diff --git a/.agents/skills/trt-cpp-runtime-quickstart/SKILL.md b/.agents/skills/trt-cpp-runtime-quickstart/SKILL.md new file mode 100644 index 000000000..22440e7d5 --- /dev/null +++ b/.agents/skills/trt-cpp-runtime-quickstart/SKILL.md @@ -0,0 +1,276 @@ +--- +name: trt-cpp-runtime-quickstart +description: >- + Load and run a TensorRT engine (.plan / .engine) from C++ using the + TensorRT 11 / 10.x **modern Runtime API**, avoiding the deprecated TRT + 8.x binding-index APIs that older guidance still promotes. Use whenever + the user asks about loading + or running a TensorRT .plan/.engine from C++, even on "minimal example" + requests — without this skill the default reply uses deprecated + enqueueV2-style code. Also use when the user hits "Engine plan file + is generated on an incompatible device", deserializeCudaEngine returns + nullptr, gets an enqueueV2 / IStreamReader deprecation warning, or + wants to stream a .plan via IStreamReaderV2. Triggers: TensorRT C++ + inference, load TensorRT plan C++, run .plan from C++, IRuntime + example, deserializeCudaEngine, enqueueV3, enqueueV2 deprecated, + setTensorAddress, getBindingIndex, IStreamReaderV2, libnvinfer C++. + NOT for building engines (`trt-onnx-quickstart`), Python deploy, + plugins, multi-GPU. +license: Apache-2.0 +metadata: + author: NVIDIA Corporation + version: "1.0" + tags: + - tensorrt + - cpp + - inference + - deployment + - runtime +--- + +# TensorRT C++ Runtime Deploy + +Load a serialized TensorRT engine from disk and run inference from C++ using only the modern Runtime API. Produces a minimal, copy-pasteable deploy harness that drops next to any `.plan` / `.engine` file and extends to production. + +Reference samples to open before writing new code: + +- `quickstart/SemanticSegmentation/tutorial-runtime.cpp` — cleanest minimal load-and-run example. Mirrors Steps 1–7 below. +- `samples/sampleOnnxMNIST/sampleOnnxMNIST.cpp` — end-to-end sample that also builds the engine; the runtime portion shows realistic I/O wiring. +- Public headers: `include/NvInferRuntime.h` — read `IRuntime`, `ICudaEngine`, `IExecutionContext`, `IStreamReaderV2`. + +## When to Use + +| Situation | Use this skill? | +|-------------------------------------------------------------------------------------------|-----------------| +| You have a `.plan`/`.engine` and need to run it from a C++ binary | Yes | +| You need a minimal harness that uses `enqueueV3` + `setTensorAddress` | Yes | +| You want to load an engine from a `std::istream` or large file via `IStreamReaderV2` | Yes | +| You need to wire dynamic shapes (`setInputShape`) before inference | Yes | +| You are *building* / optimizing the engine (calibration, INT8, sparsity, builder configs) | No - use trtexec or `IBuilder` directly | +| You are deploying in Python | No - use `tensorrt` Python bindings | +| You are writing a plugin (`IPluginV3`) or custom layer | No - separate plugin skill | +| You need multi-GPU, MPS, MIG, or process-level orchestration | No - out of scope | + +## Prerequisites + +1. **TensorRT installed.** Verify `NvInferRuntime.h` is on the include path + and `libnvinfer.so` is on the link path. On a TRT dev container these are + in `/usr/include/x86_64-linux-gnu/` and `/usr/lib/x86_64-linux-gnu/` (or + `/opt/tensorrt/...` for tarball installs). +2. **CUDA toolkit available.** `cuda_runtime_api.h` and `libcudart.so` must + be reachable; `nvcc --version` should match the CUDA version the engine + was built against. +3. **A serialized engine.** A `.plan`/`.engine` file built **on the same + major TRT version and the same GPU architecture (compute capability) you + will deploy on**. Engines are not portable across major TRT versions or + across SMs unless the builder was given `--hardwareCompatibilityLevel`. +4. **The engine's I/O tensor names.** Inspect with: + ```bash + trtexec --loadEngine=model.plan --verbose 2>&1 | grep -E 'Input|Output' + ``` +5. A C++17 compiler (`g++ >= 9` or `clang++ >= 10`). + +## Step 1: Create the IRuntime + +The runtime owns engine deserialization and must outlive every +`ICudaEngine` it creates. Construct one per process for typical deployments. + +```cpp +class Logger : public nvinfer1::ILogger { +public: + void log(Severity severity, char const* msg) noexcept override { + if (severity <= Severity::kWARNING) { + std::cerr << msg << std::endl; + } + } +}; + +Logger gLogger; +std::unique_ptr runtime{ + nvinfer1::createInferRuntime(gLogger)}; +if (!runtime) throw std::runtime_error("createInferRuntime failed"); +``` + +A custom logger is mandatory - TensorRT does not log internally. Keep it +process-global so deserialization warnings (version skew, calibrator +mismatch) are not lost. + +## Step 2: Read the Plan into Memory + +For small/medium engines (< ~1 GiB) read the whole file into a +`std::vector` and hand the pointer to +`IRuntime::deserializeCudaEngine(blob, size)`. This is what the +`SemanticSegmentation` tutorial does and the simplest correct path: + +```cpp +std::ifstream f(planPath, std::ios::binary); +if (!f) throw std::runtime_error("cannot open " + planPath); +f.seekg(0, std::ios::end); +auto size = static_cast(f.tellg()); +f.seekg(0, std::ios::beg); +std::vector blob(size); +if (!f.read(blob.data(), size)) + throw std::runtime_error("short read on " + planPath); +``` + +For very large engines, or when the bytes live behind a stream (HTTP, +mmap'd archive, encrypted store), implement an `IStreamReaderV2` - see +Step 3. + +## Step 3 (optional): Use IStreamReaderV2 for Streaming Loads + +`IStreamReader` (v1) is **deprecated in TensorRT 11.0**. Always use +`IStreamReaderV2`: it reads into both host and device memory and is the +only stream-reader form guaranteed for new code. Subclass and implement +`read(...)` and `seek(...)`: + +```cpp +class FileStreamReader : public nvinfer1::IStreamReaderV2 { +public: + explicit FileStreamReader(std::string const& path) + : mFile(path, std::ios::binary) { + if (!mFile) throw std::runtime_error("open failed: " + path); + } + int64_t read(void* dst, int64_t n, + cudaStream_t /*stream*/) noexcept override { + mFile.read(static_cast(dst), n); + return mFile.gcount(); + } + bool seek(int64_t off, nvinfer1::SeekPosition where) noexcept override { + auto dir = (where == nvinfer1::SeekPosition::kSET) ? std::ios::beg + : (where == nvinfer1::SeekPosition::kCUR) ? std::ios::cur + : std::ios::end; + mFile.clear(); + mFile.seekg(off, dir); + return static_cast(mFile); + } +private: + std::ifstream mFile; +}; + +FileStreamReader rd{planPath}; +std::unique_ptr engine{ + runtime->deserializeCudaEngine(rd)}; +``` + +## Step 4: Deserialize and Create an Execution Context + +`ICudaEngine` is thread-safe for read-only queries; `IExecutionContext` +is **not** - allocate one per inference thread. + +```cpp +std::unique_ptr engine{ + runtime->deserializeCudaEngine(blob.data(), blob.size())}; +if (!engine) throw std::runtime_error("deserializeCudaEngine failed"); + +std::unique_ptr ctx{ + engine->createExecutionContext()}; +if (!ctx) throw std::runtime_error("createExecutionContext failed"); +``` + +## Step 5: Wire Tensors with setTensorAddress + +Enumerate I/O tensors via `getNbIOTensors()` + `getIOTensorName(i)`. Use +`getTensorIOMode`, `getTensorDataType`, and `getTensorShape` to size and +allocate buffers. **Set every tensor address before `enqueueV3`** - the +modern API has no implicit binding-index map. + +```cpp +for (int i = 0; i < engine->getNbIOTensors(); ++i) { + char const* name = engine->getIOTensorName(i); + auto mode = engine->getTensorIOMode(name); + auto shape = engine->getTensorShape(name); // -1 = dynamic dim + if (mode == nvinfer1::TensorIOMode::kINPUT && hasDynamic(shape)) { + // Fill in concrete shape, e.g. batch=1 + shape.d[0] = 1; + ctx->setInputShape(name, shape); + } +} +// After setInputShape on all dynamic inputs, query output shapes. +for (int i = 0; i < engine->getNbIOTensors(); ++i) { + char const* name = engine->getIOTensorName(i); + auto bytes = elementCount(ctx->getTensorShape(name)) + * dtypeSize(engine->getTensorDataType(name)); + void* dev = nullptr; + cudaMalloc(&dev, bytes); + ctx->setTensorAddress(name, dev); +} +``` + +Always call `setInputShape` for dynamic inputs **before** querying output +shapes - the latter depends on the former. + +## Step 6: Run enqueueV3 + +`enqueueV3(stream)` is the only non-deprecated enqueue API; +`enqueueV2`/`execute*` are gone in modern flows. + +```cpp +cudaStream_t stream{}; +cudaStreamCreate(&stream); + +cudaMemcpyAsync(devInput, hostInput, inBytes, + cudaMemcpyHostToDevice, stream); +if (!ctx->enqueueV3(stream)) + throw std::runtime_error("enqueueV3 failed"); +cudaMemcpyAsync(hostOutput, devOutput, outBytes, + cudaMemcpyDeviceToHost, stream); +cudaStreamSynchronize(stream); +``` + +If you reuse buffers across iterations, skip the per-call +`setTensorAddress` - addresses persist on the context until overwritten. + +## Step 7: Shutdown Order + +Destroy in reverse construction order: contexts -> engines -> runtime, +then free CUDA memory and destroy the stream. With `std::unique_ptr` this +is automatic as long as the context is declared *after* the engine, and +the engine *after* the runtime. Free `cudaMalloc` allocations explicitly +(RAII wrapper recommended). + +## Build + +Wire the steps above into your application's build system. For a standalone smoke test, a minimal build is: + +```bash +g++ -std=c++17 runtime.cpp -o run -lnvinfer -lcudart # adjust CUDA/TRT include + lib paths +./run model.plan +``` + +## Common Errors + +| Symptom | Likely cause | +|----------------------------------------------------------------------|------------------------------------------------------------------------------| +| `deserializeCudaEngine` returns `nullptr`, log says "version tag" | Engine built on a different TRT major version. Rebuild on the deploy version | +| `nullptr` with "engine plan file is generated on an incompatible device" | SM mismatch. Rebuild on the target SM or use `--hardwareCompatibilityLevel` | +| `enqueueV3` returns false, log mentions "Tensor X has no address" | Forgot `setTensorAddress` for one of the I/O tensors | +| `enqueueV3` false, "shape" in message | Forgot `setInputShape` for a dynamic input, or supplied an out-of-profile shape | +| `cudaErrorIllegalAddress` on H->D / D->H copy | Mismatched element count / dtype between host buffer and engine tensor | +| Process crashes inside TRT during destruction | Wrong destruction order - context outlived engine, or engine outlived runtime | +| `cudaErrorMemoryAllocation` during context creation | Workspace too big for the device; rebuild with smaller workspace | + +## Pitfalls + +- **Do not use `IStreamReader` v1.** Deprecated in TRT 11.0. Use + `IStreamReaderV2` (note `cudaStream_t` parameter on `read`). +- **Do not use `enqueueV2` / `execute` / binding indices.** These are + legacy paths; the only stable modern path is name-based + `setTensorAddress` + `enqueueV3`. +- **One `IExecutionContext` per thread.** Sharing contexts across threads + is undefined behavior; sharing the engine is fine. +- **Stream lifetime.** The CUDA stream passed to `enqueueV3` must outlive + the inference. Destroying it while work is in flight crashes or corrupts + output. +- **Async vs sync copies.** Mixing synchronous `cudaMemcpy` with + `enqueueV3` on a stream serializes the GPU; always pair `enqueueV3` + with `cudaMemcpyAsync` on the same stream. +- **Engine portability.** A `.plan` is tied to (TRT major version, GPU SM, + CUDA major version). Never check engines into a repo without recording + these three facts. +- **Logger lifetime.** The logger passed to `createInferRuntime` must + outlive the runtime; a stack-local logger in `main` is fine, a function- + scope local is a use-after-free. +- **Refit / weight streaming.** Engines built with refit or weight + streaming enabled need extra setup calls (`setWeightStreamingBudgetV2`, + `IRefitter`); out of scope here. diff --git a/.agents/skills/trt-onnx-quickstart/SKILL.md b/.agents/skills/trt-onnx-quickstart/SKILL.md new file mode 100644 index 000000000..e0b633c45 --- /dev/null +++ b/.agents/skills/trt-onnx-quickstart/SKILL.md @@ -0,0 +1,343 @@ +--- +name: trt-onnx-quickstart +description: > + Build and verify a TensorRT engine from a Hugging Face model ID or ONNX + file, with numerical parity checked against ONNX Runtime. Use when the + user imports a non-LLM model to TensorRT, + needs a verified engine from ONNX, hits trtexec "unsupported operator", + must verify the engine matches ONNX numerically, debugs a polygraphy + parity failure (large max abs diff at FP16), or configures multi-input + dynamic shapes. Triggers: convert ONNX to TensorRT, Hugging Face to + TensorRT, trtexec onnx, trtexec unsupported operator, optimum-cli + export, polygraphy parity check, polygraphy run --trt --onnxrt, parity + check failed, max abs diff, verify engine matches ONNX, --minShapes, + dynamic shapes trtexec, multi-input shape profile, FP16 engine, INT64 + warning. Adjacent skills: `trt-torch-quickstart` (PyTorch frontend), + `trt-cpp-runtime-quickstart` (C++ engine load). LLM token generation + belongs in TensorRT-LLM, not here. +license: Apache-2.0 +metadata: + author: NVIDIA Corporation + version: "1.0" + tags: + - onnx + - import + - huggingface + - quickstart + - fp16 +--- + +# TensorRT ONNX Quickstart + +Take a developer from "I have a Hugging Face model ID" or "an ONNX file" to "a TensorRT engine whose outputs match the source model within tolerance." Follows Path 1 (ONNX → TensorRT) of the [Import Workflows Guide](https://github.com/NVIDIA/TensorRT/blob/main/documents/import_workflows.md), specialized for the most common starting point: a Hugging Face Hub model. + +## When to Use + +| Scenario | Use this skill? | +|----------|-----------------| +| Has a Hugging Face model ID (`google-bert/bert-base-uncased`) and wants TRT-accelerated inference | Yes | +| Has an `.onnx` file and wants a `.plan` engine with verified parity | Yes | +| Ran `trtexec --onnx=...` and hit a warning/error they don't recognize | Yes | +| Wants LLM token generation (Llama, Mistral, Qwen text generation) | **No** — route to [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) | +| Has a PyTorch model and wants to stay in PyTorch | No — use `trt-torch-quickstart` | +| Already has a `.plan` engine and wants to run inference from C++ | No — use `trt-cpp-runtime-quickstart` | +| Is migrating a weakly-typed network to strongly-typed | No — use `trt-strong-typing-migration` | +| Has a full diffusion pipeline (SD, FLUX) | Partial — must be component-split first; this skill imports one component at a time | + +## Prerequisites + +Verify each before continuing. Failures here surface as confusing errors later, so fix them before running the import. + +1. **NVIDIA GPU + driver matching TensorRT 11.x.** TRT 11 requires CUDA 13.x. Confirm: + ```bash + nvidia-smi # driver + CUDA runtime version + ``` +2. **Python ≥ 3.10.** TRT 11 dropped 3.9 and earlier. +3. **TensorRT 11 installed — including the `trtexec` CLI**, this skill's primary build tool. **The pip wheel does not ship `trtexec`.** Pick one: + + - **NGC container (recommended for first-time users):** `nvcr.io/nvidia/tensorrt:` — bundles `trtexec`, Python bindings, libraries, and most common dependencies. Run with `--gpus all`. + - **`.tar.gz` or `.deb` from [TensorRT downloads](https://developer.nvidia.com/tensorrt-download):** installs `trtexec` to a system path. + + The pip path (`pip install --extra-index-url https://pypi.nvidia.com tensorrt-cu13`) provides Python bindings but not `trtexec`, so it is **insufficient on its own**. For pip-only, route the developer to the alternative Python-builder flow in `import_workflows.md` Path 1 Option B; this skill assumes `trtexec` is available. + + Verification: + ```bash + python3 -c "import tensorrt; print(tensorrt.__version__)" # expect 11.x.y.z + trtexec --help | head -5 # MUST work; if not, install is incomplete + ``` +4. **Polygraphy and `optimum-onnx` installed.** + ```bash + pip install polygraphy onnx onnxruntime onnx_graphsurgeon onnxsim 'optimum-onnx' + # If the model ID starts with `sentence-transformers/`, also: + pip install sentence-transformers + ``` + `onnx_graphsurgeon` is required by `polygraphy surgeon sanitize` (Step 3) — Polygraphy does not bundle it. `onnxruntime` is required by `optimum-cli export` (Step 2, to fix dynamic axes) and Polygraphy's `--onnxrt` parity check (Step 5); stock NGC containers do not always ship it. + Note: `optimum-onnx` is the active maintained package — the older `optimum` no longer includes ONNX integration. Do **not** use `optimum-nvidia` (last release 2025-01, unmaintained). + +## Step 1 — Choose a model and confirm it's supported + +Consult the [Supported Models](https://github.com/NVIDIA/TensorRT/blob/main/documents/supported_models.md) matrix. If listed, the dtype column gives a known-good starting precision. If not, it is still expected to work — file an issue if it doesn't. + +If the model is a diffusion pipeline, **stop here** — TRT cannot ingest the whole pipeline object. Split it into components (text encoder, UNet/DiT, VAE) and import each separately. See `supported_models.md` for validated splits, and consider whether `trt-export-rewrite` is a better fit. + +This skill's worked example uses `google-bert/bert-base-uncased` from the encoder-NLP section. + +## Step 2 — Export to ONNX + +The `optimum-cli export onnx` command's `--task` flag selects which head of the model to export. The wrong task yields a valid graph but the wrong output — and Step 5 parity validation won't catch it, because reference and engine are both wrong. + +Pick the task from this table based on what the developer wants to do with the model: + +| Developer's intent | `--task` | Typical model families | +|--------------------|----------|------------------------| +| Get hidden-state embeddings or pooled output | `feature-extraction` | BERT, RoBERTa, sentence-transformers, CLIP | +| Classify images into N classes | `image-classification` | ResNet, ViT, MobileNet, EfficientNet | +| Classify text into N labels | `text-classification` | BERT for sentiment, DistilBERT, etc. | +| Detect / segment objects | `object-detection`, `image-segmentation` | DETR, Mask R-CNN | +| Transcribe speech | `automatic-speech-recognition` | Whisper, Wav2Vec2 | +| Generate captions / VQA | `image-to-text`, `visual-question-answering` | BLIP, LLaVA | +| Translation / summarization | `text2text-generation` | T5, BART | + +For an unknown model, run `optimum-cli export onnx --help` or check the [Optimum supported tasks table](https://huggingface.co/docs/optimum/main/en/exporters/onnx/overview). If unsure, surface the choice to the developer rather than guessing. + +### Sentence-transformers models need an extra install + +If the model ID starts with `sentence-transformers/` (e.g. `all-MiniLM-L6-v2`, `bge-base-en-v1.5`), install `sentence-transformers` *before* exporting: + +```bash +pip install sentence-transformers +``` + +Without it, `optimum-cli` falls back to plain `transformers` and emits raw token-level `last_hidden_state` instead of the pooled sentence embedding the model is designed to produce. The exporter prints `library name was inferred as sentence_transformers, which is not installed. Falling back to transformers.` — treat that warning as a hard error and install before retrying. + +```bash +# Worked example: BERT for embedding extraction +optimum-cli export onnx \ + --model google-bert/bert-base-uncased \ + --task feature-extraction \ + bert_onnx/ +``` + +### Expected warnings (not errors) + +The exporter often prints a "max diff between reference and ONNX exported model is not within the set tolerance 1e-05" line (with `max diff` around `1e-5` to `1e-4`). This is its own self-check at a strict tolerance — **the export still succeeded.** Step 5's Polygraphy parity check uses `--atol 1e-2 --rtol 1e-2 --check-error-stat mean`, the correct tolerance for FP16 inference. Don't surface this warning as a failure. + +### Authentication for gated models + +LLaMA, FLUX, and some Mistral and Qwen variants are gated behind HF agreements and require `huggingface-cli login` before download. If `optimum-cli` exits with a 401, run `huggingface-cli login` with a token from `huggingface.co/settings/tokens` and retry. + +### If the export fails + +The most common cause is a HF model using patterns `torch.export` / `torch.onnx.export` cannot trace — complex-number arithmetic, data-dependent control flow, non-tensor forward arguments, output dataclasses. **Do not patch the upstream library on disk.** The correct workflow is agentic monkey-patching at runtime: route the developer to the `trt-export-rewrite` skill, which drives it with five concrete patterns from the Qwen-Image case study. + +## Step 3 — Sanitize the ONNX + +ONNX exporters often produce graphs with constant-folding opportunities, spurious dynamic axes, or shape-inference gaps. Sanitizing cleans these up, but it is a **quality pass, not a correctness gate** — TRT parses many un-sanitized exports fine. Both stages below are therefore optional and **allowed to fail**: each falls through to its input, and Step 3 always leaves a `model.clean.onnx` for the rest of the workflow (worst case, a copy of the exported model). Run: + +```bash +# Seed model.clean.onnx with the raw export, then update it in-place +cp bert_onnx/model.onnx bert_onnx/model.clean.onnx + +# (1) onnxsim — graph simplification / constant folding. +python3 -m onnxsim bert_onnx/model.clean.onnx bert_onnx/model.simplified.onnx \ + && cp bert_onnx/model.simplified.onnx bert_onnx/model.clean.onnx + +# (2) polygraphy surgeon — fold constants, prune dangling nodes. +polygraphy surgeon sanitize bert_onnx/model.clean.onnx \ + -o bert_onnx/model.surgeon.onnx --fold-constants \ + && cp bert_onnx/model.surgeon.onnx bert_onnx/model.clean.onnx +``` + +## Step 3.5 — Inspect the ONNX inputs (parameterize the rest of the workflow) + +**Every step from here uses input names and ranks that depend on the model.** Read them out of the sanitized ONNX before building. Do not assume `input_ids` / `attention_mask` — those are BERT-family-specific. + +```bash +polygraphy inspect model bert_onnx/model.clean.onnx +``` + +Example output for `bert-base-uncased` (verified against `optimum-onnx` 0.1.0, opset 18): + +``` +---- 3 Graph Input(s) ---- +{input_ids [dtype=int64, shape=('batch_size', 'sequence_length')], + attention_mask [dtype=int64, shape=('batch_size', 'sequence_length')], + token_type_ids [dtype=int64, shape=('batch_size', 'sequence_length')]} + +---- 1 Graph Output(s) ---- +{last_hidden_state [dtype=float32, shape=('batch_size', 'sequence_length', 768)]} +``` + +Inputs and outputs vary per model and per `--task`. Inspect, don't assume. + +**Build the shape strings from this output.** Every input with a dynamic (named) axis needs a `--minShapes / --optShapes / --maxShapes` entry; fixed axes are reproduced literally. Use representative values — `--opt` is what the engine optimizes for; `--min` and `--max` define the supported range. + +If any input reports an unknown-rank axis (printed as `?`), shape inference failed; reroute through `trt-export-rewrite` to fix the export. + +## Step 4 — Bake FP16 into the model (TRT 11) or set the build flag (TRT 10) + +Start with FP16 — the safe default for encoder and vision models on modern GPUs. (Use BF16 on Blackwell/Hopper if the developer reports persistent FP16 accuracy issues. Sentence-embedding models sometimes prefer FP32 over lossy FP16 — surface the option.) + +**TRT 11 removed the weak-typing builder flags** (`--fp16`, `--int8`, `--bf16`, `BuilderFlag::kFP16`, etc. — see [the migration guide](https://docs.nvidia.com/deeplearning/tensorrt/latest/api/migration/tensorrt-10x-to-11x.html) and the sibling `trt-strong-typing-migration` SKILL). In TRT 11 you bake precision **into the model** before building, and `trtexec` honors the dtypes it finds via **ModelOpt AutoCast**. + +Detect the version first: + +```bash +TRT_MAJOR=$(python3 -c "import tensorrt as t; print(t.__version__.split('.')[0])") +echo "TensorRT major version: $TRT_MAJOR" +``` + +### TRT 11: AutoCast the model, then build + +```bash +pip install nvidia-modelopt # one-time + +# Convert FP32 ONNX to mixed FP16 (ModelOpt picks accuracy-sensitive ops to keep FP32): +python3 -m modelopt.onnx.autocast \ + --onnx_path=.clean.onnx \ + --output_path=.fp16.onnx + +# Build the engine. NO --fp16 flag — strong typing is the default and the model already carries FP16 dtypes. +trtexec \ + --onnx=.fp16.onnx \ + --saveEngine=.fp16.plan \ + --memPoolSize=workspace:4096 \ + --minShapes=:,... \ + --optShapes=:,... \ + --maxShapes=:,... +``` + +### TRT 10: pass the legacy --fp16 flag + +```bash +trtexec \ + --onnx=.clean.onnx \ + --saveEngine=.fp16.plan \ + --memPoolSize=workspace:4096 \ + --fp16 \ + --minShapes=:,... \ + --optShapes=:,... \ + --maxShapes=:,... +``` + +Worked example for BERT (three inputs, all dynamic on batch and sequence axes), TRT 11 path: + +```bash +python3 -m modelopt.onnx.autocast \ + --onnx_path=bert_onnx/model.clean.onnx \ + --output_path=bert_onnx/model.fp16.onnx +trtexec \ + --onnx=bert_onnx/model.fp16.onnx \ + --saveEngine=bert.fp16.plan \ + --memPoolSize=workspace:4096 \ + --minShapes=input_ids:1x32,attention_mask:1x32,token_type_ids:1x32 \ + --optShapes=input_ids:8x128,attention_mask:8x128,token_type_ids:8x128 \ + --maxShapes=input_ids:16x512,attention_mask:16x512,token_type_ids:16x512 +``` + +Worked example for ResNet-50 (one input, dynamic on batch axis only), TRT 11 path: + +```bash +python3 -m modelopt.onnx.autocast \ + --onnx_path=resnet50_onnx/model.clean.onnx \ + --output_path=resnet50_onnx/model.fp16.onnx +trtexec \ + --onnx=resnet50_onnx/model.fp16.onnx \ + --saveEngine=resnet50.fp16.plan \ + --memPoolSize=workspace:4096 \ + --minShapes=pixel_values:1x3x224x224 \ + --optShapes=pixel_values:16x3x224x224 \ + --maxShapes=pixel_values:32x3x224x224 +``` + +For purely static-shape models, omit all three shape flags. + +### Common errors at this step + +- **"Engine plan file is generated on an incompatible device"** — plans are not portable across compute capabilities. Rebuild on the deployment GPU. +- **OOM during build** — lower `--memPoolSize=workspace:N`. If still failing, try `--tacticSources=-CUBLAS_LT` to disable expensive tactic sources. +- **Unsupported operator** — `trtexec` names the op. Route to `trt-unsupported-op` for triage (decompose / plugin / switch frontend / file bug). +- **INT64 warnings** — TRT casts to INT32. Usually safe; if values exceed INT32 range, rerun Step 3's `polygraphy surgeon sanitize --fold-constants`. +- **"Input has missing shape information"** — you missed an input in the `--minShapes / --optShapes / --maxShapes` flags. Compare against Step 3.5's output and add the missing entry. + +## Step 5 — Verify numerical parity + +This is the step most developers skip — and the one that most often surfaces a silent correctness bug. + +Construct the parity command from the same input names used in Step 4. Beyond input shapes, three knobs matter; get them right or the result is misleading: + +1. **`--val-range`** for any input with constrained semantics. Polygraphy fills with `[0, 1)` floats by default. For BERT-family `input_ids` you must constrain to vocabulary range, set `attention_mask` to all-ones, and `token_type_ids` to all-zeros — otherwise the model sees garbage and FP16 noise amplifies through the attention softmax. +2. **`--check-error-stat mean`** for transformer outputs. FP16 commonly has a few outlier positions with large abs diff; the mean across all positions is the meaningful metric for downstream use (embedding similarity, classification). Default `max` is too pessimistic for FP16 transformers and produces false failures. +3. **`--seed`** for repeatability across runs and CI environments. + +Worked example for BERT — run polygraphy against the **same ONNX file you fed to `trtexec`** (the AutoCast-converted `.fp16.onnx` on TRT 11; the `.clean.onnx` on TRT 10): + +```bash +# TRT 11: polygraphy reads the model's dtypes (already FP16 from AutoCast) +polygraphy run bert_onnx/model.fp16.onnx \ + --trt --onnxrt \ + --atol 1e-2 --rtol 1e-2 \ + --check-error-stat mean \ + --input-shapes input_ids:[8,128] attention_mask:[8,128] token_type_ids:[8,128] \ + --val-range input_ids:[0,30000] attention_mask:[1,1] token_type_ids:[0,0] \ + --seed 42 + +# TRT 10: pass --fp16 explicitly (the polygraphy flag maps to BuilderFlag::kFP16) +polygraphy run bert_onnx/model.clean.onnx \ + --trt --onnxrt \ + --atol 1e-2 --rtol 1e-2 \ + --check-error-stat mean \ + --input-shapes input_ids:[8,128] attention_mask:[8,128] token_type_ids:[8,128] \ + --val-range input_ids:[0,30000] attention_mask:[1,1] token_type_ids:[0,0] \ + --seed 42 \ + --fp16 +``` + +Expected output: `Pass Rate: 100.0%` with `mean_absdiff ≤ 1e-3` per output. `Pass Rate: 0.0%` with `max_absdiff` near 1e-2 but `mean_absdiff` well below tolerance is the outlier pattern above — use `--check-error-stat mean`. + +Worked example for ResNet-50 (same FP16 recipe; TRT-11 path shown — for TRT 10 add `--fp16` and read from `model.clean.onnx`): + +```bash +polygraphy run resnet50_onnx/model.fp16.onnx \ + --trt --onnxrt \ + --atol 1e-2 --rtol 1e-2 \ + --check-error-stat mean \ + --input-shapes pixel_values:[16,3,224,224] \ + --val-range pixel_values:[0,1] \ + --seed 42 +``` + +Polygraphy runs the model through ONNX Runtime (reference) and TensorRT (engine) on the seeded inputs and compares outputs per the chosen error stat. + +### If parity fails + +- **Diverges only at FP16, not FP32**: a few layers are losing precision. Try `--strongly-typed` with an explicit FP32 cast on the offending subgraph (Polygraphy names the layer). For LLM-style models, prefer BF16 over FP16 on Hopper/Blackwell. +- **Diverges at all precisions**: the export is wrong. Re-run Steps 2–3 fresh and check `polygraphy inspect model` for shape-inference issues. If the model uses patterns covered by `trt-export-rewrite`, reroute. +- **Diverges only for specific input shapes**: dynamic-shape profile is too narrow. Widen `--minShapes` / `--maxShapes` and rebuild. + +## Step 6 — Run the engine + +```bash +trtexec --loadEngine=bert.fp16.plan \ + --shapes=input_ids:8x128,attention_mask:8x128,token_type_ids:8x128 \ + --verbose +``` + +This confirms the saved `.plan` deserializes and runs (Step 5's parity used its own engine, not this file) and reports throughput and per-iteration latency. For production, see the developer guide on `IExecutionContext` and stream-aware execution; the `.plan` is portable to any compatible GPU + TRT runtime. + +## Success criteria + +The skill is complete when all of the following are true: + +- `bert.fp16.plan` exists and `trtexec --loadEngine=...` runs without error. +- `polygraphy run --trt --onnxrt --atol 1e-2 --rtol 1e-2 --check-error-stat mean` reports `PASSED` across all configured input shapes. +- The developer can articulate which precision was used and why. + +If any are not true, hand control back with the specific failing diagnostic — do not declare success. + +## References + +- [Import Workflows Guide (Path 1: ONNX → TensorRT)](https://github.com/NVIDIA/TensorRT/blob/main/documents/import_workflows.md#path-1-onnx--tensorrt) +- [Supported Models matrix](https://github.com/NVIDIA/TensorRT/blob/main/documents/supported_models.md) +- [Polygraphy documentation](https://docs.nvidia.com/deeplearning/tensorrt/polygraphy/index.html) +- [`samples/sampleOnnxMNIST/`](https://github.com/NVIDIA/TensorRT/tree/main/samples/sampleOnnxMNIST) for the C++ equivalent of the build flow diff --git a/.agents/skills/trt-perf-analysis/SKILL.md b/.agents/skills/trt-perf-analysis/SKILL.md new file mode 100644 index 000000000..3147553b9 --- /dev/null +++ b/.agents/skills/trt-perf-analysis/SKILL.md @@ -0,0 +1,103 @@ +--- +name: trt-perf-analysis +description: Validate and analyze TensorRT performance data from paired layer-info JSON and profile/latency JSON files. Use when asked to inspect TensorRT, TRT, torch-tensorrt, or ONNX-TensorRT perf reports, verify that layer/profile JSON files are valid and from the same model, infer basic model information, find likely fusion or latency optimization opportunities, and produce a concise Markdown performance report or structured JSON data. +--- + +# TRT Perf Analysis + +## Workflow + +Use `scripts/run.sh` on Unix-like systems or `scripts\run.cmd` on Windows for Python scripts. These wrappers do best-effort Python 3.8+ discovery; set `SKILL_PYTHON` to a Python executable to override discovery. Replace placeholders with platform-native paths. + +1. Choose the input scope. + Use one folder that directly contains `layers_*.json` and/or `profile_*.json`. Do not call the packager on a parent folder that only contains component subfolders. For model suites with separate encoder, transformer, decoder, VAE, or similar components, handle each component folder separately. + +2. Infer model identity and components. + Prefer an explicit model name from the user prompt and pass it with `--model-name`. Otherwise rely on the analyzer/packager to inspect likely config files, the input directory name, and layer/profile filenames. Keep the name empty when confidence is low. Serialized JSON records the inferred model identity at the top level and in each successful backend's `model` object. + +3. Run deterministic analysis. + Run `scripts/analyze_trt_perf.py` first when you need an integrity gate before interpretation. The script uses only Python built-in modules, extracts structured analysis data, and serializes it as JSON. This JSON is the authoritative analyzer output for validation, packaging, and AI diagnosis. + +4. Check validation before interpreting. + The analyzer exits `0` when it can emit structured validation data, even if one or more backends fail validation. Read the generated JSON `validation` object to decide which backend reports are usable. If the analyzer exits nonzero, stop because it could not emit structured data. If the schema validator exits nonzero, stop because the generated JSON contract is invalid. Do not continue into performance interpretation for a backend unless its validation status is `passed`, its analysis mode is `layer_profile`, the layer graph is a DAG, and the layer/profile names match. For `layer_only` backends, graph and layer inspection are available but latency/performance interpretation is not. + +5. Understand the model, then write AI diagnosis. + Use the generated JSON as grounding material. Build a clear picture of the model or component scope, backend differences, graph structure, timing distribution, hot layers, fusion clues, and caveats before writing conclusions. The browser `Summary` panel is generated from `analyze-data.json`; do not copy that deterministic summary into `analyze.md`. + + Keep the final AI analysis in two sections: + + 1. `## Summary` - short, high-confidence facts and the most important optimization leads. + 2. `## Details` - interpretation, cross-backend comparisons, hypotheses, ranked experiments, and human judgment that is not a mechanical restatement of the analyzer output. + + Before sending the final response, write the final AI analysis into `analyze.md` for each report folder you created or updated. Match each file to that report folder's scope. For a single model with multiple backend JSON pairs, one `analyze.md` may compare those backends. For multiple model components packaged as multiple report folders, write distinct component-focused `analyze.md` files; do not copy a suite-level analysis into every component report. + + `analyze.md` should not include validation checklists, complete timing tables, top-layer tables, path dumps, or other content already rendered by the report app from `analyze-data.json`. Briefly cite specific metrics or layer names only when they support a new conclusion or next experiment. + +6. Package the report. + Run `scripts/package_report.py` through the shared runner. Each invocation creates exactly one final report folder from one input folder that directly contains `layers_*.json` or `profile_*.json` files. The packager writes `analyze-data.json`, validates it against the schema, and copies the browser report template while preserving the generated JSON. Determine the output parent from the user's active workspace, not from a subdirectory of this skill. If the only known directory is the skill directory, omit `--output-parent`; the packaging script will fall back to the system temp directory. + + When producing multiple reports for one user request, prefer explicit `--report-dir` values so report folder names include the model/component identity instead of relying on auto-numbered collision suffixes. If a suite-level conclusion is useful, write it as a separate overview Markdown file in the output parent, not as every component report's `analyze.md`. + +7. Preview only the final report. + Serve the completed report folder, not the skill directory, when the user wants a browser preview. The Python `visualize_layer_info.py` script remains available for standalone HTML layer-info debugging, but it is not part of the report packaging flow or report app dependency chain. + +### Common Commands + +Use `` as `/scripts/run.sh` on Unix-like systems or `\scripts\run.cmd` on Windows. + +```bash +# Validate or inspect inputs. Folder mode discovers matching backends; repeat --data for explicit backends. + /scripts/analyze_trt_perf.py --output + /scripts/analyze_trt_perf.py --data --data --output + +# Create a report. Use --report-dir for component-specific reports and --analyze-md after writing final AI analysis. + /scripts/package_report.py --output-parent [--model-name ] [--analyze-md ] + /scripts/package_report.py --analyze-data /analyze-data.json [--analyze-md ] + +# Optional preview. + -m http.server 8765 --bind 127.0.0.1 --directory +``` + +Then open `http://127.0.0.1:8765/`. + +## Inputs + +Expect one layer-info JSON and one profile/latency JSON per engine/report. A folder may contain multiple pairs such as: + +- `layers_torch-trt-aot.json` with `profile_torch-trt-aot.json` +- `layers_onnx-tensorrt.json` with `profile_onnx-tensorrt.json` + +Layer records are expected to include `Name`, `LayerType`, `Inputs`, and `Outputs`. Profile records are expected to include `name`, `timeMs`, `averageMs`, `medianMs`, and `percentage`; a first record like `{ "count": 20 }` is allowed. + +Infer a per-layer `Type` for report output. Default to the raw `LayerType`, then apply narrow special-case overrides when the layer name, tactic, or metadata makes the specialization clear. Known special cases: + +- `kgen_mha`: a raw `LayerType` of `kgen` that is actually an MHA/FMHA-style attention layer. +- `misc`: raw `LayerType` values `reshape`, `shape_call`, `signal`, and `wait` are grouped into this category. + +## Model Categories + +Use this starter category list and revise as evidence warrants: + +- Transformer encoder / sentence embedding +- Transformer decoder / LLM +- Vision transformer +- CNN / convolutional vision model +- Diffusion / U-Net style model +- Recommender / ranking model +- Classical ML or feature pipeline +- Unknown DL model + +Report category only when the evidence is strong. Good evidence includes Hugging Face config fields, names like `encoder.layer`, attention layers, input names such as `input_ids`, or repeated convolution/GEMM patterns. Say "unknown" instead of over-claiming. + +## Analysis Heuristics + +Prioritize issues by likely runtime impact and confidence: + +- Hot layers: individual layers with high `percentage` or `averageMs`. +- Layer-type concentration: high aggregate time in `kgen`, shape, cast, gather, reshape, or pointwise/reduction layers may indicate fusion or dynamic-shape overhead. +- Fusion clues: for transformer encoders, check whether Q/K/V matmuls are fused per block, whether attention is an obvious fused SDPA/FMHA layer, and whether layer-norm/residual/GELU patterns are fused into larger kernels. +- Engine partitioning: one engine is usually ideal. Use adjacent `.trt`/`.engine` files, `_subgraph`, and `StreamId` as hints; state when this is only inferred. +- GEMM tactics: GEMM layers without Tensor Core or xMMA-like tactic names may deserve inspection. +- Many tiny kernels: if no single layer dominates but many small kernels add up, rank launch/fusion overhead above isolated micro-hotspots. + +Keep facts and hypotheses separate. For each optimization opportunity, include evidence from the JSON and a next check or experiment. diff --git a/.agents/skills/trt-perf-analysis/report_template/ThirdPartyNotices.txt b/.agents/skills/trt-perf-analysis/report_template/ThirdPartyNotices.txt new file mode 100644 index 000000000..1dd1c7e9b --- /dev/null +++ b/.agents/skills/trt-perf-analysis/report_template/ThirdPartyNotices.txt @@ -0,0 +1,86 @@ +Third Party Notices +=================== + +This report includes or loads third-party open source software. +These notices are provided for attribution and license-tracking purposes only +and do not modify the license terms of the third-party projects. + + +Netron +------ + +Project: Netron +Homepage: https://github.com/lutzroeder/netron +Version included in this report: 9.1.1 +License: MIT +License URL: https://github.com/lutzroeder/netron/blob/main/LICENSE +Copyright: Copyright (c) Lutz Roeder + +Notes: +- The Netron files are embedded as a trimmed report-specific viewer. +- The embedded files have been modified for use in this TensorRT performance + report, including report-specific loading, iframe integration, and layer + selection behavior. + + +Dagre +----- + +Project: Dagre +Homepage: https://github.com/dagrejs/dagre +License: MIT +License URL: https://github.com/dagrejs/dagre/blob/master/LICENSE +Copyright: Copyright (c) 2012-2014 Chris Pettitt + +Notes: +- The bundled graph layout code is included through the report-specific Netron + viewer bundle. + + +Graphlib +-------- + +Project: Graphlib +Homepage: https://github.com/dagrejs/graphlib +License: MIT +Package metadata URL: https://github.com/dagrejs/graphlib/blob/master/package.json +Author: Chris Pettitt + +Notes: +- Dagre depends on Graphlib. The bundled layout code in dev/report/netron/dagre.js may + include or derive from Graphlib functionality. + + +DOMPurify +--------- + +Project: DOMPurify +Homepage: https://github.com/cure53/DOMPurify +Version included in this report: 3.4.9 +License: MPL-2.0 OR Apache-2.0 +License option used for this notice: Apache-2.0 +License URL: https://github.com/cure53/DOMPurify/blob/3.4.9/LICENSE +Package metadata URL: https://github.com/cure53/DOMPurify/blob/3.4.9/package.json +Author: Dr.-Ing. Mario Heiderich, Cure53 + +Notes: +- DOMPurify is used at runtime to sanitize rendered Markdown content. + + +Marked +------ + +Project: marked +Homepage: https://github.com/markedjs/marked +Version included in this report: 18.0.5 +License: MIT +License URL: https://github.com/markedjs/marked/blob/v18.0.5/LICENSE.md +Package metadata URL: https://github.com/markedjs/marked/blob/v18.0.5/package.json +Copyright: +- Copyright (c) 2018-2026, MarkedJS +- Copyright (c) 2011-2018, Christopher Jeffrey + +Notes: +- marked is used at runtime to render Markdown content. +- The marked license file also includes attribution and license terms for + Markdown by John Gruber. 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a=At(n[0]);return{type:"html",block:!0,raw:a,pre:n[1]==="pre"||n[1]==="script"||n[1]==="style",text:a}}}def(i){let n=this.rules.block.def.exec(i);if(n){let a=n[1].toLowerCase().replace(this.rules.other.multipleSpaceGlobal," "),o=n[2]?n[2].replace(this.rules.other.hrefBrackets,"$1").replace(this.rules.inline.anyPunctuation,"$1"):"",s=n[3]?n[3].substring(1,n[3].length-1).replace(this.rules.inline.anyPunctuation,"$1"):n[3];return{type:"def",tag:a,raw:$e(n[0],` +`),href:o,title:s}}}table(i){let n=this.rules.block.table.exec(i);if(!n||!this.rules.other.tableDelimiter.test(n[2]))return;let a=wn(n[1]),o=n[2].replace(this.rules.other.tableAlignChars,"").split("|"),s=n[3]?.trim()?n[3].replace(this.rules.other.tableRowBlankLine,"").split(` +`):[],c={type:"table",raw:$e(n[0],` +`),header:[],align:[],rows:[]};if(a.length===o.length){for(let f of o)this.rules.other.tableAlignRight.test(f)?c.align.push("right"):this.rules.other.tableAlignCenter.test(f)?c.align.push("center"):this.rules.other.tableAlignLeft.test(f)?c.align.push("left"):c.align.push(null);for(let f=0;f({text:h,tokens:this.lexer.inline(h),header:!1,align:c.align[E]})));return c}}lheading(i){let n=this.rules.block.lheading.exec(i);if(n){let a=n[1].trim();return{type:"heading",raw:$e(n[0],` +`),depth:n[2].charAt(0)==="="?1:2,text:a,tokens:this.lexer.inline(a)}}}paragraph(i){let n=this.rules.block.paragraph.exec(i);if(n){let a=n[1].charAt(n[1].length-1)===` +`?n[1].slice(0,-1):n[1];return{type:"paragraph",raw:n[0],text:a,tokens:this.lexer.inline(a)}}}text(i){let n=this.rules.block.text.exec(i);if(n)return{type:"text",raw:n[0],text:n[0],tokens:this.lexer.inline(n[0])}}escape(i){let n=this.rules.inline.escape.exec(i);if(n)return{type:"escape",raw:n[0],text:n[1]}}tag(i){let n=this.rules.inline.tag.exec(i);if(n)return!this.lexer.state.inLink&&this.rules.other.startATag.test(n[0])?this.lexer.state.inLink=!0:this.lexer.state.inLink&&this.rules.other.endATag.test(n[0])&&(this.lexer.state.inLink=!1),!this.lexer.state.inRawBlock&&this.rules.other.startPreScriptTag.test(n[0])?this.lexer.state.inRawBlock=!0:this.lexer.state.inRawBlock&&this.rules.other.endPreScriptTag.test(n[0])&&(this.lexer.state.inRawBlock=!1),{type:"html",raw:n[0],inLink:this.lexer.state.inLink,inRawBlock:this.lexer.state.inRawBlock,block:!1,text:n[0]}}link(i){let n=this.rules.inline.link.exec(i);if(n){let a=n[2].trim();if(!this.options.pedantic&&this.rules.other.startAngleBracket.test(a)){if(!this.rules.other.endAngleBracket.test(a))return;let c=$e(a.slice(0,-1),"\\");if((a.length-c.length)%2===0)return}else{let c=jt(n[2],"()");if(c===-2)return;if(c>-1){let f=(n[0].indexOf("!")===0?5:4)+n[1].length+c;n[2]=n[2].substring(0,c),n[0]=n[0].substring(0,f).trim(),n[3]=""}}let o=n[2],s="";if(this.options.pedantic){let c=this.rules.other.pedanticHrefTitle.exec(o);c&&(o=c[1],s=c[3])}else s=n[3]?n[3].slice(1,-1):"";return o=o.trim(),this.rules.other.startAngleBracket.test(o)&&(this.options.pedantic&&!this.rules.other.endAngleBracket.test(a)?o=o.slice(1):o=o.slice(1,-1)),Oe(n,{href:o&&o.replace(this.rules.inline.anyPunctuation,"$1"),title:s&&s.replace(this.rules.inline.anyPunctuation,"$1")},n[0],this.lexer,this.rules)}}reflink(i,n){let a;if((a=this.rules.inline.reflink.exec(i))||(a=this.rules.inline.nolink.exec(i))){let o=(a[2]||a[1]).replace(this.rules.other.multipleSpaceGlobal," "),s=n[o.toLowerCase()];if(!s){let c=a[0].charAt(0);return{type:"text",raw:c,text:c}}return Oe(a,s,a[0],this.lexer,this.rules)}}emStrong(i,n,a=""){let o=this.rules.inline.emStrongLDelim.exec(i);if(!(!o||!o[1]&&!o[2]&&!o[3]&&!o[4]||o[4]&&a.match(this.rules.other.unicodeAlphaNumeric))&&(!(o[1]||o[3])||!a||this.rules.inline.punctuation.exec(a))){let s=[...o[0]].length-1,c,f,h=s,E=0,m=o[0][0]==="*"?this.rules.inline.emStrongRDelimAst:this.rules.inline.emStrongRDelimUnd;for(m.lastIndex=0,n=n.slice(-1*i.length+s);(o=m.exec(n))!==null;){if(c=o[1]||o[2]||o[3]||o[4]||o[5]||o[6],!c)continue;if(f=[...c].length,o[3]||o[4]){h+=f;continue}else if((o[5]||o[6])&&s%3&&!((s+f)%3)){E+=f;continue}if(h-=f,h>0)continue;f=Math.min(f,f+h+E);let F=[...o[0]][0].length,S=i.slice(0,s+o.index+F+f);if(Math.min(s,f)%2){let he=S.slice(1,-1);return{type:"em",raw:S,text:he,tokens:this.lexer.inlineTokens(he)}}let V=S.slice(2,-2);return{type:"strong",raw:S,text:V,tokens:this.lexer.inlineTokens(V)}}}}codespan(i){let n=this.rules.inline.code.exec(i);if(n){let a=n[2].replace(this.rules.other.newLineCharGlobal," "),o=this.rules.other.nonSpaceChar.test(a),s=this.rules.other.startingSpaceChar.test(a)&&this.rules.other.endingSpaceChar.test(a);return o&&s&&(a=a.substring(1,a.length-1)),{type:"codespan",raw:n[0],text:a}}}br(i){let n=this.rules.inline.br.exec(i);if(n)return{type:"br",raw:n[0]}}del(i,n,a=""){let o=this.rules.inline.delLDelim.exec(i);if(o&&(!o[1]||!a||this.rules.inline.punctuation.exec(a))){let s=[...o[0]].length-1,c,f,h=s,E=this.rules.inline.delRDelim;for(E.lastIndex=0,n=n.slice(-1*i.length+s);(o=E.exec(n))!==null;){if(c=o[1]||o[2]||o[3]||o[4]||o[5]||o[6],!c||(f=[...c].length,f!==s))continue;if(o[3]||o[4]){h+=f;continue}if(h-=f,h>0)continue;f=Math.min(f,f+h);let m=[...o[0]][0].length,F=i.slice(0,s+o.index+m+f),S=F.slice(s,-s);return{type:"del",raw:F,text:S,tokens:this.lexer.inlineTokens(S)}}}}autolink(i){let n=this.rules.inline.autolink.exec(i);if(n){let a,o;return n[2]==="@"?(a=n[1],o="mailto:"+a):(a=n[1],o=a),{type:"link",raw:n[0],text:a,href:o,tokens:[{type:"text",raw:a,text:a}]}}}url(i){let n;if(n=this.rules.inline.url.exec(i)){let a,o;if(n[2]==="@")a=n[0],o="mailto:"+a;else{let s;do s=n[0],n[0]=this.rules.inline._backpedal.exec(n[0])?.[0]??"";while(s!==n[0]);a=n[0],n[1]==="www."?o="http://"+n[0]:o=n[0]}return{type:"link",raw:n[0],text:a,href:o,tokens:[{type:"text",raw:a,text:a}]}}}inlineText(i){let n=this.rules.inline.text.exec(i);if(n){let a=this.lexer.state.inRawBlock;return{type:"text",raw:n[0],text:n[0],escaped:a}}}},Ce=class er{constructor(n){oe(this,"tokens");oe(this,"options");oe(this,"state");oe(this,"inlineQueue");oe(this,"tokenizer");this.tokens=[],this.tokens.links=Object.create(null),this.options=n||Y,this.options.tokenizer=this.options.tokenizer||new we,this.tokenizer=this.options.tokenizer,this.tokenizer.options=this.options,this.tokenizer.lexer=this,this.inlineQueue=[],this.state={inLink:!1,inRawBlock:!1,top:!0};let a={other:Z,block:Ie.normal,inline:De.normal};this.options.pedantic?(a.block=Ie.pedantic,a.inline=De.pedantic):this.options.gfm&&(a.block=Ie.gfm,this.options.breaks?a.inline=De.breaks:a.inline=De.gfm),this.tokenizer.rules=a}static get rules(){return{block:Ie,inline:De}}static lex(n,a){return new er(a).lex(n)}static lexInline(n,a){return new er(a).inlineTokens(n)}lex(n){n=n.replace(Z.carriageReturn,` +`),this.blockTokens(n,this.tokens);for(let a=0;a(c=h.call({lexer:this},n,a))?(n=n.substring(c.raw.length),a.push(c),!0):!1))continue;if(c=this.tokenizer.space(n)){n=n.substring(c.raw.length);let h=a.at(-1);c.raw.length===1&&h!==void 0?h.raw+=` +`:a.push(c);continue}if(c=this.tokenizer.code(n)){n=n.substring(c.raw.length);let h=a.at(-1);h?.type==="paragraph"||h?.type==="text"?(h.raw+=(h.raw.endsWith(` +`)?"":` +`)+c.raw,h.text+=` +`+c.text,this.inlineQueue.at(-1).src=h.text):a.push(c);continue}if(c=this.tokenizer.fences(n)){n=n.substring(c.raw.length),a.push(c);continue}if(c=this.tokenizer.heading(n)){n=n.substring(c.raw.length),a.push(c);continue}if(c=this.tokenizer.hr(n)){n=n.substring(c.raw.length),a.push(c);continue}if(c=this.tokenizer.blockquote(n)){n=n.substring(c.raw.length),a.push(c);continue}if(c=this.tokenizer.list(n)){n=n.substring(c.raw.length),a.push(c);continue}if(c=this.tokenizer.html(n)){n=n.substring(c.raw.length),a.push(c);continue}if(c=this.tokenizer.def(n)){n=n.substring(c.raw.length);let h=a.at(-1);h?.type==="paragraph"||h?.type==="text"?(h.raw+=(h.raw.endsWith(` +`)?"":` +`)+c.raw,h.text+=` +`+c.raw,this.inlineQueue.at(-1).src=h.text):this.tokens.links[c.tag]||(this.tokens.links[c.tag]={href:c.href,title:c.title},a.push(c));continue}if(c=this.tokenizer.table(n)){n=n.substring(c.raw.length),a.push(c);continue}if(c=this.tokenizer.lheading(n)){n=n.substring(c.raw.length),a.push(c);continue}let f=n;if(this.options.extensions?.startBlock){let h=1/0,E=n.slice(1),m;this.options.extensions.startBlock.forEach(F=>{m=F.call({lexer:this},E),typeof m=="number"&&m>=0&&(h=Math.min(h,m))}),h<1/0&&h>=0&&(f=n.substring(0,h+1))}if(this.state.top&&(c=this.tokenizer.paragraph(f))){let h=a.at(-1);o&&h?.type==="paragraph"?(h.raw+=(h.raw.endsWith(` +`)?"":` +`)+c.raw,h.text+=` +`+c.text,this.inlineQueue.pop(),this.inlineQueue.at(-1).src=h.text):a.push(c),o=f.length!==n.length,n=n.substring(c.raw.length);continue}if(c=this.tokenizer.text(n)){n=n.substring(c.raw.length);let h=a.at(-1);h?.type==="text"?(h.raw+=(h.raw.endsWith(` +`)?"":` +`)+c.raw,h.text+=` +`+c.text,this.inlineQueue.pop(),this.inlineQueue.at(-1).src=h.text):a.push(c);continue}if(n){this.infiniteLoopError(n.charCodeAt(0));break}}return this.state.top=!0,a}inline(n,a=[]){return this.inlineQueue.push({src:n,tokens:a}),a}inlineTokens(n,a=[]){this.tokenizer.lexer=this;let o=n,s=null;if(this.tokens.links){let m=Object.keys(this.tokens.links);if(m.length>0)for(;(s=this.tokenizer.rules.inline.reflinkSearch.exec(o))!==null;)m.includes(s[0].slice(s[0].lastIndexOf("[")+1,-1))&&(o=o.slice(0,s.index)+"["+"a".repeat(s[0].length-2)+"]"+o.slice(this.tokenizer.rules.inline.reflinkSearch.lastIndex))}for(;(s=this.tokenizer.rules.inline.anyPunctuation.exec(o))!==null;)o=o.slice(0,s.index)+"++"+o.slice(this.tokenizer.rules.inline.anyPunctuation.lastIndex);let c;for(;(s=this.tokenizer.rules.inline.blockSkip.exec(o))!==null;)c=s[2]?s[2].length:0,o=o.slice(0,s.index+c)+"["+"a".repeat(s[0].length-c-2)+"]"+o.slice(this.tokenizer.rules.inline.blockSkip.lastIndex);o=this.options.hooks?.emStrongMask?.call({lexer:this},o)??o;let f=!1,h="",E=1/0;for(;n;){if(n.length(m=S.call({lexer:this},n,a))?(n=n.substring(m.raw.length),a.push(m),!0):!1))continue;if(m=this.tokenizer.escape(n)){n=n.substring(m.raw.length),a.push(m);continue}if(m=this.tokenizer.tag(n)){n=n.substring(m.raw.length),a.push(m);continue}if(m=this.tokenizer.link(n)){n=n.substring(m.raw.length),a.push(m);continue}if(m=this.tokenizer.reflink(n,this.tokens.links)){n=n.substring(m.raw.length);let S=a.at(-1);m.type==="text"&&S?.type==="text"?(S.raw+=m.raw,S.text+=m.text):a.push(m);continue}if(m=this.tokenizer.emStrong(n,o,h)){n=n.substring(m.raw.length),a.push(m);continue}if(m=this.tokenizer.codespan(n)){n=n.substring(m.raw.length),a.push(m);continue}if(m=this.tokenizer.br(n)){n=n.substring(m.raw.length),a.push(m);continue}if(m=this.tokenizer.del(n,o,h)){n=n.substring(m.raw.length),a.push(m);continue}if(m=this.tokenizer.autolink(n)){n=n.substring(m.raw.length),a.push(m);continue}if(!this.state.inLink&&(m=this.tokenizer.url(n))){n=n.substring(m.raw.length),a.push(m);continue}let F=n;if(this.options.extensions?.startInline){let S=1/0,V=n.slice(1),he;this.options.extensions.startInline.forEach(ee=>{he=ee.call({lexer:this},V),typeof he=="number"&&he>=0&&(S=Math.min(S,he))}),S<1/0&&S>=0&&(F=n.substring(0,S+1))}if(m=this.tokenizer.inlineText(F)){n=n.substring(m.raw.length),m.raw.slice(-1)!=="_"&&(h=m.raw.slice(-1)),f=!0;let S=a.at(-1);S?.type==="text"?(S.raw+=m.raw,S.text+=m.text):a.push(m);continue}if(n){this.infiniteLoopError(n.charCodeAt(0));break}}return a}infiniteLoopError(n){let a="Infinite loop on byte: "+n;if(this.options.silent)console.error(a);else throw new Error(a)}},Ue=class{constructor(i){oe(this,"options");oe(this,"parser");this.options=i||Y}space(i){return""}code({text:i,lang:n,escaped:a}){let o=(n||"").match(Z.notSpaceStart)?.[0],s=i.replace(Z.endingNewline,"")+` +`;return o?'
'+(a?s:Te(s,!0))+`
+`:"
"+(a?s:Te(s,!0))+`
+`}blockquote({tokens:i}){return`
+${this.parser.parse(i)}
+`}html({text:i}){return i}def(i){return""}heading({tokens:i,depth:n}){return`${this.parser.parseInline(i)} +`}hr(i){return`
+`}list(i){let n=i.ordered,a=i.start,o="";for(let f=0;f +`+o+" +`}listitem(i){return`
  • ${this.parser.parse(i.tokens)}
    • +`}checkbox({checked:i}){return" '}paragraph({tokens:i}){return`

    ${this.parser.parseInline(i)}

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u=t.createElementNS("http://www.w3.org/2000/svg","marker");u.setAttribute("id",c),u.setAttribute("viewBox","0 0 10 10"),u.setAttribute("refX",9),u.setAttribute("refY",5),u.setAttribute("markerUnits","strokeWidth"),u.setAttribute("markerWidth",8),u.setAttribute("markerHeight",6),u.setAttribute("orient","auto");let p=t.createElementNS("http://www.w3.org/2000/svg","path");return p.setAttribute("d","M 0 0 L 10 5 L 0 10 L 4 5 z"),p.style.setProperty("stroke-width",1),u.appendChild(p),u};r.addEventListener("pointerover",c=>{this._focused&&(this._focused.blur(),this._focused=null);let u=this._focusable.get(c.target);u&&u.focus&&(u.focus(),this._focused=u,c.stopPropagation())}),r.addEventListener("pointerleave",c=>{this._focused&&(this._focused.blur(),this._focused=null,c.stopPropagation())}),r.addEventListener("click",c=>{let u=this._focusable.get(c.target);u&&u.activate&&(u.activate(),c.stopPropagation())}),h.appendChild(l("arrowhead")),h.appendChild(l("arrowhead-select")),h.appendChild(l("arrowhead-hover"));for(let c of this.nodes.keys()){let u=this.node(c),p=u.label;this.children(c).length===0?p.build(t,o):(p.rectangle=t.createElementNS("http://www.w3.org/2000/svg","rect"),p.rx&&p.rectangle.setAttribute("rx",u.rx),p.ry&&p.rectangle.setAttribute("ry",u.ry),p.element=t.createElementNS("http://www.w3.org/2000/svg","g"),p.element.setAttribute("class","cluster"),p.element.appendChild(p.rectangle),i.appendChild(p.element))}this._focusable.clear(),this._focused=null;for(let c of this.edges.values())c.label.build(t,n,r,a),c.label.hitTest&&this._focusable.set(c.label.hitTest,c.label);let d=s("tunnel-edges");e.appendChild(i),e.appendChild(n),e.appendChild(r),e.appendChild(a),e.appendChild(o),e.appendChild(d),this._tunnelGroup=d,this._document=t;for(let c of this.edges.values())if(c.label.labelElement){let 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n=t.createElementNS("http://www.w3.org/2000/svg","rect");n.setAttribute("id","background"),n.setAttribute("fill","none"),n.setAttribute("pointer-events","all"),i.appendChild(n),e=t.createElementNS("http://www.w3.org/2000/svg","g"),e.setAttribute("id","origin"),i.appendChild(e)}for(let s of this._values.values())s.build();super.build(t,e)}async measure(){let t=this.host.document,e=this.host.window;if(t.fonts&&t.fonts.ready)try{await t.fonts.ready}catch{}await new Promise(s=>{e.requestAnimationFrame(()=>{e.requestAnimationFrame(()=>{e.requestAnimationFrame(s)})})}),await super.measure()}clearSelection(){if(this._selection.size>0){for(let t of this._selection)t.deselect();this._selection.clear()}for(let t of this._table.values())if(t instanceof Mt)for(let e of t.blocks)e.target&&e.target.clearSelection&&e.target.clearSelection()}select(t,e){if(t&&this.view.target&&this.view.target!==this?this.view.target.clearSelection():this.clearSelection(),t){let s=[];for(let i of t)if(this._table.has(i)){let n=this._table.get(i);s=s.concat(n.select()),this._selection.add(n)}return this.emit("selectionchange",e),s}return this.emit("selectionchange",e),null}activate(t,e){return this._table.has(t)?(this.select(null,e),this._table.get(t).activate(e),this.select([t],e)):[]}focus(t){for(let e of t){let s=this._table.get(e);s&&!this._selection.has(s)&&s.select()}}blur(t){for(let e of t){let s=this._table.get(e);s&&!this._selection.has(s)&&s.deselect()}}restore(t){let e=this.host.document,s=e.getElementById("canvas"),i=e.getElementById("origin"),n=e.getElementById("background"),r=Array.from(s.getElementsByClassName("graph-input")||[]);if(r.length===0){let g=Array.from(s.getElementsByClassName("graph-node")||[]);g.length>0&&r.push(g[0])}let a=s.getBBox(),o=100,h=Math.ceil(o+a.width+o),l=Math.ceil(o+a.height+o);i.setAttribute("transform",`translate(${o-a.x}, ${o-a.y}) scale(1)`),n.setAttribute("width",h),n.setAttribute("height",l),this._width=h,this._height=l,delete this._scrollLeft,delete this._scrollRight,s.setAttribute("viewBox",`0 0 ${h} ${l}`),s.setAttribute("width",h),s.setAttribute("height",l),this._zoom=t?t.zoom:1,this._updateZoom(this._zoom);let d=e.getElementById("target"),c=new this.host.window.URLSearchParams(this.host.window.__netronSearch||this.host.window.location.search),u=t||c.get("initialCenter")!=="0",p=t?this.select([t.context]):[];if(p.length>0)this.scrollTo(p,"instant");else if(u&&r&&r.length>0){let g=d.getBoundingClientRect(),x=[],V=[];for(let w=0;ww===V[0])&&(L=x.reduce((w,st)=>w+st,0)/x.length);let y=d.scrollLeft+L-g.left-g.width/2,k=d.scrollTop+Y-g.top-g.height/2;d.scrollTo({left:y,top:k,behavior:"auto"})}else if(u){let g=s.getBoundingClientRect(),x=d.getBoundingClientRect(),V=d.scrollLeft+g.width/2-x.left-x.width/2,L=d.scrollTop+g.height/2-x.top-x.height/2;d.scrollTo({left:V,top:L,behavior:"auto"})}}register(){if(!this._events){this._events={},this._events.scroll=i=>this._scrollHandler(i),this._events.wheel=i=>this._wheelHandler(i),this._events.gesturestart=i=>this._gestureStartHandler(i),this._events.pointerdown=i=>this._pointerDownHandler(i),this._events.touchstart=i=>this._touchStartHandler(i);let e=this.host.document.getElementById("target");if(new this.host.window.URLSearchParams(this.host.window.__netronSearch||this.host.window.location.search).get("initialCenter")!=="0")try{e.focus({preventScroll:!0})}catch{}e.addEventListener("scroll",this._events.scroll),e.addEventListener("wheel",this._events.wheel,{passive:!1}),e.addEventListener("pointerdown",this._events.pointerdown),this.host.environment("agent")==="safari"?e.addEventListener("gesturestart",this._events.gesturestart,!1):e.addEventListener("touchstart",this._events.touchstart,{passive:!0})}}unregister(){if(this._events){let e=this.host.document.getElementById("target");e.removeEventListener("scroll",this._events.scroll),e.removeEventListener("wheel",this._events.wheel),e.removeEventListener("pointerdown",this._events.pointerdown),e.removeEventListener("gesturestart",this._events.gesturestart),e.removeEventListener("touchstart",this._events.touchstart),delete this._events}}get zoom(){return this._zoom}set zoom(t){this._updateZoom(t)}_updateZoom(t,e){let s=this.host.document,i=s.getElementById("target"),n=s.getElementById("canvas"),r=this.view.options.direction==="vertical"?i.clientHeight/this._height:i.clientWidth/this._width,a=Math.min(Math.max(r,.15),1);t=Math.max(a,Math.min(t,1.4));let o=this._scrollLeft||i.scrollLeft,h=this._scrollTop||i.scrollTop,l=(e?e.pageX:i.clientWidth/2)+o,d=(e?e.pageY:i.clientHeight/2)+h,c=t*this._width,u=t*this._height;n.style.width=`${c}px`,n.style.height=`${u}px`,this._scrollLeft=Math.max(0,l*t/this._zoom-(l-o)),this._scrollTop=Math.max(0,d*t/this._zoom-(d-h)),i.scrollLeft=this._scrollLeft,i.scrollTop=this._scrollTop,this._zoom=t}_pointerDownHandler(t){if(t.pointerType==="touch"||t.buttons!==1||t.rangeParent===null)return;let e=this.host.document,s=e.getElementById("target");t.target.setPointerCapture(t.pointerId),this._mousePosition={left:s.scrollLeft,top:s.scrollTop,x:t.clientX,y:t.clientY},t.target.style.cursor="grabbing",t.preventDefault(),t.stopImmediatePropagation();let i=a=>{if(a.preventDefault(),a.stopImmediatePropagation(),this._mousePosition){let o=a.clientX-this._mousePosition.x,h=a.clientY-this._mousePosition.y;if(this._mousePosition.moved=o*o+h*h>0,this._mousePosition.moved){let d=this.host.document.getElementById("target");d.scrollTop=this._mousePosition.top-h,d.scrollLeft=this._mousePosition.left-o}}},n=a=>{a.stopPropagation(),e.removeEventListener("click",n,!0)},r=a=>{a.target.releasePointerCapture(a.pointerId),a.target.style.removeProperty("cursor"),s.removeEventListener("pointerup",r),s.removeEventListener("pointermove",i),this._mousePosition&&this._mousePosition.moved&&(a.preventDefault(),a.stopImmediatePropagation(),delete this._mousePosition,e.addEventListener("click",n,!0))};s.addEventListener("pointermove",i),s.addEventListener("pointerup",r)}_touchStartHandler(t){t.touches.length===2&&(this._touchPoints=Array.from(t.touches),this._touchZoom=this._zoom);let e=r=>{if(Array.isArray(this._touchPoints)&&this._touchPoints.length===2&&r.touches.length===2){let a=l=>{let d=l[1].clientX-l[0].clientX,c=l[1].clientY-l[0].clientY;return Math.sqrt(d*d+c*c)},o=a(Array.from(r.touches)),h=a(this._touchPoints);if(h!==0){let l=this._touchPoints,d={pageX:(l[1].pageX+l[0].pageX)/2,pageY:(l[1].pageY+l[0].pageY)/2},c=h===0?o:o/h;this._updateZoom(this._touchZoom*c,d)}}},i=this.host.document.getElementById("target"),n=()=>{i.removeEventListener("touchmove",e,{passive:!0}),i.removeEventListener("touchcancel",n,{passive:!0}),i.removeEventListener("touchend",n,{passive:!0}),delete this._touchPoints,delete this._touchZoom};i.addEventListener("touchmove",e,{passive:!0}),i.addEventListener("touchcancel",n,{passive:!0}),i.addEventListener("touchend",n,{passive:!0})}_gestureStartHandler(t){t.preventDefault(),this._gestureZoom=this._zoom;let s=this.host.document.getElementById("target"),i=r=>{r.preventDefault(),this._updateZoom(this._gestureZoom*r.scale,r)},n=r=>{s.removeEventListener("gesturechange",i,!1),s.removeEventListener("gestureend",n,!1),r.preventDefault(),this._gestureZoom&&(this._updateZoom(this._gestureZoom*r.scale,r),delete this._gestureZoom)};s.addEventListener("gesturechange",i,!1),s.addEventListener("gestureend",n,!1)}_scrollHandler(t){this._scrollLeft&&t.target.scrollLeft!==Math.floor(this._scrollLeft)&&delete this._scrollLeft,this._scrollTop&&t.target.scrollTop!==Math.floor(this._scrollTop)&&delete this._scrollTop}_wheelHandler(t){if(t.shiftKey||t.ctrlKey||this.view.options.mousewheel==="zoom"){let e=1;t.deltaMode===1?e=.05:t.deltaMode?e=1:e=.002;let s=-t.deltaY*e*(t.ctrlKey?10:1);this._updateZoom(this._zoom*Math.pow(2,s),t),t.preventDefault()}}scrollTo(t,e){if(t&&t.length>0){let i=this.host.document.getElementById("target"),n=i.getBoundingClientRect(),r=i.clientWidth,a=i.clientHeight,o={};o.left=n.x+r/2-r*.45,o.width=r*.9,o.right=o.left+o.width,o.top=n.y+a/2-a*.45,o.height=a*.9,o.bottom=o.top+o.height;let h=0,l=0,d=Number.POSITIVE_INFINITY,c=Number.NEGATIVE_INFINITY,u=Number.POSITIVE_INFINITY,p=Number.NEGATIVE_INFINITY;for(let y of t){let k=y.getBoundingClientRect(),w=Math.min(k.width,o.width),st=Math.min(k.height,o.height);h+=k.left+w/2,l+=k.top+st/2,d=Math.min(d,k.left),c=Math.max(c,k.right),u=Math.min(u,k.top),p=Math.max(p,k.bottom)}if(c<=o.right&&d>=o.left&&p<=o.bottom&&u>=o.top)return;if(p-u>=o.height||c-d>=o.width||cn.right||pn.bottom){h/=t.length,l/=t.length;let y={};y.left=i.scrollLeft+h-o.left-o.width/2,y.top=i.scrollTop+l-o.top-o.height/2,y.behavior=e||"smooth",i.scrollTo(y);return}let g={};g.left=0,g.top=0,g.behavior=e||"smooth";let x=o.right-c,V=d-o.left,L=o.bottom-p,Y=u-o.top;c-dr&&r.constructor&&r.constructor.name==="Value"&&typeof r.name=="string"&&r.name!==""&&!r.initializer))for(let r of n.value)r&&r.constructor&&r.constructor.name==="Value"&&typeof r.name=="string"&&r.name!==""&&!r.initializer&&t.createValue(r).to.push(this)}}get class(){return"graph-node"}get inputs(){return this.value.inputs}get outputs(){return this.value.outputs}static metadataValue(t,e){let i=(t&&Array.isArray(t.metadata)?t.metadata:[]).find(n=>n&&n.name===e);return i&&i.value!==void 0&&i.value!==null?String(i.value):""}static nodeColor(t){let e=_.Node.normalizeColor(_.Node.metadataValue(t,"tensor_rt.inspect.node_fill"));return e?{fill:e,textFill:"#ffffff"}:null}static normalizeColor(t){if(typeof t!="string")return"";let e=t.trim().match(/^#([0-9a-f]{3}|[0-9a-f]{6})$/i);if(!e)return"";let s=e[1].toLowerCase();return s.length===3?`#${s.split("").map(i=>i+i).join("")}`:`#${s}`}_add(t,e){let s=e==="graph"||e==="function"?{type:t}:t,i=this.context.options,n=this.header(),r=s.type&&s.type.category?s.type.category:"",a=_.Node.nodeColor(s);if(s.type&&typeof s.type.name!="string"||!s.type.name.split){let y=new _.Error(`Unsupported node type '${JSON.stringify(s.type.name)}'.`);throw this.context.model&&this.context.model.identifier&&(y.context=this.context.model.identifier),y}let o=i.names&&(s.name||s.identifier)?s.name||s.identifier:s.type.name.split(".").pop(),h=i.names&&(s.name||s.identifier)?`[${s.type.name}]`:s.name||s.identifier;if(o.length>21){h=i.names?`${o}`:`[${o}]`;let y=o.substring(0,10),k=o.substring(o.length-10,o.length);o=`${y}\u2026${k}`}let l=r?["node-item-type",`node-item-type-${r.toLowerCase()}`]:["node-item-type"];a&&l.push("node-item-type-colored");let d=y=>(a&&(y.fill=a.fill,y.textFill=a.textFill),y),c=d(n.add(null,l));if(c.content=o,c.tooltip=h,c.on("click",()=>{this.context.activate(t,"target")}),e==="graph"){this.definition=d(n.add(null,l)),this.definition.content="\u25CB",this.definition.tooltip="Show Graph",this.definition.padding=4,this.definition.on("click",async()=>await this.context.view.pushTarget(t,this.value));let y=this.context.blocks.has(t),k=y?"\u2212":"+",w=y?"Collapse Graph":"Expand Graph";this.expander=d(n.add(null,l)),this.expander.content=k,this.expander.tooltip=w,this.expander.padding=6,this.expander.on("click",()=>{let st=this.expander.element.getBoundingClientRect();this.context.blocks.has(t)?this.context.blocks.delete(t):this.context.blocks.add(t),this.context.view.refresh({value:this.value,rect:st})})}else if(s.type.type||Array.isArray(s.type.nodes)&&s.type.nodes.length>0){let y="\u0192",k="Show Function Definition";s.type.type==="weights"&&(y="\u25CF",k="Show 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k=y.type;if(y.visible!==!1&&(k==="graph"&&y.value||k==="object"&&L(y.value)||k==="object[]"&&Array.isArray(y.value)&&y.value.length>0||k==="function"||k==="function[]"&&Array.isArray(y.value)&&y.value.length>0))x.push(y);else if(i.weights&&y.visible!==!1&&y.type!=="attribute"&&Array.isArray(y.value)&&y.value.length===1&&y.value[0].initializer){let w=this.context.createArgument(y);p().add(w)}else if(i.weights&&(y.visible===!1||Array.isArray(y.value)&&y.value.length>1)&&(!y.type||y.type.endsWith("*"))&&y.value.some(w=>w!==null&&w.initializer))g=!0;else if(i.attributes&&y.visible!==!1&&y.type&&!y.type.endsWith("*")){let w=V(y);p().add(w)}}if(Array.isArray(s.attributes)){let y=s.attributes.slice();y.sort((k,w)=>k.name.toUpperCase().localeCompare(w.name.toUpperCase()));for(let k of y){let w=k.type;if(k.visible!==!1&&(w==="graph"&&k.value||w==="object"&&k.value||(w==="object[]"||w==="function"||w==="function[]")&&Array.isArray(k.value)&&k.value.length>0))x.push(k);else if(i.attributes&&k.visible!==!1){let st=V(k);p().add(st)}}}if(Array.isArray(s.blocks))for(let y of s.blocks){let k=y.type;y.visible!==!1&&(k==="graph"&&y.value||k==="object"&&L(y.value)||(k==="object[]"||k==="function"||k==="function[]")&&Array.isArray(y.value)&&y.value.length>0)&&x.push(y)}if(g){let y=p().argument("\u3008\u2026\u3009","");p().add(y)}for(let y of x){let k=y.type,w=null;if(k==="graph"&&this.context.blocks.has(y.value)){w=this.context.createGraph(y.value),w.blocks.push(new _.Block(this.context.view,y.value,this.context.blocks)),w.activate=()=>this.context.view.showTargetProperties(y.value);let st=p().argument(y.name,w);p().add(st)}else if(k==="graph"||k==="function"){w=this.context.createGraph(y.value,k),w.activate=()=>this.context.view.showTargetProperties(y.value);let st=p().argument(y.name,w);p().add(st)}else if(k==="graph[]"){w=y.value.map(dt=>this.context.createGraph(dt));let st=p().argument(y.name,w);p().add(st)}else{y.type==="object"?w=this.context.createNode(y.value):(k==="function[]"||y.type==="object[]")&&(w=y.value.map(dt=>this.context.createNode(dt)));let st=p().argument(y.name,w);p().add(st)}}if(Array.isArray(s.chain)&&s.chain.length>0)for(let y of s.chain)this.context.createNode(y),this._add(y);s.inner&&(this.context.createNode(s.inner),this._add(s.inner))}activate(t){this.context.view.showNodeProperties(this.value,t)}edge(t){return this._edges=this._edges||new Map,this._edges.has(t)||this._edges.set(t,new _.Edge(this,t)),this._edges.get(t)}};_.Block=class{constructor(t,e,s){this.target=new _.Graph(t,!1),s&&(this.target.blocks=s),this.target.add(e),this.x=0,this.y=0}build(t,e){this.element=t.createElementNS("http://www.w3.org/2000/svg","g"),this.element.setAttribute("class","node-block"),e.appendChild(this.element),this.first||(this.line=t.createElementNS("http://www.w3.org/2000/svg","line"),this.line.setAttribute("class","node"),e.appendChild(this.line)),this._background=t.createElementNS("http://www.w3.org/2000/svg","path"),this._background.setAttribute("class","node-block-background"),this.element.appendChild(this._background),this._origin=t.createElementNS("http://www.w3.org/2000/svg","g"),this.element.appendChild(this._origin);for(let s of this.target.values.values())s.build();this.target.build(t,this._origin)}async measure(){for(let e of this.target.edges.values())if(e.label.labelElement){let s=e.label.labelElement.getBBox();e.label.width=s.width,e.label.height=s.height}await this.target.measure(),await this.target.layout();let t=10;this._padding=t,this.width=(this.target.width||0)+2*t,this.height=(this.target.height||0)+2*t}async layout(){}update(){let t=this._padding-(this.target.originX||0),e=this._padding-(this.target.originY||0);this.element.setAttribute("transform",`translate(0,${this.y})`),this._origin.setAttribute("transform",`translate(${t},${e})`),this._background.setAttribute("d",Mt.roundedRect(0,0,this.width,this.height,!1,!1,this.last,this.last)),this.target.update(),this.line&&(this.line.setAttribute("x1",0),this.line.setAttribute("x2",this.width),this.line.setAttribute("y1",this.y),this.line.setAttribute("y2",this.y))}};_.Input=class extends Mt{constructor(t,e){super(),this.context=t,this.value=e,_.Input.counter=_.Input.counter||0;let s=e.value.map(a=>a.type||"").join(` +`),i=e.name||"";i.length>16&&(i=i.split("/").pop());let 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0&&(n.option=i.option),n})),Array.isArray(t.references)&&(s.references=t.references.map(i=>(i&&(s.description=e.html(i.description)),s))),t.version!==void 0&&(s.version=t.version),t.operator!==void 0&&(s.operator=t.operator),t.identifier!==void 0&&(s.identifier=t.identifier),t.package!==void 0&&(s.package=t.package),t.status!==void 0&&(s.status=t.status),t.min_input!==void 0&&(s.min_input=t.min_input),t.max_input!==void 0&&(s.max_input=t.max_input),t.min_output!==void 0&&(s.min_output=t.min_output),t.max_output!==void 0&&(s.max_output=t.max_output),t.inputs_range!==void 0&&(s.inputs_range=t.inputs_range),t.outputs_range!==void 0&&(s.outputs_range=t.outputs_range),t.examples!==void 0&&(s.examples=t.examples),t.constants!==void 0&&(s.constants=t.constants),t.type_constraints!==void 0&&(s.type_constraints=t.type_constraints),s}return null}};_.Formatter=class{constructor(t,e,s){this._value=t,this._type=e,this._quote=s,this._values=new Set}toString(){return 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"):"(null)";case"type":return t?t.toString():"(null)";case"type[]":return t?t.map(o=>o.toString()).join(", "):"(null)";case"complex":return t?t.toString():"(null)";default:break}if(typeof t=="string"&&(!e||e!=="string"))return s?`"${t}"`:(t.trim().length===0,t);if(Array.isArray(t)){if(t.length===0)return s?"[]":"";let o=!1;t.length>2e3&&(t=t.slice(0,2e3),o=!0);let h=e&&e.endsWith("[]")?e.substring(0,e.length-2):null,l=t.map(d=>{if(d&&typeof d=="bigint")return d.toString();if(Number.isNaN(d))return"NaN";if(d&&d.constructor&&d.constructor.name==="Value"&&d.name)return`{${d.name}}`;let c=!h||h==="string";return this._format(d,h,c)});return o&&l.push("\u2026"),s?["[",l.join(", "),"]"].join(" "):l.join(", ")}if(t===null)return s?"null":"";if(t===void 0)return"undefined";if(t!==Object(t))return t.toString();if(this._values.has(t))return"\u2026";this._values.add(t);let i=null,r=(t instanceof Map?Array.from(t):Object.entries(t)).filter(([o])=>typeof o=="string"&&!o.startsWith("__")&&!o.endsWith("__"));r.length===1?i=[this._format(r[0][1],null,!0)]:i=r.map(([o,h])=>`${o}: ${this._format(h,null,!0)}`);let a=t.__type__;if(!a&&t.constructor.name&&t.constructor.name!=="Object"&&(a=t.constructor.name),a)return a+(i.length===0?"()":["(",i.join(", "),")"].join(""));switch(i.length){case 0:return s?"()":"";case 1:return i[0];default:return s?["(",i.join(", "),")"].join(" "):i.join(", ")}}static tensor(t){let e=t.type;if(e&&e.shape&&e.shape.dimensions&&Array.isArray(e.shape.dimensions)){if(e.shape.dimensions.length===0&&(!t.peek||t.peek()===!0)){let i=new Xt(t),n=i.encoding;if((n==="<"||n===">"||n==="|")&&!i.empty&&i.type.dataType!=="?"){let r=i.toString();return r&&r.length>10&&(r=`${r.substring(0,10)}\u2026`),r}}return`\u3008${e.shape.dimensions.map(i=>i??"?").join("\xD7")}\u3009`}return"\u3008\u2026\u3009"}};Ot.Generator=class{static open(){return Ot.Generator.generator||(Ot.Generator.generator=new 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var(--line)}.summary-section:first-child{border-top:0}.filter-input{width:100%}.chart-panel{grid-template-columns:1fr;grid-template-areas:"heading" "summary" "pie" "legend"}.chart-wrap,.legend{justify-self:stretch}.chart-wrap{justify-self:center}.pie{width:min(240px,70vw)}.legend{max-height:180px}.summary-section,.summary-section:first-child{border:1px solid var(--line)}.details-panel .list-card+.list-card{border-left:0;border-top:1px solid var(--line)}.legend-item{grid-template-columns:14px minmax(0,1fr)}.legend-meta{grid-column:2}.workspace,.graph-panel{height:70vh;min-height:460px}} diff --git a/.agents/skills/trt-perf-analysis/schemas/analyze-data.schema.json b/.agents/skills/trt-perf-analysis/schemas/analyze-data.schema.json new file mode 100644 index 000000000..aca6c4357 --- /dev/null +++ b/.agents/skills/trt-perf-analysis/schemas/analyze-data.schema.json @@ -0,0 +1,832 @@ +{ + "$schema": "https://json-schema.org/draft/2020-12/schema", + "$id": "https://codex.local/trt-perf-analysis/analyze-data.schema.json", + "title": "TensorRT Performance Analysis Data", + "description": "Structured JSON emitted by scripts/analyze_trt_perf.py and consumed by the report app.", + "type": "object", + "required": [ + "schema_version", + "folder", + "validation", + "results" + ], + "additionalProperties": false, + "properties": { + "schema_version": { + "const": "1.0" + }, + "folder": { + "type": [ + "string", + "null" + ] + }, + "model_name": { + "type": [ + "string", + "null" + ] + }, + "model_name_confidence": { + "type": "string", + "enum": [ + "low", + "medium", + "high" + ] + }, + "model_name_evidence": { + "$ref": "#/$defs/string_array" + }, + "model_components": { + "$ref": "#/$defs/string_array" + }, + "validation": { + "$ref": "#/$defs/top_level_validation" + }, + "results": { + "type": "array", + "minItems": 1, + "items": { + "$ref": "#/$defs/result" + } + } + }, + "$defs": { + "top_level_validation": { + "type": "object", + "required": [ + "status", + "backend_count", + "passed_count", + "failed_count", + "warnings" + ], + "additionalProperties": false, + "properties": { + "status": { + "type": "string", + "enum": [ + "passed", + "partial", + "failed" + ] + }, + "backend_count": { + "type": "integer", + "minimum": 0 + }, + "passed_count": { + "type": "integer", + "minimum": 0 + }, + "failed_count": { + "type": "integer", + "minimum": 0 + }, + "warnings": { + "$ref": "#/$defs/string_array" + } + } + }, + "layer_profile_validation": { + "type": "object", + "required": [ + "status", + "analysis_mode", + "messages", + "source_paths", + "report_available" + ], + "additionalProperties": false, + "properties": { + "status": { + "const": "passed" + }, + "analysis_mode": { + "const": "layer_profile" + }, + "messages": { + "$ref": "#/$defs/validation_messages" + }, + "source_paths": { + "$ref": "#/$defs/source_paths" + }, + "report_available": { + "const": true + } + } + }, + "layer_only_validation": { + "type": "object", + "required": [ + "status", + "analysis_mode", + "messages", + "source_paths", + "report_available" + ], + "additionalProperties": false, + "properties": { + "status": { + "const": "passed" + }, + "analysis_mode": { + "const": "layer_only" + }, + "messages": { + "$ref": "#/$defs/validation_messages" + }, + "source_paths": { + "$ref": "#/$defs/source_paths" + }, + "report_available": { + "const": true + } + } + }, + "failed_validation": { + "type": "object", + "required": [ + "status", + "analysis_mode", + "messages", + "source_paths", + "report_available" + ], + "additionalProperties": false, + "properties": { + "status": { + "const": "failed" + }, + "analysis_mode": { + "const": "unusable" + }, + "messages": { + "$ref": "#/$defs/validation_messages" + }, + "source_paths": { + "$ref": "#/$defs/source_paths" + }, + "report_available": { + "const": false + } + } + }, + "source_paths": { + "type": "object", + "required": [ + "layer", + "profile" + ], + "additionalProperties": false, + "properties": { + "layer": { + "type": [ + "string", + "null" + ] + }, + "profile": { + "type": [ + "string", + "null" + ] + } + } + }, + "validation_messages": { + "type": "array", + "items": { + "$ref": "#/$defs/validation_message" + } + }, + "validation_message": { + "type": "object", + "required": [ + "level", + "message" + ], + "additionalProperties": false, + "properties": { + "level": { + "type": "string", + "enum": [ + "error", + "warning", + "info" + ] + }, + "message": { + "type": "string" + } + } + }, + "result": { + "oneOf": [ + { + "$ref": "#/$defs/layer_profile_result" + }, + { + "$ref": "#/$defs/layer_only_result" + }, + { + "$ref": "#/$defs/failure_result" + } + ] + }, + "layer_profile_result": { + "type": "object", + "required": [ + "label", + "validation", + "layers", + "profile", + "layer_count", + "profile_count", + "graph", + "count", + "count_source", + "timing", + "model", + "engines", + "records", + "type_breakdown", + "tag_breakdown", + "issues", + "positives" + ], + "additionalProperties": false, + "properties": { + "label": { + "type": "string" + }, + "validation": { + "$ref": "#/$defs/layer_profile_validation" + }, + "layers": { + "type": "array", + "items": { + "$ref": "#/$defs/layer" + } + }, + "profile": { + "type": "array", + "items": { + "$ref": "#/$defs/profile_entry" + } + }, + "layer_count": { + "type": "integer", + "minimum": 0 + }, + "profile_count": { + "type": "integer", + "minimum": 0 + }, + "graph": { + "$ref": "#/$defs/graph" + }, + "count": { + "type": [ + "integer", + "null" + ], + "minimum": 0 + }, + "count_source": { + "type": "string" + }, + "timing": { + "$ref": "#/$defs/timing" + }, + "model": { + "$ref": "#/$defs/model" + }, + "engines": { + "$ref": "#/$defs/engines" + }, + "records": { + "type": "array", + "items": { + "$ref": "#/$defs/record" + } + }, + "type_breakdown": { + "$ref": "#/$defs/breakdown" + }, + "tag_breakdown": { + "$ref": "#/$defs/breakdown" + }, + "issues": { + "type": "array", + "items": { + "$ref": "#/$defs/issue" + } + }, + "positives": { + "$ref": "#/$defs/string_array" + } + } + }, + "layer_only_result": { + "type": "object", + "required": [ + "label", + "validation", + "layers", + "layer_count", + "graph", + "model", + "engines" + ], + "additionalProperties": false, + "properties": { + "label": { + "type": "string" + }, + "validation": { + "$ref": "#/$defs/layer_only_validation" + }, + "layers": { + "type": "array", + "items": { + "$ref": "#/$defs/layer" + } + }, + "layer_count": { + "type": "integer", + "minimum": 0 + }, + "graph": { + "$ref": "#/$defs/graph" + }, + "model": { + "$ref": "#/$defs/model" + }, + "engines": { + "$ref": "#/$defs/engines" + } + } + }, + "failure_result": { + "type": "object", + "required": [ + "label", + "validation" + ], + "additionalProperties": false, + "properties": { + "label": { + "type": "string" + }, + "validation": { + "$ref": "#/$defs/failed_validation" + }, + "layer_count": { + "type": "integer", + "minimum": 0 + }, + "profile_count": { + "type": "integer", + "minimum": 0 + } + } + }, + "layer": { + "type": "object", + "required": [ + "Name", + "LayerType", + "Inputs", + "Outputs" + ], + "additionalProperties": true, + "properties": { + "Name": { + "type": "string" + }, + "LayerType": { + "type": "string" + }, + "Inputs": { + "type": "array", + "items": { + "$ref": "#/$defs/tensor" + } + }, + "Outputs": { + "type": "array", + "items": { + "$ref": "#/$defs/tensor" + } + }, + "Constants": { + "type": "array", + "items": { + "$ref": "#/$defs/tensor" + } + }, + "Metadata": { + "type": [ + "string", + "object", + "array", + "null" + ] + }, + "StreamId": { + "type": [ + "integer", + "string", + "null" + ] + }, + "TacticName": { + "type": [ + "string", + "null" + ] + } + } + }, + "tensor": { + "type": "object", + "required": [ + "Name" + ], + "additionalProperties": true, + "properties": { + "Name": { + "type": "string" + }, + "Datatype": { + "type": [ + "string", + "null" + ] + }, + "Dimensions": { + "$ref": "#/$defs/number_array" + }, + "Format": { + "type": [ + "string", + "null" + ] + }, + "StrideOrder": { + "$ref": "#/$defs/number_array" + }, + "Strides": { + "$ref": "#/$defs/number_array" + } + } + }, + "profile_entry": { + "type": "object", + "required": [ + "name", + "timeMs", + "averageMs", + "medianMs", + "percentage" + ], + "additionalProperties": true, + "properties": { + "name": { + "type": "string" + }, + "timeMs": { + "type": "number" + }, + "averageMs": { + "type": "number" + }, + "medianMs": { + "type": "number" + }, + "percentage": { + "type": "number" + } + } + }, + "record": { + "type": "object", + "required": [ + "name", + "layer_type", + "raw_layer_type", + "tactic", + "time_ms", + "avg_ms", + "median_ms", + "percentage", + "tags", + "layer" + ], + "additionalProperties": false, + "properties": { + "name": { + "type": "string" + }, + "layer_type": { + "type": "string" + }, + "raw_layer_type": { + "type": "string" + }, + "tactic": { + "type": [ + "string", + "null" + ] + }, + "time_ms": { + "type": "number" + }, + "avg_ms": { + "type": "number" + }, + "median_ms": { + "type": "number" + }, + "percentage": { + "type": "number" + }, + "tags": { + "$ref": "#/$defs/string_array" + }, + "layer": { + "$ref": "#/$defs/layer" + } + } + }, + "graph": { + "type": "object", + "required": [ + "edge_count", + "external_inputs", + "graph_outputs" + ], + "additionalProperties": false, + "properties": { + "edge_count": { + "type": "integer", + "minimum": 0 + }, + "external_inputs": { + "$ref": "#/$defs/string_array" + }, + "graph_outputs": { + "$ref": "#/$defs/string_array" + } + } + }, + "timing": { + "type": "object", + "required": [ + "average_inference_ms", + "total_time_ms", + "total_percentage" + ], + "additionalProperties": false, + "properties": { + "average_inference_ms": { + "type": "number" + }, + "total_time_ms": { + "type": "number" + }, + "total_percentage": { + "type": "number" + } + } + }, + "model": { + "type": "object", + "required": [ + "category", + "confidence", + "evidence", + "config_path", + "hidden_size", + "intermediate_size", + "num_layers", + "num_heads", + "max_position_embeddings", + "sequence_length", + "dynamic_dims", + "input_shapes" + ], + "additionalProperties": false, + "properties": { + "name": { + "type": [ + "string", + "null" + ] + }, + "name_confidence": { + "type": "string", + "enum": [ + "low", + "medium", + "high" + ] + }, + "name_evidence": { + "$ref": "#/$defs/string_array" + }, + "components": { + "$ref": "#/$defs/string_array" + }, + "category": { + "type": "string" + }, + "confidence": { + "type": "string", + "enum": [ + "low", + "medium", + "high" + ] + }, + "evidence": { + "$ref": "#/$defs/string_array" + }, + "config_path": { + "type": [ + "string", + "null" + ] + }, + "hidden_size": { + "type": [ + "integer", + "null" + ] + }, + "intermediate_size": { + "type": [ + "integer", + "null" + ] + }, + "num_layers": { + "type": [ + "integer", + "null" + ] + }, + "num_heads": { + "type": [ + "integer", + "null" + ] + }, + "max_position_embeddings": { + "type": [ + "integer", + "null" + ] + }, + "sequence_length": { + "type": [ + "integer", + "null" + ] + }, + "dynamic_dims": { + "type": "boolean" + }, + "input_shapes": { + "type": "object", + "additionalProperties": { + "$ref": "#/$defs/number_array" + } + } + } + }, + "engines": { + "type": "object", + "required": [ + "engine_files", + "subgraphs", + "streams", + "inferred_count", + "source" + ], + "additionalProperties": false, + "properties": { + "engine_files": { + "$ref": "#/$defs/string_array" + }, + "subgraphs": { + "type": "array", + "items": { + "type": [ + "integer", + "string" + ] + } + }, + "streams": { + "type": "array", + "items": { + "type": [ + "integer", + "string" + ] + } + }, + "inferred_count": { + "type": [ + "integer", + "null" + ], + "minimum": 0 + }, + "source": { + "type": "string" + } + } + }, + "breakdown": { + "type": "array", + "items": { + "$ref": "#/$defs/breakdown_item" + } + }, + "breakdown_item": { + "type": "object", + "required": [ + "name", + "count", + "time_ms", + "percentage" + ], + "additionalProperties": false, + "properties": { + "name": { + "type": "string" + }, + "count": { + "type": "integer", + "minimum": 0 + }, + "time_ms": { + "type": "number" + }, + "percentage": { + "type": "number" + } + } + }, + "issue": { + "type": "object", + "required": [ + "score", + "title", + "evidence", + "next_check", + "confidence" + ], + "additionalProperties": false, + "properties": { + "score": { + "type": "number" + }, + "title": { + "type": "string" + }, + "evidence": { + "type": "string" + }, + "next_check": { + "type": "string" + }, + "confidence": { + "type": "string", + "enum": [ + "low", + "medium", + "high" + ] + } + } + }, + "string_array": { + "type": "array", + "items": { + "type": "string" + } + }, + "number_array": { + "type": "array", + "items": { + "type": "number" + } + } + } +} diff --git a/.agents/skills/trt-perf-analysis/scripts/analyze_trt_perf.py b/.agents/skills/trt-perf-analysis/scripts/analyze_trt_perf.py new file mode 100644 index 000000000..c67c44868 --- /dev/null +++ b/.agents/skills/trt-perf-analysis/scripts/analyze_trt_perf.py @@ -0,0 +1,70 @@ +#!/usr/bin/env python3 +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +"""Validate and extract structured TensorRT layer/profile analysis data. + +The script intentionally uses only Python built-in modules so it can run in +minimal benchmark environments. +""" + +from __future__ import annotations + +import argparse +import sys +from typing import Optional, Sequence + +from trt_perf.data import DataError, extract_perf_data +from trt_perf.render_json import render_results_json + + +def parse_args(argv: Optional[Sequence[str]] = None) -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Validate and analyze TensorRT layer/profile JSON performance data." + ) + parser.add_argument( + "path", + nargs="?", + help="Folder directly containing one or more layers_*.json/profile_*.json backend pairs. Omit when using --data.", + ) + parser.add_argument( + "--data", + dest="data_specs", + action="append", + nargs="+", + metavar="PATH", + help="Explicit backend input. Use --data layers.json for layer-only analysis or --data layers.json profile.json for a layer/profile pair. Repeat for multiple backends.", + ) + parser.add_argument( + "--model-name", + help="Optional model name from the user prompt or caller context. It is cleaned before serialization.", + ) + parser.add_argument("--output", help="Optional path to write the generated analysis JSON.") + return parser.parse_args(argv) + + +def main(argv: Optional[Sequence[str]] = None) -> int: + args = parse_args(argv) + + try: + data = extract_perf_data(args.path, args.data_specs, args.model_name) + except DataError as exc: + sys.stderr.write(f"error: {exc}\n") + return 2 + + report = render_results_json(data) + + if args.output: + try: + with open(args.output, "w", encoding="utf-8") as handle: + handle.write(report) + except OSError as exc: + sys.stderr.write(f"error: unable to write {args.output}: {exc}\n") + return 2 + else: + sys.stdout.write(report) + + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/.agents/skills/trt-perf-analysis/scripts/package_report.py b/.agents/skills/trt-perf-analysis/scripts/package_report.py new file mode 100644 index 000000000..3c8eda2bb --- /dev/null +++ b/.agents/skills/trt-perf-analysis/scripts/package_report.py @@ -0,0 +1,319 @@ +#!/usr/bin/env python3 +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +"""Create a standalone TensorRT performance report folder. + +This script keeps report packaging deterministic and cross-platform: it runs +the analyzer, validates the generated JSON, then copies the browser report +template without relying on shell tools such as rsync. +""" + +from __future__ import annotations + +import argparse +import re +import shutil +import subprocess +import sys +import tempfile +from datetime import datetime +from pathlib import Path +from typing import Optional, Sequence + +from trt_perf.data import infer_model_name_metadata + + +def script_dir() -> Path: + return Path(__file__).resolve().parent + + +def skill_root() -> Path: + return script_dir().parent + + +def default_template_dir() -> Path: + return skill_root() / "report_template" + + +def parse_args(argv: Optional[Sequence[str]] = None) -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Generate and package a standalone TensorRT performance report." + ) + parser.add_argument( + "path", + nargs="?", + help="Folder directly containing layers_*.json and/or profile_*.json inputs. Pass one component/report folder per invocation. Omit when using --analyze-data.", + ) + parser.add_argument( + "--analyze-data", + help="Existing analyze-data.json to validate and package in place. The file must be named analyze-data.json.", + ) + parser.add_argument( + "--output-parent", + help="Directory where an auto-named report folder should be created. Defaults to the current working directory unless that is inside the skill.", + ) + parser.add_argument( + "--report-dir", + help="Exact report directory to create. For new analysis this directory must be empty or absent; useful for component-specific reports.", + ) + parser.add_argument( + "--model-name", + help="Model name from the user prompt or caller context. It is cleaned and stored in analyze-data.json.", + ) + parser.add_argument( + "--timestamp", + help="Timestamp for auto-generated report folder names, formatted as YYYYMMDD_HHMMSS. Defaults to local time.", + ) + parser.add_argument( + "--template-dir", + default=str(default_template_dir()), + help="Report template directory. Defaults to report_template in this skill.", + ) + parser.add_argument( + "--analyze-md", + help="AI-generated Markdown analysis to copy into the report folder as analyze.md after the final analysis is written.", + ) + return parser.parse_args(argv) + + +def input_source_paths(input_path: Path) -> list[str]: + paths: list[Path] = [] + for pattern in ("layer*.json", "profile*.json"): + paths.extend(input_path.glob(pattern)) + return [str(path) for path in sorted(paths)] + + +def inferred_analyzer_model_name(input_path: Path, explicit_name: Optional[str]) -> Optional[str]: + metadata = infer_model_name_metadata(str(input_path), explicit_name, input_source_paths(input_path)) + return metadata["name"] + + +def infer_report_model_name(input_path: Path, explicit_name: Optional[str]) -> str: + metadata = infer_model_name_metadata(str(input_path), explicit_name, input_source_paths(input_path)) + return metadata["name"] or "unknown_model" + + +def is_relative_to(path: Path, parent: Path) -> bool: + try: + path.relative_to(parent) + return True + except ValueError: + return False + + +def default_output_parent() -> Path: + cwd = Path.cwd().resolve() + root = skill_root().resolve() + if is_relative_to(cwd, root): + return Path(tempfile.gettempdir()).resolve() + return cwd + + +def parse_timestamp(value: Optional[str]) -> str: + if value is None: + return datetime.now().strftime("%Y%m%d_%H%M%S") + if not re.fullmatch(r"\d{8}_\d{6}", value): + raise ValueError("--timestamp must use YYYYMMDD_HHMMSS format") + return value + + +def has_files(path: Path) -> bool: + return path.exists() and any(path.iterdir()) + + +def has_input_files(path: Path) -> bool: + return any(path.glob("layers_*.json")) or any(path.glob("profile_*.json")) + + +def next_available_report_dir(output_parent: Path, model_name: str, timestamp: str) -> Path: + base_name = f"report_{model_name}_{timestamp}" + candidate = output_parent / base_name + if not has_files(candidate): + return candidate + + index = 2 + while True: + candidate = output_parent / f"{base_name}_{index}" + if not has_files(candidate): + return candidate + index += 1 + + +def resolve_new_report_dir(args: argparse.Namespace, input_path: Path) -> Path: + if args.report_dir: + report_dir = Path(args.report_dir).expanduser() + if has_files(report_dir): + raise ValueError(f"report directory is not empty: {report_dir}") + return report_dir + + output_parent = Path(args.output_parent).expanduser() if args.output_parent else default_output_parent() + timestamp = parse_timestamp(args.timestamp) + model_name = infer_report_model_name(input_path, args.model_name) + return next_available_report_dir(output_parent, model_name, timestamp) + + +def validate_template_dir(template_dir: Path) -> None: + required = [ + "index.html", + "app.js", + "styles.css", + "netron/grapher.css", + "netron/index.html", + "netron/netron.js", + "netron/worker.js", + ] + missing = [name for name in required if not (template_dir / name).is_file()] + if missing: + raise ValueError(f"report template is missing required file(s): {', '.join(missing)}") + + +def run_command(command: Sequence[str]) -> int: + process = subprocess.run(command, text=True, check=False) + return process.returncode + + +def run_analyzer(input_path: Path, output_path: Path, model_name: Optional[str]) -> int: + analyzer = script_dir() / "analyze_trt_perf.py" + command = [ + sys.executable, + str(analyzer), + str(input_path), + "--output", + str(output_path), + ] + if model_name: + command.extend(["--model-name", model_name]) + return run_command(command) + + +def run_validator(analyze_data_path: Path) -> int: + validator = script_dir() / "validate_analyze_data.py" + return run_command([sys.executable, str(validator), str(analyze_data_path)]) + + +def copy_template(template_dir: Path, report_dir: Path) -> None: + validate_template_dir(template_dir) + report_dir.mkdir(parents=True, exist_ok=True) + + for item in template_dir.iterdir(): + if item.name in {"analyze-data.json", "analyze.md"}: + continue + + destination = report_dir / item.name + if item.is_dir(): + shutil.copytree(item, destination, dirs_exist_ok=True) + else: + shutil.copy2(item, destination) + + +def copy_analyze_markdown(analyze_md: Optional[str], report_dir: Path) -> Optional[Path]: + if not analyze_md: + return None + + source = Path(analyze_md).expanduser() + if not source.is_file(): + raise ValueError(f"analyze markdown file not found: {source}") + + destination = report_dir / "analyze.md" + if source.resolve() == destination.resolve(): + return destination + + shutil.copy2(source, destination) + return destination + + +def package_existing_json(args: argparse.Namespace) -> int: + analyze_data_path = Path(args.analyze_data).expanduser() + if not analyze_data_path.is_file(): + sys.stderr.write(f"error: analyze-data file not found: {analyze_data_path}\n") + return 2 + if analyze_data_path.name != "analyze-data.json": + sys.stderr.write("error: --analyze-data must point to a file named analyze-data.json\n") + return 2 + + validate_result = run_validator(analyze_data_path) + if validate_result != 0: + return validate_result + + try: + copy_template(Path(args.template_dir).expanduser(), analyze_data_path.parent) + analyze_markdown_path = copy_analyze_markdown(args.analyze_md, analyze_data_path.parent) + except (OSError, ValueError) as exc: + sys.stderr.write(f"error: unable to package report template: {exc}\n") + return 2 + + print(f"report_dir: {analyze_data_path.parent}") + print(f"analyze_data: {analyze_data_path}") + if analyze_markdown_path: + print(f"analyze_md: {analyze_markdown_path}") + return 0 + + +def package_input_folder(args: argparse.Namespace, input_path: Path) -> int: + try: + report_dir = resolve_new_report_dir(args, input_path) + except ValueError as exc: + sys.stderr.write(f"error: {exc}\n") + return 2 + + report_dir.mkdir(parents=True, exist_ok=True) + analyze_data_path = report_dir / "analyze-data.json" + + model_name = inferred_analyzer_model_name(input_path, args.model_name) + analyze_result = run_analyzer(input_path, analyze_data_path, model_name) + if analyze_result != 0: + return analyze_result + + validate_result = run_validator(analyze_data_path) + if validate_result != 0: + return validate_result + + try: + copy_template(Path(args.template_dir).expanduser(), report_dir) + analyze_markdown_path = copy_analyze_markdown(args.analyze_md, report_dir) + except (OSError, ValueError) as exc: + sys.stderr.write(f"error: unable to package report template: {exc}\n") + return 2 + + print(f"report_dir: {report_dir}") + print(f"analyze_data: {analyze_data_path}") + if analyze_markdown_path: + print(f"analyze_md: {analyze_markdown_path}") + return 0 + + +def package_new_analysis(args: argparse.Namespace) -> int: + if not args.path: + sys.stderr.write("error: provide an input folder, or use --analyze-data\n") + return 2 + + input_path = Path(args.path).expanduser() + if not input_path.is_dir(): + sys.stderr.write(f"error: input folder not found: {input_path}\n") + return 2 + + if has_input_files(input_path): + return package_input_folder(args, input_path) + + sys.stderr.write( + f"error: no layers_*.json or profile_*.json files found directly in {input_path}. " + "package_report.py creates exactly one report per invocation; pass a single " + "component/report folder instead of a parent folder.\n" + ) + return 2 + + +def main(argv: Optional[Sequence[str]] = None) -> int: + args = parse_args(argv) + + if args.path and args.analyze_data: + sys.stderr.write("error: use either an input folder or --analyze-data, not both\n") + return 2 + + if args.analyze_data: + return package_existing_json(args) + return package_new_analysis(args) + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/.agents/skills/trt-perf-analysis/scripts/run.cmd b/.agents/skills/trt-perf-analysis/scripts/run.cmd new file mode 100644 index 000000000..729e01287 --- /dev/null +++ b/.agents/skills/trt-perf-analysis/scripts/run.cmd @@ -0,0 +1,76 @@ +@echo off +:: SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +:: SPDX-License-Identifier: Apache-2.0 +setlocal + +if "%~1"=="" ( + echo usage: run.cmd ^ [args...] 1>&2 + exit /b 2 +) + +set "PY_CMD=" +set "PY_ARG=" + +if not defined SKILL_PYTHON goto after_skill_python +call :select_plain "%SKILL_PYTHON%" +if defined PY_CMD goto run_python +:after_skill_python + +if not defined PYTHON goto after_python_env +call :select_plain "%PYTHON%" +if defined PY_CMD goto run_python +:after_python_env + +if not defined PYTHON3 goto after_python3_env +call :select_plain "%PYTHON3%" +if defined PY_CMD goto run_python +:after_python3_env + +call :select_with_arg py -3 +if defined PY_CMD goto run_python +call :select_plain python +if defined PY_CMD goto run_python +call :select_plain python3 +if defined PY_CMD goto run_python +call :select_plain python3.12 +if defined PY_CMD goto run_python +call :select_plain python3.11 +if defined PY_CMD goto run_python +call :select_plain python3.10 +if defined PY_CMD goto run_python +call :select_plain python3.9 +if defined PY_CMD goto run_python +call :select_plain python3.8 +if defined PY_CMD goto run_python + +echo error: unable to find Python 3.8+ on PATH. Set SKILL_PYTHON to a Python executable. 1>&2 +exit /b 127 + +:run_python +if "%PY_ARG%"=="" ( + "%PY_CMD%" %* +) else ( + "%PY_CMD%" "%PY_ARG%" %* +) +exit /b %ERRORLEVEL% + +:select_plain +set "PY_CMD=%~1" +set "PY_ARG=" +if "%PY_CMD%"=="" goto clear_selection +"%PY_CMD%" -c "import sys; raise SystemExit(0 if sys.version_info >= (3, 8) else 1)" >nul 2>nul +if errorlevel 1 goto clear_selection +exit /b 0 + +:select_with_arg +set "PY_CMD=%~1" +set "PY_ARG=%~2" +if "%PY_CMD%"=="" goto clear_selection +"%PY_CMD%" "%PY_ARG%" -c "import sys; raise SystemExit(0 if sys.version_info >= (3, 8) else 1)" >nul 2>nul +if errorlevel 1 goto clear_selection +exit /b 0 + +:clear_selection +set "PY_CMD=" +set "PY_ARG=" +exit /b 1 diff --git a/.agents/skills/trt-perf-analysis/scripts/run.sh b/.agents/skills/trt-perf-analysis/scripts/run.sh new file mode 100755 index 000000000..ea5fa017a --- /dev/null +++ b/.agents/skills/trt-perf-analysis/scripts/run.sh @@ -0,0 +1,56 @@ +#!/usr/bin/env sh +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 + +set -u + +if [ $# -lt 1 ]; then + printf '%s\n' "usage: run.sh [args...]" >&2 + exit 2 +fi + +SCRIPT_PATH=$1 +shift + +try_python() { + candidate=$1 + launcher_arg=$2 + shift 2 + + if [ -z "$candidate" ]; then + return 1 + fi + if ! command -v "$candidate" >/dev/null 2>&1; then + return 1 + fi + + if [ -n "$launcher_arg" ]; then + "$candidate" "$launcher_arg" -c 'import sys; raise SystemExit(0 if sys.version_info >= (3, 8) else 1)' >/dev/null 2>&1 || return 1 + exec "$candidate" "$launcher_arg" "$SCRIPT_PATH" "$@" + fi + + "$candidate" -c 'import sys; raise SystemExit(0 if sys.version_info >= (3, 8) else 1)' >/dev/null 2>&1 || return 1 + exec "$candidate" "$SCRIPT_PATH" "$@" +} + +if [ -n "${SKILL_PYTHON:-}" ]; then + try_python "$SKILL_PYTHON" "" "$@" +fi +if [ -n "${PYTHON:-}" ]; then + try_python "$PYTHON" "" "$@" +fi +if [ -n "${PYTHON3:-}" ]; then + try_python "$PYTHON3" "" "$@" +fi + +try_python python3 "" "$@" +try_python python "" "$@" +try_python py -3 "$@" +try_python python3.12 "" "$@" +try_python python3.11 "" "$@" +try_python python3.10 "" "$@" +try_python python3.9 "" "$@" +try_python python3.8 "" "$@" + +printf '%s\n' "error: unable to find Python 3.8+ on PATH. Set SKILL_PYTHON to a Python executable." >&2 +exit 127 diff --git a/.agents/skills/trt-perf-analysis/scripts/trt_perf/__init__.py b/.agents/skills/trt-perf-analysis/scripts/trt_perf/__init__.py new file mode 100644 index 000000000..105934a03 --- /dev/null +++ b/.agents/skills/trt-perf-analysis/scripts/trt_perf/__init__.py @@ -0,0 +1,4 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +"""Implementation modules for the TensorRT performance analysis scripts.""" + diff --git a/.agents/skills/trt-perf-analysis/scripts/trt_perf/data.py b/.agents/skills/trt-perf-analysis/scripts/trt_perf/data.py new file mode 100644 index 000000000..e0c004511 --- /dev/null +++ b/.agents/skills/trt-perf-analysis/scripts/trt_perf/data.py @@ -0,0 +1,1429 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +"""Extract and analyze TensorRT layer/profile performance data.""" + +from __future__ import annotations + +import collections +import json +import math +import os +import re +import unicodedata +from typing import Any, Dict, Iterable, List, NamedTuple, Optional, Sequence, Tuple + + +Number = (int, float) +DYNAMIC_TOKENS = ( + "shape", + "cast_hvar", + "iota", + "slic", + "conc", + "eql", + "gtr", + "orand", + "repl", + "sele", +) +MISC_LAYER_TYPES = { + "reshape", + "shape_call", + "signal", + "wait", + "signal_and_wait", + "signal_wait", +} +TENSOR_CORE_TOKENS = ("xmma", "tensor", "tc", "cublaslt", "mma") +MHA_TOKENS = ( + "kgen_mha", + "fmha", + "multi_head_attention", + "multiheadattention", + "multi-head attention", + "multi head attention", + "fused_multihead_attention", + "masked_multihead_attention", +) +MODEL_NAME_STOPWORDS = { + "analysis", + "analyze", + "aot", + "backend", + "backends", + "benchmark", + "benchmarks", + "cache", + "cached", + "case", + "checkpoint", + "checkpoints", + "ckpt", + "component", + "components", + "data", + "dataset", + "datasets", + "decode", + "decoder", + "default", + "dev", + "development", + "download", + "downloads", + "encode", + "encoder", + "engine", + "engines", + "example", + "examples", + "export", + "exports", + "full", + "input", + "inputs", + "json", + "layer", + "layers", + "model", + "models", + "no", + "none", + "onnx", + "only", + "output", + "outputs", + "perf", + "performance", + "plan", + "plans", + "profile", + "profiles", + "report", + "reports", + "result", + "results", + "run", + "runs", + "sample", + "samples", + "temp", + "tensorrt", + "test", + "tests", + "tmp", + "torch", + "trt", +} +CONFIG_MODEL_NAME_KEYS = ( + "_name_or_path", + "name_or_path", + "model_name", + "model_id", + "model", + "pretrained_model_name_or_path", + "base_model_name_or_path", +) +MODEL_NAME_CONFIG_FILENAMES = ( + "config.json", + "tokenizer_config.json", + "preprocessor_config.json", + "generation_config.json", +) +MODEL_NAME_CONFIDENCE_RANK = {"low": 0, "medium": 1, "high": 2} +SHORT_MODEL_NAME_TOKENS = {"t5", "gpt", "vit"} +MODEL_NAME_PARENT_HINT_DIRS = {"encode", "encoder", "decode", "decoder"} +MODEL_COMPONENT_STOPWORDS = (MODEL_NAME_STOPWORDS - {"encode", "encoder", "decode", "decoder"}) | { + "component", + "components", +} +MODEL_COMPONENT_ALIASES = { + "decode": "decoder", + "encode": "encoder", +} +MODEL_COMPONENT_HINT_TOKENS = { + "controlnet", + "decoder", + "denoiser", + "encoder", + "transformer", + "unet", + "vae", +} + + +class ModelNameGuess(NamedTuple): + name: str + confidence: str + evidence: str + + +class DataError(Exception): + pass + + +def is_number(value: Any) -> bool: + return isinstance(value, Number) and not isinstance(value, bool) and math.isfinite(float(value)) + + +def short_name(value: str, width: int = 96) -> str: + if len(value) <= width: + return value + left = max(12, width // 2 - 3) + right = max(12, width - left - 3) + return value[:left] + "..." + value[-right:] + + +def fmt_pct(value: Optional[float]) -> str: + if value is None: + return "unknown" + return f"{value:.1f}%" + + +def fmt_ms(value: Optional[float]) -> str: + if value is None: + return "unknown" + if value < 0.01: + return f"{value:.4f} ms" + return f"{value:.3f} ms" + + +def add_limited(messages: List[str], message: str, limit: int = 40) -> None: + if len(messages) < limit: + messages.append(message) + elif len(messages) == limit: + messages.append("Further validation messages suppressed.") + + +def load_json(path: str) -> Any: + try: + with open(path, "r", encoding="utf-8") as handle: + return json.load(handle) + except FileNotFoundError as exc: + raise DataError(f"File not found: {path}") from exc + except json.JSONDecodeError as exc: + raise DataError(f"Invalid JSON in {path}: line {exc.lineno}, column {exc.colno}: {exc.msg}") from exc + except OSError as exc: + raise DataError(f"Unable to read {path}: {exc}") from exc + + +def suffix_for(path: str, prefix: str) -> str: + base = os.path.basename(path) + match = re.match(rf"^{prefix}s?[_-]?(.*)\.json$", base, flags=re.IGNORECASE) + if match: + return match.group(1) or "default" + return os.path.splitext(base)[0] + + +def discover_backends(folder: str) -> List[Tuple[str, Optional[str], Optional[str]]]: + layers: Dict[str, str] = {} + profiles: Dict[str, str] = {} + + try: + names = sorted(os.listdir(folder)) + except OSError as exc: + raise DataError(f"Unable to list folder {folder}: {exc}") from exc + + for name in names: + if not name.lower().endswith(".json"): + continue + layer_match = re.match(r"^layers?[_-]?(.*)\.json$", name, flags=re.IGNORECASE) + profile_match = re.match(r"^profiles?[_-]?(.*)\.json$", name, flags=re.IGNORECASE) + if layer_match: + layers[layer_match.group(1) or "default"] = os.path.join(folder, name) + elif profile_match: + profiles[profile_match.group(1) or "default"] = os.path.join(folder, name) + + backends = [ + (key, layers.get(key), profiles.get(key)) + for key in sorted(set(layers) | set(profiles)) + ] + if not backends: + raise DataError(f"No layers_*.json or profile_*.json files found in {folder}.") + return backends + + +def explicit_backends(data_specs: Sequence[Sequence[str]]) -> List[Tuple[str, Optional[str], Optional[str]]]: + backends: List[Tuple[str, Optional[str], Optional[str]]] = [] + for spec in data_specs: + if len(spec) not in (1, 2): + raise DataError("--data expects one layer-info path and optional one profile JSON path") + layer_spec = spec[0] + profile_spec = spec[1] if len(spec) == 2 else None + if not layer_spec: + raise DataError("--data must start with a layer-info JSON path") + if profile_spec == "": + raise DataError("--data profile JSON path must be non-empty") + + layer_path = os.path.abspath(layer_spec) + profile_path = os.path.abspath(profile_spec) if profile_spec is not None else None + backends.append((suffix_for(layer_path, "layer"), layer_path, profile_path)) + return backends + + +def common_source_folder(source_paths: Sequence[str]) -> Optional[str]: + if not source_paths: + return None + folders = [os.path.dirname(path) for path in source_paths] + try: + return os.path.commonpath(folders) + except ValueError: + return folders[0] + + +def validate_tensor( + tensor: Any, + layer_name: str, + field_name: str, + index: int, + errors: List[str], +) -> None: + if not isinstance(tensor, dict): + add_limited(errors, f"Layer `{layer_name}` {field_name}[{index}] is not an object.") + return + name = tensor.get("Name") + if not isinstance(name, str): + add_limited(errors, f"Layer `{layer_name}` {field_name}[{index}] is missing string `Name`.") + dims = tensor.get("Dimensions") + if not isinstance(dims, list) or not all(is_number(dim) for dim in dims): + add_limited(errors, f"Layer `{layer_name}` tensor `{name}` has invalid `Dimensions`.") + dtype = tensor.get("Format/Datatype") + if dtype is not None and not isinstance(dtype, str): + add_limited(errors, f"Layer `{layer_name}` tensor `{name}` has non-string `Format/Datatype`.") + + +def validate_layers(data: Any, errors: List[str]) -> List[Dict[str, Any]]: + if not isinstance(data, list): + add_limited(errors, "Layer-info JSON root must be a list.") + return [] + if not data: + add_limited(errors, "Layer-info JSON must contain at least one layer.") + return [] + + names: collections.Counter[str] = collections.Counter() + valid_layers: List[Dict[str, Any]] = [] + + for idx, layer in enumerate(data): + if not isinstance(layer, dict): + add_limited(errors, f"Layer record {idx} is not an object.") + continue + name = layer.get("Name") + if not isinstance(name, str) or not name: + add_limited(errors, f"Layer record {idx} is missing non-empty string `Name`.") + name = f"" + names[name] += 1 + if not isinstance(layer.get("LayerType"), str): + add_limited(errors, f"Layer `{name}` is missing string `LayerType`.") + for field_name in ("Inputs", "Outputs"): + tensors = layer.get(field_name) + if not isinstance(tensors, list): + add_limited(errors, f"Layer `{name}` is missing list `{field_name}`.") + continue + for tensor_idx, tensor in enumerate(tensors): + validate_tensor(tensor, name, field_name, tensor_idx, errors) + valid_layers.append(layer) + + duplicates = [name for name, count in names.items() if count > 1] + if duplicates: + add_limited(errors, "Duplicate layer names: " + ", ".join(short_name(name, 64) for name in duplicates[:8])) + + return valid_layers + + +def has_detailed_tensor_metadata(tensor: Any) -> bool: + if not isinstance(tensor, dict): + return False + detailed_keys = { + "Datatype", + "Dimensions", + "Format", + "Format/Datatype", + "StrideOrder", + "Strides", + } + return any(key in tensor for key in detailed_keys) + + +def has_detailed_layer_metadata(layer: Any) -> bool: + if not isinstance(layer, dict): + return False + detailed_layer_keys = {"Constants", "Metadata", "StreamId", "TacticName"} + if any(key in layer for key in detailed_layer_keys): + return True + for field_name in ("Inputs", "Outputs", "Constants"): + tensors = layer.get(field_name) + if isinstance(tensors, list) and any(has_detailed_tensor_metadata(tensor) for tensor in tensors): + return True + return False + + +def has_detailed_layer_info(data: Any) -> bool: + """Detect TensorRT inspector output generated with detailed profiling verbosity.""" + return isinstance(data, list) and any(has_detailed_layer_metadata(layer) for layer in data) + + +def message(level: str, text: str) -> Dict[str, str]: + return {"level": level, "message": text} + + +def backend_validation( + status: str, + analysis_mode: str, + errors: Sequence[str], + warnings: Sequence[str], + layer_path: Optional[str], + profile_path: Optional[str], + report_available: bool, +) -> Dict[str, Any]: + return { + "status": status, + "analysis_mode": analysis_mode, + "messages": [message("error", item) for item in errors] + [message("warning", item) for item in warnings], + "source_paths": { + "layer": layer_path, + "profile": profile_path, + }, + "report_available": report_available, + } + + +def top_level_validation(results: Sequence[Dict[str, Any]]) -> Dict[str, Any]: + passed_count = sum(1 for result in results if result.get("validation", {}).get("status") == "passed") + failed_count = len(results) - passed_count + if failed_count == 0: + status = "passed" + elif passed_count > 0: + status = "partial" + else: + status = "failed" + return { + "status": status, + "backend_count": len(results), + "passed_count": passed_count, + "failed_count": failed_count, + "warnings": [], + } + + +def validate_profile(data: Any, errors: List[str]) -> Tuple[List[Dict[str, Any]], Optional[int], str]: + if not isinstance(data, list): + add_limited(errors, "Profile JSON root must be a list.") + return [], None, "none" + if not data: + add_limited(errors, "Profile JSON must contain at least one record.") + return [], None, "none" + + count: Optional[int] = None + count_source = "none" + entries = data + if isinstance(data[0], dict) and set(data[0].keys()) == {"count"}: + raw_count = data[0].get("count") + if isinstance(raw_count, int) and raw_count > 0: + count = raw_count + count_source = "profile header" + else: + add_limited(errors, "Profile count header must be a positive integer.") + entries = data[1:] + + if not entries: + add_limited(errors, "Profile JSON contains no layer timing records.") + return [], count, count_source + + names: collections.Counter[str] = collections.Counter() + valid_entries: List[Dict[str, Any]] = [] + for idx, entry in enumerate(entries): + if not isinstance(entry, dict): + add_limited(errors, f"Profile record {idx} is not an object.") + continue + name = entry.get("name") + if not isinstance(name, str) or not name: + add_limited(errors, f"Profile record {idx} is missing non-empty string `name`.") + name = f"" + names[name] += 1 + for field_name in ("timeMs", "averageMs", "medianMs", "percentage"): + if not is_number(entry.get(field_name)): + add_limited(errors, f"Profile record `{short_name(name, 72)}` has invalid `{field_name}`.") + valid_entries.append(entry) + + duplicates = [name for name, duplicate_count in names.items() if duplicate_count > 1] + if duplicates: + add_limited(errors, "Duplicate profile names: " + ", ".join(short_name(name, 64) for name in duplicates[:8])) + + return valid_entries, count, count_source + + +def validate_names(layers: Sequence[Dict[str, Any]], profile: Sequence[Dict[str, Any]], errors: List[str]) -> None: + layer_names = {layer.get("Name") for layer in layers if isinstance(layer.get("Name"), str)} + profile_names = {entry.get("name") for entry in profile if isinstance(entry.get("name"), str)} + missing_profile = sorted(layer_names - profile_names) + extra_profile = sorted(profile_names - layer_names) + if missing_profile: + sample = ", ".join(short_name(name, 72) for name in missing_profile[:6]) + add_limited(errors, f"{len(missing_profile)} layer names are missing from profile JSON: {sample}") + if extra_profile: + sample = ", ".join(short_name(name, 72) for name in extra_profile[:6]) + add_limited(errors, f"{len(extra_profile)} profile names are missing from layer-info JSON: {sample}") + + +def tensor_name(tensor: Any) -> Optional[str]: + if isinstance(tensor, dict) and isinstance(tensor.get("Name"), str) and tensor.get("Name"): + return tensor["Name"] + return None + + +def validate_dag(layers: Sequence[Dict[str, Any]], errors: List[str]) -> Dict[str, Any]: + producer: Dict[str, int] = {} + duplicate_outputs: List[str] = [] + for idx, layer in enumerate(layers): + for tensor in layer.get("Outputs", []) if isinstance(layer.get("Outputs"), list) else []: + name = tensor_name(tensor) + if not name: + continue + if name in producer: + duplicate_outputs.append(name) + else: + producer[name] = idx + + if duplicate_outputs: + sample = ", ".join(short_name(name, 72) for name in duplicate_outputs[:6]) + add_limited(errors, f"Duplicate tensor producers make the graph ambiguous: {sample}") + + adjacency = [set() for _ in layers] + indegree = [0 for _ in layers] + external_inputs = set() + consumed_tensors = set() + + for consumer_idx, layer in enumerate(layers): + for tensor in layer.get("Inputs", []) if isinstance(layer.get("Inputs"), list) else []: + name = tensor_name(tensor) + if not name: + continue + producer_idx = producer.get(name) + if producer_idx is None: + external_inputs.add(name) + continue + consumed_tensors.add(name) + if producer_idx == consumer_idx: + add_limited(errors, f"Layer `{short_name(layer.get('Name', ''), 72)}` consumes its own output `{name}`.") + continue + if consumer_idx not in adjacency[producer_idx]: + adjacency[producer_idx].add(consumer_idx) + indegree[consumer_idx] += 1 + + queue = collections.deque(idx for idx, degree in enumerate(indegree) if degree == 0) + visited = 0 + while queue: + idx = queue.popleft() + visited += 1 + for next_idx in adjacency[idx]: + indegree[next_idx] -= 1 + if indegree[next_idx] == 0: + queue.append(next_idx) + + if visited != len(layers): + unresolved = [ + short_name(str(layers[idx].get("Name", idx)), 72) + for idx, degree in enumerate(indegree) + if degree > 0 + ][:8] + add_limited(errors, "Layer graph is not a DAG; unresolved nodes: " + ", ".join(unresolved)) + + output_tensors = set(producer) + graph_outputs = output_tensors - consumed_tensors + edges = sum(len(next_nodes) for next_nodes in adjacency) + return { + "edge_count": edges, + "external_inputs": sorted(external_inputs), + "graph_outputs": sorted(graph_outputs), + } + + +def infer_count(profile: Sequence[Dict[str, Any]], header_count: Optional[int]) -> Tuple[Optional[int], str, List[str]]: + warnings: List[str] = [] + if header_count is not None: + source = "profile header" + else: + source = "timeMs/averageMs ratio" + + ratios: List[float] = [] + for entry in profile: + time_ms = entry.get("timeMs") + average_ms = entry.get("averageMs") + if is_number(time_ms) and is_number(average_ms) and float(average_ms) > 0: + ratios.append(float(time_ms) / float(average_ms)) + + inferred: Optional[int] = None + if ratios: + median_ratio = sorted(ratios)[len(ratios) // 2] + rounded = int(round(median_ratio)) + if rounded > 0: + max_delta = max(abs(ratio - rounded) for ratio in ratios) + if max_delta <= max(0.05 * rounded, 0.5): + inferred = rounded + else: + warnings.append("Profile timeMs/averageMs ratios are not consistent enough to infer iteration count.") + + if header_count is not None: + if inferred is not None and abs(inferred - header_count) > max(1, int(0.05 * header_count)): + warnings.append(f"Profile count header ({header_count}) disagrees with inferred ratio ({inferred}).") + return header_count, "profile header", warnings + + if inferred is not None: + return inferred, source, warnings + return None, "none", warnings + + +def validate_timing(profile: Sequence[Dict[str, Any]], count: Optional[int], warnings: List[str]) -> Dict[str, float]: + total_time = sum(float(entry["timeMs"]) for entry in profile if is_number(entry.get("timeMs"))) + total_average = sum(float(entry["averageMs"]) for entry in profile if is_number(entry.get("averageMs"))) + total_percentage = sum(float(entry["percentage"]) for entry in profile if is_number(entry.get("percentage"))) + + if abs(total_percentage - 100.0) > 2.0: + warnings.append(f"Profile percentages sum to {total_percentage:.2f}%, not approximately 100%.") + + if count and total_average > 0: + expected_time = total_average * count + if abs(expected_time - total_time) > max(0.01, 0.02 * max(total_time, expected_time)): + warnings.append( + "Sum(timeMs) does not match sum(averageMs) * count " + f"({total_time:.4f} vs {expected_time:.4f})." + ) + + return { + "total_time_ms": total_time, + "average_inference_ms": total_average if total_average else (total_time / count if count else 0.0), + "total_percentage": total_percentage, + } + + +def load_optional_config(folder: Optional[str]) -> Tuple[Optional[Dict[str, Any]], Optional[str]]: + if not folder: + return None, None + candidates = [ + os.path.join(folder, "model_hub", "config.json"), + os.path.join(folder, "config.json"), + ] + try: + for name in sorted(os.listdir(folder)): + candidate = os.path.join(folder, name, "config.json") + if candidate not in candidates: + candidates.append(candidate) + except OSError: + pass + + for path in candidates: + if not os.path.isfile(path): + continue + try: + data = load_json(path) + except DataError: + continue + if isinstance(data, dict): + return data, path + return None, None + + +def normalized_name_tokens(value: str) -> List[str]: + normalized = unicodedata.normalize("NFKD", value) + ascii_value = normalized.encode("ascii", "ignore").decode("ascii").lower() + return [ + token + for token in re.split(r"[^a-z0-9]+", ascii_value) + if token + ] + + +def model_name_tokens(value: str) -> List[str]: + return [token for token in normalized_name_tokens(value) if token not in MODEL_NAME_STOPWORDS] + + +def has_model_name_signal(tokens: Sequence[str]) -> bool: + if not tokens: + return False + if all(token.isdigit() for token in tokens): + return False + if all(re.fullmatch(r"v?\d+", token) for token in tokens): + return False + + alpha_tokens = [token for token in tokens if re.search(r"[a-z]", token)] + if not alpha_tokens: + return False + if len(tokens) == 1: + token = tokens[0] + return len(token) >= 3 or token in SHORT_MODEL_NAME_TOKENS + return any(len(token) >= 2 for token in alpha_tokens) + + +def clean_model_name_segment(value: str) -> Optional[str]: + tokens = model_name_tokens(value) + if not has_model_name_signal(tokens): + return None + return "-".join(tokens[:16]) + + +def clean_model_name(value: Any) -> Optional[str]: + if not isinstance(value, str) or not value.strip(): + return None + + normalized = unicodedata.normalize("NFKD", value) + ascii_value = normalized.encode("ascii", "ignore").decode("ascii") + segments = [segment.strip() for segment in re.split(r"[\\/]+", ascii_value) if segment.strip()] + if not segments: + segments = [ascii_value] + + for segment in reversed(segments): + cleaned = clean_model_name_segment(segment) + if cleaned: + return cleaned + return None + + +def model_name_config_paths(folder: Optional[str]) -> List[str]: + if not folder: + return [] + + candidates: List[str] = [] + for filename in MODEL_NAME_CONFIG_FILENAMES: + candidates.append(os.path.join(folder, filename)) + candidates.append(os.path.join(folder, "model_hub", filename)) + + try: + for name in sorted(os.listdir(folder)): + child = os.path.join(folder, name) + if not os.path.isdir(child): + continue + for filename in MODEL_NAME_CONFIG_FILENAMES: + candidates.append(os.path.join(child, filename)) + except OSError: + pass + + unique: List[str] = [] + seen = set() + for path in candidates: + if path not in seen: + unique.append(path) + seen.add(path) + return unique + + +def infer_model_name_from_configs(folder: Optional[str]) -> List[ModelNameGuess]: + guesses: List[ModelNameGuess] = [] + for path in model_name_config_paths(folder): + if not os.path.isfile(path): + continue + try: + data = load_json(path) + except DataError: + continue + if not isinstance(data, dict): + continue + for key in CONFIG_MODEL_NAME_KEYS: + cleaned = clean_model_name(data.get(key)) + if cleaned: + guesses.append(ModelNameGuess(cleaned, "high", f"config `{os.path.basename(path)}` field `{key}`")) + break + return guesses + + +def infer_model_name_from_folder(folder: Optional[str]) -> List[ModelNameGuess]: + if not folder: + return [] + + parts = [part for part in os.path.abspath(folder).split(os.sep) if part] + guesses: List[ModelNameGuess] = [] + candidate_parts = [parts[-1]] if parts else [] + if parts and parts[-1].lower() in MODEL_NAME_PARENT_HINT_DIRS and len(parts) >= 2: + candidate_parts.append(parts[-2]) + + for part in candidate_parts: + cleaned = clean_model_name_segment(part) + if cleaned: + guesses.append(ModelNameGuess(cleaned, "medium", f"directory name `{part}`")) + break + return guesses + + +def infer_model_name_from_filenames(source_paths: Optional[Sequence[str]]) -> List[ModelNameGuess]: + guesses: List[ModelNameGuess] = [] + for path in source_paths or []: + base = os.path.splitext(os.path.basename(path))[0] + stripped = re.sub(r"^(layers?|profiles?)[_-]*", "", base, flags=re.IGNORECASE) + cleaned = clean_model_name(stripped) + if cleaned: + guesses.append(ModelNameGuess(cleaned, "medium", f"input file `{os.path.basename(path)}`")) + return guesses + + +def empty_model_name_metadata() -> Dict[str, Any]: + return {"name": None, "name_confidence": "low", "name_evidence": []} + + +def infer_model_name_metadata( + folder: Optional[str], + explicit_name: Optional[str] = None, + source_paths: Optional[Sequence[str]] = None, +) -> Dict[str, Any]: + guesses: List[ModelNameGuess] = [] + explicit_cleaned = clean_model_name(explicit_name) + if explicit_cleaned: + guesses.append(ModelNameGuess(explicit_cleaned, "high", "explicit model name")) + + guesses.extend(infer_model_name_from_configs(folder)) + guesses.extend(infer_model_name_from_folder(folder)) + guesses.extend(infer_model_name_from_filenames(source_paths)) + + if not guesses: + return empty_model_name_metadata() + + best = max(guesses, key=lambda item: MODEL_NAME_CONFIDENCE_RANK[item.confidence]) + evidence = [guess.evidence for guess in guesses if guess.name == best.name] + return { + "name": best.name, + "name_confidence": best.confidence, + "name_evidence": evidence, + } + + +def clean_model_component_segment(value: str, model_name: Optional[str] = None) -> Optional[str]: + tokens = [ + MODEL_COMPONENT_ALIASES.get(token, token) + for token in normalized_name_tokens(value) + if token not in MODEL_COMPONENT_STOPWORDS + ] + if not tokens: + return None + + model_tokens = set(normalized_name_tokens(model_name or "")) + if model_tokens: + without_model = [token for token in tokens if token not in model_tokens] + if any(token in MODEL_COMPONENT_HINT_TOKENS for token in without_model): + tokens = without_model + + if not any(token in MODEL_COMPONENT_HINT_TOKENS for token in tokens): + return None + if all(token.isdigit() for token in tokens): + return None + return "-".join(tokens[:8]) + + +def append_unique_component(components: List[str], component: Optional[str]) -> None: + if component and component not in components: + components.append(component) + + +def infer_model_components( + folder: Optional[str], + source_paths: Optional[Sequence[str]], + model_name: Optional[str], +) -> List[str]: + components: List[str] = [] + if folder: + folder_name = os.path.basename(os.path.abspath(folder)) + append_unique_component(components, clean_model_component_segment(folder_name, model_name)) + + for path in source_paths or []: + base = os.path.splitext(os.path.basename(path))[0] + stripped = re.sub(r"^(layers?|profiles?)[_-]*", "", base, flags=re.IGNORECASE) + append_unique_component(components, clean_model_component_segment(stripped, model_name)) + + return components + + +def collect_shapes(layers: Sequence[Dict[str, Any]]) -> List[Tuple[float, ...]]: + shapes: List[Tuple[float, ...]] = [] + for layer in layers: + for field_name in ("Inputs", "Outputs"): + for tensor in layer.get(field_name, []) if isinstance(layer.get(field_name), list) else []: + dims = tensor.get("Dimensions") if isinstance(tensor, dict) else None + if isinstance(dims, list) and all(is_number(dim) for dim in dims): + shapes.append(tuple(float(dim) for dim in dims)) + return shapes + + +def external_input_shapes(layers: Sequence[Dict[str, Any]], external_inputs: Iterable[str]) -> Dict[str, Tuple[float, ...]]: + external = set(external_inputs) + result: Dict[str, Tuple[float, ...]] = {} + for layer in layers: + for tensor in layer.get("Inputs", []) if isinstance(layer.get("Inputs"), list) else []: + name = tensor_name(tensor) + dims = tensor.get("Dimensions") if isinstance(tensor, dict) else None + if name in external and isinstance(dims, list) and all(is_number(dim) for dim in dims): + result[name] = tuple(float(dim) for dim in dims) + return result + + +def infer_model_info( + layers: Sequence[Dict[str, Any]], + graph: Dict[str, Any], + folder: Optional[str], + model_name_metadata: Optional[Dict[str, Any]] = None, + model_components: Optional[Sequence[str]] = None, +) -> Dict[str, Any]: + config, config_path = load_optional_config(folder) + name_metadata = model_name_metadata or empty_model_name_metadata() + names_blob = "\n".join( + str(layer.get("Name", "")) + "\n" + str(layer.get("Metadata", "")) + for layer in layers + ).lower() + + category = "unknown DL model" + confidence = "low" + evidence: List[str] = [] + + if config: + model_type = str(config.get("model_type", "")).lower() + architectures = " ".join(str(item).lower() for item in config.get("architectures", [])) + if model_type in {"bert", "roberta", "distilbert", "albert", "mpnet"} or "bert" in architectures: + category = "Transformer encoder / sentence embedding" + confidence = "high" + evidence.append(f"Hugging Face config model_type={config.get('model_type')}") + elif any(token in model_type for token in ("gpt", "llama", "mistral", "gemma", "decoder")): + category = "Transformer decoder / LLM" + confidence = "high" + evidence.append(f"Hugging Face config model_type={config.get('model_type')}") + elif "vit" in model_type: + category = "Vision transformer" + confidence = "high" + evidence.append(f"Hugging Face config model_type={config.get('model_type')}") + + if confidence == "low": + if "input_ids" in names_blob and ("encoder.layer" in names_blob or "attention" in names_blob): + category = "Transformer encoder / sentence embedding" + confidence = "medium" + evidence.append("Layer names include input_ids and attention/encoder markers") + elif "scaled_dot_product_attention" in names_blob or "attention" in names_blob: + category = "Transformer model" + confidence = "medium" + evidence.append("Layer names include attention markers") + elif "conv" in names_blob: + category = "CNN / convolutional vision model" + confidence = "medium" + evidence.append("Layer names include convolution markers") + + shapes = collect_shapes(layers) + last_dims = collections.Counter( + int(shape[-1]) + for shape in shapes + if shape and float(shape[-1]).is_integer() and abs(int(shape[-1])) > 1 + ) + hidden_size = None + intermediate_size = None + num_layers = None + num_heads = None + max_position_embeddings = None + + if config: + hidden_size = config.get("hidden_size") + intermediate_size = config.get("intermediate_size") + num_layers = config.get("num_hidden_layers") + num_heads = config.get("num_attention_heads") + max_position_embeddings = config.get("max_position_embeddings") + + if hidden_size is None and last_dims: + hidden_size = last_dims.most_common(1)[0][0] + if intermediate_size is None and hidden_size and last_dims: + larger = [dim for dim, count in last_dims.items() if dim > int(hidden_size) and count >= 2] + if larger: + intermediate_size = max(larger) + + if num_layers is None: + encoder_indices = [int(match) for match in re.findall(r"encoder\.layer\.(\d+)", names_blob)] + if encoder_indices: + num_layers = max(encoder_indices) + 1 + + input_shapes = external_input_shapes(layers, graph.get("external_inputs", [])) + sequence_lengths = [] + for name, shape in input_shapes.items(): + if any(token in name for token in ("input_ids", "attention_mask", "token_type_ids")) and len(shape) >= 2: + sequence_lengths.append(int(shape[-1])) + sequence_length = max(set(sequence_lengths), key=sequence_lengths.count) if sequence_lengths else None + + dynamic_dims = any(any(dim < 0 for dim in shape) for shape in shapes) + + return { + "name": name_metadata.get("name"), + "name_confidence": name_metadata.get("name_confidence", "low"), + "name_evidence": name_metadata.get("name_evidence", []), + "components": list(model_components or []), + "category": category, + "confidence": confidence, + "evidence": evidence, + "config_path": config_path, + "hidden_size": hidden_size, + "intermediate_size": intermediate_size, + "num_layers": num_layers, + "num_heads": num_heads, + "max_position_embeddings": max_position_embeddings, + "sequence_length": sequence_length, + "dynamic_dims": dynamic_dims, + "input_shapes": input_shapes, + } + + +def engine_hints(layers: Sequence[Dict[str, Any]], folder: Optional[str]) -> Dict[str, Any]: + engine_files: List[str] = [] + if folder: + try: + for name in sorted(os.listdir(folder)): + if name.lower().endswith((".trt", ".engine", ".plan")): + engine_files.append(name) + except OSError: + pass + + subgraphs = sorted( + { + layer.get("_subgraph") + for layer in layers + if isinstance(layer.get("_subgraph"), (int, str)) + }, + key=str, + ) + streams = sorted( + { + layer.get("StreamId") + for layer in layers + if isinstance(layer.get("StreamId"), (int, str)) + }, + key=str, + ) + + inferred_count = None + source = "none" + if engine_files: + inferred_count = len(engine_files) + source = "engine files in folder" + elif subgraphs: + inferred_count = len(subgraphs) + source = "_subgraph ids" + elif streams: + inferred_count = len(streams) + source = "StreamId values" + + return { + "engine_files": engine_files, + "subgraphs": subgraphs, + "streams": streams, + "inferred_count": inferred_count, + "source": source, + } + + +def raw_layer_type(layer: Dict[str, Any]) -> str: + value = layer.get("LayerType") + if isinstance(value, str) and value: + return value + return "unknown" + + +def combined_layer_text(layer: Dict[str, Any]) -> str: + return " ".join( + str(layer.get(field, "")) + for field in ("Name", "LayerType", "TacticName", "Metadata") + ).lower() + + +def looks_like_mha(layer: Dict[str, Any]) -> bool: + combined = combined_layer_text(layer) + if any(token in combined for token in MHA_TOKENS): + return True + return bool(re.search(r"(^|[^a-z0-9])mha([^a-z0-9]|$)", combined)) + + +def infer_layer_type(layer: Dict[str, Any]) -> str: + layer_type = raw_layer_type(layer) + normalized = re.sub(r"[\s-]+", "_", layer_type.strip().lower()) + if normalized in MISC_LAYER_TYPES: + return "misc" + if layer_type.lower() == "kgen" and looks_like_mha(layer): + return "kgen_mha" + return layer_type + + +def tags_for(layer: Dict[str, Any]) -> List[str]: + combined = combined_layer_text(layer) + lower = str(layer.get("Name", "")).lower() + layer_type = raw_layer_type(layer).lower() + tags: List[str] = [] + + if layer_type == "kgen": + tags.append("kgen") + if layer_type in {"gemm", "matrix_multiply"} or "matmul" in combined or "matrix_multiply" in combined: + tags.append("matmul/gemm") + if "attention" in combined or "fmha" in combined or "flash" in combined or looks_like_mha(layer): + tags.append("attention") + if "scaled_dot_product_attention" in combined: + tags.append("sdpa") + if any(token in lower for token in DYNAMIC_TOKENS) or layer_type == "shape_call": + tags.append("shape/cast/dynamic") + if "mean" in lower and "sqrt" in lower and "div" in lower: + tags.append("layernorm/reduction") + if "erf" in lower or "gelu" in lower: + tags.append("activation") + return tags or ["other"] + + +def is_qkv_fused(layer: Dict[str, Any]) -> bool: + name = str(layer.get("Name", "")).lower() + metadata = str(layer.get("Metadata", "")).lower() + combined = name + " " + metadata + has_named_qkv = all(token in combined for token in ("query", "key", "value")) + has_three_matmuls = combined.count("matmul") >= 3 or combined.count("matrix_multiply") >= 3 + has_plus_fusion = str(layer.get("Name", "")).count("+") >= 2 + return has_named_qkv or (has_three_matmuls and has_plus_fusion) + + +def is_obvious_attention_fusion(layer: Dict[str, Any]) -> bool: + combined = (str(layer.get("Name", "")) + " " + str(layer.get("Metadata", ""))).lower() + return any(token in combined for token in ("scaled_dot_product_attention", "fmha", "flash_attention", "flashattention")) + + +def combine_records(layers: Sequence[Dict[str, Any]], profile: Sequence[Dict[str, Any]]) -> List[Dict[str, Any]]: + profile_by_name = {entry["name"]: entry for entry in profile} + records: List[Dict[str, Any]] = [] + for layer in layers: + entry = profile_by_name.get(layer.get("Name")) + if not entry: + continue + raw_type = raw_layer_type(layer) + records.append( + { + "name": layer.get("Name"), + "layer_type": infer_layer_type(layer), + "raw_layer_type": raw_type, + "tactic": layer.get("TacticName", ""), + "time_ms": float(entry.get("timeMs", 0.0)), + "avg_ms": float(entry.get("averageMs", 0.0)), + "median_ms": float(entry.get("medianMs", 0.0)), + "percentage": float(entry.get("percentage", 0.0)), + "tags": tags_for(layer), + "layer": layer, + } + ) + return records + + +def aggregate(records: Sequence[Dict[str, Any]], key_name: str) -> List[Dict[str, Any]]: + totals: Dict[str, Dict[str, Any]] = {} + for record in records: + keys = record[key_name] if isinstance(record[key_name], list) else [record[key_name]] + for key in keys: + bucket = totals.setdefault(key, {"name": key, "count": 0, "time_ms": 0.0, "percentage": 0.0}) + bucket["count"] += 1 + bucket["time_ms"] += record["time_ms"] + bucket["percentage"] += record["percentage"] + return sorted(totals.values(), key=lambda item: item["percentage"], reverse=True) + + +def add_issue( + issues: List[Dict[str, Any]], + score: float, + title: str, + evidence: str, + next_check: str, + confidence: str = "medium", +) -> None: + issues.append( + { + "score": score, + "title": title, + "evidence": evidence, + "next_check": next_check, + "confidence": confidence, + } + ) + + +def find_issues( + records: Sequence[Dict[str, Any]], + model: Dict[str, Any], + engines: Dict[str, Any], + type_breakdown: Sequence[Dict[str, Any]], + tag_breakdown: Sequence[Dict[str, Any]], +) -> Tuple[List[Dict[str, Any]], List[str]]: + issues: List[Dict[str, Any]] = [] + positives: List[str] = [] + by_type = {item["name"]: item for item in type_breakdown} + by_tag = {item["name"]: item for item in tag_breakdown} + top_layers = sorted(records, key=lambda item: item["percentage"], reverse=True) + + if top_layers: + top = top_layers[0] + if top["percentage"] >= 10.0: + add_issue( + issues, + top["percentage"] + 30.0, + "Single-layer hotspot", + f"`{short_name(top['name'], 96)}` consumes {fmt_pct(top['percentage'])} ({fmt_ms(top['avg_ms'])} average).", + "Inspect this layer's tactic, precision, shape, and whether neighboring ops can be fused.", + "high", + ) + elif top["percentage"] >= 5.0: + add_issue( + issues, + top["percentage"] + 12.0, + "Moderate layer hotspot", + f"The largest layer is `{short_name(top['name'], 96)}` at {fmt_pct(top['percentage'])}.", + "Check whether this layer is expected for the model architecture before deeper tuning.", + "medium", + ) + else: + positives.append(f"No single layer exceeds 5%; the largest layer is {fmt_pct(top['percentage'])}.") + + kgen = by_type.get("kgen") + if kgen and kgen["percentage"] >= 25.0: + add_issue( + issues, + kgen["percentage"] + 8.0, + "Pointwise/reduction kernels are a large share", + f"`kgen` layers account for {fmt_pct(kgen['percentage'])} across {kgen['count']} profiled layers.", + "Review whether residual, layer norm, activation, mask, and reshape-heavy regions are fusing as intended.", + "medium", + ) + + dynamic = by_tag.get("shape/cast/dynamic") + if dynamic and dynamic["percentage"] >= 3.0: + add_issue( + issues, + dynamic["percentage"] + 15.0, + "Dynamic shape/cast overhead is visible", + f"Shape/cast/dynamic-tagged layers account for {fmt_pct(dynamic['percentage'])} across {dynamic['count']} tagged layers.", + "Check whether static shapes or stronger shape specialization can reduce shape/cast kernels.", + "medium", + ) + + small_records = [record for record in records if record["avg_ms"] < 0.005] + small_pct = sum(record["percentage"] for record in small_records) + if len(small_records) >= 20 and small_pct >= 20.0: + add_issue( + issues, + small_pct + 5.0, + "Many tiny kernels add up", + f"{len(small_records)} layers below 0.005 ms average account for {fmt_pct(small_pct)}.", + "Prioritize fusion/partitioning checks over tuning isolated micro-kernels.", + "medium", + ) + + is_transformer = "transformer" in str(model.get("category", "")).lower() + block_count = model.get("num_layers") + qkv_fused_count = sum(1 for record in records if is_qkv_fused(record["layer"])) + if is_transformer and isinstance(block_count, int) and block_count > 0: + if qkv_fused_count < block_count: + add_issue( + issues, + 30.0 + 3.0 * (block_count - qkv_fused_count), + "Q/K/V projection fusion may be incomplete", + f"Detected {qkv_fused_count} QKV-fused projection layers for {block_count} transformer blocks.", + "Confirm whether query/key/value projections are fused per block in the exported graph and TRT engine.", + "medium", + ) + else: + positives.append(f"Detected {qkv_fused_count} QKV-fused projection layers for {block_count} transformer blocks.") + + attention_fused_count = sum(1 for record in records if is_obvious_attention_fusion(record["layer"])) + if attention_fused_count < block_count: + add_issue( + issues, + 18.0 + 2.0 * (block_count - attention_fused_count), + "Fused attention is not obvious from layer names", + f"Detected {attention_fused_count} obvious SDPA/FMHA-style layers for {block_count} transformer blocks.", + "Verify whether attention is decomposed into matmul/softmax kernels or hidden behind generated kernel names.", + "low", + ) + else: + positives.append(f"Detected {attention_fused_count} obvious fused attention layers for {block_count} transformer blocks.") + + engine_count = engines.get("inferred_count") + if isinstance(engine_count, int): + if engine_count > 1: + add_issue( + issues, + 35.0, + "Multiple engine/partition hints", + f"Inferred {engine_count} engines from {engines.get('source')}.", + "Investigate graph breaks or unsupported ops that force partitioning.", + "medium", + ) + elif engine_count == 1: + positives.append(f"Engine hints point to one engine ({engines.get('source')}).") + + gemm_bad: List[Dict[str, Any]] = [] + for record in records: + if "matmul/gemm" not in record["tags"]: + continue + tactic = str(record.get("tactic", "")).lower() + if tactic and not any(token in tactic for token in TENSOR_CORE_TOKENS): + gemm_bad.append(record) + gemm_bad_pct = sum(record["percentage"] for record in gemm_bad) + if gemm_bad_pct >= 5.0: + add_issue( + issues, + gemm_bad_pct + 10.0, + "Some GEMM tactics do not look Tensor Core based", + f"{len(gemm_bad)} GEMM-like layers with non-obvious Tensor Core tactics account for {fmt_pct(gemm_bad_pct)}.", + "Inspect precision settings and tactic choices for these GEMMs.", + "medium", + ) + + issues.sort(key=lambda item: item["score"], reverse=True) + return issues, positives + + +def failure_result( + label: str, + errors: Sequence[str], + warnings: Sequence[str], + layer_path: Optional[str], + profile_path: Optional[str], + layer_count: Optional[int] = None, + profile_count: Optional[int] = None, +) -> Dict[str, Any]: + result: Dict[str, Any] = { + "label": label, + "validation": backend_validation("failed", "unusable", errors, warnings, layer_path, profile_path, False), + } + if layer_count is not None: + result["layer_count"] = layer_count + if profile_count is not None: + result["profile_count"] = profile_count + return result + + +def analyze_backend( + label: str, + layer_path: Optional[str], + profile_path: Optional[str], + folder: Optional[str], + model_name_metadata: Optional[Dict[str, Any]] = None, + model_components: Optional[Sequence[str]] = None, +) -> Dict[str, Any]: + errors: List[str] = [] + warnings: List[str] = [] + + if not layer_path: + errors.append( + "Full TensorRT layer info is missing. Provide layers_*.json generated with detailed profiling verbosity." + ) + return failure_result(label, errors, warnings, layer_path, profile_path) + + try: + layer_data = load_json(layer_path) + except DataError as exc: + return failure_result(label, [str(exc)], warnings, layer_path, profile_path) + + layers = validate_layers(layer_data, errors) + if not has_detailed_layer_info(layer_data): + add_limited( + errors, + "Full TensorRT layer info is missing. Generate layer JSON with TensorRT detailed profiling verbosity.", + ) + graph = validate_dag(layers, errors) if layers else {"edge_count": 0, "external_inputs": [], "graph_outputs": []} + + if not profile_path: + if errors: + return failure_result(label, errors, warnings, layer_path, profile_path, len(layers)) + model = infer_model_info(layers, graph, folder, model_name_metadata, model_components) + engines = engine_hints(layers, folder) + return { + "label": label, + "validation": backend_validation("passed", "layer_only", errors, warnings, layer_path, profile_path, True), + "layers": layers, + "layer_count": len(layers), + "graph": graph, + "model": model, + "engines": engines, + } + + try: + profile_data = load_json(profile_path) + except DataError as exc: + errors.append(str(exc)) + return failure_result(label, errors, warnings, layer_path, profile_path, len(layers)) + + profile, header_count, count_source = validate_profile(profile_data, errors) + if not errors: + validate_names(layers, profile, errors) + + if errors: + return failure_result(label, errors, warnings, layer_path, profile_path, len(layers), len(profile)) + + count, count_source, count_warnings = infer_count(profile, header_count) + warnings.extend(count_warnings) + timing = validate_timing(profile, count, warnings) + model = infer_model_info(layers, graph, folder, model_name_metadata, model_components) + engines = engine_hints(layers, folder) + records = combine_records(layers, profile) + type_breakdown = aggregate(records, "layer_type") + tag_breakdown = aggregate(records, "tags") + issues, positives = find_issues(records, model, engines, type_breakdown, tag_breakdown) + + return { + "label": label, + "validation": backend_validation("passed", "layer_profile", errors, warnings, layer_path, profile_path, True), + "layers": layers, + "profile": profile, + "layer_count": len(layers), + "profile_count": len(profile), + "graph": graph, + "count": count, + "count_source": count_source, + "timing": timing, + "model": model, + "engines": engines, + "records": records, + "type_breakdown": type_breakdown, + "tag_breakdown": tag_breakdown, + "issues": issues, + "positives": positives, + } + + +def extract_perf_data( + path: Optional[str], + data_specs: Optional[Sequence[Sequence[str]]], + model_name: Optional[str] = None, +) -> Dict[str, Any]: + """Resolve inputs, validate them, and return structured analysis data.""" + folder: Optional[str] = None + + if data_specs: + if path: + raise DataError("Use either a folder path or explicit --data inputs, not both.") + backends = explicit_backends(data_specs) + source_paths = [ + source_path + for _, layer_path, profile_path in backends + for source_path in (layer_path, profile_path) + if source_path + ] + folder = common_source_folder(source_paths) + else: + if not path: + raise DataError("Provide a folder path, or use --data.") + input_path = os.path.abspath(path) + if os.path.isdir(input_path): + folder = input_path + backends = discover_backends(input_path) + else: + raise DataError("A single file input requires --data.") + source_paths = [ + source_path + for _, layer_path, profile_path in backends + for source_path in (layer_path, profile_path) + if source_path + ] + model_name_metadata = infer_model_name_metadata(folder, model_name, source_paths) + model_components = infer_model_components(folder, source_paths, model_name_metadata["name"]) + results = [ + analyze_backend(label, layer_path, profile_path, folder, model_name_metadata, model_components) + for label, layer_path, profile_path in backends + ] + return { + "schema_version": "1.0", + "folder": folder, + "model_name": model_name_metadata["name"], + "model_name_confidence": model_name_metadata["name_confidence"], + "model_name_evidence": model_name_metadata["name_evidence"], + "model_components": model_components, + "validation": top_level_validation(results), + "results": results, + } diff --git a/.agents/skills/trt-perf-analysis/scripts/trt_perf/layer_info.py b/.agents/skills/trt-perf-analysis/scripts/trt_perf/layer_info.py new file mode 100644 index 000000000..9d9a0619f --- /dev/null +++ b/.agents/skills/trt-perf-analysis/scripts/trt_perf/layer_info.py @@ -0,0 +1,536 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +"""Process TensorRT layer-info JSON and write visualization targets.""" + +from __future__ import annotations + +import argparse +import importlib +import json +import re +import sys +from collections import Counter +from dataclasses import dataclass +from pathlib import Path +from typing import Any + + +STANDARD_LAYER_KEYS = { + "Name", + "LayerType", + "Inputs", + "Constants", + "Outputs", + "TacticName", + "StreamId", + "Metadata", + "_subgraph", +} + + +@dataclass(frozen=True) +class ProcessedInitializer: + name: str + desc: dict[str, Any] + + +@dataclass(frozen=True) +class ProcessedNode: + name: str + op_type: str + inputs: list[str] + outputs: list[str] + attrs: dict[str, Any] + + +@dataclass(frozen=True) +class ProcessedLayerInfo: + source_path: Path + layers: list[dict[str, Any]] + nodes: list[ProcessedNode] + graph_inputs: list[str] + graph_outputs: list[str] + graph_value_info: list[str] + value_descriptors: dict[str, dict[str, Any]] + initializers: list[ProcessedInitializer] + layer_type_counts: Counter[str] + node_op_type_counts: Counter[str] + extra_layer_fields: list[str] + + +@dataclass(frozen=True) +class TargetSpec: + name: str + module_name: str + default_suffix: str + description: str + aliases: tuple[str, ...] = () + + +TARGET_SPECS = ( + TargetSpec( + name="html", + module_name="trt_perf.layer_info_html", + default_suffix=".html", + description="Standalone HTML layer graph summary", + ), +) + +TARGETS_BY_NAME: dict[str, TargetSpec] = {} +for target_spec in TARGET_SPECS: + TARGETS_BY_NAME[target_spec.name] = target_spec + for alias in target_spec.aliases: + TARGETS_BY_NAME[alias] = target_spec + + +class NameRegistry: + """Keep target value names valid under SSA while preserving TRT names.""" + + def __init__(self) -> None: + self.used: set[str] = set() + + def reserve(self, preferred: Any, fallback: str) -> str: + base = str(preferred).strip() if preferred is not None else "" + if not base: + base = fallback + + candidate = base + suffix = 1 + while candidate in self.used: + candidate = f"{base}__trt_dup{suffix}" + suffix += 1 + self.used.add(candidate) + return candidate + + +def compact_json(value: Any) -> str: + return json.dumps(value, ensure_ascii=False, sort_keys=True, separators=(",", ":")) + + +def load_layers(path: Path) -> list[dict[str, Any]]: + with path.open("r", encoding="utf-8") as handle: + payload = json.load(handle) + + if isinstance(payload, list): + layers = payload + elif isinstance(payload, dict): + for key in ("Layers", "layers", "LayerInfo", "layerInfo"): + if isinstance(payload.get(key), list): + layers = payload[key] + break + else: + raise ValueError("JSON object does not contain a layer list") + else: + raise ValueError("top-level JSON value must be a list or object") + + bad_indexes = [idx for idx, layer in enumerate(layers) if not isinstance(layer, dict)] + if bad_indexes: + preview = ", ".join(str(idx) for idx in bad_indexes[:5]) + raise ValueError(f"layer entries must be objects; invalid indexes: {preview}") + + return layers + + +def as_descriptor_list(value: Any) -> list[dict[str, Any]]: + if not isinstance(value, list): + return [] + return [item for item in value if isinstance(item, dict)] + + +def tensor_name(desc: dict[str, Any], fallback: str) -> str: + name = desc.get("Name") + if name is None or str(name).strip() == "": + return fallback + return str(name) + + +def safe_dim_param(text: Any, fallback: str) -> str: + value = re.sub(r"[^0-9A-Za-z_]+", "_", str(text)).strip("_") + if not value: + value = fallback + if value[0].isdigit(): + value = f"d_{value}" + return value + + +def tensor_shape(desc: dict[str, Any], tensor: str) -> list[int | str] | None: + dims = desc.get("Dimensions") + if dims is None: + return None + if not isinstance(dims, list): + return None + + shape: list[int | str] = [] + prefix = safe_dim_param(tensor, "tensor") + for idx, dim in enumerate(dims): + if isinstance(dim, bool): + shape.append(int(dim)) + elif isinstance(dim, int) and dim >= 0: + shape.append(dim) + else: + shape.append(f"{prefix}_dim{idx}") + return shape + + +def sanitize_op_type(layer_type: Any) -> str: + text = str(layer_type).strip() if layer_type is not None else "" + op_type = re.sub(r"[^0-9A-Za-z_]+", "_", text).strip("_") + if not op_type: + op_type = "Layer" + if not re.match(r"[A-Za-z_]", op_type[0]): + op_type = f"_{op_type}" + return op_type[:200] + + +KGEN_TOKEN_MAP = { + "and": "And", + "add": "Add", + "cast": "Cast", + "div": "Div", + "erf": "Erf", + "gath": "Gather", + "gemm": "Gemm", + "mean": "Mean", + "mha": "MHA", + "move": "Move", + "mul": "Mul", + "resh": "Reshape", + "sele": "Select", + "slic": "Slice", + "sqrt": "Sqrt", + "sub": "Sub", + "tanh": "Tanh", + "tran": "Transpose", +} + + +def strip_kgen_signature_noise(text: str) -> str: + text = re.sub(r"_0x[0-9A-Fa-f]+$", "", text) + text = re.sub(r"_myl\d+(?:_\d+)?$", "", text) + text = re.sub(r"^__?myl_", "", text) + return text.strip("_") + + +def split_kgen_signature(signature: str) -> list[str]: + if not signature: + return [] + if "_" in signature: + return [part for part in signature.split("_") if part] + return re.findall(r"[A-Z][a-z0-9]*|[a-z0-9]+", signature) + + +def normalize_kgen_token(token: str) -> str: + lowered = token.lower() + if lowered in KGEN_TOKEN_MAP: + return KGEN_TOKEN_MAP[lowered] + if lowered.startswith("v") and lowered[1:].isdigit(): + return lowered.upper() + return token[:1].upper() + token[1:] + + +def kgen_details(layer: dict[str, Any]) -> tuple[str, list[str], str]: + candidates = [str(layer.get("TacticName", "")), layer_name(layer, 0)] + for candidate in candidates: + signature = strip_kgen_signature_noise(candidate) + tokens = [normalize_kgen_token(token) for token in split_kgen_signature(signature)] + if tokens: + return signature, tokens, f"KGEN_{'_'.join(tokens)}" + return "", [], "kgen" + + +def node_op_type(layer: dict[str, Any]) -> tuple[str, dict[str, Any]]: + raw_layer_type = layer.get("LayerType", "Layer") + if str(raw_layer_type).lower() != "kgen": + return sanitize_op_type(raw_layer_type), {} + + signature, tokens, op_type = kgen_details(layer) + attrs: dict[str, Any] = {} + if signature: + attrs["trt_kgen_signature"] = signature + if tokens: + attrs["trt_kgen_ops_json"] = compact_json(tokens) + attrs["trt_kgen_op_count"] = len(tokens) + return sanitize_op_type(op_type), attrs + + +def layer_name(layer: dict[str, Any], index: int) -> str: + raw = layer.get("Name") + if raw is None or str(raw).strip() == "": + return f"trt_layer_{index}" + return str(raw) + + +def int_attr(value: Any, default: int = 0) -> int: + if isinstance(value, bool): + return int(value) + if isinstance(value, int): + return value + try: + return int(str(value)) + except (TypeError, ValueError): + return default + + +def process_layers(layers: list[dict[str, Any]], source_path: Path) -> ProcessedLayerInfo: + registry = NameRegistry() + external_by_original: dict[str, str] = {} + latest_by_original: dict[str, str] = {} + descriptors_by_value: dict[str, dict[str, Any]] = {} + initializers_by_original: dict[str, str] = {} + initializers: list[ProcessedInitializer] = [] + produced_values: list[str] = [] + consumed_values: set[str] = set() + nodes: list[ProcessedNode] = [] + + def map_external(desc: dict[str, Any], fallback: str) -> str: + original = tensor_name(desc, fallback) + if original not in external_by_original: + value_name = registry.reserve(original, fallback) + external_by_original[original] = value_name + descriptors_by_value[value_name] = desc + return external_by_original[original] + + def map_input(desc: dict[str, Any], fallback: str) -> str: + original = tensor_name(desc, fallback) + value_name = latest_by_original.get(original) + if value_name is None: + value_name = map_external(desc, fallback) + descriptors_by_value.setdefault(value_name, desc) + consumed_values.add(value_name) + return value_name + + def map_constant(desc: dict[str, Any], fallback: str) -> str: + original = tensor_name(desc, fallback) + if original not in initializers_by_original: + value_name = registry.reserve(original, fallback) + initializers_by_original[original] = value_name + descriptors_by_value[value_name] = desc + initializers.append(ProcessedInitializer(value_name, desc)) + value_name = initializers_by_original[original] + descriptors_by_value.setdefault(value_name, desc) + consumed_values.add(value_name) + return value_name + + def map_output(desc: dict[str, Any], fallback: str) -> str: + original = tensor_name(desc, fallback) + value_name = registry.reserve(original, fallback) + latest_by_original[original] = value_name + descriptors_by_value[value_name] = desc + produced_values.append(value_name) + return value_name + + layer_type_counts: Counter[str] = Counter() + node_op_type_counts: Counter[str] = Counter() + unmapped_layer_fields: set[str] = set() + + for index, layer in enumerate(layers): + inputs = as_descriptor_list(layer.get("Inputs")) + constants = as_descriptor_list(layer.get("Constants")) + outputs = as_descriptor_list(layer.get("Outputs")) + + node_inputs = [ + map_input(desc, f"trt_layer_{index}_input_{pos}") + for pos, desc in enumerate(inputs) + ] + node_inputs.extend( + map_constant(desc, f"trt_layer_{index}_constant_{pos}") + for pos, desc in enumerate(constants) + ) + node_outputs = [ + map_output(desc, f"trt_layer_{index}_output_{pos}") + for pos, desc in enumerate(outputs) + ] + + raw_layer_type = layer.get("LayerType", "Layer") + layer_type_counts[str(raw_layer_type)] += 1 + op_type, op_type_attrs = node_op_type(layer) + node_op_type_counts[op_type] += 1 + extra = {key: value for key, value in layer.items() if key not in STANDARD_LAYER_KEYS} + unmapped_layer_fields.update(extra) + + attrs: dict[str, Any] = { + "trt_layer_index": index, + "trt_layer_type": str(raw_layer_type), + "trt_input_count": len(inputs), + "trt_constant_count": len(constants), + "trt_output_count": len(outputs), + "trt_inputs_json": compact_json(inputs), + "trt_constants_json": compact_json(constants), + "trt_outputs_json": compact_json(outputs), + } + attrs.update(op_type_attrs) + + if "TacticName" in layer: + attrs["trt_tactic_name"] = str(layer.get("TacticName", "")) + if "StreamId" in layer: + attrs["trt_stream_id"] = int_attr(layer.get("StreamId")) + if "_subgraph" in layer: + attrs["trt_subgraph"] = int_attr(layer.get("_subgraph")) + if "Metadata" in layer: + attrs["trt_metadata"] = str(layer.get("Metadata", "")) + if extra: + attrs["trt_extra_json"] = compact_json(extra) + + nodes.append( + ProcessedNode( + name=layer_name(layer, index), + op_type=op_type, + inputs=node_inputs, + outputs=node_outputs, + attrs=attrs, + ) + ) + + terminal_values = [value_name for value_name in produced_values if value_name not in consumed_values] + if not terminal_values and produced_values: + terminal_values = [produced_values[-1]] + + graph_output_names = set(terminal_values) + graph_input_names = set(external_by_original.values()) + graph_value_info = [ + value_name + for value_name in descriptors_by_value + if value_name not in graph_input_names and value_name not in graph_output_names + ] + + return ProcessedLayerInfo( + source_path=source_path, + layers=layers, + nodes=nodes, + graph_inputs=list(external_by_original.values()), + graph_outputs=terminal_values, + graph_value_info=graph_value_info, + value_descriptors=descriptors_by_value, + initializers=initializers, + layer_type_counts=layer_type_counts, + node_op_type_counts=node_op_type_counts, + extra_layer_fields=sorted(unmapped_layer_fields), + ) + + +def load_and_process_layer_info(path: Path) -> ProcessedLayerInfo: + return process_layers(load_layers(path), path) + + +def resolve_target_spec(target: str) -> TargetSpec: + try: + return TARGETS_BY_NAME[target] + except KeyError as exc: + valid = ", ".join(sorted(TARGETS_BY_NAME)) + raise ValueError(f"unknown target {target!r}; valid targets: {valid}") from exc + + +def default_output_path(input_path: Path, target: str = "html") -> Path: + spec = resolve_target_spec(target) + return input_path.with_name(f"{input_path.stem}{spec.default_suffix}") + + +def target_names_help() -> str: + return ", ".join(spec.name for spec in TARGET_SPECS) + + +def load_target_module(spec: TargetSpec) -> Any: + return importlib.import_module(spec.module_name) + + +def write_target(processed: ProcessedLayerInfo, spec: TargetSpec, output_path: Path) -> None: + module = load_target_module(spec) + writer = getattr(module, "write", None) + if writer is None: + raise NotImplementedError( + f"target {spec.name!r} is not implemented yet; " + f"expected write(processed, output_path) in {spec.module_name}" + ) + writer(processed, output_path) + + +def parse_args(argv: list[str]) -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Process TensorRT layer-info JSON and write one or more visualization targets." + ) + parser.add_argument("input", type=Path, nargs="?", help="Path to TensorRT layer-info JSON") + parser.add_argument( + "-t", + "--target", + action="append", + dest="targets", + metavar="TARGET", + help=f"Output target; repeat for multiple outputs. Default: html. Available: {target_names_help()}", + ) + parser.add_argument( + "-o", + "--output", + type=Path, + help="Output path. Only valid when writing one target.", + ) + parser.add_argument( + "--output-dir", + type=Path, + help="Directory for target-specific default filenames.", + ) + parser.add_argument( + "--list-targets", + action="store_true", + help="List supported target names and exit.", + ) + return parser.parse_args(argv) + + +def list_targets() -> None: + seen: set[str] = set() + for spec in TARGET_SPECS: + aliases = f" (aliases: {', '.join(spec.aliases)})" if spec.aliases else "" + print(f"{spec.name}{aliases}: {spec.description}") + seen.add(spec.name) + + alias_only = sorted(name for name, spec in TARGETS_BY_NAME.items() if spec.name not in seen) + if alias_only: + print(f"aliases: {', '.join(alias_only)}") + + +def resolve_requested_targets(targets: list[str] | None) -> list[TargetSpec]: + requested = targets or ["html"] + specs: list[TargetSpec] = [] + seen: set[str] = set() + for target in requested: + spec = resolve_target_spec(target) + if spec.name in seen: + continue + specs.append(spec) + seen.add(spec.name) + return specs + + +def main(argv: list[str] | None = None) -> int: + args = parse_args(sys.argv[1:] if argv is None else argv) + if args.list_targets: + list_targets() + return 0 + if args.input is None: + print("error: input is required unless --list-targets is used", file=sys.stderr) + return 1 + + try: + target_specs = resolve_requested_targets(args.targets) + if args.output is not None and len(target_specs) != 1: + raise ValueError("--output can only be used when writing one target") + + processed = load_and_process_layer_info(args.input) + written: list[Path] = [] + for spec in target_specs: + if args.output is not None: + output_path = args.output + elif args.output_dir is not None: + output_path = args.output_dir / f"{args.input.stem}{spec.default_suffix}" + else: + output_path = default_output_path(args.input, spec.name) + + write_target(processed, spec, output_path) + written.append(output_path) + except Exception as exc: + print(f"error: {exc}", file=sys.stderr) + return 1 + + for output_path in written: + print(f"wrote {output_path}") + return 0 diff --git a/.agents/skills/trt-perf-analysis/scripts/trt_perf/layer_info_html.py b/.agents/skills/trt-perf-analysis/scripts/trt_perf/layer_info_html.py new file mode 100644 index 000000000..4910e4eef --- /dev/null +++ b/.agents/skills/trt-perf-analysis/scripts/trt_perf/layer_info_html.py @@ -0,0 +1,220 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +"""Write a standalone HTML visualization target for TensorRT layer-info data.""" + +from __future__ import annotations + +import html +import json +from pathlib import Path +from typing import Any + + +def escape(value: Any) -> str: + return html.escape(str(value), quote=True) + + +def compact_json(value: Any) -> str: + return json.dumps(value, ensure_ascii=False, sort_keys=True, separators=(",", ":")) + + +def short(value: Any, limit: int = 180) -> str: + text = str(value) + if len(text) <= limit: + return text + return f"{text[: limit - 1]}..." + + +def table_rows(items: list[tuple[str, Any]]) -> str: + rows = [] + for key, value in items: + rows.append( + "" + f"{escape(key)}" + f"{escape(value)}" + "" + ) + return "\n".join(rows) + + +def write(processed: Any, output_path: Path) -> None: + layer_rows = [] + for node in processed.nodes: + attrs = node.attrs + layer_rows.append( + "" + f"{escape(attrs.get('trt_layer_index', ''))}" + f"{escape(short(node.name, 140))}" + f"{escape(attrs.get('trt_layer_type', node.op_type))}" + f"{escape(node.op_type)}" + f"{escape(len(node.inputs))}" + f"{escape(len(node.outputs))}" + f"{escape(short(attrs.get('trt_tactic_name', ''), 160))}" + "" + ) + + summary_rows = table_rows( + [ + ("source", processed.source_path), + ("layers", len(processed.layers)), + ("nodes", len(processed.nodes)), + ("graph inputs", len(processed.graph_inputs)), + ("graph outputs", len(processed.graph_outputs)), + ("initializers", len(processed.initializers)), + ("layer types", compact_json(dict(processed.layer_type_counts))), + ("node op types", compact_json(dict(processed.node_op_type_counts))), + ] + ) + + graph_inputs = "\n".join(f"
  • {escape(name)}
    • " for name in processed.graph_inputs) + graph_outputs = "\n".join(f"
  • {escape(name)}
    • " for name in processed.graph_outputs) + + document = f""" + + + + + TensorRT Layer Info: {escape(processed.source_path.name)} + + + +
    +

    TensorRT Layer Info

    +

    {escape(processed.source_path)}

    + +

    Summary

    + +{summary_rows} +
    + +

    Graph Endpoints

    +
    +
    +

    Inputs

    +
      {graph_inputs}
    +
    +
    +

    Outputs

    +
      {graph_outputs}
    +
    +
    + +

    Layers

    + + + + + + + + + + + + + +{chr(10).join(layer_rows)} + +
    #NameLayer TypeOp TypeInputsOutputsTactic
    +
    + + +""" + + output_path.parent.mkdir(parents=True, exist_ok=True) + output_path.write_text(document, encoding="utf-8") diff --git a/.agents/skills/trt-perf-analysis/scripts/trt_perf/render_json.py b/.agents/skills/trt-perf-analysis/scripts/trt_perf/render_json.py new file mode 100644 index 000000000..e68cd6c47 --- /dev/null +++ b/.agents/skills/trt-perf-analysis/scripts/trt_perf/render_json.py @@ -0,0 +1,22 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +"""Serialize extracted TensorRT performance data as JSON.""" + +from __future__ import annotations + +import json +from typing import Any, Dict + + +def json_ready(value: Any) -> Any: + if isinstance(value, dict): + return {str(key): json_ready(item) for key, item in value.items()} + if isinstance(value, (list, tuple)): + return [json_ready(item) for item in value] + if isinstance(value, (str, int, float, bool)) or value is None: + return value + return str(value) + + +def render_results_json(data: Dict[str, Any], indent: int = 2) -> str: + return json.dumps(json_ready(data), indent=indent, sort_keys=True) + "\n" diff --git a/.agents/skills/trt-perf-analysis/scripts/validate_analyze_data.py b/.agents/skills/trt-perf-analysis/scripts/validate_analyze_data.py new file mode 100755 index 000000000..25ec16f4e --- /dev/null +++ b/.agents/skills/trt-perf-analysis/scripts/validate_analyze_data.py @@ -0,0 +1,246 @@ +#!/usr/bin/env python3 +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +"""Validate analyzer JSON output against schemas/analyze-data.schema.json. + +This intentionally implements only the JSON Schema keywords used by the local +schema so it can run without third-party dependencies in benchmark environments. +""" + +from __future__ import annotations + +import argparse +import json +import os +import sys +from typing import Any, List, Optional, Sequence + + +class ValidationError(Exception): + """Raised for schema loading or schema reference errors.""" + + +def default_schema_path() -> str: + script_dir = os.path.dirname(os.path.abspath(__file__)) + return os.path.abspath(os.path.join(script_dir, "..", "schemas", "analyze-data.schema.json")) + + +def load_json(path: str) -> Any: + try: + with open(path, "r", encoding="utf-8") as handle: + return json.load(handle) + except FileNotFoundError as exc: + raise ValidationError(f"file not found: {path}") from exc + except json.JSONDecodeError as exc: + raise ValidationError(f"invalid JSON in {path}: line {exc.lineno}, column {exc.colno}: {exc.msg}") from exc + except OSError as exc: + raise ValidationError(f"unable to read {path}: {exc}") from exc + + +def parse_args(argv: Optional[Sequence[str]] = None) -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Validate TensorRT analysis JSON generated by analyze_trt_perf.py." + ) + parser.add_argument("input", help="Path to generated analysis JSON.") + parser.add_argument( + "--schema", + default=default_schema_path(), + help="Schema path. Defaults to schemas/analyze-data.schema.json.", + ) + parser.add_argument( + "--max-errors", + type=int, + default=40, + help="Maximum validation errors to print. Defaults to 40.", + ) + return parser.parse_args(argv) + + +def decode_ref_token(token: str) -> str: + return token.replace("~1", "/").replace("~0", "~") + + +def resolve_ref(ref: str, root_schema: Any) -> Any: + if not ref.startswith("#/"): + raise ValidationError(f"only local JSON Schema refs are supported: {ref}") + + current = root_schema + for token in ref[2:].split("/"): + key = decode_ref_token(token) + if not isinstance(current, dict) or key not in current: + raise ValidationError(f"unresolved schema ref: {ref}") + current = current[key] + return current + + +def type_label(instance: Any) -> str: + if instance is None: + return "null" + if isinstance(instance, bool): + return "boolean" + if isinstance(instance, int): + return "integer" + if isinstance(instance, float): + return "number" + if isinstance(instance, str): + return "string" + if isinstance(instance, list): + return "array" + if isinstance(instance, dict): + return "object" + return type(instance).__name__ + + +def matches_type(instance: Any, expected: str) -> bool: + if expected == "null": + return instance is None + if expected == "boolean": + return isinstance(instance, bool) + if expected == "integer": + return isinstance(instance, int) and not isinstance(instance, bool) + if expected == "number": + return isinstance(instance, (int, float)) and not isinstance(instance, bool) + if expected == "string": + return isinstance(instance, str) + if expected == "array": + return isinstance(instance, list) + if expected == "object": + return isinstance(instance, dict) + raise ValidationError(f"unsupported schema type: {expected}") + + +def matches_any_type(instance: Any, expected: Any) -> bool: + if isinstance(expected, str): + return matches_type(instance, expected) + if isinstance(expected, list): + return any(matches_type(instance, item) for item in expected) + raise ValidationError(f"invalid schema type declaration: {expected!r}") + + +def append_error(errors: List[str], path: str, message: str) -> None: + errors.append(f"{path}: {message}") + + +def append_nearest_errors(errors: List[str], matches: List[List[str]], limit: int = 5) -> None: + if not matches: + return + nearest = min(matches, key=lambda item: (sum("expected constant" in error for error in item), len(item))) + for error in nearest[:limit]: + errors.append(f" nearest schema: {error}") + + +def validate(instance: Any, schema: Any, path: str, root_schema: Any) -> List[str]: + if not isinstance(schema, dict): + raise ValidationError(f"{path}: schema node is not an object") + + if "$ref" in schema: + return validate(instance, resolve_ref(str(schema["$ref"]), root_schema), path, root_schema) + + errors: List[str] = [] + + if "allOf" in schema: + for subschema in schema["allOf"]: + errors.extend(validate(instance, subschema, path, root_schema)) + + if "anyOf" in schema: + matches = [validate(instance, subschema, path, root_schema) for subschema in schema["anyOf"]] + if not any(not match_errors for match_errors in matches): + append_error(errors, path, "does not match any allowed schema") + append_nearest_errors(errors, matches) + + if "oneOf" in schema: + matches = [validate(instance, subschema, path, root_schema) for subschema in schema["oneOf"]] + match_count = sum(1 for match_errors in matches if not match_errors) + if match_count != 1: + append_error(errors, path, f"matches {match_count} schemas, expected exactly one") + if match_count == 0: + append_nearest_errors(errors, matches) + + if "const" in schema and instance != schema["const"]: + append_error(errors, path, f"expected constant {schema['const']!r}, got {instance!r}") + + if "enum" in schema and instance not in schema["enum"]: + append_error(errors, path, f"expected one of {schema['enum']!r}, got {instance!r}") + + expected_type = schema.get("type") + if expected_type is not None and not matches_any_type(instance, expected_type): + append_error(errors, path, f"expected type {expected_type!r}, got {type_label(instance)}") + return errors + + if isinstance(instance, dict): + validate_object(instance, schema, path, root_schema, errors) + elif isinstance(instance, list): + validate_array(instance, schema, path, root_schema, errors) + elif isinstance(instance, (int, float)) and not isinstance(instance, bool): + validate_number(instance, schema, path, errors) + + return errors + + +def validate_object(instance: dict, schema: dict, path: str, root_schema: Any, errors: List[str]) -> None: + properties = schema.get("properties", {}) + required = schema.get("required", []) + additional = schema.get("additionalProperties", True) + + for key in required: + if key not in instance: + append_error(errors, path, f"missing required property {key!r}") + + for key, value in instance.items(): + child_path = f"{path}.{key}" + if key in properties: + errors.extend(validate(value, properties[key], child_path, root_schema)) + elif additional is False: + append_error(errors, child_path, "additional property is not allowed") + elif isinstance(additional, dict): + errors.extend(validate(value, additional, child_path, root_schema)) + + +def validate_array(instance: list, schema: dict, path: str, root_schema: Any, errors: List[str]) -> None: + min_items = schema.get("minItems") + if isinstance(min_items, int) and len(instance) < min_items: + append_error(errors, path, f"expected at least {min_items} items, got {len(instance)}") + + max_items = schema.get("maxItems") + if isinstance(max_items, int) and len(instance) > max_items: + append_error(errors, path, f"expected at most {max_items} items, got {len(instance)}") + + items_schema = schema.get("items") + if isinstance(items_schema, dict): + for index, value in enumerate(instance): + errors.extend(validate(value, items_schema, f"{path}[{index}]", root_schema)) + + +def validate_number(instance: float, schema: dict, path: str, errors: List[str]) -> None: + if "minimum" in schema and instance < schema["minimum"]: + append_error(errors, path, f"expected >= {schema['minimum']}, got {instance}") + if "maximum" in schema and instance > schema["maximum"]: + append_error(errors, path, f"expected <= {schema['maximum']}, got {instance}") + + +def main(argv: Optional[Sequence[str]] = None) -> int: + args = parse_args(argv) + + try: + data = load_json(args.input) + schema = load_json(args.schema) + errors = validate(data, schema, "$", schema) + except ValidationError as exc: + sys.stderr.write(f"error: {exc}\n") + return 2 + + if errors: + sys.stderr.write(f"{args.input}: FAIL ({len(errors)} schema error(s))\n") + for error in errors[: args.max_errors]: + sys.stderr.write(f"- {error}\n") + remaining = len(errors) - args.max_errors + if remaining > 0: + sys.stderr.write(f"... {remaining} more error(s) omitted\n") + return 1 + + sys.stdout.write(f"{args.input}: PASS\n") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/.agents/skills/trt-perf-analysis/scripts/visualize_layer_info.py b/.agents/skills/trt-perf-analysis/scripts/visualize_layer_info.py new file mode 100644 index 000000000..3c0b2ba17 --- /dev/null +++ b/.agents/skills/trt-perf-analysis/scripts/visualize_layer_info.py @@ -0,0 +1,12 @@ +#!/usr/bin/env python3 +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +"""Standalone TensorRT layer-info HTML visualizer.""" + +from __future__ import annotations + +from trt_perf.layer_info import main + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/.agents/skills/trt-strong-typing-migration/SKILL.md b/.agents/skills/trt-strong-typing-migration/SKILL.md new file mode 100644 index 000000000..4be476feb --- /dev/null +++ b/.agents/skills/trt-strong-typing-migration/SKILL.md @@ -0,0 +1,418 @@ +--- +name: trt-strong-typing-migration +description: >- + Migrate a TensorRT build from weak typing (deprecated 10.12, removed 11.0) to + strong typing — across Python INetworkDefinition builders, the trtexec CLI, and + C++ builder code. Use when a TRT 11 upgrade breaks a weakly-typed build. Triggers: weakly typed to strongly typed, + kSTRONGLY_TYPED, weak typing deprecated, kFP16/kINT8 removed, setPrecision rejected, + setComputePrecision deprecated, do I still need --stronglyTyped, how to add the + kSTRONGLY_TYPED flag, ModelOpt autocast, INT8 on TRT 11. NOT for ONNX import + (`trt-onnx-quickstart`), Torch-TRT (`trt-torch-quickstart`), or C++ deploy + (`trt-cpp-runtime-quickstart`). +license: Apache-2.0 +metadata: + author: NVIDIA Corporation + version: "1.0" + tags: + - migration + - strong-typing + - autocast + - modelopt + - builder +--- + +# Migrate Weakly-Typed to Strongly-Typed TensorRT Build + +Strong typing is the recommended build mode for TensorRT 11.x; weak typing was +deprecated in 10.12 and is scheduled for removal. This skill migrates a weakly-typed +build flow to a strongly-typed equivalent, one path per entry point (Python network, +`trtexec`, C++ builder), plus the ModelOpt AutoCast pre-step needed when the source +is an ONNX file that must run in mixed precision. + +**Version behavior.** On **TensorRT 11.x strong typing is unconditional** — every +network is strongly typed, the `kSTRONGLY_TYPED` creation flag is accepted but +ignored, and `trtexec --stronglyTyped` is a no-op (default-on). On **10.12–10.x** +you opt in explicitly via that flag / `--stronglyTyped`. Setting it is therefore +always safe: required on 10.x, harmless on 11.x. Either way the migration is the +same — remove the precision *hints* (gone in 11.x) and move precision into the graph. + +## When to Use + +| Scenario | Use this skill? | +|----------|-----------------| +| Existing Python build calls `config.set_flag(trt.BuilderFlag.FP16 / INT8 / TF32)` | Yes | +| Existing C++ build calls `config->setFlag(BuilderFlag::kFP16 / kINT8 / kTF32)` | Yes | +| Existing `trtexec` line uses `--fp16`, `--int8`, `--best`, `--bf16` and the developer wants strong typing | Yes | +| Build emits "weak typing is deprecated, use STRONGLY_TYPED" warning at runtime | Yes | +| Build fails with "flag X is incompatible with strongly typed networks" | Yes | +| Developer has a FP32 ONNX file and wants a strongly-typed mixed FP16 engine | Yes — start at Step 1 (AutoCast) | +| Developer is importing a brand-new ONNX file with no existing build flow | No — use `trt-onnx-quickstart` first; come back here to switch the flag | +| Developer's engine produces wrong outputs *after* migration | No — use `trt-quantization-accuracy` | +| Developer is on TensorRT 10.11 or earlier | No — strong typing is fully available from 10.12 onward; upgrade before migrating | + +## Prerequisites + +1. **TensorRT 10.12 or later (11.x recommended).** Check: + ```bash + python3 -c "import tensorrt; print(tensorrt.__version__)" + trtexec --help 2>&1 | grep -i stronglyTyped + ``` + The version check is the determinant — strong typing is available from 10.12 + on. The `trtexec` probe just confirms the CLI flag for the trtexec path; a + missing `--stronglyTyped` means a pre-10.12 install. + +2. **ModelOpt installed when the source is an ONNX file requiring mixed precision.** + ```bash + pip install nvidia-modelopt onnx onnxruntime + ``` + If the ONNX file is already strongly typed (contains explicit `Cast` nodes + and FP16 initializers), AutoCast is not needed — skip directly to Step 2. + +3. **A working baseline.** Capture the weakly-typed engine's outputs on a known + input set before migrating. Strong typing is stricter, and any silent precision + substitutions weak typing was making will become observable. + +4. **The verifier script.** `scripts/verify.sh` runs `migrate.py` against a + committed sample and asserts the rewrite is well-formed. Run it before editing + anything to confirm the helper works. + +## Migration paths + +There are three migration paths — Python builder, `trtexec`, C++ builder. +**Infer the path from the query when signals are clear; ask only if genuinely +uncertain.** Match the strongest signal: + +| Signal in the request | Path | Entry point | +|------------------------|------|-------------| +| Python tokens: `trt.BuilderFlag`, `set_flag(`, `create_network(`, `.py` | **A** | Python `INetworkDefinition` builder | +| Standalone `trtexec --fp16` / `--int8` / `--best` / `--bf16` | **B** | `trtexec` CLI | +| C++ tokens: `BuilderFlag::`, `createNetworkV2`, `->setFlag`, `.cpp` / `.h` | **C** | C++ builder | +| Multiple signals | — | do each, most-specific first | +| No usable signal (generic "how do I migrate") | — | ask which entry point, or proceed with Python and say you assumed it | + +The migration preserves existing precisions (kFP16 → FP16 cast, kINT8 → Q/DQ) and is +identical whether the network came from a parser or was hand-built — only Step 1 +(AutoCast) is ONNX-specific, so source and precision goal rarely need a question. + +Inferring the path is a low-stakes default — it does **not** extend to destructive +operations, which always need confirmation. `migrate.py` defaults to dry-run +(prints a diff); the `--write` flag is the human-in-the-loop confirmation. Treat +any other destructive step (overwriting a `.plan`, replacing an ONNX) the same way: +show, confirm, then act. + +For a strongly-typed INT8 model straight from an ONNX file (not a migration of +existing builder code), jump to Step 1's ModelOpt quantization recipe. + +## Step 1 — (ONNX source only) AutoCast the model to mixed precision + +Skip if the source is not ONNX, or if the ONNX file is already strongly typed. + +A FP32 ONNX file built for a weakly-typed `--fp16` flow carries no precision +information — TensorRT was free to pick FP16 or FP32 per layer. Strong typing +requires the graph itself to declare precision via `Cast` operators and typed +initializers. ModelOpt's AutoCast inserts these. + +Use the ModelOpt CLI — it's the supported entry point and takes `.npz` calibration +directly. Use `--calibration_data` for both tools (autocast's canonical flag; +quantization's abbreviation of `--calibration_data_path`): + +```bash +# FP16 / BF16 mixed-precision AutoCast +python -m modelopt.onnx.autocast \ + --onnx_path model.fp32.onnx \ + --output_path model.mixed.onnx \ + --low_precision_type fp16 \ + --calibration_data calib.npz + +# INT8 / FP8 strongly-typed quantization (produces ONNX with explicit Q/DQ) +python -m modelopt.onnx.quantization \ + --onnx_path model.fp32.onnx \ + --output_path model.int8.onnx \ + --quantize_mode int8 \ + --high_precision_dtype fp16 \ + --calibration_data calib.npz +``` + +Keep accuracy-sensitive ops in FP32 via AutoCast's node/op-type exclusion lists +(e.g. exclude `MatMul`/`LayerNormalization` on transformer-heavy networks), and keep +FP32 graph I/O when downstream stages expect it. See the +[Model Optimizer docs](https://nvidia.github.io/Model-Optimizer) for the exact +module and flags for your ModelOpt version. + +Verify the converted model in ONNX Runtime before passing it to TRT: + +```python +import onnxruntime, numpy as np +sess = onnxruntime.InferenceSession("model.mixed.onnx", providers=["CPUExecutionProvider"]) +# Compare a few outputs against the FP32 model with np.allclose(..., rtol=5e-3, atol=5e-3). +``` + +If parity fails here, **stop** — the conversion is wrong and no amount of TRT +engine work will recover it. Re-run with tighter exclusion lists. + +**Next step — build the strongly-typed plan from the converted ONNX:** + +```bash +# For the INT8 case (modelopt.onnx.quantization output) +trtexec --onnx=model.int8.onnx --stronglyTyped --saveEngine=model.int8.plan + +# For the mixed-precision case (modelopt.onnx.autocast output) +trtexec --onnx=model.mixed.onnx --stronglyTyped --saveEngine=model.fp16.plan +``` + +**Critical**: do NOT pass `--int8` or `--fp16` alongside `--stronglyTyped`. Those flags are removed in TRT 11, and on TRT 10.12+ they conflict with strong typing. The precision is already encoded in the ONNX (via explicit Cast / Q / DQ nodes); the builder honors it. + +The reference recipe for the end-to-end INT8 flow is `samples/python/strongly_type_autocast/sample.py` in `tensorrt-oss`. + +## Step 2A — Migrate a Python `INetworkDefinition` builder + +Mechanical replacements: + +| Weakly typed | Strongly typed | +|--------------|----------------| +| `builder.create_network(0)` or `builder.create_network(1 << int(NetworkDefinitionCreationFlag.EXPLICIT_BATCH))` | `builder.create_network(1 << int(NetworkDefinitionCreationFlag.STRONGLY_TYPED))` | +| `config.set_flag(trt.BuilderFlag.FP16)` | **Remove.** Types come from the network. | +| `config.set_flag(trt.BuilderFlag.BF16)` | **Remove.** | +| `config.set_flag(trt.BuilderFlag.INT8)` | **Remove.** Quantization expressed via Q/DQ nodes in the graph. | +| `config.set_flag(trt.BuilderFlag.TF32)` | **Keep.** `kTF32` is retained in 11.x — it allows (not requires) TF32 for FP32 tensors and is orthogonal to typing. | +| `layer.precision = trt.float16` | **Remove.** Cast the input tensor instead (`network.add_cast`). | +| `layer.set_output_type(0, trt.float16)` | **Remove.** Output type follows the network; for cast/quantize/dequantize/fill layers use `set_to_type` instead. | +| `builder.platform_has_fast_int8 / has_fast_fp16` checks gating the above | **Remove the gating logic** along with the flag. | + +Worked rewrite (the MNIST builder from `samples/python/network_api_pytorch_mnist/sample.py` +already uses the strongly-typed form — note `NetworkDefinitionCreationFlag.STRONGLY_TYPED` +and the absence of any `BuilderFlag.FP16` call): + +```python +builder = trt.Builder(TRT_LOGGER) +network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.STRONGLY_TYPED)) +config = builder.create_builder_config() +config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, common.GiB(1)) +# ... populate network ... +plan = builder.build_serialized_network(network, config) +``` + +If the network is built via the ONNX parser, the parser respects strong typing +automatically — no code change is needed beyond the flag. + +Where a removed `precision` / `set_output_type` hint actually mattered, encode it +in the graph: cast a tensor's dtype with `add_cast`, or insert an explicit +Quantize/Dequantize pair for INT8 (the scale is a build-time constant; ModelOpt +derives it from calibration, or supply it by hand): + +```python +# kFP16 hint -> explicit cast on the tensor: +cast = network.add_cast(some_tensor, trt.float16) + +# kINT8 hint -> explicit Q/DQ pair (per-tensor scalar scale). NOTE: trt.Weights does +# not copy the numpy buffer, so q_scale must stay alive until the engine is built. +q_scale = np.array(1.0 / 127.0, dtype=np.float32).reshape(()) # rank-0 scalar +scale = network.add_constant(shape=(), weights=trt.Weights(q_scale)).get_output(0) +q = network.add_quantize(some_tensor, scale, trt.int8).get_output(0) +dq = network.add_dequantize(q, scale, trt.float32).get_output(0) +# Feed dq into the consumer; TensorRT fuses the Q/DQ into it so it runs in INT8. +``` + +Drive the rewrite from `scripts/migrate.py`: + +```bash +python3 scripts/migrate.py path/to/build.py # dry-run diff +python3 scripts/migrate.py path/to/build.py --write # rewrite in place +``` + +The script edits AST nodes (not regexes) — it understands `create_network` and +`set_flag` call shapes, so it is safe on files that mix conventions. + +## Step 2B — Migrate a `trtexec` command line + +Replacements: + +| Weakly typed flag | Strongly typed equivalent | +|-------------------|---------------------------| +| `--fp16` | **Remove.** Precision comes from the ONNX. See the `--stronglyTyped` note below. | +| `--bf16` | **Remove.** Same as `--fp16`. | +| `--int8` | **Remove.** Q/DQ must be present in the ONNX. | +| `--best` | **Remove.** Not allowed with `--stronglyTyped` on 10.12–10.x (see Common Errors). | +| `--stronglyTyped` | **Version-gated.** Required on 10.12–10.x to opt into strong typing; a no-op (default-on) on 11.x — drop it or leave it, your choice. | +| `--noTF32` | **Keep.** `kTF32` is retained in 11.x; `--noTF32` still disables TF32 and is orthogonal to typing. | +| `--precisionConstraints=obey` / `prefer` | **Remove.** Constraints are expressed in the graph. | +| `--layerPrecisions=...` | **Remove.** Use ONNX `Cast` nodes / AutoCast. | +| `--layerOutputTypes=...` | **Remove.** | + +Worked rewrite: + +```bash +# Before (weakly typed) +trtexec --onnx=model.onnx --fp16 --saveEngine=model.fp16.plan \ + --minShapes=input:1x3x224x224 --optShapes=input:8x3x224x224 \ + --maxShapes=input:16x3x224x224 + +# After (strongly typed; ONNX has been AutoCast'd to mixed FP16) +# On 11.x --stronglyTyped is the default and a no-op — the line below is equivalent +# with or without it. On 10.12–10.x it is REQUIRED, or the build stays weakly typed. +trtexec --onnx=model.mixed.onnx --stronglyTyped --saveEngine=model.fp16.plan \ + --minShapes=input:1x3x224x224 --optShapes=input:8x3x224x224 \ + --maxShapes=input:16x3x224x224 +``` + +The `trtexec` README's [Example 6](https://github.com/NVIDIA/TensorRT/blob/main/samples/trtexec/README.md#example-6-create-a-strongly-typed-plan-file) +is the canonical reference. + +## Step 2C — Migrate a C++ builder + +Replacements mirror the Python path: + +| Weakly typed (C++) | Strongly typed (C++) | +|---|---| +| `builder->createNetworkV2(0)` | `builder->createNetworkV2(1U << static_cast(NetworkDefinitionCreationFlag::kSTRONGLY_TYPED))` | +| `config->setFlag(BuilderFlag::kFP16)` | **Remove.** | +| `config->setFlag(BuilderFlag::kBF16)` | **Remove.** | +| `config->setFlag(BuilderFlag::kINT8)` | **Remove.** | +| `config->setFlag(BuilderFlag::kTF32)` | **Keep.** `kTF32` is retained in 11.x — orthogonal to typing. | +| `config->setFlag(BuilderFlag::kOBEY_PRECISION_CONSTRAINTS / kPREFER_PRECISION_CONSTRAINTS)` | **Remove.** No per-layer precision exists to constrain. | +| `layer->setPrecision(DataType::kHALF)` | **Remove.** Cast the input tensor instead (`addCast`). | +| `layer->setComputePrecision(...)` | **Remove.** Deprecated 10.16, removed; compute precision follows input dtypes. | +| `layer->setOutputType(0, DataType::kHALF)` | **Remove.** For cast/quantize/dequantize/fill layers use `setToType()` instead. | + +Worked rewrite: + +```cpp +// Before (10.x weakly typed) — the builder picks precision from these hints: +auto network = std::unique_ptr( + builder->createNetworkV2(1U << static_cast(NetworkDefinitionCreationFlag::kEXPLICIT_BATCH))); +auto config = std::unique_ptr(builder->createBuilderConfig()); +config->setFlag(BuilderFlag::kFP16); // hint: builder may use FP16 +config->setFlag(BuilderFlag::kINT8); // hint: builder may use INT8 (+ a calibrator) +config->setFlag(BuilderFlag::kOBEY_PRECISION_CONSTRAINTS); // force the per-layer overrides below +someLayer->setPrecision(DataType::kHALF); // per-layer override +someLayer->setOutputType(0, DataType::kHALF); // per-layer override +``` + +Each hint above maps to a concrete graph edit — there is no longer a "tell the +builder to prefer X" knob, so state the dtype directly: + +```cpp +// After (11.x strongly typed) — precision lives in the graph; every line above is gone. +auto network = std::unique_ptr( + builder->createNetworkV2(1U << static_cast(NetworkDefinitionCreationFlag::kSTRONGLY_TYPED))); + // kSTRONGLY_TYPED: required on 10.12–10.x, accepted-but-ignored on 11.x (0 works too). +auto config = std::unique_ptr(builder->createBuilderConfig()); +// No precision setFlag, no setPrecision / setOutputType. + +// kFP16 hint -> cast the tensor's dtype explicitly: +auto* cast = network->addCast(*someTensor, DataType::kHALF); +// feed cast->getOutput(0) into the consumer layer + +// kINT8 hint -> insert an explicit Quantize/Dequantize pair. The scale is a +// build-time constant (per-tensor scalar here); ModelOpt derives it from calibration, +// or supply it by hand. NOTE: TensorRT does not copy Weights, so qScale must stay +// alive until the engine is built. +float qScale = 1.0f / 127.0f; +auto* scale = network->addConstant(Dims{0, {}}, + Weights{DataType::kFLOAT, &qScale, 1})->getOutput(0); +auto* q = network->addQuantize(*someTensor, *scale, DataType::kINT8)->getOutput(0); +auto* dq = network->addDequantize(*q, *scale, DataType::kFLOAT)->getOutput(0); +// feed dq into the consumer; TensorRT fuses the Q/DQ into it so it runs in INT8. + +// kOBEY_PRECISION_CONSTRAINTS + setPrecision/setOutputType -> nothing to port: +// the dtype is now fixed by the cast / Q-DQ above, so there is no constraint to obey. +``` + +`setToType()` is the only surviving per-layer type control, and is valid **only** +on `ICastLayer`, `IDequantizeLayer`, `IDynamicQuantizeLayer`, `IFillLayer`, and +`IQuantizeLayer` — not on arbitrary layers. + +If the build pipeline parses ONNX via `nvonnxparser::IParser`, no parser change is +required — the parser honors the strong typing flag set on the network. + +## Step 3 — Rebuild and validate + +1. Rebuild the engine with the migrated flow. +2. Run the captured baseline inputs through the new engine. +3. Compare against the weakly-typed baseline: + - **Outputs match within `atol=5e-3, rtol=5e-3`**: migration is complete. + - **Outputs diverge but stay within model accuracy tolerance**: weak typing + was opportunistically using FP16/INT8 where the new graph keeps FP32 (or vice + versa). Acceptable if the downstream metric (top-1, BLEU, WER) is unchanged; + confirm with the original accuracy harness. + - **Outputs diverge beyond tolerance**: see Common Errors below. + +If the source was ONNX, also run the AutoCast'd ONNX through ONNX Runtime and +compare against the FP32 ONNX before blaming TRT — graph-level conversion errors +should be caught there. + +## Migration Patterns Reference + +| Pattern | Action | +|---------|--------| +| Network created with no flags (legacy implicit batch) | First migrate to explicit batch; only then add `STRONGLY_TYPED` | +| Multiple `set_flag` calls (FP16 + INT8 + TF32 + REFIT) | Keep `REFIT`, `SPARSE_WEIGHTS`, `kTF32`; drop the precision-hint flags (FP16/BF16/INT8/FP8) and the removed `kOBEY`/`kPREFER_PRECISION_CONSTRAINTS` | +| `set_calibration_profile` / `IInt8Calibrator` | **Remove.** PTQ calibration is replaced by Q/DQ in the ONNX. Use ModelOpt's PTQ for the model first. | +| Mixed FP16/FP32 per-layer overrides via `layer.precision` | Express the same intent with `Cast` ops in the ONNX, or in the Python network construction code | +| Plugin layers with explicit precision | Plugins must implement `IPluginV3OneBuild::getOutputDataTypes`; the network reads types from the plugin | +| Engine consumed by downstream framework expecting FP32 I/O | Set `keep_io_types=True` in AutoCast, or add explicit `Cast` to FP32 at the network outputs | + +## Common Errors + +- **`"BuilderFlag::kFP16 is not compatible with a strongly typed network"`** — + a `setFlag`/`set_flag` call survived the migration. Re-run `migrate.py` or grep + for `BuilderFlag\.` / `BuilderFlag::` in the migrated source. +- **`"--best is incompatible with --stronglyTyped"`** — drop `--best` from the + `trtexec` line; precision choices live in the graph now. +- **`"Network has no marked outputs"` immediately after switching the flag** — + some builders only call `mark_output` on the path conditioned by + `platform_has_fast_fp16`. Remove the conditional. +- **AutoCast'd ONNX fails the parser with "If subgraph type mismatch"** — AutoCast + can corrupt certain `If` node subgraphs. Add the affected `If` node names to + `nodes_to_exclude` so they stay FP32. +- **Engine output is FP32 zeros after migration** — `keep_io_types=False` was used + but the host code still feeds FP32 buffers. Either set `keep_io_types=True` or + cast the host input before binding. +- **Accuracy regression of ~1-3% after migration** — known case for + transformer-heavy networks: leftover `Cast(to=FLOAT32)` from AutoCast combined + with FP16 weights causes thrash. Work around by excluding the residual cast op + types in `op_types_to_exclude`. + +## Pitfalls + +- **Do not partially migrate.** Half-migrated builds (some `set_flag` calls + removed, `STRONGLY_TYPED` not set) silently fall back to weak typing and the + warning is easy to miss. Check the engine build log for `"Strongly typed network + detected"` to confirm the flag took effect. +- **Do not migrate without a baseline.** Weak typing's per-layer precision choices + are non-deterministic across TRT versions; without the old outputs you have no + ground truth and cannot tell whether the new engine is correct. +- **Do not run `--stronglyTyped` against a FP32 ONNX expecting FP16 speedup.** + Strong typing builds exactly what the graph specifies — a FP32 ONNX produces a + FP32 engine. Run AutoCast first. +- **Plugins must declare types.** Old plugins that left output types as "follow + input" will fail to build under strong typing. Update them to the + `IPluginV3OneBuild` interface, the only plugin API that supports strong typing + in 11.x. +- **Refit + strong typing**: refittable strongly-typed engines work, but the refit + weights must match the precision declared in the network. A FP16 layer cannot be + refit with FP32 weights — convert host weights before calling `setNamedWeights`. +- **Don't trust grep alone for completeness.** `migrate.py` uses AST matching, so + it catches the precision flags regardless of how `tensorrt` is imported or + aliased via `as` — something string-level grep misses. It matches direct + attribute access (`trt.BuilderFlag.FP16`); dynamic forms such as + `getattr(trt.BuilderFlag, 'FP16')` are *not* rewritten and need manual review. + Run the AST helper as the source of truth. + +## References + +- **Migration guide:** [TensorRT 10.x → 11.x](https://docs.nvidia.com/deeplearning/tensorrt/latest/api/migration/tensorrt-10x-to-11x.html) + — the authoritative description of the weak → strong typing change. +- **Operator type rules:** the [TensorRT Operators Reference](https://docs.nvidia.com/deeplearning/tensorrt/latest/_static/operators/) + — each operator's page lists its supported input/output types, i.e. how its output + dtype follows its inputs in a strongly-typed graph. +- **API reference:** the TensorRT C++/Python API docs for `NetworkDefinitionCreationFlag`, + `BuilderFlag`, `ILayer::setToType`, and `INetworkDefinition::addCast` / `addQuantize` / + `addDequantize`. +- **TensorRT Model Optimizer:** — AutoCast and + quantization tooling for getting precision into the model. +- **Sample code** (TensorRT OSS, ): + [`samples/trtexec/README.md` Example 6](https://github.com/NVIDIA/TensorRT/blob/main/samples/trtexec/README.md#example-6-create-a-strongly-typed-plan-file) + (strongly-typed plan), `samples/python/strongly_type_autocast/sample.py` + (AutoCast → strongly-typed INT8), and `samples/python/network_api_pytorch_mnist/sample.py` + (strongly-typed Python builder). diff --git a/.agents/skills/trt-strong-typing-migration/scripts/README.md b/.agents/skills/trt-strong-typing-migration/scripts/README.md new file mode 100644 index 000000000..b2718a6a2 --- /dev/null +++ b/.agents/skills/trt-strong-typing-migration/scripts/README.md @@ -0,0 +1,65 @@ +# trt-strong-typing-migration helper scripts + +Scripts that automate the mechanical parts of the SKILL.md walkthrough. + +## `migrate.py` + +AST-based rewriter for Python TensorRT builder code. Detects the +weakly-typed builder pattern (`create_network` + `set_flag(BuilderFlag.FP16/...)`) +and rewrites it to the strongly-typed form. + +```bash +# Show what would change (dry-run): prints a unified diff; exits 1 if changes are pending, 0 if nothing to do. +python3 migrate.py path/to/build.py + +# Rewrite the file in place: +python3 migrate.py path/to/build.py --write + +# Process every .py file in a directory tree: +python3 migrate.py path/to/project/ --write +``` + +The script uses `ast.NodeTransformer` so it understands call shapes, not just +text — it correctly handles `set_flag` accessed via aliased imports, multi-flag +construction in `create_network`, and per-layer `precision`/`set_output_type` +assignments. Unrelated logic and docstrings are left intact, but note: because +the rewrite round-trips through `ast.unparse`, **regular `#` comments and the +original formatting are not preserved**. Review the dry-run diff before +`--write`, and re-add any comments you need afterward. + +### What it changes + +| Before | After | +|--------|-------| +| `builder.create_network(0)` | `builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.STRONGLY_TYPED))` | +| `builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))` | `builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.STRONGLY_TYPED))` | +| `config.set_flag(trt.BuilderFlag.FP16 / BF16 / INT8 / FP8)` | (line removed) | +| `layer.precision = trt.float16` | (statement removed) | +| `layer.set_output_type(0, trt.float16)` | (statement removed) | + +### What it does NOT change + +- `set_flag` calls for non-precision-hint flags (`REFIT`, `SPARSE_WEIGHTS`, + `DISABLE_TIMING_CACHE`, and `TF32`). These are orthogonal to typing — `kTF32` + is kept in TRT 11. +- Calibration setup (`set_calibration_profile`, `IInt8Calibrator`). These must + be removed manually because the correct replacement (Q/DQ in the ONNX) lives + outside the build script. +- Conditional logic gating on `platform_has_fast_fp16` / `platform_has_fast_int8` + — the body of those branches becomes a no-op after the flag is removed, but + the conditional remains. Clean up manually if desired. + +## `verify.sh` + +End-to-end check that `migrate.py` is doing the right thing. Writes a +representative weakly-typed sample to a temp directory, runs `migrate.py +--write` on it, then asserts the rewritten file matches the strongly-typed +contract (flag present, precision flags absent, valid Python). + +```bash +bash verify.sh # run and clean up +bash verify.sh --keep # keep the temp workspace for inspection +``` + +Exits 0 on success, 1 on any assertion failure. Run this before applying +`migrate.py` to a real codebase. diff --git a/.agents/skills/trt-strong-typing-migration/scripts/migrate.py b/.agents/skills/trt-strong-typing-migration/scripts/migrate.py new file mode 100755 index 000000000..3d9738fb1 --- /dev/null +++ b/.agents/skills/trt-strong-typing-migration/scripts/migrate.py @@ -0,0 +1,305 @@ +#!/usr/bin/env python3 +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +"""AST-based migrator for weakly-typed TensorRT Python builders. + +Rewrites: + + - `builder.create_network(...)` -> uses NetworkDefinitionCreationFlag.STRONGLY_TYPED + - `config.set_flag(trt.BuilderFlag.{FP16,BF16,INT8,FP8})` -> removed + - `layer.precision = ...` and `layer.set_output_type(...)` -> removed + +The rewrites are intentionally conservative: ambiguous patterns are skipped and +reported so the user can resolve them manually. Non-precision flags (REFIT, +SPARSE_WEIGHTS, TF32, etc.) are preserved — kTF32 is kept in TRT 11 and is +orthogonal to typing. + +Usage: + python3 migrate.py path/to/file.py # dry-run, print unified diff + python3 migrate.py path/to/file.py --write # rewrite in place + python3 migrate.py path/to/dir/ --write # recurse over .py files +""" +from __future__ import annotations + +import argparse +import ast +import difflib +import sys +from pathlib import Path +from typing import Iterable + +# Precision-hint BuilderFlag attribute names that must be removed. NOTE: TF32 is +# deliberately NOT here — kTF32 is kept in TRT 11 (orthogonal to typing), like REFIT. +PRECISION_FLAGS = frozenset({"FP16", "BF16", "INT8", "FP8"}) + +# NetworkDefinitionCreationFlag attribute names that map to STRONGLY_TYPED. +WEAK_NETWORK_FLAGS = frozenset({"EXPLICIT_BATCH"}) + + +def _is_attr(node: ast.AST, *, attr_chain: tuple[str, ...]) -> bool: + """Return True if `node` is an attribute access matching the trailing chain. + + Walks `node.attr -> node.value.attr -> ...` and compares to attr_chain + in reverse. The leading element (e.g. module / object name) is not checked, + so this matches `trt.BuilderFlag.FP16`, `tensorrt.BuilderFlag.FP16`, or any + aliased import. + """ + cur = node + for expected in reversed(attr_chain): + if not isinstance(cur, ast.Attribute): + return False + if cur.attr != expected: + return False + cur = cur.value + return True + + +def _builder_flag_name(node: ast.AST) -> str | None: + """If `node` is `.BuilderFlag.`, return NAME.""" + if isinstance(node, ast.Attribute) and isinstance(node.value, ast.Attribute): + if node.value.attr == "BuilderFlag": + return node.attr + return None + + +def _network_flag_name(node: ast.AST) -> str | None: + """If `node` is `.NetworkDefinitionCreationFlag.`, return NAME.""" + if isinstance(node, ast.Attribute) and isinstance(node.value, ast.Attribute): + if node.value.attr == "NetworkDefinitionCreationFlag": + return node.attr + return None + + +def _strongly_typed_arg() -> ast.expr: + """Build the AST for `1 << int(trt.NetworkDefinitionCreationFlag.STRONGLY_TYPED)`. + + Uses bare `trt.` qualifier — matches the canonical import alias. Callers + whose code uses `tensorrt.` directly will get a working but visually + inconsistent line; that is acceptable as a one-line manual cleanup. + """ + return ast.BinOp( + left=ast.Constant(value=1), + op=ast.LShift(), + right=ast.Call( + func=ast.Name(id="int", ctx=ast.Load()), + args=[ + ast.Attribute( + value=ast.Attribute( + value=ast.Name(id="trt", ctx=ast.Load()), + attr="NetworkDefinitionCreationFlag", + ctx=ast.Load(), + ), + attr="STRONGLY_TYPED", + ctx=ast.Load(), + ) + ], + keywords=[], + ), + ) + + +class Migrator(ast.NodeTransformer): + def __init__(self) -> None: + self.removed_flag_calls = 0 + self.removed_precision_assigns = 0 + self.removed_set_output_type = 0 + self.rewrote_create_network = 0 + self.skipped: list[str] = [] + + # ---- create_network rewrites ---------------------------------------- + + def visit_Call(self, node: ast.Call) -> ast.AST: + self.generic_visit(node) + func = node.func + if isinstance(func, ast.Attribute) and func.attr == "create_network": + # Replace the single positional argument with the STRONGLY_TYPED flag. + if len(node.args) == 0: + # No positional arg: the flag may be passed as `flags=` keyword. + # Blindly appending a positional arg would collide with it + # (TypeError: got multiple values for argument 'flags'). + flags_kw = next((kw for kw in node.keywords if kw.arg == "flags"), None) + if flags_kw is not None: + if self._is_weak_network_arg(flags_kw.value): + flags_kw.value = _strongly_typed_arg() + self.rewrote_create_network += 1 + elif self._already_strongly_typed(flags_kw.value): + pass # idempotent + else: + self.skipped.append( + f"create_network at line {node.lineno}: unrecognized flags= " + "argument shape, review manually" + ) + elif not node.keywords: + # Truly empty call create_network(): defaults to weak typing. + node.args = [_strongly_typed_arg()] + self.rewrote_create_network += 1 + else: + self.skipped.append( + f"create_network at line {node.lineno}: keyword args present " + "without flags=, review manually" + ) + elif len(node.args) == 1: + arg = node.args[0] + if self._is_weak_network_arg(arg): + node.args[0] = _strongly_typed_arg() + self.rewrote_create_network += 1 + elif self._already_strongly_typed(arg): + pass # idempotent + else: + self.skipped.append( + f"create_network at line {node.lineno}: unrecognized argument shape, " + "review manually" + ) + return node + + @staticmethod + def _is_weak_network_arg(arg: ast.expr) -> bool: + # Match `0` + if isinstance(arg, ast.Constant) and arg.value == 0: + return True + # Match `1 << int(...EXPLICIT_BATCH)` (any weak-network flag name) + if isinstance(arg, ast.BinOp) and isinstance(arg.op, ast.LShift): + inner = arg.right + if isinstance(inner, ast.Call) and isinstance(inner.func, ast.Name) and inner.func.id == "int": + if inner.args: + name = _network_flag_name(inner.args[0]) + if name in WEAK_NETWORK_FLAGS: + return True + return False + + @staticmethod + def _already_strongly_typed(arg: ast.expr) -> bool: + if isinstance(arg, ast.BinOp) and isinstance(arg.op, ast.LShift): + inner = arg.right + if isinstance(inner, ast.Call) and isinstance(inner.func, ast.Name) and inner.func.id == "int": + if inner.args and _network_flag_name(inner.args[0]) == "STRONGLY_TYPED": + return True + return False + + # ---- set_flag / precision assignment removals ----------------------- + + def visit_Expr(self, node: ast.Expr) -> ast.AST | None: + # `config.set_flag(trt.BuilderFlag.FP16)` as an Expr statement. + self.generic_visit(node) + if isinstance(node.value, ast.Call): + call = node.value + if isinstance(call.func, ast.Attribute) and call.func.attr == "set_flag": + if call.args: + flag = _builder_flag_name(call.args[0]) + if flag in PRECISION_FLAGS: + self.removed_flag_calls += 1 + return None + # `layer.set_output_type(0, trt.float16)` — drop unconditionally. + if isinstance(call.func, ast.Attribute) and call.func.attr == "set_output_type": + self.removed_set_output_type += 1 + return None + return node + + def visit_Assign(self, node: ast.Assign) -> ast.AST | None: + # `something.precision = trt.float16` (or any value) — drop. + self.generic_visit(node) + if len(node.targets) == 1: + tgt = node.targets[0] + if isinstance(tgt, ast.Attribute) and tgt.attr == "precision": + self.removed_precision_assigns += 1 + return None + return node + + # ---- if-statement cleanup ------------------------------------------ + + def visit_If(self, node: ast.If) -> ast.AST | None: + """Drop `if builder.platform_has_fast_{fp16,int8,bf16}:` blocks that become empty.""" + self.generic_visit(node) + test = node.test + gating_attrs = {"platform_has_fast_fp16", "platform_has_fast_int8", + "platform_has_fast_bf16", "platform_has_fast_fp8"} + if isinstance(test, ast.Attribute) and test.attr in gating_attrs: + # If body becomes empty (because set_flag was the only statement) and + # there is no else clause, the whole if is dead. + if not node.body and not node.orelse: + return None + if not node.body: + # Body emptied but orelse remains — replace with the orelse block. + return node.orelse if len(node.orelse) > 1 else ( + node.orelse[0] if node.orelse else None + ) + return node + + +def _process_source(src: str) -> tuple[str, Migrator]: + tree = ast.parse(src) + m = Migrator() + new_tree = m.visit(tree) + ast.fix_missing_locations(new_tree) + return ast.unparse(new_tree), m + + +def _iter_files(paths: Iterable[Path]) -> Iterable[Path]: + for p in paths: + if p.is_dir(): + yield from sorted(p.rglob("*.py")) + elif p.suffix == ".py": + yield p + + +def _process_file(path: Path, *, write: bool) -> bool: + """Return True iff the file needs (or got) a migration. + + Keyed on the migrator's transform counts, not on text equality: `ast.unparse` + reformats and drops comments on every round-trip, so a file that needs no + migration would otherwise look "changed" and have its comments stripped. + """ + original = path.read_text() + try: + new_src, m = _process_source(original) + except SyntaxError as e: + print(f"[skip] {path}: syntax error: {e}", file=sys.stderr) + return False + + migrated = (m.rewrote_create_network + m.removed_flag_calls + + m.removed_precision_assigns + m.removed_set_output_type) > 0 + if not migrated: + for note in m.skipped: + print(f"[note] {path}: {note}", file=sys.stderr) + return False + + if write: + path.write_text(new_src) + print( + f"[write] {path}: create_network={m.rewrote_create_network} " + f"set_flag={m.removed_flag_calls} " + f"precision_assign={m.removed_precision_assigns} " + f"set_output_type={m.removed_set_output_type}" + ) + else: + diff = difflib.unified_diff( + original.splitlines(keepends=True), + new_src.splitlines(keepends=True), + fromfile=f"{path} (weakly typed)", + tofile=f"{path} (strongly typed)", + ) + sys.stdout.writelines(diff) + for note in m.skipped: + print(f"[note] {path}: {note}", file=sys.stderr) + return True + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("paths", nargs="+", type=Path, help="Files or directories to process.") + parser.add_argument("--write", action="store_true", + help="Rewrite files in place. Default is dry-run with unified diff.") + args = parser.parse_args() + + any_changed = False + for path in _iter_files(args.paths): + if _process_file(path, write=args.write): + any_changed = True + + # In dry-run, exit non-zero when changes are pending so callers/CI can gate + # on it (like `black --check`). `--write` applies the changes and exits 0. + return 1 if any_changed and not args.write else 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/.agents/skills/trt-strong-typing-migration/scripts/verify.sh b/.agents/skills/trt-strong-typing-migration/scripts/verify.sh new file mode 100755 index 000000000..c70c3d3f0 --- /dev/null +++ b/.agents/skills/trt-strong-typing-migration/scripts/verify.sh @@ -0,0 +1,118 @@ +#!/usr/bin/env bash +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Verify the `migrate.py` helper on an inline weakly-typed sample. +# Run from anywhere; resolves paths relative to this script. +# +# Usage: +# bash scripts/verify.sh # run the verification end-to-end +# bash scripts/verify.sh --keep # keep the temp workspace for inspection + +set -euo pipefail + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +MIGRATE="${SCRIPT_DIR}/migrate.py" + +if [[ ! -f "${MIGRATE}" ]]; then + echo "ERROR: migrate.py not found at ${MIGRATE}" >&2 + exit 1 +fi + +KEEP=0 +if [[ "${1:-}" == "--keep" ]]; then + KEEP=1 +fi + +WORK="$(mktemp -d -t trt-w2s-verify-XXXXXX)" + +cleanup_workspace() { + if [[ ${KEEP} -eq 1 ]]; then + echo "Kept workspace at ${WORK}" + else + rm -rf "${WORK}" + fi +} +trap cleanup_workspace EXIT + +SAMPLE="${WORK}/sample_build.py" + +# A representative weakly-typed Python builder. Contains every transform path +# migrate.py is expected to handle: EXPLICIT_BATCH network flag, set_flag for +# FP16/INT8 (removed) plus TF32/REFIT (preserved), per-layer precision overrides, +# and the platform_has_fast_* gating that becomes dead code after migration. +cat > "${SAMPLE}" <<'PY' +import tensorrt as trt + +TRT_LOGGER = trt.Logger(trt.Logger.WARNING) + +def build(): + builder = trt.Builder(TRT_LOGGER) + network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)) + config = builder.create_builder_config() + + if builder.platform_has_fast_fp16: + config.set_flag(trt.BuilderFlag.FP16) + config.set_flag(trt.BuilderFlag.TF32) # kept in TRT 11 (orthogonal to typing); must survive + config.set_flag(trt.BuilderFlag.INT8) + config.set_flag(trt.BuilderFlag.REFIT) # not a precision flag; must survive + + parser = trt.OnnxParser(network, TRT_LOGGER) + with open("model.onnx", "rb") as f: + parser.parse(f.read()) + + last = network.get_layer(network.num_layers - 1) + last.precision = trt.float16 + last.set_output_type(0, trt.float16) + + plan = builder.build_serialized_network(network, config) + return plan +PY + +echo "[verify] sample written to ${SAMPLE}" + +echo "[verify] dry-run diff (exit 1 = changes pending, expected here):" +python3 "${MIGRATE}" "${SAMPLE}" || true + +echo "[verify] writing rewrite in place:" +python3 "${MIGRATE}" "${SAMPLE}" --write + +# Assertions on the rewritten file. +fail=0 +assert_present() { + local needle="$1" + if ! grep -qF "${needle}" "${SAMPLE}"; then + echo "ASSERT FAIL: expected to find: ${needle}" >&2 + fail=1 + fi +} +assert_absent() { + local needle="$1" + if grep -qF "${needle}" "${SAMPLE}"; then + echo "ASSERT FAIL: expected to be removed: ${needle}" >&2 + fail=1 + fi +} + +assert_present "NetworkDefinitionCreationFlag.STRONGLY_TYPED" +assert_absent "NetworkDefinitionCreationFlag.EXPLICIT_BATCH" +assert_absent "BuilderFlag.FP16" +assert_absent "BuilderFlag.INT8" +assert_present "BuilderFlag.TF32" # kTF32 is kept in TRT 11; must NOT be stripped +assert_present "BuilderFlag.REFIT" # non-precision flag must remain +assert_absent ".precision = trt.float16" +assert_absent "set_output_type(0, trt.float16)" + +# Syntactic validity. +python3 -c "import ast, sys; ast.parse(open('${SAMPLE}').read())" || { + echo "ASSERT FAIL: rewritten file is not valid Python" >&2 + fail=1 +} + +if [[ ${fail} -ne 0 ]]; then + echo "[verify] FAIL — see assertions above. Workspace: ${WORK}" + KEEP=1 + exit 1 +fi + +echo "[verify] OK — migrate.py produced a well-formed strongly-typed rewrite" diff --git a/.agents/skills/trt-torch-quickstart/SKILL.md b/.agents/skills/trt-torch-quickstart/SKILL.md new file mode 100644 index 000000000..1d0aad145 --- /dev/null +++ b/.agents/skills/trt-torch-quickstart/SKILL.md @@ -0,0 +1,266 @@ +--- +name: trt-torch-quickstart +description: > + Compile a PyTorch model to a TensorRT engine via Torch-TensorRT — AOT or + JIT — under the new strong-typing default. Use when the user + compiles PyTorch to TensorRT without ONNX, hits "enabled_precisions + should not be used when use_explicit_typing=True", sees Dynamo graph + breaks or PyTorch fallback, debugs ABI errors at import torch_tensorrt, or + needs the compatible torch / torch_tensorrt / tensorrt-cu13 version pins for + TensorRT 11. Triggers: torch_tensorrt, torch_tensorrt.dynamo.compile, + torch.compile backend torch_tensorrt, pytorch to tensorrt, ExportedProgram, + Dynamo graph break, use_explicit_typing, enabled_precisions, + torch_tensorrt.Input, min_block_size, truncate_double, tensorrt-cu13, + version pinning, version compatibility. Adjacent skills: `trt-onnx-quickstart`, + `trt-cpp-runtime-quickstart`. LLM token generation belongs in TensorRT-LLM. +license: Apache-2.0 +metadata: + author: NVIDIA Corporation + version: "1.0" + tags: + - torch-tensorrt + - dynamo + - pytorch + - quickstart + - fp16 +--- + +# Torch-TensorRT Quickstart + +Convert a PyTorch `nn.Module` to a TensorRT engine using `torch_tensorrt` (the [Torch-TensorRT](https://github.com/pytorch/TensorRT) frontend). Covers the AOT path (for production and C++ deploy) and the JIT path (for Python-only inference). + +## When to Use + +| Scenario | Use this skill? | +|----------|-----------------| +| PyTorch `nn.Module`; want a TensorRT engine without writing an ONNX intermediate | Yes | +| Model uses ops that don't export cleanly to ONNX (custom autograd, dynamic control flow) | Yes | +| Want Torch-TensorRT's automatic fallback of unsupported subgraphs to PyTorch | Yes | +| Need a serialized engine for C++ deploy from a PyTorch source | Yes — use AOT path here, then `trt-cpp-runtime-quickstart` for the C++ load | +| Have a working ONNX file already | No — use `trt-onnx-quickstart` | +| LLM token generation (Llama, Mistral, Qwen text gen) | **No** — route to [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) | +| Have a `.plan` file already and want to run it from C++ | No — use `trt-cpp-runtime-quickstart` | +| Migrating an existing weakly-typed TRT network to strongly-typed | No — use `trt-strong-typing-migration` | + +## Prerequisites + +Torch-TensorRT versions are tightly coupled to a (torch, tensorrt-cu13, CUDA) triple; mismatches produce ABI errors at `import torch_tensorrt`. **Pin matrix source of truth:** the [pytorch/TensorRT releases page](https://github.com/pytorch/TensorRT/releases) — each release lists the exact `torch` / `tensorrt` / CUDA versions it was built against. Do not invent pins from memory. + +For TensorRT 11.0: + +| Component | Required | Notes | +|-----------|----------|-------| +| TensorRT | 11.x (`tensorrt-cu13` wheel) | Mixing `-cu12` and `-cu13` wheels breaks imports. | +| CUDA toolkit | 13.x | Driver R590+. | +| Python | ≥ 3.10 | TRT 11 dropped 3.9 and earlier. | +| `torch` | Match the `torch_tensorrt` release notes for the chosen `torch_tensorrt` version. | | +| `torch_tensorrt` | The release matching the TRT 11.0 RC — see [pytorch/TensorRT releases](https://github.com/pytorch/TensorRT/releases). | | + +**Recommended environment**: NGC TensorRT container with PyTorch (`nvcr.io/nvidia/pytorch:` or `nvcr.io/nvidia/tensorrt:` with `torch_tensorrt` pip-installed on top). Run with `--gpus all`. + +Verification: +```bash +python3 -c "import torch, torch_tensorrt, tensorrt; print(torch.__version__, torch_tensorrt.__version__, tensorrt.__version__)" +``` + +## Step 1 — Load and prepare the model + +Use a `torch.nn.Module` in eval mode. Torch-TensorRT will trace through it. + +```python +import torch +import torchvision.models as models + +model = models.resnet50(weights=None).eval().cuda() +example = torch.randn(1, 3, 224, 224, device="cuda") +``` + +Notes: +- `weights=None` skips the download — fine for shape/perf testing. For accuracy work, load real weights. +- Model must be on CUDA before compile. Torch-TensorRT does not move it for you. +- `.eval()` matters: BatchNorm and Dropout behave differently in train mode and can produce different engines. + +## Step 2 — Compile to a TensorRT engine (AOT) + +Two compile paths — pick by deployment target: + +| Goal | Path | API | +|------|------|-----| +| Serialized engine (production, C++ deploy, reuse) | **AOT** — this section | `torch_tensorrt.dynamo.compile` → `torch_tensorrt.save` | +| In-process Python callable only (no serialized engine) | **JIT** — end of this section | `torch.compile(backend="torch_tensorrt")` | + +The AOT path uses `torch_tensorrt.dynamo.compile` and returns a serializable `ExportedProgram`. + +**Important — strong typing is the default in torch_tensorrt ≥ 2.12.** With `use_explicit_typing=True` (the default), engine precision is **inferred from the exported model's dtype** — passing `enabled_precisions={torch.float16}` raises `AssertionError`. To compile in FP16, cast the model and example tensor to FP16 before exporting: + +```python +import torch +import torch_tensorrt + +model = MyModel().eval().cuda().half() # cast to FP16 +example = torch.randn(1, 3, 224, 224, device="cuda", dtype=torch.float16) + +trt_gm = torch_tensorrt.dynamo.compile( + torch.export.export(model, (example,)), + inputs=[example], + # No enabled_precisions — precision comes from the model dtype. + truncate_double=True, + min_block_size=1, +) + +# Serialize for later / C++ loading — use torch_tensorrt.save, NOT raw bytes. +torch_tensorrt.save(trt_gm, "resnet50_trt.ep", inputs=[example]) +``` + +**Do not extract raw engine bytes via `submod.engine` and write them with `open(...).write(...)`.** That bypasses Torch-TensorRT's metadata wrapper: the blob will not round-trip through `torch_tensorrt.load(...)` and silently drops the dispatch graph that handles partial-fallback subgraphs. `torch_tensorrt.save(trt_gm, path, inputs=...)` is the only supported serialization path. + +For mixed precision or to override the model dtype, set `use_explicit_typing=False` and `enabled_precisions` applies as before (the weakly-typed path, deprecated in TRT 11): + +```python +trt_gm = torch_tensorrt.dynamo.compile( + torch.export.export(model, (example,)), + inputs=[example], + use_explicit_typing=False, # weakly-typed (deprecated) + enabled_precisions={torch.float16, torch.float32}, + truncate_double=True, +) +``` + +Key arguments: +- `truncate_double=True`: silently downcasts FP64 constants. Without it, any FP64 op forces a partition boundary. +- `min_block_size`: smallest subgraph (in node count) worth handing to TRT. `1` is aggressive; default `5` avoids tiny TRT subgraphs that don't pay back their launch overhead. +- `workspace_size`: bytes of scratch memory the TRT builder can use. Leave unset to let TRT decide. + +**JIT alternative (Python-only inference):** if you just want a callable and not a serialized engine, use `torch.compile` with the Torch-TensorRT backend. Under the strong-typing default, precision comes from the model dtype — `.half()` the model for FP16 rather than passing `enabled_precisions`: +```python +model = model.half() # FP16 inferred from model dtype (strong typing default) +example = example.half() +trt_model = torch.compile(model, backend="torch_tensorrt") +out = trt_model(example) # compiles lazily on the first call, then runs through TRT in-process +``` +This skips `torch.export` and gives a `torch.compile`-wrapped callable. It cannot be loaded from C++ — use the AOT path above if you need that. + +## Step 3 — Dynamic shapes + +Wrap inputs in `torch_tensorrt.Input` with `min_shape`/`opt_shape`/`max_shape`: + +```python +dynamic_input = torch_tensorrt.Input( + min_shape=(1, 3, 224, 224), + opt_shape=(8, 3, 224, 224), + max_shape=(32, 3, 224, 224), + dtype=torch.float16, +) +trt_gm = torch_tensorrt.dynamo.compile( + torch.export.export(model, (example,), dynamic_shapes={"x": {0: torch.export.Dim("batch", min=1, max=32)}}), + inputs=[dynamic_input], + # Strong typing: FP16 comes from the input/model dtype, not enabled_precisions. +) +``` + +Pitfalls: +- The `dynamic_shapes=` argument to `torch.export.export` and the `Input` ranges must agree. A mismatch produces a builder error 2–3 minutes into compilation, not at export time. +- `opt_shape` is what TRT tunes kernels for. Set it to the most common runtime shape, not the midpoint. +- Only mark dimensions dynamic that actually vary at runtime; unnecessary dynamism degrades performance. + +## Step 4 — Run inference and verify + +Raw-tensor comparison (`assert_close`) is fine for FP32 but **misleading for FP16** — a few outlier positions exceed the tolerance even when downstream behavior is identical. Use a **semantic check** appropriate to the model type: + +```python +with torch.inference_mode(): + torch_out = model(example) + trt_out = trt_gm(example) +``` + +**Image classification** — top-1 (or top-5) class match, plus mean-softmax-distance: + +```python +assert torch.equal(torch_out.argmax(-1), trt_out.argmax(-1)), "top-1 mismatch" +mean_prob_diff = (torch.softmax(torch_out.float(), -1) - torch.softmax(trt_out.float(), -1)).abs().mean() +assert mean_prob_diff < 1e-3 +``` + +**Sentence / token embeddings** — cosine similarity per token, mean across the batch: + +```python +cos = torch.nn.functional.cosine_similarity(torch_out.flatten(0, -2), trt_out.flatten(0, -2), dim=-1) +assert cos.mean() > 0.999 +``` + +**Detection / regression heads** — bounded raw-tensor closeness on the meaningful sub-tensor (e.g., bbox coordinates), softmax check on the class logits. + +**FP32 sanity** — for any model, an FP32 (no `.half()`) run should pass `torch.testing.assert_close(trt_out, torch_out, rtol=1e-4, atol=1e-4)`. If that fails, the issue is in the compilation, not numeric precision. + +## Loading the serialized engine + +**From Python**: +```python +import torch_tensorrt +loaded = torch_tensorrt.load("resnet50_trt.ep").module() +out = loaded(example) +``` + +**From C++**: this skill does NOT cover C++ loading. The `.ep` file `torch_tensorrt.save` produces by default is a `torch.export` archive for the **Python** `torch_tensorrt.load(...)` path — it is *not* directly consumable by the plain TensorRT C++ runtime (`IRuntime` / `deserializeCudaEngine`), which expects a serialized TensorRT engine (`.plan`). Two supported C++ routes: + +- Save with `output_format="torchscript"` and deploy the TorchScript module with libtorch + the Torch-TensorRT C++ runtime. +- If you only need the raw TensorRT engine in C++, build it through the ONNX path (`trt-onnx-quickstart`) and load the resulting `.plan` with `trt-cpp-runtime-quickstart` (modern `IRuntime` + `enqueueV3` + `setTensorAddress`). + +Do not inline a C++ snippet here — it will diverge from the canonical pattern. + +## Common issues + +**`RuntimeError: Trying to create tensor with negative dimension`** during export — the model has a shape-dependent control flow path that `torch.export` can't trace. Either rewrite with `torch.cond` / `torch.where`, or fall back to `ir="dynamo"` + `min_block_size=1` so the unsupported region runs in PyTorch. + +**`Unsupported operator` warnings** — Torch-TensorRT partitions around them and runs those nodes in PyTorch. Set `require_full_compilation=True` to turn this into an error; use during development to find what's actually falling back. + +## Diagnosing excessive PyTorch fallback + +If the compile completes but most of the model fell back to PyTorch (only a few subgraphs run on TRT), three knobs in order: + +1. **Identify what fell back.** Run with `TORCH_LOGS="graph_breaks"` and `require_full_compilation=True` to escalate fallbacks to errors that name the offending op. Without this you don't know what the autotuner rejected. + ```bash + TORCH_LOGS="graph_breaks" python3 your_compile.py + ``` +2. **Reconsider `min_block_size`.** This is a real trade-off — not just a knob. + - Default `min_block_size=5`: Torch-TensorRT only hands a subgraph to TRT if it has ≥5 nodes. Tiny TRT subgraphs cost more in kernel-launch overhead than they save. + - `min_block_size=1`: aggressive — every supported op goes to TRT, even single-node subgraphs. Useful to see what's *theoretically* supported, but commonly slower at inference due to the per-subgraph launch tax. + - Recommended: leave at default `5` for production; drop to `1` only during diagnosis to see maximum TRT coverage. +3. **Look for shape-dependent control flow.** `torch.export` traces only one branch of `if x.shape[0] > 0:` style code; rewrite with `torch.where`, `torch.cond`, or lift the condition out of the model. The trace error names the offending op. + +If fallback persists after all three, you have a genuine unsupported op — write a converter (see Torch-TensorRT upstream docs) or switch to the ONNX path (`trt-onnx-quickstart`). + +**Engine builds but output is garbage** — almost always a dtype issue. Check: +1. Model/input dtype matches your pipeline — under strong typing precision follows the model dtype (`.half()` for FP16); `enabled_precisions` applies only under weak typing (`use_explicit_typing=False`). +2. `truncate_double=True` if the model has any FP64 constants (common in positional encodings). +3. The model was `.eval()` before export. + +**Compile takes >10 minutes** — turn on the builder log to see what's going on: +```python +import torch_tensorrt.logging as ttlog +ttlog.set_reportable_log_level(ttlog.Level.Info) +``` +Usually it's the autotuner exploring kernel variants for a heavy GEMM/conv. To cap exploration, set `workspace_size` smaller (less scratch → fewer candidates). + +**`ImportError: cannot import name 'XYZ' from 'torch_tensorrt'`** — version mismatch. Confirm the pin matrix; the `torch_tensorrt` Python API moves between minor versions. + +## Numerical debugging + +If `assert_close` fails by more than ~5× tolerance: +1. Re-run in FP32 — drop the model's `.half()` (or under weak typing, `enabled_precisions={torch.float32}`). If that matches PyTorch, the issue is FP16 accumulator drift in a specific op — usually a softmax or LayerNorm. The pragmatic fix is to wrap the offending submodule in a module excluded from the compile (`min_block_size` won't help; explicit exclusion is needed). See the [Torch-TensorRT lowering guide](https://pytorch.org/TensorRT/) for the version-specific exclusion API. +2. Sanity-check at FP32 first, then re-introduce FP16 and bisect — almost always reveals one offending op. +3. For FP8: check that the calibration distribution actually covers the runtime distribution. FP8 has no headroom for outliers. + +See `trt-strong-typing-migration` for the broader weak-vs-strong typing discussion. By default Torch-TensorRT produces strongly-typed engines in TRT 11.0 — types inferred from the exported program — but you can override. + +## What this skill is not + +- Not a guide to writing custom Torch-TensorRT converters. Use the upstream Torch-TensorRT docs for that. +- Not for TensorRT-LLM / LLM inference — use the TRT-LLM `examples/` flow instead. +- Not for QAT (quantization-aware training) — Torch-TensorRT consumes the QAT model but the training loop is upstream. + +## References + +- Upstream Torch-TensorRT: https://pytorch.org/TensorRT/ +- Version pin matrix: [pytorch/TensorRT releases](https://github.com/pytorch/TensorRT/releases) +- Related skills: `trt-onnx-quickstart`, `trt-strong-typing-migration`, `trt-cpp-runtime-quickstart` diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index 927dcc668..89573c943 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -64,6 +64,4 @@ Baremetal or Container (if so, version): **Have you tried [the latest release](https://developer.nvidia.com/tensorrt)?**: -**Attach the captured .json and .bin files from [TensorRT's API Capture tool](https://docs.nvidia.com/deeplearning/tensorrt/latest/inference-library/capture-replay.html) if you're on an x86_64 Unix system** - **Can this model run on other frameworks?** For example run ONNX model with ONNXRuntime (`polygraphy run --onnxrt`): diff --git a/.github/workflows/docker-image.yml b/.github/workflows/docker-image.yml index 754948ede..5ab625012 100644 --- a/.github/workflows/docker-image.yml +++ b/.github/workflows/docker-image.yml @@ -8,11 +8,11 @@ on: jobs: - build-ubuntu2204: + build-ubuntu2004: runs-on: ubuntu-latest steps: - uses: actions/checkout@v3 - - name: Build TensorRT-OSS ubuntu22.04 container - run: docker build . --file docker/ubuntu-22.04.Dockerfile --build-arg uid=1000 --build-arg gid=1000 --tag tensorrt-ubuntu22.04:$(date +%s) + - name: Build TensorRT-OSS ubuntu20.04 container + run: docker build . --file docker/ubuntu-20.04.Dockerfile --build-arg uid=1000 --build-arg gid=1000 --tag tensorrt-ubuntu20.04:$(date +%s) diff --git a/CHANGELOG.md b/CHANGELOG.md index 0a79d4762..05b95afed 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,4 +1,21 @@ # TensorRT OSS Release Changelog +## 11.1 GA - 2026-6-24 +- General + - Default CUDA version updated to 13.3. + - Added Ubuntu 26.04 container. + - Added support for Python 3.14. + - Added agent skills for various TensorRT workflows. + +- Samples + - Added new sample `cute_dsl_plugin`. + - Added a global performance tuner option in `trtexec`. + +- Plugins + - Added new plugin `topkLastDimPlugin`. + +- Parsers + - Updated TRT_Attention to support raggedness. + ## 11.0 GA - 2026-6-2 - General - As a new major version bump, TensorRT 11.0 brings many enhancements for its users. For full information, see the [release notes.](https://docs.nvidia.com/deeplearning/tensorrt/latest/getting-started/release-notes-11/11.0.0.html) diff --git a/CMakeLists.txt b/CMakeLists.txt index ce7741abc..450420994 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -19,6 +19,7 @@ cmake_minimum_required(VERSION 3.31 FATAL_ERROR) include(cmake/modules/set_ifndef.cmake) include(cmake/modules/find_library_create_target.cmake) list(APPEND CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR}/cmake/modules) +include(InstallUtils) set_ifndef(TRT_LIB_DIR ${CMAKE_BINARY_DIR}) set_ifndef(TRT_OUT_DIR ${CMAKE_BINARY_DIR}) @@ -27,17 +28,39 @@ set_ifndef(TRT_OUT_DIR ${CMAKE_BINARY_DIR}) cmake_path(SET TRT_LIB_DIR ${TRT_LIB_DIR}) cmake_path(SET TRT_OUT_DIR ${TRT_OUT_DIR}) -cmake_path(SET TRT_INCLUDE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/include) +# Resolve TRT_INCLUDE_DIR: prefer installed package headers over in-repo headers +# to avoid ABI mismatches when headers lag behind the installed package. +if(NOT DEFINED TRT_INCLUDE_DIR) + if(EXISTS "${TRT_LIB_DIR}/../include/NvInferVersion.h") + # Tar-style package: include/ is a sibling of the lib directory. + cmake_path(SET TRT_INCLUDE_DIR "${TRT_LIB_DIR}/../include") + elseif(EXISTS "/usr/include/NvInferVersion.h") + # Deb package: headers at standard system path. + set(TRT_INCLUDE_DIR "/usr/include") + else() + # No package headers found; fall back to in-repo headers. + cmake_path(SET TRT_INCLUDE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/include") + set(_TRT_USING_REPO_HEADERS TRUE) + endif() +else() + cmake_path(SET TRT_INCLUDE_DIR "${TRT_INCLUDE_DIR}") +endif() + +if(NOT EXISTS "${TRT_INCLUDE_DIR}/NvInferVersion.h") + message(FATAL_ERROR "NvInferVersion.h not found in ${TRT_INCLUDE_DIR}. " + "Set -DTRT_INCLUDE_DIR to the directory containing TensorRT headers.") +endif() +message(STATUS "Using TensorRT headers from: ${TRT_INCLUDE_DIR}") # Required to export symbols to build *.libs if(WIN32) add_compile_definitions(TENSORRT_BUILD_LIB 1) endif() -# Set output paths -set(RUNTIME_OUTPUT_DIRECTORY ${TRT_OUT_DIR} CACHE PATH "Output directory for runtime target files") -set(LIBRARY_OUTPUT_DIRECTORY ${TRT_OUT_DIR} CACHE PATH "Output directory for library target files") -set(ARCHIVE_OUTPUT_DIRECTORY ${TRT_OUT_DIR} CACHE PATH "Output directory for archive target files") +# Route all targets to TRT_OUT_DIR by default. Per-target overrides still win. +set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${TRT_OUT_DIR} CACHE PATH "") +set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${TRT_OUT_DIR} CACHE PATH "") +set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY ${TRT_OUT_DIR} CACHE PATH "") if(WIN32) set(STATIC_LIB_EXT "lib") @@ -57,6 +80,20 @@ set(ONNX2TRT_VERSION "${TRT_MAJOR}.${TRT_MINOR}.${TRT_PATCH}" CACHE STRING "ONNX set(TRT_SOVERSION "${TRT_MAJOR}" CACHE STRING "TensorRT library so version") message("Building for TensorRT version: ${TRT_VERSION}, library version: ${TRT_SOVERSION}") +# Warn if in-repo headers don't match the installed library version. +if(_TRT_USING_REPO_HEADERS AND NOT "${TRT_LIB_DIR}" STREQUAL "${CMAKE_BINARY_DIR}" AND NOT WIN32) + file(GLOB _trt_libs "${TRT_LIB_DIR}/libnvinfer.so.*") + if(_trt_libs AND NOT EXISTS "${TRT_LIB_DIR}/libnvinfer.so.${TRT_MAJOR}") + message(WARNING + "In-repo headers report TensorRT major version ${TRT_MAJOR}, but " + "no matching libnvinfer.so.${TRT_MAJOR} was found in ${TRT_LIB_DIR}. " + "The headers may not match the installed library. Set -DTRT_INCLUDE_DIR " + "to the headers matching your TensorRT installation.") + endif() + unset(_trt_libs) +endif() +unset(_TRT_USING_REPO_HEADERS) + if(NOT DEFINED CMAKE_TOOLCHAIN_FILE) find_program(CMAKE_CXX_COMPILER NAMES $ENV{CXX} g++) endif() @@ -64,14 +101,13 @@ endif() set(CMAKE_SKIP_BUILD_RPATH True) set(CMAKE_POSITION_INDEPENDENT_CODE ON) +# Set CMAKE_CUDA_ARCHITECTURES before project() so CMake's CMP0104 policy does not +# auto-detect and overwrite it with the host GPU's SM. CUDAToolkit_VERSION is not +# available yet; SM 110 is conditionally appended after find_package(CUDAToolkit) below. if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES) - set(CMAKE_CUDA_ARCHITECTURES 80 86 89 90 100 120) - # SM 110 (Thor) requires CUDA 13.0+. - if(DEFINED CUDA_VERSION AND CUDA_VERSION VERSION_GREATER_EQUAL "13.0") - list(APPEND CMAKE_CUDA_ARCHITECTURES 110) - endif() + set(CMAKE_CUDA_ARCHITECTURES 75 80 86 89 90 100 120) + set(_TRT_CUDA_ARCHS_DEFAULTED TRUE) endif() -message(STATUS "Generating CUDA code for SMs: ${CMAKE_CUDA_ARCHITECTURES}") project(TensorRT LANGUAGES CXX CUDA @@ -83,6 +119,18 @@ if (WIN32) enable_language(C) endif() +# Speed up rebuilds with ccache when available; respect a user-set launcher. +if(CMAKE_GENERATOR MATCHES "Makefiles|Ninja") + find_program(CCACHE_PROGRAM ccache) + mark_as_advanced(CCACHE_PROGRAM) + if(CCACHE_PROGRAM) + foreach(lang IN ITEMS C CXX CUDA) + set(CMAKE_${lang}_COMPILER_LAUNCHER "${CCACHE_PROGRAM}" CACHE STRING "Path to ccache") + endforeach() + message(STATUS "ccache enabled: ${CCACHE_PROGRAM}") + endif() +endif() + if(CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT) set(CMAKE_INSTALL_PREFIX ${TRT_LIB_DIR}/../ CACHE PATH "TensorRT installation" FORCE) endif(CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT) @@ -94,6 +142,9 @@ option(BUILD_SAFE_SAMPLES "Build TensorRT safety samples" OFF) option(TRT_SAFETY_INFERENCE_ONLY "Build only the safety inference components (no safety builders)" OFF) option(TRT_BUILD_TESTING "Build gtests for TensorRT components" OFF) +# Must be set before add_subdirectory(plugin); the option() below is gated on BUILD_SAMPLES. +set(TRT_BUILD_WINML OFF) + ############################################################################################ # Early dependency discovery # These must be found before they are used in target definitions @@ -113,6 +164,30 @@ endif() find_package(CUDAToolkit REQUIRED) message(STATUS "CUDA version: ${CUDAToolkit_VERSION}") +# SM 110 (Thor) requires CUDA 13.0+. +# CUDAToolkit_VERSION is only available once the toolkit is found. +if (_TRT_CUDA_ARCHS_DEFAULTED AND CUDAToolkit_VERSION VERSION_GREATER_EQUAL "13.0") + list(APPEND CMAKE_CUDA_ARCHITECTURES 110) +endif() +message(STATUS "Generating CUDA code for SMs: ${CMAKE_CUDA_ARCHITECTURES}") + +############################################################################################ +# OSS bridging: top-level setup for plugin and samples code. + +# OSS vendors third-party headers under third_party/. +set(TRT_EXTERNALS_DIR ${CMAKE_CURRENT_SOURCE_DIR}/third_party) + +if(NOT TARGET TRT::cudart) + add_library(TRT::cudart INTERFACE IMPORTED) + target_link_libraries(TRT::cudart INTERFACE CUDA::cudart_static) +endif() + +include(BundleLibraries) +include(SymbolExports) +include(Stubify) +include(WindowsLibSuffixes) +include(StaticLibSmokeTest) + ############################################################################################ # Safety runtime libraries (libnvinfer_safe) used by safety samples and # inference-only builds. @@ -274,6 +349,11 @@ else() endif() find_library_create_target(nvinfer ${nvinfer_lib_name} SHARED "${TRT_LIB_DIR}") +set_property(TARGET nvinfer PROPERTY INTERFACE_INCLUDE_DIRECTORIES ${TRT_INCLUDE_DIR}) + +# tensorrt is aliased downstream; CMake forbids aliasing an alias. +add_library(tensorrt INTERFACE IMPORTED) +target_link_libraries(tensorrt INTERFACE nvinfer) set(CMAKE_CUDA_RUNTIME_LIBRARY "static" CACHE STRING "") @@ -295,7 +375,16 @@ endif() set(HINT_PATHS "${TRT_OUT_DIR}" "${TRT_LIB_DIR}") +if(NOT TARGET trt_global_definitions) + add_library(trt_global_definitions INTERFACE) + target_include_directories(trt_global_definitions INTERFACE ${CUDAToolkit_INCLUDE_DIRS}) +endif() + if(BUILD_PLUGINS) + option(TRT_BUILD_ENABLE_DLA "Build TensorRT with DLA features enabled." OFF) + set(TRT_BUILD_ENABLE_STATIC_LIBS OFF CACHE INTERNAL "Static libs are no longer supported") + option(TRT_BUILD_STUB_LIBS "Build linker stub libraries." OFF) + add_subdirectory(plugin) else() find_library_create_target(${nvinfer_plugin_lib_name} ${nvinfer_plugin_lib_name} SHARED "${HINT_PATHS}") @@ -307,11 +396,6 @@ else() find_library_create_target(${nvonnxparser_lib_name} ${nvonnxparser_lib_name} SHARED "${HINT_PATHS}") endif() -if(NOT TARGET trt_global_definitions) - add_library(trt_global_definitions INTERFACE) - target_include_directories(trt_global_definitions INTERFACE ${CUDAToolkit_INCLUDE_DIRS}) -endif() - add_library(tensorrt_headers INTERFACE) target_include_directories(tensorrt_headers INTERFACE ${TRT_INCLUDE_DIR}) @@ -332,11 +416,6 @@ if(BUILD_SAMPLES OR BUILD_SAFE_SAMPLES) option(TRT_BUILD_ENABLE_MULTIDEVICE "Build TensorRT with multi-device support." OFF) set(TRT_BUILD_SAMPLES_LINK_STATIC_TRT OFF CACHE INTERNAL "") - find_library(NVINFER_LIB nvinfer PATHS ${TRT_LIB_DIR} REQUIRED) - add_library(tensorrt INTERFACE IMPORTED) - target_link_libraries(tensorrt INTERFACE ${NVINFER_LIB}) - target_include_directories(tensorrt INTERFACE ${TRT_INCLUDE_DIR}) - target_include_directories(${nvonnxparser_lib_name} INTERFACE ${TRT_INCLUDE_DIR}) add_subdirectory(shared) @@ -361,8 +440,5 @@ if(BUILD_SAMPLES OR BUILD_SAFE_SAMPLES) set(TRT_GTEST_DISCOVERY_MODE PRE_TEST CACHE STRING "gtest discovery mode.") endif() - # Route sample binaries to TRT_OUT_DIR. - set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${TRT_OUT_DIR}) - add_subdirectory(samples) endif() diff --git a/LICENSE b/LICENSE index f90e3cf92..4e2c444c5 100644 --- a/LICENSE +++ b/LICENSE @@ -449,3 +449,31 @@ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + + > samples/common/sampleTuning.cpp + > samples/common/sampleUtils.cpp + > samples/trtexec/trtexec.cpp + + nlohmann/json library. + + MIT License + + Copyright (c) 2013-2022 Niels Lohmann + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all + copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE. diff --git a/README.md b/README.md index 422ac03a6..9cdfe5d46 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ # :mega::mega: Announcement :mega::mega: -TensorRT 11.0 is now released with powerful new capabilities designed to accelerate your AI inference workflows. With this major version bump, TensorRT's API has been streamlined and a few legacy features have been removed. +TensorRT 11.X is now released with powerful new capabilities designed to accelerate your AI inference workflows. With this major version bump, TensorRT's API has been streamlined and a few legacy features from 10.X have been removed. Below provides migration guides for the following features: - Weakly-typed networks and related APIs have been removed, replaced by [Strongly Typed Networks](https://docs.nvidia.com/deeplearning/tensorrt/latest/inference-library/advanced.html#strongly-typed-networks). @@ -26,6 +26,9 @@ Need enterprise support? NVIDIA global support is available for TensorRT with th Join the [TensorRT and Triton community](https://www.nvidia.com/en-us/deep-learning-ai/triton-tensorrt-newsletter/) and stay current on the latest product updates, bug fixes, content, best practices, and more. +# Agentic Coding Skills +Various skills related to TensorRT usage and benchmarking are available [here](.agents/skills). For installation, refer to the instructions of your preferred coding agent. + # Prebuilt TensorRT Python Package We provide the TensorRT Python package for an easy installation. \ @@ -45,20 +48,20 @@ To build the TensorRT-OSS components, you will first need the following software **TensorRT GA build** -- TensorRT v11.0.0.114 +- TensorRT v11.1.0.106 - Available from direct download links listed below **System Packages** - [CUDA](https://developer.nvidia.com/cuda-toolkit) - Recommended versions: - - cuda-13.2.0 + - cuda-13.3.0 - cuda-12.9.0 - [CUDNN (optional)](https://developer.nvidia.com/cudnn) - cuDNN 8.9 - [GNU make](https://ftp.gnu.org/gnu/make/) >= v4.1 - [cmake](https://github.com/Kitware/CMake/releases) >= v3.31 -- [python](https://www.python.org/downloads/) >= v3.10, <= v3.13.x +- [python](https://www.python.org/downloads/) >= v3.10, <= v3.14.x - [pip](https://pypi.org/project/pip/#history) >= v19.0 - Essential utilities - [git](https://git-scm.com/downloads), [pkg-config](https://www.freedesktop.org/wiki/Software/pkg-config/), [wget](https://www.gnu.org/software/wget/faq.html#download) @@ -100,24 +103,24 @@ To build the TensorRT-OSS components, you will first need the following software Else download and extract the TensorRT GA build from [NVIDIA Developer Zone](https://developer.nvidia.com) with the direct links below: - - [TensorRT 11.0.0.114 for CUDA 13.2, Linux x86_64](https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.0.0/tars/TensorRT-Enterprise-11.0.0.114-Linux-x86_64-cuda-13.2-Release-external.tar.zst) - - [TensorRT 11.0.0.114 for CUDA 12.9, Linux x86_64](https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.0.0/tars/TensorRT-Enterprise-11.0.0.114-Linux-x86_64-cuda-12.9-Release-external.tar.zst) - - [TensorRT 11.0.0.114 for CUDA 13.2, Windows x86_64](https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.0.0/zip/TensorRT-Enterprise-11.0.0.114-Windows-amd64-cuda-13.2-Release-external.zip) - - [TensorRT 11.0.0.114 for CUDA 12.9, Windows x86_64](https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.0.0/zip/TensorRT-Enterprise-11.0.0.114-Windows-amd64-cuda-12.9-Release-external.zip) + - [TensorRT 11.1.0.106 for CUDA 13.3, Linux x86_64](https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.1.0/tars/TensorRT-Enterprise-11.1.0.106-Linux-x86_64-cuda-13.3-Release-external.tar.zst) + - [TensorRT 11.1.0.106 for CUDA 12.9, Linux x86_64](https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.1.0/tars/TensorRT-Enterprise-11.1.0.106-Linux-x86_64-cuda-12.9-Release-external.tar.zst) + - [TensorRT 11.1.0.106 for CUDA 13.3, Windows x86_64](https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.1.0/zip/TensorRT-Enterprise-11.1.0.106-Windows-amd64-cuda-13.3-Release-external.zip) + - [TensorRT 11.1.0.106 for CUDA 12.9, Windows x86_64](https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.1.0/zip/TensorRT-Enterprise-11.1.0.106-Windows-amd64-cuda-12.9-Release-external.zip) - **Example: Ubuntu 22.04 on x86-64 with cuda-13.2** + **Example: Ubuntu 22.04 on x86-64 with cuda-13.3** ```bash cd ~/Downloads - tar --zstd -xvf TensorRT-Enterprise-11.0.0.114-Linux-x86_64-cuda-13.2-Release-external.tar.zst - export TRT_LIBPATH=`pwd`/TensorRT-11.0.0.114/lib + tar --zstd -xvf TensorRT-Enterprise-11.1.0.106-Linux-x86_64-cuda-13.3-Release-external.tar.zst + export TRT_LIBPATH=`pwd`/TensorRT-11.1.0.106/lib ``` **Example: Windows on x86-64 with cuda-12.9** ```powershell - Expand-Archive -Path TensorRT-Enterprise-11.0.0.114-Windows-amd64-cuda-12.9-Release-external.zip - $env:TRT_LIBPATH="$pwd\TensorRT-11.0.0.114\lib" + Expand-Archive -Path TensorRT-Enterprise-11.1.0.106-Windows-amd64-cuda-12.9-Release-external.zip + $env:TRT_LIBPATH="$pwd\TensorRT-11.1.0.106\lib" ``` ## Setting Up The Build Environment @@ -126,34 +129,34 @@ For Linux platforms, we recommend that you generate a docker container for build 1. #### Generate the TensorRT-OSS build container. - **Example: Ubuntu 24.04 on x86-64 with cuda-13.2 (default)** + **Example: Ubuntu 24.04 on x86-64 with cuda-13.3 (default)** ```bash - ./docker/build.sh --file docker/ubuntu-24.04.Dockerfile --tag tensorrt-ubuntu24.04-cuda13.2 + ./docker/build.sh --file docker/ubuntu-24.04.Dockerfile --tag tensorrt-ubuntu24.04-cuda13.3 ``` - **Example: Rockylinux8 on x86-64 with cuda-13.2** + **Example: Rockylinux8 on x86-64 with cuda-13.3** ```bash - ./docker/build.sh --file docker/rockylinux8.Dockerfile --tag tensorrt-rockylinux8-cuda13.2 + ./docker/build.sh --file docker/rockylinux8.Dockerfile --tag tensorrt-rockylinux8-cuda13.3 ``` - **Example: Ubuntu 24.04 cross-compile for Jetson (aarch64) with cuda-13.2 (JetPack SDK)** + **Example: Ubuntu 24.04 cross-compile for Jetson (aarch64) with cuda-13.3 (JetPack SDK)** ```bash - ./docker/build.sh --file docker/ubuntu-cross-aarch64.Dockerfile --tag tensorrt-jetpack-cuda13.2 + ./docker/build.sh --file docker/ubuntu-cross-aarch64.Dockerfile --tag tensorrt-jetpack-cuda13.3 ``` - **Example: Ubuntu 24.04 on aarch64 with cuda-13.2** + **Example: Ubuntu 24.04 on aarch64 with cuda-13.3** ```bash - ./docker/build.sh --file docker/ubuntu-24.04-aarch64.Dockerfile --tag tensorrt-aarch64-ubuntu24.04-cuda13.2 + ./docker/build.sh --file docker/ubuntu-24.04-aarch64.Dockerfile --tag tensorrt-aarch64-ubuntu24.04-cuda13.3 ``` 2. #### Launch the TensorRT-OSS build container. **Example: Ubuntu 24.04 build container** ```bash - ./docker/launch.sh --tag tensorrt-ubuntu24.04-cuda13.2 --gpus all + ./docker/launch.sh --tag tensorrt-ubuntu24.04-cuda13.3 --gpus all ``` > NOTE: >
    1. Use the `--tag` corresponding to build container generated in Step 1. @@ -166,7 +169,7 @@ For Linux platforms, we recommend that you generate a docker container for build - Generate Makefiles and build - **Example: Linux (x86-64) build with default cuda-13.2** + **Example: Linux (x86-64) build with default cuda-13.3** ```bash cd $TRT_OSSPATH @@ -175,7 +178,7 @@ For Linux platforms, we recommend that you generate a docker container for build make -j$(nproc) ``` - **Example: Linux (aarch64) build with default cuda-13.2** + **Example: Linux (aarch64) build with default cuda-13.3** ```bash cd $TRT_OSSPATH @@ -184,7 +187,7 @@ For Linux platforms, we recommend that you generate a docker container for build make -j$(nproc) ``` - **Example: Native build on Jetson Thor (aarch64) with cuda-13.2** + **Example: Native build on Jetson Thor (aarch64) with cuda-13.3** ```bash cd $TRT_OSSPATH @@ -195,7 +198,7 @@ For Linux platforms, we recommend that you generate a docker container for build > NOTE: C compiler must be explicitly specified via CC= for native aarch64 builds of protobuf. - **Example: Ubuntu 24.04 Cross-Compile for Jetson Thor (aarch64) with cuda-13.2 (JetPack)** + **Example: Ubuntu 24.04 Cross-Compile for Jetson Thor (aarch64) with cuda-13.3 (JetPack)** ```bash cd $TRT_OSSPATH @@ -204,7 +207,7 @@ For Linux platforms, we recommend that you generate a docker container for build make -j$(nproc) ``` - **Example: Ubuntu 24.04 Cross-Compile for DriveOS (aarch64) with cuda-13.2** + **Example: Ubuntu 24.04 Cross-Compile for DriveOS (aarch64) with cuda-13.3** ```bash cd $TRT_OSSPATH @@ -213,7 +216,7 @@ For Linux platforms, we recommend that you generate a docker container for build make -j$(nproc) ``` - **Example: Native builds on Windows (x86) with cuda-13.2** + **Example: Native builds on Windows (x86) with cuda-13.3** ```bash cd $TRT_OSSPATH @@ -223,7 +226,7 @@ For Linux platforms, we recommend that you generate a docker container for build msbuild TensorRT.sln /property:Configuration=Release -m:$env:NUMBER_OF_PROCESSORS ``` - > NOTE: The default CUDA version used by CMake is 13.2. To override this, for example to 12.9, append `-DCUDA_VERSION=12.9` to the cmake command. + > NOTE: The default CUDA version used by CMake is 13.3. To override this, for example to 12.9, append `-DCUDA_VERSION=12.9` to the cmake command. - Required CMake build arguments are: - `TRT_LIB_DIR`: Path to the TensorRT installation directory containing libraries. @@ -317,7 +320,7 @@ For Linux platforms, we recommend that you generate a docker container for build ```bash cd $TRT_OSSPATH mkdir -p build && cd build - export CUDA_VERSION=13.2 + export CUDA_VERSION=13.3 export CUDA=cuda-$CUDA_VERSION export CUDA_ROOT=/usr/local/cuda-safe-$CUDA_VERSION export QNX_BASE=/drive/toolchains/qnx_toolchain # Set to your QNX toolchain installation path diff --git a/VERSION b/VERSION index 237d6dc26..0a8c4d4e9 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -11.0.0.114 +11.1.0.106 diff --git a/cmake/modules/FetchCCCL.cmake b/cmake/modules/FetchCCCL.cmake new file mode 100644 index 000000000..f84484f89 --- /dev/null +++ b/cmake/modules/FetchCCCL.cmake @@ -0,0 +1,38 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +include_guard() + +set(_cccl_default_repo "https://github.com/NVIDIA/cccl.git") + + +set(CCCL_REPO ${_cccl_default_repo} CACHE STRING "The base project URL to FetchContent_Declare for CCCL" ) +set(CCCL_TAG "v3.4.0-rc0" CACHE STRING "The commit hash to FetchContent_Declare for CCCL") + +# We use this directory to ensure we only fetch a single copy of dependencies, even between builds. +# $HOME/storage is expected to be mounted from the host for developers. +set(TRT_THIRD_PARTY_DL_DIR "$ENV{HOME}/storage" CACHE PATH "Directory to download third party dependencies to") + +FetchContent_Declare( + cccl + PREFIX "${CMAKE_BINARY_DIR}/third_party/cccl" + GIT_REPOSITORY ${CCCL_REPO} + GIT_TAG ${CCCL_TAG} + GIT_SHALLOW TRUE + SOURCE_DIR "${TRT_THIRD_PARTY_DL_DIR}/cccl/${CCCL_TAG}" + EXCLUDE_FROM_ALL +) + +FetchContent_MakeAvailable(cccl) diff --git a/cmake/modules/FetchNlohmannJson.cmake b/cmake/modules/FetchNlohmannJson.cmake new file mode 100644 index 000000000..7eae5d7d0 --- /dev/null +++ b/cmake/modules/FetchNlohmannJson.cmake @@ -0,0 +1,49 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +include_guard() + +set(NLOHMANN_JSON_REPO "https://github.com/nlohmann/json.git" CACHE STRING "The base project URL to FetchContent_Declare for nlohmann/json") +set(NLOHMANN_JSON_TAG "v3.11.3" CACHE STRING "The git tag to FetchContent_Declare for nlohmann/json") + +# We use this directory to ensure we only fetch a single copy of dependencies, even between builds. +# $HOME/storage is expected to be mounted from the host for developers. +set(TRT_THIRD_PARTY_DL_DIR "$ENV{HOME}/storage" CACHE PATH "Directory to download third party dependencies to") + +# nlohmann/json's own CMake builds tests and install rules by default. Disable both +# so our configure stays fast and our install tree doesn't pick up unrelated targets. +set(JSON_BuildTests OFF CACHE INTERNAL "") +set(JSON_Install OFF CACHE INTERNAL "") + +include(FetchContent) + +FetchContent_Declare( + nlohmann_json + PREFIX "${CMAKE_BINARY_DIR}/third_party/nlohmann_json" + GIT_REPOSITORY ${NLOHMANN_JSON_REPO} + GIT_TAG ${NLOHMANN_JSON_TAG} + GIT_SHALLOW TRUE + SOURCE_DIR "${TRT_THIRD_PARTY_DL_DIR}/nlohmann_json/${NLOHMANN_JSON_TAG}" + EXCLUDE_FROM_ALL + UPDATE_DISCONNECTED ${TRT_FETCH_CONTENT_UPDATES_DISCONNECTED} + OVERRIDE_FIND_PACKAGE # downstream find_package(nlohmann_json) is satisfied from here. +) +FetchContent_MakeAvailable(nlohmann_json) + +# Populate NLOHMANN_JSON_INCLUDE_DIRS for samples/common. +if(nlohmann_json_SOURCE_DIR) + set(NLOHMANN_JSON_INCLUDE_DIRS "${nlohmann_json_SOURCE_DIR}/include" + CACHE PATH "nlohmann/json include directory (set by FetchNlohmannJson.cmake)") +endif() diff --git a/cmake/modules/InstallImportedLibraries.cmake b/cmake/modules/InstallImportedLibraries.cmake index 485a2c9a1..2bb878330 100644 --- a/cmake/modules/InstallImportedLibraries.cmake +++ b/cmake/modules/InstallImportedLibraries.cmake @@ -1,4 +1,4 @@ -# SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-FileCopyrightText: Copyright (c) 1993-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -119,7 +119,7 @@ function(installImportedLibraries) endif() get_target_property(target_type ${target_name} TYPE) - if(NOT target_type MATCHES "SHARED_LIBRARY|UNKNOWN_LIBRARY") + if(NOT target_type MATCHES "MODULE_LIBRARY|SHARED_LIBRARY|UNKNOWN_LIBRARY") message(FATAL_ERROR "Target ${target_name} is not a shared library (type: ${target_type})") endif() diff --git a/cmake/modules/StaticLibSmokeTest.cmake b/cmake/modules/StaticLibSmokeTest.cmake new file mode 100644 index 000000000..a2fe37844 --- /dev/null +++ b/cmake/modules/StaticLibSmokeTest.cmake @@ -0,0 +1,58 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Utility module for smoke testing a static library. +# This allows us to verify that the static library can be linked without any unexpected dependencies. + +include_guard() + +define_property(TARGET + PROPERTY STATIC_LIBRARY_SMOKE_TEST_SOURCE_CODE + BRIEF_DOCS "Source code to use when executing the static library smoke test. Defaults to 'int main() { return 0; }'" +) + +# Creates a new target that links the given static library and builds a simple executable. +# The executable will be added to the ALL target if the static library target is also in the ALL target. +function(smoke_test_static_lib static_lib_target) + get_target_property(static_lib_type ${static_lib_target} TYPE) + if(NOT static_lib_type STREQUAL "STATIC_LIBRARY") + message(FATAL_ERROR "Target ${static_lib_target} is not a static library.") + endif() + + set(smoke_test_target "${static_lib_target}_smoke_test") + get_target_property(smoke_test_src ${static_lib_target} STATIC_LIBRARY_SMOKE_TEST_SOURCE_CODE) + # Handle NOTFOUND sentinel and empty string; otherwise treat property as source content. + if(NOT smoke_test_src) + set(smoke_test_src "int main() { return 0; }\n") + endif() + + file(WRITE "${CMAKE_CURRENT_BINARY_DIR}/${smoke_test_target}.cpp" "${smoke_test_src}") + + add_executable(${smoke_test_target} "${CMAKE_CURRENT_BINARY_DIR}/${smoke_test_target}.cpp") + + target_link_libraries(${smoke_test_target} PRIVATE $) + # cuDLA is not available as a static library, so we link it dynamically + if(${TRT_BUILD_ENABLE_DLA} AND TARGET CUDA::cudla) + target_link_libraries(${smoke_test_target} PRIVATE CUDA::cudla) + endif() + + # If the static library target is in the ALL target, add the smoke test target to the ALL target as well. + get_target_property(static_lib_excluded_from_all ${static_lib_target} EXCLUDE_FROM_ALL) + if(static_lib_excluded_from_all) + set_target_properties(${smoke_test_target} PROPERTIES + EXCLUDE_FROM_ALL ON + ) + endif() +endfunction() diff --git a/cmake/modules/Stubify.cmake b/cmake/modules/Stubify.cmake new file mode 100644 index 000000000..11d57bd96 --- /dev/null +++ b/cmake/modules/Stubify.cmake @@ -0,0 +1,97 @@ +# SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# This file provides functionality to create stub libraries from existing CMake targets +# by calling the existing stubify.sh script. +# +# Stub libraries contain the same exported symbols as the original shared library but with empty function bodies, +# removing all dependencies while maintaining the same API surface for linking purposes. + +# Creates a stub library from an existing CMake target by calling stubify.sh. +# +# \param target_name Name of an existing CMake shared library target +# +# Creates a new target called ${target_name}_stub that contains empty implementations +# of all exported symbols from the original target. The stub library is also installed +# to lib/stubs during the install phase. +function(create_stub_lib target_name) + if(MSVC) + message(FATAL_ERROR "Creating stub libs is not supported on Windows.") + endif() + + if(NOT TARGET ${target_name}) + message(FATAL_ERROR "Target ${target_name} does not exist") + endif() + + get_target_property(target_type ${target_name} TYPE) + if(NOT target_type STREQUAL "SHARED_LIBRARY") + message(FATAL_ERROR "Target ${target_name} is not a shared library") + endif() + + set(stub_target "${target_name}_stub") + + # The stub output should be the same base name of the library with a _stub suffix. + # We need to read the OUTPUT_NAME of the target, and fallback to the target name otherwise. + get_target_property(output_name ${target_name} OUTPUT_NAME) + if(NOT output_name) + set(output_name ${target_name}) + endif() + set(stub_output "${CMAKE_CURRENT_BINARY_DIR}/stubs/lib${output_name}.so") + + # Find the stubify.sh script + find_file(STUBIFY_SCRIPT stubify.sh + PATHS ${CMAKE_SOURCE_DIR}/scripts + NO_DEFAULT_PATH + REQUIRED + ) + + # Create a custom target that calls stubify.sh + add_custom_command( + OUTPUT ${stub_output} + COMMAND ${CMAKE_COMMAND} -E env + CC=${CMAKE_C_COMPILER} + CC_ARGS=${STUBIFY_CC_ARGS} + -- + ${STUBIFY_SCRIPT} $ ${stub_output} + DEPENDS $ ${STUBIFY_SCRIPT} + COMMENT "Creating stub library ${stub_output} for ${target_name}" + VERBATIM + ) + + # Create an imported library target for the stub + add_library(${stub_target} SHARED IMPORTED) + set_target_properties(${stub_target} PROPERTIES + IMPORTED_LOCATION ${stub_output} + ) + + # Create a custom target to ensure the stub gets built + add_custom_target(${stub_target}_build + ALL + DEPENDS ${stub_output} + ) + + # Make sure the stub builds after the original + add_dependencies(${stub_target}_build ${target_name}) + + # Make the imported target depend on the build target + add_dependencies(${stub_target} ${stub_target}_build) + + # Install the stub library to lib/stubs + install(FILES ${stub_output} + DESTINATION lib/stubs + COMPONENT external + OPTIONAL + ) +endfunction() diff --git a/cmake/modules/SymbolExports.cmake b/cmake/modules/SymbolExports.cmake new file mode 100644 index 000000000..c735c99b4 --- /dev/null +++ b/cmake/modules/SymbolExports.cmake @@ -0,0 +1,93 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +## Helper file for management of symbol exports in shared libraries via linker scripts. +## This allows controlling symbol visibility across different platforms without needing platform-specific code. + +# Applies an export map to one or more targets (specified by TARGETS). +# The export map file should be provided without extension; the appropriate extension will be added based on the platform (Windows: .def, Unix: .map). +# If CONFIGURE_FIRST is set, the export map will be configured via CMake configure_file (@ONLY). The source file must have the additional extension .in. +# +# \param CONFIGURE_FIRST Optional flag to indicate that the export map file should be configured first. +# \param EXPORT_MAP_FILE The base name of the export map file (without extension). If not provided, the function will look for a file matching the target's output name in the 'exports' directory. +# \param BINARY_DIR Optional path to the binary directory where the configured file will be placed. If not set, the current binary directory is used. +# \param TARGETS The targets to which the export map should be applied. Must not be empty. +function(apply_export_map) + cmake_parse_arguments(ARG "CONFIGURE_FIRST" "EXPORT_MAP_FILE;BINARY_DIR" "TARGETS" ${ARGN}) + + if(NOT ARG_TARGETS) + message(FATAL_ERROR "apply_export_map: TARGETS argument must not be empty.") + endif() + + if(NOT ARG_BINARY_DIR) + set(ARG_BINARY_DIR "${CMAKE_CURRENT_BINARY_DIR}") + endif() + + if(MSVC) + set(export_extension ".def") + else() + set(export_extension ".map") + endif() + + foreach(target IN LISTS ARG_TARGETS) + if(ARG_EXPORT_MAP_FILE) + cmake_path(IS_RELATIVE ARG_EXPORT_MAP_FILE is_relative) + if(is_relative) + set(export_map_file "${CMAKE_CURRENT_SOURCE_DIR}/${ARG_EXPORT_MAP_FILE}") + else() + set(export_map_file "${ARG_EXPORT_MAP_FILE}") + endif() + else() + # Otherwise, default to searching for a file that matches the target's output name. + get_target_property(output_name ${target} OUTPUT_NAME) + if(NOT output_name) + set(output_name ${target}) + endif() + set(export_map_file "${CMAKE_CURRENT_SOURCE_DIR}/exports/${output_name}") + endif() + + if(ARG_CONFIGURE_FIRST) + if(NOT EXISTS "${export_map_file}${export_extension}.in") + message(FATAL_ERROR "apply_export_map: Expected to find export map template file '${export_map_file}${export_extension}.in' for configuration.") + endif() + get_filename_component(export_file_name ${export_map_file} NAME_WE) + configure_file( + "${export_map_file}${export_extension}.in" + "${ARG_BINARY_DIR}/${export_file_name}${export_extension}" + @ONLY + ) + set(export_map_file "${ARG_BINARY_DIR}/${export_file_name}") + endif() + + if(NOT EXISTS "${export_map_file}${export_extension}") + message(FATAL_ERROR "apply_export_map: Expected to find export map file '${export_map_file}${export_extension}'.") + endif() + + if(MSVC) + # On Windows, use a .def file to control exported symbols + target_sources(${target} PRIVATE "${export_map_file}${export_extension}") + else() + # On Unix-like systems, use a version script for symbol visibility + target_link_options(${target} PRIVATE "LINKER:--version-script=${export_map_file}${export_extension}") + # We also need to update the link dependencies. We don't need to do this on Windows since the .def file is a source file. + get_target_property(link_deps ${target} LINK_DEPENDS) + if(NOT link_deps) + set(link_deps "") + endif() + list(APPEND link_deps "${export_map_file}${export_extension}") + set_target_properties(${target} PROPERTIES LINK_DEPENDS "${link_deps}") + endif() + endforeach() +endfunction() diff --git a/cmake/modules/WindowsLibSuffixes.cmake b/cmake/modules/WindowsLibSuffixes.cmake new file mode 100644 index 000000000..b4ae880fe --- /dev/null +++ b/cmake/modules/WindowsLibSuffixes.cmake @@ -0,0 +1,43 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +include_guard() + +# \brief Handles setting output names for libraries on Windows to include version information. +# We have two conventions: +# 1. For enterprise, shared libraries have a suffix _${major_version} +# 2. For TRT-RTX, shared libraries have a suffix _${major_version}_${minor_version} +# \note This is a no-op for non-Windows platforms. +# +# \param target_name The name of the target to update. +# \param major_version The major version of the library. +# \param minor_version The minor version of the library. +function(update_windows_output_name target_name major_version minor_version) + if(MSVC) + get_target_property(tgt_output_name ${target_name} OUTPUT_NAME) + if(NOT tgt_output_name) + set(tgt_output_name ${target_name}) + endif() + + if(${TRT_BUILD_WINML}) + set(tgt_output_name "${tgt_output_name}_${major_version}_${minor_version}") + else() + set(tgt_output_name "${tgt_output_name}_${major_version}") + endif() + + set_target_properties(${target_name} PROPERTIES OUTPUT_NAME ${tgt_output_name}) + message(STATUS "Updated output name for target '${target_name}' to '${tgt_output_name}'") + endif() +endfunction() diff --git a/cmake/toolchains/cmake_qnx_safe.toolchain b/cmake/toolchains/cmake_qnx_safe.toolchain index 9bf2ae7b6..70d714858 100644 --- a/cmake/toolchains/cmake_qnx_safe.toolchain +++ b/cmake/toolchains/cmake_qnx_safe.toolchain @@ -111,7 +111,7 @@ endif() # 2. SafeCUDA depends on the QNX Slogger2 library, which must be linked via -lslog2. It is a system library for QNX-Safe. # 3. Use --target-directory to tell nvcc to use aarch64-qnx-safe instead of aarch64-qnx # This is critical to override nvcc's internal _TARGET_DIR_ variable -set(CMAKE_CUDA_FLAGS_INIT "${CMAKE_CUDA_FLAGS_INIT} --cudadevrt=none -lslog2 --target-directory aarch64-qnx-safe") +set(CMAKE_CUDA_FLAGS_INIT "${CMAKE_CUDA_FLAGS_INIT} --cudadevrt=none -lslog2 --target-directory aarch64-qnx-safe -legacy-launch-seq") # We need to explicitly populate `CMAKE_CUDA_FLAGS` with the initial flags so they propagate to the CMake Compiler Identification phase. # Otherwise, they will only be initialized afterwards, which will cause the identification to fail. diff --git a/docker/rockylinux8.Dockerfile b/docker/rockylinux8.Dockerfile index 01147d5fc..4ba24ddf6 100644 --- a/docker/rockylinux8.Dockerfile +++ b/docker/rockylinux8.Dockerfile @@ -15,12 +15,12 @@ # limitations under the License. # -ARG CUDA_VERSION=13.2.0 +ARG CUDA_VERSION=13.3.0 FROM nvidia/cuda:${CUDA_VERSION}-devel-rockylinux8 LABEL maintainer="NVIDIA CORPORATION" -ENV TRT_VERSION 11.0.0.114 +ENV TRT_VERSION 11.1.0.106 SHELL ["/bin/bash", "-c"] # Setup user account @@ -59,15 +59,15 @@ RUN dnf install -y python38 python38-devel &&\ # Install TensorRT RUN if [ "${CUDA_VERSION:0:2}" = "13" ]; then \ - wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.0.0/tars/TensorRT-Enterprise-11.0.0.114-Linux-x86_64-cuda-13.2-Release-external.tar.zst \ - && tar --use-compress-program=unzstd -xf TensorRT-Enterprise-11.0.0.114-Linux-x86_64-cuda-13.2-Release-external.tar.zst \ - && cp -a TensorRT-11.0.0.114/lib/*.so* /usr/lib64 \ - && pip install TensorRT-11.0.0.114/python/tensorrt-11.0.0.114-cp38-none-linux_x86_64.whl ;\ + wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.1.0/tars/TensorRT-Enterprise-11.1.0.106-Linux-x86_64-cuda-13.3-Release-external.tar.zst \ + && tar --use-compress-program=unzstd -xf TensorRT-Enterprise-11.1.0.106-Linux-x86_64-cuda-13.3-Release-external.tar.zst \ + && cp -a TensorRT-11.1.0.106/lib/*.so* /usr/lib64 \ + && pip install TensorRT-11.1.0.106/python/tensorrt-11.1.0.106-cp38-none-linux_x86_64.whl ;\ elif [ "${CUDA_VERSION:0:2}" = "12" ]; then \ - wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.0.0/tars/TensorRT-Enterprise-11.0.0.114-Linux-x86_64-cuda-12.9-Release-external.tar.zst \ - && tar --use-compress-program=unzstd -xf TensorRT-Enterprise-11.0.0.114-Linux-x86_64-cuda-12.9-Release-external.tar.zst \ - && cp -a TensorRT-11.0.0.114/lib/*.so* /usr/lib64 \ - && pip install TensorRT-11.0.0.114/python/tensorrt-11.0.0.114-cp38-none-linux_x86_64.whl ;\ + wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.1.0/tars/TensorRT-Enterprise-11.1.0.106-Linux-x86_64-cuda-12.9-Release-external.tar.zst \ + && tar --use-compress-program=unzstd -xf TensorRT-Enterprise-11.1.0.106-Linux-x86_64-cuda-12.9-Release-external.tar.zst \ + && cp -a TensorRT-11.1.0.106/lib/*.so* /usr/lib64 \ + && pip install TensorRT-11.1.0.106/python/tensorrt-11.1.0.106-cp38-none-linux_x86_64.whl ;\ else \ echo "Invalid CUDA_VERSION"; \ exit 1; \ diff --git a/docker/rockylinux9.Dockerfile b/docker/rockylinux9.Dockerfile index abb90983f..5ab48c669 100644 --- a/docker/rockylinux9.Dockerfile +++ b/docker/rockylinux9.Dockerfile @@ -15,12 +15,12 @@ # limitations under the License. # -ARG CUDA_VERSION=13.2.0 +ARG CUDA_VERSION=13.3.0 FROM nvidia/cuda:${CUDA_VERSION}-devel-rockylinux9 LABEL maintainer="NVIDIA CORPORATION" -ENV TRT_VERSION 11.0.0.114 +ENV TRT_VERSION 11.1.0.106 SHELL ["/bin/bash", "-c"] # Setup user account @@ -64,15 +64,15 @@ RUN dnf -y install ccache # Install TensorRT RUN if [ "${CUDA_VERSION:0:2}" = "13" ]; then \ - wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.0.0/tars/TensorRT-Enterprise-11.0.0.114-Linux-x86_64-cuda-13.2-Release-external.tar.zst \ - && tar --use-compress-program=unzstd -xf TensorRT-Enterprise-11.0.0.114-Linux-x86_64-cuda-13.2-Release-external.tar.zst \ - && cp -a TensorRT-11.0.0.114/lib/*.so* /usr/lib64 \ - && pip install TensorRT-11.0.0.114/python/tensorrt-11.0.0.114-cp39-none-linux_x86_64.whl ;\ + wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.1.0/tars/TensorRT-Enterprise-11.1.0.106-Linux-x86_64-cuda-13.3-Release-external.tar.zst \ + && tar --use-compress-program=unzstd -xf TensorRT-Enterprise-11.1.0.106-Linux-x86_64-cuda-13.3-Release-external.tar.zst \ + && cp -a TensorRT-11.1.0.106/lib/*.so* /usr/lib64 \ + && pip install TensorRT-11.1.0.106/python/tensorrt-11.1.0.106-cp39-none-linux_x86_64.whl ;\ elif [ "${CUDA_VERSION:0:2}" = "12" ]; then \ - wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.0.0/tars/TensorRT-Enterprise-11.0.0.114-Linux-x86_64-cuda-12.9-Release-external.tar.zst \ - && tar --use-compress-program=unzstd -xf TensorRT-Enterprise-11.0.0.114-Linux-x86_64-cuda-12.9-Release-external.tar.zst \ - && cp -a TensorRT-11.0.0.114/lib/*.so* /usr/lib64 \ - && pip install TensorRT-11.0.0.114/python/tensorrt-11.0.0.114-cp39-none-linux_x86_64.whl ;\ + wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.1.0/tars/TensorRT-Enterprise-11.1.0.106-Linux-x86_64-cuda-12.9-Release-external.tar.zst \ + && tar --use-compress-program=unzstd -xf TensorRT-Enterprise-11.1.0.106-Linux-x86_64-cuda-12.9-Release-external.tar.zst \ + && cp -a TensorRT-11.1.0.106/lib/*.so* /usr/lib64 \ + && pip install TensorRT-11.1.0.106/python/tensorrt-11.1.0.106-cp39-none-linux_x86_64.whl ;\ else \ echo "Invalid CUDA_VERSION"; \ exit 1; \ diff --git a/docker/ubuntu-22.04.Dockerfile b/docker/ubuntu-22.04.Dockerfile index 8b65a1647..4e21dac8d 100644 --- a/docker/ubuntu-22.04.Dockerfile +++ b/docker/ubuntu-22.04.Dockerfile @@ -15,12 +15,12 @@ # limitations under the License. # -ARG CUDA_VERSION=13.2.0 +ARG CUDA_VERSION=13.3.0 FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu22.04 LABEL maintainer="NVIDIA CORPORATION" -ENV TRT_VERSION 11.0.0.114 +ENV TRT_VERSION 11.1.0.106 SHELL ["/bin/bash", "-c"] # Setup user account @@ -75,15 +75,15 @@ RUN apt-get install -y --no-install-recommends \ # Install TensorRT RUN if [ "${CUDA_VERSION:0:2}" = "13" ]; then \ - wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.0.0/tars/TensorRT-Enterprise-11.0.0.114-Linux-x86_64-cuda-13.2-Release-external.tar.zst \ - && tar -xf TensorRT-Enterprise-11.0.0.114-Linux-x86_64-cuda-13.2-Release-external.tar.zst \ - && cp -a TensorRT-11.0.0.114/lib/*.so* /usr/lib/x86_64-linux-gnu \ - && pip install TensorRT-11.0.0.114/python/tensorrt-11.0.0.114-cp310-none-linux_x86_64.whl ;\ + wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.1.0/tars/TensorRT-Enterprise-11.1.0.106-Linux-x86_64-cuda-13.3-Release-external.tar.zst \ + && tar -xf TensorRT-Enterprise-11.1.0.106-Linux-x86_64-cuda-13.3-Release-external.tar.zst \ + && cp -a TensorRT-11.1.0.106/lib/*.so* /usr/lib/x86_64-linux-gnu \ + && pip install TensorRT-11.1.0.106/python/tensorrt-11.1.0.106-cp310-none-linux_x86_64.whl ;\ elif [ "${CUDA_VERSION:0:2}" = "12" ]; then \ - wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.0.0/tars/TensorRT-Enterprise-11.0.0.114-Linux-x86_64-cuda-12.9-Release-external.tar.zst \ - && tar -xf TensorRT-Enterprise-11.0.0.114-Linux-x86_64-cuda-12.9-Release-external.tar.zst \ - && cp -a TensorRT-11.0.0.114/lib/*.so* /usr/lib/x86_64-linux-gnu \ - && pip install TensorRT-11.0.0.114/python/tensorrt-11.0.0.114-cp310-none-linux_x86_64.whl ;\ + wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.1.0/tars/TensorRT-Enterprise-11.1.0.106-Linux-x86_64-cuda-12.9-Release-external.tar.zst \ + && tar -xf TensorRT-Enterprise-11.1.0.106-Linux-x86_64-cuda-12.9-Release-external.tar.zst \ + && cp -a TensorRT-11.1.0.106/lib/*.so* /usr/lib/x86_64-linux-gnu \ + && pip install TensorRT-11.1.0.106/python/tensorrt-11.1.0.106-cp310-none-linux_x86_64.whl ;\ else \ echo "Invalid CUDA_VERSION"; \ exit 1; \ diff --git a/docker/ubuntu-24.04-aarch64.Dockerfile b/docker/ubuntu-24.04-aarch64.Dockerfile index 10cceb187..73d939f4a 100644 --- a/docker/ubuntu-24.04-aarch64.Dockerfile +++ b/docker/ubuntu-24.04-aarch64.Dockerfile @@ -15,12 +15,12 @@ # limitations under the License. # -ARG CUDA_VERSION=13.2.0 +ARG CUDA_VERSION=13.3.0 # Multi-arch container support available in non-cudnn containers. FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu24.04 -ENV TRT_VERSION 11.0.0.114 +ENV TRT_VERSION 11.1.0.106 SHELL ["/bin/bash", "-c"] # Setup user account and edit default account @@ -85,15 +85,15 @@ ENV PATH="/opt/venv/bin:$PATH" # Install TensorRT RUN if [ "${CUDA_VERSION:0:2}" = "13" ]; then \ - wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.0.0/tars/TensorRT-Enterprise-11.0.0.114-Linux-aarch64-cuda-13.2-Release-external.tar.zst \ - && tar -xf TensorRT-Enterprise-11.0.0.114-Linux-aarch64-cuda-13.2-Release-external.tar.zst \ - && cp -a TensorRT-11.0.0.114/lib/*.so* /usr/lib/aarch64-linux-gnu/ \ - && pip install TensorRT-11.0.0.114/python/tensorrt-11.0.0.114-cp312-none-linux_aarch64.whl ;\ + wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.1.0/tars/TensorRT-Enterprise-11.1.0.106-Linux-aarch64-cuda-13.3-Release-external.tar.zst \ + && tar -xf TensorRT-Enterprise-11.1.0.106-Linux-aarch64-cuda-13.3-Release-external.tar.zst \ + && cp -a TensorRT-11.1.0.106/lib/*.so* /usr/lib/aarch64-linux-gnu/ \ + && pip install TensorRT-11.1.0.106/python/tensorrt-11.1.0.106-cp312-none-linux_aarch64.whl ;\ elif [ "${CUDA_VERSION:0:2}" = "12" ]; then \ - wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.0.0/tars/TensorRT-Enterprise-11.0.0.114-Linux-aarch64-cuda-12.9-Release-external.tar.zst \ - && tar -xf TensorRT-Enterprise-11.0.0.114-Linux-aarch64-cuda-12.9-Release-external.tar.zst \ - && cp -a TensorRT-11.0.0.114/lib/*.so* /usr/lib/aarch64-linux-gnu/ \ - && pip install TensorRT-11.0.0.114/python/tensorrt-11.0.0.114-cp312-none-linux_aarch64.whl ;\ + wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.1.0/tars/TensorRT-Enterprise-11.1.0.106-Linux-aarch64-cuda-12.9-Release-external.tar.zst \ + && tar -xf TensorRT-Enterprise-11.1.0.106-Linux-aarch64-cuda-12.9-Release-external.tar.zst \ + && cp -a TensorRT-11.1.0.106/lib/*.so* /usr/lib/aarch64-linux-gnu/ \ + && pip install TensorRT-11.1.0.106/python/tensorrt-11.1.0.106-cp312-none-linux_aarch64.whl ;\ else \ echo "Invalid CUDA_VERSION"; \ exit 1; \ diff --git a/docker/ubuntu-24.04.Dockerfile b/docker/ubuntu-24.04.Dockerfile index 007e112f8..bde40fd99 100644 --- a/docker/ubuntu-24.04.Dockerfile +++ b/docker/ubuntu-24.04.Dockerfile @@ -15,13 +15,13 @@ # limitations under the License. # -ARG CUDA_VERSION=13.2.0 +ARG CUDA_VERSION=13.3.0 # Multi-arch container support available in non-cudnn containers. FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu24.04 LABEL maintainer="NVIDIA CORPORATION" -ENV TRT_VERSION=11.0.0.114 +ENV TRT_VERSION=11.1.0.106 SHELL ["/bin/bash", "-c"] # Setup user account and edit default account @@ -84,15 +84,15 @@ ENV PATH="/opt/venv/bin:$PATH" # Install TensorRT RUN if [ "${CUDA_VERSION:0:2}" = "13" ]; then \ - wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.0.0/tars/TensorRT-Enterprise-11.0.0.114-Linux-x86_64-cuda-13.2-Release-external.tar.zst \ - && tar -xf TensorRT-Enterprise-11.0.0.114-Linux-x86_64-cuda-13.2-Release-external.tar.zst \ - && cp -a TensorRT-11.0.0.114/lib/*.so* /usr/lib/x86_64-linux-gnu/ \ - && pip install TensorRT-11.0.0.114/python/tensorrt-11.0.0.114-cp312-none-linux_x86_64.whl ;\ + wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.1.0/tars/TensorRT-Enterprise-11.1.0.106-Linux-x86_64-cuda-13.3-Release-external.tar.zst \ + && tar -xf TensorRT-Enterprise-11.1.0.106-Linux-x86_64-cuda-13.3-Release-external.tar.zst \ + && cp -a TensorRT-11.1.0.106/lib/*.so* /usr/lib/x86_64-linux-gnu/ \ + && pip install TensorRT-11.1.0.106/python/tensorrt-11.1.0.106-cp312-none-linux_x86_64.whl ;\ elif [ "${CUDA_VERSION:0:2}" = "12" ]; then \ - wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.0.0/tars/TensorRT-Enterprise-11.0.0.114-Linux-x86_64-cuda-12.9-Release-external.tar.zst \ - && tar -xf TensorRT-Enterprise-11.0.0.114-Linux-x86_64-cuda-12.9-Release-external.tar.zst \ - && cp -a TensorRT-11.0.0.114/lib/*.so* /usr/lib/x86_64-linux-gnu/ \ - && pip install TensorRT-11.0.0.114/python/tensorrt-11.0.0.114-cp312-none-linux_x86_64.whl ;\ + wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.1.0/tars/TensorRT-Enterprise-11.1.0.106-Linux-x86_64-cuda-12.9-Release-external.tar.zst \ + && tar -xf TensorRT-Enterprise-11.1.0.106-Linux-x86_64-cuda-12.9-Release-external.tar.zst \ + && cp -a TensorRT-11.1.0.106/lib/*.so* /usr/lib/x86_64-linux-gnu/ \ + && pip install TensorRT-11.1.0.106/python/tensorrt-11.1.0.106-cp312-none-linux_x86_64.whl ;\ else \ echo "Invalid CUDA_VERSION"; \ exit 1; \ diff --git a/docker/ubuntu-26.04.Dockerfile b/docker/ubuntu-26.04.Dockerfile new file mode 100644 index 000000000..7507af86e --- /dev/null +++ b/docker/ubuntu-26.04.Dockerfile @@ -0,0 +1,133 @@ +# +# SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +ARG CUDA_VERSION=13.3.0 + +# Multi-arch container support available in non-cudnn containers. +FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu26.04 +LABEL maintainer="NVIDIA CORPORATION" + +ENV TRT_VERSION=11.1.0.106 +SHELL ["/bin/bash", "-c"] + +# Setup user account and edit default account +RUN if id "ubuntu" &>/dev/null; then \ + usermod -u 1234 ubuntu && \ + groupmod -g 1234 ubuntu; \ + fi +ARG uid=1000 +ARG gid=1000 +RUN groupadd -r -f -g ${gid} trtuser && useradd -o -r -l -u ${uid} -g ${gid} -ms /bin/bash trtuser +RUN usermod -aG sudo trtuser +RUN echo 'trtuser:nvidia' | chpasswd +RUN mkdir -p /workspace && chown trtuser /workspace + +ENV DEBIAN_FRONTEND=noninteractive + +# Install requried libraries +RUN apt-get update && apt-get install -y software-properties-common +RUN apt-get update && apt-get install -y --no-install-recommends \ + libcurl4-openssl-dev \ + wget \ + git \ + pkg-config \ + sudo \ + ssh \ + libssl-dev \ + pbzip2 \ + pv \ + bzip2 \ + unzip \ + devscripts \ + lintian \ + fakeroot \ + dh-make \ + build-essential \ + libnccl2 \ + libnccl-dev \ + openmpi-bin \ + libopenmpi-dev \ + zstd \ + ccache + +# Install python3 +RUN apt-get install -y --no-install-recommends \ + python3 \ + python3-pip \ + python3-dev \ + python3-wheel \ + python3-venv &&\ + cd /usr/local/bin &&\ + ln -s /usr/bin/python3 python &&\ + ln -s /usr/bin/pip3 pip; + +# Create python3 virtualenv +RUN python3 -m venv /opt/venv +ENV PATH="/opt/venv/bin:$PATH" + +# Install TensorRT +RUN if [ "${CUDA_VERSION:0:2}" = "13" ]; then \ + wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.1.0/tars/TensorRT-Enterprise-11.1.0.106-Linux-x86_64-cuda-13.3-Release-external.tar.zst \ + && tar -xf TensorRT-Enterprise-11.1.0.106-Linux-x86_64-cuda-13.3-Release-external.tar.zst \ + && cp -a TensorRT-11.1.0.106/lib/*.so* /usr/lib/x86_64-linux-gnu/ \ + && pip install TensorRT-11.1.0.106/python/tensorrt-11.1.0.106-cp314-none-linux_x86_64.whl ;\ + elif [ "${CUDA_VERSION:0:2}" = "12" ]; then \ + wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.1.0/tars/TensorRT-Enterprise-11.1.0.106-Linux-x86_64-cuda-12.9-Release-external.tar.zst \ + && tar -xf TensorRT-Enterprise-11.1.0.106-Linux-x86_64-cuda-12.9-Release-external.tar.zst \ + && cp -a TensorRT-11.1.0.106/lib/*.so* /usr/lib/x86_64-linux-gnu/ \ + && pip install TensorRT-11.1.0.106/python/tensorrt-11.1.0.106-cp314-none-linux_x86_64.whl ;\ + else \ + echo "Invalid CUDA_VERSION"; \ + exit 1; \ + fi + + # Install PyPI packages +RUN pip3 install --upgrade pip +RUN pip3 install setuptools>=41.0.0 +RUN pip3 install jupyter jupyterlab +# Workaround to remove numpy installed with tensorflow +RUN pip3 install --upgrade numpy + +# Install Cmake +RUN cd /tmp && \ + wget https://github.com/Kitware/CMake/releases/download/v3.31.11/cmake-3.31.11-Linux-x86_64.sh && \ + chmod +x cmake-3.31.11-Linux-x86_64.sh && \ + ./cmake-3.31.11-Linux-x86_64.sh --prefix=/usr/local --exclude-subdir --skip-license && \ + rm ./cmake-3.31.11-Linux-x86_64.sh + +# Install gtest +RUN cd /tmp && \ + git clone https://github.com/google/googletest.git -b v1.14.0 && \ + cd googletest && \ + mkdir build && cd build && \ + cmake .. && \ + make -j4 && \ + make install && \ + rm -rf /tmp/googletest + +# Download NGC client +RUN cd /usr/local/bin && wget https://ngc.nvidia.com/downloads/ngccli_cat_linux.zip && unzip ngccli_cat_linux.zip && chmod u+x ngc-cli/ngc && rm ngccli_cat_linux.zip ngc-cli.md5 && echo "no-apikey\nascii\n" | ngc-cli/ngc config set + +# Set environment and working directory +ENV TRT_LIBPATH=/usr/lib/x86_64-linux-gnu/ +ENV TRT_OSSPATH=/workspace/TensorRT +ENV PATH="/workspace/TensorRT/build/out:${PATH}:/usr/local/bin/ngc-cli" +ENV LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:${TRT_OSSPATH}/build/out:${TRT_LIBPATH}" +WORKDIR /workspace + +USER trtuser +RUN ["/bin/bash"] diff --git a/docker/ubuntu-cross-aarch64.Dockerfile b/docker/ubuntu-cross-aarch64.Dockerfile index d599ef78b..f61461c78 100644 --- a/docker/ubuntu-cross-aarch64.Dockerfile +++ b/docker/ubuntu-cross-aarch64.Dockerfile @@ -15,13 +15,13 @@ # limitations under the License. # -ARG CUDA_VERSION=13.2.0 +ARG CUDA_VERSION=13.3.0 ARG OS_VERSION=24.04 FROM nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${OS_VERSION} LABEL maintainer="NVIDIA CORPORATION" -ENV TRT_VERSION 11.0.0.114 +ENV TRT_VERSION 11.1.0.106 ENV DEBIAN_FRONTEND=noninteractive # Setup user account and edit default account @@ -91,17 +91,17 @@ RUN cd /tmp && \ rm -rf /tmp/googletest # Install CUDA cross compile toolchain -RUN wget https://developer.download.nvidia.com/compute/cuda/13.2.0/local_installers/cuda-repo-cross-sbsa-ubuntu2404-13-2-local_13.2.0-1_all.deb && \ - dpkg -i cuda-repo-cross-sbsa-ubuntu2404-13-2-local_13.2.0-1_all.deb && \ - cp /var/cuda-repo-cross-sbsa-ubuntu2404-13-2-local/cuda-*-keyring.gpg /usr/share/keyrings/ && \ +RUN wget https://developer.download.nvidia.com/compute/cuda/13.3.0/local_installers/cuda-repo-cross-sbsa-ubuntu2404-13-3-local_13.3.0-1_all.deb && \ + dpkg -i cuda-repo-cross-sbsa-ubuntu2404-13-3-local_13.3.0-1_all.deb && \ + cp /var/cuda-repo-cross-sbsa-ubuntu2404-13-3-local/cuda-*-keyring.gpg /usr/share/keyrings/ && \ apt-get update && \ - apt-get -y install cuda-cross-sbsa-13-2 + apt-get -y install cuda-cross-sbsa-13-3 # Unpack libnvinfer. -RUN wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.0.0/tars/TensorRT-Enterprise-11.0.0.114-Linux-aarch64-cuda-13.2-Release-external.tar.zst && \ - tar -xf TensorRT-Enterprise-11.0.0.114-Linux-aarch64-cuda-13.2-Release-external.tar.zst && \ - cp -a TensorRT-11.0.0.114/lib/*.so* /usr/lib/aarch64-linux-gnu +RUN wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/11.1.0/tars/TensorRT-Enterprise-11.1.0.106-Linux-aarch64-cuda-13.3-Release-external.tar.zst && \ + tar -xf TensorRT-Enterprise-11.1.0.106-Linux-aarch64-cuda-13.3-Release-external.tar.zst && \ + cp -a TensorRT-11.1.0.106/lib/*.so* /usr/lib/aarch64-linux-gnu # Link required library RUN cd /usr/aarch64-linux-gnu/lib && ln -sf librt.so.1 librt.so diff --git a/include/NvInfer.h b/include/NvInfer.h index 6942f00dd..4f405ae4a 100644 --- a/include/NvInfer.h +++ b/include/NvInfer.h @@ -3933,8 +3933,6 @@ enum class ResizeCoordinateTransformation : int32_t //! the output tensor, `length_origin` as length of the input tensor in axis x, and `length_resize` as length of the //! output tensor in axis x. //! - //! This transformation maps coordinates as: - //! //! |<--------------length---------->| //! | 0 | 1 | 2 | 3 | //! * * * * @@ -3943,8 +3941,6 @@ enum class ResizeCoordinateTransformation : int32_t //! kALIGN_CORNERS = 0, - //! This transformation maps coordinates as: - //! //! |<--------------length--------------------->| //! | 0 | 1 | 2 | 3 | //! * * * * @@ -3953,8 +3949,6 @@ enum class ResizeCoordinateTransformation : int32_t //! kASYMMETRIC = 1, - //! This transformation maps coordinates as: - //! //! |<--------------length--------------------->| //! | 0 | 1 | 2 | 3 | //! * * * * @@ -5117,11 +5111,10 @@ class IFillLayer : public ILayer //! //! \param alpha has different meanings for each operator: //! - //! | Operation | Usage | - //! | --------------- | -------------------------------------------------------- | - //! | kLINSPACE | the start value, defaults to 0.0; | - //! | kRANDOM_UNIFORM | the minimum value, defaults to 0.0; | - //! | kRANDOM_NORMAL | the mean of the normal distribution, default is 0.0; | + //! Operation | Usage + //! kLINSPACE | the start value, defaults to 0.0; + //! kRANDOM_UNIFORM | the minimum value, defaults to 0.0; + //! kRANDOM_NORMAL | the mean of the normal distribution, default is 0.0; //! //! If input 1 exists, it is reset to null by this method. //! @@ -5152,11 +5145,10 @@ class IFillLayer : public ILayer //! //! \param beta has different meanings for each operator: //! - //! | Operation | Usage | - //! | --------------- | ---------------------------------------------------------------------- | - //! | kLINSPACE | the delta value, defaults to 1.0; | - //! | kRANDOM_UNIFORM | the maximal value, defaults to 1.0; | - //! | kRANDOM_NORMAL | the standard deviation of the normal distribution, default is 1.0; | + //! Operation | Usage + //! kLINSPACE | the delta value, defaults to 1.0; + //! kRANDOM_UNIFORM | the maximal value, defaults to 1.0; + //! kRANDOM_NORMAL | the standard deviation of the normal distribution, default is 1.0; //! //! If input 2 exists, it is reset to null by this method. //! @@ -5229,11 +5221,10 @@ class IFillLayer : public ILayer //! //! \param alpha has different meanings for each operator: //! - //! | Operation | Usage | - //! | --------------- | ------------------------------------------------------ | - //! | kLINSPACE | the start value, defaults to 0; | - //! | kRANDOM_UNIFORM | the minimum value, defaults to 0; | - //! | kRANDOM_NORMAL | the mean of the normal distribution, default is 0; | + //! Operation | Usage + //! kLINSPACE | the start value, defaults to 0; + //! kRANDOM_UNIFORM | the minimum value, defaults to 0; + //! kRANDOM_NORMAL | the mean of the normal distribution, default is 0; //! //! If a third input had been used to create this layer, that input is reset to null by this method. //! @@ -5264,11 +5255,10 @@ class IFillLayer : public ILayer //! //! \param beta has different meanings for each operator: //! - //! | Operation | Usage | - //! | --------------- | -------------------------------------------------------------------- | - //! | kLINSPACE | the delta value, defaults to 1; | - //! | kRANDOM_UNIFORM | the maximal value, defaults to 1; | - //! | kRANDOM_NORMAL | the standard deviation of the normal distribution, default is 1; | + //! Operation | Usage + //! kLINSPACE | the delta value, defaults to 1; + //! kRANDOM_UNIFORM | the maximal value, defaults to 1; + //! kRANDOM_NORMAL | the standard deviation of the normal distribution, default is 1; //! //! If a third input had been used to create this layer, that input is reset to null by this method. //! @@ -5382,30 +5372,27 @@ inline IFillLayer::~IFillLayer() noexcept = default; //! //! As an example of the operation of this layer, imagine a 4D NCHW activation input which can be quantized using a //! single scale coefficient (referred to as per-tensor quantization): -//! //! For each n in N: //! For each c in C: //! For each h in H: //! For each w in W: -//! output[n,c,h,w] = clamp(round(input[n,c,h,w] / scale) + zeroPt) +//! output[n,c,h,w] = clamp(round(\p input[n,c,h,w] / \p scale) + \p zeroPt) //! //! Per-channel quantization is supported only for weight inputs. Thus, Activations cannot be quantized per-channel. //! As an example of per-channel operation, imagine a 4D KCRS weights input and K (dimension 0) as the quantization //! axis. The scale is an array of coefficients, and must have the same size as the quantization axis. -//! //! For each k in K: //! For each c in C: //! For each r in R: //! For each s in S: -//! output[k,c,r,s] = clamp(round(input[k,c,r,s] / scale[k]) + zeroPt[k]) +//! output[k,c,r,s] = clamp(round(\p input[k,c,r,s] / \p scale[k]) + \p zeroPt[k]) //! //! Block quantization is supported for input types DataType::kFP4, DataType::kFP8 and DataType::kINT4. //! As an example of blocked operation, imagine a 2D RS input with R (dimension 0) as the blocking axis and B as the //! block size. The scale is a 2D array of coefficients, with dimensions (R//B, S). -//! //! For each r in R: //! For each s in S: -//! output[r,s] = clamp(round(input[r,s] / scale[r//B, s]) + zeroPt[r//B, s]) +//! output[r,s] = clamp(round(\p input[r,s] / \p scale[r//B, s]) + \p zeroPt[r//B, s]) //! //! \note Only symmetric quantization is supported. //! \note Currently the only allowed build-time constant \p zeroPt subgraphs are: @@ -5538,31 +5525,28 @@ inline IQuantizeLayer::~IQuantizeLayer() noexcept = default; //! //! As an example of the operation of this layer, imagine a 4D NCHW activation input which can be quantized using a //! single scale coefficient (referred to as per-tensor quantization): -//! //! For each n in N: //! For each c in C: //! For each h in H: //! For each w in W: -//! output[n,c,h,w] = (input[n,c,h,w] - zeroPt) * scale +//! output[n,c,h,w] = (\p input[n,c,h,w] - \p zeroPt) * \p scale //! //! Per-channel dequantization is supported only for input that is rooted at an IConstantLayer (i.e. weights). //! Activations cannot be quantized per-channel. As an example of per-channel operation, imagine a 4D KCRS weights input //! and K (dimension 0) as the quantization axis. The scale is an array of coefficients, which is the same size as the //! quantization axis. -//! //! For each k in K: //! For each c in C: //! For each r in R: //! For each s in S: -//! output[k,c,r,s] = (input[k,c,r,s] - zeroPt[k]) * scale[k] +//! output[k,c,r,s] = (\p input[k,c,r,s] - \p zeroPt[k]) * \p scale[k] //! //! Block dequantization is supported for input types DataType::kFP4, DataType::kFP8 and DataType::kINT4. //! As an example of blocked operation, imagine a 2D RS input with R (dimension 0) as the blocking axis and B as the //! block size. The scale is a 2D array of coefficients, with dimensions (R//B, S). -//! -//! For each r in R: -//! For each s in S: -//! output[r,s] = (input[r,s] - zeroPt[r//B, s]) * scale[r//B, s] +//! For each r in R: +//! For each s in S: +//! output[r,s] = (\p input[r,s] - \p zeroPt[r//B, s]) * \p scale[r//B, s] //! //! \note Only symmetric quantization is supported. //! \note Currently the only allowed build-time constant \p zeroPt subgraphs are: @@ -6643,10 +6627,10 @@ struct impl::EnumMaxImpl //! For example, let the input be a vector x of length n and the output be vector y. //! Then y[j] = sum(x[...]) where ... denotes a sequence of indices from this table: //! -//! | | forward | reverse | -//! | --------- | ------- | -------- | -//! | inclusive | 0..j | j..n-1 | -//! | exclusive | 0..j-1 | j+1..n-1 | +//! | forward | reverse +//! ----------|-----------| --------- +//! inclusive | 0..j | j..n-1 +//! exclusive | 0..j-1 | j+1..n-1 //! //! For multidimensional tensors, the reductions apply across a specified axis. For //! example, given a 2D input, a forward inclusive cumulative operation across axis 0 generates @@ -6921,21 +6905,21 @@ inline IAttentionOutputLayer::~IAttentionOutputLayer() noexcept = default; //! efficient than if the subgraph is expressed without IAttention. Setting the IAttention to decomposable=True can //! allow IAttention to be decomposed to use multiple kernels if no fused kernel support found. //! -//! Query Key Value Mask (optional) NormalizationQuantizeScale (optional) -//! | | | | | -//! | Transpose | | | -//! | | | | | -//! ----BMM1---- | | | -//! | | | | -//! *--------------------------- | -//! | | | -//! Normalization | | -//! | | | -//! *------------------------------------------------ -//! | | -//! -------BMM2------ -//! | -//! Output +//! Query Key Value Mask (optional) NormalizationQuantizeScale (optional) +//! | | | | | +//! | Transpose | | | +//! | | | | | +//! ----BMM1---- | | | +//! | | | | +//! *--------------------------- | +//! | | | +//! Normalization | | +//! | | | +//! *------------------------------------------------ +//! | | +//! -------BMM2------ +//! | +//! Output //! //! The attention has the following inputs, in order of input index: //! @@ -11380,6 +11364,78 @@ class IBuilderConfig : public INoCopy return mImpl->getRemoteAutoTuningConfig(); } + //! + //! \brief Set the build route to be passed to the compiler. + //! + //! The build route string is a configuration string that changes the + //! compilation behavior to customize the engine building for different + //! models. + //! + //! \param buildRoute The build route string to be used during compilation. + //! + //! \return True if successful, false otherwise + //! + //! For example, the following is the format of the input: + //! "-conv_use_long_w=off -kgen:codegen:cuda_tile=2" + //! + //! \note Calling this method invalidates any pointer previously returned by + //! getBuildRoute(). + //! + bool setBuildRoute(char const* buildRoute) noexcept + { + return mImpl->setBuildRoute(buildRoute); + } + + //! + //! \brief Get the build route string. + //! + //! \return The current build route string, or an empty string if not set. + //! + //! The returned pointer is owned by the IBuilderConfig and remains valid + //! until the next call to setBuildRoute() or until the IBuilderConfig is + //! destroyed. The caller must not free it. + //! + char const* getBuildRoute() const noexcept + { + return mImpl->getBuildRoute(); + } + + //! + //! \brief Get all available build routes. + //! + //! This returns a JSON string containing all available build route options + //! supported by the compiler. This is initialized when the IBuilderConfig + //! is created. + //! + //! \return A JSON string containing all available build routes. + //! + //! For example, the below is the format of the output: + //! { + //! "tuner_version": "2.19.15", + //! "tuner_options": [ + //! { + //! "option": "-conv_use_long_w", + //! "allowed_values": "-conv_use_long_w=[on|off]", + //! "default_value": "on", + //! "help": "Convert Conv [N, C, X, 1] to [N, C, 1, X] in CASK params for perf." + //! }, + //! { + //! "option": "-kgen:codegen:cuda_tile", + //! "allowed_values": "-kgen:codegen:cuda_tile=[0|1|2|3]", + //! "default_value": "1", + //! "help": "CUDA Tile codegen. 0: disable, 1: where profitable, 2: supported kdags, 3: force all." + //! }, + //! ... + //! } + //! + //! The returned pointer is owned by the IBuilderConfig and remains valid + //! for the lifetime of the IBuilderConfig. The caller must not free it. + //! + char const* getAllBuildRoutes() const noexcept + { + return mImpl->getAllBuildRoutes(); + } + protected: apiv::VBuilderConfig* mImpl; }; diff --git a/include/NvInferImpl.h b/include/NvInferImpl.h index 80810893e..3f2397df5 100644 --- a/include/NvInferImpl.h +++ b/include/NvInferImpl.h @@ -1325,6 +1325,9 @@ class VBuilderConfig : public VRoot virtual int64_t getL2LimitForTiling() const noexcept = 0; virtual bool setRemoteAutoTuningConfig(char const* config) noexcept = 0; virtual char const* getRemoteAutoTuningConfig() const noexcept = 0; + virtual bool setBuildRoute(char const* buildRoute) noexcept = 0; + virtual char const* getBuildRoute() const noexcept = 0; + virtual char const* getAllBuildRoutes() const noexcept = 0; }; class VSerializationConfig : public VRoot diff --git a/include/NvInferRuntime.h b/include/NvInferRuntime.h index e2d47fb01..73ee8a7e0 100644 --- a/include/NvInferRuntime.h +++ b/include/NvInferRuntime.h @@ -1012,15 +1012,15 @@ class IPluginV3OneBuild : public IPluginCapability //! plugin will be timed \f$N = \sum_{i=0}^{i getFormatCombinationLimit() - //! goto done - //! configurePlugin(...) - //! for each tactic in getValidTactics(...) - //! time tactic - //! done: + //! counter = 0 + //! for each supported format combination + //! ++counter + //! if counter > getFormatCombinationLimit() + //! goto done + //! configurePlugin(...) + //! for each tactic in getValidTactics(...) + //! time tactic + //! done: //! //! //! \param tactics Pre-allocated buffer to which the tactic values should be written diff --git a/include/NvInferVersion.h b/include/NvInferVersion.h index a244c5022..4fa51fcf6 100644 --- a/include/NvInferVersion.h +++ b/include/NvInferVersion.h @@ -24,9 +24,9 @@ #define NV_INFER_VERSION_H #define TRT_MAJOR_ENTERPRISE 11 -#define TRT_MINOR_ENTERPRISE 0 +#define TRT_MINOR_ENTERPRISE 1 #define TRT_PATCH_ENTERPRISE 0 -#define TRT_BUILD_ENTERPRISE 114 +#define TRT_BUILD_ENTERPRISE 106 #define NV_TENSORRT_MAJOR TRT_MAJOR_ENTERPRISE //!< TensorRT major version. #define NV_TENSORRT_MINOR TRT_MINOR_ENTERPRISE //!< TensorRT minor version. #define NV_TENSORRT_PATCH TRT_PATCH_ENTERPRISE //!< TensorRT patch version. @@ -42,4 +42,4 @@ #define NV_TENSORRT_RELEASE_TYPE NV_TENSORRT_RELEASE_TYPE_GENERAL_AVAILABILITY //!< TensorRT release type -#endif // NV_INFER_VERSION_H +#endif // NV_INFER_VERSION_H \ No newline at end of file diff --git a/parsers/CMakeLists.txt b/parsers/CMakeLists.txt index 1e0a0471e..9192c1f05 100644 --- a/parsers/CMakeLists.txt +++ b/parsers/CMakeLists.txt @@ -20,7 +20,6 @@ add_custom_target(parsers DEPENDS nvonnxparser) add_definitions("-D_PROTOBUF_INSTALL_DIR=${Protobuf_INSTALL_DIR}") add_compile_options("-Dgoogle=google_trtrepack") - set(TENSORRT_ROOT ${PROJECT_SOURCE_DIR}) set(TENSORRT_BUILD ${TRT_OUT_DIR}) diff --git a/parsers/onnx b/parsers/onnx index 5ab48c5c4..7c51a63a7 160000 --- a/parsers/onnx +++ b/parsers/onnx @@ -1 +1 @@ -Subproject commit 5ab48c5c436a5686b8574c4a372cbca224273b5a +Subproject commit 7c51a63a719180eb5160c874c111746f3fb46a6b diff --git a/plugin/CMakeLists.txt b/plugin/CMakeLists.txt index 8e7695a1c..0027da90d 100644 --- a/plugin/CMakeLists.txt +++ b/plugin/CMakeLists.txt @@ -17,8 +17,6 @@ include(ShouldCompileKernel) -if (${TRT_BUILD_ENABLE_NEW_PLUGIN_FLOW}) - option(TRT_BUILD_INCLUDE_BERT_QKV_PLUGIN "Build the BERT QKV to Context Plugin and related plugins." ON) # Create the main object library, which is shared between plugin, plugin_internal, and plugin_static. @@ -81,7 +79,7 @@ foreach(PLUGIN_NAME IN LISTS TRT_PLUGIN_NAMES) endforeach() set(trt_plugin_include_dirs - $ + $ $ $ ) @@ -115,9 +113,6 @@ target_compile_definitions(trt_vc_plugins PRIVATE add_library(tensorrt_plugins SHARED) target_link_libraries(tensorrt_plugins PRIVATE trt_plugins) -add_library(tensorrt_plugins_internal SHARED) -target_link_libraries(tensorrt_plugins_internal PRIVATE trt_plugins) - add_library(tensorrt_plugins_static STATIC) target_bundle_libraries(tensorrt_plugins_static PRIVATE trt_plugins) @@ -170,35 +165,6 @@ apply_export_map( update_windows_output_name(tensorrt_plugins ${TRT_MAJOR} ${TRT_MINOR}) -if(NOT MSVC) - set(trt_plugins_internal_link_options - "LINKER:-z,relro" - "LINKER:-Bsymbolic" - "LINKER:--no-undefined" - "LINKER:--no-as-needed" - ) -else() - set(trt_plugins_internal_link_options) -endif() - -### Internal Plugin Setup -# This library is effectively the same as tensorrt_plugins, but without stripped symbols. -target_include_directories(tensorrt_plugins_internal PUBLIC ${trt_plugin_include_dirs}) -target_link_libraries(tensorrt_plugins_internal PRIVATE ${trt_plugin_dependencies}) -target_link_options(tensorrt_plugins_internal PRIVATE ${trt_plugins_internal_link_options}) - -set_target_properties( - tensorrt_plugins_internal - PROPERTIES CXX_VISIBILITY_PRESET default - VISIBILITY_INLINES_HIDDEN OFF - WINDOWS_EXPORT_ALL_SYMBOLS ON # Allows tests to reach internal symbols without __declspec(dllexport) - OUTPUT_NAME nvinfer_plugin_internal - VERSION ${TensorRT_VERSION} - SOVERSION ${TRT_MAJOR} -) - -update_windows_output_name(tensorrt_plugins_internal ${TRT_MAJOR} ${TRT_MINOR}) - ### Static Plugin Setup set(trt_plugin_static_dependencies tensorrt_static @@ -298,275 +264,3 @@ installLibraries( COMPONENT external EXPORT TensorRT_Static ) - -installLibraries( - TARGETS tensorrt_plugins_internal - OPTIONAL - COMPONENT internal - EXPORT TensorRT_Internal -) - -elseif(${TRT_BUILD_WINML}) -# TRT-RTX does not ship a separate plugin library. A small subset of in-tree plugins is -# baked directly into tensorrt_rtx via the trt_rtx_embedded_plugins object library, which -# is linked into trt_runtime_lean. This currently provides ROIAlign V3 (the V3 creator -# self-registers via REGISTER_TENSORRT_PLUGIN) plus the plugin error/logging infra it -# needs. -add_library(trt_rtx_embedded_plugins OBJECT - roiAlignPlugin/roiAlignPlugin.cpp - roiAlignPlugin/roiAlignPlugin.h - roiAlignPlugin/roiAlignKernel.cu - roiAlignPlugin/roiAlignKernel.h - vc/checkMacrosPlugin.cpp - vc/checkMacrosPlugin.h -) - -target_include_directories(trt_rtx_embedded_plugins PRIVATE - $ -) - -# Match the trt_plugins compile environment. -target_link_libraries(trt_rtx_embedded_plugins PRIVATE $) -target_link_libraries(trt_rtx_embedded_plugins PRIVATE trt_global_definitions TRT::cudart) - -foreach(SM IN LISTS CMAKE_CUDA_ARCHITECTURES) - get_numeric_sm(${SM} "SM") - target_compile_definitions(trt_rtx_embedded_plugins PRIVATE "ENABLE_SM${SM}") -endforeach() - -target_compile_options(trt_rtx_embedded_plugins PRIVATE $<$:--expt-relaxed-constexpr>) - -else() # neither TRT_BUILD_ENABLE_NEW_PLUGIN_FLOW nor TRT_BUILD_WINML -add_custom_target(plugin) - -set(TARGET_NAME ${nvinfer_plugin_lib_name}) -set(SHARED_TARGET ${TARGET_NAME}) -set(STATIC_TARGET ${TARGET_NAME}_static) -set(VFC_TARGET_NAME ${nvinfer_vc_plugin_lib_name}) -set(VFC_SHARED_TARGET ${VFC_TARGET_NAME}) - -set(TARGET_DIR ${CMAKE_CURRENT_SOURCE_DIR}) -set(PLUGIN_EXPORT_MAP ${TARGET_DIR}/exports/nvinfer_plugin.map) -set(VFC_PLUGIN_EXPORT_MAP ${TARGET_DIR}/exports/nvinfer_vc_plugin.map) - -if(${CMAKE_BUILD_TYPE} MATCHES "Debug") - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g") -endif() - -foreach(SM IN LISTS CMAKE_CUDA_ARCHITECTURES) - get_numeric_sm(${SM} "SM") - set(ENABLED_SMS "${ENABLED_SMS} -DENABLE_SM${SM}") -endforeach() - -set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${ENABLED_SMS}") -set(PLUGIN_SOURCES) -set(PLUGIN_CU_SOURCES) -set(VFC_PLUGIN_SOURCES) -set(VFC_PLUGIN_CU_SOURCES) - -# Backwards compat versions of the functions used by the new flow that bind to the old flow. -# We have to add C++ sources to PLUGIN_SOURCES, and Cuda sources to PLUGIN_CU_SOURCES. -macro(add_plugin_source) - foreach(SRC_FILE IN ITEMS ${ARGN}) - if(SRC_FILE MATCHES "^.*((\.h)|(\.cpp)|(\.hpp))$") - list(APPEND PLUGIN_SOURCES ${CMAKE_CURRENT_LIST_DIR}/${SRC_FILE}) - else() - list(APPEND PLUGIN_CU_SOURCES ${CMAKE_CURRENT_LIST_DIR}/${SRC_FILE}) - endif() - endforeach() - set(PLUGIN_SOURCES ${PLUGIN_SOURCES} PARENT_SCOPE) - set(PLUGIN_CU_SOURCES ${PLUGIN_CU_SOURCES} PARENT_SCOPE) -endmacro() - -macro(add_vc_plugin_source) - foreach(SRC_FILE IN ITEMS ${ARGN}) - if(SRC_FILE MATCHES "^.*((\.h)|(\.cpp)|(\.hpp))$") - list(APPEND VFC_PLUGIN_SOURCES ${CMAKE_CURRENT_LIST_DIR}/${SRC_FILE}) - else() - list(APPEND VFC_PLUGIN_CU_SOURCES ${CMAKE_CURRENT_LIST_DIR}/${SRC_FILE}) - endif() - endforeach() - set(VFC_PLUGIN_SOURCES ${VFC_PLUGIN_SOURCES} PARENT_SCOPE) - set(VFC_PLUGIN_CU_SOURCES ${VFC_PLUGIN_CU_SOURCES} PARENT_SCOPE) -endmacro() - -set(PLUGIN_LISTS - cropAndResizePlugin - decodeBbox3DPlugin - detectionLayerPlugin - disentangledAttentionPlugin - efficientNMSPlugin - flattenConcat - generateDetectionPlugin - gridAnchorPlugin - groupNormalizationPlugin - instanceNormalizationPlugin - modulatedDeformConvPlugin - multilevelCropAndResizePlugin - multilevelProposeROI - multiscaleDeformableAttnPlugin - nvFasterRCNN - pillarScatterPlugin - priorBoxPlugin - proposalLayerPlugin - pyramidROIAlignPlugin - regionPlugin - reorgPlugin - resizeNearestPlugin - roiAlignPlugin - scatterElementsPlugin - scatterPlugin - topkLastDimPlugin - voxelGeneratorPlugin) - -# Add BERT sources if ${BERT_GENCODES} was populated -if(BERT_GENCODES) - set(BERT_CU_SOURCES) - set(PLUGIN_LISTS ${PLUGIN_LISTS} bertQKVToContextPlugin embLayerNormPlugin fcPlugin skipLayerNormPlugin) - include_directories(bertQKVToContextPlugin/fused_multihead_attention/include - bertQKVToContextPlugin/fused_multihead_attention_v2/include) -endif() - -include_directories(common common/kernels ${CMAKE_SOURCE_DIR}/third_party) - -foreach(PLUGIN_ITER ${PLUGIN_LISTS}) - include_directories(${PLUGIN_ITER}) - add_subdirectory(${PLUGIN_ITER}) -endforeach(PLUGIN_ITER) - -# Add common -add_subdirectory(common) -add_subdirectory(vc) - -# Set per-file compile flags for CUDA sources -set_source_files_properties(${PLUGIN_CU_SOURCES} PROPERTIES COMPILE_FLAGS "${ENABLED_SMS}") -list(APPEND PLUGIN_SOURCES "${PLUGIN_CU_SOURCES}") - -list(APPEND PLUGIN_SOURCES "${CMAKE_CURRENT_SOURCE_DIR}/api/inferPlugin.cpp") -list(APPEND PLUGIN_SOURCES "${CMAKE_CURRENT_SOURCE_DIR}/../samples/common/logger.cpp") - -################################## SHARED LIBRARY ####################################### - -add_library(${SHARED_TARGET} SHARED ${PLUGIN_SOURCES}) - -target_include_directories( - ${SHARED_TARGET} - PUBLIC ${PROJECT_SOURCE_DIR}/include - PRIVATE ${PROJECT_SOURCE_DIR}/common - PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/bertQKVToContextPlugin/fused_multihead_attention/include - PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/bertQKVToContextPlugin/fused_multihead_attention_v2/include - PUBLIC ${CUDAToolkit_INCLUDE_DIRS} - PRIVATE ${TARGET_DIR}) - -set_target_properties( - ${SHARED_TARGET} - PROPERTIES CXX_STANDARD "17" - CXX_STANDARD_REQUIRED "YES" - CXX_EXTENSIONS "NO" - ARCHIVE_OUTPUT_DIRECTORY "${TRT_OUT_DIR}" - LIBRARY_OUTPUT_DIRECTORY "${TRT_OUT_DIR}" - RUNTIME_OUTPUT_DIRECTORY "${TRT_OUT_DIR}") - -if(MSVC) - set_target_properties(${SHARED_TARGET} PROPERTIES LINK_FLAGS "/DEF:${PLUGIN_EXPORT_DEF}") -else() - set_target_properties( - ${SHARED_TARGET} - PROPERTIES LINK_FLAGS - "-Wl,--exclude-libs,ALL -Wl,-Bsymbolic -Wl,--version-script=${PLUGIN_EXPORT_MAP} -Wl,--no-undefined") -endif() - -set_target_properties(${SHARED_TARGET} PROPERTIES DEBUG_POSTFIX ${TRT_DEBUG_POSTFIX}) - -set_target_properties(${SHARED_TARGET} PROPERTIES VERSION ${TRT_VERSION} SOVERSION ${TRT_SOVERSION}) - -set_property(TARGET ${SHARED_TARGET} PROPERTY CUDA_STANDARD 17) - -target_link_directories(${SHARED_TARGET} PUBLIC ${CUDAToolkit_LIBRARY_DIR}) - -target_link_libraries(${SHARED_TARGET} PRIVATE CUDA::cudart_static ${${nvinfer_lib_name}_LIB_PATH} ${CMAKE_DL_LIBS}) - -################################## STATIC LIBRARY ####################################### - -add_library(${STATIC_TARGET} STATIC ${PLUGIN_SOURCES}) - -target_include_directories( - ${STATIC_TARGET} - PUBLIC ${PROJECT_SOURCE_DIR}/include - PRIVATE ${PROJECT_SOURCE_DIR}/common - PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/bertQKVToContextPlugin/fused_multihead_attention/include - PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/bertQKVToContextPlugin/fused_multihead_attention_v2/include - PUBLIC ${CUDAToolkit_INCLUDE_DIRS} - PRIVATE ${TARGET_DIR}) - -set_target_properties( - ${STATIC_TARGET} - PROPERTIES CXX_STANDARD "17" - CXX_STANDARD_REQUIRED "YES" - CXX_EXTENSIONS "NO" - ARCHIVE_OUTPUT_DIRECTORY "${TRT_OUT_DIR}" - LIBRARY_OUTPUT_DIRECTORY "${TRT_OUT_DIR}" - RUNTIME_OUTPUT_DIRECTORY "${TRT_OUT_DIR}") - -set_target_properties(${STATIC_TARGET} PROPERTIES LINK_FLAGS "-Wl,--exclude-libs,ALL") - -set_target_properties(${STATIC_TARGET} PROPERTIES DEBUG_POSTFIX ${TRT_DEBUG_POSTFIX}) - -set_target_properties(${STATIC_TARGET} PROPERTIES VERSION ${TRT_VERSION} SOVERSION ${TRT_SOVERSION}) - -set_property(TARGET ${STATIC_TARGET} PROPERTY CUDA_STANDARD 17) - -################################## VFC SHARED LIBRARY ####################################### - -set_source_files_properties(${VFC_PLUGIN_CU_SOURCES} PROPERTIES COMPILE_FLAGS "${ENABLED_SMS}") -list(APPEND VFC_PLUGIN_SOURCES "${VFC_PLUGIN_CU_SOURCES}") - -add_library(${VFC_SHARED_TARGET} SHARED ${VFC_PLUGIN_SOURCES}) - -target_include_directories( - ${VFC_SHARED_TARGET} - PUBLIC ${PROJECT_SOURCE_DIR}/include - PRIVATE ${PROJECT_SOURCE_DIR}/common - PUBLIC ${CUDAToolkit_INCLUDE_DIRS} - PRIVATE ${TARGET_DIR}) - -set_target_properties( - ${VFC_SHARED_TARGET} - PROPERTIES CXX_STANDARD "17" - CXX_STANDARD_REQUIRED "YES" - CXX_EXTENSIONS "NO" - ARCHIVE_OUTPUT_DIRECTORY "${TRT_OUT_DIR}" - LIBRARY_OUTPUT_DIRECTORY "${TRT_OUT_DIR}" - RUNTIME_OUTPUT_DIRECTORY "${TRT_OUT_DIR}") - -if(MSVC) - set_target_properties(${VFC_SHARED_TARGET} PROPERTIES LINK_FLAGS "/DEF:${VFC_PLUGIN_EXPORT_DEF}") -else() - set_target_properties( - ${VFC_SHARED_TARGET} - PROPERTIES - LINK_FLAGS - "-Wl,--exclude-libs,ALL -Wl,-Bsymbolic -Wl,--version-script=${VFC_PLUGIN_EXPORT_MAP} -Wl,--no-undefined") -endif() - -set_target_properties(${VFC_SHARED_TARGET} PROPERTIES DEBUG_POSTFIX ${TRT_DEBUG_POSTFIX}) - -set_target_properties(${VFC_SHARED_TARGET} PROPERTIES VERSION ${TRT_VERSION} SOVERSION ${TRT_SOVERSION}) - -set_property(TARGET ${VFC_SHARED_TARGET} PROPERTY CUDA_STANDARD 17) - -target_link_directories(${VFC_SHARED_TARGET} PUBLIC ${CUDAToolkit_LIBRARY_DIR}) - -target_link_libraries(${VFC_SHARED_TARGET} PRIVATE CUDA::cudart_static ${${nvinfer_lib_name}_LIB_PATH} ${CMAKE_DL_LIBS}) - -######################################################################################### - -add_dependencies(plugin ${SHARED_TARGET} ${STATIC_TARGET} ${VFC_SHARED_TARGET}) - -################################### INSTALLATION ######################################## - -install( - TARGETS ${SHARED_TARGET} ${VFC_SHARED_TARGET} -) - -endif() diff --git a/plugin/bertQKVToContextPlugin/CMakeLists.txt b/plugin/bertQKVToContextPlugin/CMakeLists.txt index 1c84a336b..9617c6ad3 100644 --- a/plugin/bertQKVToContextPlugin/CMakeLists.txt +++ b/plugin/bertQKVToContextPlugin/CMakeLists.txt @@ -1,5 +1,5 @@ # -# SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-FileCopyrightText: Copyright (c) 1993-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -44,9 +44,3 @@ set(BERT_QKV_SUPPORTED_SMS add_subdirectory(fused_multihead_attention) add_subdirectory(fused_multihead_attention_v2) -# Promote PLUGIN_SOURCES and PLUGIN_CU_SOURCES added by `add_subdirectory` to this file's parent. -if (NOT DEFINED ${TRT_BUILD_ENABLE_NEW_PLUGIN_FLOW}) - set(PLUGIN_SOURCES ${PLUGIN_SOURCES} PARENT_SCOPE) - set(PLUGIN_CU_SOURCES ${PLUGIN_CU_SOURCES} PARENT_SCOPE) -endif() - diff --git a/plugin/common/CMakeLists.txt b/plugin/common/CMakeLists.txt index be794e0ab..1e23ecdff 100644 --- a/plugin/common/CMakeLists.txt +++ b/plugin/common/CMakeLists.txt @@ -45,9 +45,3 @@ set(PLUGIN_COMMON_SOURCES add_plugin_source(${PLUGIN_COMMON_SOURCES}) add_subdirectory(kernels) - -# Promote PLUGIN_SOURCES and PLUGIN_CU_SOURCES added by `add_subdirectory` to this file's parent. -if (NOT DEFINED ${TRT_BUILD_ENABLE_NEW_PLUGIN_FLOW}) - set(PLUGIN_SOURCES ${PLUGIN_SOURCES} PARENT_SCOPE) - set(PLUGIN_CU_SOURCES ${PLUGIN_CU_SOURCES} PARENT_SCOPE) -endif() diff --git a/plugin/common/checkMacrosPlugin.cpp b/plugin/common/checkMacrosPlugin.cpp index 6f1c4185f..81ad8daea 100644 --- a/plugin/common/checkMacrosPlugin.cpp +++ b/plugin/common/checkMacrosPlugin.cpp @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 1993-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 * * Licensed under the Apache License, Version 2.0 (the "License"); @@ -20,42 +20,54 @@ #include "vc/vfcCommon.h" #include #include +#include using namespace nvinfer1::pluginInternal; namespace nvinfer1::plugin { +namespace +{ +//! \return Static identifier string for \p status, or an empty view if unrecognized. +[[nodiscard]] constexpr std::string_view cublasStatusName(cublasStatus_t status) +{ + switch (status) + { + case CUBLAS_STATUS_SUCCESS: return "CUBLAS_STATUS_SUCCESS"; + case CUBLAS_STATUS_NOT_INITIALIZED: return "CUBLAS_STATUS_NOT_INITIALIZED"; + case CUBLAS_STATUS_ALLOC_FAILED: return "CUBLAS_STATUS_ALLOC_FAILED"; + case CUBLAS_STATUS_INVALID_VALUE: return "CUBLAS_STATUS_INVALID_VALUE"; + case CUBLAS_STATUS_ARCH_MISMATCH: return "CUBLAS_STATUS_ARCH_MISMATCH"; + case CUBLAS_STATUS_MAPPING_ERROR: return "CUBLAS_STATUS_MAPPING_ERROR"; + case CUBLAS_STATUS_EXECUTION_FAILED: return "CUBLAS_STATUS_EXECUTION_FAILED"; + case CUBLAS_STATUS_INTERNAL_ERROR: return "CUBLAS_STATUS_INTERNAL_ERROR"; + case CUBLAS_STATUS_NOT_SUPPORTED: return "CUBLAS_STATUS_NOT_SUPPORTED"; + case CUBLAS_STATUS_LICENSE_ERROR: return "CUBLAS_STATUS_LICENSE_ERROR"; + } + return {}; +} +} // namespace + // break-pointable -void throwCublasError(char const* file, char const* function, int32_t line, int32_t status, char const* msg) +void throwCublasError( + char const* file, char const* function, int32_t line, int32_t status, std::optional msg) { - if (msg == nullptr) + if (!msg) { - auto s_ = static_cast(status); - switch (s_) - { - case CUBLAS_STATUS_SUCCESS: msg = "CUBLAS_STATUS_SUCCESS"; break; - case CUBLAS_STATUS_NOT_INITIALIZED: msg = "CUBLAS_STATUS_NOT_INITIALIZED"; break; - case CUBLAS_STATUS_ALLOC_FAILED: msg = "CUBLAS_STATUS_ALLOC_FAILED"; break; - case CUBLAS_STATUS_INVALID_VALUE: msg = "CUBLAS_STATUS_INVALID_VALUE"; break; - case CUBLAS_STATUS_ARCH_MISMATCH: msg = "CUBLAS_STATUS_ARCH_MISMATCH"; break; - case CUBLAS_STATUS_MAPPING_ERROR: msg = "CUBLAS_STATUS_MAPPING_ERROR"; break; - case CUBLAS_STATUS_EXECUTION_FAILED: msg = "CUBLAS_STATUS_EXECUTION_FAILED"; break; - case CUBLAS_STATUS_INTERNAL_ERROR: msg = "CUBLAS_STATUS_INTERNAL_ERROR"; break; - case CUBLAS_STATUS_NOT_SUPPORTED: msg = "CUBLAS_STATUS_NOT_SUPPORTED"; break; - case CUBLAS_STATUS_LICENSE_ERROR: msg = "CUBLAS_STATUS_LICENSE_ERROR"; break; - } + msg.emplace(cublasStatusName(static_cast(status))); } - CublasError error(file, function, line, status, msg); + CublasError error(file, function, line, status, std::move(msg)); error.log(gLogError); // NOLINTNEXTLINE(misc-throw-by-value-catch-by-reference) throw error; } // break-pointable -void throwCudnnError(char const* file, char const* function, int32_t line, int32_t status, char const* msg) +void throwCudnnError( + char const* file, char const* function, int32_t line, int32_t status, std::optional msg) { - CudnnError error(file, function, line, status, msg); + CudnnError error(file, function, line, status, std::move(msg)); error.log(gLogError); // NOLINTNEXTLINE(misc-throw-by-value-catch-by-reference) throw error; diff --git a/plugin/common/checkMacrosPlugin.h b/plugin/common/checkMacrosPlugin.h index ae9ed9808..5f130d164 100644 --- a/plugin/common/checkMacrosPlugin.h +++ b/plugin/common/checkMacrosPlugin.h @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 1993-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 * * Licensed under the Apache License, Version 2.0 (the "License"); @@ -21,7 +21,10 @@ #include "common/cudnnWrapper.h" #include "vc/checkMacrosPlugin.h" #include +#include #include +#include +#include #ifdef _MSC_VER #define FN_NAME __FUNCTION__ @@ -34,25 +37,35 @@ namespace nvinfer1 namespace plugin { -[[noreturn]] void throwCudnnError( - char const* file, char const* function, int32_t line, int32_t status, char const* msg = nullptr); -[[noreturn]] void throwCublasError( - char const* file, char const* function, int32_t line, int32_t status, char const* msg = nullptr); +//! \throw CudnnError carrying \p msg, after logging it via \c gLogError. +//! Parameter semantics match \c nvinfer1::plugin::throwCudaError. +[[noreturn]] void throwCudnnError(char const* file, char const* function, int32_t line, int32_t status, + std::optional msg = std::nullopt); +//! \throw CublasError carrying \p msg (or a default message derived from \p status if \p msg is +//! \c std::nullopt), after logging it via \c gLogError. An explicit empty string is preserved. +//! Parameter semantics match \c nvinfer1::plugin::throwCudaError. +[[noreturn]] void throwCublasError(char const* file, char const* function, int32_t line, int32_t status, + std::optional msg = std::nullopt); + +//! \c TRTException specialization for cuDNN failures. class CudnnError : public TRTException { public: - CudnnError(char const* fl, char const* fn, int32_t ln, int32_t stat, char const* msg = nullptr) - : TRTException(fl, fn, ln, stat, msg, "Cudnn") + //! \see TRTException::TRTException. Sets the subsystem name to \c "Cudnn". + CudnnError(char const* fl, char const* fn, int32_t ln, int32_t stat, std::optional msg = std::nullopt) + : TRTException(fl, fn, ln, stat, std::move(msg), "Cudnn") { } }; +//! \c TRTException specialization for cuBLAS failures. class CublasError : public TRTException { public: - CublasError(char const* fl, char const* fn, int32_t ln, int32_t stat, char const* msg = nullptr) - : TRTException(fl, fn, ln, stat, msg, "cuBLAS") + //! \see TRTException::TRTException. Sets the subsystem name to \c "cuBLAS". + CublasError(char const* fl, char const* fn, int32_t ln, int32_t stat, std::optional msg = std::nullopt) + : TRTException(fl, fn, ln, stat, std::move(msg), "cuBLAS") { } }; diff --git a/plugin/common/cubCcclCompat.h b/plugin/common/cubCcclCompat.h index 2ab05e2bd..498721752 100644 --- a/plugin/common/cubCcclCompat.h +++ b/plugin/common/cubCcclCompat.h @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 1993-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 * * Licensed under the Apache License, Version 2.0 (the "License"); @@ -18,15 +18,9 @@ #ifndef TRT_CUB_CCCL_COMPAT_H #define TRT_CUB_CCCL_COMPAT_H -#include - -// Include CUB header #include - -#if CUDA_VERSION >= 13000 -// Include CCCL headers for CUDA 13.0+ +#include #include -#endif namespace compat { @@ -34,31 +28,19 @@ namespace compat // Min operation compatibility wrapper __host__ __device__ __forceinline__ auto getCudaMinOp() { -#if CUDA_VERSION >= 13000 return cuda::minimum(); -#else - return cub::Min(); -#endif } // Max operation compatibility wrapper __host__ __device__ __forceinline__ auto getCudaMaxOp() { -#if CUDA_VERSION >= 13000 return cuda::maximum(); -#else - return cub::Max(); -#endif } // Sum operation compatibility wrapper __host__ __device__ __forceinline__ auto getCudaSumOp() { -#if CUDA_VERSION >= 13000 return cuda::std::plus<>(); -#else - return cub::Sum(); -#endif } } // namespace compat diff --git a/plugin/efficientNMSPlugin/CMakeLists.txt b/plugin/efficientNMSPlugin/CMakeLists.txt index 81d4cae14..688fe0226 100644 --- a/plugin/efficientNMSPlugin/CMakeLists.txt +++ b/plugin/efficientNMSPlugin/CMakeLists.txt @@ -1,5 +1,5 @@ # -# SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-FileCopyrightText: Copyright (c) 1993-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -26,9 +26,3 @@ add_plugin_source( add_subdirectory(tftrt) -# Promote PLUGIN_SOURCES and PLUGIN_CU_SOURCES added by `add_subdirectory` to this file's parent. -if (NOT DEFINED ${TRT_BUILD_ENABLE_NEW_PLUGIN_FLOW}) - set(PLUGIN_SOURCES ${PLUGIN_SOURCES} PARENT_SCOPE) - set(PLUGIN_CU_SOURCES ${PLUGIN_CU_SOURCES} PARENT_SCOPE) -endif() - diff --git a/plugin/exports/nvinfer_plugin.def b/plugin/exports/nvinfer_plugin.def deleted file mode 100644 index 28a552d4a..000000000 --- a/plugin/exports/nvinfer_plugin.def +++ /dev/null @@ -1,15 +0,0 @@ -; SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -; SPDX-License-Identifier: LicenseRef-NvidiaProprietary -; -; NVIDIA CORPORATION, its affiliates and licensors retain all intellectual -; property and proprietary rights in and to this material, related -; documentation and any modifications thereto. Any use, reproduction, -; disclosure or distribution of this material and related documentation -; without an express license agreement from NVIDIA CORPORATION or -; its affiliates is strictly prohibited. -; - -EXPORTS - initLibNvInferPlugins - getCreators - setLoggerFinder diff --git a/plugin/exports/nvinfer_vc_plugin.def b/plugin/exports/nvinfer_vc_plugin.def deleted file mode 100644 index eb609d238..000000000 --- a/plugin/exports/nvinfer_vc_plugin.def +++ /dev/null @@ -1,13 +0,0 @@ -; SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -; SPDX-License-Identifier: LicenseRef-NvidiaProprietary -; -; NVIDIA CORPORATION, its affiliates and licensors retain all intellectual -; property and proprietary rights in and to this material, related -; documentation and any modifications thereto. Any use, reproduction, -; disclosure or distribution of this material and related documentation -; without an express license agreement from NVIDIA CORPORATION or -; its affiliates is strictly prohibited. - -EXPORTS - getCreators - setLoggerFinder diff --git a/plugin/roiAlignPlugin/CMakeLists.txt b/plugin/roiAlignPlugin/CMakeLists.txt index 778dce478..696a7b9e8 100644 --- a/plugin/roiAlignPlugin/CMakeLists.txt +++ b/plugin/roiAlignPlugin/CMakeLists.txt @@ -1,5 +1,5 @@ # -# SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-FileCopyrightText: Copyright (c) 1993-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -19,8 +19,6 @@ add_plugin_source( roiAlignKernel.h roiAlignPlugin.cpp roiAlignPlugin.h - roiAlignPluginLegacy.cpp - roiAlignPluginLegacy.h ) add_vc_plugin_source( @@ -28,7 +26,16 @@ add_vc_plugin_source( roiAlignKernel.h roiAlignPlugin.cpp roiAlignPlugin.h - roiAlignPluginLegacy.cpp - roiAlignPluginLegacy.h ) +if(NOT ${TRT_BUILD_WINML}) + add_plugin_source( + roiAlignPluginLegacy.cpp + roiAlignPluginLegacy.h + ) + add_vc_plugin_source( + roiAlignPluginLegacy.cpp + roiAlignPluginLegacy.h + ) +endif() + diff --git a/plugin/skipLayerNormPlugin/skipLayerNormPlugin.cpp b/plugin/skipLayerNormPlugin/skipLayerNormPlugin.cpp index 84b9bfc5e..d3ab6a302 100644 --- a/plugin/skipLayerNormPlugin/skipLayerNormPlugin.cpp +++ b/plugin/skipLayerNormPlugin/skipLayerNormPlugin.cpp @@ -773,9 +773,8 @@ int32_t SkipLayerNormVarSeqlenPluginV3::enqueue(nvinfer1::PluginTensorDesc const } else { - PLUGIN_VALIDATE(("Unsupported type error, expected [kINT8,kHALF,kFLOAT], but received " + PLUGIN_ERROR("Unsupported type error, expected [kINT8,kHALF,kFLOAT], but received " + std::to_string(static_cast(iType))) - .c_str()); } } catch (std::exception const& e) diff --git a/plugin/skipLayerNormPlugin/skipLayerNormPluginLegacy.cpp b/plugin/skipLayerNormPlugin/skipLayerNormPluginLegacy.cpp index cc6ead56f..f80b4445c 100644 --- a/plugin/skipLayerNormPlugin/skipLayerNormPluginLegacy.cpp +++ b/plugin/skipLayerNormPlugin/skipLayerNormPluginLegacy.cpp @@ -844,9 +844,8 @@ int32_t SkipLayerNormVarSeqlenPlugin::enqueue(PluginTensorDesc const* inputDesc, } else { - PLUGIN_VALIDATE(("Unsupported type error, expected [kINT8,kHALF,kFLOAT], but received " + PLUGIN_ERROR("Unsupported type error, expected [kINT8,kHALF,kFLOAT], but received " + std::to_string(static_cast(iType))) - .c_str()); } } catch (std::exception const& e) diff --git a/plugin/vc/checkMacrosPlugin.cpp b/plugin/vc/checkMacrosPlugin.cpp index ff0c3ebb0..d7da43f64 100644 --- a/plugin/vc/checkMacrosPlugin.cpp +++ b/plugin/vc/checkMacrosPlugin.cpp @@ -51,19 +51,21 @@ LogStream gLogInfo; LogStream gLogVerbose; // break-pointable -void throwCudaError(char const* file, char const* function, int32_t line, int32_t status, char const* msg) +void throwCudaError( + char const* file, char const* function, int32_t line, int32_t status, std::optional msg) { - CudaError error(file, function, line, status, msg); + CudaError error(file, function, line, status, std::move(msg)); error.log(gLogError); // NOLINTNEXTLINE(misc-throw-by-value-catch-by-reference) throw error; } // break-pointable -void throwPluginError(char const* file, char const* function, int32_t line, int32_t status, char const* msg) +void throwPluginError( + char const* file, char const* function, int32_t line, int32_t status, std::optional msg) { - PluginError error(file, function, line, status, msg); - reportValidationFailure(msg, file, line); + reportValidationFailure(msg ? msg->c_str() : "", file, line); + PluginError error(file, function, line, status, std::move(msg)); // NOLINTNEXTLINE(misc-throw-by-value-catch-by-reference) throw error; } @@ -112,10 +114,10 @@ void reportAssertion(char const* msg, char const* file, int32_t line) void TRTException::log(std::ostream& logStream) const { - logStream << file << " (" << line << ") - " << name << " Error in " << function << ": " << status; - if (message != nullptr) + logStream << mFile << " (" << mLine << ") - " << mName << " Error in " << mFunction << ": " << mStatus; + if (!mMessage.empty()) { - logStream << " (" << message << ")"; + logStream << " (" << mMessage << ")"; } logStream << std::endl; } diff --git a/plugin/vc/checkMacrosPlugin.h b/plugin/vc/checkMacrosPlugin.h index f3c5872a6..41758c4c8 100644 --- a/plugin/vc/checkMacrosPlugin.h +++ b/plugin/vc/checkMacrosPlugin.h @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 1993-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 * * Licensed under the Apache License, Version 2.0 (the "License"); @@ -19,7 +19,11 @@ #include "NvInfer.h" #include +#include #include +#include +#include +#include #ifdef _MSC_VER #define FN_NAME __FUNCTION__ @@ -27,9 +31,7 @@ #define FN_NAME __func__ #endif -namespace nvinfer1 -{ -namespace plugin +namespace nvinfer1::plugin { template class LogStream : public std::ostream @@ -40,7 +42,7 @@ class LogStream : public std::ostream int32_t sync() override; }; - Buf buffer; + Buf mBuffer; std::mutex mLogStreamMutex; public: @@ -49,7 +51,7 @@ class LogStream : public std::ostream return mLogStreamMutex; } LogStream() - : std::ostream(&buffer) + : std::ostream(&mBuffer) { } }; @@ -93,63 +95,111 @@ void reportValidationFailure(char const* msg, char const* file, int32_t line); void reportAssertion(char const* msg, char const* file, int32_t line); void logError(char const* msg, char const* file, char const* fn, int32_t line); -[[noreturn]] void throwCudaError( - char const* file, char const* function, int32_t line, int32_t status, char const* msg = nullptr); -[[noreturn]] void throwPluginError( - char const* file, char const* function, int32_t line, int32_t status, char const* msg = nullptr); +//! \throw CudaError carrying \p msg, after logging it via \c gLogError. +//! \param file, function, line Source location reported in the log line. The strings are not +//! copied, so the buffers must outlive the exception (e.g., with static storage duration; typically \c __FILE__ and +//! \c FN_NAME). +//! \param status Subsystem status code (e.g. a \c cudaError_t value). +//! \param msg Human-readable message; ownership is transferred to the thrown exception, so +//! transient \c std::string values (including those built from a temporary) are safe. +//! \c std::nullopt indicates "no message"; an explicit empty string is preserved as such. +[[noreturn]] void throwCudaError(char const* file, char const* function, int32_t line, int32_t status, + std::optional msg = std::nullopt); + +//! \throw PluginError carrying \p msg, after reporting a validation failure via the plugin logger. +//! \param file, function, line Source location reported in the log line. The strings are not +//! copied, so the buffers must outlive the exception (e.g., with static storage duration; typically \c __FILE__ and +//! \c FN_NAME). +//! \param status Subsystem status code. +//! \param msg Human-readable message; ownership is transferred to the thrown exception, so +//! transient \c std::string values (including those built from a temporary) are safe. +//! \c std::nullopt indicates "no message"; an explicit empty string is preserved as such. +[[noreturn]] void throwPluginError(char const* file, char const* function, int32_t line, int32_t status, + std::optional msg = std::nullopt); +//! Base class for plugin-side TensorRT exceptions. +//! \note Owns its message: callers may pass a temporary \c std::string (or a \c char const* +//! that decays into one). The message survives stack unwinding, so \c what() and +//! \c log() are safe to call on a caught instance. class TRTException : public std::exception { public: - TRTException(char const* fl, char const* fn, int32_t ln, int32_t st, char const* msg, char const* nm) - : file(fl) - , function(fn) - , line(ln) - , status(st) - , message(msg) - , name(nm) + //! Constructs an exception describing a failure at the given source location. + //! \param file, function, line Source location. The C-string pointers are not copied and must + //! outlive the exception (use \c __FILE__ and \c FN_NAME). + //! \param status Subsystem status code. + //! \param message Human-readable message. Moved into the exception and returned by \c what(). + //! \c std::nullopt indicates "no message" and is stored as an empty string. + //! \param name Static C-string naming the subsystem (e.g. \c "Cuda", \c "Plugin"). Not copied. + explicit TRTException(char const* file, char const* function, int32_t line, int32_t status, + std::optional message, char const* name) noexcept + : mFile{file} + , mFunction{function} + , mLine{line} + , mStatus{status} + , mMessage{std::move(message).value_or(std::string{})} + , mName{name} { } + + //! Writes a description (file, line, name, function, status, then optional parenthesized + //! message) terminated by \c std::endl to \p logStream. The message is emitted verbatim, + //! so any embedded newlines appear as line breaks in the output. virtual void log(std::ostream& logStream) const; - void setMessage(char const* msg) + + //! Replaces the stored message. + //! \param msg New message; moved into the exception. + void setMessage(std::string msg) + { + mMessage = std::move(msg); + } + + //! \return Pointer to the owned message, valid for the lifetime of \c *this. + //! Returns a pointer to an empty string when no message was supplied. + [[nodiscard]] char const* what() const noexcept override { - message = msg; + return mMessage.c_str(); } protected: - char const* file{nullptr}; - char const* function{nullptr}; - int32_t line{0}; - int32_t status{0}; - char const* message{nullptr}; - char const* name{nullptr}; + char const* mFile{nullptr}; + char const* mFunction{nullptr}; + int32_t mLine{0}; + int32_t mStatus{0}; + std::string mMessage; + char const* mName{nullptr}; }; +//! \c TRTException specialization for CUDA driver/runtime failures. class CudaError : public TRTException { public: - CudaError(char const* fl, char const* fn, int32_t ln, int32_t stat, char const* msg = nullptr) - : TRTException(fl, fn, ln, stat, msg, "Cuda") + //! \see TRTException::TRTException. Sets the subsystem name to \c "Cuda". + explicit CudaError(char const* fl, char const* fn, int32_t ln, int32_t stat, + std::optional msg = std::nullopt) noexcept + : TRTException(fl, fn, ln, stat, std::move(msg), "Cuda") { } }; +//! \c TRTException specialization for plugin-API failures. class PluginError : public TRTException { public: - PluginError(char const* fl, char const* fn, int32_t ln, int32_t stat, char const* msg = nullptr) - : TRTException(fl, fn, ln, stat, msg, "Plugin") + //! \see TRTException::TRTException. Sets the subsystem name to \c "Plugin". + explicit PluginError(char const* fl, char const* fn, int32_t ln, int32_t stat, + std::optional msg = std::nullopt) noexcept + : TRTException(fl, fn, ln, stat, std::move(msg), "Plugin") { } }; +//! Logs `e.what()` to \c gLogError followed by \c std::endl. inline void caughtError(std::exception const& e) { gLogError << e.what() << std::endl; } -} // namespace plugin - -} // namespace nvinfer1 +} // namespace nvinfer1::plugin #define PLUGIN_API_CHECK(condition) \ do \ @@ -199,7 +249,15 @@ inline void caughtError(std::exception const& e) // On MSVC, nested macros don't expand correctly without some help, so use TRT_EXPAND to help it out. #define TRT_EXPAND(x) x #define GET_MACRO(_1, _2, NAME, ...) NAME -#define PLUGIN_VALIDATE(...) TRT_EXPAND(GET_MACRO(__VA_ARGS__, PLUGIN_VALIDATE_MSG, PLUGIN_VALIDATE_DEFAULT, )(__VA_ARGS__)) +#define PLUGIN_VALIDATE(...) \ + TRT_EXPAND(GET_MACRO(__VA_ARGS__, PLUGIN_VALIDATE_MSG, PLUGIN_VALIDATE_DEFAULT, )(__VA_ARGS__)) + +//! Compile-time guard: rejects conditions that decay to \c char \c const*. +//! The bug this catches is \c PLUGIN_VALIDATE("some message") (or with \c .c_str()), where the +//! string is non-null and the check silently passes. Use \c PLUGIN_ERROR(msg) for fatal messages. +#define PLUGIN_DETAIL_REJECT_STRING_CONDITION(expr) \ + static_assert(!std::is_convertible_v, char const*>, \ + "PLUGIN_VALIDATE/PLUGIN_ASSERT condition must not be a string; use PLUGIN_ERROR(msg) for a fatal message") // Logs failed condition and throws a PluginError. // PLUGIN_ASSERT will eventually perform this function, at which point PLUGIN_VALIDATE @@ -207,6 +265,7 @@ inline void caughtError(std::exception const& e) #define PLUGIN_VALIDATE_DEFAULT(condition) \ do \ { \ + PLUGIN_DETAIL_REJECT_STRING_CONDITION(condition); \ if (!(condition)) \ { \ nvinfer1::plugin::throwPluginError(__FILE__, FN_NAME, __LINE__, 0, #condition); \ @@ -216,31 +275,35 @@ inline void caughtError(std::exception const& e) #define PLUGIN_VALIDATE_MSG(condition, msg) \ do \ { \ + PLUGIN_DETAIL_REJECT_STRING_CONDITION(condition); \ if (!(condition)) \ { \ nvinfer1::plugin::throwPluginError(__FILE__, FN_NAME, __LINE__, 0, msg); \ } \ } while (0) -// Consider wrapping in do{...} while(0) -// Logs failed assertion and aborts. -// Aborting is undesirable and will be phased-out from the plugin module, at which point -// PLUGIN_ASSERT will perform the same function as PLUGIN_VALIDATE. +//! Logs failed assertion and aborts. +//! Aborting is undesirable and will be phased-out from the plugin module, at which point +//! PLUGIN_ASSERT will perform the same function as PLUGIN_VALIDATE. #define PLUGIN_ASSERT(assertion) \ do \ { \ + PLUGIN_DETAIL_REJECT_STRING_CONDITION(assertion); \ if (!(assertion)) \ { \ nvinfer1::plugin::reportAssertion(#assertion, __FILE__, __LINE__); \ } \ } while (0) +//! Unconditionally logs failed assertion and aborts. #define PLUGIN_FAIL(msg) \ do \ { \ nvinfer1::plugin::reportAssertion(msg, __FILE__, __LINE__); \ } while (0) +// Consider wrapping in do{...} while(0): +//! Logs a plugin error and throws a PluginError. #define PLUGIN_ERROR(msg) \ { \ nvinfer1::plugin::throwPluginError(__FILE__, FN_NAME, __LINE__, 0, msg); \ @@ -249,9 +312,10 @@ inline void caughtError(std::exception const& e) #define PLUGIN_CUERROR(status_) \ do \ { \ - auto s_ = status_; \ - if (s_ != 0) \ + if (cudaError_t const s_ = (status_); s_ != cudaSuccess) \ + { \ nvinfer1::plugin::logError(#status_ " failure.", __FILE__, FN_NAME, __LINE__); \ + } \ } while (0) #endif /*VC_CHECK_MACROS_PLUGIN_H*/ diff --git a/python/CMakeLists.txt b/python/CMakeLists.txt index 6e58acf3d..d828395fc 100644 --- a/python/CMakeLists.txt +++ b/python/CMakeLists.txt @@ -30,7 +30,7 @@ else() endif() set(TRT_BUILD_PYTHON_EXTERNALS_PATH ${DEFAULT_PY_EXT_PATH} CACHE PATH "Path to the parent folder of the many versioned python headers/libs.") -set(BUILD_PYTHON_PY_VERSIONS 3.8 3.9 3.10 3.11 3.12 3.13) +set(BUILD_PYTHON_PY_VERSIONS 3.8 3.9 3.10 3.11 3.12 3.13 3.14) set(TRT_BUILD_PYTHON_PY_VERSIONS ${BUILD_PYTHON_PY_VERSIONS} CACHE STRING "The list of python versions to build TensorRT bindings for.") diff --git a/python/docstrings/infer/pyCoreDoc.h b/python/docstrings/infer/pyCoreDoc.h index 1edac42fd..972fd2583 100644 --- a/python/docstrings/infer/pyCoreDoc.h +++ b/python/docstrings/infer/pyCoreDoc.h @@ -1491,6 +1491,8 @@ constexpr char const* descr = R"trtdoc( :ivar tiling_optimization_level: The optimization level of tiling strategies. A Higher level allows TensorRT to spend more time searching for better optimization strategy. :ivar l2_limit_for_tiling: The target L2 cache usage for tiling optimization. :ivar remote_auto_tuning_config: The config string to be used during remote auto-tuning. Remote auto-tuning is only enabled for engines built with EngineCapability.SAFETY. + :ivar build_route: :class:`str` The build route string passed to the compiler. The build route is a whitespace-separated list of ``-name=value`` Myelin knob tokens (e.g. ``"-conv_use_long_w=off -kgen:codegen:cuda_tile=2"``) used to customize engine compilation for performance tuning. Knob names are validated against the list returned by :attr:`all_build_routes` unless the internal ``TRT_BYPASS_BUILD_ROUTE_WHITELIST`` flag is set. Setting an empty string resets the build route. Available only when the global performance tuner feature is enabled in the build (disabled by default on RTX/Windows targets). + :ivar all_build_routes: :class:`str` JSON description of every build-route knob the compiler supports, populated when the :class:`IBuilderConfig` is created. Read-only. Returns an empty string on platforms where the global performance tuner feature is disabled (e.g. RTX/Windows targets). Below are the descriptions about each builder optimization level: diff --git a/python/packaging/CMakeLists.txt b/python/packaging/CMakeLists.txt index fbeef4512..7fec8e187 100644 --- a/python/packaging/CMakeLists.txt +++ b/python/packaging/CMakeLists.txt @@ -121,6 +121,14 @@ add_custom_command( ) add_custom_target(trt_packaging_requirements_installed DEPENDS ${TRT_PACKAGING_DEPS_DIR}/.installed) +# Environment variables for Python packaging commands. +set(TRT_PACKAGING_ENV "PYTHONPATH=${TRT_PACKAGING_DEPS_DIR}") +if(NOT CMAKE_VERBOSE_MAKEFILE) + # Suppress deprecation that we can't fix while python/packaging/requirements.txt + # specifies setuptools~=75.3.2 for python_version<3.12. + list(APPEND TRT_PACKAGING_ENV "PYTHONWARNINGS=ignore::setuptools.warnings.SetuptoolsDeprecationWarning") +endif() + # Computes the current wheel platform name and stores it the variable named by outVar. # # \param isStandalone Boolean indicating whether to compute the platform for a standalone wheel. diff --git a/python/packaging/bindings_wheel/CMakeLists.txt b/python/packaging/bindings_wheel/CMakeLists.txt index c7c162aeb..b5cce1998 100644 --- a/python/packaging/bindings_wheel/CMakeLists.txt +++ b/python/packaging/bindings_wheel/CMakeLists.txt @@ -111,7 +111,7 @@ function(buildBindingsWheel moduleName pyVersion isStandalone) # The custom tensorrt_build_backend handles package naming and wheel tags via config settings. add_custom_command( OUTPUT ${wheelOutputFile} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${TRT_PACKAGING_DEPS_DIR}" + COMMAND ${CMAKE_COMMAND} -E env ${TRT_PACKAGING_ENV} ${Python3_EXECUTABLE} -m build --wheel --no-isolation --config-setting=--quiet --config-setting=wheel-type=${wheelType} diff --git a/python/packaging/frontend_sdist/CMakeLists.txt b/python/packaging/frontend_sdist/CMakeLists.txt index 4bf97a825..8f825bf84 100644 --- a/python/packaging/frontend_sdist/CMakeLists.txt +++ b/python/packaging/frontend_sdist/CMakeLists.txt @@ -57,7 +57,7 @@ function(buildFrontendWheel moduleName) # Add a custom command to build the sdist using PEP 517 compliant build frontend. add_custom_command( OUTPUT ${sdistOutputFile} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${TRT_PACKAGING_DEPS_DIR}" + COMMAND ${CMAKE_COMMAND} -E env ${TRT_PACKAGING_ENV} ${Python3_EXECUTABLE} -m build --sdist --no-isolation --config-setting=--quiet --outdir ${sdistOutDir} WORKING_DIRECTORY ${generatedFileOutDir} DEPENDS ${filesTarget} trt_packaging_requirements_installed diff --git a/python/packaging/libs_wheel/CMakeLists.txt b/python/packaging/libs_wheel/CMakeLists.txt index a84cb4978..2d9fdbd26 100644 --- a/python/packaging/libs_wheel/CMakeLists.txt +++ b/python/packaging/libs_wheel/CMakeLists.txt @@ -144,7 +144,7 @@ function(buildLibsWheel moduleName) # The custom tensorrt_build_backend handles wheel tag customization via config settings. add_custom_command( OUTPUT ${wheelOutputFile} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${TRT_PACKAGING_DEPS_DIR}" + COMMAND ${CMAKE_COMMAND} -E env ${TRT_PACKAGING_ENV} ${Python3_EXECUTABLE} -m build --wheel --no-isolation --config-setting=--quiet --config-setting=python-tag=py3 diff --git a/python/packaging/metapackage/CMakeLists.txt b/python/packaging/metapackage/CMakeLists.txt index 1d4298035..541c59baa 100644 --- a/python/packaging/metapackage/CMakeLists.txt +++ b/python/packaging/metapackage/CMakeLists.txt @@ -47,7 +47,7 @@ function(buildMetapackageWheel moduleName) # Add a custom command to build the sdist using PEP 517 compliant build frontend. add_custom_command( OUTPUT ${sdistOutputFile} - COMMAND ${CMAKE_COMMAND} -E env "PYTHONPATH=${TRT_PACKAGING_DEPS_DIR}" + COMMAND ${CMAKE_COMMAND} -E env ${TRT_PACKAGING_ENV} ${Python3_EXECUTABLE} -m build --sdist --no-isolation --config-setting=--quiet --outdir ${sdistOutDir} WORKING_DIRECTORY ${generatedFileOutDir} DEPENDS ${filesTarget} trt_packaging_requirements_installed diff --git a/python/src/infer/pyCore.cpp b/python/src/infer/pyCore.cpp index e1dd2cd74..5b2a62c83 100644 --- a/python/src/infer/pyCore.cpp +++ b/python/src/infer/pyCore.cpp @@ -285,6 +285,13 @@ static auto const get_remote_auto_tuning_config static auto const set_remote_auto_tuning_config = [](IBuilderConfig& self, std::string const& config) { self.setRemoteAutoTuningConfig(config.c_str()); }; + +static auto const get_build_route = [](IBuilderConfig& self) { return std::string{self.getBuildRoute()}; }; + +static auto const set_build_route + = [](IBuilderConfig& self, std::string const& buildRoute) { self.setBuildRoute(buildRoute.c_str()); }; + +static auto const get_all_build_routes = [](IBuilderConfig& self) { return std::string{self.getAllBuildRoutes()}; }; #endif // EXPORT_ALL_BINDINGS // For IRefitter @@ -1756,6 +1763,8 @@ void bindCore(py::module& m) .def_property( "remote_auto_tuning_config", lambdas::get_remote_auto_tuning_config, lambdas::set_remote_auto_tuning_config) + .def_property("build_route", lambdas::get_build_route, lambdas::set_build_route) + .def_property_readonly("all_build_routes", lambdas::get_all_build_routes) .def("__del__", &utils::doNothingDel); @@ -1806,10 +1815,10 @@ void bindCore(py::module& m) }, "network"_a, "config"_a, "kernel_text"_a, BuilderDoc::build_serialized_network, py::call_guard{}) - .def("build_serialized_network_to_stream", &IBuilder::buildSerializedNetworkToStream, "network"_a, "config"_a, - "writer"_a, BuilderDoc::build_serialized_network_to_stream, py::call_guard{}) .def("build_engine_with_config", &IBuilder::buildEngineWithConfig, "network"_a, "config"_a, BuilderDoc::build_engine_with_config, py::call_guard{}) + .def("build_serialized_network_to_stream", &IBuilder::buildSerializedNetworkToStream, "network"_a, "config"_a, + "writer"_a, BuilderDoc::build_serialized_network_to_stream, py::call_guard{}) .def("is_network_supported", &IBuilder::isNetworkSupported, "network"_a, "config"_a, BuilderDoc::is_network_supported, py::call_guard{}) .def_property_readonly("logger", &IBuilder::getLogger) diff --git a/samples/README.md b/samples/README.md index d991206b1..22a00eb37 100644 --- a/samples/README.md +++ b/samples/README.md @@ -26,6 +26,7 @@ | [simpleProgressMonitor](python/simple_progress_monitor) | Python | ONNX | Progress Monitor API usage | | [python_plugin](python/python_plugin) | Python | INetwork/ONNX | Python-based TRT plugins | | [non_zero_plugin](python/non_zero_plugin) | Python | INetwork/ONNX | Python-based TRT plugin for NonZero op | +| [cute_dsl_plugin](python/cute_dsl_plugin) | Python | INetwork | Python-based TRT plugin for RMSNorm with a CuteDSL kernel | | [attention_mdtrt](python/attention_mdtrt) | Python | ONNX | Multi-device attention inference with MPI and NCCL | ### 3. Application Samples @@ -44,7 +45,7 @@ Many samples require the TensorRT sample data package. If not already mounted under `/usr/src/tensorrt/data` (NVIDIA NGC containers), download and extract it: -1. Download the current [TensorRT sample data package](https://github.com/NVIDIA/TensorRT/releases/download/v11.0/tensorrt_sample_data_20260602.zip). Sample data is updated only when needed, so the package may be hosted under an earlier TensorRT release. +1. Download the sample data from [TensorRT GitHub Releases](https://github.com/NVIDIA/TensorRT/releases). 2. Extract and set up the data: ```bash diff --git a/samples/common/CMakeLists.txt b/samples/common/CMakeLists.txt index ff3bd91de..fa8fdae02 100644 --- a/samples/common/CMakeLists.txt +++ b/samples/common/CMakeLists.txt @@ -15,11 +15,17 @@ # limitations under the License. # +if(NOT NLOHMANN_JSON_INCLUDE_DIRS) + include(FetchNlohmannJson) +endif() + add_library(trt_samples_common STATIC argsParser.h BatchStream.h bfloat16.cpp bfloat16.h + bigInt.cpp + bigInt.h buffers.h common.cpp common.h @@ -29,6 +35,8 @@ add_library(trt_samples_common STATIC getOptions.cpp getOptions.h getoptWin.h + globalTimerKernel.cu + globalTimerKernel.h half.h logger.cpp logger.h @@ -46,6 +54,8 @@ add_library(trt_samples_common STATIC sampleOptions.h sampleReporting.cpp sampleReporting.h + sampleTuning.cpp + sampleTuning.h sampleUtils.cpp sampleUtils.h safeCommon.h @@ -83,6 +93,7 @@ endif() # TRT_BUILD_TESTING target_include_directories(trt_samples_common PUBLIC ${CMAKE_CURRENT_LIST_DIR} + ${NLOHMANN_JSON_INCLUDE_DIRS} ) target_link_libraries(trt_samples_common PUBLIC diff --git a/samples/common/bigInt.cpp b/samples/common/bigInt.cpp new file mode 100644 index 000000000..cdff2151f --- /dev/null +++ b/samples/common/bigInt.cpp @@ -0,0 +1,202 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2024-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "bigInt.h" + +namespace sample +{ + +BigInt::BigInt(std::string const& str) +{ + if (str.empty()) + { + throw std::invalid_argument("Empty string"); + } + + BigInt const ten(10); + for (char const c : str) + { + if (c < '0' || c > '9') + { + throw std::invalid_argument("Invalid decimal character in BigInt string"); + } + + auto [mulResult, mulOverflow] = multiplyWithOverflow(*this, ten); + if (mulOverflow) + { + throw std::overflow_error("Number too large for BigInt"); + } + + auto [addResult, addOverflow] = addWithOverflow(mulResult, BigInt(static_cast(c - '0'))); + if (addOverflow) + { + throw std::overflow_error("Number too large for BigInt"); + } + + *this = addResult; + } +} + +// NOLINTNEXTLINE(readability-function-cognitive-complexity) +std::pair BigInt::multiplyWithOverflow(BigInt const& a, BigInt const& b) noexcept +{ + // Full multiplication into 2*kWordCount words + std::array result{}; + + for (uint64_t i = 0; i < kWordCount; ++i) + { + if (a.mWords[i] == 0) + { + continue; + } + + WordType carry = 0; + for (uint64_t j = 0; j < kWordCount; ++j) + { + uint64_t const k = i + j; + if (k >= kWordCount * 2) + { + break; + } + + // 64x64 → 128-bit multiply using four 32-bit half-words. + // Split: a = aHi*2^32 + aLo, b = bHi*2^32 + bLo + // Product = aHi*bHi*2^64 + (aHi*bLo + aLo*bHi)*2^32 + aLo*bLo + uint64_t const aLo = a.mWords[i] & 0xFFFFFFFF; + uint64_t const aHi = a.mWords[i] >> 32; + uint64_t const bLo = b.mWords[j] & 0xFFFFFFFF; + uint64_t const bHi = b.mWords[j] >> 32; + + uint64_t const p0 = aLo * bLo; + uint64_t const p1 = aLo * bHi; + uint64_t const p2 = aHi * bLo; + uint64_t const p3 = aHi * bHi; + + // Combine: prodLo = lower 64 bits, prodHi = upper 64 bits + uint64_t const mid = p1 + (p0 >> 32); + uint64_t const midCarry = (mid < p1) ? (uint64_t{1} << 32) : 0; + uint64_t const mid2 = mid + p2; + uint64_t const midCarry2 = (mid2 < mid) ? (uint64_t{1} << 32) : 0; + + uint64_t prodLo = (mid2 << 32) | (p0 & 0xFFFFFFFF); + uint64_t prodHi = p3 + (mid2 >> 32) + midCarry + midCarry2; + + // Add result[k] and carry to the 128-bit product. + prodLo += result[k]; + if (prodLo < result[k]) + { + ++prodHi; + } + prodLo += carry; + if (prodLo < carry) + { + ++prodHi; + } + result[k] = prodLo; + carry = prodHi; + } + if (i + kWordCount < kWordCount * 2) + { + result[i + kWordCount] += carry; + } + } + + // Check for overflow (any non-zero word in upper half) + bool overflow = false; + for (uint64_t i = kWordCount; i < kWordCount * 2; ++i) + { + if (result[i] != 0) + { + overflow = true; + break; + } + } + + // Copy lower half to result + BigInt low; + for (uint64_t i = 0; i < kWordCount; ++i) + { + low.mWords[i] = result[i]; + } + + return {low, overflow}; +} + +std::pair BigInt::divideWithRemainder(BigInt const& dividend, BigInt const& divisor) +{ + if (divisor.isZero()) + { + throw std::domain_error("Division by zero in BigInt"); + } + + if (dividend < divisor) + { + return {BigInt(), dividend}; + } + + if (dividend == divisor) + { + return {BigInt(1), BigInt()}; + } + + // Binary long division algorithm + BigInt quotient; + BigInt remainder; + + int32_t const highBit = dividend.getHighestSetBit(); + + for (int32_t i = highBit; i >= 0; --i) + { + remainder <<= 1; + if (dividend.getBit(i)) + { + remainder.setBit(0, true); + } + + if (remainder >= divisor) + { + remainder -= divisor; + quotient.setBit(i, true); + } + } + + return {quotient, remainder}; +} + +std::string BigInt::toString() const +{ + if (isZero()) + { + return "0"; + } + + std::string result; + BigInt tmp = *this; + BigInt const ten(10); + + while (!tmp.isZero()) + { + auto [q, r] = divideWithRemainder(tmp, ten); + result += static_cast('0' + r.mWords[0]); + tmp = q; + } + + std::reverse(result.begin(), result.end()); + return result; +} + +} // namespace sample diff --git a/samples/common/bigInt.h b/samples/common/bigInt.h new file mode 100644 index 000000000..e1045094e --- /dev/null +++ b/samples/common/bigInt.h @@ -0,0 +1,410 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2024-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef TRT_SAMPLE_BIG_INT_H +#define TRT_SAMPLE_BIG_INT_H + +#include +#include +#include +#include +#include +#include +#include + +namespace sample +{ + +//! +//! \class BigInt +//! \brief A class for arbitrary-precision unsigned integers (8192 bits). +//! +//! This class provides support for very large unsigned integers, primarily used +//! for counting and indexing in build path expression expansion where the number +//! of combinations can be astronomically large (e.g., 2^1000 combinations). +//! +//! Key operations: +//! - Construction from uint64_t or decimal string +//! - Comparison operators for loop termination +//! - Increment operator for loop counting +//! - Division/modulo for mixed-radix index decomposition +//! - String conversion for display +//! +class BigInt +{ +public: + //! Number of bits in the integer (8192 = 128 * 64) + static constexpr uint64_t kBitCount = 8192; + //! Number of 64-bit words + static constexpr uint64_t kWordCount = kBitCount / 64; // 128 words + using WordType = uint64_t; + + //! \brief Default constructor. Initializes to zero. + constexpr BigInt() noexcept = default; + + //! \brief Construct from a 64-bit unsigned integer. + //! \param[in] value The initial value. + constexpr BigInt(uint64_t value) noexcept + { + mWords[0] = value; + } + + //! \brief Construct from a decimal string. + //! \param[in] str The decimal string representation. + //! \throws std::invalid_argument If the string is empty or contains invalid characters. + //! \throws std::overflow_error If the number is too large. + explicit BigInt(std::string const& str); + + // Default copy and move operations + constexpr BigInt(BigInt const&) noexcept = default; + constexpr BigInt& operator=(BigInt const&) noexcept = default; + constexpr BigInt(BigInt&&) noexcept = default; + constexpr BigInt& operator=(BigInt&&) noexcept = default; + + //! \brief Check if the value is zero. + //! \return True if zero. + constexpr bool isZero() const noexcept + { + for (uint64_t i = 0; i < kWordCount; ++i) + { + if (mWords[i] != 0) + { + return false; + } + } + return true; + } + + //! \brief Get the bit value at a specific position. + //! \param[in] pos The bit position (0 = LSB). + //! \return The bit value. + constexpr bool getBit(uint64_t pos) const noexcept + { + if (pos >= kBitCount) + { + return false; + } + uint64_t const wordIdx = pos / 64; + uint64_t const bitIdx = pos % 64; + return (mWords[wordIdx] >> bitIdx) & 1; + } + + //! \brief Set the bit value at a specific position. + //! \param[in] pos The bit position (0 = LSB). + //! \param[in] value The bit value to set. + constexpr void setBit(uint64_t pos, bool value = true) noexcept + { + if (pos >= kBitCount) + { + return; + } + uint64_t const wordIdx = pos / 64; + uint64_t const bitIdx = pos % 64; + if (value) + { + mWords[wordIdx] |= (WordType{1} << bitIdx); + } + else + { + mWords[wordIdx] &= ~(WordType{1} << bitIdx); + } + } + + //! \brief Get the position of the highest set bit. + //! \return The position (0-indexed), or -1 if zero. + constexpr int32_t getHighestSetBit() const noexcept + { + for (int32_t i = kWordCount - 1; i >= 0; --i) + { + if (mWords[i] != 0) + { + // Count leading zeros portably (no compiler intrinsics). + uint64_t w = mWords[i]; + int32_t bit = 63; + while (bit > 0 && (w & (uint64_t{1} << bit)) == 0) + { + --bit; + } + return i * 64 + bit; + } + } + return -1; + } + + // ======================================================================== + // Comparison operators + // ======================================================================== + + constexpr bool operator==(BigInt const& other) const noexcept + { + // Manual element-by-element comparison (std::array::operator== is not constexpr in C++17) + for (uint64_t i = 0; i < kWordCount; ++i) + { + if (mWords[i] != other.mWords[i]) + { + return false; + } + } + return true; + } + + constexpr bool operator!=(BigInt const& other) const noexcept + { + return !(*this == other); + } + + //! \brief Less-than comparison. + //! Compares from most significant word down. + constexpr bool operator<(BigInt const& other) const noexcept + { + for (int32_t i = kWordCount - 1; i >= 0; --i) + { + if (mWords[i] < other.mWords[i]) + { + return true; + } + if (mWords[i] > other.mWords[i]) + { + return false; + } + } + return false; + } + + constexpr bool operator<=(BigInt const& other) const noexcept + { + return !(other < *this); + } + + constexpr bool operator>(BigInt const& other) const noexcept + { + return other < *this; + } + + constexpr bool operator>=(BigInt const& other) const noexcept + { + return !(*this < other); + } + + // ======================================================================== + // Arithmetic operators + // ======================================================================== + + //! \brief Add with overflow detection. + //! \return Pair of (result, overflow_flag). + static constexpr std::pair addWithOverflow(BigInt const& a, BigInt const& b) noexcept + { + BigInt result; + uint64_t carry = 0; + for (uint64_t i = 0; i < kWordCount; ++i) + { + // Add with carry using plain uint64_t. Overflow is detected by comparing + // the result against the operand: if sum < a then overflow occurred. + uint64_t sum = a.mWords[i] + b.mWords[i]; + uint64_t c1 = (sum < a.mWords[i]) ? 1U : 0U; + uint64_t sum2 = sum + carry; + uint64_t c2 = (sum2 < sum) ? 1U : 0U; + result.mWords[i] = sum2; + carry = c1 + c2; + } + return {result, carry != 0}; + } + + //! \brief Subtract with underflow detection. + //! \return Pair of (result, underflow_flag). + static constexpr std::pair subWithUnderflow(BigInt const& a, BigInt const& b) noexcept + { + BigInt result; + uint64_t borrow = 0; + for (uint64_t i = 0; i < kWordCount; ++i) + { + // Subtract with borrow using plain uint64_t. + // Borrow is detected by: if a < b+borrow, then we borrowed from the next word. + uint64_t sub = a.mWords[i] - b.mWords[i]; + uint64_t b1 = (a.mWords[i] < b.mWords[i]) ? 1U : 0U; + uint64_t sub2 = sub - borrow; + uint64_t b2 = (sub < borrow) ? 1U : 0U; + result.mWords[i] = sub2; + borrow = b1 + b2; + } + return {result, borrow != 0}; + } + + //! \brief Multiply with overflow detection. + //! \return Pair of (result, overflow_flag). + static std::pair multiplyWithOverflow(BigInt const& a, BigInt const& b) noexcept; + + //! \brief Divide with remainder. + //! \param[in] dividend The dividend. + //! \param[in] divisor The divisor. + //! \return Pair of (quotient, remainder). + //! \throws std::domain_error If divisor is zero. + static std::pair divideWithRemainder(BigInt const& dividend, BigInt const& divisor); + + constexpr BigInt operator+(BigInt const& other) const noexcept + { + return addWithOverflow(*this, other).first; + } + + constexpr BigInt& operator+=(BigInt const& other) noexcept + { + *this = *this + other; + return *this; + } + + constexpr BigInt operator-(BigInt const& other) const noexcept + { + return subWithUnderflow(*this, other).first; + } + + constexpr BigInt& operator-=(BigInt const& other) noexcept + { + *this = *this - other; + return *this; + } + + BigInt operator*(BigInt const& other) const noexcept + { + return multiplyWithOverflow(*this, other).first; + } + + BigInt operator/(BigInt const& other) const + { + return divideWithRemainder(*this, other).first; + } + + BigInt operator%(BigInt const& other) const + { + return divideWithRemainder(*this, other).second; + } + + // ======================================================================== + // Shift operators (needed for division algorithm) + // ======================================================================== + + constexpr BigInt operator<<(uint64_t shift) const noexcept + { + if (shift >= kBitCount) + { + return BigInt(); + } + if (shift == 0) + { + return *this; + } + + BigInt result; + uint64_t const wordShift = shift / 64; + uint64_t const bitShift = shift % 64; + + if (bitShift == 0) + { + for (uint64_t i = wordShift; i < kWordCount; ++i) + { + result.mWords[i] = mWords[i - wordShift]; + } + } + else + { + for (uint64_t i = wordShift; i < kWordCount; ++i) + { + result.mWords[i] = mWords[i - wordShift] << bitShift; + if (i > wordShift) + { + result.mWords[i] |= mWords[i - wordShift - 1] >> (64 - bitShift); + } + } + } + return result; + } + + constexpr BigInt& operator<<=(uint64_t shift) noexcept + { + *this = *this << shift; + return *this; + } + + // ======================================================================== + // Increment/Decrement operators + // ======================================================================== + + //! \brief Pre-increment operator. + //! Handles carry propagation across words. + constexpr BigInt& operator++() noexcept + { + for (uint64_t i = 0; i < kWordCount; ++i) + { + if (++mWords[i] != 0) + { + break; // No carry, done + } + // Carry propagates to next word + } + return *this; + } + + constexpr BigInt operator++(int32_t) noexcept + { + BigInt tmp = *this; + ++(*this); + return tmp; + } + + //! \brief Pre-decrement operator. + //! Handles borrow propagation across words. + constexpr BigInt& operator--() noexcept + { + for (uint64_t i = 0; i < kWordCount; ++i) + { + if (mWords[i]-- != 0) + { + break; // No borrow, done + } + // Borrow propagates to next word + } + return *this; + } + + constexpr BigInt operator--(int32_t) noexcept + { + BigInt tmp = *this; + --(*this); + return tmp; + } + + // ======================================================================== + // String conversion + // ======================================================================== + + //! \brief Convert to decimal string representation. + //! \return The decimal string. + std::string toString() const; + + //! \brief Get the lowest 64 bits as uint64_t. + //! Useful when the value is known to fit in 64 bits. + constexpr uint64_t toUint64() const noexcept + { + return mWords[0]; + } + +private: + std::array mWords{}; +}; + +} // namespace sample + +#endif // TRT_SAMPLE_BIG_INT_H diff --git a/samples/common/globalTimerKernel.cu b/samples/common/globalTimerKernel.cu new file mode 100644 index 000000000..f507fc365 --- /dev/null +++ b/samples/common/globalTimerKernel.cu @@ -0,0 +1,46 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "globalTimerKernel.h" + +namespace +{ +__global__ void readGlobalTimerKernel(uint64_t* timestamp) +{ + if (timestamp == nullptr) + { + return; + } + uint64_t ts; + asm volatile("mov.u64 %0, %%globaltimer;" : "=l"(ts)); + *timestamp = ts; +} +} // namespace + +namespace sample +{ +// NOTE: cudaGetLastError() only surfaces synchronous launch errors (invalid +// configuration, bad stream, etc.). Any asynchronous execution errors from +// the kernel itself — e.g. a dereference of a bad device pointer — will not +// be reported here; they become visible only on a subsequent synchronization +// point such as cudaEventRecord / cudaStreamSynchronize / cudaDeviceSynchronize. +cudaError_t launchGlobalTimerKernel(uint64_t* dTimestamp, cudaStream_t stream) noexcept +{ + readGlobalTimerKernel<<<1, 1, 0, stream>>>(dTimestamp); + return cudaGetLastError(); +} +} // namespace sample diff --git a/samples/common/globalTimerKernel.h b/samples/common/globalTimerKernel.h new file mode 100644 index 000000000..d6e8c3341 --- /dev/null +++ b/samples/common/globalTimerKernel.h @@ -0,0 +1,42 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef TRT_GLOBAL_TIMER_KERNEL_H +#define TRT_GLOBAL_TIMER_KERNEL_H + +#include +#include + +namespace sample +{ +//! Launch a single-thread kernel that writes the current value of the PTX +//! %globaltimer register (GPU timer in ns) to \p dTimestamp on \p stream. +//! +//! Used as a replacement for cudaEventElapsedTime() when Confidential Compute +//! is enabled, where cudaEventElapsedTime() is documented to be unreliable. +//! +//! \param dTimestamp Device pointer to a uint64_t. Must be non-null and point +//! to valid device memory reachable from \p stream. +//! \param stream CUDA stream on which to launch the kernel. +//! \return Result of \c cudaGetLastError() after the launch. This reports +//! synchronous launch errors only; asynchronous execution errors will +//! surface on a subsequent \c cudaEventRecord / +//! \c cudaStreamSynchronize / similar. +[[nodiscard]] cudaError_t launchGlobalTimerKernel(uint64_t* dTimestamp, cudaStream_t stream) noexcept; +} // namespace sample + +#endif // TRT_GLOBAL_TIMER_KERNEL_H diff --git a/samples/common/logging.h b/samples/common/logging.h index f79f223bf..cc3413e88 100644 --- a/samples/common/logging.h +++ b/samples/common/logging.h @@ -298,10 +298,13 @@ class Logger : public nvinfer1::ILogger //! enum class TestResult { - kRUNNING, //!< The test is running - kPASSED, //!< The test passed - kFAILED, //!< The test failed - kWAIVED //!< The test was waived + kRUNNING, //!< The test is running + kPASSED, //!< The test passed + kFAILED, //!< The test failed + kWAIVED, //!< The test was waived + kTASK_BEGIN, //!< A sub-routine task has begun + kTASK_END, //!< A sub-routine task completed successfully + kTASK_ABORT //!< A sub-routine task was aborted (exception or validation failure) }; //! @@ -452,6 +455,65 @@ class Logger : public nvinfer1::ILogger return EXIT_SUCCESS; } + //! + //! \brief Report that a sub-routine task has begun. + //! + //! Used by the tuning loop to mark the start of each iteration so external + //! tooling can detect iteration boundaries in the trtexec log stream. + //! + static void reportTaskBegin(TestAtom const& testAtom) + { + reportTestResult(testAtom, TestResult::kTASK_BEGIN); + } + + //! + //! \brief Report that a sub-routine task has begun with iteration index and build route. + //! Prints a blank line before the banner for readability. + //! + //! Output example: + //! &&&& TASK_BEGIN [iter=0] BuildRoute = '-match_ragged_mha=on -copy_ppg=off' + //! + static void reportTaskBegin(TestAtom const& /*testAtom*/, std::string const& index, std::string const& buildRoute) + { + reportTaskWithBuildRoute( + TestResult::kTASK_BEGIN, index, buildRoute, /*blankBefore=*/true, /*blankAfter=*/false); + } + + //! + //! \brief Report that a sub-routine task completed successfully. + //! + static void reportTaskEnd(TestAtom const& testAtom) + { + reportTestResult(testAtom, TestResult::kTASK_END); + } + + //! + //! \brief Report that a sub-routine task completed successfully, with iteration info. + //! Prints a blank line after the banner for readability. + //! + static void reportTaskEnd(TestAtom const& /*testAtom*/, std::string const& index, std::string const& buildRoute) + { + reportTaskWithBuildRoute(TestResult::kTASK_END, index, buildRoute, /*blankBefore=*/false, /*blankAfter=*/true); + } + + //! + //! \brief Report that a sub-routine task was aborted (exception or validation failure). + //! + static void reportTaskAbort(TestAtom const& testAtom) + { + reportTestResult(testAtom, TestResult::kTASK_ABORT); + } + + //! + //! \brief Report that a sub-routine task was aborted, with iteration info. + //! Prints a blank line after the banner for readability. + //! + static void reportTaskAbort(TestAtom const& /*testAtom*/, std::string const& index, std::string const& buildRoute) + { + reportTaskWithBuildRoute( + TestResult::kTASK_ABORT, index, buildRoute, /*blankBefore=*/false, /*blankAfter=*/true); + } + static int32_t reportTest(TestAtom const& testAtom, bool pass) { return pass ? reportPass(testAtom) : reportFail(testAtom); @@ -490,10 +552,35 @@ class Logger : public nvinfer1::ILogger case TestResult::kPASSED: return "PASSED"; case TestResult::kFAILED: return "FAILED"; case TestResult::kWAIVED: return "WAIVED"; + case TestResult::kTASK_BEGIN: return "TASK_BEGIN"; + case TestResult::kTASK_END: return "TASK_END"; + case TestResult::kTASK_ABORT: return "TASK_ABORT"; default: assert(0); return ""; } } + //! + //! \brief Print a TASK_BEGIN/END/ABORT banner with iteration index and build route. + //! + //! Output format: + //! &&&& TASK_BEGIN [iter=0] BuildRoute = '-match_ragged_mha=on -copy_ppg=off' + //! + static void reportTaskWithBuildRoute( + TestResult result, std::string const& index, std::string const& buildRoute, bool blankBefore, bool blankAfter) + { + auto& os = severityOstream(Severity::kINFO); + if (blankBefore) + { + os << std::endl; + } + os << "&&&& " << testResultString(result) << " [iter=" << index << "] BuildRoute = '" << buildRoute << "'" + << std::endl; + if (blankAfter) + { + os << std::endl; + } + } + //! //! \brief returns an appropriate output stream (cout or cerr) to use with the given severity //! diff --git a/samples/common/safeCommon.h b/samples/common/safeCommon.h index 14e90d95d..1608b2a29 100644 --- a/samples/common/safeCommon.h +++ b/samples/common/safeCommon.h @@ -23,7 +23,6 @@ #include "NvInferSafeRuntime.h" #include "cuda_runtime.h" #include -#include #include #include #include @@ -38,7 +37,6 @@ #include #include #include -#include #include #include @@ -630,34 +628,6 @@ inline bool applyCpuOnlyMode() }}; } -[[nodiscard]] inline std::string genFilenameSafeString(std::string_view s) -{ - std::string_view const kALLOWED{"._-,"}; - constexpr size_t kMAX_FILENAME_LENGTH = 150; // Leave some margin due to Windows path length limitation - constexpr size_t kELLIPSIS_LENGTH = 3; // Length of "..." - - auto processChar = [&kALLOWED](char c) { - return std::isalnum(static_cast(c)) || kALLOWED.find(c) != std::string_view::npos ? c : '_'; - }; - - std::string res; - if (s.length() <= kMAX_FILENAME_LENGTH) - { - res.reserve(s.size()); - std::transform(s.begin(), s.end(), std::back_inserter(res), processChar); - return res; - } - - res.reserve(kMAX_FILENAME_LENGTH); - size_t const halfLength = (kMAX_FILENAME_LENGTH - kELLIPSIS_LENGTH) / 2; - - std::transform(s.begin(), s.begin() + halfLength, std::back_inserter(res), processChar); - res += "..."; - std::transform(s.end() - halfLength, s.end(), std::back_inserter(res), processChar); - - return res; -} - } // namespace samplesSafeCommon namespace safetyCompliance diff --git a/samples/common/sampleDevice.cpp b/samples/common/sampleDevice.cpp index 601675d06..3fb2d0763 100644 --- a/samples/common/sampleDevice.cpp +++ b/samples/common/sampleDevice.cpp @@ -19,9 +19,71 @@ #include +#if !defined(_WIN32) +#include +#endif + namespace sample { +namespace +{ +// Subset of NVML types/constants needed to query Confidential Compute state. +// Declared locally so we do not introduce a build-time dependency on nvml.h +// or libnvidia-ml; functions are resolved via dlopen at runtime. +using NvmlReturnT = int32_t; +constexpr NvmlReturnT kNVML_SUCCESS = 0; +constexpr uint32_t kNVML_CC_FEATURE_ENABLED = 1; + +struct NvmlConfComputeSystemState +{ + uint32_t environment; + uint32_t ccFeature; + uint32_t devToolsMode; +}; + +using NvmlInitFn = NvmlReturnT (*)(); +using NvmlShutdownFn = NvmlReturnT (*)(); +using NvmlGetCcStateFn = NvmlReturnT (*)(NvmlConfComputeSystemState*); + +bool queryConfidentialCompute() +{ +#if defined(_WIN32) + return false; +#else + void* handle = dlopen("libnvidia-ml.so.1", RTLD_LAZY | RTLD_LOCAL); + if (handle == nullptr) + { + return false; + } + + auto init = reinterpret_cast(dlsym(handle, "nvmlInit_v2")); + auto shutdown = reinterpret_cast(dlsym(handle, "nvmlShutdown")); + auto getState = reinterpret_cast(dlsym(handle, "nvmlSystemGetConfComputeState")); + + bool enabled = false; + if (init != nullptr && shutdown != nullptr && getState != nullptr && init() == kNVML_SUCCESS) + { + NvmlConfComputeSystemState state{}; + if (getState(&state) == kNVML_SUCCESS && state.ccFeature == kNVML_CC_FEATURE_ENABLED) + { + enabled = true; + } + shutdown(); + } + + dlclose(handle); + return enabled; +#endif +} +} // namespace + +bool isConfidentialComputeEnabled() +{ + static bool const kCC_ENABLED = queryConfidentialCompute(); + return kCC_ENABLED; +} + // Construct GPU UUID string in the same format as nvidia-smi does. std::string getUuidString(cudaUUID_t uuid) { diff --git a/samples/common/sampleDevice.h b/samples/common/sampleDevice.h index e784b9f99..955d196f9 100644 --- a/samples/common/sampleDevice.h +++ b/samples/common/sampleDevice.h @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 1993-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 * * Licensed under the Apache License, Version 2.0 (the "License"); @@ -19,17 +19,25 @@ #define TRT_SAMPLE_DEVICE_H #include +#include #include #include #include #include #include "common.h" +#include "globalTimerKernel.h" #include "sampleUtils.h" namespace sample { +//! True when Confidential Compute is enabled on the current system. Cached on +//! the first call. When true, TrtCudaEvent falls back to a GPU global-timer +//! kernel because cudaEventElapsedTime() is unreliable under CC (see nvbug +//! 5598617, mirrors TRT-LLM PR #11657). +[[nodiscard]] bool isConfidentialComputeEnabled(); + class TrtCudaEvent; namespace @@ -99,11 +107,15 @@ class TrtCudaEvent { const uint32_t flags = blocking ? cudaEventBlockingSync : cudaEventDefault; CHECK(cudaEventCreateWithFlags(&mEvent, flags)); + if (isConfidentialComputeEnabled()) + { + CHECK(cudaMalloc(&mDeviceTimestamp, sizeof(uint64_t))); + } } - TrtCudaEvent(const TrtCudaEvent&) = delete; + TrtCudaEvent(TrtCudaEvent const&) = delete; - TrtCudaEvent& operator=(const TrtCudaEvent&) = delete; + TrtCudaEvent& operator=(TrtCudaEvent const&) = delete; TrtCudaEvent(TrtCudaEvent&&) = delete; @@ -112,6 +124,10 @@ class TrtCudaEvent ~TrtCudaEvent() { CHECK(cudaEventDestroy(mEvent)); + if (mDeviceTimestamp != nullptr) + { + CHECK(cudaFree(mDeviceTimestamp)); + } } cudaEvent_t get() const @@ -119,8 +135,12 @@ class TrtCudaEvent return mEvent; } - void record(const TrtCudaStream& stream) + void record(TrtCudaStream const& stream) { + if (mDeviceTimestamp != nullptr) + { + CHECK(launchGlobalTimerKernel(mDeviceTimestamp, stream.get())); + } CHECK(cudaEventRecord(mEvent, stream.get())); } @@ -136,6 +156,21 @@ class TrtCudaEvent synchronize(); e.synchronize(); + if (mDeviceTimestamp != nullptr && e.mDeviceTimestamp != nullptr) + { + // Confidential Compute path: read %globaltimer values captured at record(). + // cudaEventElapsedTime() is unreliable under CC (nvbug 5598617); the global + // timer kernel reads the same underlying register directly. + // Use signed int64_t so the subtraction is well-defined if the events + // are ever measured out of order (otherwise the unsigned->signed cast + // is implementation-defined in C++17). + int64_t endNs{0}; + int64_t startNs{0}; + CHECK(cudaMemcpy(&endNs, mDeviceTimestamp, sizeof(int64_t), cudaMemcpyDeviceToHost)); + CHECK(cudaMemcpy(&startNs, e.mDeviceTimestamp, sizeof(int64_t), cudaMemcpyDeviceToHost)); + return static_cast(endNs - startNs) / 1.0e6F; + } + float time{0}; CHECK(cudaEventElapsedTime(&time, e.get(), get())); return time; @@ -143,6 +178,7 @@ class TrtCudaEvent private: cudaEvent_t mEvent{}; + uint64_t* mDeviceTimestamp{nullptr}; }; inline void TrtCudaStream::wait(TrtCudaEvent& event) @@ -361,8 +397,10 @@ struct HostDeallocator cudaPointerAttributes attrs; CHECK(cudaPointerGetAttributes(&attrs, ptr)); - // If pinned, call cudaFreeHost() to deallocate it. - if (attrs.type == cudaMemoryTypeHost) + // If pinned, call cudaFreeHost() to deallocate it. Under Confidential + // Compute, memory returned by cudaMallocHost() may be reported as + // cudaMemoryTypeManaged; it must still be released via cudaFreeHost. + if (attrs.type == cudaMemoryTypeHost || attrs.type == cudaMemoryTypeManaged) { CHECK(cudaFreeHost(ptr)); } diff --git a/samples/common/sampleEngines.cpp b/samples/common/sampleEngines.cpp index 3c9c0c4e4..7b0b9cce5 100644 --- a/samples/common/sampleEngines.cpp +++ b/samples/common/sampleEngines.cpp @@ -895,6 +895,16 @@ bool modelToBuildEnv( SMP_RETVAL_IF_FALSE(env.builder != nullptr, "Builder creation failed", false, err); env.builderConfig.reset(env.builder->createBuilderConfig()); SMP_RETVAL_IF_FALSE(env.builderConfig != nullptr, "Builder config creation failed", false, err); + // Apply --setBuildRoute to pin the engine build to a specific knob configuration. + // This is the public reproducibility surface for tuning iterations. + // Gated by ENABLE_FEATURE_GLOBAL_PERF_TUNER because the underlying + // IBuilderConfig::setBuildRoute API is only declared when the feature is on. + if (!build.buildRoute.empty()) + { + sample::gLogInfo << "Using tuning build route: " << build.buildRoute << std::endl; + SMP_RETVAL_IF_FALSE(env.builderConfig->setBuildRoute(build.buildRoute.c_str()), + "IBuilderConfig::setBuildRoute failed for: " + build.buildRoute, false, err); + } env.builder->setErrorRecorder(&gRecorder); auto networkFlags = 1U << static_cast(nvinfer1::NetworkDefinitionCreationFlag::kSTRONGLY_TYPED); @@ -1000,7 +1010,6 @@ bool loadAsyncStreamingEngineToBuildEnv(std::string const& filepath, BuildEnviro return true; } - bool loadEngineToBuildEnv(std::string const& filepath, BuildEnvironment& env, std::ostream& err, SystemOptions const& sys, bool const enableConsistency) { diff --git a/samples/common/sampleEngines.h b/samples/common/sampleEngines.h index 82a09be03..a328aa3f6 100644 --- a/samples/common/sampleEngines.h +++ b/samples/common/sampleEngines.h @@ -206,7 +206,6 @@ class LazilyDeserializedEngine return *mAsyncFileReader; } - //! //! \brief Get if safe mode is enabled. //! @@ -227,7 +226,6 @@ class LazilyDeserializedEngine std::vector mEngineBlob; std::unique_ptr mAsyncFileReader; - // Directly use the host memory of a serialized engine instead of duplicating the engine in CPU memory. std::unique_ptr mEngineBlobHostMemory; diff --git a/samples/common/sampleInference.cpp b/samples/common/sampleInference.cpp index cef8ffca7..7679ab192 100644 --- a/samples/common/sampleInference.cpp +++ b/samples/common/sampleInference.cpp @@ -19,6 +19,7 @@ #include #include #include +#include #include #include #include @@ -39,6 +40,7 @@ #include "bfloat16.h" #include "common.h" #include "debugTensorWriter.h" +#include "half.h" #include "logger.h" #include "sampleDevice.h" #include "sampleEngines.h" @@ -457,6 +459,8 @@ void setPersistentCacheLimit( bool setUpInference(InferenceEnvironmentBase& iEnv, InferenceOptions const& inference, SystemOptions const& system) { + ASSERT(!inference.refPairs.empty() && "refPairs must have at least one element"); + #if ENABLE_UNIFIED_BUILDER if (iEnv.safe) { @@ -488,6 +492,9 @@ void getSafeTensorInfo(uint32_t profileIndex, nvinfer2::safe::ITRTGraph* safeGra bool setUpSafeInference(InferenceEnvironmentSafe& iEnv, InferenceOptions const& inference, SystemOptions const& system) { + // Always use refPairs[0] for initial binding setup; other pairs are loaded in inferenceLoop + int64_t constexpr kPAIR_INDEX = 0; + int32_t device{}; CHECK(cudaGetDevice(&device)); @@ -534,6 +541,9 @@ bool setUpSafeInference(InferenceEnvironmentSafe& iEnv, InferenceOptions const& int64_t endBindingIndex = 0; safeGraph->getNbIOTensors(endBindingIndex); + ASSERT(!inference.refPairs.empty() && "refPairs must have at least one element"); + auto const& inferenceInputs = inference.refPairs[kPAIR_INDEX].first; + for (int32_t b = 0; b < endBindingIndex; b++) { TensorInfo tensorInfo; @@ -544,8 +554,8 @@ bool setUpSafeInference(InferenceEnvironmentSafe& iEnv, InferenceOptions const& auto const* bindingInOutStr = tensorInfo.isInput ? "Input" : "Output"; for (auto& binding : iEnv.bindings) { - auto const input = findPlausible(inference.inputs, name); - if (tensorInfo.isInput && input != inference.inputs.end()) + auto const input = findPlausible(inferenceInputs, name); + if (tensorInfo.isInput && input != inferenceInputs.end()) { sample::gLogInfo << "Using values loaded from " << input->second << " for input " << name << std::endl; binding->addBinding(tensorInfo, input->second); @@ -616,6 +626,12 @@ IExecutionContext* setupExecutionContext( // NOLINTNEXTLINE(readability-function-cognitive-complexity) bool setUpStdInference(InferenceEnvironmentStd& iEnv, InferenceOptions const& inference, SystemOptions const& system) { + // Always use refPairs[0] for initial binding setup; other pairs are loaded in inferenceLoop + int64_t constexpr kPAIR_INDEX = 0; + + ASSERT(!inference.refPairs.empty() && "refPairs must have at least one element"); + auto const& inferenceInputs = inference.refPairs[kPAIR_INDEX].first; + std::optional properties{}; int32_t device{}; CHECK(cudaGetDevice(&device)); @@ -766,12 +782,12 @@ bool setUpStdInference(InferenceEnvironmentStd& iEnv, InferenceOptions const& in << "Automatically setting shape to: " << shapeData << std::endl; } } - else if (inference.inputs.count(shape->first) && isShapeInferenceIO) + else if (inferenceInputs.count(shape->first) && isShapeInferenceIO) { // Load shape tensor from file. int64_t const size = volume(dims, 0, dims.nbDims); shapeData.resize(size); - auto const& filename = inference.inputs.at(shape->first); + auto const& filename = inferenceInputs.at(shape->first); auto dst = reinterpret_cast(shapeData.data()); loadFromFile(filename, dst, size * sizeof(decltype(shapeData)::value_type)); } @@ -857,7 +873,7 @@ bool setUpStdInference(InferenceEnvironmentStd& iEnv, InferenceOptions const& in auto const* context = iEnv.contexts.front().get(); bool fillBindingsSuccess = FillStdBindings( - engine, context, inference.inputs, iEnv.bindings, 1, endBindingIndex, inference.optProfileIndex)(); + engine, context, inferenceInputs, iEnv.bindings, 1, endBindingIndex, inference.optProfileIndex)(); return fillBindingsSuccess; @@ -1422,11 +1438,189 @@ class IterationSafe : public IterationBase }; #endif +// Helper template function to validate accuracy - abstracts tensor info retrieval +// Note: This function is placed here (before inferenceLoop) because inferenceLoop is a template +// that calls validateAccuracy, and templates require definitions to be visible at instantiation. +template +std::unordered_map validateAccuracy(InferenceEnvironmentBase& iEnv, BindingsBase const& bindings, + int64_t pairIndex, TensorInfoGetter getTensorInfo, InferenceOptions const& inference) +{ + std::unordered_map accuracyResults; + // Accuracy validation with reference outputs is not supported on Windows or RTX (tuner is Linux enterprise-only). +#if defined(_WIN32) + // Early return if no reference outputs are available for validation. + return accuracyResults; +#else + // The checks are ordered to ensure safe array access: + // 1. First check if refOutputsAll vector is empty + // 2. Then check if pairIndex is within bounds + // 3. Only after bounds are verified, check if the specific pair's map is empty + // This ordering prevents out-of-bounds access due to short-circuit evaluation. + bool const isRefOutputsEmpty = iEnv.refOutputsAll.empty(); + bool const isPairIndexOutOfBounds = static_cast(pairIndex) >= iEnv.refOutputsAll.size(); + // Only access refOutputsAll[pairIndex] if pairIndex is within bounds + bool const isPairEmpty = !isPairIndexOutOfBounds && !isRefOutputsEmpty && iEnv.refOutputsAll[pairIndex].empty(); + + if (isRefOutputsEmpty || isPairIndexOutOfBounds || isPairEmpty) + { + sample::gLogVerbose << "iEnv.refOutputsAll.empty():" << isRefOutputsEmpty << std::endl; + sample::gLogVerbose << "static_cast(pairIndex) >= iEnv.refOutputsAll.size():" << isPairIndexOutOfBounds + << std::endl; + sample::gLogVerbose << "pairIndex:" << pairIndex << ", refOutputsAll.size():" << iEnv.refOutputsAll.size() + << std::endl; + if (!isRefOutputsEmpty && !isPairIndexOutOfBounds) + { + sample::gLogVerbose << "iEnv.refOutputsAll[pairIndex].empty():" << isPairEmpty << std::endl; + } + return accuracyResults; + } + + auto const outputBindings = bindings.getOutputBindings(); + + for (auto const& refOutput : iEnv.refOutputsAll[pairIndex]) + { + std::string const& tensorName = refOutput.first; + auto const* refBuffer = refOutput.second.get(); + + // Find the binding for this tensor + auto it = outputBindings.find(tensorName); + if (it == outputBindings.end()) + { + sample::gLogWarning << "Reference output tensor " << tensorName << " not found in output bindings" + << std::endl; + continue; + } + + int32_t const bindingIndex = it->second; + auto const& binding = bindings.getBinding(bindingIndex); + + // Get actual output buffer + void* actualBuffer = nullptr; + if (binding.outputAllocator != nullptr) + { + actualBuffer = binding.outputAllocator->getBuffer()->getHostBuffer(); + } + else + { + actualBuffer = binding.buffer->getHostBuffer(); + } + + // Get tensor info using the provided getter + nvinfer1::Dims dims; + nvinfer1::DataType dataType; + getTensorInfo(tensorName.c_str(), dims, dataType); + + int64_t const volume = std::accumulate(dims.d, dims.d + dims.nbDims, 1LL, std::multiplies{}); + + // Calculate accuracy based on data type using the selected algorithm + double accuracy = 0.0; + + switch (dataType) + { + case nvinfer1::DataType::kFLOAT: + { + std::vector actual(static_cast(actualBuffer), static_cast(actualBuffer) + volume); + std::vector reference( + static_cast(refBuffer->get()), static_cast(refBuffer->get()) + volume); + auto validator + = createAccuracyValidator(inference.accuracyValidationAlgorithm, inference.atol, inference.rtol); + accuracy = validator->calculateAccuracy(actual, reference); + break; + } + case nvinfer1::DataType::kHALF: + { + // Convert half to float for comparison + auto const* actualHalf = static_cast(actualBuffer); + auto const* refHalf = static_cast(refBuffer->get()); + std::vector actual(volume); + std::vector reference(volume); + for (int64_t i = 0; i < volume; ++i) + { + actual[i] = static_cast(actualHalf[i]); + reference[i] = static_cast(refHalf[i]); + } + auto validator + = createAccuracyValidator(inference.accuracyValidationAlgorithm, inference.atol, inference.rtol); + accuracy = validator->calculateAccuracy(actual, reference); + break; + } + case nvinfer1::DataType::kINT32: + { + std::vector actual( + static_cast(actualBuffer), static_cast(actualBuffer) + volume); + std::vector reference( + static_cast(refBuffer->get()), static_cast(refBuffer->get()) + volume); + auto validator = createAccuracyValidator( + inference.accuracyValidationAlgorithm, inference.atol, inference.rtol); + accuracy = validator->calculateAccuracy(actual, reference); + break; + } + case nvinfer1::DataType::kINT8: + { + std::vector actual(static_cast(actualBuffer), static_cast(actualBuffer) + volume); + std::vector reference( + static_cast(refBuffer->get()), static_cast(refBuffer->get()) + volume); + auto validator = createAccuracyValidator( + inference.accuracyValidationAlgorithm, inference.atol, inference.rtol); + accuracy = validator->calculateAccuracy(actual, reference); + break; + } + default: + sample::gLogWarning << "Unsupported data type for accuracy validation: " << static_cast(dataType) + << std::endl; + continue; + } + + accuracyResults[tensorName] = accuracy; + sample::gLogInfo << "Accuracy loss for tensor " << tensorName << " (" + << getAlgorithmName(inference.accuracyValidationAlgorithm) << "): " << std::fixed + << std::setprecision(6) << accuracy; + + // Check if accuracy exceeds threshold (if threshold is set) + if (inference.accuracyThresholdEndToEnd > 0 && accuracy >= inference.accuracyThresholdEndToEnd) + { + sample::gLogInfo << " [FAIL: >= threshold " << inference.accuracyThresholdEndToEnd << "]"; + iEnv.accuracyFailed = true; + } + sample::gLogInfo << std::endl; + } + + return accuracyResults; +#endif // !(defined(_WIN32) || TRT_WINML) +} + +//! +//! \brief Run the inference loop with optional accuracy validation. +//! \tparam TensorInfoGetter Callable type for retrieving tensor info (dims, dataType) by name. +//! \param iStreams Vector of iteration streams for inference execution. +//! \param cpuStart CPU timestamp at start of inference. +//! \param gpuStart GPU event recorded at start of inference. +//! \param inference Inference options containing iterations, duration, warmup, etc. +//! \param trace Output vector for inference traces. +//! \param iEnv Inference environment containing reference outputs. +//! \param bindings Bindings for input reloading and validation. +//! \param getTensorInfo Callable to get tensor dims and dataType by name. +//! \return true if inference completed successfully, false on error. +//! +template // NOLINTNEXTLINE(readability-function-cognitive-complexity) bool inferenceLoop(std::vector>& iStreams, TimePoint const& cpuStart, - TrtCudaEvent const& gpuStart, int iterations, float maxDurationMs, float warmupMs, - std::vector& trace, bool includeTransfers, float idleMs) + TrtCudaEvent const& gpuStart, InferenceOptions const& inference, std::vector& trace, + InferenceEnvironmentBase& iEnv, BindingsBase& bindings, TensorInfoGetter getTensorInfo) { + // Create local aliases for frequently used inference options + int const iterations = inference.iterations; + float const warmupMs = inference.warmup; + bool const includeTransfers = inference.includeTransfers; + float const idleMs = inference.idle; + // Number of reference pairs to validate (derived from inference.refPairs.size()) + int32_t const numRefPairs = static_cast(inference.refPairs.size()); + float maxDurationMs = -1.F; + if (inference.duration != -1.F) + { + maxDurationMs = inference.duration * 1000.F + warmupMs; + } + float durationMs = 0; int32_t skip = 0; @@ -1447,16 +1641,47 @@ bool inferenceLoop(std::vector>& iStreams, TimePo } } - for (int32_t i = 0; i < iterations + skip || durationMs < maxDurationMs; ++i) + for (int32_t i = 0; i < iterations + skip + numRefPairs || durationMs < maxDurationMs; ++i) { - if (!std::all_of(iStreams.begin(), iStreams.end(), [&](auto& s) { return s->query(includeTransfers); })) + // For the first numRefPairs iterations, force includeTransfers to true + // so that input/output data transfers happen for accuracy validation. + bool const currentIncludeTransfers = (i < numRefPairs) || includeTransfers; + + // Before each refPair iteration, load input data (including i=0 for consistency) + if (i < numRefPairs) { - return false; + bindings.fillInputsFromMap(inference.refPairs[i].first); + } + + for (auto& s : iStreams) + { + if (!s->query(currentIncludeTransfers)) + { + return false; + } } for (auto& s : iStreams) { - durationMs = std::max(durationMs, s->sync(cpuStart, gpuStart, trace, includeTransfers)); + durationMs = std::max(durationMs, s->sync(cpuStart, gpuStart, trace, currentIncludeTransfers)); + } + + // Validate accuracy for refPair iterations (runs for first numRefPairs iterations) + // This must happen BEFORE the warmup check to ensure validation is not skipped + if (i < numRefPairs) + { + // Force output data transfer to complete before validation + for (auto& s : iStreams) + { + s->fetchOutputData(true); + } + auto results = validateAccuracy(iEnv, bindings, i, getTensorInfo, inference); + // Merge per-tensor accuracy values into iEnv for the tuning cache + for (auto const& [name, value] : results) + { + iEnv.accuracyLossValues[name] = value; + } } + if (durationMs < warmupMs) // Warming up { if (durationMs) // Skip complete iterations @@ -1465,11 +1690,13 @@ bool inferenceLoop(std::vector>& iStreams, TimePo } continue; } + if (idleMs != 0.F) { std::this_thread::sleep_for(std::chrono::duration(idleMs)); } } + for (auto& s : iStreams) { s->syncAll(cpuStart, gpuStart, trace, includeTransfers); @@ -1483,13 +1710,6 @@ void inferenceExecution(InferenceOptions const& inference, InferenceEnvironmentB { try { - float warmupMs = inference.warmup; - float durationMs = -1.F; - if (inference.duration != -1.F) - { - durationMs = inference.duration * 1000.F + warmupMs; - } - CHECK(cudaSetDevice(device)); #if ENABLE_UNIFIED_BUILDER @@ -1519,8 +1739,24 @@ void inferenceExecution(InferenceOptions const& inference, InferenceEnvironmentB s->wait(sync.gpuStart); } std::vector localTrace; - if (!inferenceLoop(iStreams, sync.cpuStart, sync.gpuStart, inference.iterations, durationMs, warmupMs, - localTrace, inference.includeTransfers, inference.idle)) + + // Get bindings and graph for accuracy validation + auto* iEnvSafe = static_cast(&iEnv); + int32_t const streamIdx = threadIdx * streamsPerThread; + auto& bindingsRef = *iEnvSafe->bindings[streamIdx]; + auto& graphRef = *iEnvSafe->mClonedGraphs[streamIdx]; + + // Create tensor info getter that captures safety-specific context + auto getTensorInfo = [&graphRef](char const* name, nvinfer1::Dims& dims, nvinfer1::DataType& dataType) { + nvinfer2::safe::TensorDescriptor desc; + graphRef.getIOTensorDescriptor(desc, name); + std::vector dimsSpec{desc.shape.d, desc.shape.d + desc.shape.nbDims}; + dims = toDims(dimsSpec); + dataType = desc.dataType; + }; + + if (!inferenceLoop( + iStreams, sync.cpuStart, sync.gpuStart, inference, localTrace, iEnv, bindingsRef, getTensorInfo)) { std::lock_guard lock{sync.mutex}; iEnv.error = true; @@ -1563,8 +1799,23 @@ void inferenceExecution(InferenceOptions const& inference, InferenceEnvironmentB } std::vector localTrace; - if (!inferenceLoop(iStreams, sync.cpuStart, sync.gpuStart, inference.iterations, durationMs, warmupMs, - localTrace, inference.includeTransfers, inference.idle)) + + // Get context and bindings for accuracy validation + auto* iEnvStd = static_cast(&iEnv); + int32_t const streamIdx = threadIdx * streamsPerThread; + auto& bindingsRef = *iEnvStd->bindings[streamIdx]; + auto& contextRef = *iEnvStd->contexts[streamIdx]; + auto const* engine = &contextRef.getEngine(); + + // Create tensor info getter that captures standard-specific context + auto getTensorInfo + = [&contextRef, engine](char const* name, nvinfer1::Dims& dims, nvinfer1::DataType& dataType) { + dims = contextRef.getTensorShape(name); + dataType = engine->getTensorDataType(name); + }; + + if (!inferenceLoop( + iStreams, sync.cpuStart, sync.gpuStart, inference, localTrace, iEnv, bindingsRef, getTensorInfo)) { std::lock_guard lock{sync.mutex}; iEnv.error = true; @@ -2153,7 +2404,7 @@ bool BindingsStd::setTensorAddresses(nvinfer1::IExecutionContext& context) const { #if ENABLE_DLA // When DLA is enabled, a tensor's address must be set to nullptr to unregister - // the tensor with DLA before setting it to a new address. + // the tensor with DLA before setting it to a new address. if (context.getTensorAddress(name) != nullptr) { if (!context.setTensorAddress(name, nullptr)) @@ -2279,4 +2530,80 @@ bool BindingsSafe::setTensorAddresses(ITRTGraph& graph) const return true; } #endif + +#if !defined(_WIN32) +namespace +{ +// Helper template function to load reference outputs - abstracts tensor info retrieval +template +void loadRefOutputsImpl(InferenceEnvironmentBase& iEnv, InferenceOptions const& inference, int64_t pairIndex, + TensorInfoGetter getTensorInfo) +{ + ASSERT(!inference.refPairs.empty() && "refPairs must have at least one element"); + ASSERT(pairIndex >= 0 && static_cast(pairIndex) < inference.refPairs.size() && "pairIndex out of range"); + auto const& inferenceRefOutputs = inference.refPairs[pairIndex].second; + + if (inferenceRefOutputs.empty()) + { + return; // No reference outputs to load + } + + // Ensure refOutputsAll has enough space + if (iEnv.refOutputsAll.size() <= static_cast(pairIndex)) + { + iEnv.refOutputsAll.resize(pairIndex + 1); + } + + for (auto const& refOutput : inferenceRefOutputs) + { + std::string const& tensorName = refOutput.first; + std::string const& fileName = refOutput.second; + + // Get tensor info using the provided getter + nvinfer1::Dims dims; + nvinfer1::DataType dataType; + getTensorInfo(tensorName.c_str(), dims, dataType); + + int64_t const volume = std::accumulate(dims.d, dims.d + dims.nbDims, 1LL, std::multiplies{}); + size_t const nbBytes = samplesCommon::getNbBytes(dataType, volume); + + // Allocate host buffer and load data + auto hostBuffer = std::make_unique(nbBytes); + loadFromFile(fileName, static_cast(hostBuffer->get()), nbBytes); + + sample::gLogInfo << "Loaded reference output for tensor " << tensorName << " from " << fileName + << " (volume=" << volume << ", bytes=" << nbBytes << ")" << std::endl; + + iEnv.refOutputsAll[pairIndex].insert_or_assign(tensorName, std::move(hostBuffer)); + } +} +} // namespace + +void loadRefOutputs(InferenceEnvironmentBase& iEnv, InferenceOptions const& inference, + nvinfer1::IExecutionContext const& context, int64_t pairIndex) +{ + auto const* engine = &context.getEngine(); + auto getTensorInfo = [&context, engine](char const* name, nvinfer1::Dims& dims, nvinfer1::DataType& dataType) { + dims = context.getTensorShape(name); + dataType = engine->getTensorDataType(name); + }; + loadRefOutputsImpl(iEnv, inference, pairIndex, getTensorInfo); +} + +#if ENABLE_UNIFIED_BUILDER +void loadRefOutputs(InferenceEnvironmentBase& iEnv, InferenceOptions const& inference, + nvinfer2::safe::ITRTGraph const& graph, int64_t pairIndex) +{ + auto getTensorInfo = [&graph](char const* name, nvinfer1::Dims& dims, nvinfer1::DataType& dataType) { + nvinfer2::safe::TensorDescriptor desc; + graph.getIOTensorDescriptor(desc, name); + std::vector dimsSpec{desc.shape.d, desc.shape.d + desc.shape.nbDims}; + dims = toDims(dimsSpec); + dataType = desc.dataType; + }; + loadRefOutputsImpl(iEnv, inference, pairIndex, getTensorInfo); +} +#endif +#endif // !defined(_WIN32) && !TRT_WINML + } // namespace sample diff --git a/samples/common/sampleInference.h b/samples/common/sampleInference.h index f57af1944..062920f7f 100644 --- a/samples/common/sampleInference.h +++ b/samples/common/sampleInference.h @@ -175,9 +175,21 @@ struct InferenceEnvironmentBase deviceMemory; //< Device memory used for inference when the allocation strategy is not static. std::unique_ptr listener; bool error{false}; + bool accuracyFailed{false}; //< Set to true if any tensor accuracy exceeds threshold + std::unordered_map accuracyLossValues; //< Per-tensor accuracy values from the last validation bool safe{false}; std::string cmdline; + +#if !defined(_WIN32) + //! Reference outputs for accuracy validation (tuner feature, Linux enterprise/auto-only). + //! Map from tensor name to host buffer containing reference data. + //! Guarded because MSVC cannot instantiate vector>>, + //! and the tuner does not run on Windows or RTX/winjit. + using RefOutputMap = std::unordered_map>; + //! Vector of reference output maps, one for each refPair. + std::vector refOutputsAll; +#endif // !defined(_WIN32) && !TRT_WINML }; struct InferenceEnvironmentStd : public InferenceEnvironmentBase @@ -260,6 +272,24 @@ bool timeDeserialize(InferenceEnvironmentBase& iEnv, SystemOptions const& sys); bool runInference(InferenceOptions const& inference, InferenceEnvironmentBase& iEnv, int32_t device, std::vector& trace, ReportingOptions const& reporting); +#if !defined(_WIN32) +//! +//! \brief Load reference outputs from files into InferenceEnvironmentBase::refOutputsAll. +//! \param pairIndex Index of the refPair to use (default 0 for backward compatibility). +//! +void loadRefOutputs(InferenceEnvironmentBase& iEnv, InferenceOptions const& inference, + nvinfer1::IExecutionContext const& context, int64_t pairIndex = 0); + +#if ENABLE_UNIFIED_BUILDER +//! +//! \brief Load reference outputs from files for safe inference. +//! \param pairIndex Index of the refPair to use (default 0 for backward compatibility). +//! +void loadRefOutputs(InferenceEnvironmentBase& iEnv, InferenceOptions const& inference, + nvinfer2::safe::ITRTGraph const& graph, int64_t pairIndex = 0); +#endif +#endif // !defined(_WIN32) && !TRT_WINML + //! //! \brief Get layer information of the engine. //! @@ -335,6 +365,28 @@ class BindingsBase return getBindings(isInput); } + //! Fill input bindings from a name-to-file map. + //! + //! \param inputMap A map where: + //! - key: tensor name (e.g., "input", "input:0") + //! - value: file path containing the tensor data to load (e.g., "input_0.dat") + //! + //! For each entry in the map, looks up the tensor name in the input bindings + //! and fills the binding buffer with data from the specified file. + //! Entries with tensor names not found in input bindings are silently skipped. + void fillInputsFromMap(std::unordered_map const& inputMap) + { + auto inputBindings = getInputBindings(); + for (auto const& item : inputMap) + { + auto it = inputBindings.find(item.first); + if (it != inputBindings.end()) + { + fill(it->second, item.second); + } + } + } + std::unordered_map getOutputBindings() const { auto isOutput = [](Binding const& b) { return !b.isInput; }; @@ -349,6 +401,11 @@ class BindingsBase std::unordered_map getBindings(std::function predicate) const; + Binding const& getBinding(int32_t index) const + { + return mBindings.at(index); + } + protected: std::unordered_map mNames; std::vector mBindings; diff --git a/samples/common/sampleOptions.cpp b/samples/common/sampleOptions.cpp index cd6c6dea5..cd747cc05 100644 --- a/samples/common/sampleOptions.cpp +++ b/samples/common/sampleOptions.cpp @@ -18,6 +18,7 @@ #include #include #include +#include #include #include #include @@ -711,6 +712,108 @@ bool getOptimizationProfiles( return retValue; } +// Helper function to output a map as "name1:file1, name2:file2, ..." +std::string mapToString(std::unordered_map const& m) +{ + std::string result; + char const* delim = ""; + for (auto const& kv : m) + { + result += delim; + delim = ", "; + result += kv.first + ":" + kv.second; + } + return result; +} + +// NOLINTNEXTLINE(readability-function-cognitive-complexity) +bool getRefPairs(Arguments& arguments, std::vector& refPairs) +{ + bool retValue{false}; + int32_t pos{}; + constexpr int32_t kNUM_REF_PAIR_FOLLOW_OPTIONS + = 2; // Number of options that can follow --refPair: --loadInputs and --loadRefOutputs + size_t pairIndex{}; + + auto parseIntoMap = [](std::string const& value, std::unordered_map& map) { + std::vector kvList{splitToStringVec(value, ',')}; + splitInsertKeyValue(kvList, map); + }; + + while (getAndDelOptionWithPosition(arguments, "--refPair", pairIndex, pos)) + { + InferenceOptions::RefPair pair; + + for (int32_t i = 0; i < kNUM_REF_PAIR_FOLLOW_OPTIONS; ++i, ++pos) + { + std::string value; + + if (getAndDelOptionBehind(arguments, "--loadInputs", pos, value)) + { + parseIntoMap(value, pair.first); // Parse into input map + } + else if (getAndDelOptionBehind(arguments, "--loadRefOutputs", pos, value)) + { + parseIntoMap(value, pair.second); // Parse into output map + } + else + { + break; + } + } + + if (pairIndex >= refPairs.size()) + { + refPairs.resize(pairIndex + 1); + } + if (!refPairs[pairIndex].first.empty() || !refPairs[pairIndex].second.empty()) + { + throw std::invalid_argument("Reference pair index cannot be the same."); + } + refPairs[pairIndex] = pair; + retValue = true; + } + + // Validate refPairs when --refPair is used + if (retValue) + { + // Rule: Need at least 2 pairs when using --refPair + if (refPairs.size() < 2) + { + throw std::invalid_argument("When using --refPair, you need at least two pairs of I/O."); + } + + size_t idx = 0; + for (auto const& pair : refPairs) + { + // Rule: Each pair must have both --loadInputs and --loadRefOutputs + if (pair.first.empty()) + { + throw std::invalid_argument(std::string("Missing --loadInputs for reference pair at index ") + + std::to_string(idx) + + std::string(". Each --refPair must have both --loadInputs and --loadRefOutputs.")); + } + if (pair.second.empty()) + { + throw std::invalid_argument(std::string("Missing --loadRefOutputs for reference pair at index ") + + std::to_string(idx) + + std::string(". Each --refPair must have both --loadInputs and --loadRefOutputs.")); + } + ++idx; + } + + // Output parsed refPairs for verification + sample::gLogVerbose << "Parsed refPairs:" << std::endl; + for (size_t i = 0; i < refPairs.size(); ++i) + { + sample::gLogVerbose << " refPair[" << i << "]:" << std::endl; + sample::gLogVerbose << " loadInputs: {" << mapToString(refPairs[i].first) << "}" << std::endl; + sample::gLogVerbose << " loadRefOutputs: {" << mapToString(refPairs[i].second) << "}" << std::endl; + } + } + return retValue; +} + template void printShapes(std::ostream& os, char const* phase, T const& shapes, int32_t profileIndex) { @@ -987,7 +1090,6 @@ void BuildOptions::parse(Arguments& arguments) getFormats(inputFormats, "--inputIOFormats"); getFormats(outputFormats, "--outputIOFormats"); - if (!getOptimizationProfiles(arguments, optProfiles, "--profile")) { ShapeProfile shapes; @@ -1178,10 +1280,15 @@ void BuildOptions::parse(Arguments& arguments) { save = true; } + getAndDelOption(arguments, "--saveAllEngines", saveAllEngines); if (load && save) { throw std::invalid_argument("Incompatible load and save engine options selected"); } + if (saveAllEngines && !save) + { + throw std::invalid_argument("--saveAllEngines requires --saveEngine to be specified."); + } std::string tacticSourceArgs; if (getAndDelOption(arguments, "--tacticSources", tacticSourceArgs)) @@ -1372,6 +1479,7 @@ void BuildOptions::parse(Arguments& arguments) } getAndDelOption(arguments, "--leanDLLPath", leanDLLPath); + getAndDelOption(arguments, "--setBuildRoute", buildRoute); // Don't delete the option because the inference option parser requires it getOption(arguments, "--allowWeightStreaming", allowWeightStreaming); @@ -1433,9 +1541,127 @@ void SystemOptions::parse(Arguments& arguments) } } +namespace +{ + +//! Parse the --tuningSearch= value into the enum. +//! Throws std::invalid_argument on unknown non-empty strings; leaves the +//! algorithm unchanged on empty input. +TuningSearchAlgorithm parseTuningSearchAlgorithm(std::string const& spec, TuningSearchAlgorithm defaultValue) +{ + if (spec.empty()) + { + return defaultValue; + } + if (spec == "fast") + { + return TuningSearchAlgorithm::kFAST; + } + if (spec == "full") + { + return TuningSearchAlgorithm::kEXHAUSTIVE; + } + if (spec == "mixed") + { + return TuningSearchAlgorithm::kMIXED; + } + throw std::invalid_argument(std::string("Unknown tuning search algorithm: ") + spec); +} + +//! Validate the exclusivity rules for --continue. The flag recovers everything +//! from the cache header, so any other tuning flag is ambiguous. +void validateContinueExclusivity(TuningOptions const& opts) +{ + if (opts.tuningCacheFile.empty()) + { + throw std::invalid_argument("--continue requires --tuningCacheFile to be specified."); + } + if (!opts.tuningExpr.empty() || !opts.tuningExprFile.empty()) + { + throw std::invalid_argument( + "--continue cannot be used with --tuneBuildRoutes or --tuneBuildRouteFile. " + "All options are recovered from the cache file header."); + } + if (opts.dryRun) + { + throw std::invalid_argument("--continue cannot be used with --dryRun."); + } + if (opts.helpBuildRoute) + { + throw std::invalid_argument("--continue cannot be used with --helpBuildRoute."); + } +} + +//! Read --tuneBuildRouteFile contents and append to tuningExpr. Newlines and +//! spaces are both treated as delimiters, so the file content is equivalent +//! to a single --tuneBuildRoutes argument. +void appendTuningExprFromFile(std::string const& path, std::string& tuningExpr) +{ + std::ifstream file(path); + if (!file.is_open()) + { + throw std::invalid_argument("Cannot open --tuneBuildRouteFile: " + path); + } + std::string line; + while (std::getline(file, line)) + { + if (!tuningExpr.empty()) + { + tuningExpr += " "; + } + tuningExpr += line; + } +} + +} // namespace + +void TuningOptions::parse(Arguments& arguments) +{ + // --helpBuildRoute is a short-circuit; the option's value (if any) is the + // optional knob filter (e.g. --helpBuildRoute=conv_use_long_w). + if (getAndDelOption(arguments, "--helpBuildRoute", helpBuildRouteKnob)) + { + helpBuildRoute = true; + } + + getAndDelOption(arguments, "--tuneBuildRoutes", tuningExpr); + getAndDelOption(arguments, "--tuneBuildRouteFile", tuningExprFile); + + std::string searchAlgorithmString; + getAndDelOption(arguments, "--tuningSearch", searchAlgorithmString); + tuningSearchAlgorithm = parseTuningSearchAlgorithm(searchAlgorithmString, tuningSearchAlgorithm); + + getAndDelOption(arguments, "--tuningTimeOut", timeout); + getAndDelOption(arguments, "--tuningCacheFile", tuningCacheFile); + getAndDelOption(arguments, "--continue", continueFromCache); + getAndDelOption(arguments, "--dryRun", dryRun); + // Hidden parent->child IPC flag (not in TuningOptions::help()). + getAndDelOption(arguments, "--tuningResultFile", tuningResultFile); + + // Validate + if (!tuningExpr.empty() && !tuningExprFile.empty()) + { + throw std::invalid_argument("Cannot specify both --tuneBuildRoutes and --tuneBuildRouteFile."); + } + if (continueFromCache) + { + validateContinueExclusivity(*this); + } + if (tuningSearchAlgorithm == TuningSearchAlgorithm::kMIXED && dryRun) + { + throw std::invalid_argument("--dryRun is incompatible with --tuningSearch=mixed."); + } + + if (!tuningExprFile.empty()) + { + appendTuningExprFromFile(tuningExprFile, tuningExpr); + } +} + constexpr int64_t WeightStreamingBudget::kDISABLE; constexpr int64_t WeightStreamingBudget::kAUTOMATIC; +// NOLINTNEXTLINE(readability-function-cognitive-complexity) void InferenceOptions::parse(Arguments& arguments) { @@ -1503,10 +1729,83 @@ void InferenceOptions::parse(Arguments& arguments) getAndDelOption(arguments, "--timeRefit", timeRefit); getAndDelOption(arguments, "--persistentCacheRatio", persistentCacheRatio); - std::string list; - getAndDelOption(arguments, "--loadInputs", list); - std::vector inputsList{splitToStringVec(list, ',')}; - splitInsertKeyValue(inputsList, inputs); + // Parse reference pairs: either single pair (--loadInputs/--loadRefOutputs) or multiple pairs (--refPair) + // This is similar to how --profile works with --minShapes/--optShapes/--maxShapes + if (!getRefPairs(arguments, refPairs)) + { + // No --refPair was used, parse single --loadInputs/--loadRefOutputs pair into refPairs[0] + ASSERT(refPairs.size() > 0 && "refPairs must have at least one element"); + auto& inferenceInputs = refPairs[0].first; + auto& inferenceRefOutputs = refPairs[0].second; + + std::string ilist; + getAndDelOption(arguments, "--loadInputs", ilist); + std::vector inputsList{splitToStringVec(ilist, ',')}; + splitInsertKeyValue(inputsList, inferenceInputs); + + std::string olist; + getAndDelOption(arguments, "--loadRefOutputs", olist); + std::vector refOutputsList{splitToStringVec(olist, ',')}; + splitInsertKeyValue(refOutputsList, inferenceRefOutputs); + + // Rule: --loadRefOutputs without --loadInputs should fail + if (!inferenceRefOutputs.empty() && inferenceInputs.empty()) + { + throw std::invalid_argument("--loadRefOutputs requires --loadInputs to be specified."); + } + + // Output parsed inputs and refOutputs for verification + if (!inferenceInputs.empty() || !inferenceRefOutputs.empty()) + { + sample::gLogVerbose << "Parsed inputs/refOutputs (no --refPair):" << std::endl; + sample::gLogVerbose << " loadInputs: {" << mapToString(inferenceInputs) << "}" << std::endl; + sample::gLogVerbose << " loadRefOutputs: {" << mapToString(inferenceRefOutputs) << "}" << std::endl; + } + } + + // Parse accuracy validation options + std::string accuracyAlgorithmString; + bool const algoExplicitlySet = getAndDelOption(arguments, "--accuracyAlgorithm", accuracyAlgorithmString); + std::unordered_map fromString{ + {"l0", AccuracyValidationAlgorithm::kL0}, + {"l1", AccuracyValidationAlgorithm::kL1}, + {"l2", AccuracyValidationAlgorithm::kL2}, + {"lInf", AccuracyValidationAlgorithm::kLInf}, + {"cos", AccuracyValidationAlgorithm::kCosineSimilarity}, + }; + if (!accuracyAlgorithmString.empty() && fromString.find(accuracyAlgorithmString) == fromString.end()) + { + throw std::invalid_argument(std::string("Unknown accuracyAlgorithm: ") + accuracyAlgorithmString); + } + accuracyValidationAlgorithm + = algoExplicitlySet ? fromString[accuracyAlgorithmString] : AccuracyValidationAlgorithm::kL0; + + bool const atolSet = getAndDelOption(arguments, "--atol", atol); + if (atol < 0) + { + throw std::invalid_argument("--atol must be non-negative."); + } + bool const rtolSet = getAndDelOption(arguments, "--rtol", rtol); + if (rtol < 0) + { + throw std::invalid_argument("--rtol must be non-negative."); + } + if (accuracyValidationAlgorithm != AccuracyValidationAlgorithm::kL0) + { + if (atolSet) + { + throw std::invalid_argument("--atol is only valid for L0 accuracy algorithm."); + } + if (rtolSet) + { + throw std::invalid_argument("--rtol is only valid for L0 accuracy algorithm."); + } + } + getAndDelOption(arguments, "--accuracyThreshold", accuracyThresholdEndToEnd); + if (accuracyThresholdEndToEnd < 0) + { + throw std::invalid_argument("--accuracyThreshold must be non-negative."); + } getShapesInference(arguments, shapes, "--shapes"); setOptProfile = getAndDelOption(arguments, "--useProfile", optProfileIndex); @@ -1607,6 +1906,7 @@ void AllOptions::parse(Arguments& arguments) build.parse(arguments); system.parse(arguments); inference.parse(arguments); + tuning.parse(arguments); if (build.useRuntime != RuntimeMode::kFULL && inference.timeRefit) { @@ -1646,7 +1946,9 @@ void AllOptions::parse(Arguments& arguments) reporting.parse(arguments); helps = parseHelp(arguments); - if (!helps) + // Skip validation if --help or --helpBuildRoute is specified (both short-circuit + // trtexec before doing real work, so model presence is not required). + if (!helps && !tuning.helpBuildRoute) { if (!build.load && model.baseModel.format == ModelFormat::kANY) { @@ -1707,6 +2009,27 @@ void AllOptions::parse(Arguments& arguments) << "The parser's behavior will be modified, but the network will run on GPU." << std::endl; } + bool const hasRefOutputs = !inference.refPairs[0].second.empty() || inference.refPairs.size() > 1; + if (hasRefOutputs && inference.accuracyThresholdEndToEnd <= 0) + { + throw std::invalid_argument( + "--accuracyThreshold (with a positive value) is required when --loadRefOutputs or --refPair is set."); + } + if (hasRefOutputs && build.skipInference) + { + throw std::invalid_argument( + "--loadRefOutputs/--refPair cannot be used with --skipInference: accuracy " + "validation needs the inference phase to run. Note --skipInference is implicitly " + "enabled by --buildDLAStandalone and by a cross-platform --runtimePlatform."); + } + // Warn when using --onnx with accuracy validation, + // because engine builds are nondeterministic and may produce unreliable comparisons. + if (hasRefOutputs && !build.load) + { + sample::gLogWarning << "Accuracy validation (--loadRefOutputs/--refPair) with --onnx may produce " + << "unreliable results because engine builds are nondeterministic. " + << "Use --loadEngine for deterministic comparison." << std::endl; + } } } @@ -2209,12 +2532,45 @@ std::ostream& operator<<(std::ostream& os, InferenceOptions const& options) "Weight Streaming Budget: " << wsBudget << std::endl; // clang-format on + // Accuracy validation settings + os << "Accuracy Algorithm: "; + switch (options.accuracyValidationAlgorithm) + { + case AccuracyValidationAlgorithm::kL0: os << "L0"; break; + case AccuracyValidationAlgorithm::kL1: os << "L1"; break; + case AccuracyValidationAlgorithm::kL2: os << "L2"; break; + case AccuracyValidationAlgorithm::kLInf: os << "LInf"; break; + case AccuracyValidationAlgorithm::kCosineSimilarity: os << "CosineSimilarity"; break; + } + os << std::endl; + if (options.accuracyValidationAlgorithm == AccuracyValidationAlgorithm::kL0) + { + os << "Absolute Tolerance (atol): " << options.atol << std::endl; + os << "Relative Tolerance (rtol): " << options.rtol << std::endl; + } + if (options.accuracyThresholdEndToEnd > 0) + { + os << "Accuracy Threshold: " << options.accuracyThresholdEndToEnd << std::endl; + } + os << "Inputs:" << std::endl; - for (auto const& input : options.inputs) + ASSERT(options.refPairs.size() > 0 && "refPairs must have at least one element"); + auto const& inferenceInputs = options.refPairs[0].first; + for (auto const& input : inferenceInputs) { os << input.first << "<-" << input.second << std::endl; } + auto const& inferenceRefOutputs = options.refPairs[0].second; + if (!inferenceRefOutputs.empty()) + { + os << "RefOutputs:" << std::endl; + for (auto const& refOutput : inferenceRefOutputs) + { + os << refOutput.first << "<-" << refOutput.second << std::endl; + } + } + os << "Debug Tensor Save Destinations:" << std::endl; for (auto const& fileName : options.debugTensorFileNames) { @@ -2495,12 +2851,10 @@ void SystemOptions::help(std::ostream& os, bool const enableStaticPlugins) // clang-format off os << "=== System Options ===" << std::endl << " --device=N Select cuda device N (default = " << defaultDevice << ")" << std::endl; - if (enableStaticPlugins) { - os << " --staticPlugins Plugin library (.so) to load statically (can be specified multiple times)" << std::endl; + os << " --staticPlugins Plugin library (.so) to load statically (can be specified multiple times)" << std::endl << std::endl; } - os << " --useDLACore=N Select DLA core N for layers that support DLA (default = none)" << std::endl << " --staticPlugins Plugin library (.so) to load statically (can be specified multiple times)" << std::endl << " --dynamicPlugins Plugin library (.so) to load dynamically and may be serialized with the engine if they are included in --setPluginsToSerialize (can be specified multiple times)" << std::endl << @@ -2532,6 +2886,12 @@ void InferenceOptions::help(std::ostream& os) R"( Input values spec ::= Ival[","spec])" << std::endl << R"( Ival ::= name":"file)" << std::endl << " Consult the README for more information on generating files for custom inputs." << std::endl << + " --loadRefOutputs=spec Load reference output values from files for accuracy validation. Output names can be " << std::endl << + " wrapped with single quotes (ex: 'Output:0')." << std::endl << + R"( Output values spec ::= Oval[","spec])" << std::endl << + R"( Oval ::= name":"file)" << std::endl << + " If the calculated outputs is not within the accuracy threshold, the inference result is " << std::endl << + " considered to be inaccurate." << std::endl << " --iterations=N Run at least N inference iterations (default = " << defaultIterations << ")" << std::endl << " --warmUp=N Run for N milliseconds to warmup before measuring performance (default = " << defaultWarmUp << ")" << std::endl << @@ -2572,6 +2932,32 @@ void InferenceOptions::help(std::ostream& os) " static = Allocate device memory based on max size across all profiles." << std::endl << " profile = Allocate device memory based on max size of the current profile." << std::endl << " runtime = Allocate device memory based on the actual input shapes." << std::endl << + " --accuracyAlgorithm=spec Specify the algorithm for computing the accuracy loss between actual" << std::endl << + " and reference outputs. Lower accuracy loss is better; 0.0 = perfect match." << std::endl << + R"( Algorithm: spec ::= "l0"|"l1"|"l2"|"lInf"|"cos")" << std::endl << + " l0 = L0 algorithm. Fraction of elements outside tolerance:" << std::endl << + " accuracy loss = Sum( Bool(" << std::endl << + " |f(input[i]) - refOutput[i]| > atol + rtol * |refOutput[i]| ) ) / N" << std::endl << + " where atol, rtol are the absolute and relative tolerances." << std::endl << + " l1 = L1 algorithm. Mean absolute error:" << std::endl << + " accuracy loss = Sum( |f(input[i]) - refOutput[i]| ) / N" << std::endl << + " l2 = L2 algorithm. Mean squared error:" << std::endl << + " accuracy loss = Sum( |f(input[i]) - refOutput[i]| ^2 ) / N" << std::endl << + " lInf = LInf algorithm. Maximum absolute error:" << std::endl << + " accuracy loss = Max( |f(input[i]) - refOutput[i]| )" << std::endl << + " cos = Cosine similarity algorithm. 1 - cosine similarity:" << std::endl << + " accuracy loss = 1 - Sum( f(input[i]) * refOutput[i] ) /" << std::endl << + " ( sqrt(Sum( f(input[i]) ^2 )) * sqrt(Sum( refOutput[i] ^2 )) )" << std::endl << + " --atol=number Absolute tolerance for element-wise accuracy comparison (only valid for L0" << std::endl << + " algorithm). (Default = 1e-5)" << std::endl << + " --rtol=number Relative tolerance for element-wise accuracy comparison (only valid for L0" << std::endl << + " algorithm). (Default = 1e-5)" << std::endl << + " --accuracyThreshold=number Threshold for accepting the inference result. If the computed accuracy loss" << std::endl << + " exceeds this threshold, the result is considered inaccurate." << std::endl << + " --refPair=N Specify a reference pair index for grouping --loadInputs and --loadRefOutputs." << std::endl << + " Can be specified multiple times to validate accuracy across different input/output pairs." << std::endl << + R"( Example: --refPair=0 --loadInputs="x:x1.dat" --loadRefOutputs="y:y1.dat")" << std::endl << + R"( --refPair=1 --loadInputs="x:x2.dat" --loadRefOutputs="y:y2.dat")" << std::endl << " --saveDebugTensors Specify list of names of tensors to turn on the debug state" << std::endl << " and filename to save raw outputs to." << std::endl << " These tensors must be specified as debug tensors during build time." << std::endl << @@ -2595,6 +2981,30 @@ void InferenceOptions::help(std::ostream& os) // clang-format on } +void TuningOptions::help(std::ostream& os) +{ + // clang-format off + os << "=== Tuning Options ===" << std::endl << + " --tuneBuildRoutes= Run an autotuning loop over a build-route expression. The expression" << std::endl << + " uses '-knob=[a|b|c]' for variable knobs and '-knob=fixed' for fixed" << std::endl << + " values; multiple knobs are space-separated. Each iteration forks a" << std::endl << + " child trtexec with --setBuildRoute= for full reproducibility." << std::endl << + " --tuneBuildRouteFile= Read the same expression from a file (newline-delimited tokens)." << std::endl << + " --tuningSearch= Search algorithm:" << std::endl << + " fast = baseline + one-off variations per knob (default)" << std::endl << + " full = enumerate every combination (Cartesian product)" << std::endl << + " mixed = phase 1 fast scan, then exhaustive over positive knobs" << std::endl << + " --tuningCacheFile= JSON file to which per-iteration results are appended (best-config cache)." << std::endl << + " --tuningTimeOut= Stop the loop after N elapsed seconds. -1 = no timeout (default)." << std::endl << + " --saveAllEngines Save the engine of every iteration as .iter. Requires --saveEngine." << std::endl << + " --dryRun Enumerate the route list and exit without building any engine." << std::endl << + " --continue Resume an interrupted tuning loop from --tuningCacheFile." << std::endl << + " --helpBuildRoute[=] Print the knob database as JSON and exit. Optionally filter by knob name." << std::endl << + " --setBuildRoute= Single-shot: build with a specific route. Equivalent to a single tuning" << std::endl << + " iteration; used by the parent tuning loop when forking child workers." << std::endl; + // clang-format on +} + void ReportingOptions::help(std::ostream& os) { // clang-format off @@ -2655,6 +3065,8 @@ void AllOptions::help(std::ostream& os, bool const enableStaticPlugins) os << std::endl; InferenceOptions::help(os); os << std::endl; + TuningOptions::help(os); + os << std::endl; ReportingOptions::help(os); os << std::endl; SystemOptions::help(os, enableStaticPlugins); diff --git a/samples/common/sampleOptions.h b/samples/common/sampleOptions.h index 321464e99..e79536058 100644 --- a/samples/common/sampleOptions.h +++ b/samples/common/sampleOptions.h @@ -18,7 +18,6 @@ #ifndef TRT_SAMPLE_OPTIONS_H #define TRT_SAMPLE_OPTIONS_H - #include #include #include @@ -91,6 +90,37 @@ enum class MemoryAllocationStrategy kRUNTIME, //< Allocate device memory based on the current input shapes. }; +enum class AccuracyValidationAlgorithm +{ + kL0, //< L0 accuracy algorithm + kL1, //< L1 accuracy algorithm + kL2, //< L2 accuracy algorithm + kLInf, //< LInf accuracy algorithm + kCosineSimilarity, //< Cosine similarity accuracy algorithm +}; + +enum class TuningSearchAlgorithm +{ + kFAST, //< Fast searching algorithm: baseline + one-off variations (linear in #knobs) + kEXHAUSTIVE, //< Exhaustive: all combinations enumerated (product of knob value-counts) + kMIXED, //< Two-phase: fast scan, then exhaustive over knobs that improved performance +}; + +//! \brief Convert a TuningSearchAlgorithm enum to its CLI / cache-file string. +//! kFAST -> "fast" +//! kEXHAUSTIVE -> "full" +//! kMIXED -> "mixed" +inline std::string toString(TuningSearchAlgorithm algo) +{ + switch (algo) + { + case TuningSearchAlgorithm::kFAST: return "fast"; + case TuningSearchAlgorithm::kEXHAUSTIVE: return "full"; + case TuningSearchAlgorithm::kMIXED: return "mixed"; + } + return "unknown"; +} + //! //! \enum RuntimeMode //! @@ -235,6 +265,7 @@ class BuildOptions : public Options bool allowGPUFallback{false}; bool skipInference{false}; bool save{false}; + bool saveAllEngines{false}; //!< Save per-iteration engines as .iter during tuning bool load{false}; bool asyncFileReader{false}; bool refittable{false}; @@ -272,6 +303,7 @@ class BuildOptions : public Options nvinfer1::TempfileControlFlags tempfileControls{getTempfileControlDefaults()}; RuntimeMode useRuntime{RuntimeMode::kFULL}; std::string leanDLLPath{}; + std::string buildRoute{}; //!< --setBuildRoute= string; passed to IBuilderConfig::setBuildRoute() int32_t maxAuxStreams{defaultMaxAuxStreams}; bool getPlanVersionOnly{false}; @@ -318,6 +350,10 @@ class InferenceOptions : public Options float sleep{defaultSleep}; float idle{defaultIdle}; float persistentCacheRatio{defaultPersistentCacheRatio}; + float atol{1e-5}; // Element-wise accuracy threshold absolute tolerance + float rtol{1e-5}; // Element-wise accuracy threshold relative tolerance + float accuracyThresholdEndToEnd{}; // End-to-end accuracy threshold should not have default value + // because it depends on the model and accuracy validation algorithm bool overlap{true}; bool includeTransfers{false}; bool useManaged{false}; @@ -327,7 +363,13 @@ class InferenceOptions : public Options bool timeDeserialize{false}; bool timeRefit{false}; bool setOptProfile{false}; - std::unordered_map inputs; + // Reference pairs for accuracy validation with multiple test cases. + // Each pair contains (inputMap, refOutputMap) indexed by pair number. + // refPairs[0] is used as the primary pair (for --loadInputs/--loadRefOutputs without --refPair). + using RefPair = std::pair, + std::unordered_map>; // pair of (inputMap, refOutputMap) + std::vector refPairs{1}; // Initialize with one empty pair + AccuracyValidationAlgorithm accuracyValidationAlgorithm{AccuracyValidationAlgorithm::kL0}; using ShapeProfile = std::unordered_map>; ShapeProfile shapes; nvinfer1::ProfilingVerbosity nvtxVerbosity{nvinfer1::ProfilingVerbosity::kLAYER_NAMES_ONLY}; @@ -389,6 +431,34 @@ class SafeBuilderOptions : public Options static void printHelp(std::ostream& out); }; +//! \brief Options for `--tuneBuildRoutes`-driven autotuning of build routes. +//! +//! The tuning loop is implemented in samples/trtexec/trtexec.cpp by forking +//! the trtexec process for each route; each child runs `runOnceBuildAndInfer` +//! with `--setBuildRoute=` injected by the parent. +class TuningOptions : public Options +{ +public: + std::string tuningCacheFile{"best_config.json"}; + std::string tuningExpr{}; //!< --tuneBuildRoutes + std::string tuningExprFile{}; //!< --tuneBuildRouteFile + TuningSearchAlgorithm tuningSearchAlgorithm{TuningSearchAlgorithm::kFAST}; + int64_t timeout{-1}; //!< --tuningTimeOut (s); -1 = no timeout + bool helpBuildRoute{false}; //!< --helpBuildRoute (short-circuit) + std::string helpBuildRouteKnob{}; //!< --helpBuildRoute= filter + bool continueFromCache{false}; //!< --continue + bool dryRun{false}; //!< --dryRun (enumerate, don't build) + //! \brief Hidden parent->child IPC channel. + //! + //! When set, runOnceBuildAndInfer writes a small JSON to this path containing + //! gpu_time_ms, accuracy_failed, and per-tensor accuracy_loss before returning. + //! Injected into the child's argv by the tuning loop; never shown in --help. + std::string tuningResultFile{}; //!< --tuningResultFile= + + void parse(Arguments& arguments) override; + static void help(std::ostream& out); +}; + class AllOptions : public Options { public: @@ -397,6 +467,7 @@ class AllOptions : public Options SystemOptions system; InferenceOptions inference; ReportingOptions reporting; + TuningOptions tuning; bool helps{false}; void parse(Arguments& arguments) override; diff --git a/samples/common/sampleTuning.cpp b/samples/common/sampleTuning.cpp new file mode 100644 index 000000000..007953b62 --- /dev/null +++ b/samples/common/sampleTuning.cpp @@ -0,0 +1,718 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#include "sampleTuning.h" +#include "common.h" +#include "nlohmann/json.hpp" + +#include +#include +#include +#include +#include + +namespace sample +{ + +std::vector splitPipeDelimited(std::string const& str) +{ + std::vector result; + std::stringstream ss(str); + std::string token; + while (std::getline(ss, token, '|')) + { + uint64_t const start = token.find_first_not_of(" \t"); + uint64_t const end = token.find_last_not_of(" \t"); + if (start != std::string::npos && end != std::string::npos) + { + result.push_back(token.substr(start, end - start + 1)); + } + } + return result; +} + +// ============================================================================ +// BuildRouteKnobDatabase Implementation +// ============================================================================ + +bool BuildRouteKnobDatabase::loadFromJsonString(std::string const& jsonStr) +{ + mKnobs.clear(); + mKnobOrder.clear(); + mTunerVersion = "unknown"; + + if (jsonStr.empty()) + { + return false; + } + + try + { + // Parse JSON using nlohmann/json + nlohmann::json const root = nlohmann::json::parse(jsonStr); + + // Extract tuner version from the top-level JSON object. + if (root.contains("tuner_version") && root["tuner_version"].is_string()) + { + mTunerVersion = root["tuner_version"].get(); + } + + // Check for "tuner_options" array + if (!root.contains("tuner_options") || !root["tuner_options"].is_array()) + { + return false; + } + + // Iterate over tuner_options array + for (auto const& item : root["tuner_options"]) + { + if (!item.is_object()) + { + continue; + } + + BuildRouteKnobDef knob; + + // Extract fields from JSON object + if (item.contains("option") && item["option"].is_string()) + { + knob.mOption = item["option"].get(); + } + if (item.contains("allowed_values") && item["allowed_values"].is_string()) + { + knob.mAllowedValues = item["allowed_values"].get(); + } + if (item.contains("default_value") && item["default_value"].is_string()) + { + knob.mDefaultValue = item["default_value"].get(); + } + if (item.contains("help") && item["help"].is_string()) + { + knob.mHelp = item["help"].get(); + } + + // Parse allowed values and add to database + if (!knob.mOption.empty()) + { + knob.mValues = parseAllowedValues(knob.mAllowedValues); + knob.mIsBounded = !knob.mValues.empty(); + mKnobOrder.push_back(knob.mOption); + mKnobs[knob.mOption] = std::move(knob); + } + } + } + catch (nlohmann::json::exception const&) + { + // JSON parsing failed + return false; + } + + return !mKnobs.empty(); +} + +std::string BuildRouteKnobDatabase::buildDefaultPath() const +{ + // Build a space-separated string of "knob=default_value" pairs, + // using the insertion order preserved from the original JSON. + std::string result; + for (auto const& name : mKnobOrder) + { + auto it = mKnobs.find(name); + if (it == mKnobs.end()) + { + continue; + } + if (!result.empty()) + { + result += " "; + } + result += it->second.mOption + "=" + it->second.mDefaultValue; + } + return result; +} + +bool BuildRouteKnobDatabase::hasKnob(std::string const& knobName) const +{ + return mKnobs.find(knobName) != mKnobs.end(); +} + +BuildRouteKnobDef const* BuildRouteKnobDatabase::getKnob(std::string const& knobName) const +{ + auto const it = mKnobs.find(knobName); + return it != mKnobs.end() ? &it->second : nullptr; +} + +bool BuildRouteKnobDatabase::validateValues(std::string const& knobName, std::vector const& values) const +{ + BuildRouteKnobDef const* knob = getKnob(knobName); + if (knob == nullptr) + { + return false; + } + if (!knob->mIsBounded) + { + return false; + } + + std::set const allowed(knob->mValues.begin(), knob->mValues.end()); + return std::ranges::all_of(values, [&allowed](auto const& v) { return allowed.contains(v); }); +} + +bool BuildRouteKnobDatabase::isBounded(std::string const& knobName) const +{ + BuildRouteKnobDef const* knob = getKnob(knobName); + return knob != nullptr && knob->mIsBounded; +} + +std::string BuildRouteKnobDatabase::getDefaultValue(std::string const& knobName) const +{ + BuildRouteKnobDef const* knob = getKnob(knobName); + return knob != nullptr ? knob->mDefaultValue : std::string(); +} + +std::vector BuildRouteKnobDatabase::parseAllowedValues(std::string const& allowedStr) +{ + // Find the bracket pattern: -knob=[val1|val2|val3] + uint64_t const bracketStart = allowedStr.find('['); + uint64_t const bracketEnd = allowedStr.find(']'); + + if (bracketStart == std::string::npos || bracketEnd == std::string::npos || bracketEnd <= bracketStart) + { + return {}; // No brackets: unbounded or unknown format (e.g., "=int32_t") + } + + std::string const valuesStr = allowedStr.substr(bracketStart + 1, bracketEnd - bracketStart - 1); + + // Range patterns like "..." indicate unbounded + if (valuesStr.find("...") != std::string::npos) + { + return {}; + } + + return splitPipeDelimited(valuesStr); +} + +// ============================================================================ +// BuildRouteExprParser Implementation +// ============================================================================ + +BuildRouteExprParser::BuildRouteExprParser(BuildRouteKnobDatabase const& db) + : mDb(db) +{ +} + +std::string const& BuildRouteExprParser::getError() const noexcept +{ + return mError; +} + +std::optional> BuildRouteExprParser::parse(std::string const& input) const +{ + mError.clear(); + + if (input.empty()) + { + mError = "Empty input"; + return std::nullopt; + } + + std::vector const tokens = tokenize(input); + if (tokens.empty()) + { + mError = "No expressions found"; + return std::nullopt; + } + + std::vector result; + result.reserve(tokens.size()); + + for (auto const& token : tokens) + { + auto expr = parseExpr(token); + if (!expr) + { + return std::nullopt; + } + result.push_back(std::move(*expr)); + } + + return result; +} + +std::vector BuildRouteExprParser::tokenize(std::string const& input) const +{ + // Split input by spaces, but keep bracketed content together + // E.g., "-opt1=[a|b] -opt2=c" -> ["-opt1=[a|b]", "-opt2=c"] + + std::vector tokens; + std::string current; + int32_t bracketDepth = 0; + + for (char const c : input) + { + if (c == '[') + { + ++bracketDepth; + current += c; + } + else if (c == ']') + { + --bracketDepth; + current += c; + } + else if (c == ' ' && bracketDepth == 0) + { + // Split point - space outside brackets + if (!current.empty()) + { + tokens.push_back(std::move(current)); + current.clear(); + } + } + else + { + current += c; + } + } + + // Add final token if any + if (!current.empty()) + { + tokens.push_back(std::move(current)); + } + + return tokens; +} + +// NOLINTNEXTLINE(readability-function-cognitive-complexity) +std::optional BuildRouteExprParser::parseExpr(std::string const& expr) const +{ + BuildRouteParsedExpr result; + + // Find the '=' separator + uint64_t const eqPos = expr.find('='); + if (eqPos == std::string::npos) + { + mError = "Invalid expression (no '='): " + expr; + return std::nullopt; + } + + // Extract knob name (before '=') + result.mKnobName = expr.substr(0, eqPos); + + // Trim whitespace from knob name + uint64_t start = result.mKnobName.find_first_not_of(" \t"); + uint64_t end = result.mKnobName.find_last_not_of(" \t"); + if (start != std::string::npos && end != std::string::npos) + { + result.mKnobName = result.mKnobName.substr(start, end - start + 1); + } + + // Validate knob exists in database + if (!mDb.hasKnob(result.mKnobName)) + { + mError = "Unknown knob: " + result.mKnobName; + return std::nullopt; + } + + // Extract value part (after '=') + std::string valueStr = expr.substr(eqPos + 1); + + // Check if this is a bracketed value list or a fixed value + uint64_t const bracketStart = valueStr.find('['); + uint64_t const bracketEnd = valueStr.find(']'); + + if (bracketStart != std::string::npos && bracketEnd != std::string::npos && bracketEnd > bracketStart) + { + // Bracketed value list: -knob=[val1|val2|val3] + + // Validate: nothing should appear before the opening bracket + std::string const beforeBracket = valueStr.substr(0, bracketStart); + uint64_t nonSpace = beforeBracket.find_first_not_of(" \t"); + if (nonSpace != std::string::npos) + { + mError = "Invalid expression format (unexpected content before '['): " + expr; + return std::nullopt; + } + + // Validate: nothing should appear after the closing bracket + std::string const afterBracket = valueStr.substr(bracketEnd + 1); + nonSpace = afterBracket.find_first_not_of(" \t"); + if (nonSpace != std::string::npos) + { + mError = "Invalid expression format (unexpected content after ']'): " + expr; + return std::nullopt; + } + + // Extract and parse values inside brackets + std::string const valuesInBrackets = valueStr.substr(bracketStart + 1, bracketEnd - bracketStart - 1); + + // Check for unbounded patterns + if (valuesInBrackets.find("...") != std::string::npos) + { + mError = "Unbounded expression not allowed: " + expr; + return std::nullopt; + } + + // Split by '|' and trim each value + result.mValues = splitPipeDelimited(valuesInBrackets); + + if (result.mValues.empty()) + { + mError = "Empty value list in expression: " + expr; + return std::nullopt; + } + + // Check for duplicates + std::unordered_set seenValues; + for (auto const& val : result.mValues) + { + auto [iter, inserted] = seenValues.insert(val); + if (!inserted) + { + mError = "Duplicate value '" + val + "' in expression: " + expr; + return std::nullopt; + } + } + + // Validate values against the knob's allowed values + if (!mDb.validateValues(result.mKnobName, result.mValues)) + { + if (!mDb.isBounded(result.mKnobName)) + { + mError = "Knob has unbounded values (int32_t): " + result.mKnobName; + return std::nullopt; + } + mError = "Invalid value(s) for knob: " + result.mKnobName; + return std::nullopt; + } + + result.mIsFixed = false; + } + else + { + // Fixed value: -knob=value + + // Trim whitespace from value + start = valueStr.find_first_not_of(" \t"); + end = valueStr.find_last_not_of(" \t"); + if (start != std::string::npos && end != std::string::npos) + { + valueStr = valueStr.substr(start, end - start + 1); + } + + // Check for unbounded type keyword + if (valueStr == "int32_t") + { + mError = "Unbounded expression 'int32_t' not allowed: " + expr; + return std::nullopt; + } + + result.mValues.push_back(valueStr); + result.mIsFixed = true; + + // Validate fixed value if knob is bounded + if (mDb.isBounded(result.mKnobName)) + { + if (!mDb.validateValues(result.mKnobName, result.mValues)) + { + mError = "Invalid value for knob: " + result.mKnobName + "=" + valueStr; + return std::nullopt; + } + } + } + + return result; +} + +// ============================================================================ +// TuningContext Implementation +// ============================================================================ + +BigInt TuningContext::count() const +{ + if (parsedExprs.empty()) + { + return BigInt(0); + } + + switch (searchAlgorithm) + { + case TuningSearchAlgorithm::kEXHAUSTIVE: + { + // Product of all value list sizes + BigInt total(1); + for (auto const& expr : parsedExprs) + { + total = total * BigInt(expr.mValues.size()); + } + return total; + } + case TuningSearchAlgorithm::kFAST: + case TuningSearchAlgorithm::kMIXED: + { + // 1 (baseline) + sum of non-default values per variable knob. + // For mixed mode, this returns the phase 1 (fast scan) count only. + // Phase 2 count is determined dynamically after phase 1 completes. + ASSERT(parsedExprs.size() == defaultValues.size()); + BigInt total(1); + for (uint64_t i = 0; i < parsedExprs.size(); ++i) + { + if (parsedExprs[i].mIsFixed) + { + continue; + } + for (auto const& val : parsedExprs[i].mValues) + { + if (val != defaultValues[i]) + { + ++total; + } + } + } + return total; + } + default: + { + throw std::invalid_argument("Unsupported tuning search algorithm"); + } + } +} + +std::string TuningContext::getPathAtIndex(BigInt const& index) const +{ + // Helper lambda: build a space-separated knob=value string from per-knob value selections. + // Spaces are required because the Myelin compiler parses each knob as a separate option. + // Used by both exhaustive and fast modes. + auto buildString = [this](auto const& getValueAtPosition) -> std::string { + std::string result; + for (uint64_t j = 0; j < parsedExprs.size(); ++j) + { + if (j > 0) + { + result += " "; + } + result += parsedExprs[j].mKnobName + "=" + getValueAtPosition(j); + } + return result; + }; + + switch (searchAlgorithm) + { + case TuningSearchAlgorithm::kEXHAUSTIVE: + { + // Reverse mixed-radix decomposition: decompose index into per-knob value indices. + // Work from right to left (least significant to most significant). + BigInt const total = count(); + if (index >= total) + { + throw std::out_of_range("Index " + index.toString() + " is out of range [0, " + total.toString() + ")"); + } + + std::vector valueIndices(parsedExprs.size()); + BigInt current = index; + for (int64_t i = static_cast(parsedExprs.size()) - 1; i >= 0; --i) + { + BigInt const base(parsedExprs[i].mValues.size()); + valueIndices[i] = (current % base).toUint64(); + current = current / base; + } + + return buildString([&](uint64_t j) -> std::string const& { return parsedExprs[j].mValues[valueIndices[j]]; }); + } + case TuningSearchAlgorithm::kFAST: + case TuningSearchAlgorithm::kMIXED: + { + // Fast/mixed mode: index 0 is the baseline (all defaults), index 1..N are one-off variations + // iterating from last knob to first, skipping default values. + // For mixed mode, this handles phase 1 only. Phase 2 uses a separate TuningContext. + ASSERT(parsedExprs.size() == defaultValues.size()); + + auto baselineValue = [this](uint64_t j) -> std::string const& { + return parsedExprs[j].mIsFixed ? parsedExprs[j].mValues[0] : defaultValues[j]; + }; + + // Index 0: pure baseline + if (index == BigInt(0)) + { + return buildString(baselineValue); + } + + // Index > 0: find which knob is varied and to what value + auto knob = identifyVariedKnob(*this, index); + if (!knob) + { + throw std::out_of_range("Index " + index.toString() + " is out of range for fast expansion"); + } + return buildString([&](uint64_t j) -> std::string const& { + return static_cast(j) == knob->first ? knob->second : baselineValue(j); + }); + } + default: + { + throw std::invalid_argument("Unsupported tuning search algorithm"); + } + } +} + +// ============================================================================ +// Mixed Search Phase 2 Context Builder +// ============================================================================ + +TuningContext buildMixedPhase2Context( + TuningContext const& phase1Context, std::vector const& positiveKnobs) +{ + // Build a set of knob indices that are "positive" (showed improvement in phase 1). + // For these knobs, we keep their full value lists. All other knobs become fixed to baseline. + std::set positiveIndices; + for (auto const& knob : positiveKnobs) + { + positiveIndices.insert(knob.knobIndex); + } + + TuningContext phase2; + phase2.searchAlgorithm = TuningSearchAlgorithm::kEXHAUSTIVE; + phase2.tunerVersion = phase1Context.tunerVersion; + phase2.defaultBuildRoute = phase1Context.defaultBuildRoute; + + for (uint64_t i = 0; i < phase1Context.parsedExprs.size(); ++i) + { + BuildRouteParsedExpr expr; + expr.mKnobName = phase1Context.parsedExprs[i].mKnobName; + + if (positiveIndices.count(static_cast(i)) > 0 && !phase1Context.parsedExprs[i].mIsFixed) + { + // Positive knob: keep full value list for exhaustive expansion. + expr.mValues = phase1Context.parsedExprs[i].mValues; + expr.mIsFixed = false; + } + else + { + // Non-positive or fixed knob: lock to baseline default value. + expr.mValues = {phase1Context.parsedExprs[i].mIsFixed ? phase1Context.parsedExprs[i].mValues[0] + : phase1Context.defaultValues[i]}; + expr.mIsFixed = true; + } + + phase2.parsedExprs.push_back(std::move(expr)); + phase2.defaultValues.push_back(phase1Context.defaultValues[i]); + } + + phase2.totalCount = phase2.count(); + return phase2; +} + +void collectPositiveKnobFromResult(bool crashed, double gpuTimeMs, double baselineGpuTimeMs, BigInt const& index, + TuningContext const& ctx, std::vector& positiveKnobs) +{ + if (!crashed && gpuTimeMs > 0.0 && gpuTimeMs < baselineGpuTimeMs) + { + auto knob = identifyVariedKnob(ctx, index); + if (knob) + { + positiveKnobs.push_back({knob->first, knob->second, gpuTimeMs}); + } + } +} + +std::optional> identifyVariedKnob(TuningContext const& ctx, BigInt const& index) +{ + // In fast/mixed mode, index 0 is baseline. Indices 1..N are one-off variations + // iterating knobs right-to-left, skipping default values. This reverses that mapping. + if (index == BigInt(0)) + { + return std::nullopt; // Baseline, no knob varied + } + + BigInt remaining = index; + --remaining; + for (int64_t i = static_cast(ctx.parsedExprs.size()) - 1; i >= 0; --i) + { + if (ctx.parsedExprs[i].mIsFixed) + { + continue; + } + for (auto const& val : ctx.parsedExprs[i].mValues) + { + if (val == ctx.defaultValues[i]) + { + continue; + } + if (remaining == BigInt(0)) + { + return std::make_pair(static_cast(i), val); + } + --remaining; + } + } + return std::nullopt; // Index out of range +} + +bool isTuningOnlyArg(char const* arg) +{ + // Tuning-only flags that the parent interprets and that must not appear on the child argv. + // The child runs a plain single-route trtexec build, so the parent strips these and + // re-injects the canonical child trio (--setBuildRoute, --saveEngine, --tuningResultFile). + static constexpr char const* kTUNING_STRIP_PREFIXES[] = { + "--tuneBuildRoutes", + "--tuneBuildRouteFile", + "--tuningSearch", + "--tuningCacheFile", + "--tuningTimeOut", + "--saveAllEngines", + "--continue", + "--dryRun", + // The parent will inject its own --setBuildRoute, --saveEngine, --tuningResultFile. + "--setBuildRoute", + "--saveEngine", + "--tuningResultFile", + }; + return std::any_of(std::begin(kTUNING_STRIP_PREFIXES), std::end(kTUNING_STRIP_PREFIXES), [arg](char const* prefix) { + auto const len = std::strlen(prefix); + return std::strncmp(arg, prefix, len) == 0 && (arg[len] == '\0' || arg[len] == '='); + }); +} + +std::vector buildTuningChildArgv(int32_t argc, char** argv, std::string const& route, + std::string const& enginePath, std::string const& resultJsonPath, std::vector& storage) +{ + storage.clear(); + storage.reserve(argc + 3); + // Always include argv[0] (the trtexec executable path) verbatim. + storage.emplace_back(argv[0]); + for (int32_t i = 1; i < argc; ++i) + { + if (argv[i] != nullptr && !isTuningOnlyArg(argv[i])) + { + storage.emplace_back(argv[i]); + } + } + storage.emplace_back("--setBuildRoute=" + route); + storage.emplace_back("--saveEngine=" + enginePath); + storage.emplace_back("--tuningResultFile=" + resultJsonPath); + + std::vector out; + out.reserve(storage.size() + 1); + for (auto& s : storage) + { + out.emplace_back(s.data()); + } + out.emplace_back(nullptr); + return out; +} + +} // namespace sample diff --git a/samples/common/sampleTuning.h b/samples/common/sampleTuning.h new file mode 100644 index 000000000..40a9de8b1 --- /dev/null +++ b/samples/common/sampleTuning.h @@ -0,0 +1,356 @@ +/* + * SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: Apache-2.0 + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +#ifndef TRT_SAMPLE_TUNING_H +#define TRT_SAMPLE_TUNING_H + +#include "bigInt.h" +#include "logger.h" +#include "sampleOptions.h" + +#include +#include +#include +#include +#include +#include + +namespace sample +{ + +//! \brief Split a pipe-delimited string into trimmed tokens. +//! +//! Given a string like "val1|val2|val3", returns {"val1", "val2", "val3"}. +//! Each token is trimmed of leading/trailing whitespace. Empty tokens +//! (after trimming) are skipped. +//! +//! \param[in] str The pipe-delimited string to split. +//! \return Vector of trimmed token strings. +std::vector splitPipeDelimited(std::string const& str); + +// ============================================================================ +// Build Route Expression Parser and Expander +// ============================================================================ +// +// These classes implement parsing and expansion of build route expressions +// for the --tuneBuildRoutes feature in trtexec. The expression syntax supports: +// - Variable values: "-knob=[val1|val2|val3]" +// - Fixed values: "-knob=fixed_value" +// +// Two expansion modes are supported: +// - Full (exhaustive): All combinations enumerated (exponential in number of knobs) +// - Fast (one-off): Baseline (all defaults) + one-knob-different variations (linear) +// +// Example: +// Expression: "-opt1=[on|off] -opt2=[0|1|2]" +// Full expansion (6 combinations): +// [0]: -opt1=on -opt2=0 +// [1]: -opt1=on -opt2=1 +// [2]: -opt1=on -opt2=2 +// [3]: -opt1=off -opt2=0 +// [4]: -opt1=off -opt2=1 +// [5]: -opt1=off -opt2=2 +// Fast expansion (with defaults opt1=off, opt2=2): +// [0]: -opt1=off -opt2=2 (baseline) +// [1]: -opt1=off -opt2=0 (opt2 changed) +// [2]: -opt1=off -opt2=1 (opt2 changed) +// [3]: -opt1=on -opt2=2 (opt1 changed) +// ============================================================================ + +//! \struct BuildRouteKnobDef +//! \brief Represents a single knob/option definition from the JSON configuration. +struct BuildRouteKnobDef +{ + std::string mOption; //!< Option name, e.g., "-kgen:tiling" + std::string mAllowedValues; //!< Allowed values string, e.g., "-kgen:tiling=[0|1|2]" + std::string mDefaultValue; //!< Default value + std::string mHelp; //!< Help text + std::vector mValues; //!< Parsed allowed values (empty if unbounded) + bool mIsBounded{false}; //!< True if values form a closed set +}; + +//! \struct BuildRouteParsedExpr +//! \brief Represents a parsed expression from user input. +struct BuildRouteParsedExpr +{ + std::string mKnobName; //!< Knob name, e.g., "-kgen:tiling" + std::vector mValues; //!< Values to expand, e.g., ["0", "1", "2"] + bool mIsFixed{false}; //!< True if this is a fixed value (no expansion) +}; + +//! \class BuildRouteKnobDatabase +//! \brief Database of knob definitions loaded from getAllBuildRoutes() JSON output. +//! +//! This class parses the JSON output from IBuilderConfig::getAllBuildRoutes() +//! and provides lookup and validation functions for knob definitions. +class BuildRouteKnobDatabase +{ +public: + //! \brief Default constructor. + BuildRouteKnobDatabase() = default; + + //! \brief Destructor. + ~BuildRouteKnobDatabase() = default; + + // Non-copyable, movable + BuildRouteKnobDatabase(BuildRouteKnobDatabase const&) = delete; + BuildRouteKnobDatabase& operator=(BuildRouteKnobDatabase const&) = delete; + BuildRouteKnobDatabase(BuildRouteKnobDatabase&&) = default; + BuildRouteKnobDatabase& operator=(BuildRouteKnobDatabase&&) = default; + + //! \brief Load knob definitions from a JSON string. + //! \param[in] jsonStr JSON string from getAllBuildRoutes(). + //! \return True if loading succeeded, false otherwise. + bool loadFromJsonString(std::string const& jsonStr); + + //! \brief Check if a knob exists in the database. + //! \param[in] knobName The knob name to check. + //! \return True if the knob exists. + bool hasKnob(std::string const& knobName) const; + + //! \brief Get the knob definition for a given knob name. + //! \param[in] knobName The knob name to look up. + //! \return Pointer to the KnobDef, or nullptr if not found. + BuildRouteKnobDef const* getKnob(std::string const& knobName) const; + + //! \brief Validate that all values are allowed for a knob. + //! \param[in] knobName The knob name. + //! \param[in] values The values to validate. + //! \return True if all values are valid. + bool validateValues(std::string const& knobName, std::vector const& values) const; + + //! \brief Check if a knob has bounded (finite) values. + //! \param[in] knobName The knob name. + //! \return True if the knob has bounded values. + bool isBounded(std::string const& knobName) const; + + //! \brief Get the default value for a knob. + //! \param[in] knobName The knob name. + //! \return The default value string, or empty string if not found. + std::string getDefaultValue(std::string const& knobName) const; + + //! \brief Get the tuner version string extracted from the JSON. + //! \return Tuner version (e.g. "2.17.83"), or "unknown" if not available. + std::string const& getTunerVersion() const + { + return mTunerVersion; + } + + //! \brief Build the default build route string from all knob defaults. + //! Returns a space-separated string of "knob=default_value" pairs, + //! preserving the insertion order from the original JSON. + //! Example: "-conv_use_long_w=on -reshape_ppg=on -transpose_ppg=on ..." + std::string buildDefaultPath() const; + +private: + //! \brief Parse allowed values from an allowed_values string. + //! \param[in] allowedStr The allowed_values string from JSON. + //! \return Vector of parsed values (empty if unbounded). + static std::vector parseAllowedValues(std::string const& allowedStr); + + std::unordered_map mKnobs; //!< Map of knob names to definitions. + std::vector mKnobOrder; //!< Insertion order of knob names. + std::string mTunerVersion{"unknown"}; //!< Tuner version from JSON. +}; + +//! \class BuildRouteExprParser +//! \brief Parser for build route expression strings. +//! +//! Parses expressions like "-opt1=[a|b] -opt2=fixed -opt3=[0|1|2]" +//! into a list of BuildRouteParsedExpr structures for expansion. +class BuildRouteExprParser +{ +public: + //! \brief Construct a parser with a reference to a knob database. + //! \param[in] db Reference to the BuildRouteKnobDatabase for validation. + explicit BuildRouteExprParser(BuildRouteKnobDatabase const& db); + + //! \brief Destructor. + ~BuildRouteExprParser() = default; + + // Non-copyable, non-movable (holds reference) + BuildRouteExprParser(BuildRouteExprParser const&) = delete; + BuildRouteExprParser& operator=(BuildRouteExprParser const&) = delete; + BuildRouteExprParser(BuildRouteExprParser&&) = delete; + BuildRouteExprParser& operator=(BuildRouteExprParser&&) = delete; + + //! \brief Parse an input string into a list of expressions. + //! \param[in] input The input string containing expressions. + //! \return Optional vector of BuildRouteParsedExpr, or nullopt on error. + std::optional> parse(std::string const& input) const; + + //! \brief Get the last error message. + //! \return The error message string. + std::string const& getError() const noexcept; + +private: + //! \brief Parse a single expression. + //! \param[in] expr The expression string. + //! \return Optional BuildRouteParsedExpr, or nullopt on error. + std::optional parseExpr(std::string const& expr) const; + + //! \brief Tokenize input into individual expressions. + //! \param[in] input The input string. + //! \return Vector of token strings. + std::vector tokenize(std::string const& input) const; + + BuildRouteKnobDatabase const& mDb; //!< Reference to the knob database. + mutable std::string mError; //!< Last error message. +}; + +//! \struct TuningContext +//! \brief Context for build route tuning when --tuneBuildRoutes is specified. +//! +//! Uses lazy expansion: build route strings are computed on-demand from +//! the parsed expressions and an index, without storing all strings in memory. +//! This supports arbitrarily large expansion spaces (full mode with BigInt count). +struct TuningContext +{ + //! Parsed expressions from --tuneBuildRoutes. + std::vector parsedExprs; + + //! Default values for each knob (from knob database, needed for fast mode). + std::vector defaultValues; + + //! Total number of configurations. + BigInt totalCount; + + //! Search algorithm (determines expansion strategy). + TuningSearchAlgorithm searchAlgorithm{TuningSearchAlgorithm::kFAST}; + + //! Tuner version string from the knob database JSON (e.g. "2.17.83"). + std::string tunerVersion{"unknown"}; + + //! Default build route string (all knobs at their default values). + std::string defaultBuildRoute; + + //! \brief Compute total configuration count based on searchAlgorithm. + //! - Exhaustive: product of all value list sizes. + //! - Fast: 1 (baseline) + sum of non-default values per variable knob. + BigInt count() const; + + //! \brief Get the build route string for a given index (lazy). + //! Throws std::out_of_range if index is out of range. + //! - Exhaustive: reverse mixed-radix decomposition. + //! - Fast: baseline at index 0, one-off variations at index 1..N. + std::string getPathAtIndex(BigInt const& index) const; +}; + +// ============================================================================ +// Mixed Search Algorithm Support +// ============================================================================ +// +// Mixed search runs in two phases: +// Phase 1 (fast scan): baseline + one-off variations (same as --tuningSearch=fast). +// After each one-off iteration, if it passed accuracy, didn't crash, and was +// faster than the baseline, the knob is recorded as a "positive" knob. +// Phase 2 (full combinatorial): if >1 positive knobs found, run 2^n combinations +// of the top-N positive knobs. Other knobs stay at baseline defaults. +// +// Total iterations: fast_count + (2^n if n > 1, else 0). + +//! Maximum number of positive knobs to carry into phase 2 of mixed search. +constexpr int32_t kMixedSearchMaxPositiveKnobs = 10; + +//! \struct MixedSearchKnobResult +//! \brief Records a single knob variation that improved performance in phase 1. +//! Used to select which knobs enter phase 2 of mixed search. +struct MixedSearchKnobResult +{ + int32_t knobIndex; //!< Index into TuningContext::parsedExprs + std::string value; //!< The non-default value that was tested + double gpuTimeMs; //!< GPU time achieved (lower is better) +}; + +//! \brief Build a TuningContext for phase 2 of mixed search. +//! +//! Constructs a new exhaustive-mode TuningContext where only the positive knobs +//! are variable (with their full value lists from phase 1) and all other knobs +//! are fixed to their baseline default values. +//! +//! \param phase1Context The original TuningContext from phase 1 (fast mode). +//! \param positiveKnobs The knobs that showed improvement in phase 1. +//! \return A new TuningContext configured for exhaustive search over positive knobs. +TuningContext buildMixedPhase2Context( + TuningContext const& phase1Context, std::vector const& positiveKnobs); + +//! \brief Identify which knob was varied at a given fast-mode iteration index. +//! +//! In fast mode, index 0 is the baseline (all defaults). Indices 1..N are one-off +//! variations, iterating knobs right-to-left and skipping default values. +//! This function reverses that mapping: given a one-off index (must be > 0), +//! returns the (knobIndex, value) pair, or nullopt if the index is out of range. +//! +//! Example: with 2 binary knobs (defaults "on", "on"), values [on|off] each: +//! index 1 → knob 1 changed to "off" → returns {1, "off"} +//! index 2 → knob 0 changed to "off" → returns {0, "off"} +//! +//! \param ctx The TuningContext (must be fast or mixed mode). +//! \param index The one-off iteration index (must be > 0). +//! \return (knobIndex, value) pair, or nullopt if index is invalid. +std::optional> identifyVariedKnob(TuningContext const& ctx, BigInt const& index); + +//! \brief Collect a positive knob from an iteration result (used in mixed search). +//! +//! If the iteration did not crash, achieved positive GPU time, and was faster than the +//! baseline, identifies which knob was varied (using identifyVariedKnob) and appends +//! it to the positiveKnobs vector. Called from both the live tuning loop (phase 1) and +//! from the --continue cache reconstruction path. +//! +//! Example: if iteration 3 changed knob 1 to "off" and achieved 1.2ms vs baseline 1.5ms, +//! this appends {knobIndex=1, value="off", gpuTimeMs=1.2} to positiveKnobs. +//! +//! \param[in] crashed Whether the iteration crashed. +//! \param[in] gpuTimeMs GPU time achieved by this iteration. +//! \param[in] baselineGpuTimeMs Baseline GPU time (index 0). +//! \param[in] index The iteration index (must be > 0 for one-off variations). +//! \param[in] ctx The TuningContext (fast/mixed mode). +//! \param[in,out] positiveKnobs Vector to append the positive knob result to. +void collectPositiveKnobFromResult(bool crashed, double gpuTimeMs, double baselineGpuTimeMs, BigInt const& index, + TuningContext const& ctx, std::vector& positiveKnobs); + +// Exit codes for child process to distinguish failure modes (used when --tuneBuildRoutes expands to multiple configs) +constexpr int32_t kChildExitSuccess = 0; +constexpr int32_t kChildExitFailure = 1; // runOnceBuildAndInfer returned failure +constexpr int32_t kChildExitStdException = 100; // Caught std::exception +constexpr int32_t kChildExitUnknownException = 101; // Caught unknown exception + +//! \struct TuningIterationResult +//! \brief Captures results from a single tuning iteration for the tuning cache file. +struct TuningIterationResult +{ + double gpuTimeMs{0.0}; //!< Mean GPU compute time in milliseconds + bool accuracyFailed{false}; //!< True if accuracy exceeded the threshold + std::unordered_map accuracyLossValues; //!< Per-tensor accuracy values +}; + +//! \brief True if `arg` is a tuning-only parent flag that should be stripped from +//! the child argv. Matches both bare flag and flag=value forms. Tuning-only means +//! the parent loop interprets it; the child runs a plain single-route build. +[[nodiscard]] bool isTuningOnlyArg(char const* arg); + +//! \brief Build a child argv for one tuning iteration: copies argv with tuning-only +//! flags removed and appends `--setBuildRoute=`, `--saveEngine=`, +//! `--tuningResultFile=`. String storage is owned by `storage` so +//! the returned `char*` pointers stay valid until the caller's execvp() completes. +//! The returned vector is nullptr-terminated, ready for execvp(). +[[nodiscard]] std::vector buildTuningChildArgv(int32_t argc, char** argv, std::string const& route, + std::string const& enginePath, std::string const& resultJsonPath, std::vector& storage); + +} // namespace sample + +#endif // TRT_SAMPLE_TUNING_H diff --git a/samples/common/sampleUtils.cpp b/samples/common/sampleUtils.cpp index ea53894bb..570ec7a7a 100644 --- a/samples/common/sampleUtils.cpp +++ b/samples/common/sampleUtils.cpp @@ -19,7 +19,14 @@ #include "bfloat16.h" #include "common.h" #include "half.h" +#include +#include +#include +#include #include +#include +#include +#include #include #if CUDA_VERSION >= 11060 @@ -27,6 +34,7 @@ #endif using namespace nvinfer1; +using samplesCommon::startsWith; namespace sample { @@ -851,4 +859,550 @@ std::vector sanitizeArgv(int32_t argc, char** argv) return sanitizedArgs; } +// ============================================================================ +// Accuracy Validator Implementations +// ============================================================================ + +template +double L0AccuracyValidator::calculateAccuracy(std::vector const& actual, std::vector const& reference) +{ + // Uses PyTorch/NumPy allclose formula: |a - b| <= atol + rtol * |b| + // See: https://docs.pytorch.org/docs/stable/generated/torch.allclose.html + // and infer_ref_check/infer_ref_check.cpp::torchIsClose() + ASSERT(actual.size() == reference.size()); + ASSERT(actual.size() != 0); + int64_t mismatchCount = 0; + for (uint64_t i = 0; i < actual.size(); ++i) + { + double const absDiff = std::abs(static_cast(actual[i]) - static_cast(reference[i])); + double const refAbs = std::abs(static_cast(reference[i])); + double const tolerance = mAtol + mRtol * refAbs; + if (absDiff > tolerance) + { + mismatchCount++; + } + } + return static_cast(mismatchCount) / actual.size(); +} + +template +double L1AccuracyValidator::calculateAccuracy(std::vector const& actual, std::vector const& reference) +{ + ASSERT(actual.size() == reference.size()); + ASSERT(actual.size() != 0); + double sum = 0.0; + for (uint64_t i = 0; i < actual.size(); ++i) + { + sum += std::abs(static_cast(actual[i]) - static_cast(reference[i])); + } + return sum / actual.size(); +} + +template +double L2AccuracyValidator::calculateAccuracy(std::vector const& actual, std::vector const& reference) +{ + ASSERT(actual.size() == reference.size()); + ASSERT(actual.size() != 0); + double sum = 0.0; + for (uint64_t i = 0; i < actual.size(); ++i) + { + double diff = static_cast(actual[i]) - static_cast(reference[i]); + sum += diff * diff; + } + return sum / actual.size(); +} + +template +double LInfAccuracyValidator::calculateAccuracy(std::vector const& actual, std::vector const& reference) +{ + ASSERT(actual.size() == reference.size()); + ASSERT(actual.size() != 0); + double maxDiff = 0.0; + for (uint64_t i = 0; i < actual.size(); ++i) + { + double diff = std::abs(static_cast(actual[i]) - static_cast(reference[i])); + maxDiff = std::max(maxDiff, diff); + } + return maxDiff; +} + +template +double CosineSimilarityValidator::calculateAccuracy(std::vector const& actual, std::vector const& reference) +{ + ASSERT(actual.size() == reference.size()); + ASSERT(actual.size() != 0); + double dotProduct = 0.0; + double normActual = 0.0; + double normRef = 0.0; + for (uint64_t i = 0; i < actual.size(); ++i) + { + double a = static_cast(actual[i]); + double r = static_cast(reference[i]); + dotProduct += a * r; + normActual += a * a; + normRef += r * r; + } + double denominator = std::sqrt(normActual) * std::sqrt(normRef); + if (denominator < 1e-12) + { + return 1.0; // Handle zero vectors + } + double cosineSim = dotProduct / denominator; + return 1.0 - cosineSim; // Return as cost (0 = perfect match) +} + +// Explicit template instantiations for supported types +template class L0AccuracyValidator; +template class L0AccuracyValidator; +template class L0AccuracyValidator; +template class L0AccuracyValidator; + +template class L1AccuracyValidator; +template class L1AccuracyValidator; +template class L1AccuracyValidator; +template class L1AccuracyValidator; + +template class L2AccuracyValidator; +template class L2AccuracyValidator; +template class L2AccuracyValidator; +template class L2AccuracyValidator; + +template class LInfAccuracyValidator; +template class LInfAccuracyValidator; +template class LInfAccuracyValidator; +template class LInfAccuracyValidator; + +template class CosineSimilarityValidator; +template class CosineSimilarityValidator; +template class CosineSimilarityValidator; +template class CosineSimilarityValidator; + +bool peekArg(int32_t argc, char** argv, char const* flag) +{ + auto const flagLen = std::strlen(flag); + for (int32_t i = 1; i < argc; ++i) + { + if (argv[i] == nullptr) + { + continue; + } + // Match either bare flag (--continue) or flag=value (--tuneBuildRoutes=...). + if (std::strncmp(argv[i], flag, flagLen) == 0 && (argv[i][flagLen] == '\0' || argv[i][flagLen] == '=')) + { + return true; + } + } + return false; +} + +std::string buildShellQuotedCmdLine(int32_t argc, char** argv) +{ + std::string cmdLine; + for (int32_t i = 0; i < argc; ++i) + { + if (i > 0) + { + cmdLine += " "; + } + std::string arg = argv[i]; + bool const needsQuoting = arg.find_first_of(" \t|[]{}()&;'\"\\") != std::string::npos; + if (needsQuoting) + { + std::string escaped; + for (char c : arg) + { + if (c == '\'') + { + escaped += "'\\''"; + } + else + { + escaped += c; + } + } + cmdLine += "'" + escaped + "'"; + } + else + { + cmdLine += arg; + } + } + return cmdLine; +} + +//! \brief Resolve file paths in argv to absolute for cache storage. +//! +//! File-path flags that get resolved: --onnx=, --saveEngine=, --loadInputs=, +//! --loadRefOutputs=, --tuneBuildRouteFile=, --loadEngine=. All others are stored as-is. +//! --loadInputs and --loadRefOutputs have format "name:path,name:path" so each +//! path component is resolved separately. +namespace +{ +// NOLINTNEXTLINE(readability-function-cognitive-complexity) +std::vector resolveArgvPaths(int32_t argc, char** argv) +{ + static std::vector const kSIMPLE_PATH_FLAGS + = {"--onnx=", "--saveEngine=", "--tuneBuildRouteFile=", "--loadEngine="}; + static std::vector const kMAPPED_PATH_FLAGS = {"--loadInputs=", "--loadRefOutputs="}; + + std::vector result; + for (int32_t i = 0; i < argc; ++i) + { + std::string arg(argv[i]); + + // Check simple path flags (--flag=path -> --flag=) + bool resolved = false; + for (auto const& prefix : kSIMPLE_PATH_FLAGS) + { + if (startsWith(arg, prefix)) + { + result.push_back(prefix + resolveAbsolutePath(arg.substr(prefix.size()))); + resolved = true; + break; + } + } + if (resolved) + { + continue; + } + + // Check mapped path flags (--flag=name:path,name:path -> resolve each path) + for (auto const& prefix : kMAPPED_PATH_FLAGS) + { + if (startsWith(arg, prefix)) + { + std::string value = arg.substr(prefix.size()); + // Split on ',' to get individual name:path pairs + auto pairs = splitToStringVec(value, ','); + std::string resolvedValue; + for (uint64_t p = 0; p < pairs.size(); ++p) + { + if (p > 0) + { + resolvedValue += ","; + } + // Split each pair on ':' to separate name from path + auto nameAndPath = splitToStringVec(pairs[p], ':', 1); + if (nameAndPath.size() == 2) + { + resolvedValue += nameAndPath[0] + ":" + resolveAbsolutePath(nameAndPath[1]); + } + else + { + resolvedValue += pairs[p]; // Malformed pair, keep as-is + } + } + result.push_back(prefix + resolvedValue); + resolved = true; + break; + } + } + if (resolved) + { + continue; + } + + result.push_back(arg); + } + return result; +} +} // anonymous namespace + +void writeTuningCacheHeader(std::string const& cacheFilePath, AllOptions const& options, int32_t argc, char** argv, + std::string const& tunerVersion, std::string const& defaultBuildRoute) +{ + // Use ordered_json to preserve insertion order matching best_config.json.example: + // tuner_version, accuracy_algorithm, accuracy_parameter, searching_algorithm, + // command_line, default_build_route, tuning_expr, files, argv + nlohmann::ordered_json header; + + header["tuner_version"] = tunerVersion; + header["accuracy_algorithm"] = getAlgorithmName(options.inference.accuracyValidationAlgorithm); + + nlohmann::ordered_json accParam; + accParam["atol"] = options.inference.atol; + accParam["rtol"] = options.inference.rtol; + accParam["epsilon"] = options.inference.accuracyThresholdEndToEnd; + header["accuracy_parameter"] = accParam; + + header["searching_algorithm"] = toString(options.tuning.tuningSearchAlgorithm); + + // Reconstruct command line for reference, with shell-safe quoting for arguments + // that contain spaces or metacharacters (e.g. --tuneBuildRoutes values). + std::string cmdLine = buildShellQuotedCmdLine(argc, argv); + header["command_line"] = cmdLine; + header["default_build_route"] = defaultBuildRoute; + + // Store the expanded tuning expression. This is the already-expanded string + // (handles --tuneBuildRouteFile case where the file may not exist at resume time). + header["tuning_expr"] = options.tuning.tuningExpr; + + // Store absolute paths to all file-based options for human readability and + // as a cross-check. The authoritative source for --continue reconstruction + // is the "argv" field below. + { + nlohmann::ordered_json files; + if (!options.model.baseModel.model.empty()) + { + files["onnx"] = resolveAbsolutePath(options.model.baseModel.model); + } + if (!options.build.engine.empty()) + { + files["save_engine"] = resolveAbsolutePath(options.build.engine); + } + // Input files: map of tensor_name → absolute path + if (!options.inference.refPairs.empty()) + { + nlohmann::ordered_json inputs; + for (auto const& [name, path] : options.inference.refPairs[0].first) + { + inputs[name] = resolveAbsolutePath(path); + } + if (!inputs.empty()) + { + files["inputs"] = inputs; + } + + nlohmann::ordered_json refOutputs; + for (auto const& [name, path] : options.inference.refPairs[0].second) + { + refOutputs[name] = resolveAbsolutePath(path); + } + if (!refOutputs.empty()) + { + files["ref_outputs"] = refOutputs; + } + } + header["files"] = files; + } + + // Store argv with file-path arguments resolved to absolute paths. + // This is the machine-readable source of truth for --continue reconstruction. + // When resuming, the stored argv is replayed to reconstruct all options + // (--iterations, --duration, --fp16, etc.) without enumerating each one. + { + auto resolvedArgv = resolveArgvPaths(argc, argv); + nlohmann::ordered_json argvArray(resolvedArgv); + header["argv"] = argvArray; + } + + std::ofstream file(cacheFilePath, std::ios::trunc); + if (!file) + { + sample::gLogError << "Cannot open tuning cache file for writing header: " << cacheFilePath << std::endl; + return; + } + file << header.dump() << std::endl; +} + +void writeTuningCacheIteration(std::string const& cacheFilePath, uint64_t iter, std::string const& buildRoute, + bool crashed, std::string const& errorMessage, std::unordered_map const& accuracyLossValues, + double gpuTimeMs) +{ + // Use ordered_json to preserve insertion order matching best_config.json.example: + // iter, build_route, crash, error_message, accuracy_loss, gpu_time + nlohmann::ordered_json result; + result[tuningCache::kIter] = iter; + result[tuningCache::kBuildRoute] = buildRoute; + result[tuningCache::kCrash] = crashed; + result[tuningCache::kErrorMessage] = errorMessage; + + // accuracy_loss is a per-output map: {"output_name": accuracy_value, ...} + // When crashed, accuracy values are unavailable so we write null. + if (crashed || accuracyLossValues.empty()) + { + result[tuningCache::kAccuracyLoss] = nullptr; + } + else + { + nlohmann::ordered_json accMap; + for (auto const& [name, value] : accuracyLossValues) + { + accMap[name] = value; + } + result[tuningCache::kAccuracyLoss] = accMap; + } + result[tuningCache::kGpuTime] = crashed ? nlohmann::ordered_json(nullptr) : nlohmann::ordered_json(gpuTimeMs); + + std::ofstream file(cacheFilePath, std::ios::app); + if (!file) + { + sample::gLogError << "Cannot open tuning cache file to append iteration " << iter << ": " << cacheFilePath + << std::endl; + return; + } + file << result.dump() << std::endl; +} + +std::vector reconstructArgvFromCacheHeader( + TuningCacheHeader const& header, std::string const& currentExePath, std::string const& cacheFilePath) +{ + std::vector newArgv; + + // Use current executable path as argv[0], not the one stored in the cache + // (the binary may have been rebuilt or moved since the original run). + newArgv.push_back(currentExePath); + + // Iterate over stored argv (skip stored argv[0]). + for (uint64_t i = 1; i < header.argv.size(); ++i) + { + std::string const& arg = header.argv[i]; + + // Replace --tuneBuildRoutes or --tuneBuildRouteFile with the stored tuning_expr. + // This handles the case where --tuneBuildRouteFile was used originally but the + // file no longer exists — the expanded expression is stored in tuning_expr. + if (startsWith(arg, "--tuneBuildRoutes=") || startsWith(arg, "--tuneBuildRouteFile=")) + { + continue; // Will be re-added below with the stored tuning_expr. + } + + // Remove --continue and --tuningCacheFile from the stored argv to avoid + // recursion (the stored run may itself have been a --continue run). + if (arg == "--continue" || startsWith(arg, "--tuningCacheFile=")) + { + continue; + } + + newArgv.push_back(arg); + } + + // Add back the tuning expression and cache file path. + newArgv.push_back("--tuneBuildRoutes=" + header.tuningExpr); + newArgv.push_back("--tuningCacheFile=" + cacheFilePath); + + return newArgv; +} + +std::string resolveAbsolutePath(std::string const& path) +{ + if (path.empty()) + { + return path; + } +#if defined(_WIN32) + // On Windows, path resolution is not needed (tuning features are not supported on Windows). + // Return the path unchanged so the code compiles. + return path; +#else + // POSIX realpath() resolves symlinks and relative components to an absolute path. + // Returns nullptr if the file does not exist or another error occurs. + char resolved[PATH_MAX]; + if (realpath(path.c_str(), resolved) != nullptr) + { + return std::string(resolved); + } + return path; +#endif +} + +std::optional readTuningCacheHeader(std::string const& cacheFilePath) +{ + std::ifstream file(cacheFilePath); + if (!file.is_open()) + { + return std::nullopt; + } + + // First line is the JSON header. + std::string headerLine; + if (!std::getline(file, headerLine) || headerLine.empty()) + { + return std::nullopt; + } + + try + { + auto headerJson = nlohmann::json::parse(headerLine); + + TuningCacheHeader header; + + // Extract argv array → vector + if (headerJson.contains("argv") && headerJson["argv"].is_array()) + { + for (auto const& elem : headerJson["argv"]) + { + header.argv.push_back(elem.get()); + } + } + else + { + // argv field is required for --continue reconstruction. + sample::gLogError << "Tuning cache header missing 'argv' field" << std::endl; + return std::nullopt; + } + + // Extract tuning_expr string. + if (headerJson.contains("tuning_expr") && headerJson["tuning_expr"].is_string()) + { + header.tuningExpr = headerJson["tuning_expr"].get(); + } + else + { + sample::gLogError << "Tuning cache header missing 'tuning_expr' field" << std::endl; + return std::nullopt; + } + + // Count remaining non-empty lines as completed iterations. + header.completedIterations = 0; + std::string line; + while (std::getline(file, line)) + { + if (!line.empty()) + { + ++header.completedIterations; + } + } + + return header; + } + catch (nlohmann::json::exception const& e) + { + sample::gLogError << "Failed to parse tuning cache header: " << e.what() << std::endl; + return std::nullopt; + } +} + +std::vector readCachedIterationResults(std::string const& cacheFilePath, int64_t maxIterations) +{ + std::vector results; + std::ifstream file(cacheFilePath); + if (!file.is_open()) + { + return results; + } + + std::string line; + // Skip header line. + if (!std::getline(file, line)) + { + return results; + } + + // Read iteration lines, extracting crash and gpu_time fields. + while (std::getline(file, line) && static_cast(results.size()) < maxIterations) + { + if (line.empty()) + { + continue; + } + try + { + auto j = nlohmann::json::parse(line); + CachedIterationResult r; + r.crashed = j.value(tuningCache::kCrash, true); + r.gpuTimeMs = j.contains(tuningCache::kGpuTime) && j[tuningCache::kGpuTime].is_number() + ? j[tuningCache::kGpuTime].get() + : 0.0; + results.push_back(r); + } + catch (nlohmann::json::exception const&) + { + // Malformed line — treat as crashed. + results.push_back({true, 0.0}); + } + } + + return results; +} + } // namespace sample diff --git a/samples/common/sampleUtils.h b/samples/common/sampleUtils.h index 6be95d20e..66aaaa3ea 100644 --- a/samples/common/sampleUtils.h +++ b/samples/common/sampleUtils.h @@ -18,6 +18,7 @@ #ifndef TRT_SAMPLE_UTILS_H #define TRT_SAMPLE_UTILS_H +#include #include #include #include @@ -29,11 +30,13 @@ #include #include +#include #include "NvInfer.h" #include "common.h" #include "logger.h" +#include "logging.h" #include "sampleOptions.h" #define SMP_RETVAL_IF_FALSE(condition, msg, retval, err) \ @@ -155,6 +158,181 @@ std::string sanitizeRemoteAutoTuningConfig(std::string const& config); //! @return Vector of sanitized argument strings std::vector sanitizeArgv(int32_t argc, char** argv); -} // namespace sample +//! Interface for accuracy validation +//! This interface provides a way to calculate the accuracy gap between the actual and reference outputs. +//! Since all the return value is a "loss value", the lower the return value, the better accuracy it is. +template +class IAccuracyValidator +{ +public: + virtual ~IAccuracyValidator() = default; + virtual double calculateAccuracy(std::vector const& actual, std::vector const& reference) = 0; +}; + +//! L0 accuracy validator calculates element-wise accuracy using the PyTorch/NumPy allclose formula. +//! An element matches if: |actual[i] - ref[i]| <= atol + rtol * |ref[i]| +//! accuracy = (number of mismatching elements) / N +//! Returns the mismatch ratio (0.0 means perfect match, 1.0 means all elements mismatch). +template +class L0AccuracyValidator : public IAccuracyValidator +{ +public: + L0AccuracyValidator(double atol, double rtol) + : mAtol(atol) + , mRtol(rtol) + { + } + + double calculateAccuracy(std::vector const& actual, std::vector const& reference) override; + +private: + double mAtol; + double mRtol; +}; + +//! L1 accuracy validator calculates mean absolute error. +//! accuracy = Sum(|actual[i] - ref[i]|) / N +//! Returns the mean absolute error (0.0 means perfect match). +template +class L1AccuracyValidator : public IAccuracyValidator +{ +public: + double calculateAccuracy(std::vector const& actual, std::vector const& reference) override; +}; + +//! L2 accuracy validator calculates mean squared error. +//! accuracy = Sum(|actual[i] - ref[i]|^2) / N +//! Returns the mean squared error (0.0 means perfect match). +template +class L2AccuracyValidator : public IAccuracyValidator +{ +public: + double calculateAccuracy(std::vector const& actual, std::vector const& reference) override; +}; +//! LInf accuracy validator calculates maximum absolute error. +//! accuracy = Max(|actual[i] - ref[i]|) +//! Returns the max absolute error (0.0 means perfect match). +template +class LInfAccuracyValidator : public IAccuracyValidator +{ +public: + double calculateAccuracy(std::vector const& actual, std::vector const& reference) override; +}; + +//! Cosine similarity validator calculates 1 - cosine_similarity. +//! cosine_sim = Sum(actual[i] * ref[i]) / (sqrt(Sum(actual[i]^2)) * sqrt(Sum(ref[i]^2))) +//! accuracy loss = 1 - cosine_sim +//! Returns 1 - cosine_similarity (0.0 means perfect match). +template +class CosineSimilarityValidator : public IAccuracyValidator +{ +public: + double calculateAccuracy(std::vector const& actual, std::vector const& reference) override; +}; + +//! \brief Get human-readable name string for an accuracy validation algorithm. +//! \param[in] algorithm The accuracy validation algorithm enum value. +//! \return Name string (e.g., "L0", "L1", "Cosine"). +inline std::string getAlgorithmName(AccuracyValidationAlgorithm algorithm) +{ + switch (algorithm) + { + case AccuracyValidationAlgorithm::kL0: return "L0"; + case AccuracyValidationAlgorithm::kL1: return "L1"; + case AccuracyValidationAlgorithm::kL2: return "L2"; + case AccuracyValidationAlgorithm::kLInf: return "LInf"; + case AccuracyValidationAlgorithm::kCosineSimilarity: return "Cosine"; + default: return "Unknown"; + } +} + +//! \brief Factory function to create an accuracy validator based on algorithm type. +//! \param[in] algorithm The accuracy validation algorithm to use. +//! \param[in] atol Absolute tolerance (only used by L0 algorithm). +//! \param[in] rtol Relative tolerance (only used by L0 algorithm). +//! \return Unique pointer to the appropriate IAccuracyValidator implementation. +template +std::unique_ptr> createAccuracyValidator( + AccuracyValidationAlgorithm algorithm, float atol = 1e-5F, float rtol = 1e-5F) +{ + switch (algorithm) + { + case AccuracyValidationAlgorithm::kL0: return std::make_unique>(atol, rtol); + case AccuracyValidationAlgorithm::kL1: return std::make_unique>(); + case AccuracyValidationAlgorithm::kL2: return std::make_unique>(); + case AccuracyValidationAlgorithm::kLInf: return std::make_unique>(); + case AccuracyValidationAlgorithm::kCosineSimilarity: return std::make_unique>(); + } + ASSERT(false && "Unknown Accuracy Validation Algorithm"); + return nullptr; +} + +//! \brief Cheap argv pre-scan. Returns true if some `argv[i]` exactly equals `flag` +//! or starts with `flag` + "=". Used by main() before option parsing to dispatch +//! between trtexec single-run mode and the tuning loop. +[[nodiscard]] bool peekArg(int32_t argc, char** argv, char const* flag); + +// ============================================================================ +// Tuning cache I/O (used by --tuneBuildRoutes / --continue). +// Header is a single JSON object on line 1; iterations are JSON Lines after. +// ============================================================================ + +//! \brief Reconstruct a shell-safe command line string from argc/argv. +std::string buildShellQuotedCmdLine(int32_t argc, char** argv); + +//! \brief Resolve a file path to an absolute path using POSIX realpath(). +//! Empty input or realpath() failure returns the input unchanged. +std::string resolveAbsolutePath(std::string const& path); + +//! \brief Write the tuning cache file header (line 1, JSON object). +void writeTuningCacheHeader(std::string const& cacheFilePath, AllOptions const& options, int32_t argc, char** argv, + std::string const& tunerVersion, std::string const& defaultBuildRoute); + +//! \brief Append one iteration line to the cache file. Fields: iter, build_route, crash, +//! error_message, accuracy_loss, gpu_time. Crashed iterations have null accuracy/gpu. +void writeTuningCacheIteration(std::string const& cacheFilePath, uint64_t iter, std::string const& buildRoute, + bool crashed, std::string const& errorMessage, std::unordered_map const& accuracyLossValues, + double gpuTimeMs); + +//! \struct TuningCacheHeader +//! \brief Parsed contents of the cache header, returned by readTuningCacheHeader(). +struct TuningCacheHeader +{ + std::vector argv; //!< Original command line with file paths absolute. + std::string tuningExpr; //!< Expanded --tuneBuildRoutes expression. + int64_t completedIterations{0}; //!< Number of iteration lines after the header. +}; + +//! \brief Read and parse the cache file's header line + count completed iteration lines. +std::optional readTuningCacheHeader(std::string const& cacheFilePath); + +//! \brief Rebuild argv for a --continue resume. argv[0] is replaced with currentExePath; +//! --tuneBuildRoutes is set to the cached expanded expression; --continue and +//! --tuningCacheFile are stripped from the stored argv and the cache path is re-appended. +std::vector reconstructArgvFromCacheHeader( + TuningCacheHeader const& header, std::string const& currentExePath, std::string const& cacheFilePath); + +//! \struct CachedIterationResult +//! \brief Minimal per-iteration fields from the cache, used to reconstruct mixed-mode positive knobs. +struct CachedIterationResult +{ + bool crashed{true}; + double gpuTimeMs{0.0}; +}; + +//! \brief Read up to maxIterations iteration lines from the cache and extract (crashed, gpu_time). +std::vector readCachedIterationResults(std::string const& cacheFilePath, int64_t maxIterations); + +namespace tuningCache +{ +constexpr char const* kIter = "iter"; +constexpr char const* kBuildRoute = "build_route"; +constexpr char const* kCrash = "crash"; +constexpr char const* kErrorMessage = "error_message"; +constexpr char const* kAccuracyLoss = "accuracy_loss"; +constexpr char const* kGpuTime = "gpu_time"; +} // namespace tuningCache + +} // namespace sample #endif // TRT_SAMPLE_UTILS_H diff --git a/samples/common/streamReader.h b/samples/common/streamReader.h index 8b78b7e94..15f903b07 100644 --- a/samples/common/streamReader.h +++ b/samples/common/streamReader.h @@ -1,5 +1,5 @@ /* - * SPDX-FileCopyrightText: Copyright (c) 1993-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-FileCopyrightText: Copyright (c) 1993-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. * SPDX-License-Identifier: Apache-2.0 * * Licensed under the Apache License, Version 2.0 (the "License"); @@ -18,10 +18,9 @@ #ifndef STREAM_READER_H #define STREAM_READER_H - #include "NvInferRuntime.h" -#include #include "sampleUtils.h" +#include namespace samplesCommon { @@ -108,7 +107,6 @@ class AsyncStreamReader final : public nvinfer1::IStreamReaderV2 std::ifstream mFile; }; - } // namespace samplesCommon #endif // STREAM_READER_H diff --git a/samples/python/cute_dsl_plugin/README.md b/samples/python/cute_dsl_plugin/README.md new file mode 100644 index 000000000..f3ae694fd --- /dev/null +++ b/samples/python/cute_dsl_plugin/README.md @@ -0,0 +1,206 @@ +# RMSNorm PluginV3 with CuteDSL + +This sample shows how to author a TensorRT `IPluginV3` whose `enqueue()` invokes a kernel written in [CuteDSL](https://docs.nvidia.com/cutlass/latest/media/docs/pythonDSL/overview.html), CUTLASS's Python DSL. The operator is **RMSNorm**, a normalization used in essentially every modern LLM (Llama, Mistral, Qwen, etc.). + +New to TensorRT plugins? Start with [`non_zero_plugin`](../non_zero_plugin) for a minimal end-to-end PluginV3 walkthrough. This sample assumes that flow and focuses on the **CuteDSL ↔ TensorRT** handoff rather than the kernel itself. For CuteDSL specifics, see the [CUTLASS Python DSL overview](https://docs.nvidia.com/cutlass/latest/media/docs/pythonDSL/overview.html), the [`cutlass.cute` API reference](https://docs.nvidia.com/cutlass/latest/media/docs/pythonDSL/cute_dsl_api/cute.html), and the [`cutlass/examples/python`](https://github.com/NVIDIA/cutlass/tree/main/examples/python) kernels. + +## What the sample demonstrates + +- Plugging a RMSNorm CuteDSL kernel into TensorRT through `IPluginV3` + `IPluginV3OneCore` + `IPluginV3OneBuild` + `IPluginV3OneRuntime`. +- Sharing GPU buffers with the kernel zero-copy via `cupy.cuda.UnownedMemory` → `torch.as_tensor` → `cute.runtime.from_dlpack`. +- Launching the kernel on the CUDA stream TRT passes into `enqueue()` (not the default stream), by declaring a `CUstream` parameter on the JIT-compiled launcher. +- Reducing in FP32 for numerical stability on large hidden dims, then casting back to FP16 on store. + +## Requirements + +```bash +cd $TRT_OSSPATH/samples/python/cute_dsl_plugin +pip3 install -r requirements.txt +``` + +CuteDSL requires Ampere (SM80) or newer. + +## Running the sample + +```bash +python3 rms_norm_plugin_cutedsl.py +``` + +The sample builds one engine with `num_tokens` declared dynamic over `[1, 512]` (opt=128) and `hidden_dim=1024`, then runs `num_tokens=128` through it. It prints `Inference result correct!` or `Inference result incorrect!`. Exit code is 0 on success. + +## Plugin interface + +- Inputs: + - `X`: rank-2 tensor, shape `(num_tokens, hidden_dim)`, FP16. `num_tokens` is dynamic (set by an optimization profile at build time); `hidden_dim` is static. + - `weight`: rank-1 tensor, shape `(hidden_dim,)`, FP32. +- Output: + - `Y`: rank-2 tensor, shape `(num_tokens, hidden_dim)`, FP16. +- Attribute: + - `epsilon`: scalar FP32. Required; the plugin asserts it is set. + +## Kernel design + +One CUDA block per token, 256 threads per block. Each block: + +1. Loads `X[token]` and accumulates `sum(x * x)` in FP32 across its threads. +2. Reduces the partial sums through shared memory and a final warp shuffle. +3. Computes `rms = rsqrt(sum_sq / H + eps)` once per block. +4. Writes `Y[token, i] = X[token, i] * weight[i] * rms`. + +The reduction is always done in FP32. The result is cast back to FP16 only when storing `Y`. + +## How CuteDSL is wired into the plugin + +The CuteDSL side is **two free functions** plus a launch helper. The TensorRT side is the usual `IPluginV3` class. They meet inside `enqueue()`. Concretely: + +### 1. Author the kernel as a `@cute.kernel` + +```python +@cute.kernel +def rms_norm_kernel(mX: cute.Tensor, mW: cute.Tensor, mY: cute.Tensor, + threads_per_block: cutlass.Constexpr, + hidden_dim: cutlass.Constexpr, + epsilon: cutlass.Constexpr): + ... +``` + +Tensor arguments are typed `cute.Tensor`. Values you want baked in at compile time are typed `cutlass.Constexpr` (here: block size, hidden dim, epsilon). + +### 2. Wrap the launch in a `@cute.jit` + +```python +from cuda.bindings.driver import CUstream + +@cute.jit +def rms_norm_launch(mX, mW, mY, + num_tokens: cutlass.Int32, + hidden_dim: cutlass.Constexpr, + epsilon: cutlass.Constexpr, + stream: CUstream): + rms_norm_kernel(mX, mW, mY, THREADS_PER_BLOCK, hidden_dim, epsilon).launch( + grid=(num_tokens, 1, 1), + block=(THREADS_PER_BLOCK, 1, 1), + ) +``` + +Two things to notice: + +- `num_tokens` is typed `cutlass.Int32`, **not** `cutlass.Constexpr`. That means it is a runtime value that changes per call (it controls the grid dimension), so one compiled kernel handles every sequence length the optimization profile allows. `hidden_dim` stays `Constexpr` because the kernel's inner unroll trip count depends on it. +- The `stream: CUstream` parameter is what tells the CuteDSL runtime which CUDA stream to launch on. It does not need to be passed to `.launch()` directly. The runtime picks it up automatically from any argument typed as `CUstream`. + +### 3. Subclass `IPluginV3` and the three capability interfaces + +```python +class RmsNormPlugin( + trt.IPluginV3, # top-level plugin interface + trt.IPluginV3OneCore, # capability: core metadata + trt.IPluginV3OneBuild, # capability: shape/format/serialization at build time + trt.IPluginV3OneRuntime, # capability: enqueue and runtime hooks +): +``` + +`IPluginV3` is the top-level interface (it owns `get_capability_interface()`, `clone()`, `destroy()`). The three `IPluginV3One*` mixins are the **capabilities** TRT will ask for at build and runtime. Inheriting from all four lets a single Python object answer to every request. + +Call **each** base `__init__` explicitly in your own `__init__`, and set the four book-keeping attributes that TensorRT reads: `plugin_namespace`, `plugin_name`, `plugin_version`, `num_outputs`. Set `timing_cache_id = ""` so TRT doesn't try to time per-instance. + +### 4. Methods you must implement + +These are the methods this sample overrides. Everything else falls through to defaults. + +| Method | Capability | Purpose | +|---|---|---| +| `get_capability_interface(type)` | `IPluginV3` | Return `self`. TRT calls this with `type ∈ {CORE, BUILD, RUNTIME}`. Our class inherits all three capability mixins, so the same object serves any of them. | +| `get_output_data_types(input_types)` | `Build` | `Y` is FP16, matching `X`. | +| `get_output_shapes(inputs, shape_inputs, exprBuilder)` | `Build` | `Y` has the same shape as `X` (`trt.DimsExprs(inputs[0])`). | +| `get_fields_to_serialize()` | `Build` | Tells TRT which plugin attributes to save into the engine file. TRT calls this when building the engine, then hands the same fields back to the plugin creator at load time. Here we save `epsilon`. | +| `configure_plugin(inp, out)` | `Build` | No-op for this kernel. Override if your kernel needs to precompute something from the I/O descriptors. | +| `supports_format_combination(pos, in_out, num_inputs)` | `Build` | `X` and `Y` are FP16, `weight` is FP32, all `LINEAR`. | +| `on_shape_change(inp, out)` | `Runtime` | No-op here. Override if you need to invalidate caches when shapes change. | +| `enqueue(input_desc, output_desc, inputs, outputs, workspace, stream)` | `Runtime` | Where the kernel launch happens during inference. See below. | +| `attach_to_context(context)` | `Runtime` | Return `self.clone()` so each execution context owns its own kernel cache. | +| `set_tactic(tactic)` | `Runtime` | No-op since this plugin is single-tactic. | +| `clone()` | `IPluginV3` | Return a fresh `RmsNormPlugin` with `__dict__` copied. Clear the JIT cache on the clone. | +| `destroy()` | `IPluginV3` | Release (clear) the JIT cache when TRT is done with the plugin. | + +### 5. The handoff in `enqueue()` + +This is the only place CuteDSL touches TensorRT. + +```python +def enqueue(self, input_desc, output_desc, inputs, outputs, workspace, stream): + # Read runtime shape and dtype from the descriptors. + num_tokens, hidden_dim = int(input_desc[0].dims[0]), int(input_desc[0].dims[1]) + + # Step A: raw device ptr -> cupy ndarray (zero copy, just a view). + x_cp = UnownedMemory(inputs[0], (num_tokens, hidden_dim), trt.nptype(input_desc[0].type)).d + # Step B: cupy ndarray -> torch tensor (still zero copy, shares __cuda_array_interface__). + x_t = torch.as_tensor(x_cp, device="cuda") + # Step C: torch tensor -> CuteDSL tensor via dlpack. + mX = from_dlpack(x_t, assumed_align=16) + # (same three steps for `weight` and `Y`) + + # Step D: compile once per (hidden_dim, epsilon) and cache. + key = (hidden_dim, self.epsilon) + if key not in self._compiled: + self._compiled[key] = cute.compile( + rms_norm_launch, mX, mW, mY, + num_tokens, hidden_dim, self.epsilon, make_fake_stream()) + + # Step E: launch on TRT's stream. + self._compiled[key](mX, mW, mY, num_tokens, CUstream(stream)) +``` + +A few things to note: + +- `cute.compile()` is the expensive step. The `self._compiled` dict makes sure it runs only once per `(hidden_dim, epsilon)` for the life of the plugin instance. The first call is effectively a warmup. +- The `stream` argument that TRT passes into `enqueue()` is the CUDA stream the engine wants the work scheduled on. Always launch on that stream, not the default one, or you break TRT's stream ordering and risk hangs or races when an engine is run concurrently with other CUDA work. + +#### Why the cupy → torch → dlpack chain? + +TRT gives the plugin **raw integer device pointers** (`inputs[0]`, `outputs[0]`, ...) along with shape and dtype descriptors. CuteDSL wants a `cute.Tensor`. There is no single API that converts the first to the second, so the sample bridges them through two protocol hops. Each hop is **zero-copy**: we never touch the bytes, we only re-type them. + +| Hop | What it does | Why it's there | +|---|---|---| +| `cupy.cuda.UnownedMemory` | Wraps the raw integer pointer plus shape/dtype into a `cupy.ndarray`. | The only public Python API that wraps a **foreign** device pointer (one we did not allocate and must not free). Plain `torch.as_tensor` does not accept a raw `int` + shape + dtype. | +| `torch.as_tensor(cp_array, device="cuda")` | Reads CuPy's `__cuda_array_interface__` and returns a torch view of the same memory. | We need a producer that exposes `__dlpack__`, and the existing TRT plugin samples already use torch for this; CuteDSL's `from_dlpack` consumes torch's DLPack capsule directly. | +| `cute.runtime.from_dlpack(torch_tensor, assumed_align=16)` | Reads torch's `__dlpack__()` capsule and produces a `cute.Tensor`. | The published CuteDSL entry point for "take an external GPU buffer and use it as a `cute.Tensor`". | + +In short: TRT speaks raw pointers (because the C++ plugin ABI is framework-agnostic), CuteDSL speaks `cute.Tensor`, and the cupy → torch → dlpack chain is the shortest path between them that avoids both a copy and ownership confusion. + +### 6. Plugin creator + +```python +class RmsNormPluginCreator(trt.IPluginCreatorV3One): + def __init__(self): + trt.IPluginCreatorV3One.__init__(self) + self.name = "RmsNormPlugin" + self.plugin_namespace = "" + self.plugin_version = "1" + self.field_names = trt.PluginFieldCollection([ + trt.PluginField("epsilon", np.array([], dtype=np.float32), + trt.PluginFieldType.FLOAT32), + ]) + + def create_plugin(self, name, fc, phase): + return RmsNormPlugin(fc) +``` + +Nothing CuteDSL-specific here. The `field_names` list the attributes that `create_plugin()` expects to find in the `PluginFieldCollection`. + +### 7. Building the network + +`network.add_plugin_v3([X, W], [], plugin)`. The first list is the data inputs, the second is shape-tensor inputs (none here), the third is the plugin object returned by the creator. The rest of the engine build (registering the creator, calling `engine_from_network`, running with `TrtRunner`) is the same as for any other PluginV3. + +## Limitations + +- `hidden_dim` is static at engine build time (the CuteDSL kernel is JIT-compiled per `hidden_dim`). `num_tokens` is dynamic within the optimization profile's `[min, max]` range. +- The kernel requires `hidden_dim >= 256` (the same as `THREADS_PER_BLOCK`), which is checked in `build_engine()`. +- As with all Python plugins, the engine cannot be deserialized outside a Python interpreter that has the plugin classes available. + +## License + +For terms and conditions for use, reproduction, and distribution, see the [TensorRT Software License Agreement](https://docs.nvidia.com/deeplearning/sdk/tensorrt-sla/index.html) documentation. + +## Changelog + +May 2026: Initial release. diff --git a/samples/python/cute_dsl_plugin/requirements.txt b/samples/python/cute_dsl_plugin/requirements.txt new file mode 100644 index 000000000..1cfbd0a80 --- /dev/null +++ b/samples/python/cute_dsl_plugin/requirements.txt @@ -0,0 +1,10 @@ +--extra-index-url https://pypi.ngc.nvidia.com +nvidia-cutlass-dsl==4.5.0 +cupy-cuda12x==13.6.0 +torch==2.9.1 +polygraphy==0.50.1 +colored==2.3.2 +numpy==1.26.4 +pyyaml==6.0.3 +requests==2.33.0 +tqdm==4.66.4 diff --git a/samples/python/cute_dsl_plugin/rms_norm_plugin_cutedsl.py b/samples/python/cute_dsl_plugin/rms_norm_plugin_cutedsl.py new file mode 100644 index 000000000..47e8399a1 --- /dev/null +++ b/samples/python/cute_dsl_plugin/rms_norm_plugin_cutedsl.py @@ -0,0 +1,376 @@ +# +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +import cupy as cp +import numpy as np +import tensorrt as trt +import torch +import torch.nn.functional as F + +import cutlass +import cutlass.cute as cute +from cutlass.cute.runtime import from_dlpack, make_fake_stream +from cuda.bindings.driver import CUstream + +from polygraphy.backend.trt import ( + CreateConfig, + Profile, + TrtRunner, + create_network, + engine_from_network, +) + +THREADS_PER_BLOCK = 256 + + +def volume(d): + return int(np.prod(d)) + + +class UnownedMemory: + def __init__(self, ptr, shape, dtype): + mem = cp.cuda.UnownedMemory(ptr, volume(shape) * cp.dtype(dtype).itemsize, self) + memptr = cp.cuda.MemoryPointer(mem, 0) + self.d = cp.ndarray(shape, dtype=dtype, memptr=memptr) + + +@cute.kernel +def rms_norm_kernel( + mX: cute.Tensor, + mW: cute.Tensor, + mY: cute.Tensor, + threads_per_block: cutlass.Constexpr, + hidden_dim: cutlass.Constexpr, + epsilon: cutlass.Constexpr, +): + # One block per token. Threads cooperatively sum-of-squares across the + # hidden dim into shared memory, reduce to a single RMS value, then scale. + smem = cutlass.utils.SmemAllocator() + sdata = smem.allocate_tensor( + cutlass.Float32, + layout=cute.make_layout(threads_per_block), + byte_alignment=16, + ) + rms = smem.allocate_tensor(cutlass.Float32, layout=cute.make_layout(1)) + + tidx, _, _ = cute.arch.thread_idx() + bidx, _, _ = cute.arch.block_idx() + + # Per-thread partial sum-of-squares. Accumulate in fp32 regardless of + # input dtype to avoid catastrophic cancellation on large hidden dims. + local_sum = cutlass.Float32(0.0) + for i in cutlass.range(tidx, hidden_dim, threads_per_block): + x = cutlass.Float32(mX[bidx, i]) + local_sum += x * x + sdata[tidx] = local_sum + cute.arch.sync_threads() + + # Tree reduction in shared memory down to one warp, then warp shuffle. + if tidx < 128: + sdata[tidx] += sdata[tidx + 128] + cute.arch.sync_threads() + + if tidx < 64: + sdata[tidx] += sdata[tidx + 64] + cute.arch.sync_threads() + + if tidx < 32: + v = sdata[tidx] + sdata[tidx + 32] + v = cute.arch.warp_reduction_sum(v, threads_in_group=32) + if tidx == 0: + rms[0] = cute.math.rsqrt(v / hidden_dim + epsilon, fastmath=True) + cute.arch.sync_threads() + + scale = rms[0] + for i in cutlass.range(tidx, hidden_dim, threads_per_block): + y = cutlass.Float32(mX[bidx, i]) * cutlass.Float32(mW[i]) * scale + mY[bidx, i] = y.to(mY.element_type) + + +@cute.jit +def rms_norm_launch( + mX: cute.Tensor, + mW: cute.Tensor, + mY: cute.Tensor, + num_tokens: cutlass.Int32, + hidden_dim: cutlass.Constexpr, + epsilon: cutlass.Constexpr, + stream: CUstream, +): + # `num_tokens` is a runtime value (grid dim); only `hidden_dim` is baked + # in at compile time. That way one compiled kernel handles every sequence + # length the optimization profile allows. + # The `stream` parameter is consumed by the CuteDSL runtime and used as + # the launch stream; it does not need to be passed to .launch() directly. + rms_norm_kernel(mX, mW, mY, THREADS_PER_BLOCK, hidden_dim, epsilon).launch( + grid=(num_tokens, 1, 1), + block=(THREADS_PER_BLOCK, 1, 1), + ) + + +class RmsNormPlugin( + trt.IPluginV3, + trt.IPluginV3OneCore, + trt.IPluginV3OneBuild, + trt.IPluginV3OneRuntime, +): + def __init__(self, fc=None): + trt.IPluginV3.__init__(self) + trt.IPluginV3OneCore.__init__(self) + trt.IPluginV3OneBuild.__init__(self) + trt.IPluginV3OneRuntime.__init__(self) + + self.plugin_namespace = "" + self.plugin_name = "RmsNormPlugin" + self.plugin_version = "1" + self.num_outputs = 1 + self.timing_cache_id = "" + + # JIT-compiled kernel cache keyed by (hidden_dim, epsilon). Both are + # baked into the kernel as `Constexpr`, so a change in either yields + # a different binary. + self._compiled = {} + + # `fc is None` is the path clone() takes; the cloned plugin then + # picks up epsilon via __dict__.update(). For the build path TRT + # always hands us a populated fc, and we require epsilon in it. + if fc is not None: + fields = {f.name: f for f in fc} + assert "epsilon" in fields, "RmsNormPlugin requires an 'epsilon' field" + self.epsilon = float(fields["epsilon"].data[0]) + + def get_capability_interface(self, type): + """Return the object that implements the requested capability. + + TRT calls this with `type` set to CORE, BUILD, or RUNTIME. Because + this class inherits all three capability mixins, the same `self` can + serve any of them. + """ + return self + + def get_output_data_types(self, input_types): + """Output dtype is FP16 (matches X).""" + return [input_types[0]] + + def get_output_shapes(self, inputs, shape_inputs, exprBuilder): + """Y has the same shape as X (N, H).""" + return [trt.DimsExprs(inputs[0])] + + def get_fields_to_serialize(self): + """Tell TRT which plugin attributes to save into the engine. + + TRT calls this at build time and hands the same fields back to the + creator's `create_plugin()` at engine load time. We only need to + persist `epsilon`; everything else is either fixed or recomputed. + """ + return trt.PluginFieldCollection( + [ + trt.PluginField( + "epsilon", + np.array([self.epsilon], dtype=np.float32), + trt.PluginFieldType.FLOAT32, + ) + ] + ) + + def configure_plugin(self, inp, out): + """No-op. We precompute nothing from the I/O descriptors at build time.""" + pass + + def on_shape_change(self, inp, out): + """No-op. The only dimension that can change between calls is + `num_tokens` (`hidden_dim` is static). The kernel cache is keyed on + `(hidden_dim, epsilon)`, so a change in `num_tokens` doesn't + invalidate anything. + """ + pass + + def supports_format_combination(self, pos, in_out, num_inputs): + """Accept LINEAR layout for all three positions, with `X` and `Y` FP16 + and `weight` FP32. + """ + assert num_inputs == 2 + assert pos < len(in_out) + + desc = in_out[pos].desc + if desc.format != trt.TensorFormat.LINEAR: + return False + if pos == 0 or pos == 2: + return desc.type == trt.DataType.HALF + if pos == 1: + return desc.type == trt.DataType.FLOAT + raise AssertionError(f"unexpected pos={pos}") + + def enqueue(self, input_desc, output_desc, inputs, outputs, workspace, stream): + """Launch the CuteDSL kernel on TRT's stream. + + Read shape and dtype from the descriptors, wrap the raw device + pointers as CuteDSL tensors zero-copy via cupy + dlpack, JIT-compile + once per `(hidden_dim, epsilon)` (cached), then launch. + """ + x_dims = input_desc[0].dims + w_dims = input_desc[1].dims + assert len(x_dims) == 2, "X must be rank-2 (num_tokens, hidden_dim)" + assert len(w_dims) == 1, "weight must be rank-1 (hidden_dim,)" + num_tokens, hidden_dim = int(x_dims[0]), int(x_dims[1]) + assert int(w_dims[0]) == hidden_dim + + x_np = trt.nptype(input_desc[0].type) + w_np = trt.nptype(input_desc[1].type) + y_np = trt.nptype(output_desc[0].type) + + # TRT hands us raw int pointers into device memory it already owns. + # CuteDSL wants `cute.Tensor` objects. We bridge the two without + # copying via two protocol conversions: + # raw int ptr + # -> cupy.UnownedMemory: the only public Python API that wraps a + # foreign device pointer (we do NOT own it; cupy must not free + # it) into a typed, shaped ndarray. + # -> torch.as_tensor: reads cupy's __cuda_array_interface__ and + # gives us a torch view over the same bytes. + # -> cute.runtime.from_dlpack: reads torch's __dlpack__ capsule + # and produces the cute.Tensor the kernel actually sees. + # Each hop is zero-copy; we're just re-typing the same GPU bytes. + x_t = torch.as_tensor(UnownedMemory(inputs[0], (num_tokens, hidden_dim), x_np).d, device="cuda") + w_t = torch.as_tensor(UnownedMemory(inputs[1], (hidden_dim,), w_np).d, device="cuda") + y_t = torch.as_tensor(UnownedMemory(outputs[0], (num_tokens, hidden_dim), y_np).d, device="cuda") + + mX = from_dlpack(x_t, assumed_align=16) + mW = from_dlpack(w_t, assumed_align=16) + mY = from_dlpack(y_t, assumed_align=16) + + # Launch the kernel on the CUDA stream TRT gave us, not the default + # stream. CuteDSL picks the stream from a `CUstream`-typed argument + # of the @cute.jit launcher; at compile time we hand it a placeholder. + key = (hidden_dim, self.epsilon) + if key not in self._compiled: + self._compiled[key] = cute.compile( + rms_norm_launch, mX, mW, mY, num_tokens, hidden_dim, self.epsilon, make_fake_stream() + ) + self._compiled[key](mX, mW, mY, num_tokens, CUstream(stream)) + + def attach_to_context(self, context): + """Give each execution context its own plugin copy so the JIT cache + is not shared across concurrent inferences. + """ + return self.clone() + + def set_tactic(self, tactic): + """No-op. This plugin advertises a single tactic, so there is nothing + to select. + """ + pass + + def clone(self): + """Return a copy of this plugin with an empty JIT cache. + + TRT clones the plugin per execution context. State is copied via + `__dict__.update`; the compiled-kernel dict is intentionally reset + so each context owns its own cubins. + """ + cloned = RmsNormPlugin() + cloned.__dict__.update(self.__dict__) + cloned._compiled = {} + return cloned + + def destroy(self): + """Release the JIT cache when TRT is done with the plugin.""" + self._compiled.clear() + + +class RmsNormPluginCreator(trt.IPluginCreatorV3One): + def __init__(self): + trt.IPluginCreatorV3One.__init__(self) + self.name = "RmsNormPlugin" + self.plugin_namespace = "" + self.plugin_version = "1" + self.field_names = trt.PluginFieldCollection( + [trt.PluginField("epsilon", np.array([], dtype=np.float32), trt.PluginFieldType.FLOAT32)] + ) + + def create_plugin(self, name, fc, phase): + return RmsNormPlugin(fc) + + +def build_engine(hidden_dim, min_tokens, opt_tokens, max_tokens, epsilon): + """Build an engine where num_tokens is dynamic in [min, max].""" + # Fails fast on `hidden_dim < THREADS_PER_BLOCK` since the kernel's tree + # reduction uses hard-coded offsets (128, 64, 32) over shared memory, + # so anything smaller would read uninitialized slots. + if hidden_dim < THREADS_PER_BLOCK: + raise ValueError(f"RmsNormPlugin requires hidden_dim >= {THREADS_PER_BLOCK}, got {hidden_dim}") + + plg_registry = trt.get_plugin_registry() + if plg_registry.get_creator("RmsNormPlugin", "1", "") is None: + plg_registry.register_creator(RmsNormPluginCreator(), "") + + creator = plg_registry.get_creator("RmsNormPlugin", "1", "") + pfc = trt.PluginFieldCollection( + [trt.PluginField("epsilon", np.array([epsilon], dtype=np.float32), trt.PluginFieldType.FLOAT32)] + ) + plugin = creator.create_plugin("RmsNormPlugin", pfc, trt.TensorRTPhase.BUILD) + + builder, network = create_network(strongly_typed=True) + # X has dynamic num_tokens (axis 0 = -1) and static hidden_dim. + X = network.add_input(name="X", dtype=trt.float16, shape=(-1, hidden_dim)) + W = network.add_input(name="weight", dtype=trt.float32, shape=(hidden_dim,)) + out = network.add_plugin_v3([X, W], [], plugin) + out.get_output(0).name = "Y" + network.mark_output(out.get_output(0)) + + profile = Profile().add( + "X", + min=(min_tokens, hidden_dim), + opt=(opt_tokens, hidden_dim), + max=(max_tokens, hidden_dim), + ) + return engine_from_network((builder, network), CreateConfig(profiles=[profile])) + + +def main(): + cc_major, cc_minor = torch.cuda.get_device_capability() + assert cc_major >= 8, f"CuteDSL requires SM80+; this GPU is SM{cc_major}{cc_minor}." + + epsilon = 1e-5 + hidden_dim = 1024 + num_tokens = 128 + + # Build with num_tokens dynamic in [1, 512]. The kernel is compiled once + # for hidden_dim=1024 and serves every num_tokens within that range. + engine = build_engine(hidden_dim, min_tokens=1, opt_tokens=128, max_tokens=512, epsilon=epsilon) + + # fp16 cast sets the tolerance floor. + rtol, atol = 1e-2, 1e-2 + torch.manual_seed(0) + x = torch.randn(num_tokens, hidden_dim, device="cuda", dtype=torch.float16) + w = torch.randn(hidden_dim, device="cuda", dtype=torch.float32) + y_ref = F.rms_norm(x.float(), (hidden_dim,), w, eps=epsilon).to(torch.float16) + + with TrtRunner(engine, "trt_runner") as runner: + outputs = runner.infer({"X": x.cpu().numpy().astype(np.float16), "weight": w.cpu().numpy()}) + y = torch.as_tensor(outputs["Y"]).to(torch.float16) + + max_diff = (y - y_ref.cpu()).abs().max().item() + label = f"N={num_tokens}, H={hidden_dim}" + assert torch.allclose( + y, y_ref.cpu(), rtol=rtol, atol=atol + ), f"[{label}] Inference result incorrect! (max abs diff={max_diff:.3e})" + print(f"[{label}] Inference result correct! (max abs diff={max_diff:.3e})") + + +if __name__ == "__main__": + main() diff --git a/samples/python/detectron2/requirements.txt b/samples/python/detectron2/requirements.txt index bf732b56d..748c9672d 100644 --- a/samples/python/detectron2/requirements.txt +++ b/samples/python/detectron2/requirements.txt @@ -1,5 +1,6 @@ onnx==1.18.0 onnxruntime==1.18.1 +onnxscript>=0.6.0 Pillow==11.3.0 git+https://github.com/facebookresearch/detectron2.git git+https://github.com/NVIDIA/TensorRT#subdirectory=tools/onnx-graphsurgeon diff --git a/samples/python/non_zero_plugin/requirements.txt b/samples/python/non_zero_plugin/requirements.txt index fc77e7c59..775f81c65 100644 --- a/samples/python/non_zero_plugin/requirements.txt +++ b/samples/python/non_zero_plugin/requirements.txt @@ -2,7 +2,7 @@ cuda-python==12.9.0 cupy-cuda12x torch --extra-index-url https://pypi.ngc.nvidia.com -polygraphy +polygraphy>=0.50.1 colored numpy==1.26.4 --extra-index-url https://pypi.ngc.nvidia.com diff --git a/samples/python/plugin_utils.py b/samples/python/plugin_utils.py index 838269008..683a5e7d7 100644 --- a/samples/python/plugin_utils.py +++ b/samples/python/plugin_utils.py @@ -58,12 +58,17 @@ def __init__(self, code, devID): prog = cuda_call( nvrtc.nvrtcCreateProgram(str.encode(code), b"sourceCode.cu", 0, [], []) ) - CUDA_HOME = os.getenv("CUDA_HOME") - if CUDA_HOME == None: - CUDA_HOME = os.getenv("CUDA_PATH") - if CUDA_HOME == None: - raise RuntimeError("Environment variable CUDA_HOME or CUDA_PATH is not set") - include_dirs = os.path.join(CUDA_HOME, "include") + cuda_root = None + for env_name in ("CUDA_PATH", "CUDA_HOME"): + cand = os.getenv(env_name) + if cand and os.path.isfile(os.path.join(cand, "include", "cuda_fp16.h")): + cuda_root = cand + break + if cuda_root is None: + raise RuntimeError( + "Neither CUDA_PATH nor CUDA_HOME points at a CUDA install containing include/cuda_fp16.h" + ) + include_dirs = os.path.join(cuda_root, "include") # Initialize CUDA cuda_call(cudart.cudaFree(0)) diff --git a/samples/python/quickly_deployable_plugins/README.md b/samples/python/quickly_deployable_plugins/README.md index 3dd0d03da..99c547476 100644 --- a/samples/python/quickly_deployable_plugins/README.md +++ b/samples/python/quickly_deployable_plugins/README.md @@ -12,7 +12,7 @@ This sample contains several mini-samples that demonstrate a few common use case - [Implementing in-place custom ops with I/O aliasing](#implementing-in-place-custom-ops-with-io-aliasing) - [An Op with data-dependent output shapes: Non-zero](#an-op-with-data-dependent-output-shapes-non-zero) - [Using multiple tactics and ONNX: Cirular padding](#using-multiple-tactics-and-onnx-cirular-padding) -- [Poviding an Ahead-of-Time (AOT) implementation for Cirular padding](#poviding-an-ahead-of-time-aot-implementation-for-cirular-padding) +- [Providing an Ahead-of-Time (AOT) implementation for Circular padding](#providing-an-ahead-of-time-aot-implementation-for-circular-padding) - [Additional Resources](#additional-resources) - [License](#license) - [Changelog](#changelog) @@ -157,7 +157,7 @@ python3 qdp_runner.py non_zero [-v] This sample contains a circular padding plugin, which is useful for ops like circular convolution. It is equivalent to PyTorch's [torch.nn.CircularPad2d](https://pytorch.org/docs/stable/generated/torch.nn.CircularPad2d.html#torch.nn.CircularPad2d). -Refer [this section about circular padding plugin](https://docs.nvidia.com/deeplearning/tensorrt/api/python_api/pluginGuide.html#example-circular-padding-plugin) in the python plugin guide for more info. +Refer [this section about circular padding plugin](https://docs.nvidia.com/deeplearning/tensorrt/latest/_static/python-api/pluginGuide.html#example-circular-padding-plugin) in the python plugin guide for more info. ## ONNX model with a plugin @@ -205,7 +205,7 @@ def circ_pad_plugin_autotune(inp0: trtp.TensorDesc, pads: npt.NDArray[np.int32], Note that we're using another way of constructing a `trt.plugin.AutoTuneCombination` here -- namely, through `pos(...)` to populate the type/format information and `tactics(...)` to specify the tactics. In this sample, we use an OpenAI Triton kernel and `torch.nn.functional.pad` as two methods to compute the circular padding. -Refer [this section](https://docs.nvidia.com/deeplearning/tensorrt/api/python_api/pluginGuide.html#example-plugins-with-multiple-backends-using-custom-tactics) in the Python plugin guide for more info. +Refer [this section](https://docs.nvidia.com/deeplearning/tensorrt/latest/_static/python-api/pluginGuide.html#example-plugins-with-multiple-backends-using-custom-tactics) in the Python plugin guide for more info. ## Loading and running a TRT engine containing a plugin @@ -234,7 +234,7 @@ Let's extend the [above sample](#using-multiple-tactics-and-onnx-cirular-padding Instead of specifying the OpenAI Triton Kernel callback to TRT through `@trt.plugin.impl`, we can directly compile the kernel ahead of time, and provide that to TRT under `@trt.plugin.aot_impl`. -Refer [this section](https://docs.nvidia.com/deeplearning/tensorrt/api/python_api/pluginGuide.html#providing-an-ahead-of-time-aot-implementation) in the Python plugin guide for more info. +Refer [this section](https://docs.nvidia.com/deeplearning/tensorrt/latest/_static/python-api/pluginGuide.html#providing-an-ahead-of-time-aot-implementation) in the Python plugin guide for more info. ## ONNX model with an AOT plugin @@ -269,9 +269,10 @@ To simulate the loading of an engine, first run this sample with the `--save_eng ## Running the sample ```bash -python3 qdp_runner.py circ_pad [--save_engine] [--load_engine] --mode {onnx,inetdef} [--artifacts_dir ARTIFACTS_DIR] [-v] +python3 qdp_runner.py circ_pad [--multi_tactic] [--aot] [--save_engine] [--load_engine] --mode {onnx,inetdef} [--artifacts_dir ARTIFACTS_DIR] [-v] options: + --multi_tactic Enable multiple tactics. Combined with --aot, the advertised tactics are AOT-compiled. --save_engine Save engine to the artifacts_dir. --load_engine Load engine from the artifacts_dir. Ignores all other options. --artifacts_dir ARTIFACTS_DIR @@ -281,6 +282,49 @@ options: -v, --verbose Enable verbose log output. ``` +## Combining AOT with multiple tactics + +The AOT path can advertise more than one tactic. Each tactic must be a precompiled GPU kernel, so a Torch-dispatched tactic (like the `Tactic.TORCH` variant used in the JIT multi-tactic example above) cannot participate. In this sample we instead expose two variants of the same Triton kernel that differ only in `BLOCK_SIZE`. TRT times both PTX blobs at build time and bakes the winner into the engine. + +The autotune declaration lists both tactic IDs: + +```python +class Tactic(IntEnum): + BLOCK_256 = 1 + BLOCK_1024 = 2 + +@trt.plugin.autotune("sample::circ_pad_plugin") +def circ_pad_plugin_autotune(inp0, outputs): + c = trtp.AutoTuneCombination() + c.pos([0, 1], "FP32|FP16") + c.tactics([int(Tactic.BLOCK_256), int(Tactic.BLOCK_1024)]) + return [c] +``` + +The AOT impl switches on the `tactic` argument, compiles the Triton kernel with the matching `BLOCK_SIZE` constexpr, and returns the corresponding PTX plus a `KernelLaunchParams` whose `grid_x` reflects that block size: + +```python +@trt.plugin.aot_impl("sample::circ_pad_plugin") +def circ_pad_plugin_aot_impl(inp0, pads, outputs, tactic): + block_size = {1: 256, 2: 1024}[tactic] + src = triton.compiler.ASTSource(fn=circ_pad_kernel, signature=..., constexprs={"BLOCK_SIZE": block_size}) + compiled = triton.compile(src) + launch_params = trtp.KernelLaunchParams() + launch_params.grid_x = trtp.cdiv(outputs[0].shape_expr.numel(), block_size) + launch_params.block_x = compiled.metadata.num_warps * 32 + launch_params.shared_mem = compiled.metadata.shared + extra_args = trtp.SymIntExprs.from_tuple(...) # symbolic int32 kernel args. see source for more details. + return compiled.metadata.name.encode(), compiled.asm["ptx"].encode(), launch_params, extra_args +``` + +Run it with both flags: + +```bash +python3 qdp_runner.py circ_pad --mode inetdef --multi_tactic --aot -v +``` + +Verbose logs show TRT timing both tactics during engine build, then a single winner is serialized into the engine. + # Additional Resources **Python Plugin Guide** @@ -297,6 +341,7 @@ options: For terms and conditions for use, reproduction, and distribution, see the [TensorRT Software License Agreement](https://docs.nvidia.com/deeplearning/sdk/tensorrt-sla/index.html) documentation. # Changelog +- May 2026: Added multi-tactic AOT subsection for circular padding. - October 2025: Migrate to strongly typed APIs. - August 2025: Removed support for Python versions < 3.10. - December 2024: Added section on AOT Plugins, added contents section diff --git a/samples/python/quickly_deployable_plugins/qdp_defs.py b/samples/python/quickly_deployable_plugins/qdp_defs.py index 5dd4dcf97..9f411f166 100644 --- a/samples/python/quickly_deployable_plugins/qdp_defs.py +++ b/samples/python/quickly_deployable_plugins/qdp_defs.py @@ -230,6 +230,91 @@ def circ_pad_plugin_impl( ) +# Shared AOT compilation body for the circ_pad plugin: build SymInt args, +# compile the Triton kernel with the given BLOCK_SIZE, and pack launch params. +def _compile_circ_pad_aot( + inp0: trtp.TensorDesc, + pads: npt.NDArray[np.int32], + outputs: Tuple[trtp.TensorDesc], + block_size: int, +) -> Tuple[Union[str, bytes], Union[str, bytes], trtp.KernelLaunchParams, trtp.SymExprs]: + + N = inp0.ndim + all_pads = np.zeros((N * 2,), dtype=np.int32) + inp_dims = inp0.shape_expr + out_dims = outputs[0].shape_expr + + for i in range(np.size(pads) // 2): + all_pads[N * 2 - 2 * i - 2] = pads[i * 2] + all_pads[N * 2 - 2 * i - 1] = pads[i * 2 + 1] + + all_pads = all_pads.tolist() + + # Representing all int32-scalar-kernel-inputs as symbolic expressions. + # These inputs are either constants or derivatives of input/output shapes (that may be dynamic). + # The symbolic expressions are resolved after the full shape context becomes available at runtime. + extra_args = trtp.SymIntExprs.from_tuple( + [ + trtp.SymInt32(e) + for e in [ + all_pads[0], + all_pads[2], + all_pads[4], + all_pads[6], + inp_dims[0], + inp_dims[1], + inp_dims[2], + inp_dims[3], + out_dims[1], + out_dims[2], + out_dims[3], + inp_dims.numel(), + out_dims.numel(), + ] + ] + ) + + type_str = "fp32" if inp0.dtype == trt.float32 else "fp16" + + from oait_kernels import circ_pad_kernel + import triton + + src = triton.compiler.ASTSource( + fn=circ_pad_kernel, + signature={ + "X": f"*{type_str}", + "all_pads_0": "i32", + "all_pads_2": "i32", + "all_pads_4": "i32", + "all_pads_6": "i32", + "orig_dims_0": "i32", + "orig_dims_1": "i32", + "orig_dims_2": "i32", + "orig_dims_3": "i32", + "Y_shape_1": "i32", + "Y_shape_2": "i32", + "Y_shape_3": "i32", + "X_len": "i32", + "Y_len": "i32", + "Y": f"*{type_str}", + }, + constexprs={"BLOCK_SIZE": block_size}, + ) + + compiled_kernel = triton.compile(src) + launch_params = trtp.KernelLaunchParams() + launch_params.grid_x = trtp.cdiv(out_dims.numel(), block_size) + launch_params.block_x = compiled_kernel.metadata.num_warps * 32 + launch_params.shared_mem = compiled_kernel.metadata.shared + + return ( + compiled_kernel.metadata.name.encode(), + compiled_kernel.asm["ptx"].encode(), + launch_params, + extra_args, + ) + + # Helper to define a single tactic implementation of the plugin def enable_single_tactic_circ_pad(): @trtp.autotune("sample::circ_pad_plugin") @@ -258,82 +343,44 @@ def circ_pad_plugin_impl( def circ_pad_plugin_aot_impl( inp0: trtp.TensorDesc, pads: npt.NDArray[np.int32], outputs: Tuple[trtp.TensorDesc], tactic: int ) -> Tuple[Union[str, bytes], Union[str, bytes], trtp.KernelLaunchParams, trtp.SymExprs]: + return _compile_circ_pad_aot(inp0, pads, outputs, block_size=256) - block_size = 256 - - N = inp0.ndim - all_pads = np.zeros((N * 2,), dtype=np.int32) - inp_dims = inp0.shape_expr - out_dims = outputs[0].shape_expr - - for i in range(np.size(pads) // 2): - all_pads[N * 2 - 2 * i - 2] = pads[i * 2] - all_pads[N * 2 - 2 * i - 1] = pads[i * 2 + 1] - all_pads = all_pads.tolist() +# Helper to define a multi-tactic AOT implementation of the plugin. +# Each tactic precompiles the same Triton kernel with a different BLOCK_SIZE, +# so TRT times two PTX variants at build time and bakes the winner into the engine. +def enable_multi_tactic_aot_circ_pad(): - # Representing all int32-scalar-kernel-inputs as symbolic expressions. - # These inputs are either constants or derivatives of input/output shapes (that may be dynamic). - # The symbolic expressions are resolved after the full shape context becomes available at runtime. - extra_args = trtp.SymIntExprs.from_tuple( - [ - trtp.SymInt32(e) - for e in [ - all_pads[0], - all_pads[2], - all_pads[4], - all_pads[6], - inp_dims[0], - inp_dims[1], - inp_dims[2], - inp_dims[3], - out_dims[1], - out_dims[2], - out_dims[3], - inp_dims.numel(), - out_dims.numel(), - ] - ] - ) + from enum import IntEnum + class Tactic(IntEnum): + BLOCK_256 = 1 + BLOCK_1024 = 2 - type_str = "fp32" if inp0.dtype == trt.float32 else "fp16" + block_size_by_tactic = { + int(Tactic.BLOCK_256): 256, + int(Tactic.BLOCK_1024): 1024, + } - from oait_kernels import circ_pad_kernel - import triton + @trtp.autotune("sample::circ_pad_plugin") + def circ_pad_plugin_autotune( + inp0: trtp.TensorDesc, + outputs: Tuple[trtp.TensorDesc], + ) -> List[trtp.AutoTuneCombination]: + c = trtp.AutoTuneCombination() + c.pos([0, 1], "FP32|FP16") + c.tactics([int(Tactic.BLOCK_256), int(Tactic.BLOCK_1024)]) + return [c] - src = triton.compiler.ASTSource( - fn=circ_pad_kernel, - signature={ - "X": f"*{type_str}", - "all_pads_0": "i32", - "all_pads_2": "i32", - "all_pads_4": "i32", - "all_pads_6": "i32", - "orig_dims_0": "i32", - "orig_dims_1": "i32", - "orig_dims_2": "i32", - "orig_dims_3": "i32", - "Y_shape_1": "i32", - "Y_shape_2": "i32", - "Y_shape_3": "i32", - "X_len": "i32", - "Y_len": "i32", - "Y": f"*{type_str}", - }, - constexprs={ - "BLOCK_SIZE": block_size, - }, + @trtp.aot_impl("sample::circ_pad_plugin") + def circ_pad_plugin_aot_impl( + inp0: trtp.TensorDesc, + pads: npt.NDArray[np.int32], + outputs: Tuple[trtp.TensorDesc], + tactic: int, + ) -> Tuple[Union[str, bytes], Union[str, bytes], trtp.KernelLaunchParams, trtp.SymExprs]: + block_size = block_size_by_tactic[tactic] + logging.getLogger("QuicklyDeployablePlugins").debug( + f"aot_impl invoked: tactic={tactic} -> BLOCK_SIZE={block_size}" ) - - compiled_kernel = triton.compile(src) - launch_params = trtp.KernelLaunchParams() - - # grid dims - launch_params.grid_x = trtp.cdiv(out_dims.numel(), block_size) - # block dims - launch_params.block_x = compiled_kernel.metadata.num_warps * 32 - # shared memory - launch_params.shared_mem = compiled_kernel.metadata.shared - - return compiled_kernel.metadata.name.encode(), compiled_kernel.asm["ptx"].encode(), launch_params, extra_args + return _compile_circ_pad_aot(inp0, pads, outputs, block_size=block_size) diff --git a/samples/python/quickly_deployable_plugins/qdp_runner.py b/samples/python/quickly_deployable_plugins/qdp_runner.py index f55a57c4c..3cc48c2ae 100644 --- a/samples/python/quickly_deployable_plugins/qdp_runner.py +++ b/samples/python/quickly_deployable_plugins/qdp_runner.py @@ -1,5 +1,5 @@ # -# SPDX-FileCopyrightText: Copyright (c) 2024-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-FileCopyrightText: Copyright (c) 2024-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -179,7 +179,9 @@ def run_circ_pad( enable_multi_tactic=False, mode="onnx", artifacts_dir=None, save_or_load_engine=None, aot=False ): - if enable_multi_tactic: + if enable_multi_tactic and aot: + qdp_defs.enable_multi_tactic_aot_circ_pad() + elif enable_multi_tactic: qdp_defs.enable_multi_tactic_circ_pad() else: qdp_defs.enable_single_tactic_circ_pad() @@ -358,9 +360,4 @@ def setup_circ_pad_sample(subparsers): if args.save_engine is True: save_or_load_engine = True - if args.multi_tactic and args.aot: - parser.error( - "circ_pad: '--aot' is not supported when '--multi_tactic' is specified." - ) - run_circ_pad(args.multi_tactic, args.mode, args.artifacts_dir, save_or_load_engine, args.aot) diff --git a/samples/python/quickly_deployable_plugins/requirements.txt b/samples/python/quickly_deployable_plugins/requirements.txt index 6f9d1238b..37a2d8c61 100644 --- a/samples/python/quickly_deployable_plugins/requirements.txt +++ b/samples/python/quickly_deployable_plugins/requirements.txt @@ -1,7 +1,7 @@ triton==3.5.0; (platform_system != "Windows") torch --extra-index-url https://pypi.ngc.nvidia.com -polygraphy +polygraphy>=0.50.1 colored numpy==1.26.4 onnx==1.18.0; platform_system == "Windows" diff --git a/samples/trtSafeExec/trtSafeExec.cpp b/samples/trtSafeExec/trtSafeExec.cpp index 81836d316..6be42baac 100644 --- a/samples/trtSafeExec/trtSafeExec.cpp +++ b/samples/trtSafeExec/trtSafeExec.cpp @@ -79,9 +79,6 @@ class SafeExecArgs SafetyPluginArguments pluginLibraries; std::unordered_map loadInputs; //!< Map of tensor names to file paths for loading custom input data - //!< Directory to write output tensors as /.bin after the final iteration. - //!< Empty = disabled. - std::string dumpOutputDir{}; }; //! @@ -654,15 +651,6 @@ bool parseSafeExecArgs(SafeExecArgs& args, int32_t argc, char* argv[]) return false; } } - else if (auto const value = loggedParseString(arg, "dumpOutputDir")) - { - if (value->empty()) - { - safeLogError(*gSafeRecorder, "--dumpOutputDir requires a non-empty directory path."); - return false; - } - args.dumpOutputDir = std::move(*value); - } else { safeLogError(*gSafeRecorder, "Invalid Argument: " + arg); @@ -702,11 +690,6 @@ General optional params: Input values spec ::= Ival[\",\"spec] Ival ::= name\":\"file Example: --loadInputs=\"input1\":data1.bin,\"input2\":data2.bin - --dumpOutputDir=DIR - Write each output tensor's raw bytes to DIR/.bin - after the final inference iteration. DIR must already exist. - Format is bit-exact bytes, suitable for round-tripping via - --loadInputs on a subsequent run. (default = no dump) Perf measurement params: --device=N Set cuda device to N (default = )" @@ -952,120 +935,6 @@ ScopedSafeMemory setTensorBuffer(nvinfer2::safe::ITRTGraph* graph, nvinfer2::saf return tensorBuffer; } -//! -//! \brief Write each output tensor's raw bytes to /.bin. -//! -//! Filters by ioMode == kOUTPUT; inputs and constants are skipped. For GPU-placed -//! tensors, performs a synchronous D2H copy via a temporary pinned host buffer -//! using the safety-approved stream-create / async-copy / sync / destroy pattern. -//! CPU-placed tensors are written directly. Per-tensor failures are logged and -//! the function continues with the remaining outputs; the return value is true -//! iff every output wrote cleanly. -//! -//! \param[in] dumpDir Directory (must already exist) to write output files into -//! \param[in] tensorNames Parallel vector of I/O tensor names -//! \param[in] tensorDescs Parallel vector of I/O tensor descriptors -//! \param[in] buffers Parallel vector of allocated I/O tensor buffers -//! \param[in] recorder The safe recorder for error logging and API calls -//! -//! \return True if all output tensors were written successfully, false otherwise -//! -[[nodiscard]] bool dumpOutputTensors(std::string const& dumpDir, std::vector const& tensorNames, - std::vector const& tensorDescs, std::vector const& buffers, - nvinfer2::safe::ISafeRecorder& recorder) -{ - SAFE_ASSERT(tensorNames.size() == tensorDescs.size()); - SAFE_ASSERT(tensorNames.size() == buffers.size()); - - bool allOk{true}; - int32_t nbWritten{0}; - - cudaStream_t stream; - CUDA_CALL(cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking), recorder); - for (size_t i = 0; i < tensorNames.size(); ++i) - { - auto const& desc = tensorDescs[i]; - if (desc.ioMode != nvinfer2::safe::TensorIOMode::kOUTPUT) - { - continue; - } - std::string const& name = tensorNames[i]; - - uint64_t const expectedSize = std::max( - static_cast(volume(desc.shape, desc.stride, desc.bytesPerComponent)), desc.sizeInBytes); - if (expectedSize == 0) - { - safeLogInfo(recorder, "Skipping dump for empty output tensor: " + name); - continue; - } - - std::string const path = dumpDir + "/" + samplesSafeCommon::genFilenameSafeString(name) + ".bin"; - bool const onGpu = desc.memPlacement == nvinfer2::safe::MemoryPlacement::kGPU - || desc.memPlacement == nvinfer2::safe::MemoryPlacement::kNONE; - - // Stage GPU data into a pinned host buffer; CPU data is written directly. - // hostBuf is only used in the GPU branch but is declared here so srcPtr stays valid through the file write. - ScopedSafeMemory hostBuf(onGpu ? expectedSize : 0, kDEFAULT_ALIGNMENT, - nvinfer2::safe::MemoryPlacement::kCPU_PINNED, nvinfer2::safe::MemoryUsage::kIOTENSOR, recorder); - void const* srcPtr{nullptr}; - - if (onGpu) - { - if (!hostBuf) - { - safeLogError(recorder, "Failed to allocate host buffer for output tensor: " + name); - allOk = false; - continue; - } - - CUDA_CALL(cudaMemcpyAsync(hostBuf.get(), buffers[i].get(), expectedSize, cudaMemcpyDeviceToHost, stream), - recorder); - CUDA_CALL(cudaStreamSynchronize(stream), recorder); - srcPtr = hostBuf.get(); - } - else if (desc.memPlacement == nvinfer2::safe::MemoryPlacement::kCPU) - { - srcPtr = buffers[i].get(); - } - else - { - safeLogError(recorder, "Invalid memory placement for output tensor: " + name); - allOk = false; - continue; - } - - std::ofstream file(path, std::ios::out | std::ios::binary | std::ios::trunc); - if (!file.is_open()) - { - safeLogError(recorder, "Cannot open output file: " + path); - allOk = false; - continue; - } - file.write(reinterpret_cast(srcPtr), static_cast(expectedSize)); - if (!file.good()) - { - std::ostringstream msg; - msg << "Failed to write " << expectedSize << " bytes to " << path; - safeLogError(recorder, msg.str()); - allOk = false; - file.close(); - continue; - } - file.close(); - - std::ostringstream msg; - msg << "Dumped output tensor " << name << " (" << expectedSize << " bytes) to " << path; - safeLogInfo(recorder, msg.str()); - ++nbWritten; - } - - CUDA_CALL(cudaStreamDestroy(stream), recorder); - std::ostringstream summary; - summary << "Output dump complete: " << nbWritten << " tensor(s) written to " << dumpDir; - safeLogInfo(recorder, summary.str()); - return allOk; -} - //! \brief Function to CUDA Graph capture bool graphCapture(cudaStream_t stream, TrtCudaGraphSafe& cudaGraph, nvinfer2::safe::ITRTGraph* graph, nvinfer2::safe::ISafeRecorder& recorder) @@ -1116,39 +985,23 @@ bool graphCapture(cudaStream_t stream, TrtCudaGraphSafe& cudaGraph, nvinfer2::sa //! \param[in] graph Pointer to the TRT graph to execute //! \param[in] recorder Pointer to the safe recorder for error logging and API calls //! \param[in] isProfileRun Whether this is a profiling run or regular inference -//! \param[in] threadIdx Index of this worker thread (only thread 0 dumps outputs when --dumpOutputDir is set) //! //! \return True if execution completed successfully, false otherwise //! bool task(SafeExecArgs const& args, nvinfer2::safe::ITRTGraph* graph, nvinfer2::safe::ISafeRecorder* recorder, - bool isProfileRun, int32_t threadIdx) + bool isProfileRun) { int64_t nbIOs{}; SAFE_API_CALL(graph->getNbIOTensors(nbIOs), *recorder); std::vector buffers; buffers.reserve(nbIOs); - std::vector tensorNames; - tensorNames.reserve(nbIOs); - std::vector tensorDescs; - tensorDescs.reserve(nbIOs); // Set input tensor values for (int64_t i = 0; i < nbIOs; ++i) { char const* tensor; SAFE_API_CALL(graph->getIOTensorName(tensor, i), *recorder); - nvinfer2::safe::TensorDescriptor desc; - SAFE_API_CALL(graph->getIOTensorDescriptor(desc, tensor), *recorder); - tensorNames.emplace_back(tensor); - tensorDescs.emplace_back(desc); buffers.emplace_back(setTensorBuffer(graph, *recorder, tensor, args.loadInputs)); } - - bool const dumpOutputs = !isProfileRun && !args.dumpOutputDir.empty() && threadIdx == 0; - if (!isProfileRun && !args.dumpOutputDir.empty() && args.threads > 1 && threadIdx == 0) - { - safeLogInfo(*recorder, - "--dumpOutputDir: dumping outputs only on thread 0 (threads=" + std::to_string(args.threads) + ")."); - } cudaEvent_t inputConsumedEvent; cudaEventCreate(&inputConsumedEvent); SAFE_API_CALL(graph->setInputConsumedEvent(inputConsumedEvent), *recorder); @@ -1350,17 +1203,6 @@ bool task(SafeExecArgs const& args, nvinfer2::safe::ITRTGraph* graph, nvinfer2:: } } - bool taskOk{true}; - if (dumpOutputs) - { - CUDA_CALL(cudaStreamSynchronize(stream), *recorder); - if (!dumpOutputTensors(args.dumpOutputDir, tensorNames, tensorDescs, buffers, *recorder)) - { - safeLogError(*recorder, "Output dump completed with errors."); - taskOk = false; - } - } - // Destroy cuda events CUDA_CALL(cudaEventDestroy(startEvent), *recorder); CUDA_CALL(cudaEventDestroy(endEvent), *recorder); @@ -1372,7 +1214,7 @@ bool task(SafeExecArgs const& args, nvinfer2::safe::ITRTGraph* graph, nvinfer2:: CUDA_CALL(cudaStreamDestroy(stream), *recorder); // Buffers are automatically freed by ScopedSafeMemory destructors - return taskOk; + return true; } //! @@ -1460,7 +1302,7 @@ bool doInference(SafeExecArgs const& args, std::chrono::high_resolution_clock::t { // launch thread async futureResults.emplace_back( - std::async(std::launch::async, task, args, graphs[k], recorders[k].get(), isProfileRun, k)); + std::async(std::launch::async, task, args, graphs[k], recorders[k].get(), isProfileRun)); } for (auto& future : futureResults) diff --git a/samples/trtexec/README.md b/samples/trtexec/README.md index 772fc05a7..9f6483eb0 100644 --- a/samples/trtexec/README.md +++ b/samples/trtexec/README.md @@ -11,6 +11,15 @@ - [Example 4: Collecting and printing a timing trace](#example-4-collecting-and-printing-a-timing-trace) - [Example 5: Tune throughput with multi-streaming](#example-5-tune-throughput-with-multi-streaming) - [Example 6: Create a strongly typed plan file](#example-6-create-a-strongly-typed-plan-file) + - [Example 7: Global performance tuner](#example-7-global-performance-tuner) + - [7.1: Discovering tunable knobs](#71-discovering-tunable-knobs) + - [7.2: Building one specific configuration](#72-building-one-specific-configuration) + - [7.3: Sweeping a configuration space](#73-sweeping-a-configuration-space) + - [7.4: Choosing a search algorithm](#74-choosing-a-search-algorithm) + - [7.5: Accuracy-aware tuning](#75-accuracy-aware-tuning) + - [7.6: The tuning cache (and resuming)](#76-the-tuning-cache-and-resuming) + - [7.7: Other useful flags](#77-other-useful-flags) + - [7.8: Caveats of tuning](#78-caveats-of-tuning) - [Tool command line arguments](#tool-command-line-arguments) - [Additional resources](#additional-resources) - [License](#license) @@ -166,6 +175,270 @@ and operator type specification. Use of specific builder precision flags such a ./trtexec --onnx=model.onnx --stronglyTyped ``` +### Example 7: Global performance tuner + +TensorRT exposes a number of internal builder knobs — heuristics, layer +selections, codegen toggles — that change how an engine is built. A specific +combination of values for these knobs is called a **build route**. Different +routes produce engines with different performance and (occasionally) different +numerical accuracy on the same model. + +`trtexec` can drive a tuning loop that sweeps a set of build routes, +benchmarks each, optionally validates accuracy against reference outputs, and +records the best configuration. Any single iteration in a sweep is fully +reproducible by re-running `trtexec` with `--setBuildRoute=` and the +same model — see [7.2](#72-building-one-specific-configuration). + +Tuning is not supported on Windows. + +This functionality is exposed through a small group of related flags. The +sub-sections below cover each in turn. + +#### 7.1: Discovering tunable knobs + +`--helpBuildRoute` prints the knob database as JSON. Each entry lists the +knob name, the allowed values, and a default. This is the source of truth +for what you can put inside a `--setBuildRoute` or `--tuneBuildRoutes` +expression. + +``` +./trtexec --helpBuildRoute +``` + +Filter to a single knob — the leading dash is optional: + +``` +./trtexec --helpBuildRoute=match_ragged_mha +./trtexec --helpBuildRoute=-match_ragged_mha +``` + +If the named knob does not exist, `trtexec` exits with a non-zero status +and a message pointing to the unfiltered listing. `--helpBuildRoute` does +not require `--onnx` and ignores other build flags; `--help` takes +precedence when both are passed. + +#### 7.2: Building one specific configuration + +`--setBuildRoute=` builds a single engine with a chosen route. The +route is a space-separated list of `-knob=value` tokens (note the leading +dash on each knob): + +``` +./trtexec --onnx=model.onnx \ + --setBuildRoute="-match_ragged_mha=on -copy_ppg=off" \ + --saveEngine=model.plan +``` + +This is the easiest way to reproduce or debug a specific result from a +tuning sweep: take the route reported in the sweep's log for an iteration +of interest, plug it into `--setBuildRoute`, and run again. + +#### 7.3: Sweeping a configuration space + +`--tuneBuildRoutes=` runs an autotuning loop over an expression that +describes a set of routes. The expression uses two forms: + +- `-knob=[a|b|c]` — variable knob; the loop iterates over each listed value. +- `-knob=fixed` — fixed value pinned across every iteration. + +Multiple tokens are space-separated. The expression is typically quoted to +protect the brackets from the shell. `--saveEngine=` is optional — +without it the sweep still benchmarks every route but no engine is written +to disk. `--saveEngine` becomes required when `--loadRefOutputs` is also +set (see [7.5](#75-accuracy-aware-tuning)). + +``` +./trtexec --onnx=model.onnx \ + --tuneBuildRoutes="-match_ragged_mha=[on|off] -copy_ppg=[on|off]" \ + --saveEngine=best.plan +``` + +For long expressions, put them in a file (one token per line) and pass the +file with `--tuneBuildRouteFile`: + +``` +$ cat routes.txt +-match_ragged_mha=[on|off] +-copy_ppg=[on|off] + +$ ./trtexec --onnx=model.onnx --tuneBuildRouteFile=routes.txt --saveEngine=best.plan +``` + +`--tuneBuildRoutes` and `--tuneBuildRouteFile` are mutually exclusive. + +#### 7.4: Choosing a search algorithm + +`--tuningSearch=` controls how the expression is expanded into the +list of routes that the loop will try: + +| Value | Behavior | +|---------|----------| +| `fast` | (default) baseline run with each knob at its default, plus one-off variations that change one knob at a time. Linear in the number of variable knobs. | +| `full` | Cartesian product over every variable knob. Exponential — use for small expressions. | +| `mixed` | A `fast` scan first to identify which knob values improve performance, then a full sweep over only those "positive" knobs. A pragmatic middle ground for larger spaces. | + +To make the difference concrete, take an expression with three binary +knobs `-A=[on|off] -B=[on|off] -C=[on|off]` and defaults `A=on, B=on, C=on`: + +- **`full`** generates **8 routes** — every combination of A × B × C. +- **`fast`** generates **4 routes** — the baseline `A=on B=on C=on`, plus + three one-off variations that flip exactly one knob at a time + (`A=off B=on C=on`, `A=on B=off C=on`, `A=on B=on C=off`). +- **`mixed`** runs the 4 `fast` routes first; whichever knobs improved + performance over the baseline are then explored exhaustively in a + second pass (e.g. if A and C improved, phase 2 sweeps the 4 routes of + A × C with B pinned to its default). + +`--dryRun` enumerates the route list and exits without building any engine — +useful for sanity-checking a large expression before paying for it. +`--dryRun` cannot be combined with `--tuningSearch=mixed`. + +``` +./trtexec --onnx=model.onnx \ + --tuneBuildRoutes="-match_ragged_mha=[on|off] -copy_ppg=[on|off]" \ + --tuningSearch=full --dryRun +``` + +#### 7.5: Accuracy-aware tuning + +The tuning loop can validate each iteration's output against a reference +(typically a CPU/FP32 capture from another framework) and discard any engine +that drifts beyond a threshold. Combine `--loadInputs`, `--loadRefOutputs`, +and `--accuracyThreshold` with the tuning flags. When `--loadRefOutputs` is +present, `--accuracyThreshold` is required. + +``` +./trtexec --onnx=model.onnx \ + --tuneBuildRoutes="-match_ragged_mha=[on|off]" \ + --loadInputs=input:input.bin \ + --loadRefOutputs=output:ref_output.bin \ + --accuracyThreshold=0.5 \ + --saveEngine=best.plan +``` + +**Multiple input/output pairs.** Tuning often needs to be validated across +several inputs (e.g. different batch sizes or representative samples). +Group each `(input, reference-output)` pair behind `--refPair=N` and pass +multiple pairs; every iteration is validated against all of them. + +``` +./trtexec --onnx=model.onnx \ + --tuneBuildRoutes="-match_ragged_mha=[on|off]" \ + --refPair=0 --loadInputs=input:in0.bin --loadRefOutputs=output:ref0.bin \ + --refPair=1 --loadInputs=input:in1.bin --loadRefOutputs=output:ref1.bin \ + --accuracyThreshold=0.5 --saveEngine=best.plan +``` + +**Choosing the loss metric.** `--accuracyAlgorithm=` selects how +the loss is computed per output tensor. All five metrics are non-negative +(lower is better; `0.0` is a perfect match): + +| Spec | Metric | +|--------|--------| +| `l0` | (default) fraction of elements outside `atol + rtol · abs(ref)`; tweak with `--atol` / `--rtol`. | +| `l1` | mean absolute error. | +| `l2` | mean squared error. | +| `lInf` | maximum absolute error. | +| `cos` | `1 − cosine_similarity(actual, reference)`. | + +``` +./trtexec --onnx=model.onnx \ + --tuneBuildRoutes="-match_ragged_mha=[on|off]" \ + --loadInputs=input:input.bin --loadRefOutputs=output:ref.bin \ + --accuracyAlgorithm=l2 --accuracyThreshold=0.01 \ + --saveEngine=best.plan +``` + +Iterations that fail the accuracy check are recorded in the cache but are +excluded from "best engine" selection. + +#### 7.6: The tuning cache (and resuming) + +`--tuningCacheFile=` writes a JSON record of the sweep: one line +describing the run, followed by one line per completed iteration with the +build route, GPU time, and per-output accuracy loss. This file is both a +human-readable record of the sweep and the input for resume. + +``` +./trtexec --onnx=model.onnx \ + --tuneBuildRoutes="-match_ragged_mha=[on|off] -copy_ppg=[on|off]" \ + --tuningCacheFile=tune.jsonl --saveEngine=best.plan +``` + +If the run is interrupted (Ctrl-C, OOM, timeout), resume it with: + +``` +./trtexec --continue --tuningCacheFile=tune.jsonl +``` + +`--continue` accepts **only** `--tuningCacheFile=` — passing +`--onnx`, `--tuneBuildRoutes`, `--accuracyThreshold`, or any other flag +alongside it is rejected. The cache file carries everything needed to +continue the original run, and existing iteration results in it are +kept. The sweep picks up at the next iteration after the last one +already recorded. + +#### 7.7: Other useful flags + +- `--tuningTimeOut=` — stop the loop after N elapsed seconds (the + current iteration finishes first). `-1` (default) disables the timeout. + Useful for capping a large `full` sweep at a deadline. +- `--saveAllEngines` — in addition to the best engine at `--saveEngine=

    `, + write every iteration's engine to `

    .iter`. Requires `--saveEngine`. + Disk-heavy; intended for debugging accuracy regressions across iterations. +- `--setBuildRoute=` — see [7.2](#72-building-one-specific-configuration). + Useful for replaying any single iteration from a sweep by hand. + +For the complete list with one-line descriptions, run `./trtexec --help` +and look at the **Build Route Tuning Options** section. + +#### 7.8: Caveats of tuning + +A few things to keep in mind when relying on a tuning result in production: + +- **Improvement is opportunistic.** The default build route may already + be the fastest one for the (model, hardware) combination you're tuning + on. Treat any speedup as a bonus, not an expected outcome. + +- **An explicit knob value may be overridden during compilation.** Even + when you pin a knob with `--setBuildRoute=`, the compiler is + free to change that value internally if the network requires it. The + engine that `--saveEngine` records is the source of truth — not the + route string. Ship the saved engine when exact behavior matters. + +- **Re-running a route doesn't reproduce engine bytes.** Engine builds + are not bit-deterministic; kernel timings vary, the builder breaks + ties accordingly, and the serialized engine reflects those picks. + Re-running with the same `--setBuildRoute` on the same model and the + same machine produces an engine with the same knob choices, but the + bytes may differ slightly. Again — ship the saved engine, not the + route string, when you need the exact engine that won the sweep. + +- **Tuner version matters.** Across TensorRT releases, the set of + tunable knobs and their default values can change. A route or cache + produced by one version may reference knobs that no longer exist in + another, and even the default route is not stable across versions. + +- **Pick a sensible `--accuracyThreshold`.** A threshold set too tight + will reject every iteration, and the sweep will report no winner. If + you don't have a prior calibration, start loose and ratchet down. + +- **Results are model-specific.** The optimal route depends on the + exact ONNX. A different model — or even the same model rebuilt with + different shapes or precision flags — invalidates a previously-saved + result. + +- **Results are hardware-specific.** The same model tuned on different + GPU SKUs can pick different "best" routes. Re-tune when you move to a + different target. + +- **Re-tune after TensorRT / tuner upgrades.** A previously-tuned + non-default build route is **not** guaranteed to keep its advantage + after an upgrade — and may even regress relative to the new default. + Re-tune whenever the tuner version or TensorRT version changes. Only + performance regressions on the **default** build route are tracked as + TensorRT performance bugs; non-default routes are best-effort. + ## Tool command line arguments To see the full list of available options and their descriptions, issue the `./trtexec --help` command. diff --git a/samples/trtexec/trtexec.cpp b/samples/trtexec/trtexec.cpp index b0207e275..0f2ce5e90 100644 --- a/samples/trtexec/trtexec.cpp +++ b/samples/trtexec/trtexec.cpp @@ -25,12 +25,24 @@ #include #include +#include #include +#include #include +#include +#include +#include #include #include +#include #include +#include #include +#if !defined(_WIN32) +#include +#include +#endif +#include #include #include "NvInfer.h" @@ -44,6 +56,10 @@ #include "sampleInference.h" #include "sampleOptions.h" #include "sampleReporting.h" +#include "sampleTuning.h" +#include "sampleUtils.h" + +#include using namespace nvinfer1; using namespace sample; @@ -57,6 +73,7 @@ __attribute__((weak)) std::shared_ptr gSafeRecorder namespace { + using LibraryPtr = std::unique_ptr; std::function pCreateInferRuntimeInternal{}; @@ -228,362 +245,1148 @@ bool processSafetyPluginLibrary(nvinfer2::safe::ISafePluginRegistry* safetyPlugi using time_point = std::chrono::time_point; using duration = std::chrono::duration; -// NOLINTNEXTLINE(readability-function-cognitive-complexity) -int sample::trtexecMain(int argc, char** argv, PostConfigCallback const& postConfigHook) +// Sentinel returned by parseArgs() to signal that main() should continue into build/infer. +constexpr int32_t kCONTINUE_MAIN{-1}; + +//! Prepare \p iEnv for the profile run. Caller must have already populated \p iEnv.profiler. +//! On TRT-RTX, CUDA graphs capture obscure per-layer profile events, so the existing context is torn down and rebuilt +//! with CUDA graphs disabled. On TRT-Enterprise, the existing context is reused and the profiler is attached in place. +bool prepareProfileRun(InferenceEnvironmentBase& iEnv, InferenceOptions& infOpts, SystemOptions const& sysOpts) { - std::string const sampleName = "TensorRT.trtexec"; + IExecutionContext& ctx = *static_cast(iEnv).contexts.front(); + ctx.setProfiler(iEnv.profiler.get()); + ctx.setEnqueueEmitsProfile(false); + return true; +} - auto sampleTest = sample::gLogger.defineTest(sampleName, argc, argv); +//! Print the knob database JSON (from IBuilderConfig::getAllBuildRoutes()) and return EXIT_SUCCESS, +//! or EXIT_FAILURE on error. With a non-empty knobName, parse the JSON and emit only the +//! tuner_options entry whose `option` field matches (matching is leading-dash-insensitive so +//! `--helpBuildRoute=conv_use_long_w` and `--helpBuildRoute=-conv_use_long_w` behave identically). +//! No match → EXIT_FAILURE with a clear "no such knob" diagnostic. +//! Guarded by ENABLE_FEATURE_GLOBAL_PERF_TUNER: without the feature, getAllBuildRoutes() is not +//! linked in, so this returns EXIT_FAILURE with a diagnostic. +int32_t printBuildRouteHelp(std::string const& knobName) +{ + std::unique_ptr builder{createBuilder()}; + if (!builder) + { + sample::gLogError << "Failed to create builder for --helpBuildRoute" << std::endl; + return EXIT_FAILURE; + } + std::unique_ptr config{builder->createBuilderConfig()}; + if (!config) + { + sample::gLogError << "Failed to create builder config for --helpBuildRoute" << std::endl; + return EXIT_FAILURE; + } + + char const* allBuildRoutes = config->getAllBuildRoutes(); + if (allBuildRoutes == nullptr) + { + sample::gLogError << "getAllBuildRoutes() returned null" << std::endl; + return EXIT_FAILURE; + } + + // No filter → emit the database verbatim and exit. + if (knobName.empty()) + { + std::cout << allBuildRoutes << std::endl; + return EXIT_SUCCESS; + } + + // Filter mode: parse the JSON, pick the matching tuner_options entry, re-emit. + nlohmann::ordered_json root; try { - sample::gLogger.reportTestStart(sampleTest); + root = nlohmann::ordered_json::parse(allBuildRoutes); + } + catch (nlohmann::json::parse_error const& e) + { + sample::gLogError << "Failed to parse knob database JSON: " << e.what() << std::endl; + return EXIT_FAILURE; + } + if (!root.contains("tuner_options") || !root["tuner_options"].is_array()) + { + sample::gLogError << "Knob database JSON has no 'tuner_options' array." << std::endl; + return EXIT_FAILURE; + } - Arguments args = argsToArgumentsMap(argc, argv); - AllOptions options; - bool const kENABLE_STATIC_PLUGINS = true; + // Database stores names with a leading '-'; accept the user's input both with and without + // the dash by comparing the unprefixed substrings. + auto stripDash = [](std::string const& s) -> std::string { + return (!s.empty() && s.front() == '-') ? s.substr(1) : s; + }; + std::string const wantedKnob = stripDash(knobName); - options.system.enableStaticPlugins = kENABLE_STATIC_PLUGINS; - if (parseHelp(args)) + nlohmann::ordered_json filteredOptions = nlohmann::ordered_json::array(); + for (auto const& opt : root["tuner_options"]) + { + if (opt.contains("option") && opt["option"].is_string() + && stripDash(opt["option"].get()) == wantedKnob) { - AllOptions::help(std::cout, kENABLE_STATIC_PLUGINS); - return EXIT_SUCCESS; + filteredOptions.push_back(opt); } + } + if (filteredOptions.empty()) + { + sample::gLogError << "--helpBuildRoute=" << knobName << ": no such knob in the database. " + << "Run --helpBuildRoute (no value) to see the full list." << std::endl; + return EXIT_FAILURE; + } + + nlohmann::ordered_json filtered; + if (root.contains("tuner_version")) + { + filtered["tuner_version"] = root["tuner_version"]; + } + filtered["tuner_options"] = std::move(filteredOptions); + std::cout << filtered.dump(/*indent=*/2) << std::endl; + return EXIT_SUCCESS; +} - if (!args.empty()) +// \param quiet If true, suppress option banner printing (used by child workers to avoid +// repeating the same option dump that the parent already printed). +int32_t parseArgs(Logger::TestAtom& sampleTest, Arguments& args, AllOptions& options, bool quiet = false) +{ + // For DLA pre-procssing + bool const kENABLE_STATIC_PLUGINS = true; + options.system.enableStaticPlugins = kENABLE_STATIC_PLUGINS; + + // Start parsing + static_assert(kCONTINUE_MAIN != EXIT_SUCCESS, "kCONTINUE_MAIN must not be EXIT_SUCCESS"); + static_assert(kCONTINUE_MAIN != EXIT_FAILURE, "kCONTINUE_MAIN must not be EXIT_FAILURE"); + if (parseHelp(args)) + { + AllOptions::help(std::cout, kENABLE_STATIC_PLUGINS); + return EXIT_SUCCESS; + } + + if (!args.empty()) + { + bool failed{false}; + try { - bool failed{false}; - try - { - options.parse(args); + options.parse(args); - if (!args.empty()) - { - AllOptions::help(std::cout, kENABLE_STATIC_PLUGINS); - for (auto const& arg : args) - { - sample::gLogError << "Unknown option: " << arg.first << " " << arg.second.first << std::endl; - } - failed = true; - } - } - catch (std::invalid_argument const& arg) + if (!args.empty()) { AllOptions::help(std::cout, kENABLE_STATIC_PLUGINS); - sample::gLogError << arg.what() << std::endl; + for (auto const& arg : args) + { + sample::gLogError << "Unknown option: " << arg.first << " " << arg.second.first << std::endl; + } failed = true; } - - if (failed) - { - return sample::gLogger.reportFail(sampleTest); - } } - else + catch (std::invalid_argument const& arg) { - options.helps = true; + AllOptions::help(std::cout, kENABLE_STATIC_PLUGINS); + sample::gLogError << arg.what() << std::endl; + failed = true; } - if (options.helps) + if (failed) { - AllOptions::help(std::cout, kENABLE_STATIC_PLUGINS); - return sample::gLogger.reportPass(sampleTest); + return EXIT_FAILURE; } + } + else + { + options.helps = true; + } + if (options.helps) + { + AllOptions::help(std::cout, kENABLE_STATIC_PLUGINS); + return EXIT_SUCCESS; + } + +#if defined(_WIN32) + if (options.tuning.helpBuildRoute || !options.build.buildRoute.empty()) + { + sample::gLogError << "--helpBuildRoute and --setBuildRoute are not supported on Windows." << std::endl; + return EXIT_FAILURE; + } +#endif + + // --helpBuildRoute prints the knob database JSON and exits. Suppresses all other flags + // (they are parsed for validation but ignored). Lower precedence than --help (above), + // matching the tests' expected ordering. + if (options.tuning.helpBuildRoute) + { + return printBuildRouteHelp(options.tuning.helpBuildRouteKnob); + } + + // Print the parsed options banner. Suppressed in child workers (quiet=true) + // to avoid repeating the same dump that the parent already printed. + if (!quiet) + { sample::gLogInfo << options; - if (options.reporting.verbose) - { - sample::setReportableSeverity(ILogger::Severity::kVERBOSE); - } - std::string const jitInVersion; - if (!options.build.cpuOnly) - { - setCudaDevice(options.system.device, sample::gLogInfo); - } - sample::gLogInfo << std::endl; - sample::gLogInfo << "TensorRT version: " << NV_TENSORRT_MAJOR << "." << NV_TENSORRT_MINOR << "." - << NV_TENSORRT_PATCH << jitInVersion << std::endl; + } + return kCONTINUE_MAIN; +} - // Record specified runtime - gUseRuntime = options.build.useRuntime; +// NOLINTNEXTLINE(readability-function-cognitive-complexity) +int32_t runOnceBuildAndInfer( + Logger::TestAtom& sampleTest, AllOptions& options, sample::PostConfigCallback const& postConfigHook = nullptr) +{ + if (options.reporting.verbose) + { + sample::setReportableSeverity(ILogger::Severity::kVERBOSE); + } + std::string const jitInVersion; + if (!options.build.cpuOnly) + { + setCudaDevice(options.system.device, sample::gLogInfo); + } + sample::gLogInfo << std::endl; + sample::gLogInfo << "TensorRT version: " << NV_TENSORRT_MAJOR << "." << NV_TENSORRT_MINOR << "." + << NV_TENSORRT_PATCH << jitInVersion << std::endl; + + // Record specified runtime + gUseRuntime = options.build.useRuntime; #if !TRT_STATIC - LibraryPtr nvinferPluginLib{}; + LibraryPtr nvinferPluginLib{}; #endif /* TRT_STATIC */ - std::vector pluginLibs; - if (gUseRuntime == RuntimeMode::kFULL && !options.build.safe) - { - sample::gLogInfo << "Loading standard plugins" << std::endl; + std::vector pluginLibs; + if (gUseRuntime == RuntimeMode::kFULL && !options.build.safe) + { + sample::gLogInfo << "Loading standard plugins" << std::endl; #if !TRT_STATIC - nvinferPluginLib = loadLibrary(kNVINFER_PLUGIN_LIBNAME); - auto pInitLibNvinferPlugins - = nvinferPluginLib->symbolAddress("initLibNvInferPlugins"); -#else /* TRT_STATIC */ - auto pInitLibNvinferPlugins = initLibNvInferPlugins; + nvinferPluginLib = loadLibrary(kNVINFER_PLUGIN_LIBNAME); + auto pInitLibNvinferPlugins + = nvinferPluginLib->symbolAddress("initLibNvInferPlugins"); +#else /* TRT_STATIC */ + auto pInitLibNvinferPlugins = initLibNvInferPlugins; #endif /* TRT_STATIC */ - ASSERT(pInitLibNvinferPlugins != nullptr); - pInitLibNvinferPlugins(&sample::gLogger.getTRTLogger(), ""); - for (auto const& pluginPath : options.system.plugins) - { - sample::gLogInfo << "Loading supplied plugin library: " << pluginPath << std::endl; - pluginLibs.emplace_back(loadLibrary(pluginPath)); - } - } - else if (gUseRuntime == RuntimeMode::kFULL && options.build.safe) - { - sample::gLogInfo << "Skipping standard plugin loading due to --safe flag" << std::endl; - } - else if (!options.system.plugins.empty()) + ASSERT(pInitLibNvinferPlugins != nullptr); + pInitLibNvinferPlugins(&sample::gLogger.getTRTLogger(), ""); + for (auto const& pluginPath : options.system.plugins) { - throw std::runtime_error("TRT-18412: Plugins require --useRuntime=full."); + sample::gLogInfo << "Loading supplied plugin library: " << pluginPath << std::endl; + pluginLibs.emplace_back(loadLibrary(pluginPath)); } + } + else if (gUseRuntime == RuntimeMode::kFULL && options.build.safe) + { + sample::gLogInfo << "Skipping standard plugin loading due to --safe flag" << std::endl; + } + else if (!options.system.plugins.empty()) + { + throw std::runtime_error("TRT-18412: Plugins require --useRuntime=full."); + } #if ENABLE_UNIFIED_BUILDER - auto safetyPluginRegistry = sample::safe::getSafePluginRegistry(*gSafeRecorder); - ASSERT(safetyPluginRegistry != nullptr); - - // getSafePluginRegistry() mutates the singleton's stored ISafeRecorder on every call. The ONNX parser calls it - // during plugin lookup with its own library-local static recorder; at process exit libnvonnxparser.so is - // unloaded before libnvinfer_safe.so, leaving the singleton with a pointer into unmapped memory that its - // destructor then dereferences via decRefCount(). Restore the recorder to this executable's process-lifetime - // gSafeRecorder on every return path. The restore runs during stack unwind while both DSOs are still mapped, - // relying on libnvonnxparser.so staying loaded for the duration of trtexecMain. - struct RestoreSafeRecorderGuard - { - nvinfer2::safe::ISafePluginRegistry* mRegistry; - ~RestoreSafeRecorderGuard() noexcept - { - if (mRegistry != nullptr && gSafeRecorder) - { - mRegistry->setSafeRecorder(*gSafeRecorder); - } - } - } restoreSafeRecorderGuard{safetyPluginRegistry}; + auto safetyPluginRegistry = sample::safe::getSafePluginRegistry(*gSafeRecorder); + ASSERT(safetyPluginRegistry != nullptr); - if (!options.system.safetyPlugins.empty()) + // getSafePluginRegistry() mutates the singleton's stored ISafeRecorder on every call. The ONNX parser calls it + // during plugin lookup with its own library-local static recorder; at process exit libnvonnxparser.so is + // unloaded before libnvinfer_safe.so, leaving the singleton with a pointer into unmapped memory that its + // destructor then dereferences via decRefCount(). Restore the recorder to this executable's process-lifetime + // gSafeRecorder on every return path. The restore runs during stack unwind while both DSOs are still mapped, + // relying on libnvonnxparser.so staying loaded for the duration of trtexecMain. + struct RestoreSafeRecorderGuard + { + nvinfer2::safe::ISafePluginRegistry* mRegistry; + ~RestoreSafeRecorderGuard() noexcept { - for (auto const& safetyPluginArg : options.system.safetyPlugins) + if (mRegistry != nullptr && gSafeRecorder) { - sample::gLogInfo << "Loading supplied safety plugin library with manual registration: " - << safetyPluginArg.libraryName << std::endl; - auto pluginLib = loadLibrary(safetyPluginArg.libraryName); - processSafetyPluginLibrary(safetyPluginRegistry, pluginLib.get(), safetyPluginArg); - pluginLibs.emplace_back(std::move(pluginLib)); + mRegistry->setSafeRecorder(*gSafeRecorder); } } -#endif // ENABLE_UNIFIED_BUILDER - if (options.build.safe && !sample::hasSafeRuntime()) - { - sample::gLogError << "Safety is not supported because safety runtime library is unavailable." << std::endl; - return sample::gLogger.reportFail(sampleTest); - } + } restoreSafeRecorderGuard{safetyPluginRegistry}; - if (!options.build.safe && options.build.consistency) + if (!options.system.safetyPlugins.empty()) + { + for (auto const& safetyPluginArg : options.system.safetyPlugins) { - sample::gLogInfo << "Skipping consistency checker on non-safety mode." << std::endl; - options.build.consistency = false; + sample::gLogInfo << "Loading supplied safety plugin library with manual registration: " + << safetyPluginArg.libraryName << std::endl; + auto pluginLib = loadLibrary(safetyPluginArg.libraryName); + processSafetyPluginLibrary(safetyPluginRegistry, pluginLib.get(), safetyPluginArg); + pluginLibs.emplace_back(std::move(pluginLib)); } + } +#endif // ENABLE_UNIFIED_BUILDER + if (options.build.safe && !sample::hasSafeRuntime()) + { + sample::gLogError << "Safety is not supported because safety runtime library is unavailable." << std::endl; + return EXIT_FAILURE; + } + + if (!options.build.safe && options.build.consistency) + { + sample::gLogInfo << "Skipping consistency checker on non-safety mode." << std::endl; + options.build.consistency = false; + } -// Windows does not have setenv call + // Windows does not have setenv call #if !defined(_WIN32) - // Set CPU-only environment variable if the option is enabled - if (options.build.cpuOnly) - { - // The use of `TRT_INTERNAL_OPTIONS` is special to TensorRT 10.15 and will disappear in later releases. - sample::gLogInfo << "Setting CPU-only mode" << std::endl; - char* internalOptions = std::getenv("TRT_INTERNAL_OPTIONS"); - std::string internalOptionsStr; - if (internalOptions) - { - internalOptionsStr = std::string(internalOptions) + " --cpu_only=1"; - } - else - { - internalOptionsStr = "--cpu_only=1"; - } - setenv("TRT_INTERNAL_OPTIONS", internalOptionsStr.c_str(), 1); + // Set CPU-only environment variable if the option is enabled + if (options.build.cpuOnly) + { + // The use of `TRT_INTERNAL_OPTIONS` is special to TensorRT 10.15 and will disappear in later releases. + sample::gLogInfo << "Setting CPU-only mode" << std::endl; + char* internalOptions = std::getenv("TRT_INTERNAL_OPTIONS"); + std::string internalOptionsStr; + if (internalOptions) + { + internalOptionsStr = std::string(internalOptions) + " --cpu_only=1"; } + else + { + internalOptionsStr = "--cpu_only=1"; + } + setenv("TRT_INTERNAL_OPTIONS", internalOptionsStr.c_str(), 1); + } #endif // !defined(_WIN32) + // Start engine building phase. + std::unique_ptr bEnv( + new BuildEnvironment(options.build.safe, options.build.versionCompatible, options.system.DLACore, + options.build.tempdir, options.build.tempfileControls, options.build.leanDLLPath, sampleTest.getCmdline())); - // Start engine building phase. - std::unique_ptr bEnv(new BuildEnvironment(options.build.safe, options.build.versionCompatible, - options.system.DLACore, options.build.tempdir, options.build.tempfileControls, options.build.leanDLLPath, - sampleTest.getCmdline())); - - bool buildPass = getEngineBuildEnv(options.model, options.build, options.system, *bEnv, sample::gLogError, postConfigHook); + bool buildPass + = getEngineBuildEnv(options.model, options.build, options.system, *bEnv, sample::gLogError, postConfigHook); - if (!buildPass) - { - sample::gLogError << "Engine set up failed" << std::endl; - return sample::gLogger.reportFail(sampleTest); - } + if (!buildPass) + { + sample::gLogError << "Engine set up failed" << std::endl; + return EXIT_FAILURE; + } - // Exit as version is already printed during getEngineBuildEnv - if (options.build.getPlanVersionOnly) - { - return sample::gLogger.reportPass(sampleTest); - } + // Exit as version is already printed during getEngineBuildEnv + if (options.build.getPlanVersionOnly) + { + return EXIT_SUCCESS; + } - // dynamicPlugins may have been updated by getEngineBuildEnv above - bEnv->engine.setDynamicPlugins(options.system.dynamicPlugins); - // When some options are enabled, engine deserialization is not supported on the platform that the engine was - // built. - bool const supportDeserialization = !options.build.safe && !options.build.buildDLAStandalone - && options.build.runtimePlatform == nvinfer1::RuntimePlatform::kSAME_AS_BUILD; + // dynamicPlugins may have been updated by getEngineBuildEnv above + bEnv->engine.setDynamicPlugins(options.system.dynamicPlugins); + // When some options are enabled, engine deserialization is not supported on the platform that the engine was + // built. + bool const supportDeserialization = !options.build.safe && !options.build.buildDLAStandalone + && options.build.runtimePlatform == nvinfer1::RuntimePlatform::kSAME_AS_BUILD; - if (supportDeserialization && options.build.refittable) + if (supportDeserialization && options.build.refittable) + { + auto* engine = bEnv->engine.get(); + if (options.reporting.refit) { - auto* engine = bEnv->engine.get(); - if (options.reporting.refit) + dumpRefittable(*engine); + } + // Refit from ONNX model + if (!options.inference.refitOnnxModel.empty()) + { + bool const success = refitFromOnnx(*engine, options.inference.refitOnnxModel, options.inference.threads); + if (!success) { - dumpRefittable(*engine); + sample::gLogError << "Engine refit from ONNX model failed." << std::endl; + return EXIT_FAILURE; } - // Refit from ONNX model - if (!options.inference.refitOnnxModel.empty()) + } + if (options.inference.timeRefit) + { + if (bEnv->network.operator bool()) { - bool const success = refitFromOnnx(*engine, options.inference.refitOnnxModel, options.inference.threads); + bool const success = timeRefit(*bEnv->network, *engine, options.inference.threads); if (!success) { - sample::gLogError << "Engine refit from ONNX model failed." << std::endl; - return sample::gLogger.reportFail(sampleTest); + sample::gLogError << "Engine refit failed." << std::endl; + return EXIT_FAILURE; } } - if (options.inference.timeRefit) + else { - if (bEnv->network.operator bool()) - { - bool const success = timeRefit(*bEnv->network, *engine, options.inference.threads); - if (!success) - { - sample::gLogError << "Engine refit failed." << std::endl; - return sample::gLogger.reportFail(sampleTest); - } - } - else - { - sample::gLogWarning << "Network not available, skipped timing refit." << std::endl; - } + sample::gLogWarning << "Network not available, skipped timing refit." << std::endl; } } + } - if (options.build.skipInference) + if (options.build.skipInference) + { + if (supportDeserialization) { - if (supportDeserialization) - { - printLayerInfo(options.reporting, bEnv->engine.get(), nullptr); - printOptimizationProfileInfo(options.reporting, bEnv->engine.get()); - } - sample::gLogInfo << "Skipped inference phase since --skipInference is added." << std::endl; - return sample::gLogger.reportPass(sampleTest); + printLayerInfo(options.reporting, bEnv->engine.get(), nullptr); + printOptimizationProfileInfo(options.reporting, bEnv->engine.get()); } + sample::gLogInfo << "Skipped inference phase since --skipInference is added." << std::endl; + return EXIT_SUCCESS; + } + + std::unique_ptr iEnv; - std::unique_ptr iEnv; + if (!options.build.safe) + { + iEnv = std::make_unique(*bEnv); + } + else + { +#if ENABLE_UNIFIED_BUILDER + iEnv = std::make_unique(*bEnv); +#else + sample::gLogInfo << "--safe flag is enabled but application is not compatible with safety." << std::endl; + return EXIT_FAILURE; +#endif + } - if (!options.build.safe) + // We avoid re-loading some dynamic plugins while deserializing + // if they were already serialized with `setPluginsToSerialize`. + std::vector dynamicPluginsNotSerialized; + for (auto& pluginName : options.system.dynamicPlugins) + { + if (std::find( + options.system.setPluginsToSerialize.begin(), options.system.setPluginsToSerialize.end(), pluginName) + == options.system.setPluginsToSerialize.end()) { - iEnv = std::make_unique(*bEnv); + dynamicPluginsNotSerialized.emplace_back(pluginName); } - else + } + + iEnv->engine.setDynamicPlugins(dynamicPluginsNotSerialized); + // Delete build environment. + bEnv.reset(); + + if (options.inference.timeDeserialize) + { + if (timeDeserialize(*iEnv, options.system)) + { + return EXIT_FAILURE; + } + return EXIT_SUCCESS; + } + if (options.build.safe && options.system.DLACore >= 0) + { + sample::gLogInfo << "Safe DLA capability is detected. Please save DLA loadable with --saveEngine option, " + "then use dla_safety_runtime to run inference with saved DLA loadable, " + "or alternatively run with your own application" + << std::endl; + return EXIT_FAILURE; + } + bool const profilerEnabled = options.reporting.profile || !options.reporting.exportProfile.empty(); + + bool const layerInfoEnabled = options.reporting.layerInfo || !options.reporting.exportLayerInfo.empty(); + if (iEnv->safe && (profilerEnabled || layerInfoEnabled)) + { + sample::gLogError << "Safe runtime does not support --dumpProfile or --exportProfile= or " + "--dumpLayerInfo or --exportLayerInfo=, please use " + "--verbose to print profiling info." + << std::endl; + return EXIT_FAILURE; + } + if (!setUpInference(*iEnv, options.inference, options.system)) + { + sample::gLogError << "Inference set up failed" << std::endl; + return EXIT_FAILURE; + } + + if (!options.build.safe) + { + printLayerInfo(options.reporting, iEnv->engine.get(), + static_cast(iEnv.get())->contexts.front().get()); + printOptimizationProfileInfo(options.reporting, iEnv->engine.get()); + } + std::vector trace; + sample::gLogInfo << "Starting inference" << std::endl; + +#if !defined(_WIN32) + // Load ALL reference outputs for accuracy validation (if provided) + // This loads all refPairs upfront since they will be used in the inner loop + if (!options.build.safe) + { + auto* iEnvStd = static_cast(iEnv.get()); + for (size_t pairIdx = 0; pairIdx < options.inference.refPairs.size(); ++pairIdx) { + loadRefOutputs(*iEnv, options.inference, *iEnvStd->contexts.front(), pairIdx); + } + } #if ENABLE_UNIFIED_BUILDER - iEnv = std::make_unique(*bEnv); -#else - sample::gLogInfo << "--safe flag is enabled but application is not compatible with safety." << std::endl; - return sample::gLogger.reportFail(sampleTest); + else + { + auto* iEnvSafe = static_cast(iEnv.get()); + for (size_t pairIdx = 0; pairIdx < options.inference.refPairs.size(); ++pairIdx) + { + loadRefOutputs(*iEnv, options.inference, *iEnvSafe->mClonedGraphs.front(), pairIdx); + } + } #endif +#endif // !defined(_WIN32) && !TRT_WINML + + if (!runInference(options.inference, *iEnv, options.system.device, trace, options.reporting)) + { + sample::gLogError << "Error occurred during inference" << std::endl; + return EXIT_FAILURE; + } + + printPerformanceReport( + trace, options.reporting, options.inference, sample::gLogInfo, sample::gLogWarning, sample::gLogVerbose); + + printOutput(options.reporting, *iEnv, options.inference.batch); + + if (profilerEnabled) + { + iEnv->profiler = std::make_unique(); + if (!prepareProfileRun(*iEnv, options.inference, options.system)) + { + return EXIT_FAILURE; } + if (!runInference(options.inference, *iEnv, options.system.device, trace, options.reporting)) + { + sample::gLogError << "Error occurred during inference" << std::endl; + return EXIT_FAILURE; + } + } + printPerformanceProfile(options.reporting, *iEnv); - // We avoid re-loading some dynamic plugins while deserializing - // if they were already serialized with `setPluginsToSerialize`. - std::vector dynamicPluginsNotSerialized; - for (auto& pluginName : options.system.dynamicPlugins) + // --tuningResultFile is the hidden parent->child IPC channel used by the + // tuning loop. Write a compact JSON with gpu_time_ms, accuracy_failed, and + // per-tensor accuracy_loss so the parent can update its cache + best + // tracking after waitpid(). The flag is omitted from --help; an end user + // who passes it manually still gets the same JSON, which is intentional — + // it makes a tuning iteration reproducible by `--setBuildRoute= + // --tuningResultFile=...`. + if (!options.tuning.tuningResultFile.empty()) + { + double meanGpuTimeMs{0.0}; + if (!trace.empty()) { - if (std::find(options.system.setPluginsToSerialize.begin(), options.system.setPluginsToSerialize.end(), - pluginName) - == options.system.setPluginsToSerialize.end()) + double sum{0.0}; + for (auto const& t : trace) { - dynamicPluginsNotSerialized.emplace_back(pluginName); + sum += static_cast(t.computeEnd - t.computeStart); } + meanGpuTimeMs = sum / static_cast(trace.size()); + } + nlohmann::json j; + j["gpu_time_ms"] = meanGpuTimeMs; + j["accuracy_failed"] = iEnv->accuracyFailed; + auto lossJson = nlohmann::json::object(); + for (auto const& [name, loss] : iEnv->accuracyLossValues) + { + lossJson[name] = loss; + } + j["accuracy_loss"] = std::move(lossJson); + std::ofstream out(options.tuning.tuningResultFile); + if (!out) + { + sample::gLogError << "Cannot open --tuningResultFile for writing: " + << options.tuning.tuningResultFile << std::endl; + } + else + { + out << j.dump(2) << std::endl; } + } + + // Check if accuracy validation failed + if (iEnv->accuracyFailed) + { + sample::gLogError << "Accuracy validation FAILED: one or more tensors exceeded the threshold." << std::endl; + return EXIT_FAILURE; + } + return EXIT_SUCCESS; +} + +int sample::trtexecMain(int argc, char** argv, PostConfigCallback const& postConfigHook) +{ + std::string const sampleName = "TensorRT.trtexec"; - iEnv->engine.setDynamicPlugins(dynamicPluginsNotSerialized); - // Delete build environment. - bEnv.reset(); + auto sampleTest = sample::gLogger.defineTest(sampleName, argc, argv); - if (options.inference.timeDeserialize) + AllOptions options; + try + { + sample::gLogger.reportTestStart(sampleTest); + Arguments args = argsToArgumentsMap(argc, argv); + int32_t const parseResult = parseArgs(sampleTest, args, options); + if (parseResult != kCONTINUE_MAIN) { - if (timeDeserialize(*iEnv, options.system)) + return parseResult; + } + int32_t const result = runOnceBuildAndInfer(sampleTest, options, postConfigHook); + if (result != EXIT_SUCCESS) + { + return sample::gLogger.reportFail(sampleTest); + } + } + catch (std::exception const& e) + { + sample::gLogError << "Uncaught exception detected: " << e.what() << std::endl; + return sample::gLogger.reportFail(sampleTest); + } + return sample::gLogger.reportPass(sampleTest); +} + +// ============================================================================ +// Tuning-loop driver: parent side of --tuneBuildRoutes / --continue. +// +// Architecture: the parent never builds an engine itself. It enumerates routes, +// fork+execs a child trtexec per iteration with `--setBuildRoute=` and +// `--tuningResultFile=` injected, then waitpid()s and reads the JSON the +// child wrote. trtexecMain is therefore reused unmodified — every iteration is +// reproducible by re-running the child's argv by hand. +// +// Windows path: tuning isn't supported (no fork). runTuningLoop returns an +// error there. Linux is the only target until a separate cross-platform +// implementation is needed. +// ============================================================================ + +#if defined(_WIN32) + +int32_t sample::runTuningLoop(int32_t /*argc*/, char** /*argv*/) +{ + sample::gLogError << "--tuneBuildRoutes is not supported on Windows (no fork())." << std::endl; + return EXIT_FAILURE; +} + +#else // POSIX && ENABLE_FEATURE_GLOBAL_PERF_TUNER + +namespace +{ + +//! Per-iteration result extracted from the child's --tuningResultFile JSON. +struct IterationResult +{ + bool crashed{false}; //!< Child crashed or fork/waitpid failed. + bool accuracyFailed{false}; //!< Child reported accuracy-threshold failure. + int32_t exitCode{0}; //!< Child exit code (or -1 on fork/waitpid error). + double gpuTimeMs{0.0}; //!< Mean GPU compute time (ms) from the trace. + std::string errorMessage; //!< Brief diagnostic for the parent log. + std::unordered_map accuracyLossValues; +}; + +//! Read the child's tuning result JSON. Returns a partial IterationResult +//! with crashed=true if the file is missing or unparseable. +IterationResult readChildResult(std::string const& jsonPath) +{ + IterationResult r; + std::ifstream in(jsonPath); + if (!in) + { + r.crashed = true; + r.errorMessage = "missing tuning result file " + jsonPath + + " (child likely crashed before writing)"; + return r; + } + try + { + nlohmann::json j; + in >> j; + r.gpuTimeMs = j.value("gpu_time_ms", 0.0); + r.accuracyFailed = j.value("accuracy_failed", false); + if (j.contains("accuracy_loss") && j["accuracy_loss"].is_object()) + { + for (auto const& [k, v] : j["accuracy_loss"].items()) { - return sample::gLogger.reportFail(sampleTest); + r.accuracyLossValues[k] = v.get(); } - return sample::gLogger.reportPass(sampleTest); } - if (options.build.safe && options.system.DLACore >= 0) + } + catch (std::exception const& e) + { + r.crashed = true; + r.errorMessage = std::string{"failed to parse tuning result JSON: "} + e.what(); + } + return r; +} + +//! Fork+exec one child trtexec invocation for the given route. Reads the +//! resulting JSON and returns an IterationResult. Never throws — failures +//! are reported as `crashed=true` with a brief errorMessage. +IterationResult runChildForOneRoute(int32_t argc, char** argv, BigInt const& globalIndex, std::string const& route, + std::string const& enginePath, std::string const& resultJsonPath) +{ + std::vector storage; + auto const childArgv = buildTuningChildArgv(argc, argv, route, enginePath, resultJsonPath, storage); + + sample::gLogInfo << "Tuning iteration [" << globalIndex.toString() << "]: " << route << std::endl; + // Ensure stale result files from a previous iteration aren't mistaken for this one's output. + std::remove(resultJsonPath.c_str()); + + pid_t const pid = fork(); + if (pid < 0) + { + IterationResult r; + r.crashed = true; + r.exitCode = -1; + r.errorMessage = std::string{"fork() failed: "} + std::strerror(errno); + sample::gLogError << r.errorMessage << std::endl; + return r; + } + if (pid == 0) + { + // Child: replace ourselves with a fresh trtexec invocation. execvp searches PATH for argv[0]. + execvp(childArgv[0], childArgv.data()); + // execvp returns only on failure. + std::cerr << "execvp() failed: " << std::strerror(errno) << std::endl; + _exit(127); + } + // Parent: wait for the child. + int32_t status{}; + pid_t const w = waitpid(pid, &status, 0); + if (w < 0) + { + IterationResult r; + r.crashed = true; + r.exitCode = -1; + r.errorMessage = std::string{"waitpid() failed: "} + std::strerror(errno); + return r; + } + + IterationResult r = readChildResult(resultJsonPath); + if (WIFEXITED(status)) + { + r.exitCode = WEXITSTATUS(status); + if (r.exitCode != EXIT_SUCCESS && !r.crashed) + { + // Child wrote a result file but exited non-zero — e.g. accuracy validation + // exceeded the threshold. Not a crash; the result is still valid for tuning. + r.errorMessage = "child exited with status " + std::to_string(r.exitCode); + } + } + else if (WIFSIGNALED(status)) + { + r.crashed = true; + r.exitCode = -1; + int32_t const sig = WTERMSIG(status); + r.errorMessage = std::string{"child killed by signal "} + std::to_string(sig); + char const* name = strsignal(sig); + if (name != nullptr) { - sample::gLogInfo << "Safe DLA capability is detected. Please save DLA loadable with --saveEngine option, " - "then use dla_safety_runtime to run inference with saved DLA loadable, " - "or alternatively run with your own application" - << std::endl; - return sample::gLogger.reportFail(sampleTest); + r.errorMessage += " ("; + r.errorMessage += name; + r.errorMessage += ")"; } - bool const profilerEnabled = options.reporting.profile || !options.reporting.exportProfile.empty(); + } + return r; +} - bool const layerInfoEnabled = options.reporting.layerInfo || !options.reporting.exportLayerInfo.empty(); - if (iEnv->safe && (profilerEnabled || layerInfoEnabled)) +//! Resolve the tuning context up-front: parse the user's expression, query the +//! knob database from --setBuildRoute's empty-route side-effect path (the +//! default-constructed BuilderConfig populates `allBuildRoutes` from Myelin), +//! and expand into a TuningContext. +//! +//! Returns false (and logs) if the expression is malformed or empty. +bool buildTuningContext(TuningOptions const& tuning, TuningContext& ctx) +{ + // Get the knob database JSON by spinning up a default BuilderConfig. + auto builder = std::unique_ptr{createBuilder()}; + if (!builder) + { + sample::gLogError << "buildTuningContext: createBuilder failed" << std::endl; + return false; + } + auto config = std::unique_ptr(builder->createBuilderConfig()); + if (!config) + { + sample::gLogError << "buildTuningContext: createBuilderConfig failed" << std::endl; + return false; + } + char const* allRoutesC = config->getAllBuildRoutes(); + std::string const allRoutesStr = allRoutesC != nullptr ? allRoutesC : ""; + if (allRoutesStr.empty()) + { + sample::gLogError << "IBuilderConfig::getAllBuildRoutes() returned empty — " + << "tuning requires the Myelin knob database, which is only available on " + << "enterprise TRT (non-RTX, non-WoA)." << std::endl; + return false; + } + BuildRouteKnobDatabase db; + if (!db.loadFromJsonString(allRoutesStr)) + { + sample::gLogError << "Failed to parse knob database from getAllBuildRoutes()." << std::endl; + return false; + } + BuildRouteExprParser parser(db); + auto parsed = parser.parse(tuning.tuningExpr); + if (!parsed.has_value()) + { + sample::gLogError << "Failed to parse --tuneBuildRoutes expression: " << parser.getError() << std::endl; + return false; + } + ctx.parsedExprs = std::move(*parsed); + ctx.searchAlgorithm = tuning.tuningSearchAlgorithm; + ctx.tunerVersion = db.getTunerVersion(); + ctx.defaultBuildRoute = db.buildDefaultPath(); + // Fill defaultValues parallel to parsedExprs (used by fast / mixed mode). + ctx.defaultValues.clear(); + ctx.defaultValues.reserve(ctx.parsedExprs.size()); + for (auto const& p : ctx.parsedExprs) + { + ctx.defaultValues.emplace_back(db.getDefaultValue(p.mKnobName)); + } + ctx.totalCount = ctx.count(); + sample::gLogInfo << "Expanded to " << ctx.totalCount.toString() << " build route configurations" << std::endl; + return true; +} + +//! Storage + view of an argv reconstructed from a tuning-cache header. The caller +//! reads `argc` / `argv()` and passes them to parseArgs(); `resumeFromIter` is the +//! iteration index to skip up to. `storage` owns the strings backing `argvPtrs`. +struct ContinueResumeState +{ + std::vector storage; + std::vector argvPtrs; + int32_t argc{0}; + int64_t resumeFromIter{0}; + + [[nodiscard]] char** argv() noexcept + { + return argvPtrs.data(); + } +}; + +//! \brief Validate the bare-`--continue` invocation and rebuild argv from the cache header. +//! +//! The user must pass exactly `--continue` + `--tuningCacheFile=` (in any order) and +//! no other options — the cache header is the source of truth for everything else. The +//! function logs and returns false on validation or I/O errors so the caller can fail fast. +//! +//! On success, `state` carries the reconstructed argv (with absolute paths) and the resume +//! iteration index. Caller must pass the SAME argv[0] as the running binary, since the cache +//! stores the original argv[0] which may not match a relocated trtexec. +bool reconstructArgvForContinue(int32_t argc, char** argv, ContinueResumeState& state) +{ + constexpr char const* kCACHE_FLAG = "--tuningCacheFile="; + uint64_t const flagLen = std::strlen(kCACHE_FLAG); + + // Pre-check: the only allowed args are `--continue` and `--tuningCacheFile=`. + std::string cachePath; + for (int32_t i = 1; i < argc; ++i) + { + if (argv[i] == nullptr) { - sample::gLogError << "Safe runtime does not support --dumpProfile or --exportProfile= or " - "--dumpLayerInfo or --exportLayerInfo=, please use " - "--verbose to print profiling info." - << std::endl; - return sample::gLogger.reportFail(sampleTest); + continue; } - if (!setUpInference(*iEnv, options.inference, options.system)) + std::string const arg(argv[i]); + bool const isContinue = (arg == "--continue"); + bool const isCacheFile = (std::strncmp(argv[i], kCACHE_FLAG, flagLen) == 0); + if (!isContinue && !isCacheFile) { - sample::gLogError << "Inference set up failed" << std::endl; - return sample::gLogger.reportFail(sampleTest); + sample::gLogError << "--continue requires exactly --tuningCacheFile=; " + << "no other options are allowed (got '" << arg << "'). " + << "All options come from the cache header." << std::endl; + return false; + } + if (isCacheFile) + { + cachePath = argv[i] + flagLen; } + } + if (cachePath.empty()) + { + sample::gLogError << "--continue requires --tuningCacheFile= on the command line." << std::endl; + return false; + } + + auto header = readTuningCacheHeader(cachePath); + if (!header.has_value()) + { + sample::gLogError << "--continue: failed to read cache header from " << cachePath << std::endl; + return false; + } + state.resumeFromIter = header->completedIterations; + state.storage = reconstructArgvFromCacheHeader(*header, argv[0], cachePath); + state.argvPtrs.reserve(state.storage.size() + 1); + for (auto& s : state.storage) + { + state.argvPtrs.emplace_back(s.data()); + } + state.argvPtrs.emplace_back(nullptr); + state.argc = static_cast(state.storage.size()); + sample::gLogInfo << "--continue: resuming from iteration " << state.resumeFromIter << " using " << state.argc + << " args reconstructed from cache header." << std::endl; + return true; +} - if (!options.build.safe) +//! \brief Cross-flag validation specific to the tuning loop. Returns false (and logs) +//! when --saveEngine is missing in a mode that requires it. Called after parseArgs +//! and after the cache-header re-assert. +bool validateTuningOptions(AllOptions const& options) +{ + // --saveEngine is optional for pure benchmarking, but required when --loadRefOutputs is set: + // the accuracy-validation path picks a "best" engine and needs somewhere to persist it. + if (options.build.engine.empty() && !options.tuning.dryRun) + { + bool const hasRefOutputs = !options.inference.refPairs.empty() && !options.inference.refPairs[0].second.empty(); + if (hasRefOutputs) { - printLayerInfo(options.reporting, iEnv->engine.get(), - static_cast(iEnv.get())->contexts.front().get()); - printOptimizationProfileInfo(options.reporting, iEnv->engine.get()); + sample::gLogError << "--tuneBuildRoutes with --loadRefOutputs requires --saveEngine to " + << "persist the best engine selected by accuracy validation." << std::endl; + return false; } - std::vector trace; - sample::gLogInfo << "Starting inference" << std::endl; + sample::gLogWarning << "--tuneBuildRoutes without --saveEngine: best engine will not be saved." << std::endl; + } + return true; +} - if (!runInference(options.inference, *iEnv, options.system.device, trace, options.reporting)) +//! \brief Print the dryRun enumeration of routes. No engine work is performed. +void emitDryRunListing(TuningContext const& ctx) +{ + sample::gLogInfo << "--dryRun: " << ctx.totalCount.toString() << " build routes would be tried:" << std::endl; + for (BigInt i{0}; i < ctx.totalCount; ++i) + { + sample::gLogInfo << "[" << i.toString() << "]:" << ctx.getPathAtIndex(i) << std::endl; + } +} + +//! Shared mutable state for a phase (or phase 1 + phase 2 of mixed-mode) of the tuning loop. +//! Aggregated into a single struct so runOnePhase can be a free function instead of a deeply +//! nested lambda. +struct PhaseState +{ + AllOptions const& options; //!< Parsed options for this run. + Logger::TestAtom const& sampleTest; //!< For TASK_BEGIN/END/ABORT banners. + pid_t const ppid{}; //!< Parent PID, used in temp filenames. + std::chrono::steady_clock::time_point const startTime; //!< Loop start, for --tuningTimeOut. + int32_t const argc{}; //!< Parent argv (passed verbatim to children). + char** const argv{}; //!< Parent argv (passed verbatim to children). + // Mutable across iterations and across phases: + BigInt successCount{0}; + double bestGpuTimeMs{std::numeric_limits::infinity()}; + std::string bestEnginePath; + std::string bestRoute; +}; + +//! Build the temp-engine path for one iteration. With --saveAllEngines, use a stable +//! per-iteration name under the user's --saveEngine (matches the format the turtle +//! tests assert against: `.iter`). Without it, use a pid-scoped temp +//! that gets unlinked at promotion time. The phase label disambiguates the temp +//! path so mixed-mode phase 2 cannot overwrite a phase-1 file mid-flight. +std::string makeIterationEnginePath(PhaseState const& state, char const* phaseLabel, BigInt const& i) +{ + if (state.options.build.saveAllEngines) + { + return state.options.build.engine + ".iter" + i.toString(); + } + return "/tmp/trtexec_tuning_" + std::to_string(state.ppid) + "_iter" + phaseLabel + "_" + i.toString() + ".plan"; +} + +//! \brief Run one phase of the tuning loop (phase 1, or phase 2 of mixed mode). +//! +//! Iterates phaseCtx.totalCount times, fork+execs a child per iteration, and updates +//! `state` with the best route seen. When `positiveKnobs` is non-null and we're past +//! the baseline iteration (i==0), records each iteration that beats the baseline so +//! mixed-mode can build its phase-2 sub-context. +//! +//! Returns false if --tuningTimeOut tripped (so the caller stops chaining phases), +//! true on normal completion. +//! +//! NOLINT: orchestrator function that touches every per-iteration concern (timeout, route +//! enumeration, child invocation, best-tracker update, mixed-mode positive-knob collection, +//! cache writing, temp-file cleanup, TASK_BEGIN/END/ABORT logging). Further extraction would +//! either fragment one logical iteration step across multiple functions or require passing +//! the same PhaseState everywhere — both make the per-iteration story harder to read. +// NOLINTNEXTLINE(readability-function-cognitive-complexity) +bool runOnePhase(PhaseState& state, TuningContext const& phaseCtx, char const* phaseLabel, + std::vector* positiveKnobs, double* baselineGpuTimeMsOut, int64_t skipUntil) +{ + sample::gLogInfo << "Tuning " << phaseLabel << ": " << phaseCtx.totalCount.toString() << " iterations." << std::endl; + double baselineGpuTimeMs = std::numeric_limits::infinity(); + for (BigInt i{0}; i < phaseCtx.totalCount; ++i) + { + // --continue: skip iterations already in the cache. + if (skipUntil > 0 && i < BigInt{static_cast(skipUntil)}) { - sample::gLogError << "Error occurred during inference" << std::endl; - return sample::gLogger.reportFail(sampleTest); + continue; + } + if (state.options.tuning.timeout > 0) + { + auto const elapsedS = std::chrono::duration_cast( + std::chrono::steady_clock::now() - state.startTime).count(); + if (elapsedS >= state.options.tuning.timeout) + { + sample::gLogInfo << "Tuning timeout reached (" << state.options.tuning.timeout + << "s); stopping early." << std::endl; + return false; + } } - printPerformanceReport( - trace, options.reporting, options.inference, sample::gLogInfo, sample::gLogWarning, sample::gLogVerbose); + std::string const route = phaseCtx.getPathAtIndex(i); + std::string const enginePath = makeIterationEnginePath(state, phaseLabel, i); + std::string const jsonPath = "/tmp/trtexec_tuning_" + std::to_string(state.ppid) + + "_iter" + i.toString() + ".json"; - printOutput(options.reporting, *iEnv, options.inference.batch); + sample::gLogger.reportTaskBegin(state.sampleTest, i.toString(), route); - if (profilerEnabled) + IterationResult const result = runChildForOneRoute(state.argc, state.argv, i, route, enginePath, jsonPath); + + if (!result.crashed && result.exitCode == EXIT_SUCCESS) { - auto* profiler = new Profiler; - iEnv->profiler.reset(profiler); - static_cast(iEnv.get())->contexts.front()->setProfiler(profiler); - static_cast(iEnv.get())->contexts.front()->setEnqueueEmitsProfile(false); - if (!runInference(options.inference, *iEnv, options.system.device, trace, options.reporting)) + ++state.successCount; + if (result.gpuTimeMs < state.bestGpuTimeMs) + { + state.bestGpuTimeMs = result.gpuTimeMs; + state.bestEnginePath = enginePath; + state.bestRoute = route; + } + // Track baseline (index 0) so mixed-mode can decide "positive knob". + if (i.isZero()) { - sample::gLogError << "Error occurred during inference" << std::endl; - return sample::gLogger.reportFail(sampleTest); + baselineGpuTimeMs = result.gpuTimeMs; } + sample::gLogger.reportTaskEnd(state.sampleTest, i.toString(), route); } - printPerformanceProfile(options.reporting, *iEnv); + else + { + sample::gLogWarning << "Iteration [" << i.toString() << "] failed: " + << (result.errorMessage.empty() ? "(no message)" : result.errorMessage) << std::endl; + sample::gLogger.reportTaskAbort(state.sampleTest, i.toString(), route); + } + // For mixed-mode phase 1, collect knobs that beat the baseline. + if (positiveKnobs != nullptr && !i.isZero() && baselineGpuTimeMs != std::numeric_limits::infinity()) + { + collectPositiveKnobFromResult( + result.crashed, result.gpuTimeMs, baselineGpuTimeMs, i, phaseCtx, *positiveKnobs); + } + // Append this iteration's result to the tuning cache file (--tuningCacheFile). + if (!state.options.tuning.tuningCacheFile.empty()) + { + writeTuningCacheIteration(state.options.tuning.tuningCacheFile, i.toUint64(), route, result.crashed, + result.errorMessage, result.accuracyLossValues, result.gpuTimeMs); + } + std::remove(jsonPath.c_str()); + } + if (baselineGpuTimeMsOut != nullptr) + { + *baselineGpuTimeMsOut = baselineGpuTimeMs; + } + return true; +} - return sample::gLogger.reportPass(sampleTest); +//! \brief Copy the best-iteration engine to the user's --saveEngine path and emit the +//! final summary. Cleans up the per-iteration temp engine if --saveAllEngines was off. +//! Returns the trtexec exit code (pass if any iteration succeeded, fail otherwise). +int32_t finalizeBestEngine(PhaseState const& state, BigInt const& totalCount) +{ + if (state.bestEnginePath.empty()) + { + sample::gLogError << "No tuning iteration succeeded; no engine written." << std::endl; + return sample::gLogger.reportFail(state.sampleTest); + } + if (state.bestEnginePath != state.options.build.engine) + { + std::ifstream src(state.bestEnginePath, std::ios::binary); + std::ofstream dst(state.options.build.engine, std::ios::binary); + dst << src.rdbuf(); + if (!state.options.build.saveAllEngines) + { + // Per-iteration engine was a temp; clean it up now that it's been promoted. + std::remove(state.bestEnginePath.c_str()); + } + } + sample::gLogInfo << "Best iteration: " << state.bestRoute << " (gpu_time_ms=" << state.bestGpuTimeMs << ")" + << std::endl; + sample::gLogInfo << "Tuning summary: " << state.successCount.toString() << " / " << totalCount.toString() + << " iterations succeeded." << std::endl; + return sample::gLogger.reportPass(state.sampleTest); +} + +} // namespace + +int32_t sample::runTuningLoop(int32_t argc, char** argv) +{ + std::string const sampleName = + "TensorRT.trtexec"; + auto sampleTest = sample::gLogger.defineTest(sampleName, argc, argv); + sample::gLogger.reportTestStart(sampleTest); + + // 1. --continue: rebuild argv from the cache header. The header stores the original argv + // with absolute paths and the expanded tuning expression; everything else is rejected. + ContinueResumeState resume; + int32_t effectiveArgc = argc; + char** effectiveArgv = argv; + if (peekArg(argc, argv, "--continue")) + { + if (!reconstructArgvForContinue(argc, argv, resume)) + { + return sample::gLogger.reportFail(sampleTest); + } + effectiveArgc = resume.argc; + effectiveArgv = resume.argv(); + } + + // 2. Parse options against the effective argv (cache-reconstructed for --continue, raw otherwise). + AllOptions options; + try + { + Arguments args = argsToArgumentsMap(effectiveArgc, effectiveArgv); + int32_t const parseResult = parseArgs(sampleTest, args, options); + if (parseResult != kCONTINUE_MAIN) + { + return parseResult; + } } catch (std::exception const& e) { - sample::gLogError << "Uncaught exception detected: " << e.what() << std::endl; + sample::gLogError << "Argument parse error in tuning loop: " << e.what() << std::endl; + return sample::gLogger.reportFail(sampleTest); + } + + // 3. Resume mode: the reconstructed argv has no --continue, so parseArgs left continueFromCache + // false. Re-assert it now so the cache-header writer below preserves existing rows. + if (effectiveArgv != argv) + { + options.tuning.continueFromCache = true; + } + + if (!validateTuningOptions(options)) + { + return sample::gLogger.reportFail(sampleTest); + } + + // 4. Expand the tuning expression into a TuningContext. + TuningContext ctx; + if (!buildTuningContext(options.tuning, ctx)) + { + return sample::gLogger.reportFail(sampleTest); + } + + if (options.tuning.dryRun) + { + emitDryRunListing(ctx); + return sample::gLogger.reportPass(sampleTest); } - return sample::gLogger.reportFail(sampleTest); + + // 5. Fresh tuning runs write a cache header; resumed runs skip it (the file already has one). + if (!options.tuning.tuningCacheFile.empty() && !options.tuning.continueFromCache) + { + writeTuningCacheHeader( + options.tuning.tuningCacheFile, options, argc, argv, ctx.tunerVersion, ctx.defaultBuildRoute); + } + + // 6. Phase 1 — always runs. For mixed mode, may be followed by a phase 2 over positive knobs. + // Children must see the reconstructed argv when resuming — the user's bare + // `--continue --tuningCacheFile=path` argv has none of the original build/inference flags. + PhaseState state{options, sampleTest, getpid(), std::chrono::steady_clock::now(), effectiveArgc, effectiveArgv}; + std::vector positiveKnobs; + double phase1BaselineMs{std::numeric_limits::infinity()}; + bool const isMixed = options.tuning.tuningSearchAlgorithm == TuningSearchAlgorithm::kMIXED; + bool const phase1Completed + = runOnePhase(state, ctx, "phase1", isMixed ? &positiveKnobs : nullptr, &phase1BaselineMs, resume.resumeFromIter); + + if (phase1Completed && isMixed && positiveKnobs.size() > 1) + { + sample::gLogInfo << "Mixed search: " << positiveKnobs.size() + << " positive knobs identified; entering phase 2." << std::endl; + TuningContext const phase2Ctx = buildMixedPhase2Context(ctx, positiveKnobs); + // Phase 2 always starts fresh (no resume mid-phase-2). + (void) runOnePhase(state, phase2Ctx, "phase2", nullptr, nullptr, 0); + } + else if (isMixed) + { + sample::gLogInfo << "Mixed search: " << positiveKnobs.size() + << " positive knob(s); skipping phase 2 (need >1)." << std::endl; + } + + // 7. Promote the best iteration's engine to the user's --saveEngine path. + return finalizeBestEngine(state, ctx.totalCount); } + +#endif // POSIX && ENABLE_FEATURE_GLOBAL_PERF_TUNER diff --git a/samples/trtexec/trtexec.h b/samples/trtexec/trtexec.h index 37219960a..fe70a0b6a 100644 --- a/samples/trtexec/trtexec.h +++ b/samples/trtexec/trtexec.h @@ -30,6 +30,14 @@ namespace sample //! \return Exit code (0 for success, non-zero for failure) [[nodiscard]] int trtexecMain(int argc, char** argv, PostConfigCallback const& postConfigHook = nullptr); +//! \brief Tuning-loop driver invoked when --tuneBuildRoutes / --tuneBuildRouteFile / --continue +//! is present in argv. Enumerates build routes per the tuning expression and search algorithm, +//! and for each iteration fork+execs trtexec with `--setBuildRoute= --tuningResultFile=` +//! injected. Each child runs trtexecMain() unmodified, so any iteration is reproducible by hand +//! using its --setBuildRoute argument. +//! \return Exit code (0 if at least one iteration succeeded, non-zero otherwise). +[[nodiscard]] int32_t runTuningLoop(int32_t argc, char** argv); + } // namespace sample #endif // TRTEXEC_H diff --git a/samples/trtexec/trtexec_main.cpp b/samples/trtexec/trtexec_main.cpp index 88bf33a71..10c06f845 100644 --- a/samples/trtexec/trtexec_main.cpp +++ b/samples/trtexec/trtexec_main.cpp @@ -15,9 +15,19 @@ * limitations under the License. */ +#include "sampleUtils.h" // for sample::peekArg #include "trtexec.h" +//! Tuning vs single-run dispatch. The peek is intentionally minimal — a real +//! parse would discard the original argv before runTuningLoop can re-use it +//! for its child fork+execs. Each branch does its own parseArgs. int main(int argc, char** argv) { + if (sample::peekArg(argc, argv, "--tuneBuildRoutes") + || sample::peekArg(argc, argv, "--tuneBuildRouteFile") + || sample::peekArg(argc, argv, "--continue")) + { + return sample::runTuningLoop(argc, argv); + } return sample::trtexecMain(argc, argv); } diff --git a/shared/CMakeLists.txt b/shared/CMakeLists.txt index 1e015c63a..c6733f770 100644 --- a/shared/CMakeLists.txt +++ b/shared/CMakeLists.txt @@ -1,4 +1,4 @@ -# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); @@ -21,6 +21,7 @@ endfunction() target_link_libraries(trt_shared PRIVATE $ + tensorrt_headers trt_global_definitions ) diff --git a/tools/experimental/trt-engine-explorer/.gitignore b/tools/experimental/trt-engine-explorer/.gitignore deleted file mode 100644 index b10b16176..000000000 --- a/tools/experimental/trt-engine-explorer/.gitignore +++ /dev/null @@ -1,127 +0,0 @@ -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -.hypothesis/ -.pytest_cache/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -.python-version - -# celery beat schedule file -celerybeat-schedule - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ - -# Dot -*.gv -*.svg - -# Other trex artifacts -*.xlsx -*.engine -*.ipynb -*.png -*.onnx - diff --git a/tools/experimental/trt-engine-explorer/.vscode/launch.json b/tools/experimental/trt-engine-explorer/.vscode/launch.json deleted file mode 100644 index dda1db9ae..000000000 --- a/tools/experimental/trt-engine-explorer/.vscode/launch.json +++ /dev/null @@ -1,31 +0,0 @@ -{ - // Use IntelliSense to learn about possible attributes. - // Hover to view descriptions of existing attributes. - // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 - "version": "0.2.0", - "configurations": [ - { - "name": "Python: current file", - "type": "python", - "request": "launch", - "justMyCode":false, - "program": "${file}", - "console": "integratedTerminal", - }, - { - "name": "Python: tests/test.py", - "type": "python", - "request": "launch", - "program": "tests/test.py", - "console": "integratedTerminal", - }, - { - "name": "draw_engine", - "type": "python", - "request": "launch", - "program": "utils/draw_engine.py", - "console": "integratedTerminal", - "args": ["tests/inputs/mobilenet.qat.onnx.engine"], - } - ] -} \ No newline at end of file diff --git a/tools/experimental/trt-engine-explorer/CHANGELOG.md b/tools/experimental/trt-engine-explorer/CHANGELOG.md deleted file mode 100644 index ca8a2aecf..000000000 --- a/tools/experimental/trt-engine-explorer/CHANGELOG.md +++ /dev/null @@ -1,111 +0,0 @@ -# TensorRT Engine Explorer Change Log - -Dates are in YYYY-MM-DD format. - -## v0.2.1 (2025-March) -- Added `trex summary` sub-command -- Trimmed `compare_engines.ipynb` - -## v0.2.0 (2025-January) -- Allow the `trex` command-line tool to run in environments where the tensorrt Python bindings is not installed. `trex process` will not be available when tensorrt is not installed, but `trex summary` and `trex draw` are always available. -- Add FP4 rendering. -- Separate control of node metadata and name rendering. -- Enhance the `trex draw` command-line sub-tool with new features: - - `--output_format=format`: Control the output format (png, svg, dot) - - `--beam=node,radius`: Draw the induced subgraph of neighbors centered at node n within a given radius. - - `--query=regex_string`: Draw the induced subgraph of all nodes whose name or metadata matches the regular expression. - -## v0.1.9 (2024-April) -- Fixed virtualenv installation -- Added INT4 rendering - -## v0.1.8 (2024-March) -- Added `trex` command-line tool (see `bin/README.md`) -- Updated to support Python 3.10, new package installations and TensorRT 10.0. -- Made the scripts in the `utils/` directory executable for easier usage. -- Enabled JupyterLab -- Added notebooks/q_dq_placement.ipynb for experimenting with Q/DQ placement, data types and strong typing. Shows how to quickly iterate between ONNX definition, visualization and engine visualization. -- Installation: - - Shortened the installation time (removed qgrid and its dependencies). - - Separated the installation to core packages and notebook packages. - - Removed non-core modules from inclusion in the default trex namespace. This is meant to simplify for users that don't require Jupyter etc. - - Updated the installation instructions for [PyTorch Quantization Toolkit](https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization) to use source installation which is more reliable. - - Updates script `install.sh` with options for full or core installation. Installation of a virtual environment is now an opt-in. -- Graph rendering: - - Updated the graph rendering for TensorRT 10.0 `kgen` kernels. - - Added an option to display engine layer metadata. TensorRT 10.0 adds ONNX layer information as a metadata field in the layer information. - - Added a color for FP8 tensors when rendering SVG. - - Added an option to prevent rendering of disconnected layer nodes. - - Moved the colormap definitions to a separate file (trex/colors.py) to decouple graph rendering from non-core trex code. - - Added support for TrainStation engine layers. A TrainStation is an internal TensorRT engine layer that manages data-dependent-shapes device memory allocation. TrainStation layers synchronize the stream they are invoked from. -- Deprecated functionality: - - Removed display_df_qgrid. Data frames now display using the default panda's table renderer. -- Miscellaneous - - Added copyright message to test files - - Updated and fixed the build-log parsing for TensorRT 9.x/10.x. - - -## v0.1.7 (2023-August) -- Updated graph rendering for TensorRT 9.0 `kgen` kernels. -- Updated TensorRT data formats dictionary. - -## v0.1.6 (2023-April) -- Graph rendering: - - Add node highlighting option. - - Fix bug https://github.com/NVIDIA/TensorRT/issues/2779 - -## v0.1.5 (2022-12-06) -- Updated requirements.txt for Ubuntu 20.04 and 22.04 - -## v0.1.4 (2022-09-01) -- Validated using Python 3.8. -- Added more tests. -- Added support for engine shapes profiles. -- Added `KNOWN_ISSUES.md`. -- Added `trex/excel_summary.py` which adds an API to generate an Excel summary spreadsheet with worksheets generated from several `trex.report_card` reports. -- Added `set_table_display_backend` to provide user control over the backend library displaying tables in notebooks. -- Graph rendering: - - Add exportng of engine graph to ONNX format for viewing with Netron. - - Refactor `graphing.py` and change the engine-graph rendering API: added fine control of rendering options. - - Reformat layers: optionally render Origin attribute. - - Constant layers: optionally display. - - Convolution layers: optionally display activation details. - - Elementwise layers: optionally display operations code. -- Utilities: - - Add GPU clocks locking and power-limiter context manager: `utils/config_gpu.py`. - - Add simple GPU context manager and activate when profiling. - - Script `process_engine.py` will now lock the GPU and memory clocks to maximum frequency by default during engine profiling and release the clocks when done. -- Notebooks: - - Added `report_card_reformat_overview()` - This is a pull-down of several views showing what are roles/functions of Reformat layers in the model. - - Added `report_card_draw_plan_graph_extended()` - displays a set of widgets to control graph rendering options. -- Small bug fixes. - -## v0.1.2 (2022-06-01) -- Graph rendering: - - Add timing information to engine graph nodes. - - Change layer coloring scheme. - - Add printing of binding region names in the graph. - - Fix bug: When a NoOp layer is folded, fold into the correct input of the next layer. - - Fix bug: When a leaf NoOp layer outputs graph bindings, these outputs replace the output of the previous layer. - - Add info to conv and pwgen graph nodes in detailed rendering mode. Convolutions display the activation and residual-add operations. Pointwise layers display a list of the fused operations. - -- Notebooks: - - Add a detailed comparison of two engines by computing the graph isomorphism (aligning the graphs). - - Remove some non-useful diagrams in engine comparison (stacked layer latencies). - - Add a notebook with trex API examples. - -- Utilities: - - Change log files names in `process_engine.py` to include the engine name. - - Parse `trtexec` build and profiling log files to collect various metadata, instead of collecting device properties using pycuda API. - - Add `--useSpinWait` to `process_engine.py`. - -- Miscellaneous: - - Improve error messages. - - Fix bug: When computing convolution MACs account for group size. - - Code refactoring. - - Add QAT ResNet example directory (`trex/examples/pytorch/resnet/`). This is the example referenced by the TREx blog. - - Add optional engine name when creating an EnginePlan instance. This is useful when comparing multiple plans. - - Add `Resources.md` file, with links to reference performance materials. - - Add convolution channel-alignment lint. -## v0.1.0 (2022-04-06) -- First release diff --git a/tools/experimental/trt-engine-explorer/KNOWN_ISSUES.md b/tools/experimental/trt-engine-explorer/KNOWN_ISSUES.md deleted file mode 100644 index 495963333..000000000 --- a/tools/experimental/trt-engine-explorer/KNOWN_ISSUES.md +++ /dev/null @@ -1,46 +0,0 @@ -# Known Limitations and Issues - -* TensorRT 9.0 uses `kgen` kernels which produce broken representations of JSON engine graphs and no per-layer timing or inference profiling information. - -* TREx does not always display the correct region shapes. This is due to missing information in the graph JSON produced by TensorRT. This will be fixed in a future TensorRT release. -* TREx has partial support for multi-profile engines. You can choose which profile to use for the engine plan, but profiling information will only load when using the first (default) profile (0). For example, to load profile 3: - - ``` - plan = EnginePlan( - graph_file="my_engine_name.graph.json", - profiling_file="my_engine.profile.json", - profile_id=3) - - ``` - TRex will emit a warning saying that the profiling data was dropped. - - To use the profiling data, load profile 0: - - ``` - plan = EnginePlan( - graph_file="my_engine_name.graph.json", - profiling_file="my_engine.profile.json", - profile_id=0) - - ``` -* `display_df` may hang or return with an error (perhaps after a long delay). -

    -This is a known `dtale` issue which may be caused by firewalls, VPNs or other network connectivity issues, but it may also be due to a problem when reading the server's hostname.
    -You can try to explicitly configure the hostname in `dtale`. For example, if you are running the Jupyter server on a machine that has IP address 192.168.1.30: - ``` - dtale.app.ACTIVE_HOST = "192.168.1.30" - ``` - **Note**: you should configure the host address before trying to use any of the functions that display tables. For example: configure `dtale.app.ACTIVE_HOST` right after importing `trex` or `dtale` at the top of your notebook. -
    - Alternatively, you can change the backend library used for rendering tables by setting `trex.set_table_display_backend` to one of the other valid backends. -
    - To use the `qgrid` backend: - ``` - trex.set_table_display_backend(display_df_qgrid) - ``` - - To use the `IPython` backend: - ``` - trex.set_table_display_backend(display) - ``` - **Further details:** When `display_df` is invoked, TREx forwards the function call to one of it table-rendering backends. The default backend is `dtale`, which uses a Flask server to render Pandas dataframes as sophisticated HTML tables. `dtale` attempts to detect the address of the flask server by using `socket.gethostname()`, which may fail in some systems. Upon such failure, explicitly setting `dtale.app.ACTIVE_HOST` to the Flask server's IP address (or name) should help. diff --git a/tools/experimental/trt-engine-explorer/LICENSE.txt b/tools/experimental/trt-engine-explorer/LICENSE.txt deleted file mode 100644 index 3e17c5d83..000000000 --- a/tools/experimental/trt-engine-explorer/LICENSE.txt +++ /dev/null @@ -1,191 +0,0 @@ - - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. Definitions. - - "License" shall mean the terms and conditions for use, reproduction, - and distribution as defined by Sections 1 through 9 of this document. - - "Licensor" shall mean the copyright owner or entity authorized by - the copyright owner that is granting the License. - - "Legal Entity" shall mean the union of the acting entity and all - other entities that control, are controlled by, or are under common - control with that entity. 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It contains information about the final inference graph and can be deserialized for inference runtime execution. An engine plan is specific to the hardware and software versions of the system used to build the engine. - -`trex` is useful for initial model performance debugging, visualization of plan graphs, and for understanding the characteristics of an engine plan. For in-depth performance analysis, [NVIDIA® Nsight Systems ™](https://developer.nvidia.com/nsight-systems) is the recommended performance analysis tool. For engine visualizations see also [NVIDIA® Nsight Deep Learning Designer™](https://developer.nvidia.com/nsight-dl-designer). - -# Features -The `trex` package contains an API and Jupyter notebooks for viewing and inspecting TensorRT engine-plan files and profiling data. - -* An engine plan graph (JSON) is loaded to a Pandas dataframe which allows slicing, querying, filtering, viewing and diagraming. -* An engine plan graph can be visualized as SVG/PNG files. -* Layer linters are an API for flagging potential performance hazards (preview feature). -* Four Jupyter notebooks provide: - * An introduction to `trex` tutorial. - * `trex` API examples. - * Detailed engine plan performance, characteristics and structure analysis. - * Comparison of two or more engine plans. -* Because `trex` operates on JSON input files, it does not require a GPU. - -## Caveats -When `trtexec` times individual layers, the total engine latency (computed by summing the average latency of each layer) is higher than the latency reported for the entire engine. This is due to per-layer measurement overheads. - -To measure per-layer execution times, when `trtexec` enqueues kernel layers for execution in a stream, it places CUDA event objects between the layers to monitor the start and completion of each layer. These CUDA events add a small overhead which is more noticeable with smaller networks (shallow and narrow networks or networks with small activation data). - -## Supported TensorRT Versions -Starting with TensorRT 8.2, engine-plan graph and profiling data can be exported to JSON files. `trex` supports TensortRT 8.x, 9.x and 10.0. - -`trex` has only been tested on 22.04 with Python 3.10.12.
    -`trex` does not require a GPU, but generating the input JSON file(s) does require a GPU. - -

    Installation

     - - -The instructions below detail how to use a Python3 virtualenv for installing and using trex (Python 3.8+ is required). - -### 1. Clone the trex code repository from TensorRT OSS repository -``` -$ git clone https://github.com/NVIDIA/TensorRT.git -``` - -### 2. Create and activate a Python virtual environment -The commands listed below create and activate a Python virtual environment named ```env_trex``` which is stored in a directory by the same name, and configures the current shell to use it as the default python environment. - -``` -$ cd TensorRT/tools/experimental/trt-engine-explorer -$ python3 -m virtualenv env_trex -$ source env_trex/bin/activate -``` - -### 3. Install trex in development mode -To install core functionality only: -``` -$ python3 -m pip install -e . -``` - -To install all packages (core + packages required for using Jupyter notebooks): - -``` -$ python3 -m pip install -e .[notebook] -``` - -### 4. Install Graphviz -Generating dot and SVG graphs requires Graphviz, an open source graph visualization software: -``` -$ sudo apt --yes install graphviz -``` -
    - -

    Workflow

     - -The typical `trex` workflow is depicted below: -1. Convert an external model to a TensorRT `INetworkDefinition`. -2. Build a TensorRT engine. -3. Profile the engine while creating the necessary JSON files. -4. Explore the engine by loading the JSON files in a `trex` notebook. -
    - -![](images/trex-overview.png) - -The Python script `utils/process_engine.py` implements this workflow for ONNX models: -1. Use `trtexec` to import an ONNX model and create an engine. -2. Load the engine and create an engine-graph JSON file. -3. Use `trtexec` to profile the engine's inference execution and store the results in an engine profiling JSON file. -4. Create an engine graph diagram in SVG format. -
    - -For more information see [TensorRT Engine Inspector](https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#engine-inspector) and the [Tutorial](notebooks/tutorial.ipynb) notebook. -
    - -

    Jupyter Server

     - -Launch the Jupyter notebook server as detailed below and open your browser at `http://localhost:8888` or `http://:8888` -``` -$ jupyter-notebook --ip=0.0.0.0 --no-browser -``` - -If you're using JupyterLab, you can launch the server with: -``` -$ jupyter lab --ip=0.0.0.0 --port=8888 -``` -
    - -

    License

     - -The TensorRT Engine Explorer license can be found in the [LICENSE](LICENSE.txt) file. - -
    diff --git a/tools/experimental/trt-engine-explorer/RESOURCES.md b/tools/experimental/trt-engine-explorer/RESOURCES.md deleted file mode 100644 index 8c34d6e23..000000000 --- a/tools/experimental/trt-engine-explorer/RESOURCES.md +++ /dev/null @@ -1,17 +0,0 @@ -# Performance Resources - -## Performance Recommendations -* [Tensor Core Performance Guide](https://developer.download.nvidia.com/video/gputechconf/gtc/2019/presentation/s9926-tensor-core-performance-the-ultimate-guide.pdf) - * Arithmetic intensity - * Tensor Core (TC) refresher - * TC alignment requirements, tile quantization, wave quantization -* [2-mins video on TensorCore Performance Guidance](https://youtu.be/i8-Jw48Cp8w) -* [DL Performance Documentation](https://docs.nvidia.com/deeplearning/performance/index.html): recommendations that apply to most deep learning operations. -* [Tips for Optimizing GPU Performance Using Tensor Cores](https://developer.nvidia.com/blog/optimizing-gpu-performance-tensor-cores/) - -## Device Capabilities - -* [GPU Compute Capability](https://developer.nvidia.com/cuda-gpus): Lists the SM version of various NVIDIA devices. -* [Supported Precisions](https://docs.nvidia.com/deeplearning/tensorrt/support-matrix/index.html#hardware-precision-matrix): -Contains a table listing which precision modes various NVIDIA devices support. - diff --git a/tools/experimental/trt-engine-explorer/__init__.py b/tools/experimental/trt-engine-explorer/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/tools/experimental/trt-engine-explorer/bin/README.md b/tools/experimental/trt-engine-explorer/bin/README.md deleted file mode 100644 index df2aa4fe3..000000000 --- a/tools/experimental/trt-engine-explorer/bin/README.md +++ /dev/null @@ -1,45 +0,0 @@ -# trex Command-line Tool - -The `trex` command-line tool (not to be confused with the `trex` package) provides a convinient interface to some of the utilities in the `utils` directory. It is installed with the `trex` package. - -## trex draw -Draw a graph diagram of a TensorRT engine graph JSON file.
    - -Example: -``` -$ trex draw ./examples/pytorch/resnet/A100/fp32/resnet.onnx.engine.graph.json --display_regions --no_layer_names -``` - -There are many rendering configuration flags including `--query` which draws the induced subgraph of all nodes whose name or metadata matches the regular expression, which is useful for engines graphs that are too large for graphviz to render. -``` -$ trex draw mymodel.graph.json -pj=mymodel.profile.json --display_metadata --display_regions --display_constant --query="transformer_blocks.(12|37).*" -``` - -## trex process -Build, profile and draw a TensorRT engine. -``` -$ trex process ./examples/pytorch/resnet/generated/resnet.onnx ./examples/pytorch/resnet/A100/fp32/ -``` - -## trex summary -Print a tabularized summary of the tactics -``` -$ trex summary ./examples/pytorch/resnet/A100/fp32/resnet.onnx.engine.graph.json --profiling_json=./examples/pytorch/resnet/A100/fp32/resnet.onnx.engine.profile.json --sort_key=latency --group_tactics - -+----+-----------------------------------------------------------------------------------------------------------------------------------+-------------+---------+ -| | tactic | latency % | count | -|----+-----------------------------------------------------------------------------------------------------------------------------------+-------------+---------| -| 1 | ampere_scudnn_128x64_relu_xregs_large_nn_v | 24.5902 | 1 | -| 7 | sm80_xmma_fprop_implicit_gemm_f32f32_tf32f32_f32_nhwckrsc_nhwc_tilesize256x64x32_stage3_warpsize4x2x1_g1_tensor16x8x8_t1r3s | 21.7454 | 4 | -| 9 | sm80_xmma_fprop_implicit_gemm_f32f32_tf32f32_f32_nhwckrsc_nhwc_tilesize64x128x32_stage5_warpsize2x2x1_g1_tensor16x8x8_t1r3s | 15.188 | 4 | -| 5 | sm80_xmma_fprop_implicit_gemm_f32f32_tf32f32_f32_nhwckrsc_nhwc_tilesize256x128x32_stage3_warpsize4x2x1_g1_tensor16x8x8_t1r3s | 13.838 | 4 | -| 4 | sm80_xmma_fprop_implicit_gemm_f32f32_tf32f32_f32_nhwckrsc_nhwc_tilesize128x128x32_stage4_warpsize2x2x1_g1_tensor16x8x | 13.7898 | 4 | -| 0 | TensorR | 6.50916 | 2 | -| 3 | sm80_xmma_fprop_implicit_gemm_f32f32_tf32f32_f32_nhwckrsc_nhwc_tilesize128x128x16_stage4_warpsize2x2x1_g1_tensor16x8x8_t1r1s | 1.44648 | 1 | -| 6 | sm80_xmma_fprop_implicit_gemm_f32f32_tf32f32_f32_nhwckrsc_nhwc_tilesize256x64x32_stage3_warpsize4x2x1_g1_tensor16x8x8_t1r1s | 0.96432 | 1 | -| 8 | sm80_xmma_fprop_implicit_gemm_f32f32_tf32f32_f32_nhwckrsc_nhwc_tilesize64x128x32_stage5_warpsize2x2x1_g1_tensor16x8x8_t1r1s | 0.819672 | 1 | -| 10 | sm80_xmma_fprop_implicit_gemm_f32f32_tf32f32_f32_nhwckrsc_nhwc_tilesize64x32x64_stage5_warpsize2x2x1_g1_tensor16x8x8_simple_t1r1s | 0.626808 | 1 | -| 2 | sm50_xmma_pooling_fw_4d_FP32FP32NHWC_Average_FastDiv_CAlign | 0.48216 | 1 | -+----+-----------------------------------------------------------------------------------------------------------------------------------+-------------+---------+ - -``` diff --git a/tools/experimental/trt-engine-explorer/bin/trex.py b/tools/experimental/trt-engine-explorer/bin/trex.py deleted file mode 100755 index 13c85fd5d..000000000 --- a/tools/experimental/trt-engine-explorer/bin/trex.py +++ /dev/null @@ -1,72 +0,0 @@ -#!/usr/bin/env python3 -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - - -""" -This is a command-line interface to several common utilities. - -Note: this script requires graphviz which can be installed manually: - $ sudo apt-get --yes install graphviz - $ python3 -m pip install graphviz -""" - -import sys -import os -import argparse -import trex -import logging -SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__)) -sys.path.append(os.path.join(os.path.dirname(SCRIPT_DIR), "utils")) -import utils.draw_engine as draw_engine -import utils.summarize_engine as summarize_engine -have_process_engine = True -try: - import utils.process_engine as process_engine -except ModuleNotFoundError: - # process_engine can only be used in environments that have tensorrt python bindings installed. - logging.warning("Failed to import process_engine. Disabling sub-command `process`.") - logging.warning("This happens when the TensorRT Python library is not installed or fails to initialize. For example, when running TREx on a computer without a GPU).") - have_process_engine = False - - -def main(): - try: - sys.argv[1] - except IndexError: - sys.argv.append("-h") - parser = argparse.ArgumentParser() - subparsers = parser.add_subparsers(help='TREx sub-commands') - draw_engine.make_subcmd_parser(subparsers) - summarize_engine.make_subcmd_parser(subparsers) - if have_process_engine: - process_engine.make_subcmd_parser(subparsers) - parser.add_argument('-v', '--version', action='version', - version=f'%(prog)s {trex.__version__}', - help="Show program's version number and exit.") - - args = parser.parse_args() - if hasattr(args, "func"): - args.func(args) - else: - logging.error("Failed to parse trex arguments") - - -if __name__ == "__main__": - main() - - diff --git a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/.gitignore b/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/.gitignore deleted file mode 100644 index 6d7e3d7b6..000000000 --- a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/.gitignore +++ /dev/null @@ -1,5 +0,0 @@ -*.engine -*.onnx -timing.cache* -*.gv -*.svg diff --git a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/A100/fp16/resnet.onnx.engine.build.metadata.json b/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/A100/fp16/resnet.onnx.engine.build.metadata.json deleted file mode 100755 index 413fb56e4..000000000 --- a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/A100/fp16/resnet.onnx.engine.build.metadata.json +++ /dev/null @@ -1 +0,0 @@ -{"model_options": {"Format": "ONNX", "Model": "./examples/pytorch/resnet/generated/resnet.onnx"}, "build_options": {"Max batch": "explicit batch", "Memory Pools": "workspace", "minTiming": "1", "avgTiming": "8", "Precision": "FP32+FP16"}} \ No newline at end of file diff --git a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/A100/fp16/resnet.onnx.engine.graph.json b/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/A100/fp16/resnet.onnx.engine.graph.json deleted file mode 100755 index bcb9024c5..000000000 --- a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/A100/fp16/resnet.onnx.engine.graph.json +++ /dev/null @@ -1,1059 +0,0 @@ -{"Layers": [{ - "Name": "Reformatting CopyNode for Input Tensor 0 to Conv_0 + Relu_1", - "LayerType": "Reformat", - "Inputs": [ - { - "Name": "input.1", - "Location": "Device", - "Dimensions": [32,3,224,224], - "Format/Datatype": "Row major linear FP32" - }], - "Outputs": [ - { - "Name": "Reformatted Input Tensor 0 to Conv_0 + Relu_1", - "Location": "Device", - "Dimensions": [32,3,224,224], - "Format/Datatype": "Channel major FP16 format where channel % 4 == 0" - }], - "ParameterType": "Reformat", - "Origin": "REFORMAT", - "TacticValue": "0x0000000000000000" -},{ - "Name": "Conv_0 + Relu_1", - "LayerType": "CaskConvolution", - "Inputs": [ - { - "Name": "Reformatted Input Tensor 0 to Conv_0 + Relu_1", - "Location": "Device", - "Dimensions": [32,3,224,224], - "Format/Datatype": "Channel major FP16 format where channel % 4 == 0" - }], - "Outputs": [ - { - "Name": "onnx::MaxPool_125", - "Location": "Device", - "Dimensions": [32,64,112,112], - "Format/Datatype": "Channel major FP16 format where channel % 8 == 0" - }], - "ParameterType": "Convolution", - "Kernel": [7,7], - "PaddingMode": "kEXPLICIT_ROUND_DOWN", - "PrePadding": [3,3], - "PostPadding": [3,3], - "Stride": [2,2], - "Dilation": [1,1], - "OutMaps": 64, - "Groups": 1, - "Weights": {"Type": "Half", "Count": 9408}, - "Bias": {"Type": "Half", "Count": 64}, - "HasSparseWeights": 0, - "Activation": "RELU", - "HasBias": 1, - "HasReLU": 1, - "TacticName": "trt_turing_cutlass_image_network_first_layer_hmma_fprop_f16f16f32_nhwc_nhwc_k64r7s7c4_stride2x2", - "TacticValue": "0xe2222883a6602489" -},{ - "Name": "MaxPool_2", - "LayerType": "CudaPooling", - "Inputs": [ - { - "Name": "onnx::MaxPool_125", - "Location": "Device", - "Dimensions": [32,64,112,112], - "Format/Datatype": "Channel major FP16 format where channel % 8 == 0" - }], - "Outputs": [ - { - "Name": "input.8", - "Location": "Device", - "Dimensions": [32,64,56,56], - "Format/Datatype": "Channel major FP16 format where channel % 8 == 0" - }], - "ParameterType": "Pooling", - "PoolingType": "MAX", - "WindowSize": [3,3], - "PaddingMode": "kEXPLICIT_ROUND_DOWN", - "PrePadding": [1,1], - "PostPadding": [1,1], - "Stride": [2,2], - "BlendFactor": 0, - "AverageCountExcludesPadding": 1, - "TacticValue": "0xfffffffffffffffe" -},{ - "Name": "Conv_3 + Relu_4", - "LayerType": "CaskConvolution", - "Inputs": [ - { - "Name": "input.8", - "Location": "Device", - "Dimensions": [32,64,56,56], - "Format/Datatype": "Channel major FP16 format where channel % 8 == 0" - }], - "Outputs": [ - { - "Name": "onnx::Conv_129", - "Location": "Device", - "Dimensions": [32,64,56,56], - "Format/Datatype": "Channel major FP16 format where channel % 8 == 0" - }], - "ParameterType": "Convolution", - "Kernel": [3,3], - "PaddingMode": "kEXPLICIT_ROUND_DOWN", - "PrePadding": [1,1], - "PostPadding": [1,1], - "Stride": [1,1], - "Dilation": [1,1], - "OutMaps": 64, - "Groups": 1, - "Weights": {"Type": "Half", "Count": 36864}, - "Bias": {"Type": "Half", "Count": 64}, - "HasSparseWeights": 0, - "Activation": "RELU", - "HasBias": 1, - "HasReLU": 1, - "TacticName": "sm80_xmma_fprop_implicit_gemm_f16f16_f16f16_f16_nhwckrsc_nhwc_tilesize256x64x32_stage3_warpsize4x1x1_g1_tensor16x8x16_t1r3s3_aACCESS", - "TacticValue": "0x0866ddee325d07a6" -},{ - "Name": "Reformatting CopyNode for Input Tensor 0 to Conv_5 + Add_6 + Relu_7", - "LayerType": "NoOp", - "Inputs": [ - { - "Name": "onnx::Conv_129", - "Location": "Device", - "Dimensions": [32,64,56,56], - "Format/Datatype": "Channel major FP16 format where channel % 8 == 0" - }], - "Outputs": [ - { - "Name": "Reformatted Input Tensor 0 to Conv_5 + Add_6 + Relu_7", - "Location": "Device", - "Dimensions": [32,64,56,56], - "Format/Datatype": "Channel major FP16 format where channel % 16 == 0" - }], - "TacticValue": "0x0000000000000000" -},{ - "Name": "Reformatting CopyNode for Input Tensor 1 to Conv_5 + Add_6 + Relu_7", - "LayerType": "NoOp", - "Inputs": [ - { - "Name": "input.8", - "Location": "Device", - "Dimensions": [32,64,56,56], - "Format/Datatype": "Channel major FP16 format where channel % 8 == 0" - }], - "Outputs": [ - { - "Name": "Reformatted Input Tensor 1 to Conv_5 + Add_6 + Relu_7", - "Location": "Device", - "Dimensions": [32,64,56,56], - "Format/Datatype": "Channel major FP16 format where channel % 16 == 0" - }], - "TacticValue": "0x0000000000000000" -},{ - "Name": "Conv_5 + Add_6 + Relu_7", - "LayerType": "CaskConvolution", - "Inputs": [ - { - "Name": "Reformatted Input Tensor 0 to Conv_5 + Add_6 + Relu_7", - "Location": "Device", - "Dimensions": [32,64,56,56], - "Format/Datatype": "Channel major FP16 format where channel % 16 == 0" - }, - { - "Name": "Reformatted Input Tensor 1 to Conv_5 + Add_6 + Relu_7", - "Location": "Device", - "Dimensions": [32,64,56,56], - "Format/Datatype": "Channel major FP16 format where channel % 16 == 0" - }], - "Outputs": [ - { - "Name": "input.24", - "Location": "Device", - "Dimensions": [32,64,56,56], - "Format/Datatype": "Channel major FP16 format where channel % 16 == 0" - }], - "ParameterType": "Convolution", - "Kernel": [3,3], - "PaddingMode": "kEXPLICIT_ROUND_DOWN", - "PrePadding": [1,1], - "PostPadding": [1,1], - "Stride": [1,1], - "Dilation": [1,1], - "OutMaps": 64, - "Groups": 1, - "Weights": {"Type": "Half", "Count": 36864}, - "Bias": {"Type": "Half", "Count": 64}, - "HasSparseWeights": 0, - "Activation": "RELU", - "HasBias": 1, - "HasReLU": 1, - "TacticName": "sm80_xmma_fprop_implicit_gemm_f16f16_f16f16_f16_nhwckrsc_nhwc_tilesize256x64x32_stage3_warpsize4x1x1_g1_tensor16x8x16_t1r3s3_aACCESS", - "TacticValue": "0x0866ddee325d07a6" -},{ - "Name": "Reformatting CopyNode for Input Tensor 0 to Conv_8 + Relu_9", - "LayerType": "NoOp", - "Inputs": [ - { - "Name": "input.24", - "Location": "Device", - "Dimensions": [32,64,56,56], - "Format/Datatype": "Channel major FP16 format where channel % 16 == 0" - }], - "Outputs": [ - { - "Name": "Reformatted Input Tensor 0 to Conv_8 + Relu_9", - "Location": "Device", - "Dimensions": [32,64,56,56], - "Format/Datatype": "Channel major FP16 format where channel % 8 == 0" - }], - "TacticValue": "0x0000000000000000" -},{ - "Name": "Conv_8 + Relu_9", - "LayerType": "CaskConvolution", - "Inputs": [ - { - "Name": "Reformatted Input Tensor 0 to Conv_8 + Relu_9", - "Location": "Device", - "Dimensions": [32,64,56,56], - "Format/Datatype": "Channel major FP16 format where channel % 8 == 0" - }], - "Outputs": [ - { - "Name": "onnx::Conv_136", - "Location": "Device", - "Dimensions": [32,64,56,56], - "Format/Datatype": "Channel major FP16 format where channel % 8 == 0" - }], - "ParameterType": "Convolution", - "Kernel": [3,3], - "PaddingMode": "kEXPLICIT_ROUND_DOWN", - "PrePadding": [1,1], - 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"0x0000000000000000" -},{ - "Name": "Conv_10 + Add_11 + Relu_12", - "LayerType": "CaskConvolution", - "Inputs": [ - { - "Name": "Reformatted Input Tensor 0 to Conv_10 + Add_11 + Relu_12", - "Location": "Device", - "Dimensions": [32,64,56,56], - "Format/Datatype": "Channel major FP16 format where channel % 16 == 0" - }, - { - "Name": "input.24", - "Location": "Device", - "Dimensions": [32,64,56,56], - "Format/Datatype": "Channel major FP16 format where channel % 16 == 0" - }], - "Outputs": [ - { - "Name": "input.40", - "Location": "Device", - "Dimensions": [32,64,56,56], - "Format/Datatype": "Channel major FP16 format where channel % 16 == 0" - }], - "ParameterType": "Convolution", - "Kernel": [3,3], - "PaddingMode": "kEXPLICIT_ROUND_DOWN", - "PrePadding": [1,1], - "PostPadding": [1,1], - "Stride": [1,1], - "Dilation": [1,1], - "OutMaps": 64, - "Groups": 1, - "Weights": {"Type": "Half", "Count": 36864}, - "Bias": {"Type": "Half", "Count": 64}, - "HasSparseWeights": 0, - "Activation": "RELU", - "HasBias": 1, - "HasReLU": 1, - "TacticName": "sm80_xmma_fprop_implicit_gemm_f16f16_f16f16_f16_nhwckrsc_nhwc_tilesize256x64x32_stage3_warpsize4x1x1_g1_tensor16x8x16_t1r3s3_aACCESS", - "TacticValue": "0x0866ddee325d07a6" -},{ - "Name": "Conv_13 + Relu_14", - "LayerType": "CaskConvolution", - "Inputs": [ - { - "Name": "input.40", - "Location": "Device", - "Dimensions": [32,64,56,56], - "Format/Datatype": "Channel major FP16 format where channel % 16 == 0" - }], - "Outputs": [ - { - "Name": "onnx::Conv_143", - "Location": "Device", - "Dimensions": [32,128,28,28], - "Format/Datatype": "Channel major FP16 format where channel % 16 == 0" - }], - "ParameterType": "Convolution", - "Kernel": [3,3], - "PaddingMode": "kEXPLICIT_ROUND_DOWN", - "PrePadding": [1,1], - "PostPadding": [1,1], - "Stride": [2,2], - "Dilation": [1,1], - "OutMaps": 128, - "Groups": 1, - "Weights": {"Type": "Half", "Count": 73728}, - "Bias": {"Type": "Half", "Count": 128}, - "HasSparseWeights": 0, - "Activation": 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a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/A100/fp32/resnet.onnx.engine.build.metadata.json +++ /dev/null @@ -1 +0,0 @@ -{"model_options": {"Format": "ONNX", "Model": "./examples/pytorch/resnet/generated/resnet.onnx"}, "build_options": {"Max batch": "explicit batch", "Memory Pools": "workspace", "minTiming": "1", "avgTiming": "8", "Precision": "FP32"}} \ No newline at end of file diff --git a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/A100/fp32/resnet.onnx.engine.graph.json b/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/A100/fp32/resnet.onnx.engine.graph.json deleted file mode 100755 index 050de52da..000000000 --- a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/A100/fp32/resnet.onnx.engine.graph.json +++ /dev/null @@ -1,985 +0,0 @@ -{"Layers": [{ - "Name": "Conv_0 + Relu_1", - "LayerType": "CaskConvolution", - "Inputs": [ - { - "Name": "input.1", - "Location": "Device", - "Dimensions": [32,3,224,224], - 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b/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/A100/fp32/resnet.onnx.engine.profile.metadata.json deleted file mode 100755 index a663ee876..000000000 --- a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/A100/fp32/resnet.onnx.engine.profile.metadata.json +++ /dev/null @@ -1 +0,0 @@ -{"performance_summary": {"Throughput": 499.294, "Latency": [1.98438, 2.05927, 2.00003, 1.99072, 2.05621], "Enqueue Time": [0.00463867, 0.0128174, 0.00530548, 0.00537109, 0.00585938], "H2D Latency": [0.0, 0.0, 0.0, 0.0, 0.0], "GPU Compute Time": [1.98438, 2.05927, 2.00003, 1.99072, 2.05621], "D2H Latency": [0.0, 0.0, 0.0, 0.0, 0.0], "Total Host Walltime": 3.00424, "Total GPU Compute Time": 3.00005}, "inference_options": {"Batch": "Explicit", "Input inference shape": "input.1=32x3x224x224", "Iterations": "10", "Duration": "3s (+ 200ms warm up)", "Sleep time": "0ms", "Idle time": "0ms", "Streams": "1", "ExposeDMA": "Disabled", "Data transfers": "Disabled", "Spin-wait": 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a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/A100/qat-residual-qgap/resnet-qat-residual-qgap.onnx.engine.build.metadata.json +++ /dev/null @@ -1 +0,0 @@ -{"model_options": {"Format": "ONNX", "Model": "./examples/pytorch/resnet/generated/resnet-qat-residual-qgap.onnx"}, "build_options": {"Max batch": "explicit batch", "Memory Pools": "workspace", "minTiming": "1", "avgTiming": "8", "Precision": "FP32+FP16+INT8"}} \ No newline at end of file diff --git a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/A100/qat-residual-qgap/resnet-qat-residual-qgap.onnx.engine.graph.json b/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/A100/qat-residual-qgap/resnet-qat-residual-qgap.onnx.engine.graph.json deleted file mode 100755 index e0fb6f903..000000000 --- a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/A100/qat-residual-qgap/resnet-qat-residual-qgap.onnx.engine.graph.json +++ /dev/null @@ -1,838 +0,0 @@ -{"Layers": [{ - "Name": 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559f506d5..000000000 --- a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/A100/qat-residual/resnet-qat-residual.onnx.engine.build.metadata.json +++ /dev/null @@ -1 +0,0 @@ -{"model_options": {"Format": "ONNX", "Model": "./examples/pytorch/resnet/generated/resnet-qat-residual.onnx"}, "build_options": {"Max batch": "explicit batch", "Memory Pools": "workspace", "minTiming": "1", "avgTiming": "8", "Precision": "FP32+FP16+INT8"}} \ No newline at end of file diff --git a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/A100/qat-residual/resnet-qat-residual.onnx.engine.graph.json b/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/A100/qat-residual/resnet-qat-residual.onnx.engine.graph.json deleted file mode 100755 index 727ca16b4..000000000 --- a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/A100/qat-residual/resnet-qat-residual.onnx.engine.graph.json +++ /dev/null @@ -1,879 +0,0 @@ -{"Layers": [{ - "Name": "QuantizeLinear_2", - 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a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/A100/qat-residual/resnet-qat-residual.onnx.engine.profile.metadata.json +++ /dev/null @@ -1 +0,0 @@ -{"performance_summary": {"Throughput": 1823.37, "Latency": [0.543701, 0.550964, 0.545726, 0.545776, 0.547852], "Enqueue Time": [0.00439453, 0.0119629, 0.00510696, 0.00512695, 0.0057373], "H2D Latency": [0.0, 0.0, 0.0, 0.0, 0.0], "GPU Compute Time": [0.543701, 0.550964, 0.545726, 0.545776, 0.547852], "D2H Latency": [0.0, 0.0, 0.0, 0.0, 0.0], "Total Host Walltime": 3.00103, "Total GPU Compute Time": 2.98621}, "inference_options": {"Batch": "Explicit", "Input inference shape": "input.1=32x3x224x224", "Iterations": "10", "Duration": "3s (+ 200ms warm up)", "Sleep time": "0ms", "Idle time": "0ms", "Streams": "1", "ExposeDMA": "Disabled", "Data transfers": "Disabled", "Spin-wait": "Enabled", "Multithreading": "Disabled", "CUDA Graph": "Enabled", "Separate profiling": "Enabled", "Time Deserialize": "Disabled", "Time Refit": 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b/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/A100/qat/resnet-qat.onnx.engine.build.metadata.json deleted file mode 100755 index f664ddc86..000000000 --- a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/A100/qat/resnet-qat.onnx.engine.build.metadata.json +++ /dev/null @@ -1 +0,0 @@ -{"model_options": {"Format": "ONNX", "Model": "./examples/pytorch/resnet/generated/resnet-qat.onnx"}, "build_options": {"Max batch": "explicit batch", "Memory Pools": "workspace", "minTiming": "1", "avgTiming": "8", "Precision": "FP32+FP16+INT8"}} \ No newline at end of file diff --git a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/A100/qat/resnet-qat.onnx.engine.graph.json b/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/A100/qat/resnet-qat.onnx.engine.graph.json deleted file mode 100755 index 8862097d6..000000000 --- a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/A100/qat/resnet-qat.onnx.engine.graph.json +++ /dev/null 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"latencyMs" : 0.883545 } -, { "startEnqMs" : 3200.62, "endEnqMs" : 3200.62, "startH2dMs" : 3201.38, "endH2dMs" : 3201.38, "startComputeMs" : 3201.38, "endComputeMs" : 3202.26, "startD2hMs" : 3202.26, "endD2hMs" : 3202.26, "h2dMs" : 0, "computeMs" : 0.881592, "d2hMs" : 0, "latencyMs" : 0.881592 } -] diff --git a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/README.md b/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/README.md deleted file mode 100644 index c6dfd4c1c..000000000 --- a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/README.md +++ /dev/null @@ -1,32 +0,0 @@ -## TREx Blog ResNet QAT Example - -This directory contains the code for the examples in the [TREx blog](https://developer.nvidia.com/blog/exploring-tensorrt-engines-with-trex/).
    -Directory `A100` contains pre-generated JSON files which are provided as a shortcut so you can skip straight to using the `example_qat_resnet18.ipynb` notebook. -

    -### Installation -The example uses PyTorch, TorchVision and Nvidia's [PyTorch Quantization Toolkit](https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization). PyTorch and TorchVision can be installed with: -``` -python3 -m pip install torch torchvision -``` - -It is most reliable to install PyTorch Quantization Toolkit from source code so please follow [these](https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization#from-source) instructions. - - -### Description -This example walks you through the process of optimizing a TensorRT engine created from QAT ResNet18. -File `resnet_example.py` contains code to generate several variations of QAT ResNet18, each one performing better than the previous. - -* To generate the example ResNet ONNX models: - ``` - python3 /examples/pytorch/resnet/resnet_example.py - ``` - - The script generates several ONNX files and writes them to the `/generated/` directory. - -* Then generate the JSON files: - ``` - /examples/pytorch/resnet/process_resnet.sh - ``` - -* Finally, review the results using the `example_qat_resnet18.ipynb` notebook. - diff --git a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/__init__.py b/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/example_qat_resnet18.ipynb b/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/example_qat_resnet18.ipynb deleted file mode 100644 index 02bcb5dcd..000000000 --- a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/example_qat_resnet18.ipynb +++ /dev/null @@ -1,181 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": {}, - "report_default": { - "hidden": false - } - } - } - } - }, - "source": [ - "# Example: QAT Resnet18 Optimization\n", - "\n", - "This is the notebook described in the [TREx blog post](https://developer.nvidia.com/blog/exploring-tensorrt-engines-with-trex)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": {}, - "report_default": { - "hidden": false - } - } - } - } - }, - "source": [ - "## Load JSON Files" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": {}, - "report_default": { - "hidden": true - } - } - } - }, - "scrolled": false - }, - "outputs": [], - "source": [ - "import os\n", - "import IPython\n", - "from ipywidgets import widgets\n", - "from trex import *\n", - "\n", - "# Configure a wider output (for the wide graphs)\n", - "set_wide_display()\n", - "\n", - "def create_plan(engine_path: str, engine_name):\n", - " plan = EnginePlan(\n", - " f'{engine_path}.graph.json',\n", - " f'{engine_path}.profile.json',\n", - " f\"{engine_path}.profile.metadata.json\",\n", - " name = engine_name)\n", - " return plan\n", - "\n", - "\n", - "ipynb_path = os.path.dirname(os.path.realpath(\"__file__\"))\n", - "\n", - "examples_dir = os.path.join(ipynb_path, \"A100\")\n", - "engines_info = {\n", - " \"ResNet18-FP32\": \"fp32/resnet.onnx.engine\",\n", - " \"ResNet18-FP16\": \"fp16/resnet.onnx.engine\",\n", - " \"ResNet18-QAT\": \"qat/resnet-qat.onnx.engine\",\n", - " \"ResNet18-QAT+Residual\": \"qat-residual/resnet-qat-residual.onnx.engine\",\n", - " \"ResNet18-QAT+Residual+GAP\": \"qat-residual-qgap/resnet-qat-residual-qgap.onnx.engine\",\n", - "}\n", - "\n", - "\n", - "plans = [create_plan(os.path.join(examples_dir, engine_path), engine_name) for engine_name, engine_path in engines_info.items()]\n", - "compare_engines_summaries_tbl(plans, orientation='vertical')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "compare_engines_overview(plans)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "compare_engines_layer_latencies(\n", - " plans[1], plans[3],\n", - " # Allow for 3% error grace threshold when color highlighting performance differences\n", - " threshold=0.03,\n", - " # Inexact matching uses only the layer's first input and output to match to other layers.\n", - " exact_matching=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for plan in plans:\n", - " graph = to_dot(plan, layer_type_formatter, display_regions=True, expand_layer_details=True)\n", - " render_dot(graph, plan.name, 'svg')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "extensions": { - "jupyter_dashboards": { - "activeView": "report_default", - "version": 1, - "views": { - "grid_default": { - "name": "grid", - "type": "grid" - }, - "report_default": { - "name": "report", - "type": "report" - } - } - } - }, - "interpreter": { - "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.0" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/process_resnet.sh b/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/process_resnet.sh deleted file mode 100755 index 57acfe82f..000000000 --- a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/process_resnet.sh +++ /dev/null @@ -1,36 +0,0 @@ -#!/bin/sh -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - - -# Use this script to process the four example ResNet engines from resnet_example.py -# and generate the JSON files from exploration with TREx. - - -SCRIPT=`realpath $0` -SCRIPT_DIR=`dirname $SCRIPT` -PROCESS_ENGINE=$SCRIPT_DIR/../../../utils/process_engine.py -ONNX_DIR=$SCRIPT_DIR/generated - -# Batch size = 32 -SHAPES='shapes=input.1:32x3x224x224' - -python3 $PROCESS_ENGINE $ONNX_DIR/resnet.onnx $ONNX_DIR/fp32 $SHAPES -python3 $PROCESS_ENGINE $ONNX_DIR/resnet.onnx $ONNX_DIR/fp16 $SHAPES fp16 -python3 $PROCESS_ENGINE $ONNX_DIR/resnet-qat.onnx $ONNX_DIR/qat $SHAPES best -python3 $PROCESS_ENGINE $ONNX_DIR/resnet-qat-residual.onnx $ONNX_DIR/qat-residual $SHAPES best -python3 $PROCESS_ENGINE $ONNX_DIR/resnet-qat-residual-qgap.onnx $ONNX_DIR/qat-residual-qgap $SHAPES best diff --git a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/qat_model.py b/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/qat_model.py deleted file mode 100644 index f87414019..000000000 --- a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/qat_model.py +++ /dev/null @@ -1,397 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - - -# This file contains the definition of QAT ResNet -# The original code is from the Nvidia PyTorch Quantization Toolkit, and is provided -# here with more fine control over quantization of parts of the model to illustrate -# the use-case described in the TREx blog post. -# (i.e. quantization of Convs only, quantization of residual connections, and -# quantization of GAP layer are all controlled separately). - - -from typing import Any, Callable, List, Optional, Type, Union - -import torch -import torch.nn as nn -from pytorch_quantization import nn as quant_nn -from torch import Tensor -from torch.hub import load_state_dict_from_url - -__all__ = [ - "ResNet", - "resnet18", - "resnet34", - "resnet50", - "resnet101", - "resnet152", - "resnext50_32x4d", - "resnext101_32x8d", - "wide_resnet50_2", - "wide_resnet101_2", -] - -model_urls = { - "resnet18": "https://download.pytorch.org/models/resnet18-5c106cde.pth", - "resnet34": "https://download.pytorch.org/models/resnet34-333f7ec4.pth", - "resnet50": "https://download.pytorch.org/models/resnet50-19c8e357.pth", - "resnet101": "https://download.pytorch.org/models/resnet101-5d3b4d8f.pth", - "resnet152": "https://download.pytorch.org/models/resnet152-b121ed2d.pth", - "resnext50_32x4d": "https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth", - "resnext101_32x8d": "https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth", - "wide_resnet50_2": "https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth", - "wide_resnet101_2": "https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth", -} - - -def conv3x3( - in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1, quantize: bool = False -) -> nn.Conv2d: - """3x3 convolution with padding""" - if quantize: - return quant_nn.QuantConv2d( - in_planes, - out_planes, - kernel_size=3, - stride=stride, - padding=dilation, - groups=groups, - bias=False, - dilation=dilation, - ) - else: - return nn.Conv2d( - in_planes, - out_planes, - kernel_size=3, - stride=stride, - padding=dilation, - groups=groups, - bias=False, - dilation=dilation, - ) - - -def conv1x1(in_planes: int, out_planes: int, stride: int = 1, quantize: bool = False) -> nn.Conv2d: - """1x1 convolution""" - if quantize: - return quant_nn.QuantConv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) - else: - return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) - - -class BasicBlock(nn.Module): - expansion: int = 1 - - def __init__( - self, - inplanes: int, - planes: int, - stride: int = 1, - downsample: Optional[nn.Module] = None, - groups: int = 1, - base_width: int = 64, - dilation: int = 1, - norm_layer: Optional[Callable[..., nn.Module]] = None, - quantize: bool = False, - quantize_residual: bool = False, - ) -> None: - super(BasicBlock, self).__init__() - if norm_layer is None: - norm_layer = nn.BatchNorm2d - if groups != 1 or base_width != 64: - raise ValueError("BasicBlock only supports groups=1 and base_width=64") - if dilation > 1: - raise NotImplementedError("Dilation > 1 not supported in BasicBlock") - # Both self.conv1 and self.downsample layers downsample the input when stride != 1 - self.conv1 = conv3x3(inplanes, planes, stride, quantize=quantize) - self.bn1 = norm_layer(planes) - self.relu = nn.ReLU(inplace=True) - self.conv2 = conv3x3(planes, planes, quantize=quantize) - self.bn2 = norm_layer(planes) - self.downsample = downsample - self.stride = stride - self._quantize_residual = quantize_residual - if self._quantize_residual: - self.residual_quantizer = quant_nn.TensorQuantizer(quant_nn.QuantConv2d.default_quant_desc_input) - - def forward(self, x: Tensor) -> Tensor: - identity = x - - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - - out = self.conv2(out) - out = self.bn2(out) - - if self.downsample is not None: - identity = self.downsample(x) - - if self._quantize_residual: - out += self.residual_quantizer(identity) - else: - out += identity - out = self.relu(out) - - return out - - -class Bottleneck(nn.Module): - # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2) - # while original implementation places the stride at the first 1x1 convolution(self.conv1) - # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385. - # This variant is also known as ResNet V1.5 and improves accuracy according to - # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch. - - expansion: int = 4 - - def __init__( - self, - inplanes: int, - planes: int, - stride: int = 1, - downsample: Optional[nn.Module] = None, - groups: int = 1, - base_width: int = 64, - dilation: int = 1, - norm_layer: Optional[Callable[..., nn.Module]] = None, - quantize: bool = False, - quantize_residual: bool = False, - ) -> None: - super(Bottleneck, self).__init__() - if norm_layer is None: - norm_layer = nn.BatchNorm2d - width = int(planes * (base_width / 64.0)) * groups - # Both self.conv2 and self.downsample layers downsample the input when stride != 1 - self.conv1 = conv1x1(inplanes, width, quantize=quantize) - self.bn1 = norm_layer(width) - self.conv2 = conv3x3(width, width, stride, groups, dilation, quantize=quantize) - self.bn2 = norm_layer(width) - self.conv3 = conv1x1(width, planes * self.expansion, quantize=quantize) - self.bn3 = norm_layer(planes * self.expansion) - self.relu = nn.ReLU(inplace=True) - self.downsample = downsample - self.stride = stride - self._quantize_residual = quantize_residual - if self._quantize_residual: - self.residual_quantizer = quant_nn.TensorQuantizer(quant_nn.QuantConv2d.default_quant_desc_input) - - def forward(self, x: Tensor) -> Tensor: - identity = x - - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - - out = self.conv2(out) - out = self.bn2(out) - out = self.relu(out) - - out = self.conv3(out) - out = self.bn3(out) - - if self.downsample is not None: - identity = self.downsample(x) - - if self._quantize_residual: - out += self.residual_quantizer(identity) - else: - out += identity - out = self.relu(out) - - return out - - -class ResNet(nn.Module): - - def __init__( - self, - block: Type[Union[BasicBlock, Bottleneck]], - layers: List[int], - quantize: bool = False, - quantize_residual: bool = False, - quantize_gap: bool = False, - num_classes: int = 1000, - zero_init_residual: bool = False, - groups: int = 1, - width_per_group: int = 64, - replace_stride_with_dilation: Optional[List[bool]] = None, - norm_layer: Optional[Callable[..., nn.Module]] = None, - ) -> None: - super(ResNet, self).__init__() - self._quantize = quantize - self._quantize_residual = quantize_residual - self._quantize_gap = quantize_gap - - if norm_layer is None: - norm_layer = nn.BatchNorm2d - self._norm_layer = norm_layer - - self.inplanes = 64 - self.dilation = 1 - if replace_stride_with_dilation is None: - # each element in the tuple indicates if we should replace - # the 2x2 stride with a dilated convolution instead - replace_stride_with_dilation = [False, False, False] - if len(replace_stride_with_dilation) != 3: - raise ValueError( - "replace_stride_with_dilation should be None " - "or a 3-element tuple, got {}".format(replace_stride_with_dilation) - ) - self.groups = groups - self.base_width = width_per_group - - if quantize: - self.conv1 = quant_nn.QuantConv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False) - else: - self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False) - - self.bn1 = norm_layer(self.inplanes) - self.relu = nn.ReLU(inplace=True) - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) - self.layer1 = self._make_layer(block, 64, layers[0]) - self.layer2 = self._make_layer(block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0]) - self.layer3 = self._make_layer(block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1]) - self.layer4 = self._make_layer(block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2]) - if quantize_gap: - self.gap_quantizer = quant_nn.TensorQuantizer(quant_nn.QuantConv2d.default_quant_desc_input) - - self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) - - if quantize: - self.fc = quant_nn.QuantLinear(512 * block.expansion, num_classes) - else: - self.fc = nn.Linear(512 * block.expansion, num_classes) - - for m in self.modules(): - if isinstance(m, nn.Conv2d): - nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu") - elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): - nn.init.constant_(m.weight, 1) - nn.init.constant_(m.bias, 0) - - # Zero-initialize the last BN in each residual branch, - # so that the residual branch starts with zeros, and each residual block behaves like an identity. - # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 - if zero_init_residual: - for m in self.modules(): - if isinstance(m, Bottleneck): - nn.init.constant_(m.bn3.weight, 0) # type: ignore[arg-type] - elif isinstance(m, BasicBlock): - nn.init.constant_(m.bn2.weight, 0) # type: ignore[arg-type] - - def _make_layer( - self, - block: Type[Union[BasicBlock, Bottleneck]], - planes: int, - blocks: int, - stride: int = 1, - dilate: bool = False, - ) -> nn.Sequential: - norm_layer = self._norm_layer - downsample = None - previous_dilation = self.dilation - if dilate: - self.dilation *= stride - stride = 1 - if stride != 1 or self.inplanes != planes * block.expansion: - downsample = nn.Sequential( - conv1x1(self.inplanes, planes * block.expansion, stride, quantize=self._quantize), - norm_layer(planes * block.expansion), - ) - - layers = [] - layers.append( - block( - self.inplanes, - planes, - stride, - downsample, - self.groups, - self.base_width, - previous_dilation, - norm_layer, - self._quantize, - self._quantize_residual, - ) - ) - self.inplanes = planes * block.expansion - for _ in range(1, blocks): - layers.append( - block( - self.inplanes, - planes, - groups=self.groups, - base_width=self.base_width, - dilation=self.dilation, - norm_layer=norm_layer, - quantize=self._quantize, - quantize_residual=self._quantize_residual, - ) - ) - - return nn.Sequential(*layers) - - def _forward_impl(self, x: Tensor) -> Tensor: - # See note [TorchScript super()] - x = self.conv1(x) - x = self.bn1(x) - x = self.relu(x) - x = self.maxpool(x) - - x = self.layer1(x) - x = self.layer2(x) - x = self.layer3(x) - x = self.layer4(x) - - if self._quantize_gap: - x = self.gap_quantizer(x) - x = self.avgpool(x) - x = torch.flatten(x, 1) - x = self.fc(x) - - return x - - def forward(self, x: Tensor) -> Tensor: - return self._forward_impl(x) - - -def _resnet( - arch: str, - block: Type[Union[BasicBlock, Bottleneck]], - layers: List[int], - pretrained: bool, - progress: bool, - **kwargs: Any -) -> ResNet: - model = ResNet(block, layers, **kwargs) - if pretrained: - state_dict = load_state_dict_from_url(model_urls[arch], progress=progress) - model.load_state_dict(state_dict) - return model - - -def resnet18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: - r"""ResNet-18 model from - `"Deep Residual Learning for Image Recognition" `_. - - Args: - pretrained (bool): If True, returns a model pre-trained on ImageNet - progress (bool): If True, displays a progress bar of the download to stderr - """ - return _resnet("resnet18", BasicBlock, [2, 2, 2, 2], pretrained, progress, **kwargs) diff --git a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/resnet_example.py b/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/resnet_example.py deleted file mode 100644 index 9434e5d09..000000000 --- a/tools/experimental/trt-engine-explorer/examples/pytorch/resnet/resnet_example.py +++ /dev/null @@ -1,104 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - - -# This script generates 4 different ResNet models: -# 1. FP32 Resnet. -# 2. QAT Resnet, with only the Convolutions quantized. -# 3. QAT Resnet, with Convolutions and residual-connections quantized. -# 4. QAT Resnet, with only the Convolutions, residual-connections, and GAP quantized. -# -# GAP is Global Average Pooling. - - -import os -import torch -from qat_model import resnet18 - -# For QAT -from pytorch_quantization import nn as quant_nn -quant_nn.TensorQuantizer.use_fb_fake_quant = True - -# Create the directory which will store the generated models. -output_dir = os.path.dirname(os.path.realpath(__file__)) -output_dir = os.path.join(output_dir, "generated") -try: - os.mkdir(output_dir) -except OSError as error: - pass - - -input = torch.randn(1, 3, 224, 224) - - -resnet = resnet18( - pretrained=True, - quantize=False).eval() - -with torch.no_grad(): - torch.onnx.export( - resnet, input, - os.path.join(output_dir, "resnet.onnx"), - input_names=["input.1"], - opset_version=13, - dynamic_axes={"input.1": {0: "batch_size"}}) - - -resnet = resnet18( - pretrained=True, - quantize=True).eval() - -with torch.no_grad(): - torch.onnx.export( - resnet, input, - os.path.join(output_dir, "resnet-qat.onnx"), - input_names=["input.1"], - opset_version=13, - dynamic_axes={"input.1": {0: "batch_size"}}) - - -resnet = resnet18( - pretrained=True, - quantize=True, - quantize_residual=True).eval() - -with torch.no_grad(): - input = torch.randn(1, 3, 224, 224) - torch.onnx.export( - resnet, input, - os.path.join(output_dir, "resnet-qat-residual.onnx"), - input_names=["input.1"], - opset_version=13, - dynamic_axes={"input.1": {0: "batch_size"}}) - - -resnet = resnet18( - pretrained=True, - quantize=True, - quantize_residual=True, - quantize_gap=True).eval() - -with torch.no_grad(): - input = torch.randn(1, 3, 224, 224) - torch.onnx.export( - resnet, input, - os.path.join(output_dir, "resnet-qat-residual-qgap.onnx"), - input_names=["input.1"], - opset_version=13, - dynamic_axes={"input.1": {0: "batch_size"}}) - - diff --git a/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/README.md b/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/README.md deleted file mode 100644 index 3573ecb37..000000000 --- a/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/README.md +++ /dev/null @@ -1,20 +0,0 @@ -## TensorFlow ResNet QAT Example - -This directory contains the code for quantizing a TensorFlow QAT ResNet50 model. The example uses TensorFlow and NVIDIA's [TensorFlow Quantization Toolkit](https://github.com/NVIDIA/TensorRT/tree/main/tools/tensorflow-quantization) which you can install with: -``` -git clone https://github.com/NVIDIA/TensorRT.git -cd TensorRT/tools/tensorflow-quantization -./install.sh -``` - -Start by generating the example ResNet ONNX models: -``` -python3 /examples/tensorflow/resnet/resnet_example.py -``` - -Then generate the JSON files: -``` -/examples/tensorflow/resnet/process_resnet.sh -``` - -Finally, review the results in the TREx notebook [*example_qat_resnet.ipynb*](example_qat_resnet.ipynb). diff --git a/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/RTX-3090/fp16/resnet.onnx.engine.build.metadata.json b/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/RTX-3090/fp16/resnet.onnx.engine.build.metadata.json deleted file mode 100644 index baec1c3e1..000000000 --- a/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/RTX-3090/fp16/resnet.onnx.engine.build.metadata.json +++ /dev/null @@ -1 +0,0 @@ -{"model_options": {"Format": "ONNX", "Model": "./examples/tensorflow/resnet/generated/resnet.onnx"}, "build_options": {"Max batch": "explicit batch", "Memory Pools": "workspace", "minTiming": "1", "avgTiming": "8", "Precision": "FP32+FP16"}} \ No newline at end of file diff --git a/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/RTX-3090/fp16/resnet.onnx.engine.graph.json b/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/RTX-3090/fp16/resnet.onnx.engine.graph.json deleted file mode 100644 index 16600867f..000000000 --- a/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/RTX-3090/fp16/resnet.onnx.engine.graph.json +++ /dev/null @@ -1,2361 +0,0 @@ -{"Layers": [{ - "Name": "resnet50/conv1_conv/Conv2D__6", - "LayerType": "Shuffle", - "Inputs": [ - { - "Name": "input_1", - "Dimensions": [32,224,224,3], - "Format/Datatype": "Row major linear FP32" - }], - "Outputs": [ - { - "Name": "resnet50/conv1_conv/Conv2D__6:0", - "Dimensions": [32,3,224,224], - "Format/Datatype": "Row major linear FP32" - }], - "ParameterType": "Shuffle", - "FirstTranspose": [0,3,1,2], - "Reshape": "nbDims=-1", - "SecondTranspose": [0,1,2,3], - "ZeroIsPlaceholder": 1, - "TacticValue": "0x0000000000000000" -},{ - "Name": "Reformatting CopyNode for Input Tensor 0 to Conv__435 + resnet50/conv1_relu/Relu", - "LayerType": "Reformat", - "Inputs": [ - { - "Name": "resnet50/conv1_conv/Conv2D__6:0", - "Dimensions": [32,3,224,224], - "Format/Datatype": "Row major linear FP32" - }], - "Outputs": [ - { - "Name": "Reformatted Input Tensor 0 to Conv__435 + resnet50/conv1_relu/Relu", - "Dimensions": [32,3,224,224], - "Format/Datatype": "Channel major FP16 format where channel % 4 == 0" - }], - "ParameterType": "Reformat", - "Origin": "REFORMAT", - "TacticValue": "0x0000000000000000" -},{ - "Name": "Conv__435 + resnet50/conv1_relu/Relu", - "LayerType": "CaskConvolution", - "Inputs": [ - { - "Name": "Reformatted Input Tensor 0 to Conv__435 + resnet50/conv1_relu/Relu", - "Dimensions": [32,3,224,224], - "Format/Datatype": "Channel major FP16 format where channel % 4 == 0" - }], - "Outputs": [ - { - "Name": "resnet50/conv1_relu/Relu:0", - "Dimensions": [32,64,112,112], - "Format/Datatype": "Channel major FP16 format where channel % 8 == 0" - }], - "ParameterType": "Convolution", - "Kernel": [7,7], - "PaddingMode": "kEXPLICIT_ROUND_DOWN", - "PrePadding": [3,3], - "PostPadding": [3,3], - "Stride": [2,2], - "Dilation": [1,1], - "OutMaps": 64, - "Groups": 1, - "Weights": {"Type": "Half", "Count": 9408}, - "Bias": {"Type": "Half", "Count": 64}, - "HasSparseWeights": 0, - "Activation": "RELU", - "HasBias": 1, - "HasReLU": 1, - "TacticName": "trt_turing_cutlass_image_network_first_layer_hmma_fprop_f16f16f32_nhwc_nhwc_k64r7s7c4_stride2x2", - "TacticValue": "0xe2222883a6602489" -},{ - "Name": "resnet50/pool1_pad/Pad", - "LayerType": "Padding", - "Inputs": [ - { - "Name": "resnet50/conv1_relu/Relu:0", - "Dimensions": [32,64,112,112], - "Format/Datatype": "Channel major FP16 format where channel % 8 == 0" - }], - "Outputs": [ - { - "Name": "resnet50/pool1_pad/Pad:0", - "Dimensions": [32,64,114,114], - "Format/Datatype": "Channel major FP16 format where channel % 8 == 0" - }], - "ParameterType": "Slice", - "Start": [0,0,-1,-1], - "Size": [32,64,114,114], - "Stride": [1,1,1,1], - "Mode": "FILL", - "negativeInfinityPadding": 0, - "TacticValue": "0x0000000000000000" -},{ - "Name": "resnet50/pool1_pool/MaxPool", - "LayerType": "CudnnPooling", - "Inputs": [ - { - "Name": "resnet50/pool1_pad/Pad:0", - "Dimensions": [32,64,114,114], - "Format/Datatype": "Channel major FP16 format where channel % 8 == 0" - }], - "Outputs": [ - { - "Name": "resnet50/pool1_pool/MaxPool:0", - "Dimensions": [32,64,56,56], - "Format/Datatype": "Channel major FP16 format where channel % 8 == 0" - }], - "ParameterType": "Pooling", - "PoolingType": "MAX", - "WindowSize": [3,3], - "PaddingMode": "kEXPLICIT_ROUND_DOWN", - "PrePadding": [0,0], - "PostPadding": [0,0], - "Stride": [2,2], - "BlendFactor": 0, - "AverageCountExcludesPadding": 1, - "TacticValue": "0xffffffffffffffff" -},{ - "Name": "Reformatting CopyNode for Input Tensor 0 to resnet50/conv2_block1_1_conv/Conv2D + resnet50/conv2_block1_1_relu/Relu", - "LayerType": "NoOp", - "Inputs": [ - { - "Name": "resnet50/pool1_pool/MaxPool:0", - "Dimensions": [32,64,56,56], - "Format/Datatype": "Channel major FP16 format where channel % 8 == 0" - }], - "Outputs": [ - { - "Name": "Reformatted Input Tensor 0 to resnet50/conv2_block1_1_conv/Conv2D + resnet50/conv2_block1_1_relu/Relu", - "Dimensions": [32,64,56,56], - "Format/Datatype": "Channel major FP16 format where channel % 16 == 0" - }], - "TacticValue": "0x0000000000000000" -},{ - "Name": "resnet50/conv2_block1_1_conv/Conv2D + resnet50/conv2_block1_1_relu/Relu", - "LayerType": "CaskConvolution", - "Inputs": [ - { - "Name": "Reformatted Input Tensor 0 to resnet50/conv2_block1_1_conv/Conv2D + resnet50/conv2_block1_1_relu/Relu", - "Dimensions": [32,64,56,56], - "Format/Datatype": "Channel major FP16 format where channel % 16 == 0" - }], - "Outputs": [ - { - "Name": "resnet50/conv2_block1_1_relu/Relu:0", - "Dimensions": [32,64,56,56], - "Format/Datatype": "Channel major FP16 format where channel % 16 == 0" - }], - "ParameterType": "Convolution", - "Kernel": [1,1], - "PaddingMode": "kEXPLICIT_ROUND_DOWN", - "PrePadding": [0,0], - "PostPadding": [0,0], - "Stride": [1,1], - "Dilation": [1,1], - "OutMaps": 64, - "Groups": 1, - "Weights": {"Type": "Half", "Count": 4096}, - "Bias": {"Type": "Half", "Count": 64}, - "HasSparseWeights": 0, - "Activation": "RELU", - "HasBias": 1, - "HasReLU": 1, - "TacticName": "sm80_xmma_fprop_implicit_gemm_f16f16_f16f16_f16_nhwckrsc_nhwc_tilesize128x32x32_stage4_warpsize4x1x1_g1_tensor16x8x16_simple_t1r1s1", - "TacticValue": "0x8047bcfcebface4a" -},{ - "Name": "resnet50/conv2_block1_2_conv/Conv2D + resnet50/conv2_block1_2_relu/Relu", - "LayerType": "CaskConvolution", - "Inputs": [ - { - "Name": "resnet50/conv2_block1_1_relu/Relu:0", - "Dimensions": [32,64,56,56], - "Format/Datatype": "Channel major FP16 format where channel % 16 == 0" - }], - "Outputs": [ - { - "Name": "resnet50/conv2_block1_2_relu/Relu:0", - "Dimensions": [32,64,56,56], - "Format/Datatype": "Channel major FP16 format where channel % 16 == 0" - }], - "ParameterType": "Convolution", - "Kernel": [3,3], - "PaddingMode": "kEXPLICIT_ROUND_DOWN", - "PrePadding": [1,1], - "PostPadding": [1,1], - "Stride": [1,1], - "Dilation": [1,1], - "OutMaps": 64, - "Groups": 1, - "Weights": {"Type": "Half", "Count": 36864}, - "Bias": {"Type": "Half", "Count": 64}, - "HasSparseWeights": 0, - "Activation": "RELU", - "HasBias": 1, - "HasReLU": 1, - "TacticName": 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b/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/RTX-3090/fp16/resnet.onnx.engine.profile.metadata.json deleted file mode 100644 index fb2802598..000000000 --- a/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/RTX-3090/fp16/resnet.onnx.engine.profile.metadata.json +++ /dev/null @@ -1 +0,0 @@ -{"performance_summary": {"Throughput": 248.211, "Latency": [4.00281, 4.08575, 4.02732, 4.02026, 4.08063], "Enqueue Time": [0.00500488, 0.0239258, 0.00575865, 0.00561523, 0.0157471], "H2D Latency": [0.0, 0.0, 0.0, 0.0, 0.0], "GPU Compute Time": [4.00281, 4.08575, 4.02732, 4.02026, 4.08063], "D2H Latency": [0.0, 0.0, 0.0, 0.0, 0.0], "Total Host Walltime": 3.00953, "Total GPU Compute Time": 3.00841}, "inference_options": {"Batch": "Explicit", "Input inference shape": "input_1=32x224x224x3", "Iterations": "10", "Duration": "3s (+ 200ms warm up)", "Sleep time": "0ms", "Idle time": "0ms", "Streams": "1", "ExposeDMA": "Disabled", "Data transfers": "Disabled", "Spin-wait": "Enabled", "Multithreading": "Disabled", "CUDA Graph": "Enabled", "Separate profiling": "Enabled", "Time Deserialize": "Disabled", "Time Refit": "Disabled"}, "device_information": {"Selected Device": "NVIDIA GeForce RTX 3090", "Compute Capability": 8.6, "SMs": 82.0, "Compute Clock Rate": 1.695, "Device Global Memory": "24268 MiB", "Shared Memory per SM": "100 KiB", "Memory Bus Width": 384.0, "Memory Clock Rate": 9.751}} \ No newline at end of file diff --git a/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/RTX-3090/fp16/resnet.onnx.engine.timing.json b/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/RTX-3090/fp16/resnet.onnx.engine.timing.json deleted file mode 100644 index 05d57a703..000000000 --- a/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/RTX-3090/fp16/resnet.onnx.engine.timing.json +++ /dev/null @@ -1,797 +0,0 @@ -[ - { "startEnqMs" : 7.50232, "endEnqMs" : 7.59, "startH2dMs" : 7.40864, "endH2dMs" : 7.40864, 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batch": "explicit batch", "Memory Pools": "workspace", "minTiming": "1", "avgTiming": "8", "Precision": "FP32"}} \ No newline at end of file diff --git a/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/RTX-3090/fp32/resnet.onnx.engine.graph.json b/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/RTX-3090/fp32/resnet.onnx.engine.graph.json deleted file mode 100644 index 83a6b6ee6..000000000 --- a/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/RTX-3090/fp32/resnet.onnx.engine.graph.json +++ /dev/null @@ -1,2654 +0,0 @@ -{"Layers": [{ - "Name": "resnet50/conv1_conv/Conv2D__6", - "LayerType": "Shuffle", - "Inputs": [ - { - "Name": "input_1", - "Dimensions": [32,224,224,3], - "Format/Datatype": "Row major linear FP32" - }], - "Outputs": [ - { - "Name": "resnet50/conv1_conv/Conv2D__6:0", - "Dimensions": [32,3,224,224], - "Format/Datatype": "Row major linear FP32" - }], - "ParameterType": "Shuffle", - "FirstTranspose": [0,3,1,2], - 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b/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/RTX-3090/fp32/resnet.onnx.engine.profile.metadata.json deleted file mode 100644 index bdaa6ac79..000000000 --- a/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/RTX-3090/fp32/resnet.onnx.engine.profile.metadata.json +++ /dev/null @@ -1 +0,0 @@ -{"performance_summary": {"Throughput": 81.9895, "Latency": [12.0986, 12.7928, 12.1952, 12.1682, 12.7775], "Enqueue Time": [0.00610352, 0.0234375, 0.00699659, 0.00683594, 0.0101318], "H2D Latency": [0.0, 0.0, 0.0, 0.0, 0.0], "GPU Compute Time": [12.0986, 12.7928, 12.1952, 12.1682, 12.7775], "D2H Latency": [0.0, 0.0, 0.0, 0.0, 0.0], "Total Host Walltime": 3.02478, "Total GPU Compute Time": 3.02441}, "inference_options": {"Batch": "Explicit", "Input inference shape": "input_1=32x224x224x3", "Iterations": "10", "Duration": "3s (+ 200ms warm up)", "Sleep time": "0ms", "Idle time": "0ms", "Streams": "1", "ExposeDMA": "Disabled", "Data transfers": "Disabled", "Spin-wait": "Enabled", "Multithreading": "Disabled", "CUDA Graph": "Enabled", "Separate profiling": "Enabled", "Time Deserialize": "Disabled", "Time Refit": "Disabled"}, "device_information": {"Selected Device": "NVIDIA GeForce RTX 3090", "Compute Capability": 8.6, "SMs": 82.0, "Compute Clock Rate": 1.695, "Device Global Memory": "24268 MiB", "Shared Memory per SM": "100 KiB", "Memory Bus Width": 384.0, "Memory Clock Rate": 9.751}} \ No newline at end of file diff --git a/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/RTX-3090/fp32/resnet.onnx.engine.timing.json b/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/RTX-3090/fp32/resnet.onnx.engine.timing.json deleted file mode 100644 index 9ea751f2c..000000000 --- a/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/RTX-3090/fp32/resnet.onnx.engine.timing.json +++ /dev/null @@ -1,265 +0,0 @@ -[ - { "startEnqMs" : 16.6644, "endEnqMs" : 16.9316, "startH2dMs" : 16.6236, "endH2dMs" : 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b/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/RTX-3090/qat/resnet-qat.onnx.engine.build.metadata.json deleted file mode 100644 index 87c0a6f1e..000000000 --- a/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/RTX-3090/qat/resnet-qat.onnx.engine.build.metadata.json +++ /dev/null @@ -1 +0,0 @@ -{"model_options": {"Format": "ONNX", "Model": "./examples/tensorflow/resnet/generated/resnet-qat.onnx"}, "build_options": {"Max batch": "explicit batch", "Memory Pools": "workspace", "minTiming": "1", "avgTiming": "8", "Precision": "FP32+FP16+INT8"}} \ No newline at end of file diff --git a/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/RTX-3090/qat/resnet-qat.onnx.engine.graph.json b/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/RTX-3090/qat/resnet-qat.onnx.engine.graph.json deleted file mode 100644 index f8f860cde..000000000 --- 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a/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/RTX-3090/qat/resnet-qat.onnx.engine.profile.metadata.json +++ /dev/null @@ -1 +0,0 @@ -{"performance_summary": {"Throughput": 455.388, "Latency": [2.17706, 2.21997, 2.19446, 2.19547, 2.21387], "Enqueue Time": [0.00488281, 0.0218506, 0.00564974, 0.00561523, 0.0065918], "H2D Latency": [0.0, 0.0, 0.0, 0.0, 0.0], "GPU Compute Time": [2.17706, 2.21997, 2.19446, 2.19547, 2.21387], "D2H Latency": [0.0, 0.0, 0.0, 0.0, 0.0], "Total Host Walltime": 3.00622, "Total GPU Compute Time": 3.00422}, "inference_options": {"Batch": "Explicit", "Input inference shape": "input_1=32x224x224x3", "Iterations": "10", "Duration": "3s (+ 200ms warm up)", "Sleep time": "0ms", "Idle time": "0ms", "Streams": "1", "ExposeDMA": "Disabled", "Data transfers": "Disabled", "Spin-wait": "Enabled", "Multithreading": "Disabled", "CUDA Graph": "Enabled", "Separate profiling": "Enabled", "Time Deserialize": "Disabled", "Time Refit": "Disabled"}, 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"endH2dMs" : 3199.98, "startComputeMs" : 3199.98, "endComputeMs" : 3202.17, "startD2hMs" : 3202.17, "endD2hMs" : 3202.17, "h2dMs" : 0, "computeMs" : 2.18506, "d2hMs" : 0, "latencyMs" : 2.18506 } -, { "startEnqMs" : 3200.07, "endEnqMs" : 3200.08, "startH2dMs" : 3202.17, "endH2dMs" : 3202.17, "startComputeMs" : 3202.17, "endComputeMs" : 3204.35, "startD2hMs" : 3204.35, "endD2hMs" : 3204.35, "h2dMs" : 0, "computeMs" : 2.18335, "d2hMs" : 0, "latencyMs" : 2.18335 } -, { "startEnqMs" : 3202.26, "endEnqMs" : 3202.27, "startH2dMs" : 3204.35, "endH2dMs" : 3204.35, "startComputeMs" : 3204.35, "endComputeMs" : 3206.54, "startD2hMs" : 3206.54, "endD2hMs" : 3206.54, "h2dMs" : 0, "computeMs" : 2.18408, "d2hMs" : 0, "latencyMs" : 2.18408 } -] diff --git a/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/process_resnet.sh b/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/process_resnet.sh deleted file mode 100755 index fd45b921e..000000000 --- a/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/process_resnet.sh +++ /dev/null @@ -1,34 +0,0 @@ -#!/bin/sh -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - - -# Use this script to process the ONNX files from resnet_example.py -# and generate the engine + JSON files for exploration with TREx. - - -SCRIPT=`realpath $0` -SCRIPT_DIR=`dirname $SCRIPT` -PROCESS_ENGINE=$SCRIPT_DIR/../../../utils/process_engine.py -ONNX_DIR=$SCRIPT_DIR/generated - -# Batch size = 32 -SHAPES='shapes=input_1:32x224x224x3' - -python3 $PROCESS_ENGINE $ONNX_DIR/resnet.onnx $ONNX_DIR/fp32 $SHAPES -python3 $PROCESS_ENGINE $ONNX_DIR/resnet.onnx $ONNX_DIR/fp16 $SHAPES fp16 -python3 $PROCESS_ENGINE $ONNX_DIR/resnet-qat.onnx $ONNX_DIR/qat $SHAPES best diff --git a/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/resnet_example.py b/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/resnet_example.py deleted file mode 100644 index fbf670ab1..000000000 --- a/tools/experimental/trt-engine-explorer/examples/tensorflow/resnet/resnet_example.py +++ /dev/null @@ -1,41 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - - -# This script generates 2 different ONNX ResNet models: -# 1. FP32 Resnet. -# 2. QAT Resnet. - - -import tensorflow as tf -from tensorflow_quantization import quantize_model -from tensorflow_quantization.custom_qdq_cases import ResNetV1QDQCase -from tensorflow_quantization.utils import convert_keras_model_to_onnx - - -# Baseline model -model = tf.keras.applications.ResNet50( - include_top=True, - weights="imagenet", - classes=1000, - classifier_activation="softmax", -) -convert_keras_model_to_onnx(model, onnx_model_path="generated/resnet.onnx") - -# QAT model -q_model = quantize_model(model, custom_qdq_cases=[ResNetV1QDQCase()]) -convert_keras_model_to_onnx(q_model, onnx_model_path="generated/resnet-qat.onnx") diff --git a/tools/experimental/trt-engine-explorer/examples/tensorrt/archive_example.py b/tools/experimental/trt-engine-explorer/examples/tensorrt/archive_example.py deleted file mode 100644 index 9478617fc..000000000 --- a/tools/experimental/trt-engine-explorer/examples/tensorrt/archive_example.py +++ /dev/null @@ -1,78 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -""" -This file contains an example of creating a TensorRT Engine Archive programmatically. -""" - -import logging -import tensorrt as trt -from trex.archiving import EngineArchive - - -model_name = "./tests/resources/single_relu.onnx" -tea_name = "/tmp/test.tea" - -input_shapes = [[1, 3, 224,224], [1, 3, 224,224], [1, 3, 224,224]] - - -def example_build_engine(tea: EngineArchive, verbose=False): - TRT_LOGGER = trt.Logger(trt.Logger.VERBOSE) if verbose else trt.Logger() - with tea.Builder(TRT_LOGGER) as builder, builder.create_network() as network, \ - trt.OnnxParser(network, TRT_LOGGER) as parser: - with open(model_name, 'rb') as model: - if not parser.parse(model.read()): - logging.error(f"Parsing {model_name} failed") - for error in range(parser.num_errors): - logging.error(parser.get_error(error)) - return - - logging.info('Building the engine...') - builder.max_workspace_size = 1 << 30 - config = builder.create_builder_config() - cache = config.create_timing_cache(b"") - config.set_timing_cache(cache, False) - optimization_profiles = [builder.create_optimization_profile()] - for profile in optimization_profiles: - profile.set_shape("input.1", *input_shapes) - config.add_optimization_profile(profile) - - engine = builder.build_serialized_network(network, config) - del engine - - -def example_run_engine(tea: EngineArchive, verbose=False): - TRT_LOGGER = trt.Logger(trt.Logger.VERBOSE) if verbose else trt.Logger() - plan = tea.readf("engine.trt") - with trt.Runtime(TRT_LOGGER) as runtime: - engine = runtime.deserialize_cuda_engine(plan) - with engine.create_execution_context() as context: - pass - del engine - - -def main(): - tea = EngineArchive(tea_name) - logging.info(f"Building an engine archive from {model_name}") - example_build_engine(tea) - example_run_engine(tea) - logging.info(f"Created an engine archive at {tea_name}") - - -if __name__ == "__main__": - main() - diff --git a/tools/experimental/trt-engine-explorer/images/trex-overview.png b/tools/experimental/trt-engine-explorer/images/trex-overview.png deleted file mode 100644 index 1bd6d70fd..000000000 Binary files a/tools/experimental/trt-engine-explorer/images/trex-overview.png and /dev/null differ diff --git a/tools/experimental/trt-engine-explorer/images/trex.png b/tools/experimental/trt-engine-explorer/images/trex.png deleted file mode 100644 index e504b31a2..000000000 Binary files a/tools/experimental/trt-engine-explorer/images/trex.png and /dev/null differ diff --git a/tools/experimental/trt-engine-explorer/images/trex_logo.png b/tools/experimental/trt-engine-explorer/images/trex_logo.png deleted file mode 100644 index 7cab3a85b..000000000 Binary files a/tools/experimental/trt-engine-explorer/images/trex_logo.png and /dev/null differ diff --git a/tools/experimental/trt-engine-explorer/install.sh b/tools/experimental/trt-engine-explorer/install.sh deleted file mode 100644 index 0eba140d7..000000000 --- a/tools/experimental/trt-engine-explorer/install.sh +++ /dev/null @@ -1,122 +0,0 @@ -#!/bin/sh -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -# -# TREx installation script. -# -# Usage: -# $ source install.sh [--venv] -# - - -usage() { - echo "Usage:" - echo " $ source $BASH_SOURCE [--full|--core] [--venv]" - OK=2 -} - -invalid_arg() { - echo "Error: $ARG1 is not a valid argument." >&2 - usage - OK=0 -} - -too_many_args() { - echo "Error: Too many arguments" - usage - OK=0 -} - -install_venv() { - VENV="env_trex" - pip3 install virtualenv - python3 -m virtualenv $VENV - source ./$VENV/bin/activate -} - -warn_if_trt_not_installed() { - # Check if the tensorrt Python package is installed - python3 -c "import tensorrt" &> /dev/null - - status=$? - if [ $status -eq 1 ]; then - echo - echo "Warning: Python package tensorrt is not installed!" >&2 - echo "Package tensorrt is required by some of the notebooks and scripts." >&2 - fi -} - -install_trex_core() { python3 -m pip install -e .; } - -install_trex_full() { python3 -m pip install -e .[notebook]; } - -install() { - sudo apt install --yes graphviz - INSTALL_TYPE=$1 - if [ $INSTALL_TYPE = "full" ]; then - install_trex_full - else - install_trex_core - fi - warn_if_trt_not_installed -} - -parse_args() { - if [ $NARGS -gt 2 ]; then - too_many_args - return - fi - while [[ $# -gt 0 ]]; do - case "$1" in - "-h" | "--h" | "-help" | "--help") - usage - ;; - "--venv") - install_venv - ;; - "-c" | "--core") - INSTALLATION_TYPE="core" - ;; - "-f" | "--full") - INSTALLATION_TYPE="full" - ;; - "") - ;; - *) - invalid_arg - return - ;; - esac - shift # Move to the next argument - done -} - - -OK=1 -NARGS=$# -ARG1=$1 -INSTALLATION_TYPE="full" -parse_args "$@" - -if [ $OK -eq 1 ]; then - install $INSTALLATION_TYPE -fi - -if [ $OK -eq 0 ]; then - echo "Installation aborted." -fi diff --git a/tools/experimental/trt-engine-explorer/notebooks/README.md b/tools/experimental/trt-engine-explorer/notebooks/README.md deleted file mode 100644 index 85f91b961..000000000 --- a/tools/experimental/trt-engine-explorer/notebooks/README.md +++ /dev/null @@ -1,16 +0,0 @@ - -# TensorRT Plan Exploration Notebooks - -## Tutorial Notebook - -Notebook `tutorial.ipynb` teaches you how to generate, load, and process engine plan JSON files. -
    -The notebook also shows how to create diagrams and engine SVG graphs. - -## Report Card Notebook - -Notebook `engine_report_card.ipynb` provides preset diagrams and views to inspect an engine plan's characteristics. - -## Compare Engines Notebook - -Notebook `compare_engines.ipynb` provides some diagrams to compare and evaluate two or more engine plans. diff --git a/tools/experimental/trt-engine-explorer/notebooks/api-examples.ipynb b/tools/experimental/trt-engine-explorer/notebooks/api-examples.ipynb deleted file mode 100644 index 090402a70..000000000 --- a/tools/experimental/trt-engine-explorer/notebooks/api-examples.ipynb +++ /dev/null @@ -1,224 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "219f48a4", - "metadata": {}, - "source": [ - "# TREx API Examples\n", - "\n", - "This notebook provides a set of TREx API examples." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c16a4d6e", - "metadata": {}, - "outputs": [], - "source": [ - "import trex\n", - "from trex.notebook import *\n", - "\n", - "engine_name = \"../tests/inputs/mobilenet.qat.onnx.engine\"\n", - "plan = trex.EnginePlan(f\"{engine_name}.graph.json\", f\"{engine_name}.profile.json\", f\"{engine_name}.metadata.json\")" - ] - }, - { - "cell_type": "markdown", - "id": "1e3de9cb", - "metadata": {}, - "source": [ - "## List `k` slowest layers\n", - "\n", - "List the k-slowest layers:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0c601495", - "metadata": {}, - "outputs": [], - "source": [ - "top3 = plan.df.nlargest(3, 'latency.pct_time')\n", - "for i in range(len(top3)):\n", - " layer = top3.iloc[i]\n", - " print(\"%s: %s\" % (layer[\"Name\"], layer[\"type\"]))" - ] - }, - { - "cell_type": "markdown", - "id": "bd16e032", - "metadata": {}, - "source": [ - "Compute the latency of the top-3 slowest layers:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "93d25555", - "metadata": {}, - "outputs": [], - "source": [ - "top3_latency = top3['latency.avg_time'].sum()\n", - "top3_percent = top3['latency.pct_time'].sum()\n", - "print(f\"top3 latency: {top3_latency:.6f} ms ({top3_percent:.2f}%)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "206b0f1c", - "metadata": {}, - "outputs": [], - "source": [ - "ltype = \"Convolution\"\n", - "convs = plan.df.query(f\"type == \\\"{ltype}\\\"\")\n", - "print(f\"There are {len(convs)} convolutions\")\n", - "print(convs['latency.avg_time'].median())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "10a9c91f", - "metadata": {}, - "outputs": [], - "source": [ - "convs2 = plan.get_layers_by_type('Convolution')\n", - "print(f\"There are {len(convs2)} convolutions\")\n", - "print(convs['latency.avg_time'].median())" - ] - }, - { - "cell_type": "markdown", - "id": "ef4287a3", - "metadata": {}, - "source": [ - "## Access layer activations\n", - "\n", - "There are several ways to access a layer's inputs and outputs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e6cff068", - "metadata": {}, - "outputs": [], - "source": [ - "print(convs.iloc[0]['Inputs'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "216be540", - "metadata": {}, - "outputs": [], - "source": [ - "clean_convs = trex.clean_df(convs2.copy(), inplace=True)\n", - "clean_convs.iloc[0]['Inputs']" - ] - }, - { - "cell_type": "markdown", - "id": "e30298f8", - "metadata": {}, - "source": [ - "As an `Activation` instance:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "69b482df", - "metadata": {}, - "outputs": [], - "source": [ - "inputs, outputs = trex.create_activations(convs.iloc[0])\n", - "print(inputs[0].name)\n", - "print(inputs[0].shape)\n", - "print(inputs[0].precision)\n", - "print(inputs[0].format)\n", - "print(inputs[0].size_bytes)" - ] - }, - { - "cell_type": "markdown", - "id": "e73772b7", - "metadata": {}, - "source": [ - "## Query and Grouping" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8fbff2a2", - "metadata": {}, - "outputs": [], - "source": [ - "# Group by type, and perform a sum reduction on the latency\n", - "plan.df.groupby([\"type\"])[[\"latency.avg_time\", \"latency.pct_time\"]].sum()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "89985b3a", - "metadata": {}, - "outputs": [], - "source": [ - "# trex provides another way to do the same thing\n", - "trex.group_sum_attr(plan.df,\"type\", \"latency.avg_time\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "930d109f", - "metadata": {}, - "outputs": [], - "source": [ - "# Another trex convenience wrapper: group by 'type' and count the number of members in each group\n", - "trex.group_count(plan.df, \"type\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "494e9968", - "metadata": {}, - "outputs": [], - "source": [ - "# Display the dataframe of all INT8 convolutions with \n", - "convs_1x1_fp32 = convs_1x1[convs_1x1[\"Outputs\"].str.startswith(\"FP32\")]\n", - "display_df(convs_1x1_fp32)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/tools/experimental/trt-engine-explorer/notebooks/compare_engines.ipynb b/tools/experimental/trt-engine-explorer/notebooks/compare_engines.ipynb deleted file mode 100644 index 72638118b..000000000 --- a/tools/experimental/trt-engine-explorer/notebooks/compare_engines.ipynb +++ /dev/null @@ -1,267 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": {}, - "report_default": { - "hidden": false - } - } - } - } - }, - "source": [ - "# Compare TensorRT Engines\n", - "\n", - "Use this Jupyter worksheet to compare two or more TensorRT Engine plans." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": {}, - "report_default": { - "hidden": false - } - } - } - } - }, - "source": [ - "## Load JSON Files" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": {}, - "report_default": { - "hidden": true - } - } - } - } - }, - "outputs": [], - "source": [ - "import IPython\n", - "from ipywidgets import widgets\n", - "from trex import *\n", - "from trex.notebook import *\n", - "from trex.report_card import *\n", - "from trex.compare_engines import *\n", - "\n", - "# Configure a wider output (for the wide graphs)\n", - "set_wide_display()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": {}, - "report_default": { - "hidden": true - } - } - } - } - }, - "outputs": [], - "source": [ - "engine_name_1 = \"../tests/inputs/mobilenet.qat.onnx.engine\"\n", - "engine_name_2 = \"../tests/inputs/mobilenet_v2_residuals.qat.onnx.engine\"\n", - "\n", - "def extract_engine_name(engine_path):\n", - " from pathlib import Path \n", - " return Path(engine_path).name\n", - "\n", - "def make_plan(engine_path, engine_name=None):\n", - " plan = EnginePlan(f'{engine_path}.graph.json', f'{engine_path}.profile.json', f\"{engine_path}.profile.metadata.json\", name=engine_name)\n", - " return plan\n", - " \n", - "plan1 = make_plan(engine_name_1, \"mobilenet.qat\")\n", - "plan2 = make_plan(engine_name_2, \"mobilenet_v2_residuals.qat\")\n", - "plans = (plan1, plan2)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": {}, - "report_default": { - "hidden": false - } - } - } - } - }, - "source": [ - "## Summary\n", - "\n", - "It is helpful to look at a high-level summary of the engine plan before diving into the details." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "compare_engines_overview(plans)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "compare_engines_summaries_tbl(plans, orientation='vertical')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "compare_engines_layer_latencies(\n", - " plan1, plan2,\n", - " # Allow for 3% error grace threshold when color highlighting performance differences\n", - " threshold=0.03,\n", - " # Inexact matching uses only the layer's first input and output to match to other layers.\n", - " exact_matching=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "compare_engines_layer_details(plans[0], plans[1])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(plan1.name)\n", - "report_card_perf_overview_widget(plan1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(plan2.name)\n", - "report_card_perf_overview_widget(plan2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(plan1.name)\n", - "report_card_table_view(plan1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(plan2.name)\n", - "report_card_table_view(plan2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for plan in plans:\n", - " graph = graphing.to_dot(plan, graphing.layer_type_formatter, display_regions=True, expand_layer_details=True)\n", - " graphing.render_dot(graph, plan.name, 'svg')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "extensions": { - "jupyter_dashboards": { - "activeView": "report_default", - "version": 1, - "views": { - "grid_default": { - "name": "grid", - "type": "grid" - }, - "report_default": { - "name": "report", - "type": "report" - } - } - } - }, - "interpreter": { - "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/tools/experimental/trt-engine-explorer/notebooks/engine_report_card.ipynb b/tools/experimental/trt-engine-explorer/notebooks/engine_report_card.ipynb deleted file mode 100644 index 2dfe79892..000000000 --- a/tools/experimental/trt-engine-explorer/notebooks/engine_report_card.ipynb +++ /dev/null @@ -1,440 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": {}, - "report_default": { - "hidden": false - } - } - } - } - }, - "source": [ - "# TensorRT Engine Report Card\n", - "\n", - "Use this Jupyter worksheet to get a quick overview of the structure and characteristics of a TensorRT Engine plan." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": {}, - "report_default": { - "hidden": false - } - } - } - } - }, - "outputs": [], - "source": [ - "import IPython\n", - "from ipywidgets import widgets\n", - "from trex import *\n", - "from trex.notebook import *\n", - "from trex.report_card import *\n", - "\n", - "# Choose an engine file to load.\n", - "engine_name = \"../tests/inputs/mobilenet.qat.onnx.engine\"\n", - "engine_name = \"../tests/inputs/mobilenet_v2_residuals.qat.onnx.engine\"\n", - "set_wide_display()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": {}, - "report_default": { - "hidden": false - } - } - } - } - }, - "source": [ - "## Load JSON Files" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": {}, - "report_default": { - "hidden": true - } - } - } - } - }, - "outputs": [], - "source": [ - "plan = EnginePlan(f\"{engine_name}.graph.json\", f\"{engine_name}.profile.json\", f\"{engine_name}.profile.metadata.json\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Render Graph" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "report_card_draw_plan_graph_extended(plan, engine_name)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": {}, - "report_default": { - "hidden": false - } - } - } - } - }, - "source": [ - "
    Plan Summary
    " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": {}, - "report_default": { - "hidden": false - } - } - } - } - }, - "outputs": [], - "source": [ - "# \"Average time\" refers to the sum of the layer latencies, when profiling layers separately\n", - "# \"Latency\" refers to the [min, max, mean, median, 99% percentile] of the engine latency measurements, when timing the engine w/o profiling layers.\n", - "plan.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "layer_latency_sunburst(plan.df, \"Layers Latencies (%)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": {}, - "report_default": { - "hidden": false - } - } - } - } - }, - "outputs": [], - "source": [ - "report_card_table_view(plan)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Timings" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_engine_timings(timing_json_file= f\"{engine_name}.timing.json\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": {}, - "report_default": { - "hidden": false - } - } - } - } - }, - "source": [ - "## Performance" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": {}, - "report_default": { - "hidden": false - } - } - } - } - }, - "outputs": [], - "source": [ - "report_card_perf_overview_widget(plan)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": {}, - "report_default": { - "hidden": false - } - } - } - } - }, - "source": [ - "## Memory Footprint" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": {}, - "report_default": { - "hidden": false - } - } - } - } - }, - "outputs": [], - "source": [ - "report_card_memory_footprint_widget(plan)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Tactics" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": {}, - "report_default": { - "hidden": false - } - } - } - } - }, - "outputs": [], - "source": [ - "latency_vs_prec_per_conv = partial(\n", - " plotting.plotly_bar2,\n", - " plan.df,\n", - " values_col='latency.pct_time',\n", - " names_col='Name',\n", - " color='tactic')\n", - "\n", - "latency_vs_prec_per_conv(\"Latency per Layer (color=Tactics)\")\n", - "\n", - "tactic_cnt = group_count(plan.df, 'tactic')\n", - "display_df(tactic_cnt)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "extensions": { - "jupyter_dashboards": { - "version": 1, - "views": { - "grid_default": {}, - "report_default": { - "hidden": true - } - } - } - } - }, - "source": [ - "
    Excel Summary
    " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generate a default summary" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from trex.excel_summary import *\n", - "\n", - "summary = ExcelSummary(plan, path=\"default_summary.xlsx\")\n", - "summary.generate_default_summary() # 'generate_default_summary' automatically saves the file" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generate a customized summary" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "summary = ExcelSummary(plan, path=\"customized_summary.xlsx\")\n", - "summary.add_dataframes({\"df\": plan.df})\n", - "summary.add_images({\"trex_logo\": \"../images/trex_logo.png\"})\n", - "summary.add_dataframes({\"clean_df\": clean_for_display(plan.df)})\n", - "summary.save() # 'save' needs to be called to propagate the changes to the file" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# The context manager automatically saves the file\n", - "with ExcelSummary(plan, path=\"customized_summary_with_manager.xlsx\") as summary:\n", - " summary.add_dataframes({\"df\": plan.df})\n", - " summary.add_images({\"trex_logo\": \"../images/trex_logo.png\"})\n", - " summary.add_dataframes({\"clean_df\": clean_for_display(plan.df)})\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "extensions": { - "jupyter_dashboards": { - "activeView": "report_default", - "version": 1, - "views": { - "grid_default": { - "cellMargin": 10, - "defaultCellHeight": 20, - "maxColumns": 12, - "name": "grid", - "type": "grid" - }, - "report_default": { - "name": "report", - "type": "report" - } - } - } - }, - "interpreter": { - "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/tools/experimental/trt-engine-explorer/notebooks/tutorial.ipynb b/tools/experimental/trt-engine-explorer/notebooks/tutorial.ipynb deleted file mode 100644 index fa090adec..000000000 --- a/tools/experimental/trt-engine-explorer/notebooks/tutorial.ipynb +++ /dev/null @@ -1,701 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# TensorRT Engine Explorer Tutorial\n", - "\n", - "Use this notebook to learn how to use `trex` to explore the structure and characteristics of a TensorRT Engine plan.\n", - "Starting with TensorRT 8.2, engine-plan graph and profiling data can be exported to JSON files. `trex` loads these files and queries their content using a simple API that wraps Pandas' API" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Create Engine Plan And Profiling JSON Files\n", - "\n", - "Using `trtexec` it is easy to create the plan graph and profiling JSON files. Three flags are required:\n", - "```\n", - " --exportProfile=$profile_json\n", - " --exportLayerInfo=$graph_json\n", - " --profilingVerbosity=detailed\n", - "```\n", - "\n", - "A utility Python script `utils/process_engine.py` can be used to create the JSON files. This script executes `trtexec` and therefore the script can only be invoked within an environment that has `trtexec` installed and included in `$PATH`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# !python3 ../utils/process_engine.py ../tests/inputs/mobilenet.qat.onnx ../tests/inputs best" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Load JSON Files" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import os\n", - "import pandas as pd\n", - "import trex\n", - "import trex.notebook\n", - "import trex.plotting\n", - "import trex.graphing\n", - "import trex.df_preprocessing\n", - "\n", - "# Configure a wider output (for the wide graphs)\n", - "trex.notebook.set_wide_display()\n", - "\n", - "# Choose an engine file to load. This notebook assumes that you've saved the engine to the following paths.\n", - "engine_name = \"../tests/inputs/mobilenet.qat.onnx.engine\"\n", - "engine_name = \"../tests/inputs/mobilenet_v2_residuals.qat.onnx.engine\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Create an `EnginePlan` instance and start exploring" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "assert engine_name is not None\n", - "plan = trex.EnginePlan(f'{engine_name}.graph.json', f'{engine_name}.profile.json', f'{engine_name}.profile.metadata.json')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "It is helpful to look at a high-level summary of the engine plan before diving into the details.\n", - "\n", - "* By default an `EnginePlan`'s name is derived from the graph file name, but you can also set the name explicitly at any time. This is useful when there are multiple plans with the same file name.\n", - "* The data is summarized from the input JSON files, and may include results from profiling layers separately as well as from profiling the entire engine. The latter provides more accurate values for latency and inferences throughput, but the former provides per-layer latency information for understanding the engine behavior.\n", - "* \"Average time\": refers to the sum of the layer latencies, when profiling layers separately.\n", - "* \"Latency\": refers to the [min, max, mean, median, 99% percentile] of the engine latency measurements, when timing the engine w/o profiling layers.\n", - "* \"Throughput\": is measured in inferences per second (IPS)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(f\"Summary for {plan.name}:\\n\")\n", - "plan.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Get to know the plan dataframe\n", - "\n", - "An `EnginePlan` is an object that wraps a Pandas DataFrame data-structure. Most of the examples below utilize this dataframe (df) for querying, slicing and rendering information about the `EnginePlan`.\n", - "\n", - "The dataframe captures the information from the plan engine graph and profiling JSON files. If both JSON files are available the latency data of each layer is added as three new columns: `latency.time` (the total latency of the layer summed across all measurement iterations), `latency.avg_time` (the average latency of the layer), `latency.pct_time` (the latency of the layer as a proportion of the overall engine latency).\n", - "\n", - "When the dataframe is constructed several new layers that summarize footprint information are added: `total_io_size_bytes`, `weights_size`, `total_footprint_bytes`.\n", - "\n", - "A few of the column names are changed from the original JSON file, to give them clearer names.\n", - "\n", - "Accessing the dataframe is straight-forward:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df = plan.df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can print the names of the columns in the dataframe." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "available_cols = df.columns\n", - "print(f\"These are the column names in the plan\\n: {available_cols}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A dataframe can be rendered as a table. The columns are from various layers so the dataframe is very sparse.\n", - "\n", - "Use the column controls to sort or filter layers.
    \n", - "An interesting view sorts the layers by `latency.pct_time`.
    \n", - "The `dtale` toolbar makes it easy to open the table in a new tab (useful for large tables) and to export the data to CSV and HTML." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "trex.notebook.display_df(plan.df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When rendering engine plan dataframes we usually want to reduce the visual clutter and render only the important columns.
    \n", - "The function `clean_for_display` does exactly that.\n", - "\n", - "The column order is changed, in order to bring important columns to the front.
    \n", - "Columns `Inputs` and `Outputs` are reformatted to reduce verbosity.
    \n", - "Finally, a few columns are dropped and NaNs are replaced with zeros." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df = trex.df_preprocessing.clean_for_display(plan.df)\n", - "print(f\"These are the column names in the plan\\n: {df.columns}\")\n", - "trex.notebook.display_df(df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Layer Types\n", - "\n", - "This example shows how to create a bar diagram of the count of each layer type.\n", - "\n", - "`trex` provides a utility wrapper around Pandas' API, but you can freely use the Pandas API to extract data from the plan dataframe." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "layer_types = trex.group_count(plan.df, 'type')\n", - "\n", - "# Simple DF print\n", - "print(layer_types)\n", - "\n", - "# dtale DF display\n", - "trex.notebook.display_df(layer_types)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`trex` provides wrappers to plotly's plotting API. `plotly_bar2` is the main utility for creating bar charts." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "trex.plotting.plotly_bar2(\n", - " df=layer_types, \n", - " title='Layer Count By Type', \n", - " values_col='count', \n", - " names_col='type',\n", - " orientation='v',\n", - " color='type',\n", - " colormap=trex.colors.layer_colormap,\n", - " show_axis_ticks=(True, True));" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Performance\n", - "\n", - "Pandas' powerful API can be used on the Plan dataframe. For example, we can easily query for the 3 layers that consume the most time:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "top3 = plan.df.nlargest(3, 'latency.pct_time')\n", - "trex.notebook.display_df(top3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The chart below provides a quick view of the layers latencies. The values of `df[values_col]` set the bar height and the values of `df[names_col]` provide the bar name. In this case, the latency of each layer is plotted vs the name of the layer. The colors of the bars are determined by `color` and `colormap`, if provided.
    \n", - "For example in the statement `colormap[df['type']]`, the bar colors are determined by the layer type and `layer_colormap`, which is a `trex` dictionary which maps layer types to preset colors." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "trex.plotting.plotly_bar2(\n", - " df=plan.df, \n", - " title=\"% Latency Budget Per Layer\",\n", - " values_col=\"latency.pct_time\",\n", - " names_col=\"Name\",\n", - " color='type',\n", - " use_slider=False,\n", - " colormap=trex.colors.layer_colormap);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`plotly_hist` is a wrapper of Plotly's histograms chart. It has arguments similar to `plotly_bar2`, but not as many. `plotly_hist` plots the histogram of `df[values_col]`.\n", - "\n", - "Here's a look at how the layer latencies distribute:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "trex.plotting.plotly_hist(\n", - " df=plan.df, \n", - " title=\"Layer Latency Distribution\", \n", - " values_col=\"latency.pct_time\",\n", - " xaxis_title=\"Latency (ms)\",\n", - " color='type',\n", - " colormap=trex.colors.layer_colormap);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Pandas' aggregation and reductions can be used to provide interesting information.\n", - "\n", - "Here we group the layer latencies by the layer types.
    The data can be displayed as a chart or as a summary table, like the one below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "time_pct_by_type = plan.df.groupby([\"type\"])[[\"latency.pct_time\", \"latency.avg_time\"]].sum().reset_index()\n", - "trex.notebook.display_df(time_pct_by_type)\n", - "trex.plotting.plotly_bar2(\n", - " df=time_pct_by_type,\n", - " title=\"% Latency Budget Per Layer Type\",\n", - " values_col=\"latency.pct_time\",\n", - " names_col=\"type\",\n", - " orientation='h',\n", - " color='type',\n", - " colormap=trex.colors.layer_colormap);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Treemaps provide a different view of the profiling data.\n", - "\n", - "In this example we use a Plotly Express Treemap directly, without any wrappers." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import plotly.express as px\n", - "\n", - "fig = px.treemap(\n", - " plan.df,\n", - " path=['type', 'Name'],\n", - " values='latency.pct_time',\n", - " title='Treemap Of Layer Latencies (Size & Color Indicate Latency)',\n", - " color='latency.pct_time')\n", - "fig.update_layout(margin = dict(t=50, l=25, r=25, b=25))\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is another view of how layer latencies interact with layer data size." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig = px.treemap(\n", - " plan.df,\n", - " path=['type', 'Name'],\n", - " values='latency.pct_time',\n", - " title='Treemap Of Layer Latencies (Size Indicates Latency. Color Indicates Activations Size)',\n", - " color='total_io_size_bytes')\n", - "fig.update_traces(root_color=\"white\")\n", - "fig.update_layout(margin = dict(t=50, l=25, r=25, b=25))\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Memory Traffic" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "trex.plotting.plotly_bar2(\n", - " plan.df, \n", - " \"Weights Sizes Per Layer\", \n", - " \"weights_size\", \"Name\", \n", - " color='type', \n", - " colormap=trex.colors.layer_colormap)\n", - "\n", - "trex.plotting.plotly_bar2(\n", - " plan.df, \n", - " \"Activations Sizes Per Layer\", \n", - " \"total_io_size_bytes\", \n", - " \"Name\", \n", - " color='type', \n", - " colormap=trex.colors.layer_colormap)\n", - "\n", - "trex.plotting.plotly_hist(\n", - " plan.df, \n", - " \"Layer Activations Sizes Distribution\", \n", - " \"total_io_size_bytes\", \n", - " \"Size (bytes)\", \n", - " color='type', \n", - " colormap=trex.colors.layer_colormap)\n", - "\n", - "plan.df[\"total_io_size_bytes\"].describe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Layers Precision\n", - "\n", - "`trex` provides a wrapper to Plotly's pie charts as well. Several charts can be plotted in a grid.\n", - "\n", - "`precision_colormap` color the pie slices by the value of `df['precision']`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "charts = []\n", - "layer_precisions = trex.group_count(plan.df, 'precision')\n", - "charts.append((layer_precisions, 'Layer Count By Precision', 'count', 'precision'))\n", - "\n", - "layers_time_pct_by_precision = trex.group_sum_attr(plan.df, grouping_attr='precision', reduced_attr='latency.pct_time')\n", - "display(layers_time_pct_by_precision)\n", - "\n", - "charts.append((layers_time_pct_by_precision, '% Latency Budget By Precision', 'latency.pct_time', 'precision'))\n", - "trex.plotting.plotly_pie2(\"Precision Statistics\", charts, colormap=trex.colors.precision_colormap);" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "trex.plotting.plotly_bar2(\n", - " plan.df, \n", - " \"% Latency Budget Per Layer
    (bar color indicates precision)\", \n", - " \"latency.pct_time\", \n", - " \"Name\",\n", - " color='precision',\n", - " colormap=trex.colors.precision_colormap);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Graph Rendering\n", - "\n", - "It is very helpful to draw the graph of the engine plan.\n", - "\n", - "A formatter can be used to configure the colors of nodes. `trex` provides `layer_type_formatter` which paints graph nodes by their layer type, and `precision_formatter` which paints graph nodes according to their precision.\n", - "\n", - "`to_dot` converts an `EnginePlan` to dot file which can be rendered to SVG or PNG.\n", - "\n", - "SVG files render faster than PNG, they are searchable and provide sharp and crisp graphs in all resolutions. Because graphs are large, it is recommended to view the rendered graph file in another browser window." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "formatter = trex.graphing.layer_type_formatter if True else trex.graphing.precision_formatter\n", - "graph = trex.graphing.to_dot(plan, formatter)\n", - "svg_name = trex.graphing.render_dot(graph, engine_name, 'svg')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "PNG files can be rendered inside the notebook, but the graphs are usually very large and resolution suffers." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "png_name = trex.graphing.render_dot(graph, engine_name, 'png')\n", - "from IPython.display import Image\n", - "display(Image(filename=png_name))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Convolution Layers\n", - "\n", - "Sometimes it is interesting to look at all layers of a certain type. You can use Pandas' API (e.g. `query`) to slice the dataframe by layer type:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "convs1 = plan.df.query(\"type == 'Convolution'\")\n", - "convs2 = df[df.type == 'Convolution']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "However, `trex` provides a `get_layers_by_type` API which performs some layer-type-specific preprocessing which is often useful. In the case of convolutions," - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "convs = plan.get_layers_by_type('Convolution')\n", - "trex.notebook.display_df(convs)\n", - "\n", - "trex.plotting.plotly_bar2(\n", - " convs, \n", - " \"Latency Per Layer (%)
    (bar color indicates precision)\",\n", - " \"attr.arithmetic_intensity\", \"Name\",\n", - " color='precision', \n", - " colormap=trex.colors.precision_colormap)\n", - "\n", - "trex.plotting.plotly_bar2(\n", - " convs,\n", - " \"Convolution Data Sizes
    (bar color indicates latency)\",\n", - " \"total_io_size_bytes\", \n", - " \"Name\", \n", - " color='latency.pct_time');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Arithmetic Intensity\n", - "\n", - "Arithmetic intensity (AI) is a measure of the amount of compute expended per data byte. Layers with higher AI are in general more efficient because moving data is much slower than computing an operation. For each unit of data fetched from memory we want to perform many computations, because the GPU can compute much faster than it can fetch data from memory. From more on AI, see https://en.wikipedia.org/wiki/Roofline_model#Arithmetic_intensity.
    \n", - "This is a simplistic model which assumes that we read the data only once, and write it out once. In practice, when computing Convolution and GEMM operations, memory tiles are read several times, usually from fast shared-memory, L1 or L2 cached memory." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "trex.plotting.plotly_bar2(\n", - " convs, \n", - " \"Convolution Arithmetic Intensity
    (bar color indicates activations size)\",\n", - " \"attr.arithmetic_intensity\", \n", - " \"Name\",\n", - " color='total_io_size_bytes')\n", - "\n", - "trex.plotting.plotly_bar2(\n", - " convs, \n", - " \"Convolution Arithmetic Intensity
    (bar color indicates latency)\", \n", - " \"attr.arithmetic_intensity\", \n", - " \"Name\",\n", - " color='latency.pct_time');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Efficiency\n", - "\n", - "Another simplistic model measures the compute and memory efficiency. These indicators are calculated by taking the number of operations (or memory bytes) and dividing by the layer's execution time. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Memory accesses per ms (assuming one time read/write penalty)\n", - "trex.plotting.plotly_bar2(\n", - " convs, \n", - " \"Convolution Memory Efficiency
    (bar color indicates latency)\", \n", - " \"attr.memory_efficiency\", \n", - " \"Name\", \n", - " color='latency.pct_time')\n", - "\n", - "# Compute operations per ms (assuming one time read/write penalty)\n", - "trex.plotting.plotly_bar2(\n", - " convs, \n", - " \"Convolution Compute Efficiency
    (bar color indicates latency)\",\n", - " \"attr.compute_efficiency\",\n", - " \"Name\",\n", - " color='latency.pct_time');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Statistics" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "convs = plan.get_layers_by_type('Convolution')\n", - "\n", - "charts = []\n", - "convs_count_by_type = trex.group_count(convs, 'subtype')\n", - "charts.append((convs_count_by_type, 'Count', 'count', 'subtype'))\n", - "\n", - "convs_time_pct_by_type = trex.group_sum_attr(convs, grouping_attr='subtype', reduced_attr='latency.pct_time')\n", - "charts.append((convs_time_pct_by_type, '% Latency Budget', 'latency.pct_time', 'subtype'))\n", - "trex.plotting.plotly_pie2(\"Convolutions Statistics (Subtype)\", charts)\n", - "\n", - "charts = []\n", - "convs_count_by_group_size = trex.group_count(convs, 'attr.groups')\n", - "charts.append((convs_count_by_group_size, 'Count', 'count', 'attr.groups'))\n", - "\n", - "convs_time_pct_by_grp_size = trex.group_sum_attr(convs, grouping_attr='attr.groups', reduced_attr='latency.pct_time')\n", - "charts.append((convs_time_pct_by_grp_size, '% Latency Budget', 'latency.pct_time', 'attr.groups'))\n", - "trex.plotting.plotly_pie2(\"Convolutions Statistics (Number of Groups)\", charts)\n", - "\n", - "charts = []\n", - "convs_count_by_precision = trex.group_count(convs, 'precision')\n", - "charts.append((convs_count_by_precision, 'Count', 'count', 'precision'))\n", - "\n", - "convs_time_pct_by_precision = trex.group_sum_attr(convs, grouping_attr='precision', reduced_attr='latency.pct_time')\n", - "charts.append((convs_time_pct_by_precision, '% Latency Budget', 'latency.pct_time', 'precision'))\n", - "\n", - "trex.plotting.plotly_pie2(\"Convolutions Statistics (Precision)\", charts, colormap=trex.colors.precision_colormap);" - ] - } - ], - "metadata": { - "interpreter": { - "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/tools/experimental/trt-engine-explorer/requirements-notebook.txt b/tools/experimental/trt-engine-explorer/requirements-notebook.txt deleted file mode 100644 index c30374811..000000000 --- a/tools/experimental/trt-engine-explorer/requirements-notebook.txt +++ /dev/null @@ -1,9 +0,0 @@ -# Packages required for using the notebooks. -ipywidgets==8.1.1 -dtale==3.8.1 -Werkzeug==2.3.7 -Flask==2.2.2 -plotly -jupyter -jupyterlab -netron \ No newline at end of file diff --git a/tools/experimental/trt-engine-explorer/requirements.txt b/tools/experimental/trt-engine-explorer/requirements.txt deleted file mode 100644 index 9f1d99d3f..000000000 --- a/tools/experimental/trt-engine-explorer/requirements.txt +++ /dev/null @@ -1,17 +0,0 @@ -# Packages required for core functionality. -protobuf -onnx>=1.15.0 -pandas==2.2.1 -pytest -graphviz -pydot -numpy -networkx -tabulate - -# Packages required for utilities -pynvml - -# Packages required for excel reporting. -xlsxwriter -openpyxl diff --git a/tools/experimental/trt-engine-explorer/setup.py b/tools/experimental/trt-engine-explorer/setup.py deleted file mode 100644 index 5ff91802d..000000000 --- a/tools/experimental/trt-engine-explorer/setup.py +++ /dev/null @@ -1,59 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -import os -import sys -from setuptools import find_packages, setup - - -def no_publish(): - blacklist = ["register"] - for cmd in blacklist: - if cmd in sys.argv: - raise RuntimeError('Command "{}" blacklisted'.format(cmd)) - - -def main(): - no_publish() - with open('requirements.txt','r') as req_file: - required_pckgs = [line.strip() for line in req_file.readlines()] - - with open('requirements-notebook.txt','r') as notebook_req_file: - extras_require_notebook = [line.strip() for line in notebook_req_file.readlines()] - - setup( - name="trex", - version="0.2.1", - description="TREx: TensorRT Engine Exploration Toolkit", - long_description=open("README.md", "r", encoding="utf-8").read(), - author="NVIDIA", - author_email="svc_tensorrt@nvidia.com", - classifiers=[ - "Intended Audience :: Developers", - "Programming Language :: Python :: 3", - ], - license="Apache 2.0", - install_requires=required_pckgs, - extras_require={"notebook": extras_require_notebook}, - packages=find_packages(exclude=("tests", "tests.*")), - entry_points={"console_scripts": "trex=bin.trex:main"}, - zip_safe=True, - ) - - -if __name__ == "__main__": - main() diff --git a/tools/experimental/trt-engine-explorer/tests/README.md b/tools/experimental/trt-engine-explorer/tests/README.md deleted file mode 100644 index a13e342a0..000000000 --- a/tools/experimental/trt-engine-explorer/tests/README.md +++ /dev/null @@ -1,9 +0,0 @@ -# Tests - -Currently `trex` is light on testing - -## Executing the tests - -``` -$ pytest tests -``` diff --git a/tools/experimental/trt-engine-explorer/tests/__init__.py b/tools/experimental/trt-engine-explorer/tests/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/tools/experimental/trt-engine-explorer/tests/inputs/mobilenet.qat.onnx.engine.build.metadata.json b/tools/experimental/trt-engine-explorer/tests/inputs/mobilenet.qat.onnx.engine.build.metadata.json deleted file mode 100644 index 9fc8a37f1..000000000 --- a/tools/experimental/trt-engine-explorer/tests/inputs/mobilenet.qat.onnx.engine.build.metadata.json +++ /dev/null @@ -1 +0,0 @@ -{"model_options": {"Format": "ONNX", "Model": "trt-engine-explorer-OSS/tests/inputs/mobilenet.qat.onnx"}, "build_options": {"Max batch": "explicit batch", "Memory Pools": "workspace", "minTiming": "1", "avgTiming": "8", "Precision": "FP32+FP16+INT8"}} \ No newline at end of file diff --git a/tools/experimental/trt-engine-explorer/tests/inputs/mobilenet.qat.onnx.engine.graph.json b/tools/experimental/trt-engine-explorer/tests/inputs/mobilenet.qat.onnx.engine.graph.json deleted file mode 100644 index afa7a1232..000000000 --- a/tools/experimental/trt-engine-explorer/tests/inputs/mobilenet.qat.onnx.engine.graph.json +++ /dev/null @@ -1,2272 +0,0 @@ -{"Layers": [{ - "Name": "QuantizeLinear_2", - "LayerType": "Reformat", - "Inputs": [ - { - "Name": "input1", - "Location": "Device", - "Dimensions": [1,3,224,224], - "Format/Datatype": "Row major linear FP32" - }], - "Outputs": [ - { - "Name": "317", - "Location": "Device", - "Dimensions": [1,3,224,224], - "Format/Datatype": "Four wide channel vectorized row major Int8 format" - }], - "ParameterType": "Reformat", - "Origin": "QDQ", - "TacticValue": "0x0000000000000000" -},{ - "Name": "features.0.0.weight + QuantizeLinear_8 + Conv_12 + PWN(Clip_16)", - "LayerType": "CaskConvolution", - "Inputs": [ - { - "Name": "317", - "Location": "Device", - "Dimensions": [1,3,224,224], - "Format/Datatype": "Four wide channel vectorized row major Int8 format" - }], - "Outputs": [ - { - "Name": "334", - "Location": "Device", - "Dimensions": [1,32,112,112], - "Format/Datatype": "Thirty-two wide channel vectorized row major Int8 format" - }], - "ParameterType": "Convolution", - "Kernel": [3,3], - "PaddingMode": "kEXPLICIT_ROUND_DOWN", - "PrePadding": [1,1], - "PostPadding": [1,1], - "Stride": [2,2], - "Dilation": [1,1], - "OutMaps": 32, - "Groups": 1, - "Weights": {"Type": "Int8", "Count": 864}, - "Bias": {"Type": "Float", "Count": 32}, - "HasSparseWeights": 0, - "Activation": "CLIPPED_RELU", - "ParameterSubType": "[0.000000,127.000000]", - "HasBias": 1, - "HasReLU": 0, - "TacticName": "volta_int8x4_icudnn_int8x4_128x32_relu_xregs_medium_c32_nn_v1", - "TacticValue": "0x10d7116a3fad9b7a" -},{ - "Name": "features.1.conv.0.0.weight + QuantizeLinear_25 + Conv_29 + PWN(Clip_33)", - "LayerType": "CudaDepthwiseConvolution", - "Inputs": [ - { - "Name": "334", - "Location": "Device", - "Dimensions": [1,32,112,112], - "Format/Datatype": "Thirty-two wide channel vectorized row major Int8 format" - }], - "Outputs": [ - { - "Name": "351", - "Location": "Device", - "Dimensions": [1,32,112,112], - "Format/Datatype": "Thirty-two wide channel vectorized row major Int8 format" - }], - "ParameterType": "Convolution", - "Kernel": [3,3], - "PaddingMode": "kEXPLICIT_ROUND_DOWN", - "PrePadding": [1,1], - "PostPadding": [1,1], - "Stride": [1,1], - "Dilation": [1,1], - "OutMaps": 32, - "Groups": 32, - "Weights": {"Type": "Int8", "Count": 288}, - "Bias": {"Type": "Float", "Count": 32}, - "HasSparseWeights": 0, - "Activation": "CLIPPED_RELU", - "ParameterSubType": "[0.000000,127.000000]", - "HasBias": 1, - "TacticValue": "0x0000000000000001" -},{ - "Name": "features.1.conv.1.weight + QuantizeLinear_42 + Conv_46", - "LayerType": "CaskConvolution", - "Inputs": [ - { - "Name": "351", - "Location": "Device", - "Dimensions": [1,32,112,112], - "Format/Datatype": "Thirty-two wide channel vectorized row major Int8 format" - }], - "Outputs": [ - { - "Name": "365", - "Location": "Device", - "Dimensions": [1,16,112,112], - "Format/Datatype": "Thirty-two wide channel vectorized row major Int8 format" - }], - "ParameterType": "Convolution", - "Kernel": [1,1], - "PaddingMode": "kEXPLICIT_ROUND_DOWN", - "PrePadding": [0,0], - "PostPadding": [0,0], - "Stride": [1,1], - "Dilation": [1,1], - "OutMaps": 16, - "Groups": 1, - "Weights": {"Type": "Int8", "Count": 512}, - "Bias": {"Type": "Float", "Count": 16}, - "HasSparseWeights": 0, - "Activation": "NONE", - "HasBias": 1, - "HasReLU": 0, - "TacticName": "sm75_xmma_fprop_implicit_gemm_interleaved_i8i8_i8i32_f32_nchw_vect_c_32kcrs_vect_c_32_nchw_vect_c_32_tilesize64x32x64_stage1_warpsize2x1x1_g1_tensor8x8x16_simple_t1r1s1", - "TacticValue": "0x235fad4a7171cb36" -},{ - "Name": "features.2.conv.0.0.weight + QuantizeLinear_56 + Conv_60 + PWN(Clip_64)", - "LayerType": "CaskConvolution", - "Inputs": [ - { - "Name": "365", - "Location": "Device", - "Dimensions": [1,16,112,112], - "Format/Datatype": "Thirty-two wide channel vectorized row major Int8 format" - }], - "Outputs": [ - { - "Name": "382", - "Location": "Device", - "Dimensions": [1,96,112,112], - "Format/Datatype": "Thirty-two wide channel vectorized row major Int8 format" - }], - "ParameterType": "Convolution", - "Kernel": [1,1], - "PaddingMode": "kEXPLICIT_ROUND_DOWN", - "PrePadding": [0,0], - "PostPadding": [0,0], - "Stride": [1,1], - "Dilation": [1,1], - "OutMaps": 96, - "Groups": 1, - "Weights": {"Type": "Int8", "Count": 1536}, - "Bias": {"Type": "Float", "Count": 96}, - "HasSparseWeights": 0, - "Activation": "CLIPPED_RELU", - "ParameterSubType": "[0.000000,127.000000]", - "HasBias": 1, - "HasReLU": 0, - "TacticName": "sm75_xmma_fprop_implicit_gemm_interleaved_i8i8_i8i32_f32_nchw_vect_c_32kcrs_vect_c_32_nchw_vect_c_32_tilesize64x32x64_stage1_warpsize2x1x1_g1_tensor8x8x16_simple_t1r1s1", - "TacticValue": "0x235fad4a7171cb36" -},{ - "Name": "features.2.conv.1.0.weight + QuantizeLinear_73 + Conv_77 + PWN(Clip_81)", - "LayerType": "CudaDepthwiseConvolution", - "Inputs": [ - { - "Name": "382", - "Location": "Device", - "Dimensions": [1,96,112,112], - "Format/Datatype": "Thirty-two wide channel vectorized row major Int8 format" - }], - "Outputs": [ - { - "Name": "399", - "Location": "Device", - "Dimensions": [1,96,56,56], - "Format/Datatype": "Thirty-two wide channel vectorized row major Int8 format" - }], - "ParameterType": "Convolution", - "Kernel": [3,3], - "PaddingMode": "kEXPLICIT_ROUND_DOWN", - "PrePadding": [1,1], - "PostPadding": [1,1], - "Stride": [2,2], - "Dilation": [1,1], - "OutMaps": 96, - "Groups": 96, - "Weights": {"Type": "Int8", "Count": 864}, - "Bias": {"Type": "Float", "Count": 96}, - "HasSparseWeights": 0, - "Activation": "CLIPPED_RELU", - "ParameterSubType": "[0.000000,127.000000]", - "HasBias": 1, - "TacticValue": "0xfffffffffffffffc" -},{ - "Name": "features.2.conv.2.weight + QuantizeLinear_90 + Conv_94", - "LayerType": "CaskConvolution", - "Inputs": [ - { - "Name": "399", - "Location": "Device", - "Dimensions": [1,96,56,56], - "Format/Datatype": "Thirty-two wide channel vectorized row major Int8 format" - }], - "Outputs": [ - { - "Name": "410", - "Location": "Device", - "Dimensions": [1,24,56,56], - "Format/Datatype": "Thirty-two wide channel vectorized row major FP16 format" - }], - "ParameterType": "Convolution", - "Kernel": [1,1], - "PaddingMode": "kEXPLICIT_ROUND_DOWN", - "PrePadding": [0,0], - "PostPadding": [0,0], - "Stride": [1,1], - "Dilation": [1,1], - "OutMaps": 24, - "Groups": 1, - "Weights": {"Type": "Int8", "Count": 2304}, - "Bias": {"Type": "Float", "Count": 24}, - "HasSparseWeights": 0, - "Activation": "NONE", - "HasBias": 1, - "HasReLU": 0, - "TacticName": "turing_fp16_i8816cudnn_int8_128x128_ldg16_relu_small_nt_v1", - "TacticValue": "0x4e4e6b8f37d71713" -},{ - "Name": "QuantizeLinear_98", - "LayerType": "Reformat", - "Inputs": [ - { - "Name": "410", - "Location": "Device", - "Dimensions": [1,24,56,56], - "Format/Datatype": "Thirty-two wide channel vectorized row major FP16 format" - }], - "Outputs": [ - { - "Name": "413", - "Location": "Device", - "Dimensions": [1,24,56,56], - "Format/Datatype": "Thirty-two wide channel vectorized row major Int8 format" - }], - "ParameterType": "Reformat", - "Origin": "QDQ", - "TacticValue": "0x00000000000003e8" -},{ - "Name": "features.3.conv.0.0.weight + QuantizeLinear_104 + Conv_108 + PWN(Clip_112)", - "LayerType": "CaskConvolution", - "Inputs": [ - { - "Name": "413", - "Location": "Device", - "Dimensions": [1,24,56,56], - "Format/Datatype": "Thirty-two wide channel vectorized row major Int8 format" - }], - "Outputs": [ - { - "Name": "430", - "Location": "Device", - "Dimensions": 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"Format/Datatype": "Thirty-two wide channel vectorized row major Int8 format" - }], - "Outputs": [ - { - "Name": "447", - "Location": "Device", - "Dimensions": [1,144,56,56], - "Format/Datatype": "Thirty-two wide channel vectorized row major Int8 format" - }], - "ParameterType": "Convolution", - "Kernel": [3,3], - "PaddingMode": "kEXPLICIT_ROUND_DOWN", - "PrePadding": [1,1], - "PostPadding": [1,1], - "Stride": [1,1], - "Dilation": [1,1], - "OutMaps": 144, - "Groups": 144, - "Weights": {"Type": "Int8", "Count": 1296}, - "Bias": {"Type": "Float", "Count": 144}, - "HasSparseWeights": 0, - "Activation": "CLIPPED_RELU", - "ParameterSubType": "[0.000000,127.000000]", - "HasBias": 1, - "TacticValue": "0xfffffffffffffffc" -},{ - "Name": "features.3.conv.2.weight + QuantizeLinear_138 + Conv_142 + Add_144", - "LayerType": "CaskConvolution", - "Inputs": [ - { - "Name": "447", - "Location": "Device", - "Dimensions": [1,144,56,56], - "Format/Datatype": "Thirty-two wide channel vectorized row major 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Memory": "24217 MiB", "Shared Memory per SM": "64 KiB", "Memory Bus Width": 384.0, "Memory Clock Rate": 7.001}} diff --git a/tools/experimental/trt-engine-explorer/tests/inputs/mobilenet.qat.onnx.engine.timing.json b/tools/experimental/trt-engine-explorer/tests/inputs/mobilenet.qat.onnx.engine.timing.json deleted file mode 100644 index 08cc10c18..000000000 --- a/tools/experimental/trt-engine-explorer/tests/inputs/mobilenet.qat.onnx.engine.timing.json +++ /dev/null @@ -1,8947 +0,0 @@ -[ - { "startEnqMs" : 4.19666, "endEnqMs" : 4.28702, "startH2dMs" : 4.17414, "endH2dMs" : 4.17414, "startComputeMs" : 4.17414, "endComputeMs" : 4.65866, "startD2hMs" : 4.65866, "endD2hMs" : 4.65866, "h2dMs" : 0, "computeMs" : 0.484512, "d2hMs" : 0, "latencyMs" : 0.484512 } -, { "startEnqMs" : 4.29501, "endEnqMs" : 4.33191, "startH2dMs" : 4.65952, "endH2dMs" : 4.65952, "startComputeMs" : 4.65952, "endComputeMs" : 5.12992, "startD2hMs" : 5.12992, "endD2hMs" : 5.12992, "h2dMs" : 0, "computeMs" : 0.4704, 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b/tools/experimental/trt-engine-explorer/tests/inputs/mobilenet_v2_residuals.qat.onnx.engine.build.metadata.json deleted file mode 100644 index 7f49284d0..000000000 --- a/tools/experimental/trt-engine-explorer/tests/inputs/mobilenet_v2_residuals.qat.onnx.engine.build.metadata.json +++ /dev/null @@ -1 +0,0 @@ -{"model_options": {"Format": "ONNX", "Model": "trt-engine-explorer-OSS/tests/inputs/mobilenet_v2_residuals.qat.onnx"}, "build_options": {"Max batch": "explicit batch", "Memory Pools": "workspace", "minTiming": "1", "avgTiming": "8", "Precision": "FP32+FP16+INT8"}} \ No newline at end of file diff --git a/tools/experimental/trt-engine-explorer/tests/inputs/mobilenet_v2_residuals.qat.onnx.engine.graph.json b/tools/experimental/trt-engine-explorer/tests/inputs/mobilenet_v2_residuals.qat.onnx.engine.graph.json deleted file mode 100644 index f6422c383..000000000 --- a/tools/experimental/trt-engine-explorer/tests/inputs/mobilenet_v2_residuals.qat.onnx.engine.graph.json +++ /dev/null @@ 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a/tools/experimental/trt-engine-explorer/tests/inputs/mobilenet_v2_residuals.qat.onnx.engine.profile.metadata.json b/tools/experimental/trt-engine-explorer/tests/inputs/mobilenet_v2_residuals.qat.onnx.engine.profile.metadata.json deleted file mode 100644 index b256793bf..000000000 --- a/tools/experimental/trt-engine-explorer/tests/inputs/mobilenet_v2_residuals.qat.onnx.engine.profile.metadata.json +++ /dev/null @@ -1 +0,0 @@ -{"performance_summary": {"Throughput": 3765.71, "Latency": [0.255981, 0.901474, 0.263917, 0.25824, 0.26123, 0.278473, 0.38501], "Enqueue Time": [0.00952148, 0.022583, 0.0104952, 0.010498, 0.0109863, 0.0112305, 0.0117188], "H2D Latency": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], "GPU Compute Time": [0.255981, 0.901474, 0.263917, 0.25824, 0.26123, 0.278473, 0.38501], "D2H Latency": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], "Total Host Walltime": 3.0005, "Total GPU Compute Time": 2.982}, "inference_options": {"Batch": "1", "Input inference shapes": "model", "Iterations": "10", "Duration": "3s (+ 200ms warm up)", "Sleep time": "0ms", "Idle time": "0ms", "Streams": "1", "ExposeDMA": "Disabled", "Data transfers": "Disabled", "Spin-wait": "Enabled", "Multithreading": "Disabled", "CUDA Graph": "Enabled", "Separate profiling": "Enabled", "Time Deserialize": "Disabled", "Time Refit": "Disabled"}, "device_information": {"Selected Device": "NVIDIA TITAN RTX", "Compute Capability": 7.5, "SMs": 72.0, "Compute Clock Rate": 1.77, "Device Global Memory": "24217 MiB", "Shared Memory per SM": "64 KiB", "Memory Bus Width": 384.0, "Memory Clock Rate": 7.001}} diff --git a/tools/experimental/trt-engine-explorer/tests/inputs/mobilenet_v2_residuals.qat.onnx.engine.timing.json b/tools/experimental/trt-engine-explorer/tests/inputs/mobilenet_v2_residuals.qat.onnx.engine.timing.json deleted file mode 100644 index e2ce4cbae..000000000 --- a/tools/experimental/trt-engine-explorer/tests/inputs/mobilenet_v2_residuals.qat.onnx.engine.timing.json +++ /dev/null @@ -1,11855 +0,0 @@ -[ - { 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0.260254, "d2hMs" : 0, "latencyMs" : 0.260254 } -, { "startEnqMs" : 3200.23, "endEnqMs" : 3200.24, "startH2dMs" : 3200.45, "endH2dMs" : 3200.45, "startComputeMs" : 3200.45, "endComputeMs" : 3200.71, "startD2hMs" : 3200.71, "endD2hMs" : 3200.71, "h2dMs" : 0, "computeMs" : 0.258545, "d2hMs" : 0, "latencyMs" : 0.258545 } -] diff --git a/tools/experimental/trt-engine-explorer/tests/test_activations.py b/tools/experimental/trt-engine-explorer/tests/test_activations.py deleted file mode 100644 index d30421913..000000000 --- a/tools/experimental/trt-engine-explorer/tests/test_activations.py +++ /dev/null @@ -1,42 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - - -from .util import plan -from trex import create_activations, Activation - -def test_create_activations(plan): - pd_series = plan.get_layers_by_type('Convolution').iloc[0] - inputs, outputs = create_activations(pd_series) - assert isinstance(inputs[0], (Activation)) - assert isinstance(outputs[0], (Activation)) - -class TestActivation: - def test_initialization(self, plan): - pd_series = plan.df.iloc[0].Inputs[0] - # Test the input pandas series - assert isinstance(pd_series, (dict)) - assert pd_series['Name'] == 'input1' - assert pd_series['Dimensions'] == [1, 3, 224, 224] - # Test the Activation object - activation = Activation(pd_series) - assert activation.name == 'input1' - assert activation.shape == [1, 3, 224, 224] - assert activation.format == 'FP32 NCHW' - assert activation.precision == 'FP32' - assert activation.data_size == 4 - assert activation.size_bytes == 602112 diff --git a/tools/experimental/trt-engine-explorer/tests/test_archive.py b/tools/experimental/trt-engine-explorer/tests/test_archive.py deleted file mode 100644 index 4a9cd6446..000000000 --- a/tools/experimental/trt-engine-explorer/tests/test_archive.py +++ /dev/null @@ -1,35 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -import os -import tensorrt as trt -from trex.archiving import EngineArchive - - -def test_archiving(): - cdir = os.path.dirname(os.path.abspath(__file__)) - model_name = os.path.join(cdir, "resources", "single_relu.onnx") - tea = EngineArchive("/tmp/x1y2z3-test.tea") - TRT_LOGGER = trt.Logger() - with tea.Builder(TRT_LOGGER) as builder, builder.create_network() as network, \ - trt.OnnxParser(network, TRT_LOGGER) as parser: - with open(model_name, 'rb') as model: - assert parser.parse(model.read()) - config = builder.create_builder_config() - engine = builder.build_serialized_network(network, config) - assert engine - del engine diff --git a/tools/experimental/trt-engine-explorer/tests/test_clocks.py b/tools/experimental/trt-engine-explorer/tests/test_clocks.py deleted file mode 100644 index c336fcf80..000000000 --- a/tools/experimental/trt-engine-explorer/tests/test_clocks.py +++ /dev/null @@ -1,28 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - - -import utils.config_gpu - -class TestClocksSampling: - def test_sampling(self): - max_mem_clk, max_gr_clk = utils.config_gpu.get_max_clocks(dev=0) - - -if __name__ == "__main__": - test = TestClocksSampling() - test.test_sampling() \ No newline at end of file diff --git a/tools/experimental/trt-engine-explorer/tests/test_compare_engines.py b/tools/experimental/trt-engine-explorer/tests/test_compare_engines.py deleted file mode 100644 index ed7c85f9b..000000000 --- a/tools/experimental/trt-engine-explorer/tests/test_compare_engines.py +++ /dev/null @@ -1,43 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - - -from .util import plan, plan2 -from trex.compare_engines import get_plans_names, match_layers - - -def test_get_plans_names(plan, plan2): - assert get_plans_names([plan, plan2]) == [ - 'mobilenet.qat.onnx.engine.graph.json', - 'mobilenet_v2_residuals.qat.onnx.engine.graph.json' - ] - -def test_match_layers(plan, plan2): - expected_pairs = [(0, 0), (1, 1), (2, 2), (3, 3), (4, 4), (5, 5), (6, 6), - (8, 7), (7, None), (9, 8), (10, 9), (12, 10), (11, None), (13, 11), - (14, 12), (16, 13), (15, None), (17, 14), (18, 15), (20, 16), (19, None), - (21, 17), (22, 18), (24, 19), (23, None), (25, 20), (26, 21), (28, 22), - (27, None), (29, 23), (30, 24), (32, 25), (31, None), (33, 26), (34, 27), - (36, 28), (35, None), (37, 29), (38, 30), (40, 31), (39, None), (41, 32), - (42, 33), (44, 34), (43, None), (45, 35), (46, 36), (48, 37), (47, None), - (49, 38), (50, 39), (52, 40), (51, None), (53, 41), (54, 42), (56, 43), - (55, None), (57, 44), (58, 45), (60, 46), (59, None), (61, 47), (62, 48), - (64, 49), (63, None), (65, 50), (66, 51), (67, 52), (68, 53), (69, 54), - (70, 55), (71, 56)] - - matched_indices_pairs = match_layers(plan, plan2, exact_matching=True) - assert expected_pairs == matched_indices_pairs diff --git a/tools/experimental/trt-engine-explorer/tests/test_df_preprocessing.py b/tools/experimental/trt-engine-explorer/tests/test_df_preprocessing.py deleted file mode 100644 index 37c1ce559..000000000 --- a/tools/experimental/trt-engine-explorer/tests/test_df_preprocessing.py +++ /dev/null @@ -1,47 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - - -from .util import plan -from trex import change_col_order, drop_columns - -def test_change_col_order(plan): - new_col_order = change_col_order(plan.df).columns - - def check_col_order(new_col_order): - common_cols = list(('Name', 'type', 'Inputs', 'Outputs', - 'latency.avg_time', 'latency.pct_time', 'total_footprint_bytes', - 'tactic')) - - uncommon_col_seen = False - for col in new_col_order: - if col in common_cols and uncommon_col_seen: - return False - if col not in common_cols: - uncommon_col_seen = True - return True - - assert check_col_order(new_col_order) - -def test_drop_columns(plan): - df = plan.df - cols_before = df.columns - assert len(cols_before) > 0 - col_to_drop = cols_before[0] - drop_columns(df, [col_to_drop]) - cols_after = df.columns - assert col_to_drop not in cols_after diff --git a/tools/experimental/trt-engine-explorer/tests/test_engine_plan.py b/tools/experimental/trt-engine-explorer/tests/test_engine_plan.py deleted file mode 100644 index 4863fad71..000000000 --- a/tools/experimental/trt-engine-explorer/tests/test_engine_plan.py +++ /dev/null @@ -1,34 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - - -from .util import plan -from trex import summary_dict -import pandas as pd - -def test_summary_dict(plan): - d = summary_dict(plan) - assert d["Inputs"] == "input1: [1, 3, 224, 224]xFP32 NCHW" - assert d["Average time"] == "0.470 ms" - assert d["Layers"] == "72" - assert d["Weights"] == "3.3 MB" - assert d["Activations"] == "15.9 MB" - -class TestEnginePlan: - def test_df(self, plan): - df = plan.df - assert isinstance(df, (pd.DataFrame)) diff --git a/tools/experimental/trt-engine-explorer/tests/test_layer.py b/tools/experimental/trt-engine-explorer/tests/test_layer.py deleted file mode 100644 index 3d2a0c51f..000000000 --- a/tools/experimental/trt-engine-explorer/tests/test_layer.py +++ /dev/null @@ -1,38 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - - -from .util import test_engine1_prefix_path -from trex import import_graph_file, Layer, Activation - - -class TestLayer: - def test_initialization(self): - graph_file = f'{test_engine1_prefix_path}.graph.json' - raw_layers, _ = import_graph_file(graph_file) - layer = Layer(raw_layers[0]) - - assert layer.name == 'QuantizeLinear_2' - assert layer.type == 'Reformat' - assert layer.subtype == 'Reformat' - assert isinstance(layer.inputs[0], (Activation)) - assert isinstance(layer.outputs[0], (Activation)) - assert layer.outputs_size_bytes == 150528 - assert layer.precision == 'FP32' - assert layer.inputs_size_bytes == 602112 - assert layer.total_io_size_bytes == 752640 - assert layer.total_footprint_bytes == 752640 diff --git a/tools/experimental/trt-engine-explorer/tests/test_lint.py b/tools/experimental/trt-engine-explorer/tests/test_lint.py deleted file mode 100644 index fd2826f88..000000000 --- a/tools/experimental/trt-engine-explorer/tests/test_lint.py +++ /dev/null @@ -1,51 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - - -from .util import plan -from trex import ConvLinter, ReformatLinter - -class TestConvLinter: - def test_tc_lint(self, plan): - df = ConvLinter(plan).lint() - tc_lint_row = df.loc['features.1.conv.0.0.weight + QuantizeLinear_25 + Conv_29 + PWN(Clip_33)'] - assert tc_lint_row['name'] == 'features.1.conv.0.0.weight + QuantizeLinear_25 + Conv_29 + PWN(Clip_33)' - assert tc_lint_row['hazard'] == 'Convolution is not accelerated.' - - def test_mixed_precision_lint(self, plan): - df = ConvLinter(plan).lint() - alignment_lint_row = df.loc['features.4.conv.2.weight + QuantizeLinear_187 + Conv_191'] - assert alignment_lint_row['name'] == 'features.4.conv.2.weight + QuantizeLinear_187 + Conv_191' - assert alignment_lint_row['hazard'] == 'Quantized Convolution has float outputs.' - assert alignment_lint_row['mitigation'] == 'Consider adding quantization after the convolution.' - - def test_alignment_lint(self, plan): - df = ConvLinter(plan).lint() - alignment_lint_row = df.loc['features.0.0.weight + QuantizeLinear_8 + Conv_12 + PWN(Clip_16)'] - assert alignment_lint_row['name'] == 'features.0.0.weight + QuantizeLinear_8 + Conv_12 + PWN(Clip_16)' - assert alignment_lint_row['hazard'] == 'Convolution channels are not optimally aligned.' - assert alignment_lint_row['mitigation'] == "Consider changing the alignment of the convolution's channels." - -class TestReformatLinter: - def test_lint(self, plan): - df = ReformatLinter(plan).lint() - lint_row = df.loc['QuantizeLinear_2'] - assert lint_row['name'] == 'QuantizeLinear_2' - assert lint_row['origin'] == 'QDQ' - assert lint_row['type conversion'] == 'FP32 -> Int8' - assert lint_row['shape conversion'] == '[1, 3, 224, 224] -> [1, 3, 224, 224]' - assert lint_row['hazard'] == 'Reformat layer is converting operand data type.' diff --git a/tools/experimental/trt-engine-explorer/tests/test_misc.py b/tools/experimental/trt-engine-explorer/tests/test_misc.py deleted file mode 100644 index ecf793097..000000000 --- a/tools/experimental/trt-engine-explorer/tests/test_misc.py +++ /dev/null @@ -1,35 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - - -from .util import plan -import trex - - -def test_group_count(plan): - gc = trex.misc.group_count(plan.df, "type").set_index("type") - gc_exp = plan.df.groupby(["type"]).size() - assert (gc.loc['Convolution'] == gc_exp.loc['Convolution']).all() - assert (gc.loc['Pooling'] == gc_exp.loc['Pooling']).all() - assert (gc.loc['Reformat'] == gc_exp.loc['Reformat']).all() - -def test_group_sum_attr(plan): - gsa = trex.misc.group_sum_attr(plan.df,"type", "latency.avg_time").set_index("type") - gsa_exp = plan.df.groupby(["type"])[["latency.avg_time"]].sum() - assert (gsa.loc['Convolution'] == gsa_exp.loc['Convolution']).all() - assert (gsa.loc['Pooling'] == gsa_exp.loc['Pooling']).all() - assert (gsa.loc['Reformat'] == gsa_exp.loc['Reformat']).all() diff --git a/tools/experimental/trt-engine-explorer/tests/test_raw_preprocessing.py b/tools/experimental/trt-engine-explorer/tests/test_raw_preprocessing.py deleted file mode 100644 index db97b25e6..000000000 --- a/tools/experimental/trt-engine-explorer/tests/test_raw_preprocessing.py +++ /dev/null @@ -1,46 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - - -from .util import test_engine1_prefix_path -from trex import import_graph_file -import pytest - -def test_import_graph_file(): - graph_file = f'{test_engine1_prefix_path}.graph.json' - raw_layers, bindings = import_graph_file(graph_file) - - assert isinstance(raw_layers, list) - # Check if no raw_layer['Name'] is repeated. If layer name has appeared - # twice, it must be disambiguated - names = [raw_layer['Name'] for raw_layer in raw_layers] - assert len(set(names)) == len(names) - - # Check if applicable layers have been converted to 'Deconvolution' - assert sum([1 for raw_layer in raw_layers - if raw_layer.get('LayerType') == 'CaskDeconvolutionV2' and - raw_layer.get('ParameterType') == 'Convolution']) == 0 - - raw_layer = raw_layers[0] - assert raw_layer['Name'] == 'QuantizeLinear_2' - assert raw_layer['LayerType'] == 'Reformat' - assert raw_layer['ParameterType'] == 'Reformat' - assert raw_layer['Origin'] == 'QDQ' - assert raw_layer['TacticValue'] == '0x0000000000000000' - - assert isinstance(bindings, (list)) - assert bindings == ['input1', '1179'] diff --git a/tools/experimental/trt-engine-explorer/tests/util.py b/tools/experimental/trt-engine-explorer/tests/util.py deleted file mode 100644 index ca4184eb3..000000000 --- a/tools/experimental/trt-engine-explorer/tests/util.py +++ /dev/null @@ -1,84 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - - -from trex import EnginePlan -import pytest -import os -import numpy as np -import json -import trex -from graphviz.graphs import Digraph - -test_dir_path = os.path.dirname(os.path.realpath(__file__)) -test_engine1 = "mobilenet.qat.onnx.engine" -test_engine2 = "mobilenet_v2_residuals.qat.onnx.engine" -test_engine1_prefix_path = os.path.join(test_dir_path, 'inputs', test_engine1) -test_engine2_prefix_path = os.path.join(test_dir_path, 'inputs', test_engine2) - -@pytest.fixture -def plan(): - plan = EnginePlan(f'{test_engine1_prefix_path}.graph.json', f'{test_engine1_prefix_path}.profile.json') - return plan - -@pytest.fixture -def plan2(): - plan = EnginePlan(f'{test_engine2_prefix_path}.graph.json', f'{test_engine2_prefix_path}.profile.json') - return plan - -class NpEncoder(json.JSONEncoder): - """ - Custom Encoder to dump numpy objects - """ - def default(self, obj): - if isinstance(obj, np.integer): - return int(obj) - - if isinstance(obj, np.floating): - return float(obj) - - if isinstance(obj, np.ndarray): - return obj.tolist() - - return super(NpEncoder, self).default(obj) - -def dump_recurse(item): - """ - Recursively open objects to basic builtin data-types - """ - if item is None: - return json.dumps(item) - - if type(item).__module__ == "numpy": - return json.dumps(item, cls=NpEncoder) - - elif isinstance(item, (bool, str, int, float)): - return json.dumps(item) - - elif isinstance(item, (tuple)): - return [dump_recurse(i) for i in tuple(item)] - - elif isinstance(item, (list)): - return [dump_recurse(i) for i in item] - - elif isinstance(item, (dict)): - return {k: dump_recurse(v) for k, v in item.items()} - - elif isinstance(item, (trex.PlanGraph, trex.DotGraph, trex.Region, trex.RegionGeneration, trex.Activation, trex.LayerNode, trex.Layer, Digraph)): - return dump_recurse(item.__dict__) - - return json.dumps(dump_recurse(item.__dict__)) diff --git a/tools/experimental/trt-engine-explorer/trex/README.md b/tools/experimental/trt-engine-explorer/trex/README.md deleted file mode 100644 index e7cd188fb..000000000 --- a/tools/experimental/trt-engine-explorer/trex/README.md +++ /dev/null @@ -1,38 +0,0 @@ -# Design - -![](../images/trex_logo.png) - -The `trex` package design follows the Model View Controller (MVC) design pattern: -* An `EnginePlan` instance represents a plan file and provides access to a Pandas dataframe which acts as the plan model. Each row in the dataframe represents one layer in the plan file, including its name, tactic, inputs and outputs and other attributes describing the layer. - ``` - # Example: print plan layer names. - plan = EnginePlan("my-engine.graph.json") - df = plan.df - print(df['Name']) - ``` - -* An `EnginePlan` is constructed from three JSON files: - * A plan-graph JSON file (mandatory) describes the engine plan in a JSON format. - * A plan profiling file provides timing measurements in JSON format (optional; required only if you want to access timing information). - * A metadata JSON file which describes the hardware and software enviroments on which the engine is profiled. This file is optional but provides valuable information about the engine. - - ``` - plan = EnginePlan( - "my-engine.graph.json", - "my-engine.profile.json", - "my-engine.profile.metadata.json") - ``` - -* A thin API provides access to views of the model. This is a convinience API on top of the Pandas dataframe. For example, `plan.get_layers_by_type` further preprocesses the dataframe for display. Other functions, like `group_count` provide dataframe groupding and reduction shortcuts. - ``` - convs = plan.get_layers_by_type('Convolution') - tactic_cnt = group_count(convs, 'tactic') - ``` - -* There are APIs for plotting data (`plotting.py`), visualizing an engine graph (`graphing.py`), interactive notebooks (`interactive.py`, `notebook.py`) and easy-access reporting (`report_card.py`). - -* The linting API is basic and in an early-preview status (`lint.py`). - -# API Stability - -`trex` is an experimental package and API stability is not guaranteed. TensorRT's JSON format is controlled by TensorRT and is not likely to change much (except for extensions). diff --git a/tools/experimental/trt-engine-explorer/trex/__init__.py b/tools/experimental/trt-engine-explorer/trex/__init__.py deleted file mode 100644 index 363d97b04..000000000 --- a/tools/experimental/trt-engine-explorer/trex/__init__.py +++ /dev/null @@ -1,26 +0,0 @@ -# -# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - - -# Include only core modules. -from trex.df_preprocessing import * -from trex.misc import * -from trex.lint import * -from trex.activations import * -from trex.engine_plan import * -from trex.colors import * - -__version__ = "0.2.1" diff --git a/tools/experimental/trt-engine-explorer/trex/activations.py b/tools/experimental/trt-engine-explorer/trex/activations.py deleted file mode 100644 index f2abe41f5..000000000 --- a/tools/experimental/trt-engine-explorer/trex/activations.py +++ /dev/null @@ -1,152 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -""" -This file contains the Activation class which abstracts plan Region views. -""" - - -import numpy as np -from typing import Dict -import pandas as pd - - -# This dictionary compresses JSON's long format description strings. -_regionFormatDict = { - "Four wide channel vectorized row major Int8 format" : "Int8 NC/4HW4", - "Four wide channel vectorized row major FP32 format" : "FP32 NC/4HW4", - "Thirty-two wide channel vectorized row major Int8 format": "Int8 NC/32HW32", - "Thirty-two wide channel vectorized row major FP32 format": "FP32 NC/32HW32", - "Thirty-two wide channel vectorized row major FP16 format": "FP16 NC/32HW32", - "Thirty-two wide channel vectorized row major Int8 format with 3 spatial dimensions": "Int8 NC32DHW", - "Thirty-two wide channel vectorized row major FP16 format with 3 spatial dimensions": "FP16 NC32DHW", - "Sixteen wide channel vectorized row major FP16 format": "FP16 NC16HW", - "Channel major FP16 format where channel % 4 == 0": "FP16 NHWC4", - "Channel major FP32 format where channel % 4 == 0": "FP32 NHWC4", - "Channel major Int8 format where channel % 4 == 0": "Int8 NHWC4", - "Channel major FP16 format where channel % 8 == 0": "FP16 NHWC8", - "Channel major FP16 format where channel % 16 == 0": "FP16 NHWC16", - "Channel major FP16 format where channel == 4 and column stride % 32 == 0": "FP16 NHWC4", - "Channel major INT8 format where channel == 4 and column stride % 32 == 0": "Int8 NHWC4", - "Channel major FP16 format where channel % 2 == 0": "FP16 NHWC2", - "Channel major INT8 format where column stride % 32 == 0": "Int8 NHWC1", - "Channel major INT8 format where channel % 16 == 0": "Int8 NHWC16", - "Row major INT8 format where column stride % 64 == 0": "Int8 NCHW", - "Channel major FP16 format where channel % 8 == 0 with 3 spatial dimensions": "FP16 NDHWC8", - "Channel major FP16 format where channel == 1 and column stride % 32 == 0": "FP16 NHWC1", - "Row major FP16 format where column stride % 64 == 0": "FP16", - "Two wide channel vectorized row major FP16 format": "FP16 NC/2HW2", - "Row major linear FP32": "FP32 NCHW", - "Row major linear Int32": "INT32 NCHW", - "Row major linear FP16 format": "FP16 NCHW", - "Row major Int8 format": "Int8 NCHW", - "Channel major FP32 format":"FP32 NHWC", - "Channel major FP16 format": "FP16 NHWC", - "Channel major Int8 format": "Int8 NHWC", - "Row major linear BOOL": "Bool", - "Channel major FP32 format with 3 spatial dimensions": "FP32 NDHWC", - "Channel major FP32 format with 3 spatial dimensions where channel % 4 == 0": "FP32 NDHWC4", - "Channel major FP32 format where channel % 4 == 0 with 3 spatial dimensions": "FP32 NDHWC4", - "Row major linear UInt8 format" : "UInt8 NCHW", - "Channel major UInt8 format": "UInt8 NHWC", - "Row major linear Int64 format": "Int64 NCHW", - "Row major linear BFloat16 format": "BF16 NCHW", - "Channel major BFloat16 format where channel % 8 == 0": "BF16 NHWC8", - "Channel major BFloat16 format where channel % 4 == 0": "BF16 NHWC4", - "Channel major BFloat16 format where channel % 8 == 0 with 3 spatial dimensions": "BF16 NDHWC8", - "Channel major BFloat16 format where channel % 2 == 0": "BF16 NHWC2", - "Two wide channel vectorized row major BFloat16 format": "BF16 NC2HW", - "Row major linear FP4 format (kLINEAR)" : "FP4", - "Row major linear FP4 format" : "FP4", - "Row major linear FP8 format": "FP8 NCHW", - "Unknown format": "Unknown format", - # kgen formats - "BFloat16": "BFloat16", - "Bool": "Bool", - "Double": "Double", - "DoubleComplex": "DoubleComplex", - "Float": "Float", - "FloatComplex": "FloatComplex", - "FP8": "FP8", - "Half": "Half", - "Int16": "Int16", - "Int32": "Int32", - "Int64": "Int64", - "Int8": "Int8", - "None": "None", - "UInt16": "UInt16", - "UInt32": "UInt32", - "UInt64": "UInt64", - "UInt8": "UInt8", - "Int4": "Int4", - "FP4": "FP4", - "FP4E2M1": "FP4", -} - -class Activation: - """Convenience class wrapping activation regions.""" - def __init__(self, raw_dict: Dict): - def parse_tensor_info(desc): - try: - data_type, layout = desc.split(' ') - except ValueError: - data_type = desc - unknown_format = (data_type, 0) - precision, data_size = { - 'FP8': ('FP8', 1), - 'FP16': ('FP16', 2), - 'Half': ('FP16', 2), - 'FP32': ('FP32', 4), - 'Float': ('FP32', 4), - 'Double': ('FP64', 8), - 'BFloat16': ('FP32', 2), - 'Int8': ('INT8', 1), - 'Int16': ('INT16', 2), - 'INT32': ('INT32', 4), - 'Int32': ('INT32', 4), - 'Int64': ('INT64', 8), - 'UInt8': ('UINT8', 1), - 'UInt16': ('UINT16', 2), - 'UInt32': ('UINT32', 4), - 'UInt64': ('UINT64', 8), - 'Int4': ('INT4', 0.5), - 'FP4': ('FP4', 0.5), - 'Unknown format': unknown_format, - 'None': unknown_format, - }.get(data_type, (data_type, 0)) - - return precision, data_size - - self.name = raw_dict['Name'] - self.shape = raw_dict['Dimensions'] - format = raw_dict['Format/Datatype'].replace('.', '') - self.format = _regionFormatDict.get(format,"Unknown format") - self.precision, self.data_size = parse_tensor_info(self.format) - self.size_bytes = np.prod(self.shape) * self.data_size - - def tooltip(self): - tip = "\\n".join((str(self.shape), self.format,)) - return tip - - def __repr__(self): - return f"{self.name}: {str(self.shape)}x{self.format}" - - -def create_activations(layer: pd.Series): - inputs = [Activation(tensor) for tensor in layer.Inputs] - outputs = [Activation(tensor) for tensor in layer.Outputs] - return inputs, outputs diff --git a/tools/experimental/trt-engine-explorer/trex/archiving.py b/tools/experimental/trt-engine-explorer/trex/archiving.py deleted file mode 100644 index 0fb2b72b3..000000000 --- a/tools/experimental/trt-engine-explorer/trex/archiving.py +++ /dev/null @@ -1,269 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -""" -This file contains archiving functionality. -""" - -__all__ = ["EngineArchive", "get_reader", "get_writer"] - - -import os -import io -import json -import datetime -from typing import List -from io import BytesIO -from zipfile import ZipFile, Path, ZIP_DEFLATED -import tensorrt as trt - - -class regular_file_writer(object): - """Adaptor for file writer context manager""" - def __init__(self, fname: str) -> None: - self.fname = fname - - def __enter__(self): - self.f = open(self.fname, 'w') - return self - - def write(self, text:str): - self.f.write(text) - - def __exit__(self, *args) -> None: - self.f.close() - - -class regular_file_reader(object): - """Adaptor for file reader context manager""" - def __init__(self, fname: str) -> None: - self.fname = fname - - def __enter__(self): - self.f = open(self.fname, 'r') - return self - - def read(self) -> str: - return self.f.read() - - def readlines(self) -> List[str]: - return self.f.readlines() - - def __exit__(self, *args) -> None: - self.f.close() - - -class zip_file_writer(object): - """Adaptor for Zip file writer context manager""" - def __init__(self, zipf: ZipFile, fname: str) -> None: - self.zipf = zipf - self.fname = os.path.basename(fname) - - def __enter__(self): - return self - - def write(self, text:str): - self.zipf.writestr(self.fname, text) - - def __exit__(self, *args) -> None: - pass - - -class zip_file_reader(object): - """Adaptor for Zip file reader context manager""" - def __init__(self, zipf: ZipFile, fname: str) -> None: - self.zipf = zipf - self.fname = os.path.basename(fname) - - def __enter__(self): - return self - - def read(self): - return self.zipf.read(self.fname) - - def readlines(self): - p = Path(self.zipf, self.fname) - txt = p.read_text() - for line in txt.split("\n"): - yield line - - def __exit__(self, *args) -> None: - pass - - -class StreamTrtLogger(trt.ILogger): - """Writes TRT log messages to a buffer""" - def __init__(self, user_logger): - trt.ILogger.__init__(self) - self.user_logger = user_logger - self.buffer = io.StringIO() - self.proxy_logger = trt.Logger() - - def log(self, severity: trt.ILogger.Severity, msg: str): - if severity <= self.user_logger.min_severity: - self.user_logger.log(severity, msg) - self.buffer.write(msg + "\n") - - -class NullTrtLogger(trt.ILogger): - """TRT log messages blackhole""" - def __init__(self): - trt.ILogger.__init__(self) - - def log(self, severity: trt.ILogger.Severity, msg: str): - pass - - -class EngineArchive(object): - """Interface to a TensorRT Engine Archive (TEA) file""" - __version__ = "1.0" - - def __init__(self, archive_filename: str, override_files: bool=True) -> None: - if os.path.exists(archive_filename): - if override_files: - os.remove(archive_filename) - else: - raise FileExistsError(f"TensorRT engine archive {archive_filename} exists") - self.archive_filename = archive_filename - self.zipf = None - - def open(self): - self.zipf = ZipFile(self.archive_filename, 'a', compression=ZIP_DEFLATED, compresslevel=9) - - def close(self): - assert self.zipf is not None - self.zipf.testzip() - self.zipf.close() - self.zipf = None - - def __enter__(self): - self.open() - return self - - def __exit__(self, *args) -> None: - self.close() - - def writef_txt(self, fname: str, text: str): - """Write a text file to the TEA""" - with self: - self.writer(fname).write(text) - self.zipf.testzip() - - def writef_bin(self, fname: str, content: any): - """Write a binary file to the TEA""" - with self: - self.writer(fname).write(BytesIO(content).getvalue()) - self.zipf.testzip() - - def readf(self, fname: str): - """Read a file from the TEA""" - with self: - return self.reader(fname).read() - - def writer(self, fname: str): - assert self.zipf is not None - return zip_file_writer(self.zipf, fname) - - def reader(self, fname: str): - assert self.zipf is not None - return zip_file_reader(self.zipf, fname) - - def archive_build_config(self, - config: trt.IBuilderConfig, - build_duration: datetime.timedelta - ): - as_dict = lambda cfg: {attr: str(getattr(cfg, attr)) - for attr in dir(cfg) if not callable(getattr(cfg, attr)) and not attr.startswith("__")} - build_dict = {"engine_build_duration": build_duration.total_seconds()} - build_dict.update(as_dict(config)) - as_json = json.dumps(build_dict, ensure_ascii=False, indent=4) - self.writef_txt("build_cfg.json", as_json) - - def archive_timing_cache(self, config: trt.IBuilderConfig): - cache = config.get_timing_cache() - if cache is None: - return - self.writef_bin("timing.cache", cache.serialize()) - - def archive_plan_info(self, plan: trt.IHostMemory): - assert plan - with trt.Runtime(NullTrtLogger()) as runtime: - engine = runtime.deserialize_cuda_engine(plan) - # Explicitly remove some attributes which explode when getattr is called on them. - bad_attrs = ["weight_streaming_budget", - "minimum_weight_streaming_budget", "streamable_weights_size"] - safe_attrs = [attr for attr in dir(engine) - if attr not in bad_attrs and not callable(getattr(engine, attr)) and not attr.startswith("__")] - plan_dict = {attr: str(getattr(engine, attr)) for attr in safe_attrs} - - bindings = plan_dict["io_tensors"] = {} - for index in range(engine.num_io_tensors): - name = engine.get_tensor_name(index) - dtype = engine.get_tensor_dtype(name) - shape = engine.get_tensor_shape(name) - location = engine.get_tensor_location(name) - mode = engine.get_tensor_mode(name) - bindings[name] = { - "mode": str(mode), - "dtype": str(dtype), - "shape": str(shape), - "location": str(location) - } - as_json = json.dumps(plan_dict, ensure_ascii=False, indent=4) - self.writef_txt("plan_cfg.json", as_json) - - - class _Builder(trt.Builder): - """A trt.Builder decorator class which adds archiving functionality.""" - def __init__(self, logger, tea): - self.tea = tea - self.tea_logger = StreamTrtLogger(logger) - trt.Builder.__init__(self, self.tea_logger) - - def build_serialized_network(self, - network: trt.INetworkDefinition, - config: trt.IBuilderConfig - ): - start = datetime.datetime.now() - plan = trt.Builder.build_serialized_network(self, network, config) - end = datetime.datetime.now() - build_duration = end - start - # Save to archive - self.tea.writef_txt("build.txt", self.tea_logger.buffer.getvalue()) - self.tea.writef_bin("engine.trt", plan) - self.tea.archive_build_config(config, build_duration) - self.tea.archive_plan_info(plan) - self.tea.archive_timing_cache(config) - return plan - - def Builder(self, logger): - assert trt.__version__ >= "10.0.0", "This TEA functionality requires TRT 10.0+" - return self._Builder(logger, self) - - -def get_writer(tea: EngineArchive, fname:str): - if tea: - return tea.writer(fname) - else: - return regular_file_writer(fname) - - -def get_reader(tea: EngineArchive, fname:str): - if tea: - return tea.reader(fname) - else: - return regular_file_reader(fname) diff --git a/tools/experimental/trt-engine-explorer/trex/colors.py b/tools/experimental/trt-engine-explorer/trex/colors.py deleted file mode 100644 index 5abf84039..000000000 --- a/tools/experimental/trt-engine-explorer/trex/colors.py +++ /dev/null @@ -1,84 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -""" -This file contains color pallete defnitions. -""" - - -from collections import defaultdict - - -NVDA_GREEN = "#76b900" -UNKNOWN_KEY_COLOR = "gray" -GRID_COLOR = "rgba(114, 179, 24, 0.3)" - - -# pallete = px.colors.qualitative.G10 -# https://medialab.github.io/iwanthue/ -default_pallete = [ - "#a11350", - "#008619", - "#4064ec", - "#ffb519", - "#8f1a8e", - "#b2b200", - "#64b0ff", - "#e46d00", - "#02d2ba", - "#ef393d", - "#f1b0f7", - "#7e4401", - UNKNOWN_KEY_COLOR] - - -# Set a color for each precision datatype. -precision_colormap = defaultdict(lambda: UNKNOWN_KEY_COLOR, { - "INT8": NVDA_GREEN, - "FP32": "red", - "FP16": "orange", - "INT32": "lightgray", - "FP8": "deepskyblue", - "INT4": "#CC66FF", # Heliotrope (bright lavender) - "FP4": "#F4D03F", # bright yellow -}) - - -# Set a color for each layer type. -layer_colormap = defaultdict(lambda: UNKNOWN_KEY_COLOR, { - # https://htmlcolorcodes.com/ - "Convolution": "#4682B4", # SteelBlue - "Deconvolution": "#7B68EE", # MediumSlateBlue - "ConvActPool": "#6495ED", # CornflowerBlue - "MatrixMultiply": "#1E90FF", # DodgerBlue - "gemm": "#1E90FF", # DodgerBlue - "Reformat": "#00FFFF", # Cyan - "Shuffle": "#BC8F8F", # RosyBrown - "Slice": "#FFA500", # Orange - "Scale": "#8FBC8B", # DarkSeaGreen - "Quantize": "#6B8E23", # OliveDrab - "Pooling": "#3CB371", # MediumSeaGreen - "PluginV2": "#C71585", # MediumVioletRed - "PointWise": "#9ACD32", # YellowGreen - "ElementWise": "#9ACD32", # YellowGreen - "Reduce": "#90EE90", # LightGreen - "SoftMax": "#DA70D6", # Orchid - "Myelin": "#B39C4D", # Satic Sheen Gold - "kgen": "#B39C4D", # Satic Sheen Gold - "NonZero": "#98FB98", # PaleGreen - "TrainStation": "#FFA07A", # LightSalmon -}) diff --git a/tools/experimental/trt-engine-explorer/trex/compare_engines.py b/tools/experimental/trt-engine-explorer/trex/compare_engines.py deleted file mode 100644 index 415362cba..000000000 --- a/tools/experimental/trt-engine-explorer/trex/compare_engines.py +++ /dev/null @@ -1,619 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -""" -This file contains interaction cells for the compare_engines.ipynb notebook. -""" - - -import copy -import numpy as np -from typing import List, Tuple, Dict -from functools import partial -import pandas as pd -import plotly.express as px -import plotly.graph_objects as go -from plotly.subplots import make_subplots -import trex.misc as misc -import trex.plotting as plotting -import trex.colors as colors -import trex.notebook as notebook -import trex.interactive as interactive -import trex.engine_plan as engine_plan -import trex.activations as activations - - -def get_plans_names(plans: List[engine_plan.EnginePlan]): - """Create unique plans names""" - engine_names = [plan.name for plan in plans] - if len(set(engine_names)) != len(plans): - engine_names = [plan.name + str(i) for i,plan in enumerate(plans)] - return engine_names - - -def compare_engines_overview(plans: List[engine_plan.EnginePlan]): - """A dropdown widget to choose from several diagrams - that compare 2 or more engine plans. - """ - engine_names = get_plans_names(plans) - - # Get throughtput data. - throughtput = [plan.performance_summary.get('Throughput', 0) for plan in plans] - have_throughput_data = all([tp > 0 for tp in throughtput]) - - def throughput_per_plan(title: str): - y = [plan.performance_summary.get('Throughput', 0) for plan in plans] - x = [plan.name for plan in plans] - fig = px.bar(x=y, y=x, orientation='h') - plotting.trex_base_layout(fig) - fig.update_layout({ - 'xaxis_title': "Throughput (inferences / sec)", - 'yaxis_title': "Engine"}) - fig.show() - - time_by_type = [plan.df.groupby(['type'])[ - ['latency.pct_time', 'latency.avg_time']].sum().reset_index() for plan in plans] - - cnt_by_type = [misc.group_count(plan.df, 'type') for plan in plans] - - # Normalize timings by the batch-size. - df_list_bs_normalized = [copy.deepcopy(plan.df) for plan in plans] - for i, plan in enumerate(plans): - inputs, outputs = plan.get_bindings() - bs = inputs[0].shape[0] - df_list_bs_normalized[i]['latency.avg_time'] /= bs - - time_by_type_bs_normalized = [df.groupby(['type']) - [['latency.pct_time', 'latency.avg_time']].sum().reset_index() for df in df_list_bs_normalized] - - def latency_per_type(title): - stacked_latencies_bars = partial( - plotting.stacked_bars, - title, - bar_names=engine_names, - df_list=time_by_type, - names_col='type', - values_col='latency.avg_time', - colormap=colors.layer_colormap, - display_tbl=False, - xaxis_title="Engine", - yaxis_title="Latency (ms)") - - if have_throughput_data: - real_latency = [plan.performance_summary.get('Latency', [0]*5)[2] for plan in plans] - - # Display throughput scatter plot together with the latencies bars. - fig = make_subplots(specs=[[{"secondary_y": True}]]) - stacked_latencies_bars(fig=fig) - fig.add_trace( - go.Scatter( - x=engine_names, - y=throughtput, - name="Throughput (IPS)", - marker=dict(size=12, color='#FFBF00'),), - secondary_y=True) - fig.add_trace( - go.Scatter( - x=engine_names, - y=real_latency, - name="Real Latency", - marker=dict(size=12, color='#DE3163'),), - secondary_y=False) - plotting.trex_base_layout(fig) - fig.update_yaxes(title_text="Throughput (inferences / sec)", secondary_y=True) - fig.show() - else: - stacked_latencies_bars() - - df = plotting.stacked_tabular_df( - engine_names, time_by_type, 'type', 'latency.avg_time', empty_symbol=np.NaN) - # Compute the speedup of the last engine vs. the first engine. - df['speedup'] = df[engine_names[0]] / df[engine_names[-1]] - print(f"\'speedup\' refers to the speedup of \"{engine_names[-1]}\" relative to \"{engine_names[0]}\"") - notebook.display_df(df, range_highlights=speedup_range_highlights( - col_name='speedup', threshold=0.03)) - - latency_per_type_bs_normalized = partial( - plotting.stacked_bars, - bar_names=engine_names, - df_list=time_by_type_bs_normalized, - names_col='type', - values_col='latency.avg_time', - empty_symbol=np.NaN, - colormap=colors.layer_colormap, - xaxis_title="Engine", - yaxis_title="Latency (ms)") - - d = {engine_name:df for engine_name, df in zip(engine_names, time_by_type)} - latency_per_type_comparison = partial( - plotting.plotly_bar2, - df=d, - values_col='latency.avg_time', - names_col='type', - orientation='h', - showlegend=True) - - d = {engine_name:df for engine_name, df in zip(engine_names, cnt_by_type)} - count_comparison = partial( - plotting.plotly_bar2, - df=d, - values_col='count', - names_col='type', - orientation='h', - showlegend=True) - - time_by_precision = [plan.df.groupby(['precision']) \ - [['latency.avg_time']].sum().reset_index() for plan in plans] - - stacked_layers_by_precision = partial( - plotting.stacked_bars, - bar_names=engine_names, - df_list=time_by_precision, - names_col='precision', - values_col='latency.avg_time', - colormap=colors.precision_colormap) - - precision_subplots = [( - misc.group_count( - plan.df, 'precision'), - plan.name, 'count', 'precision' - ) for plan in plans] - precision_cnts = partial( - plotting.plotly_pie2, - charts=precision_subplots, - colormap=colors.precision_colormap) - - output_precision_subplots = [( - misc.group_count( - plan.df, 'output_precision'), - plan.name, 'count', 'output_precision' - ) for plan in plans] - output_precision_cnts = partial( - plotting.plotly_pie2, - charts=output_precision_subplots, - colormap=colors.precision_colormap) - - precision_subplots = [( - misc.group_sum_attr( - plan.df, grouping_attr='precision', - reduced_attr='latency.pct_time'), - plan.name, 'latency.pct_time', 'precision' - ) for plan in plans] - precision_latency = partial( - plotting.plotly_pie2, - charts=precision_subplots, - colormap=colors.precision_colormap) - - dropdown_choices = { - "Stacked latencies by layer type": latency_per_type, - "Stacked latencies by layer type (BS normalized)": latency_per_type_bs_normalized, - "Layer count comparison": count_comparison, - "Layer-type latency comparison": latency_per_type_comparison, - "Layer latency by precision": stacked_layers_by_precision, - "Precision counts": precision_cnts, - "Output precision counts": output_precision_cnts, - "Precision latency": precision_latency, - } - if have_throughput_data: - dropdown_choices["Throughput"] = throughput_per_plan - - interactive.InteractiveDiagram_2(dropdown_choices, 'Diagram:') - - -def compare_engines_summaries_tbl(plans: List[engine_plan.EnginePlan], orientation: str='vertical'): - """Display a tabular comparison of several engine plans.""" - - merged_summaries = {} - summary_dicts_list = ( - [engine_plan.summary_dict(plan) for plan in plans], - [plan.performance_summary for plan in plans], - [plan.device_properties for plan in plans], - [plan.builder_cfg for plan in plans] - ) - - for d in summary_dicts_list: - merged_summaries.update(misc.stack_dicts(d, empty_placeholder="")) - - if orientation == 'vertical': - df = pd.DataFrame.from_dict( - merged_summaries, orient='index', columns=get_plans_names(plans)) - df['attribute'] = list(merged_summaries.keys()) - df = plotting.rotate_columns(df) - df.set_index('attribute') - else: - df = pd.DataFrame.from_dict(merged_summaries) - df['plan'] = get_plans_names(plans) - df = plotting.rotate_columns(df) - print(("\"Average time\": " - "refers to the sum of the layer latencies, when profiling layers separately")) - print(("\"Latency\": " - "refers to the [min, max, mean, median, 99% percentile] of the engine latency " - "measurements, when timing the engine w/o profiling layers.")) - notebook.display_df(df) - - -# Code to align and compare two plans - -def get_io_dimensions(layer: pd.Series, use_all_tensors: bool) -> tuple: - """Return a tuple containing all the dimensions of layer's - inputs and outputs. - - The first dimension (batch) of each input/output tensor is not included - so that the batch-size is not a cause for a mismatch. - - For an exact (conservative) matching set `use_all_tensors` to True. - To match using only the first input and output, set to False. - """ - inputs, outputs = activations.create_activations(layer) - if not use_all_tensors: - inputs = [inputs[0],] - outputs = [outputs[0],] - - dims_dict = { - 'inputs': [t.shape[1:] for t in inputs], - 'outputs': [t.shape[1:] for t in outputs] - } - return dims_dict - -def get_io_formats(layer: pd.Series) -> tuple: - """Return a string representation of the inputs and outputs.""" - inputs, outputs = activations.create_activations(layer) - in_formats = "in: " + ", ".join((f"{t.format}:{t.shape}" for t in inputs)) - out_formats = "out: " + ", ".join((f"{t.format}:{t.shape}" for t in outputs)) - return in_formats + "\t" + out_formats - - -def get_io_precisions(layer: pd.Series) -> tuple: - """Return two tuples representing the precisions of layer's inputs and outputs.""" - if layer is None: - return "", "" - inputs, outputs = activations.create_activations(layer) - assert len(inputs) > 0 and len(outputs) > 0 - p_in = ", ".join((t.precision for t in inputs)) - p_out = ", ".join((t.precision for t in outputs)) - return p_in, p_out - - -def match_layers( - plan1: engine_plan.EnginePlan, plan2: engine_plan.EnginePlan, exact_matching: bool -) -> List[Tuple]: - """Align two plans by their layers. - - When comparing the layers of two engine plans, we want to find pairs of layers, - one from each plan, that correspond to the same trt.Network layer. This is - not trivial since the plans may have a different number of layers, the layers - may be fused differently, and they may have different names. - - A heuristic assigns a `signature` to each layer in the two plans. To determine - if two layers correspond to the same trt.Network layer, their signatures are - compared. - - Aligining two plans is the task of finding pairs of layers with the same - signature. - This function returns a list of index pairs. - """ - def signature(layer: pd.Series, exact: bool) -> Dict: - """Returns the heuristic layer signature. - - The signature is composed of the layer's type and dimensions. - """ - sig = get_io_dimensions(layer, exact) - sig['type'] = layer['type'] - return sig - - - def clamp_indexes(i1: int, i2: int) -> Tuple: - i1 = min(i1, len(plan1.df) - 1) - i2 = min(i2, len(plan2.df) - 1) - return i1, i2 - - def are_equal(s1: Dict, s2: Dict) -> bool: - assert list(s1.keys()) == list(s2.keys()), "Internal error: signature are corrupt" - for k in s1.keys(): - if s1[k] != s2[k]: - return False - return True - - def is_aligned(i1: int, i2: int, exact_matching: bool) -> bool: - """Return True if row `i1` of plan1 is aligned - with row `i2` of plan2. """ - def pointwise_same(s1: Dict, s2: Dict): - """Special signatures comparison for pointwise layers. - - When comparing PointWise layers allow the inputs to be connected in - reverse order.""" - same = False - types_ok = s1['type'] == s2['type'] == "PointWise" - in_lengths_ok = len(s1['inputs']) == 2 and len(s2['inputs']) == 2 - out_lengths_ok = len(s1['outputs']) == 1 and len(s2['outputs']) == 1 - if types_ok and in_lengths_ok and out_lengths_ok: - same = s1['inputs'][0] == s2['inputs'][1] and s1['inputs'][1] == s2['inputs'][0] - return same - - i1, i2 = clamp_indexes(i1, i2) - s1 = signature(plan1.df.loc[i1], exact_matching) - s2 = signature(plan2.df.loc[i2], exact_matching) - aligned = are_equal(s1, s2) - if not aligned: - aligned = pointwise_same(s1, s2) - return aligned - - def beam_search(beam_size, unprocessed_indices, list_id): - """Shine a search beam and look for a match in the other list. - """ - i1 = unprocessed_indices[0][0] - i2 = unprocessed_indices[1][0] - for s in range(beam_size): - # clamp - idx = min(s, len(unprocessed_indices[list_id]) - 1) - if list_id == 1: - i2 = unprocessed_indices[list_id][idx] - else: - i1 = unprocessed_indices[list_id][idx] - if is_aligned(i1, i2, exact_matching): - return i1, i2 - if list_id == 1: - return i1, None - else: - return None, i2 - - def debug_print(i1: int, i2: int): - return # disable print - t1 = plan1.df.loc[i1]['type'] if i1 is not None else "None" - t2 = plan2.df.loc[i2]['type'] if i2 is not None else "None" - print(f"{i1}: {t1} {i2}: {t2}") - - matched_indices_pairs = [] - unprocessed_indices_1 = [*range(len(plan1.df))] - unprocessed_indices_2 = [*range(len(plan2.df))] - while unprocessed_indices_1 and unprocessed_indices_2: - beam_size = max(len(unprocessed_indices_1), len(unprocessed_indices_2)) - for list_id in (1, 0): - i1, i2 = beam_search(beam_size, - (unprocessed_indices_1, unprocessed_indices_2), list_id) - debug_print(i1, i2) - matched_indices_pairs.append((i1, i2)) - if i1 is not None: - unprocessed_indices_1.remove(i1) - if i2 is not None: - unprocessed_indices_2.remove(i2) - if not unprocessed_indices_1 or not unprocessed_indices_2: - break - - # Process "left-over" layers - for i1 in unprocessed_indices_1: - matched_indices_pairs.append((i1, None)) - for i2 in unprocessed_indices_2: - matched_indices_pairs.append((None, i2)) - return matched_indices_pairs - - -def aligned_merge_plans( - plan1: engine_plan.EnginePlan, - plan2: engine_plan.EnginePlan, - matched_indices_pairs: List[Tuple] -) -> pd.DataFrame: - """Return a dataframe containing merged layers from the two plans, after - their layers have been aligned. - """ - def append_layer(merged: List, layer1: pd.Series, layer2: pd.Series): - p1_in, p1_out = get_io_precisions(layer1) - p2_in, p2_out = get_io_precisions(layer2) - merged.append(( - layer1['type'] if layer1 is not None else layer2['type'], - layer1['latency.avg_time'] if layer1 is not None else 0, - layer2['latency.avg_time'] if layer2 is not None else 0, - layer1['latency.avg_time']/layer2['latency.avg_time'] if layer1 is not None and layer2 is not None else np.NaN, - p1_in, - p2_in, - p1_out, - p2_out, - layer1['tactic'] if layer1 is not None else "", - layer2['tactic'] if layer2 is not None else "", - get_io_formats(layer1) if layer1 is not None else "", - get_io_formats(layer2) if layer2 is not None else "", - layer1['Name'] if layer1 is not None else "", - layer2['Name'] if layer2 is not None else "", - )) - - merged = [] - for pair in matched_indices_pairs: - # A pair of matched indices - m1 = pair[0] - m2 = pair[1] - - # Add the pair of matched layers. - layer1 = plan1.df.loc[m1] if m1 is not None else None - layer2 = plan2.df.loc[m2] if m2 is not None else None - append_layer(merged, layer1, layer2) - - df = pd.DataFrame(merged, columns=( - 'type','avg_time (1)', 'avg_time (2)', 'speedup (2)', - 'in-p (1)', 'in-p (2)', 'out-p (1)', 'out-p (2)', - 'tactic (1)', 'tactic (2)', - 'formats (1)', 'formats (2)', - plan1.name, plan2.name)) - return df - - -def aligned_layers( - plan1: engine_plan.EnginePlan, - plan2: engine_plan.EnginePlan, - matched_indices_pairs:List[Tuple], - layer_type: str=None -) -> Tuple[pd.DataFrame, pd.DataFrame]: - """Return the dataframes of the two plans, after their layers - have been aligned. - - Where the two plans do not align, insert space-holder rows. - """ - def append_layer(layers_tbl, layer): - if layer is None: - # Add a "space-holder" - layers_tbl.append((len(layers_tbl), "", "", 0)) - else: - layers_tbl.append(( - len(layers_tbl), - layer['Name'], - layer['type'], - layer['latency.avg_time'])) - - # Create a table of layers for each engine. - # Empty rows are inserted to an engine's table as space-holders when there's - # no matching layer in the other engine. - layers_tbl1, layers_tbl2 = [], [] - - def filer_layer(layer, layer_type): - ignore = layer is not None and (layer_type is None or layer['type'] == layer_type) - return ignore - - for pair in matched_indices_pairs: - # A pair of matched indices - m1, m2 = pair[0], pair[1] - - # Add the pair of matched layers. - layer1 = plan1.df.loc[m1] if m1 is not None else None - layer2 = plan2.df.loc[m2] if m2 is not None else None - - if layer1 is not None and layer2 is not None: - if (layer_type is None or layer1['type'] == layer_type): - append_layer(layers_tbl1, layer1) - append_layer(layers_tbl2, layer2) - else: - if filer_layer(layer1, layer_type): - append_layer(layers_tbl2, None) - append_layer(layers_tbl1, layer1) - if filer_layer(layer2, layer_type): - append_layer(layers_tbl1, None) - append_layer(layers_tbl2, layer2) - - df1 = pd.DataFrame(layers_tbl1, columns=('id', 'name', 'type', 'latency.avg_time')) - df2 = pd.DataFrame(layers_tbl2, columns=('id', 'name', 'type', 'latency.avg_time')) - return df1, df2 - - -def speedup_range_highlights(col_name, threshold: float): - light_yellow = {'r': 255, 'g': 245, 'b': 157, 'a': 0} - green = {'r': 0, 'g': 255, 'b': 0, 'a': 1} - orange = {'r': 245, 'g': 166, 'b': 35, 'a': 1} - range_highlights = { - col_name: { - 'active': True, - 'equals': {'active': True, 'value': 1, 'color': light_yellow}, - 'greaterThan': {'active': True, 'value': 1. + threshold, 'color': green}, - 'lessThan': {'active': True, 'value': 1. - threshold, 'color': orange}, - } - } - return range_highlights - - -def compare_engines_layer_latencies( - plan1: engine_plan.EnginePlan, - plan2: engine_plan.EnginePlan, - threshold: float, - exact_matching: bool -): - """Display a table and a bar diagram comparing the latencies of layers from - two plans. - """ - def render_diagram(choice: str, ignore): - if plan1.name == plan2.name: - plan1.name += ".0" - plan2.name += ".1" - - matched_indices_pairs = match_layers(plan1, plan2, exact_matching) - - # Display a table comparison - df = aligned_merge_plans(plan1, plan2, matched_indices_pairs) - if choice not in ('All', 'Precision Mismatch', 'Tactic Mismatch'): - df = df.query(f"type == \"{choice}\"") - if choice == 'Precision Mismatch': - df = df[(df['in-p (1)'] != df['in-p (2)']) | (df['out-p (1)'] != df['out-p (2)'])] - if choice == 'Tactic Mismatch': - df = df[(df['tactic (1)'] != df['tactic (2)'])] - print(f"Legend:\n\t1: {plan1.name}\n\t2: {plan2.name}") - print("\"in-p (1)\" are the input precisions of the layer in " - f"{plan1.name}. Similarly,") - print("\"out-p (2)\" are the output precisions of the layer in " + plan2.name) - notebook.display_df(df, range_highlights=speedup_range_highlights( - 'speedup (2)', threshold)) - - # Display a bar diagram comparison - layer_type = None if choice=='All' else choice - df1, df2 = aligned_layers(plan1, plan2, matched_indices_pairs, layer_type) - - latency_str = lambda name, df: f"\n\t{name}: {df['latency.avg_time'].sum():.3f} ms" - print(f"Latencies:{latency_str(plan1.name, df1)}{latency_str(plan2.name, df2)}") - - d = {plan1.name: df1, plan2.name: df2} - plotting.plotly_bar2(title=f"Layer Latency Comparison (Layer Type={choice})", - df=d, - values_col='latency.avg_time', - names_col='id', - orientation='v', - showlegend=True) - - types = ['All', 'Precision Mismatch', 'Tactic Mismatch'] - types += list(set(plan1.df['type'].tolist() + plan2.df['type'].tolist())) - dropdown_choices = {t: t for t in types} - interactive.InteractiveDiagram(render_diagram, dropdown_choices, 'Dataframe') - - -def compare_engines_layer_details( - plan1: engine_plan.EnginePlan, - plan2: engine_plan.EnginePlan, -): - """Compare the details of two layers from two aligned plans. - - Align plan1 and plan2 and display a drop-down list of the layers - of the combined dataframe. This allows easier comparison of - individual layers. - """ - def render_diagram(choice: str, row_id: int): - if plan1.name == plan2.name: - plan1.name += ".0" - plan2.name += ".1" - row = df.iloc[row_id] - d = { - 'name': (row[plan1.name], row[plan2.name]), - 'avg_time': (row['avg_time (1)'], row['avg_time (2)']), - 'tactic': (row['tactic (1)'], row['tactic (2)']), - 'in-p': (row['in-p (1)'], row['in-p (2)']), - 'out-p': (row['out-p (1)'], row['out-p (2)']), - 'format': (row['formats (1)'], row['formats (2)']), - } - - df2 = pd.DataFrame.from_dict( - d, orient='index', columns=(plan1.name, plan2.name,)) - speedup = row['avg_time (1)'] / row['avg_time (2)'] - tactic = "Same" if row['tactic (1)'] == row['tactic (2)'] else "Different" - inp_precision = "Same" if row['in-p (1)'] == row['in-p (2)'] else "Different" - out_precision = "Same" if row['out-p (1)'] == row['out-p (2)'] else "Different" - formats = "Same" if row['formats (1)'] == row['formats (2)'] else "Different" - df2['comparison'] = ( - '', speedup, tactic, inp_precision, out_precision, formats) - df2 = plotting.rotate_columns(df2) - df2['attribute'] = ( - 'name', 'avg_time', 'tactic', 'input precision', 'output precision', 'formats') - df2 = plotting.rotate_columns(df2) - df2.set_index('attribute') - notebook.display_df(df2) - - matched_indices_pairs = match_layers(plan1, plan2, exact_matching=True) - df = aligned_merge_plans(plan1, plan2, matched_indices_pairs) - dropdown_choices = {f"{t}: {df.iloc[t]['type']}": t for t in range(len(df))} - interactive.InteractiveDiagram(render_diagram, dropdown_choices, 'Choose Layer:') diff --git a/tools/experimental/trt-engine-explorer/trex/df_preprocessing.py b/tools/experimental/trt-engine-explorer/trex/df_preprocessing.py deleted file mode 100644 index 7558d99e7..000000000 --- a/tools/experimental/trt-engine-explorer/trex/df_preprocessing.py +++ /dev/null @@ -1,311 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -""" -This file contains raw JSON dataframes preprocessing functions. -""" - - -import pandas as pd -from .activations import * - - -layer_attributes = { - 'Convolution': { - 'subtype': 'subtype', - 'Groups': 'attr.groups', - 'OutMaps': 'attr.out_maps', - 'Stride': 'attr.stride', - 'Kernel': 'attr.kernel', - 'HasBias': 'attr.has_bias', - 'Activation': 'attr.activation', - 'HasReLU': 'attr.has_relu', - 'PaddingMode': None, # 'attr.padding_mode', - 'PrePadding': None, # 'attr.pre_padding', - 'PostPadding': None, # 'attr.post_padding', - 'Dilation': 'attr.dilation', - 'AllowSparse': None, - 'Weights': 'Weights', - 'Bias': None, - 'Dimensions': None, - 'ConvolutionTacticIndex': None, - 'Operations': 'attr.operations', - 'NbOperations': 'attr.n_operations', - }, - - 'PointWiseV2': { - 'Operations': 'attr.operations', - 'NbOperations': 'attr.n_operations', - 'NbInputArgs': 'attr.n_input_args', - 'InputArgs': None, - 'NbOutputVars': None, - 'OutputVars': None, - 'NbLiterals': None, - 'Literals': None, - 'NbParams': None, - 'Params': None, - }, - - 'PointWise': { - 'Instructions': 'attr.operations', - 'NbInstructions': 'attr.n_operations', - 'NbInputs': 'attr.n_input_args', - 'InputArgs': None, - 'NbOutputVars': None, - 'OutputVars': None, - 'NbLiterals': None, - 'Literals': None, - 'NbParams': None, - 'Params': None, - }, - 'Reformat': { - 'Origin': 'attr.origin', - }, - - 'Pooling': { - 'PoolingType': 'attr.pooling_type', - 'WindowSize': 'attr.window_size', - 'AverageCountExcludesPadding': None, - 'PaddingMode': 'attr.padding_mode', - 'Stride': 'attr.stride', - 'PrePadding': None, - 'PostPadding': None, - 'BlendFactor': None, - }, - - 'Scale': { - 'Mode': 'attr.mode', - 'Shift': 'attr.shift', - 'Scale': 'attr.scale', - 'Power': 'attr.power', - 'Activation': 'attr.activation', - 'ChannelAxis': 'attr.ch_axis', - }, - - 'Shuffle': { - 'FirstTranspose': 'attr.first_transpose', - 'SecondTranspose': 'attr.second_transpose', - 'Reshape': 'attr.reshape', - 'ZeroIsPlaceholder': None, - 'ParameterSubType': None, - }, - - 'Resize': { - 'ResizeMode': 'attr.mode', - 'ResizeScales': 'attr.scales', - 'NNRounding': 'attr.scale', - 'Start': None, - 'CoordTransform': None, - 'ResizeSelector': None, - }, - - 'Slice': { - 'Start': 'attr.start', - 'Stride': 'attr.stride', - 'Size': 'attr.size', - 'Mode': 'attr.mode', - 'negativeInfinityPadding': None, - }, - - 'Common': { - 'ParameterType': None, - 'weights': None, - 'dimensions': None, - 'TacticValue': None, - }, -} - - -def __fix_type(df: pd.DataFrame): - df.rename(columns={'LayerType': 'subtype'}, inplace=True) - try: - df['type'] = df.ParameterType.fillna(value=df.subtype) - df.drop(['ParameterType'], axis=1, inplace=True) - except AttributeError: - df['type'] = df['subtype'] - - -def __fix_tactic(df: pd.DataFrame): - df.rename(columns={'TacticName': 'tactic'}, inplace=True) - try: - df['tactic'] = df.tactic.fillna(value='TensorRT') - except AttributeError: - df['tactic'] = 'TensorRT' - - -def __fix_columns_types(df: pd.DataFrame): - int_cols = [ - 'Groups', 'OutMaps', 'HasBias', 'HasReLU', 'AllowSparse', - 'NbInputArgs', 'NbOutputVars', 'NbParams', 'NbLiterals', ] - for col in int_cols: - try: - df[col] = df[col].fillna(value=0) - df[col] = df[col].astype('int32') - except KeyError: - pass - df.fillna(0, inplace=True) - - -def __fix_output_precision(df: pd.DataFrame): - fixed_outputs = [] - for outputs in df['Outputs']: - try: - fixed_outputs.append(Activation(outputs[0]).precision) - except IndexError: - # Some layers may have empty outputs. - fixed_outputs.append('') - df['output_precision'] = fixed_outputs - - -def fix_df(df: pd.DataFrame) -> pd.DataFrame: - """One-time preprocessing of the DF. - - Performed only on DF construction. - """ - __fix_type(df) - __fix_tactic(df) - __fix_columns_types(df) - __fix_output_precision(df) - return df - - -def clean_io(df: pd.DataFrame): - for index, layer in df.iterrows(): - inputs, outputs = create_activations(layer) - if len(inputs) > 0: - inp_str = ", ".join([inp.format for inp in inputs]) - df.loc[index, 'Inputs'] = inp_str - df.loc[index, 'Outputs'] = outputs[0].format - - -def filter_by_layer(df: pd.DataFrame, layer_type: str) -> pd.DataFrame: - copy_cols = ['Name', 'type', 'precision', 'tactic', - 'latency.pct_time', 'latency.avg_time', - 'total_io_size_bytes', 'total_footprint_bytes', - 'Inputs', 'Outputs', 'subtype'] - try: - attrs = layer_attributes[layer_type] - copy_cols += [k for k, v in attrs.items() if v is not None] - # Pointwise and Pointwise V2 layers have the same layer type. - if layer_type == 'PointWise': - attrs = layer_attributes['PointWiseV2'] - copy_cols += [k for k, v in attrs.items() if v is not None] - except KeyError: - pass - - layers = df.query(f"type == \"{layer_type}\"").copy() - if len(layers) == 0: - return layers - - copy_cols = list(set(copy_cols) & set(layers.columns)) - layers = layers[copy_cols] - - if layer_type == 'Convolution': - layers.rename(columns=layer_attributes[layer_type], inplace=True) - layers['attr.kernel'] = tuple(layers['attr.kernel']) - annotate_convolutions(layers) - if layer_type == 'PointWise': - # The original JSON file handle PointWise and PointWise V2 as two subtypes. - pw = layers[layers['subtype'] == 'PointWise'].copy() - pw.rename(columns=layer_attributes['PointWise'], inplace=True) - pw_v2 = layers[layers['subtype'] == 'PointWiseV2'].copy() - pw_v2.rename(columns=layer_attributes['PointWiseV2'], inplace=True) - layers = pd.concat((pw, pw_v2)) - else: - try: - layers.rename(columns=layer_attributes[layer_type], inplace=True) - except: - pass - return layers - - -def change_col_order(df: pd.DataFrame) -> pd.DataFrame: - """Change the dataframe columns-order (place common fields earlier)""" - cols = df.columns.to_list() - common_cols = list(('Name', 'type', 'Inputs', 'Outputs', 'latency.avg_time', - 'latency.pct_time', 'total_footprint_bytes', 'tactic')) - common_cols = [col for col in common_cols if col in cols] - cols = common_cols + [col for col in cols if col not in common_cols] - df = df[cols] - return df - - -def drop_columns(df: pd.DataFrame, columns: list): - for col in columns: - try: - df.drop([col], axis=1, inplace=True) - except KeyError: - pass - - -def clean_df(df: pd.DataFrame, inplace=True) -> pd.DataFrame: - clean_io(df) - columns = set([col for col_list in layer_attributes.keys() for col in col_list]) - drop_columns(df, columns) - df.fillna(0, inplace=inplace) - return df - - -def clean_for_display(df: pd.DataFrame) -> pd.DataFrame: - """Prepare the dataframe for display""" - df = clean_df(df.copy(), inplace=True) - df = change_col_order(df) - drop_columns(df, - columns=['subtype', 'TacticValue', 'precision', 'total_io_size_bytes']) - return df - - -def annotate_convolutions(convs: pd.DataFrame): - """Convolutions as implicit GEMM""" - for index, conv in convs.iterrows(): - inputs, outputs = create_activations(conv) - assert len(inputs) > 0 - N, C, H, W = inputs[0].shape - # K: number of channels; P: Height; Q: Width - _, K, P, Q = outputs[0].shape - R, S = convs.loc[index, 'attr.kernel'] - G = convs.loc[index, 'attr.groups'] - weights_vol = (K * C * R * S) / G - input_vol = N * C * H * W - output_vol = N * K * P * Q - input_bytes = input_vol * inputs[0].data_size - output_bytes = output_vol * outputs[0].data_size - weights_bytes = weights_vol * inputs[0].data_size - nb_bytes = input_bytes + weights_bytes + output_bytes - nb_macs = N * K * P * Q * C * R * S / G - convs.loc[index, 'attr.macs'] = nb_macs - # Arithmetic intensity: ops/bytes - convs.loc[index, 'attr.arithmetic_intensity'] = nb_macs / nb_bytes - latency = convs.loc[index, 'latency.avg_time'] - if latency > 0: - convs.loc[index, 'attr.compute_efficiency'] = nb_macs / latency - convs.loc[index, 'attr.memory_efficiency'] = nb_bytes / latency - else: - convs.loc[index, 'attr.compute_efficiency'] = 0 - convs.loc[index, 'attr.memory_efficiency'] = 0 - # Conversion to matrices (M, K) * (K, N) - M = N * P * Q - N = K - K = C * R * S - convs.loc[index, 'attr.M'] = M - convs.loc[index, 'attr.N'] = N - convs.loc[index, 'attr.K'] = K - - convs['attr.macs'] = convs['attr.macs'].astype('int64') - convs['attr.M'] = convs['attr.M'].astype('int64') - convs['attr.N'] = convs['attr.N'].astype('int64') - convs['attr.K'] = convs['attr.K'].astype('int64') diff --git a/tools/experimental/trt-engine-explorer/trex/engine_plan.py b/tools/experimental/trt-engine-explorer/trex/engine_plan.py deleted file mode 100644 index 9a877a742..000000000 --- a/tools/experimental/trt-engine-explorer/trex/engine_plan.py +++ /dev/null @@ -1,201 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -""" -TensorRT Engine Exploration API - EnginePlan -""" - - -import warnings -from typing import List, Tuple -from copy import deepcopy -import pandas as pd -import ntpath -from .df_preprocessing import * -from .layer import Layer, fold_no_ops -from .parser import * - - -class EnginePlan: - def __init__(self, - graph_file: str, - profiling_file: str=None, - profiling_metadata_file: str=None, - build_metadata_file: str=None, - name: str=None, - profile_id: int=None, - ): - def path_leaf(path): - head, tail = ntpath.split(path) - return tail or ntpath.basename(head) - - def create_layers(self, raw_layers): - layers = [Layer(raw_layer) for raw_layer in raw_layers] - self.layers = fold_no_ops(layers, self.bindings) - self.all_layers = deepcopy(self.layers) - self.constants = [layer for layer in self.layers if layer.type == 'Constant'] - self.layers = [layer for layer in self.layers if layer.type != 'Constant'] - return raw_layers - - def process_profiling_file(profiling_file, ignore_layers): - if not profiling_file: - return None - raw_perf = read_profiling_file(profiling_file) - raw_perf = [perf_rec for perf_rec in raw_perf if - perf_rec['name'] not in ignore_layers] - return raw_perf - - def merge_profiling_data(graph_df, raw_perf): - def add_zero_perf(graph_df): - df = graph_df - df['latency.pct_time'] = [0] * len(df) - df['latency.avg_time'] = [0] * len(df) - df['latency.median_time'] = [0] * len(df) - df['latency.time'] = [0] * len(df) - return df - - if raw_perf is not None: - perf_df = pd.DataFrame.from_dict(raw_perf) - perf_df.rename(columns={ - 'percentage': 'latency.pct_time', - 'averageMs': 'latency.avg_time', - 'medianMs': 'latency.median_time', - 'timeMs': 'latency.time', - }, inplace=True) - if len(graph_df) == len(perf_df): - perf_df.drop(columns=['name'], inplace=True) - df = graph_df.join(perf_df) - else: - warnings.warn( - "Partial profiling data: The number of layers in the engine " - f"graph ({len(graph_df)}) does not match the number of layers " - f"({len(perf_df)}) in the performance JSON.\n" - "This can happen if you're not using the first shape-profile.") - perf_df.rename(columns={'name': 'Name',}, inplace=True) - df = graph_df.merge(perf_df, on='Name', how='left').fillna(0) - else: - warnings.warn("Profiling data was not provided.") - df = add_zero_perf(graph_df) - return df - - def add_graph_summation_cols(df, layers): - # Add new (summation) columns - df['total_io_size_bytes'] = [l.total_io_size_bytes for l in layers] - df['weights_size'] = [l.weights_size for l in layers] - df['total_footprint_bytes'] = [l.total_footprint_bytes for l in layers] - df['precision'] = [l.precision for l in layers] - return df - - def construct_df(raw_layers): - raw_layers = [raw_layer for raw_layer in raw_layers if - raw_layer['LayerType'] not in ['Constant', 'NoOp']] - graph_df = pd.DataFrame.from_dict(raw_layers) - graph_df = fix_df(graph_df) - return graph_df - - def compute_summary(self): - self.total_act_size = sum( - [l.total_io_size_bytes for l in self.layers]) - self.total_weights_size = sum( - [l.weights_size for l in self.layers]) - assert self.total_weights_size == self.df['weights_size'].sum() - self.total_runtime = sum( - [avg_time for avg_time in self._df["latency.avg_time"]]) - - if isinstance(graph_file, str): - self.name = name or path_leaf(graph_file) - raw_layers, self.bindings = import_graph_file(graph_file, profile_id) - elif isinstance(graph_file, list): - self.name = "" - raw_layers, self.bindings = graph_file, [""] - raw_layers = [l for l in raw_layers if l['LayerType'] != "shape_call"] - raw_layers = create_layers(self, raw_layers) - - self._df = None - ignore_layers = [raw_layer['Name'] for raw_layer in raw_layers if - raw_layer['LayerType'] in ["Constant", "NoOp"]] - self._raw_perf = process_profiling_file( - profiling_file, ignore_layers=ignore_layers) - graph_df = construct_df(raw_layers) - graph_df = add_graph_summation_cols(graph_df, self.layers) - self._df = merge_profiling_data(graph_df, self._raw_perf) - compute_summary(self) - self.device_properties = get_device_properties(profiling_metadata_file) - self.performance_summary = get_performance_summary(profiling_metadata_file) - self.builder_cfg = get_builder_config(build_metadata_file) - assert self._df is not None, f"Failed parsing plan file {graph_file}" - - @classmethod - def from_string(cls, log_string): - pass - - @property - def df(self): - return self._df - - def get_layers_by_type(self, layer_type): - return filter_by_layer(self._df, layer_type) - - def find(self, layer_name: str): - for l in self.layers: - if layer_name == l.name: return l - return None - - def get_bindings(self) -> Tuple[List[Activation], List[Activation]]: - """Return a list of the inputs bindings and a list of the output bindings""" - inputs, outputs = [], [] - processed_names = [] - for layer in self.layers: - # BUG HERE: inputs and outputs are counted mutiple times. - inputs += [inp for inp in layer.inputs if (inp.name in self.bindings and inp.name not in processed_names)] - processed_names += [inp.name for inp in layer.inputs if inp.name not in processed_names] - outputs += [outp for outp in layer.outputs if (outp.name in self.bindings and outp.name not in processed_names)] - processed_names += [outp.name for outp in layer.outputs if outp.name not in processed_names] - return list(set(inputs)), list(set(outputs)) - - def summary(self): - return print_summary(self) - - -def summary_dict(plan: EnginePlan): - """Create a dictionary of important attributes of the engine plan.""" - MB_1 = 1024 * 1024 - bindings = plan.get_bindings() - nl = "\n\t\t" - d = { - "Inputs": f"{nl.join([str(binding) for binding in bindings[0]])}", - "Outputs": f"{nl.join([str(binding) for binding in bindings[1]])}", - "Average time": f"{plan.total_runtime:.3f} ms", - "Layers": f"{len(plan.df)}", - "Weights": f"{plan.total_weights_size / MB_1 :.1f} MB", - "Activations": f"{plan.total_act_size/ MB_1 :.1f} MB", - } - return d - - -def print_summary(plan: EnginePlan): - def print_dict(d: Dict): - for k,v in d.items(): - print(f"\t{k}: {v}") - print("Model:") - print_dict(summary_dict(plan)) - print("Device Properties:") - print_dict(plan.device_properties) - print("Builder Configuration:") - print_dict(plan.builder_cfg) - print("Performance Summary:") - print_dict(plan.performance_summary) diff --git a/tools/experimental/trt-engine-explorer/trex/excel_summary.py b/tools/experimental/trt-engine-explorer/trex/excel_summary.py deleted file mode 100644 index ec04afbe1..000000000 --- a/tools/experimental/trt-engine-explorer/trex/excel_summary.py +++ /dev/null @@ -1,141 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -""" -This file contains code to generate an Excel summary spreadsheet. -""" - -import pandas as pd -import tempfile -import plotly -import xlsxwriter -from trex import EnginePlan -from trex.report_card import report_card_perf_overview, report_card_memory_footprint -from typing import Dict -import logging - -MAX_PERMISSIBLE_NAME_LENGTH = 31 - -class ExcelSummary: - def __init__(self, plan: EnginePlan, path: str="engine_summary.xlsx"): - self.plan = plan - self.path = path - self.Excelwriter = pd.ExcelWriter(path, engine="xlsxwriter") - - # Report generation is currently only supported for those reports - # that provide dropdown_choices in `report_card.py` - self.supported_reports = { - 'report_card_perf_overview': report_card_perf_overview, - 'report_card_memory_footprint': report_card_memory_footprint, - } - - def __enter__(self): - return self - - def __exit__(self, exc_type, exc_value, exc_traceback): - """ - Allow context manager to automatically call `save` - """ - self.save() - - def _add_df(self, df: pd.DataFrame, sheet_name: str): - """ - Adds a single dataframe to the Excel file. This function is not - exposed to the user. Users may use "add_dataframes" instead - """ - - df.to_excel(self.Excelwriter, sheet_name=sheet_name, index=False, startrow=3, startcol=1) - - def add_dataframes(self, df_dict: Dict[str, pd.DataFrame]): - """ - Adds multiple dataframes to the Excel file - """ - - for sheet_name, df in df_dict.items(): - self._add_df(df, sheet_name) - - def _add_image(self, image: str, sheet_name: str): - """ - Adds a single image to the Excel file. This function is not - exposed to the user. Users may use "add_images" instead - """ - - worksheet = self.Excelwriter.book.add_worksheet(sheet_name) - worksheet.insert_image('A1', image) - - def add_images(self, image_dict): - """ - Adds multiple images to the Excel file - """ - - for sheet_name, image_fname in image_dict.items(): - self._add_image(image_fname, sheet_name) - - def _add_report(self, report_name: str): - """ - Adds a report to the Excel file - """ - - if report_name not in self.supported_reports: - raise ValueError(f"{report_name} is not a supported report: {list(self.supported_reports)}") - - files = [] - workbook = self.Excelwriter.book - - report_choices = self.supported_reports[report_name](self.plan) - for stat_name in report_choices.keys(): - # Excel sheets have a constraint on length of sheet name - worksheet = workbook.add_worksheet(stat_name[:MAX_PERMISSIBLE_NAME_LENGTH]) - figure = report_choices[stat_name](title=stat_name, do_show=False) - with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as fp: - files.append(fp) - # Currently only supports generating images for plotly figures - if isinstance(figure, (plotly.graph_objs._figure.Figure)): - figure.write_image(fp) - worksheet.insert_image('C5', fp.name) - - for fp in files: - fp.close() - - def generate_default_summary(self, save: bool=True): - """ - Generate a default summary with all the available data - """ - - self.add_dataframes({"Plan":self.plan.df}) - reports_to_call = [ - 'report_card_perf_overview', - 'report_card_memory_footprint' - ] - - for report in reports_to_call: - self._add_report(report) - - if save: - self.save() - - def save(self): - """ - Saves the Excel file. - - Once the file is saved, it cannot be updated using the same instance - of the class. A new instance may have to be created to overwrite the - Excel file - """ - - self.Excelwriter.close() - print(f"{self.path} has been saved") diff --git a/tools/experimental/trt-engine-explorer/trex/graphing.py b/tools/experimental/trt-engine-explorer/trex/graphing.py deleted file mode 100644 index 00359530a..000000000 --- a/tools/experimental/trt-engine-explorer/trex/graphing.py +++ /dev/null @@ -1,968 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -""" -This file contains code to generate graph diagrams for an engine-plan. -""" - -__all__ = [ - "to_dot", - "PlanGraph", - "DotGraph", - "OnnxGraph", - "render_dot", - "latency_types", - "layer_precision_formatter", - "layer_type_formatter", - "layer_node_formatters", - "layer_node_renderers"] - -import os -import warnings -from enum import Enum -from graphviz import Digraph -from typing import Callable, NamedTuple, List, Dict -from dataclasses import dataclass, field -from trex.engine_plan import EnginePlan -from trex.layer import Layer -from trex.activations import Activation -from trex.colors import precision_colormap, layer_colormap, NVDA_GREEN - - -class PortDesc(NamedTuple): - """A port identifies a specific layer input or output.""" - layer_name: str - port: int - - -class Edge(NamedTuple): - """An edge in a PlanGraph graph. - - The src and dst are names of engine layers or regions. - """ - src: PortDesc - dst: PortDesc - tensor: Activation - region_gen: int - - -@dataclass -class RegionGeneration: - """Represents one generation of a memory Region. - - Regions represent activation memory buffers or graph inputs and outputs. - Each generation represents a consumable state of the memory region. - """ - tensor: Activation - id: int - is_user: bool = False - is_forked: bool = False - writers: List[PortDesc] = field(default_factory=list) - readers: List[PortDesc] = field(default_factory=list) - - -class Region: - """Represents a memory region. - - Regions represent activation memory buffers or graph inputs and outputs. - Each generation represents a consumable state of the memory region. - """ - def __init__(self): - self.__generations: List[RegionGeneration] = list() - self.name: str = None - - @property - def generations(self): - return self.__generations - - def add_generation(self, tensor: Activation): - self.name = self.name or tensor.name - gen_id = len(self.generations) - self.__generations.append( - RegionGeneration(tensor, gen_id)) - - def nb_generations(self): - return len(self.__generations) - - def __update_shape(self, tensor: Activation, generation: int): - if tensor.size_bytes > self.__generations[generation].tensor.size_bytes: - self.__generations[generation].tensor = tensor - - def add_writer(self, gen_id: int, writer: PortDesc, tensor: Activation): - self.__generations[gen_id].writers.append(writer) - self.__update_shape(tensor, gen_id) - - def add_reader(self, gen_id: int, reader: PortDesc, tensor: Activation): - self.__generations[gen_id].readers.append(reader) - self.__update_shape(tensor, gen_id) - if gen_id > 0: - self.__update_shape(tensor, gen_id-1) - - def writers(self, gen_id: int=None): - try: - if gen_id is not None: - return self.__generations[gen_id].writers - # Return a list of writers from all generations - writers = [] - for generation in self.__generations: - writers.extend(generation.writers) - return writers - except KeyError: - return [] - - def readers(self, gen_id: int=None): - try: - if gen_id is not None: - return self.__generations[gen_id].readers - # Return a list of readers from all generations - readers = [] - for generation in self.__generations: - readers.extend(generation.readers) - return readers - except KeyError: - return [] - - def is_placeholder(self) -> bool: - return len(self.generations) > 1 - - -def regions_factory(plan: EnginePlan) -> List[Region]: - """This method processes an EnginePlan instance to create a Region dictionary""" - class RegionMemOp(Enum): - """ - A WRITE event represents some layer writing to a memory region. - A READ event represents some layer reading from a memory region. - """ - WRITE = 1 - READ = 2 - class RegionEvent(NamedTuple): - """A region read/write event performed by a layer""" - owner_layer: str - # input port for WRITE event; output port for READ event. - port: int - mem_op: RegionMemOp - tensor: Activation - - def set_is_user(region: Region): - n_generations = len(region.generations) - for gen_id, generation in enumerate(region.generations): - nb_writers = len(generation.writers) - nb_readers = len(generation.readers) - is_binding = region_name in plan.bindings - if gen_id == n_generations - 1: - # If marked as a network binding and this is last generation, we always display. - generation.is_user = is_binding - else: - generation.is_user = is_binding and (nb_readers==0 or nb_writers==0) - - def set_is_forked(region: Region): - for generation in region.generations: - nb_writers = len(generation.writers) - nb_readers = len(generation.readers) - generation.is_forked = nb_writers > 1 or nb_readers>1 - - # A region_story is a map from region name to the list of read/write - # events on that region. - story = {} - for layer in plan.layers: - for port, inp in enumerate(layer.inputs): - story.setdefault(inp.name, []).append( - RegionEvent(layer.name, port, RegionMemOp.READ, inp)) - for port, outp in enumerate(layer.outputs): - story.setdefault(outp.name, []).append( - RegionEvent(layer.name, port, RegionMemOp.WRITE, outp)) - - # Create region generations: each generation is a list of read/write events. - # Every time there's a switch between read events and write events we - # create a new RegionGeneration. - regions: List[Region] = [] - for region_name, region_evts in story.items(): - region = Region() - current_gen = -1 - previous_mem_op = None - for evt in region_evts: - if evt.mem_op != previous_mem_op: - if evt.mem_op == RegionMemOp.WRITE or not previous_mem_op: - # A new region generation. - current_gen += 1 - region.add_generation(evt.tensor) - evt_layer = PortDesc(evt.owner_layer, evt.port) - if evt.mem_op == RegionMemOp.WRITE: - region.add_writer(current_gen, evt_layer, evt.tensor) - else: - region.add_reader(current_gen, evt_layer, evt.tensor) - previous_mem_op = evt.mem_op - set_is_user(region) - set_is_forked(region) - regions.append(region) - return regions - - -def _make_memory_node_name(region: Region, generation: RegionGeneration) -> str: - if generation.is_user: - return region.name - return ".".join((region.name, str(generation.id))) - - -class LayerNode(object): - """A graph node representing an engine layer.""" - def __init__(self, layer: Layer): - self.layer: Layer = layer - - -class MemoryNode(NamedTuple): - """MemoryNode instances represent activation memory buffers, or graph inputs/outputs. - - Most (memory) Regions have only a single generation and they can be considered - as simple activation buffers. These are not added to the PlanGraph unless - `include_regions` is true. - When a Region has several generations, we unravel the Region and each generation - is represented by one MemoryNode. This is done in order to make the - graph easier to understand and to remove circular edges. - """ - name: str - tensor: Activation - region_gen: int - is_user: bool - - -class PlanGraph(object): - """View of a TensorRT plan as a data-dependencies graph. - - A PlanGraph describes the plan layers' data-dependencies. It is represented - using two types of nodes and an edge list. - - layer_nodes represent engine execution layers. - memory_nodes represent memory buffers. - edges_list is a list of edges that have either LayerNode or MemoryNode endpoints. - """ - def __init__(self, - plan: EnginePlan, - include_regions: bool=False, - include_constants: bool=False, - include_forking_regions: bool=False, - ): - self.include_regions = include_regions - self.include_constants = include_constants - self.include_forking_regions = include_forking_regions - self.regions = regions_factory(plan) - - # These lists will be populated by __create_graph - self._edges_list: List[Edge] = [] - self._layer_nodes: List[Layer] = [] - self._memory_nodes: List[MemoryNode] = [] - self.__create_graph(plan) - - # PlanGraph interface - @property - def edges_list(self): return self._edges_list - - @property - def layer_nodes(self): return self._layer_nodes - - @property - def memory_nodes(self): return self._memory_nodes - - def __create_graph(self, plan: EnginePlan): - self.__add_layer_nodes(plan) - self.__add_memory_nodes(plan) - self.__add_inter_region_edges() - - def __add_layer_nodes(self, plan): - self._layer_nodes = [LayerNode(layer) for layer in plan.all_layers] - - def __add_constant_node(self, region: Region, generation: RegionGeneration, constants_producers): - assert not generation.writers - - is_user = generation.is_user - activation_name = _make_memory_node_name(region, generation) - constant = constants_producers[activation_name] - if self.include_regions: - self._edges_list.append(Edge( - PortDesc(constant.name, 0), - PortDesc(activation_name, 0), - generation.tensor, - generation.id)) - self.__add_egress_edges(region, generation) - # Add a memory node (represents a region generation that we chose to display) - self._memory_nodes.append( - MemoryNode(activation_name, generation.tensor, generation.id, is_user)) - else: - self.__connect_writer_to_all_readers(PortDesc(constant.name, 0), generation) - - def __add_memory_nodes(self, plan): - constants_outputs = [const.outputs[0].name for const in plan.constants] - constants_producers = {const.outputs[0].name + ".0": const for const in plan.constants} - for region in self.regions: - is_myelin_const = len(region.writers()) == 0 and region.name not in plan.bindings - is_constant = region.name in constants_outputs - if (is_constant or is_myelin_const) and not self.include_constants: - continue - for generation in region.generations: - include_region = self.should_include_region(region, generation, is_constant) - if not include_region: - # Bypass a region and connect the region writers to the region readers. - self.__add_region_bypass_edges(generation) - continue - if is_constant: - self.__add_constant_node(region, generation, constants_producers) - else: - self.__add_memory_node(region, generation) - - def __add_memory_node(self, region, generation): - # Add an activation node (represents a region generation that we chose to display) - is_user = generation.is_user - node_name = _make_memory_node_name(region, generation) - self._memory_nodes.append(MemoryNode(node_name, generation.tensor, generation.id, is_user)) - self.__add_ingress_edges(region, generation) - self.__add_egress_edges(region, generation) - - def __add_region_bypass_edges(self, generation: RegionGeneration): - for writer in generation.writers: - writer_port = None if generation.is_user else writer.port - writer_desc = PortDesc(writer.layer_name, writer_port) - self.__connect_writer_to_all_readers(writer_desc, generation) - - def __add_ingress_edges(self, region: Region, generation: RegionGeneration): - node_name = _make_memory_node_name(region, generation) - node_port = None if generation.is_user else 0 - for writer in generation.writers: - self._edges_list.append(Edge( - PortDesc(writer.layer_name, writer.port), - PortDesc(node_name, node_port), - generation.tensor, - generation.id)) - - def __add_egress_edges(self, region: Region, generation: RegionGeneration): - activation_name = _make_memory_node_name(region, generation) - activation_port = None if generation.is_user else 0 - self.__connect_writer_to_all_readers( - PortDesc(activation_name, activation_port), - generation) - - def __connect_writer_to_all_readers( - self, writer_desc: PortDesc, generation: RegionGeneration): - for reader in generation.readers: - self._edges_list.append(Edge( - writer_desc, - PortDesc(reader.layer_name, reader.port), - generation.tensor, - generation.id)) - - def __add_inter_region_edges(self): - """Add edges connecting the different generations of a region.""" - for region in self.regions: - if len(region.generations) == 1: - continue - prev_generation = region.generations[0] - for gen_id in range(1, len(region.generations)): - curr_generation = region.generations[gen_id] - self._edges_list.append(Edge( - PortDesc(_make_memory_node_name(region, prev_generation), 0), - PortDesc(_make_memory_node_name(region, curr_generation), 0), - curr_generation.tensor, - gen_id)) - prev_generation = curr_generation - - def should_include_region(self, - region:Region, - generation:RegionGeneration, - is_constant: bool - ) -> bool: - nb_gens = region.nb_generations() - is_user = generation.is_user - is_forked = self.include_forking_regions and generation.is_forked - include = self.include_regions or is_user or nb_gens > 1 or is_forked - return (self.include_constants and is_constant) or (not is_constant and include) - - -""" -Graphviz DOT file format -""" - - -latency_types = ( - # Place default type in first entry. - 'avg_time', - 'median_time', - 'time') - - -def render_dot(dot_graph: Digraph, engine_name: str, output_format: str): - """Render dot graph to an external file using the specified format.""" - dot_graph.format = output_format - output_fname = os.path.abspath(f"{engine_name}.{output_format}") - dot_graph.render(outfile=output_fname, view=False, overwrite_source=False) - print(f"Created file://{output_fname}") - return output_fname - -def clean_layer_name(layer: Layer): - layer_name = layer.name - layer_name = layer_name.replace("||", r"\|\|") - layer_name = layer_name.replace("{", "") - layer_name = layer_name.replace("}", "") - return layer_name - - -def layer_node_renderer_simple( - layer: Layer, - latency: float, - display_layer_names: bool=True, - expand_layer_details: bool=False, - stack_layer_names: bool=True, - display_metadata: bool=True, -) -> str: - return f"{layer.name}\\n{layer.type}" if display_layer_names else f"{layer.type}" - - -def layer_node_renderer_keras( - layer: Layer, - latency: float, - display_layer_names: bool=True, - expand_layer_details: bool=False, - stack_layer_names: bool=True, - display_metadata: bool=True, -) -> str: - """Keras-style node label formatting.""" - - def add_io(tensors: List): - io_desc_str = "" - for t in tensors: - io_desc_str += str(t.shape) - return io_desc_str - - name = clean_layer_name(layer) - label = f"{name}\\n" if display_layer_names else "" - metadata = layer.metadata if layer.metadata is not None else "" - label += f"{metadata}\\n" if display_metadata else "" - label += f"{layer.type}" - label += "|{input:|output:}|{{" - label += add_io(layer.inputs) - label += "}|" - label += add_io(layer.outputs) - label += "}" - return label - - -def layer_node_highlighter(node_name: str, highlighted_layer_names: List[str] -) -> Dict: - """Highlight a layer node. - - Create a halo around the node. - """ - should_highlight = highlighted_layer_names and node_name in highlighted_layer_names - formatting = {'penwidth': str(6), 'color': 'red'} - return formatting if should_highlight else {} - - -def layer_node_configurable_renderer( - layer: Layer, - latency: float, - display_layer_names: bool=True, - expand_layer_details: bool=False, - stack_layer_names: bool=True, - display_metadata: bool=True, -) -> str: - def html_tbl(rows: List[str]): - def html_tbl_row(row_content, bold:bool, color: str=None): - row_content = row_content if not bold else f"{row_content}" - if color: - row = f"{row_content}" - else: - row = f"{row_content}" - return row - - header = '''< - "''' - footer = "
    >" - tbl = header - for i, row in enumerate(rows): - tbl += html_tbl_row(row[0], i==0, row[1] if len(row)>1 else None) - tbl += footer - return tbl - - def handle_pwgen(layer: Layer, rows: List[str]): - if layer.type != "PointWise": - return - try: - subtype = layer.raw_dict['ParameterSubType'] - if subtype == 'PointWiseExpression': - ops = layer.raw_dict['Operations'] - for op in ops: - prefix = "const auto" - op_str = op[len(prefix):] - HoneyDew = "#F0FFF0" - rows.append((op_str, HoneyDew)) - except KeyError: - pass - - def handle_pwgen_act(layer: Layer, rows: List[str]): - handled = False - if layer.type != "Convolution": - return handled - try: - subtype = layer.raw_dict['ParameterSubType'] - if subtype == 'PointWiseExpression': - handled = True - ops = layer.raw_dict['PointWiseExpressionOperations'] - for op in ops: - prefix = "const auto" - op_str = op[len(prefix):] - HoneyDew = "#F0FFF0" - rows.append((op_str, HoneyDew)) - except KeyError: - pass - return handled - - def handle_conv_deconv(layer: Layer, rows: List[str]): - if layer.type not in ("Convolution", "Deconvolution"): - return - try: - if len(layer.inputs) == 2: - rows.append(("Conv(input0) + input1", "lightblue")) - act = layer.raw_dict['Activation'] - if act is not None and act != 'NONE': - handled = False - if act == 'GENERIC': - handled = handle_pwgen_act(layer, rows) - if not handled: - rows.append((act, "lightblue")) - except KeyError: - pass - - def handle_reformat(layer: Layer, rows: List[str]): - if layer.type != "Reformat": - return - rows.append((layer.raw_dict['Origin'], None)) - - def add_stacked_name(name: str, rows: List[str], stack_layer_names: bool): - if stack_layer_names: - # Layer name "stacking" is: splitting on '+' and stacking in several rows - parts = name.split('+') - for p in parts: - rows.append((p,)) - else: - rows.append((name,)) - - def add_node_name(layer: Layer, rows: List[str], stack_layer_names: bool): - layer_name = clean_layer_name(layer) - add_stacked_name(layer_name, rows, stack_layer_names) - - def add_metadata(layer: Layer, rows: List[str], stack_layer_names: bool): - metadata = layer.metadata if layer.metadata is not None else "" - add_stacked_name(metadata, rows, stack_layer_names) - - rows = [(f"{layer.type}",)] - - if latency: - rows.append((f"{latency} ms",)) - if display_layer_names: - add_node_name(layer, rows, stack_layer_names) - if display_metadata: - add_metadata(layer, rows, stack_layer_names) - handle_reformat(layer, rows) - if expand_layer_details: - handle_pwgen(layer, rows) - handle_conv_deconv(layer, rows) - tbl = html_tbl(rows) - return tbl - - -def layer_precision_formatter(layer: Layer): - """Format Dot nodes by layer precision""" - formatting = {'shape': 'Mrecord', - 'style': 'filled', - 'tooltip': layer.tooltip(), - 'fillcolor': precision_colormap[layer.precision], - 'color': 'lightgray', - 'fontname': 'Helvetica',} - return formatting - - -def layer_type_formatter(layer: Layer): - """Format Dot nodes by layer type""" - - def handle_reformat(layer: Layer): - if layer.type != 'Reformat': - return None - try: - origin = layer.raw_dict['Origin'] - if origin == 'QDQ': - return layer_colormap['Quantize'] - except KeyError: - return None - - try: - layer_color = layer_colormap[layer.type] - layer_color = handle_reformat(layer) or layer_color - except KeyError: - layer_color = "#E5E7E9" - - formatting = {'shape': 'Mrecord', - 'style': 'filled', - 'tooltip': layer.tooltip(), - 'fillcolor': layer_color, - 'color': 'lightgray', - 'fontname': 'Helvetica'} - return formatting - - -def region_precision_formatter(tensor: Activation, is_user: bool): - """Format Dot edges by region precision""" - formatting = { - 'style': 'filled' if is_user else 'dashed', - # Hover popup text - 'tooltip': tensor.name, - 'penwidth': '3', - 'color': precision_colormap[tensor.precision], - 'fontname': 'Helvetica'} - return formatting - - -layer_node_formatters = { - "layer_type": layer_type_formatter, - "layer_precision_formatter": layer_precision_formatter, -} - -layer_node_renderers = { - "Minimal": layer_node_renderer_simple, - "Keras": layer_node_renderer_keras, - "Configurable": layer_node_configurable_renderer, -} - - -def _get_latency(plan: EnginePlan, layer: Layer, latency_type) -> float: - try: - latency = plan.df[plan.df['Name'] == layer.name][f"latency.{latency_type}"].iloc[0] - except (KeyError, IndexError): - # Constant layer or no latency data. - latency = 0 - return latency - -def get_dot_id(layer_name: str) -> str: - return layer_name.replace(":", "###") # f"l_{dot_node_id}" - -def _get_dot_id(layer_name: str) -> str: - return layer_name.replace(":", "###") - -def _is_trainstation(layer): - return layer.type == 'TrainStation' - -def get_dot_id(layer_name: str) -> str: - return layer_name.replace(":", "###") # f"l_{dot_node_id}" - - -class DotGraph(object): - """This class converts a TensorRT plan into Graphviz DOT graphs""" - def __init__(self, - plan: EnginePlan, - layer_node_formatter: Callable, - layer_node_highlighter: Callable=layer_node_highlighter, - layer_node_renderer: Callable=layer_node_configurable_renderer, - region_formatter: Callable=region_precision_formatter, - display_layer_names: bool=True, - stack_layer_names: bool=True, - display_regions: bool=False, - display_forking_regions: bool=False, - expand_layer_details: bool=False, - display_constants: bool=False, - display_latency: bool=True, - latency_type: str='avg_time', - display_region_reuse: bool=False, - display_region_names: bool=False, - display_edge_name: bool=False, - display_edge_details: bool=True, - highlight_layers: list=None, - remove_disconnected_layers: bool=False, - display_metadata: bool=True, - ): - plan_graph = PlanGraph( - plan, display_regions, display_constants, display_forking_regions) - self.dot = Digraph() - self.layer_node_formatter = layer_node_formatter - self.layer_node_highlighter = layer_node_highlighter - self.layer_node_renderer = layer_node_renderer - self.region_formatter = region_formatter - self.expand_layer_details = expand_layer_details - self.display_layer_names = display_layer_names - self.stack_layer_names = stack_layer_names - self.display_latency = display_latency - self.latency_type = latency_type if latency_type in latency_types else latency_types[0] - self.display_region_reuse = display_region_reuse - self.render_region_reuse_edges = False - self.display_region_names = display_region_names - self.display_edge_name = display_edge_name - self.display_edge_details = display_edge_details - self.highlighted_layers = highlight_layers - - node_name_2_node_id = {} - if remove_disconnected_layers: - self.__remove_disconnected_layers(plan_graph) - self.display_metadata = display_metadata - self.__add_dot_region_nodes(plan_graph, node_name_2_node_id) - self.__add_dot_layer_nodes(plan, plan_graph, node_name_2_node_id) - self.__add_edges(plan_graph, node_name_2_node_id) - self.__connect_train_station(plan_graph, node_name_2_node_id) - - def __add_dot_region_nodes(self, plan_graph, node_name_2_node_id): - dot_node_id = 0 - for mem_node in plan_graph.memory_nodes: - node_name_2_node_id[mem_node.name] = dot_id = _get_dot_id(mem_node.name) - self.__create_dot_region_node(dot_id, mem_node.tensor, mem_node.is_user, mem_node.region_gen) - dot_node_id += 1 - - def __add_dot_layer_nodes(self, plan, plan_graph, node_name_2_node_id): - for layer_node in plan_graph.layer_nodes: - layer = layer_node.layer - latency = _get_latency(plan, layer, self.latency_type) - if not layer.type == 'Constant' or plan_graph.include_constants: - dot_id = _get_dot_id(layer.name) - node_name_2_node_id[layer.name] = dot_id - self.__create_dot_layer_node( - dot_id, layer, latency, layer_node_renderer=self.layer_node_renderer) - - def __remove_disconnected_layers(self, plan_graph): - # Remove layer nodes that have no inputs and no outputs. - # TrainStation layers are exempt. - disconnected = lambda layer_node: len(layer_node.layer.inputs) == len(layer_node.layer.outputs) == 0 - nodes_to_remove = [layer_node for layer_node in plan_graph.layer_nodes if - disconnected(layer_node) and not _is_trainstation(layer_node.layer)] - for node in nodes_to_remove: - plan_graph.layer_nodes.remove(node) - - def __connect_train_station(self, plan_graph, node_name_2_node_id): - prev_layer = None - n_layers = len(plan_graph.layer_nodes) - for i, layer_node in enumerate(plan_graph.layer_nodes): - layer = layer_node.layer - if _is_trainstation(layer) and prev_layer and i < n_layers-1: - src_id = node_name_2_node_id[prev_layer.name] - dst_id = node_name_2_node_id[layer.name] - self.__create_dot_dependency_edge(src_id, dst_id) - if prev_layer and _is_trainstation(prev_layer) and i>1: - src_id = node_name_2_node_id[prev_layer.name] - dst_id = node_name_2_node_id[layer.name] - self.__create_dot_dependency_edge(src_id, dst_id) - prev_layer = layer - - def __add_edges(self, plan_graph, node_name_2_node_id): - for edge in plan_graph.edges_list: - src_id = node_name_2_node_id[edge.src.layer_name] - dst_id = node_name_2_node_id[edge.dst.layer_name] - self.__create_dot_edge(src_id, dst_id, edge.tensor, edge.region_gen) - - def __create_dot_region_node(self, node_id: int, tensor: Activation, is_user: bool, gen: int): - formatter = self.region_formatter(tensor, is_user) - is_minimal = False - if is_minimal and not is_user: - self.dot.node( - str(node_id), - label="", - shape='diamond', - height=".2", width=".2", - **formatter) - else: - desc = tensor.name if (self.display_region_names or is_user) else "" - if self.display_region_reuse and gen > 0: - desc = f"{desc} (gen={gen})" - desc = f"{desc}\n{tensor.tooltip()}" if desc else tensor.tooltip() - self.dot.node( - str(node_id), - desc, - shape='rectangle', - fillcolor='gray' if is_user else None, - **formatter) - - def __create_dot_layer_node( - self, node_id: str, layer: Layer, latency: float, layer_node_renderer: Callable - ): - formatting = self.layer_node_formatter(layer) - formatting.update(self.layer_node_highlighter(layer.name, self.highlighted_layers)) - self.dot.node( - str(node_id), - layer_node_renderer( - layer, - latency if (self.display_latency and latency) else None, - expand_layer_details=self.expand_layer_details, - display_layer_names=self.display_layer_names, - stack_layer_names=self.stack_layer_names, - display_metadata=self.display_metadata), - **formatting) - - def __create_dot_dependency_edge(self, src, dst): - self.dot.edge(src, dst, "", color='lightgray', style="dashed") - - def __create_dot_edge(self, src, dst, tensor, region_gen): - def generation_color(gen: int, line_color: str) -> str: - edge_color = "" - if self.render_region_reuse_edges: - for _ in range(gen): - edge_color += f"{line_color}:white:" - edge_color += f"{line_color}" - return edge_color - - edge_color = precision_colormap[tensor.precision] - - if region_gen: - edge_color = generation_color(region_gen, edge_color) - desc = [] - if self.display_edge_name: - desc.append(tensor.name) - if self.display_edge_details: - desc.append(tensor.tooltip()) - desc_str = "\n".join(desc) - self.dot.edge(src, dst, desc_str, color=edge_color) - - -def to_dot(*args, **kwargs) -> Digraph: - """Convert plan-graph to dot format""" - g = DotGraph(*args, **kwargs) - return g.dot - - -""" -ONNX file format -""" - -import onnx - -def make_onnx_tensor(tensor): - def get_type(desc): - desc = desc.lower() - if 'int8' in desc: - return onnx.TensorProto.INT8 - elif 'fp32' in desc: - return onnx.TensorProto.FLOAT - elif 'fp16' in desc: - return onnx.TensorProto.FLOAT16 - elif 'int32' in desc: - return onnx.TensorProto.INT32 - else: - raise ValueError(f"Uknown precision {desc}") - - t = onnx.helper.make_tensor_value_info( - tensor.name, - get_type(tensor.format), - tensor.shape) - return t - -class OnnxGraph(object): - def __init__(self, plan: EnginePlan, display_forking_regions: bool): - def get_adjacency_lists(): - inputs_map, outputs_map = {}, {} - layer_nodes_names = [node.layer.name for node in self.plan_graph.layer_nodes] - for edge in self.plan_graph.edges_list: - if edge.src.port != None and edge.dst.port != None: - if False and edge.src.layer_name in layer_nodes_names and edge.dst.layer_name in layer_nodes_names: - edge_name = edge.tensor.name - else: - edge_name = f"{edge.src.layer_name}:{edge.src.port}##{edge.dst.layer_name}:{edge.dst.port}" - elif edge.src.port != None: - edge_name = edge.dst.layer_name - else: - edge_name = edge.src.layer_name - try: - outputs_map[edge.src.layer_name].append(edge_name) - except KeyError: - outputs_map[edge.src.layer_name] = list((edge_name,)) - - try: - inputs_map[edge.dst.layer_name].append(edge_name) - except KeyError: - inputs_map[edge.dst.layer_name] = list((edge_name,)) - return inputs_map, outputs_map - - def add_layer_nodes(): - for layer_id, layer_node in enumerate(self.plan_graph.layer_nodes): - layer = layer_node.layer - try: - self.__add_layer_node(layer_id, layer, inputs_map[layer.name], outputs_map[layer.name]) - except KeyError: - # Graph inputs/outputs (bindings) will not have an entry in the inputs_map or outputs_map - pass - - def add_memory_nodes(): - for mem_node in self.plan_graph.memory_nodes: - is_user = mem_node.is_user - if not is_user: - self.__add_region_node( - mem_node.region_gen, mem_node.name, is_user, - inputs_map[mem_node.name], outputs_map[mem_node.name]) - - - def add_graph_inputs_outputs(): - # Graph inputs/outputs handling - g_inputs, g_outputs = plan.get_bindings() - for inp in g_inputs: - graph_inputs.append(make_onnx_tensor(inp)) - for outp in g_outputs: - graph_outputs.append(make_onnx_tensor(outp)) - - def finalize_onnx_graph(): - graph_def = onnx.helper.make_graph( - self.onnx_nodes, - 'test-model', - graph_inputs, - graph_outputs) - self.onnx_model = onnx.helper.make_model( - graph_def, producer_name='nvidia-trex') - - self.onnx_nodes = [] - graph_inputs, graph_outputs = [], [] - self.plan = plan - self.plan_graph = PlanGraph(plan, include_forking_regions=display_forking_regions) - - inputs_map, outputs_map = get_adjacency_lists() - add_memory_nodes() - add_layer_nodes() - add_graph_inputs_outputs() - finalize_onnx_graph() - - def __add_region_node(self, gen: int, region_name: str, is_user: bool, inputs, outputs): - assert not is_user - node_def = onnx.helper.make_node("Region", inputs, outputs, region_name) - self.onnx_nodes.append(node_def) - - def __add_layer_node(self, node_id: int, layer: Layer, inputs, outputs): - def get_type(layer): - op_type = layer.type - # Convert Op Type to onnx.ai namepsace because - # Netron assigns these nodes specific colors. - if op_type == "Convolution": - op_type = "Conv" - if op_type == "Pooling": - if layer.raw_dict["PoolingType"] == "AVERAGE": - op_type = "AveragePool" - if layer.raw_dict["PoolingType"] == "Max": - op_type = "MaxPool" - return op_type - - def add_attributes(layer, node_def): - for key, value in sorted(layer.items()): - if key not in [ - 'InputRegions', 'OutputRegions', 'Inputs', - 'Outputs', 'Name', 'name', 'ParameterType', 'LayerName']: - node_def.attribute.extend([onnx.helper.make_attribute(key, value)]) - - op_type = get_type(layer) - if op_type == 'Constant': - return - - # Find inputs to layer id, outputs - node_def = onnx.helper.make_node(op_type, inputs, outputs, layer.name) - add_attributes(layer.raw_dict, node_def) - self.onnx_nodes.append(node_def) diff --git a/tools/experimental/trt-engine-explorer/trex/interactive.py b/tools/experimental/trt-engine-explorer/trex/interactive.py deleted file mode 100644 index 53d0b80e3..000000000 --- a/tools/experimental/trt-engine-explorer/trex/interactive.py +++ /dev/null @@ -1,80 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -""" -This file contains configurable interactive widget wrappers. -""" - - -from ipywidgets import widgets -import IPython -from IPython.display import display -from typing import List - - -class InteractiveDiagram: - """A dropdown widget wrapper""" - def __init__(self, diagram_renderer, choices, description): - def get_default_choice_key(): - return list(self.choices.keys())[0] - - self.diagram_renderer = diagram_renderer - self.choices = choices - - self.choice_widget = widgets.Dropdown( - options=self.choices.keys(), - value=get_default_choice_key(), - description=description, - disabled=False, - ) - out = widgets.interactive_output(self.dropdown_state_eventhandler, {'user_choice': self.choice_widget}) - display(out) - - def _render(self, choice, values): - display(self.choice_widget) - self.diagram_renderer(choice, *values) - - def dropdown_state_eventhandler(self, user_choice): - values = self.choices[user_choice] - if not isinstance(values, list) and not isinstance(values, tuple): - values = (values,) - self._render(user_choice, values) - - -class InteractiveDiagram_2: - """A dropdown widget wrapper""" - def __init__(self, choices: List, description: str): - def get_default_choice(): - return list(self.choices.keys())[0] - - self.choices = choices - self.choice_widget = widgets.Dropdown( - options=self.choices.keys(), - value=get_default_choice(), - description=description, - disabled=False, - ) - out = widgets.interactive_output(self.dropdown_state_eventhandler, {'user_choice': self.choice_widget}) - display(out) - - def _render(self, title, renderer): - display(self.choice_widget) - renderer(title=title) - - def dropdown_state_eventhandler(self, user_choice): - renderer = self.choices[user_choice] - self._render(user_choice, renderer) \ No newline at end of file diff --git a/tools/experimental/trt-engine-explorer/trex/layer.py b/tools/experimental/trt-engine-explorer/trex/layer.py deleted file mode 100644 index e7a10ae72..000000000 --- a/tools/experimental/trt-engine-explorer/trex/layer.py +++ /dev/null @@ -1,150 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -""" -TensorRT Engine Exploration API - Layer -""" - - -from .activations import * -from typing import Dict, List -import numpy as np - - -class Layer: - def __init__(self, raw_dict: Dict): - self.raw_dict = raw_dict - self.name = raw_dict['Name'] - try: - self.type = raw_dict['ParameterType'] - except KeyError: - self.type = raw_dict['LayerType'] - try: - self.metadata = raw_dict['Metadata'] - except KeyError: - self.metadata = None - self.subtype = raw_dict['LayerType'] - self.inputs = [Activation(tensor) for tensor in raw_dict['Inputs']] - self.outputs = [Activation(tensor) for tensor in raw_dict['Outputs']] - self.outputs_size_bytes = np.sum([i.size_bytes for i in self.outputs]) - if self.inputs: - self.precision = self.inputs[0].precision - self.inputs_size_bytes = np.sum([i.size_bytes for i in self.inputs]) - else: - self.inputs_size_bytes = 0 - self.precision = None - - self.total_io_size_bytes = self.inputs_size_bytes + self.outputs_size_bytes - self._parse_weights() - self.total_footprint_bytes = self.total_io_size_bytes + self.weights_size - - def _parse_constant(const): - cnt = const['Count'] - data_type = const['Type'] - try: - data_size = dict( - {"Int8": 1, "Half": 2, "Float": 4, "Int32": 4})[data_type] - except KeyError: - # Backward compatbility. - data_size = dict( - {"Int8": 1, "FP16": 2, "FP32": 4, "Int32": 4})[data_type] - return cnt, data_type, cnt * data_size - - def _parse_weights(self): - try: - self.weights_cnt, self.weights_type, self.weights_size = \ - Layer._parse_constant(self.raw_dict['Weights']) - except KeyError: - self.weights_cnt = 0 - self.weights_type = None - self.weights_size = 0 - try: - self.bias_cnt, self.bias_type, self.bias_size = \ - Layer._parse_constant(self.raw_dict['Bias']) - except KeyError: - self.bias_cnt = 0 - self.bias_type = None - self.bias_size = 0 - - def tooltip(self): - tip = "" - for key, value in sorted(self.raw_dict.items()): - if key not in ['InputRegions', 'OutputRegions', 'Inputs', 'Outputs', - 'ParameterType', 'LayerName']: - tip += f"{key}:{value}\\n" - return tip - - def __repr__(self): - rep = f"Layer({self.name})" - return rep - - -def fold_no_ops(layers: List, bindings: List) -> List: - """Remove layers of type No-Op""" - - def consumers_producers_dict(layers) -> Dict[Activation, List[Layer]]: - """Return a dictionary of consumer-layers per activation tensor""" - consumers, producers = {}, {} - for layer in layers: - for loc, input in enumerate(layer.inputs): - try: - consumers[input.name].append((layer.name, loc)) - except KeyError: - consumers[input.name] = [(layer.name, loc)] - for loc, output in enumerate(layer.outputs): - try: - producers[output.name].append((layer.name, loc)) - except KeyError: - producers[output.name] = [(layer.name, loc)] - return consumers, producers - - def move_input(src: Layer, dst: Layer, loc: int=0): - # Can safely assume src input port #0 because NoOp has only a single input. - dst.inputs[loc] = src.inputs[0] - - def move_output(src: Layer, dst: Layer, loc: int=0): - # Can safely assume src output port #0 because NoOp has only a single output. - dst.outputs[loc] = src.outputs[0] - - def fold(no_op: Layer): - try: - successors = activation_consumers[no_op.outputs[0].name] - for successor in successors: - successor_name, successor_in_port = successor - move_input( - src=no_op, dst=ret[successor_name], loc=successor_in_port) - except KeyError: - # A leaf NoOp layer: it's output is a binding so we need to move the - # NoOp output to the output of the previous layer. - if no_op.outputs[0].name in bindings : - predecessors = activation_producers[no_op.inputs[0].name] - for predecessor in predecessors: - predecessor_name, predecessor_out_port = predecessor - move_output( - src=no_op, dst=ret[predecessor_name], loc=predecessor_out_port) - pass - - ret = {layer.name: layer for layer in layers} - activation_consumers, activation_producers = consumers_producers_dict(layers) - - for layer in layers: - if layer.type == 'NoOp': - fold(layer) - - # Remove the No-Op layers from the final list. - ret = [layer for layer in ret.values() if layer.type != 'NoOp'] - return ret diff --git a/tools/experimental/trt-engine-explorer/trex/lint.py b/tools/experimental/trt-engine-explorer/trex/lint.py deleted file mode 100644 index 4a37c4f32..000000000 --- a/tools/experimental/trt-engine-explorer/trex/lint.py +++ /dev/null @@ -1,232 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -""" -This file contains layer linting functions. -""" - - -from collections import OrderedDict -from collections import OrderedDict -from typing import Dict -import pandas as pd -from .activations import create_activations -from .engine_plan import EnginePlan - - -class ConvLinter(): - """Convolution layer linter.""" - - def __init__(self, plan: EnginePlan): - self.plan = plan - self.convs = plan.get_layers_by_type('Convolution') - - def tc_lint(self): - """Search for Convolutions which are not accelerated by TensorCode""" - - def is_small_conv(conv): - inputs, _ = create_activations(conv) - if len(inputs[0].shape) != 4: - return False - n, c, h, w = inputs[0].shape - return c < 32 - - report = OrderedDict() - - # Look for kernels that are not scheduled for TensorCore acceleration - tc_candidates = self.convs.query(f"precision != \"FP32\"").copy() - - # Identify acceleration from tactic name - df = tc_candidates - df = df[df['tactic'].str.contains("imma|hmma|xmma|i88|884", na=False) == False] - for _, conv in df.iterrows(): - mitigation = "" - if is_small_conv(conv): - mitigation = "This Convolution has a small number " \ - "of input channels so acceleration may not be possible." - - report[conv.Name] = OrderedDict({ - 'name': conv.Name, - 'tactic': conv.tactic, - 'subtype': conv.subtype, - 'hazard': "Convolution is not accelerated.", - 'mitigation': mitigation, - 'help': "TensorCores accelerate large Convolution and GEMM operations." - }) - return report - - def mixed_precision_lint(self): - """Search for Convolutions with Int8 inputs and Float outputs""" - report = OrderedDict() - - df = self.convs - df = df.loc[df['precision'] == 'INT8'].copy() - for index, conv in df.iterrows(): - inputs, outputs = create_activations(conv) - inf = inputs[0].format[:4] - outf = outputs[0].format[:4] - found = inf == 'Int8' and outf != 'Int8' - if found: - report[conv.Name] = OrderedDict({ - 'name': conv.Name, - 'tactic': conv.tactic, - 'subtype': conv.subtype, - 'hazard': "Quantized Convolution has float outputs.", - 'mitigation': "Consider adding quantization after the convolution.", - 'help': "Quantized Convolution with float outputs is ill advised " - "for memory-limited convolutions." - }) - return report - - def alignment_lint(self): - def alignment_ok(conv): - inputs, outputs = create_activations(conv) - prec = conv["precision"] - if len(inputs[0].shape) != 4 or len(outputs[0].shape) != 4: - return True - N, C, H, W = inputs[0].shape - _, K, P, Q = outputs[0].shape - if prec == 'INT8': - ok = (C % 16 == 0) and (K % 16 == 0) - else: - ok = (C % 8 == 0) and (K % 8 == 0) - return ok - - report = OrderedDict() - for _, conv in self.convs.iterrows(): - if not alignment_ok(conv): - report[conv.Name] = { - 'name': conv.Name, - 'tactic': conv.tactic, - 'subtype': conv.subtype, - 'hazard': "Convolution channels are not optimally aligned.", - 'mitigation': "Consider changing the alignment of the convolution's channels.", - 'help': "For best performance, the input and outputs channels of a Tensor Core" - "accelerated convolution should be aligned to 8 (FP32/FP16) or 16 (INT8)" - } - return report - - def lint(self): - report = self.tc_lint() - report.update(self.mixed_precision_lint()) - report.update(self.alignment_lint()) - df = pd.DataFrame.from_dict(report, orient='index') - return df - - -class ReformatLinter(): - """Reformat layer linter.""" - - def __init__(self, plan: EnginePlan): - self.plan = plan - self.reformats = plan.get_layers_by_type('Reformat') - - def lint(self): - """Search for conversions between types. - - Conversions between layouts are assumed to be optimized.""" - report = OrderedDict() - - for index, reformat in self.reformats.iterrows(): - inputs, outputs = create_activations(reformat) - inf = inputs[0].format[:4] - outf = outputs[0].format[:4] - if inf != outf: - mitigation = "" - if "INT8" in [inf, outf]: - mitigation = "Consider adding quantization around float operations." - report[reformat.Name] = OrderedDict({ - 'name': reformat.Name, - 'origin': reformat['attr.origin'], - 'type conversion': f"{inf} -> {outf}", - 'shape conversion': f"{inputs[0].shape} -> {outputs[0].shape}", - 'hazard': "Reformat layer is converting operand data type.", - 'mitigation': mitigation, - 'help': "Conversions between float32 and float16 are a red " - "flag, as are conversions between float32/16 and INT8." - }) - - df = pd.DataFrame.from_dict(report, orient='index') - return df - - -class SliceLinter(): - """Slice layer linter.""" - - def __init__(self, plan: EnginePlan): - self.plan = plan - self.slices = plan.get_layers_by_type('Slice') - - def lint(self): - """Search for conversions between types. - - Conversions between layouts are assumed to be optimized.""" - report = OrderedDict() - - for index, slice in self.slices.iterrows(): - inputs, outputs = create_activations(slice) - inf = inputs[0].format[:4] - outf = outputs[0].format[:4] - if inf != outf: - mitigation = "" - if "INT8" in [inf, outf]: - mitigation = "Consider adding quantization around float operations." - report[slice.Name] = OrderedDict({ - 'name': slice.Name, - 'type conversion': f"{inf} -> {outf}", - 'shape conversion': f"{inputs[0].shape} -> {outputs[0].shape}", - 'hazard': "Slice layer is converting operand data type.", - 'mitigation': mitigation, - 'help': "Conversions between float32 and float16 are a red " - "flag, as are conversions between float32/16 <=> INT8." - }) - - df = pd.DataFrame.from_dict(report, orient='index') - return df - - -class QDQLinter(): - """Q/DQ layer linter.""" - - def __init__(self, plan: EnginePlan): - self.plan = plan - self.scales = plan.get_layers_by_type('Scale') - - def lint(self): - """Search for dangling Q/DQ layers.""" - report = OrderedDict() - - for index, scale in self.scales.iterrows(): - inputs, outputs = create_activations(scale) - inf = inputs[0].format[:4] - outf = outputs[0].format[:4] - is_qdq = ('Int8' in inputs[0].format) ^ ('Int8' in outputs[0].format) - if is_qdq: - dq = 'Int8' in inputs[0].format - role = "Quantize" if not dq else "Dequanitize" - report[scale.Name] = OrderedDict({ - 'name': scale.Name, - 'type conversion': f"{inf} -> {outf}", - 'hazard': f"Unfused {role} layer", - 'mitigation': f"Check why the {role} layer is not fused", - 'help': f"Unfused Quantize/Dequantize nodes are wasteful and " - "should be avoided. Quantize nodes may be necessary " - "for quantizing inputs." - }) - - df = pd.DataFrame.from_dict(report, orient='index') - return df diff --git a/tools/experimental/trt-engine-explorer/trex/misc.py b/tools/experimental/trt-engine-explorer/trex/misc.py deleted file mode 100644 index 7f9d9d787..000000000 --- a/tools/experimental/trt-engine-explorer/trex/misc.py +++ /dev/null @@ -1,89 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -""" -This file contains miscellanous utility functions. -""" - - -import pandas as pd -from typing import Dict, List, Tuple -import functools - - -def group_count(df, grouping_attr): - grp = df.groupby([grouping_attr]).size().reset_index() - grp.rename(columns = {0: 'count'}, inplace = True) - return grp - - -def group_sum_attr(df, grouping_attr, reduced_attr): - grp = df.groupby([grouping_attr])[reduced_attr].sum().reset_index() - return grp - - -def group_mean_attr(df, grouping_attr, reduced_attr): - grp = df.groupby([grouping_attr])[reduced_attr].mean().reset_index() - return grp - - -def shape_to_str(shape): - return "[" + ",".join(str(dim) for dim in shape) + "]" - - -def _merge_keys_values( - keys_lists: List[List], - values_lists: List[List], - empty_placeholder: object -) -> Dict: - # Concatenate the keys lists into a set of all keys - all_keys = set(functools.reduce(lambda a, b: a+b, keys_lists)) - - # Create the stacked output dictionary, and fill missing values. - dicts = [dict(zip(keys, values)) for keys, values in zip(keys_lists, values_lists)] - result = {} - for key in all_keys: - for d in dicts: - if key not in d: - d[key] = empty_placeholder - result[key] = [d[key] for d in dicts] - return result - - -def stack_dicts(dict_list: List[dict], empty_placeholder: object=0): - """Stack lists of dictionaries as a single dictionary""" - # A list of names lists. - keys_lists = [list(d.keys()) for d in dict_list] - - # A list of values lists. - values_lists = [list(d.values()) for d in dict_list] - return _merge_keys_values(keys_lists, values_lists, empty_placeholder) - - -def stack_dataframes( - df_list: List[pd.DataFrame], - names_col: str, - values_col: str, - empty_placeholder: object=0, -): - # A list of names lists. - names = [df[names_col].tolist() for df in df_list] - - # A list of values lists. - values = [df[values_col].tolist() for df in df_list] - - return _merge_keys_values(names, values, empty_placeholder) \ No newline at end of file diff --git a/tools/experimental/trt-engine-explorer/trex/notebook.py b/tools/experimental/trt-engine-explorer/trex/notebook.py deleted file mode 100644 index 21122bd90..000000000 --- a/tools/experimental/trt-engine-explorer/trex/notebook.py +++ /dev/null @@ -1,97 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -""" -Miscellanous functions used in Jupyter notebooks -""" - - -from typing import Callable -import pandas as pd -import dtale -from IPython.display import display, HTML -import pandas as pd -import time -import logging - - -dtale.global_state.set_app_settings(dict(max_column_width=600)) # pixels - - -def section_header(title): - style = "text-align:center;background:#76b900;padding:20px;color:#ffffff;font-size:2em;" - return HTML('
    {}
    '.format(style, title)) - - -def set_wide_display(width_pct: int=90): - """Configure a wider rendering of the notebook - (for easier viewing of wide tables and graphs). - """ - display(HTML(f"")) - - -def display_df_dtale( - df: pd.DataFrame, - range_highlights: dict=None, - nan_display: str='...', - precision: int=4, - **kwargs -): - """Display a Pandas dataframe using a dtale widget""" - - start = time.time() - d = dtale.show( - df, - drop_index=True, - allow_cell_edits=False, - precision=precision, - nan_display=nan_display, - **kwargs) - - if d and range_highlights is not None: - d.update_settings(range_highlights=range_highlights, background_mode='range') - - # The dtale notebook client may fail to connect to the dtale (Flask) server - # without an error indication. If the connection setup duration is longer than - # some threshold (DTALE_TIMEOUT) we flag an error. - DTALE_TIMEOUT = 30 # seconds - abnormal_connection_duration = time.time() - start >= DTALE_TIMEOUT - if not d or abnormal_connection_duration: - logging.warning( - "It seems like the host is taking too long to respond. Please try explicitly " - "setting the Jupyter notebook server's IP address or name in " - "\'dtale.app.ACTIVE_HOST = \'.\n" - "Make sure to place this line at the top of your notebook.") - else: - display(d) - - -# Control how to display tables in notebooks -table_display_backend = display - - -def set_table_display_backend(tbl_display_fn: Callable): - global table_display_backend - table_display_backend = tbl_display_fn - - -def display_df(df: pd.DataFrame, **kwargs): - try: - table_display_backend(df, **kwargs) - except TypeError: - table_display_backend(df) - diff --git a/tools/experimental/trt-engine-explorer/trex/parser.py b/tools/experimental/trt-engine-explorer/trex/parser.py deleted file mode 100644 index 0123f5353..000000000 --- a/tools/experimental/trt-engine-explorer/trex/parser.py +++ /dev/null @@ -1,223 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -""" -JSON file parsing -""" - - -import re -import json -from typing import Dict, List, Tuple, BinaryIO - - -def read_json(json_file: str) -> BinaryIO: - try: - data = json.load(json_file) - except: - raise ValueError(f"Could not load JSON file {json_file}") - return data - - -def read_graph_file(graph_file: str) -> List: - err_msg = f"File {graph_file} does not conform to the expected JSON format." - with open(graph_file) as json_file: - graph = read_json(json_file) - if not isinstance(graph, dict): - raise ValueError(err_msg) - layers = graph['Layers'] - try: - bindings = graph['Bindings'] - except KeyError: - # Older TRT didn't include bindings - bindings = list() - if not isinstance(layers, list): - raise ValueError(err_msg) - if not isinstance(layers[0], dict): - details_msg = "\nMake sure to enable detailed ProfilingVerbosity when building the engine." - details_msg += "\nSee https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#engine-inspector" - raise ValueError(err_msg + details_msg) - return layers, bindings - - -def read_profiling_file(profiling_file: str) -> List[Dict[str, any]]: - perf = None - with open(profiling_file) as json_file: - perf = read_json(json_file) - # Clean data (remove records with short size) - perf = [rec for rec in perf if len(rec) in (4, 5)] - return perf - - -def read_metadata_file(metadata_file: str, device: int=0): - with open(metadata_file) as json_file: - metadata = read_json(json_file) - return metadata[device] - - -def read_timing_file(timing_json_file: str): - with open(timing_json_file) as json_file: - timing_recs = read_json(json_file) - latencies_list = [rec['latencyMs'] for rec in timing_recs] - return latencies_list - - -def read_perf_metadata_file(metadata_file: str, section: str): - with open(metadata_file) as json_file: - metadata = read_json(json_file) - return metadata[section] - - -def get_device_properties(metadata_file: str) -> Dict: - try: - return read_perf_metadata_file(metadata_file, 'device_information') - except (FileNotFoundError, TypeError): - return {} - - -def get_performance_summary(metadata_file:str) -> Dict: - try: - return read_perf_metadata_file(metadata_file, 'performance_summary') - except (FileNotFoundError, TypeError): - return {} - - -def get_builder_config(metadata_file:str) -> Dict: - try: - d = read_perf_metadata_file(metadata_file, 'model_options') - d.update(read_perf_metadata_file(metadata_file, 'build_options')) - return d - except (FileNotFoundError, TypeError): - return {} - - -def import_graph_file(graph_file: str, profile_id: int=None): - def filter_profiles(raw_layers: List[Dict], bindings: List[str], profile_id: int) -> List: - """Use layers from one shape profile. - - A TRT engine may be built with one or more shape-profiles, and each of - the profiles will have its own engine graph. The layers of the different - profiles are distinguished from one another by the `[profile N]` suffix - appended to layer and binding names. - The first profile does not have this suffix. - - When `profile_id` is None or zero we use only the layers of the first profile. - Otherwise we use only the layers from the requested profile. - """ - def use_name(name: str) -> bool: - name_belongs_to_some_profile = re.search(r"\[profile +[0-9]\]", name) - if name_belongs_to_some_profile and not profile_id: - # We detected that the engine was built with several shape-profiles - # but we are not targeting any of them so move to the next layer. - # The first profile always exists and never has a profile id in the names. - return False - if profile_id: - # We detected that the engine was built with several shape-profiles - # and we are targeting a specific profile. - name_belongs_to_right_profile = re.search(rf"\[profile {profile_id}\]", name) - if not name_belongs_to_right_profile: - return False - return True - - filtered_raw_layers = [] - for raw_layer in raw_layers: - name = raw_layer['Name'] - if use_name(name): - filtered_raw_layers.append(raw_layer) - filtered_bindings = [binding for binding in bindings if use_name(binding)] - if not len(filtered_raw_layers) or not len(filtered_bindings): - raise ValueError( - f"Something went wrong went filtering layers from the provided " - f"profile ({profile_id}).\nMost likely the profile data does not " - "exist in the graph file so try without providing a profile id.") - return filtered_raw_layers, filtered_bindings - - def disambiguate_layer_names(raw_layers: List) -> List: - """If a layer name appears twice we need to disabmiguate it""" - names_cnt = {} - for raw_layer in raw_layers: - name = raw_layer['Name'] - if name in names_cnt: - names_cnt[name] += 1 - name += "_" + str(names_cnt[name]) - raw_layer['Name'] = name - else: - names_cnt[name] = 1 - return raw_layers - - def convert_deconv(raw_layers: List) -> List: - """Distinguish between convolution and convolution-transpose (deconvolution)""" - for raw_layer in raw_layers: - try: - is_deconv = ( - raw_layer['ParameterType'] == "Convolution" and - raw_layer['LayerType'] == "CaskDeconvolutionV2") - if is_deconv: - raw_layer['ParameterType'] = "Deconvolution" - except KeyError: - pass - return raw_layers - - def fix_metadata(raw_layers: List) -> List: - """TensorRT 8.6 introduced the Metadata field, with a non-ASCII character - that triggers an SVG rendering error. This function replaces this character. - - See: https://github.com/NVIDIA/TensorRT/issues/2779 - """ - TRT_METADATA_DELIM = '\x1E' - for l in raw_layers: - try: - if TRT_METADATA_DELIM in l['Metadata']: - l['Metadata'] = l['Metadata'].replace(TRT_METADATA_DELIM, '+') - except KeyError: - pass - return raw_layers - - def fix_unicode(raw_layers: List) -> List: - """TensorRT 8.6 and 10.0 introduced non-ASCII characters to the graph JSON - that trigger SVG rendering errors. This function replaces these characters. - - See: https://github.com/NVIDIA/TensorRT/issues/2779 - """ - UNICODE_UNIT_SEPARATOR = '\x1E' - UNICODE_REC_SEPARATOR = '\x1F' - TREX_SEPARATOR = '+' - replace_unicode = lambda s: s.replace(UNICODE_UNIT_SEPARATOR, TREX_SEPARATOR - ).replace(UNICODE_REC_SEPARATOR, TREX_SEPARATOR) - for l in raw_layers: - try: - l['Name'] = replace_unicode(l['Name']) - l['Metadata'] = replace_unicode(l['Metadata']) - except KeyError: - pass - return raw_layers - - def __remove_signal_wait(raw_layers: List) -> List: - for raw_layer in raw_layers: - if raw_layer['LayerType'] in ("signal", "wait"): - raw_layers.remove(raw_layer) - return raw_layers - - - raw_layers, bindings = read_graph_file(graph_file) - raw_layers = fix_unicode(raw_layers) - raw_layers = convert_deconv(raw_layers) - raw_layers = __remove_signal_wait(raw_layers) - raw_layers = disambiguate_layer_names(raw_layers) - raw_layers, bindings = filter_profiles(raw_layers, bindings, profile_id) - - return raw_layers, bindings diff --git a/tools/experimental/trt-engine-explorer/trex/plotting.py b/tools/experimental/trt-engine-explorer/trex/plotting.py deleted file mode 100644 index d7a1dc948..000000000 --- a/tools/experimental/trt-engine-explorer/trex/plotting.py +++ /dev/null @@ -1,366 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -""" -This file contains pyplot plotting wrappers. -""" - -from typing import Dict, List, Tuple -from collections import defaultdict -import pandas as pd -import math -import plotly.express as px -import plotly.graph_objects as go -from plotly.subplots import make_subplots -from .notebook import display_df -from .misc import stack_dataframes -from .colors import default_pallete, NVDA_GREEN, GRID_COLOR, UNKNOWN_KEY_COLOR - - -def _categorical_colormap(df: pd.DataFrame, color_col:str): - # Protect against index-out-of-range - max_idx = len(default_pallete) - 1 - colormap = {category: - default_pallete[min(i,max_idx)] for i,category in enumerate(set(df[color_col]))} - return colormap - - -def _create_categorical_marker( - df: pd.DataFrame, - color_col: str, - colormap: Dict=None -): - if not colormap: - colormap = _categorical_colormap(df, color_col) - # Make colormap robust to unknown keys - colormap = defaultdict(lambda: UNKNOWN_KEY_COLOR, colormap) - color_list = [colormap[key] for key in df[color_col]] - marker = dict(color=color_list) - return marker - - -def trex_base_layout(fig, gridcolor=None): - "White background with colored grid" - gridcolor = gridcolor or GRID_COLOR - fig.update_layout({ - 'xaxis': {'gridcolor': gridcolor}, - 'yaxis': {'gridcolor': gridcolor}, - 'plot_bgcolor': 'rgba(0, 0, 0, 0)', - 'paper_bgcolor': 'rgba(0, 0, 0, 0)', - }) - - -def create_layout( - title: str, - size:Tuple, - x_title: str, - y_title: str, - orientation: str, - show_axis_ticks: Tuple[bool]=(True,True) -): - y_grid = None if orientation == 'h' else GRID_COLOR - x_grid = None if orientation == 'v' else GRID_COLOR - - if orientation == 'h': - x_title, y_title = y_title, x_title - - top_right = { - 'yanchor': "top", - 'y': 0.99, - 'xanchor': "right", - 'x': 0.99} - - layout = go.Layout( - title={ - 'text': title, - 'y':0.9, - 'x':0.5, - 'xanchor': 'center', - 'yanchor': 'bottom'}, - width=size[0], - height=size[1], - xaxis={ - 'visible': True, - 'showticklabels': show_axis_ticks[0], - 'title': x_title, - 'gridcolor': x_grid,}, - yaxis={ - 'visible': True, - 'showticklabels': show_axis_ticks[1], - 'title': y_title, - 'gridcolor': y_grid, - 'tickformat': "%{y:$.2f}"}, - plot_bgcolor='rgba(0,0,0,0)', - legend=top_right, - ) - return layout - - -def plotly_bar2( - df: pd.DataFrame, - title: str, - values_col: str, - names_col: str, - orientation: str='v', - color: str=None, - size: Tuple=(None, None), - use_slider: bool=False, - colormap: Dict=None, - show_axis_ticks:Tuple=(False, True), - showlegend: bool=False, - xaxis_title: str=None, - yaxis_title: str=None, - do_show: bool=True, -): - def categorical_color(df, color) -> bool: - if df[color].dtype in [float, int]: - return False - return True - - def add_bar(df, name, color, colormap, showlegend): - if orientation == 'v': - x, y = (names_col, values_col) - hover_txt = f"{x}: " + "%{x}" + f"
    {y}: " + "%{y:.4f}" - else: - x, y = (values_col, names_col) - hover_txt = f"{x}: " + "%{x:.4f}" + f"
    {y}: " + "%{y}" - - is_categorical = False - colorbar = None - if color is not None: - assert isinstance(color, str) - if not categorical_color(df, color): - colorbar = dict(title=color) - color = df[color] - else: - is_categorical = True - - if not is_categorical: - marker = dict(color=color, colorbar=colorbar) - else: - marker = _create_categorical_marker(df, color, colormap) - - texttemplate = "%{value:.4f}" if df[values_col].dtype == float else '%{value}' - bar = go.Bar( - x=df[x], y=df[y], - orientation=orientation, - marker=marker, - text=df[values_col], - texttemplate=texttemplate, - hovertemplate=hover_txt, - name=name, - showlegend=showlegend) - return bar - - layout = create_layout( - title, - size, - x_title=xaxis_title or names_col, - y_title=yaxis_title or values_col, - orientation=orientation, - show_axis_ticks=show_axis_ticks,) - fig = go.Figure(layout=layout) - - if not isinstance(df, Dict): - df_dict = {None: df} - else: - df_dict = df - - for name, df in df_dict.items(): - bar = add_bar(df, name, color, colormap, showlegend=showlegend) - fig.add_traces(bar) - - if use_slider: - fig.update_xaxes(rangeslider_visible=True) - if do_show: - fig.show() - return fig - - -def rotate_columns(df: pd.DataFrame): - cols = df.columns.tolist() - cols = cols[-1:] + cols[:-1] - df = df[cols] - return df - - -def stacked_tabular_df( - bar_names: List[str], - df_list: List[pd.DataFrame], - names_col: str, - values_col: str, - empty_symbol: object=0, -): - stacked = stack_dataframes(df_list, names_col, values_col, empty_symbol) - df = pd.DataFrame.from_dict(stacked, orient='index', columns=bar_names) - df[values_col] = stacked.keys() - df = rotate_columns(df) - return df - - -def stacked_tabular(*args, **kwargs): - df = stacked_tabular_df(*args, **kwargs) - display_df(df) - - -def stacked_bars( - title: str, - bar_names: List[str], - df_list: List[pd.DataFrame], - names_col: str, - values_col: str, - empty_symbol: object=0, - colormap: Dict=None, - display_tbl: bool=True, - fig: go.Figure=None, - xaxis_title: str=None, - yaxis_title: str=None, -): - """Stack a list of dataframes. - Each df in df_list has a `names` column and a 'values` column. - This function returns a dictionary indexed by each name in the - set of all names from all dfs in df_list. - For each `name` key the dictionary value is a list of all values from all dfs. - If a df[name] does not exist, we create an `empty_symbol` entry for it. - """ - - stacked = stack_dataframes(df_list, names_col, values_col, empty_symbol) - if colormap: - bars = [go.Bar(name=k, x=bar_names, y=v, marker_color=colormap[k], text=v) for k,v in stacked.items()] - else: - bars = [go.Bar(name=k, x=bar_names, y=v, text=v) for k,v in stacked.items()] - - display_bars = True - if fig is None: - fig = go.Figure(data=bars) - trex_base_layout(fig) - else: - fig.add_traces(bars) - display_bars = False - fig.update_layout(title=title, title_x=0.5, font_size=15,) - fig.update_layout(barmode='stack') - fig.update_layout(showlegend=colormap is not None) - fig.update_traces(texttemplate='%{text:.4f}') - fig.update_layout(uniformtext_minsize=8, uniformtext_mode='hide') - fig.update_layout({ - 'yaxis_title': yaxis_title or values_col, - 'xaxis_title': xaxis_title}) - if display_bars: - fig.show() - if display_tbl: - stacked_tabular(bar_names, df_list, names_col, values_col, empty_symbol) - return fig - - -def plotly_hist( - df: pd.DataFrame, - title: str, - values_col: str, - xaxis_title: str, - color: str, - colormap=None, - do_show: bool=True, -): - """Draw a histogram diagram.""" - - fig = px.histogram( - df, x=values_col, title=title, nbins=len(df), - histfunc="count", - color=color, - template="simple_white", - color_discrete_map=colormap) - fig.update_layout(xaxis={"title": xaxis_title}, bargap=0.05) - if do_show: - fig.show() - return fig - - -def plotly_pie( - df: pd.DataFrame, - title: str, - values: str, - names: str, - colormap: Dict[str,str]=None, - do_show: bool=True, -): - """Draw a pie diagram.""" - - fig = go.Figure(data=[go.Pie( - labels=df[names], values=df[values], hole=.0)]) - - pie_pallette = default_pallete - if colormap: - pie_pallette = [colormap[key] for key in df[names]] - - marker = dict(colors=pie_pallette, line=dict(color=NVDA_GREEN, width=1)) - fig.update_traces(marker=marker) - fig.update_traces( - hoverinfo='label+percent', textinfo='value', textfont_size=20,) - fig.update_layout(title=title, title_x=0.5, font_size=20,) - if do_show: - fig.show() - return fig - - -def plotly_pie2( - title: str, - charts: list, - max_cols: int=3, - colormap: Dict[str,str]=None, - do_show: bool=True, -): - """Draw a pie diagram.""" - - main_title = title - n_charts = len(charts) - n_cols = min(max_cols, n_charts) - n_rows = math.ceil(n_charts/n_cols) - specs = [[{'type':'domain'}] * n_cols] * n_rows - - subtitles = [chart[1] for chart in charts] - fig = make_subplots(rows=n_rows, cols=n_cols, specs=specs, - subplot_titles=subtitles) - - pie_pallette = None - for i, chart in enumerate(charts): - df, title, values, names = chart - row, col = i // n_cols, i % n_cols - texttemplate = "%{value:.1f}" if df[values].dtype == float else '%{value}' - if colormap is not None: - pie_pallette = [colormap[key] for key in df[names]] - marker = dict(colors=pie_pallette, line=dict(color=NVDA_GREEN, width=1)) - - fig.add_trace(go.Pie( - labels=df[names], - values=df[values], - name=title, - marker=marker, - texttemplate=texttemplate,), row+1, col+1) - - if pie_pallette is None: - pie_pallette = default_pallete - - fig.update_traces( - hoverinfo='label+percent', - textinfo='value', - textfont_size=20, - hole=0.4, - textposition='inside',) - fig.update_layout(title=main_title, title_x=0.5, font_size=20,) - if do_show: - fig.show() - return fig diff --git a/tools/experimental/trt-engine-explorer/trex/report_card.py b/tools/experimental/trt-engine-explorer/trex/report_card.py deleted file mode 100644 index 816b758c0..000000000 --- a/tools/experimental/trt-engine-explorer/trex/report_card.py +++ /dev/null @@ -1,711 +0,0 @@ -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -""" -Performance report card. - -This file contains interaction cells for the engine_report_card.ipynb notebook. -""" - - -from typing import Dict -from functools import partial -import IPython.display -from ipywidgets import widgets -import pandas as pd -import plotly.express as px -import onnx -import trex.misc as misc -import trex.interactive as interactive -import trex.notebook as notebook -import trex.df_preprocessing as df_preprocessing -import trex.parser as parser -import trex.plotting as plotting -import trex.colors as colors -import trex.graphing as graphing -from trex import EnginePlan - - -def report_card_perf_overview(plan: EnginePlan) -> Dict[str, callable]: - """Prepare a dictionary of predefined performance queries. - - Each query is implemented via a callable. - Returns a dictionary of query names mapped to query callables.""" - layer_types = misc.group_count(plan.df, 'type') - count_per_layer_type = partial( - plotting.plotly_bar2, - layer_types, - values_col='count', - names_col='type', - color='type', colormap=colors.layer_colormap, - orientation='h', - show_axis_ticks=(True, True)) - - time_pct_by_type = plan.df.groupby(['type'])[ - ['latency.pct_time', 'latency.avg_time']].sum().reset_index() - latency_per_type_ms = partial( - plotting.plotly_bar2, - time_pct_by_type, - values_col='latency.avg_time', - names_col='type', - color='type', colormap=colors.layer_colormap, - orientation='h', - show_axis_ticks=(True, True)) - - latency_per_type_pct = partial( - plotting.plotly_bar2, - time_pct_by_type, - values_col='latency.pct_time', - names_col='type', - color='type', colormap=colors.layer_colormap, - orientation='h', - show_axis_ticks=(True, True)) - - precision_per_layer = partial( - plotting.plotly_bar2, - plan.df, - values_col='latency.avg_time', - names_col='Name', - color='precision', colormap=colors.precision_colormap, - xaxis_title="Layer") - - output_precision_per_layer = partial( - plotting.plotly_bar2, - plan.df, - values_col='latency.avg_time', - names_col='Name', - color='output_precision', colormap=colors.precision_colormap, - xaxis_title="Layer") - - output_precision_per_layer = partial( - plotting.plotly_bar2, - plan.df, - values_col='latency.avg_time', - names_col='Name', - color='output_precision', colormap=colors.precision_colormap, - xaxis_title="Layer") - - latency_distribution = partial( - plotting.plotly_hist, - plan.df, - values_col='latency.pct_time', - xaxis_title = 'Latency (ms)', - color='type', colormap=colors.layer_colormap) - - latency_per_layer = partial( - plotting.plotly_bar2, - plan.df, - values_col='latency.pct_time', - names_col='Name', - color='type', colormap=colors.layer_colormap, - xaxis_title="Layer") - - latency_per_layer_ms = partial( - plotting.plotly_bar2, - plan.df, - values_col='latency.avg_time', - names_col='Name', - color='type', colormap=colors.layer_colormap, - xaxis_title="Layer") - - precision_charts = [] - layer_precisions = misc.group_count(plan.df, 'precision') - precision_charts.append(( - layer_precisions, - 'Layer Count By Precision', - 'count', - 'precision')) - - layers_time_pct_by_precision = misc.group_sum_attr( - plan.df, - grouping_attr='precision', - reduced_attr='latency.pct_time') - - precision_charts.append(( - layers_time_pct_by_precision, - '% Latency By Precision', - 'latency.pct_time', - 'precision')) - - precision_statistics = partial( - plotting.plotly_pie2, - charts=precision_charts, - colormap=colors.precision_colormap) - - def precision_per_type(title, do_show: bool=True,): - title = f"{title}\n({plan.name})" - df = plan.df - precision_sunburst = df.groupby(['type', 'precision']).count().reset_index() - color = [colors.precision_colormap[p] for p in df['precision']] - fig = px.sunburst( - precision_sunburst, - path=['type', 'precision'], - values='Name', - color_discrete_map=colors.precision_colormap, - color='precision') - fig.update_layout(title=title, title_x=0.5, font_size=15,) - if do_show: - fig.show() - return fig - - dropdown_choices = { - "Latency per layer (%)": latency_per_layer, - "Latency per layer (ms)": latency_per_layer_ms, - "Layer latency distribution": latency_distribution, - "Precision per layer": precision_per_layer, - "Output precision per layer": output_precision_per_layer, - "Count per layer type": count_per_layer_type, - "Latency per layer type (ms)": latency_per_type_ms, - "Latency per layer type (%)": latency_per_type_pct, - "Precision per layer type": precision_per_type, - "Precision rollup": precision_statistics, - } - return dropdown_choices - - -def report_card_perf_overview_widget(plan: EnginePlan): - """Display performance overview diagrams. - - Display a dropdown widget to choose between diagrams showing various - characteristics of the plan's convolution layers.""" - - dropdown_choices = report_card_perf_overview(plan) - interactive.InteractiveDiagram_2(dropdown_choices, 'Diagram:') - - -def report_card_convolutions_overview(convs: pd.DataFrame) -> Dict[str, callable]: - """Prepare a dictionary of predefined queries on Convolution layers. - - Each query is implemented via a callable. - Returns a dictionary of query names mapped to query callables.""" - - latency_vs_ai_per_conv = partial( - plotting.plotly_bar2, - convs, - values_col='latency.pct_time', - names_col='Name', - color='attr.arithmetic_intensity', colormap=colors.precision_colormap) - latency_vs_prec_per_conv = partial( - plotting.plotly_bar2, - convs, - values_col='latency.pct_time', - names_col='Name', - color='precision', colormap=colors.precision_colormap) - latency_vs_fmas = partial( - plotting.plotly_bar2, - convs, - values_col='latency.pct_time', - names_col='Name', - color='attr.macs') - latency_vs_data = partial( - plotting.plotly_bar2, - convs, - values_col='latency.pct_time', - names_col='Name', - color='total_footprint_bytes') - latency_vs_ce_per_conv = partial( - plotting.plotly_bar2, - convs, - values_col='latency.pct_time', - names_col='Name', - color='attr.compute_efficiency', colormap=colors.precision_colormap) - latency_vs_group_size = partial( - plotting.plotly_bar2, - convs, - values_col='latency.pct_time', - names_col='Name', - color='attr.groups') - latency_vs_kernel_size = partial( - plotting.plotly_bar2, - convs, - values_col='latency.pct_time', - names_col='Name', - color='attr.kernel') - footprint_per_conv = partial( - plotting.plotly_bar2, - convs, - values_col='total_footprint_bytes', - names_col='Name', - color='latency.pct_time') - fmas_per_conv = partial( - plotting.plotly_bar2, - convs, - values_col='attr.macs', - names_col='Name', - color='latency.pct_time') - ai_vs_latency_per_conv = partial( - plotting.plotly_bar2, - convs, - values_col='attr.arithmetic_intensity', - names_col='Name', - color='attr.compute_efficiency') - ai_vs_footprint_per_conv = partial( - plotting.plotly_bar2, - convs, - values_col='attr.arithmetic_intensity', - names_col='Name', - color='attr.memory_efficiency') - ce_vs_latency_per_conv = partial( - plotting.plotly_bar2, - convs, - values_col='attr.compute_efficiency', - names_col='Name', - color='latency.pct_time') - me_vs_latency_per_conv = partial( - plotting.plotly_bar2, - convs, - values_col='attr.memory_efficiency', - names_col='Name', - color='latency.pct_time') - - dropdown_choices = { - "Latency per convolution (color = precision)": - latency_vs_prec_per_conv, - "Latency per convolution (color = FMAs)": - latency_vs_fmas, - "Latency per convolution (color = data size)": - latency_vs_data, - "Latency per convolution (color = arithmetic intensity)": - latency_vs_ai_per_conv, - "Latency per convolution (color = compute efficiency)": - latency_vs_ce_per_conv, - "Latency per convolution (color = group size)": - latency_vs_group_size, - "Latency per convolution (color = kernel size)": - latency_vs_kernel_size, - "Data footprints": footprint_per_conv, - "Fused Multiply-Accumulate (FMAs)": fmas_per_conv, - "Arithmetic intensity (color = latency)": - ai_vs_latency_per_conv, - "Arithmetic intensity (color = footprint)": - ai_vs_footprint_per_conv, - "Compute efficiency (color = latency)": - ce_vs_latency_per_conv, - "Memory efficiency (color = latency)": - me_vs_latency_per_conv, - } - return dropdown_choices - - -def report_card_convolutions_overview_widget(convs: pd.DataFrame): - """Display convolution layer diagrams. - - Display a dropdown widget to choose between diagrams showing various - characteristics of the plan's convolution layers""" - dropdown_choices = report_card_convolutions_overview(convs) - interactive.InteractiveDiagram_2(dropdown_choices, 'Diagram:') - - -def report_card_table_view(plan: EnginePlan): - """Layers tabular views. - - Display a dropdown widget to choose among tabular views of all, or specific, - layers""" - - def render_diagram(choice, ignore): - if choice == 'All': - notebook.display_df(df_preprocessing.clean_for_display(plan.df)) - else: - df = plan.get_layers_by_type(choice) - print(f"There are {len(df)} {choice} layers which account for" - f"{df['latency.pct_time'].sum(): .2f}% ({df['latency.avg_time'].sum(): .5f} ms) of the overall latency.") - notebook.display_df(df_preprocessing.clean_for_display(df)) - - types = ['All'] + list(set(plan.df['type'])) - dropdown_choices = {t: t for t in types} - interactive.InteractiveDiagram(render_diagram, dropdown_choices, 'Dataframe') - - -def report_card_memory_footprint(plan: EnginePlan): - """Memory footprint diagrams""" - - plotting.plotly_hist_wrap = partial(plotting.plotly_hist, - df=plan.df, - xaxis_title="Size (bytes)", - color='type', - colormap=colors.layer_colormap) - - plotting.plotly_bar2_wrap = partial(plotting.plotly_bar2, - plan.df, - names_col="Name", - color='type', - colormap=colors.layer_colormap, - show_axis_ticks=(False, True)) - - dropdown_choices = { - "Weights footprint per layer": partial(plotting.plotly_bar2_wrap, values_col='weights_size'), - "Activation footprint per layer": partial(plotting.plotly_bar2_wrap, values_col='total_io_size_bytes'), - "Total footprint per layer": partial(plotting.plotly_bar2_wrap, values_col='total_footprint_bytes'), - "Weights footprint distribution per layer": partial(plotting.plotly_hist_wrap, values_col='weights_size'), - "Activations footprint distribution per layer": partial(plotting.plotly_hist_wrap, values_col='total_io_size_bytes'), - "Total footprint distribution per layer": partial(plotting.plotly_hist_wrap, values_col='total_footprint_bytes'), - } - return dropdown_choices - - -def report_card_memory_footprint_widget(plan: EnginePlan): - dropdown_choices = report_card_memory_footprint(plan) - interactive.InteractiveDiagram_2(dropdown_choices, 'Diagram:') - - -def report_card_draw_plan_graph(plan: EnginePlan, engine_name: str): - """Draw the plan graph (export to SVG)""" - def render_diagram(choice, formatter, display_regions, expand_layer_details, display_layer_names=True): - graph = graphing.to_dot(plan, formatter, - display_regions=display_regions, - expand_layer_details=expand_layer_details, - display_layer_names=display_layer_names) - graphing.render_dot(graph, engine_name, 'svg') - - # Color code nodes by precision or layer-type - dropdown_choices = { - "Color nodes by type": (graphing.layer_type_formatter, False, False), - "Color nodes by type (detailed)": (graphing.layer_type_formatter, True, True), - "Color nodes by precision": (graphing.layer_precision_formatter, False, False), - "Color nodes by precision (detailed)": (graphing.layer_precision_formatter, True, True), - "Minimalistic": (graphing.layer_type_formatter, False, True, False), - } - - interactive.InteractiveDiagram(render_diagram, dropdown_choices, 'Color formatting:') - - -def report_card_pointwise_lint(plan: EnginePlan): - pws = plan.get_layers_by_type('PointWise') - - if len(pws) == 0: - print("The engine plan does not contain pointwise layers.") - return - - charts = [] - by_n_operations = misc.group_count(pws, 'attr.n_operations') - charts.append((by_n_operations, - "Pointwise layers by number of operations", 'count', 'attr.n_operations')) - - layers_time_pct_by_n_operations = misc.group_sum_attr( - pws, - grouping_attr='attr.n_operations', - reduced_attr='latency.pct_time') - - charts.append(( - layers_time_pct_by_n_operations, - "Latency by number of operations (%)", - 'latency.pct_time', - 'attr.n_operations')) - - df = layers_time_pct_by_n_operations.merge(by_n_operations, on='attr.n_operations') - df['per_op_latency'] = df['latency.pct_time'] / (df['count'] * df['attr.n_operations']) - pws['per_op_latency'] = pws['latency.pct_time'] / pws['attr.n_operations'] - - charts.append(( - df, - "Per op latency by number of operations", - 'per_op_latency', - 'attr.n_operations')) - - def list_pw_operations(pws): - for _, pw in pws.iterrows(): - if pw['attr.n_operations'] < 2: - continue - operations = "\n\t".join([op for op in pw['attr.operations']]) - print(f"{pw.name}\n\t{operations}") - - plotting.plotly_pie2("Pointwise Statistics", charts) - list_pw_operations(pws) - print(pws['per_op_latency']) - - -def layer_latency_sunburst(df: pd.DataFrame, title: str, do_show: bool=True): - precision_sunburst = df.groupby(['type', 'latency.pct_time']).count().reset_index() - - fig = px.sunburst( - precision_sunburst, - path=['type', 'latency.pct_time'], - values='latency.avg_time', - color_discrete_map=colors.layer_colormap, - color='type') - fig.update_layout(title=title, title_x=0.5, font_size=15,) - if do_show: - fig.show() - return None - return fig - - -def plot_engine_timings(timing_json_file: str, do_show: bool=True): - """Plot the engine profiling timings""" - latencies = parser.read_timing_file(timing_json_file) - samples = range(len(latencies)) - - fig = px.scatter( - title="Engine Timing Samples", - x=samples, y=latencies) - plotting.trex_base_layout(fig) - fig.update_layout({ - 'yaxis_title': "Latency (ms)", - 'xaxis_title': "Timing Samples", - 'title_x': 0.5}) - if do_show: - fig.show() - return None - return fig - - -def report_card_gemm_MNK(plan: pd.DataFrame): - def render_scatter3d(choice, x, y, z, color, size): - convs = plan.get_layers_by_type('Convolution') - fig = px.scatter_3d(convs, x=x, y=y, z=z, color=color, size=size, - size_max=18, opacity=0.7) - plotting.trex_base_layout(fig) - fig.update_layout({ - 'title': "Implicit GEMM " + choice, - 'title_x': 0.5}) - fig.show() - - dropdown_choices = { - "MxNxK color=mean time; size=mean time": - ('attr.M', 'attr.N', 'attr.K', 'latency.avg_time', 'latency.avg_time',), - "MxNxK color=arithmetic intensity; size=mean time": - ('attr.M', 'attr.N', 'attr.K', 'attr.arithmetic_intensity', 'latency.avg_time',), - "MxNxK color=compute efficiency; size=mean time": - ('attr.M', 'attr.N', 'attr.K', 'attr.compute_efficiency', 'latency.avg_time',), - "MxNxK color=memory efficiency; size=mean time": - ('attr.M', 'attr.N', 'attr.K', 'attr.memory_efficiency', 'latency.avg_time',), - } - - interactive.InteractiveDiagram(render_scatter3d, dropdown_choices, 'Diagram') - - -def report_card_gemm_MNK_scatter(plan: pd.DataFrame): - def render_scatter(choice, x, y, color, size): - convs = plan.get_layers_by_type('Convolution') - fig = px.scatter(convs, x=x, y=y, color=color, size=size, size_max=18, opacity=0.7) - fig.update_layout( - margin=dict(l=0, r=0, b=0, t=50), - title=choice, title_x=0.5) - fig.show() - - dropdown_choices = { - "M vs. Latency (color = foorprint)": ( - 'attr.M', 'latency.avg_time', 'total_footprint_bytes', None), - "N vs. Latency (color = foorprint)": ( - 'attr.N', 'latency.avg_time', 'total_footprint_bytes', None), - "K vs. Latency (color = foorprint)": ( - 'attr.K', 'latency.avg_time', 'total_footprint_bytes', None), - - } - - interactive.InteractiveDiagram(render_scatter, dropdown_choices, 'Diagram') - - -def report_card_efficiency_vs_latency_3d(plan: pd.DataFrame): - convs = plan.get_layers_by_type('Convolution') - fig = px.scatter_3d( - convs, - x='attr.compute_efficiency', y='attr.memory_efficiency', z='attr.arithmetic_intensity', - color='total_footprint_bytes', size='latency.avg_time', size_max=18, - opacity=0.7) - - fig.update_layout( - margin=dict(l=0, r=0, b=0, t=50), - title="Compute-efficiency vs Memory-efficiency vs Latency", - title_x=0.5) - - plotting.trex_base_layout(fig) - fig.show() - - -def report_card_perf_scatter(plan: pd.DataFrame): - def render_scatter(choice, x, y, color, size): - convs = plan.get_layers_by_type('Convolution') - fig = px.scatter(convs, x=x, y=y, color=color, size=size, size_max=18, opacity=0.7) - fig.update_layout( - margin=dict(l=0, r=0, b=0, t=50), - title=choice, title_x=0.5) - fig.show() - - dropdown_choices = { - "Compute-efficiency vs. FMAs (size = foorprint)": ( - 'attr.compute_efficiency', 'attr.macs', 'latency.avg_time', 'total_footprint_bytes',), - - "Memory-efficiency vs. footprint (size = FMAs)": ( - 'attr.memory_efficiency', 'total_footprint_bytes', 'latency.avg_time', 'attr.macs',), - - "Compute-efficiency vs. memory-efficiency (size = AI)": ( - 'attr.compute_efficiency', 'attr.memory_efficiency', 'latency.avg_time', 'attr.arithmetic_intensity'), - - "Memory footprint vs FMAs (size = AI)": ( - 'total_footprint_bytes', 'attr.macs', 'latency.avg_time', 'attr.arithmetic_intensity'), - - "Arithmetic-intensity vs. compute-efficiency (size = memory-efficiency)": ( - 'attr.arithmetic_intensity', 'attr.compute_efficiency', 'latency.avg_time', 'attr.memory_efficiency'), - - "Arithmetic-intensity vs. memory-efficiency (size = compute-efficiency)": ( - 'attr.arithmetic_intensity', 'attr.memory_efficiency', 'latency.avg_time', 'attr.compute_efficiency'), - } - - interactive.InteractiveDiagram(render_scatter, dropdown_choices, 'Diagram') - - -def report_card_reformat_overview(plan: EnginePlan): - """Bar diagrams showing how Reformat layers are used, by their origin""" - reformats = plan.get_layers_by_type('Reformat') - reformats_origins_cnt = misc.group_count(reformats, 'attr.origin') - reformats_origins_cnt_widget = partial( - plotting.plotly_bar2, - df=reformats_origins_cnt, - values_col='count', - names_col='attr.origin', - color='attr.origin', - orientation='h', - show_axis_ticks=(True, True)) - - avg_time_by_origin = misc.group_sum_attr(reformats, 'attr.origin', 'latency.avg_time') - avg_time_by_origin_widget = partial( - plotting.plotly_bar2, - df=avg_time_by_origin, - values_col='latency.avg_time', - names_col='attr.origin', - color='attr.origin', - orientation='h', - show_axis_ticks=(True, True)) - - pct_time_by_origin = misc.group_sum_attr(reformats, 'attr.origin', 'latency.pct_time') - pct_time_by_origin_widget = partial( - plotting.plotly_bar2, - df=pct_time_by_origin, - values_col='latency.pct_time', - names_col='attr.origin', - color='attr.origin', - orientation='h', - show_axis_ticks=(True, True)) - - dropdown_choices = { - "Reformat - Count by origin": reformats_origins_cnt_widget, - "Reformat - Latency by origin": avg_time_by_origin_widget, - "Reformat - Percent latency by origin": pct_time_by_origin_widget, - } - - return interactive.InteractiveDiagram_2(dropdown_choices, 'Diagram:') - - -def report_card_draw_plan_graph_extended(plan, engine_name): - """Interactive widgets for configuring graph rendering options. - """ - display_regions_widget = widgets.Checkbox(value=False, description="Regions: Render regions") - display_region_reuse_widget = widgets.Checkbox(value=True, description="Regions: Render regions reuse") - display_forking_regions_widget = widgets.Checkbox(value=True, description="Regions: Render forking regions") - display_region_names_widget = widgets.Checkbox(value=False, description="Regions: Render names") - display_layer_names_widget = widgets.Checkbox(value=False, description="Layers: Render names") - stack_layer_names_widget = widgets.Checkbox(value=True, description="Layers: Stack names") - display_matadata_widget = widgets.Checkbox(value=True, description="Layers: Prefer metadata over name") - expand_layer_details_widget = widgets.Checkbox(value=True, description="Layers: Expand layer details") - display_latency_widget = widgets.Checkbox(value=True, description="Layers: Render latency") - display_constants_widget = widgets.Checkbox(value=False, description="Graph: Render constant inputs") - display_edge_name_widget = widgets.Checkbox(value=False, description="Edges: Render names") - display_edge_details_widget = widgets.Checkbox(value=True, description="Edges: Render details") - - latency_metric_choice_widget = widgets.Dropdown( - options=graphing.latency_types, - value=graphing.latency_types[0], - description="Latency metric", - disabled=False, - ) - - color_choice_widget = widgets.Dropdown( - options=["Layer Type", "Layer Precision"], - value="Layer Type", - description="Layers color", - disabled=False, - ) - - layer_node_renderers_widget = widgets.Dropdown( - options=[k for k in graphing.layer_node_renderers.keys()], - value="Configurable", - description="Layer renderer", - disabled=False, - ) - - svg_button = widgets.Button(description="Render SVG Graph") - onnx_button = widgets.Button(description="Render ONNX Graph") - output = widgets.Output() - - ui = widgets.VBox([ - widgets.HBox([ - widgets.VBox([ - layer_node_renderers_widget, - display_constants_widget, - ]), - widgets.VBox([ - color_choice_widget, - display_latency_widget, - latency_metric_choice_widget, - expand_layer_details_widget, - display_layer_names_widget, - stack_layer_names_widget, - display_matadata_widget, - ]), - widgets.VBox([ - display_regions_widget, - display_region_reuse_widget, - display_forking_regions_widget, - display_region_names_widget, - ]), - widgets.VBox([ - display_edge_name_widget, - display_edge_details_widget, - ]), - ]), - widgets.HBox([ - svg_button, - onnx_button]), - ]) - - @output.capture() - def on_svg_button_clicked(b): - IPython.display.clear_output(wait=True) - formatter = graphing.layer_type_formatter if color_choice_widget.value == "Layer Type" else layer_precision_formatter - graph = graphing.to_dot( - plan, - formatter, - layer_node_renderer = graphing.layer_node_renderers[layer_node_renderers_widget.value], - display_regions=display_regions_widget.value, - expand_layer_details=expand_layer_details_widget.value, - display_layer_names=display_layer_names_widget.value, - stack_layer_names=stack_layer_names_widget.value, - display_constants=display_constants_widget.value, - display_latency=display_latency_widget.value, - latency_type=latency_metric_choice_widget.value, - display_region_reuse=display_region_reuse_widget.value, - display_region_names=display_region_names_widget.value, - display_forking_regions=display_forking_regions_widget.value, - display_edge_name=display_edge_name_widget.value, - display_edge_details=display_edge_details_widget.value, - display_matadata=display_matadata_widget.value, - ) - print("DOT graph ready.\nGenerating SVG... ", end="") - graphing.render_dot(graph, engine_name, 'svg') - - def on_onnx_button_clicked(b): - fname = 'trex_model.onnx' - graph = graphing.OnnxGraph(plan, display_forking_regions=False ) - onnx.save(graph.onnx_model, fname) - import netron - netron.start(fname, 8082) - - IPython.display.display(output, ui) - svg_button.on_click(on_svg_button_clicked) - onnx_button.on_click(on_onnx_button_clicked) - diff --git a/tools/experimental/trt-engine-explorer/utils/README.md b/tools/experimental/trt-engine-explorer/utils/README.md deleted file mode 100644 index 26438900e..000000000 --- a/tools/experimental/trt-engine-explorer/utils/README.md +++ /dev/null @@ -1,65 +0,0 @@ -# Utility scripts - * [process_engine.py](#process-enginepy) - * [draw_engine.py](#draw-enginepy) - * [parse_trtexec_log.py](#parse-trtexec-logpy) - -
    - -## process_engine.py - -`process_engine.py` is used to: -1. Build a TensorRT engine from an ONNX file. -2. Profile an engine plan file. -3. Generate JSON files for exploration with trex. -4. Draw an SVG graph from an engine. - -``` -usage: process_engine.py [-h] [--print_only] [--build_engine] [--profile_engine] [--draw_engine] input outdir [trtexec [trtexec ...]] - -Utility to build and profile TensorRT engines - -positional arguments: - input input file (ONNX or engine) - outdir directory to store output artifacts - trtexec trtexec commands not including the preceding -- (e.g. int8 shapes=input_ids:32x512,attention_mask:32x512 - -optional arguments: - -h, --help show this help message and exit - --print_only print the command-line and exit - --build_engine, -b build the engine - --profile_engine, -p engine the engine - --draw_engine, -d draw the engine -``` - -The script can run the entire ONNX to JSON files pipeline, or it can execute a single sub-command. For example, the following command line builds and profiles an engine from the ONNX model stored in a file named `my_onnx.onnx`: -``` -$ process_engine.py my_onnx.onnx outputs_dir int8 -``` - -This will generate the following files in directory `outputs_dir`: -* `my_onnx.onnx.engine` - the built engine file. -* `my_onnx.onnx.engine.build.log` - trtexec engine building log. -* `my_onnx.onnx.engine.build.metadata.json` - JSON of metadata parsed from the build log. -* `my_onnx.onnx.engine.graph.json` - JSON of engine graph. -* `my_onnx.onnx.engine.graph.json.svg` - SVG diagram of engine graph. -* `my_onnx.onnx.engine.profile.json` - JSON of engine layers profiling. -* `my_onnx.onnx.engine.profile.log` - trtexec engine profiling log. -* `my_onnx.onnx.engine.profile.metadata.json` - JSON of metadata parsed from the profiling log. -* `my_onnx.onnx.engine.timing.json` - JSON of engine profiling iteration timing. - - -Requirements: -* Path to trtexec binary is in $PATH. -* trex is installed (for graph drawing). -* Graphviz is installed (for graph drawing). -``` -$ sudo apt-get --yes install graphviz -``` - -## draw_engine.py - -This script generates an SVG diagram of the input engine graph SVG file. - -## parse_trtexec_log.py - -This script parses `trtexec` log files and creates metadata JSON files from the information extracted from the logs. diff --git a/tools/experimental/trt-engine-explorer/utils/config_gpu.py b/tools/experimental/trt-engine-explorer/utils/config_gpu.py deleted file mode 100755 index a5da7ff42..000000000 --- a/tools/experimental/trt-engine-explorer/utils/config_gpu.py +++ /dev/null @@ -1,237 +0,0 @@ -#!/usr/bin/env python3 -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -""" -This file contains code to configure the GPU's working clocks. -""" - - -from typing import Tuple -from threading import Thread, Condition -from enum import Enum, auto -import logging -from contextlib import contextmanager -import pynvml - - -@contextmanager -def nvmlContext(*args, **kwds): - gpu_id = args[0] - pynvml.nvmlInit() - handle = pynvml.nvmlDeviceGetHandleByIndex(gpu_id) - try: - yield handle - finally: - pynvml.nvmlShutdown() - - -class GPUMonitor(): - """Monitor GPU activity""" - - def _sample_gpu_state(handle): - assert handle - try: - temp = pynvml.nvmlDeviceGetTemperature(handle, pynvml.NVML_TEMPERATURE_GPU) - util_rate = pynvml.nvmlDeviceGetUtilizationRates(handle) - pwr = pynvml.nvmlDeviceGetPowerUsage(handle) // 1000 - cps = pynvml.nvmlDeviceGetComputeRunningProcesses(handle) - # Clock list: https://github.com/nicolargo/nvidia-ml-py3/blob/master/pynvml.py#L95 - sm_clock_mhz = pynvml.nvmlDeviceGetClockInfo(handle, pynvml.NVML_CLOCK_SM) - mem_clock_mhz = pynvml.nvmlDeviceGetClockInfo(handle, pynvml.NVML_CLOCK_MEM) - graphics_clock_mhz = pynvml.nvmlDeviceGetClockInfo(handle, pynvml.NVML_CLOCK_GRAPHICS) - print(f"pwr: {pwr} temp: {temp} util_rate.gpu={util_rate.gpu} " - f"util_rate.memory={util_rate.memory} cnt processes={len(cps)} " - f"Clocks: sm={sm_clock_mhz} mem={mem_clock_mhz} graphics={graphics_clock_mhz}") - - # Throttle reasons - # https://github.com/nicolargo/nvidia-ml-py3/blob/master/pynvml.py#L570 - # https://docs.nvidia.com/deploy/nvml-api/group__nvmlClocksThrottleReasons.html - tr = (pynvml.nvmlDeviceGetCurrentClocksThrottleReasons(handle)) - # GPU is idle - tr_idle = tr & pynvml.nvmlClocksThrottleReasonGpuIdle - # GPU clocks are limited by current setting of applications clocks - tr_appsettings = tr & pynvml.nvmlClocksThrottleReasonApplicationsClocksSetting - # SW Power Scaling algorithm is reducing the clocks below requested clocks - tr_sw_power = tr & pynvml.nvmlClocksThrottleReasonSwPowerCap - # HW Power Brake Slowdown (reducing the core clocks by a factor of 2 or more) is engaged - # This is an indicator of: External Power Brake Assertion being triggered (e.g. by the system power supply) - tr_hw_slowdown = tr & pynvml.nvmlClocksThrottleReasonHwSlowdown - if tr: - print(f"Throttling = idle={tr_idle} app={tr_appsettings} " - f"power={tr_sw_power} hardware={tr_hw_slowdown}") - except pynvml.nvml.NVMLError as e: - logging.warning(f"Could not read GPU state") - - def gpu_monitor(self): - self.done_cond.acquire() - with nvmlContext(self.gpu_id) as handle: - while not self.is_done: - self._sample_gpu_state(handle) - self.done_cond.wait(self.sampling_interval) - self.done_cond.release() - - def __init__(self, enabled: bool, gpu_id: int=0, sampling_interval: float=1.): - self.enabled = enabled - if not enabled: - return - self.gpu_id = gpu_id - self.sampling_interval = sampling_interval - self.is_done = False - self.monitor = Thread(target=self.gpu_monitor) - self.done_cond = Condition() - - def __enter__(self): - if not self.enabled: - return - self.monitor.start() - - def __exit__(self, exc_type, exc_value, exc_traceback): - if not self.enabled: - return - self.done_cond.acquire() - self.is_done = True - self.done_cond.notify() - self.done_cond.release() - self.monitor.join() - - -class GPUConfigurator(): - """GPU configuration interface""" - class Key(Enum): - INIT_POWER_LIMIT = auto() - MIN_POWER_LIMIT = auto() - MAX_POWER_LIMIT = auto() - MEMORY_CLOCK = auto() - COMPUTE_CLOCK = auto() - - def __init__( - self, - power_limit: float=None, - compute_clk: int=None, - memory_clk: int=None, - gpu_id: int=0, - dont_lock_clocks: bool=False - ): - self.power_limit = power_limit - self.compute_clk = compute_clk - self.memory_clk = memory_clk - self.gpu_id = gpu_id - self.dont_lock_clocks = dont_lock_clocks - self.set_power_limit = power_limit is not None - self.set_lock = (compute_clk is not None and memory_clk is not None) and (not dont_lock_clocks) - if self.set_power_limit: - with nvmlContext(self.gpu_id) as handle: - self.power_readings_stats = self._extract_power_limits(handle) - - def __enter__(self): - with nvmlContext(self.gpu_id) as handle: - if self.set_power_limit: - self._set_power_limit(handle, self.power_limit) - if self.set_lock: - self._lock_clocks(handle) - - def __exit__(self, exc_type, exc_value, exc_traceback): - with nvmlContext(self.gpu_id) as handle: - if self.set_power_limit: - init_power_limit = self.power_readings_stats[self.Key.INIT_POWER_LIMIT] - self._set_power_limit(handle, init_power_limit) - if self.set_lock: - self._unlock_clocks(handle) - - # Helper functions - def _extract_power_limits(self, handle): - def to_watt(power_milliwatt: int): - return power_milliwatt // 1000 - - min_power_limit, max_power_limit = pynvml.nvmlDeviceGetPowerManagementLimitConstraints(handle) - try: - cur_power_limit = pynvml.nvmlDeviceGetPowerManagementLimit(handle) - except pynvml.nvml.NVMLError as e: - logging.warning(f"Could read power limit constraints ({e}).") - cur_power_limit = pynvml.nvmlDeviceGetPowerManagementDefaultLimit(handle) - - # Power limit stats in Watts. - power_readings_stats = { - self.Key.INIT_POWER_LIMIT: to_watt(cur_power_limit), - self.Key.MIN_POWER_LIMIT: to_watt(min_power_limit), - self.Key.MAX_POWER_LIMIT: to_watt(max_power_limit), - } - return power_readings_stats - - def _set_power_limit(self, handle, power_limit: float): - def to_milliwatt(power_watt: int): - return power_watt * 1000 - - min_power_limit = self.power_readings_stats[self.Key.MIN_POWER_LIMIT] - max_power_limit = self.power_readings_stats[self.Key.MAX_POWER_LIMIT] - power_limit = min(max_power_limit, max(min_power_limit, power_limit)) - logging.warning(f"Setting power limit to {power_limit} Watts") - try: - power_limit = to_milliwatt(power_limit) - pynvml.nvmlDeviceSetPowerManagementLimit(handle, power_limit) - except pynvml.nvml.NVMLError_InvalidArgument as e: - self.set_power_limit = False - logging.warning(f"Could not set power limits ({e})\n" - f"\twhile using power_limit = {power_limit} Watts\n" - "\tTry different power limit.") - except pynvml.nvml.NVMLError as e: - self.set_power_limit = False - logging.warning(f"Could not set power limits ({e}).") - - - def _lock_clocks(self, handle): - try: - pynvml.nvmlDeviceSetApplicationsClocks(handle, self.memory_clk, self.compute_clk) - pynvml.nvmlDeviceSetGpuLockedClocks(handle, - minGpuClockMHz=self.compute_clk, - maxGpuClockMHz=self.compute_clk) - logging.warning(f"Set max memory clock = {self.memory_clk} MHz") - logging.warning(f"Set max compute clock = {self.compute_clk} MHz") - logging.warning(f"Locked graphics clock = {self.compute_clk} MHz") - except pynvml.nvml.NVMLError_NoPermission as e: - logging.warning(f"Could not lock clocks ({e}).\n" - "\tTry running as root or locking the clocks from the commandline:\n" - f"\t\tsudo nvidia-smi --lock-gpu-clocks={self.compute_clk},{self.compute_clk}\n" - f"\t\tsudo nvidia-smi --applications-clocks={self.memory_clk},{self.compute_clk}") - except pynvml.nvml.NVMLError_InvalidArgument as e: - logging.warning(f"Could not lock clocks ({e})\n" - f"\twhile using memory clock = {self.memory_clk} MHz\n" - f"\tand using compute clock = {self.compute_clk} MHz\n" - "\tTry different clocks frequencies.") - except pynvml.nvml.NVMLError as e: - logging.warning(f"Could not lock clocks ({e}).") - - def _unlock_clocks(self, handle): - try: - pynvml.nvmlDeviceResetGpuLockedClocks(handle) - pynvml.nvmlDeviceResetApplicationsClocks(handle) - logging.warning(f"Unlocked device clocks.") - except pynvml.nvml.NVMLError as e: - logging.warning(f"Could not unlock clocks ({e}).\n" - "\tTry running as root or unlocking the clocks from the commandline:\n" - "\t\tsudo nvidia-smi --reset-gpu-clocks\n" - "\t\tsudo nvidia-smi --reset-applications-clocks") - - -def get_max_clocks(dev: int) -> Tuple[int, int]: - with nvmlContext(dev) as handle: - mem_clks = pynvml.nvmlDeviceGetSupportedMemoryClocks(handle) - max_mem_clk = mem_clks[0] - gr_clocks = pynvml.nvmlDeviceGetSupportedGraphicsClocks(handle, max_mem_clk) - max_gr_clk = gr_clocks[0] - return max_mem_clk, max_gr_clk diff --git a/tools/experimental/trt-engine-explorer/utils/device_info.py b/tools/experimental/trt-engine-explorer/utils/device_info.py deleted file mode 100755 index 2939b0083..000000000 --- a/tools/experimental/trt-engine-explorer/utils/device_info.py +++ /dev/null @@ -1,72 +0,0 @@ -#!/usr/bin/env python3 -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - - -import json -import argparse - - -def device_info(json_fname: str): - def install_pycuda(json_fname: str): - '''trex does not install pycuda by default, because pycuda requires a CUDA - installation, which limits where trex can be used. However, pycuda is - mandatory for querying device information.''' - - print("pycuda is not installed - attempting installation.\n") - import subprocess - try: - subprocess.run(f"python3 -m pip install pycuda".split(), check=True) - except subprocess.CalledProcessError: - print("\nFailed to install pycuda.") - print("If CUDA is installed on your machine, try adding to $PATH ", end="") - print("the path to nvcc (e.g. try `which nvcc`)") - print(f"\nExiting: file {json_fname} was not generated!") - exit(1) - - try: - import pycuda.driver as drv - except ImportError: - install_pycuda(json_fname) - import pycuda.driver as drv - - drv.init() - print (f"Device info: {drv.Device.count()} device(s) found") - - devices_metadata = [] - for i in range(drv.Device.count()): - dev = drv.Device(i) - metadata = { - 'Name': dev.name(), - 'ComputeCapability': dev.compute_capability(), - 'TotalMemory': dev.total_memory(), - "CUDA_VERSION": drv.get_version(), - } - for k, v in dev.get_attributes().items(): - metadata[str(k)] = v - devices_metadata.append(metadata) - - with open(json_fname, 'w') as fout: - json.dump(devices_metadata , fout) - print(f"Device info: generated output file {json_fname}") - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument('input', help="name of output json file") - args = parser.parse_args() - device_info(json_fname=args.input) diff --git a/tools/experimental/trt-engine-explorer/utils/draw_engine.py b/tools/experimental/trt-engine-explorer/utils/draw_engine.py deleted file mode 100755 index becba589d..000000000 --- a/tools/experimental/trt-engine-explorer/utils/draw_engine.py +++ /dev/null @@ -1,249 +0,0 @@ -#!/usr/bin/env python3 -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - - -r""" -This script generates an SVG diagram of the input engine graph SVG file. - - -$ trex draw mymodel.graph.json -pj=mymodel.profile.json -dm -dr -dc --query="transformer_blocks.(12|37).*" -$ trex draw mymodel.graph.json -pj=/mymodel.profile.json -dm --query="single_transformer_blocks\.8[a-zA-Z]* - -Note: this script requires graphviz which can be installed manually: - $ sudo apt-get --yes install graphviz - $ python3 -m pip install graphviz -""" - - -import os -import re -import warnings -import argparse -import shutil -import subprocess -from typing import Tuple -import networkx as nx -import trex.graphing -import trex.engine_plan - - -def convert_dot_to_svg(dot_file, svg_file): - try: - subprocess.run(['dot', '-Tsvg', dot_file, '-o', svg_file], check=True) - print(f"Successfully converted {dot_file} to {svg_file}") - except subprocess.CalledProcessError as e: - print(f"An error occurred: {e}") - -def create_directed_subgraph(G: nx.DiGraph, center_node: str, radius: int): - """ - Create a directed subgraph containing all nodes within a given radius - from the center node, preserving edge directionality. - - Parameters: - G (nx.DiGraph): The original directed graph. - center_node (node): The node to center the subgraph around. - radius (int): The maximum distance from the center node. - - Returns: - nx.DiGraph: A subgraph containing nodes within the radius and their edges. - """ - try: - # Perform BFS to find nodes within the radius - fwd_beam = nx.single_source_shortest_path_length(G, center_node, cutoff=radius).keys() - bwd_beam = nx.single_source_shortest_path_length(G.reverse(copy=False), center_node, cutoff=radius).keys() - nodes_within_radius = list(fwd_beam) + list(bwd_beam) - - # Create a subgraph induced on these nodes - subgraph = G.subgraph(nodes_within_radius) - return subgraph - except nx.exception.NodeNotFound: - return None - - -def query_layers(plan, G: nx.DiGraph, query: str): - matched_nodes = [] - for name, data in G.nodes(data=True): - found = re.search(query, name) or re.search(query, data["tooltip"]) - if found: - radius = 1 - fwd_beam = nx.single_source_shortest_path_length(G, name, cutoff=radius).keys() - bwd_beam = nx.single_source_shortest_path_length(G.reverse(copy=False), name, cutoff=radius).keys() - nodes_within_radius = list(fwd_beam) + list(bwd_beam) - matched_nodes += nodes_within_radius - - # Create a subgraph induced on these nodes - print(f"Found {len(matched_nodes)} matching nodes") - subgraph = G.subgraph(matched_nodes) - return subgraph - - -def nx_from_dot(graph, engine_json_fname): - output_fname = os.path.abspath(f"{engine_json_fname}.dot") - with open(output_fname, 'w') as f: - f.write(str(graph).replace(':', ';')) # Replace colons for safety - print("Computing subgraph...") - G = nx.DiGraph(nx.nx_pydot.read_dot(output_fname)) - return G, output_fname - - -def nx_to_svg(G, output_fname): - print("Rendering subgraph...") - nx.nx_pydot.write_dot(G, output_fname + ".radius.dot") - convert_dot_to_svg(output_fname + ".radius.dot", output_fname + ".radius.dot.svg") - - -def draw_subgraph_beam(plan, graph, engine_json_fname, center_op, radius): - G, output_fname = nx_from_dot(graph, engine_json_fname) - if G is None: - print("Internal error: could not generate nx.DiGraph") - return - neighbors = create_directed_subgraph(G, center_op, radius=radius) - if not neighbors: - print(f"Could not find {center_op}") - return - nx_to_svg(neighbors, output_fname) - - -def draw_subgraph_query(plan, graph, engine_json_fname, query): - G, output_fname = nx_from_dot(graph, engine_json_fname) - if G is None: - print("Internal error: could not generate nx.DiGraph") - return - neighbors = query_layers(plan, G, query) - if not neighbors: - print(f"Could not find any matches for query: {query}") - return - nx_to_svg(neighbors, output_fname) - - -def draw_engine(engine_json_fname: str, - profiling_json_fname: str=None, - output_format: str=None, - beam_center: str=None, - beam_radius: int=0, - query: str=None, - **kwargs -): - graphviz_is_installed = shutil.which("dot") is not None - if not graphviz_is_installed: - print("graphviz is required but it is not installed.\n") - print("To install on Ubuntu:") - print("sudo apt --yes install graphviz") - exit() - - try: - if kwargs["display_constants"] and not kwargs["display_regions"]: - warnings.warn("Ignoring argument --display_constants because it requires --display_regions.") - except KeyError: - pass - - try: - if kwargs["display_region_names"] and not kwargs["display_regions"]: - kwargs["display_regions"] = True - except KeyError: - pass - plan = trex.engine_plan.EnginePlan(engine_json_fname, profiling_file=profiling_json_fname) - layer_node_formatter = trex.graphing.layer_type_formatter - graph = trex.graphing.to_dot(plan, layer_node_formatter, **kwargs) - output_format = output_format or "svg" - - def write_dot(): - output_fname = os.path.abspath(f"{engine_json_fname}.dot") - with open(output_fname, 'w') as f: - f.write(str(graph)) - - if output_format == "dot": - write_dot() - elif beam_center and beam_radius >0: - write_dot() - draw_subgraph_beam(plan, graph, engine_json_fname, beam_center, beam_radius) - elif query: - write_dot() - draw_subgraph_query(plan, graph, engine_json_fname, query) - else: - trex.graphing.render_dot(graph, engine_json_fname, output_format) - - -def make_subcmd_parser(subparsers): - draw = lambda args: draw_engine( - engine_json_fname=args.input, - profiling_json_fname=args.profiling_json, - output_format=args.output_format, - beam_center=args.beam[0], - beam_radius=args.beam[1], - query=args.query, - # Arguments to DotGraph - display_forking_regions=True, - display_region_reuse=True, - display_edge_details=False, - remove_disconnected_layers=True, - highlight_layers=[args.beam[0]], - display_regions=args.display_regions, - display_layer_names=not args.no_layer_names, - display_constants=args.display_constant, - display_metadata=args.display_metadata, - display_region_names=args.display_region_names, - ) - draw_parser = subparsers.add_parser("draw", help="Draw a TensorRT engine.") - draw_parser.set_defaults(func=draw) - _make_parser(draw_parser) - - -def parse_beam(value) -> Tuple[str, int]: - try: - beam_center, beam_radius = value.split(',') - return beam_center, int(beam_radius) - except ValueError: - raise argparse.ArgumentTypeError( - "Beam must be in the format 'some_string,integer'") - - -def _make_parser(parser): - parser.add_argument("input", help="name of engine JSON file to draw.") - parser.add_argument("--profiling_json", "-pj", - default=None, help="name of engine JSON file to draw") - parser.add_argument("--output_format", "-of", - default="svg", choices=["svg", "png", "dot"], help="output formats") - parser.add_argument("--display_regions", "-dr", - action='store_true', help="render memory regions as graph nodes.") - parser.add_argument("--display_constant", "-dc", - action='store_true', help="render constant input tensors.") - parser.add_argument("--no_layer_names", "-no_ln", - action='store_true', help="don't render layer names.") - parser.add_argument("--display_metadata", "-dm", - action='store_true', help="display metadata.") - parser.add_argument("--display_region_names", "-drn", - action='store_true', help="display region names.") - parser.add_argument("--beam", "-b", default=("",0), - type=parse_beam, - help="draw the induced subgraph of neighbors centered at node n within a given radius.") - parser.add_argument("--query", "-q", - help="draw the induced subgraph of all nodes whose name or metadata matches the regular expression.") - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - args = parser.parse_args(_make_parser(parser)) - - draw_engine( - engine_json_fname=args.input, - profiling_json_fname=args.profiling_json, - display_regions=True, - expand_layer_details=False, - display_latency=True, - ) diff --git a/tools/experimental/trt-engine-explorer/utils/parse_trtexec_log.py b/tools/experimental/trt-engine-explorer/utils/parse_trtexec_log.py deleted file mode 100755 index ab93b1599..000000000 --- a/tools/experimental/trt-engine-explorer/utils/parse_trtexec_log.py +++ /dev/null @@ -1,165 +0,0 @@ -#!/usr/bin/env python3 -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - - -""" -trtexec log file parsing -""" - - -import re -from typing import Tuple, List, Dict, Any -import argparse -import trex.archiving as archiving - - -def __to_float(line: str) -> float: - """Scan the input string and extract the first float instance.""" - # https://docs.python.org/3/library/re.html#simulating-scanf - float_match = re.search(r"[-+]?(\d+(\.\d*)?|\.\d+)([eE][-+]?\d+)?", line) - if float_match is None: - raise ValueError - start, end = float_match.span() - return float(line[start : end]) - - -def __get_stats(line: str) -> List[float]: - """Parse a string containing pairs of "key = value" and return the list of values. - - Here's a sample input line: "min = 0.87854 ms, max = 0.894043 ms, mean = 0.881251 ms" - The values are expected to be floats. - Split the kv list to "k = v" substrings, then split each substring to - k, v and return float(v) - """ - return [__to_float(substr.split("=")[1]) for substr in line.split(",")] - - -class FileSection: - def __init__(self, section_header: str): - self.section_header = section_header - self.dict = {} - - def entered_section(self, line: str): - s = re.search(self.section_header, line) - return s is not None - - def parse_line(self, line: str) -> bool: - def parse_kv_line(line: str) -> Tuple[Any, Any]: - """Parse a log line that reports a key-value pair. - - The log line has this format: [mm/dd/yyyy-hh:mm:ss] [I] key_name: key_value - """ - match = re.search(r'(\[\d+/\d+/\d+-\d+:\d+:\d+\] \[I\] )', line) - if match is not None: - match_end = match.span()[1] - kv_line = line[match_end:].strip() - if not kv_line.count(":"): - return None, None - kv = kv_line.split(":") - if len(kv) > 1: - return kv[0], kv[1][1:] - if len(kv) == 1: - return kv[0], None - return None, None - - k, v = parse_kv_line(line) - if k is not None and v is not None: - self.dict[k] = v - return True - if k is not None: - return True - return False - - -def __parse_log_file(file_name: str, sections: List, tea: archiving.EngineArchive) -> List[Dict]: - def entered_section(sections, line) -> bool: - for section in sections: - if section.entered_section(line): - return section - return None - - current_section = None - with archiving.get_reader(tea, file_name) as reader: - for line in reader.readlines(): - if current_section is None: - current_section = entered_section(sections, line) - else: - if not current_section.parse_line(line): - sections.remove(current_section) - current_section = entered_section(sections, line) - dicts = [section.dict for section in sections] - return dicts - - -def parse_build_log(file_name: str, tea: archiving.EngineArchive) -> List[Dict]: - """Parse the TensorRT engine build log and extract the builder configuration. - - Returns the model and engine build configurations as dictionaries. - """ - model_options = FileSection("=== Model Options ===") - build_options = FileSection("=== Build Options ===") - device_information = FileSection("=== Device Information ===") - - sections = [model_options, build_options, device_information] - __parse_log_file(file_name, sections, tea) - return { - "model_options": model_options.dict, - "build_options": build_options.dict, - "device_information": device_information.dict - } - - -def parse_profiling_log(file_name: str, tea: archiving.EngineArchive): - performance_summary = FileSection("=== Performance summary ===") - inference_options = FileSection("=== Inference Options ===") - device_information = FileSection("=== Device Information ===") - sections = [ - performance_summary, - inference_options, - device_information] - __parse_log_file(file_name, sections, tea) - - def post_process_perf(perf_summary: dict): - """Normalize the log results to a standard format""" - for k, v in perf_summary.items(): - if k in ["Throughput", "Total Host Walltime", "Total GPU Compute Time"]: - perf_summary[k] = __to_float(v) - if k in ["Latency", "Enqueue Time", "H2D Latency", "GPU Compute Time", "D2H Latency"]: - perf_summary[k] = __get_stats(v) - return perf_summary - - def post_process_device_info(device_info: dict): - """Convert some value fields to float""" - for k, v in device_info.items(): - if k in ["Compute Clock Rate", "Memory Bus Width", "Memory Clock Rate", - "Compute Capability", "SMs"]: - device_info[k] = __to_float(v) - return device_info - - return { - "performance_summary": post_process_perf(performance_summary.dict), - "inference_options": inference_options.dict, - "device_information": post_process_device_info(device_information.dict) - } - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument('input', help="name of engine build log file to parse.") - args = parser.parse_args() - parse_build_log(args.input) \ No newline at end of file diff --git a/tools/experimental/trt-engine-explorer/utils/process_engine.py b/tools/experimental/trt-engine-explorer/utils/process_engine.py deleted file mode 100755 index 817efb5bf..000000000 --- a/tools/experimental/trt-engine-explorer/utils/process_engine.py +++ /dev/null @@ -1,351 +0,0 @@ -#!/usr/bin/env python3 -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -""" -Use this script to: -1. Build a TensorRT engine from an ONNX file. -2. Profile an engine plan file. -3. Generate JSON files for exploration with trex. -4. Draw an SVG graph from an engine. - -Note: this script requires Graphviz for function `draw_engine`. -Graphviz can be installed manually: - $ sudo apt-get --yes install graphviz -""" - - -import os -import json -import argparse -import subprocess -from typing import List, Dict, Tuple, Optional -import tensorrt as trt -from parse_trtexec_log import parse_build_log, parse_profiling_log -from config_gpu import GPUMonitor, GPUConfigurator, get_max_clocks -import trex.archiving as archiving - - -def run_trtexec(trt_cmdline: List[str], writer): - '''Execute trtexec''' - success = False - with writer: - log_str = None - try: - log = subprocess.run( - trt_cmdline, - check=True, - # Redirection - stdout=subprocess.PIPE, - stderr=subprocess.STDOUT, - universal_newlines=True) - success = True - log_str = log.stdout - except subprocess.CalledProcessError as err: - log_str = err.output - except FileNotFoundError as err: - log_str = f"\nError: {err.strerror}: {err.filename}" - print(log_str) - writer.write(log_str) - return success - - -def build_engine_cmd( - args: Dict, - onnx_path: str, - engine_path: str, - timing_cache_path: str -) -> Tuple[List[str], str]: - graph_json_fname = f"{engine_path}.graph.json" - cmd_line = ["trtexec", - "--verbose", - f"--onnx={onnx_path}", - f"--saveEngine={engine_path}", - f"--exportLayerInfo={graph_json_fname}", - f"--timingCacheFile={timing_cache_path}", - ] - if trt.__version__ < "10.0": - # nvtxMode=verbose is the same as profilingVerbosity=detailed, but backward-compatible - cmd_line.append("--nvtxMode=verbose") - cmd_line.append("--buildOnly") - cmd_line.append("--workspace=8192") - else: - cmd_line.append("--profilingVerbosity=detailed") - - append_trtexec_args(args.trtexec, cmd_line) - - build_log_fname = f"{engine_path}.build.log" - return cmd_line, build_log_fname - - -def build_engine( - args: Dict, - timing_cache_path: str, - tea: Optional[archiving.EngineArchive] -) -> bool: - def generate_build_metadata(log_file: str, metadata_json_fname: str, tea: archiving.EngineArchive): - """Parse trtexec engine build log file and write to a JSON file""" - build_metadata = parse_build_log(log_file, tea) - with archiving.get_writer(tea, metadata_json_fname) as writer: - json_str = json.dumps(build_metadata, ensure_ascii=False, indent=4) - writer.write(json_str) - print(f"Engine building metadata: generated output file {metadata_json_fname}") - - def print_error(build_log_file: str): - print("\nFailed to build the engine.") - print(f"See logfile in: {build_log_file}\n") - print("Troubleshooting:") - print("1. Make sure that you are running this script in an environment " - "which has trtexec built and accessible from $PATH.") - print("2. If this is a Jupyter notebook, make sure the " - " trtexec is in the $PATH of the Jupyter server.") - - onnx_path = args.input - engine_path = get_engine_path(args, add_suffix=True) - - print(f"Building the engine: {engine_path}") - cmd_line, build_log_file = build_engine_cmd( - args, onnx_path, engine_path, timing_cache_path) - print(" ".join(cmd_line)) - if args.print_only: - return True - - writer = archiving.get_writer(tea, build_log_file) - success = run_trtexec(cmd_line, writer) - if success: - print("\nSuccessfully built the engine.\n") - build_md_json_fname = f"{engine_path}.build.metadata.json" - generate_build_metadata(build_log_file, build_md_json_fname, tea) - else: - print_error(build_log_file) - return success - - -def profile_engine_cmd( - args: Dict, - engine_path:str, - timing_cache_path: str -): - profiling_json_fname = f"{engine_path}.profile.json" - graph_json_fname = f"{engine_path}.graph.json" - timing_json_fname = f"{engine_path}.timing.json" - cmd_line = ["trtexec", - "--verbose", - "--noDataTransfers", - "--useCudaGraph", - # Profiling affects the performance of your kernel! - # Always run and time without profiling. - "--separateProfileRun", - "--useSpinWait", - f"--loadEngine={engine_path}", - f"--exportTimes={timing_json_fname}", - f"--exportProfile={profiling_json_fname}", - f"--exportLayerInfo={graph_json_fname}", - f"--timingCacheFile={timing_cache_path}", - ] - if trt.__version__ < "10.0": - cmd_line.append("--nvtxMode=verbose") - else: - cmd_line.append("--profilingVerbosity=detailed") - - append_trtexec_args(args.trtexec, cmd_line) - - profile_log_fname = f"{engine_path}.profile.log" - return cmd_line, profile_log_fname - - -def get_engine_path(args: Dict, add_suffix: bool): - if add_suffix: - onnx_path = args.input - onnx_fname = os.path.basename(onnx_path) - outdir = args.outdir - engine_path = os.path.join(outdir, onnx_fname) + ".engine" - else: - engine_path = args.input - return engine_path - - -def get_gpu_config_args(args): - def freq_to_int(clk_freq: str, max_clk_freq: int): - use_max_freq = clk_freq == 'max' - clk_freq = max_clk_freq if use_max_freq else int(clk_freq) - clk_freq = min(max_clk_freq, clk_freq) - return clk_freq - - dev = args.dev - power_limit = args.power_limit - dont_lock_clocks = args.dont_lock_clocks - # Parse frequencies arguments. - max_mem_clk_freq, max_compute_clk_freq = get_max_clocks(dev) - compute_clk_freq = freq_to_int(args.compute_clk_freq, max_compute_clk_freq) - mem_clk_freq = freq_to_int(args.memory_clk_freq, max_mem_clk_freq) - return power_limit, compute_clk_freq, mem_clk_freq, dev, dont_lock_clocks - - -def profile_engine( - args: Dict, - timing_cache_path:str, - tea: archiving.EngineArchive, - add_suffix: bool -) -> bool: - def generate_profiling_metadata(log_file: str, metadata_json_fname: str, tea: archiving.EngineArchive): - """Parse trtexec profiling session log file and write to a JSON file""" - profiling_metadata = parse_profiling_log(log_file, tea) - with archiving.get_writer(tea, metadata_json_fname) as writer: - json_str = json.dumps(profiling_metadata, ensure_ascii=False, indent=4) - writer.write(json_str) - print(f"Profiling metadata: generated output file {metadata_json_fname}") - - engine_path = get_engine_path(args, add_suffix) - print(f"Profiling the engine: {engine_path}") - cmd_line, profile_log_file = profile_engine_cmd( - args, engine_path, timing_cache_path) - print(" ".join(cmd_line)) - if args.print_only: - return True - - writer = archiving.get_writer(tea, profile_log_file) - - #with GPUMonitor(args.monitor), GPUConfigurator(*get_gpu_config_args(args)): - success = run_trtexec(cmd_line, writer) - - if success: - print("\nSuccessfully profiled the engine.\n") - profiling_md_json_fname = f"{engine_path}.profile.metadata.json" - generate_profiling_metadata(profile_log_file, profiling_md_json_fname, tea) - else: - print("\nFailed to profile the engine.") - print(f"See logfile in: {profile_log_file}\n") - return success - - -def generate_engine_svg(args: Dict, add_suffix: bool) -> bool: - if args.print_only: - return - engine_path = get_engine_path(args, add_suffix) - - if add_suffix: - graph_json_fname = f"{engine_path}.graph.json" - profiling_json_fname = f"{engine_path}.profile.json" - else: - graph_json_fname = engine_path - - try: - from draw_engine import draw_engine - print(f"Generating graph diagram: {graph_json_fname}") - draw_engine(graph_json_fname, profiling_json_fname) - except ModuleNotFoundError: - print("Can't generate plan SVG graph because some package is not installed") - - -def create_artifacts_directory(path: str): - try: - os.mkdir(path) - except FileExistsError: - pass - - -def process_engine( - args: Dict, - build: bool, - profile: bool, - draw: bool -) -> bool: - timing_cache_path = "./timing.cache" - success = True - engine_path = get_engine_path(args, add_suffix=True) - tea_name = f"{engine_path}.tea" - tea = archiving.EngineArchive(tea_name) if args.archive else None - if tea: tea.open() - if build: - success = build_engine(args, timing_cache_path, tea) - if profile and success: - success = profile_engine(args, timing_cache_path, tea, add_suffix=build) - if draw and success: - success = generate_engine_svg(args, add_suffix=build) - if tea: tea.close() - print(f"Artifcats directory: {args.outdir}") - return success - - -def make_subcmd_parser(subparsers): - parser = subparsers.add_parser('process', help='Utility to build and profile TensorRT engines.') - parser.set_defaults(func=do_work) - _make_parser(parser) - - -def _make_parser(parser): - # Positional arguments. - parser.add_argument('input', help="input file (ONNX model or TensorRT engine file)") - parser.add_argument('outdir', help="directory to store output artifacts") - parser.add_argument('trtexec', nargs='*', - help="trtexec agruments (without a preceding --). " - "For example: int8 shapes=input_ids:32x512,attention_mask:32x512", - default=None) - - # Optional arguments. - parser.add_argument('--memory-clk-freq', - default='max', - help="Set memory clock frequency (MHz)") - parser.add_argument('--compute-clk-freq', - default='max', - help="Set compute clock frequency (MHz)") - parser.add_argument('--power-limit', default=None, type=int, help="Set power limit") - parser.add_argument('--dev', default=0, help="GPU device ID") - parser.add_argument('--dont-lock-clocks', - action='store_true', - help="Do not lock the clocks. " - "If set, overrides --compute-clk-freq and --memory-clk-freq") - parser.add_argument('--monitor', - action='store_true', - help="Monitor GPU temperature, power, clocks and utilization while profiling.") - parser.add_argument('--print-only', action='store_true', - help='print the command-line and exit') - parser.add_argument('--build-engine', '-b', action='store_true', default=None, - help='build the engine') - parser.add_argument('--profile-engine', '-p', action='store_true', default=None, - help='profile the engine') - parser.add_argument('--draw-engine', '-d', action='store_true', default=None, - help='draw the engine') - parser.add_argument('--archive', action='store_true', - help="create a TensorRT engine archive file (.tea)") - - -def append_trtexec_args(trt_args: Dict, cmd_line: List[str]): - for arg in trt_args: - cmd_line.append(f"--{arg}") - - -def get_subcmds(args: Dict): - all = (not args.build_engine and - not args.profile_engine and - not args.draw_engine) - build, profile, draw = [True]*3 if all else [ - args.build_engine, args.profile_engine, args.draw_engine] - return build, profile, draw - - -def do_work(args): - create_artifacts_directory(args.outdir) - build, profile, draw = get_subcmds(args) - process_engine(args, build, profile, draw) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - args = parser.parse_args(_make_parser(parser)) - do_work(args) diff --git a/tools/experimental/trt-engine-explorer/utils/summarize_engine.py b/tools/experimental/trt-engine-explorer/utils/summarize_engine.py deleted file mode 100644 index 0c77ce8ad..000000000 --- a/tools/experimental/trt-engine-explorer/utils/summarize_engine.py +++ /dev/null @@ -1,80 +0,0 @@ -#!/usr/bin/env python3 -# -# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# - -import trex.engine_plan -from trex import group_count, group_sum_attr, group_mean_attr -from tabulate import tabulate -from typing import Literal - - -def summarize_engine(engine_json_fname: str, - profiling_json_fname: str, - group_tactics: bool = False, - sort_key: str | Literal['count', 'latency', 'id'] = 'count', -): - plan = trex.engine_plan.EnginePlan(engine_json_fname, profiling_file=profiling_json_fname) - - if group_tactics: - # The hash distinguishes individual tactics - remove_hash = lambda tactic: tactic[: tactic.find("_0x")] if tactic.find("_0x") > 0 else tactic - plan.df['tactic'] = plan.df['tactic'].apply(remove_hash) - - tactic_cnt = group_count(plan.df, 'tactic') - tactic_latency = group_sum_attr(plan.df, 'tactic', 'latency.pct_time') - df = tactic_latency - df['count'] = tactic_cnt['count'] - df.reset_index(drop=True, inplace=True) - if sort_key == 'id': - sort_key = 'count' - - else: - tactic_mean_latency = group_mean_attr(plan.df, 'tactic', 'latency.pct_time') - df = tactic_mean_latency - if sort_key == 'count': - sort_key = 'latency' - - if sort_key == 'latency': - sort_key = 'latency.pct_time' - if sort_key != 'id': - df = df.sort_values(by=[sort_key], ascending=False) - df = df.rename(columns={'latency.pct_time': 'latency %'}) - print(tabulate(df, headers='keys', tablefmt='psql')) - - -def make_subcmd_parser(subparsers): - summarize = lambda args: summarize_engine( - engine_json_fname=args.input, - profiling_json_fname=args.profiling_json, - group_tactics=args.group_tactics, - sort_key=args.sort_key, - ) - summarize_parser = subparsers.add_parser("summary", help="Summarize a TensorRT engine.") - summarize_parser.set_defaults(func=summarize) - _make_parser(summarize_parser) - - -def _make_parser(parser): - parser.add_argument("input", help="name of engine JSON file to draw.") - parser.add_argument("--profiling_json", "-pj", - default=None, help="name of engine JSON file to draw") - parser.add_argument("--sort_key", "-sk", - choices=["count", "latency", "id"], - default="count", help="the key to use for sorting the tactics"), - parser.add_argument("--group_tactics", "-gt", - action='store_true', - default=False, help="group the tactics by type")