Implement offline correlation between profiler rows and captured FX regions

.github/workflows/ci.yml

@@ -0,0 +1,48 @@
+name: CI
+
+on:
+  pull_request:
+  push:
+    branches:
+      - main
+
+permissions:
+  contents: read
+
+jobs:
+  python-syntax:
+    name: Python syntax (${{ matrix.python-version }})
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version:
+          - "3.10"
+          - "3.13"
+
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Compile Python sources
+        run: python -m compileall -q .
+
+  cpu-tests:
+    name: CPU tests
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          persist-credentials: false
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.10"
+      - uses: astral-sh/setup-uv@v5
+        with:
+          enable-cache: true
+      - name: Install development dependencies
+        run: uv sync --extra dev
+      - name: Run CPU test suite
+        run: uv run pytest -m "not gpu"

.gitignore

@@ -31,3 +31,4 @@ traces/
 
 # Workspace (runtime artifacts)
 workspace/
+uv.lock

CHANGELOG.md

@@ -1,5 +1,119 @@
 # Changelog
 
+## Unreleased (downstream)
+
+### Universal workload and discovery foundation
+
+- Added versioned, metadata-only FastVideo workload manifests under
+  `workloads/` (Wan 2.1 T2V 1.3B 480p and LTX 480p), a resume-safe
+  native-versus-optimized launcher bridge, structured generation results with
+  full-frame parity enforcement, and the `workload.py` CLI
+  (`validate`, `show`, `run-ab`, `validate-result`)
+- Added the discovery layer under `autokernel/discovery/`: metadata-only
+  discovery report schema with stable graph fingerprints, fail-closed
+  pure-tensor safety checks, model-independent CPU FX region capture,
+  profiler-export ingestion, profiler-to-region timing correlation, and
+  Amdahl-style impact ranking with a configurable end-to-end floor through
+  the `discovery.py` CLI (`validate`, `rank`, `ingest-profiler`)
+- Profiled Wan 2.1 T2V 1.3B and LTX-2 distilled T2V generations on GB200
+  through the model-agnostic path and ingested both into validated, ranked
+  discovery reports
+
+### Model optimization campaigns
+
+- Added a versioned, metadata-only campaign contract with strict validation,
+  impact ranking, legacy orchestration-plan generation, and trusted
+  starter-kernel preparation through `campaign.py`
+- Added a one-command, time-bounded and resumable overnight campaign runner
+  with per-target benchmark instructions, durable logs, terminal receipts, and
+  a consolidated morning report
+
+### MotionKernel identity
+
+- Renamed the downstream distribution to MotionKernel and reset its independent
+  package version to `0.1.0`
+- Repositioned the project around verified GPU kernel optimization for video
+  generation models, with FastVideo as the first target integration
+- Preserved the `autokernel` Python import namespace as a temporary
+  compatibility boundary and retained the upstream MIT license and attribution
+
+### Custom operation registry
+
+- Added the `autokernel` package with `autokernel/specs/`: a typed `KernelSpec`
+  that owns one operation's reference, deterministic inputs, sizes, dtypes,
+  tolerances, edge cases, FLOP/byte accounting, profiler shape aliases and
+  starter kernels
+- Added `KernelRegistry` with deterministic ordering, duplicate detection and
+  per-command isolation (`create_builtin_registry()`); registry discovery imports
+  no `torch` and initializes no GPU, so it works on CPU-only machines
+- Migrated all nine built-in operations (`matmul`, `softmax`, `layernorm`,
+  `flash_attention`, `fused_mlp`, `cross_entropy`, `rotary_embedding`,
+  `rmsnorm`, `reduce`) to specifications; `bench.py` and `extract.py` now read
+  metadata only from those specs
+- Removed the duplicated metadata maps from `extract.py` (`SHAPE_KEYS`,
+  `SHAPE_ALIAS_MAP`, `TOLERANCES_MAP`, `FLOPS_FN_SRC`, `BYTES_FN_SRC`,
+  `SPEEDUP_ESTIMATES`, hard-coded default shapes). FLOP/byte accounting is a
+  serializable expression tree instead of stored Python source strings
+- Added `--spec LOCATOR` and `--spec-override` to `bench.py` and `extract.py`.
+  Precedence is `--spec`, then `--kernel`, then `kernel.py::KERNEL_TYPE`, so
+  existing invocations are unchanged. `--help` never imports an external spec
+- Added `examples/custom_ops/add.py` (external spec) and
+  `examples/custom_ops/add_kernel.py` (its starter kernel)
+- Added a CPU test suite (`uv run pytest -m "not gpu"`) that freezes the
+  built-in metadata against its pre-refactor values, plus a `dev` extra and a
+  CI job to run it. GPU tests are marked `gpu`
+- `bench.py` keeps a deprecated `KERNEL_CONFIGS` view derived from the registry
+  for out-of-tree callers
+- `Tolerance` rejects negative, NaN, and infinite `atol`/`rtol` values, so a
+  malformed specification cannot silently disable the correctness gate
+- All declared built-in, edge-case and default-shape dimensions must be
+  positive integers; empty starter-kernel mappings are explicitly supported
+  for benchmark-only external specifications
+
+### Generalized verification
+
+- Added deterministic comparison for tensor, tuple, list, dictionary,
+  named-tuple and nested output trees, including exact metadata comparison and
+  per-leaf NaN, infinity and error diagnostics
+- Added versioned production shape corpora, append and corpus-only benchmark
+  modes, and weighted aggregates that remain separated by dtype
+- Added optional `BackwardSpec` gradient verification with deterministic
+  upstream gradients and per-input diagnostics
+- Added optional `CompileSpec` verification and `--check-compile`; candidates
+  compile with full-graph mode by default, run at least twice, reuse one
+  compiled callable for dynamic shapes and compare through the normal output
+  tree gate outside performance timing
+- Added `FORWARD_CORRECTNESS`, `BACKWARD_CORRECTNESS` and
+  `COMPILE_CORRECTNESS` console verdicts
+- Added schema-versioned, atomic JSON results under
+  `workspace/bench_result.json`, configurable with `--result-json`
+- Added `examples/custom_ops/affine.py`, its candidate and a metadata-only
+  shape corpus as a structured-output, backward and compile fixture
+- Made the float32 matmul starter request IEEE dot inputs instead of Triton's
+  TF32 default, kept strict BF16 LayerNorm parity through an explicit PyTorch
+  fallback pending a Welford Triton implementation, and made the affine
+  fixture's residual rounding stable under Inductor fusion
+- Kept the top-level `profile.py` CLI compatible with the standard-library
+  `profile` API so importing `cProfile` and initializing `torch.compile` from
+  the repository root no longer fails
+
+### Wan kernel fusion
+
+- Added the first production video-DiT operation specification: Wan's
+  post-self-attention gated residual update plus FP32 affine LayerNorm
+- Added specifications, production corpora, and Triton starters for Wan's
+  modulated pre-attention LayerNorm and post-MLP gated residual, completing the
+  first three-target Wan optimization pack
+- Validated both new starters across their complete production corpora on
+  GB200, with all correctness stages passing and weighted isolated speedups of
+  10.449x and 10.628x respectively
+- Added a metadata-only shape corpus covering Wan 2.1 1.3B and 14B at common
+  480p token counts, including four-way sequence-parallel layouts
+- Added a structured-output Triton baseline that returns both the normalized
+  activation and updated residual stream in the model dtype
+- Validated the full production corpus on GB200 with all correctness stages
+  passing and a weighted 8.638x operator speedup over eager PyTorch
+
 ## v1.3.0 -- 2026-03-13
 
 ### AMD ROCm GPU Support (PR #3 by @andyluo7)

CONTRIBUTING.md

@@ -0,0 +1,53 @@
+# Contributing to MotionKernel
+
+## Development workflow
+
+1. Start from an up-to-date `main`.
+2. Create a focused branch.
+3. Keep framework changes separate from generated kernel experiments.
+4. Run CPU validation before pushing.
+5. Run the relevant GPU correctness and performance suites before promoting a
+   kernel.
+
+Do not run autonomous experiments in a checkout containing unrelated or
+uncommitted work. Use a disposable clone or Git worktree so an experiment can
+be abandoned without affecting development state.
+
+## Validation levels
+
+### CPU baseline
+
+The baseline check requires no GPU and verifies that tracked Python sources
+compile:
+
+```bash
+python -m compileall -q .
+```
+
+### GPU correctness
+
+GPU changes must run the relevant benchmark correctness stages across their
+declared shapes, dtypes, layouts, and edge cases. Multi-output or training
+operations must also validate every returned tensor and requested gradient.
+
+### Performance
+
+Performance claims must include:
+
+- GPU model and compute capability;
+- PyTorch, Triton, CUDA, and driver versions;
+- input shapes, dtypes, and layouts;
+- warmup and measurement methodology;
+- median latency and variance; and
+- the exact baseline being compared.
+
+Generated candidates are experimental until their correctness and performance
+results are reproducible.
+
+## Git safety
+
+- Never force-push shared branches.
+- Never push changes to the `upstream` remote.
+- Never use a destructive reset outside an isolated experiment branch or
+  disposable worktree.
+- Preserve the MIT license and upstream attribution.

DOWNSTREAM.md

@@ -0,0 +1,60 @@
+# MotionKernel provenance
+
+MotionKernel is an independently maintained, MIT-licensed downstream fork of
+[RightNow-AI/AutoKernel](https://github.com/RightNow-AI/autokernel). Its focus
+is GPU kernel discovery, optimization, verification, and packaging for video
+generation models.
+
+## Provenance
+
+- Upstream repository: `https://github.com/RightNow-AI/autokernel`
+- Initial downstream base: `7843582` (`test hf kernels export`)
+- License: MIT
+- Original copyright: Copyright (c) 2026 RightNow AI
+
+The upstream `LICENSE` file is preserved. Source files substantially derived
+from upstream remain covered by that notice.
+
+## MotionKernel direction
+
+MotionKernel is intended to become a video-first, framework-agnostic platform
+for discovering, testing, tuning, and exporting production GPU kernels. Its
+initial work focuses on:
+
+- external custom-operation specifications;
+- multi-output, backward, determinism, and compile verification;
+- production shape corpora captured from real models;
+- modulated normalization, gated-residual, attention, and layout fusion;
+- architecture-aware tuning and reproducible experiment records; and
+- clean export into runtime kernel packages for FastVideo, Diffusers, and other
+  PyTorch video runtimes.
+
+The optimization platform and shipped runtime kernels are separate products:
+the platform searches and validates candidates, while downstream applications
+consume only promoted kernel implementations.
+
+The initial model families are Wan, LTX-Video, Cosmos, and Kandinsky. Listing a
+model as a target does not imply complete support: support is earned through a
+published integration, representative workload corpus, correctness results,
+and an end-to-end benchmark.
+
+## Compatibility identity
+
+The distribution is named `motionkernel`. The Python import namespace remains
+`autokernel` temporarily so existing specifications, scripts, and downstream
+users do not break during the project transition. A future namespace migration
+will include a compatibility release and explicit upgrade instructions.
+
+## Upstream relationship
+
+Useful upstream changes can be incorporated without making upstream a release
+dependency:
+
+```bash
+git fetch upstream
+git switch main
+git merge upstream/main
+```
+
+MotionKernel features do not require upstream approval. Improvements may still
+be offered upstream when doing so benefits both projects.