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Merge/dev to main 20260807#97
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zhaoyinglia:merge/dev-to-main-20260807

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Description

Please include a brief summary of the changes, relevant motivation and context.

Fixes # (issue)

Type of change

  • Documentation change (change only to the documentation, either a fix or a new content)
  • Bug fix (non-breaking change which fixes an issue)
  • New feature (non-breaking change which adds functionality)
  • Breaking change (fix or feature that would cause existing functionality to not work as expected)
  • Infra/Build change
  • Code refactoring

Changes

Please list the changes introduced in this PR:

  • Change A
  • Change B

Checklist:

  • I have read and followed the contributing guidelines
  • The functionality is complete
  • I have commented my code, particularly in hard-to-understand areas
  • I have made corresponding changes to the documentation
  • My changes generate no new warnings
  • I have added tests that prove my fix is effective or that my feature works
  • New and existing unit tests pass locally with my changes

ptrendx and others added 30 commits March 13, 2026 01:46
…tensors. NVIDIA#2120" (NVIDIA#2673)

* Adds dst.dtype information in copy_ method of quantized tensors.

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* Update transformer_engine/pytorch/tensor/quantized_tensor.py

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* Update transformer_engine/pytorch/quantized_tensor.py

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* Fix reference tensor copy

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* Fuse scale + 0 + cumulative sum for splits to offsets calc

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* Add unit test and fix bug in kernel for >256 size

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* Removing duplicate

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… parallelism (NVIDIA#2688)

* Error out if constructing LayerNormLinear with row tensor parallelism

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* Disable Userbuffers test for row-TP LayerNormLinear

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* Enable cgemm + FP8 tests

* Implement CGEMM + MXFP8

* added size check for mxfp8

* added tols for assertions

* update tests with recipes

* enable tests + is_quantize_recipe_supported

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---------

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…kScaling and Float8BlockScaling quantized model init. (NVIDIA#2753)

* Updates FusedAdam with FSDP2 and MXFP8

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* removes xfailing unit test for MXFPr MXFP8

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* attempts to add float8block scaling fsdp hooks

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* removes unused block_len param

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- Add host-side validation: A and B must use same scaling mode (both MXFP8 or both tensor scaling)
- Add host-side validation: A and B must both be FP8 or both non-FP8; restrict inputs to FP8/BF16
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- Use void* + NVTEScalingMode in setup kernel instead of separate float*/char* scale params
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- Split set_fp8_scale_pointers into set_fp8_scale_pointers / set_mxfp8_scale_pointers
- Remove scale_inv_ptrs from GroupedOperandSelection; pass workspace pointers directly
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…mel > INT_MAX (NVIDIA#3136)

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Resolved conflicts strategy:
- Attention files: accept upstream base, will re-apply fork patches in Stage 5
- Module/ops/tensor files: accept upstream (fork changes are te_device_type only)
- pytorch/__init__.py: merged (torch_nv + upstream imports)
- permutation.py: accept upstream, will re-apply device checks in Stage 5
- Deleted upstream files: removed (fused_attn_f16_max512_seqlen.cu/h, experimental/quantization_nvfp4.py)
- Custom recipes: kept fork's quantization_nvfp4.py

Next: Stage 4 (build verification) and Stage 5 (re-apply fork patches)
- Remove convert_host_pointers_to_tensor (replaced by copy_data_ptrs_to_device)
- Remove get_device_pointer_for_data_and_scales (functionality merged)

Both methods removed from:
- transformer_engine/plugin/core/backends/vendor/cuda/cuda.py
- transformer_engine/plugin/core/backends/vendor/cuda/register_ops.py
Added new pybind APIs to CUDABackend and registry:

Memory Management (2):
- bulk_allocate: bulk allocate multiple tensors
- copy_data_ptrs_to_device: copy host pointers to device

Quantization (5):
- create_empty_quantized_tensor: create empty quantized tensor
- group_dequantize: dequantize grouped tensors
- nvfp4_quantize_with_amax: NVFP4 quantization (Ada/Hopper+)
- nvfp4_group_quantize_with_amax: NVFP4 group quantization
- swizzle_scales_and_pack_ptrs_for_discrete_weights: pointer packing

Multi-Tensor Operations (3):
- multi_tensor_pad_last_dim: pad last dim for multiple tensors
- multi_tensor_swizzle_scales_for_gemm_: in-place scale swizzle
- multi_tensor_transpose_to_bhsd: batch transpose to BHSD layout

GEMM & Solver (3):
- get_grouped_gemm_setup_workspace_size: workspace size calculation
- cusolvermp_ctx_create/destroy: cuSolverMP context management

Utility (2):
- splits_to_offsets_multi: convert splits to offsets
- newton_schulz: Newton-Schulz iteration for matrix ops

All methods include hardware capability checks where needed.
Modified APIs to match upstream changes:

1. fused_topk_with_score_function_fwd:
   + Add routing_map_format parameter (default BYTEMAP)

2. fused_topk_with_score_function_bwd:
   - Remove num_tokens and num_experts parameters
   + Add routing_map_format parameter (default BYTEMAP)

3. fused_score_for_moe_aux_loss_fwd:
   + Add routing_map_format parameter (default BYTEMAP)

4. fused_score_for_moe_aux_loss_bwd:
   - Remove num_tokens and num_experts parameters

The routing_map_format parameter enables flexible routing map
formats (BYTEMAP vs other layouts) for MOE operations.
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