GitLab

* Add GEMM logic for blockwise quantized tensors.

GEMM test cases included in pytorch integration.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Update NVTE_BLOCK_SCALING for GEMM.
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* Gate feature on CUDA 12.9
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* Gemm typo.
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* Remove unecessary type converter change.
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* Reflect epilogue availability and test supported epilogues.
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* GEMM simplifications from recipe branch.
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* Format py code.
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* Update GEMM DGelu tests to match support depending on output dtype.
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* Force pow2Scales in GEMM
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* Add GEMM test to pytorch test suite.
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* Add copyright to GEMM test.
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* Update import for GEMM test.
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* Add license.
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* Update test gemm supported predicate.
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* Use sgemm like interfaces and naming.
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* Rewrite GEMM comment.
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* MR Feedback.
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* Recipe setup for Linear modules.
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* Use 12.9 feature test.
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* Run against tensor dumps from internal library.
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* Update FIXME to TODO with linked issue.
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* Update full recompute feature to save recipe.

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* MR Feedback. Avoid reusing quantizer objects.
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* Update logic in module.
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* Format py.
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* Update for PP bug.
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* Update test numerics.
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* Update force_power_of_2 scales in the recipe.
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* Update usage method to satisfy upstream changes.
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* fix subchannel recipe in distributed test with bf16 gather
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* Edit and cleanup BF16 gather code.
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* Update test import.
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* support columnwise only mode to 1D quantize kernel
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* Format and move enum
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* Skip alloc.
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* try async bf16 gather
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* Format python code.
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* Document and type code.
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* Update pytorch lint errors.
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* Dont set high precision dtype.
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* Add test for sanity and CG; fix CG for sequential?
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* Keep make_quantizers API stable

Update num_quantizers instead to pass cuda_graph tests.
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* Fix import name.
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* Skip grouped linear sanity test.
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* Set usage before BF16 gather.
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* refactor for nvte_quantize_v2
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* Format code.
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* Cleanup nvte_quantize_v2
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* Test fp32 scales.
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* Disable CUDA graph.
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* Simplify layernorm linear
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* Cleanup layernorm linear.
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* LayerNorm linear bwd gather logic.
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* Communication updates.
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* Update transformer_engine/pytorch/ops/op.py

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* Lint fix.
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* MR feedback.
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* Enable cuda graph tests.
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* Reduce chance of spurious failure and reword.
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* Review suggestions from @timmoon10
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* Update CPP tests.
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* Update common.h
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* Update test_float8blockwisetensor.py
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* Overlap input all-gather with dgrad GEMM in FP8 linear layers
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* Add missing docstring
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* Coalesce NCCL all-gathers for MXFP8 all-gather
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* Add missing import
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* Cache quantized input tensor after linear module forward pass
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* Fix linter warnings
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* Avoid unnecessarily allocating layernorm output in LayerNormLinear/LayerNormMLP
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* Update full recompute feature to save recipe.

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* Formatted python code.
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* Simplify code by relying on recipe in ctx
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* Delete row-wise data in single-GPU linear forward
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* Fix moe cases, lint, rm unused ctx
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* Fix CPU activation offloading and use consistent logic for save/restore
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* Fix typo
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* RM stray file
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* Fix distributed and cpp tests
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* Fix norm cpp tests
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* RM stray file
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* Fix MXFP8 AG
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* Fix UB bulk dgrad
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finish linear layer fwd bwd test, determined error with bf16
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achieved zero tolerance for Linear by specify gemm use_split_accumulator config
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enable layernormlinear with current scaling, pass bitwise test
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bug fix: use cached fp8 recipe in backward
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support detailed numerical settings from recipe to quantization kernel
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---------
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---------
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

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* fixes
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---------
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---------
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