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.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Gate feature on CUDA 12.9
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Gemm typo.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Remove unecessary type converter change.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* 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.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Force pow2Scales in GEMM
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Add GEMM test to pytorch test suite.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* 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.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Rewrite GEMM comment.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* 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.

The recompute context uses the same recipe
and fp8 settings as the original fwd pass.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* MR Feedback. Avoid reusing quantizer objects.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Update logic in module.
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* Format py.
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* Update for PP bug.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* 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.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Update test import.
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* support columnwise only mode to 1D quantize kernel
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* Format and move enum
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* 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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* Rename recipe method.
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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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* Debug checkpointing with te.Sequential
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Explicitly specify quantized tensor usages needed for linear op backward
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* scaling enum abstract

* rm NVTE_ from ScalingMode names

* rework scaling mode enum in grouped gemm

* fix norm sharding

---------
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* Minor stylistic tweaks and typo fixes

Review suggestions from @ptrendx
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* Fix incorrect col strides for MXFP8 matrices
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* Use dummy wgrads for lower memory consumption
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* Bug fix to avoid sharing gradients.
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* Disable automatic use of batch_p2p_comm for CP2
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* 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.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* 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.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* MR Feedback.
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* Refactor GEMM param canonicalization

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* Prune number of tests.
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* rm no scaling enum
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* update jax enum
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Support fp8 primary weight in fsdp training
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* fix cpp warning
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* more fix
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* refactor to add cp support for sbhd/bshd
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* rename QuantizeAxis to QuantizeLayout, get_layout to get_data_layout, q_axis to q_layout

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* sharding constraint fixes

* fix padding when flattening first dim needs to be padded

* update test sizes so that padding is tested

* rm output sharding as it can be done in the flax module

* sharding scale_inv for mxfp8

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MXFP8 flax layer tests
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* Blockwise float8 quantizer and quantized tensor class.

The classes are configurable for 128x128 blocksize
and 1x128 blocksize via setting block_scaling_dim == 2,1 respectively.

Scale tensors are stored in a format emenable for matrix multiplication,
however the integration of matmul is deferred as a separate story.

Fusions of quantization and DBIAS or activation functions are not yet
implemented, and the dequantization is currently implemented in torch.

Tests for quantization are included in C++ and pytorch layers, with
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to hit interesting branches through the API such as tensor creation
in pytorch and CPP and dequantization of row and columnwise usage.

Two CUDA kernels for quantization are included, and are direct ports
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* Apply linting changes.
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* Alignment for 1D scaling for GEMM edge case.
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* Fix typo.
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* Update some call sites.
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* Tests for torch tensor API surface.

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* Reuse scale calculation between quantizer refs.
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* Save memory by dropping reference to saved tensors.

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Make sure that weight matrix has required usages for dgrad GEMM
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* Fix
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* fix lint
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for more information, see https://pre-commit.ci



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for more information, see https://pre-commit.ci



* fix merge
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* fix FA import comments
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for more information, see https://pre-commit.ci



* update FA3 to its latest commit on main
Signed-off-by: Charlene Yang <8636796+cyanguwa@users.noreply.github.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci



* add default values to IP and assertion to graph.py
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* add more comments in attention
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Signed-off-by: Charlene Yang <8636796+cyanguwa@users.noreply.github.com>

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---------
Signed-off-by: Charlene Yang <8636796+cyanguwa@users.noreply.github.com>
Signed-off-by: Charlene Yang <charleney@nvidia.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>