GitLab

* refactor the multi_stream utils + implement nvte_multi_tensor_quantize in TE/Common

* implement GroupedQuantizer and grouped_quantize in jaxx

* fix logical_axes_names for transpose tensor in ScaledTensor
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

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Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>
Co-authored-by: Hua Huang <huah@nvidia.com>
Co-authored-by: Ming Huang <mingh@nvidia.com>

* Changed the Tensor allocation strategy
Signed-off-by: Przemek Tredak <ptredak@nvidia.com>

* Fixes
Signed-off-by: Przemek Tredak <ptredak@nvidia.com>

* Disable debug flag
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* Fix the double free error
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* Fix
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* Cleaning
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* Fix permutation
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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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* 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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* Rewrite GEMM comment.
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* MR Feedback.
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* Recipe setup for Linear modules.
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* MR Feedback. Avoid reusing quantizer objects.
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* Update usage method to satisfy upstream changes.
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* Update test import.
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* Document and type code.
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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

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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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* Perform scale-inv update in cast-transpose kernels
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* Perform scale-inv update in cast and activation kernels
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* Fix typos
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* Debug amax update in layernorm kernels
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* Debug test failures
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* Debug ONNX export

Use quantization scaling factor in ONNX quantize op.
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* Review suggestion from @ptrendx
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* Apply formatting
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* Move scale inverse calculation to framework
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* cleanup
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* Add NVTX to TE modules
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* Fix
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