GitLab

* add noop to comp amax
Signed-off-by: zhongboz <zhongboz@nvidia.com>

* fix for fp8 blockwise recipe
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* resolve comments
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[PyTorch] FP8 Subchannel Recipe With FP8 Gather And Configurable Scaling Factor Tensor Swizzling (#1707)

* functional kernel for columnwise + no-transpose option, still hacky
Signed-off-by: zhongboz <zhongboz@nvidia.com>

* pass all quantizer unit tests
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* refactor, add gemm ready api
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* make format options private members, simplify api
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* swizzle scales right before gemm
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* bug fix of single layer test
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* attempt to fix lint issue
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* fp8 gather pass, need minor refine
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* fix return_layernorm_output_gathered case
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* remove special cases, add sanity check before gemm
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* fix lint
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* Implement dequantize for compact 1D blocks.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* add more unit test with dequantize compact supported
Signed-off-by: zhongboz <zhongboz@nvidia.com>

* lint again
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* make ag for subchannel respect async
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* zero tolerance in distributed test
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* fix zero tolerance test
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* resolve rebase issues
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* lint & format
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* fix lint
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* clean up
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* bug fix
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* relax rtol for fp32 distributed test
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* fix some ci issue
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* fix ci test failure in debug mode
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* Force row-wise and column-wise data to have same data format

Prototype "all-gather usage" in quantizer.
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* Remove dead logic for high-precision AGs
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* Handle case where LayerNormLinear returns gathered norm output
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* Add GEMM logic for blockwise quantized tensors.

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* Add license.
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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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* Update FIXME to TODO with linked issue.
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* Update full recompute feature to save recipe.

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

* 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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* 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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* 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
exact comparison to reference quantizer behavior as well as an attempt
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
of equivalents in the kitchen repository, where a subchannel recipe
has been used for end to end training.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Apply linting changes.
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* Alignment for 1D scaling for GEMM edge case.
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* MR feedback.
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* Change API name.
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* Fix merge conflict with name change.
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* Change API to use two scaling mode enums.
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* Fix typo.
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* Update some call sites.
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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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* Add shape implementations for block scaling.

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* Move benchmark to te_playground
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* Remove amax_epsilon and pow_2_scales from tensor.

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* Documentation prose.
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* Reuse compute_scale function from Current Scaling.
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* MR feedback.
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* Skip tests on Lovelace
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