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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
Signed-off-by: zhongboz <zhongboz@nvidia.com>

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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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* lint ungrouped imports
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* Implement dequantize for compact 1D blocks.
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* add more unit test with dequantize compact supported
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* 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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* Debug FP8 block-wise tensor tests
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* Debug distributed test
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* Handle case where LayerNormLinear returns gathered norm output
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* fix debug mode
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* rm unused swizzle extensions
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* Fix swizzle
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* Consistent namespaces and first refactor
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* format and lint
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* transformer_engine
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* revert accidental perm change
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* rtx5090 arch fix support
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* apprend `nvte` to the function name so that its visible in framework specific dirs
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* fix typo
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* add filter for nvte_is_supported_nontn_fp8_gemm
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* properly expose the api
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* feedback from PR
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* Enable MXFP8 and Per-Tensor Current Scaling for Grouped Linear
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* enable float8blockwise
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* resolve comments
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* Add GEMM logic for blockwise quantized tensors.

GEMM test cases included in pytorch integration.
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* Update NVTE_BLOCK_SCALING for GEMM.
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* Gate feature on CUDA 12.9
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* Reflect epilogue availability and test supported epilogues.
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* GEMM simplifications from recipe branch.
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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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* 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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* 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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* 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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* 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

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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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* 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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* 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.
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* Apply linting changes.
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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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* support tp-comm-overlap in Current Scaling recipe
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fix layernorm_mlp with current scaling, fix activation_helper with current scaling
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support detailed numerical settings from recipe to quantization kernel
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resolving MR comments
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recipe naming
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* [pre-commit.ci] auto fixes from pre-commit.com hooks

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



* resolve mr comments, remove IS_CURRENT_SCALING template from kernels
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* resolve mr comments, make current scaling c++ test cases
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* add current scaling to test_numerics.py, skip act recomp and grouped linear
Signed-off-by: zhongboz <zhongboz@nvidia.com>

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



* add benchmark for quantizer
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* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* add benchmarks for linear layer
Signed-off-by: zhongboz <zhongboz@nvidia.com>

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* bug fix, typo
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* resolve more mr comments
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* resolve more comments
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* Debug linter warnings and license check
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* Debug import error in FP8 tensor test
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* resolve mr comments, fix activation cast fusion
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* resolve comments, add NVTEQuantizationParams for compute scale
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* remove benchmarks, will contribute in another repo
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* adjust comments in test
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* Remove current scaling mode from core lib
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* Fix compilation error in C++ test
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---------
Signed-off-by: zhongboz <zhongboz@nvidia.com>
Signed-off-by: Tim Moon <tmoon@nvidia.com>
Co-authored-by: zhongboz <zhongboz@nvidia.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>
Co-authored-by: Tim Moon <tmoon@nvidia.com>

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