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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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* 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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* Changed the Tensor allocation strategy
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* Fixes
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* Fix the double free error
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* Fix
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* Fix
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* Fix
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* Hide TensorAllocator and fix the usage in LayerNorm
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* Cleaning
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* Fix
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* Fix permutation
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* Move multi tensors kernels from PyTorch extensions to core
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* Add int16 type to core (for storing fp32 param remainders)
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* Fix perf, memory, vars
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* Re-add device guard for multi-device
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* Fix junk output dtype for non-per tensor
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* Fixes for test and upgrade mcore version
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* Fix core tests
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* Reapply "Allow NVTEShape to own data." (#1703)

This reverts commit 91405eb4

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* Update code so that data is replaced by an array.
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* Specify unambiguous Tensor constructor in tests.
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* Fix assumption in test of 2D shape.
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* Remove row and col
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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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* add more info
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Revert "Allow NVTEShape to own data. (#1674)"

This reverts commit e61ce77c

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* move swizzle scaling factor to cpp
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* Allow NVTEShape to own data.
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* Convert repeated copy paths to nvte_make_shape calls.
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* Apply suggestions from code review
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* Build fixes.
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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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* 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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* MR Feedback.
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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 force_power_of_2 scales in the recipe.
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* Update test import.
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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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* 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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* Use common tensor map API.
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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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* 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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* Remove constexpr parameters from kernel.

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* Merge conflict from rebase.
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* Add shape implementations for block scaling.

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