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Revert "Allow NVTEShape to own data. (#1674)"

This reverts commit e61ce77c

.
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* move swizzle scaling factor to cpp
Signed-off-by: Xin Yao <xiny@nvidia.com>

* resolve comments
Signed-off-by: Xin Yao <xiny@nvidia.com>

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* Allow NVTEShape to own data.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Convert repeated copy paths to nvte_make_shape calls.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Apply suggestions from code review
Signed-off-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>

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

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

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Signed-off-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>
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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.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

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

* 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.
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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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* 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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* 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.
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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.

Since the quantized tensor is a tensor
subclass, these tests exercise torch hooks.
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* Reuse scale calculation between quantizer refs.
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* Save memory by dropping reference to saved tensors.

Issues previously observed are solved.
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* Remove constexpr parameters from kernel.

Code size is reduced with fewer constexpr params.
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* Merge conflict from rebase.
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* Add shape implementations for block scaling.

nvte_shape was added upstream. Logic added
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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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