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

* 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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* Refactor GEMM param canonicalization

Configure A and B matrices separately. Have separate code path for each scaling mode.
Signed-off-by: Tim Moon <tmoon@nvidia.com>

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* Prune number of tests.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

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

* Reuse scale calculation between quantizer refs.
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* Save memory by dropping reference to saved tensors.

Issues previously observed are solved.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* 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
for block scaled fp8.
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* Move benchmark to te_playground
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* Remove amax_epsilon and pow_2_scales from tensor.

Hardcodes the default values.
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* Lint changes.
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* Fixup MR changes that broke.
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* Safer ifdef in kernel.
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* Documentation prose.
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* Reuse compute_scale function from Current Scaling.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Bugfix on inf_value scale refactor.
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* Remove qopt calls from test.
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* Update pytest list.
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* Add copyright to reference scale calc.
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* Use ptx.cuh functions instead of cde.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Update shape logic with allocation and reuse shape.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Usage defaults MR feedback.
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* Copyright and header guard.
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* Updating torch dispatch code.
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* Fix exception type.
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* Use TypeInfo
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* MR feedback.
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* Update CS scale update test to use updated ref impl
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Update JAX scaling mode enum
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* Skip tests on Lovelace
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---------
Signed-off-by: Keith Wyss <kwyss@nvidia.com>
Signed-off-by: Tim Moon <tmoon@nvidia.com>
Co-authored-by: Tim Moon <tmoon@nvidia.com>
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* Enable fp8_primary_weights for current scaling
Signed-off-by: kunlunl <kunlunl@nvidia.com>

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* Use different cast_master_weights_to_fp8 functions depending on the type of quantizer
Signed-off-by: kunlunl <kunlunl@nvidia.com>

* All amaxes of model_weights should participate in reduce-max
Signed-off-by: kunlunl <kunlunl@nvidia.com>

* Clear _high_precision_init_val automatically in cast_master_weights_to_fp8 function
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* Merge all all-reduce on amaxes into one NCCL kernel
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* Add unit tests for multi_tensor_compute_scale_and_scale_inv and preserve_high_precision_init_val
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* Fix conflicts
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* Add unit test for cast_master_weights_to_fp8
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* use mock group to initialize fp8_autocast to avoid reduction of amax_history by fp8_autocast_exit
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* Remove with_computing_amax and with_computing_scale
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* Move replace_raw_data from QuantizedTensor to utils.py
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* Remove allow_empty_output argument from nvte_compute_amax and set it always be true
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* Rename import guard of recipe_common.cuh to be align with other import guards
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* Add unit test for replace_raw_data
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* Add test_replace_raw_data into qa/L0_pytorch_unittest/test.sh
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* Minor changes in comments
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* Add randomness to the unit test of replace_raw_data
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* (Maybe need revert) Add tex.quantize_to_fragment
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* (Maybe needsto rrevert) Use nvte_quantize_noop in quantize_to_fragment
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* Fix lint error
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* Move high_precision_init_val test and replace_raw_data test to test_sanity.py
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* Remove test_fp8_model_init.py and test_replace_raw_data.py
Signed-off-by: kunlunl <kunlunl@nvidia.com>

* Remove cast_master_weights_to_fp8 and replace_raw_data from __all__ of tensor.__init__.py
Signed-off-by: kunlunl <kunlunl@nvidia.com>

* Move FP8 casting logic back from C++ tex funcs to Python
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* Remove unimplemented function from header
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* check in per-tensor current scaling full recipe
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setup basics of current scaling quantizer in python level
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Signed-off-by: zhongboz <zhongboz@nvidia.com>

add test case for current scaling dequantize
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finish linear layer fwd bwd test, determined error with bf16
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achieved zero tolerance for Linear by specify gemm use_split_accumulator config
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enable layernormlinear with current scaling, pass bitwise test
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refactor test case code
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make current scaling quantizers distrbuted, pass distributed linear&layernormlinear tests
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bug fix: use cached fp8 recipe in backward
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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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* 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
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* add benchmark for quantizer
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* add benchmarks for linear layer
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* bug fix, typo
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* resolve more mr comments
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* avoid potential race condition by not using from_blob to construct amax tensor in C++
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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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* Debug compilation error with CUDA 12.1 for Turing
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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 is_current_scaling check totally from common folder
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* remove benchmarks, will contribute in another repo
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* adjust cs default recipe config
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* adjust comments in test
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* Remove current scaling mode from core lib
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* Refactor current-scaling-specific logic in core C++ lib

Move amax and scale update functions out of casting functions, and put into dedicated current-scaling source file. Add general API for accessing quantization config object.
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* Add missing header in C++ tests
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* Disable test config with FP8 transpose on Blackwell
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* Fix compilation error in C++ test
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