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

* fix for fp8 blockwise recipe
Signed-off-by: zhongboz <zhongboz@nvidia.com>

* resolve comments
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* add checks to cuda kernel launch and cuda API calls
Signed-off-by: Xin Yao <xiny@nvidia.com>

* Remove exceptions from destructors
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* fix weired dispatch in ln/rmsnorm
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* Changed the Tensor allocation strategy
Signed-off-by: Przemek Tredak <ptredak@nvidia.com>

* Fixes
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* Disable debug flag
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* Fix the double free error
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* Fix
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* Fixed pyTorch recipe extension
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* Fix
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* Fix
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* Cleaning
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* Fix
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* Fix permutation
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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.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* Alignment for 1D scaling for GEMM edge case.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* 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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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
for block scaled fp8.
Signed-off-by: Keith Wyss <kwyss@nvidia.com>

* 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.
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* Bugfix on inf_value scale refactor.
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* Add copyright to reference scale calc.
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* Use ptx.cuh functions instead of cde.
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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
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* Update JAX scaling mode enum
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* Skip tests on Lovelace
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* Cleanup sanity tests and add CS recipe tests
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Fix sanity test
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* Fix CG capture with CS recipe
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* Fix ops for CG
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@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
Signed-off-by: kunlunl <kunlunl@nvidia.com>

* Merge all all-reduce on amaxes into one NCCL kernel
Signed-off-by: kunlunl <kunlunl@nvidia.com>

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* Add unit tests for multi_tensor_compute_scale_and_scale_inv and preserve_high_precision_init_val
Signed-off-by: kunlunl <kunlunl@nvidia.com>

* 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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* Add test_replace_raw_data into qa/L0_pytorch_unittest/test.sh
Signed-off-by: kunlunl <kunlunl@nvidia.com>

* Minor changes in comments
Signed-off-by: kunlunl <kunlunl@nvidia.com>

* Add randomness to the unit test of replace_raw_data
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* (Maybe need revert) Add tex.quantize_to_fragment
Signed-off-by: kunlunl <kunlunl@nvidia.com>

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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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* Remove test_fp8_model_init.py and test_replace_raw_data.py
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* Remove cast_master_weights_to_fp8 and replace_raw_data from __all__ of tensor.__init__.py
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* 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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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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* 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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* 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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* Remove current scaling mode from core lib
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