GitLab

quantize_transpose_vector_blockwise function use lds exceeding 64kb when
input type is fp32. But max size of lds in dcu is 64kb, thus we use lds
as bfp16 for workaround.
Signed-off-by: wenjh <wenjh@sugon.com>

Signed-off-by: wenjh <wenjh@sugon.com>

Use ocp fp8.
Workaround: test_cast_float8blockwise.cu link wrong std::max
Signed-off-by: wenjh <wenjh@sugon.com>

* Move multi tensors kernels from PyTorch extensions to core
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Add int16 type to core (for storing fp32 param remainders)
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* Fix core build
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* same fix to scale
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* Fix perf, memory, vars
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* Fix core tests
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---------
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* Reapply "Allow NVTEShape to own data." (#1703)

This reverts commit 91405eb4

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

* 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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---------
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Introduce nvte_memset to provide a fill kernel that is faster than cudaMemsetAsync for small sizes (#1716)

* nvte_memset fills single float value
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Support larger sizes than a single value and add tests
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---------
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Revert "Allow NVTEShape to own data. (#1674)"

This reverts commit e61ce77c

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

* Allow NVTEShape to own data.
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* Convert repeated copy paths to nvte_make_shape calls.
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---------
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* Add GEMM logic for blockwise quantized tensors.

GEMM test cases included in pytorch integration.
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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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