GitLab

Add rules of cuda_runtime.h, cuda_driver.h and cuda_nvml.h to hip.
Signed-off-by: wenjh <wenjh@sugon.com>

Default use of hipMallocAsync rather than hipMalloc in rocblas_gemm and
add support of fp16_fp16_fp32 in rocblas_gemm.
Signed-off-by: wenjh <wenjh@sugon.com>

Fix launch params are larger than launch bounds(256) for kernels in
rocm_gemm.cu
Signed-off-by: wenjh <wenjh@sugon.com>

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

[RocblasGemm] Provide support of AB(bf16)D(fp32)
Signed-off-by: wenjh <wenjh@sugon.com>

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

Ref params of rmsnorm will make program corruption with 'nil' error.
Signed-off-by: wenjh <wenjh@sugon.com>

Due to the difference of warp size between nvidia(32) and dtk(64), the
OperatorTest/CTDBiasTestSuite.TestCTDBias/* are all failed except:

* OperatorTest/CTDBiasTestSuite.TestCTDBias/bfloat16Xfloat32X65536X128
* OperatorTest/CTDBiasTestSuite.TestCTDBias/bfloat16Xfloat16X65536X128
* OperatorTest/CTDBiasTestSuite.TestCTDBias/bfloat16Xbfloat16X65536X128
* OperatorTest/CTDBiasTestSuite.TestCTDBias/bfloat16Xfloat8e5m2X65536X128
* OperatorTest/CTDBiasTestSuite.TestCTDBias/bfloat16Xfloat8e4m3X65536X128

This commit is intended to fix this.
Signed-off-by: wenjh <wenjh@sugon.com>

Due to the compiler compiling incorrect code. The following test case crashed:

* OperatorTest/CTTestSuite.TestCastTranspose/bfloat16Xbfloat16X2048X12288
* OperatorTest/CTTestSuite.TestCastTranspose/bfloat16Xbfloat16X65536X128
* OperatorTest/CTTestSuite.TestCastTranspose/bfloat16Xbfloat16X256X65536

This commit is intended to fix these test cases.
Signed-off-by: wenjh <wenjh@sugon.com>

* move swizzle scaling factor to cpp
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* resolve comments
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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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* Add a flag to support computing zero-centered gamma in weight dtype or compute dtype for CuDNN
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* Address comments
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* add support for new recipe on permute_fusion, rm fp unpermute
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* fix lint
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Repeated calls to nvte_shape should not invalidate
previous data pointers.

It would be possible to avoid unnecessary comparisons
by duplicating some of the logic from shape() so that
the cache is only relevant when columnwise shapes are
involved. Whether this code duplication is preferable
to the comparisons that arise from by value semantics
of reusing shape is a judgment call.
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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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* 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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* Add license.
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* Update for PP bug.
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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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* 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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* refactor for nvte_quantize_v2
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* LayerNorm linear bwd gather logic.
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* Communication updates.
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* Lint fix.
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* MR feedback.
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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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* Minor stylistic tweaks and typo fixes

Review suggestions from @ptrendx
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* Fix incorrect col strides for MXFP8 matrices
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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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* 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 license.
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* Rewrite GEMM comment.
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* MR Feedback.
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* rm no scaling enum
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* update jax enum
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---------
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* fix cpp warning
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* more fix
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* refactor to add cp support for sbhd/bshd
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* Blockwise float8 quantizer and quantized tensor class.

The classes are configurable for 128x128 blocksize
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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
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Tests for quantization are included in C++ and pytorch layers, with
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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
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* Fix typo.
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* refactor + mxfp8

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* rename linear to dense

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* Fix ops for CG
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