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Add env to chose blocklen of blockwise quantize.
Signed-off-by: wenjh <wenjh@sugon.com>

Fix pytest of blockwise error
Signed-off-by: wenjh <wenjh@sugon.com>

Resolve new api in  int8 gemm test
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Fix incorrect launch parm
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Fix 1D blockwise(64) acc error
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Signed-off-by: wenjh <wenjh@sugon.com>

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Same intention of commit 3e38a2ea

.
This commit is to improve acc.
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Signed-off-by: wenjh <wenjh@sugon.com>

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

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.
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Use ocp fp8.
Workaround: test_cast_float8blockwise.cu link wrong std::max
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* Added token ignoring for CE loss
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* Added tests
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* [JAX] Update flax module param initialization to support logical partitioning axes
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* Fix ffn1 intermediate result being replicated
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* Add documentation and assert when logical_axes=None
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* Fix bias in LayerNormMLP flax module
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* Fix layer tests to not use nn_partitioning and instead use nn.with_logical_axes
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* disable sm89 and cuDNN < 9.11 for KV caching
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* First pass refactor
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The acc problem in test_grouped_linear_accuracy and test_grouped_gemm is
because calc test out and ref out using diff kernel.
Make NVTE_FORCE_ROCM_GEMM=1 can force these tests to call rocm gemm using
same kernel.
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Default use of hipMallocAsync rather than hipMalloc in rocblas_gemm and
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* Update LayerNormLinear
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* Fix incorrect usage for GEMM input with block-scaled FP8
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* Fix RS out dims
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* Restore support for internal quantized tensors
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* Changes to Linear
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* Add QuantizedTensorBase class
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* Change the blockwise tensor.
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* Fix L2 test_custom_call_compute.py L2 tests
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* Reapply "Allow NVTEShape to own data." (#1703)

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