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

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

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

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

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

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

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

Fix incorrect launch parm
Signed-off-by: wenjh <wenjh@sugon.com>

Fix 1D blockwise(64) acc error
Signed-off-by: wenjh <wenjh@sugon.com>

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* add support for head dim > 128
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* remove debugging
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* raise tols slightly to tolerate 1/2048 mismatches
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* fix is_training for test_te_layer
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* add bprop support for blackwell
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* minor tweak for format
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* fix backend selection results
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* bump sm100 to sm100+
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* add sq=1 test for MLA
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* enable sq=1 for bprop
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* minor tweak in comments
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* fix head_dim logic and remove pytest skip
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* add FE fix for d>128
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* update FE again to take in small fixes
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* add cuDNN version info in L0 tests
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* increase tols for Unfused + large dim
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* Revert "add cuDNN version info in L0 tests"

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* fix tols for Unfused
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* Added support of FP4 data type
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* Refactoring to BitsNum in progress
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* Fixed compilation errors. All C++ tests passed
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* Fixed a typo
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* Added FP4 guard to TMA tensor descriptor data type
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* Fixed errors in JAX C++ extensions
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* Removed dummy NVFP4 C++ test file
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* Make pytorch changes
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* Refactored the code per the review notes. Fixed JAX build error.
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* Removed unnecessary static casts
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* Typo fix
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* Pass correct num bits to create_2D_tensor_map; fixes CI
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* inline funcs
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* Implemented GroupedDense and TestGroupedDense for BF16, FP16, and FP8 
* Fix GroupedGemmFFI cuBLAS workspace alignment bug
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Revert "[JAX] GroupedDense v.2 without dynamic shape (#1721)"

This reverts commit 5d01ef21

.
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* Implemented GroupedDense and TestGroupedDense for BF16, FP16, and FP8 
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Signed-off-by: wenjh <wenjh@sugon.com>

Same intention of commit 3e38a2ea

.
This commit is to improve acc.
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* added missing deallocs in Userbuffers destroyer
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* refactor the multi_stream utils + implement nvte_multi_tensor_quantize in TE/Common

* implement GroupedQuantizer and grouped_quantize in jaxx

* fix logical_axes_names for transpose tensor in ScaledTensor
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

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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>

[PyTorch] FP8 Subchannel Recipe With FP8 Gather And Configurable Scaling Factor Tensor Swizzling (#1707)

* functional kernel for columnwise + no-transpose option, still hacky
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* pass all quantizer unit tests
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* refactor, add gemm ready api
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* make format options private members, simplify api
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* swizzle scales right before gemm
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* bug fix of single layer test
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* attempt to fix lint issue
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* fp8 gather pass, need minor refine
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* fix return_layernorm_output_gathered case
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* remove special cases, add sanity check before gemm
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* Implement dequantize for compact 1D blocks.
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* add more unit test with dequantize compact supported
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* resolve rebase issues
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* fix lint
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* clean up
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* bug fix
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* relax rtol for fp32 distributed test
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* fix some ci issue
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* fix ci test failure in debug mode
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* Force row-wise and column-wise data to have same data format

Prototype "all-gather usage" in quantizer.
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* Remove dead logic for high-precision AGs
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* Debug FP8 block-wise tensor tests
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* Handle case where LayerNormLinear returns gathered norm output
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* Use versioned flavor of get driver entrypoint function
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* Error checking
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