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Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>
Co-authored-by: Phuong Nguyen <phuonguyen@nvidia.com>

* reuse amax for current scaling
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

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Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

* add amax input to DBiasQuantizePrimitive and FFI
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* make sure amax is init with zero
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* fix sharding rule
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* rm cudaGraph compatible trait from GroupedGEMM and groupedQuantize
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* add grouped_gemm jitting in the unit test
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* memset for the mxfp8 scale padding
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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
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* Fix NVTE_FRAMEWORK=all
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* fix
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* Fix
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* Update test_helper.py and add QuantizeConfig class for CurrentScaling
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* WIP distributed current scaling
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* Distributed Current Scaling (debugging). Distributed implementation with replicated scale_inv works for layernorm_mlp but feels like a hack

Has different per-device scale_inv values, but jax.debug.print only shows one of them. Since we're telling JAX/XLA that this scale is replicated, I think it assumes all the values are equal. However, it doesn't actually check this, so it seems we are able to get away with per-device scales for current scaling but I am not sure how stable this will be and may randomly fail if us or the user changes partitioning at all or if XLA decides to actually act on the assumption that all these scale_invs are the same.
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Implement distributed current scaling by computing a global amax and scale before quantization
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* Add encoder and mnist tests for current scaling
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* Add primitive prefix to shardy unique_vars to prevent factor conflicts when performing unfused primitives for current scaling
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* Remove scale_shape primitive arg that is no longer used
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* Format
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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
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* Support larger sizes than a single value and add tests
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* [JAX-Q] Single GPU current scaling for JAX
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* Fix scale check dtype for MXFP8 scales affecting tests using assert_bitwise_scaled_tensors
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* Address comments
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* Remove cast to fp32 for norm primitives now that zero-centered gamma dtype issue is fixed
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* Fix lint issue
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* Remove unnecessary cast to fp32
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* Lint
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* scaling enum abstract

* rm NVTE_ from ScalingMode names

* rework scaling mode enum in grouped gemm

* fix norm sharding

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* rename QuantizeAxis to QuantizeLayout, get_layout to get_data_layout, q_axis to q_layout

* add fatten_axis option

* added gated act to test encoder

* sharding constraint fixes

* fix padding when flattening first dim needs to be padded

* update test sizes so that padding is tested

* rm output sharding as it can be done in the flax module

* sharding scale_inv for mxfp8

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* refactor + mxfp8

* added grouped gemm

* rename linear to dense

* added cublas init phase for groupedGemm

* relax the tol of test encoder multiprocessing mxfp8 by 0.001
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Co-authored-by: Hua Huang <huah@nvidia.com>
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Signed-off-by: Przemek Tredak <ptredak@nvidia.com>

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* FFI for all softmax functions
Signed-off-by: Hua Huang <huah@nvidia.com>

* FFI for FusedAttnBackward and Dequantize

FusedAttnBackward passed all testes in test_fused_attn.py.
Dequantize is not used currently; finish it for completeness.
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* Revert changes to tests/jax/test_fused_attn.py
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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[JAX] XLA Custom Calls with FFI for FusedAttnFwd, Quantize, Transpose, ActLuFP8, LayerNormForwardFP8FFI, and LayerNormBackwardFFI (#1263)

* Add TransposeFFI, test passed
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* Add ActLuFP8FFI; fix TransposeFFI
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* Add FusedAttnForwardFFI and some unit tests
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* Minor fix
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* Add LayerNormForwardFP8FFI & LayerNormBackwardFFI
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* Revise FusedAttnForwardFFI()
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* Add FFI_CudaGraph_Traits

All tests passed, ready for merge
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* Bug fix for FFI data type mismatch

Also add a safeguard on the entrance to FFI function
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