GitLab

* added XLA FFI custom op for TE/common nvte_cublas_gemm
Signed-off-by: Alp Dener <adener@nvidia.com>

started GemmPrimitive, abstract done
Signed-off-by: Alp Dener <adener@nvidia.com>

gemm custom op working with BF16, needs testing for FP8/MXFP8
Signed-off-by: Alp Dener <adener@nvidia.com>

converted TE GEMM API to use ScaledTensor and added os ENV flag to use TE GEMM under general gemm() call
Signed-off-by: Alp Dener <adener@nvidia.com>

BF16 tests passing, FP8 tests should be passing but contracting_dims has a scoping issue
Signed-off-by: Alp Dener <adener@nvidia.com>

fp8 tests passing for E4M3, getting CUBLAS_STATUS_NOT_SUPPORTED for E5M2
Signed-off-by: Alp Dener <adener@nvidia.com>

updated GEMM API to use separate LHS and RHS quantizers instead of a QuantizerSet
Signed-off-by: Alp Dener <adener@nvidia.com>

new GemmPrimitive passing all Dense tests
Signed-off-by: Alp Dener <adener@nvidia.com>

import cleanup and reverted code chunk movement
Signed-off-by: Alp Dener <adener@nvidia.com>

removed unused .transpose() implementations from ScaledTensors
Signed-off-by: Alp Dener <adener@nvidia.com>

all custom call tests passing on Hopper, GEMM-related tests cover both GemmPrimitive and native JAX impl
Signed-off-by: Alp Dener <adener@nvidia.com>

removed direct calls to GemmPrimitive.enabled() from outside of cpp_extensions
Signed-off-by: Alp Dener <adener@nvidia.com>

removed unused changes to ScaledTensor classes and debug prints
Signed-off-by: Alp Dener <adener@nvidia.com>

* minor unit test cleanup
Signed-off-by: Alp Dener <adener@nvidia.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* FP8 tests passing on Blackwell but MXFP8 outputs NaN
Signed-off-by: Alp Dener <adener@nvidia.com>

* reverted dense and fuseddense changes, FP8 test passing on Hopper and Blackwell, MXFP8 has issues with E5M2
Signed-off-by: Alp Dener <adener@nvidia.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* MXFP8 issue traced to scale factor padding with NaNs instead of zeros
Signed-off-by: Alp Dener <adener@nvidia.com>

* padding scale with 2^-127 instead of nans
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

* fix bug on rhs_scale_inv usage
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

* cleanup E8M0 type converter use it in gemm.cpp
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

* segfault fixed, passing all unittests on Blackwell
Signed-off-by: Alp Dener <adener@nvidia.com>

* fix for fuseddense tests
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

* fix workspace alignment
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* fixed GemmPrimitive custom partitioning to match jax.nn.scaled_matmul
Signed-off-by: Alp Dener <adener@nvidia.com>

all unit tests passing on H100x8 node
Signed-off-by: Alp Dener <adener@nvidia.com>

[pre-commit.ci] auto fixes from pre-commit.com hooks

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linting fixes
Signed-off-by: Alp Dener <adener@nvidia.com>

fixed batch dimension numbers
Signed-off-by: Alp Dener <adener@nvidia.com>

fixed FP8 scale sharding rule when there are no FP8 scales
Signed-off-by: Alp Dener <adener@nvidia.com>

added error message for unsupported Shardy partitioner
Signed-off-by: Alp Dener <adener@nvidia.com>

fixed test tolerances for FP8 cases
Signed-off-by: Alp Dener <adener@nvidia.com>

fixed shardy test skip cases
Signed-off-by: Alp Dener <adener@nvidia.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* moved reshape of encoder output in encoder examples to make custom partitioning rules work correctly
Signed-off-by: Alp Dener <adener@nvidia.com>

* added helper functions for padding and unpadding block scales, changed GemmPrimitive to accept unpadded scales and pad them after sharding
Signed-off-by: Alp Dener <adener@nvidia.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* updated shardy rules for all custom ops to decouple block scale rules from their tensors
Signed-off-by: Alp Dener <adener@nvidia.com>

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* fixed linting errors
Signed-off-by: Alp Dener <adener@nvidia.com>

* changed unit test use_jax_gemm option to be a context to preserve external custom op settings, tightened multi-GPU encoder test tolerances, changed gemm() API to use contracting_dims and batched_dims separately instead of dimension_numbers
Signed-off-by: Alp Dener <adener@nvidia.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* fixed typo in test utils
Signed-off-by: Alp Dener <adener@nvidia.com>

* added sequence-first input warnings
Signed-off-by: Alp Dener <adener@nvidia.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* fixed datasets version for JAX examples
Signed-off-by: Alp Dener <adener@nvidia.com>

* reverting modification to force_1x_quantization decision
Signed-off-by: Alp Dener <adener@nvidia.com>

* corrected gemm function syntax in unit tests
Signed-off-by: Alp Dener <adener@nvidia.com>

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Signed-off-by: Alp Dener <adener@nvidia.com>
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Phuong Nguyen <phuonguyen@nvidia.com>

Update test tolerance for L40
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Update test_helper.py and add QuantizeConfig class for CurrentScaling
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* WIP distributed current scaling
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* 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
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Add encoder and mnist tests for current scaling
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Add primitive prefix to shardy unique_vars to prevent factor conflicts when performing unfused primitives for current scaling
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Remove scale_shape primitive arg that is no longer used
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Format
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Fix expected result on multiprocessing encoder test
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Lint fix
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Update multiprocessing current scaling tolerances
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Uncomment test case that was disabled for testing
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Remove commented out debug line
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

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Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Add experimental Shardy support.

Production use is not yet recommended.

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Signed-off-by: Johannes Reifferscheid <jreiffers@nvidia.com>

* scaling enum abstract

* rm NVTE_ from ScalingMode names

* rework scaling mode enum in grouped gemm

* fix norm sharding

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

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

MXFP8 flax layer tests
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

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

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

flax module with compute dtype inferred from the inputs
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Apply formatting
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Apply formatting
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

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Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Splitted cpp_extensions.py, renamed mlp.py and fused_attn.py
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

* fixed import in tests
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

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

* Reformat FP8 Meta

1. Reformat FP8 meta to be one-set-per-tensor.
2. Remove fp8_max and scale_inv.
3. Remove unused functions in fp8.py
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Fix unit-tests
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Remove ShardingType and MajorShardingType
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Fix lint errors
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Fixed unittests.
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Rename few variables.
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Add jit to update_amax_list
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Fixed naming error in LayernormMLP
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Fixed bugs in test_distributed_layernorm_mlp.py
Signed-off-by: Ming Huang <mingh@nvidia.com>

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Signed-off-by: Ming Huang <mingh@nvidia.com>

* added distributed test for ln_mlp primitive

* added distributed test for LayerNorm layer

* changed error messages

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