GitLab

* fp8 gemm with direct quant
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

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

Update jax_scaled_masked_softmax to match TE kernel implementation
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

Make primitive names more granular for better disabling granularity
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Build support for cuda 13
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* Fix build for cudnn 8.9*; cuda 12.1
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* readd include
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---------
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* [JAX] Update flax module param initialization to support logical partitioning axes
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Fix ffn1 intermediate result being replicated
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Lint
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* Add documentation and assert when logical_axes=None
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Fix bias in LayerNormMLP flax module
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Fix layer tests to not use nn_partitioning and instead use nn.with_logical_axes
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

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

* Cleanup runtime library loading
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* Better comments and logic
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* Fix catching stray builds
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* Fix missing fw case
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* minor grammar
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* Improve error msg
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---------
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

This reverts commit 5bee81e2

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

* Fix L2 test_custom_call_compute.py L2 tests
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

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

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

* removes unneccessary reshapes for FP8 GEMM

* use nn.jax.scaled_matmul
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

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

* Enforce input sharding of norm primitive does not shard hidden dim
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Fix partitioning issue in dact primitive causing NaN and add better shape checks before calling TE API
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Move dact shape assertion from cpp to python
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

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

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

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

Fix distributed layernorm test failure
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* Update test_helper.py and add QuantizeConfig class for CurrentScaling
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* 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.
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* 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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* Fix expected result on multiprocessing encoder test
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* Lint fix
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* Update multiprocessing current scaling tolerances
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* Remove commented out debug line
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---------
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* Add support for nvidia cu* lib wheels
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* Small cleanup
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* Loose
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* Fix jax wheel install in no toolkit env [wip]
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* Add missing headers via pip
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* Load SOs, revert CMake
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* Proper fix got get_te_path
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* Fix JAX exec without cudatk
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* Fix lint and typo
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---------
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

Introduce nvte_memset to provide a fill kernel that is faster than cudaMemsetAsync for small sizes (#1716)

* nvte_memset fills single float value
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Support larger sizes than a single value and add tests
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

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

* [JAX-Q] Single GPU current scaling for JAX
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

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

Check CuDNN version and apply unfused norm if
below a version with the fix
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* Move jaxx cuda kernels to core
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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 test cases for full coverage in jax/test_layer.py
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- no_mask and window size default (2,2)
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* Correct the condition where padding_causal_mask was being mapped to scaled upper triangle
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* Fix Issue #1524
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* Add experimental Shardy support.

Production use is not yet recommended.

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

* New GroupedGemmPrimitive using variadic args

* Remove squeeze() to reduce D2D memcpy

* Revert to the list append fashion to simplify code

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

* scaling enum abstract

* rm NVTE_ from ScalingMode names

* rework scaling mode enum in grouped gemm

* fix norm sharding

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

* rm no scaling enum
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* rename QuantizeAxis to QuantizeLayout, get_layout to get_data_layout, q_axis to q_layout

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

MXFP8 flax layer tests
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Hardcodes the default values.
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* refactor + mxfp8

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