GitLab

* 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

.
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* Fix L2 test_custom_call_compute.py L2 tests
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* Fix test_helper.py
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* Address comments
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---------
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* removes unneccessary reshapes for FP8 GEMM

* use nn.jax.scaled_matmul
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---------
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* Enforce input sharding of norm primitive does not shard hidden dim
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* Fix partitioning issue in dact primitive causing NaN and add better shape checks before calling TE API
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* Move dact shape assertion from cpp to python
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---------
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* exclude GroupedGemm APIs
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---------
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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
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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.
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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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* Remove scale_shape primitive arg that is no longer used
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* Format
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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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---------
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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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Check CuDNN version and apply unfused norm if
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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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* Fix Issue #1524
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* Add experimental Shardy support.

Production use is not yet recommended.

---------
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* New GroupedGemmPrimitive using variadic args

* Remove squeeze() to reduce D2D memcpy

* Revert to the list append fashion to simplify code

---------
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* scaling enum abstract

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

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---------
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MXFP8 flax layer tests
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