GitLab

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

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

---------
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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.
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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* Fix expected result on multiprocessing encoder test
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* Lint fix
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* Add support for nvidia cu* lib wheels
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* Small cleanup
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* Fixes
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* Fix
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* rm req
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* rm debug ms
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* Loose
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* Fix lint and typo
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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
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Support larger sizes than a single value and add tests
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---------
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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 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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* 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.

---------
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* 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>
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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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* rm no scaling enum
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* update jax enum
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---------
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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

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* sharding scale_inv for mxfp8

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

MXFP8 flax layer tests
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* Blockwise float8 quantizer and quantized tensor class.

The classes are configurable for 128x128 blocksize
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Scale tensors are stored in a format emenable for matrix multiplication,
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Fusions of quantization and DBIAS or activation functions are not yet
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in pytorch and CPP and dequantization of row and columnwise usage.

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of equivalents in the kitchen repository, where a subchannel recipe
has been used for end to end training.
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