GitLab

* 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
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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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* 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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* Uncomment test case that was disabled for testing
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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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* Fixes
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* Fix
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* rm req
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* Specify exact package versions
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* rm debug ms
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* Loose
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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
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---------
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* [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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* 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 (-1,1)
- 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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* Explicit SWA conditons in comments. Fix Typo
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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>
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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* 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

* 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

---------
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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Hardcodes the default values.
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