GitLab

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

* 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>

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

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

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

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

Fix distributed layernorm test failure
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
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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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* [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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* Add test cases for full coverage in jax/test_layer.py
- causal and window size None
- causal and window size default (-1,1)
- no_mask and window size default (-1,1)
- no_mask and window size default (2,2)
- padding and window size None
- padding_causal and window_size (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 a runner and test cases for jax.flax.module.Softmax class for fwd pass only
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* Simplify logic when picking softmax primitives and softmax jax framework calls
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* Simplify the logic for performing jax based softmax
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* Add support table for mask, SWA and Softmax type. Code linting
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* Explicit SWA conditons in comments. Fix Typo
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* Resolve typo to remove None in SWA comments section
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* Add experimental Shardy support.

Production use is not yet recommended.

---------
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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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* 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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MXFP8 flax layer tests
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* 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
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Remove xla_ignore_channel_id check and ignore Scan loop warning in unit test
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* Fix wheel install after src install
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* Fix JAX imports
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* Fix lint
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* Support THD + ring attention for self attn
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* Consolidate reorder strategy
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* Fix dataclass frozen issue
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* Use AttnBiasType, AttnMaskType, QKVLayout in cpp_extension
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flax module with compute dtype inferred from the inputs
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Fix issues when mask/sequence_descriptor is None
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* fixes L1 test

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* POC for segment_ids/segment_pos
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* Change segment_pos position
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* Use RemainingArgs to solve number of parameters mismatches
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* Test mask_descriptor for accomendating different mask representations
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Consolidate the distributed fused attention tests to shared input generation and execition logic.
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Correct fused attention output after each step to reduce intermediate memory use.
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* Add util functions to qkv_layout
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Scale sequence length in CP tests to avoid tiny sizes.
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[JAX] XLA Custom Calls with FFI for FusedAttnFwd, Quantize, Transpose, ActLuFP8, LayerNormForwardFP8FFI, and LayerNormBackwardFFI (#1263)

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* Minor fix
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* Add FFI_CudaGraph_Traits

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[JAX] Fix correctness of JAX fused attention with CP and improve numerics check in unit tests (#1282)

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* Revised following Reese Wang's comments

Still need further debugging:
FAILED test_fused_attn.py::TestFusedAttn::test_backward[NO_SWA-DROP_0.0-4-128-256-16-16-64-BF16-CROSS-KV_PACKED-NO_MASK-NO_BIAS] - AssertionError:
FAILED test_fused_attn.py::TestFusedAttn::test_backward[NO_SWA-DROP_0.0-4-128-256-16-16-64-BF16-CROSS-KV_PACKED-NO_MASK-POST_SCALE_BIAS-1HSS] - AssertionError:
FAILED test_fused_attn.py::TestFusedAttn::test_backward[NO_SWA-DROP_0.0-4-128-256-16-16-64-BF16-CROSS-SEPARATE-NO_MASK-NO_BIAS] - AssertionError:
FAILED test_fused_attn.py::TestFusedAttn::test_backward[NO_SWA-DROP_0.0-4-128-256-16-16-64-BF16-CROSS-SEPARATE-NO_MASK-POST_SCALE_BIAS-1HSS] - AssertionError:

These errors does not exist in the previous commit
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* Fix no-SWA test case errors in previous commit
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Implementation of context parallel fused attention using all-gather.
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