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

This reverts commit 5bee81e2

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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>

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

* Add primitive prefix to shardy unique_vars to prevent factor conflicts when performing unfused primitives for current scaling
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Remove scale_shape primitive arg that is no longer used
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

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

* Fix expected result on multiprocessing encoder test
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

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

* Update multiprocessing current scaling tolerances
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Uncomment test case that was disabled for testing
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Remove commented out debug line
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

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

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

* Remove cast to fp32 for norm primitives now that zero-centered gamma dtype issue is fixed
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

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

* Remove unnecessary cast to fp32
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

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

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

* Add a flag to support computing zero-centered gamma in weight dtype or compute dtype for CuDNN
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

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

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

* 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
Segregate runner classes for Softmax module and softmax primitives
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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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* 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

* rm NVTE_ from ScalingMode names

* rework scaling mode enum in grouped gemm

* fix norm sharding

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

* rm no scaling enum
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* update jax enum
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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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* 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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* Fix softmax shape for THD format.
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Make ffi compatible with jax 0.4
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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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* Use existing dir src build
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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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* Remove redundant code
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* Use AttnBiasType, AttnMaskType, QKVLayout in cpp_extension
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* Fix lint
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* Fixup
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* Add dual chunk swap example
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* Expose THD to flex MHA module
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* Enhance docs
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* fixes L1 test

* fix test_multigpu_encoder

* fixes for other multi-encoder tests

* jax.extend.ffi to jax.ffi

* initialization with float32

* add init_dtype as an optional arg to all modules

* update use_scan query from xla flags

* relax threshold for test_encoder fp8

* relax the tols

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* flax module to init params with given dtype
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* all tests passed
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* remove unneccessary reshape for kernel
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* remove casting output of dot
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* clean up
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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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* Fix bugs
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* Use descriptor in bwd
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* Primitives only accepts pure jnp array
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* segment_ids/pos support POC
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* Move seqlens/offsets generation to mask descriptor
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* Rename MaskDescriptor to SequenceDescriptor
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* Generalize get_seqlens_and_offsets
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* Utilize sequence desc on FA bwd
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* Remove small inputs and test different input format
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Correct fused attention output after each step to reduce intermediate memory use.
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* softmax custom calls with correct encapsulates

* rm jax deprecated features

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* fix ctx.aval_out indexing for workspace
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* cuDNN normalization integration
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* FFI for all softmax functions
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* FFI for FusedAttnBackward and Dequantize

FusedAttnBackward passed all testes in test_fused_attn.py.
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* Revert changes to tests/jax/test_fused_attn.py
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* added prepare phase for the FusedAttnForwardFFI

* enabled FusedAttnForwardFFI by default

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rm default value for NVTE_JAX_FUSED_ATTN_WITH_FFI
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