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[JAX] XLA Custom Calls with FFI for FusedAttnFwd, Quantize, Transpose, ActLuFP8, LayerNormForwardFP8FFI, and LayerNormBackwardFFI (#1263)

* Add TransposeFFI, test passed
Signed-off-by: Hua Huang <huah@nvidia.com>

* Add ActLuFP8FFI; fix TransposeFFI
Signed-off-by: Hua Huang <huah@nvidia.com>

* Add QuantizeFFI
Signed-off-by: Hua Huang <huah@nvidia.com>

* Add FusedAttnForwardFFI and some unit tests
Signed-off-by: Hua Huang <huah@nvidia.com>

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* Minor fix
Signed-off-by: Hua Huang <huah@nvidia.com>

* Add LayerNormForwardFP8FFI & LayerNormBackwardFFI
Signed-off-by: Hua Huang <huah@nvidia.com>

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* Revise FusedAttnForwardFFI()
Signed-off-by: Hua Huang <huah@nvidia.com>

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* Add FFI_CudaGraph_Traits

All tests passed, ready for merge
Signed-off-by: Hua Huang <huah@nvidia.com>

* Bug fix for FFI data type mismatch

Also add a safeguard on the entrance to FFI function
Signed-off-by: Hua Huang <huah@nvidia.com>

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

Fix correctness of JAX fused attention with CP.
Signed-off-by: Michael Goldfarb <mgoldfarb@nvidia.com>

Add THD + GQA supports for cuDNN >= 9.6
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Reorganize PyTorch L1 tests
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Move ONNX tests to L1
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* Move FA version test to L3
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* Limit parallel build jobs in FA version test
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* WIP: make FA2 optional
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* WIP: fix logic
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* fix lint
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* minor fixes
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* minor tweak
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* add L1 test to test all supported FA versions
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* update version to 2.1.1 and trim L1 tests
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* update onnxruntime version
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* remove onnxruntime from L1 FA versions tests
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* Build custom ORT ops before running ONNX tests
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* Remove ONNX from context parallelism tests
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* Export ONNX ops that do compute in FP32

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* Add build script for custom ORT ops
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Update test to check support for context parallel attention.
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* Let Fused RoPE support THD with CP
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* add comment
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* Expose JAX sliding window attn API
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* No SWA in context parallel; fix RNG seed in test
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* Handle SAW API discrepancy in cuDNN and Python
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* Add SAW API for flax, all tests passed

Will update tests/jax/test_praxis_layers.py next
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* Update test_praxis_layers.py for SWA, test passed
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* Use tuple window_size; update for PR #1212
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* Add and adjust some pytest.skip
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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
Signed-off-by: Hua Huang <huah@nvidia.com>

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* Fix no-SWA test case errors in previous commit
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* Add Padding mask w/ sliding windows sanity tests
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* Use float32 for the reference code softmax calculation
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* add extra_state change description for different TE versions
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* add FAQ page
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* fix extra_state tests
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* minor fixes
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* Handle Float8Tensor when casting module dtype

Keep data in Float8Tensor and only change nominal dtype. Monkey-patch PyTorch module casting functions to handle Float8Tensor. Add tests.
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* Respect autocast dtype in linear op
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* add qkv descales to FA3
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* fix sbhd shapes
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* force the same dtype when comparing FA3 and cuDNN FP8
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* Revert "force the same dtype when comparing FA3 and cuDNN FP8"

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* force the same dtype when comparing FA3 and cuDNN FP8
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* add try/except for FA3 when custom qkv descales are not supported
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* fix lint
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* remove unused imports
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* add SWA support for FA3
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* minor fix
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skip FP8 CP tests if hardware does not support FP8
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