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Fix NVTETensor leak in attention.cpp
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Use 1x quantization + jax transpose on BW for performance
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* Undo architecture check helper function
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* Lint
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---------
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* Support SWA in CP Ring Attn THD striped sharding
Signed-off-by: Hua Huang <huah@nvidia.com>

* Add some comments; move check to _FusedAttnCPWithP2PHelper.check_supported()
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Remove unused check
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* Changed the Tensor allocation strategy
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* removes unnecessary reshapes for FP8 GEMM

* use nn.jax.scaled_matmul
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

---------
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* fp8 gemm with direct quant
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Update jax_scaled_masked_softmax to match TE kernel implementation
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Make primitive names more granular for better disabling granularity
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* [JAX] Update flax module param initialization to support logical partitioning axes
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* Fix ffn1 intermediate result being replicated
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* Fix bias in LayerNormMLP flax module
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This reverts commit 5bee81e2

.
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* removes unneccessary reshapes for FP8 GEMM

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* Enforce input sharding of norm primitive does not shard hidden dim
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* exclude GroupedGemm APIs
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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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Introduce nvte_memset to provide a fill kernel that is faster than cudaMemsetAsync for small sizes (#1716)

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* [JAX-Q] Single GPU current scaling for JAX
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Check CuDNN version and apply unfused norm if
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Production use is not yet recommended.

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