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* TensorUsage + FP8 GEMM with all layouts handling on BW
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>


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

* Support MXFP8 and handle empty matrices
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* Add support for Fused Attn MLA head_dim_qk != head_dim_v
	Modify is_fused_attn_kernel_available() to accept different head_dims for qk and v
	Modify FusedAttnHelper to accept different head_dims for qk and v and modify assert dims checks in parse_qkv_aval()
	Modify FusedAttnFwdPrimitive and FusedAttnBwdPrimitive to accept different head_dims for qk and v
	Modify Fused Attn related cpp and csrc extension API calls to accept different head_dims for qk and v
	Modify DotProductAttention call() to extract head dims separately for qk and v
	Modify the FusedAttn Tests to accommodate for API changes in FusedAttn API
	Add test case for head_dim_qk != head_dim_v (failing)
	Modify the baseline JAX appropriately to reshape the output vector based on v dims and not q dims
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* Fix context dims in general DPA in test_fused_attn
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* Fix dim for output tensor by replacing with v head dim rather than q head dim
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* Modify the fused attn jax unit test case for head dim qk != head dim v
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* Use new FusedAttnRunner function signature for separate hidden dim for qk and v in Fused Attn distributed tests
Code clean up
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* Fix usage of is_fused_attn signature in distributed tests
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* Remove unnecessary assert
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* add support for head dim > 128
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* remove debugging
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* raise tols slightly to tolerate 1/2048 mismatches
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* fix is_training for test_te_layer
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* add bprop support for blackwell
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* minor tweak for format
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* fix backend selection results
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* bump sm100 to sm100+
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* add sq=1 test for MLA
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* enable sq=1 for bprop
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* minor tweak in comments
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* fix head_dim logic and remove pytest skip
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* add FE fix for d>128
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* update FE again to take in small fixes
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* add cuDNN version info in L0 tests
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* increase tols for Unfused + large dim
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* Revert "add cuDNN version info in L0 tests"

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* fix tols for Unfused
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* fixes for jittable grouped_quantize

* fixes for jittable grouped_gemm

* fix contracting_dim for wgrad gemm

* exclude jitted grouped_gemm from the unit test as it does not work cudaGraph

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* Implemented GroupedDense and TestGroupedDense for BF16, FP16, and FP8 
* Fix GroupedGemmFFI cuBLAS workspace alignment bug
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Revert "[JAX] GroupedDense v.2 without dynamic shape (#1721)"

This reverts commit 5d01ef21

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* Implemented GroupedDense and TestGroupedDense for BF16, FP16, and FP8 
* Fix GroupedGemmFFI cuBLAS workspace alignment bug
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* refactor the multi_stream utils + implement nvte_multi_tensor_quantize in TE/Common

* implement GroupedQuantizer and grouped_quantize in jaxx

* fix logical_axes_names for transpose tensor in ScaledTensor
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* fix otype for fp8 gemm
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* Fix 1x quantize kernel availability check on hopper

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* Use 1x quantization + jax transpose on BW for performance
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* Use 1x quantization on Hopper as well as it is also faster
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* Undo architecture check helper function
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* Lint
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* Support SWA in CP Ring Attn THD striped sharding
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* Add some comments; move check to _FusedAttnCPWithP2PHelper.check_supported()
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Remove unused check
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* removes unnecessary reshapes for FP8 GEMM

* use nn.jax.scaled_matmul
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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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This reverts commit 5bee81e2

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* removes unneccessary reshapes for FP8 GEMM

* use nn.jax.scaled_matmul
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* 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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* 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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* 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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* [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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Check CuDNN version and apply unfused norm if
below a version with the fix
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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
- 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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* New GroupedGemmPrimitive using variadic args

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* scaling enum abstract

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* fix norm sharding

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* rm no scaling enum
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* refactor + mxfp8

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* Fix softmax shape for THD format.
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* Fix dataclass frozen issue
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* Fix lint
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* Fixup
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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

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* relax the tols

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