GitLab

[JAX] Remove unused TE DPA module dtype which fixes cuDNN backend detection to properly use input dtypes (#2485)

* Remove unused TE DPA module dtype which fixes cuDNN backend detection to properly use input dtypes
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Warning fallback
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* adjust test tolerances slightly for encoder tests due to change in backend
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

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

* Try to use pre-downloaded dataset artifacts first
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Set HF_HUB_OFFLINE to disable any network calls to HF when the
pre-downloaded dataset is available
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

---------
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* [JAX] Support recipe flags for disabling SR, RHT, and 2D quantization
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* lint
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* Fix issue with SR state being erased due to pytree handling of NVFP4Quantizer
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Add test for SR state preservation across VJP boundaries
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Fix sharding of SR rng state
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* lint
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* update tolerances slightly now that SR is enabled
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* lint
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* Use hashlib for deterministic hashes across runs for SR
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* rename uses_rht on scaled tensors to has_applied_rht
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

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

* Move decision of whether to use RHT into helper.py and add dedicated RHT tests
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* lint
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* fix use_rht attr usage
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* fix pure-jax rht usage criteria
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* Adjust tolerances after rebase
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

---------
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HF login in JAX examples
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Initial API change
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Change all imports and api
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* format
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* fixes
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* fix typo
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* fix recipe tets
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* fix more tests
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* fix docs, tests, and make Jax change as well
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* Change internal uses of fp8_autocast
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* Address nits
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* rename file
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* CG function, and small test fixes
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* Change instances of make_graphed_callables internally
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* Fix distributed tests
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* Review
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* Review
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* Fix test and add more docs
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* Cleanup test imports and minimize internal file imports
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* Make is_bf16_available public
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* fixes
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* fix tests
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* Better docs and better api
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* format
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* Apply suggestions from code review
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* fix nvfp4 test
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* add xml export for test_multiprocessing_encoder and test_cgemm
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---------
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* init cgemm + unit tests

* UB bootstrap with NCCL, no MPI dependency

* add NVLINK-P2P check + error message

* skip tests if no NVLINK available

* use std::vector to store ncclComm_t

* update misuse of TP warning
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---------
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* clean up sharding
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* added tpsp_resource
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* update tests
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* rework test for MeshResource
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* add mesh_resource into fp8_autocast in test_helper.py
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---------
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

[JAX] Error checking for mesh resource and update GemmPrimitive to use global_mesh_resource().fsdp_resource (#2088)

* Enforce global MeshResource is set
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Use global_mesh_resource().fsdp_resource in gemm primitive
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* Update tests
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* Update gemm.py
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* Update test_layer.py
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---------
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tighten encoder test tols
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* Support flax sharding constraints
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* Add warning for deprecated TE logical axes
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* Update examples
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* added XLA FFI custom op for TE/common nvte_cublas_gemm
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started GemmPrimitive, abstract done
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gemm custom op working with BF16, needs testing for FP8/MXFP8
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converted TE GEMM API to use ScaledTensor and added os ENV flag to use TE GEMM under general gemm() call
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BF16 tests passing, FP8 tests should be passing but contracting_dims has a scoping issue
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fp8 tests passing for E4M3, getting CUBLAS_STATUS_NOT_SUPPORTED for E5M2
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updated GEMM API to use separate LHS and RHS quantizers instead of a QuantizerSet
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new GemmPrimitive passing all Dense tests
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import cleanup and reverted code chunk movement
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removed unused .transpose() implementations from ScaledTensors
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all custom call tests passing on Hopper, GEMM-related tests cover both GemmPrimitive and native JAX impl
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removed direct calls to GemmPrimitive.enabled() from outside of cpp_extensions
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removed unused changes to ScaledTensor classes and debug prints
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* minor unit test cleanup
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* FP8 tests passing on Blackwell but MXFP8 outputs NaN
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* reverted dense and fuseddense changes, FP8 test passing on Hopper and Blackwell, MXFP8 has issues with E5M2
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* MXFP8 issue traced to scale factor padding with NaNs instead of zeros
Signed-off-by: Alp Dener <adener@nvidia.com>

* padding scale with 2^-127 instead of nans
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

* fix bug on rhs_scale_inv usage
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* cleanup E8M0 type converter use it in gemm.cpp
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* segfault fixed, passing all unittests on Blackwell
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* fix for fuseddense tests
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* fix workspace alignment
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* fixed GemmPrimitive custom partitioning to match jax.nn.scaled_matmul
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all unit tests passing on H100x8 node
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linting fixes
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fixed batch dimension numbers
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fixed FP8 scale sharding rule when there are no FP8 scales
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added error message for unsupported Shardy partitioner
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fixed test tolerances for FP8 cases
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fixed shardy test skip cases
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* moved reshape of encoder output in encoder examples to make custom partitioning rules work correctly
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* added helper functions for padding and unpadding block scales, changed GemmPrimitive to accept unpadded scales and pad them after sharding
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* updated shardy rules for all custom ops to decouple block scale rules from their tensors
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* fixed linting errors
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* changed unit test use_jax_gemm option to be a context to preserve external custom op settings, tightened multi-GPU encoder test tolerances, changed gemm() API to use contracting_dims and batched_dims separately instead of dimension_numbers
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* fixed typo in test utils
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* added sequence-first input warnings
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* fixed datasets version for JAX examples
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* reverting modification to force_1x_quantization decision
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* corrected gemm function syntax in unit tests
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* capped JAX encoder example datasets version at below 4.0
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Use keyword args for jit in_shardings and out_shardings
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* include previously accidentally excluded tests

* Execute run_test_multiprocessing_encoder with nested bash + exit code for inner bash shell

* Adapt run_test_multiprocessing to handle segfault
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---------
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* fix otype for fp8 gemm
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---------
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* fix model parallel encoder to be properly sharded
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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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* Lint
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* Add documentation and assert when logical_axes=None
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* Fix bias in LayerNormMLP flax module
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* Fix layer tests to not use nn_partitioning and instead use nn.with_logical_axes
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---------
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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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* script improvement

* add wait

* add return code back

* relax tols for FP8 test in test_multiprocessing_ by 0.001

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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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* 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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import te before te_jax
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* Fix wheel install after src install
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* Fix JAX imports
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* switch order of dirs for finding so
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* Use existing dir src build
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* Fix lint
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flax module with compute dtype inferred from the inputs
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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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* add test_multiprocessing_encoder with processing spawning in bash

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* Skip encoder tests on V100

* Fix mulitprocessing jax.distributed.init

* Remove XLA xla_gpu_deterministic_ops which causes segfault

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* Use jit instead of pjit

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* Switch to nltk>3.8.1 and new data
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* fix nltk install
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* rm tensor check if the workspace is empty
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* add trust_remote=true for load_dataset() in the mnist test
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* Apply formatting
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* Apply formatting
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* Reformat FP8 Meta

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3. Remove unused functions in fp8.py
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* Fix unit-tests
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* Remove ShardingType and MajorShardingType
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* Fix lint errors
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* Fixed unittests.
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* Rename few variables.
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* Add jit to update_amax_list
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* Fixed naming error in LayernormMLP
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* Fixed bugs in test_distributed_layernorm_mlp.py
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* Allow multi-dims for dgamma and dbeta in LN descriptor.
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* Fix the jit error in examples/jax
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* fixed static argnums for jax.jit in single gpu encoder test, changed warning filtering for pytest
Signed-off-by: Alp Dener <adener@nvidia.com>

* propagating the fix to the JAX mnist example
Signed-off-by: Alp Dener <adener@nvidia.com>

* fixed missing space ibetween flags i QAA scripts
Signed-off-by: Alp Dener <adener@nvidia.com>

* added TE warnings into the ignore list
Signed-off-by: Alp Dener <adener@nvidia.com>

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Signed-off-by: Alp Dener <adener@nvidia.com>