GitLab

* Implemented GroupedDense and TestGroupedDense for BF16, FP16, and FP8 
* Fix GroupedGemmFFI cuBLAS workspace alignment bug
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

---------
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>
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Revert "[JAX] GroupedDense v.2 without dynamic shape (#1721)"

This reverts commit 5d01ef21

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

* 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 incorrectly skipped test_quantize_dbias tests
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* Fix L2 test_custom_call_compute.py L2 tests
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* Fix test_helper.py
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* Address comments
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---------
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* exclude GroupedGemm APIs
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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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* Remove unnecessary cast to fp32
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* Lint
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---------
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Check CuDNN version and apply unfused norm if
below a version with the fix
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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

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* relax the tol of test encoder multiprocessing mxfp8 by 0.001
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---------
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* FFI for some transpose & activation functions
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* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Remove comments in transformer_engine/jax/csrc/extensions/activation.cpp
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[JAX] XLA Custom Calls with FFI for FusedAttnFwd, Quantize, Transpose, ActLuFP8, LayerNormForwardFP8FFI, and LayerNormBackwardFFI (#1263)

* Add TransposeFFI, test passed
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* Add ActLuFP8FFI; fix TransposeFFI
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* Add FusedAttnForwardFFI and some unit tests
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* Minor fix
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* Add LayerNormForwardFP8FFI & LayerNormBackwardFFI
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* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Add FFI_CudaGraph_Traits

All tests passed, ready for merge
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* Bug fix for FFI data type mismatch

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Signed-off-by: Hua Huang <huah@nvidia.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

* implemented custom call with ffi in csrc

* moved headers of misc to misc.h, add ffi.h

* ActLu and DActLu lowering with ffi_lowering

* CastTranspose with ffi_lowering

* enabled cudaGraph

* added 4d input test case to TestActivationLu

* added operand_output_aliases for CastTranspose

* added env var NVTE_JAX_WITH_FFI, default value = 1

* replace casting ActivationEnum by taking its value

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

* Add enabled() to BasePrimitive

* Add layernorm/rmsnorm fallback

* Add cast_fp8 fallback

* Add transpose/cast_transpose XLA fall back

* Act_lu fallback

* Add transpose fallback

* Add softmax fallback

* Unify the use of _cast_fp8

* Add tests for NVTE_JAX_CUSTOM_CALLS_RE

---------
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* Apply formatting
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* Apply formatting
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---------
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* fixed import in tests
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---------
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* Reformat FP8 Meta

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* Fixed bugs in test_distributed_layernorm_mlp.py
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---------
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* renamed gelu to act

* added relu, srelu, qgelu

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* Moved NVTE_Activation_Enum to common/.../activation.h

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

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---------
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* Add layernorm_fp8_dot unit test
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* Add doc string for test_layer
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* Fix conflicts of test_layer
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* Avoid to left bias parameters in graph when use_bias=False
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---------
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* Implemented swiglu and silu
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* Renamed nvte-*silu to nvte-*swish + generalized GetDBiasDact functions
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* combined layernorm_geglu with layernorm_gelu into fused_layernorm
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* cleaning and formatting
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* renaming based on reviewers suggestions
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* geglu + bias passed tests
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* added partial fused calculation for dbias_1
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* clean up
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* value_and_grad requires same shape for input and gradients
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* Use high precision layernorm
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* Remove local_device_ids as it caused unexpected behaviors
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* Revert "Remove local_device_ids as it caused unexpected behaviors"

This reverts commit c54349b2ce1e96ae696cf0d74f5210e55002cf72.
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---------
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* fixed linting errors for blank lines
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---------
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* Adding Cast custom call
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---------
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* Update README and Add deprecating warning to *ShardingType
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Canonicalize the dtype
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Adding assertion for non-supported batch dims.
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Adding doc/examples to _multidim_transpose
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Set FP8 meta as WeightHParamsCollection.OVERWRITE_WITH_GRADIENT in Praxis modules.
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Set FP8 meta as WeightHParamsCollection.OVERWRITE_WITH_GRADIENT in Praxis modules.
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Apply dtype-based rtol/atol to UTs
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Deprecate QKV_INTERLEAVED enum
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Skip test_distributed_custom_ops.py
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Fix the wrong sharding of bias in SelfAttn
Signed-off-by: Ming Huang <mingh@nvidia.com>

* WAR to fix the wrong cu_seqlen of MHA when DP/FSDP enabled
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Adding distributed ops unit-tests
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Adding license to test_distributed_*
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Follow review feedback to modify
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Use total bytes involved in collective ops as criteria.
Signed-off-by: Ming Huang <mingh@nvidia.com>

---------
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>
Co-authored-by: Donglin Yang <dongliny@nvidia.com>

[C/JAX] Support more mask types for the arbitrary seqlen kernels and minor changes of JAX bias (#469)

* Move bias to float32
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Enable varlen
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Increase neg infinity abs values
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Enable varlen tests
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Remove unnecessary code
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Fix lint
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Support variable sequence length after cuDNN 8.9.6
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Use unique_ptr instead of shared_ptr
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Add a new mask type: PADDING_CAUSAL_MASK
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Support flash padding mask after 8.9.6
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Enhance the Max512 handling for causal masking and add the related tests
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Update the fused attn support lists
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Remove padding_aware from the caching
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Fix libtransformer.so issue
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Reduce the pad ratio tests
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Fix a bug with cuDNN 8.9.5
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Release backend resource after the module level unit test
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Clean the jax live arrays before running the unit tests
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Fix too-few-public-methods lint
Signed-off-by: Reese Wang <rewang@nvidia.com>

---------
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Use FP8 tolerances in JAX FP8 tests
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Programmatically compute expected floating point error
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Loosen tolerance for MNIST test
Signed-off-by: Tim Moon <tmoon@nvidia.com>

---------
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Debug PyTorch and Paddle tests on Ada
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Only run Paddle layer tests with cuDNN fMHA on supported archs
Signed-off-by: Tim Moon <tmoon@nvidia.com>

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

* Reduce JAX FP8 GEMM sizes

Avoid split-k kernels on Ada.
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Disable JAX fused self-attention test on Ada
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Run supported fused attention tests on Ada
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Run supported fused attention JAX tests on Ada
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Enable Paddle fused attention on Ada
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Update reference scale calculation in TensorFlow test
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Restore backend support to reference FP8 attention impl in PyT test

Review suggestion from @cyanguwa
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Fix merge conflicts
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Debug Paddle tests on Ada
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Loosen tolerances for Paddle attention tests
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Assume causal mask implies equal seqlens in Paddle attention tests
Signed-off-by: Tim Moon <tmoon@nvidia.com>

---------
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* add mp example
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* refactor
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* update doc-string
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* better FP8 checker
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* update readme
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* replace te.* with te.flax* to remove deprecated warning
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* remove nouse os.environ
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* remove nouse
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* fix typo
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* Update examples/jax/encoder/README.md
Co-authored-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>
Signed-off-by: Jeng Bai-Cheng <jeng1220@users.noreply.github.com>
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* Update examples/jax/encoder/README.md
Co-authored-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>
Signed-off-by: Jeng Bai-Cheng <jeng1220@users.noreply.github.com>
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* Update examples/jax/encoder/README.md
Co-authored-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>
Signed-off-by: Jeng Bai-Cheng <jeng1220@users.noreply.github.com>
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* Update examples/jax/encoder/README.md
Co-authored-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>
Signed-off-by: Jeng Bai-Cheng <jeng1220@users.noreply.github.com>
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* Update examples/jax/encoder/test_multiprocessing_encoder.py
Co-authored-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>
Signed-off-by: Jeng Bai-Cheng <jeng1220@users.noreply.github.com>
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* remove cuda-python
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* adjust readme
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* Update examples/jax/encoder/README.md
Signed-off-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* fix cpp lint

fix issue of "Could not find a newline character at the end of the file."
Signed-off-by: Jeng Bai-Cheng <jeng1220@users.noreply.github.com>
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* fix AssertionError: 1 GPU per process
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* replace tfds with datasets

The Flax application crash if it use TensorFlow Dataset (tfds) in NVIDIA JAX container.
The tfds is very useful for downloading well-knwon dataset (e.g., MNIST, GLUE) and commonly used by TF/JAX community.
However, it seems like that it is NOT compatible with NVIDIA TensorFlow in NVIDIA JAX container and somehow affects JAX.
It triggers random errors at JAX initialization depending on different versions, and make CI unstable.
Thus, this commit replaces tfds with "huggingface datasets" to download needed datasets.
See "nvbugs 4039266" for more details.
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* fix input sharding

Unlike SPMD mode, in multiprocessing mode, the input tensor must be sharded manually.
Using DP=4, TP=2 as an example, the device mesh looks like:

mesh.device_ids = [[0, 1],
                   [2, 3],
                   [4, 5],
                   [6, 7]]

Assume that the process ID is mapped to GPU ID.
The process 0 and process 1 are grouped for model parallelism,
process 2 and process 3 are grouped together too, and so on.

The process 0 and process 1 need to share the same micro-batch in the training step,
process 0 and process 2, 4, and 6 have different micro-batch.

Thus, `shard_array_wrapper` partitions inputs to 4 parts (and setup
needed arguments for jax.make_array_from_single_device_arrays).
The process 0 and process 1 take the first quarter,
process 2 and process 3 take the second quarter, and so on.
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* refactor UT for multiprocess example

Use Python `multiprocessing` to test the multiprocessing example,
if the system has multiple GPU. 1 GPU per process.

Because `jax.distributed.initialize` must be called before any other JAX or Flax API,
GPU info cannot be queried by calling jax.local_devices() in TestEncoder.
Thus, `unittest_query_gpu()` forks another process to query number of GPUs and
FP8 capability.
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* remove nouse arg `--num-gpu`

JAX doesn't have an API to setup number of GPU used in SPMD mode.
The only way is to use `CUDA_VISIBLE_DEVICES` for now.
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* fix typo
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* fix ut
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* simplify the mask setting
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* increase batch-size for multigpu example

The batch-size 64 is too small to be partitioned for 8xH100.
If batch-size is 64, the GEMM shape is 256x8192x8 per GPU.
The 8 is too small for FP8 GEMM kernel, and
cuBLASLt will throw "Failed to query heuristics".
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* fix downloading mnist error

To download MNIST via `huggingface datasets`, it requires Pillow.
Otherwise, it throws `An error occurred while generating the
dataset`
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

---------
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>
Signed-off-by: Jeng Bai-Cheng <jeng1220@users.noreply.github.com>
Signed-off-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>
Co-authored-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>
Co-authored-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>

* Add zero_center_gamma/functional pass
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Add zero_centered_gamma for fp8_ln_mlp
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Add zero_centered_gamma to modules
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Add zero_centered_gamma to TransformerLayer
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Refactored code style for improved readability and consistency
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Docs enhancement for zero_centered_gamma
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Add escape for line break and remove some bad if conditions
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Revise scale_init docs
Signed-off-by: Reese Wang <rewang@nvidia.com>

---------
Signed-off-by: Reese Wang <rewang@nvidia.com>
Co-authored-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>

* add transformer module , unittests and examples
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* Update tests/jax/test_sharding.py
Co-authored-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>
Signed-off-by: Jeng Bai-Cheng <jeng1220@users.noreply.github.com>

* Update transformer_engine/jax/transformer.py
Co-authored-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>
Signed-off-by: Jeng Bai-Cheng <jeng1220@users.noreply.github.com>

* remove pylint: disable=line-too-long
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* remove pylint: disable=too-many-func-args
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* Fix the wrong broadcasting dim to dropout masks when enable transpose_bs.
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* Enable 2xACC for WGRAD and DGRAD by default
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* rename LayerNormMlpBlock as LayerNormMLP
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* refactor to avoid line-too-long
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* rename amax_history_size to amax_history_len
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* align dropout mask to TE/PyTorch as default
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* enlarge atol for decoder unittests

Two decoder unittests can pass in old JAX container(e.g., 23.02)
but can't in latest container (devel).

1. The actual(-0.020264) and desired(-0.020386) are very close.
2. The TE kernels are not changed, the diff should come from
   new codegen behavior of XLA.

Thus, it is a common floating-point accumulated error.
Enlarge atol to avoid unittest failures.
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* Adding Amax History Support

1. hide amax update in custom_vjp
2. replace amax indexing with roll(using circular buffer)
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* move kernel_init to __post_init__
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* refactor encoder examples
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* Update transformer_engine/jax/fp8.py
Co-authored-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>
Signed-off-by: Jeng Bai-Cheng <jeng1220@users.noreply.github.com>

* Update transformer_engine/jax/fp8.py
Co-authored-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>
Signed-off-by: Jeng Bai-Cheng <jeng1220@users.noreply.github.com>

* remove envvar regarding 2xACC
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* remove unused import
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

---------
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>
Signed-off-by: Jeng Bai-Cheng <jeng1220@users.noreply.github.com>
Co-authored-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>
Co-authored-by: Ming-Xu Huang <mingh@nvidia.com>
Co-authored-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* add building workflow for jax modules
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* replace bit_cast with reinterpret_cast
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* add nvtx to cmake check list
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* refactor layernorm fwd
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* refactor rmsnorm fwd
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* refactor layernorm_bwd
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* set pytorch as default in setup.py
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* rename extension from *.cc to *.cpp

cpplint cannot recognize *.cc file, so rename the extension
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* refactor style, to align TE/PyTorch
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* add pybinding, unittest and qa
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* fix license
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* disable c-extension-no-member and no-name-in-module
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* add dataclass avoid pylint error
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* Update transformer_engine/__init__.py
Co-authored-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>
Signed-off-by: Jeng Bai-Cheng <jeng1220@users.noreply.github.com>

* Update tests/jax/test_custom_call_shape.py

fix typo
Co-authored-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>
Signed-off-by: Jeng Bai-Cheng <jeng1220@users.noreply.github.com>

* Update tests/jax/test_custom_call_shape.py
Co-authored-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>
Signed-off-by: Jeng Bai-Cheng <jeng1220@users.noreply.github.com>

* add building workflow for jax modules
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* replace bit_cast with reinterpret_cast
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* add nvtx to cmake check list
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* refactor layernorm fwd
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* refactor rmsnorm fwd
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* refactor layernorm_bwd
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* set pytorch as default in setup.py
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* rename extension from *.cc to *.cpp

cpplint cannot recognize *.cc file, so rename the extension
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* refactor style, to align TE/PyTorch
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* add pybinding, unittest and qa
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* fix license
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* disable c-extension-no-member and no-name-in-module
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* add dataclass avoid pylint error
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* Update transformer_engine/__init__.py
Co-authored-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>
Signed-off-by: Jeng Bai-Cheng <jeng1220@users.noreply.github.com>

* Update tests/jax/test_custom_call_shape.py

fix typo
Co-authored-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>
Signed-off-by: Jeng Bai-Cheng <jeng1220@users.noreply.github.com>

* Update tests/jax/test_custom_call_shape.py
Co-authored-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>
Signed-off-by: Jeng Bai-Cheng <jeng1220@users.noreply.github.com>

* fix conflict due to PR62
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* fix c-extension-no-member and no-name-in-module

1. add transformer_engine_jax into extension-pkg-whitelist
2. convert pylintrc from CRLF to LF format
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* Update setup.py
Co-authored-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>
Signed-off-by: Jeng Bai-Cheng <jeng1220@users.noreply.github.com>

* remove pylint:disable and refactor import order
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

---------
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>
Signed-off-by: Jeng Bai-Cheng <jeng1220@users.noreply.github.com>
Co-authored-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>
Co-authored-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>