GitLab

Support layernorm sm_margin through environment variables
Signed-off-by: Reese Wang <rewang@nvidia.com>

Use relative idx to ScaledUpperTriangMaskedSoftmaxFwdPrimitive.abstract to support batching.
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Refactor sharding.py for the further custom_partitioning migration
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Migrating both FWD and BWD of LayerNorm/RMSNorm from xmap to custom_partitioning.
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Migrating both FWD and BWD of all kinds of softmax from xmap to custom_partitioning.
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Fix the wrong order of parameters to LN/RMSN bwd in ln_mlp_fp8.
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* WAR to LN/RMSN_fp8 before migrating to CP.
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Fix the wrong order of parameters of bwd of LN/RMSN_fp8.
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

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

* Force the hidden dim in Norm ops to no sharding and add warning msg.
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Reuse fwd_rule in VJP functions
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Migrating both FWD and BWD of self-fused-attn from xmap to custom_partitioning.
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Migrating both FWD and BWD of cross-fused-attn from xmap to custom_partitioning.
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* add gelu and dgelu.
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Reuse fwd_rule in VJP functions for attentions
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Apply native FP8 Dtypes to fp8.py
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Migrating cast_and_transpose from xmap to custom_partitioning
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Migrating transpose from xmap to custom_partitioning
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Apply XLA pattern match to perform FP8 GEMM.
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* migrate layernorm_fp8 to custom_partitioning.
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Unify code style
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Extend supported of Transpose with FP8
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Implementing layernorm_fp8_dot based on migrated custom calls.
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Renaming variables and publish NVTE_FP8_COLLECTION_NAME
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Replace Q/DQ custom calls with native XLA implementations
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* migrate gelu_fp to custom_partitioning.
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Miner fix
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Support custom calls with mutli-dims
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Support gerneral dot indices in _fp8_dot_impl
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Implementing layernrom_geglu_fp8_mlp
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Remove GEMM custom calls
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Remove xmap related code
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Fix typo and add query-function to FP8MetaPackage
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Fix some bugs of custom calls
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Fix CT's bugs
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Update UTs/eaxmaples to adapt to the API changes.
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Unify kernel initilization in MLP.
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Modifing with code review's feedback
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* 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>

* Deprecate QKV_INTERLEAVED use in JAX
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Deprecate QKV_INTERLEAVED use in Paddle
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Enhance qkv enum mappings
Signed-off-by: rewang <rewang@nvidia.com>

* Fix LD_LIBRARY_PATH issue
Signed-off-by: rewang <rewang@nvidia.com>

* Arbitrary seqlen kernels only support self attention currently
Signed-off-by: rewang <rewang@nvidia.com>

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

canonicalize the dtype for the better user experience
Signed-off-by: Reese Wang <rewang@nvidia.com>

Signed-off-by: Frederic Bastien <fbastien@nvidia.com>

Use the new API when it is available.
Signed-off-by: Frederic Bastien <fbastien@nvidia.com>

* Fused attention kernel only supports sm80 and sm90
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Update transformer_engine/jax/csrc/modules.cpp
Co-authored-by: Tim Moon <4406448+timmoon10@users.noreply.github.com>
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* arbitary fused kernel supports sm86/sm89 after 8.9.3
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Skip sm70
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Forward is_fused_attn_kernel_available to cpp backend
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Remove cpp is_fused_attn_available API
Signed-off-by: Reese Wang <rewang@nvidia.com>

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

* Fix flash attention dropout probability with inference
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Add output as the fused attention ctx tensor
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Add rng_state as the fused attention ctx tensors
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Add flash attention supported lengths to the fused attention
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Refactor attention primitive to reuse abstract shaped array
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Detect backend type to allocate appropriate ctx size
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Skip dropout correctness instead of return success
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Use cudaMemsetAsync and enhance the error handling
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Add flash attention kernel elts_per_thread update
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Remove redundant max 512 suffix
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Keep only DType and remove NVTEDType from python
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Fix a float32_attention_logits bugs
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Re-calculate workspace size for self attention
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Enhance bias/dbias shape guard
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Enhance the seed/rng_state checker
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Use jax.core.ShapedArray as jax.abstract_arrays is deprecated
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Enhance the unittest docs
Signed-off-by: Reese Wang <rewang@nvidia.com>

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

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

* Cast the uint32 key/counter to int64
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Update dropout support in fused attention docs
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Revise devPtrCuSeqlen* to align the naming
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Support different Jax PRNG impls
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Revert CastAsync since it is not used
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Implement is_training for 16-bit fused attn
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Add fused attn with dropout sanity unit tests
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Enhance the comments readability and rng_state checker
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Change the attention dropout shape to align other frameworks
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Make encoder tests deterministic
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Change the default seed for the jax encoder tests
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Maintain offset in TE
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Enhance the resource safety
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Revert rng_state type to allow only i64
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Handle the corner case for elts_per_threads calculation
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Populate rng state by kernels
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Rename rng_state as seed in cpp_extensions
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Update the attention dropout comment
Signed-off-by: Reese Wang <rewang@nvidia.com>

---------
Signed-off-by: Reese Wang <rewang@nvidia.com>
Co-authored-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Ignore IDE files
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

* Fix typing errors
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

* Ignore devcontainer files
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

* Avoid import from private module
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

* Apply @timmoon10 's suggestions
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

---------
Signed-off-by: Jan Bielak <jbielak@nvidia.com>

bugfix for softmax lowering
Signed-off-by: Ryan Jeng <rjeng@nvidia.com>

* Add fused attention unit tests
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Use NVTE_* enums
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Use NVTE_Mask_Type and remove FMHADescriptor
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Move common functions to utils
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Change namespace to fused_attn
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Move fused_attn_max_512_fwd_qkvpacked under the general APIs
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Add fused_attn_max_512_bwd_qkvpacked
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Move fused_attn_max_512_bwd_qkvpacked under the general APIs
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Remove redundant blank line
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Fix a potential bug for cu_seqlen converter
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Reformat fused_attn_max_512
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Refine the unfused attention warning message
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Rename to fused_attn_max_512
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Remove the deprecated header
Signed-off-by: Reese Wang <rewang@nvidia.com>

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

* Rename to fused attn
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Add attention related mask
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Add attn_mask_type and attn_bias_type
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Refactor jax primitive API
* Merge q_cu_seqlen and kv_cu_seqlen
* Remove is_causal_masking
* Replace seed with rng_state
* Add is_training argument
Signed-off-by: Reese Wang <rewang@nvidia.com>

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

* Add None guard for bias and dropout_rng
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Add version guard
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Add is_fused_attn_kernel_available() to correctly dispatch the attention impl
Signed-off-by: Reese Wang <rewang@nvidia.com>

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

* Adjust the code style
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Add the missing blank lines
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Change the order of FADescriptor members
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Enhance the readability of fused_attn_max_512.cu
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Generalize the input dimension unpacking
Signed-off-by: Reese Wang <rewang@nvidia.com>

* 16 bits fused attention requires 8.9.1
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Update fused attention support matrix
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Handle None type when sharding
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Change to the padding ratio
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Performance optimization for non-bias cases
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Revert the cudnn-frontend PRIVATE keyword which was used for debugging
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Revert "Update fused attention support matrix"

This reverts commit 4effe67d0f08f733919a329ce5ab421958740f4a.
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Treat b * s as total_seqs to align ragged cases
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Add FP16/BF16 max_seqlen <= 512 fused attention to the support matrix
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Refine test_fused_attn.py

* Replace reference code with flax.linen
* Remove unnecessary comments
* Use AttnMaskType
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Unify the cuDNN compile version
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Add dropout to the support matrix
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Slightly adjust the headers
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Typo fix: remove redundant either
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Consolidating fused attention requirements
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Replace cudnn_frontend::throw_if with NVTE_CHECK for the better error line report
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Rename to fused_attn_fp16_bf16_max_seqlen_512 for the better readability
Signed-off-by: Reese Wang <rewang@nvidia.com>

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

* Add more annotations to the custom calls
Signed-off-by: Reese Wang <rewang@nvidia.com>

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