GitLab

Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Support actlen = 0 after cuDNN 9.3.0
Signed-off-by: Reese Wang <rewang@nvidia.com>

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

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

* Integrate experimental ragged offset
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Use per sequence based offsets
Signed-off-by: Reese Wang <rewang@nvidia.com>

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

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

* Add FP16 sanity tests and remove forward tests from the automatically run tests
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Enhance input checks
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Separate fused attn to 2 differnt APIs and add the docs
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Add experimental to the docs
Signed-off-by: Reese Wang <rewang@nvidia.com>

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

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

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

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

* Apply formatting
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Apply formatting
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

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Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

Signed-off-by: Przemek Tredak <ptredak@nvidia.com>

* [JAX] Fix incorrect sharding when only enable FSDP.
Signed-off-by: Ming Huang <mingh@nvidia.com>

* [JAX] Add WAR to memory misaligned issues of LN BWD.
Signed-off-by: Ming Huang <mingh@nvidia.com>

* [JAX] Reuse sm_arch for avoiding duplicate code.
Signed-off-by: Ming Huang <mingh@nvidia.com>

* [JAX] Support multiple sizes allocation in WorkspaceManager.
Signed-off-by: Ming Huang <mingh@nvidia.com>

* [JAX] Use template and ariadic arguments to improve multple sizes allocator.
Signed-off-by: Ming Huang <mingh@nvidia.com>

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Signed-off-by: Ming Huang <mingh@nvidia.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>

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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>

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