GitLab

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

---------
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>
Co-authored-by: Hua Huang <huah@nvidia.com>
Co-authored-by: Jeremy Berchtold <jberchtold@nvidia.com>

import te before te_jax
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

* Fix wheel install after src install
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Fix JAX imports
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* switch order of dirs for finding so
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Use existing dir src build
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

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

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* Add support for UB MNNVL
Signed-off-by: Nicolas Castet <ncastet@nvidia.com>

* Address review comments
Signed-off-by: Nicolas Castet <ncastet@nvidia.com>

* Fix lint
Signed-off-by: Nicolas Castet <ncastet@nvidia.com>

* Dlopen nvml lib since it comes with the cuda driver
Signed-off-by: Nicolas Castet <ncastet@nvidia.com>

* Add initial copyright date
Signed-off-by: Nicolas Castet <ncastet@nvidia.com>

---------
Signed-off-by: Nicolas Castet <ncastet@nvidia.com>

flax module with compute dtype inferred from the inputs
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

* 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

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

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

* add test_multiprocessing_encoder with processing spawning in bash

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

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

* Skip encoder tests on V100

* Fix mulitprocessing jax.distributed.init

* Remove XLA xla_gpu_deterministic_ops which causes segfault

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

* Create README.md

added all PyT examples
Signed-off-by: Santosh Bhavani <santosh.bhavani@live.com>

* Update README.md

- Added JAX, PaddlePaddle, and third-party examples
- Fixed DL framework links
- Removed issue request for new PRs
Signed-off-by: Santosh Bhavani <sbhavani@nvidia.com>

---------
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Co-authored-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Use jit instead of pjit

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Signed-off-by: Frederic Bastien <fbastien@nvidia.com>
Co-authored-by: Phuong Nguyen <36155692+phu0ngng@users.noreply.github.com>

* Switch to nltk>3.8.1 and new data
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* fix nltk install
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

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[C/PyTorch] Fixed incorrect use of `torch.distributed.new_group()` when creating intra-node group in `initialize_ub()` (#1087)

* updated initialize_ub() to use new_subgroups_by_enumeration() to generate intra-node groups, added new unit tests for TE layers with comm overlap
Signed-off-by: Alp Dener <adener@nvidia.com>

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[PyTorch] Fixing hang in `initialize_ub()` for multi-node runs after PR901 removal of MPI-dependence (#986)

* Re-implementing PR901 (removing MPI-dependence in Userbuffers) with multi-node fixes

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* multi-node example working with UB_SKIPMC=1 but not with multicast
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* fixed multi-node hang in initialize_ub(), updated comm+GEMM overlap example to support multi-node mixed tensor/data parallelism, added README
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* fixed use case when Userbuffers is asked to allocate the TP overlap buffer with UB_SKIPMC=1
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* corrected example problem to set device by local ordinal instead of global process rank
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* double-free fix in userbuffers destructor
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* removed unnecessary and incorrect torch.cuda.set_device(...)
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* corrected inter-node ranks logic
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* generalized node ID logic in initialize_ub to handle arbitrary world rank layouts within node
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* added single-node comm+GEMM overlap unit tests
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* LayerNormMLP example confirmed working with 2 nodes on Eos
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* unit test cleanup
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* corrected DP group ranks logic in LNMLP comm+GEMM overlap example
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* corrected enums in unit test
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* fixed incorrect Ubuf object init signature
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* switched default backend for Userbuffer bootstrapping to Gloo with MPI and NCCL fallbacks, and initialize_ub option to manually select backend
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* fixed all comm+GEMM overlap unit tests
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* corrected all_gather use for Gloo backend
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* changed userbuffers allgather callback to always use all_gather() instead of all_gather_into_tensor()
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* restored and verified old MPI-based bootstrapping via NVTE_UB_WITH_MPI=1 option at compile time
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* disabled scoped GIL release for comm+GEMM overlap algorithms
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* avoid dist.init_device_mesh in comm+GEMM overlap example to support older PyTorch versions
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* applied RS overlap FP8 fix from PR1004
Signed-off-by: Alp Dener <adener@nvidia.com>

* fixed segfault in Userbuffers destructor
Signed-off-by: Alp Dener <adener@nvidia.com>

* corrected comm+GEMM overlap unit test arguments
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* fixed unit test run command for when Userbuffers is compiled with MPI
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* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci



* Refactored torch.distributed collectives into pure C++ callbacks
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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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---------
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

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

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

* added DL framework callbacks for bootstrapping userbuffers without MPI
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* removed userbuffers availability check in TE modules since userbuffers is now always compiled
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* added comm+GEMM overlap example with LayerNormMLP
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* lintin and review fixes
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* linting and review fixes
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* added header guards
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* removed defunct userbuffers checks in build_utils and setup.py
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* added exposed API in modules/base.py to __all__
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* removed transformer_engine/CMakeLists.txt and shifted all TE/common compile into transformer_engine/common/CmakeLists.txt
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* Reformat FP8 Meta

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2. Remove fp8_max and scale_inv.
3. Remove unused functions in fp8.py
Signed-off-by: Ming Huang <mingh@nvidia.com>

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

* Remove ShardingType and MajorShardingType
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Fix lint errors
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Fixed unittests.
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Rename few variables.
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Add jit to update_amax_list
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Fixed naming error in LayernormMLP
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* Fixed bugs in test_distributed_layernorm_mlp.py
Signed-off-by: Ming Huang <mingh@nvidia.com>

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

* New TE wrapper for PyTorch FullyShardedDataParallel to make TE modules distribute their activations after the forward pass and gather them before the backward pass
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* simplified TE module setup for FSDP comms
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* FSDP scatter/gather for tensors saved into autograd ctx now working for base TE modules
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* make sure activation recompute disables FSDP scatter/gather
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* make sure Fp8 weight buffers are sharded at the end of the backward pass and gathered before forward
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* Fixed typo in attribute name
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* fixed bug in finding FSDP-wrapped TE modules
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* fixed typo in fp8 weight tensor name
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* fixed incorrect # of gradients
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* Added fp8 amax gradient hook tensor to the parameter reset
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* get rid of erroneous dummy tensor leftover from incorrect rebase
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* Linting fixes
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* fixing git snafu and removing debug statements
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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
Signed-off-by: Ming Huang <mingh@nvidia.com>

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

* fixed static argnums for jax.jit in single gpu encoder test, changed warning filtering for pytest
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* propagating the fix to the JAX mnist example
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* fixed missing space ibetween flags i QAA scripts
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* added TE warnings into the ignore list
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* Adding support of sequence parallelism
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Adding RoPE
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* Fix wrong batch_logical_axes
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* Rnaming FSDP outer env var
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* Poring RoPE to Praxis layers.
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* Porting GeLU + [FP8 Cast].
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* WAR to make XLA successfully match FP8 GEMM on FFN1 with GeLU.
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* Adding checkpoint_name to fused functions of mlp.py to get better perf with nn.scan.
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* Modify with review feedback.
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* Fix bugs
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* Fix typo.
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* Fixed for lint
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* Fix typo.
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* Port SP to Praxis
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* Fix an issue when enabling both GQA and RoPE.
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* Update docs
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---------
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

Signed-off-by: hugo-syn <hugo.vincent@synacktiv.com>

* Implemented deferred initialization via `device='meta'` option for te.Linear and added new PyTorch example to demonstrate its use with FullyShardedDataParallel execution.
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* correcting Float8Tensor initialization and fixing linting errors
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* removed duplicate code from upstream rebase, local tests passing
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* improved comments/documentation for FSDP example
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* converted reset_parameters() into a base module function
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* fixed Float8Tensor creation with deferred init, all tests passing locally
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* extended deferred initialization to all TE modules
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* fixed linting errors
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* removed unnecessary reference to the parent module of parameter, added clarifying comments in parameter reset
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* Refactor sharding.py for the further custom_partitioning migration
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* Migrating both FWD and BWD of all kinds of softmax from xmap to custom_partitioning.
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* Fix the wrong order of parameters to LN/RMSN bwd in ln_mlp_fp8.
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* WAR to LN/RMSN_fp8 before migrating to CP.
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* Fix the wrong order of parameters of bwd of LN/RMSN_fp8.
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* Following review feedback to modify
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* Migrating both FWD and BWD of self-fused-attn from xmap to custom_partitioning.
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* add gelu and dgelu.
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* Reuse fwd_rule in VJP functions for attentions
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* Apply native FP8 Dtypes to fp8.py
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* Migrating cast_and_transpose from xmap to custom_partitioning
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* Migrating transpose from xmap to custom_partitioning
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* Apply XLA pattern match to perform FP8 GEMM.
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---------
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>

* 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>

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

* [Paddle] Add TP, DP, PP, FSDP
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Minor fix
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Fix CI failure
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Remove set_nccl_overlap_warning_if_tp
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Improve variable naming
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Refactor FP8 Buffer
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Stylic changes
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Fix FP32 parallel training
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Fix numel performance issue
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Squashed commit of the following:

commit 79e2e5fd774e67dcdda9aae01a9f31a6479c5d70
Author: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>
Date:   Sun Aug 20 14:39:16 2023 +0000

    Add TP test
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

commit 1d40ad60540490f97ed82ba877cc6eda8902cbf6
Author: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>
Date:   Sun Aug 20 14:22:25 2023 +0000

    Fix tp_size when disabled
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

commit 6632f735a0c8251862355fc74622af59fae3a509
Author: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>
Date:   Sun Aug 20 05:52:18 2023 +0000

    Add TP for attention and transformer layer
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Add shape check
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Add FSDP check for stage 1,2,3
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Review changes
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Fix group_sharding test
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Support NVTE_FUSE_ATTN
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Fix CI errors
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

---------
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>
Co-authored-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Cast Flax collections to FrozenDict as WAR to adapt Flax 0.7.1.
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Adding min version of flax to requirements.txt in examples
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Fix praxis tests and rename compare_frozen_dict to compare_dict
Signed-off-by: Ming Huang <mingh@nvidia.com>

* [Paddle] Refactor FP8 state (#350)

Refactor fp8 state
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Store FP8 checkpointing data in CPU (#351)
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Make test_layer be able to run on both Flax >=0.7.1 and <=0.7.0
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Update Flax version
Signed-off-by: Tim Moon <tmoon@nvidia.com>

---------
Signed-off-by: Ming Huang <mingh@nvidia.com>
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>
Signed-off-by: Tim Moon <tmoon@nvidia.com>
Co-authored-by: Tian Zheng <tizheng@nvidia.com>
Co-authored-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>
Co-authored-by: Tim Moon <tmoon@nvidia.com>

* Add FP8 support

- Add FP8 recipe
- Add FP8 path for nn layers
- Add MNIST FP8 example
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Update README
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Fix LayerNormMLP FP8 backward
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Fix FP8 training in float32 accumulation
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Fix FP8 checkpointing for non forward execution cases (same as #323)
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Refactors and improvements for better code stype, readability and organization
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Remove unnecassary pylint override
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

---------
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Add Linear layer (FP16)
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

- Add BF16 training example
- Add fp8_autocast (only supports non-fp8 for now)
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Remove FP8 stuff
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Simplify Linear layer forward
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Add LayerNorm layer (BF16)
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Add LayerNormLinear layer
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Store weights in BF16
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Add LayerNormMLP layer
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Add BF16 MNIST example
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Remove in-place cast for compatibility with Paddle AMP mechanism
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* README correction
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Add Paddle op as a backend option
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Fix code format
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Fix dtype change between iterations
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Minor fixes
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Move forward function out of base layer
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Use Paddle nvtx bindings
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

---------
Signed-off-by: Tian Zheng (Engrg-Hardware 1) <tizheng@nvidia.com>

* Fully remove attn_type and set self_attn_mask_type default to 'causal'
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Fix tests with new arguments
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Explicit self_attn_mask_type for examples
Signed-off-by: Reese Wang <rewang@nvidia.com>

* Update transformer_engine/jax/flax/transformer.py
Co-authored-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>
Signed-off-by: zlsh80826 <rewang@nvidia.com>

* Update transformer_engine/jax/flax/transformer.py
Co-authored-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>
Signed-off-by: zlsh80826 <rewang@nvidia.com>

---------
Signed-off-by: Reese Wang <rewang@nvidia.com>
Signed-off-by: zlsh80826 <rewang@nvidia.com>
Co-authored-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

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

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

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

* Adding JAX/Praxis modules and dependencies.
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Adding UTs to JAX/Praxis modules.
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Remove praxis as a dependency due to not strictly needed
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Repalce is_fp8_supported to is_fp8_available
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Make Praxis as an optional dependency.

1. Removed 'from . import praxis' in __init__.py.
    1.1 Noted, keep 'from . import flax' for deprecated warning.
2. Changed te.flax to te_flax in examples and README.rst.
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Adding a workaround to FP8 training on Praxis.
Signed-off-by: Ming Huang <mingh@nvidia.com>

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

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