GitLab

* capped JAX encoder example datasets version at below 4.0
Signed-off-by: Alp Dener <adener@nvidia.com>

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

Use keyword args for jit in_shardings and out_shardings
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* include previously accidentally excluded tests

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

* Adapt run_test_multiprocessing to handle segfault
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

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

* fix otype for fp8 gemm
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>
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Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

* fix model parallel encoder to be properly sharded
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* [JAX] Update flax module param initialization to support logical partitioning axes
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Fix ffn1 intermediate result being replicated
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

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

* Add documentation and assert when logical_axes=None
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Fix bias in LayerNormMLP flax module
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Fix layer tests to not use nn_partitioning and instead use nn.with_logical_axes
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

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

* Update test_helper.py and add QuantizeConfig class for CurrentScaling
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* WIP distributed current scaling
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Distributed Current Scaling (debugging). Distributed implementation with replicated scale_inv works for layernorm_mlp but feels like a hack

Has different per-device scale_inv values, but jax.debug.print only shows one of them. Since we're telling JAX/XLA that this scale is replicated, I think it assumes all the values are equal. However, it doesn't actually check this, so it seems we are able to get away with per-device scales for current scaling but I am not sure how stable this will be and may randomly fail if us or the user changes partitioning at all or if XLA decides to actually act on the assumption that all these scale_invs are the same.
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Implement distributed current scaling by computing a global amax and scale before quantization
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Add encoder and mnist tests for current scaling
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Add primitive prefix to shardy unique_vars to prevent factor conflicts when performing unfused primitives for current scaling
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Remove scale_shape primitive arg that is no longer used
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

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

* Fix expected result on multiprocessing encoder test
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Lint fix
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Update multiprocessing current scaling tolerances
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Uncomment test case that was disabled for testing
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Remove commented out debug line
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

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

Update FSDP example instructions
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* script improvement

* add wait

* add return code back

* relax tols for FP8 test in test_multiprocessing_ by 0.001

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

* Add experimental Shardy support.

Production use is not yet recommended.

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Signed-off-by: Johannes Reifferscheid <jreiffers@nvidia.com>

* scaling enum abstract

* rm NVTE_ from ScalingMode names

* rework scaling mode enum in grouped gemm

* fix norm sharding

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

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

* 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
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* Use existing dir src build
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* 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>

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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

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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

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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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Signed-off-by: Santosh Bhavani <santosh.bhavani@live.com>
Signed-off-by: Santosh Bhavani <sbhavani@nvidia.com>
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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Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

[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

* passing data-parallel rank/size info from torch.distributed to userbuffers
Signed-off-by: Alp Dener <adener@nvidia.com>

* multi-node example working with UB_SKIPMC=1 but not with multicast
Signed-off-by: Alp Dener <adener@nvidia.com>

* fixed multi-node hang in initialize_ub(), updated comm+GEMM overlap example to support multi-node mixed tensor/data parallelism, added README
Signed-off-by: Alp Dener <adener@nvidia.com>

* fixed use case when Userbuffers is asked to allocate the TP overlap buffer with UB_SKIPMC=1
Signed-off-by: Alp Dener <adener@nvidia.com>

* corrected example problem to set device by local ordinal instead of global process rank
Signed-off-by: Alp Dener <adener@nvidia.com>

* double-free fix in userbuffers destructor
Signed-off-by: Alp Dener <adener@nvidia.com>

* removed unnecessary and incorrect torch.cuda.set_device(...)
Signed-off-by: Alp Dener <adener@nvidia.com>

* corrected inter-node ranks logic
Signed-off-by: Alp Dener <adener@nvidia.com>

* generalized node ID logic in initialize_ub to handle arbitrary world rank layouts within node
Signed-off-by: Alp Dener <adener@nvidia.com>

* added single-node comm+GEMM overlap unit tests
Signed-off-by: Alp Dener <adener@nvidia.com>

* LayerNormMLP example confirmed working with 2 nodes on Eos
Signed-off-by: Alp Dener <adener@nvidia.com>

* unit test cleanup
Signed-off-by: Alp Dener <adener@nvidia.com>

* corrected DP group ranks logic in LNMLP comm+GEMM overlap example
Signed-off-by: Alp Dener <adener@nvidia.com>

* corrected enums in unit test
Signed-off-by: Alp Dener <adener@nvidia.com>

* fixed incorrect Ubuf object init signature
Signed-off-by: Alp Dener <adener@nvidia.com>

* switched default backend for Userbuffer bootstrapping to Gloo with MPI and NCCL fallbacks, and initialize_ub option to manually select backend
Signed-off-by: Alp Dener <adener@nvidia.com>

* fixed all comm+GEMM overlap unit tests
Signed-off-by: Alp Dener <adener@nvidia.com>

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

* 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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* Refactored torch.distributed collectives into pure C++ callbacks
Signed-off-by: Alp Dener <adener@nvidia.com>

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* rm tensor check if the workspace is empty
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

* add trust_remote=true for load_dataset() in the mnist test
Signed-off-by: Phuong Nguyen <phuonguyen@nvidia.com>

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

* Apply formatting
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* Apply formatting
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* added DL framework callbacks for bootstrapping userbuffers without MPI
Signed-off-by: Alp Dener <adener@nvidia.com>

* removed userbuffers availability check in TE modules since userbuffers is now always compiled
Signed-off-by: Alp Dener <adener@nvidia.com>

* 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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* Reformat FP8 Meta

1. Reformat FP8 meta to be one-set-per-tensor.
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 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.
Signed-off-by: Ming Huang <mingh@nvidia.com>

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

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

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

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* 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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* Allowing arbitrary dimension of NVShape for the workspace allocation
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* Adding checkpoint_name to fused functions of mlp.py to get better perf with nn.scan.
Signed-off-by: Ming Huang <mingh@nvidia.com>

* Modify with review feedback.
Signed-off-by: Ming Huang <mingh@nvidia.com>

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

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

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

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

* Fix typo.
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* Port SP to Praxis
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

* Fix an issue when enabling both GQA and RoPE.
Signed-off-by: Ming-Xu Huang <mingh@nvidia.com>

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

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

* 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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* Migrating both FWD and BWD of LayerNorm/RMSNorm from xmap to custom_partitioning.
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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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* Force the hidden dim in Norm ops to no sharding and add warning msg.
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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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* 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>