GitLab

Rm unused import causing CI failures
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

add deterministic option
Signed-off-by: Shijie Wang <jaywan@nvidia.com>
Co-authored-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

Update Paddle image
Signed-off-by: Tian Zheng <tizheng@nvidia.com>

fix tp_size for GQA
Signed-off-by: Charlene Yang <8636796+cyanguwa@users.noreply.github.com>

This bug will cause bug [ERROR] failed (exitcode: -11) local_rank: 0 (pid: 1761020) of binary: ~/megatron/bin/python.

That is because we miss the rng_states that is required in attention recompute (for dropout), but no hint is provided.  

It is very very very difficult to trace and cost me two weeks.

```python
before the start of training step] datetime: 2024-07-22 18:26:45 
[2024-07-22 18:27:00,941] torch.distributed.elastic.multiprocessing.api: [ERROR] failed (exitcode: -11) local_rank: 0 (pid: 1761020) of binary: /home//miniconda3/envs/megatron/bin/python
Traceback (most recent call last):
  File "/home//miniconda3/envs/megatron/bin/torchrun", line 33, in <module>
    sys.exit(load_entry_point('torch==2.2.1+cu121', 'console_scripts', 'torchrun')())
  File "/home//miniconda3/envs/megatron/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 347, in wrapper
    return f(*args, **kwargs)
  File "/home//miniconda3/envs/megatron/lib/python3.10/site-packages/torch/distributed/run.py", line 812, in main
    run(args)
  File "/home//miniconda3/envs/megatron/lib/python3.10/site-packages/torch/distributed/run.py", line 803, in run
    elastic_launch(
  File "/home//miniconda3/envs/megatron/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 135, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/home//miniconda3/envs/megatron/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 268, in launch_agent
    raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
```
Signed-off-by: 李金梁 <975761915@qq.com>

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

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

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

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

* Add classifiers for python
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Add utils to build wheels
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

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

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

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

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

* PaddlePaddle only builds for x86
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Add optional fwk deps
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Python3.8; catch install error
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* [wip] cudnn9 compile with paddle support
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* [wip] dont link cudnn
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

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

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

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

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

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

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

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* Lint
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* remove residual packages; exclude stub from nvrtc .so search
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Exclude builtins from nvrtc .so search
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* properly include files for sdist
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* paddle wheel tie to python version
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Fix paddle build from src [wip]
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

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

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

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

* fix lint from pr986
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

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

* Add sanity wheel test
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Add sanity import to wheel test
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* remove upper limit on paddlepaddle version
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

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

* Remove pybind11 dependency
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Fix cpp tests
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* Search .sos in cuda home
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* fixes
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* CLeanup, remove residual code
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

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Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

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

* Fix build error with Paddle >2.6.1
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* Remove extra args to fused attention func
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Add missing arg to fused attention func
Signed-off-by: Tim Moon <tmoon@nvidia.com>

---------
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* i
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* .
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---------
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* Set minimum CMake version to 3.21

Stop linking to nvtx.
Signed-off-by: Tim Moon <tmoon@nvidia.com>

* Update .github/workflows/build.yml
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* Revert Python version to 3.9
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---------
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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
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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
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* fixed segfault in Userbuffers destructor
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* 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
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* removed unwanted memcpyDtoD/fixed weight parametrisation
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* Fixed convergence issues
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* Update transformer_engine/pytorch/module/layernorm_linear.py
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* Update transformer_engine/pytorch/module/layernorm_mlp.py
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* Update transformer_engine/pytorch/module/linear.py
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* Update sequential container constructor to handle modules in plain dicts
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* Avoid initializing Sequential with dicts
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---------
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* initialize output tensors to 0 for THD while waiting for cuDNN bug fix
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* move fill_() to F16 loop
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* fix fused_attn_bwd()
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* correct typo in check_set_window_size
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* use nvtx3 instead
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Update nvtx header
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* FP8 type switch macro now wraps only the FP8 kernel to avoid invalid type errors
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fix 261 compile
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* DGRAD_RS UB overlap Bug fixes
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* Add option to pass kwargs to CUDA graph module
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* Debug unit tests
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* Tweak comments
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* Add enabled() to BasePrimitive

* Add layernorm/rmsnorm fallback

* Add cast_fp8 fallback

* Add transpose/cast_transpose XLA fall back

* Act_lu fallback

* Add transpose fallback

* Add softmax fallback

* Unify the use of _cast_fp8

* Add tests for NVTE_JAX_CUSTOM_CALLS_RE

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* add cuDNN swa
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* fix SWA
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* add set_deterministic and minor fixes for swa
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* add AttentionParams
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* change window_size to int64_t; fix swa/determinism tests; cache _attention_backends
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* add window_size to get_backend; fix jax and paddle
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* minor fixes; add set_deter to bwd_impl
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* fix unit tests
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* fix FP8 tests due to determinism
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* add support matrix for SWA and bias
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* minor fixes and lint
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* minor fixes
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* add wording on window_size special cases
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* minor tweak on wording
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* fix jax assertion error
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* fix wording
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Reduce CUDA driver API calls when choosing transpose kernels
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* Add basic infrastructure for Sequential module
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* Add linear op
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* Add bias op
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* Add unfused linear op

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* Debug unfused linear op
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* Use fused cast-transpose in linear ops
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* Disable GEMM+bias fusion with FP32 activations

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* Refactor parallel tests to reduce job launches
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* Add all-reduce, all-gather, and reduce-scatter ops
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* Remove unused file
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* Debug multi-GPU FP8 test
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* Add support for FP8 scale updates

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* Add license boilerplate
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* Fuse GEMM+bias in row TP

Add documentation for unfused ops
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* Rename pipeline to fuser

Expand documentation
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* Tweak documentation
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* Preserve cached FP8 transpose between ops
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* Add option for fused wgrad accumulation
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* Directly output FP8 from linear if needed
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* Fix cuDNN front-end commit
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* Use updated FP8 tensor API for transpose caching
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* Use updated API for FP8 scale updates
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* Add tests for non-default FP8 recipes
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* Rename UnfusedOperation to BasicOperation
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* Add unit test to check amax reduction with fusable op
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* Operator autograd state no longer needs to be initialized
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