fix imports in components/checkpoint.py

[saforem2]

I was seeing:

• ModuleNotFoundError in components/checkpoint.py:

  raceback (most recent call last):
   File "/opt/aurora/25.190.0/frameworks/aurora_frameworks-2025.2.0/lib/python3.10/runpy.py", line 196, in _run_module_as_main
     return _run_code(code, main_globals, None,
   File "/opt/aurora/25.190.0/frameworks/aurora_frameworks-2025.2.0/lib/python3.10/runpy.py", line 86, in _run_code
     exec(code, run_globals)
   File "/lus/flare/projects/AuroraGPT/AuroraGPT-v1/Experiments/AuroraGPT-2B/tt/saforem2/tt/torchtitan/experiments/blendcorpus/train.py", line 21, in <module>
     from torchtitan.components.checkpoint import CheckpointManager
   File "/lus/flare/projects/AuroraGPT/AuroraGPT-v1/Experiments/AuroraGPT-2B/tt/saforem2/tt/torchtitan/components/checkpoint.py", line 26, in <module>
     from torch.distributed.checkpoint._consolidate_hf_safetensors import (
  oduleNotFoundError: No module named 'torch.distributed.checkpoint._consolidate_hf_safetensors'
  

• ImportError issue in torchtitan/distributed/pipeline_parallel.py:

  raceback (most recent call last):
   File "/opt/aurora/25.190.0/frameworks/aurora_frameworks-2025.2.0/lib/python3.10/runpy.py", line 187, in _run_module_as_main
     mod_name, mod_spec, code = _get_module_details(mod_name, _Error)
   File "/opt/aurora/25.190.0/frameworks/aurora_frameworks-2025.2.0/lib/python3.10/runpy.py", line 110, in _get_module_details
     __import__(pkg_name)
   File "/lus/flare/projects/AuroraGPT/AuroraGPT-v1/Experiments/AuroraGPT-2B/tt/saforem2/tt/torchtitan/experiments/blendcorpus/__init__.py", line 16, in <module>
     from torchtitan.experiments.blendcorpus.infra.pipeline import pipeline_llama
   File "/lus/flare/projects/AuroraGPT/AuroraGPT-v1/Experiments/AuroraGPT-2B/tt/saforem2/tt/torchtitan/experiments/blendcorpus/infra/pipeline.py", line 22, in <module>
     from torchtitan.distributed.pipeline_parallel import (
   File "/lus/flare/projects/AuroraGPT/AuroraGPT-v1/Experiments/AuroraGPT-2B/tt/saforem2/tt/torchtitan/distributed/pipeline_parallel.py", line 15, in <module>
     from torch.distributed.pipelining.schedules import (
  mportError: cannot import name 'ScheduleDualPipeV' from 'torch.distributed.pipelining.schedules' (/opt/aurora/25.190.0/frameworks/aurora_frameworks-2025.2.0/lib/python3.10/site-packages/torch/distributed/pipelining/schedules.py)
  C^C^C
  1]    66162 interrupt  ezpz-launch python3 -m torchtitan.experiments.blendcorpus.train     |
  

• PyTorch Config:

  ; python3 -c 'import torch; print(*torch.__config__.show().split("\n", sep="\n")'
  PyTorch built with:
    - GCC 13.3
    - C++ Version: 201703
    - Intel(R) oneAPI Math Kernel Library Version 2025.2-Product Build 20250620 for Intel(R) 64 architecture applications
    - Intel(R) MKL-DNN v3.7.1 (Git Hash 8d263e693366ef8db40acc569cc7d8edf644556d)
    - OpenMP 201511 (a.k.a. OpenMP 4.5)
    - LAPACK is enabled (usually provided by MKL)
    - CPU capability usage: AVX512
  XPU backend  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=RelWithDebInfo, COMMIT_SHA=ba56102387ef21a3b04b357e5b183d48f0afefc7, CXX_COMPILER=/opt/aurora/25.190.0/spack/unified/0.10.0/install/linux-sles15-x86_64/gcc-13.3.0/gcc-13.3.0-4enwbrb/bin/g++, CXX_FLAGS= -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=OFF -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -DC10_NODEPRECATED -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=range-loop-construct -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=old-style-cast -faligned-new -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-dangling-reference -Wno-error=dangling-reference -DUSE_XPU -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, TORCH_VERSION=2.8.0, USE_CUDA=0, USE_CUDNN=OFF, USE_CUSPARSELT=OFF, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=1, USE_MPI=0, USE_NCCL=OFF, USE_NNPACK=0, USE_OPENMP=ON, USE_ROCM=0, USE_ROCM_KERNEL_ASSERT=OFF, USE_XCCL=1, USE_XPU=1,
  

Copilot Summary

This pull request introduces several updates to the checkpointing and pipeline parallel scheduling logic to improve compatibility, simplify configuration, and streamline checkpoint saving. The most significant changes involve refactoring how checkpoint staging and consolidation are handled, updating the pipeline schedule import logic for better fallback, and removing obsolete or redundant configuration options.

Checkpointing improvements and refactoring:

• Refactored the import of DefaultStager and StagingOptions in checkpoint.py to use a fallback implementation if the primary import fails, improving compatibility across different PyTorch versions. (torchtitan/components/checkpoint.py)
• Simplified the logic for creating HuggingFaceStorageWriter by removing conditional consolidation and always enabling consolidation with a fixed thread count, streamlining the checkpoint saving process. (torchtitan/components/checkpoint.py)
• Removed the call to consolidate_safetensors_files_on_every_rank after saving, as consolidation is now always handled during the save operation itself. (torchtitan/components/checkpoint.py)
• Removed the load_only option and all related checks, as well as redundant state management for staging, to simplify the checkpointing interface and behavior. (torchtitan/components/checkpoint.py) [1] [2] [3] [4]

Pipeline parallel scheduling updates:

• Updated the import logic for ScheduleDualPipeV to provide a fallback to ScheduleZBVZeroBubble if the primary schedule is unavailable, increasing robustness to upstream changes. (torchtitan/distributed/pipeline_parallel.py)
• Simplified pipeline schedule construction by removing the loss rescaling wrapper, passing the loss function directly instead. (torchtitan/distributed/pipeline_parallel.py)

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[saforem2]

ahhhh, okay. I saw from the README

To use the latest features of torchtitan, we recommend using the most recent PyTorch nightly.

but didn't know it was a strict requirement.

The only reason I bring it up is because I'm currently testing on Intel XPU devices, but our production (user-facing) environments are currently at 2.8