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FMS Acceleration for Mixture-of-Experts

This library contains plugins to accelerate finetuning with the following optimizations:

  1. Expert-Parallel MoE with Triton Kernels from ScatterMoE, and some extracted from megablocks.
    • Megablocks kernels for gather and scatter

Plugins

Plugin Description Depends Loading Augmentation Callbacks
scattermoe MoE Expert Parallel with Triton Kernels from scattermoe (& megablocks) ScatterMoE / extracted kernels from megablocks ✅ ✅

Adding New Models

Our ScatterMoe implementation is a module-swap; to add new models we need to update the specifications in scattermoe_constants.py.

  • See the code documentation within to understand how to add new models.

Using ScatterMoE Saved Checkpoints

ScatterMoE checkpoints are saved using torch.distributed.checkpoint (DCP) and which is by default StateDictType.SHARDED_STATE_DICT:

  • DTensors limited support for full state dicts.
  • sharded state dicts are the extremely efficient, and require little comms overhead when saving.

We provide a script to recover back the original checkpoint:

  • currently the script is only tested in the case where DCP has saved the model in a single node.

If the checkpoint is stored in hf/checkpoint-10, call the following to have the converted checkpoint written into output_dir:

python -m fms_acceleration_moe.utils.checkpoint_utils \
    hf/checkpoint-10 output_dir \
    mistralai/Mixtral-8x7B-Instruct-v0.1

Code Extracted from Megablocks

Notes on code extraction:

Running Benchmarks

Run the below in the top-level directory of this repo:

  • the scattermoe dep is not included by default, so the -x switch installs it.
  • consider disabling the torch memory logging to see improved speeds.
tox -e run-benches \
    -x testenv:run-benches.deps+="-r plugins/accelerated-moe/requirements-khd.txt" \
    -x testenv:run-benches.setenv+="MEMORY_LOGGING=nvidia" \
    -- \
    "1 2 4" 128 benchmark_outputs scenarios-moe.yaml accelerated-moe-scatter

or run the larger Mixtral-8x7B bench:

tox ... \
    8 128 benchmark_outputs scenarios-moe.yaml accelerated-moe-scatter-mixtral

NOTE: if FileNotFoundError is observed on the triton cache, similar to issues like these:

then somehow tox is causing problems with triton and multiprocessing (there is some race condition). But the workaound is to first activate the tox env and running in bash:

# if FileNotFoundError in the triton cache is observed
# - then activate the env and run the script manually

source .tox/run-benches/bin/activate
bash scripts/run_benchmarks.sh \
    ....

Triton Kernel Dependencies

Currently we do not copy the scattermoe kernels into this respository, to this is an additional manual install:

# this will install the kernel-hyperdrive fork with the scattermoe triton kernels
pip install -r requirements-khd.txt

Known Issues

These are currently some known issues not yet resolved:

  • should eventually remove the dependency on an external kernel-hyperdrive repository.
  • now support only loading sharded safetensor non-GGUF MoE checkpoints. This is a reasonable assumption since MoE checkpoints are typically above the size limit that prevents it being saved into a single checkpoint filed.
  • when used together with FSDP, the FSDP's clip_grad_norm will not properly compute for ScatterMoE, see issue here.