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README.md

Monarch Mixer BERT

This folder shows an example of adapting M2-BERT to use FlashFFTConv. The original files are sourced from the M2-BERT implementation.

Requirements

Install model-specific requirements:

pip install -r requirements.txt

Usage

We have sample configs for M2-BERT models of different sizes that you can benchmark:

python benchmark_fwd.py configs/m2-110M.yaml
python benchmark_fwd.py configs/m2-110M-flashfftconv.yaml

Changes to Use FlashFFTConv in M2-BERT

We describe the changes necessary to use FlashFFTConv in M2-BERT:

Create an instance of FlashFFTConv in BERTEncoder. In bert_layers.py, lines 294-301:

seqlen = config.max_position_embeddings
if config.use_flashfftconv:
    self.flashfftconv = FlashFFTConv(seqlen * 2, dtype=torch.float16) # 2x for padding, may need bfloat16
self.layer = nn.ModuleList(
    [copy.deepcopy(layer) for _ in range(config.num_hidden_layers)])
if config.use_flashfftconv:
    for layer in self.layer:
        layer.attention.flashfftconv = self.flashfftconv # add it to the layers

Then, we adapt the actual sequence mixer to use flashfftconv in monarch_mixer_sequence_mixer_flashfftconv.py.

We make a couple more optimizations:

  • We use our fast depthwise kernel.
  • We introduce an "inference mode" that simply loads the convolution kernel from weights, instead of recomputing it every time (which is especially expensive for short kernels). An alternative is to use a fast kernel to generate the convolution kernel, as in the M2 repo.