support grouped query attention(MQA & GQA) for flash_attn #22
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…v, total_attention)
tongxin
approved these changes
May 16, 2024
| # --------------------------- public API --------------------------- | ||
| class FlashAttention(torch.autograd.Function): | ||
| @staticmethod | ||
| def forward(ctx, q, k, v, causal, sm_scale, return_log_normalizer, return_total_attention): | ||
| # size, stride, dtype checking | ||
| Dq, Dk, Dv = q.shape[-1], k.shape[-1], v.shape[-1] |
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I think we could first run an all equal test over the input shapes, using iterator, as a fast path, to avoid interpreter overhead.
… flash attention.
iclementine
changed the title
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support grouped query attention(GQA) for flash_attn(related kernels: fwd, bwd, split_kv, total_attention)
The MQA paper
The GQA paper
Mind the layout of the heads in the query.