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Merge pull request #10266 from nero-dv/dev

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Update sub_quadratic_attention.py
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AUTOMATIC1111 2023-05-11 07:21:18 +03:00 committed by GitHub
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1 changed files with 15 additions and 6 deletions
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modules/sub_quadratic_attention.py
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@ -202,13 +202,22 @@ def efficient_dot_product_attention(
value=value, value=value,
) )
# TODO: maybe we should use torch.empty_like(query) to allocate storage in-advance, # slices of res tensor are mutable, modifications made
# and pass slices to be mutated, instead of torch.cat()ing the returned slices # to the slices will affect the original tensor.
res = torch.cat([ # if output of compute_query_chunk_attn function has same number of
compute_query_chunk_attn( # dimensions as input query tensor, we initialize tensor like this:
num_query_chunks = int(np.ceil(q_tokens / query_chunk_size))
query_shape = get_query_chunk(0).shape
res_shape = (query_shape[0], query_shape[1] * num_query_chunks, *query_shape[2:])
res_dtype = get_query_chunk(0).dtype
res = torch.zeros(res_shape, dtype=res_dtype)
for i in range(num_query_chunks):
attn_scores = compute_query_chunk_attn(
query=get_query_chunk(i * query_chunk_size), query=get_query_chunk(i * query_chunk_size),
key=key, key=key,
value=value, value=value,
) for i in range(math.ceil(q_tokens / query_chunk_size)) )
], dim=1) res[:, i * query_chunk_size:(i + 1) * query_chunk_size, :] = attn_scores
return res return res