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31dc8dc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | import torch
import torch.nn
def scaled_dot_product_attention(query, key, value, att_mask=None, is_causal=False, scale=None, enable_gqa=False):
# Query: B, H_q, L, D
# Key: B, H_k, S, D
# Value: B, H_v, S, D
L, S = query.size(-2), key.size(-2)
if scale is None:
scale = query.size(-1) ** -0.5
attn_bias = torch.zeros((L, S), dtype=query.dtype, device=query.device)
if is_causal:
assert att_mask is None
att_mask = (query.new_ones(L, S).tril(diagonal=0) == 1)
attn_bias.masked_fill_(~att_mask, float('-inf'))
if enable_gqa:
key = key.repeat_interleave(query.size(-3)// key.size(-3), dim=-3)
value = value.repeat_interleave(query.size(-3) // value.size(-3), dim=-3)
attn_weights = query @ key.transpose(-2, -1) * scale
attn_weights += attn_bias
attn_weights = torch.nn.softmax(attn_weights, dim=-1)
attn_weights = attn_weights @ value
return attn_weights
def group_query_attention(x, Wq, Wk, Wv, att_mask = None, is_causal = False, enable_gqa=False):
query = torch.einsum('bld,dh->blh', x, Wq)
key = torch.einsum('bld,dh->blh', x, Wk)
value = torch.einsum('bld,dh->blh', x, Wv)
query = query.view(B, Q_L, H_q, -1).transpose(1, 2)
key = key.view(B, K_L, H_kv, -1).transpose(1, 2)
value = value.view(B, V_L, H_vv, -1).transpose(1, 2)
query, key = rotary_emb(query, key)
attention = scaled_dot_product_attention(
query, key, value,
att_mask=att_mask,
is_causal=is_causal,
scale=None,
enable_gqa=enable_gqa
)
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