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https://github.com/RVC-Boss/GPT-SoVITS.git
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94 lines
3.5 KiB
Python
94 lines
3.5 KiB
Python
from torch.nn.functional import *
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from torch.nn.functional import (
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_canonical_mask,
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)
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from typing import Tuple, Optional
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def multi_head_attention_forward_patched(
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query,
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key,
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value,
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embed_dim_to_check: int,
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num_heads: int,
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in_proj_weight,
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in_proj_bias: Optional[Tensor],
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bias_k: Optional[Tensor],
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bias_v: Optional[Tensor],
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add_zero_attn: bool,
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dropout_p: float,
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out_proj_weight: Tensor,
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out_proj_bias: Optional[Tensor],
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training: bool = True,
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key_padding_mask: Optional[Tensor] = None,
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need_weights: bool = True,
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attn_mask: Optional[Tensor] = None,
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use_separate_proj_weight: bool = False,
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q_proj_weight: Optional[Tensor] = None,
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k_proj_weight: Optional[Tensor] = None,
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v_proj_weight: Optional[Tensor] = None,
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static_k: Optional[Tensor] = None,
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static_v: Optional[Tensor] = None,
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average_attn_weights: bool = True,
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is_causal: bool = False,
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cache=None,
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) -> Tuple[Tensor, Optional[Tensor]]:
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# set up shape vars
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_, _, embed_dim = query.shape
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attn_mask = _canonical_mask(
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mask=attn_mask,
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mask_name="attn_mask",
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other_type=None,
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other_name="",
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target_type=query.dtype,
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check_other=False,
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)
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head_dim = embed_dim // num_heads
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proj_qkv = linear(query, in_proj_weight, in_proj_bias)
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proj_qkv = proj_qkv.unflatten(-1, (3, query.size(-1))).unsqueeze(0).transpose(0, -2).squeeze(-2).contiguous()
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q, k, v = proj_qkv[0], proj_qkv[1], proj_qkv[2]
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# 使用动态形状推断来统一处理kv cache首步和后续步骤形状差异
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# # k,v : [N, 1, 512] at first time, [1, 1, 512] afterwards
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# # cache_k, cache_v : [N, 1, 512] for one head, N size increasement is prepared outside
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# cache["k"][:, cache["stage"]:cache["stage"]+1, :]
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# cache["v"][:, cache["stage"]:cache["stage"]+1, :]
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# Magic to get an index of either -1 or -N according to if first_infer_mask is set
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minus_one = torch.tensor([-1]).to(k.device).to(torch.int64)
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multipled = minus_one * cache["first_infer"] * (cache['x_seq_len'] + cache['y_seq_len'])
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index_offset = torch.min(minus_one, multipled)
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# 首次时 index 为 -N，后续index 为 -1
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cache["k"][index_offset:, cache["stage"]:cache["stage"]+1, :] = k
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cache["v"][index_offset:, cache["stage"]:cache["stage"]+1, :] = v
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k = cache["k"][:, cache["stage"]:cache["stage"]+1, :]
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v = cache["v"][:, cache["stage"]:cache["stage"]+1, :]
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cache["stage"] = (cache["stage"] + 1) % cache["all_stage"]
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attn_mask = _canonical_mask(
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mask=attn_mask,
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mask_name="attn_mask",
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other_type=None,
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other_name="",
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target_type=q.dtype,
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check_other=False,
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)
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attn_mask = attn_mask.unsqueeze(0)
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q = q.view(-1, num_heads, head_dim).transpose(0, 1)
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k = k.view(-1, num_heads, head_dim).transpose(0, 1)
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v = v.view(-1, num_heads, head_dim).transpose(0, 1)
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dropout_p = 0.0
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attn_mask = attn_mask.unsqueeze(0)
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q = q.view(num_heads, -1, head_dim).unsqueeze(0)
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k = k.view(num_heads, -1, head_dim).unsqueeze(0)
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v = v.view(num_heads, -1, head_dim).unsqueeze(0)
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attn_output = scaled_dot_product_attention(q, k, v, attn_mask, dropout_p, is_causal)
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attn_output = attn_output.permute(2, 0, 1, 3).contiguous().view(-1, embed_dim)
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attn_output = linear(attn_output, out_proj_weight, out_proj_bias)
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attn_output = attn_output.view(-1, 1, attn_output.size(1))
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return attn_output
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