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https://github.com/RVC-Boss/GPT-SoVITS.git
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894 lines
33 KiB
Python
894 lines
33 KiB
Python
# modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_model.py
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# reference: https://github.com/lifeiteng/vall-e
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import math
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from typing import List, Optional
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import torch
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from tqdm import tqdm
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from AR.models.utils import make_pad_mask, make_pad_mask_left
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from AR.models.utils import (
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topk_sampling,
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sample,
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logits_to_probs,
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multinomial_sample_one_no_sync,
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dpo_loss,
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make_reject_y,
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get_batch_logps
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)
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from AR.modules.embedding import SinePositionalEmbedding
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from AR.modules.embedding import TokenEmbedding
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from AR.modules.transformer import LayerNorm
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from AR.modules.transformer import TransformerEncoder
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from AR.modules.transformer import TransformerEncoderLayer
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from torch import nn
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from torch.nn import functional as F
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from torchmetrics.classification import MulticlassAccuracy
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default_config = {
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"embedding_dim": 512,
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"hidden_dim": 512,
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"num_head": 8,
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"num_layers": 12,
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"num_codebook": 8,
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"p_dropout": 0.0,
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"vocab_size": 1024 + 1,
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"phoneme_vocab_size": 512,
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"EOS": 1024,
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}
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# @torch.jit.script ## 使用的话首次推理会非常慢，而且推理速度不稳定
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# Efficient implementation equivalent to the following:
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def scaled_dot_product_attention(query:torch.Tensor, key:torch.Tensor, value:torch.Tensor, attn_mask:Optional[torch.Tensor]=None, scale:Optional[torch.Tensor]=None) -> torch.Tensor:
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B, H, L, S =query.size(0), query.size(1), query.size(-2), key.size(-2)
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if scale is None:
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scale_factor = torch.tensor(1 / math.sqrt(query.size(-1)))
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else:
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scale_factor = scale
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attn_bias = torch.zeros(B, H, L, S, dtype=query.dtype, device=query.device)
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if attn_mask is not None:
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if attn_mask.dtype == torch.bool:
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attn_bias.masked_fill_(attn_mask, float("-inf"))
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else:
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attn_bias += attn_mask
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attn_weight = query @ key.transpose(-2, -1) * scale_factor
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attn_weight += attn_bias
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attn_weight = torch.softmax(attn_weight, dim=-1)
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if attn_mask is not None:
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if attn_mask.dtype == torch.bool:
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attn_weight.masked_fill_(attn_mask, 0)
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else:
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attn_mask[attn_mask!=float("-inf")] =0
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attn_mask[attn_mask==float("-inf")] =1
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attn_weight.masked_fill_(attn_mask, 0)
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return attn_weight @ value
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@torch.jit.script
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class T2SMLP:
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def __init__(self, w1, b1, w2, b2):
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self.w1 = w1
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self.b1 = b1
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self.w2 = w2
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self.b2 = b2
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def forward(self, x):
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x = F.relu(F.linear(x, self.w1, self.b1))
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x = F.linear(x, self.w2, self.b2)
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return x
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@torch.jit.script
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class T2SBlock:
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def __init__(
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self,
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num_heads,
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hidden_dim: int,
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mlp: T2SMLP,
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qkv_w,
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qkv_b,
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out_w,
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out_b,
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norm_w1,
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norm_b1,
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norm_eps1,
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norm_w2,
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norm_b2,
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norm_eps2,
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):
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self.num_heads = num_heads
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self.mlp = mlp
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self.hidden_dim: int = hidden_dim
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self.qkv_w = qkv_w
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self.qkv_b = qkv_b
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self.out_w = out_w
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self.out_b = out_b
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self.norm_w1 = norm_w1
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self.norm_b1 = norm_b1
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self.norm_eps1 = norm_eps1
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self.norm_w2 = norm_w2
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self.norm_b2 = norm_b2
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self.norm_eps2 = norm_eps2
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self.false = torch.tensor(False, dtype=torch.bool)
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@torch.jit.ignore
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def to_mask(self, x:torch.Tensor, padding_mask:Optional[torch.Tensor]):
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if padding_mask is None:
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return x
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if padding_mask.dtype == torch.bool:
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return x.masked_fill(padding_mask, 0)
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else:
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return x * padding_mask
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def process_prompt(self, x:torch.Tensor, attn_mask : torch.Tensor, padding_mask:Optional[torch.Tensor]=None, torch_sdpa:bool=True):
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q, k, v = F.linear(self.to_mask(x, padding_mask), self.qkv_w, self.qkv_b).chunk(3, dim=-1)
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batch_size = q.shape[0]
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q_len = q.shape[1]
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kv_len = k.shape[1]
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q = self.to_mask(q, padding_mask)
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k_cache = self.to_mask(k, padding_mask)
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v_cache = self.to_mask(v, padding_mask)
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q = q.view(batch_size, q_len, self.num_heads, -1).transpose(1, 2)
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k = k_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
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v = v_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
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if torch_sdpa:
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attn = F.scaled_dot_product_attention(q, k, v, ~attn_mask)
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else:
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attn = scaled_dot_product_attention(q, k, v, attn_mask)
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attn = attn.transpose(1, 2).reshape(batch_size, q_len, -1)
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attn = F.linear(self.to_mask(attn, padding_mask), self.out_w, self.out_b)
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x = x + attn
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x = F.layer_norm(
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x, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1
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)
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x = x + self.mlp.forward(x)
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x = F.layer_norm(
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x,
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[self.hidden_dim],
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self.norm_w2,
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self.norm_b2,
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self.norm_eps2,
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)
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return x, k_cache, v_cache
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def decode_next_token(self, x:torch.Tensor, k_cache:torch.Tensor, v_cache:torch.Tensor, attn_mask:torch.Tensor=None, torch_sdpa:bool=True):
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q, k, v = F.linear(x, self.qkv_w, self.qkv_b).chunk(3, dim=-1)
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k_cache = torch.cat([k_cache, k], dim=1)
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v_cache = torch.cat([v_cache, v], dim=1)
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batch_size = q.shape[0]
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q_len = q.shape[1]
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kv_len = k_cache.shape[1]
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q = q.view(batch_size, q_len, self.num_heads, -1).transpose(1, 2)
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k = k_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
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v = v_cache.view(batch_size, kv_len, self.num_heads, -1).transpose(1, 2)
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if torch_sdpa:
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attn = F.scaled_dot_product_attention(q, k, v, (~attn_mask) if attn_mask is not None else None)
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else:
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attn = scaled_dot_product_attention(q, k, v, attn_mask)
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attn = attn.transpose(1, 2).reshape(batch_size, q_len, -1)
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attn = F.linear(attn, self.out_w, self.out_b)
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x = x + attn
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x = F.layer_norm(
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x, [self.hidden_dim], self.norm_w1, self.norm_b1, self.norm_eps1
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)
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x = x + self.mlp.forward(x)
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x = F.layer_norm(
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x,
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[self.hidden_dim],
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self.norm_w2,
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self.norm_b2,
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self.norm_eps2,
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)
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return x, k_cache, v_cache
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@torch.jit.script
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class T2STransformer:
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def __init__(self, num_blocks : int, blocks: List[T2SBlock]):
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self.num_blocks : int = num_blocks
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self.blocks = blocks
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def process_prompt(
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self, x:torch.Tensor, attn_mask : torch.Tensor,
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padding_mask : Optional[torch.Tensor]=None,
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torch_sdpa:bool=True
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):
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k_cache : List[torch.Tensor] = []
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v_cache : List[torch.Tensor] = []
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for i in range(self.num_blocks):
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x, k_cache_, v_cache_ = self.blocks[i].process_prompt(x, attn_mask, padding_mask, torch_sdpa)
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k_cache.append(k_cache_)
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v_cache.append(v_cache_)
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return x, k_cache, v_cache
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def decode_next_token(
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self, x:torch.Tensor,
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k_cache: List[torch.Tensor],
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v_cache: List[torch.Tensor],
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attn_mask : torch.Tensor=None,
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torch_sdpa:bool=True
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):
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for i in range(self.num_blocks):
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x, k_cache[i], v_cache[i] = self.blocks[i].decode_next_token(x, k_cache[i], v_cache[i], attn_mask, torch_sdpa)
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return x, k_cache, v_cache
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class Text2SemanticDecoder(nn.Module):
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def __init__(self, config, norm_first=False, top_k=3):
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super(Text2SemanticDecoder, self).__init__()
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self.model_dim = config["model"]["hidden_dim"]
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self.embedding_dim = config["model"]["embedding_dim"]
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self.num_head = config["model"]["head"]
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self.num_layers = config["model"]["n_layer"]
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self.norm_first = norm_first
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self.vocab_size = config["model"]["vocab_size"]
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self.phoneme_vocab_size = config["model"]["phoneme_vocab_size"]
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self.p_dropout = config["model"]["dropout"]
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self.EOS = config["model"]["EOS"]
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self.norm_first = norm_first
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assert self.EOS == self.vocab_size - 1
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# should be same as num of kmeans bin
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# assert self.EOS == 1024
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self.bert_proj = nn.Linear(1024, self.embedding_dim)
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self.ar_text_embedding = TokenEmbedding(
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self.embedding_dim, self.phoneme_vocab_size, self.p_dropout
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)
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self.ar_text_position = SinePositionalEmbedding(
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self.embedding_dim, dropout=0.1, scale=False, alpha=True
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)
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self.ar_audio_embedding = TokenEmbedding(
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self.embedding_dim, self.vocab_size, self.p_dropout
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)
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self.ar_audio_position = SinePositionalEmbedding(
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self.embedding_dim, dropout=0.1, scale=False, alpha=True
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)
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self.h = TransformerEncoder(
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TransformerEncoderLayer(
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d_model=self.model_dim,
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nhead=self.num_head,
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dim_feedforward=self.model_dim * 4,
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dropout=0.1,
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batch_first=True,
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norm_first=norm_first,
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),
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num_layers=self.num_layers,
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norm=LayerNorm(self.model_dim) if norm_first else None,
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)
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self.ar_predict_layer = nn.Linear(self.model_dim, self.vocab_size, bias=False)
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self.loss_fct = nn.CrossEntropyLoss(reduction="sum")
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self.ar_accuracy_metric = MulticlassAccuracy(
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self.vocab_size,
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top_k=top_k,
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average="micro",
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multidim_average="global",
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ignore_index=self.EOS,
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)
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blocks = []
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for i in range(self.num_layers):
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layer = self.h.layers[i]
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t2smlp = T2SMLP(
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layer.linear1.weight,
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layer.linear1.bias,
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layer.linear2.weight,
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layer.linear2.bias
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)
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block = T2SBlock(
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self.num_head,
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self.model_dim,
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t2smlp,
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layer.self_attn.in_proj_weight,
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layer.self_attn.in_proj_bias,
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layer.self_attn.out_proj.weight,
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layer.self_attn.out_proj.bias,
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layer.norm1.weight,
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layer.norm1.bias,
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layer.norm1.eps,
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layer.norm2.weight,
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layer.norm2.bias,
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layer.norm2.eps
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)
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blocks.append(block)
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self.t2s_transformer = T2STransformer(self.num_layers, blocks)
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def make_input_data(self, x, x_lens, y, y_lens, bert_feature):
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x = self.ar_text_embedding(x)
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x = x + self.bert_proj(bert_feature.transpose(1, 2))
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x = self.ar_text_position(x)
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x_mask = make_pad_mask(x_lens)
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y_mask = make_pad_mask(y_lens)
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y_mask_int = y_mask.type(torch.int64)
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codes = y.type(torch.int64) * (1 - y_mask_int)
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# Training
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# AR Decoder
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y, targets = self.pad_y_eos(codes, y_mask_int, eos_id=self.EOS)
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x_len = x_lens.max()
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y_len = y_lens.max()
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y_emb = self.ar_audio_embedding(y)
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y_pos = self.ar_audio_position(y_emb)
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xy_padding_mask = torch.concat([x_mask, y_mask], dim=1)
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ar_xy_padding_mask = xy_padding_mask
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x_attn_mask = F.pad(
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torch.zeros((x_len, x_len), dtype=torch.bool, device=x.device),
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(0, y_len),
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value=True,
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)
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# x_attn_mask[:, x_len]=False
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y_attn_mask = F.pad(
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torch.triu(
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torch.ones(y_len, y_len, dtype=torch.bool, device=x.device),
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diagonal=1,
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),
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(x_len, 0),
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value=False,
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)
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xy_attn_mask = torch.concat([x_attn_mask, y_attn_mask], dim=0)
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bsz, src_len = x.shape[0], x_len + y_len
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_xy_padding_mask = (
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ar_xy_padding_mask.view(bsz, 1, 1, src_len)
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.expand(-1, self.num_head, -1, -1)
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.reshape(bsz * self.num_head, 1, src_len)
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)
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xy_attn_mask = xy_attn_mask.logical_or(_xy_padding_mask)
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new_attn_mask = torch.zeros_like(xy_attn_mask, dtype=x.dtype)
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new_attn_mask.masked_fill_(xy_attn_mask, float("-inf"))
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xy_attn_mask = new_attn_mask
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# x 和完整的 y 一次性输入模型
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xy_pos = torch.concat([x, y_pos], dim=1)
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return xy_pos, xy_attn_mask, targets
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def forward(self, x, x_lens, y, y_lens, bert_feature):
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"""
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x: phoneme_ids
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y: semantic_ids
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"""
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reject_y, reject_y_lens = make_reject_y(y, y_lens)
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xy_pos, xy_attn_mask, targets = self.make_input_data(x, x_lens, y, y_lens, bert_feature)
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xy_dec, _ = self.h(
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(xy_pos, None),
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mask=xy_attn_mask,
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)
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x_len = x_lens.max()
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logits = self.ar_predict_layer(xy_dec[:, x_len:])
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###### DPO #############
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reject_xy_pos, reject_xy_attn_mask, reject_targets = self.make_input_data(x, x_lens, reject_y, reject_y_lens, bert_feature)
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reject_xy_dec, _ = self.h(
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(reject_xy_pos, None),
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mask=reject_xy_attn_mask,
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)
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x_len = x_lens.max()
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reject_logits = self.ar_predict_layer(reject_xy_dec[:, x_len:])
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# loss
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# from feiteng: 每次 duration 越多, 梯度更新也应该更多, 所以用 sum
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loss_1 = F.cross_entropy(logits.permute(0, 2, 1), targets, reduction="sum")
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acc = self.ar_accuracy_metric(logits.permute(0, 2, 1).detach(), targets).item()
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A_logits, R_logits = get_batch_logps(logits, reject_logits, targets, reject_targets)
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loss_2, _, _ = dpo_loss(A_logits, R_logits, 0, 0, 0.2, reference_free=True)
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loss = loss_1 + loss_2
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return loss, acc
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def forward_old(self, x, x_lens, y, y_lens, bert_feature):
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"""
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x: phoneme_ids
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y: semantic_ids
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"""
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x = self.ar_text_embedding(x)
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x = x + self.bert_proj(bert_feature.transpose(1, 2))
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x = self.ar_text_position(x)
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x_mask = make_pad_mask(x_lens)
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y_mask = make_pad_mask(y_lens)
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y_mask_int = y_mask.type(torch.int64)
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codes = y.type(torch.int64) * (1 - y_mask_int)
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# Training
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# AR Decoder
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y, targets = self.pad_y_eos(codes, y_mask_int, eos_id=self.EOS)
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x_len = x_lens.max()
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y_len = y_lens.max()
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y_emb = self.ar_audio_embedding(y)
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y_pos = self.ar_audio_position(y_emb)
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xy_padding_mask = torch.concat([x_mask, y_mask], dim=1)
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ar_xy_padding_mask = xy_padding_mask
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x_attn_mask = F.pad(
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torch.zeros((x_len, x_len), dtype=torch.bool, device=x.device),
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(0, y_len),
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value=True,
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)
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y_attn_mask = F.pad(
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torch.triu(
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torch.ones(y_len, y_len, dtype=torch.bool, device=x.device),
|
||
diagonal=1,
|
||
),
|
||
(x_len, 0),
|
||
value=False,
|
||
)
|
||
xy_attn_mask = torch.concat([x_attn_mask, y_attn_mask], dim=0)
|
||
bsz, src_len = x.shape[0], x_len + y_len
|
||
_xy_padding_mask = (
|
||
ar_xy_padding_mask.view(bsz, 1, 1, src_len)
|
||
.expand(-1, self.num_head, -1, -1)
|
||
.reshape(bsz * self.num_head, 1, src_len)
|
||
)
|
||
xy_attn_mask = xy_attn_mask.logical_or(_xy_padding_mask)
|
||
new_attn_mask = torch.zeros_like(xy_attn_mask, dtype=x.dtype)
|
||
new_attn_mask.masked_fill_(xy_attn_mask, float("-inf"))
|
||
xy_attn_mask = new_attn_mask
|
||
# x 和完整的 y 一次性输入模型
|
||
xy_pos = torch.concat([x, y_pos], dim=1)
|
||
xy_dec, _ = self.h(
|
||
(xy_pos, None),
|
||
mask=xy_attn_mask,
|
||
)
|
||
logits = self.ar_predict_layer(xy_dec[:, x_len:]).permute(0, 2, 1)
|
||
# loss
|
||
# from feiteng: 每次 duration 越多, 梯度更新也应该更多, 所以用 sum
|
||
loss = F.cross_entropy(logits, targets, reduction="sum")
|
||
acc = self.ar_accuracy_metric(logits.detach(), targets).item()
|
||
return loss, acc
|
||
|
||
# 需要看下这个函数和 forward 的区别以及没有 semantic 的时候 prompts 输入什么
|
||
def infer(
|
||
self,
|
||
x,
|
||
x_lens,
|
||
prompts,
|
||
bert_feature,
|
||
top_k: int = -100,
|
||
early_stop_num: int = -1,
|
||
temperature: float = 1.0,
|
||
):
|
||
x = self.ar_text_embedding(x)
|
||
x = x + self.bert_proj(bert_feature.transpose(1, 2))
|
||
x = self.ar_text_position(x)
|
||
|
||
# AR Decoder
|
||
y = prompts
|
||
prefix_len = y.shape[1]
|
||
x_len = x.shape[1]
|
||
x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool)
|
||
stop = False
|
||
for _ in tqdm(range(1500)):
|
||
y_emb = self.ar_audio_embedding(y)
|
||
y_pos = self.ar_audio_position(y_emb)
|
||
# x 和逐渐增长的 y 一起输入给模型
|
||
xy_pos = torch.concat([x, y_pos], dim=1)
|
||
y_len = y.shape[1]
|
||
x_attn_mask_pad = F.pad(
|
||
x_attn_mask,
|
||
(0, y_len),
|
||
value=True,
|
||
)
|
||
y_attn_mask = F.pad(
|
||
torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1),
|
||
(x_len, 0),
|
||
value=False,
|
||
)
|
||
xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0).to(
|
||
y.device
|
||
)
|
||
|
||
xy_dec, _ = self.h(
|
||
(xy_pos, None),
|
||
mask=xy_attn_mask,
|
||
)
|
||
logits = self.ar_predict_layer(xy_dec[:, -1])
|
||
samples = topk_sampling(
|
||
logits, top_k=top_k, top_p=1.0, temperature=temperature
|
||
)
|
||
|
||
if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
|
||
print("use early stop num:", early_stop_num)
|
||
stop = True
|
||
|
||
if torch.argmax(logits, dim=-1)[0] == self.EOS or samples[0, 0] == self.EOS:
|
||
# print(torch.argmax(logits, dim=-1)[0] == self.EOS, samples[0, 0] == self.EOS)
|
||
stop = True
|
||
if stop:
|
||
if prompts.shape[1] == y.shape[1]:
|
||
y = torch.concat([y, torch.zeros_like(samples)], dim=1)
|
||
print("bad zero prediction")
|
||
print(f"T2S Decoding EOS [{prefix_len} -> {y.shape[1]}]")
|
||
break
|
||
# 本次生成的 semantic_ids 和之前的 y 构成新的 y
|
||
# print(samples.shape)#[1,1]#第一个1是bs
|
||
# import os
|
||
# os._exit(2333)
|
||
y = torch.concat([y, samples], dim=1)
|
||
return y
|
||
|
||
def pad_y_eos(self, y, y_mask_int, eos_id):
|
||
targets = F.pad(y, (0, 1), value=0) + eos_id * F.pad(
|
||
y_mask_int, (0, 1), value=1
|
||
)
|
||
# 错位
|
||
return targets[:, :-1], targets[:, 1:]
|
||
|
||
def infer_panel_batch_infer(
|
||
self,
|
||
x:List[torch.LongTensor], #####全部文本token
|
||
x_lens:torch.LongTensor,
|
||
prompts:torch.LongTensor, ####参考音频token
|
||
bert_feature:List[torch.LongTensor],
|
||
top_k: int = -100,
|
||
top_p: int = 100,
|
||
early_stop_num: int = -1,
|
||
temperature: float = 1.0,
|
||
repetition_penalty: float = 1.35,
|
||
**kwargs,
|
||
):
|
||
if prompts is None:
|
||
print("Warning: Prompt free is not supported batch_infer! switch to naive_infer")
|
||
return self.infer_panel_naive_batched(x, x_lens, prompts, bert_feature, top_k=top_k, top_p=top_p, early_stop_num=early_stop_num, temperature=temperature, **kwargs)
|
||
|
||
|
||
max_len = kwargs.get("max_len",x_lens.max())
|
||
x_list = []
|
||
for x_item, bert_item in zip(x, bert_feature):
|
||
# max_len = max(max_len, x_item.shape[0], bert_item.shape[1])
|
||
x_item = self.ar_text_embedding(x_item.unsqueeze(0))
|
||
x_item = x_item + self.bert_proj(bert_item.transpose(0, 1).unsqueeze(0))
|
||
x_item = self.ar_text_position(x_item).squeeze(0)
|
||
# x_item = F.pad(x_item,(0,0,0,max_len-x_item.shape[0]),value=0) if x_item.shape[0]<max_len else x_item ### padding right
|
||
x_item = F.pad(x_item,(0,0,max_len-x_item.shape[0],0),value=0) if x_item.shape[0]<max_len else x_item ### padding left
|
||
x_list.append(x_item)
|
||
x:torch.Tensor = torch.stack(x_list, dim=0)
|
||
|
||
|
||
# AR Decoder
|
||
y = prompts
|
||
|
||
x_len = x.shape[1]
|
||
stop = False
|
||
|
||
k_cache = None
|
||
v_cache = None
|
||
################### first step ##########################
|
||
assert y is not None, "Error: Prompt free is not supported batch_infer!"
|
||
ref_free = False
|
||
|
||
y_emb = self.ar_audio_embedding(y)
|
||
y_len = y_emb.shape[1]
|
||
prefix_len = y.shape[1]
|
||
y_lens = torch.LongTensor([y_emb.shape[1]]*y_emb.shape[0]).to(x.device)
|
||
y_pos = self.ar_audio_position(y_emb)
|
||
xy_pos = torch.concat([x, y_pos], dim=1)
|
||
|
||
|
||
|
||
##### create mask #####
|
||
bsz = x.shape[0]
|
||
src_len = x_len + y_len
|
||
y_paddind_mask = make_pad_mask_left(y_lens, y_len)
|
||
x_paddind_mask = make_pad_mask_left(x_lens, max_len)
|
||
|
||
# (bsz, x_len + y_len)
|
||
padding_mask = torch.concat([x_paddind_mask, y_paddind_mask], dim=1)
|
||
|
||
x_mask = F.pad(
|
||
torch.zeros(x_len, x_len, dtype=torch.bool, device=x.device),
|
||
(0, y_len),
|
||
value=True,
|
||
)
|
||
|
||
y_mask = F.pad( ###yy的右上1扩展到左边xy的0,(y,x+y)
|
||
torch.triu(torch.ones(y_len, y_len, dtype=torch.bool, device=x.device), diagonal=1),
|
||
(x_len, 0),
|
||
value=False,
|
||
)
|
||
|
||
causal_mask = torch.concat([x_mask, y_mask], dim=0).view(1 , src_len, src_len).repeat(bsz, 1, 1).to(x.device)
|
||
# padding_mask = padding_mask.unsqueeze(1) * padding_mask.unsqueeze(2) ### [b, x+y, x+y]
|
||
### 上面是错误的，会导致padding的token被"看见"
|
||
|
||
# 正确的padding_mask应该是：
|
||
# | pad_len | x_len | y_len |
|
||
# [[PAD, PAD, PAD, 1, 2, 3, 4, 5, 6],
|
||
# [PAD, PAD, PAD, 1, 2, 3, 4, 5, 6],
|
||
# [PAD, PAD, PAD, 1, 2, 3, 4, 5, 6], 前3行按理说也应该被mask掉，但是为了防止计算attention时不出现nan，还是保留了，不影响结果
|
||
# [PAD, PAD, PAD, 1, 2, 3, 4, 5, 6],
|
||
# [PAD, PAD, PAD, 1, 2, 3, 4, 5, 6],
|
||
# [PAD, PAD, PAD, 1, 2, 3, 4, 5, 6],
|
||
# [PAD, PAD, PAD, 1, 2, 3, 4, 5, 6],
|
||
# [PAD, PAD, PAD, 1, 2, 3, 4, 5, 6],
|
||
# [PAD, PAD, PAD, 1, 2, 3, 4, 5, 6]]
|
||
|
||
padding_mask = padding_mask.view(bsz, 1, src_len).repeat(1, src_len, 1)
|
||
|
||
attn_mask:torch.Tensor = causal_mask.logical_or(padding_mask)
|
||
attn_mask = attn_mask.unsqueeze(1).expand(-1, self.num_head, -1, -1).bool()
|
||
|
||
|
||
# 正确的attn_mask应该是这样的：
|
||
# | pad_len | x_len | y_len |
|
||
# [[PAD, PAD, PAD, 1, 2, 3, EOS, EOS, EOS],
|
||
# [PAD, PAD, PAD, 1, 2, 3, EOS, EOS, EOS],
|
||
# [PAD, PAD, PAD, 1, 2, 3, EOS, EOS, EOS], 前3行按理说也应该被mask掉，但是为了防止计算attention时不出现nan，还是保留了，不影响结果
|
||
# [PAD, PAD, PAD, 1, 2, 3, EOS, EOS, EOS],
|
||
# [PAD, PAD, PAD, 1, 2, 3, EOS, EOS, EOS],
|
||
# [PAD, PAD, PAD, 1, 2, 3, EOS, EOS, EOS],
|
||
# [PAD, PAD, PAD, 1, 2, 3, 4, EOS, EOS],
|
||
# [PAD, PAD, PAD, 1, 2, 3, 4, 5, EOS],
|
||
# [PAD, PAD, PAD, 1, 2, 3, 4, 5, 6]]
|
||
|
||
|
||
###### decode #####
|
||
y_list = [None]*y.shape[0]
|
||
batch_idx_map = list(range(y.shape[0]))
|
||
idx_list = [None]*y.shape[0]
|
||
for idx in tqdm(range(1500)):
|
||
if idx == 0:
|
||
xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, attn_mask, None)
|
||
else:
|
||
xy_dec, k_cache, v_cache = self.t2s_transformer.decode_next_token(xy_pos, k_cache, v_cache, attn_mask)
|
||
logits = self.ar_predict_layer(
|
||
xy_dec[:, -1]
|
||
)
|
||
|
||
if idx == 0:
|
||
attn_mask = F.pad(attn_mask[:,:,-1].unsqueeze(-2),(0,1),value=False)
|
||
logits = logits[:, :-1]
|
||
else:
|
||
attn_mask = F.pad(attn_mask,(0,1),value=False)
|
||
|
||
samples = sample(
|
||
logits, y, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature
|
||
)[0]
|
||
|
||
y = torch.concat([y, samples], dim=1)
|
||
|
||
####### 移除batch中已经生成完毕的序列,进一步优化计算量
|
||
tokens = torch.argmax(logits, dim=-1)
|
||
reserved_idx_of_batch_for_y = None
|
||
if (self.EOS in samples[:, 0]) or \
|
||
(self.EOS in tokens): ###如果生成到EOS，则停止
|
||
l1 = samples[:, 0]==self.EOS
|
||
l2 = tokens==self.EOS
|
||
l = l1.logical_or(l2)
|
||
removed_idx_of_batch_for_y = torch.where(l==True)[0].tolist()
|
||
reserved_idx_of_batch_for_y = torch.where(l==False)[0]
|
||
# batch_indexs = torch.tensor(batch_idx_map, device=y.device)[removed_idx_of_batch_for_y]
|
||
for i in removed_idx_of_batch_for_y:
|
||
batch_index = batch_idx_map[i]
|
||
idx_list[batch_index] = idx
|
||
y_list[batch_index] = y[i, :-1]
|
||
|
||
batch_idx_map = [batch_idx_map[i] for i in reserved_idx_of_batch_for_y.tolist()]
|
||
|
||
# 只保留batch中未生成完毕的序列
|
||
if reserved_idx_of_batch_for_y is not None:
|
||
# index = torch.LongTensor(batch_idx_map).to(y.device)
|
||
y = torch.index_select(y, dim=0, index=reserved_idx_of_batch_for_y)
|
||
attn_mask = torch.index_select(attn_mask, dim=0, index=reserved_idx_of_batch_for_y)
|
||
if k_cache is not None :
|
||
for i in range(len(k_cache)):
|
||
k_cache[i] = torch.index_select(k_cache[i], dim=0, index=reserved_idx_of_batch_for_y)
|
||
v_cache[i] = torch.index_select(v_cache[i], dim=0, index=reserved_idx_of_batch_for_y)
|
||
|
||
|
||
if (early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num) or idx==1499:
|
||
print("use early stop num:", early_stop_num)
|
||
stop = True
|
||
for i, batch_index in enumerate(batch_idx_map):
|
||
batch_index = batch_idx_map[i]
|
||
idx_list[batch_index] = idx
|
||
y_list[batch_index] = y[i, :-1]
|
||
|
||
if not (None in idx_list):
|
||
stop = True
|
||
|
||
if stop:
|
||
if y.shape[1]==0:
|
||
y = torch.concat([y, torch.zeros_like(samples)], dim=1)
|
||
print("bad zero prediction")
|
||
print(f"T2S Decoding EOS [{prefix_len} -> {y.shape[1]}]")
|
||
break
|
||
|
||
####################### update next step ###################################
|
||
y_emb = self.ar_audio_embedding(y[:, -1:])
|
||
xy_pos = y_emb * self.ar_audio_position.x_scale + self.ar_audio_position.alpha * self.ar_audio_position.pe[:, y_len + idx].to( dtype= y_emb.dtype,device=y_emb.device)
|
||
|
||
if (None in idx_list):
|
||
for i in range(x.shape[0]):
|
||
if idx_list[i] is None:
|
||
idx_list[i] = 1500-1 ###如果没有生成到EOS，就用最大长度代替
|
||
|
||
if ref_free:
|
||
return y_list, [0]*x.shape[0]
|
||
# print(idx_list)
|
||
return y_list, idx_list
|
||
|
||
def infer_panel_naive_batched(self,
|
||
x:List[torch.LongTensor], #####全部文本token
|
||
x_lens:torch.LongTensor,
|
||
prompts:torch.LongTensor, ####参考音频token
|
||
bert_feature:List[torch.LongTensor],
|
||
top_k: int = -100,
|
||
top_p: int = 100,
|
||
early_stop_num: int = -1,
|
||
temperature: float = 1.0,
|
||
repetition_penalty: float = 1.35,
|
||
**kwargs
|
||
):
|
||
y_list = []
|
||
idx_list = []
|
||
for i in range(len(x)):
|
||
y, idx = self.infer_panel_naive(x[i].unsqueeze(0),
|
||
x_lens[i],
|
||
prompts[i].unsqueeze(0) if prompts is not None else None,
|
||
bert_feature[i].unsqueeze(0),
|
||
top_k,
|
||
top_p,
|
||
early_stop_num,
|
||
temperature,
|
||
repetition_penalty,
|
||
**kwargs)
|
||
y_list.append(y[0])
|
||
idx_list.append(idx)
|
||
|
||
return y_list, idx_list
|
||
|
||
def infer_panel_naive(
|
||
self,
|
||
x:torch.LongTensor, #####全部文本token
|
||
x_lens:torch.LongTensor,
|
||
prompts:torch.LongTensor, ####参考音频token
|
||
bert_feature:torch.LongTensor,
|
||
top_k: int = -100,
|
||
top_p: int = 100,
|
||
early_stop_num: int = -1,
|
||
temperature: float = 1.0,
|
||
repetition_penalty: float = 1.35,
|
||
**kwargs
|
||
):
|
||
x = self.ar_text_embedding(x)
|
||
x = x + self.bert_proj(bert_feature.transpose(1, 2))
|
||
x = self.ar_text_position(x)
|
||
|
||
# AR Decoder
|
||
y = prompts
|
||
|
||
x_len = x.shape[1]
|
||
x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool)
|
||
stop = False
|
||
# print(1111111,self.num_layers)
|
||
|
||
k_cache = None
|
||
v_cache = None
|
||
################### first step ##########################
|
||
if y is not None:
|
||
y_emb = self.ar_audio_embedding(y)
|
||
y_len = y_emb.shape[1]
|
||
prefix_len = y.shape[1]
|
||
y_pos = self.ar_audio_position(y_emb)
|
||
xy_pos = torch.concat([x, y_pos], dim=1)
|
||
ref_free = False
|
||
else:
|
||
y_emb = None
|
||
y_len = 0
|
||
prefix_len = 0
|
||
y_pos = None
|
||
xy_pos = x
|
||
y = torch.zeros(x.shape[0], 0, dtype=torch.int, device=x.device)
|
||
ref_free = True
|
||
|
||
bsz = x.shape[0]
|
||
src_len = x_len + y_len
|
||
x_attn_mask_pad = F.pad(
|
||
x_attn_mask,
|
||
(0, y_len), ###xx的纯0扩展到xx纯0+xy纯1，(x,x+y)
|
||
value=True,
|
||
)
|
||
y_attn_mask = F.pad( ###yy的右上1扩展到左边xy的0,(y,x+y)
|
||
torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1),
|
||
(x_len, 0),
|
||
value=False,
|
||
)
|
||
xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0)\
|
||
.unsqueeze(0)\
|
||
.expand(bsz*self.num_head, -1, -1)\
|
||
.view(bsz, self.num_head, src_len, src_len)\
|
||
.to(device=x.device, dtype=torch.bool)
|
||
|
||
for idx in tqdm(range(1500)):
|
||
if xy_attn_mask is not None:
|
||
xy_dec, k_cache, v_cache = self.t2s_transformer.process_prompt(xy_pos, xy_attn_mask, None)
|
||
else:
|
||
xy_dec, k_cache, v_cache = self.t2s_transformer.decode_next_token(xy_pos, k_cache, v_cache)
|
||
|
||
logits = self.ar_predict_layer(
|
||
xy_dec[:, -1]
|
||
)
|
||
|
||
if idx == 0:
|
||
xy_attn_mask = None
|
||
if(idx<11):###至少预测出10个token不然不给停止（0.4s）
|
||
logits = logits[:, :-1]
|
||
|
||
samples = sample(
|
||
logits, y, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, temperature=temperature
|
||
)[0]
|
||
|
||
y = torch.concat([y, samples], dim=1)
|
||
|
||
if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num:
|
||
print("use early stop num:", early_stop_num)
|
||
stop = True
|
||
|
||
if torch.argmax(logits, dim=-1)[0] == self.EOS or samples[0, 0] == self.EOS:
|
||
stop = True
|
||
if stop:
|
||
if y.shape[1] == 0:
|
||
y = torch.concat([y, torch.zeros_like(samples)], dim=1)
|
||
print("bad zero prediction")
|
||
print(f"T2S Decoding EOS [{prefix_len} -> {y.shape[1]}]")
|
||
break
|
||
|
||
####################### update next step ###################################
|
||
y_emb = self.ar_audio_embedding(y[:, -1:])
|
||
xy_pos = y_emb * self.ar_audio_position.x_scale + self.ar_audio_position.alpha * self.ar_audio_position.pe[:, y_len + idx].to(dtype=y_emb.dtype,device=y_emb.device)
|
||
|
||
if ref_free:
|
||
return y[:, :-1], 0
|
||
return y[:, :-1], idx
|
||
|
||
|
||
def infer_panel(
|
||
self,
|
||
x:torch.LongTensor, #####全部文本token
|
||
x_lens:torch.LongTensor,
|
||
prompts:torch.LongTensor, ####参考音频token
|
||
bert_feature:torch.LongTensor,
|
||
top_k: int = -100,
|
||
top_p: int = 100,
|
||
early_stop_num: int = -1,
|
||
temperature: float = 1.0,
|
||
repetition_penalty: float = 1.35,
|
||
**kwargs
|
||
):
|
||
return self.infer_panel_naive(x, x_lens, prompts, bert_feature, top_k, top_p, early_stop_num, temperature, repetition_penalty, **kwargs)
|