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lyuxiang.lx
208
cosyvoice/flow/DiT/dit_model.py
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208
cosyvoice/flow/DiT/dit_model.py
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"""
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ein notation:
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b - batch
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n - sequence
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nt - text sequence
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nw - raw wave length
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d - dimension
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"""
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from __future__ import annotations
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import torch
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from torch import nn
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import torch.nn.functional as F
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from einops import repeat
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from x_transformers.x_transformers import RotaryEmbedding
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from funasr.models.transformer.utils.mask import causal_block_mask
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from cosyvoice.flow.DiT.dit_modules import (
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TimestepEmbedding,
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ConvNeXtV2Block,
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CausalConvPositionEmbedding,
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DiTBlock,
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AdaLayerNormZero_Final,
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precompute_freqs_cis,
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get_pos_embed_indices,
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)
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# Text embedding
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class TextEmbedding(nn.Module):
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def __init__(self, text_num_embeds, text_dim, conv_layers=0, conv_mult=2):
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super().__init__()
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self.text_embed = nn.Embedding(text_num_embeds + 1, text_dim) # use 0 as filler token
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if conv_layers > 0:
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self.extra_modeling = True
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self.precompute_max_pos = 4096 # ~44s of 24khz audio
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self.register_buffer("freqs_cis", precompute_freqs_cis(text_dim, self.precompute_max_pos), persistent=False)
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self.text_blocks = nn.Sequential(
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*[ConvNeXtV2Block(text_dim, text_dim * conv_mult) for _ in range(conv_layers)]
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)
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else:
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self.extra_modeling = False
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def forward(self, text: int["b nt"], seq_len, drop_text=False): # noqa: F722
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batch, text_len = text.shape[0], text.shape[1]
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text = text + 1 # use 0 as filler token. preprocess of batch pad -1, see list_str_to_idx()
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text = text[:, :seq_len] # curtail if character tokens are more than the mel spec tokens
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text = F.pad(text, (0, seq_len - text_len), value=0)
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if drop_text: # cfg for text
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text = torch.zeros_like(text)
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text = self.text_embed(text) # b n -> b n d
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# possible extra modeling
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if self.extra_modeling:
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# sinus pos emb
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batch_start = torch.zeros((batch,), dtype=torch.long)
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pos_idx = get_pos_embed_indices(batch_start, seq_len, max_pos=self.precompute_max_pos)
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text_pos_embed = self.freqs_cis[pos_idx]
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text = text + text_pos_embed
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# convnextv2 blocks
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text = self.text_blocks(text)
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return text
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# noised input audio and context mixing embedding
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class InputEmbedding(nn.Module):
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def __init__(self, mel_dim, text_dim, out_dim, spk_dim=None):
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super().__init__()
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spk_dim = 0 if spk_dim is None else spk_dim
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self.spk_dim = spk_dim
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self.proj = nn.Linear(mel_dim * 2 + text_dim + spk_dim, out_dim)
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self.conv_pos_embed = CausalConvPositionEmbedding(dim=out_dim)
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def forward(
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self,
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x: float["b n d"],
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cond: float["b n d"],
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text_embed: float["b n d"],
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spks: float["b d"],
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):
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to_cat = [x, cond, text_embed]
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if self.spk_dim > 0:
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spks = repeat(spks, "b c -> b t c", t=x.shape[1])
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to_cat.append(spks)
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x = self.proj(torch.cat(to_cat, dim=-1))
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x = self.conv_pos_embed(x) + x
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return x
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# Transformer backbone using DiT blocks
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class DiT(nn.Module):
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def __init__(
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self,
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*,
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dim,
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depth=8,
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heads=8,
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dim_head=64,
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dropout=0.1,
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ff_mult=4,
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mel_dim=80,
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mu_dim=None,
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long_skip_connection=False,
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spk_dim=None,
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**kwargs
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):
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super().__init__()
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self.time_embed = TimestepEmbedding(dim)
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if mu_dim is None:
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mu_dim = mel_dim
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self.input_embed = InputEmbedding(mel_dim, mu_dim, dim, spk_dim)
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self.rotary_embed = RotaryEmbedding(dim_head)
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self.dim = dim
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self.depth = depth
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self.transformer_blocks = nn.ModuleList(
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[DiTBlock(dim=dim, heads=heads, dim_head=dim_head, ff_mult=ff_mult, dropout=dropout) for _ in range(depth)]
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)
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self.long_skip_connection = nn.Linear(dim * 2, dim, bias=False) if long_skip_connection else None
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self.norm_out = AdaLayerNormZero_Final(dim) # final modulation
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self.proj_out = nn.Linear(dim, mel_dim)
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self.causal_mask_type = kwargs.get("causal_mask_type", None)
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def build_mix_causal_mask(self, attn_mask, rand=None, ratio=None):
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b, _, _, t = attn_mask.shape
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if rand is None:
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rand = torch.rand((b, 1, 1, 1), device=attn_mask.device, dtype=torch.float32)
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mixed_mask = attn_mask.clone()
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for item in self.causal_mask_type:
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prob_min, prob_max = item["prob_min"], item["prob_max"]
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_ratio = 1
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if "ratio" in item:
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_ratio = item["ratio"]
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if ratio is not None:
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_ratio = ratio
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block_size = item["block_size"] * _ratio
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if block_size <= 0:
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causal_mask = attn_mask
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else:
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causal_mask = causal_block_mask(
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t, block_size, attn_mask.device, torch.float32
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).unsqueeze(0).unsqueeze(1) # 1,1,T,T
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flag = (prob_min <= rand) & (rand < prob_max)
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mixed_mask = mixed_mask * (~flag) + (causal_mask * attn_mask) * flag
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return mixed_mask
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def forward(
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self,
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x: float["b n d"], # nosied input audio
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cond: float["b n d"], # masked cond audio
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mu: int["b nt d"], # mu
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spks: float["b 1 d"], # spk xvec
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time: float["b"] | float[""], # time step
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return_hidden: bool = False,
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mask: bool["b 1 n"] | None = None,
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mask_rand: float["b 1 1"] = None, # for mask flag type
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**kwargs,
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):
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batch, seq_len = x.shape[0], x.shape[1]
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if time.ndim == 0:
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time = time.repeat(batch)
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# t: conditioning time, c: context (text + masked cond audio), x: noised input audio
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t = self.time_embed(time)
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x = self.input_embed(x, cond, mu, spks.squeeze(1))
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rope = self.rotary_embed.forward_from_seq_len(seq_len)
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if self.long_skip_connection is not None:
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residual = x
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mask = mask.unsqueeze(1) # B,1,1,T
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if self.causal_mask_type is not None:
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mask = self.build_mix_causal_mask(mask, rand=mask_rand.unsqueeze(-1))
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for block in self.transformer_blocks:
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# mask-out padded values for amp training
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x = x * mask[:, 0, -1, :].unsqueeze(-1)
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x = block(x, t, mask=mask.bool(), rope=rope)
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if self.long_skip_connection is not None:
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x = self.long_skip_connection(torch.cat((x, residual), dim=-1))
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x = self.norm_out(x, t)
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output = self.proj_out(x)
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if return_hidden:
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return output, None
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return output
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615
cosyvoice/flow/DiT/dit_modules.py
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615
cosyvoice/flow/DiT/dit_modules.py
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@@ -0,0 +1,615 @@
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"""
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ein notation:
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b - batch
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n - sequence
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nt - text sequence
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nw - raw wave length
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d - dimension
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"""
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from __future__ import annotations
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from typing import Optional
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import math
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import torch
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from torch import nn
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import torch.nn.functional as F
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import torchaudio
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from x_transformers.x_transformers import apply_rotary_pos_emb
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# raw wav to mel spec
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class MelSpec(nn.Module):
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def __init__(
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self,
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filter_length=1024,
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hop_length=256,
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win_length=1024,
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n_mel_channels=100,
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target_sample_rate=24_000,
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normalize=False,
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power=1,
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norm=None,
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center=True,
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):
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super().__init__()
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self.n_mel_channels = n_mel_channels
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self.mel_stft = torchaudio.transforms.MelSpectrogram(
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sample_rate=target_sample_rate,
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n_fft=filter_length,
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win_length=win_length,
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hop_length=hop_length,
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n_mels=n_mel_channels,
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power=power,
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center=center,
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normalized=normalize,
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norm=norm,
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)
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self.register_buffer("dummy", torch.tensor(0), persistent=False)
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def forward(self, inp):
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if len(inp.shape) == 3:
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inp = inp.squeeze(1) # 'b 1 nw -> b nw'
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assert len(inp.shape) == 2
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if self.dummy.device != inp.device:
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self.to(inp.device)
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mel = self.mel_stft(inp)
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mel = mel.clamp(min=1e-5).log()
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return mel
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# sinusoidal position embedding
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class SinusPositionEmbedding(nn.Module):
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def __init__(self, dim):
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super().__init__()
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self.dim = dim
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def forward(self, x, scale=1000):
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device = x.device
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half_dim = self.dim // 2
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emb = math.log(10000) / (half_dim - 1)
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emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb)
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emb = scale * x.unsqueeze(1) * emb.unsqueeze(0)
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emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
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return emb
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# convolutional position embedding
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class ConvPositionEmbedding(nn.Module):
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def __init__(self, dim, kernel_size=31, groups=16):
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super().__init__()
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assert kernel_size % 2 != 0
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self.conv1d = nn.Sequential(
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nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2),
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nn.Mish(),
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nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=kernel_size // 2),
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nn.Mish(),
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)
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def forward(self, x: float["b n d"], mask: bool["b n"] | None = None): # noqa: F722
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if mask is not None:
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mask = mask[..., None]
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x = x.masked_fill(~mask, 0.0)
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x = x.permute(0, 2, 1)
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x = self.conv1d(x)
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out = x.permute(0, 2, 1)
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if mask is not None:
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out = out.masked_fill(~mask, 0.0)
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return out
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class CausalConvPositionEmbedding(nn.Module):
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def __init__(self, dim, kernel_size=31, groups=16):
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super().__init__()
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assert kernel_size % 2 != 0
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self.kernel_size = kernel_size
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self.conv1 = nn.Sequential(
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nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=0),
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nn.Mish(),
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)
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self.conv2 = nn.Sequential(
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nn.Conv1d(dim, dim, kernel_size, groups=groups, padding=0),
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nn.Mish(),
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)
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def forward(self, x: float["b n d"], mask: bool["b n"] | None = None): # noqa: F722
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if mask is not None:
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mask = mask[..., None]
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x = x.masked_fill(~mask, 0.0)
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x = x.permute(0, 2, 1)
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x = F.pad(x, (self.kernel_size - 1, 0, 0, 0))
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x = self.conv1(x)
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x = F.pad(x, (self.kernel_size - 1, 0, 0, 0))
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x = self.conv2(x)
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out = x.permute(0, 2, 1)
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if mask is not None:
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out = out.masked_fill(~mask, 0.0)
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return out
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# rotary positional embedding related
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def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0, theta_rescale_factor=1.0):
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# proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
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# has some connection to NTK literature
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# https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
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# https://github.com/lucidrains/rotary-embedding-torch/blob/main/rotary_embedding_torch/rotary_embedding_torch.py
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theta *= theta_rescale_factor ** (dim / (dim - 2))
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freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
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t = torch.arange(end, device=freqs.device) # type: ignore
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freqs = torch.outer(t, freqs).float() # type: ignore
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freqs_cos = torch.cos(freqs) # real part
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freqs_sin = torch.sin(freqs) # imaginary part
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return torch.cat([freqs_cos, freqs_sin], dim=-1)
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def get_pos_embed_indices(start, length, max_pos, scale=1.0):
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# length = length if isinstance(length, int) else length.max()
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scale = scale * torch.ones_like(start, dtype=torch.float32) # in case scale is a scalar
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pos = (
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start.unsqueeze(1)
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+ (torch.arange(length, device=start.device, dtype=torch.float32).unsqueeze(0) * scale.unsqueeze(1)).long()
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)
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# avoid extra long error.
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pos = torch.where(pos < max_pos, pos, max_pos - 1)
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return pos
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# Global Response Normalization layer (Instance Normalization ?)
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class GRN(nn.Module):
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def __init__(self, dim):
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super().__init__()
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self.gamma = nn.Parameter(torch.zeros(1, 1, dim))
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self.beta = nn.Parameter(torch.zeros(1, 1, dim))
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def forward(self, x):
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Gx = torch.norm(x, p=2, dim=1, keepdim=True)
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Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)
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return self.gamma * (x * Nx) + self.beta + x
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# ConvNeXt-V2 Block https://github.com/facebookresearch/ConvNeXt-V2/blob/main/models/convnextv2.py
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# ref: https://github.com/bfs18/e2_tts/blob/main/rfwave/modules.py#L108
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class ConvNeXtV2Block(nn.Module):
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def __init__(
|
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self,
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dim: int,
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intermediate_dim: int,
|
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dilation: int = 1,
|
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):
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super().__init__()
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padding = (dilation * (7 - 1)) // 2
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self.dwconv = nn.Conv1d(
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dim, dim, kernel_size=7, padding=padding, groups=dim, dilation=dilation
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) # depthwise conv
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self.norm = nn.LayerNorm(dim, eps=1e-6)
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self.pwconv1 = nn.Linear(dim, intermediate_dim) # pointwise/1x1 convs, implemented with linear layers
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self.act = nn.GELU()
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self.grn = GRN(intermediate_dim)
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self.pwconv2 = nn.Linear(intermediate_dim, dim)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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residual = x
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x = x.transpose(1, 2) # b n d -> b d n
|
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x = self.dwconv(x)
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x = x.transpose(1, 2) # b d n -> b n d
|
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x = self.norm(x)
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x = self.pwconv1(x)
|
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x = self.act(x)
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x = self.grn(x)
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x = self.pwconv2(x)
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return residual + x
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# AdaLayerNormZero
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# return with modulated x for attn input, and params for later mlp modulation
|
||||
|
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class AdaLayerNormZero(nn.Module):
|
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def __init__(self, dim):
|
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super().__init__()
|
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self.silu = nn.SiLU()
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||||
self.linear = nn.Linear(dim, dim * 6)
|
||||
|
||||
self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
|
||||
|
||||
def forward(self, x, emb=None):
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emb = self.linear(self.silu(emb))
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shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = torch.chunk(emb, 6, dim=1)
|
||||
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x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]
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return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
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|
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|
||||
# AdaLayerNormZero for final layer
|
||||
# return only with modulated x for attn input, cuz no more mlp modulation
|
||||
|
||||
|
||||
class AdaLayerNormZero_Final(nn.Module):
|
||||
def __init__(self, dim):
|
||||
super().__init__()
|
||||
|
||||
self.silu = nn.SiLU()
|
||||
self.linear = nn.Linear(dim, dim * 2)
|
||||
|
||||
self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
|
||||
|
||||
def forward(self, x, emb):
|
||||
emb = self.linear(self.silu(emb))
|
||||
scale, shift = torch.chunk(emb, 2, dim=1)
|
||||
|
||||
x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]
|
||||
return x
|
||||
|
||||
|
||||
# FeedForward
|
||||
|
||||
|
||||
class FeedForward(nn.Module):
|
||||
def __init__(self, dim, dim_out=None, mult=4, dropout=0.0, approximate: str = "none"):
|
||||
super().__init__()
|
||||
inner_dim = int(dim * mult)
|
||||
dim_out = dim_out if dim_out is not None else dim
|
||||
|
||||
activation = nn.GELU(approximate=approximate)
|
||||
project_in = nn.Sequential(nn.Linear(dim, inner_dim), activation)
|
||||
self.ff = nn.Sequential(project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out))
|
||||
|
||||
def forward(self, x):
|
||||
return self.ff(x)
|
||||
|
||||
|
||||
# Attention with possible joint part
|
||||
# modified from diffusers/src/diffusers/models/attention_processor.py
|
||||
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
processor: JointAttnProcessor | AttnProcessor,
|
||||
dim: int,
|
||||
heads: int = 8,
|
||||
dim_head: int = 64,
|
||||
dropout: float = 0.0,
|
||||
context_dim: Optional[int] = None, # if not None -> joint attention
|
||||
context_pre_only=None,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
if not hasattr(F, "scaled_dot_product_attention"):
|
||||
raise ImportError("Attention equires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
|
||||
|
||||
self.processor = processor
|
||||
|
||||
self.dim = dim
|
||||
self.heads = heads
|
||||
self.inner_dim = dim_head * heads
|
||||
self.dropout = dropout
|
||||
|
||||
self.context_dim = context_dim
|
||||
self.context_pre_only = context_pre_only
|
||||
|
||||
self.to_q = nn.Linear(dim, self.inner_dim)
|
||||
self.to_k = nn.Linear(dim, self.inner_dim)
|
||||
self.to_v = nn.Linear(dim, self.inner_dim)
|
||||
|
||||
if self.context_dim is not None:
|
||||
self.to_k_c = nn.Linear(context_dim, self.inner_dim)
|
||||
self.to_v_c = nn.Linear(context_dim, self.inner_dim)
|
||||
if self.context_pre_only is not None:
|
||||
self.to_q_c = nn.Linear(context_dim, self.inner_dim)
|
||||
|
||||
self.to_out = nn.ModuleList([])
|
||||
self.to_out.append(nn.Linear(self.inner_dim, dim))
|
||||
self.to_out.append(nn.Dropout(dropout))
|
||||
|
||||
if self.context_pre_only is not None and not self.context_pre_only:
|
||||
self.to_out_c = nn.Linear(self.inner_dim, dim)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x: float["b n d"], # noised input x # noqa: F722
|
||||
c: float["b n d"] = None, # context c # noqa: F722
|
||||
mask: bool["b n"] | None = None, # noqa: F722
|
||||
rope=None, # rotary position embedding for x
|
||||
c_rope=None, # rotary position embedding for c
|
||||
) -> torch.Tensor:
|
||||
if c is not None:
|
||||
return self.processor(self, x, c=c, mask=mask, rope=rope, c_rope=c_rope)
|
||||
else:
|
||||
return self.processor(self, x, mask=mask, rope=rope)
|
||||
|
||||
|
||||
# Attention processor
|
||||
|
||||
|
||||
class AttnProcessor:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
attn: Attention,
|
||||
x: float["b n d"], # noised input x # noqa: F722
|
||||
mask: bool["b n"] | None = None, # noqa: F722
|
||||
rope=None, # rotary position embedding
|
||||
) -> torch.FloatTensor:
|
||||
batch_size = x.shape[0]
|
||||
|
||||
# `sample` projections.
|
||||
query = attn.to_q(x)
|
||||
key = attn.to_k(x)
|
||||
value = attn.to_v(x)
|
||||
|
||||
# apply rotary position embedding
|
||||
if rope is not None:
|
||||
freqs, xpos_scale = rope
|
||||
q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0)
|
||||
|
||||
query = apply_rotary_pos_emb(query, freqs, q_xpos_scale)
|
||||
key = apply_rotary_pos_emb(key, freqs, k_xpos_scale)
|
||||
|
||||
# attention
|
||||
inner_dim = key.shape[-1]
|
||||
head_dim = inner_dim // attn.heads
|
||||
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
||||
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
||||
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
||||
|
||||
# mask. e.g. inference got a batch with different target durations, mask out the padding
|
||||
if mask is not None:
|
||||
attn_mask = mask
|
||||
if attn_mask.dim() == 2:
|
||||
attn_mask = attn_mask.unsqueeze(1).unsqueeze(1) # 'b n -> b 1 1 n'
|
||||
attn_mask = attn_mask.expand(batch_size, attn.heads, query.shape[-2], key.shape[-2])
|
||||
else:
|
||||
attn_mask = None
|
||||
|
||||
x = F.scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=0.0, is_causal=False)
|
||||
x = x.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
|
||||
x = x.to(query.dtype)
|
||||
|
||||
# linear proj
|
||||
x = attn.to_out[0](x)
|
||||
# dropout
|
||||
x = attn.to_out[1](x)
|
||||
|
||||
if mask is not None:
|
||||
if mask.dim() == 2:
|
||||
mask = mask.unsqueeze(-1)
|
||||
else:
|
||||
mask = mask[:, 0, -1].unsqueeze(-1)
|
||||
x = x.masked_fill(~mask, 0.0)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
# Joint Attention processor for MM-DiT
|
||||
# modified from diffusers/src/diffusers/models/attention_processor.py
|
||||
|
||||
|
||||
class JointAttnProcessor:
|
||||
def __init__(self):
|
||||
pass
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
attn: Attention,
|
||||
x: float["b n d"], # noised input x # noqa: F722
|
||||
c: float["b nt d"] = None, # context c, here text # noqa: F722
|
||||
mask: bool["b n"] | None = None, # noqa: F722
|
||||
rope=None, # rotary position embedding for x
|
||||
c_rope=None, # rotary position embedding for c
|
||||
) -> torch.FloatTensor:
|
||||
residual = x
|
||||
|
||||
batch_size = c.shape[0]
|
||||
|
||||
# `sample` projections.
|
||||
query = attn.to_q(x)
|
||||
key = attn.to_k(x)
|
||||
value = attn.to_v(x)
|
||||
|
||||
# `context` projections.
|
||||
c_query = attn.to_q_c(c)
|
||||
c_key = attn.to_k_c(c)
|
||||
c_value = attn.to_v_c(c)
|
||||
|
||||
# apply rope for context and noised input independently
|
||||
if rope is not None:
|
||||
freqs, xpos_scale = rope
|
||||
q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0)
|
||||
query = apply_rotary_pos_emb(query, freqs, q_xpos_scale)
|
||||
key = apply_rotary_pos_emb(key, freqs, k_xpos_scale)
|
||||
if c_rope is not None:
|
||||
freqs, xpos_scale = c_rope
|
||||
q_xpos_scale, k_xpos_scale = (xpos_scale, xpos_scale**-1.0) if xpos_scale is not None else (1.0, 1.0)
|
||||
c_query = apply_rotary_pos_emb(c_query, freqs, q_xpos_scale)
|
||||
c_key = apply_rotary_pos_emb(c_key, freqs, k_xpos_scale)
|
||||
|
||||
# attention
|
||||
query = torch.cat([query, c_query], dim=1)
|
||||
key = torch.cat([key, c_key], dim=1)
|
||||
value = torch.cat([value, c_value], dim=1)
|
||||
|
||||
inner_dim = key.shape[-1]
|
||||
head_dim = inner_dim // attn.heads
|
||||
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
||||
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
||||
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
||||
|
||||
# mask. e.g. inference got a batch with different target durations, mask out the padding
|
||||
if mask is not None:
|
||||
attn_mask = F.pad(mask, (0, c.shape[1]), value=True) # no mask for c (text)
|
||||
attn_mask = attn_mask.unsqueeze(1).unsqueeze(1) # 'b n -> b 1 1 n'
|
||||
attn_mask = attn_mask.expand(batch_size, attn.heads, query.shape[-2], key.shape[-2])
|
||||
else:
|
||||
attn_mask = None
|
||||
|
||||
x = F.scaled_dot_product_attention(query, key, value, attn_mask=attn_mask, dropout_p=0.0, is_causal=False)
|
||||
x = x.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
|
||||
x = x.to(query.dtype)
|
||||
|
||||
# Split the attention outputs.
|
||||
x, c = (
|
||||
x[:, : residual.shape[1]],
|
||||
x[:, residual.shape[1] :],
|
||||
)
|
||||
|
||||
# linear proj
|
||||
x = attn.to_out[0](x)
|
||||
# dropout
|
||||
x = attn.to_out[1](x)
|
||||
if not attn.context_pre_only:
|
||||
c = attn.to_out_c(c)
|
||||
|
||||
if mask is not None:
|
||||
mask = mask.unsqueeze(-1)
|
||||
x = x.masked_fill(~mask, 0.0)
|
||||
# c = c.masked_fill(~mask, 0.) # no mask for c (text)
|
||||
|
||||
return x, c
|
||||
|
||||
|
||||
# DiT Block
|
||||
|
||||
|
||||
class DiTBlock(nn.Module):
|
||||
def __init__(self, dim, heads, dim_head, ff_mult=4, dropout=0.1):
|
||||
super().__init__()
|
||||
|
||||
self.attn_norm = AdaLayerNormZero(dim)
|
||||
self.attn = Attention(
|
||||
processor=AttnProcessor(),
|
||||
dim=dim,
|
||||
heads=heads,
|
||||
dim_head=dim_head,
|
||||
dropout=dropout,
|
||||
)
|
||||
|
||||
self.ff_norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
|
||||
self.ff = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh")
|
||||
|
||||
def forward(self, x, t, mask=None, rope=None): # x: noised input, t: time embedding
|
||||
# pre-norm & modulation for attention input
|
||||
norm, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.attn_norm(x, emb=t)
|
||||
|
||||
# attention
|
||||
attn_output = self.attn(x=norm, mask=mask, rope=rope)
|
||||
|
||||
# process attention output for input x
|
||||
x = x + gate_msa.unsqueeze(1) * attn_output
|
||||
|
||||
ff_norm = self.ff_norm(x) * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
|
||||
ff_output = self.ff(ff_norm)
|
||||
x = x + gate_mlp.unsqueeze(1) * ff_output
|
||||
|
||||
return x
|
||||
|
||||
|
||||
# MMDiT Block https://arxiv.org/abs/2403.03206
|
||||
|
||||
|
||||
class MMDiTBlock(nn.Module):
|
||||
r"""
|
||||
modified from diffusers/src/diffusers/models/attention.py
|
||||
|
||||
notes.
|
||||
_c: context related. text, cond, etc. (left part in sd3 fig2.b)
|
||||
_x: noised input related. (right part)
|
||||
context_pre_only: last layer only do prenorm + modulation cuz no more ffn
|
||||
"""
|
||||
|
||||
def __init__(self, dim, heads, dim_head, ff_mult=4, dropout=0.1, context_pre_only=False):
|
||||
super().__init__()
|
||||
|
||||
self.context_pre_only = context_pre_only
|
||||
|
||||
self.attn_norm_c = AdaLayerNormZero_Final(dim) if context_pre_only else AdaLayerNormZero(dim)
|
||||
self.attn_norm_x = AdaLayerNormZero(dim)
|
||||
self.attn = Attention(
|
||||
processor=JointAttnProcessor(),
|
||||
dim=dim,
|
||||
heads=heads,
|
||||
dim_head=dim_head,
|
||||
dropout=dropout,
|
||||
context_dim=dim,
|
||||
context_pre_only=context_pre_only,
|
||||
)
|
||||
|
||||
if not context_pre_only:
|
||||
self.ff_norm_c = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
|
||||
self.ff_c = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh")
|
||||
else:
|
||||
self.ff_norm_c = None
|
||||
self.ff_c = None
|
||||
self.ff_norm_x = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
|
||||
self.ff_x = FeedForward(dim=dim, mult=ff_mult, dropout=dropout, approximate="tanh")
|
||||
|
||||
def forward(self, x, c, t, mask=None, rope=None, c_rope=None): # x: noised input, c: context, t: time embedding
|
||||
# pre-norm & modulation for attention input
|
||||
if self.context_pre_only:
|
||||
norm_c = self.attn_norm_c(c, t)
|
||||
else:
|
||||
norm_c, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.attn_norm_c(c, emb=t)
|
||||
norm_x, x_gate_msa, x_shift_mlp, x_scale_mlp, x_gate_mlp = self.attn_norm_x(x, emb=t)
|
||||
|
||||
# attention
|
||||
x_attn_output, c_attn_output = self.attn(x=norm_x, c=norm_c, mask=mask, rope=rope, c_rope=c_rope)
|
||||
|
||||
# process attention output for context c
|
||||
if self.context_pre_only:
|
||||
c = None
|
||||
else: # if not last layer
|
||||
c = c + c_gate_msa.unsqueeze(1) * c_attn_output
|
||||
|
||||
norm_c = self.ff_norm_c(c) * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
|
||||
c_ff_output = self.ff_c(norm_c)
|
||||
c = c + c_gate_mlp.unsqueeze(1) * c_ff_output
|
||||
|
||||
# process attention output for input x
|
||||
x = x + x_gate_msa.unsqueeze(1) * x_attn_output
|
||||
|
||||
norm_x = self.ff_norm_x(x) * (1 + x_scale_mlp[:, None]) + x_shift_mlp[:, None]
|
||||
x_ff_output = self.ff_x(norm_x)
|
||||
x = x + x_gate_mlp.unsqueeze(1) * x_ff_output
|
||||
|
||||
return c, x
|
||||
|
||||
|
||||
# time step conditioning embedding
|
||||
|
||||
|
||||
class TimestepEmbedding(nn.Module):
|
||||
def __init__(self, dim, freq_embed_dim=256):
|
||||
super().__init__()
|
||||
self.time_embed = SinusPositionEmbedding(freq_embed_dim)
|
||||
self.time_mlp = nn.Sequential(nn.Linear(freq_embed_dim, dim), nn.SiLU(), nn.Linear(dim, dim))
|
||||
|
||||
def forward(self, timestep: float["b"]): # noqa: F821
|
||||
time_hidden = self.time_embed(timestep)
|
||||
time_hidden = time_hidden.to(timestep.dtype)
|
||||
time = self.time_mlp(time_hidden) # b d
|
||||
return time
|
||||
@@ -37,14 +37,11 @@ class MaskedDiffWithXvec(torch.nn.Module):
|
||||
'cfm_params': DictConfig({'sigma_min': 1e-06, 'solver': 'euler', 't_scheduler': 'cosine',
|
||||
'training_cfg_rate': 0.2, 'inference_cfg_rate': 0.7, 'reg_loss_type': 'l1'}),
|
||||
'decoder_params': {'channels': [256, 256], 'dropout': 0.0, 'attention_head_dim': 64,
|
||||
'n_blocks': 4, 'num_mid_blocks': 12, 'num_heads': 8, 'act_fn': 'gelu'}},
|
||||
mel_feat_conf: Dict = {'n_fft': 1024, 'num_mels': 80, 'sampling_rate': 22050,
|
||||
'hop_size': 256, 'win_size': 1024, 'fmin': 0, 'fmax': 8000}):
|
||||
'n_blocks': 4, 'num_mid_blocks': 12, 'num_heads': 8, 'act_fn': 'gelu'}}):
|
||||
super().__init__()
|
||||
self.input_size = input_size
|
||||
self.output_size = output_size
|
||||
self.decoder_conf = decoder_conf
|
||||
self.mel_feat_conf = mel_feat_conf
|
||||
self.vocab_size = vocab_size
|
||||
self.output_type = output_type
|
||||
self.input_frame_rate = input_frame_rate
|
||||
@@ -165,14 +162,11 @@ class CausalMaskedDiffWithXvec(torch.nn.Module):
|
||||
'cfm_params': DictConfig({'sigma_min': 1e-06, 'solver': 'euler', 't_scheduler': 'cosine',
|
||||
'training_cfg_rate': 0.2, 'inference_cfg_rate': 0.7, 'reg_loss_type': 'l1'}),
|
||||
'decoder_params': {'channels': [256, 256], 'dropout': 0.0, 'attention_head_dim': 64,
|
||||
'n_blocks': 4, 'num_mid_blocks': 12, 'num_heads': 8, 'act_fn': 'gelu'}},
|
||||
mel_feat_conf: Dict = {'n_fft': 1024, 'num_mels': 80, 'sampling_rate': 22050,
|
||||
'hop_size': 256, 'win_size': 1024, 'fmin': 0, 'fmax': 8000}):
|
||||
'n_blocks': 4, 'num_mid_blocks': 12, 'num_heads': 8, 'act_fn': 'gelu'}}):
|
||||
super().__init__()
|
||||
self.input_size = input_size
|
||||
self.output_size = output_size
|
||||
self.decoder_conf = decoder_conf
|
||||
self.mel_feat_conf = mel_feat_conf
|
||||
self.vocab_size = vocab_size
|
||||
self.output_type = output_type
|
||||
self.input_frame_rate = input_frame_rate
|
||||
@@ -279,3 +273,158 @@ class CausalMaskedDiffWithXvec(torch.nn.Module):
|
||||
feat = feat[:, :, mel_len1:]
|
||||
assert feat.shape[2] == mel_len2
|
||||
return feat.float(), None
|
||||
|
||||
|
||||
class CausalMaskedDiffWithDiT(torch.nn.Module):
|
||||
def __init__(self,
|
||||
input_size: int = 512,
|
||||
output_size: int = 80,
|
||||
spk_embed_dim: int = 192,
|
||||
output_type: str = "mel",
|
||||
vocab_size: int = 4096,
|
||||
input_frame_rate: int = 50,
|
||||
only_mask_loss: bool = True,
|
||||
token_mel_ratio: int = 2,
|
||||
pre_lookahead_len: int = 3,
|
||||
pre_lookahead_layer: torch.nn.Module = None,
|
||||
decoder: torch.nn.Module = None,
|
||||
decoder_conf: Dict = {'in_channels': 240, 'out_channel': 80, 'spk_emb_dim': 80, 'n_spks': 1,
|
||||
'cfm_params': DictConfig({'sigma_min': 1e-06, 'solver': 'euler', 't_scheduler': 'cosine',
|
||||
'training_cfg_rate': 0.2, 'inference_cfg_rate': 0.7, 'reg_loss_type': 'l1'}),
|
||||
'decoder_params': {'channels': [256, 256], 'dropout': 0.0, 'attention_head_dim': 64,
|
||||
'n_blocks': 4, 'num_mid_blocks': 12, 'num_heads': 8, 'act_fn': 'gelu'}}):
|
||||
super().__init__()
|
||||
self.input_size = input_size
|
||||
self.output_size = output_size
|
||||
self.decoder_conf = decoder_conf
|
||||
self.vocab_size = vocab_size
|
||||
self.output_type = output_type
|
||||
self.input_frame_rate = input_frame_rate
|
||||
logging.info(f"input frame rate={self.input_frame_rate}")
|
||||
self.input_embedding = nn.Embedding(vocab_size, input_size)
|
||||
self.spk_embed_affine_layer = torch.nn.Linear(spk_embed_dim, output_size)
|
||||
self.pre_lookahead_len = pre_lookahead_len
|
||||
self.pre_lookahead_layer = pre_lookahead_layer
|
||||
self.decoder = decoder
|
||||
self.only_mask_loss = only_mask_loss
|
||||
self.token_mel_ratio = token_mel_ratio
|
||||
|
||||
def forward(
|
||||
self,
|
||||
batch: dict,
|
||||
device: torch.device,
|
||||
) -> Dict[str, Optional[torch.Tensor]]:
|
||||
token = batch['speech_token'].to(device)
|
||||
token_len = batch['speech_token_len'].to(device)
|
||||
feat = batch['speech_feat'].to(device)
|
||||
feat_len = batch['speech_feat_len'].to(device)
|
||||
embedding = batch['embedding'].to(device)
|
||||
|
||||
# NOTE unified training, static_chunk_size > 0 or = 0
|
||||
streaming = True if random.random() < 0.5 else False
|
||||
|
||||
# xvec projection
|
||||
embedding = F.normalize(embedding, dim=1)
|
||||
embedding = self.spk_embed_affine_layer(embedding)
|
||||
|
||||
# concat text and prompt_text
|
||||
mask = (~make_pad_mask(token_len)).float().unsqueeze(-1).to(device)
|
||||
token = self.input_embedding(torch.clamp(token, min=0)) * mask
|
||||
|
||||
# text encode
|
||||
h, h_lengths = self.encoder(token, token_len, streaming=streaming)
|
||||
h = self.encoder_proj(h)
|
||||
|
||||
# get conditions
|
||||
conds = torch.zeros(feat.shape, device=token.device)
|
||||
for i, j in enumerate(feat_len):
|
||||
if random.random() < 0.5:
|
||||
continue
|
||||
index = random.randint(0, int(0.3 * j))
|
||||
conds[i, :index] = feat[i, :index]
|
||||
conds = conds.transpose(1, 2)
|
||||
|
||||
mask = (~make_pad_mask(h_lengths.sum(dim=-1).squeeze(dim=1))).to(h)
|
||||
loss, _ = self.decoder.compute_loss(
|
||||
feat.transpose(1, 2).contiguous(),
|
||||
mask.unsqueeze(1),
|
||||
h.transpose(1, 2).contiguous(),
|
||||
embedding,
|
||||
cond=conds,
|
||||
streaming=streaming,
|
||||
)
|
||||
return {'loss': loss}
|
||||
|
||||
@torch.inference_mode()
|
||||
def inference(self,
|
||||
token,
|
||||
token_len,
|
||||
prompt_token,
|
||||
prompt_token_len,
|
||||
prompt_feat,
|
||||
prompt_feat_len,
|
||||
embedding,
|
||||
streaming,
|
||||
finalize):
|
||||
assert token.shape[0] == 1
|
||||
# xvec projection
|
||||
embedding = F.normalize(embedding, dim=1)
|
||||
embedding = self.spk_embed_affine_layer(embedding)
|
||||
|
||||
# concat text and prompt_text
|
||||
token, token_len = torch.concat([prompt_token, token], dim=1), prompt_token_len + token_len
|
||||
mask = (~make_pad_mask(token_len)).unsqueeze(-1).to(embedding)
|
||||
token = self.input_embedding(torch.clamp(token, min=0)) * mask
|
||||
|
||||
# text encode
|
||||
if finalize is True:
|
||||
h = self.pre_lookahead_layer(token)
|
||||
else:
|
||||
h = self.pre_lookahead_layer(token[:, :-self.pre_lookahead_len], context=token[:, -self.pre_lookahead_len:])
|
||||
h = h.repeat_interleave(self.token_mel_ratio, dim=1)
|
||||
mel_len1, mel_len2 = prompt_feat.shape[1], h.shape[1] - prompt_feat.shape[1]
|
||||
|
||||
# get conditions
|
||||
conds = torch.zeros([1, mel_len1 + mel_len2, self.output_size], device=token.device).to(h.dtype)
|
||||
conds[:, :mel_len1] = prompt_feat
|
||||
conds = conds.transpose(1, 2)
|
||||
|
||||
mask = (~make_pad_mask(torch.tensor([mel_len1 + mel_len2]))).to(h)
|
||||
feat, _ = self.decoder(
|
||||
mu=h.transpose(1, 2).contiguous(),
|
||||
mask=mask.unsqueeze(1),
|
||||
spks=embedding,
|
||||
cond=conds,
|
||||
n_timesteps=10,
|
||||
streaming=streaming
|
||||
)
|
||||
feat = feat[:, :, mel_len1:]
|
||||
assert feat.shape[2] == mel_len2
|
||||
return feat.float(), None
|
||||
|
||||
if __name__ == '__main__':
|
||||
torch.backends.cudnn.deterministic = True
|
||||
torch.backends.cudnn.benchmark = False
|
||||
from hyperpyyaml import load_hyperpyyaml
|
||||
with open('./pretrained_models/CosyVoice3-0.5B/cosyvoice3.yaml', 'r') as f:
|
||||
configs = load_hyperpyyaml(f, overrides={'llm': None, 'hift': None})
|
||||
model = configs['flow']
|
||||
device = 'cuda' if torch.cuda.is_available() else 'cpu'
|
||||
model.to(device)
|
||||
model.eval()
|
||||
max_len = 10 * model.decoder.estimator.static_chunk_size
|
||||
chunk_size = model.decoder.estimator.static_chunk_size
|
||||
context_size = model.pre_lookahead_layer.pre_lookahead_len
|
||||
token = torch.randint(0, 6561, size=(1, max_len)).to(device)
|
||||
token_len = torch.tensor([max_len]).to(device)
|
||||
prompt_token = torch.randint(0, 6561, size=(1, chunk_size)).to(device)
|
||||
prompt_token_len = torch.tensor([chunk_size]).to(device)
|
||||
prompt_feat = torch.rand(1, chunk_size * 2, 80).to(device)
|
||||
prompt_feat_len = torch.tensor([chunk_size * 2]).to(device)
|
||||
prompt_embedding = torch.rand(1, 192).to(device)
|
||||
pred_gt, _ = model.inference(token, token_len, prompt_token, prompt_token_len, prompt_feat, prompt_feat_len, prompt_embedding, streaming=True, finalize=True)
|
||||
for i in range(0, max_len, chunk_size):
|
||||
finalize = True if i + chunk_size + context_size >= max_len else False
|
||||
pred_chunk, _ = model.inference(token[:, :i + chunk_size + context_size], torch.tensor([token[:, :i + chunk_size + context_size].shape[1]]).to(device), prompt_token, prompt_token_len, prompt_feat, prompt_feat_len, prompt_embedding, streaming=True, finalize=finalize)
|
||||
pred_chunk = pred_chunk[:, :, i * model.token_mel_ratio:]
|
||||
print((pred_gt[:, :, i * model.token_mel_ratio: i * model.token_mel_ratio + pred_chunk.shape[2]] - pred_chunk).abs().max().item())
|
||||
Reference in New Issue
Block a user