third_party/GLM-4-Voice/cosyvoice/flow/stable/dit_v2.py

import typing as tp

import torch

from einops import rearrange
from torch import nn
from torch.nn import functional as F
from x_transformers import ContinuousTransformerWrapper, Encoder

from .blocks import FourierFeatures
from .transformer import ContinuousTransformer
from model.stable import transformer_use_mask


class DiffusionTransformerV2(nn.Module):
    def __init__(self,
                 io_channels=32,
                 patch_size=1,
                 embed_dim=768,
                 cond_token_dim=0,
                 project_cond_tokens=True,
                 global_cond_dim=0,
                 project_global_cond=True,
                 input_concat_dim=0,
                 prepend_cond_dim=0,
                 depth=12,
                 num_heads=8,
                 transformer_type: tp.Literal["x-transformers", "continuous_transformer"] = "x-transformers",
                 global_cond_type: tp.Literal["prepend", "adaLN"] = "prepend",
                 **kwargs):

        super().__init__()
        d_model = embed_dim
        n_head = num_heads
        n_layers = depth
        encoder_layer = torch.nn.TransformerEncoderLayer(batch_first=True,
                                                         norm_first=True,
                                                         d_model=d_model,
                                                         nhead=n_head)
        self.transformer = torch.nn.TransformerEncoder(encoder_layer, num_layers=n_layers)

        # ===================================== timestep embedding
        timestep_features_dim = 256
        self.timestep_features = FourierFeatures(1, timestep_features_dim)
        self.to_timestep_embed = nn.Sequential(
            nn.Linear(timestep_features_dim, embed_dim, bias=True),
            nn.SiLU(),
            nn.Linear(embed_dim, embed_dim, bias=True),
        )


    def _forward(
            self,
            Xt_btd,
            t, #(1d)
            mu_btd,
            ):

        timestep_embed = self.to_timestep_embed(self.timestep_features(t[:, None]))  # (b, embed_dim)
        cated_input = torch.cat([t,mu,x_t])

        ### 1. 需要重新写过以适应不同长度的con
        if cross_attn_cond is not None:
            cross_attn_cond = self.to_cond_embed(cross_attn_cond)

        if global_embed is not None:
            # Project the global conditioning to the embedding dimension
            global_embed = self.to_global_embed(global_embed)

        prepend_inputs = None
        prepend_mask = None
        prepend_length = 0
        if prepend_cond is not None:
            # Project the prepend conditioning to the embedding dimension
            prepend_cond = self.to_prepend_embed(prepend_cond)

            prepend_inputs = prepend_cond
            if prepend_cond_mask is not None:
                prepend_mask = prepend_cond_mask

        if input_concat_cond is not None:

            # Interpolate input_concat_cond to the same length as x
            if input_concat_cond.shape[2] != x.shape[2]:
                input_concat_cond = F.interpolate(input_concat_cond, (x.shape[2],), mode='nearest')

            x = torch.cat([x, input_concat_cond], dim=1)

        # Get the batch of timestep embeddings
        try:
            timestep_embed = self.to_timestep_embed(self.timestep_features(t[:, None]))  # (b, embed_dim)
        except Exception as e:
            print("t.shape:", t.shape, "x.shape", x.shape)
            print("t:", t)
            raise e

        # Timestep embedding is considered a global embedding. Add to the global conditioning if it exists
        if global_embed is not None:
            global_embed = global_embed + timestep_embed
        else:
            global_embed = timestep_embed

        # Add the global_embed to the prepend inputs if there is no global conditioning support in the transformer
        if self.global_cond_type == "prepend":
            if prepend_inputs is None:
                # Prepend inputs are just the global embed, and the mask is all ones
                prepend_inputs = global_embed.unsqueeze(1)
                prepend_mask = torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)
            else:
                # Prepend inputs are the prepend conditioning + the global embed
                prepend_inputs = torch.cat([prepend_inputs, global_embed.unsqueeze(1)], dim=1)
                prepend_mask = torch.cat([prepend_mask, torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)],
                                         dim=1)

            prepend_length = prepend_inputs.shape[1]

        x = self.preprocess_conv(x) + x

        x = rearrange(x, "b c t -> b t c")

        extra_args = {}

        if self.global_cond_type == "adaLN":
            extra_args["global_cond"] = global_embed

        if self.patch_size > 1:
            x = rearrange(x, "b (t p) c -> b t (c p)", p=self.patch_size)

        if self.transformer_type == "x-transformers":
            output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond,
                                      context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask,
                                      **extra_args, **kwargs)
        elif self.transformer_type in ["continuous_transformer", "continuous_transformer_with_mask"]:
            output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond,
                                      context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask,
                                      return_info=return_info, **extra_args, **kwargs)

            if return_info:
                output, info = output
        elif self.transformer_type == "mm_transformer":
            output = self.transformer(x, context=cross_attn_cond, mask=mask, context_mask=cross_attn_cond_mask,
                                      **extra_args, **kwargs)

        output = rearrange(output, "b t c -> b c t")[:, :, prepend_length:]

        if self.patch_size > 1:
            output = rearrange(output, "b (c p) t -> b c (t p)", p=self.patch_size)

        output = self.postprocess_conv(output) + output

        if return_info:
            return output, info

        return output

    def forward(
            self,
            x,
            t,
            cross_attn_cond=None,
            cross_attn_cond_mask=None,
            negative_cross_attn_cond=None,
            negative_cross_attn_mask=None,
            input_concat_cond=None,
            global_embed=None,
            negative_global_embed=None,
            prepend_cond=None,
            prepend_cond_mask=None,
            cfg_scale=1.0,
            cfg_dropout_prob=0.0,
            causal=False,
            scale_phi=0.0,
            mask=None,
            return_info=False,
            **kwargs):

        assert causal == False, "Causal mode is not supported for DiffusionTransformer"

        if cross_attn_cond_mask is not None:
            cross_attn_cond_mask = cross_attn_cond_mask.bool()

            cross_attn_cond_mask = None  # Temporarily disabling conditioning masks due to kernel issue for flash attention

        if prepend_cond_mask is not None:
            prepend_cond_mask = prepend_cond_mask.bool()

        # CFG dropout
        if cfg_dropout_prob > 0.0:
            if cross_attn_cond is not None:
                null_embed = torch.zeros_like(cross_attn_cond, device=cross_attn_cond.device)
                dropout_mask = torch.bernoulli(
                    torch.full((cross_attn_cond.shape[0], 1, 1), cfg_dropout_prob, device=cross_attn_cond.device)).to(
                    torch.bool)
                cross_attn_cond = torch.where(dropout_mask, null_embed, cross_attn_cond)

            if prepend_cond is not None:
                null_embed = torch.zeros_like(prepend_cond, device=prepend_cond.device)
                dropout_mask = torch.bernoulli(
                    torch.full((prepend_cond.shape[0], 1, 1), cfg_dropout_prob, device=prepend_cond.device)).to(
                    torch.bool)
                prepend_cond = torch.where(dropout_mask, null_embed, prepend_cond)

        if cfg_scale != 1.0 and (cross_attn_cond is not None or prepend_cond is not None):
            # Classifier-free guidance
            # Concatenate conditioned and unconditioned inputs on the batch dimension
            batch_inputs = torch.cat([x, x], dim=0)
            batch_timestep = torch.cat([t, t], dim=0)

            if global_embed is not None:
                batch_global_cond = torch.cat([global_embed, global_embed], dim=0)
            else:
                batch_global_cond = None

            if input_concat_cond is not None:
                batch_input_concat_cond = torch.cat([input_concat_cond, input_concat_cond], dim=0)
            else:
                batch_input_concat_cond = None

            batch_cond = None
            batch_cond_masks = None

            # Handle CFG for cross-attention conditioning
            if cross_attn_cond is not None:

                null_embed = torch.zeros_like(cross_attn_cond, device=cross_attn_cond.device)

                # For negative cross-attention conditioning, replace the null embed with the negative cross-attention conditioning
                if negative_cross_attn_cond is not None:

                    # If there's a negative cross-attention mask, set the masked tokens to the null embed
                    if negative_cross_attn_mask is not None:
                        negative_cross_attn_mask = negative_cross_attn_mask.to(torch.bool).unsqueeze(2)

                        negative_cross_attn_cond = torch.where(negative_cross_attn_mask, negative_cross_attn_cond,
                                                               null_embed)

                    batch_cond = torch.cat([cross_attn_cond, negative_cross_attn_cond], dim=0)

                else:
                    batch_cond = torch.cat([cross_attn_cond, null_embed], dim=0)

                if cross_attn_cond_mask is not None:
                    batch_cond_masks = torch.cat([cross_attn_cond_mask, cross_attn_cond_mask], dim=0)

            batch_prepend_cond = None
            batch_prepend_cond_mask = None

            if prepend_cond is not None:

                null_embed = torch.zeros_like(prepend_cond, device=prepend_cond.device)

                batch_prepend_cond = torch.cat([prepend_cond, null_embed], dim=0)

                if prepend_cond_mask is not None:
                    batch_prepend_cond_mask = torch.cat([prepend_cond_mask, prepend_cond_mask], dim=0)

            if mask is not None:
                batch_masks = torch.cat([mask, mask], dim=0)
            else:
                batch_masks = None

            batch_output = self._forward(
                batch_inputs,
                batch_timestep,
                cross_attn_cond=batch_cond,
                cross_attn_cond_mask=batch_cond_masks,
                mask=batch_masks,
                input_concat_cond=batch_input_concat_cond,
                global_embed=batch_global_cond,
                prepend_cond=batch_prepend_cond,
                prepend_cond_mask=batch_prepend_cond_mask,
                return_info=return_info,
                **kwargs)

            if return_info:
                batch_output, info = batch_output

            cond_output, uncond_output = torch.chunk(batch_output, 2, dim=0)
            cfg_output = uncond_output + (cond_output - uncond_output) * cfg_scale

            # CFG Rescale
            if scale_phi != 0.0:
                cond_out_std = cond_output.std(dim=1, keepdim=True)
                out_cfg_std = cfg_output.std(dim=1, keepdim=True)
                output = scale_phi * (cfg_output * (cond_out_std / out_cfg_std)) + (1 - scale_phi) * cfg_output
            else:
                output = cfg_output

            if return_info:
                return output, info

            return output

        else:
            return self._forward(
                x,
                t,
                cross_attn_cond=cross_attn_cond,
                cross_attn_cond_mask=cross_attn_cond_mask,
                input_concat_cond=input_concat_cond,
                global_embed=global_embed,
                prepend_cond=prepend_cond,
                prepend_cond_mask=prepend_cond_mask,
                mask=mask,
                return_info=return_info,
                **kwargs
            )