643 lines
21 KiB
Python
643 lines
21 KiB
Python
# Modified from Flux
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#
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# Copyright 2024 Black Forest Labs
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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import math # noqa: I001
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from dataclasses import dataclass
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from functools import partial
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import torch
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import torch.nn.functional as F
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from einops import rearrange
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# from liger_kernel.ops.rms_norm import LigerRMSNormFunction
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from torch import Tensor, nn
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try:
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import flash_attn
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from flash_attn.flash_attn_interface import (
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_flash_attn_forward,
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flash_attn_varlen_func,
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)
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except ImportError:
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flash_attn = None
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flash_attn_varlen_func = None
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_flash_attn_forward = None
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MEMORY_LAYOUT = {
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"flash": (
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lambda x: x.view(x.shape[0] * x.shape[1], *x.shape[2:]),
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lambda x: x,
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),
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"torch": (
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lambda x: x.transpose(1, 2),
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lambda x: x.transpose(1, 2),
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),
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"vanilla": (
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lambda x: x.transpose(1, 2),
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lambda x: x.transpose(1, 2),
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),
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}
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def attention(
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q,
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k,
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v,
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mode="flash",
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drop_rate=0,
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attn_mask=None,
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causal=False,
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cu_seqlens_q=None,
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cu_seqlens_kv=None,
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max_seqlen_q=None,
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max_seqlen_kv=None,
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batch_size=1,
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):
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"""
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Perform QKV self attention.
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Args:
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q (torch.Tensor): Query tensor with shape [b, s, a, d], where a is the number of heads.
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k (torch.Tensor): Key tensor with shape [b, s1, a, d]
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v (torch.Tensor): Value tensor with shape [b, s1, a, d]
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mode (str): Attention mode. Choose from 'self_flash', 'cross_flash', 'torch', and 'vanilla'.
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drop_rate (float): Dropout rate in attention map. (default: 0)
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attn_mask (torch.Tensor): Attention mask with shape [b, s1] (cross_attn), or [b, a, s, s1] (torch or vanilla).
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(default: None)
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causal (bool): Whether to use causal attention. (default: False)
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cu_seqlens_q (torch.Tensor): dtype torch.int32. The cumulative sequence lengths of the sequences in the batch,
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used to index into q.
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cu_seqlens_kv (torch.Tensor): dtype torch.int32. The cumulative sequence lengths of the sequences in the batch,
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used to index into kv.
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max_seqlen_q (int): The maximum sequence length in the batch of q.
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max_seqlen_kv (int): The maximum sequence length in the batch of k and v.
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Returns:
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torch.Tensor: Output tensor after self attention with shape [b, s, ad]
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"""
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pre_attn_layout, post_attn_layout = MEMORY_LAYOUT[mode]
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q = pre_attn_layout(q)
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k = pre_attn_layout(k)
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v = pre_attn_layout(v)
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if mode == "torch":
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if attn_mask is not None and attn_mask.dtype != torch.bool:
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attn_mask = attn_mask.to(q.dtype)
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x = F.scaled_dot_product_attention(
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q, k, v, attn_mask=attn_mask, dropout_p=drop_rate, is_causal=causal
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)
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elif mode == "flash":
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assert flash_attn_varlen_func is not None
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x: torch.Tensor = flash_attn_varlen_func(
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q,
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k,
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v,
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cu_seqlens_q,
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cu_seqlens_kv,
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max_seqlen_q,
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max_seqlen_kv,
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) # type: ignore
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# x with shape [(bxs), a, d]
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x = x.view(batch_size, max_seqlen_q, x.shape[-2], x.shape[-1]) # type: ignore # reshape x to [b, s, a, d]
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elif mode == "vanilla":
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scale_factor = 1 / math.sqrt(q.size(-1))
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b, a, s, _ = q.shape
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s1 = k.size(2)
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attn_bias = torch.zeros(b, a, s, s1, dtype=q.dtype, device=q.device)
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if causal:
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# Only applied to self attention
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assert attn_mask is None, (
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"Causal mask and attn_mask cannot be used together"
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)
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temp_mask = torch.ones(b, a, s, s, dtype=torch.bool, device=q.device).tril(
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diagonal=0
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)
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attn_bias.masked_fill_(temp_mask.logical_not(), float("-inf"))
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attn_bias.to(q.dtype)
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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.logical_not(), float("-inf"))
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else:
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attn_bias += attn_mask
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# TODO: Maybe force q and k to be float32 to avoid numerical overflow
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attn = (q @ k.transpose(-2, -1)) * scale_factor
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attn += attn_bias
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attn = attn.softmax(dim=-1)
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attn = torch.dropout(attn, p=drop_rate, train=True)
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x = attn @ v
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else:
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raise NotImplementedError(f"Unsupported attention mode: {mode}")
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x = post_attn_layout(x)
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b, s, a, d = x.shape
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out = x.reshape(b, s, -1)
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return out
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def apply_gate(x, gate=None, tanh=False):
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"""AI is creating summary for apply_gate
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Args:
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x (torch.Tensor): input tensor.
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gate (torch.Tensor, optional): gate tensor. Defaults to None.
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tanh (bool, optional): whether to use tanh function. Defaults to False.
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Returns:
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torch.Tensor: the output tensor after apply gate.
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"""
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if gate is None:
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return x
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if tanh:
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return x * gate.unsqueeze(1).tanh()
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else:
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return x * gate.unsqueeze(1)
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class MLP(nn.Module):
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"""MLP as used in Vision Transformer, MLP-Mixer and related networks"""
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def __init__(
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self,
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in_channels,
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hidden_channels=None,
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out_features=None,
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act_layer=nn.GELU,
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norm_layer=None,
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bias=True,
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drop=0.0,
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use_conv=False,
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device=None,
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dtype=None,
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):
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super().__init__()
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out_features = out_features or in_channels
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hidden_channels = hidden_channels or in_channels
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bias = (bias, bias)
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drop_probs = (drop, drop)
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linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear
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self.fc1 = linear_layer(
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in_channels, hidden_channels, bias=bias[0], device=device, dtype=dtype
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)
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self.act = act_layer()
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self.drop1 = nn.Dropout(drop_probs[0])
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self.norm = (
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norm_layer(hidden_channels, device=device, dtype=dtype)
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if norm_layer is not None
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else nn.Identity()
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)
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self.fc2 = linear_layer(
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hidden_channels, out_features, bias=bias[1], device=device, dtype=dtype
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)
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self.drop2 = nn.Dropout(drop_probs[1])
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def forward(self, x):
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x = self.fc1(x)
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x = self.act(x)
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x = self.drop1(x)
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x = self.norm(x)
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x = self.fc2(x)
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x = self.drop2(x)
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return x
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class TextProjection(nn.Module):
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"""
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Projects text embeddings. Also handles dropout for classifier-free guidance.
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Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py
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"""
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def __init__(self, in_channels, hidden_size, act_layer, dtype=None, device=None):
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factory_kwargs = {"dtype": dtype, "device": device}
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super().__init__()
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self.linear_1 = nn.Linear(
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in_features=in_channels,
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out_features=hidden_size,
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bias=True,
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**factory_kwargs,
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)
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self.act_1 = act_layer()
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self.linear_2 = nn.Linear(
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in_features=hidden_size,
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out_features=hidden_size,
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bias=True,
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**factory_kwargs,
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)
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def forward(self, caption):
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hidden_states = self.linear_1(caption)
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hidden_states = self.act_1(hidden_states)
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hidden_states = self.linear_2(hidden_states)
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return hidden_states
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class TimestepEmbedder(nn.Module):
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"""
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Embeds scalar timesteps into vector representations.
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"""
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def __init__(
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self,
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hidden_size,
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act_layer,
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frequency_embedding_size=256,
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max_period=10000,
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out_size=None,
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dtype=None,
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device=None,
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):
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factory_kwargs = {"dtype": dtype, "device": device}
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super().__init__()
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self.frequency_embedding_size = frequency_embedding_size
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self.max_period = max_period
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if out_size is None:
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out_size = hidden_size
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self.mlp = nn.Sequential(
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nn.Linear(
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frequency_embedding_size, hidden_size, bias=True, **factory_kwargs
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),
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act_layer(),
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nn.Linear(hidden_size, out_size, bias=True, **factory_kwargs),
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)
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nn.init.normal_(self.mlp[0].weight, std=0.02) # type: ignore
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nn.init.normal_(self.mlp[2].weight, std=0.02) # type: ignore
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@staticmethod
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def timestep_embedding(t, dim, max_period=10000):
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"""
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Create sinusoidal timestep embeddings.
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Args:
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t (torch.Tensor): a 1-D Tensor of N indices, one per batch element. These may be fractional.
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dim (int): the dimension of the output.
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max_period (int): controls the minimum frequency of the embeddings.
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Returns:
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embedding (torch.Tensor): An (N, D) Tensor of positional embeddings.
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.. ref_link: https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
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"""
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half = dim // 2
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freqs = torch.exp(
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-math.log(max_period)
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* torch.arange(start=0, end=half, dtype=torch.float32)
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/ half
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).to(device=t.device)
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args = t[:, None].float() * freqs[None]
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embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
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if dim % 2:
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embedding = torch.cat(
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[embedding, torch.zeros_like(embedding[:, :1])], dim=-1
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)
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return embedding
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def forward(self, t):
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t_freq = self.timestep_embedding(
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t, self.frequency_embedding_size, self.max_period
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).type(self.mlp[0].weight.dtype) # type: ignore
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t_emb = self.mlp(t_freq)
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return t_emb
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class EmbedND(nn.Module):
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def __init__(self, dim: int, theta: int, axes_dim: list[int]):
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super().__init__()
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self.dim = dim
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self.theta = theta
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self.axes_dim = axes_dim
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def forward(self, ids: Tensor) -> Tensor:
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n_axes = ids.shape[-1]
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emb = torch.cat(
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[rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)],
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dim=-3,
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)
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return emb.unsqueeze(1)
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class MLPEmbedder(nn.Module):
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def __init__(self, in_dim: int, hidden_dim: int):
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super().__init__()
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self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True)
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self.silu = nn.SiLU()
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self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True)
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def forward(self, x: Tensor) -> Tensor:
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return self.out_layer(self.silu(self.in_layer(x)))
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def rope(pos, dim: int, theta: int):
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assert dim % 2 == 0
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scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
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omega = 1.0 / (theta**scale)
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out = torch.einsum("...n,d->...nd", pos, omega)
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out = torch.stack(
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[torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1
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)
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out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
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return out.float()
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def attention_after_rope(q, k, v, pe):
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q, k = apply_rope(q, k, pe)
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from .attention import attention
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x = attention(q, k, v, mode="flash")
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return x
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@torch.compile(mode="max-autotune-no-cudagraphs", dynamic=True)
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def apply_rope(xq, xk, freqs_cis):
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# 将 num_heads 和 seq_len 的维度交换回原函数的处理顺序
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xq = xq.transpose(1, 2) # [batch, num_heads, seq_len, head_dim]
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xk = xk.transpose(1, 2)
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# 将 head_dim 拆分为复数部分（实部和虚部）
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xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
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xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
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# 应用旋转位置编码（复数乘法）
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xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
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xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
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# 恢复张量形状并转置回目标维度顺序
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xq_out = xq_out.reshape(*xq.shape).type_as(xq).transpose(1, 2)
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xk_out = xk_out.reshape(*xk.shape).type_as(xk).transpose(1, 2)
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return xq_out, xk_out
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@torch.compile(mode="max-autotune-no-cudagraphs", dynamic=True)
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def scale_add_residual(
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x: torch.Tensor, scale: torch.Tensor, residual: torch.Tensor
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) -> torch.Tensor:
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return x * scale + residual
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@torch.compile(mode="max-autotune-no-cudagraphs", dynamic=True)
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def layernorm_and_scale_shift(
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x: torch.Tensor, scale: torch.Tensor, shift: torch.Tensor
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) -> torch.Tensor:
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return torch.nn.functional.layer_norm(x, (x.size(-1),)) * (scale + 1) + shift
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class SelfAttention(nn.Module):
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def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False):
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super().__init__()
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.norm = QKNorm(head_dim)
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self.proj = nn.Linear(dim, dim)
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def forward(self, x: Tensor, pe: Tensor) -> Tensor:
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qkv = self.qkv(x)
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q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.num_heads)
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q, k = self.norm(q, k, v)
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x = attention_after_rope(q, k, v, pe=pe)
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x = self.proj(x)
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return x
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@dataclass
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class ModulationOut:
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shift: Tensor
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scale: Tensor
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gate: Tensor
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class RMSNorm(torch.nn.Module):
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def __init__(self, dim: int):
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super().__init__()
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self.scale = nn.Parameter(torch.ones(dim))
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# @staticmethod
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# def rms_norm_fast(x, weight, eps):
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# return LigerRMSNormFunction.apply(
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# x,
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# weight,
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# eps,
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# 0.0,
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# "gemma",
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# True,
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# )
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@staticmethod
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def rms_norm(x, weight, eps):
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x_dtype = x.dtype
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x = x.float()
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rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + eps)
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return (x * rrms).to(dtype=x_dtype) * weight
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def forward(self, x: Tensor):
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# return self.rms_norm_fast(x, self.scale.to(x.dtype), 1e-6)
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# Because windows does not support liger_kernel, So I delete it for windows users.
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return self.rms_norm(x, self.scale.to(x.dtype), 1e-6)
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class QKNorm(torch.nn.Module):
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def __init__(self, dim: int):
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super().__init__()
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self.query_norm = RMSNorm(dim)
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self.key_norm = RMSNorm(dim)
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def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple[Tensor, Tensor]:
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q = self.query_norm(q)
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k = self.key_norm(k)
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return q.to(v), k.to(v)
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class Modulation(nn.Module):
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def __init__(self, dim: int, double: bool):
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super().__init__()
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self.is_double = double
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self.multiplier = 6 if double else 3
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self.lin = nn.Linear(dim, self.multiplier * dim, bias=True)
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def forward(self, vec: Tensor) -> tuple[ModulationOut, ModulationOut | None]:
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out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(
|
|
self.multiplier, dim=-1
|
|
)
|
|
|
|
return (
|
|
ModulationOut(*out[:3]),
|
|
ModulationOut(*out[3:]) if self.is_double else None,
|
|
)
|
|
|
|
|
|
class DoubleStreamBlock(nn.Module):
|
|
def __init__(
|
|
self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False
|
|
):
|
|
super().__init__()
|
|
|
|
mlp_hidden_dim = int(hidden_size * mlp_ratio)
|
|
self.num_heads = num_heads
|
|
self.hidden_size = hidden_size
|
|
self.img_mod = Modulation(hidden_size, double=True)
|
|
self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
self.img_attn = SelfAttention(
|
|
dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias
|
|
)
|
|
|
|
self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
self.img_mlp = nn.Sequential(
|
|
nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
|
|
nn.GELU(approximate="tanh"),
|
|
nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
|
|
)
|
|
|
|
self.txt_mod = Modulation(hidden_size, double=True)
|
|
self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
self.txt_attn = SelfAttention(
|
|
dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias
|
|
)
|
|
|
|
self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
self.txt_mlp = nn.Sequential(
|
|
nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
|
|
nn.GELU(approximate="tanh"),
|
|
nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
|
|
)
|
|
|
|
def forward(
|
|
self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor
|
|
) -> tuple[Tensor, Tensor]:
|
|
img_mod1, img_mod2 = self.img_mod(vec)
|
|
txt_mod1, txt_mod2 = self.txt_mod(vec)
|
|
|
|
# prepare image for attention
|
|
img_modulated = self.img_norm1(img)
|
|
img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
|
|
img_qkv = self.img_attn.qkv(img_modulated)
|
|
img_q, img_k, img_v = rearrange(
|
|
img_qkv, "B L (K H D) -> K B L H D", K=3, H=self.num_heads
|
|
)
|
|
img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
|
|
|
|
# prepare txt for attention
|
|
txt_modulated = self.txt_norm1(txt)
|
|
txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
|
|
txt_qkv = self.txt_attn.qkv(txt_modulated)
|
|
txt_q, txt_k, txt_v = rearrange(
|
|
txt_qkv, "B L (K H D) -> K B L H D", K=3, H=self.num_heads
|
|
)
|
|
txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
|
|
|
|
# run actual attention
|
|
q = torch.cat((txt_q, img_q), dim=1)
|
|
k = torch.cat((txt_k, img_k), dim=1)
|
|
v = torch.cat((txt_v, img_v), dim=1)
|
|
|
|
attn = attention_after_rope(q, k, v, pe=pe)
|
|
txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
|
|
|
|
# calculate the img bloks
|
|
img = img + img_mod1.gate * self.img_attn.proj(img_attn)
|
|
img_mlp = self.img_mlp(
|
|
(1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift
|
|
)
|
|
img = scale_add_residual(img_mlp, img_mod2.gate, img)
|
|
|
|
# calculate the txt bloks
|
|
txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn)
|
|
txt_mlp = self.txt_mlp(
|
|
(1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift
|
|
)
|
|
txt = scale_add_residual(txt_mlp, txt_mod2.gate, txt)
|
|
return img, txt
|
|
|
|
|
|
class SingleStreamBlock(nn.Module):
|
|
"""
|
|
A DiT block with parallel linear layers as described in
|
|
https://arxiv.org/abs/2302.05442 and adapted modulation interface.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
hidden_size: int,
|
|
num_heads: int,
|
|
mlp_ratio: float = 4.0,
|
|
qk_scale: float | None = None,
|
|
):
|
|
super().__init__()
|
|
self.hidden_dim = hidden_size
|
|
self.num_heads = num_heads
|
|
head_dim = hidden_size // num_heads
|
|
self.scale = qk_scale or head_dim**-0.5
|
|
|
|
self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
|
|
# qkv and mlp_in
|
|
self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim)
|
|
# proj and mlp_out
|
|
self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size)
|
|
|
|
self.norm = QKNorm(head_dim)
|
|
|
|
self.hidden_size = hidden_size
|
|
self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
|
|
self.mlp_act = nn.GELU(approximate="tanh")
|
|
self.modulation = Modulation(hidden_size, double=False)
|
|
|
|
def forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> Tensor:
|
|
mod, _ = self.modulation(vec)
|
|
x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
|
|
qkv, mlp = torch.split(
|
|
self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1
|
|
)
|
|
|
|
q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.num_heads)
|
|
q, k = self.norm(q, k, v)
|
|
|
|
# compute attention
|
|
attn = attention_after_rope(q, k, v, pe=pe)
|
|
# compute activation in mlp stream, cat again and run second linear layer
|
|
output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
|
|
return scale_add_residual(output, mod.gate, x)
|
|
|
|
|
|
class LastLayer(nn.Module):
|
|
def __init__(self, hidden_size: int, patch_size: int, out_channels: int):
|
|
super().__init__()
|
|
self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
|
|
self.linear = nn.Linear(
|
|
hidden_size, patch_size * patch_size * out_channels, bias=True
|
|
)
|
|
self.adaLN_modulation = nn.Sequential(
|
|
nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True)
|
|
)
|
|
|
|
def forward(self, x: Tensor, vec: Tensor) -> Tensor:
|
|
shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1)
|
|
x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
|
|
x = self.linear(x)
|
|
return x
|