559 lines
23 KiB
Python
559 lines
23 KiB
Python
# 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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# --------------------------------------------------------
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# References:
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# GLIDE: https://github.com/openai/glide-text2im
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# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py
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# --------------------------------------------------------
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from typing import Final, Optional, Tuple, Union
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import torch
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import torch.nn as nn
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import numpy as np
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import math
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import torch.utils.checkpoint as cp
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from einops import rearrange, repeat
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from timm.layers import use_fused_attn, to_2tuple
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# from timm.models.vision_transformer import PatchEmbed, Attention, Mlp
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from timm.models.vision_transformer import PatchEmbed, Mlp
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from torch.nn import functional as F
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from .VisionRoPE import VisionRotaryEmbeddingFast
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from torch.nn import functional as F
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try:
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# needs to have https://github.com/corl-team/rebased/ installed
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from fla.ops.triton.rebased_fast import parallel_rebased
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except:
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REBASED_IS_AVAILABLE = False
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try:
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# needs to have https://github.com/lucidrains/ring-attention-pytorch installed
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from ring_attention_pytorch.ring_flash_attention_cuda import ring_flash_attn_cuda
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except:
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RING_ATTENTION_IS_AVAILABLE = False
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class Attention(nn.Module):
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fused_attn: Final[bool]
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def __init__(
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self,
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dim: int,
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num_heads: int = 8,
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qkv_bias: bool = False,
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qk_norm: bool = False,
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attn_drop: float = 0.,
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proj_drop: float = 0.,
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norm_layer: nn.Module = nn.LayerNorm,
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attention_mode='math',
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attention_pe_mode=None,
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hw: Union[int, Tuple[int, int]] = 16, # (h, w)
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pt_hw: Union[int, Tuple[int, int]] = 16, # (h, w)
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intp_vfreq: bool = True, # vision position interpolation
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) -> None:
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super().__init__()
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assert dim % num_heads == 0, 'dim should be divisible by num_heads'
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self.num_heads = num_heads
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self.head_dim = dim // num_heads
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self.scale = self.head_dim ** -0.5
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self.attention_mode = attention_mode
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self.attention_pe_mode = attention_pe_mode
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self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
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self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
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self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(dim, dim)
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self.proj_drop = nn.Dropout(proj_drop)
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if self.attention_pe_mode == '2d_rope':
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half_head_dim = dim // num_heads // 2
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self.hw = to_2tuple(hw)
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self.rope = VisionRotaryEmbeddingFast(
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dim=half_head_dim,
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pt_hw=to_2tuple(pt_hw),
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ft_hw=self.hw if intp_vfreq else None,
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)
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def forward(self, x: torch.Tensor, attn_mask) -> torch.Tensor:
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B, N, C = x.shape
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qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4).contiguous()
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q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple) b h n c
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if attn_mask is not None:
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attn_mask = attn_mask.repeat(1, self.num_heads, 1, 1).to(q.dtype)
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if self.attention_pe_mode == '2d_rope':
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q_t = q.view(B, self.num_heads, -1, self.hw[0] * self.hw[1], C // self.num_heads)
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ro_q_t = self.rope(q_t)
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q = ro_q_t.view(B, self.num_heads, N, C // self.num_heads)
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k_t = k.view(B, self.num_heads, -1, self.hw[0] * self.hw[1], C // self.num_heads)
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ro_k_t = self.rope(k_t)
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k = ro_k_t.view(B, self.num_heads, N, C // self.num_heads)
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if self.attention_mode == 'xformers': # require pytorch 2.0
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with torch.backends.cuda.sdp_kernel(enable_math=False, enable_flash=False, enable_mem_efficient=True):
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x = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask,
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dropout_p=self.attn_drop.p, scale=self.scale).reshape(B, N, C)
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elif self.attention_mode == 'flash': # require pytorch 2.0
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# https://github.com/PKU-YuanGroup/Open-Sora-Plan/issues/109
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if attn_mask is None or torch.all(attn_mask.bool()):
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with torch.backends.cuda.sdp_kernel(enable_math=False, enable_flash=True, enable_mem_efficient=False):
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x = F.scaled_dot_product_attention(q, k, v,
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dropout_p=self.attn_drop.p, scale=self.scale).reshape(B, N, C)
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else: # turn to xformers
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with torch.backends.cuda.sdp_kernel(enable_math=False, enable_flash=False, enable_mem_efficient=True):
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x = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask,
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dropout_p=self.attn_drop.p, scale=self.scale).reshape(B, N, C)
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elif self.attention_mode == 'math':
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attn = (q @ k.transpose(-2, -1)) * self.scale
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if attn_mask is not None:
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attn_bias = self.make_attn_bias(attn_mask)
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attn = attn + attn_bias
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attn = attn.softmax(dim=-1)
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if torch.any(torch.isnan(attn)):
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print('torch.any(torch.isnan(attn))')
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attn = attn.masked_fill(torch.isnan(attn), float(0.))
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attn = self.attn_drop(attn)
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x = (attn @ v).transpose(1, 2).reshape(B, N, C)
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elif self.attention_mode == 'rebased':
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x = parallel_rebased(q, k, v, self.eps, True, True).reshape(B, N, C)
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elif self.attention_mode == 'ring':
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x = ring_flash_attn_cuda(q, k, v, causal=self.causal, bucket_size=self.ring_bucket_size).reshape(B, N, C)
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else:
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raise NotImplemented
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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def make_attn_bias(self, attn_mask):
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# The numerical range of bfloat16, float16 can't conver -1e8
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# Refer to https://discuss.pytorch.org/t/runtimeerror-value-cannot-be-converted-to-type-at-half-without-overflow-1e-30/109768
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attn_bias = torch.where(attn_mask == 0, -1e8 if attn_mask.dtype == torch.float32 else -1e4, attn_mask)
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attn_bias = torch.where(attn_mask == 1, 0., attn_bias)
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return attn_bias
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def modulate(x, shift, scale):
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return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
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#################################################################################
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# Embedding Layers for Timesteps and Class Labels #
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#################################################################################
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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__(self, hidden_size, frequency_embedding_size=256):
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super().__init__()
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self.mlp = nn.Sequential(
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nn.Linear(frequency_embedding_size, hidden_size, bias=True),
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nn.SiLU(),
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nn.Linear(hidden_size, hidden_size, bias=True),
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)
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self.frequency_embedding_size = frequency_embedding_size
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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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:param t: a 1-D Tensor of N indices, one per batch element.
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These may be fractional.
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:param dim: the dimension of the output.
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:param max_period: controls the minimum frequency of the embeddings.
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:return: an (N, C) Tensor of positional embeddings.
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"""
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# https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
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half = dim // 2
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freqs = torch.exp(
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-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / 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([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
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return embedding
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def forward(self, t):
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t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
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t_emb = self.mlp(t_freq)
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return t_emb
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class LabelEmbedder(nn.Module):
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"""
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Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
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"""
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def __init__(self, num_classes, hidden_size, dropout_prob):
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super().__init__()
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use_cfg_embedding = dropout_prob > 0
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self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size)
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self.num_classes = num_classes
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self.dropout_prob = dropout_prob
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def token_drop(self, labels, force_drop_ids=None):
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"""
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Drops labels to enable classifier-free guidance.
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"""
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if force_drop_ids is None:
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drop_ids = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob
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else:
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drop_ids = force_drop_ids == 1
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labels = torch.where(drop_ids, self.num_classes, labels)
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return labels
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def forward(self, labels, train, force_drop_ids=None):
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use_dropout = self.dropout_prob > 0
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if (train and use_dropout) or (force_drop_ids is not None):
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labels = self.token_drop(labels, force_drop_ids)
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embeddings = self.embedding_table(labels)
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return embeddings
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#################################################################################
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# Core DiT Model #
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#################################################################################
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class DiTBlock(nn.Module):
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"""
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A DiT block with adaptive layer norm zero (adaLN-Zero) conDiTioning.
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"""
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def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, **block_kwargs):
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super().__init__()
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self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
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self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, **block_kwargs)
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self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
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mlp_hidden_dim = int(hidden_size * mlp_ratio)
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approx_gelu = lambda: nn.GELU(approximate="tanh")
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self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0)
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self.adaLN_modulation = nn.Sequential(
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nn.SiLU(),
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nn.Linear(hidden_size, 6 * hidden_size, bias=True)
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)
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def forward(self, x, c, attention_mask):
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shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1)
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x = x + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(x), shift_msa, scale_msa), attention_mask)
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x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
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return x
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class FinalLayer(nn.Module):
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"""
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The final layer of DiT.
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"""
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def __init__(self, hidden_size, patch_size_t, patch_size, out_channels):
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super().__init__()
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self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
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self.linear = nn.Linear(hidden_size, patch_size_t * patch_size * patch_size * out_channels, bias=True)
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self.adaLN_modulation = nn.Sequential(
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nn.SiLU(),
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nn.Linear(hidden_size, 2 * hidden_size, bias=True)
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)
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def forward(self, x, c):
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shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
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x = modulate(self.norm_final(x), shift, scale)
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x = self.linear(x)
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return x
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class DiT(nn.Module):
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"""
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Diffusion model with a Transformer backbone.
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"""
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def __init__(
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self,
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input_size=32,
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patch_size=2,
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patch_size_t=1,
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in_channels=256,
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hidden_size=1152,
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depth=28,
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num_heads=16,
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mlp_ratio=4.0,
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num_frames=16,
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class_dropout_prob=0.1,
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num_classes=1000,
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learn_sigma=True,
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extras=1,
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attention_mode='math',
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attention_pe_mode=None,
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pt_input_size: Union[int, Tuple[int, int]] = None, # (h, w)
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intp_vfreq: bool = True, # vision position interpolation
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):
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super().__init__()
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self.gradient_checkpointing = False
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self.learn_sigma = learn_sigma
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self.in_channels = in_channels
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self.out_channels = in_channels * 2 if learn_sigma else in_channels
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self.patch_size = patch_size
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self.patch_size_t = patch_size_t
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self.num_heads = num_heads
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self.extras = extras
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self.hidden_size = hidden_size
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# import ipdb;ipdb.set_trace()
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self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, bias=True)
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self.t_embedder = TimestepEmbedder(hidden_size)
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if self.extras == 2:
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self.y_embedder = LabelEmbedder(num_classes, hidden_size, class_dropout_prob)
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num_patches = self.x_embedder.num_patches
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# Will use fixed sin-cos embedding:
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self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, hidden_size), requires_grad=False)
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self.temp_embed = nn.Parameter(torch.zeros(1, num_frames, hidden_size), requires_grad=False)
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if pt_input_size is None:
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pt_input_size = input_size
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self.blocks = nn.ModuleList([
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DiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio, attention_mode=attention_mode,
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attention_pe_mode=attention_pe_mode, hw=input_size, pt_hw=pt_input_size,
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intp_vfreq=intp_vfreq) for _ in range(depth)
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])
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self.final_layer = FinalLayer(hidden_size, patch_size_t, patch_size, self.out_channels)
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self.initialize_weights()
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def initialize_weights(self):
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# Initialize transformer layers:
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def _basic_init(module):
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if isinstance(module, nn.Linear):
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torch.nn.init.xavier_uniform_(module.weight)
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if module.bias is not None:
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nn.init.constant_(module.bias, 0)
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self.apply(_basic_init)
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# Initialize (and freeze) pos_embed by sin-cos embedding:
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pos_embed = get_2d_sincos_pos_embed(self.pos_embed.shape[-1], int(self.x_embedder.num_patches ** 0.5))
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self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))
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temp_embed = get_1d_sincos_temp_embed(self.temp_embed.shape[-1], self.temp_embed.shape[-2])
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self.temp_embed.data.copy_(torch.from_numpy(temp_embed).float().unsqueeze(0))
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# Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
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w = self.x_embedder.proj.weight.data
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nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
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nn.init.constant_(self.x_embedder.proj.bias, 0)
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# Initialize label embedding table:
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if self.extras == 2:
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nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02)
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# Initialize timestep embedding MLP:
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nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
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nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
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# Zero-out adaLN modulation layers in DiT blocks:
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for block in self.blocks:
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nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
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nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
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# Zero-out output layers:
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nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
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nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
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nn.init.constant_(self.final_layer.linear.weight, 0)
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nn.init.constant_(self.final_layer.linear.bias, 0)
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def unpatchify(self, x):
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"""
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x: (N, T, patch_size**2 * C)
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imgs: (N, H, W, C)
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"""
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c = self.out_channels
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p = self.x_embedder.patch_size[0]
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h = w = int(x.shape[1] ** 0.5)
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assert h * w == x.shape[1]
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x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
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x = torch.einsum('nhwpqc->nchpwq', x)
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imgs = x.reshape(shape=(x.shape[0], c, h * p, h * p))
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return imgs
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def ckpt_wrapper(self, module):
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def ckpt_forward(*inputs):
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outputs = module(*inputs)
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return outputs
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return ckpt_forward
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def make_mask(self, attention_mask):
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attention_mask = attention_mask.flatten(1).unsqueeze(-1) # bs t h w -> bs thw 1
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attention_mask = attention_mask @ attention_mask.transpose(1, 2) # bs thw 1 @ bs 1 thw = bs thw thw
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attention_mask = attention_mask.unsqueeze(1)
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return attention_mask
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def forward(self, x, t, y, attention_mask=None):
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"""
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Forward pass of DiT.
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x: (B, T, C, H, W) tensor of spatial inputs (images or latent representations of images)
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t: (B,) tensor of diffusion timesteps
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y: (B,) tensor of class labels
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"""
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if x.ndim == 4:
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raise NotImplementedError
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B, T, C, H, W = x.shape
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self.t = T // self.patch_size_t # 4 // 1
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if attention_mask is not None:
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attention_mask = self.make_mask(attention_mask)
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# print(num_patches_height, num_patches_width, x.shape, pos_embed.shape)
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x = rearrange(x, 'b t c h w -> (b t) c h w')
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x = self.x_embedder(x) + self.pos_embed # (BT, N, C), where N = H * W / patch_size ** 2
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x = rearrange(x, '(b t) n c -> (b n) t c', t=self.t)
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x = x + self.temp_embed
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x = rearrange(x, '(b n) t c -> b (t n) c', b=B)
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|
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t = self.t_embedder(t) # (B, C)
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if self.extras == 2:
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y = self.y_embedder(y, self.training) # (B, C)
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c = t + y # (B, C)
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else:
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c = t
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for block in self.blocks: # (B, N, C)
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if self.gradient_checkpointing and self.training:
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x = torch.utils.checkpoint.checkpoint(self.ckpt_wrapper(block), x, c, attention_mask) # (B, N, C)
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else:
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x = block(x, c, attention_mask)
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|
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x = rearrange(x, 'b (t n) c -> (b t) n c', t=self.t)
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c = repeat(c, 'b c -> (b t) c', t=self.t)
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x = self.final_layer(x, c) # (B, N, patch_size ** 2 * out_channels)
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x = self.unpatchify(x) # (B, out_channels, H, W)
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x = rearrange(x, '(b t) c h w -> b t c h w', t=self.t)
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return x
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|
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def forward_with_cfg(self, x, t, y, cfg_scale, attention_mask):
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"""
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Forward pass of DiT, but also batches the unconDiTional forward pass for classifier-free guidance.
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"""
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# https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
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half = x[: len(x) // 2]
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combined = torch.cat([half, half], dim=0)
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model_out = self.forward(combined, t, y, attention_mask)
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|
# For exact reproducibility reasons, we apply classifier-free guidance on only
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|
# three channels by default. The standard approach to cfg applies it to all channels.
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|
# This can be done by uncommenting the following line and commenting-out the line following that.
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|
eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:]
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# eps, rest = model_out[:, :3], model_out[:, 3:]
|
|
cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
|
|
half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)
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|
eps = torch.cat([half_eps, half_eps], dim=0)
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|
return torch.cat([eps, rest], dim=1)
|
|
|
|
|
|
#################################################################################
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|
# Sine/Cosine Positional Embedding Functions #
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|
#################################################################################
|
|
# https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py
|
|
|
|
def get_1d_sincos_temp_embed(embed_dim, length):
|
|
pos = torch.arange(0, length).unsqueeze(1)
|
|
return get_1d_sincos_pos_embed_from_grid(embed_dim, pos)
|
|
|
|
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0):
|
|
"""
|
|
grid_size: int of the grid height and width
|
|
return:
|
|
pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
|
|
"""
|
|
grid_h = np.arange(grid_size, dtype=np.float32)
|
|
grid_w = np.arange(grid_size, dtype=np.float32)
|
|
grid = np.meshgrid(grid_w, grid_h) # here w goes first
|
|
grid = np.stack(grid, axis=0)
|
|
|
|
grid = grid.reshape([2, 1, grid_size, grid_size])
|
|
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
|
|
if cls_token and extra_tokens > 0:
|
|
pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
|
|
return pos_embed
|
|
|
|
|
|
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
|
|
assert embed_dim % 2 == 0
|
|
|
|
# use half of dimensions to encode grid_h
|
|
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])
|
|
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])
|
|
|
|
emb = np.concatenate([emb_h, emb_w], axis=1)
|
|
return emb
|
|
|
|
|
|
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
|
|
"""
|
|
embed_dim: output dimension for each position
|
|
pos: a list of positions to be encoded: size (M,)
|
|
out: (M, D)
|
|
"""
|
|
assert embed_dim % 2 == 0
|
|
omega = np.arange(embed_dim // 2, dtype=np.float64)
|
|
omega /= embed_dim / 2.
|
|
omega = 1. / 10000**omega
|
|
|
|
pos = pos.reshape(-1)
|
|
out = np.einsum('m,d->md', pos, omega)
|
|
|
|
emb_sin = np.sin(out)
|
|
emb_cos = np.cos(out)
|
|
|
|
emb = np.concatenate([emb_sin, emb_cos], axis=1)
|
|
return emb
|
|
|
|
|
|
|
|
#################################################################################
|
|
# DiT Configs #
|
|
#################################################################################
|
|
|
|
def DiT_XL_122(**kwargs):
|
|
return DiT(depth=28, hidden_size=1152, patch_size_t=1, patch_size=2, num_heads=16, **kwargs)
|
|
|
|
def DiT_XL_144(**kwargs):
|
|
return DiT(depth=28, hidden_size=1152, patch_size_t=1, patch_size=4, num_heads=16, **kwargs)
|
|
|
|
def DiT_XL_188(**kwargs):
|
|
return DiT(depth=28, hidden_size=1152, patch_size_t=1, patch_size=8, num_heads=16, **kwargs)
|
|
|
|
def DiT_L_122(**kwargs):
|
|
return DiT(depth=24, hidden_size=1024, patch_size_t=1, patch_size=2, num_heads=16, **kwargs)
|
|
|
|
def DiT_L_144(**kwargs):
|
|
return DiT(depth=24, hidden_size=1024, patch_size_t=1, patch_size=4, num_heads=16, **kwargs)
|
|
|
|
def DiT_L_188(**kwargs):
|
|
return DiT(depth=24, hidden_size=1024, patch_size_t=1, patch_size=8, num_heads=16, **kwargs)
|
|
|
|
def DiT_B_122(**kwargs):
|
|
return DiT(depth=12, hidden_size=768, patch_size_t=1, patch_size=2, num_heads=12, **kwargs)
|
|
|
|
def DiT_B_144(**kwargs):
|
|
return DiT(depth=12, hidden_size=768, patch_size_t=1, patch_size=4, num_heads=12, **kwargs)
|
|
|
|
def DiT_B_188(**kwargs):
|
|
return DiT(depth=12, hidden_size=768, patch_size_t=1, patch_size=8, num_heads=12, **kwargs)
|
|
|
|
def DiT_S_122(**kwargs):
|
|
return DiT(depth=12, hidden_size=384, patch_size_t=1, patch_size=2, num_heads=6, **kwargs)
|
|
|
|
def DiT_S_144(**kwargs):
|
|
return DiT(depth=12, hidden_size=384, patch_size_t=1, patch_size=4, num_heads=6, **kwargs)
|
|
|
|
def DiT_S_188(**kwargs):
|
|
return DiT(depth=12, hidden_size=384, patch_size_t=1, patch_size=8, num_heads=6, **kwargs)
|
|
|
|
|
|
DiT_models = {
|
|
'DiT-XL/122': DiT_XL_122, 'DiT-XL/144': DiT_XL_144, 'DiT-XL/188': DiT_XL_188,
|
|
'DiT-L/122': DiT_L_122, 'DiT-L/144': DiT_L_144, 'DiT-L/188': DiT_L_188,
|
|
'DiT-B/122': DiT_B_122, 'DiT-B/144': DiT_B_144, 'DiT-B/188': DiT_B_188,
|
|
'DiT-S/122': DiT_S_122, 'DiT-S/144': DiT_S_144, 'DiT-S/188': DiT_S_188,
|
|
} |