1067 lines
42 KiB
Python
1067 lines
42 KiB
Python
from wan.modules.attention import attention
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from wan.modules.model import (
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WanRMSNorm,
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rope_apply,
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WanLayerNorm,
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WAN_CROSSATTENTION_CLASSES,
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rope_params,
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MLPProj,
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sinusoidal_embedding_1d
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)
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from torch.nn.attention.flex_attention import create_block_mask, flex_attention
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from diffusers.configuration_utils import ConfigMixin, register_to_config
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from torch.nn.attention.flex_attention import BlockMask
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from diffusers.models.modeling_utils import ModelMixin
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import torch.nn as nn
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import torch
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import math
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import torch.distributed as dist
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# wan 1.3B model has a weird channel / head configurations and require max-autotune to work with flexattention
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# see https://github.com/pytorch/pytorch/issues/133254
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# change to default for other models
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flex_attention = torch.compile(
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flex_attention, dynamic=False, mode="max-autotune-no-cudagraphs")
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def causal_rope_apply(x, grid_sizes, freqs, start_frame=0):
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n, c = x.size(2), x.size(3) // 2
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# split freqs
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freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1)
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# loop over samples
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output = []
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for i, (f, h, w) in enumerate(grid_sizes.tolist()):
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seq_len = f * h * w
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# precompute multipliers
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x_i = torch.view_as_complex(x[i, :seq_len].to(torch.float64).reshape(
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seq_len, n, -1, 2))
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freqs_i = torch.cat([
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freqs[0][start_frame:start_frame + f].view(f, 1, 1, -1).expand(f, h, w, -1),
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freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
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freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)
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],
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dim=-1).reshape(seq_len, 1, -1)
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# apply rotary embedding
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x_i = torch.view_as_real(x_i * freqs_i).flatten(2)
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x_i = torch.cat([x_i, x[i, seq_len:]])
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# append to collection
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output.append(x_i)
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return torch.stack(output).type_as(x)
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class CausalWanSelfAttention(nn.Module):
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def __init__(self,
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dim,
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num_heads,
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local_attn_size=-1,
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sink_size=0,
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qk_norm=True,
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eps=1e-6):
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assert dim % num_heads == 0
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super().__init__()
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self.dim = dim
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self.num_heads = num_heads
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self.head_dim = dim // num_heads
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self.local_attn_size = local_attn_size
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self.sink_size = sink_size
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self.qk_norm = qk_norm
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self.eps = eps
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self.max_attention_size = 32760 if local_attn_size == -1 else local_attn_size * 1560
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# layers
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self.q = nn.Linear(dim, dim)
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self.k = nn.Linear(dim, dim)
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self.v = nn.Linear(dim, dim)
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self.o = nn.Linear(dim, dim)
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self.norm_q = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
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self.norm_k = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
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def forward(
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self,
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x,
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seq_lens,
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grid_sizes,
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freqs,
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block_mask,
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kv_cache=None,
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current_start=0,
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cache_start=None
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):
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r"""
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Args:
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x(Tensor): Shape [B, L, num_heads, C / num_heads]
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seq_lens(Tensor): Shape [B]
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grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W)
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freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
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block_mask (BlockMask)
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"""
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b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
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if cache_start is None:
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cache_start = current_start
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# print(f"x sum: {x.float().sum().item()}, shape: {x.shape}")
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# query, key, value function
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def qkv_fn(x):
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q = self.norm_q(self.q(x)).view(b, s, n, d)
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k = self.norm_k(self.k(x)).view(b, s, n, d)
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v = self.v(x).view(b, s, n, d)
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return q, k, v
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q, k, v = qkv_fn(x)
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if kv_cache is None:
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# if it is teacher forcing training?
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is_tf = (s == seq_lens[0].item() * 2)
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if is_tf:
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q_chunk = torch.chunk(q, 2, dim=1)
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k_chunk = torch.chunk(k, 2, dim=1)
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roped_query = []
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roped_key = []
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# rope should be same for clean and noisy parts
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for ii in range(2):
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rq = rope_apply(q_chunk[ii], grid_sizes, freqs).type_as(v)
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rk = rope_apply(k_chunk[ii], grid_sizes, freqs).type_as(v)
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roped_query.append(rq)
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roped_key.append(rk)
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roped_query = torch.cat(roped_query, dim=1)
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roped_key = torch.cat(roped_key, dim=1)
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padded_length = math.ceil(q.shape[1] / 128) * 128 - q.shape[1]
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padded_roped_query = torch.cat(
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[roped_query,
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torch.zeros([q.shape[0], padded_length, q.shape[2], q.shape[3]],
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device=q.device, dtype=v.dtype)],
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dim=1
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)
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padded_roped_key = torch.cat(
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[roped_key, torch.zeros([k.shape[0], padded_length, k.shape[2], k.shape[3]],
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device=k.device, dtype=v.dtype)],
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dim=1
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)
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padded_v = torch.cat(
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[v, torch.zeros([v.shape[0], padded_length, v.shape[2], v.shape[3]],
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device=v.device, dtype=v.dtype)],
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dim=1
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)
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x = flex_attention(
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query=padded_roped_query.transpose(2, 1),
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key=padded_roped_key.transpose(2, 1),
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value=padded_v.transpose(2, 1),
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block_mask=block_mask
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)[:, :, :-padded_length].transpose(2, 1)
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else:
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roped_query = rope_apply(q, grid_sizes, freqs).type_as(v)
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roped_key = rope_apply(k, grid_sizes, freqs).type_as(v)
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padded_length = math.ceil(q.shape[1] / 128) * 128 - q.shape[1]
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padded_roped_query = torch.cat(
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[roped_query,
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torch.zeros([q.shape[0], padded_length, q.shape[2], q.shape[3]],
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device=q.device, dtype=v.dtype)],
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dim=1
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)
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padded_roped_key = torch.cat(
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[roped_key, torch.zeros([k.shape[0], padded_length, k.shape[2], k.shape[3]],
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device=k.device, dtype=v.dtype)],
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dim=1
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)
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padded_v = torch.cat(
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[v, torch.zeros([v.shape[0], padded_length, v.shape[2], v.shape[3]],
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device=v.device, dtype=v.dtype)],
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dim=1
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)
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x = flex_attention(
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query=padded_roped_query.transpose(2, 1),
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key=padded_roped_key.transpose(2, 1),
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value=padded_v.transpose(2, 1),
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block_mask=block_mask
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)[:, :, :-padded_length].transpose(2, 1)
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else:
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frame_seqlen = math.prod(grid_sizes[0][1:]).item()
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current_start_frame = current_start // frame_seqlen
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roped_query = causal_rope_apply(
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q, grid_sizes, freqs, start_frame=current_start_frame).type_as(v)
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roped_key = causal_rope_apply(
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k, grid_sizes, freqs, start_frame=current_start_frame).type_as(v)
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current_end = current_start + roped_query.shape[1]
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sink_tokens = self.sink_size * frame_seqlen
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# If we are using local attention and the current KV cache size is larger than the local attention size, we need to truncate the KV cache
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kv_cache_size = kv_cache["k"].shape[1]
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num_new_tokens = roped_query.shape[1]
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if self.local_attn_size != -1 and (current_end > kv_cache["global_end_index"].item()) and (
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num_new_tokens + kv_cache["local_end_index"].item() > kv_cache_size):
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assert False, "Not implemented"
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# Calculate the number of new tokens added in this step
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# Shift existing cache content left to discard oldest tokens
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# Clone the source slice to avoid overlapping memory error
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num_evicted_tokens = num_new_tokens + kv_cache["local_end_index"].item() - kv_cache_size
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num_rolled_tokens = kv_cache["local_end_index"].item() - num_evicted_tokens - sink_tokens
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kv_cache["k"][:, sink_tokens:sink_tokens + num_rolled_tokens] = \
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kv_cache["k"][:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
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kv_cache["v"][:, sink_tokens:sink_tokens + num_rolled_tokens] = \
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kv_cache["v"][:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
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# Insert the new keys/values at the end
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local_end_index = kv_cache["local_end_index"].item() + current_end - \
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kv_cache["global_end_index"].item() - num_evicted_tokens
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local_start_index = local_end_index - num_new_tokens
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kv_cache["k"][:, local_start_index:local_end_index] = roped_key
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kv_cache["v"][:, local_start_index:local_end_index] = v
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else:
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# Assign new keys/values directly up to current_end
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local_end_index = kv_cache["local_end_index"].item() + current_end - kv_cache["global_end_index"].item()
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local_start_index = local_end_index - num_new_tokens
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kv_cache["k"][:, local_start_index:local_end_index] = roped_key
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kv_cache["v"][:, local_start_index:local_end_index] = v
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x = attention(
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roped_query,
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kv_cache["k"][:, max(0, local_end_index - self.max_attention_size):local_end_index],
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kv_cache["v"][:, max(0, local_end_index - self.max_attention_size):local_end_index]
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)
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kv_cache["global_end_index"].fill_(current_end)
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kv_cache["local_end_index"].fill_(local_end_index)
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# output
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x = x.flatten(2)
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x = self.o(x)
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return x
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class CausalWanAttentionBlock(nn.Module):
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def __init__(self,
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cross_attn_type,
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dim,
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ffn_dim,
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num_heads,
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local_attn_size=-1,
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sink_size=0,
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qk_norm=True,
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cross_attn_norm=False,
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eps=1e-6):
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super().__init__()
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self.dim = dim
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self.ffn_dim = ffn_dim
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self.num_heads = num_heads
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self.local_attn_size = local_attn_size
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self.qk_norm = qk_norm
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self.cross_attn_norm = cross_attn_norm
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self.eps = eps
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# layers
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self.norm1 = WanLayerNorm(dim, eps)
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self.self_attn = CausalWanSelfAttention(dim, num_heads, local_attn_size, sink_size, qk_norm, eps)
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self.norm3 = WanLayerNorm(
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dim, eps,
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elementwise_affine=True) if cross_attn_norm else nn.Identity()
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self.cross_attn = WAN_CROSSATTENTION_CLASSES[cross_attn_type](dim,
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num_heads,
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(-1, -1),
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qk_norm,
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eps)
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self.norm2 = WanLayerNorm(dim, eps)
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self.ffn = nn.Sequential(
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nn.Linear(dim, ffn_dim), nn.GELU(approximate='tanh'),
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nn.Linear(ffn_dim, dim))
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# modulation
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self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)
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def forward(
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self,
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x,
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e,
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seq_lens,
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grid_sizes,
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freqs,
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context,
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context_lens,
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block_mask,
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kv_cache=None,
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crossattn_cache=None,
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current_start=0,
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cache_start=None
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):
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r"""
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Args:
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x(Tensor): Shape [B, L, C]
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e(Tensor): Shape [B, F, 6, C]
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seq_lens(Tensor): Shape [B], length of each sequence in batch
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grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W)
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freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
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"""
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num_frames, frame_seqlen = e.shape[1], x.shape[1] // e.shape[1]
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# assert e.dtype == torch.float32
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# with amp.autocast(dtype=torch.float32):
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# print(f"e sum: {e.float().sum().item()}, dtype: {e.dtype}")
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e = (self.modulation.unsqueeze(1) + e).chunk(6, dim=2)
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# assert e[0].dtype == torch.float32
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# print(f"e[1] sum: {e[1].float().sum().item()}, dtype: {e[1].dtype}")
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# print(f"e[0] sum: {e[0].float().sum().item()}, dtype: {e[0].dtype}")
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# self-attention
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y = self.self_attn(
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(self.norm1(x).unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * (1 + e[1]) + e[0]).flatten(1, 2),
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seq_lens, grid_sizes,
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freqs, block_mask, kv_cache, current_start, cache_start)
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# with amp.autocast(dtype=torch.float32):
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x = x + (y.unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * e[2]).flatten(1, 2)
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# cross-attention & ffn function
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def cross_attn_ffn(x, context, context_lens, e, crossattn_cache=None):
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x = x + self.cross_attn(self.norm3(x), context,
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context_lens, crossattn_cache=crossattn_cache)
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y = self.ffn(
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(self.norm2(x).unflatten(dim=1, sizes=(num_frames,
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frame_seqlen)) * (1 + e[4]) + e[3]).flatten(1, 2)
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)
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# with amp.autocast(dtype=torch.float32):
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x = x + (y.unflatten(dim=1, sizes=(num_frames,
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frame_seqlen)) * e[5]).flatten(1, 2)
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return x
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x = cross_attn_ffn(x, context, context_lens, e, crossattn_cache)
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return x
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class CausalHead(nn.Module):
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def __init__(self, dim, out_dim, patch_size, eps=1e-6):
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super().__init__()
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self.dim = dim
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self.out_dim = out_dim
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self.patch_size = patch_size
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self.eps = eps
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# layers
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out_dim = math.prod(patch_size) * out_dim
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self.norm = WanLayerNorm(dim, eps)
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self.head = nn.Linear(dim, out_dim)
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# modulation
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self.modulation = nn.Parameter(torch.randn(1, 2, dim) / dim**0.5)
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def forward(self, x, e):
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r"""
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Args:
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x(Tensor): Shape [B, L1, C]
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e(Tensor): Shape [B, F, 1, C]
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"""
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# assert e.dtype == torch.float32
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# with amp.autocast(dtype=torch.float32):
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num_frames, frame_seqlen = e.shape[1], x.shape[1] // e.shape[1]
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e = (self.modulation.unsqueeze(1) + e).chunk(2, dim=2)
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x = (self.head(self.norm(x).unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * (1 + e[1]) + e[0]))
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return x
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class CausalWanModel(ModelMixin, ConfigMixin):
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r"""
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Wan diffusion backbone supporting both text-to-video and image-to-video.
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"""
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ignore_for_config = [
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'patch_size', 'cross_attn_norm', 'qk_norm', 'text_dim'
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]
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_no_split_modules = ['WanAttentionBlock']
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_supports_gradient_checkpointing = True
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@register_to_config
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def __init__(self,
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model_type='t2v',
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patch_size=(1, 2, 2),
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text_len=512,
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in_dim=16,
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dim=2048,
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ffn_dim=8192,
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freq_dim=256,
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text_dim=4096,
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out_dim=16,
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num_heads=16,
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num_layers=32,
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local_attn_size=-1,
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sink_size=0,
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qk_norm=True,
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cross_attn_norm=True,
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eps=1e-6):
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r"""
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Initialize the diffusion model backbone.
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Args:
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model_type (`str`, *optional*, defaults to 't2v'):
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Model variant - 't2v' (text-to-video) or 'i2v' (image-to-video)
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patch_size (`tuple`, *optional*, defaults to (1, 2, 2)):
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3D patch dimensions for video embedding (t_patch, h_patch, w_patch)
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text_len (`int`, *optional*, defaults to 512):
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Fixed length for text embeddings
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in_dim (`int`, *optional*, defaults to 16):
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Input video channels (C_in)
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dim (`int`, *optional*, defaults to 2048):
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Hidden dimension of the transformer
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ffn_dim (`int`, *optional*, defaults to 8192):
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Intermediate dimension in feed-forward network
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freq_dim (`int`, *optional*, defaults to 256):
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Dimension for sinusoidal time embeddings
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text_dim (`int`, *optional*, defaults to 4096):
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Input dimension for text embeddings
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out_dim (`int`, *optional*, defaults to 16):
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Output video channels (C_out)
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num_heads (`int`, *optional*, defaults to 16):
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Number of attention heads
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num_layers (`int`, *optional*, defaults to 32):
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Number of transformer blocks
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local_attn_size (`int`, *optional*, defaults to -1):
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Window size for temporal local attention (-1 indicates global attention)
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sink_size (`int`, *optional*, defaults to 0):
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Size of the attention sink, we keep the first `sink_size` frames unchanged when rolling the KV cache
|
|
qk_norm (`bool`, *optional*, defaults to True):
|
|
Enable query/key normalization
|
|
cross_attn_norm (`bool`, *optional*, defaults to False):
|
|
Enable cross-attention normalization
|
|
eps (`float`, *optional*, defaults to 1e-6):
|
|
Epsilon value for normalization layers
|
|
"""
|
|
|
|
super().__init__()
|
|
|
|
assert model_type in ['t2v', 'i2v']
|
|
self.model_type = model_type
|
|
|
|
self.patch_size = patch_size
|
|
self.text_len = text_len
|
|
self.in_dim = in_dim
|
|
self.dim = dim
|
|
self.ffn_dim = ffn_dim
|
|
self.freq_dim = freq_dim
|
|
self.text_dim = text_dim
|
|
self.out_dim = out_dim
|
|
self.num_heads = num_heads
|
|
self.num_layers = num_layers
|
|
self.local_attn_size = local_attn_size
|
|
self.qk_norm = qk_norm
|
|
self.cross_attn_norm = cross_attn_norm
|
|
self.eps = eps
|
|
|
|
# embeddings
|
|
self.patch_embedding = nn.Conv3d(
|
|
in_dim, dim, kernel_size=patch_size, stride=patch_size)
|
|
self.text_embedding = nn.Sequential(
|
|
nn.Linear(text_dim, dim), nn.GELU(approximate='tanh'),
|
|
nn.Linear(dim, dim))
|
|
|
|
self.time_embedding = nn.Sequential(
|
|
nn.Linear(freq_dim, dim), nn.SiLU(), nn.Linear(dim, dim))
|
|
self.time_projection = nn.Sequential(
|
|
nn.SiLU(), nn.Linear(dim, dim * 6))
|
|
|
|
# blocks
|
|
cross_attn_type = 't2v_cross_attn' if model_type == 't2v' else 'i2v_cross_attn'
|
|
self.blocks = nn.ModuleList([
|
|
CausalWanAttentionBlock(cross_attn_type, dim, ffn_dim, num_heads,
|
|
local_attn_size, sink_size, qk_norm, cross_attn_norm, eps)
|
|
for _ in range(num_layers)
|
|
])
|
|
|
|
# head
|
|
self.head = CausalHead(dim, out_dim, patch_size, eps)
|
|
|
|
# buffers (don't use register_buffer otherwise dtype will be changed in to())
|
|
assert (dim % num_heads) == 0 and (dim // num_heads) % 2 == 0
|
|
d = dim // num_heads
|
|
self.freqs = torch.cat([
|
|
rope_params(1024, d - 4 * (d // 6)),
|
|
rope_params(1024, 2 * (d // 6)),
|
|
rope_params(1024, 2 * (d // 6))
|
|
],
|
|
dim=1)
|
|
|
|
if model_type == 'i2v':
|
|
self.img_emb = MLPProj(1280, dim)
|
|
|
|
# initialize weights
|
|
self.init_weights()
|
|
|
|
self.gradient_checkpointing = False
|
|
|
|
self.block_mask = None
|
|
|
|
self.num_frame_per_block = 1
|
|
self.independent_first_frame = False
|
|
|
|
def _set_gradient_checkpointing(self, module, value=False):
|
|
self.gradient_checkpointing = value
|
|
|
|
@staticmethod
|
|
def _prepare_blockwise_causal_attn_mask(
|
|
device: torch.device | str, num_frames: int = 21,
|
|
frame_seqlen: int = 1560, num_frame_per_block=1, local_attn_size=-1
|
|
) -> BlockMask:
|
|
"""
|
|
we will divide the token sequence into the following format
|
|
[1 latent frame] [1 latent frame] ... [1 latent frame]
|
|
We use flexattention to construct the attention mask
|
|
"""
|
|
total_length = num_frames * frame_seqlen
|
|
|
|
# we do right padding to get to a multiple of 128
|
|
padded_length = math.ceil(total_length / 128) * 128 - total_length
|
|
|
|
ends = torch.zeros(total_length + padded_length,
|
|
device=device, dtype=torch.long)
|
|
|
|
# Block-wise causal mask will attend to all elements that are before the end of the current chunk
|
|
frame_indices = torch.arange(
|
|
start=0,
|
|
end=total_length,
|
|
step=frame_seqlen * num_frame_per_block,
|
|
device=device
|
|
)
|
|
|
|
for tmp in frame_indices:
|
|
ends[tmp:tmp + frame_seqlen * num_frame_per_block] = tmp + \
|
|
frame_seqlen * num_frame_per_block
|
|
|
|
def attention_mask(b, h, q_idx, kv_idx):
|
|
if local_attn_size == -1:
|
|
return (kv_idx < ends[q_idx]) | (q_idx == kv_idx)
|
|
else:
|
|
return ((kv_idx < ends[q_idx]) & (kv_idx >= (ends[q_idx] - local_attn_size * frame_seqlen))) | (q_idx == kv_idx)
|
|
# return ((kv_idx < total_length) & (q_idx < total_length)) | (q_idx == kv_idx) # bidirectional mask
|
|
|
|
block_mask = create_block_mask(attention_mask, B=None, H=None, Q_LEN=total_length + padded_length,
|
|
KV_LEN=total_length + padded_length, _compile=False, device=device)
|
|
|
|
import torch.distributed as dist
|
|
if not dist.is_initialized() or dist.get_rank() == 0:
|
|
print(
|
|
f" cache a block wise causal mask with block size of {num_frame_per_block} frames")
|
|
print(block_mask)
|
|
|
|
# import imageio
|
|
# import numpy as np
|
|
# from torch.nn.attention.flex_attention import create_mask
|
|
|
|
# mask = create_mask(attention_mask, B=None, H=None, Q_LEN=total_length +
|
|
# padded_length, KV_LEN=total_length + padded_length, device=device)
|
|
# import cv2
|
|
# mask = cv2.resize(mask[0, 0].cpu().float().numpy(), (1024, 1024))
|
|
# imageio.imwrite("mask_%d.jpg" % (0), np.uint8(255. * mask))
|
|
|
|
return block_mask
|
|
|
|
@staticmethod
|
|
def _prepare_teacher_forcing_mask(
|
|
device: torch.device | str, num_frames: int = 21,
|
|
frame_seqlen: int = 1560, num_frame_per_block=1
|
|
) -> BlockMask:
|
|
"""
|
|
we will divide the token sequence into the following format
|
|
[1 latent frame] [1 latent frame] ... [1 latent frame]
|
|
We use flexattention to construct the attention mask
|
|
"""
|
|
# debug
|
|
DEBUG = False
|
|
if DEBUG:
|
|
num_frames = 9
|
|
frame_seqlen = 256
|
|
|
|
total_length = num_frames * frame_seqlen * 2
|
|
|
|
# we do right padding to get to a multiple of 128
|
|
padded_length = math.ceil(total_length / 128) * 128 - total_length
|
|
|
|
clean_ends = num_frames * frame_seqlen
|
|
# for clean context frames, we can construct their flex attention mask based on a [start, end] interval
|
|
context_ends = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
|
|
# for noisy frames, we need two intervals to construct the flex attention mask [context_start, context_end] [noisy_start, noisy_end]
|
|
noise_context_starts = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
|
|
noise_context_ends = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
|
|
noise_noise_starts = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
|
|
noise_noise_ends = torch.zeros(total_length + padded_length, device=device, dtype=torch.long)
|
|
|
|
# Block-wise causal mask will attend to all elements that are before the end of the current chunk
|
|
attention_block_size = frame_seqlen * num_frame_per_block
|
|
frame_indices = torch.arange(
|
|
start=0,
|
|
end=num_frames * frame_seqlen,
|
|
step=attention_block_size,
|
|
device=device, dtype=torch.long
|
|
)
|
|
|
|
# attention for clean context frames
|
|
for start in frame_indices:
|
|
context_ends[start:start + attention_block_size] = start + attention_block_size
|
|
|
|
noisy_image_start_list = torch.arange(
|
|
num_frames * frame_seqlen, total_length,
|
|
step=attention_block_size,
|
|
device=device, dtype=torch.long
|
|
)
|
|
noisy_image_end_list = noisy_image_start_list + attention_block_size
|
|
|
|
# attention for noisy frames
|
|
for block_index, (start, end) in enumerate(zip(noisy_image_start_list, noisy_image_end_list)):
|
|
# attend to noisy tokens within the same block
|
|
noise_noise_starts[start:end] = start
|
|
noise_noise_ends[start:end] = end
|
|
# attend to context tokens in previous blocks
|
|
# noise_context_starts[start:end] = 0
|
|
noise_context_ends[start:end] = block_index * attention_block_size
|
|
|
|
def attention_mask(b, h, q_idx, kv_idx):
|
|
# first design the mask for clean frames
|
|
clean_mask = (q_idx < clean_ends) & (kv_idx < context_ends[q_idx])
|
|
# then design the mask for noisy frames
|
|
# noisy frames will attend to all clean preceeding clean frames + itself
|
|
C1 = (kv_idx < noise_noise_ends[q_idx]) & (kv_idx >= noise_noise_starts[q_idx])
|
|
C2 = (kv_idx < noise_context_ends[q_idx]) & (kv_idx >= noise_context_starts[q_idx])
|
|
noise_mask = (q_idx >= clean_ends) & (C1 | C2)
|
|
|
|
eye_mask = q_idx == kv_idx
|
|
return eye_mask | clean_mask | noise_mask
|
|
|
|
block_mask = create_block_mask(attention_mask, B=None, H=None, Q_LEN=total_length + padded_length,
|
|
KV_LEN=total_length + padded_length, _compile=False, device=device)
|
|
|
|
if DEBUG:
|
|
print(block_mask)
|
|
import imageio
|
|
import numpy as np
|
|
from torch.nn.attention.flex_attention import create_mask
|
|
|
|
mask = create_mask(attention_mask, B=None, H=None, Q_LEN=total_length +
|
|
padded_length, KV_LEN=total_length + padded_length, device=device)
|
|
import cv2
|
|
mask = cv2.resize(mask[0, 0].cpu().float().numpy(), (1024, 1024))
|
|
imageio.imwrite("mask_%d.jpg" % (0), np.uint8(255. * mask))
|
|
|
|
return block_mask
|
|
|
|
@staticmethod
|
|
def _prepare_blockwise_causal_attn_mask_i2v(
|
|
device: torch.device | str, num_frames: int = 21,
|
|
frame_seqlen: int = 1560, num_frame_per_block=4, local_attn_size=-1
|
|
) -> BlockMask:
|
|
"""
|
|
we will divide the token sequence into the following format
|
|
[1 latent frame] [N latent frame] ... [N latent frame]
|
|
The first frame is separated out to support I2V generation
|
|
We use flexattention to construct the attention mask
|
|
"""
|
|
total_length = num_frames * frame_seqlen
|
|
|
|
# we do right padding to get to a multiple of 128
|
|
padded_length = math.ceil(total_length / 128) * 128 - total_length
|
|
|
|
ends = torch.zeros(total_length + padded_length,
|
|
device=device, dtype=torch.long)
|
|
|
|
# special handling for the first frame
|
|
ends[:frame_seqlen] = frame_seqlen
|
|
|
|
# Block-wise causal mask will attend to all elements that are before the end of the current chunk
|
|
frame_indices = torch.arange(
|
|
start=frame_seqlen,
|
|
end=total_length,
|
|
step=frame_seqlen * num_frame_per_block,
|
|
device=device
|
|
)
|
|
|
|
for idx, tmp in enumerate(frame_indices):
|
|
ends[tmp:tmp + frame_seqlen * num_frame_per_block] = tmp + \
|
|
frame_seqlen * num_frame_per_block
|
|
|
|
def attention_mask(b, h, q_idx, kv_idx):
|
|
if local_attn_size == -1:
|
|
return (kv_idx < ends[q_idx]) | (q_idx == kv_idx)
|
|
else:
|
|
return ((kv_idx < ends[q_idx]) & (kv_idx >= (ends[q_idx] - local_attn_size * frame_seqlen))) | \
|
|
(q_idx == kv_idx)
|
|
|
|
block_mask = create_block_mask(attention_mask, B=None, H=None, Q_LEN=total_length + padded_length,
|
|
KV_LEN=total_length + padded_length, _compile=False, device=device)
|
|
|
|
if not dist.is_initialized() or dist.get_rank() == 0:
|
|
print(
|
|
f" cache a block wise causal mask with block size of {num_frame_per_block} frames")
|
|
print(block_mask)
|
|
|
|
# import imageio
|
|
# import numpy as np
|
|
# from torch.nn.attention.flex_attention import create_mask
|
|
|
|
# mask = create_mask(attention_mask, B=None, H=None, Q_LEN=total_length +
|
|
# padded_length, KV_LEN=total_length + padded_length, device=device)
|
|
# import cv2
|
|
# mask = cv2.resize(mask[0, 0].cpu().float().numpy(), (1024, 1024))
|
|
# imageio.imwrite("mask_%d.jpg" % (0), np.uint8(255. * mask))
|
|
|
|
return block_mask
|
|
|
|
def _forward_inference(
|
|
self,
|
|
x,
|
|
t,
|
|
context,
|
|
seq_len,
|
|
clip_fea=None,
|
|
y=None,
|
|
kv_cache: dict = None,
|
|
crossattn_cache: dict = None,
|
|
current_start: int = 0,
|
|
cache_start: int = 0
|
|
):
|
|
r"""
|
|
Run the diffusion model with kv caching.
|
|
See Algorithm 2 of CausVid paper https://arxiv.org/abs/2412.07772 for details.
|
|
This function will be run for num_frame times.
|
|
Process the latent frames one by one (1560 tokens each)
|
|
|
|
Args:
|
|
x (List[Tensor]):
|
|
List of input video tensors, each with shape [C_in, F, H, W]
|
|
t (Tensor):
|
|
Diffusion timesteps tensor of shape [B]
|
|
context (List[Tensor]):
|
|
List of text embeddings each with shape [L, C]
|
|
seq_len (`int`):
|
|
Maximum sequence length for positional encoding
|
|
clip_fea (Tensor, *optional*):
|
|
CLIP image features for image-to-video mode
|
|
y (List[Tensor], *optional*):
|
|
Conditional video inputs for image-to-video mode, same shape as x
|
|
|
|
Returns:
|
|
List[Tensor]:
|
|
List of denoised video tensors with original input shapes [C_out, F, H / 8, W / 8]
|
|
"""
|
|
|
|
if self.model_type == 'i2v':
|
|
assert clip_fea is not None and y is not None
|
|
# params
|
|
device = self.patch_embedding.weight.device
|
|
if self.freqs.device != device:
|
|
self.freqs = self.freqs.to(device)
|
|
|
|
if y is not None:
|
|
x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)]
|
|
|
|
# embeddings
|
|
x = [self.patch_embedding(u.unsqueeze(0)) for u in x]
|
|
grid_sizes = torch.stack(
|
|
[torch.tensor(u.shape[2:], dtype=torch.long) for u in x])
|
|
x = [u.flatten(2).transpose(1, 2) for u in x]
|
|
seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long)
|
|
assert seq_lens.max() <= seq_len
|
|
x = torch.cat(x)
|
|
"""
|
|
torch.cat([
|
|
torch.cat([u, u.new_zeros(1, seq_len - u.size(1), u.size(2))],
|
|
dim=1) for u in x
|
|
])
|
|
"""
|
|
|
|
# time embeddings
|
|
# with amp.autocast(dtype=torch.float32):
|
|
e = self.time_embedding(
|
|
sinusoidal_embedding_1d(self.freq_dim, t.flatten()).type_as(x))
|
|
e0 = self.time_projection(e).unflatten(
|
|
1, (6, self.dim)).unflatten(dim=0, sizes=t.shape)
|
|
# assert e.dtype == torch.float32 and e0.dtype == torch.float32
|
|
|
|
# context
|
|
context_lens = None
|
|
context = self.text_embedding(
|
|
torch.stack([
|
|
torch.cat(
|
|
[u, u.new_zeros(self.text_len - u.size(0), u.size(1))])
|
|
for u in context
|
|
]))
|
|
|
|
if clip_fea is not None:
|
|
context_clip = self.img_emb(clip_fea) # bs x 257 x dim
|
|
context = torch.concat([context_clip, context], dim=1)
|
|
|
|
# arguments
|
|
kwargs = dict(
|
|
e=e0,
|
|
seq_lens=seq_lens,
|
|
grid_sizes=grid_sizes,
|
|
freqs=self.freqs,
|
|
context=context,
|
|
context_lens=context_lens,
|
|
block_mask=self.block_mask
|
|
)
|
|
|
|
def create_custom_forward(module):
|
|
def custom_forward(*inputs, **kwargs):
|
|
return module(*inputs, **kwargs)
|
|
return custom_forward
|
|
|
|
for block_index, block in enumerate(self.blocks):
|
|
if torch.is_grad_enabled() and self.gradient_checkpointing:
|
|
kwargs.update(
|
|
{
|
|
"kv_cache": kv_cache[block_index],
|
|
"current_start": current_start,
|
|
"cache_start": cache_start
|
|
}
|
|
)
|
|
x = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(block),
|
|
x, **kwargs,
|
|
use_reentrant=False,
|
|
)
|
|
else:
|
|
kwargs.update(
|
|
{
|
|
"kv_cache": kv_cache[block_index],
|
|
"crossattn_cache": crossattn_cache[block_index],
|
|
"current_start": current_start,
|
|
"cache_start": cache_start
|
|
}
|
|
)
|
|
x = block(x, **kwargs)
|
|
|
|
# head
|
|
x = self.head(x, e.unflatten(dim=0, sizes=t.shape).unsqueeze(2))
|
|
# unpatchify
|
|
x = self.unpatchify(x, grid_sizes)
|
|
return torch.stack(x)
|
|
|
|
def _forward_train(
|
|
self,
|
|
x,
|
|
t,
|
|
context,
|
|
seq_len,
|
|
clean_x=None,
|
|
aug_t=None,
|
|
clip_fea=None,
|
|
y=None,
|
|
):
|
|
r"""
|
|
Forward pass through the diffusion model
|
|
|
|
Args:
|
|
x (List[Tensor]):
|
|
List of input video tensors, each with shape [C_in, F, H, W]
|
|
t (Tensor):
|
|
Diffusion timesteps tensor of shape [B]
|
|
context (List[Tensor]):
|
|
List of text embeddings each with shape [L, C]
|
|
seq_len (`int`):
|
|
Maximum sequence length for positional encoding
|
|
clip_fea (Tensor, *optional*):
|
|
CLIP image features for image-to-video mode
|
|
y (List[Tensor], *optional*):
|
|
Conditional video inputs for image-to-video mode, same shape as x
|
|
|
|
Returns:
|
|
List[Tensor]:
|
|
List of denoised video tensors with original input shapes [C_out, F, H / 8, W / 8]
|
|
"""
|
|
if self.model_type == 'i2v':
|
|
assert clip_fea is not None and y is not None
|
|
# params
|
|
device = self.patch_embedding.weight.device
|
|
if self.freqs.device != device:
|
|
self.freqs = self.freqs.to(device)
|
|
|
|
# Construct blockwise causal attn mask
|
|
if self.block_mask is None:
|
|
if clean_x is not None:
|
|
if self.independent_first_frame:
|
|
raise NotImplementedError()
|
|
else:
|
|
self.block_mask = self._prepare_teacher_forcing_mask(
|
|
device, num_frames=x.shape[2],
|
|
frame_seqlen=x.shape[-2] * x.shape[-1] // (self.patch_size[1] * self.patch_size[2]),
|
|
num_frame_per_block=self.num_frame_per_block
|
|
)
|
|
else:
|
|
if self.independent_first_frame:
|
|
self.block_mask = self._prepare_blockwise_causal_attn_mask_i2v(
|
|
device, num_frames=x.shape[2],
|
|
frame_seqlen=x.shape[-2] * x.shape[-1] // (self.patch_size[1] * self.patch_size[2]),
|
|
num_frame_per_block=self.num_frame_per_block,
|
|
local_attn_size=self.local_attn_size
|
|
)
|
|
else:
|
|
self.block_mask = self._prepare_blockwise_causal_attn_mask(
|
|
device, num_frames=x.shape[2],
|
|
frame_seqlen=x.shape[-2] * x.shape[-1] // (self.patch_size[1] * self.patch_size[2]),
|
|
num_frame_per_block=self.num_frame_per_block,
|
|
local_attn_size=self.local_attn_size
|
|
)
|
|
|
|
if y is not None:
|
|
x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)]
|
|
|
|
# embeddings
|
|
x = [self.patch_embedding(u.unsqueeze(0)) for u in x]
|
|
|
|
grid_sizes = torch.stack(
|
|
[torch.tensor(u.shape[2:], dtype=torch.long) for u in x])
|
|
x = [u.flatten(2).transpose(1, 2) for u in x]
|
|
|
|
seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long)
|
|
assert seq_lens.max() <= seq_len
|
|
x = torch.cat([
|
|
torch.cat([u, u.new_zeros(1, seq_lens[0] - u.size(1), u.size(2))],
|
|
dim=1) for u in x
|
|
])
|
|
|
|
# time embeddings
|
|
# with amp.autocast(dtype=torch.float32):
|
|
e = self.time_embedding(
|
|
sinusoidal_embedding_1d(self.freq_dim, t.flatten()).type_as(x))
|
|
e0 = self.time_projection(e).unflatten(
|
|
1, (6, self.dim)).unflatten(dim=0, sizes=t.shape)
|
|
# assert e.dtype == torch.float32 and e0.dtype == torch.float32
|
|
|
|
# context
|
|
context_lens = None
|
|
context = self.text_embedding(
|
|
torch.stack([
|
|
torch.cat(
|
|
[u, u.new_zeros(self.text_len - u.size(0), u.size(1))])
|
|
for u in context
|
|
]))
|
|
|
|
if clip_fea is not None:
|
|
context_clip = self.img_emb(clip_fea) # bs x 257 x dim
|
|
context = torch.concat([context_clip, context], dim=1)
|
|
|
|
if clean_x is not None:
|
|
clean_x = [self.patch_embedding(u.unsqueeze(0)) for u in clean_x]
|
|
clean_x = [u.flatten(2).transpose(1, 2) for u in clean_x]
|
|
|
|
seq_lens_clean = torch.tensor([u.size(1) for u in clean_x], dtype=torch.long)
|
|
assert seq_lens_clean.max() <= seq_len
|
|
clean_x = torch.cat([
|
|
torch.cat([u, u.new_zeros(1, seq_lens_clean[0] - u.size(1), u.size(2))], dim=1) for u in clean_x
|
|
])
|
|
|
|
x = torch.cat([clean_x, x], dim=1)
|
|
if aug_t is None:
|
|
aug_t = torch.zeros_like(t)
|
|
e_clean = self.time_embedding(
|
|
sinusoidal_embedding_1d(self.freq_dim, aug_t.flatten()).type_as(x))
|
|
e0_clean = self.time_projection(e_clean).unflatten(
|
|
1, (6, self.dim)).unflatten(dim=0, sizes=t.shape)
|
|
e0 = torch.cat([e0_clean, e0], dim=1)
|
|
|
|
# arguments
|
|
kwargs = dict(
|
|
e=e0,
|
|
seq_lens=seq_lens,
|
|
grid_sizes=grid_sizes,
|
|
freqs=self.freqs,
|
|
context=context,
|
|
context_lens=context_lens,
|
|
block_mask=self.block_mask)
|
|
|
|
def create_custom_forward(module):
|
|
def custom_forward(*inputs, **kwargs):
|
|
return module(*inputs, **kwargs)
|
|
return custom_forward
|
|
|
|
for block in self.blocks:
|
|
if torch.is_grad_enabled() and self.gradient_checkpointing:
|
|
x = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(block),
|
|
x, **kwargs,
|
|
use_reentrant=False,
|
|
)
|
|
else:
|
|
x = block(x, **kwargs)
|
|
|
|
if clean_x is not None:
|
|
x = x[:, x.shape[1] // 2:]
|
|
|
|
# head
|
|
x = self.head(x, e.unflatten(dim=0, sizes=t.shape).unsqueeze(2))
|
|
|
|
# unpatchify
|
|
x = self.unpatchify(x, grid_sizes)
|
|
return torch.stack(x)
|
|
|
|
def forward(
|
|
self,
|
|
*args,
|
|
**kwargs
|
|
):
|
|
if kwargs.get('kv_cache', None) is not None:
|
|
return self._forward_inference(*args, **kwargs)
|
|
else:
|
|
return self._forward_train(*args, **kwargs)
|
|
|
|
def unpatchify(self, x, grid_sizes):
|
|
r"""
|
|
Reconstruct video tensors from patch embeddings.
|
|
|
|
Args:
|
|
x (List[Tensor]):
|
|
List of patchified features, each with shape [L, C_out * prod(patch_size)]
|
|
grid_sizes (Tensor):
|
|
Original spatial-temporal grid dimensions before patching,
|
|
shape [B, 3] (3 dimensions correspond to F_patches, H_patches, W_patches)
|
|
|
|
Returns:
|
|
List[Tensor]:
|
|
Reconstructed video tensors with shape [C_out, F, H / 8, W / 8]
|
|
"""
|
|
|
|
c = self.out_dim
|
|
out = []
|
|
for u, v in zip(x, grid_sizes.tolist()):
|
|
u = u[:math.prod(v)].view(*v, *self.patch_size, c)
|
|
u = torch.einsum('fhwpqrc->cfphqwr', u)
|
|
u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size)])
|
|
out.append(u)
|
|
return out
|
|
|
|
def init_weights(self):
|
|
r"""
|
|
Initialize model parameters using Xavier initialization.
|
|
"""
|
|
|
|
# basic init
|
|
for m in self.modules():
|
|
if isinstance(m, nn.Linear):
|
|
nn.init.xavier_uniform_(m.weight)
|
|
if m.bias is not None:
|
|
nn.init.zeros_(m.bias)
|
|
|
|
# init embeddings
|
|
nn.init.xavier_uniform_(self.patch_embedding.weight.flatten(1))
|
|
for m in self.text_embedding.modules():
|
|
if isinstance(m, nn.Linear):
|
|
nn.init.normal_(m.weight, std=.02)
|
|
for m in self.time_embedding.modules():
|
|
if isinstance(m, nn.Linear):
|
|
nn.init.normal_(m.weight, std=.02)
|
|
|
|
# init output layer
|
|
nn.init.zeros_(self.head.head.weight)
|