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1461 lines
53 KiB
Python
1461 lines
53 KiB
Python
# Adopted from https://github.com/guandeh17/Self-Forcing
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# SPDX-License-Identifier: CC-BY-NC-SA-4.0
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import math
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import torch
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import torch.nn as nn
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from diffusers.configuration_utils import ConfigMixin, register_to_config
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from diffusers.models.modeling_utils import ModelMixin
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from torch.nn.attention.flex_attention import (
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BlockMask,
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create_block_mask,
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flex_attention,
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)
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from .attention import attention
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from .model import (
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WAN_CROSSATTENTION_CLASSES,
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MLPProj,
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WanLayerNorm,
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WanRMSNorm,
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rope_apply,
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rope_params,
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sinusoidal_embedding_1d,
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)
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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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)
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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(
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x[i, :seq_len].to(torch.float64).reshape(seq_len, n, -1, 2)
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)
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freqs_i = torch.cat(
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[
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freqs[0][start_frame : start_frame + f]
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.view(f, 1, 1, -1)
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.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,
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).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__(
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self, dim, num_heads, local_attn_size=-1, sink_size=0, qk_norm=True, eps=1e-6
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):
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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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# Support list/tuple local_attn_size by converting to list first (handles OmegaConf ListConfig)
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if not isinstance(local_attn_size, int) and hasattr(
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local_attn_size, "__iter__"
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):
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values = list(local_attn_size)
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else:
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values = [int(local_attn_size)]
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non_neg_vals = [int(v) for v in values if int(v) != -1]
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max_local = max(non_neg_vals) if len(non_neg_vals) > 0 else -1
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self.max_attention_size = 32760 if max_local == -1 else max_local * 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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# 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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[
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roped_query,
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torch.zeros(
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[q.shape[0], padded_length, q.shape[2], q.shape[3]],
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device=q.device,
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dtype=v.dtype,
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),
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],
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dim=1,
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)
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padded_roped_key = torch.cat(
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[
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roped_key,
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torch.zeros(
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[k.shape[0], padded_length, k.shape[2], k.shape[3]],
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device=k.device,
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dtype=v.dtype,
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),
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],
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dim=1,
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)
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padded_v = torch.cat(
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[
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v,
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torch.zeros(
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[v.shape[0], padded_length, v.shape[2], v.shape[3]],
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device=v.device,
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dtype=v.dtype,
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),
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],
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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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[
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roped_query,
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torch.zeros(
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[q.shape[0], padded_length, q.shape[2], q.shape[3]],
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device=q.device,
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dtype=v.dtype,
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),
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],
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dim=1,
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)
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padded_roped_key = torch.cat(
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[
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roped_key,
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torch.zeros(
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[k.shape[0], padded_length, k.shape[2], k.shape[3]],
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device=k.device,
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dtype=v.dtype,
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),
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],
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dim=1,
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)
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padded_v = torch.cat(
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[
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v,
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torch.zeros(
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[v.shape[0], padded_length, v.shape[2], v.shape[3]],
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device=v.device,
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dtype=v.dtype,
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),
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],
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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
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).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
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).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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# Compute cache update parameters without modifying kv_cache directly
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cache_update_info = None
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is_recompute = (
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current_end <= kv_cache["global_end_index"].item() and current_start > 0
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)
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if (
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self.local_attn_size != -1
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and (current_end > kv_cache["global_end_index"].item())
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and (
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num_new_tokens + kv_cache["local_end_index"].item() > kv_cache_size
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)
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):
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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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num_evicted_tokens = (
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num_new_tokens + kv_cache["local_end_index"].item() - kv_cache_size
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)
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num_rolled_tokens = (
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kv_cache["local_end_index"].item()
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- num_evicted_tokens
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- sink_tokens
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)
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# Compute updated local indices
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local_end_index = (
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kv_cache["local_end_index"].item()
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+ current_end
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- kv_cache["global_end_index"].item()
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- num_evicted_tokens
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)
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local_start_index = local_end_index - num_new_tokens
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# Construct full k, v for attention computation (without modifying the original cache)
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# Create temporary k, v for computation
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temp_k = kv_cache["k"].clone()
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temp_v = kv_cache["v"].clone()
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# Apply rolling update to the temporary cache
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temp_k[:, sink_tokens : sink_tokens + num_rolled_tokens] = temp_k[
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:,
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sink_tokens + num_evicted_tokens : sink_tokens
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+ num_evicted_tokens
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+ num_rolled_tokens,
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].clone()
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temp_v[:, sink_tokens : sink_tokens + num_rolled_tokens] = temp_v[
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:,
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sink_tokens + num_evicted_tokens : sink_tokens
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+ num_evicted_tokens
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+ num_rolled_tokens,
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].clone()
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# Insert new key/value into the temporary cache
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# Protect sink_tokens only during recomputation; regular forward generation allows writing into the initial sink region
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write_start_index = (
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max(local_start_index, sink_tokens)
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if is_recompute
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else local_start_index
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)
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roped_offset = max(0, write_start_index - local_start_index)
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write_len = max(0, local_end_index - write_start_index)
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if write_len > 0:
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temp_k[:, write_start_index:local_end_index] = roped_key[
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:, roped_offset : roped_offset + write_len
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]
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temp_v[:, write_start_index:local_end_index] = v[
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:, roped_offset : roped_offset + write_len
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]
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# Save cache update info for later use
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cache_update_info = {
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"action": "roll_and_insert",
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"sink_tokens": sink_tokens,
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"num_rolled_tokens": num_rolled_tokens,
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"num_evicted_tokens": num_evicted_tokens,
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"local_start_index": local_start_index,
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"local_end_index": local_end_index,
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"write_start_index": write_start_index,
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"write_end_index": local_end_index,
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"new_k": roped_key[:, roped_offset : roped_offset + write_len],
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"new_v": v[:, roped_offset : roped_offset + write_len],
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"current_end": current_end,
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"is_recompute": is_recompute,
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}
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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 = (
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kv_cache["local_end_index"].item()
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+ current_end
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- kv_cache["global_end_index"].item()
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)
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local_start_index = local_end_index - num_new_tokens
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# Construct full k, v for attention computation (without modifying the original cache)
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temp_k = kv_cache["k"].clone()
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temp_v = kv_cache["v"].clone()
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# Protect sink_tokens only during recomputation; regular forward generation allows writing into the initial sink region
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write_start_index = (
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max(local_start_index, sink_tokens)
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if is_recompute
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else local_start_index
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)
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roped_offset = max(0, write_start_index - local_start_index)
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write_len = max(0, local_end_index - write_start_index)
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if write_len > 0:
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temp_k[:, write_start_index:local_end_index] = roped_key[
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:, roped_offset : roped_offset + write_len
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]
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temp_v[:, write_start_index:local_end_index] = v[
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:, roped_offset : roped_offset + write_len
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]
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# Save cache update info for later use
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cache_update_info = {
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"action": "direct_insert",
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"local_start_index": local_start_index,
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"local_end_index": local_end_index,
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"write_start_index": write_start_index,
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"write_end_index": local_end_index,
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"new_k": roped_key[:, roped_offset : roped_offset + write_len],
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"new_v": v[:, roped_offset : roped_offset + write_len],
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"current_end": current_end,
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"is_recompute": is_recompute,
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}
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# Use temporary k, v to compute attention
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if sink_tokens > 0:
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# Concatenate sink tokens and local window tokens, keeping total length strictly below max_attention_size
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local_budget = self.max_attention_size - sink_tokens
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k_sink = temp_k[:, :sink_tokens]
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v_sink = temp_v[:, :sink_tokens]
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if local_budget > 0:
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local_start_for_window = max(
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sink_tokens, local_end_index - local_budget
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)
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k_local = temp_k[:, local_start_for_window:local_end_index]
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v_local = temp_v[:, local_start_for_window:local_end_index]
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k_cat = torch.cat([k_sink, k_local], dim=1)
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v_cat = torch.cat([v_sink, v_local], dim=1)
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else:
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k_cat = k_sink
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v_cat = v_sink
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x = attention(roped_query, k_cat, v_cat)
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else:
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window_start = max(0, local_end_index - self.max_attention_size)
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x = attention(
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roped_query,
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temp_k[:, window_start:local_end_index],
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temp_v[:, window_start:local_end_index],
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)
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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 both output and cache update info
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if kv_cache is not None:
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return x, (current_end, local_end_index, cache_update_info)
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else:
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return x
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|
|
|
|
class CausalWanAttentionBlock(nn.Module):
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def __init__(
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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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):
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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(
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dim, num_heads, local_attn_size, sink_size, qk_norm, eps
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)
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self.norm3 = (
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WanLayerNorm(dim, eps, elementwise_affine=True)
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if cross_attn_norm
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else nn.Identity()
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)
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self.cross_attn = WAN_CROSSATTENTION_CLASSES[cross_attn_type](
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dim, num_heads, (-1, -1), qk_norm, eps
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)
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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),
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nn.GELU(approximate="tanh"),
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nn.Linear(ffn_dim, dim),
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)
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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,
|
|
):
|
|
r"""
|
|
Args:
|
|
x(Tensor): Shape [B, L, C]
|
|
e(Tensor): Shape [B, F, 6, C]
|
|
seq_lens(Tensor): Shape [B], length of each sequence in batch
|
|
grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W)
|
|
freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
|
|
"""
|
|
num_frames, frame_seqlen = e.shape[1], x.shape[1] // e.shape[1]
|
|
# assert e.dtype == torch.float32
|
|
# with amp.autocast(dtype=torch.float32):
|
|
e = (self.modulation.unsqueeze(1) + e).chunk(6, dim=2)
|
|
# assert e[0].dtype == torch.float32
|
|
|
|
# self-attention
|
|
self_attn_result = self.self_attn(
|
|
(
|
|
self.norm1(x).unflatten(dim=1, sizes=(num_frames, frame_seqlen))
|
|
* (1 + e[1])
|
|
+ e[0]
|
|
).flatten(1, 2),
|
|
seq_lens,
|
|
grid_sizes,
|
|
freqs,
|
|
block_mask,
|
|
kv_cache,
|
|
current_start,
|
|
cache_start,
|
|
)
|
|
|
|
if kv_cache is not None:
|
|
y, cache_update_info = self_attn_result
|
|
else:
|
|
y = self_attn_result
|
|
cache_update_info = None
|
|
|
|
# with amp.autocast(dtype=torch.float32):
|
|
x = x + (y.unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * e[2]).flatten(
|
|
1, 2
|
|
)
|
|
|
|
# cross-attention & ffn function
|
|
def cross_attn_ffn(x, context, context_lens, e, crossattn_cache=None):
|
|
x = x + self.cross_attn(
|
|
self.norm3(x), context, context_lens, crossattn_cache=crossattn_cache
|
|
)
|
|
y = self.ffn(
|
|
(
|
|
self.norm2(x).unflatten(dim=1, sizes=(num_frames, frame_seqlen))
|
|
* (1 + e[4])
|
|
+ e[3]
|
|
).flatten(1, 2)
|
|
)
|
|
# with amp.autocast(dtype=torch.float32):
|
|
x = x + (
|
|
y.unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * e[5]
|
|
).flatten(1, 2)
|
|
return x
|
|
|
|
x = cross_attn_ffn(x, context, context_lens, e, crossattn_cache)
|
|
|
|
if cache_update_info is not None:
|
|
# cache_update_info is already in the format (current_end, local_end_index, cache_update_info)
|
|
return x, cache_update_info
|
|
else:
|
|
return x
|
|
|
|
|
|
class CausalHead(nn.Module):
|
|
def __init__(self, dim, out_dim, patch_size, eps=1e-6):
|
|
super().__init__()
|
|
self.dim = dim
|
|
self.out_dim = out_dim
|
|
self.patch_size = patch_size
|
|
self.eps = eps
|
|
|
|
# layers
|
|
out_dim = math.prod(patch_size) * out_dim
|
|
self.norm = WanLayerNorm(dim, eps)
|
|
self.head = nn.Linear(dim, out_dim)
|
|
|
|
# modulation
|
|
self.modulation = nn.Parameter(torch.randn(1, 2, dim) / dim**0.5)
|
|
|
|
def forward(self, x, e):
|
|
r"""
|
|
Args:
|
|
x(Tensor): Shape [B, L1, C]
|
|
e(Tensor): Shape [B, F, 1, C]
|
|
"""
|
|
# assert e.dtype == torch.float32
|
|
# with amp.autocast(dtype=torch.float32):
|
|
num_frames, frame_seqlen = e.shape[1], x.shape[1] // e.shape[1]
|
|
e = (self.modulation.unsqueeze(1) + e).chunk(2, dim=2)
|
|
x = self.head(
|
|
self.norm(x).unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * (1 + e[1])
|
|
+ e[0]
|
|
)
|
|
return x
|
|
|
|
|
|
class CausalWanModel(ModelMixin, ConfigMixin):
|
|
r"""
|
|
Wan diffusion backbone supporting both text-to-video and image-to-video.
|
|
"""
|
|
|
|
ignore_for_config = ["patch_size", "cross_attn_norm", "qk_norm", "text_dim"]
|
|
_no_split_modules = ["WanAttentionBlock"]
|
|
_supports_gradient_checkpointing = True
|
|
|
|
@register_to_config
|
|
def __init__(
|
|
self,
|
|
model_type="t2v",
|
|
patch_size=(1, 2, 2),
|
|
text_len=512,
|
|
in_dim=16,
|
|
dim=2048,
|
|
ffn_dim=8192,
|
|
freq_dim=256,
|
|
text_dim=4096,
|
|
out_dim=16,
|
|
num_heads=16,
|
|
num_layers=32,
|
|
local_attn_size=-1,
|
|
sink_size=0,
|
|
qk_norm=True,
|
|
cross_attn_norm=True,
|
|
eps=1e-6,
|
|
):
|
|
r"""
|
|
Initialize the diffusion model backbone.
|
|
|
|
Args:
|
|
model_type (`str`, *optional*, defaults to 't2v'):
|
|
Model variant - 't2v' (text-to-video) or 'i2v' (image-to-video)
|
|
patch_size (`tuple`, *optional*, defaults to (1, 2, 2)):
|
|
3D patch dimensions for video embedding (t_patch, h_patch, w_patch)
|
|
text_len (`int`, *optional*, defaults to 512):
|
|
Fixed length for text embeddings
|
|
in_dim (`int`, *optional*, defaults to 16):
|
|
Input video channels (C_in)
|
|
dim (`int`, *optional*, defaults to 2048):
|
|
Hidden dimension of the transformer
|
|
ffn_dim (`int`, *optional*, defaults to 8192):
|
|
Intermediate dimension in feed-forward network
|
|
freq_dim (`int`, *optional*, defaults to 256):
|
|
Dimension for sinusoidal time embeddings
|
|
text_dim (`int`, *optional*, defaults to 4096):
|
|
Input dimension for text embeddings
|
|
out_dim (`int`, *optional*, defaults to 16):
|
|
Output video channels (C_out)
|
|
num_heads (`int`, *optional*, defaults to 16):
|
|
Number of attention heads
|
|
num_layers (`int`, *optional*, defaults to 32):
|
|
Number of transformer blocks
|
|
local_attn_size (`int`, *optional*, defaults to -1):
|
|
Window size for temporal local attention (-1 indicates global attention)
|
|
sink_size (`int`, *optional*, defaults to 0):
|
|
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,
|
|
)
|
|
|
|
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, strict=False)
|
|
):
|
|
# 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,
|
|
)
|
|
|
|
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,
|
|
)
|
|
|
|
return block_mask
|
|
|
|
def _apply_cache_updates(self, kv_cache, cache_update_infos):
|
|
"""
|
|
Applies cache updates collected from multiple blocks.
|
|
Args:
|
|
kv_cache: List of cache dictionaries for each block
|
|
cache_update_infos: List of (block_index, cache_update_info) tuples
|
|
"""
|
|
for block_index, (
|
|
current_end,
|
|
local_end_index,
|
|
update_info,
|
|
) in cache_update_infos:
|
|
if update_info is not None:
|
|
cache = kv_cache[block_index]
|
|
|
|
if update_info["action"] == "roll_and_insert":
|
|
# Apply rolling update
|
|
sink_tokens = update_info["sink_tokens"]
|
|
num_rolled_tokens = update_info["num_rolled_tokens"]
|
|
num_evicted_tokens = update_info["num_evicted_tokens"]
|
|
local_start_index = update_info["local_start_index"]
|
|
local_end_index = update_info["local_end_index"]
|
|
write_start_index = update_info.get(
|
|
"write_start_index", local_start_index
|
|
)
|
|
write_end_index = update_info.get(
|
|
"write_end_index", local_end_index
|
|
)
|
|
new_k = update_info["new_k"]
|
|
new_v = update_info["new_v"]
|
|
|
|
# Perform the rolling operation
|
|
cache["k"][:, sink_tokens : sink_tokens + num_rolled_tokens] = (
|
|
cache["k"][
|
|
:,
|
|
sink_tokens + num_evicted_tokens : sink_tokens
|
|
+ num_evicted_tokens
|
|
+ num_rolled_tokens,
|
|
].clone()
|
|
)
|
|
cache["v"][:, sink_tokens : sink_tokens + num_rolled_tokens] = (
|
|
cache["v"][
|
|
:,
|
|
sink_tokens + num_evicted_tokens : sink_tokens
|
|
+ num_evicted_tokens
|
|
+ num_rolled_tokens,
|
|
].clone()
|
|
)
|
|
|
|
# Insert new key/value
|
|
if write_end_index > write_start_index and new_k.shape[1] == (
|
|
write_end_index - write_start_index
|
|
):
|
|
cache["k"][:, write_start_index:write_end_index] = new_k
|
|
cache["v"][:, write_start_index:write_end_index] = new_v
|
|
|
|
elif update_info["action"] == "direct_insert":
|
|
# Direct insert
|
|
local_start_index = update_info["local_start_index"]
|
|
local_end_index = update_info["local_end_index"]
|
|
write_start_index = update_info.get(
|
|
"write_start_index", local_start_index
|
|
)
|
|
write_end_index = update_info.get(
|
|
"write_end_index", local_end_index
|
|
)
|
|
new_k = update_info["new_k"]
|
|
new_v = update_info["new_v"]
|
|
|
|
# Insert new key/value
|
|
if write_end_index > write_start_index and new_k.shape[1] == (
|
|
write_end_index - write_start_index
|
|
):
|
|
cache["k"][:, write_start_index:write_end_index] = new_k
|
|
cache["v"][:, write_start_index:write_end_index] = new_v
|
|
|
|
# Update indices: do not roll back pointers during recomputation
|
|
is_recompute = (
|
|
False if update_info is None else update_info.get("is_recompute", False)
|
|
)
|
|
if not is_recompute:
|
|
kv_cache[block_index]["global_end_index"].fill_(current_end)
|
|
kv_cache[block_index]["local_end_index"].fill_(local_end_index)
|
|
|
|
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, strict=False)]
|
|
|
|
# print(f"x.device: {x[0].device}, t.device: {t.device}, context.device: {context.device}, seq_len: {seq_len}")
|
|
|
|
# embeddings
|
|
x = [self.patch_embedding(u.unsqueeze(0)) for u in x]
|
|
# print("patch embedding done")
|
|
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
|
|
# print("time embedding done")
|
|
# 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
|
|
]
|
|
)
|
|
)
|
|
# print("text embedding done")
|
|
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,
|
|
)
|
|
|
|
# print("kwargs done")
|
|
def create_custom_forward(module):
|
|
def custom_forward(*inputs, **kwargs):
|
|
return module(*inputs, **kwargs)
|
|
|
|
return custom_forward
|
|
|
|
cache_update_info = None
|
|
cache_update_infos = [] # Collect cache update info for all blocks
|
|
for block_index, block in enumerate(self.blocks):
|
|
# print(f"block_index: {block_index}")
|
|
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,
|
|
}
|
|
)
|
|
# print(f"forward checkpointing")
|
|
result = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(block),
|
|
x,
|
|
**kwargs,
|
|
use_reentrant=False,
|
|
)
|
|
# Handle the result
|
|
if kv_cache is not None and isinstance(result, tuple):
|
|
x, block_cache_update_info = result
|
|
cache_update_infos.append((block_index, block_cache_update_info))
|
|
# Extract base info for subsequent blocks (without concrete cache update details)
|
|
cache_update_info = block_cache_update_info[
|
|
:2
|
|
] # (current_end, local_end_index)
|
|
else:
|
|
x = result
|
|
else:
|
|
kwargs.update(
|
|
{
|
|
"kv_cache": kv_cache[block_index],
|
|
"crossattn_cache": crossattn_cache[block_index],
|
|
"current_start": current_start,
|
|
"cache_start": cache_start,
|
|
}
|
|
)
|
|
# print(f"forward no checkpointing")
|
|
result = block(x, **kwargs)
|
|
# Handle the result
|
|
if kv_cache is not None and isinstance(result, tuple):
|
|
x, block_cache_update_info = result
|
|
cache_update_infos.append((block_index, block_cache_update_info))
|
|
# Extract base info for subsequent blocks (without concrete cache update details)
|
|
cache_update_info = block_cache_update_info[
|
|
:2
|
|
] # (current_end, local_end_index)
|
|
else:
|
|
x = result
|
|
# log_gpu_memory(f"in _forward_inference: {x[0].device}")
|
|
# After all blocks are processed, apply cache updates in a single pass
|
|
if kv_cache is not None and cache_update_infos:
|
|
self._apply_cache_updates(kv_cache, cache_update_infos)
|
|
|
|
# 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]
|
|
"""
|
|
pass
|
|
raise NotImplementedError()
|
|
|
|
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, strict=False)]
|
|
|
|
# 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(), strict=False):
|
|
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, strict=False)])
|
|
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=0.02)
|
|
for m in self.time_embedding.modules():
|
|
if isinstance(m, nn.Linear):
|
|
nn.init.normal_(m.weight, std=0.02)
|
|
|
|
# init output layer
|
|
nn.init.zeros_(self.head.head.weight)
|