from wan.modules.attention import attention, flash_attention from wan.modules.model import ( WanRMSNorm, rope_apply, WanLayerNorm, WAN_CROSSATTENTION_CLASSES, rope_params, MLPProj, sinusoidal_embedding_1d, WanT2VCrossAttention, ) from torch.nn.attention.flex_attention import create_block_mask, flex_attention from diffusers.configuration_utils import ConfigMixin, register_to_config from torch.nn.attention.flex_attention import BlockMask from diffusers.models.modeling_utils import ModelMixin import torch.nn as nn import torch.nn.functional as F import torch import math import copy import torch.distributed as dist # wan 1.3B model has a weird channel / head configurations and require max-autotune to work with flexattention # see https://github.com/pytorch/pytorch/issues/133254 # change to default for other models flex_attention = torch.compile( flex_attention, dynamic=False, mode="default" ) class DualFullExpertLinear(nn.Linear): """Route memory and generation tokens to separate linear weights.""" def __init__(self, in_features, out_features, bias=True): super().__init__(in_features, out_features, bias=bias) self.memory_weight = nn.Parameter(torch.empty_like(self.weight)) self.memory_bias = ( nn.Parameter(torch.empty_like(self.bias)) if self.bias is not None else None ) @classmethod def from_linear(cls, linear): routed = cls( linear.in_features, linear.out_features, bias=linear.bias is not None, ).to(device=linear.weight.device, dtype=linear.weight.dtype) with torch.no_grad(): routed.weight.copy_(linear.weight) routed.memory_weight.copy_(linear.weight) if linear.bias is not None: routed.bias.copy_(linear.bias) routed.memory_bias.copy_(linear.bias) return routed def forward(self, x, memory_token_count=0): if memory_token_count == 0: return F.linear(x, self.weight, self.bias) if not 0 <= memory_token_count <= x.shape[1]: raise ValueError( f"memory_token_count={memory_token_count} is incompatible with input {x.shape}" ) memory_output = F.linear( x[:, :memory_token_count], self.memory_weight, self.memory_bias ) if memory_token_count == x.shape[1]: return memory_output target_output = F.linear(x[:, memory_token_count:], self.weight, self.bias) return torch.cat([memory_output, target_output], dim=1) class MemoryRoutedFFN(nn.Sequential): """Token-routed FFN compatible with FSDP wrapping.""" def forward(self, x, memory_token_count=0): x = _memory_routed_linear(self[0], x, memory_token_count) x = self[1](x) return _memory_routed_linear(self[2], x, memory_token_count) def _memory_routed_linear(linear, x, memory_token_count): if isinstance(linear, DualFullExpertLinear): return linear(x, memory_token_count) return linear(x) def _memory_routed_norm(norm, x, memory_token_count): if isinstance(norm, nn.Identity) or memory_token_count == 0: return norm(x) if isinstance(norm, nn.LayerNorm) and norm.weight is None and norm.bias is None: return norm(x) memory_output = _memory_only_norm(norm, x[:, :memory_token_count]) if memory_token_count == x.shape[1]: return memory_output return torch.cat([memory_output, norm(x[:, memory_token_count:])], dim=1) def _memory_only_linear(linear, x): return F.linear(x, linear.memory_weight, linear.memory_bias) def _memory_only_norm(norm, x): if isinstance(norm, nn.Identity): return x memory_weight = getattr(norm, "memory_weight", None) memory_bias = getattr(norm, "memory_bias", None) if isinstance(norm, WanRMSNorm): weight = memory_weight if memory_weight is not None else norm.weight.detach() return norm._norm(x.float()).type_as(x) * weight if isinstance(norm, nn.LayerNorm): weight = ( memory_weight if memory_weight is not None else (norm.weight.detach() if norm.weight is not None else None) ) bias = ( memory_bias if memory_bias is not None else (norm.bias.detach() if norm.bias is not None else None) ) return F.layer_norm(x, norm.normalized_shape, weight, bias, norm.eps).type_as(x) raise TypeError(f"Unsupported memory-only norm: {type(norm)}") def _memory_t2v_cross_attention( module, x, context, context_lens, crossattn_cache=None ): b, n, d = x.size(0), module.num_heads, module.head_dim q = _memory_only_norm(module.norm_q, _memory_only_linear(module.q, x)).view( b, -1, n, d ) if crossattn_cache is not None and crossattn_cache.get("is_init", False): k = crossattn_cache["k"] v = crossattn_cache["v"] else: k = _memory_only_norm( module.norm_k, _memory_only_linear(module.k, context) ).view(b, -1, n, d) v = _memory_only_linear(module.v, context).view(b, -1, n, d) if crossattn_cache is not None: crossattn_cache["is_init"] = True crossattn_cache["k"] = k crossattn_cache["v"] = v output = flash_attention(q, k, v, k_lens=context_lens).flatten(2) return _memory_only_linear(module.o, output) def _memory_routed_cross_attention( module, x, context, context_lens, memory_token_count, crossattn_cache=None, memory_context=None, ): if memory_token_count == 0: return module(x, context, context_lens, crossattn_cache=crossattn_cache) if crossattn_cache is not None and memory_token_count != x.shape[1]: raise ValueError("Differentiable writer routing does not support a cross-attention cache") if not isinstance(module, WanT2VCrossAttention): raise TypeError(f"Writer routing currently supports T2V cross-attention, got {type(module)}") memory_output = _memory_t2v_cross_attention( module, x[:, :memory_token_count], memory_context if memory_context is not None else context.detach(), context_lens, crossattn_cache=crossattn_cache, ) if memory_token_count == x.shape[1]: return memory_output target_output = module( x[:, memory_token_count:], context, context_lens, crossattn_cache=None ) return torch.cat([memory_output, target_output], dim=1) def causal_rope_apply(x, grid_sizes, freqs, start_frame=0): n, c = x.size(2), x.size(3) // 2 # split freqs freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1) # loop over samples output = [] for i, (f, h, w) in enumerate(grid_sizes.tolist()): seq_len = f * h * w # precompute multipliers x_i = torch.view_as_complex(x[i, :seq_len].to(torch.float64).reshape( seq_len, n, -1, 2)) freqs_i = torch.cat([ freqs[0][start_frame:start_frame + f].view(f, 1, 1, -1).expand(f, h, w, -1), freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1), freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1) ], dim=-1).reshape(seq_len, 1, -1) # apply rotary embedding x_i = torch.view_as_real(x_i * freqs_i).flatten(2) x_i = torch.cat([x_i, x[i, seq_len:]]) # append to collection output.append(x_i) return torch.stack(output).type_as(x) def causal_rope_apply_frames(x, grid_sizes, freqs, frame_indices): """RoPE variant where every latent frame is given an *explicit* temporal index (instead of a contiguous ``start_frame:start_frame+f`` range). This is used by the streaming long-video KV cache: keys/values are stored *before* RoPE is applied, and on every step we re-apply RoPE with relative indices ``frame_indices`` so the temporal position never leaves the model's trained range (e.g. 0-20). Args: x (Tensor): Shape [B, L, n, d], with ``L = f*h*w`` for each sample. grid_sizes (Tensor): Shape [B, 3] with (f, h, w) per sample. frame_indices (LongTensor): Shape [f], the temporal RoPE index for each of the ``f`` latent frames. """ n, c = x.size(2), x.size(3) // 2 # split freqs freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1) frame_indices = frame_indices.to(freqs[0].device) output = [] for i, (f, h, w) in enumerate(grid_sizes.tolist()): seq_len = f * h * w x_i = torch.view_as_complex(x[i, :seq_len].to(torch.float64).reshape( seq_len, n, -1, 2)) freqs_i = torch.cat([ freqs[0][frame_indices].view(f, 1, 1, -1).expand(f, h, w, -1), freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1), freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1) ], dim=-1).reshape(seq_len, 1, -1) x_i = torch.view_as_real(x_i * freqs_i).flatten(2) x_i = torch.cat([x_i, x[i, seq_len:]]) output.append(x_i) return torch.stack(output).type_as(x) class CausalWanSelfAttention(nn.Module): def __init__(self, dim, num_heads, local_attn_size=-1, sink_size=0, qk_norm=True, eps=1e-6): assert dim % num_heads == 0 super().__init__() self.dim = dim self.num_heads = num_heads self.head_dim = dim // num_heads self.local_attn_size = local_attn_size self.sink_size = sink_size self.qk_norm = qk_norm self.eps = eps self.max_attention_size = 32760 if local_attn_size == -1 else local_attn_size * 1560 # ── Streaming long-video KV cache (relative RoPE) ────────────────── # When `kv_rope_relative` is True the self-attention stores *un-roped* # K/V in the cache and re-applies RoPE every step with contiguous # relative frame indices, so the temporal position stays in # [0, kv_cache_max_frames-1] (never exceeding the trained range). # `kv_cache_max_frames` is the size of the attention window *including* # the frame currently being generated: with the defaults below the # window is the sink frame (idx 0) + 19 FIFO context frames (idx 1-19) # + the current frame (idx 20). The first `kv_cache_sink` frame(s) are # never evicted; the rest roll FIFO. # Default off → ordinary (short-video) inference is unchanged. self.kv_rope_relative = False self.kv_cache_max_frames = 21 self.kv_cache_sink = 1 # Trained temporal range (position-encoding ceiling). RoPE positions are # placed to match training: sink at {0..sink-1}, recent window pinned to # the top of this range, gap preserved; beyond it the geometry freezes. self.kv_cache_train_frames = 21 self.kv_cache_position_mode = "top_aligned" # layers self.q = nn.Linear(dim, dim) self.k = nn.Linear(dim, dim) self.v = nn.Linear(dim, dim) self.o = nn.Linear(dim, dim) self.norm_q = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity() self.norm_k = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity() def forward( self, x, seq_lens, grid_sizes, freqs, block_mask, kv_cache=None, current_start=0, cache_start=None, memory_token_count=0, ): r""" Args: x(Tensor): Shape [B, L, num_heads, C / num_heads] seq_lens(Tensor): Shape [B] grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W) freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2] block_mask (BlockMask) """ b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim if cache_start is None: cache_start = current_start # query, key, value function def qkv_fn(x): q = _memory_routed_linear(self.q, x, memory_token_count) k = _memory_routed_linear(self.k, x, memory_token_count) q = _memory_routed_norm(self.norm_q, q, memory_token_count).view(b, s, n, d) k = _memory_routed_norm(self.norm_k, k, memory_token_count).view(b, s, n, d) v = _memory_routed_linear(self.v, x, memory_token_count).view(b, s, n, d) return q, k, v q, k, v = qkv_fn(x) if kv_cache is None: # if it is teacher forcing training? is_tf = (s == seq_lens[0].item() * 2) if is_tf: q_chunk = torch.chunk(q, 2, dim=1) k_chunk = torch.chunk(k, 2, dim=1) roped_query = [] roped_key = [] # rope should be same for clean and noisy parts for ii in range(2): rq = rope_apply(q_chunk[ii], grid_sizes, freqs).type_as(v) rk = rope_apply(k_chunk[ii], grid_sizes, freqs).type_as(v) roped_query.append(rq) roped_key.append(rk) roped_query = torch.cat(roped_query, dim=1) roped_key = torch.cat(roped_key, dim=1) padded_length = math.ceil(q.shape[1] / 128) * 128 - q.shape[1] padded_roped_query = torch.cat( [roped_query, torch.zeros([q.shape[0], padded_length, q.shape[2], q.shape[3]], device=q.device, dtype=v.dtype)], dim=1 ) padded_roped_key = torch.cat( [roped_key, torch.zeros([k.shape[0], padded_length, k.shape[2], k.shape[3]], device=k.device, dtype=v.dtype)], dim=1 ) padded_v = torch.cat( [v, torch.zeros([v.shape[0], padded_length, v.shape[2], v.shape[3]], device=v.device, dtype=v.dtype)], dim=1 ) x = flex_attention( query=padded_roped_query.transpose(2, 1), key=padded_roped_key.transpose(2, 1), value=padded_v.transpose(2, 1), block_mask=block_mask )[:, :, :-padded_length].transpose(2, 1) else: roped_query = rope_apply(q, grid_sizes, freqs).type_as(v) roped_key = rope_apply(k, grid_sizes, freqs).type_as(v) padded_length = math.ceil(q.shape[1] / 128) * 128 - q.shape[1] padded_roped_query = torch.cat( [roped_query, torch.zeros([q.shape[0], padded_length, q.shape[2], q.shape[3]], device=q.device, dtype=v.dtype)], dim=1 ) padded_roped_key = torch.cat( [roped_key, torch.zeros([k.shape[0], padded_length, k.shape[2], k.shape[3]], device=k.device, dtype=v.dtype)], dim=1 ) padded_v = torch.cat( [v, torch.zeros([v.shape[0], padded_length, v.shape[2], v.shape[3]], device=v.device, dtype=v.dtype)], dim=1 ) x = flex_attention( query=padded_roped_query.transpose(2, 1), key=padded_roped_key.transpose(2, 1), value=padded_v.transpose(2, 1), block_mask=block_mask )[:, :, :-padded_length].transpose(2, 1) elif self.kv_rope_relative: # ── Streaming long-video path ────────────────────────────────── # Store raw (pre-RoPE) K/V; re-apply RoPE with relative indices so # the temporal position always lives in [0, kv_cache_max_frames-1]. fs = math.prod(grid_sizes[0][1:]).item() cur_frames = q.shape[1] // fs cur_abs = current_start // fs max_frames = self.kv_cache_max_frames sink = self.kv_cache_sink # Per-cache bookkeeping: chronological list of the absolute frame # index occupying each slot (sink frames first, then recent window). abs_list = kv_cache.get("stream_abs", None) if abs_list is None: abs_list = [] kv_cache["stream_abs"] = abs_list new_block_abs = [cur_abs + i for i in range(cur_frames)] # Re-running the same frame (extra denoising / context-update step) # overwrites the tail slot instead of appending a new frame. is_rewrite = (len(abs_list) >= cur_frames and abs_list[-cur_frames:] == new_block_abs) if is_rewrite: tail_slot = len(abs_list) - cur_frames else: n_after = len(abs_list) + cur_frames if n_after > max_frames: # Evict the oldest non-sink frame(s); shift the kept window # left, leaving the first `sink` frame(s) pinned in place. num_evict = n_after - max_frames src_lo = (sink + num_evict) * fs src_hi = len(abs_list) * fs dst_lo = sink * fs dst_hi = dst_lo + (src_hi - src_lo) if src_hi > src_lo: kv_cache["k"][:, dst_lo:dst_hi] = kv_cache["k"][:, src_lo:src_hi].clone() kv_cache["v"][:, dst_lo:dst_hi] = kv_cache["v"][:, src_lo:src_hi].clone() del abs_list[sink:sink + num_evict] abs_list.extend(new_block_abs) tail_slot = len(abs_list) - cur_frames # Write the current block's *un-roped* K/V into its tail slot. tail_lo = tail_slot * fs tail_hi = tail_lo + cur_frames * fs kv_cache["k"][:, tail_lo:tail_hi] = k kv_cache["v"][:, tail_lo:tail_hi] = v num_occ = len(abs_list) if self.kv_cache_position_mode == "contiguous": # Compress the retained cache into the training prefix. With # sink=0, max_frames=12 this yields {0..11} once full and then # keeps that exact RoPE geometry forever. pos = torch.arange(num_occ, device=q.device, dtype=torch.long) else: # RoPE positions that MATCH the teacher-forcing training geometry: # sink frames pinned at {0 .. sink-1}; # recent window pinned to the TOP of the trained range so the # query/window relative distances equal those seen in training; # the gap (dropped middle frames) is preserved. # Query is clamped to train_frames-1, so beyond the trained range # the whole geometry freezes (e.g. sink={0,1,2}, window={9..20} # forever) instead of being compressed into {0..num_occ-1}. n_win = num_occ - sink if n_win <= 0: pos = torch.arange(num_occ, device=q.device, dtype=torch.long) else: q_abs = cur_abs + cur_frames - 1 Q = min(int(q_abs), self.kv_cache_train_frames - 1) pos = torch.cat([ torch.arange(sink, device=q.device, dtype=torch.long), torch.arange(Q - (n_win - 1), Q + 1, device=q.device, dtype=torch.long), ]) window_grid = grid_sizes.clone() window_grid[:, 0] = num_occ roped_key = causal_rope_apply_frames( kv_cache["k"][:, :num_occ * fs], window_grid, freqs, pos).type_as(v) q_pos = pos[tail_slot:tail_slot + cur_frames] roped_query = causal_rope_apply_frames( q, grid_sizes, freqs, q_pos).type_as(v) x = attention( roped_query, roped_key, kv_cache["v"][:, :num_occ * fs] ) # Keep the legacy counters roughly in sync (unused by this path). kv_cache["global_end_index"].fill_(current_start + q.shape[1]) kv_cache["local_end_index"].fill_(num_occ * fs) else: frame_seqlen = math.prod(grid_sizes[0][1:]).item() current_start_frame = current_start // frame_seqlen roped_query = causal_rope_apply( q, grid_sizes, freqs, start_frame=current_start_frame).type_as(v) roped_key = causal_rope_apply( k, grid_sizes, freqs, start_frame=current_start_frame).type_as(v) current_end = current_start + roped_query.shape[1] sink_tokens = self.sink_size * frame_seqlen # 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 kv_cache_size = kv_cache["k"].shape[1] num_new_tokens = roped_query.shape[1] if self.local_attn_size != -1 and (current_end > kv_cache["global_end_index"].item()) and ( num_new_tokens + kv_cache["local_end_index"].item() > kv_cache_size): # Calculate the number of new tokens added in this step # Shift existing cache content left to discard oldest tokens # Clone the source slice to avoid overlapping memory error num_evicted_tokens = num_new_tokens + kv_cache["local_end_index"].item() - kv_cache_size num_rolled_tokens = kv_cache["local_end_index"].item() - num_evicted_tokens - sink_tokens kv_cache["k"][:, sink_tokens:sink_tokens + num_rolled_tokens] = \ kv_cache["k"][:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone() kv_cache["v"][:, sink_tokens:sink_tokens + num_rolled_tokens] = \ kv_cache["v"][:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone() # Insert the new keys/values at the end local_end_index = kv_cache["local_end_index"].item() + current_end - \ kv_cache["global_end_index"].item() - num_evicted_tokens local_start_index = local_end_index - num_new_tokens kv_cache["k"][:, local_start_index:local_end_index] = roped_key kv_cache["v"][:, local_start_index:local_end_index] = v else: # Assign new keys/values directly up to current_end local_end_index = kv_cache["local_end_index"].item() + current_end - kv_cache["global_end_index"].item() local_start_index = local_end_index - num_new_tokens kv_cache["k"][:, local_start_index:local_end_index] = roped_key kv_cache["v"][:, local_start_index:local_end_index] = v win_lo = max(0, local_end_index - self.max_attention_size) if self.local_attn_size != -1 and sink_tokens > 0 and win_lo > sink_tokens: # Disjoint sink + recent window (a real gap separates them). # Eviction above keeps [0, sink_tokens) un-rolled, so this slice is # always the original first `sink_size` frames. k_read = torch.cat( [kv_cache["k"][:, :sink_tokens], kv_cache["k"][:, win_lo:local_end_index]], dim=1) v_read = torch.cat( [kv_cache["v"][:, :sink_tokens], kv_cache["v"][:, win_lo:local_end_index]], dim=1) else: # Contiguous: when a sink exists and the window already reaches back # into/before it, read from 0 so the sink is not skipped (e.g. frame # 12 must see frame 0). Otherwise the plain recent slice (pure window, # or full attention when local_attn_size == -1). lo = 0 if (self.local_attn_size != -1 and sink_tokens > 0) else win_lo k_read = kv_cache["k"][:, lo:local_end_index] v_read = kv_cache["v"][:, lo:local_end_index] x = attention(roped_query, k_read, v_read) kv_cache["global_end_index"].fill_(current_end) kv_cache["local_end_index"].fill_(local_end_index) # output x = x.flatten(2) # x.shape is [1, 65520, 1536] x = _memory_routed_linear(self.o, x, memory_token_count) return x class CausalWanAttentionBlock(nn.Module): def __init__(self, cross_attn_type, dim, ffn_dim, num_heads, local_attn_size=-1, sink_size=0, qk_norm=True, cross_attn_norm=False, eps=1e-6): super().__init__() self.dim = dim self.ffn_dim = ffn_dim self.num_heads = num_heads self.local_attn_size = local_attn_size self.qk_norm = qk_norm self.cross_attn_norm = cross_attn_norm self.eps = eps # layers self.norm1 = WanLayerNorm(dim, eps) self.self_attn = CausalWanSelfAttention(dim, num_heads, local_attn_size, sink_size, qk_norm, eps) self.norm3 = WanLayerNorm( dim, eps, elementwise_affine=True) if cross_attn_norm else nn.Identity() self.cross_attn = WAN_CROSSATTENTION_CLASSES[cross_attn_type](dim, num_heads, (-1, -1), qk_norm, eps) self.norm2 = WanLayerNorm(dim, eps) self.ffn = nn.Sequential( nn.Linear(dim, ffn_dim), nn.GELU(approximate='tanh'), nn.Linear(ffn_dim, dim)) # modulation self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5) def forward( self, x, e, seq_lens, grid_sizes, freqs, context, context_lens, block_mask, kv_cache=None, crossattn_cache=None, current_start=0, cache_start=None, memory_token_count=0, memory_context=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] if memory_token_count % frame_seqlen != 0: raise ValueError( f"memory_token_count={memory_token_count} is not frame-aligned " f"for frame_seqlen={frame_seqlen}" ) memory_frame_count = memory_token_count // frame_seqlen # assert e.dtype == torch.float32 # with amp.autocast(dtype=torch.float32): if memory_frame_count == 0: modulated_e = self.modulation.unsqueeze(1) + e else: memory_modulation = getattr( self, "memory_modulation", self.modulation.detach() ) memory_e = memory_modulation.unsqueeze(1) + e[:, :memory_frame_count] if memory_frame_count == num_frames: modulated_e = memory_e else: target_e = self.modulation.unsqueeze(1) + e[:, memory_frame_count:] modulated_e = torch.cat([memory_e, target_e], dim=1) e = modulated_e.chunk(6, dim=2) # assert e[0].dtype == torch.float32 # self-attention y = self.self_attn( (_memory_routed_norm(self.norm1, x, memory_token_count) .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, memory_token_count=memory_token_count) # 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): normalized_x = _memory_routed_norm(self.norm3, x, memory_token_count) x = x + _memory_routed_cross_attention( self.cross_attn, normalized_x, context, context_lens, memory_token_count, crossattn_cache=crossattn_cache, memory_context=memory_context, ) ffn_input = ( _memory_routed_norm(self.norm2, x, memory_token_count) .unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * (1 + e[4]) + e[3] ).flatten(1, 2) if getattr(self, "dual_full_expert_enabled", False): y = self.ffn( ffn_input, memory_token_count=memory_token_count ) else: y = self.ffn(ffn_input) # 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) 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.sink_size = sink_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 self.dual_full_expert_enabled = False @staticmethod def _add_memory_norm_parameters(norm): if isinstance(norm, nn.Identity): return 0 count = 0 if getattr(norm, "weight", None) is not None: norm.register_parameter( "memory_weight", nn.Parameter(norm.weight.detach().clone()) ) count += norm.weight.numel() if getattr(norm, "bias", None) is not None: norm.register_parameter( "memory_bias", nn.Parameter(norm.bias.detach().clone()) ) count += norm.bias.numel() return count def enable_dual_full_expert(self): if self.dual_full_expert_enabled: raise RuntimeError("Dual full experts are already enabled") if self.model_type != 't2v': raise NotImplementedError("Dual full experts currently support T2V only") memory_params = 0 for block in self.blocks: for name in ("q", "k", "v", "o"): linear = getattr(block.self_attn, name) routed = DualFullExpertLinear.from_linear(linear) setattr(block.self_attn, name, routed) memory_params += routed.memory_weight.numel() if routed.memory_bias is not None: memory_params += routed.memory_bias.numel() for name in ("q", "k", "v", "o"): linear = getattr(block.cross_attn, name) routed = DualFullExpertLinear.from_linear(linear) setattr(block.cross_attn, name, routed) memory_params += routed.memory_weight.numel() if routed.memory_bias is not None: memory_params += routed.memory_bias.numel() for index in (0, 2): routed = DualFullExpertLinear.from_linear(block.ffn[index]) block.ffn[index] = routed memory_params += routed.memory_weight.numel() if routed.memory_bias is not None: memory_params += routed.memory_bias.numel() block.ffn = MemoryRoutedFFN(*block.ffn.children()) block.dual_full_expert_enabled = True for norm in ( block.norm1, block.norm2, block.norm3, block.self_attn.norm_q, block.self_attn.norm_k, block.cross_attn.norm_q, block.cross_attn.norm_k, ): memory_params += self._add_memory_norm_parameters(norm) block.memory_modulation = nn.Parameter(block.modulation.detach().clone()) memory_params += block.memory_modulation.numel() self.memory_patch_embedding = copy.deepcopy(self.patch_embedding) self.memory_text_embedding = copy.deepcopy(self.text_embedding) self.memory_time_embedding = copy.deepcopy(self.time_embedding) self.memory_time_projection = copy.deepcopy(self.time_projection) self.memory_head = copy.deepcopy(self.head) memory_params += sum( parameter.numel() for module in ( self.memory_patch_embedding, self.memory_text_embedding, self.memory_time_embedding, self.memory_time_projection, self.memory_head, ) for parameter in module.parameters() ) self.dual_full_expert_enabled = True return memory_params 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, local_attn_size: int = -1, sink_size: int = 0 ) -> 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 # Block-wise causal mask attends to elements before the end of the current chunk. attention_block_size = frame_seqlen * num_frame_per_block # Streaming attention = sink + sliding window (StreamingLLM-style), token units. # local_attn_size = -1 -> unbounded recent window (original full behaviour) # local_attn_size = W -> attend to the last W frames INCLUDING the current one # sink_size = S -> the first S frames are ALWAYS attended (pinned) # When local_attn_size == -1 and sink_size == 0 this reduces exactly to the # original blockwise-causal teacher-forcing mask. # sink/window are measured in LATENTS (frame_seqlen), independent of the # chunk size, so chunk=1 and chunk=3 share the same temporal extent. The # intra-chunk bidirectional + cross-chunk causal structure still comes from # attention_block_size below. sink/window must be multiples of the chunk so # the window stays chunk-aligned (matches the chunk-granular cache eviction). assert sink_size % num_frame_per_block == 0, \ f"sink_size ({sink_size}) must be a multiple of num_frame_per_block ({num_frame_per_block})" if local_attn_size != -1: assert local_attn_size % num_frame_per_block == 0, \ f"local_attn_size ({local_attn_size}) must be a multiple of num_frame_per_block ({num_frame_per_block})" win_tokens = local_attn_size * frame_seqlen if local_attn_size != -1 else None sink_tokens = sink_size * frame_seqlen # clean context frames: blockwise-causal self-attention in [context_starts, context_ends) context_ends = torch.zeros(total_length + padded_length, device=device, dtype=torch.long) context_starts = torch.zeros(total_length + padded_length, device=device, dtype=torch.long) # noisy frames: recent clean-context window [noise_context_starts, noise_context_ends) + own block 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) 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 (self-attention building their K/V representation) for start in frame_indices: end = int(start) + attention_block_size context_ends[start:start + attention_block_size] = end # a clean frame looks back at most `local_attn_size` frames (incl. its own block) context_starts[start:start + attention_block_size] = \ max(0, end - win_tokens) if win_tokens is not None else 0 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 # clean context = all frames strictly before this block ... ctx_end = block_index * attention_block_size noise_context_ends[start:end] = ctx_end # ... limited to the most recent (local_attn_size - 1) clean frames if win_tokens is not None: noise_context_starts[start:end] = max(0, ctx_end - (win_tokens - attention_block_size)) else: noise_context_starts[start:end] = 0 def attention_mask(b, h, q_idx, kv_idx): # clean self-attention: CAUSAL (kv block <= q block) AND in the recent # window OR the sink. The causal bound is applied to BOTH terms, so the # sink frames themselves stay causal (clean frame 0 -> {0}, 1 -> {0,1}). clean_causal = kv_idx < context_ends[q_idx] clean_keep = (kv_idx >= context_starts[q_idx]) | (kv_idx < sink_tokens) clean_mask = (q_idx < clean_ends) & clean_causal & clean_keep # noisy frames: own block (C1) + clean context STRICTLY BEFORE this block # (causal), restricted to the recent window OR the sink. C1 = (kv_idx < noise_noise_ends[q_idx]) & (kv_idx >= noise_noise_starts[q_idx]) ctx_causal = kv_idx < noise_context_ends[q_idx] ctx_keep = (kv_idx >= noise_context_starts[q_idx]) | (kv_idx < sink_tokens) noise_mask = (q_idx >= clean_ends) & (C1 | (ctx_causal & ctx_keep)) 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, use_memory_expert: bool = False, ): 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)] use_memory_expert = ( self.dual_full_expert_enabled and use_memory_expert ) # embeddings patch_embedding = ( self.memory_patch_embedding if use_memory_expert else self.patch_embedding ) x = [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 ]) """ memory_token_count = 0 if self.dual_full_expert_enabled and use_memory_expert: memory_token_count = x.shape[1] # time embeddings # with amp.autocast(dtype=torch.float32): time_embedding = ( self.memory_time_embedding if use_memory_expert else self.time_embedding ) time_projection = ( self.memory_time_projection if use_memory_expert else self.time_projection ) e = time_embedding( sinusoidal_embedding_1d(self.freq_dim, t.flatten()).type_as(x)) e0 = 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 padded_context = torch.stack([ torch.cat( [u, u.new_zeros(self.text_len - u.size(0), u.size(1))]) for u in context ]) text_embedding = ( self.memory_text_embedding if use_memory_expert else self.text_embedding ) context = text_embedding(padded_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, memory_token_count=memory_token_count, memory_context=context if use_memory_expert else None, ) 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): block_crossattn_cache = crossattn_cache[block_index] if self.dual_full_expert_enabled: expert_name = ( "memory_expert" if use_memory_expert else "generation_expert" ) block_crossattn_cache = block_crossattn_cache.setdefault( expert_name, {"is_init": False} ) if torch.is_grad_enabled() and self.gradient_checkpointing: kwargs.update( { "kv_cache": kv_cache[block_index], "crossattn_cache": block_crossattn_cache, "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": block_crossattn_cache, "current_start": current_start, "cache_start": cache_start } ) x = block(x, **kwargs) # head head = self.memory_head if use_memory_expert else self.head x = 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, use_memory_expert: bool = False, ): 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: # TF 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, local_attn_size=self.local_attn_size, sink_size=self.sink_size, ) else: # DF? 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 ]) memory_token_count = 0 # time embeddings # with amp.autocast(dtype=torch.float32): time_embedding = self.time_embedding time_projection = self.time_projection e = time_embedding( sinusoidal_embedding_1d(self.freq_dim, t.flatten()).type_as(x)) e0 = 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 padded_context = torch.stack([ torch.cat( [u, u.new_zeros(self.text_len - u.size(0), u.size(1))]) for u in context ]) context = self.text_embedding(padded_context) memory_context = None if self.dual_full_expert_enabled and ( clean_x is not None or use_memory_expert): memory_context = self.memory_text_embedding(padded_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.detach() if self.dual_full_expert_enabled: clean_x = [ self.memory_patch_embedding(u.unsqueeze(0)) for u in clean_x ] else: 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 ]) if self.dual_full_expert_enabled: memory_token_count = clean_x.shape[1] x = torch.cat([clean_x, x], dim=1) if aug_t is None: aug_t = torch.zeros_like(t) if self.dual_full_expert_enabled: e_clean = self.memory_time_embedding( sinusoidal_embedding_1d( self.freq_dim, aug_t.flatten() ).type_as(x) ) e0_clean = self.memory_time_projection(e_clean).unflatten( 1, (6, self.dim)).unflatten(dim=0, sizes=t.shape) else: 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) elif self.dual_full_expert_enabled and use_memory_expert: memory_token_count = x.shape[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, memory_token_count=memory_token_count, memory_context=memory_context) 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:] # [1,32760,1536] # 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: # TF or DF 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)