771 lines
35 KiB
Python
771 lines
35 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import copy
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import math
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from typing import Any
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import torch
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import torch.nn as nn
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import fastvideo.envs as envs
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from fastvideo.attention import (DistributedAttention, DistributedAttention_VSA, LocalAttention)
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from fastvideo.configs.models.dits import WanVideoConfig
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from fastvideo.distributed.communication_op import (sequence_model_parallel_all_gather_with_unpad,
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sequence_model_parallel_shard)
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from fastvideo.layers.layernorm import (FP32LayerNorm, LayerNormScaleShift, RMSNorm, ScaleResidual,
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ScaleResidualLayerNormScaleShift)
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from fastvideo.layers.linear import ReplicatedLinear
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# from torch.nn import RMSNorm
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# TODO: RMSNorm ....
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from fastvideo.layers.mlp import MLP
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from fastvideo.layers.rotary_embedding import get_rotary_pos_embed
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from fastvideo.layers.visual_embedding import (ModulateProjection, PatchEmbed, TimestepEmbedder)
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from fastvideo.logger import init_logger
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from fastvideo.models.dits.base import BaseDiT
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from fastvideo.platforms import AttentionBackendEnum, current_platform
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from fastvideo.layers.quantization import QuantizationConfig
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from fastvideo.distributed.parallel_state import get_sp_world_size
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logger = init_logger(__name__)
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class WanImageEmbedding(torch.nn.Module):
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def __init__(self, in_features: int, out_features: int):
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super().__init__()
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self.norm1 = FP32LayerNorm(in_features)
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self.ff = MLP(in_features, in_features, out_features, act_type="gelu")
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self.norm2 = FP32LayerNorm(out_features)
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def forward(self, encoder_hidden_states_image: torch.Tensor) -> torch.Tensor:
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dtype = encoder_hidden_states_image.dtype
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hidden_states = self.norm1(encoder_hidden_states_image)
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hidden_states = self.ff(hidden_states)
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hidden_states = self.norm2(hidden_states).to(dtype)
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return hidden_states
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class WanTimeTextImageEmbedding(nn.Module):
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def __init__(
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self,
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dim: int,
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time_freq_dim: int,
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text_embed_dim: int,
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image_embed_dim: int | None = None,
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*,
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r_embedder: bool = False,
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r_embedder_fusion: str = "additive",
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r_embedder_gate_value: float = 0.25,
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r_embedder_deltatime_type: str = "r",
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):
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super().__init__()
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self.time_embedder = TimestepEmbedder(dim, frequency_embedding_size=time_freq_dim, act_layer="silu")
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self.time_modulation = ModulateProjection(dim, factor=6, act_layer="silu")
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self.text_embedder = MLP(text_embed_dim, dim, dim, bias=True,
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act_type="gelu_pytorch_tanh") if text_embed_dim > 0 else None
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self.image_embedder = None
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if image_embed_dim is not None:
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self.image_embedder = WanImageEmbedding(image_embed_dim, dim)
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# AnyFlow dual-timestep support. When r_embedder is False the forward
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# path bypasses delta_embedder entirely and the output is byte-identical
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# to the legacy single-timestep implementation.
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self._r_embedder_enabled = bool(r_embedder)
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self._r_embedder_fusion = r_embedder_fusion
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self._r_embedder_deltatime_type = r_embedder_deltatime_type
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if self._r_embedder_enabled:
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if r_embedder_fusion not in ("additive", "gated"):
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raise ValueError("r_embedder_fusion must be one of {additive, gated}, "
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f"got {r_embedder_fusion!r}")
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if r_embedder_deltatime_type not in ("r", "t-r"):
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raise ValueError("r_embedder_deltatime_type must be one of {r, t-r}, "
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f"got {r_embedder_deltatime_type!r}")
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# Deep-copy preserves identical initialization with time_embedder,
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# matching AnyFlow reference setup_flowmap_model() behavior.
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self.delta_embedder = copy.deepcopy(self.time_embedder)
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# Non-persistent buffer — gate is a hyperparameter, not learned.
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self.register_buffer(
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"_r_embedder_gate",
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torch.tensor(float(r_embedder_gate_value)),
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persistent=False,
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)
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else:
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self.delta_embedder = None
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def forward(
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self,
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timestep: torch.Tensor,
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encoder_hidden_states: torch.Tensor,
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encoder_hidden_states_image: torch.Tensor | None = None,
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timestep_seq_len: int | None = None,
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r_timestep: torch.Tensor | None = None,
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):
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temb = self.time_embedder(timestep, timestep_seq_len)
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if self._r_embedder_enabled and r_timestep is not None:
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assert self.delta_embedder is not None
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if self._r_embedder_deltatime_type == "r":
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delta_input = r_timestep
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else:
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delta_input = timestep - r_timestep
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delta_emb = self.delta_embedder(delta_input, timestep_seq_len)
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gate = self._r_embedder_gate
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if self._r_embedder_fusion == "gated":
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temb = (1.0 - gate) * temb + gate * delta_emb
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else:
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# Additive (additional channel, no convex blend).
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temb = temb + gate * delta_emb
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timestep_proj = self.time_modulation(temb)
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if self.text_embedder is not None:
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encoder_hidden_states = self.text_embedder(encoder_hidden_states)
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else:
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encoder_hidden_states = torch.zeros((timestep.shape[0], 0, temb.shape[-1]),
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device=temb.device,
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dtype=temb.dtype)
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if encoder_hidden_states_image is not None:
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assert self.image_embedder is not None
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encoder_hidden_states_image = self.image_embedder(encoder_hidden_states_image)
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return temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image
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class WanSelfAttention(nn.Module):
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def __init__(self,
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dim: int,
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num_heads: int,
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window_size=(-1, -1),
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qk_norm=True,
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eps=1e-6,
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parallel_attention=False,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "") -> None:
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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.window_size = window_size
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self.qk_norm = qk_norm
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self.eps = eps
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self.parallel_attention = parallel_attention
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# layers
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self.to_q = ReplicatedLinear(dim, dim, quant_config=quant_config, prefix=f"{prefix}.to_q")
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self.to_k = ReplicatedLinear(dim, dim, quant_config=quant_config, prefix=f"{prefix}.to_k")
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self.to_v = ReplicatedLinear(dim, dim, quant_config=quant_config, prefix=f"{prefix}.to_v")
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self.to_out = ReplicatedLinear(dim, dim, quant_config=quant_config, prefix=f"{prefix}.to_out")
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self.norm_q = RMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
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self.norm_k = RMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
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# Scaled dot product attention
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self.attn = LocalAttention(num_heads=num_heads,
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head_size=self.head_dim,
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dropout_rate=0,
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softmax_scale=None,
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causal=False,
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supported_attention_backends=(AttentionBackendEnum.FLASH_ATTN,
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AttentionBackendEnum.TORCH_SDPA))
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def forward(self, x: torch.Tensor, context: torch.Tensor, context_lens: int):
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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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"""
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pass
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class WanT2VCrossAttention(WanSelfAttention):
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def forward(self, x, context, context_lens, crossattn_cache=None):
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r"""
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Args:
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x(Tensor): Shape [B, L1, C]
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context(Tensor): Shape [B, L2, C]
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context_lens(Tensor): Shape [B]
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"""
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b, n, d = x.size(0), self.num_heads, self.head_dim
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# compute query, key, value
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q = self.norm_q(self.to_q(x)[0]).view(b, -1, n, d)
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if crossattn_cache is not None:
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if not crossattn_cache["is_init"]:
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crossattn_cache["is_init"] = True
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k = self.norm_k(self.to_k(context)[0]).view(b, -1, n, d)
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v = self.to_v(context)[0].view(b, -1, n, d)
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crossattn_cache["k"] = k
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crossattn_cache["v"] = v
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else:
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k = crossattn_cache["k"]
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v = crossattn_cache["v"]
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else:
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k = self.norm_k(self.to_k(context)[0]).view(b, -1, n, d)
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v = self.to_v(context)[0].view(b, -1, n, d)
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# compute attention
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x = self.attn(q, k, v) if k.size(1) > 0 else torch.zeros_like(q)
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# output
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x = x.flatten(2)
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x, _ = self.to_out(x)
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return x
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class WanI2VCrossAttention(WanSelfAttention):
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def __init__(
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self,
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dim: int,
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num_heads: int,
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window_size=(-1, -1),
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qk_norm=True,
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eps=1e-6,
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supported_attention_backends: tuple[AttentionBackendEnum, ...]
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| None = None,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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) -> None:
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super().__init__(dim,
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num_heads,
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window_size,
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qk_norm,
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eps,
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supported_attention_backends,
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quant_config=quant_config,
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prefix=prefix)
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self.add_k_proj = ReplicatedLinear(dim, dim, quant_config=quant_config, prefix=f"{prefix}.add_k_proj")
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self.add_v_proj = ReplicatedLinear(dim, dim, quant_config=quant_config, prefix=f"{prefix}.add_v_proj")
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self.norm_added_k = RMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
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self.norm_added_q = RMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
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def forward(self, x, context, context_lens):
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r"""
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Args:
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x(Tensor): Shape [B, L1, C]
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context(Tensor): Shape [B, L2, C]
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context_lens(Tensor): Shape [B]
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"""
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context_img = context[:, :257]
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context = context[:, 257:]
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b, n, d = x.size(0), self.num_heads, self.head_dim
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# compute query, key, value
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q = self.norm_q(self.to_q(x)[0]).view(b, -1, n, d)
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k = self.norm_k(self.to_k(context)[0]).view(b, -1, n, d)
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v = self.to_v(context)[0].view(b, -1, n, d)
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k_img = self.norm_added_k(self.add_k_proj(context_img)[0]).view(b, -1, n, d)
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v_img = self.add_v_proj(context_img)[0].view(b, -1, n, d)
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img_x = self.attn(q, k_img, v_img)
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# compute attention
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x = self.attn(q, k, v) if k.size(1) > 0 else torch.zeros_like(q)
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# output
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x = x.flatten(2)
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img_x = img_x.flatten(2)
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x = x + img_x
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x, _ = self.to_out(x)
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return x
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class WanTransformerBlock(nn.Module):
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def __init__(self,
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dim: int,
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ffn_dim: int,
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num_heads: int,
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qk_norm: str = "rms_norm_across_heads",
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cross_attn_norm: bool = False,
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eps: float = 1e-6,
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added_kv_proj_dim: int | None = None,
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supported_attention_backends: tuple[AttentionBackendEnum, ...]
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| None = None,
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quant_config: QuantizationConfig | None = None,
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prefix: str = ""):
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super().__init__()
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# 1. Self-attention
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self.norm1 = FP32LayerNorm(dim, eps, elementwise_affine=False)
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self.to_q = ReplicatedLinear(dim, dim, bias=True, quant_config=quant_config, prefix=f"{prefix}.to_q")
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self.to_k = ReplicatedLinear(dim, dim, bias=True, quant_config=quant_config, prefix=f"{prefix}.to_k")
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self.to_v = ReplicatedLinear(dim, dim, bias=True, quant_config=quant_config, prefix=f"{prefix}.to_v")
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self.to_out = ReplicatedLinear(dim, dim, bias=True, quant_config=quant_config, prefix=f"{prefix}.to_out")
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self.attn1 = DistributedAttention(num_heads=num_heads,
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head_size=dim // num_heads,
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causal=False,
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supported_attention_backends=supported_attention_backends,
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prefix=f"{prefix}.attn1")
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self.hidden_dim = dim
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self.num_attention_heads = num_heads
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dim_head = dim // num_heads
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if qk_norm == "rms_norm":
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self.norm_q = RMSNorm(dim_head, eps=eps)
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self.norm_k = RMSNorm(dim_head, eps=eps)
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elif qk_norm == "rms_norm_across_heads":
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# LTX applies qk norm across all heads
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self.norm_q = RMSNorm(dim, eps=eps)
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self.norm_k = RMSNorm(dim, eps=eps)
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else:
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print("QK Norm type not supported")
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raise Exception
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assert cross_attn_norm is True
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self.self_attn_residual_norm = ScaleResidualLayerNormScaleShift(dim,
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norm_type="layer",
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eps=eps,
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elementwise_affine=True,
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dtype=torch.float32,
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compute_dtype=torch.float32)
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# 2. Cross-attention
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if added_kv_proj_dim is not None:
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# I2V
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self.attn2 = WanI2VCrossAttention(dim,
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num_heads,
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qk_norm=qk_norm,
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eps=eps,
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quant_config=quant_config,
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prefix=f"{prefix}.attn2")
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else:
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# T2V
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self.attn2 = WanT2VCrossAttention(dim,
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num_heads,
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qk_norm=qk_norm,
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eps=eps,
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quant_config=quant_config,
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prefix=f"{prefix}.attn2")
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self.cross_attn_residual_norm = ScaleResidualLayerNormScaleShift(dim,
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norm_type="layer",
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eps=eps,
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elementwise_affine=False,
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dtype=torch.float32,
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compute_dtype=torch.float32)
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# 3. Feed-forward
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self.ffn = MLP(dim, ffn_dim, act_type="gelu_pytorch_tanh", quant_config=quant_config, prefix=f"{prefix}.ffn")
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self.mlp_residual = ScaleResidual()
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self.scale_shift_table = 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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hidden_states: torch.Tensor,
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encoder_hidden_states: torch.Tensor,
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temb: torch.Tensor,
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freqs_cis: tuple[torch.Tensor, torch.Tensor],
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original_seq_len: int,
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) -> torch.Tensor:
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if hidden_states.dim() == 4:
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hidden_states = hidden_states.squeeze(1)
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bs, seq_length, _ = hidden_states.shape
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orig_dtype = hidden_states.dtype
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# assert orig_dtype != torch.float32
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if temb.dim() == 4:
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# temb: batch_size, seq_len, 6, inner_dim (wan2.2 ti2v)
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shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (
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self.scale_shift_table.unsqueeze(0) + temb.float()).chunk(6, dim=2)
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# batch_size, seq_len, 1, inner_dim
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shift_msa = shift_msa.squeeze(2)
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scale_msa = scale_msa.squeeze(2)
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gate_msa = gate_msa.squeeze(2)
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c_shift_msa = c_shift_msa.squeeze(2)
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c_scale_msa = c_scale_msa.squeeze(2)
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c_gate_msa = c_gate_msa.squeeze(2)
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else:
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# temb: batch_size, 6, inner_dim (wan2.1/wan2.2 14B)
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e = self.scale_shift_table + temb.float()
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shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = e.chunk(6, dim=1)
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assert shift_msa.dtype == torch.float32
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# 1. Self-attention
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norm_hidden_states = (self.norm1(hidden_states.float()) * (1 + scale_msa) + shift_msa).to(orig_dtype)
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query, _ = self.to_q(norm_hidden_states)
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key, _ = self.to_k(norm_hidden_states)
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value, _ = self.to_v(norm_hidden_states)
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if self.norm_q is not None:
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query = self.norm_q(query)
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if self.norm_k is not None:
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key = self.norm_k(key)
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query = query.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
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key = key.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
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value = value.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
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attn_output, _ = self.attn1(
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query,
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key,
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value,
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original_seq_len,
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freqs_cis=freqs_cis,
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)
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attn_output = attn_output.flatten(2)
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attn_output, _ = self.to_out(attn_output)
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attn_output = attn_output.squeeze(1)
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null_shift = null_scale = torch.tensor([0], device=hidden_states.device)
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norm_hidden_states, hidden_states = self.self_attn_residual_norm(hidden_states, attn_output, gate_msa,
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null_shift, null_scale)
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norm_hidden_states, hidden_states = norm_hidden_states.to(orig_dtype), hidden_states.to(orig_dtype)
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# 2. Cross-attention
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attn_output = self.attn2(norm_hidden_states, context=encoder_hidden_states, context_lens=None)
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norm_hidden_states, hidden_states = self.cross_attn_residual_norm(hidden_states, attn_output, 1, c_shift_msa,
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c_scale_msa)
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norm_hidden_states, hidden_states = norm_hidden_states.to(orig_dtype), hidden_states.to(orig_dtype)
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# 3. Feed-forward
|
|
ff_output = self.ffn(norm_hidden_states)
|
|
hidden_states = self.mlp_residual(hidden_states, ff_output, c_gate_msa)
|
|
hidden_states = hidden_states.to(orig_dtype)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class WanTransformerBlock_VSA(nn.Module):
|
|
|
|
def __init__(self,
|
|
dim: int,
|
|
ffn_dim: int,
|
|
num_heads: int,
|
|
qk_norm: str = "rms_norm_across_heads",
|
|
cross_attn_norm: bool = False,
|
|
eps: float = 1e-6,
|
|
added_kv_proj_dim: int | None = None,
|
|
supported_attention_backends: tuple[AttentionBackendEnum, ...]
|
|
| None = None,
|
|
quant_config: QuantizationConfig | None = None,
|
|
prefix: str = ""):
|
|
super().__init__()
|
|
|
|
# 1. Self-attention
|
|
self.norm1 = FP32LayerNorm(dim, eps, elementwise_affine=False)
|
|
self.to_q = ReplicatedLinear(dim, dim, bias=True, quant_config=quant_config, prefix=f"{prefix}.to_q")
|
|
self.to_k = ReplicatedLinear(dim, dim, bias=True, quant_config=quant_config, prefix=f"{prefix}.to_k")
|
|
self.to_v = ReplicatedLinear(dim, dim, bias=True, quant_config=quant_config, prefix=f"{prefix}.to_v")
|
|
self.to_gate_compress = ReplicatedLinear(dim,
|
|
dim,
|
|
bias=True,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.to_gate_compress")
|
|
self.to_out = ReplicatedLinear(dim, dim, bias=True, quant_config=quant_config, prefix=f"{prefix}.to_out")
|
|
self.attn1 = DistributedAttention_VSA(num_heads=num_heads,
|
|
head_size=dim // num_heads,
|
|
causal=False,
|
|
supported_attention_backends=supported_attention_backends,
|
|
prefix=f"{prefix}.attn1")
|
|
self.hidden_dim = dim
|
|
self.num_attention_heads = num_heads
|
|
dim_head = dim // num_heads
|
|
if qk_norm == "rms_norm":
|
|
self.norm_q = RMSNorm(dim_head, eps=eps)
|
|
self.norm_k = RMSNorm(dim_head, eps=eps)
|
|
elif qk_norm == "rms_norm_across_heads":
|
|
# LTX applies qk norm across all heads
|
|
self.norm_q = RMSNorm(dim, eps=eps)
|
|
self.norm_k = RMSNorm(dim, eps=eps)
|
|
else:
|
|
print("QK Norm type not supported")
|
|
raise Exception
|
|
assert cross_attn_norm is True
|
|
self.self_attn_residual_norm = ScaleResidualLayerNormScaleShift(dim,
|
|
norm_type="layer",
|
|
eps=eps,
|
|
elementwise_affine=True,
|
|
dtype=torch.float32,
|
|
compute_dtype=torch.float32)
|
|
|
|
# 2. Cross-attention
|
|
if added_kv_proj_dim is not None:
|
|
# I2V
|
|
self.attn2 = WanI2VCrossAttention(dim,
|
|
num_heads,
|
|
qk_norm=qk_norm,
|
|
eps=eps,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.attn2")
|
|
else:
|
|
# T2V
|
|
self.attn2 = WanT2VCrossAttention(dim,
|
|
num_heads,
|
|
qk_norm=qk_norm,
|
|
eps=eps,
|
|
quant_config=quant_config,
|
|
prefix=f"{prefix}.attn2")
|
|
self.cross_attn_residual_norm = ScaleResidualLayerNormScaleShift(dim,
|
|
norm_type="layer",
|
|
eps=eps,
|
|
elementwise_affine=False,
|
|
dtype=torch.float32,
|
|
compute_dtype=torch.float32)
|
|
|
|
# 3. Feed-forward
|
|
self.ffn = MLP(dim, ffn_dim, act_type="gelu_pytorch_tanh", quant_config=quant_config, prefix=f"{prefix}.ffn")
|
|
self.mlp_residual = ScaleResidual()
|
|
|
|
self.scale_shift_table = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
encoder_hidden_states: torch.Tensor,
|
|
temb: torch.Tensor,
|
|
freqs_cis: tuple[torch.Tensor, torch.Tensor],
|
|
original_seq_len: int,
|
|
) -> torch.Tensor:
|
|
if hidden_states.dim() == 4:
|
|
hidden_states = hidden_states.squeeze(1)
|
|
bs, seq_length, _ = hidden_states.shape
|
|
orig_dtype = hidden_states.dtype
|
|
# assert orig_dtype != torch.float32
|
|
e = self.scale_shift_table + temb.float()
|
|
shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = e.chunk(6, dim=1)
|
|
assert shift_msa.dtype == torch.float32
|
|
|
|
# 1. Self-attention
|
|
norm_hidden_states = (self.norm1(hidden_states.float()) * (1 + scale_msa) + shift_msa).to(orig_dtype)
|
|
query, _ = self.to_q(norm_hidden_states)
|
|
key, _ = self.to_k(norm_hidden_states)
|
|
value, _ = self.to_v(norm_hidden_states)
|
|
gate_compress, _ = self.to_gate_compress(norm_hidden_states)
|
|
|
|
if self.norm_q is not None:
|
|
query = self.norm_q(query)
|
|
if self.norm_k is not None:
|
|
key = self.norm_k(key)
|
|
|
|
query = query.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
|
|
key = key.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
|
|
value = value.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
|
|
gate_compress = gate_compress.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
|
|
|
|
attn_output, _ = self.attn1(
|
|
query,
|
|
key,
|
|
value,
|
|
original_seq_len,
|
|
freqs_cis=freqs_cis,
|
|
gate_compress=gate_compress,
|
|
)
|
|
attn_output = attn_output.flatten(2)
|
|
attn_output, _ = self.to_out(attn_output)
|
|
attn_output = attn_output.squeeze(1)
|
|
|
|
null_shift = null_scale = torch.tensor([0], device=hidden_states.device)
|
|
norm_hidden_states, hidden_states = self.self_attn_residual_norm(hidden_states, attn_output, gate_msa,
|
|
null_shift, null_scale)
|
|
norm_hidden_states, hidden_states = norm_hidden_states.to(orig_dtype), hidden_states.to(orig_dtype)
|
|
|
|
# 2. Cross-attention
|
|
attn_output = self.attn2(norm_hidden_states, context=encoder_hidden_states, context_lens=None)
|
|
norm_hidden_states, hidden_states = self.cross_attn_residual_norm(hidden_states, attn_output, 1, c_shift_msa,
|
|
c_scale_msa)
|
|
norm_hidden_states, hidden_states = norm_hidden_states.to(orig_dtype), hidden_states.to(orig_dtype)
|
|
|
|
# 3. Feed-forward
|
|
ff_output = self.ffn(norm_hidden_states)
|
|
hidden_states = self.mlp_residual(hidden_states, ff_output, c_gate_msa)
|
|
hidden_states = hidden_states.to(orig_dtype)
|
|
|
|
return hidden_states
|
|
|
|
|
|
class WanTransformer3DModel(BaseDiT):
|
|
_fsdp_shard_conditions = WanVideoConfig()._fsdp_shard_conditions
|
|
_compile_conditions = WanVideoConfig()._compile_conditions
|
|
_supported_attention_backends = WanVideoConfig()._supported_attention_backends
|
|
param_names_mapping = WanVideoConfig().param_names_mapping
|
|
reverse_param_names_mapping = WanVideoConfig().reverse_param_names_mapping
|
|
lora_param_names_mapping = WanVideoConfig().lora_param_names_mapping
|
|
|
|
def __init__(self, config: WanVideoConfig, hf_config: dict[str, Any]) -> None:
|
|
super().__init__(config=config, hf_config=hf_config)
|
|
self.quant_config = config.quant_config
|
|
|
|
inner_dim = config.num_attention_heads * config.attention_head_dim
|
|
self.hidden_size = config.hidden_size
|
|
self.num_attention_heads = config.num_attention_heads
|
|
self.in_channels = config.in_channels
|
|
self.out_channels = config.out_channels
|
|
self.num_channels_latents = config.num_channels_latents
|
|
self.patch_size = config.patch_size
|
|
self.text_len = config.text_len
|
|
|
|
assert config.num_attention_heads % get_sp_world_size(
|
|
) == 0, f"The number of attention heads ({config.num_attention_heads}) must be divisible by the sequence parallel size ({get_sp_world_size()})"
|
|
|
|
# 1. Patch & position embedding
|
|
self.patch_embedding = PatchEmbed(in_chans=config.in_channels,
|
|
embed_dim=inner_dim,
|
|
patch_size=config.patch_size,
|
|
flatten=False)
|
|
|
|
# 2. Condition embeddings
|
|
self.condition_embedder = WanTimeTextImageEmbedding(
|
|
dim=inner_dim,
|
|
time_freq_dim=config.freq_dim,
|
|
text_embed_dim=config.text_dim,
|
|
image_embed_dim=config.image_dim,
|
|
r_embedder=config.r_embedder,
|
|
r_embedder_fusion=config.r_embedder_fusion,
|
|
r_embedder_gate_value=config.r_embedder_gate_value,
|
|
r_embedder_deltatime_type=config.r_embedder_deltatime_type,
|
|
)
|
|
|
|
# 3. Transformer blocks
|
|
attn_backend = envs.FASTVIDEO_ATTENTION_BACKEND
|
|
transformer_block = WanTransformerBlock_VSA if attn_backend == "VIDEO_SPARSE_ATTN" else WanTransformerBlock
|
|
self.blocks = nn.ModuleList([
|
|
transformer_block(inner_dim,
|
|
config.ffn_dim,
|
|
config.num_attention_heads,
|
|
config.qk_norm,
|
|
config.cross_attn_norm,
|
|
config.eps,
|
|
config.added_kv_proj_dim,
|
|
self._supported_attention_backends,
|
|
quant_config=config.quant_config,
|
|
prefix=f"{config.prefix}.blocks.{i}") for i in range(config.num_layers)
|
|
])
|
|
|
|
# 4. Output norm & projection
|
|
self.norm_out = LayerNormScaleShift(inner_dim,
|
|
norm_type="layer",
|
|
eps=config.eps,
|
|
elementwise_affine=False,
|
|
dtype=torch.float32,
|
|
compute_dtype=torch.float32)
|
|
self.proj_out = nn.Linear(inner_dim, config.out_channels * math.prod(config.patch_size))
|
|
self.scale_shift_table = nn.Parameter(torch.randn(1, 2, inner_dim) / inner_dim**0.5)
|
|
|
|
self.gradient_checkpointing = False
|
|
self.__post_init__()
|
|
|
|
def forward(self,
|
|
hidden_states: torch.Tensor,
|
|
encoder_hidden_states: torch.Tensor | list[torch.Tensor],
|
|
timestep: torch.LongTensor,
|
|
encoder_hidden_states_image: torch.Tensor | list[torch.Tensor]
|
|
| None = None,
|
|
guidance=None,
|
|
r_timestep: torch.Tensor | None = None,
|
|
**kwargs) -> torch.Tensor:
|
|
orig_dtype = hidden_states.dtype
|
|
if encoder_hidden_states is not None and not isinstance(encoder_hidden_states, torch.Tensor):
|
|
encoder_hidden_states = encoder_hidden_states[0]
|
|
if isinstance(encoder_hidden_states_image, list):
|
|
encoder_hidden_states_image = (encoder_hidden_states_image[0]
|
|
if len(encoder_hidden_states_image) > 0 else None)
|
|
|
|
batch_size, num_channels, num_frames, height, width = hidden_states.shape
|
|
p_t, p_h, p_w = self.patch_size
|
|
post_patch_num_frames = num_frames // p_t
|
|
post_patch_height = height // p_h
|
|
post_patch_width = width // p_w
|
|
|
|
# Get rotary embeddings
|
|
d = self.hidden_size // self.num_attention_heads
|
|
rope_dim_list = [d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)]
|
|
freqs_cos, freqs_sin = get_rotary_pos_embed((post_patch_num_frames, post_patch_height, post_patch_width),
|
|
self.hidden_size,
|
|
self.num_attention_heads,
|
|
rope_dim_list,
|
|
dtype=torch.float32 if current_platform.is_mps() else torch.float64,
|
|
rope_theta=10000)
|
|
freqs_cis = (freqs_cos.to(hidden_states.device).float(), freqs_sin.to(hidden_states.device).float())
|
|
|
|
hidden_states = self.patch_embedding(hidden_states)
|
|
hidden_states = hidden_states.flatten(2).transpose(1, 2)
|
|
|
|
# Shard with padding support - returns (sharded_tensor, original_seq_len)
|
|
hidden_states, original_seq_len = sequence_model_parallel_shard(hidden_states, dim=1)
|
|
|
|
current_seq_len = hidden_states.shape[1]
|
|
sp_world_size = get_sp_world_size()
|
|
padded_seq_len = current_seq_len * sp_world_size
|
|
|
|
# timestep shape: batch_size, or batch_size, seq_len (wan 2.2 ti2v)
|
|
if timestep.dim() == 2:
|
|
ts_seq_len = timestep.shape[1]
|
|
timestep = timestep.flatten() # batch_size * seq_len
|
|
else:
|
|
ts_seq_len = None
|
|
|
|
# AnyFlow dual-timestep — match timestep's flattening so embedder
|
|
# sees aligned shapes.
|
|
if r_timestep is not None and r_timestep.dim() == 2:
|
|
r_timestep = r_timestep.flatten()
|
|
|
|
temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
|
|
timestep,
|
|
encoder_hidden_states,
|
|
encoder_hidden_states_image,
|
|
timestep_seq_len=ts_seq_len,
|
|
r_timestep=r_timestep)
|
|
if ts_seq_len is not None:
|
|
# batch_size, seq_len, 6, inner_dim
|
|
timestep_proj = timestep_proj.unflatten(2, (6, -1))
|
|
else:
|
|
# batch_size, 6, inner_dim
|
|
timestep_proj = timestep_proj.unflatten(1, (6, -1))
|
|
|
|
if encoder_hidden_states_image is not None:
|
|
if encoder_hidden_states is not None:
|
|
encoder_hidden_states = torch.concat([encoder_hidden_states_image, encoder_hidden_states], dim=1)
|
|
else:
|
|
encoder_hidden_states = encoder_hidden_states_image
|
|
|
|
if current_platform.is_mps() or current_platform.is_npu():
|
|
encoder_hidden_states = encoder_hidden_states.to(orig_dtype)
|
|
else:
|
|
encoder_hidden_states = encoder_hidden_states # cast to orig_dtype for MPS & NPU
|
|
|
|
assert encoder_hidden_states.dtype == orig_dtype
|
|
|
|
# 4. Transformer blocks
|
|
if torch.is_grad_enabled() and self.gradient_checkpointing:
|
|
for block in self.blocks:
|
|
hidden_states = self._gradient_checkpointing_func(block, hidden_states, encoder_hidden_states,
|
|
timestep_proj, freqs_cis, original_seq_len)
|
|
else:
|
|
for block in self.blocks:
|
|
hidden_states = block(hidden_states, encoder_hidden_states, timestep_proj, freqs_cis, original_seq_len)
|
|
# 5. Output norm, projection & unpatchify
|
|
if temb.dim() == 3:
|
|
# batch_size, seq_len, inner_dim (wan 2.2 ti2v)
|
|
shift, scale = (self.scale_shift_table.unsqueeze(0) + temb.unsqueeze(2)).chunk(2, dim=2)
|
|
shift = shift.squeeze(2)
|
|
scale = scale.squeeze(2)
|
|
else:
|
|
# batch_size, inner_dim
|
|
shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2, dim=1)
|
|
|
|
hidden_states = self.norm_out(hidden_states, shift, scale)
|
|
|
|
# Gather and unpad in one operation
|
|
hidden_states = sequence_model_parallel_all_gather_with_unpad(hidden_states, original_seq_len, dim=1)
|
|
hidden_states = self.proj_out(hidden_states)
|
|
|
|
hidden_states = hidden_states.reshape(batch_size, post_patch_num_frames, post_patch_height, post_patch_width,
|
|
p_t, p_h, p_w, -1)
|
|
hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
|
|
output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)
|
|
|
|
return output
|
|
|
|
|
|
# Entry point for model registry
|
|
EntryClass = WanTransformer3DModel
|