530 lines
19 KiB
Python
530 lines
19 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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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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from fastvideo.attention import DistributedAttention
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from fastvideo.configs.models.dits.matrixgame2 import MatrixGame2WanVideoConfig
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from fastvideo.distributed.parallel_state import get_sp_world_size
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from fastvideo.layers.layernorm import (
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FP32LayerNorm,
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LayerNormScaleShift,
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RMSNorm,
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ScaleResidual,
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ScaleResidualLayerNormScaleShift,
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)
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from fastvideo.layers.linear import ReplicatedLinear
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from fastvideo.layers.mlp import MLP
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from fastvideo.layers.rotary_embedding import (
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_apply_rotary_emb,
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get_rotary_pos_embed,
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)
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from fastvideo.layers.visual_embedding import (
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PatchEmbed,
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TimestepEmbedder,
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ModulateProjection,
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)
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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.models.dits.wanvideo import (
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WanSelfAttention,
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WanI2VCrossAttention,
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WanT2VCrossAttention,
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WanImageEmbedding,
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)
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from fastvideo.platforms import AttentionBackendEnum, current_platform
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# Import ActionModule
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from .action_module import ActionModule
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logger = init_logger(__name__)
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class MatrixGame2TimeImageEmbedding(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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image_embed_dim: int | None = None,
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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.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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def forward(
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self,
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timestep: torch.Tensor,
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encoder_hidden_states: torch.Tensor, # Kept for interface compatibility
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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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):
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temb = self.time_embedder(timestep, timestep_seq_len)
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timestep_proj = self.time_modulation(temb)
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# Matrix-Game 2.0 does not use text embeddings, so we ignore encoder_hidden_states
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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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encoder_hidden_states = torch.zeros(
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(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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)
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return (
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temb,
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timestep_proj,
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encoder_hidden_states,
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encoder_hidden_states_image,
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)
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class MatrixGame2CrossAttention(WanSelfAttention):
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def forward(self, x, context, context_lens=None, 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] - typically 257 image tokens
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context_lens(Tensor): Shape [B]
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crossattn_cache(dict): Optional cache for k/v during inference
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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)
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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 MatrixGame2TransformerBlock(nn.Module):
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def __init__(
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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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prefix: str = "",
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action_config: dict | None = None,
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):
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super().__init__()
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action_config = action_config or {}
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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)
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self.to_k = ReplicatedLinear(dim, dim, bias=True)
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self.to_v = ReplicatedLinear(dim, dim, bias=True)
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self.to_out = ReplicatedLinear(dim, dim, bias=True)
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self.attn1 = DistributedAttention(
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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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)
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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(
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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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)
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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, num_heads, qk_norm=qk_norm, eps=eps)
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else:
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# T2V
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self.attn2 = WanT2VCrossAttention(dim, num_heads, qk_norm=qk_norm, eps=eps)
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self.cross_attn_residual_norm = ScaleResidualLayerNormScaleShift(
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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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)
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# 2.1. Action Module Integration
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self.use_action_module = len(action_config) > 0
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if self.use_action_module:
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self.action_model = ActionModule(**action_config)
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else:
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self.action_model = None
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# 3. Feed-forward
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self.ffn = MLP(dim, ffn_dim, act_type="gelu_pytorch_tanh")
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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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# Action Module specific args
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grid_sizes: torch.Tensor,
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mouse_cond: torch.Tensor | None = None,
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keyboard_cond: torch.Tensor | None = None,
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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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orig_dtype = hidden_states.dtype
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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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(
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shift_msa,
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scale_msa,
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gate_msa,
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c_shift_msa,
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c_scale_msa,
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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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(
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shift_msa,
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scale_msa,
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gate_msa,
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c_shift_msa,
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c_scale_msa,
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c_gate_msa,
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) = 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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# Apply rotary embeddings
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cos, sin = freqs_cis
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query, key = (
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_apply_rotary_emb(query, cos, sin, is_neox_style=False),
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_apply_rotary_emb(key, cos, sin, is_neox_style=False),
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)
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attn_output, _ = self.attn1(query, key, value)
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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 = (
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norm_hidden_states.to(orig_dtype),
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hidden_states.to(orig_dtype),
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)
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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 = (
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norm_hidden_states.to(orig_dtype),
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hidden_states.to(orig_dtype),
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)
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# ================= Action Module =================
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if self.action_model is not None:
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if mouse_cond is not None or keyboard_cond is not None:
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# grid_sizes is expected to be [F, H, W]
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# ActionModule implementation takes hidden_states directly
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hidden_states = self.action_model(
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hidden_states,
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int(grid_sizes[0]),
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int(grid_sizes[1]),
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int(grid_sizes[2]),
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mouse_cond,
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keyboard_cond,
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num_frame_per_block=int(grid_sizes[0]),
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)
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# =================================================
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# 3. Feed-forward
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ff_output = self.ffn(norm_hidden_states)
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hidden_states = self.mlp_residual(hidden_states, ff_output, c_gate_msa)
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hidden_states = hidden_states.to(orig_dtype)
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return hidden_states
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_DEFAULT_MATRIXGAME2_CONFIG = MatrixGame2WanVideoConfig()
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class MatrixGame2WanModel(BaseDiT):
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# Marker for action input support (Matrix-Game)
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supports_action_input = True
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_fsdp_shard_conditions = _DEFAULT_MATRIXGAME2_CONFIG._fsdp_shard_conditions
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_compile_conditions = _DEFAULT_MATRIXGAME2_CONFIG._compile_conditions
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_supported_attention_backends = (_DEFAULT_MATRIXGAME2_CONFIG._supported_attention_backends)
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param_names_mapping = _DEFAULT_MATRIXGAME2_CONFIG.param_names_mapping
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reverse_param_names_mapping = (_DEFAULT_MATRIXGAME2_CONFIG.reverse_param_names_mapping)
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lora_param_names_mapping = (_DEFAULT_MATRIXGAME2_CONFIG.lora_param_names_mapping)
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def __init__(
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self,
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config: MatrixGame2WanVideoConfig,
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hf_config: dict[str, Any],
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**kwargs,
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) -> None:
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super().__init__(config=config, hf_config=hf_config)
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inner_dim = config.num_attention_heads * config.attention_head_dim
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self.hidden_size = config.hidden_size
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self.num_attention_heads = config.num_attention_heads
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self.num_channels_latents = config.out_channels
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self.patch_size = config.patch_size
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# 1. Patch & position embedding
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self.patch_embedding = PatchEmbed(
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in_chans=config.in_channels,
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embed_dim=inner_dim,
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patch_size=config.patch_size,
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flatten=False,
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)
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# 2. Condition embeddings
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self.condition_embedder = MatrixGame2TimeImageEmbedding(
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dim=inner_dim,
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time_freq_dim=config.freq_dim,
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image_embed_dim=config.image_dim,
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)
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# 2.1. Get action config
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self.action_config = getattr(config, "action_config", {})
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# 3. Transformer blocks
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self.blocks = nn.ModuleList([
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MatrixGame2TransformerBlock(
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inner_dim,
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config.ffn_dim,
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config.num_attention_heads,
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config.qk_norm,
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config.cross_attn_norm,
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config.eps,
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config.added_kv_proj_dim,
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self._supported_attention_backends,
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prefix=f"{getattr(config, 'prefix', 'Wan')}.blocks.{i}",
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action_config=self.action_config,
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) for i in range(config.num_layers)
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])
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# 4. Output norm & projection
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self.norm_out = LayerNormScaleShift(
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inner_dim,
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norm_type="layer",
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eps=config.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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)
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self.proj_out = nn.Linear(inner_dim, config.out_channels * math.prod(config.patch_size))
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self.scale_shift_table = nn.Parameter(torch.randn(1, 2, inner_dim) / inner_dim**0.5)
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self.gradient_checkpointing = False
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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 | list[torch.Tensor],
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timestep: torch.LongTensor,
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encoder_hidden_states_image: torch.Tensor
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| list[torch.Tensor]
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| None = None,
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# Action inputs
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mouse_cond: torch.Tensor | None = None,
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keyboard_cond: torch.Tensor | None = None,
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**kwargs,
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) -> torch.Tensor:
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if encoder_hidden_states is not None and not isinstance(encoder_hidden_states, torch.Tensor):
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encoder_hidden_states = encoder_hidden_states[0]
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if (isinstance(encoder_hidden_states_image, list) and len(encoder_hidden_states_image) > 0):
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encoder_hidden_states_image = encoder_hidden_states_image[0]
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elif not isinstance(encoder_hidden_states_image, torch.Tensor):
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encoder_hidden_states_image = None
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batch_size, num_channels, num_frames, height, width = (hidden_states.shape)
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p_t, p_h, p_w = self.patch_size
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post_patch_num_frames = num_frames // p_t
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post_patch_height = height // p_h
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post_patch_width = width // p_w
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# Get rotary embeddings
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d = self.hidden_size // self.num_attention_heads
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rope_dim_list = [d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)]
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freqs_cos, freqs_sin = get_rotary_pos_embed(
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(
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post_patch_num_frames * get_sp_world_size(),
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post_patch_height,
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post_patch_width,
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),
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self.hidden_size,
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self.num_attention_heads,
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rope_dim_list,
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dtype=torch.float32 if current_platform.is_mps() else torch.float64,
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rope_theta=10000,
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do_sp_sharding=True,
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)
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freqs_cos = freqs_cos.to(hidden_states.device)
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freqs_sin = freqs_sin.to(hidden_states.device)
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freqs_cis = ((freqs_cos.float(), freqs_sin.float()) if freqs_cos is not None else None)
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hidden_states = self.patch_embedding(hidden_states)
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hidden_states = hidden_states.flatten(2).transpose(1, 2)
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if timestep.dim() == 2:
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timestep = timestep.flatten()
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(
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temb,
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timestep_proj,
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encoder_hidden_states,
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encoder_hidden_states_image,
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) = self.condition_embedder(timestep, encoder_hidden_states, encoder_hidden_states_image)
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timestep_proj = timestep_proj.unflatten(1, (6, -1))
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if encoder_hidden_states is not None:
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if isinstance(encoder_hidden_states, list):
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encoder_hidden_states = encoder_hidden_states[0]
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elif encoder_hidden_states.ndim == 2:
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encoder_hidden_states = encoder_hidden_states.unsqueeze(0)
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else:
|
|
# encoder_hidden_states is None (e.g. no text encoder)
|
|
# Matrix-Game 2.0 uses image-action cross-attn.
|
|
pass
|
|
|
|
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
|
|
|
|
# This is [F, H, W] for the ActionModule
|
|
grid_sizes = torch.tensor(
|
|
[post_patch_num_frames, post_patch_height, post_patch_width],
|
|
device=hidden_states.device,
|
|
)
|
|
|
|
# Blocks
|
|
for block in self.blocks:
|
|
if torch.is_grad_enabled() and self.gradient_checkpointing:
|
|
hidden_states = self._gradient_checkpointing_func(
|
|
block,
|
|
hidden_states,
|
|
encoder_hidden_states,
|
|
timestep_proj,
|
|
freqs_cis,
|
|
grid_sizes=grid_sizes,
|
|
mouse_cond=mouse_cond,
|
|
keyboard_cond=keyboard_cond,
|
|
)
|
|
else:
|
|
hidden_states = block(
|
|
hidden_states,
|
|
encoder_hidden_states,
|
|
timestep_proj,
|
|
freqs_cis,
|
|
grid_sizes=grid_sizes,
|
|
mouse_cond=mouse_cond,
|
|
keyboard_cond=keyboard_cond,
|
|
)
|
|
|
|
# Output
|
|
shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2, dim=1)
|
|
hidden_states = self.norm_out(hidden_states, shift, scale)
|
|
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
|