mirror of
https://github.com/modelscope/DiffSynth-Studio.git
synced 2026-03-19 14:58:12 +00:00
788 lines
24 KiB
Python
788 lines
24 KiB
Python
import math
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import torch
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import torch.amp as amp
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import torch.nn as nn
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from tqdm import tqdm
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from .utils import hash_state_dict_keys
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try:
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import flash_attn_interface
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FLASH_ATTN_3_AVAILABLE = True
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except ModuleNotFoundError:
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FLASH_ATTN_3_AVAILABLE = False
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try:
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import flash_attn
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FLASH_ATTN_2_AVAILABLE = True
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except ModuleNotFoundError:
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FLASH_ATTN_2_AVAILABLE = False
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import warnings
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__all__ = ['WanModel']
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def flash_attention(
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q,
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k,
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v,
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q_lens=None,
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k_lens=None,
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dropout_p=0.,
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softmax_scale=None,
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q_scale=None,
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causal=False,
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window_size=(-1, -1),
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deterministic=False,
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dtype=torch.bfloat16,
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version=None,
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):
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"""
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q: [B, Lq, Nq, C1].
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k: [B, Lk, Nk, C1].
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v: [B, Lk, Nk, C2]. Nq must be divisible by Nk.
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q_lens: [B].
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k_lens: [B].
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dropout_p: float. Dropout probability.
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softmax_scale: float. The scaling of QK^T before applying softmax.
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causal: bool. Whether to apply causal attention mask.
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window_size: (left right). If not (-1, -1), apply sliding window local attention.
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deterministic: bool. If True, slightly slower and uses more memory.
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dtype: torch.dtype. Apply when dtype of q/k/v is not float16/bfloat16.
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"""
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half_dtypes = (torch.float16, torch.bfloat16)
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assert dtype in half_dtypes
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assert q.device.type == 'cuda' and q.size(-1) <= 256
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# params
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b, lq, lk, out_dtype = q.size(0), q.size(1), k.size(1), q.dtype
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def half(x):
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return x if x.dtype in half_dtypes else x.to(dtype)
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# preprocess query
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if q_lens is None:
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q = half(q.flatten(0, 1))
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q_lens = torch.tensor(
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[lq] * b, dtype=torch.int32).to(
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device=q.device, non_blocking=True)
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else:
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q = half(torch.cat([u[:v] for u, v in zip(q, q_lens)]))
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# preprocess key, value
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if k_lens is None:
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k = half(k.flatten(0, 1))
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v = half(v.flatten(0, 1))
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k_lens = torch.tensor(
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[lk] * b, dtype=torch.int32).to(
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device=k.device, non_blocking=True)
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else:
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k = half(torch.cat([u[:v] for u, v in zip(k, k_lens)]))
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v = half(torch.cat([u[:v] for u, v in zip(v, k_lens)]))
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q = q.to(v.dtype)
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k = k.to(v.dtype)
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if q_scale is not None:
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q = q * q_scale
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if version is not None and version == 3 and not FLASH_ATTN_3_AVAILABLE:
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warnings.warn(
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'Flash attention 3 is not available, use flash attention 2 instead.'
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)
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# apply attention
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if (version is None or version == 3) and FLASH_ATTN_3_AVAILABLE:
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# Note: dropout_p, window_size are not supported in FA3 now.
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x = flash_attn_interface.flash_attn_varlen_func(
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q=q,
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k=k,
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v=v,
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cu_seqlens_q=torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum(
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0, dtype=torch.int32).to(q.device, non_blocking=True),
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cu_seqlens_k=torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum(
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0, dtype=torch.int32).to(q.device, non_blocking=True),
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seqused_q=None,
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seqused_k=None,
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max_seqlen_q=lq,
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max_seqlen_k=lk,
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softmax_scale=softmax_scale,
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causal=causal,
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deterministic=deterministic)[0].unflatten(0, (b, lq))
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elif FLASH_ATTN_2_AVAILABLE:
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x = flash_attn.flash_attn_varlen_func(
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q=q,
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k=k,
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v=v,
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cu_seqlens_q=torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum(
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0, dtype=torch.int32).to(q.device, non_blocking=True),
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cu_seqlens_k=torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum(
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0, dtype=torch.int32).to(q.device, non_blocking=True),
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max_seqlen_q=lq,
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max_seqlen_k=lk,
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dropout_p=dropout_p,
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softmax_scale=softmax_scale,
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causal=causal,
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window_size=window_size,
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deterministic=deterministic).unflatten(0, (b, lq))
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else:
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q = q.unsqueeze(0).transpose(1, 2).to(dtype)
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k = k.unsqueeze(0).transpose(1, 2).to(dtype)
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v = v.unsqueeze(0).transpose(1, 2).to(dtype)
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x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
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x = x.transpose(1, 2).contiguous()
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# output
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return x.type(out_dtype)
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def create_sdpa_mask(q, k, q_lens, k_lens, causal=False):
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b, lq, lk = q.size(0), q.size(1), k.size(1)
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if q_lens is None:
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q_lens = torch.tensor([lq] * b, dtype=torch.int32)
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if k_lens is None:
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k_lens = torch.tensor([lk] * b, dtype=torch.int32)
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attn_mask = torch.zeros((b, lq, lk), dtype=torch.bool)
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for i in range(b):
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q_len, k_len = q_lens[i], k_lens[i]
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attn_mask[i, q_len:, :] = True
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attn_mask[i, :, k_len:] = True
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if causal:
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causal_mask = torch.triu(torch.ones((lq, lk), dtype=torch.bool), diagonal=1)
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attn_mask[i, :, :] = torch.logical_or(attn_mask[i, :, :], causal_mask)
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attn_mask = attn_mask.logical_not().to(q.device, non_blocking=True)
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return attn_mask
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def attention(
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q,
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k,
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v,
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q_lens=None,
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k_lens=None,
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dropout_p=0.,
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softmax_scale=None,
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q_scale=None,
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causal=False,
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window_size=(-1, -1),
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deterministic=False,
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dtype=torch.bfloat16,
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fa_version=None,
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):
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if FLASH_ATTN_2_AVAILABLE or FLASH_ATTN_3_AVAILABLE:
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return flash_attention(
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q=q,
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k=k,
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v=v,
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q_lens=q_lens,
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k_lens=k_lens,
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dropout_p=dropout_p,
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softmax_scale=softmax_scale,
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q_scale=q_scale,
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causal=causal,
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window_size=window_size,
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deterministic=deterministic,
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dtype=dtype,
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version=fa_version,
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)
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else:
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if q_lens is not None or k_lens is not None:
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warnings.warn('Padding mask is disabled when using scaled_dot_product_attention. It can have a significant impact on performance.')
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attn_mask = None
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q = q.transpose(1, 2).to(dtype)
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k = k.transpose(1, 2).to(dtype)
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v = v.transpose(1, 2).to(dtype)
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out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask, is_causal=causal, dropout_p=dropout_p)
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out = out.transpose(1, 2).contiguous()
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return out
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def sinusoidal_embedding_1d(dim, position):
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# preprocess
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assert dim % 2 == 0
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half = dim // 2
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position = position.type(torch.float64)
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# calculation
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sinusoid = torch.outer(
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position, torch.pow(10000, -torch.arange(half).to(position).div(half)))
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x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1)
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return x
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@amp.autocast(enabled=False, device_type="cuda")
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def rope_params(max_seq_len, dim, theta=10000):
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assert dim % 2 == 0
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freqs = torch.outer(
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torch.arange(max_seq_len),
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1.0 / torch.pow(theta,
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torch.arange(0, dim, 2).to(torch.float64).div(dim)))
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freqs = torch.polar(torch.ones_like(freqs), freqs)
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return freqs
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@amp.autocast(enabled=False, device_type="cuda")
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def rope_apply(x, grid_sizes, freqs):
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n, c = x.size(2), x.size(3) // 2
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# split freqs
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freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1)
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# loop over samples
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output = []
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for i, (f, h, w) in enumerate(grid_sizes.tolist()):
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seq_len = f * h * w
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# precompute multipliers
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x_i = torch.view_as_complex(x[i, :seq_len].to(torch.float64).reshape(
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seq_len, n, -1, 2))
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freqs_i = torch.cat([
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freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1),
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freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
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freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)
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],
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dim=-1).reshape(seq_len, 1, -1)
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# apply rotary embedding
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x_i = torch.view_as_real(x_i * freqs_i).flatten(2)
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x_i = torch.cat([x_i, x[i, seq_len:]])
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# append to collection
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output.append(x_i)
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return torch.stack(output).float()
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class WanRMSNorm(nn.Module):
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def __init__(self, dim, eps=1e-5):
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super().__init__()
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self.dim = dim
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self.eps = eps
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self.weight = nn.Parameter(torch.ones(dim))
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def forward(self, x):
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return self._norm(x.float()).type_as(x) * self.weight
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def _norm(self, x):
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return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)
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class WanLayerNorm(nn.LayerNorm):
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def __init__(self, dim, eps=1e-6, elementwise_affine=False):
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super().__init__(dim, elementwise_affine=elementwise_affine, eps=eps)
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def forward(self, x):
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return super().forward(x.float()).type_as(x)
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class WanSelfAttention(nn.Module):
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def __init__(self,
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dim,
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num_heads,
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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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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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# layers
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self.q = nn.Linear(dim, dim)
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self.k = nn.Linear(dim, dim)
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self.v = nn.Linear(dim, dim)
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self.o = nn.Linear(dim, dim)
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self.norm_q = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
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self.norm_k = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
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def forward(self, x, seq_lens, grid_sizes, freqs):
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b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
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# query, key, value function
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def qkv_fn(x):
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q = self.norm_q(self.q(x)).view(b, s, n, d)
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k = self.norm_k(self.k(x)).view(b, s, n, d)
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v = self.v(x).view(b, s, n, d)
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return q, k, v
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q, k, v = qkv_fn(x)
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x = flash_attention(
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q=rope_apply(q, grid_sizes, freqs),
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k=rope_apply(k, grid_sizes, freqs),
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v=v,
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k_lens=seq_lens,
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window_size=self.window_size)
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# output
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x = x.flatten(2)
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x = self.o(x)
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return x
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class WanT2VCrossAttention(WanSelfAttention):
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def forward(self, x, context, context_lens):
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"""
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x: [B, L1, C].
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context: [B, L2, C].
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context_lens: [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.q(x)).view(b, -1, n, d)
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k = self.norm_k(self.k(context)).view(b, -1, n, d)
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v = self.v(context).view(b, -1, n, d)
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# compute attention
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x = flash_attention(q, k, v, k_lens=context_lens)
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# output
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x = x.flatten(2)
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x = self.o(x)
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return x
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class WanI2VCrossAttention(WanSelfAttention):
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def __init__(self,
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dim,
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num_heads,
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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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super().__init__(dim, num_heads, window_size, qk_norm, eps)
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self.k_img = nn.Linear(dim, dim)
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self.v_img = nn.Linear(dim, dim)
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# self.alpha = nn.Parameter(torch.zeros((1, )))
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self.norm_k_img = WanRMSNorm(
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dim, eps=eps) if qk_norm else nn.Identity()
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def forward(self, x, context, context_lens):
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"""
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x: [B, L1, C].
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context: [B, L2, C].
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context_lens: [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.q(x)).view(b, -1, n, d)
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k = self.norm_k(self.k(context)).view(b, -1, n, d)
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v = self.v(context).view(b, -1, n, d)
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k_img = self.norm_k_img(self.k_img(context_img)).view(b, -1, n, d)
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v_img = self.v_img(context_img).view(b, -1, n, d)
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img_x = flash_attention(q, k_img, v_img, k_lens=None)
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# compute attention
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x = flash_attention(q, k, v, k_lens=context_lens)
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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.o(x)
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return x
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WANX_CROSSATTENTION_CLASSES = {
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't2v_cross_attn': WanT2VCrossAttention,
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'i2v_cross_attn': WanI2VCrossAttention,
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}
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class WanAttentionBlock(nn.Module):
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def __init__(self,
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cross_attn_type,
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dim,
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ffn_dim,
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num_heads,
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window_size=(-1, -1),
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qk_norm=True,
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cross_attn_norm=False,
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eps=1e-6):
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super().__init__()
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self.dim = dim
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self.ffn_dim = ffn_dim
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self.num_heads = num_heads
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self.window_size = window_size
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self.qk_norm = qk_norm
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self.cross_attn_norm = cross_attn_norm
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self.eps = eps
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# layers
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self.norm1 = WanLayerNorm(dim, eps)
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self.self_attn = WanSelfAttention(dim, num_heads, window_size, qk_norm,
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eps)
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self.norm3 = WanLayerNorm(
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dim, eps,
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elementwise_affine=True) if cross_attn_norm else nn.Identity()
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self.cross_attn = WANX_CROSSATTENTION_CLASSES[cross_attn_type](
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dim, num_heads, (-1, -1), qk_norm, eps)
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self.norm2 = WanLayerNorm(dim, eps)
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self.ffn = nn.Sequential(
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nn.Linear(dim, ffn_dim), nn.GELU(approximate='tanh'),
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nn.Linear(ffn_dim, dim))
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# modulation
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self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)
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def forward(
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self,
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x,
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e,
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seq_lens,
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grid_sizes,
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freqs,
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context,
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context_lens,
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):
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assert e.dtype == torch.float32
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with amp.autocast(dtype=torch.float32, device_type="cuda"):
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e = (self.modulation.to(dtype=e.dtype, device=e.device) + e).chunk(6, dim=1)
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assert e[0].dtype == torch.float32
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# self-attention
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y = self.self_attn(
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self.norm1(x).float() * (1 + e[1]) + e[0], seq_lens, grid_sizes,
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freqs)
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with amp.autocast(dtype=torch.float32, device_type="cuda"):
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x = x + y * e[2]
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# cross-attention & ffn function
|
|
def cross_attn_ffn(x, context, context_lens, e):
|
|
x = x + self.cross_attn(self.norm3(x), context, context_lens)
|
|
y = self.ffn(self.norm2(x).float() * (1 + e[4]) + e[3])
|
|
with amp.autocast(dtype=torch.float32, device_type="cuda"):
|
|
x = x + y * e[5]
|
|
return x
|
|
|
|
x = cross_attn_ffn(x, context, context_lens, e)
|
|
return x
|
|
|
|
|
|
class Head(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):
|
|
assert e.dtype == torch.float32
|
|
with amp.autocast(dtype=torch.float32, device_type="cuda"):
|
|
e = (self.modulation.to(dtype=e.dtype, device=e.device) + e.unsqueeze(1)).chunk(2, dim=1)
|
|
x = (self.head(self.norm(x) * (1 + e[1]) + e[0]))
|
|
return x
|
|
|
|
|
|
class MLPProj(torch.nn.Module):
|
|
|
|
def __init__(self, in_dim, out_dim):
|
|
super().__init__()
|
|
|
|
self.proj = torch.nn.Sequential(
|
|
torch.nn.LayerNorm(in_dim), torch.nn.Linear(in_dim, in_dim),
|
|
torch.nn.GELU(), torch.nn.Linear(in_dim, out_dim),
|
|
torch.nn.LayerNorm(out_dim))
|
|
|
|
def forward(self, image_embeds):
|
|
clip_extra_context_tokens = self.proj(image_embeds)
|
|
return clip_extra_context_tokens
|
|
|
|
|
|
class WanModel(nn.Module):
|
|
|
|
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,
|
|
window_size=(-1, -1),
|
|
qk_norm=True,
|
|
cross_attn_norm=False,
|
|
eps=1e-6):
|
|
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.window_size = window_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([
|
|
WanAttentionBlock(cross_attn_type, dim, ffn_dim, num_heads,
|
|
window_size, qk_norm, cross_attn_norm, eps)
|
|
for _ in range(num_layers)
|
|
])
|
|
|
|
# head
|
|
self.head = Head(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()
|
|
|
|
def forward(
|
|
self,
|
|
x,
|
|
timestep,
|
|
context,
|
|
seq_len,
|
|
clip_fea=None,
|
|
y=None,
|
|
use_gradient_checkpointing=False,
|
|
**kwargs,
|
|
):
|
|
"""
|
|
x: A list of videos each with shape [C, T, H, W].
|
|
t: [B].
|
|
context: A list of text embeddings each with shape [L, C].
|
|
"""
|
|
if self.model_type == 'i2v':
|
|
assert clip_fea is not None and y is not None
|
|
# params
|
|
device = x[0].device
|
|
if self.freqs.device != device:
|
|
self.freqs = self.freqs.to(device)
|
|
|
|
if y is not None:
|
|
x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)]
|
|
|
|
# embeddings
|
|
x = [self.patch_embedding(u.unsqueeze(0)) for u in x]
|
|
grid_sizes = torch.stack(
|
|
[torch.tensor(u.shape[2:], dtype=torch.long) for u in x])
|
|
x = [u.flatten(2).transpose(1, 2) for u in x]
|
|
seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long)
|
|
assert seq_lens.max() <= seq_len
|
|
x = torch.cat([
|
|
torch.cat([u, u.new_zeros(1, seq_len - u.size(1), u.size(2))],
|
|
dim=1) for u in x
|
|
])
|
|
|
|
# time embeddings
|
|
with amp.autocast(dtype=torch.float32, device_type="cuda"):
|
|
e = self.time_embedding(
|
|
sinusoidal_embedding_1d(self.freq_dim, timestep).float())
|
|
e0 = self.time_projection(e).unflatten(1, (6, self.dim))
|
|
assert e.dtype == torch.float32 and e0.dtype == torch.float32
|
|
|
|
# context
|
|
context_lens = None
|
|
context = self.text_embedding(
|
|
torch.stack([
|
|
torch.cat(
|
|
[u, u.new_zeros(self.text_len - u.size(0), u.size(1))])
|
|
for u in context
|
|
]))
|
|
|
|
if clip_fea is not None:
|
|
context_clip = self.img_emb(clip_fea) # bs x 257 x dim
|
|
context = torch.concat([context_clip, context], dim=1)
|
|
|
|
# arguments
|
|
kwargs = dict(
|
|
e=e0,
|
|
seq_lens=seq_lens,
|
|
grid_sizes=grid_sizes,
|
|
freqs=self.freqs,
|
|
context=context,
|
|
context_lens=context_lens)
|
|
|
|
def create_custom_forward(module):
|
|
def custom_forward(*inputs, **kwargs):
|
|
return module(*inputs, **kwargs)
|
|
return custom_forward
|
|
|
|
for block in self.blocks:
|
|
if self.training and use_gradient_checkpointing:
|
|
x = torch.utils.checkpoint.checkpoint(
|
|
create_custom_forward(block),
|
|
x, **kwargs,
|
|
use_reentrant=False,
|
|
)
|
|
else:
|
|
x = block(x, **kwargs)
|
|
|
|
# head
|
|
x = self.head(x, e)
|
|
|
|
# unpatchify
|
|
x = self.unpatchify(x, grid_sizes)
|
|
x = torch.stack(x).float()
|
|
return x
|
|
|
|
def unpatchify(self, x, grid_sizes):
|
|
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):
|
|
# 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)
|
|
|
|
@staticmethod
|
|
def state_dict_converter():
|
|
return WanModelStateDictConverter()
|
|
|
|
|
|
class WanModelStateDictConverter:
|
|
def __init__(self):
|
|
pass
|
|
|
|
def from_diffusers(self, state_dict):
|
|
return state_dict
|
|
|
|
def from_civitai(self, state_dict):
|
|
if hash_state_dict_keys(state_dict) == "9269f8db9040a9d860eaca435be61814":
|
|
config = {
|
|
"model_type": "t2v",
|
|
"patch_size": (1, 2, 2),
|
|
"text_len": 512,
|
|
"in_dim": 16,
|
|
"dim": 1536,
|
|
"ffn_dim": 8960,
|
|
"freq_dim": 256,
|
|
"text_dim": 4096,
|
|
"out_dim": 16,
|
|
"num_heads": 12,
|
|
"num_layers": 30,
|
|
"window_size": (-1, -1),
|
|
"qk_norm": True,
|
|
"cross_attn_norm": True,
|
|
"eps": 1e-6,
|
|
}
|
|
elif hash_state_dict_keys(state_dict) == "aafcfd9672c3a2456dc46e1cb6e52c70":
|
|
config = {
|
|
"model_type": "t2v",
|
|
"patch_size": (1, 2, 2),
|
|
"text_len": 512,
|
|
"in_dim": 16,
|
|
"dim": 5120,
|
|
"ffn_dim": 13824,
|
|
"freq_dim": 256,
|
|
"text_dim": 4096,
|
|
"out_dim": 16,
|
|
"num_heads": 40,
|
|
"num_layers": 40,
|
|
"window_size": (-1, -1),
|
|
"qk_norm": True,
|
|
"cross_attn_norm": True,
|
|
"eps": 1e-6,
|
|
}
|
|
elif hash_state_dict_keys(state_dict) == "6bfcfb3b342cb286ce886889d519a77e":
|
|
config = {
|
|
"model_type": "i2v",
|
|
"patch_size": (1, 2, 2),
|
|
"text_len": 512,
|
|
"in_dim": 36,
|
|
"dim": 5120,
|
|
"ffn_dim": 13824,
|
|
"freq_dim": 256,
|
|
"text_dim": 4096,
|
|
"out_dim": 16,
|
|
"num_heads": 40,
|
|
"num_layers": 40,
|
|
"window_size": (-1, -1),
|
|
"qk_norm": True,
|
|
"cross_attn_norm": True,
|
|
"eps": 1e-6,
|
|
}
|
|
else:
|
|
config = {}
|
|
return state_dict, config
|