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fix: WanAttentionBlock gradient checkpointing + FSDP2 dtype mismatch #354

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fix: WanAttentionBlock gradient checkpointing + FSDP2 dtype mismatch #354

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44 changes: 27 additions & 17 deletions wan/modules/model.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
import math
from functools import partial

import torch
import torch.nn as nn
Expand All @@ -11,6 +12,15 @@
__all__ = ['WanModel']


def _gc_autocast(dtype=torch.bfloat16):
"""Context function for gradient checkpointing that provides autocast
for both forward and recompute phases, ensuring dtype consistency."""
return (
torch.amp.autocast('cuda', dtype=dtype),
torch.amp.autocast('cuda', dtype=dtype),
)


def sinusoidal_embedding_1d(dim, position):
# preprocess
assert dim % 2 == 0
Expand Down Expand Up @@ -235,27 +245,21 @@ def forward(
freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
"""
assert e.dtype == torch.float32
with torch.amp.autocast('cuda', dtype=torch.float32):
e = (self.modulation.unsqueeze(0) + e).chunk(6, dim=2)
compute_dtype = x.dtype
e = (self.modulation.float().unsqueeze(0) + e).chunk(6, dim=2)
assert e[0].dtype == torch.float32

# self-attention
y = self.self_attn(
self.norm1(x).float() * (1 + e[1].squeeze(2)) + e[0].squeeze(2),
(self.norm1(x).float() * (1 + e[1].squeeze(2)) + e[0].squeeze(2)).to(dtype=compute_dtype),
seq_lens, grid_sizes, freqs)
with torch.amp.autocast('cuda', dtype=torch.float32):
x = x + y * e[2].squeeze(2)

# 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].squeeze(2)) + e[3].squeeze(2))
with torch.amp.autocast('cuda', dtype=torch.float32):
x = x + y * e[5].squeeze(2)
return x

x = cross_attn_ffn(x, context, context_lens, e)
x = (x.float() + y.float() * e[2].squeeze(2)).to(dtype=compute_dtype)

# cross-attention & ffn
x = x + self.cross_attn(self.norm3(x), context, context_lens)
y = self.ffn(
(self.norm2(x).float() * (1 + e[4].squeeze(2)) + e[3].squeeze(2)).to(dtype=compute_dtype))
x = (x.float() + y.float() * e[5].squeeze(2)).to(dtype=compute_dtype)
return x


Expand Down Expand Up @@ -358,6 +362,7 @@ def __init__(self,

assert model_type in ['t2v', 'i2v', 'ti2v', 's2v']
self.model_type = model_type
self.gradient_checkpointing = False

self.patch_size = patch_size
self.text_len = text_len
Expand Down Expand Up @@ -487,7 +492,12 @@ def forward(
context_lens=context_lens)

for block in self.blocks:
x = block(x, **kwargs)
if self.gradient_checkpointing and self.training:
x = torch.utils.checkpoint.checkpoint(
block, x, use_reentrant=False,
context_fn=partial(_gc_autocast, dtype=x.dtype), **kwargs)
else:
x = block(x, **kwargs)

# head
x = self.head(x, e)
Expand Down