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Cache RoPE freqs on device to avoid repeated CPU-GPU copy in QwenImage #13406

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Cache RoPE freqs on device to avoid repeated CPU-GPU copy in QwenImage #13406

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33 changes: 23 additions & 10 deletions src/diffusers/models/transformers/transformer_qwenimage.py
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Original file line number Diff line number Diff line change
Expand Up @@ -222,6 +222,7 @@ def __init__(self, theta: int, axes_dim: list[int], scale_rope=False):

# DO NOT USING REGISTER BUFFER HERE, IT WILL CAUSE COMPLEX NUMBERS LOSE ITS IMAGINARY PART
self.scale_rope = scale_rope
self._device_freq_cache: dict[torch.device, tuple[torch.Tensor, torch.Tensor]] = {}

def rope_params(self, index, dim, theta=10000):
"""
Expand All @@ -233,6 +234,12 @@ def rope_params(self, index, dim, theta=10000):
freqs = torch.polar(torch.ones_like(freqs), freqs)
return freqs

def _get_device_freqs(self, device: torch.device) -> tuple[torch.Tensor, torch.Tensor]:
"""Return pos_freqs and neg_freqs on the given device, caching the transfer."""
if device not in self._device_freq_cache:
self._device_freq_cache[device] = (self.pos_freqs.to(device), self.neg_freqs.to(device))
return self._device_freq_cache[device]

def forward(
self,
video_fhw: tuple[int, int, int, list[tuple[int, int, int]]],
Expand Down Expand Up @@ -300,8 +307,9 @@ def forward(
max_vid_index = max(height, width, max_vid_index)

max_txt_seq_len_int = int(max_txt_seq_len)
# Create device-specific copy for text freqs without modifying self.pos_freqs
txt_freqs = self.pos_freqs.to(device)[max_vid_index : max_vid_index + max_txt_seq_len_int, ...]
# Use cached device-transferred freqs to avoid CPU→GPU sync every forward call
pos_freqs_device, _ = self._get_device_freqs(device)
txt_freqs = pos_freqs_device[max_vid_index : max_vid_index + max_txt_seq_len_int, ...]
vid_freqs = torch.cat(vid_freqs, dim=0)

return vid_freqs, txt_freqs
Expand All @@ -311,8 +319,7 @@ def _compute_video_freqs(
self, frame: int, height: int, width: int, idx: int = 0, device: torch.device = None
) -> torch.Tensor:
seq_lens = frame * height * width
pos_freqs = self.pos_freqs.to(device) if device is not None else self.pos_freqs
neg_freqs = self.neg_freqs.to(device) if device is not None else self.neg_freqs
pos_freqs, neg_freqs = self._get_device_freqs(device) if device is not None else (self.pos_freqs, self.neg_freqs)

freqs_pos = pos_freqs.split([x // 2 for x in self.axes_dim], dim=1)
freqs_neg = neg_freqs.split([x // 2 for x in self.axes_dim], dim=1)
Expand Down Expand Up @@ -356,6 +363,7 @@ def __init__(self, theta: int, axes_dim: list[int], scale_rope=False):
)

self.scale_rope = scale_rope
self._device_freq_cache: dict[torch.device, tuple[torch.Tensor, torch.Tensor]] = {}

def rope_params(self, index, dim, theta=10000):
"""
Expand All @@ -367,6 +375,12 @@ def rope_params(self, index, dim, theta=10000):
freqs = torch.polar(torch.ones_like(freqs), freqs)
return freqs

def _get_device_freqs(self, device: torch.device) -> tuple[torch.Tensor, torch.Tensor]:
"""Return pos_freqs and neg_freqs on the given device, caching the transfer."""
if device not in self._device_freq_cache:
self._device_freq_cache[device] = (self.pos_freqs.to(device), self.neg_freqs.to(device))
return self._device_freq_cache[device]

def forward(
self,
video_fhw: tuple[int, int, int, list[tuple[int, int, int]]],
Expand Down Expand Up @@ -421,17 +435,17 @@ def forward(

max_vid_index = max(max_vid_index, layer_num)
max_txt_seq_len_int = int(max_txt_seq_len)
# Create device-specific copy for text freqs without modifying self.pos_freqs
txt_freqs = self.pos_freqs.to(device)[max_vid_index : max_vid_index + max_txt_seq_len_int, ...]
# Use cached device-transferred freqs to avoid CPU→GPU sync every forward call
pos_freqs_device, _ = self._get_device_freqs(device)
txt_freqs = pos_freqs_device[max_vid_index : max_vid_index + max_txt_seq_len_int, ...]
vid_freqs = torch.cat(vid_freqs, dim=0)

return vid_freqs, txt_freqs

@lru_cache_unless_export(maxsize=None)
def _compute_video_freqs(self, frame, height, width, idx=0, device: torch.device = None):
seq_lens = frame * height * width
pos_freqs = self.pos_freqs.to(device) if device is not None else self.pos_freqs
neg_freqs = self.neg_freqs.to(device) if device is not None else self.neg_freqs
pos_freqs, neg_freqs = self._get_device_freqs(device) if device is not None else (self.pos_freqs, self.neg_freqs)

freqs_pos = pos_freqs.split([x // 2 for x in self.axes_dim], dim=1)
freqs_neg = neg_freqs.split([x // 2 for x in self.axes_dim], dim=1)
Expand All @@ -452,8 +466,7 @@ def _compute_video_freqs(self, frame, height, width, idx=0, device: torch.device
@lru_cache_unless_export(maxsize=None)
def _compute_condition_freqs(self, frame, height, width, device: torch.device = None):
seq_lens = frame * height * width
pos_freqs = self.pos_freqs.to(device) if device is not None else self.pos_freqs
neg_freqs = self.neg_freqs.to(device) if device is not None else self.neg_freqs
pos_freqs, neg_freqs = self._get_device_freqs(device) if device is not None else (self.pos_freqs, self.neg_freqs)

freqs_pos = pos_freqs.split([x // 2 for x in self.axes_dim], dim=1)
freqs_neg = neg_freqs.split([x // 2 for x in self.axes_dim], dim=1)
Expand Down