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Fix SwinLayer / DonutSwinLayer / ClapAudioLayer attention mask device (#31295)

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Fix DonutSwinLayer attention mask device
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Alex Gorodnitskiy 2024-06-06 21:52:14 +01:00 committed by GitHub
parent b6c9f47fd6
commit 3b4d3d09fd
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3 changed files with 15 additions and 15 deletions
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10
src/transformers/models/clap/modeling_clap.py
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@ -593,10 +593,10 @@ class ClapAudioLayer(nn.Module):
self.shift_size = 0 self.shift_size = 0
self.window_size = min(input_resolution) self.window_size = min(input_resolution)
def get_attn_mask(self, height, width, dtype): def get_attn_mask(self, height, width, dtype, device):
if self.shift_size > 0: if self.shift_size > 0:
# calculate attention mask for SW-MSA # calculate attention mask for SW-MSA
img_mask = torch.zeros((1, height, width, 1), dtype=dtype) img_mask = torch.zeros((1, height, width, 1), dtype=dtype, device=device)
height_slices = ( height_slices = (
slice(0, -self.window_size), slice(0, -self.window_size),
slice(-self.window_size, -self.shift_size), slice(-self.window_size, -self.shift_size),
@ -661,9 +661,9 @@ class ClapAudioLayer(nn.Module):
# partition windows # partition windows
hidden_states_windows = window_partition(shifted_hidden_states, self.window_size) hidden_states_windows = window_partition(shifted_hidden_states, self.window_size)
hidden_states_windows = hidden_states_windows.view(-1, self.window_size * self.window_size, channels) hidden_states_windows = hidden_states_windows.view(-1, self.window_size * self.window_size, channels)
attn_mask = self.get_attn_mask(height_pad, width_pad, dtype=hidden_states.dtype) attn_mask = self.get_attn_mask(
if attn_mask is not None: height_pad, width_pad, dtype=hidden_states.dtype, device=hidden_states_windows.device
attn_mask = attn_mask.to(hidden_states_windows.device) )
attention_outputs = self.attention( attention_outputs = self.attention(
hidden_states_windows, attn_mask, head_mask, output_attentions=output_attentions hidden_states_windows, attn_mask, head_mask, output_attentions=output_attentions

10
src/transformers/models/donut/modeling_donut_swin.py
View File

@ -565,10 +565,10 @@ class DonutSwinLayer(nn.Module):
self.shift_size = 0 self.shift_size = 0
self.window_size = min(input_resolution) self.window_size = min(input_resolution)
def get_attn_mask(self, height, width, dtype): def get_attn_mask(self, height, width, dtype, device):
if self.shift_size > 0: if self.shift_size > 0:
# calculate attention mask for SW-MSA # calculate attention mask for SW-MSA
img_mask = torch.zeros((1, height, width, 1), dtype=dtype) img_mask = torch.zeros((1, height, width, 1), dtype=dtype, device=device)
height_slices = ( height_slices = (
slice(0, -self.window_size), slice(0, -self.window_size),
slice(-self.window_size, -self.shift_size), slice(-self.window_size, -self.shift_size),
@ -633,9 +633,9 @@ class DonutSwinLayer(nn.Module):
# partition windows # partition windows
hidden_states_windows = window_partition(shifted_hidden_states, self.window_size) hidden_states_windows = window_partition(shifted_hidden_states, self.window_size)
hidden_states_windows = hidden_states_windows.view(-1, self.window_size * self.window_size, channels) hidden_states_windows = hidden_states_windows.view(-1, self.window_size * self.window_size, channels)
attn_mask = self.get_attn_mask(height_pad, width_pad, dtype=hidden_states.dtype) attn_mask = self.get_attn_mask(
if attn_mask is not None: height_pad, width_pad, dtype=hidden_states.dtype, device=hidden_states_windows.device
attn_mask = attn_mask.to(hidden_states_windows.device) )
attention_outputs = self.attention( attention_outputs = self.attention(
hidden_states_windows, attn_mask, head_mask, output_attentions=output_attentions hidden_states_windows, attn_mask, head_mask, output_attentions=output_attentions

10
src/transformers/models/swin/modeling_swin.py
View File

@ -642,10 +642,10 @@ class SwinLayer(nn.Module):
self.shift_size = 0 self.shift_size = 0
self.window_size = min(input_resolution) self.window_size = min(input_resolution)
def get_attn_mask(self, height, width, dtype): def get_attn_mask(self, height, width, dtype, device):
if self.shift_size > 0: if self.shift_size > 0:
# calculate attention mask for SW-MSA # calculate attention mask for SW-MSA
img_mask = torch.zeros((1, height, width, 1), dtype=dtype) img_mask = torch.zeros((1, height, width, 1), dtype=dtype, device=device)
height_slices = ( height_slices = (
slice(0, -self.window_size), slice(0, -self.window_size),
slice(-self.window_size, -self.shift_size), slice(-self.window_size, -self.shift_size),
@ -710,9 +710,9 @@ class SwinLayer(nn.Module):
# partition windows # partition windows
hidden_states_windows = window_partition(shifted_hidden_states, self.window_size) hidden_states_windows = window_partition(shifted_hidden_states, self.window_size)
hidden_states_windows = hidden_states_windows.view(-1, self.window_size * self.window_size, channels) hidden_states_windows = hidden_states_windows.view(-1, self.window_size * self.window_size, channels)
attn_mask = self.get_attn_mask(height_pad, width_pad, dtype=hidden_states.dtype) attn_mask = self.get_attn_mask(
if attn_mask is not None: height_pad, width_pad, dtype=hidden_states.dtype, device=hidden_states_windows.device
attn_mask = attn_mask.to(hidden_states_windows.device) )
attention_outputs = self.attention( attention_outputs = self.attention(
hidden_states_windows, attn_mask, head_mask, output_attentions=output_attentions hidden_states_windows, attn_mask, head_mask, output_attentions=output_attentions