riaz.somc/transformers
1
0
Fork 0
mirror of https://github.com/huggingface/transformers.git synced 2025-08-03 03:31:05 +06:00
Code Issues Actions 2 Packages Projects Releases Wiki Activity

Fix TVLT (torch device issue) (#21710)

Browse Source 
* fix tvlt ci

* fix tvlt ci

* fix tvlt ci

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar 2023-02-21 11:37:49 +01:00 committed by GitHub
parent 4c6346cc3e
commit 03aaac3502
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 7 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

9
src/transformers/models/tvlt/modeling_tvlt.py
View File

@ -153,7 +153,7 @@ def generate_pixel_mask_noise(pixel_values, pixel_mask=None, mask_ratio=0.75):
"""Generate noise for audio masking."""
batch_size, seq_len = pixel_values.shape[:2]
noise = torch.rand((batch_size, seq_len)) # noise in [0, 1]
noise = torch.rand((batch_size, seq_len), device=pixel_values.device) # noise in [0, 1]
len_keep = int(seq_len * (1 - mask_ratio))
return noise, len_keep
@ -165,10 +165,13 @@ def generate_audio_mask_noise(audio_values, audio_mask=None, mask_ratio=0.75, ma
if mask_type == "frame-level":
num_time_patches = seq_len // freq_len
noise = (
torch.rand(batch_size, num_time_patches).unsqueeze(-1).repeat(1, 1, freq_len).view(batch_size, seq_len)
torch.rand(batch_size, num_time_patches, device=audio_values.device)
.unsqueeze(-1)
.repeat(1, 1, freq_len)
.view(batch_size, seq_len)
) # noise in [0, 1]
elif mask_type == "patch-level":
noise = torch.rand(batch_size, seq_len) # noise in [0, 1]
noise = torch.rand(batch_size, seq_len, device=audio_values.device) # noise in [0, 1]
len_keep = int(seq_len * (1 - mask_ratio))
return noise, len_keep

2
tests/models/tvlt/test_modeling_tvlt.py
View File

@ -590,7 +590,7 @@ class TvltModelIntegrationTest(unittest.TestCase):
outputs = model(**inputs)
# verify the logits
expected_last_hidden_state_slice = torch.tensor([[-0.0186, -0.0691], [0.0242, -0.0398]])
expected_last_hidden_state_slice = torch.tensor([[-0.0186, -0.0691], [0.0242, -0.0398]], device=torch_device)
self.assertTrue(
torch.allclose(outputs.last_hidden_state[:, :2, :2], expected_last_hidden_state_slice, atol=1e-4)
)