Skip to content

disable test_conversion_when_using_device_map #7620

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
yiyixuxu merged 2 commits into from
Apr 9, 2024
Merged

disable test_conversion_when_using_device_map #7620

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
20f4e1a
disable test
Apr 9, 2024
7a981b4
update
Apr 9, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
79 changes: 39 additions & 40 deletions tests/models/test_attention_processor.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,10 +1,7 @@
import tempfile
import unittest

import numpy as np
import torch

from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor


Expand Down Expand Up @@ -80,40 +77,42 @@ def test_only_cross_attention(self):

class DeprecatedAttentionBlockTests(unittest.TestCase):
def test_conversion_when_using_device_map(self):
pipe = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None)

pre_conversion = pipe(
"foo",
num_inference_steps=2,
generator=torch.Generator("cpu").manual_seed(0),
output_type="np",
).images

# the initial conversion succeeds
pipe = DiffusionPipeline.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-pipe", device_map="sequential", safety_checker=None
)

conversion = pipe(
"foo",
num_inference_steps=2,
generator=torch.Generator("cpu").manual_seed(0),
output_type="np",
).images

with tempfile.TemporaryDirectory() as tmpdir:
# save the converted model
pipe.save_pretrained(tmpdir)

# can also load the converted weights
pipe = DiffusionPipeline.from_pretrained(tmpdir, device_map="sequential", safety_checker=None)

after_conversion = pipe(
"foo",
num_inference_steps=2,
generator=torch.Generator("cpu").manual_seed(0),
output_type="np",
).images

self.assertTrue(np.allclose(pre_conversion, conversion, atol=1e-5))
self.assertTrue(np.allclose(conversion, after_conversion, atol=1e-5))
# To-DO for Sayak: enable this test again and to test `device_map='balanced'` once we have this in accelerate https://github.com/huggingface/accelerate/pull/2641
Copy link
Collaborator Author

@yiyixuxu yiyixuxu Apr 9, 2024

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

cc @sayakpaul here

pass
# pipe = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None)

# pre_conversion = pipe(
# "foo",
# num_inference_steps=2,
# generator=torch.Generator("cpu").manual_seed(0),
# output_type="np",
# ).images

# # the initial conversion succeeds
# pipe = DiffusionPipeline.from_pretrained(
# "hf-internal-testing/tiny-stable-diffusion-pipe", device_map="sequential", safety_checker=None
# )

# conversion = pipe(
# "foo",
# num_inference_steps=2,
# generator=torch.Generator("cpu").manual_seed(0),
# output_type="np",
# ).images

# with tempfile.TemporaryDirectory() as tmpdir:
# # save the converted model
# pipe.save_pretrained(tmpdir)

# # can also load the converted weights
# pipe = DiffusionPipeline.from_pretrained(tmpdir, device_map="sequential", safety_checker=None)

# after_conversion = pipe(
# "foo",
# num_inference_steps=2,
# generator=torch.Generator("cpu").manual_seed(0),
# output_type="np",
# ).images

# self.assertTrue(np.allclose(pre_conversion, conversion, atol=1e-5))
# self.assertTrue(np.allclose(conversion, after_conversion, atol=1e-5))