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Merged
merged 10 commits into from
May 9, 2025
Merged

enable 7 cases on XPU #11503

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7cd66d2
enable 7 cases on XPU
yao-matrix May 6, 2025
ccf542b
Merge branch 'main' into misc-xpu
yao-matrix May 6, 2025
b8be6d8
Merge branch 'main' into misc-xpu
yao-matrix May 6, 2025
2f2b2fa
calibrate A100 expectations
yao-matrix May 6, 2025
690fce1
Merge branch 'main' into misc-xpu
yao-matrix May 6, 2025
c3c042f
Merge branch 'main' into misc-xpu
yao-matrix May 6, 2025
5faa3cd
Merge branch 'main' into misc-xpu
yao-matrix May 7, 2025
50e5070
Merge branch 'main' into misc-xpu
yao-matrix May 8, 2025
a37bd67
Merge branch 'main' into misc-xpu
yao-matrix May 9, 2025
7dc40d1
Merge branch 'main' into misc-xpu
yao-matrix May 9, 2025
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15 changes: 8 additions & 7 deletions tests/pipelines/consisid/test_consisid.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,9 +24,10 @@
from diffusers import AutoencoderKLCogVideoX, ConsisIDPipeline, ConsisIDTransformer3DModel, DDIMScheduler
from diffusers.utils import load_image
from diffusers.utils.testing_utils import (
backend_empty_cache,
enable_full_determinism,
numpy_cosine_similarity_distance,
require_torch_gpu,
require_torch_accelerator,
slow,
torch_device,
)
Expand Down Expand Up @@ -316,19 +317,19 @@ def test_vae_tiling(self, expected_diff_max: float = 0.4):


@slow
@require_torch_gpu
@require_torch_accelerator
class ConsisIDPipelineIntegrationTests(unittest.TestCase):
prompt = "A painting of a squirrel eating a burger."

def setUp(self):
super().setUp()
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

def test_consisid(self):
generator = torch.Generator("cpu").manual_seed(0)
Expand All @@ -338,8 +339,8 @@ def test_consisid(self):

prompt = self.prompt
image = load_image("https://github.com/PKU-YuanGroup/ConsisID/blob/main/asserts/example_images/2.png?raw=true")
id_vit_hidden = [torch.ones([1, 2, 2])] * 1
id_cond = torch.ones(1, 2)
id_vit_hidden = [torch.ones([1, 577, 1024])] * 5
id_cond = torch.ones(1, 1280)
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the size of the id_vit_hidden and id_cond is wrong, both A100 and XPU will report below error

def forward(self, input: Tensor) -> Tensor:
  return F.linear(input, self.weight, self.bias)
    E       RuntimeError: mat1 and mat2 shapes cannot be multiplied (1x2 and 1280x1024)

I checked the needed size for these 2 tensors and use the correct tensor


videos = pipe(
image=image,
Expand All @@ -357,5 +358,5 @@ def test_consisid(self):
video = videos[0]
expected_video = torch.randn(1, 16, 480, 720, 3).numpy()
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expected_video here is generated by RNG, so suppose this is a in-complete case, so for this PR, I enable the case to run on XPU, need re-check the numerical correctness after expected_video correctly set.


max_diff = numpy_cosine_similarity_distance(video, expected_video)
max_diff = numpy_cosine_similarity_distance(video.cpu(), expected_video)
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need to cpu, since expected_video is in cpu, numpy_cosine_similarity_distance only supports 2 tensors in same device.

assert max_diff < 1e-3, f"Max diff is too high. got {video}"
9 changes: 5 additions & 4 deletions tests/pipelines/easyanimate/test_easyanimate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,9 +27,10 @@
FlowMatchEulerDiscreteScheduler,
)
from diffusers.utils.testing_utils import (
backend_empty_cache,
enable_full_determinism,
numpy_cosine_similarity_distance,
require_torch_gpu,
require_torch_accelerator,
slow,
torch_device,
)
Expand Down Expand Up @@ -256,19 +257,19 @@ def test_encode_prompt_works_in_isolation(self):


@slow
@require_torch_gpu
@require_torch_accelerator
class EasyAnimatePipelineIntegrationTests(unittest.TestCase):
prompt = "A painting of a squirrel eating a burger."

def setUp(self):
super().setUp()
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

def test_EasyAnimate(self):
generator = torch.Generator("cpu").manual_seed(0)
Expand Down
12 changes: 6 additions & 6 deletions tests/pipelines/mochi/test_mochi.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,8 +27,8 @@
enable_full_determinism,
nightly,
numpy_cosine_similarity_distance,
require_big_gpu_with_torch_cuda,
require_torch_gpu,
require_big_accelerator,
require_torch_accelerator,
torch_device,
)

Expand Down Expand Up @@ -266,9 +266,9 @@ def test_vae_tiling(self, expected_diff_max: float = 0.2):


@nightly
@require_torch_gpu
@require_big_gpu_with_torch_cuda
@pytest.mark.big_gpu_with_torch_cuda
@require_torch_accelerator
@require_big_accelerator
@pytest.mark.big_accelerator
class MochiPipelineIntegrationTests(unittest.TestCase):
prompt = "A painting of a squirrel eating a burger."

Expand Down Expand Up @@ -302,5 +302,5 @@ def test_mochi(self):
video = videos[0]
expected_video = torch.randn(1, 19, 480, 848, 3).numpy()

max_diff = numpy_cosine_similarity_distance(video, expected_video)
max_diff = numpy_cosine_similarity_distance(video.cpu(), expected_video)
assert max_diff < 1e-3, f"Max diff is too high. got {video}"
73 changes: 55 additions & 18 deletions tests/pipelines/omnigen/test_pipeline_omnigen.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,8 +7,10 @@

from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, OmniGenPipeline, OmniGenTransformer2DModel
from diffusers.utils.testing_utils import (
Expectations,
backend_empty_cache,
numpy_cosine_similarity_distance,
require_torch_gpu,
require_torch_accelerator,
slow,
torch_device,
)
Expand Down Expand Up @@ -87,20 +89,20 @@ def test_inference(self):


@slow
@require_torch_gpu
@require_torch_accelerator
class OmniGenPipelineSlowTests(unittest.TestCase):
pipeline_class = OmniGenPipeline
repo_id = "shitao/OmniGen-v1-diffusers"

def setUp(self):
super().setUp()
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

def get_inputs(self, device, seed=0):
if str(device).startswith("mps"):
Expand All @@ -125,21 +127,56 @@ def test_omnigen_inference(self):
image = pipe(**inputs).images[0]
image_slice = image[0, :10, :10]

expected_slice = np.array(
[
[0.1783447, 0.16772744, 0.14339337],
[0.17066911, 0.15521264, 0.13757327],
[0.17072496, 0.15531206, 0.13524258],
[0.16746324, 0.1564025, 0.13794944],
[0.16490817, 0.15258026, 0.13697758],
[0.16971767, 0.15826806, 0.13928896],
[0.16782972, 0.15547255, 0.13783783],
[0.16464645, 0.15281534, 0.13522372],
[0.16535294, 0.15301755, 0.13526791],
[0.16365296, 0.15092957, 0.13443318],
],
dtype=np.float32,
expected_slices = Expectations(
{
("xpu", 3): np.array(
[
[0.05859375, 0.05859375, 0.04492188],
[0.04882812, 0.04101562, 0.03320312],
[0.04882812, 0.04296875, 0.03125],
[0.04296875, 0.0390625, 0.03320312],
[0.04296875, 0.03710938, 0.03125],
[0.04492188, 0.0390625, 0.03320312],
[0.04296875, 0.03710938, 0.03125],
[0.04101562, 0.03710938, 0.02734375],
[0.04101562, 0.03515625, 0.02734375],
[0.04101562, 0.03515625, 0.02929688],
],
dtype=np.float32,
),
("cuda", 7): np.array(
[
[0.1783447, 0.16772744, 0.14339337],
[0.17066911, 0.15521264, 0.13757327],
[0.17072496, 0.15531206, 0.13524258],
[0.16746324, 0.1564025, 0.13794944],
[0.16490817, 0.15258026, 0.13697758],
[0.16971767, 0.15826806, 0.13928896],
[0.16782972, 0.15547255, 0.13783783],
[0.16464645, 0.15281534, 0.13522372],
[0.16535294, 0.15301755, 0.13526791],
[0.16365296, 0.15092957, 0.13443318],
],
dtype=np.float32,
),
("cuda", 8): np.array(
[
[0.0546875, 0.05664062, 0.04296875],
[0.046875, 0.04101562, 0.03320312],
[0.05078125, 0.04296875, 0.03125],
[0.04296875, 0.04101562, 0.03320312],
[0.0390625, 0.03710938, 0.02929688],
[0.04296875, 0.03710938, 0.03125],
[0.0390625, 0.03710938, 0.02929688],
[0.0390625, 0.03710938, 0.02734375],
[0.0390625, 0.03320312, 0.02734375],
[0.0390625, 0.03320312, 0.02734375],
],
dtype=np.float32,
),
}
)
expected_slice = expected_slices.get_expectation()

max_diff = numpy_cosine_similarity_distance(expected_slice.flatten(), image_slice.flatten())

Expand Down
9 changes: 5 additions & 4 deletions tests/pipelines/paint_by_example/test_paint_by_example.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,11 +25,12 @@
from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNet2DConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils.testing_utils import (
backend_empty_cache,
enable_full_determinism,
floats_tensor,
load_image,
nightly,
require_torch_gpu,
require_torch_accelerator,
torch_device,
)

Expand Down Expand Up @@ -174,19 +175,19 @@ def test_inference_batch_single_identical(self):


@nightly
@require_torch_gpu
@require_torch_accelerator
class PaintByExamplePipelineIntegrationTests(unittest.TestCase):
def setUp(self):
# clean up the VRAM before each test
super().setUp()
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

def test_paint_by_example(self):
# make sure here that pndm scheduler skips prk
Expand Down
27 changes: 20 additions & 7 deletions tests/pipelines/stable_audio/test_stable_audio.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,14 @@
StableAudioProjectionModel,
)
from diffusers.utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, torch_device
from diffusers.utils.testing_utils import (
Expectations,
backend_empty_cache,
enable_full_determinism,
nightly,
require_torch_accelerator,
torch_device,
)

from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
Expand Down Expand Up @@ -419,17 +426,17 @@ def test_encode_prompt_works_in_isolation(self):


@nightly
@require_torch_gpu
@require_torch_accelerator
class StableAudioPipelineIntegrationTests(unittest.TestCase):
def setUp(self):
super().setUp()
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
Expand Down Expand Up @@ -459,9 +466,15 @@ def test_stable_audio(self):
# check the portion of the generated audio with the largest dynamic range (reduces flakiness)
audio_slice = audio[0, 447590:447600]
# fmt: off
expected_slice = np.array(
[-0.0278, 0.1096, 0.1877, 0.3178, 0.5329, 0.6990, 0.6972, 0.6186, 0.5608, 0.5060]
expected_slices = Expectations(
{
("xpu", 3): np.array([-0.0285, 0.1083, 0.1863, 0.3165, 0.5312, 0.6971, 0.6958, 0.6177, 0.5598, 0.5048]),
("cuda", 7): np.array([-0.0278, 0.1096, 0.1877, 0.3178, 0.5329, 0.6990, 0.6972, 0.6186, 0.5608, 0.5060]),
("cuda", 8): np.array([-0.0285, 0.1082, 0.1862, 0.3163, 0.5306, 0.6964, 0.6953, 0.6172, 0.5593, 0.5044]),
}
)
# fmt: one
# fmt: on

expected_slice = expected_slices.get_expectation()
max_diff = np.abs(expected_slice - audio_slice.detach().cpu().numpy()).max()
assert max_diff < 1.5e-3
10 changes: 5 additions & 5 deletions tests/quantization/bnb/test_4bit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -389,7 +389,7 @@ def test_bnb_4bit_logs_warning_for_no_quantization(self):
class BnB4BitTrainingTests(Base4bitTests):
def setUp(self):
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

nf4_config = BitsAndBytesConfig(
load_in_4bit=True,
Expand Down Expand Up @@ -657,7 +657,7 @@ def get_dummy_tensor_inputs(device=None, seed: int = 0):
class SlowBnb4BitFluxTests(Base4bitTests):
def setUp(self) -> None:
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

model_id = "hf-internal-testing/flux.1-dev-nf4-pkg"
t5_4bit = T5EncoderModel.from_pretrained(model_id, subfolder="text_encoder_2")
Expand All @@ -674,7 +674,7 @@ def tearDown(self):
del self.pipeline_4bit

gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

def test_quality(self):
# keep the resolution and max tokens to a lower number for faster execution.
Expand Down Expand Up @@ -722,7 +722,7 @@ def test_lora_loading(self):
class SlowBnb4BitFluxControlWithLoraTests(Base4bitTests):
def setUp(self) -> None:
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

self.pipeline_4bit = FluxControlPipeline.from_pretrained("eramth/flux-4bit", torch_dtype=torch.float16)
self.pipeline_4bit.enable_model_cpu_offload()
Expand All @@ -731,7 +731,7 @@ def tearDown(self):
del self.pipeline_4bit

gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)

def test_lora_loading(self):
self.pipeline_4bit.load_lora_weights("black-forest-labs/FLUX.1-Canny-dev-lora")
Expand Down
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