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Fix nondeterministic tests for GPU runs #314

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Sep 1, 2022
Merged

Fix nondeterministic tests for GPU runs #314

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995802c
Fix nondeterministic tests for GPU runs
anton-l Sep 1, 2022
8445bed
force SD fast tests to the CPU
anton-l Sep 1, 2022
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81 changes: 37 additions & 44 deletions tests/test_pipelines.py
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Original file line number Diff line number Diff line change
Expand Up @@ -236,6 +236,7 @@ def test_ldm_text2img(self):
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

def test_stable_diffusion_ddim(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
scheduler = DDIMScheduler(
beta_start=0.00085,
Expand All @@ -259,14 +260,11 @@ def test_stable_diffusion_ddim(self):
safety_checker=self.dummy_safety_checker,
feature_extractor=self.dummy_extractor,
)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe = sd_pipe.to(device)

prompt = "A painting of a squirrel eating a burger"
generator = torch.Generator(device=torch_device).manual_seed(0)
with torch.autocast("cuda"):
output = sd_pipe(
[prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np"
)
generator = torch.Generator(device=device).manual_seed(0)
output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")

image = output["sample"]

Expand All @@ -277,6 +275,7 @@ def test_stable_diffusion_ddim(self):
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

def test_stable_diffusion_pndm(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
scheduler = PNDMScheduler(tensor_format="pt", skip_prk_steps=True)
vae = self.dummy_vae
Expand All @@ -293,14 +292,11 @@ def test_stable_diffusion_pndm(self):
safety_checker=self.dummy_safety_checker,
feature_extractor=self.dummy_extractor,
)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe = sd_pipe.to(device)

prompt = "A painting of a squirrel eating a burger"
generator = torch.Generator(device=torch_device).manual_seed(0)
with torch.autocast("cuda"):
output = sd_pipe(
[prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np"
)
generator = torch.Generator(device=device).manual_seed(0)
output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")

image = output["sample"]

Expand All @@ -311,8 +307,8 @@ def test_stable_diffusion_pndm(self):
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

def test_stable_diffusion_k_lms(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
scheduler = PNDMScheduler(tensor_format="pt", skip_prk_steps=True)
scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")
vae = self.dummy_vae
bert = self.dummy_text_encoder
Expand All @@ -328,14 +324,11 @@ def test_stable_diffusion_k_lms(self):
safety_checker=self.dummy_safety_checker,
feature_extractor=self.dummy_extractor,
)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe = sd_pipe.to(device)

prompt = "A painting of a squirrel eating a burger"
generator = torch.Generator(device=torch_device).manual_seed(0)
with torch.autocast("cuda"):
output = sd_pipe(
[prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np"
)
generator = torch.Generator(device=device).manual_seed(0)
output = sd_pipe([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="np")

image = output["sample"]

Expand Down Expand Up @@ -395,13 +388,14 @@ def test_karras_ve_pipeline(self):
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

def test_stable_diffusion_img2img(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
scheduler = PNDMScheduler(tensor_format="pt", skip_prk_steps=True)
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

init_image = self.dummy_image
init_image = self.dummy_image.to(device)

# make sure here that pndm scheduler skips prk
sd_pipe = StableDiffusionImg2ImgPipeline(
Expand All @@ -413,19 +407,18 @@ def test_stable_diffusion_img2img(self):
safety_checker=self.dummy_safety_checker,
feature_extractor=self.dummy_extractor,
)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe = sd_pipe.to(device)

prompt = "A painting of a squirrel eating a burger"
generator = torch.Generator(device=torch_device).manual_seed(0)
with torch.autocast("cuda"):
output = sd_pipe(
[prompt],
generator=generator,
guidance_scale=6.0,
num_inference_steps=2,
output_type="np",
init_image=init_image,
)
generator = torch.Generator(device=device).manual_seed(0)
output = sd_pipe(
[prompt],
generator=generator,
guidance_scale=6.0,
num_inference_steps=2,
output_type="np",
init_image=init_image,
)

image = output["sample"]

Expand All @@ -436,13 +429,14 @@ def test_stable_diffusion_img2img(self):
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

def test_stable_diffusion_inpaint(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
scheduler = PNDMScheduler(tensor_format="pt", skip_prk_steps=True)
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

image = self.dummy_image.permute(0, 2, 3, 1)[0]
image = self.dummy_image.to(device).permute(0, 2, 3, 1)[0]
init_image = Image.fromarray(np.uint8(image)).convert("RGB")
mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128))

Expand All @@ -456,20 +450,19 @@ def test_stable_diffusion_inpaint(self):
safety_checker=self.dummy_safety_checker,
feature_extractor=self.dummy_extractor,
)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe = sd_pipe.to(device)

prompt = "A painting of a squirrel eating a burger"
generator = torch.Generator(device=torch_device).manual_seed(0)
with torch.autocast("cuda"):
output = sd_pipe(
[prompt],
generator=generator,
guidance_scale=6.0,
num_inference_steps=2,
output_type="np",
init_image=init_image,
mask_image=mask_image,
)
generator = torch.Generator(device=device).manual_seed(0)
output = sd_pipe(
[prompt],
generator=generator,
guidance_scale=6.0,
num_inference_steps=2,
output_type="np",
init_image=init_image,
mask_image=mask_image,
)

image = output["sample"]

Expand Down
17 changes: 8 additions & 9 deletions tests/test_training.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,8 +18,8 @@
import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNet2DModel
from diffusers.testing_utils import slow, torch_device
from diffusers.training_utils import enable_full_determinism, set_seed
from diffusers.testing_utils import slow
from diffusers.training_utils import set_seed


torch.backends.cuda.matmul.allow_tf32 = False
Expand All @@ -34,8 +34,7 @@ def get_model_optimizer(self, resolution=32):

@slow
def test_training_step_equality(self):
enable_full_determinism(0)

device = "cpu" # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
ddpm_scheduler = DDPMScheduler(
num_train_timesteps=1000,
beta_start=0.0001,
Expand All @@ -57,13 +56,13 @@ def test_training_step_equality(self):

# shared batches for DDPM and DDIM
set_seed(0)
clean_images = [torch.randn((4, 3, 32, 32)).clip(-1, 1).to(torch_device) for _ in range(4)]
noise = [torch.randn((4, 3, 32, 32)).to(torch_device) for _ in range(4)]
timesteps = [torch.randint(0, 1000, (4,)).long().to(torch_device) for _ in range(4)]
clean_images = [torch.randn((4, 3, 32, 32)).clip(-1, 1).to(device) for _ in range(4)]
noise = [torch.randn((4, 3, 32, 32)).to(device) for _ in range(4)]
timesteps = [torch.randint(0, 1000, (4,)).long().to(device) for _ in range(4)]

# train with a DDPM scheduler
model, optimizer = self.get_model_optimizer(resolution=32)
model.train().to(torch_device)
model.train().to(device)
for i in range(4):
optimizer.zero_grad()
ddpm_noisy_images = ddpm_scheduler.add_noise(clean_images[i], noise[i], timesteps[i])
Expand All @@ -75,7 +74,7 @@ def test_training_step_equality(self):

# recreate the model and optimizer, and retry with DDIM
model, optimizer = self.get_model_optimizer(resolution=32)
model.train().to(torch_device)
model.train().to(device)
for i in range(4):
optimizer.zero_grad()
ddim_noisy_images = ddim_scheduler.add_noise(clean_images[i], noise[i], timesteps[i])
Expand Down