502 lines
20 KiB
Python
502 lines
20 KiB
Python
import contextlib
|
|
import gc
|
|
import inspect
|
|
import io
|
|
import re
|
|
import tempfile
|
|
import unittest
|
|
from typing import Callable, Union
|
|
|
|
import numpy as np
|
|
import torch
|
|
|
|
import diffusers
|
|
from diffusers import DiffusionPipeline
|
|
from diffusers.utils import logging
|
|
from diffusers.utils.import_utils import is_accelerate_available, is_accelerate_version, is_xformers_available
|
|
from diffusers.utils.testing_utils import require_torch, torch_device
|
|
|
|
|
|
torch.backends.cuda.matmul.allow_tf32 = False
|
|
|
|
|
|
@require_torch
|
|
class PipelineTesterMixin:
|
|
"""
|
|
This mixin is designed to be used with unittest.TestCase classes.
|
|
It provides a set of common tests for each PyTorch pipeline, e.g. saving and loading the pipeline,
|
|
equivalence of dict and tuple outputs, etc.
|
|
"""
|
|
|
|
allowed_required_args = [
|
|
"source_prompt",
|
|
"prompt",
|
|
"image",
|
|
"mask_image",
|
|
"example_image",
|
|
"class_labels",
|
|
"token_indices",
|
|
]
|
|
required_optional_params = ["generator", "num_inference_steps", "return_dict"]
|
|
num_inference_steps_args = ["num_inference_steps"]
|
|
|
|
# set these parameters to False in the child class if the pipeline does not support the corresponding functionality
|
|
test_attention_slicing = True
|
|
test_cpu_offload = True
|
|
test_xformers_attention = True
|
|
|
|
def get_generator(self, seed):
|
|
device = torch_device if torch_device != "mps" else "cpu"
|
|
generator = torch.Generator(device).manual_seed(seed)
|
|
return generator
|
|
|
|
@property
|
|
def pipeline_class(self) -> Union[Callable, DiffusionPipeline]:
|
|
raise NotImplementedError(
|
|
"You need to set the attribute `pipeline_class = ClassNameOfPipeline` in the child test class. "
|
|
"See existing pipeline tests for reference."
|
|
)
|
|
|
|
def get_dummy_components(self):
|
|
raise NotImplementedError(
|
|
"You need to implement `get_dummy_components(self)` in the child test class. "
|
|
"See existing pipeline tests for reference."
|
|
)
|
|
|
|
def get_dummy_inputs(self, device, seed=0):
|
|
raise NotImplementedError(
|
|
"You need to implement `get_dummy_inputs(self, device, seed)` in the child test class. "
|
|
"See existing pipeline tests for reference."
|
|
)
|
|
|
|
def tearDown(self):
|
|
# clean up the VRAM after each test in case of CUDA runtime errors
|
|
super().tearDown()
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
|
|
def test_save_load_local(self):
|
|
components = self.get_dummy_components()
|
|
pipe = self.pipeline_class(**components)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
# Warmup pass when using mps (see #372)
|
|
if torch_device == "mps":
|
|
_ = pipe(**self.get_dummy_inputs(torch_device))
|
|
|
|
inputs = self.get_dummy_inputs(torch_device)
|
|
output = pipe(**inputs)[0]
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
pipe.save_pretrained(tmpdir)
|
|
pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
|
|
pipe_loaded.to(torch_device)
|
|
pipe_loaded.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_dummy_inputs(torch_device)
|
|
output_loaded = pipe_loaded(**inputs)[0]
|
|
|
|
max_diff = np.abs(output - output_loaded).max()
|
|
self.assertLess(max_diff, 1e-4)
|
|
|
|
def test_pipeline_call_implements_required_args(self):
|
|
assert hasattr(self.pipeline_class, "__call__"), f"{self.pipeline_class} should have a `__call__` method"
|
|
parameters = inspect.signature(self.pipeline_class.__call__).parameters
|
|
required_parameters = {k: v for k, v in parameters.items() if v.default == inspect._empty}
|
|
required_parameters.pop("self")
|
|
required_parameters = set(required_parameters)
|
|
optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty})
|
|
|
|
for param in required_parameters:
|
|
if param == "kwargs":
|
|
# kwargs can be added if arguments of pipeline call function are deprecated
|
|
continue
|
|
assert param in self.allowed_required_args
|
|
|
|
optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty})
|
|
|
|
for param in self.required_optional_params:
|
|
assert param in optional_parameters
|
|
|
|
def test_inference_batch_consistent(self):
|
|
self._test_inference_batch_consistent()
|
|
|
|
def _test_inference_batch_consistent(self, batch_sizes=[2, 4, 13]):
|
|
components = self.get_dummy_components()
|
|
pipe = self.pipeline_class(**components)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_dummy_inputs(torch_device)
|
|
|
|
logger = logging.get_logger(pipe.__module__)
|
|
logger.setLevel(level=diffusers.logging.FATAL)
|
|
|
|
# batchify inputs
|
|
for batch_size in batch_sizes:
|
|
batched_inputs = {}
|
|
for name, value in inputs.items():
|
|
if name in self.allowed_required_args:
|
|
# prompt is string
|
|
if name == "prompt":
|
|
len_prompt = len(value)
|
|
# make unequal batch sizes
|
|
batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]
|
|
|
|
# make last batch super long
|
|
batched_inputs[name][-1] = 2000 * "very long"
|
|
# or else we have images
|
|
else:
|
|
batched_inputs[name] = batch_size * [value]
|
|
elif name == "batch_size":
|
|
batched_inputs[name] = batch_size
|
|
else:
|
|
batched_inputs[name] = value
|
|
|
|
for arg in self.num_inference_steps_args:
|
|
batched_inputs[arg] = inputs[arg]
|
|
|
|
batched_inputs["output_type"] = None
|
|
|
|
if self.pipeline_class.__name__ == "DanceDiffusionPipeline":
|
|
batched_inputs.pop("output_type")
|
|
|
|
output = pipe(**batched_inputs)
|
|
|
|
assert len(output[0]) == batch_size
|
|
|
|
batched_inputs["output_type"] = "np"
|
|
|
|
if self.pipeline_class.__name__ == "DanceDiffusionPipeline":
|
|
batched_inputs.pop("output_type")
|
|
|
|
output = pipe(**batched_inputs)[0]
|
|
|
|
assert output.shape[0] == batch_size
|
|
|
|
logger.setLevel(level=diffusers.logging.WARNING)
|
|
|
|
def test_inference_batch_single_identical(self):
|
|
self._test_inference_batch_single_identical()
|
|
|
|
def _test_inference_batch_single_identical(
|
|
self, test_max_difference=None, test_mean_pixel_difference=None, relax_max_difference=False
|
|
):
|
|
if test_max_difference is None:
|
|
# TODO(Pedro) - not sure why, but not at all reproducible at the moment it seems
|
|
# make sure that batched and non-batched is identical
|
|
test_max_difference = torch_device != "mps"
|
|
|
|
if test_mean_pixel_difference is None:
|
|
# TODO same as above
|
|
test_mean_pixel_difference = torch_device != "mps"
|
|
|
|
components = self.get_dummy_components()
|
|
pipe = self.pipeline_class(**components)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_dummy_inputs(torch_device)
|
|
|
|
logger = logging.get_logger(pipe.__module__)
|
|
logger.setLevel(level=diffusers.logging.FATAL)
|
|
|
|
# batchify inputs
|
|
batched_inputs = {}
|
|
batch_size = 3
|
|
for name, value in inputs.items():
|
|
if name in self.allowed_required_args:
|
|
# prompt is string
|
|
if name == "prompt":
|
|
len_prompt = len(value)
|
|
# make unequal batch sizes
|
|
batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]
|
|
|
|
# make last batch super long
|
|
batched_inputs[name][-1] = 2000 * "very long"
|
|
# or else we have images
|
|
else:
|
|
batched_inputs[name] = batch_size * [value]
|
|
elif name == "batch_size":
|
|
batched_inputs[name] = batch_size
|
|
elif name == "generator":
|
|
batched_inputs[name] = [self.get_generator(i) for i in range(batch_size)]
|
|
else:
|
|
batched_inputs[name] = value
|
|
|
|
for arg in self.num_inference_steps_args:
|
|
batched_inputs[arg] = inputs[arg]
|
|
|
|
if self.pipeline_class.__name__ != "DanceDiffusionPipeline":
|
|
batched_inputs["output_type"] = "np"
|
|
|
|
output_batch = pipe(**batched_inputs)
|
|
assert output_batch[0].shape[0] == batch_size
|
|
|
|
inputs["generator"] = self.get_generator(0)
|
|
|
|
output = pipe(**inputs)
|
|
|
|
logger.setLevel(level=diffusers.logging.WARNING)
|
|
if test_max_difference:
|
|
if relax_max_difference:
|
|
# Taking the median of the largest <n> differences
|
|
# is resilient to outliers
|
|
diff = np.abs(output_batch[0][0] - output[0][0])
|
|
diff = diff.flatten()
|
|
diff.sort()
|
|
max_diff = np.median(diff[-5:])
|
|
else:
|
|
max_diff = np.abs(output_batch[0][0] - output[0][0]).max()
|
|
assert max_diff < 1e-4
|
|
|
|
if test_mean_pixel_difference:
|
|
assert_mean_pixel_difference(output_batch[0][0], output[0][0])
|
|
|
|
def test_dict_tuple_outputs_equivalent(self):
|
|
components = self.get_dummy_components()
|
|
pipe = self.pipeline_class(**components)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
# Warmup pass when using mps (see #372)
|
|
if torch_device == "mps":
|
|
_ = pipe(**self.get_dummy_inputs(torch_device))
|
|
|
|
output = pipe(**self.get_dummy_inputs(torch_device))[0]
|
|
output_tuple = pipe(**self.get_dummy_inputs(torch_device), return_dict=False)[0]
|
|
|
|
max_diff = np.abs(output - output_tuple).max()
|
|
self.assertLess(max_diff, 1e-4)
|
|
|
|
def test_components_function(self):
|
|
init_components = self.get_dummy_components()
|
|
pipe = self.pipeline_class(**init_components)
|
|
|
|
self.assertTrue(hasattr(pipe, "components"))
|
|
self.assertTrue(set(pipe.components.keys()) == set(init_components.keys()))
|
|
|
|
@unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA")
|
|
def test_float16_inference(self):
|
|
components = self.get_dummy_components()
|
|
pipe = self.pipeline_class(**components)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
for name, module in components.items():
|
|
if hasattr(module, "half"):
|
|
components[name] = module.half()
|
|
pipe_fp16 = self.pipeline_class(**components)
|
|
pipe_fp16.to(torch_device)
|
|
pipe_fp16.set_progress_bar_config(disable=None)
|
|
|
|
output = pipe(**self.get_dummy_inputs(torch_device))[0]
|
|
output_fp16 = pipe_fp16(**self.get_dummy_inputs(torch_device))[0]
|
|
|
|
max_diff = np.abs(output - output_fp16).max()
|
|
self.assertLess(max_diff, 1e-2, "The outputs of the fp16 and fp32 pipelines are too different.")
|
|
|
|
@unittest.skipIf(torch_device != "cuda", reason="float16 requires CUDA")
|
|
def test_save_load_float16(self):
|
|
components = self.get_dummy_components()
|
|
for name, module in components.items():
|
|
if hasattr(module, "half"):
|
|
components[name] = module.to(torch_device).half()
|
|
pipe = self.pipeline_class(**components)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_dummy_inputs(torch_device)
|
|
output = pipe(**inputs)[0]
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
pipe.save_pretrained(tmpdir)
|
|
pipe_loaded = self.pipeline_class.from_pretrained(tmpdir, torch_dtype=torch.float16)
|
|
pipe_loaded.to(torch_device)
|
|
pipe_loaded.set_progress_bar_config(disable=None)
|
|
|
|
for name, component in pipe_loaded.components.items():
|
|
if hasattr(component, "dtype"):
|
|
self.assertTrue(
|
|
component.dtype == torch.float16,
|
|
f"`{name}.dtype` switched from `float16` to {component.dtype} after loading.",
|
|
)
|
|
|
|
inputs = self.get_dummy_inputs(torch_device)
|
|
output_loaded = pipe_loaded(**inputs)[0]
|
|
|
|
max_diff = np.abs(output - output_loaded).max()
|
|
self.assertLess(max_diff, 3e-3, "The output of the fp16 pipeline changed after saving and loading.")
|
|
|
|
def test_save_load_optional_components(self):
|
|
if not hasattr(self.pipeline_class, "_optional_components"):
|
|
return
|
|
|
|
components = self.get_dummy_components()
|
|
pipe = self.pipeline_class(**components)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
# Warmup pass when using mps (see #372)
|
|
if torch_device == "mps":
|
|
_ = pipe(**self.get_dummy_inputs(torch_device))
|
|
|
|
# set all optional components to None
|
|
for optional_component in pipe._optional_components:
|
|
setattr(pipe, optional_component, None)
|
|
|
|
inputs = self.get_dummy_inputs(torch_device)
|
|
output = pipe(**inputs)[0]
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdir:
|
|
pipe.save_pretrained(tmpdir)
|
|
pipe_loaded = self.pipeline_class.from_pretrained(tmpdir)
|
|
pipe_loaded.to(torch_device)
|
|
pipe_loaded.set_progress_bar_config(disable=None)
|
|
|
|
for optional_component in pipe._optional_components:
|
|
self.assertTrue(
|
|
getattr(pipe_loaded, optional_component) is None,
|
|
f"`{optional_component}` did not stay set to None after loading.",
|
|
)
|
|
|
|
inputs = self.get_dummy_inputs(torch_device)
|
|
output_loaded = pipe_loaded(**inputs)[0]
|
|
|
|
max_diff = np.abs(output - output_loaded).max()
|
|
self.assertLess(max_diff, 1e-4)
|
|
|
|
@unittest.skipIf(torch_device != "cuda", reason="CUDA and CPU are required to switch devices")
|
|
def test_to_device(self):
|
|
components = self.get_dummy_components()
|
|
pipe = self.pipeline_class(**components)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
pipe.to("cpu")
|
|
model_devices = [component.device.type for component in components.values() if hasattr(component, "device")]
|
|
self.assertTrue(all(device == "cpu" for device in model_devices))
|
|
|
|
output_cpu = pipe(**self.get_dummy_inputs("cpu"))[0]
|
|
self.assertTrue(np.isnan(output_cpu).sum() == 0)
|
|
|
|
pipe.to("cuda")
|
|
model_devices = [component.device.type for component in components.values() if hasattr(component, "device")]
|
|
self.assertTrue(all(device == "cuda" for device in model_devices))
|
|
|
|
output_cuda = pipe(**self.get_dummy_inputs("cuda"))[0]
|
|
self.assertTrue(np.isnan(output_cuda).sum() == 0)
|
|
|
|
def test_attention_slicing_forward_pass(self):
|
|
self._test_attention_slicing_forward_pass()
|
|
|
|
def _test_attention_slicing_forward_pass(self, test_max_difference=True):
|
|
if not self.test_attention_slicing:
|
|
return
|
|
|
|
components = self.get_dummy_components()
|
|
pipe = self.pipeline_class(**components)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
# Warmup pass when using mps (see #372)
|
|
if torch_device == "mps":
|
|
_ = pipe(**self.get_dummy_inputs(torch_device))
|
|
|
|
inputs = self.get_dummy_inputs(torch_device)
|
|
output_without_slicing = pipe(**inputs)[0]
|
|
|
|
pipe.enable_attention_slicing(slice_size=1)
|
|
inputs = self.get_dummy_inputs(torch_device)
|
|
output_with_slicing = pipe(**inputs)[0]
|
|
|
|
if test_max_difference:
|
|
max_diff = np.abs(output_with_slicing - output_without_slicing).max()
|
|
self.assertLess(max_diff, 1e-3, "Attention slicing should not affect the inference results")
|
|
|
|
assert_mean_pixel_difference(output_with_slicing[0], output_without_slicing[0])
|
|
|
|
@unittest.skipIf(
|
|
torch_device != "cuda" or not is_accelerate_available() or is_accelerate_version("<", "0.14.0"),
|
|
reason="CPU offload is only available with CUDA and `accelerate v0.14.0` or higher",
|
|
)
|
|
def test_cpu_offload_forward_pass(self):
|
|
if not self.test_cpu_offload:
|
|
return
|
|
|
|
components = self.get_dummy_components()
|
|
pipe = self.pipeline_class(**components)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_dummy_inputs(torch_device)
|
|
output_without_offload = pipe(**inputs)[0]
|
|
|
|
pipe.enable_sequential_cpu_offload()
|
|
inputs = self.get_dummy_inputs(torch_device)
|
|
output_with_offload = pipe(**inputs)[0]
|
|
|
|
max_diff = np.abs(output_with_offload - output_without_offload).max()
|
|
self.assertLess(max_diff, 1e-4, "CPU offloading should not affect the inference results")
|
|
|
|
@unittest.skipIf(
|
|
torch_device != "cuda" or not is_xformers_available(),
|
|
reason="XFormers attention is only available with CUDA and `xformers` installed",
|
|
)
|
|
def test_xformers_attention_forwardGenerator_pass(self):
|
|
self._test_xformers_attention_forwardGenerator_pass()
|
|
|
|
def _test_xformers_attention_forwardGenerator_pass(self, test_max_difference=True):
|
|
if not self.test_xformers_attention:
|
|
return
|
|
|
|
components = self.get_dummy_components()
|
|
pipe = self.pipeline_class(**components)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_dummy_inputs(torch_device)
|
|
output_without_offload = pipe(**inputs)[0]
|
|
|
|
pipe.enable_xformers_memory_efficient_attention()
|
|
inputs = self.get_dummy_inputs(torch_device)
|
|
output_with_offload = pipe(**inputs)[0]
|
|
|
|
if test_max_difference:
|
|
max_diff = np.abs(output_with_offload - output_without_offload).max()
|
|
self.assertLess(max_diff, 1e-4, "XFormers attention should not affect the inference results")
|
|
|
|
assert_mean_pixel_difference(output_with_offload[0], output_without_offload[0])
|
|
|
|
def test_progress_bar(self):
|
|
components = self.get_dummy_components()
|
|
pipe = self.pipeline_class(**components)
|
|
pipe.to(torch_device)
|
|
|
|
inputs = self.get_dummy_inputs(torch_device)
|
|
with io.StringIO() as stderr, contextlib.redirect_stderr(stderr):
|
|
_ = pipe(**inputs)
|
|
stderr = stderr.getvalue()
|
|
# we can't calculate the number of progress steps beforehand e.g. for strength-dependent img2img,
|
|
# so we just match "5" in "#####| 1/5 [00:01<00:00]"
|
|
max_steps = re.search("/(.*?) ", stderr).group(1)
|
|
self.assertTrue(max_steps is not None and len(max_steps) > 0)
|
|
self.assertTrue(
|
|
f"{max_steps}/{max_steps}" in stderr, "Progress bar should be enabled and stopped at the max step"
|
|
)
|
|
|
|
pipe.set_progress_bar_config(disable=True)
|
|
with io.StringIO() as stderr, contextlib.redirect_stderr(stderr):
|
|
_ = pipe(**inputs)
|
|
self.assertTrue(stderr.getvalue() == "", "Progress bar should be disabled")
|
|
|
|
|
|
# Some models (e.g. unCLIP) are extremely likely to significantly deviate depending on which hardware is used.
|
|
# This helper function is used to check that the image doesn't deviate on average more than 10 pixels from a
|
|
# reference image.
|
|
def assert_mean_pixel_difference(image, expected_image):
|
|
image = np.asarray(DiffusionPipeline.numpy_to_pil(image)[0], dtype=np.float32)
|
|
expected_image = np.asarray(DiffusionPipeline.numpy_to_pil(expected_image)[0], dtype=np.float32)
|
|
avg_diff = np.abs(image - expected_image).mean()
|
|
assert avg_diff < 10, f"Error image deviates {avg_diff} pixels on average"
|