diffusers/tests/test_pipelines_common.py

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 (
    CycleDiffusionPipeline,
    DanceDiffusionPipeline,
    DiffusionPipeline,
    RePaintPipeline,
    StableDiffusionDepth2ImgPipeline,
    StableDiffusionImg2ImgPipeline,
)
from diffusers.utils import logging
from diffusers.utils.import_utils import is_accelerate_available, 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):
        if torch_device == "mps" and self.pipeline_class in (
            DanceDiffusionPipeline,
            CycleDiffusionPipeline,
            RePaintPipeline,
            StableDiffusionImg2ImgPipeline,
        ):
            # FIXME: inconsistent outputs on MPS
            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 = 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 self.pipeline_class.__name__ in [
            "CycleDiffusionPipeline",
            "RePaintPipeline",
            "StableDiffusionPix2PixZeroPipeline",
        ]:
            # RePaint can hardly be made deterministic since the scheduler is currently always
            # nondeterministic
            # CycleDiffusion is also slightly nondeterministic
            # There's a training loop inside Pix2PixZero and is guided by edit directions. This is
            # why the slight non-determinism.
            return

        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):
        if torch_device == "mps" and self.pipeline_class in (
            DanceDiffusionPipeline,
            CycleDiffusionPipeline,
            RePaintPipeline,
            StableDiffusionImg2ImgPipeline,
        ):
            # FIXME: inconsistent outputs on MPS
            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))

        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

        if torch_device == "mps" and self.pipeline_class in (
            DanceDiffusionPipeline,
            CycleDiffusionPipeline,
            RePaintPipeline,
            StableDiffusionImg2ImgPipeline,
        ):
            # FIXME: inconsistent outputs on MPS
            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

        if torch_device == "mps" and self.pipeline_class in (
            DanceDiffusionPipeline,
            CycleDiffusionPipeline,
            RePaintPipeline,
            StableDiffusionImg2ImgPipeline,
            StableDiffusionDepth2ImgPipeline,
        ):
            # FIXME: inconsistent outputs on MPS
            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(),
        reason="CPU offload is only available with CUDA and `accelerate` installed",
    )
    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"