test_pipelines_common.py

import contextlib
import gc
import inspect
import io
import re
import tempfile
import time
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"]
    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"]:
            # RePaint can hardly be made deterministic since the scheduler is currently always
            # nondeterministic
            # CycleDiffusion is also slightly nondeterministic
            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_num_inference_steps_consistent(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))

        outputs = []
        times = []
        for num_steps in [9, 6, 3]:
            inputs = self.get_dummy_inputs(torch_device)

            for arg in self.num_inference_steps_args:
                inputs[arg] = num_steps

            start_time = time.time()
            output = pipe(**inputs)[0]
            inference_time = time.time() - start_time

            outputs.append(output)
            times.append(inference_time)

        # check that all outputs have the same shape
        self.assertTrue(all(outputs[0].shape == output.shape for output in outputs))
        # check that the inference time increases with the number of inference steps
        self.assertTrue(all(times[i] < times[i - 1] for i in range(1, len(times))))

    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):
        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]

        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")

    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"