test_modeling_common.py

# coding=utf-8
# Copyright 2022 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import inspect
import tempfile
import unittest
from typing import Dict, List, Tuple

import numpy as np
import torch

from diffusers.modeling_utils import ModelMixin
from diffusers.training_utils import EMAModel
from diffusers.utils import torch_device


class ModelTesterMixin:
    def test_from_pretrained_save_pretrained(self):
        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()

        model = self.model_class(**init_dict)
        model.to(torch_device)
        model.eval()

        with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(tmpdirname)
            new_model = self.model_class.from_pretrained(tmpdirname)
            new_model.to(torch_device)

        with torch.no_grad():
            # Warmup pass when using mps (see #372)
            if torch_device == "mps" and isinstance(model, ModelMixin):
                _ = model(**self.dummy_input)
                _ = new_model(**self.dummy_input)

            image = model(**inputs_dict)
            if isinstance(image, dict):
                image = image.sample

            new_image = new_model(**inputs_dict)

            if isinstance(new_image, dict):
                new_image = new_image.sample

        max_diff = (image - new_image).abs().sum().item()
        self.assertLessEqual(max_diff, 5e-5, "Models give different forward passes")

    def test_determinism(self):
        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
        model = self.model_class(**init_dict)
        model.to(torch_device)
        model.eval()

        with torch.no_grad():
            # Warmup pass when using mps (see #372)
            if torch_device == "mps" and isinstance(model, ModelMixin):
                model(**self.dummy_input)

            first = model(**inputs_dict)
            if isinstance(first, dict):
                first = first.sample

            second = model(**inputs_dict)
            if isinstance(second, dict):
                second = second.sample

        out_1 = first.cpu().numpy()
        out_2 = second.cpu().numpy()
        out_1 = out_1[~np.isnan(out_1)]
        out_2 = out_2[~np.isnan(out_2)]
        max_diff = np.amax(np.abs(out_1 - out_2))
        self.assertLessEqual(max_diff, 1e-5)

    def test_output(self):
        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
        model = self.model_class(**init_dict)
        model.to(torch_device)
        model.eval()

        with torch.no_grad():
            output = model(**inputs_dict)

            if isinstance(output, dict):
                output = output.sample

        self.assertIsNotNone(output)
        expected_shape = inputs_dict["sample"].shape
        self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")

    def test_forward_with_norm_groups(self):
        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()

        init_dict["norm_num_groups"] = 16
        init_dict["block_out_channels"] = (16, 32)

        model = self.model_class(**init_dict)
        model.to(torch_device)
        model.eval()

        with torch.no_grad():
            output = model(**inputs_dict)

            if isinstance(output, dict):
                output = output.sample

        self.assertIsNotNone(output)
        expected_shape = inputs_dict["sample"].shape
        self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")

    def test_forward_signature(self):
        init_dict, _ = self.prepare_init_args_and_inputs_for_common()

        model = self.model_class(**init_dict)
        signature = inspect.signature(model.forward)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        arg_names = [*signature.parameters.keys()]

        expected_arg_names = ["sample", "timestep"]
        self.assertListEqual(arg_names[:2], expected_arg_names)

    def test_model_from_pretrained(self):
        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()

        model = self.model_class(**init_dict)
        model.to(torch_device)
        model.eval()

        # test if the model can be loaded from the config
        # and has all the expected shape
        with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(tmpdirname)
            new_model = self.model_class.from_pretrained(tmpdirname)
            new_model.to(torch_device)
            new_model.eval()

        # check if all parameters shape are the same
        for param_name in model.state_dict().keys():
            param_1 = model.state_dict()[param_name]
            param_2 = new_model.state_dict()[param_name]
            self.assertEqual(param_1.shape, param_2.shape)

        with torch.no_grad():
            output_1 = model(**inputs_dict)

            if isinstance(output_1, dict):
                output_1 = output_1.sample

            output_2 = new_model(**inputs_dict)

            if isinstance(output_2, dict):
                output_2 = output_2.sample

        self.assertEqual(output_1.shape, output_2.shape)

    @unittest.skipIf(torch_device == "mps", "Training is not supported in mps")
    def test_training(self):
        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()

        model = self.model_class(**init_dict)
        model.to(torch_device)
        model.train()
        output = model(**inputs_dict)

        if isinstance(output, dict):
            output = output.sample

        noise = torch.randn((inputs_dict["sample"].shape[0],) + self.output_shape).to(torch_device)
        loss = torch.nn.functional.mse_loss(output, noise)
        loss.backward()

    @unittest.skipIf(torch_device == "mps", "Training is not supported in mps")
    def test_ema_training(self):
        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()

        model = self.model_class(**init_dict)
        model.to(torch_device)
        model.train()
        ema_model = EMAModel(model, device=torch_device)

        output = model(**inputs_dict)

        if isinstance(output, dict):
            output = output.sample

        noise = torch.randn((inputs_dict["sample"].shape[0],) + self.output_shape).to(torch_device)
        loss = torch.nn.functional.mse_loss(output, noise)
        loss.backward()
        ema_model.step(model)

    def test_outputs_equivalence(self):
        def set_nan_tensor_to_zero(t):
            # Temporary fallback until `aten::_index_put_impl_` is implemented in mps
            # Track progress in https://github.com/pytorch/pytorch/issues/77764
            device = t.device
            if device.type == "mps":
                t = t.to("cpu")
            t[t != t] = 0
            return t.to(device)

        def recursive_check(tuple_object, dict_object):
            if isinstance(tuple_object, (List, Tuple)):
                for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object.values()):
                    recursive_check(tuple_iterable_value, dict_iterable_value)
            elif isinstance(tuple_object, Dict):
                for tuple_iterable_value, dict_iterable_value in zip(tuple_object.values(), dict_object.values()):
                    recursive_check(tuple_iterable_value, dict_iterable_value)
            elif tuple_object is None:
                return
            else:
                self.assertTrue(
                    torch.allclose(
                        set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
                    ),
                    msg=(
                        "Tuple and dict output are not equal. Difference:"
                        f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
                        f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
                        f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
                    ),
                )

        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()

        model = self.model_class(**init_dict)
        model.to(torch_device)
        model.eval()

        with torch.no_grad():
            # Warmup pass when using mps (see #372)
            if torch_device == "mps" and isinstance(model, ModelMixin):
                model(**self.dummy_input)

            outputs_dict = model(**inputs_dict)
            outputs_tuple = model(**inputs_dict, return_dict=False)

        recursive_check(outputs_tuple, outputs_dict)

    @unittest.skipIf(torch_device == "mps", "Gradient checkpointing skipped on MPS")
    def test_enable_disable_gradient_checkpointing(self):
        if not self.model_class._supports_gradient_checkpointing:
            return  # Skip test if model does not support gradient checkpointing

        init_dict, _ = self.prepare_init_args_and_inputs_for_common()

        # at init model should have gradient checkpointing disabled
        model = self.model_class(**init_dict)
        self.assertFalse(model.is_gradient_checkpointing)

        # check enable works
        model.enable_gradient_checkpointing()
        self.assertTrue(model.is_gradient_checkpointing)

        # check disable works
        model.disable_gradient_checkpointing()
        self.assertFalse(model.is_gradient_checkpointing)

    def test_deprecated_kwargs(self):
        has_kwarg_in_model_class = "kwargs" in inspect.signature(self.model_class.__init__).parameters
        has_deprecated_kwarg = len(self.model_class._deprecated_kwargs) > 0

        if has_kwarg_in_model_class and not has_deprecated_kwarg:
            raise ValueError(
                f"{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs"
                " under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are"
                " no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ="
                " [<deprecated_argument>]`"
            )

        if not has_kwarg_in_model_class and has_deprecated_kwarg:
            raise ValueError(
                f"{self.model_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated kwargs"
                " under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to"
                f" {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument"
                " from `_deprecated_kwargs = [<deprecated_argument>]`"
            )