move test_hooks.py to pytest

tests/hooks/test_hooks.py

@@ -13,8 +13,8 @@
 # limitations under the License.
 
 import gc
-import unittest
 
+import pytest
 import torch
 
 from diffusers.hooks import HookRegistry, ModelHook
@@ -134,20 +134,18 @@ class SkipLayerHook(ModelHook):
         return output
 
 
-class HookTests(unittest.TestCase):
+class TestHooks:
     in_features = 4
     hidden_features = 8
     out_features = 4
     num_layers = 2
 
-    def setUp(self):
+    def setup_method(self):
         params = self.get_module_parameters()
         self.model = DummyModel(**params)
         self.model.to(torch_device)
 
-    def tearDown(self):
-        super().tearDown()
-
+    def teardown_method(self):
         del self.model
         gc.collect()
         free_memory()
@@ -171,20 +169,20 @@ class HookTests(unittest.TestCase):
         registry_repr = repr(registry)
         expected_repr = "HookRegistry(\n  (0) add_hook - AddHook\n  (1) multiply_hook - MultiplyHook(value=2)\n)"
 
-        self.assertEqual(len(registry.hooks), 2)
-        self.assertEqual(registry._hook_order, ["add_hook", "multiply_hook"])
-        self.assertEqual(registry_repr, expected_repr)
+        assert len(registry.hooks) == 2
+        assert registry._hook_order == ["add_hook", "multiply_hook"]
+        assert registry_repr == expected_repr
 
         registry.remove_hook("add_hook")
 
-        self.assertEqual(len(registry.hooks), 1)
-        self.assertEqual(registry._hook_order, ["multiply_hook"])
+        assert len(registry.hooks) == 1
+        assert registry._hook_order == ["multiply_hook"]
 
     def test_stateful_hook(self):
         registry = HookRegistry.check_if_exists_or_initialize(self.model)
         registry.register_hook(StatefulAddHook(1), "stateful_add_hook")
 
-        self.assertEqual(registry.hooks["stateful_add_hook"].increment, 0)
+        assert registry.hooks["stateful_add_hook"].increment == 0
 
         input = torch.randn(1, 4, device=torch_device, generator=self.get_generator())
         num_repeats = 3
@@ -194,13 +192,13 @@ class HookTests(unittest.TestCase):
             if i == 0:
                 output1 = result
 
-        self.assertEqual(registry.get_hook("stateful_add_hook").increment, num_repeats)
+        assert registry.get_hook("stateful_add_hook").increment == num_repeats
 
         registry.reset_stateful_hooks()
         output2 = self.model(input)
 
-        self.assertEqual(registry.get_hook("stateful_add_hook").increment, 1)
-        self.assertTrue(torch.allclose(output1, output2))
+        assert registry.get_hook("stateful_add_hook").increment == 1
+        assert torch.allclose(output1, output2)
 
     def test_inference(self):
         registry = HookRegistry.check_if_exists_or_initialize(self.model)
@@ -218,9 +216,9 @@ class HookTests(unittest.TestCase):
         new_input = input * 2 + 1
         output3 = self.model(new_input).mean().detach().cpu().item()
 
-        self.assertAlmostEqual(output1, output2, places=5)
-        self.assertAlmostEqual(output1, output3, places=5)
-        self.assertAlmostEqual(output2, output3, places=5)
+        assert output1 == pytest.approx(output2, abs=5e-6)
+        assert output1 == pytest.approx(output3, abs=5e-6)
+        assert output2 == pytest.approx(output3, abs=5e-6)
 
     def test_skip_layer_hook(self):
         registry = HookRegistry.check_if_exists_or_initialize(self.model)
@@ -228,30 +226,29 @@ class HookTests(unittest.TestCase):
 
         input = torch.zeros(1, 4, device=torch_device)
         output = self.model(input).mean().detach().cpu().item()
-        self.assertEqual(output, 0.0)
+        assert output == 0.0
 
         registry.remove_hook("skip_layer_hook")
         registry.register_hook(SkipLayerHook(skip_layer=False), "skip_layer_hook")
         output = self.model(input).mean().detach().cpu().item()
-        self.assertNotEqual(output, 0.0)
+        assert output != 0.0
 
     def test_skip_layer_internal_block(self):
         registry = HookRegistry.check_if_exists_or_initialize(self.model.linear_1)
         input = torch.zeros(1, 4, device=torch_device)
 
         registry.register_hook(SkipLayerHook(skip_layer=True), "skip_layer_hook")
-        with self.assertRaises(RuntimeError) as cm:
+        with pytest.raises(RuntimeError, match="mat1 and mat2 shapes cannot be multiplied"):
             self.model(input).mean().detach().cpu().item()
-        self.assertIn("mat1 and mat2 shapes cannot be multiplied", str(cm.exception))
 
         registry.remove_hook("skip_layer_hook")
         output = self.model(input).mean().detach().cpu().item()
-        self.assertNotEqual(output, 0.0)
+        assert output != 0.0
 
         registry = HookRegistry.check_if_exists_or_initialize(self.model.blocks[1])
         registry.register_hook(SkipLayerHook(skip_layer=True), "skip_layer_hook")
         output = self.model(input).mean().detach().cpu().item()
-        self.assertNotEqual(output, 0.0)
+        assert output != 0.0
 
     def test_invocation_order_stateful_first(self):
         registry = HookRegistry.check_if_exists_or_initialize(self.model)
@@ -278,7 +275,7 @@ class HookTests(unittest.TestCase):
             .replace(" ", "")
             .replace("\n", "")
         )
-        self.assertEqual(output, expected_invocation_order_log)
+        assert output == expected_invocation_order_log
 
         registry.remove_hook("add_hook")
         with CaptureLogger(logger) as cap_logger:
@@ -289,7 +286,7 @@ class HookTests(unittest.TestCase):
             .replace(" ", "")
             .replace("\n", "")
         )
-        self.assertEqual(output, expected_invocation_order_log)
+        assert output == expected_invocation_order_log
 
     def test_invocation_order_stateful_middle(self):
         registry = HookRegistry.check_if_exists_or_initialize(self.model)
@@ -316,7 +313,7 @@ class HookTests(unittest.TestCase):
             .replace(" ", "")
             .replace("\n", "")
         )
-        self.assertEqual(output, expected_invocation_order_log)
+        assert output == expected_invocation_order_log
 
         registry.remove_hook("add_hook")
         with CaptureLogger(logger) as cap_logger:
@@ -327,7 +324,7 @@ class HookTests(unittest.TestCase):
             .replace(" ", "")
             .replace("\n", "")
         )
-        self.assertEqual(output, expected_invocation_order_log)
+        assert output == expected_invocation_order_log
 
         registry.remove_hook("add_hook_2")
         with CaptureLogger(logger) as cap_logger:
@@ -336,7 +333,7 @@ class HookTests(unittest.TestCase):
         expected_invocation_order_log = (
             ("MultiplyHook pre_forward\nMultiplyHook post_forward\n").replace(" ", "").replace("\n", "")
         )
-        self.assertEqual(output, expected_invocation_order_log)
+        assert output == expected_invocation_order_log
 
     def test_invocation_order_stateful_last(self):
         registry = HookRegistry.check_if_exists_or_initialize(self.model)
@@ -363,7 +360,7 @@ class HookTests(unittest.TestCase):
             .replace(" ", "")
             .replace("\n", "")
         )
-        self.assertEqual(output, expected_invocation_order_log)
+        assert output == expected_invocation_order_log
 
         registry.remove_hook("add_hook")
         with CaptureLogger(logger) as cap_logger:
@@ -374,4 +371,4 @@ class HookTests(unittest.TestCase):
             .replace(" ", "")
             .replace("\n", "")
         )
-        self.assertEqual(output, expected_invocation_order_log)
+        assert output == expected_invocation_order_log

tests/modular_pipelines/test_modular_pipelines_common.py

@@ -5,7 +5,6 @@ from typing import Callable
 
 import pytest
 import torch
-from huggingface_hub import hf_hub_download
 
 import diffusers
 from diffusers import AutoModel, ComponentsManager, ModularPipeline, ModularPipelineBlocks
@@ -33,33 +32,6 @@ from ..testing_utils import (
 )
 
 
-def _get_specified_components(path_or_repo_id, cache_dir=None):
-    if os.path.isdir(path_or_repo_id):
-        config_path = os.path.join(path_or_repo_id, "modular_model_index.json")
-    else:
-        try:
-            config_path = hf_hub_download(
-                repo_id=path_or_repo_id,
-                filename="modular_model_index.json",
-                local_dir=cache_dir,
-            )
-        except Exception:
-            return None
-
-    with open(config_path) as f:
-        config = json.load(f)
-
-    components = set()
-    for k, v in config.items():
-        if isinstance(v, (str, int, float, bool)):
-            continue
-        for entry in v:
-            if isinstance(entry, dict) and (entry.get("repo") or entry.get("pretrained_model_name_or_path")):
-                components.add(k)
-                break
-    return components
-
-
 class ModularPipelineTesterMixin:
     """
     It provides a set of common tests for each modular pipeline,
@@ -388,39 +360,6 @@ class ModularPipelineTesterMixin:
 
         assert torch.abs(image_slices[0] - image_slices[1]).max() < 1e-3
 
-    def test_load_expected_components_from_pretrained(self, tmp_path):
-        pipe = self.get_pipeline()
-        expected = _get_specified_components(self.pretrained_model_name_or_path, cache_dir=tmp_path)
-        if not expected:
-            pytest.skip("Skipping test as we couldn't fetch the expected components.")
-
-        actual = {
-            name
-            for name in pipe.components
-            if getattr(pipe, name, None) is not None
-            and getattr(getattr(pipe, name), "_diffusers_load_id", None) not in (None, "null")
-        }
-        assert expected == actual, f"Component mismatch: missing={expected - actual}, unexpected={actual - expected}"
-
-    def test_load_expected_components_from_save_pretrained(self, tmp_path):
-        pipe = self.get_pipeline()
-        save_dir = str(tmp_path / "saved-pipeline")
-        pipe.save_pretrained(save_dir)
-
-        expected = _get_specified_components(save_dir)
-        loaded_pipe = ModularPipeline.from_pretrained(save_dir)
-        loaded_pipe.load_components(torch_dtype=torch.float32)
-
-        actual = {
-            name
-            for name in loaded_pipe.components
-            if getattr(loaded_pipe, name, None) is not None
-            and getattr(getattr(loaded_pipe, name), "_diffusers_load_id", None) not in (None, "null")
-        }
-        assert expected == actual, (
-            f"Component mismatch after save/load: missing={expected - actual}, unexpected={actual - expected}"
-        )
-
     def test_modular_index_consistency(self, tmp_path):
         pipe = self.get_pipeline()
         components_spec = pipe._component_specs