refactor unet_1d tests (#13898)

* refactor unet_1d tests

* use per-sample output_shape for unet_1d tests

---------

Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

Author: akshan-main

tests/models/unets/test_models_unet_1d.py

@@ -13,77 +13,37 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import unittest
-
-import pytest
 import torch
 
 from diffusers import UNet1DModel
+from diffusers.utils.torch_utils import randn_tensor
 
-from ...testing_utils import (
-    backend_manual_seed,
-    floats_tensor,
-    slow,
-    torch_device,
-)
-from ..test_modeling_common import ModelTesterMixin, UNetTesterMixin
-
+from ...testing_utils import backend_manual_seed, enable_full_determinism, slow, torch_device
+from ..testing_utils import BaseModelTesterConfig, ModelTesterMixin
 
-class UNet1DModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
-    model_class = UNet1DModel
-    main_input_name = "sample"
-
-    @property
-    def dummy_input(self):
-        batch_size = 4
-        num_features = 14
-        seq_len = 16
 
-        noise = floats_tensor((batch_size, num_features, seq_len)).to(torch_device)
-        time_step = torch.tensor([10] * batch_size).to(torch_device)
+enable_full_determinism()
 
-        return {"sample": noise, "timestep": time_step}
 
+class UNet1DModelTesterConfig(BaseModelTesterConfig):
     @property
-    def input_shape(self):
-        return (4, 14, 16)
+    def model_class(self):
+        return UNet1DModel
 
     @property
-    def output_shape(self):
-        return (4, 14, 16)
-
-    @unittest.skip("Test not supported.")
-    def test_ema_training(self):
-        pass
-
-    @unittest.skip("Test not supported.")
-    def test_training(self):
-        pass
-
-    @unittest.skip("Test not supported.")
-    def test_layerwise_casting_training(self):
-        pass
-
-    def test_determinism(self):
-        super().test_determinism()
-
-    def test_outputs_equivalence(self):
-        super().test_outputs_equivalence()
-
-    def test_from_save_pretrained(self):
-        super().test_from_save_pretrained()
-
-    def test_from_save_pretrained_variant(self):
-        super().test_from_save_pretrained_variant()
+    def main_input_name(self) -> str:
+        return "sample"
 
-    def test_model_from_pretrained(self):
-        super().test_model_from_pretrained()
+    @property
+    def output_shape(self) -> tuple:
+        return (14, 16)
 
-    def test_output(self):
-        super().test_output()
+    @property
+    def generator(self):
+        return torch.Generator("cpu").manual_seed(0)
 
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = {
+    def get_init_dict(self) -> dict:
+        return {
             "block_out_channels": (8, 8, 16, 16),
             "in_channels": 14,
             "out_channels": 14,
@@ -97,19 +57,26 @@ def prepare_init_args_and_inputs_for_common(self):
             "up_block_types": ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D"),
             "act_fn": "swish",
         }
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
 
+    def get_dummy_inputs(self) -> dict:
+        batch_size = 4
+        num_features = 14
+        seq_len = 16
+        noise = randn_tensor((batch_size, num_features, seq_len), generator=self.generator, device=torch_device)
+        timestep = torch.tensor([10] * batch_size, device=torch_device)
+        return {"sample": noise, "timestep": timestep}
+
+
+class TestUNet1DModel(UNet1DModelTesterConfig, ModelTesterMixin):
     def test_from_pretrained_hub(self):
         model, loading_info = UNet1DModel.from_pretrained(
             "bglick13/hopper-medium-v2-value-function-hor32", output_loading_info=True, subfolder="unet"
         )
-        self.assertIsNotNone(model)
-        self.assertEqual(len(loading_info["missing_keys"]), 0)
+        assert model is not None
+        assert len(loading_info["missing_keys"]) == 0
 
         model.to(torch_device)
-        image = model(**self.dummy_input)
-
+        image = model(**self.get_dummy_inputs())
         assert image is not None, "Make sure output is not None"
 
     def test_output_pretrained(self):
@@ -119,9 +86,7 @@ def test_output_pretrained(self):
 
         num_features = model.config.in_channels
         seq_len = 16
-        noise = torch.randn((1, seq_len, num_features)).permute(
-            0, 2, 1
-        )  # match original, we can update values and remove
+        noise = torch.randn((1, seq_len, num_features)).permute(0, 2, 1)
         time_step = torch.full((num_features,), 0)
 
         with torch.no_grad():
@@ -131,12 +96,7 @@ def test_output_pretrained(self):
         # fmt: off
         expected_output_slice = torch.tensor([-2.137172, 1.1426016, 0.3688687, -0.766922, 0.7303146, 0.11038864, -0.4760633, 0.13270172, 0.02591348])
         # fmt: on
-        self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-3))
-
-    @unittest.skip("Test not supported.")
-    def test_forward_with_norm_groups(self):
-        # Not implemented yet for this UNet
-        pass
+        assert torch.allclose(output_slice, expected_output_slice, rtol=1e-3)
 
     @slow
     def test_unet_1d_maestro(self):
@@ -157,98 +117,26 @@ def test_unet_1d_maestro(self):
         assert (output_sum - 224.0896).abs() < 0.5
         assert (output_max - 0.0607).abs() < 4e-4
 
-    @pytest.mark.xfail(
-        reason=(
-            "RuntimeError: 'fill_out' not implemented for 'Float8_e4m3fn'. The error is caused due to certain torch.float8_e4m3fn and torch.float8_e5m2 operations "
-            "not being supported when using deterministic algorithms (which is what the tests run with). To fix:\n"
-            "1. Wait for next PyTorch release: https://github.com/pytorch/pytorch/issues/137160.\n"
-            "2. Unskip this test."
-        ),
-    )
-    def test_layerwise_casting_inference(self):
-        super().test_layerwise_casting_inference()
-
-    @pytest.mark.xfail(
-        reason=(
-            "RuntimeError: 'fill_out' not implemented for 'Float8_e4m3fn'. The error is caused due to certain torch.float8_e4m3fn and torch.float8_e5m2 operations "
-            "not being supported when using deterministic algorithms (which is what the tests run with). To fix:\n"
-            "1. Wait for next PyTorch release: https://github.com/pytorch/pytorch/issues/137160.\n"
-            "2. Unskip this test."
-        ),
-    )
-    def test_layerwise_casting_memory(self):
-        pass
-
-
-class UNetRLModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
-    model_class = UNet1DModel
-    main_input_name = "sample"
 
+class UNetRLModelTesterConfig(BaseModelTesterConfig):
     @property
-    def dummy_input(self):
-        batch_size = 4
-        num_features = 14
-        seq_len = 16
-
-        noise = floats_tensor((batch_size, num_features, seq_len)).to(torch_device)
-        time_step = torch.tensor([10] * batch_size).to(torch_device)
-
-        return {"sample": noise, "timestep": time_step}
+    def model_class(self):
+        return UNet1DModel
 
     @property
-    def input_shape(self):
-        return (4, 14, 16)
+    def main_input_name(self) -> str:
+        return "sample"
 
     @property
-    def output_shape(self):
-        return (4, 14, 1)
-
-    def test_determinism(self):
-        super().test_determinism()
-
-    def test_outputs_equivalence(self):
-        super().test_outputs_equivalence()
-
-    def test_from_save_pretrained(self):
-        super().test_from_save_pretrained()
-
-    def test_from_save_pretrained_variant(self):
-        super().test_from_save_pretrained_variant()
-
-    def test_model_from_pretrained(self):
-        super().test_model_from_pretrained()
-
-    def test_output(self):
-        # UNetRL is a value-function is different output shape
-        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 = torch.Size((inputs_dict["sample"].shape[0], 1))
-        self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
-
-    @unittest.skip("Test not supported.")
-    def test_ema_training(self):
-        pass
+    def output_shape(self) -> tuple:
+        return (1,)
 
-    @unittest.skip("Test not supported.")
-    def test_training(self):
-        pass
-
-    @unittest.skip("Test not supported.")
-    def test_layerwise_casting_training(self):
-        pass
+    @property
+    def generator(self):
+        return torch.Generator("cpu").manual_seed(0)
 
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = {
+    def get_init_dict(self) -> dict:
+        return {
             "in_channels": 14,
             "out_channels": 14,
             "down_block_types": ["DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"],
@@ -264,19 +152,36 @@ def prepare_init_args_and_inputs_for_common(self):
             "time_embedding_type": "positional",
             "act_fn": "mish",
         }
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
+
+    def get_dummy_inputs(self) -> dict:
+        batch_size = 4
+        num_features = 14
+        seq_len = 16
+        noise = randn_tensor((batch_size, num_features, seq_len), generator=self.generator, device=torch_device)
+        timestep = torch.tensor([10] * batch_size, device=torch_device)
+        return {"sample": noise, "timestep": timestep}
+
+
+class TestUNetRLModel(UNetRLModelTesterConfig, ModelTesterMixin):
+    # UNetRL is a value function, so it has a different output shape.
+    def test_output(self):
+        model = self.model_class(**self.get_init_dict()).to(torch_device).eval()
+
+        inputs = self.get_dummy_inputs()
+        with torch.no_grad():
+            output = model(**inputs).sample
+
+        assert output.shape == (inputs["sample"].shape[0], 1), "Input and output shapes do not match"
 
     def test_from_pretrained_hub(self):
         value_function, vf_loading_info = UNet1DModel.from_pretrained(
             "bglick13/hopper-medium-v2-value-function-hor32", output_loading_info=True, subfolder="value_function"
         )
-        self.assertIsNotNone(value_function)
-        self.assertEqual(len(vf_loading_info["missing_keys"]), 0)
+        assert value_function is not None
+        assert len(vf_loading_info["missing_keys"]) == 0
 
         value_function.to(torch_device)
-        image = value_function(**self.dummy_input)
-
+        image = value_function(**self.get_dummy_inputs())
         assert image is not None, "Make sure output is not None"
 
     def test_output_pretrained(self):
@@ -288,9 +193,7 @@ def test_output_pretrained(self):
 
         num_features = value_function.config.in_channels
         seq_len = 14
-        noise = torch.randn((1, seq_len, num_features)).permute(
-            0, 2, 1
-        )  # match original, we can update values and remove
+        noise = torch.randn((1, seq_len, num_features)).permute(0, 2, 1)
         time_step = torch.full((num_features,), 0)
 
         with torch.no_grad():
@@ -299,31 +202,4 @@ def test_output_pretrained(self):
         # fmt: off
         expected_output_slice = torch.tensor([165.25] * seq_len)
         # fmt: on
-        self.assertTrue(torch.allclose(output, expected_output_slice, rtol=1e-3))
-
-    @unittest.skip("Test not supported.")
-    def test_forward_with_norm_groups(self):
-        # Not implemented yet for this UNet
-        pass
-
-    @pytest.mark.xfail(
-        reason=(
-            "RuntimeError: 'fill_out' not implemented for 'Float8_e4m3fn'. The error is caused due to certain torch.float8_e4m3fn and torch.float8_e5m2 operations "
-            "not being supported when using deterministic algorithms (which is what the tests run with). To fix:\n"
-            "1. Wait for next PyTorch release: https://github.com/pytorch/pytorch/issues/137160.\n"
-            "2. Unskip this test."
-        ),
-    )
-    def test_layerwise_casting_inference(self):
-        pass
-
-    @pytest.mark.xfail(
-        reason=(
-            "RuntimeError: 'fill_out' not implemented for 'Float8_e4m3fn'. The error is caused due to certain torch.float8_e4m3fn and torch.float8_e5m2 operations "
-            "not being supported when using deterministic algorithms (which is what the tests run with). To fix:\n"
-            "1. Wait for next PyTorch release: https://github.com/pytorch/pytorch/issues/137160.\n"
-            "2. Unskip this test."
-        ),
-    )
-    def test_layerwise_casting_memory(self):
-        pass
+        assert torch.allclose(output, expected_output_slice, rtol=1e-3)