Move the top level 'tests/' into src/maxdiffusion/tests/ as a legacy (#322)

* Move the top level 'tests/' into `src/maxdiffusion/tests/` as a legacy

* Format by pyink

Author: michelle-yooh

src/maxdiffusion/tests/legacy_hf_tests/__init__.py

@@ -0,0 +1,15 @@
+"""
+Copyright 2024 Google LLC
+
+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
+
+     https://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.
+"""

tests/conftest.py …fusion/tests/legacy_hf_tests/conftest.pytests/conftest.py renamed to src/maxdiffusion/tests/legacy_hf_tests/conftest.py tests/conftest.py renamed to src/maxdiffusion/tests/legacy_hf_tests/conftest.py

@@ -31,14 +31,14 @@
 
 
 def pytest_addoption(parser):
-    from maxdiffusion.utils.testing_utils import pytest_addoption_shared
+  from maxdiffusion.utils.testing_utils import pytest_addoption_shared
 
-    pytest_addoption_shared(parser)
+  pytest_addoption_shared(parser)
 
 
 def pytest_terminal_summary(terminalreporter):
-    from maxdiffusion.utils.testing_utils import pytest_terminal_summary_main
+  from maxdiffusion.utils.testing_utils import pytest_terminal_summary_main
 
-    make_reports = terminalreporter.config.getoption("--make-reports")
-    if make_reports:
-        pytest_terminal_summary_main(terminalreporter, id=make_reports)
+  make_reports = terminalreporter.config.getoption("--make-reports")
+  if make_reports:
+    pytest_terminal_summary_main(terminalreporter, id=make_reports)

src/maxdiffusion/tests/legacy_hf_tests/models/__init__.py

@@ -0,0 +1,15 @@
+"""
+Copyright 2024 Google LLC
+
+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
+
+     https://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.
+"""

src/maxdiffusion/tests/legacy_hf_tests/models/test_modeling_common_flax.py

@@ -0,0 +1,83 @@
+"""
+Copyright 2024 Google LLC
+
+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
+
+     https://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
+
+from maxdiffusion.utils import is_flax_available
+from maxdiffusion.utils.testing_utils import require_flax
+
+
+if is_flax_available():
+  import jax
+
+
+@require_flax
+class FlaxModelTesterMixin:
+
+  def test_output(self):
+    init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+
+    model = self.model_class(**init_dict)
+    variables = model.init(inputs_dict["prng_key"], inputs_dict["sample"])
+    jax.lax.stop_gradient(variables)
+
+    output = model.apply(variables, inputs_dict["sample"])
+
+    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)
+    variables = model.init(inputs_dict["prng_key"], inputs_dict["sample"])
+    jax.lax.stop_gradient(variables)
+
+    output = model.apply(variables, inputs_dict["sample"])
+
+    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_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>]`"
+      )

src/maxdiffusion/tests/legacy_hf_tests/models/test_models_unet_2d_flax.py

@@ -0,0 +1,118 @@
+"""
+Copyright 2024 Google LLC
+
+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
+
+     https://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 gc
+import unittest
+
+from maxdiffusion import FlaxUNet2DConditionModel
+from maxdiffusion.utils import is_flax_available
+from maxdiffusion.utils.testing_utils import load_hf_numpy, require_flax, slow
+from parameterized import parameterized
+
+
+if is_flax_available():
+  import jax
+  import jax.numpy as jnp
+
+
+@slow
+@require_flax
+class FlaxUNet2DConditionModelIntegrationTests(unittest.TestCase):
+
+  def get_file_format(self, seed, shape):
+    return f"gaussian_noise_s={seed}_shape={'_'.join([str(s) for s in shape])}.npy"
+
+  def tearDown(self):
+    # clean up the VRAM after each test
+    super().tearDown()
+    gc.collect()
+
+  def get_latents(self, seed=0, shape=(4, 4, 64, 64), fp16=False):
+    dtype = jnp.bfloat16 if fp16 else jnp.float32
+    image = jnp.array(load_hf_numpy(self.get_file_format(seed, shape)), dtype=dtype)
+    return image
+
+  def get_unet_model(self, fp16=False, model_id="CompVis/stable-diffusion-v1-4"):
+    dtype = jnp.bfloat16 if fp16 else jnp.float32
+    revision = "bf16" if fp16 else None
+
+    model, params = FlaxUNet2DConditionModel.from_pretrained(model_id, subfolder="unet", dtype=dtype, revision=revision)
+    return model, params
+
+  def get_encoder_hidden_states(self, seed=0, shape=(4, 77, 768), fp16=False):
+    dtype = jnp.bfloat16 if fp16 else jnp.float32
+    hidden_states = jnp.array(load_hf_numpy(self.get_file_format(seed, shape)), dtype=dtype)
+    return hidden_states
+
+  @parameterized.expand(
+      [
+          # fmt: off
+            [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
+            [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
+            [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
+            [3, 1000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
+          # fmt: on
+      ]
+  )
+  def test_compvis_sd_v1_4_flax_vs_torch_fp16(self, seed, timestep, expected_slice):
+    model, params = self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4", fp16=True)
+    latents = self.get_latents(seed, fp16=True)
+    encoder_hidden_states = self.get_encoder_hidden_states(seed, fp16=True)
+
+    sample = model.apply(
+        {"params": params},
+        latents,
+        jnp.array(timestep, dtype=jnp.int32),
+        encoder_hidden_states=encoder_hidden_states,
+    ).sample
+
+    assert sample.shape == latents.shape
+
+    output_slice = jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())), dtype=jnp.float32)
+    expected_output_slice = jnp.array(expected_slice, dtype=jnp.float32)
+
+    # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
+    assert jnp.allclose(output_slice, expected_output_slice, atol=1e-2)
+
+  @parameterized.expand(
+      [
+          # fmt: off
+            [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
+            [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
+            [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
+            [3, 1000, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
+          # fmt: on
+      ]
+  )
+  def test_stabilityai_sd_v2_flax_vs_torch_fp16(self, seed, timestep, expected_slice):
+    model, params = self.get_unet_model(model_id="stabilityai/stable-diffusion-2", fp16=True)
+    latents = self.get_latents(seed, shape=(4, 4, 96, 96), fp16=True)
+    encoder_hidden_states = self.get_encoder_hidden_states(seed, shape=(4, 77, 1024), fp16=True)
+
+    sample = model.apply(
+        {"params": params},
+        latents,
+        jnp.array(timestep, dtype=jnp.int32),
+        encoder_hidden_states=encoder_hidden_states,
+    ).sample
+
+    assert sample.shape == latents.shape
+
+    output_slice = jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())), dtype=jnp.float32)
+    expected_output_slice = jnp.array(expected_slice, dtype=jnp.float32)
+
+    # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
+    assert jnp.allclose(output_slice, expected_output_slice, atol=1e-2)

src/maxdiffusion/tests/legacy_hf_tests/models/test_models_vae_flax.py

@@ -0,0 +1,55 @@
+"""
+Copyright 2024 Google LLC
+
+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
+
+     https://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 unittest
+
+from maxdiffusion import FlaxAutoencoderKL
+from maxdiffusion.utils import is_flax_available
+from maxdiffusion.utils.testing_utils import require_flax
+
+from .test_modeling_common_flax import FlaxModelTesterMixin
+
+
+if is_flax_available():
+  import jax
+
+
+@require_flax
+class FlaxAutoencoderKLTests(FlaxModelTesterMixin, unittest.TestCase):
+  model_class = FlaxAutoencoderKL
+
+  @property
+  def dummy_input(self):
+    batch_size = 4
+    num_channels = 3
+    sizes = (32, 32)
+
+    prng_key = jax.random.PRNGKey(0)
+    image = jax.random.uniform(prng_key, ((batch_size, num_channels) + sizes))
+
+    return {"sample": image, "prng_key": prng_key}
+
+  def prepare_init_args_and_inputs_for_common(self):
+    init_dict = {
+        "block_out_channels": [32, 64],
+        "in_channels": 3,
+        "out_channels": 3,
+        "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
+        "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
+        "latent_channels": 4,
+    }
+    inputs_dict = self.dummy_input
+    return init_dict, inputs_dict

src/maxdiffusion/tests/legacy_hf_tests/schedulers/__init__.py

@@ -0,0 +1,15 @@
+"""
+Copyright 2024 Google LLC
+
+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
+
+     https://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.
+"""