[CI] Refactor Z Image Transformer Tests

tests/models/transformers/test_models_transformer_z_image.py

@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2025 HuggingFace Inc.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -13,16 +12,23 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import gc
 import os
-import unittest
 
+import pytest
 import torch
 
 from diffusers import ZImageTransformer2DModel
-
-from ...testing_utils import IS_GITHUB_ACTIONS, torch_device
-from ..test_modeling_common import ModelTesterMixin, TorchCompileTesterMixin
+from diffusers.utils.torch_utils import randn_tensor
+
+from ...testing_utils import assert_tensors_close, torch_device
+from ..testing_utils import (
+    BaseModelTesterConfig,
+    LoraTesterMixin,
+    MemoryTesterMixin,
+    ModelTesterMixin,
+    TorchCompileTesterMixin,
+    TrainingTesterMixin,
+)
 
 
 # Z-Image requires torch.use_deterministic_algorithms(False) due to complex64 RoPE operations
@@ -36,44 +42,38 @@
     torch.backends.cuda.matmul.allow_tf32 = False
 
 
-@unittest.skipIf(
-    IS_GITHUB_ACTIONS,
-    reason="Skipping test-suite inside the CI because the model has `torch.empty()` inside of it during init and we don't have a clear way to override it in the modeling tests.",
-)
-class ZImageTransformerTests(ModelTesterMixin, unittest.TestCase):
-    model_class = ZImageTransformer2DModel
-    main_input_name = "x"
-    # We override the items here because the transformer under consideration is small.
-    model_split_percents = [0.9, 0.9, 0.9]
-
-    def prepare_dummy_input(self, height=16, width=16):
-        batch_size = 1
-        num_channels = 16
-        embedding_dim = 16
-        sequence_length = 16
-
-        hidden_states = [torch.randn((num_channels, 1, height, width)).to(torch_device) for _ in range(batch_size)]
-        encoder_hidden_states = [
-            torch.randn((sequence_length, embedding_dim)).to(torch_device) for _ in range(batch_size)
-        ]
-        timestep = torch.tensor([0.0]).to(torch_device)
+def _concat_list_output(output):
+    """Model output `sample` is a list of tensors. Concatenate them for comparison."""
+    return torch.cat([t.flatten() for t in output])
 
-        return {"x": hidden_states, "cap_feats": encoder_hidden_states, "t": timestep}
 
+class ZImageTransformerTesterConfig(BaseModelTesterConfig):
     @property
-    def dummy_input(self):
-        return self.prepare_dummy_input()
+    def model_class(self):
+        return ZImageTransformer2DModel
 
     @property
-    def input_shape(self):
+    def output_shape(self) -> tuple[int, ...]:
         return (4, 32, 32)
 
     @property
-    def output_shape(self):
+    def input_shape(self) -> tuple[int, ...]:
         return (4, 32, 32)
 
-    def prepare_init_args_and_inputs_for_common(self):
-        init_dict = {
+    @property
+    def model_split_percents(self) -> list:
+        return [0.9, 0.9, 0.9]
+
+    @property
+    def main_input_name(self) -> str:
+        return "x"
+
+    @property
+    def generator(self):
+        return torch.Generator("cpu").manual_seed(0)
+
+    def get_init_dict(self):
+        return {
             "all_patch_size": (2,),
             "all_f_patch_size": (1,),
             "in_channels": 16,
@@ -89,83 +89,223 @@ def prepare_init_args_and_inputs_for_common(self):
             "axes_dims": [8, 4, 4],
             "axes_lens": [256, 32, 32],
         }
-        inputs_dict = self.dummy_input
-        return init_dict, inputs_dict
-
-    def setUp(self):
-        gc.collect()
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-            torch.cuda.synchronize()
+
+    def get_dummy_inputs(self) -> dict[str, torch.Tensor | list]:
+        batch_size = 1
+        num_channels = 16
+        embedding_dim = 16
+        sequence_length = 16
+        height = 16
+        width = 16
+
+        hidden_states = [
+            randn_tensor((num_channels, 1, height, width), generator=self.generator, device=torch_device)
+            for _ in range(batch_size)
+        ]
+        encoder_hidden_states = [
+            randn_tensor((sequence_length, embedding_dim), generator=self.generator, device=torch_device)
+            for _ in range(batch_size)
+        ]
+        timestep = torch.tensor([0.0]).to(torch_device)
+
+        return {"x": hidden_states, "cap_feats": encoder_hidden_states, "t": timestep}
+
+
+class TestZImageTransformer(ZImageTransformerTesterConfig, ModelTesterMixin):
+    """Core model tests for Z-Image Transformer."""
+
+    @torch.no_grad()
+    def test_determinism(self, atol=1e-5, rtol=0):
+        model = self.model_class(**self.get_init_dict())
+        model.to(torch_device)
+        model.eval()
+
+        inputs_dict = self.get_dummy_inputs()
+        first = _concat_list_output(model(**inputs_dict, return_dict=False)[0])
+        second = _concat_list_output(model(**inputs_dict, return_dict=False)[0])
+
+        mask = ~(torch.isnan(first) | torch.isnan(second))
+        assert_tensors_close(
+            first[mask], second[mask], atol=atol, rtol=rtol, msg="Model outputs are not deterministic"
+        )
+
+    def test_from_save_pretrained(self, tmp_path, atol=5e-5, rtol=5e-5):
         torch.manual_seed(0)
-        if torch.cuda.is_available():
-            torch.cuda.manual_seed_all(0)
-
-    def tearDown(self):
-        super().tearDown()
-        gc.collect()
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-            torch.cuda.synchronize()
+        model = self.model_class(**self.get_init_dict())
+        model.to(torch_device)
+        model.eval()
+
+        model.save_pretrained(tmp_path)
+        new_model = self.model_class.from_pretrained(tmp_path)
+        new_model.to(torch_device)
+
+        for param_name in model.state_dict().keys():
+            param_1 = model.state_dict()[param_name]
+            param_2 = new_model.state_dict()[param_name]
+            assert param_1.shape == param_2.shape
+
+        inputs_dict = self.get_dummy_inputs()
+        image = _concat_list_output(model(**inputs_dict, return_dict=False)[0])
+        new_image = _concat_list_output(new_model(**inputs_dict, return_dict=False)[0])
+
+        assert_tensors_close(image, new_image, atol=atol, rtol=rtol, msg="Models give different forward passes.")
+
+    @torch.no_grad()
+    def test_from_save_pretrained_variant(self, tmp_path, atol=5e-5, rtol=0):
+        model = self.model_class(**self.get_init_dict())
+        model.to(torch_device)
+        model.eval()
+
+        model.save_pretrained(tmp_path, variant="fp16")
+        new_model = self.model_class.from_pretrained(tmp_path, variant="fp16")
+
+        with pytest.raises(OSError) as exc_info:
+            self.model_class.from_pretrained(tmp_path)
+
+        assert "Error no file named diffusion_pytorch_model.bin found in directory" in str(exc_info.value)
+
+        new_model.to(torch_device)
+
+        inputs_dict = self.get_dummy_inputs()
+        image = _concat_list_output(model(**inputs_dict, return_dict=False)[0])
+        new_image = _concat_list_output(new_model(**inputs_dict, return_dict=False)[0])
+
+        assert_tensors_close(image, new_image, atol=atol, rtol=rtol, msg="Models give different forward passes.")
+
+    @pytest.mark.skip("Model output `sample` is a list of tensors, not a single tensor.")
+    def test_outputs_equivalence(self, atol=1e-5, rtol=0):
+        pass
+
+    def test_sharded_checkpoints_with_parallel_loading(self, tmp_path, atol=1e-5, rtol=0):
+        from diffusers.utils import SAFE_WEIGHTS_INDEX_NAME, constants
+
+        from ..testing_utils.common import calculate_expected_num_shards, compute_module_persistent_sizes
+
         torch.manual_seed(0)
-        if torch.cuda.is_available():
-            torch.cuda.manual_seed_all(0)
+        config = self.get_init_dict()
+        inputs_dict = self.get_dummy_inputs()
+        model = self.model_class(**config).eval()
+        model = model.to(torch_device)
 
-    def test_gradient_checkpointing_is_applied(self):
-        expected_set = {"ZImageTransformer2DModel"}
-        super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
+        base_output = _concat_list_output(model(**inputs_dict, return_dict=False)[0])
 
-    @unittest.skip("Test is not supported for handling main inputs that are lists.")
-    def test_training(self):
-        super().test_training()
+        model_size = compute_module_persistent_sizes(model)[""]
+        max_shard_size = int((model_size * 0.75) / (2**10))
 
-    @unittest.skip("Test is not supported for handling main inputs that are lists.")
-    def test_ema_training(self):
-        super().test_ema_training()
+        original_parallel_loading = constants.HF_ENABLE_PARALLEL_LOADING
+        original_parallel_workers = getattr(constants, "HF_PARALLEL_WORKERS", None)
 
-    @unittest.skip("Test is not supported for handling main inputs that are lists.")
-    def test_effective_gradient_checkpointing(self):
-        super().test_effective_gradient_checkpointing()
+        try:
+            model.cpu().save_pretrained(tmp_path, max_shard_size=f"{max_shard_size}KB")
+            assert os.path.exists(os.path.join(tmp_path, SAFE_WEIGHTS_INDEX_NAME))
 
-    @unittest.skip(
-        "Test needs to be revisited. But we need to ensure `x_pad_token` and `cap_pad_token` are cast to the same dtype as the destination tensor before they are assigned to the padding indices."
+            expected_num_shards = calculate_expected_num_shards(os.path.join(tmp_path, SAFE_WEIGHTS_INDEX_NAME))
+            actual_num_shards = len([file for file in os.listdir(tmp_path) if file.endswith(".safetensors")])
+            assert actual_num_shards == expected_num_shards
+
+            constants.HF_ENABLE_PARALLEL_LOADING = False
+            self.model_class.from_pretrained(tmp_path).eval().to(torch_device)
+
+            constants.HF_ENABLE_PARALLEL_LOADING = True
+            constants.DEFAULT_HF_PARALLEL_LOADING_WORKERS = 2
+
+            torch.manual_seed(0)
+            model_parallel = self.model_class.from_pretrained(tmp_path).eval()
+            model_parallel = model_parallel.to(torch_device)
+
+            output_parallel = _concat_list_output(model_parallel(**inputs_dict, return_dict=False)[0])
+
+            assert_tensors_close(
+                base_output, output_parallel, atol=atol, rtol=rtol, msg="Output should match with parallel loading"
+            )
+        finally:
+            constants.HF_ENABLE_PARALLEL_LOADING = original_parallel_loading
+            if original_parallel_workers is not None:
+                constants.HF_PARALLEL_WORKERS = original_parallel_workers
+
+
+class TestZImageTransformerMemory(ZImageTransformerTesterConfig, MemoryTesterMixin):
+    """Memory optimization tests for Z-Image Transformer."""
+
+    @pytest.mark.skip(
+        "Ensure `x_pad_token` and `cap_pad_token` are cast to the same dtype as the destination tensor before they are assigned to the padding indices."
     )
     def test_layerwise_casting_training(self):
-        super().test_layerwise_casting_training()
+        pass
 
-    @unittest.skip("Test is not supported for handling main inputs that are lists.")
-    def test_outputs_equivalence(self):
-        super().test_outputs_equivalence()
 
-    @unittest.skip("Test will pass if we change to deterministic values instead of empty in the DiT.")
-    def test_group_offloading(self):
-        super().test_group_offloading()
+class TestZImageTransformerTraining(ZImageTransformerTesterConfig, TrainingTesterMixin):
+    """Training tests for Z-Image Transformer."""
 
-    @unittest.skip("Test will pass if we change to deterministic values instead of empty in the DiT.")
-    def test_group_offloading_with_disk(self):
-        super().test_group_offloading_with_disk()
+    def test_gradient_checkpointing_is_applied(self):
+        super().test_gradient_checkpointing_is_applied(expected_set={"ZImageTransformer2DModel"})
+
+    @pytest.mark.skip("Test is not supported for handling main inputs that are lists.")
+    def test_training(self):
+        pass
 
+    @pytest.mark.skip("Test is not supported for handling main inputs that are lists.")
+    def test_training_with_ema(self):
+        pass
 
-class ZImageTransformerCompileTests(TorchCompileTesterMixin, unittest.TestCase):
-    model_class = ZImageTransformer2DModel
-    different_shapes_for_compilation = [(4, 4), (4, 8), (8, 8)]
+    @pytest.mark.skip("Test is not supported for handling main inputs that are lists.")
+    def test_gradient_checkpointing_equivalence(self, loss_tolerance=1e-5, param_grad_tol=5e-5, skip=None):
+        pass
 
-    def prepare_init_args_and_inputs_for_common(self):
-        return ZImageTransformerTests().prepare_init_args_and_inputs_for_common()
 
-    def prepare_dummy_input(self, height, width):
-        return ZImageTransformerTests().prepare_dummy_input(height=height, width=width)
+class TestZImageTransformerLoRA(ZImageTransformerTesterConfig, LoraTesterMixin):
+    """LoRA adapter tests for Z-Image Transformer."""
+
+    @pytest.mark.skip("Model output `sample` is a list of tensors, not a single tensor.")
+    def test_save_load_lora_adapter(self, tmp_path, rank=4, lora_alpha=4, use_dora=False, atol=1e-4, rtol=1e-4):
+        pass
+
+
+# TODO: Add pretrained_model_name_or_path once a tiny Z-Image model is available on the Hub
+# class TestZImageTransformerBitsAndBytes(ZImageTransformerTesterConfig, BitsAndBytesTesterMixin):
+#     """BitsAndBytes quantization tests for Z-Image Transformer."""
+
+
+# TODO: Add pretrained_model_name_or_path once a tiny Z-Image model is available on the Hub
+# class TestZImageTransformerTorchAo(ZImageTransformerTesterConfig, TorchAoTesterMixin):
+#     """TorchAo quantization tests for Z-Image Transformer."""
+
+
+class TestZImageTransformerCompile(ZImageTransformerTesterConfig, TorchCompileTesterMixin):
+    """Torch compile tests for Z-Image Transformer."""
+
+    @property
+    def different_shapes_for_compilation(self):
+        return [(4, 4), (4, 8), (8, 8)]
+
+    def get_dummy_inputs(self, height: int = 16, width: int = 16) -> dict[str, torch.Tensor | list]:
+        batch_size = 1
+        num_channels = 16
+        embedding_dim = 16
+        sequence_length = 16
+
+        hidden_states = [
+            randn_tensor((num_channels, 1, height, width), generator=self.generator, device=torch_device)
+            for _ in range(batch_size)
+        ]
+        encoder_hidden_states = [
+            randn_tensor((sequence_length, embedding_dim), generator=self.generator, device=torch_device)
+            for _ in range(batch_size)
+        ]
+        timestep = torch.tensor([0.0]).to(torch_device)
+
+        return {"x": hidden_states, "cap_feats": encoder_hidden_states, "t": timestep}
 
-    @unittest.skip(
-        "The repeated block in this model is ZImageTransformerBlock, which is used for noise_refiner, context_refiner, and layers. As a consequence of this, the inputs recorded for the block would vary during compilation and full compilation with fullgraph=True would trigger recompilation at least thrice."
+    @pytest.mark.skip(
+        "The repeated block in this model is ZImageTransformerBlock, which is used for noise_refiner, context_refiner, and layers. The inputs recorded for the block would vary during compilation and full compilation with fullgraph=True would trigger recompilation at least thrice."
     )
     def test_torch_compile_recompilation_and_graph_break(self):
-        super().test_torch_compile_recompilation_and_graph_break()
+        pass
 
-    @unittest.skip("Fullgraph AoT is broken")
-    def test_compile_works_with_aot(self):
-        super().test_compile_works_with_aot()
+    @pytest.mark.skip("Fullgraph AoT is broken")
+    def test_compile_works_with_aot(self, tmp_path):
+        pass
 
-    @unittest.skip("Fullgraph is broken")
+    @pytest.mark.skip("Fullgraph is broken")
     def test_compile_on_different_shapes(self):
-        super().test_compile_on_different_shapes()
+        pass