refactor hotswap tester.

Author: sayakpaul

tests/models/test_modeling_common.py

@@ -59,7 +59,6 @@
 from diffusers.utils.testing_utils import (
     CaptureLogger,
     backend_empty_cache,
-    floats_tensor,
     get_python_version,
     is_torch_compile,
     numpy_cosine_similarity_distance,
@@ -1720,7 +1719,7 @@ def test_push_to_hub_library_name(self):
 @require_peft_backend
 @require_peft_version_greater("0.14.0")
 @is_torch_compile
-class TestLoraHotSwappingForModel(unittest.TestCase):
+class LoraHotSwappingForModelTesterMixin:
     """Test that hotswapping does not result in recompilation on the model directly.
 
     We're not extensively testing the hotswapping functionality since it is implemented in PEFT and is extensively
@@ -1741,48 +1740,24 @@ def tearDown(self):
         gc.collect()
         backend_empty_cache(torch_device)
 
-    def get_small_unet(self):
-        # from diffusers UNet2DConditionModelTests
-        torch.manual_seed(0)
-        init_dict = {
-            "block_out_channels": (4, 8),
-            "norm_num_groups": 4,
-            "down_block_types": ("CrossAttnDownBlock2D", "DownBlock2D"),
-            "up_block_types": ("UpBlock2D", "CrossAttnUpBlock2D"),
-            "cross_attention_dim": 8,
-            "attention_head_dim": 2,
-            "out_channels": 4,
-            "in_channels": 4,
-            "layers_per_block": 1,
-            "sample_size": 16,
-        }
-        model = UNet2DConditionModel(**init_dict)
-        return model.to(torch_device)
-
-    def get_unet_lora_config(self, lora_rank, lora_alpha, target_modules):
+    def get_lora_config(self, lora_rank, lora_alpha, target_modules):
         # from diffusers test_models_unet_2d_condition.py
         from peft import LoraConfig
 
-        unet_lora_config = LoraConfig(
+        lora_config = LoraConfig(
             r=lora_rank,
             lora_alpha=lora_alpha,
             target_modules=target_modules,
             init_lora_weights=False,
             use_dora=False,
         )
-        return unet_lora_config
-
-    def get_dummy_input(self):
-        # from UNet2DConditionModelTests
-        batch_size = 4
-        num_channels = 4
-        sizes = (16, 16)
-
-        noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
-        time_step = torch.tensor([10]).to(torch_device)
-        encoder_hidden_states = floats_tensor((batch_size, 4, 8)).to(torch_device)
+        return lora_config
 
-        return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}
+    def get_linear_module_name_other_than_attn(self, model):
+        linear_names = [
+            name for name, module in model.named_modules() if isinstance(module, nn.Linear) and "to_" not in name
+        ]
+        return linear_names[0]
 
     def check_model_hotswap(self, do_compile, rank0, rank1, target_modules0, target_modules1=None):
         """
@@ -1800,23 +1775,26 @@ def check_model_hotswap(self, do_compile, rank0, rank1, target_modules0, target_
         fine.
         """
         # create 2 adapters with different ranks and alphas
-        dummy_input = self.get_dummy_input()
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**init_dict).to(torch_device)
+
         alpha0, alpha1 = rank0, rank1
         max_rank = max([rank0, rank1])
         if target_modules1 is None:
             target_modules1 = target_modules0[:]
-        lora_config0 = self.get_unet_lora_config(rank0, alpha0, target_modules0)
-        lora_config1 = self.get_unet_lora_config(rank1, alpha1, target_modules1)
+        lora_config0 = self.get_lora_config(rank0, alpha0, target_modules0)
+        lora_config1 = self.get_lora_config(rank1, alpha1, target_modules1)
 
-        unet = self.get_small_unet()
-        unet.add_adapter(lora_config0, adapter_name="adapter0")
+        model.add_adapter(lora_config0, adapter_name="adapter0")
         with torch.inference_mode():
-            output0_before = unet(**dummy_input)["sample"]
+            torch.manual_seed(0)
+            output0_before = model(**inputs_dict)["sample"]
 
-        unet.add_adapter(lora_config1, adapter_name="adapter1")
-        unet.set_adapter("adapter1")
+        model.add_adapter(lora_config1, adapter_name="adapter1")
+        model.set_adapter("adapter1")
         with torch.inference_mode():
-            output1_before = unet(**dummy_input)["sample"]
+            torch.manual_seed(0)
+            output1_before = model(**inputs_dict)["sample"]
 
         # sanity checks:
         tol = 5e-3
@@ -1826,40 +1804,44 @@ def check_model_hotswap(self, do_compile, rank0, rank1, target_modules0, target_
 
         with tempfile.TemporaryDirectory() as tmp_dirname:
             # save the adapter checkpoints
-            unet.save_lora_adapter(os.path.join(tmp_dirname, "0"), safe_serialization=True, adapter_name="adapter0")
-            unet.save_lora_adapter(os.path.join(tmp_dirname, "1"), safe_serialization=True, adapter_name="adapter1")
-            del unet
+            model.save_lora_adapter(os.path.join(tmp_dirname, "0"), safe_serialization=True, adapter_name="adapter0")
+            model.save_lora_adapter(os.path.join(tmp_dirname, "1"), safe_serialization=True, adapter_name="adapter1")
+            del model
 
             # load the first adapter
-            unet = self.get_small_unet()
+            init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+            model = self.model_class(**init_dict).to(torch_device)
+
             if do_compile or (rank0 != rank1):
                 # no need to prepare if the model is not compiled or if the ranks are identical
-                unet.enable_lora_hotswap(target_rank=max_rank)
+                model.enable_lora_hotswap(target_rank=max_rank)
 
             file_name0 = os.path.join(os.path.join(tmp_dirname, "0"), "pytorch_lora_weights.safetensors")
             file_name1 = os.path.join(os.path.join(tmp_dirname, "1"), "pytorch_lora_weights.safetensors")
-            unet.load_lora_adapter(file_name0, safe_serialization=True, adapter_name="adapter0", prefix=None)
+            model.load_lora_adapter(file_name0, safe_serialization=True, adapter_name="adapter0", prefix=None)
 
             if do_compile:
-                unet = torch.compile(unet, mode="reduce-overhead")
+                model = torch.compile(model, mode="reduce-overhead")
 
             with torch.inference_mode():
-                output0_after = unet(**dummy_input)["sample"]
+                torch.manual_seed(0)
+                output0_after = model(**inputs_dict)["sample"]
             assert torch.allclose(output0_before, output0_after, atol=tol, rtol=tol)
 
             # hotswap the 2nd adapter
-            unet.load_lora_adapter(file_name1, adapter_name="adapter0", hotswap=True, prefix=None)
+            model.load_lora_adapter(file_name1, adapter_name="adapter0", hotswap=True, prefix=None)
 
             # we need to call forward to potentially trigger recompilation
             with torch.inference_mode():
-                output1_after = unet(**dummy_input)["sample"]
+                torch.manual_seed(0)
+                output1_after = model(**inputs_dict)["sample"]
             assert torch.allclose(output1_before, output1_after, atol=tol, rtol=tol)
 
             # check error when not passing valid adapter name
             name = "does-not-exist"
             msg = f"Trying to hotswap LoRA adapter '{name}' but there is no existing adapter by that name"
             with self.assertRaisesRegex(ValueError, msg):
-                unet.load_lora_adapter(file_name1, adapter_name=name, hotswap=True, prefix=None)
+                model.load_lora_adapter(file_name1, adapter_name=name, hotswap=True, prefix=None)
 
     @parameterized.expand([(11, 11), (7, 13), (13, 7)])  # important to test small to large and vice versa
     def test_hotswapping_model(self, rank0, rank1):
@@ -1877,58 +1859,86 @@ def test_hotswapping_compiled_model_linear(self, rank0, rank1):
     @parameterized.expand([(11, 11), (7, 13), (13, 7)])  # important to test small to large and vice versa
     def test_hotswapping_compiled_model_conv2d(self, rank0, rank1):
         # It's important to add this context to raise an error on recompilation
+        if "unet" not in self.model_class.__name__.lower():
+            return
+
         target_modules = ["conv", "conv1", "conv2"]
         with torch._dynamo.config.patch(error_on_recompile=True):
             self.check_model_hotswap(do_compile=True, rank0=rank0, rank1=rank1, target_modules0=target_modules)
 
     @parameterized.expand([(11, 11), (7, 13), (13, 7)])  # important to test small to large and vice versa
     def test_hotswapping_compiled_model_both_linear_and_conv2d(self, rank0, rank1):
         # It's important to add this context to raise an error on recompilation
+        if "unet" not in self.model_class.__name__.lower():
+            return
+
         target_modules = ["to_q", "conv"]
         with torch._dynamo.config.patch(error_on_recompile=True):
             self.check_model_hotswap(do_compile=True, rank0=rank0, rank1=rank1, target_modules0=target_modules)
 
+    @parameterized.expand([(11, 11), (7, 13), (13, 7)])  # important to test small to large and vice versa
+    def test_hotswapping_compiled_model_both_linear_and_other(self, rank0, rank1):
+        # In `test_hotswapping_compiled_model_both_linear_and_conv2d()`, we check if we can do hotswapping
+        # with `torch.compile()` for models that have both linear and conv layers. In this test, we check
+        # if we can target a linear layer from the transformer blocks and another linear layer from non-attention
+        # block.
+        # It's important to add this context to raise an error on recompilation
+        target_modules = ["to_q"]
+        init_dict, _ = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**init_dict)
+
+        target_modules.append(self.get_linear_module_name_other_than_attn(model))
+        del model
+
+        with torch._dynamo.config.patch(error_on_recompile=True):
+            self.check_model_hotswap(do_compile=True, rank0=rank0, rank1=rank1, target_modules0=target_modules)
+
     def test_enable_lora_hotswap_called_after_adapter_added_raises(self):
         # ensure that enable_lora_hotswap is called before loading the first adapter
-        lora_config = self.get_unet_lora_config(8, 8, target_modules=["to_q"])
-        unet = self.get_small_unet()
-        unet.add_adapter(lora_config)
+        lora_config = self.get_lora_config(8, 8, target_modules=["to_q"])
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**init_dict).to(torch_device)
+        model.add_adapter(lora_config)
+
         msg = re.escape("Call `enable_lora_hotswap` before loading the first adapter.")
         with self.assertRaisesRegex(RuntimeError, msg):
-            unet.enable_lora_hotswap(target_rank=32)
+            model.enable_lora_hotswap(target_rank=32)
 
     def test_enable_lora_hotswap_called_after_adapter_added_warning(self):
         # ensure that enable_lora_hotswap is called before loading the first adapter
         from diffusers.loaders.peft import logger
 
-        lora_config = self.get_unet_lora_config(8, 8, target_modules=["to_q"])
-        unet = self.get_small_unet()
-        unet.add_adapter(lora_config)
+        lora_config = self.get_lora_config(8, 8, target_modules=["to_q"])
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**init_dict).to(torch_device)
+        model.add_adapter(lora_config)
         msg = (
             "It is recommended to call `enable_lora_hotswap` before loading the first adapter to avoid recompilation."
         )
         with self.assertLogs(logger=logger, level="WARNING") as cm:
-            unet.enable_lora_hotswap(target_rank=32, check_compiled="warn")
+            model.enable_lora_hotswap(target_rank=32, check_compiled="warn")
             assert any(msg in log for log in cm.output)
 
     def test_enable_lora_hotswap_called_after_adapter_added_ignore(self):
         # check possibility to ignore the error/warning
-        lora_config = self.get_unet_lora_config(8, 8, target_modules=["to_q"])
-        unet = self.get_small_unet()
-        unet.add_adapter(lora_config)
+        lora_config = self.get_lora_config(8, 8, target_modules=["to_q"])
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**init_dict).to(torch_device)
+        model.add_adapter(lora_config)
         with warnings.catch_warnings(record=True) as w:
             warnings.simplefilter("always")  # Capture all warnings
-            unet.enable_lora_hotswap(target_rank=32, check_compiled="warn")
+            model.enable_lora_hotswap(target_rank=32, check_compiled="warn")
             self.assertEqual(len(w), 0, f"Expected no warnings, but got: {[str(warn.message) for warn in w]}")
 
     def test_enable_lora_hotswap_wrong_check_compiled_argument_raises(self):
         # check that wrong argument value raises an error
-        lora_config = self.get_unet_lora_config(8, 8, target_modules=["to_q"])
-        unet = self.get_small_unet()
-        unet.add_adapter(lora_config)
+        lora_config = self.get_lora_config(8, 8, target_modules=["to_q"])
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**init_dict).to(torch_device)
+        model.add_adapter(lora_config)
         msg = re.escape("check_compiles should be one of 'error', 'warn', or 'ignore', got 'wrong-argument' instead.")
         with self.assertRaisesRegex(ValueError, msg):
-            unet.enable_lora_hotswap(target_rank=32, check_compiled="wrong-argument")
+            model.enable_lora_hotswap(target_rank=32, check_compiled="wrong-argument")
 
     def test_hotswap_second_adapter_targets_more_layers_raises(self):
         # check the error and log

tests/models/transformers/test_models_transformer_flux.py

@@ -22,7 +22,7 @@
 from diffusers.models.embeddings import ImageProjection
 from diffusers.utils.testing_utils import enable_full_determinism, torch_device
 
-from ..test_modeling_common import ModelTesterMixin
+from ..test_modeling_common import LoraHotSwappingForModelTesterMixin, ModelTesterMixin
 
 
 enable_full_determinism()
@@ -78,7 +78,7 @@ def create_flux_ip_adapter_state_dict(model):
     return ip_state_dict
 
 
-class FluxTransformerTests(ModelTesterMixin, unittest.TestCase):
+class FluxTransformerTests(ModelTesterMixin, LoraHotSwappingForModelTesterMixin, unittest.TestCase):
     model_class = FluxTransformer2DModel
     main_input_name = "hidden_states"
     # We override the items here because the transformer under consideration is small.

tests/models/unets/test_models_unet_2d_condition.py

@@ -53,7 +53,7 @@
     torch_device,
 )
 
-from ..test_modeling_common import ModelTesterMixin, UNetTesterMixin
+from ..test_modeling_common import LoraHotSwappingForModelTesterMixin, ModelTesterMixin, UNetTesterMixin
 
 
 if is_peft_available():
@@ -350,7 +350,9 @@ def create_custom_diffusion_layers(model, mock_weights: bool = True):
     return custom_diffusion_attn_procs
 
 
-class UNet2DConditionModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
+class UNet2DConditionModelTests(
+    ModelTesterMixin, LoraHotSwappingForModelTesterMixin, UNetTesterMixin, unittest.TestCase
+):
     model_class = UNet2DConditionModel
     main_input_name = "sample"
     # We override the items here because the unet under consideration is small.