Enable Gradient Checkpointing for UNet2DModel (New) (huggingface#7201)

* Port UNet2DModel gradient checkpointing code from huggingface#6718.


---------

Co-authored-by: Sayak Paul <[email protected]>
Co-authored-by: Vincent Neemie <[email protected]>
Co-authored-by: Patrick von Platen <[email protected]>
Co-authored-by: Dhruv Nair <[email protected]>
Co-authored-by: hlky <[email protected]>

Author: 6 people

src/diffusers/models/unets/unet_2d.py

@@ -93,6 +93,8 @@ class UNet2DModel(ModelMixin, ConfigMixin):
             The number of discrete classes. The number of class types per each class is provided in tuple-like array.
     """
 
+    _supports_gradient_checkpointing = True
+
     @register_to_config
     def __init__(
         self,
@@ -267,6 +269,10 @@ def __init__(
         self.conv_act = nn.SiLU()
         self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, kernel_size=3, padding=1)
 
+    def _set_gradient_checkpointing(self, module, value=False):
+        if hasattr(module, "gradient_checkpointing"):
+            module.gradient_checkpointing = value
+
     def forward(
         self,
         sample: torch.Tensor,

src/diffusers/models/unets/unet_2d_blocks.py

@@ -731,12 +731,35 @@ def __init__(
         self.attentions = nn.ModuleList(attentions)
         self.resnets = nn.ModuleList(resnets)
 
+        self.gradient_checkpointing = False
+
     def forward(self, hidden_states: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:
         hidden_states = self.resnets[0](hidden_states, temb)
         for attn, resnet in zip(self.attentions, self.resnets[1:]):
-            if attn is not None:
-                hidden_states = attn(hidden_states, temb=temb)
-            hidden_states = resnet(hidden_states, temb)
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                if attn is not None:
+                    hidden_states = attn(hidden_states, temb=temb)
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(resnet),
+                    hidden_states,
+                    temb,
+                    **ckpt_kwargs,
+                )
+            else:
+                if attn is not None:
+                    hidden_states = attn(hidden_states, temb=temb)
+                hidden_states = resnet(hidden_states, temb)
 
         return hidden_states
 
@@ -1116,6 +1139,8 @@ def __init__(
         else:
             self.downsamplers = None
 
+        self.gradient_checkpointing = False
+
     def forward(
         self,
         hidden_states: torch.Tensor,
@@ -1130,9 +1155,30 @@ def forward(
         output_states = ()
 
         for resnet, attn in zip(self.resnets, self.attentions):
-            hidden_states = resnet(hidden_states, temb)
-            hidden_states = attn(hidden_states, **cross_attention_kwargs)
-            output_states = output_states + (hidden_states,)
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(resnet),
+                    hidden_states,
+                    temb,
+                    **ckpt_kwargs,
+                )
+                hidden_states = attn(hidden_states, **cross_attention_kwargs)
+                output_states = output_states + (hidden_states,)
+            else:
+                hidden_states = resnet(hidden_states, temb)
+                hidden_states = attn(hidden_states, **cross_attention_kwargs)
+                output_states = output_states + (hidden_states,)
 
         if self.downsamplers is not None:
             for downsampler in self.downsamplers:
@@ -2354,6 +2400,7 @@ def __init__(
         else:
             self.upsamplers = None
 
+        self.gradient_checkpointing = False
         self.resolution_idx = resolution_idx
 
     def forward(
@@ -2375,8 +2422,28 @@ def forward(
             res_hidden_states_tuple = res_hidden_states_tuple[:-1]
             hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
 
-            hidden_states = resnet(hidden_states, temb)
-            hidden_states = attn(hidden_states)
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(resnet),
+                    hidden_states,
+                    temb,
+                    **ckpt_kwargs,
+                )
+                hidden_states = attn(hidden_states)
+            else:
+                hidden_states = resnet(hidden_states, temb)
+                hidden_states = attn(hidden_states)
 
         if self.upsamplers is not None:
             for upsampler in self.upsamplers:

src/diffusers/pipelines/deprecated/versatile_diffusion/modeling_text_unet.py

@@ -2223,12 +2223,35 @@ def __init__(
         self.attentions = nn.ModuleList(attentions)
         self.resnets = nn.ModuleList(resnets)
 
+        self.gradient_checkpointing = False
+
     def forward(self, hidden_states: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:
         hidden_states = self.resnets[0](hidden_states, temb)
         for attn, resnet in zip(self.attentions, self.resnets[1:]):
-            if attn is not None:
-                hidden_states = attn(hidden_states, temb=temb)
-            hidden_states = resnet(hidden_states, temb)
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                if attn is not None:
+                    hidden_states = attn(hidden_states, temb=temb)
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(resnet),
+                    hidden_states,
+                    temb,
+                    **ckpt_kwargs,
+                )
+            else:
+                if attn is not None:
+                    hidden_states = attn(hidden_states, temb=temb)
+                hidden_states = resnet(hidden_states, temb)
 
         return hidden_states
 

tests/models/autoencoders/test_models_autoencoder_kl.py

@@ -146,7 +146,7 @@ def test_enable_disable_slicing(self):
         )
 
     def test_gradient_checkpointing_is_applied(self):
-        expected_set = {"Decoder", "Encoder"}
+        expected_set = {"Decoder", "Encoder", "UNetMidBlock2D"}
         super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
 
     def test_from_pretrained_hub(self):

tests/models/autoencoders/test_models_autoencoder_kl_temporal_decoder.py

@@ -65,7 +65,7 @@ def prepare_init_args_and_inputs_for_common(self):
         return init_dict, inputs_dict
 
     def test_gradient_checkpointing_is_applied(self):
-        expected_set = {"Encoder", "TemporalDecoder"}
+        expected_set = {"Encoder", "TemporalDecoder", "UNetMidBlock2D"}
         super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
 
     @unittest.skip("Test unsupported.")

tests/models/test_modeling_common.py

@@ -803,7 +803,7 @@ def test_enable_disable_gradient_checkpointing(self):
         self.assertFalse(model.is_gradient_checkpointing)
 
     @require_torch_accelerator_with_training
-    def test_effective_gradient_checkpointing(self, loss_tolerance=1e-5, param_grad_tol=5e-5):
+    def test_effective_gradient_checkpointing(self, loss_tolerance=1e-5, param_grad_tol=5e-5, skip: set[str] = {}):
         if not self.model_class._supports_gradient_checkpointing:
             return  # Skip test if model does not support gradient checkpointing
 
@@ -850,6 +850,8 @@ def test_effective_gradient_checkpointing(self, loss_tolerance=1e-5, param_grad_
         for name, param in named_params.items():
             if "post_quant_conv" in name:
                 continue
+            if name in skip:
+                continue
             self.assertTrue(torch_all_close(param.grad.data, named_params_2[name].grad.data, atol=param_grad_tol))
 
     @unittest.skipIf(torch_device == "mps", "This test is not supported for MPS devices.")

tests/models/unets/test_models_unet_2d.py

@@ -105,6 +105,23 @@ def test_mid_block_attn_groups(self):
         expected_shape = inputs_dict["sample"].shape
         self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
 
+    def test_gradient_checkpointing_is_applied(self):
+        expected_set = {
+            "AttnUpBlock2D",
+            "AttnDownBlock2D",
+            "UNetMidBlock2D",
+            "UpBlock2D",
+            "DownBlock2D",
+        }
+
+        # NOTE: unlike UNet2DConditionModel, UNet2DModel does not currently support tuples for `attention_head_dim`
+        attention_head_dim = 8
+        block_out_channels = (16, 32)
+
+        super().test_gradient_checkpointing_is_applied(
+            expected_set=expected_set, attention_head_dim=attention_head_dim, block_out_channels=block_out_channels
+        )
+
 
 class UNetLDMModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
     model_class = UNet2DModel
@@ -220,6 +237,17 @@ def test_output_pretrained(self):
 
         self.assertTrue(torch_all_close(output_slice, expected_output_slice, rtol=1e-3))
 
+    def test_gradient_checkpointing_is_applied(self):
+        expected_set = {"DownBlock2D", "UNetMidBlock2D", "UpBlock2D"}
+
+        # NOTE: unlike UNet2DConditionModel, UNet2DModel does not currently support tuples for `attention_head_dim`
+        attention_head_dim = 32
+        block_out_channels = (32, 64)
+
+        super().test_gradient_checkpointing_is_applied(
+            expected_set=expected_set, attention_head_dim=attention_head_dim, block_out_channels=block_out_channels
+        )
+
 
 class NCSNppModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
     model_class = UNet2DModel
@@ -329,3 +357,17 @@ def test_output_pretrained_ve_large(self):
     def test_forward_with_norm_groups(self):
         # not required for this model
         pass
+
+    def test_gradient_checkpointing_is_applied(self):
+        expected_set = {
+            "UNetMidBlock2D",
+        }
+
+        block_out_channels = (32, 64, 64, 64)
+
+        super().test_gradient_checkpointing_is_applied(
+            expected_set=expected_set, block_out_channels=block_out_channels
+        )
+
+    def test_effective_gradient_checkpointing(self):
+        super().test_effective_gradient_checkpointing(skip={"time_proj.weight"})