device map legacy attention block weight conversion

src/diffusers/models/attention_processor.py

@@ -78,6 +78,7 @@ def __init__(
         self.upcast_softmax = upcast_softmax
         self.rescale_output_factor = rescale_output_factor
         self.residual_connection = residual_connection
+        self.dropout = dropout
 
         # we make use of this private variable to know whether this class is loaded
         # with an deprecated state dict so that we can convert it on the fly

src/diffusers/models/modeling_utils.py

@@ -22,7 +22,7 @@
 from typing import Any, Callable, List, Optional, Tuple, Union
 
 import torch
-from torch import Tensor, device
+from torch import Tensor, device, nn
 
 from .. import __version__
 from ..utils import (
@@ -646,15 +646,47 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                 else:  # else let accelerate handle loading and dispatching.
                     # Load weights and dispatch according to the device_map
                     # by default the device_map is None and the weights are loaded on the CPU
-                    accelerate.load_checkpoint_and_dispatch(
-                        model,
-                        model_file,
-                        device_map,
-                        max_memory=max_memory,
-                        offload_folder=offload_folder,
-                        offload_state_dict=offload_state_dict,
-                        dtype=torch_dtype,
-                    )
+                    try:
+                        accelerate.load_checkpoint_and_dispatch(
+                            model,
+                            model_file,
+                            device_map,
+                            max_memory=max_memory,
+                            offload_folder=offload_folder,
+                            offload_state_dict=offload_state_dict,
+                            dtype=torch_dtype,
+                        )
+                    except AttributeError as e:
+                        # When using accelerate loading, we do not have the ability to load the state
+                        # dict and rename the weight names manually. Additionally, accelerate skips
+                        # torch loading conventions and directly writes into `module.{_buffers, _parameters}`
+                        # (which look like they should be private variables?), so we can't use the standard hooks
+                        # to rename parameters on load. We need to mimic the original weight names so the correct
+                        # attributes are available. After we have loaded the weights, we convert the deprecated
+                        # names to the new non-deprecated names. Then we _greatly encourage_ the user to convert
+                        # the weights so we don't have to do this again.
+
+                        if "'Attention' object has no attribute" in str(e):
+                            logger.warn(
+                                f"Taking `{str(e)}` while using `accelerate.load_checkpoint_and_dispatch` to mean {pretrained_model_name_or_path}"
+                                " was saved with deprecated attention block weight names. We will load it with the deprecated attention block"
+                                " names and convert them on the fly to the new attention block format. Please re-save the model after this conversion,"
+                                " so we don't have to do the on the fly renaming in the future. If the model is from a hub checkpoint,"
+                                " please also re-upload it or open a PR on the original repository."
+                            )
+                            model._temp_convert_self_to_deprecated_attention_blocks()
+                            accelerate.load_checkpoint_and_dispatch(
+                                model,
+                                model_file,
+                                device_map,
+                                max_memory=max_memory,
+                                offload_folder=offload_folder,
+                                offload_state_dict=offload_state_dict,
+                                dtype=torch_dtype,
+                            )
+                            model._undo_temp_convert_self_to_deprecated_attention_blocks()
+                        else:
+                            raise e
 
                 loading_info = {
                     "missing_keys": [],
@@ -889,3 +921,53 @@ def recursive_find_attn_block(name, module):
                 state_dict[f"{path}.to_out.0.weight"] = state_dict.pop(f"{path}.proj_attn.weight")
             if f"{path}.proj_attn.bias" in state_dict:
                 state_dict[f"{path}.to_out.0.bias"] = state_dict.pop(f"{path}.proj_attn.bias")
+
+    def _temp_convert_self_to_deprecated_attention_blocks(self):
+        deprecated_attention_block_modules = []
+
+        def recursive_find_attn_block(module):
+            if hasattr(module, "_from_deprecated_attn_block") and module._from_deprecated_attn_block:
+                deprecated_attention_block_modules.append(module)
+
+            for sub_module in module.children():
+                recursive_find_attn_block(sub_module)
+
+        recursive_find_attn_block(self)
+
+        for module in deprecated_attention_block_modules:
+            module.query = module.to_q
+            module.key = module.to_k
+            module.value = module.to_v
+            module.proj_attn = module.to_out[0]
+
+            # We don't _have_ to delete the old attributes, but it's helpful to ensure
+            # that _all_ the weights are loaded into the new attributes and we're not
+            # making an incorrect assumption that this model should be converted when
+            # it really shouldn't be.
+            del module.to_q
+            del module.to_k
+            del module.to_v
+            del module.to_out
+
+    def _undo_temp_convert_self_to_deprecated_attention_blocks(self):
+        deprecated_attention_block_modules = []
+
+        def recursive_find_attn_block(module):
+            if hasattr(module, "_from_deprecated_attn_block") and module._from_deprecated_attn_block:
+                deprecated_attention_block_modules.append(module)
+
+            for sub_module in module.children():
+                recursive_find_attn_block(sub_module)
+
+        recursive_find_attn_block(self)
+
+        for module in deprecated_attention_block_modules:
+            module.to_q = module.query
+            module.to_k = module.key
+            module.to_v = module.value
+            module.to_out = nn.ModuleList([module.proj_attn, nn.Dropout(module.dropout)])
+
+            del module.query
+            del module.key
+            del module.value
+            del module.proj_attn

tests/models/test_attention_processor.py

@@ -1,7 +1,10 @@
+import tempfile
 import unittest
 
+import numpy as np
 import torch
 
+from diffusers import DiffusionPipeline
 from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor
 
 
@@ -73,3 +76,44 @@ def test_only_cross_attention(self):
         only_cross_attn_out = attn(**forward_args)
 
         self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all())
+
+
+class DeprecatedAttentionBlockTests(unittest.TestCase):
+    def test_conversion_when_using_device_map(self):
+        pipe = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None)
+
+        pre_conversion = pipe(
+            "foo",
+            num_inference_steps=2,
+            generator=torch.Generator("cpu").manual_seed(0),
+            output_type="np",
+        ).images
+
+        # the initial conversion succeeds
+        pipe = DiffusionPipeline.from_pretrained(
+            "hf-internal-testing/tiny-stable-diffusion-pipe", device_map="sequential", safety_checker=None
+        )
+
+        conversion = pipe(
+            "foo",
+            num_inference_steps=2,
+            generator=torch.Generator("cpu").manual_seed(0),
+            output_type="np",
+        ).images
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            # save the converted model
+            pipe.save_pretrained(tmpdir)
+
+            # can also load the converted weights
+            pipe = DiffusionPipeline.from_pretrained(tmpdir, device_map="sequential", safety_checker=None)
+
+        after_conversion = pipe(
+            "foo",
+            num_inference_steps=2,
+            generator=torch.Generator("cpu").manual_seed(0),
+            output_type="np",
+        ).images
+
+        self.assertTrue(np.allclose(pre_conversion, conversion))
+        self.assertTrue(np.allclose(conversion, after_conversion))