Move accelerate to a soft-dependency (huggingface#1134)

* finish

* finish

* Update src/diffusers/modeling_utils.py

* Update src/diffusers/pipeline_utils.py

Co-authored-by: Anton Lozhkov <[email protected]>

* more fixes

* fix

Co-authored-by: Anton Lozhkov <[email protected]>

Author: patrickvonplaten

__init__.py

@@ -1,5 +1,4 @@
 from .utils import (
-    is_accelerate_available,
     is_flax_available,
     is_inflect_available,
     is_onnx_available,
@@ -17,13 +16,6 @@
 from .utils import logging
 
 
-# This will create an extra dummy file "dummy_torch_and_accelerate_objects.py"
-# TODO: (patil-suraj, anton-l) maybe import everything under is_torch_and_accelerate_available
-if is_torch_available() and not is_accelerate_available():
-    error_msg = "Please install the `accelerate` library to use Diffusers with PyTorch. You can do so by running `pip install diffusers[torch]`. Or if torch is already installed, you can run `pip install accelerate`."  # noqa: E501
-    raise ImportError(error_msg)
-
-
 if is_torch_available():
     from .modeling_utils import ModelMixin
     from .models import AutoencoderKL, Transformer2DModel, UNet1DModel, UNet2DConditionModel, UNet2DModel, VQModel

modeling_utils.py

@@ -21,15 +21,20 @@
 import torch
 from torch import Tensor, device
 
-import accelerate
-from accelerate.utils import set_module_tensor_to_device
-from accelerate.utils.versions import is_torch_version
 from huggingface_hub import hf_hub_download
 from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError
 from requests import HTTPError
 
 from . import __version__
-from .utils import CONFIG_NAME, DIFFUSERS_CACHE, HUGGINGFACE_CO_RESOLVE_ENDPOINT, WEIGHTS_NAME, logging
+from .utils import (
+    CONFIG_NAME,
+    DIFFUSERS_CACHE,
+    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
+    WEIGHTS_NAME,
+    is_accelerate_available,
+    is_torch_version,
+    logging,
+)
 
 
 logger = logging.get_logger(__name__)
@@ -41,6 +46,12 @@
     _LOW_CPU_MEM_USAGE_DEFAULT = False
 
 
+if is_accelerate_available():
+    import accelerate
+    from accelerate.utils import set_module_tensor_to_device
+    from accelerate.utils.versions import is_torch_version
+
+
 def get_parameter_device(parameter: torch.nn.Module):
     try:
         return next(parameter.parameters()).device
@@ -319,6 +330,21 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         device_map = kwargs.pop("device_map", None)
         low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
 
+        if low_cpu_mem_usage and not is_accelerate_available():
+            low_cpu_mem_usage = False
+            logger.warn(
+                "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
+                " environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
+                " `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
+                " install accelerate\n```\n."
+            )
+
+        if device_map is not None and not is_accelerate_available():
+            raise NotImplementedError(
+                "Loading and dispatching requires `accelerate`. Please make sure to install accelerate or set"
+                " `device_map=None`. You can install accelerate with `pip install accelerate`."
+            )
+
         # Check if we can handle device_map and dispatching the weights
         if device_map is not None and not is_torch_version(">=", "1.9.0"):
             raise NotImplementedError(

pipeline_utils.py

@@ -25,7 +25,6 @@
 
 import diffusers
 import PIL
-from accelerate.utils.versions import is_torch_version
 from huggingface_hub import snapshot_download
 from packaging import version
 from PIL import Image
@@ -43,6 +42,8 @@
     WEIGHTS_NAME,
     BaseOutput,
     deprecate,
+    is_accelerate_available,
+    is_torch_version,
     is_transformers_available,
     logging,
 )
@@ -397,6 +398,15 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         device_map = kwargs.pop("device_map", None)
         low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
 
+        if low_cpu_mem_usage and not is_accelerate_available():
+            low_cpu_mem_usage = False
+            logger.warn(
+                "Cannot initialize model with low cpu memory usage because `accelerate` was not found in the"
+                " environment. Defaulting to `low_cpu_mem_usage=False`. It is strongly recommended to install"
+                " `accelerate` for faster and less memory-intense model loading. You can do so with: \n```\npip"
+                " install accelerate\n```\n."
+            )
+
         if device_map is not None and not is_torch_version(">=", "1.9.0"):
             raise NotImplementedError(
                 "Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or set"

utils/__init__.py

@@ -31,6 +31,7 @@
     is_scipy_available,
     is_tf_available,
     is_torch_available,
+    is_torch_version,
     is_transformers_available,
     is_unidecode_available,
     requires_backends,

utils/dummy_pt_objects.py

@@ -272,21 +272,6 @@ def from_pretrained(cls, *args, **kwargs):
         requires_backends(cls, ["torch"])
 
 
-class VQDiffusionPipeline(metaclass=DummyObject):
-    _backends = ["torch"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["torch"])
-
-    @classmethod
-    def from_config(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-    @classmethod
-    def from_pretrained(cls, *args, **kwargs):
-        requires_backends(cls, ["torch"])
-
-
 class DDIMScheduler(metaclass=DummyObject):
     _backends = ["torch"]