[Low cpu memory] Correct naming and improve default usage

src/diffusers/modeling_utils.py

@@ -35,6 +35,12 @@
 logger = logging.get_logger(__name__)
 
 
+if is_torch_version(">=", "1.9.0"):
+    _LOW_CPU_MEM_USAGE_DEFAULT = True
+else:
+    _LOW_CPU_MEM_USAGE_DEFAULT = False
+
+
 def get_parameter_device(parameter: torch.nn.Module):
     try:
         return next(parameter.parameters()).device
@@ -278,11 +284,11 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                 To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For
                 more information about each option see [designing a device
                 map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
-            fast_load (`bool`, *optional*, defaults to `True`):
+            low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
                 Speed up model loading by not initializing the weights and only loading the pre-trained weights. This
                 also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the
                 model. This is only supported when torch version >= 1.9.0. If you are using an older version of torch,
-                this argument will be ignored and the model will be loaded normally.
+                setting this argument to `True` will raise an error.
 
         <Tip>
 
@@ -311,16 +317,26 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         torch_dtype = kwargs.pop("torch_dtype", None)
         subfolder = kwargs.pop("subfolder", None)
         device_map = kwargs.pop("device_map", None)
-        fast_load = kwargs.pop("fast_load", True)
+        low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
 
         # 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("Loading and dispatching requires torch >= 1.9.0")
+            raise NotImplementedError(
+                "Loading and dispatching requires torch >= 1.9.0. Please either update your PyTorch version or sets"
+                " `device_map=None`."
+            )
 
-        # Fast init is only possible if torch version is >= 1.9.0
-        _INIT_EMPTY_WEIGHTS = fast_load or device_map is not None
-        if _INIT_EMPTY_WEIGHTS and not is_torch_version(">=", "1.9.0"):
-            logger.warn("Loading with `fast_load` requires torch >= 1.9.0. Falling back to normal loading.")
+        if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
+            raise NotImplementedError(
+                "Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or sets"
+                " `low_cpu_mem_usage=False`."
+            )
+
+        if low_cpu_mem_usage is False and device_map is not None:
+            raise ValueError(
+                f"You cannot set `low_cpu_mem_usage` to False while using device_map={device_map} for loading and"
+                " dispatching. Please make sure to set `low_cpu_mem_usage=True`."
+            )
 
         user_agent = {
             "diffusers": __version__,
@@ -403,7 +419,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
 
             # restore default dtype
 
-        if _INIT_EMPTY_WEIGHTS:
+        if low_cpu_mem_usage:
             # Instantiate model with empty weights
             with accelerate.init_empty_weights():
                 model, unused_kwargs = cls.from_config(

src/diffusers/pipeline_utils.py

@@ -25,6 +25,7 @@
 
 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
@@ -33,6 +34,7 @@
 from .configuration_utils import ConfigMixin
 from .dynamic_modules_utils import get_class_from_dynamic_module
 from .hub_utils import http_user_agent
+from .modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT
 from .schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
 from .utils import (
     CONFIG_NAME,
@@ -328,6 +330,19 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                 Mirror source to accelerate downloads in China. If you are from China and have an accessibility
                 problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
                 Please refer to the mirror site for more information. specify the folder name here.
+            device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):
+                A map that specifies where each submodule should go. It doesn't need to be refined to each
+                parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the
+                same device.
+
+                To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For
+                more information about each option see [designing a device
+                map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
+            low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
+                Speed up model loading by not initializing the weights and only loading the pre-trained weights. This
+                also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the
+                model. This is only supported when torch version >= 1.9.0. If you are using an older version of torch,
+                setting this argument to `True` will raise an error.
 
             kwargs (remaining dictionary of keyword arguments, *optional*):
                 Can be used to overwrite load - and saveable variables - *i.e.* the pipeline components - of the
@@ -380,7 +395,25 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         provider = kwargs.pop("provider", None)
         sess_options = kwargs.pop("sess_options", None)
         device_map = kwargs.pop("device_map", None)
-        fast_load = kwargs.pop("fast_load", True)
+        low_cpu_mem_usage = kwargs.pop("low_cpu_mem_usage", _LOW_CPU_MEM_USAGE_DEFAULT)
+
+        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 sets"
+                " `device_map=None`."
+            )
+
+        if low_cpu_mem_usage is True and not is_torch_version(">=", "1.9.0"):
+            raise NotImplementedError(
+                "Low memory initialization requires torch >= 1.9.0. Please either update your PyTorch version or sets"
+                " `low_cpu_mem_usage=False`."
+            )
+
+        if low_cpu_mem_usage is False and device_map is not None:
+            raise ValueError(
+                f"You cannot set `low_cpu_mem_usage` to False while using device_map={device_map} for loading and"
+                " dispatching. Please make sure to set `low_cpu_mem_usage=True`."
+            )
 
         # 1. Download the checkpoints and configs
         # use snapshot download here to get it working from from_pretrained
@@ -573,17 +606,12 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                     and version.parse(version.parse(transformers.__version__).base_version) >= version.parse("4.20.0")
                 )
 
-                if is_diffusers_model:
-                    loading_kwargs["fast_load"] = fast_load
-
                 # When loading a transformers model, if the device_map is None, the weights will be initialized as opposed to diffusers.
-                # To make default loading faster we set the `low_cpu_mem_usage=fast_load` flag which is `True` by default.
+                # To make default loading faster we set the `low_cpu_mem_usage=low_cpu_mem_usage` flag which is `True` by default.
                 # This makes sure that the weights won't be initialized which significantly speeds up loading.
-                if is_transformers_model and device_map is None:
-                    loading_kwargs["low_cpu_mem_usage"] = fast_load
-
                 if is_diffusers_model or is_transformers_model:
                     loading_kwargs["device_map"] = device_map
+                    loading_kwargs["low_cpu_mem_usage"] = low_cpu_mem_usage
 
                 # check if the module is in a subdirectory
                 if os.path.isdir(os.path.join(cached_folder, name)):

tests/models/test_models_unet_2d.py

@@ -133,7 +133,7 @@ def test_from_pretrained_accelerate(self):
 
     @unittest.skipIf(torch_device != "cuda", "This test is supposed to run on GPU")
     def test_from_pretrained_accelerate_wont_change_results(self):
-        # by defautl model loading will use accelerate as `fast_load=True`
+        # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
         model_accelerate, _ = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update", output_loading_info=True)
         model_accelerate.to(torch_device)
         model_accelerate.eval()
@@ -156,7 +156,7 @@ def test_from_pretrained_accelerate_wont_change_results(self):
         gc.collect()
 
         model_normal_load, _ = UNet2DModel.from_pretrained(
-            "fusing/unet-ldm-dummy-update", output_loading_info=True, fast_init=False
+            "fusing/unet-ldm-dummy-update", output_loading_info=True, low_cpu_mem_usage=False
         )
         model_normal_load.to(torch_device)
         model_normal_load.eval()
@@ -170,7 +170,7 @@ def test_memory_footprint_gets_reduced(self):
         gc.collect()
 
         tracemalloc.start()
-        # by defautl model loading will use accelerate as `fast_load=True`
+        # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
         model_accelerate, _ = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update", output_loading_info=True)
         model_accelerate.to(torch_device)
         model_accelerate.eval()
@@ -181,7 +181,7 @@ def test_memory_footprint_gets_reduced(self):
         gc.collect()
 
         model_normal_load, _ = UNet2DModel.from_pretrained(
-            "fusing/unet-ldm-dummy-update", output_loading_info=True, fast_init=False
+            "fusing/unet-ldm-dummy-update", output_loading_info=True, low_cpu_mem_usage=False
         )
         model_normal_load.to(torch_device)
         model_normal_load.eval()

tests/pipelines/stable_diffusion/test_stable_diffusion.py

@@ -823,23 +823,23 @@ def test_callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> No
         assert test_callback_fn.has_been_called
         assert number_of_steps == 51
 
-    def test_stable_diffusion_fast_load(self):
+    def test_stable_diffusion_low_cpu_mem_usage(self):
         pipeline_id = "CompVis/stable-diffusion-v1-4"
 
         start_time = time.time()
-        pipeline_fast_load = StableDiffusionPipeline.from_pretrained(
+        pipeline_low_cpu_mem_usage = StableDiffusionPipeline.from_pretrained(
             pipeline_id, revision="fp16", torch_dtype=torch.float16
         )
-        pipeline_fast_load.to(torch_device)
-        fast_load_time = time.time() - start_time
+        pipeline_low_cpu_mem_usage.to(torch_device)
+        low_cpu_mem_usage_time = time.time() - start_time
 
         start_time = time.time()
         _ = StableDiffusionPipeline.from_pretrained(
-            pipeline_id, revision="fp16", torch_dtype=torch.float16, use_auth_token=True, fast_load=False
+            pipeline_id, revision="fp16", torch_dtype=torch.float16, use_auth_token=True, low_cpu_mem_usage=False
         )
         normal_load_time = time.time() - start_time
 
-        assert 2 * fast_load_time < normal_load_time
+        assert 2 * low_cpu_mem_usage_time < normal_load_time
 
     @unittest.skipIf(torch_device == "cpu", "This test is supposed to run on GPU")
     def test_stable_diffusion_pipeline_with_unet_on_gpu_only(self):