[pipeline] A simple fix for half-precision & 8bit models

docs/source/en/pipeline_tutorial.mdx

@@ -105,6 +105,8 @@ If the model is too large for a single GPU, you can set `device_map="auto"` to a
 generator(model="openai/whisper-large", device_map="auto")
 ```
 
+Note that if  `device_map="auto"` is passed, there is no need to add the argument `device=device` when instantiating your `pipeline` as you may encounter some unexpected behavior!
+
 ### Batch size
 
 By default, pipelines will not batch inference for reasons explained in detail [here](https://huggingface.co/docs/transformers/main_classes/pipelines#pipeline-batching). The reason is that batching is not necessarily faster, and can actually be quite slower in some cases.
@@ -257,4 +259,32 @@ sudo apt install -y tesseract-ocr
 pip install pytesseract
 ```
 
-</Tip>
+</Tip>
+
+## Using `pipeline` on large models with 🤗 `accelerate`:
+
+You can easily run `pipeline` on large models using 🤗 `accelerate`! First make sure you have installed `accelerate` with `pip install accelerate`. 
+
+First load your model using `device_map="auto"`! We will use `facebook/opt-1.3b` for our example.
+
+```py
+# pip install accelerate
+import torch
+from transformers import pipeline
+
+pipe = pipeline(model="facebook/opt-1.3b", torch_dtype=torch.bfloat16, device_map="auto")
+output = pipe("This is a cool example!", do_sample=True, top_p=0.95)
+```
+
+You can also pass 8-bit loaded models if you install `bitsandbytes` and add the argument `load_in_8bit=True`
+
+```py
+# pip install accelerate bitsandbytes
+import torch
+from transformers import pipeline
+
+pipe = pipeline(model="facebook/opt-1.3b", device_map="auto", model_kwargs={"load_in_8bit": True})
+output = pipe("This is a cool example!", do_sample=True, top_p=0.95)
+```
+
+Note that you can replace the checkpoint with any of the Hugging Face model that supports large model loading such as BLOOM!

src/transformers/pipelines/__init__.py

@@ -741,6 +741,11 @@ def pipeline(
                 'You cannot use both `pipeline(... device_map=..., model_kwargs={"device_map":...})` as those'
                 " arguments might conflict, use only one.)"
             )
+        if device is not None:
+            logger.warning(
+                "Both `device` and `device_map` are specified. `device` will override `device_map`. You"
+                " will most likely encounter unexpected behavior. Please remove `device` and keep `device_map`."
+            )
         model_kwargs["device_map"] = device_map
     if torch_dtype is not None:
         if "torch_dtype" in model_kwargs:

src/transformers/pipelines/automatic_speech_recognition.py

@@ -287,9 +287,9 @@ class AutomaticSpeechRecognitionPipeline(ChunkPipeline):
             installed. If no framework is specified, will default to the one currently installed. If no framework is
             specified and both frameworks are installed, will default to the framework of the `model`, or to PyTorch if
             no model is provided.
-        device (`int`, *optional*, defaults to -1):
-            Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
-            the associated CUDA device id.
+        device (Union[`int`, `torch.device`], *optional*):
+            Device ordinal for CPU/GPU supports. Setting this to `None` will leverage CPU, a positive will run the
+            model on the associated CUDA device id.
         decoder (`pyctcdecode.BeamSearchDecoderCTC`, *optional*):
             [PyCTCDecode's
             BeamSearchDecoderCTC](https://github.com/kensho-technologies/pyctcdecode/blob/2fd33dc37c4111417e08d89ccd23d28e9b308d19/pyctcdecode/decoder.py#L180)

src/transformers/pipelines/base.py

@@ -749,7 +749,7 @@ def __init__(
         framework: Optional[str] = None,
         task: str = "",
         args_parser: ArgumentHandler = None,
-        device: Union[int, str, "torch.device"] = -1,
+        device: Union[int, str, "torch.device"] = None,
         torch_dtype: Optional[Union[str, "torch.dtype"]] = None,
         binary_output: bool = False,
         **kwargs,
@@ -764,6 +764,19 @@ def __init__(
         self.image_processor = image_processor
         self.modelcard = modelcard
         self.framework = framework
+
+        if self.framework == "pt" and device is not None:
+            self.model = self.model.to(device=device)
+
+        if device is None:
+            # `accelerate` device map
+            hf_device_map = getattr(self.model, "hf_device_map", None)
+            if hf_device_map is not None:
+                # Take the first device used by `accelerate`.
+                device = next(iter(hf_device_map.values()))
+            else:
+                device = -1
+
         if is_torch_available() and self.framework == "pt":
             if isinstance(device, torch.device):
                 self.device = device
@@ -774,14 +787,10 @@ def __init__(
             else:
                 self.device = torch.device(f"cuda:{device}")
         else:
-            self.device = device
+            self.device = device if device is not None else -1
         self.torch_dtype = torch_dtype
         self.binary_output = binary_output
 
-        # Special handling
-        if self.framework == "pt" and self.device.type != "cpu":
-            self.model = self.model.to(self.device)
-
         # Update config with task specific parameters
         task_specific_params = self.model.config.task_specific_params
         if task_specific_params is not None and task in task_specific_params:

src/transformers/pipelines/question_answering.py

@@ -255,7 +255,6 @@ def __init__(
         tokenizer: PreTrainedTokenizer,
         modelcard: Optional[ModelCard] = None,
         framework: Optional[str] = None,
-        device: int = -1,
         task: str = "",
         **kwargs,
     ):
@@ -264,7 +263,6 @@ def __init__(
             tokenizer=tokenizer,
             modelcard=modelcard,
             framework=framework,
-            device=device,
             task=task,
             **kwargs,
         )

tests/pipelines/test_pipelines_text_generation.py

@@ -312,3 +312,12 @@ def test_small_model_fp16(self):
 
         pipe = pipeline(model="hf-internal-testing/tiny-random-bloom", device=0, torch_dtype=torch.float16)
         pipe("This is a test")
+
+    @require_torch
+    @require_accelerate
+    @require_torch_gpu
+    def test_pipeline_accelerate_top_p(self):
+        import torch
+
+        pipe = pipeline(model="hf-internal-testing/tiny-random-bloom", device_map="auto", torch_dtype=torch.float16)
+        pipe("This is a test", do_sample=True, top_p=0.5)