langchain_huggingface: Fix multiple GPU usage bug in from_model_id function (#23628)

- [ ]  **Description:**   
   - pass the device_map into model_kwargs 
- removing the unused device_map variable in the hf_pipeline function
call
- [ ] **Issue:** issue #13128 
When using the from_model_id function to load a Hugging Face model for
text generation across multiple GPUs, the model defaults to loading on
the CPU despite multiple GPUs being available using the expected format
``` python
llm = HuggingFacePipeline.from_model_id(
    model_id="model-id",
    task="text-generation",
    device_map="auto",
)
```
Currently, to enable multiple GPU , we have to pass in variable in this
format instead
``` python
llm = HuggingFacePipeline.from_model_id(
    model_id="model-id",
    task="text-generation",
    device=None,
    model_kwargs={
        "device_map": "auto",
    }
)
```
This issue arises due to improper handling of the device and device_map
parameters.

- [ ] **Explanation:**
1. In from_model_id, the model is created using model_kwargs and passed
as the model variable of the pipeline function. So at this moment, to
load the model with multiple GPUs, "device_map" needs to be set to
"auto" within model_kwargs. Otherwise, the model defaults to loading on
the CPU.
2. The device_map variable in from_model_id is not utilized correctly.
In the pipeline function's source code of tnansformer:
- The device_map variable is stored in the model_kwargs dictionary
(lines 867-878 of transformers/src/transformers/pipelines/\__init__.py).
```python
    if device_map is not None:
        ......
        model_kwargs["device_map"] = device_map
```
- The model is constructed with model_kwargs containing the device_map
value ONLY IF it is a string (lines 893-903 of
transformers/src/transformers/pipelines/\__init__.py).
```python
    if isinstance(model, str) or framework is None:
        model_classes = {"tf": targeted_task["tf"], "pt": targeted_task["pt"]}
        framework, model = infer_framework_load_model( ... , **model_kwargs, )
```
- Consequently, since a model object is already passed to the pipeline
function, the device_map variable from from_model_id is never used.

3. The device_map variable in from_model_id not only appears unused but
also causes errors. Without explicitly setting device=None, attempting
to load the model on multiple GPUs may result in the following error:
 ```
Device has 2 GPUs available. Provide device={deviceId} to
`from_model_id` to use available GPUs for execution. deviceId is -1
(default) for CPU and can be a positive integer associated with CUDA
device id.
  Traceback (most recent call last):
    File "foo.py", line 15, in <module>
      llm = HuggingFacePipeline.from_model_id(
File
"foo\site-packages\langchain_huggingface\llms\huggingface_pipeline.py",
line 217, in from_model_id
      pipeline = hf_pipeline(
File "foo\lib\site-packages\transformers\pipelines\__init__.py", line
1108, in pipeline
return pipeline_class(model=model, framework=framework, task=task,
**kwargs)
File "foo\lib\site-packages\transformers\pipelines\text_generation.py",
line 96, in __init__
      super().__init__(*args, **kwargs)
File "foo\lib\site-packages\transformers\pipelines\base.py", line 835,
in __init__
      raise ValueError(
ValueError: The model has been loaded with `accelerate` and therefore
cannot be moved to a specific device. Please discard the `device`
argument when creating your pipeline object.
```
This error occurs because, in from_model_id, the default values in from_model_id for device and device_map are -1 and None, respectively. It would passes the statement (`device_map is not None and device < 0`) and keep the device as -1 so the pipeline function later raises an error when trying to move a GPU-loaded model back to the CPU. 
19eb82e68b/libs/community/langchain_community/llms/huggingface_pipeline.py (L204-L213)




If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.

---------

Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: vbarda <vadym@langchain.dev>

libs/partners/huggingface/langchain_huggingface/llms/huggingface_pipeline.py

@@ -74,7 +74,7 @@ class HuggingFacePipeline(BaseLLM):
         model_id: str,
         task: str,
         backend: str = "default",
-        device: Optional[int] = -1,
+        device: Optional[int] = None,
         device_map: Optional[str] = None,
         model_kwargs: Optional[dict] = None,
         pipeline_kwargs: Optional[dict] = None,
@@ -96,7 +96,21 @@ class HuggingFacePipeline(BaseLLM):
                 "Please install it with `pip install transformers`."
             )
 
-        _model_kwargs = model_kwargs or {}
+        _model_kwargs = model_kwargs.copy() if model_kwargs else {}
+        if device_map is not None:
+            if device is not None:
+                raise ValueError(
+                    "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`."
+                )
+
+            if "device_map" in _model_kwargs:
+                raise ValueError("`device_map` is already specified in `model_kwargs`.")
+
+            _model_kwargs["device_map"] = device_map
         tokenizer = AutoTokenizer.from_pretrained(model_id, **_model_kwargs)
 
         try:
@@ -218,7 +232,6 @@ class HuggingFacePipeline(BaseLLM):
             model=model,
             tokenizer=tokenizer,
             device=device,
-            device_map=device_map,
             batch_size=batch_size,
             model_kwargs=_model_kwargs,
             **_pipeline_kwargs,