community[minor]: Add openvino backend support (#11591)

- **Description:** add openvino backend support by HuggingFace Optimum
Intel,
  - **Dependencies:** “optimum[openvino]”,

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>

docs/docs/integrations/llms/huggingface_pipelines.ipynb

@@ -192,6 +192,94 @@
     "for answer in answers:\n",
     "    print(answer)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df1d41d9",
+   "metadata": {},
+   "source": [
+    "### Inference with OpenVINO backend\n",
+    "\n",
+    "To deploy a model with OpenVINO, you can specify the `backend=\"openvino\"` parameter to trigger OpenVINO as backend inference framework.\n",
+    "\n",
+    "If you have an Intel GPU, you can specify `model_kwargs={\"device\": \"GPU\"}` to run inference on it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "efb73dd7-77bf-4436-92e5-51306af45bd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install --upgrade-strategy eager \"optimum[openvino,nncf]\" --quiet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "70f6826c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ov_config = {\"PERFORMANCE_HINT\": \"LATENCY\", \"NUM_STREAMS\": \"1\", \"CACHE_DIR\": \"\"}\n",
+    "\n",
+    "ov_llm = HuggingFacePipeline.from_model_id(\n",
+    "    model_id=\"gpt2\",\n",
+    "    task=\"text-generation\",\n",
+    "    backend=\"openvino\",\n",
+    "    model_kwargs={\"device\": \"CPU\", \"ov_config\": ov_config},\n",
+    "    pipeline_kwargs={\"max_new_tokens\": 10},\n",
+    ")\n",
+    "\n",
+    "ov_chain = prompt | ov_llm\n",
+    "\n",
+    "question = \"What is electroencephalography?\"\n",
+    "\n",
+    "print(ov_chain.invoke({\"question\": question}))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12524837-e9ab-455a-86be-66b95f4f893a",
+   "metadata": {},
+   "source": [
+    "### Inference with local OpenVINO model\n",
+    "\n",
+    "It is possible to [export your model](https://github.com/huggingface/optimum-intel?tab=readme-ov-file#export) to the OpenVINO IR format with the CLI, and load the model from local folder.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3d1104a2-79c7-43a6-aa1c-8076a5ad7747",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!optimum-cli export openvino --model gpt2 ov_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac71e60d-5595-454e-8602-03ebb0248205",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ov_llm = HuggingFacePipeline.from_model_id(\n",
+    "    model_id=\"ov_model\",\n",
+    "    task=\"text-generation\",\n",
+    "    backend=\"openvino\",\n",
+    "    model_kwargs={\"device\": \"CPU\", \"ov_config\": ov_config},\n",
+    "    pipeline_kwargs={\"max_new_tokens\": 10},\n",
+    ")\n",
+    "\n",
+    "ov_chain = prompt | ov_llm\n",
+    "\n",
+    "question = \"What is electroencephalography?\"\n",
+    "\n",
+    "print(ov_chain.invoke({\"question\": question}))"
+   ]
   }
  ],
  "metadata": {
@@ -210,7 +298,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/integrations/platforms/huggingface.mdx

@@ -40,6 +40,22 @@ See a [usage example](/docs/integrations/llms/huggingface_pipelines).
 from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
 ```
 
+To use the OpenVINO backend in local pipeline wrapper, please install the optimum library and set HuggingFacePipeline's backend as `openvino`:
+
+```bash
+pip install --upgrade-strategy eager "optimum[openvino,nncf]"
+```
+
+See a [usage example](/docs/integrations/llms/huggingface_pipelines)
+
+To export your model to the OpenVINO IR format with the CLI:
+
+```bash
+optimum-cli export openvino --model gpt2 ov_model
+```
+
+To apply [weight-only quantization](https://github.com/huggingface/optimum-intel?tab=readme-ov-file#export) when exporting your model.
+
 ### Hugging Face TextGen Inference
 
 >[Text Generation Inference](https://github.com/huggingface/text-generation-inference) is 

libs/community/langchain_community/llms/huggingface_pipeline.py

@@ -68,6 +68,7 @@ class HuggingFacePipeline(BaseLLM):
         cls,
         model_id: str,
         task: str,
+        backend: str = "default",
         device: Optional[int] = -1,
         device_map: Optional[str] = None,
         model_kwargs: Optional[dict] = None,
@@ -95,9 +96,57 @@ class HuggingFacePipeline(BaseLLM):
 
         try:
             if task == "text-generation":
-                model = AutoModelForCausalLM.from_pretrained(model_id, **_model_kwargs)
+                if backend == "openvino":
+                    try:
+                        from optimum.intel.openvino import OVModelForCausalLM
+
+                    except ImportError:
+                        raise ValueError(
+                            "Could not import optimum-intel python package. "
+                            "Please install it with: "
+                            "pip install 'optimum[openvino,nncf]' "
+                        )
+                    try:
+                        # use local model
+                        model = OVModelForCausalLM.from_pretrained(
+                            model_id, **_model_kwargs
+                        )
+
+                    except Exception:
+                        # use remote model
+                        model = OVModelForCausalLM.from_pretrained(
+                            model_id, export=True, **_model_kwargs
+                        )
+                else:
+                    model = AutoModelForCausalLM.from_pretrained(
+                        model_id, **_model_kwargs
+                    )
             elif task in ("text2text-generation", "summarization"):
-                model = AutoModelForSeq2SeqLM.from_pretrained(model_id, **_model_kwargs)
+                if backend == "openvino":
+                    try:
+                        from optimum.intel.openvino import OVModelForSeq2SeqLM
+
+                    except ImportError:
+                        raise ValueError(
+                            "Could not import optimum-intel python package. "
+                            "Please install it with: "
+                            "pip install 'optimum[openvino,nncf]' "
+                        )
+                    try:
+                        # use local model
+                        model = OVModelForSeq2SeqLM.from_pretrained(
+                            model_id, **_model_kwargs
+                        )
+
+                    except Exception:
+                        # use remote model
+                        model = OVModelForSeq2SeqLM.from_pretrained(
+                            model_id, export=True, **_model_kwargs
+                        )
+                else:
+                    model = AutoModelForSeq2SeqLM.from_pretrained(
+                        model_id, **_model_kwargs
+                    )
             else:
                 raise ValueError(
                     f"Got invalid task {task}, "
@@ -112,9 +161,13 @@ class HuggingFacePipeline(BaseLLM):
             tokenizer.pad_token_id = model.config.eos_token_id
 
         if (
-            getattr(model, "is_loaded_in_4bit", False)
+            (
-            or getattr(model, "is_loaded_in_8bit", False)
+                getattr(model, "is_loaded_in_4bit", False)
-        ) and device is not None:
+                or getattr(model, "is_loaded_in_8bit", False)
+            )
+            and device is not None
+            and backend == "default"
+        ):
             logger.warning(
                 f"Setting the `device` argument to None from {device} to avoid "
                 "the error caused by attempting to move the model that was already "
@@ -123,7 +176,11 @@ class HuggingFacePipeline(BaseLLM):
             )
             device = None
 
-        if device is not None and importlib.util.find_spec("torch") is not None:
+        if (
+            device is not None
+            and importlib.util.find_spec("torch") is not None
+            and backend == "default"
+        ):
             import torch
 
             cuda_device_count = torch.cuda.device_count()
@@ -142,6 +199,8 @@ class HuggingFacePipeline(BaseLLM):
                     "can be a positive integer associated with CUDA device id.",
                     cuda_device_count,
                 )
+        if device is not None and device_map is not None and backend == "openvino":
+            logger.warning("Please set device for OpenVINO through: " "'model_kwargs'")
         if "trust_remote_code" in _model_kwargs:
             _model_kwargs = {
                 k: v for k, v in _model_kwargs.items() if k != "trust_remote_code"

libs/community/tests/integration_tests/llms/test_huggingface_pipeline.py

@@ -80,3 +80,45 @@ def test_huggingface_pipeline_runtime_kwargs() -> None:
     prompt = "Say foo:"
     output = llm(prompt, pipeline_kwargs={"max_new_tokens": 2})
     assert len(output) < 10
+
+
+ov_config = {"PERFORMANCE_HINT": "LATENCY", "NUM_STREAMS": "1", "CACHE_DIR": ""}
+
+
+def test_huggingface_pipeline_text_generation_ov() -> None:
+    """Test valid call to HuggingFace text generation model with openvino."""
+    llm = HuggingFacePipeline.from_model_id(
+        model_id="gpt2",
+        task="text-generation",
+        backend="openvino",
+        model_kwargs={"device": "CPU", "ov_config": ov_config},
+        pipeline_kwargs={"max_new_tokens": 64},
+    )
+    output = llm("Say foo:")
+    assert isinstance(output, str)
+
+
+def test_huggingface_pipeline_text2text_generation_ov() -> None:
+    """Test valid call to HuggingFace text2text generation model with openvino."""
+    llm = HuggingFacePipeline.from_model_id(
+        model_id="google/flan-t5-small",
+        task="text2text-generation",
+        backend="openvino",
+        model_kwargs={"device": "CPU", "ov_config": ov_config},
+        pipeline_kwargs={"max_new_tokens": 64},
+    )
+    output = llm("Say foo:")
+    assert isinstance(output, str)
+
+
+def text_huggingface_pipeline_summarization_ov() -> None:
+    """Test valid call to HuggingFace summarization model with openvino."""
+    llm = HuggingFacePipeline.from_model_id(
+        model_id="facebook/bart-large-cnn",
+        task="summarization",
+        backend="openvino",
+        model_kwargs={"device": "CPU", "ov_config": ov_config},
+        pipeline_kwargs={"max_new_tokens": 64},
+    )
+    output = llm("Say foo:")
+    assert isinstance(output, str)