Add HuggingFacePipeline LLM (#353)

https://github.com/hwchase17/langchain/issues/354

Add support for running your own HF pipeline locally. This would allow
you to get a lot more dynamic with what HF features and models you
support since you wouldn't be beholden to what is hosted in HF hub. You
could also do stuff with HF Optimum to quantize your models and stuff to
get pretty fast inference even running on a laptop.

tests/integration_tests/llms/test_huggingface_pipeline.py

@@ -0,0 +1,41 @@
+"""Test HuggingFace Pipeline wrapper."""
+
+from pathlib import Path
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+from langchain.llms.huggingface_pipeline import HuggingFacePipeline
+from langchain.llms.loading import load_llm
+from tests.integration_tests.llms.utils import assert_llm_equality
+
+
+def test_huggingface_pipeline_text_generation() -> None:
+    """Test valid call to HuggingFace text generation model."""
+    llm = HuggingFacePipeline.from_model_id(
+        model_id="gpt2", task="text-generation", model_kwargs={"max_new_tokens": 10}
+    )
+    output = llm("Say foo:")
+    assert isinstance(output, str)
+
+
+def test_saving_loading_llm(tmp_path: Path) -> None:
+    """Test saving/loading an HuggingFaceHub LLM."""
+    llm = HuggingFacePipeline.from_model_id(
+        model_id="gpt2", task="text-generation", model_kwargs={"max_new_tokens": 10}
+    )
+    llm.save(file_path=tmp_path / "hf.yaml")
+    loaded_llm = load_llm(tmp_path / "hf.yaml")
+    assert_llm_equality(llm, loaded_llm)
+
+
+def test_init_with_pipeline() -> None:
+    """Test initialization with a HF pipeline."""
+    model_id = "gpt2"
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    model = AutoModelForCausalLM.from_pretrained(model_id)
+    pipe = pipeline(
+        "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10
+    )
+    llm = HuggingFacePipeline(pipeline=pipe)
+    output = llm("Say foo:")
+    assert isinstance(output, str)

tests/integration_tests/llms/utils.py

@@ -10,7 +10,7 @@ def assert_llm_equality(llm: LLM, loaded_llm: LLM) -> None:
     # Client field can be session based, so hash is different despite
     # all other values being the same, so just assess all other fields
     for field in llm.__fields__.keys():
-        if field != "client":
+        if field != "client" and field != "pipeline":
             val = getattr(llm, field)
             new_val = getattr(loaded_llm, field)
             assert new_val == val