community: add runtime kwargs to HuggingFacePipeline (#17005)

This PR enables changing the behaviour of huggingface pipeline between
different calls. For example, before this PR there's no way of changing
maximum generation length between different invocations of the chain.
This is desirable in cases, such as when we want to scale the maximum
output size depending on a dynamic prompt size.

Usage example:

```python
from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_id = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
hf = HuggingFacePipeline(pipeline=pipe)

hf("Say foo:", pipeline_kwargs={"max_new_tokens": 42})
```

---------

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

libs/community/langchain_community/llms/huggingface_pipeline.py

@@ -195,12 +195,13 @@ class HuggingFacePipeline(BaseLLM):
     ) -> LLMResult:
         # List to hold all results
         text_generations: List[str] = []
+        pipeline_kwargs = kwargs.get("pipeline_kwargs", {})
 
         for i in range(0, len(prompts), self.batch_size):
             batch_prompts = prompts[i : i + self.batch_size]
 
             # Process batch of prompts
-            responses = self.pipeline(batch_prompts)
+            responses = self.pipeline(batch_prompts, **pipeline_kwargs)
 
             # Process each response in the batch
             for j, response in enumerate(responses):

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

@@ -69,3 +69,14 @@ def test_init_with_pipeline() -> None:
     llm = HuggingFacePipeline(pipeline=pipe)
     output = llm("Say foo:")
     assert isinstance(output, str)
+
+
+def test_huggingface_pipeline_runtime_kwargs() -> None:
+    """Test pipelines specifying the device map parameter."""
+    llm = HuggingFacePipeline.from_model_id(
+        model_id="gpt2",
+        task="text-generation",
+    )
+    prompt = "Say foo:"
+    output = llm(prompt, pipeline_kwargs={"max_new_tokens": 2})
+    assert len(output) < 10