mirror of
https://github.com/hwchase17/langchain
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481d3855dc
- `llm(prompt)` -> `llm.invoke(prompt)`
- `llm(prompt=prompt` -> `llm.invoke(prompt)` (same with `messages=`)
- `llm(prompt, callbacks=callbacks)` -> `llm.invoke(prompt,
config={"callbacks": callbacks})`
- `llm(prompt, **kwargs)` -> `llm.invoke(prompt, **kwargs)`
125 lines
4.3 KiB
Python
Executable File
125 lines
4.3 KiB
Python
Executable File
"""Test HuggingFace Pipeline wrapper."""
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from pathlib import Path
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from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
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from langchain_community.llms.loading import load_llm
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from tests.integration_tests.llms.utils import assert_llm_equality
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def test_huggingface_pipeline_text_generation() -> None:
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"""Test valid call to HuggingFace text generation model."""
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llm = HuggingFacePipeline.from_model_id(
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model_id="gpt2", task="text-generation", pipeline_kwargs={"max_new_tokens": 10}
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)
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output = llm.invoke("Say foo:")
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assert isinstance(output, str)
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def test_huggingface_pipeline_text2text_generation() -> None:
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"""Test valid call to HuggingFace text2text generation model."""
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llm = HuggingFacePipeline.from_model_id(
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model_id="google/flan-t5-small", task="text2text-generation"
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)
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output = llm.invoke("Say foo:")
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assert isinstance(output, str)
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def test_huggingface_pipeline_device_map() -> None:
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"""Test pipelines specifying the device map parameter."""
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llm = HuggingFacePipeline.from_model_id(
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model_id="gpt2",
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task="text-generation",
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device_map="auto",
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pipeline_kwargs={"max_new_tokens": 10},
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)
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output = llm.invoke("Say foo:")
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assert isinstance(output, str)
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def text_huggingface_pipeline_summarization() -> None:
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"""Test valid call to HuggingFace summarization model."""
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llm = HuggingFacePipeline.from_model_id(
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model_id="facebook/bart-large-cnn", task="summarization"
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)
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output = llm.invoke("Say foo:")
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assert isinstance(output, str)
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def test_saving_loading_llm(tmp_path: Path) -> None:
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"""Test saving/loading an HuggingFaceHub LLM."""
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llm = HuggingFacePipeline.from_model_id(
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model_id="gpt2", task="text-generation", pipeline_kwargs={"max_new_tokens": 10}
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)
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llm.save(file_path=tmp_path / "hf.yaml")
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loaded_llm = load_llm(tmp_path / "hf.yaml")
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assert_llm_equality(llm, loaded_llm)
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def test_init_with_pipeline() -> None:
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"""Test initialization with a HF pipeline."""
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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model_id = "gpt2"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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pipe = pipeline(
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"text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10
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)
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llm = HuggingFacePipeline(pipeline=pipe)
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output = llm.invoke("Say foo:")
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assert isinstance(output, str)
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def test_huggingface_pipeline_runtime_kwargs() -> None:
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"""Test pipelines specifying the device map parameter."""
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llm = HuggingFacePipeline.from_model_id(
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model_id="gpt2",
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task="text-generation",
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)
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prompt = "Say foo:"
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output = llm.invoke(prompt, pipeline_kwargs={"max_new_tokens": 2})
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assert len(output) < 10
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ov_config = {"PERFORMANCE_HINT": "LATENCY", "NUM_STREAMS": "1", "CACHE_DIR": ""}
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def test_huggingface_pipeline_text_generation_ov() -> None:
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"""Test valid call to HuggingFace text generation model with openvino."""
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llm = HuggingFacePipeline.from_model_id(
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model_id="gpt2",
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task="text-generation",
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backend="openvino",
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model_kwargs={"device": "CPU", "ov_config": ov_config},
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pipeline_kwargs={"max_new_tokens": 64},
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)
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output = llm.invoke("Say foo:")
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assert isinstance(output, str)
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def test_huggingface_pipeline_text2text_generation_ov() -> None:
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"""Test valid call to HuggingFace text2text generation model with openvino."""
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llm = HuggingFacePipeline.from_model_id(
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model_id="google/flan-t5-small",
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task="text2text-generation",
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backend="openvino",
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model_kwargs={"device": "CPU", "ov_config": ov_config},
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pipeline_kwargs={"max_new_tokens": 64},
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)
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output = llm.invoke("Say foo:")
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assert isinstance(output, str)
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def text_huggingface_pipeline_summarization_ov() -> None:
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"""Test valid call to HuggingFace summarization model with openvino."""
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llm = HuggingFacePipeline.from_model_id(
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model_id="facebook/bart-large-cnn",
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task="summarization",
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backend="openvino",
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model_kwargs={"device": "CPU", "ov_config": ov_config},
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pipeline_kwargs={"max_new_tokens": 64},
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)
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output = llm.invoke("Say foo:")
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assert isinstance(output, str)
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