mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
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)`
89 lines
3.1 KiB
Python
89 lines
3.1 KiB
Python
# flake8: noqa
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"""Test Llama.cpp wrapper."""
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import os
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from typing import Generator
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from urllib.request import urlretrieve
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import pytest
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from langchain_community.llms import LlamaCpp
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from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
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def get_model() -> str:
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"""Download model. f
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From https://huggingface.co/Sosaka/Alpaca-native-4bit-ggml/,
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convert to new ggml format and return model path."""
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model_url = "https://huggingface.co/Sosaka/Alpaca-native-4bit-ggml/resolve/main/ggml-alpaca-7b-q4.bin"
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tokenizer_url = "https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/tokenizer.model"
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conversion_script = "https://github.com/ggerganov/llama.cpp/raw/master/convert-unversioned-ggml-to-ggml.py"
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local_filename = model_url.split("/")[-1]
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if not os.path.exists("convert-unversioned-ggml-to-ggml.py"):
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urlretrieve(conversion_script, "convert-unversioned-ggml-to-ggml.py")
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if not os.path.exists("tokenizer.model"):
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urlretrieve(tokenizer_url, "tokenizer.model")
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if not os.path.exists(local_filename):
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urlretrieve(model_url, local_filename)
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os.system(f"python convert-unversioned-ggml-to-ggml.py . tokenizer.model")
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return local_filename
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def test_llamacpp_inference() -> None:
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"""Test valid llama.cpp inference."""
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model_path = get_model()
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llm = LlamaCpp(model_path=model_path)
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output = llm.invoke("Say foo:")
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assert isinstance(output, str)
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assert len(output) > 1
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def test_llamacpp_streaming() -> None:
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"""Test streaming tokens from LlamaCpp."""
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model_path = get_model()
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llm = LlamaCpp(model_path=model_path, max_tokens=10)
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generator = llm.stream("Q: How do you say 'hello' in German? A:'", stop=["'"])
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stream_results_string = ""
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assert isinstance(generator, Generator)
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for chunk in generator:
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assert not isinstance(chunk, str)
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# Note that this matches the OpenAI format:
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assert isinstance(chunk["choices"][0]["text"], str)
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stream_results_string += chunk["choices"][0]["text"]
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assert len(stream_results_string.strip()) > 1
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def test_llamacpp_streaming_callback() -> None:
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"""Test that streaming correctly invokes on_llm_new_token callback."""
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MAX_TOKENS = 5
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OFF_BY_ONE = 1 # There may be an off by one error in the upstream code!
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callback_handler = FakeCallbackHandler()
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llm = LlamaCpp(
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model_path=get_model(),
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callbacks=[callback_handler],
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verbose=True,
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max_tokens=MAX_TOKENS,
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)
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llm.invoke("Q: Can you count to 10? A:'1, ")
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assert callback_handler.llm_streams <= MAX_TOKENS + OFF_BY_ONE
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def test_llamacpp_model_kwargs() -> None:
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llm = LlamaCpp(model_path=get_model(), model_kwargs={"n_gqa": None})
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assert llm.model_kwargs == {"n_gqa": None}
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def test_llamacpp_invalid_model_kwargs() -> None:
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with pytest.raises(ValueError):
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LlamaCpp(model_path=get_model(), model_kwargs={"n_ctx": 1024})
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def test_llamacpp_incorrect_field() -> None:
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with pytest.warns(match="not default parameter"):
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llm = LlamaCpp(model_path=get_model(), n_gqa=None)
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llm.model_kwargs == {"n_gqa": None}
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