2023-04-02 21:57:45 +00:00
|
|
|
# flake8: noqa
|
|
|
|
"""Test Llama.cpp wrapper."""
|
|
|
|
import os
|
2023-04-24 23:27:51 +00:00
|
|
|
from typing import Generator
|
2023-04-02 21:57:45 +00:00
|
|
|
from urllib.request import urlretrieve
|
|
|
|
|
|
|
|
from langchain.llms import LlamaCpp
|
2023-04-24 23:27:51 +00:00
|
|
|
from langchain.callbacks.base import CallbackManager
|
|
|
|
|
|
|
|
from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler
|
2023-04-02 21:57:45 +00:00
|
|
|
|
|
|
|
|
|
|
|
def get_model() -> str:
|
|
|
|
"""Download model. f
|
|
|
|
From https://huggingface.co/Sosaka/Alpaca-native-4bit-ggml/,
|
|
|
|
convert to new ggml format and return model path."""
|
|
|
|
model_url = "https://huggingface.co/Sosaka/Alpaca-native-4bit-ggml/resolve/main/ggml-alpaca-7b-q4.bin"
|
|
|
|
tokenizer_url = "https://huggingface.co/decapoda-research/llama-7b-hf/resolve/main/tokenizer.model"
|
|
|
|
conversion_script = "https://github.com/ggerganov/llama.cpp/raw/master/convert-unversioned-ggml-to-ggml.py"
|
|
|
|
local_filename = model_url.split("/")[-1]
|
|
|
|
|
|
|
|
if not os.path.exists("convert-unversioned-ggml-to-ggml.py"):
|
|
|
|
urlretrieve(conversion_script, "convert-unversioned-ggml-to-ggml.py")
|
|
|
|
if not os.path.exists("tokenizer.model"):
|
|
|
|
urlretrieve(tokenizer_url, "tokenizer.model")
|
|
|
|
if not os.path.exists(local_filename):
|
|
|
|
urlretrieve(model_url, local_filename)
|
|
|
|
os.system(f"python convert-unversioned-ggml-to-ggml.py . tokenizer.model")
|
|
|
|
|
|
|
|
return local_filename
|
|
|
|
|
|
|
|
|
|
|
|
def test_llamacpp_inference() -> None:
|
|
|
|
"""Test valid llama.cpp inference."""
|
|
|
|
model_path = get_model()
|
|
|
|
llm = LlamaCpp(model_path=model_path)
|
|
|
|
output = llm("Say foo:")
|
|
|
|
assert isinstance(output, str)
|
2023-04-24 23:27:51 +00:00
|
|
|
assert len(output) > 1
|
|
|
|
|
|
|
|
|
|
|
|
def test_llamacpp_streaming() -> None:
|
|
|
|
"""Test streaming tokens from LlamaCpp."""
|
|
|
|
model_path = get_model()
|
|
|
|
llm = LlamaCpp(model_path=model_path, max_tokens=10)
|
|
|
|
generator = llm.stream("Q: How do you say 'hello' in German? A:'", stop=["'"])
|
|
|
|
stream_results_string = ""
|
|
|
|
assert isinstance(generator, Generator)
|
|
|
|
|
|
|
|
for chunk in generator:
|
|
|
|
assert not isinstance(chunk, str)
|
|
|
|
# Note that this matches the OpenAI format:
|
|
|
|
assert isinstance(chunk["choices"][0]["text"], str)
|
|
|
|
stream_results_string += chunk["choices"][0]["text"]
|
|
|
|
assert len(stream_results_string.strip()) > 1
|
|
|
|
|
|
|
|
|
|
|
|
def test_llamacpp_streaming_callback() -> None:
|
|
|
|
"""Test that streaming correctly invokes on_llm_new_token callback."""
|
|
|
|
MAX_TOKENS = 5
|
|
|
|
OFF_BY_ONE = 1 # There may be an off by one error in the upstream code!
|
|
|
|
|
|
|
|
callback_handler = FakeCallbackHandler()
|
|
|
|
callback_manager = CallbackManager([callback_handler])
|
|
|
|
llm = LlamaCpp(
|
|
|
|
model_path=get_model(),
|
|
|
|
callback_manager=callback_manager,
|
|
|
|
verbose=True,
|
|
|
|
max_tokens=MAX_TOKENS,
|
|
|
|
)
|
|
|
|
llm("Q: Can you count to 10? A:'1, ")
|
|
|
|
assert callback_handler.llm_streams <= MAX_TOKENS + OFF_BY_ONE
|