200 lines
6 KiB
Python
200 lines
6 KiB
Python
import ctypes
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import pytest
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import llama_cpp
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MODEL = "./vendor/llama.cpp/models/ggml-vocab-llama.gguf"
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def test_llama_cpp_tokenization():
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llama = llama_cpp.Llama(model_path=MODEL, vocab_only=True, verbose=False)
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assert llama
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assert llama._ctx.ctx is not None
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text = b"Hello World"
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tokens = llama.tokenize(text)
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assert tokens[0] == llama.token_bos()
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assert tokens == [1, 15043, 2787]
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detokenized = llama.detokenize(tokens)
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assert detokenized == text
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tokens = llama.tokenize(text, add_bos=False)
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assert tokens[0] != llama.token_bos()
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assert tokens == [15043, 2787]
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detokenized = llama.detokenize(tokens)
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assert detokenized != text
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text = b"Hello World</s>"
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tokens = llama.tokenize(text)
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assert tokens[-1] != llama.token_eos()
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assert tokens == [1, 15043, 2787, 829, 29879, 29958]
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tokens = llama.tokenize(text, special=True)
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assert tokens[-1] == llama.token_eos()
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assert tokens == [1, 15043, 2787, 2]
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def test_llama_patch(monkeypatch):
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n_ctx = 128
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llama = llama_cpp.Llama(model_path=MODEL, vocab_only=True, n_ctx=n_ctx)
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n_vocab = llama_cpp.llama_n_vocab(llama._model.model)
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assert n_vocab == 32000
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## Set up mock function
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def mock_decode(*args, **kwargs):
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return 0
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def mock_get_logits(*args, **kwargs):
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size = n_vocab * n_ctx
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return (llama_cpp.c_float * size)()
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monkeypatch.setattr("llama_cpp.llama_cpp.llama_decode", mock_decode)
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monkeypatch.setattr("llama_cpp.llama_cpp.llama_get_logits", mock_get_logits)
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text = "The quick brown fox"
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text_tokens = llama.tokenize(text.encode("utf-8"), add_bos=True, special=True)
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output_text = " jumps over the lazy dog."
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all_text_tokens = llama.tokenize((text + output_text).encode("utf-8"), add_bos=True, special=True)
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output_tokens = all_text_tokens[len(text_tokens):]
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token_eos = llama.token_eos()
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n = 0
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def mock_sample(*args, **kwargs):
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nonlocal n
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if n < len(output_tokens):
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n += 1
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return output_tokens[n - 1]
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else:
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return token_eos
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monkeypatch.setattr("llama_cpp.llama_cpp.llama_sample_token", mock_sample)
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## Test basic completion until eos
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n = 0 # reset
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completion = llama.create_completion(text, max_tokens=20)
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assert completion["choices"][0]["text"] == output_text
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assert completion["choices"][0]["finish_reason"] == "stop"
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## Test streaming completion until eos
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n = 0 # reset
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chunks = list(llama.create_completion(text, max_tokens=20, stream=True))
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assert "".join(chunk["choices"][0]["text"] for chunk in chunks) == output_text
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assert chunks[-1]["choices"][0]["finish_reason"] == "stop"
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## Test basic completion until stop sequence
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n = 0 # reset
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completion = llama.create_completion(text, max_tokens=20, stop=["lazy"])
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assert completion["choices"][0]["text"] == " jumps over the "
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assert completion["choices"][0]["finish_reason"] == "stop"
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## Test streaming completion until stop sequence
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n = 0 # reset
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chunks = list(llama.create_completion(text, max_tokens=20, stream=True, stop=["lazy"]))
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assert (
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"".join(chunk["choices"][0]["text"] for chunk in chunks) == " jumps over the "
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)
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assert chunks[-1]["choices"][0]["finish_reason"] == "stop"
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## Test basic completion until length
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n = 0 # reset
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completion = llama.create_completion(text, max_tokens=2)
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assert completion["choices"][0]["text"] == " jumps"
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assert completion["choices"][0]["finish_reason"] == "length"
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## Test streaming completion until length
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n = 0 # reset
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chunks = list(llama.create_completion(text, max_tokens=2, stream=True))
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assert "".join(chunk["choices"][0]["text"] for chunk in chunks) == " jumps"
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assert chunks[-1]["choices"][0]["finish_reason"] == "length"
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def test_llama_pickle():
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import pickle
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import tempfile
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fp = tempfile.TemporaryFile()
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llama = llama_cpp.Llama(model_path=MODEL, vocab_only=True)
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pickle.dump(llama, fp)
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fp.seek(0)
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llama = pickle.load(fp)
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assert llama
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assert llama.ctx is not None
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text = b"Hello World"
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assert llama.detokenize(llama.tokenize(text)) == text
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def test_utf8(monkeypatch):
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n_ctx = 512
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llama = llama_cpp.Llama(model_path=MODEL, vocab_only=True, n_ctx=n_ctx, logits_all=True)
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n_vocab = llama.n_vocab()
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## Set up mock function
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def mock_decode(*args, **kwargs):
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return 0
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def mock_get_logits(*args, **kwargs):
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size = n_vocab * n_ctx
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return (llama_cpp.c_float * size)()
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monkeypatch.setattr("llama_cpp.llama_cpp.llama_decode", mock_decode)
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monkeypatch.setattr("llama_cpp.llama_cpp.llama_get_logits", mock_get_logits)
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output_text = "😀"
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output_tokens = llama.tokenize(output_text.encode("utf-8"))
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token_eos = llama.token_eos()
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n = 0
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def mock_sample(*args, **kwargs):
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nonlocal n
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if n < len(output_tokens):
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n += 1
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return output_tokens[n - 1]
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else:
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return token_eos
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monkeypatch.setattr("llama_cpp.llama_cpp.llama_sample_token", mock_sample)
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## Test basic completion with utf8 multibyte
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n = 0 # reset
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completion = llama.create_completion("", max_tokens=4)
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assert completion["choices"][0]["text"] == output_text
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## Test basic completion with incomplete utf8 multibyte
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n = 0 # reset
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completion = llama.create_completion("", max_tokens=1)
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assert completion["choices"][0]["text"] == ""
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def test_llama_server():
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from fastapi.testclient import TestClient
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from llama_cpp.server.app import create_app, Settings
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settings = Settings(
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model=MODEL,
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vocab_only=True,
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)
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app = create_app(settings)
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client = TestClient(app)
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response = client.get("/v1/models")
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assert response.json() == {
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"object": "list",
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"data": [
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{
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"id": MODEL,
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"object": "model",
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"owned_by": "me",
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"permissions": [],
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}
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],
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}
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def test_llama_cpp_version():
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assert llama_cpp.__version__
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