293 lines
10 KiB
Python
293 lines
10 KiB
Python
import ctypes
|
|
|
|
import numpy as np
|
|
import pytest
|
|
from scipy.special import log_softmax
|
|
|
|
import llama_cpp
|
|
|
|
MODEL = "./vendor/llama.cpp/models/ggml-vocab-llama.gguf"
|
|
|
|
|
|
def test_llama_cpp_tokenization():
|
|
llama = llama_cpp.Llama(model_path=MODEL, vocab_only=True, verbose=False)
|
|
|
|
assert llama
|
|
assert llama._ctx.ctx is not None
|
|
|
|
text = b"Hello World"
|
|
|
|
tokens = llama.tokenize(text)
|
|
assert tokens[0] == llama.token_bos()
|
|
assert tokens == [1, 15043, 2787]
|
|
detokenized = llama.detokenize(tokens)
|
|
assert detokenized == text
|
|
|
|
tokens = llama.tokenize(text, add_bos=False)
|
|
assert tokens[0] != llama.token_bos()
|
|
assert tokens == [15043, 2787]
|
|
|
|
detokenized = llama.detokenize(tokens)
|
|
assert detokenized != text
|
|
|
|
text = b"Hello World</s>"
|
|
tokens = llama.tokenize(text)
|
|
assert tokens[-1] != llama.token_eos()
|
|
assert tokens == [1, 15043, 2787, 829, 29879, 29958]
|
|
|
|
tokens = llama.tokenize(text, special=True)
|
|
assert tokens[-1] == llama.token_eos()
|
|
assert tokens == [1, 15043, 2787, 2]
|
|
|
|
text = b""
|
|
tokens = llama.tokenize(text, add_bos=True, special=True)
|
|
assert tokens[-1] != llama.token_eos()
|
|
assert tokens == [llama.token_bos()]
|
|
assert text == llama.detokenize(tokens)
|
|
|
|
|
|
@pytest.fixture
|
|
def mock_llama(monkeypatch):
|
|
def setup_mock(llama: llama_cpp.Llama, output_text: str):
|
|
n_ctx = llama.n_ctx()
|
|
n_vocab = llama.n_vocab()
|
|
output_tokens = llama.tokenize(
|
|
output_text.encode("utf-8"), add_bos=True, special=True
|
|
)
|
|
logits = (llama_cpp.c_float * (n_vocab * n_ctx))(-100.0)
|
|
for i in range(n_ctx):
|
|
output_idx = i + 1 # logits for first tokens predict second token
|
|
if output_idx < len(output_tokens):
|
|
logits[i * n_vocab + output_tokens[output_idx]] = 100.0
|
|
else:
|
|
logits[i * n_vocab + llama.token_eos()] = 100.0
|
|
n = 0
|
|
last_n_tokens = 0
|
|
|
|
def mock_decode(ctx: llama_cpp.llama_context_p, batch: llama_cpp.llama_batch):
|
|
# Test some basic invariants of this mocking technique
|
|
assert ctx == llama._ctx.ctx, "context does not match mock_llama"
|
|
assert batch.n_tokens > 0, "no tokens in batch"
|
|
assert all(
|
|
batch.n_seq_id[i] == 1 for i in range(batch.n_tokens)
|
|
), "n_seq >1 not supported by mock_llama"
|
|
assert all(
|
|
batch.seq_id[i][0] == 0 for i in range(batch.n_tokens)
|
|
), "n_seq >1 not supported by mock_llama"
|
|
assert batch.logits[
|
|
batch.n_tokens - 1
|
|
], "logits not allocated for last token"
|
|
# Update the mock context state
|
|
nonlocal n
|
|
nonlocal last_n_tokens
|
|
n = max(batch.pos[i] for i in range(batch.n_tokens)) + 1
|
|
last_n_tokens = batch.n_tokens
|
|
return 0
|
|
|
|
def mock_get_logits(ctx: llama_cpp.llama_context_p):
|
|
# Test some basic invariants of this mocking technique
|
|
assert ctx == llama._ctx.ctx, "context does not match mock_llama"
|
|
assert n > 0, "mock_llama_decode not called"
|
|
assert last_n_tokens > 0, "mock_llama_decode not called"
|
|
# Return view of logits for last_n_tokens
|
|
return (llama_cpp.c_float * (last_n_tokens * n_vocab)).from_address(
|
|
ctypes.addressof(logits)
|
|
+ (n - last_n_tokens) * n_vocab * ctypes.sizeof(llama_cpp.c_float)
|
|
)
|
|
|
|
monkeypatch.setattr("llama_cpp.llama_cpp.llama_decode", mock_decode)
|
|
monkeypatch.setattr("llama_cpp.llama_cpp.llama_get_logits", mock_get_logits)
|
|
|
|
def mock_kv_cache_clear(ctx: llama_cpp.llama_context_p):
|
|
# Test some basic invariants of this mocking technique
|
|
assert ctx == llama._ctx.ctx, "context does not match mock_llama"
|
|
return
|
|
|
|
def mock_kv_cache_seq_rm(
|
|
ctx: llama_cpp.llama_context_p,
|
|
seq_id: llama_cpp.llama_seq_id,
|
|
pos0: llama_cpp.llama_pos,
|
|
pos1: llama_cpp.llama_pos,
|
|
):
|
|
# Test some basic invariants of this mocking technique
|
|
assert ctx == llama._ctx.ctx, "context does not match mock_llama"
|
|
return
|
|
|
|
def mock_kv_cache_seq_cp(
|
|
ctx: llama_cpp.llama_context_p,
|
|
seq_id_src: llama_cpp.llama_seq_id,
|
|
seq_id_dst: llama_cpp.llama_seq_id,
|
|
pos0: llama_cpp.llama_pos,
|
|
pos1: llama_cpp.llama_pos,
|
|
):
|
|
# Test some basic invariants of this mocking technique
|
|
assert ctx == llama._ctx.ctx, "context does not match mock_llama"
|
|
return
|
|
|
|
def mock_kv_cache_seq_keep(
|
|
ctx: llama_cpp.llama_context_p,
|
|
seq_id: llama_cpp.llama_seq_id,
|
|
):
|
|
# Test some basic invariants of this mocking technique
|
|
assert ctx == llama._ctx.ctx, "context does not match mock_llama"
|
|
return
|
|
|
|
def mock_kv_cache_seq_shift(
|
|
ctx: llama_cpp.llama_context_p,
|
|
seq_id: llama_cpp.llama_seq_id,
|
|
pos0: llama_cpp.llama_pos,
|
|
pos1: llama_cpp.llama_pos,
|
|
):
|
|
# Test some basic invariants of this mocking technique
|
|
assert ctx == llama._ctx.ctx, "context does not match mock_llama"
|
|
return
|
|
|
|
monkeypatch.setattr("llama_cpp.llama_cpp.llama_kv_cache_clear", mock_kv_cache_clear)
|
|
monkeypatch.setattr("llama_cpp.llama_cpp.llama_kv_cache_seq_rm", mock_kv_cache_seq_rm)
|
|
monkeypatch.setattr("llama_cpp.llama_cpp.llama_kv_cache_seq_cp", mock_kv_cache_seq_cp)
|
|
monkeypatch.setattr("llama_cpp.llama_cpp.llama_kv_cache_seq_keep", mock_kv_cache_seq_keep)
|
|
monkeypatch.setattr("llama_cpp.llama_cpp.llama_kv_cache_seq_shift", mock_kv_cache_seq_shift)
|
|
|
|
return setup_mock
|
|
|
|
|
|
def test_llama_patch(mock_llama):
|
|
n_ctx = 128
|
|
llama = llama_cpp.Llama(model_path=MODEL, vocab_only=True, n_ctx=n_ctx)
|
|
n_vocab = llama_cpp.llama_n_vocab(llama._model.model)
|
|
assert n_vocab == 32000
|
|
|
|
text = "The quick brown fox"
|
|
output_text = " jumps over the lazy dog."
|
|
all_text = text + output_text
|
|
|
|
## Test basic completion from bos until eos
|
|
mock_llama(llama, all_text)
|
|
completion = llama.create_completion("", max_tokens=36)
|
|
assert completion["choices"][0]["text"] == all_text
|
|
assert completion["choices"][0]["finish_reason"] == "stop"
|
|
|
|
## Test basic completion until eos
|
|
mock_llama(llama, all_text)
|
|
completion = llama.create_completion(text, max_tokens=20)
|
|
assert completion["choices"][0]["text"] == output_text
|
|
assert completion["choices"][0]["finish_reason"] == "stop"
|
|
|
|
## Test streaming completion until eos
|
|
mock_llama(llama, all_text)
|
|
chunks = list(llama.create_completion(text, max_tokens=20, stream=True))
|
|
assert "".join(chunk["choices"][0]["text"] for chunk in chunks) == output_text
|
|
assert chunks[-1]["choices"][0]["finish_reason"] == "stop"
|
|
|
|
## Test basic completion until stop sequence
|
|
mock_llama(llama, all_text)
|
|
completion = llama.create_completion(text, max_tokens=20, stop=["lazy"])
|
|
assert completion["choices"][0]["text"] == " jumps over the "
|
|
assert completion["choices"][0]["finish_reason"] == "stop"
|
|
|
|
## Test streaming completion until stop sequence
|
|
mock_llama(llama, all_text)
|
|
chunks = list(
|
|
llama.create_completion(text, max_tokens=20, stream=True, stop=["lazy"])
|
|
)
|
|
assert (
|
|
"".join(chunk["choices"][0]["text"] for chunk in chunks) == " jumps over the "
|
|
)
|
|
assert chunks[-1]["choices"][0]["finish_reason"] == "stop"
|
|
|
|
## Test basic completion until length
|
|
mock_llama(llama, all_text)
|
|
completion = llama.create_completion(text, max_tokens=2)
|
|
assert completion["choices"][0]["text"] == " jumps"
|
|
assert completion["choices"][0]["finish_reason"] == "length"
|
|
|
|
## Test streaming completion until length
|
|
mock_llama(llama, all_text)
|
|
chunks = list(llama.create_completion(text, max_tokens=2, stream=True))
|
|
assert "".join(chunk["choices"][0]["text"] for chunk in chunks) == " jumps"
|
|
assert chunks[-1]["choices"][0]["finish_reason"] == "length"
|
|
|
|
|
|
def test_llama_pickle():
|
|
import pickle
|
|
import tempfile
|
|
|
|
fp = tempfile.TemporaryFile()
|
|
llama = llama_cpp.Llama(model_path=MODEL, vocab_only=True)
|
|
pickle.dump(llama, fp)
|
|
fp.seek(0)
|
|
llama = pickle.load(fp)
|
|
|
|
assert llama
|
|
assert llama.ctx is not None
|
|
|
|
text = b"Hello World"
|
|
|
|
assert llama.detokenize(llama.tokenize(text)) == text
|
|
|
|
|
|
def test_utf8(mock_llama):
|
|
llama = llama_cpp.Llama(model_path=MODEL, vocab_only=True, logits_all=True)
|
|
|
|
output_text = "😀"
|
|
|
|
## Test basic completion with utf8 multibyte
|
|
mock_llama(llama, output_text)
|
|
completion = llama.create_completion("", max_tokens=4)
|
|
assert completion["choices"][0]["text"] == output_text
|
|
|
|
## Test basic completion with incomplete utf8 multibyte
|
|
mock_llama(llama, output_text)
|
|
completion = llama.create_completion("", max_tokens=1)
|
|
assert completion["choices"][0]["text"] == ""
|
|
|
|
|
|
def test_llama_server():
|
|
from fastapi.testclient import TestClient
|
|
from llama_cpp.server.app import create_app, Settings
|
|
|
|
settings = Settings(
|
|
model=MODEL,
|
|
vocab_only=True,
|
|
)
|
|
app = create_app(settings)
|
|
client = TestClient(app)
|
|
response = client.get("/v1/models")
|
|
assert response.json() == {
|
|
"object": "list",
|
|
"data": [
|
|
{
|
|
"id": MODEL,
|
|
"object": "model",
|
|
"owned_by": "me",
|
|
"permissions": [],
|
|
}
|
|
],
|
|
}
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"size_and_axis",
|
|
[
|
|
((32_000,), -1), # last token's next-token logits
|
|
((10, 32_000), -1), # many tokens' next-token logits, or batch of last tokens
|
|
((4, 10, 32_000), -1), # batch of texts
|
|
],
|
|
)
|
|
@pytest.mark.parametrize("convert_to_list", [True, False])
|
|
def test_logits_to_logprobs(size_and_axis, convert_to_list: bool, atol: float = 1e-7):
|
|
size, axis = size_and_axis
|
|
logits: np.ndarray = -np.random.uniform(low=0, high=60, size=size)
|
|
logits = logits.astype(np.single)
|
|
if convert_to_list:
|
|
# Currently, logits are converted from arrays to lists. This may change soon
|
|
logits = logits.tolist()
|
|
log_probs = llama_cpp.Llama.logits_to_logprobs(logits, axis=axis)
|
|
log_probs_correct = log_softmax(logits, axis=axis)
|
|
assert log_probs.dtype == np.single
|
|
assert log_probs.shape == size
|
|
assert np.allclose(log_probs, log_probs_correct, atol=atol)
|
|
|
|
|
|
def test_llama_cpp_version():
|
|
assert llama_cpp.__version__
|