Repository navigation
Expand file tree
/
Copy pathtest_llama.py
More file actions
173 lines (134 loc) · 4.67 KB
/
Copy pathtest_llama.py
File metadata and controls
173 lines (134 loc) · 4.67 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
import ctypes
import multiprocessing
import numpy as np
from scipy.special import log_softmax
from huggingface_hub import hf_hub_download
import pytest
import llama_cpp
import llama_cpp._internals as internals
MODEL = "./vendor/llama.cpp/models/ggml-vocab-llama-spm.gguf"
def test_llama_cpp_version():
assert llama_cpp.__version__
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 llama_cpp_model_path():
repo_id = "Qwen/Qwen2-0.5B-Instruct-GGUF"
filename = "qwen2-0_5b-instruct-q8_0.gguf"
model_path = hf_hub_download(repo_id, filename)
return model_path
def test_real_model(llama_cpp_model_path):
import os
assert os.path.exists(llama_cpp_model_path)
params = llama_cpp.llama_model_default_params()
params.use_mmap = llama_cpp.llama_supports_mmap()
params.use_mlock = llama_cpp.llama_supports_mlock()
params.check_tensors = False
model = internals.LlamaModel(path_model=llama_cpp_model_path, params=params)
cparams = llama_cpp.llama_context_default_params()
cparams.n_ctx = 16
cparams.n_batch = 16
cparams.n_ubatch = 16
cparams.n_threads = multiprocessing.cpu_count()
cparams.n_threads_batch = multiprocessing.cpu_count()
cparams.logits_all = False
cparams.flash_attn = True
context = internals.LlamaContext(model=model, params=cparams)
tokens = model.tokenize(b"Hello, world!", add_bos=True, special=True)
assert tokens == [9707, 11, 1879, 0]
tokens = model.tokenize(b"The quick brown fox jumps", add_bos=True, special=True)
batch = internals.LlamaBatch(n_tokens=len(tokens), embd=0, n_seq_max=1)
seed = 1337
sampler = internals.LlamaSampler()
sampler.add_top_k(50)
sampler.add_top_p(0.9, 1)
sampler.add_temp(0.8)
sampler.add_dist(seed)
result = tokens
n_eval = 0
for _ in range(4):
batch.set_batch(tokens, n_past=n_eval, logits_all=False)
context.decode(batch)
n_eval += len(tokens)
token_id = sampler.sample(context, -1)
tokens = [token_id]
result += tokens
output = result[5:]
output_text = model.detokenize(output, special=True)
assert output_text == b" over the lazy dog"
def test_real_llama(llama_cpp_model_path):
model = llama_cpp.Llama(
llama_cpp_model_path,
n_ctx=32,
n_batch=32,
n_ubatch=32,
n_threads=multiprocessing.cpu_count(),
n_threads_batch=multiprocessing.cpu_count(),
logits_all=False,
flash_attn=True,
)
output = model.create_completion(
"The quick brown fox jumps",
max_tokens=4,
top_k=50,
top_p=0.9,
temperature=0.8,
seed=1337
)
assert output["choices"][0]["text"] == " over the lazy dog"
output = model.create_completion(
"The capital of france is paris, 'true' or 'false'?:\n",
max_tokens=4,
top_k=50,
top_p=0.9,
temperature=0.8,
seed=1337,
grammar=llama_cpp.LlamaGrammar.from_string("""
root ::= "true" | "false"
""")
)
assert output["choices"][0]["text"] == "true"
suffix = b"rot"
tokens = model.tokenize(suffix, add_bos=True, special=True)
def logit_processor_func(input_ids, logits):
for token in tokens:
logits[token] *= 1000
return logits
logit_processors = llama_cpp.LogitsProcessorList(
[logit_processor_func]
)
output = model.create_completion(
"The capital of france is par",
max_tokens=4,
top_k=50,
top_p=0.9,
temperature=0.8,
seed=1337,
logits_processor=logit_processors
)
assert output["choices"][0]["text"].lower().startswith("rot")