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
|
#include "common.h"
#include "llama.h"
#include "build-info.h"
#include <cmath>
#include <ctime>
std::vector<float> softmax(const std::vector<float>& logits) {
std::vector<float> probs(logits.size());
float max_logit = logits[0];
for (float v : logits) max_logit = std::max(max_logit, v);
double sum_exp = 0.0;
for (size_t i = 0; i < logits.size(); i++) {
// Subtract the maximum logit value from the current logit value for numerical stability
const float logit = logits[i] - max_logit;
const float exp_logit = expf(logit);
sum_exp += exp_logit;
probs[i] = exp_logit;
}
for (size_t i = 0; i < probs.size(); i++) probs[i] /= sum_exp;
return probs;
}
void perplexity(llama_context * ctx, const gpt_params & params) {
// Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
// Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
// Output: `perplexity: 13.5106 [114/114]`
auto tokens = ::llama_tokenize(ctx, params.prompt, true);
int count = 0;
int seq_count = tokens.size() / params.n_ctx;
int n_vocab = llama_n_vocab(ctx);
double nll = 0.0;
fprintf(stderr, "%s : calculating perplexity over %d chunks, batch_size=%d\n", __func__, seq_count, params.n_batch);
for (int i = 0; i < seq_count; ++i) {
int start = i * params.n_ctx;
int end = start + params.n_ctx;
std::vector<float> logits;
int num_batches = (params.n_ctx + params.n_batch - 1) / params.n_batch;
auto start_t = std::chrono::high_resolution_clock::now();
for (int j = 0; j < num_batches; ++j) {
int batch_start = start + j * params.n_batch;
int batch_size = std::min(end - batch_start, params.n_batch);
if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * params.n_batch, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return;
}
auto batch_logits = llama_get_logits(ctx);
logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
}
auto end_t = std::chrono::high_resolution_clock::now();
if (i == 0) {
const float seconds = std::chrono::duration<float>(end_t - start_t).count();
printf("%.2f seconds per pass - ETA ", seconds);
int total_seconds = (int)(seconds * seq_count);
if (total_seconds >= 60*60) {
printf("%d hours ", total_seconds / (60*60));
total_seconds = total_seconds % (60*60);
}
printf("%d minutes\n", total_seconds / 60);
}
// We get the logits for all the tokens in the context window (params.n_ctx)
// from llama_eval above. Now, based on https://huggingface.co/docs/transformers/perplexity,
// calculate the perplexity over the last half the window (so the model always has
// some context to predict the token).
//
// We rely on the fact that attention in the forward pass only looks at previous
// tokens here, so the logits returned for each token are an accurate representation
// of what the model would have predicted at that point.
//
// Example, we have a context window of 512, we will compute perplexity for each of the
// last 256 tokens. Then, we split the input up into context window size chunks to
// process the entire prompt.
for (int j = std::min(512, params.n_ctx / 2); j < params.n_ctx - 1; ++j) {
// Calculate probability of next token, given the previous ones.
std::vector<float> tok_logits(
logits.begin() + j * n_vocab,
logits.begin() + (j + 1) * n_vocab);
float prob = softmax(tok_logits)[tokens[start + j + 1]];
nll += -std::log(prob);
++count;
}
// perplexity is e^(average negative log-likelihood)
printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
fflush(stdout);
}
printf("\n");
}
int main(int argc, char ** argv) {
gpt_params params;
params.model = "models/llama-7B/ggml-model.bin";
params.n_batch = 512;
if (gpt_params_parse(argc, argv, params) == false) {
return 1;
}
params.perplexity = true;
params.n_batch = std::min(params.n_batch, params.n_ctx);
if (params.n_ctx > 2048) {
fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"
"expect poor results\n", __func__, params.n_ctx);
}
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
if (params.seed <= 0) {
params.seed = time(NULL);
}
fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
std::mt19937 rng(params.seed);
if (params.random_prompt) {
params.prompt = gpt_random_prompt(rng);
}
llama_context * ctx;
// load the model
{
auto lparams = llama_context_default_params();
lparams.n_ctx = params.n_ctx;
lparams.n_parts = params.n_parts;
lparams.seed = params.seed;
lparams.f16_kv = params.memory_f16;
lparams.logits_all = params.perplexity;
lparams.use_mmap = params.use_mmap;
lparams.use_mlock = params.use_mlock;
lparams.embedding = params.embedding;
ctx = llama_init_from_file(params.model.c_str(), lparams);
if (ctx == NULL) {
fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
return 1;
}
}
if (!params.lora_adapter.empty()) {
int err = llama_apply_lora_from_file(ctx,
params.lora_adapter.c_str(),
params.lora_base.empty() ? NULL : params.lora_base.c_str(),
params.n_threads);
if (err != 0) {
fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
return 1;
}
}
// print system information
{
fprintf(stderr, "\n");
fprintf(stderr, "system_info: n_threads = %d / %d | %s\n",
params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
}
perplexity(ctx, params);
llama_print_timings(ctx);
llama_free(ctx);
return 0;
}
|
