Skip to content
Navigation Menu
{{ message }}
forked from ggml-org/llama.cpp
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathtest-sampling.cpp
More file actions
400 lines (309 loc) · 16.2 KB
/
Copy pathtest-sampling.cpp
File metadata and controls
400 lines (309 loc) · 16.2 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
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
#include "ggml.h"
#include "llama.h"
#ifdef NDEBUG
#undef NDEBUG
#endif
#include <algorithm>
#include <cmath>
#include <string>
#include <vector>
extern struct llama_sampler * llama_sampler_init_dry_testing(int32_t context_size, float dry_multiplier, float dry_base, int32_t dry_allowed_length, int32_t dry_penalty_last_n, const std::vector<std::vector<llama_token>>& seq_breakers);
static void dump(const llama_token_data_array * cur_p) {
for (size_t i = 0; i < cur_p->size; i++) {
printf("%d: %f (%f)\n", cur_p->data[i].id, cur_p->data[i].p, cur_p->data[i].logit);
}
}
#define DUMP(__cur_p) do { printf("%s:%d (%s)\n", __FILE__, __LINE__, __func__); dump((__cur_p)); printf("-\n"); } while(0)
struct sampler_tester {
sampler_tester(size_t n_vocab) {
cur.reserve(n_vocab);
for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
const float logit = logf(token_id);
cur.emplace_back(llama_token_data{token_id, logit, 0.0f});
}
cur_p = llama_token_data_array { cur.data(), cur.size(), -1, false };
}
sampler_tester(const std::vector<float> & probs, const std::vector<float> & probs_expected) : probs_expected(probs_expected) {
cur.reserve(probs.size());
for (llama_token token_id = 0; token_id < (llama_token)probs.size(); token_id++) {
const float logit = logf(probs[token_id]);
cur.emplace_back(llama_token_data{token_id, logit, probs[token_id]});
}
cur_p = llama_token_data_array { cur.data(), cur.size(), -1, false };
}
void apply(llama_sampler * sampler) {
llama_sampler_apply(sampler, &cur_p);
llama_sampler_free(sampler);
}
void check() {
GGML_ASSERT(cur_p.size == probs_expected.size());
for (size_t i = 0; i < cur_p.size; i++) {
GGML_ASSERT(fabs(cur_p.data[i].p - probs_expected[i]) < 1e-5);
}
}
llama_token_data_array cur_p;
private:
const std::vector<float> probs_expected;
std::vector<llama_token_data> cur;
};
static void test_temp(const std::vector<float> & probs, const std::vector<float> & probs_expected, float temp) {
sampler_tester tester(probs, probs_expected);
DUMP(&tester.cur_p);
tester.apply(llama_sampler_init_temp(temp));
tester.apply(llama_sampler_init_dist(0));
DUMP(&tester.cur_p);
tester.check();
}
static void test_temp_ext(const std::vector<float> & probs, const std::vector<float> & probs_expected, float temp, float delta, float exponent) {
sampler_tester tester(probs, probs_expected);
DUMP(&tester.cur_p);
tester.apply(llama_sampler_init_temp_ext(temp, delta, exponent));
tester.apply(llama_sampler_init_dist (0));
DUMP(&tester.cur_p);
tester.check();
}
static void test_top_k(const std::vector<float> & probs, const std::vector<float> & probs_expected, int k) {
sampler_tester tester(probs, probs_expected);
DUMP(&tester.cur_p);
tester.apply(llama_sampler_init_top_k(k));
tester.apply(llama_sampler_init_dist (0));
DUMP(&tester.cur_p);
tester.check();
}
static void test_top_p(const std::vector<float> & probs, const std::vector<float> & probs_expected, float p) {
sampler_tester tester(probs, probs_expected);
DUMP(&tester.cur_p);
tester.apply(llama_sampler_init_top_p(p, 0));
tester.apply(llama_sampler_init_dist (0));
DUMP(&tester.cur_p);
tester.check();
}
static void test_min_p(const std::vector<float> & probs, const std::vector<float> & probs_expected, float p) {
sampler_tester tester(probs, probs_expected);
DUMP(&tester.cur_p);
tester.apply(llama_sampler_init_min_p(p, 0));
tester.apply(llama_sampler_init_dist (0));
DUMP(&tester.cur_p);
tester.check();
}
static void test_xtc(const std::vector<float> & probs, const std::vector<float> & probs_expected, float p, float t) {
sampler_tester tester(probs, probs_expected);
DUMP(&tester.cur_p);
tester.apply(llama_sampler_init_xtc(p, t, 0, 0));
DUMP(&tester.cur_p);
tester.check();
}
static void test_typical(const std::vector<float> & probs, const std::vector<float> & probs_expected, float p) {
sampler_tester tester(probs, probs_expected);
DUMP(&tester.cur_p);
tester.apply(llama_sampler_init_typical(p, 0));
DUMP(&tester.cur_p);
tester.check();
}
static void test_penalties(
const std::vector<float> & probs, const std::vector<llama_token> & last_tokens,
const std::vector<float> & probs_expected, float repeat_penalty, float alpha_frequency, float alpha_presence
) {
GGML_ASSERT(probs.size() == probs_expected.size());
sampler_tester tester(probs, probs_expected);
auto * sampler = llama_sampler_init_penalties(last_tokens.size(), repeat_penalty, alpha_frequency, alpha_presence);
for (size_t i = 0; i < last_tokens.size(); i++) {
llama_sampler_accept(sampler, last_tokens[i]);
}
DUMP(&tester.cur_p);
tester.apply(sampler);
tester.apply(llama_sampler_init_dist(0));
DUMP(&tester.cur_p);
tester.check();
}
static void test_dry(
const std::vector<float> & probs, const std::vector<llama_token> & last_tokens,
const std::vector<float> & expected_probs, float dry_multiplier, float dry_base,
int dry_allowed_length, int dry_penalty_last_n,
const std::vector<std::vector<llama_token>> & seq_breakers
) {
GGML_ASSERT(probs.size() == expected_probs.size());
sampler_tester tester(probs, expected_probs);
auto * sampler = llama_sampler_init_dry_testing(1024, dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n, seq_breakers);
for (size_t i = 0; i < last_tokens.size(); i++) {
llama_sampler_accept(sampler, last_tokens[i]);
}
DUMP(&tester.cur_p);
tester.apply(sampler);
tester.apply(llama_sampler_init_dist(0));
DUMP(&tester.cur_p);
tester.check();
}
static void test_top_n_sigma(const std::vector<float> & probs, const std::vector<float> & probs_expected, int n) {
sampler_tester tester(probs, probs_expected);
DUMP(&tester.cur_p);
tester.apply(llama_sampler_init_top_n_sigma(n));
tester.apply(llama_sampler_init_dist (0));
DUMP(&tester.cur_p);
tester.check();
}
static void test_sampler_queue(const size_t n_vocab, const std::string & samplers_sequence, const int top_k, const float top_p, const float min_p
) {
sampler_tester tester(n_vocab);
llama_token min_token_id = 0;
const llama_token max_token_id = n_vocab - 1;
for (auto s : samplers_sequence) {
switch (s) {
case 'k': tester.apply(llama_sampler_init_top_k(top_k)); break;
case 'y': GGML_ABORT("typical test not implemented");
case 'p': tester.apply(llama_sampler_init_top_p(top_p, 1)); break;
case 'm': tester.apply(llama_sampler_init_min_p(min_p, 1)); break;
case 't': GGML_ABORT("temperature test not implemented");
default : GGML_ABORT("Unknown sampler");
}
tester.apply(llama_sampler_init_dist(0));
auto & cur_p = tester.cur_p;
const int size = cur_p.size;
if (s == 'k') {
const int expected_size = std::min(size, top_k);
min_token_id = std::max(min_token_id, (llama_token)(n_vocab - top_k));
GGML_ASSERT(size == expected_size);
GGML_ASSERT(cur_p.data[0].id == max_token_id);
GGML_ASSERT(cur_p.data[expected_size-1].id == min_token_id);
} else if (s == 'p') {
const int softmax_divisor = n_vocab * (n_vocab-1) / 2 - min_token_id * (min_token_id-1) / 2;
const int softmax_numerator_target = ceilf(top_p * softmax_divisor);
min_token_id = n_vocab;
int expected_size = 0;
int cumsum = 0;
do { // do-while because always at least one token is sampled
min_token_id--;
expected_size++;
cumsum += min_token_id;
} while (cumsum < softmax_numerator_target);
// token 0 has p == 0, need special consideration for cumsum because top_p immediately returns
if (min_token_id == 1) {
min_token_id--;
expected_size += 1;
}
GGML_ASSERT(size == expected_size);
GGML_ASSERT(!cur_p.sorted || cur_p.data[0].id == max_token_id);
GGML_ASSERT(!cur_p.sorted || cur_p.data[expected_size-1].id == min_token_id);
} else if (s == 'm') {
int expected_size = ceilf((1.0f - min_p) * n_vocab);
expected_size = std::max(expected_size, 1);
expected_size = std::min(expected_size, size);
min_token_id = floorf(min_p * n_vocab);
min_token_id = std::max(min_token_id, 1);
min_token_id = std::max(min_token_id, (llama_token)(n_vocab - size));
min_token_id = std::min(min_token_id, (llama_token)(n_vocab - 1));
GGML_ASSERT(size == expected_size);
GGML_ASSERT(!cur_p.sorted || cur_p.data[0].id == max_token_id);
GGML_ASSERT(!cur_p.sorted || cur_p.data[expected_size-1].id == min_token_id);
} else {
GGML_ABORT("fatal error");
}
}
printf("Sampler queue %3s OK with n_vocab=%05zu top_k=%5d top_p=%f min_p=%f\n",
samplers_sequence.c_str(), n_vocab, top_k, top_p, min_p);
}
static void bench(llama_sampler * cnstr, const char * cnstr_name, const std::vector<llama_token_data> & data, int n_iter) {
std::vector<llama_token_data> cur(data.size());
std::copy(data.begin(), data.end(), cur.begin());
llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
llama_sampler_apply(cnstr, &cur_p);
llama_sampler_reset(cnstr);
const int64_t t_start = ggml_time_us();
for (int i = 0; i < n_iter; i++) {
std::copy(data.begin(), data.end(), cur.begin());
llama_token_data_array cur_p = { cur.data(), cur.size(), -1, false };
llama_sampler_apply(cnstr, &cur_p);
llama_sampler_reset(cnstr);
}
const int64_t t_end = ggml_time_us();
llama_sampler_free(cnstr);
printf("%-43s: %8.3f us/iter\n", cnstr_name, (t_end - t_start) / (float)n_iter);
}
#define BENCH(__cnstr, __data, __n_iter) bench((__cnstr), #__cnstr, (__data), (__n_iter))
static void test_perf() {
const int n_vocab = 1 << 17;
std::vector<llama_token_data> data;
data.reserve(n_vocab);
for (int i = 0; i < n_vocab; i++) {
const float logit = 2.0f*((double)(rand())/RAND_MAX - 0.5);
data.emplace_back(llama_token_data{i, logit, 0.0f});
}
BENCH(llama_sampler_init_top_k (40), data, 32);
BENCH(llama_sampler_init_top_p (0.8f, 1), data, 32);
BENCH(llama_sampler_init_min_p (0.2f, 1), data, 32);
BENCH(llama_sampler_init_typical(0.5f, 1), data, 32);
BENCH(llama_sampler_init_xtc (1.0f, 0.1f, 1, 1), data, 32);
}
int main(void) {
ggml_time_init();
test_temp({0.1f, 0.2f, 0.3f, 0.4f}, {0.1f, 0.2f, 0.3f, 0.4f}, 1.0f);
test_temp({0.1f, 0.2f, 0.3f, 0.4f}, {0.0f, 0.0f, 0.0f, 1.0f}, 0.0f);
test_temp_ext({0.1f, 0.2f, 0.3f, 0.4f}, {0.1f, 0.2f, 0.3f, 0.4f}, 1.0f, 0.0f, 1.0f);
test_temp_ext({0.1f, 0.2f, 0.3f, 0.4f}, {0.0f, 0.0f, 0.0f, 1.0f}, 0.0f, 0.0f, 1.0f);
test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {1.0f}, 1);
test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.44444f, 0.33333f, 0.22222f}, 3);
test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 4);
test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.1f, 0.2f, 0.3f, 0.4f}, 0);
test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {1.0f}, 0);
test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.571429f, 0.428571f}, 0.7f);
test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.44444f, 0.33333f, 0.22222f}, 0.8f);
test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.1f, 0.2f, 0.3f, 0.4f}, 1.0f);
test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.1f/1.0f, 0.2f/1.0f, 0.3f/1.0f, 0.4f/1.0f}, 0.00f);
test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.1f/1.0f, 0.2f/1.0f, 0.3f/1.0f, 0.4f/1.0f}, 0.24f);
test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.2f/0.9f, 0.3f/0.9f, 0.4f/0.9f}, 0.26f);
test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.2f/0.9f, 0.3f/0.9f, 0.4f/0.9f}, 0.49f);
test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.3f/0.7f, 0.4f/0.7f}, 0.51f);
test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.3f/0.7f, 0.4f/0.7f}, 0.74f);
test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.4f}, 0.76f);
test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.4f}, 1.00f);
test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.4f}, 1.05f);
printf("XTC should:\n");
test_xtc({0.4f, 0.3f, 0.2f, 0.1f}, {0.1f}, 0.99f, 0.09f);
test_xtc({0.4f, 0.3f, 0.2f, 0.1f}, {0.2f, 0.1f}, 0.99f, 0.19f);
test_xtc({0.4f, 0.3f, 0.2f, 0.1f}, {0.3f, 0.2f, 0.1f}, 0.99f, 0.29f);
printf("XTC should not:\n");
test_xtc({0.4f, 0.3f, 0.2f, 0.1f}, {0.4f, 0.3f, 0.2f, 0.1f}, 0.99f, 0.39f);
test_typical({0.97f, 0.01f, 0.01f, 0.01f}, {0.97f}, 0.5f);
test_typical({0.4f, 0.2f, 0.2f, 0.2f}, {0.2f, 0.2f, 0.2f}, 0.5f);
test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0}, {0, 0.25f, 0.25f, 0.25f, 0.25f}, 50.0f, 0.0f, 0.0f);
test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0, 0, 0, 0.5f, 0.5f}, 50.0f, 0.0f, 0.0f);
test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0, 0, 0, 0.5f, 0.5f}, 50.0f, 0.0f, 0.0f);
test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0}, {0.000011f, 0.249997f, 0.249997f, 0.249997f, 0.249997f}, 1.0f, 5.0f, 5.0f);
test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.000023f, 0.000023f, 0.000023f, 0.499966f, 0.499966f}, 1.0f, 5.0f, 5.0f);
test_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.000000f, 0.000023f, 0.000023f, 0.499977f, 0.499977f}, 1.0f, 5.0f, 5.0f);
test_dry({0.25f, 0.25f, 0.25f, 0.25f}, {0, 1}, {0.25f, 0.25f, 0.25f, 0.25f}, 1.0f, 1.1f, 2, 4, {});
test_dry({0.25f, 0.25f, 0.25f, 0.25f}, {0, 1, 2, 0, 1}, {0.296923f, 0.296923f, 0.109232f, 0.296923f}, 1.0f, 1.1f, 2, 5, {});
test_dry({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 3, 4, 0, 1}, {0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, 1.0f, 1.1f, 2, 6, {{3}});
test_dry({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 1}, {0.241818f, 0.241818f, 0.032727f, 0.241818f, 0.241818f}, 2.0f, 1.1f, 2, 5, {});
test_dry({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 3, 4, 0, 1}, {0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, 1.0f, 1.1f, 4, 7, {});
test_top_n_sigma({0.1f, 0.2f, 0.3f, 0.4f}, {0.571429f, 0.428571f, 0.0f, 0.0f}, 1.00f);
test_top_n_sigma({0.1f, 0.2f, 0.3f, 0.4f}, {0.1f, 0.2f, 0.3f, 0.4f}, 0.00f); // top_n_sigma == 0 now represents a no-op rather than greedy decoding as of PR#13345
test_top_n_sigma({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 3.00f);
test_sampler_queue(10000, "k", 10000, 1.0f, 1.0f);
test_sampler_queue(10000, "k", 1, 1.0f, 1.0f);
test_sampler_queue(10000, "p", 10000, 1.0f, 1.0f);
test_sampler_queue(10000, "p", 10000, 0.0f, 1.0f);
test_sampler_queue(10000, "m", 10000, 1.0f, 1.0f);
test_sampler_queue(10000, "m", 10000, 1.0f, 1e-12);
test_sampler_queue(10000, "k", 100, 1.0000f, 1.0f);
test_sampler_queue(10000, "p", 10000, 0.0003f, 1.0f);
test_sampler_queue(10000, "p", 10000, 0.8000f, 1.0f);
test_sampler_queue(10000, "m", 10000, 1.0000f, 9997.9f/9999.0f);
test_sampler_queue(10000, "m", 10000, 1.0000f, 0.1f);
test_sampler_queue(10000, "kp", 100, 0.8f, 0.1f);
test_sampler_queue(10000, "km", 100, 0.8f, 0.1f);
test_sampler_queue(10000, "pk", 100, 0.8f, 0.1f);
test_sampler_queue(10000, "pm", 100, 0.8f, 0.1f);
test_sampler_queue(10000, "mk", 100, 0.8f, 0.1f);
test_sampler_queue(10000, "mp", 100, 0.8f, 9997.9f/9999.0f);
test_sampler_queue(10000, "mp", 100, 0.8f, 0.1f);
test_sampler_queue(10000, "kpm", 100, 0.8f, 0.1f);
test_sampler_queue(10000, "kmp", 100, 0.8f, 0.1f);
test_sampler_queue(10000, "pkm", 100, 0.8f, 0.1f);
test_sampler_queue(10000, "pmk", 100, 0.8f, 0.1f);
test_sampler_queue(10000, "mkp", 100, 0.8f, 0.1f);
test_sampler_queue(10000, "mpk", 100, 0.8f, 0.1f);
printf("OK\n");
test_perf();
return 0;
}
You can’t perform that action at this time.