forked from ggml-org/llama.cpp
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathsave-load-state.cpp
246 lines (192 loc) · 8 KB
/
save-load-state.cpp
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
#include "common.h"
#include "llama.h"
#include <vector>
#include <cstdio>
#include <chrono>
int main(int argc, char ** argv) {
gpt_params params;
params.prompt = "The quick brown fox";
if (!gpt_params_parse(argc, argv, params)) {
return 1;
}
print_build_info();
if (params.n_predict < 0) {
params.n_predict = 16;
}
auto n_past = 0;
std::string result0;
std::string result1;
std::string result2;
// init
llama_model * model;
llama_context * ctx;
std::tie(model, ctx) = llama_init_from_gpt_params(params);
if (model == nullptr || ctx == nullptr) {
fprintf(stderr, "%s : failed to init\n", __func__);
return 1;
}
// tokenize prompt
auto tokens = llama_tokenize(ctx, params.prompt, true);
// evaluate prompt
llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), n_past, 0));
n_past += tokens.size();
// save state (rng, logits, embedding and kv_cache) to file
{
std::vector<uint8_t> state_mem(llama_state_get_size(ctx));
const size_t written = llama_state_get_data(ctx, state_mem.data());
FILE *fp_write = fopen("dump_state.bin", "wb");
fwrite(state_mem.data(), 1, written, fp_write);
fclose(fp_write);
fprintf(stderr, "%s : serialized state into %zd out of a maximum of %zd bytes\n", __func__, written, state_mem.size());
}
// save state (last tokens)
const auto n_past_saved = n_past;
// first run
printf("\nfirst run: %s", params.prompt.c_str());
for (auto i = 0; i < params.n_predict; i++) {
auto * logits = llama_get_logits(ctx);
auto n_vocab = llama_n_vocab(model);
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
}
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
auto next_token = llama_sample_token(ctx, &candidates_p);
auto next_token_str = llama_token_to_piece(ctx, next_token);
printf("%s", next_token_str.c_str());
result0 += next_token_str;
if (llama_decode(ctx, llama_batch_get_one(&next_token, 1, n_past, 0))) {
fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
llama_free(ctx);
llama_free_model(model);
return 1;
}
n_past += 1;
}
printf("\n\n");
// free old context
llama_free(ctx);
// make new context
auto * ctx2 = llama_new_context_with_model(model, llama_context_params_from_gpt_params(params));
printf("\nsecond run: %s", params.prompt.c_str());
// load state (rng, logits, embedding and kv_cache) from file
{
std::vector<uint8_t> state_mem(llama_state_get_size(ctx2));
FILE * fp_read = fopen("dump_state.bin", "rb");
const size_t read = fread(state_mem.data(), 1, state_mem.size(), fp_read);
fclose(fp_read);
if (read != llama_state_set_data(ctx2, state_mem.data())) {
fprintf(stderr, "\n%s : failed to read state\n", __func__);
llama_free(ctx2);
llama_free_model(model);
return 1;
}
fprintf(stderr, "%s : deserialized state from %zd out of a maximum of %zd bytes\n", __func__, read, state_mem.size());
}
// restore state (last tokens)
n_past = n_past_saved;
// second run
for (auto i = 0; i < params.n_predict; i++) {
auto * logits = llama_get_logits(ctx2);
auto n_vocab = llama_n_vocab(model);
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
}
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
auto next_token = llama_sample_token(ctx2, &candidates_p);
auto next_token_str = llama_token_to_piece(ctx2, next_token);
printf("%s", next_token_str.c_str());
result1 += next_token_str;
if (llama_decode(ctx2, llama_batch_get_one(&next_token, 1, n_past, 0))) {
fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
llama_free(ctx2);
llama_free_model(model);
return 1;
}
n_past += 1;
}
printf("\n\n");
llama_free(ctx2);
if (result0 != result1) {
fprintf(stderr, "\n%s : error : the 2 generations are different\n", __func__);
return 1;
}
// make new context
auto* ctx3 = llama_new_context_with_model(model, llama_context_params_from_gpt_params(params));
printf("\nsingle seq run: %s", params.prompt.c_str());
// load state (rng, logits, embedding and kv_cache) from file
{
std::vector<uint8_t> state_mem(llama_state_get_size(ctx3));
FILE * fp_read = fopen("dump_state.bin", "rb");
const size_t read = fread(state_mem.data(), 1, state_mem.size(), fp_read);
fclose(fp_read);
if (read != llama_state_set_data(ctx3, state_mem.data())) {
fprintf(stderr, "\n%s : failed to read state\n", __func__);
llama_free(ctx3);
llama_free_model(model);
return 1;
}
fprintf(stderr, "%s : deserialized state from %zd out of a maximum of %zd bytes\n", __func__, read, state_mem.size());
}
// restore state (last tokens)
n_past = n_past_saved;
// save seq 0 and load into seq 1
{
// save kv of seq 0
std::vector<uint8_t> seq_store(llama_state_seq_get_size(ctx3, 0));
const size_t ncopy = llama_state_seq_get_data(ctx3, seq_store.data(), 0);
if (ncopy != seq_store.size()) {
fprintf(stderr, "\n%s : seq copy data length %zd does not match expected length %zd\n", __func__, ncopy, seq_store.size());
llama_free(ctx3);
llama_free_model(model);
return 1;
}
fprintf(stderr, "%s : seq 0 copied, %zd bytes\n", __func__, ncopy);
// erase whole kv
llama_kv_cache_clear(ctx3);
fprintf(stderr, "%s : kv cache cleared\n", __func__);
// restore kv into seq 1
const size_t nset = llama_state_seq_set_data(ctx3, seq_store.data(), 1);
if (nset != seq_store.size()) {
fprintf(stderr, "\n%s : seq set data length %zd does not match expected length %zd\n", __func__, nset, seq_store.size());
llama_free(ctx3);
llama_free_model(model);
return 1;
}
fprintf(stderr, "%s : seq 1 restored, %zd bytes\n", __func__, nset);
}
// third run with seq 1 instead of 0
for (auto i = 0; i < params.n_predict; i++) {
auto * logits = llama_get_logits(ctx3);
auto n_vocab = llama_n_vocab(model);
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
}
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
auto next_token = llama_sample_token(ctx3, &candidates_p);
auto next_token_str = llama_token_to_piece(ctx3, next_token);
printf("%s", next_token_str.c_str());
result2 += next_token_str;
if (llama_decode(ctx3, llama_batch_get_one(&next_token, 1, n_past, 1))) {
fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
llama_free(ctx3);
llama_free_model(model);
return 1;
}
n_past += 1;
}
printf("\n");
llama_free(ctx3);
llama_free_model(model);
if (result0 != result2) {
fprintf(stderr, "\n%s : error : the seq restore generation is different\n", __func__);
return 1;
}
fprintf(stderr, "\n%s : success\n", __func__);
return 0;
}