vbatts/llama.cpp
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shorten code using a variable

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xaedes 2023-05-07 19:48:38 +02:00
parent 49d6daa11e
commit e0de09d77e
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1 changed files with 18 additions and 17 deletions
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examples/baby-llama/baby-llama.cpp
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@ -1179,6 +1179,7 @@ int main(int argc, char ** argv) {
int n_examples = 128;
int n_tokens = model.hparams.n_ctx;
int n_vocab = model.hparams.n_vocab;
for (int ex=0; ex<n_examples; ++ex) {
struct ggml_init_params params = {
@ -1190,18 +1191,18 @@ int main(int argc, char ** argv) {
struct ggml_context * ctx0 = ggml_init(params);
// struct ggml_tensor * before_opt_best_samples = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
// struct ggml_tensor * before_opt_probs = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, model.hparams.n_vocab, n_tokens);
struct ggml_tensor * before_opt_best_samples = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
struct ggml_tensor * before_opt_probs = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_vocab, n_tokens);
struct ggml_tensor * after_opt_best_samples = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
struct ggml_tensor * after_opt_probs = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, model.hparams.n_vocab, n_tokens);
struct ggml_tensor * after_opt_probs = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_vocab, n_tokens);
struct ggml_tensor * tokens_input1 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
struct ggml_tensor * tokens_input2 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
// struct ggml_tensor * tokens_input3 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
// struct ggml_tensor * tokens_input4 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
struct ggml_tensor * targets1 = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, model.hparams.n_vocab, n_tokens);
struct ggml_tensor * targets2 = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, model.hparams.n_vocab, n_tokens);
// struct ggml_tensor * targets3 = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, model.hparams.n_vocab, n_tokens);
// struct ggml_tensor * targets4 = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, model.hparams.n_vocab, n_tokens);
struct ggml_tensor * targets1 = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_vocab, n_tokens);
struct ggml_tensor * targets2 = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_vocab, n_tokens);
// struct ggml_tensor * targets3 = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_vocab, n_tokens);
// struct ggml_tensor * targets4 = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_vocab, n_tokens);
int n_past = 0;
@ -1213,7 +1214,7 @@ int main(int argc, char ** argv) {
// get_example_targets(64*ex+32, tokens_input3, targets3);
// get_example_targets(64*ex+48, tokens_input4, targets4);
// print_probs(targets);
// print_tokens(tokens_input, model.hparams.n_vocab);
// print_tokens(tokens_input, n_vocab);
struct ggml_tensor * logits1 = forward(&model, &kv_self, ctx0, &gf, tokens_input1, n_tokens, n_past);
struct ggml_tensor * logits2 = forward(&model, &kv_self, ctx0, &gf, tokens_input2, n_tokens, n_past);
@ -1246,7 +1247,7 @@ int main(int argc, char ** argv) {
// printf("probabilities before optimization:\n");
// print_probs(before_opt_probs);
// printf("best samples before optimization:\n");
// print_tokens(before_opt_best_samples, model.hparams.n_vocab);
// print_tokens(before_opt_best_samples, n_vocab);
struct ggml_opt_params opt_params_adam = ggml_opt_default_params(GGML_OPT_ADAM);
struct ggml_opt_params opt_params_lbfgs = ggml_opt_default_params(GGML_OPT_LBFGS);
@ -1276,28 +1277,28 @@ int main(int argc, char ** argv) {
// printf("probabilities after optimization:\n");
// print_probs(after_opt_probs);
printf("best samples after optimization:\n");
print_tokens(after_opt_best_samples, model.hparams.n_vocab);
print_tokens(after_opt_best_samples, n_vocab);
}
ggml_free(ctx0);
}
{
int n_gen = 64;
int sample_ctx = n_tokens/2;
int n_gen = 128;
int sample_ctx = n_tokens/2-n_tokens/16;
printf("Generating %d tokens.\n", n_gen);
struct ggml_tensor * tokens_input = ggml_new_tensor_1d(model.ctx, GGML_TYPE_I32, n_tokens);
struct ggml_tensor * targets = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, model.hparams.n_vocab, n_tokens);
struct ggml_tensor * targets = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, n_vocab, n_tokens);
get_example_targets(137, tokens_input, targets);
for (int i=sample_ctx; i<n_tokens; ++i) {
ggml_set_i32_1d(tokens_input, i, model.hparams.n_vocab/2);
ggml_set_i32_1d(tokens_input, i, n_vocab/2);
}
for (int i=0; i<sample_ctx-1; ++i) {
print_token(ggml_get_i32_1d(tokens_input, i), model.hparams.n_vocab);
print_token(ggml_get_i32_1d(tokens_input, i), n_vocab);
}
printf("---\n");
for (int i=0; i<n_gen; ++i) {
@ -1318,7 +1319,7 @@ int main(int argc, char ** argv) {
ggml_graph_compute(ctx0, &gf);
struct ggml_tensor * best_samples = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, sample_ctx);
struct ggml_tensor * probs = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, model.hparams.n_vocab, sample_ctx);
struct ggml_tensor * probs = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_vocab, sample_ctx);
sample_softmax(logits, probs, best_samples);
@ -1326,7 +1327,7 @@ int main(int argc, char ** argv) {
int token = ggml_get_i32_1d(best_samples, sample_ctx-1);
// print_probs1(probs, sample_at);
print_token(token, model.hparams.n_vocab);
print_token(token, n_vocab);
lshift_examples(tokens_input, targets, 1);
ggml_set_i32_1d(tokens_input, 0, 0);