vbatts/llama.cpp
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train with two examples, creating new tensors each time..

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xaedes 2023-05-01 22:22:00 +02:00
parent 5f23052eb2
commit bc1c13bb66
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1 changed files with 86 additions and 66 deletions
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152
examples/baby-llama/baby-llama.cpp
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@ -539,7 +539,7 @@ void print_probs(struct ggml_tensor * probs) {
for (int i=0; i<probs->ne[1]; ++i) {
for (int k = 0; k < probs->ne[0]; ++k) {
float p = ggml_get_f32_1d(probs, i*probs->ne[0] + k);
printf(" %.1f", p);
printf(" %.2f", p);
}
printf("\n");
}
@ -559,6 +559,21 @@ void print_tokens(struct ggml_tensor * tokens, int n_vocab) {
}
}
void get_example_targets(int example_id, struct ggml_tensor * tokens_input, struct ggml_tensor * targets) {
int n_tokens = tokens_input->ne[0];
int n_vocab = targets->ne[0];
ggml_set_zero(targets);
for (int i=0; i<n_tokens; ++i) {
float x = example_id + i * 3.14159f * 2.0f * 4.0f / n_tokens;
float y = sinf(x);//*cosf(x*1.1f+1.0f);
float z = (y+1.0f)*0.5f; // scale to [0..1]
z = (z < 0.0f) ? 0.0f : (z > 1.0f) ? 1.0f : z; // clamp to [0..1]
int token = (int)(z*(float)(n_vocab-1));
ggml_set_f32_1d(targets, i*n_vocab + token, +1.0f);
ggml_set_i32_1d(tokens_input, i, token);
}
}
int main(int argc, char ** argv) {
struct ggml_init_params lcparams;
lcparams.mem_size = 1024*1024*1024;
@ -566,19 +581,26 @@ int main(int argc, char ** argv) {
lcparams.no_alloc = false;
struct llama_model model;
model.hparams.n_vocab = 16;
model.hparams.n_ctx = 64;
model.hparams.n_embd = 64;
model.hparams.n_vocab = 8;
model.hparams.n_ctx = 32;
model.hparams.n_embd = 32;
model.hparams.n_mult = 2;
model.hparams.n_head = 8;
model.hparams.n_layer = 16;
model.hparams.n_layer = 8;
model.hparams.n_rot = 16;
// model.hparams.n_embd = 32;
// model.hparams.n_mult = 2;
// model.hparams.n_head = 4;
// model.hparams.n_layer = 8;
// model.hparams.n_rot = 8;
model.ctx = ggml_init(lcparams);
printf("init model\n");
init_model(&model);
set_param_model(&model);
randomize_model(&model, 1337, 0.0f, 2.0f, -1.0f, +1.0f);
randomize_model(&model, 1337, 0.0f, 1.0f, -1.0f, +1.0f);
// key + value cache for the self attention
struct llama_kv_cache kv_self;
@ -593,68 +615,66 @@ int main(int argc, char ** argv) {
struct ggml_context * ctx0 = model.ctx; // ggml_init(c0params);
int n_tokens = 64;
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 * 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 * tokens_input = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
struct ggml_tensor * targets = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, model.hparams.n_vocab, n_tokens);
for (int i=0; i<n_tokens; ++i) {
float x = i * 3.14159f * 2.0f * 4.0f / n_tokens;
float y = sinf(x);
float z = (y+1.0f)*0.5f;
int token = (int)(z*(float)(model.hparams.n_vocab-1));
for (int k = 0; k < token; ++k) {
ggml_set_f32_1d(targets, i*model.hparams.n_vocab + k, 0.0f);
}
ggml_set_f32_1d(targets, i*model.hparams.n_vocab + token, +1.0f);
for (int k = token+1; k < model.hparams.n_vocab; ++k) {
ggml_set_f32_1d(targets, i*model.hparams.n_vocab + k, 0.0f);
}
ggml_set_i32_1d(tokens_input, i, token);
int n_examples = 2;
int n_tokens = model.hparams.n_ctx;
for (int ex=0; ex<n_examples; ++ex) {
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 * 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 * tokens_input = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
struct ggml_tensor * targets = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, model.hparams.n_vocab, n_tokens);
int n_past = 0;
ggml_cgraph gf = {};
gf.n_threads = 1;
get_example_targets(ex, tokens_input, targets);
printf("Example %d\n", (ex+1));
print_probs(targets);
print_tokens(tokens_input, model.hparams.n_vocab);
struct ggml_tensor * logits = forward(&model, &kv_self, ctx0, &gf, tokens_input, n_tokens, n_past);
struct ggml_tensor * e = ggml_sum(ctx0, ggml_sqr(ctx0, ggml_sub(ctx0, targets, logits)));
ggml_build_forward_expand(&gf, e);
ggml_graph_compute(ctx0, &gf);
float error_before_opt = ggml_get_f32_1d(e, 0);
sample_softmax(logits, before_opt_probs, before_opt_best_samples);
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);
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);
opt_params_adam.print_forward_graph = false;
opt_params_adam.print_backward_graph = false;
opt_params_lbfgs.print_forward_graph = false;
opt_params_lbfgs.print_backward_graph = false;
// ggml_opt(ctx0, opt_params_adam, e);
ggml_opt(ctx0, opt_params_lbfgs, e);
//
ggml_build_forward_expand(&gf, e);
ggml_graph_compute(ctx0, &gf);
float error_after_opt = ggml_get_f32_1d(e, 0);
sample_softmax(logits, after_opt_probs, after_opt_best_samples);
printf("error_before_opt: %.2f\n", error_before_opt);
printf("error_after_opt: %.2f\n", error_after_opt);
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_probs(targets);
print_tokens(tokens_input, model.hparams.n_vocab);
int n_past = 0;
ggml_cgraph gf = {};
gf.n_threads = 1;
struct ggml_tensor * logits = forward(&model, &kv_self, ctx0, &gf, tokens_input, n_tokens, n_past);
struct ggml_tensor * e = ggml_sum(ctx0, ggml_sqr(ctx0, ggml_sub(ctx0, targets, logits)));
ggml_build_forward_expand(&gf, e);
ggml_graph_compute(ctx0, &gf);
float error_before_opt = ggml_get_f32_1d(e, 0);
sample_softmax(logits, before_opt_probs, before_opt_best_samples);
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);
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);
ggml_opt(ctx0, opt_params_lbfgs, e);
// ggml_opt(ctx0, opt_params_adam, e);
//
ggml_build_forward_expand(&gf, e);
ggml_graph_compute(ctx0, &gf);
float error_after_opt = ggml_get_f32_1d(e, 0);
sample_softmax(logits, after_opt_probs, after_opt_best_samples);
printf("error_before_opt: %.2f\n", error_before_opt);
printf("error_after_opt: %.2f\n", error_after_opt);
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);
ggml_free(ctx0);