vbatts/llama.cpp
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Merge branch 'master' into gg/flash-attn

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Georgi Gerganov 2024-03-27 10:24:09 +02:00
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1
examples/CMakeLists.txt
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@ -34,6 +34,7 @@ else()
add_subdirectory(perplexity)
add_subdirectory(quantize)
add_subdirectory(quantize-stats)
add_subdirectory(retrieval)
add_subdirectory(save-load-state)
add_subdirectory(simple)
add_subdirectory(passkey)

2
examples/convert-llama2c-to-ggml/README.md
View file

@ -21,6 +21,8 @@ An example command using a model from [karpathy/tinyllamas](https://huggingface.
`$ ./convert-llama2c-to-ggml --copy-vocab-from-model llama-2-7b-chat.gguf.q2_K.bin --llama2c-model stories42M.bin --llama2c-output-model stories42M.gguf.bin`
Note: The vocabulary for `stories260K.bin` should be its own tokenizer `tok512.bin` found in [karpathy/tinyllamas/stories260K](https://huggingface.co/karpathy/tinyllamas/tree/main/stories260K).
Now you can use the model with a command like:
`$ ./main -m stories42M.gguf.bin -p "One day, Lily met a Shoggoth" -n 500 -c 256`

387
examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
View file

@ -1,6 +1,7 @@
#include "ggml.h"
#include "llama.h"
#include "common.h"
#include "log.h"
#include <unordered_map>
#include <vector>
@ -78,111 +79,101 @@ typedef struct {
struct TransformerWeights {
// token embedding table
float* token_embedding_table; // (vocab_size, dim)
std::vector<float> token_embedding_table; // (vocab_size, dim)
// weights for rmsnorms
float* rms_att_weight; // (layer, dim) rmsnorm weights
float* rms_ffn_weight; // (layer, dim)
std::vector<float> rms_att_weight; // (layer, dim) rmsnorm weights
std::vector<float> rms_ffn_weight; // (layer, dim)
// weights for matmuls
float* wq; // (layer, dim, dim)
float* wk; // (layer, dim, dim)
float* wv; // (layer, dim, dim)
float* wo; // (layer, dim, dim)
std::vector<float> wq; // (layer, dim, dim)
std::vector<float> wk; // (layer, dim, dim)
std::vector<float> wv; // (layer, dim, dim)
std::vector<float> wo; // (layer, dim, dim)
// weights for ffn
float* w1; // (layer, hidden_dim, dim)
float* w2; // (layer, dim, hidden_dim)
float* w3; // (layer, hidden_dim, dim)
std::vector<float> w1; // (layer, hidden_dim, dim)
std::vector<float> w2; // (layer, dim, hidden_dim)
std::vector<float> w3; // (layer, hidden_dim, dim)
// final rmsnorm
float* rms_final_weight; // (dim,)
std::vector<float> rms_final_weight; // (dim,)
// freq_cis for RoPE relatively positional embeddings
// float* freq_cis_real; // (seq_len, dim/2)
// float* freq_cis_imag; // (seq_len, dim/2)
// std::vector<float> freq_cis_real; // (seq_len, dim/2)
// std::vector<float> freq_cis_imag; // (seq_len, dim/2)
// (optional) classifier weights for the logits, on the last layer
float* wcls;
~TransformerWeights() {
delete[] token_embedding_table;
delete[] rms_att_weight;
delete[] rms_ffn_weight;
delete[] wq;
delete[] wk;
delete[] wv;
delete[] wo;
delete[] w1;
delete[] w2;
delete[] w3;
delete[] rms_final_weight;
delete[] wcls;
}
std::vector<float> wcls;
};
static void malloc_weights(TransformerWeights* w, Config* p, bool shared_weights) {
// we calloc instead of malloc to keep valgrind happy
w->token_embedding_table = new float[p->vocab_size * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->token_embedding_table\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim);
static void alloc_weights(TransformerWeights * w, const Config * p, bool shared_weights) {
const int n_multiqueries = p->n_kv_heads <= 0 || p->n_kv_heads >= p->n_heads ? 1 : p->n_heads / p->n_kv_heads;
try {
w->token_embedding_table.resize(p->vocab_size * p->dim);
LOG("%s: Allocating [%d] x [%d] = [%d] float space for w->token_embedding_table\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim);
w->rms_att_weight = new float[p->n_layers * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->rms_att_weight\n",__func__,p->n_layers, p->dim, p->n_layers * p->dim);
w->rms_att_weight.resize(p->n_layers * p->dim);
LOG("%s: Allocating [%d] x [%d] = [%d] float space for w->rms_att_weight\n",__func__,p->n_layers, p->dim, p->n_layers * p->dim);
w->rms_ffn_weight = new float[p->n_layers * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->rms_ffn_weight\n",__func__,p->n_layers , p->dim, p->n_layers * p->dim);
w->rms_ffn_weight.resize(p->n_layers * p->dim);
LOG("%s: Allocating [%d] x [%d] = [%d] float space for w->rms_ffn_weight\n",__func__,p->n_layers , p->dim, p->n_layers * p->dim);
w->wq = new float[p->n_layers * p->dim * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wq\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
w->wq.resize(p->n_layers * p->dim * p->dim);
LOG("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->wq\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
w->wk = new float[p->n_layers * p->dim * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wk\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
w->wk.resize(p->n_layers * p->dim * p->dim / n_multiqueries);
LOG("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->wk\n",__func__,p->n_layers, p->dim, p->dim / n_multiqueries, p->n_layers * p->dim * p->dim / n_multiqueries);
w->wv = new float[p->n_layers * p->dim * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wv\n",__func__, p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
w->wv.resize(p->n_layers * p->dim * p->dim / n_multiqueries);
LOG("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->wv\n",__func__, p->n_layers, p->dim, p->dim / n_multiqueries, p->n_layers * p->dim * p->dim / n_multiqueries);
w->wo = new float[p->n_layers * p->dim * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->wo\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
w->wo.resize(p->n_layers * p->dim * p->dim);
LOG("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->wo\n",__func__,p->n_layers, p->dim, p->dim, p->n_layers * p->dim * p->dim);
w->w1 = new float[p->n_layers * p->hidden_dim * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->w1\n",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim);
w->w1.resize(p->n_layers * p->hidden_dim * p->dim);
LOG("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->w1\n",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim);
w->w2 = new float[p->n_layers * p->hidden_dim * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->w2\n",__func__,p->n_layers, p->dim, p->hidden_dim, p->n_layers * p->hidden_dim * p->dim);
w->w2.resize(p->n_layers * p->hidden_dim * p->dim);
LOG("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->w2\n",__func__,p->n_layers, p->dim, p->hidden_dim, p->n_layers * p->hidden_dim * p->dim);
w->w3 = new float[p->n_layers * p->hidden_dim * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] x [%d] = [%d] float space for w->w3\n",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim);
w->w3.resize(p->n_layers * p->hidden_dim * p->dim);
LOG("%s: Allocating [%d] x [%d] x [%d] = [%d] float space for w->w3\n",__func__,p->n_layers, p->hidden_dim, p->dim, p->n_layers * p->hidden_dim * p->dim);
w->rms_final_weight = new float[p->dim]();
printf("[%s:AK] Allocating [%d] float space for w->rms_final_weight\n",__func__,p->dim);
w->rms_final_weight.resize(p->dim);
LOG("%s: Allocating [%d] float space for w->rms_final_weight\n",__func__,p->dim);
if (shared_weights) {
w->wcls = NULL;
} else {
w->wcls = new float[p->vocab_size * p->dim]();
printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->wcls\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim);
if (shared_weights) {
w->wcls = {};
} else {
w->wcls.resize(p->vocab_size * p->dim);
LOG("%s: Allocating [%d] x [%d] = [%d] float space for w->wcls\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim);
}
}
catch (std::length_error &) {
die("Invalid configuration. Failed to allocate memory for weights");
}
}
static int checkpoint_init_weights(TransformerWeights *w, Config* p, FILE* f, bool shared_weights) {
if (fread(w->token_embedding_table, sizeof(float), p->vocab_size * p->dim, f) != static_cast<size_t>(p->vocab_size * p->dim)) return 1;
if (fread(w->rms_att_weight, sizeof(float), p->n_layers * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim)) return 1;
if (fread(w->wq, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
if (fread(w->wk, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
if (fread(w->wv, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
if (fread(w->wo, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
if (fread(w->rms_ffn_weight, sizeof(float), p->n_layers * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim)) return 1;
if (fread(w->w1, sizeof(float), p->n_layers * p->dim * p->hidden_dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->hidden_dim)) return 1;
if (fread(w->w2, sizeof(float), p->n_layers * p->hidden_dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->hidden_dim * p->dim)) return 1;
if (fread(w->w3, sizeof(float), p->n_layers * p->dim * p->hidden_dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->hidden_dim)) return 1;
if (fread(w->rms_final_weight, sizeof(float), p->dim, f) != static_cast<size_t>(p->dim)) return 1;
static int checkpoint_init_weights(TransformerWeights * w, const Config * p, FILE * f, bool shared_weights) {
if (fread(w->token_embedding_table.data(), sizeof(float), w->token_embedding_table.size(), f) != w->token_embedding_table.size()) return 1;
if (fread(w->rms_att_weight.data(), sizeof(float), w->rms_att_weight.size(), f) != w->rms_att_weight.size()) return 1;
if (fread(w->wq.data(), sizeof(float), w->wq.size(), f) != w->wq.size()) return 1;
if (fread(w->wk.data(), sizeof(float), w->wk.size(), f) != w->wk.size()) return 1;
if (fread(w->wv.data(), sizeof(float), w->wv.size(), f) != w->wv.size()) return 1;
if (fread(w->wo.data(), sizeof(float), w->wo.size(), f) != w->wo.size()) return 1;
if (fread(w->rms_ffn_weight.data(), sizeof(float), w->rms_ffn_weight.size(), f) != w->rms_ffn_weight.size()) return 1;
if (fread(w->w1.data(), sizeof(float), w->w1.size(), f) != w->w1.size()) return 1;
if (fread(w->w2.data(), sizeof(float), w->w2.size(), f) != w->w2.size()) return 1;
if (fread(w->w3.data(), sizeof(float), w->w3.size(), f) != w->w3.size()) return 1;
if (fread(w->rms_final_weight.data(), sizeof(float), w->rms_final_weight.size(), f) != w->rms_final_weight.size()) return 1;
// Skip freq_cis_real & freq_cis_imag
int head_size = p->dim / p->n_heads;
fseek(f, p->seq_len * head_size * sizeof(float), SEEK_CUR);
if (!shared_weights && fread(w->wcls, sizeof(float), p->vocab_size * p->dim, f) != static_cast<size_t>(p->vocab_size * p->dim)) return 1;
if (!shared_weights && fread(w->wcls.data(), sizeof(float), w->wcls.size(), f) != w->wcls.size()) return 1;
// Check we didn't forget to read anything
auto curr = ftell(f);
fseek(f, 0, SEEK_END);
auto end = ftell(f);
if (curr != end) {
printf("Error: failed to read the checkpoint file to the end (curr = %ld, end = %ld)\n", curr, end);
LOG("%s: Error: failed to read the checkpoint file to the end (curr = %ld, end = %ld)\n", __func__, curr, end);
return 1;
}
@ -190,20 +181,20 @@ static int checkpoint_init_weights(TransformerWeights *w, Config* p, FILE* f, bo
}
static void print_sample_weights(TransformerWeights *w){
printf("----- Quick print of first of the weight vales of all the variables\n");
printf("%f\n", w->token_embedding_table[0]);
printf("%f\n", w->rms_att_weight[0]);
printf("%f\n", w->rms_ffn_weight[0]);
LOG("----- Quick print of first of the weight vales of all the variables\n");
LOG("%f\n", w->token_embedding_table[0]);
LOG("%f\n", w->rms_att_weight[0]);
LOG("%f\n", w->rms_ffn_weight[0]);
printf("%f\n", w->wq[0]);
printf("%f\n", w->wk[0]);
printf("%f\n", w->wv[0]);
printf("%f\n", w->wo[0]);
printf("%f\n", w->w1[0]);
printf("%f\n", w->w2[0]);
printf("%f\n", w->w3[0]);
printf("%f\n", w->rms_att_weight[0]);
if (w->wcls) printf("%f\n", w->wcls[0]);
LOG("%f\n", w->wq[0]);
LOG("%f\n", w->wk[0]);
LOG("%f\n", w->wv[0]);
LOG("%f\n", w->wo[0]);
LOG("%f\n", w->w1[0]);
LOG("%f\n", w->w2[0]);
LOG("%f\n", w->w3[0]);
LOG("%f\n", w->rms_att_weight[0]);
if (!w->wcls.empty()) LOG("%f\n", w->wcls[0]);
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////
@ -225,14 +216,16 @@ struct llama_vocab {
};
struct my_llama_hparams {
uint32_t n_vocab = 32000;
uint32_t n_ctx = 512; // this is provided as user input?
uint32_t n_embd = 4096;
uint32_t n_ff = 11008;
uint32_t n_mult = 4;
uint32_t n_head = 32;
uint32_t n_layer = 32;
uint32_t n_rot = 64;
uint32_t n_vocab = 32000;
uint32_t n_ctx = 512; // this is provided as user input?
uint32_t n_embd = 4096;
uint32_t n_ff = 11008;
uint32_t n_mult = 4;
uint32_t n_head = 32;
uint32_t n_head_kv = 32;
uint32_t n_layer = 32;
uint32_t n_rot = 64;
bool operator!=(const my_llama_hparams& other) const {
return memcmp(this, &other, sizeof(my_llama_hparams));
}
@ -325,14 +318,30 @@ struct train_params {
};
static void print_params(struct my_llama_hparams * params) {
printf("%s: n_vocab: %u\n", __func__, params->n_vocab);
printf("%s: n_ctx: %u\n", __func__, params->n_ctx);
printf("%s: n_embd: %u\n", __func__, params->n_embd);
printf("%s: n_mult: %u\n", __func__, params->n_mult);
printf("%s: n_head: %u\n", __func__, params->n_head);
printf("%s: n_ff: %u\n", __func__, params->n_ff);
printf("%s: n_layer: %u\n", __func__, params->n_layer);
printf("%s: n_rot: %u\n", __func__, params->n_rot);
LOG("%s: n_vocab: %u\n", __func__, params->n_vocab);
LOG("%s: n_ctx: %u\n", __func__, params->n_ctx);
LOG("%s: n_embd: %u\n", __func__, params->n_embd);
LOG("%s: n_mult: %u\n", __func__, params->n_mult);
LOG("%s: n_head: %u\n", __func__, params->n_head);
LOG("%s: n_head_kv: %u\n", __func__, params->n_head_kv);
LOG("%s: n_ff: %u\n", __func__, params->n_ff);
LOG("%s: n_layer: %u\n", __func__, params->n_layer);
LOG("%s: n_rot: %u\n", __func__, params->n_rot);
}
static void print_tensor_info(const struct ggml_context * ctx) {
for (auto t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
LOG("%s: Allocating ", __func__);
int64_t total = 1;
int i = 0;
for (; i < ggml_n_dims(t); ++i) {
if (i > 0) LOG("x ");
LOG("[%" PRId64 "] ", t->ne[i]);
total *= t->ne[i];
}
if (i > 1) LOG("= [%" PRId64 "] ", total);
LOG("float space for %s\n", ggml_get_name(t));
}
}
static void init_model(struct my_llama_model * model) {
@ -342,6 +351,8 @@ static void init_model(struct my_llama_model * model) {
const uint32_t n_layer = hparams.n_layer;
const uint32_t n_vocab = hparams.n_vocab;
const uint32_t n_multiqueries = hparams.n_head_kv <= 0 || hparams.n_head_kv >= hparams.n_head ? 1 : hparams.n_head / hparams.n_head_kv;
const uint32_t n_ff = hparams.n_ff;
struct ggml_context * ctx = model->ctx;
@ -350,25 +361,8 @@ static void init_model(struct my_llama_model * model) {
model->train_tokens = 0;
model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab);
printf("[%s:GG] Allocating [%u] x [%u] = [%u] float space for model->tok_embeddings\n",__func__,n_embd , n_vocab, n_embd * n_vocab);
model->norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
printf("[%s:GG] Allocating [%u] float space for model->norm\n",__func__,n_embd);
model->output = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab);
printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for model->output\n",__func__,n_embd, n_vocab, n_embd * n_vocab);
// printing the per-layer allocations here so we dont print in the for loop.
printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.wq for [%u] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer);
printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.wk for [%u] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer);
printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.wv for [%u] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer);
printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.wo for [%u] layers\n",__func__, n_embd, n_embd, n_embd * n_embd, n_layer);
printf("[%s:GG] Allocating [%u] float space for layer.ffn_norm for [%u] layers\n",__func__,n_embd, n_layer);
printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.w1 for [%u] layers\n",__func__, n_ff, n_embd, n_embd * n_ff, n_layer);
printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.w2 for [%u] layers\n",__func__, n_embd, n_ff, n_ff * n_embd, n_layer);
printf("[%s:GG] Allocating [%u] x[%u] = [%u] float space for layer.w3 for [%u] layers\n",__func__, n_ff, n_embd, n_embd * n_ff, n_layer);
ggml_set_name(model->tok_embeddings, "tok_embeddings.weight");
ggml_set_name(model->norm, "norm.weight");
@ -383,8 +377,8 @@ static void init_model(struct my_llama_model * model) {
layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
layer.wq = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
layer.wk = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
layer.wv = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
layer.wk = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd / n_multiqueries);
layer.wv = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd / n_multiqueries);
layer.wo = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
@ -406,6 +400,8 @@ static void init_model(struct my_llama_model * model) {
ggml_format_name(layer.w2, "%s.feed_forward.w2.weight", layers_i.c_str());
ggml_format_name(layer.w3, "%s.feed_forward.w3.weight", layers_i.c_str());
}
print_tensor_info(ctx);
}
static float get_f32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1) {
@ -421,9 +417,9 @@ static int32_t get_i32_2d(struct ggml_tensor * tensor, int64_t i0, int64_t i1) {
static void print_row(struct ggml_tensor * probs, int i) {
for (int k = 0; k < probs->ne[0]; ++k) {
float p = get_f32_2d(probs, k, i);
printf(" %f", p);
LOG(" %f", p);
}
printf("\n");
LOG("\n");
}
static void print_matrix(struct ggml_tensor * probs) {
@ -431,33 +427,12 @@ static void print_matrix(struct ggml_tensor * probs) {
for (int i = 0; i < probs->ne[1]; ++i) {
for (int k = 0; k < probs->ne[0]; ++k) {
float p = get_f32_2d(probs, k, i);
printf(" %.2f", p);
LOG(" %.2f", p);
}
printf("\n");
LOG("\n");
}
}
#ifdef __GNUC__
#ifdef __MINGW32__
__attribute__((format(gnu_printf, 1, 2)))
#else
__attribute__((format(printf, 1, 2)))
#endif
#endif
static std::string format(const char * fmt, ...) {
va_list ap, ap2;
va_start(ap, fmt);
va_copy(ap2, ap);
int size = vsnprintf(NULL, 0, fmt, ap);
GGML_ASSERT(size >= 0 && size < INT_MAX);
std::vector<char> buf(size + 1);
int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
GGML_ASSERT(size2 == size);
va_end(ap2);
va_end(ap);
return std::string(buf.data(), size);
}
struct llama_file {
// use FILE * so we don't have to re-open the file to mmap
FILE * fp;
@ -549,8 +524,9 @@ static std::string llama_escape_whitespaces(const std::string & text) {
return out.str();
}
static void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) {
static void load_vocab(const char * filename, const Config * config, struct llama_vocab * vocab) {
if (is_ggml_file(filename)) {
LOG("%s: Loading vocabulary from gguf file %s\n", __func__, filename);
struct ggml_context * ctx_data = NULL;
struct gguf_init_params params = {
@ -578,6 +554,9 @@ static void load_vocab(const char *filename, Config *config, struct llama_vocab
const int * toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx);
const uint32_t n_vocab = gguf_get_arr_n(ctx, token_idx);
if (n_vocab != static_cast<uint32_t>(config->vocab_size)) {
die_fmt("vocab size mismatch: (gguf) %u != (llama2c) %d", n_vocab, config->vocab_size);
}
vocab->id_to_token.resize(n_vocab);
@ -595,7 +574,7 @@ static void load_vocab(const char *filename, Config *config, struct llama_vocab
gguf_free(ctx);
} else {
// assume llama2.c vocabulary
printf("Assuming llama2.c vocabulary since %s is not a gguf file\n", filename);
LOG("%s: Assuming llama2.c vocabulary since %s is not a gguf file\n", __func__, filename);
llama_file file(filename, "rb");
if (!file.fp) {
die_fmt("%s: %s", strerror(errno), filename);
@ -638,38 +617,15 @@ static void load_vocab(const char *filename, Config *config, struct llama_vocab
}
static void convert_weights_ak_to_gg(struct ggml_tensor * gg_weights, const float * karpathy_weights) {
int ct;
switch (ggml_n_dims(gg_weights)) {
case 1:
ct = 0;
for (int i0 = 0; i0 < gg_weights->ne[0]; i0++){
float * ptr = (float *) ((char *) gg_weights->data + i0*gg_weights->nb[0]);
*ptr = karpathy_weights[ct];
ct++;
}
break;
case 2:
ct = 0;
for (int i1 = 0; i1 < gg_weights->ne[1]; i1++) {
for (int i0 = 0; i0 < gg_weights->ne[0]; i0++) {
float * ptr = (float *) ((char *) gg_weights->data + i0*gg_weights->nb[0] + i1*gg_weights->nb[1]);
*ptr = karpathy_weights[ct];
ct++;
}
}
break;
case 3:
ct = 0;
for (int i2 = 0; i2 < gg_weights->ne[2]; i2++) {
for (int i1 = 0; i1 < gg_weights->ne[1]; i1++) {
for (int i0 = 0; i0 < gg_weights->ne[0]; i0++) {
float * ptr = (float *) ((char *) gg_weights->data + i0*gg_weights->nb[0] + i1*gg_weights->nb[1] + i2*gg_weights->nb[2]);
*ptr = karpathy_weights[ct];
ct++;
}
}
}
break;
int size = 1;
for (int dim = 0; dim < ggml_n_dims(gg_weights); ++dim) {
size *= gg_weights->ne[dim];
}
for (int ct = 0; ct < size; ++ct) {
int64_t i0 = 0; int64_t i1 = 0;
int64_t i2 = 0; int64_t i3 = 0;
ggml_unravel_index(gg_weights, ct, &i0, &i1, &i2, &i3);
ggml_set_f32_nd(gg_weights, i0, i1, i2, i3, karpathy_weights[ct]);
}
}
@ -679,16 +635,18 @@ static void save_as_llama_model(
// convert AK weights into GG weights one by one.
// w->token_embedding_table -> model->tok_embeddings
// float* -> struct ggml_tensor
convert_weights_ak_to_gg(model->tok_embeddings, w->token_embedding_table);
convert_weights_ak_to_gg(model->output, w->wcls ? w->wcls : w->token_embedding_table);
convert_weights_ak_to_gg(model->tok_embeddings, w->token_embedding_table.data());
convert_weights_ak_to_gg(model->output, !w->wcls.empty() ? w->wcls.data() : w->token_embedding_table.data());
convert_weights_ak_to_gg(model->norm, w->rms_final_weight);
convert_weights_ak_to_gg(model->norm, w->rms_final_weight.data());
//print_row(model->norm, 0);
// for rms-att-weight
int row_length = model->hparams.n_embd;
int n_ff = model->hparams.n_ff;
const uint32_t n_multiqueries = model->hparams.n_head_kv <= 0 || model->hparams.n_head_kv >= model->hparams.n_head ? 1 : model->hparams.n_head / model->hparams.n_head_kv;
for (uint32_t i = 0; i < model->hparams.n_layer; ++i){
auto & layer = model->layers[i];
// 1d
@ -697,9 +655,10 @@ static void save_as_llama_model(
// from 3d matrix layer x dim x dim to 2d matrix dim x dim
convert_weights_ak_to_gg(layer.wq , &w->wq[i*row_length*row_length]);
convert_weights_ak_to_gg(layer.wk , &w->wk[i*row_length*row_length]);
convert_weights_ak_to_gg(layer.wv , &w->wv[i*row_length*row_length]);
convert_weights_ak_to_gg(layer.wo , &w->wo[i*row_length*row_length]);
// from 3d matrix layer x dim x dim to 2d matrix dim x dim / n_multiqueries
convert_weights_ak_to_gg(layer.wk , &w->wk[i*row_length*row_length/n_multiqueries]);
convert_weights_ak_to_gg(layer.wv , &w->wv[i*row_length*row_length/n_multiqueries]);
convert_weights_ak_to_gg(layer.w1 , &w->w1[i*row_length*n_ff]);
convert_weights_ak_to_gg(layer.w2 , &w->w2[i*n_ff*row_length]);
@ -736,8 +695,8 @@ static void save_as_llama_model(
gguf_set_val_u32(ctx, KV_EMBEDDING_LENGTH, model->hparams.n_embd);
gguf_set_val_u32(ctx, KV_FEED_FORWARD_LENGTH, model->hparams.n_ff);
gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT, model->hparams.n_head);
// n_head_kv is optional, default to n_head
// gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT_KV, ...);
gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT, model->hparams.n_head);
gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT_KV, model->hparams.n_head_kv);
gguf_set_val_u32(ctx, KV_BLOCK_COUNT, model->hparams.n_layer);
gguf_set_val_u32(ctx, KV_ROPE_DIMENSION_COUNT, model->hparams.n_rot);
gguf_set_val_f32(ctx, KV_ATTENTION_LAYERNORM_RMS_EPS, 1e-5f);
@ -789,12 +748,12 @@ static void save_as_llama_model(
static struct train_params get_default_train_params() {
struct train_params params;
params.fn_vocab_model = "models/7B/ggml-model-f16.gguf";
params.fn_vocab_model = "models/7B/ggml-model-f16.gguf";
params.fn_llama2c_output_model = "ak_llama_model.bin";
params.fn_train_data = "shakespeare.txt";
params.fn_checkpoint_in = "checkpoint.bin";
params.fn_checkpoint_out = "checkpoint.bin";
params.fn_model_out = "ggml-checkpoint-f32.bin";
params.fn_train_data = "shakespeare.txt";
params.fn_checkpoint_in = "checkpoint.bin";
params.fn_checkpoint_out = "checkpoint.bin";
params.fn_model_out = "ggml-checkpoint-f32.bin";
params.seed = -1;
@ -829,8 +788,8 @@ static struct train_params get_default_train_params() {
params.adam_alpha = 1e-3f;
params.adam_decay = 1e-3f;
params.mem_model_gb = 2;
params.mem_compute_gb = 24;
params.mem_model_gb = 2;
params.mem_compute_gb = 24;
params.mem_compute0_gb = 8;
params.mem_compute1_gb = 2;
@ -916,19 +875,30 @@ int main(int argc, char ** argv) {
if (!params_parse(argc, argv, &params)) {
return 1;
}
log_set_target(stdout);
Config config;
TransformerWeights weights = {};
{
FILE *file = fopen(params.fn_llama2c_model, "rb");
if (!file) { printf("Unable to open the checkpoint file %s!\n", params.fn_llama2c_model); return 1; }
LOG("%s: Loading llama2c model from %s\n", __func__, params.fn_llama2c_model);
FILE * file = fopen(params.fn_llama2c_model, "rb");
if (!file) {
LOG("%s: Unable to open the checkpoint file %s!\n", __func__, params.fn_llama2c_model);
return 1;
}
// read in the config header
if(fread(&config, sizeof(Config), 1, file) != 1) { return 1; }
if (fread(&config, sizeof(Config), 1, file) != 1) {
LOG("%s: Unable to read llama2c config from %s!\n",__func__,params.fn_llama2c_model);
return 1;
}
auto shared_weights = config.vocab_size > 0;
config.vocab_size = abs(config.vocab_size);
// read in the Transformer weights
malloc_weights(&weights, &config, shared_weights);
if(checkpoint_init_weights(&weights, &config, file, shared_weights)) { return 1; }
alloc_weights(&weights, &config, shared_weights);
if (checkpoint_init_weights(&weights, &config, file, shared_weights)) {
LOG("%s: Unable to initialize transformer weights from %s!",__func__,params.fn_llama2c_model);
return 1;
}
fclose(file);
}
@ -936,15 +906,18 @@ int main(int argc, char ** argv) {
load_vocab(params.fn_vocab_model, &config, &vocab);
struct my_llama_model model;
model.hparams.n_vocab = config.vocab_size; //llama_n_vocab(lctx);
model.hparams.n_ctx = params.n_ctx;
model.hparams.n_embd = config.dim; //params.n_embd;
model.hparams.n_ff = config.hidden_dim;
model.hparams.n_mult = 32;//params.n_mult;
model.hparams.n_head = config.n_heads; //params.n_head;
model.hparams.n_layer = config.n_layers; //params.n_layer;
model.hparams.n_rot = std::min((uint32_t)params.n_rotmax, model.hparams.n_embd / model.hparams.n_head);
model.hparams.n_vocab = config.vocab_size; //llama_n_vocab(lctx);
model.hparams.n_ctx = params.n_ctx;
model.hparams.n_embd = config.dim; //params.n_embd;
model.hparams.n_ff = config.hidden_dim;
model.hparams.n_mult = 32;//params.n_mult;
model.hparams.n_head = config.n_heads; //params.n_head;
model.hparams.n_head_kv = config.n_kv_heads;
model.hparams.n_layer = config.n_layers; //params.n_layer;
model.hparams.n_rot = std::min((uint32_t)params.n_rotmax, model.hparams.n_embd / model.hparams.n_head);
print_params(&model.hparams);
struct ggml_init_params lcparams;
lcparams.mem_size = 1024ll*1024ll*1024ll*((size_t) params.mem_model_gb);
lcparams.mem_buffer = NULL;
@ -956,7 +929,7 @@ int main(int argc, char ** argv) {
model.name = basename(params.fn_llama2c_model);
save_as_llama_model(&vocab, &model, &weights, params.fn_llama2c_output_model);
printf("Saving llama.c model file %s in ggml format at %s\n", params.fn_llama2c_model, params.fn_llama2c_output_model);
LOG("%s: Saving llama.c model file %s in ggml format at %s\n", __func__, params.fn_llama2c_model, params.fn_llama2c_output_model);
ggml_free(model.ctx);
return 0;

6
examples/embedding/embedding.cpp
View file

@ -61,6 +61,8 @@ int main(int argc, char ** argv) {
}
params.embedding = true;
// For non-causal models, batch size must be equal to ubatch size
params.n_ubatch = params.n_batch;
print_build_info();
@ -114,7 +116,9 @@ int main(int argc, char ** argv) {
for (const auto & prompt : prompts) {
auto inp = ::llama_tokenize(ctx, prompt, true, false);
if (inp.size() > n_batch) {
inp.resize(n_batch);
fprintf(stderr, "%s: error: number of tokens in input line (%lld) exceeds batch size (%lld), increase batch size and re-run\n",
__func__, (long long int) inp.size(), (long long int) n_batch);
return 1;
}
inputs.push_back(inp);
}

149
examples/gguf-split/gguf-split.cpp
View file

@ -1,32 +1,31 @@
#include "llama.h"
#include "ggml.h"
#include "common.h"
#include <algorithm>
#include <cmath>
#include <cstdint>
#include <cstdlib>
#include <fstream>
#include <ios>
#include <string>
#include <vector>
#include <stdio.h>
#include <fcntl.h>
#include <string.h>
#include <climits>
#include <stdexcept>
#if defined(_WIN32)
#include <windows.h>
#ifndef PATH_MAX
#define PATH_MAX MAX_PATH
#endif
#include <io.h>
#endif
enum split_operation : uint8_t {
SPLIT_OP_SPLIT,
SPLIT_OP_MERGE,
};
static const char * const LLM_KV_GENERAL_SPLIT_I_SPLIT = "general.split";
static const char * const LLM_KV_GENERAL_SPLIT_N_SPLIT = "general.split_count";
static const int SPLIT_FILENAME_MAX = 256;
static const char * const SPLIT_FILENAME_FORMAT = "%s-%05d-of-%05d.gguf";
struct split_params {
split_operation operation = SPLIT_OP_SPLIT;
int n_split_tensors = 128;
@ -116,13 +115,13 @@ static bool split_params_parse(int argc, const char ** argv, split_params & para
try {
if (!split_params_parse_ex(argc, argv, params)) {
split_print_usage(argv[0]);
exit(1);
exit(EXIT_FAILURE);
}
}
catch (const std::invalid_argument & ex) {
fprintf(stderr, "%s\n", ex.what());
split_print_usage(argv[0]);
exit(1);
exit(EXIT_FAILURE);
}
return result;
}
@ -134,12 +133,6 @@ static void zeros(std::ofstream & file, size_t n) {
}
}
static std::string split_file_name(const std::string & path, int i_split, int n_split) {
char f_split[SPLIT_FILENAME_MAX] = {0};
snprintf(f_split, sizeof(f_split), SPLIT_FILENAME_FORMAT, path.c_str(), i_split + 1, n_split);
return std::string(f_split);
}
struct split_strategy {
const split_params params;
std::ifstream & f_input;
@ -180,8 +173,9 @@ struct split_strategy {
if (i_split == 0) {
gguf_set_kv(ctx_out, ctx_gguf);
}
gguf_set_val_u8(ctx_out, LLM_KV_GENERAL_SPLIT_I_SPLIT, i_split);
gguf_set_val_u8(ctx_out, LLM_KV_GENERAL_SPLIT_N_SPLIT, n_split);
gguf_set_val_u16(ctx_out, LLM_KV_SPLIT_NO, i_split);
gguf_set_val_u16(ctx_out, LLM_KV_SPLIT_COUNT, n_split);
gguf_set_val_i32(ctx_out, LLM_KV_SPLIT_TENSORS_COUNT, n_tensors);
// populate the original tensors, so we get an initial metadata
for (int i = i_split * params.n_split_tensors; i < n_tensors && i < (i_split + 1) * params.n_split_tensors; ++i) {
@ -189,10 +183,11 @@ struct split_strategy {
gguf_add_tensor(ctx_out, meta);
}
auto split_name = split_file_name(params.output, i_split, n_split);
char split_path[PATH_MAX] = {0};
llama_split_path(split_path, sizeof(split_path), params.output.c_str(), i_split, n_split);
fprintf(stderr, "%s: %s ...", __func__, split_name.c_str());
fout = std::ofstream(split_name, std::ios::binary);
fprintf(stderr, "%s: %s ...", __func__, split_path);
fout = std::ofstream(split_path, std::ios::binary);
fout.exceptions(std::ofstream::failbit); // fail fast on write errors
auto meta_size = gguf_get_meta_size(ctx_out);
@ -250,19 +245,23 @@ static void gguf_split(const split_params & split_params) {
std::ifstream f_input(split_params.input.c_str(), std::ios::binary);
if (!f_input.is_open()) {
fprintf(stderr, "%s: failed to open input GGUF from %s\n", __func__, split_params.input.c_str());
exit(1);
exit(EXIT_FAILURE);
}
auto * ctx_gguf = gguf_init_from_file(split_params.input.c_str(), params);
if (!ctx_gguf) {
fprintf(stderr, "%s: failed to load input GGUF from %s\n", __func__, split_params.input.c_str());
exit(1);
exit(EXIT_FAILURE);
}
split_strategy strategy(split_params, f_input, ctx_gguf, ctx_meta);
char first_split_path[PATH_MAX] = {0};
llama_split_path(first_split_path, sizeof(first_split_path),
split_params.output.c_str(), strategy.i_split, strategy.n_split);
fprintf(stderr, "%s: %s -> %s (%d tensors per file)\n",
__func__, split_params.input.c_str(),
split_file_name(split_params.output, strategy.i_split, strategy.n_split).c_str(),
first_split_path,
split_params.n_split_tensors);
strategy.split_start();
@ -298,7 +297,9 @@ static void gguf_merge(const split_params & split_params) {
std::vector<ggml_context *> ctx_metas;
std::vector<gguf_context *> ctx_ggufs;
std::string split_prefix;
char split_path[PATH_MAX] = {0};
strncpy(split_path, split_params.input.c_str(), sizeof(split_path) - 1);
char split_prefix[PATH_MAX] = {0};
// First pass to find KV and tensors metadata
for (int i_split = 0; i_split < n_split; i_split++) {
@ -309,89 +310,66 @@ static void gguf_merge(const split_params & split_params) {
/*.ctx = */ &ctx_meta,
};
auto split_name = split_params.input;
if (i_split > 0) {
split_name = split_file_name(split_prefix, i_split, n_split);
llama_split_path(split_path, sizeof(split_path), split_prefix, i_split, n_split);
}
fprintf(stderr, "%s: reading metadata %s ...", __func__, split_name.c_str());
fprintf(stderr, "%s: reading metadata %s ...", __func__, split_path);
auto * ctx_gguf = gguf_init_from_file(split_name.c_str(), params);
auto * ctx_gguf = gguf_init_from_file(split_path, params);
if (!ctx_gguf) {
fprintf(stderr, "\n%s: failed to load input GGUF from %s\n", __func__, split_params.input.c_str());
exit(1);
exit(EXIT_FAILURE);
}
ctx_ggufs.push_back(ctx_gguf);
ctx_metas.push_back(ctx_meta);
if (i_split == 0) {
auto key_n_split = gguf_find_key(ctx_gguf, LLM_KV_GENERAL_SPLIT_N_SPLIT);
auto key_n_split = gguf_find_key(ctx_gguf, LLM_KV_SPLIT_COUNT);
if (key_n_split < 0) {
fprintf(stderr,
"\n%s: input file does not contain %s metadata\n",
__func__,
LLM_KV_GENERAL_SPLIT_N_SPLIT);
LLM_KV_SPLIT_COUNT);
gguf_free(ctx_gguf);
ggml_free(ctx_meta);
gguf_free(ctx_out);
fout.close();
exit(1);
exit(EXIT_FAILURE);
}
n_split = gguf_get_val_u8(ctx_gguf, key_n_split);
n_split = gguf_get_val_u16(ctx_gguf, key_n_split);
if (n_split < 1) {
fprintf(stderr,
"\n%s: input file does not contain a valid split count %d\n",
__func__,
n_split);
gguf_free(ctx_gguf);
ggml_free(ctx_meta);
gguf_free(ctx_out);
fout.close();
exit(1);
exit(EXIT_FAILURE);
}
// Verify the file naming and extract split_prefix
if (!llama_split_prefix(split_prefix, sizeof (split_prefix), split_path, i_split, n_split)) {
fprintf(stderr, "\n%s: unexpected input file name: %s"
" i_split=%d"
" n_split=%d\n", __func__,
split_path, i_split, n_split);
gguf_free(ctx_gguf);
ggml_free(ctx_meta);
gguf_free(ctx_out);
fout.close();
exit(EXIT_FAILURE);
}
// Do not trigger merge if we try to merge again the output
gguf_set_val_u8(ctx_out, LLM_KV_GENERAL_SPLIT_N_SPLIT, 0);
gguf_set_val_u16(ctx_gguf, LLM_KV_SPLIT_COUNT, 0);
// Set metadata from the first split
gguf_set_kv(ctx_out, ctx_gguf);
}
// Verify the file naming
{
int i_split_file = 0;
int n_split_file = 0;
const char * i_split_format = "-00000-of-00000.gguf";
if (split_name.size() < strlen(i_split_format)) {
fprintf(stderr, "\n%s: unexpected input file name: %s\n", __func__, split_params.input.c_str());
for (auto * _ctx_gguf : ctx_ggufs) {
gguf_free(_ctx_gguf);
}
gguf_free(ctx_out);
fout.close();
exit(1);
}
split_prefix = split_name.substr(0, split_name.size() - strlen(i_split_format));
const char * split_name_c_str = split_name.c_str();
int n_part = sscanf(&split_name_c_str[0] + split_prefix.size(), "-%d-of-%d", &i_split_file, &n_split_file);
if (n_part != 2 || i_split_file - 1 != i_split || n_split_file != n_split) {
fprintf(stderr, "\n%s: unexpected input file name: %s"
" i_split=%d i_split_file=%d"
" n_split=%d n_split_file=%d\n", __func__,
split_params.input.c_str(),
i_split, i_split_file,
n_split, n_split_file);
for (auto * _ctx_gguf : ctx_ggufs) {
gguf_free(_ctx_gguf);
}
gguf_free(ctx_out);
fout.close();
exit(1);
}
}
auto n_tensors = gguf_get_n_tensors(ctx_gguf);
for (int i_tensor = 0; i_tensor < n_tensors; i_tensor++) {
const char * t_name = gguf_get_tensor_name(ctx_gguf, i_tensor);
@ -411,18 +389,19 @@ static void gguf_merge(const split_params & split_params) {
// Write tensors data
for (int i_split = 0; i_split < n_split; i_split++) {
auto split_name = split_file_name(split_prefix, i_split, n_split);
std::ifstream f_input(split_name.c_str(), std::ios::binary);
llama_split_path(split_path, sizeof(split_path), split_prefix, i_split, n_split);
std::ifstream f_input(split_path, std::ios::binary);
if (!f_input.is_open()) {
fprintf(stderr, "%s: failed to open input GGUF from %s\n", __func__, split_name.c_str());
for (auto * _ctx_gguf : ctx_ggufs) {
gguf_free(_ctx_gguf);
fprintf(stderr, "%s: failed to open input GGUF from %s\n", __func__, split_path);
for (uint32_t i = 0; i < ctx_ggufs.size(); i++) {
gguf_free(ctx_ggufs[i]);
ggml_free(ctx_metas[i]);
}
gguf_free(ctx_out);
fout.close();
exit(1);
exit(EXIT_FAILURE);
}
fprintf(stderr, "%s: writing tensors %s ...", __func__, split_name.c_str());
fprintf(stderr, "%s: writing tensors %s ...", __func__, split_path);
auto * ctx_gguf = ctx_ggufs[i_split];
auto * ctx_meta = ctx_metas[i_split];
@ -481,8 +460,8 @@ int main(int argc, const char ** argv) {
break;
case SPLIT_OP_MERGE: gguf_merge(params);
break;
default:split_print_usage(argv[0]);
exit(1);
default: split_print_usage(argv[0]);
exit(EXIT_FAILURE);
}
return 0;

2
examples/imatrix/README.md
View file

@ -22,7 +22,7 @@ For faster computation, make sure to use GPU offloading via the `-ngl` argument
## Example
```bash
LLAMA_CUBLAS=1 make -j
LLAMA_CUDA=1 make -j
# generate importance matrix (imatrix.dat)
./imatrix -m ggml-model-f16.gguf -f train-data.txt -ngl 99

40
examples/imatrix/imatrix.cpp
View file

@ -50,29 +50,31 @@ private:
void keep_imatrix(int ncall) const;
};
// remove any prefix and suffixes from the name
// CUDA0#blk.0.attn_k.weight#0 => blk.0.attn_k.weight
static std::string filter_tensor_name(const char * name) {
std::string wname;
const char * p = strchr(name, '#');
if (p != NULL) {
p = p + 1;
const char * q = strchr(p, '#');
if (q != NULL) {
wname = std::string(p, q - p);
} else {
wname = p;
}
} else {
wname = name;
}
return wname;
}
bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data) {
GGML_UNUSED(user_data);
const struct ggml_tensor * src0 = t->src[0];
const struct ggml_tensor * src1 = t->src[1];
std::string wname;
{
// remove any prefix and suffixes from the name
// CUDA0#blk.0.attn_k.weight#0 => blk.0.attn_k.weight
const char * p = strchr(src0->name, '#');
if (p != NULL) {
p = p + 1;
const char * q = strchr(p, '#');
if (q != NULL) {
wname = std::string(p, q - p);
} else {
wname = p;
}
} else {
wname = src0->name;
}
}
std::string wname = filter_tensor_name(src0->name);
// when ask is true, the scheduler wants to know if we are interested in data from this tensor
// if we return true, a follow-up call will be made with ask=false in which we can do the actual collection
@ -112,6 +114,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
// this is necessary to guarantee equal number of "ncall" for each tensor
for (int ex = 0; ex < n_as; ++ex) {
src0 = t->src[2 + ex];
wname = filter_tensor_name(src0->name);
auto& e = m_stats[wname];
if (e.values.empty()) {
e.values.resize(src1->ne[0], 0);
@ -421,6 +424,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool
tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
}
// TODO: use batch.logits to save computations instead of relying on logits_all == true
if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return false;

4
examples/llama-bench/llama-bench.cpp
View file

@ -113,7 +113,7 @@ static std::string get_cpu_info() {
static std::string get_gpu_info() {
std::string id;
#ifdef GGML_USE_CUBLAS
#ifdef GGML_USE_CUDA
int count = ggml_backend_cuda_get_device_count();
for (int i = 0; i < count; i++) {
char buf[128];
@ -808,7 +808,7 @@ struct test {
const std::string test::build_commit = LLAMA_COMMIT;
const int test::build_number = LLAMA_BUILD_NUMBER;
const bool test::cuda = !!ggml_cpu_has_cublas();
const bool test::cuda = !!ggml_cpu_has_cuda();
const bool test::opencl = !!ggml_cpu_has_clblast();
const bool test::vulkan = !!ggml_cpu_has_vulkan();
const bool test::kompute = !!ggml_cpu_has_kompute();

2
examples/llava/MobileVLM-README.md
View file

@ -124,7 +124,7 @@ llama_print_timings: total time = 34570.79 ms
## Orin compile and run
### compile
```sh
make LLAMA_CUBLAS=1 CUDA_DOCKER_ARCH=sm_87 LLAMA_CUDA_F16=1 -j 32
make LLAMA_CUDA=1 CUDA_DOCKER_ARCH=sm_87 LLAMA_CUDA_F16=1 -j 32
```
### run on Orin

4
examples/llava/clip.cpp
View file

@ -7,7 +7,7 @@
#include "ggml-alloc.h"
#include "ggml-backend.h"
#ifdef GGML_USE_CUBLAS
#ifdef GGML_USE_CUDA
#include "ggml-cuda.h"
#endif
@ -968,7 +968,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
}
}
#ifdef GGML_USE_CUBLAS
#ifdef GGML_USE_CUDA
new_clip->backend = ggml_backend_cuda_init(0);
printf("%s: CLIP using CUDA backend\n", __func__);
#endif

18
examples/lookup/CMakeLists.txt
View file

@ -3,3 +3,21 @@ add_executable(${TARGET} lookup.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)
set(TARGET lookup-create)
add_executable(${TARGET} lookup-create.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)
set(TARGET lookup-merge)
add_executable(${TARGET} lookup-merge.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)
set(TARGET lookup-stats)
add_executable(${TARGET} lookup-stats.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)

43
examples/lookup/lookup-create.cpp Normal file
View file

@ -0,0 +1,43 @@
#include "ggml.h"
#include "llama.h"
#include "common.h"
#include "ngram-cache.h"
#include <cstdint>
#include <fstream>
#include <iostream>
#include <string>
#include <unordered_map>
#include <vector>
int main(int argc, char ** argv){
gpt_params params;
if (!gpt_params_parse(argc, argv, params)) {
return 1;
}
// init llama.cpp
llama_backend_init();
llama_numa_init(params.numa);
llama_model * model = NULL;
llama_context * ctx = NULL;
// load the model
std::tie(model, ctx) = llama_init_from_gpt_params(params);
GGML_ASSERT(model != nullptr);
// tokenize the prompt
const bool add_bos = llama_should_add_bos_token(model);
std::vector<llama_token> inp;
inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
fprintf(stderr, "%s: tokenization done\n", __func__);
llama_ngram_cache ngram_cache;
llama_ngram_cache_update(ngram_cache, LLAMA_NGRAM_STATIC, LLAMA_NGRAM_STATIC, inp, inp.size(), true);
fprintf(stderr, "%s: hashing done, writing file to %s\n", __func__, params.lookup_cache_static.c_str());
llama_ngram_cache_save(ngram_cache, params.lookup_cache_static);
}

47
examples/lookup/lookup-merge.cpp Normal file
View file

@ -0,0 +1,47 @@
#include "ggml.h"
#include "llama.h"
#include "common.h"
#include "ngram-cache.h"
#include <cstdint>
#include <cstdio>
#include <fstream>
#include <iostream>
#include <string>
#include <unordered_map>
#include <vector>
static void print_usage() {
fprintf(stderr, "Merges multiple lookup cache files into a single one.\n");
fprintf(stderr, "Usage: lookup-merge [--help] lookup_part_1.bin lookup_part_2.bin ... lookup_merged.bin\n");
}
int main(int argc, char ** argv){
if (argc < 3) {
print_usage();
exit(1);
}
std::vector<std::string> args;
args.resize(argc-1);
for (int i = 0; i < argc-1; ++i) {
args[i] = argv[i+1];
if (args[i] == "-h" || args[i] == "--help") {
print_usage();
exit(0);
}
}
fprintf(stderr, "lookup-merge: loading file %s\n", args[0].c_str());
llama_ngram_cache ngram_cache_merged = llama_ngram_cache_load(args[0]);
for (size_t i = 1; i < args.size()-1; ++i) {
fprintf(stderr, "lookup-merge: loading file %s\n", args[i].c_str());
llama_ngram_cache ngram_cache = llama_ngram_cache_load(args[i]);
llama_ngram_cache_merge(ngram_cache_merged, ngram_cache);
}
fprintf(stderr, "lookup-merge: saving file %s\n", args.back().c_str());
llama_ngram_cache_save(ngram_cache_merged, args.back());
}

163
examples/lookup/lookup-stats.cpp Normal file
View file

@ -0,0 +1,163 @@
#include "ggml.h"
#include "common.h"
#include "llama.h"
#include "log.h"
#include "ngram-cache.h"
#include <cmath>
#include <cstdint>
#include <cstdio>
#include <fstream>
#include <string>
#include <vector>
#include <unordered_map>
int main(int argc, char ** argv){
gpt_params params;
if (!gpt_params_parse(argc, argv, params)) {
return 1;
}
const int n_draft = params.n_draft;
// init llama.cpp
llama_backend_init();
llama_numa_init(params.numa);
llama_model * model = NULL;
llama_context * ctx = NULL;
// load the model
std::tie(model, ctx) = llama_init_from_gpt_params(params);
llama_set_rng_seed(ctx, params.seed);
GGML_ASSERT(llama_n_vocab(model) < (1 << 16));
// tokenize the prompt
const bool add_bos = llama_should_add_bos_token(model);
LOG("add_bos tgt: %d\n", add_bos);
std::vector<llama_token> inp;
inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
llama_ngram_cache ngram_cache_context;
llama_ngram_cache ngram_cache_dynamic;
llama_ngram_cache ngram_cache_static;
int64_t t_draft_flat_us = 0;
int64_t t_draft_us = 0;
{
const int64_t t_start_draft_us = ggml_time_us();
if (!params.lookup_cache_static.empty()) {
try {
ngram_cache_static = llama_ngram_cache_load(params.lookup_cache_static);
} catch (std::ifstream::failure const &) {
fprintf(stderr, "error: failed to open static lookup cache: %s", params.lookup_cache_static.c_str());
exit(1);
}
}
if (!params.lookup_cache_dynamic.empty()) {
try {
ngram_cache_dynamic = llama_ngram_cache_load(params.lookup_cache_dynamic);
} catch (std::ifstream::failure const &) {} // if the file does not exist it will simply be created at the end of the program
}
t_draft_flat_us += ggml_time_us() - t_start_draft_us;
}
const int n_input = inp.size();
const int n_ctx = params.n_ctx;
int n_drafted = 0;
int n_accept = 0;
const int64_t t_start_ms = ggml_time_ms();
// Iterate over input tokens in chunks of size n_ctx.
// Each chunk is treated as if a sequential generation but with pre-determined tokens to ensure reproducibility.
for (int i_start = 0; i_start + n_ctx < n_input; i_start += n_ctx) {
const std::vector<llama_token> inp_slice(inp.begin() + i_start, inp.begin() + i_start + n_ctx);
std::vector<llama_token> pseudo_output;
pseudo_output.push_back(inp_slice[0]);
while ((int) pseudo_output.size() < n_ctx) {
// Simulate drafting and decoding from draft:
std::vector<llama_token> draft;
draft.push_back(pseudo_output.back());
{
const int64_t t_start_draft_us = ggml_time_us();
llama_ngram_cache_draft(pseudo_output, draft, n_draft, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, ngram_cache_context, ngram_cache_dynamic, ngram_cache_static);
t_draft_us += ggml_time_us() - t_start_draft_us;
}
n_drafted += draft.size() - 1;
for (size_t j = 1; j < draft.size() && (int) pseudo_output.size() < n_ctx; ++j) {
const llama_token ground_truth = inp_slice[pseudo_output.size()];
const llama_token drafted = draft[j];
if (ground_truth != drafted) {
break;
}
++n_accept;
pseudo_output.push_back(ground_truth);
{
const int64_t t_start_draft_us = ggml_time_us();
llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, pseudo_output, 1, false);
t_draft_us += ggml_time_us() - t_start_draft_us;
}
}
// After each simulated batch decoding simulate the sampling of a single token:
if ((int) pseudo_output.size() < n_ctx) {
pseudo_output.push_back(inp_slice[pseudo_output.size()]);
{
const int64_t t_start_draft_us = ggml_time_us();
llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, pseudo_output, 1, false);
t_draft_us += ggml_time_us() - t_start_draft_us;
}
}
draft.erase(draft.begin());
}
if (i_start > 0 && i_start / 100000 != (i_start - n_ctx) / 100000) {
const int64_t t_now_ms = ggml_time_ms();
const int64_t eta_ms = (n_input - i_start) * (t_now_ms - t_start_ms) / i_start;
const int64_t eta_min = eta_ms / (60*1000);
const int64_t eta_s = (eta_ms - 60*1000*eta_min) / 1000;
LOG_TEE("lookup-stats: %d/%d done, ETA: %02" PRId64 ":%02" PRId64 "\n", i_start, n_input, eta_min, eta_s);
}
// After each chunk, update the dynamic ngram cache with the context ngram cache:
llama_ngram_cache_merge(ngram_cache_dynamic, ngram_cache_context);
ngram_cache_context.clear();
}
LOG_TEE("\n");
LOG_TEE("\n");
LOG_TEE("n_draft = %d\n", n_draft);
LOG_TEE("n_predict = %d\n", n_input - n_input % n_ctx);
LOG_TEE("n_drafted = %d\n", n_drafted);
LOG_TEE("t_draft_flat = %.2f ms\n", t_draft_flat_us*1e-3);
LOG_TEE("t_draft = %.2f ms, %.2f us per token, %.2f tokens per second\n",
t_draft_us*1e-3, 1.0f*t_draft_us/n_drafted, n_drafted/(1e-6*t_draft_us));
LOG_TEE("n_accept = %d\n", n_accept);
LOG_TEE("accept = %.3f%%\n", 100.0f * n_accept / n_drafted);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
fprintf(stderr, "\n\n");
return 0;
}

116
examples/lookup/lookup.cpp
View file

@ -1,12 +1,15 @@
#include "common.h"
#include "ggml.h"
#include "llama.h"
#include "common.h"
#include "ngram-cache.h"
#include <cmath>
#include <cstdint>
#include <cstdio>
#include <fstream>
#include <string>
#include <vector>
#include <unordered_map>
int main(int argc, char ** argv){
gpt_params params;
@ -15,11 +18,7 @@ int main(int argc, char ** argv){
return 1;
}
// max/min n-grams size to search for in prompt
const int ngram_max = 4;
const int ngram_min = 1;
// length of the candidate / draft sequence, if match is found
// max. number of additional tokens to draft if match is found
const int n_draft = params.n_draft;
const bool dump_kv_cache = params.dump_kv_cache;
@ -39,6 +38,8 @@ int main(int argc, char ** argv){
// load the model
std::tie(model, ctx) = llama_init_from_gpt_params(params);
llama_set_rng_seed(ctx, params.seed);
GGML_ASSERT(llama_n_vocab(model) < (1 << 16));
// tokenize the prompt
const bool add_bos = llama_should_add_bos_token(model);
@ -47,6 +48,35 @@ int main(int argc, char ** argv){
std::vector<llama_token> inp;
inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
llama_ngram_cache ngram_cache_context;
llama_ngram_cache ngram_cache_dynamic;
llama_ngram_cache ngram_cache_static;
int64_t t_draft_flat_us = 0;
int64_t t_draft_us = 0;
{
// Fill up context ngram cache with tokens from user input:
const int64_t t_start_draft_us = ggml_time_us();
llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, inp.size(), false);
if (!params.lookup_cache_static.empty()) {
try {
ngram_cache_static = llama_ngram_cache_load(params.lookup_cache_static);
} catch (std::ifstream::failure const &) {
fprintf(stderr, "error: failed to open static lookup cache: %s", params.lookup_cache_static.c_str());
exit(1);
}
}
if (!params.lookup_cache_dynamic.empty()) {
try {
ngram_cache_dynamic = llama_ngram_cache_load(params.lookup_cache_dynamic);
} catch (std::ifstream::failure const &) {} // if the file does not exist it will simply be created at the end of the program
}
t_draft_flat_us += ggml_time_us() - t_start_draft_us;
}
const int max_context_size = llama_n_ctx(ctx);
const int max_tokens_list_size = max_context_size - 4;
@ -76,8 +106,6 @@ int main(int argc, char ** argv){
int n_drafted = 0;
int n_accept = 0;
int64_t t_draft_us = 0;
int n_past = inp.size();
bool has_eos = false;
@ -129,6 +157,12 @@ int main(int argc, char ** argv){
++n_past;
++i_dft;
inp.push_back(id);
{
// Update context ngram cache with the newly accepted token:
const int64_t t_start_draft_us = ggml_time_us();
llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, 1, false);
t_draft_us += ggml_time_us() - t_start_draft_us;
}
if (params.use_color) {
// color accepted draft token
@ -149,6 +183,12 @@ int main(int argc, char ** argv){
draft.clear();
draft.push_back(id);
inp.push_back(id);
{
// Update context ngram cache with the newly accepted token:
const int64_t t_start_draft_us = ggml_time_us();
llama_ngram_cache_update(ngram_cache_context, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, inp, 1, false);
t_draft_us += ggml_time_us() - t_start_draft_us;
}
break;
}
@ -163,44 +203,19 @@ int main(int argc, char ** argv){
llama_batch_clear(batch_tgt);
llama_batch_add(batch_tgt, draft[0], n_past, { 0 }, true);
// generate n_pred tokens through prompt lookup
auto prompt_lookup = [&]() -> void {
const int inp_size = inp.size();
for (int ngram_size = ngram_max ; ngram_size > ngram_min; --ngram_size){
const llama_token * ngram = &inp[inp_size - ngram_size];
for (int i = 0; i <= (int) inp_size - (ngram_size * 2); ++i) {
bool match = true;
for (int j = 0; j < ngram_size; ++j) {
if (inp[i + j] != ngram[j]) {
match = false;
break;
}
}
if (match) {
const int startIdx = i + ngram_size;
const int endIdx = startIdx + n_draft;
if (endIdx < inp_size) {
for (int j = startIdx; j < endIdx; ++j) {
LOG(" - draft candidate %d: %d\n", j, inp[j]);
draft.push_back(inp[j]);
llama_batch_add(batch_tgt, inp[j], n_past + (j - startIdx) + 1, { 0 }, true);
++n_drafted;
}
return;
}
}
}
}
return;
};
// Draft already contains a single token sampled from the model:
GGML_ASSERT(draft.size() == 1);
GGML_ASSERT(draft[0] == inp.back());
const int64_t t_start_draft_us = ggml_time_us();
prompt_lookup();
llama_ngram_cache_draft(inp, draft, n_draft, LLAMA_NGRAM_MIN, LLAMA_NGRAM_MAX, ngram_cache_context, ngram_cache_dynamic, ngram_cache_static);
for (size_t i = 1; i < draft.size(); ++i) {
llama_batch_add(batch_tgt, draft[i], n_past + i, { 0 }, true);
}
t_draft_us += ggml_time_us() - t_start_draft_us;
n_drafted += draft.size() - 1;
llama_decode(ctx, batch_tgt);
++n_past;
@ -210,19 +225,24 @@ int main(int argc, char ** argv){
auto t_dec_end = ggml_time_us();
// Update dynamic ngram cache with context ngram cache and save it to disk:
llama_ngram_cache_merge(ngram_cache_dynamic, ngram_cache_context);
llama_ngram_cache_save(ngram_cache_dynamic, params.lookup_cache_dynamic);
LOG_TEE("\n\n");
LOG_TEE("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input, (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f));
LOG_TEE("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict / ((t_dec_end - t_dec_start) / 1e6f));
LOG_TEE("\n");
LOG_TEE("n_draft = %d\n", n_draft);
LOG_TEE("n_predict = %d\n", n_predict);
LOG_TEE("n_drafted = %d\n", n_drafted);
LOG_TEE("t_draft = %.2f ms, %.2f us per token, %.2f tokens per second\n",
LOG_TEE("n_draft = %d\n", n_draft);
LOG_TEE("n_predict = %d\n", n_predict);
LOG_TEE("n_drafted = %d\n", n_drafted);
LOG_TEE("t_draft_flat = %.2f ms\n", t_draft_flat_us*1e-3);
LOG_TEE("t_draft = %.2f ms, %.2f us per token, %.2f tokens per second\n",
t_draft_us*1e-3, 1.0f*t_draft_us/n_drafted, n_drafted/(1e-6*t_draft_us));
LOG_TEE("n_accept = %d\n", n_accept);
LOG_TEE("accept = %.3f%%\n", 100.0f * n_accept / n_drafted);
LOG_TEE("n_accept = %d\n", n_accept);
LOG_TEE("accept = %.3f%%\n", 100.0f * n_accept / n_drafted);
LOG_TEE("\ntarget:\n");
llama_print_timings(ctx);

2
examples/main-cmake-pkg/README.md
View file

@ -8,7 +8,7 @@ Because this example is "outside of the source tree", it is important to first b
### Considerations
When hardware acceleration libraries are used (e.g. CUBlas, Metal, CLBlast, etc.), CMake must be able to locate the associated CMake package. In the example below, when building _main-cmake-pkg_ notice the `CMAKE_PREFIX_PATH` includes the Llama CMake package location _in addition to_ the CLBlast package—which was used when compiling _llama.cpp_.
When hardware acceleration libraries are used (e.g. CUDA, Metal, CLBlast, etc.), CMake must be able to locate the associated CMake package. In the example below, when building _main-cmake-pkg_ notice the `CMAKE_PREFIX_PATH` includes the Llama CMake package location _in addition to_ the CLBlast package—which was used when compiling _llama.cpp_.
### Build llama.cpp and install to C:\LlamaCPP directory

6
examples/main/README.md
View file

@ -316,8 +316,8 @@ These options provide extra functionality and customization when running the LLa
- `-h, --help`: Display a help message showing all available options and their default values. This is particularly useful for checking the latest options and default values, as they can change frequently, and the information in this document may become outdated.
- `--verbose-prompt`: Print the prompt before generating text.
- `-ngl N, --n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
- `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS.
- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. Requires cuBLAS.
- `-ngl N, --n-gpu-layers N`: When compiled with GPU support, this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
- `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used.
- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance.
- `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains.
- `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation.

1
examples/parallel/parallel.cpp
View file

@ -132,7 +132,6 @@ int main(int argc, char ** argv) {
llama_context * ctx = NULL;
// load the target model
params.logits_all = true;
std::tie(model, ctx) = llama_init_from_gpt_params(params);
// load the prompts from an external file if there are any

129
examples/perplexity/perplexity.cpp
View file

@ -380,6 +380,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
const int batch_size = std::min(end - batch_start, n_batch);
//fprintf(stderr, " Batch %d: starts at %d, size is %d, n_past is %d\n",j,batch_start,batch_size,j * n_batch);
// TODO: use llama_batch.logits instead of relying on logits_all == true
if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
//fprintf(stderr, "%s : failed to eval\n", __func__);
return {tokens, -1, logit_history, prob_history};
@ -552,6 +553,8 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
const int batch_start = start + j * n_batch;
const int batch_size = std::min(end - batch_start, n_batch);
int n_outputs = 0;
batch.n_tokens = 0;
for (int seq = 0; seq < n_seq_batch; seq++) {
int seq_start = batch_start + seq*n_ctx;
@ -566,11 +569,13 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
for (int k = 0; k < batch_size; ++k) {
const int idx = seq*n_ctx + k;
batch.token[idx] = tokens[seq_start + k];
batch.pos[idx] = j*n_batch + k;
batch.n_seq_id[idx] = 1;
batch.seq_id[idx][0] = seq;
batch.logits[idx] = batch.pos[idx] >= first ? 1 : 0;
batch.token [idx] = tokens[seq_start + k];
batch.pos [idx] = j*n_batch + k;
batch.n_seq_id[idx] = 1;
batch.seq_id [idx][0] = seq;
batch.logits [idx] = batch.pos[idx] >= first ? 1 : 0;
n_outputs += batch.logits[idx] != 0;
}
batch.n_tokens += batch_size;
@ -583,9 +588,9 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
return {tokens, -1, logit_history, prob_history};
}
if (num_batches > 1) {
if (num_batches > 1 && n_outputs > 0) {
const auto * batch_logits = llama_get_logits(ctx);
logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
logits.insert(logits.end(), batch_logits, batch_logits + n_outputs * n_vocab);
}
}
@ -604,14 +609,15 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
}
for (int seq = 0; seq < n_seq_batch; seq++) {
const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits_ith(ctx, seq*n_ctx);
const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits_ith(ctx, seq*n_ctx + first);
llama_token * tokens_data = tokens.data() + start + seq*n_ctx + first;
if (!params.logits_file.empty()) {
process_logits(logits_stream, n_vocab, all_logits + first*n_vocab,
process_logits(logits_stream, n_vocab, all_logits,
tokens_data, n_ctx - 1 - first,
workers, log_probs, nll, nll2);
} else {
process_logits(n_vocab, all_logits + first*n_vocab,
process_logits(n_vocab, all_logits,
tokens_data, n_ctx - 1 - first,
workers, nll, nll2,
logit_history.data() + start + seq*n_ctx + first,
@ -652,6 +658,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
}
static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector<float> & batch_logits, int32_t n_batch, int32_t n_vocab) {
int prev_outputs = 0;
for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) {
const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
@ -672,7 +679,14 @@ static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector<
return false;
}
memcpy(batch_logits.data() + i*n_vocab, llama_get_logits(ctx), n_tokens*n_vocab*sizeof(float));
int n_outputs = 0;
for (int i = 0; i < n_tokens; ++i) {
n_outputs += batch_view.logits[i] != 0;
}
memcpy(batch_logits.data() + prev_outputs*n_vocab, llama_get_logits(ctx), n_outputs*n_vocab*sizeof(float));
prev_outputs += n_outputs;
}
return true;
@ -779,7 +793,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
size_t ending_logprob_count[4];
double ending_logprob[4];
size_t i_batch; // starting index in the llama_batch
size_t i_logits; // starting index of logits in the llama_batch
size_t common_prefix; // max number of initial tokens that are the same in all sentences
size_t required_tokens; // needed number of tokens to evaluate all 4 endings
std::vector<llama_token> seq_tokens[4];
@ -844,9 +858,10 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
const int max_tasks_per_batch = 32;
const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
llama_batch batch = llama_batch_init(n_ctx, 0, 4);
std::vector<float> tok_logits(n_vocab);
// TODO: this could be made smaller; it's currently the worst-case size
std::vector<float> batch_logits(n_vocab*n_ctx);
std::vector<std::pair<size_t, llama_token>> eval_pairs;
@ -857,16 +872,17 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
int n_cur = 0;
size_t i1 = i0;
size_t i_batch = 0; // this tells us where in `llama_batch` we are currently
size_t i_logits = 0; // this tells us how many logits were needed before this point in the batch
llama_batch_clear(batch);
// batch as much tasks as possible into the available context
// each task has 4 unique seuqnce ids - one for each ending
// each task has 4 unique sequence ids - one for each ending
// the common prefix is shared among the 4 sequences to save tokens
// we extract logits only from the last common token and from all ending tokens of each sequence
while (n_cur + (int) hs_data[i1].required_tokens <= n_ctx) {
auto & hs_cur = hs_data[i1];
int n_logits = 0;
const int s0 = 4*(i1 - i0);
if (s0 + 4 > max_seq) {
@ -874,18 +890,23 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
}
for (size_t i = 0; i < hs_cur.common_prefix; ++i) {
llama_batch_add(batch, hs_cur.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3}, false);
llama_batch_add(batch, hs_cur.seq_tokens[0][i], i, { s0 + 0, s0 + 1, s0 + 2, s0 + 3 }, false);
}
batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix
n_logits += 1;
for (int s = 0; s < 4; ++s) {
for (size_t i = hs_cur.common_prefix; i < hs_cur.seq_tokens[s].size(); ++i) {
llama_batch_add(batch, hs_cur.seq_tokens[s][i], i, { s0 + s }, true);
const size_t seq_tokens_size = hs_cur.seq_tokens[s].size();
// TODO: don't evaluate the last token of each sequence
for (size_t i = hs_cur.common_prefix; i < seq_tokens_size; ++i) {
const bool needs_logits = i < seq_tokens_size - 1;
llama_batch_add(batch, hs_cur.seq_tokens[s][i], i, { s0 + s }, needs_logits);
n_logits += needs_logits;
}
}
hs_cur.i_batch = i_batch;
i_batch += hs_cur.required_tokens;
hs_cur.i_logits = i_logits;
i_logits += n_logits;
n_cur += hs_data[i1].required_tokens;
if (++i1 == hs_task_count) {
@ -911,12 +932,11 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
eval_pairs.clear();
for (size_t i = i0; i < i1; ++i) {
auto & hs_cur = hs_data[i];
size_t li = hs_cur.common_prefix;
size_t li = 1; // skip the last logit of the common prefix (computed separately below)
for (int s = 0; s < 4; ++s) {
for (size_t j = hs_cur.common_prefix; j < hs_cur.seq_tokens[s].size() - 1; j++) {
eval_pairs.emplace_back(hs_cur.i_batch + li++, hs_cur.seq_tokens[s][j + 1]);
eval_pairs.emplace_back(hs_cur.i_logits + li++, hs_cur.seq_tokens[s][j + 1]);
}
++li;
}
}
// Then we do the actual calculation
@ -928,7 +948,8 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
for (size_t i = i0; i < i1; ++i) {
auto & hs_cur = hs_data[i];
std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*(hs_cur.i_batch + hs_cur.common_prefix - 1), n_vocab*sizeof(float));
// get the logits of the last token of the common prefix
std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*hs_cur.i_logits, n_vocab*sizeof(float));
const auto first_probs = softmax(tok_logits);
@ -978,7 +999,7 @@ struct winogrande_entry {
std::array<std::string, 2> choices;
int answer;
size_t i_batch;
size_t i_logits;
size_t common_prefix;
size_t required_tokens;
size_t n_base1; // number of tokens for context + choice 1
@ -1104,6 +1125,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
task.common_prefix++;
}
// TODO: the last token of each of the sequences don't need to be evaluated
task.required_tokens = task.common_prefix +
task.seq_tokens[0].size() - task.common_prefix +
task.seq_tokens[1].size() - task.common_prefix;
@ -1121,9 +1143,10 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
const int max_tasks_per_batch = 128;
const int max_seq = std::min(2*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
llama_batch batch = llama_batch_init(n_ctx, 0, 2);
std::vector<float> tok_logits(n_vocab);
// TODO: this could be made smaller; it's currently the worst-case size
std::vector<float> batch_logits(n_vocab*n_ctx);
std::vector<std::pair<size_t, llama_token>> eval_pairs;
@ -1137,29 +1160,33 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
int n_cur = 0;
size_t i1 = i0;
size_t i_batch = 0;
size_t i_logits = 0;
llama_batch_clear(batch);
while (n_cur + (int) data[i1].required_tokens <= n_ctx) {
int n_logits = 0;
const int s0 = 2*(i1 - i0);
if (s0 + 2 > max_seq) {
break;
}
for (size_t i = 0; i < data[i1].common_prefix; ++i) {
llama_batch_add(batch, data[i1].seq_tokens[0][i], i, { s0 + 0, s0 + 1}, false);
llama_batch_add(batch, data[i1].seq_tokens[0][i], i, { s0 + 0, s0 + 1 }, false);
}
batch.logits[batch.n_tokens - 1] = true;
n_logits += 1;
for (int s = 0; s < 2; ++s) {
// TODO: end before the last token, no need to predict past the end of the sequences
for (size_t i = data[i1].common_prefix; i < data[i1].seq_tokens[s].size(); ++i) {
llama_batch_add(batch, data[i1].seq_tokens[s][i], i, { s0 + s }, true);
n_logits += 1;
}
}
data[i1].i_batch = i_batch;
i_batch += data[i1].required_tokens;
data[i1].i_logits = i_logits;
i_logits += n_logits;
n_cur += data[i1].required_tokens;
if (++i1 == data.size()) {
@ -1190,15 +1217,16 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
const auto& n_base1 = skip_choice ? task.n_base1 : task.common_prefix;
const int last_1st = task.seq_tokens[0].size() - n_base1 > 1 ? 1 : 0;
size_t li = n_base1 - 1;
size_t li = n_base1 - task.common_prefix;
for (size_t j = n_base1-1; j < task.seq_tokens[0].size()-1-last_1st; ++j) {
eval_pairs.emplace_back(task.i_batch + li++, task.seq_tokens[0][j+1]);
eval_pairs.emplace_back(task.i_logits + li++, task.seq_tokens[0][j+1]);
}
const auto& n_base2 = skip_choice ? task.n_base2 : task.common_prefix;
const int last_2nd = task.seq_tokens[1].size() - n_base2 > 1 ? 1 : 0;
li = task.seq_tokens[0].size() - task.common_prefix + n_base2 - 1;
// FIXME: this uses the wrong first logits when not skipping the choice word
li = task.seq_tokens[0].size() - task.common_prefix + n_base2 - task.common_prefix;
for (size_t j = n_base2-1; j < task.seq_tokens[1].size()-1-last_2nd; ++j) {
eval_pairs.emplace_back(task.i_batch + li++, task.seq_tokens[1][j+1]);
eval_pairs.emplace_back(task.i_logits + li++, task.seq_tokens[1][j+1]);
}
}
compute_logprobs(batch_logits.data(), n_vocab, workers, eval_pairs, eval_results);
@ -1287,7 +1315,7 @@ struct multiple_choice_task {
}
// For evaluation
size_t i_batch; // starting index in the llama_batch
size_t i_logits; // starting index of logits in the llama_batch
size_t common_prefix; // max number of initial tokens that are the same in all sentences
size_t required_tokens; // needed number of tokens to evaluate all answers
std::vector<std::vector<llama_token>> seq_tokens;
@ -1366,7 +1394,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
std::vector<uint32_t> task_pos(n_task);
strstream.read((char *)task_pos.data(), task_pos.size()*sizeof(uint32_t));
if (strstream.fail()) {
printf("%s: failed to raad task positions from prompt\n", __func__);
printf("%s: failed to read task positions from prompt\n", __func__);
return;
}
@ -1447,7 +1475,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
return;
}
} else {
int n_dot = n_task/100;
int n_dot = std::max((int) n_task/100, 1);
int i_task = 0;
for (auto& task : tasks) {
++i_task;
@ -1491,17 +1519,18 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
int n_cur = 0;
size_t i1 = i0;
size_t i_batch = 0; // this tells us where in `llama_batch` we are currently
size_t i_logits = 0; // this tells us how many logits were needed before this point in the batch
llama_batch_clear(batch);
// batch as much tasks as possible into the available context
// each task has 4 unique seuqnce ids - one for each ending
// each task has 4 unique sequence ids - one for each ending
// the common prefix is shared among the 4 sequences to save tokens
// we extract logits only from the last common token and from all ending tokens of each sequence
int s0 = 0;
while (n_cur + (int) tasks[i1].required_tokens <= n_ctx) {
auto& cur_task = tasks[i1];
int n_logits = 0;
int num_answers = cur_task.seq_tokens.size();
if (s0 + num_answers > max_seq) {
@ -1518,17 +1547,22 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
llama_batch_add(batch, cur_task.seq_tokens[0][i], i, batch_indeces, false);
}
batch.logits[batch.n_tokens - 1] = true; // we need logits for the last token of the common prefix
n_logits += 1;
for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) {
for (size_t i = cur_task.common_prefix; i < cur_task.seq_tokens[s].size(); ++i) {
llama_batch_add(batch, cur_task.seq_tokens[s][i], i, { s0 + s }, true);
const size_t seq_tokens_size = cur_task.seq_tokens[s].size();
// TODO: don't evaluate the last token of each sequence
for (size_t i = cur_task.common_prefix; i < seq_tokens_size; ++i) {
const bool needs_logits = i < seq_tokens_size - 1;
llama_batch_add(batch, cur_task.seq_tokens[s][i], i, { s0 + s }, needs_logits);
n_logits += needs_logits;
}
}
s0 += num_answers;
cur_task.i_batch = i_batch;
i_batch += cur_task.required_tokens;
cur_task.i_logits = i_logits;
i_logits += n_logits;
n_cur += cur_task.required_tokens;
if (++i1 == tasks.size()) {
@ -1554,12 +1588,11 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
eval_pairs.clear();
for (size_t i = i0; i < i1; ++i) {
auto& cur_task = tasks[i];
size_t li = cur_task.common_prefix;
size_t li = 1; // skip the last logit of the common prefix (computed separately below)
for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) {
for (size_t j = cur_task.common_prefix; j < cur_task.seq_tokens[s].size() - 1; j++) {
eval_pairs.emplace_back(cur_task.i_batch + li++, cur_task.seq_tokens[s][j + 1]);
eval_pairs.emplace_back(cur_task.i_logits + li++, cur_task.seq_tokens[s][j + 1]);
}
++li;
}
}
// Then we do the actual calculation
@ -1578,7 +1611,8 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
//}
//printf("\n common_prefix: %zu\n", cur_task.common_prefix);
std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*(cur_task.i_batch + cur_task.common_prefix - 1), n_vocab*sizeof(float));
// get the logits of the last token of the common prefix
std::memcpy(tok_logits.data(), batch_logits.data() + n_vocab*cur_task.i_logits, n_vocab*sizeof(float));
const auto first_probs = softmax(tok_logits);
@ -1730,6 +1764,7 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
}
// TODO: use llama_batch.logits instead of relying on logits_all == true
if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return;

119
examples/quantize/quantize.cpp
View file

@ -26,6 +26,7 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
{ "IQ2_S", LLAMA_FTYPE_MOSTLY_IQ2_S, " 2.5 bpw quantization", },
{ "IQ2_M", LLAMA_FTYPE_MOSTLY_IQ2_M, " 2.7 bpw quantization", },
{ "IQ1_S", LLAMA_FTYPE_MOSTLY_IQ1_S, " 1.56 bpw quantization", },
{ "IQ1_M", LLAMA_FTYPE_MOSTLY_IQ1_M, " 1.75 bpw quantization", },
{ "Q2_K", LLAMA_FTYPE_MOSTLY_Q2_K, " 2.63G, +0.6717 ppl @ LLaMA-v1-7B", },
{ "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.16G, +9.0634 ppl @ LLaMA-v1-7B", },
{ "IQ3_XXS",LLAMA_FTYPE_MOSTLY_IQ3_XXS," 3.06 bpw quantization", },
@ -87,13 +88,17 @@ static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftyp
//
[[noreturn]]
static void usage(const char * executable) {
printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights] [--exclude-weights] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable);
printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights] [--exclude-weights] [--output-tensor-type] [--token-embedding-type] [--override-kv] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable);
printf(" --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n");
printf(" --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n");
printf(" --pure: Disable k-quant mixtures and quantize all tensors to the same type\n");
printf(" --imatrix file_name: use data in file_name as importance matrix for quant optimizations\n");
printf(" --include-weights tensor_name: use importance matrix for this/these tensor(s)\n");
printf(" --exclude-weights tensor_name: use importance matrix for this/these tensor(s)\n");
printf(" --output-tensor-type ggml_type: use this ggml_type for the output.weight tensor\n");
printf(" --token-embedding-type ggml_type: use this ggml_type for the token embeddings tensor\n");
printf(" --override-kv KEY=TYPE:VALUE\n");
printf(" Advanced option to override model metadata by key in the quantized model. May be specified multiple times.\n");
printf("Note: --include-weights and --exclude-weights cannot be used together\n");
printf("\nAllowed quantization types:\n");
for (auto & it : QUANT_OPTIONS) {
@ -107,14 +112,14 @@ static void usage(const char * executable) {
exit(1);
}
static void load_imatrix(const std::string& imatrix_file, std::unordered_map<std::string, std::vector<float>>& imatrix_data) {
static void load_imatrix(const std::string & imatrix_file, std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
std::ifstream in(imatrix_file.c_str(), std::ios::binary);
if (!in) {
printf("%s: failed to open %s\n",__func__,imatrix_file.c_str());
printf("%s: failed to open %s\n",__func__, imatrix_file.c_str());
return;
}
int n_entries;
in.read((char*)&n_entries, sizeof(n_entries));
in.read((char *)&n_entries, sizeof(n_entries));
if (in.fail() || n_entries < 1) {
printf("%s: no data in file %s\n", __func__, imatrix_file.c_str());
return;
@ -124,25 +129,25 @@ static void load_imatrix(const std::string& imatrix_file, std::unordered_map<std
std::vector<char> name_as_vec(len+1);
in.read((char *)name_as_vec.data(), len);
if (in.fail()) {
printf("%s: failed reading name for entry %d from %s\n",__func__,i+1,imatrix_file.c_str());
printf("%s: failed reading name for entry %d from %s\n", __func__, i+1, imatrix_file.c_str());
return;
}
name_as_vec[len] = 0;
std::string name{name_as_vec.data()};
auto& e = imatrix_data[std::move(name)];
auto & e = imatrix_data[std::move(name)];
int ncall;
in.read((char*)&ncall, sizeof(ncall));
in.read((char *)&ncall, sizeof(ncall));
int nval;
in.read((char *)&nval, sizeof(nval));
if (in.fail() || nval < 1) {
printf("%s: failed reading number of values for entry %d\n",__func__,i);
printf("%s: failed reading number of values for entry %d\n", __func__, i);
imatrix_data = {};
return;
}
e.resize(nval);
in.read((char*)e.data(), nval*sizeof(float));
in.read((char *)e.data(), nval*sizeof(float));
if (in.fail()) {
printf("%s: failed reading data for entry %d\n",__func__,i);
printf("%s: failed reading data for entry %d\n", __func__, i);
imatrix_data = {};
return;
}
@ -150,13 +155,13 @@ static void load_imatrix(const std::string& imatrix_file, std::unordered_map<std
for (auto& v : e) v /= ncall;
}
}
printf("%s: loaded %d importance matrix entries from %s\n",__func__,int(imatrix_data.size()),imatrix_file.c_str());
printf("%s: loaded %d importance matrix entries from %s\n", __func__, int(imatrix_data.size()), imatrix_file.c_str());
}
static void prepare_imatrix(const std::string& imatrix_file,
const std::vector<std::string>& included_weights,
const std::vector<std::string>& excluded_weights,
std::unordered_map<std::string, std::vector<float>>& imatrix_data) {
static void prepare_imatrix(const std::string & imatrix_file,
const std::vector<std::string> & included_weights,
const std::vector<std::string> & excluded_weights,
std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
if (!imatrix_file.empty()) {
load_imatrix(imatrix_file, imatrix_data);
}
@ -189,6 +194,55 @@ static void prepare_imatrix(const std::string& imatrix_file,
}
}
static ggml_type parse_ggml_type(const char * arg) {
ggml_type result = GGML_TYPE_COUNT;
for (int j = 0; j < GGML_TYPE_COUNT; ++j) {
auto type = ggml_type(j);
const auto * name = ggml_type_name(type);
if (name && strcmp(arg, name) == 0) {
result = type; break;
}
}
return result;
}
static bool parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides) {
const char* sep = strchr(data, '=');
if (sep == nullptr || sep - data >= 128) {
fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data);
return false;
}
llama_model_kv_override kvo;
std::strncpy(kvo.key, data, sep - data);
kvo.key[sep - data] = 0;
sep++;
if (strncmp(sep, "int:", 4) == 0) {
sep += 4;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
kvo.int_value = std::atol(sep);
} else if (strncmp(sep, "float:", 6) == 0) {
sep += 6;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT;
kvo.float_value = std::atof(sep);
} else if (strncmp(sep, "bool:", 5) == 0) {
sep += 5;
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL;
if (std::strcmp(sep, "true") == 0) {
kvo.bool_value = true;
} else if (std::strcmp(sep, "false") == 0) {
kvo.bool_value = false;
} else {
fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data);
return false;
}
} else {
fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data);
return false;
}
overrides.emplace_back(std::move(kvo));
return true;
}
int main(int argc, char ** argv) {
if (argc < 3) {
usage(argv[0]);
@ -199,10 +253,27 @@ int main(int argc, char ** argv) {
int arg_idx = 1;
std::string imatrix_file;
std::vector<std::string> included_weights, excluded_weights;
std::vector<llama_model_kv_override> kv_overrides;
for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) {
if (strcmp(argv[arg_idx], "--leave-output-tensor") == 0) {
params.quantize_output_tensor = false;
} else if (strcmp(argv[arg_idx], "--output-tensor-type") == 0) {
if (arg_idx < argc-1) {
params.output_tensor_type = parse_ggml_type(argv[++arg_idx]);
} else {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--token-embedding-type") == 0) {
if (arg_idx < argc-1) {
params.token_embedding_type = parse_ggml_type(argv[++arg_idx]);
} else {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--override-kv") == 0) {
if (arg_idx == argc-1 || !parse_kv_override(argv[++arg_idx], kv_overrides)) {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--allow-requantize") == 0) {
params.allow_requantize = true;
} else if (strcmp(argv[arg_idx], "--pure") == 0) {
@ -243,6 +314,11 @@ int main(int argc, char ** argv) {
if (!imatrix_data.empty()) {
params.imatrix = &imatrix_data;
}
if (!kv_overrides.empty()) {
kv_overrides.emplace_back();
kv_overrides.back().key[0] = 0;
params.kv_overrides = &kv_overrides;
}
llama_backend_init();
@ -264,8 +340,7 @@ int main(int argc, char ** argv) {
if (ftype_str == "COPY") {
params.only_copy = true;
}
}
else {
} else {
fname_out = argv[arg_idx];
arg_idx++;
@ -296,10 +371,12 @@ int main(int argc, char ** argv) {
if ((params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS ||
params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_S ||
params.ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S || params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) && imatrix_data.empty()) {
fprintf(stderr, "\n===============================================================================================\n");
fprintf(stderr, "Please do not use IQ1_S, IQ2_XXS, IQ2_XS or Q2_K_S quantization without an importance matrix\n");
fprintf(stderr, "===============================================================================================\n\n\n");
params.ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S ||
params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) && imatrix_data.empty()) {
fprintf(stderr, "\n==========================================================================================================\n");
fprintf(stderr, "Please do not use IQ1_S, IQ1_M, IQ2_S, IQ2_XXS, IQ2_XS or Q2_K_S quantization without an importance matrix\n");
fprintf(stderr, "==========================================================================================================\n\n\n");
return 1;
}

5
examples/retrieval/CMakeLists.txt Normal file
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@ -0,0 +1,5 @@
set(TARGET retrieval)
add_executable(${TARGET} retrieval.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)

69
examples/retrieval/README.md Normal file
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@ -0,0 +1,69 @@
# llama.cpp/examples/retrieval
Demonstration of simple retrieval technique based on cosine similarity
More info:
https://github.com/ggerganov/llama.cpp/pull/6193
### How to use
`retieval.cpp` has parameters of its own:
- `--context-file`: file to be embedded - state this option multiple times to embed multiple files
- `--chunk-size`: minimum size of each text chunk to be embedded
- `--chunk-separator`: STRING to divide chunks by. newline by default
`retrieval` example can be tested as follows:
```bash
make -j && ./retrieval --model ./models/bge-base-en-v1.5-f16.gguf --top-k 3 --context-file README.md --context-file License --chunk-size 100 --chunk-separator .
```
This chunks and embeds all given files and starts a loop requesting query inputs:
```
Enter query:
```
On each query input, top k chunks are shown along with file name, chunk position within file and original text:
```
Enter query: describe the mit license
batch_decode: n_tokens = 6, n_seq = 1
Top 3 similar chunks:
filename: README.md
filepos: 119
similarity: 0.762334
textdata:
png)
[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
[Roadmap](https://github.
--------------------
filename: License
filepos: 0
similarity: 0.725146
textdata:
MIT License
Copyright (c) 2023 Georgi Gerganov
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
--------------------
filename: README.md
filepos: 9178
similarity: 0.621722
textdata:
com/cztomsik/ava) (MIT)
- [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal)
- [pythops/tenere](https://github.
--------------------
```

350
examples/retrieval/retrieval.cpp Normal file
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@ -0,0 +1,350 @@
#include "common.h"
#include "llama.h"
#include <algorithm>
#include <fstream>
struct retrieval_params {
std::vector<std::string> context_files; // context files to embed
int32_t chunk_size = 64; // chunk size for context embedding
std::string chunk_separator = "\n"; // chunk separator for context embedding
};
static void retrieval_params_print_usage(int argc, char ** argv, gpt_params & gpt_params, retrieval_params & params) {
gpt_print_usage(argc, argv, gpt_params);
printf("retrieval options:\n");
printf(" --context-file FNAME file containing context to embed.\n");
printf(" specify multiple files by providing --context-file option multiple times.\n");
printf(" --chunk-size N minimum length of embedded text chunk (default:%d)\n", params.chunk_size);
printf(" --chunk-separator STRING\n");
printf(" string to separate chunks (default: \"\\n\")\n");
printf("\n");
}
static void retrieval_params_parse(int argc, char ** argv, gpt_params & gpt_params, retrieval_params & retrieval_params) {
int i = 1;
std::string arg;
while (i < argc) {
arg = argv[i];
bool invalid_gpt_param = false;
if(gpt_params_find_arg(argc, argv, argv[i], gpt_params, i, invalid_gpt_param)) {
if (invalid_gpt_param) {
fprintf(stderr, "error: invalid argument: %s\n", arg.c_str());
retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
exit(1);
}
// option was parsed by gpt_params_find_arg
} else if (arg == "--context-file") {
if (++i >= argc) {
fprintf(stderr, "error: missing argument for --context-file\n");
retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
exit(1);
}
std::ifstream file(argv[i]);
if (!file) {
fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
exit(1);
}
// store the external file name in params
retrieval_params.context_files.push_back(argv[i]);
} else if (arg == "--chunk-size") {
if (++i >= argc) {
fprintf(stderr, "error: missing argument for --chunk-size\n");
retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
exit(1);
}
retrieval_params.chunk_size = std::stoi(argv[i]);
} else if (arg == "--chunk-separator") {
if (++i >= argc) {
fprintf(stderr, "error: missing argument for --chunk-separator\n");
retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
exit(1);
}
retrieval_params.chunk_separator = argv[i];
} else {
// unknown argument
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
exit(1);
}
i++;
}
}
struct chunk {
// filename
std::string filename;
// original file position
size_t filepos;
// original text data
std::string textdata = "";
// tokenized text data
std::vector<llama_token> tokens;
// embedding
std::vector<float> embedding;
};
// chunk file data to chunks of size >= chunk_size
// chunk_separator is the separator between chunks
static std::vector<chunk> chunk_file(const std::string & filename, int chunk_size, const std::string & chunk_separator) {
std::vector<chunk> chunks;
std::ifstream f(filename.c_str());
if (!f.is_open()) {
fprintf(stderr, "Error: could not open file %s\n", filename.c_str());
return chunks;
}
chunk current_chunk;
char buffer[1024];
int64_t filepos = 0;
std::string current = "";
while (f.read(buffer, 1024)) {
current += std::string(buffer, f.gcount());
size_t pos;
while ((pos = current.find(chunk_separator)) != std::string::npos) {
current_chunk.textdata += current.substr(0, pos + chunk_separator.size());
if ((int) current_chunk.textdata.size() > chunk_size) {
// save chunk
current_chunk.filepos = filepos;
current_chunk.filename = filename;
chunks.push_back(current_chunk);
// update filepos
filepos += (int) current_chunk.textdata.size();
// reset current_chunk
current_chunk = chunk();
}
current = current.substr(pos + chunk_separator.size());
}
}
// add leftover data to last chunk
if (current_chunk.textdata.size() > 0) {
if (chunks.empty()) {
current_chunk.filepos = filepos;
current_chunk.filename = filename;
chunks.push_back(current_chunk);
} else {
chunks.back().textdata += current_chunk.textdata;
}
}
f.close();
return chunks;
}
static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, int seq_id) {
for (size_t i = 0; i < tokens.size(); i++) {
llama_batch_add(batch, tokens[i], i, { seq_id }, i == tokens.size() - 1);
}
}
static void batch_decode(llama_context * ctx, llama_batch & batch, float * output, int n_seq, int n_embd) {
// clear previous kv_cache values (irrelevant for embeddings)
llama_kv_cache_clear(ctx);
// run model
fprintf(stderr, "%s: n_tokens = %d, n_seq = %d\n", __func__, batch.n_tokens, n_seq);
if (llama_decode(ctx, batch) < 0) {
fprintf(stderr, "%s : failed to decode\n", __func__);
}
for (int i = 0; i < batch.n_tokens; i++) {
if (!batch.logits[i]) {
continue;
}
// try to get sequence embeddings - supported only when pooling_type is not NONE
const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
if (embd == NULL) {
embd = llama_get_embeddings_ith(ctx, i);
if (embd == NULL) {
fprintf(stderr, "%s: failed to get embeddings for token %d\n", __func__, i);
continue;
}
}
float * out = output + batch.seq_id[i][0] * n_embd;
llama_embd_normalize(embd, out, n_embd);
}
}
int main(int argc, char ** argv) {
gpt_params params;
retrieval_params retrieval_params;
retrieval_params_parse(argc, argv, params, retrieval_params);
// For BERT models, batch size must be equal to ubatch size
params.n_ubatch = params.n_batch;
if (retrieval_params.chunk_size <= 0) {
fprintf(stderr, "chunk_size must be positive\n");
return 1;
}
if (retrieval_params.context_files.empty()) {
fprintf(stderr, "context_files must be specified\n");
return 1;
}
params.embedding = true;
print_build_info();
printf("processing files:\n");
for (auto & context_file : retrieval_params.context_files) {
printf("%s\n", context_file.c_str());
}
std::vector<chunk> chunks;
for (auto & context_file : retrieval_params.context_files) {
std::vector<chunk> file_chunk = chunk_file(context_file, retrieval_params.chunk_size, retrieval_params.chunk_separator);
chunks.insert(chunks.end(), file_chunk.begin(), file_chunk.end());
}
printf("Number of chunks: %ld\n", chunks.size());
llama_backend_init();
llama_numa_init(params.numa);
llama_model * model;
llama_context * ctx;
// load the model
std::tie(model, ctx) = llama_init_from_gpt_params(params);
if (model == NULL) {
fprintf(stderr, "%s: error: unable to load model\n", __func__);
return 1;
}
const int n_ctx_train = llama_n_ctx_train(model);
const int n_ctx = llama_n_ctx(ctx);
if (n_ctx > n_ctx_train) {
fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n",
__func__, n_ctx_train, n_ctx);
}
// print system information
{
fprintf(stderr, "\n");
fprintf(stderr, "%s\n", get_system_info(params).c_str());
}
// max batch size
const uint64_t n_batch = params.n_batch;
GGML_ASSERT(params.n_batch >= params.n_ctx);
// tokenize the prompts and trim
for (auto & chunk : chunks) {
auto inp = ::llama_tokenize(ctx, chunk.textdata, true, false);
if (inp.size() > n_batch) {
fprintf(stderr, "%s: error: chunk size (%lld) exceeds batch size (%lld), increase batch size and re-run\n",
__func__, (long long int) inp.size(), (long long int) n_batch);
return 1;
}
// add eos if not present
if (inp.empty() || inp.back() != llama_token_eos(model)) {
inp.push_back(llama_token_eos(model));
}
chunk.tokens = inp;
}
// tokenization stats
if (params.verbose_prompt) {
for (int i = 0; i < (int) chunks.size(); i++) {
fprintf(stderr, "%s: prompt %d: '%s'\n", __func__, i, chunks[i].textdata.c_str());
fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, chunks[i].tokens.size());
for (int j = 0; j < (int) chunks[i].tokens.size(); j++) {
fprintf(stderr, "%6d -> '%s'\n", chunks[i].tokens[j], llama_token_to_piece(ctx, chunks[i].tokens[j]).c_str());
}
fprintf(stderr, "\n\n");
}
}
// initialize batch
const int n_chunks = chunks.size();
struct llama_batch batch = llama_batch_init(n_batch, 0, 1);
// allocate output
const int n_embd = llama_n_embd(model);
std::vector<float> embeddings(n_chunks * n_embd, 0);
float * emb = embeddings.data();
// break into batches
int p = 0; // number of prompts processed already
int s = 0; // number of prompts in current batch
for (int k = 0; k < n_chunks; k++) {
// clamp to n_batch tokens
auto & inp = chunks[k].tokens;
const uint64_t n_toks = inp.size();
// encode if at capacity
if (batch.n_tokens + n_toks > n_batch) {
float * out = emb + p * n_embd;
batch_decode(ctx, batch, out, s, n_embd);
llama_batch_clear(batch);
p += s;
s = 0;
}
// add to batch
batch_add_seq(batch, inp, s);
s += 1;
}
// final batch
float * out = emb + p * n_embd;
batch_decode(ctx, batch, out, s, n_embd);
// save embeddings to chunks
for (int i = 0; i < n_chunks; i++) {
chunks[i].embedding = std::vector<float>(emb + i * n_embd, emb + (i + 1) * n_embd);
// clear tokens as they are no longer needed
chunks[i].tokens.clear();
}
// start loop, receive query and return top k similar chunks based on cosine similarity
std::string query;
while (true) {
printf("Enter query: ");
std::getline(std::cin, query);
std::vector<int32_t> query_tokens = llama_tokenize(ctx, query, true);
struct llama_batch query_batch = llama_batch_init(n_batch, 0, 1);
batch_add_seq(query_batch, query_tokens, 0);
std::vector<float> query_emb(n_embd, 0);
batch_decode(ctx, query_batch, query_emb.data(), 1, n_embd);
llama_batch_clear(query_batch);
// compute cosine similarities
{
std::vector<std::pair<int, float>> similarities;
for (int i = 0; i < n_chunks; i++) {
float sim = llama_embd_similarity_cos(chunks[i].embedding.data(), query_emb.data(), n_embd);
similarities.push_back(std::make_pair(i, sim));
}
// sort similarities
std::sort(similarities.begin(), similarities.end(), [](const std::pair<int, float> & a, const std::pair<int, float> & b) {
return a.second > b.second;
});
printf("Top %d similar chunks:\n", params.sparams.top_k);
for (int i = 0; i < std::min(params.sparams.top_k, (int) chunks.size()); i++) {
printf("filename: %s\n", chunks[similarities[i].first].filename.c_str());
printf("filepos: %lld\n", (long long int) chunks[similarities[i].first].filepos);
printf("similarity: %f\n", similarities[i].second);
printf("textdata:\n%s\n", chunks[similarities[i].first].textdata.c_str());
printf("--------------------\n");
}
}
}
// clean up
llama_print_timings(ctx);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
}

21
examples/server/README.md
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@ -16,17 +16,20 @@ The project is under active development, and we are [looking for feedback and co
**Command line options:**
- `--threads N`, `-t N`: Set the number of threads to use during generation.
- `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation.
- `--threads N`, `-t N`: Set the number of threads to use during generation. Not used if model layers are offloaded to GPU. The server is using batching, this parameter is used only if one token is to be processed on CPU backend.
- `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation. Not used if model layers are offloaded to GPU.
- `--threads-http N`: number of threads in the http server pool to process requests (default: `max(std::thread::hardware_concurrency() - 1, --parallel N + 2)`)
- `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`).
- `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (e.g https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf).
- `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (default: unused).
- `-hfr REPO, --hf-repo REPO`: Hugging Face model repository (default: unused).
- `-hff FILE, --hf-file FILE`: Hugging Face model file (default: unused).
- `-a ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses.
- `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096.
- `-ngl N`, `--n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
- `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS.
- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. Requires cuBLAS.
- `-b N`, `--batch-size N`: Set the batch size for prompt processing. Default: `512`.
- `-ngl N`, `--n-gpu-layers N`: When compiled with GPU support, this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
- `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used.
- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance.
- `-b N`, `--batch-size N`: Set the batch size for prompt processing. Default: `2048`.
- `-ub N`, `--ubatch-size N`: physical maximum batch size. Default: `512`.
- `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. Not recommended.
- `--mlock`: Lock the model in memory, preventing it from being swapped out when memory-mapped.
- `--no-mmap`: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed.
@ -57,7 +60,7 @@ see https://github.com/ggerganov/llama.cpp/issues/1437
- `--slots-endpoint-disable`: To disable slots state monitoring endpoint. Slots state may contain user data, prompts included.
- `--metrics`: enable prometheus `/metrics` compatible endpoint (default: disabled)
- `--chat-template JINJA_TEMPLATE`: Set custom jinja chat template. This parameter accepts a string, not a file name (default: template taken from model's metadata). We only support [some pre-defined templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template)
- `--log-disable`: Output logs to stdout only, default: enabled.
- `--log-disable`: Output logs to stdout only, not to `llama.log`. default: enabled.
- `--log-format FORMAT`: Define the log output to FORMAT: json or text (default: json)
**If compiled with `LLAMA_SERVER_SSL=ON`**
@ -357,7 +360,7 @@ Notice that each `probs` is an array of length `n_probs`.
- `default_generation_settings` - the default generation settings for the `/completion` endpoint, has the same fields as the `generation_settings` response object from the `/completion` endpoint.
- `total_slots` - the total number of slots for process requests (defined by `--parallel` option)
- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint.
- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only model with [supported chat template](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) can be used optimally with this endpoint. By default, ChatML template will be used.
*Options:*

866
examples/server/completion.js.hpp
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0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x5f, 0x67, 0x65, 0x6e, 0x65,
0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, 0x74, 0x74, 0x69,
0x6e, 0x67, 0x73, 0x3b, 0x0a, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x72,
0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61,
0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67,
0x73, 0x3b, 0x0a, 0x7d, 0x0a
};
size_t completion_js_len = 5796;
unsigned int completion_js_len = 5909;

4828
examples/server/index.html.hpp
View file

File diff suppressed because it is too large Load diff

2
examples/server/index.js.hpp
View file

@ -1928,4 +1928,4 @@ unsigned char index_js[] = {
0x45, 0x66, 0x66, 0x65, 0x63, 0x74, 0x2c, 0x50, 0x74, 0x20, 0x61, 0x73,
0x20, 0x75, 0x73, 0x65, 0x53, 0x74, 0x61, 0x74, 0x65, 0x7d, 0x3b, 0x0a
};
size_t index_js_len = 23136;
unsigned int index_js_len = 23136;

8
examples/server/public/completion.js
View file

@ -21,6 +21,7 @@ let generation_settings = null;
//
export async function* llama(prompt, params = {}, config = {}) {
let controller = config.controller;
const api_url = config.api_url || "";
if (!controller) {
controller = new AbortController();
@ -28,7 +29,7 @@ export async function* llama(prompt, params = {}, config = {}) {
const completionParams = { ...paramDefaults, ...params, prompt };
const response = await fetch("/completion", {
const response = await fetch(`${api_url}/completion`, {
method: 'POST',
body: JSON.stringify(completionParams),
headers: {
@ -193,9 +194,10 @@ export const llamaComplete = async (params, controller, callback) => {
}
// Get the model info from the server. This is useful for getting the context window and so on.
export const llamaModelInfo = async () => {
export const llamaModelInfo = async (config = {}) => {
if (!generation_settings) {
const props = await fetch("/props").then(r => r.json());
const api_url = config.api_url || "";
const props = await fetch(`${api_url}/props`).then(r => r.json());
generation_settings = props.default_generation_settings;
}
return generation_settings;

8
examples/server/public/index.html
View file

@ -199,10 +199,10 @@
<script type="module">
import {
html, h, signal, effect, computed, render, useSignal, useEffect, useRef, Component
} from '/index.js';
} from './index.js';
import { llama } from '/completion.js';
import { SchemaConverter } from '/json-schema-to-grammar.mjs';
import { llama } from './completion.js';
import { SchemaConverter } from './json-schema-to-grammar.mjs';
let selected_image = false;
var slot_id = -1;
@ -405,7 +405,7 @@
throw new Error("already running");
}
controller.value = new AbortController();
for await (const chunk of llama(prompt, llamaParams, { controller: controller.value })) {
for await (const chunk of llama(prompt, llamaParams, { controller: controller.value, api_url: document.baseURI.replace(/\/+$/, '') })) {
const data = chunk.data;
if (data.stop) {

55
examples/server/server.cpp
View file

@ -99,6 +99,7 @@ struct slot_params {
uint32_t seed = -1; // RNG seed
int32_t n_keep = 0; // number of tokens to keep from initial prompt
int32_t n_discard = 0; // number of tokens after n_keep that may be discarded when shifting context, 0 defaults to half
int32_t n_predict = -1; // new tokens to predict
std::vector<std::string> antiprompt;
@ -746,7 +747,8 @@ struct server_context {
{
const int32_t n_batch = llama_n_batch(ctx);
batch = llama_batch_init(n_batch, 0, params.n_parallel);
// only a single seq_id per token is needed
batch = llama_batch_init(n_batch, 0, 1);
}
metrics.init();
@ -846,10 +848,18 @@ struct server_context {
slot.sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta);
slot.sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl);
slot.params.n_keep = json_value(data, "n_keep", slot.params.n_keep);
slot.params.n_discard = json_value(data, "n_discard", default_params.n_discard);
slot.params.seed = json_value(data, "seed", default_params.seed);
if (data.contains("json_schema") && !data.contains("grammar")) {
slot.sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs);
slot.sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep);
// process "json_schema" and "grammar"
if (data.contains("json_schema") && data.contains("grammar")) {
send_error(task, "Either \"json_schema\" or \"grammar\" can be specified, but not both", ERROR_TYPE_INVALID_REQUEST);
return false;
} else if (data.contains("json_schema") && !data.contains("grammar")) {
try {
auto schema = json_value(data, "json_schema", json::object());
auto schema = json_value(data, "json_schema", json::object());
slot.sparams.grammar = json_schema_to_grammar(schema);
} catch (const std::exception & e) {
send_error(task, std::string("\"json_schema\": ") + e.what(), ERROR_TYPE_INVALID_REQUEST);
@ -858,8 +868,6 @@ struct server_context {
} else {
slot.sparams.grammar = json_value(data, "grammar", default_sparams.grammar);
}
slot.sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs);
slot.sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep);
if (slot.params.cache_prompt && slot.ga_n != 1) {
LOG_WARNING("cache_prompt is not supported with group-attention", {});
@ -1248,6 +1256,7 @@ struct server_context {
{"stop", slot.params.antiprompt},
{"n_predict", slot.params.n_predict}, // TODO: fix duplicate key n_predict
{"n_keep", slot.params.n_keep},
{"n_discard", slot.params.n_discard},
{"ignore_eos", ignore_eos},
{"stream", slot.params.stream},
{"logit_bias", slot.sparams.logit_bias},
@ -1691,7 +1700,7 @@ struct server_context {
// Shift context
const int n_keep = slot.params.n_keep + add_bos_token;
const int n_left = (int) system_tokens.size() + slot.n_past - n_keep;
const int n_discard = n_left / 2;
const int n_discard = slot.params.n_discard ? slot.params.n_discard : (n_left / 2);
LOG_INFO("slot context shift", {
{"id_slot", slot.id},
@ -2208,7 +2217,11 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co
printf(" -m FNAME, --model FNAME\n");
printf(" model path (default: %s)\n", params.model.c_str());
printf(" -mu MODEL_URL, --model-url MODEL_URL\n");
printf(" model download url (default: %s)\n", params.model_url.c_str());
printf(" model download url (default: unused)\n");
printf(" -hfr REPO, --hf-repo REPO\n");
printf(" Hugging Face model repository (default: unused)\n");
printf(" -hff FILE, --hf-file FILE\n");
printf(" Hugging Face model file (default: unused)\n");
printf(" -a ALIAS, --alias ALIAS\n");
printf(" set an alias for the model, will be added as `model` field in completion response\n");
printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n");
@ -2337,6 +2350,18 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
break;
}
params.model_url = argv[i];
} else if (arg == "-hfr" || arg == "--hf-repo") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.hf_repo = argv[i];
} else if (arg == "-hff" || arg == "--hf-file") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.hf_file = argv[i];
} else if (arg == "-a" || arg == "--alias") {
if (++i >= argc) {
invalid_param = true;
@ -2489,15 +2514,15 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
invalid_param = true;
break;
}
#ifndef GGML_USE_CUBLAS
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Setting the split mode has no effect.\n");
#endif // GGML_USE_CUBLAS
#ifndef GGML_USE_CUDA
fprintf(stderr, "warning: llama.cpp was compiled without CUDA. Setting the split mode has no effect.\n");
#endif // GGML_USE_CUDA
} else if (arg == "--tensor-split" || arg == "-ts") {
if (++i >= argc) {
invalid_param = true;
break;
}
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL)
#if defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL)
std::string arg_next = argv[i];
// split string by , and /
@ -2514,17 +2539,17 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
}
}
#else
LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n", {});
#endif // GGML_USE_CUBLAS
LOG_WARNING("llama.cpp was compiled without CUDA. It is not possible to set a tensor split.\n", {});
#endif // GGML_USE_CUDA
} else if (arg == "--main-gpu" || arg == "-mg") {
if (++i >= argc) {
invalid_param = true;
break;
}
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL)
#if defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL)
params.main_gpu = std::stoi(argv[i]);
#else
LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.", {});
LOG_WARNING("llama.cpp was compiled without CUDA. It is not possible to set a main GPU.", {});
#endif
} else if (arg == "--lora") {
if (++i >= argc) {

3
examples/server/tests/features/parallel.feature
View file

@ -4,7 +4,8 @@ Feature: Parallel
Background: Server startup
Given a server listening on localhost:8080
And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
And a model file tinyllamas/split/stories15M-00001-of-00003.gguf from HF repo ggml-org/models
And a model file test-model-00001-of-00003.gguf
And 42 as server seed
And 128 as batch size
And 256 KV cache size

4
examples/server/tests/features/server.feature
View file

@ -4,8 +4,8 @@ Feature: llama.cpp server
Background: Server startup
Given a server listening on localhost:8080
And a model url https://huggingface.co/ggml-org/models/resolve/main/tinyllamas/stories260K.gguf
And a model file stories260K.gguf
And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
And a model file test-model.gguf
And a model alias tinyllama-2
And 42 as server seed
# KV Cache corresponds to the total amount of tokens

13
examples/server/tests/features/steps/steps.py
View file

@ -16,7 +16,6 @@ import numpy as np
import openai
from behave import step
from behave.api.async_step import async_run_until_complete
from huggingface_hub import hf_hub_download
from prometheus_client import parser
@ -39,6 +38,8 @@ def step_server_config(context, server_fqdn, server_port):
context.model_alias = None
context.model_file = None
context.model_hf_repo = None
context.model_hf_file = None
context.model_url = None
context.n_batch = None
context.n_ubatch = None
@ -68,9 +69,9 @@ def step_server_config(context, server_fqdn, server_port):
@step('a model file {hf_file} from HF repo {hf_repo}')
def step_download_hf_model(context, hf_file, hf_repo):
context.model_file = hf_hub_download(repo_id=hf_repo, filename=hf_file)
if context.debug:
print(f"model file: {context.model_file}")
context.model_hf_repo = hf_repo
context.model_hf_file = hf_file
context.model_file = os.path.basename(hf_file)
@step('a model file {model_file}')
@ -1079,6 +1080,10 @@ def start_server_background(context):
server_args.extend(['--model', context.model_file])
if context.model_url:
server_args.extend(['--model-url', context.model_url])
if context.model_hf_repo:
server_args.extend(['--hf-repo', context.model_hf_repo])
if context.model_hf_file:
server_args.extend(['--hf-file', context.model_hf_file])
if context.n_batch:
server_args.extend(['--batch-size', context.n_batch])
if context.n_ubatch:

88
examples/server/utils.hpp
View file

@ -95,8 +95,8 @@ static inline void server_log(const char *level, const char *function, int line,
const std::string str = ss.str();
printf("%.*s\n", (int)str.size(), str.data());
fflush(stdout);
}
fflush(stdout);
}
//
@ -352,51 +352,71 @@ static json oaicompat_completion_params_parse(
// https://platform.openai.com/docs/api-reference/chat/create
llama_sampling_params default_sparams;
llama_params["model"] = json_value(body, "model", std::string("unknown"));
llama_params["prompt"] = format_chat(model, chat_template, body["messages"]);
llama_params["cache_prompt"] = json_value(body, "cache_prompt", false);
llama_params["temperature"] = json_value(body, "temperature", 0.0);
llama_params["top_k"] = json_value(body, "top_k", default_sparams.top_k);
llama_params["top_p"] = json_value(body, "top_p", 1.0);
llama_params["n_predict"] = json_value(body, "max_tokens", -1);
llama_params["logit_bias"] = json_value(body, "logit_bias", json::object());
llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0);
llama_params["logit_bias"] = json_value(body, "logit_bias", json::object());
llama_params["n_predict"] = json_value(body, "max_tokens", -1);
llama_params["presence_penalty"] = json_value(body, "presence_penalty", 0.0);
llama_params["seed"] = json_value(body, "seed", LLAMA_DEFAULT_SEED);
llama_params["stream"] = json_value(body, "stream", false);
llama_params["mirostat"] = json_value(body, "mirostat", default_sparams.mirostat);
llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", default_sparams.mirostat_tau);
llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", default_sparams.mirostat_eta);
llama_params["penalize_nl"] = json_value(body, "penalize_nl", default_sparams.penalize_nl);
llama_params["typical_p"] = json_value(body, "typical_p", default_sparams.typical_p);
llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", default_sparams.penalty_last_n);
llama_params["ignore_eos"] = json_value(body, "ignore_eos", false);
llama_params["tfs_z"] = json_value(body, "tfs_z", default_sparams.tfs_z);
llama_params["n_keep"] = json_value(body, "n_keep", 0);
llama_params["temperature"] = json_value(body, "temperature", 0.0);
llama_params["top_p"] = json_value(body, "top_p", 1.0);
if (body.contains("grammar")) {
llama_params["grammar"] = json_value(body, "grammar", json::object());
}
// Apply chat template to the list of messages
llama_params["prompt"] = format_chat(model, chat_template, body["messages"]);
if (body.contains("response_format")) {
auto response_format = json_value(body, "response_format", json::object());
if (response_format.contains("type")) {
if (response_format["type"] == "json_object") {
llama_params["json_schema"] = json_value(response_format, "schema", json::object());
} else {
throw std::runtime_error("response_format type not supported: " + response_format["type"].dump());
}
}
}
// Handle 'stop' field
// Handle "stop" field
if (body.contains("stop") && body["stop"].is_string()) {
llama_params["stop"] = json::array({body["stop"].get<std::string>()});
} else {
llama_params["stop"] = json_value(body, "stop", json::array());
}
// Some chat templates don't use EOS token to stop generation
// We must add their end sequences to list of stop words
llama_params["stop"].push_back("<|im_end|>"); // chatml
llama_params["stop"].push_back("<end_of_turn>"); // gemma
// Ensure there is ChatML-specific end sequence among stop words
llama_params["stop"].push_back("<|im_end|>");
// Handle "response_format" field
if (body.contains("response_format")) {
json response_format = json_value(body, "response_format", json::object());
std::string response_type = json_value(response_format, "type", std::string());
if (response_type == "json_object") {
llama_params["json_schema"] = json_value(response_format, "schema", json::object());
} else if (!response_type.empty() && response_type != "text") {
throw std::runtime_error("response_format type must be one of \"text\" or \"json_object\", but got: " + response_type);
}
}
// Handle "n" field
int n_choices = json_value(body, "n", 1);
if (n_choices != 1) {
throw std::runtime_error("Only one completion choice is allowed");
}
// Handle "logprobs" field
// TODO: The response format of this option is not yet OAI-compatible, but seems like no one really using it; We may need to fix it in the future
if (body.contains("logprobs")) {
llama_params["n_probs"] = json_value(body, "top_logprobs", 20);
} else if (body.contains("top_logprobs")) {
throw std::runtime_error("top_logprobs requires logprobs to be set to true");
}
// Params supported by OAI but unsupported by llama.cpp
static const std::vector<std::string> unsupported_params { "tools", "tool_choice" };
for (auto & param : unsupported_params) {
if (body.contains(param)) {
throw std::runtime_error("Unsupported param: " + param);
}
}
// Copy remaining properties to llama_params
// This allows user to use llama.cpp-specific params like "mirostat", "tfs_z",... via OAI endpoint.
// See "launch_slot_with_task()" for a complete list of params supported by llama.cpp
for (const auto & item : body.items()) {
// Exception: if "n_predict" is present, we overwrite the value specified earlier by "max_tokens"
if (!llama_params.contains(item.key()) || item.key() == "n_predict") {
llama_params[item.key()] = item.value();
}
}
return llama_params;
}

4
examples/speculative/speculative.cpp
View file

@ -65,7 +65,6 @@ int main(int argc, char ** argv) {
llama_context * ctx_dft = NULL;
// load the target model
params.logits_all = true;
std::tie(model_tgt, ctx_tgt) = llama_init_from_gpt_params(params);
// load the draft model
@ -219,7 +218,8 @@ int main(int argc, char ** argv) {
if (params.sparams.temp > 0) {
// stochastic verification
llama_token_data_array dist_tgt = llama_sampling_probability_distribution(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft]);
llama_token_data_array dist_tgt = llama_sampling_prepare(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft], true, NULL);
llama_sample_softmax(ctx_tgt, &dist_tgt);
float p_tgt = 0, p_dft = 0;
// GGML_ASSERT(dist_tgt.size() == dist_dft.size());

17
examples/sycl/win-build-sycl.bat
View file

@ -3,9 +3,13 @@
:: Copyright (C) 2024 Intel Corporation
:: SPDX-License-Identifier: MIT
mkdir -p build
IF not exist build (mkdir build)
cd build
if %errorlevel% neq 0 goto ERROR
@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
if %errorlevel% neq 0 goto ERROR
:: for FP16
:: faster for long-prompt inference
@ -13,11 +17,18 @@ cd build
:: for FP32
cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release
if %errorlevel% neq 0 goto ERROR
:: build example/main only
:: make main
:: build all binary
make -j
if %errorlevel% neq 0 goto ERROR
cd ..
exit /B 0
:ERROR
echo comomand error: %errorlevel%
exit /B %errorlevel%