vbatts/llama.cpp
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ggml_debug: use common gpt_params to pass cb eval.

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Fix get tensor SIGV random.
This commit is contained in:
Pierrick HYMBERT 2024-04-10 20:52:38 +02:00
parent 8fe3be8d68
commit 01dd5e9776
1 changed files with 16 additions and 30 deletions
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46
examples/ggml-debug/ggml-debug.cpp
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@ -3,7 +3,9 @@
#include "ggml.h"
#include <cstdio>
#include <random>
#include <string>
#include <tuple>
#include <vector>
/**
@ -11,7 +13,7 @@
* Later on we can for example add operation or tensor name filter from the CLI arg, or a file descriptor to dump the tensor.
*/
struct callback_data {
std::vector<float> data;
std::vector<int8_t> data;
};
static std::string ggml_ne_string(const ggml_tensor * t) {
@ -25,7 +27,7 @@ static std::string ggml_ne_string(const ggml_tensor * t) {
return str;
}
static void ggml_print_tensor(const float * data, const int64_t * ne, const size_t * nb, int64_t n) {
static void ggml_print_tensor(int8_t * data, const int64_t * ne, const size_t * nb, int64_t n) {
for (int64_t i3 = 0; i3 < ne[3]; i3++) {
printf(" [\n");
for (int64_t i2 = 0; i2 < ne[2] && i2 < n; i2++) {
@ -34,7 +36,7 @@ static void ggml_print_tensor(const float * data, const int64_t * ne, const size
printf(" [");
for (int64_t i0 = 0; i0 < ne[0] && i0 < n; i0++) {
size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0];
float v = *(data + i);
float v = *(float *)(data + i);
printf("%8.4f", v);
if (i0 < ne[0] - 1 && i0 < n - 1) printf(", ");
}
@ -55,6 +57,7 @@ static void ggml_print_tensor(const float * data, const int64_t * ne, const size
* @param t current tensor
* @param ask 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.
* see ggml_backend_sched_eval_callback
* @param user_data user data to pass at each call back
* @return true to receive data or continue the graph, false otherwise
*/
@ -85,12 +88,12 @@ static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) {
if (!is_host) {
auto n_bytes = ggml_nbytes(t);
cb_data->data.resize(n_bytes / sizeof(float));
cb_data->data.resize(n_bytes);
ggml_backend_tensor_get(t, cb_data->data.data(), 0, n_bytes);
}
if (t->type == GGML_TYPE_F32 || t->type == GGML_TYPE_F16) {
const float * data = is_host ? (const float *) t->data : cb_data->data.data();
int8_t * data = is_host ? (int8_t *) t->data : cb_data->data.data();
ggml_print_tensor(data, t->ne, t->nb, 3);
}
@ -131,34 +134,17 @@ int main(int argc, char ** argv) {
llama_backend_init();
llama_numa_init(params.numa);
auto mparams = llama_model_params_from_gpt_params(params);
llama_model * model = nullptr;
if (!params.hf_repo.empty() && !params.hf_file.empty()) {
model = llama_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), mparams);
} else if (!params.model_url.empty()) {
model = llama_load_model_from_url(params.model_url.c_str(), params.model.c_str(), mparams);
} else {
model = llama_load_model_from_file(params.model.c_str(), mparams);
}
if (model == NULL) {
fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
return 1;
}
auto cparams = llama_context_params_from_gpt_params(params);
// pass the callback to the backend scheduler
// it will be executed for each node during the graph computation
cparams.cb_eval = ggml_debug;
cparams.cb_eval_user_data = &cb_data;
params.cb_eval = ggml_debug;
params.cb_eval_user_data = &cb_data;
llama_context * ctx = llama_new_context_with_model(model, cparams);
if (ctx == NULL) {
fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str());
llama_free_model(model);
// init
llama_model * model;
llama_context * ctx;
std::tie(model, ctx) = llama_init_from_gpt_params(params);
if (model == nullptr || ctx == nullptr) {
fprintf(stderr, "%s : failed to init\n", __func__);
return 1;
}