eval-callback: Example how to use eval callback for debugging (#6576)
* gguf-debug: Example how to use ggml callback for debugging * gguf-debug: no mutex, verify type, fix stride. * llama: cv eval: move cb eval field in common gpt_params * ggml_debug: use common gpt_params to pass cb eval. Fix get tensor SIGV random. * ggml_debug: ci: add tests * ggml_debug: EOL in CMakeLists.txt * ggml_debug: Remove unused param n_batch, no batching here * ggml_debug: fix trailing spaces * ggml_debug: fix trailing spaces * common: fix cb_eval and user data not initialized * ci: build revert label * ggml_debug: add main test label * doc: add a model: add a link to ggml-debug * ggml-debug: add to make toolchain * ggml-debug: tests add the main label * ggml-debug: ci add test curl label * common: allow the warmup to be disabled in llama_init_from_gpt_params * ci: add curl test * ggml-debug: better tensor type support * gitignore : ggml-debug * ggml-debug: printing also the sum of each tensor * ggml-debug: remove block size * eval-callback: renamed from ggml-debug * eval-callback: fix make toolchain --------- Co-authored-by: slaren <slarengh@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Pierrick Hymbert
GitHub
parent
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commit
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12 changed files with 319 additions and 22 deletions
185
examples/eval-callback/eval-callback.cpp
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185
examples/eval-callback/eval-callback.cpp
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#include "common.h"
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#include "llama.h"
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#include "ggml.h"
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#include <cstdio>
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#include <random>
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#include <string>
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#include <tuple>
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#include <vector>
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/**
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* This the arbitrary data which will be passed to each callback.
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* Later on we can for example add operation or tensor name filter from the CLI arg, or a file descriptor to dump the tensor.
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*/
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struct callback_data {
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std::vector<uint8_t> data;
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};
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static std::string ggml_ne_string(const ggml_tensor * t) {
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std::string str;
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for (int i = 0; i < GGML_MAX_DIMS; ++i) {
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str += std::to_string(t->ne[i]);
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if (i + 1 < GGML_MAX_DIMS) {
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str += ", ";
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}
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}
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return str;
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}
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static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n) {
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float sum = 0;
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for (int64_t i3 = 0; i3 < ne[3]; i3++) {
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printf(" [\n");
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for (int64_t i2 = 0; i2 < ne[2] && i2 < n; i2++) {
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printf(" [\n");
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for (int64_t i1 = 0; i1 < ne[1] && i1 < n; i1++) {
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printf(" [");
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for (int64_t i0 = 0; i0 < ne[0] && i0 < n; i0++) {
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size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0];
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float v;
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if (type == GGML_TYPE_F16) {
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v = ggml_fp16_to_fp32(*(ggml_fp16_t *) data + i);
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} else if (type == GGML_TYPE_F32) {
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v = *(float *) data + i;
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} else if (type == GGML_TYPE_I32) {
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v = (float) *(int32_t *) data + i;
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} else if (type == GGML_TYPE_I16) {
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v = (float) *(int16_t *) data + i;
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} else if (type == GGML_TYPE_I8) {
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v = (float) *(int8_t *) data + i;
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} else {
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GGML_ASSERT(false);
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}
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printf("%8.4f", v);
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sum += v;
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if (i0 < ne[0] - 1 && i0 < n - 1) printf(", ");
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}
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if (ne[0] > n) printf(", ...");
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printf("],\n");
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}
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if (ne[1] > n) printf(" ...\n");
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printf(" ],\n");
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}
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if (ne[2] > n) printf(" ...\n");
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printf(" ]\n");
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printf(" sum = %f\n", sum);
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}
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}
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/**
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* GGML operations callback during the graph execution.
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*
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* @param t current tensor
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* @param ask when ask is true, the scheduler wants to know if we are interested in data from this tensor
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* if we return true, a follow-up call will be made with ask=false in which we can do the actual collection.
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* see ggml_backend_sched_eval_callback
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* @param user_data user data to pass at each call back
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* @return true to receive data or continue the graph, false otherwise
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*/
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static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) {
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auto * cb_data = (callback_data *) user_data;
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const struct ggml_tensor * src0 = t->src[0];
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const struct ggml_tensor * src1 = t->src[1];
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if (ask) {
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return true; // Always retrieve data
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}
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char src1_str[128] = {0};
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if (src1) {
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sprintf(src1_str, "%s{%s}", src1->name, ggml_ne_string(src1).c_str());
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}
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printf("%s: %24s = (%s) %10s(%s{%s}, %s}) = {%s}\n", __func__,
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t->name, ggml_type_name(t->type), ggml_op_name(t->op),
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src0->name, ggml_ne_string(src0).c_str(),
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src1 ? src1_str : "",
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ggml_ne_string(t).c_str());
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// copy the data from the GPU memory if needed
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const bool is_host = ggml_backend_buffer_is_host(t->buffer);
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if (!is_host) {
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auto n_bytes = ggml_nbytes(t);
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cb_data->data.resize(n_bytes);
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ggml_backend_tensor_get(t, cb_data->data.data(), 0, n_bytes);
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}
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if (!ggml_is_quantized(t->type)) {
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uint8_t * data = is_host ? (uint8_t *) t->data : cb_data->data.data();
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ggml_print_tensor(data, t->type, t->ne, t->nb, 3);
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}
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return true;
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}
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static bool run(llama_context * ctx, const gpt_params & params) {
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const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
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std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
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if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), 0, 0))) {
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fprintf(stderr, "%s : failed to eval\n", __func__);
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return false;
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}
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return true;
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}
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int main(int argc, char ** argv) {
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callback_data cb_data;
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gpt_params params;
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if (!gpt_params_parse(argc, argv, params)) {
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return 1;
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}
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print_build_info();
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std::mt19937 rng(params.seed);
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if (params.random_prompt) {
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params.prompt = gpt_random_prompt(rng);
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}
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llama_backend_init();
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llama_numa_init(params.numa);
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// pass the callback to the backend scheduler
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// it will be executed for each node during the graph computation
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params.cb_eval = ggml_debug;
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params.cb_eval_user_data = &cb_data;
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params.warmup = false;
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// init
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llama_model * model;
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llama_context * ctx;
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std::tie(model, ctx) = llama_init_from_gpt_params(params);
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if (model == nullptr || ctx == nullptr) {
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fprintf(stderr, "%s : failed to init\n", __func__);
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return 1;
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}
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// print system information
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{
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fprintf(stderr, "\n");
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fprintf(stderr, "%s\n", get_system_info(params).c_str());
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}
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bool OK = run(ctx, params);
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if (!OK) {
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return 1;
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}
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llama_print_timings(ctx);
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llama_free(ctx);
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llama_free_model(model);
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llama_backend_free();
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return 0;
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}
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