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llama.cpp
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llama.cpp/examples/model-backend-compare/model-backend-compare.cpp

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#include "arg.h"
#include "common.h"
#include "log.h"
#include "common.h"
#include "llama.h"
#include "ggml.h"
#include <vector>
#include <cstdint>
#include <unordered_map>
#include <string>
#include <sstream>
#include <cstring>
#include <cmath>
namespace {
constexpr double nmse_threshold = 1e-2;
struct callback_data {
std::vector<uint8_t> data;
std::vector<float> device_results;
std::unordered_map<std::string, std::vector<float>> cpu_results;
};
bool gather = true;
// normalized mean squared error = mse(a, b) / mse(a, 0)
double nmse(const float * a, const float * b, size_t n) {
double mse_a_b = 0.0;
double mse_a_0 = 0.0;
for (size_t i = 0; i < n; i++) {
float a_i = a[i];
float b_i = b[i];
mse_a_b += (a_i - b_i) * (a_i - b_i);
mse_a_0 += a_i * a_i;
}
return mse_a_b / mse_a_0;
}
void ggml_print_tensor(const ggml_tensor * t, const std::vector<float> data, int64_t n) {
GGML_ASSERT(n > 0);
float sum = 0;
for (int64_t i3 = 0; i3 < t->ne[3]; i3++) {
for (int64_t i2 = 0; i2 < t->ne[2]; i2++) {
for (int64_t i1 = 0; i1 < t->ne[1]; i1++) {
for (int64_t i0 = 0; i0 < t->ne[0]; i0++) {
const float v = data[i3 * t->ne[2] * t->ne[1] * t->ne[0] + i2 * t->ne[1] * t->ne[0] + i1 * t->ne[0] + i0];
sum += v;
}
}
}
}
for (int64_t i3 = 0; i3 < t->ne[3]; i3++) {
LOG(" [\n");
for (int64_t i2 = 0; i2 < t->ne[2]; i2++) {
if (i2 == n && t->ne[2] > 2*n) {
LOG(" ..., \n");
i2 = t->ne[2] - n;
}
LOG(" [\n");
for (int64_t i1 = 0; i1 < t->ne[1]; i1++) {
if (i1 == n && t->ne[1] > 2*n) {
LOG(" ..., \n");
i1 = t->ne[1] - n;
}
LOG(" [");
for (int64_t i0 = 0; i0 < t->ne[0]; i0++) {
if (i0 == n && t->ne[0] > 2*n) {
LOG("..., ");
i0 = t->ne[0] - n;
}
const float v = data[i3 * t->ne[2] * t->ne[1] * t->ne[0] + i2 * t->ne[1] * t->ne[0] + i1 * t->ne[0] + i0];
LOG("%12.4f", v);
if (i0 < t->ne[0] - 1) LOG(", ");
}
LOG("],\n");
}
LOG(" ],\n");
}
LOG(" ]\n");
LOG(" sum = %f\n", sum);
}
if (std::isnan(sum)) {
LOG_ERR("encountered NaN - aborting\n");
exit(0);
}
}
inline float ggml_compute_bf16_to_fp32(ggml_bf16_t h) {
union {
float f;
uint32_t i;
} u;
u.i = (uint32_t)h.bits << 16;
return u.f;
}
float to_float(const uint8_t * ptr, ggml_type type) {
switch (type) {
case GGML_TYPE_F32:
return *(const float *)ptr;
case GGML_TYPE_F16:
return ggml_fp16_to_fp32(*(const ggml_fp16_t *)ptr);
case GGML_TYPE_BF16:
return ggml_compute_bf16_to_fp32(*(const ggml_bf16_t *)ptr);
case GGML_TYPE_I8:
return static_cast<float>(*(const int8_t *)ptr);
case GGML_TYPE_I16:
return static_cast<float>(*(const int16_t *)ptr);
case GGML_TYPE_I32:
return static_cast<float>(*(const int32_t *)ptr);
case GGML_TYPE_I64:
return static_cast<float>(*(const int64_t *)ptr);
default:
GGML_ABORT("unsupported ggml_type %d in to_float", type);
}
return 0.0f;
}
void tensor_to_float_array(const ggml_tensor * t, const void * data, std::vector<float> & out) {
const size_t n_elements = ggml_nelements(t);
out.resize(n_elements);
// convert to float
size_t idx = 0;
for (int64_t i3 = 0; i3 < t->ne[3]; i3++) {
for (int64_t i2 = 0; i2 < t->ne[2]; i2++) {
for (int64_t i1 = 0; i1 < t->ne[1]; i1++) {
if (!ggml_is_quantized(t->type)) {
for (int64_t i0 = 0; i0 < t->ne[0]; i0++) {
const uint8_t * ptr = ((const uint8_t *)data) + i3 * t->nb[3] + i2 * t->nb[2] + i1 * t->nb[1] + i0 * t->nb[0];
out[idx] = to_float(ptr, t->type);
idx++;
}
} else {
GGML_ABORT("quantized types are not supported in tensor_to_float_array");
}
}
}
}
}
bool tensor_is_empty(ggml_tensor * node) {
return ggml_is_empty(node) || node->op == GGML_OP_NONE || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE;
}
std::string remove_device_from_name(const std::string & name) {
// Remove prefix and suffix
// Example: Vulkan0#inp_embd#0 -> inp_embd
size_t start = name.find_first_of('#');
size_t end = name.find_last_of('#');
if (start != std::string::npos && end != std::string::npos &&
end > start) {
return name.substr(start + 1, end - start - 1);
}
return name;
}
std::string tensor_name(ggml_tensor * t) {
const std::string tname(t->name, strnlen(t->name, GGML_MAX_NAME));
std::stringstream ss;
ss << tname << "[";
// Get last source
size_t last_src = 0;
for (size_t i = 0; i < GGML_MAX_SRC; i++) {
if (t->src[i]) {
last_src = i;
}
}
for (size_t i = 0; i < GGML_MAX_SRC; i++) {
if (t->src[i]) {
const std::string src_name(t->src[i]->name, strnlen(t->src[i]->name, GGML_MAX_NAME));
ss << remove_device_from_name(src_name);
if (i < last_src) {
ss << ", ";
}
}
}
ss << "]";
return ss.str();
}
bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) {
auto * cb_data = (callback_data *) user_data;
if (ask || tensor_is_empty(t)) {
return true; // Always retrieve data
}
const std::string name = tensor_name(t);
if (gather) {
// CPU data should be host-visible
GGML_ASSERT(ggml_backend_buffer_is_host(t->buffer));
// Make sure this tensor does not exist yet
if (cb_data->cpu_results.find(name) != cb_data->cpu_results.end())
{
LOG_ERR("%s : tensor '%s' already exists in CPU reference data\n", __func__, name.c_str());
GGML_ABORT("fatal error");
}
std::vector<float>& result = cb_data->cpu_results[name];
// LOG("gathering CPU reference data for tensor '%s'\n", name.c_str());
// for (size_t i = 0; i < GGML_MAX_DIMS; i++) {
// LOG(" ne[%zu] = %lld\n", i, t->ne[i]);
// }
// for (size_t i = 0; i < GGML_MAX_SRC; i++) {
// if (t->src[i]) {
// const std::string src_name(t->src[i]->name, strnlen(t->src[i]->name, GGML_MAX_NAME));
// LOG(" src[%zu] = %s\n", i, src_name.c_str());
// }
// }
tensor_to_float_array(t, t->data, result);
return true;
}
// Compare with CPU data if available
auto it = cb_data->cpu_results.find(name);
if (it == cb_data->cpu_results.end()) {
LOG_ERR("no CPU reference data for tensor '%s'\n", name.c_str());
return true;
}
const bool is_host = ggml_backend_buffer_is_host(t->buffer);
const size_t n_bytes = ggml_nbytes(t);
const uint8_t * data;
if (!is_host) {
if (cb_data->data.size() < n_bytes) {
cb_data->data.resize(n_bytes);
}
ggml_backend_tensor_get(t, cb_data->data.data(), 0, n_bytes);
data = cb_data->data.data();
} else {
data = (const uint8_t *) t->data;
}
tensor_to_float_array(t, data, cb_data->device_results);
const std::vector<float>& ref_data = it->second;
double error = nmse(ref_data.data(), cb_data->device_results.data(), ref_data.size());
if (error > nmse_threshold) {
LOG_ERR("nmse = %.12f tensor '%s' op=%s\n", error, name.c_str(), ggml_op_name(t->op));
LOG_ERR(" ne: ");
for (int i = 0; i < GGML_MAX_DIMS; i++) {
LOG_ERR("%ld ", t->ne[i]);
}
LOG_ERR("\n nb: ");
for (int i = 0; i < GGML_MAX_DIMS; i++) {
LOG_ERR("%zu ", t->nb[i]);
}
LOG_ERR("\n\n");
for (int i = 0; i < GGML_MAX_SRC; i++) {
if (t->src[i]) {
const std::string src_name(t->src[i]->name, strnlen(t->src[i]->name, GGML_MAX_NAME));
LOG_ERR(" src%d: %s\n", i, src_name.c_str());
}
}
LOG_ERR("CPU reference data for tensor '%s':\n", name.c_str());
ggml_print_tensor(t, ref_data, 2);
LOG_ERR("Device data for tensor '%s':\n", name.c_str());
ggml_print_tensor(t, cb_data->device_results, 2);
return false;
} else {
LOG("nmse = %.12f tensor '%s' op = %s\n", error, name.c_str(), ggml_op_name(t->op));
}
return true;
}
bool run(llama_context * ctx, const common_params & params) {
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
const bool add_bos = llama_vocab_get_add_bos(vocab);
std::vector<llama_token> tokens = common_tokenize(ctx, params.prompt, add_bos);
if (tokens.empty()) {
LOG_ERR("%s : there are not input tokens to process - (try to provide a prompt with '-p')\n", __func__);
return false;
}
if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size()))) {
LOG_ERR("%s : failed to eval\n", __func__);
return false;
}
return true;
}
} // namespace
int main(int argc, char ** argv) {
callback_data cb_data;
common_params params;
params.prompt = "The quick brown fox";
params.sampling.seed = 1234;
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
return 1;
}
common_init();
llama_backend_init();
// pass the callback to the backend scheduler
// it will be executed for each node during the graph computation
params.cb_eval = ggml_debug;
params.cb_eval_user_data = &cb_data;
params.warmup = false;
params.split_mode = LLAMA_SPLIT_MODE_NONE;
const size_t n_dev = ggml_backend_dev_count();
for (size_t i = 0; i < n_dev * 2; i++) {
ggml_backend_dev_t device = ggml_backend_dev_get(i % ggml_backend_dev_count());
// Run CPU-only first to gather reference results
if ((i < n_dev && ggml_backend_dev_type(device) != GGML_BACKEND_DEVICE_TYPE_CPU) ||
(i >= n_dev && ggml_backend_dev_type(device) == GGML_BACKEND_DEVICE_TYPE_CPU)) {
continue;
}
params.devices.clear();
params.devices.push_back(device);
if (i < n_dev) {
LOG_INF("=== Running on device %zu (gathering reference results) ===\n", i);
gather = true;
} else {
LOG_INF("=== Running on device %zu ===\n", i - n_dev);
gather = false;
}
// init
common_init_result llama_init = common_init_from_params(params);
llama_model * model = llama_init.model.get();
llama_context * ctx = llama_init.context.get();
if (model == nullptr || ctx == nullptr) {
LOG_ERR("%s : failed to init\n", __func__);
return 1;
}
// print system information
{
LOG_INF("\n");
LOG_INF("%s\n", common_params_get_system_info(params).c_str());
LOG_INF("\n");
}
bool OK = run(ctx, params);
if (!OK) {
return 1;
}
LOG("\n");
llama_perf_context_print(ctx);
llama_backend_free();
}
return 0;
}
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