#include "arg.h"
#include "common.h"
#include "log.h"
#include "llama-cpp.h"
#include "../src/llama-ext.h"
#include "ggml.h"
#include "gguf-model-data.h"
#include "gguf.h"
#include "ggml-backend.h"
#include "download.h"
#include <array>
#include <vector>
#include <set>
#include <fstream>
#include <iostream>
#include <random>
// Noop because weights are not needed
static void set_tensor_data(struct ggml_tensor * tensor, void * userdata) {
GGML_UNUSED(tensor);
GGML_UNUSED(userdata);
}
struct input_tensor {
ggml_type type;
std::array<int64_t, 4> ne;
std::array<size_t, 4> nb;
input_tensor(ggml_type type, int64_t * ne, size_t * nb): type(type) {
memcpy(this->ne.data(), ne, 4 * sizeof(int64_t));
memcpy(this->nb.data(), nb, 4 * sizeof(size_t));
}
bool operator<(const input_tensor &b) const {
return std::tie(type, ne, nb) <
std::tie(b.type, b.ne, b.nb);
}
void serialize(std::ostream& out) const {
out << type << ' ';
for (size_t i = 0; i < 4; i++) {
out << ne[i] << ' ';
}
for (size_t i = 0; i < 4; i++) {
out << nb[i] << ' ';
}
}
};
struct test_object {
ggml_op op;
ggml_type type;
std::array<int64_t, 4> ne;
std::vector<int32_t> op_params;
std::vector<input_tensor> sources;
std::string name;
void serialize(std::ostream& out) const {
out << op << ' ' << type << ' ';
for (size_t i = 0; i < 4; i++) {
out << ne[i] << ' ';
}
out << op_params.size() << ' ';
for (size_t i = 0; i < op_params.size(); i++) {
out << op_params[i] << ' ';
}
out << sources.size() << ' ';
for (size_t s = 0; s < sources.size(); s++) {
sources[s].serialize(out);
}
if (!name.empty()) {
out << name;
} else {
out << '-';
}
out << '\n';
}
bool operator<(const test_object &b) const {
return std::tie(op, type, ne, op_params, sources) <
std::tie(b.op, b.type, b.ne, b.op_params, b.sources);
}
};
static void extract_graph_ops(ggml_cgraph * cgraph, const char * label, std::set<test_object> & tests) {
int n_nodes = ggml_graph_n_nodes(cgraph);
int n_skipped = 0;
int n_before = (int) tests.size();
for (int i = 0; i < n_nodes; i++) {
ggml_tensor * node = ggml_graph_node(cgraph, i);
if (node->op == GGML_OP_NONE || node->op == GGML_OP_VIEW || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_TRANSPOSE) {
n_skipped++;
continue;
}
test_object test;
test.op = node->op;
test.type = node->type;
memcpy(&test.ne, node->ne, 4 * sizeof(int64_t));
test.op_params.resize(GGML_MAX_OP_PARAMS / sizeof(int32_t));
memcpy(test.op_params.data(), node->op_params, GGML_MAX_OP_PARAMS);
for (size_t s = 0; s < GGML_MAX_SRC; s++) {
if (node->src[s] == nullptr) {
break;
}
test.sources.emplace_back(node->src[s]->type, node->src[s]->ne, node->src[s]->nb);
}
test.name = node->name;
tests.insert(test);
}
int n_new = (int) tests.size() - n_before;
LOG_INF("%s: %d unique ops, %d total nodes, %d skipped (view ops)\n",
label, n_new, n_nodes, n_skipped);
}
int main(int argc, char ** argv) {
common_params params;
params.out_file = "tests.txt";
common_init();
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_EXPORT_GRAPH_OPS)) {
return 1;
}
// Load CPU-only
ggml_backend_dev_t cpu_device = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
params.devices = { cpu_device, nullptr };
params.fit_params = false;
params.n_gpu_layers = 0;
params.warmup = false;
llama_context * ctx;
common_init_result_ptr init_result;
llama_context_ptr ctx2;
llama_model_ptr model;
if (params.model.hf_repo.empty()) {
init_result = common_init_from_params(params);
ctx = init_result->context();
if (!ctx) {
LOG_ERR("failed to initialize params\n");
return 1;
}
} else {
#ifdef LLAMA_HF_FETCH
auto [hf_repo, hf_quant] = common_download_split_repo_tag(params.model.hf_repo);
if (hf_quant.empty() || hf_quant == "latest") {
hf_quant = "Q4_K_M";
}
gguf_context_ptr gguf_ctx = gguf_fetch_gguf_ctx(hf_repo, hf_quant);
if (!gguf_ctx) {
LOG_ERR("failed to fetch GGUF metadata from %s\n", hf_repo.c_str());
return 1;
}
llama_model_params model_params = llama_model_default_params();
model_params.devices = params.devices.data();
model_params.no_alloc = true;
model.reset(llama_model_init_from_user(gguf_ctx.get(), set_tensor_data, nullptr, model_params));
if (!model) {
LOG_ERR("failed to create llama_model from %s\n", hf_repo.c_str());
return 1;
}
llama_context_params ctx_params = llama_context_default_params();
ctx2.reset(llama_init_from_model(model.get(), ctx_params));
ctx = ctx2.get();
if (!ctx) {
LOG_ERR("failed to create llama_context\n");
return 1;
}
#else
LOG_ERR("test-export-graph-ops compiled without HF fetch support\n");
return 1;
#endif
}
const uint32_t n_seqs = llama_n_seq_max(ctx);
const uint32_t n_tokens = std::min(llama_n_ctx(ctx), llama_n_ubatch(ctx));
std::set<test_object> tests;
auto * gf_pp = llama_graph_reserve(ctx, n_tokens, n_seqs, n_tokens);
if (!gf_pp) {
LOG_ERR("failed to reserve prompt processing graph\n");
return 1;
}
extract_graph_ops(gf_pp, "pp", tests);
auto * gf_tg = llama_graph_reserve(ctx, n_seqs, n_seqs, n_seqs);
if (!gf_tg) {
LOG_ERR("failed to reserve token generation graph\n");
return 1;
}
extract_graph_ops(gf_tg, "tg", tests);
LOG_INF("%d unique ops total\n", (int) tests.size());
std::ofstream f(params.out_file);
if (!f.is_open()) {
LOG_ERR("unable to open output file: %s\n", params.out_file.c_str());
return 1;
}
for (const auto& test : tests) {
test.serialize(f);
}
return 0;
}