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// Copyright Verizon Media. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
#include <vespa/eval/onnx/onnx_wrapper.h>
#include <vespa/eval/eval/tensor_spec.h>
#include <vespa/eval/eval/value_codec.h>
#include <vespa/eval/eval/fast_value.h>
#include <vespa/vespalib/util/benchmark_timer.h>
#include <vespa/vespalib/util/require.h>
#include <vespa/vespalib/util/guard.h>
#include <vespa/vespalib/util/stringfmt.h>
using vespalib::make_string_short::fmt;
using vespalib::FilePointer;
using namespace vespalib::eval;
bool read_line(FilePointer &file, vespalib::string &line) {
char line_buffer[1024];
char *res = fgets(line_buffer, sizeof(line_buffer), file.fp());
if (res == nullptr) {
line.clear();
return false;
}
line = line_buffer;
while (!line.empty() && isspace(line[line.size() - 1])) {
line.pop_back();
}
return true;
}
void extract(const vespalib::string &str, const vespalib::string &prefix, vespalib::string &dst) {
if (starts_with(str, prefix)) {
size_t pos = prefix.size();
while ((str.size() > pos) && isspace(str[pos])) {
++pos;
}
dst = str.substr(pos);
}
}
void report_memory_usage(const vespalib::string &desc) {
vespalib::string vm_size = "unknown";
vespalib::string vm_rss = "unknown";
vespalib::string line;
FilePointer file(fopen("/proc/self/status", "r"));
while (read_line(file, line)) {
extract(line, "VmSize:", vm_size);
extract(line, "VmRSS:", vm_rss);
}
fprintf(stderr, "vm_size: %s, vm_rss: %s (%s)\n", vm_size.c_str(), vm_rss.c_str(), desc.c_str());
}
struct Options {
size_t pos = 0;
std::vector<vespalib::string> opt_list;
void add_option(const vespalib::string &opt) {
opt_list.push_back(opt);
}
vespalib::string get_option(const vespalib::string &desc, const vespalib::string &fallback) {
vespalib::string opt;
if (pos < opt_list.size()) {
opt = opt_list[pos];
fprintf(stderr, "option[%zu](%s): %s\n",
pos, desc.c_str(), opt.c_str());
} else {
opt = fallback;
fprintf(stderr, "unspecified option[%zu](%s), fallback: %s\n",
pos, desc.c_str(), fallback.c_str());
}
++pos;
return opt;
}
bool get_bool_opt(const vespalib::string &desc, const vespalib::string &fallback) {
auto opt = get_option(desc, fallback);
REQUIRE((opt == "true") || (opt == "false"));
return (opt == "true");
}
size_t get_size_opt(const vespalib::string &desc, const vespalib::string &fallback) {
auto opt = get_option(desc, fallback);
size_t value = atoi(opt.c_str());
REQUIRE(value > 0);
return value;
}
};
void dump_model_info(const Onnx &model) {
fprintf(stderr, "model meta-data:\n");
for (size_t i = 0; i < model.inputs().size(); ++i) {
fprintf(stderr, " input[%zu]: '%s' %s\n", i, model.inputs()[i].name.c_str(), model.inputs()[i].type_as_string().c_str());
}
for (size_t i = 0; i < model.outputs().size(); ++i) {
fprintf(stderr, " output[%zu]: '%s' %s\n", i, model.outputs()[i].name.c_str(), model.outputs()[i].type_as_string().c_str());
}
}
void dump_wire_info(const Onnx::WireInfo &wire) {
fprintf(stderr, "test setup:\n");
REQUIRE_EQ(wire.vespa_inputs.size(), wire.onnx_inputs.size());
for (size_t i = 0; i < wire.vespa_inputs.size(); ++i) {
fprintf(stderr, " input[%zu]: %s -> %s\n", i, wire.vespa_inputs[i].to_spec().c_str(), wire.onnx_inputs[i].type_as_string().c_str());
}
REQUIRE_EQ(wire.onnx_outputs.size(), wire.vespa_outputs.size());
for (size_t i = 0; i < wire.onnx_outputs.size(); ++i) {
fprintf(stderr, " output[%zu]: %s -> %s\n", i, wire.onnx_outputs[i].type_as_string().c_str(), wire.vespa_outputs[i].to_spec().c_str());
}
}
struct MakeInputType {
Options &opts;
std::map<vespalib::string,int> symbolic_sizes;
MakeInputType(Options &opts_in) : opts(opts_in), symbolic_sizes() {}
ValueType operator()(const Onnx::TensorInfo &info) {
int d = 0;
std::vector<ValueType::Dimension> dim_list;
for (const auto &dim: info.dimensions) {
REQUIRE(d <= 9);
size_t size = 0;
if (dim.is_known()) {
size = dim.value;
} else if (dim.is_symbolic()) {
size = symbolic_sizes[dim.name];
if (size == 0) {
size = opts.get_size_opt(fmt("symbolic size '%s'", dim.name.c_str()), "1");
symbolic_sizes[dim.name] = size;
}
} else {
size = opts.get_size_opt(fmt("size of input '%s' dimension %d", info.name.c_str(), d), "1");
}
dim_list.emplace_back(fmt("d%d", d), size);
++d;
}
return ValueType::make_type(Onnx::WirePlanner::best_cell_type(info.elements), std::move(dim_list));
}
};
Onnx::WireInfo make_plan(Options &opts, const Onnx &model) {
Onnx::WirePlanner planner;
MakeInputType make_input_type(opts);
for (const auto &input: model.inputs()) {
auto type = make_input_type(input);
REQUIRE(planner.bind_input_type(type, input));
}
for (const auto &output: model.outputs()) {
REQUIRE(!planner.make_output_type(output).is_error());
}
return planner.get_wire_info(model);
}
struct MyEval {
Onnx::EvalContext context;
std::vector<Value::UP> inputs;
MyEval(const Onnx &model, const Onnx::WireInfo &wire) : context(model, wire), inputs() {
for (const auto &input_type: wire.vespa_inputs) {
TensorSpec spec(input_type.to_spec());
inputs.push_back(value_from_spec(spec, FastValueBuilderFactory::get()));
}
}
void eval() {
for (size_t i = 0; i < inputs.size(); ++i) {
context.bind_param(i, *inputs[i]);
}
context.eval();
}
};
int usage(const char *self) {
fprintf(stderr, "usage: %s <onnx-model> [options...]\n", self);
fprintf(stderr, " load onnx model and report memory usage\n");
fprintf(stderr, " options are used to specify unknown values, like dimension sizes\n");
fprintf(stderr, " options are accepted in the order in which they are needed\n");
fprintf(stderr, " tip: run without options first, to see which you need\n");
return 1;
}
int main(int argc, char **argv) {
if (argc < 2) {
return usage(argv[0]);
}
Options opts;
for (int i = 2; i < argc; ++i) {
opts.add_option(argv[i]);
}
Onnx::Optimize optimize = opts.get_bool_opt("optimize model", "true")
? Onnx::Optimize::ENABLE : Onnx::Optimize::DISABLE;
report_memory_usage("before loading model");
Onnx model(argv[1], optimize);
report_memory_usage("after loading model");
dump_model_info(model);
auto wire_info = make_plan(opts, model);
dump_wire_info(wire_info);
std::vector<std::unique_ptr<MyEval>> eval_list;
size_t max_concurrent = opts.get_size_opt("max concurrent evaluations", "1");
report_memory_usage("no evaluations yet");
for (size_t i = 1; i <= max_concurrent; ++i) {
eval_list.push_back(std::make_unique<MyEval>(model, wire_info));
eval_list.back()->eval();
if ((i % 8) == 0) {
report_memory_usage(fmt("concurrent evaluations: %zu", i));
}
}
if ((max_concurrent % 8) != 0) {
report_memory_usage(fmt("concurrent evaluations: %zu", max_concurrent));
}
eval_list.resize(1);
double min_time_s = vespalib::BenchmarkTimer::benchmark([&e = *eval_list.back()](){ e.eval(); }, 10.0);
fprintf(stderr, "estimated model evaluation time: %g ms\n", min_time_s * 1000.0);
return 0;
}
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