happyz
/
gemma.cpp
mirror of https://github.com/google/gemma.cpp.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Major duplicated code reduction in test/benchmarks 

Helper functions to tokenize/wrap
Move LayersOutputFunc into RuntimeConfig
AcceptFunc passes the probability
Implement StringFromType using the parser, and verify results match

PiperOrigin-RevId: 643255119
This commit is contained in:
Jan Wassenberg 2024-06-14 00:15:36 -07:00 committed by Copybara-Service
parent c15ff9529c
commit d3c6a45b59
17 changed files with 626 additions and 814 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

58
BUILD.bazel
View File

@ -113,12 +113,8 @@ cc_library(
cc_library( cc_library(
name = "cross_entropy", name = "cross_entropy",
srcs = [ srcs = ["gemma/cross_entropy.cc"],
"gemma/cross_entropy.cc", hdrs = ["gemma/cross_entropy.h"],
],
hdrs = [
"gemma/cross_entropy.h",
],
deps = [ deps = [
":common", ":common",
":gemma_lib", ":gemma_lib",
@ -149,6 +145,25 @@ cc_library(
], ],
) )
cc_library(
name = "benchmark_helper",
srcs = ["gemma/benchmark_helper.cc"],
hdrs = ["gemma/benchmark_helper.h"],
deps = [
":app",
":args",
":common",
":cross_entropy",
":gemma_lib",
# Placeholder for internal dep, do not remove.,
"@benchmark//:benchmark",
"//compression:compress",
"@hwy//:hwy",
"@hwy//:nanobenchmark",
"@hwy//:thread_pool",
],
)
cc_test( cc_test(
name = "gemma_test", name = "gemma_test",
srcs = ["gemma/gemma_test.cc"], srcs = ["gemma/gemma_test.cc"],
@ -161,11 +176,11 @@ cc_test(
deps = [ deps = [
":app", ":app",
":args", ":args",
":benchmark_helper",
":common", ":common",
":cross_entropy", ":cross_entropy",
":gemma_lib", ":gemma_lib",
":ops", ":ops",
# Placeholder for internal dep, do not remove.,
"@googletest//:gtest_main", "@googletest//:gtest_main",
"//compression:io", "//compression:io",
"@hwy//:hwy_test_util", "@hwy//:hwy_test_util",
@ -179,6 +194,7 @@ cc_binary(
deps = [ deps = [
":app", ":app",
":args", ":args",
":benchmark_helper",
":common", ":common",
":gemma_lib", ":gemma_lib",
# Placeholder for internal dep, do not remove., # Placeholder for internal dep, do not remove.,
@ -213,10 +229,10 @@ cc_binary(
deps = [ deps = [
":app", ":app",
":args", ":args",
":benchmark_helper",
":common", ":common",
":cross_entropy", ":cross_entropy",
":gemma_lib", ":gemma_lib",
# Placeholder for internal dep, do not remove.,
"//compression:io", "//compression:io",
"@hwy//:hwy", "@hwy//:hwy",
"@hwy//:nanobenchmark", "@hwy//:nanobenchmark",
@ -230,7 +246,6 @@ cc_binary(
srcs = ["gemma/benchmarks.cc"], srcs = ["gemma/benchmarks.cc"],
deps = [ deps = [
":benchmark_helper", ":benchmark_helper",
# Placeholder for internal dep, do not remove.,
"@benchmark//:benchmark", "@benchmark//:benchmark",
], ],
) )
@ -243,8 +258,8 @@ cc_binary(
deps = [ deps = [
":app", ":app",
":args", ":args",
":benchmark_helper",
":gemma_lib", ":gemma_lib",
# Placeholder for internal dep, do not remove.,
"//compression:io", "//compression:io",
"@hwy//:hwy", "@hwy//:hwy",
"@hwy//:thread_pool", "@hwy//:thread_pool",
@ -257,8 +272,10 @@ cc_binary(
srcs = ["gemma/run_mmlu.cc"], srcs = ["gemma/run_mmlu.cc"],
deps = [ deps = [
":app", ":app",
":args",
":benchmark_helper",
":gemma_lib", ":gemma_lib",
# Placeholder for internal dep, do not remove., "//compression:io",
"@hwy//:hwy", "@hwy//:hwy",
"@hwy//:profiler", "@hwy//:profiler",
"@hwy//:thread_pool", "@hwy//:thread_pool",
@ -318,25 +335,6 @@ cc_library(
], ],
) )
cc_library(
name = "benchmark_helper",
srcs = [
"gemma/benchmark_helper.cc",
],
hdrs = [
"gemma/benchmark_helper.h",
],
deps = [
":app",
":common",
":gemma_lib",
"@benchmark//:benchmark",
"@hwy//:hwy",
"@hwy//:nanobenchmark",
"@hwy//:thread_pool",
],
)
cc_test( cc_test(
name = "backward_scalar_test", name = "backward_scalar_test",
size = "large", size = "large",

20
compression/io.h
View File

@ -23,6 +23,8 @@
#include <string> #include <string>
#include <utility> // std::move #include <utility> // std::move
#include "hwy/base.h"
namespace gcpp { namespace gcpp {
// Forward-declare to break the circular dependency: OpenFileOrNull returns // Forward-declare to break the circular dependency: OpenFileOrNull returns
@ -77,12 +79,30 @@ struct Path {
return path; return path;
} }
bool Empty() const { return path.empty(); }
// Returns whether the file existed when this was called. // Returns whether the file existed when this was called.
bool Exists() const { return !!OpenFileOrNull(*this, "r"); } bool Exists() const { return !!OpenFileOrNull(*this, "r"); }
std::string path; std::string path;
}; };
static inline HWY_MAYBE_UNUSED std::string ReadFileToString(const Path& path) {
std::unique_ptr<File> file = OpenFileOrNull(path, "r");
if (!file) {
HWY_ABORT("Failed to open %s", path.path.c_str());
}
const size_t size = file->FileSize();
if (size == 0) {
HWY_ABORT("Empty file %s", path.path.c_str());
}
std::string content(size, ' ');
if (!file->Read(0, size, content.data())) {
HWY_ABORT("Failed to read %s", path.path.c_str());
}
return content;
}
} // namespace gcpp } // namespace gcpp
#endif // THIRD_PARTY_GEMMA_CPP_COMPRESSION_IO_H_ #endif // THIRD_PARTY_GEMMA_CPP_COMPRESSION_IO_H_

155
debug_prompt.cc
View File

@ -1,146 +1,83 @@
// Copyright 2024 Google LLC
// SPDX-License-Identifier: Apache-2.0
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// https://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <cstdlib>
#include <fstream> #include <fstream>
#include <iostream> #include <iostream>
#include <random>
#include <string> #include <string>
#include <utility>
#include <vector> #include <vector>
// Placeholder for internal header, do not modify.
#include "compression/io.h" #include "compression/io.h"
#include "gemma/gemma.h" #include "gemma/benchmark_helper.h"
#include "util/app.h" #include "gemma/gemma.h" // LayersOutputFunc
#include "util/args.h" #include "util/args.h"
#include "hwy/base.h" #include "hwy/base.h"
#include "hwy/contrib/thread_pool/thread_pool.h"
#include "nlohmann/json.hpp" #include "nlohmann/json.hpp"
using json = nlohmann::json; using json = nlohmann::json;
class PromptArgs : public gcpp::ArgsBase<PromptArgs> { namespace gcpp {
class PromptArgs : public ArgsBase<PromptArgs> {
public: public:
PromptArgs(int argc, char* argv[]) { InitAndParse(argc, argv); } PromptArgs(int argc, char* argv[]) { InitAndParse(argc, argv); }
gcpp::Path layers_output; Path layers_output; // optional
std::string prompt; std::string prompt;
// Returns error string or nullptr if OK.
const char* Validate() const {
if (prompt.empty()) return "Must specify --prompt";
return nullptr;
}
template <class Visitor> template <class Visitor>
void ForEach(const Visitor& visitor) { void ForEach(const Visitor& visitor) {
visitor(layers_output.path, "layers_output", std::string(""), visitor(layers_output, "layers_output", Path(""),
"Path to store layers output", 2); "Path to store layers output", 2);
visitor(prompt, "prompt", std::string(""), "Prompt to the model", 2); visitor(prompt, "prompt", std::string(""), "Prompt to the model", 2);
} }
}; };
std::pair<std::string, int> QueryModel( int Run(int argc, char** argv) {
gcpp::Gemma& model, gcpp::InferenceArgs& args, gcpp::AppArgs& app, PromptArgs prompt_args(argc, argv);
gcpp::KVCache& kv_cache, hwy::ThreadPool& pool, const std::string& input, AbortIfInvalidArgs(prompt_args);
gcpp::LayersOutputT* layers_output) {
std::vector<int> prompt;
HWY_ASSERT(model.Tokenizer().Encode(input, &prompt));
// For both pre-trained and instruction-tuned models: prepend "<bos>" token
// if needed.
prompt.insert(prompt.begin(), gcpp::BOS_ID);
std::string res;
size_t total_tokens = 0;
std::mt19937 gen;
gen.seed(42);
auto stream_token = [&res, &total_tokens, &model](int token, float) {
++total_tokens;
std::string token_text;
HWY_ASSERT(model.Tokenizer().Decode(std::vector<int>{token}, &token_text));
res += token_text;
return true;
};
if (app.verbosity >= 2) {
std::cout << args.max_tokens << " " << args.max_generated_tokens << " "
<< args.temperature;
}
gcpp::TimingInfo timing_info;
gcpp::RuntimeConfig runtime_config = {
.max_tokens = args.max_tokens,
.max_generated_tokens = args.max_generated_tokens,
.temperature = args.temperature,
.verbosity = app.verbosity,
.gen = &gen,
.stream_token = stream_token,
};
model.Generate(runtime_config, prompt, /*start_pos=*/0, kv_cache, timing_info,
layers_output);
return {res, total_tokens};
}
class OutputJsonLogger {
public:
json json_output; json json_output;
GemmaEnv env(argc, argv);
gcpp::LayersOutputT layers_output_log_f = env.MutableConfig().layers_output =
[this](int pos, const std::string& key, const float* values, prompt_args.layers_output.Empty()
? LayersOutputFunc()
: [&json_output](int pos, const std::string& key, const float* values,
size_t values_len) { size_t values_len) {
std::vector<float> v{values, values + values_len}; std::vector<float> v{values, values + values_len};
json_output[std::to_string(pos)][key] = v; json_output[std::to_string(pos)][key] = v;
}; };
};
/* Run this in the same way as gemma, p.ex.: const auto [answer, token_count] = env.QueryModel(prompt_args.prompt);
./debug_prompt --tokenizer tokenizer.spm --model 2b-it --weights \ std::cout << answer.substr(prompt_args.prompt.size()) << "\n" << std::flush;
2b-it-sfp.sbs --prompt "..." --layers_output [path]
*/
int main(int argc, char** argv) {
{
// Placeholder for internal init, do not modify.
}
gcpp::LoaderArgs loader(argc, argv); if (env.MutableConfig().layers_output) {
gcpp::InferenceArgs args(argc, argv); // inference
gcpp::AppArgs app(argc, argv);
PromptArgs prompt_args(argc, argv);
if (const char* error = loader.Validate()) {
HWY_ABORT("\nInvalid loader args: %s", error);
}
if (const char* error = args.Validate()) {
HWY_ABORT("\nInvalid inference args: %s", error);
}
const bool log_layers_output = !prompt_args.layers_output.path.empty();
OutputJsonLogger json_logger;
gcpp::LayersOutputT* layers_output =
log_layers_output ? &json_logger.layers_output_log_f : nullptr;
hwy::ThreadPool pool(app.num_threads);
// For many-core, pinning workers to cores helps.
if (app.num_threads > 10) {
gcpp::PinWorkersToCores(pool);
}
gcpp::Gemma model = gcpp::CreateGemma(loader, pool);
gcpp::KVCache kv_cache = gcpp::KVCache::Create(loader.ModelType());
const std::string& prompt = prompt_args.prompt;
if (prompt.empty()) {
std::cout << "Please specify --prompt" << std::endl;
return EXIT_FAILURE;
}
const auto [answer, token_count] = QueryModel(
model, args, app, kv_cache, pool, prompt, layers_output);
std::cout << answer.substr(prompt.size()) << "\n" << std::flush;
if (log_layers_output) {
std::ofstream output_f(prompt_args.layers_output.path, std::ofstream::out); std::ofstream output_f(prompt_args.layers_output.path, std::ofstream::out);
if (!output_f) { if (!output_f) HWY_ABORT("Opening layer output file failed");
std::cout << "Opening file failed" << std::endl; output_f << json_output.dump();
return EXIT_FAILURE; if (!output_f) HWY_ABORT("Writing to layer output file failed");
}
output_f << json_logger.json_output.dump();
if (!output_f) {
std::cout << "Writing to file failed" << std::endl;
return EXIT_FAILURE;
}
output_f.close(); output_f.close();
} }
return 0;
return EXIT_SUCCESS;
} }
} // namespace gcpp
int main(int argc, char** argv) { return gcpp::Run(argc, argv); }

201
gemma/benchmark.cc
View File

@ -1,36 +1,36 @@
#include <stdio.h>
#include <algorithm> #include <algorithm>
#include <cstdlib> // EXIT_FAILURE #include <cstdlib> // EXIT_FAILURE
#include <fstream> #include <fstream>
#include <iostream> #include <iostream>
#include <ostream> #include <ostream>
#include <random>
#include <sstream>
#include <string> #include <string>
#include <utility> // std::pair #include <utility> // std::pair
#include <vector> #include <vector>
// Placeholder for internal header, do not modify.
#include "compression/io.h" // Path #include "compression/io.h" // Path
#include "gemma/benchmark_helper.h"
#include "gemma/common.h"
#include "gemma/cross_entropy.h" #include "gemma/cross_entropy.h"
#include "gemma/gemma.h" #include "gemma/gemma.h"
#include "util/app.h"
#include "util/args.h" #include "util/args.h"
#include "hwy/base.h" #include "hwy/base.h"
#include "hwy/contrib/thread_pool/thread_pool.h"
#include "hwy/highway.h"
#include "hwy/timer.h" #include "hwy/timer.h"
#include "nlohmann/json.hpp" #include "nlohmann/json.hpp"
namespace gcpp {
using json = nlohmann::json; using json = nlohmann::json;
class BenchmarkArgs : public gcpp::ArgsBase<BenchmarkArgs> { class BenchmarkArgs : public ArgsBase<BenchmarkArgs> {
public: public:
BenchmarkArgs(int argc, char* argv[]) { InitAndParse(argc, argv); } BenchmarkArgs(int argc, char* argv[]) { InitAndParse(argc, argv); }
gcpp::Path goldens; Path goldens;
gcpp::Path summarize_text; Path summarize_text;
gcpp::Path cross_entropy; Path cross_entropy;
gcpp::Path trivia_qa; Path trivia_qa;
size_t max_questions; size_t max_questions;
size_t batch_tokens; size_t batch_tokens;
@ -53,61 +53,6 @@ class BenchmarkArgs : public gcpp::ArgsBase<BenchmarkArgs> {
} }
}; };
void LogSpeedStats(const double time_start, size_t total_tokens) {
const double time_end = hwy::platform::Now();
const double time_elapsed = time_end - time_start;
const double tok_sec = total_tokens / time_elapsed;
std::cout << total_tokens << " tokens in " << time_elapsed << " seconds"
<< " [" << tok_sec << " tokens / sec" << "]\n";
}
std::pair<std::string, int> QueryModel(
gcpp::Gemma& model, gcpp::InferenceArgs& args, gcpp::AppArgs& app,
gcpp::KVCache& kv_cache, hwy::ThreadPool& pool, const std::string& input) {
std::vector<int> prompt;
HWY_ASSERT(model.Tokenizer().Encode(input, &prompt));
// For both pre-trained and instruction-tuned models: prepend "<bos>" token
// if needed.
prompt.insert(prompt.begin(), 2);
std::string res;
size_t total_tokens = 0;
std::mt19937 gen;
gen.seed(42);
const double time_start = hwy::platform::Now();
auto stream_token = [&res, &total_tokens, &time_start, &app, &model](
int token, float) {
++total_tokens;
std::string token_text;
HWY_ASSERT(model.Tokenizer().Decode(std::vector<int>{token}, &token_text));
res += token_text;
if (app.verbosity >= 1 && total_tokens % 100 == 0) {
LogSpeedStats(time_start, total_tokens);
}
return true;
};
if (app.verbosity >= 2) {
std::cout << args.max_tokens << " " << args.max_generated_tokens << " "
<< args.temperature;
}
gcpp::TimingInfo timing_info;
gcpp::RuntimeConfig runtime_config = {
.max_tokens = args.max_tokens,
.max_generated_tokens = args.max_generated_tokens,
.temperature = args.temperature,
.verbosity = app.verbosity,
.gen = &gen,
.stream_token = stream_token,
};
model.Generate(runtime_config, prompt, /*start_pos=*/0, kv_cache, timing_info,
/*layers_output=*/nullptr);
if (app.verbosity >= 1) {
LogSpeedStats(time_start, total_tokens);
}
return {res, total_tokens};
}
std::vector<std::pair<std::string, std::string>> load_goldens( std::vector<std::pair<std::string, std::string>> load_goldens(
const std::string& path) { const std::string& path) {
std::ifstream goldens_file(path); std::ifstream goldens_file(path);
@ -129,28 +74,14 @@ std::vector<std::pair<std::string, std::string>> load_goldens(
return res; return res;
} }
std::string ReadFile(const gcpp::Path& path) { int BenchmarkGoldens(GemmaEnv& env, const std::string& golden_path) {
std::ifstream text_file(path.path); std::vector<std::pair<std::string, std::string>> queries_answers =
if (!text_file) {
std::cout << "Could not open file: " << path.path << "\n" << std::flush;
return {};
}
std::stringstream buffer;
buffer << text_file.rdbuf();
return buffer.str();
}
int BenchmarkGoldens(gcpp::Gemma& model, gcpp::InferenceArgs& args,
gcpp::AppArgs& app, gcpp::KVCache& kv_cache,
hwy::ThreadPool& pool, const std::string& golden_path) {
const std::vector<std::pair<std::string, std::string>> queries_answers =
load_goldens(golden_path); load_goldens(golden_path);
int correct_answers = 0; size_t correct_answers = 0;
int total_tokens = 0; size_t total_tokens = 0;
const double time_start = hwy::platform::Now(); const double time_start = hwy::platform::Now();
for (const auto& [question, expected_answer] : queries_answers) { for (auto& [question, expected_answer] : queries_answers) {
const auto [answer, token_count] = const auto [answer, token_count] = env.QueryModel(question);
QueryModel(model, args, app, kv_cache, pool, question);
total_tokens += token_count; total_tokens += token_count;
if (answer.find(expected_answer) != std::string::npos) { if (answer.find(expected_answer) != std::string::npos) {
correct_answers++; correct_answers++;
@ -172,28 +103,22 @@ int BenchmarkGoldens(gcpp::Gemma& model, gcpp::InferenceArgs& args,
return EXIT_SUCCESS; return EXIT_SUCCESS;
} }
int BenchmarkSummary(gcpp::Gemma& model, gcpp::InferenceArgs& args, int BenchmarkSummary(GemmaEnv& env, const Path& text) {
gcpp::AppArgs& app, gcpp::KVCache& kv_cache,
hwy::ThreadPool& pool, const gcpp::Path& text) {
std::string prompt("Here is some text to summarize:\n"); std::string prompt("Here is some text to summarize:\n");
prompt.append(ReadFile(text)); prompt.append(ReadFileToString(text));
prompt.append("\nSummarize this text.\n"); prompt.append("\nSummarize this text.\n");
const double time_start = hwy::platform::Now(); const double time_start = hwy::platform::Now();
const auto [answer, token_count] = const auto [answer, token_count] = env.QueryModel(prompt);
QueryModel(model, args, app, kv_cache, pool, prompt);
std::cout << answer.substr(prompt.size()) << "\n" << std::flush; std::cout << answer.substr(prompt.size()) << "\n" << std::flush;
LogSpeedStats(time_start, token_count); LogSpeedStats(time_start, token_count);
return EXIT_SUCCESS; return EXIT_SUCCESS;
} }
int BenchmarkCrossEntropy(gcpp::Gemma& model, gcpp::Model model_type, int BenchmarkCrossEntropy(GemmaEnv& env, const Path& text,
gcpp::InferenceArgs& args, gcpp::AppArgs& app,
hwy::ThreadPool& pool, const gcpp::Path& text,
size_t batch_tokens) { size_t batch_tokens) {
std::string input = ReadFile(text); std::string input = ReadFileToString(text);
std::vector<int> prompt; std::vector<int> prompt = env.Tokenize(input);
HWY_ASSERT(model.Tokenizer().Encode(input, &prompt)); prompt.resize(std::min<size_t>(env.MaxTokens(), prompt.size()));
prompt.resize(std::min<size_t>(args.max_tokens, prompt.size()));
std::cout << "Number of input tokens: " << prompt.size() << "\n"; std::cout << "Number of input tokens: " << prompt.size() << "\n";
const double time_start = hwy::platform::Now(); const double time_start = hwy::platform::Now();
float total_entropy = 0.0f; float total_entropy = 0.0f;
@ -203,13 +128,12 @@ int BenchmarkCrossEntropy(gcpp::Gemma& model, gcpp::Model model_type,
size_t num_tokens = std::min<size_t>(prompt.size() - pos, batch_tokens); size_t num_tokens = std::min<size_t>(prompt.size() - pos, batch_tokens);
std::vector<int> prompt_slice(prompt.begin() + pos, std::vector<int> prompt_slice(prompt.begin() + pos,
prompt.begin() + pos + num_tokens); prompt.begin() + pos + num_tokens);
gcpp::KVCache kv_cache = gcpp::KVCache::Create(model_type); KVCache kv_cache = KVCache::Create(env.ModelType());
float entropy = ComputeCrossEntropy(model, num_tokens, prompt_slice, float entropy = ComputeCrossEntropy(
kv_cache, app.verbosity); *env.GetModel(), num_tokens, prompt_slice, kv_cache, env.Verbosity());
total_entropy += entropy; total_entropy += entropy;
LogSpeedStats(time_start, pos + num_tokens); LogSpeedStats(time_start, pos + num_tokens);
std::string text_slice; std::string text_slice = env.StringFromTokens(prompt_slice);
HWY_ASSERT(model.Tokenizer().Decode(prompt_slice, &text_slice));
total_input_len += text_slice.size(); total_input_len += text_slice.size();
printf("Total cross entropy: %f [cumulative: %f]\n", printf("Total cross entropy: %f [cumulative: %f]\n",
entropy, total_entropy); entropy, total_entropy);
@ -219,23 +143,19 @@ int BenchmarkCrossEntropy(gcpp::Gemma& model, gcpp::Model model_type,
return EXIT_SUCCESS; return EXIT_SUCCESS;
} }
int BenchmarkTriviaQA(gcpp::Gemma& model, gcpp::InferenceArgs& args, int BenchmarkTriviaQA(GemmaEnv& env, const Path& json_file,
gcpp::AppArgs& app, gcpp::KVCache& kv_cache,
hwy::ThreadPool& pool, const gcpp::Path& json_file,
size_t max_questions) { size_t max_questions) {
std::ifstream trivia_file(json_file.path); std::ifstream trivia_file(json_file.path);
if (!trivia_file) { if (!trivia_file) {
std::cout << "Could not load file: " << json_file.path << "\n" HWY_ABORT("Could not load file %s\n", json_file.path.c_str());
<< std::flush;
return EXIT_FAILURE;
} }
std::string line; std::string line;
size_t correct_answers = 0; size_t correct_answers = 0;
size_t i = 0; size_t i = 0;
while (std::getline(trivia_file, line)) { while (std::getline(trivia_file, line)) {
json data = json::parse(line); json data = json::parse(line);
const auto [answer, token_count] = QueryModel( std::string q(data["question"]);
model, args, app, kv_cache, pool, data["question"]); const auto [answer, token_count] = env.QueryModel(q);
std::cout << answer << "\n"; std::cout << answer << "\n";
bool correct = false; bool correct = false;
for (const std::string expected : data["answer"]["aliases"]) { for (const std::string expected : data["answer"]["aliases"]) {
@ -256,52 +176,25 @@ int BenchmarkTriviaQA(gcpp::Gemma& model, gcpp::InferenceArgs& args,
return EXIT_SUCCESS; return EXIT_SUCCESS;
} }
/* Run this in the same way as gemma, p.ex.: } // namespace gcpp
./benchmark --tokenizer tokenizer.spm --model 2b-it --weights \
2b-it-sfp.sbs --goldens_dir "../goldens"
*/
int main(int argc, char** argv) { int main(int argc, char** argv) {
{ gcpp::GemmaEnv env(argc, argv);
// Placeholder for internal init, do not modify. gcpp::BenchmarkArgs benchmark_args(argc, argv);
}
gcpp::LoaderArgs loader(argc, argv); if (!benchmark_args.goldens.Empty()) {
gcpp::InferenceArgs args(argc, argv); // inference
gcpp::AppArgs app(argc, argv);
BenchmarkArgs benchmark_args(argc, argv);
if (const char* error = loader.Validate()) {
HWY_ABORT("\nInvalid loader args: %s", error);
}
if (const char* error = args.Validate()) {
HWY_ABORT("\nInvalid inference args: %s", error);
}
hwy::ThreadPool pool(app.num_threads);
// For many-core, pinning workers to cores helps.
if (app.num_threads > 10) {
gcpp::PinWorkersToCores(pool);
}
gcpp::Gemma model = gcpp::CreateGemma(loader, pool);
gcpp::KVCache kv_cache = gcpp::KVCache::Create(loader.ModelType());
if (!benchmark_args.goldens.path.empty()) {
const std::string golden_path = const std::string golden_path =
benchmark_args.goldens.path + "/" + loader.model_type_str + ".txt"; benchmark_args.goldens.path + "/" +
return BenchmarkGoldens(model, args, app, kv_cache, pool, golden_path); gcpp::ModelString(env.ModelType(), env.ModelTrainingType()) + ".txt";
} else if (!benchmark_args.summarize_text.path.empty()) { return BenchmarkGoldens(env, golden_path);
return BenchmarkSummary(model, args, app, kv_cache, pool, } else if (!benchmark_args.summarize_text.Empty()) {
benchmark_args.summarize_text); return BenchmarkSummary(env, benchmark_args.summarize_text);
} else if (!benchmark_args.cross_entropy.path.empty()) { } else if (!benchmark_args.cross_entropy.Empty()) {
return BenchmarkCrossEntropy(model, loader.ModelType(), args, app, return BenchmarkCrossEntropy(env, benchmark_args.cross_entropy,
pool, benchmark_args.cross_entropy,
benchmark_args.batch_tokens); benchmark_args.batch_tokens);
} else if (!benchmark_args.trivia_qa.path.empty()) { } else if (!benchmark_args.trivia_qa.Empty()) {
return BenchmarkTriviaQA(model, args, app, kv_cache, pool, return BenchmarkTriviaQA(env, benchmark_args.trivia_qa,
benchmark_args.trivia_qa,
benchmark_args.max_questions); benchmark_args.max_questions);
} }
std::cout << "No benchmark command given." << "\n" << std::flush; HWY_ABORT("No benchmark command given.");
return EXIT_FAILURE;
} }

162
gemma/benchmark_helper.cc
View File

@ -14,70 +14,95 @@
// limitations under the License. // limitations under the License.
#include "gemma/benchmark_helper.h" #include "gemma/benchmark_helper.h"
#include <cstdlib> // EXIT_FAILURE
#include <stdio.h>
#include <time.h>
#include <iostream> #include <iostream>
#include <memory> #include <memory>
#include <ostream> #include <ostream>
#include <random> #include <random>
#include <string> #include <string>
#include <thread> // NOLINT
#include <utility> // std::pair #include <utility> // std::pair
#include <vector> #include <vector>
#include "gemma/common.h" // Placeholder for internal header, do not modify.
#include "compression/compress.h" // TypeName
#include "gemma/common.h" // StringFromType
#include "gemma/cross_entropy.h"
#include "gemma/gemma.h" #include "gemma/gemma.h"
#include "util/app.h" #include "util/app.h"
#include "util/args.h"
#include "hwy/base.h" #include "hwy/base.h"
#include "hwy/contrib/thread_pool/thread_pool.h" #include "hwy/contrib/thread_pool/thread_pool.h"
#include "hwy/highway.h" #include "hwy/highway.h"
#include "hwy/per_target.h"
#include "hwy/timer.h" #include "hwy/timer.h"
namespace gcpp { namespace gcpp {
void InitGenerator(const InferenceArgs& inference, std::mt19937& gen) {
if (inference.deterministic) {
// Nothing up my sleeve number, at least some upper bits set.
gen.seed(0x12345678);
} else {
// Depending on the library implementation, this may still be deterministic.
std::random_device rd;
gen.seed(rd());
}
}
GemmaEnv::GemmaEnv(int argc, char** argv) GemmaEnv::GemmaEnv(int argc, char** argv)
: loader_(argc, argv), inference_args_(argc, argv), app_(argc, argv), : loader_(argc, argv),
inference_args_(argc, argv),
app_(argc, argv),
pool_(app_.num_threads) { pool_(app_.num_threads) {
if (const char* error = loader_.Validate()) { {
HWY_ABORT("\nInvalid loader args: %s", error); // Placeholder for internal init, do not modify.
}
if (const char* error = inference_args_.Validate()) {
HWY_ABORT("\nInvalid inference args: %s", error);
} }
// For many-core, pinning workers to cores helps. // For many-core, pinning workers to cores helps.
if (app_.num_threads > 10) { if (app_.num_threads > 10) {
gcpp::PinWorkersToCores(pool_); gcpp::PinWorkersToCores(pool_);
} }
AbortIfInvalidArgs(inference_args_);
if (const char* err = loader_.Validate()) {
loader_.Help();
fprintf(stderr, "Skipping model load because: %s\n", err);
} else {
fprintf(stderr, "Loading model...\n");
model_ = AllocateGemma(loader_, pool_); model_ = AllocateGemma(loader_, pool_);
kv_cache_ = KVCache::Create(loader_.ModelType()); kv_cache_ = KVCache::Create(loader_.ModelType());
gen_.seed(42);
} }
std::pair<std::string, int> GemmaEnv::QueryModel(const std::string& input) { InitGenerator(inference_args_, gen_);
std::string prompt_string = input;
if (loader_.ModelTrainingType() == ModelTraining::GEMMA_IT) {
// For instruction-tuned models: add control tokens.
prompt_string = "<start_of_turn>user\n" + input +
"<end_of_turn>\n<start_of_turn>model\n";
}
std::vector<int> prompt;
HWY_ASSERT(model_->Tokenizer().Encode(input, &prompt));
// For both pre-trained and instruction-tuned models: prepend "<bos>" token runtime_config_ = {
// if needed. .max_tokens = inference_args_.max_tokens,
prompt.insert(prompt.begin(), gcpp::BOS_ID); .max_generated_tokens = inference_args_.max_generated_tokens,
.temperature = inference_args_.temperature,
.verbosity = app_.verbosity,
.gen = &gen_,
};
}
std::pair<std::string, size_t> GemmaEnv::QueryModel(
const std::vector<int>& tokens) {
std::string res; std::string res;
size_t total_tokens = 0; size_t total_tokens = 0;
auto accept_token = [](int) { return true; };
std::mt19937 gen;
gen.seed(42);
const double time_start = hwy::platform::Now(); const double time_start = hwy::platform::Now();
auto stream_token = [&res, &total_tokens, &time_start, this]( const StreamFunc stream_token = [&res, &total_tokens, &time_start, this](
int token, float) { int token, float) {
++total_tokens; ++total_tokens;
std::string token_text; std::string token_text;
HWY_ASSERT(model_->Tokenizer().Decode(std::vector<int>{token}, HWY_ASSERT(
&token_text)); model_->Tokenizer().Decode(std::vector<int>{token}, &token_text));
res += token_text; res += token_text;
if (app_.verbosity >= 1 && total_tokens % 100 == 0) { if (app_.verbosity >= 1 && total_tokens % 128 == 0) {
LogSpeedStats(time_start, total_tokens); LogSpeedStats(time_start, total_tokens);
} }
return true; return true;
@ -88,24 +113,32 @@ std::pair<std::string, int> GemmaEnv::QueryModel(const std::string& input) {
<< inference_args_.temperature; << inference_args_.temperature;
} }
gcpp::TimingInfo timing_info; gcpp::TimingInfo timing_info;
gcpp::RuntimeConfig runtime_config = { runtime_config_.stream_token = stream_token;
.max_tokens = inference_args_.max_tokens, model_->Generate(runtime_config_, tokens, /*start_pos=*/0, kv_cache_,
.max_generated_tokens = inference_args_.max_generated_tokens, timing_info);
.temperature = inference_args_.temperature,
.verbosity = app_.verbosity,
.gen = &gen,
.stream_token = stream_token,
.accept_token = accept_token,
};
model_->Generate(runtime_config, prompt, /*start_pos=*/0, kv_cache_,
timing_info, /*layers_output=*/nullptr);
if (app_.verbosity >= 1) { if (app_.verbosity >= 1) {
LogSpeedStats(time_start, total_tokens); LogSpeedStats(time_start, total_tokens);
} }
return {res, total_tokens}; return {res, total_tokens};
} }
void GemmaEnv::LogSpeedStats(double time_start, size_t total_tokens) const { std::pair<std::string, size_t> GemmaEnv::QueryModel(std::string& input) {
const std::vector<int> prompt =
WrapAndTokenize(model_->Tokenizer(), loader_.ModelTrainingType(),
/*pos=*/0, input);
return QueryModel(prompt);
}
float GemmaEnv::CrossEntropy(const std::string& input) {
std::vector<int> prompt = Tokenize(input);
prompt.insert(prompt.begin(), BOS_ID);
return ComputeCrossEntropy(*GetModel(), /*max_tokens=*/3072, prompt,
MutableKVCache(),
/*verbosity=*/0) /
static_cast<int>(input.size());
}
void LogSpeedStats(double time_start, size_t total_tokens) {
const double time_end = hwy::platform::Now(); const double time_end = hwy::platform::Now();
const double time_elapsed = time_end - time_start; const double time_elapsed = time_end - time_start;
const double tok_sec = total_tokens / time_elapsed; const double tok_sec = total_tokens / time_elapsed;
@ -113,6 +146,53 @@ void GemmaEnv::LogSpeedStats(double time_start, size_t total_tokens) const {
<< " [" << tok_sec << " tokens / sec" << "]\n"; << " [" << tok_sec << " tokens / sec" << "]\n";
} }
void ShowConfig(LoaderArgs& loader, InferenceArgs& inference, AppArgs& app) {
loader.Print(app.verbosity);
inference.Print(app.verbosity);
app.Print(app.verbosity);
if (app.verbosity >= 2) {
time_t now = time(nullptr);
char* dt = ctime(&now); // NOLINT
std::cout << "Date & Time : " << dt
<< "Prefill Token Batch Size : " << gcpp::kPrefillBatchSize
<< "\n"
<< "Hardware concurrency : "
<< std::thread::hardware_concurrency() << "\n"
<< "Instruction set : "
<< hwy::TargetName(hwy::DispatchedTarget()) << " ("
<< hwy::VectorBytes() * 8 << " bits)" << "\n";
char cpu100[100];
if (hwy::platform::GetCpuString(cpu100)) {
std::cout << "CPU : " << cpu100 << "\n";
}
std::cout << "Compiled config : " << CompiledConfig() << "\n"
<< "Weight Type : "
<< gcpp::StringFromType(loader.WeightType()) << "\n"
<< "EmbedderInput Type : "
<< gcpp::TypeName(gcpp::EmbedderInputT()) << "\n";
}
}
void ShowHelp(gcpp::LoaderArgs& loader, gcpp::InferenceArgs& inference,
gcpp::AppArgs& app) {
std::cerr
<< "\n\ngemma.cpp : a lightweight, standalone C++ inference engine\n"
"==========================================================\n\n"
"To run gemma.cpp, you need to "
"specify 3 required model loading arguments:\n"
" --tokenizer\n"
" --weights\n"
" --model.\n";
std::cerr << "\n*Example Usage*\n\n./gemma --tokenizer tokenizer.spm "
"--weights 2b-it-sfp.sbs --model 2b-it\n";
std::cerr << "\n*Model Loading Arguments*\n\n";
loader.Help();
std::cerr << "\n*Inference Arguments*\n\n";
inference.Help();
std::cerr << "\n*Application Arguments*\n\n";
app.Help();
std::cerr << "\n";
}
} // namespace gcpp } // namespace gcpp

60
gemma/benchmark_helper.h
View File

@ -16,11 +16,15 @@
#ifndef THIRD_PARTY_GEMMA_CPP_GEMMA_BENCHMARK_HELPER_H_ #ifndef THIRD_PARTY_GEMMA_CPP_GEMMA_BENCHMARK_HELPER_H_
#define THIRD_PARTY_GEMMA_CPP_GEMMA_BENCHMARK_HELPER_H_ #define THIRD_PARTY_GEMMA_CPP_GEMMA_BENCHMARK_HELPER_H_
#include <stddef.h>
#include <memory> #include <memory>
#include <random> #include <random>
#include <string> #include <string>
#include <utility> #include <utility>
#include <vector>
#include "gemma/common.h"
#include "gemma/gemma.h" #include "gemma/gemma.h"
#include "util/app.h" #include "util/app.h"
#include "hwy/base.h" #include "hwy/base.h"
@ -28,24 +32,60 @@
namespace gcpp { namespace gcpp {
void InitGenerator(const InferenceArgs& inference, std::mt19937& gen);
// Convenience class to load a model and run inference. // Convenience class to load a model and run inference.
class GemmaEnv { class GemmaEnv {
public: public:
GemmaEnv(int argc, char** argv); GemmaEnv(int argc, char** argv);
size_t MaxTokens() const { return inference_args_.max_tokens; }
// Sets the maximum number of output tokens to generate. // Sets the maximum number of output tokens to generate.
void set_max_generated_tokens(int max_tokens) { void SetMaxGeneratedTokens(size_t max_tokens) {
inference_args_.max_generated_tokens = max_tokens; inference_args_.max_generated_tokens = max_tokens;
} }
std::vector<int> Tokenize(const std::string& input) const {
std::vector<int> tokens;
HWY_ASSERT(model_->Tokenizer().Encode(input, &tokens));
return tokens;
}
std::vector<int> TokenizeAndPrependBOS(const std::string& input) const {
std::vector<int> tokens = Tokenize(input);
tokens.insert(tokens.begin(), BOS_ID);
return tokens;
}
std::string StringFromTokens(const std::vector<int>& tokens) const {
std::string string;
HWY_ASSERT(model_->Tokenizer().Decode(tokens, &string));
return string;
}
// Runs inference on the given input and returns the top-1 result string and // Runs inference on the given input and returns the top-1 result string and
// the number of tokens that were generated. // the number of tokens that were generated.
std::pair<std::string, int> QueryModel(const std::string& input); std::pair<std::string, size_t> QueryModel(const std::vector<int>& tokens);
// Adds turn structure to input, tokenizes and calls the above overload.
std::pair<std::string, size_t> QueryModel(std::string& input);
// Runs inference on the given input and returns the cross entropy, a measure
// of how well the model predicts the correct output. It is the average
// number of bits per token.
float CrossEntropy(const std::string& input);
// Returns nullptr if the model failed to load.
Gemma* GetModel() const { return model_.get(); }
Model ModelType() const { return loader_.ModelType(); }
ModelTraining ModelTrainingType() const {
return loader_.ModelTrainingType();
}
int Verbosity() const { return app_.verbosity; }
gcpp::RuntimeConfig& MutableConfig() { return runtime_config_; }
std::mt19937& MutableGen() { return gen_; }
KVCache& MutableKVCache() { return kv_cache_; }
private: private:
// Logs the inference speed in tokens/sec.
void LogSpeedStats(double time_start, size_t total_tokens) const;
// Arguments to the model loader: file locations, etc. // Arguments to the model loader: file locations, etc.
LoaderArgs loader_; LoaderArgs loader_;
// Arguments to the inference function: max tokens, etc. // Arguments to the inference function: max tokens, etc.
@ -60,10 +100,16 @@ class GemmaEnv {
std::unique_ptr<Gemma> model_; std::unique_ptr<Gemma> model_;
// The KV cache to use for inference. // The KV cache to use for inference.
KVCache kv_cache_; KVCache kv_cache_;
gcpp::RuntimeConfig runtime_config_;
}; };
// Logs the inference speed in tokens/sec.
void LogSpeedStats(double time_start, size_t total_tokens);
void ShowConfig(LoaderArgs& loader, InferenceArgs& inference, AppArgs& app);
void ShowHelp(gcpp::LoaderArgs& loader, gcpp::InferenceArgs& inference,
gcpp::AppArgs& app);
} // namespace gcpp } // namespace gcpp
#endif // THIRD_PARTY_GEMMA_CPP_GEMMA_BENCHMARK_HELPER_H_ #endif // THIRD_PARTY_GEMMA_CPP_GEMMA_BENCHMARK_HELPER_H_

83
gemma/benchmarks.cc
View File

@ -13,86 +13,60 @@
// See the License for the specific language governing permissions and // See the License for the specific language governing permissions and
// limitations under the License. // limitations under the License.
#include <fstream> #include <stddef.h>
#include <iostream> #include <stdio.h>
#include <ostream>
#include <random>
#include <sstream>
#include <string> #include <string>
// Placeholder for internal header, do not modify.
#include "benchmark/benchmark.h" #include "benchmark/benchmark.h"
#include "gemma/benchmark_helper.h" #include "gemma/benchmark_helper.h"
void run_gemma_prompt(const std::string& prompt_string, namespace gcpp {
gcpp::GemmaEnv& env,
benchmark::State& state) {
std::mt19937 gen;
if (prompt_string.empty()) return; // Shared state for benchmarks - unfortunately the library does not allow
// passing context nor closures. Raw pointer because style guide forbids
// non-local static objects with dtors.t
GemmaEnv* s_env = nullptr;
int token_counter = 0; void RunPrompt(const std::string& original_prompt, benchmark::State& state) {
size_t total_tokens = 0;
for (auto s : state) { for (auto s : state) {
auto [response, n] = env.QueryModel(prompt_string); std::string prompt = original_prompt; // reset from original
std::cout << "response: " << response << "\n"; auto [response, n] = s_env->QueryModel(prompt);
std::cout << "n: " << n << "\n"; if (s_env->Verbosity() != 0) {
token_counter += n; fprintf(stdout, "|%s|\n", response.c_str());
}
total_tokens += n;
} }
state.SetItemsProcessed(token_counter); state.SetItemsProcessed(total_tokens);
} }
// Awkward global because benchmarks don't support additional state, so it is } // namespace gcpp
// either this or cast to int64_t.
gcpp::GemmaEnv* global_env = nullptr;
static void BM_short_prompt(benchmark::State& state) { static void BM_short_prompt(benchmark::State& state) {
run_gemma_prompt("What is the capital of Spain?", *global_env, gcpp::RunPrompt("What is the capital of Spain?", state);
state);
} }
static void BM_factuality_prompt(benchmark::State& state) { static void BM_factuality_prompt(benchmark::State& state) {
run_gemma_prompt("How does an inkjet printer work?", gcpp::RunPrompt("How does an inkjet printer work?", state);
*global_env, state);
} }
static void BM_creative_prompt(benchmark::State& state) { static void BM_creative_prompt(benchmark::State& state) {
run_gemma_prompt( gcpp::RunPrompt("Tell me a story about a magical bunny and their TRS-80.",
"Tell me a story about a magical bunny and their TRS-80.", state);
*global_env, state);
} }
static void BM_coding_prompt(benchmark::State& state) { static void BM_coding_prompt(benchmark::State& state) {
run_gemma_prompt( gcpp::RunPrompt("Write a python program to generate a fibonacci sequence.",
"Write a python program to generate a fibonacci sequence.", state);
*global_env, state);
} }
static void BM_long_coding_prompt(benchmark::State& state) {
std::ifstream t("benchmarks.cc", std::ios_base::in);
std::stringstream buffer;
buffer << t.rdbuf();
std::string prompt_string = buffer.str();
t.close();
run_gemma_prompt("Make improvements to the following code:\n " +
prompt_string, *global_env, state);
}
int main(int argc, char** argv) {
{
// Placeholder for internal init, do not modify.
}
gcpp::GemmaEnv env(argc, argv);
env.set_max_generated_tokens(128);
global_env = &env;
BENCHMARK(BM_short_prompt) BENCHMARK(BM_short_prompt)
->Iterations(3) ->Iterations(3)
->Unit(benchmark::kMillisecond) ->Unit(benchmark::kMillisecond)
->UseRealTime(); ->UseRealTime();
env.set_max_generated_tokens(256);
BENCHMARK(BM_factuality_prompt) BENCHMARK(BM_factuality_prompt)
->Iterations(3) ->Iterations(3)
->Unit(benchmark::kMillisecond) ->Unit(benchmark::kMillisecond)
@ -108,11 +82,10 @@ int main(int argc, char** argv) {
->Unit(benchmark::kMillisecond) ->Unit(benchmark::kMillisecond)
->UseRealTime(); ->UseRealTime();
env.set_max_generated_tokens(1024); int main(int argc, char** argv) {
BENCHMARK(BM_long_coding_prompt) gcpp::GemmaEnv env(argc, argv);
->Iterations(3) env.SetMaxGeneratedTokens(256);
->Unit(benchmark::kMillisecond) gcpp::s_env = &env;
->UseRealTime();
::benchmark::RunSpecifiedBenchmarks(); ::benchmark::RunSpecifiedBenchmarks();
::benchmark::Shutdown(); ::benchmark::Shutdown();

41
gemma/common.cc
View File

@ -28,7 +28,8 @@
namespace gcpp { namespace gcpp {
namespace { const char* ParseModelTypeAndTraining(const std::string& model_flag,
Model& model, ModelTraining& training) {
constexpr const char* kModelFlags[] = {"2b-pt", "7b-pt", "gr2b-pt", "2b-it", constexpr const char* kModelFlags[] = {"2b-pt", "7b-pt", "gr2b-pt", "2b-it",
"7b-it", "gr2b-it", "tiny"}; "7b-it", "gr2b-it", "tiny"};
constexpr Model kModelTypes[] = { constexpr Model kModelTypes[] = {
@ -38,10 +39,7 @@ constexpr ModelTraining kModelTraining[] = {
ModelTraining::GEMMA_PT, ModelTraining::GEMMA_PT, ModelTraining::GEMMA_PT, ModelTraining::GEMMA_PT, ModelTraining::GEMMA_PT, ModelTraining::GEMMA_PT,
ModelTraining::GEMMA_IT, ModelTraining::GEMMA_IT, ModelTraining::GEMMA_IT, ModelTraining::GEMMA_IT, ModelTraining::GEMMA_IT, ModelTraining::GEMMA_IT,
ModelTraining::GEMMA_IT}; ModelTraining::GEMMA_IT};
} // namespace
const char* ParseModelTypeAndTraining(const std::string& model_flag,
Model& model, ModelTraining& training) {
constexpr size_t kNum = std::end(kModelFlags) - std::begin(kModelFlags); constexpr size_t kNum = std::end(kModelFlags) - std::begin(kModelFlags);
static char kErrorMessageBuffer[kNum * 8 + 1024] = static char kErrorMessageBuffer[kNum * 8 + 1024] =
"Invalid or missing model flag, need to specify one of "; "Invalid or missing model flag, need to specify one of ";
@ -51,37 +49,58 @@ const char* ParseModelTypeAndTraining(const std::string& model_flag,
} }
strcat(kErrorMessageBuffer, kModelFlags[kNum - 1]); // NOLINT strcat(kErrorMessageBuffer, kModelFlags[kNum - 1]); // NOLINT
strcat(kErrorMessageBuffer, "."); // NOLINT strcat(kErrorMessageBuffer, "."); // NOLINT
std::string model_type_lc = model_flag; std::string model_type_lc = model_flag;
std::transform(begin(model_type_lc), end(model_type_lc), begin(model_type_lc), std::transform(begin(model_type_lc), end(model_type_lc), begin(model_type_lc),
[](unsigned char c) { return std::tolower(c); }); [](unsigned char c) { return std::tolower(c); });
for (size_t i = 0; i < kNum; i++) { for (size_t i = 0; i < kNum; i++) {
if (kModelFlags[i] == model_type_lc) { if (kModelFlags[i] == model_type_lc) {
model = kModelTypes[i]; model = kModelTypes[i];
training = kModelTraining[i]; training = kModelTraining[i];
HWY_ASSERT(std::string(ModelString(model, training)) == model_type_lc);
return nullptr; return nullptr;
} }
} }
return kErrorMessageBuffer; return kErrorMessageBuffer;
} }
const char* ModelString(Model model, ModelTraining training) {
if (model == Model::GEMMA_TINY) return "tiny";
static_assert(static_cast<size_t>(ModelTraining::GEMMA_IT) == 0);
constexpr const char* k2B[] = {"2b-it", "2b-pt"};
constexpr const char* k7B[] = {"7b-it", "7b-pt"};
constexpr const char* kGr2B[] = {"gr2b-it", "gr2b-pt"};
if (model == Model::GEMMA_2B) return k2B[static_cast<size_t>(training)];
if (model == Model::GEMMA_7B) return k7B[static_cast<size_t>(training)];
if (model == Model::GRIFFIN_2B) return kGr2B[static_cast<size_t>(training)];
HWY_ABORT("Unknown model %d training %d\n", static_cast<int>(model),
static_cast<int>(training));
}
constexpr const char* kTypeStrings[] = {"f32", "bf16", "sfp"};
const char* StringFromType(Type type) {
return kTypeStrings[static_cast<size_t>(type)];
}
const char* ParseType(const std::string& type_string, Type& type) { const char* ParseType(const std::string& type_string, Type& type) {
constexpr Type kTypes[] = {Type::kF32, Type::kBF16, Type::kSFP}; constexpr size_t kNum = std::end(kTypeStrings) - std::begin(kTypeStrings);
constexpr const char* kStrings[] = {"f32", "bf16", "sfp"};
constexpr size_t kNum = std::end(kStrings) - std::begin(kStrings);
static char kErrorMessageBuffer[kNum * 8 + 100] = static char kErrorMessageBuffer[kNum * 8 + 100] =
"Invalid or missing type, need to specify one of "; "Invalid or missing type, need to specify one of ";
for (size_t i = 0; i + 1 < kNum; i++) { for (size_t i = 0; i + 1 < kNum; i++) {
strcat(kErrorMessageBuffer, kStrings[i]); // NOLINT strcat(kErrorMessageBuffer, kTypeStrings[i]); // NOLINT
strcat(kErrorMessageBuffer, ", "); // NOLINT strcat(kErrorMessageBuffer, ", "); // NOLINT
} }
strcat(kErrorMessageBuffer, kStrings[kNum - 1]); // NOLINT strcat(kErrorMessageBuffer, kTypeStrings[kNum - 1]); // NOLINT
strcat(kErrorMessageBuffer, "."); // NOLINT strcat(kErrorMessageBuffer, "."); // NOLINT
std::string type_lc = type_string; std::string type_lc = type_string;
std::transform(begin(type_lc), end(type_lc), begin(type_lc), std::transform(begin(type_lc), end(type_lc), begin(type_lc),
[](unsigned char c) { return std::tolower(c); }); [](unsigned char c) { return std::tolower(c); });
for (size_t i = 0; i < kNum; i++) { for (size_t i = 0; i < kNum; i++) {
if (kStrings[i] == type_lc) { if (kTypeStrings[i] == type_lc) {
type = kTypes[i]; type = static_cast<Type>(i);
HWY_ASSERT(std::string(StringFromType(type)) == type_lc);
return nullptr; return nullptr;
} }
} }

18
gemma/common.h
View File

@ -154,18 +154,12 @@ const char* ParseModelTypeAndTraining(const std::string& model_flag,
Model& model, ModelTraining& training); Model& model, ModelTraining& training);
const char* ParseType(const std::string& type_string, Type& type); const char* ParseType(const std::string& type_string, Type& type);
static inline const char* StringFromType(Type type) { // Inverse of ParseModelTypeAndTraining.
switch (type) { const char* ModelString(Model model, ModelTraining training);
case Type::kF32: const char* StringFromType(Type type);
return "f32";
case Type::kBF16: // ----------------------------------------------------------------------------
return "bf16"; //
case Type::kSFP:
return "sfp";
default:
return "?";
}
}
// __builtin_sqrt is not constexpr as of Clang 17. // __builtin_sqrt is not constexpr as of Clang 17.
#if HWY_COMPILER_GCC_ACTUAL #if HWY_COMPILER_GCC_ACTUAL

2
gemma/cross_entropy.cc
View File

@ -111,7 +111,7 @@ float ComputeCrossEntropy(Gemma& gemma, size_t max_tokens,
}; };
TimingInfo timing_info; TimingInfo timing_info;
gemma.Generate(runtime, prompt0, 0, kv_cache, timing_info, nullptr); gemma.Generate(runtime, prompt0, 0, kv_cache, timing_info);
const float scale = 1.0f / std::log(2.0f); const float scale = 1.0f / std::log(2.0f);
return cross_entropy * scale; return cross_entropy * scale;

54
gemma/gemma.cc
View File

@ -17,7 +17,6 @@
// Compiles this file for multiple architectures via "foreach_target.h", to // Compiles this file for multiple architectures via "foreach_target.h", to
// which we pass the filename via macro 'argument'. // which we pass the filename via macro 'argument'.
#include <cstdio>
#undef HWY_TARGET_INCLUDE #undef HWY_TARGET_INCLUDE
#define HWY_TARGET_INCLUDE "gemma/gemma.cc" // NOLINT #define HWY_TARGET_INCLUDE "gemma/gemma.cc" // NOLINT
#include "hwy/foreach_target.h" // IWYU pragma: keep #include "hwy/foreach_target.h" // IWYU pragma: keep
@ -208,8 +207,8 @@ bool GemmaTokenizer::Encode(const std::string& input,
} }
bool GemmaTokenizer::Encode(const std::string& input, bool GemmaTokenizer::Encode(const std::string& input,
std::vector<int>* pieces) const { std::vector<int>* ids) const {
return impl_->Encode(input, pieces); return impl_->Encode(input, ids);
} }
// Given a sequence of ids, decodes it into a detokenized output. // Given a sequence of ids, decodes it into a detokenized output.
@ -653,12 +652,12 @@ HWY_NOINLINE void Transformer(const int *tokens, size_t num_tokens, size_t pos,
const WeightArrayT& weights, const WeightArrayT& weights,
Activations<TConfig, kBatchSize>& activations, Activations<TConfig, kBatchSize>& activations,
KVCache& kv_cache, hwy::ThreadPool& pool, KVCache& kv_cache, hwy::ThreadPool& pool,
LayersOutputT* layers_output) { const LayersOutputFunc& layers_output) {
HWY_ASSERT(num_tokens <= kBatchSize); HWY_ASSERT(num_tokens <= kBatchSize);
if (layers_output != nullptr) { if (layers_output) {
for (size_t token_idx = 0; token_idx < num_tokens; ++token_idx) { for (size_t token_idx = 0; token_idx < num_tokens; ++token_idx) {
float token_f = tokens[token_idx]; float token_f = tokens[token_idx];
(*layers_output)(pos + token_idx, "Tokens", &token_f, 1); layers_output(pos + token_idx, "Tokens", &token_f, 1);
} }
} }
static constexpr size_t kModelDim = TConfig::kModelDim; static constexpr size_t kModelDim = TConfig::kModelDim;
@ -713,12 +712,11 @@ HWY_NOINLINE void Transformer(const int *tokens, size_t num_tokens, size_t pos,
} }
AddFromBatched<kBatchSize>(num_tokens, activations.ffw_out.data(), AddFromBatched<kBatchSize>(num_tokens, activations.ffw_out.data(),
activations.x.data(), kModelDim); activations.x.data(), kModelDim);
if (layers_output != nullptr) { if (layers_output) {
std::string block_name = "blocks." + std::to_string(layer); std::string block_name = "blocks." + std::to_string(layer);
for (size_t token_idx = 0; token_idx < num_tokens; ++token_idx) { for (size_t token_idx = 0; token_idx < num_tokens; ++token_idx) {
(*layers_output)(pos + token_idx, block_name, layers_output(pos + token_idx, block_name,
activations.x.data() + token_idx * kModelDim, activations.x.data() + token_idx * kModelDim, kModelDim);
kModelDim);
} }
} }
} }
@ -727,9 +725,9 @@ HWY_NOINLINE void Transformer(const int *tokens, size_t num_tokens, size_t pos,
RMSNormInplaceBatched<kBatchSize>(num_tokens, weights.final_norm_scale.data(), RMSNormInplaceBatched<kBatchSize>(num_tokens, weights.final_norm_scale.data(),
activations.x.data(), kModelDim); activations.x.data(), kModelDim);
if (layers_output != nullptr) { if (layers_output) {
for (size_t token_idx = 0; token_idx < num_tokens; ++token_idx) { for (size_t token_idx = 0; token_idx < num_tokens; ++token_idx) {
(*layers_output)(pos + token_idx, "final_norm", layers_output(pos + token_idx, "final_norm",
activations.x.data() + token_idx * kModelDim, kModelDim); activations.x.data() + token_idx * kModelDim, kModelDim);
} }
} }
@ -782,8 +780,7 @@ void GenerateT(const ByteStorageT& weights_u8, const ByteStorageT& prefill_u8,
const ByteStorageT& decode_u8, const ByteStorageT& decode_u8,
const RuntimeConfig& runtime_config, const RuntimeConfig& runtime_config,
const std::vector<int>& prompt, size_t pos, KVCache& kv_cache, const std::vector<int>& prompt, size_t pos, KVCache& kv_cache,
hwy::ThreadPool& pool, TimingInfo& timing_info, hwy::ThreadPool& pool, TimingInfo& timing_info) {
LayersOutputT* layers_output) {
const CompressedWeights<TConfig>& weights = GetWeights<TConfig>(weights_u8); const CompressedWeights<TConfig>& weights = GetWeights<TConfig>(weights_u8);
auto& prefill_activations = auto& prefill_activations =
GetActivations<TConfig, kPrefillBatchSize>(prefill_u8); GetActivations<TConfig, kPrefillBatchSize>(prefill_u8);
@ -860,7 +857,8 @@ void GenerateT(const ByteStorageT& weights_u8, const ByteStorageT& prefill_u8,
pos < max_tokens && generate_pos < max_generated_tokens; pos < max_tokens && generate_pos < max_generated_tokens;
++pos, ++pos_offset, ++generate_pos) { ++pos, ++pos_offset, ++generate_pos) {
Transformer<kDecodeBatchSize>(&token, kDecodeBatchSize, pos, weights, Transformer<kDecodeBatchSize>(&token, kDecodeBatchSize, pos, weights,
activations, kv_cache, pool, layers_output); activations, kv_cache, pool,
runtime_config.layers_output);
float token_logit = 0.0f; float token_logit = 0.0f;
// The condition below is always true if we are doing Prefill above. // The condition below is always true if we are doing Prefill above.
// We keep it here for clarity so that the code is correct even if Prefill // We keep it here for clarity so that the code is correct even if Prefill
@ -953,17 +951,37 @@ Gemma::~Gemma() {
void Gemma::Generate(const RuntimeConfig& runtime_config, void Gemma::Generate(const RuntimeConfig& runtime_config,
const std::vector<int>& prompt, size_t start_pos, const std::vector<int>& prompt, size_t start_pos,
KVCache& kv_cache, TimingInfo& timing_info, KVCache& kv_cache, TimingInfo& timing_info) {
LayersOutputT* layers_output) {
pool_.SetWaitMode(hwy::PoolWaitMode::kSpin); pool_.SetWaitMode(hwy::PoolWaitMode::kSpin);
GEMMA_EXPORT_AND_DISPATCH( GEMMA_EXPORT_AND_DISPATCH(
model_type_, weight_type_, GenerateT, model_type_, weight_type_, GenerateT,
(weights_u8_, prefill_u8_, decode_u8_, runtime_config, prompt, start_pos, (weights_u8_, prefill_u8_, decode_u8_, runtime_config, prompt, start_pos,
kv_cache, pool_, timing_info, layers_output)); kv_cache, pool_, timing_info));
pool_.SetWaitMode(hwy::PoolWaitMode::kBlock); pool_.SetWaitMode(hwy::PoolWaitMode::kBlock);
} }
std::vector<int> WrapAndTokenize(const GemmaTokenizer& tokenizer,
const ModelTraining training, size_t pos,
std::string& prompt) {
// Instruction-tuned models are trained to expect control tokens.
if (training == ModelTraining::GEMMA_IT) {
// Prepend "<end_of_turn>" if this is a multi-turn dialogue continuation.
const std::string start = (pos == 0)
? "<start_of_turn>user\n"
: "<end_of_turn>\n<start_of_turn>user\n";
prompt = start + prompt + "<end_of_turn>\n<start_of_turn>model\n";
}
std::vector<int> tokens;
HWY_ASSERT(tokenizer.Encode(prompt, &tokens));
// Both pre-trained and instruction-tuned require BOS as first token.
if (pos == 0) {
tokens.insert(tokens.begin(), gcpp::BOS_ID);
}
return tokens;
}
} // namespace gcpp } // namespace gcpp
#endif // HWY_ONCE #endif // HWY_ONCE

36
gemma/gemma.h
View File

@ -74,10 +74,17 @@ class GemmaTokenizer {
using StreamFunc = std::function<bool(int, float)>; using StreamFunc = std::function<bool(int, float)>;
// If not empty, AcceptFunc is called with token. It should return false for // If not empty, AcceptFunc is called with token. It should return false for
// tokens you don't want to generate and true for tokens you want to generate. // tokens you don't want to generate and true for tokens you want to generate.
using AcceptFunc = std::function<bool(int)>; using AcceptFunc = std::function<bool(int, float)>;
// If not empty, SampleFunc is called with the probability distribution for the // If not empty, SampleFunc is called with the probability distribution for the
// next token, and its return value is used as the next generated token. // next token, and its return value is used as the next generated token.
using SampleFunc = std::function<int(const float*, size_t)>; using SampleFunc = std::function<int(const float*, size_t)>;
// Will be called for layers output with:
// - position in the tokens sequence
// - name of the data, p.ex. "tokens", "block.1", "final_norm"
// - pointer to the data array
// - size of the data array
using LayersOutputFunc =
std::function<void(int, const std::string&, const float*, size_t)>;
struct RuntimeConfig { struct RuntimeConfig {
size_t max_tokens; size_t max_tokens;
@ -88,6 +95,7 @@ struct RuntimeConfig {
StreamFunc stream_token; StreamFunc stream_token;
AcceptFunc accept_token; // if empty, accepts all tokens. AcceptFunc accept_token; // if empty, accepts all tokens.
SampleFunc sample_func; // if empty, uses SampleTopK. SampleFunc sample_func; // if empty, uses SampleTopK.
LayersOutputFunc layers_output; // if not empty, called after each layer.
int eos_id = EOS_ID; int eos_id = EOS_ID;
}; };
@ -97,14 +105,6 @@ struct TimingInfo {
double time_to_first_token = 0.0; double time_to_first_token = 0.0;
}; };
// Will be called for layers output with:
// - position in the tokens sequence
// - name of the data, p.ex. "tokens", "block.1", "final_norm"
// - pointer to the data array
// - size of the data array
using LayersOutputT =
std::function<void(int, const std::string&, const float*, size_t)>;
class Gemma { class Gemma {
public: public:
Gemma(const Path& tokenizer_path, const Path& weights, Model model_type, Gemma(const Path& tokenizer_path, const Path& weights, Model model_type,
@ -121,12 +121,9 @@ class Gemma {
const ByteStorageT& Prefill() const { return prefill_u8_; } const ByteStorageT& Prefill() const { return prefill_u8_; }
const ByteStorageT& Decode() const { return decode_u8_; } const ByteStorageT& Decode() const { return decode_u8_; }
// layers_output is optional; if set - it will be called with the activations
// output after applying each layer.
void Generate(const RuntimeConfig& runtime_config, void Generate(const RuntimeConfig& runtime_config,
const std::vector<int>& prompt, size_t start_pos, const std::vector<int>& prompt, size_t start_pos,
KVCache& kv_cache, TimingInfo& timing_info, KVCache& kv_cache, TimingInfo& timing_info);
LayersOutputT* layers_output = nullptr);
private: private:
hwy::ThreadPool& pool_; hwy::ThreadPool& pool_;
@ -141,14 +138,19 @@ class Gemma {
Type weight_type_; Type weight_type_;
}; };
// Adds BOS token and possibly 'turn' annotations, which depend on `training`
// and `pos`, the number of tokens decoded so far; returns the corresponding
// tokens. Asserts that tokenization is successful.
std::vector<int> WrapAndTokenize(const GemmaTokenizer& tokenizer,
ModelTraining training, size_t pos,
std::string& prompt);
// DEPRECATED, call Gemma::Generate directly. // DEPRECATED, call Gemma::Generate directly.
HWY_INLINE void GenerateGemma(Gemma& gemma, const RuntimeConfig& runtime_config, HWY_INLINE void GenerateGemma(Gemma& gemma, const RuntimeConfig& runtime_config,
const std::vector<int>& prompt, size_t start_pos, const std::vector<int>& prompt, size_t start_pos,
KVCache& kv_cache, hwy::ThreadPool& /*pool*/, KVCache& kv_cache, hwy::ThreadPool& /*pool*/,
TimingInfo& timing_info, TimingInfo& timing_info) {
LayersOutputT* layers_output) { gemma.Generate(runtime_config, prompt, start_pos, kv_cache, timing_info);
gemma.Generate(runtime_config, prompt, start_pos, kv_cache, timing_info,
layers_output);
} }
} // namespace gcpp } // namespace gcpp

129
gemma/gemma_test.cc
View File

@ -17,89 +17,35 @@
#include <stdio.h> #include <stdio.h>
#include <memory>
#include <random>
#include <string> #include <string>
#include <vector> #include <vector>
// Placeholder for internal header, do not modify. #include "gemma/benchmark_helper.h"
#include "gemma/common.h" #include "gemma/common.h"
#include "gemma/cross_entropy.h" #include "hwy/tests/hwy_gtest.h"
#include "gemma/ops.h"
#include "util/app.h"
#include "hwy/contrib/thread_pool/thread_pool.h"
#include "hwy/tests/test_util-inl.h"
namespace gcpp { namespace gcpp {
namespace { namespace {
int s_argc = 0; // Shared state. Requires argc/argv, so construct in main and use the same raw
char** s_argv = nullptr; // pointer approach as in benchmarks.cc. Note that the style guide forbids
// non-local static variables with dtors.
GemmaEnv* s_env = nullptr;
class GemmaTest : public ::testing::Test { class GemmaTest : public ::testing::Test {
protected: protected:
static void SetUpTestSuite() { std::string GemmaReply(const std::string& prompt) {
gcpp::AppArgs app(s_argc, s_argv); s_env->SetMaxGeneratedTokens(2048);
gcpp::LoaderArgs loader(s_argc, s_argv); s_env->MutableConfig().temperature = 0.0f; // deterministic
if (const char* err = loader.Validate()) { s_env->MutableConfig().verbosity = 0;
fprintf(stderr, "Insufficient LoaderArgs, skipping e2e tests.\n"); // Using the turn structure worsens results.
} else { const std::vector<int> tokens = s_env->TokenizeAndPrependBOS(prompt);
fprintf(stderr, "Loading model..\n"); auto [response, n] = s_env->QueryModel(tokens);
s_pool = std::make_unique<hwy::ThreadPool>(app.num_threads); return response;
s_gemma = AllocateGemma(loader, *s_pool);
s_kv_cache = KVCache::Create(loader.ModelType());
s_model = loader.ModelType();
}
}
static void TearDownTestSuite() {
s_pool.reset();
s_gemma.reset();
}
std::string GemmaReply(const std::string& prompt_string) {
std::mt19937 gen;
gen.seed(42);
std::vector<int> prompt;
HWY_ASSERT(s_gemma->Tokenizer().Encode(prompt_string, &prompt));
// For both pre-trained and instruction-tuned models: prepend "<bos>" token
// if needed.
prompt.insert(prompt.begin(), BOS_ID);
std::vector<int> response;
auto stream_token = [&response](int token, float) {
response.push_back(token);
return true;
};
gcpp::RuntimeConfig runtime_config = {
.max_tokens = 3072,
.max_generated_tokens = 2048,
.temperature = 1.0,
.verbosity = 0,
.gen = &gen,
.stream_token = stream_token,
};
gcpp::TimingInfo timing_info;
s_gemma->Generate(runtime_config, prompt, /*start_pos=*/0, s_kv_cache,
timing_info, /*layers_output=*/nullptr);
std::string response_text;
HWY_ASSERT(s_gemma->Tokenizer().Decode(response, &response_text));
return response_text;
}
float GemmaCrossEntropy(const std::string& prompt_string) {
std::vector<int> prompt;
HWY_ASSERT(s_gemma->Tokenizer().Encode(prompt_string, &prompt));
prompt.insert(prompt.begin(), BOS_ID);
return ComputeCrossEntropy(*s_gemma, /*max_tokens=*/3072, prompt,
s_kv_cache,
/*verbosity=*/0) /
prompt_string.size();
} }
void TestQuestions(const char* kQA[][2], size_t num_questions) { void TestQuestions(const char* kQA[][2], size_t num_questions) {
if (!s_gemma) return; if (!s_env->GetModel()) return;
for (size_t i = 0; i < num_questions; ++i) { for (size_t i = 0; i < num_questions; ++i) {
fprintf(stderr, "Question %zu\n\n", i + 1); fprintf(stderr, "Question %zu\n\n", i + 1);
std::string response = GemmaReply(kQA[i][0]); std::string response = GemmaReply(kQA[i][0]);
@ -107,18 +53,8 @@ class GemmaTest : public ::testing::Test {
EXPECT_TRUE(response.find(kQA[i][1]) != std::string::npos); // NOLINT EXPECT_TRUE(response.find(kQA[i][1]) != std::string::npos); // NOLINT
} }
} }
static std::unique_ptr<hwy::ThreadPool> s_pool;
static std::unique_ptr<gcpp::Gemma> s_gemma;
static gcpp::KVCache s_kv_cache;
static gcpp::Model s_model;
}; };
/*static*/ std::unique_ptr<hwy::ThreadPool> GemmaTest::s_pool;
/*static*/ std::unique_ptr<gcpp::Gemma> GemmaTest::s_gemma;
/*static*/ gcpp::KVCache GemmaTest::s_kv_cache;
/*static*/ gcpp::Model GemmaTest::s_model;
TEST_F(GemmaTest, Geography) { TEST_F(GemmaTest, Geography) {
static const char* kQA[][2] = { static const char* kQA[][2] = {
{"What is the capital of Hungary?", "Budapest"}, {"What is the capital of Hungary?", "Budapest"},
@ -130,7 +66,7 @@ TEST_F(GemmaTest, Geography) {
TEST_F(GemmaTest, History) { TEST_F(GemmaTest, History) {
static const char* kQA[][2] = { static const char* kQA[][2] = {
{"When was the Battle of Hastings?", "1066"}, {"When was the battle of Hastings?", "1066"},
}; };
static const size_t kNum = sizeof(kQA) / sizeof(kQA[0]); static const size_t kNum = sizeof(kQA) / sizeof(kQA[0]);
TestQuestions(kQA, kNum); TestQuestions(kQA, kNum);
@ -181,42 +117,39 @@ static const char kGettysburg[] = {
"people, for the people, shall not perish from the earth.\n"}; "people, for the people, shall not perish from the earth.\n"};
TEST_F(GemmaTest, CrossEntropySmall) { TEST_F(GemmaTest, CrossEntropySmall) {
if (!s_gemma) return; if (!s_env->GetModel()) return;
static const char kSmall[] = static const char kSmall[] =
"The capital of Hungary is Budapest which is located in Europe."; "The capital of Hungary is Budapest which is located in Europe.";
float entropy = GemmaCrossEntropy(kSmall); float entropy = s_env->CrossEntropy(kSmall);
fprintf(stderr, "per-byte entropy: %f\n", entropy); fprintf(stderr, "per-byte entropy: %f\n", entropy);
EXPECT_LT(entropy, (s_model == gcpp::Model::GEMMA_7B) ? 2.1f : 2.0f); EXPECT_LT(entropy,
(s_env->ModelType() == gcpp::Model::GEMMA_7B) ? 2.1f : 2.0f);
} }
TEST_F(GemmaTest, CrossEntropyJingleBells) { TEST_F(GemmaTest, CrossEntropyJingleBells) {
if (!s_gemma) return; if (!s_env->GetModel()) return;
float entropy = GemmaCrossEntropy(kJingleBells); float entropy = s_env->CrossEntropy(kJingleBells);
fprintf(stderr, "per-byte entropy: %f\n", entropy); fprintf(stderr, "per-byte entropy: %f\n", entropy);
EXPECT_LT(entropy, (s_model == gcpp::Model::GEMMA_7B) ? 0.9f : 1.8f); EXPECT_LT(entropy,
(s_env->ModelType() == gcpp::Model::GEMMA_7B) ? 0.9f : 1.8f);
} }
TEST_F(GemmaTest, CrossEntropyGettysburg) { TEST_F(GemmaTest, CrossEntropyGettysburg) {
if (!s_gemma) return; if (!s_env->GetModel()) return;
float entropy = GemmaCrossEntropy(kGettysburg); float entropy = s_env->CrossEntropy(kGettysburg);
fprintf(stderr, "per-byte entropy: %f\n", entropy); fprintf(stderr, "per-byte entropy: %f\n", entropy);
EXPECT_LT(entropy, (s_model == gcpp::Model::GEMMA_7B) ? 0.8f : 1.2f); EXPECT_LT(entropy,
(s_env->ModelType() == gcpp::Model::GEMMA_7B) ? 0.8f : 1.2f);
} }
} // namespace } // namespace
} // namespace gcpp } // namespace gcpp
int main(int argc, char** argv) { int main(int argc, char** argv) {
{ gcpp::GemmaEnv env(argc, argv);
// Placeholder for internal init, do not modify. gcpp::s_env = &env;
}
// For later use by SetUp. testing::InitGoogleTest(&argc, argv);
gcpp::s_argc = argc;
gcpp::s_argv = argv;
// Probably should be called before SetUpTestSuite.
testing::InitGoogleTest(&gcpp::s_argc, argv);
return RUN_ALL_TESTS(); return RUN_ALL_TESTS();
} }

5
gemma/ops.h
View File

@ -1668,12 +1668,13 @@ static HWY_NOINLINE HWY_MAYBE_UNUSED int SampleTopK(
std::array<int, k> indices{}; std::array<int, k> indices{};
for (size_t i = 0; i < vocab_size; ++i) { for (size_t i = 0; i < vocab_size; ++i) {
if (probabilities[i] < top_k[k - 1] && if (probabilities[i] < top_k[k - 1] &&
(!accept_token || accept_token(StaticCast<int>(i)))) { (!accept_token || accept_token(StaticCast<int>(i), probabilities[i]))) {
continue; continue;
} }
for (size_t j = 0; j < k; ++j) { for (size_t j = 0; j < k; ++j) {
if (probabilities[i] > top_k[j] && if (probabilities[i] > top_k[j] &&
(!accept_token || accept_token(StaticCast<int>(i)))) { (!accept_token ||
accept_token(StaticCast<int>(i), probabilities[i]))) {
// shift elements by 1, insert the new value, move on to next value // shift elements by 1, insert the new value, move on to next value
for (size_t idx = k - 1; idx > j; --idx) { for (size_t idx = k - 1; idx > j; --idx) {
top_k[idx] = top_k[idx - 1]; top_k[idx] = top_k[idx - 1];

97
gemma/run.cc
View File

@ -15,27 +15,22 @@
// Command line text interface to gemma. // Command line text interface to gemma.
#include <ctime>
#include <iostream> #include <iostream>
#include <random> #include <random>
#include <string> #include <string>
#include <string_view> #include <string_view>
#include <thread> // NOLINT
#include <vector> #include <vector>
// Placeholder for internal header, do not modify. // Placeholder for internal header, do not modify.
#include "compression/compress.h" #include "gemma/benchmark_helper.h"
#include "gemma/common.h" #include "gemma/common.h"
#include "gemma/configs.h"
#include "gemma/gemma.h" // Gemma #include "gemma/gemma.h" // Gemma
#include "util/app.h" #include "util/app.h"
#include "util/args.h" // HasHelp #include "util/args.h" // HasHelp
#include "hwy/base.h" #include "hwy/base.h"
#include "hwy/contrib/thread_pool/thread_pool.h" #include "hwy/contrib/thread_pool/thread_pool.h"
#include "hwy/highway.h" #include "hwy/highway.h"
#include "hwy/per_target.h"
#include "hwy/profiler.h" #include "hwy/profiler.h"
#include "hwy/timer.h"
#if (!defined(HWY_VERSION_LT) || HWY_VERSION_LT(1, 2)) && !HWY_IDE #if (!defined(HWY_VERSION_LT) || HWY_VERSION_LT(1, 2)) && !HWY_IDE
#error "Please update to version 1.2 of github.com/google/highway." #error "Please update to version 1.2 of github.com/google/highway."
@ -57,56 +52,6 @@ static constexpr std::string_view kAsciiArtBanner = R""(
|___/ |_| |_| |___/ |_| |_|
)""; )"";
void ShowConfig(LoaderArgs& loader, InferenceArgs& inference, AppArgs& app) {
loader.Print(app.verbosity);
inference.Print(app.verbosity);
app.Print(app.verbosity);
if (app.verbosity >= 2) {
time_t now = time(nullptr);
char* dt = ctime(&now); // NOLINT
std::cout << "Date & Time : " << dt
<< "Prefill Token Batch Size : " << gcpp::kPrefillBatchSize
<< "\n"
<< "Hardware concurrency : "
<< std::thread::hardware_concurrency() << "\n"
<< "Instruction set : "
<< hwy::TargetName(hwy::DispatchedTarget()) << " ("
<< hwy::VectorBytes() * 8 << " bits)" << "\n";
char cpu100[100];
if (hwy::platform::GetCpuString(cpu100)) {
std::cout << "CPU : " << cpu100 << "\n";
}
std::cout << "Compiled config : " << CompiledConfig() << "\n"
<< "Weight Type : "
<< gcpp::StringFromType(loader.WeightType()) << "\n"
<< "EmbedderInput Type : "
<< gcpp::TypeName(gcpp::EmbedderInputT()) << "\n";
}
}
void ShowHelp(gcpp::LoaderArgs& loader, gcpp::InferenceArgs& inference,
gcpp::AppArgs& app) {
std::cerr
<< kAsciiArtBanner
<< "\n\ngemma.cpp : a lightweight, standalone C++ inference engine\n"
"==========================================================\n\n"
"To run gemma.cpp, you need to "
"specify 3 required model loading arguments:\n"
" --tokenizer\n"
" --weights\n"
" --model.\n";
std::cerr << "\n*Example Usage*\n\n./gemma --tokenizer tokenizer.spm "
"--weights 2b-it-sfp.sbs --model 2b-it\n";
std::cerr << "\n*Model Loading Arguments*\n\n";
loader.Help();
std::cerr << "\n*Inference Arguments*\n\n";
inference.Help();
std::cerr << "\n*Application Arguments*\n\n";
app.Help();
std::cerr << "\n";
}
// The main Read-Eval-Print Loop. // The main Read-Eval-Print Loop.
void ReplGemma(gcpp::Gemma& model, ModelTraining training, void ReplGemma(gcpp::Gemma& model, ModelTraining training,
gcpp::KVCache& kv_cache, hwy::ThreadPool& pool, gcpp::KVCache& kv_cache, hwy::ThreadPool& pool,
@ -118,12 +63,7 @@ void ReplGemma(gcpp::Gemma& model, ModelTraining training,
int prompt_size{}; int prompt_size{};
std::mt19937 gen; std::mt19937 gen;
if (args.deterministic) { InitGenerator(args, gen);
gen.seed(42);
} else {
std::random_device rd;
gen.seed(rd());
}
// callback function invoked for each generated token. // callback function invoked for each generated token.
auto stream_token = [&abs_pos, &current_pos, &args, &gen, &prompt_size, auto stream_token = [&abs_pos, &current_pos, &args, &gen, &prompt_size,
@ -162,7 +102,6 @@ void ReplGemma(gcpp::Gemma& model, ModelTraining training,
while (abs_pos < args.max_tokens) { while (abs_pos < args.max_tokens) {
std::string prompt_string; std::string prompt_string;
std::vector<int> prompt;
current_pos = 0; current_pos = 0;
{ {
PROFILER_ZONE("Gen.input"); PROFILER_ZONE("Gen.input");
@ -192,30 +131,11 @@ void ReplGemma(gcpp::Gemma& model, ModelTraining training,
continue; continue;
} }
if (training == ModelTraining::GEMMA_IT) { const std::vector<int> prompt =
// For instruction-tuned models: add control tokens. WrapAndTokenize(model.Tokenizer(), training, abs_pos, prompt_string);
prompt_string = "<start_of_turn>user\n" + prompt_string +
"<end_of_turn>\n<start_of_turn>model\n";
if (abs_pos != 0) {
// Prepend "<end_of_turn>" token if this is a multi-turn dialogue
// continuation.
prompt_string = "<end_of_turn>\n" + prompt_string;
}
}
HWY_ASSERT(model.Tokenizer().Encode(prompt_string, &prompt));
// For both pre-trained and instruction-tuned models: prepend "<bos>" token
// if needed.
if (abs_pos == 0) {
prompt.insert(prompt.begin(), gcpp::BOS_ID);
}
prompt_size = prompt.size(); prompt_size = prompt.size();
std::cerr << "\n" std::cerr << "\n"
<< "[ Reading prompt ] " << std::flush; << "[ Reading prompt ] " << std::flush;
if constexpr (kVerboseLogTokens) { if constexpr (kVerboseLogTokens) {
for (int i = 0; i < prompt_size; ++i) { for (int i = 0; i < prompt_size; ++i) {
fprintf(stderr, "DDD TOKEN %3d: %6d\n", i, prompt[i]); fprintf(stderr, "DDD TOKEN %3d: %6d\n", i, prompt[i]);
@ -301,17 +221,20 @@ int main(int argc, char** argv) {
gcpp::AppArgs app(argc, argv); gcpp::AppArgs app(argc, argv);
if (gcpp::HasHelp(argc, argv)) { if (gcpp::HasHelp(argc, argv)) {
ShowHelp(loader, inference, app); std::cerr << gcpp::kAsciiArtBanner;
gcpp::ShowHelp(loader, inference, app);
return 0; return 0;
} }
if (const char* error = loader.Validate()) { if (const char* error = loader.Validate()) {
ShowHelp(loader, inference, app); std::cerr << gcpp::kAsciiArtBanner;
gcpp::ShowHelp(loader, inference, app);
HWY_ABORT("\nInvalid args: %s", error); HWY_ABORT("\nInvalid args: %s", error);
} }
if (const char* error = inference.Validate()) { if (const char* error = inference.Validate()) {
ShowHelp(loader, inference, app); std::cerr << gcpp::kAsciiArtBanner;
gcpp::ShowHelp(loader, inference, app);
HWY_ABORT("\nInvalid args: %s", error); HWY_ABORT("\nInvalid args: %s", error);
} }

231
gemma/run_mmlu.cc
View File

@ -13,19 +13,16 @@
// See the License for the specific language governing permissions and // See the License for the specific language governing permissions and
// limitations under the License. // limitations under the License.
// Command line text interface to gemma. #include <stdio.h>
#include <fstream> #include <algorithm>
#include <iostream>
#include <random>
#include <set>
#include <sstream>
#include <string> #include <string>
#include <vector> #include <vector>
// Placeholder for internal header, do not modify. #include "compression/io.h" // Path
#include "gemma/benchmark_helper.h"
#include "gemma/gemma.h" // Gemma #include "gemma/gemma.h" // Gemma
#include "util/app.h" #include "util/args.h"
#include "hwy/base.h" #include "hwy/base.h"
#include "hwy/contrib/thread_pool/thread_pool.h" #include "hwy/contrib/thread_pool/thread_pool.h"
#include "hwy/highway.h" #include "hwy/highway.h"
@ -34,164 +31,134 @@
namespace gcpp { namespace gcpp {
void JsonGemma(gcpp::Gemma& model, gcpp::KVCache& kv_cache, struct JsonArgs : public ArgsBase<JsonArgs> {
hwy::ThreadPool& pool, JsonArgs(int argc, char* argv[]) { InitAndParse(argc, argv); }
const InferenceArgs& args, int verbosity,
std::string& eot_line) {
PROFILER_ZONE("Gen.misc");
// token index within the current turn
int max_tokens = 4096;
std::mt19937 gen; Path input;
if (args.deterministic) {
gen.seed(42); // Returns error string or nullptr if OK.
} else { const char* Validate() const {
std::random_device rd; if (input.Empty()) return "Must specify --input";
gen.seed(rd()); if (!input.Exists()) return "--input file does not exist";
return nullptr;
} }
float answers = 0.0; template <class Visitor>
float correct_answers = 0.0; void ForEach(const Visitor& visitor) {
visitor(input, "input", Path(), "Full pathname of mmlu.json.");
};
};
std::ifstream fJson("/tmp/mmlu.json"); // Linear search for a few tokens is faster than std::set.
std::stringstream buffer; // TODO: instead of accepting for each vocab entry, filter the logits once.
buffer << fJson.rdbuf(); class TokenSet {
auto json = nlohmann::json::parse(buffer.str()); public:
TokenSet(const GemmaTokenizer& tokenizer,
std::vector<std::string> accept_tokens = {"A", "B", "C", "D"}; const std::vector<std::string>& strings) {
std::set<int> accept_token_set{}; all_tokens_.reserve(strings.size());
for (const std::string& accept_token : accept_tokens) { for (const std::string& str : strings) {
std::vector<int> accept_token_ids; std::vector<int> tokens;
HWY_ASSERT(model.Tokenizer().Encode(accept_token, &accept_token_ids)); fprintf(stderr, "%s -> ", str.c_str());
accept_token_set.insert(accept_token_ids.begin(), accept_token_ids.end()); HWY_ASSERT(tokenizer.Encode(str, &tokens));
for (int token : tokens) {
fprintf(stderr, "%d, ", token);
all_tokens_.push_back(token);
}
fprintf(stderr, "\n");
}
} }
for (auto sample : json["samples"]) { bool Contains(int token) const {
int abs_pos = 0; // absolute token index over all turns return std::find(all_tokens_.begin(), all_tokens_.end(), token) !=
int current_pos = 0; all_tokens_.end();
int prompt_size{}; }
// cout << "prompt:" << sample["prompt"] << endl; private:
const std::string& prompt_string = sample["prompt"]; std::vector<int> all_tokens_;
std::vector<int> prompt; };
HWY_ASSERT(model.Tokenizer().Encode(prompt_string, &prompt)); void Run(GemmaEnv& env, JsonArgs& json) {
prompt_size = prompt.size(); PROFILER_ZONE("Run.all");
const std::string& correct_answer = accept_tokens[sample["input_label"]]; float answers = 0.0f;
float correct_answers = 0.0f;
// max_tokens = prompt_size + max_tokens; auto json_data = nlohmann::json::parse(ReadFileToString(json.input));
const std::vector<std::string> accept_strings = {
"A", "B", "C", "D", //
" A", " B", " C", " D", //
"**", "**:", ":**", "The", "Answer", "is", ":", "."};
const TokenSet accept_set(env.GetModel()->Tokenizer(), accept_strings);
for (auto sample : json_data["samples"]) {
const int id = sample["i"];
fprintf(stderr, "Processing question %d\n", id);
const std::string& correct_answer = accept_strings[sample["input_label"]];
std::string prompt_string = sample["prompt"];
// AcceptFunc restricts the output to one of these four tokens, so make an
// effort to steer the model towards that. See
// https://huggingface.co/blog/open-llm-leaderboard-mmlu
prompt_string +=
"What is start of the line with the correct answer? "
"Do not include any justifications or explanations. Reply only with a "
"letter.";
const std::vector<int> prompt =
WrapAndTokenize(env.GetModel()->Tokenizer(), env.ModelTrainingType(),
/*pos=*/0, prompt_string);
const size_t prompt_size = prompt.size();
std::vector<int> predicted_token_ids; std::vector<int> predicted_token_ids;
predicted_token_ids.reserve(max_tokens); predicted_token_ids.reserve(4096);
auto stream_token = [&current_pos, &prompt_size, &predicted_token_ids, size_t current_pos = 0;
&accept_token_set](int token, float proba) { const StreamFunc stream_token = [&current_pos, prompt_size,
&predicted_token_ids](int token,
float proba) {
PROFILER_ZONE("Stream");
++current_pos; ++current_pos;
if (current_pos > prompt_size) { if (current_pos > prompt_size) {
predicted_token_ids.push_back(token); predicted_token_ids.push_back(token);
// If the generated token is in the accepted token set, return False.
// This will stop further generation.
return accept_token_set.find(token) == accept_token_set.end();
} }
return true; return true;
}; };
const AcceptFunc accept_token = [&current_pos, &prompt_size, // Although " A" is a token, it is difficult to associate that with the
&accept_token_set](int token) { // correct answer. Only accepting certain tokens is risky: (A) is easily
// i.e. we have no constraints on accepted tokens // confused with the word "A".
if (accept_token_set.empty()) {
return true;
}
if (current_pos >= prompt_size) {
return accept_token_set.find(token) != accept_token_set.end();
} else {
// auto-accept early tokens
return true;
}
};
gcpp::TimingInfo timing_info; gcpp::TimingInfo timing_info;
gcpp::RuntimeConfig runtime_config = { gcpp::RuntimeConfig runtime_config = {
.max_tokens = args.max_tokens, .max_tokens = env.MaxTokens(),
.max_generated_tokens = args.max_generated_tokens, .max_generated_tokens = 30,
.temperature = args.temperature, .temperature = 0.0f,
.verbosity = verbosity, .verbosity = env.Verbosity(),
.gen = &gen, .gen = &env.MutableGen(),
.stream_token = stream_token, .stream_token = stream_token,
.accept_token = accept_token,
}; };
model.Generate(runtime_config, prompt, abs_pos, kv_cache, timing_info); env.GetModel()->Generate(runtime_config, prompt, /*pos=*/0,
env.MutableKVCache(), timing_info);
std::string output_string; std::string output_string = env.StringFromTokens(predicted_token_ids);
HWY_ASSERT(model.Tokenizer().Decode(predicted_token_ids, &output_string)); fprintf(stderr, "Correct %s, model '%s'\n", correct_answer.c_str(),
std::cout << "QuestionId: " << sample["i"] << "; " output_string.c_str());
<< "Predicted Answer: " << output_string << "; "
<< "Correct Answer: " << correct_answer << std::endl;
answers += 1.0; answers += 1.0f;
if (output_string == correct_answer) { if (output_string == correct_answer) {
correct_answers += 1.0; correct_answers += 1.0f;
} }
std::cout << "Running accuracy = " << "[" fprintf(stderr, "%.0f/%.0f = %.2f%%\n", correct_answers, answers,
<< static_cast<int>(correct_answers) << "/" correct_answers / answers);
<< static_cast<int>(answers) << "]" << " = "
<< correct_answers / answers << std::endl;
} }
} }
void ShowConfig(LoaderArgs& loader, InferenceArgs& inference, AppArgs& app) {
loader.Print(app.verbosity);
inference.Print(app.verbosity);
app.Print(app.verbosity);
}
void Run(LoaderArgs& loader, InferenceArgs& inference, AppArgs& app) {
PROFILER_ZONE("Run.misc");
hwy::ThreadPool pool(app.num_threads);
// For many-core, pinning workers to cores helps.
if (app.num_threads > 10) {
PinWorkersToCores(pool);
}
gcpp::Gemma model = gcpp::CreateGemma(loader, pool);
gcpp::KVCache kv_cache = gcpp::KVCache::Create(loader.ModelType());
JsonGemma(model, kv_cache, pool, inference, app.verbosity, app.eot_line);
}
} // namespace gcpp } // namespace gcpp
int main(int argc, char** argv) { int main(int argc, char** argv) {
{ {
PROFILER_ZONE("Startup.misc"); PROFILER_ZONE("Startup.all");
gcpp::GemmaEnv env(argc, argv);
// Placeholder for internal init, do not modify. gcpp::JsonArgs json(argc, argv);
gcpp::AbortIfInvalidArgs(json);
gcpp::LoaderArgs loader(argc, argv); gcpp::Run(env, json);
gcpp::InferenceArgs inference(argc, argv);
gcpp::AppArgs app(argc, argv);
if (const char* error = loader.Validate()) {
fprintf(stderr,
"\ngemma.cpp\n---------\n\nTo run gemma.cpp, you need to "
"specify 3 required model loading arguments: --tokenizer, "
"--compressed_weights, "
"and --model.\n\nModel Loading Arguments\n\n");
loader.Help();
fprintf(stderr, "\nInference Arguments\n\n");
inference.Help();
fprintf(stderr, "\nApplication Arguments\n\n");
app.Help();
fprintf(stderr, "\n\n");
HWY_ABORT("\nInvalid args: %s", error);
}
gcpp::Run(loader, inference, app);
} }
PROFILER_PRINT_RESULTS(); // Must call outside the zone above. PROFILER_PRINT_RESULTS(); // Must call outside the zone above.
return 0; return 0;

8
util/args.h
View File

@ -197,6 +197,14 @@ static inline HWY_MAYBE_UNUSED bool HasHelp(int argc, char* argv[]) {
return false; return false;
} }
template <class TArgs>
static inline HWY_MAYBE_UNUSED void AbortIfInvalidArgs(TArgs& args) {
if (const char* err = args.Validate()) {
args.Help();
HWY_ABORT("Problem with args: %s\n", err);
}
}
} // namespace gcpp } // namespace gcpp
#endif // THIRD_PARTY_GEMMA_CPP_UTIL_ARGS_H_ #endif // THIRD_PARTY_GEMMA_CPP_UTIL_ARGS_H_