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// 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.
// Command line text interface to gemma.
#include <iostream>
#include <random>
#include <string>
#include <string_view>
#include <vector>
// Placeholder for internal header, do not modify.
#include "compression/shared.h" // PromptWrapping
#include "evals/benchmark_helper.h"
#include "gemma/common.h"
#include "gemma/gemma.h" // Gemma
#include "ops/matmul.h" // MatMulEnv
#include "paligemma/image.h"
#include "util/app.h"
#include "util/args.h" // HasHelp
#include "util/threading.h"
#include "hwy/base.h"
#include "hwy/highway.h"
#include "hwy/profiler.h"
#if (!defined(HWY_VERSION_LT) || HWY_VERSION_LT(1, 2)) && !HWY_IDE
#error "Please update to version 1.2 of github.com/google/highway."
#endif
#if HWY_CXX_LANG < 201703L
#error "Gemma.cpp requires C++17, please pass -std=c++17."
#endif
static constexpr bool kVerboseLogTokens = false;
namespace gcpp {
static constexpr std::string_view kAsciiArtBanner = R""(
__ _ ___ _ __ ___ _ __ ___ __ _ ___ _ __ _ __
/ _` |/ _ \ '_ ` _ \| '_ ` _ \ / _` | / __| '_ \| '_ \
| (_| | __/ | | | | | | | | | | (_| || (__| |_) | |_) |
\__, |\___|_| |_| |_|_| |_| |_|\__,_(_)___| .__/| .__/
__/ | | | | |
|___/ |_| |_|
)"";
std::string GetPrompt(std::istream& input, int verbosity,
std::string_view eot_line) {
PROFILER_ZONE("Gen.input");
if (verbosity >= 1) {
std::cout << "> " << std::flush;
}
std::string prompt_string;
if (eot_line.empty()) {
std::getline(input, prompt_string);
} else {
std::string line;
while (std::getline(input, line)) {
if (line == eot_line) {
break;
}
prompt_string += line + "\n";
}
}
return prompt_string;
}
// The main Read-Eval-Print Loop.
void ReplGemma(Gemma& model, KVCache& kv_cache, const AppArgs& app,
const InferenceArgs& args, const AcceptFunc& accept_token,
std::string& eot_line) {
PROFILER_ZONE("Gen.misc");
size_t abs_pos = 0; // across turns
size_t tokens_generated_this_turn = 0; // differentiates prefill from reply
size_t prompt_size = 0;
bool end_of_turn_seen = false;
std::mt19937 gen;
InitGenerator(args, gen);
const bool have_image = !args.image_file.path.empty();
Image image;
ImageTokens image_tokens;
if (have_image) {
size_t pool_dim = model.GetModelConfig().vit_config.pool_dim;
image_tokens = ImageTokens(Extents2D(
model.GetModelConfig().vit_config.seq_len / (pool_dim * pool_dim),
model.GetModelConfig().model_dim));
HWY_ASSERT(model.Info().wrapping == PromptWrapping::PALIGEMMA ||
model.Info().wrapping == PromptWrapping::GEMMA_VLM);
HWY_ASSERT(image.ReadPPM(args.image_file.path));
const size_t image_size = model.GetModelConfig().vit_config.image_size;
image.Resize(image_size, image_size);
RuntimeConfig runtime_config = {
.gen = &gen, .verbosity = app.verbosity, .use_spinning = app.spin};
double image_tokens_start = hwy::platform::Now();
model.GenerateImageTokens(runtime_config, image, image_tokens);
if (app.verbosity >= 1) {
double image_tokens_duration = hwy::platform::Now() - image_tokens_start;
fprintf(stderr,
"\n\n[ Timing info ] Image token generation took: %d ms\n",
static_cast<int>(image_tokens_duration * 1000));
}
}
// callback function invoked for each generated token.
auto stream_token = [&](int token, float) {
++abs_pos;
if (model.GetModelConfig().IsEOS(token)) {
if (app.verbosity >= 2) {
std::cout << "\n[ End ]\n";
}
return true;
}
const bool in_prompt = tokens_generated_this_turn < prompt_size;
const bool first_response_token = tokens_generated_this_turn == prompt_size;
++tokens_generated_this_turn;
if (in_prompt) {
if (app.verbosity >= 1) {
std::cerr << "." << std::flush;
}
return true;
}
std::string token_text;
HWY_ASSERT(model.Tokenizer().Decode(std::vector<int>{token}, &token_text));
if (first_response_token) {
token_text.erase(0, token_text.find_first_not_of(" \t\n"));
if (app.verbosity >= 1) {
std::cout << "\n\n";
}
}
if (token_text == "<end_of_turn>") {
// We don't want to show the <end_of_turn> token to the user.
// We also need to remember that we've seen it, so that we can rewind
// abs_pos appropriately. We expect EOS as the next token.
end_of_turn_seen = true;
return true;
}
std::cout << token_text << std::flush;
return true;
};
while (true) { // Loop until user quits.
tokens_generated_this_turn = 0;
// Read prompt and handle special commands.
std::string prompt_string = GetPrompt(std::cin, app.verbosity, eot_line);
if (!std::cin) return;
// If !eot_line.empty(), we append \n, so only look at the first 2 chars.
if (prompt_string.size() >= 2 && prompt_string[0] == '%') {
if (prompt_string[1] == 'q' || prompt_string[1] == 'Q') return;
if (prompt_string[1] == 'c' || prompt_string[1] == 'C') {
abs_pos = 0;
continue;
}
}
if (prompt_string.empty()) {
std::cout << "Use '%q' to quit.\n";
continue;
}
// Wrap, tokenize and maybe log prompt tokens.
std::vector<int> prompt = WrapAndTokenize(
model.Tokenizer(), model.Info(), abs_pos, prompt_string);
prompt_size = prompt.size();
if constexpr (kVerboseLogTokens) {
for (int i = 0; i < prompt_size; ++i) {
fprintf(stderr, "DDD TOKEN %3d: %6d\n", i, prompt[i]);
}
}
// Set up runtime config.
TimingInfo timing_info = {.verbosity = app.verbosity};
RuntimeConfig runtime_config = {.gen = &gen,
.verbosity = app.verbosity,
.stream_token = stream_token,
.accept_token = accept_token,
.use_spinning = app.spin};
args.CopyTo(runtime_config);
size_t prefix_end = 0;
if (have_image) {
runtime_config.image_tokens = &image_tokens;
if (model.Info().wrapping == PromptWrapping::PALIGEMMA) {
prompt.insert(prompt.begin(), image_tokens.BatchSize(), 0);
} else if (model.Info().wrapping == PromptWrapping::GEMMA_VLM) {
size_t seq_len = model.GetModelConfig().vit_config.seq_len;
size_t pool_dim = model.GetModelConfig().vit_config.pool_dim;
prompt =
WrapVLM(model.Tokenizer(), model.Info(), abs_pos, prompt,
image_tokens.BatchSize(), seq_len / (pool_dim * pool_dim));
}
prompt_size = prompt.size();
// The end of the prefix for prefix-LM style attention in Paligemma.
// See Figure 2 of https://arxiv.org/abs/2407.07726.
prefix_end = prompt_size;
// We need to look at all the tokens for the prefix.
runtime_config.prefill_tbatch_size = prompt_size;
}
// Generate until EOS or max_generated_tokens.
if (app.verbosity >= 1) {
std::cerr << "\n[ Reading prompt ] " << std::flush;
}
model.Generate(runtime_config, prompt, abs_pos, prefix_end, kv_cache,
timing_info);
std::cout << "\n\n";
// Prepare for the next turn. Works only for PaliGemma.
if (!args.multiturn || model.Info().wrapping == PromptWrapping::PALIGEMMA) {
abs_pos = 0; // Start a new turn at position 0.
InitGenerator(args, gen);
} else {
// The last token was either EOS, then it should be ignored because it is
// never part of the dialog, see Table 5 in the Gemma-2 paper:
// https://arxiv.org/pdf/2408.00118
// Or we have hit max_generated_tokens, then the last token will be lost.
// (We could store it in stream_token, and then prepend to the next turn,
// but it's not worth the complexity, as multi-turn with max_generated is
// not a common use case.)
// In either case, we need to rewind abs_pos by one.
HWY_ASSERT(abs_pos > 0);
abs_pos--;
}
if (end_of_turn_seen && abs_pos > 0) {
// If we have seen an end_of_turn token, we need to rewind abs_pos by one
// more, because we will prepend it again to the prompt in
// WrapAndTokenize.
abs_pos--;
}
end_of_turn_seen = false;
}
}
void Run(LoaderArgs& loader, InferenceArgs& inference, AppArgs& app) {
PROFILER_ZONE("Run.misc");
// Note that num_threads is an upper bound; we also limit to the number of
// detected and enabled cores.
const BoundedTopology topology = CreateTopology(app);
NestedPools pools = CreatePools(topology, app);
MatMulEnv env(topology, pools);
if (app.verbosity >= 2) env.print_best = true;
Gemma model = CreateGemma(loader, env);
KVCache kv_cache =
KVCache::Create(model.GetModelConfig(), inference.prefill_tbatch_size);
if (app.verbosity >= 1) {
std::string instructions =
"*Usage*\n"
" Enter an instruction and press enter (%C resets conversation, "
"%Q quits).\n";
const std::string multiturn =
inference.multiturn == 0
? std::string(
" Since multiturn is set to 0, conversation will "
"automatically reset every turn.\n\n")
: "\n";
const std::string examples =
"*Examples*\n"
" - Write an email to grandma thanking her for the cookies.\n"
" - What are some historical attractions to visit around "
"Massachusetts?\n"
" - Compute the nth fibonacci number in javascript.\n"
" - Write a standup comedy bit about GPU programming.\n";
instructions += multiturn;
instructions += examples;
std::cout << "\033[2J\033[1;1H" // clear screen
<< kAsciiArtBanner << "\n\n";
ShowConfig(loader, inference, app, topology, pools);
std::cout << "\n" << instructions << "\n";
}
ReplGemma(model, kv_cache, app, inference, AcceptFunc(), app.eot_line);
}
} // namespace gcpp
int main(int argc, char** argv) {
{
PROFILER_ZONE("Startup.misc");
// Placeholder for internal init, do not modify.
gcpp::LoaderArgs loader(argc, argv);
gcpp::InferenceArgs inference(argc, argv);
gcpp::AppArgs app(argc, argv);
if (gcpp::HasHelp(argc, argv)) {
std::cerr << gcpp::kAsciiArtBanner;
gcpp::ShowHelp(loader, inference, app);
return 0;
}
if (const char* error = loader.Validate()) {
std::cerr << gcpp::kAsciiArtBanner;
gcpp::ShowHelp(loader, inference, app);
HWY_ABORT("\nInvalid args: %s", error);
}
if (const char* error = inference.Validate()) {
std::cerr << gcpp::kAsciiArtBanner;
gcpp::ShowHelp(loader, inference, app);
HWY_ABORT("\nInvalid args: %s", error);
}
gcpp::Run(loader, inference, app);
}
PROFILER_PRINT_RESULTS(); // Must call outside the zone above.
return 0;
}
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