mirror of https://github.com/google/gemma.cpp.git
libgemma refactor - review changes
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@ -14,7 +14,6 @@
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cmake_minimum_required(VERSION 3.11)
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project(hello_world)
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set(CMAKE_CXX_STANDARD 17)
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set(CMAKE_CXX_STANDARD_REQUIRED ON)
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include(FetchContent)
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@ -31,18 +30,18 @@ if (NOT BUILD_MODE)
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set(BUILD_MODE "remote")
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endif()
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if (BUILD_MODE STREQUAL "local")
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# Relative path to gemma.cpp from examples/hello_world/build/
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FetchContent_Declare(gemma SOURCE_DIR ../../..)
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else()
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FetchContent_Declare(gemma GIT_REPOSITORY https://github.com/google/gemma.cpp.git GIT_TAG 8c7b2cf61b9794b806de091685dc6739dd3db837)
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endif()
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FetchContent_MakeAvailable(gemma)
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FetchContent_MakeAvailabl(gemma)
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if(NOT CMAKE_BUILD_TYPE)
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set(CMAKE_BUILD_TYPE "Release")
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endif()
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add_executable(hello_world run.cc)
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set_property(TARGET hello_world PROPERTY CXX_STANDARD 17)
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target_link_libraries(hello_world hwy hwy_contrib sentencepiece libgemma)
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FetchContent_GetProperties(sentencepiece)
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target_include_directories(hello_world PRIVATE ${sentencepiece_SOURCE_DIR})
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@ -1,23 +1,29 @@
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# Hello World Example
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This is a minimal/template project for using `gemma.cpp` as a library. Instead of an interactive interface, it sets up the model state and generates text for a single hard coded prompt.
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This is a minimal/template project for using `gemma.cpp` as a library. Instead
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of an interactive interface, it sets up the model state and generates text for a
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single hard coded prompt.
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Build steps are similar to the main `gemma` executable. From inside the top-level directory. For now only `cmake`/`make` is available for builds (PRs welcome for other build options).
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Build steps are similar to the main `gemma` executable. For now only
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`cmake`/`make` is available for builds (PRs welcome for other build options).
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First use `cmake` to configure the project, assuming you are in the `hello_world` example directory (`gemma.cpp/examples/hello_world`):
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First use `cmake` to configure the project, starting from the `hello_world`
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example directory (`gemma.cpp/examples/hello_world`):
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```sh
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cmake -B build
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```
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This sets up a build configuration in `gemma.cpp/examples/hello_world/build`. Note that this fetches `libgemma` from a git commit hash on github. Alternatively if you want to build using the local version of `gemma.cpp` use:
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This sets up a build configuration in `gemma.cpp/examples/hello_world/build`.
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Note that this fetches `libgemma` from a git commit hash on github.
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Alternatively if you want to build using the local version of `gemma.cpp` use:
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```sh
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cmake -B build -DBUILD_MODE=local
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```
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Make sure you delete the contents of the build directory before changing configurations.
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Make sure you delete the contents of the build directory before changing
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configurations.
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Then use `make` to build the project:
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@ -26,9 +32,13 @@ cd build
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make hello_world
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```
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As with the top-level `gemma.cpp` project you can use the `make` commands `-j` flag to use parallel threads for faster builds.
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As with the top-level `gemma.cpp` project you can use the `make` commands `-j`
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flag to use parallel threads for faster builds.
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From inside the `gemma.cpp/examples/hello_world/build` directory, there should be a `hello_world` executable. You can run it with the same 3 model arguments as gemma.cpp specifying the tokenizer, compressed weights file, and model type, for example:
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From inside the `gemma.cpp/examples/hello_world/build` directory, there should
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be a `hello_world` executable. You can run it with the same 3 model arguments as
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gemma.cpp specifying the tokenizer, compressed weights file, and model type, for
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example:
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```sh
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./hello_world --tokenizer tokenizer.spm --compressed_weights 2b-it-sfp.sbs --model 2b-it
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@ -24,12 +24,12 @@
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#include "hwy/contrib/thread_pool/thread_pool.h"
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std::vector<int> tokenize(
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std::string prompt_string,
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const std::string& prompt_string,
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const sentencepiece::SentencePieceProcessor* tokenizer) {
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prompt_string = "<start_of_turn>user\n" + prompt_string +
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std::string formatted = "<start_of_turn>user\n" + prompt_string +
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"<end_of_turn>\n<start_of_turn>model\n";
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std::vector<int> tokens;
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HWY_ASSERT(tokenizer->Encode(prompt_string, &tokens).ok());
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HWY_ASSERT(tokenizer->Encode(formatted, &tokens).ok());
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tokens.insert(tokens.begin(), 2); // BOS token
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return tokens;
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}
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17
gemma.cc
17
gemma.cc
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@ -261,8 +261,7 @@ KVCache CreateKVCache(Model type) {
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template <class Config>
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struct GemmaImpl : public GemmaInterface {
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GemmaImpl( // const LoaderArgs& args,
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std::unique_ptr<sentencepiece::SentencePieceProcessor>& tokenizer,
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GemmaImpl(std::unique_ptr<sentencepiece::SentencePieceProcessor>& tokenizer,
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hwy::AlignedFreeUniquePtr<uint8_t[]>& compressed_weights,
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hwy::ThreadPool& pool);
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@ -767,16 +766,10 @@ GemmaImpl<Config>::GemmaImpl(
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std::unique_ptr<sentencepiece::SentencePieceProcessor>& tokenizer,
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hwy::AlignedFreeUniquePtr<uint8_t[]>& compressed_weights,
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hwy::ThreadPool& pool)
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// GemmaImpl<Config>::GemmaImpl(const LoaderArgs& args, hwy::ThreadPool&
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// pool)
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: compressed_weights(std::move(compressed_weights)),
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// HWY_DYNAMIC_DISPATCH(GetCompressedWeightsT)(args, pool)),
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prefill(hwy::MakeUniqueAligned<Activations<Config, kPrefillBatchSize>>()),
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state(hwy::MakeUniqueAligned<Activations<Config, 1>>()),
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tokenizer(std::move(tokenizer)) {
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// PROFILER_ZONE("Startup.tokenizer");
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// HWY_ASSERT(tokenizer.Load(args.tokenizer.path).ok());
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}
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tokenizer(std::move(tokenizer)) {}
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template <>
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void GemmaImpl<ConfigGemma2B>::Generate(
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@ -804,10 +797,14 @@ void GemmaImpl<ConfigGemma7B>::Generate(
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Gemma::Gemma(const Path& tokenizer_path, const Path& compressed_weights_path,
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const Path& weights_path, Model model_type,
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hwy::ThreadPool& pool) {
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{
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PROFILER_ZONE("Startup.tokenizer");
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std::unique_ptr<sentencepiece::SentencePieceProcessor> tokenizer =
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std::make_unique<sentencepiece::SentencePieceProcessor>();
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HWY_ASSERT(tokenizer->Load(tokenizer_path.path).ok());
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if (!tokenizer->Load(tokenizer_path.path).ok()) {
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HWY_ABORT("Failed to load the tokenizer file.");
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}
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}
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auto compressed_weights = HWY_DYNAMIC_DISPATCH(GetCompressedWeightsT)(
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model_type, weights_path, compressed_weights_path, pool);
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switch (model_type) {
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1
run.cc
1
run.cc
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@ -190,7 +190,6 @@ void ReplGemma(gcpp::Gemma& model, gcpp::KVCache& kv_cache,
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}
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}
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// HWY_ASSERT(model.Tokenizer().Encode(prompt_string, &prompt).ok());
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HWY_ASSERT(model.Tokenizer()->Encode(prompt_string, &prompt).ok());
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// For both pre-trained and instruction-tuned models: prepend "<bos>" token
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