Add Voxtral runner

examples/models/voxtral/CMakeLists.txt

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+#
+# Simple CMake build system for voxtral runner.
+#
+cmake_minimum_required(VERSION 3.24)
+project(voxtral)
+
+set(EXECUTORCH_ROOT ${CMAKE_CURRENT_SOURCE_DIR}/../../..)
+
+include(${EXECUTORCH_ROOT}/tools/cmake/Utils.cmake)
+
+if(CMAKE_TOOLCHAIN_FILE MATCHES ".*(iOS|ios\.toolchain)\.cmake$")
+  set(CMAKE_TOOLCHAIN_IOS ON)
+else()
+  set(CMAKE_TOOLCHAIN_IOS OFF)
+endif()
+
+# Let files say "include <executorch/path/to/header.h>"
+set(_common_include_directories ${EXECUTORCH_ROOT}/..)
+
+# Need this for gflags for some reason
+set(gflags_DIR ${CMAKE_CURRENT_BINARY_DIR}/../../../third-party/gflags)
+find_package(gflags REQUIRED)
+
+# Find `executorch` libraries, same as for gflags
+list(APPEND CMAKE_FIND_ROOT_PATH ${CMAKE_CURRENT_BINARY_DIR}/../../..)
+find_package(executorch CONFIG REQUIRED FIND_ROOT_PATH_BOTH)
+executorch_target_link_options_shared_lib(executorch)
+
+set(link_libraries executorch gflags)
+set(_srcs multimodal.cpp)
+
+list(
+  APPEND
+  link_libraries
+  optimized_native_cpu_ops_lib
+  quantized_ops_lib
+  custom_ops
+  cpublas
+  eigen_blas
+)
+executorch_target_link_options_shared_lib(optimized_native_cpu_ops_lib)
+executorch_target_link_options_shared_lib(quantized_ops_lib)
+executorch_target_link_options_shared_lib(custom_ops)
+
+# XNNPACK
+if(TARGET xnnpack_backend)
+  set(xnnpack_backend_libs xnnpack_backend XNNPACK xnnpack-microkernels-prod)
+  if(TARGET kleidiai)
+    list(APPEND xnnpack_backend_libs kleidiai)
+  endif()
+  list(APPEND link_libraries ${xnnpack_backend_libs})
+  executorch_target_link_options_shared_lib(xnnpack_backend)
+endif()
+
+# Add LLM runner and extension module
+if(NOT TARGET extension_llm_runner)
+  message(
+    FATAL_ERROR
+      "ExecuTorch must be installed with EXECUTORCH_BUILD_EXTENSION_LLM_RUNNER enabled."
+  )
+endif()
+
+# Needed for cpuinfo where it uses android specific log lib
+if(ANDROID)
+  list(APPEND link_libraries log)
+endif()
+
+# Add the required ExecuTorch extensions for multimodal LLM runner
+list(
+  APPEND
+  link_libraries
+  extension_llm_runner
+  extension_module
+  extension_data_loader
+  extension_tensor
+  extension_flat_tensor
+)
+
+# Add tokenizers
+list(APPEND link_libraries tokenizers::tokenizers)
+
+add_executable(voxtral_runner ${_srcs})
+if(NOT CMAKE_BUILD_TYPE STREQUAL "Debug")
+  target_link_options_gc_sections(voxtral_runner)
+  if(NOT APPLE)
+    target_link_options(voxtral_runner PRIVATE "LINKER:-s")
+  endif()
+endif()
+
+target_include_directories(voxtral_runner PUBLIC ${_common_include_directories})
+target_link_libraries(voxtral_runner PUBLIC ${link_libraries})
+target_compile_options(voxtral_runner PUBLIC ${_common_compile_options})

examples/models/voxtral/multimodal.cpp

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+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#include <cmath>
+#include <cstring>
+#include <fstream>
+
+#include <gflags/gflags.h>
+
+#include <executorch/extension/llm/runner/audio.h>
+#include <executorch/extension/llm/runner/image.h>
+#include <executorch/extension/llm/runner/llm_runner_helper.h>
+#include <executorch/extension/llm/runner/multimodal_input.h>
+#include <executorch/extension/llm/runner/multimodal_runner.h>
+#include <executorch/runtime/core/error.h>
+#include <executorch/runtime/platform/log.h>
+
+#if defined(ET_USE_THREADPOOL)
+#include <executorch/extension/threadpool/cpuinfo_utils.h>
+#include <executorch/extension/threadpool/threadpool.h>
+#endif
+
+DEFINE_string(
+    model_path,
+    "multimodal.pte",
+    "Model serialized in flatbuffer format.");
+
+DEFINE_string(tokenizer_path, "tekken.json", "Tokenizer stuff.");
+
+DEFINE_string(prompt, "What is happening in this audio?", "Text prompt.");
+
+DEFINE_string(audio_path, "", "Path to input audio file.");
+
+DEFINE_double(
+    temperature,
+    0.8f,
+    "Temperature; Default is 0.8f. 0 = greedy argmax sampling (deterministic). Lower temperature = more deterministic");
+
+DEFINE_int32(
+    cpu_threads,
+    -1,
+    "Number of CPU threads for inference. Defaults to -1, which implies we'll use a heuristic to derive the # of performant cores for a specific device.");
+
+DEFINE_bool(warmup, false, "Whether to run a warmup run.");
+
+namespace {
+
+using ::executorch::extension::llm::Image;
+using ::executorch::extension::llm::make_image_input;
+using ::executorch::extension::llm::make_text_input;
+using ::executorch::extension::llm::MultimodalInput;
+
+bool ends_with(const std::string& str, const std::string& suffix) {
+  return str.size() >= suffix.size() &&
+      str.compare(str.size() - suffix.size(), suffix.size(), suffix) == 0;
+}
+
+/**
+ * @brief Loads preprocessed audio data from a binary file
+ *
+ * Reads mel spectrogram features that have been pre-computed and saved as a
+ * binary file. The audio data is expected to be stored as float values in
+ * binary format, typically saved using:
+ *   with open("tensor.bin", "wb") as f:
+ *       f.write(t.numpy().tobytes())
+ *
+ * @param audio_path Path to the binary audio file (.bin)
+ * @return MultimodalInput containing the loaded audio data
+ */
+MultimodalInput loadPreprocessedAudio(const std::string& audio_path) {
+  std::ifstream f(audio_path, std::ios::binary | std::ios::ate);
+  int32_t n_bins = 128;
+  int32_t n_frames = 3000;
+  std::size_t n_floats =
+      f.tellg() / sizeof(float); // Number of floats in the audio file.
+  f.seekg(0, std::ios::beg);
+  int32_t batch_size = ceil(
+      n_floats /
+      (n_bins * n_frames)); // Batch in increments of n_frames, rounding up.
+  std::vector<float> audio_data(batch_size * n_bins * n_frames);
+  f.read(
+      reinterpret_cast<char*>(audio_data.data()),
+      audio_data.size() * sizeof(float));
+
+  ET_LOG(Info, "audio_data len = %d", audio_data.size());
+
+  auto audio = std::make_unique<::executorch::extension::llm::Audio>();
+  audio->batch_size = batch_size;
+  audio->n_bins = n_bins;
+  audio->n_frames = n_frames;
+  audio->data.resize(audio_data.size() * sizeof(float));
+  std::memcpy(
+      audio->data.data(), audio_data.data(), audio_data.size() * sizeof(float));
+  return ::executorch::extension::llm::make_audio_input(std::move(*audio));
+}
+
+/**
+ * @brief Processes audio files for multimodal input
+ *
+ * Dispatches audio file processing based on file extension:
+ * - .bin files: Loads preprocessed mel spectrogram features directly
+ * - .wav/.mp3 files: Currently unsupported, throws runtime_error
+ *
+ * This function provides a interface for different audio input formats
+ * and can be extended to support raw audio processing in the future.
+ *
+ * @param audio_path Path to the audio file
+ * @return MultimodalInput containing the processed audio data
+ * @throws std::runtime_error if file format is unsupported or processing fails
+ */
+MultimodalInput processAudioFile(const std::string& audio_path) {
+  if (ends_with(audio_path, ".bin")) {
+    // Current behavior - load preprocessed audio stored as a binary file.
+    return loadPreprocessedAudio(audio_path);
+  } else if (ends_with(audio_path, ".wav") || ends_with(audio_path, ".mp3")) {
+    // New: Process raw audio files - unsupported for now
+    ET_LOG(Error, "Raw audio file processing (.wav/.mp3) is not yet supported");
+    throw std::runtime_error("Raw audio file processing not supported");
+  } else {
+    ET_LOG(Error, "Unsupported audio file format: %s", audio_path.c_str());
+    throw std::runtime_error("Unsupported audio file format");
+  }
+}
+
+} // namespace
+
+int32_t main(int32_t argc, char** argv) {
+  gflags::ParseCommandLineFlags(&argc, &argv, true);
+
+  const char* model_path = FLAGS_model_path.c_str();
+
+  const char* tokenizer_path = FLAGS_tokenizer_path.c_str();
+  const char* prompt = FLAGS_prompt.c_str();
+  const char* audio_path = FLAGS_audio_path.c_str();
+  float temperature = FLAGS_temperature;
+  int32_t cpu_threads = FLAGS_cpu_threads;
+  bool warmup = FLAGS_warmup;
+
+#if defined(ET_USE_THREADPOOL)
+  uint32_t num_performant_cores = cpu_threads == -1
+      ? ::executorch::extension::cpuinfo::get_num_performant_cores()
+      : static_cast<uint32_t>(cpu_threads);
+  ET_LOG(
+      Info, "Resetting threadpool with num threads = %d", num_performant_cores);
+  if (num_performant_cores > 0) {
+    ::executorch::extension::threadpool::get_threadpool()
+        ->_unsafe_reset_threadpool(num_performant_cores);
+  }
+#endif
+
+  // Load tokenizer
+  std::unique_ptr<::tokenizers::Tokenizer> tokenizer =
+      ::executorch::extension::llm::load_tokenizer(tokenizer_path);
+  if (tokenizer == nullptr) {
+    ET_LOG(Error, "Failed to load tokenizer from: %s", tokenizer_path);
+    return 1;
+  }
+
+  // Create multimodal runner
+  std::unique_ptr<::executorch::extension::llm::MultimodalRunner> runner =
+      ::executorch::extension::llm::create_multimodal_runner(
+          model_path, std::move(tokenizer));
+  if (runner == nullptr) {
+    ET_LOG(Error, "Failed to create multimodal runner");
+    return 1;
+  }
+
+  // Load runner
+  auto load_error = runner->load();
+  if (load_error != ::executorch::runtime::Error::Ok) {
+    ET_LOG(Error, "Failed to load multimodal runner");
+    return 1;
+  }
+
+  // Prepare inputs
+  std::vector<MultimodalInput> inputs = {
+      make_text_input("<s>[INST][BEGIN_AUDIO]"),
+      processAudioFile(audio_path),
+      make_text_input(std::string(prompt) + "[/INST]"),
+  };
+
+  ::executorch::extension::llm::GenerationConfig config;
+  config.max_new_tokens = 100;
+  config.temperature = temperature;
+
+  // Run warmup if requested
+  if (warmup) {
+    ET_LOG(Info, "Running warmup...");
+    auto warmup_error = runner->generate(inputs, config);
+    if (warmup_error != ::executorch::runtime::Error::Ok) {
+      ET_LOG(Error, "Failed to run warmup");
+      return 1;
+    }
+    runner->reset();
+  }
+
+  // Generate
+  ET_LOG(Info, "Starting generation...");
+  auto error = runner->generate(inputs, config);
+  if (error != ::executorch::runtime::Error::Ok) {
+    ET_LOG(Error, "Failed to generate with multimodal runner");
+    return 1;
+  }
+
+  printf("\n");
+  return 0;
+}