/*
 * 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.
 */

/**
 * @file
 *
 * This tool can run ExecuTorch model files that only use operators that
 * are covered by the portable kernels, with possible delegate to the
 * test_backend_compiler_lib.
 *
 * It sets all input tensor data to ones, and assumes that the outputs are
 * all fp32 tensors.
 */

#include <iostream>
#include <memory>

#include <gflags/gflags.h>

#include <executorch/extension/data_loader/file_data_loader.h>
#include <executorch/extension/evalue_util/print_evalue.h>
#include <executorch/extension/runner_util/inputs.h>
#include <executorch/runtime/executor/method.h>
#include <executorch/runtime/executor/program.h>
#include <executorch/runtime/platform/log.h>
#include <executorch/runtime/platform/runtime.h>

static uint8_t method_allocator_pool[4 * 1024U * 1024U]; // 4 MB

DEFINE_string(
    model_path,
    "model.pte",
    "Model serialized in flatbuffer format.");

using executorch::extension::FileDataLoader;
using executorch::runtime::Error;
using executorch::runtime::EValue;
using executorch::runtime::HierarchicalAllocator;
using executorch::runtime::MemoryAllocator;
using executorch::runtime::MemoryManager;
using executorch::runtime::Method;
using executorch::runtime::MethodMeta;
using executorch::runtime::Program;
using executorch::runtime::Result;
using executorch::runtime::Span;

int main(int argc, char** argv) {
  executorch::runtime::runtime_init();

  gflags::ParseCommandLineFlags(&argc, &argv, true);
  if (argc != 1) {
    std::string msg = "Extra commandline args:";
    for (int i = 1 /* skip argv[0] (program name) */; i < argc; i++) {
      msg += std::string(" ") + argv[i];
    }
    ET_LOG(Error, "%s", msg.c_str());
    return 1;
  }

  // Create a loader to get the data of the program file. There are other
  // DataLoaders that use mmap() or point to data that's already in memory, and
  // users can create their own DataLoaders to load from arbitrary sources.
  const char* model_path = FLAGS_model_path.c_str();
  Result<FileDataLoader> loader = FileDataLoader::from(model_path);
  ET_CHECK_MSG(
      loader.ok(),
      "FileDataLoader::from() failed: 0x%" PRIx32,
      (uint32_t)loader.error());

  // Parse the program file. This is immutable, and can also be reused between
  // multiple execution invocations across multiple threads.
  Result<Program> program = Program::load(&loader.get());
  if (!program.ok()) {
    ET_LOG(Error, "Failed to parse model file %s", model_path);
    return 1;
  }
  ET_LOG(Info, "Model file %s is loaded.", model_path);

  // Use the first method in the program.
  const char* method_name = nullptr;
  {
    const auto method_name_result = program->get_method_name(0);
    ET_CHECK_MSG(method_name_result.ok(), "Program has no methods");
    method_name = *method_name_result;
  }
  ET_LOG(Info, "Using method %s", method_name);

  // MethodMeta describes the memory requirements of the method.
  Result<MethodMeta> method_meta = program->method_meta(method_name);
  ET_CHECK_MSG(
      method_meta.ok(),
      "Failed to get method_meta for %s: 0x%" PRIx32,
      method_name,
      (uint32_t)method_meta.error());

  //
  // The runtime does not use malloc/new; it allocates all memory using the
  // MemoryManger provided by the client. Clients are responsible for allocating
  // the memory ahead of time, or providing MemoryAllocator subclasses that can
  // do it dynamically.
  //

  // The method allocator is used to allocate all dynamic C++ metadata/objects
  // used to represent the loaded method. This allocator is only used during
  // loading a method of the program, which will return an error if there was
  // not enough memory.
  //
  // The amount of memory required depends on the loaded method and the runtime
  // code itself. The amount of memory here is usually determined by running the
  // method and seeing how much memory is actually used, though it's possible to
  // subclass MemoryAllocator so that it calls malloc() under the hood (see
  // MallocMemoryAllocator).
  //
  // In this example we use a statically allocated memory pool.
  MemoryAllocator method_allocator{
      MemoryAllocator(sizeof(method_allocator_pool), method_allocator_pool)};

  // The memory-planned buffers will back the mutable tensors used by the
  // method. The sizes of these buffers were determined ahead of time during the
  // memory-planning pasees.
  //
  // Each buffer typically corresponds to a different hardware memory bank. Most
  // mobile environments will only have a single buffer. Some embedded
  // environments may have more than one for, e.g., slow/large DRAM and
  // fast/small SRAM, or for memory associated with particular cores.
  std::vector<std::unique_ptr<uint8_t[]>> planned_buffers; // Owns the memory
  std::vector<Span<uint8_t>> planned_spans; // Passed to the allocator
  size_t num_memory_planned_buffers = method_meta->num_memory_planned_buffers();
  for (size_t id = 0; id < num_memory_planned_buffers; ++id) {
    // .get() will always succeed because id < num_memory_planned_buffers.
    size_t buffer_size =
        static_cast<size_t>(method_meta->memory_planned_buffer_size(id).get());
    ET_LOG(Info, "Setting up planned buffer %zu, size %zu.", id, buffer_size);
    planned_buffers.push_back(std::make_unique<uint8_t[]>(buffer_size));
    planned_spans.push_back({planned_buffers.back().get(), buffer_size});
  }
  HierarchicalAllocator planned_memory(
      {planned_spans.data(), planned_spans.size()});

  // Assemble all of the allocators into the MemoryManager that the Executor
  // will use.
  MemoryManager memory_manager(&method_allocator, &planned_memory);

  //
  // Load the method from the program, using the provided allocators. Running
  // the method can mutate the memory-planned buffers, so the method should only
  // be used by a single thread at at time, but it can be reused.
  //

  Result<Method> method = program->load_method(method_name, &memory_manager);
  ET_CHECK_MSG(
      method.ok(),
      "Loading of method %s failed with status 0x%" PRIx32,
      method_name,
      (uint32_t)method.error());
  ET_LOG(Info, "Method loaded.");

  // Allocate input tensors and set all of their elements to 1. The `inputs`
  // variable owns the allocated memory and must live past the last call to
  // `execute()`.
  auto inputs = executorch::extension::prepare_input_tensors(*method);
  ET_CHECK_MSG(
      inputs.ok(),
      "Could not prepare inputs: 0x%" PRIx32,
      (uint32_t)inputs.error());
  ET_LOG(Info, "Inputs prepared.");

  // Run the model.
  Error status = method->execute();
  ET_CHECK_MSG(
      status == Error::Ok,
      "Execution of method %s failed with status 0x%" PRIx32,
      method_name,
      (uint32_t)status);
  ET_LOG(Info, "Model executed successfully.");

  // Print the outputs.
  std::vector<EValue> outputs(method->outputs_size());
  ET_LOG(Info, "%zu outputs: ", outputs.size());
  status = method->get_outputs(outputs.data(), outputs.size());
  ET_CHECK(status == Error::Ok);
  // Print the first and last 100 elements of long lists of scalars.
  std::cout << executorch::extension::evalue_edge_items(100);
  for (int i = 0; i < outputs.size(); ++i) {
    std::cout << "Output " << i << ": " << outputs[i] << std::endl;
  }

  return 0;
}