method.cpp

/*
 * 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 <executorch/runtime/executor/method.h>

#include <c10/util/irange.h>
#include <array>
#include <cinttypes> // @donotremove
#include <cstdint>
#include <cstdio>

#include <executorch/runtime/backend/interface.h>
#include <executorch/runtime/core/event_tracer_hooks.h>
#include <executorch/runtime/core/exec_aten/util/tensor_util.h>
#include <executorch/runtime/core/named_data_map.h>
#include <executorch/runtime/core/span.h>
#include <executorch/runtime/executor/memory_manager.h>
#include <executorch/runtime/executor/merged_data_map.h>
#include <executorch/runtime/executor/platform_memory_allocator.h>
#include <executorch/runtime/executor/program.h>
#include <executorch/runtime/executor/tensor_parser.h>
#include <executorch/runtime/kernel/kernel_runtime_context.h>
#include <executorch/runtime/kernel/operator_registry.h>
#include <executorch/runtime/platform/assert.h>
#include <executorch/runtime/platform/compiler.h>
#include <executorch/runtime/platform/log.h>
#include <executorch/runtime/platform/profiler.h>
#include <executorch/schema/program_generated.h>

namespace executorch {
namespace ET_RUNTIME_NAMESPACE {

using internal::PlatformMemoryAllocator;

/**
 * Runtime state for a backend delegate.
 */
// This lint wants to wrap the class in an anonymous namespace, but it must be
// visible because it's forward-declared and used in Executor.h.
// @lint-ignore CLANGTIDY facebook-hte-ShadowingClass
class BackendDelegate final {
 public:
  /**
   * Initializes an already-allocated BackendDelegate from its serialized
   * representation.
   *
   * @param[in] delegate The serialized backend delegate to load.
   * @param[in] program The serialized program to load from.
   * @param[in] backend_init_context The context pointer to pass to the
   *     backend's init() method.
   * @param[out] out The BackendDelegate to initialize.
   *
   * @returns Error::Ok if the initialization succeeded, or an error otherwise.
   */
  static Error Init(
      const executorch_flatbuffer::BackendDelegate& delegate,
      const Program* program,
      BackendInitContext& backend_init_context,
      BackendDelegate* out) {
    // Look up the backend.
    ET_CHECK_OR_RETURN_ERROR(
        delegate.id() != nullptr, InvalidProgram, "Missing backend id");
    const char* backend_id = delegate.id()->c_str();
    BackendInterface* backend = get_backend_class(backend_id);
    ET_CHECK_OR_RETURN_ERROR(
        backend != nullptr,
        NotFound,
        "Backend %s is not registered.",
        backend_id);
    ET_CHECK_OR_RETURN_ERROR(
        backend->is_available(),
        NotFound,
        "Backend %s is not available.",
        backend_id);

    // Get the delegate data.
    Result<FreeableBuffer> processed_data = GetProcessedData(delegate, program);
    if (!processed_data.ok()) {
      ET_LOG(Error, "Failed to load data for backend %s", backend_id);
      return processed_data.error();
    }

    // Parse compilation specs from program
    CompileSpec* compile_specs;
    Error err = PopulateCompileSpecs(
        delegate.compile_specs(), backend_init_context, &compile_specs);
    if (err != Error::Ok) {
      ET_LOG(Error, "Failed to get compile specs for backend %s", backend_id);
      return err;
    }
    size_t num_compile_specs = delegate.compile_specs()->size();

    out->backend_ = backend;
    out->handle_ = nullptr;
    // Pass a pointer to this buffer to the backend. It's safe for the backend
    // to point its handle to this object, since it will outlive the backend.
    new (&out->segment_) FreeableBuffer(std::move(processed_data.get()));

    // Initialize the delegate.
    Result<DelegateHandle*> handle = backend->init(
        backend_init_context,
        &out->segment_,
        ArrayRef<CompileSpec>(compile_specs, num_compile_specs));
    if (!handle.ok()) {
      ET_LOG(
          Error,
          "Init failed for backend %s: 0x%" PRIx32,
          backend_id,
          static_cast<uint32_t>(handle.error()));
      out->segment_.Free();
      return handle.error();
    }
    out->handle_ = handle.get();
    return Error::Ok;
  }

  ~BackendDelegate() {
    if (backend_ != nullptr) {
      backend_->destroy(handle_);
    }
  }

  Error Execute(
      BackendExecutionContext& backend_execution_context,
      Span<EValue*> args) const {
    EXECUTORCH_SCOPE_PROF("delegate_execute");
    return backend_->execute(backend_execution_context, handle_, args);
  }

 private:
  // Not constructible.
  BackendDelegate() = delete;

  // Disallow copy/move.
  BackendDelegate(const BackendDelegate&) = delete;
  BackendDelegate& operator=(const BackendDelegate&) = delete;
  BackendDelegate(BackendDelegate&&) = delete;
  BackendDelegate& operator=(BackendDelegate&&) = delete;

  static Error PopulateCompileSpecs(
      const flatbuffers::Vector<flatbuffers::Offset<
          executorch_flatbuffer::CompileSpec>>* compile_specs_in_program,
      BackendInitContext& backend_init_context,
      CompileSpec** out_spec) {
    auto number_of_compile_specs = compile_specs_in_program->size();

    CompileSpec* compile_specs_list =
        backend_init_context.get_runtime_allocator()->allocateList<CompileSpec>(
            number_of_compile_specs);
    if (compile_specs_list == nullptr) {
      return Error::MemoryAllocationFailed;
    }

    // Initialize the spec list for each method spec
    for (size_t j = 0; j < number_of_compile_specs; j++) {
      auto compile_spec_in_program = compile_specs_in_program->Get(j);

      compile_specs_list[j].key = compile_spec_in_program->key()->c_str();
      compile_specs_list[j].value = {
          /*buffer*/ static_cast<void*>(
              const_cast<uint8_t*>(compile_spec_in_program->value()->Data())),
          /*nbytes*/ compile_spec_in_program->value()->size(),
      };
    }

    *out_spec = compile_specs_list;
    return Error::Ok;
  }

  static Result<FreeableBuffer> GetProcessedData(
      const executorch_flatbuffer::BackendDelegate& delegate,
      const Program* program) {
    const executorch_flatbuffer::BackendDelegateDataReference* processed =
        delegate.processed();
    switch (processed->location()) {
      case executorch_flatbuffer::DataLocation::INLINE: {
        const void* data;
        size_t size;
        Error err = program->get_backend_delegate_data(
            processed->index(), &data, &size);
        if (err != Error::Ok) {
          return err;
        }
        return FreeableBuffer(
            data,
            size,
            /*free_fn=*/nullptr);
      }
      case executorch_flatbuffer::DataLocation::SEGMENT: {
        const char* backend_id = delegate.id()->c_str();
        return program->LoadSegment(DataLoader::SegmentInfo(
            DataLoader::SegmentInfo::Type::Backend,
            processed->index(),
            backend_id));
      }
      default:
        ET_LOG(
            Error,
            "Unknown data location %u",
            static_cast<unsigned int>(processed->location()));
        return Error::Internal;
    }
  }

  FreeableBuffer segment_;
  const BackendInterface* backend_;
  DelegateHandle* handle_;
};

/**
 * Runtime state for a chain of instructions.
 */
struct Chain {
  /// Pointer to the associated flatbuffer chain.
  const executorch_flatbuffer::Chain* s_chain_;

  /// Each entry is a list of parameters for a kernel or delegate call.
  Span<InstructionArgs> argument_lists_;
  /// Each instruction will have one kernel (not for delegate).
  OpFunction* kernels_;
};

namespace {

Result<InstructionArgs> gen_instruction_arguments(
    MemoryAllocator* method_allocator,
    size_t num_values,
    EValue* values,
    size_t num_args,
    const int32_t* arg_idxs) {
  EValue** arg_list = method_allocator->allocateList<EValue*>(num_args);
  if (arg_list == nullptr) {
    return Error::MemoryAllocationFailed;
  }
  for (size_t i = 0; i < num_args; ++i) {
    int32_t arg_idx = arg_idxs[i];
    ET_CHECK_OR_RETURN_ERROR(
        static_cast<size_t>(arg_idx) < num_values,
        InvalidProgram,
        "Arg index %zd >= %" ET_PRIsize_t,
        static_cast<ssize_t>(arg_idx),
        num_values);
    arg_list[i] = &values[arg_idx];
  }
  return InstructionArgs(arg_list, num_args);
}

Result<bool> parse_cond_value(const EValue& cond_value) {
  // The cond value attached to the JF instruction at the beginning of an
  // if/else branch is a Tensor which we parse and decide whether to continue
  // to execute the if branch or jump to the else branch.
  // The cond value attached to the JF instruction at the end of the if branch
  // is a Bool Scalar which resolves to false and points us to the instruction
  // to jump to which will take us to a point that is after the else branch.
  if (cond_value.isTensor()) {
    const executorch::aten::Tensor& cond_val = cond_value.toTensor();

    // All the tensors and scalar cond values should be of bool type
    // currently. If that's not the case then something is wrong in the model
    // and we should exit.
    ET_CHECK_OR_RETURN_ERROR(
        executorch::aten::ScalarType::Bool == cond_val.scalar_type(),
        InvalidProgram,
        "Expected dtype of %" PRId8 " got %" PRId8,
        static_cast<int8_t>(executorch::aten::ScalarType::Bool),
        static_cast<int8_t>(cond_val.scalar_type()));

    const bool* cond_data = cond_val.const_data_ptr<bool>();
    ET_CHECK_OR_RETURN_ERROR(
        cond_data != nullptr, InvalidState, "Tensor data is null");
    for (size_t i = 0; i < static_cast<size_t>(cond_val.numel()); i++) {
      if (!cond_data[i]) {
        return false;
      }
    }
  } else if (cond_value.isBool()) {
    if (!cond_value.toBool()) {
      return false;
    }
  } else {
    ET_LOG(
        Error, "Unsupported JF EValue type %" PRIu32, (uint32_t)cond_value.tag);
    return Error::InvalidProgram;
  }

  return true;
}

} // namespace

Result<size_t> Method::get_num_external_constants() {
  auto flatbuffer_values = serialization_plan_->values();
  size_t n_value = flatbuffer_values->size();

  size_t n_external_constants = 0;
  for (size_t i = 0; i < n_value; ++i) {
    auto serialization_value = flatbuffer_values->Get(i);
    // Ensure values are non-null.
    // Note that as a side-effect of this check, we're guaranteed that all
    // values are non-null, so later loops can skip that check.
    ET_CHECK_OR_RETURN_ERROR(
        serialization_value != nullptr &&
            (serialization_value->val_type() ==
                 executorch_flatbuffer::KernelTypes::Null ||
             serialization_value->val() != nullptr),
        InvalidProgram,
        "Null value at index %" ET_PRIsize_t,
        i);
    // Ignore non-tensor types.
    if (serialization_value->val_type() !=
        executorch_flatbuffer::KernelTypes::Tensor) {
      continue;
    }
    const auto s_tensor = static_cast<const executorch_flatbuffer::Tensor*>(
        serialization_value->val());

    // An external constant is tagged with EXTERNAL and has no
    // allocation_info.
    if (s_tensor->extra_tensor_info() != nullptr &&
        s_tensor->extra_tensor_info()->location() ==
            executorch_flatbuffer::TensorDataLocation::EXTERNAL &&
        s_tensor->allocation_info() == nullptr) {
      n_external_constants++;
    }
  }
  return n_external_constants;
}

Error Method::parse_external_constants(const NamedDataMap* external_data_map) {
  ET_CHECK_OR_RETURN_ERROR(
      external_data_map != nullptr, InvalidState, "external_data_map is null");
  auto flatbuffer_values = serialization_plan_->values();
  size_t n_value = flatbuffer_values->size();

  // n_external_constants_ counts the number of successfully-initialized
  // external constants for ~Method() to clean up, and is incremented at the
  // bottom of the loop. This makes it safe for errors to return without
  // updating any state.
  n_external_constants_ = 0;
  for (size_t i = 0; i < n_value; ++i) {
    auto serialization_value = flatbuffer_values->Get(i);
    // Ignore non-tensor types.
    if (serialization_value->val_type() !=
        executorch_flatbuffer::KernelTypes::Tensor) {
      continue;
    }
    const auto s_tensor = static_cast<const executorch_flatbuffer::Tensor*>(
        serialization_value->val());
    // Constant tensors are resolved here; tensors with allocation_info are
    // mutable and are resolved in parse_values.
    if (s_tensor->extra_tensor_info() == nullptr ||
        s_tensor->extra_tensor_info()->location() !=
            executorch_flatbuffer::TensorDataLocation::EXTERNAL ||
        s_tensor->allocation_info() != nullptr) {
      continue;
    }
    ET_CHECK_OR_RETURN_ERROR(
        s_tensor->extra_tensor_info()->fully_qualified_name() != nullptr,
        InvalidExternalData,
        "Fully qualified name of external tensor is null at index %zu",
        i);

    const char* key =
        s_tensor->extra_tensor_info()->fully_qualified_name()->c_str();

    // Check if this tensor has already been resolved.
    if (get_data_by_key(
            key, Span<NamedData>(external_constants_, n_external_constants_)) !=
        nullptr) {
      continue;
    }
    Result<const TensorLayout> tensor_layout =
        external_data_map->get_tensor_layout(key);
    if (!tensor_layout.ok()) {
      ET_LOG(Info, "Failed to get metadata for key %s", key);
      return tensor_layout.error();
    }
    // Check external tensor compatibility.
    Error err =
        deserialization::validateTensorLayout(s_tensor, tensor_layout.get());
    if (err != Error::Ok) {
      return err;
    }
    // Save the key.
    external_constants_[n_external_constants_].key = key;

    // Save the buffer.
    Result<FreeableBuffer> buffer = external_data_map->get_data(key);
    ET_CHECK_OR_RETURN_ERROR(
        buffer.ok(),
        InvalidExternalData,
        "Buffer retrieved from get_data is not valid");
    new (&external_constants_[n_external_constants_].buffer)
        FreeableBuffer(std::move(buffer.get()));

    n_external_constants_ += 1;
  }
  return Error::Ok;
}

Error Method::parse_values(const NamedDataMap* external_data_map) {
  auto flatbuffer_values = serialization_plan_->values();
  ET_CHECK_OR_RETURN_ERROR(
      flatbuffer_values != nullptr, InvalidProgram, "Missing values");
  const size_t n_value = flatbuffer_values->size();
  values_ = memory_manager_->method_allocator()->allocateList<EValue>(n_value);
  if (values_ == nullptr) {
    return Error::MemoryAllocationFailed;
  }
  const size_t n_input = inputs_size();
  if (n_input > 0) {
    input_set_ =
        memory_manager_->method_allocator()->allocateList<bool>(n_input);
    if (input_set_ == nullptr) {
      return Error::MemoryAllocationFailed;
    }
    for (size_t i = 0; i < n_input; ++i) {
      input_set_[i] = false;
    }
  }

  // Count the number of tensors marked as EXTERNAL for this method. The actual
  // number of external constants may be smaller, eg. if multiple tensors point
  // to the same underlying data buffer.
  // This function also ensures that all flatbuffer_values entries
  // are non-null, so `val_as_X()` calls below are guaranteed to return
  // non-null pointers.
  Result<size_t> max_external_constants = get_num_external_constants();
  if (!max_external_constants.ok()) {
    return max_external_constants.error();
  }
  if (max_external_constants.get() > 0) {
    // Allocate space for external tensors.
    external_constants_ =
        memory_manager_->method_allocator()->allocateList<NamedData>(
            max_external_constants.get());
    if (external_constants_ == nullptr) {
      return Error::MemoryAllocationFailed;
    }
    Error err = parse_external_constants(external_data_map);
    if (err != Error::Ok) {
      return err;
    }
  }

  // n_value_ counts the number of successfully-initialized values for ~Method()
  // to clean up, and is incremented at the bottom of the loop. This makes it
  // safe for errors to return without updating any state.
  n_value_ = 0;

  for (size_t i = 0; i < n_value; ++i) {
    auto serialization_value = flatbuffer_values->Get(i);
    const auto val = serialization_value->val();

    switch (serialization_value->val_type()) {
      case executorch_flatbuffer::KernelTypes::Null: {
        // Placement new as the list elements are not initialized, so calling
        // copy assignment is not defined if its non trivial (Imagine the
        // garbage in values_[i] thinks its an at::Tensor).
        new (&values_[i]) EValue();
      } break;
      case executorch_flatbuffer::KernelTypes::Int: {
        new (&values_[i]) EValue(
            static_cast<const executorch_flatbuffer::Int*>(val)->int_val());
      } break;
      case executorch_flatbuffer::KernelTypes::Double: {
        new (&values_[i])
            EValue(static_cast<const executorch_flatbuffer::Double*>(val)
                       ->double_val());
      } break;
      case executorch_flatbuffer::KernelTypes::Bool: {
        new (&values_[i]) EValue(
            static_cast<const executorch_flatbuffer::Bool*>(val)->bool_val());
      } break;
      case executorch_flatbuffer::KernelTypes::IntList: {
        const auto items =
            static_cast<const executorch_flatbuffer::IntList*>(val)->items();
        ET_CHECK_OR_RETURN_ERROR(
            items != nullptr,
            InvalidProgram,
            "Missing list at index %" ET_PRIsize_t,
            i);
        // Allocate space for boxed and unboxed list representations using
        // values_ as source of truth
        auto* evalp_list =
            memory_manager_->method_allocator()->allocateList<EValue*>(
                items->size());
        auto* int_list =
            memory_manager_->method_allocator()->allocateList<int64_t>(
                items->size());

        // initialize boxed list
        for (size_t j = 0; j < items->size(); j++) {
          auto value_index = items->Get(j);
          ET_CHECK_OR_RETURN_ERROR(
              value_index >= 0 && static_cast<size_t>(value_index) < n_value,
              InvalidProgram,
              "Invalid value index %" PRId64 " for IntList %" ET_PRIsize_t
              " index %" ET_PRIsize_t,
              value_index,
              i,
              j);
          evalp_list[j] = &values_[static_cast<size_t>(value_index)];
        }
        auto* boxed_list_mem =
            memory_manager_->method_allocator()
                ->allocateInstance<BoxedEvalueList<int64_t>>();
        auto boxed_list = new (boxed_list_mem)
            BoxedEvalueList<int64_t>(evalp_list, int_list, items->size());
        new (&values_[i]) EValue(boxed_list);
      } break;
      case executorch_flatbuffer::KernelTypes::BoolList: {
        const auto items =
            static_cast<const executorch_flatbuffer::BoolList*>(val)->items();
        ET_CHECK_OR_RETURN_ERROR(
            items != nullptr,
            InvalidProgram,
            "Missing list at index %" ET_PRIsize_t,
            i);
        // NOTE: This is technically not portable. A platform could technically
        // define boolean as something longer than a byte. This would be an
        // exceptionally rare case, and this type is currently unused in any
        // operators in ATen that we would need to support. To be properly
        // portable here we need to allocate a new array of bool and copy cast
        // the flatbuffer data into it, but because of how exceptionally rare
        // this case is its low prio TODO: jakeszwe
        auto* bool_list_mem =
            memory_manager_->method_allocator()
                ->allocateInstance<executorch::aten::ArrayRef<bool>>();
        auto bool_list = new (bool_list_mem) executorch::aten::ArrayRef<bool>(
            (const bool*)items->data(), items->size());
        new (&values_[i]) EValue(bool_list);
      } break;
      case executorch_flatbuffer::KernelTypes::DoubleList: {
        const auto items =
            static_cast<const executorch_flatbuffer::DoubleList*>(val)->items();
        ET_CHECK_OR_RETURN_ERROR(
            items != nullptr,
            InvalidProgram,
            "Missing list at index %" ET_PRIsize_t,
            i);
        auto* double_list_mem =
            memory_manager_->method_allocator()
                ->allocateInstance<executorch::aten::ArrayRef<double>>();
        auto double_list = new (double_list_mem)
            executorch::aten::ArrayRef<double>(items->data(), items->size());
        new (&values_[i]) EValue(double_list);
      } break;
      case executorch_flatbuffer::KernelTypes::String: {
        const auto fb_str =
            static_cast<const executorch_flatbuffer::String*>(val)
                ->string_val();
        ET_CHECK_OR_RETURN_ERROR(
            fb_str != nullptr,
            InvalidProgram,
            "Missing string at index %" ET_PRIsize_t,
            i);
        auto* char_list_mem =
            memory_manager_->method_allocator()
                ->allocateInstance<executorch::aten::ArrayRef<char>>();
        auto char_list = new (char_list_mem)
            executorch::aten::ArrayRef<char>(fb_str->c_str(), fb_str->size());
        new (&values_[i]) EValue(char_list);
      } break;
      case executorch_flatbuffer::KernelTypes::Tensor: {
        auto t = deserialization::parseTensor(
            program_,
            memory_manager_,
            static_cast<const executorch_flatbuffer::Tensor*>(val),
            external_data_map,
            Span<NamedData>(external_constants_, n_external_constants_));
        if (!t.ok()) {
          ET_LOG(
              Error,
              "Failed parsing tensor at index %" ET_PRIsize_t ": 0x%" PRIx32,
              i,
              static_cast<uint32_t>(t.error()));
          return t.error();
        }
        new (&values_[i]) EValue(t.get());
      } break;
      case executorch_flatbuffer::KernelTypes::TensorList: {
        const auto items =
            static_cast<const executorch_flatbuffer::TensorList*>(val)->items();
        ET_CHECK_OR_RETURN_ERROR(
            items != nullptr, InvalidProgram, "Missing list at index %zu", i);
        // get list of serialization tensors and allocate storage for executor
        // tensors
        auto tensors = deserialization::parseTensorList(
            items,
            values_,
            n_value, // The size of the full array.
            memory_manager_);
        if (!tensors.ok()) {
          ET_LOG(
              Error,
              "Failed parsing tensor list at index %" ET_PRIsize_t
              ": 0x%" PRIx32,
              i,
              static_cast<uint32_t>(tensors.error()));
          return tensors.error();
        }
        auto* boxed_tensor_list_mem =
            memory_manager_->method_allocator()
                ->allocateInstance<BoxedEvalueList<executorch::aten::Tensor>>();
        auto boxed_tensor_list = new (boxed_tensor_list_mem)
            BoxedEvalueList<executorch::aten::Tensor>(std::move(tensors.get()));
        new (&values_[i]) EValue(boxed_tensor_list);
      } break;
      case executorch_flatbuffer::KernelTypes::OptionalTensorList: {
        const auto items =
            static_cast<const executorch_flatbuffer::OptionalTensorList*>(val)
                ->items();
        ET_CHECK_OR_RETURN_ERROR(
            items != nullptr, InvalidProgram, "Missing list at index %zu", i);
        // Same as TensorList but optional<Tensor> instead of Tensor
        auto tensors =
            deserialization::parseListOptionalType<executorch::aten::Tensor>(
                items,
                values_,
                n_value, // The size of the full array.
                memory_manager_);
        if (!tensors.ok()) {
          ET_LOG(
              Error,
              "Failed parsing optional tensor list at index %" ET_PRIsize_t
              ": 0x%" PRIx32,
              i,
              static_cast<uint32_t>(tensors.error()));
          return tensors.error();
        }
        auto* boxed_optional_tensor_list_mem =
            memory_manager_->method_allocator()
                ->allocateInstance<
                    BoxedEvalueList<std::optional<executorch::aten::Tensor>>>();
        auto boxed_optional_tensor_list = new (boxed_optional_tensor_list_mem)
            BoxedEvalueList<std::optional<executorch::aten::Tensor>>(
                std::move(tensors.get()));
        new (&values_[i]) EValue(boxed_optional_tensor_list);
      } break;
      default:
        // flatbuffer enums start at 0, but they generate a hidden NONE enum
        // and give it that value. schema.fbs doesnt show this type, so I
        // subtract one to keep the output in 0 based indexing for a
        // disgruntled debugger seeing this error message and checking
        // schema.fbs
        ET_LOG(
            Error,
            "Unknown KernelTypes value %" PRIu32 " at index %" ET_PRIsize_t,
            static_cast<uint32_t>(serialization_value->val_type()) - 1,
            i);
        return Error::InvalidProgram;
    }

    // ~Method() will try to clean up n_value_ entries in the values_ array.
    // Only increment this once we know the entry is valid, so that we don't try
    // to clean up an uninitialized entry.
    n_value_ = i + 1;
  }
  return Error::Ok;
}

namespace {
/**
 * Private/helper method for populating operator_name from the Operator.
 * operator_name is a char pointer that is already allocated. The size of
 * of this buffer is of size operator_name_size.
 */
Error populate_operator_name(
    const executorch_flatbuffer::Operator* const& op,
    const size_t operator_name_size,
    char* operator_name) {
  const bool has_overload =
      op->overload() != nullptr && op->overload()->size() > 0;

  ET_CHECK_OR_RETURN_ERROR(
      op->name() != nullptr, InvalidProgram, "Missing operator name");
  int cx = snprintf(
      operator_name,
      operator_name_size,
      "%s%s%s",
      op->name()->c_str(),
      // Don't append any overload if the overload string is empty.
      has_overload ? "." : "",
      has_overload ? op->overload()->c_str() : "");
  ET_CHECK_OR_RETURN_ERROR(cx >= 0, Internal, "snprintf failed: %d", cx);
  ET_CHECK_OR_RETURN_ERROR(
      static_cast<size_t>(cx) < operator_name_size,
      Internal,
      "Operator name %s%s%s with length %d "
      "truncated to %" ET_PRIsize_t " due to internal buffer limit.",
      op->name()->c_str(),
      has_overload ? "." : "",
      has_overload ? op->overload()->c_str() : "",
      cx,
      operator_name_size);

  return Error::Ok;
}
} // namespace

Error Method::resolve_operator(
    int32_t op_index,
    OpFunction* kernels,
    size_t kernel_index,
    InstructionArgs args,
    size_t n_args) {
  // TODO(T153506819) Investigate optimizing this function for both
  // space and time.

  // resolve name
  constexpr size_t kTempBufferSizeForName = 100;
  char operator_name[kTempBufferSizeForName];
  const auto ops = serialization_plan_->operators();
  ET_CHECK_OR_RETURN_ERROR(
      ops != nullptr &&
          static_cast<flatbuffers::uoffset_t>(op_index) < ops->size(),
      InvalidProgram,
      "Op index %" PRIu32 " out of range",
      op_index);
  const auto& op = ops->Get(op_index);

  Error err = populate_operator_name(op, kTempBufferSizeForName, operator_name);
  if (err != Error::Ok) {
    return err;
  }

  // resolve tensor meta
  // Since temp allocator can be freed, we optimistically
  // try to use that allocator first.
  auto allocator = memory_manager_->temp_allocator();
  // However, it does not have to be provided, so if it
  // is not provided (or an empty one is provided), we
  // fall back to the method allocator.
  if (allocator == nullptr || allocator->size() == 0) {
    allocator = memory_manager_->method_allocator();
  }
  TensorMeta* meta = allocator->allocateList<TensorMeta>(n_args);
  if (meta == nullptr) {
    if (allocator == memory_manager_->temp_allocator()) {
      memory_manager_->temp_allocator()->reset();
    }
    return Error::MemoryAllocationFailed;
  }

  size_t count = 0;
  for (size_t i = 0; i < n_args; i++) {
    EValue* eval = args[i];
    // handle tensor list as well
    if (eval->isTensor()) {
      auto tensor = eval->toTensor();
      meta[count].dtype_ = tensor.scalar_type();
      executorch::aten::DimOrderType* dim_order_ptr =
          allocator->allocateList<executorch::aten::DimOrderType>(tensor.dim());
      if (dim_order_ptr == nullptr) {
        if (allocator == memory_manager_->temp_allocator()) {
          memory_manager_->temp_allocator()->reset();
        }
        return Error::MemoryAllocationFailed;
      }
      size_t size = tensor.dim();
      err = get_dim_order(tensor, dim_order_ptr, size);
      ET_CHECK_OR_RETURN_ERROR(
          err == Error::Ok,
          InvalidArgument,
          "Error setting dim_order %" ET_PRIsize_t ": 0x%" PRIx32,
          i,
          static_cast<uint32_t>(err));
      meta[count].dim_order_ =
          Span<executorch::aten::DimOrderType>(dim_order_ptr, size);
      count++;
    }
  }

  // Find a kernel with the matching name and tensor meta.
  Result<OpFunction> op_function =
      get_op_function_from_registry(operator_name, {meta, count});
  if (!op_function.ok()) {
    ET_LOG(
        Error,
        "Missing operator: [%" ET_PRIssize_t "] %s",
        static_cast<ssize_t>(op_index),
        operator_name);
    if (allocator == memory_manager_->temp_allocator()) {
      memory_manager_->temp_allocator()->reset();
    }
    return op_function.error();
  }
  kernels[kernel_index] = op_function.get();

  // If we used the temp allocator here, reset it.
  if (allocator == memory_manager_->temp_allocator()) {
    memory_manager_->temp_allocator()->reset();
  }

  return Error::Ok;
}

Result<Method> Method::load(
    executorch_flatbuffer::ExecutionPlan* s_plan,
    const Program* program,
    MemoryManager* memory_manager,
    EventTracer* event_tracer,
    const NamedDataMap* external_data_map) {
  MemoryAllocator* temp_allocator = memory_manager->temp_allocator();
  if (temp_allocator == nullptr) {
    PlatformMemoryAllocator* platform_allocator =
        memory_manager->method_allocator()
            ->allocateInstance<PlatformMemoryAllocator>();
    if (platform_allocator == nullptr) {
      return Error::MemoryAllocationFailed;
    }
    new (platform_allocator) PlatformMemoryAllocator();
    temp_allocator = platform_allocator;
  }
  Method method(program, memory_manager, event_tracer, temp_allocator);
  ET_LOG(Debug, "Loading method: %s.", s_plan->name()->c_str());
  Error err = method.init(s_plan, external_data_map);
  if (err != Error::Ok) {
    return err;
  } else {
    ET_CHECK(method.initialized());
    return method;
  }
}

Error Method::init(
    executorch_flatbuffer::ExecutionPlan* s_plan,
    const NamedDataMap* external_data_map) {
  EXECUTORCH_SCOPE_PROF("Method::init");
  internal::EventTracerProfileMethodScope event_tracer_profile_scope =
      internal::EventTracerProfileMethodScope(event_tracer_, "Method::init");
  ET_CHECK_OR_RETURN_ERROR(
      // Don't use !initialized() here because we also want to fail on the
      // InitializationFailed state.
      init_state_ == InitializationState::Uninitialized,
      InvalidState,
      "Method already initialized, or previously failed to initialize.");
  init_state_ =
      InitializationState::InitializationFailed; // Until proven otherwise
  serialization_plan_ = s_plan;
  auto method_allocator = memory_manager_->method_allocator();

  {
    // Parse the elements of the values_ array.
    Error err = parse_values(external_data_map);
    if (err != Error::Ok) {
      return err;
    }
  }

  {
    // Resolve delegates
    const auto delegates = serialization_plan_->delegates();
    ET_CHECK_OR_RETURN_ERROR(
        delegates != nullptr, InvalidProgram, "Missing delegates field");
    size_t n_delegate = delegates->size();
    delegates_ = method_allocator->allocateList<BackendDelegate>(n_delegate);
    if (delegates_ == nullptr) {
      return Error::MemoryAllocationFailed;
    }

    // Get PTE data map, if it exists.
    auto pte_data_map = program_->get_named_data_map();
    ET_CHECK_OR_RETURN_ERROR(
        pte_data_map.ok() || pte_data_map.error() == Error::NotFound,
        InvalidProgram,
        "Failed to get named data map from program: 0x%" PRIx32,
        static_cast<uint32_t>(pte_data_map.error()));

    const NamedDataMap* named_data_map = nullptr;
    if (external_data_map && pte_data_map.ok()) {
      // Merge external_data_map and pte_data_map if both are present.
      auto merged =
          internal::MergedDataMap::load(external_data_map, pte_data_map.get());
      if (!merged.ok()) {
        return merged.error();
      }
      // Allocate memory for the merged data map.
      merged_data_map_ =
          method_allocator->allocateInstance<internal::MergedDataMap>();
      if (merged_data_map_ == nullptr) {
        return Error::MemoryAllocationFailed;
      }
      new (merged_data_map_) internal::MergedDataMap(std::move(merged.get()));
      named_data_map = merged_data_map_;
    } else if (external_data_map) {
      named_data_map = external_data_map;
    } else if (pte_data_map.ok()) {
      named_data_map = pte_data_map.get();
    }

    // n_delegate_ counts the number of successfully-initialized delegates for
    // ~Method() to clean up, and is incremented at the bottom of the loop. This
    // makes it safe for errors to return without updating any state.
    n_delegate_ = 0;

    for (size_t i = 0; i < n_delegate; ++i) {
      const auto& delegate = *delegates->Get(i);
      BackendInitContext backend_init_context(
          method_allocator,
          /*event_tracer=*/event_tracer_,
          /*method_name=*/serialization_plan_->name()->c_str(),
          /*named_data_map=*/named_data_map);
      Error err = BackendDelegate::Init(
          delegate, program_, backend_init_context, &delegates_[i]);
      if (err != Error::Ok) {
        return err;
      }
      // ~Method() will try to clean up n_delegate_ entries in the delegates_
      // array. Only increment this once we know the entry is valid, so that
      // we don't try to clean up an uninitialized entry.
      n_delegate_ = i + 1;
    }
  }

  {
    // Load chains
    const auto chains = serialization_plan_->chains();
    ET_CHECK_OR_RETURN_ERROR(
        chains != nullptr && chains->size() > 0, InvalidProgram, "No chains");
    n_chains_ = chains->size();
    chains_ = method_allocator->allocateList<Chain>(n_chains_);
    if (chains_ == nullptr) {
      return Error::MemoryAllocationFailed;
    }

    // Try resolving all operators before failing, to make it easier to debug
    // multiple problems at once.
    Error delayed_error = Error::Ok;
    int32_t num_instructions_missing_op = 0;
    for (size_t i = 0; i < n_chains_; ++i) {
      auto s_chain = chains->Get(i);
      auto s_instructions = s_chain->instructions();
      ET_CHECK_OR_RETURN_ERROR(
          s_instructions != nullptr,
          InvalidProgram,
          "Missing instructions in chain %" ET_PRIsize_t,
          i);
      auto num_instructions = s_instructions->size();
      auto chain_instruction_kernels =
          method_allocator->allocateList<OpFunction>(num_instructions);
      if (chain_instruction_kernels == nullptr) {
        return Error::MemoryAllocationFailed;
      }
      auto chain_instruction_arg_lists =
          method_allocator->allocateList<InstructionArgs>(num_instructions);
      if (chain_instruction_arg_lists == nullptr) {
        return Error::MemoryAllocationFailed;
      }

      // Set up the argument lists ahead of time and store pointers to them to
      // use when the instructions are called
      for (size_t instr_idx = 0; instr_idx < s_instructions->size();
           ++instr_idx) {
        const auto instruction = s_instructions->Get(instr_idx);
        // Ensure that the `instr_args_as_X()` calls will return non-null.
        ET_CHECK_OR_RETURN_ERROR(
            instruction != nullptr && instruction->instr_args() != nullptr,
            InvalidProgram,
            "Null instruction at index %" ET_PRIsize_t,
            instr_idx);

        const void* instr_args = instruction->instr_args();
        switch (instruction->instr_args_type()) {
          case executorch_flatbuffer::InstructionArguments::KernelCall: {
            const auto* instr_args_as_KernelCall =
                static_cast<const executorch_flatbuffer::KernelCall*>(
                    instr_args);
            const auto arg_idxs = instr_args_as_KernelCall->args();
            ET_CHECK_OR_RETURN_ERROR(
                arg_idxs != nullptr, InvalidProgram, "KernelCall args missing");
            auto res = gen_instruction_arguments(
                method_allocator,
                n_value_,
                values_,
                arg_idxs->size(),
                arg_idxs->data());
            if (!res.ok()) {
              return res.error();
            }
            chain_instruction_arg_lists[instr_idx] = res.get();
            auto err = resolve_operator(
                instr_args_as_KernelCall->op_index(),
                chain_instruction_kernels,
                instr_idx,
                res.get(),
                arg_idxs->size());
            if (err == Error::OperatorMissing) {
              num_instructions_missing_op++;
            } else if (err == Error::MemoryAllocationFailed) {
              return err;
            } else {
              delayed_error = err;
            }
          } break;
          case executorch_flatbuffer::InstructionArguments::DelegateCall: {
            const auto arg_idxs =
                static_cast<const executorch_flatbuffer::DelegateCall*>(
                    instr_args)
                    ->args();
            ET_CHECK_OR_RETURN_ERROR(
                arg_idxs != nullptr,
                InvalidProgram,
                "DelegateCall args missing");
            auto res = gen_instruction_arguments(
                method_allocator,
                n_value_,
                values_,
                arg_idxs->size(),
                arg_idxs->data());
            if (!res.ok()) {
              return res.error();
            }
            chain_instruction_arg_lists[instr_idx] = res.get();
          } break;
          case executorch_flatbuffer::InstructionArguments::JumpFalseCall: {
            // Validate the index at load time so we can trust it during
            // execution.
            auto index =
                static_cast<const executorch_flatbuffer::JumpFalseCall*>(
                    instr_args)
                    ->cond_value_index();
            ET_CHECK_OR_RETURN_ERROR(
                index >= 0 && static_cast<size_t>(index) < n_value_,
                InvalidProgram,
                "Index %zd negative or >= %" ET_PRIsize_t,
                static_cast<ssize_t>(index),
                n_value_);
            chain_instruction_arg_lists[instr_idx] = InstructionArgs();
          } break;
          default: {
            chain_instruction_arg_lists[instr_idx] = InstructionArgs();
          } break;
        }
      }
      chains_[i] = Chain{
          s_chain,
          Span<InstructionArgs>(chain_instruction_arg_lists, num_instructions),
          chain_instruction_kernels,
      };
    }
    ET_CHECK_OR_RETURN_ERROR(
        num_instructions_missing_op == 0,
        OperatorMissing,
        "There are %zu instructions don't have corresponding operator registered. "
        "See logs for details",
        static_cast<size_t>(num_instructions_missing_op));
    if (delayed_error != Error::Ok) {
      return delayed_error;
    }
  }

  step_state_ = StepState{0, 0};

  init_state_ = InitializationState::Initialized;
  return Error::Ok;
}

ET_NODISCARD Error
Method::set_input(const EValue& input_evalue, size_t input_idx) {
  ET_CHECK_OR_RETURN_ERROR(
      initialized(),
      InvalidState,
      "Input can not be set until method has been initialized.");

  ET_CHECK_OR_RETURN_ERROR(
      step_state_.instr_idx == 0 && step_state_.chain_idx == 0,
      InvalidState,
      "Inputs can not be set mid execution.");

  ET_CHECK_OR_RETURN_ERROR(
      input_idx < inputs_size(),
      InvalidArgument,
      "Input index (%" ET_PRIsize_t
      ") must be less than the number of inputs in method (%" ET_PRIsize_t ").",
      input_idx,
      inputs_size());

  const auto& e = get_value(get_input_index(input_idx));

  if (!(e.isNone() || e.isTensor() || e.isScalar() || e.isString())) {
#if ET_LOG_ENABLED
    std::array<char, kTagNameBufferSize> tag_name;
    tag_to_string(e.tag, tag_name.data(), tag_name.size());
    ET_LOG(
        Error,
        "Input %" ET_PRIsize_t
        " was expected to be a Tensor or primitive but was %s.",
        input_idx,
        tag_name.data());
#endif

    return Error::InvalidArgument;
  }

  if (e.tag != input_evalue.tag) {
#if ET_LOG_ENABLED
    std::array<char, kTagNameBufferSize> e_tag_name;
    std::array<char, kTagNameBufferSize> input_tag_name;
    tag_to_string(e.tag, e_tag_name.data(), e_tag_name.size());
    tag_to_string(
        input_evalue.tag, input_tag_name.data(), input_tag_name.size());
    ET_LOG(
        Error,
        "Input %zu was expected to have type %s but was %s.",
        input_idx,
        e_tag_name.data(),
        input_tag_name.data());
#endif

    return Error::InvalidArgument;
  }

  if (e.isNone()) {
    // no-op
  } else if (e.isTensor()) {
    const auto& t_dst = e.toTensor();
    const auto& t_src = input_evalue.toTensor();

    ET_CHECK_OR_RETURN_ERROR(
        t_dst.scalar_type() == t_src.scalar_type(),
        InvalidArgument,
        "Input %" ET_PRIsize_t
        " has unexpected scalar type: expected %s but was %s.",
        input_idx,
        executorch::runtime::toString(t_dst.scalar_type()),
        executorch::runtime::toString(t_src.scalar_type()));
    // Reset the shape for the Method's input as the size of forwarded input
    // tensor for shape dynamism. Also is a safety check if need memcpy.
    ET_CHECK_OK_OR_RETURN_ERROR(
        resize_tensor(t_dst, t_src.sizes()),
        "Error resizing tensor at input %" ET_PRIsize_t,
        input_idx);
    auto tensor_meta = this->method_meta().input_tensor_meta(input_idx);
    if (tensor_meta->is_memory_planned()) {
      ET_CHECK_OK_OR_RETURN_ERROR(
          internal::copy_tensor_data(t_dst, t_src),
          "Error copying tensor data at input %" ET_PRIsize_t,
          input_idx);
    } else {
      ET_CHECK_OK_OR_RETURN_ERROR(
          internal::share_tensor_data(t_dst, t_src),
          "Error sharing tensor data at input %" ET_PRIsize_t,
          input_idx);
    }
    // Prims have to be the same as what was traced
  } else if (e.isInt()) {
    ET_CHECK_OR_RETURN_ERROR(
        e.toInt() == input_evalue.toInt(),
        InvalidArgument,
        "The %" ET_PRIsize_t
        "-th input of method should have the same value as the input_evalue, but got %" PRId64
        " and %" PRId64,
        input_idx,
        e.toInt(),
        input_evalue.toInt());
  } else if (e.isBool()) {
    ET_CHECK_OR_RETURN_ERROR(
        e.toBool() == input_evalue.toBool(),
        InvalidArgument,
        "The %" ET_PRIsize_t
        "-th input of method should have the same value as the input_evalue, but got %" PRId64
        " and %" PRId64,
        input_idx,
        (int64_t)e.toBool(),
        (int64_t)input_evalue.toBool());
  } else if (e.isDouble()) {
    double lhs = input_evalue.toDouble();
    double rhs = e.toDouble();
    double atol = 1e-4;
    double rtol = 1e-5;
    bool is_equal = true;
    if (std::isnan(lhs) && std::isnan(rhs)) {
      // NaN == NaN
    } else if (
        !std::isfinite(lhs) && !std::isfinite(rhs) &&
        ((lhs > 0) == (rhs > 0))) {
      // -Inf == -Inf
      // +Inf == +Inf
    } else {
      auto allowed_error = atol + std::abs(rtol * rhs);
      auto actual_error = std::abs(lhs - rhs);
      if (!std::isfinite(actual_error) || actual_error > allowed_error) {
        is_equal = false;
      }
    }
    ET_CHECK_OR_RETURN_ERROR(
        is_equal,
        InvalidArgument,
        "The %" ET_PRIsize_t
        "-th input of method should have the same value as the input_evalue, but get %f and %f",
        input_idx,
        lhs,
        rhs);
  } else if (e.isString()) {
    ET_CHECK_OR_RETURN_ERROR(
        e.toString() == input_evalue.toString(),
        InvalidArgument,
        "The %" ET_PRIsize_t
        "-th input of method should have the same value as the input_evalue, but get %s and %s",
        input_idx,
        e.toString().data(),
        input_evalue.toString().data());
  } else {
#if ET_LOG_ENABLED
    std::array<char, kTagNameBufferSize> tag_name;
    tag_to_string(e.tag, tag_name.data(), tag_name.size());
    ET_LOG(Error, "Unsupported input type: %s", tag_name.data());
#endif

    return Error::InvalidArgument;
  }
  input_set_[input_idx] = true;

  return Error::Ok;
}

ET_NODISCARD Error
Method::set_inputs(const executorch::aten::ArrayRef<EValue>& input_evalues) {
  const size_t n_input = inputs_size();
  ET_CHECK_OR_RETURN_ERROR(
      input_evalues.size() == n_input,
      InvalidArgument,
      "Invalid number of inputs provided. Expected %" ET_PRIsize_t
      ", but got %" ET_PRIsize_t,
      n_input,
      input_evalues.size());
  for (size_t i = 0; i < n_input; ++i) {
    ET_CHECK_OK_OR_RETURN_ERROR(set_input(input_evalues[i], i));
  }
  return Error::Ok;
}

ET_NODISCARD Error
Method::set_output_data_ptr(void* buffer, size_t size, size_t output_idx) {
  // Check method state
  ET_CHECK_OR_RETURN_ERROR(
      initialized(),
      InvalidState,
      "Outputs can not be retrieved until method has been initialized.");

  // Check the args
  ET_CHECK_OR_RETURN_ERROR(
      output_idx < outputs_size(),
      InvalidArgument,
      "output_idx: %" ET_PRIsize_t " > num_outputs: %" ET_PRIsize_t,
      output_idx,
      outputs_size());

  auto& output = mutable_value(get_output_index(output_idx));
  if (!output.isTensor()) {
#if ET_LOG_ENABLED
    std::array<char, kTagNameBufferSize> tag_name;
    tag_to_string(output.tag, tag_name.data(), tag_name.size());
    ET_LOG(Error, "Output type: %s is not a tensor.", tag_name.data());
#endif

    return Error::InvalidArgument;
  }

  auto tensor_meta = this->method_meta().output_tensor_meta(output_idx);
  if (tensor_meta->is_memory_planned()) {
    ET_LOG(
        Error,
        "Output %" ET_PRIsize_t
        " is memory planned, or is a constant. Cannot override "
        "the existing data pointer.",
        output_idx);
    return Error::InvalidState;
  }

  auto& t = output.toTensor();
  if (!output.isTensor()) {
#if ET_LOG_ENABLED
    std::array<char, kTagNameBufferSize> tag_name;
    tag_to_string(output.tag, tag_name.data(), tag_name.size());
    ET_LOG(Error, "output type: %s is not a tensor.", tag_name.data());
#endif

    return Error::InvalidArgument;
  }

  ET_CHECK_OR_RETURN_ERROR(
      t.nbytes() <= size,
      InvalidArgument,
      "buffer size: %" ET_PRIsize_t
      " is smaller then expected tensor size: %" ET_PRIsize_t,
      size,
      t.nbytes());

  // Set data
  return internal::set_tensor_data(t, buffer, size);
}

ET_NODISCARD Error Method::get_outputs(EValue* output_evalues, size_t length) {
  ET_CHECK_OR_RETURN_ERROR(
      initialized(),
      InvalidState,
      "Outputs can not be retrieved until method has been initialized.");
  const size_t n_output = outputs_size();
  ET_CHECK_OR_RETURN_ERROR(
      length >= n_output,
      InvalidArgument,
      "The given array is not large enough to hold all outputs.");
  for (size_t i = 0; i < n_output; ++i) {
    output_evalues[i] = get_output(i);
  }
  for (size_t i = n_output; i < length; ++i) {
    output_evalues[i] = EValue();
  }
  return Error::Ok;
}

ET_NODISCARD Error Method::get_inputs(EValue* input_evalues, size_t length) {
  ET_CHECK_OR_RETURN_ERROR(
      initialized(),
      InvalidState,
      "Inputs can not be retrieved until method has been initialized.");
  const size_t n_input = inputs_size();
  ET_CHECK_OR_RETURN_ERROR(
      length >= n_input,
      InvalidArgument,
      "The given array is not large enough to hold all inputs.");

  for (size_t i = 0; i < n_input; ++i) {
    input_evalues[i] = values_[get_input_index(i)];
    // Accessing inputs this way is deprecated.
    // We assume the users to be responsible to set the inputs they get.
    input_set_[i] = true;
  }
  for (size_t i = n_input; i < length; ++i) {
    input_evalues[i] = EValue();
  }
  return Error::Ok;
}

Error Method::execute_instruction() {
  auto& chain = chains_[step_state_.chain_idx];
  auto instructions = chain.s_chain_->instructions();

  ET_CHECK_OR_RETURN_ERROR(
      step_state_.instr_idx < instructions->size(),
      Internal,
      "Instr index %" ET_PRIsize_t " >= chain[%" ET_PRIsize_t
      "] instr count %" ET_PRIsize_t,
      step_state_.instr_idx,
      step_state_.chain_idx,
      (size_t)instructions->size());

  auto instruction = instructions->Get(step_state_.instr_idx);
  size_t next_instr_idx = step_state_.instr_idx + 1;
  Error err = Error::Ok;

  switch (instruction->instr_args_type()) {
    case executorch_flatbuffer::InstructionArguments::KernelCall: {
      EXECUTORCH_SCOPE_PROF("OPERATOR_CALL");
      internal::EventTracerProfileOpScope event_tracer_op_scope =
          internal::EventTracerProfileOpScope(event_tracer_, "OPERATOR_CALL");
      // TODO(T147221312): Also expose tensor resizer via the context.
      KernelRuntimeContext context(event_tracer_, temp_allocator_);
      auto args = chain.argument_lists_[step_state_.instr_idx];
      chain.kernels_[step_state_.instr_idx](context, args);
      // We reset the temp_allocator after the switch statement
      err = context.failure_state();
      if (err != Error::Ok) {
        // We know that instr_args_as_KernelCall is non-null because it was
        // checked at init time.
        auto op_index = instruction->instr_args_as_KernelCall()->op_index();
        ET_UNUSED auto op = serialization_plan_->operators()->Get(op_index);
        ET_LOG(
            Error,
            "KernelCall failed at instruction %" ET_PRIsize_t ":%" ET_PRIsize_t
            " in operator %s.%s: 0x%x",
            step_state_.chain_idx,
            step_state_.instr_idx,
            op->name()->c_str(),
            op->overload()->c_str(),
            (unsigned int)err);
        for (size_t i = 0; i < args.size(); ++i) {
          ET_LOG(
              Error,
              "arg %u with type id %u",
              (unsigned int)i,
              (unsigned int)args[i]->tag);
        }
        // TODO(T153804650): Consider logging the EValues to help with
        // debugging. This is a failure path, and it doesn't matter if it's a
        // little slow. Do the same for DelegateCall errors.
      }
    } break;
    case executorch_flatbuffer::InstructionArguments::DelegateCall: {
      EXECUTORCH_SCOPE_PROF("DELEGATE_CALL");
      internal::EventTracerProfileOpScope event_tracer_op_scope =
          internal::EventTracerProfileOpScope(event_tracer_, "DELEGATE_CALL");
      // We know that instr_args_as_DelegateCall is non-null because it was
      // checked at init time.
      auto delegate_idx =
          instruction->instr_args_as_DelegateCall()->delegate_index();
      ET_CHECK_OR_RETURN_ERROR(
          static_cast<size_t>(delegate_idx) < n_delegate_,
          Internal,
          "DELEGATE_CALL index %" PRIu32 " >= num delegates %" ET_PRIsize_t
          " at instruction %" ET_PRIsize_t,
          delegate_idx,
          n_delegate_,
          step_state_.instr_idx);
      BackendExecutionContext backend_execution_context(
          /*event_tracer=*/event_tracer_,
          /*temp_allocator=*/temp_allocator_,
          /*method_name=*/serialization_plan_->name()->c_str());
      err = delegates_[delegate_idx].Execute(
          backend_execution_context,
          chain.argument_lists_[step_state_.instr_idx]);
      if (err != Error::Ok) {
        ET_LOG(
            Error,
            "CALL_DELEGATE execute failed at instruction %" ET_PRIsize_t
            ": 0x%" PRIx32,
            step_state_.instr_idx,
            static_cast<uint32_t>(err));
      }

      // Log all the arguments of the delegate call. Ideally we'd only like to
      // log the outputs of the delegate, but currently we cannot know from the
      // arguments which are the inputs and which are the outputs, so we just
      // log everything. This will be changed in the future when the inputs and
      // ouputs are separate lists.
#ifdef ET_EVENT_TRACER_ENABLED
      for (size_t i = 0;
           i < chain.argument_lists_[step_state_.instr_idx].size();
           i++) {
        EValue* arg = chain.argument_lists_[step_state_.instr_idx].data()[i];
        internal::event_tracer_log_evalue(event_tracer_, *arg);
      }
#endif
    } break;
    case executorch_flatbuffer::InstructionArguments::JumpFalseCall: {
      EXECUTORCH_SCOPE_PROF("JF_CALL");
      internal::EventTracerProfileOpScope event_tracer_op_scope =
          internal::EventTracerProfileOpScope(event_tracer_, "JF_CALL");
      // We know that instr_args_as_JumpFalseCall is non-null because it was
      // checked at init time.
      auto jf_call = instruction->instr_args_as_JumpFalseCall();
      // We know that index is a valid values_ index because it was checked at
      // init time.
      auto index = jf_call->cond_value_index();
      Result<bool> jf_result = parse_cond_value(values_[index]);
      if (jf_result.ok()) {
        if (!jf_result.get()) {
          next_instr_idx = jf_call->destination_instruction();
        }
      } else {
        err = jf_result.error();
      }
    } break;
    case executorch_flatbuffer::InstructionArguments::MoveCall: {
      EXECUTORCH_SCOPE_PROF("MOVE_CALL");
      internal::EventTracerProfileOpScope event_tracer_op_scope =
          internal::EventTracerProfileOpScope(event_tracer_, "MOVE_CALL");
      // We know that instr_args_as_MoveCall is non-null because it was checked
      // at init time.
      auto move_call = instruction->instr_args_as_MoveCall();
      mutable_value(move_call->move_to()) = get_value(move_call->move_from());
    } break;
    case executorch_flatbuffer::InstructionArguments::FreeCall: {
      EXECUTORCH_SCOPE_PROF("FREE_CALL");
      internal::EventTracerProfileOpScope event_tracer_op_scope =
          internal::EventTracerProfileOpScope(event_tracer_, "FREE_CALL");
      // We know that instr_args_as_FreeCall is non-null because it was checked
      // at init time.
      auto free_call = instruction->instr_args_as_FreeCall();
      auto t = values_[free_call->value_index()].toTensor();
      internal::reset_data_ptr(t);
    } break;
    default:
      ET_LOG(
          Error,
          "Unknown instruction: %hhu",
          static_cast<uint8_t>(instruction->instr_args_type()));
      err = Error::InvalidProgram;
  }
  // Reset the temp allocator for every instruction.
  if (temp_allocator_ != nullptr) {
    temp_allocator_->reset();
  }
  if (err == Error::Ok) {
    step_state_.instr_idx = next_instr_idx;
  }
  return err;
}

Error Method::reset_execution() {
  ET_CHECK_OR_RETURN_ERROR(
      step_state_.chain_idx == n_chains_,
      InvalidState,
      "Cannot reset until EndOfMethod has been reached.");
  step_state_ = StepState{0, 0};
  return Error::Ok;
}

Error Method::experimental_reset_execution() {
  return reset_execution(); // @lint-ignore CLANGTIDY facebook-hte-Deprecated
}

// Log all the outputs of this method to the event tracer.
void Method::log_outputs() {
#ifdef ET_EVENT_TRACER_ENABLED
  if (event_tracer_ != nullptr) {
    if (event_tracer_->event_tracer_debug_level() >=
        EventTracerDebugLogLevel::kProgramOutputs) {
      for (size_t i = 0; i < outputs_size(); i++) {
        internal::event_tracer_log_evalue_output(event_tracer_, get_output(i));
      }
    }
  }
#endif
}

Error Method::step() {
  EXECUTORCH_PROFILE_INSTRUCTION_SCOPE(
      static_cast<int32_t>(step_state_.chain_idx),
      static_cast<uint32_t>(step_state_.instr_idx));
  internal::EventTracerProfileInstructionScope event_tracer_instr_scope =
      internal::EventTracerProfileInstructionScope(
          event_tracer_,
          static_cast<int32_t>(step_state_.chain_idx),
          static_cast<uint32_t>(step_state_.instr_idx));
  EXECUTORCH_SCOPE_PROF("Method::step");
  EventTracerEntry event_tracer_entry =
      internal::event_tracer_begin_profiling_event(
          event_tracer_, "Method::step");
  ET_CHECK_OR_RETURN_ERROR(
      initialized(),
      InvalidState,
      "Cannot execute until method has been initialized.");

  // If chain_step_ is on n_chains_, then we have no instructions run.
  if (step_state_.chain_idx == n_chains_) {
    return Error::EndOfMethod;
  }

  auto num_instructions =
      chains_[step_state_.chain_idx].s_chain_->instructions()->size();

  // Special case chains with no instructions. These appear for example in a
  // model that just returns the input/a constant.
  if (num_instructions == 0) {
    step_state_.chain_idx += 1;
    return Error::Ok;
  }

  auto status = execute_instruction();
  if (status != Error::Ok) {
    return status;
  }

  internal::event_tracer_end_profiling_event(event_tracer_, event_tracer_entry);
  // end of the current chain, advance to the next chain
  if (step_state_.instr_idx == num_instructions) {
    step_state_.instr_idx = 0;
    step_state_.chain_idx += 1;
    log_outputs();
  }
  return Error::Ok;
}

Error Method::experimental_step() {
  return step();
}

bool Method::in_progress() const {
  return (step_state_.chain_idx != 0 || step_state_.instr_idx != 0) &&
      step_state_.chain_idx < n_chains_;
}

Error Method::execute() {
  internal::event_tracer_create_event_block(event_tracer_, "Execute");
  EventTracerEntry event_tracer_entry =
      internal::event_tracer_begin_profiling_event(
          event_tracer_, "Method::execute");
  EXECUTORCH_SCOPE_PROF("Method::execute");
  ET_CHECK_OR_RETURN_ERROR(
      initialized(),
      NotSupported,
      "Cannot execute until method has been initialized.");
  ET_CHECK_OR_RETURN_ERROR(
      !in_progress(), InvalidState, "Method execution is in progress");
  const size_t n_input = inputs_size();
  for (size_t i = 0; i < n_input; ++i) {
    ET_CHECK_OR_RETURN_ERROR(
        input_set_[i],
        InvalidArgument,
        "Input %" ET_PRIsize_t " has not been set.",
        i);
  }
  ET_LOG(Debug, "Executing method: %s.", method_meta().name());
  if (temp_allocator_ != nullptr) {
    temp_allocator_->reset();
  }

  // Chains are executed sequentially today, but future async designs may
  // branch and run many in parallel or out of order.
  for (step_state_.chain_idx = 0; step_state_.chain_idx < n_chains_;
       ++step_state_.chain_idx) {
    Chain& chain = chains_[step_state_.chain_idx];
    auto instructions = chain.s_chain_->instructions();
    ET_CHECK_OR_RETURN_ERROR(
        instructions != nullptr,
        Internal,
        "chain %" ET_PRIsize_t " has no instructions field",
        step_state_.chain_idx);

    // Loop over instructions
    step_state_.instr_idx = 0;
    while (step_state_.instr_idx < chain.s_chain_->instructions()->size()) {
      EXECUTORCH_PROFILE_INSTRUCTION_SCOPE(
          static_cast<int32_t>(step_state_.chain_idx),
          static_cast<uint32_t>(step_state_.instr_idx));
      internal::EventTracerProfileInstructionScope event_tracer_instr_scope =
          internal::EventTracerProfileInstructionScope(
              event_tracer_,
              static_cast<ChainID>(step_state_.chain_idx),
              static_cast<DebugHandle>(step_state_.instr_idx));
      auto status = execute_instruction();
      if (status != Error::Ok) {
        step_state_ = StepState{0, 0};
        return status;
      }
    }
  }
  internal::event_tracer_end_profiling_event(event_tracer_, event_tracer_entry);
  log_outputs();

  // TODO(jakeszwe, dbort): Decide on calling execute back to back without
  // going through the reset api first.
  return reset_execution(); // @lint-ignore CLANGTIDY facebook-hte-Deprecated
}

MethodMeta Method::method_meta() const {
  auto name = serialization_plan_->name()->c_str();
  auto method_meta = program_->method_meta(name);
  ET_CHECK_MSG(
      method_meta.ok(),
      "Internal error: method_meta(%s) returned 0x%" PRIx32,
      name,
      static_cast<uint32_t>(method_meta.error()));
  return method_meta.get();
}

const EValue& Method::get_value(size_t i) const {
  ET_CHECK_MSG(
      i < n_value_, "%" ET_PRIsize_t " >= %" ET_PRIsize_t, i, n_value_);
  return values_[i];
}

EValue& Method::mutable_value(size_t i) {
  ET_CHECK_MSG(
      i < n_value_, "%" ET_PRIsize_t " >= %" ET_PRIsize_t, i, n_value_);
  return values_[i];
}

size_t Method::inputs_size() const {
  const auto* inputs = serialization_plan_->inputs();
  return inputs == nullptr ? 0 : inputs->size();
}

size_t Method::get_input_index(size_t i) const {
  ET_CHECK_MSG(
      i < inputs_size(),
      "%" ET_PRIsize_t " >= %" ET_PRIsize_t,
      i,
      inputs_size());
  return static_cast<size_t>(serialization_plan_->inputs()->Get(i));
}

const EValue& Method::get_input(size_t i) const {
  // Accessing inputs this way is deprecated.
  // We assume the users to be responsible to set the inputs they get.
  input_set_[i] = true;
  return get_value(get_input_index(i));
}

EValue& Method::mutable_input(size_t i) {
  // Accessing inputs this way is deprecated.
  // We assume the users to be responsible to set the inputs they get.
  input_set_[i] = true;
  return mutable_value(get_input_index(i));
}

Result<executorch::aten::Tensor> Method::get_attribute(std::string_view name) {
  auto flatbuffer_values = serialization_plan_->values();
  size_t counter = 0;

  for (size_t i = 0; i < flatbuffer_values->size(); ++i) {
    auto serialization_value = flatbuffer_values->Get(i);
    if (serialization_value->val_type() ==
        executorch_flatbuffer::KernelTypes::Tensor) {
      const auto s_tensor = static_cast<const executorch_flatbuffer::Tensor*>(
          serialization_value->val());
      if (s_tensor->extra_tensor_info() != nullptr &&
          s_tensor->extra_tensor_info()->fully_qualified_name() != nullptr &&
          strcmp(
              s_tensor->extra_tensor_info()->fully_qualified_name()->c_str(),
              name.data()) == 0) {
        if (!this->values_[counter].isTensor()) {
          ET_LOG(
              Error,
              "Attribute tensor not at the expected location. The .pte is likely malformed. Please file a bug report on https://github.com/pytorch/executorch/issues");
          return Error::Internal;
        }
        return this->values_[counter].toTensor();
      }
    }
    ++counter;
  }

  return Error::NotFound;
}

size_t Method::outputs_size() const {
  const auto* outputs = serialization_plan_->outputs();
  return outputs == nullptr ? 0 : outputs->size();
}

size_t Method::get_output_index(size_t i) const {
  ET_CHECK_MSG(
      i < outputs_size(),
      "%" ET_PRIsize_t " >= %" ET_PRIsize_t,
      i,
      outputs_size());
  return static_cast<size_t>(serialization_plan_->outputs()->Get(i));
}

const EValue& Method::get_output(size_t i) const {
  return get_value(get_output_index(i));
}

EValue& Method::mutable_output(size_t i) {
  return mutable_value(get_output_index(i));
}

EventTracer* Method::get_event_tracer() {
  return event_tracer_;
}

Method::~Method() {
  // Destroy the values. It's necessary in ATen mode, where the refcount of
  // Tensors needs to be decremented properly.
  if (values_ != nullptr) {
    for (size_t i = 0; i < n_value_; ++i) {
      values_[i].~EValue();
    }
  }
  // Free any resources associated with delegate backends.
  if (delegates_ != nullptr) {
    for (size_t i = 0; i < n_delegate_; i++) {
      delegates_[i].~BackendDelegate();
    }
  }
  // Free resources associated with external constants.
  for (const auto i : c10::irange(n_external_constants_)) {
    external_constants_[i].buffer.~FreeableBuffer();
  }
  // Free the MergedDataMap.
  if (merged_data_map_ != nullptr) {
    merged_data_map_->~MergedDataMap();
  }
  // All other fields are trivially destructible.
}
} // namespace ET_RUNTIME_NAMESPACE
} // namespace executorch