xref: /aosp_15_r20/external/pytorch/torch/csrc/jit/runtime/static/memory_planner.cpp (revision da0073e96a02ea20f0ac840b70461e3646d07c45)

#include <c10/core/alignment.h>
#include <torch/csrc/jit/runtime/static/memory_planner.h>

#include <ATen/Tensor.h>
#include <torch/csrc/jit/ir/alias_analysis.h>
#include <torch/csrc/jit/jit_log.h>
#include <torch/csrc/jit/runtime/static/impl.h>
#include <iterator>

namespace torch::jit {

namespace {

bool isUnmanagedSpecialCase(const ProcessedNode& pnode, size_t output_idx) {
  DCHECK(output_idx < pnode.outputs().size());
  static const auto to_maybe_copy_out_symbol =
      c10::Symbol::fromQualString("static_runtime::to_maybe_copy_out");
  // Heuristic and special case:
  // If to_maybe_copy_out did not actually do anything in the
  // first iteration, assume it will continue to not do anything
  // and avoid managing its output.
  return pnode.node()->kind() == to_maybe_copy_out_symbol &&
      pnode.Output(output_idx).isNone();
}

c10::FastMap<const Value*, at::Tensor*> tensorValueToTensor(
    const std::vector<ProcessedNode>& nodes,
    const c10::FastSet<const Value*>& managed_tensor_values) {
  c10::FastMap<const Value*, at::Tensor*> tensor_value_to_tensor;
  for (auto& pnode : nodes) {
    auto* node = pnode.node();
    for (const auto output_idx : c10::irange(node->outputs().size())) {
      auto* output = node->output(output_idx);

      if (managed_tensor_values.find(output) == managed_tensor_values.end()) {
        continue;
      }

      auto& ival = pnode.Output(output_idx);

      // ival is allowed to be None in special cases, e.g. to_maybe_copy_out
      DCHECK(
          ival.isTensor() ||
          (ival.isNone() && isUnmanagedSpecialCase(pnode, output_idx)));

      if (ival.isTensor()) {
        tensor_value_to_tensor.emplace(
            output,
            // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
            const_cast<at::Tensor*>(&ival.toTensor()));
      }
    }
  }
  return tensor_value_to_tensor;
}

// Don't change the size if it is already aligned, otherwise increase the size
// to make it aligned.
size_t compute_aligned_tensor_size(size_t nbytes) {
  // Note: everything below is size_t
  return (nbytes + c10::gAlignment - 1) & (~(c10::gAlignment - 1));
}

at::DataPtr allocate_buffer(size_t size) {
  at::Allocator* allocator = c10::GetCPUCachingAllocator();
  return allocator->allocate(size);
}

} // namespace

std::vector<StorageGroup> assignStorageToManagedTensors(
    graph_node_list nodes,
    const ManagedTensorRanges& ranges,
    const c10::FastMap<const Value*, at::Tensor*>& tensor_value_to_tensor) {
  std::vector<StorageGroup> managed_tensor_groups;
  // This set maps each Value* to its assigned storage group.
  c10::FastMap<const Value*, size_t> storage_group_mapping;
  // On each iteration, this vector stores the set of storage groups that
  // are available for re-use.
  std::vector<size_t> free_storage_groups;

  auto makeNewStorageGroup = [&](const Value* value) {
    const auto storage_group = managed_tensor_groups.size();
    storage_group_mapping.emplace(value, storage_group);
    auto* tensor_ptr = tensor_value_to_tensor.at(value);
    managed_tensor_groups.emplace_back(tensor_ptr);
  };

  auto assignToAvailableStorageGroup = [&](const Value* value) {
    DCHECK(!free_storage_groups.empty());
    const auto storage_group = free_storage_groups.back();
    TORCH_DCHECK_LT(storage_group, managed_tensor_groups.size());
    storage_group_mapping.emplace(value, storage_group);
    auto* tensor_ptr = tensor_value_to_tensor.at(value);
    managed_tensor_groups[storage_group].addTensor(tensor_ptr);
    free_storage_groups.pop_back();
  };

  auto isManagedTensor = [&](const Value* value) {
    return tensor_value_to_tensor.find(value) != tensor_value_to_tensor.end();
  };

  for (auto* node : nodes) {
    // Assign storage groups to outputs
    for (const auto output_idx : c10::irange(node->outputs().size())) {
      Value* output = node->output(output_idx);
      if (!isManagedTensor(output)) {
        continue;
      }
      if (free_storage_groups.empty()) {
        makeNewStorageGroup(output);
        continue;
      }
      assignToAvailableStorageGroup(output);
    }

    // This node may be the last use of some managed tensors. If so, we
    // can mark the corresponding storage groups as free.
    if (ranges.nodeFreesManagedTensors(node)) {
      const auto& new_free_tensors =
          ranges.availableTensorValuesAfterNode(node);
      for (auto* tensor_value : new_free_tensors) {
        // We need to check this here to handle special cases like
        // to_maybe_copy_out. We don't know if the tensor value is managed until
        // after the first iter, but `ranges` is initialized at load time!
        if (!isManagedTensor(tensor_value)) {
          continue;
        }
        const auto storage_group = storage_group_mapping.at(tensor_value);
        free_storage_groups.push_back(storage_group);
      }
    }
  }
  return managed_tensor_groups;
}

ManagedStorages::ManagedStorages()
    : storages_(nullptr), size_(0), capacity_(0) {}

ManagedStorages::~ManagedStorages() {
  deallocate();
}

void ManagedStorages::allocate(size_t capacity) {
  TORCH_CHECK(!is_allocated(), "Must deallocate before allocating again");
  // `size_` should already be 0 if not allocated, so double check it here
  TORCH_INTERNAL_ASSERT(size_ == 0);
  capacity_ = capacity;
  storages_ = reinterpret_cast<at::StorageImpl*>(
      new unsigned char[capacity_ * sizeof(at::StorageImpl)]);
}

void ManagedStorages::deallocate() {
  if (is_allocated()) {
    for (const size_t idx : c10::irange(size_)) {
      storages_[idx].~StorageImpl();
    }
    delete[] reinterpret_cast<unsigned char*>(storages_);
    capacity_ = 0;
    size_ = 0;
    storages_ = nullptr;
  }
}

void ManagedStorages::append(at::StorageImpl& storageImpl) {
  TORCH_INTERNAL_ASSERT(size_ < capacity_);
  new (&storages_[size_]) at::StorageImpl(
      at::StorageImpl::use_byte_size_t(),
      storageImpl.nbytes(),
      storageImpl.allocator(),
      storageImpl.resizable());
  size_++;
}

namespace {

bool setIncludes(const c10::FastSet<const Value*>& set, const Value* v) {
  return set.find(v) != set.end();
}

std::vector<std::pair<size_t, at::Tensor*>> assignStorageToOutputTensors(
    BlockRunner* block_runner,
    const c10::FastSet<const Value*>& managed_output_tensor_values) {
  std::vector<std::pair<size_t, at::Tensor*>> managed_output_tensors;
  for (auto& pnode : block_runner->nodes()) {
    for (const auto i : c10::irange(pnode.outputs().size())) {
      auto& ival = pnode.Output(i);
      const auto* val = pnode.node()->outputs()[i];
      if (!setIncludes(managed_output_tensor_values, val) ||
          isUnmanagedSpecialCase(pnode, i)) {
        continue;
      }
      TORCH_CHECK(ival.isTensor());
      at::Tensor* tensor = &ival.toTensor();
      managed_output_tensors.emplace_back(0, tensor);
    }
  }
  return managed_output_tensors;
}

} // namespace

MemoryPlanner::MemoryPlanner(
    BlockRunner* block_runner,
    const BlockInfo& block_info,
    bool enable_out_variant,
    bool manage_output_tensors) {
  const auto& managed_tensor_values = block_info.managed_tensor_values();
  const auto& managed_output_tensor_values =
      block_info.managed_output_tensor_values();
  const auto& leaked_values = block_info.leaked_values();

  // collect unmanaged output ivalues
  c10::FastSet<IValue*> unmanaged_ivalues;
  c10::FastSet<IValue*> unmanaged_borrowed_ivalues;
  for (ProcessedNode& pnode : block_runner->nodes()) {
    const auto borrows_outputs = borrowsOutputs(pnode.node()->kind());
    for (const auto i : c10::irange(pnode.outputs().size())) {
      const Value* out_v = pnode.node()->outputs()[i];
      const bool in_managed_tensors = setIncludes(managed_tensor_values, out_v);
      const bool is_unmanaged_special_case = isUnmanagedSpecialCase(pnode, i);
      if (in_managed_tensors && !is_unmanaged_special_case) {
        ++num_managed_tensors_;
      }
      const bool in_managed_sets = in_managed_tensors ||
          // Manage output tensors might have been turned off, so we have to
          // check the flag here
          (manage_output_tensors &&
           setIncludes(managed_output_tensor_values, out_v)) ||
          setIncludes(leaked_values, out_v);

      if (in_managed_sets && !is_unmanaged_special_case) {
        continue;
      }
      if (doesNotHeapAllocateWhenStoredInIValue(*out_v->type())) {
        // Scalars do not need to be freed after each iteration.
        num_unmanaged_scalar_ivalues_++;
      } else if (borrows_outputs) {
        IValue& out = pnode.Output(i);
        unmanaged_borrowed_ivalues.insert(&out);
      } else {
        IValue& out = pnode.Output(i);
        unmanaged_ivalues.insert(&out);
      }
    }
  }
  for (IValue* output : block_runner->outputs()) {
    auto it = unmanaged_borrowed_ivalues.find(output);
    if (it != unmanaged_borrowed_ivalues.end()) {
      borrowed_ivalues_needing_incref_.push_back(output);
      unmanaged_borrowed_ivalues.erase(it);
    } else {
      unmanaged_ivalues.erase(output);
    }
  }

  // copy to unmanaged_ivalues_
  unmanaged_ivalues_.reserve(unmanaged_ivalues.size());
  unmanaged_ivalues_.insert(
      unmanaged_ivalues_.begin(),
      unmanaged_ivalues.begin(),
      unmanaged_ivalues.end());
  unmanaged_borrowed_ivalues_.reserve(unmanaged_borrowed_ivalues.size());
  unmanaged_borrowed_ivalues_.insert(
      unmanaged_borrowed_ivalues_.begin(),
      unmanaged_borrowed_ivalues.begin(),
      unmanaged_borrowed_ivalues.end());

  if (enable_out_variant && manage_output_tensors) {
    managed_output_tensors_ = assignStorageToOutputTensors(
        block_runner, managed_output_tensor_values);
  }
}

uint8_t* MemoryPlanner::allocateBuffer(size_t num_bytes) {
  buffer_ = allocate_buffer(num_bytes);
  uint8_t* start = static_cast<uint8_t*>(buffer_.get());
  buffer_start_ = start;
  buffer_end_ = start + num_bytes;
  return start;
}

void MemoryPlanner::allocateOutputTensors() {
  if (output_buffer_bytes_ == 0) {
    return;
  }
  TORCH_CHECK(
      !output_buffer_,
      "Previously allocated output_buffer_ was not deallocated properly.");
  output_buffer_ = allocate_buffer(output_buffer_bytes_);

  size_t offset = 0;
  uint8_t* start = static_cast<uint8_t*>(output_buffer_.get());

  for (const auto& ms : managed_output_tensors_) {
    auto tensor_size = ms.first;
    auto* tensor = ms.second;
    if (tensor_size == 0) {
      continue;
    }
    TORCH_DCHECK_LE(offset + tensor_size, output_buffer_bytes_);
    void* src = static_cast<void*>(start + offset);
    // NOTE: Populating `ctx` enables clients to take the ownership of a
    // tensor managed by Static Runtime. Some clients use "move" semantics to
    // pass a Tensor object to another holding object (e.g., a thrift message)
    // to avoid `memcpy`.
    // `torch::distributed::detail::WireDumpOp::dumpTensorData is a concrete
    // example of doing this (See `torch::distributed::detail::hasDeleter`).
    // Since this output Tensor object is permanently owned by Static Runtime,
    // this ownership passing does *not* have an intended effect of keeping the
    // Tensor alive till the "owner" releases it: A premature call to
    // `StaticRuntime::deallocateOutputTensors` can destruct such a Tensor
    // object that a holding object believes to retain, causing it to read
    // corrupted values from an already destructed Tensor object. Therefore, a
    // client of receiving Static Runtime-managed Tensors needs to be very
    // careful to call `StaticRuntime::deallocateOutputTensors` after these
    // holding objects are gone.
    tensor->storage().set_data_ptr_noswap(
        at::DataPtr(src, /*ctx=*/src, nullptr, tensor->device()));
    tensor->storage().set_nbytes(tensor_size);
    offset += tensor_size;
  }
  TORCH_DCHECK_EQ(offset, output_buffer_bytes_);
}

void MemoryPlanner::allocate() {
  // TODO: Improve this once D31357486 is landed.
  allocateManagedTensors();
  allocateOutputTensors();
}

void MemoryPlanner::deallocate() {
  for (auto& iv : borrowed_ivalues_needing_incref_) {
    auto old = std::move(*iv);
    *iv = IValue(old);
    c10::MaybeOwnedTraits<c10::IValue>::destroyBorrow(old);
  }
  // for unmanaged ivalues (either tensor or non-tensor), we reset the *iv so
  // that the objects pointed to by *iv may be reclaimed by reference counting
  for (auto& iv : unmanaged_ivalues_) {
    *iv = IValue();
  }
  for (auto& iv : unmanaged_borrowed_ivalues_) {
    c10::MaybeOwnedTraits<c10::IValue>::destroyBorrow(*iv);
  }
  // It's important to call this function after all other owning refs
  // of the managed StorageImpls are cleaned up. It can reset the
  // the StorageImpl's refcount to (# tensors in storage group),
  // so destructing any owning refs afterwards will bring the refcount
  // lower than expected and trigger the debug assertion in
  // ~intrusive_ptr_target.
  deallocateManagedTensors();
  buffer_ = {};
}

void MemoryPlanner::deallocateOutputTensors() {
  size_t output_buffer_bytes = 0;
  for (auto& ms : managed_output_tensors_) {
    auto* tensor = ms.second;
    size_t current_size =
        compute_aligned_tensor_size(tensor->storage().nbytes());
    tensor->storage().unsafeGetStorageImpl()->reset();
    if (current_size > ms.first) {
      ms.first = current_size;
    }
    output_buffer_bytes += ms.first;
  }
  output_buffer_bytes_ = output_buffer_bytes;
  output_buffer_ = {};
}

StandardMemoryPlanner::StandardMemoryPlanner(
    BlockRunner* block_runner,
    const BlockInfo& block_info,
    bool enable_out_variant,
    bool manage_output_tensors,
    bool optimize_memory)
    : MemoryPlanner(
          block_runner,
          block_info,
          enable_out_variant,
          manage_output_tensors) {
  const auto& managed_tensor_values = block_info.managed_tensor_values();
  if (enable_out_variant) {
    const auto tensor_value_to_tensor =
        tensorValueToTensor(block_runner->nodes(), managed_tensor_values);
    if (optimize_memory) {
      managed_tensors_ = assignStorageToManagedTensors(
          block_info.node_ptrs(),
          block_info.managed_tensor_ranges(),
          tensor_value_to_tensor);
    } else {
      for (auto& tensor : tensor_value_to_tensor) {
        managed_tensors_.emplace_back(tensor.second);
      }
    }
  }
}

void StandardMemoryPlanner::allocateManagedTensors() {
  if (managed_bytes_ == 0) {
    return;
  }
  DCHECK(!storages_.empty());
  size_t offset = 0;
  auto* start = allocateBuffer(managed_bytes_);

  reused_tensors_ = 0;
  size_t group_idx = 0;
  for (const size_t storages_idx : c10::irange(storages_.size())) {
    auto tensor_size = storages_nbytes_[storages_idx];
    if (tensor_size == 0) {
      group_idx++;
      continue;
    }
    at::StorageImpl* storageImpl = &storages_[storages_idx];
    TORCH_DCHECK_LE(offset + tensor_size, managed_bytes_);
    void* src = static_cast<void*>(start + offset);

#ifndef NDEBUG
    TORCH_DCHECK_EQ(tensor_size, managed_tensors_[group_idx].maxTensorSize());
    for (auto* tensor : managed_tensors_[group_idx].group()) {
      TORCH_DCHECK_EQ(storageImpl, tensor->storage().unsafeGetStorageImpl());
    }
#endif
    TORCH_DCHECK_NE(managed_tensors_[group_idx].numManagedTensors(), 0);
    reused_tensors_ += managed_tensors_[group_idx].numManagedTensors() - 1;
    storageImpl->set_data_ptr_noswap(
        at::DataPtr(src, src, nullptr, c10::Device(c10::DeviceType::CPU)));
    storageImpl->set_nbytes(tensor_size);

    offset += tensor_size;
    group_idx++;
  }
  TORCH_DCHECK_EQ(offset, managed_bytes_);
}

void StandardMemoryPlanner::deallocateManagedTensors() {
  managed_bytes_ = 0;
  // free memory used by outputs of ops in out variants
  // but keep the TensorImpl and StorageImpl around.

  // We don't have any guarantee that the model doesn't change the
  // Storage for managed tensors out from under us during execution,
  // so we have to check the Storages each time we deallocate.
  unsigned group_idx = 0;
  const bool first_time = storages_.empty();
  if (C10_UNLIKELY(first_time)) {
    if (storages_.is_allocated()) {
      storages_.deallocate();
    }
    storages_.allocate(managed_tensors_.size());
    storages_nbytes_.reserve(managed_tensors_.size());
  }
  for (auto& ms : managed_tensors_) {
    const auto& tensors = ms.group();
    size_t max = ms.maxTensorSize();
    for (auto& tensor : tensors) {
      const auto& storage = tensor->storage();
      size_t current_size = compute_aligned_tensor_size(storage.nbytes());
      at::StorageImpl* tensorStorageImpl = storage.unsafeGetStorageImpl();
      if (C10_UNLIKELY(first_time)) {
        tensorStorageImpl->reset();

        DCHECK(
            storages_.size() == group_idx || storages_.size() == group_idx + 1);
        if (storages_.size() == group_idx) {
          storages_.append(*tensorStorageImpl);
          storages_nbytes_.emplace_back(0);
        }
        at::StorageImpl* newImpl = &storages_[storages_.size() - 1];

        // We want to manage StorageImpls' lifetimes ourselves, but TensorImpl
        // expects to refcount them. unsafe_adapt_non_heap_allocated is our
        // escape hatch: it sets the reference count for the StorageImpl to an
        // impractically high value so that it will never get deallocated by
        // intrusive_ptr, leaving us free to manage its lifetime as we see fit.
        // (Note that allowing it to be deallocated by intrusive_ptr would be
        // UB, because that would entail deleting an object that wasn't
        // allocated with operator new.)
        //
        // For more information, see the doc comment for
        // intrusive_ptr::unsafe_adapt_non_heap_allocated.
        tensor->unsafeGetTensorImpl()->set_storage_keep_dtype(at::Storage(
            c10::intrusive_ptr<at::StorageImpl>::
                unsafe_adapt_non_heap_allocated(newImpl, tensors.size())));
      } else if (C10_UNLIKELY(tensorStorageImpl != &storages_[group_idx])) {
        tensorStorageImpl->reset();

        // If somehow the tensor got different storage, put it back to
        // the shared impl for this group.
        tensor->unsafeGetTensorImpl()->set_storage_keep_dtype(
            at::Storage(c10::intrusive_ptr<at::StorageImpl>::
                            unsafe_adapt_non_heap_allocated(
                                &storages_[group_idx], tensors.size())));
      }
      TORCH_DCHECK_EQ(
          tensor->storage().unsafeGetStorageImpl(), &storages_[group_idx]);
      max = std::max(max, current_size);
    }
    // Static runtime does not know the size of tensors statically, so we use
    // the tensor size from the previous run to allocate tensors for the next
    // run (following C2 tradition), exploiting the fact that tensor storage
    // size does not have to match that of real tensor size. The following logic
    // records the tensor storage size for the next run.
    storages_nbytes_[group_idx++] = max;
    ms.setMaxTensorSize(max);
    managed_bytes_ += max;
  }

  TORCH_DCHECK_EQ(storages_.size(), managed_tensors_.size());
  VLOG(1) << "managed_bytes: " << managed_bytes_;
}

} // namespace torch::jit