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#include <torch/csrc/profiler/data_flow.h>
#include <c10/util/overloaded.h>
#include <torch/csrc/profiler/collection.h>
namespace torch::profiler::impl {
namespace {
static constexpr TensorImplAddress NoTensorImpl{nullptr};
// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
struct RawTensorInfo {
TensorImplAddress impl_;
StorageImplData storage_;
c10::Device device_;
bool is_free_;
// Used to assign back to the original structs.
std::reference_wrapper<std::optional<AllocationID>> allocation_id_ref_;
std::reference_wrapper<std::optional<TensorID>> id_ref_;
};
struct RawTensors {
std::vector<RawTensorInfo>& get() {
return tensors_;
}
void operator()(TensorMetadata& t) {
tensors_.emplace_back(RawTensorInfo{
t.impl(), t.data_, t.device_, false, t.allocation_id_, t.id_});
}
void operator()(std::optional<TensorMetadata>& t) {
if (t.has_value()) {
(*this)(*t);
}
}
void operator()(ExtraFields<EventType::Allocation>& a) {
const StorageImplData ptr{a.ptr_};
const auto is_free = a.alloc_size_ < 0;
tensors_.emplace_back(RawTensorInfo{
NoTensorImpl, ptr, a.device(), is_free, a.allocation_id_, a.id_});
}
void operator()(std::vector<TensorMetadata>& t) {
for (auto& ti : t) {
(*this)(ti);
}
}
template <typename T>
void operator()(T&) {}
std::vector<RawTensorInfo> tensors_;
};
} // namespace
void calculateUniqueTensorIDs(
std::vector<std::shared_ptr<Result>>& sorted_results) {
// This task is equivilent to https://leetcode.com/problems/number-of-islands/
// We first cluster events with a greedy index assignment, and then merge
// groups that overlap.
std::vector<RawTensorInfo> tensors;
// Flatten results to a uniform representation.
// --------------------------------------------------------------------------
{
RawTensors raw_tensors;
// The python tracer caches values, so it's only safe to use the first case.
ska::flat_hash_set<PyModuleSelf> seen_modules;
ska::flat_hash_set<PyOptimizerSelf> seen_optimizers;
for (auto& result : sorted_results) {
result->visit(c10::overloaded(
[&](ExtraFields<EventType::TorchOp>& torch_op) {
for (auto& i : torch_op.inputs_) {
std::visit(raw_tensors, i);
}
},
[&](ExtraFields<EventType::PyCall>& py_call) {
// torch.nn.Module
if (py_call.module_.has_value() &&
seen_modules.insert(py_call.module_->self_).second) {
for (auto& p : py_call.module_->parameters_) {
raw_tensors(p.metadata_);
raw_tensors(p.grad_metadata_);
}
}
// torch.optim.Optimizer
if (py_call.optimizer_.has_value() &&
seen_optimizers.insert(py_call.optimizer_->self_).second) {
for (auto& p : py_call.optimizer_->parameters_) {
raw_tensors(p.metadata_);
raw_tensors(p.grad_metadata_);
for (auto& state_i : p.state_) {
raw_tensors(state_i.second);
}
}
}
},
[&](auto& i) { raw_tensors(i); }));
}
tensors = std::move(raw_tensors.tensors_);
}
// Assign IDs to solve ABA for Storage.
// --------------------------------------------------------------------------
{
size_t counter{1};
using key_t = std::pair<StorageImplData, c10::Device>;
ska::flat_hash_map<key_t, size_t, HashCombine> versions;
for (auto& t : tensors) {
auto inserted = versions.insert({{t.storage_, t.device_}, counter});
counter += inserted.second;
t.allocation_id_ref_.get().emplace(AllocationID(inserted.first->second));
if (t.is_free_) {
versions.erase(inserted.first);
}
}
}
// Handle any allocation events which we cannot prove are for Tensor storage.
// --------------------------------------------------------------------------
{
ska::flat_hash_set<AllocationID> tensor_set;
for (const auto& t : tensors) {
if (t.impl_ != NoTensorImpl) {
// NOLINTNEXTLINE(bugprone-unchecked-optional-access)
tensor_set.insert(t.allocation_id_ref_.get().value());
}
}
tensors.erase(
std::remove_if(
tensors.begin(),
tensors.end(),
[&tensor_set](const auto& i) {
auto it = tensor_set.find(i.allocation_id_ref_.get().value());
return it == tensor_set.end();
}),
tensors.end());
}
// Handle the case that the storage of a TensorImpl changed.
// --------------------------------------------------------------------------
using storage_id_pair_t = std::pair<AllocationID, AllocationID>;
ska::flat_hash_set<storage_id_pair_t, HashCombine> same_group_set;
{
ska::flat_hash_map<TensorImplAddress, AllocationID> impl_map;
for (const auto& t : tensors) {
// Storage allocations / frees don't have an associated TensorImpl, so
// we don't want all storages to merge through nullptr.
if (!t.impl_) {
continue;
}
// NOLINTNEXTLINE(bugprone-unchecked-optional-access)
const auto allocation_id = *t.allocation_id_ref_.get();
const auto it = impl_map.insert({t.impl_, allocation_id}).first;
// The pair needs to be sorted for the coalesce step to work properly.
it->second < allocation_id
? same_group_set.insert({it->second, allocation_id})
: same_group_set.insert({allocation_id, it->second});
}
}
// Coalesce groups and assign final IDs.
// --------------------------------------------------------------------------
ska::flat_hash_map<AllocationID, size_t> id_map;
{
std::vector<storage_id_pair_t> unique_pairs;
for (const auto& i : same_group_set) {
unique_pairs.push_back(i);
}
std::sort(unique_pairs.begin(), unique_pairs.end());
size_t current_id{0};
for (const auto& i : unique_pairs) {
auto inserted = id_map.insert({i.first, current_id});
current_id += inserted.second;
id_map.insert({i.second, inserted.first->second});
}
}
// Write back to Tensor IDs.
// --------------------------------------------------------------------------
for (const auto& t : tensors) {
// NOLINTNEXTLINE(bugprone-unchecked-optional-access)
const auto id = id_map.at(t.allocation_id_ref_.get().value());
t.id_ref_.get().emplace(TensorID(id));
}
}
} // namespace torch::profiler::impl
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