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Expose size, dtype and device in HashMap #403

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Merged
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Feb 10, 2025
Merged

Expose size, dtype and device in HashMap #403

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59 changes: 39 additions & 20 deletions pyg_lib/csrc/classes/cpu/hash_map.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,8 @@ struct HashMapImpl {
virtual ~HashMapImpl() = default;
virtual at::Tensor get(const at::Tensor& query) = 0;
virtual at::Tensor keys() = 0;
virtual int64_t size() = 0;
virtual at::ScalarType dtype() = 0;
};

template <typename KeyType>
Expand Down Expand Up @@ -60,16 +62,8 @@ struct CPUHashMapImpl : HashMapImpl {
}

at::Tensor keys() override {
const auto size = static_cast<int64_t>(map_.size());

at::Tensor key;
if (std::is_same<KeyType, int16_t>::value) {
key = at::empty({size}, at::TensorOptions().dtype(at::kShort));
} else if (std::is_same<KeyType, int32_t>::value) {
key = at::empty({size}, at::TensorOptions().dtype(at::kInt));
} else {
key = at::empty({size}, at::TensorOptions().dtype(at::kLong));
}
const at::Tensor key =
at::empty({size()}, at::TensorOptions().dtype(dtype()));
const auto key_data = key.data_ptr<KeyType>();

for (const auto& pair : map_) { // No efficient multi-threading possible :(
Expand All @@ -79,6 +73,18 @@ struct CPUHashMapImpl : HashMapImpl {
return key;
}

int64_t size() override { return static_cast<int64_t>(map_.size()); }

at::ScalarType dtype() override {
if (std::is_same<KeyType, int16_t>::value) {
return at::kShort;
} else if (std::is_same<KeyType, int32_t>::value) {
return at::kInt;
} else {
return at::kLong;
}
}

private:
phmap::flat_hash_map<KeyType, ValueType> map_;
};
Expand Down Expand Up @@ -129,16 +135,8 @@ struct ParallelCPUHashMapImpl : HashMapImpl {
}

at::Tensor keys() override {
const auto size = static_cast<int64_t>(map_.size());

at::Tensor key;
if (std::is_same<KeyType, int16_t>::value) {
key = at::empty({size}, at::TensorOptions().dtype(at::kShort));
} else if (std::is_same<KeyType, int32_t>::value) {
key = at::empty({size}, at::TensorOptions().dtype(at::kInt));
} else {
key = at::empty({size}, at::TensorOptions().dtype(at::kLong));
}
const at::Tensor key =
at::empty({size()}, at::TensorOptions().dtype(dtype()));
const auto key_data = key.data_ptr<KeyType>();

for (const auto& pair : map_) { // No efficient multi-threading possible :(
Expand All @@ -148,6 +146,18 @@ struct ParallelCPUHashMapImpl : HashMapImpl {
return key;
}

int64_t size() override { return static_cast<int64_t>(map_.size()); }

at::ScalarType dtype() override {
if (std::is_same<KeyType, int16_t>::value) {
return at::kShort;
} else if (std::is_same<KeyType, int32_t>::value) {
return at::kInt;
} else {
return at::kLong;
}
}

private:
phmap::parallel_flat_hash_map<
KeyType,
Expand Down Expand Up @@ -234,6 +244,12 @@ struct CPUHashMap : torch::CustomClassHolder {

at::Tensor keys() { return map_->keys(); }

int64_t size() { return map_->size(); }

at::ScalarType dtype() { return map_->dtype(); }

at::Device device() { return at::Device(at::kCPU); }

private:
std::unique_ptr<HashMapImpl> map_;
};
Expand All @@ -245,6 +261,9 @@ TORCH_LIBRARY_FRAGMENT(pyg, m) {
.def(torch::init<at::Tensor&, int64_t>())
.def("get", &CPUHashMap::get)
.def("keys", &CPUHashMap::keys)
.def("size", &CPUHashMap::size)
.def("dtype", &CPUHashMap::dtype)
.def("device", &CPUHashMap::device)
.def_pickle(
// __getstate__
[](const c10::intrusive_ptr<CPUHashMap>& self) -> at::Tensor {
Expand Down
54 changes: 39 additions & 15 deletions pyg_lib/csrc/classes/cuda/hash_map.cu
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Original file line number Diff line number Diff line change
@@ -1,4 +1,5 @@
#include <ATen/ATen.h>
#include <ATen/cuda/CUDAContext.h>
#include <torch/library.h>
#include <limits>

Expand All @@ -23,6 +24,9 @@ struct HashMapImpl {
virtual ~HashMapImpl() = default;
virtual at::Tensor get(const at::Tensor& query) = 0;
virtual at::Tensor keys() = 0;
virtual int64_t size() = 0;
virtual at::ScalarType dtype() = 0;
virtual at::Device device() = 0;
};

#ifndef _WIN32
Expand All @@ -31,7 +35,10 @@ struct CUDAHashMapImpl : HashMapImpl {
public:
using ValueType = int64_t;

CUDAHashMapImpl(const at::Tensor& key, double load_factor) {
CUDAHashMapImpl(const at::Tensor& key, double load_factor)
: device_(key.device()) {
c10::cuda::MaybeSetDevice(key.get_device());

KeyType constexpr empty_key_sentinel = std::numeric_limits<KeyType>::min();
ValueType constexpr empty_value_sentinel = -1;

Expand All @@ -52,6 +59,8 @@ struct CUDAHashMapImpl : HashMapImpl {
}

at::Tensor get(const at::Tensor& query) override {
c10::cuda::MaybeSetDevice(query.get_device());

const auto options =
query.options().dtype(c10::CppTypeToScalarType<ValueType>::value);
const auto out = at::empty({query.numel()}, options);
Expand All @@ -64,21 +73,12 @@ struct CUDAHashMapImpl : HashMapImpl {
}

at::Tensor keys() override {
// TODO This will not work in multi-GPU scenarios.
const auto options = at::TensorOptions()
.device(at::DeviceType::CUDA)
.dtype(c10::CppTypeToScalarType<ValueType>::value);
const auto size = static_cast<int64_t>(map_->size());
c10::cuda::MaybeSetDevice(device_.index());

at::Tensor key;
if (std::is_same<KeyType, int16_t>::value) {
key = at::empty({size}, options.dtype(at::kShort));
} else if (std::is_same<KeyType, int32_t>::value) {
key = at::empty({size}, options.dtype(at::kInt));
} else {
key = at::empty({size}, options);
}
const auto value = at::empty({size}, options);
const auto options = at::TensorOptions().device(device_);
const at::Tensor key = at::empty({size()}, options.dtype(dtype()));
const at::Tensor value = at::empty(
{size()}, options.dtype(c10::CppTypeToScalarType<ValueType>::value));
const auto key_data = key.data_ptr<KeyType>();
const auto value_data = value.data_ptr<ValueType>();

Expand All @@ -90,8 +90,23 @@ struct CUDAHashMapImpl : HashMapImpl {
return key.index_select(0, perm);
}

int64_t size() override { return static_cast<int64_t>(map_->size()); }

at::ScalarType dtype() override {
if (std::is_same<KeyType, int16_t>::value) {
return at::kShort;
} else if (std::is_same<KeyType, int32_t>::value) {
return at::kInt;
} else {
return at::kLong;
}
}

at::Device device() override { return device_; }

private:
std::unique_ptr<cuco::static_map<KeyType, ValueType>> map_;
at::Device device_;
};
#endif

Expand Down Expand Up @@ -135,6 +150,12 @@ struct CUDAHashMap : torch::CustomClassHolder {
#endif
}

int64_t size() { return map_->size(); }

at::ScalarType dtype() { return map_->dtype(); }

at::Device device() { return map_->device(); }

private:
#ifndef _WIN32
std::unique_ptr<HashMapImpl> map_;
Expand All @@ -148,6 +169,9 @@ TORCH_LIBRARY_FRAGMENT(pyg, m) {
.def(torch::init<at::Tensor&, double>())
.def("get", &CUDAHashMap::get)
.def("keys", &CUDAHashMap::keys)
.def("size", &CUDAHashMap::size)
.def("dtype", &CUDAHashMap::dtype)
.def("device", &CUDAHashMap::device)
.def_pickle(
// __getstate__
[](const c10::intrusive_ptr<CUDAHashMap>& self) -> at::Tensor {
Expand Down
9 changes: 9 additions & 0 deletions test/classes/test_hash_map.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,12 @@

from pyg_lib.testing import withCUDA

INT_TO_DTPYE = {
2: torch.short,
3: torch.int,
4: torch.long,
}


@withCUDA
@pytest.mark.parametrize('dtype', [torch.short, torch.int, torch.long])
Expand All @@ -22,6 +28,9 @@ def test_hash_map(dtype, device):
else:
raise NotImplementedError(f"Unsupported device '{device}'")

assert hash_map.size() == 4
assert INT_TO_DTPYE[hash_map.dtype()] == dtype
assert hash_map.device() == device
assert hash_map.keys().equal(key)
assert hash_map.keys().dtype == dtype
expected = torch.tensor([2, 1, 3, -1], device=device)
Expand Down
3 changes: 3 additions & 0 deletions test/csrc/classes/test_hash_map.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,9 @@ TEST(HashMapTest, BasicAssertions) {
auto key = at::tensor({0, 10, 30, 20}, options);

auto map = pyg::classes::CPUHashMap(key);
EXPECT_EQ(map.size(), 4);
EXPECT_EQ(map.dtype(), at::kLong);
EXPECT_EQ(map.device(), at::Device(at::kCPU));
EXPECT_TRUE(at::equal(map.keys(), key));

auto query = at::tensor({30, 10, 20, 40}, options);
Expand Down
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