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/*
* Copyright (c) 2020 NVIDIA CORPORATION.
* Copyright (c) Chris Choy (chrischoy@ai.stanford.edu).
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
* IN THE SOFTWARE.
*
* Please cite "4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural
* Networks", CVPR'19 (https://arxiv.org/abs/1904.08755) if you use any part
* of the code.
*/
#include "coordinate_map_manager.hpp"
#include "coordinate_map_key.hpp"
#include "errors.hpp"
#include "kernel_region.hpp"
#include "utils.hpp"
#include <pybind11/pybind11.h>
#include <string>
#include <unordered_map>
namespace py = pybind11;
namespace minkowski {
namespace detail {
default_types::stride_type zeros(size_t const len) { return _fill_vec<0>(len); }
default_types::stride_type ones(size_t const len) { return _fill_vec<1>(len); }
} // namespace detail
/*******************************
* Initialization
*******************************/
namespace detail {
template <typename coordinate_type, typename coordinate_field_type>
struct insert_and_map_functor<coordinate_type, coordinate_field_type,
std::allocator, CoordinateMapCPU> {
std::pair<at::Tensor, at::Tensor>
operator()(coordinate_map_key_type &map_key, at::Tensor const &th_coordinate,
CoordinateMapManager<coordinate_type, coordinate_field_type,
std::allocator, CoordinateMapCPU> &manager) {
LOG_DEBUG("initialize_and_map");
uint32_t const N = th_coordinate.size(0);
uint32_t const coordinate_size = th_coordinate.size(1);
coordinate_type *p_coordinate = th_coordinate.data_ptr<coordinate_type>();
auto map = CoordinateMapCPU<coordinate_type, std::allocator>(
N, coordinate_size, map_key.first);
auto map_inverse_map = map.template insert_and_map<true>(
p_coordinate, p_coordinate + N * coordinate_size);
LOG_DEBUG("mapping size:", map_inverse_map.first.size());
// insert moves map
THRUST_CHECK(manager.insert(map_key, map));
auto const &mapping = map_inverse_map.first;
auto const &inverse_mapping = map_inverse_map.second;
// return tensors
at::Tensor th_mapping = torch::empty(
{(int64_t)mapping.size()},
torch::TensorOptions().requires_grad(false).dtype(torch::kInt64));
at::Tensor th_inverse_mapping = torch::empty(
{(int64_t)inverse_mapping.size()},
torch::TensorOptions().requires_grad(false).dtype(torch::kInt64));
// copy_n to int to long
int64_t *p_mapping = th_mapping.data_ptr<int64_t>();
for (default_types::index_type i = 0; i < mapping.size(); ++i) {
p_mapping[i] = mapping[i];
}
int64_t *p_inverse_mapping = th_inverse_mapping.data_ptr<int64_t>();
for (default_types::index_type i = 0; i < inverse_mapping.size(); ++i) {
p_inverse_mapping[i] = inverse_mapping[i];
}
return std::make_pair(std::move(th_mapping), std::move(th_inverse_mapping));
}
};
template <typename coordinate_type, typename coordinate_field_type>
struct insert_field_functor<
coordinate_type, coordinate_field_type, std::allocator, CoordinateMapCPU,
CoordinateFieldMapCPU<coordinate_field_type, coordinate_type,
std::allocator>> {
void
operator()(coordinate_map_key_type &map_key, at::Tensor const &th_coordinate,
CoordinateMapManager<coordinate_type, coordinate_field_type,
std::allocator, CoordinateMapCPU> &manager) {
LOG_DEBUG("insert field");
uint32_t const N = th_coordinate.size(0);
uint32_t const coordinate_size = th_coordinate.size(1);
coordinate_field_type *p_coordinate =
th_coordinate.data_ptr<coordinate_field_type>();
auto map = CoordinateFieldMapCPU<coordinate_field_type, coordinate_type,
std::allocator>(N, coordinate_size,
map_key.first);
THRUST_CHECK(map.insert(p_coordinate, p_coordinate + N * coordinate_size));
LOG_DEBUG("insert map with tensor_stride", map_key.first);
manager.insert_field_map(map_key, map);
}
};
} // namespace detail
/*
* coords: coordinates in IntTensor
* mapping: output mapping in IntTensor
* tensor_strides: current tensor strides this coords will be initializeds
* force_creation: even when there's a duplicate coords with the same tensor
* strides.
* force_remap: if there's duplicate coords, remap
* allow_duplicate_coords: create map when there are duplicates in the
* coordinates
*/
template <typename coordinate_type, typename coordinate_field_type,
template <typename C> class TemplatedAllocator,
template <typename T, template <typename Q> class A>
class CoordinateMapType>
py::object CoordinateMapManager<coordinate_type, coordinate_field_type,
TemplatedAllocator, CoordinateMapType>::
insert_field(at::Tensor const &coordinates,
default_types::stride_type const tensor_stride,
std::string const string_id) {
torch::TensorArg arg_coordinate(coordinates, "coordinates", 0);
torch::CheckedFrom c = "initialize";
torch::checkContiguous(c, arg_coordinate);
// must match coordinate_type
torch::checkScalarType(c, arg_coordinate, torch::kFloat);
torch::checkBackend(c, arg_coordinate.tensor,
detail::is_cpu_coordinate_map<CoordinateMapType>::value
? torch::Backend::CPU
: torch::Backend::CUDA);
torch::checkDim(c, arg_coordinate, 2);
auto const coordinate_size = (index_type)coordinates.size(1);
// Basic assertions
ASSERT(coordinate_size - 1 == tensor_stride.size(),
"The coordinate dimension (coordinate_size - 1):", coordinate_size - 1,
" must match the size of tensor stride:", ArrToString(tensor_stride));
// generate the map_key
coordinate_map_key_type map_key = std::make_pair(tensor_stride, string_id);
if (m_field_coordinates.find(map_key) != m_field_coordinates.end()) {
LOG_DEBUG("CoordinateMapKey collision detected:", map_key,
"generating new string id.");
map_key = get_random_string_id(tensor_stride, string_id);
}
LOG_DEBUG("initializing a map with tensor stride:", map_key.first,
"string id:", map_key.second);
// Create the concurrent coords map
detail::insert_field_functor<coordinate_type, coordinate_field_type,
TemplatedAllocator, CoordinateMapType,
field_map_type>()(map_key, coordinates, *this);
py::object py_key = py::cast(new CoordinateMapKey(coordinate_size, map_key));
return py_key;
}
/* to_sparse_and_map */
/*
* coords: coordinates in IntTensor
* tensor_strides: current tensor strides this coords will be initializeds
*/
template <typename coordinate_type, typename coordinate_field_type,
template <typename C> class TemplatedAllocator,
template <typename T, template <typename Q> class A>
class CoordinateMapType>
std::pair<py::object, std::pair<at::Tensor, at::Tensor>>
CoordinateMapManager<coordinate_type, coordinate_field_type, TemplatedAllocator,
CoordinateMapType>::
field_to_sparse_insert_and_map(
CoordinateMapKey const *p_in_field_map_key,
default_types::stride_type const sparse_tensor_stride,
std::string const sparse_tensor_string_id) {
auto const coordinate_size = p_in_field_map_key->get_coordinate_size();
// Basic assertions
ASSERT(coordinate_size - 1 == sparse_tensor_stride.size(),
"The coordinate dimension (coordinate_size - 1):", coordinate_size - 1,
" must match the size of tensor stride:",
ArrToString(sparse_tensor_stride));
// Find coordinate field
auto const it = m_field_coordinates.find(p_in_field_map_key->get_key());
ASSERT(it != m_field_coordinates.end(), ERROR_MAP_NOT_FOUND);
auto const &field_map = it->second;
auto options = torch::TensorOptions().dtype(torch::kInt).requires_grad(false);
if (!detail::is_cpu_coordinate_map<CoordinateMapType>::value) {
#ifndef CPU_ONLY
auto device_id = at::cuda::current_device();
options = options.device(torch::kCUDA, device_id);
#else
ASSERT(false, ERROR_CPU_ONLY);
#endif
}
// generate the map_key
coordinate_map_key_type map_key =
std::make_pair(sparse_tensor_stride, sparse_tensor_string_id);
if (m_coordinate_maps.find(map_key) != m_coordinate_maps.end()) {
LOG_DEBUG("CoordinateMapKey collision detected:", map_key,
"generating new string id.");
map_key =
get_random_string_id(sparse_tensor_stride, sparse_tensor_string_id);
}
LOG_DEBUG("initializing a field map with tensor stride:", map_key.first,
"string id:", map_key.second);
// Quantize the field with tensor stride.
// The coordinate must be a tensor. Wrap a pointer with a tensor.
at::Tensor int_coordinates =
at::empty({field_map.size(), coordinate_size}, options);
field_map.quantize_coordinates(int_coordinates.data_ptr<coordinate_type>(),
sparse_tensor_stride);
auto const map_inverse_map =
detail::insert_and_map_functor<coordinate_type, coordinate_field_type,
TemplatedAllocator, CoordinateMapType>()(
map_key, int_coordinates, *this);
auto const field_to_sparse_map_key =
std::pair<coordinate_map_key_type, coordinate_map_key_type>{
p_in_field_map_key->get_key(), map_key};
auto result = m_field_to_sparse_maps.insert(
std::pair<
const std::pair<coordinate_map_key_type, coordinate_map_key_type>,
const std::pair<at::Tensor, at::Tensor>>{field_to_sparse_map_key,
map_inverse_map});
LOG_DEBUG("field to sparse tensor map insertion", result.second);
py::object py_key = py::cast(new CoordinateMapKey(coordinate_size, map_key));
return std::make_pair(py_key, map_inverse_map);
}
/*
* Return existing field to sparse map. Raise an except if the map doesn't
* exist
*/
template <typename coordinate_type, typename coordinate_field_type,
template <typename C> class TemplatedAllocator,
template <typename T, template <typename Q> class A>
class CoordinateMapType>
std::pair<at::Tensor, at::Tensor>
CoordinateMapManager<coordinate_type, coordinate_field_type, TemplatedAllocator,
CoordinateMapType>::
get_field_to_sparse_map(CoordinateMapKey const *p_field_key,
CoordinateMapKey const *p_sparse_key) const {
auto key = std::pair<coordinate_map_key_type, coordinate_map_key_type>{
p_field_key->get_key(), p_sparse_key->get_key()};
auto it = m_field_to_sparse_maps.find(key);
ASSERT(it != m_field_to_sparse_maps.end(),
"Field To Sparse Map doesn't exist");
return it->second;
}
/*
* Create a field to sparse map if it doesn't exist.
*/
template <typename coordinate_type, typename coordinate_field_type,
template <typename C> class TemplatedAllocator,
template <typename T, template <typename Q> class A>
class CoordinateMapType>
std::pair<at::Tensor, at::Tensor>
CoordinateMapManager<coordinate_type, coordinate_field_type, TemplatedAllocator,
CoordinateMapType>::
field_to_sparse_map(CoordinateMapKey const *p_in_field_map_key,
CoordinateMapKey const *p_out_sparse_map_key) {
auto const coordinate_size = p_in_field_map_key->get_coordinate_size();
// Basic assertions
ASSERT(coordinate_size == p_out_sparse_map_key->get_coordinate_size(),
"The coordinate dimension mismatch.", coordinate_size,
"!=", p_out_sparse_map_key->get_coordinate_size());
// Find coordinate field
auto const it_field = m_field_coordinates.find(p_in_field_map_key->get_key());
ASSERT(it_field != m_field_coordinates.end(), ERROR_MAP_NOT_FOUND);
auto const &field_map = it_field->second;
auto const it_sparse =
m_coordinate_maps.find(p_out_sparse_map_key->get_key());
ASSERT(it_sparse != m_coordinate_maps.end(), ERROR_MAP_NOT_FOUND);
auto const &sparse_map = it_sparse->second;
auto options = torch::TensorOptions().dtype(torch::kInt).requires_grad(false);
if (!detail::is_cpu_coordinate_map<CoordinateMapType>::value) {
#ifndef CPU_ONLY
auto device_id = at::cuda::current_device();
options = options.device(torch::kCUDA, device_id);
#else
ASSERT(false, ERROR_CPU_ONLY);
#endif
}
auto const map_inverse_map =
sparse_map.field_map(field_map.const_coordinate_data(), field_map.size());
auto const field_to_sparse_map_key =
std::pair<coordinate_map_key_type, coordinate_map_key_type>{
p_in_field_map_key->get_key(), p_out_sparse_map_key->get_key()};
auto result = m_field_to_sparse_maps.insert(
std::pair<
const std::pair<coordinate_map_key_type, coordinate_map_key_type>,
const std::pair<at::Tensor, at::Tensor>>{field_to_sparse_map_key,
map_inverse_map});
LOG_DEBUG("field to sparse tensor map insertion", result.second);
return map_inverse_map;
}
/*
* coords: coordinates in IntTensor
* tensor_strides: current tensor strides this coords will be initializeds
*/
template <typename coordinate_type, typename coordinate_field_type,
template <typename C> class TemplatedAllocator,
template <typename T, template <typename Q> class A>
class CoordinateMapType>
std::pair<py::object, std::pair<at::Tensor, at::Tensor>>
CoordinateMapManager<coordinate_type, coordinate_field_type, TemplatedAllocator,
CoordinateMapType>::
insert_and_map(at::Tensor const &coordinate,
default_types::stride_type const tensor_stride,
std::string const string_id) {
torch::TensorArg arg_coordinate(coordinate, "coordinates", 0);
torch::CheckedFrom c = "initialize";
torch::checkContiguous(c, arg_coordinate);
// must match coordinate_type
torch::checkScalarType(c, arg_coordinate, torch::kInt);
torch::checkBackend(c, arg_coordinate.tensor,
detail::is_cpu_coordinate_map<CoordinateMapType>::value
? torch::Backend::CPU
: torch::Backend::CUDA);
torch::checkDim(c, arg_coordinate, 2);
auto const coordinate_size = (index_type)coordinate.size(1);
// Basic assertions
ASSERT(coordinate_size - 1 == tensor_stride.size(),
"The coordinate dimension (coordinate_size - 1):", coordinate_size - 1,
" must match the size of tensor stride:", ArrToString(tensor_stride));
// generate the map_key
coordinate_map_key_type map_key = std::make_pair(tensor_stride, string_id);
if (m_coordinate_maps.find(map_key) != m_coordinate_maps.end()) {
LOG_DEBUG("CoordinateMapKey collision detected:", map_key,
"generating new string id.");
map_key = get_random_string_id(tensor_stride, string_id);
}
LOG_DEBUG("initializing a map with tensor stride:", map_key.first,
"string id:", map_key.second);
// Create the concurrent coords map
auto const map_inverse_map =
detail::insert_and_map_functor<coordinate_type, coordinate_field_type,
TemplatedAllocator, CoordinateMapType>()(
map_key, coordinate, *this);
LOG_DEBUG("map_inverse_map initialized");
py::object py_key = py::cast(new CoordinateMapKey(coordinate_size, map_key));
LOG_DEBUG("py key initialized");
return std::make_pair(py_key, map_inverse_map);
}
// stride
template <typename coordinate_type, typename coordinate_field_type,
template <typename C> class TemplatedAllocator,
template <typename T, template <typename Q> class A>
class CoordinateMapType>
std::pair<coordinate_map_key_type, bool> CoordinateMapManager<
coordinate_type, coordinate_field_type, TemplatedAllocator,
CoordinateMapType>::stride(coordinate_map_key_type const &in_map_key,
stride_type const &kernel_stride,
std::string const string_id) {
ASSERT(exists(in_map_key), ERROR_MAP_NOT_FOUND);
// check if the key exists.
LOG_DEBUG("In tensor stride:", in_map_key.first,
"kernel stride:", kernel_stride);
coordinate_map_key_type out_map_key(
detail::stride_tensor_stride(in_map_key.first, kernel_stride, false),
string_id == "" ? in_map_key.second : string_id);
LOG_DEBUG("Out stride map key:", out_map_key);
bool const exists_out_map = exists(out_map_key);
if (!exists_out_map) {
// operator[] required mapped_type(), which is not defined.
// ASSERTION already checked that in_map_key exists.
map_type const &in_map = m_coordinate_maps.find(in_map_key)->second;
map_type out_map = in_map.stride(kernel_stride);
insert(out_map_key, out_map);
}
// (key, new map generated flag)
return std::make_pair(out_map_key, !exists_out_map);
}
template <typename coordinate_type, typename coordinate_field_type,
template <typename C> class TemplatedAllocator,
template <typename T, template <typename Q> class A>
class CoordinateMapType>
std::pair<coordinate_map_key_type, bool>
CoordinateMapManager<coordinate_type, coordinate_field_type, TemplatedAllocator,
CoordinateMapType>::
stride_region(coordinate_map_key_type const &in_map_key,
cpu_kernel_region<coordinate_type> &kernel,
stride_type const &out_tensor_stride,
bool const expand_coordinates) {
ASSERT(exists(in_map_key), ERROR_MAP_NOT_FOUND);
LOG_DEBUG("stride_region");
// kernel.tensor_stride must be set to out tensor stride.
// stride_type out_tensor_stride{kernel.tensor_stride(),
// kernel.tensor_stride() +
// kernel.coordinate_size() - 1};
// check if the key exists.
coordinate_map_key_type out_map_key(out_tensor_stride, "");
bool const exists_out_map = exists(out_map_key);
if (!exists_out_map || expand_coordinates) {
LOG_DEBUG("Create a new stride region map for tensor_stride:",
out_tensor_stride);
map_type const &in_map = m_coordinate_maps.find(in_map_key)->second;
map_type out_map = in_map.stride_region(kernel, out_tensor_stride);
if (exists_out_map) {
LOG_DEBUG("coordinate map exists for tensor_stride:", out_tensor_stride);
out_map_key = get_random_string_id(out_tensor_stride, "");
LOG_DEBUG("created a random key:", out_map_key);
}
insert(out_map_key, out_map);
}
// (key, new map generated flag)
return std::make_pair(out_map_key, !exists_out_map || expand_coordinates);
}
template <typename coordinate_type, typename coordinate_field_type,
template <typename C> class TemplatedAllocator,
template <typename T, template <typename Q> class A>
class CoordinateMapType>
std::pair<coordinate_map_key_type, bool>
CoordinateMapManager<coordinate_type, coordinate_field_type, TemplatedAllocator,
CoordinateMapType>::origin() {
ASSERT(m_coordinate_maps.size() > 0, "No coordinate map found");
// check if the key exists.
map_type const &random_map = m_coordinate_maps.begin()->second;
stride_type origin_tensor_stride(random_map.coordinate_size() - 1);
std::for_each(origin_tensor_stride.begin(), origin_tensor_stride.end(),
[](auto &i) { i = 0; });
LOG_DEBUG("origin tensor stride:", origin_tensor_stride);
coordinate_map_key_type origin_map_key(origin_tensor_stride, "");
bool const exists_origin_map = exists(origin_map_key);
if (!exists_origin_map) {
LOG_DEBUG("origin coordinate map not found");
map_type const *p_min_coordinate_map{nullptr};
size_type min_size = std::numeric_limits<size_type>::max();
for (auto map_it = m_coordinate_maps.begin();
map_it != m_coordinate_maps.end(); ++map_it) {
if (min_size > map_it->second.size()) {
p_min_coordinate_map = &(map_it->second);
}
}
if (p_min_coordinate_map != nullptr) {
map_type origin_map = p_min_coordinate_map->origin();
LOG_DEBUG("origin map with size:", origin_map.size(), " inserted");
insert(origin_map_key, origin_map);
} else {
ASSERT(false, "Invalid origin map");
}
}
LOG_DEBUG("return origin()");
// (key, new map generated flag)
return std::make_pair(origin_map_key, !exists_origin_map);
}
template <typename coordinate_type, typename coordinate_field_type,
template <typename C> class TemplatedAllocator,
template <typename T, template <typename Q> class A>
class CoordinateMapType>
std::pair<coordinate_map_key_type, bool>
CoordinateMapManager<coordinate_type, coordinate_field_type, TemplatedAllocator,
CoordinateMapType>::origin_field() {
ASSERT(m_field_coordinates.size() > 0, "No coordinate map found");
// check if the key exists.
field_map_type const &random_map = m_field_coordinates.begin()->second;
stride_type origin_tensor_stride(random_map.coordinate_size() - 1);
std::for_each(origin_tensor_stride.begin(), origin_tensor_stride.end(),
[](auto &i) { i = 0; });
LOG_DEBUG("origin tensor stride:", origin_tensor_stride);
coordinate_map_key_type origin_map_key(origin_tensor_stride, "");
bool const exists_origin_map = exists(origin_map_key);
if (!exists_origin_map) {
LOG_DEBUG("origin coordinate map not found");
field_map_type const *p_min_coordinate_map{nullptr};
size_type min_size = std::numeric_limits<size_type>::max();
for (auto map_it = m_field_coordinates.begin();
map_it != m_field_coordinates.end(); ++map_it) {
if (min_size > map_it->second.size()) {
p_min_coordinate_map = &(map_it->second);
}
}
if (p_min_coordinate_map != nullptr) {
map_type origin_map = p_min_coordinate_map->origin();
LOG_DEBUG("origin map with size:", origin_map.size(), " inserted");
insert(origin_map_key, origin_map);
} else {
ASSERT(false, "Invalid origin map");
}
}
// (key, new map generated flag)
return std::make_pair(origin_map_key, !exists_origin_map);
}
template <typename coordinate_type, typename coordinate_field_type,
template <typename C> class TemplatedAllocator,
template <typename T, template <typename Q> class A>
class CoordinateMapType>
coordinate_map_key_type
CoordinateMapManager<coordinate_type, coordinate_field_type, TemplatedAllocator,
CoordinateMapType>::prune(coordinate_map_key_type const
&in_key,
bool const *keep_begin,
bool const *keep_end) {
auto const map_it = m_coordinate_maps.find(in_key);
ASSERT(map_it != m_coordinate_maps.end(), ERROR_MAP_NOT_FOUND);
// create a coordinate_map_key
coordinate_map_key_type map_key = std::make_pair(in_key.first, "pruned");
if (m_coordinate_maps.find(map_key) != m_coordinate_maps.end()) {
map_key = get_random_string_id(map_key.first, map_key.second);
}
map_type pruned_map = map_it->second.prune(keep_begin, keep_end);
LOG_DEBUG("pruned map with size:", pruned_map.size(), " inserted");
insert(map_key, pruned_map);
return map_key;
}
// Kernel map
namespace detail {
template <typename coordinate_type>
struct kernel_map_functor<coordinate_type, std::allocator, CoordinateMapCPU,
cpu_kernel_map> {
cpu_kernel_map
operator()(CoordinateMapCPU<coordinate_type, std::allocator> const &in_map,
CoordinateMapCPU<coordinate_type, std::allocator> const &out_map,
CUDAKernelMapMode::Mode kernel_map_mode,
cpu_kernel_region<coordinate_type> &kernel) {
return in_map.kernel_map(out_map, kernel);
}
};
template <typename coordinate_type>
struct stride_map_functor<coordinate_type, std::allocator, CoordinateMapCPU,
cpu_kernel_map> {
cpu_kernel_map
operator()(CoordinateMapCPU<coordinate_type, std::allocator> const &in_map,
CoordinateMapCPU<coordinate_type, std::allocator> const &out_map,
default_types::stride_type const &out_tensor_stride) {
return in_map.stride_map(out_map, out_tensor_stride);
}
};
// a partial specialization functor for kernel map in/out swap
template <> struct swap_in_out_map_functor<cpu_kernel_map> {
cpu_kernel_map operator()(cpu_kernel_map const &kernel_map) {
return std::make_pair(kernel_map.second, kernel_map.first);
}
};
template <typename coordinate_type>
struct empty_map_functor<coordinate_type, std::allocator, CoordinateMapCPU,
cpu_kernel_map> {
cpu_kernel_map operator()() { return cpu_kernel_map{}; }
};
} // namespace detail
/*
* Given tensor_stride_src and tensor_stride_dst, find the respective coord_maps
* and return the indices of the coord_map_ind in coord_map_dst
*/
template <typename coordinate_type, typename coordinate_field_type,
template <typename C> class TemplatedAllocator,
template <typename T, template <typename Q> class A>
class CoordinateMapType>
typename CoordinateMapManager<coordinate_type, coordinate_field_type,
TemplatedAllocator,
CoordinateMapType>::kernel_map_type const &
CoordinateMapManager<
coordinate_type, coordinate_field_type, TemplatedAllocator,
CoordinateMapType>::kernel_map(CoordinateMapKey const *p_in_map_key,
CoordinateMapKey const *p_out_map_key) {
// when kernel has volume 1
auto const &map_it = m_coordinate_maps.find(p_in_map_key->get_key());
ASSERT(map_it != m_coordinate_maps.end(), ERROR_MAP_NOT_FOUND);
auto const coordinate_size = map_it->second.coordinate_size();
auto const one_vec = detail::ones(coordinate_size - 1);
auto const offset = torch::empty(
{0}, torch::TensorOptions().dtype(torch::kInt32).requires_grad(false));
return kernel_map(p_in_map_key, p_out_map_key, one_vec, one_vec, one_vec,
RegionType::HYPER_CUBE, offset, false, false);
}
/*
* Given tensor_stride_src and tensor_stride_dst, find the respective coord_maps
* and return the indices of the coord_map_ind in coord_map_dst
*/
template <typename coordinate_type, typename coordinate_field_type,
template <typename C> class TemplatedAllocator,
template <typename T, template <typename Q> class A>
class CoordinateMapType>
typename CoordinateMapManager<coordinate_type, coordinate_field_type,
TemplatedAllocator,
CoordinateMapType>::kernel_map_type const &
CoordinateMapManager<
coordinate_type, coordinate_field_type, TemplatedAllocator,
CoordinateMapType>::kernel_map(CoordinateMapKey const *p_in_map_key,
CoordinateMapKey const *p_out_map_key,
stride_type const &kernel_size, //
stride_type const &kernel_stride,
stride_type const &kernel_dilation,
RegionType::Type const region_type,
at::Tensor const &offset, bool is_transpose,
bool is_pool) {
ASSERT(region_type != RegionType::CUSTOM, "Not implemented yet.");
if (region_type == RegionType::CUSTOM)
ASSERT(offset.is_cuda() ==
!detail::is_cpu_coordinate_map<CoordinateMapType>::value,
"Invalid device for offset");
size_type kernel_dim = kernel_size.size();
ASSERT(kernel_dim == kernel_stride.size(), "kernel size mismatch");
ASSERT(kernel_dim == kernel_dilation.size(), "kernel size mismatch");
// in_coords_key->tensor_stride * kernel_stride ==
// out_coords_key->tensor_stride
kernel_map_key_type const kernel_map_key =
std::make_tuple(p_in_map_key->get_key(), p_out_map_key->get_key(), // maps
kernel_size, kernel_stride, kernel_dilation, // kernels
region_type, is_transpose, is_pool);
const auto &kernel_map_iter = m_kernel_maps.find(kernel_map_key);
LOG_DEBUG("set kernel map key for kernel map:", p_in_map_key->get_key(), "->",
p_out_map_key->get_key());
if (kernel_map_iter == m_kernel_maps.end()) {
// create a kernel map if it exists
auto const in_map_it = m_coordinate_maps.find(p_in_map_key->get_key());
auto const out_map_it = m_coordinate_maps.find(p_out_map_key->get_key());
ASSERT(in_map_it != m_coordinate_maps.end(), "in_map", ERROR_MAP_NOT_FOUND);
ASSERT(out_map_it != m_coordinate_maps.end(), "out_map",
ERROR_MAP_NOT_FOUND);
auto const &in_map = in_map_it->second;
auto const &out_map = out_map_it->second;
LOG_DEBUG("coordinate_size:", in_map.coordinate_size(),
"in tensor_stride:", in_map.get_tensor_stride(),
"out tensor_stride:", out_map.get_tensor_stride());
// +1 for batch index
ASSERT(kernel_dim + 1 == in_map.coordinate_size(), "kernel size mismatch");
ASSERT(kernel_dim + 1 == out_map.coordinate_size(), "kernel size mismatch");
// If either coordinate map is empty
if (in_map.size() == 0 || out_map.size() == 0) {
return detail::empty_map_functor<coordinate_type, TemplatedAllocator,
CoordinateMapType, kernel_map_type>()();
}
if (!is_transpose) {
if (is_pool && (kernel_stride == kernel_size)) {
LOG_DEBUG("generating stride_map");
auto const stride_map =
detail::stride_map_functor<coordinate_type, TemplatedAllocator,
CoordinateMapType, kernel_map_type>()(
in_map, out_map, out_map.get_tensor_stride());
m_kernel_maps[kernel_map_key] = std::move(stride_map);
} else {
LOG_DEBUG("generating kernel map");
// Default kernel map
LOG_DEBUG(
"kernel region with kernel: ",
PtrToString(kernel_size.data(), in_map.coordinate_size() - 1));
LOG_DEBUG(
"kernel region with dilation: ",
PtrToString(kernel_dilation.data(), in_map.coordinate_size() - 1));
auto kernel_region = cpu_kernel_region<coordinate_type>(
region_type, //
in_map.coordinate_size(), //
in_map.get_tensor_stride().data(), //
kernel_size.data(), //
kernel_dilation.data(), //
0, offset.data_ptr<coordinate_type>(), offset.size(0));
auto const kernel_map =
detail::kernel_map_functor<coordinate_type, TemplatedAllocator,
CoordinateMapType, kernel_map_type>()(
in_map, out_map, m_kernel_map_mode, kernel_region);
LOG_DEBUG("kernel_map done");
m_kernel_maps[kernel_map_key] = std::move(kernel_map);
LOG_DEBUG("kernel_map saved");
}
} else { // is_transpose == true
// Check first if the out2in kernel map exists
//
// Create temporary key for the flipped in/out
kernel_map_key_type const swapped_kernel_map_key = std::make_tuple(
p_out_map_key->get_key(), p_in_map_key->get_key(), // maps
kernel_size, kernel_stride, kernel_dilation, // kernels
region_type, false, is_pool);
// Check if the temporary key exists and return swapped in/out
if (m_kernel_maps.find(swapped_kernel_map_key) != m_kernel_maps.end()) {
// copy the in out maps from the existing maps
LOG_DEBUG("found existing kernel_map_key for transposed kernel map");
m_kernel_maps[kernel_map_key] =
detail::swap_in_out_map_functor<kernel_map_type>()(
m_kernel_maps[swapped_kernel_map_key]);
} else { // create in out kernel if it doesn't exist
LOG_DEBUG("No existing kernel_map_key for transposed kernel map");
if (is_pool && kernel_stride == kernel_size) {
// e.g. out_map has tensor stride 2 in_map has tensor stride 4.
// Thus, create a stride map from 2 to 4, out to in.
auto const stride_map =
detail::stride_map_functor<coordinate_type, TemplatedAllocator,
CoordinateMapType, kernel_map_type>()(
out_map, in_map, in_map.get_tensor_stride());
// TODO Replace the kernel_map values to shared pointers.
m_kernel_maps[kernel_map_key] =
detail::swap_in_out_map_functor<kernel_map_type>()(stride_map);
} else {
// Default kernel map
auto kernel_region = cpu_kernel_region<coordinate_type>(
region_type, //
out_map.coordinate_size(), //
out_map.get_tensor_stride().data(), //
kernel_size.data(), //
kernel_dilation.data(), //
0, offset.data_ptr<coordinate_type>(), offset.size(0),
true // is_transpose
);
// out to in kernel map
auto const kernel_map =
detail::kernel_map_functor<coordinate_type, TemplatedAllocator,
CoordinateMapType, kernel_map_type>()(
out_map, in_map, m_kernel_map_mode, kernel_region);
LOG_DEBUG("kernel_map done");
m_kernel_maps[kernel_map_key] =
detail::swap_in_out_map_functor<kernel_map_type>()(
std::move(kernel_map));
LOG_DEBUG("kernel_map saved");
}
}
}
}
#ifdef DEBUG
else {
LOG_DEBUG("kernel map found");
}
#endif
// TODO check if it copies or moves the internal data
return m_kernel_maps[kernel_map_key];
}
namespace detail {
template <typename coordinate_type>
struct origin_map_functor<coordinate_type, std::allocator, CoordinateMapCPU,
cpu_kernel_map> {
std::pair<at::Tensor, std::vector<at::Tensor>>
operator()(CoordinateMapCPU<coordinate_type, std::allocator> const
&origin_coordinate_map,
cpu_kernel_map const &origin_map) {
auto options =
torch::TensorOptions().dtype(torch::kLong).requires_grad(false);
auto const out_size = origin_coordinate_map.size();
auto const coordinate_size = origin_coordinate_map.coordinate_size();
at::Tensor batch_indices =
torch::empty({origin_coordinate_map.size()}, options);
int64_t *p_batch_indices = batch_indices.data_ptr<int64_t>();
LOG_DEBUG("Copying", origin_coordinate_map.size(), "batch indices");
for (default_types::index_type i = 0; i < out_size; ++i) {
p_batch_indices[i] =
origin_coordinate_map.const_coordinate_data()[i * coordinate_size];
}
// WARNING: this is an inclusive max index
coordinate_type const max_batch_index =
*std::max_element(p_batch_indices, p_batch_indices + out_size);
std::vector<at::Tensor> in_maps;
for (auto i = 0; i <= max_batch_index; ++i) {
at::Tensor row_indices = torch::empty({0}, options);
in_maps.push_back(std::move(row_indices));
}
ASSERT(origin_map.first.size() == origin_map.second.size(),
"invalid kernel_map");
LOG_DEBUG("Iterating over", origin_map.first.size(), "unique maps");
for (uint32_t out_row_index = 0; out_row_index < origin_map.first.size();
++out_row_index) {
auto const &in_map = origin_map.first[out_row_index];
int32_t const curr_size = in_map.size();
ASSERT(curr_size > 0, "invalid kernel map for index", out_row_index);
auto const curr_batch_index = p_batch_indices[out_row_index];
ASSERT(curr_batch_index <= max_batch_index, "invalid batch index");
at::Tensor &row_indices = in_maps[curr_batch_index];
row_indices.resize_({curr_size});
int64_t *p_row_indices = row_indices.data_ptr<int64_t>();
LOG_DEBUG("Copying", curr_size, "elements to batch index",
curr_batch_index, "and row index", out_row_index);
for (auto i = 0; i < curr_size; ++i) {
p_row_indices[i] = in_map[i];
}
}
return std::make_pair(batch_indices, in_maps);
}
};
} // namespace detail
template <typename coordinate_type, typename coordinate_field_type,
template <typename C> class TemplatedAllocator,
template <typename T, template <typename Q> class A>
class CoordinateMapType>
typename CoordinateMapManager<coordinate_type, coordinate_field_type,
TemplatedAllocator,
CoordinateMapType>::kernel_map_type const &
CoordinateMapManager<coordinate_type, coordinate_field_type, TemplatedAllocator,
CoordinateMapType>::origin_map(CoordinateMapKey const
*p_in_map_key) {
ASSERT(exists(p_in_map_key), ERROR_MAP_NOT_FOUND);
kernel_map_key_type const kernel_map_key =
origin_map_key(p_in_map_key->get_key());
coordinate_map_key_type const origin_key = std::get<1>(kernel_map_key);
if (m_kernel_maps.find(kernel_map_key) == m_kernel_maps.end()) {
auto const key = origin().first;
auto const &origin_coordinate_map = m_coordinate_maps.find(key)->second;
auto origin_map = m_coordinate_maps.find(p_in_map_key->get_key())
->second.origin_map(origin_coordinate_map);
m_kernel_maps[kernel_map_key] = std::move(origin_map);
}
return m_kernel_maps[kernel_map_key];
}
template <typename coordinate_type, typename coordinate_field_type,
template <typename C> class TemplatedAllocator,
template <typename T, template <typename Q> class A>
class CoordinateMapType>
typename CoordinateMapManager<coordinate_type, coordinate_field_type,
TemplatedAllocator,
CoordinateMapType>::kernel_map_type const &
CoordinateMapManager<coordinate_type, coordinate_field_type, TemplatedAllocator,
CoordinateMapType>::origin_field_map(CoordinateMapKey const
*p_in_map_key) {
ASSERT(exists_field(p_in_map_key), ERROR_MAP_NOT_FOUND);
kernel_map_key_type const kernel_map_key =
origin_map_key(p_in_map_key->get_key());
coordinate_map_key_type const origin_key = std::get<1>(kernel_map_key);
if (m_field_kernel_maps.find(kernel_map_key) == m_field_kernel_maps.end()) {
auto const key = origin_field().first;
auto const &origin_coordinate_map = m_coordinate_maps.find(key)->second;
auto origin_map = m_field_coordinates.find(p_in_map_key->get_key())
->second.origin_map(origin_coordinate_map);
m_field_kernel_maps[kernel_map_key] = std::move(origin_map);
}
return m_field_kernel_maps[kernel_map_key];
}
namespace detail {
template <typename coordinate_type>
struct stride_map2tensor_functor<coordinate_type, std::allocator,
CoordinateMapCPU, cpu_kernel_map> {
std::pair<at::Tensor, at::Tensor>
operator()(cpu_kernel_map const &stride_kernel_map) {
ASSERT(stride_kernel_map.first.size() == 1, "Invalid kernel_map");
ASSERT(stride_kernel_map.first.size() == stride_kernel_map.second.size(),
"invalid kernel_map");
auto const &in_map = stride_kernel_map.first[0];
auto const &out_map = stride_kernel_map.second[0];
auto options =
torch::TensorOptions().dtype(torch::kLong).requires_grad(false);
int64_t const out_size = (int64_t)in_map.size();
at::Tensor th_in_map = torch::empty({out_size}, options);
at::Tensor th_out_map = torch::empty({out_size}, options);
int64_t *p_in_map = th_in_map.data_ptr<int64_t>();
int64_t *p_out_map = th_out_map.data_ptr<int64_t>();
// from int32_t to long type
for (uint32_t i = 0; i < out_size; ++i)
p_in_map[i] = in_map[i];
for (uint32_t i = 0; i < out_size; ++i)
p_out_map[i] = out_map[i];
return std::make_pair(std::move(th_in_map), std::move(th_out_map));
}
};
} // namespace detail
template <typename coordinate_type, typename coordinate_field_type,
template <typename C> class TemplatedAllocator,
template <typename T, template <typename Q> class A>
class CoordinateMapType>
std::pair<at::Tensor, at::Tensor>
CoordinateMapManager<coordinate_type, coordinate_field_type, TemplatedAllocator,
CoordinateMapType>::stride_map_th(CoordinateMapKey const
*p_in_map_key,
CoordinateMapKey const
*p_strided_map_key) {
ASSERT(exists(p_in_map_key), ERROR_MAP_NOT_FOUND);
ASSERT(exists(p_strided_map_key), ERROR_MAP_NOT_FOUND);
map_type const &in_map =
m_coordinate_maps.find(p_in_map_key->get_key())->second;
map_type const &strided_map =
m_coordinate_maps.find(p_strided_map_key->get_key())->second;
// Get tensor strides and find kernel stride size
// Check if the kernel map key exists
auto const &in_map_stride = in_map.get_tensor_stride();
auto const &strided_map_stride = strided_map.get_tensor_stride();
stride_type kernel_stride(in_map_stride.size());
for (index_type i = 0; i < kernel_stride.size(); ++i) {
ASSERT(strided_map_stride[i] % in_map_stride[i] == 0,
"The tensor stride of the strided map must be divisible by the "
"tensor stride of the input map. strided_map_stride:",
ArrToString(strided_map_stride),
" in_map_stride:", ArrToString(in_map_stride));
kernel_stride[i] = strided_map_stride[i] / in_map_stride[i];
}
auto const one_vec = detail::ones(in_map.coordinate_size() - 1);
kernel_map_key_type const kernel_map_key = std::make_tuple(
p_in_map_key->get_key(), p_strided_map_key->get_key(), // maps
kernel_stride, kernel_stride, one_vec, // kernels
RegionType::HYPER_CUBE /* region_type */, 0 /* is_transpose */,
true /* is_pool */);
if (m_kernel_maps.find(kernel_map_key) == m_kernel_maps.end()) {
LOG_DEBUG("Creating stride kernel map with kernel size:",
ArrToString(kernel_stride));
auto const stride_map =
detail::stride_map_functor<coordinate_type, TemplatedAllocator,
CoordinateMapType, kernel_map_type>()(
in_map, strided_map, strided_map.get_tensor_stride());
m_kernel_maps[kernel_map_key] = std::move(stride_map);
}
// copy the kernel map to tensors
return detail::stride_map2tensor_functor<coordinate_type, TemplatedAllocator,
CoordinateMapType,
kernel_map_type>()(
m_kernel_maps[kernel_map_key]);
}
template <typename coordinate_type, typename coordinate_field_type,
template <typename C> class TemplatedAllocator,
template <typename T, template <typename Q> class A>
class CoordinateMapType>
std::pair<at::Tensor, std::vector<at::Tensor>>
CoordinateMapManager<coordinate_type, coordinate_field_type, TemplatedAllocator,
CoordinateMapType>::origin_map_th(CoordinateMapKey const
*p_in_map_key) {
kernel_map_type const &kernel_map = origin_map(p_in_map_key);
coordinate_map_key_type const origin_key = origin().first;
map_type const &origin_map = m_coordinate_maps.find(origin_key)->second;
return detail::origin_map_functor<coordinate_type, TemplatedAllocator,
CoordinateMapType, kernel_map_type>()(
origin_map, kernel_map);
}
template <typename coordinate_type, typename coordinate_field_type,
template <typename C> class TemplatedAllocator,
template <typename T, template <typename Q> class A>
class CoordinateMapType>
std::pair<at::Tensor, std::vector<at::Tensor>>
CoordinateMapManager<coordinate_type, coordinate_field_type, TemplatedAllocator,
CoordinateMapType>::
origin_field_map_th(CoordinateMapKey const *p_in_map_key) {
kernel_map_type const &kernel_map = origin_field_map(p_in_map_key);
coordinate_map_key_type const origin_key = origin_field().first;
map_type const &origin_map = m_coordinate_maps.find(origin_key)->second;
return detail::origin_map_functor<coordinate_type, TemplatedAllocator,
CoordinateMapType, kernel_map_type>()(
origin_map, kernel_map);
}
// Interpolation map
template <typename coordinate_type, typename coordinate_field_type,
template <typename C> class TemplatedAllocator,
template <typename T, template <typename Q> class A>
class CoordinateMapType>
std::vector<at::Tensor>
CoordinateMapManager<coordinate_type, coordinate_field_type, TemplatedAllocator,
CoordinateMapType>::
interpolation_map_weight(at::Tensor const &tfield,
CoordinateMapKey const *p_in_map_key) {
ASSERT(exists(p_in_map_key), ERROR_MAP_NOT_FOUND);
return m_coordinate_maps.find(p_in_map_key->get_key())
->second.interpolation_map_weight(tfield);
}
/*********************************/
/*
template <typename MapType>
uint64_t
CoordsManager<MapType>::createUnionCoords(vector<py::object> py_in_coords_keys,
py::object py_out_coords_key) {
vector<CoordsKey *> p_in_coords_keys;
CoordsKey *p_in_coords_key = py_in_coords_keys[0].cast<CoordsKey *>();
auto tensor_strides = p_in_coords_key->getTensorStride();
for (const auto &py_in_coords_key : py_in_coords_keys) {
// Set the tensor strides to the smallest elements.
p_in_coords_key = py_in_coords_key.cast<CoordsKey *>();
p_in_coords_keys.push_back(p_in_coords_key);
transform(tensor_strides.begin(), // In1 begin
tensor_strides.end(), // In1 end
p_in_coords_key->getTensorStride().begin(), // In2 begin
tensor_strides.begin(), // out begin
[](int a, int b) -> int { return std::min(a, b); } // binary op
);
const uint64_t in_coords_key = p_in_coords_key->getKey();
ASSERT(existsCoordsKey(in_coords_key),
"The coord map doesn't exist for the given coords_key: ",
to_string(in_coords_key), ".");
}
CoordsKey *p_out_coords_key = py_out_coords_key.cast<CoordsKey *>();
vector<reference_wrapper<CoordsMap<MapType>>> in_coords_maps;
for (const CoordsKey *p_in_coords_key : p_in_coords_keys) {
CoordsMap<MapType> &curr_map = coords_maps[p_in_coords_key->getKey()];
in_coords_maps.push_back(ref(curr_map));
}
// set a random coords key
const uint64_t out_coords_key = getRandomCoordsKey();
// Set the pycoordskey using the last coords_key
p_out_coords_key->setDimension(p_in_coords_key->getDimension());
p_out_coords_key->setKey(out_coords_key);
p_out_coords_key->setTensorStride(tensor_strides);
coords_maps[out_coords_key] =
CoordsMap<MapType>::union_coords(in_coords_maps);
return out_coords_key;
}
template <typename MapType>
const InOutMapKey
CoordsManager<MapType>::getUnionMapHashKey(vector<py::object> py_in_coords_keys,
py::object py_out_coords_key) const {
CoordsKey *p_out_coords_key = py_out_coords_key.cast<CoordsKey *>();
ASSERT(py_in_coords_keys.size() > 1, "Number of input coords must be > 1");
vector<CoordsKey *> p_in_coords_keys;
// We use sum of coords key (even with overflow, it will be unique with high
// prob). We use sum to make the key invariant to the order of the keys.
uint64_t sum_in_coords_key = 0;
CoordsKey *p_in_coords_key = py_in_coords_keys[0].cast<CoordsKey *>();
for (auto &py_in_coords_key : py_in_coords_keys) {
p_in_coords_key = py_in_coords_key.cast<CoordsKey *>();
const uint64_t in_coords_key = p_in_coords_key->getKey();
ASSERT(existsCoordsKey(in_coords_key),
"The coord map doesn't exist for the given coords_key: ",
to_string(in_coords_key), ".");
sum_in_coords_key += in_coords_key;
}
ASSERT(p_out_coords_key->key_set, "Key is not set. out_coords_key: ",
to_string(p_out_coords_key->getKey()));
const uint64_t out_coords_key = p_out_coords_key->getKey();
const vector<int> zero_vec(p_in_coords_key->getDimension(), 0);
const uint64_t zero_hash = hash_vec(zero_vec);
InOutMapKey map_key = {sum_in_coords_key,
out_coords_key,
zero_hash,
zero_hash,
zero_hash,
0,
false,
true};
return map_key;
}
*/
/**
* Entry function for coords map generation and the associated kernel maps.
*/
/*
template <typename MapType>
const InOutMapsRefPair<int>
CoordsManager<MapType>::getPruningInOutMaps(at::Tensor use_feat,
py::object py_in_coords_key,
py::object py_out_coords_key) {
CoordsKey *p_in_coords_key = py_in_coords_key.cast<CoordsKey *>();
CoordsKey *p_out_coords_key = py_out_coords_key.cast<CoordsKey *>();
// Create output coordinates if it doesn't exist
if (!p_out_coords_key->key_set) {
// The following function setup py_out_coords_key
createPrunedCoords(use_feat, py_in_coords_key, py_out_coords_key);
}
const uint64_t in_coords_key = p_in_coords_key->getKey();
const uint64_t out_coords_key = p_out_coords_key->getKey();
// Use the map key for origin hash map (stride, dilation, kernel are all
// NULL)
const InOutMapKey map_key =
getOriginMapHashKey(py_in_coords_key, py_out_coords_key);
// For non transpose case
// make a kernel mapping. The kernel will be saved with the map_key.
if (in_maps.find(map_key) == in_maps.end()) {
const auto in_out = coords_maps[in_coords_key].pruned_kernel_map(
coords_maps[out_coords_key]);
in_maps[map_key] = in_out.first;
out_maps[map_key] = in_out.second;
}
return make_pair(ref(in_maps[map_key]), ref(out_maps[map_key]));
}
template <typename MapType>
const InOutMapsRefPair<int>
CoordsManager<MapType>::getUnionInOutMaps(vector<py::object> py_in_coords_keys,
py::object py_out_coords_key) {
CoordsKey *p_out_coords_key = py_out_coords_key.cast<CoordsKey *>();
// Create output coordinates if it doesn't exist
if (!p_out_coords_key->key_set)
createUnionCoords(py_in_coords_keys, py_out_coords_key);
const uint64_t out_coords_key = p_out_coords_key->getKey();
// Map key for origin hash map
const InOutMapKey map_key =
getUnionMapHashKey(py_in_coords_keys, py_out_coords_key);
vector<reference_wrapper<CoordsMap<MapType>>> in_coords_maps;
for (const auto &py_in_coords_key : py_in_coords_keys) {
const CoordsKey *p_in_coords_key = py_in_coords_key.cast<CoordsKey *>();
uint64_t in_coords_key = p_in_coords_key->getKey();
in_coords_maps.push_back(ref(coords_maps[in_coords_key]));
}
// For non transpose case
// make a kernel mapping. The kernel will be saved with the map_key.
if (in_maps.find(map_key) == in_maps.end()) {
const auto in_out = CoordsMap<MapType>::union_map(
in_coords_maps, coords_maps[out_coords_key]);
in_maps[map_key] = in_out.first;
out_maps[map_key] = in_out.second;
}
return make_pair(ref(in_maps[map_key]), ref(out_maps[map_key]));
}
*/
template <typename coordinate_type, typename coordinate_field_type,
template <typename C> class TemplatedAllocator,
template <typename T, template <typename Q> class A>
class CoordinateMapType>
coordinate_map_key_type
CoordinateMapManager<coordinate_type, coordinate_field_type, TemplatedAllocator,
CoordinateMapType>::
merge(std::vector<coordinate_map_key_type> const &map_keys) {
ASSERT(map_keys.size() > 1, "Got one or zero map. Merge at least 2 maps.");
// Aggregate all coords maps
std::vector<std::reference_wrapper<map_type>> maps;
auto const tensor_stride_size = map_keys[0].first.size();
stride_type merged_map_tensor_stride{map_keys[0].first};
for (const auto &key : map_keys) {
ASSERT(exists(key), ERROR_MAP_NOT_FOUND);
auto &map = m_coordinate_maps.find(key)->second;
maps.push_back(map);
for (int k = 0; k < tensor_stride_size; ++k) {
merged_map_tensor_stride[k] =
std::min(merged_map_tensor_stride[k], map.get_tensor_stride()[k]);
}
}
// Create a merged map with the smallest tensor stride
coordinate_map_key_type merged_map_key =
get_random_string_id(merged_map_tensor_stride, "merge");
map_type const &map = m_coordinate_maps.find(map_keys[0])->second;
map_type merged_map = map.merge(maps);
insert(merged_map_key, merged_map);
return merged_map_key;
}
template <typename coordinate_type, typename coordinate_field_type,
template <typename C> class TemplatedAllocator,
template <typename T, template <typename Q> class A>
class CoordinateMapType>
std::pair<coordinate_map_key_type, std::vector<at::Tensor>>
CoordinateMapManager<coordinate_type, coordinate_field_type, TemplatedAllocator,
CoordinateMapType>::
union_map(std::vector<coordinate_map_key_type> const &map_keys) {
// Create a merged map
auto const merged_key = merge(map_keys);
map_type const &merged_map = m_coordinate_maps.find(merged_key)->second;
std::vector<std::reference_wrapper<map_type>> maps;
for (const auto &key : map_keys) {
ASSERT(exists(key), ERROR_MAP_NOT_FOUND);
maps.push_back(std::ref(m_coordinate_maps.find(key)->second));
}
return std::make_pair(merged_key, merged_map.union_map(maps));
}
template <typename coordinate_type, typename coordinate_field_type,
template <typename C> class TemplatedAllocator,
template <typename T, template <typename Q> class A>
class CoordinateMapType>
std::vector<at::Tensor>
CoordinateMapManager<coordinate_type, coordinate_field_type, TemplatedAllocator,
CoordinateMapType>::
union_map_th(std::vector<CoordinateMapKey *> const &p_map_keys,
CoordinateMapKey *p_out_key) {
ASSERT(!p_out_key->is_key_set(),
"Out coordinate map key should be uninitialized");
std::vector<coordinate_map_key_type> map_keys;
map_keys.reserve(p_map_keys.size());
std::for_each(
p_map_keys.begin(), p_map_keys.end(),
[&](CoordinateMapKey *p_key) { map_keys.push_back(p_key->get_key()); });
auto union_pair = union_map(map_keys);
p_out_key->set_key(union_pair.first);
return union_pair.second;
}
/* Helper functions */
template <typename coordinate_type, typename coordinate_field_type,
template <typename C> class TemplatedAllocator,
template <typename T, template <typename Q> class A>
class CoordinateMapType>
at::Tensor
CoordinateMapManager<coordinate_type, coordinate_field_type, TemplatedAllocator,
CoordinateMapType>::get_coordinates(CoordinateMapKey const
*p_key) const {
ASSERT(exists(p_key), ERROR_MAP_NOT_FOUND);
auto const it = m_coordinate_maps.find(p_key->get_key());
ASSERT(it != m_coordinate_maps.end(), ERROR_MAP_NOT_FOUND);
auto const &map = it->second;
auto const nrows = map.size();
auto const ncols = map.coordinate_size();
LOG_DEBUG("coordinate map nrows:", nrows, "ncols:", ncols);
// CPU torch.IntTensor
auto options = torch::TensorOptions().dtype(torch::kInt).requires_grad(false);
if (!detail::is_cpu_coordinate_map<CoordinateMapType>::value) {
#ifndef CPU_ONLY
auto device_id = at::cuda::current_device();
options = options.device(torch::kCUDA, device_id);
#else
ASSERT(false, ERROR_CPU_ONLY);
#endif
}
at::Tensor coordinates =
torch::empty({(int64_t)nrows, (int64_t)ncols}, options);
LOG_DEBUG("Initialized coordinates");
// copy to the out coords
map.copy_coordinates(coordinates.template data_ptr<coordinate_type>());
LOG_DEBUG("Copied coordinates");
return coordinates;
}
namespace detail {
template <typename coordinate_type>
struct kernel_map_to_tensors<coordinate_type, std::allocator, CoordinateMapCPU,
cpu_kernel_map> {
std::unordered_map<int64_t, at::Tensor>
operator()(cpu_kernel_map const &kernel_map) {
const auto &in_maps = kernel_map.first;
const auto &out_maps = kernel_map.second;
auto options =
torch::TensorOptions().dtype(torch::kInt).requires_grad(false);
std::unordered_map<int64_t, at::Tensor> th_kernel_maps;
for (auto k = 0; k < in_maps.size(); ++k) {
const auto &in_map = in_maps[k];
const auto &out_map = out_maps[k];
const int64_t N = in_map.size();
if (N > 0) {
at::Tensor kernel_map = torch::empty({2, N}, options);
int32_t *p_kernel_map = kernel_map.data_ptr<int32_t>();
std::copy_n(&in_map[0], N, p_kernel_map);
std::copy_n(&out_map[0], N, p_kernel_map + N);
th_kernel_maps[k] = std::move(kernel_map);
}
}
return th_kernel_maps;
}
};
} // namespace detail
template <typename coordinate_type, typename coordinate_field_type,
template <typename C> class TemplatedAllocator,
template <typename T, template <typename Q> class A>
class CoordinateMapType>
std::unordered_map<int64_t, at::Tensor> CoordinateMapManager<
coordinate_type, coordinate_field_type, TemplatedAllocator,
CoordinateMapType>::kernel_map_th(CoordinateMapKey const *p_in_map_key,
CoordinateMapKey const *p_out_map_key,
stride_type const &kernel_size, //
stride_type const &kernel_stride,
stride_type const &kernel_dilation,
RegionType::Type const region_type,
at::Tensor const &offset,
bool is_transpose, bool is_pool) {
auto const &curr_kernel_map =
kernel_map(p_in_map_key, p_out_map_key, // maps
kernel_size, kernel_stride, kernel_dilation, // kernels
region_type, offset, is_transpose, is_pool);
return detail::kernel_map_to_tensors<coordinate_type, TemplatedAllocator,
CoordinateMapType, kernel_map_type>()(
curr_kernel_map);
}
template <typename coordinate_type, typename coordinate_field_type,
template <typename C> class TemplatedAllocator,
template <typename T, template <typename Q> class A>
class CoordinateMapType>
at::Tensor CoordinateMapManager<coordinate_type, coordinate_field_type,
TemplatedAllocator, CoordinateMapType>::
get_coordinate_field(CoordinateMapKey const *p_key) const {
auto const it = m_field_coordinates.find(p_key->get_key());
ASSERT(it != m_field_coordinates.end(), ERROR_MAP_NOT_FOUND);
auto const &map = it->second;
auto const nrows = map.size();
auto const ncols = map.coordinate_size();
auto options = torch::TensorOptions()
.dtype(std::is_same<float, coordinate_field_type>::value
? torch::kFloat
: torch::kDouble)
.requires_grad(false);
if (!detail::is_cpu_coordinate_map<CoordinateMapType>::value) {
#ifndef CPU_ONLY
auto device_id = at::cuda::current_device();
options = options.device(torch::kCUDA, device_id);
#else
ASSERT(false, ERROR_CPU_ONLY);
#endif
}
at::Tensor coordinates =
torch::empty({(int64_t)nrows, (int64_t)ncols}, options);
// copy to the out coords
map.copy_coordinates(coordinates.template data_ptr<coordinate_field_type>());
return coordinates;
}
template class CoordinateMapManager<default_types::dcoordinate_type,
default_types::ccoordinate_type,
std::allocator, CoordinateMapCPU>;
} // end namespace minkowski
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