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#include <mutex>
#include <ATen/Parallel.h>
#include "operator.cuh"
#include "rspmm.h"
namespace at {
// In PyTorch 1.4.0, parallel_for depends on some functions from at::internal in ATen/Parallel.h
// which are not explicitly included
// This is fixed in some new PyTorch release
using namespace at::internal;
void rspmm_forward_check(CheckedFrom c, const TensorArg &edge_index_arg, const TensorArg &edge_type_arg,
const TensorArg &edge_weight_arg, const TensorArg &relation_arg, const TensorArg &input_arg) {
checkDim(c, edge_index_arg, 2);
checkDim(c, edge_type_arg, 1);
checkDim(c, edge_weight_arg, 1);
checkDim(c, relation_arg, 2);
checkDim(c, input_arg, 2);
checkSameType(c, edge_index_arg, edge_type_arg);
checkAllSameType(c, {edge_weight_arg, relation_arg, input_arg});
checkSize(c, edge_index_arg, 0, 2);
checkSize(c, edge_type_arg, {edge_index_arg->size(1)});
checkSize(c, edge_weight_arg, {edge_index_arg->size(1)});
checkSize(c, relation_arg, 1, input_arg->size(1));
}
void rspmm_backward_check(CheckedFrom c, const TensorArg &edge_index_arg, const TensorArg &edge_type_arg,
const TensorArg &edge_weight_arg, const TensorArg &relation_arg, const TensorArg &input_arg,
const TensorArg &output_arg, const TensorArg &output_grad_arg) {
rspmm_forward_check(c, edge_index_arg, edge_type_arg, edge_weight_arg, relation_arg, input_arg);
checkDim(c, output_arg, 2);
checkSameSize(c, output_arg, output_grad_arg);
checkAllSameType(c, {input_arg, output_arg, output_grad_arg});
checkSize(c, output_arg, 1, input_arg->size(1));
}
Tensor ind2ptr(const Tensor &index, int size) {
// scatter_add is super slow for int64, due to non-hardware atomic operations
// use int32 instead
Tensor num_per_index = at::zeros({size}, index.options().dtype(at::ScalarType::Int));
num_per_index.scatter_add_(0, index, at::ones(index.sizes(), num_per_index.options()));
num_per_index = num_per_index.toType(at::ScalarType::Long);
Tensor pointer = num_per_index.cumsum(0) - num_per_index;
return pointer;
}
template <class scalar_t, class NaryOp, class BinaryOp>
void rspmm_forward_out_cpu(const int64_t *row_ptr, const int64_t *col_ind, const int64_t *layer_ind,
const scalar_t *weight, const scalar_t *relation, const scalar_t *input,
scalar_t *output,
int64_t num_row, int64_t nnz, int64_t dim) {
parallel_for(0, num_row, 0, [&](int64_t row_start, int64_t row_end) {
for (int64_t row = row_start; row < row_end; row++) {
for (int64_t d = 0; d < dim; d++)
output[row * dim + d] = NaryOp::zero;
int64_t ptr_start = row_ptr[row];
int64_t ptr_end = row + 1 < num_row ? row_ptr[row + 1] : nnz;
for (int64_t ptr = ptr_start; ptr < ptr_end; ptr++) {
int64_t col = col_ind[ptr];
int64_t layer = layer_ind[ptr];
scalar_t w = weight[ptr];
for (int64_t d = 0; d < dim; d++) {
scalar_t x = BinaryOp::forward(relation[layer * dim + d], input[col * dim + d]);
scalar_t y = w * x;
scalar_t &out = output[row * dim + d];
out = NaryOp::forward(out, y);
}
}
}
});
}
template <class scalar_t, class NaryOp, class BinaryOp>
void rspmm_backward_out_cpu(const int64_t *row_ptr, const int64_t *col_ind, const int64_t *layer_ind,
const scalar_t *weight, const scalar_t *relation, const scalar_t *input,
const scalar_t *output, const scalar_t *output_grad,
scalar_t *weight_grad, scalar_t *relation_grad, scalar_t *input_grad,
int64_t num_row, int64_t nnz, int64_t dim,
std::vector<std::mutex> &relation_mutex, std::vector<std::mutex> &input_mutex) {
parallel_for(0, num_row, 0, [&](int64_t row_start, int64_t row_end) {
for (int64_t row = row_start; row < row_end; row++) {
int64_t ptr_start = row_ptr[row];
int64_t ptr_end = row + 1 < num_row ? row_ptr[row + 1] : nnz;
for (int64_t ptr = ptr_start; ptr < ptr_end; ptr++) {
int64_t col = col_ind[ptr];
int64_t layer = layer_ind[ptr];
scalar_t w = weight[ptr];
scalar_t w_grad = 0;
for (int64_t d = 0; d < dim; d++) {
scalar_t rel = relation[layer * dim + d];
scalar_t in = input[col * dim + d];
scalar_t out = output[row * dim + d];
scalar_t out_grad = output_grad[row * dim + d];
scalar_t x = BinaryOp::forward(rel, in);
scalar_t y = w * x;
scalar_t dx_drel = BinaryOp::backward_lhs(rel, in);
scalar_t dx_din = BinaryOp::backward_rhs(rel, in);
scalar_t dout_dy = NaryOp::backward(out, y);
scalar_t dy_dw = x;
scalar_t dy_dx = w;
w_grad += out_grad * dout_dy * dy_dw;
{
std::lock_guard<std::mutex> lock(relation_mutex[layer * dim + d]);
relation_grad[layer * dim + d] += out_grad * dout_dy * dy_dx * dx_drel;
}
{
std::lock_guard<std::mutex> lock(input_mutex[col * dim + d]);
input_grad[col * dim + d] += out_grad * dout_dy * dy_dx * dx_din;
}
}
weight_grad[ptr] = w_grad;
}
}
});
}
template <template<class> class NaryOp, template<class> class BinaryOp>
Tensor rspmm_forward_cpu(const Tensor &edge_index_, const Tensor &edge_type_, const Tensor &edge_weight_,
const Tensor &relation_, const Tensor &input_) {
constexpr const char *fn_name = "rspmm_forward_cpu";
TensorArg edge_index_arg(edge_index_, "edge_index", 1), edge_type_arg(edge_type_, "edge_type", 2),
edge_weight_arg(edge_weight_, "edge_weight", 3), relation_arg(relation_, "relation", 4),
input_arg(input_, "input", 5);
rspmm_forward_check(fn_name, edge_index_arg, edge_type_arg, edge_weight_arg, relation_arg, input_arg);
checkDeviceType(fn_name, {edge_index_, edge_type_, edge_weight_, relation_, input_}, kCPU);
const Tensor edge_index = edge_index_.contiguous();
const Tensor edge_type = edge_type_.contiguous();
const Tensor edge_weight = edge_weight_.contiguous();
const Tensor relation = relation_.contiguous();
const Tensor input = input_.contiguous();
int64_t nnz = edge_index.size(0);
int64_t num_row = input.size(0);
int64_t dim = input.size(1);
Tensor output = at::empty({num_row, dim}, input.options());
Tensor row_ind = edge_index.select(0, 0);
Tensor row_ptr = ind2ptr(row_ind, num_row);
Tensor col_ind = edge_index.select(0, 1);
Tensor layer_ind = edge_type;
AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "rspmm_forward_cpu", [&] {
rspmm_forward_out_cpu<scalar_t, NaryOp<scalar_t>, BinaryOp<scalar_t>>(
row_ptr.data_ptr<int64_t>(),
col_ind.data_ptr<int64_t>(),
layer_ind.data_ptr<int64_t>(),
edge_weight.data_ptr<scalar_t>(),
relation.data_ptr<scalar_t>(),
input.data_ptr<scalar_t>(),
output.data_ptr<scalar_t>(),
num_row, nnz, dim
);
});
return output;
}
template <template<class> class NaryOp, template<class> class BinaryOp>
std::tuple<Tensor, Tensor, Tensor> rspmm_backward_cpu(
const Tensor &edge_index_, const Tensor &edge_type_, const Tensor &edge_weight_,
const Tensor &relation_, const Tensor &input_, const Tensor &output_, const Tensor &output_grad_) {
constexpr const char *fn_name = "rspmm_backward_cpu";
TensorArg edge_index_arg(edge_index_, "edge_index", 1), edge_type_arg(edge_type_, "edge_type", 2),
edge_weight_arg(edge_weight_, "edge_weight", 3), relation_arg(relation_, "relation", 4),
input_arg(input_, "input", 5), output_arg(output_, "output", 6),
output_grad_arg(output_grad_, "output_grad", 7);
rspmm_backward_check(fn_name, edge_index_arg, edge_type_arg, edge_weight_arg, relation_arg, input_arg,
output_arg, output_grad_arg);
checkDeviceType(fn_name, {edge_index_, edge_type_, edge_weight_, relation_, input_, output_, output_grad_}, kCPU);
const Tensor edge_index = edge_index_.contiguous();
const Tensor edge_type = edge_type_.contiguous();
const Tensor edge_weight = edge_weight_.contiguous();
const Tensor relation = relation_.contiguous();
const Tensor input = input_.contiguous();
const Tensor output = output_.contiguous();
const Tensor output_grad = output_grad_.contiguous();
int64_t nnz = edge_index.size(0);
int64_t num_row = input.size(0);
int64_t dim = input.size(1);
Tensor weight_grad = at::zeros_like(edge_weight);
Tensor relation_grad = at::zeros_like(relation);
Tensor input_grad = at::zeros_like(input);
Tensor row_ind = edge_index.select(0, 0);
Tensor row_ptr = ind2ptr(row_ind, num_row);
Tensor col_ind = edge_index.select(0, 1);
Tensor layer_ind = edge_type;
std::vector<std::mutex> relation_mutex(relation.numel());
std::vector<std::mutex> input_mutex(input.numel());
AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "rspmm_backward_cpu", [&] {
rspmm_backward_out_cpu<scalar_t, NaryOp<scalar_t>, BinaryOp<scalar_t>>(
row_ptr.data_ptr<int64_t>(),
col_ind.data_ptr<int64_t>(),
layer_ind.data_ptr<int64_t>(),
edge_weight.data_ptr<scalar_t>(),
relation.data_ptr<scalar_t>(),
input.data_ptr<scalar_t>(),
output.data_ptr<scalar_t>(),
output_grad.data_ptr<scalar_t>(),
weight_grad.data_ptr<scalar_t>(),
relation_grad.data_ptr<scalar_t>(),
input_grad.data_ptr<scalar_t>(),
num_row, nnz, dim,
relation_mutex, input_mutex
);
});
return std::make_tuple(weight_grad, relation_grad, input_grad);
}
#define DECLARE_FORWARD_IMPL(ADD, MUL, NARYOP, BINARYOP) \
Tensor rspmm_##ADD##_##MUL##_forward_cpu( \
const Tensor &edge_index, const Tensor &edge_type, const Tensor &edge_weight, \
const Tensor &relation, const Tensor &input) { \
return rspmm_forward_cpu<NARYOP, BINARYOP>(edge_index, edge_type, edge_weight, relation, input); \
}
#define DECLARE_BACKWARD_IMPL(ADD, MUL, NARYOP, BINARYOP) \
std::tuple<Tensor, Tensor, Tensor> rspmm_##ADD##_##MUL##_backward_cpu( \
const Tensor &edge_index, const Tensor &edge_type, const Tensor &edge_weight, \
const Tensor &relation, const Tensor &input, const Tensor &output, const Tensor &output_grad) { \
return rspmm_backward_cpu<NARYOP, BINARYOP>(edge_index, edge_type, edge_weight, relation, input, \
output, output_grad); \
}
DECLARE_FORWARD_IMPL(add, mul, NaryAdd, BinaryMul)
DECLARE_BACKWARD_IMPL(add, mul, NaryAdd, BinaryMul)
DECLARE_FORWARD_IMPL(min, mul, NaryMin, BinaryMul)
DECLARE_BACKWARD_IMPL(min, mul, NaryMin, BinaryMul)
DECLARE_FORWARD_IMPL(max, mul, NaryMax, BinaryMul)
DECLARE_BACKWARD_IMPL(max, mul, NaryMax, BinaryMul)
DECLARE_FORWARD_IMPL(add, add, NaryAdd, BinaryAdd)
DECLARE_BACKWARD_IMPL(add, add, NaryAdd, BinaryAdd)
DECLARE_FORWARD_IMPL(min, add, NaryMin, BinaryAdd)
DECLARE_BACKWARD_IMPL(min, add, NaryMin, BinaryAdd)
DECLARE_FORWARD_IMPL(max, add, NaryMax, BinaryAdd)
DECLARE_BACKWARD_IMPL(max, add, NaryMax, BinaryAdd)
} // namespace at
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("rspmm_add_mul_forward_cpu", &at::rspmm_add_mul_forward_cpu);
m.def("rspmm_add_mul_backward_cpu", &at::rspmm_add_mul_backward_cpu);
m.def("rspmm_min_mul_forward_cpu", &at::rspmm_min_mul_forward_cpu);
m.def("rspmm_min_mul_backward_cpu", &at::rspmm_min_mul_backward_cpu);
m.def("rspmm_max_mul_forward_cpu", &at::rspmm_max_mul_forward_cpu);
m.def("rspmm_max_mul_backward_cpu", &at::rspmm_max_mul_backward_cpu);
m.def("rspmm_add_add_forward_cpu", &at::rspmm_add_add_forward_cpu);
m.def("rspmm_add_add_backward_cpu", &at::rspmm_add_add_backward_cpu);
m.def("rspmm_min_add_forward_cpu", &at::rspmm_min_add_forward_cpu);
m.def("rspmm_min_add_backward_cpu", &at::rspmm_min_add_backward_cpu);
m.def("rspmm_max_add_forward_cpu", &at::rspmm_max_add_forward_cpu);
m.def("rspmm_max_add_backward_cpu", &at::rspmm_max_add_backward_cpu);
#ifdef CUDA_OP
m.def("rspmm_add_mul_forward_cuda", &at::rspmm_add_mul_forward_cuda);
m.def("rspmm_add_mul_backward_cuda", &at::rspmm_add_mul_backward_cuda);
m.def("rspmm_min_mul_forward_cuda", &at::rspmm_min_mul_forward_cuda);
m.def("rspmm_min_mul_backward_cuda", &at::rspmm_min_mul_backward_cuda);
m.def("rspmm_max_mul_forward_cuda", &at::rspmm_max_mul_forward_cuda);
m.def("rspmm_max_mul_backward_cuda", &at::rspmm_max_mul_backward_cuda);
m.def("rspmm_add_add_forward_cuda", &at::rspmm_add_add_forward_cuda);
m.def("rspmm_add_add_backward_cuda", &at::rspmm_add_add_backward_cuda);
m.def("rspmm_min_add_forward_cuda", &at::rspmm_min_add_forward_cuda);
m.def("rspmm_min_add_backward_cuda", &at::rspmm_min_add_backward_cuda);
m.def("rspmm_max_add_forward_cuda", &at::rspmm_max_add_forward_cuda);
m.def("rspmm_max_add_backward_cuda", &at::rspmm_max_add_backward_cuda);
#endif
} |