123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122 |
- #pragma once
- #include <ATen/core/Tensor.h>
- #include <ATen/EmptyTensor.h>
- #include <ATen/TensorIterator.h>
- #include <ATen/native/DispatchStub.h>
- #ifndef AT_PER_OPERATOR_HEADERS
- #include <ATen/Functions.h>
- #else
- #include <ATen/ops/scalar_tensor.h>
- #endif
- namespace at { namespace native {
- // Different combinations of row, col, and offset can lead to two cases:
- //
- // Case 1 - Trapezoid (Triangle as a special case): row + offset <= col
- // Example A: offset > 0
- // 1 1 0 0 0
- // 1 1 1 0 0
- // 1 1 1 1 0
- // Example B: offset <= 0
- // 0 0 0
- // 1 0 0
- // 1 1 0
- // In this case, we calculate the number of elements in the first row and
- // last row of the tril respectively, and then compute the tril size.
- //
- // Case 2 - Trapezoid + Rectangle: row + offset > col
- // Example:
- // 1 1 0
- // 1 1 1
- // 1 1 1
- // In this case, we first calculate the size of top trapezoid, and then
- // calculate the size of the bottom rectangle.
- inline int64_t get_tril_size(int64_t row, int64_t col, int64_t offset) {
- // If either dimension is 0 then the there is no tril
- if (row == 0 || col == 0) {
- return 0;
- }
- // number of elements in the first row of the tril
- auto m_first_row = offset > 0 ?
- std::min<int64_t>(col, 1 + offset) : // upper bounded by col
- row + offset > 0; // either 0 or 1
- // number of elements in the last row of the tril, bounded by [0, col]
- auto m_last_row = std::max<int64_t>(0, std::min<int64_t>(col, row + offset));
- // number of rows, bounded by [0, row]
- auto n_row_all = std::max<int64_t>(0, std::min<int64_t>(row, row + offset));
- auto n_row_trapezoid = (m_last_row - m_first_row + 1);
- // calculate # of elements in the top trapezoid
- auto tril_size = (m_first_row + m_last_row) * n_row_trapezoid >> 1;
- // calculate # of elements in the bottom rectangle if there is any
- auto diff_row = n_row_all - n_row_trapezoid;
- if (diff_row > 0) {
- tril_size += diff_row * col;
- }
- return tril_size;
- }
- inline void check_args(
- int64_t row, int64_t col, c10::optional<Layout> layout_opt) {
- TORCH_CHECK(row >= 0, "row must be non-negative, got", row);
- TORCH_CHECK(col >= 0, "col must be non-negative, got", col);
- if (layout_opt.has_value()) {
- TORCH_CHECK(
- *layout_opt == at::kStrided,
- "only support layout=torch.strided, got",
- *layout_opt)
- }
- }
- using at::check_size_nonnegative;
- // assumes maximum value in created tensor is n-1 (e.g., torch.randperm(n))
- inline void check_supported_max_int_with_precision(int64_t n, const Tensor& tensor) {
- // match defined() to behavior of checks below
- TORCH_CHECK(at::scalar_tensor(n>0?n-1:n, tensor.options()).defined(),
- "n is too large for result tensor type: '", tensor.toString(), "'");
- // Ensure sufficient precision for floating point representation.
- switch (tensor.scalar_type()) {
- case at::ScalarType::Half:
- TORCH_CHECK(n <= (int64_t(1) << 11) + 1, "n cannot be greater than 2049 for Half type.");
- break;
- case at::ScalarType::Float:
- TORCH_CHECK(n <= (int64_t(1) << 24) + 1, "n cannot be greater than 2^24+1 for Float type.");
- break;
- case at::ScalarType::Double: // Unlikely to happen, but doesn't hurt to check
- TORCH_CHECK(n <= (int64_t(1) << 53) + 1, "n cannot be greater than 2^53+1 for Double type.");
- break;
- default:
- break;
- }
- }
- // The ZeroTensor allocator ignores whatever allocation is requested and always
- // gives you nullptr
- struct ZeroTensorAllocator final : public at::Allocator {
- ZeroTensorAllocator(at::Device device) : device_(device) {};
- ~ZeroTensorAllocator() override = default;
- static void deleter(void* const pointer) {
- TORCH_INTERNAL_ASSERT(!pointer);
- }
- DataPtr allocate(const size_t /*nbytes*/) const override {
- return {nullptr, nullptr, &deleter, device_};
- }
- DeleterFnPtr raw_deleter() const override {
- return deleter;
- }
- at::Device device_;
- };
- using binary_fn = void (*)(TensorIterator&);
- DECLARE_DISPATCH(binary_fn, complex_stub);
- DECLARE_DISPATCH(binary_fn, polar_stub);
- } // namespace native
- } // namespace at
|