#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