chore: import upstream snapshot with attribution
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// Copyright (c) 2026 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#include <ATen/AccumulateType.h>
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#include <c10/core/Scalar.h>
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#include <limits>
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namespace at::native {
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inline void arange_check_bounds(const c10::Scalar& start,
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const c10::Scalar& end,
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const c10::Scalar& step) {
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// use double precision for validation to avoid precision issues
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double dstart = start.to<double>();
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double dend = end.to<double>();
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double dstep = step.to<double>();
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TORCH_CHECK(dstep > 0 || dstep < 0, "step must be nonzero");
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TORCH_CHECK(std::isfinite(dstart) && std::isfinite(dend),
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"unsupported range: ",
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dstart,
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" -> ",
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dend);
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TORCH_CHECK(
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((dstep > 0) && (dend >= dstart)) || ((dstep < 0) && (dend <= dstart)),
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"upper bound and lower bound inconsistent with step sign");
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}
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template <typename scalar_t>
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int64_t compute_arange_size(const Scalar& start,
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const Scalar& end,
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const Scalar& step) {
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arange_check_bounds(start, end, step);
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// we use double precision for (start - end) / step
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// to compute size_d for consistency across devices.
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// The problem with using accscalar_t is that accscalar_t might be float32 on
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// gpu for a float32 scalar_t, but double on cpu for the same, and the
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// effective output size starts differing on CPU vs GPU because of precision
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// issues, which we dont want. the corner-case we do want to take into account
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// is int64_t, which has higher precision than double
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double size_d;
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if constexpr (std::is_same_v<scalar_t, int64_t>) {
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using accscalar_t = at::acc_type<scalar_t, false>;
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auto xstart = start.to<accscalar_t>();
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auto xend = end.to<accscalar_t>();
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auto xstep = step.to<accscalar_t>();
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int64_t sgn = (xstep > 0) - (xstep < 0);
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size_d = std::ceil((xend - xstart + xstep - sgn) / xstep);
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} else {
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size_d =
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std::ceil((end.to<double>() - start.to<double>()) / step.to<double>());
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}
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TORCH_CHECK(size_d >= 0 && size_d <= static_cast<double>(
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std::numeric_limits<int64_t>::max()),
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"invalid size, possible overflow?");
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return static_cast<int64_t>(size_d);
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}
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} // namespace at::native
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@@ -0,0 +1,19 @@
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// Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#pragma once
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#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
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#include <c10/cuda/CUDAGuard.h>
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#endif
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