wehub-resource-sync
97 lines
3.1 KiB
C++
97 lines
3.1 KiB
C++
// Copyright (c) 2022 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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#include "paddle/phi/kernels/cum_kernel.h"
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#include "paddle/phi/backends/xpu/enforce_xpu.h"
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#include "paddle/phi/core/kernel_registry.h"
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namespace phi {
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template <typename T, typename Context>
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void CumsumKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const Scalar& axis,
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bool flatten,
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bool exclusive,
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bool reverse,
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DenseTensor* out) {
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using XPUType = typename XPUTypeTrait<T>::Type;
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if (out && out->numel() == 0) {
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dev_ctx.template Alloc<T>(out);
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return;
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}
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dev_ctx.template Alloc<T>(out);
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if (x.numel() == 1) {
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int r = xpu::copy<XPUType>(dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(x.data<T>()),
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reinterpret_cast<XPUType*>(out->data<T>()),
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x.numel());
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "copy");
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return;
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}
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// prepare for call xdnn api
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std::vector<int64_t> x_shape = vectorize<int64_t>(x.dims());
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int axis_as_int = axis.to<int>();
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if (flatten) {
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// flatten to 1-dim vector
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x_shape = {x.numel()};
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axis_as_int = 0;
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} else {
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// not flatten
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// check axis_as_int
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auto out_dims = out->dims();
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PADDLE_ENFORCE_EQ(
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axis_as_int < out_dims.size() && axis_as_int >= (0 - out_dims.size()),
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true,
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common::errors::OutOfRange(
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"Attr(axis) is out of range, It's expected "
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"to be in range of [-%d, %d]. But received Attr(axis) = %d.",
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out_dims.size(),
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out_dims.size() - 1,
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axis_as_int));
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if (axis_as_int < 0) {
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axis_as_int += out_dims.size();
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}
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}
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// template<typename T> DLL_EXPORT int cumsum(Context* xpu_ctx, const T* x, T*
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// y, const std::vector<int>& xshape, bool reverse, bool exclusive, int
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// axis);
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int r = xpu::cumsum<XPUType>(dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(x.data<T>()),
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reinterpret_cast<XPUType*>(out->data<T>()),
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x_shape,
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reverse,
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exclusive,
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axis_as_int);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "cumsum");
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}
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} // namespace phi
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PD_REGISTER_KERNEL(cumsum,
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XPU,
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ALL_LAYOUT,
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phi::CumsumKernel,
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float,
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int,
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int64_t,
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phi::float16,
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phi::bfloat16) {}
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