// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. #include "paddle/phi/kernels/arg_min_max_kernel.h" #include "paddle/phi/backends/xpu/xpu_context.h" #include "paddle/phi/core/ddim.h" #include "paddle/phi/core/kernel_registry.h" #include "paddle/phi/core/utils/data_type.h" namespace phi { template void ArgMaxKernel(const Context& dev_ctx, const DenseTensor& x, const Scalar& axis, bool keepdims, bool flatten, int dtype, DenseTensor* out) { PADDLE_ENFORCE_EQ( (dtype < 0 || dtype == 2 || dtype == 3), true, errors::InvalidArgument( "The attribute of dtype in xpu argmin/argmax must be [%s] or [%s], " "but " "received [%s]", DataType::INT64, DataType::INT32, dtype)); dev_ctx.template Alloc(out); DDim x_dims; int axis_val = axis.to(); if (flatten) { x_dims = phi::make_ddim({x.numel()}); // if flatten, the axis just as 0 axis_val = 0; } else { x_dims = x.dims(); if (axis_val < 0) axis_val += x_dims.size(); } auto xdims_vec = phi::vectorize(x_dims); int r = xpu::argmax(dev_ctx.x_context(), x.data(), out->data(), xdims_vec, axis_val); PADDLE_ENFORCE_EQ( r, XPU_SUCCESS, errors::External("XPU argmax kernel return wrong value[%d %s].", r, XPUAPIErrorMsg[r])); } } // namespace phi PD_REGISTER_KERNEL(argmax, XPU, ALL_LAYOUT, phi::ArgMaxKernel, float) {}