chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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// 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/arg_min_max_kernel.h"
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#include "paddle/common/ddim.h"
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/core/utils/data_type.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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namespace phi {
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enum ArgMinMaxType { kArgMin, kArgMax };
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template <typename Context,
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typename T,
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typename Tout,
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int64_t Rank,
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ArgMinMaxType argMinMaxValue>
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struct ArgMinMaxFunctor {};
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#define DECLARE_ARG_MIN_MAX_FUNCTOR(eigen_op_type, enum_argminmax_value) \
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template <typename Context, typename T, typename Tout, int64_t Rank> \
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struct ArgMinMaxFunctor<Context, T, Tout, Rank, enum_argminmax_value> { \
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void operator()(const Context& dev_ctx, \
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const DenseTensor& in, \
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DenseTensor* out, \
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DDim x_dims, \
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DDim out_dims, \
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int64_t axis, \
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bool keepdims, \
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bool flatten) { \
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auto in_eigen = EigenTensor<T, Rank>::From(in, x_dims); \
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if (flatten) { \
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auto out_eigen = EigenTensor<Tout, 0>::From(*out, out_dims); \
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out_eigen.device(*(dev_ctx.eigen_device())) = \
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in_eigen.eigen_op_type(axis).template cast<Tout>(); \
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} else { \
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if (keepdims) { \
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auto out_eigen = EigenTensor<Tout, Rank>::From(*out, out_dims); \
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out_eigen.device(*(dev_ctx.eigen_device())) = \
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in_eigen.eigen_op_type(axis).template cast<Tout>(); \
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} else { \
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auto out_eigen = EigenTensor<Tout, Rank - 1>::From(*out, out_dims); \
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out_eigen.device(*(dev_ctx.eigen_device())) = \
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in_eigen.eigen_op_type(axis).template cast<Tout>(); \
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} \
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} \
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} \
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}
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DECLARE_ARG_MIN_MAX_FUNCTOR(argmin, ArgMinMaxType::kArgMin);
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DECLARE_ARG_MIN_MAX_FUNCTOR(argmax, ArgMinMaxType::kArgMax);
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template <typename Context, typename T, ArgMinMaxType EnumArgMinMaxValue>
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struct VisitDataArgMinMaxFunctor {
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const Context& dev_ctx;
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const DenseTensor& x;
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int64_t axis;
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bool keepdims;
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bool flatten;
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DenseTensor* out;
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explicit VisitDataArgMinMaxFunctor(const Context& dev_ctx,
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const DenseTensor& x,
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int64_t axis,
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bool keepdims,
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bool flatten,
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DenseTensor* out)
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: dev_ctx(dev_ctx),
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x(x),
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axis(axis),
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keepdims(keepdims),
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flatten(flatten),
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out(out) {}
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template <typename Tout>
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void apply() const {
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dev_ctx.template Alloc<Tout>(out);
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if (x.numel() == 0) return;
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// if flatten, will construct the new dims for the calculation
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DDim x_dims;
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DDim out_dims;
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int new_axis = axis;
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if (flatten) {
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// always reduce 1D -> 0D
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x_dims = make_ddim({x.numel()});
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out_dims = make_ddim({});
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new_axis = 0;
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} else {
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x_dims = x.dims();
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out_dims = out->dims();
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if (axis < 0) new_axis = axis + x_dims.size();
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}
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#define CALL_ARG_MINMAX_FUNCTOR(rank) \
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ArgMinMaxFunctor<Context, T, Tout, rank, EnumArgMinMaxValue> functor##rank; \
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functor##rank(dev_ctx, x, out, x_dims, out_dims, new_axis, keepdims, flatten)
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switch (x_dims.size()) {
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case 0:
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funcs::set_constant(dev_ctx, out, static_cast<Tout>(0));
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return;
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case 1:
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CALL_ARG_MINMAX_FUNCTOR(1);
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break;
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case 2:
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CALL_ARG_MINMAX_FUNCTOR(2);
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break;
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case 3:
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CALL_ARG_MINMAX_FUNCTOR(3);
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break;
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case 4:
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CALL_ARG_MINMAX_FUNCTOR(4);
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break;
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case 5:
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CALL_ARG_MINMAX_FUNCTOR(5);
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break;
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case 6:
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CALL_ARG_MINMAX_FUNCTOR(6);
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break;
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default:
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PADDLE_ENFORCE_LE(
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x_dims.size(),
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6,
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common::errors::InvalidArgument(
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"%s operator doesn't supports tensors whose ranks are greater "
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"than 6.",
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(EnumArgMinMaxValue == kArgMin ? "argmin" : "argmax")));
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break;
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#undef CALL_ARG_MINMAX_FUNCTOR
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}
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}
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};
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template <typename Context, typename T, ArgMinMaxType EnumArgMinMaxValue>
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void ArgMinMaxKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const Scalar& axis,
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bool keepdims,
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bool flatten,
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DataType dtype,
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DenseTensor* out) {
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PADDLE_ENFORCE_GE(
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x.numel(),
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0,
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common::errors::InvalidArgument(
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"argmin/argmax input numel must > 0, bug got %d", x.numel()));
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if (dtype == DataType::UNDEFINED) {
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VisitDataTypeTiny(
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DataType::INT64,
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VisitDataArgMinMaxFunctor<Context, T, EnumArgMinMaxValue>(
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dev_ctx, x, axis.to<int64_t>(), keepdims, flatten, out));
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return;
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}
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VisitDataTypeTiny(
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dtype,
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VisitDataArgMinMaxFunctor<Context, T, EnumArgMinMaxValue>(
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dev_ctx, x, axis.to<int64_t>(), keepdims, flatten, out));
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}
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template <typename T, typename Context>
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void ArgMinKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const Scalar& axis,
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bool keepdims,
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bool flatten,
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DataType dtype,
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DenseTensor* out) {
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ArgMinMaxKernel<Context, T, ArgMinMaxType::kArgMin>(
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dev_ctx, x, axis, keepdims, flatten, dtype, out);
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}
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template <typename T, typename Context>
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void ArgMaxKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const Scalar& axis,
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bool keepdims,
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bool flatten,
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DataType dtype,
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DenseTensor* out) {
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ArgMinMaxKernel<Context, T, ArgMinMaxType::kArgMax>(
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dev_ctx, x, axis, keepdims, flatten, dtype, out);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(argmin,
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CPU,
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ALL_LAYOUT,
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phi::ArgMinKernel,
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float,
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double,
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int32_t,
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int64_t,
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int16_t,
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uint8_t) {
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kernel->OutputAt(0).SetDataType(phi::DataType::UNDEFINED);
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}
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PD_REGISTER_KERNEL(argmax,
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CPU,
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ALL_LAYOUT,
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phi::ArgMaxKernel,
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float,
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double,
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int32_t,
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int64_t,
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int16_t,
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uint8_t) {
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kernel->OutputAt(0).SetDataType(phi::DataType::UNDEFINED);
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}
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