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[API Compatibility] add aminmax op-part (#78441)
* develop paddle.aminmax to align paddle with torch.Tensor.aminmax * refine tests, improve en docs * fix * try fix coverage * add out test and add out in docs. * add CINN test, follow the test routine, remove name in docs and remove extra in op_compat.yaml * enable CINN, remove the redundant wrapper, and restructure the if-elif branch * remove example code and add coverage for CINN * try conver * add back aminmax in __init__ * add AminmaxOpInferSymbolicShape coverage test to CINN sym shape test file * enable all device to run test for aminmax, disabled the example code check in docs * add SKIP per example code * delete example code
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‎paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc‎

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@@ -307,6 +307,24 @@ bool AminOpInferSymbolicShape(pir::Operation *op,
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axis.size() == 0 /*reduce_all*/);
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}
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bool AminmaxOpInferSymbolicShape(
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pir::Operation *op, pir::InferSymbolicShapeContext *infer_context) {
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const auto &axis = details::GetVectorAttr(op, "axis");
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bool keepdim = GetBoolAttr(op, "keepdim");
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bool reduce_all = axis.size() == 0;
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// ReduceInferDim only sets result(0). We need the same shape for both
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// outputs, so call it for result(0) then copy to result(1).
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bool ret =
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details::ReduceInferDim(op, infer_context, axis, keepdim, reduce_all);
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if (ret) {
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const auto &out_shape =
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infer_context->GetShapeOrDataForValue(op->result(0));
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infer_context->SetShapeOrDataForValue(op->result(1), out_shape);
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}
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return ret;
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}
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bool AnyOpInferSymbolicShape(pir::Operation *op,
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pir::InferSymbolicShapeContext *infer_context) {
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const auto &axis = details::GetVectorAttr(op, "axis");

‎paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.h‎

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@@ -21,6 +21,7 @@ OP_DECLARE_INFER_SYMBOLIC_SHAPE(AffineGrid)
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OP_DECLARE_INFER_SYMBOLIC_SHAPE(All)
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OP_DECLARE_INFER_SYMBOLIC_SHAPE(Amax)
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OP_DECLARE_INFER_SYMBOLIC_SHAPE(Amin)
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OP_DECLARE_INFER_SYMBOLIC_SHAPE(Aminmax)
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OP_DECLARE_INFER_SYMBOLIC_SHAPE(Any)
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OP_DECLARE_INFER_SYMBOLIC_SHAPE(Argmax)
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OP_DECLARE_INFER_SYMBOLIC_SHAPE(Argmin)

‎paddle/phi/infermeta/unary.cc‎

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@@ -4291,6 +4291,24 @@ void ReduceInferMeta(const MetaTensor& x,
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ReduceInferMetaBase(x, axis, keep_dim, reduce_all, out);
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}
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void AMinMaxInferMeta(const MetaTensor& x,
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const std::vector<int64_t>& axis,
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bool keep_dim,
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MetaTensor* min,
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MetaTensor* max) {
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bool reduce_all = false;
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if (axis.empty()) {
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reduce_all = true;
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}
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DDim out_dim = ReduceInferDim(x, axis, keep_dim, reduce_all);
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min->set_dims(out_dim);
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min->set_dtype(x.dtype());
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min->set_layout(x.layout());
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max->set_dims(out_dim);
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max->set_dtype(x.dtype());
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max->set_layout(x.layout());
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}
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DDim ReduceInferDimForIntArrayAxis(const MetaTensor& x,
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const IntArray& axis,
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bool keep_dim,

‎paddle/phi/infermeta/unary.h‎

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@@ -69,6 +69,12 @@ PADDLE_API void ArgMinMaxInferMeta(const MetaTensor& x,
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MetaTensor* out,
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MetaConfig config = MetaConfig());
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PADDLE_API void AMinMaxInferMeta(const MetaTensor& x,
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const std::vector<int64_t>& axis,
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bool keep_dim,
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MetaTensor* min,
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MetaTensor* max);
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PADDLE_API void MinMaxWithIndexInferMeta(const MetaTensor& x,
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const Scalar& axis,
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bool keepdims,

‎paddle/phi/kernels/aminmax_grad_kernel.cc‎

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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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#include "paddle/phi/kernels/aminmax_grad_kernel.h"
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#include "paddle/phi/backends/all_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/elementwise_add_kernel.h"
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#include "paddle/phi/kernels/reduce_amax_grad_kernel.h"
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#include "paddle/phi/kernels/reduce_amin_grad_kernel.h"
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namespace phi {
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template <typename T, typename Context>
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void AMinMaxGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& min,
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const DenseTensor& max,
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const DenseTensor& min_grad,
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const DenseTensor& max_grad,
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const std::vector<int64_t>& dims,
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bool keep_dim,
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bool reduce_all,
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DenseTensor* x_grad) {
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if (x_grad && x_grad->numel() == 0) {
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dev_ctx.template Alloc<T>(x_grad);
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return;
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}
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reduce_all = recompute_reduce_all(x, dims, reduce_all);
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// Compute amax grad contribution into x_grad
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ReduceAMaxGradKernel<T, Context>(
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dev_ctx, x, max, max_grad, dims, keep_dim, reduce_all, x_grad);
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// Compute amin grad contribution into a temporary tensor
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DenseTensor amin_x_grad;
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amin_x_grad.Resize(x_grad->dims());
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dev_ctx.template Alloc<T>(&amin_x_grad);
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ReduceAMinGradKernel<T, Context>(
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dev_ctx, x, min, min_grad, dims, keep_dim, reduce_all, &amin_x_grad);
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// x_grad = amax_grad_result + amin_grad_result
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Add<T, Context>(dev_ctx, *x_grad, amin_x_grad, x_grad);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(aminmax_grad,
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CPU,
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ALL_LAYOUT,
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phi::AMinMaxGradKernel,
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float,
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double,
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int,
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int64_t) {}
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#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
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PD_REGISTER_KERNEL(aminmax_grad,
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GPU,
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ALL_LAYOUT,
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phi::AMinMaxGradKernel,
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float,
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double,
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int,
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int64_t) {}
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#endif

‎paddle/phi/kernels/aminmax_grad_kernel.h‎

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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 "paddle/phi/core/dense_tensor.h"
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namespace phi {
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template <typename T, typename Context>
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void AMinMaxGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& min,
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const DenseTensor& max,
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const DenseTensor& min_grad,
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const DenseTensor& max_grad,
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const std::vector<int64_t>& axis,
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bool keep_dim,
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bool reduce_all,
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DenseTensor* x_grad);
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} // namespace phi

‎paddle/phi/kernels/aminmax_kernel.cc‎

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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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#include "paddle/phi/kernels/aminmax_kernel.h"
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#include "paddle/phi/backends/all_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/reduce_amax_kernel.h"
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#include "paddle/phi/kernels/reduce_amin_kernel.h"
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namespace phi {
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template <typename T, typename Context>
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void AMinMaxKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const std::vector<int64_t>& dims,
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bool keep_dim,
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DenseTensor* min,
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DenseTensor* max) {
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bool reduce_all = recompute_reduce_all(x, dims);
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AMinRawKernel<T>(dev_ctx, x, dims, keep_dim, reduce_all, min);
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AMaxRawKernel<T>(dev_ctx, x, dims, keep_dim, reduce_all, max);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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aminmax, CPU, ALL_LAYOUT, phi::AMinMaxKernel, float, double, int, int64_t) {
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}
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#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
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PD_REGISTER_KERNEL(
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aminmax, GPU, ALL_LAYOUT, phi::AMinMaxKernel, float, double, int, int64_t) {
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}
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#endif
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#if defined(PADDLE_WITH_XPU_KP)
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PD_REGISTER_KERNEL(aminmax, KPS, ALL_LAYOUT, phi::AMinMaxKernel, float) {}
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#endif

‎paddle/phi/kernels/aminmax_kernel.h‎

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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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#include "paddle/phi/core/dense_tensor.h"
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namespace phi {
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template <typename T, typename Context>
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void AMinMaxKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const std::vector<int64_t>& dims,
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bool keep_dim,
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DenseTensor* min,
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DenseTensor* max);
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} // namespace phi

‎paddle/phi/ops/yaml/backward.yaml‎

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@@ -141,6 +141,16 @@
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kernel :
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func : amin_grad
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- backward_op : aminmax_grad
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forward : aminmax (Tensor x, int64_t[] axis={}, bool keepdim=false) -> Tensor(min), Tensor(max)
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args : (Tensor x, Tensor min, Tensor max, Tensor min_grad, Tensor max_grad, int64_t[] axis={}, bool keepdim=false, bool reduce_all=false)
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output : Tensor(x_grad)
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infer_meta :
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func : UnchangedInferMeta
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param : [x]
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kernel :
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func : aminmax_grad
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- backward_op : angle_grad
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forward : angle (Tensor x) -> Tensor(out)
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args : (Tensor x, Tensor out_grad)

‎paddle/phi/ops/yaml/op_compat.yaml‎

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amin_grad : GetReduceGradExpectedKernelType
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manual_signature : [amin]
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- op : aminmax
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backward : aminmax_grad
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inputs :
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x : X
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outputs :
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{min : Min, max : Max}
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manual_signature : [aminmax]
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- op : anchor_generator
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inputs:
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input : Input

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