new api/op repeat_interleave

paddle/fluid/operators/repeat_interleave_op.cc

@@ -0,0 +1,174 @@
+// Copyright (c) 2021 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/fluid/operators/repeat_interleave_op.h"
+#include <memory>
+
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+
+class RepeatInterleaveOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE_EQ(
+        ctx->HasInput("X"), true,
+        platform::errors::InvalidArgument(
+            "Input(X) of RepeatInterleaveOp should not be null."));
+    PADDLE_ENFORCE_EQ(
+        ctx->HasOutput("Out"), true,
+        platform::errors::InvalidArgument(
+            "Output(Out) of RepeatInterleaveOp should not be null."));
+
+    auto input_dim = ctx->GetInputDim("X");
+    auto dim = ctx->Attrs().Get<int>("dim");
+    auto output_dim = framework::vectorize(input_dim);
+    PADDLE_ENFORCE_EQ(
+        dim < input_dim.size() && dim >= (0 - input_dim.size()), true,
+        platform::errors::OutOfRange(
+            "Attr(dim) is out of range, It's expected "
+            "to be in range of [-%d, %d]. But received Attr(dim) = %d.",
+            input_dim.size(), input_dim.size() - 1, dim));
+
+    auto repeats = ctx->Attrs().Get<int>("Repeats");
+    if (ctx->HasInput("RepeatsTensor")) {
+      auto repeats_dim = ctx->GetInputDim("RepeatsTensor");
+
+      PADDLE_ENFORCE_EQ(
+          repeats_dim.size() == 1 ||
+              (repeats_dim.size() == 2 && repeats_dim[1] == 1),
+          true, platform::errors::InvalidArgument(
+                    "The 'shape' of Input(RepeatsTensor) must be 1-D tensor. "
+                    "But received: the 'shape' of Input(Index) is [%s], "
+                    "the dimension of Input(Index) is [%d].",
+                    repeats_dim, repeats_dim.size()));
+
+      PADDLE_ENFORCE_EQ(repeats_dim[0] != 0, true,
+                        platform::errors::InvalidArgument(
+                            "The length of Input(RepeatsTensor) can't be 0."));
+
+      if (dim < 0) {
+        dim += input_dim.size();
+      }
+      output_dim[dim] = -1;
+    } else if (repeats > 0) {
+      output_dim[dim] = input_dim[dim] * repeats;
+    }
+    VLOG(3) << "infershap out " << output_dim[dim];
+    ctx->SetOutputDim("Out", framework::make_ddim(output_dim));
+    auto type = ctx->GetInputsVarType("X")[0];
+    if (type == framework::proto::VarType::LOD_TENSOR) {
+      ctx->ShareLoD("X", /*->*/ "Out");
+    }
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    auto data_type = OperatorWithKernel::IndicateVarDataType(ctx, "X");
+    return framework::OpKernelType(data_type, ctx.device_context());
+  }
+};
+
+class RepeatInterleaveGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE_EQ(ctx->HasInput(framework::GradVarName("Out")), true,
+                      platform::errors::InvalidArgument(
+                          "Input(Out@GRAD) should be not null."));
+    PADDLE_ENFORCE_EQ(ctx->HasOutput(framework::GradVarName("X")), true,
+                      platform::errors::InvalidArgument(
+                          "Output(X@GRAD) should be not null."));
+
+    ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(OperatorWithKernel::IndicateVarDataType(
+                                       ctx, framework::GradVarName("Out")),
+                                   ctx.device_context());
+  }
+};
+
+class RepeatInterleaveOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X", "(Tensor) the input tensor.");
+    AddInput("RepeatsTensor",
+             "the 1-D tensor containing the repeats alongsize the axis.")
+        .AsDispensable();
+    AddOutput("Out", "the output tensor.");
+    AddAttr<int>("Repeats", "the number of repetitions for each element.")
+        .SetDefault(0);
+    AddAttr<int>("dim", "the dimension in which we repeat.").SetDefault(0);
+    AddComment(R"DOC(
+Returns a new tensor which repeats the input tensor
+along dimension dim using the entries in repeats which
+is a Tensor or int.
+
+The returned tensor has the same number of dimensions
+as the original tensor (input), except along the given axis.
+    )DOC");
+  }
+};
+
+template <typename T>
+class RepeatInterleaveGradMaker : public framework::SingleGradOpMaker<T> {
+ public:
+  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
+
+ protected:
+  void Apply(GradOpPtr<T> op) const override {
+    op->SetType("repeat_interleave_grad");
+
+    op->SetInput("X", this->Input("X"));
+    op->SetInput("RepeatsTensor", this->Input("RepeatsTensor"));
+    op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
+    op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
+    op->SetAttrMap(this->Attrs());
+  }
+};
+
+DECLARE_NO_NEED_BUFFER_VARS_INFERER(RepeatInterleaveGradNoNeedBufferVarsInferer,
+                                    "X");
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(repeat_interleave, ops::RepeatInterleaveOp,
+                  ops::RepeatInterleaveOpMaker,
+                  ops::RepeatInterleaveGradMaker<paddle::framework::OpDesc>,
+                  ops::RepeatInterleaveGradMaker<paddle::imperative::OpBase>);
+REGISTER_OPERATOR(repeat_interleave_grad, ops::RepeatInterleaveGradOp,
+                  ops::RepeatInterleaveGradNoNeedBufferVarsInferer);
+REGISTER_OP_CPU_KERNEL(
+    repeat_interleave,
+    ops::RepeatInterleaveKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::RepeatInterleaveKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::RepeatInterleaveKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::RepeatInterleaveKernel<paddle::platform::CPUDeviceContext, int64_t>);
+REGISTER_OP_CPU_KERNEL(
+    repeat_interleave_grad,
+    ops::RepeatInterleaveGradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::RepeatInterleaveGradKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::RepeatInterleaveGradKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::RepeatInterleaveGradKernel<paddle::platform::CPUDeviceContext,
+                                    int64_t>);