"exported scatter to python" (#9038)

* "exported scatter to python"

* Revert ""exported scatter to python""

This reverts commit 38745a62.

* "polish scatter and export to python"

paddle/fluid/operators/scatter_op.cc

@@ -23,24 +23,24 @@ class ScatterOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("Ref"),
-                   "Input(Ref) of ScatterOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("Index"),
-                   "Input(Index) of ScatterOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of ScatterOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Ids"),
+                   "Input(Ids) of ScatterOp should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("Updates"),
                    "Input(Updates) of ScatterOp should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
                    "Output(Out) of ScatterOp should not be null.");
 
     auto updates_dims = ctx->GetInputDim("Updates");
-    auto ref_dims = ctx->GetInputDim("Ref");
-    PADDLE_ENFORCE_EQ(ctx->GetInputDim("Index").size(), 1,
-                      "Update Index should be 1-D.");
+    auto ref_dims = ctx->GetInputDim("X");
+    PADDLE_ENFORCE_EQ(ctx->GetInputDim("Ids").size(), 1,
+                      "Update Ids should be 1-D.");
     PADDLE_ENFORCE_EQ(ref_dims.size(), updates_dims.size(),
-                      "Reference and Updates should have the same shape size");
+                      "Xerence and Updates should have the same shape size");
     PADDLE_ENFORCE_EQ(ctx->GetInputDim("Updates")[0],
-                      ctx->GetInputDim("Index")[0],
-                      "Updates and Index should have same batch-size.");
+                      ctx->GetInputDim("Ids")[0],
+                      "Updates and Ids should have same batch-size.");
     framework::DDim data_dim(updates_dims);
     for (int i = 1; i < data_dim.size(); ++i) {
       PADDLE_ENFORCE_EQ(data_dim[i], updates_dims[i]);
@@ -52,7 +52,7 @@ class ScatterOp : public framework::OperatorWithKernel {
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
     return framework::OpKernelType(
-        framework::ToDataType(ctx.Input<Tensor>("Ref")->type()),
+        framework::ToDataType(ctx.Input<Tensor>("X")->type()),
         ctx.device_context());
   }
 };
@@ -64,14 +64,14 @@ class ScatterGradOp : public framework::OperatorWithKernel {
   void InferShape(framework::InferShapeContext* ctx) const override {
     ctx->SetOutputDim(framework::GradVarName("Updates"),
                       ctx->GetInputDim("Updates"));
-    ctx->SetOutputDim(framework::GradVarName("Ref"), ctx->GetInputDim("Ref"));
+    ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
   }
 
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
     return framework::OpKernelType(
-        framework::ToDataType(ctx.Input<Tensor>("Ref")->type()),
+        framework::ToDataType(ctx.Input<Tensor>("X")->type()),
         ctx.device_context());
   }
 };
@@ -80,9 +80,8 @@ class ScatterOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   ScatterOpMaker(OpProto* proto, OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("Ref", "The source input of scatter op");
-    AddInput("Index",
-             "The index input of scatter op where Ref will be updated");
+    AddInput("X", "The source input of scatter op");
+    AddInput("Ids", "The index input of scatter op where X will be updated");
     AddInput("Updates", "The updated value of updates op");
     AddOutput("Out", "The output of add op");
     AddComment(R"DOC(
@@ -91,8 +90,8 @@ Scatter Operator.
 This operator obtains output by updating the input on selected indices on the first axis:
 
 $$
-Out = Ref \\
-Out[Index] = Ref[Index] + Updates
+Out = X \\
+Out[Ids] = X[Ids] + Updates
 $$
 
 )DOC");

paddle/fluid/operators/scatter_op.cu

@@ -25,14 +25,14 @@ class ScatterOpCUDAKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext &ctx) const override {
     PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
                    "This kernel only runs on GPU device.");
-    auto *Ref = ctx.Input<Tensor>("Ref");
-    auto *Index = ctx.Input<Tensor>("Index");
+    auto *X = ctx.Input<Tensor>("X");
+    auto *Ids = ctx.Input<Tensor>("Ids");
     auto *Updates = ctx.Input<Tensor>("Updates");
     auto *Out = ctx.Output<Tensor>("Out");
 
-    Out->ShareDataWith(*Ref);
+    Out->ShareDataWith(*X);
 
-    GPUScatterAssign<T>(ctx.device_context(), *Updates, *Index, Out);
+    GPUScatterAssign<T>(ctx.device_context(), *Updates, *Ids, Out);
   }
 };
 
@@ -42,16 +42,16 @@ class ScatterGradOpCUDAKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext &ctx) const override {
     PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
                    "This kernel only runs on GPU device.");
-    auto *dRef = ctx.Output<Tensor>(framework::GradVarName("Ref"));
+    auto *dX = ctx.Output<Tensor>(framework::GradVarName("X"));
     auto *dUpdates = ctx.Output<Tensor>(framework::GradVarName("Updates"));
-    auto *Index = ctx.Input<Tensor>("Index");
+    auto *Ids = ctx.Input<Tensor>("Ids");
     auto *dOut = ctx.Input<Tensor>(framework::GradVarName("Out"));
 
-    // In place gradient: dRef = dO
-    dRef->ShareDataWith(*dOut);
+    // In place gradient: dX = dO
+    dX->ShareDataWith(*dOut);
     dUpdates->mutable_data<T>(ctx.GetPlace());
-    // Gradient by Gather: dUpdates = dO[Index]
-    GPUGather<T>(ctx.device_context(), *dOut, *Index, dUpdates);
+    // Gradient by Gather: dUpdates = dO[Ids]
+    GPUGather<T>(ctx.device_context(), *dOut, *Ids, dUpdates);
   }
 };
 

paddle/fluid/operators/scatter_op.h

@@ -29,15 +29,15 @@ class ScatterOpKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext &ctx) const override {
     PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()),
                    "This kernel only runs on CPU.");
-    auto *Ref = ctx.Input<Tensor>("Ref");
-    auto *Index = ctx.Input<Tensor>("Index");
+    auto *X = ctx.Input<Tensor>("X");
+    auto *Ids = ctx.Input<Tensor>("Ids");
     auto *Updates = ctx.Input<Tensor>("Updates");
     auto *Out = ctx.Output<Tensor>("Out");
 
-    // In place output: Out = Ref, Out[Index] += Updates
-    Out->ShareDataWith(*Ref);
+    // In place output: Out = X, Out[Ids] += Updates
+    Out->ShareDataWith(*X);
     // Apply ScatterUpdate: Out[index] += Updates[:]
-    ScatterAssign<T>(ctx.device_context(), *Updates, *Index, Out);
+    ScatterAssign<T>(ctx.device_context(), *Updates, *Ids, Out);
   }
 };
 
@@ -47,16 +47,16 @@ class ScatterGradientOpKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext &ctx) const override {
     PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()),
                    "This kernel only runs on CPU.");
-    auto *dRef = ctx.Output<Tensor>(framework::GradVarName("Ref"));
+    auto *dX = ctx.Output<Tensor>(framework::GradVarName("X"));
     auto *dUpdates = ctx.Output<Tensor>(framework::GradVarName("Updates"));
-    auto *Index = ctx.Input<Tensor>("Index");
+    auto *Ids = ctx.Input<Tensor>("Ids");
     auto *dOut = ctx.Input<Tensor>(framework::GradVarName("Out"));
 
-    // In place gradient: dRef = dO
-    dRef->ShareDataWith(*dOut);
+    // In place gradient: dX = dO
+    dX->ShareDataWith(*dOut);
     dUpdates->mutable_data<T>(ctx.GetPlace());
-    // Gradient by Gather: dUpdates += dO[Index]
-    CPUGather<T>(ctx.device_context(), *dOut, *Index, dUpdates);
+    // Gradient by Gather: dUpdates += dO[Ids]
+    CPUGather<T>(ctx.device_context(), *dOut, *Ids, dUpdates);
   }
 };
 

python/paddle/fluid/layers/ops.py

@@ -45,31 +45,13 @@ __activations__ = [
 ]
 
 __all__ = [
-    'mean',
-    'mul',
-    'reshape',
-    'scale',
-    'sigmoid_cross_entropy_with_logits',
-    'elementwise_add',
-    'elementwise_div',
-    'elementwise_sub',
-    'elementwise_mul',
-    'elementwise_max',
-    'elementwise_min',
-    'elementwise_pow',
-    'clip',
-    'clip_by_norm',
-    'softmax',
-    'sequence_softmax',
-    'logical_and',
-    'logical_or',
-    'logical_xor',
-    'logical_not',
-    'uniform_random',
-    'uniform_random_batch_size_like',
-    'gaussian_random',
-    'gaussian_random_batch_size_like',
-    'cumsum',
+    'mean', 'mul', 'reshape', 'scale', 'sigmoid_cross_entropy_with_logits',
+    'elementwise_add', 'elementwise_div', 'elementwise_sub', 'elementwise_mul',
+    'elementwise_max', 'elementwise_min', 'elementwise_pow', 'clip',
+    'clip_by_norm', 'softmax', 'sequence_softmax', 'logical_and', 'logical_or',
+    'logical_xor', 'logical_not', 'uniform_random',
+    'uniform_random_batch_size_like', 'gaussian_random',
+    'gaussian_random_batch_size_like', 'cumsum', 'scatter'
 ] + __activations__
 
 for _OP in set(__all__):

python/paddle/fluid/tests/unittests/test_scatter_op.py

@@ -25,7 +25,7 @@ class TestScatterOp(OpTest):
         updates_np = np.random.random((2, 3)).astype("float32")
         output_np = np.copy(ref_np)
         output_np[index_np] = updates_np
-        self.inputs = {'Ref': ref_np, 'Index': index_np, 'Updates': updates_np}
+        self.inputs = {'X': ref_np, 'Ids': index_np, 'Updates': updates_np}
         self.outputs = {'Out': output_np}
 
     def test_check_output(self):