roll_op: support Tensor as input for shifts (#36727)

paddle/fluid/operators/roll_op.cc

@@ -40,21 +40,23 @@ class RollOp : public framework::OperatorWithKernel {
     auto dims = ctx->Attrs().Get<std::vector<int64_t>>("axis");
     auto shifts = ctx->Attrs().Get<std::vector<int64_t>>("shifts");
 
-    if (dims.size() != 0) {
-      PADDLE_ENFORCE_EQ(dims.size(), shifts.size(),
-                        platform::errors::InvalidArgument(
-                            "When dims.size() != 0, dims.size() "
-                            "should be equal to "
-                            "shifts.size(). But received "
-                            "dims.size() = %d, shifts.size() = %d",
-                            dims.size(), shifts.size()));
-    } else {
-      PADDLE_ENFORCE_EQ(shifts.size(), 1,
-                        platform::errors::InvalidArgument(
-                            "When dims.size() == 0, shifts.size() "
-                            "should be equal to 1, But received "
-                            "shifts.size() = %d",
-                            shifts.size()));
+    if (!ctx->HasInput("ShiftsTensor")) {
+      if (dims.size() != 0) {
+        PADDLE_ENFORCE_EQ(dims.size(), shifts.size(),
+                          platform::errors::InvalidArgument(
+                              "When dims.size() != 0, dims.size() "
+                              "should be equal to "
+                              "shifts.size(). But received "
+                              "dims.size() = %d, shifts.size() = %d",
+                              dims.size(), shifts.size()));
+      } else {
+        PADDLE_ENFORCE_EQ(shifts.size(), 1,
+                          platform::errors::InvalidArgument(
+                              "When dims.size() == 0, shifts.size() "
+                              "should be equal to 1, But received "
+                              "shifts.size() = %d",
+                              shifts.size()));
+      }
     }
 
     ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
@@ -105,6 +107,10 @@ class RollOpMaker : public framework::OpProtoAndCheckerMaker {
                                   "The number of places by which the elements "
                                   "of the tensor are shifted.")
         .SetDefault({});
+    AddInput("ShiftsTensor",
+             "The number of places by which the elements of the tensor "
+             "are shifted.")
+        .AsDispensable();
     AddAttr<std::vector<int64_t>>(
         "axis",
         "Axis along which to roll. It must have the same size "
@@ -129,6 +135,9 @@ class RollGradMaker : public framework::SingleGradOpMaker<T> {
   void Apply(GradOpPtr<T> op) const override {
     op->SetType("roll_grad");
     op->SetInput("X", this->Input("X"));
+    if (this->HasInput("ShiftsTensor")) {
+      op->SetInput("ShiftsTensor", this->Input("ShiftsTensor"));
+    }
     op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
     op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
     op->SetAttrMap(this->Attrs());

paddle/fluid/operators/roll_op.cu

@@ -59,6 +59,16 @@ class RollKernel<platform::CUDADeviceContext, T>
     auto* in = context.Input<LoDTensor>("X");
     auto* out = context.Output<LoDTensor>("Out");
     std::vector<int64_t> shifts = context.Attr<std::vector<int64_t>>("shifts");
+    if (context.HasInput("ShiftsTensor")) {
+      const auto* shifts_tensor =
+          context.Input<framework::Tensor>("ShiftsTensor");
+      PADDLE_ENFORCE_EQ(
+          shifts_tensor->dims().size(), 1,
+          platform::errors::InvalidArgument(
+              "The rank of ShiftsTensor is expected to be 1, got %s",
+              shifts_tensor->dims().size()));
+      shifts = GetDataFromTensor<int64_t>(shifts_tensor);
+    }
     std::vector<int64_t> dims = context.Attr<std::vector<int64_t>>("axis");
 
     auto* in_data = in->data<T>();
@@ -134,6 +144,16 @@ class RollGradKernel<platform::CUDADeviceContext, T>
     auto* in = context.Input<LoDTensor>(framework::GradVarName("Out"));
     auto* out = context.Output<LoDTensor>(framework::GradVarName("X"));
     std::vector<int64_t> shifts = context.Attr<std::vector<int64_t>>("shifts");
+    if (context.HasInput("ShiftsTensor")) {
+      const auto* shifts_tensor =
+          context.Input<framework::Tensor>("ShiftsTensor");
+      PADDLE_ENFORCE_EQ(
+          shifts_tensor->dims().size(), 1,
+          platform::errors::InvalidArgument(
+              "The rank of ShiftsTensor is expected to be 1, got %s",
+              shifts_tensor->dims().size()));
+      shifts = GetDataFromTensor<int64_t>(shifts_tensor);
+    }
     std::vector<int64_t> dims = context.Attr<std::vector<int64_t>>("axis");
 
     auto* in_data = in->data<T>();

paddle/fluid/operators/roll_op.h

@@ -16,6 +16,8 @@
 #include <memory>
 #include <vector>
 #include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/utils.h"
+#include "paddle/fluid/platform/enforce.h"
 
 namespace paddle {
 namespace operators {
@@ -85,6 +87,16 @@ class RollKernel : public framework::OpKernel<T> {
     auto& input = input_var->Get<LoDTensor>();
     auto* output = output_var->GetMutable<LoDTensor>();
     std::vector<int64_t> shifts = context.Attr<std::vector<int64_t>>("shifts");
+    if (context.HasInput("ShiftsTensor")) {
+      const auto* shifts_tensor =
+          context.Input<framework::Tensor>("ShiftsTensor");
+      PADDLE_ENFORCE_EQ(
+          shifts_tensor->dims().size(), 1,
+          platform::errors::InvalidArgument(
+              "The rank of ShiftsTensor is expected to be 1, got %s",
+              shifts_tensor->dims().size()));
+      shifts = GetDataFromTensor<int64_t>(shifts_tensor);
+    }
     std::vector<int64_t> dims = context.Attr<std::vector<int64_t>>("axis");
 
     std::vector<T> out_vec;
@@ -123,6 +135,11 @@ class RollGradKernel : public framework::OpKernel<T> {
     auto& input = input_var->Get<LoDTensor>();
     auto* output = output_var->GetMutable<LoDTensor>();
     std::vector<int64_t> shifts = context.Attr<std::vector<int64_t>>("shifts");
+    if (context.HasInput("ShiftsTensor")) {
+      const auto* shifts_tensor =
+          context.Input<framework::Tensor>("ShiftsTensor");
+      shifts = GetDataFromTensor<int64_t>(shifts_tensor);
+    }
     std::vector<int64_t> dims = context.Attr<std::vector<int64_t>>("axis");
 
     std::vector<T> out_vec;

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

@@ -122,6 +122,34 @@ class TestRollAPI(unittest.TestCase):
 
         self.assertRaises(ValueError, test_axis_out_range)
 
+    def test_shifts_as_tensor_dygraph(self):
+        with fluid.dygraph.guard():
+            x = paddle.arange(9).reshape([3, 3])
+            shape = paddle.shape(x)
+            shifts = shape // 2
+            axes = [0, 1]
+            out = paddle.roll(x, shifts=shifts, axis=axes).numpy()
+            expected_out = np.array([[8, 6, 7], [2, 0, 1], [5, 3, 4]])
+            self.assertTrue(np.allclose(out, expected_out))
+
+    def test_shifts_as_tensor_static(self):
+        with program_guard(Program(), Program()):
+            x = paddle.arange(9).reshape([3, 3]).astype('float32')
+            shape = paddle.shape(x)
+            shifts = shape // 2
+            axes = [0, 1]
+            out = paddle.roll(x, shifts=shifts, axis=axes)
+            expected_out = np.array([[8, 6, 7], [2, 0, 1], [5, 3, 4]])
+
+            exe = fluid.Executor(fluid.CPUPlace())
+            [out_np] = exe.run(fetch_list=[out])
+            self.assertTrue(np.allclose(out_np, expected_out))
+
+            if paddle.is_compiled_with_cuda():
+                exe = fluid.Executor(fluid.CPUPlace())
+                [out_np] = exe.run(fetch_list=[out])
+                self.assertTrue(np.allclose(out_np, expected_out))
+
 
 if __name__ == "__main__":
     unittest.main()

python/paddle/tensor/manipulation.py

@@ -696,15 +696,24 @@ def roll(x, shifts, axis=None, name=None):
 
     helper = LayerHelper("roll", **locals())
     check_type(axis, 'axis', (list, tuple), 'roll')
-    check_type(shifts, 'shifts', (list, tuple), 'roll')
+
     out = helper.create_variable_for_type_inference(x.dtype)
 
-    helper.append_op(
-        type='roll',
-        inputs={'X': x},
-        outputs={'Out': out},
-        attrs={'axis': axis,
-               'shifts': shifts})
+    if isinstance(shifts, Variable):
+        helper.append_op(
+            type='roll',
+            inputs={'X': x,
+                    "ShiftsTensor": shifts},
+            outputs={'Out': out},
+            attrs={'axis': axis})
+    else:
+        check_type(shifts, 'shifts', (list, tuple), 'roll')
+        helper.append_op(
+            type='roll',
+            inputs={'X': x},
+            outputs={'Out': out},
+            attrs={'axis': axis,
+                   'shifts': shifts})
     return out