modify roll test=develop

paddle/fluid/operators/roll_op.cc

@@ -33,7 +33,7 @@ class RollOp : public framework::OperatorWithKernel {
  platform::errors::InvalidArgument(
  "Output(Out) of RollOp should not be null."));
 
- auto dims = ctx->Attrs().Get<std::vector<int64_t>>("dims");
+ auto dims = ctx->Attrs().Get<std::vector<int64_t>>("axis");
  auto shifts = ctx->Attrs().Get<std::vector<int64_t>>("shifts");
 
  PADDLE_ENFORCE_EQ(dims.size(), shifts.size(),
@@ -92,7 +92,7 @@ class RollOpMaker : public framework::OpProtoAndCheckerMaker {
  "of the tensor are shifted.")
  .SetDefault({});
  AddAttr<std::vector<int64_t>>(
- "dims",
+ "axis",
  "Axis along which to roll. It must have the same size "
  "with shifts.")
  .SetDefault({});

paddle/fluid/operators/roll_op.h

@@ -82,7 +82,7 @@ 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");
- std::vector<int64_t> dims = context.Attr<std::vector<int64_t>>("dims");
+ std::vector<int64_t> dims = context.Attr<std::vector<int64_t>>("axis");
 
  std::vector<T> out_vec;
  TensorToVector(input, context.device_context(), &out_vec);
@@ -94,8 +94,8 @@ class RollKernel : public framework::OpKernel<T> {
  PADDLE_ENFORCE_EQ(
  dims[i] < input_dim.size() && dims[i] >= (0 - input_dim.size()), true,
  platform::errors::OutOfRange(
- "Attr(dims[%d]) is out of range, It's expected "
- "to be in range of [-%d, %d]. But received Attr(dims[%d]) = %d.",
+ "Attr(axis[%d]) is out of range, It's expected "
+ "to be in range of [-%d, %d]. But received Attr(axis[%d]) = %d.",
  i, input_dim.size(), input_dim.size() - 1, i, dims[i]));
  shift_along_dim(out_vec.data(), input_dim, dims[i], shifts[i]);
  }
@@ -114,7 +114,7 @@ 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");
- std::vector<int64_t> dims = context.Attr<std::vector<int64_t>>("dims");
+ std::vector<int64_t> dims = context.Attr<std::vector<int64_t>>("axis");
 
  std::vector<T> out_vec;
  TensorToVector(input, context.device_context(), &out_vec);

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

@@ -28,17 +28,17 @@ def setUp(self):
  self.op_type = "roll"
  self.init_dtype_type()
  self.inputs = {'X': np.random.random(self.x_shape).astype(self.dtype)}
- self.attrs = {'shifts': self.shifts, 'dims': self.dims}
+ self.attrs = {'shifts': self.shifts, 'axis': self.axis}
  self.outputs = {
  'Out': np.roll(self.inputs['X'], self.attrs['shifts'],
- self.attrs['dims'])
+ self.attrs['axis'])
  }
 
  def init_dtype_type(self):
  self.dtype = np.float64
  self.x_shape = (100, 4, 5)
  self.shifts = [101, -1]
- self.dims = [0, -2]
+ self.axis = [0, -2]
 
  def test_check_output(self):
  self.check_output()
@@ -52,7 +52,7 @@ def init_dtype_type(self):
  self.dtype = np.float32
  self.x_shape = (100, 10, 5)
  self.shifts = [8, -1]
- self.dims = [-1, -2]
+ self.axis = [-1, -2]
 
 
 class TestRollAPI(unittest.TestCase):
@@ -78,7 +78,7 @@ def test_roll_op_api(self):
  # case 2:
  with program_guard(Program(), Program()):
  x = fluid.layers.data(name='x', shape=[-1, 3])
- z = paddle.roll(x, shifts=1, dims=0)
+ z = paddle.roll(x, shifts=1, axis=0)
  exe = fluid.Executor(fluid.CPUPlace())
  res, = exe.run(feed={'x': self.data_x},
  fetch_list=[z.name],
@@ -101,7 +101,7 @@ def test_dygraph_api(self):
  # case 2:
  with fluid.dygraph.guard():
  x = fluid.dygraph.to_variable(self.data_x)
- z = paddle.roll(x, shifts=1, dims=0)
+ z = paddle.roll(x, shifts=1, axis=0)
  np_z = z.numpy()
  expect_out = np.array([[7.0, 8.0, 9.0], [1.0, 2.0, 3.0],
  [4.0, 5.0, 6.0]])

python/paddle/tensor/manipulation.py

@@ -104,7 +104,7 @@ def flip(input, dims, name=None):
  return out
 
 
-def roll(input, shifts, dims=None):
+def roll(input, shifts, axis=None, name=None):
  """
 :alias_main: paddle.roll
 :alias: paddle.roll,paddle.tensor.roll,paddle.tensor.manipulation.roll
@@ -117,7 +117,7 @@ def roll(input, shifts, dims=None):
  input (Variable): The input tensor variable.
  shifts (int|list|tuple): The number of places by which the elements
  of the `input` tensor are shifted.
- dims (int|list|tuple|None): Dimentions along which to roll.
+ axis (int|list|tuple|None): Dimentions along which to roll.
 
  Returns:
  Variable: A Tensor with same data type as `input`.
@@ -138,7 +138,7 @@ def roll(input, shifts, dims=None):
  #[[9. 1. 2.]
  # [3. 4. 5.]
  # [6. 7. 8.]]
- out_z2 = paddle.roll(x, shifts=1, dims=0)
+ out_z2 = paddle.roll(x, shifts=1, axis=0)
  print(out_z2.numpy())
  #[[7. 8. 9.]
  # [1. 2. 3.]
@@ -148,31 +148,31 @@ def roll(input, shifts, dims=None):
  origin_shape = input.shape
  if type(shifts) == int:
  shifts = [shifts]
- if type(dims) == int:
- dims = [dims]
+ if type(axis) == int:
+ axis = [axis]
 
- if dims:
- check_type(dims, 'dims', (list, tuple), 'roll')
+ if axis:
+ check_type(axis, 'axis', (list, tuple), 'roll')
  check_type(shifts, 'shifts', (list, tuple), 'roll')
 
  if in_dygraph_mode():
- if dims is None:
+ if axis is None:
  input = core.ops.reshape(input, 'shape', [-1, 1])
- dims = [0]
- out = core.ops.roll(input, 'dims', dims, 'shifts', shifts)
+ axis = [0]
+ out = core.ops.roll(input, 'axis', axis, 'shifts', shifts)
  return core.ops.reshape(out, 'shape', origin_shape)
 
  out = helper.create_variable_for_type_inference(input.dtype)
 
- if dims is None:
+ if axis is None:
  input = reshape(input, shape=[-1, 1])
- dims = [0]
+ axis = [0]
 
  helper.append_op(
  type='roll',
  inputs={'X': input},
  outputs={'Out': out},
- attrs={'dims': dims,
+ attrs={'axis': axis,
  'shifts': shifts})
  out = reshape(out, shape=origin_shape, inplace=True)
  return out