diff --git a/paddle/fluid/operators/reshape_op.cc b/paddle/fluid/operators/reshape_op.cc index 6e8b962488a56800eb0ad985acd0113fe9fd2422..e980aa66e7ca33467cfe216fbf04e3b5649d9c15 100644 --- a/paddle/fluid/operators/reshape_op.cc +++ b/paddle/fluid/operators/reshape_op.cc @@ -114,11 +114,6 @@ class ReshapeOp : public framework::OperatorWithKernel { return; } - PADDLE_ENFORCE_EQ(!shape.empty(), - true, - platform::errors::InvalidArgument( - "The parameter 'shape' in ReshapeOp must be set. " - "But received 'shape' is empty.")); auto x_dims = ctx->GetInputDim("X"); auto out_dims = ValidateShape(shape, x_dims); ctx->SetOutputDim("Out", out_dims); diff --git a/python/paddle/fluid/tests/unittests/test_reshape_op.py b/python/paddle/fluid/tests/unittests/test_reshape_op.py index a31749d744aead800a038300b3eeafad51b175c7..887ce9ff3f7411bb01115e8d648b53be0ec7de31 100755 --- a/python/paddle/fluid/tests/unittests/test_reshape_op.py +++ b/python/paddle/fluid/tests/unittests/test_reshape_op.py @@ -49,20 +49,20 @@ class TestReshapeOp(OpTest): class TestReshapeOp_ZeroDim1(OpTest): def init_data(self): self.ori_shape = () - self.new_shape = 1 - self.infered_shape = 1 + self.new_shape = (1,) + self.infered_shape = (1,) class TestReshapeOp_ZeroDim2(OpTest): def init_data(self): self.ori_shape = () - self.new_shape = -1 - self.infered_shape = 1 + self.new_shape = (-1,) + self.infered_shape = (1,) class TestReshapeOp_ZeroDim3(OpTest): def init_data(self): - self.ori_shape = 1 + self.ori_shape = (1,) self.new_shape = () self.infered_shape = () diff --git a/python/paddle/fluid/tests/unittests/test_zero_dim_tensor.py b/python/paddle/fluid/tests/unittests/test_zero_dim_tensor.py index 887a04f10c3aa71cef0591d2de0311927ec7a065..8523fb44b982fe60140efc7e8ef25c15d891126a 100644 --- a/python/paddle/fluid/tests/unittests/test_zero_dim_tensor.py +++ b/python/paddle/fluid/tests/unittests/test_zero_dim_tensor.py @@ -756,6 +756,105 @@ class TestSundryAPI(unittest.TestCase): np.testing.assert_array_equal(out3_1.numpy(), out3_2.numpy()) np.testing.assert_array_equal(out3_2.numpy(), np.asarray(1)) + def test_reshape_list(self): + x = paddle.rand([]) + x.stop_gradient = False + + out = paddle.reshape(x, []) + out.backward() + self.assertEqual(x.grad.shape, []) + self.assertEqual(out.shape, []) + self.assertEqual(out.grad.shape, []) + + out = paddle.reshape(x, [1]) + out.backward() + self.assertEqual(x.grad.shape, []) + self.assertEqual(out.shape, [1]) + self.assertEqual(out.grad.shape, [1]) + + out = paddle.reshape(x, [-1]) + out.backward() + self.assertEqual(x.grad.shape, []) + self.assertEqual(out.shape, [1]) + self.assertEqual(out.grad.shape, [1]) + + out = paddle.reshape(x, [-1, 1]) + out.backward() + self.assertEqual(x.grad.shape, []) + self.assertEqual(out.shape, [1, 1]) + self.assertEqual(out.grad.shape, [1, 1]) + + def test_reshape_tensor(self): + x = paddle.rand([1, 1]) + x.stop_gradient = False + + out = paddle.reshape(x, []) + out.backward() + self.assertEqual(x.grad.shape, [1, 1]) + self.assertEqual(out.shape, []) + self.assertEqual(out.grad.shape, []) + + new_shape = paddle.full([1], 1, "int32") + out = paddle.reshape(x, new_shape) + out.backward() + self.assertEqual(x.grad.shape, [1, 1]) + self.assertEqual(out.shape, [1]) + self.assertEqual(out.grad.shape, [1]) + + new_shape = paddle.full([1], -1, "int32") + out = paddle.reshape(x, new_shape) + out.backward() + self.assertEqual(x.grad.shape, [1, 1]) + self.assertEqual(out.shape, [1]) + self.assertEqual(out.grad.shape, [1]) + + new_shape = [paddle.full([], -1, "int32"), paddle.full([], 1, "int32")] + out = paddle.reshape(x, new_shape) + out.backward() + self.assertEqual(x.grad.shape, [1, 1]) + self.assertEqual(out.shape, [1, 1]) + self.assertEqual(out.grad.shape, [1, 1]) + + def test_reshape__list(self): + x = paddle.rand([]) + out = paddle.reshape_(x, []) + self.assertEqual(out.shape, []) + + out = paddle.reshape_(x, [1]) + self.assertEqual(out.shape, [1]) + + out = paddle.reshape_(x, [-1]) + self.assertEqual(out.shape, [1]) + + out = paddle.reshape_(x, [-1, 1]) + self.assertEqual(out.shape, [1, 1]) + + def test_reshape__tensor(self): + x = paddle.rand([1, 1]) + out = paddle.reshape_(x, []) + self.assertEqual(out.shape, []) + + new_shape = paddle.full([1], 1, "int32") + out = paddle.reshape_(x, new_shape) + self.assertEqual(out.shape, [1]) + + new_shape = paddle.full([1], -1, "int32") + out = paddle.reshape_(x, new_shape) + self.assertEqual(out.shape, [1]) + + new_shape = [paddle.full([], -1, "int32"), paddle.full([], 1, "int32")] + out = paddle.reshape_(x, new_shape) + self.assertEqual(out.shape, [1, 1]) + + def test_reverse(self): + x = paddle.rand([]) + x.stop_gradient = False + out = paddle.reverse(x, axis=[]) + out.backward() + self.assertEqual(x.shape, []) + self.assertEqual(out.shape, []) + self.assertEqual(out.grad.shape, []) + class TestSundryAPIStatic(unittest.TestCase): def setUp(self): @@ -1011,6 +1110,78 @@ class TestSundryAPIStatic(unittest.TestCase): np.testing.assert_array_equal(out3_1, out3_2) np.testing.assert_array_equal(out3_2, np.asarray(1)) + @prog_scope() + def test_reshape_list(self): + x1 = paddle.rand([]) + x2 = paddle.rand([]) + x3 = paddle.rand([]) + x4 = paddle.rand([]) + x1.stop_gradient = False + x2.stop_gradient = False + x3.stop_gradient = False + x4.stop_gradient = False + + out1 = paddle.reshape(x1, []) + paddle.static.append_backward(out1) + + out2 = paddle.reshape(x2, [1]) + paddle.static.append_backward(out2) + + out3 = paddle.reshape(x3, [-1]) + paddle.static.append_backward(out3) + + out4 = paddle.reshape(x4, [-1, 1]) + paddle.static.append_backward(out4) + + program = paddle.static.default_main_program() + res1, res2, res3, res4 = self.exe.run( + program, fetch_list=[out1, out2, out3, out4] + ) + self.assertEqual(res1.shape, ()) + self.assertEqual(res2.shape, (1,)) + self.assertEqual(res3.shape, (1,)) + self.assertEqual(res4.shape, (1, 1)) + + @prog_scope() + def test_reshape_tensor(self): + x1 = paddle.rand([]) + x2 = paddle.rand([]) + x3 = paddle.rand([]) + x1.stop_gradient = False + x2.stop_gradient = False + x3.stop_gradient = False + + new_shape = paddle.full([1], 1, "int32") + out1 = paddle.reshape(x1, new_shape) + paddle.static.append_backward(out1) + + new_shape = paddle.full([1], -1, "int32") + out2 = paddle.reshape(x2, new_shape) + paddle.static.append_backward(out2) + + new_shape = [paddle.full([], -1, "int32"), paddle.full([], 1, "int32")] + out3 = paddle.reshape(x3, new_shape) + paddle.static.append_backward(out3) + + program = paddle.static.default_main_program() + res1, res2, res3 = self.exe.run(program, fetch_list=[out1, out2, out3]) + self.assertEqual(res1.shape, (1,)) + self.assertEqual(res2.shape, (1,)) + self.assertEqual(res3.shape, (1, 1)) + + @prog_scope() + def test_reverse(self): + x = paddle.rand([]) + x.stop_gradient = False + + out = paddle.reverse(x, axis=[]) + paddle.static.append_backward(out) + + program = paddle.static.default_main_program() + res1, res2 = self.exe.run(program, fetch_list=[x, out]) + self.assertEqual(res1.shape, ()) + self.assertEqual(res2.shape, ()) + # Use to test API whose zero-dim input tensors don't have grad and not need to test backward in OpTest. class TestNoBackwardAPI(unittest.TestCase): diff --git a/python/paddle/fluid/tests/unittests/xpu/test_zero_dim_tensor_xpu.py b/python/paddle/fluid/tests/unittests/xpu/test_zero_dim_tensor_xpu.py index b561b775f29d0c0393fce1832a59893209235d49..8ceee04c206b10c31f863447c528bc978f9ceb1e 100644 --- a/python/paddle/fluid/tests/unittests/xpu/test_zero_dim_tensor_xpu.py +++ b/python/paddle/fluid/tests/unittests/xpu/test_zero_dim_tensor_xpu.py @@ -556,6 +556,96 @@ class TestSundryAPI(unittest.TestCase): np.testing.assert_array_equal(out3_1.numpy(), out3_2.numpy()) np.testing.assert_array_equal(out3_2.numpy(), np.asarray(1)) + def test_reshape_list(self): + x = paddle.rand([]) + x.stop_gradient = False + + out = paddle.reshape(x, []) + out.backward() + self.assertEqual(x.grad.shape, []) + self.assertEqual(out.shape, []) + self.assertEqual(out.grad.shape, []) + + out = paddle.reshape(x, [1]) + out.backward() + self.assertEqual(x.grad.shape, []) + self.assertEqual(out.shape, [1]) + self.assertEqual(out.grad.shape, [1]) + + out = paddle.reshape(x, [-1]) + out.backward() + self.assertEqual(x.grad.shape, []) + self.assertEqual(out.shape, [1]) + self.assertEqual(out.grad.shape, [1]) + + out = paddle.reshape(x, [-1, 1]) + out.backward() + self.assertEqual(x.grad.shape, []) + self.assertEqual(out.shape, [1, 1]) + self.assertEqual(out.grad.shape, [1, 1]) + + def test_reshape_tensor(self): + x = paddle.rand([1, 1]) + x.stop_gradient = False + + out = paddle.reshape(x, []) + out.backward() + self.assertEqual(x.grad.shape, [1, 1]) + self.assertEqual(out.shape, []) + self.assertEqual(out.grad.shape, []) + + new_shape = paddle.full([], 1, "int32") + out = paddle.reshape(x, new_shape) + out.backward() + self.assertEqual(x.grad.shape, [1, 1]) + self.assertEqual(out.shape, [1]) + self.assertEqual(out.grad.shape, [1]) + + new_shape = paddle.full([], -1, "int32") + out = paddle.reshape(x, new_shape) + out.backward() + self.assertEqual(x.grad.shape, [1, 1]) + self.assertEqual(out.shape, [1]) + self.assertEqual(out.grad.shape, [1]) + + new_shape = [paddle.full([], -1, "int32"), paddle.full([], 1, "int32")] + out = paddle.reshape(x, new_shape) + out.backward() + self.assertEqual(x.grad.shape, [1, 1]) + self.assertEqual(out.shape, [1, 1]) + self.assertEqual(out.grad.shape, [1, 1]) + + def test_reshape__list(self): + x = paddle.rand([]) + out = paddle.reshape_(x, []) + self.assertEqual(out.shape, []) + + out = paddle.reshape_(x, [1]) + self.assertEqual(out.shape, [1]) + + out = paddle.reshape_(x, [-1]) + self.assertEqual(out.shape, [1]) + + out = paddle.reshape_(x, [-1, 1]) + self.assertEqual(out.shape, [1, 1]) + + def test_reshape__tensor(self): + x = paddle.rand([1, 1]) + out = paddle.reshape_(x, []) + self.assertEqual(out.shape, []) + + new_shape = paddle.full([1], 1, "int32") + out = paddle.reshape_(x, new_shape) + self.assertEqual(out.shape, [1]) + + new_shape = paddle.full([1], -1, "int32") + out = paddle.reshape_(x, new_shape) + self.assertEqual(out.shape, [1]) + + new_shape = [paddle.full([], -1, "int32"), paddle.full([], 1, "int32")] + out = paddle.reshape_(x, new_shape) + self.assertEqual(out.shape, [1, 1]) + # Use to test API whose zero-dim input tensors don't have grad and not need to test backward in OpTest. class TestNoBackwardAPI(unittest.TestCase): diff --git a/python/paddle/tensor/manipulation.py b/python/paddle/tensor/manipulation.py index 9b6d0fecf617247de4cbb2237db85923c23f1b8f..842deaac991a9c0ef006d21175e86e6c7b5767a4 100644 --- a/python/paddle/tensor/manipulation.py +++ b/python/paddle/tensor/manipulation.py @@ -3450,7 +3450,7 @@ def reshape(x, shape, name=None): Args: x (Tensor): An N-D Tensor. The data type is ``float32``, ``float64``, ``int32``, ``int64`` or ``bool`` shape (list|tuple|Tensor): Define the target shape. At most one dimension of the target shape can be -1. - The data type is ``int32`` . If ``shape`` is a list or tuple, the elements of it should be integers or Tensors with shape [1]. + The data type is ``int32`` . If ``shape`` is a list or tuple, the elements of it should be integers or Tensors with shape []. If ``shape`` is an Tensor, it should be an 1-D Tensor . name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. @@ -3574,10 +3574,6 @@ def reshape(x, shape, name=None): shape.stop_gradient = True inputs["Shape"] = shape elif isinstance(shape, (list, tuple)): - assert len(shape) > 0, ( - "The size of 'shape' in reshape can't be zero, " - "but received %s." % len(shape) - ) attrs["shape"] = get_attr_shape(shape) if utils._contain_var(shape): inputs['ShapeTensor'] = utils._convert_to_tensor_list(shape)