add empty_like op (python, and unit test), use c++ implementation of empty op, (#27287)

and optimize the c++ implmentation of empty op as PR#26659 reviews,
and add bool for shape op.

paddle/fluid/operators/empty_op.cc

@@ -55,31 +55,38 @@ class EmptyOp : public framework::OperatorWithKernel {
     OP_INOUT_CHECK(context->HasOutput("Out"), "Output", "Out", "empty");
 
     if (context->HasInput("ShapeTensor")) {
-      auto dims = context->GetInputDim("ShapeTensor");
+      auto shape_dims = context->GetInputDim("ShapeTensor");
       int num_ele = 1;
-      for (int i = 0; i < dims.size(); ++i) {
-        num_ele *= dims[i];
+      for (int i = 0; i < shape_dims.size(); ++i) {
+        num_ele *= shape_dims[i];
       }
-
-      context->SetOutputDim("Out", framework::make_ddim({num_ele}));
+      auto vec_dims = std::vector<int>(num_ele, -1);
+      context->SetOutputDim("Out", framework::make_ddim(vec_dims));
     } else if (context->HasInputs("ShapeTensorList")) {
       std::vector<int> out_dims;
       auto dims_list = context->GetInputsDim("ShapeTensorList");
       for (size_t i = 0; i < dims_list.size(); ++i) {
         auto& dims = dims_list[i];
-        PADDLE_ENFORCE_EQ(
-            dims, framework::make_ddim({1}),
-            "ShapeError: The shape of Tensor in list must be [1]. "
-            "But received the shape "
-            "is [%s]",
-            dims);
-
-        out_dims.push_back(dims[0]);
+        PADDLE_ENFORCE_EQ(dims, framework::make_ddim({1}),
+                          platform::errors::InvalidArgument(
+                              "The shape of Tensor in list must be [1]. "
+                              "But received the shape is [%s]",
+                              dims));
+
+        out_dims.push_back(-1);
       }
 
       context->SetOutputDim("Out", framework::make_ddim(out_dims));
     } else {
       auto& shape = context->Attrs().Get<std::vector<int64_t>>("shape");
+      for (size_t i = 0; i < shape.size(); ++i) {
+        PADDLE_ENFORCE_GE(
+            shape[i], 0,
+            platform::errors::InvalidArgument(
+                "Each value of attribute 'shape' is expected to be no less "
+                "than 0. But recieved: shape[%u] = %d; shape = [%s].",
+                i, shape[i], framework::make_ddim(shape)));
+      }
       context->SetOutputDim("Out", framework::make_ddim(shape));
     }
   }

paddle/fluid/operators/shape_op.cc

@@ -68,6 +68,6 @@ REGISTER_OPERATOR(
     shape, ops::ShapeOp, ops::ShapeOpMaker,
     paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
     paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>);
-REGISTER_OP_CPU_KERNEL(shape, ops::ShapeKernel<int>, ops::ShapeKernel<int32_t>,
+REGISTER_OP_CPU_KERNEL(shape, ops::ShapeKernel<bool>, ops::ShapeKernel<int>,
                        ops::ShapeKernel<int64_t>, ops::ShapeKernel<float>,
                        ops::ShapeKernel<double>);

paddle/fluid/operators/shape_op.cu

@@ -15,8 +15,8 @@ limitations under the License. */
 #include "paddle/fluid/operators/shape_op.h"
 
 REGISTER_OP_CUDA_KERNEL(
-    shape, paddle::operators::ShapeKernel<int>,
-    paddle::operators::ShapeKernel<int32_t>,
+    shape, paddle::operators::ShapeKernel<bool>,
+    paddle::operators::ShapeKernel<int>,
     paddle::operators::ShapeKernel<int64_t>,
     paddle::operators::ShapeKernel<float>,
     paddle::operators::ShapeKernel<double>,

python/paddle/__init__.py

@@ -77,6 +77,7 @@ from .tensor.creation import triu  #DEFINE_ALIAS
 from .tensor.creation import tril  #DEFINE_ALIAS
 from .tensor.creation import meshgrid  #DEFINE_ALIAS
 from .tensor.creation import empty  #DEFINE_ALIAS
+from .tensor.creation import empty_like  #DEFINE_ALIAS
 from .tensor.linalg import matmul  #DEFINE_ALIAS
 from .tensor.linalg import dot  #DEFINE_ALIAS
 # from .tensor.linalg import einsum        #DEFINE_ALIAS

python/paddle/fluid/layers/nn.py

@@ -11229,7 +11229,7 @@ def shape(input):
                 input.shape = [3, 2]
 
     Args:
-        input (Variable): The input can be N-D Tensor or SelectedRows with data type float16, float32, float64, int32, int64.
+        input (Variable): The input can be N-D Tensor or SelectedRows with data type bool, float16, float32, float64, int32, int64.
                           If input variable is type of SelectedRows, returns the shape of it's inner tensor.
 
     Returns:
@@ -11253,8 +11253,8 @@ def shape(input):
             print(res) # [array([  3, 100, 100], dtype=int32)]
     """
     check_variable_and_dtype(
-        input, 'input', ['float16', 'float32', 'float64', 'int32', 'int64'],
-        'shape')
+        input, 'input',
+        ['bool', 'float16', 'float32', 'float64', 'int32', 'int64'], 'shape')
     helper = LayerHelper('shape', **locals())
     out = helper.create_variable_for_type_inference(dtype='int32')
     helper.append_op(

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

+#  Copyright (c) 2020 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.data_feeder import convert_dtype
+import paddle.fluid.core as core
+from paddle.static import program_guard, Program
+
+
+class TestEmptyLikeAPICommon(unittest.TestCase):
+    def __check_out__(self, out):
+        data_type = convert_dtype(out.dtype)
+        self.assertEqual(data_type, self.dst_dtype,
+                         'dtype should be %s, but get %s' %
+                         (self.dst_dtype, data_type))
+
+        shape = out.shape
+        self.assertTupleEqual(shape, self.dst_shape,
+                              'shape should be %s, but get %s' %
+                              (self.dst_shape, shape))
+
+        if data_type in ['float32', 'float64', 'int32', 'int64']:
+            max_value = np.nanmax(out)
+            min_value = np.nanmin(out)
+            always_non_full_zero = max_value > min_value
+            always_full_zero = max_value == 0.0 and min_value == 0.0
+            self.assertTrue(always_full_zero or always_non_full_zero,
+                            'always_full_zero or always_non_full_zero.')
+        elif data_type in ['bool']:
+            total_num = out.size
+            true_num = np.sum(out == True)
+            false_num = np.sum(out == False)
+            self.assertTrue(total_num == true_num + false_num,
+                            'The value should always be True or False.')
+        else:
+            self.assertTrue(False, 'invalid data type')
+
+
+class TestEmptyLikeAPI(TestEmptyLikeAPICommon):
+    def setUp(self):
+        self.init_config()
+
+    def test_dygraph_api_out(self):
+        paddle.disable_static()
+        out = paddle.empty_like(self.x, self.dtype)
+        self.__check_out__(out.numpy())
+        paddle.enable_static()
+
+    def init_config(self):
+        self.x = np.random.random((200, 3)).astype("float32")
+        self.dtype = self.x.dtype
+        self.dst_shape = self.x.shape
+        self.dst_dtype = self.dtype
+
+
+class TestEmptyLikeAPI2(TestEmptyLikeAPI):
+    def init_config(self):
+        self.x = np.random.random((200, 3)).astype("float64")
+        self.dtype = self.x.dtype
+        self.dst_shape = self.x.shape
+        self.dst_dtype = self.dtype
+
+
+class TestEmptyLikeAPI3(TestEmptyLikeAPI):
+    def init_config(self):
+        self.x = np.random.random((200, 3)).astype("int")
+        self.dtype = self.x.dtype
+        self.dst_shape = self.x.shape
+        self.dst_dtype = self.dtype
+
+
+class TestEmptyLikeAPI4(TestEmptyLikeAPI):
+    def init_config(self):
+        self.x = np.random.random((200, 3)).astype("int64")
+        self.dtype = self.x.dtype
+        self.dst_shape = self.x.shape
+        self.dst_dtype = self.dtype
+
+
+class TestEmptyLikeAPI5(TestEmptyLikeAPI):
+    def init_config(self):
+        self.x = np.random.random((200, 3)).astype("bool")
+        self.dtype = self.x.dtype
+        self.dst_shape = self.x.shape
+        self.dst_dtype = self.dtype
+
+
+class TestEmptyLikeAPI6(TestEmptyLikeAPI):
+    def init_config(self):
+        self.x = np.random.random((200, 3)).astype("float64")
+        self.dtype = "float32"
+        self.dst_shape = self.x.shape
+        self.dst_dtype = self.dtype
+
+
+class TestEmptyLikeAPI7(TestEmptyLikeAPI):
+    def init_config(self):
+        self.x = np.random.random((200, 3)).astype("int")
+        self.dtype = "float32"
+        self.dst_shape = self.x.shape
+        self.dst_dtype = self.dtype
+
+
+class TestEmptyLikeAPI8(TestEmptyLikeAPI):
+    def init_config(self):
+        self.x = np.random.random((200, 3)).astype("int64")
+        self.dtype = "float32"
+        self.dst_shape = self.x.shape
+        self.dst_dtype = self.dtype
+
+
+class TestEmptyLikeAPI9(TestEmptyLikeAPI):
+    def init_config(self):
+        self.x = np.random.random((200, 3)).astype("bool")
+        self.dtype = "float32"
+        self.dst_shape = self.x.shape
+        self.dst_dtype = self.dtype
+
+
+class TestEmptyLikeAPI10(TestEmptyLikeAPI):
+    def init_config(self):
+        self.x = np.random.random((200, 3)).astype("float32")
+        self.dtype = "bool"
+        self.dst_shape = self.x.shape
+        self.dst_dtype = self.dtype
+
+
+class TestEmptyLikeAPI_Static(TestEmptyLikeAPICommon):
+    def setUp(self):
+        self.init_config()
+
+    def test_static_graph(self):
+        dtype = 'float32'
+
+        train_program = Program()
+        startup_program = Program()
+
+        with program_guard(train_program, startup_program):
+            x = np.random.random(self.x_shape).astype(dtype)
+            data_x = paddle.static.data(
+                'x', shape=self.data_x_shape, dtype=dtype)
+
+            out = paddle.empty_like(data_x)
+
+        place = paddle.CUDAPlace(0) if core.is_compiled_with_cuda(
+        ) else paddle.CPUPlace()
+        exe = paddle.static.Executor(place)
+        res = exe.run(train_program, feed={'x': x}, fetch_list=[out])
+
+        self.dst_dtype = dtype
+        self.dst_shape = x.shape
+        self.__check_out__(res[0])
+
+    def init_config(self):
+        self.x_shape = (200, 3)
+        self.data_x_shape = [200, 3]
+
+
+class TestEmptyLikeAPI_Static2(TestEmptyLikeAPI_Static):
+    def init_config(self):
+        self.x_shape = (3, 200, 3)
+        self.data_x_shape = [-1, 200, 3]
+
+
+class TestEmptyError(unittest.TestCase):
+    def test_attr(self):
+        def test_dtype():
+            x = np.random.random((200, 3)).astype("float64")
+            dtype = 'uint8'
+            result = paddle.empty_like(x, dtype=dtype)
+
+        self.assertRaises(TypeError, test_dtype)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/tensor/__init__.py

@@ -41,6 +41,7 @@ from .creation import triu  #DEFINE_ALIAS
 from .creation import tril  #DEFINE_ALIAS
 from .creation import meshgrid  #DEFINE_ALIAS
 from .creation import empty  #DEFINE_ALIAS
+from .creation import empty_like  #DEFINE_ALIAS
 from .io import save  #DEFINE_ALIAS
 from .io import load  #DEFINE_ALIAS
 from .linalg import matmul  #DEFINE_ALIAS

python/paddle/tensor/creation.py

@@ -49,6 +49,7 @@ __all__ = [
     'full',
     'full_like',
     'empty',
+    'empty_like',
     'triu',
     'tril',
     'meshgrid'
@@ -1068,3 +1069,70 @@ def empty(shape, dtype=None, name=None):
         stop_gradient=True)
     out.stop_gradient = True
     return out
+
+
+def empty_like(x, dtype=None, name=None):
+    """
+    This Op returns a Tensor with uninitialized data which has identical shape of ``x`` and ``dtype``.
+    If the ``dtype`` is None, the data type of Tensor is same with ``x``.
+    
+    Args:
+        x(Tensor): The input tensor which specifies shape and data type. The data type can be bool, float16, float32, float64, int32, int64.
+        dtype(np.dtype|str, optional): The data type of output. The data type can be one
+            of bool, float16, float32, float64, int32, int64. The default value is None, which means the output 
+            data type is the same as input.
+        name(str, optional): The default value is None. Normally there is no need for user to set this
+            property. For more information, please refer to :ref:`api_guide_Name`.
+    
+    Returns:
+        Tensor: Tensor which is created according to ``x`` and ``dtype``, and is uninitialized.
+
+    Examples:
+        .. code-block:: python
+
+          import paddle
+          import numpy as np
+
+          paddle.disable_static()   # Now we are in imperative mode
+          paddle.set_device("cpu")  # and use cpu device
+
+          x = paddle.randn([2, 3], 'float32')
+          output = paddle.empty_like(x)
+          #[[1.8491974e+20 1.8037303e+28 1.7443726e+28]     # uninitialized
+          # [4.9640171e+28 3.0186127e+32 5.6715899e-11]]    # uninitialized
+    """
+
+    if dtype is None:
+        dtype = x.dtype
+    dtype = convert_dtype(dtype)
+
+    if in_dygraph_mode():
+        out = core.ops.empty('shape', x.shape, 'dtype',
+                             convert_np_dtype_to_dtype_(dtype))
+        out.stop_gradient = True
+        return out
+
+    helper = LayerHelper("empty_like", **locals())
+    check_variable_and_dtype(
+        x, 'x', ['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
+        'empty_like')
+    check_dtype(dtype, 'dtype',
+                ['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
+                'empty_like')
+    out = helper.create_variable_for_type_inference(dtype=dtype)
+
+    inputs = {}
+    attrs = {}
+    attrs['dtype'] = convert_np_dtype_to_dtype_(dtype)
+    shape = paddle.shape(x)
+    utils.get_shape_tensor_inputs(
+        inputs=inputs, attrs=attrs, shape=shape, op_type='empty_like')
+
+    helper.append_op(
+        type='empty',
+        inputs=inputs,
+        outputs={'Out': [out]},
+        attrs=attrs,
+        stop_gradient=True)
+    out.stop_gradient = True
+    return out