Add fill_constant_batch_size YAML and UT (#41474)

python/paddle/fluid/layers/tensor.py

@@ -846,6 +846,18 @@ def fill_constant_batch_size_like(input,
                     input=like, shape=[1], value=0, dtype='int64') #like=[[10, 10]] data=[0]
 
     """
+    if in_dygraph_mode():
+        if not isinstance(dtype, core.VarDesc.VarType):
+            dtype = convert_np_dtype_to_dtype_(dtype)
+
+        place = _current_expected_place()
+        if force_cpu:
+            place = core.CPUPlace()
+        out = _C_ops.final_state_full_batch_size_like(
+            input, shape, dtype, value, input_dim_idx, output_dim_idx, place)
+        out.stop_gradient = True
+        return out
+
     helper = LayerHelper("fill_constant_batch_size_like", **locals())
     out = helper.create_variable_for_type_inference(dtype=dtype)
     attrs = {

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

+#   Copyright (c) 2019 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 paddle
+import paddle.fluid.core as core
+from paddle.static import program_guard, Program
+import paddle.compat as cpt
+import unittest
+import numpy as np
+from op_test import OpTest
+from paddle.fluid.framework import convert_np_dtype_to_dtype_
+
+paddle.enable_static()
+
+
+def fill_constant_batch_size_like(input,
+                                  shape,
+                                  value,
+                                  data_type,
+                                  input_dim_idx=0,
+                                  output_dim_idx=0,
+                                  force_cpu=False):
+    return paddle.fluid.layers.fill_constant_batch_size_like(
+        input, shape, data_type, value, input_dim_idx, output_dim_idx,
+        force_cpu)
+
+
+class TestFillConstatnBatchSizeLike1(OpTest):
+    # test basic
+    def setUp(self):
+        self.op_type = "fill_constant_batch_size_like"
+        self.python_api = fill_constant_batch_size_like
+        self.init_data()
+
+        input = np.zeros(self.shape)
+        out = np.full_like(input, self.value, self.dtype)
+
+        self.inputs = {'Input': input}
+        self.outputs = {'Out': out}
+        self.attrs = {
+            'shape': self.shape,
+            'dtype': convert_np_dtype_to_dtype_(self.dtype),
+            'value': self.value,
+            'input_dim_idx': self.input_dim_idx,
+            'output_dim_idx': self.output_dim_idx,
+            'force_cpu': self.force_cpu
+        }
+
+    def init_data(self):
+        self.shape = [10, 10]
+        self.dtype = np.float32
+        self.value = 100
+        self.input_dim_idx = 0
+        self.output_dim_idx = 0
+        self.force_cpu = False
+
+    def test_check_output(self):
+        self.check_output(check_eager=True)
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/utils/code_gen/api.yaml

@@ -718,6 +718,18 @@
     data_type : dtype
     backend : place
 
+- api : full_batch_size_like
+  args : (Tensor input, int[] shape, DataType dtype, Scalar value, int input_dim_idx, int output_dim_idx, Place place=CPUPlace())
+  output: Tensor
+  infer_meta :
+    func : FullBatchSizeLikeInferMeta
+    param : [input, shape, value, dtype, input_dim_idx, output_dim_idx]
+  kernel :
+    func : full_batch_size_like
+    param : [input, shape, value, dtype, input_dim_idx, output_dim_idx]
+    data_type : dtype
+    backend : place
+
 - api : full_like
   args : (Tensor x, Scalar value, DataType dtype = DataType::UNDEFINED, Place place = {})
   output: Tensor