Merge pull request #11850 from guochaorong/revert_11496

Revert "Extend fill_zeros_like_op for zero-filling an LoDTensorArray …

paddle/fluid/framework/operator.cc

@@ -748,10 +748,6 @@ proto::VarType::Type OperatorWithKernel::IndicateDataType(
           t = &var->Get<LoDTensor>();
         } else if (var->IsType<SelectedRows>()) {
           t = &(var->Get<SelectedRows>().value());
-        } else if (var->IsType<LoDTensorArray>()) {
-          const LoDTensorArray& arr = var->Get<LoDTensorArray>();
-          PADDLE_ENFORCE(arr.size() > 0);
-          t = &(arr[0]);
         }
         if (t != nullptr) {
           int tmp = static_cast<int>(ToDataType(t->type()));

paddle/fluid/operators/fill_zeros_like_op.cc

@@ -26,12 +26,8 @@ class FillZerosLikeOp : public framework::OperatorWithKernel {
                    "Input(X) of FillZerosLikeOp should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
                    "Output(Out) of FillZerosLikeOp should not be null.");
-
-    if (ctx->IsRuntime() &&
-        ctx->GetOutputsVarType("Out")[0] ==
-            framework::proto::VarType::LOD_TENSOR_ARRAY) {
-      return;  // skip runtime infershape when is tensor array;
-    }
+    ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
+    ctx->ShareLoD("X", /*->*/ "Out");
   }
 };
 
@@ -43,7 +39,7 @@ class FillZerosLikeOpMaker : public framework::OpProtoAndCheckerMaker {
     AddComment(R"DOC(
 FillZerosLike Operator.
 
-Fill up a variable with zeros, supporting both LoDTensor and LoDTensorArray.
+Fill up a variable with zeros.
 The output will have the same size as the input.
 
 )DOC");

paddle/fluid/operators/fill_zeros_like_op.h

@@ -13,7 +13,6 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
-#include "paddle/fluid/framework/lod_tensor_array.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/operators/math/math_function.h"
 
@@ -24,29 +23,12 @@ template <typename DeviceContext, typename T>
 class FillZerosLikeKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    auto var = context.InputVar("X");
-    if (var->IsType<framework::LoDTensor>()) {
-      auto& input = *context.Input<framework::LoDTensor>("X");
-      auto& output = *context.Output<framework::LoDTensor>("Out");
-      output.Resize(input.dims());
-      output.set_lod(input.lod());
-      output.mutable_data<T>(context.GetPlace());
-      math::SetConstant<DeviceContext, T> setter;
-      setter(context.template device_context<DeviceContext>(), &(output),
-             static_cast<T>(0));
-    } else if (var->IsType<framework::LoDTensorArray>()) {
-      auto& input = *context.Input<framework::LoDTensorArray>("X");
-      auto& output = *context.Output<framework::LoDTensorArray>("Out");
-      output.resize(input.size());
-      for (auto i = 0; i < input.size(); i++) {
-        output[i].Resize(input[i].dims());
-        output[i].set_lod(input[i].lod());
-        output[i].mutable_data<T>(context.GetPlace());
-        math::SetConstant<DeviceContext, T> setter;
-        setter(context.template device_context<DeviceContext>(), &(output[i]),
-               static_cast<T>(0));
-      }
-    }
+    auto* out = context.Output<framework::Tensor>("Out");
+    out->mutable_data<T>(context.GetPlace());
+
+    math::SetConstant<DeviceContext, T> setter;
+    setter(context.template device_context<DeviceContext>(), out,
+           static_cast<T>(0));
   }
 };
 

python/paddle/fluid/layers/nn.py

@@ -95,7 +95,6 @@ __all__ = [
     'relu',
     'log',
     'crop',
-    'fill_zeros_like',
 ]
 
 
@@ -5185,40 +5184,3 @@ def crop(x, shape=None, offsets=None, name=None):
         outputs={'Out': out},
         attrs=None if len(attrs) == 0 else attrs)
     return out
-
-
-def fill_zeros_like(x):
-    """
-    This layer takes an input and outputs a variable that has the same structure as
-    the input and with all the element values as zero. The variable can be a Tensor
-    or TensorArray.
-
-    .. code-block:: text
-
-
-       Given
-          X = [[0, 1, 2, 0],
-               [0, 3, 4, 0],
-               [0, 0, 0, 0]],
-       output is:
-          Out = [[0, 0, 0, 0],
-                 [0, 0, 0, 0],
-                 [0, 0, 0, 0]].
-
-    Args:
-        x (Variable): The input variable, which could be a tensor or tensor array
-
-    Returns:
-        Variable: The zero-filled variable, which has the same type and shape as
-                  the input variable.
-
-    Examples:
-
-        .. code-block:: python
-            y = fluid.layers.fill_zeros_like(x)
-    """
-    helper = LayerHelper('fill_zeros_like', **locals())
-    out = helper.create_tmp_variable(dtype=x.dtype)
-    helper.append_op(
-        type='fill_zeros_like', inputs={'X': [x]}, outputs={'Out': [out]})
-    return out

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

-#   Copyright (c) 2018 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.
-
-import unittest
-import paddle.fluid.core as core
-import numpy
-import paddle.fluid.layers as layers
-from paddle.fluid.framework import Program, program_guard
-from paddle.fluid.executor import Executor
-
-import paddle.fluid as fluid
-import paddle.fluid.core as core
-
-
-class TestFillZerosLikeOpForTensorArray(unittest.TestCase):
-    def place(self):
-        return core.CPUPlace()
-
-    def test_zero_filling_lod_tensor_array(self):
-        tensor = core.LoDTensor()
-        tensor.set(
-            numpy.arange(20).reshape(20, 1).astype('int32'), self.place())
-        tensor.set_lod([[0, 2, 5], [0, 3, 9, 11, 17, 20]])
-
-        expect = [
-            numpy.array(
-                [0, 0, 0, 0, 0], dtype='int32'), numpy.array(
-                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype='int32'),
-            numpy.array(
-                [0, 0, 0], dtype='int32')
-        ]
-
-        lod = [[[0, 2, 5]], [[0, 6, 12]], [[0, 3]]]
-        self.main(
-            tensor=tensor,
-            expect_array=expect,
-            expect_lod=lod,
-            expect_max_len=3)
-
-    def main(self, tensor, expect_array, expect_lod, expect_max_len, level=0):
-        place = self.place()
-        program = Program()
-        with program_guard(program):
-            x = layers.data(name='x', shape=[10])
-            x.persistable = True
-            table = layers.lod_rank_table(x, level=level)
-            max_len = layers.max_sequence_len(table)
-            max_len.persistable = True
-            array = layers.lod_tensor_to_array(x, table)
-            array = layers.fill_zeros_like(array)
-            array.persistable = True
-
-            result = layers.array_to_lod_tensor(array, table)
-            result.persistable = True
-        exe = Executor(place)
-        scope = core.Scope()
-        exe.run(program, feed={'x': tensor}, scope=scope)
-        var = scope.find_var(array.name)
-        array = var.get_lod_tensor_array()
-        if expect_array is not None and expect_lod is not None:
-            self.check_array_same(array, expect_array, expect_lod)
-
-        self.assertEqual(
-            numpy.array(scope.find_var(max_len.name).get_tensor())[0],
-            expect_max_len)
-
-    def check_array_same(self, array, expect_tensor, expect_lod):
-        self.assertEqual(len(expect_tensor), len(array))
-        for i, exp in enumerate(zip(expect_tensor, expect_lod)):
-            exp_tensor, exp_lod = exp
-            exp_tensor = numpy.expand_dims(exp_tensor, axis=1)
-            self.assertTrue(numpy.allclose(exp_tensor, numpy.array(array[i])))
-            self.assertEqual(exp_lod, array[i].lod())
-
-
-if __name__ == '__main__':
-    unittest.main()