Add row conv operator (#6013)

* Fix documentation

* Address review comments

paddle/operators/row_conv_op.h

@@ -0,0 +1,33 @@
+/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
+
+   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. */
+
+#pragma once
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename Place, typename T>
+class RowConvKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &context) const override;
+};
+
+template <typename Place, typename T>
+class RowConvGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &context) const override;
+};
+}  // namespace operators
+}  // namespace paddle

python/paddle/v2/fluid/tests/test_row_conv_op.py

@@ -0,0 +1,95 @@
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+def row_conv_forward(x, lod, wt):
+    out = np.zeros_like(x)
+    seq_info = lod[0]
+    num_sequences = len(seq_info) - 1
+    context_length = wt.shape[0]
+
+    for i in range(num_sequences):  # loop over number of sequences
+        start = seq_info[i]
+        end = seq_info[i + 1]
+        curinput = x[start:end, :]
+        curoutput = out[start:end, :]
+
+        cur_timesteps = end - start
+        for j in range(cur_timesteps):  # loop over different timesteps
+            for k in range(context_length):
+
+                if j + k >= cur_timesteps:
+                    continue
+                curoutput[j, :] += curinput[j + k, :] * wt[k, :]
+
+    return out
+
+
+class TestRowConvOp1(OpTest):
+    def setUp(self):
+
+        self.op_type = "row_conv"
+        lod = [[0, 2, 5, 7]]
+        T = lod[0][-1]
+        D = 16
+        context_length = 2
+
+        x = np.random.random((T, D)).astype("float32")
+        wt = np.random.random((context_length, D)).astype("float32")
+        self.inputs = {'X': (x, lod), 'Filter': wt}
+
+        out = row_conv_forward(x, lod, wt)
+        self.outputs = {'Out': (out, lod)}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['X', 'Filter'], 'Out', max_relative_error=0.05)
+
+    def test_check_grad_ignore_x(self):
+        self.check_grad(
+            ['Filter'], 'Out', max_relative_error=0.05, no_grad_set=set('X'))
+
+    def test_check_grad_ignore_wt(self):
+        self.check_grad(
+            ['X'], 'Out', max_relative_error=0.05, no_grad_set=set('Filter'))
+
+
+class TestRowConvOp2(OpTest):
+    def setUp(self):
+
+        self.op_type = "row_conv"
+        lod = [[0, 20, 50, 100]]
+        T = lod[0][-1]
+        D = 35
+        context_length = 35
+
+        x = np.random.random((T, D)).astype("float32")
+        wt = np.random.random((context_length, D)).astype("float32")
+        self.inputs = {'X': (x, lod), 'Filter': wt}
+
+        out = row_conv_forward(x, lod, wt)
+        self.outputs = {'Out': (out, lod)}
+
+    def test_check_output(self):
+        self.check_output()
+
+    #max_relative_error is increased from 0.05 to 0.06 as for higher
+    #dimensional input, the dX on CPU for some values has max_rel_error 
+    #slightly more than 0.05
+    def test_check_grad_normal(self):
+        self.check_grad(['X', 'Filter'], 'Out', max_relative_error=0.06)
+
+    def test_check_grad_ignore_x(self):
+        self.check_grad(
+            ['Filter'], 'Out', max_relative_error=0.06, no_grad_set=set('X'))
+
+    def test_check_grad_ignore_wt(self):
+        self.check_grad(
+            ['X'], 'Out', max_relative_error=0.06, no_grad_set=set('Filter'))
+
+
+if __name__ == '__main__':
+    unittest.main()