init attention lstm op test

python/paddle/fluid/tests/unittests/test_attention_lstm_op.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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+from op_test import OpTest
+from test_fusion_lstm_op import fc, ACTIVATION
+
+
+def attention_lstm(
+        x,  # T x M
+        lod,  # 1 x N
+        h0,  # N x D
+        c0,  # N x D
+        fcws,  # (M+D) x 1, 1x1
+        fcbs,  # 1 x 1, 1x1
+        w,  # (M+D) x 4D
+        b,  # 1 x 4D
+        act_gate,
+        act_cell,
+        act_cand):
+    hidden
+    cell
+    return hidden, cell
+
+
+class TestAttentionLSTMOp(OpTest):
+    def set_conf(self):
+        self.lod = [[3]]
+
+    def setUp(self):
+        self.op_type = 'attention_lstm'
+        self.lod = [[3]]
+        self.M = 30
+        self.D = 15
+        self.has_initial_hidden = True
+        self.act_gate = 'sigmoid'
+        self.act_cell = 'tanh'
+        self.act_cand = 'tanh'
+        self.set_conf()
+
+        T = sum(self.lod[0])
+        bs = len(self.lod[0])
+
+        x = np.random.normal(size=(T, self.M)).astype('float32')
+        c0 = np.random.normal(size=(bs, self.D)).astype('float32')
+        if self.has_initial_hidden:
+            h0 = np.random.normal(size=(bs, self.D)).astype('float32')
+        else:
+            h0 = np.zeros((bs, self.D)).astype('float32')
+
+        fcw1 = np.random.normal(size=(self.M + self.D, 1)).astype('float32')
+        fcb1 = np.random.normal(size=(1, 1)).astype('float32')
+        fcw2 = np.random.normal(size=(1, 1)).astype('float32')
+        fcb2 = np.random.normal(size=(1, 1)).astype('float32')
+
+        # lstm weight and bias
+        w = np.random.normal(size=(self.M + self.D,
+                                   self.D * 4)).astype('float32')
+        b = np.random.normal(size=(1, self.D * 4)).astype('float32')
+
+        h, c = attention_lstm(x, self.lod, h0, c0, [fcw1, fcw2], [fcb1, fcb2],
+                              ACTIVATION[self.act_gate],
+                              ACTIVATION[self.act_cell],
+                              ACTIVATION[self.act_cand])
+
+        self.inputs = {
+            'X': (x, self.lod),
+            'C0': c0,
+            'AttentionWeight': fcw1,
+            'AttentionBias': fcb1,
+            'AttentionScalar': fcw2,
+            'AttentionScalarBias': fcb2,
+            'LSTMWeight': w,
+            'LSTMBias': b
+        }
+
+        if self.has_initial_hidden:
+            self.inputs['H0'] = h0
+
+        self.outputs = {
+            'Hidden': (h, self.lod),
+            'Cell': (c, self.lod),
+        }
+        self.attrs = {
+            'gate_activation': self.act_gate,
+            'cell_activation': self.act_cell,
+            'candidate_activation': self.act_cand
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestAttentionOpNonInit(TestAttentionLSTMOp):
+    def set_conf(self):
+        self.has_initial_hidden = False
+
+
+class TestAttentionOpMD1(TestAttentionLSTMOp):
+    def set_conf(self):
+        self.M = 36
+        self.D = 8
+
+
+class TestAttentionOpMD2(TestAttentionLSTMOp):
+    def set_conf(self):
+        self.M = 8
+        self.D = 8
+
+
+class TestAttentionOpMD3(TestAttentionLSTMOp):
+    def set_conf(self):
+        self.M = 15
+        self.D = 30
+
+
+class TestAttentionOpBS1(TestAttentionLSTMOp):
+    def set_conf(self):
+        self.lod = [[5]]
+        self.M = 16
+        self.D = 32
+
+
+class TestAttentionOpBS2(TestAttentionLSTMOp):
+    def set_conf(self):
+        self.lod = [[3, 6]]
+
+
+class TestAttentionOpBS5(TestAttentionLSTMOp):
+    def set_conf(self):
+        self.lod = [[3, 2, 4, 7, 5]]
+
+
+if __name__ == '__main__':
+    unittest.main()