add unit test of fusion lstm op

paddle/fluid/operators/fusion_lstm_op.cc

@@ -158,7 +158,8 @@ void FusionLSTMOpMaker::Make() {
             "(LoDTensor) the result after X * WeightX (size is T x 4D)"
             " or batched_X (size is T x M), this will be automatically chosen,"
             " where T is the total time steps in this mini-batch,"
-            " D is the hidden size, M is the dim size of x input.");
+            " D is the hidden size, M is the dim size of x input.")
+      .AsIntermediate();
   AddOutput("BatchedGate", "(LoDTensor) (same as LSTMOp).").AsIntermediate();
   AddOutput("BatchCellPreAct", "(LoDTensor) (same as LSTMOp).")
       .AsIntermediate();
@@ -251,7 +252,6 @@ class FuisonLSTMKernel : public framework::OpKernel<T> {
     math::LoDTensor2BatchFunctor<DeviceContext, T> to_batch;
     auto& dev_ctx = ctx.template device_context<DeviceContext>();
     auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
-    // TODO(TJ): op test these two cases
     if (x_dims[1] > wx_dims[1]) {
       SimpleFC<DeviceContext, T>(blas, x_dims[0], wx_dims[1], x_dims[1], x_data,
                                  wx_data, xx_data, bias->data<T>());

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

@@ -0,0 +1,143 @@
+#   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 numpy as np
+from op_test import OpTest
+from test_lstm_op import lstm, ACTIVATION
+
+
+def fc(x, w, b):
+    return np.dot(x, w) + b
+
+
+def fusion_lstm(
+        x,  # T x M
+        lod,  # 1 x N
+        wx=None,  # M x 4D
+        bx=None,  # 1 x 4D
+        h0=None,  # N x D
+        c0=None,  # N x D
+        w_h=None,  # D x 4D
+        w_b=None,  # 1 x 4D
+        w_c=None,  # 1 x 3D
+        is_reverse=False,
+        act_gate=None,
+        act_cell=None,
+        act_cand=None):
+    return lstm(
+        fc(x, wx, bx), lod, h0, c0, w_h, w_b, w_c, is_reverse, act_gate,
+        act_cell, act_cand)
+
+
+class TestLstmOp(OpTest):
+    def set_argument(self):
+        self.lod = [[2, 3, 2]]
+
+    def setUp(self):
+        self.op_type = 'fusion_lstm'
+        self.lod = [[2, 3, 2]]
+        self.M = 8
+        self.D = 16
+        self.has_initial_state = False
+        self.is_reverse = False
+        self.act_gate = 'sigmoid'
+        self.act_cell = 'tanh'
+        self.act_cand = 'tanh'
+        self.use_peepholes = False
+        self.set_argument()
+
+        T = sum(self.lod[0])
+        bs = len(self.lod[0])
+
+        x = np.random.normal(size=(T, self.M)).astype('float32')
+        if self.has_initial_state:
+            h0 = np.random.normal(size=(bs, self.D)).astype('float32')
+            c0 = np.random.normal(size=(bs, self.D)).astype('float32')
+        else:
+            h0 = np.zeros((bs, self.D)).astype('float32')
+            c0 = np.zeros((bs, self.D)).astype('float32')
+
+        wh = np.random.normal(size=(self.D, 4 * self.D)).astype('float32')
+
+        if self.use_peepholes:
+            b = np.random.normal(size=(1, 7 * self.D)).astype('float32')
+        else:
+            b = np.random.normal(size=(1, 4 * self.D)).astype('float32')
+        w_b = np.copy(b[:, 0:4 * self.D])
+        w_c = b[:, 4 * self.D:] if self.use_peepholes else None
+
+        # this is the weight of fc
+        wx = np.random.normal(size=(self.M, 4 * self.D)).astype('float32')
+        # this is the bias of fc
+        # and it should be manually added into the bias of this fusion LSTM
+        bx = np.random.normal(size=(1, 4 * self.D)).astype('float32')
+        b[0, 0:4 * self.D] += bx[0, :]
+        h, c = fusion_lstm(x, self.lod, wx, bx, h0, c0, wh, w_b, w_c,
+                           self.is_reverse, ACTIVATION[self.act_gate],
+                           ACTIVATION[self.act_cell], ACTIVATION[self.act_cand])
+
+        self.inputs = {
+            'X': (x, self.lod),
+            'WeightX': wx,
+            'WeightH': wh,
+            'Bias': b
+        }
+
+        if self.has_initial_state:
+            self.inputs['H0'] = h0
+            self.inputs['C0'] = c0
+
+        self.outputs = {
+            'Hidden': (h, self.lod),
+            'Cell': (c, self.lod),
+        }
+        self.attrs = {
+            'use_peepholes': self.use_peepholes,
+            'is_reverse': self.is_reverse,
+            'gate_activation': self.act_gate,
+            'cell_activation': self.act_cell,
+            'candidate_activation': self.act_cand
+        }
+
+    def test_check_output(self):
+        self.check_output(atol=1e-8)
+
+
+class TestLstmOpInitReverse(TestLstmOp):
+    def set_argument(self):
+        self.has_initial_state = True
+        self.is_reverse = True
+
+
+class TestLstmOpMD1(TestLstmOp):
+    def set_argument(self):
+        self.M = 35
+        self.D = 8
+
+
+class TestLstmOpMD2(TestLstmOp):
+    def set_argument(self):
+        self.M = 36
+        self.D = 8
+
+
+class TestLstmOpBS1(TestLstmOp):
+    def set_argument(self):
+        self.lod = [[3]]
+        self.D = 16
+
+
+if __name__ == '__main__':
+    unittest.main()