complete attention lstm op test

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

@@ -18,6 +18,7 @@ import unittest
 import numpy as np
 from op_test import OpTest
 from test_fusion_lstm_op import fc, ACTIVATION
+from test_softmax_op import stable_softmax
 
 
 def attention_lstm(
@@ -32,8 +33,56 @@ def attention_lstm(
         act_gate,
         act_cell,
         act_cand):
-    hidden
-    cell
+
+    T = sum(lod[0])
+    N = len(lod[0])
+    M = x.shape[1]
+    D = b.shape[1] / 4
+    assert T == x.shape[0]
+    assert len(fcws) == len(fcbs)
+
+    hidden = []
+    cell = []
+
+    start_offset = 0
+    for bid in range(N):
+        seq_len = lod[0][bid]
+        xi = np.copy(x[start_offset:seq_len, :]).reshape(seq_len, M)
+        prev_cell = np.copy(c0[bid]).reshape([1, D])
+        prev_hidden = np.copy(h0[bid]).reshape([1, D])
+        for step in range(seq_len):
+            expanded_cell = np.repeat(prev_cell, seq_len, axis=0)
+            tmp = np.concatenate((xi, expanded_cell), axis=1)
+            assert tmp.shape[1] == M + D
+            for fcid in range(len(fcbs)):
+                tmp = fc(tmp, fcws[fcid], fcbs[fcid])
+                tmp = ACTIVATION['relu'](tmp)
+            tmp = np.reshape(tmp, (1, seq_len))
+            tmp = stable_softmax(tmp).reshape(seq_len, 1)
+            lstmx = xi * tmp  # seq * M
+            lstmx = np.sum(lstmx.reshape(seq_len, M), axis=0).reshape([1, M])
+            lstmin = np.concatenate((prev_hidden, lstmx), axis=1)
+            lstmout = np.dot(lstmin, w).reshape([1, 4 * D])
+
+            g_f, g_i, g_o, cand = np.split(lstmout, 4, axis=1)
+            g_f = act_gate(g_f).reshape([1, D])
+            g_i = act_gate(g_i).reshape([1, D])
+            g_o = act_gate(g_o).reshape([1, D])
+            cand = act_cand(cand).reshape([1, D])
+
+            cell_t = (prev_cell * g_f) + (g_i * cand)
+            hidden_t = g_o * act_cell(cell_t)
+
+            hidden.append(hidden_t.flatten())
+            cell.append(cell_t.flatten())
+
+            prev_cell = cell_t.reshape([1, D])
+            prev_hidden = hidden_t.reshape([1, D])
+
+        start_offset += seq_len
+
+    hidden = np.array(hidden).astype('float32').reshape([T, D])
+    cell = np.array(cell).astype('float32').reshape([T, D])
     return hidden, cell
 
 
@@ -73,7 +122,7 @@ class TestAttentionLSTMOp(OpTest):
         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],
+                              w, b, ACTIVATION[self.act_gate],
                               ACTIVATION[self.act_cell],
                               ACTIVATION[self.act_cand])