fix compiling warning.

paddle/operators/lstm_op.h

@@ -155,7 +155,7 @@ class LSTMGradKernel : public framework::OpKernel<T> {
     auto* batch_cell_pre_act = ctx.Input<LoDTensor>("BatchCellPreAct");
 
     auto* hidden_g = ctx.Input<LoDTensor>(framework::GradVarName("Hidden"));
-    auto* cell_g = ctx.Input<LoDTensor>(framework::GradVarName("Cell"));
+    // auto* cell_g = ctx.Input<LoDTensor>(framework::GradVarName("Cell"));
 
     auto* in_g = ctx.Output<LoDTensor>(framework::GradVarName("Input"));
     auto* weight_g = ctx.Output<Tensor>(framework::GradVarName("Weight"));
@@ -219,8 +219,8 @@ class LSTMGradKernel : public framework::OpKernel<T> {
     LoDTensor batch_cell_g;
     batch_cell_g.mutable_data<T>(out_dims, ctx.GetPlace());
     batch_cell_g.set_lod(batch_gate->lod());
-    to_batch(device_ctx, *cell_g, batch_cell_g, false);
     // TODO(qingqing) support the case output cell has gradient.
+    // to_batch(device_ctx, *cell_g, batch_cell_g, false);
     zero(device_ctx, &batch_cell_g, static_cast<T>(0.0));
 
     LoDTensor batch_gate_g;

paddle/operators/math/sequence2batch.h

@@ -58,7 +58,8 @@ class LoDTensor2BatchFunctor {
     if (!is_cal_batch_lod) {
       auto lods = batch.lod();
       PADDLE_ENFORCE_EQ(lods.size(), 2UL);
-      PADDLE_ENFORCE_EQ(lods[1].size(), lod_tensor.dims()[0]);
+      PADDLE_ENFORCE_EQ(lods[1].size(),
+                        static_cast<size_t>(lod_tensor.dims()[0]));
       CopyMatrixRowsFunctor<Place, T> to_batch;
       to_batch(context, lod_tensor, lods[1].data(), batch, true);
       return;
@@ -111,10 +112,10 @@ class LoDTensor2BatchFunctor {
     size_t* batch_starts = batch_lods[0].data();
     size_t* seq2batch_idx = batch_lods[1].data();
     batch_starts[0] = 0;
-    for (size_t n = 0; n < num_batch; n++) {
+    for (int n = 0; n < num_batch; n++) {
       auto batch_id = static_cast<int>(batch_starts[n]);
       for (size_t i = 0; i < seq_info.size(); ++i) {
-        size_t seq_len = seq_info[i].length;
+        int seq_len = seq_info[i].length;
         int start = seq_info[i].start;
         if (n < seq_len) {
           seq2batch_idx[batch_id] =

python/paddle/v2/framework/tests/test_lstm_op.py

@@ -52,7 +52,7 @@ def lstm(
         g = np.dot(h_pre, w_h)  # 1 x 4D
         g = g + x
         g = np.reshape(g, (1, g.size))
-        c_tmp, g_i, g_f, g_o = np.split(g, 4, axis=1)
+        c, g_i, g_f, g_o = np.split(g, 4, axis=1)
         if w_c is None:
             g_i = act_gate(g_i)  # 1 x D
             g_f = act_gate(g_f)  # 1 x D
@@ -60,7 +60,7 @@ def lstm(
             w_ic, w_fc, w_oc = np.split(w_c, 3, axis=1)
             g_i = act_gate(g_i + w_ic * c_pre)  # 1 x D
             g_f = act_gate(g_f + w_fc * c_pre)  # 1 x D
-        c = g_f * c_pre + g_i * act_cand(c_tmp)  # 1 x D
+        c = g_f * c_pre + g_i * act_cand(c)  # 1 x D
 
         if w_c is None:
             g_o = act_gate(g_o)  # 1 x D
@@ -68,8 +68,7 @@ def lstm(
             _, _, w_oc = np.split(w_c, 3, axis=1)
             g_o = act_gate(g_o + w_oc * c)  # 1 x D
         h = g_o * act_cell(c)
-        bg = np.concatenate((act_cand(c_tmp), g_i, g_f, g_o), axis=1)
-        return h, c, bg
+        return h, c
 
     def _reverse(x, lod):
         y = np.zeros_like(x)
@@ -82,7 +81,6 @@ def lstm(
     batch_size = len(offset) - 1
     hidden = []
     cell = []
-    gate = []
     input = _reverse(input, offset) if is_reverse else input
     if w_b is not None:
         input = input + np.tile(w_b, (offset[-1], 1))
@@ -94,30 +92,26 @@ def lstm(
         c_pre = c0[i]  # 1 x D
         for j in range(seq_len):
             # compute one step
-            h_pre, c_pre, g_pre = _step(x[j], w_h, w_c, h_pre, c_pre, act_gate,
+            h_pre, c_pre = _step(x[j], w_h, w_c, h_pre, c_pre, act_gate,
                                  act_cell, act_cand)
             hidden.append(h_pre.flatten())
             cell.append(c_pre.flatten())
-            gate.append(g_pre.flatten())
 
     hidden = np.array(hidden).astype('float64')
     cell = np.array(cell).astype('float64')
-    gate = np.array(gate).astype('float64')
 
     hidden = _reverse(hidden, offset) if is_reverse else hidden
     cell = _reverse(cell, offset) if is_reverse else cell
 
-    assert gate.shape == input.shape
     assert hidden.shape == (input.shape[0], input.shape[1] / 4)
     assert cell.shape == (input.shape[0], input.shape[1] / 4)
-    return hidden, cell, gate
+    return hidden, cell
 
 
 class TestLstmOp(OpTest):
     def set_argument(self):
-        self.lod = [[0, 2, 6, 9]]
+        self.lod = [[0, 2, 6]]
         self.D = 16
-        self.sort_idx = [2, 6, 0, 3, 7, 1, 4, 8, 5]
 
         self.act_gate = 'sigmoid'
         self.act_cell = 'tanh'
@@ -141,22 +135,18 @@ class TestLstmOp(OpTest):
 
         w_b = b[:, 0:4 * self.D]
         w_c = b[:, 4 * self.D:]
-        h, c, g = lstm(x, self.lod, h0, c0, w, w_b, w_c, self.is_reverse,
+        h, c = lstm(x, self.lod, h0, c0, w, w_b, w_c, self.is_reverse,
                     ACTVATION[self.act_gate], ACTVATION[self.act_cell],
                     ACTVATION[self.act_cand])
 
-        g_sort = np.zeros_like(x)
-        for i, j in enumerate(self.sort_idx):
-            g_sort[i, :] = g[j, :]
-
         self.inputs = {'Input': (x, self.lod), 'Weight': w, '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),
-            'BatchGate': g_sort,
         }
         self.attrs = {
             'usePeepholes': True,
@@ -179,9 +169,8 @@ class TestLstmOp(OpTest):
 
 class TestLstmOpHasNoInitial(TestLstmOp):
     def set_argument(self):
-        self.lod = [[0, 2, 6, 9]]
-        self.D = 64
-        self.sort_idx = [2, 6, 0, 3, 7, 1, 4, 8, 5]
+        self.lod = [[0, 2, 6]]
+        self.D = 16
 
         self.act_gate = 'sigmoid'
         self.act_cell = 'tanh'
@@ -193,9 +182,8 @@ class TestLstmOpHasNoInitial(TestLstmOp):
 
 class TestLstmOpRerverse(TestLstmOp):
     def set_argument(self):
-        self.lod = [[0, 2, 6, 9]]
-        self.D = 64
-        self.sort_idx = [2, 6, 0, 3, 7, 1, 4, 8, 5]
+        self.lod = [[0, 2, 6]]
+        self.D = 16
 
         self.act_gate = 'sigmoid'
         self.act_cell = 'tanh'