add seqlastin/seqfirstin for seq_pool op

paddle/operators/sequence_pool_op.h

@@ -15,6 +15,7 @@ limitations under the License. */
 #pragma once
 #include "paddle/framework/eigen.h"
 #include "paddle/framework/op_registry.h"
+#include "paddle/operators/math/math_function.h"
 
 namespace paddle {
 namespace operators {
@@ -81,6 +82,12 @@ class SequencePoolKernel : public framework::OpKernel<T> {
           out_e.device(place) = in_e.sum(Eigen::array<int, 1>({{0}})) /
                                 std::sqrt(static_cast<T>(h));
           break;
+        case LAST:
+          out_e.device(place) = in_e.chip(h - 1, 0);
+          break;
+        case FIRST:
+          out_e.device(place) = in_e.chip(0, 0);
+          break;
         default:
           PADDLE_THROW("unsupported pooling strategy");
       }
@@ -102,6 +109,10 @@ class SequencePoolGradKernel : public framework::OpKernel<T> {
     int64_t w = in->numel() / dims[0];
 
     in_g->mutable_data<T>(context.GetPlace());
+    if (strategy > 2) {
+      // set X@Grad be zero at first when strategy is LAST/FIRST/MAX
+      math::SetConstant<Place, T>(context.device_context(), in_g, 0);
+    }
     auto place = context.GetEigenDevice<Place>();
     for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
       auto in_g_t = in_g->Slice<T>(static_cast<int>(lod[i]),
@@ -123,6 +134,12 @@ class SequencePoolGradKernel : public framework::OpKernel<T> {
           in_g_e.device(place) =
               (out_g_e / std::sqrt(static_cast<T>(h))).broadcast(bcast);
           break;
+        case LAST:
+          in_g_e.chip(h - 1, 0).device(place) = out_g_e;
+          break;
+        case FIRST:
+          in_g_e.chip(0, 0).device(place) = out_g_e;
+          break;
         default:
           PADDLE_THROW("unsupported pooling strategy");
       }

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

@@ -107,5 +107,45 @@ class TestSeqSqrtPool2D(TestSeqAvgPool2D):
         self.check_grad(["X"], "Out", max_relative_error=0.06)
 
 
+class TestSeqLastPool(TestSeqAvgPool):
+    def compute(self):
+        self.attrs = {'strategy': SeqPoolType.LAST}
+        x, lod = self.inputs['X']
+        out = self.outputs['Out']
+        for i in range(4):
+            sub_x = x[lod[0][i]:lod[0][i + 1], :]
+            out[i] = sub_x[-1, :]
+
+
+class TestSeqLastPool2D(TestSeqAvgPool2D):
+    def compute(self):
+        self.attrs = {'strategy': SeqPoolType.LAST}
+        x, lod = self.inputs['X']
+        out = self.outputs['Out']
+        for i in range(4):
+            sub_x = np.reshape(x[lod[0][i]:lod[0][i + 1], :], (-1, 3 * 17))
+            out[i] = np.reshape(sub_x[-1, :], (3, 17))
+
+
+class TestSeqFirstPool(TestSeqAvgPool):
+    def compute(self):
+        self.attrs = {'strategy': SeqPoolType.FIRST}
+        x, lod = self.inputs['X']
+        out = self.outputs['Out']
+        for i in range(4):
+            sub_x = x[lod[0][i]:lod[0][i + 1], :]
+            out[i] = sub_x[0, :]
+
+
+class TestSeqFirstPool2D(TestSeqAvgPool2D):
+    def compute(self):
+        self.attrs = {'strategy': SeqPoolType.FIRST}
+        x, lod = self.inputs['X']
+        out = self.outputs['Out']
+        for i in range(4):
+            sub_x = np.reshape(x[lod[0][i]:lod[0][i + 1], :], (-1, 3 * 17))
+            out[i] = np.reshape(sub_x[0, :], (3, 17))
+
+
 if __name__ == '__main__':
     unittest.main()