refine seq_pool

paddle/fluid/operators/math/sequence_pooling.cu

@@ -135,7 +135,7 @@ class SequencePoolFunctor<platform::CUDADeviceContext, T> {
                   const std::string pooltype, const framework::LoDTensor& input,
                   framework::Tensor* output,
                   framework::Tensor* index = nullptr) {
-    auto lod = input.lod()[0];
+    auto& lod = input.lod()[0];
     const size_t item_dim = output->numel() / output->dims()[0];
     dim3 threads(1024, 1);
     dim3 grid(lod.size(), 1);
@@ -297,7 +297,7 @@ class SequencePoolGradFunctor<platform::CUDADeviceContext, T> {
                   framework::LoDTensor* in_grad,
                   /* max pool has index */
                   const framework::Tensor* index = nullptr) {
-    auto lod = in_grad->lod()[0];
+    auto& lod = in_grad->lod()[0];
     const size_t item_dim = in_grad->numel() / in_grad->dims()[0];
     dim3 threads(1024, 1);
     dim3 grid(lod.size(), 1);

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

@@ -31,11 +31,11 @@ class TestSeqAvgPool(OpTest):
         self.op_type = 'sequence_pool'
         # one level, batch size is 4
         x = np.random.uniform(0.1, 1, [11, 23]).astype('float32')
-        lod = [[4, 1, 3, 3]]
+        lod = [[11]]
         self.inputs = {'X': (x, lod)}
         offset = self.convert_to_offset(lod)
 
-        out = np.zeros((4, 23)).astype('float32')
+        out = np.zeros((len(lod[0]), 23)).astype('float32')
         self.outputs = {'Out': out}
         return x, offset, out
 
@@ -71,7 +71,7 @@ class TestSeqMaxPool(TestSeqAvgPool):
     def set_data(self):
         self.op_type = 'sequence_pool'
         x = np.random.uniform(0.1, 1, [13, 23]).astype('float32')
-        lod = [[4, 1, 3, 5]]
+        lod = [[13]]
         offset = self.convert_to_offset(lod)
         for i in range(len(offset[0]) - 1):
             l = offset[0][i + 1] - offset[0][i]
@@ -79,7 +79,7 @@ class TestSeqMaxPool(TestSeqAvgPool):
 
         self.inputs = {'X': (x, lod)}
 
-        out = np.zeros((4, 23)).astype('float32')
+        out = np.zeros((1, 23)).astype('float32')
         self.outputs = {'Out': out}
         return x, offset, out