change offset and length's rank to 2, dim[0] for batch size

paddle/operators/sequence_slice_op.cc

@@ -32,6 +32,14 @@ class SequenceSliceOp : public framework::OperatorWithKernel {
                    "Output(Out) of SequenceSliceOp should not be null.");
     auto input_dims = ctx->GetInputDim("X");
 
+    auto offset_dim = ctx->GetInputDim("Offset");
+    auto length_dim = ctx->GetInputDim("Length");
+
+    PADDLE_ENFORCE_EQ(offset_dim.size(), 2UL,
+                      "Only support one level sequence now.");
+    PADDLE_ENFORCE_EQ(length_dim.size(), 2UL,
+                      "Only support one level sequence now.");
+
     ctx->SetOutputDim("Out", input_dims);
     }
 
@@ -95,7 +103,7 @@ It only supports sequence (LoD Tensor with level number is 1).
        [d1, d2;
         e1, e2]]
     LoD(X) = {{0, 3, 5}}; Dims(X) = (5, 2)
-    Offset = [0, 1]; Length = [2, 1]
+    Offset = [[0], [1]]; Length = [[2], [1]]
 
     Out = [[a1, a2;
             b1, b2]

paddle/operators/sequence_slice_op.h

@@ -48,42 +48,42 @@ class SequenceSliceOpKernel : public framework::OpKernel<T> {
     auto* length = ctx.Input<Tensor>("Length");
     auto* out = ctx.Output<LoDTensor>("Out");
 
+    auto lod = in->lod();
+    auto n = lod[0].size() - 1;
+
+    PADDLE_ENFORCE_EQ(lod.size(), 1UL,
+                      "Only support one level sequence now.");
+    PADDLE_ENFORCE_EQ(
+        n, length->dims()[0],
+        "The size of input-sequence and length-array should be the same")
+    PADDLE_ENFORCE_EQ(
+        n, offset->dims()[0],
+        "The size of input-sequence and offset-array should be the same")
+
     const int64_t* offset_data = offset->data<int64_t>();
     const int64_t* length_data = length->data<int64_t>();
+    framework::Tensor offset_cpu;
+    framework::Tensor length_cpu;
 
     if (platform::is_gpu_place(ctx.GetPlace())) {
-      framework::Tensor offset_cpu;
       offset_cpu.mutable_data<T>(offset->dims(), platform::CPUPlace());
       offset_cpu.CopyFrom(*offset, platform::CPUPlace(), ctx.device_context());
       offset_data = offset_cpu.data<int64_t>();
 
-      framework::Tensor length_cpu;
       length_cpu.mutable_data<T>(length->dims(), platform::CPUPlace());
       length_cpu.CopyFrom(*length, platform::CPUPlace(), ctx.device_context());
       length_data = length_cpu.data<int64_t>();
     }
 
-    auto lod = in->lod();
-    auto n = lod[0].size() - 1;
-
-    PADDLE_ENFORCE_EQ(lod.size(), 1UL, "Only support one level sequence now.");
-    PADDLE_ENFORCE_EQ(offset->dims().size(), 1UL,
-                      "Only support one level sequence now.");
-    PADDLE_ENFORCE_EQ(length->dims().size(), 1UL,
-                      "Only support one level sequence now.");
-    PADDLE_ENFORCE_EQ(
-        n, length->dims()[0],
-        "The size of input-sequence and length-array should be the same")
-    PADDLE_ENFORCE_EQ(
-        n, offset->dims()[0],
-        "The size of input-sequence and offset-array should be the same")
-
     for (size_t i = 0; i < n; ++i) {
-      PADDLE_ENFORCE_LT(0, offset_data[i], "The offset must greater than zero")
+      PADDLE_ENFORCE_LT(0, offset_data[i],
-      PADDLE_ENFORCE_LT(0, length_data[i], "The length must greater than zero")
+                "The offset must greater than zero")
-      PADDLE_ENFORCE_LT(lod[0][i] + offset_data[i] + length_data[i],
+      PADDLE_ENFORCE_LT(0, length_data[i],
-                        lod[0][i + 1], "The target tensor's length overflow")
+                "The length must greater than zero")
-    }
+      PADDLE_ENFORCE_LT(
+          lod[0][i] + offset_data[i] + length_data[i],
+          lod[0][i + 1],
+          "The target tensor's length overflow")}
 
     out->mutable_data<T>(ctx.GetPlace());
     auto out_lod = SequenceSliceLoD(*in, offset_data, length_data);

python/paddle/v2/fluid/tests/test_beam_search_op.py

@@ -1,6 +1,6 @@
 import logging
-from paddle.v2.framework.op import Operator, DynamicRecurrentOp
+from paddle.v2.fluid.op import Operator, DynamicRecurrentOp
-import paddle.v2.framework.core as core
+import paddle.v2.fluid.core as core
 import unittest
 import numpy as np
 

python/paddle/v2/fluid/tests/test_sequence_slice_op.py

@@ -9,16 +9,16 @@ class TestSequenceSliceOp(OpTest):
         # only supprot one level LoD
         x = np.random.random(self.x_dim).astype('float32')
         lod = self.x_lod
-        offset = np.array(self.offset).flatten().astype("int64")
+        offset = np.array(self.offset).astype("int64")
-        length = np.array(self.length).flatten().astype("int64")
+        length = np.array(self.length).astype("int64")
 
         self.inputs = {'X': (x, lod), 'Offset': offset, 'Length': length}
         outs = [] #np.zeros((100, 3, 2)).astype('float32')
         out_lod = [[0]]
         out_lod_offset = 0
         for i in range(len(offset)):
-            sub_x = x[lod[0][i] + offset[i]: lod[0]
+            sub_x = x[lod[0][i] + offset[i, 0]: lod[0]
-                      [i] + offset[i] + length[i], :]
+                      [i] + offset[i, 0] + length[i, 0], :]
             out_lod_offset = out_lod_offset + len(sub_x)
             outs.append(sub_x)
             out_lod[0].append(out_lod_offset)
@@ -28,8 +28,8 @@ class TestSequenceSliceOp(OpTest):
     def init_test_case(self):
         self.x_dim = (100, 3, 2)
         self.x_lod = [[0, 20, 40, 60, 80, 100]]
-        self.offset = [1, 2, 3, 4, 5]
+        self.offset = [[1], [2], [3], [4], [5]]
-        self.length = [10, 8, 6, 4, 2]
+        self.length = [[10], [8], [6], [4], [2]]
 
     def setUp(self):
         self.op_type = "sequence_slice"