Add document for sequence_erase_op

paddle/operators/sequence_erase_op.cc

@@ -26,7 +26,11 @@ class SequenceEraseOp : public framework::OperatorWithKernel {
                    "Input(X) of SequenceEraseOp should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
                    "Output(Out) of SequenceEraseOp should not be null.");
-    ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
+    auto x_dims = ctx->GetInputDim("X");
+    PADDLE_ENFORCE(x_dims.size() == 2 && x_dims[1] == 1,
+                   "Input(X) of SequenceEraseOp should be a 2-D LoDTensor "
+                   "with the 2nd dimension equal to 1.");
+    ctx->SetOutputDim("Out", x_dims);
   }
 };
 
@@ -35,17 +39,41 @@ class SequenceEraseOpMaker : public framework::OpProtoAndCheckerMaker {
   SequenceEraseOpMaker(OpProto* proto, OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X",
-             "(LoDTensor) 2-D input LoDTensor with the 2-nd dimension "
-             "of length 1.");
+             "(2-D LoDTensor with the 2nd dim. equal to 1) "
+             "Input LoDTensor of SequenceEraseOp.");
     AddOutput("Out",
-              "(LoDTensor) 2-D output LoDTensor with the 2-nd dimension "
-              "of length 1.");
+              "(2-D LoDTensor with the 2nd dim. equal to 1) "
+              "Output LoDTensor of SequenceEraseOp.");
     AddAttr<std::vector<int>>("tokens",
-                              "(vector<int>) "
-                              "Tokens to be removed from input.");
+                              "(vector<int>) Tokens need to be erased from "
+                              "input sequences.");
     AddComment(R"DOC(
 Sequence Erase Operator.
 
+Sequence erase operator erases tokens specified by Attr(tokens) in the input 
+sequences Input(X), and outputs the remaining data and modifies the LoD 
+information at the same time. For example, given a 2-D LoDTensor
+
+    X = [[2, 2, 6, 1, 3, 9, 6, 1, 0, 1]]^T
+
+with lod = [[0, 3, 6, 10]], there are three sequences in the input:
+   
+     X1 = [[2, 2, 6]]^T, X2 = [[1, 3, 9]]^T and X3 = [[6, 1, 0, 1]]^T.
+
+If the tokens to be erased are Attr(tokens) = [2, 3, 5], after the erasing 
+operation, the three sequences become
+
+    X1' = [[6]]^T, X2' = [[1, 9]]^T and X3' = [[6, 1, 0, 1]]^T.
+
+Hence the LoDTensor Output(Out) should be
+
+    Out = [[6, 1, 9, 6, 1, 0, 1]]^T,
+
+with lod = [[0, 1, 3, 7]].
+
+An example usage for this operator is to remove the special tokens when 
+computing the edit distance between two strings, such as blank, start token, 
+and end token.
 )DOC");
   }
 };

paddle/operators/sequence_erase_op.cu

@@ -13,17 +13,13 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include <thrust/device_vector.h>
-#include <thrust/execution_policy.h>
 #include <thrust/host_vector.h>
-#include <thrust/reduce.h>
 #include "paddle/operators/sequence_erase_op.h"
 #include "paddle/platform/cuda_helper.h"
-#include "paddle/platform/gpu_info.h"
 
 namespace paddle {
 namespace operators {
 using platform::PADDLE_CUDA_NUM_THREADS;
-using Tensor = framework::Tensor;
 using LoDTensor = framework::LoDTensor;
 
 template <typename T>
@@ -97,7 +93,7 @@ class SequenceEraseOpCUDAKernel : public framework::OpKernel<T> {
     thrust::inclusive_scan(num_erased.begin() + 1, num_erased.end(),
                            num_erased.begin() + 1);
 
-    // Reset LoD
+    // Calc LoD
     auto lod_len = lod0.size();
     thrust::host_vector<int> host_lod(lod_len);
     for (size_t i = 0; i < lod_len; ++i) {
@@ -117,15 +113,14 @@ class SequenceEraseOpCUDAKernel : public framework::OpKernel<T> {
     }
     framework::LoD out_lod;
     out_lod.push_back(out_lod0);
+    out->set_lod(out_lod);
 
-    out->Resize({out_lod0.back(), 1});
     // Set output
+    out->Resize({out_lod0.back(), 1});
     auto out_dat = out->mutable_data<T>(ctx.GetPlace());
     SetOutput<<<(in_len - 1) / PADDLE_CUDA_NUM_THREADS + 1,
                 PADDLE_CUDA_NUM_THREADS, 0, stream>>>(in_dat, in_len,
                                                       num_erased_ptr, out_dat);
-    // Set LoD
-    out->set_lod(out_lod);
   }
 };
 

paddle/operators/sequence_erase_op.h

@@ -15,14 +15,10 @@ limitations under the License. */
 #pragma once
 
 #include "paddle/framework/op_registry.h"
-#include "paddle/operators/math/softmax.h"
 
 namespace paddle {
 namespace operators {
 
-using Tensor = framework::Tensor;
-using LoDTensor = framework::LoDTensor;
-
 template <typename DeviceContext, typename T>
 class SequenceEraseKernel : public framework::OpKernel<T> {
  public:
@@ -32,17 +28,6 @@ class SequenceEraseKernel : public framework::OpKernel<T> {
 
     auto lod = in->lod();
     PADDLE_ENFORCE_EQ(lod.size(), 1UL, "Only support one level sequence now.");
-    // auto dims = x->dims();
-    /*
-    const size_t level = lod.size() - 1;
-    PADDLE_ENFORCE_EQ(dims[0], static_cast<int64_t>(lod[level].back()),
-                      "The first dimension of Input(X) should be equal to the "
-                      "sum of all sequences' lengths.");
-    PADDLE_ENFORCE_EQ(dims[0], x->numel(),
-                      "The width of each timestep in Input(X) of "
-                      "SequenceEraseOp should be 1.");
-    out->mutable_data<T>(ctx.GetPlace());
-    */
     auto tokens = ctx.Attr<std::vector<int>>("tokens");
     auto in_len = in->numel();
     auto in_dat = in->data<T>();
@@ -65,7 +50,7 @@ class SequenceEraseKernel : public framework::OpKernel<T> {
     out->Resize({static_cast<int64_t>(out_len), 1});
     auto out_dat = out->mutable_data<T>(ctx.GetPlace());
 
-    for (size_t i = 0; i < in_len; ++i) {
+    for (int64_t i = 0; i < in_len; ++i) {
       if (num_erased[i] == num_erased[i + 1]) {
         out_dat[i - num_erased[i]] = in_dat[i];
       }

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

@@ -28,9 +28,9 @@ def sequence_erase(in_seq, lod0, tokens):
 class TestSequenceEraseOp(OpTest):
     def setUp(self):
         self.op_type = "sequence_erase"
-        in_seq = np.random.randint(0, 10, (30, 1)).astype("int32")
-        lod = [[0, 5, 15, 30]]
-        tokens = [2, 5]
+        in_seq = np.random.randint(0, 10, (10, 1)).astype("int32")
+        lod = [[0, 3, 6, 10]]
+        tokens = [2, 3, 5]
         out_seq, new_lod0 = sequence_erase(in_seq, lod[0], tokens)
         self.attrs = {'tokens': tokens}
         self.inputs = {'X': (in_seq, lod)}