1. Add sequence_num as edit distance op's output

2. Fix evaluator using 'reduce_sum' op instead of 'mean' op

Author: wanghaoshuang

paddle/operators/CMakeLists.txt

@@ -156,6 +156,7 @@ op_library(parallel_do_op DEPS executor)
 # Regist multiple Kernel to pybind
 if (WITH_GPU)
 op_library(conv_op SRCS conv_op.cc conv_op.cu.cc conv_cudnn_op.cu.cc DEPS vol2col)
+op_library(edit_distance_op SRCS edit_distance_op.cc edit_distance_op.cu DEPS math_function)
 op_library(pool_op SRCS pool_op.cc pool_op.cu.cc pool_cudnn_op.cu.cc DEPS pooling)
 op_library(conv_transpose_op SRCS conv_transpose_op.cc conv_transpose_op.cu.cc
   conv_transpose_cudnn_op.cu.cc DEPS vol2col)

paddle/operators/edit_distance_op.cc

@@ -25,6 +25,8 @@ class EditDistanceOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(ctx->HasInput("Hyps"), "Input(Hyps) shouldn't be null.");
     PADDLE_ENFORCE(ctx->HasInput("Refs"), "Input(Refs) shouldn't be null.");
     PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) shouldn't be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("SequenceNum"),
+                   "Output(SequenceNum) shouldn't be null.");
     auto hyp_dims = ctx->GetInputDim("Hyps");
     auto ref_dims = ctx->GetInputDim("Refs");
     PADDLE_ENFORCE(hyp_dims.size() == 2 && hyp_dims[1] == 1,
@@ -34,6 +36,7 @@ class EditDistanceOp : public framework::OperatorWithKernel {
                    "Input(Refs) must be a 2-D LoDTensor with the 2nd dimension "
                    "equal to 1.");
     ctx->SetOutputDim("Out", ctx->GetInputDim("Refs"));
+    ctx->SetOutputDim("SequenceNum", {1});
   }
 
  protected:
@@ -54,6 +57,7 @@ class EditDistanceOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("Refs",
              "(2-D LoDTensor<int64_t>, 2nd dim. equal to 1) "
              "The indices for reference strings.");
+    AddOutput("SequenceNum", "The sequence count of current batch");
     AddAttr<bool>("normalized",
                   "(bool, default false) Indicated whether to normalize "
                   "the edit distance by the length of reference string.")

paddle/operators/edit_distance_op.cu

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #include <algorithm>
 #include "paddle/framework/op_registry.h"
+#include "paddle/operators/math/math_function.h"
 #include "paddle/platform/cuda_helper.h"
 #include "paddle/platform/gpu_info.h"
 
@@ -72,6 +73,8 @@ class EditDistanceGPUKernel : public framework::OpKernel<T> {
 
     auto* x1_t = ctx.Input<framework::LoDTensor>("Hyps");
     auto* x2_t = ctx.Input<framework::LoDTensor>("Refs");
+    auto* sequence_num = ctx.Output<framework::Tensor>("SequenceNum");
+    sequence_num->mutable_data<int64_t>(ctx.GetPlace());
 
     auto normalized = ctx.Attr<bool>("normalized");
     auto stream = reinterpret_cast<const platform::CUDADeviceContext&>(
@@ -88,7 +91,11 @@ class EditDistanceGPUKernel : public framework::OpKernel<T> {
                      "Reference string %d is empty.", i);
     }
 
-    auto num_strs = hyp_lod.size() - 1;
+    const size_t num_strs = hyp_lod.size() - 1;
+    math::SetConstant<platform::CUDADeviceContext, int64_t> set_constant;
+    set_constant(ctx.template device_context<platform::CUDADeviceContext>(),
+                 sequence_num, static_cast<int64_t>(num_strs));
+
     out_t->Resize({static_cast<int64_t>(num_strs), 1});
     out_t->mutable_data<T>(ctx.GetPlace());
     auto out = out_t->data<T>();

paddle/operators/edit_distance_op.h

@@ -16,7 +16,6 @@ limitations under the License. */
 #include <algorithm>
 #include "paddle/framework/eigen.h"
 #include "paddle/framework/op_registry.h"
-
 namespace paddle {
 namespace operators {
 
@@ -28,6 +27,8 @@ class EditDistanceKernel : public framework::OpKernel<T> {
 
     auto* x1_t = ctx.Input<framework::LoDTensor>("Hyps");
     auto* x2_t = ctx.Input<framework::LoDTensor>("Refs");
+    auto* sequence_num = ctx.Output<framework::Tensor>("SequenceNum");
+    int64_t* seq_num_data = sequence_num->mutable_data<int64_t>(ctx.GetPlace());
 
     auto normalized = ctx.Attr<bool>("normalized");
 
@@ -41,6 +42,7 @@ class EditDistanceKernel : public framework::OpKernel<T> {
                      "Reference string %d is empty.", i);
     }
     auto num_strs = hyp_lod.size() - 1;
+    *seq_num_data = static_cast<int64_t>(num_strs);
 
     out_t->Resize({static_cast<int64_t>(num_strs), 1});
     out_t->mutable_data<float>(ctx.GetPlace());

python/paddle/v2/fluid/evaluator.py

@@ -219,22 +219,22 @@ def __init__(self, input, label, k=1, **kwargs):
 
         self.total_error = self.create_state(
             dtype='float32', shape=[1], suffix='total')
-        self.batch_num = self.create_state(
-            dtype='float32', shape=[1], suffix='total')
-        error = layers.edit_distance(input=input, label=label)
-        error = layers.cast(x=error, dtype='float32')
-        mean_error = layers.mean(x=error)
-        layers.sums(input=[self.total_error, mean_error], out=self.total_error)
-        const1 = layers.fill_constant(shape=[1], value=1.0, dtype="float32")
-        layers.sums(input=[self.batch_num, const1], out=self.batch_num)
-        self.metrics.append(mean_error)
+        self.seq_num = self.create_state(
+            dtype='int64', shape=[1], suffix='total')
+        error, seq_num = layers.edit_distance(input=input, label=label)
+        #error = layers.cast(x=error, dtype='float32')
+        sum_error = layers.reduce_sum(error)
+        layers.sums(input=[self.total_error, sum_error], out=self.total_error)
+        layers.sums(input=[self.seq_num, seq_num], out=self.seq_num)
+        self.metrics.append(sum_error)
 
     def eval(self, executor, eval_program=None):
         if eval_program is None:
             eval_program = Program()
         block = eval_program.current_block()
         with program_guard(main_program=eval_program):
             total_error = _clone_var_(block, self.total_error)
-            batch_num = _clone_var_(block, self.batch_num)
-            out = layers.elementwise_div(x=total_error, y=batch_num)
+            seq_num = _clone_var_(block, self.seq_num)
+            seq_num = layers.cast(x=seq_num, dtype='float32')
+            out = layers.elementwise_div(x=total_error, y=seq_num)
         return np.array(executor.run(eval_program, fetch_list=[out])[0])

python/paddle/v2/fluid/layers/nn.py

@@ -1918,14 +1918,16 @@ def edit_distance(input, label, normalized=False, tokens=None, name=None):
 
     # edit distance op
     edit_distance_out = helper.create_tmp_variable(dtype="int64")
+    sequence_num = helper.create_tmp_variable(dtype="int64")
     helper.append_op(
         type="edit_distance",
         inputs={"Hyps": [input],
                 "Refs": [label]},
-        outputs={"Out": [edit_distance_out]},
+        outputs={"Out": [edit_distance_out],
+                 "SequenceNum": [sequence_num]},
         attrs={"normalized": normalized})
 
-    return edit_distance_out
+    return edit_distance_out, sequence_num
 
 
 def ctc_greedy_decoder(input, blank, name=None):

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

@@ -60,6 +60,7 @@ def setUp(self):
 
         num_strs = len(x1_lod) - 1
         distance = np.zeros((num_strs, 1)).astype("float32")
+        sequence_num = np.array(2).astype("int64")
         for i in range(0, num_strs):
             distance[i] = Levenshtein(
                 hyp=x1[x1_lod[i]:x1_lod[i + 1]],
@@ -69,7 +70,7 @@ def setUp(self):
                 distance[i] = distance[i] / len_ref
         self.attrs = {'normalized': normalized}
         self.inputs = {'Hyps': (x1, [x1_lod]), 'Refs': (x2, [x2_lod])}
-        self.outputs = {'Out': distance}
+        self.outputs = {'Out': distance, 'SequenceNum': sequence_num}
 
     def test_check_output(self):
         self.check_output()
@@ -88,6 +89,7 @@ def setUp(self):
 
         num_strs = len(x1_lod) - 1
         distance = np.zeros((num_strs, 1)).astype("float32")
+        sequence_num = np.array(3).astype("int64")
         for i in range(0, num_strs):
             distance[i] = Levenshtein(
                 hyp=x1[x1_lod[i]:x1_lod[i + 1]],
@@ -97,7 +99,7 @@ def setUp(self):
                 distance[i] = distance[i] / len_ref
         self.attrs = {'normalized': normalized}
         self.inputs = {'Hyps': (x1, [x1_lod]), 'Refs': (x2, [x2_lod])}
-        self.outputs = {'Out': distance}
+        self.outputs = {'Out': distance, 'SequenceNum': sequence_num}
 
     def test_check_output(self):
         self.check_output()