Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into poolmaxpool_with_mask

paddle/operators/sequence_concat_op.cc

@@ -68,38 +68,42 @@ class SequenceConcatOpMaker : public framework::OpProtoAndCheckerMaker {
                  "The level should be less than the level number of inputs.")
         .SetDefault(0);
     AddComment(R"DOC(
-Sequence Concat Operator.
-
-The sequence_concat operator concatenates multiple LoDTensors.
-It supports a sequence (LoD Tensor with level number is 1)
+The sequence_concat operator concatenates multiple LoDTensors. 
+It only supports sequence (LoD Tensor with level number is 1) 
 or a nested sequence (LoD tensor with level number is 2) as its input.
-The following examples explain how the operator works:
 - Case1:
   If the axis is other than 0(here, axis is 1 and level is 1),
-  each input should have the same LoD information and the LoD
+  each input should have the same LoD information and the LoD 
   information of the output keeps the same as the input.
 
-    LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)
-    LoD(x1) = {{0,2,4}, {0,1,2,3,4}}; Dims(x1) = (4,4,4)
-    LoD(Out) = {{0,2,4}, {0,1,2,3,4}}; Dims(Out) = (4,7,4)
+  LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)
+  LoD(x1) = {{0,2,4}, {0,1,2,3,4}}; Dims(x1) = (4,4,4)
+  LoD(Out) = {{0,2,4}, {0,1,2,3,4}}; Dims(Out) = (4,7,4)
 
 - Case2:
-  If the axis is 0(here, leve is 0), the inputs are concatenated along
+  If the axis is 0(here, leve is 0), the inputs are concatenated along 
   time steps, the LoD information of the output need to re-compute.
+  The LoD information of level-1 should be same.
 
-    LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)
-    LoD(x1) = {{0,3,5}, {0,1,2,3,5}}; Dims(x1) = (5,3,4)
-    LoD(Out) = {{0,5,9}, {0,1,2,3,4,5,6,7,9}}; Dims(Out) = (9,3,4)
+  LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)
+  LoD(x1) = {{0,2,4}, {0,1,3,5,7}}; Dims(x1) = (7,3,4)
+  LoD(Out) = {{0,2,4}, {0,2,5,8,11}}; Dims(Out) = (11,3,4)
 
 - Case3:
   If the axis is 0(here, level is 1).
 
-    LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)
-    LoD(x1) = {{0,3,5}, {0,1,3,4,5}}; Dims(x1) = (5,3,4)
-    LoD(Out) = {{0,5,9}, {0,2,5,7,9}}; Dims(Out) = (9,3,4)
+  LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)
+  LoD(x1) = {{0,3,4}, {0,1,3,5,7}}; Dims(x1) = (7,3,4)
+  LoD(Out) = {{0,5,8}, {0,1,2,3,5,7,8,9,11}}; Dims(Out) = (11,3,4)
 
-NOTE: The levels of all the inputs should be the same.
+- Case4:
+  If the LoD number is 1, axis is 0, level is 0
 
+  LoD(x0) = {{0,1,2,3,4}}; Dims(x0) = (4,3,4)
+  LoD(x1) = {{0,1,3,5,7}}; Dims(x1) = (7,3,4)
+  LoD(Out) = {{0,2,5,8,11}}; Dims(Out) = (11,3,4)
+
+NOTE: The levels of all the inputs should be the same.
     )DOC");
   }
 };

paddle/operators/sequence_concat_op.h

@@ -24,28 +24,38 @@ using LoDTensor = framework::LoDTensor;
 using LoD = framework::LoD;
 
 template <typename T>
-LoD concatLoD(const std::vector<const T*> ins, const size_t axis,
-              const size_t level) {
+LoD ConcatLoD(const std::vector<const T*> ins, const size_t level) {
   auto out_lod = ins[0]->lod();
+  auto numLevels = ins[0]->NumLevels();
   const size_t n = ins.size();
-  if (axis == 0UL) {
-    for (size_t i = 1; i < n; ++i) {
-      for (size_t j = 0; j < ins[i]->lod()[0].size(); ++j) {
-        out_lod[0][j] += ins[i]->lod()[0][j];
-      }
+  const size_t level_idx = ins[0]->NumLevels() - 1 - level;
+  for (size_t i = 1; i < n; ++i) {
+    for (size_t j = 0; j < ins[i]->lod()[level_idx].size(); ++j) {
+      out_lod[level_idx][j] += ins[i]->lod()[level_idx][j];
+    }
+  }
 
-      if (ins[0]->NumLevels() == 2) {
-        for (size_t j = 1; j < ins[i]->lod()[1].size(); ++j) {
-          if (level == 0UL) {
-            out_lod[1].push_back(out_lod[1].back() + ins[i]->lod()[1][j] -
-                                 ins[i]->lod()[1][j - 1]);
-          } else if (level == 1UL) {
-            out_lod[1][j] += ins[1]->lod()[1][j];
-          }
+  for (size_t i = level_idx; i < numLevels - 1; ++i) {
+    size_t lod_len = 1;
+    for (size_t j = 0; j < n; ++j) {
+      lod_len += ins[j]->lod()[i + 1].size() - 1;
+    }
+    out_lod[i + 1].clear();
+    out_lod[i + 1].resize(lod_len);
+
+    size_t idx = 1;
+    for (size_t j = 0; j < ins[0]->lod()[i].size() - 1; ++j) {
+      for (size_t k = 0; k < n; ++k) {
+        for (size_t m = ins[k]->lod()[i][j]; m < ins[k]->lod()[i][j + 1]; ++m) {
+          out_lod[i + 1][idx] = out_lod[i + 1][idx - 1] +
+                                ins[k]->lod()[i + 1][m + 1] -
+                                ins[k]->lod()[i + 1][m];
+          idx++;
         }
       }
     }
   }
+
   return out_lod;
 }
 
@@ -82,18 +92,21 @@ class SequenceConcatOpKernel : public framework::OpKernel<T> {
                       "should be greater than the specify level");
 
     out->mutable_data<T>(ctx.GetPlace());
-    auto out_lod = concatLoD<LoDTensor>(ins, axis, level);
+    auto out_lod = ins[0]->lod();
+    if (axis == 0) {
+      out_lod = ConcatLoD<LoDTensor>(ins, level);
+    }
     out->set_lod(out_lod);
 
-    auto out_lod_level = out_lod[level];
+    const size_t level_idx = out_lod.size() - level - 1;
+    auto out_lod_level = framework::ToAbsOffset(out_lod)[level_idx];
     for (size_t i = 0; i < out_lod_level.size() - 1; ++i) {
       Tensor out_t = out->Slice(static_cast<int>(out_lod_level[i]),
                                 static_cast<int>(out_lod_level[i + 1]));
       auto out_stride = framework::stride(out_t.dims());
       size_t offset = 0;
-
       for (size_t j = 0; j < n; ++j) {
-        auto in_lod_level = ins[j]->lod()[level];
+        auto in_lod_level = framework::ToAbsOffset(ins[j]->lod())[level_idx];
         auto in_stride = framework::stride(ins[j]->dims());
         Tensor in_t = ins[j]->Slice(static_cast<int>(in_lod_level[i]),
                                     static_cast<int>(in_lod_level[i + 1]));
@@ -124,9 +137,12 @@ class SequenceConcatGradOpKernel : public framework::OpKernel<T> {
       x_grads[i]->set_lod(ins[i]->lod());
       x_grads[i]->mutable_data<T>(ctx.GetPlace());
     }
-
-    auto out_lod = concatLoD<LoDTensor>(ins, axis, level);
-    auto out_lod_level = out_lod[level];
+    auto out_lod = ins[0]->lod();
+    if (axis == 0UL) {
+      out_lod = ConcatLoD<LoDTensor>(ins, level);
+    }
+    const size_t level_idx = out_lod.size() - level - 1;
+    auto out_lod_level = framework::ToAbsOffset(out_lod)[level_idx];
 
     for (size_t i = 0; i < out_lod_level.size() - 1; ++i) {
       Tensor out_grad_t =
@@ -136,7 +152,8 @@ class SequenceConcatGradOpKernel : public framework::OpKernel<T> {
       size_t offset = 0;
 
       for (size_t j = 0; j < n; ++j) {
-        auto x_grad_lod_level = x_grads[j]->lod()[level];
+        auto x_grad_lod_level =
+            framework::ToAbsOffset(x_grads[j]->lod())[level_idx];
         auto x_grad_stride = framework::stride(x_grads[j]->dims());
         Tensor x_grad_t =
             x_grads[j]->Slice(static_cast<int>(x_grad_lod_level[i]),

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

@@ -215,7 +215,11 @@ class OpTest(unittest.TestCase):
             if isinstance(input_vars[var_name], list):
                 for name, np_value in self.inputs[var_name]:
                     tensor = core.LoDTensor()
-                    tensor.set(np_value, place)
+                    if isinstance(np_value, tuple):
+                        tensor.set(np_value[0], place)
+                        tensor.set_lod(np_value[1])
+                    else:
+                        tensor.set(np_value, place)
                     feed_map[name] = tensor
             else:
                 tensor = core.LoDTensor()
@@ -236,7 +240,6 @@ class OpTest(unittest.TestCase):
 
         inputs = append_input_output(block, op_proto, self.inputs, True)
         outputs = append_input_output(block, op_proto, self.outputs, False)
-
         op = block.append_op(
             type=self.op_type,
             inputs=inputs,
@@ -397,9 +400,11 @@ class OpTest(unittest.TestCase):
                 if not isinstance(item[0], basestring):
                     item = [[param_name] + list(item)]
                 if len(item) == 2:
-                    # only set var name and value, set lod to None
-                    var[i] = list(item) + [None]
-
+                    if isinstance(item[1], tuple):
+                        var[i] = [item[0], item[1][0], item[1][1]]
+                    else:
+                        # only set var name and value, set lod to None
+                        var[i] = list(item) + [None]
             var_descs = [(block.create_var(
                 name=name, shape=each.shape, dtype=each.dtype), each, lod)
                          for name, each, lod in var]

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

@@ -4,7 +4,33 @@ import sys
 from op_test import OpTest
 
 
-class TestConcatOp(OpTest):
+def to_abs_lod(lod):
+    if len(lod) == 0 or len(lod) == 1:
+        return lod
+    import copy
+    new_lod = copy.deepcopy(lod)
+    for idx, val in enumerate(lod[0]):
+        new_lod[0][idx] = lod[1][val]
+    return new_lod
+
+
+def seq_concat(inputs, level):
+    lod0 = inputs['X'][0][1][1]
+    lod1 = inputs['X'][1][1][1]
+    x0 = inputs['X'][0][1][0]
+    x1 = inputs['X'][1][1][0]
+    level_idx = len(lod0) - level - 1
+    outs = []
+    for i in range(len(lod0[level_idx]) - 1):
+        sub_x0 = x0[to_abs_lod(lod0)[level_idx][i]:to_abs_lod(lod0)[level_idx][
+            i + 1], :]
+        sub_x1 = x1[to_abs_lod(lod1)[level_idx][i]:to_abs_lod(lod1)[level_idx][
+            i + 1], :]
+        outs.append(np.concatenate((sub_x0, sub_x1), axis=0))
+    return np.concatenate(outs, axis=0)
+
+
+class TestSeqConcatOp(OpTest):
     def set_data(self):
         # two level, batch size is 3
         x0 = np.random.random((4, 6, 3)).astype('float32')
@@ -15,13 +41,7 @@ class TestConcatOp(OpTest):
         level = 1
         self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
         self.attrs = {'axis': axis, 'level': level}
-        outs = []
-        for i in range(4):
-            sub_x0 = x0[lod0[level][i]:lod0[level][i + 1], :]
-            sub_x1 = x1[lod1[level][i]:lod1[level][i + 1], :]
-            outs.append(np.concatenate((sub_x0, sub_x1), axis=axis))
-
-        self.outputs = {'Out': np.concatenate(outs, axis=0)}
+        self.outputs = {'Out': (np.concatenate([x0, x1], axis=1), lod0)}
 
     def setUp(self):
         self.op_type = "sequence_concat"
@@ -34,46 +54,50 @@ class TestConcatOp(OpTest):
         self.check_grad(['x0'], 'Out')
 
 
-class TestConcatOpDiffLod(TestConcatOp):
+class TestSeqConcatOpLevelZeroNestedSequence(TestSeqConcatOp):
     def set_data(self):
         # two level, batch size is 3
         x0 = np.random.random((4, 6, 3)).astype('float32')
         lod0 = [[0, 2, 4], [0, 1, 2, 3, 4]]
-        x1 = np.random.random((5, 6, 3)).astype('float32')
-        lod1 = [[0, 3, 5], [0, 1, 2, 3, 5]]
+        x1 = np.random.random((7, 6, 3)).astype('float32')
+        lod1 = [[0, 2, 4], [0, 1, 3, 5, 7]]
         axis = 0
-        level = 1
+        level = 0
         self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
         self.attrs = {'axis': axis, 'level': level}
-        outs = []
-        for i in range(4):
-            sub_x0 = x0[lod0[level][i]:lod0[level][i + 1], :]
-            sub_x1 = x1[lod1[level][i]:lod1[level][i + 1], :]
-            outs.append(np.concatenate((sub_x0, sub_x1), axis=axis))
+        out_lod = [[0, 2, 4], [0, 2, 5, 8, 11]]
+        self.outputs = {'Out': (seq_concat(self.inputs, level), out_lod)}
 
-        self.outputs = {'Out': np.concatenate(outs, axis=0)}
+
+class TestSeqConcatOplevelOneNestedSequence(TestSeqConcatOp):
+    def set_data(self):
+        # two level, batch size is 3
+        x0 = np.random.random((4, 6, 3)).astype('float32')
+        lod0 = [[0, 2, 4], [0, 1, 2, 3, 4]]
+        x1 = np.random.random((7, 6, 3)).astype('float32')
+        lod1 = [[0, 3, 4], [0, 1, 3, 5, 7]]
+        axis = 0
+        level = 1
+        self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
+        self.attrs = {'axis': axis, 'level': level}
+        out_lod = [[0, 5, 8], [0, 1, 2, 3, 5, 7, 8, 9, 11]]
+        self.outputs = {'Out': (seq_concat(self.inputs, level), out_lod)}
 
 
-class TestConcatOpLevelZero(TestConcatOp):
+class TestSeqConcatOpLevelZeroSequence(TestSeqConcatOp):
     def set_data(self):
         # two level, batch size is 3
         x0 = np.random.random((4, 3, 4)).astype('float32')
-        lod0 = [[0, 2, 4], [0, 1, 2, 3, 4]]
-        x1 = np.random.random((5, 3, 4)).astype('float32')
-        lod1 = [[0, 3, 5], [0, 1, 3, 4, 5]]
+        lod0 = [[0, 1, 2, 3, 4]]
+        x1 = np.random.random((7, 3, 4)).astype('float32')
+        lod1 = [[0, 1, 3, 5, 7]]
         axis = 0
         level = 0
         self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
         self.attrs = {'axis': axis, 'level': level}
-        outs = []
-        for i in range(2):
-            sub_x0 = x0[lod0[level][i]:lod0[level][i + 1], :]
-            sub_x1 = x1[lod1[level][i]:lod1[level][i + 1], :]
-            outs.append(np.concatenate((sub_x0, sub_x1), axis=axis))
-
-        self.outputs = {'Out': np.concatenate(outs, axis=0)}
+        out_lod = [[0, 2, 5, 8, 11]]
+        self.outputs = {'Out': (seq_concat(self.inputs, level), out_lod)}
 
 
 if __name__ == '__main__':
-    sys.exit(0)
     unittest.main()