add seqpool concat unit test

paddle/fluid/operators/fused/fusion_seqpool_concat_op.cc

@@ -29,8 +29,6 @@ void FusionSeqPoolConcatOp::InferShape(
   int axis = ctx->Attrs().Get<int>("axis");
   PADDLE_ENFORCE_EQ(axis, 1,
                     "FusionSeqPoolConcatOp only supports concat axis=1 yet.");
-  PADDLE_ENFORCE_EQ(ctx->Attrs().Get<std::string>("pooltype"), "SUM",
-                    "FusionSeqPoolConcatOp only supports sum pool type yet.");
 
   auto ins_dims = ctx->GetInputsDim("X");
   const size_t n = ins_dims.size();
@@ -74,6 +72,7 @@ class FusionSeqPoolConcatKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto ins = ctx.MultiInput<LoDTensor>("X");
     auto* out = ctx.Output<LoDTensor>("Out");
+    std::string pooltype = ctx.Attr<std::string>("pooltype");
     auto x0_lod = ins[0]->lod();
     auto x0_dims = ins[0]->dims();
     auto y_dims = out->dims();
@@ -92,6 +91,11 @@ class FusionSeqPoolConcatKernel : public framework::OpKernel<T> {
     PADDLE_ENFORCE_EQ(y_dims[1] % w, 0,
                       "The output of dims[1] should be dividable of w");
     jit::seq_pool_attr_t attr(w, jit::SeqPoolType::kSum);
+    if (pooltype == "AVERAGE") {
+      attr.type = jit::SeqPoolType::kAvg;
+    } else if (pooltype == "SQRT") {
+      attr.type = jit::SeqPoolType::kSqrt;
+    }
     auto seqpool =
         jit::Get<jit::kSeqPool, jit::SeqPoolTuples<T>, platform::CPUPlace>(
             attr);

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

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+from op_test import OpTest
+from test_reorder_lod_tensor import convert_to_offset
+from test_seq_pool import compute_seqpool_sum, compute_seqpool_avg, compute_seqpool_sqrt
+
+
+class TestFusionSeqPoolConcatOp(OpTest):
+    def setUp(self):
+        self.w = 11
+        self.lods = [[[2, 3, 5]], [[1, 5, 2]]]
+        self.set_conf()
+        self.set_pooltype()
+        self.op_type = 'fusion_seqpool_concat'
+        self.axis = 1
+        bs = len(self.lods[0][0])
+        inputs = []
+        outs = []
+        i = 0
+        for lod in self.lods:
+            assert bs == len(lod[0]), 'All lod size should be equal'
+            x = np.random.uniform(0.1, 1,
+                                  [sum(lod[0]), self.w]).astype('float32')
+            offset = convert_to_offset(lod)
+            out = np.zeros((bs, self.w)).astype('float32')
+            if self.pooltype == "SUM":
+                compute_seqpool_sum(x, offset, out)
+            elif self.pooltype == "AVERAGE":
+                compute_seqpool_avg(x, offset, out)
+            elif self.pooltype == "SQRT":
+                compute_seqpool_sqrt(x, offset, out)
+            else:
+                raise Exception("Unsupported pool type!")
+            inputs.append(('x_{0}'.format(i), (x, lod)))
+            outs.append(out)
+            i = i + 1
+
+        self.inputs = {'X': inputs}
+        self.outputs = {'Out': np.concatenate(outs, axis=self.axis)}
+        self.attrs = {
+            'pooltype': self.pooltype,
+            'axis': self.axis,
+        }
+
+    def set_pooltype(self):
+        self.pooltype = "SUM"
+
+    def set_conf(self):
+        pass
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestFusionSeqPoolConcatOpCase1(TestFusionSeqPoolConcatOp):
+    def set_conf(self):
+        self.lods = [[[1]]]
+
+
+class TestFusionSeqPoolConcatOpCase2(TestFusionSeqPoolConcatOp):
+    def set_conf(self):
+        self.lods = [[[1]], [[1]], [[1]]]
+
+
+class TestFusionSeqPoolConcatOpCase3(TestFusionSeqPoolConcatOp):
+    def set_conf(self):
+        self.lods = [[[1, 3, 4, 6]]]
+        self.w = 10
+
+
+class TestFusionSeqPoolConcatOpCase4(TestFusionSeqPoolConcatOp):
+    def set_conf(self):
+        self.lods = [[[2, 13, 4]], [[1, 1, 1]], [[5, 3, 1]], [[9, 10, 3]]]
+        self.w = 3
+
+
+## test avg pool and sqrt
+def create_test_avg_sqrt_class(parent):
+    class TestSeqPoolAvgCase(parent):
+        def set_pooltype(self):
+            self.pooltype = "AVERAGE"
+
+    class TestSeqPoolSqrtCase(parent):
+        def set_pooltype(self):
+            self.pooltype = "SQRT"
+
+    cls_name_avg = "{0}_{1}".format(parent.__name__, "avg")
+    cls_name_sqrt = "{0}_{1}".format(parent.__name__, "sqrt")
+    TestSeqPoolAvgCase.__name__ = cls_name_avg
+    TestSeqPoolSqrtCase.__name__ = cls_name_sqrt
+    globals()[cls_name_avg] = TestSeqPoolAvgCase
+    globals()[cls_name_sqrt] = TestSeqPoolSqrtCase
+
+
+create_test_avg_sqrt_class(TestFusionSeqPoolConcatOp)
+create_test_avg_sqrt_class(TestFusionSeqPoolConcatOpCase1)
+create_test_avg_sqrt_class(TestFusionSeqPoolConcatOpCase2)
+create_test_avg_sqrt_class(TestFusionSeqPoolConcatOpCase3)
+create_test_avg_sqrt_class(TestFusionSeqPoolConcatOpCase4)
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -22,6 +22,14 @@ import numpy
 import functools
 
 
+def convert_to_offset(lod):
+    offset = [[0] for i in lod]
+    for i, level in enumerate(lod):
+        for seq_len in level:
+            offset[i].append(offset[i][-1] + seq_len)
+    return offset
+
+
 class TestReorderLoDTensor(unittest.TestCase):
     num_seq = 5
     # [name, shape, lod_level] pair indicating data info of source and target
@@ -91,13 +99,6 @@ class TestReorderLoDTensor(unittest.TestCase):
             self.inputs[desc[0]] = tensor
 
     def reorder(self):
-        def convert_to_offset(lod):
-            offset_lod = [[0] for i in lod]
-            for i, level in enumerate(lod):
-                for seq_len in level:
-                    offset_lod[i].append(offset_lod[i][-1] + seq_len)
-            return offset_lod
-
         level = 0
         # compute the rank_table according to ref_lod
         ref_lod = self.data[self.data_desc[1][0]][1][level]

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

@@ -17,33 +17,43 @@ from __future__ import print_function
 import unittest
 import numpy as np
 from op_test import OpTest
+from test_reorder_lod_tensor import convert_to_offset
 
 
-class TestSeqAvgPool(OpTest):
-    def convert_to_offset(self, lod):
-        offset = [[0] for i in lod]
-        for i, level in enumerate(lod):
-            for seq_len in level:
-                offset[i].append(offset[i][-1] + seq_len)
-        return offset
+def compute_seqpool_sum(x, offset, out):
+    for i in range(len(offset[0]) - 1):
+        sub_x = x[offset[0][i]:offset[0][i + 1], :]
+        out[i] = sub_x.sum(axis=0)
+
+
+def compute_seqpool_avg(x, offset, out):
+    for i in range(len(offset[0]) - 1):
+        sub_x = x[offset[0][i]:offset[0][i + 1], :]
+        out[i] = sub_x.mean(axis=0)
+
 
+def compute_seqpool_sqrt(x, offset, out):
+    for i in range(len(offset[0]) - 1):
+        sub_x = x[offset[0][i]:offset[0][i + 1], :]
+        seq_len = offset[0][i + 1] - offset[0][i]
+        out[i] = sub_x.sum(axis=0) / np.sqrt(seq_len)
+
+
+class TestSeqAvgPool(OpTest):
     def set_data(self):
         self.op_type = 'sequence_pool'
         # one level, batch size is 4
         x = np.random.uniform(0.1, 1, [11, 23]).astype('float32')
         lod = [[11]]
         self.inputs = {'X': (x, lod)}
-        offset = self.convert_to_offset(lod)
-
+        offset = convert_to_offset(lod)
         out = np.zeros((len(lod[0]), 23)).astype('float32')
         self.outputs = {'Out': out}
         return x, offset, out
 
     def compute(self, x, offset, out):
         self.attrs = {'pooltype': "AVERAGE"}
-        for i in range(len(offset[0]) - 1):
-            sub_x = x[offset[0][i]:offset[0][i + 1], :]
-            out[i] = sub_x.mean(axis=0)
+        compute_seqpool_avg(x, offset, out)
 
     def setUp(self):
         x, offset, out = self.set_data()
@@ -62,9 +72,7 @@ class TestSeqAvgPool(OpTest):
 class TestSeqSumPool(TestSeqAvgPool):
     def compute(self, x, offset, out):
         self.attrs = {'pooltype': "SUM"}
-        for i in range(len(offset[0]) - 1):
-            sub_x = x[offset[0][i]:offset[0][i + 1], :]
-            out[i] = sub_x.sum(axis=0)
+        compute_seqpool_sum(x, offset, out)
 
 
 class TestSeqMaxPool(TestSeqAvgPool):
@@ -72,7 +80,7 @@ class TestSeqMaxPool(TestSeqAvgPool):
         self.op_type = 'sequence_pool'
         x = np.random.uniform(0.1, 1, [13, 23]).astype('float32')
         lod = [[13]]
-        offset = self.convert_to_offset(lod)
+        offset = convert_to_offset(lod)
         for i in range(len(offset[0]) - 1):
             l = offset[0][i + 1] - offset[0][i]
             x[offset[0][i] + np.random.randint(l), :] += 2.0
@@ -93,10 +101,7 @@ class TestSeqMaxPool(TestSeqAvgPool):
 class TestSeqSqrtPool(TestSeqAvgPool):
     def compute(self, x, offset, out):
         self.attrs = {'pooltype': "SQRT"}
-        for i in range(len(offset[0]) - 1):
-            sub_x = x[offset[0][i]:offset[0][i + 1], :]
-            seq_len = offset[0][i + 1] - offset[0][i]
-            out[i] = sub_x.sum(axis=0) / np.sqrt(seq_len)
+        compute_seqpool_sqrt(x, offset, out)
 
 
 class TestSeqLastPool(TestSeqAvgPool):
@@ -122,7 +127,7 @@ class TestSeqAvgPool2D(TestSeqAvgPool):
         x = np.random.uniform(0.1, 1, [13, 3, 17]).astype('float32')
         lod = [[4, 1, 3, 5]]
         self.inputs = {'X': (x, lod)}
-        offset = self.convert_to_offset(lod)
+        offset = convert_to_offset(lod)
 
         out = np.zeros((4, 3, 17)).astype('float32')
         self.outputs = {'Out': out}
@@ -167,7 +172,7 @@ class TestSeqMaxPool2D(TestSeqAvgPool2D):
         x = np.random.uniform(0.1, 1, [13, 3, 11]).astype('float32')
         lod = [[4, 1, 3, 5]]
         self.inputs = {'X': (x, lod)}
-        offset = self.convert_to_offset(lod)
+        offset = convert_to_offset(lod)
         for i in range(len(offset[0]) - 1):
             l = offset[0][i + 1] - offset[0][i]
             x[offset[0][i] + np.random.randint(l), :] += 1.0