add batch_size_1_input data function for sequence ops unittests (#22172)

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -207,8 +207,8 @@ set(TEST_OPS_WITH_GC
   test_pad2d_op
   test_scatter_op
   test_sequence_concat
-  test_seq_conv
-  test_seq_pool
+  test_sequence_conv
+  test_sequence_pool
   test_sequence_expand_as
   test_sequence_expand
   test_sequence_pad_op

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

@@ -374,6 +374,24 @@ class OpTest(unittest.TestCase):
         else:
             return fluid.dygraph.base.to_variable(value)
 
+    def get_sequence_batch_size_1_input(self, lod=None, shape=None):
+        """Get LoD input data whose batch size is 1.
+        All sequence related OP unittests should call this function to contain the case of batch size = 1.
+        Args:
+            lod (list[list of int], optional): Length-based LoD, length of lod[0] should be 1. Default: [[13]].
+            shape (list, optional): Shape of input, shape[0] should be equals to lod[0][0]. Default: [13, 23].
+        Returns:
+            tuple (ndarray, lod) : LoD input data whose batch size is 1.
+        """
+        if lod is None:
+            lod = [[13]]
+        if shape is None:
+            shape = [13, 23]
+        assert len(lod[0]) == 1
+        assert lod[0][0] == shape[0]
+        x = np.random.uniform(0.1, 1, shape).astype('float32')
+        return (x, lod)
+
     def append_input_output_for_dygraph(self, op_proto, np_list, is_input,
                                         if_return_inputs_grad_dict, block):
         def create_var(np_value, name, is_input, if_return_inputs_grad_dict):

python/paddle/fluid/tests/unittests/sequence/test_sequence_pool.py

@@ -57,26 +57,30 @@ class TestSeqAvgPool(OpTest):
     def set_lod(self):
         return [[11]]
 
-    def set_data(self):
-        self.op_type = 'sequence_pool'
+    def set_lod_data(self):
         x = np.random.uniform(0.1, 1, [11, 23]).astype('float32')
+        return x
+
+    def set_data(self):
+        x = self.set_lod_data()
         lod = self.set_lod()
         level = len(lod) - 1
         self.inputs = {'X': (x, lod)}
         offset = convert_to_offset(lod)
-        out = np.zeros((len(lod[level]), 23)).astype('float32')
+        out = np.zeros((len(lod[level]), x.shape[1])).astype('float32')
         self.outputs = {'Out': out}
-        return x, offset, out
+        return x, lod, offset, out
 
     def compute(self, x, offset, out):
         self.attrs = {"pad_value": 0.0, 'pooltype': "AVERAGE"}
         compute_seqpool_avg(x, offset, out, self.attrs["pad_value"])
 
     def setUp(self):
-        x, offset, out = self.set_data()
+        self.op_type = 'sequence_pool'
+        x, lod, offset, out = self.set_data()
         self.compute(x, offset, out)
         if len(offset) > 1:
-            self.outputs = {'Out': (out, [self.set_lod()[0]])}
+            self.outputs = {'Out': (out, [lod[0]])}
 
     def test_check_output(self):
         self.check_output(check_dygraph=False)
@@ -90,6 +94,17 @@ class TestSeqAvgPool(OpTest):
         self.check_grad(["X"], "Out", check_dygraph=False)
 
 
+class TestSeqAvgPoolBatch1(TestSeqAvgPool):
+    def set_lod(self):
+        return [[11]]
+
+    def set_lod_data(self):
+        lod = self.set_lod()
+        x, _ = self.get_sequence_batch_size_1_input(
+            lod=lod, shape=[lod[0][0], 23])
+        return x
+
+
 class TestSeqAvgPoolLen0(TestSeqAvgPool):
     def set_lod(self):
         return [[0, 4, 0, 7, 0]]
@@ -135,7 +150,7 @@ class TestSeqMaxPool(TestSeqAvgPool):
 
         out = np.zeros((len(lod[level]), 23)).astype('float32')
         self.outputs = {'Out': out}
-        return x, offset, out
+        return x, lod, offset, out
 
     def compute(self, x, offset, out):
         self.attrs = {"pad_value": 0.5, 'pooltype': "MAX"}
@@ -232,7 +247,7 @@ class TestSeqAvgPool2D(TestSeqAvgPool):
 
         out = np.zeros((len(lod[level]), 3, 17)).astype('float32')
         self.outputs = {'Out': out}
-        return x, offset, out
+        return x, lod, offset, out
 
     def compute(self, x, offset, out):
         self.attrs = {"pad_value": 0.0, 'pooltype': "AVERAGE"}
@@ -321,19 +336,19 @@ class TestSeqMaxPool2D(TestSeqAvgPool2D):
     def set_data(self):
         self.op_type = 'sequence_pool'
         x = np.random.uniform(0.1, 1, [13, 3, 11]).astype('float32')
-        self.lod = self.set_lod()
-        level = len(self.lod) - 1
-        self.inputs = {'X': (x, self.lod)}
-        offset = convert_to_offset(self.lod)
+        lod = self.set_lod()
+        level = len(lod) - 1
+        self.inputs = {'X': (x, lod)}
+        offset = convert_to_offset(lod)
         for i in range(len(offset[level]) - 1):
             l = offset[level][i + 1] - offset[level][i]
             if l == 0:
                 continue
             x[offset[level][i] + np.random.randint(l), :] += 1.0
 
-        out = np.zeros((len(self.lod[level]), 3, 11)).astype('float32')
+        out = np.zeros((len(lod[level]), 3, 11)).astype('float32')
         self.outputs = {'Out': out}
-        return x, offset, out
+        return x, lod, offset, out
 
     def compute(self, x, offset, out):
         self.attrs = {"pad_value": 0.0, 'pooltype': "MAX"}

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

@@ -18,7 +18,7 @@ import unittest
 import numpy as np
 import random
 from op_test import OpTest
-from sequence.test_seq_conv import seqconv
+from sequence.test_sequence_conv import seqconv
 
 
 class TestSeqConvEltAddRelu(OpTest):

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

@@ -18,7 +18,7 @@ import unittest
 import numpy as np
 from op_test import OpTest
 from test_reorder_lod_tensor import convert_to_offset
-from sequence.test_seq_pool import compute_seqpool_sum, compute_seqpool_avg, compute_seqpool_sqrt
+from sequence.test_sequence_pool import compute_seqpool_sum, compute_seqpool_avg, compute_seqpool_sqrt
 
 
 class TestFusionSeqPoolConcatOp(OpTest):

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

@@ -18,7 +18,7 @@ import unittest
 import numpy as np
 from op_test import OpTest
 from test_reorder_lod_tensor import convert_to_offset
-from sequence.test_seq_pool import compute_seqpool_sum, compute_seqpool_avg, compute_seqpool_sqrt
+from sequence.test_sequence_pool import compute_seqpool_sum, compute_seqpool_avg, compute_seqpool_sqrt
 from test_cvm_op import cvm_compute