Support LoDTensorArray in stack/concat api for transformation of list dygraph_to_static (#22987)

* Support that the input(x) of stack api is a LoDTensorArray. test=develop

* Support that the input of concat api is a LoDTensorArray. test=develop

* Add tests to test stack/concat called in dygraph_to_static. test=develop

python/paddle/fluid/layers/nn.py

@@ -28,7 +28,7 @@ from ..framework import Variable, OpProtoHolder, in_dygraph_mode, dygraph_only,
 from .. import dygraph_utils
 from ..param_attr import ParamAttr
 from .layer_function_generator import autodoc, templatedoc, _generate_doc_string_
-from .tensor import concat, assign, fill_constant, zeros
+from .tensor import concat, assign, fill_constant, zeros, tensor_array_to_tensor
 from . import utils
 from .. import unique_name
 from functools import reduce
@@ -9137,10 +9137,24 @@ def stack(x, axis=0):
 
     if not isinstance(x, list) and not isinstance(x, tuple):
         x = [x]
-
     out = helper.create_variable_for_type_inference(x[0].dtype)
+    if not in_dygraph_mode() and \
+            x[0].desc.type() == core.VarDesc.VarType.LOD_TENSOR_ARRAY:
+        assert len(x) == 1, "If the elements of 'x' in stack are Variable(LoDTensorArray), " \
+                            "number of the elements must be 1, but received %s." % len(x)
+        out_index = helper.create_variable_for_type_inference(dtype="int32")
+        helper.append_op(
+            type='tensor_array_to_tensor',
+            inputs={'X': x[0]},
+            outputs={'Out': [out],
+                     'OutIndex': [out_index]},
+            attrs={'axis': axis,
+                   'use_stack': True})
+    else:
         helper.append_op(
-        type='stack', inputs={'X': x}, outputs={'Y': out},
+            type='stack',
+            inputs={'X': x},
+            outputs={'Y': out},
             attrs={'axis': axis})
 
     return out

python/paddle/fluid/layers/tensor.py

@@ -273,6 +273,22 @@ def concat(input, axis=0, name=None):
             x, 'input[' + str(id) + ']',
             ['float16', 'float32', 'float64', 'int32', 'int64'], 'concat')
     check_type(axis, 'axis', (int, Variable), 'concat')
+
+    helper = LayerHelper('concat', **locals())
+    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
+
+    if input[0].desc.type() == core.VarDesc.VarType.LOD_TENSOR_ARRAY:
+        assert len(input) == 1, "If the elements of 'input' in concat are Variable(LoDTensorArray), " \
+                            "number of the elements must be 1, but received %s." % len(x)
+        out_index = helper.create_variable_for_type_inference(dtype="int32")
+        helper.append_op(
+            type='tensor_array_to_tensor',
+            inputs={'X': input[0]},
+            outputs={'Out': [out],
+                     'OutIndex': [out_index]},
+            attrs={'axis': axis,
+                   'use_stack': False})
+    else:
         inputs = {'X': input}
         attrs = {}
         if isinstance(axis, Variable):
@@ -281,8 +297,6 @@ def concat(input, axis=0, name=None):
         else:
             attrs['axis'] = axis
 
-    helper = LayerHelper('concat', **locals())
-    out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
         helper.append_op(
             type='concat', inputs=inputs, outputs={'Out': [out]}, attrs=attrs)
     return out

python/paddle/fluid/tests/unittests/dygraph_to_static/test_list.py

@@ -52,6 +52,16 @@ def test_list_in_for_loop(x, iter_num):
     return a
 
 
+def test_list_in_for_loop_with_concat(x, iter_num):
+    # Note: for_loop can't be transformed before PR22867 merged.
+    x = fluid.dygraph.to_variable(x)
+    a = []
+    for i in range(iter_num):
+        a.append(x)
+    out = fluid.layers.concat(a, axis=0)
+    return out
+
+
 def test_list_in_while_loop(x, iter_num):
     x = fluid.dygraph.to_variable(x)
     iter_num = fluid.layers.fill_constant(
@@ -67,12 +77,28 @@ def test_list_in_while_loop(x, iter_num):
     return a
 
 
+def test_list_in_while_loop_with_stack(x, iter_num):
+    x = fluid.dygraph.to_variable(x)
+    iter_num = fluid.layers.fill_constant(
+        shape=[1], value=iter_num, dtype="int32")
+    a = []
+    i = 0
+    while i < iter_num.numpy()[0]:
+        a.append(x)
+        i += 1
+    out = fluid.layers.stack(a, axis=1)
+    return out
+
+
 class TestListWithoutControlFlow(unittest.TestCase):
     def setUp(self):
         self.input = np.random.random((3)).astype('int32')
-        self.dygraph_func = test_list_without_control_flow
         self.place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
         ) else fluid.CPUPlace()
+        self.init_dygraph_func()
+
+    def init_dygraph_func(self):
+        self.dygraph_func = test_list_without_control_flow
 
     def run_dygraph_mode(self):
         with fluid.dygraph.guard():
@@ -100,11 +126,8 @@ class TestListWithoutControlFlow(unittest.TestCase):
 
 
 class TestListInIf(TestListWithoutControlFlow):
-    def setUp(self):
-        self.input = np.random.random((3)).astype('int32')
+    def init_dygraph_func(self):
         self.dygraph_func = test_list_in_if
-        self.place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
-        ) else fluid.CPUPlace()
 
     def run_static_mode(self):
         main_program = fluid.Program()
@@ -120,13 +143,16 @@ class TestListInIf(TestListWithoutControlFlow):
         return numpy_res[0]
 
 
-class TestListInWhileLoop(unittest.TestCase):
+class TestListInWhileLoop(TestListWithoutControlFlow):
     def setUp(self):
         self.iter_num = 3
         self.input = np.random.random((3)).astype('int32')
-        self.dygraph_func = test_list_in_while_loop
         self.place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
         ) else fluid.CPUPlace()
+        self.init_dygraph_func()
+
+    def init_dygraph_func(self):
+        self.dygraph_func = test_list_in_while_loop
 
     def run_dygraph_mode(self):
         with fluid.dygraph.guard():
@@ -146,56 +172,40 @@ class TestListInWhileLoop(unittest.TestCase):
                         tensor_array,
                         i=fluid.layers.fill_constant(
                             shape=[1], value=i, dtype='int64')))
+
         exe = fluid.Executor(self.place)
         numpy_res = exe.run(main_program, fetch_list=static_outs)
         return numpy_res
 
-    def test_transformed_static_result(self):
-        static_res = self.run_static_mode()
-        dygraph_res = self.run_dygraph_mode()
-        self.assertTrue(
-            np.array_equal(dygraph_res, static_res),
-            msg='dygraph res is {}\nstatic_res is {}'.format(dygraph_res,
-                                                             static_res))
-
 
-class TestListInForLoop(unittest.TestCase):
-    def setUp(self):
-        self.iter_num = 3
-        self.input = np.random.random((3)).astype('int32')
-        self.dygraph_func = test_list_in_for_loop
-        self.place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
-        ) else fluid.CPUPlace()
+class TestListInWhileLoopWithStack(TestListInWhileLoop):
+    def init_dygraph_func(self):
+        self.dygraph_func = test_list_in_while_loop_with_stack
 
     def run_dygraph_mode(self):
         with fluid.dygraph.guard():
             var_res = self.dygraph_func(self.input, self.iter_num)
-            numpy_res = [ele.numpy() for ele in var_res]
+            numpy_res = var_res.numpy()
             return numpy_res
 
     def run_static_mode(self):
         main_program = fluid.Program()
         with fluid.program_guard(main_program):
-            tensor_array = dygraph_to_static_graph(self.dygraph_func)(
-                self.input, self.iter_num)
-            static_outs = []
-            for i in range(self.iter_num):
-                static_outs.append(
-                    fluid.layers.array_read(
-                        tensor_array,
-                        i=fluid.layers.fill_constant(
-                            shape=[1], value=i, dtype='int64')))
+            out_var = dygraph_to_static_graph(self.dygraph_func)(self.input,
+                                                                 self.iter_num)
         exe = fluid.Executor(self.place)
-        numpy_res = exe.run(main_program, fetch_list=static_outs)
-        return numpy_res
+        numpy_res = exe.run(main_program, fetch_list=out_var)
+        return numpy_res[0]
 
-    def test_transformed_static_result(self):
-        static_res = self.run_static_mode()
-        dygraph_res = self.run_dygraph_mode()
-        self.assertTrue(
-            np.array_equal(dygraph_res, static_res),
-            msg='dygraph res is {}\nstatic_res is {}'.format(dygraph_res,
-                                                             static_res))
+
+class TestListInForLoop(TestListInWhileLoop):
+    def init_dygraph_func(self):
+        self.dygraph_func = test_list_in_for_loop
+
+
+class TestListInForLoopWithConcat(TestListInWhileLoopWithStack):
+    def init_dygraph_func(self):
+        self.dygraph_func = test_list_in_for_loop_with_concat
 
 
 if __name__ == '__main__':

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

@@ -211,5 +211,42 @@ class TestConcatAPI(unittest.TestCase):
         assert np.array_equal(res_3, np.concatenate((input_2, input_3), axis=1))
 
 
+class TestConcatAPIWithLoDTensorArray(unittest.TestCase):
+    """
+    Test concat api when the input(x) is a LoDTensorArray.
+    """
+
+    def setUp(self):
+        self.axis = 1
+        self.iter_num = 3
+        self.input_shape = [2, 3]
+        self.x = np.random.random(self.input_shape).astype("float32")
+        self.place = fluid.CUDAPlace(0) \
+            if fluid.is_compiled_with_cuda() else fluid.CPUPlace()
+        self.set_program()
+
+    def set_program(self):
+        self.program = fluid.Program()
+        with fluid.program_guard(self.program):
+            input = fluid.layers.assign(self.x)
+            tensor_array = fluid.layers.create_array(dtype='float32')
+            zero = fluid.layers.fill_constant(shape=[1], value=0, dtype="int64")
+
+            for i in range(self.iter_num):
+                fluid.layers.array_write(input, zero + i, tensor_array)
+
+            self.out_var = fluid.layers.concat(tensor_array, axis=self.axis)
+
+    def test_case(self):
+        self.assertTrue(self.out_var.shape[self.axis] == -1)
+        exe = fluid.Executor(self.place)
+        res = exe.run(self.program, fetch_list=self.out_var)
+        self.assertTrue(
+            np.array_equal(
+                res[0],
+                np.concatenate(
+                    [self.x] * self.iter_num, axis=self.axis)))
+
+
 if __name__ == '__main__':
     unittest.main()

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

@@ -12,9 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from op_test import OpTest
 import numpy as np
 import unittest
+import paddle.fluid as fluid
+from op_test import OpTest
 
 
 class TestStackOpBase(OpTest):
@@ -88,5 +89,41 @@ class TestStackOp6(TestStackOpBase):
         self.axis = 3
 
 
+class TestStackAPIWithLoDTensorArray(unittest.TestCase):
+    """
+    Test stack api when the input(x) is a LoDTensorArray.
+    """
+
+    def setUp(self):
+        self.axis = 1
+        self.iter_num = 3
+        self.input_shape = [2, 3]
+        self.x = np.random.random(self.input_shape).astype("float32")
+        self.place = fluid.CUDAPlace(0) \
+            if fluid.is_compiled_with_cuda() else fluid.CPUPlace()
+        self.set_program()
+
+    def set_program(self):
+        self.program = fluid.Program()
+        with fluid.program_guard(self.program):
+            input = fluid.layers.assign(self.x)
+            tensor_array = fluid.layers.create_array(dtype='float32')
+            zero = fluid.layers.fill_constant(shape=[1], value=0, dtype="int64")
+
+            for i in range(self.iter_num):
+                fluid.layers.array_write(input, zero + i, tensor_array)
+
+            self.out_var = fluid.layers.stack(tensor_array, axis=self.axis)
+
+    def test_case(self):
+        self.assertTrue(self.out_var.shape[self.axis] == -1)
+        exe = fluid.Executor(self.place)
+        res = exe.run(self.program, fetch_list=self.out_var)
+        self.assertTrue(
+            np.array_equal(
+                res[0], np.stack(
+                    [self.x] * self.iter_num, axis=self.axis)))
+
+
 if __name__ == '__main__':
     unittest.main()