diff --git a/python/paddle/fluid/dygraph/dygraph_to_static/loop_transformer.py b/python/paddle/fluid/dygraph/dygraph_to_static/loop_transformer.py index 140c57f710a3d21df40ab6bf37fbfec68f249a1b..979808267c77dfa8cfa949fce80917b0583579a3 100644 --- a/python/paddle/fluid/dygraph/dygraph_to_static/loop_transformer.py +++ b/python/paddle/fluid/dygraph/dygraph_to_static/loop_transformer.py @@ -39,8 +39,35 @@ FOR_CONDITION_PREFIX = 'for_loop_condition' FOR_BODY_PREFIX = 'for_loop_body' GENERATE_VARIABLE_PREFIX = 'generate_variable' +ATTRIBUTE_VARIABLE_PREFIX = '__attribute_variable' -def create_while_node(condition_name, body_name, loop_var_names): + +def create_while_nodes(condition_name, body_name, loop_var_names): + """ + Returns a list of gast.Node which represents the calling of Paddle + controlflow while_loop. + + Usually, the list just contain 1 statement such as: + + [a, b, c] = paddle.jit.dy2static.convert_while_loop( + condition_name, body_name, [a, b, c]) + + where a, b, c are in loop_var_names. + + However, if loop_var_names contains attribute such as foo.x, we cannot + assign the attribute as output of convert_while_loop because Python + property is a kind of read-only attribute. To handle the case, we replace + the attributes which are output of convert_while_loop with generated + variables, then if we know the attribute is not read-only at runtime, we + assign the attribute. The created statements are like: + + [a, b, __attribute_variable_1] = paddle.jit.dy2static.convert_while_loop( + condition_name, body_name, [a, b, foo.x]) + if not isinstance(getattr(type(foo), x, None), property): foo.x = __attribute_variable_1 + + The number of above statements is not only 1, that's why the return type is + a list of gast.Node. + """ # NOTE(liym27): # It's better to parse the source code into an AST node than to customize an AST node # including child nodes, because it is easy to mistake the ast node type when customizing the node. @@ -48,14 +75,37 @@ def create_while_node(condition_name, body_name, loop_var_names): # For example: loop_var_names = [a, b, foo.x], the type of `a` or `b` is gast.Name, # but the type of `foo.x` gast.Attribute. + unique_name_to_origin = {} + # We have to make loop_var_names and assign_loop_var_names with same order + # set doesn't have order so we convert it to list + loop_var_names = list(loop_var_names) + assign_loop_var_names = [] + for name in (loop_var_names): + if "." in name: + # name is an attribute variable such as foo.x + tmp_attr_name = unique_name.generate(ATTRIBUTE_VARIABLE_PREFIX) + unique_name_to_origin[tmp_attr_name] = name + assign_loop_var_names.append(tmp_attr_name) + else: + assign_loop_var_names.append(name) + while_func_name = "paddle.jit.dy2static.convert_while_loop" while_node_str = "[{}] = {}({}, {}, [{}])".format( - ",".join(loop_var_names), while_func_name, condition_name, body_name, - ",".join(loop_var_names)) - + ",".join(assign_loop_var_names), while_func_name, condition_name, + body_name, ",".join(loop_var_names)) while_node = gast.parse(while_node_str).body[0] - return while_node + ret = [while_node] + for tmp_attr_name in unique_name_to_origin: + origin_attr_var = unique_name_to_origin[tmp_attr_name] + dot_pos = origin_attr_var.rindex(".") + obj_name = origin_attr_var[0:dot_pos] + attr_name = origin_attr_var[dot_pos + 1:] + assign_if_not_prop_str = "if not isinstance(getattr(type({}), '{}', None), property): {} = {}".format( + obj_name, attr_name, origin_attr_var, tmp_attr_name) + assign_if_not_prop_node = gast.parse(assign_if_not_prop_str).body[0] + ret.append(assign_if_not_prop_node) + return ret class NameVisitor(gast.NodeVisitor): @@ -573,9 +623,9 @@ class LoopTransformer(gast.NodeTransformer): new_stmts.append(body_func_node) # 7. create & append while loop node - while_loop_node = create_while_node(condition_func_node.name, - body_func_node.name, loop_var_names) - new_stmts.append(while_loop_node) + while_loop_nodes = create_while_nodes( + condition_func_node.name, body_func_node.name, loop_var_names) + new_stmts.extend(while_loop_nodes) return new_stmts @@ -655,7 +705,7 @@ class LoopTransformer(gast.NodeTransformer): name, unique_name.generate(GENERATE_VARIABLE_PREFIX)) new_stmts.append(body_func_node) - while_loop_node = create_while_node(condition_func_node.name, - body_func_node.name, loop_var_names) - new_stmts.append(while_loop_node) + while_loop_nodes = create_while_nodes( + condition_func_node.name, body_func_node.name, loop_var_names) + new_stmts.extend(while_loop_nodes) return new_stmts diff --git a/python/paddle/fluid/dygraph/dygraph_to_static/tensor_shape_transformer.py b/python/paddle/fluid/dygraph/dygraph_to_static/tensor_shape_transformer.py index 7cbe86b60c81e3b023e44fc4ed297b1f2e7d3078..2a0b2cadb5979673a736f153b8c88e18b397e8d1 100644 --- a/python/paddle/fluid/dygraph/dygraph_to_static/tensor_shape_transformer.py +++ b/python/paddle/fluid/dygraph/dygraph_to_static/tensor_shape_transformer.py @@ -340,8 +340,8 @@ class TensorShapeTransformer(gast.NodeTransformer): static_shape_value_node = copy.deepcopy(value_node) # x.shape becomes convert_var_shape_simple(x) - ShapeAttributeTransformer().visit( - static_shape_value_node) + static_shape_value_node = ShapeAttributeTransformer( + ).visit(static_shape_value_node) index_value_node = gast.Constant(value=idx, kind=None) slice_index_node = gast.Index(value=index_value_node) sub_node = gast.Subscript( @@ -382,7 +382,8 @@ class TensorShapeTransformer(gast.NodeTransformer): 0].value static_shape_value_node = copy.deepcopy(value_node) # x.shape becomes convert_var_shape_simple(x) - ShapeAttributeTransformer().visit(static_shape_value_node) + static_shape_value_node = ShapeAttributeTransformer().visit( + static_shape_value_node) update_static_shape_var_node = [ gast.Assign( targets=[static_shape_var_node], diff --git a/python/paddle/fluid/layers/control_flow.py b/python/paddle/fluid/layers/control_flow.py index b735ae247f94d2e9a69ce98046a3a96b1dd5f2c6..3a06b84d111c4f332fdaa491bb7bc60e1e048d08 100755 --- a/python/paddle/fluid/layers/control_flow.py +++ b/python/paddle/fluid/layers/control_flow.py @@ -1098,6 +1098,10 @@ def assign_skip_lod_tensor_array(input, output): """ Assign input to output, but skip the process of copying LoDTensorArray unless it's created in while_block. """ + if not isinstance(input, Variable) and not isinstance(input, core.VarBase): + output = input + return + if input.type == core.VarDesc.VarType.LOD_TENSOR_ARRAY: main_program = input.block.program parent_block = main_program.block(main_program.current_block() diff --git a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_loop.py b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_loop.py index bc235ca8606499aa02743f4b627d478f007f4ed8..fe86d5d636811e83f7768e5414930e82da46ca1f 100644 --- a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_loop.py +++ b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_loop.py @@ -17,6 +17,7 @@ from __future__ import print_function import gast import inspect import numpy as np +import paddle import paddle.fluid as fluid import unittest @@ -157,6 +158,16 @@ def while_loop_class_var(x): return foo.c +def loop_var_contains_property(x): + a = paddle.zeros(shape=[1], dtype='float32') + i = paddle.to_tensor(x) + s = i.shape + while i < 10 and s[0] >= 1: + a += i.shape[0] + i += 1 + return a + + def for_loop_class_var(max_len): class Foo(object): def __init__(self): @@ -240,9 +251,7 @@ class TestNameVisitor(unittest.TestCase): name_visitor = NameVisitor(gast_root) self.loop_var_names = [ - set(["j", "two"]), - set(["i", "three", "b"]), - set(["i", "j"]), + set(["j", "two"]), set(["i", "three", "b"]), set(["i", "j"]) ] self.create_var_names = [set(), set(["b"]), set()] @@ -326,6 +335,11 @@ class TestWhileLoopClassVar(TestTransformWhileLoop): self.dyfunc = while_loop_class_var +class TestLoopVarContainsProperty(TestTransformWhileLoop): + def _init_dyfunc(self): + self.dyfunc = loop_var_contains_property + + class TestTransformForLoop(unittest.TestCase): def setUp(self): self.place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda( diff --git a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_tensor_shape.py b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_tensor_shape.py index b84a13be9b3213d18ce8087db5bb0454aff3c1b1..be571aaf2b75dd2a569b44b23e928906b2eaf196 100644 --- a/python/paddle/fluid/tests/unittests/dygraph_to_static/test_tensor_shape.py +++ b/python/paddle/fluid/tests/unittests/dygraph_to_static/test_tensor_shape.py @@ -144,11 +144,6 @@ def dyfunc_with_for_2(x): def dyfunc_with_for_3(x): - # TODO(liym27): - # It will fail to run because `for i in range(len(x.shape))` will be transformed into Paddle while_loop. - # Here the python list x.shape will be added to loop_vars. However, loop_vars doesn't support python list. - # And the condition of `for i in range(len(x.shape))` only uses the length of x.shape, so it doesn't have to be transformed into Paddle while_loop. - # After the AST tranformation of for loop is improved, add TestTensorShapeInFor3. x = fluid.dygraph.to_variable(x) res = fluid.layers.fill_constant(value=0, shape=[1], dtype="int32") # `len(x.shape)` is not transformed. @@ -282,6 +277,11 @@ class TestTensorShapeBasic2(TestTensorShapeBasic): def init_test_func(self): self.dygraph_func = dyfunc_tensor_shape_2 + def _set_expected_op_num(self): + self.expected_op_num = 3 + self.expected_shape_op_num = 1 + self.expected_slice_op_num = 0 + class TestTensorShapeBasic3(TestTensorShapeBasic): def init_test_func(self): @@ -319,6 +319,11 @@ class TestTupleShape1(TestTensorShapeBasic): self.input_spec = [paddle.static.InputSpec(shape=[5, 7], dtype="int32")] self.dygraph_func = dyfunc_tuple_shape_1 + def _set_expected_op_num(self): + self.expected_op_num = 6 + self.expected_shape_op_num = 2 + self.expected_slice_op_num = 2 + class TestTupleShape2(TestTensorShapeBasic): def init_test_func(self): @@ -326,6 +331,11 @@ class TestTupleShape2(TestTensorShapeBasic): self.input_spec = [paddle.static.InputSpec(shape=[5, 7], dtype="int32")] self.dygraph_func = dyfunc_tuple_shape_2 + def _set_expected_op_num(self): + self.expected_op_num = 5 + self.expected_shape_op_num = 1 + self.expected_slice_op_num = 2 + class TestPaddleShapeApi(TestTensorShapeBasic): def init_test_func(self): @@ -381,6 +391,16 @@ class TestTensorShapeInFor2(TestTensorShapeInFor1): self.expected_slice_op_num = 1 +class TestTensorShapeInFor3(TestTensorShapeInFor1): + def init_test_func(self): + self.dygraph_func = dyfunc_with_for_3 + + def _set_expected_op_num(self): + self.expected_op_num = 25 + self.expected_shape_op_num = 6 + self.expected_slice_op_num = 3 + + # 4. Tests with control flow while loop class TestTensorShapeInWhile1(TestTensorShapeInFor1): def init_test_func(self): @@ -402,8 +422,8 @@ class TestTensorShapeInWhile3(TestTensorShapeBasic): self.dygraph_func = dyfunc_with_while_3 def _set_expected_op_num(self): - self.expected_op_num = 2 - self.expected_shape_op_num = 0 + self.expected_op_num = 3 + self.expected_shape_op_num = 1 self.expected_slice_op_num = 0 @@ -474,9 +494,9 @@ class TestOpNumWithTensorShapeTuple1(TestOpNumBasicWithTensorShape): self.dygraph_func = dyfunc_tuple_shape_1 def _set_expected_op_num(self): - self.expected_op_num = 2 - self.expected_shape_op_num = 0 - self.expected_slice_op_num = 0 + self.expected_op_num = 7 + self.expected_shape_op_num = 2 + self.expected_slice_op_num = 2 class TestOpNumWithTensorShapeInIf1(TestOpNumBasicWithTensorShape): @@ -516,9 +536,9 @@ class TestChangeShapeAfterAssign(TestTensorShapeBasic): self.dygraph_func = dyfunc_change_shape_after_assign def _set_expected_op_num(self): - self.expected_op_num = 3 - self.expected_shape_op_num = 0 - self.expected_slice_op_num = 0 + self.expected_op_num = 7 + self.expected_shape_op_num = 2 + self.expected_slice_op_num = 2 if __name__ == '__main__':