[cherry-pick] [Dy2Stat] Don't convert to paddle.shape if var_x.shape is not...

[cherry-pick] [Dy2Stat] Don't convert to paddle.shape if var_x.shape is not negetive #29965 (#30235)

* [Cherry-Pick 2.0] [Dy2Stat] Don't convert to paddle.shape if var_x.shape is not negetive (#29965)

1. When x is Variable, call nn.shape(x) only in following cases:
 1）The shape of x is used in control flow condition.
 2）The dim to be used is negetive
2. When x is Variable, but x.shape or x.shape[idx] doesn't contain negetive value, don't convert to paddle.shape()

* [Cherry-Pick 2.0] [Dy2Stat] Use Paddle2.0 api paddle.tensor.array_* (#30156)

python/paddle/fluid/dygraph/dygraph_to_static/convert_operators.py

@@ -262,14 +262,34 @@ def convert_len(var):
         return len(var)
 
 
-def convert_var_shape(x):
+def convert_var_shape(x, idx=None, in_control_flow=False):
     """
     A function representation of the shape of variable.
     """
-    if isinstance(x, Variable):
-        return nn.shape(x)
+
+    def has_negetive(list_shape, idx=None):
+        if idx is not None:
+            return list_shape[idx] < 0
+
+        num_negetive = sum([1 if i < 0 else 0 for i in list_shape])
+        return num_negetive > 0
+
+    # When `x` is Variable, call nn.shape(x) in following cases:
+    #  (1) The shape of `x` is used in control flow condition.
+    #      ```
+    #      if x.shape[0] == 1:
+    #          y = XX
+    #      ```
+    #  (2) The dim to be used is negetive
+    #      ```
+    #      # Assume x.shape=[3, -1] in static mode
+    #      y = paddle.reshape(x, shape=[1, x.shape[1]])
+    #      ```
+    if isinstance(x, Variable) and (in_control_flow or has_negetive(x.shape,
+                                                                    idx)):
+        return nn.shape(x) if idx is None else nn.shape(x)[idx]
     else:
-        return x.shape
+        return x.shape if idx is None else x.shape[idx]
 
 
 def convert_shape_compare(left, *args):

python/paddle/fluid/dygraph/dygraph_to_static/list_transformer.py

@@ -126,7 +126,7 @@ class ListTransformer(gast.NodeTransformer):
             i = "paddle.cast(" \
                 "x=paddle.jit.dy2static.to_static_variable({})," \
                 "dtype='int64')".format(ast_to_source_code(slice_node))
-            assign_code = "{} = fluid.layers.array_write(x={}, i={}, array={})" \
+            assign_code = "{} = paddle.tensor.array_write(x={}, i={}, array={})" \
                 .format(target_name, value_code, i, target_name)
             assign_node = gast.parse(assign_code).body[0]
         return assign_node
@@ -168,7 +168,7 @@ class ListTransformer(gast.NodeTransformer):
         #         return False
         #     if NodeVarType.TENSOR not in var_type_set and NodeVarType.PADDLE_RETURN_TYPES not in var_type_set:
         #         return False
-        # # TODO: Consider that `arg` may be a gast.Call about Paddle Api. eg: list_a.append(fluid.layers.reshape(x))
+        # # TODO: Consider that `arg` may be a gast.Call about Paddle Api. eg: list_a.append(paddle.reshape(x))
         # # else:
         # # return True
         self.list_name_to_updated[value_name.strip()] = True
@@ -187,7 +187,7 @@ class ListTransformer(gast.NodeTransformer):
 
     def _create_tensor_array(self):
         # Although `dtype='float32'`, other types such as `int32` can also be supported
-        func_code = "fluid.layers.create_array(dtype='float32')"
+        func_code = "paddle.tensor.create_array(dtype='float32')"
         func_node = gast.parse(func_code).body[0].value
         return func_node
 
@@ -195,8 +195,8 @@ class ListTransformer(gast.NodeTransformer):
         assert isinstance(node, gast.Call)
         array = astor.to_source(gast.gast_to_ast(node.func.value))
         x = astor.to_source(gast.gast_to_ast(node.args[0]))
-        i = "fluid.layers.array_length({})".format(array)
-        func_code = "fluid.layers.array_write(x={}, i={}, array={})".format(
+        i = "paddle.tensor.array_length({})".format(array)
+        func_code = "paddle.tensor.array_write(x={}, i={}, array={})".format(
             x, i, array)
         return gast.parse(func_code).body[0].value
 

python/paddle/fluid/dygraph/dygraph_to_static/tensor_shape_transformer.py

@@ -24,21 +24,26 @@ from paddle.fluid.dygraph.dygraph_to_static.static_analysis import AstNodeWrappe
 from paddle.fluid.dygraph.dygraph_to_static.static_analysis import StaticAnalysisVisitor
 
 
-def create_convert_shape_node(var_shape_node):
+def create_convert_shape_node(var_shape_node,
+                              slice_node=None,
+                              in_control_flow=False):
     assert isinstance(var_shape_node, (gast.Attribute, gast.Subscript))
 
-    convert_var_shape_func = "paddle.jit.dy2static.convert_var_shape"
-
     if isinstance(var_shape_node, gast.Attribute):
-        api_shape_node = gast.Call(
-            func=gast.parse(convert_var_shape_func).body[0].value,
-            args=[var_shape_node.value],
-            keywords=[])
+        args = [ast_to_source_code(var_shape_node.value).strip()]
+        if slice_node:
+            args.append(ast_to_source_code(slice_node).strip())
+
+        convert_var_shape_func = "paddle.jit.dy2static.convert_var_shape({}, in_control_flow={})".format(
+            ",".join(args), in_control_flow)
+
+        api_shape_node = gast.parse(convert_var_shape_func).body[0].value
         return api_shape_node
 
     if isinstance(var_shape_node, gast.Subscript):
         result_node = copy.deepcopy(var_shape_node)
-        result_node.value = create_convert_shape_node(result_node.value)
+        result_node = create_convert_shape_node(
+            result_node.value, result_node.slice, in_control_flow)
         return result_node
 
 
@@ -72,14 +77,30 @@ class TensorShapeTransformer(gast.NodeTransformer):
         self.generic_visit(node)
         return node
 
+    def visit_Subscript(self, node):
+        value_node = node.value
+        slice_node = node.slice
+        if isinstance(value_node, gast.Name):
+            if self._is_var_shape(value_node) and self._used_by_paddle_api(
+                    value_node):
+                var_shape_node = self.name_to_var_shape[value_node.id]
+                return create_convert_shape_node(var_shape_node, slice_node)
+
+        if isinstance(value_node, gast.Attribute):
+            if self._used_by_paddle_api(value_node) and self._is_var_shape(
+                    value_node):
+                return create_convert_shape_node(value_node, slice_node)
+
+        return node
+
     def visit_Attribute(self, node):
         if self._used_by_paddle_api(node):
-            if self.is_var_shape(node):
+            if self._is_var_shape(node):
                 return create_convert_shape_node(node)
         return node
 
     def visit_Name(self, node):
-        if node.id in self.name_to_var_shape:
+        if self._is_var_shape(node):
             if self._used_by_paddle_api(node):
                 var_shape_node = self.name_to_var_shape[node.id]
                 return create_convert_shape_node(var_shape_node)
@@ -126,7 +147,7 @@ class TensorShapeTransformer(gast.NodeTransformer):
             return False
         args = node.iter.args
         for idx, arg in enumerate(args):
-            if isinstance(arg, gast.Name) and arg.id in self.name_to_var_shape:
+            if isinstance(arg, gast.Name) and self._is_var_shape(arg):
                 args[idx] = create_convert_shape_node(self.name_to_var_shape[
                     arg.id])
 
@@ -136,11 +157,11 @@ class TensorShapeTransformer(gast.NodeTransformer):
         need_transformed = False
         for child_node in gast.walk(cond):
             var_shape_node = None
-            if isinstance(child_node, (gast.Attribute)):
-                if self.is_var_shape(child_node):
+            if isinstance(child_node, (gast.Attribute, gast.Subscript)):
+                if self._is_var_shape(child_node):
                     var_shape_node = child_node
             elif isinstance(child_node, (gast.Name)):
-                if child_node.id in self.name_to_var_shape:
+                if self._is_var_shape(child_node):
                     var_shape_node = self.name_to_var_shape[child_node.id]
 
             if var_shape_node:
@@ -150,7 +171,8 @@ class TensorShapeTransformer(gast.NodeTransformer):
                 for field, value in gast.iter_fields(parent_node):
                     if child_node is value:
                         setattr(parent_node, field,
-                                create_convert_shape_node(var_shape_node))
+                                create_convert_shape_node(var_shape_node, None,
+                                                          True))
                         break
                     # Some child_node may be in a list such as gast.Compare
                     if isinstance(value, list):
@@ -158,7 +180,7 @@ class TensorShapeTransformer(gast.NodeTransformer):
                         for i, v in enumerate(value):
                             if child_node is v:
                                 value[i] = create_convert_shape_node(
-                                    var_shape_node)
+                                    var_shape_node, None, True)
                                 has_converted_shape = True
                                 break
                         if has_converted_shape:
@@ -182,24 +204,30 @@ class TensorShapeTransformer(gast.NodeTransformer):
 
         return False
 
-    def is_var_shape(self, node):
+    def _is_var_shape(self, node):
         """
-        Return True if node is like `x.shape`, return False otherwise.
+        Return True if node is like `x.shape` or `x.shape[0]`, return False otherwise.
         """
-        assert isinstance(node, gast.Attribute)
+        if not isinstance(node, (gast.Name, gast.Attribute, gast.Subscript)):
+            return False
 
+        if isinstance(node, gast.Name) and node.id in self.name_to_var_shape:
+            return True
+
+        if isinstance(node, gast.Attribute):
             if node.attr != 'shape':
                 return False
 
-        try:
-            value_id = node.value.id
-        except AttributeError:
+            if not isinstance(node.value, gast.Name):
                 return False
 
-        if value_id in self.name_to_var_shape:
             return True
 
-        return True
+        if isinstance(node, gast.Subscript):
+            value_node = node.value
+            return self._is_var_shape(value_node)
+
+        return False
 
     def _update_name_to_var_shape(self, node):
         assert isinstance(node, gast.Assign)
@@ -223,7 +251,7 @@ class TensorShapeTransformer(gast.NodeTransformer):
                         self.name_to_var_shape[target_id] = sub_node
                         has_updated = True
                 if isinstance(value_node, gast.Attribute):
-                    if self.is_var_shape(value_node):  # eg: x.shape
+                    if self._is_var_shape(value_node):  # eg: x.shape
                         index_value_node = gast.Constant(value=idx, kind=None)
                         slice_index_node = gast.Index(value=index_value_node)
                         sub_node = gast.Subscript(
@@ -238,17 +266,17 @@ class TensorShapeTransformer(gast.NodeTransformer):
             target_id = ast_to_source_code(target_node).strip()
 
             if isinstance(value_node, gast.Name):
-                if value_node.id in self.name_to_var_shape:
+                if self._is_var_shape(value_node):
                     self.name_to_var_shape[target_id] = self.name_to_var_shape[
                         value_node.id]
                     return True
             if isinstance(value_node, gast.Attribute):
-                if self.is_var_shape(value_node):  # eg: x.shape
+                if self._is_var_shape(value_node):  # eg: x.shape
                     self.name_to_var_shape[target_id] = value_node
                     return True
             if isinstance(value_node, gast.Subscript):
                 if isinstance(value_node.value, gast.Attribute):
-                    if self.is_var_shape(value_node.value):  # eg: x.shape[0]
+                    if self._is_var_shape(value_node.value):  # eg: x.shape[0]
                         self.name_to_var_shape[target_id] = value_node
                         return True
         return False

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

@@ -192,11 +192,16 @@ class TestTensorShapeBasic(unittest.TestCase):
         self.input = numpy.ones(5).astype("int32")
         self.place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
         ) else fluid.CPUPlace()
+        self._set_input_spec()
+        self._set_expected_op_num()
         self.init_test_func()
 
     def init_test_func(self):
         self.dygraph_func = dyfunc_tensor_shape_1
 
+    def _set_input_spec(self):
+        self.input_spec = [paddle.static.InputSpec(shape=[5], dtype="int32")]
+
     def _run(self, to_static):
         with fluid.dygraph.guard():
             if to_static:
@@ -219,6 +224,30 @@ class TestTensorShapeBasic(unittest.TestCase):
             msg='dygraph res is {}\nstatic_res is {}'.format(dygraph_res,
                                                              static_res))
 
+    def _set_expected_op_num(self):
+        self.expected_op_num = 2
+        self.expected_shape_op_num = 0
+        self.expected_slice_op_num = 0
+
+    def _compute_op_num(self, program):
+        self.op_num = sum([len(block.ops) for block in program.blocks])
+        self.shape_op_num = 0
+        self.slice_op_num = 0
+
+        for block in program.blocks:
+            self.shape_op_num += len(
+                [op for op in block.ops if op.type == "shape"])
+            self.slice_op_num += len(
+                [op for op in block.ops if op.type == "slice"])
+
+    def test_op_num(self):
+        static_layer = paddle.jit.to_static(self.dygraph_func, self.input_spec)
+        program = static_layer.main_program
+        self._compute_op_num(program)
+        self.assertEqual(self.op_num, self.expected_op_num)
+        self.assertEqual(self.shape_op_num, self.expected_shape_op_num)
+        self.assertEqual(self.slice_op_num, self.expected_slice_op_num)
+
 
 class TestTensorShapeBasic2(TestTensorShapeBasic):
     def init_test_func(self):
@@ -243,12 +272,14 @@ class TestTensorShapeBasic5(TestTensorShapeBasic):
 class TestTupleShape1(TestTensorShapeBasic):
     def init_test_func(self):
         self.input = numpy.ones((5, 7)).astype("int32")
+        self.input_spec = [paddle.static.InputSpec(shape=[5, 7], dtype="int32")]
         self.dygraph_func = dyfunc_tuple_shape_1
 
 
 class TestTupleShape2(TestTensorShapeBasic):
     def init_test_func(self):
         self.input = numpy.ones((5, 7)).astype("int32")
+        self.input_spec = [paddle.static.InputSpec(shape=[5, 7], dtype="int32")]
         self.dygraph_func = dyfunc_tuple_shape_2
 
 
@@ -257,30 +288,45 @@ class TestTensorShapeInIf1(TestTensorShapeBasic):
     def init_test_func(self):
         self.dygraph_func = dyfunc_with_if_1
 
+    def _set_expected_op_num(self):
+        self.expected_op_num = 26
+        self.expected_shape_op_num = 2
+        self.expected_slice_op_num = 2
+
 
 class TestTensorShapeInIf2(TestTensorShapeBasic):
     def init_test_func(self):
         self.dygraph_func = dyfunc_with_if_2
 
+    def _set_expected_op_num(self):
+        self.expected_op_num = 14
+        self.expected_shape_op_num = 2
+        self.expected_slice_op_num = 1
+
 
 # 3. Tests with control flow for loop
 class TestTensorShapeInFor1(TestTensorShapeBasic):
     def init_test_func(self):
         self.dygraph_func = dyfunc_with_for_1
 
+    def _set_expected_op_num(self):
+        self.expected_op_num = 22
+        self.expected_shape_op_num = 3
+        self.expected_slice_op_num = 3
+
 
-class TestTensorShapeInFor2(TestTensorShapeBasic):
+class TestTensorShapeInFor2(TestTensorShapeInFor1):
     def init_test_func(self):
         self.dygraph_func = dyfunc_with_for_2
 
 
 # 4. Tests with control flow while loop
-class TestTensorShapeInWhile1(TestTensorShapeBasic):
+class TestTensorShapeInWhile1(TestTensorShapeInFor1):
     def init_test_func(self):
         self.dygraph_func = dyfunc_with_while_1
 
 
-class TestTensorShapeInWhile2(TestTensorShapeBasic):
+class TestTensorShapeInWhile2(TestTensorShapeInFor1):
     def init_test_func(self):
         self.dygraph_func = dyfunc_with_while_2
 
@@ -289,11 +335,113 @@ class TestTensorShapeInWhile3(TestTensorShapeBasic):
     def init_test_func(self):
         self.dygraph_func = dyfunc_with_while_3
 
+    def _set_expected_op_num(self):
+        self.expected_op_num = 25
+        self.expected_shape_op_num = 6
+        self.expected_slice_op_num = 3
+
 
 class TestTensorShapeInWhile4(TestTensorShapeBasic):
     def init_test_func(self):
         self.dygraph_func = dyfunc_with_while_4
 
+    def _set_expected_op_num(self):
+        self.expected_op_num = 5
+        self.expected_shape_op_num = 0
+        self.expected_slice_op_num = 0
+
+
+# 5. Test op num for negetive dim
+class TestOpNumBasicWithTensorShape(unittest.TestCase):
+    def setUp(self):
+        self._set_input_spec()
+        self._set_test_func()
+        self._set_expected_op_num()
+
+    def _set_input_spec(self):
+        self.input_spec = [
+            paddle.static.InputSpec(
+                shape=[-1, 5], dtype="int32")
+        ]
+
+    def _set_test_func(self):
+        self.dygraph_func = dyfunc_tensor_shape_1
+
+    def _set_expected_op_num(self):
+        self.expected_op_num = 3
+        self.expected_shape_op_num = 1
+        self.expected_slice_op_num = 0
+
+    def _compute_op_num(self, program):
+        self.op_num = sum([len(block.ops) for block in program.blocks])
+        self.shape_op_num = 0
+        self.slice_op_num = 0
+
+        for block in program.blocks:
+            self.shape_op_num += len(
+                [op for op in block.ops if op.type == "shape"])
+            self.slice_op_num += len(
+                [op for op in block.ops if op.type == "slice"])
+
+    def test_op_num(self):
+        static_layer = paddle.jit.to_static(self.dygraph_func, self.input_spec)
+        program = static_layer.main_program
+
+        self._compute_op_num(program)
+        self.assertEqual(self.op_num, self.expected_op_num)
+        self.assertEqual(self.shape_op_num, self.expected_shape_op_num)
+        self.assertEqual(self.slice_op_num, self.expected_slice_op_num)
+
+
+class TestOpNumBasicWithTensorShape4(TestOpNumBasicWithTensorShape):
+    def _set_test_func(self):
+        self.dygraph_func = dyfunc_tensor_shape_4
+
+    def _set_expected_op_num(self):
+        self.expected_op_num = 6
+        self.expected_shape_op_num = 1
+        self.expected_slice_op_num = 1
+
+
+class TestOpNumWithTensorShapeTuple1(TestOpNumBasicWithTensorShape):
+    def _set_test_func(self):
+        self.dygraph_func = dyfunc_tuple_shape_1
+
+    def _set_expected_op_num(self):
+        self.expected_op_num = 5
+        self.expected_shape_op_num = 1
+        self.expected_slice_op_num = 1
+
+
+class TestOpNumWithTensorShapeInIf1(TestOpNumBasicWithTensorShape):
+    def _set_test_func(self):
+        self.dygraph_func = dyfunc_with_if_1
+
+    def _set_expected_op_num(self):
+        self.expected_op_num = 28
+        self.expected_shape_op_num = 4
+        self.expected_slice_op_num = 2
+
+
+class TestOpNumWithTensorShapeInFor1(TestOpNumBasicWithTensorShape):
+    def _set_test_func(self):
+        self.dygraph_func = dyfunc_with_for_1
+
+    def _set_expected_op_num(self):
+        self.expected_op_num = 22
+        self.expected_shape_op_num = 3
+        self.expected_slice_op_num = 3
+
+
+class TestOpNumWithTensorShapeInWhile1(TestOpNumBasicWithTensorShape):
+    def _set_test_func(self):
+        self.dygraph_func = dyfunc_with_while_1
+
+    def _set_expected_op_num(self):
+        self.expected_op_num = 22
+        self.expected_shape_op_num = 3
+        self.expected_slice_op_num = 3
+
 
 if __name__ == '__main__':
     unittest.main()