[Dy2stat] Support len syntax (#24638)

* [dy2stat] Support len

* add convert_call func

* refine code test=develop

paddle/fluid/operators/shape_op.h

@@ -20,15 +20,23 @@ namespace paddle {
 namespace operators {
 
 using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+using SelectedRows = framework::SelectedRows;
 
 template <typename T>
 class ShapeKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    auto* in_t = ctx.Input<Tensor>("Input");
+    auto* in_var = ctx.InputVar("Input");
+    framework::DDim in_dims;
+    if (in_var->IsType<SelectedRows>()) {
+      in_dims = in_var->Get<SelectedRows>().value().dims();
+    } else {
+      in_dims = in_var->Get<LoDTensor>().dims();
+    }
     auto* out_t = ctx.Output<Tensor>("Out");
+    out_t->Resize({in_dims.size()});
     auto out_data = out_t->mutable_data<int32_t>(platform::CPUPlace());
-    auto in_dims = in_t->dims();
     for (int i = 0; i < in_dims.size(); ++i) {
       out_data[i] = in_dims[i];
     }

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

@@ -32,12 +32,23 @@ class CallTransformer(gast.NodeTransformer):
         self.wrapper_root = wrapper_root
         self.root = wrapper_root.node
 
-    def _is_builtin_call(self, node):
+    def _no_need_convert_call(self, node):
+        """
+        Determines whether a function needs to be transformed by `convert_call`.
+        It doesn't need to be transformed when a function satisfies the following conditions:
+          1. It's a api of paddle
+          2. It's a python builtin function not include `len`
+        """
         assert isinstance(node, gast.Call)
+        if is_paddle_api(node):
+            return True
+
         func_str = ast_to_source_code(node.func).strip()
         try:
-            from paddle.fluid.dygraph.dygraph_to_static.convert_call_func import is_builtin
-            return eval("is_builtin({})".format(func_str))
+            from paddle.fluid.dygraph.dygraph_to_static.convert_call_func import is_builtin_len, is_builtin
+            is_builtin = eval("is_builtin({})".format(func_str))
+            is_builtin_len = eval("is_builtin_len({})".format(func_str))
+            return is_builtin and not is_builtin_len
         except Exception:
             return False
 
@@ -46,10 +57,8 @@ class CallTransformer(gast.NodeTransformer):
 
     def visit_Call(self, node):
         self.generic_visit(node)
-        if is_paddle_api(node):
-            return node
 
-        if self._is_builtin_call(node):
+        if self._no_need_convert_call(node):
             return node
 
         func_str = ast_to_source_code(node.func).strip()

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

@@ -0,0 +1,47 @@
+# Copyright (c) 2020 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
+
+from paddle.fluid import framework
+from paddle.fluid import core
+from paddle.fluid.layers import nn
+from paddle.fluid.layers import control_flow
+
+
+def convert_len(var):
+    """
+    return variable(length) from shape ops based on var.type
+
+    Note: In addition to some ast transformations, some block-related
+          operations are added in `len` transformation, such as appending
+          `shape_op` in var.block.
+    """
+    if isinstance(var, framework.Variable):
+        if var.type in [
+                core.VarDesc.VarType.LOD_TENSOR,
+                core.VarDesc.VarType.SELECTED_ROWS
+        ]:
+            # Note: Length of var may be known ahead of time in dygraph,
+            # but it probably represents batch size which can be variant.
+            # so we return a variable dynamically inferred from var.shape.
+            return nn.shape(var)[0]
+        elif var.type == core.VarDesc.VarType.LOD_TENSOR_ARRAY:
+            return control_flow.array_length(var)
+        else:
+            raise TypeError(
+                'len(var) only supports LoDTensor/LoDTensorArray/SelectedRows, but received %s.'
+                % type(var))
+    else:
+        return len(var)

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

@@ -29,6 +29,7 @@ import six
 
 from paddle.fluid.dygraph.dygraph_to_static import ProgramTranslator
 from paddle.fluid.dygraph.layers import Layer
+from paddle.fluid.dygraph.dygraph_to_static.convert_builtins_func import convert_len
 
 DECORATOR_NAMES = ['declarative', 'dygraph_to_static_func']
 program_translator = ProgramTranslator()
@@ -49,6 +50,12 @@ def is_builtin(func):
         return False
 
 
+def is_builtin_len(func):
+    if isinstance(func, types.BuiltinFunctionType) and func.__name__ == 'len':
+        return True
+    return False
+
+
 def is_paddle_func(func):
     m = inspect.getmodule(func)
     return m is not None and m.__name__.startswith("paddle")
@@ -91,10 +98,10 @@ def convert_call(func):
     func_self = None
     converted_call = None
 
-    if is_builtin(func):
-        return func
+    if is_builtin_len(func):
+        return convert_len
 
-    if is_paddle_func(func):
+    if is_builtin(func) or is_paddle_func(func):
         return func
 
     if inspect.isfunction(func):

python/paddle/fluid/layers/nn.py

@@ -11045,8 +11045,26 @@ def shape(input):
 
     Get the shape of the input.
 
+    .. code-block:: text
+
+        Case1:
+            Given N-D Tensor:
+                input = [ [1, 2, 3, 4], [5, 6, 7, 8] ]
+
+            Then:
+                input.shape = [2, 4]
+
+        Case2:
+            Given SelectedRows:
+                input.rows = [0, 4, 19]
+                input.height = 20
+                input.value = [ [1, 2], [3, 4], [5, 6] ]  # inner tensor
+            Then:
+                input.shape = [3, 2]
+
     Args:
-        input (Variable): The input N-D Tensor. Datatype can be float32, float64, int32, int64.
+        input (Variable): The input can be N-D Tensor or SelectedRows with data type float32, float64, int32, int64.
+                          If input variable is type of SelectedRows, returns the shape of it's inner tensor.
 
     Returns:
         Variable (Tensor): The shape of the input variable.
@@ -11057,7 +11075,7 @@ def shape(input):
             import paddle.fluid as fluid
             import numpy as np
 
-            inputs = fluid.layers.data(name="x", shape=[3, 100, 100], dtype="float32")
+            inputs = fluid.data(name="x", shape=[3, 100, 100], dtype="float32")
             output = fluid.layers.shape(inputs)
 
             exe = fluid.Executor(fluid.CPUPlace())

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

@@ -0,0 +1,122 @@
+# Copyright (c) 2020 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
+import paddle.fluid as fluid
+from paddle.fluid.dygraph import declarative
+from paddle.fluid.dygraph.dygraph_to_static import convert_call
+
+SEED = 2020
+np.random.seed(SEED)
+
+
+def len_with_tensor(x):
+    x = fluid.dygraph.to_variable(x)
+    x_len = len(x)
+    return x_len
+
+
+def len_with_lod_tensor_array(x):
+    x = fluid.dygraph.to_variable(x)
+
+    i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
+    arr = fluid.layers.array_write(x, i=i)
+    arr_len = len(arr)
+
+    return arr_len
+
+
+class TestLen(unittest.TestCase):
+    def setUp(self):
+        self.place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
+        ) else fluid.CPUPlace()
+        self.x_data = np.random.random([10, 16]).astype('float32')
+        self.init_func()
+
+    def init_func(self):
+        self.func = len_with_tensor
+
+    def _run(self, to_static):
+        with fluid.dygraph.guard(self.place):
+            if to_static:
+                out = declarative(self.func)(self.x_data)
+            else:
+                out = self.func(self.x_data)
+
+            if isinstance(out, fluid.core.VarBase):
+                out = out.numpy()
+            return out
+
+    def test_len(self):
+        dygraph_res = self._run(to_static=False)
+        static_res = self._run(to_static=True)
+        self.assertTrue(np.allclose(dygraph_res, static_res))
+
+
+class TestLenWithTensorArray(TestLen):
+    def init_func(self):
+        self.func = len_with_lod_tensor_array
+
+
+# Note: Variable(SelectedRows) is not exposed directly in dygraph.
+# The unittest is used to test coverage by fake transformed code.
+def len_with_selected_rows(place):
+    block = fluid.default_main_program().global_block()
+    # create selected_rows variable
+    var = block.create_var(
+        name="X",
+        dtype="float32",
+        persistable=True,
+        type=fluid.core.VarDesc.VarType.SELECTED_ROWS)
+    # y is Variable(SelectedRows)
+    y = fluid.layers.merge_selected_rows(var)
+    y_len = convert_call(len)(y)
+
+    # z is inner tensor with shape [4, 2]
+    z = fluid.layers.get_tensor_from_selected_rows(y)
+    z_len = convert_call(len)(z)
+
+    # set data for selected_rows
+    x_rows = [0, 2, 2, 4, 19]
+    row_numel = 2
+    np_array = np.ones((len(x_rows), row_numel)).astype("float32")
+
+    x_var = fluid.global_scope().var("X").get_selected_rows()
+    x_var.set_rows(x_rows)
+    x_var.set_height(20)
+    x_tensor = x_var.get_tensor()
+    x_tensor.set(np_array, place)
+
+    exe = fluid.Executor(place=place)
+    result = exe.run(fluid.default_main_program(), fetch_list=[y_len, z_len])
+    return result
+
+
+class TestLenWithSelectedRows(unittest.TestCase):
+    def setUp(self):
+        self.place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
+        ) else fluid.CPUPlace()
+
+    def test_len(self):
+        selected_rows_var_len, var_tensor_len = len_with_selected_rows(
+            self.place)
+        self.assertEqual(selected_rows_var_len, var_tensor_len)
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -17,6 +17,8 @@ from __future__ import print_function
 import unittest
 import numpy as np
 from op_test import OpTest
+from paddle.fluid import core
+from paddle.fluid.op import Operator
 
 
 class TestShapeOp(OpTest):
@@ -45,5 +47,41 @@ class case2(TestShapeOp):
         self.shape = [1, 2, 3]
 
 
+class TestShapeWithSelectedRows(unittest.TestCase):
+    def get_places(self):
+        places = [core.CPUPlace()]
+        if core.is_compiled_with_cuda():
+            places.append(core.CUDAPlace(0))
+        return places
+
+    def check_with_place(self, place):
+        scope = core.Scope()
+        x_rows = [0, 1, 5, 4, 19]
+        height = 20
+        row_numel = 2
+
+        np_array = np.ones((len(x_rows), row_numel)).astype("float32")
+
+        # initialize input variable X
+        x = scope.var('X').get_selected_rows()
+        x.set_rows(x_rows)
+        x.set_height(height)
+        x_tensor = x.get_tensor()
+        x_tensor.set(np_array, place)
+
+        # initialize input variable Out
+        out_shape = scope.var("Out").get_tensor()
+        op = Operator("shape", Input="X", Out="Out")
+
+        op.run(scope, place)
+
+        out_shape = np.array(out_shape).tolist()
+        self.assertListEqual([5, 2], out_shape)
+
+    def test_check_output(self):
+        for place in self.get_places():
+            self.check_with_place(place)
+
+
 if __name__ == '__main__':
     unittest.main()