Merge pull request #12097 from reyoung/feature/hide_api_cont

Hide internal API of LoDTensors, Clipping, etc.

Author: reyoung

paddle/fluid/pybind/pybind.cc

@@ -87,37 +87,37 @@ PYBIND11_PLUGIN(core) {
   py::class_<Tensor>(m, "Tensor", py::buffer_protocol())
       .def_buffer(
           [](Tensor &self) -> py::buffer_info { return CastToPyBuffer(self); })
-      .def("get_dims",
+      .def("_get_dims",
            [](const Tensor &self) { return vectorize(self.dims()); })
-      .def("set_dims",
+      .def("_set_dims",
            [](Tensor &self, const std::vector<int64_t> &dim) {
              self.Resize(make_ddim(dim));
            })
-      .def("set_layout",
+      .def("_set_layout",
            [](Tensor &self, const std::string &layout) {
              self.set_layout(StringToDataLayout(layout));
            })
-      .def("alloc_float",
+      .def("_alloc_float",
            [](Tensor &self, paddle::platform::CUDAPlace &place) {
              self.mutable_data<float>(place);
            })
-      .def("alloc_float",
+      .def("_alloc_float",
            [](Tensor &self, paddle::platform::CPUPlace &place) {
              self.mutable_data<float>(place);
            })
-      .def("alloc_int",
+      .def("_alloc_int",
            [](Tensor &self, paddle::platform::CPUPlace &place) {
              self.mutable_data<int>(place);
            })
-      .def("alloc_int",
+      .def("_alloc_int",
            [](Tensor &self, paddle::platform::CUDAPlace &place) {
              self.mutable_data<int>(place);
            })
-      .def("alloc_int",
+      .def("_alloc_int",
            [](Tensor &self, paddle::platform::CUDAPinnedPlace &place) {
              self.mutable_data<int>(place);
            })
-      .def("alloc_float",
+      .def("_alloc_float",
            [](Tensor &self, paddle::platform::CUDAPinnedPlace &place) {
              self.mutable_data<float>(place);
            })
@@ -145,11 +145,11 @@ PYBIND11_PLUGIN(core) {
       .def("set", PyCUDAPinnedTensorSetFromArray<uint8_t>)
 #endif
       .def("shape", [](Tensor &self) { return vectorize(self.dims()); })
-      .def("set_float_element", TensorSetElement<float>)
-      .def("get_float_element", TensorGetElement<float>)
-      .def("set_double_element", TensorSetElement<double>)
-      .def("get_double_element", TensorGetElement<double>)
-      .def("dtype", [](Tensor &self) { return ToDataType(self.type()); });
+      .def("_set_float_element", TensorSetElement<float>)
+      .def("_get_float_element", TensorGetElement<float>)
+      .def("_set_double_element", TensorSetElement<double>)
+      .def("_get_double_element", TensorGetElement<double>)
+      .def("_dtype", [](Tensor &self) { return ToDataType(self.type()); });
 
   py::class_<LoDTensor, Tensor>(m, "LoDTensor")
       .def_buffer(

python/paddle/fluid/clip.py

@@ -31,7 +31,7 @@ class BaseErrorClipAttr(object):
     def __str__(self):
         raise NotImplementedError()
 
-    def append_clip_op(self, block, grad_name):
+    def _append_clip_op(self, block, grad_name):
         raise NotImplementedError()
 
 
@@ -67,7 +67,7 @@ def __init__(self, max, min=None):
     def __str__(self):
         return "ByValue, min=%f, max=%f" % (self.min, self.max)
 
-    def append_clip_op(self, block, grad_name):
+    def _append_clip_op(self, block, grad_name):
         clip_op_desc = block.desc.append_op()
         clip_op_desc.set_type("clip")
         clip_op_desc.set_input("X", [grad_name])
@@ -90,28 +90,28 @@ def error_clip_callback(block, context):
                 "Variable's error_clip should be an instance of BaseErrorClipAttr or None."
             )
         if error_clip is not None:
-            error_clip.append_clip_op(block, grad_n)
+            error_clip._append_clip_op(block, grad_n)
 
 
 class BaseGradientClipAttr(object):
     def __str__(self):
         raise NotImplementedError()
 
-    def process_context(self, context, param, grad):
+    def _process_context(self, context, param, grad):
         raise NotImplementedError()
 
-    def create_operators(self, param, grad):
+    def _create_operators(self, param, grad):
         raise NotImplementedError()
 
 
 class NullGradientClipAttr(BaseGradientClipAttr):
     def __str__(self):
         return "Null"
 
-    def process_context(self, context, param, grad):
+    def _process_context(self, context, param, grad):
         pass
 
-    def create_operators(self, param, grad):
+    def _create_operators(self, param, grad):
         return param, grad
 
 
@@ -153,10 +153,10 @@ def __init__(self, max, min=None):
     def __str__(self):
         return "ByValue, min=%f, max=%f" % (self.min, self.max)
 
-    def process_context(self, context, param, grad):
+    def _process_context(self, context, param, grad):
         pass
 
-    def create_operators(self, param, grad):
+    def _create_operators(self, param, grad):
         new_grad = layers.clip(x=grad, min=self.min, max=self.max)
         return param, new_grad
 
@@ -199,10 +199,10 @@ def __init__(self, clip_norm):
     def __str__(self):
         return "ByNorm, clip_norm=%f" % self.clip_norm
 
-    def process_context(self, context, param, grad):
+    def _process_context(self, context, param, grad):
         pass
 
-    def create_operators(self, param, grad):
+    def _create_operators(self, param, grad):
         new_grad = layers.clip_by_norm(x=grad, max_norm=self.clip_norm)
         return param, new_grad
 
@@ -257,7 +257,7 @@ def __str__(self):
         return "ByGlobalNorm, group_name=%s, clip_norm=%f" % (self.group_name,
                                                               self.clip_norm)
 
-    def process_context(self, context, param, grad):
+    def _process_context(self, context, param, grad):
         if self.group_name not in context:
             context[self.group_name] = []
             context[self.group_name + "_clip_value"] = self.clip_norm
@@ -274,7 +274,7 @@ def process_context(self, context, param, grad):
 
         self.context = context
 
-    def create_operators(self, param, grad):
+    def _create_operators(self, param, grad):
         group_scale_name = self.group_name + "_scale"
         if group_scale_name not in self.context:
             group_norm_var = layers.sums(input=self.context[self.group_name])
@@ -336,12 +336,12 @@ def append_gradient_clip_ops(param_grad):
                     "clip attribute should be an instance of BaseGradientClipAttr"
                 )
 
-            clip_attr.process_context(context=context, param=p, grad=g)
+            clip_attr._process_context(context=context, param=p, grad=g)
 
     res = []
     for p, g in param_grad:
         with p.block.program.optimized_guard(p):
-            res.append(clip_attr.create_operators(param=p, grad=g))
+            res.append(clip_attr._create_operators(param=p, grad=g))
 
     return res
 

python/paddle/fluid/layer_helper.py

@@ -68,11 +68,11 @@ def input(self, input_param_name='input'):
 
     @property
     def param_attr(self):
-        return ParamAttr.to_attr(self.kwargs.get('param_attr', None))
+        return ParamAttr._to_attr(self.kwargs.get('param_attr', None))
 
     @property
     def bias_attr(self):
-        return ParamAttr.to_attr(self.kwargs.get('bias_attr', None))
+        return ParamAttr._to_attr(self.kwargs.get('bias_attr', None))
 
     def multiple_param_attr(self, length):
         param_attr = self.param_attr
@@ -262,11 +262,11 @@ def __weight_normalize(g, v, dim):
         g_param = self.startup_program.global_block().create_parameter(
             dtype=dtype,
             shape=g_param_shape,
-            **g_param_attr.to_kwargs(with_initializer=False))
+            **g_param_attr._to_kwargs(with_initializer=False))
         v_param = self.startup_program.global_block().create_parameter(
             dtype=dtype,
             shape=v_param_shape,
-            **v_param_attr.to_kwargs(with_initializer=True))
+            **v_param_attr._to_kwargs(with_initializer=True))
         __norm_except_dim(
             x=v_param,
             out=g_param,
@@ -275,9 +275,9 @@ def __weight_normalize(g, v, dim):
 
         # Add weight normalization to main_program
         g_param = self.main_program.global_block().create_parameter(
-            dtype=dtype, shape=g_param_shape, **g_param_attr.to_kwargs())
+            dtype=dtype, shape=g_param_shape, **g_param_attr._to_kwargs())
         v_param = self.main_program.global_block().create_parameter(
-            dtype=dtype, shape=v_param_shape, **v_param_attr.to_kwargs())
+            dtype=dtype, shape=v_param_shape, **v_param_attr._to_kwargs())
         w_param = __weight_normalize(g_param, v_param, dim=attr.dim)
         return w_param
 
@@ -296,11 +296,11 @@ def create_parameter(self,
 
         if default_initializer is None and attr.initializer is None:
             if is_bias:
-                attr.set_default_bias_initializer()
+                attr._set_default_bias_initializer()
             else:
-                attr.set_default_param_initializer()
+                attr._set_default_param_initializer()
         else:
-            attr.set_default_initializer(default_initializer)
+            attr._set_default_initializer(default_initializer)
 
         # If weight normalization is set, insert extra parameters and ops.
         # Refer to https://arxiv.org/pdf/1602.07868.pdf
@@ -310,9 +310,9 @@ def create_parameter(self,
             return param
 
         self.startup_program.global_block().create_parameter(
-            dtype=dtype, shape=shape, **attr.to_kwargs(with_initializer=True))
+            dtype=dtype, shape=shape, **attr._to_kwargs(with_initializer=True))
         return self.main_program.global_block().create_parameter(
-            dtype=dtype, shape=shape, **attr.to_kwargs())
+            dtype=dtype, shape=shape, **attr._to_kwargs())
 
     def get_parameter(self, name):
         param = self.main_program.global_block().var(name)

python/paddle/fluid/param_attr.py

@@ -67,7 +67,7 @@ def __init__(self,
         self.gradient_clip = gradient_clip
         self.model_average = do_model_average
 
-    def set_default_initializer(self, initializer):
+    def _set_default_initializer(self, initializer):
         """
         Set the default initializer, the initializer should be Constant,
         Uniform, Normal, Xavier, MSRA.
@@ -88,7 +88,7 @@ def set_default_initializer(self, initializer):
 
         self.initializer = initializer
 
-    def set_default_param_initializer(self):
+    def _set_default_param_initializer(self):
         """
         Set the default initializer for the parameter with Xavier.
 
@@ -98,9 +98,9 @@ def set_default_param_initializer(self):
         Returns:
             None.
         """
-        self.set_default_initializer(Xavier())
+        self._set_default_initializer(Xavier())
 
-    def set_default_bias_initializer(self):
+    def _set_default_bias_initializer(self):
         """
         Set the default initializer for the bias with Constant(0.0).
 
@@ -110,10 +110,10 @@ def set_default_bias_initializer(self):
         Returns:
             None.
         """
-        self.set_default_initializer(Constant(0.0))
+        self._set_default_initializer(Constant(0.0))
 
     @staticmethod
-    def to_attr(arg):
+    def _to_attr(arg):
         """
         Create ParamAttr[s].
 
@@ -131,7 +131,7 @@ def to_attr(arg):
         if arg is None:
             return ParamAttr()
         elif isinstance(arg, list) or isinstance(arg, tuple):
-            return [ParamAttr.to_attr(a) for a in arg]
+            return [ParamAttr._to_attr(a) for a in arg]
         elif isinstance(arg, ParamAttr):
             return arg
         elif isinstance(arg, str) or isinstance(arg, unicode):
@@ -141,11 +141,11 @@ def to_attr(arg):
         elif isinstance(arg, WeightDecayRegularizer):
             return ParamAttr(regularizer=arg)
         elif isinstance(arg, bool):
-            return ParamAttr.to_attr(None) if arg else False
+            return ParamAttr._to_attr(None) if arg else False
         else:
             raise TypeError("{0} cast to ParamAttr".format(type(arg)))
 
-    def to_kwargs(self, with_initializer=False):
+    def _to_kwargs(self, with_initializer=False):
         """
         Returns the attributes of this parameter.
 

python/paddle/fluid/regularizer.py

@@ -15,10 +15,7 @@
 import framework
 from . import core
 
-__all__ = [
-    'append_regularization_ops', 'L1Decay', 'L2Decay', 'L1DecayRegularizer',
-    'L2DecayRegularizer'
-]
+__all__ = ['L1Decay', 'L2Decay', 'L1DecayRegularizer', 'L2DecayRegularizer']
 
 
 def append_regularization_ops(parameters_and_grads, regularization=None):

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

@@ -60,8 +60,8 @@ def get_output():
         return np.array(sum).mean()
 
     tensor_to_check = scope.find_var(input_to_check).get_tensor()
-    tensor_size = product(tensor_to_check.get_dims())
-    tensor_to_check_dtype = tensor_to_check.dtype()
+    tensor_size = product(tensor_to_check.shape())
+    tensor_to_check_dtype = tensor_to_check._dtype()
     if tensor_to_check_dtype == core.VarDesc.VarType.FP32:
         tensor_to_check_dtype = np.float32
     elif tensor_to_check_dtype == core.VarDesc.VarType.FP64:
@@ -74,15 +74,15 @@ def get_output():
 
     def __get_elem__(tensor, i):
         if tensor_to_check_dtype == np.float32:
-            return tensor.get_float_element(i)
+            return tensor._get_float_element(i)
         else:
-            return tensor.get_double_element(i)
+            return tensor._get_double_element(i)
 
     def __set_elem__(tensor, i, e):
         if tensor_to_check_dtype == np.float32:
-            tensor.set_float_element(i, e)
+            tensor._set_float_element(i, e)
         else:
-            tensor.set_double_element(i, e)
+            tensor._set_double_element(i, e)
 
     # we only compute gradient of one element each time.
     # we use a for loop to compute the gradient of every element.
@@ -107,7 +107,7 @@ def __set_elem__(tensor, i, e):
         __set_elem__(tensor_to_check, i, origin)
         gradient_flat[i] = (y_pos - y_neg) / delta / 2
 
-    return gradient_flat.reshape(tensor_to_check.get_dims())
+    return gradient_flat.reshape(tensor_to_check.shape())
 
 
 class OpTest(unittest.TestCase):
@@ -125,7 +125,7 @@ def setUpClass(cls):
 
     @classmethod
     def tearDownClass(cls):
-        '''Restore random seeds'''
+        """Restore random seeds"""
         np.random.set_state(cls._np_rand_state)
         random.setstate(cls._py_rand_state)
 

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

@@ -129,7 +129,6 @@ def create_or_get_tensor(scope, var_name, var, place):
     if var is not None:
         assert isinstance(var, np.ndarray)
         tensor.set_recursive_sequence_lengths([])
-        tensor.set_dims(var.shape)
         tensor.set(var, place)
     return tensor