Merge pull request #12097 from reyoung/feature/hide_api_cont

Hide internal API of LoDTensors, Clipping, etc.

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 @@ class ErrorClipByValue(BaseErrorClipAttr):
     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])
@@ -90,17 +90,17 @@ 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()
 
 
@@ -108,10 +108,10 @@ 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 @@ class GradientClipByValue(BaseGradientClipAttr):
     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 @@ class GradientClipByNorm(BaseGradientClipAttr):
     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 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
         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 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
 
         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 @@ class LayerHelper(object):
 
     @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 @@ class LayerHelper(object):
         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 @@ class LayerHelper(object):
 
         # 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 @@ class LayerHelper(object):
 
         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 @@ class LayerHelper(object):
             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 @@ class ParamAttr(object):
         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 @@ class ParamAttr(object):
 
         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 @@ class ParamAttr(object):
         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 @@ class ParamAttr(object):
         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 @@ class ParamAttr(object):
         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 @@ class ParamAttr(object):
         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_numeric_gradient(place,
         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_numeric_gradient(place,
 
     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 get_numeric_gradient(place,
         __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 @@ class OpTest(unittest.TestCase):
 
     @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
 

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

@@ -65,10 +65,10 @@ class TestDyRnnStaticInput(unittest.TestCase):
         return self._lodtensor_to_ndarray(fetch_outs[0])
 
     def _lodtensor_to_ndarray(self, lod_tensor):
-        dims = lod_tensor.get_dims()
+        dims = lod_tensor.shape()
         ndarray = np.zeros(shape=dims).astype('float32')
         for i in xrange(np.product(dims)):
-            ndarray.ravel()[i] = lod_tensor.get_float_element(i)
+            ndarray.ravel()[i] = lod_tensor._get_float_element(i)
         return ndarray, lod_tensor.recursive_sequence_lengths()
 
     def build_graph(self, only_forward=False):
@@ -185,19 +185,19 @@ class TestDyRnnStaticInput(unittest.TestCase):
 
         actual_gradients, actual_lod = self.fetch_value(static_input_grad)
 
-        static_input_shape = self.static_input_tensor.get_dims()
+        static_input_shape = self.static_input_tensor.shape()
         numeric_gradients = np.zeros(shape=static_input_shape).astype('float32')
         # calculate numeric gradients
         tensor_size = np.product(static_input_shape)
         for i in xrange(tensor_size):
-            origin = self.static_input_tensor.get_float_element(i)
+            origin = self.static_input_tensor._get_float_element(i)
             x_pos = origin + self._delta
-            self.static_input_tensor.set_float_element(i, x_pos)
+            self.static_input_tensor._set_float_element(i, x_pos)
             y_pos = self.fetch_value(loss)[0][0]
             x_neg = origin - self._delta
-            self.static_input_tensor.set_float_element(i, x_neg)
+            self.static_input_tensor._set_float_element(i, x_neg)
             y_neg = self.fetch_value(loss)[0][0]
-            self.static_input_tensor.set_float_element(i, origin)
+            self.static_input_tensor._set_float_element(i, origin)
             numeric_gradients.ravel()[i] = (y_pos - y_neg) / self._delta / 2
         self.assertTrue(np.allclose(actual_gradients, numeric_gradients, 0.001))
         self.assertTrue(

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

@@ -40,12 +40,12 @@ class TestSelectedRows(unittest.TestCase):
 
         # compare tensor
         self.assertAlmostEqual(2.0,
-                               selected_rows.get_tensor().get_float_element(0))
+                               selected_rows.get_tensor()._get_float_element(0))
         self.assertAlmostEqual(1.0,
-                               selected_rows.get_tensor().get_float_element(1))
+                               selected_rows.get_tensor()._get_float_element(1))
         self.assertAlmostEqual(
             4.0,
-            selected_rows.get_tensor().get_float_element(2 * row_numel + 8))
+            selected_rows.get_tensor()._get_float_element(2 * row_numel + 8))
 
 
 if __name__ == "__main__":

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

@@ -45,8 +45,8 @@ class TestShrinkRNNMemoryBase(unittest.TestCase):
 
     def sum_lodtensor(self, tensor):
         sum_res = 0.0
-        for i in xrange(np.product(tensor.get_dims())):
-            sum_res += tensor.get_float_element(i)
+        for i in xrange(np.product(tensor.shape())):
+            sum_res += tensor._get_float_element(i)
         return sum_res
 
 

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

@@ -25,8 +25,8 @@ class TestTensor(unittest.TestCase):
 
         tensor = var.get_tensor()
 
-        tensor.set_dims([1000, 784])
-        tensor.alloc_int(place)
+        tensor._set_dims([1000, 784])
+        tensor._alloc_int(place)
         tensor_array = numpy.array(tensor)
         self.assertEqual((1000, 784), tensor_array.shape)
         tensor_array[3, 9] = 1
@@ -44,8 +44,8 @@ class TestTensor(unittest.TestCase):
 
         tensor = var.get_tensor()
 
-        tensor.set_dims([1000, 784])
-        tensor.alloc_float(place)
+        tensor._set_dims([1000, 784])
+        tensor._alloc_float(place)
 
         tensor_array = numpy.array(tensor)
         self.assertEqual((1000, 784), tensor_array.shape)
@@ -63,8 +63,8 @@ class TestTensor(unittest.TestCase):
         var_lod = scope.var("test_lod_tensor")
         lod_tensor = var_lod.get_tensor()
 
-        lod_tensor.set_dims([4, 4, 6])
-        lod_tensor.alloc_int(place)
+        lod_tensor._set_dims([4, 4, 6])
+        lod_tensor._alloc_int(place)
         array = numpy.array(lod_tensor)
         array[0, 0, 0] = 3
         array[3, 3, 5] = 10
@@ -84,8 +84,8 @@ class TestTensor(unittest.TestCase):
         var_lod = scope.var("test_lod_tensor")
 
         lod_tensor = var_lod.get_tensor()
-        lod_tensor.set_dims([5, 2, 3, 4])
-        lod_tensor.alloc_float(place)
+        lod_tensor._set_dims([5, 2, 3, 4])
+        lod_tensor._alloc_float(place)
 
         tensor_array = numpy.array(lod_tensor)
         self.assertEqual((5, 2, 3, 4), tensor_array.shape)
@@ -104,14 +104,13 @@ class TestTensor(unittest.TestCase):
         self.assertListEqual(lod_py, lod)
 
     def test_lod_tensor_init(self):
-        scope = core.Scope()
         place = core.CPUPlace()
         lod_py = [[2, 1], [1, 2, 2]]
         lod_tensor = core.LoDTensor()
 
-        lod_tensor.set_dims([5, 2, 3, 4])
+        lod_tensor._set_dims([5, 2, 3, 4])
         lod_tensor.set_recursive_sequence_lengths(lod_py)
-        lod_tensor.alloc_float(place)
+        lod_tensor._alloc_float(place)
         tensor_array = numpy.array(lod_tensor)
         tensor_array[0, 0, 0, 0] = 1.0
         tensor_array[0, 0, 0, 1] = 2.0
@@ -129,9 +128,9 @@ class TestTensor(unittest.TestCase):
         lod_py = [[2, 1], [1, 2, 2]]
         lod_tensor = core.LoDTensor()
 
-        lod_tensor.set_dims([5, 2, 3, 4])
+        lod_tensor._set_dims([5, 2, 3, 4])
         lod_tensor.set_recursive_sequence_lengths(lod_py)
-        lod_tensor.alloc_float(place)
+        lod_tensor._alloc_float(place)
         tensor_array = numpy.array(lod_tensor)
         tensor_array[0, 0, 0, 0] = 1.0
         tensor_array[0, 0, 0, 1] = 2.0
@@ -149,15 +148,15 @@ class TestTensor(unittest.TestCase):
 
         tensor = var.get_tensor()
 
-        tensor.set_dims([0, 1])
-        tensor.alloc_float(place)
+        tensor._set_dims([0, 1])
+        tensor._alloc_float(place)
 
         tensor_array = numpy.array(tensor)
         self.assertEqual((0, 1), tensor_array.shape)
 
         if core.is_compiled_with_cuda():
             gpu_place = core.CUDAPlace(0)
-            tensor.alloc_float(gpu_place)
+            tensor._alloc_float(gpu_place)
             tensor_array = numpy.array(tensor)
             self.assertEqual((0, 1), tensor_array.shape)
 

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

@@ -75,7 +75,7 @@ def set_input(scope, op, inputs, place):
             if isinstance(var, tuple):
                 tensor.set_recursive_sequence_lengths(var[1])
                 var = var[0]
-            tensor.set_dims(var.shape)
+            tensor._set_dims(var.shape)
             tensor.set(var, place)
         elif isinstance(var, float):
             scope.find_var(var_name).set_float(var)