Merge pull request #4684 from reyoung/feature/parameter

Feature/parameter

doc/design/python_api.md

@@ -22,7 +22,7 @@ Whenever we create a block, we need to set its parent block to the current block
 ```python
 class Program(objects):
     def __init__(self):
-        self.proto = core.NewProgram() # a C++ ProgramDesc pointer.
+        self.desc = core.NewProgram() # a C++ ProgramDesc pointer.
         self.blocks = vector<Block>()
         self.blocks.append(Block(self, -1)) # the global block
         self.current_block = 0          # initialized to the global block
@@ -57,7 +57,7 @@ A [Block](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/block.m
 ```python
 class Block(objects):
     def __init__(self, program, parent_idx):
-        self.proto = core.NewBlock(program.proto)
+        self.desc = core.NewBlock(program.desc)
         self.program = program
         self.vars = map<string, Variable>()
         self.ops = vector<Operator>()
@@ -98,11 +98,11 @@ class Operator(object):
                  outputs,# dict<stirng, Variable>
                  attrs   # dict<string, Any>
                  ):
-        self.proto = core.NewOpDesc(block.proto, type, inputs, outputs, attrs)
-        core.infer_shape(self.proto, inputs, outputs)
+        self.desc = core.NewOpDesc(block.desc, type, inputs, outputs, attrs)
+        core.infer_shape(self.desc, inputs, outputs)
 
     def type(self):
-        return self.proto.type()
+        return self.desc.type()
 ```
 
 `Operator` creates the `OpDesc` message in C++ space, so that it can call the `InferShape` function, which is in C++.
@@ -124,7 +124,7 @@ class Variable(object):
             name = unique_name_generator()
         self.name = name
         self.block = block
-        self.proto = core.NewVarDesc(block.proto, name, shape, lod_level)
+        self.desc = core.NewVarDesc(block.desc, name, shape, lod_level)
         self.writer = None
 ```
 

paddle/framework/var_desc.cc

@@ -32,5 +32,13 @@ std::vector<int64_t> VarDescBind::Shape() const {
 DataType VarDescBind::GetDataType() const {
   return desc_.lod_tensor().data_type();
 }
+
+void VarDescBind::SetLoDLevel(int32_t lod_level) {
+  desc_.mutable_lod_tensor()->set_lod_level(lod_level);
+}
+
+int32_t VarDescBind::GetLodLevel() const {
+  return desc_.lod_tensor().lod_level();
+}
 }  // namespace framework
 }  // namespace paddle

paddle/framework/var_desc.h

@@ -66,6 +66,10 @@ class VarDescBind {
 
   DataType GetDataType() const;
 
+  void SetLoDLevel(int32_t lod_level);
+
+  int32_t GetLodLevel() const;
+
  private:
   VarDesc desc_;
 };

paddle/pybind/protobuf.cc

@@ -166,7 +166,9 @@ void BindVarDsec(py::module &m) {
       .def("set_shape", &VarDescBind::SetShape)
       .def("set_data_type", &VarDescBind::SetDataType)
       .def("shape", &VarDescBind::Shape, py::return_value_policy::reference)
-      .def("data_type", &VarDescBind::GetDataType);
+      .def("data_type", &VarDescBind::GetDataType)
+      .def("lod_level", &VarDescBind::GetLodLevel)
+      .def("set_lod_level", &VarDescBind::SetLoDLevel);
 }
 
 void BindOpDesc(py::module &m) {

python/paddle/v2/framework/graph.py

 import paddle.v2.framework.core as core
 import collections
+import numpy as np
+import copy
 
 __all__ = ['Block', 'Variable', 'Program', 'Operator']
 
 
 class Variable(object):
-    def __init__(self, block, name=None, shape=None, dtype=None,
-                 lod_level=None):
+    def __init__(self,
+                 block,
+                 name=None,
+                 shape=None,
+                 dtype=None,
+                 lod_level=None,
+                 **kwargs):
         self.block = block
 
         if name is None:
             name = Variable._unique_var_name_()
-        self.proto = self.block.proto.new_var(name)
+        try:
+            self.desc = self.block.desc.var(name)
+            is_new_var = False
+        except core.EnforceNotMet:
+            self.desc = self.block.desc.new_var(name)
+            is_new_var = True
 
         if shape is not None:
-            self.proto.set_shape(shape)
-
+            if is_new_var:
+                self.desc.set_shape(shape)
+            else:
+                old_shape = self.shape
+                shape = tuple(shape)
+                if shape != old_shape:
+                    raise ValueError(
+                        "Variable {0} has been created before. the previous "
+                        "shape is {1}; the new shape is {2}. They are not "
+                        "matched.".format(self.name, old_shape, shape))
         if dtype is not None:
-            # TODO(yuyang18): Convert dtype from numpy.dtype
-            self.proto.set_data_type(dtype)
+            if not isinstance(dtype, core.DataType):
+                dtype = Variable._convert_np_dtype_to_dtype_(dtype)
+            if is_new_var:
+                self.desc.set_data_type(dtype)
+            else:
+                old_dtype = self.data_type()
+                if dtype != old_shape:
+                    raise ValueError("Variable {0} has been created before. "
+                                     "The previous data type is {1}; the new "
+                                     "data type is {2}. They are not "
+                                     "matched.".format(self.name, old_dtype,
+                                                       dtype))
 
         if lod_level is not None:
-            # TODO(yuyang18): set_lod_level is not defined.
-            self.proto.set_lod_level(lod_level)
-
+            if is_new_var:
+                self.desc.set_lod_level(lod_level)
+            else:
+                if lod_level != self.lod_level:
+                    raise ValueError("Variable {0} has been created before. "
+                                     "The previous lod_level is {1}; the new "
+                                     "lod_level is {2}. They are not "
+                                     "matched".format(self.name, self.lod_level,
+                                                      lod_level))
         self.block.vars[name] = self
         self.op = None
 
-    # TODO(yuyang18): Get methods
+    @property
+    def name(self):
+        return self.desc.name()
+
+    @property
+    def shape(self):
+        # convert to tuple, make it as same as numpy API.
+        return tuple(self.desc.shape())
+
+    @property
+    def data_type(self):
+        return self.desc.data_type()
+
+    @property
+    def lod_level(self):
+        return self.desc.lod_level()
 
     @staticmethod
     def _unique_var_name_():
         uid = core.unique_integer()  # unique during whole process.
         return "_generated_var_%d" % uid
 
+    @staticmethod
+    def _convert_np_dtype_to_dtype_(np_dtype):
+        dtype = np.dtype(np_dtype)
+        if dtype == np.float32:
+            return core.DataType.FP32
+        elif dtype == np.float64:
+            return core.DataType.FP64
+        elif dtype == np.float16:
+            return core.DataType.FP16
+        elif dtype == np.int32:
+            return core.DataType.INT32
+        elif dtype == np.int16:
+            return core.DataType.INT16
+        elif dtype == np.int64:
+            return core.DataType.INT64
+        elif dtype == np.bool:
+            return core.DataType.BOOL
+        else:
+            raise ValueError("Not supported numpy dtype " + str(dtype))
+
 
 class Operator(object):
     def __init__(self,
                  block,
-                 proto,
+                 desc,
                  type=None,
                  inputs=None,
                  outputs=None,
                  attrs=None):
         self.block = block
-        self.proto = proto
+        self.desc = desc
         if type is not None:
             # TODO.
             pass
@@ -58,36 +129,40 @@ class Operator(object):
             # TODO
             pass
 
-        # TODO: Getters
+            # TODO: Getters
 
 
 class Block(object):
     def __init__(self, program, idx):
-        self.proto = program.proto.block(idx)
+        self.desc = program.desc.block(idx)
         self.vars = dict()  # var_name --> var
         self.ops = collections.deque()  # operator list
         self.program = program
 
     @property
     def parent_idx(self):
-        return self.proto.parent
+        return self.desc.parent
 
     @property
     def idx(self):
-        return self.proto.id
+        return self.desc.id
 
     def create_var(self, *args, **kwargs):
         return Variable(self, *args, **kwargs)
 
+    def create_parameter(self, *args, **kwargs):
+        global_block = self.program.global_block()
+        return Parameter(global_block, *args, **kwargs)
+
     def append_op(self, *args, **kwargs):
-        op_proto = self.proto.append_op()
-        op = Operator(self, op_proto, *args, **kwargs)
+        op_desc = self.desc.append_op()
+        op = Operator(self, op_desc, *args, **kwargs)
         self.ops.append(op)
         return op
 
     def prepend_op(self, *args, **kwargs):
-        op_proto = self.proto.prepend_op()
-        op = Operator(self, op_proto, *args, **kwargs)
+        op_desc = self.desc.prepend_op()
+        op = Operator(self, op_desc, *args, **kwargs)
         self.ops.appendleft(op)
         return op
 
@@ -104,7 +179,7 @@ class Program(object):
     def __init__(self):
         assert not hasattr(self.__class__,
                            '_instance'), 'Do not call constructor directly!'
-        self.proto = core.ProgramDesc.instance()
+        self.desc = core.ProgramDesc.instance()
         self.blocks = [Block(self, 0)]
         self.current_block_idx = 0
 
@@ -116,7 +191,7 @@ class Program(object):
 
     def create_block(self):
         new_block_idx = len(self.blocks)
-        self.proto.append_block(self.current_block().proto)
+        self.desc.append_block(self.current_block().desc)
         self.current_block_idx = new_block_idx
         self.blocks.append(Block(self, self.current_block_idx))
         return self.current_block()
@@ -125,5 +200,41 @@ class Program(object):
         self.current_block_idx = self.current_block().parent_idx
 
 
+class Parameter(Variable):
+    def __init__(self, block, shape, dtype, **kwargs):
+        if shape is None or dtype is None:
+            raise ValueError("Parameter must set shape and dtype")
+        if len(shape) == 0:
+            raise ValueError("Parameter shape cannot be empty")
+
+        for each in shape:
+            if each < 0:
+                raise ValueError("Parameter shape should not be related with "
+                                 "batch-size")
+
+        Variable.__init__(self, block, shape=shape, dtype=dtype, **kwargs)
+        self.trainable = kwargs.get('trainable', True)
+        self.init_attr = kwargs.get('initialize_attr', {
+            'type': 'uniform_random',
+            'min': -1.0,
+            'max': 1.0
+        })
+
+        self.optimize_attr = kwargs.get('optimize_attr', {'learning_rate': 1.0})
+        self._append_initialize_ops_()
+
+    def _append_initialize_ops_(self):
+        attr = copy.deepcopy(self.init_attr)
+        op_type = attr.pop('type', None)
+        block = self.block
+        assert isinstance(block, Block)
+        shape = self.shape
+        attr['dims'] = shape
+        attr['data_type'] = int(self.data_type)
+        op = block.prepend_op(
+            type=op_type, inputs=None, outputs={'Out': [self]}, attrs=attr)
+        self.op = op
+
+
 # program is a global instance.
 g_program = Program.instance()

python/paddle/v2/framework/tests/test_parameter.py

+import unittest
+from paddle.v2.framework.graph import g_program
+import paddle.v2.framework.core as core
+
+
+class TestParameter(unittest.TestCase):
+    def test_param(self):
+        b = g_program.create_block()
+        param = b.create_parameter(
+            name='fc.w',
+            shape=[784, 100],
+            dtype='float32',
+            initialize_attr={
+                'type': 'uniform_random',
+                'seed': 13,
+                'min': -5.0,
+                'max': 5.0
+            })
+        self.assertIsNotNone(param)
+        self.assertEqual('fc.w', param.name)
+        self.assertEqual((784, 100), param.shape)
+        self.assertEqual(core.DataType.FP32, param.data_type)
+        self.assertEqual(0, param.block.idx)
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/v2/framework/tests/test_variable.py

+import unittest
+from paddle.v2.framework.graph import Variable, g_program
+import paddle.v2.framework.core as core
+import numpy as np
+
+
+class TestVariable(unittest.TestCase):
+    def test_np_dtype_convert(self):
+        DT = core.DataType
+        convert = Variable._convert_np_dtype_to_dtype_
+        self.assertEqual(DT.FP32, convert(np.float32))
+        self.assertEqual(DT.FP16, convert("float16"))
+        self.assertEqual(DT.FP64, convert("float64"))
+        self.assertEqual(DT.INT32, convert("int32"))
+        self.assertEqual(DT.INT16, convert("int16"))
+        self.assertEqual(DT.INT64, convert("int64"))
+        self.assertEqual(DT.BOOL, convert("bool"))
+        self.assertRaises(ValueError, lambda: convert("int8"))
+
+    def test_var(self):
+        b = g_program.current_block()
+        w = b.create_var(
+            dtype="float64", shape=[784, 100], lod_level=0, name="fc.w")
+        self.assertEqual(core.DataType.FP64, w.data_type)
+        self.assertEqual((784, 100), w.shape)
+        self.assertEqual("fc.w", w.name)
+        self.assertEqual(0, w.lod_level)
+
+        w = b.create_var(name='fc.w')
+        self.assertEqual(core.DataType.FP64, w.data_type)
+        self.assertEqual((784, 100), w.shape)
+        self.assertEqual("fc.w", w.name)
+        self.assertEqual(0, w.lod_level)
+
+        self.assertRaises(ValueError,
+                          lambda: b.create_var(name="fc.w", shape=(24, 100)))
+
+
+if __name__ == '__main__':
+    unittest.main()