# Copyright (c) 2018 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. import collections import contextlib import re import numpy as np import proto.framework_pb2 as framework_pb2 from . import core import unique_name __all__ = [ 'Block', 'Variable', 'Program', 'Operator', 'default_startup_program', 'default_main_program', 'program_guard', 'get_var', ] EMPTY_VAR_NAME = core.kEmptyVarName() TEMP_VAR_NAME = core.kTempVarName() GRAD_VAR_SUFFIX = core.kGradVarSuffix() ZERO_VAR_SUFFIX = core.kZeroVarSuffix() def grad_var_name(var_name): """ Returns: str: gradient name for a certain var name """ return var_name + GRAD_VAR_SUFFIX def convert_np_dtype_to_dtype_(np_dtype): """ Convert the data type in numpy to the data type in Paddle Args: np_dtype(np.dtype): the data type in numpy. Returns: core.VarDesc.VarType: the data type in Paddle. """ dtype = np.dtype(np_dtype) if dtype == np.float32: return core.VarDesc.VarType.FP32 elif dtype == np.float64: return core.VarDesc.VarType.FP64 elif dtype == np.float16: return core.VarDesc.VarType.FP16 elif dtype == np.int32: return core.VarDesc.VarType.INT32 elif dtype == np.int16: return core.VarDesc.VarType.INT16 elif dtype == np.int64: return core.VarDesc.VarType.INT64 elif dtype == np.bool: return core.VarDesc.VarType.BOOL elif dtype == np.uint16: return core.VarDesc.VarType.INT16 elif dtype == np.uint8: return core.VarDesc.VarType.UINT8 else: raise ValueError("Not supported numpy dtype " + str(dtype)) def dtype_is_floating(dtype): """ Check the data type is floating or not. Args: dtype(np.dtype|core.VarDesc.VarType): data type. Could be numpy format or Paddle format Returns(bool): True if data type is a float value """ if not isinstance(dtype, core.VarDesc.VarType): dtype = convert_np_dtype_to_dtype_(dtype) return dtype in [ core.VarDesc.VarType.FP16, core.VarDesc.VarType.FP32, core.VarDesc.VarType.FP64 ] def _debug_string_(proto, throw_on_error=True): """ Get the debug string of a protobuf message. The message could be not initialized. Args: proto(google.protobuf.message.Message): The protobuf message throw_on_error(bool): True if raise an error when the protobuf message is not initialized. Returns(str): The debug string of the protobuf message """ error_fields = list() if not proto.IsInitialized(error_fields) and throw_on_error: raise ValueError("{0} are not initialized.\nThe message is {1}:\n". format(error_fields, proto)) return proto.__str__() class Variable(object): """ In Fluid, every input and output of an operator is a variable. In most cases, variables are used for holding different kinds of data or training labels. A variable belongs to a block. All variable has its own name and two variables in different blocks could have the same name. There are many kinds of variables. Each kind of them has its own attributes and usages. Please reference the framework.proto for details. Most of a Variable's member variables can be setted to be None. It mean it is not available or will be specified later. Args: block(Block): The block that the variable belongs to. type(core.VarDesc.VarType): Variable type. Please reference the framework.proto for details. name(str|None): The name of the variable. If setted None, it will be generated automatically. Default: None shape(tuple|list|None): The shape of the variable. -1 means the batch size. Some kinds of variable do not contain shape, just set it to None. Default: None dtype(np.dtype|core.VarDesc.VarType|str|None): The data type of variable. Default: None lod_level (int|None): The level of lod tensor. 0 means it is not a time series data. Default: None capacity (int|None): The capacity of Channel variable. Ignored for other types. Default: None persistable (bool|None): True if the variable is persistable. A persistable variable will not be deleted after an iteration ending. Defaults: None. error_clip (BaseErrorClipAttr|None): The error clip attributes of the corresponding gradient variable. Default: None stop_gradient (bool): True if the variable will stop to calculate its gradients when backward. Default: False. is_data (bool): True if the variable is an input data. Default: False Notes: The constructor of Variable should not be invoked directly. Please use `Block.create_var` to create a variable. Examples: .. code-block:: python cur_program = Program() cur_block = cur_program.current_block() new_variable = cur_block.create_var(name="X", shape=[-1, 23, 48], dtype='float32') """ def __init__(self, block, type=core.VarDesc.VarType.LOD_TENSOR, name=None, shape=None, dtype=None, lod_level=None, capacity=None, persistable=None, error_clip=None, stop_gradient=False, is_data=False, **kwargs): self.block = block self.error_clip = error_clip if name is None: name = unique_name.generate('_generated_var') is_new_var = False self.desc = self.block.desc.find_var(name) if self.desc is None: self.desc = self.block.desc.var(name) is_new_var = True if is_new_var: self.desc.set_type(type) elif self.desc.type() != type: raise ValueError("Variable {0} has been created before. The " "previous type is {1}; the new type is {2}. They" " are not matched".format(self.name, self.desc.type(), type)) if shape is not None: 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: if not isinstance(dtype, core.VarDesc.VarType): dtype = convert_np_dtype_to_dtype_(dtype) if is_new_var: self.desc.set_dtype(dtype) else: old_dtype = self.dtype if dtype != old_dtype: 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: 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)) if persistable is not None: if is_new_var: self.desc.set_persistable(persistable) else: if persistable != self.persistable: raise ValueError( "Variable {0} has been created before." "The previous persistable is {1}; the new " "persistable is {2}. They are not matched".format( self.name, self.persistable, persistable)) if capacity is not None: if is_new_var: self.desc.set_capacity(capacity) else: # TODO(abhinavarora) : Compare with set capacity once, # get_capacity is implemented pass self.block.vars[name] = self self.op = None self.stop_gradient = stop_gradient self.is_data = is_data def __str__(self): return self.to_string(True) def to_string(self, throw_on_error, with_details=False): """ Get debug string. Args: throw_on_error(bool): True if raise an exception when self is not initialized. with_details(bool): more details about variables and parameters (e.g. trainable, optimize_attr, ...) will be printed when with_details is True. Default False; Returns: str: The debug string. """ assert isinstance(throw_on_error, bool) and isinstance(with_details, bool) protostr = self.desc.serialize_to_string() proto = framework_pb2.VarDesc.FromString(str(protostr)) res_str = _debug_string_(proto, throw_on_error) if with_details: additional_attr = ("error_clip", "stop_gradient") for attr_name in additional_attr: res_str += "%s: %s\n" % (attr_name, str(getattr(self, attr_name))) return res_str __repr__ = __str__ def set_desc(self, input): """ Set the variable description. Args: input(core.VarDesc): The new VarDesc. Returns: None """ self.desc = input @property def persistable(self): return self.desc.persistable() @persistable.setter def persistable(self, p): self.desc.set_persistable(p) @property def name(self): return self.desc.name() @name.setter def name(self, new_name): self.desc.set_name(new_name) @property def shape(self): # convert to tuple, make it as same as numpy API. return tuple(self.desc.shape()) @property def dtype(self): return self.desc.dtype() @property def lod_level(self): return self.desc.lod_level() @property def type(self): return self.desc.type() def set_error_clip(self, error_clip): """ Set the error_clip. Args: error_clip(BaseErrorClipAttr) : The new error_clip. Returns: None """ self.error_clip = error_clip def get_all_op_protos(): """ Get all registered op proto from PaddlePaddle C++ end. Returns: list: list of OpProto. """ protostrs = core.get_all_op_protos() ret_values = [] for pbstr in protostrs: op_proto = framework_pb2.OpProto.FromString(str(pbstr)) ret_values.append(op_proto) return ret_values class OpProtoHolder(object): """ A global variable to hold all OpProtos from C++ as a map """ @classmethod def instance(cls): if not hasattr(cls, '_instance'): cls._instance = cls() return cls._instance def __init__(self): assert not hasattr( self.__class__, '_instance'), 'Please use `instance()` to get OpProtoHolder object!' op_protos = get_all_op_protos() self.op_proto_map = {} for proto in op_protos: self.op_proto_map[proto.type] = proto def get_op_proto(self, type): """ Get OpProto by a type string. Args: type(str): The type that operator registered in C++ side. Returns(framework_pb2.OpProto): The OpProto """ if type not in self.op_proto_map: raise ValueError("Operator \"%s\" has not been registered." % type) return self.op_proto_map[type] @staticmethod def generated_op_attr_names(): return { core.op_proto_and_checker_maker.kOpRoleAttrName(), core.op_proto_and_checker_maker.kOpRoleVarAttrName() } class Operator(object): """ In Fluid, all the operation are represented by Operator, and Operator is regarded as a build in an instruction of a Block. Users can use the build in instructions to describe their neural network. Args: block(Block): The block has the current operator. desc(core.OpDesc): The protobuf description of Operator. type(str): The type of operator. Default None. inputs(dict): The input of this Operator. it is a dictionary, for every element, key is the input parameter name, and value is a list of variables. Default None. outputs(dict): The output of this Operator. it is a dictionary, for every element, key is the input parameter name, and value is a list of variables. Default None. attrs(dict): The attributes of this Operator. it is a dictionary, for every element, key is attribute name, and value is the attribute value. The attribute type should be as same as the type registered in C++ side. Default None. Returns: Operator: The initialized Operator. Raises: ValueError: If the passed input, output and attrs doesn't match the initializing Operator's that registered in C++ side. Notes: The constructor of operator should not be invoked directly. Use Block.append_op or Block.prepend_op instead. Examples: .. code-block:: python cur_program = Program() cur_block = cur_program.current_block() # var1 += var2 + var3 cur_block.append_op(type="sum", inputs={"X": [var1, var2, var3]}, outputs={"Out": [var1]}) """ OP_WITHOUT_KERNEL_SET = { 'feed', 'fetch', 'save', 'load', 'recurrent', 'go', 'rnn_memory_helper_grad', 'conditional_block', 'while', 'send', 'recv', 'listen_and_serv', 'parallel_do', 'save_combine', 'load_combine', 'ncclInit', 'channel_create', 'channel_close', 'channel_send', 'channel_recv', 'select', 'gen_nccl_id' } def __init__(self, block, desc, type=None, inputs=None, outputs=None, attrs=None): self.block = block self.desc = desc self.attrs = attrs if self.attrs is None: self.attrs = dict() del attrs op_maker = core.op_proto_and_checker_maker if op_maker.kOpRoleAttrName() not in self.attrs: self.attrs[op_maker.kOpRoleAttrName()] = self.block.program.op_role role_var_name = op_maker.kOpRoleVarAttrName() if len(self.block.program. op_role_var) != 0 and role_var_name not in self.attrs: self.attrs[role_var_name] = self.block.program.op_role_var if role_var_name in self.attrs and len(self.attrs[role_var_name]) == 0: del self.attrs[role_var_name] if len(self.desc.type()) != 0: return if type is None: raise ValueError( "`type` to initilized an Operator can not be None.") self.desc.set_type(type) proto = OpProtoHolder.instance().get_op_proto(type) def find_name(var_list, name): for var_name in var_list: if var_list[var_name] is not None and var_name == name: return True return False if inputs is not None: for in_proto in proto.inputs: found = find_name(inputs, in_proto.name) assert found or in_proto.dispensable, "Input {} not found".format( in_proto.name) if found: in_args = inputs[in_proto.name] if not isinstance(in_args, list): in_args = [in_args] if not in_proto.duplicable and len(in_args) > 1: raise ValueError( "Input %s expects only one input, but %d are given." % (in_proto.name, len(in_args))) in_arg_names = [] for arg in in_args: if isinstance(arg, basestring): in_arg_names.append(arg) else: in_arg_names.append(arg.name) self.desc.set_input(in_proto.name, in_arg_names) else: self.desc.set_input(in_proto.name, []) if outputs is not None: given = set() need = set() for n in outputs: given.add(n) for m in proto.outputs: need.add(m.name) if not given == need: raise ValueError(("Incorrect setting for output(s) of " "operator \"%s\". Need: [%s] Given: [%s]") % (type, ", ".join(str(e) for e in need), ", ".join(str(e) for e in given))) for out_proto in proto.outputs: out_args = outputs[out_proto.name] if not isinstance(out_args, list): out_args = [out_args] if not out_proto.duplicable and len(out_args) > 1: raise ValueError( "Output %s expects only one output, but %d are given." % (out_proto.name, len(out_args))) out_arg_names = [] for arg in out_args: out_arg_names.append(arg.name) arg.op = self self.desc.set_output(out_proto.name, out_arg_names) if self.attrs is not None: if not isinstance(self.attrs, dict): raise TypeError("'attrs' should be a dict.") for attr in proto.attrs: attr_name = attr.name if (attr_name not in self.attrs) or ( self.attrs[attr_name] is None): continue attr_val = self.attrs[attr_name] if isinstance(attr_val, Block): self.desc.set_block_attr(attr_name, self.attrs[attr_name].desc) elif isinstance(attr_val, list) and attr_val and \ all(isinstance(v, Block) for v in attr_val): self.desc.set_blocks_attr(attr_name, [v.desc for v in attr_val]) elif isinstance(attr_val, core.BlockDesc) or \ isinstance(attr_val, core.ProgramDesc): self.desc.set_serialized_attr( attr_name, attr_val.serialize_to_string()) else: self.desc.set_attr(attr_name, attr_val) self.desc.check_attrs() if self.has_kernel(type): self.desc.infer_var_type(self.block.desc) self.desc.infer_shape(self.block.desc) def has_kernel(self, op_type): return op_type not in self.OP_WITHOUT_KERNEL_SET def to_string(self, throw_on_error): """ Get debug string. Args: throw_on_error(bool): Whether to raise exception if self is not initialized. Returns: str: The debug string. """ protostr = self.desc.serialize_to_string() proto = framework_pb2.OpDesc.FromString(str(protostr)) return _debug_string_(proto, throw_on_error) def __str__(self): return self.to_string(True) __repr__ = __str__ @property def type(self): return self.desc.type() def input(self, name): """ Get the input arguments according to the input parameter name. Args: name(str): The input parameter name. Returns: list: return the list of argument names that associated with \ the specific parameter name. """ return self.desc.input(name) def rename_input(self, old_name, new_name): """ Rename the `old_name` to `new_name`. Args: old_name(str): The old name of the Operator's input. new_name(str): The new name of the Operator's input. Returns: None """ self.desc.rename_input(old_name, new_name) def rename_output(self, old_name, new_name): """ Rename the `old_name` to `new_name`. Args: old_name(str): The old name of the Operator's output. new_name(str): The new name of the Operator's output. Returns: None """ self.desc.rename_output(old_name, new_name) @property def input_names(self): return self.desc.input_names() @property def input_arg_names(self): return self.desc.input_arg_names() @property def output_arg_names(self): return self.desc.output_arg_names() def output(self, name): """ Get output arguments by the output parameter name. Args: name(str): The output parameter name. Returns: list: return the list of argument names associated with \ the specific parameter name. """ return self.desc.output(name) @property def output_names(self): return self.desc.output_names() @property def idx(self): for i, op in enumerate(self.block.ops): if op == self: return i raise ValueError( "Can't find op itself in it's block. It could be a bug of Paddle.") def has_attr(self, name): """ Whether this Operator has the attribute with name or not. Args: name(str): the attribute name. Returns: bool: True if has this attribute. """ return self.desc.has_attr(name) def attr_type(self, name): """ Get the type of attribute by attribute's name. Args: name(str): the attribute name. Returns: core.AttrType: the attribute type. """ return self.desc.attr_type(name) def set_attr(self, name, val): """ Set the value of attribute by attribute's name. Args: name(str): the attribute name. val(bool|int|str|float|list): the value of the attribute. Raises: ValueError: If the type of value doesn't match with desc.attr_type(name). """ self.attrs[name] = val if isinstance(val, Block): self.desc.set_block_attr(name, val.desc) elif isinstance(val, list) and val and all( isinstance(v, Block) for v in val): self.desc.set_blocks_attr(name, [v.desc for v in val]) elif isinstance(val, core.BlockDesc) or \ isinstance(val, core.ProgramDesc): self.desc.set_serialized_attr(name, val.serialize_to_string()) else: self.desc.set_attr(name, val) @property def attr_names(self): return self.desc.attr_names() def attr(self, name): """ Get the attribute by name. Args: name(str): the attribute name. Returns: bool|int|str|float|list: The attribute value. The return value can be any valid attribute type. """ return self.desc.attr(name) def block_attr(self, name): """ Get the block attribute by name. Args: name(str): the attribute name. Returns: int: the block index. """ return self.desc.block_attr(name) def all_attrs(self): """ Get the attribute dict. Returns: dict: The Operator's attribute dict. """ attr_names = self.attr_names attr_map = {} for n in attr_names: if n == 'sub_block': attr_map[n] = self.block_attr(n) else: attr_map[n] = self.attr(n) return attr_map class Block(object): """ In Fluid, a Program is consistence of multi-Block, and Block stores VarDesc and OpDesc. In a specific Block, a VarDesc have a unique name. One block could have some child blocks, and child block's name scopes should inherit the parent's so that OpDesc in child block can reference a VarDesc that is stored in the parent block. Please reference the framework.proto for details. Args: program(Program): The Program that the Block belongs to. idx(int): The block's id in the Program. Notes: The constructor of Block should not be invoked directly. Please use `Program.create_block()` to create a block. Examples: .. code-block:: python cur_program = Program() cur_block = cur_program.current_block() var = cur_block.create_var(name="X", shape=[-1, 23, 48], dtype='float32') cur_block.append_op(type="abs", inputs={"X": [var]}, outputs={"Out": [var]}) """ def __init__(self, program, idx): self.desc = program.desc.block(idx) self.vars = collections.OrderedDict() # var_name --> var self.ops = list() # operator list self.program = program self.removed_vars = collections.OrderedDict() def __str__(self): return self.to_string(True) def to_string(self, throw_on_error, with_details=False): """ Get debug string. Args: throw_on_error(bool): raise exception when self is not initialized when throw_on_error is True. with_details(bool): more details about variables and parameters (e.g. trainable, optimize_attr, ...) will be printed when with_details is True. Default False. Returns: str: The debug string. """ assert isinstance(throw_on_error, bool) and isinstance(with_details, bool) if with_details: re_add_indent = re.compile(r"\n(.)") res_str = "blocks {\n idx: %d\n parent_idx: %d" % ( self.idx, self.parent_idx) for var in self.vars.itervalues(): res_str += "\n vars {\n %s }" % re_add_indent.sub( r"\n \1", var.to_string(throw_on_error, with_details)) for op in self.ops: res_str += "\n ops {\n %s }" % re_add_indent.sub( r"\n \1", op.to_string(throw_on_error)) res_str += "\n}" else: protostr = self.desc.serialize_to_string() proto = framework_pb2.BlockDesc.FromString(str(protostr)) res_str = _debug_string_(proto, throw_on_error) return res_str __repr__ = __str__ @property def parent_idx(self): return self.desc.parent @property def forward_block_idx(self): return self.desc.get_forward_block_idx() def set_forward_block_idx(self, idx): """ Set the forward block Idx. Args: idx(int): the block index. Returns: None """ self.desc.set_forward_block_idx(idx) @property def idx(self): return self.desc.id def var(self, name): """ Get a Variable by name from this block. Args: name(str): the Variable's name. Raises: ValueError: The If input's type is not str, or this block doesn't have a Variable with the giving name. Returns: Variable: the Variable with the giving name. """ if not isinstance(name, basestring): raise TypeError( "var require string as parameter, but get %s instead." % (type(name))) v = self.vars.get(name, None) if v is None: raise ValueError("var %s not in this block" % name) return v def var_recursive(self, name): """ Get a Variable by name from this block recursively. Args: name(str): the Variable's name. Raises: ValueError: this block and this parent block doesn't have a Variable with the giving name. Returns: Variable: the Variable with the giving name. """ frontier = list() visited = set() frontier.append(self) prog = self.program while len(frontier) != 0: # BFS cur = frontier[0] frontier = frontier[1:] if id(cur) in visited: continue if cur.has_var(name): return cur.var(name) if cur.parent_idx != -1: frontier.append(prog.block(cur.parent_idx)) if cur.forward_block_idx != -1: frontier.append(prog.block(cur.forward_block_idx)) visited.add(id(cur)) raise ValueError("Var {0} is not found recursively".format(name)) def all_parameters(self): return list(self.iter_parameters()) def iter_parameters(self): return (item[1] for item in self.vars.iteritems() if isinstance(item[1], Parameter)) def create_var(self, *args, **kwargs): var = Variable(block=self, *args, **kwargs) if 'initializer' in kwargs: kwargs['initializer'](var, self) return var def has_var(self, name): return name in self.vars def rename_var(self, name, new_name): """ Rename variable in vars and ops' inputs and outputs Args: name(str): the name that need to be renamed. new_name(str): the name that need to rename to. Raises: ValueError: If this block doesn't have this the giving name, or the type of the var with the giving name is not Parameter or Variable. Returns: Variable: the Variable with the giving name. """ if not self.has_var(name): raise ValueError("var %s is not in current block" % name) v = self.var(name) if type(v) == Parameter: var_type = "Parameter" stop_gradient = v.stop_gradient trainable = v.trainable optimize_attr = v.optimize_attr regularizer = v.regularizer gradient_clip_attr = v.gradient_clip_attr error_clip = v.error_clip elif type(v) == Variable: var_type = "Variable" error_clip = v.error_clip stop_gradient = v.stop_gradient else: raise ValueError("unsupported var type: %s", type(v)) orig_var_type = v.type self.desc.rename_var(name, new_name) # NOTE: v is destroyed by C++ after calling rename_var. d = self.desc.find_var(new_name) if var_type == "Parameter": var = Parameter( self, d.shape(), d.dtype(), type=orig_var_type, name=new_name, stop_gradient=stop_gradient, trainable=trainable, optimize_attr=optimize_attr, regularizer=regularizer, gradient_clip_attr=gradient_clip_attr, error_clip=error_clip) elif var_type == "Variable": var = Variable( self, type=orig_var_type, name=new_name, error_clip=error_clip, stop_gradient=stop_gradient) # rename the python side, sync_with_cpp will only add # new vars/ops to python side. self.vars[new_name] = var del self.vars[name] self.sync_with_cpp() return var def remove_var(self, name): self.sync_with_cpp() self.desc.remove_var(name) del self.vars[name] def create_parameter(self, *args, **kwargs): global_block = self.program.global_block() param = Parameter(global_block, *args, **kwargs) if 'initializer' in kwargs: kwargs['initializer'](param, self) return param def append_op(self, *args, **kwargs): """ Appends a new Operator according to the giving arguments. Returns: Operator: the append Operator. """ op_desc = self.desc.append_op() op = Operator(block=self, desc=op_desc, *args, **kwargs) self.ops.append(op) return op def insert_op(self, index, *args, **kwargs): """ Insert a Operator according to the giving arguments. Args: index(int): the place that the operator to insert. Returns: Operator: the insert Operator. """ self.sync_with_cpp() op_desc = self.desc.insert_op(index) op = Operator(block=self, desc=op_desc, *args, **kwargs) self.ops.insert(index, op) return op def remove_op(self, index): """ Remove the specific position operator. Args: index(int): the position that the operator to insert. Returns: None """ self.sync_with_cpp() self.desc.remove_op(index, index + 1) del self.ops[index] def slice_ops(self, start, end): """ Return the Operator between start and end. Args: start(int): the start position. end(int): the end position. Returns: list: the Operators between start and end. """ return self.ops[start:end] def prepend_op(self, *args, **kwargs): op_desc = self.desc.prepend_op() op = Operator(self, op_desc, *args, **kwargs) self.ops.insert(0, op) return op def sync_with_cpp(self): """ Sync from the desc on the c++ end. This method is used to synchronize the c++ desc instance generated by backward. """ # sync variables from cpp for var in self.desc.all_vars(): if not self.has_var(var.name()): self.create_var(name=var.name(), desc=var, type=var.type()) # sync variables removed from c++ end for var in self.vars.keys(): if not self.desc.find_var(var): self.vars.pop(var) # sync operators from cpp ops_in_cpp = [] for op_idx in range(0, self.desc.op_size()): ops_in_cpp.append(self.desc.op(op_idx)) if len(self.ops) != 0: first_op_in_python = self.ops[0].desc last_op_in_python = self.ops[len(self.ops) - 1].desc start_index = None end_index = None for index in range(len(ops_in_cpp)): if first_op_in_python == ops_in_cpp[index]: start_index = index if last_op_in_python == ops_in_cpp[index]: end_index = index assert start_index is not None assert end_index is not None assert start_index <= end_index else: start_index = 0 end_index = -1 # sync ops append to the head of cpp_ops for index in range((start_index - 1 - 1), -1, -1): op_desc = ops_in_cpp[index] op = Operator(self, op_desc) self.ops.insert(0, op) # sync ops append to the end of cpp_ops for index in range((end_index + 1), len(ops_in_cpp)): op_desc = ops_in_cpp[index] op = Operator(self, op_desc) self.ops.append(op) # sync ops removed from c++ end if end_index != -1 and end_index < len(self.ops): ops_in_cpp_index = 0 ops_in_python_index = 0 while ops_in_python_index < len( self.ops) and ops_in_cpp_index < len(ops_in_cpp): if self.ops[ops_in_python_index].desc != ops_in_cpp[ ops_in_cpp_index]: del self.ops[ops_in_python_index] else: ops_in_cpp_index += 1 ops_in_python_index += 1 assert len(self.ops) == len(ops_in_cpp) for index in range(len(self.ops)): assert self.ops[index].desc == ops_in_cpp[index] def copy_param_info_from(self, other): """ Copy the information of parameters from the other block. Args: other(Block): the other block. Raises: ValueError: If type of input is not Block, or the `other` and this block is not in the same topology. Returns: None """ if not isinstance(other, Block): raise TypeError("copy_param_info_from should be invoked with Block") for p in other.iter_parameters(): assert isinstance(p, Parameter) v = self.vars.get(p.name, None) if v is None: raise ValueError("copy_param_info_from should be invoked with " "same topology") assert isinstance(v, Variable) new_p = Parameter( block=self, shape=v.shape, dtype=v.dtype, type=v.type, lod_level=v.lod_level, stop_gradient=p.stop_gradient, trainable=p.trainable, optimize_attr=p.optimize_attr, regularizer=p.regularizer, gradient_clip_attr=p.gradient_clip_attr, error_clip=p.error_clip, name=v.name) self.vars[new_p.name] = new_p def clone_variable(self, var): """ Clone a variable into current block. Args: var: the variable to be cloned. Returns: Variable: the new variable cloned from 'var' in current block. """ assert isinstance(var, Variable) ret_var = None # make STEP_SCOPES var can be safely cloned. if var.type == core.VarDesc.VarType.STEP_SCOPES: ret_var = self.create_var( name=var.name, persistable=var.persistable, type=var.type) elif var.type == core.VarDesc.VarType.SELECTED_ROWS: ret_var = self.create_var( name=var.name, shape=var.shape, dtype=var.dtype, type=var.type, persistable=True, is_data=var.is_data) else: ret_var = self.create_var( name=var.name, shape=var.shape, dtype=var.dtype, type=var.type, lod_level=var.lod_level, persistable=True, is_data=var.is_data) return ret_var class Program(object): """ Python Program. Beneath it is a ProgramDesc, which is used for create c++ Program. A program is a self-contained programing language like container. It has at least one Block, when the control flow op like conditional_block, while_op is included, it will contains nested block. Please reference the framework.proto for details. Notes: we have default_startup_program and default_main_program by default, a pair of them will shared the parameters. The default_startup_program only run once to initialize parameters, default_main_program run in every mini batch and adjust the weights. Returns: A empty program. Examples: >>> main_program = fluid.Program() >>> startup_program = fluid.Program() >>> with fluid.program_guard(main_program=main_program, startup_program=startup_program): >>> fluid.layers.data(name="x", shape=[-1, 784], dtype='float32') >>> fluid.layers.data(name="y", shape=[-1, 1], dtype='int32') >>> fluid.layers.fc(name="fc", shape=[10], dtype='float32', act="relu") """ def __init__(self): self.desc = core.ProgramDesc() self.blocks = [Block(self, 0)] self.current_block_idx = 0 self._seed = 0 self._current_role = core.op_proto_and_checker_maker.OpRole.Forward self._op_role_var = [] @property def op_role(self): """ The operator role. In a enum {Forward, Backward, Optimize}. Notes: this is a low level API. It is used only for ParallelExecutor to duplicate or schedule operator to devices. For example, the forward operator should be executed on every device. The backward operator should be executed on every device and the parameter gradient of backward (use :code:`op_role_var` to get this variable) operator should be merged to one device. The optimization operators should be executed on only one device and broadcast the optimization result, i.e., the new parameter, to every other device. """ return self._current_role @op_role.setter def set_op_role(self, role): self._current_role = role @property def op_role_var(self): """ The auxiliary variables for :code:`op_role` property. See Also: :code:`Program.op_role`'s documentation for details. Notes: This is a very low-level API. Users should not use it directly. """ return self._op_role_var @op_role_var.setter def set_op_role_var(self, var_name): self._op_role_var = [var_name] @contextlib.contextmanager def optimized_guard(self, var): """ A with guard to set :code:`Optimization` :code:`OpRole` and :code:`OpRoleVar` automatically. Notes: This is a very low level API. Users should not use it directly. Args: var(Variable|str): The variable (name) to be optimized. Examples: >>> p, g = backward(...) >>> with program.optimized_guard(p): >>> p = p - 0.001 * g """ OpRole = core.op_proto_and_checker_maker.OpRole self._current_role = OpRole.Optimize self._op_role_var = [var.name if isinstance(var, Variable) else var] yield self._op_role_var = [] self._current_role = OpRole.Forward def __str__(self): """ Get the protobuf debug string of this Program. Returns: (str): The protobuf debug string. Raises: ValueError: If any of required fields is not set. """ return self.to_string(True) def to_string(self, throw_on_error, with_details=False): """ To debug string. Args: throw_on_error(bool): raise Value error when any of required fields is not set. with_details(bool): True if more details about variables and parameters, e.g., :code:`trainable`, :code:`optimize_attr`, need to print. Returns (str): The debug string. Raises: ValueError: If any of required fields is not set and throw_on_error is True. """ assert isinstance(throw_on_error, bool) and isinstance(with_details, bool) if with_details: res_str = "" for block in self.blocks: res_str += block.to_string(throw_on_error, with_details) else: protostr = self.desc.serialize_to_string() proto = framework_pb2.ProgramDesc.FromString(str(protostr)) res_str = _debug_string_(proto, throw_on_error) return res_str def get_desc(self): """ Get the C++ side of `ProgramDesc` object pointer. The C++ object is exposed by :code:`pybind`. Notes: This is a very low level API. Users should not use this API directly. """ return self.desc def clone(self, for_test=False): """ Create a new, duplicated program. Some operators, e.g., :code:`batch_norm`, behave differently between training and testing. They have an attribute, :code:`is_test`, to control this behaviour. This method will change the :code:`is_test` attribute of them to :code:`True` when :code:`for_test=True`. * Set for_test to False when we want to clone the program for training. * Set for_test to True when we want to clone the program for testing. Notes: This API DOES NOT prune any operator. Use :code:`clone(for_test=True)` before backward and optimization please. Args: for_test(bool): True if change the :code:`is_test` attribute of operators to :code:`True`. Returns: Program: The new, duplicated Program object. Examples: 1. To clone a test program, the sample code is: >>> import paddle.fluid as fluid >>> train_program = fluid.Program() >>> startup_program = fluid.Program() >>> with fluid.program_guard(train_program, startup_program): >>> img = fluid.layers.data(name='image', shape=[784]) >>> hidden = fluid.layers.fc(input=img, size=200, act='relu') >>> hidden = fluid.layers.dropout(hidden, dropout_prob=0.5) >>> loss = fluid.layers.cross_entropy( >>> input=fluid.layers.fc(hidden, size=10, act='softmax'), >>> label=fluid.layers.data(name='label', shape=[1], dtype='int64')) >>> >>> test_program = train_program.clone(for_test=True) >>> >>> sgd = fluid.optimizer.SGD(learning_rate=1e-3) >>> with fluid.program_guard(train_program, startup_program): >>> sgd.minimize(loss) 2. The :code:`clone` method can be avoid if you create program for training and program for testing individually. >>> import paddle.fluid as fluid >>> >>> def network(is_test): >>> img = fluid.layers.data(name='image', shape=[784]) >>> hidden = fluid.layers.fc(input=img, size=200, act='relu') >>> hidden = fluid.layers.dropout(hidden, dropout_prob=0.5, is_test=is_test) >>> loss = fluid.layers.cross_entropy( >>> input=fluid.layers.fc(hidden, size=10, act='softmax'), >>> label=fluid.layers.data(name='label', shape=[1], dtype='int64')) >>> return loss >>> >>> train_program = fluid.Program() >>> startup_program = fluid.Program() >>> test_program = fluid.Program() >>> >>> with fluid.program_guard(train_program, startup_program): >>> with fluid.unique_name.guard(): >>> loss = network(is_test=False) >>> sgd = fluid.optimizer.SGD(learning_rate=1e-3) >>> sgd.minimize(loss) >>> >>> # the test startup program is not used. >>> with fluid.program_guard(test_program, fluid.Program()): >>> with fluid.unique_name.guard(): >>> loss = network(is_test=True) The two code snippets above will generate same programs. """ if for_test: p = self.inference_optimize() else: p = Program() p.desc = core.ProgramDesc(self.desc) p.blocks = [Block(p, i) for i in xrange(self.desc.num_blocks())] p.sync_with_cpp() p.copy_param_info_from(self) p.copy_data_info_from(self) return p def prune(self, targets): """ Prune operators and variables which are not needed to generate :code:`targets`. Notes: This is a very low level API. Users should not use this API directly. This API is in flux and not stable. Args: targets(list|Variable|Operator): A list of variables or operators need to be pruned Returns: Program: A new, pruned program. """ if not isinstance(targets, list): targets = [targets] targets_idx = [] for t in targets: if not isinstance(t, Operator): if isinstance(t, Variable): # After transpiler processing, the op that output this # variable maybe has been changed, so t.op is not reliable # and we need to find the current op that generate this # variable here. t.op = None global_block = self.global_block() for idx, op in enumerate(global_block.ops): if t.name in op.output_arg_names: t.op = op break t = t.op if t is None: raise ValueError( "The target variable must have an " "associated operator that generates it.") else: raise ValueError("All targets of prune() can only be " "Variable or Operator.") targets_idx.append([t.block.idx, t.idx]) res = Program() res.desc = core.prune(self.desc, targets_idx) res.blocks = [Block(res, i) for i in xrange(res.desc.num_blocks())] res.sync_with_cpp() return res def inference_optimize(self): """ This method will create a new program and change the :code:`is_test` attribute of operators to :code:`True`. All the :code:`Parameter` information will be lost. Notes: This API is a very low level API. Use :code:`Program.clone(for_test=True)` instead. Returns: Program: The new program. """ # this is an alternative implement before # core.inference_optimize being fixed. res = Program() res.desc = core.ProgramDesc(self.desc) for i in xrange(res.desc.num_blocks()): block = res.desc.block(i) for j in xrange(block.op_size()): op = block.op(j) if op.has_attr('is_test'): op.set_attr('is_test', True) res.blocks = [Block(res, i) for i in xrange(res.desc.num_blocks())] res.sync_with_cpp() return res @staticmethod def parse_from_string(binary_str): """ Deserialize a program desc from protobuf binary string. Notes: All information about parameters will be lost after serialization and deserialization. Args: binary_str(str): The binary prootbuf string. Returns: Program: A deserialized program desc. """ p = Program() p.desc = core.ProgramDesc(binary_str) p.blocks = [Block(p, i) for i in xrange(p.desc.num_blocks())] p.sync_with_cpp() return p @property def random_seed(self): """ The default random seed for random operators in Program. Zero means get the random seed from random device. Notes: It must be set before the operators have been added. """ return self._seed @property def num_blocks(self): """ The number of blocks in this program. """ return self.desc.num_blocks() @random_seed.setter def random_seed(self, seed): if not isinstance(seed, int): raise ValueError("Seed must be a integer.") self._seed = seed def __repr__(self): return str(self) def global_block(self): """ Get the first block of this program. """ return self.blocks[0] def block(self, index): """ Get the :code:`index` block of this program Args: index(int): The index of block to get Returns: Block: The :code:`index` block """ return self.blocks[index] def current_block(self): """ Get the current block. The :code:`current` block is the block to append operators. """ return self.blocks[self.current_block_idx] def create_block(self, parent_idx=None): """ Create a new block with the :code:`parent_idx` and change the current block to new block. Args: parent_idx(int): The parent block index. Returns: Block: The new block. """ new_block_idx = len(self.blocks) parent = self.current_block() if parent_idx is None else self.block( parent_idx) self.desc.append_block(parent.desc) self.current_block_idx = new_block_idx self.blocks.append(Block(self, self.current_block_idx)) return self.current_block() def rollback(self): """ Exit a code block, i.e., roll back to the parent block. Returns: None """ self.current_block_idx = self.current_block().parent_idx def sync_with_cpp(self): """ Synchronize Python instance to its binding C++ object instance. If the program is modified in C++ space, this method should be invoked. Notes: This is a very low level API. Users should not invoke it directly. Returns: None """ for block_idx in range(len(self.blocks), self.desc.num_blocks()): self.blocks.append(Block(self, block_idx)) for block in self.blocks: block.sync_with_cpp() def copy_param_info_from(self, other): """ Copy the information of parameters from other program. Notes: This is a very low level API. Users should not invoke it directly. Args: other(Program): Other program Returns: None """ if not isinstance(other, Program): raise TypeError("copy_param_info_from should be invoked with " "Program") if len(self.blocks) != len(other.blocks): raise ValueError("copy_param_info_from should be invoked with two " "program, with represent the same topology") self.global_block().copy_param_info_from(other.global_block()) def copy_data_info_from(self, other): """ Copy the information of data variables from other program. Notes: This is a very low level API. Users should not invoke it directly. Args: other(Program): Other program Returns: None """ if not isinstance(other, Program): raise TypeError("copy_param_info_from should be invoked with " "Program") if len(self.blocks) != len(other.blocks): raise ValueError("copy_param_info_from should be invoked with two " "program, with represent the same topology") for var in other.global_block().vars.itervalues(): if var.is_data: self.global_block().var(var.name).is_data = True def list_vars(self): """ Get all variables from this Program. A iterable object is returned. Returns: iterable: The generator will yield every variable in this program. """ for each_block in self.blocks: for each_var in each_block.vars.itervalues(): yield each_var class Parameter(Variable): """ Parameter is derived from Variable. A parameter is a persistable Variable, and will be updated by optimizers after each iteration. The training of a neural network is essentially the updating of its parameters. Relative to a general Variable, a Parameter has several its own member variables: Args: trainable(bool): True if the parameter need to be updated after iterations. optimize_attr(map): Parameter attributes related with optimizing. Currently, it only contains 'learning_rate'. Default: {'learning_rate': 1.0} regularizer(WeightDecayRegularizer): The Regularizer which will be applied on the parameter. Default: None gradient_clip_attr(BaseGradientClipAttr): The gradint clip strategy which will be applied on the parameter. Default: None do_model_average(bool): True if the model average strategy will be applied on this parameter. """ 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, persistable=True, shape=shape, dtype=dtype, **kwargs) self.trainable = kwargs.get('trainable', True) self.optimize_attr = kwargs.get('optimize_attr', {'learning_rate': 1.0}) self.regularizer = kwargs.get('regularizer', None) self.gradient_clip_attr = kwargs.get('gradient_clip_attr', None) self.do_model_average = kwargs.get('do_model_average', None) def __str__(self): return self.to_string(True) def to_string(self, throw_on_error, with_details=False): """ To debug string. Args: throw_on_error(bool): raise exception when self is not initialized when throw_on_error is True with_details(bool): more details about variables and parameters (e.g. trainable, optimize_attr, ...) will be printed when with_details is True Returns(str): The debug string. """ assert isinstance(throw_on_error, bool) and isinstance(with_details, bool) if with_details: res_str = Variable.to_string(self, throw_on_error, True) additional_attr = ("trainable", "optimize_attr", "regularizer", "gradient_clip_attr", "do_model_average") for attr_name in additional_attr: res_str += "%s: %s\n" % (attr_name, str(getattr(self, attr_name))) else: res_str = Variable.to_string(self, throw_on_error, False) return res_str __repr__ = __str__ # program is a global instance. _main_program_ = Program() _startup_program_ = Program() def default_startup_program(): """ Get default/global startup program. The layer function in :code:`fluid.layers` will create parameters, readers, NCCL handles as global variables. The :code:`startup_program` will initialize them by the operators in startup program. The layer function will append these initialization operators into startup program. This method will return the :code:`default` or the :code:`current` startup program. Users can use :code:`fluid.program_guard` to switch program. Returns: Program: startup program """ return _startup_program_ def default_main_program(): """ Get default/global main program. The main program is used for training or testing. All layer function in :code:`fluid.layers` will append operators and variables to the :code:`default_main_program`. The :code:`default_main_program` is the default program in a lot of APIs. For example, the :code:`Executor.run()` will execute the :code:`default_main_program` when the program is not specified. Returns: Program: main program """ return _main_program_ def switch_main_program(program): """ Switch the main program to a new program. Args: program(Program): The new main program Returns: Program: The previous main program """ global _main_program_ prev_program = _main_program_ _main_program_ = program return prev_program def switch_startup_program(program): """ Switch the startup program to a new program Args: program(Program): The new startup program Returns: Program: The previous startup program """ global _startup_program_ prev_program = _startup_program_ _startup_program_ = program return prev_program @contextlib.contextmanager def program_guard(main_program, startup_program=None): """ Change the global main program and startup program with `with` statement. Layer functions in the Python `with` block will append operators and variables to the new main programs. Examples: >>> import paddle.fluid as fluid >>> main_program = fluid.Program() >>> startup_program = fluid.Program() >>> with fluid.program_guard(main_program, startup_program): >>> data = fluid.layers.data(...) >>> hidden = fluid.layers.fc(...) Notes: The temporary :code:`Program` can be used if the user does not need to construct either of startup program or main program. Examples: >>> import paddle.fluid as fluid >>> main_program = fluid.Program() >>> # does not care about startup program. Just pass a temporary value. >>> with fluid.program_guard(main_program, fluid.Program()): >>> data = ... Args: main_program(Program): New main program inside `with` statement. startup_program(Program): New startup program inside `with` statement. None means do not change startup program. """ if not isinstance(main_program, Program): raise TypeError("main_program should be Program") main_program = switch_main_program(main_program) if startup_program is not None: if not isinstance(startup_program, Program): raise TypeError("startup_program should be Program") startup_program = switch_startup_program(startup_program) yield switch_main_program(main_program) if startup_program is not None: switch_startup_program(startup_program) def get_var(name, program=None): """ Get a variable by name from the global block of a program. Args: name(str): name of the variable program(Program|None): program object. If None, default_global_program() will be used. Returns: Variable """ if program is None: program = default_main_program() assert isinstance(name, str) assert isinstance(program, Program) return program.global_block().var(name)