from ..layer_helper import LayerHelper, unique_name from ..framework import Program, Variable, Operator from .. import core from tensor import assign, fill_constant import contextlib __all__ = [ 'split_lod_tensor', 'merge_lod_tensor', 'BlockGuard', 'StaticRNNGuard', 'StaticRNNMemoryLink', 'WhileGuard', 'While', 'lod_rank_table', 'max_sequence_len', 'topk', 'lod_tensor_to_array', 'array_to_lod_tensor', 'increment', 'array_write', 'create_array', 'less_than', 'array_read', 'shrink_memory', 'array_length', 'IfElse', 'DynamicRNN', 'ConditionalBlock', 'StaticRNN' ] def split_lod_tensor(input, mask, level=0): helper = LayerHelper('split_lod_tensor', **locals()) out_true = helper.create_tmp_variable(dtype=input.dtype) out_false = helper.create_tmp_variable(dtype=input.dtype) helper.append_op( type='split_lod_tensor', inputs={ 'X': input, 'Mask': mask, }, outputs={'OutTrue': out_true, 'OutFalse': out_false}, attrs={'level': level}) return out_true, out_false def merge_lod_tensor(in_true, in_false, x, mask, level=0): helper = LayerHelper('merge_lod_tensor', **locals()) out = helper.create_tmp_variable(dtype=in_true.dtype) helper.append_op( type='merge_lod_tensor', inputs={'X': x, 'Mask': mask, 'InTrue': in_true, 'InFalse': in_false}, outputs={'Out': out}, attrs={'level': level}) return out class BlockGuard(object): """ BlockGuard class. BlockGuard class is used to create a sub-block in a program by using the Python `with` keyword. """ def __init__(self, main_program): if not isinstance(main_program, Program): raise TypeError("BlockGuard takes a program") self.main_program = main_program def __enter__(self): self.main_program.create_block() def __exit__(self, exc_type, exc_val, exc_tb): self.main_program.rollback() if exc_type is not None: return False # re-raise exception return True class StaticRNNGuard(BlockGuard): """ StaticRNNGuard class. StaticRNNGuard class is used to create a StaticRNN block in a program. """ def __init__(self, rnn): if not isinstance(rnn, StaticRNN): raise TypeError("StaticRNNGuard takes a StaticRNN") super(StaticRNNGuard, self).__init__(rnn.helper.main_program) self.rnn = rnn def __enter__(self): self.rnn.status = StaticRNN.IN_RNN_BLOCK return super(StaticRNNGuard, self).__enter__() def __exit__(self, exc_type, exc_val, exc_tb): if exc_type is not None: return False self.rnn.status = StaticRNN.AFTER_RNN_BLOCK self.rnn.complete_rnn_op() return super(StaticRNNGuard, self).__exit__(exc_type, exc_val, exc_tb) class StaticRNNMemoryLink(object): """ StaticRNNMemoryLink class. Args: init: the initial variable for Memory init: Variable pre_mem: the memory variable in previous time step pre_mem: Variable mem: the memory variable in current time step mem: Variable StaticRNNMemoryLink class is used to create a link between two memory cells of a StaticRNN. """ def __init__(self, init, pre_mem, mem=None): self.init = init self.pre_mem = pre_mem self.mem = mem class StaticRNN(object): """ StaticRNN class. StaticRNN class is used to create a StaticRNN. The RNN will have its own parameters like inputs, outputs, memories, status and length. """ BEFORE_RNN_BLOCK = 0 IN_RNN_BLOCK = 1 AFTER_RNN_BLOCK = 2 def __init__(self, name=None): self.helper = LayerHelper("static_rnn", name=name) self.memories = {} # memory map, from pre_mem.name --> MemoryLink self.inputs = [] # input variable list in current block self.outputs = [] # output variable list in parent block self.status = StaticRNN.BEFORE_RNN_BLOCK # status flag. # sequence length, since it is a static RNN, sequence length are fixed. self.seq_len = None def step(self): return StaticRNNGuard(self) def _assert_in_rnn_block_(self, method): if self.status != StaticRNN.IN_RNN_BLOCK: raise ValueError("You must invoke {0} in rnn block".format(method)) def memory(self, init=None, shape=None, batch_ref=None, init_value=0.0, init_batch_dim_idx=0, ref_batch_dim_idx=1): """ Args: init: boot memory, if not set, a shape, batch_ref must be provided shape: shape of the boot memory batch_ref: batch size reference variable init_value: the init value of boot memory init_batch_dim_idx: the index of batch size in init's dimension ref_batch_dim_idx: the index of batch size in batch_ref's dimension """ self._assert_in_rnn_block_('memory') if init is None: if shape is None or batch_ref is None: raise ValueError( "if init is None, memory at least need shape and batch_ref") parent_block = self.parent_block() var_name = unique_name("@".join([self.helper.name, "memory_boot"])) boot_var = parent_block.create_var( name=var_name, shape=shape, dtype=batch_ref.dtype, persistable=False) parent_block.append_op( type="fill_constant_batch_size_like", inputs={'Input': [batch_ref]}, outputs={'Out': [boot_var]}, attrs={ 'value': init_value, 'shape': boot_var.shape, 'dtype': boot_var.dtype, 'input_dim_idx': ref_batch_dim_idx, 'output_dim_idx': init_batch_dim_idx }) return self.memory(init=boot_var) else: pre_mem = self.helper.create_variable( name=unique_name("@".join([self.helper.name, "mem"])), dtype=init.dtype, shape=init.shape) self.memories[pre_mem.name] = StaticRNNMemoryLink( init=init, pre_mem=pre_mem) return pre_mem def step_input(self, x): self._assert_in_rnn_block_('step_input') if not isinstance(x, Variable): raise TypeError("step input takes a Variable") if self.seq_len is None: self.seq_len = x.shape[0] elif self.seq_len != x.shape[0]: raise ValueError("Static RNN only take fix seq_len input") ipt = self.helper.create_variable( name=x.name, dtype=x.dtype, shape=list(x.shape[1:]), type=x.type) self.inputs.append(ipt) return ipt def step_output(self, o): self._assert_in_rnn_block_('step_output') if not isinstance(o, Variable): raise TypeError("step output takes a Variable") tmp_o = self.helper.create_tmp_variable(dtype=o.dtype) self.helper.append_op( type='rnn_memory_helper', inputs={'X': [o]}, outputs={'Out': tmp_o}, attrs={'dtype': o.dtype}) out_var = self.parent_block().create_var( name=tmp_o.name, shape=[self.seq_len] + list(tmp_o.shape), dtype=tmp_o.dtype) self.outputs.append(out_var) def output(self, *outputs): for each in outputs: self.step_output(each) def update_memory(self, mem, var): if not isinstance(mem, Variable) or not isinstance(var, Variable): raise TypeError("update memory should take variables") self.memories[mem.name].mem = var def parent_block(self): prog = self.helper.main_program parent_idx = prog.current_block().parent_idx assert parent_idx >= 0 parent_block = prog.block(parent_idx) return parent_block def __call__(self, *args, **kwargs): if self.status != StaticRNN.AFTER_RNN_BLOCK: raise ValueError("RNN output can only be retrieved after rnn block") if len(self.outputs) == 0: raise ValueError("RNN has no output") elif len(self.outputs) == 1: return self.outputs[0] else: return self.outputs def complete_rnn_op(self): main_program = self.helper.main_program rnn_block = main_program.current_block() parent_block = self.parent_block() local_inputs = set() for op in rnn_block.ops: assert isinstance(op, Operator) for oname in op.output_names: for out_var_name in op.output(oname): local_inputs.add(out_var_name) for var in self.inputs: local_inputs.add(var.name) for m in self.memories: local_inputs.add(m) params = list() for op in rnn_block.ops: assert isinstance(op, Operator) for iname in op.input_names: for in_var_name in op.input(iname): if in_var_name not in local_inputs: params.append(in_var_name) parameters = [parent_block.var(name) for name in params] step_scope = parent_block.create_var( type=core.VarDesc.VarType.STEP_SCOPES) inlinks = [parent_block.var(i.name) for i in self.inputs] outlinks = self.outputs boot_memories = [] pre_memories = [] memories = [] for _, mem in self.memories.iteritems(): boot_memories.append(mem.init) pre_memories.append(mem.pre_mem.name) mem_var = rnn_block.var(mem.mem.name) assert isinstance(mem_var, Variable) new_mem = self.helper.create_tmp_variable(dtype=mem_var.dtype) rnn_block.append_op( type='rnn_memory_helper', inputs={'X': [mem_var]}, outputs={'Out': [new_mem]}, attrs={'dtype': mem_var.dtype}) memories.append(new_mem.name) parent_block.append_op( type='recurrent', inputs={ 'inputs': inlinks, 'initial_states': boot_memories, 'parameters': parameters }, outputs={'outputs': outlinks, 'step_scopes': [step_scope]}, attrs={ 'ex_states': pre_memories, 'states': memories, 'sub_block': rnn_block }) class WhileGuard(BlockGuard): def __init__(self, while_op): if not isinstance(while_op, While): raise TypeError("WhileGuard takes a while op") super(WhileGuard, self).__init__(while_op.helper.main_program) self.while_op = while_op def __enter__(self): self.while_op.status = While.IN_WHILE_BLOCK return super(WhileGuard, self).__enter__() def __exit__(self, exc_type, exc_val, exc_tb): if exc_type is not None: return False self.while_op.status = While.AFTER_WHILE_BLOCK self.while_op.complete() return super(WhileGuard, self).__exit__(exc_type, exc_val, exc_tb) class While(object): BEFORE_WHILE_BLOCK = 0 IN_WHILE_BLOCK = 1 AFTER_WHILE_BLOCK = 2 def __init__(self, cond, name=None): self.helper = LayerHelper("while", name=name) self.status = While.BEFORE_WHILE_BLOCK if not isinstance(cond, Variable): raise TypeError("condition should be a variable") assert isinstance(cond, Variable) if cond.dtype != core.DataType.BOOL: raise TypeError("condition should be a bool variable") if reduce(lambda a, b: a * b, cond.shape, 1) != 1: raise TypeError("condition should be a bool scalar") self.cond_var = cond def block(self): return WhileGuard(self) def complete(self): main_program = self.helper.main_program while_block = main_program.current_block() parent_block = main_program.block(main_program.current_block() .parent_idx) inner_outputs = {self.cond_var.name} x_name_list = set() for op in while_block.ops: for iname in op.input_names: for in_var_name in op.input(iname): if in_var_name not in inner_outputs: x_name_list.add(in_var_name) for oname in op.output_names: for out_var_name in op.output(oname): inner_outputs.add(out_var_name) out_vars = [] for inner_out_name in inner_outputs: if inner_out_name in parent_block.vars: out_vars.append(parent_block.var(inner_out_name)) step_scope = parent_block.create_var( type=core.VarDesc.VarType.STEP_SCOPES) parent_block.append_op( type='while', inputs={ 'X': [parent_block.var(x_name) for x_name in x_name_list], 'Condition': [self.cond_var] }, outputs={'Out': out_vars, 'StepScopes': [step_scope]}, attrs={'sub_block': while_block}) def lod_rank_table(x, level=0): """ This function creates an operator for creating a LOD_RANK_TABLE using the input x. """ helper = LayerHelper("lod_rank_table", **locals()) table = helper.create_variable( type=core.VarDesc.VarType.LOD_RANK_TABLE, name=unique_name("lod_rank_table")) helper.append_op( type='lod_rank_table', inputs={'X': x}, outputs={'Out': table}, attrs={'level': level}) return table def max_sequence_len(rank_table): """ This function creates an operator to calculate the length of max seqence through input rank_table(should be a lod_rank_table) """ helper = LayerHelper("max_seqence_len", **locals()) res = helper.create_tmp_variable(dtype="int64") helper.append_op( type="max_sequence_len", inputs={"RankTable": rank_table}, outputs={"Out": res}) return res def topk(input, k): helper = LayerHelper('topk', **locals()) topk_out = helper.create_tmp_variable(dtype=input.data_type) topk_indices = helper.create_tmp_variable(dtype='int64') helper.append_op( type='top_k', inputs={'X': [input]}, outputs={'Out': [topk_out], 'Indices': [topk_indices]}, attrs={'k': k}) return topk_out, topk_indices def lod_tensor_to_array(x, table): """ This function creates an operator to convert an LOD_Tensor to an array. """ helper = LayerHelper("lod_tensor_to_array", **locals()) array = helper.create_variable( name=unique_name("lod_tensor_to_array"), type=core.VarDesc.VarType.LOD_TENSOR_ARRAY, dtype=x.dtype) helper.append_op( type='lod_tensor_to_array', inputs={'X': x, 'RankTable': table}, outputs={'Out': array}) return array def array_to_lod_tensor(x, table): """ This function creates an operator to convert an array to a LOD_Tensor. """ helper = LayerHelper("array_to_lod_tensor", **locals()) tmp = helper.create_tmp_variable(dtype=x.dtype) helper.append_op( type="array_to_lod_tensor", inputs={'X': x, 'RankTable': table}, outputs={'Out': tmp}) return tmp def increment(x, value=1.0, in_place=True): """ This function creates an operator to increment each value in the input `x` by an amount: `value` as mentioned in the input parameter. This operation is performed in-place by default. """ helper = LayerHelper("increment", **locals()) if not in_place: out = helper.create_tmp_variable(dtype=x.dtype) else: out = x helper.append_op( type='increment', inputs={'X': [x]}, outputs={'Out': [out]}, attrs={'step': float(value)}) return out def array_write(x, i, array=None): """This function performs the operation to write the data out as an LOD_TENSOR_ARRAY. Args: x (Variable|list): The input tensor from which the data will be read. i (Variable|list): The subscript index in tensor array, that points the place from which data will be read. array (Variable|list): The data can be read into this variable if this is assigned. Returns: Variable: The tensor type variable that has the data written to it. Examples: .. code-block::python tmp = fluid.layers.zeros(shape=[10], dtype='int32') i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10) arr = layers.array_write(tmp, i=i) """ helper = LayerHelper('array_write', **locals()) if array is None: array = helper.create_variable( name="{0}.out".format(helper.name), type=core.VarDesc.VarType.LOD_TENSOR_ARRAY, dtype=x.dtype) helper.append_op( type='write_to_array', inputs={'X': [x], 'I': [i]}, outputs={'Out': [array]}) return array def create_array(dtype): helper = LayerHelper("array", **locals()) return helper.create_variable( name="{0}.out".format(helper.name), type=core.VarDesc.VarType.LOD_TENSOR_ARRAY, dtype=dtype) def less_than(x, y, cond=None, **ignored): """ **Less than** This layer returns the truth value of :math:`x < y` elementwise. Args: x(Variable): First operand of *less_than* y(Variable): Second operand of *less_than* cond(Variable|None): Optional output variable to store the result of *less_than* Returns: Variable: The tensor variable storing the output of *less_than*. Examples: .. code-block:: python less = fluid.layers.less_than(x=label, y=limit) """ helper = LayerHelper("less_than", **locals()) if cond is None: cond = helper.create_tmp_variable(dtype='bool') cond.stop_gradient = True helper.append_op( type='less_than', inputs={'X': [x], 'Y': [y]}, outputs={'Out': [cond]}) return cond def array_read(array, i): """This function performs the operation to read the data in as an LOD_TENSOR_ARRAY. Args: array (Variable|list): The input tensor that will be written to an array. i (Variable|list): The subscript index in tensor array, that points the place where data will be written to. Returns: Variable: The tensor type variable that has the data written to it. Examples: .. code-block::python tmp = fluid.layers.zeros(shape=[10], dtype='int32') i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10) arr = layers.array_read(tmp, i=i) """ helper = LayerHelper('array_read', **locals()) if not isinstance( array, Variable) or array.type != core.VarDesc.VarType.LOD_TENSOR_ARRAY: raise TypeError("array should be tensor array vairable") out = helper.create_tmp_variable(dtype=array.dtype) helper.append_op( type='read_from_array', inputs={'X': [array], 'I': [i]}, outputs={'Out': [out]}) return out def shrink_memory(x, i, table): """ This function creates an operator to shrink_rnn_memory using the RankTable as mentioned in the input parameter. """ helper = LayerHelper('shrink_memory', **locals()) out = helper.create_tmp_variable(dtype=x.dtype) helper.append_op( type='shrink_rnn_memory', inputs={'X': [x], 'I': [i], 'RankTable': [table]}, outputs={'Out': [out]}, attrs={}) return out def array_length(array): """This function performs the operation to find the length of the input LOD_TENSOR_ARRAY. Args: array (LOD_TENSOR_ARRAY): The input array that will be used to compute the length. Returns: Variable: The length of the input LoDTensorArray. Examples: .. code-block::python tmp = fluid.layers.zeros(shape=[10], dtype='int32') i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10) arr = fluid.layers.array_write(tmp, i=i) arr_len = fluid.layers.array_length(arr) """ helper = LayerHelper('array_length', **locals()) tmp = helper.create_tmp_variable(dtype='int64') tmp.stop_gradient = True helper.append_op( type='lod_array_length', inputs={'X': [array]}, outputs={'Out': [tmp]}) return tmp class ConditionalBlockGuard(BlockGuard): def __init__(self, block): if not isinstance(block, ConditionalBlock): raise TypeError("block should be conditional block") super(ConditionalBlockGuard, self).__init__(block.helper.main_program) self.block = block def __enter__(self): return super(ConditionalBlockGuard, self).__enter__() def __exit__(self, exc_type, exc_val, exc_tb): self.block.complete() return super(ConditionalBlockGuard, self).__exit__(exc_type, exc_val, exc_tb) class ConditionalBlock(object): def __init__(self, inputs, name=None): for each_input in inputs: if not isinstance(each_input, Variable): raise TypeError("Each input should be variable") self.inputs = inputs self.helper = LayerHelper('conditional_block', name=name) def block(self): return ConditionalBlockGuard(self) def complete(self): inside_block = self.helper.main_program.current_block() parent_block = self.helper.main_program.block(inside_block.parent_idx) intermediate = set() params = set() for each_op in inside_block.ops: assert isinstance(each_op, Operator) for iname in each_op.input_names: for in_var_name in each_op.input(iname): if in_var_name not in intermediate: params.add(in_var_name) for oname in each_op.output_names: for out_var_name in each_op.output(oname): intermediate.add(out_var_name) input_set = set([ipt.name for ipt in self.inputs]) param_list = [ parent_block.var(each_name) for each_name in params if each_name not in input_set ] out_list = [ parent_block.var(var_name) for var_name in parent_block.vars if var_name not in intermediate ] step_scope = parent_block.create_var( type=core.VarDesc.VarType.STEP_SCOPES) parent_block.append_op( type='conditional_block', inputs={ 'X': self.inputs, 'Params': param_list, }, outputs={'Out': out_list, 'Scope': [step_scope]}, attrs={'sub_block': inside_block}) class IfElseBlockGuard(object): def __init__(self, is_true, ifelse): if not isinstance(ifelse, IfElse): raise TypeError("ifelse must be an instance of IfElse class") if ifelse.status != IfElse.OUT_IF_ELSE_BLOCKS: raise ValueError("You cannot invoke IfElse.block() inside a block") self.is_true = is_true self.ie = ifelse if is_true: self.cond_block = ifelse.conditional_true_block else: self.cond_block = ifelse.conditional_false_block if not isinstance(self.cond_block, ConditionalBlock): raise TypeError("Unexpected situation") self.cond_block = self.cond_block.block() def __enter__(self): self.ie.status = IfElse.IN_IF_ELSE_TRUE_BLOCKS if self.is_true else IfElse.IN_IF_ELSE_FALSE_BLOCKS self.cond_block.__enter__() def __exit__(self, exc_type, exc_val, exc_tb): if not self.cond_block.__exit__(exc_type, exc_val, exc_tb): # re-raise inside exception return False if len(self.ie.output_table[1 if self.is_true else 0]) == 0: raise ValueError("Must set output inside block") self.ie.status = IfElse.OUT_IF_ELSE_BLOCKS class IfElse(object): OUT_IF_ELSE_BLOCKS = 0 IN_IF_ELSE_TRUE_BLOCKS = 1 IN_IF_ELSE_FALSE_BLOCKS = 2 def __init__(self, cond, name=None): if not isinstance(cond, Variable): raise TypeError("cond must be a Variable") self.helper = LayerHelper('ifelse', name=name) self.cond = cond self.input_table = {} self.status = IfElse.OUT_IF_ELSE_BLOCKS self.conditional_true_block = ConditionalBlock(inputs=[self.cond]) self.conditional_false_block = ConditionalBlock(inputs=[self.cond]) self.output_table = ([], []) # (true_outs, false_outs) def input(self, x): if self.status == IfElse.OUT_IF_ELSE_BLOCKS: raise ValueError("input must in true/false blocks") if id(x) not in self.input_table: parent_block = self.parent_block() out_true = parent_block.create_var( name=unique_name('ifelse_input' + self.helper.name), dtype=x.dtype) out_false = parent_block.create_var( name=unique_name('ifelse_input' + self.helper.name), dtype=x.dtype) parent_block.append_op( type='split_lod_tensor', inputs={ 'X': x, 'Mask': self.cond, }, outputs={'OutTrue': out_true, 'OutFalse': out_false}, attrs={'level': 0}) self.input_table[id(x)] = (out_true, out_false) else: out_true, out_false = self.input_table[id(x)] if self.status == IfElse.IN_IF_ELSE_TRUE_BLOCKS: return out_true else: return out_false def parent_block(self): current_block = self.helper.main_program.current_block() return self.helper.main_program.block(current_block.parent_idx) def true_block(self): return IfElseBlockGuard(True, self) def false_block(self): return IfElseBlockGuard(False, self) def output(self, *outs): if self.status == self.OUT_IF_ELSE_BLOCKS: raise ValueError("output can only be invoked in the sub-block") out_table = self.output_table[1 if self.status == self.IN_IF_ELSE_TRUE_BLOCKS else 0] parent_block = self.parent_block() for each_out in outs: if not isinstance(each_out, Variable): raise TypeError("Each output should be a variable") # create outside tensor outside_out = parent_block.create_var( name=unique_name("_".join([self.helper.name, 'output'])), dtype=each_out.dtype) out_table.append(outside_out) # assign local var to outside assign(input=each_out, output=outside_out) def __call__(self): if self.status != self.OUT_IF_ELSE_BLOCKS: raise ValueError("IfElse::__call__ must be out of sub-block") false_len, true_len = map(len, self.output_table) if false_len == 0 and true_len == 0: raise ValueError("Must invoke true_block/false_block before " "__call__") elif false_len != true_len and false_len != 0 and true_len != 0: raise ValueError("The output side must be same") elif false_len == 0 or true_len == 0: return self.output_table[0 if false_len != 0 else 1] # else none of false_len/true_len is zero # merge together rlist = [] for false_var, true_var in zip(*self.output_table): rlist.append( merge_lod_tensor( in_true=true_var, in_false=false_var, mask=self.cond, x=self.cond, level=0)) return rlist class DynamicRNN(object): BEFORE_RNN = 0 IN_RNN = 1 AFTER_RNN = 2 def __init__(self, name=None): self.helper = LayerHelper('dynamic_rnn', name=name) self.status = DynamicRNN.BEFORE_RNN self.lod_rank_table = None self.max_seq_len = None self.step_idx = None self.zero_idx = fill_constant(shape=[1], value=0, dtype='int64') self.mem_dict = dict() self.output_array = [] self.outputs = [] self.cond = self.helper.create_tmp_variable(dtype='bool') self.cond.stop_gradient = False self.while_op = While(self.cond) self.input_array = [] self.mem_link = [] def step_input(self, x): self._assert_in_rnn_block_("step_input") if not isinstance(x, Variable): raise TypeError( "step_input() can only take a Variable as its input") parent_block = self._parent_block_() if self.lod_rank_table is None: self.lod_rank_table = parent_block.create_var( name=unique_name('lod_rank_table'), type=core.VarDesc.VarType.LOD_RANK_TABLE) self.lod_rank_table.stop_gradient = True parent_block.append_op( type='lod_rank_table', inputs={"X": x}, outputs={"Out": self.lod_rank_table}) self.max_seq_len = parent_block.create_var( name=unique_name('dynamic_rnn_max_seq_len'), dtype='int64') self.max_seq_len.stop_gradient = False parent_block.append_op( type='max_sequence_len', inputs={'RankTable': self.lod_rank_table}, outputs={"Out": self.max_seq_len}) self.cond.stop_gradient = True parent_block.append_op( type='less_than', inputs={'X': self.step_idx, 'Y': self.max_seq_len}, outputs={'Out': self.cond}) input_array = parent_block.create_var( name=unique_name('dynamic_rnn_input_array'), type=core.VarDesc.VarType.LOD_TENSOR_ARRAY, dtype=x.dtype) self.input_array.append((input_array, x.dtype)) parent_block.append_op( type='lod_tensor_to_array', inputs={'X': x, 'RankTable': self.lod_rank_table}, outputs={'Out': input_array}) return array_read(array=input_array, i=self.step_idx) @contextlib.contextmanager def block(self): if self.status != DynamicRNN.BEFORE_RNN: raise ValueError("rnn.block() can only be invoke once") self.step_idx = fill_constant(shape=[1], dtype='int64', value=0) self.step_idx.stop_gradient = False self.status = DynamicRNN.IN_RNN with self.while_op.block(): yield increment(x=self.step_idx, value=1.0, in_place=True) for new_mem, mem_array in self.mem_link: array_write(x=new_mem, i=self.step_idx, array=mem_array) less_than(x=self.step_idx, y=self.max_seq_len, cond=self.cond) self.status = DynamicRNN.AFTER_RNN for each_array in self.output_array: self.outputs.append( array_to_lod_tensor( x=each_array, table=self.lod_rank_table)) def __call__(self, *args, **kwargs): if self.status != DynamicRNN.AFTER_RNN: raise ValueError( "Dynamic RNN outputs can only be retrieved after rnn block") if len(self.outputs) == 1: return self.outputs[0] else: return self.outputs def memory(self, init=None, shape=None, value=0.0, dtype='float32'): self._assert_in_rnn_block_('memory') if init is not None: if not isinstance(init, Variable): raise TypeError( "The input arg `init` of memory() must be a Variable") parent_block = self._parent_block_() mem_array = parent_block.create_var( name=unique_name('dynamic_rnn_mem_array'), type=core.VarDesc.VarType.LOD_TENSOR_ARRAY, dtype=init.dtype) parent_block.append_op( type='write_to_array', inputs={'X': init, 'I': self.zero_idx}, outputs={'Out': mem_array}) retv = array_read(array=mem_array, i=self.step_idx) retv = shrink_memory( x=retv, i=self.step_idx, table=self.lod_rank_table) self.mem_dict[retv.name] = mem_array return retv else: if len(self.input_array) == 0: raise ValueError( "step_input should be invoked before memory(shape=..., value=...)" ) parent_block = self._parent_block_() init = parent_block.create_var( name=unique_name('mem_init'), dtype=dtype) arr, dtype = self.input_array[0] in0 = parent_block.create_var(name=unique_name('in0'), dtype=dtype) parent_block.append_op( type='read_from_array', inputs={'X': [arr], 'I': [self.zero_idx]}, outputs={'Out': [in0]}) parent_block.append_op( type='fill_constant_batch_size_like', inputs={'Input': [in0]}, outputs={'Out': [init]}, attrs={ 'shape': [-1] + shape, 'value': float(value), 'dtype': init.dtype }) return self.memory(init=init) def update_memory(self, ex_mem, new_mem): self._assert_in_rnn_block_('update_memory') if not isinstance(ex_mem, Variable): raise TypeError("The input arg `ex_mem` of update_memory() must " "be a Variable") if not isinstance(new_mem, Variable): raise TypeError("The input arg `new_mem` of update_memory() must " "be a Variable") mem_array = self.mem_dict.get(ex_mem.name, None) if mem_array is None: raise ValueError("Please invoke memory before update_memory") if self.lod_rank_table is None: raise ValueError("Please invoke step_input before update_memory") self.mem_link.append((new_mem, mem_array)) def output(self, *outputs): self._assert_in_rnn_block_('output') parent_block = self._parent_block_() for each in outputs: outside_array = parent_block.create_var( name=unique_name("_".join( [self.helper.name, "output_array", each.name])), type=core.VarDesc.VarType.LOD_TENSOR_ARRAY, dtype=each.dtype) array_write(x=each, i=self.step_idx, array=outside_array) self.output_array.append(outside_array) def _parent_block_(self): prog = self.helper.main_program parent_idx = prog.current_block().parent_idx assert parent_idx >= 0 parent_block = prog.block(parent_idx) return parent_block def _assert_in_rnn_block_(self, method): if self.status != DynamicRNN.IN_RNN: raise ValueError("{0} can only be invoked inside rnn block.".format( method))