import core import proto.framework_pb2 as framework_pb2 from framework import OpProtoHolder, Variable, Program, Operator from initializer import Constant, Normal, Xavier, Initializer from paddle.v2.fluid.layer_helper import LayerHelper, unique_name import re import cStringIO from param_attr import ParamAttr __all__ = [ 'fc', 'data', 'cross_entropy', 'conv2d', 'pool2d', 'embedding', 'concat', 'StaticRNN', 'cast', 'sequence_conv', 'sequence_pool', 'sums', 'cos_sim', 'batch_norm', 'accuracy', 'split_lod_tensor', 'While' ] def fc(input, size, num_flatten_dims=1, param_attr=None, bias_attr=None, act=None, name=None, main_program=None, startup_program=None): """ Fully Connected Layer. Args: input: The input tensor to the function size: The size of the layer num_flatten_dims: Number of columns in input param_attr: The parameters/weights to the FC Layer param_initializer: Initializer used for the weight/parameter. If None, XavierInitializer() is used bias_attr: The bias parameter for the FC layer bias_initializer: Initializer used for the bias. If None, then ConstantInitializer() is used act: Activation to be applied to the output of FC layer name: Name/alias of the function main_program: Name of the main program that calls this startup_program: Name of the startup program This function can take in multiple inputs and performs the Fully Connected function (linear transformation) on top of each of them. So for input x, the output will be : Wx + b. Where W is the parameter, b the bias and x is the input. The function also applies an activation (non-linearity) on top of the output, if activation is passed in the input. All the input variables of this function are passed in as local variables to the LayerHelper constructor. """ helper = LayerHelper('fc', **locals()) dtype = helper.input_dtype() mul_results = [] for input_var, param_attr in helper.iter_inputs_and_params(): input_shape = input_var.shape param_shape = [ reduce(lambda a, b: a * b, input_shape[num_flatten_dims:], 1) ] + [size] w = helper.create_parameter( attr=param_attr, shape=param_shape, dtype=dtype, is_bias=False) tmp = helper.create_tmp_variable(dtype) helper.append_op( type="mul", inputs={ "X": input_var, "Y": w, }, outputs={"Out": tmp}, attrs={'x_num_col_dims': num_flatten_dims, 'y_num_col_dims': 1}) mul_results.append(tmp) # sum if len(mul_results) == 1: pre_bias = mul_results[0] else: pre_bias = helper.create_tmp_variable(dtype) helper.append_op( type="sum", inputs={"X": mul_results}, outputs={"Out": pre_bias}) # add bias pre_activation = helper.append_bias_op(pre_bias) # add activation return helper.append_activation(pre_activation) def embedding(input, size, is_sparse=False, param_attr=None, dtype='float32', main_program=None, startup_program=None): """ Embedding Layer. Args: param_initializer: input: The input to the function size: The size of the layer is_sparse: A flag that decleares whether the input is sparse param_attr: Parameters for this layer dtype: The type of data : float32, float_16, int etc main_program: Name of the main program that calls this startup_program: Name of the startup program This function can take in the input (which is a vector of IDs) and performs a lookup in the lookup_table using these IDs, to result into the embedding of each ID in the input. All the input variables of this function are passed in as local variables to the LayerHelper constructor. """ helper = LayerHelper('embedding', **locals()) w = helper.create_parameter( attr=helper.param_attr, shape=size, dtype=dtype, is_bias=False) tmp = helper.create_tmp_variable(dtype) helper.append_op( type='lookup_table', inputs={'Ids': input, 'W': w}, outputs={'Out': tmp}, attrs={'is_sparse': is_sparse}) return tmp # TODO(qijun): expose H0 and C0 def dynamic_lstm(input, size, param_attr=None, bias_attr=None, use_peepholes=True, is_reverse=False, gate_activation='sigmoid', cell_activation='tanh', candidate_activation='tanh', dtype='float32', main_program=None, startup_program=None): helper = LayerHelper('lstm', **locals()) size = size / 4 weight = helper.create_parameter( attr=helper.param_attr, shape=[size, 4 * size], dtype=dtype) bias_size = [1, 7 * size] if not use_peepholes: bias_size[1] = 4 * size bias = helper.create_parameter( attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True) hidden = helper.create_tmp_variable(dtype) cell = helper.create_tmp_variable(dtype) batch_gate = helper.create_tmp_variable(dtype) batch_cell_pre_act = helper.create_tmp_variable(dtype) helper.append_op( type='lstm', inputs={'Input': input, 'Weight': weight, 'Bias': bias}, outputs={ 'Hidden': hidden, 'Cell': cell, 'BatchGate': batch_gate, 'BatchCellPreAct': batch_cell_pre_act }, attrs={ 'use_peepholes': use_peepholes, 'is_reverse': is_reverse, 'gate_activation': gate_activation, 'cell_activation': cell_activation, 'candidate_activation': candidate_activation }) return hidden, cell def data(name, shape, append_batch_size=True, dtype='float32', lod_level=0, type=core.VarDesc.VarType.LOD_TENSOR, main_program=None, startup_program=None, stop_gradient=True): """ Data Layer. Args: name: The name/alias of the function shape: Tuple declaring the shape. append_batch_size: Whether or not to append the data as a batch. dtype: The type of data : float32, float_16, int etc type: The output type. By default it is LOD_TENSOR. lod_level(int): The LoD Level. 0 means the input data is not a sequence. main_program: Name of the main program that calls this startup_program: Name of the startup program stop_gradient: A boolean that mentions whether gradient should flow. This function takes in input and based on whether data has to be returned back as a minibatch, it creates the global variable using the helper functions. The global variables can be accessed by all the following operations and layers in the graph. All the input variables of this function are passed in as local variables to the LayerHelper constructor. """ helper = LayerHelper('data', **locals()) shape = list(shape) for i in xrange(len(shape)): if shape[i] is None: shape[i] = -1 append_batch_size = False elif shape[i] < 0: append_batch_size = False if append_batch_size: shape = [-1] + shape # append batch size as -1 return helper.create_global_variable( name=name, shape=shape, dtype=dtype, type=type, stop_gradient=stop_gradient, lod_level=lod_level) def create_tensor(dtype, name=None, main_program=None, startup_program=None): helper = LayerHelper("create_tensor", **locals()) return helper.create_variable(name=helper.name, dtype=dtype) def _convert_(name): """ Formatting. Args: name: The name/alias This function takes in a name and converts it to a standard format of group1_group2. Where as per the regular expression, group1 can have alphabets and numbers and group2 has capital alphabets. """ s1 = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', name) return re.sub('([a-z0-9])([A-Z])', r'\1_\2', s1).lower() def _generate_doc_string_(op_proto): """ Generate docstring by OpProto Args: op_proto (framework_pb2.OpProto): a protobuf message typed OpProto Returns: str: the document string """ def _type_to_str_(tp): return framework_pb2.AttrType.Name(tp) if not isinstance(op_proto, framework_pb2.OpProto): raise TypeError("OpProto should be `framework_pb2.OpProto`") buf = cStringIO.StringIO() buf.write(op_proto.comment) buf.write('\nArgs:\n') for each_input in op_proto.inputs: line_begin = ' {0}: '.format(_convert_(each_input.name)) buf.write(line_begin) buf.write(each_input.comment) buf.write('\n') buf.write(' ' * len(line_begin)) buf.write('Duplicable: ') buf.write(str(each_input.duplicable)) buf.write(' Optional: ') buf.write(str(each_input.dispensable)) buf.write('\n') for each_attr in op_proto.attrs: buf.write(' ') buf.write(each_attr.name) buf.write(' (') buf.write(_type_to_str_(each_attr.type)) buf.write('): ') buf.write(each_attr.comment) buf.write('\n') if len(op_proto.outputs) != 0: buf.write('\nReturns:\n') buf.write(' ') for each_opt in op_proto.outputs: if not each_opt.intermediate: break buf.write(each_opt.comment) return buf.getvalue() def _create_op_func_(op_type): """ Create an Operator for a Function. Args: op_type: The name of the operator to be created This function takes in the operator type (sigmoid, mean , average etc) and creates the operator functionality. """ op_proto = OpProtoHolder.instance().get_op_proto(op_type) not_intermediate_outputs = \ filter(lambda output: not output.intermediate, op_proto.outputs) intermediate_outputs = \ filter(lambda output: output.intermediate, op_proto.outputs) if len(not_intermediate_outputs) != 1: raise ValueError("Only one non intermediate output operator can be", "automatically generated") if not_intermediate_outputs[0].duplicable: raise ValueError( "Only non duplicable op can be automatically generated") for output in intermediate_outputs: if output.duplicable: raise ValueError("The op can be automatically generated only when ", "all intermediate ops are not duplicable") o_name = not_intermediate_outputs[0].name intermediate_output_names = [output.name for output in intermediate_outputs] def infer_and_check_dtype(op_proto, **kwargs): """ This function performs the sanity check for dtype and instance type. """ dtype = None for ipt in op_proto.inputs: name = _convert_(ipt.name) val = kwargs.pop(name, []) if not isinstance(val, list) and not isinstance(val, tuple): val = [val] for each in val: if not isinstance(each, Variable): raise ValueError("input of {0} must be variable".format( op_type)) if dtype is None: dtype = each.dtype elif dtype != each.dtype: raise ValueError( "operator {0} must input same dtype".format(op_type)) return dtype def func(**kwargs): helper = LayerHelper(op_type, **kwargs) dtype = infer_and_check_dtype(op_proto, **kwargs) inputs = dict() for ipt in op_proto.inputs: name = _convert_(ipt.name) val = kwargs.pop(name, []) if not isinstance(val, list) and not isinstance(val, tuple): val = [val] inputs[ipt.name] = val outputs = dict() out = helper.create_tmp_variable(dtype=dtype) outputs[o_name] = [out] for name in intermediate_output_names: outputs[name] = [helper.create_tmp_variable(dtype=dtype)] helper.append_op( type=op_type, inputs=inputs, outputs=outputs, attrs=kwargs) return helper.append_activation(out) func.__name__ = op_type globals()[op_type] = func func.__doc__ = _generate_doc_string_(op_proto) global __all__ __all__.append(op_type) _create_op_func_('mean') _create_op_func_('mul') _create_op_func_('elementwise_add') _create_op_func_('elementwise_div') _create_op_func_('dropout') _create_op_func_('reshape') _create_op_func_('sigmoid') _create_op_func_('scale') _create_op_func_('reshape') _create_op_func_('transpose') _create_op_func_('sigmoid_cross_entropy_with_logits') def cast(x, dtype, main_program=None): """ This function takes in the input with input_dtype and casts it to the output_dtype as the output. """ helper = LayerHelper('cast', **locals()) out = helper.create_tmp_variable(dtype=dtype) helper.append_op( type='cast', inputs={'X': [x]}, outputs={'Out': [out]}, attrs={'in_dtype': x.dtype, 'out_dtype': out.dtype}) return out def concat(input, axis, main_program=None, startup_program=None): """ This function concats the input along the axis mentioned and returns that as the output. """ helper = LayerHelper('concat', **locals()) out = helper.create_tmp_variable(dtype=helper.input_dtype()) helper.append_op( type='concat', inputs={'X': input}, outputs={'Out': [out]}, attrs={'axis': axis}) return out def sums(input, out=None, main_program=None, startup_program=None): """ This function takes in the input and performs the sum operation on it and returns that as the output. """ helper = LayerHelper('sum', **locals()) if out is None: out = helper.create_tmp_variable(dtype=helper.input_dtype()) helper.append_op(type='sum', inputs={'X': input}, outputs={'Out': out}) return out def linear_chain_crf(input, label, param_attr=None, main_program=None, startup_program=None): helper = LayerHelper('linear_chain_crf', **locals()) size = input.shape[1] transition = helper.create_parameter( attr=helper.param_attr, shape=[size + 2, size], dtype=helper.input_dtype()) alpha = helper.create_tmp_variable(dtype=helper.input_dtype()) emission_exps = helper.create_tmp_variable(dtype=helper.input_dtype()) transition_exps = helper.create_tmp_variable(dtype=helper.input_dtype()) log_likelihood = helper.create_tmp_variable(dtype=helper.input_dtype()) helper.append_op( type='linear_chain_crf', inputs={"Emission": [input], "Transition": transition, "Label": label}, outputs={ "Alpha": [alpha], "EmissionExps": [emission_exps], "TransitionExps": transition_exps, "LogLikelihood": log_likelihood }) return log_likelihood def assign(input, output, main_program=None, startup_program=None): helper = LayerHelper('assign', **locals()) helper.append_op( type='scale', inputs={'X': [input]}, outputs={'Out': [output]}, attrs={'scale': 1.0}) return output def split_lod_tensor(input, mask, level=0, main_program=None, startup_program=None): 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, main_program=None, startup_program=None): 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 def cos_sim(X, Y, **kwargs): """ This function performs the cosine similarity between two tensors X and Y and returns that as the output. """ helper = LayerHelper('cos_sim', **kwargs) out = helper.create_tmp_variable(dtype=X.dtype) xnorm = helper.create_tmp_variable(dtype=X.dtype) ynorm = helper.create_tmp_variable(dtype=X.dtype) helper.append_op( type='cos_sim', inputs={'X': [X], 'Y': [Y]}, outputs={'Out': [out], 'XNorm': [xnorm], 'YNorm': [ynorm]}) return out def cross_entropy(input, label, **kwargs): """ This function computes cross_entropy using the input and label. """ helper = LayerHelper('cross_entropy', **kwargs) out = helper.create_tmp_variable(dtype=input.dtype) helper.append_op( type='cross_entropy', inputs={'X': [input], 'Label': [label]}, outputs={'Y': [out]}, attrs=kwargs) return out def square_error_cost(input, label, **kwargs): """ This functions returns the squared error cost using the input and label. The output is appending the op to do the above. """ helper = LayerHelper('square_error_cost', **kwargs) minus_out = helper.create_tmp_variable(dtype=input.dtype) helper.append_op( type='elementwise_sub', inputs={'X': [input], 'Y': [label]}, outputs={'Out': [minus_out]}) square_out = helper.create_tmp_variable(dtype=input.dtype) helper.append_op( type='square', inputs={'X': [minus_out]}, outputs={'Y': [square_out]}) return square_out def accuracy(input, label, k=1, correct=None, total=None, **kwargs): """ This function computes the accuracy using the input and label. The output is the top_k inputs and their indices. """ helper = LayerHelper("accuracy", **kwargs) topk_out = helper.create_tmp_variable(dtype=input.dtype) 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}) acc_out = helper.create_tmp_variable(dtype="float32") if correct is None: correct = helper.create_tmp_variable(dtype="int64") if total is None: total = helper.create_tmp_variable(dtype="int64") helper.append_op( type="accuracy", inputs={ "Out": [topk_out], "Indices": [topk_indices], "Label": [label] }, outputs={ "Accuracy": [acc_out], "Correct": [correct], "Total": [total], }) return acc_out def sequence_conv(input, num_filters, filter_size=3, filter_stride=1, padding=None, bias_attr=None, param_attr=None, act=None, main_program=None, startup_program=None): """ This function creates the op for sequence_conv, using the inputs and other convolutional configurations for the filters and stride as given in the input parameters to the function. """ # FIXME(dzh) : want to unify the argument of python layer # function. So we ignore some unecessary attributes. # such as, padding_trainable, context_start. helper = LayerHelper('sequence_conv', **locals()) dtype = helper.input_dtype() filter_shape = [filter_size * input.shape[1], num_filters] filter = helper.create_parameter( attr=helper.param_attr, shape=filter_shape, dtype=dtype) pre_bias = helper.create_tmp_variable(dtype) helper.append_op( type='sequence_conv', inputs={ 'X': [input], 'Filter': [filter], }, outputs={"Out": pre_bias}, attrs={ 'contextStride': filter_stride, 'contextStart': -int(filter_size / 2), 'contextLength': filter_size }) pre_act = helper.append_bias_op(pre_bias) return helper.append_activation(pre_act) def conv2d(input, num_filters, filter_size, stride=[1, 1], padding=None, groups=None, param_attr=None, bias_attr=None, act=None, name=None, main_program=None, startup_program=None): """ This function creates the op for a 2-dimensional Convolution. This is performed using the parameters of filters(size, dimensionality etc) , stride and other configurations for a Convolution operation. This funciton can also append an activation on top of the conv-2d output, if mentioned in the input parameters. """ helper = LayerHelper('conv2d', **locals()) dtype = helper.input_dtype() num_channels = input.shape[1] if groups is None: num_filter_channels = num_channels else: if num_channels % groups != 0: raise ValueError("num_channels must be divisible by groups.") num_filter_channels = num_channels / groups if isinstance(filter_size, int): filter_size = [filter_size, filter_size] if isinstance(stride, int): stride = [stride, stride] if isinstance(padding, int): padding = [padding, padding] input_shape = input.shape filter_shape = [num_filters, num_filter_channels] + filter_size def _get_default_param_initializer(): std = (2.0 / (filter_size[0]**2 * num_channels))**0.5 return Normal(0.0, std, 0) filter = helper.create_parameter( attr=helper.param_attr, shape=filter_shape, dtype=dtype, default_initializer=_get_default_param_initializer()) pre_bias = helper.create_tmp_variable(dtype) helper.append_op( type='conv2d', inputs={ 'Input': input, 'Filter': filter, }, outputs={"Output": pre_bias}, attrs={'strides': stride, 'paddings': padding, 'groups': groups}) pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2) return helper.append_activation(pre_act) def sequence_pool(input, pool_type, **kwargs): """ This function add the operator for sequence pooling. This is applied on top of the input using pool_type mentioned in the parameters. """ helper = LayerHelper('sequence_pool', input=input, **kwargs) dtype = helper.input_dtype() pool_out = helper.create_tmp_variable(dtype) max_index = helper.create_tmp_variable(dtype) helper.append_op( type="sequence_pool", inputs={"X": input}, outputs={"Out": pool_out, "MaxIndex": max_index}, attrs={"pooltype": pool_type.upper()}) return pool_out def pool2d(input, pool_size, pool_type, pool_stride=[1, 1], pool_padding=[0, 0], global_pooling=False, main_program=None, startup_program=None): """ This function adds the operator for pooling in 2 dimensions, using the pooling configurations mentioned in input parameters. """ if pool_type not in ["max", "avg"]: raise ValueError( "Unknown pool_type: '%s'. It can only be 'max' or 'avg'.", str(pool_type)) if isinstance(pool_size, int): pool_size = [pool_size, pool_size] if isinstance(pool_stride, int): pool_stride = [pool_stride, pool_stride] if isinstance(pool_padding, int): pool_padding = [pool_padding, pool_padding] helper = LayerHelper('pool2d', **locals()) dtype = helper.input_dtype() pool_out = helper.create_tmp_variable(dtype) helper.append_op( type="pool2d", inputs={"X": input}, outputs={"Out": pool_out}, attrs={ "pooling_type": pool_type, "ksize": pool_size, "global_pooling": global_pooling, "strides": pool_stride, "paddings": pool_padding }) return pool_out def batch_norm(input, act=None, is_test=False, momentum=0.9, epsilon=1e-05, param_attr=None, bias_attr=None, data_layout='NCHW', main_program=None, startup_program=None): """ This function helps create an operator to implement the BatchNorm layer using the configurations from the input parameters. """ helper = LayerHelper('batch_norm', **locals()) dtype = helper.input_dtype() input_shape = input.shape if data_layout == 'NCHW': channel_num = input_shape[1] else: if data_layout == 'NHWC': channel_num = input_shape[-1] else: raise ValueError("unsupported data layout:" + data_layout) param_shape = [channel_num] # create parameter scale = helper.create_parameter( attr=helper.param_attr, shape=param_shape, dtype=dtype, default_initializer=Constant(1.0)) bias = helper.create_parameter( attr=helper.param_attr, shape=param_shape, dtype=dtype, is_bias=True) mean = helper.create_global_variable( dtype=input.dtype, shape=param_shape, persistable=True) helper.set_variable_initializer(var=mean, initializer=Constant(0.0)) variance = helper.create_global_variable( dtype=input.dtype, shape=param_shape, persistable=True) helper.set_variable_initializer(var=variance, initializer=Constant(1.0)) # create output # mean and mean_out share the same memory mean_out = mean # variance and variance out share the same memory variance_out = variance saved_mean = helper.create_tmp_variable(dtype) saved_variance = helper.create_tmp_variable(dtype) batch_norm_out = helper.create_tmp_variable(dtype) helper.append_op( type="batch_norm", inputs={ "X": input, "Scale": scale, "Bias": bias, "Mean": mean, "Variance": variance }, outputs={ "Y": batch_norm_out, "MeanOut": mean_out, "VarianceOut": variance_out, "SavedMean": saved_mean, "SavedVariance": saved_variance }, attrs={"momentum": momentum, "epsilon": epsilon, "is_test": is_test}) return helper.append_activation(batch_norm_out) def beam_search_decode(ids, scores, main_program=None, startup_program=None): helper = LayerHelper('beam_search_decode', **locals()) sentence_ids = helper.create_tmp_variable(dtype=ids.dtype) sentence_scores = helper.create_tmp_variable(dtype=ids.dtype) helper.append_op( type="beam_search_decode", inputs={"Ids": ids, "Scores": scores}, outputs={ "SentenceIds": sentence_ids, "SentenceScores": sentence_scores }) return sentence_ids, sentence_scores 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, main_program=None): self.helper = LayerHelper( "static_rnn", name=name, main_program=main_program) 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, 'step_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, main_program=None): self.helper = LayerHelper("while", name=name, main_program=main_program) 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={'step_block': while_block}) def lstm(x, c_pre_init, hidden_dim, forget_bias=None, main_program=None, startup_program=None): """ This function helps create an operator for the LSTM (Long Short Term Memory) cell that can be used inside an RNN. """ helper = LayerHelper('lstm_unit', **locals()) rnn = StaticRNN() with rnn.step(): c_pre = rnn.memory(init=c_pre_init) x_t = rnn.step_input(x) before_fc = concat( input=[x_t, c_pre], axis=1, main_program=main_program, startup_program=startup_program) after_fc = fc(input=before_fc, size=hidden_dim * 4, main_program=main_program, startup_program=startup_program) dtype = x.dtype c = helper.create_tmp_variable(dtype) h = helper.create_tmp_variable(dtype) helper.append_op( type='lstm_unit', inputs={"X": after_fc, "C_prev": c_pre}, outputs={"C": c, "H": h}, attrs={"forget_bias": forget_bias}) rnn.update_memory(c_pre, c) rnn.output(h) return rnn() def lod_rank_table(x, level=0, main_program=None): """ 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, main_program=None): """ 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, main_program=None, startup_program=None): 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, main_program=None): """ 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, main_program=None): """ 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 fill_constant(shape, dtype, value, out=None, main_program=None, startup_program=None): """ This function creates a tensor , with shape as mentioned in the input and specified dtype and fills this up with a constant value that comes in the input. It also sets the stop_gradient to be True. """ helper = LayerHelper("fill_constant", **locals()) if out is None: out = helper.create_tmp_variable(dtype=dtype) helper.append_op( type='fill_constant', inputs={}, outputs={'Out': [out]}, attrs={'shape': shape, 'dtype': out.dtype, 'value': float(value)}) out.stop_gradient = True return out def fill_constant_batch_size_like(input, shape, dtype, value, input_dim_idx=0, output_dim_idx=0, main_program=None, startup_program=None): helper = LayerHelper("fill_constant_batch_size_like", **locals()) out = helper.create_tmp_variable(dtype=dtype) helper.append_op( type='fill_constant_batch_size_like', inputs={'Input': input}, outputs={'Out': [out]}, attrs={ 'shape': shape, 'dtype': out.dtype, 'value': float(value), 'input_dim_idx': input_dim_idx, 'output_dim_idx': output_dim_idx }) out.stop_gradient = True return out def ones(shape, dtype, main_program=None): """ This function performs the same function as fill_constant() declared above with the constant value being 1.0. """ return fill_constant(value=1.0, **locals()) def zeros(shape, dtype, main_program=None): """ This function performs the same function as fill_constant() declared above with the constant value being 0.0. """ return fill_constant(value=0.0, **locals()) def increment(x, value=1.0, in_place=True, main_program=None): """ 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, main_program=None): """ This function creates an operator to write the data out as a LOD_TENSOR_ARRAY. """ 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, main_program=None): 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, main_program=None, **ignored): 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, main_program=None): """ This function creates an operator to read the data in as a LOD_TENSOR_ARRAY. """ 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, main_program=None): """ 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, main_program=None): """ This function creates an operator to find the length of the LOD_TENSOR_ARRAY. """ 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 def conv2d_transpose(input, num_filters, output_size=None, filter_size=None, padding=None, stride=None, param_attr=None, main_program=None, startup_program=None): """ The transpose of conv2d layer. This layer is also known as deconvolution layer. Args: input(Variable): The input image with [N, C, H, W] format. num_filters(int): The number of filter. It is as same as the output image channel. output_size(int|tuple|None): The output image size. If output size is a tuple, it must contain two integers, (image_H, image_W). This parameter only works when filter_size is None. filter_size(int|tuple|None): The filter size. If filter_size is a tuple, it must contain two integers, (filter_size_H, filter_size_W). Otherwise, the filter will be a square. None if use output size to calculate filter_size padding(int|tuple): The padding size. If padding is a tuple, it must contain two integers, (padding_H, padding_W). Otherwise, the padding_H = padding_W = padding. stride(int|tuple): The stride size. If stride is a tuple, it must contain two integers, (stride_H, stride_W). Otherwise, the stride_H = stride_W = stride. param_attr: Parameter Attribute. main_program(Program): the main program startup_program(Program): the startup program Returns: Variable: Output image. """ helper = LayerHelper("conv2d_transpose", **locals()) if not isinstance(input, Variable): raise TypeError("Input of conv2d_transpose must be Variable") input_channel = input.shape[1] op_attr = dict() if isinstance(padding, int): op_attr['paddings'] = [padding, padding] elif padding is not None: op_attr['paddings'] = padding if isinstance(stride, int): op_attr['strides'] = stride elif stride is not None: op_attr['strides'] = stride if filter_size is None: if output_size is None: raise ValueError("output_size must be set when filter_size is None") if isinstance(output_size, int): output_size = [output_size, output_size] padding = op_attr.get('paddings', [0, 0]) stride = op_attr.get('strides', [1, 1]) h_in = input.shape[2] w_in = input.shape[3] filter_size_h = output_size[0] - (h_in - 1) * stride[0] + 2 * padding[0] filter_size_w = output_size[1] - (w_in - 1) * stride[1] + 2 * padding[1] filter_size = [filter_size_h, filter_size_w] elif isinstance(filter_size, int): filter_size = [filter_size, filter_size] filter_shape = [input_channel, num_filters] + filter_size img_filter = helper.create_parameter( dtype=input.dtype, shape=filter_shape, attr=helper.param_attr) out = helper.create_tmp_variable(dtype=input.dtype) helper.append_op( type='conv2d_transpose', inputs={'Input': [input], 'Filter': [img_filter]}, outputs={'Output': out}, attrs=op_attr) return out 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, main_program=None, startup_program=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, main_program=main_program, startup_program=startup_program) 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={'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, main_program=None, startup_program=None): if not isinstance(cond, Variable): raise TypeError("cond must be a Variable") self.helper = LayerHelper( 'ifelse', name=name, main_program=main_program, startup_program=startup_program) 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, main_program=self.helper.main_program, startup_program=self.helper.startup_program) 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, main_program=self.helper.main_program, startup_program=self.helper.startup_program)) return rlist