# 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. from ..wrapped_decorator import signature_safe_contextmanager from .layer_function_generator import templatedoc from .tensor import fill_constant from .. import core from ..framework import ( Program, Variable, Operator, static_only, in_dygraph_mode, ) from ..layer_helper import LayerHelper, unique_name from ...utils import ( assert_same_structure, map_structure, hold_mutable_vars, copy_mutable_vars, is_sequence, pack_sequence_as, flatten, to_sequence, ) import numpy import warnings from functools import reduce, partial from ..data_feeder import ( convert_dtype, check_variable_and_dtype, check_type, check_dtype, ) from ..backward import _infer_var_data_type_shape_ import paddle from paddle import _C_ops, _legacy_C_ops __all__ = [ 'Switch', 'StaticRNN', 'Print', 'while_loop', ] def select_output(input, outputs, mask): """ **select_output** This API takes in one input and multiple outputs and an integer mask. It selects the output specified by the mask and copy the input to selected output. It is useful in control flow. Args: input(Variable): The input variable outputs(tuple|list): The output variables mask(Variable): A tensor containing 1 integer number selecting which output to be copied with input Returns: Variable: The outputs variables """ helper = LayerHelper('select_output', **locals()) check_type(input, 'input', (Variable), 'select_output') check_variable_and_dtype(mask, 'mask', ['int32'], 'select_output') check_type(outputs, 'outputs', (list, tuple), 'select_output') helper.append_op( type='select_output', inputs={'X': input, 'Mask': mask}, outputs={'Out': outputs}, ) return outputs def _select_input_infer_shape(first_shape, second_shape): """ This function infer the output shape by following algorithm: 1. if the dims is different, raise a error. 2. compare axis one by one: if a == b: we set axis to a if a != b: we set axis to -1 for compatibility, non declarative mode, we just return second_shape. """ if len(first_shape) != len(second_shape): warnings.warn( f"the input shapes of select_input should have the same rank, but get {first_shape}, {second_shape}" ) return second_shape out_shape = list( map(lambda a, b: a if a == b else -1, first_shape, second_shape) ) return out_shape def select_input(inputs, mask): """ **select_input** This API takes in multiple inputs and uses an integer mask to select one input to output. It is useful in control flow. Args: inputs(tuple|list): The input variables mask(Variable): A tensor containing 1 integer number selecting which input to output Returns: Variable: The selected input variable """ helper = LayerHelper('select_input', **locals()) check_type(inputs, 'inputs', (list, tuple), 'select_input') check_variable_and_dtype(mask, 'mask', ['int32'], 'select_input') # Select input should expand the shape. If it is - 1 and valid number, use - 1 first. If the dim is different, an error will be reported directly # assert inputs[0].dtype == inputs[1].dtype, f"Expect the inputs should have the same dtype, but get {inputs[0].dtype} and {inputs[1].dtype}" output_shape = _select_input_infer_shape(inputs[0].shape, inputs[1].shape) output_dtype = inputs[1].dtype output_type = inputs[1].type out = helper.create_variable( dtype=output_dtype, shape=output_shape, type=output_type ) helper.append_op( type='select_input', inputs={'X': inputs, 'Mask': mask}, outputs={'Out': out}, ) return out @static_only def Print( input, first_n=-1, message=None, summarize=20, print_tensor_name=True, print_tensor_type=True, print_tensor_shape=True, print_tensor_layout=True, print_tensor_lod=True, print_phase='both', ): ''' :api_attr: Static Graph **Print operator** This creates a print op that will print when a tensor is accessed. Wraps the tensor passed in so that whenever that a tensor is accessed, the message `message` is printed, along with the current value of the tensor `t`. Args: input (Tensor): A Tensor to print. first_n (int, optional): Only log `first_n` number of times. Default: -1. message (str, optional): A string message to print as a prefix. Default: None. summarize (int, optional): Number of elements in the tensor to be print. If it's value is -1, then all elements in the tensor will be print. print_tensor_name (bool, optional): Print the tensor name. Default: True. print_tensor_type (bool, optional): Print the tensor type. Defaultt: True. print_tensor_shape (bool, optional): Print the tensor shape. Default: True. print_tensor_layout (bool, optional): Print the tensor layout. Default: True. print_tensor_lod (bool, optional): Print the tensor lod. Default: True. print_phase (str, optional): Which phase to displace, including 'forward', 'backward' and 'both'. Default: 'both'. If set to 'backward', will only print the gradients of input tensor; If set to 'both', will both print the input tensor itself and the gradients of input tensor. Returns: Tensor: Output tensor. NOTES: The input and output are two different Tensor, and in the following process, you should use the output Tensor but not the input, otherwise, the print layer doesn't have backward. Examples: .. code-block:: python import paddle paddle.enable_static() x = paddle.full(shape=[2, 3], fill_value=3, dtype='int64') out = paddle.static.Print(x, message="The content of input layer:") main_program = paddle.static.default_main_program() exe = paddle.static.Executor(place=paddle.CPUPlace()) res = exe.run(main_program, fetch_list=[out]) # Variable: fill_constant_1.tmp_0 # - message: The content of input layer: # - lod: {} # - place: CPUPlace # - shape: [2, 3] # - layout: NCHW # - dtype: long # - data: [3 3 3 3 3 3] ''' check_variable_and_dtype( input, 'input', ['float32', 'float64', 'int32', 'int64', 'bool'], 'fluid.layers.Print', ) helper = LayerHelper('print' + "_" + input.name, **locals()) output = helper.create_variable_for_type_inference(input.dtype) helper.append_op( type='print', inputs={'In': input}, outputs={'Out': output}, attrs={ 'first_n': first_n, 'summarize': summarize, 'message': message or "", 'print_tensor_name': print_tensor_name, 'print_tensor_type': print_tensor_type, 'print_tensor_shape': print_tensor_shape, 'print_tensor_layout': print_tensor_layout, 'print_tensor_lod': print_tensor_lod, 'print_phase': print_phase.upper(), }, ) return output # (TODO: Mine) There exists dependency. It will be removed later. class BlockGuard: """ 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 # (TODO: Mine) There exists dependency. It will be removed later. class BlockGuardWithCompletion(BlockGuard): """ BlockGuardWithCompletion class. BlockGuardWithCompletion class is used to create an op with a block in a program. """ def __init__(self, rnn): if not isinstance(rnn, StaticRNN): raise TypeError("BlockGuardWithCompletion takes a StaticRNN") super().__init__(rnn.helper.main_program) self.rnn = rnn def __enter__(self): self.rnn.status = StaticRNN.IN_RNN_BLOCK return super().__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_op() return super().__exit__(exc_type, exc_val, exc_tb) class StaticRNNMemoryLink: """ StaticRNNMemoryLink class. StaticRNNMemoryLink class is used to create a link between two memory cells of a StaticRNN. NOTE: This is a internal data structure of a very low-level API. Please use StaticRNN instead. Args: init(Variable): the initial variable for Memory. pre_mem(Variable): the memory variable in previous time step. mem(Variable): the memory variable in current time step. """ def __init__(self, init, pre_mem, mem=None): self.init = init self.pre_mem = pre_mem self.mem = mem class StaticRNN: """ :api_attr: Static Graph StaticRNN class. The StaticRNN can process a batch of sequence data. The first dimension of inputs represents sequence length, the length of each input sequence must be equal. StaticRNN will unfold sequence into time steps, user needs to define how to process each time step during the :code:`with` step. Args: name (str, optional): Please refer to :ref:`api_guide_Name`, Default None. Examples: .. code-block:: python import paddle import paddle.fluid as fluid import paddle.fluid.layers as layers vocab_size, hidden_size=10000, 200 paddle.enable_static() x = fluid.data(name="x", shape=[None, 1, 1], dtype='int64') # create word sequence x_emb = layers.embedding( input=x, size=[vocab_size, hidden_size], dtype='float32', is_sparse=False) # transform batch size to dim 1 x_emb = paddle.transpose(x_emb, perm=[1, 0, 2]) rnn = fluid.layers.StaticRNN() with rnn.step(): # mark created x_emb as input, each step process a word word = rnn.step_input(x_emb) # create prev memory parameter, batch size comes from word prev = rnn.memory(shape=[-1, hidden_size], batch_ref = word) hidden = paddle.static.nn.fc(x=[word, prev], size=hidden_size, activation='relu') # use hidden to update prev rnn.update_memory(prev, hidden) # mark hidden as output rnn.step_output(hidden) # get StaticrNN final output result = rnn() """ BEFORE_RNN_BLOCK = 0 IN_RNN_BLOCK = 1 AFTER_RNN_BLOCK = 2 def __init__(self, name=None): check_type(name, "name", (str, type(None)), "fluid.layers.StaticRNN") 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): """ Define operators in each step. step is used in :code:`with` block, OP in :code:`with` block will be executed sequence_len times (sequence_len is the length of input) """ return BlockGuardWithCompletion(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, ): """ Create a memory variable for static rnn. If the :code:`init` is not None, :code:`memory` will be initialized by this Variable. If the :code:`init` is None, :code:`shape` and :code:`batch_ref` must be set, and this function will create a new variable with shape and batch_ref to initialize :code:`init` Variable. Args: init(Variable, optional): Tensor used to init memory. If it is not set, :code:`shape` and :code:`batch_ref` must be provided. Default: None. shape(list|tuple): When :code:`init` is None use this arg to initialize memory shape. NOTE the shape does not contain batch_size. Default: None. batch_ref(Variable, optional): When :code:`init` is None, memory's batch size will be set as batch_ref's ref_batch_dim_idx value. Default: None. init_value(float, optional): When :code:`init` is None, used to init memory's value. Default: 0.0. init_batch_dim_idx(int, optional): the batch_size axis of the :code:`init` Variable. Default: 0. ref_batch_dim_idx(int, optional): the batch_size axis of the :code:`batch_ref` Variable. Default: 1. Returns: Variable: The memory variable. Examples 1: .. code-block:: python import paddle import paddle.fluid as fluid import paddle.fluid.layers as layers vocab_size, hidden_size=10000, 200 paddle.enable_static() x = fluid.data(name="x", shape=[None, 1, 1], dtype='int64') # create word sequence x_emb = layers.embedding( input=x, size=[vocab_size, hidden_size], dtype='float32', is_sparse=False) # transform batch size to dim 1 x_emb = paddle.transpose(x_emb, perm=[1, 0, 2]) rnn = fluid.layers.StaticRNN() with rnn.step(): # mark created x_emb as input, each step process a word word = rnn.step_input(x_emb) # create prev memory parameter, batch size comes from word prev = rnn.memory(shape=[-1, hidden_size], batch_ref = word) hidden = paddle.static.nn.fc(x=[word, prev], size=hidden_size, activation='relu') # use hidden to update prev rnn.update_memory(prev, hidden) Examples 2: .. code-block:: python import paddle import paddle.fluid as fluid import paddle.fluid.layers as layers vocab_size, hidden_size=10000, 200 paddle.enable_static() x = fluid.data(name="x", shape=[None, 1, 1], dtype='int64') # create word sequence x_emb = layers.embedding( input=x, size=[vocab_size, hidden_size], dtype='float32', is_sparse=False) # transform batch size to dim 1 x_emb = paddle.transpose(x_emb, perm=[1, 0, 2]) boot_memory = paddle.static.data(name='boot', shape=[-1, hidden_size], dtype='float32', lod_level=1) rnn = fluid.layers.StaticRNN() with rnn.step(): # mark created x_emb as input, each step process a word word = rnn.step_input(x_emb) # init memory prev = rnn.memory(init=boot_memory) hidden = paddle.static.nn.fc(x=[word, prev], size=hidden_size, activation='relu') # update hidden with prev rnn.update_memory(prev, hidden) """ self._assert_in_rnn_block_('memory') check_type( init, "init", (Variable, type(None)), "fluid.layers.StaticRNN.memory", ) check_type( shape, "shape", (list, tuple, type(None)), "fluid.layers.StaticRNN.memory", ) check_type( batch_ref, "batch_ref", (Variable, type(None)), "fluid.layers.StaticRNN.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.generate_with_ignorable_key( "@".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.generate_with_ignorable_key( "@".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): """ Mark a sequence as a StaticRNN input. Args: x(Variable): The input sequence, the shape of x should be [seq_len, ...]. Returns: Variable: The current time step data in the input sequence. Examples: .. code-block:: python import paddle import paddle.fluid as fluid import paddle.fluid.layers as layers vocab_size, hidden_size=10000, 200 paddle.enable_static() x = fluid.data(name="x", shape=[None, 1, 1], dtype='int64') # create word sequence x_emb = layers.embedding( input=x, size=[vocab_size, hidden_size], dtype='float32', is_sparse=False) # transform batch size to dim 1 x_emb = paddle.transpose(x_emb, perm=[1, 0, 2]) rnn = fluid.layers.StaticRNN() with rnn.step(): # mark created x_emb as input, each step process a word word = rnn.step_input(x_emb) # create prev memory parameter, batch size comes from word prev = rnn.memory(shape=[-1, hidden_size], batch_ref = word) hidden = paddle.static.nn.fc(x=[word, prev], size=hidden_size, activation='relu') # use hidden to update prev rnn.update_memory(prev, hidden) """ self._assert_in_rnn_block_('step_input') check_type(x, "x", Variable, "fluid.layers.StaticRNN.step_input") if self.seq_len is None: self.seq_len = x.shape[0] elif x.shape[0] != -1 and 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): """ Mark a sequence as a StaticRNN output. Args: o(Variable): The output sequence. Returns: None. Examples: .. code-block:: python import paddle import paddle.fluid as fluid import paddle.fluid.layers as layers vocab_size, hidden_size=10000, 200 paddle.enable_static() x = fluid.data(name="x", shape=[None, 1, 1], dtype='int64') # create word sequence x_emb = layers.embedding( input=x, size=[vocab_size, hidden_size], dtype='float32', is_sparse=False) # transform batch size to dim 1 x_emb = paddle.transpose(x_emb, perm=[1, 0, 2]) rnn = fluid.layers.StaticRNN() with rnn.step(): # mark created x_emb as input, each step process a word word = rnn.step_input(x_emb) # create prev memory parameter, batch size comes from word prev = rnn.memory(shape=[-1, hidden_size], batch_ref = word) hidden = paddle.static.nn.fc(x=[word, prev], size=hidden_size, activation='relu') # use hidden to update prev rnn.update_memory(prev, hidden) rnn.step_output(hidden) result = rnn() """ self._assert_in_rnn_block_('step_output') check_type(o, "o", Variable, "fluid.layers.StaticRNN.step_output") tmp_o = self.helper.create_variable_for_type_inference(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): """ Mark the StaticRNN output variables. Args: outputs: The output Tensor, can mark multiple variables as output Returns: None Examples: .. code-block:: python import paddle import paddle.fluid as fluid import paddle.fluid.layers as layers vocab_size, hidden_size=10000, 200 paddle.enable_static() x = fluid.data(name="x", shape=[None, 1, 1], dtype='int64') # create word sequence x_emb = layers.embedding( input=x, size=[vocab_size, hidden_size], dtype='float32', is_sparse=False) # transform batch size to dim 1 x_emb = paddle.transpose(x_emb, perm=[1, 0, 2]) rnn = fluid.layers.StaticRNN() with rnn.step(): # mark created x_emb as input, each step process a word word = rnn.step_input(x_emb) # create prev memory parameter, batch size comes from word prev = rnn.memory(shape=[-1, hidden_size], batch_ref = word) hidden = paddle.static.nn.fc(x=[word, prev], size=hidden_size, activation='relu') # use hidden to update prev rnn.update_memory(prev, hidden) # mark each step's hidden and word as output rnn.output(hidden, word) result = rnn() """ for each in outputs: self.step_output(each) def update_memory(self, mem, var): """ Update the memory from :code:`mem` to :code:`var`. Args: mem(Variable): the memory variable. var(Variable): the plain variable generated in RNN block, used to update memory. var and mem should have same dims and data type. Returns: None """ check_type(mem, "mem", Variable, "fluid.layers.StaticRNN.update_memory") check_type(var, "var", Variable, "fluid.layers.StaticRNN.update_memory") 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_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) # NOTE(zcd): the params have two categories of variables. # - the variables that are the out of StaticRnn. # - the variables that are the parameters of some layers, for example, conv2d. 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._find_var_recursive(name) for name in set(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 # NOTE(zcd): the states maybe empty in some case. boot_memories = [] pre_memories = [] memories = [] for _, mem in self.memories.items(): boot_memories.append(mem.init) pre_memories.append(mem.pre_mem.name) assert ( mem.mem is not None ), "%s should be updated in every step." % (mem.init.name) mem_var = rnn_block.var(mem.mem.name) assert isinstance(mem_var, Variable) new_mem = self.helper.create_variable_for_type_inference( 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={ 'has_states': len(pre_memories) > 0, 'ex_states': pre_memories, 'states': memories, 'sub_block': rnn_block, }, ) # (TODO: Mine) There exists dependency. It will be removed later. class WhileGuard(BlockGuard): def __init__(self, while_op): if not isinstance(while_op, While): raise TypeError("WhileGuard takes a while op") super().__init__(while_op.helper.main_program) self.while_op = while_op def __enter__(self): self.while_op.status = While.IN_WHILE_BLOCK return super().__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().__exit__(exc_type, exc_val, exc_tb) # (TODO: Mine) There exists dependency. It will be removed later. def get_inputs_outputs_in_block( current_block, inner_inputs, inner_outputs, helper ): """ Find inputs and outputs in current control flow block. :param current_block: Current control flow block. :param inner_inputs: Input var name of ops in current block. :param inner_outputs: Output var name of ops in current block. :return: inner_inputs, inner_outputs """ def is_ignore_vars(op, var_name): # NOTE(dev): There are some persistable var created in some non-standard API # such as "contrib.layers.shuffle_batch". It create a "Seed" used both in # Input and Output. This var shall not be considered as a loop_var in # control_flow. IGNORE_VAR_NAMES = {"shuffle_batch": ["shuffle_batch_seed"]} if op.type in IGNORE_VAR_NAMES: var_names = IGNORE_VAR_NAMES[op.type] for name in var_names: if name in var_name: return True return False # Step1: update inner_inputs and inner_outputs # NOTE: Here assumes that all variables are input or output of Ops, # but some variables are created without appendding a real op. # For example, in `arr = create_array(dtype)`, `arr` is not a output of a op. for op in current_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 inner_outputs and not is_ignore_vars( op, in_var_name ): inner_inputs.add(in_var_name) for oname in op.output_names: for out_var_name in op.output(oname): inner_outputs.add(out_var_name) # Step2: Remove LOD_TENSOR_ARRAY created in current control flow block. remove_inner_inputs = set() parent_block = helper.main_program.block(current_block.parent_idx) for in_var_name in inner_inputs: parent_block_var = parent_block._find_var_recursive(in_var_name) current_block_var = None if current_block.has_var(in_var_name): current_block_var = current_block.var(in_var_name) if ( not parent_block_var and current_block_var and current_block_var.type == core.VarDesc.VarType.LOD_TENSOR_ARRAY ): remove_inner_inputs.add(in_var_name) inner_inputs = inner_inputs - remove_inner_inputs return inner_inputs, inner_outputs # (TODO: Mine) There exists dependency. It will be removed later. class While: """ :api_attr: Static Graph while loop control flow. Repeat while body until cond is False. Note: A new OP :ref:`api_fluid_layers_while_loop` is highly recommended instead of ``While`` if the shape of parameter ``cond`` is [1]. OP :ref:`api_fluid_layers_while_loop` is easier to use and is called with less code but does the same thing as ``While`` . Notice: Local variables created in ``While`` are similar to that created in while of C++, and cannot be referenced externally. As a result, they cannot be obtained through ``fetch_list`` of ``Executor``. If you would like to access the variable out of ``while`` , PaddlePaddle provides ``assign`` API to assign local variables to external. Please refer to example code 2 or refer to `issue#22724 `_. Args: cond(Variable): A Tensor whose data type is bool controlling whether to continue looping. is_test(bool, optional): A flag indicating whether execution is in test phase. Default value is False. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Examples 1: .. code-block:: python import paddle.fluid as fluid import numpy as np i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0) # loop counter loop_len = fluid.layers.fill_constant(shape=[1],dtype='int64', value=10) # loop length cond = paddle.less_than(x=i, y=loop_len) while_op = fluid.layers.While(cond=cond) with while_op.block(): i = paddle.increment(x=i, value=1) paddle.assign(paddle.less_than(x=i, y=loop_len), cond) exe = fluid.Executor(fluid.CPUPlace()) exe.run(fluid.default_startup_program()) res = exe.run(fluid.default_main_program(), feed={}, fetch_list=[i]) print(res) # [array([10])] Examples 2: .. code-block:: python import paddle import paddle.fluid as fluid import numpy as np paddle.enable_static() i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0) loop_len = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10) one = fluid.layers.fill_constant(shape=[1], dtype='float32', value=1) data = fluid.data(name='data', shape=[1], dtype='float32') sums = fluid.layers.fill_constant(shape=[1], dtype='float32', value=0) # Define the variable to be obtained ouside of While, which name should be different from the variable inside the While to be obtained cond = paddle.less_than(x=i, y=loop_len) while_op = fluid.layers.While(cond=cond) with while_op.block(): sums_tensor = paddle.add(x=data, y=data) fluid.layers.assign(sums_tensor, sums) # Update the value of sums_tensor defined in While to the sums which defined outside of While through layers.assign i = paddle.increment(x=i, value=1) data = paddle.add(x=data, y=one) paddle.assign(paddle.less_than(x=i, y=loop_len), cond) feed_data = np.ones(1).astype('float32') exe = fluid.Executor(fluid.CPUPlace()) exe.run(fluid.default_startup_program()) res = exe.run(fluid.default_main_program(), feed={'data': feed_data}, fetch_list=sums) print(res[0]) # [2.] # Because the data in While does not update the value outside the While, the value of sums is [2.] after the loop """ BEFORE_WHILE_BLOCK = 0 IN_WHILE_BLOCK = 1 AFTER_WHILE_BLOCK = 2 def __init__(self, cond, is_test=False, name=None): self.helper = LayerHelper("while", name=name) self.status = While.BEFORE_WHILE_BLOCK check_variable_and_dtype(cond, 'cond', ['bool'], 'fluid.layers.While') if reduce(lambda a, b: a * b, cond.shape, 1) != 1: raise TypeError( "condition expected shape as [1], but given shape as {0}.".format( list(cond.shape) ) ) self.cond_var = cond self.is_test = is_test 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() x_name_list, inner_outputs = get_inputs_outputs_in_block( while_block, x_name_list, inner_outputs, self.helper ) out_vars = [] for inner_out_name in inner_outputs: inner_var = parent_block._find_var_recursive(inner_out_name) if inner_var: out_vars.append(inner_var) x_name_list |= set(map(lambda x: x.name, out_vars)) # NOTE(dev): cond_var has been contained in Input('Condition'), so # we remove it from Input('X') x_name_list -= {self.cond_var.name} step_scope = parent_block.create_var( type=core.VarDesc.VarType.STEP_SCOPES ) parent_block.append_op( type='while', inputs={ 'X': [ parent_block._var_recursive(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, "is_test": self.is_test}, ) support_ret_buildin_type = (bool, float, int) # (TODO: Mine) There exists dependency. It will be removed later. def assign_skip_lod_tensor_array(input, output): """ Assign input to output, but skip the process of copying LoDTensorArray unless it's created in while_block. """ def has_shape_diff(x_var, y_var): if len(x_var.shape) != len(y_var.shape): return True for x_dim, y_dim in zip(x_var.shape, y_var.shape): if x_dim != y_dim and -1 not in [x_dim, y_dim]: return True return False if not isinstance(input, (Variable, core.VarBase)): if isinstance(output, Variable) and isinstance( input, support_ret_buildin_type ): paddle.assign(input, output) else: output = input return if input.type == core.VarDesc.VarType.LOD_TENSOR_ARRAY: main_program = input.block.program parent_block = main_program.block( main_program.current_block().parent_idx ) if parent_block and not parent_block._find_var_recursive(input.name): paddle.assign(input, output) else: if ( isinstance(output, Variable) and isinstance(input, Variable) and has_shape_diff(input, output) ): warnings.warn( "In dy2static mode, we attemp to assign a variable with shape {} into a variable with shape{}, which is not always right.".format( input.shape, output.shape ) ) paddle.assign(input, output) # (TODO: Mine) There exists dependency (jit.dy2static.convert_operators). It will be removed later. def while_loop(cond, body, loop_vars, is_test=False, name=None): """ :api_attr: Static Graph while_loop is one of the control flows. Repeats while_loop `body` until `cond` returns False. Notice: Local variables defined in ``body`` cannot be obtained through ``fetch_list`` of ``Executor`` , variables should be defined outside ``body`` and placed in ``loop_vars`` for looping, then these variables can be fetched by ``fetch_list`` . Args: cond(Callable): A callable returning a boolean tensor controlling whether to continue looping. And ``cond`` takes as many arguments as ``loop_vars`` . body(Callable): A callable returning a tuple or list of tensors or LoDTensorArrays of the same arity (length and structure) and types as ``loops_vars`` . And ``body`` takes as many arguments as ``loop_vars`` . loop_vars(list|tuple): A list or tuple of tensors or LoDTensorArrays that is passed to both ``cond`` and ``body`` . is_test(bool, optional): A flag indicating whether execution is in test phase. Default value is False. name(str, optional): Normally there is no need for users to set this property. For more information, please refer to :ref:`api_guide_Name`. Default is None. Returns: A list or tuple of Tensors or LoDTensorArrays which returned by ``body`` . Examples: .. code-block:: python import paddle paddle.enable_static() def cond(i, ten): return i < ten def body(i, ten): i = i + 1 return [i, ten] main_program = paddle.static.default_main_program() startup_program = paddle.static.default_startup_program() with paddle.static.program_guard(main_program, startup_program): i = paddle.full(shape=[1], fill_value=0, dtype='int64') # loop counter ten = paddle.full(shape=[1], fill_value=10, dtype='int64') # loop length i, ten = paddle.static.nn.while_loop(cond, body, [i, ten]) exe = paddle.static.Executor(paddle.CPUPlace()) res = exe.run(main_program, feed={}, fetch_list=[i]) print(res) # [array([10])] """ helper = LayerHelper('while_loop', **locals()) if not callable(cond): raise TypeError("cond in while_loop should be callable") if not callable(body): raise TypeError("body in while_loop should be callable") check_type(loop_vars, 'loop_vars', (list, tuple), 'fluid.layers.while_loop') if len(loop_vars) == 0: raise ValueError("loop_vars in while_loop should not be empty") pre_cond = cond(*loop_vars) if reduce(lambda a, b: a * b, pre_cond.shape, 1) != 1: raise TypeError( "the shape of the variable returned by cond should be [1]," "but given shape as {0}.".format(list(pre_cond.shape)) ) if in_dygraph_mode(): now_cond = pre_cond.numpy().item() while now_cond: output_vars = body(*loop_vars) if not isinstance(output_vars, (list, tuple)): output_vars = [output_vars] if len(output_vars) != len(loop_vars): raise ValueError( "body in while_loop should return the same arity " "(length and structure) and types as loop_vars" ) now_cond = cond(*output_vars).numpy().item() map_structure(assign_skip_lod_tensor_array, output_vars, loop_vars) return loop_vars else: check_variable_and_dtype( pre_cond, 'var of cond returned', ['bool'], 'fluid.layers.while_loop', ) while_loop_block = While(pre_cond, is_test, name) has_mutable_vars_in_loop = hold_mutable_vars(loop_vars) with while_loop_block.block(): # If a variable with mutable type is included in loop_vars, like `dict/list`, # modifying it in the body function will cause origin variable to be modified # synchronously. This will raise an assignment error out of while block. # Here we make a copy of the mutable vars to avoid this problem. if has_mutable_vars_in_loop: new_loop_vars = copy_mutable_vars(loop_vars) output_vars = body(*new_loop_vars) else: output_vars = body(*loop_vars) if not isinstance(output_vars, (list, tuple)): output_vars = [output_vars] try: loop_vars = _deal_with_undefined_var(output_vars, loop_vars) assert_same_structure(output_vars, loop_vars, check_types=False) except ValueError as e: raise ValueError( "body in while_loop should return the same arity " "(length and structure) as loop_vars: {0}".format(e) ) now_cond = cond(*output_vars) map_structure(assign_skip_lod_tensor_array, output_vars, loop_vars) paddle.assign(now_cond, pre_cond) return loop_vars # (TODO: Mine) There exists dependency. It will be removed later. def _deal_with_undefined_var(output_vars, loop_vars): """Deal with undefined var cases, We create undefined variable based on the results of body(). In Dy2Static, we use undefined var to represent the var created in control flow. This function expand the loop_vars and replace original loop_vars. 1. UndefinedVar = Variable # create a variable 2. UndefinedVar = None # create a undefined var with RETURN_NO_VALUE_MAGIC_NUM 3. UndefinedVar = List(int) # create a list of variable 4. UndefinedVar = value # create a variable """ from paddle.jit.dy2static.utils import ( UndefinedVar, create_undefined_variable, ) def create_var_like(o_var): if ( isinstance(o_var, (Variable,) + support_ret_buildin_type) or o_var is None ): return create_undefined_variable() if is_sequence(o_var): """ Create a complex container class inside the body of while, including Python list and python Dict """ return map_structure(lambda x: create_undefined_variable(), o_var) if len(output_vars) != len(loop_vars): raise ValueError("The length of loop_vars should be the same.") results = [] for o_var, l_var in zip(output_vars, loop_vars): if isinstance(l_var, UndefinedVar) or l_var is None: results.append(create_var_like(o_var)) else: results.append(l_var) return results class ConditionalBlockGuard(BlockGuard): """ ConditionalBlockGuard is derived from BlockGuard. It is dedicated for holding a ConditionalBlock, and helping users entering and exiting the ConditionalBlock via Python's 'with' keyword. However, ConditionalBlockGuard is generally an internal component of IfElse, users should not use it directly. """ def __init__(self, block): check_type(block, "block", ConditionalBlock, "ConditionalBlockGuard") super().__init__(block.helper.main_program) self.block = block def __enter__(self): return super().__enter__() def __exit__(self, exc_type, exc_val, exc_tb): self.block.complete() return super().__exit__(exc_type, exc_val, exc_tb) class ConditionalBlock: ''' **ConditionalBlock** ConditionalBlock is an operator that bind a block to a specific condition, if the condition matches, the corresponding block will be executed. Args: inputs (Variable): bool conditions. is_scalar_condition (bool): whether the branch is controlled by a scalar. name(str): name of this ConditionalBlock. Examples: .. code-block:: python import paddle import paddle.fluid as fluid cond = paddle.less_than(x=label, y=limit) true_image, false_image = layers.split_lod_tensor( input=image, mask=cond) true_cond = layers.ConditionalBlock([true_image]) with true_cond.block(): ... with false_cond.block(): ... ''' def __init__(self, inputs, is_scalar_condition=False, name=None): for each_input in inputs: check_type(each_input, "input", Variable, "ConditionalBlock") self.inputs = inputs self.is_scalar_condition = is_scalar_condition 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() params, intermediate = get_inputs_outputs_in_block( inside_block, params, intermediate, helper=self.helper ) # Todo(liym27) Here assume that all params are in recursive parent block # but when minimize() called in control flow, some params may be in # conditional grad block param_list = [ parent_block._var_recursive(each_name) for each_name in params ] out_list = [] for inner_out_name in intermediate: inner_var = parent_block._find_var_recursive(inner_out_name) if inner_var: out_list.append(inner_var) step_scope = parent_block.create_var( type=core.VarDesc.VarType.STEP_SCOPES ) conditional_block_op = parent_block.append_op( type='conditional_block', inputs={ 'Cond': self.inputs, 'Input': param_list, }, outputs={'Out': out_list, 'Scope': [step_scope]}, attrs={ 'sub_block': inside_block, 'is_scalar_condition': self.is_scalar_condition, }, ) if self.need_append_conditional_block_grad(inside_block): self.append_conditional_block_grad( parent_block, inside_block, conditional_block_op ) def need_append_conditional_block_grad(self, inside_block): grad_sub_block_idx = inside_block.backward_block_idx inside_block_idx = inside_block.idx # if inside_block have grad_block and grad_block is not itself, # we will append conditional block grad. return ( grad_sub_block_idx != -1 and grad_sub_block_idx != inside_block_idx ) def append_conditional_block_grad( self, parent_block, inside_block, conditional_block_op ): ''' Append op `conditional_block_grad` manually. When `optimizer.minimize/append_backward` is called in Paddle control flow, grad ops will be appended before appending op `conditional_block` so that op `conditional_block_grad` can't be appended when calling `optimizer.minimize/append_backward`. After appending op `conditional_block`, `conditional_block_grad` is appended manually. Args: parent_block (Block): The block that `conditional_block_op` blongs to. inside_block (Block): The sub block of `conditional_block_op`. conditional_block_op (Operator): The forward op conditional_block. ''' grad_sub_block_idx = inside_block.backward_block_idx grad_sub_block = self.helper.main_program.block(grad_sub_block_idx) intermediate = set() params = set() for each_op in grad_sub_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) param_list = [] for inner_input_name in params: inner_var = parent_block._find_var_recursive(inner_input_name) if inner_var: param_list.append(inner_var.name) grad_op_desc, op_grad_to_var = core.get_grad_op_desc( conditional_block_op.desc, set(), [grad_sub_block.desc] ) # append op_desc in grad_op_descs to target_block op_role_attr_name = core.op_proto_and_checker_maker.kOpRoleAttrName() backward = core.op_proto_and_checker_maker.OpRole.Backward new_op_desc = parent_block.desc.append_op() new_op_desc.copy_from(grad_op_desc[0]) new_op_desc._set_attr(op_role_attr_name, backward) # set input and output manually new_op_desc.set_input('Input', param_list) new_op_desc.set_output( 'Input@GRAD', [param + "@GRAD" for param in param_list] ) new_vars = set() for grad_var_name in new_op_desc.output_arg_names(): if ( grad_sub_block.desc.has_var_recursive(grad_var_name.encode()) or grad_var_name == core.empty_var_name() ): continue grad_sub_block.desc.var(grad_var_name.encode()) new_vars.add(grad_var_name) if grad_var_name not in op_grad_to_var: continue # infer_shape and infer_type new_op_desc.infer_var_type(grad_sub_block.desc) new_op_desc.infer_shape(grad_sub_block.desc) for arg in new_op_desc.output_arg_names(): if arg in new_vars: _infer_var_data_type_shape_(arg, grad_sub_block) self.helper.main_program._sync_with_cpp() def _to_sequence_except_dict(x): """ In this function, dict is not viewed as sequence. """ if isinstance(x, dict): return [x] return to_sequence(x) def _is_sequence_except_dict(x): """ In this function, dict is not viewed as sequence. """ if isinstance(x, dict): return False return is_sequence(x) def expand_undefined_var(nest1, nest2, names): """TODO: make this function recursively. nest1: Var1, (UndefinedVar, [1,2,3]) nest2: Var2, ([1,2,3,4], UndefinedVar) In this case, we should not expand recursively. """ from paddle.jit.dy2static.utils import UndefinedVar from paddle.jit.dy2static.return_transformer import ( RETURN_VALUE_PREFIX, ) def pack_undefined_var_as(seq): return pack_sequence_as( seq, [UndefinedVar("padding") for i in flatten(seq)] ) def map_fn(n1, n2, name, order): if not name.startswith(RETURN_VALUE_PREFIX) and ( isinstance(n1, UndefinedVar) or n1 is None ): if n1 is None and n2 is not None: if order == 0: warnings.warn( "In cond : Var '{}' or part of it is set differently in ifelse branchs, " "<{}, {}> in true branch and <{}, {}> in false branch. Set var to " "'None' in ifelse block might lead to error.".format( name, type(n1), n1, type(n2), n2 ) ) else: warnings.warn( "In cond : Var '{}' or part of it is set differently in ifelse branchs, " "<{}, {}> in true branch and <{}, {}> in false branch. Set var to " "'None' in ifelse block might lead to error.".format( name, type(n2), n2, type(n1), n1 ) ) return pack_undefined_var_as(n2) return n1 nest1_out = list( map( map_fn, _to_sequence_except_dict(nest1), _to_sequence_except_dict(nest2), _to_sequence_except_dict(names), [0 for i in _to_sequence_except_dict(names)], ) ) nest2_out = list( map( map_fn, _to_sequence_except_dict(nest2), _to_sequence_except_dict(nest1), _to_sequence_except_dict(names), [1 for i in _to_sequence_except_dict(names)], ) ) if not _is_sequence_except_dict(nest1): nest1_out = nest1_out[0] if not _is_sequence_except_dict(nest2): nest2_out = nest2_out[0] return nest1_out, nest2_out class Switch: """ :api_attr: Static Graph This class is used to implement Switch branch control function. Switch branch contains several case branches and one default branch. Switch control flow checks whether the case branch conditions are satisfied in turn, and only executes the statement after the first case branch that satisfies the conditions. If there is no case branch that satisfies the condition, only the statement following the default branch is executed. Note: A new OP :ref:`api_fluid_layers_case` is highly recommended instead of ``Switch`` if the shape of parameter ``cond`` is [1]. OP :ref:`api_fluid_layers_case` is easier to use and is called with less code but does the same thing as ``Switch`` . Member Functions: case(condition): The case branch of Switch whose parameter cond is a scalar Variable of bool type. Only if the cond of the current case branch is True and the cond of the previous case branch is False, the statement after the case branch will be executed, and the statement after the case branch will not be executed. default(): The default branch of Switch. When cond of all case branches is False, the statement after default branch is executed. Case and default functions can only be used inside the scope of Switch, as shown below: .. code-block:: python ''' with fluid.layers.Switch() as switch: with switch.case(cond1): i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=1) with switch.case(cond2): i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=2) with switch.default(): i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0) ''' Args: name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Examples: .. code-block:: python import paddle import paddle.fluid as fluid lr = paddle.static.create_global_var( shape=[1], value=0.0, dtype='float32', persistable=True, name="learning_rate") zero_var = fluid.layers.fill_constant( shape=[1], dtype='float32', value=0.0) one_var = fluid.layers.fill_constant( shape=[1], dtype='float32', value=1.0) two_var = fluid.layers.fill_constant( shape=[1], dtype='float32', value=2.0) global_step = fluid.layers.autoincreased_step_counter(counter_name='@LR_DECAY_COUNTER@', begin=0, step=1) with fluid.layers.control_flow.Switch() as switch: with switch.case(global_step == zero_var): fluid.layers.assign(input=one_var, output=lr) with switch.default(): fluid.layers.assign(input=two_var, output=lr) exe = fluid.Executor(fluid.CPUPlace()) exe.run(fluid.default_startup_program()) res = exe.run(fluid.default_main_program(), feed={}, fetch_list=[lr]) print(res) # [array([1.], dtype=float32)] """ def __init__(self, name=None): self.helper = LayerHelper('switch', name=name) self.inside_scope = False self.pre_not_conditions = [] def case(self, condition): if not self.inside_scope: raise ValueError("case should be called inside with") check_variable_and_dtype( condition, 'condition', ['bool'], 'the member function case of fluid.layers.Switch', ) if len(self.pre_not_conditions) == 0: cond_block = ConditionalBlock([condition], is_scalar_condition=True) not_cond = paddle.logical_not(x=condition) self.pre_not_conditions.append(not_cond) else: pre_cond_num = len(self.pre_not_conditions) pre_not_cond = self.pre_not_conditions[pre_cond_num - 1] new_not_cond = paddle.logical_and( x=pre_not_cond, y=paddle.logical_not(x=condition) ) self.pre_not_conditions.append(new_not_cond) cond_block = ConditionalBlock( [paddle.logical_and(x=pre_not_cond, y=condition)], is_scalar_condition=True, ) return ConditionalBlockGuard(cond_block) def default(self): pre_cond_num = len(self.pre_not_conditions) if pre_cond_num == 0: raise ValueError("there should be at least one condition") cond_block = ConditionalBlock( [self.pre_not_conditions[pre_cond_num - 1]], is_scalar_condition=True, ) return ConditionalBlockGuard(cond_block) def __enter__(self): """ set flag that now is inside switch.block {} :return: """ self.inside_scope = True return self def __exit__(self, exc_type, exc_val, exc_tb): self.inside_scope = False if exc_type is not None: return False # re-raise exception return True