# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import collections import contextlib import sys import numpy as np import six import re import copy import weakref import warnings from copy import deepcopy import inspect import paddle import paddle.profiler as profiler from paddle.profiler.utils import in_profiler_mode from . import parallel_helper from .. import unique_name from paddle.fluid import core from .layer_object_helper import LayerObjectHelper from .layer_hooks import record_program_ops_pre_hook, set_op_customized_attrs_post_hook, LayerOpsRecoder from .base import program_desc_tracing_guard, param_guard, in_declarative_mode, _convert_into_variable from paddle.fluid import framework from ..param_attr import ParamAttr from paddle.fluid.executor import Executor, global_scope from paddle.fluid.framework import _non_static_mode, convert_np_dtype_to_dtype_, in_dygraph_mode from paddle.fluid.framework import Program, program_guard from paddle.fluid.framework import _current_expected_place as _get_device from paddle.fluid.core import VarDesc from paddle.fluid.dygraph import no_grad import paddle.utils.deprecated as deprecated __all__ = ['Layer'] _first_cap_re = re.compile('(.)([A-Z][a-z]+)') _all_cap_re = re.compile('([a-z])([A-Z])') def _convert_camel_to_snake(name): s1 = _first_cap_re.sub(r'\1_\2', name) return _all_cap_re.sub(r'\1_\2', s1).lower() def _addindent(string, indent): s1 = string.split('\n') if len(s1) == 1: return string s2 = [] for idx, line in enumerate(s1): if idx > 0: s2.append(str((indent * ' ') + line)) return s1[0] + '\n' + '\n'.join(s2) class HookRemoveHelper(object): """ A HookRemoveHelper that can be used to remove hook. """ next_hook_id = 0 def __init__(self, hooks): self._hooks_ref = weakref.ref(hooks) self._hook_id = HookRemoveHelper.next_hook_id HookRemoveHelper.next_hook_id += 1 def remove(self): hooks = self._hooks_ref() if hooks is not None and self._hook_id in hooks: del hooks[self._hook_id] class Layer(object): """ Dynamic graph Layer based on OOD, includes the parameters of the layer, the structure of the forward graph and so on. Parameters: name_scope (str, optional): prefix name used by the layer to name parameters. If prefix is "my_layer", parameter name in MyLayer can be "my_layer_0.w_n", where "w" is the parameter base name and "n" is an unique suffix auto-generated. If None, prefix name will be snake cased class name. Default: None. dtype(str, optional): data type of this parameter. If set str, it can be "bool", "float16", "float32", "float64", "int8", "int16", "int32", "int64", "uint8" or "uint16". Default: "float32" Returns: None Examples: .. code-block:: python import paddle class MyLayer(paddle.nn.Layer): def __init__(self): super(MyLayer, self).__init__() self._linear = paddle.nn.Linear(1, 1) self._dropout = paddle.nn.Dropout(p=0.5) def forward(self, input): temp = self._linear(input) temp = self._dropout(temp) return temp x = paddle.randn([10, 1], 'float32') mylayer = MyLayer() mylayer.eval() # set mylayer._dropout to eval mode out = mylayer(x) mylayer.train() # set mylayer._dropout to train mode out = mylayer(x) """ def __init__(self, name_scope=None, dtype="float32"): self.training = True if name_scope is None: name_scope = _convert_camel_to_snake(self.__class__.__name__) self._full_name = unique_name.generate(name_scope) self._helper = LayerObjectHelper(self._full_name) self._built = False self._dtype = dtype self._init_in_dynamic_mode = framework._non_static_mode() self._parameters = collections.OrderedDict() # Buffers the variable (not parameter) created in layer self._buffers = collections.OrderedDict() self._non_persistable_buffer_names_set = set() self._sub_layers = collections.OrderedDict() self._loaddict_holder = collections.OrderedDict() # Record generated op_descs in this layer self._op_recorder = LayerOpsRecoder(ops=[], hooks=[]) self._customized_attrs = {} self._forward_pre_hooks = collections.OrderedDict() self._forward_post_hooks = collections.OrderedDict() self._casted_by_pure_fp16 = False self._state_dict_hooks = collections.OrderedDict() # Records orignal functions after @to_static to support to rollback self._original_funcs = collections.OrderedDict() def train(self): """ Sets this Layer and all its sublayers to training mode. This only effects certain modules like `Dropout` and `BatchNorm`. Returns: None Example:: .. code-block:: python import paddle class MyLayer(paddle.nn.Layer): def __init__(self): super(MyLayer, self).__init__() self._linear = paddle.nn.Linear(1, 1) self._dropout = paddle.nn.Dropout(p=0.5) def forward(self, input): temp = self._linear(input) temp = self._dropout(temp) return temp x = paddle.randn([10, 1], 'float32') mylayer = MyLayer() mylayer.eval() # set mylayer._dropout to eval mode out = mylayer(x) mylayer.train() # set mylayer._dropout to train mode out = mylayer(x) """ # global setting in dygraph # NOTE(chenweihang): nn.Layer also can be used in static mode, # but _dygraph_tracer() can not be called in static mode if _non_static_mode(): framework._dygraph_tracer().train_mode() # Layer-level setting self.training = True for layer in self.sublayers(): layer.training = True def eval(self): """ Sets this Layer and all its sublayers to evaluation mode. This only effects certain modules like `Dropout` and `BatchNorm`. Returns: None Example:: .. code-block:: python import paddle class MyLayer(paddle.nn.Layer): def __init__(self): super(MyLayer, self).__init__() self._linear = paddle.nn.Linear(1, 1) self._dropout = paddle.nn.Dropout(p=0.5) def forward(self, input): temp = self._linear(input) temp = self._dropout(temp) return temp x = paddle.randn([10, 1], 'float32') mylayer = MyLayer() mylayer.eval() # set mylayer._dropout to eval mode out = mylayer(x) print(out) """ # global setting in dygraph # NOTE(chenweihang): nn.Layer also can be used in static mode, # but _dygraph_tracer() can not be called in static mode if _non_static_mode(): framework._dygraph_tracer().eval_mode() # Layer-level setting self.training = False for layer in self.sublayers(): layer.training = False def apply(self, fn): """ Applies ``fn`` recursively to every sublayer (as returned by ``.sublayers()``) as well as self. Typical use includes initializing the parameters of a model. Parameters: fn (function): a function to be applied to each sublayer Returns: Layer: self Example:: .. code-block:: python import paddle import paddle.nn as nn net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2)) def init_weights(layer): if type(layer) == nn.Linear: print('before init weight:', layer.weight.numpy()) new_weight = paddle.full(shape=layer.weight.shape, dtype=layer.weight.dtype, fill_value=0.9) layer.weight.set_value(new_weight) print('after init weight:', layer.weight.numpy()) net.apply(init_weights) print(net.state_dict()) """ for layer in self.children(): layer.apply(fn) fn(self) return self def full_name(self): """Full name for this layer, composed by name_scope + "/" + MyLayer.__class__.__name__ Returns: str: full name of this layer. Example:: .. code-block:: python import paddle class LinearNet(paddle.nn.Layer): def __init__(self): super(LinearNet, self).__init__(name_scope = "demo_linear_net") self._linear = paddle.nn.Linear(1, 1) def forward(self, x): return self._linear(x) linear_net = LinearNet() print(linear_net.full_name()) # demo_linear_net_0 """ return self._full_name def register_forward_post_hook(self, hook): """Register a forward post-hook for Layer. The hook will be called after `forward` function has been computed. It should have the following form, `input` and `output` of the `hook` is `input` and `output` of the `Layer` respectively. User can use forward post-hook to change the output of the Layer or perform information statistics tasks on the Layer. hook(Layer, input, output) -> None or modified output Parameters: hook(function): a function registered as a forward post-hook Returns: HookRemoveHelper: a HookRemoveHelper object that can be used to remove the added hook by calling `hook_remove_helper.remove()` . Examples: .. code-block:: python import paddle import numpy as np # the forward_post_hook change the output of the layer: output = output * 2 def forward_post_hook(layer, input, output): # user can use layer, input and output for information statistis tasks # change the output return output * 2 linear = paddle.nn.Linear(13, 5) # register the hook forward_post_hook_handle = linear.register_forward_post_hook(forward_post_hook) value1 = np.arange(26).reshape(2, 13).astype("float32") in1 = paddle.to_tensor(value1) out0 = linear(in1) # remove the hook forward_post_hook_handle.remove() out1 = linear(in1) # hook change the linear's output to output * 2, so out0 is equal to out1 * 2. assert (out0.numpy() == (out1.numpy()) * 2).any() """ hook_remove_helper = HookRemoveHelper(self._forward_post_hooks) self._forward_post_hooks[hook_remove_helper._hook_id] = hook return hook_remove_helper def register_forward_pre_hook(self, hook): """Register a forward pre-hook for Layer. The hook will be called before `forward` function has been computed. It should have the following form, `input` of the `hook` is `input` of the `Layer`, hook can either return a tuple or a single modified value in the hook. We will wrap the value into a tuple if a single value is returned(unless that value is already a tuple). User can use forward pre-hook to change the input of the Layer or perform information statistics tasks on the Layer. hook(Layer, input) -> None or modified input Parameters: hook(function): a function registered as a forward pre-hook Returns: HookRemoveHelper: a HookRemoveHelper object that can be used to remove the added hook by calling `hook_remove_helper.remove()` . Examples: .. code-block:: python import paddle import numpy as np # the forward_pre_hook change the input of the layer: input = input * 2 def forward_pre_hook(layer, input): # user can use layer and input for information statistis tasks # change the input input_return = (input[0] * 2) return input_return linear = paddle.nn.Linear(13, 5) # register the hook forward_pre_hook_handle = linear.register_forward_pre_hook(forward_pre_hook) value0 = np.arange(26).reshape(2, 13).astype("float32") in0 = paddle.to_tensor(value0) out0 = linear(in0) # remove the hook forward_pre_hook_handle.remove() value1 = value0 * 2 in1 = paddle.to_tensor(value1) out1 = linear(in1) # hook change the linear's input to input * 2, so out0 is equal to out1. assert (out0.numpy() == out1.numpy()).any() """ hook_remove_helper = HookRemoveHelper(self._forward_pre_hooks) self._forward_pre_hooks[hook_remove_helper._hook_id] = hook return hook_remove_helper def create_parameter(self, shape, attr=None, dtype=None, is_bias=False, default_initializer=None): """Create parameters for this layer. Parameters: shape(list): Shape of the parameter. attr(ParamAttr, optional): Parameter attribute of weight. Please refer to :ref:`api_paddle_ParamAttr`. Default: None. dtype(str, optional): Data type of this parameter. If set str, it can be "bool", "float16", "float32", "float64", "int8", "int16", "int32", "int64", "uint8" or "uint16". Default: "float32". is_bias(bool, optional): if this is a bias parameter. Default: False. default_initializer(Initializer, optional): the default initializer for this parameter. If set None, default initializer will be set to paddle.nn.initializer.Xavier and paddle.nn.initializer.Constant for non-bias and bias parameter, respectively. Default: None. Returns: :Tensor, created parameter. Examples: .. code-block:: python import paddle class MyLayer(paddle.nn.Layer): def __init__(self): super(MyLayer, self).__init__() self._linear = paddle.nn.Linear(1, 1) w_tmp = self.create_parameter([1,1]) self.add_parameter("w_tmp", w_tmp) def forward(self, input): return self._linear(input) mylayer = MyLayer() for name, param in mylayer.named_parameters(): print(name, param) # will print w_tmp,_linear.weight,_linear.bias """ temp_attr = copy.deepcopy(attr) if isinstance(temp_attr, six.string_types) and temp_attr == "": temp_attr = None return self._helper.create_parameter(temp_attr, shape, dtype, is_bias, default_initializer) @deprecated(since="2.0.0", update_to="paddle.nn.Layer.create_tensor", reason="New api in create_tensor, easier to use.") def create_variable(self, name=None, persistable=None, dtype=None): """ Create Tensor for this layer. Parameters: name(str, optional): name of the tensor. Please refer to :ref:`api_guide_Name` . Default: None persistable(bool, optional): if set this tensor persistable. Default: False dtype(str, optional): data type of this parameter. If set str, it can be "bool", "float16", "float32", "float64","int8", "int16", "int32", "int64", "uint8" or "uint16". If set None, it will be "float32". Default: None Returns: Tensor, created Tensor. Examples: .. code-block:: python import paddle class MyLinear(paddle.nn.Layer): def __init__(self, in_features, out_features): super(MyLinear, self).__init__() self.linear = paddle.nn.Linear( 10, 10) self.back_var = self.create_variable(name = "linear_tmp_0", dtype=self._dtype) def forward(self, input): out = self.linear(input) paddle.assign( out, self.back_var) return out """ if name is not None: var_name = ".".join([self._full_name, name]) else: var_name = unique_name.generate(".".join( [self._full_name, "_generated_var"])) return self._helper.main_program.current_block().create_var( name=var_name, persistable=persistable, dtype=dtype, type=core.VarDesc.VarType.LOD_TENSOR) # TODO: Add more parameter list when we need them def create_tensor(self, name=None, persistable=None, dtype=None): """ Create Tensor for this layer. Parameters: name(str, optional): name of the tensor. Please refer to :ref:`api_guide_Name` . Default: None persistable(bool, optional): if set this tensor persistable. Default: False dtype(str, optional): data type of this parameter. If set str, it can be "bool", "float16", "float32", "float64", "int8", "int16", "int32", "int64", "uint8" or "uint16". If set None, it will be "float32". Default: None Returns: Tensor, created Tensor. Examples: .. code-block:: python import paddle class MyLinear(paddle.nn.Layer): def __init__(self, in_features, out_features): super(MyLinear, self).__init__() self.linear = paddle.nn.Linear( 10, 10) self.back_var = self.create_tensor(name = "linear_tmp_0", dtype=self._dtype) def forward(self, input): out = self.linear(input) paddle.assign( out, self.back_var) return out """ if name is not None: var_name = ".".join([self._full_name, name]) else: var_name = unique_name.generate(".".join( [self._full_name, "_generated_var"])) return self._helper.main_program.current_block().create_var( name=var_name, persistable=persistable, dtype=dtype, type=core.VarDesc.VarType.LOD_TENSOR) def parameters(self, include_sublayers=True): """Returns a list of all Parameters from current layer and its sub-layers. Returns: list of Tensor : a list of Parameters. Examples: .. code-block:: python import paddle linear = paddle.nn.Linear(1,1) print(linear.parameters()) # print linear_0.w_0 and linear_0.b_0 """ ret = [ param for _, param in self.named_parameters( include_sublayers=include_sublayers) ] return ret def children(self): """Returns an iterator over immediate children layers. Yields: Layer: a child layer Examples: .. code-block:: python import paddle linear1 = paddle.nn.Linear(10, 3) linear2 = paddle.nn.Linear(3, 10, bias_attr=False) model = paddle.nn.Sequential(linear1, linear2) layer_list = list(model.children()) print(layer_list) # [, ] """ for _, layer in self.named_children(): yield layer def named_children(self): """Returns an iterator over immediate children layers, yielding both the name of the layer as well as the layer itself. Yields: (string, Layer): Tuple containing a name and child layer Examples: .. code-block:: python import paddle linear1 = paddle.nn.Linear(10, 3) linear2 = paddle.nn.Linear(3, 10, bias_attr=False) model = paddle.nn.Sequential(linear1, linear2) for prefix, layer in model.named_children(): print(prefix, layer) # ('0', ) # ('1', ) """ memo = set() for name, layer in self._sub_layers.items(): if layer is not None and layer not in memo: memo.add(layer) yield name, layer def sublayers(self, include_self=False): """Returns a list of sub layers. Parameters: include_self(bool, optional): Whether return self as sublayers. Default: False Returns: list of Layer : a list of sub layers. Examples: .. code-block:: python import paddle class MyLayer(paddle.nn.Layer): def __init__(self): super(MyLayer, self).__init__() self._linear = paddle.nn.Linear(1, 1) self._dropout = paddle.nn.Dropout(p=0.5) def forward(self, input): temp = self._linear(input) temp = self._dropout(temp) return temp mylayer = MyLayer() print(mylayer.sublayers()) # [, ] """ ret = [ layer for _, layer in self.named_sublayers(include_self=include_self) ] return ret def named_parameters(self, prefix='', include_sublayers=True): """ Returns an iterator over all parameters in the Layer, yielding tuple of name and parameter. Parameters: prefix(str, optional): Prefix to prepend to all parameter names. Default: ''. include_sublayers(bool, optional): Whether include the parameters of sublayers. If True, also include the named parameters from sublayers. Default: True. Yields: (string, Parameter): Tuple of name and Parameter Examples: .. code-block:: python import paddle fc1 = paddle.nn.Linear(10, 3) fc2 = paddle.nn.Linear(3, 10, bias_attr=False) model = paddle.nn.Sequential(fc1, fc2) for name, param in model.named_parameters(): print(name, param) """ params_set = set() named_sublayers = self.named_sublayers( prefix=prefix, include_self=True) if include_sublayers else zip( [prefix], [self]) for layer_prefix, sublayer in named_sublayers: params = sublayer._parameters.items() for key, param in params: if param is None or param in params_set: continue params_set.add(param) name = layer_prefix + ('.' if layer_prefix else '') + key yield name, param def named_sublayers(self, prefix='', include_self=False, layers_set=None): """ Returns an iterator over all sublayers in the Layer, yielding tuple of name and sublayer. The duplicate sublayer will only be yielded once. Parameters: prefix(str, optional): Prefix to prepend to all parameter names. Default: ''. include_self(bool, optional): Whether include the Layer itself. Default: False. layers_set(set, optional): The set to record duplicate sublayers. Default: None. Yields: (string, Layer): Tuple of name and Layer Examples: .. code-block:: python import paddle fc1 = paddle.nn.Linear(10, 3) fc2 = paddle.nn.Linear(3, 10, bias_attr=False) model = paddle.nn.Sequential(fc1, fc2) for prefix, layer in model.named_sublayers(): print(prefix, layer) """ if layers_set is None: layers_set = set() if include_self and self not in layers_set: layers_set.add(self) yield prefix, self for key, layer in self._sub_layers.items(): if layer is None: continue layer_prefix = prefix + ('.' if prefix else '') + key for p, l in layer.named_sublayers(prefix=layer_prefix, include_self=True, layers_set=layers_set): yield p, l def register_buffer(self, name, tensor, persistable=True): """ Registers a tensor as buffer into the layer. `buffer` is a non-trainable tensor and will not be updated by optimizer, but is necessary for evaluation and inference. For example, the mean and variance in BatchNorm layers. The registered buffer is persistable by default, and will be saved into `state_dict` alongside parameters. If set persistable=False, it registers a non-persistable buffer, so that it will not be a part of `state_dict` . Buffers can be accessed as attributes using given names. Parameters: name (string): name of the buffer. The buffer can be accessed from this layer using the given name tensor (Tensor): the tensor to be registered as buffer. persistable (bool): whether the buffer is part of this layer's state_dict. Returns: None Examples: .. code-block:: python import numpy as np import paddle linear = paddle.nn.Linear(10, 3) value = np.array([0]).astype("float32") buffer = paddle.to_tensor(value) linear.register_buffer("buf_name", buffer, persistable=True) # get the buffer by attribute. print(linear.buf_name) """ if '_buffers' not in self.__dict__: raise ValueError( "super(YourLayer, self).__init__() should be called first") elif not isinstance(name, six.string_types): raise TypeError( "The name of buffer should be a string, but received {}.". format(type(name).__name__)) elif '.' in name: raise KeyError( "The name of buffer can not contain `.`, " "because when you access the newly added buffer in the " "form of `self.**.**`, it will cause AttributeError.") elif name == '': raise KeyError("The name of buffer can not be empty.") elif hasattr(self, name) and name not in self._buffers: raise KeyError("attribute '{}' already exists.".format(name)) elif tensor is not None and not (type(tensor) == core.VarBase or type(tensor) == core.eager.Tensor): raise TypeError( "The registered buffer should be a Paddle.Tensor, but received {}." .format(type(tensor).__name__)) else: self._buffers[name] = tensor if persistable: self._non_persistable_buffer_names_set.discard(name) else: self._non_persistable_buffer_names_set.add(name) def buffers(self, include_sublayers=True): """ Returns a list of all buffers from current layer and its sub-layers. Parameters: include_sublayers(bool, optional): Whether include the buffers of sublayers. If True, also include the buffers from sublayers. Default: True Returns: list of Tensor : a list of buffers. Examples: .. code-block:: python import numpy as np import paddle linear = paddle.nn.Linear(10, 3) value = np.array([0]).astype("float32") buffer = paddle.to_tensor(value) linear.register_buffer("buf_name", buffer, persistable=True) print(linear.buffers()) # == print([linear.buf_name]) """ ret = [ buffer for _, buffer in self.named_buffers( include_sublayers=include_sublayers) ] return ret def named_buffers(self, prefix='', include_sublayers=True): """ Returns an iterator over all buffers in the Layer, yielding tuple of name and Tensor. Parameters: prefix(str, optional): Prefix to prepend to all buffer names. Default: ''. include_sublayers(bool, optional): Whether include the buffers of sublayers. If True, also include the named buffers from sublayers. Default: True. Yields: (string, Tensor): Tuple of name and tensor Examples: .. code-block:: python import numpy as np import paddle fc1 = paddle.nn.Linear(10, 3) buffer1 = paddle.to_tensor(np.array([0]).astype("float32")) # register a tensor as buffer by specific `persistable` fc1.register_buffer("buf_name_1", buffer1, persistable=True) fc2 = paddle.nn.Linear(3, 10) buffer2 = paddle.to_tensor(np.array([1]).astype("float32")) # register a buffer by assigning an attribute with Tensor. # The `persistable` can only be False by this way. fc2.buf_name_2 = buffer2 model = paddle.nn.Sequential(fc1, fc2) # get all named buffers for name, buffer in model.named_buffers(): print(name, buffer) """ buffers_set = set() named_sublayers = self.named_sublayers( prefix=prefix, include_self=True) if include_sublayers else zip( [prefix], [self]) for layer_prefix, sublayer in named_sublayers: buffers = sublayer._buffers.items() for key, buffer in buffers: if buffer is None or buffer in buffers_set: continue buffers_set.add(buffer) name = layer_prefix + ('.' if layer_prefix else '') + key yield name, buffer def clear_gradients(self): """ Clear the gradients of all parameters for this layer. Returns: None Examples: .. code-block:: python import paddle import numpy as np value = np.arange(26).reshape(2, 13).astype("float32") a = paddle.to_tensor(value) linear = paddle.nn.Linear(13, 5) adam = paddle.optimizer.Adam(learning_rate=0.01, parameters=linear.parameters()) out = linear(a) out.backward() adam.step() linear.clear_gradients() """ for p in self.parameters(): if p.trainable: p.clear_gradient() def _build_once(self, *args, **kwargs): pass def _dygraph_call_func(self, *inputs, **kwargs): for forward_pre_hook in self._forward_pre_hooks.values(): hook_result = forward_pre_hook(self, inputs) if hook_result is not None: if not isinstance(hook_result, tuple): hook_result = (hook_result, ) inputs = hook_result if not self._built: with program_desc_tracing_guard(False): self._build_once(*inputs, **kwargs) # TODO(liuyuhui) Only xpu broadcast parameters here. # The other device is to call _sync_params_buffers in DataParallel # to realize the parameter synchronization among multiply cards. if parallel_helper._is_data_parallel_mode( ) and paddle.is_compiled_with_xpu(): parallel_helper._broadcast_parameters( self._parameters.values()) self._built = True if in_profiler_mode(): with profiler.RecordEvent(self.__class__.__name__, profiler.TracerEventType.Forward): outputs = self.forward(*inputs, **kwargs) else: outputs = self.forward(*inputs, **kwargs) for forward_post_hook in self._forward_post_hooks.values(): hook_result = forward_post_hook(self, inputs, outputs) if hook_result is not None: outputs = hook_result return outputs def __call__(self, *inputs, **kwargs): if (not in_declarative_mode()) and (not self._forward_pre_hooks) \ and (not self._forward_post_hooks) and (not self._built) and in_dygraph_mode() and (not in_profiler_mode()): self._build_once(*inputs, **kwargs) return self.forward(*inputs, **kwargs) else: return self._dygraph_call_func(*inputs, **kwargs) def forward(self, *inputs, **kwargs): """ Defines the computation performed at every call. Should be overridden by all subclasses. Parameters: *inputs(tuple): unpacked tuple arguments **kwargs(dict): unpacked dict arguments """ raise NotImplementedError def backward(self, *inputs): raise ValueError("Layer shouldn't implement backward") def add_sublayer(self, name, sublayer): """Adds a sub Layer instance. Added sublayer can be accessed by self.name Parameters: name(str): name of this sublayer. sublayer(Layer): an instance of Layer. Returns: Layer: the sublayer passed in. Examples: .. code-block:: python import paddle class MySequential(paddle.nn.Layer): def __init__(self, *layers): super(MySequential, self).__init__() if len(layers) > 0 and isinstance(layers[0], tuple): for name, layer in layers: self.add_sublayer(name, layer) else: for idx, layer in enumerate(layers): self.add_sublayer(str(idx), layer) def forward(self, input): for layer in self._sub_layers.values(): input = layer(input) return input fc1 = paddle.nn.Linear(10, 3) fc2 = paddle.nn.Linear(3, 10, bias_attr=False) model = MySequential(fc1, fc2) for prefix, layer in model.named_sublayers(): print(prefix, layer) """ assert (isinstance(sublayer, Layer) or sublayer == None) self._sub_layers[name] = sublayer return sublayer def add_parameter(self, name, parameter): """Adds a Parameter instance. Added parameter can be accessed by self.name Parameters: name(str): name of this sublayer. parameter(Parameter): an instance of Parameter. Returns: Parameter: the parameter passed in. Examples: .. code-block:: python import paddle class MyLayer(paddle.nn.Layer): def __init__(self): super(MyLayer, self).__init__() self._linear = paddle.nn.Linear(1, 1) w_tmp = self.create_parameter([1,1]) self.add_parameter("w_tmp", w_tmp) def forward(self, input): return self._linear(input) mylayer = MyLayer() for name, param in mylayer.named_parameters(): print(name, param) # will print w_tmp,_linear.weight,_linear.bias """ if '_parameters' not in self.__dict__: raise RuntimeError( "super(YourLayer, self).__init__() should be called firstly.") elif not isinstance(name, six.string_types): raise TypeError( "The name of parameter should be a string, but received {}.". format(type(name).__name__)) elif '.' in name: raise KeyError( "The name of parameter can not contain `.`, " "because when you access the newly added parameter in the " "form of `self.**.**`, it will cause AttributeError.") elif name == '': raise KeyError("The name of parameter can not be empty.") elif hasattr(self, name) and name not in self._parameters: raise KeyError("The parameter '{}' already exists.".format(name)) elif parameter is not None and not isinstance(parameter, framework.Parameter): raise TypeError( "The parameter to be added should be a Parameter, but received {}." .format(type(parameter).__name__)) else: if parameter is None: self._parameters[name] = None if len(self._loaddict_holder) > 0: assert parameter.name in self._loaddict_holder, "Parameter not found, Can't not find [ {} ] in state_dict".format( parameter.name) parameter.set_value(self._loaddict_holder[parameter.name]) self._parameters[name] = parameter return parameter def _set_op_attrs(self, attrs): """ Add customized attribute while append_op. In case of quantization, we want to save some attributes into op_desc while exporting inference model by @to_static. Arguments: attrs(dict): customized attributes that will be added into op_descs. NOTE: The interface is only exposed to developers. """ def is_already_registered(is_pre_hook): layers_hooks = self._forward_pre_hooks if is_pre_hook else self._forward_post_hooks candidate_hook = record_program_ops_pre_hook if is_pre_hook else set_op_customized_attrs_post_hook already_registed = False if layers_hooks: last_key = next(reversed(layers_hooks)) already_registed = (layers_hooks[last_key] == candidate_hook) return already_registed if not isinstance(attrs, dict): raise TypeError( "attrs should be type(dict), but received {}".format( type(attrs).__name__)) # NOTE: Overwrite behavior for same key. self._customized_attrs.update(attrs) if not is_already_registered(is_pre_hook=True): pre_hook_helper = self.register_forward_pre_hook( record_program_ops_pre_hook) assert len(self._op_recorder.hooks) == 0 self._op_recorder.hooks = [pre_hook_helper] # manually register post_hook to ensure it is inserted into the head. if not is_already_registered(is_pre_hook=False): post_hook_helper = self.register_forward_post_hook( set_op_customized_attrs_post_hook) if len(self._forward_post_hooks) > 1: self._forward_post_hooks.move_to_end(post_hook_helper._hook_id, last=False) assert len(self._op_recorder.hooks) == 1 # hooks that need to be removed once we finish executing them. self._op_recorder.hooks.append(post_hook_helper) def __getstate__(self): return self.__dict__ def __setstate__(self, state): self.__dict__.update(state) def __getattr__(self, name): if '_parameters' in self.__dict__: _parameters = self.__dict__['_parameters'] if name in self._parameters: if in_declarative_mode(): return _convert_into_variable(self._parameters[name]) return self._parameters[name] if '_sub_layers' in self.__dict__: _sub_layers = self.__dict__['_sub_layers'] if name in self._sub_layers: return self._sub_layers[name] if '_buffers' in self.__dict__: _buffers = self.__dict__['_buffers'] if name in _buffers: if in_declarative_mode(): return _convert_into_variable(_buffers[name]) return _buffers[name] return object.__getattribute__(self, name) def __setattr__(self, name, value): def _remove_if_exist(*dicts): for d in dicts: if name in d: del d[name] if isinstance(getattr(type(self), name, None), property): object.__setattr__(self, name, value) params = self.__dict__.get('_parameters', None) if isinstance(value, framework.Parameter): if params is None: raise ValueError( "super(YourLayer, self).__init__() should be called first") if len(self._loaddict_holder) > 0: assert value.name in self._loaddict_holder, "Parameter not found, Can't not find [ {} ] in state_dict".format( value.name) value.set_value(self._loaddict_holder[value.name]) _remove_if_exist(self.__dict__, self._buffers, self._sub_layers) params[name] = value elif params is not None and name in params: if value is not None: raise TypeError( "assignment to parameter '{}' should be of type Parameter or None, but got '{}'" .format(name, type(value).__name__)) params[name] = None else: layers = self.__dict__.get('_sub_layers', None) if isinstance(value, Layer): if layers is None: raise ValueError( "super(YourLayer, self).__init__() should be called first" ) _remove_if_exist(self.__dict__, self._parameters, self._buffers) layers[name] = value elif layers is not None and name in layers: if value is not None: raise TypeError( "assignment to sublayer '{}' should be of type Layer or None, but got '{}'" .format(name, type(value).__name__)) layers[name] = None else: _buffers = self.__dict__.get('_buffers', None) if isinstance(value, (core.VarBase, core.eager.Tensor)): if _buffers is None: raise ValueError( "super(YourLayer, self).__init__() should be called first" ) _remove_if_exist(self.__dict__, self._parameters, self._sub_layers) # Set persistable=False by default. Only `register_buffer` can # add a persistable buffer. if name not in self._buffers: self._non_persistable_buffer_names_set.add(name) if not value.name: value.name = unique_name.generate('_buffers_' + name) _buffers[name] = value elif _buffers is not None and name in _buffers: # Note(Aurelius84): In Dy2stat, the value of the Buffer may be modified in # decorated function, such as `self.buffer = new_tensor`. So we update its # value via `assign`. if type(value) == framework.Variable: from paddle import assign # Note(zhhsplendid): the condition below happens in PaddleGan model, # but should all non-Variable _buffers[name] be re-assign? We # should consider it in the future. I current wrote this as # conservative code. if in_declarative_mode() and _buffers[name] is None: raise RuntimeError( 'In Dy2stat, self.{0} is a buffer and self.{0} is ' 'not allowed to be set to Variable when self.{0} is None.' .format(name)) elif _buffers[name] is None or type(getattr( self, name)) == core.VarBase: _buffers[name] = assign(value) else: assign(value, getattr(self, name)) elif value is not None: raise TypeError( "assignment to buffers '{}' should be of type core.VarBase or None, but got '{}'" .format(name, type(value).__name__)) else: # Assigning None will remove the buffer, but if re-assign a new varBase to it, # it will be remarked as a buffer with same `persistable` attribute. _buffers[name] = None else: object.__setattr__(self, name, value) def __delattr__(self, name): if name in self._parameters: del self._parameters[name] elif name in self._sub_layers: del self._sub_layers[name] elif name in self._buffers: del self._buffers[name] self._non_persistable_buffer_names_set.discard(name) else: object.__delattr__(self, name) def __dir__(self): """ Return a list. Get all parameters, buffers(non-parameter tensors), sublayers, method and attr of Layer. Examples: .. code-block:: python import paddle import numpy as np class Mylayer(paddle.nn.Layer): def __init__(self): super(Mylayer, self).__init__() self.linear1 = paddle.nn.Linear(10, 10) self.linear2 = paddle.nn.Linear(5, 5) self.conv2d = paddle.nn.Conv2D(3, 2, 3) self.embedding = paddle.nn.Embedding(128, 16) self.h_0 = paddle.to_tensor(np.zeros([10, 10]).astype('float32')) mylayer = Mylayer() print(dir(mylayer)) # only parts are shown, because of list have too much content # ['__call__', '__class__', ... , 'conv2d', 'embedding', 'h_0', 'linear1', 'linear2', ... , 'sublayers', 'train'] """ method = dir(self.__class__) attrs = list(self.__dict__.keys()) parameters = list(self._parameters.keys()) sublayers = list(self._sub_layers.keys()) buffers = list(self._buffers.keys()) keys = method + attrs + parameters + sublayers + buffers return keys def extra_repr(self): """ Extra representation of this layer, you can have custom implementation of your own layer. """ return '' def __repr__(self): extra_lines = [] extra_repr = self.extra_repr() extra_lines = extra_repr.split('\n') sublayer_lines = [] for name, layer in self._sub_layers.items(): sublayer_str = repr(layer) sublayer_str = _addindent(sublayer_str, 2) sublayer_lines.append('(' + name + '): ' + sublayer_str) final_str = self.__class__.__name__ + '(' if extra_lines: if len(extra_lines) > 1: final_str += '\n ' + '\n '.join(extra_lines) + '\n' elif len(extra_lines) == 1: final_str += extra_lines[0] if sublayer_lines: final_str += '\n ' + '\n '.join(sublayer_lines) + '\n' final_str += ')' return final_str def register_state_dict_hook(self, hook): hook_remove_helper = HookRemoveHelper(self._state_dict_hooks) self._state_dict_hooks[hook_remove_helper._hook_id] = hook return hook_remove_helper def _obtain_parameters_buffers(self, destination=None, include_sublayers=True, structured_name_prefix=""): """ The difference from state_dict() is that state_dict_hook will not be called, but the original types of parameters and buffers will be maintained. """ if destination is None: destination = collections.OrderedDict() for name, data in self._parameters.items(): if data is not None: destination[structured_name_prefix + name] = data for name, buffer in self._buffers.items(): if buffer is not None and name not in self._non_persistable_buffer_names_set: destination[structured_name_prefix + name] = buffer if include_sublayers: for layer_name, layer_item in self._sub_layers.items(): if layer_item is not None: destination_temp = destination.copy() destination_temp.update( layer_item._obtain_parameters_buffers( destination_temp, include_sublayers, structured_name_prefix + layer_name + ".")) destination = destination_temp return destination def _state_dict_impl(self, destination=None, include_sublayers=True, structured_name_prefix="", include_non_persistable_buffer=False, use_hook=True): """ Get all parameters and persistable buffers of current layer and its sub-layers. And set them into a dict Parameters: destination(dict, optional) : If provide, all the parameters and persistable buffers will be set to this dict . Default: None include_sublayers(bool, optional) : If true, also include the parameters and persistable buffers from sublayers. Default: True include_non_persistable_buffer(bool, optional): If true, include non persistable buffers of current layer and its sub-layers, it is used in pure fp16 and jit.save. Default: False use_hook(bool, optional) : If true, the operations contained in _state_dict_hooks will be appended to the destination. Default: True """ if destination is None: destination = collections.OrderedDict() for name, data in self._parameters.items(): if data is not None: destination[structured_name_prefix + name] = data for name, buffer in self._buffers.items(): if not include_non_persistable_buffer: if buffer is not None and name not in self._non_persistable_buffer_names_set: destination[structured_name_prefix + name] = buffer else: if buffer is not None: destination[structured_name_prefix + name] = buffer if include_sublayers: for layer_name, layer_item in self._sub_layers.items(): if layer_item is not None: destination_temp = destination.copy() destination_temp.update( layer_item._state_dict_impl( destination_temp, include_sublayers, structured_name_prefix + layer_name + ".", include_non_persistable_buffer, use_hook)) destination = destination_temp if use_hook: for state_dict_hook in self._state_dict_hooks.values(): hook_result = state_dict_hook(destination) if hook_result is not None: destination = hook_result return destination def to_static_state_dict(self, destination=None, include_sublayers=True, structured_name_prefix="", use_hook=True): ''' Get all parameters and buffers of current layer and its sub-layers. And set them into a dict Parameters: destination(dict, optional) : If provide, all the parameters and persistable buffers will be set to this dict . Default: None include_sublayers(bool, optional) : If true, also include the parameters and persistable buffers from sublayers. Default: True use_hook(bool, optional) : If true, the operations contained in _state_dict_hooks will be appended to the destination. Default: True Retruns: dict: a dict contains all the parameters and persistable buffers. Examples: .. code-block:: python import paddle emb = paddle.nn.Embedding(10, 10) state_dict = emb.to_static_state_dict() paddle.save( state_dict, "paddle_dy.pdparams") ''' return self._state_dict_impl( destination=destination, include_sublayers=include_sublayers, structured_name_prefix=structured_name_prefix, include_non_persistable_buffer=True, use_hook=use_hook) def state_dict(self, destination=None, include_sublayers=True, structured_name_prefix="", use_hook=True): ''' Get all parameters and persistable buffers of current layer and its sub-layers. And set them into a dict Parameters: destination(dict, optional) : If provide, all the parameters and persistable buffers will be set to this dict . Default: None include_sublayers(bool, optional) : If true, also include the parameters and persistable buffers from sublayers. Default: True use_hook(bool, optional) : If true, the operations contained in _state_dict_hooks will be appended to the destination. Default: True Retruns: dict: a dict contains all the parameters and persistable buffers. Examples: .. code-block:: python import paddle emb = paddle.nn.Embedding(10, 10) state_dict = emb.state_dict() paddle.save( state_dict, "paddle_dy.pdparams") ''' return self._state_dict_impl( destination=destination, include_sublayers=include_sublayers, structured_name_prefix=structured_name_prefix, include_non_persistable_buffer=False, use_hook=use_hook) @framework.deprecate_stat_dict def set_state_dict(self, state_dict, use_structured_name=True): ''' Set parameters and persistable buffers from state_dict. All the parameters and buffers will be reset by the tensor in the state_dict Parameters: state_dict(dict) : Dict contains all the parameters and persistable buffers. use_structured_name(bool, optional) : If true, use structured name as key, otherwise, use parameter or buffer name as key. Default: True Returns: None Examples: .. code-block:: python import paddle emb = paddle.nn.Embedding(10, 10) state_dict = emb.state_dict() paddle.save(state_dict, "paddle_dy.pdparams") para_state_dict = paddle.load("paddle_dy.pdparams") emb.set_state_dict(para_state_dict) ''' def _check_match(key, param): state = state_dict.get(key, None) if state is None: raise ValueError( "{} is not found in the provided dict.".format(key)) if (isinstance(state, dict) or isinstance(state, list)): if (len(state) != len(param)): raise ValueError("{} receieves the length of {}, " "but the expected shape is {}".format( key, len(state), len(param))) else: return param, state else: state_shape = state.shape() if inspect.ismethod( state.shape) else state.shape if list(state_shape) != list(param.shape): raise ValueError( "{} receives a shape {}, but the expected shape is {}.". format(key, list(state_shape), list(param.shape))) return param, state matched_param_state = [] for key, param in self.state_dict(use_hook=False).items(): key_name = key if use_structured_name else param.name try: match_res = _check_match(key_name, param) matched_param_state.append(match_res) except ValueError as err: warnings.warn(("Skip loading for {}. ".format(key) + str(err))) if _non_static_mode(): for param, state in matched_param_state: param.set_value(state) else: def _set_var(var, ndarray): t = global_scope().find_var(var.name).get_tensor() p = t._place() if p.is_cpu_place(): place = core.CPUPlace() elif p.is_cuda_pinned_place(): place = core.CUDAPinnedPlace() elif p.is_xpu_place(): p = core.Place() p.set_place(t._place()) place = core.XPUPlace(p.xpu_device_id()) else: p = core.Place() p.set_place(t._place()) place = core.CUDAPlace(p.gpu_device_id()) t.set(ndarray, place) executor = Executor(_get_device())._default_executor # restore parameter states core._create_loaded_parameter( [param for param, state in matched_param_state], global_scope(), executor) for param, state in matched_param_state: _set_var(param, state) def to(self, device=None, dtype=None, blocking=None): ''' Cast the parameters and buffers of Layer by the give device, dtype and blocking. Parameters: device(str|paddle.CPUPlace()|paddle.CUDAPlace()|paddle.CUDAPinnedPlace()|paddle.XPUPlace()|None, optional): The device of the Layer which want to be stored. If None, the device is the same with the original Tensor. If device is string, it can be ``cpu``, ``gpu:x`` and ``xpu:x``, where ``x`` is the index of the GPUs or XPUs. Default: None. dtype(str|numpy.dtype|paddle.dtype|None, optional): The type of the data. If None, the dtype is the same with the original Tensor. Default: None. blocking(bool|None, optional): If False and the source is in pinned memory, the copy will be asynchronous with respect to the host. Otherwise, the argument has no effect. If None, the blocking is set True. Default: None. Returns: self Examples: .. code-block:: python # required: skip import paddle linear=paddle.nn.Linear(2, 2) linear.weight #Parameter containing: #Tensor(shape=[2, 2], dtype=float32, place=CUDAPlace(0), stop_gradient=False, # [[-0.32770029, 0.38653070], # [ 0.46030545, 0.08158520]]) linear.to(dtype='float64') linear.weight #Tenor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=False, # [[-0.32770029, 0.38653070], # [ 0.46030545, 0.08158520]]) linear.to(device='cpu') linear.weight #Tensor(shape=[2, 2], dtype=float64, place=CPUPlace, stop_gradient=False, # [[-0.32770029, 0.38653070], # [ 0.46030545, 0.08158520]]) linear.to(device=paddle.CUDAPinnedPlace(), blocking=False) linear.weight #Tensor(shape=[2, 2], dtype=float64, place=CUDAPinnedPlace, stop_gradient=False, # [[-0.04989364, -0.56889004], # [ 0.33960250, 0.96878713]]) ''' return self._to_impl(device=device, dtype=dtype, blocking=blocking, include_sublayers=True, floating_only=False) def _apply(self, func, device, dtype, blocking, include_sublayers=True): if include_sublayers: for layer in self.children(): layer._apply(func, device, dtype, blocking, include_sublayers) for key, param in self._parameters.items(): if param is not None: with no_grad(): param_applied = func(param, device, dtype, blocking) if param.grad is not None: with no_grad(): grad_applied = func(param._grad_ivar(), device, dtype, blocking) for key, buf in self._buffers.items(): if buf is not None: self._buffers[key] = func(buf, device, dtype, blocking) self._dtype = dtype def _transform(self, t, device, dtype, blocking): if device is None: device = t.place if dtype is None: dtype = t.dtype if type(dtype) is not VarDesc.VarType: dtype = convert_np_dtype_to_dtype_(dtype) # 1. gpu place need to determine whether the memory is sufficient for allocation: if t.place.is_gpu_place(): # for gpu, minimum memory allocation unit is 256 bytes. size_dtype = core.size_of_dtype(dtype) # Note(zhangbo): Paddle GPU minimum memory allocation unit is 256 bytes, waiting_alloc_memory will comput ‘t’ occupied memory space. # Coefficient 1.2 is used to avoid OOM that may occur in this critical state when the memory is just enough. waiting_alloc_memory = ( (np.prod(t.shape) * size_dtype) / 256 + 1) * 256 * 1.2 gpu_memory_available = core.gpu_memory_available() if gpu_memory_available < waiting_alloc_memory: # Copy param / Tensor to cpu t_used = t._copy_to(paddle.CPUPlace(), blocking) # k-v type will error # Release mem of t t.value().get_tensor()._clear() else: t_used = t else: t_used = t # 2. cast param / Tensor to dtype if dtype is not None and dtype != t_used.dtype: with paddle.fluid.framework._dygraph_place_guard( place=t_used.place): t_casted = t_used.cast(dtype=dtype) else: t_casted = t_used # 3. Copy casted cpu param / Tensor to device if device is not None and not t_casted.place._equals(device): new_t = t_casted._copy_to(device, blocking) else: new_t = t_casted # 4. share Tensor to origin param / Tensor dst_tensor = t.value().get_tensor() src_tensor = new_t.value().get_tensor() dst_tensor._share_data_with(src_tensor) return t def _to_impl(self, device=None, dtype=None, blocking=None, include_sublayers=True, floating_only=False): ''' Cast the parameters and buffers of Layer by the give device, dtype and blocking. Parameters: device(str|paddle.CPUPlace()|paddle.CUDAPlace()|paddle.CUDAPinnedPlace()|paddle.XPUPlace()|None, optional): The device of the Layer which want to be stored. If None, the device is the same with the original Tensor. If device is string, it can be ``cpu``, ``gpu:x`` and ``xpu:x``, where ``x`` is the index of the GPUs or XPUs. Default: None. dtype(str|numpy.dtype|paddle.dtype|None, optional): The type of the data. If None, the dtype is the same with the original Tensor. Default: None. blocking(bool|None, optional): If False and the source is in pinned memory, the copy will be asynchronous with respect to the host. Otherwise, the argument has no effect. If None, the blocking is set True. Default: None. include_sublayers(bool|True, optional): If True, deal with self and all sublayers parameters and buffers, if not only deal with self parameters and buffers. Default: True. floating_only(bool|False, optional): If True, only cast all floating point parameters and buffers of Layer by the give device, dtype and blocking. Returns: self ''' if device is None and dtype is None and blocking is None: return self if device is not None: if isinstance(device, str): device = paddle.device._convert_to_place(device) elif isinstance(device, (core.CPUPlace, core.CUDAPlace, core.CUDAPinnedPlace, core.XPUPlace)): pass else: raise ValueError( "device value error, must be str, paddle.CPUPlace(), paddle.CUDAPlace(), paddle.CUDAPinnedPlace() or paddle.XPUPlace(), but the type of device is " + type(device).__name__) if blocking is None: blocking = True else: assert isinstance( blocking, bool), "blocking value error, must be the True, False or None" def transform(t, device, dtype, blocking): if floating_only and (not paddle.is_floating_point(t)): return t return self._transform(t, device, dtype, blocking) with warnings.catch_warnings(): warnings.filterwarnings("ignore", category=UserWarning) self._apply(transform, device, dtype, blocking, include_sublayers) self._dtype = dtype return self def _startup_program(self): """ Return starup program containing initialization operations of all parameters. NOTE(dev): This is a very low level API and only for inner developer. """ startup_program = Program() for param in self.parameters(): param._create_init_op(startup_program.global_block()) return startup_program # [aliases] Compatible with old method names set_dict = set_state_dict load_dict = set_state_dict