Transformer

Transformer for contextual representation of entities.

Parameters

• model

• tokenizer

• entities

• relations

• scoring – defaults to TransE scoring

• hidden_dim – defaults to None

• max_length – defaults to None

• gamma – defaults to 9

• device – defaults to cuda

Attributes

• embeddings

  Extracts embeddings.

• name

Examples

>>> import torch
>>> _ = torch.manual_seed(42)

>>> from ckb import models
>>> from ckb import datasets

>>> from transformers import BertTokenizer
>>> from transformers import BertModel

>>> dataset = datasets.Semanlink(1)

>>> model = models.Transformer(
...    model = BertModel.from_pretrained('bert-base-uncased'),
...    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased'),
...    entities = dataset.entities,
...    relations = dataset.relations,
...    gamma = 9,
...    device = 'cpu',
... )

>>> sample = torch.tensor([[0, 0, 0], [2, 2, 2]])
>>> model(sample)
tensor([[3.5500],
        [3.2861]], grad_fn=<ViewBackward>)

>>> sample = torch.tensor([[0, 0, 1], [2, 2, 1]])
>>> model(sample)
tensor([[-227.8486],
        [-197.0484]], grad_fn=<ViewBackward>)

>>> sample = torch.tensor([[1, 0, 0], [1, 2, 2]])
>>> model(sample)
tensor([[-227.8378],
        [-196.5193]], grad_fn=<ViewBackward>)

>>> sample = torch.tensor([[0, 0, 0], [2, 2, 2]])
>>> negative_sample = torch.tensor([[1], [1]])

>>> model(sample, negative_sample, mode='head-batch')
tensor([[-227.8378],
        [-196.5193]], grad_fn=<ViewBackward>)

>>> model(sample, negative_sample, mode='tail-batch')
tensor([[-227.8486],
        [-197.0484]], grad_fn=<ViewBackward>)

Methods

call

Call self as a function.

Parameters

• input
• kwargs

add_module

Adds a child module to the current module.

The module can be accessed as an attribute using the given name. Args: name (string): name of the child module. The child module can be accessed from this module using the given name module (Module): child module to be added to the module.

Parameters

• name (str)
• module (Union[ForwardRef('Module'), NoneType])

apply

Applies fn recursively to every submodule (as returned by .children()) as well as self. Typical use includes initializing the parameters of a model (see also :ref:nn-init-doc).

Args: fn (:class:Module -> None): function to be applied to each submodule Returns: Module: self Example:: >>> @torch.no_grad() >>> def init_weights(m): >>> print(m) >>> if type(m) == nn.Linear: >>> m.weight.fill_(1.0) >>> print(m.weight) >>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2)) >>> net.apply(init_weights) Linear(in_features=2, out_features=2, bias=True) Parameter containing: tensor([[ 1., 1.], [ 1., 1.]]) Linear(in_features=2, out_features=2, bias=True) Parameter containing: tensor([[ 1., 1.], [ 1., 1.]]) Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=True) ) Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=True) )

Parameters

• fn (Callable[[ForwardRef('Module')], NoneType])

batch

Process input sample.

Parameters

• sample
• negative_sample – defaults to None
• mode – defaults to None

bfloat16

Casts all floating point parameters and buffers to bfloat16 datatype.

Returns: Module: self

buffers

Returns an iterator over module buffers.

Args: recurse (bool): if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. Yields: torch.Tensor: module buffer Example:: >>> for buf in model.buffers(): >>> print(type(buf), buf.size()) (20L,) (20L, 1L, 5L, 5L)

Parameters

• recurse (bool) – defaults to True

children

Returns an iterator over immediate children modules.

Yields: Module: a child module

cpu

Moves all model parameters and buffers to the CPU.

Returns: Module: self

cuda

Moves all model parameters and buffers to the GPU.

This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on GPU while being optimized. Arguments: device (int, optional): if specified, all parameters will be copied to that device Returns: Module: self

Parameters

• device (Union[int, torch.device, NoneType]) – defaults to None

default_batch

distill

Default distillation method

Parameters

• sample
• negative_sample – defaults to None
• mode – defaults to None

double

Casts all floating point parameters and buffers to double datatype.

Returns: Module: self

encode

Encode input sample, negative sample with respect to the mode.

Parameters

• sample
• negative_sample – defaults to None
• mode – defaults to None

encoder

Encode input entities descriptions.

Parameters: e (list): List of description of entities. Returns: Torch tensor of encoded entities.

Parameters

• e

eval

Sets the module in evaluation mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. :class:Dropout, :class:BatchNorm, etc. This is equivalent with :meth:self.train(False) <torch.nn.Module.train>. Returns: Module: self

extra_repr

Set the extra representation of the module

To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.

float

Casts all floating point parameters and buffers to float datatype.

Returns: Module: self

format_sample

Adapt input tensor to compute scores.

Parameters

• sample
• negative_sample – defaults to None

forward

Compute scores of input sample, negative sample with respect to the mode.

Parameters

• sample
• negative_sample – defaults to None
• mode – defaults to None

half

Casts all floating point parameters and buffers to half datatype.

Returns: Module: self

head_batch

Used to get faster when computing scores for negative samples.

Parameters

• sample
• negative_sample

load_state_dict

Copies parameters and buffers from :attr:state_dict into this module and its descendants. If :attr:strict is True, then the keys of :attr:state_dict must exactly match the keys returned by this module's :meth:~torch.nn.Module.state_dict function.

Arguments: state_dict (dict): a dict containing parameters and persistent buffers. strict (bool, optional): whether to strictly enforce that the keys in :attr:state_dict match the keys returned by this module's :meth:~torch.nn.Module.state_dict function. Default: True Returns: NamedTuple with missing_keys and unexpected_keys fields: * missing_keys is a list of str containing the missing keys * unexpected_keys is a list of str containing the unexpected keys

Parameters

• state_dict (Dict[str, torch.Tensor])
• strict (bool) – defaults to True

modules

Returns an iterator over all modules in the network.

Yields: Module: a module in the network Note: Duplicate modules are returned only once. In the following example, l will be returned only once. Example:: >>> l = nn.Linear(2, 2) >>> net = nn.Sequential(l, l) >>> for idx, m in enumerate(net.modules()): print(idx, '->', m) 0 -> Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=True) ) 1 -> Linear(in_features=2, out_features=2, bias=True)

named_buffers

Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

Args: prefix (str): prefix to prepend to all buffer names. recurse (bool): if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. Yields: (string, torch.Tensor): Tuple containing the name and buffer Example:: >>> for name, buf in self.named_buffers(): >>> if name in ['running_var']: >>> print(buf.size())

Parameters

• prefix (str) – defaults to ``
• recurse (bool) – defaults to True

named_children

Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.

Yields: (string, Module): Tuple containing a name and child module Example:: >>> for name, module in model.named_children(): >>> if name in ['conv4', 'conv5']: >>> print(module)

named_modules

Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.

Yields: (string, Module): Tuple of name and module Note: Duplicate modules are returned only once. In the following example, l will be returned only once. Example:: >>> l = nn.Linear(2, 2) >>> net = nn.Sequential(l, l) >>> for idx, m in enumerate(net.named_modules()): print(idx, '->', m) 0 -> ('', Sequential( (0): Linear(in_features=2, out_features=2, bias=True) (1): Linear(in_features=2, out_features=2, bias=True) )) 1 -> ('0', Linear(in_features=2, out_features=2, bias=True))

Parameters

• memo (Union[Set[ForwardRef('Module')], NoneType]) – defaults to None
• prefix (str) – defaults to ``

named_parameters

Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

Args: prefix (str): prefix to prepend to all parameter names. recurse (bool): if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module. Yields: (string, Parameter): Tuple containing the name and parameter Example:: >>> for name, param in self.named_parameters(): >>> if name in ['bias']: >>> print(param.size())

Parameters

• prefix (str) – defaults to ``
• recurse (bool) – defaults to True

negative_encoding

parameters

Returns an iterator over module parameters.

This is typically passed to an optimizer. Args: recurse (bool): if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module. Yields: Parameter: module parameter Example:: >>> for param in model.parameters(): >>> print(type(param), param.size()) (20L,) (20L, 1L, 5L, 5L)

Parameters

• recurse (bool) – defaults to True

register_backward_hook

Registers a backward hook on the module.

.. warning :: The current implementation will not have the presented behavior for complex :class:Module that perform many operations. In some failure cases, :attr:grad_input and :attr:grad_output will only contain the gradients for a subset of the inputs and outputs. For such :class:Module, you should use :func:torch.Tensor.register_hook directly on a specific input or output to get the required gradients. The hook will be called every time the gradients with respect to module inputs are computed. The hook should have the following signature:: hook(module, grad_input, grad_output) -> Tensor or None The :attr:grad_input and :attr:grad_output may be tuples if the module has multiple inputs or outputs. The hook should not modify its arguments, but it can optionally return a new gradient with respect to input that will be used in place of :attr:grad_input in subsequent computations. :attr:grad_input will only correspond to the inputs given as positional arguments. Returns: :class:torch.utils.hooks.RemovableHandle: a handle that can be used to remove the added hook by calling handle.remove()

Parameters

• hook (Callable[[ForwardRef('Module'), Union[Tuple[torch.Tensor, ...], torch.Tensor], Union[Tuple[torch.Tensor, ...], torch.Tensor]], Union[NoneType, torch.Tensor]])

register_buffer

Adds a buffer to the module.

This is typically used to register a buffer that should not to be considered a model parameter. For example, BatchNorm's running_mean is not a parameter, but is part of the module's state. Buffers, by default, are persistent and will be saved alongside parameters. This behavior can be changed by setting :attr:persistent to False. The only difference between a persistent buffer and a non-persistent buffer is that the latter will not be a part of this module's :attr:state_dict. Buffers can be accessed as attributes using given names. Args: name (string): name of the buffer. The buffer can be accessed from this module using the given name tensor (Tensor): buffer to be registered. persistent (bool): whether the buffer is part of this module's :attr:state_dict. Example:: >>> self.register_buffer('running_mean', torch.zeros(num_features))

Parameters

• name (str)
• tensor (Union[torch.Tensor, NoneType])
• persistent (bool) – defaults to True

register_forward_hook

Registers a forward hook on the module.

The hook will be called every time after :func:forward has computed an output. It should have the following signature:: hook(module, input, output) -> None or modified output The input contains only the positional arguments given to the module. Keyword arguments won't be passed to the hooks and only to the forward. The hook can modify the output. It can modify the input inplace but it will not have effect on forward since this is called after :func:forward is called. Returns: :class:torch.utils.hooks.RemovableHandle: a handle that can be used to remove the added hook by calling handle.remove()

Parameters

• hook (Callable[..., NoneType])

register_forward_pre_hook

Registers a forward pre-hook on the module.

The hook will be called every time before :func:forward is invoked. It should have the following signature:: hook(module, input) -> None or modified input The input contains only the positional arguments given to the module. Keyword arguments won't be passed to the hooks and only to the forward. The hook can modify the input. User 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). Returns: :class:torch.utils.hooks.RemovableHandle: a handle that can be used to remove the added hook by calling handle.remove()

Parameters

• hook (Callable[..., NoneType])

register_parameter

Adds a parameter to the module.

The parameter can be accessed as an attribute using given name. Args: name (string): name of the parameter. The parameter can be accessed from this module using the given name param (Parameter): parameter to be added to the module.

Parameters

• name (str)
• param (Union[torch.nn.parameter.Parameter, NoneType])

requires_grad_

Change if autograd should record operations on parameters in this module.

This method sets the parameters' :attr:requires_grad attributes in-place. This method is helpful for freezing part of the module for finetuning or training parts of a model individually (e.g., GAN training). Args: requires_grad (bool): whether autograd should record operations on parameters in this module. Default: True. Returns: Module: self

Parameters

• requires_grad (bool) – defaults to True

save

share_memory

state_dict

Returns a dictionary containing a whole state of the module.

Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names. Returns: dict: a dictionary containing a whole state of the module Example:: >>> module.state_dict().keys() ['bias', 'weight']

Parameters

• destination – defaults to None
• prefix – defaults to ``
• keep_vars – defaults to False

tail_batch

Used to get faster when computing scores for negative samples.

Parameters

• sample
• negative_sample

to

Moves and/or casts the parameters and buffers.

This can be called as .. function:: to(device=None, dtype=None, non_blocking=False) .. function:: to(dtype, non_blocking=False) .. function:: to(tensor, non_blocking=False) .. function:: to(memory_format=torch.channels_last) Its signature is similar to :meth:torch.Tensor.to, but only accepts floating point desired :attr:dtype s. In addition, this method will only cast the floating point parameters and buffers to :attr:dtype (if given). The integral parameters and buffers will be moved :attr:device, if that is given, but with dtypes unchanged. When :attr:non_blocking is set, it tries to convert/move asynchronously with respect to the host if possible, e.g., moving CPU Tensors with pinned memory to CUDA devices. See below for examples. .. note:: This method modifies the module in-place. Args: device (:class:torch.device): the desired device of the parameters and buffers in this module dtype (:class:torch.dtype): the desired floating point type of the floating point parameters and buffers in this module tensor (torch.Tensor): Tensor whose dtype and device are the desired dtype and device for all parameters and buffers in this module memory_format (:class:torch.memory_format): the desired memory format for 4D parameters and buffers in this module (keyword only argument) Returns: Module: self Example:: >>> linear = nn.Linear(2, 2) >>> linear.weight Parameter containing: tensor([[ 0.1913, -0.3420], [-0.5113, -0.2325]]) >>> linear.to(torch.double) Linear(in_features=2, out_features=2, bias=True) >>> linear.weight Parameter containing: tensor([[ 0.1913, -0.3420], [-0.5113, -0.2325]], dtype=torch.float64) >>> gpu1 = torch.device("cuda:1") >>> linear.to(gpu1, dtype=torch.half, non_blocking=True) Linear(in_features=2, out_features=2, bias=True) >>> linear.weight Parameter containing: tensor([[ 0.1914, -0.3420], [-0.5112, -0.2324]], dtype=torch.float16, device='cuda:1') >>> cpu = torch.device("cpu") >>> linear.to(cpu) Linear(in_features=2, out_features=2, bias=True) >>> linear.weight Parameter containing: tensor([[ 0.1914, -0.3420], [-0.5112, -0.2324]], dtype=torch.float16)

Parameters

• args
• kwargs

train

Sets the module in training mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. :class:Dropout, :class:BatchNorm, etc. Args: mode (bool): whether to set training mode (True) or evaluation mode (False). Default: True. Returns: Module: self

Parameters

• mode (bool) – defaults to True

type

Casts all parameters and buffers to :attr:dst_type.

Arguments: dst_type (type or string): the desired type Returns: Module: self

Parameters

• dst_type (Union[torch.dtype, str])

zero_grad

Sets gradients of all model parameters to zero. See similar function under :class:torch.optim.Optimizer for more context.

Arguments: set_to_none (bool): instead of setting to zero, set the grads to None. See :meth:torch.optim.Optimizer.zero_grad for details.

Parameters

• set_to_none (bool) – defaults to False