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ColBERT

ColBERT model.

Parameters

  • model_name_or_path (str)

    Path to the model or the model name.

  • embedding_size (int) – defaults to 128

    Size of the embeddings in output of ColBERT model.

  • device (str) – defaults to None

    Device to use for the model. CPU or CUDA.

  • max_length_query (int) – defaults to 32

  • max_length_document (int) – defaults to 350

  • kwargs

    Additional parameters to the SentenceTransformer model.

Examples

>>> from neural_cherche import models
>>> import torch

>>> _ = torch.manual_seed(42)

>>> queries = ["Berlin", "Paris", "London"]

>>> documents = [
...     "Berlin is the capital of Germany",
...     "Paris is the capital of France and France is in Europe",
...     "London is the capital of England",
... ]

>>> encoder = models.ColBERT(
...     model_name_or_path="raphaelsty/neural-cherche-colbert",
...     embedding_size=128,
...     max_length_query=32,
...     max_length_document=350,
...     device="mps",
... )

>>> scores = encoder.scores(
...    queries=queries,
...    documents=documents,
... )

>>> scores
tensor([24.5880, 19.3780, 22.6769], device='mps:0', grad_fn=<CatBackward0>)

>>> _ = encoder.save_pretrained("checkpoint")

>>> encoder = models.ColBERT(
...     model_name_or_path="checkpoint",
...     embedding_size=64,
...     device="cpu",
... )

>>> scores = encoder.scores(
...    queries=queries,
...    documents=documents,
... )

>>> scores
tensor([24.5880, 19.3781, 22.6769], grad_fn=<CatBackward0>)

>>> embeddings = encoder(
...     texts=queries,
...     query_mode=True
... )

>>> embeddings["embeddings"].shape
torch.Size([3, 32, 128])

>>> embeddings = encoder(
...     texts=queries,
...     query_mode=False
... )

>>> embeddings["embeddings"].shape
torch.Size([3, 350, 128])

Methods

call

Call self as a function.

Parameters

  • args
  • kwargs
    Additional parameters to the SentenceTransformer model.
add_module

Adds a child module to the current module.

The module can be accessed as an attribute using the given name. Args: name (str): 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 (Optional[ForwardRef('Module')])
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.]], requires_grad=True) Linear(in_features=2, out_features=2, bias=True) Parameter containing: tensor([[1., 1.], [1., 1.]], requires_grad=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])
bfloat16

Casts all floating point parameters and buffers to bfloat16 datatype.

.. note:: This method modifies the module in-place. 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:: >>> # xdoctest: +SKIP("undefined vars") >>> 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.

.. note:: This method modifies the module in-place. 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. .. note:: This method modifies the module in-place. Args: 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
    Device to use for the model. CPU or CUDA.
double

Casts all floating point parameters and buffers to double datatype.

.. note:: This method modifies the module in-place. Returns: Module: self

encode

Encode documents

Parameters

  • texts (list[str])
  • truncation (bool) – defaults to True
  • add_special_tokens (bool) – defaults to False
  • query_mode (bool) – defaults to True
  • kwargs
    Additional parameters to the SentenceTransformer model.
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>. See :ref:locally-disable-grad-doc for a comparison between .eval() and several similar mechanisms that may be confused with it. 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.

.. note:: This method modifies the module in-place. Returns: Module: self

forward

Pytorch forward method.

Parameters

  • texts (list[str])
  • query_mode (bool) – defaults to True
  • kwargs
    Additional parameters to the SentenceTransformer model.
get_buffer

Returns the buffer given by target if it exists, otherwise throws an error.

See the docstring for get_submodule for a more detailed explanation of this method's functionality as well as how to correctly specify target. Args: target: The fully-qualified string name of the buffer to look for. (See get_submodule for how to specify a fully-qualified string.) Returns: torch.Tensor: The buffer referenced by target Raises: AttributeError: If the target string references an invalid path or resolves to something that is not a buffer

Parameters

  • target (str)
get_extra_state

Returns any extra state to include in the module's state_dict. Implement this and a corresponding :func:set_extra_state for your module if you need to store extra state. This function is called when building the module's state_dict().

Note that extra state should be picklable to ensure working serialization of the state_dict. We only provide provide backwards compatibility guarantees for serializing Tensors; other objects may break backwards compatibility if their serialized pickled form changes. Returns: object: Any extra state to store in the module's state_dict

get_parameter

Returns the parameter given by target if it exists, otherwise throws an error.

See the docstring for get_submodule for a more detailed explanation of this method's functionality as well as how to correctly specify target. Args: target: The fully-qualified string name of the Parameter to look for. (See get_submodule for how to specify a fully-qualified string.) Returns: torch.nn.Parameter: The Parameter referenced by target Raises: AttributeError: If the target string references an invalid path or resolves to something that is not an nn.Parameter

Parameters

  • target (str)
get_submodule

Returns the submodule given by target if it exists, otherwise throws an error.

For example, let's say you have an nn.Module A that looks like this: .. code-block:: text A( (net_b): Module( (net_c): Module( (conv): Conv2d(16, 33, kernel_size=(3, 3), stride=(2, 2)) ) (linear): Linear(in_features=100, out_features=200, bias=True) ) ) (The diagram shows an nn.Module A. A has a nested submodule net_b, which itself has two submodules net_c and linear. net_c then has a submodule conv.) To check whether or not we have the linear submodule, we would call get_submodule("net_b.linear"). To check whether we have the conv submodule, we would call get_submodule("net_b.net_c.conv"). The runtime of get_submodule is bounded by the degree of module nesting in target. A query against named_modules achieves the same result, but it is O(N) in the number of transitive modules. So, for a simple check to see if some submodule exists, get_submodule should always be used. Args: target: The fully-qualified string name of the submodule to look for. (See above example for how to specify a fully-qualified string.) Returns: torch.nn.Module: The submodule referenced by target Raises: AttributeError: If the target string references an invalid path or resolves to something that is not an nn.Module

Parameters

  • target (str)
half

Casts all floating point parameters and buffers to half datatype.

.. note:: This method modifies the module in-place. Returns: Module: self

ipu

Moves all model parameters and buffers to the IPU.

This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on IPU while being optimized. .. note:: This method modifies the module in-place. 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
    Device to use for the model. CPU or CUDA.
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.

Args: 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 Note: If a parameter or buffer is registered as None and its corresponding key exists in :attr:state_dict, :meth:load_state_dict will raise a RuntimeError.

Parameters

  • state_dict (Mapping[str, Any])
  • 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, optional): if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module. Defaults to True. remove_duplicate (bool, optional): whether to remove the duplicated buffers in the result. Defaults to True. Yields: (str, torch.Tensor): Tuple containing the name and buffer Example:: >>> # xdoctest: +SKIP("undefined vars") >>> 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
  • remove_duplicate (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: (str, Module): Tuple containing a name and child module Example:: >>> # xdoctest: +SKIP("undefined vars") >>> 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.

Args: memo: a memo to store the set of modules already added to the result prefix: a prefix that will be added to the name of the module remove_duplicate: whether to remove the duplicated module instances in the result or not Yields: (str, 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 (Optional[Set[ForwardRef('Module')]]) – defaults to None
  • prefix (str) – defaults to ``
  • remove_duplicate (bool) – defaults to True
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. remove_duplicate (bool, optional): whether to remove the duplicated parameters in the result. Defaults to True. Yields: (str, Parameter): Tuple containing the name and parameter Example:: >>> # xdoctest: +SKIP("undefined vars") >>> for name, param in self.named_parameters(): >>> if name in ['bias']: >>> print(param.size())

Parameters

  • prefix (str) – defaults to ``
  • recurse (bool) – defaults to True
  • remove_duplicate (bool) – defaults to True
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:: >>> # xdoctest: +SKIP("undefined vars") >>> 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.

This function is deprecated in favor of :meth:~torch.nn.Module.register_full_backward_hook and the behavior of this function will change in future versions. 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, Tuple[torch.Tensor, ...], 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 (str): name of the buffer. The buffer can be accessed from this module using the given name tensor (Tensor or None): buffer to be registered. If None, then operations that run on buffers, such as :attr:cuda, are ignored. If None, the buffer is not included in the module's :attr:state_dict. persistent (bool): whether the buffer is part of this module's :attr:state_dict. Example:: >>> # xdoctest: +SKIP("undefined vars") >>> self.register_buffer('running_mean', torch.zeros(num_features))

Parameters

  • name (str)
  • tensor (Optional[torch.Tensor])
  • 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. If with_kwargs is False or not specified, 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. The hook should have the following signature:: hook(module, args, output) -> None or modified output If with_kwargs is True, the forward hook will be passed the kwargs given to the forward function and be expected to return the output possibly modified. The hook should have the following signature:: hook(module, args, kwargs, output) -> None or modified output Args: hook (Callable): The user defined hook to be registered. prepend (bool): If True, the provided hook will be fired before all existing forward hooks on this :class:torch.nn.modules.Module. Otherwise, the provided hook will be fired after all existing forward hooks on this :class:torch.nn.modules.Module. Note that global forward hooks registered with :func:register_module_forward_hook will fire before all hooks registered by this method. Default: False with_kwargs (bool): If True, the hook will be passed the kwargs given to the forward function. Default: False Returns: :class:torch.utils.hooks.RemovableHandle: a handle that can be used to remove the added hook by calling handle.remove()

Parameters

  • hook (Union[Callable[[~T, Tuple[Any, ...], Any], Optional[Any]], Callable[[~T, Tuple[Any, ...], Dict[str, Any], Any], Optional[Any]]])
  • prepend (bool) – defaults to False
  • with_kwargs (bool) – defaults to False
register_forward_pre_hook

Registers a forward pre-hook on the module.

The hook will be called every time before :func:forward is invoked. If with_kwargs is false or not specified, 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). The hook should have the following signature:: hook(module, args) -> None or modified input If with_kwargs is true, the forward pre-hook will be passed the kwargs given to the forward function. And if the hook modifies the input, both the args and kwargs should be returned. The hook should have the following signature:: hook(module, args, kwargs) -> None or a tuple of modified input and kwargs Args: hook (Callable): The user defined hook to be registered. prepend (bool): If true, the provided hook will be fired before all existing forward_pre hooks on this :class:torch.nn.modules.Module. Otherwise, the provided hook will be fired after all existing forward_pre hooks on this :class:torch.nn.modules.Module. Note that global forward_pre hooks registered with :func:register_module_forward_pre_hook will fire before all hooks registered by this method. Default: False with_kwargs (bool): If true, the hook will be passed the kwargs given to the forward function. Default: False Returns: :class:torch.utils.hooks.RemovableHandle: a handle that can be used to remove the added hook by calling handle.remove()

Parameters

  • hook (Union[Callable[[~T, Tuple[Any, ...]], Optional[Any]], Callable[[~T, Tuple[Any, ...], Dict[str, Any]], Optional[Tuple[Any, Dict[str, Any]]]]])
  • prepend (bool) – defaults to False
  • with_kwargs (bool) – defaults to False
register_full_backward_hook

Registers a backward hook on the module.

The hook will be called every time the gradients with respect to a module are computed, i.e. the hook will execute if and only if the gradients with respect to module outputs are computed. The hook should have the following signature:: hook(module, grad_input, grad_output) -> tuple(Tensor) or None The :attr:grad_input and :attr:grad_output are tuples that contain the gradients with respect to the inputs and outputs respectively. The hook should not modify its arguments, but it can optionally return a new gradient with respect to the 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 and all kwarg arguments are ignored. Entries in :attr:grad_input and :attr:grad_output will be None for all non-Tensor arguments. For technical reasons, when this hook is applied to a Module, its forward function will receive a view of each Tensor passed to the Module. Similarly the caller will receive a view of each Tensor returned by the Module's forward function. .. warning :: Modifying inputs or outputs inplace is not allowed when using backward hooks and will raise an error. Args: hook (Callable): The user-defined hook to be registered. prepend (bool): If true, the provided hook will be fired before all existing backward hooks on this :class:torch.nn.modules.Module. Otherwise, the provided hook will be fired after all existing backward hooks on this :class:torch.nn.modules.Module. Note that global backward hooks registered with :func:register_module_full_backward_hook will fire before all hooks registered by this method. 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, Tuple[torch.Tensor, ...], torch.Tensor]])
  • prepend (bool) – defaults to False
register_full_backward_pre_hook

Registers a backward pre-hook on the module.

The hook will be called every time the gradients for the module are computed. The hook should have the following signature:: hook(module, grad_output) -> Tensor or None The :attr:grad_output is a tuple. The hook should not modify its arguments, but it can optionally return a new gradient with respect to the output that will be used in place of :attr:grad_output in subsequent computations. Entries in :attr:grad_output will be None for all non-Tensor arguments. For technical reasons, when this hook is applied to a Module, its forward function will receive a view of each Tensor passed to the Module. Similarly the caller will receive a view of each Tensor returned by the Module's forward function. .. warning :: Modifying inputs inplace is not allowed when using backward hooks and will raise an error. Args: hook (Callable): The user-defined hook to be registered. prepend (bool): If true, the provided hook will be fired before all existing backward_pre hooks on this :class:torch.nn.modules.Module. Otherwise, the provided hook will be fired after all existing backward_pre hooks on this :class:torch.nn.modules.Module. Note that global backward_pre hooks registered with :func:register_module_full_backward_pre_hook will fire before all hooks registered by this method. 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[NoneType, Tuple[torch.Tensor, ...], torch.Tensor]])
  • prepend (bool) – defaults to False
register_load_state_dict_post_hook

Registers a post hook to be run after module's load_state_dict is called.

It should have the following signature:: hook(module, incompatible_keys) -> None The module argument is the current module that this hook is registered on, and the incompatible_keys argument is a NamedTuple consisting of attributes missing_keys and unexpected_keys. missing_keys is a list of str containing the missing keys and unexpected_keys is a list of str containing the unexpected keys. The given incompatible_keys can be modified inplace if needed. Note that the checks performed when calling :func:load_state_dict with strict=True are affected by modifications the hook makes to missing_keys or unexpected_keys, as expected. Additions to either set of keys will result in an error being thrown when strict=True, and clearing out both missing and unexpected keys will avoid an error. Returns: :class:torch.utils.hooks.RemovableHandle: a handle that can be used to remove the added hook by calling handle.remove()

Parameters

  • hook
register_module

Alias for :func:add_module.

Parameters

  • name (str)
  • module (Optional[ForwardRef('Module')])
register_parameter

Adds a parameter to the module.

The parameter can be accessed as an attribute using given name. Args: name (str): name of the parameter. The parameter can be accessed from this module using the given name param (Parameter or None): parameter to be added to the module. If None, then operations that run on parameters, such as :attr:cuda, are ignored. If None, the parameter is not included in the module's :attr:state_dict.

Parameters

  • name (str)
  • param (Optional[torch.nn.parameter.Parameter])
register_state_dict_pre_hook

These hooks will be called with arguments: self, prefix, and keep_vars before calling state_dict on self. The registered hooks can be used to perform pre-processing before the state_dict call is made.

Parameters

  • hook
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). See :ref:locally-disable-grad-doc for a comparison between .requires_grad_() and several similar mechanisms that may be confused with it. 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_pretrained

Save model the model.

Parameters

  • path (str)
scores

Score queries and documents.

Parameters

  • queries (list[str])
  • documents (list[str])
  • batch_size (int) – defaults to 2
  • tqdm_bar (bool) – defaults to True
  • kwargs
    Additional parameters to the SentenceTransformer model.
set_extra_state

This function is called from :func:load_state_dict to handle any extra state found within the state_dict. Implement this function and a corresponding :func:get_extra_state for your module if you need to store extra state within its state_dict.

Args: state (dict): Extra state from the state_dict

Parameters

  • state (Any)
share_memory

See :meth:torch.Tensor.share_memory_

state_dict

Returns a dictionary containing references to the whole state of the module.

Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names. Parameters and buffers set to None are not included. .. note:: The returned object is a shallow copy. It contains references to the module's parameters and buffers. .. warning:: Currently state_dict() also accepts positional arguments for destination, prefix and keep_vars in order. However, this is being deprecated and keyword arguments will be enforced in future releases. .. warning:: Please avoid the use of argument destination as it is not designed for end-users. Args: destination (dict, optional): If provided, the state of module will be updated into the dict and the same object is returned. Otherwise, an OrderedDict will be created and returned. Default: None. prefix (str, optional): a prefix added to parameter and buffer names to compose the keys in state_dict. Default: ''. keep_vars (bool, optional): by default the :class:~torch.Tensor s returned in the state dict are detached from autograd. If it's set to True, detaching will not be performed. Default: False. Returns: dict: a dictionary containing a whole state of the module Example:: >>> # xdoctest: +SKIP("undefined vars") >>> module.state_dict().keys() ['bias', 'weight']

Parameters

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

Moves and/or casts the parameters and buffers.

This can be called as .. function:: to(device=None, dtype=None, non_blocking=False) :noindex: .. function:: to(dtype, non_blocking=False) :noindex: .. function:: to(tensor, non_blocking=False) :noindex: .. function:: to(memory_format=torch.channels_last) :noindex: Its signature is similar to :meth:torch.Tensor.to, but only accepts floating point or complex :attr:dtype\ s. In addition, this method will only cast the floating point or complex 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 or complex dtype of the 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 Examples:: >>> # xdoctest: +IGNORE_WANT("non-deterministic") >>> 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) >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA1) >>> 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) >>> linear = nn.Linear(2, 2, bias=None).to(torch.cdouble) >>> linear.weight Parameter containing: tensor([[ 0.3741+0.j, 0.2382+0.j], [ 0.5593+0.j, -0.4443+0.j]], dtype=torch.complex128) >>> linear(torch.ones(3, 2, dtype=torch.cdouble)) tensor([[0.6122+0.j, 0.1150+0.j], [0.6122+0.j, 0.1150+0.j], [0.6122+0.j, 0.1150+0.j]], dtype=torch.complex128)

Parameters

  • args
  • kwargs
    Additional parameters to the SentenceTransformer model.
to_empty

Moves the parameters and buffers to the specified device without copying storage.

Args: device (:class:torch.device): The desired device of the parameters and buffers in this module. Returns: Module: self

Parameters

  • device (Union[str, torch.device])
    Device to use for the model. CPU or CUDA.
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.

.. note:: This method modifies the module in-place. Args: dst_type (type or string): the desired type Returns: Module: self

Parameters

  • dst_type (Union[torch.dtype, str])
xpu

Moves all model parameters and buffers to the XPU.

This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on XPU while being optimized. .. note:: This method modifies the module in-place. 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
    Device to use for the model. CPU or CUDA.
zero_grad

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

Args: 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 True