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util

allennlp.nn.util

[SOURCE]

Assorted utilities for working with neural networks in AllenNLP.

T

T = TypeVar("T")

StateDictType

StateDictType = Union[Dict[str, torch.Tensor], "OrderedDict[str, torch.Tensor]"]

move_to_device

def move_to_device(obj, device: Union[torch.device, int])

Given a structure (possibly) containing Tensors, move all the Tensors to the specified device (or do nothing, if they are already on the target device).

clamp_tensor

def clamp_tensor(tensor, minimum, maximum)

Supports sparse and dense tensors. Returns a tensor with values clamped between the provided minimum and maximum, without modifying the original tensor.

batch_tensor_dicts

def batch_tensor_dicts(
    tensor_dicts: List[Dict[str, torch.Tensor]],
    remove_trailing_dimension: bool = False
) -> Dict[str, torch.Tensor]

Takes a list of tensor dictionaries, where each dictionary is assumed to have matching keys, and returns a single dictionary with all tensors with the same key batched together.

Parameters

  • tensor_dicts : List[Dict[str, torch.Tensor]]
    The list of tensor dictionaries to batch.
  • remove_trailing_dimension : bool
    If True, we will check for a trailing dimension of size 1 on the tensors that are being batched, and remove it if we find it.

get_lengths_from_binary_sequence_mask

def get_lengths_from_binary_sequence_mask(
    mask: torch.BoolTensor
) -> torch.LongTensor

Compute sequence lengths for each batch element in a tensor using a binary mask.

Parameters

  • mask : torch.BoolTensor
    A 2D binary mask of shape (batch_size, sequence_length) to calculate the per-batch sequence lengths from.

Returns

  • torch.LongTensor
    A torch.LongTensor of shape (batch_size,) representing the lengths of the sequences in the batch.

get_mask_from_sequence_lengths

def get_mask_from_sequence_lengths(
    sequence_lengths: torch.Tensor,
    max_length: int
) -> torch.BoolTensor

Given a variable of shape (batch_size,) that represents the sequence lengths of each batch element, this function returns a (batch_size, max_length) mask variable. For example, if our input was [2, 2, 3], with a max_length of 4, we'd return [[1, 1, 0, 0], [1, 1, 0, 0], [1, 1, 1, 0]].

We require max_length here instead of just computing it from the input sequence_lengths because it lets us avoid finding the max, then copying that value from the GPU to the CPU so that we can use it to construct a new tensor.

sort_batch_by_length

def sort_batch_by_length(
    tensor: torch.Tensor,
    sequence_lengths: torch.Tensor
)

Sort a batch first tensor by some specified lengths.

Parameters

  • tensor : torch.FloatTensor
    A batch first Pytorch tensor.
  • sequence_lengths : torch.LongTensor
    A tensor representing the lengths of some dimension of the tensor which we want to sort by.

Returns

  • sorted_tensor : torch.FloatTensor
    The original tensor sorted along the batch dimension with respect to sequence_lengths.
  • sorted_sequence_lengths : torch.LongTensor
    The original sequence_lengths sorted by decreasing size.
  • restoration_indices : torch.LongTensor
    Indices into the sorted_tensor such that sorted_tensor.index_select(0, restoration_indices) == original_tensor
  • permutation_index : torch.LongTensor
    The indices used to sort the tensor. This is useful if you want to sort many tensors using the same ordering.

get_final_encoder_states

def get_final_encoder_states(
    encoder_outputs: torch.Tensor,
    mask: torch.BoolTensor,
    bidirectional: bool = False
) -> torch.Tensor

Given the output from a Seq2SeqEncoder, with shape (batch_size, sequence_length, encoding_dim), this method returns the final hidden state for each element of the batch, giving a tensor of shape (batch_size, encoding_dim). This is not as simple as encoder_outputs[:, -1], because the sequences could have different lengths. We use the mask (which has shape (batch_size, sequence_length)) to find the final state for each batch instance.

Additionally, if bidirectional is True, we will split the final dimension of the encoder_outputs into two and assume that the first half is for the forward direction of the encoder and the second half is for the backward direction. We will concatenate the last state for each encoder dimension, giving encoder_outputs[:, -1, :encoding_dim/2] concatenated with encoder_outputs[:, 0, encoding_dim/2:].

get_dropout_mask

def get_dropout_mask(
    dropout_probability: float,
    tensor_for_masking: torch.Tensor
)

Computes and returns an element-wise dropout mask for a given tensor, where each element in the mask is dropped out with probability dropout_probability. Note that the mask is NOT applied to the tensor - the tensor is passed to retain the correct CUDA tensor type for the mask.

Parameters

  • dropout_probability : float
    Probability of dropping a dimension of the input.
  • tensor_for_masking : torch.Tensor

Returns

  • torch.FloatTensor
    A torch.FloatTensor consisting of the binary mask scaled by 1/ (1 - dropout_probability). This scaling ensures expected values and variances of the output of applying this mask and the original tensor are the same.

masked_softmax

def masked_softmax(
    vector: torch.Tensor,
    mask: torch.BoolTensor,
    dim: int = -1,
    memory_efficient: bool = False
) -> torch.Tensor

torch.nn.functional.softmax(vector) does not work if some elements of vector should be masked. This performs a softmax on just the non-masked portions of vector. Passing None in for the mask is also acceptable; you'll just get a regular softmax.

vector can have an arbitrary number of dimensions; the only requirement is that mask is broadcastable to vector's shape. If mask has fewer dimensions than vector, we will unsqueeze on dimension 1 until they match. If you need a different unsqueezing of your mask, do it yourself before passing the mask into this function.

If memory_efficient is set to true, we will simply use a very large negative number for those masked positions so that the probabilities of those positions would be approximately 0. This is not accurate in math, but works for most cases and consumes less memory.

In the case that the input vector is completely masked and memory_efficient is false, this function returns an array of 0.0. This behavior may cause NaN if this is used as the last layer of a model that uses categorical cross-entropy loss. Instead, if memory_efficient is true, this function will treat every element as equal, and do softmax over equal numbers.

masked_log_softmax

def masked_log_softmax(
    vector: torch.Tensor,
    mask: torch.BoolTensor,
    dim: int = -1
) -> torch.Tensor

torch.nn.functional.log_softmax(vector) does not work if some elements of vector should be masked. This performs a log_softmax on just the non-masked portions of vector. Passing None in for the mask is also acceptable; you'll just get a regular log_softmax.

vector can have an arbitrary number of dimensions; the only requirement is that mask is broadcastable to vector's shape. If mask has fewer dimensions than vector, we will unsqueeze on dimension 1 until they match. If you need a different unsqueezing of your mask, do it yourself before passing the mask into this function.

In the case that the input vector is completely masked, the return value of this function is arbitrary, but not nan. You should be masking the result of whatever computation comes out of this in that case, anyway, so the specific values returned shouldn't matter. Also, the way that we deal with this case relies on having single-precision floats; mixing half-precision floats with fully-masked vectors will likely give you nans.

If your logits are all extremely negative (i.e., the max value in your logit vector is -50 or lower), the way we handle masking here could mess you up. But if you've got logit values that extreme, you've got bigger problems than this.

masked_max

def masked_max(
    vector: torch.Tensor,
    mask: torch.BoolTensor,
    dim: int,
    keepdim: bool = False
) -> torch.Tensor

To calculate max along certain dimensions on masked values

Parameters

  • vector : torch.Tensor
    The vector to calculate max, assume unmasked parts are already zeros
  • mask : torch.BoolTensor
    The mask of the vector. It must be broadcastable with vector.
  • dim : int
    The dimension to calculate max
  • keepdim : bool
    Whether to keep dimension

Returns

  • torch.Tensor
    A torch.Tensor of including the maximum values.

masked_mean

def masked_mean(
    vector: torch.Tensor,
    mask: torch.BoolTensor,
    dim: int,
    keepdim: bool = False
) -> torch.Tensor

To calculate mean along certain dimensions on masked values

Parameters

  • vector : torch.Tensor
    The vector to calculate mean.
  • mask : torch.BoolTensor
    The mask of the vector. It must be broadcastable with vector.
  • dim : int
    The dimension to calculate mean
  • keepdim : bool
    Whether to keep dimension

Returns

  • torch.Tensor
    A torch.Tensor of including the mean values.

masked_flip

def masked_flip(
    padded_sequence: torch.Tensor,
    sequence_lengths: List[int]
) -> torch.Tensor

Flips a padded tensor along the time dimension without affecting masked entries.

Parameters

  • padded_sequence : torch.Tensor
    The tensor to flip along the time dimension. Assumed to be of dimensions (batch size, num timesteps, ...)
  • sequence_lengths : torch.Tensor
    A list containing the lengths of each unpadded sequence in the batch.

Returns

  • torch.Tensor
    A torch.Tensor of the same shape as padded_sequence.

viterbi_decode

def viterbi_decode(
    tag_sequence: torch.Tensor,
    transition_matrix: torch.Tensor,
    tag_observations: Optional[List[int]] = None,
    allowed_start_transitions: torch.Tensor = None,
    allowed_end_transitions: torch.Tensor = None,
    top_k: int = None
)

Perform Viterbi decoding in log space over a sequence given a transition matrix specifying pairwise (transition) potentials between tags and a matrix of shape (sequence_length, num_tags) specifying unary potentials for possible tags per timestep.

Parameters

  • tag_sequence : torch.Tensor
    A tensor of shape (sequence_length, num_tags) representing scores for a set of tags over a given sequence.
  • transition_matrix : torch.Tensor
    A tensor of shape (num_tags, num_tags) representing the binary potentials for transitioning between a given pair of tags.
  • tag_observations : Optional[List[int]], optional (default = None)
    A list of length sequence_length containing the class ids of observed elements in the sequence, with unobserved elements being set to -1. Note that it is possible to provide evidence which results in degenerate labelings if the sequences of tags you provide as evidence cannot transition between each other, or those transitions are extremely unlikely. In this situation we log a warning, but the responsibility for providing self-consistent evidence ultimately lies with the user.
  • allowed_start_transitions : torch.Tensor, optional (default = None)
    An optional tensor of shape (num_tags,) describing which tags the START token may transition to. If provided, additional transition constraints will be used for determining the start element of the sequence.
  • allowed_end_transitions : torch.Tensor, optional (default = None)
    An optional tensor of shape (num_tags,) describing which tags may transition to the end tag. If provided, additional transition constraints will be used for determining the end element of the sequence.
  • top_k : int, optional (default = None)
    Optional integer specifying how many of the top paths to return. For top_k>=1, returns a tuple of two lists: top_k_paths, top_k_scores, For top_k==None, returns a flattened tuple with just the top path and its score (not in lists, for backwards compatibility).

Returns

  • viterbi_path : List[int]
    The tag indices of the maximum likelihood tag sequence.
  • viterbi_score : torch.Tensor
    The score of the viterbi path.

get_text_field_mask

def get_text_field_mask(
    text_field_tensors: Dict[str, Dict[str, torch.Tensor]],
    num_wrapping_dims: int = 0,
    padding_id: int = 0
) -> torch.BoolTensor

Takes the dictionary of tensors produced by a TextField and returns a mask with 0 where the tokens are padding, and 1 otherwise. padding_id specifies the id of padding tokens. We also handle TextFields wrapped by an arbitrary number of ListFields, where the number of wrapping ListFields is given by num_wrapping_dims.

If num_wrapping_dims == 0, the returned mask has shape (batch_size, num_tokens). If num_wrapping_dims > 0 then the returned mask has num_wrapping_dims extra dimensions, so the shape will be (batch_size, ..., num_tokens).

There could be several entries in the tensor dictionary with different shapes (e.g., one for word ids, one for character ids). In order to get a token mask, we use the tensor in the dictionary with the lowest number of dimensions. After subtracting num_wrapping_dims, if this tensor has two dimensions we assume it has shape (batch_size, ..., num_tokens), and use it for the mask. If instead it has three dimensions, we assume it has shape (batch_size, ..., num_tokens, num_features), and sum over the last dimension to produce the mask. Most frequently this will be a character id tensor, but it could also be a featurized representation of each token, etc.

If the input text_field_tensors contains the "mask" key, this is returned instead of inferring the mask.

get_token_ids_from_text_field_tensors

def get_token_ids_from_text_field_tensors(
    text_field_tensors: Dict[str, Dict[str, torch.Tensor]]
) -> torch.Tensor

Our TextFieldTensors are complex output structures, because they try to handle a lot of potential variation. Sometimes, you just want to grab the token ids from this data structure, and that's not trivial without hard-coding assumptions about your data processing, which defeats the entire purpose of that generality. This method tries to let you get the token ids out of the data structure in your model without hard-coding any assumptions.

weighted_sum

def weighted_sum(
    matrix: torch.Tensor,
    attention: torch.Tensor
) -> torch.Tensor

Takes a matrix of vectors and a set of weights over the rows in the matrix (which we call an "attention" vector), and returns a weighted sum of the rows in the matrix. This is the typical computation performed after an attention mechanism.

Note that while we call this a "matrix" of vectors and an attention "vector", we also handle higher-order tensors. We always sum over the second-to-last dimension of the "matrix", and we assume that all dimensions in the "matrix" prior to the last dimension are matched in the "vector". Non-matched dimensions in the "vector" must be directly after the batch dimension.

For example, say I have a "matrix" with dimensions (batch_size, num_queries, num_words, embedding_dim). The attention "vector" then must have at least those dimensions, and could have more. Both:

- `(batch_size, num_queries, num_words)` (distribution over words for each query)
- `(batch_size, num_documents, num_queries, num_words)` (distribution over words in a
  query for each document)

are valid input "vectors", producing tensors of shape: (batch_size, num_queries, embedding_dim) and (batch_size, num_documents, num_queries, embedding_dim) respectively.

sequence_cross_entropy_with_logits

def sequence_cross_entropy_with_logits(
    logits: torch.FloatTensor,
    targets: torch.LongTensor,
    weights: Union[torch.FloatTensor, torch.BoolTensor],
    average: str = "batch",
    label_smoothing: float = None,
    gamma: float = None,
    alpha: Union[float, List[float], torch.FloatTensor] = None
) -> torch.FloatTensor

Computes the cross entropy loss of a sequence, weighted with respect to some user provided weights. Note that the weighting here is not the same as in the torch.nn.CrossEntropyLoss() criterion, which is weighting classes; here we are weighting the loss contribution from particular elements in the sequence. This allows loss computations for models which use padding.

Parameters

  • logits : torch.FloatTensor
    A torch.FloatTensor of size (batch_size, sequence_length, num_classes) which contains the unnormalized probability for each class.
  • targets : torch.LongTensor
    A torch.LongTensor of size (batch, sequence_length) which contains the index of the true class for each corresponding step.
  • weights : Union[torch.FloatTensor, torch.BoolTensor]
    A torch.FloatTensor of size (batch, sequence_length)
  • average : str, optional (default = "batch")
    If "batch", average the loss across the batches. If "token", average the loss across each item in the input. If None, return a vector of losses per batch element.
  • label_smoothing : float, optional (default = None)
    Whether or not to apply label smoothing to the cross-entropy loss. For example, with a label smoothing value of 0.2, a 4 class classification target would look like [0.05, 0.05, 0.85, 0.05] if the 3rd class was the correct label.
  • gamma : float, optional (default = None)
    Focal loss[*] focusing parameter gamma to reduces the relative loss for well-classified examples and put more focus on hard. The greater value gamma is, the more focus on hard examples.
  • alpha : Union[float, List[float]], optional (default = None)
    Focal loss[] weighting factor alpha to balance between classes. Can be used independently with gamma. If a single float is provided, it is assumed binary case using alpha and 1 - alpha for positive and negative respectively. If a list of float is provided, with the same length as the number of classes, the weights will match the classes. [] T. Lin, P. Goyal, R. Girshick, K. He and P. Dollár, "Focal Loss for Dense Object Detection," 2017 IEEE International Conference on Computer Vision (ICCV), Venice, 2017, pp. 2999-3007.

Returns

  • torch.FloatTensor
    A torch.FloatTensor representing the cross entropy loss. If average=="batch" or average=="token", the returned loss is a scalar. If average is None, the returned loss is a vector of shape (batch_size,).

replace_masked_values

def replace_masked_values(
    tensor: torch.Tensor,
    mask: torch.BoolTensor,
    replace_with: float
) -> torch.Tensor

Replaces all masked values in tensor with replace_with. mask must be broadcastable to the same shape as tensor. We require that tensor.dim() == mask.dim(), as otherwise we won't know which dimensions of the mask to unsqueeze.

This just does tensor.masked_fill(), except the pytorch method fills in things with a mask value of 1, where we want the opposite. You can do this in your own code with tensor.masked_fill(~mask, replace_with).

tensors_equal

def tensors_equal(
    tensor1: torch.Tensor,
    tensor2: torch.Tensor,
    tolerance: float = 1e-12
) -> bool

A check for tensor equality (by value). We make sure that the tensors have the same shape, then check all of the entries in the tensor for equality. We additionally allow the input tensors to be lists or dictionaries, where we then do the above check on every position in the list / item in the dictionary. If we find objects that aren't tensors as we're doing that, we just defer to their equality check.

This is kind of a catch-all method that's designed to make implementing __eq__ methods easier, in a way that's really only intended to be useful for tests.

device_mapping

def device_mapping(cuda_device: int)

In order to torch.load() a GPU-trained model onto a CPU (or specific GPU), you have to supply a map_location function. Call this with the desired cuda_device to get the function that torch.load() needs.

read_state_dict

def read_state_dict(
    path: Union[PathLike, str],
    strip_prefix: Optional[str] = None,
    ignore: Optional[List[str]] = None,
    strict: bool = True,
    cuda_device: int = -1
) -> Dict[str, torch.Tensor]

Read a PyTorch model state dictionary from a checkpoint at the given path.

Parameters

  • path : Union[PathLike, str]

  • strip_prefix : Optional[str], optional (default = None)
    A prefix to remove from all of the state dict keys.

  • ignore : Optional[List[str]], optional (default = None)
    Optional list of regular expressions. Keys that match any of these will be removed from the state dict.

    Note

    If strip_prefix is given, the regular expressions in ignore are matched before the prefix is stripped.

  • strict : bool, optional (default = True)
    If True (the default) and strip_prefix was never used or any of the regular expressions in ignore never matched, a ValueError will be raised.

  • cuda_device : int, optional (default = -1)
    The device to load the parameters onto. Use -1 (the default) for CPU.

Returns

  • Dict[str, torch.Tensor]
    An ordered dictionary of the state.

combine_tensors

def combine_tensors(
    combination: str,
    tensors: List[torch.Tensor]
) -> torch.Tensor

Combines a list of tensors using element-wise operations and concatenation, specified by a combination string. The string refers to (1-indexed) positions in the input tensor list, and looks like "1,2,1+2,3-1".

We allow the following kinds of combinations : x, x*y, x+y, x-y, and x/y, where x and y are positive integers less than or equal to len(tensors). Each of the binary operations is performed elementwise. You can give as many combinations as you want in the combination string. For example, for the input string "1,2,1*2", the result would be [1;2;1*2], as you would expect, where [;] is concatenation along the last dimension.

If you have a fixed, known way to combine tensors that you use in a model, you should probably just use something like torch.cat([x_tensor, y_tensor, x_tensor * y_tensor]). This function adds some complexity that is only necessary if you want the specific combination used to be configurable.

If you want to do any element-wise operations, the tensors involved in each element-wise operation must have the same shape.

This function also accepts x and y in place of 1 and 2 in the combination string.

combine_tensors_and_multiply

def combine_tensors_and_multiply(
    combination: str,
    tensors: List[torch.Tensor],
    weights: torch.nn.Parameter
) -> torch.Tensor

Like combine_tensors, but does a weighted (linear) multiplication while combining. This is a separate function from combine_tensors because we try to avoid instantiating large intermediate tensors during the combination, which is possible because we know that we're going to be multiplying by a weight vector in the end.

Parameters

  • combination : str
    Same as in combine_tensors
  • tensors : List[torch.Tensor]
    A list of tensors to combine, where the integers in the combination are (1-indexed) positions in this list of tensors. These tensors are all expected to have either three or four dimensions, with the final dimension being an embedding. If there are four dimensions, one of them must have length 1.
  • weights : torch.nn.Parameter
    A vector of weights to use for the combinations. This should have shape (combined_dim,), as calculated by get_combined_dim.

get_combined_dim

def get_combined_dim(combination: str, tensor_dims: List[int]) -> int

For use with combine_tensors. This function computes the resultant dimension when calling combine_tensors(combination, tensors), when the tensor dimension is known. This is necessary for knowing the sizes of weight matrices when building models that use combine_tensors.

Parameters

  • combination : str
    A comma-separated list of combination pieces, like "1,2,1*2", specified identically to combination in combine_tensors.
  • tensor_dims : List[int]
    A list of tensor dimensions, where each dimension is from the last axis of the tensors that will be input to combine_tensors.

logsumexp

def logsumexp(
    tensor: torch.Tensor,
    dim: int = -1,
    keepdim: bool = False
) -> torch.Tensor

A numerically stable computation of logsumexp. This is mathematically equivalent to tensor.exp().sum(dim, keep=keepdim).log(). This function is typically used for summing log probabilities.

Parameters

  • tensor : torch.FloatTensor
    A tensor of arbitrary size.
  • dim : int, optional (default = -1)
    The dimension of the tensor to apply the logsumexp to.
  • keepdim : bool, optional (default = False)
    Whether to retain a dimension of size one at the dimension we reduce over.

get_device_of

def get_device_of(tensor: torch.Tensor) -> int

Returns the device of the tensor.

flatten_and_batch_shift_indices

def flatten_and_batch_shift_indices(
    indices: torch.Tensor,
    sequence_length: int
) -> torch.Tensor

This is a subroutine for batched_index_select. The given indices of size (batch_size, d_1, ..., d_n) indexes into dimension 2 of a target tensor, which has size (batch_size, sequence_length, embedding_size). This function returns a vector that correctly indexes into the flattened target. The sequence length of the target must be provided to compute the appropriate offsets.

    indices = torch.ones([2,3], dtype=torch.long)
    # Sequence length of the target tensor.
    sequence_length = 10
    shifted_indices = flatten_and_batch_shift_indices(indices, sequence_length)
    # Indices into the second element in the batch are correctly shifted
    # to take into account that the target tensor will be flattened before
    # the indices are applied.
    assert shifted_indices == [1, 1, 1, 11, 11, 11]

Parameters

  • indices : torch.LongTensor
  • sequence_length : int
    The length of the sequence the indices index into. This must be the second dimension of the tensor.

Returns

  • offset_indices : torch.LongTensor

batched_index_select

def batched_index_select(
    target: torch.Tensor,
    indices: torch.LongTensor,
    flattened_indices: Optional[torch.LongTensor] = None
) -> torch.Tensor

The given indices of size (batch_size, d_1, ..., d_n) indexes into the sequence dimension (dimension 2) of the target, which has size (batch_size, sequence_length, embedding_size).

This function returns selected values in the target with respect to the provided indices, which have size (batch_size, d_1, ..., d_n, embedding_size). This can use the optionally precomputed flattened_indices with size (batch_size * d_1 * ... * d_n) if given.

An example use case of this function is looking up the start and end indices of spans in a sequence tensor. This is used in the CoreferenceResolver model to select contextual word representations corresponding to the start and end indices of mentions.

The key reason this can't be done with basic torch functions is that we want to be able to use look-up tensors with an arbitrary number of dimensions (for example, in the coref model, we don't know a-priori how many spans we are looking up).

Parameters

  • target : torch.Tensor
    A 3 dimensional tensor of shape (batch_size, sequence_length, embedding_size). This is the tensor to be indexed.
  • indices : torch.LongTensor
    A tensor of shape (batch_size, ...), where each element is an index into the sequence_length dimension of the target tensor.
  • flattened_indices : Optional[torch.Tensor], optional (default = None)
    An optional tensor representing the result of calling flatten_and_batch_shift_indices on indices. This is helpful in the case that the indices can be flattened once and cached for many batch lookups.

Returns

  • selected_targets : torch.Tensor
    A tensor with shape [indices.size(), target.size(-1)] representing the embedded indices extracted from the batch flattened target tensor.

masked_index_fill

def masked_index_fill(
    target: torch.Tensor,
    indices: torch.LongTensor,
    mask: torch.BoolTensor,
    fill_value: int = 1
) -> torch.Tensor

The given indices in target will be will be filled with fill_value given a mask.

Parameters

  • target : torch.Tensor
    A 2 dimensional tensor of shape (batch_size, sequence_length). This is the tensor to be filled.
  • indices : torch.LongTensor
    A 2 dimensional tensor of shape (batch_size, num_indices), These are the indices that will be filled in the original tensor.
  • mask : torch.Tensor
    A 2 dimensional tensor of shape (batch_size, num_indices), mask.sum() == nonzero_indices.
  • fill_value : int, optional (default = 1)
    The value we fill the tensor with.

Returns

  • filled_target : torch.Tensor
    A tensor with shape (batch_size, sequence_length) where 'indices' are filled with fill_value

masked_index_replace

def masked_index_replace(
    target: torch.Tensor,
    indices: torch.LongTensor,
    mask: torch.BoolTensor,
    replace: torch.Tensor
) -> torch.Tensor

The given indices in target will be will be replaced with corresponding index from the replace tensor given a mask.

Parameters

  • target : torch.Tensor
    A 3 dimensional tensor of shape (batch_size, sequence_length, embedding_dim). This is the tensor to be replaced into.
  • indices : torch.LongTensor
    A 2 dimensional tensor of shape (batch_size, num_indices), These are the indices that will be replaced in the original tensor.
  • mask : torch.Tensor
    A 2 dimensional tensor of shape (batch_size, num_indices), mask.sum() == nonzero_indices.
  • replace : torch.Tensor
    A 3 dimensional tensor of shape (batch_size, num_indices, embedding_dim), The tensor to perform scatter from.

Returns

  • replaced_target : torch.Tensor
    A tensor with shape (batch_size, sequence_length, embedding_dim) where 'indices' are replaced with the corrosponding vector from replace

batched_span_select

def batched_span_select(
    target: torch.Tensor,
    spans: torch.LongTensor
) -> torch.Tensor

The given spans of size (batch_size, num_spans, 2) indexes into the sequence dimension (dimension 2) of the target, which has size (batch_size, sequence_length, embedding_size).

This function returns segmented spans in the target with respect to the provided span indices.

Parameters

  • target : torch.Tensor
    A 3 dimensional tensor of shape (batch_size, sequence_length, embedding_size). This is the tensor to be indexed.
  • indices : torch.LongTensor
    A 3 dimensional tensor of shape (batch_size, num_spans, 2) representing start and end indices (both inclusive) into the sequence_length dimension of the target tensor.

Returns

  • span_embeddings : torch.Tensor
    A tensor with shape (batch_size, num_spans, max_batch_span_width, embedding_size] representing the embedded spans extracted from the batch flattened target tensor.
  • span_mask : torch.BoolTensor
    A tensor with shape (batch_size, num_spans, max_batch_span_width) representing the mask on the returned span embeddings.

flattened_index_select

def flattened_index_select(
    target: torch.Tensor,
    indices: torch.LongTensor
) -> torch.Tensor

The given indices of size (set_size, subset_size) specifies subsets of the target that each of the set_size rows should select. The target has size (batch_size, sequence_length, embedding_size), and the resulting selected tensor has size (batch_size, set_size, subset_size, embedding_size).

Parameters

  • target : torch.Tensor
    A Tensor of shape (batch_size, sequence_length, embedding_size).
  • indices : torch.LongTensor
    A LongTensor of shape (set_size, subset_size). All indices must be < sequence_length as this tensor is an index into the sequence_length dimension of the target.

Returns

  • selected : torch.Tensor, required.
    A Tensor of shape (batch_size, set_size, subset_size, embedding_size).

get_range_vector

def get_range_vector(size: int, device: int) -> torch.Tensor

Returns a range vector with the desired size, starting at 0. The CUDA implementation is meant to avoid copy data from CPU to GPU.

bucket_values

def bucket_values(
    distances: torch.Tensor,
    num_identity_buckets: int = 4,
    num_total_buckets: int = 10
) -> torch.Tensor

Places the given values (designed for distances) into num_total_bucketssemi-logscale buckets, with num_identity_buckets of these capturing single values.

The default settings will bucket values into the following buckets: [0, 1, 2, 3, 4, 5-7, 8-15, 16-31, 32-63, 64+].

Parameters

  • distances : torch.Tensor
    A Tensor of any size, to be bucketed.
  • num_identity_buckets : int, optional (default = 4)
    The number of identity buckets (those only holding a single value).
  • num_total_buckets : int, optional (default = 10)
    The total number of buckets to bucket values into.

Returns

  • torch.Tensor
    A tensor of the same shape as the input, containing the indices of the buckets the values were placed in.

add_sentence_boundary_token_ids

def add_sentence_boundary_token_ids(
    tensor: torch.Tensor,
    mask: torch.BoolTensor,
    sentence_begin_token: Any,
    sentence_end_token: Any
) -> Tuple[torch.Tensor, torch.BoolTensor]

Add begin/end of sentence tokens to the batch of sentences. Given a batch of sentences with size (batch_size, timesteps) or (batch_size, timesteps, dim) this returns a tensor of shape (batch_size, timesteps + 2) or (batch_size, timesteps + 2, dim) respectively.

Returns both the new tensor and updated mask.

Parameters

  • tensor : torch.Tensor
    A tensor of shape (batch_size, timesteps) or (batch_size, timesteps, dim)
  • mask : torch.BoolTensor
    A tensor of shape (batch_size, timesteps)
  • sentence_begin_token : Any
    Can be anything that can be broadcast in torch for assignment. For 2D input, a scalar with the <S> id. For 3D input, a tensor with length dim.
  • sentence_end_token : Any
    Can be anything that can be broadcast in torch for assignment. For 2D input, a scalar with the </S> id. For 3D input, a tensor with length dim.

Returns

  • tensor_with_boundary_tokens : torch.Tensor
    The tensor with the appended and prepended boundary tokens. If the input was 2D, it has shape (batch_size, timesteps + 2) and if the input was 3D, it has shape (batch_size, timesteps + 2, dim).
  • new_mask : torch.BoolTensor
    The new mask for the tensor, taking into account the appended tokens marking the beginning and end of the sentence.

remove_sentence_boundaries

def remove_sentence_boundaries(
    tensor: torch.Tensor,
    mask: torch.BoolTensor
) -> Tuple[torch.Tensor, torch.Tensor]

Remove begin/end of sentence embeddings from the batch of sentences. Given a batch of sentences with size (batch_size, timesteps, dim) this returns a tensor of shape (batch_size, timesteps - 2, dim) after removing the beginning and end sentence markers. The sentences are assumed to be padded on the right, with the beginning of each sentence assumed to occur at index 0 (i.e., mask[:, 0] is assumed to be 1).

Returns both the new tensor and updated mask.

This function is the inverse of add_sentence_boundary_token_ids.

Parameters

  • tensor : torch.Tensor
    A tensor of shape (batch_size, timesteps, dim)
  • mask : torch.BoolTensor
    A tensor of shape (batch_size, timesteps)

Returns

  • tensor_without_boundary_tokens : torch.Tensor
    The tensor after removing the boundary tokens of shape (batch_size, timesteps - 2, dim)
  • new_mask : torch.BoolTensor
    The new mask for the tensor of shape (batch_size, timesteps - 2).

add_positional_features

def add_positional_features(
    tensor: torch.Tensor,
    min_timescale: float = 1.0,
    max_timescale: float = 1.0e4
)

Implements the frequency-based positional encoding described in Attention is All you Need.

Adds sinusoids of different frequencies to a Tensor. A sinusoid of a different frequency and phase is added to each dimension of the input Tensor. This allows the attention heads to use absolute and relative positions.

The number of timescales is equal to hidden_dim / 2 within the range (min_timescale, max_timescale). For each timescale, the two sinusoidal signals sin(timestep / timescale) and cos(timestep / timescale) are generated and concatenated along the hidden_dim dimension.

Parameters

  • tensor : torch.Tensor
    a Tensor with shape (batch_size, timesteps, hidden_dim).
  • min_timescale : float, optional (default = 1.0)
    The smallest timescale to use.
  • max_timescale : float, optional (default = 1.0e4)
    The largest timescale to use.

Returns

  • torch.Tensor
    The input tensor augmented with the sinusoidal frequencies.

clone

def clone(
    module: torch.nn.Module,
    num_copies: int
) -> torch.nn.ModuleList

Produce N identical layers.

combine_initial_dims

def combine_initial_dims(tensor: torch.Tensor) -> torch.Tensor

Given a (possibly higher order) tensor of ids with shape (d1, ..., dn, sequence_length) Return a view that's (d1 * ... * dn, sequence_length). If original tensor is 1-d or 2-d, return it as is.

uncombine_initial_dims

def uncombine_initial_dims(
    tensor: torch.Tensor,
    original_size: torch.Size
) -> torch.Tensor

Given a tensor of embeddings with shape (d1 * ... * dn, sequence_length, embedding_dim) and the original shape (d1, ..., dn, sequence_length), return the reshaped tensor of embeddings with shape (d1, ..., dn, sequence_length, embedding_dim). If original size is 1-d or 2-d, return it as is.

inspect_parameters

def inspect_parameters(
    module: torch.nn.Module,
    quiet: bool = False
) -> Dict[str, Any]

Inspects the model/module parameters and their tunability. The output is structured in a nested dict so that parameters in same sub-modules are grouped together. This can be helpful to setup module path based regex, for example in initializer. It prints it by default (optional) and returns the inspection dict. Eg. output::

{
    "_text_field_embedder": {
        "token_embedder_tokens": {
            "_projection": {
                "bias": "tunable",
                "weight": "tunable"
            },
            "weight": "frozen"
        }
    }
}

find_text_field_embedder

def find_text_field_embedder(
    model: torch.nn.Module
) -> torch.nn.Module

Takes a Model and returns the Module that is a TextFieldEmbedder. We return just the first one, as it's very rare to have more than one. If there isn't a TextFieldEmbedder in the given Model, we raise a ValueError.

find_embedding_layer

def find_embedding_layer(model: torch.nn.Module) -> torch.nn.Module

Takes a model (typically an AllenNLP Model, but this works for any torch.nn.Module) and makes a best guess about which module is the embedding layer. For typical AllenNLP models, this often is the TextFieldEmbedder, but if you're using a pre-trained contextualizer, we really want layer 0 of that contextualizer, not the output. So there are a bunch of hacks in here for specific pre-trained contextualizers.

get_token_offsets_from_text_field_inputs

def get_token_offsets_from_text_field_inputs(
    text_field_inputs: List[Any]
) -> Optional[torch.Tensor]

Given a list of inputs to a TextFieldEmbedder, tries to find token offsets from those inputs, if there are any. You will have token offsets if you are using a mismatched token embedder; if you're not, the return value from this function should be None. This function is intended to be called from a forward_hook attached to a TextFieldEmbedder, so the inputs are formatted just as a list.

It's possible in theory that you could have multiple offsets as inputs to a single call to a TextFieldEmbedder, but that's an extremely rare use case (I can't really imagine anyone wanting to do that). In that case, we'll only return the first one. If you need different behavior for your model, open an issue on github describing what you're doing.

extend_layer

def extend_layer(layer: torch.nn.Module, new_dim: int) -> None

masked_topk

def masked_topk(
    input_: torch.FloatTensor,
    mask: torch.BoolTensor,
    k: Union[int, torch.LongTensor],
    dim: int = -1
) -> Tuple[torch.LongTensor, torch.LongTensor, torch.FloatTensor]

Extracts the top-k items along a certain dimension. This is similar to torch.topk except: (1) we allow of a mask that makes the function not consider certain elements; (2) the returned top input, mask, and indices are sorted in their original order in the input; (3) May use the same k for all dimensions, or different k for each.

Parameters

  • input_ : torch.FloatTensor
    A tensor containing the items that we want to prune.
  • mask : torch.BoolTensor
    A tensor with the same shape as input_ that makes the function not consider masked out (i.e. False) elements.
  • k : Union[int, torch.LongTensor]
    If a tensor of shape as input_ except without dimension dim, specifies the number of items to keep for each dimension. If an int, keep the same number of items for all dimensions.

Returns

  • top_input : torch.FloatTensor
    The values of the top-k scoring items. Has the same shape as input_ except dimension dim has value k when it's an int or k.max() when it's a tensor.
  • top_mask : torch.BoolTensor
    The corresponding mask for top_input. Has the shape as top_input.
  • top_indices : torch.IntTensor
    The indices of the top-k scoring items into the original input_ tensor. This is returned because it can be useful to retain pointers to the original items, if each item is being scored by multiple distinct scorers, for instance. Has the shape as top_input.

info_value_of_dtype

def info_value_of_dtype(dtype: torch.dtype)

Returns the finfo or iinfo object of a given PyTorch data type. Does not allow torch.bool.

min_value_of_dtype

def min_value_of_dtype(dtype: torch.dtype)

Returns the minimum value of a given PyTorch data type. Does not allow torch.bool.

max_value_of_dtype

def max_value_of_dtype(dtype: torch.dtype)

Returns the maximum value of a given PyTorch data type. Does not allow torch.bool.

tiny_value_of_dtype

def tiny_value_of_dtype(dtype: torch.dtype)

Returns a moderately tiny value for a given PyTorch data type that is used to avoid numerical issues such as division by zero. This is different from info_value_of_dtype(dtype).tiny because it causes some NaN bugs. Only supports floating point dtypes.

distributed_device

def distributed_device() -> torch.device

Get the correct torch.device of the current process to use for distributed point-to-point communication.

dist_reduce

def dist_reduce(value: _V, reduce_op) -> _V

Reduces the given value across all distributed worker nodes according the given reduction operation.

If called outside of a distributed context, it will just return value.

Parameters

  • value : _V
    The value to reduce across distributed nodes.
  • reduce_op : torch.distributed.ReduceOp
    The reduction operation to use.
  • **kwargs : Any
    Additional arguments used to construct the tensor that will wrap value.

Returns

  • _V
    The final value.

dist_reduce_sum

def dist_reduce_sum(value: _V) -> _V

Sums the given value across distributed worker nodes. This is equivalent to calling dist_reduce(v, dist.ReduceOp.SUM).

load_state_dict_distributed

def load_state_dict_distributed(
    module: torch.nn.Module,
    state_dict: Optional[StateDictType],
    strict: bool = True,
    prefix: str = ""
) -> _IncompatibleKeys

Load a state_dict to the module within a distributed process. Only the global primary process requires the state_dict to not be None. All other processes will have the state tensors broadcasted to them one-by-one.

If strict is True, then the keys of state_dict must exactly match the keys returned by module.state_dict().

Note

The returned missing_keys and unexpected_keys will only be accurate in the primary process.

Returns

  • A NamedTuple with missing_keys and unexpected_keys fields, both of which
  • are lists of strings.

Raises

  • RuntimeError
    If strict is True and there are missing or unexpected keys.