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vectors.py
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vectors.py
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import logging
import os
import torch
from torch import Tensor
import torch.nn as nn
from tqdm import tqdm
from torchtext.utils import (
download_from_url,
extract_archive
)
logger = logging.getLogger(__name__)
def _infer_shape(f, delimiter=" "):
num_lines, vector_dim = 0, None
for line in f:
if vector_dim is None:
# token and entries are seperated by delimeter
token, entries = line.rstrip().split(bytes(delimiter, "utf-8"), 1)
# we assume entries are always seperated by " "
vector = entries.split(b" ")
# Assuming word, [vector] format
if len(vector) > 2:
# The header present in some (w2v) formats contains two elements.
vector_dim = len(vector)
num_lines += 1 # First element read
else:
num_lines += 1
f.seek(0)
return num_lines, vector_dim
def _load_token_and_vectors_from_file(file_path, delimiter=" "):
with open(file_path, "rb") as f:
num_lines, dim = _infer_shape(f, delimiter=delimiter)
stoi, tokens, vectors, dup_tokens = {}, [], [], []
vectors = torch.zeros((num_lines, dim))
vectors_loaded = 0
for line in tqdm(f, unit_scale=0, unit="lines", total=num_lines):
# token and entries are seperated by delimeter
token, entries = line.rstrip().split(bytes(delimiter, "utf-8"), 1)
# we assume entries are always seperated by " "
entries = entries.split(b" ")
if dim is None and len(entries) > 1:
dim = len(entries)
elif len(entries) == 1:
logger.warning("Skipping token {} with 1-dimensional "
"vector {}; likely a header".format(token, entries))
continue
elif dim != len(entries):
raise RuntimeError(
"Vector for token {} has {} dimensions, but previously "
"read vectors have {} dimensions. All vectors must have "
"the same number of dimensions.".format(token, len(entries),
dim))
try:
if isinstance(token, bytes):
token = token.decode("utf-8")
except UnicodeDecodeError:
logger.info("Skipping non-UTF8 token {}".format(repr(token)))
continue
if token in stoi:
dup_tokens.append((token, len(vectors) + 1))
continue
stoi[token] = len(vectors)
tokens.append(token)
vectors[vectors_loaded] = torch.tensor([float(c) for c in entries], dtype=torch.float)
vectors_loaded += 1
vectors = vectors[:vectors_loaded]
return tokens, vectors, dup_tokens
def FastText(language="en", unk_tensor=None, root=".data", validate_file=True):
r"""Create a FastText Vectors object.
Args:
language (str): the language to use for FastText. The list of supported languages options
can be found at https://fasttext.cc/docs/en/language-identification.html
unk_tensor (Tensor): a 1d tensor representing the vector associated with an unknown token
root (str): folder used to store downloaded files in (.data)
validate_file (bool): flag to determine whether to validate the downloaded files checksum.
Should be `False` when running tests with a local asset.
Returns:
Vectors: a Vectors object.
Raises:
ValueError: if duplicate tokens are found in FastText file.
"""
url = "https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.{}.vec".format(language)
file_name = os.path.basename(url)
cached_vectors_file_path = os.path.join(root, file_name + ".pt")
if os.path.isfile(cached_vectors_file_path):
logger.info("Loading from cached file {}".format(str(cached_vectors_file_path)))
return torch.load(cached_vectors_file_path)
checksum = None
if validate_file:
checksum = CHECKSUMS_FAST_TEXT.get(url, None)
downloaded_file_path = download_from_url(url, root=root, hash_value=checksum)
tokens, vectors, dup_tokens = _load_token_and_vectors_from_file(downloaded_file_path)
if dup_tokens:
raise ValueError("Found duplicate tokens in file: {}".format(str(dup_tokens)))
vectors_obj = Vectors(tokens, vectors, unk_tensor=unk_tensor)
torch.save(vectors_obj, cached_vectors_file_path)
return vectors_obj
def GloVe(name="840B", dim=300, unk_tensor=None, root=".data", validate_file=True):
r"""Create a GloVe Vectors object.
Args:
name (str): the name of the GloVe dataset to use. Options are:
- 42B
- 840B
- twitter.27B
- 6B
dim (int): the dimension for the GloVe dataset to load. Options are:
42B:
- 300
840B:
- 300
twitter.27B:
- 25
- 50
- 100
- 200
6B:
- 50
- 100
- 200
- 300
unk_tensor (Tensor): a 1d tensor representing the vector associated with an unknown token.
root (str): folder used to store downloaded files in (.data)
validate_file (bool): flag to determine whether to validate the downloaded files checksum.
Should be `False` when running tests with a local asset.
Returns:
Vectors: a Vectors object.
Raises:
ValueError: if unexpected duplicate tokens are found in GloVe file.
"""
dup_token_glove_840b = [("����������������������������������������������������������������������"
"����������������������������������������������������������������������"
"����������������������������������������������������������������������"
"����������������������������������������������������������������������"
"������������������������������������������������������", 140649)]
urls = {
"42B": "https://nlp.stanford.edu/data/glove.42B.300d.zip",
"840B": "https://nlp.stanford.edu/data/glove.840B.300d.zip",
"twitter.27B": "https://nlp.stanford.edu/data/glove.twitter.27B.zip",
"6B": "https://nlp.stanford.edu/data/glove.6B.zip",
}
valid_glove_file_names = {
"glove.42B.300d.txt",
"glove.840B.300d.txt",
"glove.twitter.27B.25d.txt",
"glove.twitter.27B.50d.txt",
"glove.twitter.27B.100d.txt",
"glove.twitter.27B.200d.txt",
"glove.6B.50d.txt",
"glove.6B.100d.txt",
"glove.6B.200d.txt",
"glove.6B.300d.txt"
}
file_name = "glove.{}.{}d.txt".format(name, str(dim))
if file_name not in valid_glove_file_names:
raise ValueError("Could not find GloVe file with name {}. Please check that `name` and `dim`"
"are valid.".format(str(file_name)))
url = urls[name]
cached_vectors_file_path = os.path.join(root, file_name + '.pt')
if os.path.isfile(cached_vectors_file_path):
logger.info("Loading from cached file {}".format(str(cached_vectors_file_path)))
return torch.load(cached_vectors_file_path)
checksum = None
if validate_file:
checksum = CHECKSUMS_GLOVE.get(url, None)
downloaded_file_path = download_from_url(url, root=root, hash_value=checksum)
extracted_file_paths = extract_archive(downloaded_file_path)
# need to get the full path to the correct file in the case when multiple files are extracted with different dims
extracted_file_path_with_correct_dim = [path for path in extracted_file_paths if file_name in path][0]
tokens, vectors, dup_tokens = _load_token_and_vectors_from_file(extracted_file_path_with_correct_dim)
# Ensure there is only 1 expected duplicate token present for 840B dataset
if dup_tokens and dup_tokens != dup_token_glove_840b:
raise ValueError("Found duplicate tokens in file: {}".format(str(dup_tokens)))
vectors_obj = Vectors(tokens, vectors, unk_tensor=unk_tensor)
torch.save(vectors_obj, cached_vectors_file_path)
return vectors_obj
def vectors_from_file_object(file_like_object, delimiter=",", unk_tensor=None):
r"""Create a Vectors object from a csv file like object.
Note that the tensor corresponding to each vector is of type `torch.float`.
Format for csv file:
token1<delimiter>num1 num2 num3
token2<delimiter>num4 num5 num6
...
token_n<delimiter>num_m num_j num_k
Args:
file_like_object (FileObject): a file like object to read data from.
delimiter (char): a character to delimit between the token and the vector. Default value is ","
unk_tensor (Tensor): a 1d tensor representing the vector associated with an unknown token.
Returns:
Vectors: a Vectors object.
Raises:
ValueError: if duplicate tokens are found in FastText file.
"""
tokens, vectors, dup_tokens = _load_token_and_vectors_from_file(file_like_object.name, delimiter=delimiter)
if dup_tokens:
raise ValueError("Found duplicate tokens in file: {}".format(str(dup_tokens)))
return Vectors(tokens, vectors, unk_tensor=unk_tensor)
class Vectors(nn.Module):
r"""Creates a vectors object which maps tokens to vectors.
Arguments:
tokens (List[str]): a list of tokens.
vectors (torch.Tensor): a 2d tensor representing the vector associated with each token.
unk_tensor (torch.Tensor): a 1d tensors representing the vector associated with an unknown token.
Raises:
ValueError: if `vectors` is empty and a default `unk_tensor` isn't provided.
RuntimeError: if `tokens` and `vectors` have different sizes or `tokens` has duplicates.
TypeError: if all tensors within`vectors` are not of data type `torch.float`.
"""
def __init__(self, tokens, vectors, unk_tensor=None):
super(Vectors, self).__init__()
if unk_tensor is None and (vectors is None or not len(vectors)):
raise ValueError("The vectors list is empty and a default unk_tensor wasn't provided.")
if not vectors.dtype == torch.float:
raise TypeError("`vectors` should be of data type `torch.float`.")
unk_tensor = unk_tensor if unk_tensor is not None else torch.zeros(vectors[0].size(), dtype=torch.float)
self.vectors = torch.classes.torchtext.Vectors(tokens, vectors, unk_tensor)
@torch.jit.export
def __getitem__(self, token: str) -> Tensor:
r"""
Args:
token (str): the token used to lookup the corresponding vector.
Returns:
vector (Tensor): a tensor (the vector) corresponding to the associated token.
"""
return self.vectors[token]
@torch.jit.export
def __setitem__(self, token: str, vector: Tensor) -> None:
r"""
Args:
token (str): the token used to lookup the corresponding vector.
vector (Tensor): a 1d tensor representing a vector associated with the token.
Raises:
TypeError: if `vector` is not of data type `torch.float`.
"""
if vector.dtype != torch.float:
raise TypeError("`vector` should be of data type `torch.float` but it's of type " + vector.dtype)
self.vectors[token] = vector.float()
@torch.jit.export
def __len__(self) -> int:
r"""Get length of vectors object.
Returns:
length (int): the length of the vectors.
"""
return len(self.vectors)
CHECKSUMS_GLOVE = {
"https://nlp.stanford.edu/data/glove.42B.300d.zip":
"03d5d7fa28e58762ace4b85fb71fe86a345ef0b5ff39f5390c14869da0fc1970",
"https://nlp.stanford.edu/data/glove.840B.300d.zip":
"c06db255e65095393609f19a4cfca20bf3a71e20cc53e892aafa490347e3849f",
"https://nlp.stanford.edu/data/glove.twitter.27B.zip":
"792af52f795d1a32c9842a3240f5f3fe5e941a8ff6df5eb0f9d668092ebc019c",
"https://nlp.stanford.edu/data/glove.6B.zip":
"617afb2fe6cbd085c235baf7a465b96f4112bd7f7ccb2b2cbd649fed9cbcf2fb"
}
CHECKSUMS_FAST_TEXT = {
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.am.vec":
"b532c57a74628fb110b48b9d8ae2464eb971df2ecc43b89c2eb92803b8ac92bf",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.als.vec":
"056a359a2651a211817dbb7885ea3e6f69e0d6048d7985eab173858c59ee1adf",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.af.vec":
"87ecbfea969eb707eab72a7156b4318d341c0652e6e5c15c21bc08f5cf458644",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.an.vec":
"57db91d8c307c45613092ebfd405061ccfdec5905035d9a8ad364f6b8ce41b29",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.ar.vec":
"5527041ce04fa66e45e27d7bd278f00425d97fde8c67755392d70f112fecc356",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.arz.vec":
"0b6c261fd179e5d030f2b363f9f7a4db0a52e6241a910b39fb3332d39bcfbec3",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.as.vec":
"4475daa38bc1e8501e54dfcd79a1a58bb0771b347ad9092ce9e57e9ddfdd3b07",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.av.vec":
"1292eed7f649687403fac18e0ee97202e163f9ab50f6efa885aa2db9760a967e",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.ast.vec":
"fbba958174ced32fde2593f628c3cf4f00d53cd1d502612a34e180a0d13ce037",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.be.vec":
"3b36ba86f5b76c40dabe1c7fc3214338d53ce7347c28bb2fba92b6acc098c6ad",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.az.vec":
"93ebe624677a1bfbb57de001d373e111ef9191cd3186f42cad5d52886b8c6467",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.ba.vec":
"b739fd6f9fe57205314d67a7975a2fc387b55679399a6b2bda0d1835b1fdd5a8",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.azb.vec":
"05709ce8abc91115777f3cc2574d24d9439d3f6905500163295d695d41260a06",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.bar.vec":
"3f58304eb0345d96c0abbffb61621c1f6ec2ca39e13272b434cc6cc2bde052a1",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.bcl.vec":
"309bb74a85647ac3a5be53fd9d3be3196cff385d257561f4183a0d91a67f0c8b",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.bg.vec":
"16f1a02f3b708f2cbc04971258b0febdfc9ed4e64fcc3818cc6a397e3db5cf81",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.bh.vec":
"ab0819c155fd1609393f8af74794de8d5b49db0787edf136e938ea2c87993ab5",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.bn.vec":
"3dd27b9b271c203a452de1c533fdf975ebec121f17f945ef234370358db2bae6",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.bpy.vec":
"2ba9f046d70bdaae2cbd9d33f9a1d2913637c00126588cc3223ba58ca80d49fe",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.bo.vec":
"c5ed2a28edf39bc100f4200cdf1c9d3c1448efefcb3d78db8becea613a2fb2eb",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.br.vec":
"fe858e2be787351cce96c206a9034c361e45f8b9e0a385aacfce3c73f844e923",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.da.vec":
"397b0c3e18f710fb8aa1caf86441a25af2f247335e8560dbe949feb3613ef5cc",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.bs.vec":
"ee065fe168c0a4f1a0b9fbd8854be4572c138a414fd7200381d0135ce6c03b49",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.bxr.vec":
"0bc0e47a669aa0d9ad1c665593f7257c4b27a4e3becce457a7348da716bdabb4",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.ca.vec":
"1600696088c7f2fe555eb6a4548f427f969a450ed0313d68e859d6024242db5f",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.cbk.vec":
"e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.ceb.vec":
"7fbe4474043e4f656eb2f81ee03d1e863cef8e62ad4e3bd9a3a4143785752568",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.ce.vec":
"2a321e2de98d0abb5a12599d9567dd5ac93f9e2599251237026acff35f23cef8",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.cs.vec":
"0eba2ac0852b1057909d4e8e5e3fa75470f9cb9408b364433ac4747eb2b568a9",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.cv.vec":
"67f09d353f2561b16c385187789eb6ff43fa125d3cc81081b2bc7d062c9f0b8a",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.cy.vec":
"1023affdcb7e84dd59b1b7de892f65888b6403e2ed4fd77cb836face1c70ee68",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.co.vec":
"7f16f06c19c8528dc48a0997f67bf5f0d79da2d817247776741b54617b6053d9",
"https://dl.fbaipublicfiles.com/fasttext/vectors-wiki/wiki.ckb.vec":
"ef3a8472cc2ac86976a1a91cde3edc7fcd1d1affd3c6fb6441451e9fbc6c3ae8",
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