id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
18,835 | import cv2
import numpy as np
def enhance_level_to_args(MAX_LEVEL):
def level_to_args(level):
return ((level / MAX_LEVEL) * 1.8 + 0.1,)
return level_to_args | null |
18,836 | import cv2
import numpy as np
def shear_level_to_args(MAX_LEVEL, replace_value):
def level_to_args(level):
level = (level / MAX_LEVEL) * 0.3
if np.random.random() > 0.5: level = -level
return (level, replace_value)
return level_to_args | null |
18,837 | import cv2
import numpy as np
def translate_level_to_args(translate_const, MAX_LEVEL, replace_value):
def level_to_args(level):
level = (level / MAX_LEVEL) * float(translate_const)
if np.random.random() > 0.5: level = -level
return (level, replace_value)
return level_to_args | null |
18,838 | import cv2
import numpy as np
def cutout_level_to_args(cutout_const, MAX_LEVEL, replace_value):
def level_to_args(level):
level = int((level / MAX_LEVEL) * cutout_const)
return (level, replace_value)
return level_to_args | null |
18,839 | import cv2
import numpy as np
def solarize_level_to_args(MAX_LEVEL):
def level_to_args(level):
level = int((level / MAX_LEVEL) * 256)
return (level, )
return level_to_args | null |
18,840 | import cv2
import numpy as np
def none_level_to_args(level):
return () | null |
18,841 | import cv2
import numpy as np
def posterize_level_to_args(MAX_LEVEL):
def level_to_args(level):
level = int((level / MAX_LEVEL) * 4)
return (level, )
return level_to_args | null |
18,842 | import cv2
import numpy as np
def rotate_level_to_args(MAX_LEVEL, replace_value):
def level_to_args(level):
level = (level / MAX_LEVEL) * 30
if np.random.random() < 0.5:
level = -level
return (level, replace_value)
return level_to_args | null |
18,843 | from typing import Optional
from torch import Tensor
from PIL import Image
from dataset import vg_transforms as T
import os
import re
import sys
import json
import torch
import numpy as np
import os.path as osp
import scipy.io as sio
import torch.utils.data as data
from models.tokenization_bert import BertTokenizer
fro... | Read a list of `InputExample`s from an input file. |
18,844 | from typing import Optional
from torch import Tensor
from PIL import Image
from dataset import vg_transforms as T
import os
import re
import sys
import json
import torch
import numpy as np
import os.path as osp
import scipy.io as sio
import torch.utils.data as data
from models.tokenization_bert import BertTokenizer
fro... | Loads a data file into a list of `InputBatch`s. |
18,845 | from typing import Optional
from torch import Tensor
from PIL import Image
from dataset import vg_transforms as T
import os
import re
import sys
import json
import torch
import numpy as np
import os.path as osp
import scipy.io as sio
import torch.utils.data as data
from models.tokenization_bert import BertTokenizer
fro... | null |
18,846 | from typing import Optional
from torch import Tensor
from PIL import Image
from dataset import vg_transforms as T
import os
import re
import sys
import json
import torch
import numpy as np
import os.path as osp
import scipy.io as sio
import torch.utils.data as data
from models.tokenization_bert import BertTokenizer
fro... | null |
18,847 | from typing import Optional
from torch import Tensor
from PIL import Image
from dataset import vg_transforms as T
import os
import re
import sys
import json
import torch
import numpy as np
import os.path as osp
import scipy.io as sio
import torch.utils.data as data
from models.tokenization_bert import BertTokenizer
fro... | null |
18,848 | from typing import Optional
from torch import Tensor
from PIL import Image
from dataset import vg_transforms as T
import os
import re
import sys
import json
import torch
import numpy as np
import os.path as osp
import scipy.io as sio
import torch.utils.data as data
from models.tokenization_bert import BertTokenizer
fro... | null |
18,849 | from torch.utils.data import Dataset
from torchvision.datasets.utils import download_url
from PIL import Image
import torch
import numpy as np
import random
import decord
from decord import VideoReader
import json
import os
from dataset.utils import pre_caption
def load_jsonl(filename):
with open(filename, "r") as... | null |
18,850 | import os
import json
import random
import torch
import numpy as np
from PIL import Image
from PIL import ImageFile
from torch.utils.data import Dataset
from dataset.utils import pre_question
import decord
from decord import VideoReader
import oss2
from io import BytesIO
def load_jsonl(filename):
with open(filenam... | null |
18,851 | import math
import torch
import random
from PIL import Image, ImageEnhance, ImageFilter
import numpy as np
import torchvision.transforms as T
import torchvision.transforms.functional as F
from vgTools.utils.box_utils import xyxy2xywh
from vgTools.utils.misc import interpolate
def crop(image, box, region):
cropped_... | null |
18,852 | import math
import torch
import random
from PIL import Image, ImageEnhance, ImageFilter
import numpy as np
import torchvision.transforms as T
import torchvision.transforms.functional as F
from vgTools.utils.box_utils import xyxy2xywh
from vgTools.utils.misc import interpolate
def resize_according_to_long_side(img, box... | null |
18,853 | import math
import torch
import random
from PIL import Image, ImageEnhance, ImageFilter
import numpy as np
import torchvision.transforms as T
import torchvision.transforms.functional as F
from vgTools.utils.box_utils import xyxy2xywh
from vgTools.utils.misc import interpolate
def resize_according_to_short_side(img, bo... | null |
18,854 | import argparse
import os
import ruamel_yaml as yaml
import numpy as np
import random
import time
import datetime
import json
from pathlib import Path
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
import torch.backends.cudnn as cudnn
import torch.distributed ... | null |
18,855 | import argparse
import os
import ruamel_yaml as yaml
import numpy as np
import random
import time
import datetime
import json
from pathlib import Path
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
import torch.backends.cudnn as cudnn
import torch.distributed ... | null |
18,856 | import argparse
import os
import ruamel_yaml as yaml
import numpy as np
import random
import time
import datetime
import json
from pathlib import Path
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
import torch.backends.cudnn as cudnn
import torch.distributed ... | null |
18,857 | import torch
from torch import optim as optim
from .adafactor import Adafactor
from .adahessian import Adahessian
from .adamp import AdamP
from .lookahead import Lookahead
from .nadam import Nadam
from .novograd import NovoGrad
from .nvnovograd import NvNovoGrad
from .radam import RAdam
from .rmsprop_tf import RMSpropT... | null |
18,858 | import torch
from torch import optim as optim
from .adafactor import Adafactor
from .adahessian import Adahessian
from .adamp import AdamP
from .lookahead import Lookahead
from .nadam import Nadam
from .novograd import NovoGrad
from .nvnovograd import NvNovoGrad
from .radam import RAdam
from .rmsprop_tf import RMSpropT... | null |
18,859 | from functools import partial
from pickle import NONE, TRUE
from turtle import forward
from matplotlib.transforms import Transform
from models.vit import VisionTransformer, interpolate_pos_embed
from models.modeling_mplug import BertConfig, BertModel, BertPrefixModel,BertEncoder, BertPrefixModelForGrounding, FusionMode... | Performs all_gather operation on the provided tensors. *** Warning ***: torch.distributed.all_gather has no gradient. |
18,860 | from functools import partial
from models.vit import VisionTransformer
from models.modeling_mplug import BertConfig, BertModel, BertLMHeadModel, FusionModel
from models.visual_transformers import initialize_clip
import torch
from torch import nn
import torch.nn.functional as F
The provided code snippet includes necess... | Performs all_gather operation on the provided tensors. *** Warning ***: torch.distributed.all_gather has no gradient. |
18,861 | import copy
import json
import logging
import math
import os
import shutil
import tarfile
import tempfile
import sys
from io import open
import torch.nn.functional as F
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, SmoothL1Loss
import numpy as np
def resize_pos_embed(posemb, posemb_new):
... | null |
18,862 | import copy
import json
import logging
import math
import os
import shutil
import tarfile
import tempfile
import sys
from io import open
import torch.nn.functional as F
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, SmoothL1Loss
import numpy as np
def resize_pos_embed(posemb, posemb_new):
... | null |
18,863 | import copy
import json
import logging
import math
import os
import shutil
import tarfile
import tempfile
import sys
from io import open
import torch.nn.functional as F
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, SmoothL1Loss
import numpy as np
from torch.optim import Optimizer
def initial... | null |
18,864 | import copy
import json
import logging
import math
import os
import shutil
import tarfile
import tempfile
import sys
from io import open
import torch.nn.functional as F
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, SmoothL1Loss
import numpy as np
from torch.optim import Optimizer
The provide... | Decay the learning rate based on schedule |
18,865 | from functools import partial
from models.vit import VisionTransformer
from models.modeling_mplug import BertConfig, BertModel, BertPrefixModel, FusionModel
from models.visual_transformers import initialize_clip
from models.predictor import TextGenerator
import torch
from torch import nn
import torch.nn.functional as F... | null |
18,866 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from timm.models.vision_transformer import _cfg, PatchEmbed
from timm.models.registry import register_model
from timm.models.layers import trunc_normal_, DropPath
def interpolate_pos_embed(pos_embed_checkpoint,... | null |
18,867 | from functools import partial
from models.vit import VisionTransformer
from models.modeling_mplug import BertConfig, BertModel, BertLMHeadModel, FusionModel
from models.visual_transformers import initialize_clip
from models.predictor import TextGenerator
import torch
from torch import nn
import torch.nn.functional as F... | null |
18,868 | import math
import os
import warnings
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import Tensor, device, dtype, nn
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
import torch.nn.functional as F
from transformers.activati... | Load tf checkpoints in a pytorch model. |
18,869 | import math
import os
import warnings
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import Tensor, device, dtype, nn
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
import torch.nn.functional as F
from transformers.activati... | null |
18,870 | from __future__ import print_function
import torch.nn as nn
import torch.nn.functional as F
import os
import math
import json
import torch
The provided code snippet includes necessary dependencies for implementing the `_make_causal_mask` function. Write a Python function `def _make_causal_mask(input_ids_shape: torch.S... | Make causal mask used for bi-directional self-attention. |
18,871 | from __future__ import print_function
import torch.nn as nn
import torch.nn.functional as F
import os
import math
import json
import torch
class TextGenerator(object):
def __init__(self,
args,
model,
vocab=None,
symbols=None,
... | null |
18,872 | from __future__ import print_function
import torch.nn as nn
import torch.nn.functional as F
import os
import math
import json
import torch
def top_k_top_p_filtering(logits, top_k=10, top_p=1.0, filter_value=-float("Inf"), min_tokens_to_keep=1):
if top_k > 0:
top_k = min(max(top_k, min_tokens_to_keep),... | null |
18,873 | from __future__ import print_function
import torch.nn as nn
import torch.nn.functional as F
import os
import math
import json
import torch
The provided code snippet includes necessary dependencies for implementing the `tile` function. Write a Python function `def tile(x, count, dim=0)` to solve the following problem:
... | Tiles x on dimension dim count times. |
18,875 | import hashlib
import os
import urllib
import warnings
from typing import Union, List
import torch
from PIL import Image
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
from tqdm import tqdm
from .model import build_model
from .simple_tokenizer import SimpleTokenizer as _Tokenizer
_t... | null |
18,876 | from collections import OrderedDict
from typing import Tuple, Union
import torch
import torch.nn.functional as F
from torch import nn
The provided code snippet includes necessary dependencies for implementing the `convert_weights` function. Write a Python function `def convert_weights(model: nn.Module)` to solve the f... | Convert applicable model parameters to fp16 |
18,877 | import gzip
import html
import os
from functools import lru_cache
import ftfy
import regex as re
def default_bpe():
return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz") | null |
18,878 | import gzip
import html
import os
from functools import lru_cache
import ftfy
import regex as re
The provided code snippet includes necessary dependencies for implementing the `bytes_to_unicode` function. Write a Python function `def bytes_to_unicode()` to solve the following problem:
Returns list of utf-8 byte and a ... | Returns list of utf-8 byte and a corresponding list of unicode strings. The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. This ... |
18,879 | import gzip
import html
import os
from functools import lru_cache
import ftfy
import regex as re
The provided code snippet includes necessary dependencies for implementing the `get_pairs` function. Write a Python function `def get_pairs(word)` to solve the following problem:
Return set of symbol pairs in a word. Word ... | Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). |
18,880 | import gzip
import html
import os
from functools import lru_cache
import ftfy
import regex as re
def basic_clean(text):
text = ftfy.fix_text(text)
text = html.unescape(html.unescape(text))
return text.strip() | null |
18,881 | import gzip
import html
import os
from functools import lru_cache
import ftfy
import regex as re
def whitespace_clean(text):
text = re.sub(r'\s+', ' ', text)
text = text.strip()
return text | null |
18,882 | import collections
import os
import unicodedata
from typing import List, Optional, Tuple
from transformers.tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from transformers.utils import logging
The provided code snippet includes necessary dependencies for implementing the `l... | Loads a vocabulary file into a dictionary. |
18,883 | import collections
import os
import unicodedata
from typing import List, Optional, Tuple
from transformers.tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from transformers.utils import logging
The provided code snippet includes necessary dependencies for implementing the `w... | Runs basic whitespace cleaning and splitting on a piece of text. |
18,884 | import argparse
import os
import ruamel_yaml as yaml
import numpy as np
import random
import time
import datetime
import json
from pathlib import Path
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from models.model_retrieval_mplu... | null |
18,885 | import argparse
import os
import ruamel_yaml as yaml
import numpy as np
import random
import time
import datetime
import json
from pathlib import Path
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from models.model_retrieval_mplu... | null |
18,886 | import argparse
import os
import ruamel_yaml as yaml
import numpy as np
import random
import time
import datetime
import json
from pathlib import Path
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from models.model_retrieval_mplu... | null |
18,887 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import pickle
import modeling
import modeling_labert
import optimization
import tokenization
import tokenization_labert
import tensorflow as tf
import numpy as np
import tf_metrics
import random
import... | Convert a set of `InputExample`s to a TFRecord file. |
18,888 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import pickle
import modeling
import modeling_labert
import optimization
import tokenization
import tokenization_labert
import tensorflow as tf
import numpy as np
import tf_metrics
import random
import... | Creates an `input_fn` closure to be passed to TPUEstimator. |
18,889 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import pickle
import modeling
import modeling_labert
import optimization
import tokenization
import tokenization_labert
import tensorflow as tf
import numpy as np
import tf_metrics
import random
import... | Returns `model_fn` closure for TPUEstimator. |
18,890 | import tensorflow as tf
import collections
import tokenization
def write_lattice_instance_to_example_file(
instance, tokenizer, writer, max_seq_length,
max_predictions_per_seq,
position_embedding_names=('start', 'end'), do_dump_example=False):
"""Create TF example files from `TrainingInstance`s."""
inpu... | Create TF example files from `TrainingInstance`s. |
18,891 | import tensorflow as tf
import collections
import tokenization
def create_int_feature(values):
feature = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
return feature
def create_float_feature(values):
feature = tf.train.Feature(float_list=tf.train.FloatList(value=list(values)))
return featu... | Create TF example files from `TrainingInstance`s. |
18,892 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import csv
import os
import pickle
import modeling
import modeling_labert
import optimization
import tokenization
import tokenization_labert
import tensorflow as tf
import numpy as np
import r... | Reads a tab separated value file. |
18,893 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import csv
import os
import pickle
import modeling
import modeling_labert
import optimization
import tokenization
import tokenization_labert
import tensorflow as tf
import numpy as np
import r... | Convert a set of `InputExample`s to a TFRecord file. |
18,894 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import csv
import os
import pickle
import modeling
import modeling_labert
import optimization
import tokenization
import tokenization_labert
import tensorflow as tf
import numpy as np
import r... | Creates an `input_fn` closure to be passed to TPUEstimator. |
18,895 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import csv
import os
import pickle
import modeling
import modeling_labert
import optimization
import tokenization
import tokenization_labert
import tensorflow as tf
import numpy as np
import r... | Returns `model_fn` closure for TPUEstimator. |
18,896 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import csv
import os
import pickle
import modeling
import modeling_labert
import optimization
import tokenization
import tokenization_labert
import tensorflow as tf
import numpy as np
import r... | Convert a set of `InputExample`s to a list of `InputFeatures`. |
18,897 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import math
import tensorflow as tf
from modeling import (BertConfig,
reshape_to_matrix,
reshape_from_matrix,
get_shape_list,
... | null |
18,898 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import math
import tensorflow as tf
from modeling import (BertConfig,
reshape_to_matrix,
reshape_from_matrix,
get_shape_list,
... | Performs various post-processing on a word embedding tensor. Args: input_tensor: float Tensor of shape [batch_size, seq_length, embedding_size]. use_token_type: bool. Whether to add embeddings for `token_type_ids`. token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length]. Must be specified if `use_toke... |
18,899 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import math
import tensorflow as tf
from modeling import (BertConfig,
reshape_to_matrix,
reshape_from_matrix,
get_shape_list,
... | Transformer model from "On layer normalization in the transformer architecture". See the original paper: https://openreview.net/pdf?id=B1x8anVFPr Args: input_tensor: float Tensor of shape [batch_size, seq_length, hidden_size]. attention_mask: (optional) int32 Tensor of shape [batch_size, seq_length, seq_length], with 1... |
18,900 | import tensorflow as tf
def float32_variable_storage_getter(getter, name, shape=None, dtype=None,
initializer=None, regularizer=None,
trainable=True,
*args, **kwargs):
def get_custom_getter(compute_type):
re... | null |
18,901 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
import modeling
import modeling_labert
import optimization
import tensorflow as tf
import shutil
import random
tf.flags = tf.compat.v1.flags
FLAGS = flags.FLAGS
tf.flags.DEFINE_string(
"t... | Returns `model_fn` closure for TPUEstimator. |
18,902 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
import modeling
import modeling_labert
import optimization
import tensorflow as tf
import shutil
import random
tf.flags = tf.compat.v1.flags
FLAGS = flags.FLAGS
tf.flags.DEFINE_string(
"t... | Creates an `input_fn` closure to be passed to TPUEstimator. |
18,903 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import re
import unicodedata
import six
import tensorflow as tf
The provided code snippet includes necessary dependencies for implementing the `validate_case_matches_checkpoint` function. Wri... | Checks whether the casing config is consistent with the checkpoint name. |
18,904 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import re
import unicodedata
import six
import tensorflow as tf
def convert_to_unicode(text):
"""Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
if six.PY3:
... | Loads a vocabulary file into a dictionary. |
18,905 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import re
import unicodedata
import six
import tensorflow as tf
def convert_by_vocab(vocab, items):
"""Converts a sequence of [tokens|ids] using the vocab."""
output = []
for item in ite... | null |
18,906 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import re
import unicodedata
import six
import tensorflow as tf
The provided code snippet includes necessary dependencies for implementing the `whitespace_tokenize` function. Write a Python f... | Runs basic whitespace cleaning and splitting on a piece of text. |
18,907 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import re
import unicodedata
import six
import tensorflow as tf
The provided code snippet includes necessary dependencies for implementing the `_is_whitespace` function. Write a Python functi... | Checks whether `chars` is a whitespace character. |
18,908 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import re
import unicodedata
import six
import tensorflow as tf
The provided code snippet includes necessary dependencies for implementing the `_is_control` function. Write a Python function ... | Checks whether `chars` is a control character. |
18,909 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import re
import unicodedata
import six
import tensorflow as tf
The provided code snippet includes necessary dependencies for implementing the `_is_punctuation` function. Write a Python funct... | Checks whether `chars` is a punctuation character. |
18,910 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import random
import tokenization
import tokenization_labert
from tokenization_labert import LatticeEncoding
import tensorflow as tf
import numpy as np
from create_pretraining_data_utils import (
write_lattice... | null |
18,911 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import copy
import json
import math
import re
import numpy as np
import six
import tensorflow as tf
from gpu_environment import get_custom_getter
def create_initializer(initializer_range=0.02)... | Looks up words embeddings for id tensor. Args: input_ids: int32 Tensor of shape [batch_size, seq_length] containing word ids. vocab_size: int. Size of the embedding vocabulary. embedding_size: int. Width of the word embeddings. initializer_range: float. Embedding initialization range. word_embedding_name: string. Name ... |
18,912 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import copy
import json
import math
import re
import numpy as np
import six
import tensorflow as tf
from gpu_environment import get_custom_getter
def layer_norm_and_dropout(input_tensor, dropo... | Performs various post-processing on a word embedding tensor. Args: input_tensor: float Tensor of shape [batch_size, seq_length, embedding_size]. use_token_type: bool. Whether to add embeddings for `token_type_ids`. token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length]. Must be specified if `use_toke... |
18,913 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import copy
import json
import math
import re
import numpy as np
import six
import tensorflow as tf
from gpu_environment import get_custom_getter
def gelu(x):
"""Gaussian Error Linear Unit.
... | Transformer model from "On layer normalization in the transformer architecture". See the original paper: https://openreview.net/pdf?id=B1x8anVFPr Args: input_tensor: float Tensor of shape [batch_size, seq_length, hidden_size]. attention_mask: (optional) int32 Tensor of shape [batch_size, seq_length, seq_length], with 1... |
18,914 | import os
import random
import time
import numpy as np
import torch
from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP
import mpu
import model
The provided code snippet includes necessary dependencies for implementing the `print_args` function. Write a Python function `def print_args(args)` ... | Print arguments. |
18,915 | import os
import random
import time
import numpy as np
import torch
from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP
import mpu
import model
def print_rank_0(message):
if torch.distributed.is_initialized():
if torch.distributed.get_rank() == 0:
print(message, flush=T... | Simple GPU memory report. |
18,916 | import os
import random
import time
import numpy as np
import torch
from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP
import mpu
import model
def get_checkpoint_name(checkpoints_path, iteration, release=False, zero=False):
if release:
d = 'release'
else:
d = 'iter_{:0... | null |
18,917 | import os
import random
import numpy as np
import torch
import torch.nn.functional as F
from arguments import get_args
import deepspeed
from data_utils import make_tokenizer
from configure_data import configure_data
import mpu
from fp16 import FP16_Module
from data_utils.wordpiece import BertTokenizer
from model import... | null |
18,918 | import os
import random
import numpy as np
import torch
import torch.nn.functional as F
from arguments import get_args
import deepspeed
from data_utils import make_tokenizer
from configure_data import configure_data
import mpu
from fp16 import FP16_Module
from data_utils.wordpiece import BertTokenizer
from model import... | null |
18,919 | import os
import random
import numpy as np
import torch
import torch.nn.functional as F
from arguments import get_args
import deepspeed
from data_utils import make_tokenizer
from configure_data import configure_data
import mpu
from fp16 import FP16_Module
from data_utils.wordpiece import BertTokenizer
from model import... | Main training program. |
18,924 | import math
import torch
import torch.nn.init as init
from apex.normalization.fused_layer_norm import FusedLayerNorm as LayerNorm
from .initialize import get_model_parallel_world_size
from .layers import ColumnParallelLinear
from .layers import RowParallelLinear
from .mappings import gather_from_model_parallel_region
i... | null |
18,925 | import math
import torch
import torch.nn.init as init
from apex.normalization.fused_layer_norm import FusedLayerNorm as LayerNorm
from .initialize import get_model_parallel_world_size
from .layers import ColumnParallelLinear
from .layers import RowParallelLinear
from .mappings import gather_from_model_parallel_region
i... | Init method based on N(0, sigma). |
18,926 | import math
import torch
import torch.nn.init as init
from apex.normalization.fused_layer_norm import FusedLayerNorm as LayerNorm
from .initialize import get_model_parallel_world_size
from .layers import ColumnParallelLinear
from .layers import RowParallelLinear
from .mappings import gather_from_model_parallel_region
i... | Init method based on N(0, sigma/sqrt(2*num_layers). |
18,932 | import torch
from .initialize import get_model_parallel_group
from .utils import split_tensor_along_last_dim
from deepspeed.utils.timer import SynchronizedWallClockTimer
class _ScatterToModelParallelRegion(torch.autograd.Function):
def forward(ctx, input_):
def backward(ctx, grad_output):
def scatter_to_mode... | null |
18,933 | import torch
from .initialize import get_model_parallel_group
from .utils import split_tensor_along_last_dim
from deepspeed.utils.timer import SynchronizedWallClockTimer
class _GatherFromModelParallelRegion(torch.autograd.Function):
def forward(ctx, input_):
def backward(ctx, grad_output):
def gather_from_mo... | null |
18,937 | import contextlib
import torch.distributed as dist
import torch
from torch import _C
from torch.cuda import _lazy_call, device as device_ctx_manager
import torch.distributed as dist
from .initialize import get_data_parallel_rank
from .initialize import get_model_parallel_rank
from .initialize import get_model_parallel_... | null |
18,938 | import contextlib
import torch.distributed as dist
import torch
from torch import _C
from torch.cuda import _lazy_call, device as device_ctx_manager
import torch.distributed as dist
from .initialize import get_data_parallel_rank
from .initialize import get_model_parallel_rank
from .initialize import get_model_parallel_... | null |
18,939 | import contextlib
import torch.distributed as dist
import torch
from torch import _C
from torch.cuda import _lazy_call, device as device_ctx_manager
import torch.distributed as dist
from .initialize import get_data_parallel_rank
from .initialize import get_model_parallel_rank
from .initialize import get_model_parallel_... | Sets the random number generator state of the current GPU. Argumentss: new_state (torch.ByteTensor): The desired state This function is adapted from PyTorch repo (torch.cuda.set_rng_state) with a single change: the input state is not cloned. Cloning caused major performance issues for +4 GPU cases. |
18,940 | import contextlib
import torch.distributed as dist
import torch
from torch import _C
from torch.cuda import _lazy_call, device as device_ctx_manager
import torch.distributed as dist
from .initialize import get_data_parallel_rank
from .initialize import get_model_parallel_rank
from .initialize import get_model_parallel_... | Get cuda rng tracker. |
18,941 | import contextlib
import torch.distributed as dist
import torch
from torch import _C
from torch.cuda import _lazy_call, device as device_ctx_manager
import torch.distributed as dist
from .initialize import get_data_parallel_rank
from .initialize import get_model_parallel_rank
from .initialize import get_model_parallel_... | Initialize model parallel cuda seed. This function should be called after the model parallel is initialized. Also, no torch.cuda.manual_seed should be called after this function. Basically, this is replacement for that function. Two set of RNG states are tracked: default state: This is for data parallelism and is the s... |
18,942 | import contextlib
import torch.distributed as dist
import torch
from torch import _C
from torch.cuda import _lazy_call, device as device_ctx_manager
import torch.distributed as dist
from .initialize import get_data_parallel_rank
from .initialize import get_model_parallel_rank
from .initialize import get_model_parallel_... | null |
18,943 | import contextlib
import torch.distributed as dist
import torch
from torch import _C
from torch.cuda import _lazy_call, device as device_ctx_manager
import torch.distributed as dist
from .initialize import get_data_parallel_rank
from .initialize import get_model_parallel_rank
from .initialize import get_model_parallel_... | null |
18,944 | import contextlib
import torch.distributed as dist
import torch
from torch import _C
from torch.cuda import _lazy_call, device as device_ctx_manager
import torch.distributed as dist
from .initialize import get_data_parallel_rank
from .initialize import get_model_parallel_rank
from .initialize import get_model_parallel_... | Checkpoint a model or part of the model. This has been directly copied from torch.utils.checkpoint. |
18,945 | import contextlib
import torch.distributed as dist
import torch
from torch import _C
from torch.cuda import _lazy_call, device as device_ctx_manager
import torch.distributed as dist
PARTITION_ACTIVATIONS = False
from .initialize import get_data_parallel_rank
from .initialize import get_model_parallel_rank
from .initial... | null |
18,946 | import math
import torch
import torch.nn.functional as F
import torch.nn.init as init
from torch.nn.parameter import Parameter
from apex.normalization.fused_layer_norm import FusedLayerNorm as LayerNorm
from .initialize import get_model_parallel_rank
from .initialize import get_model_parallel_world_size
from .mappings ... | Initialize affine weight for model parallel. Build the master weight on all processes and scatter the relevant chunk. |
18,947 | import queue
import threading
import tensorflow as tf
import torch
import numpy as np
def convert_tf_example_to_torch_tensors(example):
item = {k: (v.numpy()) for k,v in example.items()}
mask = np.zeros_like(item['input_ids'])
mask_labels = np.ones_like(item['input_ids'])*-1
for b, row in enumerate(item... | null |
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