id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
8,761 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import random
from albert import tokenization
import numpy as np
import six
from six.moves import range
from six.moves import zip
import tensorflow.compat.v1 as tf
FLAGS = flags.FLAGS
def crea... | Create `TrainingInstance`s from raw text. |
8,762 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import os
import random
import time
from albert import fine_tuning_utils
from albert import modeling
from albert import squad_utils
import six
import tensorflow.compat.v1 as tf
from tensorflow.contri... | Validate the input FLAGS or throw an exception. |
8,763 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import os
import time
from albert import classifier_utils
from albert import fine_tuning_utils
from albert import modeling
import tensorflow.compat.v1 as tf
from tensorflow.compat.v1 import estimator... | Creates an input function for serving. |
8,764 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import os
import time
from albert import classifier_utils
from albert import fine_tuning_utils
from albert import modeling
import tensorflow.compat.v1 as tf
from tensorflow.compat.v1 import estimator... | Adds the classifier threshold to the given model_fn. |
8,765 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import json
import os
from albert import classifier_utils
from albert import fine_tuning_utils
from albert import modeling
from albert import optimization
from albert import tokenization
impor... | Convert a set of `InputExample`s to a TFRecord file. |
8,766 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import json
import os
from albert import classifier_utils
from albert import fine_tuning_utils
from albert import modeling
from albert import optimization
from albert import tokenization
impor... | Returns `model_fn` closure for TPUEstimator. |
8,767 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import re
from albert import lamb_optimizer
import six
from six.moves import zip
import tensorflow.compat.v1 as tf
from tensorflow.contrib import tpu as contrib_tpu
class AdamWeightDecayOptimizer(tf.train.Optimi... | Creates an optimizer training op. |
8,768 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
from albert import modeling
from albert import optimization
from six.moves import range
import tensorflow.compat.v1 as tf
from tensorflow.compat.v1 import estimator as tf_estimator
from ten... | Returns `model_fn` closure for TPUEstimator. |
8,769 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
from albert import modeling
from albert import optimization
from six.moves import range
import tensorflow.compat.v1 as tf
from tensorflow.compat.v1 import estimator as tf_estimator
from ten... | Creates an `input_fn` closure to be passed to TPUEstimator. |
8,770 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import csv
import os
from albert import fine_tuning_utils
from albert import modeling
from albert import optimization
from albert import tokenization
import tensorflow.compat.v1 as tf
from ten... | Convert a set of `InputExample`s to a TFRecord file. |
8,771 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import csv
import os
from albert import fine_tuning_utils
from albert import modeling
from albert import optimization
from albert import tokenization
import tensorflow.compat.v1 as tf
from ten... | Creates an `input_fn` closure to be passed to TPUEstimator. |
8,772 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import csv
import os
from albert import fine_tuning_utils
from albert import modeling
from albert import optimization
from albert import tokenization
import tensorflow.compat.v1 as tf
from ten... | Returns `model_fn` closure for TPUEstimator. |
8,773 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import csv
import os
from albert import fine_tuning_utils
from albert import modeling
from albert import optimization
from albert import tokenization
import tensorflow.compat.v1 as tf
from ten... | Creates an `input_fn` closure to be passed to TPUEstimator. |
8,774 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import csv
import os
from albert import fine_tuning_utils
from albert import modeling
from albert import optimization
from albert import tokenization
import tensorflow.compat.v1 as tf
from ten... | Convert a set of `InputExample`s to a list of `InputFeatures`. |
8,775 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from absl import app
from absl import flags
from albert import modeling
import tensorflow.compat.v1 as tf
import tensorflow_hub as hub
FLAGS = flags.FLAGS
def get_mlm_logits(model, albert_config, mlm_p... | Module function. |
8,776 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import json
import math
import re
import string
import sys
from albert import fine_tuning_utils
from albert import modeling
from albert import optimization
from albert import tokenization
impo... | Read a SQuAD json file into a list of SquadExample. |
8,777 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import json
import math
import re
import string
import sys
from albert import fine_tuning_utils
from albert import modeling
from albert import optimization
from albert import tokenization
impo... | Loads a data file into a list of `InputBatch`s. |
8,778 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import json
import math
import re
import string
import sys
from albert import fine_tuning_utils
from albert import modeling
from albert import optimization
from albert import tokenization
impo... | Creates an `input_fn` closure to be passed to TPUEstimator. |
8,779 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import json
import math
import re
import string
import sys
from albert import fine_tuning_utils
from albert import modeling
from albert import optimization
from albert import tokenization
impo... | Returns `model_fn` closure for TPUEstimator. |
8,780 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import json
import math
import re
import string
import sys
from albert import fine_tuning_utils
from albert import modeling
from albert import optimization
from albert import tokenization
impo... | accumulate predictions for each positions in a dictionary. |
8,781 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import json
import math
import re
import string
import sys
from albert import fine_tuning_utils
from albert import modeling
from albert import optimization
from albert import tokenization
impo... | Write final predictions to the json file and log-odds of null if needed. |
8,782 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import json
import math
import re
import string
import sys
from albert import fine_tuning_utils
from albert import modeling
from albert import optimization
from albert import tokenization
impo... | null |
8,783 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import json
import math
import re
import string
import sys
from albert import fine_tuning_utils
from albert import modeling
from albert import optimization
from albert import tokenization
impo... | null |
8,784 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import json
import math
import re
import string
import sys
from albert import fine_tuning_utils
from albert import modeling
from albert import optimization
from albert import tokenization
impo... | accumulate predictions for each positions in a dictionary. |
8,785 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import json
import math
import re
import string
import sys
from albert import fine_tuning_utils
from albert import modeling
from albert import optimization
from albert import tokenization
impo... | Returns `model_fn` closure for TPUEstimator. |
8,786 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import json
import math
import re
import string
import sys
from albert import fine_tuning_utils
from albert import modeling
from albert import optimization
from albert import tokenization
impo... | null |
8,787 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from absl import app
from absl import flags
from albert import modeling
import tensorflow.compat.v1 as tf
FLAGS = flags.FLAGS
def get_mlm_logits(input_tensor, albert_config, mlm_positions, output_weigh... | Module function. |
8,788 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import unicodedata
import six
from six.moves import range
import tensorflow.compat.v1 as tf
import tensorflow_hub as hub
import sentencepiece as spm
The provided code snippet includes necessa... | preprocess data by removing extra space and normalize data. |
8,789 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import unicodedata
import six
from six.moves import range
import tensorflow.compat.v1 as tf
import tensorflow_hub as hub
import sentencepiece as spm
def encode_pieces(sp_model, text, return_un... | null |
8,790 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import unicodedata
import six
from six.moves import range
import tensorflow.compat.v1 as tf
import tensorflow_hub as hub
import sentencepiece as spm
def convert_to_unicode(text):
"""Converts... | Loads a vocabulary file into a dictionary. |
8,791 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import unicodedata
import six
from six.moves import range
import tensorflow.compat.v1 as tf
import tensorflow_hub as hub
import sentencepiece as spm
def convert_by_vocab(vocab, items):
"""Co... | null |
8,792 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import unicodedata
import six
from six.moves import range
import tensorflow.compat.v1 as tf
import tensorflow_hub as hub
import sentencepiece as spm
def convert_by_vocab(vocab, items):
"""Co... | null |
8,793 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import unicodedata
import six
from six.moves import range
import tensorflow.compat.v1 as tf
import tensorflow_hub as hub
import sentencepiece as spm
The provided code snippet includes necessa... | Runs basic whitespace cleaning and splitting on a piece of text. |
8,794 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import unicodedata
import six
from six.moves import range
import tensorflow.compat.v1 as tf
import tensorflow_hub as hub
import sentencepiece as spm
The provided code snippet includes necessa... | Checks whether `chars` is a whitespace character. |
8,795 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import unicodedata
import six
from six.moves import range
import tensorflow.compat.v1 as tf
import tensorflow_hub as hub
import sentencepiece as spm
The provided code snippet includes necessa... | Checks whether `chars` is a control character. |
8,796 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import unicodedata
import six
from six.moves import range
import tensorflow.compat.v1 as tf
import tensorflow_hub as hub
import sentencepiece as spm
The provided code snippet includes necessa... | Checks whether `chars` is a punctuation character. |
8,797 | 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
from six.moves import range
import tensorflow.compat.v1 as tf
from tensorflow.contrib import layers as contrib_layer... | Maps a string to a Python function, e.g., "relu" => `tf.nn.relu`. Args: activation_string: String name of the activation function. Returns: A Python function corresponding to the activation function. If `activation_string` is None, empty, or "linear", this will return None. If `activation_string` is not a string, it wi... |
8,798 | 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
from six.moves import range
import tensorflow.compat.v1 as tf
from tensorflow.contrib import layers as contrib_layer... | Compute the union of the current variables and checkpoint variables. |
8,799 | 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
from six.moves import range
import tensorflow.compat.v1 as tf
from tensorflow.contrib import layers as contrib_layer... | Get sinusoids of diff frequencies, with timing position given. Adapted from add_timing_signal_1d_given_position in //third_party/py/tensor2tensor/layers/common_attention.py Args: channels: scalar, size of timing embeddings to create. The number of different timescales is equal to channels / 2. position: a Tensor with s... |
8,800 | 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
from six.moves import range
import tensorflow.compat.v1 as tf
from tensorflow.contrib import layers as contrib_layer... | 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... |
8,801 | 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
from six.moves import range
import tensorflow.compat.v1 as tf
from tensorflow.contrib import layers as contrib_layer... | Multi-headed, multi-layer Transformer from "Attention is All You Need". This is almost an exact implementation of the original Transformer encoder. See the original paper: https://arxiv.org/abs/1706.03762 Also see: https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/models/transformer.py Args: input_t... |
8,802 | 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
from six.moves import range
import tensorflow.compat.v1 as tf
from tensorflow.contrib import layers as contrib_layer... | Reshapes a >= rank 2 tensor to a rank 2 tensor (i.e., a matrix). |
8,803 | 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
from six.moves import range
import tensorflow.compat.v1 as tf
from tensorflow.contrib import layers as contrib_layer... | Reshapes a rank 2 tensor back to its original rank >= 2 tensor. |
8,804 | import re
import sys
import codecs
import socket
import hashlib
from subprocess import Popen
from calibre.utils.logging import Log
from ..lib.cssselect import GenericTranslator, SelectorError
def css(seletor):
try:
return GenericTranslator().css_to_xpath(seletor, prefix='self::x:')
except SelectorError... | null |
8,805 | import re
import sys
import codecs
import socket
import hashlib
from subprocess import Popen
from calibre.utils.logging import Log
from ..lib.cssselect import GenericTranslator, SelectorError
def chunk(items, length=0):
if length < 1:
for item in items:
yield [item]
return
item_leng... | null |
8,806 | import re
import sys
import codecs
import socket
import hashlib
from subprocess import Popen
from calibre.utils.logging import Log
from ..lib.cssselect import GenericTranslator, SelectorError
def group(numbers):
ranges = []
current_range = []
numbers = sorted(numbers)
for number in numbers:
if ... | null |
8,807 | import re
import sys
import codecs
import socket
import hashlib
from subprocess import Popen
from calibre.utils.logging import Log
from ..lib.cssselect import GenericTranslator, SelectorError
def sorted_mixed_keys(s):
# https://docs.python.org/3/reference/expressions.html#value-comparisons
return [int(s) if s.... | null |
8,808 | import re
import sys
import codecs
import socket
import hashlib
from subprocess import Popen
from calibre.utils.logging import Log
from ..lib.cssselect import GenericTranslator, SelectorError
def is_proxy_availiable(host, port, timeout=1):
try:
host = host.replace('http://', '')
socket.create_conne... | null |
8,809 | import re
import sys
import codecs
import socket
import hashlib
from subprocess import Popen
from calibre.utils.logging import Log
from ..lib.cssselect import GenericTranslator, SelectorError
def size_by_unit(number, unit='KB'):
unit = unit.upper()
multiple = {'KB': 1, 'MB': 2}
if unit not in multiple:
... | null |
8,810 | import re
import sys
import codecs
import socket
import hashlib
from subprocess import Popen
from calibre.utils.logging import Log
from ..lib.cssselect import GenericTranslator, SelectorError
def open_path(path):
cmd = 'open'
if sys.platform.startswith('win32'):
cmd = 'explorer'
if sys.platform.sta... | null |
8,811 | import re
import sys
import codecs
import socket
import hashlib
from subprocess import Popen
from calibre.utils.logging import Log
from ..lib.cssselect import GenericTranslator, SelectorError
def dummy(*args, **kwargs):
pass | null |
8,812 | import os
import shutil
import os.path
from types import MethodType
from tempfile import gettempdir
from calibre.gui2 import Dispatcher
from calibre.constants import DEBUG, __version__
from calibre.ebooks.conversion.plumber import Plumber
from calibre.ptempfile import PersistentTemporaryFile
from calibre.ebooks.metadat... | null |
8,813 | import os
import shutil
import os.path
from types import MethodType
from tempfile import gettempdir
from calibre.gui2 import Dispatcher
from calibre.constants import DEBUG, __version__
from calibre.ebooks.conversion.plumber import Plumber
from calibre.ptempfile import PersistentTemporaryFile
from calibre.ebooks.metadat... | The following parameters need attention: :cache_only: Only use the translation which exists in the cache. :notification: It is automatically added by arbitrary_n. |
8,814 | from calibre.utils.config import JSONConfig
from .. import EbookTranslator
from ..engines import (
GoogleFreeTranslate, ChatgptTranslate, AzureChatgptTranslate)
def get_config():
preferences = JSONConfig('plugins/ebook_translator')
preferences.defaults = defaults
return Configuration(preferences)
def ve... | null |
8,815 | import re
import json
import copy
from lxml import etree
from calibre import prepare_string_for_xml as xml_escape
from .utils import ns, css, uid, trim, sorted_mixed_keys, open_file
from .config import get_config
def trim(text):
def get_string(element, remove_ns=False):
element.text = element.text or '' # preven... | null |
8,816 | import re
import json
import copy
from lxml import etree
from calibre import prepare_string_for_xml as xml_escape
from .utils import ns, css, uid, trim, sorted_mixed_keys, open_file
from .config import get_config
def get_name(element):
return etree.QName(element).localname | null |
8,817 | import os
import re
import json
import shutil
import sqlite3
import os.path
import tempfile
from glob import glob
from .utils import size_by_unit
from .config import get_config
def default_cache_path():
path = os.path.join(
tempfile.gettempdir(), 'com.bookfere.Calibre.EbookTranslator')
not os.path.exist... | null |
8,818 | import sys
import re
import operator
The provided code snippet includes necessary dependencies for implementing the `ascii_lower` function. Write a Python function `def ascii_lower(string)` to solve the following problem:
Lower-case, but only in the ASCII range.
Here is the function:
def ascii_lower(string):
"""... | Lower-case, but only in the ASCII range. |
8,819 | import sys
import re
import operator
class Selector(object):
"""
Represents a parsed selector.
:meth:`~GenericTranslator.selector_to_xpath` accepts this object,
but ignores :attr:`pseudo_element`. It is the user’s responsibility
to account for pseudo-elements and reject selectors with unknown
or... | Parse a CSS *group of selectors*. If you don't care about pseudo-elements or selector specificity, you can skip this and use :meth:`~GenericTranslator.css_to_xpath`. :param css: A *group of selectors* as an Unicode string. :raises: :class:`SelectorSyntaxError` on invalid selectors. :returns: A list of parsed :class:`Se... |
8,820 | import sys
import re
import operator
The provided code snippet includes necessary dependencies for implementing the `parse_series` function. Write a Python function `def parse_series(tokens)` to solve the following problem:
Parses the arguments for :nth-child() and friends. :raises: A list of tokens :returns: :``(a, b... | Parses the arguments for :nth-child() and friends. :raises: A list of tokens :returns: :``(a, b)`` |
8,821 | import sys
import re
import operator
class TokenMacros:
unicode_escape = r'\\([0-9a-f]{1,6})(?:\r\n|[ \n\r\t\f])?'
escape = unicode_escape + r'|\\[^\n\r\f0-9a-f]'
string_escape = r'\\(?:\n|\r\n|\r|\f)|' + escape
nonascii = r'[^\0-\177]'
nmchar = '[_a-z0-9-]|%s|%s' % (escape, nonascii)
nmstart = ... | null |
8,822 | import sys
import re
import operator
_sub_simple_escape = re.compile(r'\\(.)').sub
_sub_unicode_escape = re.compile(TokenMacros.unicode_escape, re.I).sub
_replace_simple = operator.methodcaller('group', 1)
def _replace_unicode(match):
codepoint = int(match.group(1), 16)
if codepoint > sys.maxunicode:
co... | null |
8,823 | import sys
import re
from .parser import parse, parse_series, SelectorError
def _unicode_safe_getattr(obj, name, default=None):
# getattr() with a non-ASCII name fails on Python 2.x
name = name.encode('ascii', 'replace').decode('ascii')
return getattr(obj, name, default) | null |
8,824 | import json
from lxml import etree
from ..lib.utils import is_str
from . import builtin_engines
from .base import Base
def create_engine_template(name):
return """{
"name": "%s",
"languages": {
"source": {
"Source Language": "code"
},
"target": {
"Target Lang... | null |
8,825 | import json
from lxml import etree
from ..lib.utils import is_str
from . import builtin_engines
from .base import Base
def is_str(data):
return type(data).__name__ in ('str', 'unicode')
def load_engine_data(text):
# json format
try:
json_data = json.loads(text)
except Exception:
return... | null |
8,826 | from calibre.utils.localization import get_lang
from calibre_plugins.ebook_translator import EbookTranslator
def layout_info():
widget = QWidget()
widget.setStyleSheet('color:grey')
layout = QHBoxLayout(widget)
layout.setContentsMargins(0, 0, 0, 0)
app_author = EbookTranslator.author
site = QLa... | null |
8,827 | import argparse
import os
import subprocess
import sys
from pathlib import Path
from typing import List
from setuptools import find_packages, setup
ROOT_DIR = Path(__file__).parent.resolve()
def _get_version():
try:
cmd = ["git", "rev-parse", "HEAD"]
sha = subprocess.check_output(cmd, cwd=str(ROOT_... | null |
8,828 | import argparse
import os
import subprocess
import sys
from pathlib import Path
from typing import List
from setuptools import find_packages, setup
ROOT_DIR = Path(__file__).parent.resolve()
def _export_version(version, sha):
version_path = ROOT_DIR / "torchrec" / "version.py"
with open(version_path, "w") as f... | null |
8,829 | import argparse
import os
import subprocess
import sys
from pathlib import Path
from typing import List
from setuptools import find_packages, setup
def parse_args(argv: List[str]) -> argparse.Namespace:
parser = argparse.ArgumentParser(description="torchrec setup")
return parser.parse_known_args(argv) | null |
8,830 | import queue
import threading
from typing import Dict, List, Union
import torch
from torch.utils.data._utils import MP_STATUS_CHECK_INTERVAL
from torchrec import EmbeddingBagConfig, EmbeddingConfig
from torchrec.distributed.model_parallel import DistributedModelParallel
from torchrec.sparse.jagged_tensor import KeyedJa... | null |
8,831 | import queue
import threading
from typing import Dict, List, Union
import torch
from torch.utils.data._utils import MP_STATUS_CHECK_INTERVAL
from torchrec import EmbeddingBagConfig, EmbeddingConfig
from torchrec.distributed.model_parallel import DistributedModelParallel
from torchrec.sparse.jagged_tensor import KeyedJa... | DataLoader to transform data from global id to cache id. Args: url: configuration for PS, e.g. redis://127.0.0.1:6379/?prefix=model. dataloader: dataloader to transform. module: DMP module that need dynamic embedding. configs_dict: a dictionary that maps the module path of the sharded module to its embedding configs or... |
8,832 | import queue
import threading
from typing import Dict, List, Union
import torch
from torch.utils.data._utils import MP_STATUS_CHECK_INTERVAL
from torchrec import EmbeddingBagConfig, EmbeddingConfig
from torchrec.distributed.model_parallel import DistributedModelParallel
from torchrec.sparse.jagged_tensor import KeyedJa... | Save the dynamic embedding part of the model. |
8,833 | from typing import Dict
import torch.nn as nn
from torchrec.distributed.types import ShardingPlan
class ShardingPlan:
"""
Representation of sharding plan. This uses the FQN of the larger wrapped model (i.e the model that is wrapped using `DistributedModelParallel`)
EmbeddingModuleShardingPlan should be use... | Get all sharded modules of module from `plan`. |
8,834 | import queue
import threading
from typing import Dict, List, Union
from torchrec import EmbeddingBagConfig, EmbeddingConfig, KeyedJaggedTensor
from torchrec.distributed.model_parallel import DistributedModelParallel
from .id_transformer_collection import IDTransformerCollection
from .ps import PSCollection
from .utils ... | Create a thread for transformer. |
8,835 | from typing import List, Optional
import torch
import torch.distributed as dist
def gather_global_ids(global_ids: List[torch.Tensor], group):
world_size = dist.get_world_size()
rank = dist.get_rank()
concat_global_ids = torch.cat(global_ids)
concat_numel = torch.tensor(concat_global_ids.numel(), dtyp... | null |
8,836 | from typing import List, Optional
import torch
import torch.distributed as dist
def scatter_cache_ids(
cache_ids_list: Optional[List[torch.Tensor]], concat_numel_list: List[int], group
):
world_size = dist.get_world_size()
rank = dist.get_rank()
max_numel = max(concat_numel_list)
concat_cache_ids... | null |
8,837 | from typing import List, Optional
import torch
import torch.distributed as dist
def broadcast_transform_result(
success: bool, ids_to_fetch: Optional[torch.Tensor], group
):
if dist.get_rank() == 0:
success_and_numel = torch.tensor(
[1 if success else 0, ids_to_fetch.numel()], dtype=torch.i... | null |
8,838 | from typing import List, Optional
import torch
import torch.distributed as dist
def broadcast_ids_to_evict(ids, group):
if dist.get_rank() == 0:
numel = torch.tensor(ids.numel(), dtype=torch.int64)
dist.broadcast(numel, src=0, group=group)
else:
numel = torch.tensor(0, dtype=torch.int64... | null |
8,839 | import argparse
import logging
import sys
import grpc
import torch
from torch.utils.data import DataLoader
from torchrec.datasets.criteo import DEFAULT_CAT_NAMES, DEFAULT_INT_NAMES
from torchrec.datasets.random import RandomRecDataset
from torchrec.datasets.utils import Batch
from gen.torchrec.inference import predicto... | null |
8,840 | import argparse
import logging
import sys
import grpc
import torch
from torch.utils.data import DataLoader
from torchrec.datasets.criteo import DEFAULT_CAT_NAMES, DEFAULT_INT_NAMES
from torchrec.datasets.random import RandomRecDataset
from torchrec.datasets.utils import Batch
from gen.torchrec.inference import predicto... | null |
8,841 | import argparse
import sys
from typing import List
from dlrm_predict import DLRMModelConfig, DLRMPredictFactory
from torch.package import PackageExporter
from torchrec.datasets.criteo import DEFAULT_CAT_NAMES, DEFAULT_INT_NAMES
from torchrec.inference.model_packager import PredictFactoryPackager
DEFAULT_INT_NAMES: Lis... | null |
8,842 | import os
from typing import List, Optional
import torch
from torch import distributed as dist
from torch.distributed.elastic.multiprocessing.errors import record
from torch.distributed.optim import (
_apply_optimizer_in_backward as apply_optimizer_in_backward,
)
from torch.utils.data import IterableDataset
from to... | Constructs and trains a DLRM model (using random dummy data). Each script is run on each process (rank) in SPMD fashion. The embedding layers will be sharded across available ranks qcomm_forward_precision: Compression used in forwards pass. FP16 is the recommended usage. INT8 and FP8 are in development, but feel free t... |
8,843 | import os
import torch
import torch.nn.functional as F
from torch.distributed import all_reduce, get_rank, get_world_size, init_process_group
The provided code snippet includes necessary dependencies for implementing the `compute_world_size` function. Write a Python function `def compute_world_size() -> int` to solve ... | Dummy script to compute world_size. Meant to test if can run Ray + Pytorch DDP |
8,844 | import os
from typing import cast, List, Optional
import torch
from fbgemm_gpu.split_embedding_configs import EmbOptimType as OptimType
from torch import distributed as dist, nn
from torch.utils.data import DataLoader
from torchrec.datasets.criteo import DEFAULT_CAT_NAMES, DEFAULT_INT_NAMES
from torchrec.datasets.rando... | Constructs and trains a DLRM model (using random dummy data). Each script is run on each process (rank) in SPMD fashion. The embedding layers will be sharded across available ranks |
8,845 | import copyreg
import io
import os
import pickle
import uuid
from typing import cast, List, Optional
import torch
import torch.distributed as dist
import torch.distributed.launcher as pet
import torchrec
from fbgemm_gpu.split_embedding_configs import EmbOptimType
from torch import nn
from torch.multiprocessing.reductio... | Share a tensor via shared memory with local peers. This is a collective function that must be called by all processes within the global process group. Rank `src_rank` must pass in the tensor it wants to share. NOTE: this is a simple implementation that only supports the single-host, multi-process environment. Multi-hos... |
8,846 | import copyreg
import io
import os
import pickle
import uuid
from typing import cast, List, Optional
import torch
import torch.distributed as dist
import torch.distributed.launcher as pet
import torchrec
from fbgemm_gpu.split_embedding_configs import EmbOptimType
from torch import nn
from torch.multiprocessing.reductio... | null |
8,847 | import os
import torchx.specs as specs
from torchx.components.dist import ddp
The provided code snippet includes necessary dependencies for implementing the `run_dlrm_main` function. Write a Python function `def run_dlrm_main(num_trainers: int = 8, *script_args: str) -> specs.AppDef` to solve the following problem:
Ar... | Args: num_trainers: The number of trainers to use. script_args: A variable number of parameters to provide dlrm_main.py. |
8,848 | import argparse
import os
import sys
import time
from typing import cast, Iterator, List, Tuple
import torch
import torch.distributed as dist
import torch.nn as nn
import torchmetrics as metrics
import torchrec
import torchrec.distributed as trec_dist
import torchrec.optim as trec_optim
from nvt_binary_dataloader impor... | null |
8,849 | import argparse
import os
import sys
import time
from typing import cast, Iterator, List, Tuple
import torch
import torch.distributed as dist
import torch.nn as nn
import torchmetrics as metrics
import torchrec
import torchrec.distributed as trec_dist
import torchrec.optim as trec_optim
from nvt_binary_dataloader impor... | null |
8,850 | import argparse
import os
import sys
from typing import Any, cast, Dict, List, Union
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as data_utils
from fbgemm_gpu.split_embedding_configs import EmbOptimType
from torch import distributed as dist
from torch.nn.par... | null |
8,851 | import argparse
import os
import sys
from typing import Any, cast, Dict, List, Union
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as data_utils
from fbgemm_gpu.split_embedding_configs import EmbOptimType
from torch import distributed as dist
from torch.nn.par... | Train/validation/test loop. Ensure the dataloader will do the shuffling on each rank and will output the performance metrics like recalls and ndcgs Args: model (Union[DDP, DMP]): DMP or DDP model contains the Bert4Rec. train_loader (data_utils.DataLoader): DataLoader used for training. val_loader (data_utils.DataLoader... |
8,852 | import random
from collections import Counter
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
import numpy as np
import pandas as pd
def _get_dataframe_random(
user_count: int = 50, item_count: int = 5000, size: int = 20000, min_rating: int = 2
) -> pd.DataFrame:
uids = [random.choi... | Gets raw dataframe of both random and movielens Args: name (int): the random or movielens dataset name user_count (int): the random user count of the random set item_count (int): the random item count of the random set size (int): the random sample count of the random set min_rating (int): the minimum rating of the ran... |
8,853 | import copy
import math
from typing import Callable, Optional, Tuple
import torch
import torch.nn as nn
from torchrec.modules.embedding_configs import EmbeddingConfig
from torchrec.modules.embedding_modules import EmbeddingCollection
from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
The provided code snippet... | Clone the module to N copies Args: module (nn.Module): module to clone N (int): number of copies Returns: nn.ModuleList of module copies |
8,854 | from typing import List, Optional
import click
import faiss
import faiss.contrib.torch_utils
import torch
from torchrec import inference as trec_infer
from torchrec.datasets.movielens import DEFAULT_RATINGS_COLUMN_NAMES
from torchrec.distributed.embedding_types import EmbeddingComputeKernel
from torchrec.distributed.... | Loads the serialized model and FAISS index from `two_tower_train.py`. A `TwoTowerRetrieval` model is instantiated, which wraps the `KNNIndex`, the query (user) tower and the candidate item (movie) tower inside an `nn.Module`. The retreival model is quantized using [`torchrec.quant`](https://pytorch.org/torchrec/torchre... |
8,855 | import os
from typing import List, Optional
import click
import faiss
import faiss.contrib.torch_utils
import torch
from torch import distributed as dist
from torch.distributed.optim import (
_apply_optimizer_in_backward as apply_optimizer_in_backward,
)
from torchrec import inference as trec_infer
from torchrec.... | Trains a simple Two Tower (UV) model, which is a simplified version of [A Dual Augmented Two-tower Model for Online Large-scale Recommendation](https://dlp-kdd.github.io/assets/pdf/DLP-KDD_2021_paper_4.pdf). Torchrec is used to shard the model, and is pipelined so that dataloading, data-parallel to model-parallel comms... |
8,856 | import time
from typing import Dict, List, Optional, Tuple
import numpy as np
import torch
from torch.utils.data.dataset import IterableDataset
from torchrec.datasets.random import RandomRecDataset
from torchrec.datasets.utils import Batch
from torchrec.modules.embedding_configs import EmbeddingBagConfig
class RandomR... | null |
8,857 | import time
from typing import Dict, List, Optional, Tuple
import numpy as np
import torch
from torch.utils.data.dataset import IterableDataset
from torchrec.datasets.random import RandomRecDataset
from torchrec.datasets.utils import Batch
from torchrec.modules.embedding_configs import EmbeddingBagConfig
def train_one_... | null |
8,858 | import argparse
import sys
from typing import List, Tuple
import torch
from fbgemm_gpu.split_table_batched_embeddings_ops_training import EmbeddingLocation
from torchrec.github.benchmarks import ebc_benchmarks_utils
from torchrec.modules.embedding_configs import EmbeddingBagConfig
from torchrec.modules.embedding_module... | null |
8,859 | import argparse
import sys
from typing import List, Tuple
import torch
from fbgemm_gpu.split_table_batched_embeddings_ops_training import EmbeddingLocation
from torchrec.github.benchmarks import ebc_benchmarks_utils
from torchrec.modules.embedding_configs import EmbeddingBagConfig
from torchrec.modules.embedding_module... | null |
8,860 | import argparse
import sys
from typing import List, Tuple
import torch
from fbgemm_gpu.split_table_batched_embeddings_ops_training import EmbeddingLocation
from torchrec.github.benchmarks import ebc_benchmarks_utils
from torchrec.modules.embedding_configs import EmbeddingBagConfig
from torchrec.modules.embedding_module... | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.