repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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pytorch | pytorch-main/torch/utils/tensorboard/_pytorch_graph.py | from collections import OrderedDict
import contextlib
from typing import Dict, Any
from tensorboard.compat.proto.config_pb2 import RunMetadata
from tensorboard.compat.proto.graph_pb2 import GraphDef
from tensorboard.compat.proto.step_stats_pb2 import StepStats, DeviceStepStats
from tensorboard.compat.proto.versions_pb... | 13,988 | 35.054124 | 120 | py |
pytorch | pytorch-main/torch/utils/tensorboard/summary.py | import json
import logging
import os
from typing import Optional
import torch
import numpy as np
from google.protobuf import struct_pb2
from tensorboard.compat.proto.summary_pb2 import (
HistogramProto,
Summary,
SummaryMetadata,
)
from tensorboard.compat.proto.tensor_pb2 import TensorProto
from tensorboa... | 33,399 | 33.791667 | 119 | py |
pytorch | pytorch-main/torch/utils/benchmark/__init__.py | from torch.utils.benchmark.utils.common import * # noqa: F403
from torch.utils.benchmark.utils.timer import * # noqa: F403
from torch.utils.benchmark.utils.compare import * # noqa: F403
from torch.utils.benchmark.utils.fuzzer import * # noqa: F403
from torch.utils.benchmark.utils.valgrind_wrapper.timer_interface im... | 411 | 57.857143 | 88 | py |
pytorch | pytorch-main/torch/utils/benchmark/examples/end_to_end.py | # -*- coding: utf-8 -*-
"""End-to-end example to test a PR for regressions:
$ python -m examples.end_to_end --pr 39850
$ python -m examples.end_to_end --pr 39967
$ python -m examples.end_to_end --pr 39744
NOTE:
This example assumes that you have and environment prefixed with
`ref_`, and another prefixed with `pr_... | 14,780 | 33.535047 | 118 | py |
pytorch | pytorch-main/torch/utils/benchmark/examples/simple_timeit.py | """Trivial use of Timer API:
$ python -m examples.simple_timeit
"""
import torch
import torch.utils.benchmark as benchmark_utils
def main():
timer = benchmark_utils.Timer(
stmt="x + y",
globals={"x": torch.ones((4, 8)), "y": torch.ones((1, 8))},
label="Broadcasting add (4x8)",
)
... | 533 | 19.538462 | 67 | py |
pytorch | pytorch-main/torch/utils/benchmark/examples/op_benchmark.py | """Example use of Timer and op fuzzers to measure kernel performance.
$ python -m examples.op_benchmark
"""
import numpy as np
import torch
from torch.utils.benchmark import Timer
from torch.utils.benchmark.op_fuzzers.binary import BinaryOpFuzzer
from torch.utils.benchmark.op_fuzzers.unary import UnaryOpFuzzer
_ME... | 4,176 | 39.163462 | 98 | py |
pytorch | pytorch-main/torch/utils/benchmark/examples/fuzzer.py | """Example of the Timer and Fuzzer APIs:
$ python -m examples.fuzzer
"""
import sys
import torch.utils.benchmark as benchmark_utils
def main():
add_fuzzer = benchmark_utils.Fuzzer(
parameters=[
[
benchmark_utils.FuzzedParameter(
name=f"k{i}",
... | 2,623 | 29.511628 | 92 | py |
pytorch | pytorch-main/torch/utils/benchmark/examples/blas_compare_setup.py | import collections
import os
import shutil
import subprocess
try:
# no type stub for conda command line interface
import conda.cli.python_api # type: ignore[import]
from conda.cli.python_api import Commands as conda_commands
except ImportError:
# blas_compare.py will fail to import these when it's ins... | 7,018 | 31.345622 | 112 | py |
pytorch | pytorch-main/torch/utils/benchmark/examples/blas_compare.py | import argparse
import datetime
import itertools as it
import multiprocessing
import multiprocessing.dummy
import os
import queue
import pickle
import shutil
import subprocess
import sys
import tempfile
import threading
import time
from typing import Tuple, Dict
from . import blas_compare_setup
MIN_RUN_TIME = 1
NUM_... | 7,776 | 32.521552 | 104 | py |
pytorch | pytorch-main/torch/utils/benchmark/examples/compare.py | """Example of Timer and Compare APIs:
$ python -m examples.compare
"""
import pickle
import sys
import time
import torch
import torch.utils.benchmark as benchmark_utils
class FauxTorch:
"""Emulate different versions of pytorch.
In normal circumstances this would be done with multiple processes
writin... | 2,888 | 28.181818 | 97 | py |
pytorch | pytorch-main/torch/utils/benchmark/examples/spectral_ops_fuzz_test.py | """Microbenchmarks for the torch.fft module"""
from argparse import ArgumentParser
from collections import namedtuple
from collections.abc import Iterable
import torch
import torch.fft
from torch.utils import benchmark
from torch.utils.benchmark.op_fuzzers.spectral import SpectralOpFuzzer
def _dim_options(ndim):
... | 4,724 | 40.447368 | 106 | py |
pytorch | pytorch-main/torch/utils/benchmark/examples/sparse/op_benchmark.py | """Example use of Timer and sparse op fuzzers to measure kernel performance.
$ python -m examples.sparse.op_benchmark
"""
import numpy as np
import torch
from torch.utils.benchmark import Timer
from torch.utils.benchmark.op_fuzzers.sparse_unary import UnaryOpSparseFuzzer
from torch.utils.benchmark.op_fuzzers.sparse_... | 4,220 | 41.636364 | 98 | py |
pytorch | pytorch-main/torch/utils/benchmark/examples/sparse/fuzzer.py | """Example of the Timer and Sparse Fuzzer APIs:
$ python -m examples.sparse.fuzzer
"""
import sys
import torch.utils.benchmark as benchmark_utils
def main():
add_fuzzer = benchmark_utils.Fuzzer(
parameters=[
[
benchmark_utils.FuzzedParameter(
name=f"k{i}",... | 3,417 | 32.509804 | 96 | py |
pytorch | pytorch-main/torch/utils/benchmark/examples/sparse/compare.py | """Example of Timer and Compare APIs:
$ python -m examples.sparse.compare
"""
import pickle
import sys
import time
import torch
import torch.utils.benchmark as benchmark_utils
class FauxTorch:
"""Emulate different versions of pytorch.
In normal circumstances this would be done with multiple processes
... | 3,873 | 29.503937 | 98 | py |
pytorch | pytorch-main/torch/utils/benchmark/op_fuzzers/sparse_binary.py | import numpy as np
import torch
from torch.utils.benchmark import Fuzzer, FuzzedParameter, ParameterAlias, FuzzedSparseTensor
_MIN_DIM_SIZE = 16
_MAX_DIM_SIZE = 16 * 1024 ** 2
_POW_TWO_SIZES = tuple(2 ** i for i in range(
int(np.log2(_MIN_DIM_SIZE)),
int(np.log2(_MAX_DIM_SIZE)) + 1,
))
class BinaryOpSparse... | 4,191 | 38.17757 | 107 | py |
pytorch | pytorch-main/torch/utils/benchmark/op_fuzzers/sparse_unary.py |
import numpy as np
import torch
from torch.utils.benchmark import Fuzzer, FuzzedParameter, ParameterAlias, FuzzedSparseTensor
_MIN_DIM_SIZE = 16
_MAX_DIM_SIZE = 16 * 1024 ** 2
_POW_TWO_SIZES = tuple(2 ** i for i in range(
int(np.log2(_MIN_DIM_SIZE)),
int(np.log2(_MAX_DIM_SIZE)) + 1,
))
class UnaryOpSparseFu... | 3,219 | 37.795181 | 107 | py |
pytorch | pytorch-main/torch/utils/benchmark/op_fuzzers/binary.py | import numpy as np
import torch
from torch.utils.benchmark import Fuzzer, FuzzedParameter, ParameterAlias, FuzzedTensor
_MIN_DIM_SIZE = 16
_MAX_DIM_SIZE = 16 * 1024 ** 2
_POW_TWO_SIZES = tuple(2 ** i for i in range(
int(np.log2(_MIN_DIM_SIZE)),
int(np.log2(_MAX_DIM_SIZE)) + 1,
))
class BinaryOpFuzzer(Fuzze... | 4,109 | 37.411215 | 107 | py |
pytorch | pytorch-main/torch/utils/benchmark/op_fuzzers/unary.py | import numpy as np
import torch
from torch.utils.benchmark import Fuzzer, FuzzedParameter, ParameterAlias, FuzzedTensor
_MIN_DIM_SIZE = 16
_MAX_DIM_SIZE = 16 * 1024 ** 2
_POW_TWO_SIZES = tuple(2 ** i for i in range(
int(np.log2(_MIN_DIM_SIZE)),
int(np.log2(_MAX_DIM_SIZE)) + 1,
))
class UnaryOpFuzzer(Fuzzer... | 3,119 | 37.04878 | 107 | py |
pytorch | pytorch-main/torch/utils/benchmark/op_fuzzers/spectral.py | import math
import torch
from torch.utils import benchmark
from torch.utils.benchmark import FuzzedParameter, FuzzedTensor, ParameterAlias
__all__ = ['SpectralOpFuzzer']
MIN_DIM_SIZE = 16
MAX_DIM_SIZE = 16 * 1024
def power_range(upper_bound, base):
return (base ** i for i in range(int(math.log(upper_bound, bas... | 3,597 | 37.276596 | 94 | py |
pytorch | pytorch-main/torch/utils/benchmark/utils/timer.py | """Timer class based on the timeit.Timer class, but torch aware."""
import enum
import timeit
import textwrap
from typing import overload, Any, Callable, Dict, List, NoReturn, Optional, Tuple, Type, Union
import torch
from torch.utils.benchmark.utils import common, cpp_jit
from torch.utils.benchmark.utils._stubs impor... | 19,513 | 38.263581 | 100 | py |
pytorch | pytorch-main/torch/utils/benchmark/utils/sparse_fuzzer.py | from typing import Optional, Tuple, Union
from numbers import Number
import torch
from torch.utils.benchmark import FuzzedTensor
import math
class FuzzedSparseTensor(FuzzedTensor):
def __init__(
self,
name: str,
size: Tuple[Union[str, int], ...],
min_elements: Optional[int] = None,
... | 5,165 | 41.694215 | 114 | py |
pytorch | pytorch-main/torch/utils/benchmark/utils/fuzzer.py | import functools
import itertools as it
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np
import torch
__all__ = [
"Fuzzer",
"FuzzedParameter", "ParameterAlias",
"FuzzedTensor",
]
_DISTRIBUTIONS = (
"loguniform",
"uniform",
)
class FuzzedParameter:
""... | 18,218 | 38.779476 | 100 | py |
pytorch | pytorch-main/torch/utils/benchmark/utils/cpp_jit.py | """JIT C++ strings into executables."""
import atexit
import os
import re
import shutil
import textwrap
import threading
from typing import Any, List, Optional
import torch
from torch.utils.benchmark.utils._stubs import CallgrindModuleType, TimeitModuleType
from torch.utils.benchmark.utils.common import _make_temp_dir... | 6,824 | 38.450867 | 111 | py |
pytorch | pytorch-main/torch/utils/benchmark/utils/compile.py | import torch
__all__ = ["bench_all", "benchmark_compile"]
import torch._dynamo
from torch._dynamo.testing import CompileCounterWithBackend
from torch.utils.benchmark import Timer
from typing import Optional, List, Callable, Union, Any, cast
_warned_tensor_cores = False
_default_float_32_precision = torch.get_float3... | 7,523 | 39.021277 | 116 | py |
pytorch | pytorch-main/torch/utils/benchmark/utils/compare.py | """Display class to aggregate and print the results of many measurements."""
import collections
import enum
import itertools as it
from typing import DefaultDict, List, Optional, Tuple
from torch.utils.benchmark.utils import common
from torch import tensor as _tensor
__all__ = ["Colorize", "Compare"]
BEST = "\033[92... | 12,321 | 37.386293 | 119 | py |
pytorch | pytorch-main/torch/utils/benchmark/utils/common.py | """Base shared classes and utilities."""
import collections
import contextlib
import dataclasses
import os
import shutil
import tempfile
import textwrap
import time
from typing import cast, Any, DefaultDict, Dict, Iterable, Iterator, List, Optional, Tuple
import uuid
import torch
__all__ = ["TaskSpec", "Measurement... | 13,666 | 37.390449 | 169 | py |
pytorch | pytorch-main/torch/utils/benchmark/utils/_stubs.py | from typing import Any, Callable, Dict, Protocol, runtime_checkable
class TimerClass(Protocol):
"""This is the portion of the `timeit.Timer` API used by benchmark utils."""
def __init__(
self,
stmt: str,
setup: str,
timer: Callable[[], float],
globals: Dict[str, Any],
... | 976 | 22.829268 | 80 | py |
pytorch | pytorch-main/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py | """Intermediate layer between `Timer` and `valgrind`."""
import collections
import enum
import dataclasses
import itertools as it
import os
import pickle
import re
import shutil
import subprocess
import sys
import textwrap
from typing import (
cast, Any, Callable, DefaultDict, Dict, Generator, List, NamedTuple,
... | 36,792 | 39.745293 | 126 | py |
pytorch | pytorch-main/torch/utils/data/sampler.py | import torch
from torch import Tensor
from typing import Iterator, Iterable, Optional, Sequence, List, TypeVar, Generic, Sized, Union
__all__ = [
"BatchSampler",
"RandomSampler",
"Sampler",
"SequentialSampler",
"SubsetRandomSampler",
"WeightedRandomSampler",
]
T_co = TypeVar('T_co', covariant... | 12,801 | 40.564935 | 120 | py |
pytorch | pytorch-main/torch/utils/data/graph_settings.py | import inspect
import warnings
from typing import Any, List, Optional, Set
import torch
from torch.utils.data.datapipes.iter.sharding import (
_ShardingIterDataPipe,
SHARDING_PRIORITIES,
)
from torch.utils.data.graph import DataPipe, DataPipeGraph, traverse_dps
__all__ = [
"apply_random_seed",
"appl... | 5,463 | 33.802548 | 124 | py |
pytorch | pytorch-main/torch/utils/data/dataloader.py | r"""Definition of the DataLoader and associated iterators that subclass _BaseDataLoaderIter
To support these two classes, in `./_utils` we define many utility methods and
functions to be run in multiprocessing. E.g., the data loading worker loop is
in `./_utils/worker.py`.
"""
import functools
import itertools
import... | 74,325 | 49.152497 | 131 | py |
pytorch | pytorch-main/torch/utils/data/dataset.py | import bisect
import warnings
import math
from typing import (
Generic,
Iterable,
Iterator,
List,
Optional,
Sequence,
Tuple,
TypeVar,
Union,
Dict
)
# No 'default_generator' in torch/__init__.pyi
from torch import default_generator, randperm
from torch._utils import _accumulate
... | 17,140 | 39.522459 | 120 | py |
pytorch | pytorch-main/torch/utils/data/graph.py | import io
import pickle
import warnings
from collections.abc import Collection
from typing import Dict, List, Optional, Set, Tuple, Type, Union
from torch.utils.data import IterDataPipe, MapDataPipe
from torch.utils.data._utils.serialization import DILL_AVAILABLE
__all__ = ["traverse", "traverse_dps"]
DataPipe = U... | 5,810 | 38.530612 | 117 | py |
pytorch | pytorch-main/torch/utils/data/distributed.py | import math
from typing import TypeVar, Optional, Iterator
import torch
from . import Sampler, Dataset
import torch.distributed as dist
__all__ = ["DistributedSampler", ]
T_co = TypeVar('T_co', covariant=True)
class DistributedSampler(Sampler[T_co]):
r"""Sampler that restricts data loading to a subset of the d... | 5,971 | 42.591241 | 105 | py |
pytorch | pytorch-main/torch/utils/data/__init__.py | # TODO(VitalyFedyunin): Rearranging this imports leads to crash,
# need to cleanup dependencies and fix it
from torch.utils.data.sampler import (
BatchSampler,
RandomSampler,
Sampler,
SequentialSampler,
SubsetRandomSampler,
WeightedRandomSampler,
)
from torch.utils.data.dataset import (
Chai... | 1,956 | 24.415584 | 64 | py |
pytorch | pytorch-main/torch/utils/data/_utils/collate.py | r""""Contains definitions of the methods used by the _BaseDataLoaderIter workers to
collate samples fetched from dataset into Tensor(s).
These **needs** to be in global scope since Py2 doesn't support serializing
static methods.
`default_collate` and `default_convert` are exposed to users via 'dataloader.py'.
"""
im... | 12,595 | 46.353383 | 122 | py |
pytorch | pytorch-main/torch/utils/data/_utils/__init__.py | r"""Utility classes & functions for data loading. Code in this folder is mostly
used by ../dataloder.py.
A lot of multiprocessing is used in data loading, which only supports running
functions defined in global environment (py2 can't serialize static methods).
Therefore, for code tidiness we put these functions into d... | 1,596 | 29.132075 | 117 | py |
pytorch | pytorch-main/torch/utils/data/_utils/pin_memory.py | r""""Contains definitions of the methods used by the _BaseDataLoaderIter to put
fetched tensors into pinned memory.
These **needs** to be in global scope since Py2 doesn't support serializing
static methods.
"""
import collections
import queue
import torch
from . import MP_STATUS_CHECK_INTERVAL
from torch._utils imp... | 3,134 | 37.703704 | 118 | py |
pytorch | pytorch-main/torch/utils/data/_utils/signal_handling.py | r""""Signal handling for multiprocessing data loading.
NOTE [ Signal handling in multiprocessing data loading ]
In cases like DataLoader, if a worker process dies due to bus error/segfault
or just hang, the main process will hang waiting for data. This is difficult
to avoid on PyTorch side as it can be caused by limi... | 3,156 | 42.246575 | 103 | py |
pytorch | pytorch-main/torch/utils/data/_utils/worker.py | r""""Contains definitions of the methods used by the _BaseDataLoaderIter workers.
These **needs** to be in global scope since Py2 doesn't support serializing
static methods.
"""
import torch
import random
import os
import queue
from dataclasses import dataclass
from torch._utils import ExceptionWrapper
from typing im... | 13,425 | 39.684848 | 111 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/_hook_iterator.py | import inspect
import functools
from enum import Enum
import torch.autograd
class _SnapshotState(Enum):
r"""
These are the snapshotting-related states that IterDataPipes can be in.
`NotStarted` - allows you to restore a snapshot and create an iterator with reset
`Restored` - cannot restore again, all... | 11,643 | 45.390438 | 116 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/_decorator.py | import inspect
from functools import wraps
from typing import Any, Callable, Optional, Type, Union, get_type_hints
from torch.utils.data.datapipes.datapipe import IterDataPipe, MapDataPipe
from torch.utils.data.datapipes._typing import _DataPipeMeta
######################################################
# Functional ... | 7,646 | 39.893048 | 120 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/_typing.py | # Taking reference from official Python typing
# https://github.com/python/cpython/blob/master/Lib/typing.py
import collections
import functools
import numbers
import sys
from torch.utils.data.datapipes._hook_iterator import hook_iterator, _SnapshotState
from typing import (Any, Dict, Iterator, Generic, List, Set, Tu... | 15,907 | 36.081585 | 129 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/datapipe.py | import functools
import pickle
from typing import Dict, Callable, Optional, TypeVar, Generic, Iterator
from torch.utils.data.datapipes._typing import _DataPipeMeta, _IterDataPipeMeta
from torch.utils.data.datapipes._hook_iterator import _SnapshotState
from torch.utils.data.datapipes.utils.common import (
_deprecat... | 17,031 | 41.263027 | 118 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/dataframe/datapipes.py | import random
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.datapipe import DFIterDataPipe, IterDataPipe
from torch.utils.data.datapipes.dataframe import dataframe_wrapper as df_wrapper
__all__ = [
"ConcatDataFramesPipe",
"DataFramesAsTuplesPipe",
"... | 4,435 | 32.606061 | 109 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/dataframe/structures.py | from torch.utils.data.datapipes.datapipe import DataChunk
from torch.utils.data.datapipes.dataframe import dataframe_wrapper as df_wrapper
__all__ = ["DataChunkDF", ]
class DataChunkDF(DataChunk):
"""
DataChunkDF iterating over individual items inside of DataFrame containers,
to access DataFrames... | 599 | 26.272727 | 83 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/dataframe/__init__.py | from torch.utils.data.datapipes.dataframe.dataframes import (
CaptureDataFrame, DFIterDataPipe,
)
from torch.utils.data.datapipes.dataframe.datapipes import (
DataFramesAsTuplesPipe,
)
__all__ = ['CaptureDataFrame', 'DFIterDataPipe', 'DataFramesAsTuplesPipe']
# Please keep this list sorted
assert __all__ == s... | 335 | 27 | 74 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/dataframe/dataframes.py | from typing import Any, Dict, List
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.datapipe import DFIterDataPipe, IterDataPipe
from torch.utils.data.datapipes.dataframe.structures import DataChunkDF
# TODO(VitalyFedyunin): Add error when two different traces get... | 13,434 | 29.956221 | 115 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/iter/routeddecoder.py | from io import BufferedIOBase
from typing import Any, Callable, Iterable, Iterator, Sized, Tuple
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.datapipe import IterDataPipe
from torch.utils.data.datapipes.utils.common import _deprecation_warning
from torch.utils.d... | 2,730 | 40.378788 | 94 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/iter/selecting.py | from typing import Callable, Iterator, Tuple, TypeVar
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.datapipe import IterDataPipe
from torch.utils.data.datapipes.dataframe import dataframe_wrapper as df_wrapper
from torch.utils.data.datapipes.utils.common import (... | 3,208 | 32.427083 | 118 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/iter/filelister.py | from typing import Iterator, List, Sequence, Union
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.datapipe import IterDataPipe
from torch.utils.data.datapipes.iter import IterableWrapper
from torch.utils.data.datapipes.utils.common import get_file_pathnames_from... | 2,520 | 37.19697 | 123 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/iter/utils.py | import copy
import warnings
from torch.utils.data.datapipes.datapipe import IterDataPipe
__all__ = ["IterableWrapperIterDataPipe", ]
class IterableWrapperIterDataPipe(IterDataPipe):
r"""
Wraps an iterable object to create an IterDataPipe.
Args:
iterable: Iterable object to be wrapped into an Ite... | 1,781 | 33.941176 | 88 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/iter/fileopener.py | from io import IOBase
from typing import Iterable, Tuple, Optional
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.datapipe import IterDataPipe
from torch.utils.data.datapipes.utils.common import get_file_binaries_from_pathnames
__all__ = [
"FileOpenerIterData... | 2,787 | 37.191781 | 96 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/iter/sharding.py | from typing import (
Dict,
Sized,
Tuple,
)
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.datapipe import IterDataPipe
from enum import IntEnum
__all__ = [
"SHARDING_PRIORITIES",
"ShardingFilterIterDataPipe",
]
class SHARDING_PRIORITIES(IntE... | 3,286 | 38.130952 | 119 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/iter/streamreader.py | from typing import Tuple
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.datapipe import IterDataPipe
__all__ = ["StreamReaderIterDataPipe", ]
@functional_datapipe('read_from_stream')
class StreamReaderIterDataPipe(IterDataPipe[Tuple[str, bytes]]):
r"""
G... | 1,402 | 34.974359 | 103 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/iter/grouping.py | import warnings
from collections import defaultdict
from typing import Any, Callable, DefaultDict, Iterator, List, Optional, Sized, TypeVar
import torch.utils.data.datapipes.iter.sharding
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.datapipe import DataChunk, I... | 12,269 | 40.452703 | 124 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/iter/__init__.py | from torch.utils.data.datapipes.iter.utils import (
IterableWrapperIterDataPipe as IterableWrapper,
)
from torch.utils.data.datapipes.iter.callable import (
CollatorIterDataPipe as Collator,
MapperIterDataPipe as Mapper,
)
from torch.utils.data.datapipes.iter.combinatorics import (
SamplerIterDataPipe a... | 1,942 | 28.892308 | 59 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/iter/combinatorics.py | import random
import torch
from torch.utils.data import Sampler, SequentialSampler
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.datapipe import IterDataPipe
from typing import Dict, Iterator, List, Optional, Sized, Tuple, Type, TypeVar
__all__ = [
"SamplerI... | 6,496 | 34.895028 | 122 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/iter/callable.py | import functools
from collections import namedtuple
from typing import Callable, Iterator, Sized, TypeVar, Optional, Union, Any, Dict, List
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data._utils.collate import default_collate
from torch.utils.data.datapipes.dataframe import... | 9,041 | 37.476596 | 112 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/iter/combining.py | import warnings
from abc import ABC, abstractmethod
from collections import deque
import copy as copymodule
from typing import Any, Callable, Iterator, List, Literal, Optional, Sized, Tuple, TypeVar, Deque
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes._hook_iter... | 26,513 | 41.019017 | 123 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/utils/snapshot.py | from torch.utils.data.datapipes._hook_iterator import _SnapshotState
from torch.utils.data.datapipes.datapipe import IterDataPipe
from torch.utils.data.graph_settings import apply_random_seed
# TODO: Caveats
# 1. Caller (either the ReadingService or DataLoader) must pass in the initial RNG
# 2. `in_batch_shuffle`... | 3,137 | 52.186441 | 113 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/utils/common.py | import fnmatch
import functools
import inspect
import os
import warnings
from io import IOBase
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
from torch.utils.data._utils.serialization import DILL_AVAILABLE
__all__ = [
"validate_input_col",
"Str... | 13,463 | 33.880829 | 118 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/utils/decoder.py | # This file takes partial of the implementation from NVIDIA's webdataset at here:
# https://github.com/tmbdev/webdataset/blob/master/webdataset/autodecode.py
import io
import json
import os.path
import pickle
import tempfile
import torch
from torch.utils.data.datapipes.utils.common import StreamWrapper
__all__ = [
... | 11,023 | 32.609756 | 122 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/map/utils.py | import copy
import warnings
from torch.utils.data.datapipes.datapipe import MapDataPipe
__all__ = ["SequenceWrapperMapDataPipe", ]
class SequenceWrapperMapDataPipe(MapDataPipe):
r"""
Wraps a sequence object into a MapDataPipe.
Args:
sequence: Sequence object to be wrapped into an MapDataPipe
... | 1,547 | 29.96 | 87 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/map/grouping.py | from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.datapipe import MapDataPipe, DataChunk
from typing import List, Sized, TypeVar
__all__ = ["BatcherMapDataPipe", ]
T = TypeVar('T')
@functional_datapipe('batch')
class BatcherMapDataPipe(MapDataPipe[DataChunk]):
... | 2,449 | 35.029412 | 97 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/map/__init__.py | # Functional DataPipe
from torch.utils.data.datapipes.map.callable import MapperMapDataPipe as Mapper
from torch.utils.data.datapipes.map.combinatorics import ShufflerIterDataPipe as Shuffler
from torch.utils.data.datapipes.map.combining import (
ConcaterMapDataPipe as Concater,
ZipperMapDataPipe as Zipper
)
fr... | 656 | 35.5 | 94 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/map/combinatorics.py | import random
import torch
from torch.utils.data.datapipes.datapipe import IterDataPipe, MapDataPipe
from typing import Iterator, List, Optional, TypeVar
__all__ = ["ShufflerIterDataPipe", ]
T_co = TypeVar('T_co', covariant=True)
# @functional_datapipe('shuffle')
class ShufflerIterDataPipe(IterDataPipe[T_co]):
... | 4,168 | 32.087302 | 106 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/map/callable.py | from torch.utils.data.datapipes.utils.common import _check_unpickable_fn
from typing import Callable, TypeVar
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.datapipe import MapDataPipe
__all__ = ["MapperMapDataPipe", "default_fn"]
T_co = TypeVar('T_co', covariant... | 1,824 | 29.416667 | 96 | py |
pytorch | pytorch-main/torch/utils/data/datapipes/map/combining.py | from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.datapipe import MapDataPipe
from typing import Sized, Tuple, TypeVar
__all__ = ["ConcaterMapDataPipe", "ZipperMapDataPipe"]
T_co = TypeVar('T_co', covariant=True)
@functional_datapipe('concat')
class ConcaterMapDat... | 3,609 | 36.604167 | 113 | py |
pytorch | pytorch-main/torch/contrib/_tensorboard_vis.py | import time
from collections import defaultdict
from functools import partial
from typing import DefaultDict
import torch
# Unfortunately it doesn't seem as if there was any way to get TensorBoard to do
# anything without having TF installed, and so this file has a hard dependency on it
# as well. It really is a deb... | 5,925 | 40.440559 | 94 | py |
pytorch | pytorch-main/torch/profiler/_pattern_matcher.py | import json
import math
import os
import re
from typing import Dict, List, Optional, Set
import torch
from torch.profiler import profile
import torch.utils.benchmark as benchmark
from torch.profiler._utils import index_of_first_match, traverse_bfs, traverse_dfs
from torch._C._profiler import (_ProfilerEvent, _ExtraFie... | 25,213 | 37.377473 | 129 | py |
pytorch | pytorch-main/torch/profiler/_utils.py | from collections import deque
from dataclasses import dataclass
import functools
import re
from typing import Dict, List
from torch.profiler import DeviceType
from torch.autograd.profiler import profile
from torch.autograd import _KinetoEvent
def _traverse(tree, next_fn, children_fn=lambda x: x.children, reverse: bo... | 13,319 | 36.206704 | 91 | py |
pytorch | pytorch-main/torch/profiler/_memory_profiler.py | import collections
import dataclasses
import enum
import itertools as it
import logging
from typing import (
Any,
cast,
DefaultDict,
Dict,
Iterator,
List,
Optional,
Set,
Tuple,
Union,
)
import torch
from torch._C import FunctionSchema
from torch._C._autograd import _ProfilerResu... | 46,578 | 39.858772 | 112 | py |
pytorch | pytorch-main/torch/profiler/python_tracer.py | import os
import site
import sys
import typing
import torch
def _prefix_regex() -> typing.List[str]:
raw_paths = (
site.getsitepackages() +
sys.path +
[site.getuserbase()] +
[site.getusersitepackages()] +
[os.path.dirname(os.path.dirname(torch.__file__))]
)
path_p... | 497 | 22.714286 | 81 | py |
pytorch | pytorch-main/torch/profiler/itt.py | from contextlib import contextmanager
try:
from torch._C import _itt
except ImportError:
class _ITTStub:
@staticmethod
def _fail(*args, **kwargs):
raise RuntimeError("ITT functions not installed. Are you sure you have a ITT build?")
@staticmethod
def is_available():... | 1,723 | 21.684211 | 97 | py |
pytorch | pytorch-main/torch/profiler/__init__.py | r"""
PyTorch Profiler is a tool that allows the collection of performance metrics during training and inference.
Profiler's context manager API can be used to better understand what model operators are the most expensive,
examine their input shapes and stack traces, study device kernel activity and visualize the execut... | 1,432 | 29.489362 | 110 | py |
pytorch | pytorch-main/torch/profiler/profiler.py | import gzip
import json
import os
import tempfile
from enum import Enum
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
from warnings import warn
import torch
import torch.autograd.profiler as prof
from torch._C._profiler import (
_add_execution_trace_observer,... | 28,334 | 39.887446 | 126 | py |
pytorch | pytorch-main/torch/quantization/fake_quantize.py | # flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
`torch/ao/quantization/fake_quantize.py`, while adding an import statement
here.
""... | 1,015 | 29.787879 | 74 | py |
pytorch | pytorch-main/torch/quantization/fuse_modules.py | # flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
`torch/ao/quantization/fuse_modules.py`, while adding an import statement
here.
"""... | 913 | 35.56 | 73 | py |
pytorch | pytorch-main/torch/quantization/quantize_jit.py | # flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
`torch/ao/quantization/quantize_jit.py`, while adding an import statement
here.
"""... | 713 | 25.444444 | 73 | py |
pytorch | pytorch-main/torch/quantization/quant_type.py | # flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
`torch/ao/quantization/quant_type.py`, while adding an import statement
here.
"""
... | 443 | 36 | 72 | py |
pytorch | pytorch-main/torch/quantization/stubs.py | # flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
`torch/ao/quantization/stubs.py`, while adding an import statement
here.
"""
from ... | 408 | 26.266667 | 72 | py |
pytorch | pytorch-main/torch/quantization/utils.py | # flake8: noqa: F401
r"""
Utils shared by different modes of quantization (eager/graph)
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
`torch/ao/quantizati... | 833 | 26.8 | 72 | py |
pytorch | pytorch-main/torch/quantization/quantize_fx.py | # flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
`torch/ao/quantization/quantize_fx.py`, while adding an import statement
here.
"""
... | 746 | 23.9 | 72 | py |
pytorch | pytorch-main/torch/quantization/qconfig.py | # flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
`torch/ao/quantization/qconfig.py`, while adding an import statement
here.
"""
from... | 909 | 28.354839 | 72 | py |
pytorch | pytorch-main/torch/quantization/_numeric_suite.py | # flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
`torch/ao/ns/_numeric_suite.py`, while adding an import statement
here.
"""
from t... | 779 | 25.896552 | 72 | py |
pytorch | pytorch-main/torch/quantization/quantization_mappings.py | # flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
`torch/ao/quantization/quantization_mappings.py`, while adding an import statement
... | 1,147 | 37.266667 | 82 | py |
pytorch | pytorch-main/torch/quantization/_numeric_suite_fx.py | # flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
`torch/ao/ns/_numeric_suite_fx.py`, while adding an import statement
here.
"""
fro... | 752 | 26.888889 | 72 | py |
pytorch | pytorch-main/torch/quantization/__init__.py | from .quantize import * # noqa: F403
from .observer import * # noqa: F403
from .qconfig import * # noqa: F403
from .fake_quantize import * # noqa: F403
from .fuse_modules import fuse_modules
from .stubs import * # noqa: F403
from .quant_type import * # noqa: F403
from .quantize_jit import * # noqa: F403
# from .... | 2,565 | 39.730159 | 87 | py |
pytorch | pytorch-main/torch/quantization/quantize.py | # flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
`torch/ao/quantization/quantize.py`, while adding an import statement
here.
"""
fr... | 1,479 | 50.034483 | 81 | py |
pytorch | pytorch-main/torch/quantization/fuser_method_mappings.py | # flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
`torch/ao/quantization/fuser_method_mappings.py`, while adding an import statement
... | 511 | 31 | 82 | py |
pytorch | pytorch-main/torch/quantization/observer.py | # flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
`torch/ao/quantization/observer.py`, while adding an import statement
here.
"""
fro... | 1,078 | 28.162162 | 72 | py |
pytorch | pytorch-main/torch/quantization/fx/_equalize.py | # flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
appropriate files under `torch/ao/quantization/fx/`, while adding an import stateme... | 1,250 | 31.076923 | 85 | py |
pytorch | pytorch-main/torch/quantization/fx/fusion_patterns.py | # flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
appropriate files under `torch/ao/quantization/fx/`, while adding an import stateme... | 428 | 32 | 85 | py |
pytorch | pytorch-main/torch/quantization/fx/pattern_utils.py | # flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
appropriate files under `torch/ao/quantization/fx/`, while adding an import stateme... | 1,288 | 38.060606 | 100 | py |
pytorch | pytorch-main/torch/quantization/fx/utils.py | # flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
appropriate files under `torch/ao/quantization/fx/`, while adding an import stateme... | 722 | 33.428571 | 85 | py |
pytorch | pytorch-main/torch/quantization/fx/graph_module.py | # flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
appropriate files under `torch/ao/quantization/fx/`, while adding an import stateme... | 572 | 30.833333 | 85 | py |
pytorch | pytorch-main/torch/quantization/fx/fuse.py | # flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
appropriate files under `torch/ao/quantization/fx/`, while adding an import stateme... | 380 | 37.1 | 85 | py |
pytorch | pytorch-main/torch/quantization/fx/prepare.py | # flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
appropriate files under `torch/ao/quantization/fx/`, while adding an import stateme... | 394 | 31.916667 | 85 | py |
pytorch | pytorch-main/torch/quantization/fx/quantization_patterns.py | # flake8: noqa: F401
r"""
This file is in the process of migration to `torch/ao/quantization`, and
is kept here for compatibility while the migration process is ongoing.
If you are adding a new entry/functionality, please, add it to the
appropriate files under `torch/ao/quantization/fx/`, while adding an import stateme... | 2,053 | 50.35 | 97 | py |
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