python_code stringlengths 0 1.02M | repo_name stringlengths 9 48 | file_path stringlengths 5 114 |
|---|---|---|
# Copyright 2019 Kakao Brain
#
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
"""Utilities for eliminating boilerplate code to handle abstract streams with
CPU device.
"... | pytorch-master | torch/distributed/pipeline/sync/stream.py |
from torch import nn
from typing import List
def partition_model(
module: nn.Sequential,
balance: List[int],
devices: List[int] = None):
"""
Given an :class:`nn.Sequential <torch.nn.Sequential>` module, partitions
the model across multiple GPU devices according the provided ``balanc... | pytorch-master | torch/distributed/pipeline/sync/utils.py |
# -*- coding: utf-8 -*-
# Copyright 2019 Kakao Brain
#
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
"""The pipeline parallelism of Pipe."""
from queue import Queue
fro... | pytorch-master | torch/distributed/pipeline/sync/pipeline.py |
# Copyright 2019 Kakao Brain
#
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
"""Manipulation of micro-batches."""
import typing
from typing import Any, Callable, List, ... | pytorch-master | torch/distributed/pipeline/sync/microbatch.py |
# Copyright 2019 Kakao Brain
#
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
"""Arbitrary dependency between two autograd lanes."""
from typing import List, Tuple
impo... | pytorch-master | torch/distributed/pipeline/sync/dependency.py |
# Copyright 2019 Kakao Brain
#
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
"""The Pipe interface."""
from collections import OrderedDict
from typing import TYPE_CHECK... | pytorch-master | torch/distributed/pipeline/sync/pipe.py |
# -*- coding: utf-8 -*-
# Copyright 2019 Kakao Brain
#
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
"""Portal keeps a tensor in the pocket plane. The tensor becomes hi... | pytorch-master | torch/distributed/pipeline/sync/skip/portal.py |
# Copyright 2019 Kakao Brain
#
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
"""Static skip connection layout of ``@skippable`` modules."""
from typing import Dict, Ite... | pytorch-master | torch/distributed/pipeline/sync/skip/layout.py |
# Copyright 2019 Kakao Brain
#
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
"""Supports efficiency with skip connections."""
from .namespace import Namespace
from .ski... | pytorch-master | torch/distributed/pipeline/sync/skip/__init__.py |
# Copyright 2019 Kakao Brain
#
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
"""Tracks skip tensors on a thread."""
from contextlib import contextmanager
import threadi... | pytorch-master | torch/distributed/pipeline/sync/skip/tracker.py |
# Copyright 2019 Kakao Brain
#
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
"""Provides isolated namespace of skip tensors."""
import abc
from functools import total_o... | pytorch-master | torch/distributed/pipeline/sync/skip/namespace.py |
# -*- coding: utf-8 -*-
# Copyright 2019 Kakao Brain
#
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
"""The user interface to define skip connections."""
from typing im... | pytorch-master | torch/distributed/pipeline/sync/skip/skippable.py |
# Copyright 2019 Kakao Brain
#
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
"""Per-layer profilers."""
import copy
import time
from typing import Any, Generator, List,... | pytorch-master | torch/distributed/pipeline/sync/_balance/profile.py |
# Copyright 2019 Kakao Brain
#
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
"""A helper to roughly balance a sequential module.
Usage::
import torch
from tor... | pytorch-master | torch/distributed/pipeline/sync/_balance/__init__.py |
# -*- coding: utf-8 -*-
# Copyright 2019 Kakao Brain
#
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
"""Implements "Block Partitions of Sequences" by Imre Bárány et al.... | pytorch-master | torch/distributed/pipeline/sync/_balance/blockpartition.py |
#!/usr/bin/env/python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
from torch.distributed.launcher.api import ( # noqa: F401
LaunchConfig,
elastic... | pytorch-master | torch/distributed/launcher/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import sys
import uuid
from dataclasses import dataclass, field
from typing import Any, Ca... | pytorch-master | torch/distributed/launcher/api.py |
# Keep old package for BC purposes, this file should be removed once
# everything moves to the `torch.distributed._shard` package.
import sys
import torch
import warnings
from torch.distributed._shard.sharded_tensor import * # noqa: F403
warnings.warn(
"torch.distributed._sharded_tensor will be deprecated, use to... | pytorch-master | torch/distributed/_sharded_tensor/__init__.py |
import torch
if torch.distributed.rpc.is_available():
from .api.remote_module import RemoteModule
from .functional import * # noqa: F403
| pytorch-master | torch/distributed/nn/__init__.py |
import torch
import torch.distributed as dist
from torch.autograd import Function
# The two imports below are not always available depending on the
# USE_DISTRIBUTED compile flag. Make sure they raise import error
# if we're trying to use them.
from torch.distributed import group, ReduceOp
def broadcast(tensor, src, g... | pytorch-master | torch/distributed/nn/functional.py |
#!/usr/bin/python3
import importlib
import logging
import os
import sys
import tempfile
from typing import Optional
import torch
from torch.distributed.nn.jit.templates.remote_module_template import (
get_remote_module_template,
)
logger = logging.getLogger(__name__)
_FILE_PREFIX = "_remote_module_"
_TEMP_DIR ... | pytorch-master | torch/distributed/nn/jit/instantiator.py |
pytorch-master | torch/distributed/nn/jit/__init__.py | |
pytorch-master | torch/distributed/nn/jit/templates/__init__.py | |
#!/usr/bin/python3
def get_remote_module_template(enable_moving_cpu_tensors_to_cuda: bool):
return _TEMPLATE_PREFIX + (
_REMOTE_FORWARD_TEMPLATE_ENABLE_MOVING_CPU_TENSORS_TO_CUDA
if enable_moving_cpu_tensors_to_cuda
else _REMOTE_FORWARD_TEMPLATE
)
_TEMPLATE_PREFIX = """from typing im... | pytorch-master | torch/distributed/nn/jit/templates/remote_module_template.py |
#!/usr/bin/python3
import collections
import io
import sys
import types
from typing import (
Any,
Callable,
Dict,
Iterator,
List,
Mapping,
Optional,
Set,
Tuple,
Type,
TypeVar,
Union,
)
import torch
import torch.distributed.rpc as rpc
from torch import Tensor, device, dty... | pytorch-master | torch/distributed/nn/api/remote_module.py |
pytorch-master | torch/distributed/nn/api/__init__.py | |
#!/usr/bin/env/python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
"""
Torchelastic agent and user worker failover contract:
**TL;DR;**:
* TE(torchelasti... | pytorch-master | torch/distributed/elastic/__init__.py |
#!/usr/bin/env/python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
"""Metrics API
**Overview**:
The metrics API in torchelastic is used to publish telemet... | pytorch-master | torch/distributed/elastic/metrics/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import abc
import time
import warnings
from collections import namedtuple
from functools ... | pytorch-master | torch/distributed/elastic/metrics/api.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import datetime
import random
import time
from base64 import b64decode, b64encode
from typing import Optional
im... | pytorch-master | torch/distributed/elastic/rendezvous/etcd_store.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
from .api import RendezvousHandler, RendezvousParameters
from .api import rendezvous_handler_registry as handler_... | pytorch-master | torch/distributed/elastic/rendezvous/registry.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
"""
In the context of Torch Distributed Elastic we use the term *rendezvous* to
refer to a particular functionali... | pytorch-master | torch/distributed/elastic/rendezvous/__init__.py |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import json
import logging
import sys
import threading
import tim... | pytorch-master | torch/distributed/elastic/rendezvous/etcd_rendezvous.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import binascii
import logging
import os
import tempfile
from base64 import b64decode, b64encode
from datetime im... | pytorch-master | torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
from abc import ABC, abstractmethod
from typing import Any, Callable, Dict, Optional, Tuple
from torch.distribut... | pytorch-master | torch/distributed/elastic/rendezvous/api.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import binascii
from base64 import b64decode, b64encode
from typing import Optional, Tuple, cast
import urllib3.... | pytorch-master | torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import ipaddress
import random
import re
import socket
import time
import weakref
from datetime import timedelta
... | pytorch-master | torch/distributed/elastic/rendezvous/utils.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import inspect
import logging
import os
import pickle
import socket
import threading
import time
import weakref
f... | pytorch-master | torch/distributed/elastic/rendezvous/dynamic_rendezvous.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import atexit
import logging
import os
import shlex
import shutil
import socket
import sub... | pytorch-master | torch/distributed/elastic/rendezvous/etcd_server.py |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import datetime
import logging
from typing import Tuple, cast, Op... | pytorch-master | torch/distributed/elastic/rendezvous/static_tcp_rendezvous.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import logging
import os
import time
from concurrent.futures._base import Future
from con... | pytorch-master | torch/distributed/elastic/multiprocessing/tail_log.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
"""
Library that launches and manages ``n`` copies of worker subprocesses
either specifie... | pytorch-master | torch/distributed/elastic/multiprocessing/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import abc
import logging
import os
import re
import signal
import subprocess
import sys
... | pytorch-master | torch/distributed/elastic/multiprocessing/api.py |
# !/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
# Taken and modified from original source:
# https://eli.thegreenplace.net/2015/redirect... | pytorch-master | torch/distributed/elastic/multiprocessing/redirects.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
# Multiprocessing error-reporting module
from torch.distributed.elastic.multiprocessing.... | pytorch-master | torch/distributed/elastic/multiprocessing/errors/handlers.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
"""
Each host in a distributed PyTorch job runs with a single TorchElastic agent,
and mul... | pytorch-master | torch/distributed/elastic/multiprocessing/errors/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import faulthandler
import json
import logging
import os
import time
import traceback
impo... | pytorch-master | torch/distributed/elastic/multiprocessing/errors/error_handler.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import logging
import multiprocessing as mp
import os
import signal
import time
from queue import Empty
from typin... | pytorch-master | torch/distributed/elastic/timer/local_timer.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
"""
Expiration timers are set up on the same process as the agent and
used from your script to deal with stuck wo... | pytorch-master | torch/distributed/elastic/timer/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import abc
import logging
import threading
import time
from contextlib import contextmanager
from inspect import g... | pytorch-master | torch/distributed/elastic/timer/api.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
from datetime import timedelta
from typing import List
def get_all(store, rank: int, pr... | pytorch-master | torch/distributed/elastic/utils/store.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import inspect
import logging
import os
import warnings
from typing import Optional
from... | pytorch-master | torch/distributed/elastic/utils/logging.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
from .api import get_env_variable_or_raise, get_socket_with_port, macros # noqa: F401
| pytorch-master | torch/distributed/elastic/utils/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
def get_log_level() -> str:
"""
Return default log level for pytorch.
"""
... | pytorch-master | torch/distributed/elastic/utils/log_level.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import os
import socket
from string import Template
from typing import List, Any
def ge... | pytorch-master | torch/distributed/elastic/utils/api.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import datetime
import socket
from contextlib import closing
import torch.distributed as ... | pytorch-master | torch/distributed/elastic/utils/distributed.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
from torch.utils.data.distributed import DistributedSampler
c... | pytorch-master | torch/distributed/elastic/utils/data/elastic_distributed_sampler.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
class CyclingIterator:
"""
An iterator decorator that cycles through the
und... | pytorch-master | torch/distributed/elastic/utils/data/cycling_iterator.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
from .cycling_iterator import CyclingIterator # noqa: F401
from .elastic_distributed_sam... | pytorch-master | torch/distributed/elastic/utils/data/__init__.py |
pytorch-master | torch/distributed/elastic/agent/__init__.py | |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
"""
The elastic agent is the control plane of torchelastic. It is a process
that launches... | pytorch-master | torch/distributed/elastic/agent/server/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import abc
import functools
import json
import os
import signal
import socket
import time... | pytorch-master | torch/distributed/elastic/agent/server/api.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import os
import shutil
import signal
import tempfile
from typing import Any, Dict, Opti... | pytorch-master | torch/distributed/elastic/agent/server/local_elastic_agent.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import logging
from typing import Dict
_log_handlers: Dict[str, logging.Handler] = {
... | pytorch-master | torch/distributed/elastic/events/handlers.py |
#!/usr/bin/env/python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
"""
Module contains events processing mechanisms that are integrated with the standard py... | pytorch-master | torch/distributed/elastic/events/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import json
from dataclasses import asdict, dataclass, field
from enum import Enum
from t... | pytorch-master | torch/distributed/elastic/events/api.py |
import sys
import torch
def is_available():
return hasattr(torch._C, "_dist_autograd_init")
if is_available() and not torch._C._dist_autograd_init():
raise RuntimeError("Failed to initialize torch.distributed.autograd")
if is_available():
from torch._C._distributed_autograd import (
get_gradie... | pytorch-master | torch/distributed/autograd/__init__.py |
from .join import Join
from .join import Joinable
from .join import JoinHook
| pytorch-master | torch/distributed/algorithms/__init__.py |
import warnings
from abc import ABC, abstractmethod
from types import TracebackType
from typing import Any, List, NamedTuple, Optional, Type
import torch
import torch.distributed as dist
__all__ = ['JoinHook', 'Joinable', 'Join']
class JoinHook():
r"""
This defines a join hook, which provides two entry point... | pytorch-master | torch/distributed/algorithms/join.py |
from . import default_hooks as default
LOW_PRECISION_HOOKS = [
default.fp16_compress_hook,
default.bf16_compress_hook,
]
| pytorch-master | torch/distributed/algorithms/_comm_hooks/__init__.py |
import functools
import torch
import torch.distributed as dist
from torch.distributed import distributed_c10d
class DefaultState(object):
r"""
Stores state needed to perform the default ``all_reduce`` algorithm
within a communication hook.
Args:
process_group (ProcessGroup): The process group... | pytorch-master | torch/distributed/algorithms/_comm_hooks/default_hooks.py |
from enum import Enum, auto
from contextlib import suppress
import torch
from torch.autograd.graph import save_on_cpu
from torch.utils.checkpoint import checkpoint
from torch.distributed.utils import _replace_by_prefix
import torch.nn as nn
from typing import Any, Dict, Iterator, Tuple
from functools import partial
_... | pytorch-master | torch/distributed/algorithms/_checkpoint/checkpoint_wrapper.py |
pytorch-master | torch/distributed/algorithms/_checkpoint/__init__.py | |
pytorch-master | torch/distributed/algorithms/model_averaging/__init__.py | |
import warnings
from abc import ABC, abstractmethod
from typing import Union, Iterable, Dict
import torch
import torch.distributed as dist
import torch.distributed.algorithms.model_averaging.utils as utils
__all__ = ['ModelAverager', 'PeriodicModelAverager']
class ModelAverager(ABC):
r"""Base class for all model ... | pytorch-master | torch/distributed/algorithms/model_averaging/averagers.py |
# flake8: noqa C101
import itertools
from typing import Union, Iterable, Dict, Iterator
import torch
import torch.distributed as dist
# The two imports below are not always available depending on the
# USE_DISTRIBUTED compile flag. Make sure they raise import error
# if we're trying to use them.
from torch.distributed... | pytorch-master | torch/distributed/algorithms/model_averaging/utils.py |
# Copyright 2022 Cruise LLC
import logging
import warnings
from collections import OrderedDict
from typing import Union, Iterable, Dict
import torch
import torch.distributed as dist
import torch.distributed.algorithms.model_averaging.averagers as averagers
import torch.distributed.algorithms.model_averaging.utils as u... | pytorch-master | torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py |
import torch
import torch.distributed as dist
from torch import nn
def _quantize_per_tensor_cuda(x, scale, zero_point):
y = torch.round(x / scale) + zero_point
y = torch.clamp(y, 0, 255).to(torch.uint8)
return y
def _dequantize_per_tensor_cuda(y, scale, zero_point):
x = scale * (y.to(torch.float32) ... | pytorch-master | torch/distributed/algorithms/ddp_comm_hooks/quantization_hooks.py |
from typing import Any, Callable
import torch
import torch.distributed as dist
_FUNCTIONAL_OPTIM_STEP_METHOD_NAME = "step_param"
class _OptimizerHookState(object):
"""
Holds state for running optimizer in-line after DDP communication hook.
Currently contains only optimizer class which must have a method ... | pytorch-master | torch/distributed/algorithms/ddp_comm_hooks/optimizer_overlap_hooks.py |
import logging
import torch
import torch.distributed as dist
from . import default_hooks as default
logger = logging.getLogger(__name__)
class PostLocalSGDState(object):
r"""
Stores the state for all-reducing gradients globally using ``process_group`` until step ``start_localSGD_iter``,
and all-reducin... | pytorch-master | torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py |
from enum import Enum
from functools import partial
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel
from . import (
debugging_hooks as debugging,
default_hooks as default,
powerSGD_hook as powerSGD,
quantization_hooks as quantization,
optimizer_overlap_hooks ... | pytorch-master | torch/distributed/algorithms/ddp_comm_hooks/__init__.py |
from typing import Any
import torch
from torch.distributed import GradBucket
def noop_hook(_: Any, bucket: GradBucket) -> torch.futures.Future[torch.Tensor]:
"""
This DDP communication hook returns a future that wraps the input,
so it is a noop that does not incur any communication overheads.
This ... | pytorch-master | torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.py |
import weakref
from typing import Any, Callable, List, Optional
import torch
import torch.distributed as dist
from torch.distributed.optim import ZeroRedundancyOptimizer
from torch.distributed.optim.zero_redundancy_optimizer import (
_get_global_rank,
_OverlapStatus,
)
from torch.nn.parallel.distributed import... | pytorch-master | torch/distributed/algorithms/ddp_comm_hooks/ddp_zero_hook.py |
from typing import Any, Callable
import torch
import torch.distributed as dist
def _allreduce_fut(
process_group: dist.ProcessGroup, tensor: torch.Tensor
) -> torch.futures.Future[torch.Tensor]:
"Averages the input gradient tensor by allreduce and returns a future."
group_to_use = process_group if proces... | pytorch-master | torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py |
from collections import defaultdict
import logging
import math
from typing import Dict
import numpy as np
import torch
import torch.distributed as dist
from . import default_hooks as default
from torch.distributed import distributed_c10d
__all__ = [
"PowerSGDState", "powerSGD_hook", "batched_powerSGD_hook"
]
lo... | pytorch-master | torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py |
from abc import ABC
import inspect
from typing import Dict, Type
from torch.distributed.fsdp import FullyShardedDataParallel
from torch.nn.parallel import DistributedDataParallel
from torch.optim import Optimizer
from torch.distributed.optim import as_functional_optim
from torch.distributed.algorithms.ddp_comm_hooks.... | pytorch-master | torch/distributed/algorithms/_optimizer_overlap/optimizer_overlap.py |
from .optimizer_overlap import _as_overlapped_optim
| pytorch-master | torch/distributed/algorithms/_optimizer_overlap/__init__.py |
import functools
import torch
import torch.distributed as dist
from enum import Enum
TORCH_HALF_MIN = torch.finfo(torch.float16).min
TORCH_HALF_MAX = torch.finfo(torch.float16).max
class DQuantType(Enum):
"""
Different quantization methods for auto_quantize API are identified here.
auto_quantize API cu... | pytorch-master | torch/distributed/algorithms/_quantization/quantization.py |
pytorch-master | torch/distributed/algorithms/_quantization/__init__.py | |
"""
:mod:`torch.distributed.optim` exposes DistributedOptimizer, which takes a list
of remote parameters (:class:`~torch.distributed.rpc.RRef`) and runs the
optimizer locally on the workers where the parameters live. The distributed
optimizer can use any of the local optimizer :ref:`optimizer-algorithms` to
apply the ... | pytorch-master | torch/distributed/optim/__init__.py |
from typing import List, Optional, Dict
import torch
import torch.optim._functional as F
from torch import Tensor
__all__ : List[str] = []
# Define a TorchScript compatible Functional SGD Optimizer
# where we use these optimizer in a functional way.
# Instead of using the `param.grad` when updating parameters,
# we ... | pytorch-master | torch/distributed/optim/functional_sgd.py |
from typing import List, Dict, Optional
import torch
import torch.optim._functional as F
from torch import Tensor
__all__ : List[str] = []
# Define a TorchScript compatible Functional Adagrad Optimizer
# where we use these optimizer in a functional way.
# Instead of using the `param.grad` when updating parameters,
#... | pytorch-master | torch/distributed/optim/functional_adagrad.py |
from typing import List, Dict, Optional, Tuple
import torch
import torch.optim._functional as F
from torch import Tensor
__all__ : List[str] = []
# Define a TorchScript compatible Functional AdamW Optimizer
# where we use these optimizer in a functional way.
# Instead of using the `param.grad` when updating paramete... | pytorch-master | torch/distributed/optim/functional_adamw.py |
from typing import List, Dict, Optional, Tuple
import torch
import torch.optim._functional as F
from torch import Tensor
__all__ : List[str] = []
# Define a TorchScript compatible Functional Adam Optimizer
# where we use these optimizer in a functional way.
# Instead of using the `param.grad` when updating parameter... | pytorch-master | torch/distributed/optim/functional_adam.py |
from typing import Type
from torch import optim
from .functional_adagrad import _FunctionalAdagrad
from .functional_adam import _FunctionalAdam
from .functional_adamw import _FunctionalAdamW
from .functional_sgd import _FunctionalSGD
from .functional_adadelta import _FunctionalAdadelta
from .functional_rmsprop import _... | pytorch-master | torch/distributed/optim/utils.py |
from typing import List, Dict, Optional, Tuple
import torch
import torch.optim._functional as F
from torch import Tensor
__all__ : List[str] = []
# Define a TorchScript compatible Functional Rprop Optimizer
# where we use these optimizer in a functional way.
# Instead of using the `param.grad` when updating paramete... | pytorch-master | torch/distributed/optim/functional_rprop.py |
import torch
import torch.distributed.algorithms.model_averaging.averagers as averagers
import warnings
class PostLocalSGDOptimizer(torch.optim.Optimizer):
r"""
Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD <https://arxiv.org/abs/1808.07217>`_,
This optimizer runs local optimi... | pytorch-master | torch/distributed/optim/post_localSGD_optimizer.py |
from typing import List, Optional
import logging
import torch
import torch.distributed.rpc as rpc
import torch.jit as jit
import torch.nn as nn
from torch import Tensor
from torch.distributed.rpc import RRef
from .utils import functional_optim_map
import torch.distributed.autograd as dist_autograd
from collections i... | pytorch-master | torch/distributed/optim/optimizer.py |
from typing import List, Dict, Optional
import torch
import torch.optim._functional as F
from torch import Tensor
__all__ : List[str] = []
# Define a TorchScript compatible Functional Adadelta Optimizer
# where we use these optimizer in a functional way.
# Instead of using the `param.grad` when updating parameters,
... | pytorch-master | torch/distributed/optim/functional_adadelta.py |
from typing import List, Dict, Optional
import torch
import torch.optim._functional as F
from torch import Tensor
__all__ : List[str] = []
# Define a TorchScript compatible Functional RMSprop Optimizer
# where we use these optimizer in a functional way.
# Instead of using the `param.grad` when updating parameters,
#... | pytorch-master | torch/distributed/optim/functional_rmsprop.py |
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