id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
10,254 | import os
from typing import Union
import torch
import json
import copy
import base64
from .constants import *
from .fp16.loss_scaler import (
INITIAL_LOSS_SCALE,
SCALE_WINDOW,
DELAYED_SHIFT,
MIN_LOSS_SCALE,
)
from .config_utils import (
get_scalar_param,
dict_raise_error_on_duplicate_keys,
... | null |
10,255 | import os
from typing import Union
import torch
import json
import copy
import base64
from .constants import *
from .fp16.loss_scaler import (
INITIAL_LOSS_SCALE,
SCALE_WINDOW,
DELAYED_SHIFT,
MIN_LOSS_SCALE,
)
from .config_utils import (
get_scalar_param,
dict_raise_error_on_duplicate_keys,
... | null |
10,256 | import os
from typing import Union
import torch
import json
import copy
import base64
from .constants import *
from .fp16.loss_scaler import (
INITIAL_LOSS_SCALE,
SCALE_WINDOW,
DELAYED_SHIFT,
MIN_LOSS_SCALE,
)
from .config_utils import (
get_scalar_param,
dict_raise_error_on_duplicate_keys,
... | null |
10,257 | import os
from typing import Union
import torch
import json
import copy
import base64
from .constants import *
from .fp16.loss_scaler import (
INITIAL_LOSS_SCALE,
SCALE_WINDOW,
DELAYED_SHIFT,
MIN_LOSS_SCALE,
)
from .config_utils import (
get_scalar_param,
dict_raise_error_on_duplicate_keys,
... | null |
10,258 | import os
from typing import Union
import torch
import json
import copy
import base64
from .constants import *
from .fp16.loss_scaler import (
INITIAL_LOSS_SCALE,
SCALE_WINDOW,
DELAYED_SHIFT,
MIN_LOSS_SCALE,
)
from .config_utils import (
get_scalar_param,
dict_raise_error_on_duplicate_keys,
... | Parses pipeline engine configuration. |
10,259 | import os
from typing import Union
import torch
import json
import copy
import base64
from .constants import *
from .fp16.loss_scaler import (
INITIAL_LOSS_SCALE,
SCALE_WINDOW,
DELAYED_SHIFT,
MIN_LOSS_SCALE,
)
from .config_utils import (
get_scalar_param,
dict_raise_error_on_duplicate_keys,
... | null |
10,260 | import os
from typing import Union
import torch
import json
import copy
import base64
from .constants import *
from .fp16.loss_scaler import (
INITIAL_LOSS_SCALE,
SCALE_WINDOW,
DELAYED_SHIFT,
MIN_LOSS_SCALE,
)
from .config_utils import (
get_scalar_param,
dict_raise_error_on_duplicate_keys,
... | null |
10,261 | import os
from typing import Union
import torch
import json
import copy
import base64
from .constants import *
from .fp16.loss_scaler import (
INITIAL_LOSS_SCALE,
SCALE_WINDOW,
DELAYED_SHIFT,
MIN_LOSS_SCALE,
)
from .config_utils import (
get_scalar_param,
dict_raise_error_on_duplicate_keys,
... | null |
10,262 | import os
from typing import Union
import torch
import json
import copy
import base64
from .constants import *
from .fp16.loss_scaler import (
INITIAL_LOSS_SCALE,
SCALE_WINDOW,
DELAYED_SHIFT,
MIN_LOSS_SCALE,
)
from .config_utils import (
get_scalar_param,
dict_raise_error_on_duplicate_keys,
... | null |
10,263 | import os
from typing import Union
import torch
import json
import copy
import base64
from .constants import *
from .fp16.loss_scaler import (
INITIAL_LOSS_SCALE,
SCALE_WINDOW,
DELAYED_SHIFT,
MIN_LOSS_SCALE,
)
from .config_utils import (
get_scalar_param,
dict_raise_error_on_duplicate_keys,
... | null |
10,264 | import os
from typing import Union
import torch
import json
import copy
import base64
from .constants import *
from .fp16.loss_scaler import (
INITIAL_LOSS_SCALE,
SCALE_WINDOW,
DELAYED_SHIFT,
MIN_LOSS_SCALE,
)
from .config_utils import (
get_scalar_param,
dict_raise_error_on_duplicate_keys,
... | null |
10,265 | import os
from typing import Union
import torch
import json
import copy
import base64
from .constants import *
from .fp16.loss_scaler import (
INITIAL_LOSS_SCALE,
SCALE_WINDOW,
DELAYED_SHIFT,
MIN_LOSS_SCALE,
)
from .config_utils import (
get_scalar_param,
dict_raise_error_on_duplicate_keys,
... | null |
10,266 | import os
from typing import Union
import torch
import json
import copy
import base64
from .constants import *
from .fp16.loss_scaler import (
INITIAL_LOSS_SCALE,
SCALE_WINDOW,
DELAYED_SHIFT,
MIN_LOSS_SCALE,
)
from .config_utils import (
get_scalar_param,
dict_raise_error_on_duplicate_keys,
... | null |
10,267 | import os
from typing import Union
import torch
import json
import copy
import base64
from .constants import *
from .fp16.loss_scaler import (
INITIAL_LOSS_SCALE,
SCALE_WINDOW,
DELAYED_SHIFT,
MIN_LOSS_SCALE,
)
from .config_utils import (
get_scalar_param,
dict_raise_error_on_duplicate_keys,
... | null |
10,268 | import os
from typing import Union
import torch
import json
import copy
import base64
from .constants import *
from .fp16.loss_scaler import (
INITIAL_LOSS_SCALE,
SCALE_WINDOW,
DELAYED_SHIFT,
MIN_LOSS_SCALE,
)
from .config_utils import (
get_scalar_param,
dict_raise_error_on_duplicate_keys,
... | null |
10,269 | import os
from typing import Union
import torch
import json
import copy
import base64
from .constants import *
from .fp16.loss_scaler import (
INITIAL_LOSS_SCALE,
SCALE_WINDOW,
DELAYED_SHIFT,
MIN_LOSS_SCALE,
)
from .config_utils import (
get_scalar_param,
dict_raise_error_on_duplicate_keys,
... | null |
10,270 | import os
from typing import Union
import torch
import json
import copy
import base64
from .constants import *
from .fp16.loss_scaler import (
INITIAL_LOSS_SCALE,
SCALE_WINDOW,
DELAYED_SHIFT,
MIN_LOSS_SCALE,
)
from .config_utils import (
get_scalar_param,
dict_raise_error_on_duplicate_keys,
... | null |
10,271 | import os
from typing import Union
import torch
import json
import copy
import base64
from .constants import *
from .fp16.loss_scaler import (
INITIAL_LOSS_SCALE,
SCALE_WINDOW,
DELAYED_SHIFT,
MIN_LOSS_SCALE,
)
from .config_utils import (
get_scalar_param,
dict_raise_error_on_duplicate_keys,
... | null |
10,272 | import os
from typing import Union
import torch
import json
import copy
import base64
from .constants import *
from .fp16.loss_scaler import (
INITIAL_LOSS_SCALE,
SCALE_WINDOW,
DELAYED_SHIFT,
MIN_LOSS_SCALE,
)
from .config_utils import (
get_scalar_param,
dict_raise_error_on_duplicate_keys,
... | null |
10,273 | from dataclasses import dataclass
import collections
from collections import UserDict
from typing import Deque, Set
from torch.cuda import Event, Stream
from deepspeed import comm as dist
from deepspeed.utils.logging import logger
from deepspeed.runtime.zero.offload_config import OffloadDeviceEnum
from deepspeed.runtim... | null |
10,274 | from dataclasses import dataclass
import collections
from collections import UserDict
from typing import Deque, Set
from torch.cuda import Event, Stream
from deepspeed import comm as dist
from deepspeed.utils.logging import logger
from deepspeed.runtime.zero.offload_config import OffloadDeviceEnum
from deepspeed.runtim... | null |
10,275 | import os
from typing import List
import torch
from deepspeed import comm as dist
from deepspeed.utils import logger
from deepspeed.ops.adam import DeepSpeedCPUAdam
from deepspeed.ops.adam import FusedAdam
from deepspeed.utils.nvtx import instrument_w_nvtx
def _initialize_parameter_parallel_groups(parameter_parallel_s... | null |
10,276 | import os
from typing import List
import torch
from deepspeed import comm as dist
from deepspeed.utils import logger
from deepspeed.ops.adam import DeepSpeedCPUAdam
from deepspeed.ops.adam import FusedAdam
from deepspeed.utils.nvtx import instrument_w_nvtx
ZERO_SUPPORTED_OPTIMIZERS = [
torch.optim.Adam,
torch.o... | null |
10,277 | import os
from typing import List
import torch
from deepspeed import comm as dist
from deepspeed.utils import logger
from deepspeed.ops.adam import DeepSpeedCPUAdam
from deepspeed.ops.adam import FusedAdam
from deepspeed.utils.nvtx import instrument_w_nvtx
def get_lst_from_rank0(lst: List[int]) -> None:
"""
NOT... | NOTE: creates both communication and synchronization overhead so should be used sparingly takes a list of ints from each rank and ensures that they are the same across ranks, throwing an exception if they are not. |
10,278 | import torch
import os
from deepspeed import comm as dist
from torch._six import inf
from packaging import version as pkg_version
from collections import OrderedDict
from deepspeed.runtime import ZeROOptimizer
from deepspeed.runtime.fp16.loss_scaler import LossScaler, DynamicLossScaler
from deepspeed.runtime.utils impo... | null |
10,279 | import torch
import os
from deepspeed import comm as dist
from torch._six import inf
from packaging import version as pkg_version
from collections import OrderedDict
from deepspeed.runtime import ZeROOptimizer
from deepspeed.runtime.fp16.loss_scaler import LossScaler, DynamicLossScaler
from deepspeed.runtime.utils impo... | null |
10,280 | import torch
import os
from deepspeed import comm as dist
from torch._six import inf
from packaging import version as pkg_version
from collections import OrderedDict
from deepspeed.runtime import ZeROOptimizer
from deepspeed.runtime.fp16.loss_scaler import LossScaler, DynamicLossScaler
from deepspeed.runtime.utils impo... | null |
10,281 | import torch
import os
from deepspeed import comm as dist
from torch._six import inf
from packaging import version as pkg_version
from collections import OrderedDict
from deepspeed.runtime import ZeROOptimizer
from deepspeed.runtime.fp16.loss_scaler import LossScaler, DynamicLossScaler
from deepspeed.runtime.utils impo... | null |
10,282 | import torch
import os
from deepspeed import comm as dist
from torch._six import inf
from packaging import version as pkg_version
from collections import OrderedDict
from deepspeed.runtime import ZeROOptimizer
from deepspeed.runtime.fp16.loss_scaler import LossScaler, DynamicLossScaler
from deepspeed.runtime.utils impo... | null |
10,283 | import torch
import os
from deepspeed import comm as dist
from torch._six import inf
from packaging import version as pkg_version
from collections import OrderedDict
from deepspeed.runtime import ZeROOptimizer
from deepspeed.runtime.fp16.loss_scaler import LossScaler, DynamicLossScaler
from deepspeed.runtime.utils impo... | null |
10,284 | import torch
import os
from deepspeed import comm as dist
from torch._six import inf
from packaging import version as pkg_version
from collections import OrderedDict
from deepspeed.runtime import ZeROOptimizer
from deepspeed.runtime.fp16.loss_scaler import LossScaler, DynamicLossScaler
from deepspeed.runtime.utils impo... | null |
10,285 | import torch
import os
from deepspeed import comm as dist
from torch._six import inf
from packaging import version as pkg_version
from collections import OrderedDict
from deepspeed.runtime import ZeROOptimizer
from deepspeed.runtime.fp16.loss_scaler import LossScaler, DynamicLossScaler
from deepspeed.runtime.utils impo... | null |
10,286 | import torch
import os
from deepspeed import comm as dist
from torch._six import inf
from packaging import version as pkg_version
from collections import OrderedDict
from deepspeed.runtime import ZeROOptimizer
from deepspeed.runtime.fp16.loss_scaler import LossScaler, DynamicLossScaler
from deepspeed.runtime.utils impo... | null |
10,287 | import torch
import os
from deepspeed import comm as dist
from torch._six import inf
from packaging import version as pkg_version
from collections import OrderedDict
from deepspeed.runtime import ZeROOptimizer
from deepspeed.runtime.fp16.loss_scaler import LossScaler, DynamicLossScaler
from deepspeed.runtime.utils impo... | Print out estimates on memory usage requirements for ZeRO 2 params, optim states and gradients for a given ``model`` and hardware setup. If you have an actual model object, use this function and everything will be derived automatically. If it's a hypothetical model, use ``estimate_zero2_model_states_mem_needs_all_cold`... |
10,288 | import torch
from deepspeed import comm as dist
def print_rank_0(message):
if dist.get_rank() == 0:
print(message) | null |
10,289 | import sys
import gc
import collections
from typing import Deque, Dict, Tuple
from torch.cuda import Event, Stream
from torch._six import inf
from deepspeed.runtime import ZeROOptimizer
from deepspeed.utils import logger
from deepspeed.runtime.fp16.loss_scaler import LossScaler, DynamicLossScaler
from deepspeed.runtime... | null |
10,290 | import sys
import gc
import collections
from typing import Deque, Dict, Tuple
from torch.cuda import Event, Stream
from torch._six import inf
from deepspeed.runtime import ZeROOptimizer
from deepspeed.utils import logger
from deepspeed.runtime.fp16.loss_scaler import LossScaler, DynamicLossScaler
from deepspeed.runtime... | null |
10,291 | import sys
import gc
import collections
from typing import Deque, Dict, Tuple
from torch.cuda import Event, Stream
from torch._six import inf
from deepspeed.runtime import ZeROOptimizer
from deepspeed.utils import logger
from deepspeed.runtime.fp16.loss_scaler import LossScaler, DynamicLossScaler
from deepspeed.runtime... | null |
10,292 | import sys
import gc
import collections
from typing import Deque, Dict, Tuple
from torch.cuda import Event, Stream
from torch._six import inf
from deepspeed.runtime import ZeROOptimizer
from deepspeed.utils import logger
from deepspeed.runtime.fp16.loss_scaler import LossScaler, DynamicLossScaler
from deepspeed.runtime... | null |
10,293 | import sys
import gc
import collections
from typing import Deque, Dict, Tuple
from torch.cuda import Event, Stream
from torch._six import inf
from deepspeed.runtime import ZeROOptimizer
from deepspeed.utils import logger
from deepspeed.runtime.fp16.loss_scaler import LossScaler, DynamicLossScaler
from deepspeed.runtime... | null |
10,294 | import sys
import gc
import collections
from typing import Deque, Dict, Tuple
from torch.cuda import Event, Stream
from torch._six import inf
from deepspeed.runtime import ZeROOptimizer
from deepspeed.utils import logger
from deepspeed.runtime.fp16.loss_scaler import LossScaler, DynamicLossScaler
from deepspeed.runtime... | null |
10,295 | import sys
import gc
import collections
from typing import Deque, Dict, Tuple
from torch.cuda import Event, Stream
from torch._six import inf
from deepspeed.runtime import ZeROOptimizer
from deepspeed.utils import logger
from deepspeed.runtime.fp16.loss_scaler import LossScaler, DynamicLossScaler
from deepspeed.runtime... | Print out estimates on memory usage requirements for ZeRO 3 params, optim states and gradients for a given ``model`` and hardware setup. If you have an actual model object, use this function and everything will be derived automatically. If it's a hypothetical model, use ``estimate_zero3_model_states_mem_needs_all_cold`... |
10,296 | from pydantic import Field, validator
import sys
from typing import Optional
from enum import Enum
from deepspeed.runtime.config_utils import get_scalar_param, pp_int, DeepSpeedConfigModel
from deepspeed.utils import logger
from .offload_config import DeepSpeedZeroOffloadParamConfig, DeepSpeedZeroOffloadOptimizerConfig... | null |
10,297 | import sys
import torch
from torch.cuda import Stream
from collections import OrderedDict
from deepspeed.runtime.utils import see_memory_usage
from deepspeed.runtime.zero.offload_config import OffloadDeviceEnum
from deepspeed.runtime.zero.partition_parameters import _init_external_params
from deepspeed.runtime.zero.par... | null |
10,298 | import sys
import torch
from torch.cuda import Stream
from collections import OrderedDict
from deepspeed.runtime.utils import see_memory_usage
from deepspeed.runtime.zero.offload_config import OffloadDeviceEnum
from deepspeed.runtime.zero.partition_parameters import _init_external_params
from deepspeed.runtime.zero.par... | null |
10,299 | import sys
import torch
from torch.cuda import Stream
from collections import OrderedDict
from deepspeed.runtime.utils import see_memory_usage
from deepspeed.runtime.zero.offload_config import OffloadDeviceEnum
from deepspeed.runtime.zero.partition_parameters import _init_external_params
from deepspeed.runtime.zero.par... | null |
10,300 | import math
import os
import types
from typing import Callable, Iterable
from enum import Enum
import functools
import itertools
from typing import List
import torch
from torch import Tensor
from deepspeed import comm as dist
from torch.nn import Module
from torch.nn import Parameter
from .linear import zero3_linear_wr... | null |
10,301 | import math
import os
import types
from typing import Callable, Iterable
from enum import Enum
import functools
import itertools
from typing import List
import torch
from torch import Tensor
from deepspeed import comm as dist
from torch.nn import Module
from torch.nn import Parameter
from .linear import zero3_linear_wr... | null |
10,302 | import math
import os
import types
from typing import Callable, Iterable
from enum import Enum
import functools
import itertools
from typing import List
import torch
from torch import Tensor
from deepspeed import comm as dist
from torch.nn import Module
from torch.nn import Parameter
from .linear import zero3_linear_wr... | null |
10,303 | import math
import os
import types
from typing import Callable, Iterable
from enum import Enum
import functools
import itertools
from typing import List
import torch
from torch import Tensor
from deepspeed import comm as dist
from torch.nn import Module
from torch.nn import Parameter
from .linear import zero3_linear_wr... | null |
10,304 | import math
import os
import types
from typing import Callable, Iterable
from enum import Enum
import functools
import itertools
from typing import List
import torch
from torch import Tensor
from deepspeed import comm as dist
from torch.nn import Module
from torch.nn import Parameter
from .linear import zero3_linear_wr... | Instruct DeepSpeed to coordinate ``parameter``'s collection and partitioning in the forward and backward passes of ``module``. This is used when a parameter is accessed outside of its owning module's ``forward()``. DeepSpeed must know to collect it from its partitioned state and when to release the memory. .. note:: Th... |
10,305 | import math
import os
import types
from typing import Callable, Iterable
from enum import Enum
import functools
import itertools
from typing import List
import torch
from torch import Tensor
from deepspeed import comm as dist
from torch.nn import Module
from torch.nn import Parameter
from .linear import zero3_linear_wr... | Reverses the effects of :meth:`register_external_parameter`. Args: module (``torch.nn.Module``): The module to affect. parameter (``torch.nn.Parameter``): The parameter to unregister. Raises: RuntimeError: If ``parameter`` is not of type ``torch.nn.Parameter``. RuntimeError: If ``parameter`` is not a registered externa... |
10,306 | import math
import os
import types
from typing import Callable, Iterable
from enum import Enum
import functools
import itertools
from typing import List
import torch
from torch import Tensor
from deepspeed import comm as dist
from torch.nn import Module
from torch.nn import Parameter
from .linear import zero3_linear_wr... | null |
10,307 | import math
import os
import types
from typing import Callable, Iterable
from enum import Enum
import functools
import itertools
from typing import List
import torch
from torch import Tensor
from deepspeed import comm as dist
from torch.nn import Module
from torch.nn import Parameter
from .linear import zero3_linear_wr... | null |
10,308 | import math
import os
import types
from typing import Callable, Iterable
from enum import Enum
import functools
import itertools
from typing import List
import torch
from torch import Tensor
from deepspeed import comm as dist
from torch.nn import Module
from torch.nn import Parameter
from .linear import zero3_linear_wr... | Free underlying storage of a parameter. |
10,309 | import math
import os
import types
from typing import Callable, Iterable
from enum import Enum
import functools
import itertools
from typing import List
import torch
from torch import Tensor
from deepspeed import comm as dist
from torch.nn import Module
from torch.nn import Parameter
from .linear import zero3_linear_wr... | null |
10,310 | import torch
import deepspeed
from deepspeed.runtime.utils import partition_uniform as partition
The provided code snippet includes necessary dependencies for implementing the `split_tensor_along_last_dim` function. Write a Python function `def split_tensor_along_last_dim(tensor, partitions, contiguous_split_chunks=Fa... | Split a tensor along its last dimension. Adapted from Megatron-LM. Arguments: tensor: input tensor. partitions: list of partition sizes to supply to torch.split contiguous_split_chunks: If True, make each chunk contiguous in memory. |
10,311 | import math
import torch
from torch import Tensor
from torch.nn.parameter import Parameter
from torch.nn import init
from torch.nn.modules.module import Module
from deepspeed.runtime.utils import noop_decorator
from deepspeed import comm as dist
def print_rank_0(message, debug=False, force=False):
if dist.get_rank... | null |
10,312 | import math
import torch
from torch import Tensor
from torch.nn.parameter import Parameter
from torch.nn import init
from torch.nn.modules.module import Module
from deepspeed.runtime.utils import noop_decorator
from deepspeed import comm as dist
class LinearFunctionForZeroStage3(torch.autograd.Function):
def forwa... | null |
10,313 |
def to_python_float(t):
if hasattr(t, 'item'):
return t.item()
return t[0] | null |
10,314 | import torch
from deepspeed.utils.logging import logger
from deepspeed import comm as dist
def swap_in_tensors(swap_handle, tensor_buffers, swap_paths):
for buffer, path in zip(tensor_buffers, swap_paths):
assert (swap_handle.async_pread(buffer, path) == 0) | null |
10,315 | import torch
from deepspeed.utils.logging import logger
from deepspeed import comm as dist
def swap_out_tensors(swap_handle, tensor_buffers, swap_paths):
for buffer, path in zip(tensor_buffers, swap_paths):
assert (swap_handle.async_pwrite(buffer, path) == 0) | null |
10,316 | import torch
from deepspeed.utils.logging import logger
from deepspeed import comm as dist
logger = LoggerFactory.create_logger(name="DeepSpeed", level=logging.INFO)
def print_object(obj, name, exclude_list=[]):
logger.info('{}:'.format(name))
for arg in sorted(vars(obj)):
if not arg in exclude_list:
... | null |
10,317 | import torch
from deepspeed.utils.logging import logger
from deepspeed import comm as dist
def get_sized_buffer(buffer, num_elems):
assert num_elems <= buffer.numel(), \
f'num_elems {num_elems} > buffer {buffer.numel()}'
return buffer.narrow(0, 0, num_elems) if num_elems < buffer.numel() else buffer
de... | null |
10,318 | from deepspeed.runtime.config_utils import get_scalar_param
from deepspeed.runtime.swap_tensor.constants import *
AIO_DEFAULT_DICT = {
AIO_BLOCK_SIZE: AIO_BLOCK_SIZE_DEFAULT,
AIO_QUEUE_DEPTH: AIO_QUEUE_DEPTH_DEFAULT,
AIO_THREAD_COUNT: AIO_THREAD_COUNT_DEFAULT,
AIO_SINGLE_SUBMIT: AIO_SINGLE_SUBMIT_DEFAUL... | null |
10,319 | import os
import shutil
from enum import Enum
import torch
from deepspeed import comm as dist
from deepspeed.ops.aio import AsyncIOBuilder
from .constants import *
from .utils import swap_in_tensors, swap_out_tensors, MIN_AIO_BYTES, AIO_ALIGNED_BYTES, print_object, SwapBufferPool
def print_rank_0(message, debug=False,... | null |
10,320 | import pickle
import typing
import torch
from deepspeed import comm as dist
from packaging.version import Version
from deepspeed.git_version_info import torch_info
_groups = None
_grid = None
def can_send_recv() -> bool:
torch_version = Version(torch_info['version'])
sendrecv_min = Version('1.8')
return tor... | null |
10,321 | import pickle
import typing
import torch
from deepspeed import comm as dist
from packaging.version import Version
from deepspeed.git_version_info import torch_info
_async = []
def wait():
global _async
for op in _async:
op.wait()
_async = []
torch.cuda.synchronize() | null |
10,322 | import pickle
import typing
import torch
from deepspeed import comm as dist
from packaging.version import Version
from deepspeed.git_version_info import torch_info
def send(tensor, dest_stage, async_op=False):
global _groups
assert async_op == False, "Doesn't support async_op true"
src_stage = _grid.get_sta... | Send an arbitrary python object to ``dest``. Note: ``msg`` must be pickleable. WARN: This incurs a CPU -> GPU transfer and should be used sparingly for performance reasons. Args: msg (typing.Any): The object to send. dest (int): Destination rank. |
10,323 | import pickle
import typing
import torch
from deepspeed import comm as dist
from packaging.version import Version
from deepspeed.git_version_info import torch_info
def recv(tensor, src_stage, async_op=False):
global _groups
assert async_op == False, "Doesn't support async_op true"
dest_stage = _grid.get_sta... | Receive an arbitrary python object from ``sender``. WARN: This incur a CPU <-> GPU transfers and should be used sparingly for performance reasons. Args: sender (int): The rank sending the message. |
10,324 | from ..utils import call_to_str
from abc import ABC, abstractmethod
def _is_even(x):
return x % 2 == 0 | null |
10,325 | from ..utils import call_to_str
from abc import ABC, abstractmethod
def _is_odd(x):
return x % 2 != 0 | null |
10,326 | from deepspeed import comm as dist
from collections import namedtuple
from itertools import product as cartesian_product
The provided code snippet includes necessary dependencies for implementing the `_prime_factors` function. Write a Python function `def _prime_factors(N)` to solve the following problem:
Returns the ... | Returns the prime factorization of positive integer N. |
10,327 | from types import MethodType
import torch
from deepspeed import comm as dist
from deepspeed.utils import logger
from deepspeed.utils.timer import ThroughputTimer
from ..engine import DeepSpeedEngine, MEMORY_OPT_ALLREDUCE_SIZE
from ..utils import PartitionedTensor
from ..dataloader import RepeatingLoader
from .module im... | null |
10,328 | from types import MethodType
import torch
from deepspeed import comm as dist
from deepspeed.utils import logger
from deepspeed.utils.timer import ThroughputTimer
from ..engine import DeepSpeedEngine, MEMORY_OPT_ALLREDUCE_SIZE
from ..utils import PartitionedTensor
from ..dataloader import RepeatingLoader
from .module im... | null |
10,329 | import os
import torch
import torch_nebula
from deepspeed.runtime.checkpoint_engine.checkpoint_engine import \
CheckpointEngine
from deepspeed.utils import logger, log_dist
from deepspeed.nebula.constants import *
def _get_tag_from_path(path):
return os.path.basename(os.path.dirname(path)) | null |
10,330 | import copy
import torch
import contextlib
from deepspeed import comm as dist
import mmap
from torch import _C
from torch.cuda import _lazy_call, device as device_ctx_manager
from deepspeed.runtime.config import DeepSpeedConfig
from deepspeed.utils import logger
from deepspeed.runtime.utils import copy_to_device, move_... | null |
10,331 | import copy
import torch
import contextlib
from deepspeed import comm as dist
import mmap
from torch import _C
from torch.cuda import _lazy_call, device as device_ctx_manager
from deepspeed.runtime.config import DeepSpeedConfig
from deepspeed.utils import logger
from deepspeed.runtime.utils import copy_to_device, move_... | Sets the random number generator state of the current GPU. Arguments: new_state (torch.ByteTensor): The desired state This function is adapted from PyTorch repo (torch.cuda.set_rng_state) with a single change: the input state is not cloned. Cloning caused major performance issues for +4 GPU cases. |
10,332 | import copy
import torch
import contextlib
from deepspeed import comm as dist
import mmap
from torch import _C
from torch.cuda import _lazy_call, device as device_ctx_manager
from deepspeed.runtime.config import DeepSpeedConfig
from deepspeed.utils import logger
from deepspeed.runtime.utils import copy_to_device, move_... | Get cuda rng tracker. |
10,333 | import copy
import torch
import contextlib
from deepspeed import comm as dist
import mmap
from torch import _C
from torch.cuda import _lazy_call, device as device_ctx_manager
from deepspeed.runtime.config import DeepSpeedConfig
from deepspeed.utils import logger
from deepspeed.runtime.utils import copy_to_device, move_... | Initialize model parallel cuda seed. This function should be called after the model parallel is initialized. Also, no torch.cuda.manual_seed should be called after this function. Basically, this is replacement for that function. Two set of RNG states are tracked: default state: This is for data parallelism and is the s... |
10,334 | import copy
import torch
import contextlib
from deepspeed import comm as dist
import mmap
from torch import _C
from torch.cuda import _lazy_call, device as device_ctx_manager
from deepspeed.runtime.config import DeepSpeedConfig
from deepspeed.utils import logger
from deepspeed.runtime.utils import copy_to_device, move_... | null |
10,335 | import copy
import torch
import contextlib
from deepspeed import comm as dist
import mmap
from torch import _C
from torch.cuda import _lazy_call, device as device_ctx_manager
from deepspeed.runtime.config import DeepSpeedConfig
from deepspeed.utils import logger
from deepspeed.runtime.utils import copy_to_device, move_... | Separate objects in list/tuple into tensors and non-tensors and create a mapping to enable re-aggregation. The order of tensors and non-tensors is preserved in their respective output groups. Parameters: all_objects (list/tuple): Objects containing tensors and non-tensors to be split. Returns: tuple: Containing tensors... |
10,336 | import copy
import torch
import contextlib
from deepspeed import comm as dist
import mmap
from torch import _C
from torch.cuda import _lazy_call, device as device_ctx_manager
from deepspeed.runtime.config import DeepSpeedConfig
from deepspeed.utils import logger
from deepspeed.runtime.utils import copy_to_device, move_... | Merge two lists (or tuples) of tensors and non-tensors using a mapping of positions in merged list (or tuple). Parameters: tensor_objects (list/tuple): Tensors to merge. non_tensor_objects (list/tuple): Non-tensors to merge. tensor_flags (list/tuple): Indicates whether each position in output is a tensor. Returns: tupl... |
10,337 | import copy
import torch
import contextlib
from deepspeed import comm as dist
import mmap
from torch import _C
from torch.cuda import _lazy_call, device as device_ctx_manager
from deepspeed.runtime.config import DeepSpeedConfig
from deepspeed.utils import logger
from deepspeed.runtime.utils import copy_to_device, move_... | null |
10,338 | import copy
import torch
import contextlib
from deepspeed import comm as dist
import mmap
from torch import _C
from torch.cuda import _lazy_call, device as device_ctx_manager
from deepspeed.runtime.config import DeepSpeedConfig
from deepspeed.utils import logger
from deepspeed.runtime.utils import copy_to_device, move_... | null |
10,339 | import copy
import torch
import contextlib
from deepspeed import comm as dist
import mmap
from torch import _C
from torch.cuda import _lazy_call, device as device_ctx_manager
from deepspeed.runtime.config import DeepSpeedConfig
from deepspeed.utils import logger
from deepspeed.runtime.utils import copy_to_device, move_... | Checkpoint a model or part of the model. This has been directly copied from torch.utils.checkpoint. |
10,340 | import copy
import torch
import contextlib
from deepspeed import comm as dist
import mmap
from torch import _C
from torch.cuda import _lazy_call, device as device_ctx_manager
from deepspeed.runtime.config import DeepSpeedConfig
from deepspeed.utils import logger
from deepspeed.runtime.utils import copy_to_device, move_... | null |
10,341 | import copy
import torch
import contextlib
from deepspeed import comm as dist
import mmap
from torch import _C
from torch.cuda import _lazy_call, device as device_ctx_manager
from deepspeed.runtime.config import DeepSpeedConfig
from deepspeed.utils import logger
from deepspeed.runtime.utils import copy_to_device, move_... | null |
10,342 | import copy
import torch
import contextlib
from deepspeed import comm as dist
import mmap
from torch import _C
from torch.cuda import _lazy_call, device as device_ctx_manager
from deepspeed.runtime.config import DeepSpeedConfig
from deepspeed.utils import logger
from deepspeed.runtime.utils import copy_to_device, move_... | Configure DeepSpeed Activation Checkpointing. Arguments: mpu_: Optional: An object that implements the following methods get_model_parallel_rank/group/world_size, and get_data_parallel_rank/group/world_size deepspeed_config: Optional: DeepSpeed Config json file when provided will be used to configure DeepSpeed Activati... |
10,343 | import copy
import torch
import contextlib
from deepspeed import comm as dist
import mmap
from torch import _C
from torch.cuda import _lazy_call, device as device_ctx_manager
from deepspeed.runtime.config import DeepSpeedConfig
from deepspeed.utils import logger
from deepspeed.runtime.utils import copy_to_device, move_... | True if deepspeed activation checkpointing has been configured by calling deepspeed.checkpointing.configure, else returns false Arguments: None Return: True of configured, else False |
10,344 | import os
import re
import stat
import torch
import hashlib
from collections import defaultdict, OrderedDict
from shutil import copyfile
from torch.nn.modules import Module
from torch.nn.parameter import Parameter
from torch.optim import Optimizer
from torch.optim.lr_scheduler import _LRScheduler
from typing import Cal... | null |
10,345 | import os
import re
import stat
import torch
import hashlib
from collections import defaultdict, OrderedDict
from shutil import copyfile
from torch.nn.modules import Module
from torch.nn.parameter import Parameter
from torch.optim import Optimizer
from torch.optim.lr_scheduler import _LRScheduler
from typing import Cal... | null |
10,346 | import re
import collections.abc
import os
import json
from deepspeed.runtime.constants import GRADIENT_ACCUMULATION_STEPS, TRAIN_MICRO_BATCH_SIZE_PER_GPU
import itertools
import copy
from ..utils import logger
def search_error(filename):
if not os.path.exists(filename):
return "stderr.log does not exist"
... | null |
10,347 | import re
import collections.abc
import os
import json
from deepspeed.runtime.constants import GRADIENT_ACCUMULATION_STEPS, TRAIN_MICRO_BATCH_SIZE_PER_GPU
import itertools
import copy
from ..utils import logger
def was_interruptted(filename):
if not os.path.exists(filename):
return "stderr.log does not exi... | null |
10,348 | import re
import collections.abc
import os
import json
from deepspeed.runtime.constants import GRADIENT_ACCUMULATION_STEPS, TRAIN_MICRO_BATCH_SIZE_PER_GPU
import itertools
import copy
from ..utils import logger
def find_replace_str(value, replace_dict):
if not isinstance(value, str):
return str(value)
m... | null |
10,349 | import re
import collections.abc
import os
import json
from deepspeed.runtime.constants import GRADIENT_ACCUMULATION_STEPS, TRAIN_MICRO_BATCH_SIZE_PER_GPU
import itertools
import copy
from ..utils import logger
def get_list(val):
if not isinstance(val, list):
return [val]
else:
return val
def c... | null |
10,350 | import re
import collections.abc
import os
import json
from deepspeed.runtime.constants import GRADIENT_ACCUMULATION_STEPS, TRAIN_MICRO_BATCH_SIZE_PER_GPU
import itertools
import copy
from ..utils import logger
def get_val_by_key(d: dict, k):
if k in d:
return d[k]
for v in d.values():
if isins... | null |
10,351 | import re
import collections.abc
import os
import json
from deepspeed.runtime.constants import GRADIENT_ACCUMULATION_STEPS, TRAIN_MICRO_BATCH_SIZE_PER_GPU
import itertools
import copy
from ..utils import logger
def set_val_by_key(d: dict, k, vv):
if k in d:
d[k] = vv
for v in d.values():
if isi... | null |
10,352 | import re
import collections.abc
import os
import json
from deepspeed.runtime.constants import GRADIENT_ACCUMULATION_STEPS, TRAIN_MICRO_BATCH_SIZE_PER_GPU
import itertools
import copy
from ..utils import logger
def fetch_hostfile(hostfile_path):
if not os.path.isfile(hostfile_path):
logger.warning("Unable ... | null |
10,353 | import re
import collections.abc
import os
import json
from deepspeed.runtime.constants import GRADIENT_ACCUMULATION_STEPS, TRAIN_MICRO_BATCH_SIZE_PER_GPU
import itertools
import copy
from ..utils import logger
def validate_ds_config(config: dict):
def is_False(config: dict, key):
if config is None:
... | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.