id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
10,154 | import argparse
import dataclasses
from attr import define, field
from attr.setters import frozen
import functools
import gc
import math
import os
from typing import Tuple, Union, Optional, Any, Sequence, List
import numpy as np
import torch
def piecewise_linear_func_ret_func(xs, ys, x):
assert x >= xs[0] and x <= ... | Return a function created by linear inerpolation. |
10,155 | import argparse
import dataclasses
from attr import define, field
from attr.setters import frozen
import functools
import gc
import math
import os
from typing import Tuple, Union, Optional, Any, Sequence, List
import numpy as np
import torch
def sample_from_range(n, k):
assert n >= 1
if k == -1:
ret =... | null |
10,156 | import argparse
import dataclasses
from attr import define, field
from attr.setters import frozen
import functools
import gc
import math
import os
from typing import Tuple, Union, Optional, Any, Sequence, List
import numpy as np
import torch
def cpu_mem_stats():
objects = gc.get_objects()
tensors = [obj for ob... | null |
10,157 | import argparse
import dataclasses
from attr import define, field
from attr.setters import frozen
import functools
import gc
import math
import os
from typing import Tuple, Union, Optional, Any, Sequence, List
import numpy as np
import torch
def torch_mem_stats():
objects = gc.get_objects()
tensors = [obj for ... | null |
10,158 | import argparse
import dataclasses
from attr import define, field
from attr.setters import frozen
import functools
import gc
import math
import os
from typing import Tuple, Union, Optional, Any, Sequence, List
import numpy as np
import torch
def array_1d(a, cls):
return [cls() for _ in range(a)] | null |
10,159 | import argparse
import dataclasses
from attr import define, field
from attr.setters import frozen
import functools
import gc
import math
import os
from typing import Tuple, Union, Optional, Any, Sequence, List
import numpy as np
import torch
def array_2d(a, b, cls):
return [[cls() for _ in range(b)] for _ in range... | null |
10,160 | import argparse
import dataclasses
from attr import define, field
from attr.setters import frozen
import functools
import gc
import math
import os
from typing import Tuple, Union, Optional, Any, Sequence, List
import numpy as np
import torch
def array_3d(a, b, c, cls):
return [[[cls() for _ in range(c)] for _ in r... | null |
10,161 | import argparse
import dataclasses
from attr import define, field
from attr.setters import frozen
import functools
import gc
import math
import os
from typing import Tuple, Union, Optional, Any, Sequence, List
import numpy as np
import torch
def array_4d(a, b, c, d, cls):
return [[[[cls() for _ in range(d)] for _ ... | null |
10,162 | import argparse
import dataclasses
from attr import define, field
from attr.setters import frozen
import functools
import gc
import math
import os
from typing import Tuple, Union, Optional, Any, Sequence, List
import numpy as np
import torch
class BenchmarkResult:
def read_benchmark_log(filename):
with open(filena... | null |
10,163 | import numpy as np
import torch
from flexgen.profile_bandwidth import benchmark_func
def benchmark_func(func, number, repeat, warmup=3):
for i in range(warmup):
func()
costs = [0]
for i in range(repeat):
torch.cuda.synchronize()
tic = time.time()
for i in range(number):
... | null |
10,164 | import dataclasses
import torch
import numpy as np
from flexgen.pytorch_backend import (TorchTensor, TorchDevice,
DeviceType, general_copy, fix_recursive_import)
from flexgen.utils import np_dtype_to_torch_dtype
default_cache_config = CompressionConfig(
num_bits=0, group_size=0, group_dim=0, symmetric=False, en... | null |
10,165 | import dataclasses
import torch
import numpy as np
from flexgen.pytorch_backend import (TorchTensor, TorchDevice,
DeviceType, general_copy, fix_recursive_import)
from flexgen.utils import np_dtype_to_torch_dtype
default_cache_config = CompressionConfig(
num_bits=0, group_size=0, group_dim=0, symmetric=False, en... | null |
10,166 | import dataclasses
import torch
import numpy as np
from flexgen.pytorch_backend import (TorchTensor, TorchDevice,
DeviceType, general_copy, fix_recursive_import)
from flexgen.utils import np_dtype_to_torch_dtype
def compress(tensor, config):
"""Simulate group-wise quantization."""
if not config.enabled:
... | null |
10,167 | import dataclasses
import torch
import numpy as np
from flexgen.pytorch_backend import (TorchTensor, TorchDevice,
DeviceType, general_copy, fix_recursive_import)
from flexgen.utils import np_dtype_to_torch_dtype
class CompressionConfig:
"""Group-wise quantization."""
num_bits: int
group_size: int
gr... | null |
10,168 | import dataclasses
import torch
import numpy as np
from flexgen.pytorch_backend import (TorchTensor, TorchDevice,
DeviceType, general_copy, fix_recursive_import)
from flexgen.utils import np_dtype_to_torch_dtype
class CompressionConfig:
"""Group-wise quantization."""
num_bits: int
group_size: int
gr... | null |
10,169 | from enum import Enum, auto
from functools import partial
from itertools import count
import os
import queue
import shutil
import time
import threading
from typing import Optional, Union, Tuple
import torch
import torch.nn.functional as F
import numpy as np
from flexgen.utils import (GB, T, cpu_mem_stats, vector_gather... | null |
10,170 | from enum import Enum, auto
from functools import partial
from itertools import count
import os
import queue
import shutil
import time
import threading
from typing import Optional, Union, Tuple
import torch
import torch.nn.functional as F
import numpy as np
from flexgen.utils import (GB, T, cpu_mem_stats, vector_gather... | The copy worker thread. |
10,171 | import argparse
from dataclasses import asdict, replace
import json
import math
import os
import time
from flexgen.flex_opt import (Policy, OptLM, ExecutionEnv, CompressionConfig,
str2bool)
from helm.benchmark.presentation.run_entry import RunEntry
from helm.benchmark.run import run_entries_to_run_specs
from he... | null |
10,172 | import argparse
from tqdm import tqdm
import json
import math
import logging
from pathlib import Path
import time
import numpy as np
from transformers import AutoTokenizer, AutoConfig
import flexgen.apps.data_wrangle.utils.data_utils as data_utils
import flexgen.apps.data_wrangle.utils.prompt_utils as prompt_utils
from... | Generate args. |
10,173 | import argparse
from tqdm import tqdm
import json
import math
import logging
from pathlib import Path
import time
import numpy as np
from transformers import AutoTokenizer, AutoConfig
import flexgen.apps.data_wrangle.utils.data_utils as data_utils
import flexgen.apps.data_wrangle.utils.prompt_utils as prompt_utils
from... | null |
10,174 | import argparse
from tqdm import tqdm
import json
import math
import logging
from pathlib import Path
import time
import numpy as np
from transformers import AutoTokenizer, AutoConfig
import flexgen.apps.data_wrangle.utils.data_utils as data_utils
import flexgen.apps.data_wrangle.utils.prompt_utils as prompt_utils
from... | null |
10,175 | import logging
from pathlib import Path
from typing import List
from rich.logging import RichHandler
import logging
The provided code snippet includes necessary dependencies for implementing the `setup_logger` function. Write a Python function `def setup_logger(log_dir: str)` to solve the following problem:
Create lo... | Create log directory and logger. |
10,176 | import logging
from functools import partial
from pathlib import Path
from typing import Dict, List
import pandas as pd
from flexgen.apps.data_wrangle.utils import constants
logger = logging.getLogger(__name__)
def read_raw_data(
data_dir: str,
add_instruction: bool = False,
task_instruction_idx: int = 0,
... | Read in data where each directory is unique for a task. |
10,177 | import torch
import torch.distributed as dist
_COMM_DEVICE = None
_PIPELINE_PARALLEL_PRED_GROUP = None
_PIPELINE_PARALLEL_SUCC_GROUP = None
def suppress_output(rank):
"""Suppress printing on the current device. Force printing with `force=True`."""
import builtins as __builtin__
builtin_print = __builtin__.p... | null |
10,178 | import torch
import torch.distributed as dist
_PIPELINE_PARALLEL_PRED_GROUP = None
def get_pipeline_parallel_pred_group():
return _PIPELINE_PARALLEL_PRED_GROUP | null |
10,179 | import torch
import torch.distributed as dist
_PIPELINE_PARALLEL_SUCC_GROUP = None
def get_pipeline_parallel_succ_group():
return _PIPELINE_PARALLEL_SUCC_GROUP | null |
10,180 | import torch
import torch.distributed as dist
_COMM_DEVICE = None
def get_comm_device():
return _COMM_DEVICE | null |
10,181 | import argparse
import dataclasses
import os
import pickle
import time
from typing import Union, List, Optional
import numpy as np
from tqdm import tqdm
import torch
from transformers import AutoTokenizer
from flexgen.compression import CompressionConfig
from flexgen.opt_config import OptConfig, get_opt_config, downloa... | null |
10,182 | import argparse
import dataclasses
import os
import pickle
import time
from typing import Union, List, Optional
import numpy as np
from tqdm import tqdm
import torch
from transformers import AutoTokenizer
from flexgen.compression import CompressionConfig
from flexgen.opt_config import OptConfig, get_opt_config, downloa... | null |
10,183 | import argparse
import dataclasses
import os
import pickle
import time
from typing import Union, List, Optional
import numpy as np
from tqdm import tqdm
import torch
from transformers import AutoTokenizer
from flexgen.compression import CompressionConfig
from flexgen.opt_config import OptConfig, get_opt_config, downloa... | null |
10,184 | import argparse
from itertools import count
import os
import pickle
import traceback
from typing import Union, List, Optional
import numpy as np
import torch
import torch.distributed as dist
from transformers import AutoTokenizer
from flexgen.compression import CompressionConfig
from flexgen.dist_utils import initializ... | null |
10,185 | import argparse
from itertools import count
import os
import pickle
import traceback
from typing import Union, List, Optional
import numpy as np
import torch
import torch.distributed as dist
from transformers import AutoTokenizer
from flexgen.compression import CompressionConfig
from flexgen.dist_utils import initializ... | null |
10,186 | import time
from argparse import ArgumentParser
from statistics import mean
import torch
from petals import DistributedBloomConfig, DistributedBloomForCausalLM
from torch.multiprocessing import Process, Event, Queue
from transformers import AutoTokenizer, BloomConfig, OPTConfig
def _patch_bloom_config(bloom_config: Bl... | null |
10,187 | import time
from argparse import ArgumentParser
from statistics import mean
import torch
from petals import DistributedBloomConfig, DistributedBloomForCausalLM
from torch.multiprocessing import Process, Event, Queue
from transformers import AutoTokenizer, BloomConfig, OPTConfig
def client_process(
finished_warmup,
... | null |
10,188 | import argparse
import multiprocessing as mp
import os
import pickle
import time
import numpy as np
from accelerate import (infer_auto_device_map, init_empty_weights,
load_checkpoint_and_dispatch)
from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM
from transformers import OPTForCausalLM
import... | null |
10,189 | import argparse
import multiprocessing as mp
import os
import pickle
import time
import numpy as np
from accelerate import (infer_auto_device_map, init_empty_weights,
load_checkpoint_and_dispatch)
from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM
from transformers import OPTForCausalLM
import... | null |
10,190 | import argparse
from dataclasses import dataclass
import time
from flexgen.utils import run_cmd
def run_huggingface(model, prompt_len, gen_len, cut_gen_len, batch_size,
num_nodes, num_gpus_per_node,
use_ds, cpu, disk, dummy, log_file=None, pkl_file=None):
assert num_nodes == ... | null |
10,191 | import os
import sys
import subprocess
from setuptools import setup, find_packages
from setuptools.command import egg_info
import time
from op_builder import ALL_OPS, get_default_compute_capabilities, OpBuilder
from op_builder.builder import installed_cuda_version
ERROR = f"{RED_START} [ERROR] {RED_END}"
print(f"DS_BUI... | null |
10,192 | import os
import sys
import subprocess
from setuptools import setup, find_packages
from setuptools.command import egg_info
import time
from op_builder import ALL_OPS, get_default_compute_capabilities, OpBuilder
from op_builder.builder import installed_cuda_version
with open('deepspeed/git_version_info_installed.py', 'w... | null |
10,193 | import os
import sys
import subprocess
from setuptools import setup, find_packages
from setuptools.command import egg_info
import time
from op_builder import ALL_OPS, get_default_compute_capabilities, OpBuilder
from op_builder.builder import installed_cuda_version
if sys.platform == "win32":
# This creates a symbol... | null |
10,194 | import os
import sys
import subprocess
from setuptools import setup, find_packages
from setuptools.command import egg_info
import time
from op_builder import ALL_OPS, get_default_compute_capabilities, OpBuilder
from op_builder.builder import installed_cuda_version
BUILD_OP_DEFAULT = int(os.environ.get('DS_BUILD_OPS', B... | null |
10,195 | import os
import sys
import subprocess
from setuptools import setup, find_packages
from setuptools.command import egg_info
import time
from op_builder import ALL_OPS, get_default_compute_capabilities, OpBuilder
from op_builder.builder import installed_cuda_version
if 'DS_BUILD_STRING' in os.environ:
# Build string ... | null |
10,196 | import os
from .constants import (MODEL_FILE_PREFIX,
MODEL_FILE_SUFFIX,
OPTIM_FILE_SUFFIX,
ZERO_FILE_PREFIX)
MODEL_FILE_PREFIX = 'mp_rank_'
MODEL_FILE_SUFFIX = '_model_states.pt'
def get_model_ckpt_name_for_rank(base_folder, mp_rank_str):
ckp... | null |
10,197 | import os
from .constants import (MODEL_FILE_PREFIX,
MODEL_FILE_SUFFIX,
OPTIM_FILE_SUFFIX,
ZERO_FILE_PREFIX)
MODEL_FILE_PREFIX = 'mp_rank_'
ZERO_FILE_PREFIX = 'zero_pp_rank_'
OPTIM_FILE_SUFFIX = '_optim_states.pt'
def get_zero_ckpt_name_for_rank(... | null |
10,198 | import os
from .constants import (MODEL_FILE_PREFIX,
MODEL_FILE_SUFFIX,
OPTIM_FILE_SUFFIX,
ZERO_FILE_PREFIX)
MODEL_FILE_SUFFIX = '_model_states.pt'
def get_layer_ckpt_name_for_rank(base_folder, layer_id, tp_rank):
ckpt_file = f'{layer_id}-mod... | null |
10,199 | from .reshape_utils import partition_data
class meg_2d_parallel_map(object):
def __init__(self, pp_degree, tp_degree):
self.pp_degree = pp_degree
self.tp_degree = tp_degree
self.map = {}
def simple_init(self):
self.map = {
self._make_key(i // self.tp_degree,
... | null |
10,200 | from .reshape_utils import partition_data
def get_mpu_ranks(tp_size=1, pp_size=1, dp_size=1, virtual_pp_size=None):
"""
Initialize model data parallel groups.
Arguments:
tp_size: number of GPUs used to parallelize model tensor.
pp_size: number of GPUs used to parallelize model pipeline.
... | reshape([tp_size_src, pp_size_src, dp_size_src], [tp_size_tgt, pp_size_tgt, dp_size_tgt]) |
10,201 | import os
import torch
from collections import OrderedDict
from .constants import (ZERO_FILE_PREFIX, FP16_ZERO_FILE_PREFIX, BF16_ZERO_FILE_PREFIX)
def basic_folder_validation(dir):
assert os.path.exists(dir), f'{dir} path does not exist'
assert os.path.isdir(dir), f'{dir} is not a folder' | null |
10,202 | import os
import torch
from collections import OrderedDict
from .constants import (ZERO_FILE_PREFIX, FP16_ZERO_FILE_PREFIX, BF16_ZERO_FILE_PREFIX)
def validate_files(file_list):
for file in file_list:
if not os.path.isfile(file):
print(f'Error: {file} is not existent') | null |
10,203 | import os
import torch
import types
from .constants import (FP32_WEIGHT_KEY,
PARAM,
VOCAB_DIVISIBILITY_PADDING_TENSOR,
CAT_DIM)
def load_hp_checkpoint_state(self, folder, tp_rank, tp_world_size):
hp_mapping = self._hp_mapping
optim_state_ke... | null |
10,204 | from .reshape_utils import (get_files,
get_files_with_prefix,
partition_data,
get_zero_files)
from .constants import (MODEL_FILE_PREFIX, LAYER_FILE_PREFIX)
from .reshape_meg_2d import (reshape_meg_2d_parallel, meg_2d_parallel_map)
class... | null |
10,205 | from .reshape_utils import (get_files,
get_files_with_prefix,
partition_data,
get_zero_files)
from .constants import (MODEL_FILE_PREFIX, LAYER_FILE_PREFIX)
from .reshape_meg_2d import (reshape_meg_2d_parallel, meg_2d_parallel_map)
clas... | null |
10,206 | from .reshape_utils import (get_files,
get_files_with_prefix,
partition_data,
get_zero_files)
from .constants import (MODEL_FILE_PREFIX, LAYER_FILE_PREFIX)
from .reshape_meg_2d import (reshape_meg_2d_parallel, meg_2d_parallel_map)
def ... | null |
10,207 | from collections import OrderedDict
import torch
import sys
import os
from deepspeed import comm as dist
from deepspeed.runtime.constants import PIPE_REPLICATED
from deepspeed.ops.op_builder import UtilsBuilder
from deepspeed.runtime import ZeROOptimizer
from packaging import version as pkg_version
from deepspeed.git_v... | null |
10,208 | from collections.abc import Iterable
from deepspeed.moe.utils import is_moe_param
import os
import psutil
import gc
from math import sqrt
from math import floor
from bisect import bisect_left
import torch
from torch._six import inf
from deepspeed import comm as dist
from deepspeed.utils import groups, logger
from deeps... | null |
10,209 | from collections.abc import Iterable
from deepspeed.moe.utils import is_moe_param
import os
import psutil
import gc
from math import sqrt
from math import floor
from bisect import bisect_left
import torch
from torch._six import inf
from deepspeed import comm as dist
from deepspeed.utils import groups, logger
from deeps... | Create the directory path to ``filename`` if it does not already exist. Args: filename (str): A file path. |
10,210 | from collections.abc import Iterable
from deepspeed.moe.utils import is_moe_param
import os
import psutil
import gc
from math import sqrt
from math import floor
from bisect import bisect_left
import torch
from torch._six import inf
from deepspeed import comm as dist
from deepspeed.utils import groups, logger
from deeps... | Set the random seed for common PRNGs used during training: random, numpy, and torch. Args: seed (int): the seed to use |
10,211 | from collections.abc import Iterable
from deepspeed.moe.utils import is_moe_param
import os
import psutil
import gc
from math import sqrt
from math import floor
from bisect import bisect_left
import torch
from torch._six import inf
from deepspeed import comm as dist
from deepspeed.utils import groups, logger
from deeps... | Return a copy of tensor on specified device. Works on individual tensors, and tensors contained/nested in lists, tuples, and dicts. Parameters: item: tensor to copy or (possibly nested) container of tensors to copy. device: target device criterion_func: Function to restrict copy operation to items meet criterion Return... |
10,212 | from collections.abc import Iterable
from deepspeed.moe.utils import is_moe_param
import os
import psutil
import gc
from math import sqrt
from math import floor
from bisect import bisect_left
import torch
from torch._six import inf
from deepspeed import comm as dist
from deepspeed.utils import groups, logger
from deeps... | Move tensor on to specified device by changing the storage. Works on individual tensors, and tensors contained/nested in lists, tuples, and dicts. Parameters: item: tensor to move or (possibly nested) container of tensors to move. device: target device criterion_func: Function to restrict move operation to items meet c... |
10,213 | from collections.abc import Iterable
from deepspeed.moe.utils import is_moe_param
import os
import psutil
import gc
from math import sqrt
from math import floor
from bisect import bisect_left
import torch
from torch._six import inf
from deepspeed import comm as dist
from deepspeed.utils import groups, logger
from deeps... | null |
10,214 | from collections.abc import Iterable
from deepspeed.moe.utils import is_moe_param
import os
import psutil
import gc
from math import sqrt
from math import floor
from bisect import bisect_left
import torch
from torch._six import inf
from deepspeed import comm as dist
from deepspeed.utils import groups, logger
from deeps... | Compute total from a list of norms |
10,215 | from collections.abc import Iterable
from deepspeed.moe.utils import is_moe_param
import os
import psutil
import gc
from math import sqrt
from math import floor
from bisect import bisect_left
import torch
from torch._six import inf
from deepspeed import comm as dist
from deepspeed.utils import groups, logger
from deeps... | Clips gradient norm of an iterable of parameters. This has been adapted from Nvidia megatron. We add norm averaging to consider MoE params when calculating norm as they will result in different norms across different ranks. This is adapted from torch.nn.utils.clip_grad.clip_grad_norm_ and added functionality to handle ... |
10,216 | from collections.abc import Iterable
from deepspeed.moe.utils import is_moe_param
import os
import psutil
import gc
from math import sqrt
from math import floor
from bisect import bisect_left
import torch
from torch._six import inf
from deepspeed import comm as dist
from deepspeed.utils import groups, logger
from deeps... | Compute the number of grads with zero values. This is adapted from get_grad_norm Arguments: parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a single Tensor that will have gradients normalized Returns: Total number of params with zero values (viewed as a single vector). |
10,217 | from collections.abc import Iterable
from deepspeed.moe.utils import is_moe_param
import os
import psutil
import gc
from math import sqrt
from math import floor
from bisect import bisect_left
import torch
from torch._six import inf
from deepspeed import comm as dist
from deepspeed.utils import groups, logger
from deeps... | Get norm of an iterable of parameters. This is adapted from torch.nn.utils.clip_grad.clip_grad_norm_ and added functionality to handle model parallel parameters. Note that the gradients are modified in place. Taken from Nvidia Megatron. Arguments: parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a sin... |
10,218 | from collections.abc import Iterable
from deepspeed.moe.utils import is_moe_param
import os
import psutil
import gc
from math import sqrt
from math import floor
from bisect import bisect_left
import torch
from torch._six import inf
from deepspeed import comm as dist
from deepspeed.utils import groups, logger
from deeps... | null |
10,219 | from collections.abc import Iterable
from deepspeed.moe.utils import is_moe_param
import os
import psutil
import gc
from math import sqrt
from math import floor
from bisect import bisect_left
import torch
from torch._six import inf
from deepspeed import comm as dist
from deepspeed.utils import groups, logger
from deeps... | null |
10,220 | from collections.abc import Iterable
from deepspeed.moe.utils import is_moe_param
import os
import psutil
import gc
from math import sqrt
from math import floor
from bisect import bisect_left
import torch
from torch._six import inf
from deepspeed import comm as dist
from deepspeed.utils import groups, logger
from deeps... | null |
10,221 | from collections.abc import Iterable
from deepspeed.moe.utils import is_moe_param
import os
import psutil
import gc
from math import sqrt
from math import floor
from bisect import bisect_left
import torch
from torch._six import inf
from deepspeed import comm as dist
from deepspeed.utils import groups, logger
from deeps... | null |
10,222 | from collections.abc import Iterable
from deepspeed.moe.utils import is_moe_param
import os
import psutil
import gc
from math import sqrt
from math import floor
from bisect import bisect_left
import torch
from torch._six import inf
from deepspeed import comm as dist
from deepspeed.utils import groups, logger
from deeps... | Construct a string representation of a call. Args: base (str): name of the call args (tuple, optional): args to ``base`` kwargs (dict, optional): kwargs supplied to ``base`` Returns: str: A string representation of base(*args, **kwargs) |
10,223 | from collections.abc import Iterable
from deepspeed.moe.utils import is_moe_param
import os
import psutil
import gc
from math import sqrt
from math import floor
from bisect import bisect_left
import torch
from torch._six import inf
from deepspeed import comm as dist
from deepspeed.utils import groups, logger
from deeps... | null |
10,224 | from collections.abc import Iterable
from deepspeed.moe.utils import is_moe_param
import os
import psutil
import gc
from math import sqrt
from math import floor
from bisect import bisect_left
import torch
from torch._six import inf
from deepspeed import comm as dist
from deepspeed.utils import groups, logger
from deeps... | Clip the gradient of a list of parameters. Args: parameters: List of parameters whose .grad will be clipped. global_grad_norm (float, optional): Precomputed gradient norm. Defaults to None. mpu (optional): model parallelism unit. Defaults to None. eps (float, optional): epsilon value added to grad norm. Defaults to 1e-... |
10,225 | from collections.abc import Iterable
from deepspeed.moe.utils import is_moe_param
import os
import psutil
import gc
from math import sqrt
from math import floor
from bisect import bisect_left
import torch
from torch._six import inf
from deepspeed import comm as dist
from deepspeed.utils import groups, logger
from deeps... | Clip list of tensors by global norm. Args: input_tensors: List of tensors to be clipped global_norm (float, optional): Precomputed norm. Defaults to None. mpu (optional): model parallelism unit. Defaults to None. eps (float, optional): epsilon value added to grad norm. Defaults to 1e-6 Returns: float: the global norm |
10,226 | from collections.abc import Iterable
from deepspeed.moe.utils import is_moe_param
import os
import psutil
import gc
from math import sqrt
from math import floor
from bisect import bisect_left
import torch
from torch._six import inf
from deepspeed import comm as dist
from deepspeed.utils import groups, logger
from deeps... | null |
10,227 | from collections.abc import Iterable
from deepspeed.moe.utils import is_moe_param
import os
import psutil
import gc
from math import sqrt
from math import floor
from bisect import bisect_left
import torch
from torch._six import inf
from deepspeed import comm as dist
from deepspeed.utils import groups, logger
from deeps... | null |
10,228 | import argparse
from torch.optim import Optimizer
import math
from deepspeed.utils import logger
def add_tuning_arguments(parser):
group = parser.add_argument_group('Convergence Tuning',
'Convergence tuning configurations')
# LR scheduler
group.add_argument('--lr_schedu... | null |
10,229 | import argparse
from torch.optim import Optimizer
import math
from deepspeed.utils import logger
def override_lr_range_test_params(args, params):
if hasattr(args, LR_RANGE_TEST_MIN_LR) and args.lr_range_test_min_lr is not None:
params[LR_RANGE_TEST_MIN_LR] = args.lr_range_test_min_lr
if hasattr(args,
... | null |
10,230 | import argparse
from torch.optim import Optimizer
import math
from deepspeed.utils import logger
LR_SCHEDULE = 'lr_schedule'
LR_RANGE_TEST = 'LRRangeTest'
ONE_CYCLE = 'OneCycle'
VALID_LR_SCHEDULES = [LR_RANGE_TEST, ONE_CYCLE, WARMUP_LR, WARMUP_DECAY_LR]
def override_lr_range_test_params(args, params):
if hasattr(ar... | null |
10,231 | import argparse
from torch.optim import Optimizer
import math
from deepspeed.utils import logger
LR_RANGE_TEST = 'LRRangeTest'
ONE_CYCLE = 'OneCycle'
VALID_LR_SCHEDULES = [LR_RANGE_TEST, ONE_CYCLE, WARMUP_LR, WARMUP_DECAY_LR]
LR_RANGE_TEST_MIN_LR = 'lr_range_test_min_lr'
CYCLE_MAX_LR = 'cycle_max_lr'
WARMUP_MAX_LR = 'w... | null |
10,232 | import argparse
from torch.optim import Optimizer
import math
from deepspeed.utils import logger
def get_torch_optimizer(optimizer):
if isinstance(optimizer, Optimizer):
return optimizer
if hasattr(optimizer, 'optimizer') and isinstance(optimizer.optimizer, Optimizer):
return optimizer.optimiz... | null |
10,233 | import json
import collections
import collections.abc
from functools import reduce
from pydantic import BaseModel
from deepspeed.utils import logger
def get_list_param(param_dict, param_name, param_default_value):
return param_dict.get(param_name, param_default_value) | null |
10,234 | import json
import collections
import collections.abc
from functools import reduce
from pydantic import BaseModel
from deepspeed.utils import logger
def get_dict_param(param_dict, param_name, param_default_value):
return param_dict.get(param_name, param_default_value) | null |
10,235 | import math
from typing import List
import torch
from torch import Tensor
from deepspeed import comm as dist
from torch.distributed import ProcessGroup
import torch.nn.functional
from deepspeed.utils import instrument_w_nvtx
def _torch_reduce_scatter_fn(input_tensor: Tensor,
output_tensor: ... | simultaneously reduce-scatter a list of tensors - this can be done more efficiently than individual reduce scatter calls TODO. see if PyTorch team wants a c++ version of this for ProcessGroupNCCL |
10,236 | 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 |
10,237 | 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 |
10,238 | 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 |
10,239 | 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 |
10,240 | 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 |
10,241 | 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 |
10,242 | 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 |
10,243 | 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 |
10,244 | 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 |
10,245 | 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 |
10,246 | 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 |
10,247 | 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 |
10,248 | 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 |
10,249 | 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 |
10,250 | 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 |
10,251 | 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 |
10,252 | 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 |
10,253 | 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 |
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