id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
141,212 | import contextlib
import hashlib
import json
import random
import string
import time
from typing import Optional, Literal, Union, Tuple
from nonebot import logger as nb_logger
from tortoise.queryset import Q
from LittlePaimon.config import config
from LittlePaimon.database import PublicCookie, PrivateCookie, CookieCach... | null |
141,213 | import contextlib
import hashlib
import json
import random
import string
import time
from typing import Optional, Literal, Union, Tuple
from nonebot import logger as nb_logger
from tortoise.queryset import Q
from LittlePaimon.config import config
from LittlePaimon.database import PublicCookie, PrivateCookie, CookieCach... | null |
141,214 | from difflib import get_close_matches
from typing import Union, Literal, List, Optional, Dict
from .files import load_json
from .path import JSON_DATA
def load_json(path: Union[Path, str], encoding: str = 'utf-8'):
"""
读取本地json文件，返回文件数据。
:param path: 文件路径
:param encoding: 编码，默认为utf-8
:return: 数据
... | 根据角色id获取角色名 :param role_id: 角色id :return: 角色名字符串 |
141,215 | from difflib import get_close_matches
from typing import Union, Literal, List, Optional, Dict
from .files import load_json
from .path import JSON_DATA
def load_json(path: Union[Path, str], encoding: str = 'utf-8'):
"""
读取本地json文件，返回文件数据。
:param path: 文件路径
:param encoding: 编码，默认为utf-8
:return: 数据
... | null |
141,216 | from difflib import get_close_matches
from typing import Union, Literal, List, Optional, Dict
from .files import load_json
from .path import JSON_DATA
def load_json(path: Union[Path, str], encoding: str = 'utf-8'):
"""
读取本地json文件，返回文件数据。
:param path: 文件路径
:param encoding: 编码，默认为utf-8
:return: 数据
... | null |
141,217 | from difflib import get_close_matches
from typing import Union, Literal, List, Optional, Dict
from .files import load_json
from .path import JSON_DATA
def load_json(path: Union[Path, str], encoding: str = 'utf-8'):
"""
读取本地json文件，返回文件数据。
:param path: 文件路径
:param encoding: 编码，默认为utf-8
:return: 数据
... | null |
141,218 | from pathlib import Path
from typing import Union
from nonebot.adapters.onebot.v11 import Message
from .path import JSON_DATA
text_filter = DFAFilter()
text_filter.parse(JSON_DATA / 'ban_word.txt')
The provided code snippet includes necessary dependencies for implementing the `filter_msg` function. Write a Python func... | 过滤违禁词 :param message: 过滤的消息 :param repl: 替换词 |
141,219 | import asyncio
import datetime
import functools
import hashlib
import inspect
import time
import zipfile
from collections import defaultdict
from pathlib import Path
from LittlePaimon.config import config
from .logger import logger
from .requests import aiorequests
The provided code snippet includes necessary dependen... | 缓存装饰器 :param ttl: 过期时间 |
141,220 | import asyncio
import datetime
import functools
import hashlib
import inspect
import time
import zipfile
from collections import defaultdict
from pathlib import Path
from LittlePaimon.config import config
from .logger import logger
from .requests import aiorequests
RESOURCE_BASE_PATH = Path() / 'resources'
class logge... | null |
141,221 | import datetime
import re
from pathlib import Path
import git
from git.exc import InvalidGitRepositoryError, GitCommandError
from nonebot.utils import run_sync
from . import __version__, NICKNAME
from .requests import aiorequests
from .logger import logger
class aiorequests:
async def get(url: str,
... | null |
141,222 | from pathlib import Path
from ssl import SSLCertVerificationError
from typing import Union
from ruamel.yaml import YAML
from .requests import aiorequests
def load_json(path: Union[Path, str], encoding: str = 'utf-8'):
"""
读取本地json文件，返回文件数据。
:param path: 文件路径
:param encoding: 编码，默认为utf-8
:return: 数据
... | 从网络url中读取json，当有path参数时，如果path文件不存在，就会从url下载保存到path，如果path文件存在，则直接读取path :param url: url :param path: 本地json文件路径 :param force_refresh: 是否强制重新下载 :return: json字典 |
141,223 | from pathlib import Path
from ssl import SSLCertVerificationError
from typing import Union
from ruamel.yaml import YAML
from .requests import aiorequests
The provided code snippet includes necessary dependencies for implementing the `load_yaml` function. Write a Python function `def load_yaml(path: Union[Path, str], e... | 读取本地yaml文件，返回字典。 :param path: 文件路径 :param encoding: 编码，默认为utf-8 :return: 字典 |
141,224 | from pathlib import Path
from ssl import SSLCertVerificationError
from typing import Union
from ruamel.yaml import YAML
from .requests import aiorequests
The provided code snippet includes necessary dependencies for implementing the `save_yaml` function. Write a Python function `def save_yaml(data: dict, path: Union[P... | 保存yaml文件 :param data: 数据 :param path: 保存路径 :param encoding: 编码 |
141,225 | import logging
from apscheduler.schedulers.asyncio import AsyncIOScheduler
from nonebot import get_driver
from nonebot.log import LoguruHandler, logger
from pydantic import Field, BaseSettings
scheduler = AsyncIOScheduler()
scheduler.configure(plugin_config.apscheduler_config)
async def _start_scheduler():
if not ... | null |
141,226 | import logging
from apscheduler.schedulers.asyncio import AsyncIOScheduler
from nonebot import get_driver
from nonebot.log import LoguruHandler, logger
from pydantic import Field, BaseSettings
scheduler = AsyncIOScheduler()
scheduler.configure(plugin_config.apscheduler_config)
async def _shutdown_scheduler():
if s... | null |
141,227 | import random
import re
import time
from io import BytesIO
from pathlib import Path
from typing import Union, Optional, Tuple, List
from PIL import Image
from nonebot import get_bot
from nonebot.adapters.onebot.v11 import MessageEvent, Message, MessageSegment, GroupMessageEvent
from nonebot.rule import Rule
from nonebo... | null |
141,228 | import random
import re
import time
from io import BytesIO
from pathlib import Path
from typing import Union, Optional, Tuple, List
from PIL import Image
from nonebot import get_bot
from nonebot.adapters.onebot.v11 import MessageEvent, Message, MessageSegment, GroupMessageEvent
from nonebot.rule import Rule
from nonebo... | 获取查询操作中的user_id、uid和图片，并将过滤uid后的msg存放到T_State中 :param limit: 限制个数 :param only_cn: 是否只接受国服uid :return: 查询对象列表 |
141,229 | import random
import re
import time
from io import BytesIO
from pathlib import Path
from typing import Union, Optional, Tuple, List
from PIL import Image
from nonebot import get_bot
from nonebot.adapters.onebot.v11 import MessageEvent, Message, MessageSegment, GroupMessageEvent
from nonebot.rule import Rule
from nonebo... | 从消息中提取uid :param only_cn: 是否只接受国服uid :return: uid |
141,230 | import random
import re
import time
from io import BytesIO
from pathlib import Path
from typing import Union, Optional, Tuple, List
from PIL import Image
from nonebot import get_bot
from nonebot.adapters.onebot.v11 import MessageEvent, Message, MessageSegment, GroupMessageEvent
from nonebot.rule import Rule
from nonebo... | 从命令中提取出原神的角色，需配合CommandUID使用 :param limit: 限制个数 :return: 角色名列表 |
141,231 | import random
import re
import time
from io import BytesIO
from pathlib import Path
from typing import Union, Optional, Tuple, List
from PIL import Image
from nonebot import get_bot
from nonebot.adapters.onebot.v11 import MessageEvent, Message, MessageSegment, GroupMessageEvent
from nonebot.rule import Rule
from nonebo... | 根据消息事件的类型获取对象id 私聊->用户id 群聊->群id 频道->子频道id :return: 对象id |
141,232 | import random
import re
import time
from io import BytesIO
from pathlib import Path
from typing import Union, Optional, Tuple, List
from PIL import Image
from nonebot import get_bot
from nonebot.adapters.onebot.v11 import MessageEvent, Message, MessageSegment, GroupMessageEvent
from nonebot.rule import Rule
from nonebo... | 获取消息中的开关类型，如果没有则返回None :return: Optional[bool] |
141,233 | import random
import re
import time
from io import BytesIO
from pathlib import Path
from typing import Union, Optional, Tuple, List
from PIL import Image
from nonebot import get_bot
from nonebot.adapters.onebot.v11 import MessageEvent, Message, MessageSegment, GroupMessageEvent
from nonebot.rule import Rule
from nonebo... | null |
141,234 | import random
import re
import time
from io import BytesIO
from pathlib import Path
from typing import Union, Optional, Tuple, List
from PIL import Image
from nonebot import get_bot
from nonebot.adapters.onebot.v11 import MessageEvent, Message, MessageSegment, GroupMessageEvent
from nonebot.rule import Rule
from nonebo... | 获取消息中的小时:分钟格式时间元组，如果没有则返回None :return: (小时, 分钟) |
141,235 | import random
import re
import time
from io import BytesIO
from pathlib import Path
from typing import Union, Optional, Tuple, List
from PIL import Image
from nonebot import get_bot
from nonebot.adapters.onebot.v11 import MessageEvent, Message, MessageSegment, GroupMessageEvent
from nonebot.rule import Rule
from nonebo... | 检查时间戳是否在指定天数内 :param time_stamp: 时间戳 :param days: 天数 :return: True/False |
141,236 | import random
import re
import time
from io import BytesIO
from pathlib import Path
from typing import Union, Optional, Tuple, List
from PIL import Image
from nonebot import get_bot
from nonebot.adapters.onebot.v11 import MessageEvent, Message, MessageSegment, GroupMessageEvent
from nonebot.rule import Rule
from nonebo... | 撤回指定群消息（需管理员权限且权限大于发送者） :param event: 消息事件 :return: 是否撤回成功 |
141,237 | import random
import re
import time
from io import BytesIO
from pathlib import Path
from typing import Union, Optional, Tuple, List
from PIL import Image
from nonebot import get_bot
from nonebot.adapters.onebot.v11 import MessageEvent, Message, MessageSegment, GroupMessageEvent
from nonebot.rule import Rule
from nonebo... | null |
141,238 | from contextlib import asynccontextmanager
from contextlib import suppress
from typing import Optional, Literal, Tuple, Union, List, AsyncGenerator, AsyncIterator
from playwright.async_api import Page, Browser, Playwright, async_playwright, Error
from . import DRIVER
from .logger import logger
from LittlePaimon.config ... | null |
141,239 | from contextlib import asynccontextmanager
from contextlib import suppress
from typing import Optional, Literal, Tuple, Union, List, AsyncGenerator, AsyncIterator
from playwright.async_api import Page, Browser, Playwright, async_playwright, Error
from . import DRIVER
from .logger import logger
from LittlePaimon.config ... | null |
141,240 | import os
import sys
import torch
from setuptools import Extension, find_packages, setup
from torch.utils.cpp_extension import (
CppExtension,
CUDAExtension,
BuildExtension,
CUDA_HOME,
)
version = write_version_py()
with open("README.md") as f:
readme = f.read()
def write_version_py():
with ope... | null |
141,241 | import os
import sys
import torch
from setuptools import Extension, find_packages, setup
from torch.utils.cpp_extension import (
CppExtension,
CUDAExtension,
BuildExtension,
CUDA_HOME,
)
version = write_version_py()
extension_modules = [
NumpyExtension(
"metaseq.data.data_utils_fast",
... | null |
141,242 | import torch
import torch.nn as nn
import torch.nn.functional as F
try:
from apex.normalization import FusedLayerNorm as _FusedLayerNorm
has_fused_layernorm = True
class FusedLayerNorm(_FusedLayerNorm):
def forward(self, x):
if not x.is_cuda:
return super().forward(x)
... | null |
141,243 |
def checkpoint_wrapper(module, *args, **kwargs):
try:
from metaseq.modules.checkpoint_activation_wrapper.checkpoint_activations import (
checkpoint_wrapper as _checkpoint_wrapper,
)
except ImportError:
raise ImportError(
"Cannot find fairscale.nn.misc.checkpoint... | null |
141,244 | import math
from typing import Dict, Optional
import torch
import torch.nn as nn
from torch import Tensor
from metaseq import utils
from metaseq.modules import (
ActivationFn,
ModelParallelMultiheadAttention,
Dropout,
FeedForward,
LayerNorm,
)
from metaseq.modules.megatron.mpu import (
ColumnPar... | null |
141,245 | import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Callable, List
def relu(x):
return F.relu(x)
def relu_squared(x: torch.Tensor):
return F.relu(x).pow(2) | null |
141,246 | import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Callable, List
def gelu_back(g, x):
tanh_out = torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x))
# sqrt(2/pi) * 3 * 0.044715 -> 0.1070322243
ff = 0.5 * x * (
(1 - tanh_out * tanh_out) * (0.79788456 + 0.107032224... | null |
141,247 | import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Callable, List
def relu_back(g, x):
return g.masked_fill_(x <= 0, 0) | null |
141,248 | import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Callable, List
def swiglu(x: torch.Tensor, gate: torch.Tensor):
return F.silu(x) * gate | null |
141,249 | import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Callable, List
def gelu(x):
return x * 0.5 * (1.0 + torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x)))
def geglu(x: torch.Tensor, gate: torch.Tensor):
return gelu(x) * gate | null |
141,250 | import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Callable, List
def get_available_activation_fns() -> List:
return [
"relu",
"relu_squared",
"gelu",
"tanh",
"linear",
"swiglu",
"geglu",
] | null |
141,251 | from typing import Optional
import torch
from torch import nn as nn
The provided code snippet includes necessary dependencies for implementing the `Embedding` function. Write a Python function `def Embedding( num_embeddings, embedding_dim, padding_idx, initialize_params_on_gpu=False, dtype: Optiona... | Returns an embedding initialized to normal(0, 1/sqrt(embedding_dim)) with the padding token embedding initialized to 0. |
141,252 | import functools
import threading
import weakref
from contextlib import contextmanager
from typing import Any, Dict, Generator, Optional, Tuple
import torch
import torch.nn as nn
import torch.utils.checkpoint as torch_checkpoint
from fairscale.nn.checkpoint.checkpoint_utils import patch_batchnorm
from fairscale.utils.c... | Makes :func:`is_checkpointing_disabled` return :data:`True` within a context. |
141,253 | import functools
import threading
import weakref
from contextlib import contextmanager
from typing import Any, Dict, Generator, Optional, Tuple
import torch
import torch.nn as nn
import torch.utils.checkpoint as torch_checkpoint
from fairscale.nn.checkpoint.checkpoint_utils import patch_batchnorm
from fairscale.utils.c... | Makes :func:`is_checkpointing` return :data:`True` within a context. |
141,254 | import functools
import threading
import weakref
from contextlib import contextmanager
from typing import Any, Dict, Generator, Optional, Tuple
import torch
import torch.nn as nn
import torch.utils.checkpoint as torch_checkpoint
from fairscale.nn.checkpoint.checkpoint_utils import patch_batchnorm
from fairscale.utils.c... | Makes :func:`is_recomputing` return :data:`True` within a context. |
141,255 | import functools
import threading
import weakref
from contextlib import contextmanager
from typing import Any, Dict, Generator, Optional, Tuple
import torch
import torch.nn as nn
import torch.utils.checkpoint as torch_checkpoint
from fairscale.nn.checkpoint.checkpoint_utils import patch_batchnorm
from fairscale.utils.c... | A friendlier wrapper for performing activation checkpointing. Compared to the PyTorch version, this version: - wraps an nn.Module, so that all subsequent calls will use checkpointing - handles keyword arguments in the forward - handles non-Tensor outputs from the forward - supports offloading activations to CPU Usage::... |
141,256 | import functools
import threading
import weakref
from contextlib import contextmanager
from typing import Any, Dict, Generator, Optional, Tuple
import torch
import torch.nn as nn
import torch.utils.checkpoint as torch_checkpoint
from fairscale.nn.checkpoint.checkpoint_utils import patch_batchnorm
from fairscale.utils.c... | null |
141,257 | import functools
import threading
import weakref
from contextlib import contextmanager
from typing import Any, Dict, Generator, Optional, Tuple
import torch
import torch.nn as nn
import torch.utils.checkpoint as torch_checkpoint
from fairscale.nn.checkpoint.checkpoint_utils import patch_batchnorm
from fairscale.utils.c... | null |
141,258 | import functools
import threading
import weakref
from contextlib import contextmanager
from typing import Any, Dict, Generator, Optional, Tuple
import torch
import torch.nn as nn
import torch.utils.checkpoint as torch_checkpoint
from fairscale.nn.checkpoint.checkpoint_utils import patch_batchnorm
from fairscale.utils.c... | Similar to torch.is_autocast_enabled, but compatible with torch 1.5.1 |
141,259 | import functools
import threading
import weakref
from contextlib import contextmanager
from typing import Any, Dict, Generator, Optional, Tuple
import torch
import torch.nn as nn
import torch.utils.checkpoint as torch_checkpoint
from fairscale.nn.checkpoint.checkpoint_utils import patch_batchnorm
from fairscale.utils.c... | Similar to torch.cuda.amp.autocast, but compatible with torch 1.5.1 |
141,261 | import torch
import torch.nn as nn
from .learned_positional_embedding import LearnedPositionalEmbedding
from .sinusoidal_positional_embedding import SinusoidalPositionalEmbedding
class LearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
Padding... | null |
141,262 | import torch
from .utils import ensure_divisibility
_TENSOR_MODEL_PARALLEL_GROUP = None
_PIPELINE_MODEL_PARALLEL_GROUP = None
_MODEL_PARALLEL_GROUP = None
_EMBEDDING_GROUP = None
_POSITION_EMBEDDING_GROUP = None
_DATA_PARALLEL_GROUP = None
_VIRTUAL_PIPELINE_MODEL_PARALLEL_RANK = None
_VIRTUAL_PIPELINE_MODEL_PARALLEL_WO... | Initialize model data parallel groups. Arguments: tensor_model_parallel_size: number of GPUs used for tensor model parallelism. pipeline_model_parallel_size: number of GPUs used for pipeline model parallelism. virtual_pipeline_model_parallel_size: number of virtual stages (interleaved pipeline). pipeline_model_parallel... |
141,264 | import torch
from .utils import ensure_divisibility
_TENSOR_MODEL_PARALLEL_GROUP = None
_PIPELINE_MODEL_PARALLEL_GROUP = None
_MODEL_PARALLEL_GROUP = None
_EMBEDDING_GROUP = None
_POSITION_EMBEDDING_GROUP = None
_DATA_PARALLEL_GROUP = None
The provided code snippet includes necessary dependencies for implementing the ... | Set the groups to none. |
141,265 | import os
import torch
from metaseq.modules.megatron.global_vars import get_global_memory_buffer
from .initialize import (
get_tensor_model_parallel_group,
get_tensor_model_parallel_world_size,
get_tensor_model_parallel_rank,
)
from .utils import split_tensor_along_last_dim
def get_tensor_model_parallel_gr... | All-reduce the input tensor across model parallel group. |
141,266 | import os
import torch
from metaseq.modules.megatron.global_vars import get_global_memory_buffer
from .initialize import (
get_tensor_model_parallel_group,
get_tensor_model_parallel_world_size,
get_tensor_model_parallel_rank,
)
from .utils import split_tensor_along_last_dim
def get_tensor_model_parallel_wo... | Split the tensor along its last dimension and keep the corresponding slice. |
141,267 | import os
import torch
from metaseq.modules.megatron.global_vars import get_global_memory_buffer
from .initialize import (
get_tensor_model_parallel_group,
get_tensor_model_parallel_world_size,
get_tensor_model_parallel_rank,
)
from .utils import split_tensor_along_last_dim
def get_tensor_model_parallel_wo... | Split the tensor along its first dimension and keep the corresponding slice. |
141,268 | import os
import torch
from metaseq.modules.megatron.global_vars import get_global_memory_buffer
from .initialize import (
get_tensor_model_parallel_group,
get_tensor_model_parallel_world_size,
get_tensor_model_parallel_rank,
)
from .utils import split_tensor_along_last_dim
def get_tensor_model_parallel_gr... | Gather tensors and concatinate along the last dimension. |
141,269 | import os
import torch
from metaseq.modules.megatron.global_vars import get_global_memory_buffer
from .initialize import (
get_tensor_model_parallel_group,
get_tensor_model_parallel_world_size,
get_tensor_model_parallel_rank,
)
from .utils import split_tensor_along_last_dim
def get_global_memory_buffer():
... | Gather tensors and concatinate along the first dimension. |
141,270 | import os
import torch
from metaseq.modules.megatron.global_vars import get_global_memory_buffer
from .initialize import (
get_tensor_model_parallel_group,
get_tensor_model_parallel_world_size,
get_tensor_model_parallel_rank,
)
from .utils import split_tensor_along_last_dim
def get_tensor_model_parallel_gr... | Reduce-scatter the input tensor across model parallel group. |
141,271 | import os
import torch
from metaseq.modules.megatron.global_vars import get_global_memory_buffer
from .initialize import (
get_tensor_model_parallel_group,
get_tensor_model_parallel_world_size,
get_tensor_model_parallel_rank,
)
from .utils import split_tensor_along_last_dim
class _CopyToModelParallelRegion(... | null |
141,272 | import os
import torch
from metaseq.modules.megatron.global_vars import get_global_memory_buffer
from .initialize import (
get_tensor_model_parallel_group,
get_tensor_model_parallel_world_size,
get_tensor_model_parallel_rank,
)
from .utils import split_tensor_along_last_dim
class _ReduceFromModelParallelReg... | null |
141,273 | import os
import torch
from metaseq.modules.megatron.global_vars import get_global_memory_buffer
from .initialize import (
get_tensor_model_parallel_group,
get_tensor_model_parallel_world_size,
get_tensor_model_parallel_rank,
)
from .utils import split_tensor_along_last_dim
class _ScatterToModelParallelRegi... | null |
141,274 | import os
import torch
from metaseq.modules.megatron.global_vars import get_global_memory_buffer
from .initialize import (
get_tensor_model_parallel_group,
get_tensor_model_parallel_world_size,
get_tensor_model_parallel_rank,
)
from .utils import split_tensor_along_last_dim
class _GatherFromModelParallelReg... | null |
141,275 | import os
import torch
from metaseq.modules.megatron.global_vars import get_global_memory_buffer
from .initialize import (
get_tensor_model_parallel_group,
get_tensor_model_parallel_world_size,
get_tensor_model_parallel_rank,
)
from .utils import split_tensor_along_last_dim
class _ScatterToSequenceParallelR... | null |
141,276 | import os
import torch
from metaseq.modules.megatron.global_vars import get_global_memory_buffer
from .initialize import (
get_tensor_model_parallel_group,
get_tensor_model_parallel_world_size,
get_tensor_model_parallel_rank,
)
from .utils import split_tensor_along_last_dim
class _GatherFromSequenceParallel... | null |
141,277 | import os
import torch
from metaseq.modules.megatron.global_vars import get_global_memory_buffer
from .initialize import (
get_tensor_model_parallel_group,
get_tensor_model_parallel_world_size,
get_tensor_model_parallel_rank,
)
from .utils import split_tensor_along_last_dim
class _ReduceScatterToSequencePar... | null |
141,278 | import torch
from .initialize import (
get_tensor_model_parallel_group,
get_tensor_model_parallel_rank,
)
from .utils import VocabUtility
class _VocabParallelCrossEntropy(torch.autograd.Function):
def forward(ctx, vocab_parallel_logits, target):
# Maximum value along vocab dimension across all GPUs.... | Helper function for the cross entropy. |
141,279 | import contextlib
import torch
from torch import _C
from torch.cuda import _lazy_call, device as device_ctx_manager
from .initialize import (
get_data_parallel_rank,
get_tensor_model_parallel_group,
get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size,
)
The provided code snippet includ... | Sets the random number generator state of the current GPU. Argumentss: 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. |
141,280 | import contextlib
import torch
from torch import _C
from torch.cuda import _lazy_call, device as device_ctx_manager
from .initialize import (
get_data_parallel_rank,
get_tensor_model_parallel_group,
get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size,
)
def get_tensor_model_parallel_wo... | Break a tensor into equal 1D chunks. |
141,281 | import contextlib
import torch
from torch import _C
from torch.cuda import _lazy_call, device as device_ctx_manager
from .initialize import (
get_data_parallel_rank,
get_tensor_model_parallel_group,
get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size,
)
def get_tensor_model_parallel_gr... | Opposite of above function, gather values from model parallel ranks. |
141,282 | import contextlib
import torch
from torch import _C
from torch.cuda import _lazy_call, device as device_ctx_manager
from .initialize import (
get_data_parallel_rank,
get_tensor_model_parallel_group,
get_tensor_model_parallel_rank,
get_tensor_model_parallel_world_size,
)
_MODEL_PARALLEL_RNG_TRACKER_NAME ... | 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... |
141,283 | import torch
import torch.nn.functional as F
import torch.nn.init as init
from torch.nn.parameter import Parameter
from metaseq.modules.megatron.global_vars import get_global_memory_buffer
from .initialize import (
get_tensor_model_parallel_group,
get_tensor_model_parallel_rank,
get_tensor_model_parallel_wo... | Initialize affine weight for model parallel on GPU. |
141,284 | import torch
import torch.nn.functional as F
import torch.nn.init as init
from torch.nn.parameter import Parameter
from metaseq.modules.megatron.global_vars import get_global_memory_buffer
from .initialize import (
get_tensor_model_parallel_group,
get_tensor_model_parallel_rank,
get_tensor_model_parallel_wo... | Initialize affine weight for model parallel. Build the master weight on all processes and scatter the relevant chunk. |
141,285 | import torch
import torch.nn.functional as F
import torch.nn.init as init
from torch.nn.parameter import Parameter
from metaseq.modules.megatron.global_vars import get_global_memory_buffer
from .initialize import (
get_tensor_model_parallel_group,
get_tensor_model_parallel_rank,
get_tensor_model_parallel_wo... | Initialize affine weight for model parallel. Build the master weight on all processes and scatter the relevant chunk. |
141,286 | import torch
import torch.nn.functional as F
import torch.nn.init as init
from torch.nn.parameter import Parameter
from metaseq.modules.megatron.global_vars import get_global_memory_buffer
from .initialize import (
get_tensor_model_parallel_group,
get_tensor_model_parallel_rank,
get_tensor_model_parallel_wo... | Initialize affine weight for model parallel. Build the master weight on all processes and scatter the relevant chunk. |
141,287 | import operator
from functools import reduce
import torch
_GLOBAL_ARGS = None
def _ensure_var_is_initialized(var, name):
"""Make sure the input variable is not None."""
assert var is not None, "{} is not initialized.".format(name)
The provided code snippet includes necessary dependencies for implementing the `... | Return arguments. |
141,288 | from torch import nn as nn
from metaseq.modules import Linear
The provided code snippet includes necessary dependencies for implementing the `FeedForward` function. Write a Python function `def FeedForward(x, fc1, activation_fn, fc2, dropout_module)` to solve the following problem:
Feedforward network consisting of tw... | Feedforward network consisting of two linear layers (fc1, fc2), where activation_fn is applied between the two layers and dropout_module is applied at the end. |
141,289 | import ast
import collections
import logging
import os
import re
import socket
from typing import Any, Dict, List, Optional, Tuple
import math
import torch
from omegaconf import OmegaConf
from metaseq.dataclass.configs import CheckpointConfig
from metaseq.dataclass.utils import overwrite_args_by_name, overwrite_keys_no... | Load a checkpoint and restore the training iterator. *passthrough_args* will be passed through to ``trainer.get_train_iterator``. |
141,290 | import ast
import collections
import logging
import os
import re
import socket
from typing import Any, Dict, List, Optional, Tuple
import math
import torch
from omegaconf import OmegaConf
from metaseq.dataclass.configs import CheckpointConfig
from metaseq.dataclass.utils import overwrite_args_by_name, overwrite_keys_no... | Retrieves all checkpoints found in `path` directory. Checkpoints are identified by matching filename to the specified pattern. If the pattern contains groups, the result will be sorted by the first group in descending order. |
141,291 | import ast
import collections
import logging
import os
import re
import socket
from typing import Any, Dict, List, Optional, Tuple
import math
import torch
from omegaconf import OmegaConf
from metaseq.dataclass.configs import CheckpointConfig
from metaseq.dataclass.utils import overwrite_args_by_name, overwrite_keys_no... | null |
141,292 | import copy
import importlib
import logging
import math
import os
import random
import re
import sys
import warnings
from itertools import accumulate
from typing import List, Optional
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from metaseq.distributed import utils a... | null |
141,293 | import copy
import importlib
import logging
import math
import os
import random
import re
import sys
import warnings
from itertools import accumulate
from typing import List, Optional
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from metaseq.distributed import utils a... | Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. |
141,294 | import copy
import importlib
import logging
import math
import os
import random
import re
import sys
import warnings
from itertools import accumulate
from typing import List, Optional
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from metaseq.distributed import utils a... | null |
141,295 | import copy
import importlib
import logging
import math
import os
import random
import re
import sys
import warnings
from itertools import accumulate
from typing import List, Optional
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from metaseq.distributed import utils a... | FP16-compatible function that fills a tensor with -inf. |
141,296 | import copy
import importlib
import logging
import math
import os
import random
import re
import sys
import warnings
from itertools import accumulate
from typing import List, Optional
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from metaseq.distributed import utils a... | Resolve max position constraints from multiple sources. |
141,297 | import copy
import importlib
import logging
import math
import os
import random
import re
import sys
import warnings
from itertools import accumulate
from typing import List, Optional
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from metaseq.distributed import utils a... | null |
141,298 | import copy
import importlib
import logging
import math
import os
import random
import re
import sys
import warnings
from itertools import accumulate
from typing import List, Optional
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from metaseq.distributed import utils a... | null |
141,299 | import copy
import importlib
import logging
import math
import os
import random
import re
import sys
import warnings
from itertools import accumulate
from typing import List, Optional
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from metaseq.distributed import utils a... | null |
141,300 | import copy
import importlib
import logging
import math
import os
import random
import re
import sys
import warnings
from itertools import accumulate
from typing import List, Optional
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from metaseq.distributed import utils a... | null |
141,301 | import copy
import importlib
import logging
import math
import os
import random
import re
import sys
import warnings
from itertools import accumulate
from typing import List, Optional
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from metaseq.distributed import utils a... | null |
141,302 | import copy
import importlib
import logging
import math
import os
import random
import re
import sys
import warnings
from itertools import accumulate
from typing import List, Optional
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from metaseq.distributed import utils a... | null |
141,303 | import copy
import importlib
import logging
import math
import os
import random
import re
import sys
import warnings
from itertools import accumulate
from typing import List, Optional
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from metaseq.distributed import utils a... | null |
141,304 | import copy
import importlib
import logging
import math
import os
import random
import re
import sys
import warnings
from itertools import accumulate
from typing import List, Optional
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from metaseq.distributed import utils a... | null |
141,305 | import copy
import importlib
import logging
import math
import os
import random
import re
import sys
import warnings
from itertools import accumulate
from typing import List, Optional
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from metaseq.distributed import utils a... | Convert a tensor x into the desired dtype. Also sanity checks combinations of options. |
141,306 | import copy
import importlib
import logging
import math
import os
import random
import re
import sys
import warnings
from itertools import accumulate
from typing import List, Optional
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from metaseq.distributed import utils a... | null |
141,307 | import copy
import importlib
import logging
import math
import os
import random
import re
import sys
import warnings
from itertools import accumulate
from typing import List, Optional
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from metaseq.distributed import utils a... | null |
141,308 | import copy
import importlib
import logging
import math
import os
import random
import re
import sys
import warnings
from itertools import accumulate
from typing import List, Optional
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from metaseq.distributed import utils a... | null |
141,309 | import copy
import importlib
import logging
import math
import os
import random
import re
import sys
import warnings
from itertools import accumulate
from typing import List, Optional
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from metaseq.distributed import utils a... | Init method based on N(0, sigma). |
141,310 | import copy
import importlib
import logging
import math
import os
import random
import re
import sys
import warnings
from itertools import accumulate
from typing import List, Optional
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from metaseq.distributed import utils a... | Init method based on N(0, sigma/sqrt(2*num_layers). |
141,311 | import copy
import importlib
import logging
import math
import os
import random
import re
import sys
import warnings
from itertools import accumulate
from typing import List, Optional
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from metaseq.distributed import utils a... | null |
141,312 | import copy
import importlib
import logging
import math
import os
import random
import re
import sys
import warnings
from itertools import accumulate
from typing import List, Optional
import numpy as np
import torch
import torch.distributed as dist
import torch.nn.functional as F
from metaseq.distributed import utils a... | null |
141,313 | from metaseq.logging.progress_bar.base_progress_bar import (
BaseProgressBar,
logger,
)
def get_aim_run(repo, run_hash):
from aim import Run
return Run(run_hash=run_hash, repo=repo) | null |
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