id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
143,474 | import random
from typing import List
from tqdm import tqdm
import sys
import os
import multiprocessing as mp
from evaluate import Evaluator
from sphinx import SPHINXModel
from data.conversation.lib import conv_templates, SeparatorStyle
import argparse
import torch
import torch.distributed as dist
from PIL import Image... | null |
143,475 | import random
from typing import List
from tqdm import tqdm
import sys
import os
import multiprocessing as mp
from evaluate import Evaluator
from sphinx import SPHINXModel
from data.conversation.lib import conv_templates, SeparatorStyle
import argparse
import torch
import torch.distributed as dist
from PIL import Image... | null |
143,476 | import random
from typing import List
from tqdm import tqdm
import sys
import os
import multiprocessing as mp
from evaluate import Evaluator
from sphinx import SPHINXModel
from data.conversation.lib import conv_templates, SeparatorStyle
import argparse
import torch
import torch.distributed as dist
from PIL import Image... | null |
143,477 | import random
from typing import List
from tqdm import tqdm
import sys
import os
import multiprocessing as mp
from evaluate import Evaluator
from sphinx import SPHINXModel
from data.conversation.lib import conv_templates, SeparatorStyle
import argparse
import torch
import torch.distributed as dist
from PIL import Image... | null |
143,478 | import itertools
import random
import time
from functools import partial
from typing import Optional, List, Tuple
from tqdm import tqdm
from utils.vqa import VQA
from utils.vqa_eval import VQAEval
from utils.utils import save_result
from utils.metric import relaxed_correctness, evaluate_relaxed_accuracy, evaluate_exact... | null |
143,479 | import itertools
import random
import time
from functools import partial
from typing import Optional, List, Tuple
from tqdm import tqdm
from utils.vqa import VQA
from utils.vqa_eval import VQAEval
from utils.utils import save_result
from utils.metric import relaxed_correctness, evaluate_relaxed_accuracy, evaluate_exact... | null |
143,480 | import os
def save_result(args, info, prompt, global_config, ds_collections, result_path='', dataset=''):
os.makedirs('results', exist_ok=True)
if isinstance(args.pretrained_path, list):
pre_path = args.pretrained_path[0]
else:
pre_path = args.pretrained_path
with open(f'results/{pre_pa... | null |
143,481 | import torch
import pickle
from nltk.tokenize.treebank import TreebankWordDetokenizer
from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix
from typing import Tuple, List, Optional
The provided code snippet includes necessary dependencies for implementing the `scores_to_ranks` fun... | Convert model output scores into ranks. |
143,482 | import torch
import pickle
from nltk.tokenize.treebank import TreebankWordDetokenizer
from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix
from typing import Tuple, List, Optional
def relaxed_correctness(target: str,
prediction: str,
... | null |
143,483 | import torch
import pickle
from nltk.tokenize.treebank import TreebankWordDetokenizer
from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix
from typing import Tuple, List, Optional
def evaluate_exact_match_accuracy(entries):
scores = []
for elem in entries:
pred = ... | null |
143,484 | import torch
import pickle
from nltk.tokenize.treebank import TreebankWordDetokenizer
from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix
from typing import Tuple, List, Optional
def parse_pred_ans(pred_ans):
pred_label = None
if pred_ans in ["yes", "no"]:
pred_l... | null |
143,485 | import torch
import pickle
from nltk.tokenize.treebank import TreebankWordDetokenizer
from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix
from typing import Tuple, List, Optional
def compute_mme_metric(gts, preds):
assert len(gts) == len(preds)
label_map = {
"ye... | null |
143,486 | import json
import re
from transformers import GenerationConfig
from io import StringIO
from contextlib import redirect_stdout
import math
import multiprocessing
import threading
from functools import lru_cache
import os
import torch
def format_code(code_str: str):
code = 'def run_it():\n'
for line in code_str... | null |
143,487 | import json
import re
from transformers import GenerationConfig
from io import StringIO
from contextlib import redirect_stdout
import math
import multiprocessing
import threading
from functools import lru_cache
import os
import torch
def read_jsonl(path: str):
with open(path, "r", encoding='utf-8') as fh:
... | null |
143,488 | import json
import re
from transformers import GenerationConfig
from io import StringIO
from contextlib import redirect_stdout
import math
import multiprocessing
import threading
from functools import lru_cache
import os
import torch
def extract_nums(s):
s = s.replace(",", "")
nums = re.findall(r"[+-]? *(?:\d+... | null |
143,489 | import json
import re
from transformers import GenerationConfig
from io import StringIO
from contextlib import redirect_stdout
import math
import multiprocessing
import threading
from functools import lru_cache
import os
import torch
def find_formula(step):
assert step.count("<<") == step.count(">>") == 1
left... | null |
143,490 | import json
import re
from transformers import GenerationConfig
from io import StringIO
from contextlib import redirect_stdout
import math
import multiprocessing
import threading
from functools import lru_cache
import os
import torch
def extract_answer(completion):
ANS_RE = re.compile(r"#### (\-?[0-9\.\,]+)")
... | null |
143,491 | import json
import re
from transformers import GenerationConfig
from io import StringIO
from contextlib import redirect_stdout
import math
import multiprocessing
import threading
from functools import lru_cache
import os
import torch
def delete_extra_zero(n):
'''删除小数点后多余的0'''
try:
n=float(n)
except:... | null |
143,492 | import json
import re
from transformers import GenerationConfig
from io import StringIO
from contextlib import redirect_stdout
import math
import multiprocessing
import threading
from functools import lru_cache
import os
import torch
class CodeExecutor:
def __init__(self, code, timeout, use_process: bool):
... | null |
143,493 | import json
import re
from transformers import GenerationConfig
from io import StringIO
from contextlib import redirect_stdout
import math
import multiprocessing
import threading
from functools import lru_cache
import os
import torch
def number_it(num: str):
if 'frac' in num:
pattern = r"\\frac\{([^{}]+)\}\... | null |
143,494 | import json
import re
from transformers import GenerationConfig
from io import StringIO
from contextlib import redirect_stdout
import math
import multiprocessing
import threading
from functools import lru_cache
import os
import torch
def process_question_with_flan_tag(questions: list, stem_flan_type: str):
if stem... | null |
143,495 | import json
import re
from transformers import GenerationConfig
from io import StringIO
from contextlib import redirect_stdout
import math
import multiprocessing
import threading
from functools import lru_cache
import os
import torch
def remove_flan_tag(question: str, stem_flan_type: str):
if stem_flan_type == "po... | null |
143,496 | import json
import re
from transformers import GenerationConfig
from io import StringIO
from contextlib import redirect_stdout
import math
import multiprocessing
import threading
from functools import lru_cache
import os
import torch
def recover_options(input_str: str, combined: bool = False):
options = input_str.... | null |
143,497 | from PIL import Image
from io import BytesIO
import time
Image.MAX_IMAGE_PIXELS = None
def init_ceph_client_if_needed():
client = None
def read_img_general(img_path):
if "s3://" in img_path:
init_ceph_client_if_needed()
img_bytes = client.get(img_path)
image = Image.open(BytesIO(img_bytes))... | null |
143,498 | from typing import Dict
def format_prompt(format_dict: Dict, sys_name="alpaca"):
if sys_name == "alpaca":
prompt_dict = {
"prompt_input": (
"Below is an instruction that describes a task, paired with an input that provides further context. "
"Write a response tha... | null |
143,499 | import random
import warnings
from time import sleep
from typing import List, Callable
import torch
import yaml
from torch.utils.data import Dataset
from ..data_reader import read_img_general
import json
import h5py
from accessory.model.tokenizer import Tokenizer
import os
from pathlib import Path
import copy
from . im... | null |
143,500 | import dataclasses
from enum import auto, Enum
from typing import List, Tuple
class SeparatorStyle(Enum):
class Conversation:
def process(self):
def get_prompt(self):
def append_message(self, role, message):
def copy(self):
def load_qas(self, qas: List[List[str]]):
def response_end_signal(... | null |
143,501 | import dataclasses
from enum import auto, Enum
from typing import List, Tuple
class SeparatorStyle(Enum):
"""Different separator style."""
SINGLE = auto()
TWO = auto()
class Conversation:
"""A class that keeps all conversation history."""
system: str
roles: Tuple[str, str]
messages: List
... | null |
143,502 | import dataclasses
from enum import auto, Enum
from typing import List, Tuple
class SeparatorStyle(Enum):
"""Different separator style."""
SINGLE = auto()
TWO = auto()
class Conversation:
"""A class that keeps all conversation history."""
system: str
roles: Tuple[str, str]
messages: List
... | null |
143,503 | import dataclasses
from enum import auto, Enum
from typing import List, Tuple
class SeparatorStyle(Enum):
"""Different separator style."""
SINGLE = auto()
TWO = auto()
class Conversation:
"""A class that keeps all conversation history."""
system: str
roles: Tuple[str, str]
messages: List
... | null |
143,504 | import dataclasses
from enum import auto, Enum
from typing import List, Tuple
class SeparatorStyle(Enum):
"""Different separator style."""
SINGLE = auto()
TWO = auto()
class Conversation:
"""A class that keeps all conversation history."""
system: str
roles: Tuple[str, str]
messages: List
... | null |
143,505 | import dataclasses
from enum import auto, Enum
from typing import List, Tuple
class SeparatorStyle(Enum):
"""Different separator style."""
SINGLE = auto()
TWO = auto()
class Conversation:
"""A class that keeps all conversation history."""
system: str
roles: Tuple[str, str]
messages: List
... | null |
143,506 | import dataclasses
from enum import auto, Enum
from typing import List, Tuple
class SeparatorStyle(Enum):
"""Different separator style."""
SINGLE = auto()
TWO = auto()
class Conversation:
"""A class that keeps all conversation history."""
system: str
roles: Tuple[str, str]
messages: List
... | null |
143,507 | import math
import sys
import contextlib
import torch
import accessory.util.misc as misc
import accessory.util.lr_sched as lr_sched
from fairscale.nn.model_parallel import initialize as fs_init
def train_one_epoch(model: torch.nn.Module,
data_loader, optimizer: torch.optim.Optimizer,
... | null |
143,508 | import sys
import os
import argparse
import datetime
import json
import warnings
import numpy as np
import time
from pathlib import Path
import functools
from functools import partial
import torch
from torch.utils.data import Dataset
import torch.distributed as dist
import torch.backends.cudnn as cudnn
from torch.utils... | null |
143,509 | import random
import sys
import os
import traceback
import argparse
import multiprocessing as mp
import numpy as np
from typing import List, Optional
import torch
import torch.distributed as dist
from fairscale.nn.model_parallel import initialize as fs_init
import gradio as gr
from accessory.util.misc import setup_for_... | The worker function that manipulates the GPU to run the inference. Exact n_gpu workers are started, with each one operating on a separate GPU. Args: rank (int): Distributed rank of the worker. args (argparse.Namespace): All command line arguments. barrier (multiprocessing.Barrier): A barrier used to delay the start of ... |
143,510 | import random
import sys
import os
import traceback
import argparse
import multiprocessing as mp
import numpy as np
from typing import List, Optional
import torch
import torch.distributed as dist
from fairscale.nn.model_parallel import initialize as fs_init
import gradio as gr
from accessory.util.misc import setup_for_... | The gradio worker is responsible for displaying the WebUI and relay the requests to model workers. It should be launched only once. Args: request_queues (List[mp.Queue]): A list of request queues (one for each model worker). args (argparse.Namespace): All command line arguments. barrier (multiprocessing.Barrier): A bar... |
143,511 | import sys
import os
import argparse
from PIL import Image
from accessory.model.meta import MetaModel
from accessory.data.transform import get_transform
from accessory.data.system_prompt import format_prompt
def get_transform(transform_type: str, size=224):
if transform_type == "random_resized_crop":
trans... | null |
143,512 | import random
import sys
import os
import argparse
import multiprocessing as mp
import numpy as np
from typing import List, Optional
import traceback
import torch
import torch.distributed as dist
from fairscale.nn.model_parallel import initialize as fs_init
import gradio as gr
from accessory.util.misc import setup_for_... | The worker function that manipulates the GPU to run the inference. Exact n_gpu workers are started, with each one operating on a separate GPU. Args: rank (int): Distributed rank of the worker. args (argparse.Namespace): All command line arguments. barrier (multiprocessing.Barrier): A barrier used to delay the start of ... |
143,513 | import random
import sys
import os
import argparse
import multiprocessing as mp
import numpy as np
from typing import List, Optional
import traceback
import torch
import torch.distributed as dist
from fairscale.nn.model_parallel import initialize as fs_init
import gradio as gr
from accessory.util.misc import setup_for_... | The gradio worker is responsible for displaying the WebUI and relay the requests to model workers. It should be launched only once. Args: request_queues (List[mp.Queue]): A list of request queues (one for each model worker). args (argparse.Namespace): All command line arguments. barrier (multiprocessing.Barrier): A bar... |
143,514 | import sys
import os
from accessory.model.meta import MetaModel
import argparse
import torch
import torch.distributed as dist
import gradio as gr
import numpy as np
import random
from accessory.util import misc
from fairscale.nn.model_parallel import initialize as fs_init
from accessory.data.alpaca import format_prompt... | null |
143,515 | import sys
import os
from accessory.model.meta import MetaModel
import argparse
import torch
import torch.distributed as dist
import gradio as gr
import numpy as np
import random
from accessory.util import misc
from fairscale.nn.model_parallel import initialize as fs_init
from accessory.data.alpaca import format_prompt... | null |
143,516 | import sys
import os
from accessory.model.meta import MetaModel
import argparse
import torch
import torch.distributed as dist
import gradio as gr
import numpy as np
import random
from accessory.util import misc
from fairscale.nn.model_parallel import initialize as fs_init
from accessory.data.alpaca import format_prompt... | null |
143,517 | import sys
import os
from accessory.model.meta import MetaModel
import argparse
import torch
import torch.distributed as dist
import gradio as gr
import numpy as np
import random
from PIL import Image
from accessory.util import misc
from fairscale.nn.model_parallel import initialize as fs_init
from accessory.data.alpac... | null |
143,518 | import sys
import os
from accessory.model.meta import MetaModel
import argparse
import torch
import torch.distributed as dist
import gradio as gr
import numpy as np
import random
from PIL import Image
from accessory.util import misc
from fairscale.nn.model_parallel import initialize as fs_init
from accessory.data.alpac... | null |
143,519 | import sys
import os
from accessory.model.meta import MetaModel
import argparse
import torch
import torch.distributed as dist
import gradio as gr
import numpy as np
import random
from PIL import Image
from accessory.util import misc
from fairscale.nn.model_parallel import initialize as fs_init
from accessory.data.alpac... | null |
143,520 | import random
import sys
import os
import traceback
import argparse
import multiprocessing as mp
import numpy as np
from typing import List, Optional
import torch
import torch.distributed as dist
from fairscale.nn.model_parallel import initialize as fs_init
import gradio as gr
from accessory.util.misc import setup_for_... | The worker function that manipulates the GPU to run the inference. Exact n_gpu workers are started, with each one operating on a separate GPU. Args: rank (int): Distributed rank of the worker. args (argparse.Namespace): All command line arguments. barrier (multiprocessing.Barrier): A barrier used to delay the start of ... |
143,521 | import random
import sys
import os
import traceback
import argparse
import multiprocessing as mp
import numpy as np
from typing import List, Optional
import torch
import torch.distributed as dist
from fairscale.nn.model_parallel import initialize as fs_init
import gradio as gr
from accessory.util.misc import setup_for_... | The gradio worker is responsible for displaying the WebUI and relay the requests to model workers. It should be launched only once. Args: request_queues (List[mp.Queue]): A list of request queues (one for each model worker). args (argparse.Namespace): All command line arguments. barrier (multiprocessing.Barrier): A bar... |
143,522 | import sys
import os
import glob
import os
import pandas as pd
import tqdm
from multiprocessing import Pool
from accessory.model.tokenizer import Tokenizer
import pickle
max_len = 2048
os.makedirs(save_dir, exist_ok=True)
def pack_tokens(filename, save_dir, tokenizer):
print(f"{filename} start")
texts = pd.re... | null |
143,523 | import argparse
import os
from huggingface_hub import hf_hub_download
from huggingface_hub import snapshot_download
def download_file(repo_id, subfolder, filename, local_dir):
def download_files(repo_id, subfolder, file_names, output_path):
for file_name in file_names:
download_file(repo_id, subfolder, fil... | null |
143,524 | import argparse
import os
from huggingface_hub import hf_hub_download
from huggingface_hub import snapshot_download
def get_file_names(prefix, model_size):
return [prefix + 'tokenizer.model', prefix + f"{model_size}_params.json"] | null |
143,525 | import argparse
import os
from huggingface_hub import hf_hub_download
from huggingface_hub import snapshot_download
model_list = {
'convert': {
'sg': ['InternLM','Falcon','Falcon_180b','mixtral-8x7b-32kseqlen']
},
'finetune': {
'mm': ['alpacaLlava_llamaQformerv2', 'alpacaLlava_llamaQformerv2... | null |
143,526 | import argparse
import os
from huggingface_hub import hf_hub_download
from huggingface_hub import snapshot_download
def get_args_parser():
parser = argparse.ArgumentParser('Download the weights of the model.', add_help=False)
parser.add_argument('--train_type', default=None, choices=['finetune', 'convert'])
... | null |
143,527 | import os
import argparse
import torch
def get_args_parser():
parser = argparse.ArgumentParser('Combine or separate the weights of the model.', add_help=False)
parser.add_argument('--pretrained_path', default='/path/to/pretrained', type=str,
help='directory containing pretrained checkpo... | null |
143,528 | import os
import argparse
import torch
args = get_args_parser().parse_args()
if not os.path.exists(args.output_path):
os.makedirs(args.output_path)
def calculate_weight_delta(original_model, fine_tuned_model, num, max_num):
original_state_dict = {key: val.float() for key, val in original_model.items()}
fin... | null |
143,529 | import os
import argparse
import torch
args = get_args_parser().parse_args()
if not os.path.exists(args.output_path):
os.makedirs(args.output_path)
def merge_weights_and_save(original_model, delta_weights, num, max_num):
original_state_dict = {key: val.float() for key, val in original_model.items()}
delta_... | null |
143,530 | import argparse
import json
import os
from typing import Any, Dict, List
import re
import torch
from accessory.util.tensor_parallel import (
infer_checkpoint_format_and_mp_size,
load_tensor_parallel_shard_state_dict,
ShardedTensorLoader,
)
print("transformers version:", transformers.__version__)
def load_t... | null |
143,531 | import argparse
import json
import os
from typing import Any, Dict, List
import re
import torch
from accessory.util.tensor_parallel import (
infer_checkpoint_format_and_mp_size,
load_tensor_parallel_shard_state_dict,
ShardedTensorLoader,
)
def convert_merged_ckpt_to_hf(
merged_state_dict: Dict[str, tor... | null |
143,532 | import argparse
import json
import os
from typing import Any, Dict, List
import re
import torch
from accessory.util.tensor_parallel import (
infer_checkpoint_format_and_mp_size,
load_tensor_parallel_shard_state_dict,
ShardedTensorLoader,
)
print("transformers version:", transformers.__version__)
def write_m... | null |
143,533 | from sentencepiece import SentencePieceProcessor
from transformers import AutoTokenizer
from logging import getLogger
from typing import List, Optional
import os
from pathlib import Path
def probe_tokenizer_path_from_pretrained(pretrained_path: str):
tokenizer_path = None
# try find spm-style tokenizer
pr... | null |
143,534 | from typing import Optional, Tuple, Union
from dataclasses import dataclass
import math
import functools
import torch
from torch import nn
import torch.nn.functional as F
import fairscale.nn.model_parallel.initialize as fs_init
from fairscale.nn.model_parallel.layers import (
ParallelEmbedding,
RowParallelLinea... | null |
143,535 | from typing import Optional, Tuple, Union
from dataclasses import dataclass
import math
import functools
import torch
from torch import nn
import torch.nn.functional as F
import fairscale.nn.model_parallel.initialize as fs_init
from fairscale.nn.model_parallel.layers import (
ParallelEmbedding,
RowParallelLinea... | null |
143,536 | from typing import Optional, Tuple, Union
from dataclasses import dataclass
import math
import functools
import torch
from torch import nn
import torch.nn.functional as F
import fairscale.nn.model_parallel.initialize as fs_init
from fairscale.nn.model_parallel.layers import (
ParallelEmbedding,
RowParallelLinea... | torch.repeat_interleave(x, dim=2, repeats=n_rep) |
143,537 | from typing import Optional, Tuple, Union
from dataclasses import dataclass
import math
import functools
import torch.nn as nn
import torch
import torch.nn.functional as F
from torch.nn.init import normal_
from einops import rearrange
import fairscale.nn.model_parallel.initialize as fs_init
from fairscale.nn.model_para... | null |
143,538 | from typing import Optional, Tuple, Union
from dataclasses import dataclass
import torch.nn as nn
import torch
import torch.nn.functional as F
from torch.nn import LayerNorm
import fairscale.nn.model_parallel.initialize as fs_init
from fairscale.nn.model_parallel.layers import (
ParallelEmbedding,
RowParallelLi... | null |
143,539 | from typing import Optional, Tuple, Union
from dataclasses import dataclass
import torch.nn as nn
import torch
import torch.nn.functional as F
from torch.nn import LayerNorm
import fairscale.nn.model_parallel.initialize as fs_init
from fairscale.nn.model_parallel.layers import (
ParallelEmbedding,
RowParallelLi... | null |
143,540 | from typing import Optional, Tuple, Union, Dict, List
from importlib import resources as impresources
from dataclasses import dataclass, field
import math
import functools
import numpy as np
import torch
from torch import nn
import torch.distributed as dist
import torch.nn.functional as F
import fairscale.nn.model_para... | null |
143,541 | from typing import Optional, Tuple, Union, Dict, List
from importlib import resources as impresources
from dataclasses import dataclass, field
import math
import functools
import numpy as np
import torch
from torch import nn
import torch.distributed as dist
import torch.nn.functional as F
import fairscale.nn.model_para... | null |
143,542 | from typing import Optional, Tuple, Union, Dict, List
from importlib import resources as impresources
from dataclasses import dataclass, field
import math
import functools
import numpy as np
import torch
from torch import nn
import torch.distributed as dist
import torch.nn.functional as F
import fairscale.nn.model_para... | null |
143,543 | from typing import Optional, Tuple, Union, Dict, List
from dataclasses import dataclass, field
import math
import functools
import numpy as np
import torch
from torch import nn
import torch.distributed as dist
import torch.nn.functional as F
import fairscale.nn.model_parallel.initialize as fs_init
from fairscale.nn.mod... | null |
143,544 | from typing import Optional, Tuple, Union, Dict, List
from dataclasses import dataclass, field
import math
import functools
import numpy as np
import torch
from torch import nn
import torch.distributed as dist
import torch.nn.functional as F
import fairscale.nn.model_parallel.initialize as fs_init
from fairscale.nn.mod... | null |
143,545 | from typing import Optional, Tuple, Union, Dict, List
from dataclasses import dataclass, field
import math
import functools
import numpy as np
import torch
from torch import nn
import torch.distributed as dist
import torch.nn.functional as F
import fairscale.nn.model_parallel.initialize as fs_init
from fairscale.nn.mod... | null |
143,549 | import atexit
import sys
import os
import warnings
import random
import numpy as np
import builtins
import traceback
import subprocess
import torch
import torch.distributed as dist
from typing import List, Optional, Tuple
import accessory.util.misc
from accessory.model.meta import MetaModel
REQUESTS_WITH_STREAM_RESPONS... | null |
143,550 | import atexit
import sys
import os
import warnings
import random
import numpy as np
import builtins
import traceback
import subprocess
import torch
import torch.distributed as dist
from typing import List, Optional, Tuple
import accessory.util.misc
from accessory.model.meta import MetaModel
def _reset_world():
for... | null |
143,551 | import atexit
import sys
import os
import warnings
import random
import numpy as np
import builtins
import traceback
import subprocess
import torch
import torch.distributed as dist
from typing import List, Optional, Tuple
import accessory.util.misc
from accessory.model.meta import MetaModel
def _save_world():
save... | null |
143,552 | import atexit
import sys
import os
import warnings
import random
import numpy as np
import builtins
import traceback
import subprocess
import torch
import torch.distributed as dist
from typing import List, Optional, Tuple
import accessory.util.misc
from accessory.model.meta import MetaModel
def _load_world(load_dict, ... | null |
143,553 | from types import TracebackType
from typing import Any, Optional
import torch
import torch.nn as nn
def promote_trainable_params_to_fp32(model: nn.Module) -> None:
for param in model.parameters():
if param.requires_grad:
if param.is_floating_point() and torch.finfo(param.dtype).bits < 32:
... | null |
143,554 | from typing import Any, Callable, Dict, List, Optional
import torch
import torch.nn as nn
BlockwiseParamGroupFuncType = Callable[[Dict[str, torch.Tensor]],
List[List[str]]]
_LAYERWISE_PARAM_GROUP_FUNCS: Dict[str, BlockwiseParamGroupFuncType] = {}
The provided code snippet include... | r"""Decorator to define a method that generate the block-wise parameter group for all keys starting with a specific prefix. |
143,555 | from typing import Any, Callable, Dict, List, Optional
import torch
import torch.nn as nn
The provided code snippet includes necessary dependencies for implementing the `_make_default_param_group` function. Write a Python function `def _make_default_param_group( meta_param_dict: Dict[str, torch.Tensor] ) -> List[L... | r"""Generate the default param group. As we group parameters according to longest name prefix match, this function defines the grouping of the empty prefix to act as the catch-all grouping. |
143,556 | from typing import Any, Callable, Dict, List, Optional
import torch
import torch.nn as nn
The provided code snippet includes necessary dependencies for implementing the `_clip_make_layerwise_param_groups` function. Write a Python function `def _clip_make_layerwise_param_groups( meta_param_dict: Dict[str, torch.Ten... | r"""Generate a list of param groups for the clip visual encoder. .. note: This also serves as a reference for implementing other vision encoder grouping in the future. All grouping methods should comply with this spec. Args: meta_param_dict (Dict[str, torch.Tensor]): The param dict received from the caller. The values ... |
143,557 | from typing import Any, Callable, Dict, List, Optional
import torch
import torch.nn as nn
_LAYERWISE_PARAM_GROUP_FUNCS: Dict[str, BlockwiseParamGroupFuncType] = {}
The provided code snippet includes necessary dependencies for implementing the `make_param_groups` function. Write a Python function `def make_param_groups... | r"""This method sets up param groups of different learning rate or weight decay configurations. Currently, the supported functions are: * Disable weight decay by the default criterion: parameters with names ending with ``.bias`` and parameters with dimensions <= 1 (following ``timm``, controlled by argument ``bias_and_... |
143,558 | import builtins
import datetime
import os
import shutil
import socket
import dataclasses
import random
import time
from collections import defaultdict, deque
from pathlib import Path
import subprocess
from types import SimpleNamespace
import json
import numpy as np
from huggingface_hub import snapshot_download
import t... | null |
143,559 | import builtins
import datetime
import os
import shutil
import socket
import dataclasses
import random
import time
from collections import defaultdict, deque
from pathlib import Path
import subprocess
from types import SimpleNamespace
import json
import numpy as np
from huggingface_hub import snapshot_download
import t... | split resume into two separate stages since resuming from a full model state has to be done before FSDP model init :param args: :param model_without_FSDP: :return: |
143,560 | import builtins
import datetime
import os
import shutil
import socket
import dataclasses
import random
import time
from collections import defaultdict, deque
from pathlib import Path
import subprocess
from types import SimpleNamespace
import json
import numpy as np
from huggingface_hub import snapshot_download
import t... | null |
143,561 | import builtins
import datetime
import os
import shutil
import socket
import dataclasses
import random
import time
from collections import defaultdict, deque
from pathlib import Path
import subprocess
from types import SimpleNamespace
import json
import numpy as np
from huggingface_hub import snapshot_download
import t... | null |
143,562 | import builtins
import datetime
import os
import shutil
import socket
import dataclasses
import random
import time
from collections import defaultdict, deque
from pathlib import Path
import subprocess
from types import SimpleNamespace
import json
import numpy as np
from huggingface_hub import snapshot_download
import t... | null |
143,563 | import builtins
import datetime
import os
import shutil
import socket
import dataclasses
import random
import time
from collections import defaultdict, deque
from pathlib import Path
import subprocess
from types import SimpleNamespace
import json
import numpy as np
from huggingface_hub import snapshot_download
import t... | null |
143,564 | import builtins
import datetime
import os
import shutil
import socket
import dataclasses
import random
import time
from collections import defaultdict, deque
from pathlib import Path
import subprocess
from types import SimpleNamespace
import json
import numpy as np
from huggingface_hub import snapshot_download
import t... | null |
143,565 | import builtins
import datetime
import os
import shutil
import socket
import dataclasses
import random
import time
from collections import defaultdict, deque
from pathlib import Path
import subprocess
from types import SimpleNamespace
import json
import numpy as np
from huggingface_hub import snapshot_download
import t... | null |
143,566 | import functools
import math
import warnings
from typing import (
Iterable,
List,
Union,
)
import torch
import torch.distributed as dist
import torch.distributed.fsdp._traversal_utils as traversal_utils
import torch.nn as nn
from torch.distributed.fsdp._common_utils import (
TrainingState,
)
from torch.... | Clips the gradient norm of all parameters. The norm is computed over all parameters' gradients as viewed as a single vector, and the gradients are modified in-place. Args: max_norm (float or int): max norm of the gradients norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for infinity norm. Returns: T... |
143,567 | from collections import OrderedDict
import os
import re
from typing import Dict, List, Set, Tuple, Type
import torch
import torch.nn as nn
import fairscale.nn.model_parallel.initialize as fs_init
from fairscale.nn.model_parallel.layers import (
ColumnParallelLinear,
RowParallelLinear,
ParallelEmbedding,
)
i... | r""""This method calls ``load_tensor_parallel_model_state_dict`` (which handles multiple formats / unmatched tensor parallel size) and load the converted checkpoint into a model. Args: model (nn.Module): The model to load the checkpoint into. path (str): A path containing checkpoint files. format (str): Format of the c... |
143,568 | from collections import OrderedDict
import os
import re
from typing import Dict, List, Set, Tuple, Type
import torch
import torch.nn as nn
import fairscale.nn.model_parallel.initialize as fs_init
from fairscale.nn.model_parallel.layers import (
ColumnParallelLinear,
RowParallelLinear,
ParallelEmbedding,
)
i... | r"""A helper function to partially load a tensor. This can save memory sometimes as this allows tensor parallel shards to stream into memory ( without being concatenated into a full model first). Args: target (``torch.Tensor``): The target tensor to load the values into. parallel_dim (int): Tensor parallel dimension of... |
143,569 | import argparse
import json
import jsonlines
import re
from tqdm import tqdm
import torch
import os
import sys
from model.meta import MetaModel
from util import misc
from fairscale.nn.model_parallel import initialize as fs_init
from util.tensor_parallel import load_tensor_parallel_model_list
from util.quant import quan... | null |
143,570 | import argparse
import json
import jsonlines
import re
from tqdm import tqdm
import torch
import os
import sys
from model.meta import MetaModel
from util import misc
from fairscale.nn.model_parallel import initialize as fs_init
from util.tensor_parallel import load_tensor_parallel_model_list
from util.quant import quan... | null |
143,571 | import argparse
import json
import jsonlines
import re
from tqdm import tqdm
import torch
import os
import sys
from model.meta import MetaModel
from util import misc
from fairscale.nn.model_parallel import initialize as fs_init
from util.tensor_parallel import load_tensor_parallel_model_list
from util.quant import quan... | null |
143,572 | import re
import argparse
import json
import jsonlines
from fractions import Fraction
import re
from tqdm import tqdm
import torch
import os
import sys
from model.meta import MetaModel
from util import misc
from fairscale.nn.model_parallel import initialize as fs_init
from util.tensor_parallel import load_tensor_parall... | null |
143,573 | import re
import argparse
import json
import jsonlines
from fractions import Fraction
import re
from tqdm import tqdm
import torch
import os
import sys
from model.meta import MetaModel
from util import misc
from fairscale.nn.model_parallel import initialize as fs_init
from util.tensor_parallel import load_tensor_parall... | null |
143,574 | import re
import argparse
import json
import jsonlines
from fractions import Fraction
import re
from tqdm import tqdm
import torch
import os
import sys
from model.meta import MetaModel
from util import misc
from fairscale.nn.model_parallel import initialize as fs_init
from util.tensor_parallel import load_tensor_parall... | null |
143,575 | import re
import argparse
import json
import jsonlines
from fractions import Fraction
import re
from tqdm import tqdm
import torch
import os
import sys
from model.meta import MetaModel
from util import misc
from fairscale.nn.model_parallel import initialize as fs_init
from util.tensor_parallel import load_tensor_parall... | null |
143,576 | def last_boxed_only_string(string):
idx = string.rfind("\\boxed")
if idx < 0:
idx = string.rfind("\\fbox")
if idx < 0:
return None
i = idx
right_brace_idx = None
num_left_braces_open = 0
while i < len(string):
if string[i] == "{":
num_left_braces_o... | null |
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