id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
38,093 | import json
import logging
import os
import pickle
import time
from functools import partial
import numpy as np
import paddle
import paddle.optimizer
import paddle.static
from datasets import load_dataset
from modeling import (
BertModel,
DeviceScope,
IpuBertConfig,
IpuBertForQuestionAnswering,
IpuB... | null |
38,094 | import json
import logging
import os
import pickle
import time
from functools import partial
import numpy as np
import paddle
import paddle.optimizer
import paddle.static
from datasets import load_dataset
from modeling import (
BertModel,
DeviceScope,
IpuBertConfig,
IpuBertForQuestionAnswering,
IpuB... | null |
38,095 | import argparse
import os
import tqdm
from paddle.utils.cpp_extension import load
from paddlenlp.trainer.argparser import strtobool
def load_custom_ops():
cur_dir = os.path.dirname(os.path.realpath(__file__))
custom_dir = cur_dir + "/custom_ops"
sources = [
f"{custom_dir}/custom_shape_infer.cc",
... | null |
38,096 | import argparse
import os
import tqdm
from paddle.utils.cpp_extension import load
from paddlenlp.trainer.argparser import strtobool
def str_to_bool(val):
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--task",
type=str,
default="PRETRAINING",
help="ta... | null |
38,097 | import os
from logging import getLogger
import numpy as np
logger = getLogger(__name__)
def get_tf_mapping(args):
squad_mapping = {"cls/squad/output_weights": "linear_72.w_0", "cls/squad/output_bias": "linear_72.b_0"}
tf_to_pdmodel = {
"bert/embeddings/word_embeddings": "ipu_bert_embeddings_0.w_0",
... | Loads weights, etc. from Tensorflow files into a dictionary of Numpy Arrays. Can read either checkpoint files, or frozen graphs, according to the `is_checkpoint` flag, passed in as the second argument. |
38,098 | import logging
import multiprocessing
import threading
from queue import Queue
import h5py
import numpy as np
import paddle
def shuffle_dict(dic, len):
idxs = np.arange(len)
np.random.shuffle(idxs)
for k, v in dic.items():
dic[k] = v[idxs] | null |
38,099 | import logging
import os
import pickle
import random
import time
import numpy as np
import paddle
import paddle.optimizer
import paddle.static
from dataset_ipu import PretrainingHDF5DataLoader
from modeling import (
BertModel,
DeviceScope,
IpuBertConfig,
IpuBertPretrainingMLMAccAndLoss,
IpuBertPretr... | Use the same data seed(for data shuffle) for all procs to guarantee data consistency after sharding. |
38,100 | import logging
import os
import pickle
import random
import time
import numpy as np
import paddle
import paddle.optimizer
import paddle.static
from dataset_ipu import PretrainingHDF5DataLoader
from modeling import (
BertModel,
DeviceScope,
IpuBertConfig,
IpuBertPretrainingMLMAccAndLoss,
IpuBertPretr... | null |
38,101 | import logging
import os
import pickle
import random
import time
import numpy as np
import paddle
import paddle.optimizer
import paddle.static
from dataset_ipu import PretrainingHDF5DataLoader
from modeling import (
BertModel,
DeviceScope,
IpuBertConfig,
IpuBertPretrainingMLMAccAndLoss,
IpuBertPretr... | Initialize the parameter from the bert config, and set the parameter by reseting the state dict." |
38,102 | import logging
import os
import pickle
import random
import time
import numpy as np
import paddle
import paddle.optimizer
import paddle.static
from dataset_ipu import PretrainingHDF5DataLoader
from modeling import (
BertModel,
DeviceScope,
IpuBertConfig,
IpuBertPretrainingMLMAccAndLoss,
IpuBertPretr... | null |
38,103 | from dataclasses import dataclass, field
import numpy as np
import paddle
from datasets import load_dataset
from paddle.metric import Accuracy
from paddlenlp.data import DataCollatorWithPadding
from paddlenlp.metrics import AccuracyAndF1, Mcc, PearsonAndSpearman
from paddlenlp.trainer import PdArgumentParser, Trainer, ... | null |
38,104 | from __future__ import absolute_import, division, print_function, unicode_literals
import argparse
import collections
import os
import random
from io import open
import h5py
import numpy as np
from tqdm import tqdm
from paddlenlp.transformers import BertTokenizer
from paddlenlp.transformers.tokenizer_utils import conve... | Create example files from `TrainingInstance`s. |
38,105 | from __future__ import absolute_import, division, print_function, unicode_literals
import argparse
import collections
import os
import random
from io import open
import h5py
import numpy as np
from tqdm import tqdm
from paddlenlp.transformers import BertTokenizer
from paddlenlp.transformers.tokenizer_utils import conve... | Create `TrainingInstance`s from raw text. |
38,106 | import argparse
import os
import random
import time
from functools import partial
import numpy as np
import paddle
import paddle.distributed.fleet as fleet
from paddle.io import DataLoader
from paddle.metric import Accuracy
from paddlenlp.data import Pad, Stack, Tuple
from paddlenlp.datasets import load_dataset
from pa... | null |
38,107 | import argparse
import os
from functools import partial
import paddle
from run_glue import METRIC_CLASSES, MODEL_CLASSES, convert_example
from paddlenlp.data import Pad, Tuple
from paddlenlp.datasets import load_dataset
METRIC_CLASSES = {
"cola": Mcc,
"sst-2": Accuracy,
"sts-b": PearsonAndSpearman,
"mn... | null |
38,108 | import argparse
import os
import random
import time
from concurrent.futures import ThreadPoolExecutor
import numpy as np
import paddle
import paddle.distributed.fleet as fleet
from dataset import create_data_holder, create_pretraining_dataset
from paddlenlp.trainer.argparser import strtobool
from paddlenlp.transformers... | null |
38,109 | import argparse
import os
import random
import time
from concurrent.futures import ThreadPoolExecutor
import numpy as np
import paddle
import paddle.distributed.fleet as fleet
from dataset import create_data_holder, create_pretraining_dataset
from paddlenlp.trainer.argparser import strtobool
from paddlenlp.transformers... | null |
38,110 | import argparse
import os
import random
import time
from functools import partial
import numpy as np
import paddle
from paddle.incubate import asp
from paddle.io import DataLoader
from paddle.metric import Accuracy
from paddlenlp.data import Pad, Stack, Tuple
from paddlenlp.datasets import load_dataset
from paddlenlp.m... | null |
38,111 | import argparse
import os
import random
import time
from functools import partial
import numpy as np
import paddle
from paddle.incubate import asp
from paddle.io import DataLoader
from paddle.metric import Accuracy
from paddlenlp.data import Pad, Stack, Tuple
from paddlenlp.datasets import load_dataset
from paddlenlp.m... | null |
38,112 | import argparse
import os
import random
import sys
import time
from concurrent.futures import ThreadPoolExecutor
import h5py
import numpy as np
import paddle
from paddle.io import DataLoader, Dataset
from paddlenlp.data import Stack
from paddlenlp.trainer.argparser import strtobool
from paddlenlp.transformers import (
... | null |
38,113 | import argparse
import os
import random
import sys
import time
from concurrent.futures import ThreadPoolExecutor
import h5py
import numpy as np
import paddle
from paddle.io import DataLoader, Dataset
from paddlenlp.data import Stack
from paddlenlp.trainer.argparser import strtobool
from paddlenlp.transformers import (
... | null |
38,114 | import os
from dataclasses import dataclass, field
import h5py
import numpy as np
import paddle
from paddle.io import Dataset
from paddlenlp.data import Stack
from paddlenlp.trainer import PdArgumentParser, Trainer, TrainingArguments
from paddlenlp.trainer.argparser import strtobool
from paddlenlp.transformers import (... | null |
38,115 | import argparse
import hashlib
import os
import paddle
def get_md5sum(file_path):
md5sum = None
if os.path.isfile(file_path):
with open(file_path, "rb") as f:
md5_obj = hashlib.md5()
md5_obj.update(f.read())
hash_code = md5_obj.hexdigest()
md5sum = str(hash_c... | null |
38,116 | import argparse
import paddle
import paddle_serving_client.io as serving_io
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--inference_model_dir", type=str, required=True, help="input inference model dir")
parser.add_argument("--model_file", type=str, required=True, help="input i... | null |
38,117 | import argparse
import os
import time
import numpy as np
from paddle_serving_client import Client
from paddlenlp.transformers import ElectraTokenizer
from paddlenlp.utils.log import logger
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--client_config_file", type=str, required=True, ... | null |
38,118 | import argparse
import os
import time
import numpy as np
from paddle_serving_client import Client
from paddlenlp.transformers import ElectraTokenizer
from paddlenlp.utils.log import logger
def read_sentences(paths=[]):
sentences = []
for sen_path in paths:
assert os.path.isfile(sen_path), "The {} isn't ... | Args: sentences (list[str]): each string is a sentence. If have sentences then no need paths paths (list[str]): The paths of file which contain sentences. If have paths then no need sentences Returns: res (list(numpy.ndarray)): The result of sentence, indicate whether each word is replaced, same shape with sentences. |
38,119 | import argparse
import os
import time
import numpy as np
from paddle import inference
from paddlenlp.transformers import ElectraTokenizer
from paddlenlp.utils.log import logger
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--model_file", type=str, required=True, help="model filename... | null |
38,120 | import argparse
import os
import time
import numpy as np
from paddle import inference
from paddlenlp.transformers import ElectraTokenizer
from paddlenlp.utils.log import logger
def read_sentences(paths=[]):
sentences = []
for sen_path in paths:
assert os.path.isfile(sen_path), "The {} isn't a valid file... | Args: sentences (list[str]): each string is a sentence. If sentences not paths paths (list[str]): The paths of file which contain sentences. If paths not sentences Returns: res (list(numpy.ndarray)): The result of sentence, indicate whether each word is replaced, same shape with sentences. |
38,121 | import argparse
import fileinput
import io
import os
import shutil
import time
import numpy as np
from paddlenlp.transformers import ElectraTokenizer
from paddlenlp.utils.log import logger
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--lite_lib_path", type=str, required=Tr... | null |
38,122 | import argparse
import fileinput
import io
import os
import shutil
import time
import numpy as np
from paddlenlp.transformers import ElectraTokenizer
from paddlenlp.utils.log import logger
def read_sentences(paths=[]):
sentences = []
for sen_path in paths:
assert os.path.isfile(sen_path), "The {} isn't ... | Args: sentences (list[str]): each string is a sentence. If sentences not paths paths (list[str]): The paths of file which contain sentences. If paths not sentences |
38,123 | from __future__ import absolute_import, division, print_function
import argparse
import hashlib
import os
import paddle
from paddle.static import InputSpec
from paddlenlp.transformers import ElectraForSequenceClassification
def get_md5sum(file_path):
md5sum = None
if os.path.isfile(file_path):
with ope... | null |
38,124 | import argparse
import logging
import os
import random
import time
from functools import partial
import numpy as np
import paddle
from paddle.io import DataLoader
from paddle.metric import Accuracy
from paddlenlp.data import Pad, Stack, Tuple
from paddlenlp.datasets import load_dataset
from paddlenlp.metrics import Acc... | null |
38,125 | import argparse
import logging
import os
import random
import time
from functools import partial
import numpy as np
import paddle
from paddle.io import DataLoader
from paddle.metric import Accuracy
from paddlenlp.data import Pad, Stack, Tuple
from paddlenlp.datasets import load_dataset
from paddlenlp.metrics import Acc... | null |
38,126 | import argparse
import logging
import os
import random
import time
from functools import partial
import numpy as np
import paddle
from paddle.io import DataLoader
from paddle.metric import Accuracy
from paddlenlp.data import Pad, Stack, Tuple
from paddlenlp.datasets import load_dataset
from paddlenlp.metrics import Acc... | print arguments |
38,127 | import argparse
import copy
import io
import json
import logging
import os
import random
import time
import numpy as np
import paddle
from paddlenlp.trainer.argparser import strtobool
from paddlenlp.transformers import (
ElectraForTotalPretraining,
ElectraPretrainingCriterion,
ElectraTokenizer,
LinearDe... | null |
38,128 | import argparse
import copy
import io
import json
import logging
import os
import random
import time
import numpy as np
import paddle
from paddlenlp.trainer.argparser import strtobool
from paddlenlp.transformers import (
ElectraForTotalPretraining,
ElectraPretrainingCriterion,
ElectraTokenizer,
LinearDe... | null |
38,129 | import argparse
import copy
import io
import json
import logging
import os
import random
import time
import numpy as np
import paddle
from paddlenlp.trainer.argparser import strtobool
from paddlenlp.transformers import (
ElectraForTotalPretraining,
ElectraPretrainingCriterion,
ElectraTokenizer,
LinearDe... | print arguments |
38,130 | import argparse
import os
import time
from pprint import pprint
import paddle
from paddlenlp.data import DataCollatorWithPadding
from paddlenlp.ops import enable_ft_para, get_ft_para_conf
from paddlenlp.trainer.argparser import strtobool
from paddlenlp.transformers import (
UnifiedTransformerLMHeadModel,
Unifie... | null |
38,131 | import argparse
import os
import time
from pprint import pprint
import paddle
from paddlenlp.data import DataCollatorWithPadding
from paddlenlp.ops import enable_ft_para, get_ft_para_conf
from paddlenlp.trainer.argparser import strtobool
from paddlenlp.transformers import (
UnifiedTransformerLMHeadModel,
Unifie... | null |
38,132 | import argparse
import os
import time
from pprint import pprint
import paddle
from paddlenlp.data import DataCollatorWithPadding
from paddlenlp.ops import enable_ft_para, get_ft_para_conf
from paddlenlp.trainer.argparser import strtobool
from paddlenlp.transformers import (
UnifiedTransformerLMHeadModel,
Unifie... | Post-process the decoded sequence. Truncate from the first <eos>. |
38,133 | import astroid
from pylint.checkers import BaseChecker, utils
from pylint.interfaces import IAstroidChecker
from collections import defaultdict
import re
class DocstringChecker(BaseChecker):
"""DosstringChecker is pylint checker to
check docstring style.
"""
__implements__ = (IAstroidChecker, )
POSI... | Register checkers. |
38,134 | from __future__ import absolute_import, division, print_function
import argparse
import os
import sys
import paddle.distributed.fleet as fleet
from ppfleetx.core.engine.inference_engine import InferenceEngine
from ppfleetx.data import tokenizers
def parse_args():
parser = argparse.ArgumentParser()
parser.add_a... | null |
38,135 | import argparse
import os
import sys
import time
import numpy as np
import paddle.distributed.fleet as fleet
from ppfleetx.core.engine.inference_engine import InferenceEngine
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--seq_len", default=128, type=int, required=False, help="seq l... | null |
38,136 | import argparse
import os
import sys
import time
import numpy as np
import paddle.distributed.fleet as fleet
from ppfleetx.core.engine.inference_engine import InferenceEngine
def predict(engine, data, args):
with engine._static_guard:
for d, name in zip(data, engine.input_names()):
handle = en... | null |
38,137 | import multiprocessing
import os
def run(func):
p = multiprocessing.Process(target=func)
p.start()
p.join() | null |
38,138 | import multiprocessing
import os
def get_gencode_flags():
import paddle
prop = paddle.device.cuda.get_device_properties()
cc = prop.major * 10 + prop.minor
return ["-gencode", "arch=compute_{0},code=sm_{0}".format(cc)]
def change_pwd():
path = os.path.dirname(__file__)
if path:
os.chdir(... | null |
38,139 | import multiprocessing
import os
def get_gencode_flags():
import paddle
prop = paddle.device.cuda.get_device_properties()
cc = prop.major * 10 + prop.minor
return ["-gencode", "arch=compute_{0},code=sm_{0}".format(cc)]
def change_pwd():
path = os.path.dirname(__file__)
if path:
os.chdir(... | null |
38,140 | import json
import math
import os
import re
import time
import numpy as np
import paddle
from ppfleetx.data.tokenizers import GPTTokenizer
from ppfleetx.distributed.apis import env
from ppfleetx.utils.log import logger
import paddlenlp
from paddlenlp.transformers.gpt.tokenizer import GPTChineseTokenizer
logger = Logg... | null |
38,141 | import json
import math
import os
import re
import time
import numpy as np
import paddle
from ppfleetx.data.tokenizers import GPTTokenizer
from ppfleetx.distributed.apis import env
from ppfleetx.utils.log import logger
import paddlenlp
from paddlenlp.transformers.gpt.tokenizer import GPTChineseTokenizer
The provided ... | Get dataset splits from comma or '/' separated string list. |
38,142 | import json
import math
import os
import re
import time
import numpy as np
import paddle
from ppfleetx.data.tokenizers import GPTTokenizer
from ppfleetx.distributed.apis import env
from ppfleetx.utils.log import logger
import paddlenlp
from paddlenlp.transformers.gpt.tokenizer import GPTChineseTokenizer
def _num_token... | documents: document index from 0 to len(docs) sizes: the length list of all docs. num_samples: total step*bs iterations of data. seq_length: the sequence length. sum(sizes) = tokens_per_epoch data_nums = num_samples * micro_batch_size num_epochs = (data_nums + 1) // sum(sizes) len(doc_idx) = num_epochs * sum(sizes) |
38,143 | import json
import math
import os
import re
import time
import numpy as np
import paddle
from ppfleetx.data.tokenizers import GPTTokenizer
from ppfleetx.distributed.apis import env
from ppfleetx.utils.log import logger
import paddlenlp
from paddlenlp.transformers.gpt.tokenizer import GPTChineseTokenizer
The provided ... | num_samples + 1, pos of bs data the distance between two points for sample idx is bs tokens. |
38,144 | from __future__ import absolute_import, division, print_function, unicode_literals
import json
import logging
import os
import sys
import warnings
from io import open
import regex as re
from ppfleetx.utils.download import cached_path
from ppfleetx.utils.log import logger
def lru_cache():
return lambda func: fu... | null |
38,145 | from __future__ import absolute_import, division, print_function, unicode_literals
import json
import logging
import os
import sys
import warnings
from io import open
import regex as re
from ppfleetx.utils.download import cached_path
from ppfleetx.utils.log import logger
The provided code snippet includes necessary de... | Returns list of utf-8 byte and a corresponding list of unicode strings. The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. This ... |
38,146 | from __future__ import absolute_import, division, print_function, unicode_literals
import json
import logging
import os
import sys
import warnings
from io import open
import regex as re
from ppfleetx.utils.download import cached_path
from ppfleetx.utils.log import logger
The provided code snippet includes necessary de... | Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). |
38,147 | import copy
import importlib
import json
import os
import re
import warnings
from collections import OrderedDict, UserDict
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass, field
from typing import (
TYPE_CHECKING,
Any,
Dict,
List,
NamedTupl... | null |
38,148 | import copy
import importlib
import json
import os
import re
import warnings
from collections import OrderedDict, UserDict
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass, field
from typing import (
TYPE_CHECKING,
Any,
Dict,
List,
NamedTupl... | null |
38,149 | import copy
import importlib
import json
import os
import re
import warnings
from collections import OrderedDict, UserDict
from collections.abc import Mapping
from contextlib import contextmanager
from dataclasses import dataclass, field
from typing import (
TYPE_CHECKING,
Any,
Dict,
List,
NamedTupl... | Converts a config key to the corresponding module. |
38,150 | import os
import subprocess
path = os.path.abspath(os.path.dirname(__file__))
The provided code snippet includes necessary dependencies for implementing the `compile_helper` function. Write a Python function `def compile_helper()` to solve the following problem:
Compile helper function ar runtime. Make sure this is in... | Compile helper function ar runtime. Make sure this is invoked on a single process. |
38,151 | import argparse
import io
import json
import multiprocessing
import os
import re
import sys
import time
import numpy as np
from tqdm import tqdm
CHINESE_SEG_FUNC = {}
def lexical_analysis_fn():
from LAC import LAC
lac = LAC(mode="lac")
def process(line):
words, _ = lac.run(line)
return words... | null |
38,152 | import argparse
import io
import json
import multiprocessing
import os
import re
import sys
import time
import numpy as np
from tqdm import tqdm
The provided code snippet includes necessary dependencies for implementing the `get_whole_word_mask_tokens` function. Write a Python function `def get_whole_word_mask_tokens(... | Do whole word mask on Chinese word. First, we do Chinese word segmentation on the sequence of tokens, which are from the WordPiece tokenization. Then, we add the '##' mark on chinese characters which are in the middle of Chinese words. And if the tokens are not chinese characters, we just exploit the results of WordPie... |
38,153 | import argparse
import json
import multiprocessing
import os
import shutil
import sys
import time
from functools import partial
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--input_path", type=str, required=True, help="Path to you raw files. Folder or file path.")
parser.add_argu... | null |
38,154 | import argparse
import json
import multiprocessing
import os
import shutil
import sys
import time
from functools import partial
def raw_text_to_json(path, doc_spliter="", json_key="text", min_doc_length=10):
path = os.path.abspath(path)
if not os.path.exists(path):
print("No found file %s" % path)
... | null |
38,155 | import argparse
import json
import multiprocessing
import os
import shutil
import sys
import time
from functools import partial
def merge_file(file_paths, output_path):
if not output_path.endswith(".jsonl"):
output_path = output_path + ".jsonl"
print("Merging files into %s" % output_path)
with open... | null |
38,156 | import argparse
import json
import multiprocessing
import os
import shutil
import sys
import time
from functools import partial
def shuffle_file(output_path):
print("Shuffling the jsonl file...")
if os.path.exists(output_path):
os.system("shuf %s -o %s" % (output_path, output_path))
print("File... | null |
38,157 | from . import preprocess
The provided code snippet includes necessary dependencies for implementing the `transform` function. Write a Python function `def transform(data, ops=[])` to solve the following problem:
transform
Here is the function:
def transform(data, ops=[]):
"""transform"""
for op in ops:
... | transform |
38,158 | from . import preprocess
The provided code snippet includes necessary dependencies for implementing the `create_preprocess_operators` function. Write a Python function `def create_preprocess_operators(params)` to solve the following problem:
create operators based on the config Args: params(list): a dict list, used to... | create operators based on the config Args: params(list): a dict list, used to create some operators |
38,159 | import numbers
import numpy as np
import paddle
from ppfleetx.data.sampler import Stack, Tuple
def collate_fn(batch):
def default_collate_fn(batch_transform=None):
if batch_transform is not None:
# batch_ops = create_preprocess_operators(batch_transform)
# def inner_collate_fn(batch):
# ... | null |
38,160 | import numbers
import numpy as np
import paddle
from ppfleetx.data.sampler import Stack, Tuple
def gpt_collate_fn(batch):
return Tuple([Stack() for raw in zip(*batch)])(batch) | null |
38,161 | import os
import sys
import numpy as np
import paddle
import paddle.base.core as core
import paddle.nn as nn
from paddle.distributed.fleet import auto
from paddle.profiler import SummaryView
from paddle.profiler.utils import job_schedule_profiler_range
from ppfleetx.core.engine import BasicEngine
from ppfleetx.core.mod... | null |
38,162 | import distutils.util
import importlib
import os
import paddle
from paddle import _C_ops
def try_import(module_name, func_name=None):
if func_name is None:
func_name = module_name
try:
m = importlib.import_module(module_name)
return m
# return getattr(m, func_name)
except Import... | null |
38,163 | import distutils.util
import importlib
import os
import paddle
from paddle import _C_ops
def check_normalized_shape(normalized_shape):
if isinstance(normalized_shape, (list, tuple)):
assert len(normalized_shape) == 1 | null |
38,164 | import distutils.util
import importlib
import os
import paddle
from paddle import _C_ops
class FusedLayerNorm(OriginLayerNorm):
def __init__(self,
normalized_shape,
epsilon=1e-05,
weight_attr=None,
bias_attr=None,
name=None):
... | null |
38,165 | import os
import random
import numpy as np
import paddle
import paddle.distributed as dist
from paddle.distributed import fleet
from paddle.distributed.fleet.meta_parallel import get_rng_state_tracker
from ppfleetx.distributed.apis import comm_groups
from ppfleetx.utils.log import logger
from paddlenlp.trainer.trainer_... | null |
38,166 | import os
import random
import numpy as np
import paddle
import paddle.distributed as dist
from paddle.distributed import fleet
from paddle.distributed.fleet.meta_parallel import get_rng_state_tracker
from ppfleetx.distributed.apis import comm_groups
from ppfleetx.utils.log import logger
from paddlenlp.trainer.trainer_... | null |
38,167 | import os
import random
import numpy as np
import paddle
import paddle.distributed as dist
from paddle.distributed import fleet
from paddle.distributed.fleet.meta_parallel import get_rng_state_tracker
from ppfleetx.distributed.apis import comm_groups
from ppfleetx.utils.log import logger
from paddlenlp.trainer.trainer_... | null |
38,168 | import os
import random
import numpy as np
import paddle
import paddle.distributed as dist
from paddle.distributed import fleet
from paddle.distributed.fleet.meta_parallel import get_rng_state_tracker
from ppfleetx.distributed.apis import comm_groups
from ppfleetx.utils.log import logger
from paddlenlp.trainer.trainer_... | null |
38,169 | import os
import random
import numpy as np
import paddle
import paddle.distributed as dist
from paddle.distributed import fleet
from paddle.distributed.fleet.meta_parallel import get_rng_state_tracker
from ppfleetx.distributed.apis import comm_groups
from ppfleetx.utils.log import logger
from paddlenlp.trainer.trainer_... | null |
38,170 | import os
import random
import numpy as np
import paddle
import paddle.distributed as dist
from paddle.distributed import fleet
from paddle.distributed.fleet.meta_parallel import get_rng_state_tracker
from ppfleetx.distributed.apis import comm_groups
from ppfleetx.utils.log import logger
from paddlenlp.trainer.trainer_... | null |
38,171 | import os
import random
import numpy as np
import paddle
import paddle.distributed as dist
from paddle.distributed import fleet
from paddle.distributed.fleet.meta_parallel import get_rng_state_tracker
from ppfleetx.distributed.apis import comm_groups
from ppfleetx.utils.log import logger
from paddlenlp.trainer.trainer_... | null |
38,172 | import os
import random
import numpy as np
import paddle
import paddle.distributed as dist
from paddle.distributed import fleet
from paddle.distributed.fleet.meta_parallel import get_rng_state_tracker
from ppfleetx.distributed.apis import comm_groups
from ppfleetx.utils.log import logger
from paddlenlp.trainer.trainer_... | null |
38,173 | import os
import random
from collections import namedtuple
import numpy as np
import paddle
import paddle.distributed as dist
import paddle.distributed.auto_parallel as auto
from paddlenlp.ops import Topology
from paddlenlp.trainer.trainer_utils import _get_distributed_seeds
from ppfleetx.utils.log import logger
def se... | null |
38,174 | import os
import random
from collections import namedtuple
import numpy as np
import paddle
import paddle.distributed as dist
import paddle.distributed.auto_parallel as auto
from paddlenlp.ops import Topology
from paddlenlp.trainer.trainer_utils import _get_distributed_seeds
from ppfleetx.utils.log import logger
_mesh ... | null |
38,175 | import os
import paddle
import paddle.distributed as dist
from paddle.incubate.distributed.utils.io import save_for_auto_inference
from ppfleetx.distributed.apis import env
from ppfleetx.utils.log import logger
logger = Logger()
The provided code snippet includes necessary dependencies for implementing the `save` fun... | save the state dicts of model and optimizer into an checkpoint. |
38,176 | import os
import paddle
import paddle.distributed as dist
from paddle.incubate.distributed.utils.io import save_for_auto_inference
from ppfleetx.distributed.apis import env
from ppfleetx.utils.log import logger
logger = Logger()
def load(ckpt_dir, model, optimizer=None, mode="train", load_recovery=None):
nranks =... | null |
38,177 | import paddle.distributed.fleet as fleet
from paddle.distributed.fleet.meta_parallel import TensorParallel
from paddle.distributed.parallel import sync_params_buffers
from paddle.distributed.sharding import group_sharded_parallel
from ppfleetx.distributed.apis import env
def wrap_sharding_2_3(dist_config, model, optimi... | null |
38,178 | import logging
import paddle
import paddle.distributed as dist
from paddle.base import core
from ppfleetx.utils.log import logger
def process_inference_configs(config):
"""
process inference configs for hybrid parallel
"""
if "Inference" not in config.keys():
return
configs = config["Inferen... | null |
38,179 | import copy
import logging
import math
import os
import numpy as np
import paddle
import ppfleetx.models.language_model.gpt as gpt
from paddle.static import InputSpec
from ppfleetx.core.module.basic_module import BasicModule
from ppfleetx.data.tokenizers import GPTTokenizer
from ppfleetx.distributed.apis import env
fro... | null |
38,180 | import copy
import logging
import math
import os
import numpy as np
import paddle
import ppfleetx.models.language_model.gpt as gpt
from paddle.static import InputSpec
from ppfleetx.core.module.basic_module import BasicModule
from ppfleetx.data.tokenizers import GPTTokenizer
from ppfleetx.distributed.apis import env
fro... | null |
38,181 | def process_optim_configs(config):
"""
process optim configs for auto parallel
"""
config["Optimizer"]["lr"]["decay_steps"] *= config["Global"]["global_batch_size"]
def process_model_configs(config):
"""
process model configs for auto parallel
"""
cfg_model = config["Model"]
if cfg_m... | null |
38,182 | import paddle
from paddle import distributed as dist
from paddle.autograd import PyLayer
from paddle.distributed.fleet.meta_parallel import get_rng_state_tracker
from paddle.distributed.fleet.utils.hybrid_parallel_util import (
fused_allreduce_gradients_with_group,
)
from paddle.base import core
from paddle.nn impo... | null |
38,183 | import paddle
from paddle import distributed as dist
from paddle.autograd import PyLayer
from paddle.distributed.fleet.meta_parallel import get_rng_state_tracker
from paddle.distributed.fleet.utils.hybrid_parallel_util import (
fused_allreduce_gradients_with_group,
)
from paddle.base import core
from paddle.nn impo... | null |
38,184 | import paddle
from paddle import distributed as dist
from paddle.autograd import PyLayer
from paddle.distributed.fleet.meta_parallel import get_rng_state_tracker
from paddle.distributed.fleet.utils.hybrid_parallel_util import (
fused_allreduce_gradients_with_group,
)
from paddle.base import core
from paddle.nn impo... | null |
38,185 | import paddle
from paddle import distributed as dist
from paddle.autograd import PyLayer
from paddle.distributed.fleet.meta_parallel import get_rng_state_tracker
from paddle.distributed.fleet.utils.hybrid_parallel_util import (
fused_allreduce_gradients_with_group,
)
from paddle.base import core
from paddle.nn impo... | null |
38,186 | import paddle
from paddle import distributed as dist
from paddle.autograd import PyLayer
from paddle.distributed.fleet.meta_parallel import get_rng_state_tracker
from paddle.distributed.fleet.utils.hybrid_parallel_util import (
fused_allreduce_gradients_with_group,
)
from paddle.base import core
from paddle.nn impo... | null |
38,187 | import paddle
from paddle import distributed as dist
from paddle.autograd import PyLayer
from paddle.distributed.fleet.meta_parallel import get_rng_state_tracker
from paddle.distributed.fleet.utils.hybrid_parallel_util import (
fused_allreduce_gradients_with_group,
)
from paddle.base import core
from paddle.nn impo... | null |
38,188 | import os
import collections
import logging
import math
import numpy as np
import paddle
import paddle.distributed.fleet as fleet
import paddle.incubate as incubate
import paddle.nn as nn
import paddle.nn.functional as F
import paddle.tensor as tensor
from paddle.autograd import PyLayer
from paddle.common_ops_import im... | null |
38,189 | import os
import collections
import logging
import math
import numpy as np
import paddle
import paddle.distributed.fleet as fleet
import paddle.incubate as incubate
import paddle.nn as nn
import paddle.nn.functional as F
import paddle.tensor as tensor
from paddle.autograd import PyLayer
from paddle.common_ops_import im... | null |
38,190 | import os
import collections
import logging
import math
import numpy as np
import paddle
import paddle.distributed.fleet as fleet
import paddle.incubate as incubate
import paddle.nn as nn
import paddle.nn.functional as F
import paddle.tensor as tensor
from paddle.autograd import PyLayer
from paddle.common_ops_import im... | null |
38,191 | import collections
import math
import paddle
import paddle.incubate as incubate
import paddle.nn as nn
import paddle.nn.functional as F
import paddle.tensor as tensor
from paddle.common_ops_import import convert_dtype
from paddle.distributed.fleet.utils import recompute
from paddle.base import layers
from paddle.incuba... | null |
38,192 | import collections
import math
import paddle
import paddle.distributed.auto_parallel as auto
import paddle.incubate as incubate
import paddle.nn as nn
import paddle.nn.functional as F
import paddle.tensor as tensor
from paddle.common_ops_import import convert_dtype
from paddle.base import layers
from paddle.nn.layer.tr... | null |
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