python_code stringlengths 0 992k | repo_name stringlengths 8 46 | file_path stringlengths 5 162 |
|---|---|---|
from pathlib import Path
import pytest
from models.openai_model import Model
from transformers import GPT2TokenizerFast
from services.usage_service import UsageService
# Non-ChatGPT -> TODO: make generic test and loop through text models
@pytest.mark.asyncio
async def test_send_req():
usage_service = UsageServi... | SwarmsDiscord-main | tests/test_requests.py |
Speculative-Decoding-main | sd/__init__.py | |
import torch
import torch.nn.functional as F
class SpeculativeDecoder:
def __init__(self, Mp, Mq, gamma):
"""
Initialize the SpeculativeDecoder.
Parameters:
- Mp (nn.Module): The target model.
- Mq (nn.Module): The more efficient approximation model.
- gamma... | Speculative-Decoding-main | sd/main.py |
import json
import warnings
# warning raised by pkg_resources used in a lot of google packages
warnings.filterwarnings("ignore", message=r".*declare_namespace\(\'.*google.*", category=DeprecationWarning)
# base warning raised when warning above are raised
warnings.filterwarnings("ignore", message=r".*pkg_resources is ... | dolma-main | python/dolma/__init__.py |
import math
from abc import abstractmethod, abstractproperty
from typing import Dict, List, NamedTuple, Optional, Tuple, Union
import numpy as np
import numpy.typing as npt
# # # OLD IMPORT # # #
# from sortedcontainers import SortedDict
class SummaryTuple(NamedTuple):
counts: List[int]
bins: List[float]
... | dolma-main | python/dolma/core/binning.py |
import glob
import re
from functools import partial
from itertools import chain
from pathlib import Path
from typing import Any, Dict, Iterable, Iterator, List, Tuple, Union
from urllib.parse import urlparse
from fsspec import AbstractFileSystem, get_filesystem_class
__all__ = [
"glob_path",
"sub_prefix",
... | dolma-main | python/dolma/core/paths.py |
# flake8: noqa
# type: ignore
import argparse
import json
import os
from contextlib import ExitStack
from typing import Dict, List, Optional
import msgspec
import yaml
from .data_types import DocResult, InputSpec, OutputSpec
class Visualizer:
BASE_S3_PREFIX = "s3://ai2-llm/pretraining-data/sources"
def __... | dolma-main | python/dolma/core/vizualizer.py |
from typing import Callable, Dict, Generator, Tuple, Type, TypeVar
from .taggers import BaseTagger
T = TypeVar("T", bound=BaseTagger)
class TaggerRegistry:
__taggers: Dict[str, Type[BaseTagger]] = {}
@classmethod
def taggers(cls) -> Generator[Tuple[str, Type[BaseTagger]], None, None]:
yield fro... | dolma-main | python/dolma/core/registry.py |
import logging
def get_logger(name: str) -> logging.Logger:
name = f"dolma.{name}"
logger = logging.getLogger(name)
logger.setLevel(logging.WARN)
if not logger.handlers:
handler = logging.StreamHandler()
handler.setFormatter(logging.Formatter("%(asctime)s %(levelname)s %(name)s %(mess... | dolma-main | python/dolma/core/loggers.py |
import multiprocessing
import re
import shutil
from contextlib import ExitStack
from tempfile import TemporaryDirectory
from typing import Dict, List, Optional
import msgspec
import smart_open
import tqdm
from msgspec.json import Decoder
from rich.console import Console
from rich.table import Table
from .binning impo... | dolma-main | python/dolma/core/analyzer.py |
from .data_types import DocResult, Document, Span
from .registry import TaggerRegistry
from .taggers import BaseTagger
__all__ = [
"BaseTagger",
"DocResult",
"Document",
"Span",
"TaggerRegistry",
]
| dolma-main | python/dolma/core/__init__.py |
"""
Data types assumed by Filters.
@kylel, @soldni
"""
from typing import Any, Dict, List, Optional, Tuple
from msgspec import Struct
from typing_extensions import TypeAlias
TaggerOutputValueType: TypeAlias = Tuple[int, int, float]
TaggerOutputType: TypeAlias = List[TaggerOutputValueType]
TaggerOutputDictType: Ty... | dolma-main | python/dolma/core/data_types.py |
import logging
import multiprocessing
import tempfile
from contextlib import ExitStack, contextmanager
from typing import (
IO,
Any,
Dict,
Generator,
Iterable,
List,
NamedTuple,
Optional,
Set,
Union,
)
import msgspec
import smart_open
from .data_types import InputSpec, OutputSp... | dolma-main | python/dolma/core/runtime.py |
"""
Base implementation for a fasttext tagger; all fasttext taggers should inherit from this class.
@kylel, @soldni
"""
import os
from tempfile import NamedTemporaryFile
from typing import Iterable, Literal, NamedTuple, Optional
import smart_open
from cached_path import cached_path
from fasttext import train_superv... | dolma-main | python/dolma/core/ft_tagger.py |
"""
Builds a dataset for training a FastText model from 2 or more pretraining datasets.
@rauthur
"""
import argparse
import gzip
import json
import os
import random
from dataclasses import dataclass
from functools import partial
from multiprocessing import Manager, Pool, Process, Queue
from threading import Event
f... | dolma-main | python/dolma/core/ft_dataset.py |
"""
Filters.
@kylel, @soldni
"""
from abc import abstractmethod
from typing import List
from .data_types import DocResult, Document, InputSpec, TaggerOutputDictType
# digits after the decimal point
TAGGER_SCORE_PRECISION = 5
class BaseTagger:
FIELDS: List[str] = ["text"]
@classmethod
def train(cls, ... | dolma-main | python/dolma/core/taggers.py |
import re
import string
from typing import List
try:
import blingfire
BLINGFIRE_AVAILABLE = True
except Exception:
BLINGFIRE_AVAILABLE = False
import nltk
from nltk.tokenize.punkt import PunktSentenceTokenizer
try:
nltk.data.find("tokenizers/punkt")
except LookupError:
nltk.download("punkt")
f... | dolma-main | python/dolma/core/utils.py |
class DolmaError(Exception):
"""Base class for all errors"""
class DolmaFatalError(DolmaError):
"""Fatal error. Abort the entire process"""
class DolmaShardError(DolmaError):
"""Fail the shard and continue"""
class DolmaRetryableFailure(DolmaError):
"""Retry if a shard throws this error"""
class... | dolma-main | python/dolma/core/errors.py |
"""
Code to train a Filter.
@kylel
"""
| dolma-main | python/dolma/core/trainer.py |
import inspect
import itertools
import multiprocessing
import os
import pickle
import random
import re
import time
from contextlib import ExitStack
from datetime import datetime
from functools import partial
from queue import Queue
from threading import Thread
from typing import Any, Dict, List, Optional, Tuple, Union
... | dolma-main | python/dolma/core/parallel.py |
import multiprocessing
from typing import List, TypeVar
from cached_path import cached_path
from omegaconf.omegaconf import OmegaConf as om
from omegaconf.omegaconf import Resolver
from ..core.paths import glob_path
__all__ = ["cache", "glob", "processes"]
C = TypeVar("C", bound=Resolver)
def resolver(resolver: ... | dolma-main | python/dolma/cli/resolvers.py |
from dataclasses import dataclass
from typing import List, Optional
from dolma.cli import BaseCli, field, print_config
from dolma.cli.shared import WorkDirConfig, make_workdirs
from dolma.core.analyzer import create_and_run_analyzer
from dolma.core.errors import DolmaConfigError
from dolma.core.loggers import get_logg... | dolma-main | python/dolma/cli/analyzer.py |
"""
Utilities to work with a OmegaConf structured config object
Author: Luca Soldaini (@soldni)
"""
from argparse import ArgumentParser, Namespace
from collections.abc import Iterable
from copy import deepcopy
from dataclasses import Field
from dataclasses import field as dataclass_field
from dataclasses import is_d... | dolma-main | python/dolma/cli/__init__.py |
import copy
import tempfile
from contextlib import ExitStack, contextmanager
from dataclasses import dataclass
from typing import Generator, Optional
from dolma.cli import field
@dataclass
class WorkDirConfig:
input: Optional[str] = field(default=None, help="Path to the input directory.")
output: Optional[st... | dolma-main | python/dolma/cli/shared.py |
from dataclasses import dataclass
from typing import List, Optional
from rich.console import Console
from rich.table import Table
from dolma.cli import BaseCli, field, print_config
from dolma.cli.shared import WorkDirConfig, make_workdirs
from dolma.core.errors import DolmaConfigError
from dolma.core.loggers import g... | dolma-main | python/dolma/cli/tagger.py |
from dataclasses import dataclass
from typing import Any, Dict, List, Optional
from omegaconf import OmegaConf as om
from dolma import deduper
from dolma.cli import BaseCli, field, print_config
from dolma.cli.shared import WorkDirConfig, make_workdirs
from dolma.core.errors import DolmaConfigError
from dolma.core.log... | dolma-main | python/dolma/cli/deduper.py |
from dataclasses import dataclass
from typing import Any, Dict, List, Optional
from dolma import mixer
from dolma.cli import BaseCli, field, print_config
from dolma.cli.shared import WorkDirConfig, make_workdirs
from dolma.core.errors import DolmaConfigError
from dolma.core.loggers import get_logger
from dolma.core.pa... | dolma-main | python/dolma/cli/mixer.py |
from argparse import ArgumentParser
from pathlib import Path
from typing import List, Optional
from yaml import safe_load
from .analyzer import AnalyzerCli
from .deduper import DeduperCli
from .mixer import MixerCli
# must import these to register the resolvers
from .resolvers import * # noqa: F401,F403
from .tagge... | dolma-main | python/dolma/cli/__main__.py |
import logging
import os
from dataclasses import dataclass
from typing import List, Set
from ..core.data_types import DocResult, Document, Span
from ..core.registry import TaggerRegistry
from ..core.taggers import BaseTagger
MIN_WORDS_PER_LINE = 3
naughty_lines = (
open(os.path.join(os.path.dirname(os.path.dirnam... | dolma-main | python/dolma/taggers/c4.py |
import logging
from collections import Counter
from dataclasses import dataclass
from statistics import median
from typing import Counter as CounterType
from typing import List, Tuple
from ..core.data_types import DocResult, Document, Span
from ..core.registry import TaggerRegistry
from ..core.taggers import BaseTagge... | dolma-main | python/dolma/taggers/gopher.py |
"""
Filters.
@kylel, @soldni
"""
import regex
import uniseg.wordbreak
from tokenizers import Regex, pre_tokenizers
from ..core.data_types import DocResult, Document, Span
from ..core.registry import TaggerRegistry
from ..core.taggers import BaseTagger
from ..core.utils import split_paragraphs
@TaggerRegistry.add... | dolma-main | python/dolma/taggers/length.py |
"""
Code-related taggers.
@akshitab
"""
import logging
import re
from typing import Generator, List
import numpy as np
import regex
from detect_secrets import SecretsCollection
from detect_secrets.core.scan import (
PotentialSecret,
_process_line_based_plugins,
get_plugins,
)
from detect_secrets.setting... | dolma-main | python/dolma/taggers/code.py |
from . import c4, code, gopher, jigsaw, language, length, pii, sampling
| dolma-main | python/dolma/taggers/__init__.py |
import random
from multiprocessing import current_process
from ..core.data_types import DocResult, Document, Span
from ..core.registry import TaggerRegistry
from ..core.taggers import BaseTagger
@TaggerRegistry.add("random_number_v1")
class RandomNumberTagger(BaseTagger):
def __init__(self, seed: int = 1) -> Non... | dolma-main | python/dolma/taggers/sampling.py |
"""
Filters.
@kylel, @soldni
"""
from typing import Iterable
from ..core.data_types import TextSlice
from ..core.ft_tagger import BaseFastTextTagger, Prediction
from ..core.registry import TaggerRegistry
@TaggerRegistry.add("jigsaw_hatespeech_document_v2")
class FastTextJigsawHatespeechDocumentTagger(BaseFastTex... | dolma-main | python/dolma/taggers/jigsaw.py |
"""
Filters.
@kylel, @soldni
"""
from typing import Iterable, List, Tuple
try:
import cld3
CLD3_AVAILABLE = True
except ImportError:
CLD3_AVAILABLE = False
import pycld2 as cld2
import regex
from anyascii import anyascii
from ..core.data_types import DocResult, Document, Span, TextSlice
from ..core.f... | dolma-main | python/dolma/taggers/language.py |
"""
Filters.
@kylel, @soldni
"""
try:
import re2 as re
except ImportError:
import re
else:
re.set_fallback_notification(re.FALLBACK_WARNING)
from typing import List
from warnings import warn
from presidio_analyzer import AnalyzerEngine
from ..core.data_types import DocResult, Document, Span, TextSli... | dolma-main | python/dolma/taggers/pii.py |
from argparse import ArgumentParser
from dataclasses import dataclass
from unittest import TestCase
from omegaconf import MissingMandatoryValue
from dolma.cli import field, make_parser, namespace_to_nested_omegaconf
@dataclass
class _1:
a: int = field(help="a")
b: str = field(help="b")
@dataclass
class _2... | dolma-main | tests/python/test_omegaconf.py |
"""
Tests for the utils module.
@kylel
"""
from unittest import TestCase
from dolma.core.data_types import TextSlice
from dolma.core.utils import split_paragraphs, split_sentences
class TestUtils(TestCase):
def test_make_variable_name(self):
pass
def test_split_paragraphs(self):
text = ... | dolma-main | tests/python/test_utils.py |
import unittest
import numpy as np
from dolma.core.binning import (
FixedBucketsValTracker,
InferBucketsValTracker,
merge_bins,
)
class TestBinning(unittest.TestCase):
def setUp(self) -> None:
np.random.seed(0)
def test_binning(self):
bin_a = np.arange(0, 10_000, 55).astype(np.f... | dolma-main | tests/python/test_binning.py |
import json
import os
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest import TestCase
import smart_open
from dolma.core.runtime import (
_make_paths_from_prefix,
_make_paths_from_substitution,
create_and_run_tagger,
)
LOCAL_DATA = Path(__file__).parent.parent / "data"
cla... | dolma-main | tests/python/test_runtime.py |
import json
from pathlib import Path
from tempfile import NamedTemporaryFile
from unittest import TestCase
from dolma.cli.__main__ import main
from .utils import (
clean_test_data,
download_s3_prefix,
get_test_prefix,
load_jsonl,
skip_aws_tests,
upload_s3_prefix,
)
EMAIL_SPANS = Path(__file__... | dolma-main | tests/python/test_mixer.py |
import warnings
# warning raised by pkg_resources used in a lot of google packages
warnings.filterwarnings("ignore", message=r".*declare_namespace\(\'.*google.*", category=DeprecationWarning)
# base warning raised when warning above are raised
warnings.filterwarnings("ignore", message=r".*pkg_resources is deprecated.*... | dolma-main | tests/python/__init__.py |
import json
import shutil
from contextlib import ExitStack
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from unittest import TestCase
from dolma.cli.__main__ import main
from .utils import (
clean_test_data,
download_s3_prefix,
get_test_prefix,
load_jsonl,
s... | dolma-main | tests/python/test_deduper.py |
# mypy: disable-error-code="unused-ignore"
import os
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import Any
from unittest import TestCase
import smart_open
from dolma.core.parallel import BaseParallelProcessor, QueueType
LOCAL_DATA = Path(__file__).parent.parent / "data"
class Moc... | dolma-main | tests/python/test_parallel.py |
import json
import os
import re
import uuid
from typing import List, Tuple
from urllib.parse import urlparse
import boto3
import smart_open
from smart_open import open
from dolma.core.paths import glob_path, mkdir_p
DOLMA_TESTS_S3_PREFIX_ENV_VAR = "DOLMA_TESTS_S3_PREFIX"
DOLMA_TESTS_SKIP_AWS_ENV_VAR = "DOLMA_TESTS_S... | dolma-main | tests/python/utils.py |
"""
Unit tests for core/data_types.py
@kylel
"""
from unittest import TestCase
from dolma.core.data_types import DocResult, Document, InputSpec, Span, TextSlice
class TestDocument(TestCase):
def test_document_to_from_json(self):
doc = Document(source="source", version="version", id="id", text="text")... | dolma-main | tests/python/test_data_types.py |
import itertools
import os
from pathlib import Path
from unittest import TestCase
from dolma.core.paths import (
_escape_glob,
_pathify,
_unescape_glob,
add_suffix,
glob_path,
is_glob,
join_path,
make_relative,
split_glob,
split_path,
sub_prefix,
sub_suffix,
)
from .uti... | dolma-main | tests/python/test_paths.py |
"""
Unit tests for taggers/*.py
@kylel
"""
from unittest import TestCase
from dolma.core.data_types import Document
from dolma.taggers.gopher import GopherTagger
class TestGopherTagger(TestCase):
def test_predict_short(self):
tagger = GopherTagger()
doc = Document(source="", version="", id=""... | dolma-main | tests/python/test_taggers.py |
import argparse
import bisect
import copy
import hashlib
import json
import multiprocessing
import os
from collections import defaultdict
from contextlib import ExitStack
from copy import deepcopy
from dataclasses import dataclass, field
from itertools import chain
from queue import Queue
from tempfile import Temporary... | dolma-main | scripts/stats.py |
blockwise-parallel-transformer-1-main | bpt/__init__.py | |
# coding=utf-8
# Copyright 2021 The EleutherAI and The HuggingFace Inc. team.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requi... | blockwise-parallel-transformer-1-main | bpt/model.py |
import dataclasses
import pprint
from functools import partial
import re
from tqdm import tqdm, trange
import numpy as np
import bpt.tools.utils as utils
import jax
import jax.numpy as jnp
from jax.experimental.pjit import pjit
from jax.sharding import PartitionSpec as PS
import flax
from flax import linen as nn
from... | blockwise-parallel-transformer-1-main | bpt/train.py |
import dataclasses
import pprint
import time
from functools import partial
import json
from multiprocessing import Pool
import h5py
import bpt.tools.utils as utils
from ml_collections.config_dict import config_dict
from ml_collections import ConfigDict
from tqdm import tqdm, trange
import numpy as np
from datasets im... | blockwise-parallel-transformer-1-main | bpt/data.py |
import functools
import json
import math
from functools import partial
from typing import Optional, Tuple
import flax.linen as nn
import jax
import jax.numpy as jnp
import numpy as np
from einops import rearrange
from flax.linen import combine_masks, make_causal_mask
from jax import lax
from jax import numpy as jnp
... | blockwise-parallel-transformer-1-main | bpt/blocks/vanilla.py |
import functools
import json
import math
from functools import partial
from typing import Callable, NamedTuple, Optional
import flax.linen as nn
import jax
import jax.numpy as jnp
import numpy as np
from einops import rearrange
from flax.linen import combine_masks, make_causal_mask
from jax import lax
from jax import ... | blockwise-parallel-transformer-1-main | bpt/blocks/blockwise_parallel_v1.py |
blockwise-parallel-transformer-1-main | bpt/blocks/__init__.py | |
import functools
import json
import math
from functools import partial
from typing import Callable, NamedTuple, Optional
import flax.linen as nn
import jax
import jax.numpy as jnp
import numpy as np
from einops import rearrange
from flax.linen import combine_masks, make_causal_mask
from jax import lax
from jax import ... | blockwise-parallel-transformer-1-main | bpt/blocks/blockwise_parallel.py |
import functools
import json
import math
from functools import partial
from typing import Callable, NamedTuple, Optional
import flax.linen as nn
import jax
import jax.numpy as jnp
import numpy as np
from einops import rearrange
from flax.linen import combine_masks, make_causal_mask
from jax import lax
from jax import ... | blockwise-parallel-transformer-1-main | bpt/blocks/memeff.py |
import os
import numpy as np
from ml_collections import ConfigDict
import bpt.tools.utils as utils
import jax
import jax.numpy as jnp
import flax
from flax.serialization import (
from_bytes, to_bytes, to_state_dict, from_state_dict
)
from flax.traverse_util import flatten_dict, unflatten_dict, empty_node
import msg... | blockwise-parallel-transformer-1-main | bpt/tools/checkpoint.py |
from datasets import load_dataset
import json
from multiprocessing import Pool, cpu_count
dataset = load_dataset("openwebtext")
split_dataset = dataset["train"].train_test_split(test_size=0.0005, seed=2357, shuffle=True)
split_dataset['val'] = split_dataset.pop('test')
def save_split(split):
with open(f"openwebt... | blockwise-parallel-transformer-1-main | bpt/tools/prepare_owt.py |
blockwise-parallel-transformer-1-main | bpt/tools/__init__.py | |
import os
import math
from typing import Any, Mapping, Text, Tuple, Union, NamedTuple
from functools import partial
import re
import dataclasses
import random
import dill
import flax
import jax
import jax.numpy as jnp
from jax.sharding import PartitionSpec as PS
from jax.sharding import Mesh
from jax.experimental.pjit... | blockwise-parallel-transformer-1-main | bpt/tools/jax_utils.py |
import os
import time
from typing import Any, Mapping, Text, Tuple, Union, NamedTuple
from functools import partial
import re
import dataclasses
import random
from ml_collections.config_dict import config_dict
from ml_collections import ConfigDict
import jax
import jax.numpy as jnp
import numpy as np
from absl import ... | blockwise-parallel-transformer-1-main | bpt/tools/optimizers.py |
import inspect
import logging
import os
import pprint
import random
import tempfile
import time
import uuid
from concurrent.futures import ThreadPoolExecutor
from copy import copy
from io import BytesIO
from socket import gethostname
import dataclasses
import absl.flags
import absl.logging
import cloudpickle as pickle... | blockwise-parallel-transformer-1-main | bpt/tools/utils.py |
# Python file for Paperspace Gradient NLP Text Generation Tutorial example
# It runs the GPT-2 model from HuggingFace: https://huggingface.co/gpt2
#
# The Workflow is triggered when its YAML file is present in the .gradient/workflows/ directory
# in a GitHub repository linked to the user's Gradient project
# It clones ... | kosmos-model-main | nlp_text_generation.py |
import os
INITIAL_PEERS = os.environ.get("INITIAL_PEERS")
if not INITIAL_PEERS:
raise RuntimeError("Must specify INITIAL_PEERS environment variable with one or more peer ids")
INITIAL_PEERS = INITIAL_PEERS.split()
MODEL_NAME = os.environ.get("MODEL_NAME")
if not MODEL_NAME:
raise RuntimeError("Must specify M... | TheGrid-main | tests/test_utils.py |
import asyncio
import gc
from contextlib import suppress
import psutil
import pytest
from hivemind.utils.crypto import RSAPrivateKey
from hivemind.utils.logging import get_logger
from hivemind.utils.mpfuture import MPFuture
logger = get_logger(__name__)
@pytest.fixture
def event_loop():
"""
This overrides t... | TheGrid-main | tests/conftest.py |
import random
import pytest
import torch
import transformers
from tensor_parallel import TensorParallel
from tensor_parallel.slicing_configs import get_bloom_config
from grid.server.from_pretrained import load_pretrained_block
from test_utils import MODEL_NAME
@pytest.mark.forked
@pytest.mark.parametrize("custom_co... | TheGrid-main | tests/test_tensor_parallel.py |
import subprocess
import sys
import pytest
import torch
from grid import AutoDistributedConfig
from grid.server.throughput import measure_compute_rps
from grid.utils.convert_block import QuantType
from test_utils import MODEL_NAME
def test_bnb_not_imported_when_unnecessary():
"""
We avoid importing bitsandb... | TheGrid-main | tests/test_aux_functions.py |
import os
import shutil
import pytest
from huggingface_hub import snapshot_download
from grid.utils.peft import check_peft_repository, load_peft
UNSAFE_PEFT_REPO = "artek0chumak/bloom-560m-unsafe-peft"
SAFE_PEFT_REPO = "artek0chumak/bloom-560m-safe-peft"
TMP_CACHE_DIR = "tmp_cache/"
def clear_dir(path_to_dir):
... | TheGrid-main | tests/test_peft.py |
import multiprocessing as mp
import time
import pytest
import torch
from hivemind.moe.server.runtime import Runtime
from grid.server.task_pool import PrioritizedTaskPool
@pytest.mark.forked
def test_priority_pools():
outputs_queue = mp.SimpleQueue()
results_valid = mp.Event()
def dummy_pool_func(x):
... | TheGrid-main | tests/test_priority_pool.py |
import time
import hivemind
import pytest
import torch
from grid import DistributedBloomConfig, RemoteSequential
from grid.server.handler import CACHE_TOKENS_AVAILABLE
from test_utils import *
@pytest.mark.forked
def test_server_info(block_from: int = 22, block_to: int = 24, max_length: int = 100, max_length2: int ... | TheGrid-main | tests/test_server_stats.py |
import pytest
import torch
import torch.nn.functional as F
from hivemind import DHT, BatchTensorDescriptor, get_logger
from hivemind.proto import runtime_pb2
from grid import DistributedBloomConfig
from grid.client import RemoteSequenceManager, RemoteSequential
from grid.data_structures import UID_DELIMITER
from grid.... | TheGrid-main | tests/test_remote_sequential.py |
import peft
import pytest
import torch
import transformers
from hivemind import get_logger
from transformers.generation import BeamSearchScorer
from transformers.models.bloom import BloomForCausalLM
from grid import DistributedBloomForCausalLM
from test_utils import *
logger = get_logger(__name__)
@pytest.mark.fork... | TheGrid-main | tests/test_full_model.py |
######
# Warning:torch this test is a work in progress. It will be modified soon.
# - if you want more stable tests, see test_block_exact_match
# - if you want to figure out chained inference, ask yozh
import pytest
import torch
from grid import DistributedBloomConfig
from grid.client.remote_sequential import Remote... | TheGrid-main | tests/test_chained_calls.py |
import threading
import time
import pytest
import torch
from hivemind import DHT, get_logger
from grid import DistributedBloomConfig
from grid.client import RemoteSequenceManager, RemoteSequential
from grid.data_structures import UID_DELIMITER
from test_utils import *
logger = get_logger(__name__)
@pytest.mark.for... | TheGrid-main | tests/test_sequence_manager.py |
import pytest
import torch
from grid.server.block_utils import resolve_block_dtype
from grid.server.from_pretrained import load_pretrained_block
from grid.utils.auto_config import AutoDistributedConfig
from test_utils import MODEL_NAME
@pytest.mark.forked
@pytest.mark.parametrize("torch_dtype", [torch.float32, torch... | TheGrid-main | tests/test_dtype.py |
import random
import pytest
import torch
from grid import DistributedBloomConfig, RemoteSequential
from grid.server.from_pretrained import load_pretrained_block
from test_utils import *
@pytest.mark.forked
def test_remote_block_exact_match(atol_forward=1e-4, atol_inference=1e-3):
config = DistributedBloomConfig... | TheGrid-main | tests/test_block_exact_match.py |
#!/usr/bin/env python3
import argparse
import multiprocessing as mp
from time import perf_counter
import numpy as np
import torch
from hivemind.utils.logging import get_logger
from grid import AutoDistributedModelForCausalLM, AutoDistributedModelForSequenceClassification
from grid.constants import DTYPE_MAP, PUBLIC_... | TheGrid-main | benchmarks/benchmark_training.py |
#!/usr/bin/env python3
import argparse
import multiprocessing as mp
from time import perf_counter
import numpy as np
import torch
from hivemind.utils.logging import get_logger
from transformers import AutoTokenizer
from grid import AutoDistributedModelForCausalLM
from grid.constants import DTYPE_MAP, PUBLIC_INITIAL_... | TheGrid-main | benchmarks/benchmark_inference.py |
#!/usr/bin/env python3
import argparse
import multiprocessing as mp
from time import perf_counter
import numpy as np
import torch
from hivemind.utils.logging import get_logger
from grid import AutoDistributedModel
from grid.constants import DTYPE_MAP, PUBLIC_INITIAL_PEERS
logger = get_logger()
def main():
par... | TheGrid-main | benchmarks/benchmark_forward.py |
"""
Utilities for declaring and retrieving active model layers using a shared DHT.
"""
from __future__ import annotations
import math
from functools import partial
from typing import Dict, List, Optional, Sequence, Union
from hivemind.dht import DHT, DHTNode, DHTValue
from hivemind.p2p import PeerID
from hivemind.uti... | TheGrid-main | grid/dht_utils.py |
import torch
PUBLIC_INITIAL_PEERS = [
# IPv4 DNS addresses
"/dns/bootstrap1.grid.dev/tcp/31337/p2p/QmedTaZXmULqwspJXz44SsPZyTNKxhnnFvYRajfH7MGhCY",
"/dns/bootstrap2.grid.dev/tcp/31338/p2p/QmQGTqmM7NKjV6ggU1ZCap8zWiyKR89RViDXiqehSiCpY5",
# IPv6 DNS addresses
"/dns6/bootstrap1.grid.dev/tcp/31337/p2p/... | TheGrid-main | grid/constants.py |
import os
os.environ.setdefault("BITSANDBYTES_NOWELCOME", "1")
import hivemind
import transformers
from packaging import version
from grid.client import *
from grid.models import *
from grid.utils import *
from grid.utils.logging import initialize_logs as _initialize_logs
__version__ = "2.0.1"
if not os.getenv("G... | TheGrid-main | grid/__init__.py |
import dataclasses
from enum import Enum
from typing import Any, Dict, Optional, Sequence, Tuple
import pydantic
from hivemind import PeerID
from hivemind.moe.expert_uid import ExpertUID
from grid.server.memory_cache import Handle
ModuleUID = str
UID_DELIMITER = "." # delimits parts of one module uid, e.g. "bloom.t... | TheGrid-main | grid/data_structures.py |
"""
A pytorch memory cache that can be allocated by ConnectionHandler (on cpu) and used over multiple calls to Runtime.
For now, the only purpose of this code is to ensure that allocated memory will be deleted properly.
"""
import asyncio
import contextlib
import ctypes
import multiprocessing as mp
import os
import t... | TheGrid-main | grid/server/memory_cache.py |
import ctypes
import multiprocessing as mp
import threading
import time
from concurrent.futures._base import PENDING
from dataclasses import dataclass, field
from queue import PriorityQueue
from typing import Any, List, Optional, Sequence, Tuple, Union
import torch
from hivemind import get_logger
from hivemind.moe.ser... | TheGrid-main | grid/server/task_pool.py |
from __future__ import annotations
import gc
import math
import multiprocessing as mp
import random
import threading
import time
from typing import Dict, List, Optional, Sequence, Union
import hivemind
import torch
from hivemind import DHT, MAX_DHT_TIME_DISCREPANCY_SECONDS, BatchTensorDescriptor, get_dht_time
from hi... | TheGrid-main | grid/server/server.py |
from abc import ABC, abstractmethod
import torch
class TaskPrioritizerBase(ABC):
"""Abstract class for TaskPrioritizer whose responsibility is to evaluate task priority"""
@abstractmethod
def prioritize(self, *input: torch.Tensor, points: float = 0.0, **kwargs) -> float:
"""Evaluates task value ... | TheGrid-main | grid/server/task_prioritizer.py |
from __future__ import annotations
from collections import Counter
from itertools import chain
from typing import Any, Dict, Optional, Sequence, Tuple, Union
import torch
from hivemind import BatchTensorDescriptor, TensorDescriptor
from hivemind.moe.expert_uid import ExpertUID
from hivemind.moe.server.module_backend ... | TheGrid-main | grid/server/backend.py |
from __future__ import annotations
import asyncio
import contextlib
import multiprocessing as mp
import sys
from enum import Enum
from itertools import chain
from typing import Any, AsyncIterator, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from async_timeout import timeout
from hivemind impor... | TheGrid-main | grid/server/handler.py |
import fcntl
import json
import math
import multiprocessing as mp
import os
import time
from collections import Counter
from pathlib import Path
from typing import Dict, Optional, Sequence, Union
import torch
from hivemind.utils.logging import get_logger
from transformers import PretrainedConfig
from grid.server.bloc... | TheGrid-main | grid/server/throughput.py |
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import numpy as np
from hivemind import PeerID, get_logger
from grid.data_structures import RemoteModuleInfo, ServerState
__all__ = ["choose_best_blocks", "should_choose_other_blocks"]
logger = get_logger(__name__)
@dataclass
class S... | TheGrid-main | grid/server/block_selection.py |
from typing import Optional, Union
import torch
from accelerate import init_empty_weights
from transformers import PretrainedConfig
from grid.utils.convert_block import QuantType
def resolve_block_dtype(config: PretrainedConfig, dtype: Union[str, torch.dtype]) -> torch.dtype:
"""If dtype is "auto", resolves it ... | TheGrid-main | grid/server/block_utils.py |
TheGrid-main | grid/server/__init__.py | |
import asyncio
import math
import threading
import time
from concurrent.futures import Future
from contextlib import asynccontextmanager
from functools import partial
from typing import Optional
import requests
from hivemind.dht import DHT, DHTNode
from hivemind.moe.client.remote_expert_worker import RemoteExpertWorke... | TheGrid-main | grid/server/reachability.py |
"""
Utils for fetching pretrained model parts. Currently, this relies on huggingface transformers' from_pretrained code.
If necessary, one can rewrite this to implement a different behavior, such as:
- loading files from a local data source (e.g. S3)
- load files via BitTorrent ( https://pypi.org/project/libtorrent/ ... | TheGrid-main | grid/server/from_pretrained.py |
import os
from hivemind.utils import logging as hm_logging
def initialize_logs():
"""Initialize Grid logging tweaks. This function is called when you import the `grid` module."""
# Env var GRID_LOGGING=False prohibits Grid do anything with logs
if os.getenv("GRID_LOGGING", "True").lower() in ("false", "... | TheGrid-main | grid/utils/logging.py |
import torch
DUMMY = torch.empty(0) # dummy tensor that replaces empty prompt or adapter parameters
def is_dummy(tensor: torch.Tensor):
return tensor.numel() == 0
| TheGrid-main | grid/utils/misc.py |
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