reduce test concurrency to avoid HF rate limiting, test suite parity (#2128)
* reduce test concurrency to avoid HF rate limiting, test suite parity * make val_set_size smaller to speed up e2e tests * more retries for pytest fixture downloads * val_set_size was too small * move retry_on_request_exceptions to data utils and add retry strategy * pre-download ultrafeedback as a test fixture * refactor download retry into it's own fn * don't import from data utils * use retry mechanism now for fixtures
This commit is contained in:
7
.github/workflows/tests-nightly.yml
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7
.github/workflows/tests-nightly.yml
vendored
@@ -23,9 +23,15 @@ jobs:
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runs-on: ubuntu-latest
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runs-on: ubuntu-latest
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strategy:
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strategy:
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fail-fast: false
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fail-fast: false
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max-parallel: 2
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matrix:
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matrix:
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python_version: ["3.10", "3.11"]
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python_version: ["3.10", "3.11"]
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pytorch_version: ["2.3.1", "2.4.1", "2.5.1"]
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pytorch_version: ["2.3.1", "2.4.1", "2.5.1"]
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exclude:
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- python_version: "3.10"
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pytorch_version: "2.4.1"
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- python_version: "3.10"
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pytorch_version: "2.5.1"
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timeout-minutes: 20
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timeout-minutes: 20
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steps:
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steps:
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@@ -55,6 +61,7 @@ jobs:
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pip3 install --upgrade pip
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pip3 install --upgrade pip
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pip3 install --upgrade packaging
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pip3 install --upgrade packaging
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pip3 install -U -e .
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pip3 install -U -e .
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python scripts/unsloth_install.py | sh
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python scripts/cutcrossentropy_install.py | sh
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python scripts/cutcrossentropy_install.py | sh
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pip3 install -r requirements-dev.txt -r requirements-tests.txt
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pip3 install -r requirements-dev.txt -r requirements-tests.txt
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9
.github/workflows/tests.yml
vendored
9
.github/workflows/tests.yml
vendored
@@ -45,9 +45,15 @@ jobs:
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runs-on: ubuntu-latest
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runs-on: ubuntu-latest
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strategy:
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strategy:
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fail-fast: false
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fail-fast: false
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max-parallel: 2
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matrix:
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matrix:
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python_version: ["3.10", "3.11"]
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python_version: ["3.10", "3.11"]
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pytorch_version: ["2.3.1", "2.4.1", "2.5.1"]
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pytorch_version: ["2.3.1", "2.4.1", "2.5.1"]
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exclude:
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- python_version: "3.10"
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pytorch_version: "2.4.1"
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- python_version: "3.10"
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pytorch_version: "2.5.1"
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timeout-minutes: 20
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timeout-minutes: 20
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steps:
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steps:
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@@ -95,6 +101,7 @@ jobs:
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runs-on: ubuntu-latest
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runs-on: ubuntu-latest
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strategy:
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strategy:
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fail-fast: false
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fail-fast: false
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max-parallel: 1
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matrix:
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matrix:
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python_version: ["3.11"]
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python_version: ["3.11"]
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pytorch_version: ["2.4.1", "2.5.1"]
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pytorch_version: ["2.4.1", "2.5.1"]
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@@ -124,6 +131,8 @@ jobs:
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pip3 show torch
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pip3 show torch
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python3 setup.py sdist
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python3 setup.py sdist
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pip3 install dist/axolotl*.tar.gz
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pip3 install dist/axolotl*.tar.gz
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python scripts/unsloth_install.py | sh
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python scripts/cutcrossentropy_install.py | sh
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pip3 install -r requirements-dev.txt -r requirements-tests.txt
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pip3 install -r requirements-dev.txt -r requirements-tests.txt
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- name: Ensure axolotl CLI was installed
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- name: Ensure axolotl CLI was installed
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@@ -2,11 +2,9 @@
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import functools
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import functools
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import logging
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import logging
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import time
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from pathlib import Path
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from pathlib import Path
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from typing import List, Optional, Tuple, Union
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from typing import List, Optional, Tuple, Union
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import requests
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from datasets import (
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from datasets import (
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Dataset,
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Dataset,
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DatasetDict,
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DatasetDict,
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@@ -44,7 +42,11 @@ from axolotl.prompters import (
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UnsupportedPrompter,
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UnsupportedPrompter,
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)
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)
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from axolotl.utils.data.pretraining import wrap_pretraining_dataset
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from axolotl.utils.data.pretraining import wrap_pretraining_dataset
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from axolotl.utils.data.utils import deduplicate_and_log_datasets, md5
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from axolotl.utils.data.utils import (
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deduplicate_and_log_datasets,
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md5,
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retry_on_request_exceptions,
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)
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from axolotl.utils.dict import DictDefault
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from axolotl.utils.dict import DictDefault
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from axolotl.utils.distributed import is_local_main_process, zero_first
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from axolotl.utils.distributed import is_local_main_process, zero_first
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from axolotl.utils.trainer import (
|
from axolotl.utils.trainer import (
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@@ -55,27 +57,6 @@ from axolotl.utils.trainer import (
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LOG = logging.getLogger("axolotl")
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LOG = logging.getLogger("axolotl")
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def retry_on_request_exceptions(max_retries=3, delay=1):
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def decorator(func):
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@functools.wraps(func)
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def wrapper(*args, **kwargs): # pylint: disable=inconsistent-return-statements
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for attempt in range(max_retries):
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try:
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return func(*args, **kwargs)
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except (
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requests.exceptions.ReadTimeout,
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requests.exceptions.ConnectionError,
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) as exc:
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if attempt < max_retries - 1:
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time.sleep(delay)
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else:
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raise exc
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return wrapper
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return decorator
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@retry_on_request_exceptions(max_retries=3, delay=5)
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@retry_on_request_exceptions(max_retries=3, delay=5)
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def prepare_dataset(cfg, tokenizer, processor=None):
|
def prepare_dataset(cfg, tokenizer, processor=None):
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prompters = []
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prompters = []
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@@ -1,13 +1,57 @@
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"""data handling helpers"""
|
"""data handling helpers"""
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import functools
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import hashlib
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import hashlib
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import logging
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import logging
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import time
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from enum import Enum
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import huggingface_hub
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import requests
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from datasets import Dataset
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from datasets import Dataset
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LOG = logging.getLogger("axolotl")
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LOG = logging.getLogger("axolotl")
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class RetryStrategy(Enum):
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|
"""
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|
Enum for retry strategies.
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"""
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CONSTANT = 1
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LINEAR = 2
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EXPONENTIAL = 3
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def retry_on_request_exceptions(
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max_retries=3, delay=1, retry_strategy: RetryStrategy = RetryStrategy.LINEAR
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|
):
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|
def decorator(func):
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|
@functools.wraps(func)
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|
def wrapper(*args, **kwargs): # pylint: disable=inconsistent-return-statements
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|
for attempt in range(max_retries):
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try:
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return func(*args, **kwargs)
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|
except (
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|
requests.exceptions.ReadTimeout,
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|
requests.exceptions.ConnectionError,
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huggingface_hub.errors.HfHubHTTPError,
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) as exc:
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if attempt < max_retries - 1:
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if retry_strategy == RetryStrategy.EXPONENTIAL:
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step_delay = delay * 2**attempt
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elif retry_strategy == RetryStrategy.LINEAR:
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step_delay = delay * (attempt + 1)
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else:
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step_delay = delay # Use constant delay.
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time.sleep(step_delay)
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|
else:
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raise exc
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return wrapper
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return decorator
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def md5(to_hash: str, encoding: str = "utf-8") -> str:
|
def md5(to_hash: str, encoding: str = "utf-8") -> str:
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try:
|
try:
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return hashlib.md5(to_hash.encode(encoding), usedforsecurity=False).hexdigest()
|
return hashlib.md5(to_hash.encode(encoding), usedforsecurity=False).hexdigest()
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@@ -1,47 +1,77 @@
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"""
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"""
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shared pytest fixtures
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shared pytest fixtures
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"""
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"""
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import functools
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import shutil
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import shutil
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import tempfile
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import tempfile
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import time
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import pytest
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import pytest
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import requests
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from huggingface_hub import snapshot_download
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from huggingface_hub import snapshot_download
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|
def retry_on_request_exceptions(max_retries=3, delay=1):
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|
# pylint: disable=duplicate-code
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|
def decorator(func):
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|
@functools.wraps(func)
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|
def wrapper(*args, **kwargs): # pylint: disable=inconsistent-return-statements
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|
for attempt in range(max_retries):
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|
try:
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|
return func(*args, **kwargs)
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|
except (
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requests.exceptions.ReadTimeout,
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|
requests.exceptions.ConnectionError,
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|
) as exc:
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|
if attempt < max_retries - 1:
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|
time.sleep(delay)
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|
else:
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|
raise exc
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|
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||||||
|
return wrapper
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return decorator
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|
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|
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|
@retry_on_request_exceptions(max_retries=3, delay=5)
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|
def snapshot_download_w_retry(*args, **kwargs):
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|
return snapshot_download(*args, **kwargs)
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|
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|
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@pytest.fixture(scope="session", autouse=True)
|
@pytest.fixture(scope="session", autouse=True)
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def download_smollm2_135m_model():
|
def download_smollm2_135m_model():
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# download the model
|
# download the model
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snapshot_download("HuggingFaceTB/SmolLM2-135M")
|
snapshot_download_w_retry("HuggingFaceTB/SmolLM2-135M")
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|
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|
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@pytest.fixture(scope="session", autouse=True)
|
@pytest.fixture(scope="session", autouse=True)
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||||||
def download_llama_68m_random_model():
|
def download_llama_68m_random_model():
|
||||||
# download the model
|
# download the model
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||||||
snapshot_download("JackFram/llama-68m")
|
snapshot_download_w_retry("JackFram/llama-68m")
|
||||||
|
|
||||||
|
|
||||||
@pytest.fixture(scope="session", autouse=True)
|
@pytest.fixture(scope="session", autouse=True)
|
||||||
def download_qwen_2_5_half_billion_model():
|
def download_qwen_2_5_half_billion_model():
|
||||||
# download the model
|
# download the model
|
||||||
snapshot_download("Qwen/Qwen2.5-0.5B")
|
snapshot_download_w_retry("Qwen/Qwen2.5-0.5B")
|
||||||
|
|
||||||
|
|
||||||
@pytest.fixture(scope="session", autouse=True)
|
@pytest.fixture(scope="session", autouse=True)
|
||||||
def download_tatsu_lab_alpaca_dataset():
|
def download_tatsu_lab_alpaca_dataset():
|
||||||
# download the dataset
|
# download the dataset
|
||||||
snapshot_download("tatsu-lab/alpaca", repo_type="dataset")
|
snapshot_download_w_retry("tatsu-lab/alpaca", repo_type="dataset")
|
||||||
|
|
||||||
|
|
||||||
@pytest.fixture(scope="session", autouse=True)
|
@pytest.fixture(scope="session", autouse=True)
|
||||||
def download_mhenrichsen_alpaca_2k_dataset():
|
def download_mhenrichsen_alpaca_2k_dataset():
|
||||||
# download the dataset
|
# download the dataset
|
||||||
snapshot_download("mhenrichsen/alpaca_2k_test", repo_type="dataset")
|
snapshot_download_w_retry("mhenrichsen/alpaca_2k_test", repo_type="dataset")
|
||||||
|
|
||||||
|
|
||||||
@pytest.fixture(scope="session", autouse=True)
|
@pytest.fixture(scope="session", autouse=True)
|
||||||
def download_mhenrichsen_alpaca_2k_w_revision_dataset():
|
def download_mhenrichsen_alpaca_2k_w_revision_dataset():
|
||||||
# download the dataset
|
# download the dataset
|
||||||
snapshot_download(
|
snapshot_download_w_retry(
|
||||||
"mhenrichsen/alpaca_2k_test", repo_type="dataset", revision="d05c1cb"
|
"mhenrichsen/alpaca_2k_test", repo_type="dataset", revision="d05c1cb"
|
||||||
)
|
)
|
||||||
|
|
||||||
@@ -49,21 +79,29 @@ def download_mhenrichsen_alpaca_2k_w_revision_dataset():
|
|||||||
@pytest.fixture(scope="session", autouse=True)
|
@pytest.fixture(scope="session", autouse=True)
|
||||||
def download_mlabonne_finetome_100k_dataset():
|
def download_mlabonne_finetome_100k_dataset():
|
||||||
# download the dataset
|
# download the dataset
|
||||||
snapshot_download("mlabonne/FineTome-100k", repo_type="dataset")
|
snapshot_download_w_retry("mlabonne/FineTome-100k", repo_type="dataset")
|
||||||
|
|
||||||
|
|
||||||
@pytest.fixture
|
@pytest.fixture(scope="session", autouse=True)
|
||||||
def download_argilla_distilabel_capybara_dpo_7k_binarized_dataset():
|
def download_argilla_distilabel_capybara_dpo_7k_binarized_dataset():
|
||||||
# download the dataset
|
# download the dataset
|
||||||
snapshot_download(
|
snapshot_download_w_retry(
|
||||||
"argilla/distilabel-capybara-dpo-7k-binarized", repo_type="dataset"
|
"argilla/distilabel-capybara-dpo-7k-binarized", repo_type="dataset"
|
||||||
)
|
)
|
||||||
|
|
||||||
|
|
||||||
@pytest.fixture
|
@pytest.fixture(scope="session", autouse=True)
|
||||||
|
def download_argilla_ultrafeedback_binarized_preferences_cleaned_dataset():
|
||||||
|
# download the dataset
|
||||||
|
snapshot_download_w_retry(
|
||||||
|
"argilla/ultrafeedback-binarized-preferences-cleaned", repo_type="dataset"
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
@pytest.fixture(scope="session", autouse=True)
|
||||||
def download_arcee_ai_distilabel_intel_orca_dpo_pairs_dataset():
|
def download_arcee_ai_distilabel_intel_orca_dpo_pairs_dataset():
|
||||||
# download the dataset
|
# download the dataset
|
||||||
snapshot_download(
|
snapshot_download_w_retry(
|
||||||
"arcee-ai/distilabel-intel-orca-dpo-pairs-binarized", repo_type="dataset"
|
"arcee-ai/distilabel-intel-orca-dpo-pairs-binarized", repo_type="dataset"
|
||||||
)
|
)
|
||||||
|
|
||||||
|
|||||||
@@ -42,7 +42,7 @@ class Test4dMultipackLlama(unittest.TestCase):
|
|||||||
"lora_dropout": 0.05,
|
"lora_dropout": 0.05,
|
||||||
"lora_target_linear": True,
|
"lora_target_linear": True,
|
||||||
"sequence_len": 1024,
|
"sequence_len": 1024,
|
||||||
"val_set_size": 0.1,
|
"val_set_size": 0.02,
|
||||||
"datasets": [
|
"datasets": [
|
||||||
{
|
{
|
||||||
"path": "mhenrichsen/alpaca_2k_test",
|
"path": "mhenrichsen/alpaca_2k_test",
|
||||||
@@ -86,7 +86,7 @@ class Test4dMultipackLlama(unittest.TestCase):
|
|||||||
"lora_alpha": 16,
|
"lora_alpha": 16,
|
||||||
"lora_dropout": 0.05,
|
"lora_dropout": 0.05,
|
||||||
"lora_target_linear": True,
|
"lora_target_linear": True,
|
||||||
"val_set_size": 0.1,
|
"val_set_size": 0.02,
|
||||||
"datasets": [
|
"datasets": [
|
||||||
{
|
{
|
||||||
"path": "mhenrichsen/alpaca_2k_test",
|
"path": "mhenrichsen/alpaca_2k_test",
|
||||||
|
|||||||
@@ -40,7 +40,7 @@ class TestFalconPatched(unittest.TestCase):
|
|||||||
"lora_dropout": 0.1,
|
"lora_dropout": 0.1,
|
||||||
"lora_target_linear": True,
|
"lora_target_linear": True,
|
||||||
"lora_modules_to_save": ["word_embeddings", "lm_head"],
|
"lora_modules_to_save": ["word_embeddings", "lm_head"],
|
||||||
"val_set_size": 0.1,
|
"val_set_size": 0.05,
|
||||||
"special_tokens": {
|
"special_tokens": {
|
||||||
"bos_token": "<|endoftext|>",
|
"bos_token": "<|endoftext|>",
|
||||||
"pad_token": "<|endoftext|>",
|
"pad_token": "<|endoftext|>",
|
||||||
@@ -80,7 +80,7 @@ class TestFalconPatched(unittest.TestCase):
|
|||||||
"flash_attention": True,
|
"flash_attention": True,
|
||||||
"sample_packing": True,
|
"sample_packing": True,
|
||||||
"sequence_len": 2048,
|
"sequence_len": 2048,
|
||||||
"val_set_size": 0.1,
|
"val_set_size": 0.05,
|
||||||
"special_tokens": {
|
"special_tokens": {
|
||||||
"bos_token": "<|endoftext|>",
|
"bos_token": "<|endoftext|>",
|
||||||
"pad_token": "<|endoftext|>",
|
"pad_token": "<|endoftext|>",
|
||||||
|
|||||||
@@ -38,7 +38,7 @@ class TestFusedLlama(unittest.TestCase):
|
|||||||
"flash_attn_fuse_mlp": True,
|
"flash_attn_fuse_mlp": True,
|
||||||
"sample_packing": True,
|
"sample_packing": True,
|
||||||
"sequence_len": 1024,
|
"sequence_len": 1024,
|
||||||
"val_set_size": 0.1,
|
"val_set_size": 0.02,
|
||||||
"special_tokens": {
|
"special_tokens": {
|
||||||
"unk_token": "<unk>",
|
"unk_token": "<unk>",
|
||||||
"bos_token": "<s>",
|
"bos_token": "<s>",
|
||||||
|
|||||||
@@ -98,7 +98,7 @@ class TestLoraLlama(unittest.TestCase):
|
|||||||
"lora_alpha": 64,
|
"lora_alpha": 64,
|
||||||
"lora_dropout": 0.05,
|
"lora_dropout": 0.05,
|
||||||
"lora_target_linear": True,
|
"lora_target_linear": True,
|
||||||
"val_set_size": 0.1,
|
"val_set_size": 0.02,
|
||||||
"special_tokens": {
|
"special_tokens": {
|
||||||
"unk_token": "<unk>",
|
"unk_token": "<unk>",
|
||||||
"bos_token": "<s>",
|
"bos_token": "<s>",
|
||||||
|
|||||||
@@ -39,7 +39,7 @@ class TestMistral(unittest.TestCase):
|
|||||||
"lora_alpha": 64,
|
"lora_alpha": 64,
|
||||||
"lora_dropout": 0.05,
|
"lora_dropout": 0.05,
|
||||||
"lora_target_linear": True,
|
"lora_target_linear": True,
|
||||||
"val_set_size": 0.1,
|
"val_set_size": 0.05,
|
||||||
"special_tokens": {
|
"special_tokens": {
|
||||||
"unk_token": "<unk>",
|
"unk_token": "<unk>",
|
||||||
"bos_token": "<s>",
|
"bos_token": "<s>",
|
||||||
@@ -80,7 +80,7 @@ class TestMistral(unittest.TestCase):
|
|||||||
"flash_attention": True,
|
"flash_attention": True,
|
||||||
"sample_packing": True,
|
"sample_packing": True,
|
||||||
"sequence_len": 1024,
|
"sequence_len": 1024,
|
||||||
"val_set_size": 0.1,
|
"val_set_size": 0.05,
|
||||||
"special_tokens": {
|
"special_tokens": {
|
||||||
"unk_token": "<unk>",
|
"unk_token": "<unk>",
|
||||||
"bos_token": "<s>",
|
"bos_token": "<s>",
|
||||||
|
|||||||
@@ -40,7 +40,7 @@ class TestMixtral(unittest.TestCase):
|
|||||||
"lora_alpha": 32,
|
"lora_alpha": 32,
|
||||||
"lora_dropout": 0.1,
|
"lora_dropout": 0.1,
|
||||||
"lora_target_linear": True,
|
"lora_target_linear": True,
|
||||||
"val_set_size": 0.1,
|
"val_set_size": 0.05,
|
||||||
"special_tokens": {},
|
"special_tokens": {},
|
||||||
"datasets": [
|
"datasets": [
|
||||||
{
|
{
|
||||||
@@ -78,7 +78,7 @@ class TestMixtral(unittest.TestCase):
|
|||||||
"flash_attention": True,
|
"flash_attention": True,
|
||||||
"sample_packing": True,
|
"sample_packing": True,
|
||||||
"sequence_len": 2048,
|
"sequence_len": 2048,
|
||||||
"val_set_size": 0.1,
|
"val_set_size": 0.05,
|
||||||
"special_tokens": {},
|
"special_tokens": {},
|
||||||
"datasets": [
|
"datasets": [
|
||||||
{
|
{
|
||||||
|
|||||||
@@ -38,7 +38,7 @@ class TestPhiMultipack(unittest.TestCase):
|
|||||||
"pad_to_sequence_len": True,
|
"pad_to_sequence_len": True,
|
||||||
"load_in_8bit": False,
|
"load_in_8bit": False,
|
||||||
"adapter": None,
|
"adapter": None,
|
||||||
"val_set_size": 0.1,
|
"val_set_size": 0.05,
|
||||||
"special_tokens": {
|
"special_tokens": {
|
||||||
"pad_token": "<|endoftext|>",
|
"pad_token": "<|endoftext|>",
|
||||||
},
|
},
|
||||||
|
|||||||
Reference in New Issue
Block a user