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
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@@ -23,9 +23,15 @@ jobs:
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runs-on: ubuntu-latest
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strategy:
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fail-fast: false
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max-parallel: 2
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matrix:
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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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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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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 packaging
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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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pip3 install -r requirements-dev.txt -r requirements-tests.txt
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9
.github/workflows/tests.yml
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9
.github/workflows/tests.yml
vendored
@@ -45,9 +45,15 @@ jobs:
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runs-on: ubuntu-latest
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strategy:
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fail-fast: false
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max-parallel: 2
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matrix:
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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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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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steps:
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@@ -95,6 +101,7 @@ jobs:
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runs-on: ubuntu-latest
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strategy:
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fail-fast: false
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max-parallel: 1
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matrix:
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python_version: ["3.11"]
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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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python3 setup.py sdist
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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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- 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 logging
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import time
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from pathlib import Path
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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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Dataset,
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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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)
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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.distributed import is_local_main_process, zero_first
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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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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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def prepare_dataset(cfg, tokenizer, processor=None):
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prompters = []
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@@ -1,13 +1,57 @@
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"""data handling helpers"""
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import functools
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import hashlib
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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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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:
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try:
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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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shared pytest fixtures
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"""
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import functools
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import shutil
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import tempfile
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import time
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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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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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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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def snapshot_download_w_retry(*args, **kwargs):
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return snapshot_download(*args, **kwargs)
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@pytest.fixture(scope="session", autouse=True)
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def download_smollm2_135m_model():
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# download the model
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snapshot_download("HuggingFaceTB/SmolLM2-135M")
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snapshot_download_w_retry("HuggingFaceTB/SmolLM2-135M")
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@pytest.fixture(scope="session", autouse=True)
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def download_llama_68m_random_model():
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# download the model
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snapshot_download("JackFram/llama-68m")
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snapshot_download_w_retry("JackFram/llama-68m")
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@pytest.fixture(scope="session", autouse=True)
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def download_qwen_2_5_half_billion_model():
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# download the model
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snapshot_download("Qwen/Qwen2.5-0.5B")
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snapshot_download_w_retry("Qwen/Qwen2.5-0.5B")
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@pytest.fixture(scope="session", autouse=True)
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def download_tatsu_lab_alpaca_dataset():
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# download the dataset
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snapshot_download("tatsu-lab/alpaca", repo_type="dataset")
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snapshot_download_w_retry("tatsu-lab/alpaca", repo_type="dataset")
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@pytest.fixture(scope="session", autouse=True)
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def download_mhenrichsen_alpaca_2k_dataset():
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# download the dataset
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snapshot_download("mhenrichsen/alpaca_2k_test", repo_type="dataset")
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snapshot_download_w_retry("mhenrichsen/alpaca_2k_test", repo_type="dataset")
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@pytest.fixture(scope="session", autouse=True)
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def download_mhenrichsen_alpaca_2k_w_revision_dataset():
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# download the dataset
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snapshot_download(
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snapshot_download_w_retry(
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"mhenrichsen/alpaca_2k_test", repo_type="dataset", revision="d05c1cb"
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)
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@@ -49,21 +79,29 @@ def download_mhenrichsen_alpaca_2k_w_revision_dataset():
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@pytest.fixture(scope="session", autouse=True)
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def download_mlabonne_finetome_100k_dataset():
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# download the dataset
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snapshot_download("mlabonne/FineTome-100k", repo_type="dataset")
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snapshot_download_w_retry("mlabonne/FineTome-100k", repo_type="dataset")
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@pytest.fixture
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@pytest.fixture(scope="session", autouse=True)
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def download_argilla_distilabel_capybara_dpo_7k_binarized_dataset():
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# download the dataset
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snapshot_download(
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snapshot_download_w_retry(
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"argilla/distilabel-capybara-dpo-7k-binarized", repo_type="dataset"
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)
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@pytest.fixture
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@pytest.fixture(scope="session", autouse=True)
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def download_argilla_ultrafeedback_binarized_preferences_cleaned_dataset():
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# download the dataset
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snapshot_download_w_retry(
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"argilla/ultrafeedback-binarized-preferences-cleaned", repo_type="dataset"
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)
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@pytest.fixture(scope="session", autouse=True)
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def download_arcee_ai_distilabel_intel_orca_dpo_pairs_dataset():
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# download the dataset
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snapshot_download(
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snapshot_download_w_retry(
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"arcee-ai/distilabel-intel-orca-dpo-pairs-binarized", repo_type="dataset"
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)
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@@ -42,7 +42,7 @@ class Test4dMultipackLlama(unittest.TestCase):
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"lora_dropout": 0.05,
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"lora_target_linear": True,
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"sequence_len": 1024,
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"val_set_size": 0.1,
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"val_set_size": 0.02,
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"datasets": [
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{
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"path": "mhenrichsen/alpaca_2k_test",
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@@ -86,7 +86,7 @@ class Test4dMultipackLlama(unittest.TestCase):
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"lora_alpha": 16,
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"lora_dropout": 0.05,
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"lora_target_linear": True,
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"val_set_size": 0.1,
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"val_set_size": 0.02,
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"datasets": [
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{
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"path": "mhenrichsen/alpaca_2k_test",
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@@ -40,7 +40,7 @@ class TestFalconPatched(unittest.TestCase):
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"lora_dropout": 0.1,
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"lora_target_linear": True,
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"lora_modules_to_save": ["word_embeddings", "lm_head"],
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"val_set_size": 0.1,
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"val_set_size": 0.05,
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"special_tokens": {
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"bos_token": "<|endoftext|>",
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"pad_token": "<|endoftext|>",
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@@ -80,7 +80,7 @@ class TestFalconPatched(unittest.TestCase):
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"flash_attention": True,
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"sample_packing": True,
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"sequence_len": 2048,
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"val_set_size": 0.1,
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"val_set_size": 0.05,
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"special_tokens": {
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"bos_token": "<|endoftext|>",
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"pad_token": "<|endoftext|>",
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@@ -38,7 +38,7 @@ class TestFusedLlama(unittest.TestCase):
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"flash_attn_fuse_mlp": True,
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"sample_packing": True,
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"sequence_len": 1024,
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"val_set_size": 0.1,
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"val_set_size": 0.02,
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"special_tokens": {
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"unk_token": "<unk>",
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"bos_token": "<s>",
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@@ -98,7 +98,7 @@ class TestLoraLlama(unittest.TestCase):
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"lora_alpha": 64,
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"lora_dropout": 0.05,
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"lora_target_linear": True,
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"val_set_size": 0.1,
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"val_set_size": 0.02,
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"special_tokens": {
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"unk_token": "<unk>",
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"bos_token": "<s>",
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@@ -39,7 +39,7 @@ class TestMistral(unittest.TestCase):
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"lora_alpha": 64,
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"lora_dropout": 0.05,
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"lora_target_linear": True,
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"val_set_size": 0.1,
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"val_set_size": 0.05,
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"special_tokens": {
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"unk_token": "<unk>",
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"bos_token": "<s>",
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@@ -80,7 +80,7 @@ class TestMistral(unittest.TestCase):
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"flash_attention": True,
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"sample_packing": True,
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"sequence_len": 1024,
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"val_set_size": 0.1,
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"val_set_size": 0.05,
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"special_tokens": {
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"unk_token": "<unk>",
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"bos_token": "<s>",
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@@ -40,7 +40,7 @@ class TestMixtral(unittest.TestCase):
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"lora_alpha": 32,
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"lora_dropout": 0.1,
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"lora_target_linear": True,
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"val_set_size": 0.1,
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"val_set_size": 0.05,
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"special_tokens": {},
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"datasets": [
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{
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@@ -78,7 +78,7 @@ class TestMixtral(unittest.TestCase):
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"flash_attention": True,
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"sample_packing": True,
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"sequence_len": 2048,
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"val_set_size": 0.1,
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"val_set_size": 0.05,
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"special_tokens": {},
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"datasets": [
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{
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@@ -38,7 +38,7 @@ class TestPhiMultipack(unittest.TestCase):
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"pad_to_sequence_len": True,
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"load_in_8bit": False,
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"adapter": None,
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"val_set_size": 0.1,
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"val_set_size": 0.05,
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"special_tokens": {
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"pad_token": "<|endoftext|>",
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},
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Reference in New Issue
Block a user