diff --git a/.github/workflows/build-and-publish.yaml b/.github/workflows/build-and-publish.yaml index 7bb377df8..fc52fea53 100644 --- a/.github/workflows/build-and-publish.yaml +++ b/.github/workflows/build-and-publish.yaml @@ -43,6 +43,8 @@ jobs: run: | python -m pip install --upgrade pip python -m pip install tox + python -m pip install poetry --user + echo "PATH=$PATH:~/.local/bin" >> "$GITHUB_ENV" - name: Build and test with tox run: tox -e ${{ matrix.python-version.tox }} - name: Build and check wheel package diff --git a/.github/workflows/coverage.yaml b/.github/workflows/coverage.yaml index e5a7a0eb4..a6fdbed09 100644 --- a/.github/workflows/coverage.yaml +++ b/.github/workflows/coverage.yaml @@ -18,5 +18,7 @@ jobs: run: | python -m pip install --upgrade pip python -m pip install tox + python -m pip install poetry --user + echo "PATH=$PATH:~/.local/bin" >> "$GITHUB_ENV" - name: Check Coverage run: tox -e coverage \ No newline at end of file diff --git a/.github/workflows/format.yml b/.github/workflows/format.yml index 574aac18a..980953685 100644 --- a/.github/workflows/format.yml +++ b/.github/workflows/format.yml @@ -33,6 +33,8 @@ jobs: run: | python -m pip install --upgrade pip python -m pip install tox + python -m pip install poetry --user + echo "PATH=$PATH:~/.local/bin" >> "$GITHUB_ENV" - name: Check formatting run: tox -e fmt - name: Run pylint diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index f8e24265c..3f4961202 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -23,5 +23,7 @@ jobs: run: | python -m pip install --upgrade pip python -m pip install tox + python -m pip install poetry --user + echo "PATH=$PATH:~/.local/bin" >> "$GITHUB_ENV" - name: Run unit tests run: tox -e py \ No newline at end of file diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 6d20b395c..d99311f2c 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -42,6 +42,8 @@ If additional new Python module dependencies are required, think about where to - If they're optional dependencies for additional functionality, then put them in the pyproject.toml file like were done for [flash-attn](https://github.com/foundation-model-stack/fms-hf-tuning/blob/main/pyproject.toml#L44) or [aim](https://github.com/foundation-model-stack/fms-hf-tuning/blob/main/pyproject.toml#L45). - If it's an additional dependency for development, then add it to the [dev](https://github.com/foundation-model-stack/fms-hf-tuning/blob/main/pyproject.toml#L43) dependencies. +if using `poetry` to install dependencies, you can optionally leverage [poetry add](https://python-poetry.org/docs/cli/#add) to add new dependencies to pyproject. + #### Code Review Once you've [created a pull request](#how-can-i-contribute), maintainers will review your code and may make suggestions to fix before merging. It will be easier for your pull request to receive reviews if you consider the criteria the reviewers follow while working. Remember to: @@ -82,12 +84,45 @@ The following tools are required: - [git](https://git-scm.com) - [python](https://www.python.org) (v3.8+) - [pip](https://pypi.org/project/pip/) (v23.0+) +- [poetry](https://python-poetry.org/docs/#installation) (v1.8.3+) + - Poetry should always be installed in a dedicated virtual environment to isolate it from the rest of your system. It should in no case be installed in the environment of the project that is to be managed by Poetry. This ensures that Poetry’s own dependencies will not be accidentally upgraded or uninstalled. +- [tox](https://tox.wiki/en/4.15.1/installation.html) (v4.15.1+) + - Just like `poetry` install `tox` in an isolated virtual environment Installation: -``` -pip install -U datasets -pip install -e . + +```bash +: Install poetry and tox in an isolated virtual environment +python3 -m venv isolated +./isolated/bin/pip install -U pip setuptools +./isolated/bin/pip install poetry tox + +: Ensure you can access poetry and tox without activating the +: the isolated virtual environment +export PATH=$PATH:`pwd`/isolated/bin + +: Create your development virtual environment +python3 -m venv venv +. venv/bin/activate + +: Install a dev version (similar to pip -e ".[dev]") of fms-hf-tuning +poetry install --extras dev +``` + + +> Note: After installing, if you wish to use [FlashAttention](https://github.com/Dao-AILab/flash-attention), then you need to install these requirements: + +``` +poetry install --extras dev,flash-attn +``` + +If you wish to use [aim](https://github.com/aimhubio/aim), then you need to install it: ``` +poetry install --extras aim +``` + +If you wish to use [fms-acceleration](https://github.com/foundation-model-stack/fms-acceleration) follow the instructions in [this section of README.md](README.md#fms-acceleration). +
Linting diff --git a/README.md b/README.md index 7a6bbd7d2..ece625601 100644 --- a/README.md +++ b/README.md @@ -7,6 +7,7 @@ This repo provides basic tuning scripts with support for specific models. The re ## Installation +### 1. Install from wheel ``` pip install fms-hf-tuning ``` @@ -27,7 +28,13 @@ If you wish to use [fms-acceleration](https://github.com/foundation-model-stack/ ``` pip install git+https://github.com/foundation-model-stack/fms-acceleration.git#subdirectory=plugins/framework ``` -`fms-acceleration` is a collection of plugins that packages that accelerate fine-tuning / training of large models, as part of the `fms-hf-tuning` suite. For more details on see [this section below](#fms-acceleration). +`fms-acceleration` is a collection of plugins that packages that accelerate fine-tuning / training of large models, as part of the `fms-hf-tuning` suite. For more details see [this section below](#fms-acceleration). + +### 2. Build from source + +We have committed a `poetry.lock` file to allow reproducible enviornments. If building from source, you can clone the repository and use poetry to install as mentioned in [development docs](/CONTRIBUTING.md#development) + +If building in a dockerfile use `poetry export --format requirements.txt` can install same dependencies from the lock file. Maintainers regularly update the lock file. ## Data format We support two data formats: @@ -385,7 +392,7 @@ Equally you can pass in a JSON configuration for running tuning. See [build doc] ### FMS Acceleration -`fms-acceleration` is fuss-free approach to access a curated collection of acceleration plugins that acclerate your `tuning/sft-trainer.py` experience. Accelerations that apply to a variety of use-cases, e.g., PeFT / full-finetuning, are being planned for. As such, the accelerations are grouped into *plugins*; only install the plugins needed for the acceleration of interest. The plugins are housed in the [seperate repository found here](https://github.com/foundation-model-stack/fms-acceleration). +`fms-acceleration` is fuss-free approach to access a curated collection of acceleration plugins that accelerate your `tuning/sft-trainer.py` experience. Accelerations that apply to a variety of use-cases, e.g., PeFT / full-finetuning, are being planned for. As such, the accelerations are grouped into *plugins*; only install the plugins needed for the acceleration of interest. The plugins are housed in the [separate repository found here](https://github.com/foundation-model-stack/fms-acceleration). To access `fms-acceleration` features the `[fms-accel]` dependency must first be installed: ``` diff --git a/build/Dockerfile b/build/Dockerfile index 6ccd21e96..1720a6cd1 100644 --- a/build/Dockerfile +++ b/build/Dockerfile @@ -110,29 +110,44 @@ RUN dnf install -y git && \ rm -f /usr/share/doc/perl-Net-SSLeay/examples/server_key.pem && \ dnf clean all USER ${USER} -WORKDIR /tmp +# Ensure that git directory is owned by current user, otherwise git raises +# "fatal: detected dubious ownership" for `/tmp` +WORKDIR /tmp/fms-hf-tuning + +# Install poetry and its dependencies inside an isolated virtual environment which we +# will not copy into the release-base layer RUN --mount=type=cache,target=/home/${USER}/.cache/pip,uid=${USER_UID} \ - python -m pip install --user build -COPY --chown=${USER}:root tuning tuning -COPY .git .git -COPY pyproject.toml pyproject.toml + python -m venv venv /tmp/isolated && \ + /tmp/isolated/bin/pip install poetry poetry-plugin-export -# Build a wheel if PyPi wheel_version is empty else download the wheel from PyPi -RUN if [[ -z "${WHEEL_VERSION}" ]]; \ - then python -m build --wheel --outdir /tmp; \ - else pip download fms-hf-tuning==${WHEEL_VERSION} --dest /tmp --only-binary=:all: --no-deps; \ - fi && \ - ls /tmp/*.whl >/tmp/bdist_name +COPY --chown=${USER}:root tuning tuning +COPY --chown=${USER}:root .git .git +COPY --chown=${USER}:root pyproject.toml pyproject.toml +COPY --chown=${USER}:root poetry.lock poetry.lock +COPY README.md README.md -# Install from the wheel +# Install using poetry if PyPi wheel_version is empty else download the wheel from PyPi RUN --mount=type=cache,target=/home/${USER}/.cache/pip,uid=${USER_UID} \ - python -m pip install --user wheel && \ - python -m pip install --user "$(head bdist_name)" && \ - python -m pip install --user "$(head bdist_name)[flash-attn]" && \ - # Clean up the wheel module. It's only needed by flash-attn install - python -m pip uninstall wheel build -y && \ - # Cleanup the bdist whl file - rm $(head bdist_name) /tmp/bdist_name + if [[ -z "${WHEEL_VERSION}" ]]; then \ + # Extract requirements from poetry and install them in ~/.local \ + # Need wheel and build for the flash-attn package \ + python -m pip install --user wheel build && \ + python -m pip install --user --requirement <(/tmp/isolated/bin/poetry export --format requirements.txt) && \ + # Next install the package with flash-attn \ + python -m pip install --user ".[flash-attn]" && \ + python -m pip uninstall wheel build -y ; \ + else \ + # This will use whatever dependencies versions satisfy the pyproject.toml constraints \ + # but they won't necessarily be the exact same versions as present in poetry.lock \ + # First, install fms-hf-tuning to get its dependencies which include torch. \ + # Then install with the flash-attn extras as the latter expects torch to be present \ + python -m pip install --user wheel build && \ + python -m pip install --user "fms-hf-tuning==${WHEEL_VERSION}" && \ + python -m pip install --user "fms-hf-tuning[flash-attn]==${WHEEL_VERSION}" && \ + python -m pip uninstall wheel build -y ; \ + fi + +RUN python -m pip freeze ## Final image ################################################ FROM release-base as release diff --git a/build/accelerate_launch.py b/build/accelerate_launch.py index 9816dde02..22ab2a1c9 100644 --- a/build/accelerate_launch.py +++ b/build/accelerate_launch.py @@ -18,30 +18,33 @@ """ # Standard -import os +from pathlib import Path import logging +import os +import shutil import subprocess import sys -import traceback import tempfile -import shutil -from pathlib import Path +import traceback # Third Party from accelerate.commands.launch import launch_command +import torch.distributed.elastic.multiprocessing.errors -# Local +# First Party from build.utils import ( + get_highest_checkpoint, process_accelerate_launch_args, serialize_args, - get_highest_checkpoint, ) -from tuning.utils.config_utils import get_json_config + +# Local from tuning.config.tracker_configs import FileLoggingTrackerConfig +from tuning.utils.config_utils import get_json_config from tuning.utils.error_logging import ( - write_termination_log, - USER_ERROR_EXIT_CODE, INTERNAL_ERROR_EXIT_CODE, + USER_ERROR_EXIT_CODE, + write_termination_log, ) ERROR_LOG = "/dev/termination-log" @@ -89,6 +92,20 @@ def main(): # Launch training # ########## + + def handle_sft_trainer_exit_error(return_code): + # If the subprocess throws an exception, the base exception is hidden in the + # subprocess call and is difficult to access at this level. However, that is not + # an issue because sft_trainer.py would have already written the exception + # message to termination log. + logging.error(traceback.format_exc()) + # The exit code that sft_trainer.py threw is captured in e.returncode + + if return_code not in [INTERNAL_ERROR_EXIT_CODE, USER_ERROR_EXIT_CODE]: + return_code = INTERNAL_ERROR_EXIT_CODE + write_termination_log(f"Unhandled exception during training. {e}") + sys.exit(return_code) + original_output_dir = job_config.get("output_dir") with tempfile.TemporaryDirectory() as tempdir: try: @@ -98,19 +115,13 @@ def main(): os.environ["SFT_TRAINER_CONFIG_JSON_ENV_VAR"] = updated_args launch_command(args) + except torch.distributed.elastic.multiprocessing.errors.ChildFailedError as e: + # This is what accelerate.commands.launch.multi_gpu_launcher() raises + # (when using >1 GPUs) + handle_sft_trainer_exit_error(e.get_first_failure()[1].exitcode) except subprocess.CalledProcessError as e: - # If the subprocess throws an exception, the base exception is hidden in the - # subprocess call and is difficult to access at this level. However, that is not - # an issue because sft_trainer.py would have already written the exception - # message to termination log. - logging.error(traceback.format_exc()) - # The exit code that sft_trainer.py threw is captured in e.returncode - - return_code = e.returncode - if return_code not in [INTERNAL_ERROR_EXIT_CODE, USER_ERROR_EXIT_CODE]: - return_code = INTERNAL_ERROR_EXIT_CODE - write_termination_log(f"Unhandled exception during training. {e}") - sys.exit(return_code) + # This is what accelerate.commands.launch.simple_launcher() raises + handle_sft_trainer_exit_error(e.returncode) except Exception as e: # pylint: disable=broad-except logging.error(traceback.format_exc()) write_termination_log(f"Unhandled exception during training. {e}") diff --git a/build/utils.py b/build/utils.py index 96c432d90..e9ed22a2f 100644 --- a/build/utils.py +++ b/build/utils.py @@ -13,14 +13,14 @@ # limitations under the License. # Standard -import os +import base64 import logging +import os import 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b/pyproject.toml @@ -1,22 +1,33 @@ [build-system] -build-backend = "setuptools.build_meta" -requires = [ - "setuptools>=60", - "setuptools-scm>=8.0" - ] +requires = ["poetry-core>=1.0.0", "poetry-dynamic-versioning>=1.0.0,<2.0.0"] +build-backend = "poetry_dynamic_versioning.backend" + +[tool.poetry-dynamic-versioning] +enable = true +vcs = "git" +style = "semver" + +[tool.poetry-dynamic-versioning.files."tuning/_version.py"] +persistent-substitution = true +initial-content = """ + # These version placeholders will be replaced later during substitution. + __version__ = "0.0.0" + __version_tuple__ = (0, 0, 0) +""" + +[tool.poetry] +# poetry-dynamic-versioning will auto-generate this when running commands like `poetry build` +version = "0.0.0" -[project] name = "fms-hf-tuning" -dynamic = ["version"] description = "FMS HF Tuning" -authors = [ - {name = "Sukriti Sharma", email = "sukriti.sharma4@ibm.com"}, - {name = "Anh Uong", email = "anh.uong@ibm.com"}, -] -license = {text = "Apache-2.0"} +authors = ["Sukriti Sharma ", "Anh Uong "] readme = "README.md" -requires-python = "~=3.9" + +exclude = ["tests", "venv"] +license = "Apache-2.0" keywords = ['fms-hf-tuning', 'python', 'tuning'] + classifiers=[ "License :: OSI Approved :: Apache Software License", "Development Status :: 4 - Beta", @@ -25,32 +36,49 @@ classifiers=[ "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", ] -dependencies = [ -"numpy>=1.26.4,<2.0", -"accelerate>=0.20.3,<0.40", -"transformers>=4.34.1,<=4.40.2,!=4.38.2", -"torch>=2.2.0,<3.0", -"sentencepiece>=0.1.99,<0.3", -"tokenizers>=0.13.3,<1.0", -"tqdm>=4.66.2,<5.0", -"trl>=0.9.3,<1.0", -"peft>=0.8.0,<0.13", -"datasets>=2.15.0,<3.0", -"fire>=0.5.0,<1.0", -"simpleeval>=0.9.13,<1.0", -] -[project.optional-dependencies] -dev = ["wheel>=0.42.0,<1.0", "packaging>=23.2,<24", "ninja>=1.11.1.1,<2.0", "scikit-learn>=1.0, <2.0", "boto3>=1.34, <2.0"] -flash-attn = ["flash-attn>=2.5.3,<3.0"] -aim = ["aim>=3.19.0,<4.0"] 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default list, # see https://doc.pytest.org/en/latest/reference/reference.html#confval-norecursedirs -norecursedirs = *.egg .* _darcs CVS dist node_modules venv {arch} +norecursedirs = *.egg .* _darcs CVS dist node_modules venv {arch} \ No newline at end of file diff --git a/tests/acceleration/test_acceleration_framework.py b/tests/acceleration/test_acceleration_framework.py index c907b8f06..fa7600124 100644 --- a/tests/acceleration/test_acceleration_framework.py +++ b/tests/acceleration/test_acceleration_framework.py @@ -24,7 +24,7 @@ import torch # First Party -from tests.test_sft_trainer import DATA_ARGS, MODEL_ARGS, PEFT_LORA_ARGS, TRAIN_ARGS +from tests.test_sft_trainer import DATA_ARGS, MODEL_ARGS, PEFT_LORA_ARGS, get_train_args # Local from .spying_utils import create_mock_plugin_class_and_spy @@ -218,8 +218,7 @@ def test_framework_raises_if_used_with_missing_package(): """Ensure that trying the use the framework, without first installing fms_acceleration will raise. """ - train_args = copy.deepcopy(TRAIN_ARGS) - train_args.output_dir = None + train_args = get_train_args(output_dir=None) quantized_lora_config = QuantizedLoraConfig(auto_gptq=AutoGPTQLoraConfig()) @@ -236,7 +235,7 @@ def test_framework_raises_if_used_with_missing_package(): sft_trainer.train( MODEL_ARGS, DATA_ARGS, - TRAIN_ARGS, + train_args, PEFT_LORA_ARGS, quantized_lora_config=quantized_lora_config, ) @@ -281,8 +280,7 @@ def test_framework_raises_due_to_invalid_arguments( with tempfile.TemporaryDirectory() as tempdir: model_args = copy.deepcopy(MODEL_ARGS) model_args = replace(model_args, **bad_kwargs) - train_args = copy.deepcopy(TRAIN_ARGS) - train_args.output_dir = tempdir + train_args = get_train_args(output_dir=tempdir) quantized_lora_config = QuantizedLoraConfig(auto_gptq=AutoGPTQLoraConfig()) @@ -347,10 +345,7 @@ def test_framework_intialized_properly_peft( model_args = copy.deepcopy(MODEL_ARGS) model_args.model_name_or_path = model_name_or_path model_args.torch_dtype = torch.float16 - train_args = copy.deepcopy(TRAIN_ARGS) - train_args.output_dir = tempdir - train_args.save_strategy = "no" - train_args.fp16 = True + train_args = get_train_args(output_dir=tempdir, save_strategy="no", fp16=True) installation_path, (MockedPlugin, spy) = mock_and_spy @@ -395,10 +390,7 @@ def test_framework_intialized_properly_foak(): model_args = copy.deepcopy(MODEL_ARGS) model_args.model_name_or_path = "TheBloke/TinyLlama-1.1B-Chat-v0.3-GPTQ" model_args.torch_dtype = torch.float16 - train_args = copy.deepcopy(TRAIN_ARGS) - train_args.output_dir = tempdir - train_args.save_strategy = "no" - train_args.fp16 = True + train_args = get_train_args(output_dir=tempdir, save_strategy="no", fp16=True) # setup default quantized lora args dataclass # - with auth gptq as the quantized method diff --git a/tests/build/test_launch_script.py b/tests/build/test_launch_script.py index 824b7125c..f55f75176 100644 --- a/tests/build/test_launch_script.py +++ b/tests/build/test_launch_script.py @@ -16,9 +16,10 @@ """ # Standard +import glob import os +import socket import tempfile -import glob # Third Party import pytest @@ -27,10 +28,9 @@ from build.accelerate_launch import main from build.utils import serialize_args from tests.data import TWITTER_COMPLAINTS_DATA -from tuning.utils.error_logging import ( - USER_ERROR_EXIT_CODE, - INTERNAL_ERROR_EXIT_CODE, -) + +# Local +from tuning.utils.error_logging import INTERNAL_ERROR_EXIT_CODE, USER_ERROR_EXIT_CODE SCRIPT = "tuning/sft_trainer.py" MODEL_NAME = "Maykeye/TinyLLama-v0" @@ -89,11 +89,30 @@ def cleanup_env(): os.environ.pop("TERMINATION_LOG_FILE", None) -def test_successful_ft(): +# Use name="free_port" to avoid W0621: redefined-outer-name pylint warning +@pytest.fixture(scope="function", name="free_port") +def fixture_free_port() -> int: + # This does not guarantee that a different process doesn't use this port + # immediately after sock.close() + sock = socket.socket() + sock.bind(("", 0)) + + port = sock.getsockname()[1] + sock.close() + + return port + + +def test_successful_ft(free_port: int): """Check if we can bootstrap and fine tune causallm models""" with tempfile.TemporaryDirectory() as tempdir: setup_env(tempdir) - TRAIN_KWARGS = {**BASE_KWARGS, **{"output_dir": tempdir}} + TRAIN_KWARGS = { + **BASE_KWARGS, + **{"output_dir": tempdir}, + "accelerate_launch_args": {"main_process_port": free_port}, + } + serialized_args = serialize_args(TRAIN_KWARGS) os.environ["SFT_TRAINER_CONFIG_JSON_ENV_VAR"] = serialized_args @@ -105,11 +124,15 @@ def test_successful_ft(): assert len(glob.glob(f"{tempdir}/model*.safetensors")) > 0 -def test_successful_pt(): +def test_successful_pt(free_port: int): """Check if we can bootstrap and peft tune causallm models""" with tempfile.TemporaryDirectory() as tempdir: setup_env(tempdir) - TRAIN_KWARGS = {**BASE_PEFT_KWARGS, **{"output_dir": tempdir}} + TRAIN_KWARGS = { + **BASE_PEFT_KWARGS, + **{"output_dir": tempdir}, + "accelerate_launch_args": {"main_process_port": free_port}, + } serialized_args = serialize_args(TRAIN_KWARGS) os.environ["SFT_TRAINER_CONFIG_JSON_ENV_VAR"] = serialized_args @@ -121,11 +144,15 @@ def test_successful_pt(): assert os.path.exists(tempdir + "/adapter_config.json") is True -def test_successful_lora(): +def test_successful_lora(free_port: int): """Check if we can bootstrap and LoRA tune causallm models""" with tempfile.TemporaryDirectory() as tempdir: setup_env(tempdir) - TRAIN_KWARGS = {**BASE_LORA_KWARGS, **{"output_dir": tempdir}} + TRAIN_KWARGS = { + **BASE_LORA_KWARGS, + **{"output_dir": tempdir}, + "accelerate_launch_args": {"main_process_port": free_port}, + } serialized_args = serialize_args(TRAIN_KWARGS) os.environ["SFT_TRAINER_CONFIG_JSON_ENV_VAR"] = serialized_args @@ -137,11 +164,15 @@ def test_successful_lora(): assert os.path.exists(tempdir + "/adapter_config.json") is True -def test_bad_script_path(): +def test_bad_script_path(free_port: int): """Check for appropriate error for an invalid training script location""" with tempfile.TemporaryDirectory() as tempdir: setup_env(tempdir) - TRAIN_KWARGS = {**BASE_PEFT_KWARGS, **{"output_dir": tempdir}} + TRAIN_KWARGS = { + **BASE_PEFT_KWARGS, + **{"output_dir": tempdir}, + "accelerate_launch_args": {"main_process_port": free_port}, + } serialized_args = serialize_args(TRAIN_KWARGS) os.environ["SFT_TRAINER_CONFIG_JSON_ENV_VAR"] = serialized_args os.environ["TRAINING_SCRIPT"] = "/not/here" @@ -164,13 +195,14 @@ def test_blank_env_var(): assert os.stat(tempdir + "/termination-log").st_size > 0 -def test_faulty_file_path(): +def test_faulty_file_path(free_port: int): with tempfile.TemporaryDirectory() as tempdir: setup_env(tempdir) faulty_path = os.path.join(tempdir, "non_existent_file.pkl") TRAIN_KWARGS = { **BASE_PEFT_KWARGS, **{"training_data_path": faulty_path, "output_dir": tempdir}, + "accelerate_launch_args": {"main_process_port": free_port}, } serialized_args = serialize_args(TRAIN_KWARGS) os.environ["SFT_TRAINER_CONFIG_JSON_ENV_VAR"] = serialized_args @@ -181,12 +213,13 @@ def test_faulty_file_path(): assert os.stat(tempdir + "/termination-log").st_size > 0 -def test_bad_base_model_path(): +def test_bad_base_model_path(free_port: int): with tempfile.TemporaryDirectory() as tempdir: setup_env(tempdir) TRAIN_KWARGS = { **BASE_PEFT_KWARGS, **{"model_name_or_path": "/wrong/path"}, + "accelerate_launch_args": {"main_process_port": free_port}, } serialized_args = serialize_args(TRAIN_KWARGS) os.environ["SFT_TRAINER_CONFIG_JSON_ENV_VAR"] = serialized_args @@ -197,12 +230,13 @@ def test_bad_base_model_path(): assert os.stat(tempdir + "/termination-log").st_size > 0 -def test_config_parsing_error(): +def test_config_parsing_error(free_port: int): with tempfile.TemporaryDirectory() as tempdir: setup_env(tempdir) TRAIN_KWARGS = { **BASE_PEFT_KWARGS, **{"num_train_epochs": "five"}, + "accelerate_launch_args": {"main_process_port": free_port}, } # Intentional type error serialized_args = serialize_args(TRAIN_KWARGS) os.environ["SFT_TRAINER_CONFIG_JSON_ENV_VAR"] = serialized_args diff --git a/tests/build/test_utils.py b/tests/build/test_utils.py index dddd861cb..97fe71861 100644 --- a/tests/build/test_utils.py +++ b/tests/build/test_utils.py @@ -13,15 +13,15 @@ # limitations under the License. # Standard +from unittest.mock import patch import copy import json import os -from unittest.mock import patch # Third Party import pytest -# Local +# First Party from build.utils import process_accelerate_launch_args HAPPY_PATH_DUMMY_CONFIG_PATH = os.path.join( diff --git a/tests/test_sft_trainer.py b/tests/test_sft_trainer.py index d7508d8de..ed7b21d97 100644 --- a/tests/test_sft_trainer.py +++ b/tests/test_sft_trainer.py @@ -50,22 +50,22 @@ response_template="\n### Label:", dataset_text_field="output", ) -TRAIN_ARGS = configs.TrainingArguments( - num_train_epochs=5, - per_device_train_batch_size=4, - per_device_eval_batch_size=4, - gradient_accumulation_steps=4, - learning_rate=0.00001, - weight_decay=0, - warmup_ratio=0.03, - lr_scheduler_type="cosine", - logging_steps=1, - include_tokens_per_second=True, - packing=False, - max_seq_length=4096, - save_strategy="epoch", - output_dir="tmp", -) +TRAIN_ARGS = { + "num_train_epochs": 5, + "per_device_train_batch_size": 4, + "per_device_eval_batch_size": 4, + "gradient_accumulation_steps": 4, + "learning_rate": 0.00001, + "weight_decay": 0, + "warmup_ratio": 0.03, + "lr_scheduler_type": "cosine", + "logging_steps": 1, + "include_tokens_per_second": True, + "packing": False, + "max_seq_length": 4096, + "save_strategy": "epoch", + "output_dir": "tmp", +} PEFT_PT_ARGS = peft_config.PromptTuningConfig( prompt_tuning_init="RANDOM", num_virtual_tokens=8, @@ -76,12 +76,17 @@ PEFT_LORA_ARGS = peft_config.LoraConfig(r=8, lora_alpha=32, lora_dropout=0.05) +def get_train_args(**kwargs) -> configs.TrainingArguments: + args = copy.deepcopy(TRAIN_ARGS) + args.update(kwargs) + + return configs.TrainingArguments(**args) + + def test_run_train_requires_output_dir(): """Check fails when output dir not provided.""" - updated_output_dir_train_args = copy.deepcopy(TRAIN_ARGS) - updated_output_dir_train_args.output_dir = None with pytest.raises(TypeError): - sft_trainer.train(MODEL_ARGS, DATA_ARGS, updated_output_dir_train_args, None) + sft_trainer.train(MODEL_ARGS, DATA_ARGS, get_train_args(output_dir=None), None) def test_run_train_fails_training_data_path_not_exist(): @@ -89,7 +94,7 @@ def test_run_train_fails_training_data_path_not_exist(): updated_data_path_args = copy.deepcopy(DATA_ARGS) updated_data_path_args.training_data_path = "fake/path" with pytest.raises(FileNotFoundError): - sft_trainer.train(MODEL_ARGS, updated_data_path_args, TRAIN_ARGS, None) + sft_trainer.train(MODEL_ARGS, updated_data_path_args, get_train_args(), None) HAPPY_PATH_DUMMY_CONFIG_PATH = os.path.join( @@ -166,8 +171,7 @@ def test_parse_arguments_peft_method(job_config): def test_run_causallm_pt_and_inference(): """Check if we can bootstrap and peft tune causallm models""" with tempfile.TemporaryDirectory() as tempdir: - train_args = copy.deepcopy(TRAIN_ARGS) - train_args.output_dir = tempdir + train_args = get_train_args(output_dir=tempdir) sft_trainer.train(MODEL_ARGS, DATA_ARGS, train_args, PEFT_PT_ARGS) @@ -199,8 +203,7 @@ def test_run_causallm_pt_and_inference_with_formatting_data(): "### Text: {{Tweet text}} \n\n### Label: {{text_label}}" ) - train_args = copy.deepcopy(TRAIN_ARGS) - train_args.output_dir = tempdir + train_args = get_train_args(output_dir=tempdir) sft_trainer.train(MODEL_ARGS, data_formatting_args, train_args, PEFT_PT_ARGS) @@ -224,8 +227,8 @@ def test_run_causallm_pt_and_inference_with_formatting_data(): def test_run_causallm_pt_and_inference_JSON_file_formatter(): """Check if we can bootstrap and peft tune causallm models with JSON train file format""" with tempfile.TemporaryDirectory() as tempdir: - train_args = copy.deepcopy(TRAIN_ARGS) - train_args.output_dir = tempdir + train_args = get_train_args(output_dir=tempdir) + data_args = copy.deepcopy(DATA_ARGS) data_args.training_data_path = TWITTER_COMPLAINTS_JSON_FORMAT data_args.dataset_text_field = None @@ -255,8 +258,7 @@ def test_run_causallm_pt_and_inference_JSON_file_formatter(): def test_run_causallm_pt_init_text(): """Check if we can bootstrap and peft tune causallm models with init text as 'TEXT'""" with tempfile.TemporaryDirectory() as tempdir: - train_args = copy.deepcopy(TRAIN_ARGS) - train_args.output_dir = tempdir + train_args = get_train_args(output_dir=tempdir) tuning_config = peft_config.PromptTuningConfig( prompt_tuning_init="TEXT", @@ -291,20 +293,23 @@ def test_run_causallm_pt_init_text(): def test_run_causallm_pt_invalid_train_params(param_name, param_val, exc_msg): """Check if error is raised when invalid params are used to peft tune causallm models""" with tempfile.TemporaryDirectory() as tempdir: - invalid_params = copy.deepcopy(TRAIN_ARGS) - invalid_params.output_dir = tempdir - setattr(invalid_params, param_name, param_val) + train_args = get_train_args(output_dir=tempdir, **{param_name: param_val}) with pytest.raises(ValueError, match=exc_msg): - sft_trainer.train(MODEL_ARGS, DATA_ARGS, invalid_params, PEFT_PT_ARGS) + sft_trainer.train(MODEL_ARGS, DATA_ARGS, train_args, PEFT_PT_ARGS) def test_run_causallm_pt_with_validation(): """Check if we can bootstrap and peft tune causallm models with validation dataset""" with tempfile.TemporaryDirectory() as tempdir: - train_args = copy.deepcopy(TRAIN_ARGS) - train_args.output_dir = tempdir - train_args.evaluation_strategy = "epoch" + if "eval_strategy" in dir(configs.TrainingArguments): + # in transformers 4.39.3 the field is called eval_strategy + eval_strategy = {"eval_strategy": "epoch"} + else: + eval_strategy = {"evaluation_strategy": "epoch"} + + train_args = get_train_args(output_dir=tempdir, **eval_strategy) + data_args = copy.deepcopy(DATA_ARGS) data_args.validation_data_path = TWITTER_COMPLAINTS_DATA @@ -315,9 +320,14 @@ def test_run_causallm_pt_with_validation(): def test_run_causallm_pt_with_validation_data_formatting(): """Check if we can bootstrap and peft tune causallm models with validation dataset""" with tempfile.TemporaryDirectory() as tempdir: - train_args = copy.deepcopy(TRAIN_ARGS) - train_args.output_dir = tempdir - train_args.evaluation_strategy = "epoch" + if "eval_strategy" in dir(configs.TrainingArguments): + # in transformers 4.39.3 the field is called eval_strategy + eval_strategy = {"eval_strategy": "epoch"} + else: + eval_strategy = {"evaluation_strategy": "epoch"} + + train_args = get_train_args(output_dir=tempdir, **eval_strategy) + data_args = copy.deepcopy(DATA_ARGS) data_args.validation_data_path = TWITTER_COMPLAINTS_DATA data_args.dataset_text_field = None @@ -352,8 +362,7 @@ def test_run_causallm_pt_with_validation_data_formatting(): def test_run_causallm_lora_and_inference(request, target_modules, expected): """Check if we can bootstrap and lora tune causallm models""" with tempfile.TemporaryDirectory() as tempdir: - train_args = copy.deepcopy(TRAIN_ARGS) - train_args.output_dir = tempdir + train_args = get_train_args(output_dir=tempdir) base_lora_args = copy.deepcopy(PEFT_LORA_ARGS) if "default" not in request._pyfuncitem.callspec.id: base_lora_args.target_modules = target_modules @@ -386,8 +395,7 @@ def test_run_causallm_lora_and_inference(request, target_modules, expected): def test_run_causallm_ft_and_inference(): """Check if we can bootstrap and finetune tune causallm models""" with tempfile.TemporaryDirectory() as tempdir: - train_args = copy.deepcopy(TRAIN_ARGS) - train_args.output_dir = tempdir + train_args = get_train_args(output_dir=tempdir) sft_trainer.train(MODEL_ARGS, DATA_ARGS, train_args, None) @@ -466,11 +474,9 @@ def test_tokenizer_has_no_eos_token(): with tempfile.TemporaryDirectory() as tempdir: tokenizer.save_pretrained(tempdir) model.save_pretrained(tempdir) - - train_args = copy.deepcopy(TRAIN_ARGS) - train_args.output_dir = tempdir model_args = copy.deepcopy(MODEL_ARGS) - train_args.model_name_or_path = tempdir + + train_args = get_train_args(output_dir=tempdir) # If we handled this badly, we would probably get something like a # TypeError: can only concatenate str (not "NoneType") to str error @@ -487,7 +493,7 @@ def test_invalid_dataset_text_field(): data_args.dataset_text_field = "not found" with pytest.raises(KeyError): - sft_trainer.train(MODEL_ARGS, data_args, TRAIN_ARGS, PEFT_PT_ARGS) + sft_trainer.train(MODEL_ARGS, data_args, get_train_args(), PEFT_PT_ARGS) ### Tests that giving dataset_text_field as well as formatter template gives error @@ -499,7 +505,7 @@ def test_invalid_dataset_text_field_and_formatter_template(): ) with pytest.raises(ValueError): - sft_trainer.train(MODEL_ARGS, data_args, TRAIN_ARGS, PEFT_PT_ARGS) + sft_trainer.train(MODEL_ARGS, data_args, get_train_args(), PEFT_PT_ARGS) ### Tests passing formatter with invalid keys gives error @@ -511,7 +517,7 @@ def test_invalid_formatter_template(): ) with pytest.raises(KeyError): - sft_trainer.train(MODEL_ARGS, data_args, TRAIN_ARGS, PEFT_PT_ARGS) + sft_trainer.train(MODEL_ARGS, data_args, get_train_args(), PEFT_PT_ARGS) ### Tests for bad training data (i.e., data_path is an unhappy value or points to an unhappy thing) @@ -521,7 +527,7 @@ def test_malformatted_data(): data_args.training_data_path = MALFORMATTED_DATA with pytest.raises(DatasetGenerationError): - sft_trainer.train(MODEL_ARGS, data_args, TRAIN_ARGS, PEFT_PT_ARGS) + sft_trainer.train(MODEL_ARGS, data_args, get_train_args(), PEFT_PT_ARGS) def test_empty_data(): @@ -530,7 +536,7 @@ def test_empty_data(): data_args.training_data_path = EMPTY_DATA with pytest.raises(DatasetGenerationError): - sft_trainer.train(MODEL_ARGS, data_args, TRAIN_ARGS, PEFT_PT_ARGS) + sft_trainer.train(MODEL_ARGS, data_args, get_train_args(), PEFT_PT_ARGS) def test_data_path_is_a_directory(): @@ -543,15 +549,15 @@ def test_data_path_is_a_directory(): # FileNotFoundError saying "unable to find ''", since it can't # find a matchable file in the path. with pytest.raises(FileNotFoundError): - sft_trainer.train(MODEL_ARGS, data_args, TRAIN_ARGS, PEFT_PT_ARGS) + sft_trainer.train(MODEL_ARGS, data_args, get_train_args(), PEFT_PT_ARGS) ### Tests for bad tuning module configurations def test_run_causallm_lora_with_invalid_modules(): """Check that we throw a value error if the target modules for lora don't exist.""" with tempfile.TemporaryDirectory() as tempdir: - train_args = copy.deepcopy(TRAIN_ARGS) - train_args.output_dir = tempdir + train_args = get_train_args(output_dir=tempdir) + # Defaults are q_proj / v_proj; this will fail lora as the torch module doesn't have them lora_config = copy.deepcopy(PEFT_LORA_ARGS) lora_config.target_modules = ["foo", "bar"] @@ -564,7 +570,7 @@ def test_run_causallm_lora_with_invalid_modules(): def test_no_packing_needs_dataset_text_field_or_data_formatter_template(): """Ensure we need to set the dataset text field if packing is False""" with tempfile.TemporaryDirectory() as tempdir: - train_args = copy.deepcopy(TRAIN_ARGS) + train_args = get_train_args() train_args.output_dir = tempdir data_args = copy.deepcopy(DATA_ARGS) # One of dataset_text_field or data_formatter_template should be set @@ -580,7 +586,7 @@ def test_no_packing_needs_dataset_text_field_or_data_formatter_template(): def test_no_packing_needs_reponse_template(): """Ensure we need to set the response template if packing is False""" with tempfile.TemporaryDirectory() as tempdir: - train_args = copy.deepcopy(TRAIN_ARGS) + train_args = get_train_args() train_args.output_dir = tempdir data_args = copy.deepcopy(DATA_ARGS) data_args.response_template = None @@ -598,7 +604,7 @@ def test_bf16_still_tunes_if_unsupported(): """Ensure that even if bf16 is not supported, tuning still works without problems.""" assert not torch.cuda.is_bf16_supported() with tempfile.TemporaryDirectory() as tempdir: - train_args = copy.deepcopy(TRAIN_ARGS) + train_args = get_train_args() train_args.output_dir = tempdir model_args = copy.deepcopy(MODEL_ARGS) model_args.torch_dtype = "bfloat16" @@ -610,7 +616,7 @@ def test_bf16_still_tunes_if_unsupported(): def test_bad_torch_dtype(): """Ensure that specifying an invalid torch dtype yields a ValueError.""" with tempfile.TemporaryDirectory() as tempdir: - train_args = copy.deepcopy(TRAIN_ARGS) + train_args = get_train_args() train_args.output_dir = tempdir model_args = copy.deepcopy(MODEL_ARGS) model_args.torch_dtype = "not a type" @@ -623,8 +629,7 @@ def test_run_with_additional_callbacks(): """Ensure that train() can work with additional_callbacks""" with tempfile.TemporaryDirectory() as tempdir: - train_args = copy.deepcopy(TRAIN_ARGS) - train_args.output_dir = tempdir + train_args = get_train_args(output_dir=tempdir) model_args = copy.deepcopy(MODEL_ARGS) sft_trainer.train( diff --git a/tox.ini b/tox.ini index 41a107639..66f4248fd 100644 --- a/tox.ini +++ b/tox.ini @@ -3,36 +3,44 @@ envlist = py, lint, fmt [testenv] description = run unit tests -deps = - pytest>=7 +allowlist_externals= + pytest + poetry +commands_pre = + poetry install --extras dev --no-root commands = + pip freeze pytest {posargs:tests} [testenv:fmt] description = format with pre-commit -deps = - pre-commit -commands = ./scripts/fmt.sh -allowlist_externals = ./scripts/fmt.sh +commands_pre = + poetry install --extras dev --no-root +commands = + ./scripts/fmt.sh +allowlist_externals = + poetry + ./scripts/fmt.sh [testenv:lint] description = lint with pylint -deps = - pylint>=2.16.2,<=3.1.0 - pytest - .[dev] +commands_pre = + poetry install --extras dev --no-root commands = pylint tuning scripts/*.py build/*.py tests -allowlist_externals = pylint +allowlist_externals = + poetry + pylint [testenv:build] description = build wheel -deps = - build -commands = python -m build -w +allowlist_externals= + poetry +commands = poetry build --format wheel skip_install = True [testenv:twinecheck] description = check wheel +commands_pre = [] deps = twine commands = twine check dist/* @@ -40,13 +48,14 @@ skip_install = True [testenv:coverage] description = report unit test coverage -deps = +allowlist_externals= + poetry coverage - pytest - genbadge[coverage] + genbadge +commands_pre = + poetry install --extras dev commands = - coverage run \ - --omit=*/_version.py,*/launch_training.py \ + coverage run --omit='*/_version.py,*/launch_training.py' \ --source=tuning,build \ --module pytest tests/ coverage report -m diff --git a/tuning/sft_trainer.py b/tuning/sft_trainer.py index b221db898..0db19c25e 100644 --- a/tuning/sft_trainer.py +++ b/tuning/sft_trainer.py @@ -510,7 +510,7 @@ def main(**kwargs): # pylint: disable=unused-argument exp_metadata, ) except Exception as e: # pylint: disable=broad-except - logging.error(traceback.format_exc()) + logging.get_logger("error").error(traceback.format_exc()) write_termination_log( f"Exception raised during training. This may be a problem with your input: {e}" )