repo_id stringlengths 15 89 | file_path stringlengths 27 180 | content stringlengths 1 2.23M | __index_level_0__ int64 0 0 |
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hf_public_repos | hf_public_repos/trl/setup.py | """ trl is an open library for RL with transformer models.
Note:
VERSION needs to be formatted following the MAJOR.MINOR.PATCH convention
(we need to follow this convention to be able to retrieve versioned scripts)
Simple check list for release from AllenNLP repo: https://github.com/allenai/allennlp/blob/master/setup.py
To create the package for pypi.
0. Prerequisites:
- Dependencies:
- twine: "pip install twine"
- Create an account in (and join the 'trl' project):
- PyPI: https://pypi.org/
- Test PyPI: https://test.pypi.org/
1. Change the version in:
- __init__.py
- setup.py
2. Commit these changes: "git commit -m 'Release: VERSION'"
3. Add a tag in git to mark the release: "git tag VERSION -m 'Add tag VERSION for pypi'"
Push the tag to remote: git push --tags origin main
4. Build both the sources and the wheel. Do not change anything in setup.py between
creating the wheel and the source distribution (obviously).
First, delete any "build" directory that may exist from previous builds.
For the wheel, run: "python setup.py bdist_wheel" in the top level directory.
(this will build a wheel for the python version you use to build it).
For the sources, run: "python setup.py sdist"
You should now have a /dist directory with both .whl and .tar.gz source versions.
5. Check that everything looks correct by uploading the package to the pypi test server:
twine upload dist/* -r pypitest --repository-url=https://test.pypi.org/legacy/
Check that you can install it in a virtualenv/notebook by running:
pip install huggingface_hub fsspec aiohttp
pip install -U tqdm
pip install -i https://testpypi.python.org/pypi evaluate
6. Upload the final version to actual pypi:
twine upload dist/* -r pypi
7. Fill release notes in the tag in github once everything is looking hunky-dory.
8. Change the version in __init__.py and setup.py to X.X.X+1.dev0 (e.g. VERSION=1.18.3 -> 1.18.4.dev0).
Then push the change with a message 'set dev version'
"""
from setuptools import find_packages, setup
__version__ = "0.7.5.dev0" # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
REQUIRED_PKGS = [
"torch>=1.4.0",
"transformers>=4.18.0",
"numpy>=1.18.2",
"accelerate",
"datasets",
"tyro>=0.5.11",
]
EXTRAS = {
"test": ["parameterized", "pytest", "pytest-xdist", "accelerate"],
"peft": ["peft>=0.4.0"],
"diffusers": ["diffusers>=0.18.0"],
"deepspeed": ["deepspeed>=0.9.5"],
"benchmark": ["wandb", "ghapi", "openrlbenchmark==0.2.1a5", "requests", "deepspeed"],
"quantization": ["bitsandbytes<=0.41.1"],
}
EXTRAS["dev"] = []
for reqs in EXTRAS.values():
EXTRAS["dev"].extend(reqs)
setup(
name="trl",
license="Apache 2.0",
classifiers=[
"Development Status :: 2 - Pre-Alpha",
"Intended Audience :: Developers",
"Intended Audience :: Science/Research",
"License :: OSI Approved :: Apache Software License",
"Natural Language :: English",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
],
url="https://github.com/huggingface/trl",
packages=find_packages(),
include_package_data=True,
install_requires=REQUIRED_PKGS,
extras_require=EXTRAS,
python_requires=">=3.7",
long_description=open("README.md", encoding="utf-8").read(),
long_description_content_type="text/markdown",
zip_safe=False,
version=__version__,
description="A Pytorch implementation of Proximal Policy Optimization for transfomer language models.",
keywords="ppo, transformers, huggingface, gpt2, language modeling, rlhf",
author="Leandro von Werra",
author_email="leandro.vonwerra@gmail.com",
)
| 0 |
hf_public_repos | hf_public_repos/trl/CONTRIBUTING.md | # How to contribute
## How to get started
Before you start contributing make sure you installed all the dev tools:
```bash
pip install -e ".[dev]"
```
## Did you find a bug?
* Ensure the bug was not already reported by searching on GitHub under Issues.
* If you're unable to find an open issue addressing the problem, open a new one. Be sure to include a title and clear description, as much relevant information as possible, and a code sample or an executable test case demonstrating the expected behavior that is not occurring.
* Be sure to add the complete error messages.
#### Did you write a patch that fixes a bug?
* Open a new GitHub pull request with the patch.
* Ensure that your PR includes a test that fails without your patch, and pass with it.
* Ensure the PR description clearly describes the problem and solution. Include the relevant issue number if applicable.
## PR submission guidelines
* Keep each PR focused. While it's more convenient, do not combine several unrelated fixes together. Create as many branches as needing to keep each PR focused.
* Do not mix style changes/fixes with "functional" changes. It's very difficult to review such PRs and it most likely get rejected.
* Do not add/remove vertical whitespace. Preserve the original style of the file you edit as much as you can.
* Do not turn an already submitted PR into your development playground. If after you submitted PR, you discovered that more work is needed - close the PR, do the required work and then submit a new PR. Otherwise each of your commits requires attention from maintainers of the project.
* If, however, you submitted a PR and received a request for changes, you should proceed with commits inside that PR, so that the maintainer can see the incremental fixes and won't need to review the whole PR again. In the exception case where you realize it'll take many many commits to complete the requests, then it's probably best to close the PR, do the work and then submit it again. Use common sense where you'd choose one way over another.
### Before you submit a PR
First you want to make sure that all the tests pass:
```bash
make test
```
Then before submitting your PR make sure the code quality follows the standards. You can run the following command to format:
```bash
make precommit
```
Make sure to install `pre-commit` before running the command:
```bash
pip install pre-commit
```
## Do you want to contribute to the documentation?
* Docs are in the `docs/` folder and can be updated there.
| 0 |
hf_public_repos | hf_public_repos/trl/setup.cfg | [metadata]
license_file = LICENSE
[isort]
ensure_newline_before_comments = True
force_grid_wrap = 0
include_trailing_comma = True
line_length = 119
lines_after_imports = 2
multi_line_output = 3
use_parentheses = True
| 0 |
hf_public_repos/trl | hf_public_repos/trl/benchmark/benchmark_level1_plot.sh | # pip install openrlbenchmark==0.2.1a5
# see https://github.com/openrlbenchmark/openrlbenchmark#get-started for documentation
echo "we deal with $TAGS_STRING"
python -m openrlbenchmark.rlops_multi_metrics \
--filters '?we=huggingface&wpn=trl&xaxis=_step&ceik=trl_ppo_trainer_config.value.reward_model&cen=trl_ppo_trainer_config.value.exp_name&metrics=env/reward_mean&metrics=objective/kl' \
"ppo$TAGS_STRING" \
--env-ids sentiment-analysis:lvwerra/distilbert-imdb \
--no-check-empty-runs \
--pc.ncols 2 \
--pc.ncols-legend 1 \
--output-filename benchmark/trl/$FOLDER_STRING/hello_world \
--scan-history
python benchmark/upload_benchmark.py \
--folder_path="benchmark/trl/$FOLDER_STRING" \
--path_in_repo="images/benchmark/$FOLDER_STRING" \
--repo_id="trl-internal-testing/example-images" \
--repo_type="dataset"
| 0 |
hf_public_repos/trl | hf_public_repos/trl/benchmark/post_github_comment.sbatch | #!/bin/bash
#SBATCH --job-name=trl
#SBATCH --partition=production-cluster
#SBATCH --ntasks=1
#SBATCH --output=slurm/logs/%x_%j.out
sleep 2m
bash $BENCHMARK_PLOT_SCRIPT
srun python benchmark/post_github_comment.py
| 0 |
hf_public_repos/trl | hf_public_repos/trl/benchmark/benchmark.py | import argparse
import math
import os
import shlex
import subprocess
import uuid
from distutils.util import strtobool
import requests
def parse_args():
# fmt: off
parser = argparse.ArgumentParser()
parser.add_argument("--command", type=str, default="",
help="the command to run")
parser.add_argument("--num-seeds", type=int, default=3,
help="the number of random seeds")
parser.add_argument("--start-seed", type=int, default=1,
help="the number of the starting seed")
parser.add_argument("--workers", type=int, default=0,
help="the number of workers to run benchmark experimenets")
parser.add_argument("--auto-tag", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
help="if toggled, the runs will be tagged with git tags, commit, and pull request number if possible")
parser.add_argument("--slurm-template-path", type=str, default=None,
help="the path to the slurm template file (see docs for more details)")
parser.add_argument("--slurm-gpus-per-task", type=int, default=1,
help="the number of gpus per task to use for slurm jobs")
parser.add_argument("--slurm-total-cpus", type=int, default=50,
help="the number of gpus per task to use for slurm jobs")
parser.add_argument("--slurm-ntasks", type=int, default=1,
help="the number of tasks to use for slurm jobs")
parser.add_argument("--slurm-nodes", type=int, default=None,
help="the number of nodes to use for slurm jobs")
args = parser.parse_args()
# fmt: on
return args
def run_experiment(command: str):
command_list = shlex.split(command)
print(f"running {command}")
# Use subprocess.PIPE to capture the output
fd = subprocess.Popen(command_list, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
output, errors = fd.communicate()
return_code = fd.returncode
assert return_code == 0, f"Command failed with error: {errors.decode('utf-8')}"
# Convert bytes to string and strip leading/trailing whitespaces
return output.decode("utf-8").strip()
def autotag() -> str:
wandb_tag = ""
print("autotag feature is enabled")
git_tag = ""
try:
git_tag = subprocess.check_output(["git", "describe", "--tags"]).decode("ascii").strip()
print(f"identified git tag: {git_tag}")
except subprocess.CalledProcessError as e:
print(e)
if len(git_tag) == 0:
try:
count = int(subprocess.check_output(["git", "rev-list", "--count", "HEAD"]).decode("ascii").strip())
hash = subprocess.check_output(["git", "rev-parse", "--short", "HEAD"]).decode("ascii").strip()
git_tag = f"no-tag-{count}-g{hash}"
print(f"identified git tag: {git_tag}")
except subprocess.CalledProcessError as e:
print(e)
wandb_tag = f"{git_tag}"
git_commit = subprocess.check_output(["git", "rev-parse", "--verify", "HEAD"]).decode("ascii").strip()
try:
# try finding the pull request number on github
prs = requests.get(f"https://api.github.com/search/issues?q=repo:huggingface/trl+is:pr+{git_commit}")
if prs.status_code == 200:
prs = prs.json()
if len(prs["items"]) > 0:
pr = prs["items"][0]
pr_number = pr["number"]
wandb_tag += f",pr-{pr_number}"
print(f"identified github pull request: {pr_number}")
except Exception as e:
print(e)
return wandb_tag
if __name__ == "__main__":
args = parse_args()
if args.auto_tag:
existing_wandb_tag = os.environ.get("WANDB_TAGS", "")
wandb_tag = autotag()
if len(wandb_tag) > 0:
if len(existing_wandb_tag) > 0:
os.environ["WANDB_TAGS"] = ",".join([existing_wandb_tag, wandb_tag])
else:
os.environ["WANDB_TAGS"] = wandb_tag
print("WANDB_TAGS: ", os.environ.get("WANDB_TAGS", ""))
commands = []
for seed in range(0, args.num_seeds):
commands += [" ".join([args.command, "--seed", str(args.start_seed + seed)])]
print("======= commands to run:")
for command in commands:
print(command)
if args.workers > 0 and args.slurm_template_path is None:
from concurrent.futures import ThreadPoolExecutor
executor = ThreadPoolExecutor(max_workers=args.workers, thread_name_prefix="cleanrl-benchmark-worker-")
for command in commands:
executor.submit(run_experiment, command)
executor.shutdown(wait=True)
else:
print("not running the experiments because --workers is set to 0; just printing the commands to run")
# SLURM logic
if args.slurm_template_path is not None:
if not os.path.exists("slurm"):
os.makedirs("slurm")
if not os.path.exists("slurm/logs"):
os.makedirs("slurm/logs")
print("======= slurm commands to run:")
with open(args.slurm_template_path) as f:
slurm_template = f.read()
slurm_template = slurm_template.replace("{{array}}", f"0-{len(commands) - 1}%{args.workers}")
slurm_template = slurm_template.replace(
"{{seeds}}", f"({' '.join([str(args.start_seed + int(seed)) for seed in range(args.num_seeds)])})"
)
slurm_template = slurm_template.replace("{{len_seeds}}", f"{args.num_seeds}")
slurm_template = slurm_template.replace("{{command}}", args.command)
slurm_template = slurm_template.replace("{{gpus_per_task}}", f"{args.slurm_gpus_per_task}")
total_gpus = args.slurm_gpus_per_task * args.slurm_ntasks
slurm_cpus_per_gpu = math.ceil(args.slurm_total_cpus / total_gpus)
slurm_template = slurm_template.replace("{{cpus_per_gpu}}", f"{slurm_cpus_per_gpu}")
slurm_template = slurm_template.replace("{{ntasks}}", f"{args.slurm_ntasks}")
if args.slurm_nodes is not None:
slurm_template = slurm_template.replace("{{nodes}}", f"#SBATCH --nodes={args.slurm_nodes}")
else:
slurm_template = slurm_template.replace("{{nodes}}", "")
filename = str(uuid.uuid4())
open(os.path.join("slurm", f"{filename}.slurm"), "w").write(slurm_template)
slurm_path = os.path.join("slurm", f"{filename}.slurm")
print(f"saving command in {slurm_path}")
if args.workers > 0:
job_id = run_experiment(f"sbatch --parsable {slurm_path}")
print(f"Job ID: {job_id}")
| 0 |
hf_public_repos/trl | hf_public_repos/trl/benchmark/upload_benchmark.py | from dataclasses import dataclass
import tyro
from huggingface_hub import HfApi
@dataclass
class Args:
folder_path: str = "benchmark/trl"
path_in_repo: str = "images/benchmark"
repo_id: str = "trl-internal-testing/example-images"
repo_type: str = "dataset"
args = tyro.cli(Args)
api = HfApi()
api.upload_folder(
folder_path=args.folder_path,
path_in_repo=args.path_in_repo,
repo_id=args.repo_id,
repo_type=args.repo_type,
)
| 0 |
hf_public_repos/trl | hf_public_repos/trl/benchmark/benchmark_level1.sh | # hello world experiment
python benchmark/benchmark.py \
--command "python examples/scripts/ppo.py --ppo_config.log_with wandb" \
--num-seeds 3 \
--start-seed 1 \
--workers 10 \
--slurm-nodes 1 \
--slurm-gpus-per-task 1 \
--slurm-ntasks 1 \
--slurm-total-cpus 12 \
--slurm-template-path benchmark/trl.slurm_template
| 0 |
hf_public_repos/trl | hf_public_repos/trl/benchmark/benchmark_level2_plot.sh | # pip install openrlbenchmark==0.2.1a5
# see https://github.com/openrlbenchmark/openrlbenchmark#get-started for documentation
echo "we deal with $TAGS_STRING"
python -m openrlbenchmark.rlops_multi_metrics \
--filters '?we=huggingface&wpn=trl&xaxis=_step&ceik=trl_ppo_trainer_config.value.reward_model&cen=trl_ppo_trainer_config.value.exp_name&metrics=env/reward_mean&metrics=objective/kl' \
"ppo$TAGS_STRING" \
"ppo_gpt2xl_grad_accu$TAGS_STRING" \
--env-ids sentiment-analysis:lvwerra/distilbert-imdb \
--no-check-empty-runs \
--pc.ncols 2 \
--pc.ncols-legend 1 \
--output-filename benchmark/trl/$FOLDER_STRING/different_models \
--scan-history
python -m openrlbenchmark.rlops_multi_metrics \
--filters '?we=huggingface&wpn=trl&xaxis=_step&ceik=trl_ppo_trainer_config.value.reward_model&cen=trl_ppo_trainer_config.value.exp_name&metrics=env/reward_mean&metrics=objective/kl' \
"ppo_Cerebras-GPT-6.7B_grad_accu_deepspeed_stage2$TAGS_STRING" \
--env-ids sentiment-analysis:cerebras/Cerebras-GPT-6.7B \
--no-check-empty-runs \
--pc.ncols 2 \
--pc.ncols-legend 1 \
--output-filename benchmark/trl/$FOLDER_STRING/deepspeed \
--scan-history
python benchmark/upload_benchmark.py \
--folder_path="benchmark/trl/$FOLDER_STRING" \
--path_in_repo="images/benchmark/$FOLDER_STRING" \
--repo_id="trl-internal-testing/example-images" \
--repo_type="dataset"
| 0 |
hf_public_repos/trl | hf_public_repos/trl/benchmark/trl.slurm_template | #!/bin/bash
#SBATCH --job-name=trl
#SBATCH --partition=production-cluster
#SBATCH --gpus-per-task={{gpus_per_task}}
#SBATCH --cpus-per-gpu={{cpus_per_gpu}}
#SBATCH --ntasks={{ntasks}}
#SBATCH --output=slurm/logs/%x_%j.out
#SBATCH --array={{array}}
#SBATCH --exclude=ip-26-0-156-239,ip-26-0-148-151,ip-26-0-146-212,ip-26-0-145-137,ip-26-0-146-249,ip-26-0-146-149,ip-26-0-147-233,ip-26-0-145-154,ip-26-0-144-35,ip-26-0-144-189,ip-26-0-146-183,ip-26-0-147-120,ip-26-0-144-95,ip-26-0-145-193
{{nodes}}
seeds={{seeds}}
seed=${seeds[$SLURM_ARRAY_TASK_ID % {{len_seeds}}]}
echo "Running task $SLURM_ARRAY_TASK_ID with seed: $seed"
srun {{command}} --ppo_config.seed $seed
| 0 |
hf_public_repos/trl | hf_public_repos/trl/benchmark/benchmark_and_report.sh | #### Step 1: create a work directory:
# this is necessary because another github action job will remove
# the entire directory, which slurm depends on.
# https://stackoverflow.com/questions/4632028/how-to-create-a-temporary-directory
MY_SLURM_TMP_DIR=/fsx/costa/slurm_tmpdir
mkdir -p $MY_SLURM_TMP_DIR
WORK_DIR=`mktemp -d -p "$MY_SLURM_TMP_DIR"`
cp -r "$PWD" "$WORK_DIR"
cd "$WORK_DIR/$(basename "$PWD")"
echo WORK_DIR: $WORK_DIR
#### Step 2: actual work starts:
echo PATH is $PATH
echo PYTHONPATH is $PYTHONPATH
echo whcih python is $(which python)
export WANDB_ENTITY=huggingface
bash $BENCHMARK_SCRIPT > output.txt
# Extract Job IDs into an array
job_ids=($(grep "Job ID:" output.txt | awk '{print $3}'))
# Extract WANDB_TAGS into an array
WANDB_TAGS=($(grep "WANDB_TAGS:" output.txt | awk '{print $2}'))
WANDB_TAGS=($(echo $WANDB_TAGS | tr "," "\n"))
# Print to verify
echo "Job IDs: ${job_ids[@]}"
echo "WANDB_TAGS: ${WANDB_TAGS[@]}"
TAGS_STRING="?tag=${WANDB_TAGS[0]}"
FOLDER_STRING="${WANDB_TAGS[0]}"
for tag in "${WANDB_TAGS[@]:1}"; do
TAGS_STRING+="&tag=$tag"
FOLDER_STRING+="_$tag"
done
echo "TAGS_STRING: $TAGS_STRING"
echo "FOLDER_STRING: $FOLDER_STRING"
TAGS_STRING=$TAGS_STRING FOLDER_STRING=$FOLDER_STRING BENCHMARK_PLOT_SCRIPT=$BENCHMARK_PLOT_SCRIPT sbatch --dependency=afterany:$job_ids benchmark/post_github_comment.sbatch
| 0 |
hf_public_repos/trl | hf_public_repos/trl/benchmark/plot.sh | # pip install openrlbenchmark==0.2.1a5
# see https://github.com/openrlbenchmark/openrlbenchmark#get-started for documentation
BASELINE_PR_TAG=v0.4.7-55-g110e672
BASELINE_PR_NAME=PR-662
python -m openrlbenchmark.rlops_multi_metrics \
--filters '?we=huggingface&wpn=trl&xaxis=_step&ceik=trl_ppo_trainer_config.value.reward_model&cen=trl_ppo_trainer_config.value.exp_name&metrics=env/reward_mean&metrics=objective/kl' \
"sentiment_tuning?tag=$BASELINE_PR_TAG&cl=sentiment lvwerra/gpt2-imdb ($BASELINE_PR_NAME)" \
--env-ids sentiment-analysis:lvwerra/distilbert-imdb \
--no-check-empty-runs \
--pc.ncols 2 \
--pc.ncols-legend 1 \
--output-filename benchmark/trl/$BASELINE_PR_TAG/sentiment \
--scan-history
python -m openrlbenchmark.rlops_multi_metrics \
--filters '?we=huggingface&wpn=trl&xaxis=_step&ceik=trl_ppo_trainer_config.value.reward_model&cen=trl_ppo_trainer_config.value.exp_name&metrics=env/reward_mean&metrics=objective/kl' \
"sentiment_tuning?tag=$BASELINE_PR_TAG&cl=sentiment lvwerra/gpt2-imdb ($BASELINE_PR_NAME)" \
"sentiment_tuning_step_grad_accu?tag=$BASELINE_PR_TAG&cl=sentiment lvwerra/gpt2-imdb gradient accumulation ($BASELINE_PR_NAME)" \
--env-ids sentiment-analysis:lvwerra/distilbert-imdb \
--no-check-empty-runs \
--pc.ncols 2 \
--pc.ncols-legend 1 \
--output-filename benchmark/trl/$BASELINE_PR_TAG/gradient_accu \
--scan-history
python -m openrlbenchmark.rlops_multi_metrics \
--filters '?we=huggingface&wpn=trl&xaxis=_step&ceik=trl_ppo_trainer_config.value.reward_model&cen=trl_ppo_trainer_config.value.exp_name&metrics=env/reward_mean&metrics=objective/kl' \
"sentiment_tuning?tag=$BASELINE_PR_TAG&cl=sentiment lvwerra/gpt2-imdb ($BASELINE_PR_NAME)" \
"sentiment_tuning_gpt2?tag=$BASELINE_PR_TAG&cl=sentiment gpt2 ($BASELINE_PR_NAME)" \
"sentiment_tuning_falcon_rw_1b?tag=$BASELINE_PR_TAG&cl=sentiment tiiuae/falcon-rw-1b ($BASELINE_PR_NAME)" \
"sentiment_tuning_gpt2xl_grad_accu?tag=$BASELINE_PR_TAG&cl=sentiment gpt2xl ($BASELINE_PR_NAME)" \
--env-ids sentiment-analysis:lvwerra/distilbert-imdb \
--no-check-empty-runs \
--pc.ncols 2 \
--pc.ncols-legend 1 \
--output-filename benchmark/trl/$BASELINE_PR_TAG/different_models \
--scan-history
python -m openrlbenchmark.rlops_multi_metrics \
--filters '?we=huggingface&wpn=trl&xaxis=_step&ceik=trl_ppo_trainer_config.value.reward_model&cen=trl_ppo_trainer_config.value.exp_name&metrics=env/reward_mean&metrics=objective/kl' \
"sentiment_tuning?tag=$BASELINE_PR_TAG&cl=sentiment lvwerra/gpt2-imdb ($BASELINE_PR_NAME)" \
"sentiment_tuning_peft?tag=$BASELINE_PR_TAG&cl=sentiment lvwerra/gpt2-imdb w/ peft ($BASELINE_PR_NAME)" \
--env-ids sentiment-analysis:lvwerra/distilbert-imdb \
--no-check-empty-runs \
--pc.ncols 2 \
--pc.ncols-legend 1 \
--output-filename benchmark/trl/$BASELINE_PR_TAG/peft \
--scan-history
python benchmark/upload_benchmark.py \
--folder_path="benchmark/trl/$BASELINE_PR_TAG" \
--path_in_repo="images/benchmark/$BASELINE_PR_TAG" \
--repo_id="trl-internal-testing/example-images" \
--repo_type="dataset" | 0 |
hf_public_repos/trl | hf_public_repos/trl/benchmark/benchmark_level2.sh | # compound experiments: gpt2xl + grad_accu
python benchmark/benchmark.py \
--command "python examples/scripts/ppo.py --ppo_config.exp_name ppo_gpt2xl_grad_accu --ppo_config.model_name gpt2-xl --ppo_config.mini_batch_size 16 --ppo_config.gradient_accumulation_steps 8 --ppo_config.log_with wandb" \
--num-seeds 3 \
--start-seed 1 \
--workers 10 \
--slurm-nodes 1 \
--slurm-gpus-per-task 1 \
--slurm-ntasks 1 \
--slurm-total-cpus 12 \
--slurm-template-path benchmark/trl.slurm_template
# compound experiments: Cerebras-GPT-6.7B + deepspeed zero2 + grad_accu
python benchmark/benchmark.py \
--command "accelerate launch --config_file examples/accelerate_configs/deepspeed_zero2.yaml examples/scripts/ppo.py --ppo_config.exp_name ppo_Cerebras-GPT-6.7B_grad_accu_deepspeed_stage2 --ppo_config.batch_size 32 --ppo_config.mini_batch_size 32 --ppo_config.log_with wandb --ppo_config.model_name cerebras/Cerebras-GPT-6.7B --ppo_config.reward_model sentiment-analysis:cerebras/Cerebras-GPT-6.7B" \
--num-seeds 3 \
--start-seed 1 \
--workers 10 \
--slurm-nodes 1 \
--slurm-gpus-per-task 8 \
--slurm-ntasks 1 \
--slurm-total-cpus 90 \
--slurm-template-path benchmark/trl.slurm_template
| 0 |
hf_public_repos/trl | hf_public_repos/trl/benchmark/post_github_comment.py | import json
import os
from ghapi.all import GhApi
FOLDER_STRING = os.environ.get("FOLDER_STRING", "")
folder = f"benchmark/trl/{FOLDER_STRING}"
host_url = f"https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/benchmark/{FOLDER_STRING}"
# Create a GitHub API instance
github_context = json.loads(os.environ["GITHUB_CONTEXT"])
token = os.environ["PERSONAL_ACCESS_TOKEN_GITHUB"] # this needs to refreshed every 12 months
status_message = "**[COSTA BENCHMARK BOT]**: Here are the results"
body = status_message
repo = github_context["repository"]
owner, repo = repo.split("/")
api = GhApi(owner=owner, repo=repo, token=token)
# for each `.png` file in the folder, add it to the comment
for file in os.listdir(folder):
if file.endswith(".png"):
body += f"\n"
# Create a comment on the issue
api.issues.create_comment(issue_number=github_context["event"]["issue"]["number"], body=body)
| 0 |
hf_public_repos/trl | hf_public_repos/trl/benchmark/benchmark_level3.sh | ## w/ and w/o gradient accumulation
python benchmark/benchmark.py \
--command "python examples/scripts/ppo.py --ppo_config.exp_name ppo_step_grad_accu --ppo_config.mini_batch_size 1 --ppo_config.gradient_accumulation_steps 128 --ppo_config.log_with wandb" \
--num-seeds 3 \
--start-seed 1 \
--workers 10 \
--slurm-nodes 1 \
--slurm-gpus-per-task 1 \
--slurm-ntasks 1 \
--slurm-total-cpus 12 \
--slurm-template-path benchmark/trl.slurm_template
## w/ different models (gpt2, gpt2-xl, falcon, llama2)
python benchmark/benchmark.py \
--command "python examples/scripts/ppo.py --ppo_config.exp_name ppo_gpt2 --ppo_config.log_with wandb" \
--num-seeds 3 \
--start-seed 1 \
--workers 10 \
--slurm-nodes 1 \
--slurm-gpus-per-task 1 \
--slurm-ntasks 1 \
--slurm-total-cpus 12 \
--slurm-template-path benchmark/trl.slurm_template
python benchmark/benchmark.py \
--command "python examples/scripts/ppo.py --ppo_config.exp_name ppo_falcon_rw_1b --ppo_config.model_name tiiuae/falcon-rw-1b --ppo_config.log_with wandb" \
--num-seeds 3 \
--start-seed 1 \
--workers 10 \
--slurm-nodes 1 \
--slurm-gpus-per-task 1 \
--slurm-ntasks 1 \
--slurm-total-cpus 12 \
--slurm-template-path benchmark/trl.slurm_template
## w/ and w/o PEFT
python benchmark/benchmark.py \
--command "python examples/scripts/ppo.py --ppo_config.exp_name ppo_peft --use_peft --ppo_config.log_with wandb" \
--num-seeds 3 \
--start-seed 1 \
--workers 10 \
--slurm-nodes 1 \
--slurm-gpus-per-task 1 \
--slurm-ntasks 1 \
--slurm-total-cpus 12 \
--slurm-template-path benchmark/trl.slurm_template | 0 |
hf_public_repos/trl | hf_public_repos/trl/tests/test_sft_trainer.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import os
import tempfile
import unittest
import numpy as np
import torch
from datasets import Dataset
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments
from trl import SFTTrainer
from trl.import_utils import is_peft_available
from trl.trainer import ConstantLengthDataset, DataCollatorForCompletionOnlyLM
from .testing_utils import require_peft
def formatting_prompts_func(example):
text = f"### Question: {example['question']}\n ### Answer: {example['answer']}"
return text
def formatting_prompts_func_batched(example):
output_text = []
for i, question in enumerate(example["question"]):
text = f"### Question: {question}\n ### Answer: {example['answer'][i]}"
output_text.append(text)
return output_text
if is_peft_available():
from peft import LoraConfig, PeftModel
class SFTTrainerTester(unittest.TestCase):
r""" """
@classmethod
def setUpClass(cls):
cls.model_id = "trl-internal-testing/dummy-GPT2-correct-vocab"
cls.model = AutoModelForCausalLM.from_pretrained(cls.model_id)
cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_id)
cls.tokenizer.pad_token = cls.tokenizer.eos_token
cls.dummy_dataset = Dataset.from_dict(
{
"question": [
"Does llamas know how to code?",
"Does llamas know how to fly?",
"Does llamas know how to talk?",
"Does llamas know how to code?",
"Does llamas know how to fly?",
"Does llamas know how to talk?",
"Does llamas know how to swim?",
],
"answer": [
"Yes, llamas are very good at coding.",
"No, llamas can't fly.",
"Yes, llamas are very good at talking.",
"Yes, llamas are very good at coding.",
"No, llamas can't fly.",
"Yes, llamas are very good at talking.",
"No, llamas can't swim.",
],
"text": [
"### Question: Does llamas know how to code?\n ### Answer: Yes, llamas are very good at coding.",
"### Question: Does llamas know how to fly?\n ### Answer: No, llamas can't fly.",
"### Question: Does llamas know how to talk?\n ### Answer: Yes, llamas are very good at talking.",
"### Question: Does llamas know how to code?\n ### Answer: Yes, llamas are very good at coding.",
"### Question: Does llamas know how to fly?\n ### Answer: No, llamas can't fly.",
"### Question: Does llamas know how to talk?\n ### Answer: Yes, llamas are very good at talking.",
"### Question: Does llamas know how to swim?\n ### Answer: No, llamas can't swim.",
],
}
)
cls.train_dataset = ConstantLengthDataset(
cls.tokenizer,
cls.dummy_dataset,
dataset_text_field=None,
formatting_func=formatting_prompts_func,
seq_length=16,
num_of_sequences=16,
)
cls.eval_dataset = ConstantLengthDataset(
cls.tokenizer,
cls.dummy_dataset,
dataset_text_field=None,
formatting_func=formatting_prompts_func,
seq_length=16,
num_of_sequences=16,
)
def test_constant_length_dataset(self):
formatted_dataset = ConstantLengthDataset(
self.tokenizer,
self.dummy_dataset,
dataset_text_field=None,
formatting_func=formatting_prompts_func,
)
self.assertTrue(len(formatted_dataset) == len(self.dummy_dataset))
self.assertTrue(len(formatted_dataset) > 0)
for example in formatted_dataset:
self.assertTrue("input_ids" in example)
self.assertTrue("labels" in example)
self.assertTrue(len(example["input_ids"]) == formatted_dataset.seq_length)
self.assertTrue(len(example["labels"]) == formatted_dataset.seq_length)
decoded_text = self.tokenizer.decode(example["input_ids"])
self.assertTrue(("Question" in decoded_text) and ("Answer" in decoded_text))
def test_sft_trainer(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
dataloader_drop_last=True,
evaluation_strategy="steps",
max_steps=4,
eval_steps=2,
save_steps=2,
per_device_train_batch_size=2,
)
trainer = SFTTrainer(
model=self.model_id,
args=training_args,
train_dataset=self.train_dataset,
eval_dataset=self.eval_dataset,
packing=True,
)
trainer.train()
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
self.assertIsNotNone(trainer.state.log_history[0]["eval_loss"])
self.assertTrue("model.safetensors" in os.listdir(tmp_dir + "/checkpoint-2"))
def test_sft_trainer_uncorrect_data(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
dataloader_drop_last=True,
evaluation_strategy="steps",
max_steps=2,
eval_steps=1,
save_steps=1,
per_device_train_batch_size=2,
)
with self.assertRaises(ValueError):
_ = SFTTrainer(
model=self.model,
args=training_args,
train_dataset=self.dummy_dataset,
packing=True,
)
# This should work
_ = SFTTrainer(
model=self.model,
args=training_args,
train_dataset=self.dummy_dataset,
formatting_func=formatting_prompts_func,
max_seq_length=32, # make sure there is at least 1 packed sequence
packing=True,
)
with self.assertRaises(ValueError):
# This should not work because not enough data for one sample
_ = SFTTrainer(
model=self.model,
args=training_args,
train_dataset=self.dummy_dataset,
formatting_func=formatting_prompts_func,
max_seq_length=1024, # make sure there is NOT at least 1 packed sequence
packing=True,
)
# This should not work as well
with self.assertRaises(ValueError):
_ = SFTTrainer(
model=self.model,
args=training_args,
train_dataset=self.dummy_dataset,
formatting_func=formatting_prompts_func,
packing=False,
)
# but this should work
_ = SFTTrainer(
model=self.model,
args=training_args,
train_dataset=self.dummy_dataset,
formatting_func=formatting_prompts_func_batched,
packing=False,
)
def test_sft_trainer_with_model_num_train_epochs(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
dataloader_drop_last=True,
evaluation_strategy="steps",
max_steps=2,
eval_steps=1,
save_steps=1,
num_train_epochs=2,
per_device_train_batch_size=2,
)
trainer = SFTTrainer(
model=self.model,
args=training_args,
train_dataset=self.train_dataset,
eval_dataset=self.eval_dataset,
packing=True,
)
trainer.train()
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
self.assertIsNotNone(trainer.state.log_history[0]["eval_loss"])
self.assertTrue("model.safetensors" in os.listdir(tmp_dir + "/checkpoint-2"))
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
dataloader_drop_last=True,
evaluation_strategy="steps",
max_steps=2,
save_steps=1,
num_train_epochs=2,
per_device_train_batch_size=2,
)
trainer = SFTTrainer(
model=self.model,
args=training_args,
train_dataset=self.dummy_dataset,
dataset_text_field="text",
max_seq_length=16,
num_of_sequences=16,
packing=True,
)
trainer.train()
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
self.assertTrue("model.safetensors" in os.listdir(tmp_dir + "/checkpoint-2"))
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
dataloader_drop_last=True,
evaluation_strategy="steps",
max_steps=2,
save_steps=1,
num_train_epochs=2,
per_device_train_batch_size=2,
)
trainer = SFTTrainer(
model=self.model,
args=training_args,
train_dataset=self.dummy_dataset,
dataset_text_field="text",
max_seq_length=16,
)
trainer.train()
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
self.assertTrue("model.safetensors" in os.listdir(tmp_dir + "/checkpoint-1"))
def test_sft_trainer_with_model(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
dataloader_drop_last=True,
evaluation_strategy="steps",
max_steps=2,
eval_steps=1,
save_steps=1,
per_device_train_batch_size=2,
)
trainer = SFTTrainer(
model=self.model,
args=training_args,
train_dataset=self.train_dataset,
eval_dataset=self.eval_dataset,
packing=True,
)
trainer.train()
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
self.assertIsNotNone(trainer.state.log_history[0]["eval_loss"])
self.assertTrue("model.safetensors" in os.listdir(tmp_dir + "/checkpoint-2"))
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
dataloader_drop_last=True,
evaluation_strategy="steps",
max_steps=2,
save_steps=1,
per_device_train_batch_size=2,
)
trainer = SFTTrainer(
model=self.model,
args=training_args,
train_dataset=self.dummy_dataset,
dataset_text_field="text",
max_seq_length=16,
num_of_sequences=16,
packing=True,
)
trainer.train()
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
self.assertTrue("model.safetensors" in os.listdir(tmp_dir + "/checkpoint-2"))
# with formatting_func + packed
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
dataloader_drop_last=True,
evaluation_strategy="steps",
max_steps=2,
save_steps=1,
per_device_train_batch_size=2,
)
trainer = SFTTrainer(
model=self.model,
args=training_args,
train_dataset=self.dummy_dataset,
formatting_func=formatting_prompts_func,
max_seq_length=16,
num_of_sequences=16,
packing=True,
)
trainer.train()
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
self.assertTrue("model.safetensors" in os.listdir(tmp_dir + "/checkpoint-2"))
# with formatting_func + packed
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
dataloader_drop_last=True,
evaluation_strategy="steps",
max_steps=2,
save_steps=1,
per_device_train_batch_size=2,
)
trainer = SFTTrainer(
model=self.model,
args=training_args,
train_dataset=self.dummy_dataset,
formatting_func=formatting_prompts_func_batched,
max_seq_length=16,
)
trainer.train()
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
self.assertTrue("model.safetensors" in os.listdir(tmp_dir + "/checkpoint-2"))
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
dataloader_drop_last=True,
evaluation_strategy="steps",
max_steps=2,
save_steps=1,
per_device_train_batch_size=2,
)
trainer = SFTTrainer(
model=self.model,
args=training_args,
train_dataset=self.dummy_dataset,
dataset_text_field="text",
max_seq_length=16,
)
trainer.train()
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
self.assertTrue("model.safetensors" in os.listdir(tmp_dir + "/checkpoint-1"))
def test_sft_trainer_with_multiple_eval_datasets(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
dataloader_drop_last=True,
evaluation_strategy="steps",
max_steps=1,
eval_steps=1,
save_steps=1,
per_device_train_batch_size=2,
)
trainer = SFTTrainer(
model=self.model_id,
args=training_args,
train_dataset=self.train_dataset,
eval_dataset={
"data1": self.eval_dataset,
"data2": self.eval_dataset,
},
packing=True,
)
trainer.train()
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
self.assertIsNotNone(trainer.state.log_history[0]["eval_data1_loss"])
self.assertIsNotNone(trainer.state.log_history[1]["eval_data2_loss"])
self.assertTrue("model.safetensors" in os.listdir(tmp_dir + "/checkpoint-1"))
def test_data_collator_completion_lm(self):
response_template = "### Response:\n"
data_collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=self.tokenizer, mlm=False)
text = """\n\n### Instructions:\nHello all this should be masked\n\n### Response:\nI have not been masked correctly."""
encoded_text = self.tokenizer(text)
examples = [encoded_text]
batch = data_collator(examples)
labels = batch["labels"]
last_pad_idx = np.where(labels == -100)[1][-1]
result_text = self.tokenizer.decode(batch["input_ids"][0, last_pad_idx + 1 :])
self.assertEqual(result_text, "I have not been masked correctly.")
def test_data_collator_completion_lm_with_multiple_text(self):
tokenizer = copy.deepcopy(self.tokenizer)
tokenizer.padding_side = "left"
response_template = "### Response:\n"
data_collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer, mlm=False)
text1 = """\n\n### Instructions:\nHello all this should be masked\n\n### Response:\nI have not been masked correctly."""
text2 = """\n\n### Instructions:\nThis is another longer text that should also be masked. This text is significantly longer than the previous one.\n\n### Response:\nI have not been masked correctly."""
encoded_text1 = tokenizer(text1)
encoded_text2 = tokenizer(text2)
examples = [encoded_text1, encoded_text2]
batch = data_collator(examples)
for i in range(2):
labels = batch["labels"][i]
last_pad_idx = np.where(labels == -100)[0][-1]
result_text = tokenizer.decode(batch["input_ids"][i, last_pad_idx + 1 :])
self.assertEqual(result_text, "I have not been masked correctly.")
def test_data_collator_chat_completion_lm(self):
instruction_template = "### Human:"
assistant_template = "### Assistant:"
data_collator = DataCollatorForCompletionOnlyLM(
response_template=assistant_template,
instruction_template=instruction_template,
tokenizer=self.tokenizer,
mlm=False,
)
text = """### Human: Hello all this should be masked.### Assistant: I should not be masked.### Human: All this should be masked too.### Assistant: I should not be masked too."""
encoded_text = self.tokenizer(text)
examples = [encoded_text]
batch = data_collator(examples)
labels = batch["labels"]
non_masked_tokens = batch["input_ids"][labels != -100]
result_text = self.tokenizer.decode(non_masked_tokens)
self.assertEqual(result_text, " I should not be masked. I should not be masked too.")
def test_data_collator_chat_completion_lm_with_multiple_text(self):
tokenizer = copy.deepcopy(self.tokenizer)
tokenizer.padding_side = "left"
instruction_template = "### Human:"
assistant_template = "### Assistant:"
data_collator = DataCollatorForCompletionOnlyLM(
response_template=assistant_template,
instruction_template=instruction_template,
tokenizer=tokenizer,
mlm=False,
)
text1 = """### Human: Hello all this should be masked.### Assistant: I should not be masked."""
text2 = """### Human: Hello all this should be masked.### Assistant: I should not be masked.### Human: All this should be masked too.### Assistant: I should not be masked too."""
encoded_text1 = tokenizer(text1)
encoded_text2 = tokenizer(text2)
examples = [encoded_text1, encoded_text2]
batch = data_collator(examples)
labels = batch["labels"]
input_ids = batch["input_ids"]
non_masked_tokens1 = input_ids[0][labels[0] != -100]
result_text1 = tokenizer.decode(non_masked_tokens1)
self.assertEqual(result_text1, " I should not be masked.")
non_masked_tokens2 = input_ids[1][labels[1] != -100]
result_text2 = tokenizer.decode(non_masked_tokens2)
self.assertEqual(result_text2, " I should not be masked. I should not be masked too.")
def test_sft_trainer_infinite_with_model(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
dataloader_drop_last=True,
evaluation_strategy="steps",
max_steps=5,
eval_steps=1,
save_steps=1,
per_device_train_batch_size=2,
)
trainer = SFTTrainer(
model=self.model,
args=training_args,
train_dataset=self.train_dataset,
eval_dataset=self.eval_dataset,
packing=True,
max_seq_length=500,
)
self.assertTrue(trainer.train_dataset.infinite)
trainer.train()
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
self.assertIsNotNone(trainer.state.log_history[0]["eval_loss"])
# make sure the trainer did 5 steps
self.assertTrue("model.safetensors" in os.listdir(tmp_dir + "/checkpoint-5"))
def test_sft_trainer_infinite_with_model_epochs(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
dataloader_drop_last=True,
num_train_epochs=1,
per_device_train_batch_size=2,
save_strategy="epoch",
)
trainer = SFTTrainer(
model=self.model,
args=training_args,
train_dataset=self.train_dataset,
eval_dataset=self.eval_dataset,
packing=True,
max_seq_length=500,
)
self.assertFalse(trainer.train_dataset.infinite)
trainer.train()
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
# make sure the trainer did 5 steps
self.assertTrue("model.safetensors" in os.listdir(tmp_dir + "/checkpoint-4"))
def test_sft_trainer_with_model_neftune(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
dataloader_drop_last=True,
evaluation_strategy="steps",
max_steps=2,
eval_steps=1,
save_steps=1,
per_device_train_batch_size=2,
)
trainer = SFTTrainer(
model=self.model,
args=training_args,
train_dataset=self.train_dataset,
eval_dataset=self.eval_dataset,
neftune_noise_alpha=5,
packing=True,
)
trainer.model = trainer._trl_activate_neftune(trainer.model)
device = trainer.model.get_input_embeddings().weight.device
trainer.model.train()
torch.random.manual_seed(42)
embeds_neftune = trainer.model.get_input_embeddings()(torch.LongTensor([[1, 0, 1]]).to(device))
torch.random.manual_seed(24)
embeds_neftune_2 = trainer.model.get_input_embeddings()(torch.LongTensor([[1, 0, 1]]).to(device))
self.assertFalse(torch.allclose(embeds_neftune, embeds_neftune_2))
self.assertTrue(len(trainer.model.get_input_embeddings()._forward_hooks) > 0)
trainer.neftune_hook_handle.remove()
trainer.train()
# Make sure forward pass works fine
_ = trainer.model(torch.LongTensor([[1, 0, 1]]).to(device))
self.assertTrue(len(trainer.model.get_input_embeddings()._forward_hooks) == 0)
@require_peft
def test_peft_sft_trainer(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
dataloader_drop_last=True,
evaluation_strategy="steps",
max_steps=4,
eval_steps=2,
save_steps=2,
per_device_train_batch_size=2,
)
peft_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
trainer = SFTTrainer(
model=self.model_id,
args=training_args,
train_dataset=self.train_dataset,
eval_dataset=self.eval_dataset,
peft_config=peft_config,
packing=True,
)
self.assertTrue(isinstance(trainer.model, PeftModel))
trainer.train()
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
self.assertIsNotNone(trainer.state.log_history[0]["eval_loss"])
self.assertTrue("adapter_model.safetensors" in os.listdir(tmp_dir + "/checkpoint-2"))
self.assertTrue("adapter_config.json" in os.listdir(tmp_dir + "/checkpoint-2"))
self.assertTrue("model.safetensors" not in os.listdir(tmp_dir + "/checkpoint-2"))
@require_peft
def test_peft_sft_trainer_gc(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
dataloader_drop_last=True,
evaluation_strategy="steps",
max_steps=4,
eval_steps=2,
save_steps=2,
per_device_train_batch_size=2,
gradient_checkpointing=True,
)
peft_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
trainer = SFTTrainer(
model=self.model_id,
args=training_args,
train_dataset=self.train_dataset,
eval_dataset=self.eval_dataset,
peft_config=peft_config,
packing=True,
)
self.assertTrue(isinstance(trainer.model, PeftModel))
trainer.train()
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
self.assertIsNotNone(trainer.state.log_history[0]["eval_loss"])
self.assertTrue("adapter_model.safetensors" in os.listdir(tmp_dir + "/checkpoint-2"))
self.assertTrue("adapter_config.json" in os.listdir(tmp_dir + "/checkpoint-2"))
self.assertTrue("model.safetensors" not in os.listdir(tmp_dir + "/checkpoint-2"))
@require_peft
def test_peft_sft_trainer_neftune(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
dataloader_drop_last=True,
evaluation_strategy="steps",
max_steps=4,
eval_steps=2,
save_steps=2,
per_device_train_batch_size=2,
)
peft_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
trainer = SFTTrainer(
model=self.model_id,
args=training_args,
train_dataset=self.train_dataset,
eval_dataset=self.eval_dataset,
peft_config=peft_config,
neftune_noise_alpha=5,
packing=True,
)
trainer.model = trainer._trl_activate_neftune(trainer.model)
self.assertTrue(isinstance(trainer.model, PeftModel))
device = trainer.model.get_input_embeddings().weight.device
trainer.model.train()
torch.random.manual_seed(42)
embeds_neftune = trainer.model.get_input_embeddings()(torch.LongTensor([[1, 0, 1]]).to(device))
torch.random.manual_seed(24)
embeds_neftune_2 = trainer.model.get_input_embeddings()(torch.LongTensor([[1, 0, 1]]).to(device))
self.assertFalse(torch.allclose(embeds_neftune, embeds_neftune_2))
self.assertTrue(len(trainer.model.get_input_embeddings()._forward_hooks) > 0)
trainer.neftune_hook_handle.remove()
trainer.train()
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
self.assertIsNotNone(trainer.state.log_history[0]["eval_loss"])
self.assertTrue("adapter_model.safetensors" in os.listdir(tmp_dir + "/checkpoint-2"))
self.assertTrue("adapter_config.json" in os.listdir(tmp_dir + "/checkpoint-2"))
self.assertTrue("model.safetensors" not in os.listdir(tmp_dir + "/checkpoint-2"))
# Make sure forward pass works fine to check if embeddings forward is not broken.
_ = trainer.model(torch.LongTensor([[1, 0, 1]]).to(device))
self.assertTrue(len(trainer.model.get_input_embeddings()._forward_hooks) == 0)
| 0 |
hf_public_repos/trl | hf_public_repos/trl/tests/testing_constants.py | # Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
CI_HUB_USER = "__DUMMY_TRANSFORMERS_USER__"
CI_HUB_USER_FULL_NAME = "Dummy User"
CI_HUB_USER_TOKEN = "hf_94wBhPGp6KrrTH3KDchhKpRxZwd6dmHWLL"
CI_HUB_ENDPOINT = "https://hub-ci.huggingface.co"
| 0 |
hf_public_repos/trl | hf_public_repos/trl/tests/testing_utils.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import torch
from trl import is_diffusers_available, is_peft_available, is_wandb_available, is_xpu_available
def require_peft(test_case):
"""
Decorator marking a test that requires peft. Skips the test if peft is not available.
"""
if not is_peft_available():
test_case = unittest.skip("test requires peft")(test_case)
return test_case
def require_diffusers(test_case):
"""
Decorator marking a test that requires diffusers. Skips the test if diffusers is not available.
"""
if not is_diffusers_available():
test_case = unittest.skip("test requires diffusers")(test_case)
return test_case
def require_wandb(test_case, required: bool = True):
"""
Decorator marking a test that requires wandb. Skips the test if wandb is not available.
"""
# XOR, i.e.:
# skip if available and required = False and
# skip if not available and required = True
if is_wandb_available() ^ required:
test_case = unittest.skip("test requires wandb")(test_case)
return test_case
def require_no_wandb(test_case):
"""
Decorator marking a test that requires no wandb. Skips the test if wandb is available.
"""
return require_wandb(test_case, required=False)
def require_bitsandbytes(test_case):
"""
Decorator marking a test that requires bitsandbytes. Skips the test if bitsandbytes is not available.
"""
try:
import bitsandbytes # noqa: F401
except ImportError:
test_case = unittest.skip("test requires bitsandbytes")(test_case)
return test_case
def require_torch_multi_gpu(test_case):
"""
Decorator marking a test that requires multiple GPUs. Skips the test if there aren't enough GPUs.
"""
if torch.cuda.device_count() < 2:
test_case = unittest.skip("test requires multiple GPUs")(test_case)
return test_case
def require_torch_multi_xpu(test_case):
"""
Decorator marking a test that requires multiple XPUs. Skips the test if there aren't enough XPUs.
"""
if torch.xpu.device_count() < 2 and is_xpu_available():
test_case = unittest.skip("test requires multiple XPUs")(test_case)
return test_case
| 0 |
hf_public_repos/trl | hf_public_repos/trl/tests/test_core.py | # Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import torch
from trl.core import masked_mean, masked_var, masked_whiten, whiten
class CoreTester(unittest.TestCase):
"""
A wrapper class for testing core utils functions
"""
@classmethod
def setUpClass(cls):
cls.test_input = torch.Tensor([1, 2, 3, 4])
cls.test_mask = torch.Tensor([0, 1, 1, 0])
cls.test_input_unmasked = cls.test_input[1:3]
def test_masked_mean(self):
self.assertEqual(torch.mean(self.test_input_unmasked), masked_mean(self.test_input, self.test_mask))
def test_masked_var(self):
self.assertEqual(torch.var(self.test_input_unmasked), masked_var(self.test_input, self.test_mask))
def test_masked_whiten(self):
whiten_unmasked = whiten(self.test_input_unmasked)
whiten_masked = masked_whiten(self.test_input, self.test_mask)[1:3]
diffs = (whiten_unmasked - whiten_masked).sum()
self.assertAlmostEqual(diffs, 0)
| 0 |
hf_public_repos/trl | hf_public_repos/trl/tests/test_data_collator_completion_only.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import torch
from transformers import AutoTokenizer
from trl import DataCollatorForCompletionOnlyLM
class DataCollatorForCompletionOnlyLMTester(unittest.TestCase):
def test_data_collator_finds_response_template_llama2_tokenizer(self):
# this should ideally be tested with meta-llama/Llama-2-7b-hf
self.tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/dummy-GPT2-correct-vocab")
self.instruction = """### System: You are a helpful assistant.
### User: How much is 2+2?
### Assistant: 2+2 equals 4"""
self.instruction_template = "\n### User:"
self.response_template = "\n### Assistant:"
# GPT2Tokenizer: [198, 21017, 11787, 25] -> [11787, 25]
# Llama2Tokenizer: [29871, 13, 2277, 29937, 4911, 29901] -> [2277, 29937, 4911, 29901]
self.tokenized_instruction_w_context = self.tokenizer.encode(
self.instruction_template, add_special_tokens=False
)[2:]
# GPT2Tokenizer: [198, 21017, 15286, 25] -> [15286, 25]
# Llama2Tokenizer: [29871, 13, 2277, 29937, 4007, 22137, 29901] -> [2277, 29937, 4007, 22137, 29901]
self.tokenized_response_w_context = self.tokenizer.encode(self.response_template, add_special_tokens=False)[2:]
# Plain check on string
self.assertIn(self.response_template, self.instruction)
self.tokenized_instruction = self.tokenizer.encode(self.instruction, add_special_tokens=False)
# Test the fix for #598
# Pass already tokenized (w context) and truncated response_template so token_ids are like in the instruction + response
self.collator = DataCollatorForCompletionOnlyLM(self.tokenized_response_w_context, tokenizer=self.tokenizer)
self.collator.torch_call([self.tokenized_instruction])
# Test for PR #749
# Pass already tokenized (w context) instruction and response both so token_ids are like in the instruction + response
self.collator = DataCollatorForCompletionOnlyLM(
self.tokenized_response_w_context, self.tokenized_instruction_w_context, tokenizer=self.tokenizer
)
self.collator.torch_call([self.tokenized_instruction])
def test_data_collator_handling_of_long_sequences(self):
self.tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/dummy-GPT2-correct-vocab")
self.instruction = """### System: You are a helpful assistant.
### User: How much is 2+2? I'm asking because I'm not sure. And I'm not sure because I'm not good at math.
"""
self.response_template = "\n### Assistant:"
# check DataCollatorForCompletionOnlyLM using response template only
self.tokenized_instruction = self.tokenizer.encode(self.instruction, add_special_tokens=False)
self.collator = DataCollatorForCompletionOnlyLM(self.response_template, tokenizer=self.tokenizer)
encoded_instance = self.collator.torch_call([self.tokenized_instruction])
result = torch.all(encoded_instance["labels"] == -100)
self.assertTrue(result, "Not all values in the tensor are -100.")
# check DataCollatorForCompletionOnlyLM using response template and instruction template
self.instruction_template = "\n### User:"
self.collator = DataCollatorForCompletionOnlyLM(
self.response_template, self.instruction_template, tokenizer=self.tokenizer
)
encoded_instance = self.collator.torch_call([self.tokenized_instruction])
result = torch.all(encoded_instance["labels"] == -100)
self.assertTrue(result, "Not all values in the tensor are -100.")
| 0 |
hf_public_repos/trl | hf_public_repos/trl/tests/test_environments.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from unittest.mock import patch
import torch
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead, TextEnvironment, TextHistory
class DummyTool:
def __call__(self, text):
return text
def dummy_generate(histories):
for i in range(len(histories)):
histories[i].append_segment("<request><DummyTool>test<call>", torch.tensor([1, 2, 3]), system=False)
return histories
class TextHistoryTest(unittest.TestCase):
def test_text_history_init(self):
text = "Hello there!"
tokens = torch.tensor([1, 2, 3])
history = TextHistory(text, tokens)
self.assertEqual(history.text, text)
self.assertTrue(torch.equal(history.tokens, tokens))
self.assertTrue(torch.equal(history.token_masks, torch.zeros_like(tokens)))
history = TextHistory(text, tokens, system=False)
self.assertTrue(torch.equal(history.token_masks, torch.ones_like(tokens)))
def test_text_history_append_segment(self):
text = "Hello there!"
tokens = torch.tensor([1, 2, 3])
history = TextHistory(text, tokens)
history.append_segment("General Kenobi!", torch.tensor([4, 5, 6]), system=False)
self.assertEqual(history.text, text + "General Kenobi!")
self.assertTrue(torch.equal(history.tokens, torch.tensor([1, 2, 3, 4, 5, 6])))
self.assertTrue(torch.equal(history.token_masks, torch.tensor([0, 0, 0, 1, 1, 1])))
history.append_segment("You are a bold one!", torch.tensor([7, 8, 9]))
self.assertEqual(history.text, text + "General Kenobi!" + "You are a bold one!")
self.assertTrue(torch.equal(history.tokens, torch.tensor([1, 2, 3, 4, 5, 6, 7, 8, 9])))
self.assertTrue(torch.equal(history.token_masks, torch.tensor([0, 0, 0, 1, 1, 1, 0, 0, 0])))
def test_text_history_complete(self):
text = "Hello there!"
tokens = torch.tensor([1, 2, 3])
history = TextHistory(text, tokens)
history.complete()
self.assertTrue(history.completed)
self.assertFalse(history.truncated)
history.complete(truncated=True)
self.assertTrue(history.completed)
self.assertTrue(history.truncated)
def test_text_history_last_segment(self):
text = "Hello there!"
tokens = torch.tensor([1, 2, 3])
history = TextHistory(text, tokens)
history.append_segment("General Kenobi!", torch.tensor([4, 5, 6]))
history.append_segment("You are a bold one!", torch.tensor([7, 8, 9]))
self.assertEqual(history.last_text_segment, "You are a bold one!")
def test_text_history_split_query_response(self):
text = "Hello there!"
tokens = torch.tensor([1, 2, 3])
history = TextHistory(text, tokens)
history.append_segment("General Kenobi!", torch.tensor([4, 5, 6]), system=False)
history.append_segment("You are a bold one!", torch.tensor([7, 8, 9]), system=True)
query, response, mask = history.split_query_response_tokens()
self.assertTrue(torch.equal(query, torch.tensor([1, 2, 3])))
self.assertTrue(torch.equal(response, torch.tensor([4, 5, 6, 7, 8, 9])))
self.assertTrue(torch.equal(mask, torch.tensor([1, 1, 1, 0, 0, 0])))
class TextEnvironmentTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
# model_id
cls.model_id = "trl-internal-testing/dummy-GPT2-correct-vocab"
# get models and tokenizer
cls.gpt2_model = AutoModelForCausalLMWithValueHead.from_pretrained(cls.model_id)
cls.gpt2_tokenizer = AutoTokenizer.from_pretrained(cls.model_id)
cls.gpt2_tokenizer.pad_token = cls.gpt2_tokenizer.eos_token
def test_text_environment_setup(self):
env = TextEnvironment(
self.gpt2_model,
self.gpt2_tokenizer,
tools=[DummyTool()],
reward_fn=lambda x: torch.tensor(1),
prompt="I am a prompt!\n",
)
self.assertEqual(env.prompt, "I am a prompt!\n")
self.assertEqual(list(env.tools.keys()), ["DummyTool"])
self.assertTrue(isinstance(env.tools["DummyTool"], DummyTool))
self.assertEqual(env.reward_fn("Hello there!"), 1)
def test_text_environment_generate(self):
generation_kwargs = {"do_sample": False, "max_new_tokens": 4, "pad_token_id": self.gpt2_tokenizer.eos_token_id}
env = TextEnvironment(
self.gpt2_model,
self.gpt2_tokenizer,
tools=[DummyTool()],
reward_fn=lambda x: torch.tensor(1),
prompt="I am a prompt!\n",
generation_kwargs=generation_kwargs,
)
input_texts = ["this is a test", "this is another, longer test"]
model_inputs = [self.gpt2_tokenizer(txt, return_tensors="pt").input_ids.squeeze() for txt in input_texts]
generations_batched = env._generate_batched(model_inputs, batch_size=2)
generations_batched = self.gpt2_tokenizer.batch_decode(generations_batched)
generations_single = [env._generate_batched([inputs], batch_size=1)[0] for inputs in model_inputs]
generations_single = self.gpt2_tokenizer.batch_decode(generations_single)
self.assertEqual(generations_single, generations_batched)
def test_text_environment_tool_call_parsing(self):
string_valid = "Something something <request><Tool1>Hello there!<call>"
string_invalid_request = "Something something <Tool1>Hello there!<call>"
string_invalid_call = "Something something <request><Tool1>Hello there!"
string_invalid_tool = "Something something <request>|Tool2|Hello there!<call>"
string_invalid_random = "<>abcdefghijklm<>nopqrstuvwxyz<>"
env = TextEnvironment(
self.gpt2_model,
self.gpt2_tokenizer,
tools=[DummyTool()],
reward_fn=lambda x: torch.tensor(1),
prompt="I am a prompt!\n",
)
tool, response = env.parse_tool_call(string_valid)
self.assertEqual(tool, "Tool1")
self.assertEqual(response, "Hello there!")
tool, response = env.parse_tool_call(string_invalid_request)
self.assertEqual(tool, None)
self.assertEqual(response, None)
tool, response = env.parse_tool_call(string_invalid_call)
self.assertEqual(tool, None)
self.assertEqual(response, None)
tool, response = env.parse_tool_call(string_invalid_tool)
self.assertEqual(tool, None)
self.assertEqual(response, None)
tool, response = env.parse_tool_call(string_invalid_random)
self.assertEqual(tool, None)
self.assertEqual(response, None)
def test_text_environment_tool_truncation(self):
env = TextEnvironment(
self.gpt2_model,
self.gpt2_tokenizer,
tools={"dummy": lambda x: "a" * 1000},
reward_fn=lambda x: torch.tensor(1),
prompt="I am a prompt!\n",
)
env.max_tool_response = 100
history = env.step(TextHistory("<request><dummy>Hello there!<call>", torch.tensor([1, 2, 3])))
self.assertEqual(len(history.last_text_segment) - len(env.response_token), 100)
env.max_tool_response = 500
history = env.step(TextHistory("<request><dummy>Hello there!<call>", torch.tensor([1, 2, 3])))
self.assertEqual(len(history.last_text_segment) - len(env.response_token), 500)
env.max_tool_response = 1001
history = env.step(TextHistory("<request><dummy>Hello there!<call>", torch.tensor([1, 2, 3])))
self.assertEqual(len(history.last_text_segment) - len(env.response_token), 1000)
env.max_tool_response = 2000
history = env.step(TextHistory("<request><dummy>Hello there!<call>", torch.tensor([1, 2, 3])))
self.assertEqual(len(history.last_text_segment) - len(env.response_token), 1000)
@patch.object(TextEnvironment, "generate", side_effect=dummy_generate)
def test_text_environment_max_calls(self, mock_generate):
env = TextEnvironment(
self.gpt2_model,
self.gpt2_tokenizer,
tools={"DummyTool": DummyTool()},
reward_fn=lambda x: [torch.tensor(1) for _ in x],
prompt="I am a prompt!\n",
)
env.max_turns = 1
_, _, _, _, histories = env.run(["test"])
self.assertEqual(
histories[0].text, "I am a prompt!\n" + "test" + 1 * "<request><DummyTool>test<call>test<response>"
)
env.max_turns = 2
_, _, _, _, histories = env.run(["test"])
self.assertEqual(
histories[0].text, "I am a prompt!\n" + "test" + 2 * "<request><DummyTool>test<call>test<response>"
)
env.max_turns = 4
_, _, _, _, histories = env.run(["test"])
self.assertEqual(
histories[0].text, "I am a prompt!\n" + "test" + 4 * "<request><DummyTool>test<call>test<response>"
)
def test_text_environment_compute_rewards(self):
env = TextEnvironment(
self.gpt2_model,
self.gpt2_tokenizer,
tools={"DummyTool": DummyTool()},
reward_fn=lambda x: [torch.tensor(i) for i, _ in enumerate(x)],
prompt="I am a prompt!\n",
)
histories = [TextHistory("<request><DummyTool>test<call>", torch.tensor([1, 2, 3])) for _ in range(8)]
histories = env.compute_reward(histories)
for i in range(8):
self.assertEqual(histories[i].reward, i)
@patch.object(TextEnvironment, "generate", side_effect=dummy_generate)
def test_text_environment_run(self, mock_generate):
env = TextEnvironment(
self.gpt2_model,
self.gpt2_tokenizer,
tools={"DummyTool": DummyTool()},
reward_fn=lambda x: [torch.tensor(i) for i, _ in enumerate(x)],
prompt="I am a prompt!\n",
max_turns=2,
)
task_1 = "Hello there!"
task_2 = "Hello there! General Kenobi!"
query, response, response_mask, reward, histories = env.run([task_1, task_2])
self.assertEqual(len(query[0]), 9)
self.assertEqual(len(query[1]), 12)
self.assertEqual(len(response[0]), 14)
self.assertEqual(len(response[1]), 14)
self.assertEqual(response_mask[0].sum(), 2 * 3) # mocked generate always adds 3 toknes
self.assertEqual(response_mask[1].sum(), 2 * 3) # mocked generate always adds 3 toknes
self.assertEqual(reward[0], 0)
self.assertEqual(reward[1], 1)
self.assertEqual(
histories[0].text, "I am a prompt!\n" + "Hello there!" + 2 * "<request><DummyTool>test<call>test<response>"
)
self.assertEqual(
histories[1].text,
"I am a prompt!\n" + "Hello there! General Kenobi!" + 2 * "<request><DummyTool>test<call>test<response>",
)
| 0 |
hf_public_repos/trl | hf_public_repos/trl/tests/test_iterative_sft_trainer.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tempfile
import unittest
import torch
from datasets import Dataset
from parameterized import parameterized
from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoTokenizer, TrainingArguments
from trl import IterativeSFTTrainer
class IterativeTrainerTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.model_id = "trl-internal-testing/dummy-GPT2-correct-vocab"
cls.model = AutoModelForCausalLM.from_pretrained(cls.model_id)
cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_id)
cls.tokenizer.pad_token = cls.tokenizer.eos_token
# get t5 as seq2seq example:
model_id = "trl-internal-testing/tiny-T5ForConditionalGeneration-correct-vocab"
cls.t5_model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
cls.t5_tokenizer = AutoTokenizer.from_pretrained(model_id)
def _init_tensor_dummy_dataset(self):
dummy_dataset_dict = {
"input_ids": [torch.tensor([5303, 3621]), torch.tensor([3666, 1438, 318]), torch.tensor([5303, 3621])],
"attention_mask": [torch.tensor([1, 1]), torch.tensor([1, 1, 1]), torch.tensor([1, 1])],
"labels": [torch.tensor([5303, 3621]), torch.tensor([3666, 1438, 318]), torch.tensor([5303, 3621])],
}
dummy_dataset = Dataset.from_dict(dummy_dataset_dict)
dummy_dataset.set_format("torch")
return dummy_dataset
def _init_textual_dummy_dataset(self):
dummy_dataset_dict = {
"texts": ["Testing the IterativeSFTTrainer.", "This is a test of the IterativeSFTTrainer"],
"texts_labels": ["Testing the IterativeSFTTrainer.", "This is a test of the IterativeSFTTrainer"],
}
dummy_dataset = Dataset.from_dict(dummy_dataset_dict)
dummy_dataset.set_format("torch")
return dummy_dataset
def setUp(self):
# initialize trainer
self.model.train()
return super().setUp()
@parameterized.expand(
[
["gpt2", "tensor"],
["gpt2", "text"],
["t5", "tensor"],
["t5", "text"],
]
)
def test_iterative_step_from_tensor(self, model_name, input_name):
with tempfile.TemporaryDirectory() as tmp_dir:
# initialize dataset
if input_name == "tensor":
dummy_dataset = self._init_tensor_dummy_dataset()
inputs = {
"input_ids": dummy_dataset["input_ids"],
"attention_mask": dummy_dataset["attention_mask"],
"labels": dummy_dataset["labels"],
}
else:
dummy_dataset = self._init_textual_dummy_dataset()
inputs = {
"texts": dummy_dataset["texts"],
"texts_labels": dummy_dataset["texts_labels"],
}
if model_name == "gpt2":
model = self.model
tokenizer = self.tokenizer
else:
model = self.t5_model
tokenizer = self.t5_tokenizer
args = TrainingArguments(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=2,
)
iterative_trainer = IterativeSFTTrainer(model=model, args=args, tokenizer=tokenizer)
iterative_trainer.step(**inputs)
for param in iterative_trainer.model.parameters():
assert param.grad is not None
| 0 |
hf_public_repos/trl | hf_public_repos/trl/tests/test_ddpo_trainer.py | # Copyright 2023 metric-space, The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import unittest
import torch
from trl import is_diffusers_available
from .testing_utils import require_diffusers
if is_diffusers_available():
from trl import DDPOConfig, DDPOTrainer, DefaultDDPOStableDiffusionPipeline
def scorer_function(images, prompts, metadata):
return torch.randn(1) * 3.0, {}
def prompt_function():
return ("cabbages", {})
@require_diffusers
class DDPOTrainerTester(unittest.TestCase):
"""
Test the DDPOTrainer class.
"""
def setUp(self):
self.ddpo_config = DDPOConfig(
num_epochs=2,
train_gradient_accumulation_steps=1,
per_prompt_stat_tracking_buffer_size=32,
sample_num_batches_per_epoch=2,
sample_batch_size=2,
mixed_precision=None,
save_freq=1000000,
)
pretrained_model = "hf-internal-testing/tiny-stable-diffusion-torch"
pretrained_revision = "main"
pipeline = DefaultDDPOStableDiffusionPipeline(
pretrained_model, pretrained_model_revision=pretrained_revision, use_lora=False
)
self.trainer = DDPOTrainer(self.ddpo_config, scorer_function, prompt_function, pipeline)
return super().setUp()
def tearDown(self) -> None:
gc.collect()
def test_loss(self):
advantage = torch.tensor([-1.0])
clip_range = 0.0001
ratio = torch.tensor([1.0])
loss = self.trainer.loss(advantage, clip_range, ratio)
self.assertEqual(loss.item(), 1.0)
def test_generate_samples(self):
samples, output_pairs = self.trainer._generate_samples(1, 2)
self.assertEqual(len(samples), 1)
self.assertEqual(len(output_pairs), 1)
self.assertEqual(len(output_pairs[0][0]), 2)
def test_calculate_loss(self):
samples, _ = self.trainer._generate_samples(1, 2)
sample = samples[0]
latents = sample["latents"][0, 0].unsqueeze(0)
next_latents = sample["next_latents"][0, 0].unsqueeze(0)
log_probs = sample["log_probs"][0, 0].unsqueeze(0)
timesteps = sample["timesteps"][0, 0].unsqueeze(0)
prompt_embeds = sample["prompt_embeds"]
advantage = torch.tensor([1.0], device=prompt_embeds.device)
self.assertEqual(latents.shape, (1, 4, 64, 64))
self.assertEqual(next_latents.shape, (1, 4, 64, 64))
self.assertEqual(log_probs.shape, (1,))
self.assertEqual(timesteps.shape, (1,))
self.assertEqual(prompt_embeds.shape, (2, 77, 32))
loss, approx_kl, clipfrac = self.trainer.calculate_loss(
latents, timesteps, next_latents, log_probs, advantage, prompt_embeds
)
self.assertTrue(torch.isfinite(loss.cpu()))
| 0 |
hf_public_repos/trl | hf_public_repos/trl/tests/test_no_peft.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
import unittest
from unittest.mock import patch
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from .testing_utils import is_peft_available, require_peft
class DummyDataset(torch.utils.data.Dataset):
def __init__(self, query_data, response_data):
self.query_data = query_data
self.response_data = response_data
def __len__(self):
return len(self.query_data)
def __getitem__(self, idx):
return self.query_data[idx], self.response_data[idx]
EXPECTED_STATS = [
"objective/kl",
"objective/kl_dist",
"objective/logprobs",
"objective/ref_logprobs",
"objective/kl_coef",
"objective/entropy",
"ppo/mean_non_score_reward",
"ppo/loss/policy",
"ppo/loss/value",
"ppo/loss/total",
"ppo/policy/entropy",
"ppo/policy/approxkl",
"ppo/policy/policykl",
"ppo/policy/clipfrac",
"ppo/policy/advantages",
"ppo/policy/advantages_mean",
"ppo/policy/ratio",
"ppo/returns/mean",
"ppo/returns/var",
"ppo/val/vpred",
"ppo/val/error",
"ppo/val/clipfrac",
"ppo/val/mean",
"ppo/val/var",
"ppo/val/var_explained",
"time/ppo/forward_pass",
"time/ppo/compute_rewards",
"time/ppo/optimize_step",
"time/ppo/calc_stats",
"time/ppo/total",
"ppo/learning_rate",
]
@require_peft
class TestPeftDependancy(unittest.TestCase):
def setUp(self):
self.causal_lm_model_id = "trl-internal-testing/tiny-random-GPTNeoXForCausalLM"
self.seq_to_seq_model_id = "trl-internal-testing/tiny-random-T5ForConditionalGeneration"
if is_peft_available():
from peft import LoraConfig, get_peft_model
lora_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
causal_lm_model = AutoModelForCausalLM.from_pretrained(self.causal_lm_model_id)
self.peft_model = get_peft_model(causal_lm_model, lora_config)
def test_no_peft(self):
with patch.dict(sys.modules, {"peft": None}):
from trl import AutoModelForCausalLMWithValueHead, AutoModelForSeq2SeqLMWithValueHead
# Check that loading a model with `peft` will raise an error
with self.assertRaises(ModuleNotFoundError):
import peft # noqa
trl_model = AutoModelForCausalLMWithValueHead.from_pretrained(self.causal_lm_model_id) # noqa
trl_seq2seq_model = AutoModelForSeq2SeqLMWithValueHead.from_pretrained(self.seq_to_seq_model_id) # noqa
def test_imports_no_peft(self):
with patch.dict(sys.modules, {"peft": None}):
from trl import ( # noqa
AutoModelForCausalLMWithValueHead,
AutoModelForSeq2SeqLMWithValueHead,
PPOConfig,
PPOTrainer,
PreTrainedModelWrapper,
)
def test_ppo_trainer_no_peft(self):
with patch.dict(sys.modules, {"peft": None}):
from trl import AutoModelForCausalLMWithValueHead, PPOConfig, PPOTrainer
ppo_model_id = "trl-internal-testing/dummy-GPT2-correct-vocab"
trl_model = AutoModelForCausalLMWithValueHead.from_pretrained(ppo_model_id)
tokenizer = AutoTokenizer.from_pretrained(ppo_model_id)
tokenizer.pad_token_id = tokenizer.eos_token_id
ppo_config = PPOConfig(batch_size=2, mini_batch_size=1, log_with=None)
dummy_dataset = DummyDataset(
[torch.LongTensor([0, 1, 0, 1, 0, 1]), torch.LongTensor([0, 1, 0, 1, 0, 1])],
[torch.LongTensor([1, 0, 1, 0, 1, 0]), torch.LongTensor([0, 1, 0, 1, 0, 1])],
)
ppo_trainer = PPOTrainer(
config=ppo_config,
model=trl_model,
ref_model=None,
tokenizer=tokenizer,
dataset=dummy_dataset,
)
dummy_dataloader = ppo_trainer.dataloader
for query_tensor, response_tensor in dummy_dataloader:
# define a reward for response
# (this could be any reward such as human feedback or output from another model)
reward = [torch.tensor(1.0), torch.tensor(0.0)]
# train model
train_stats = ppo_trainer.step([q for q in query_tensor], [r for r in response_tensor], reward)
break
# check gradients are not None
for _, param in trl_model.named_parameters():
if param.requires_grad:
self.assertIsNotNone(param.grad)
# check expected stats
for stat in EXPECTED_STATS:
self.assertIn(stat, train_stats)
| 0 |
hf_public_repos/trl | hf_public_repos/trl/tests/test_dpo_trainer.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tempfile
import unittest
import torch
from datasets import Dataset
from parameterized import parameterized
from pytest import mark
from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoTokenizer, TrainingArguments
from trl import DPOTrainer
from .testing_utils import require_no_wandb, require_peft
class DPOTrainerTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.model_id = "trl-internal-testing/dummy-GPT2-correct-vocab"
cls.model = AutoModelForCausalLM.from_pretrained(cls.model_id)
cls.ref_model = AutoModelForCausalLM.from_pretrained(cls.model_id)
cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_id)
cls.tokenizer.pad_token = cls.tokenizer.eos_token
# get t5 as seq2seq example:
model_id = "trl-internal-testing/tiny-T5ForConditionalGeneration-correct-vocab"
cls.t5_model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
cls.t5_ref_model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
cls.t5_tokenizer = AutoTokenizer.from_pretrained(model_id)
def _init_dummy_dataset(self):
# fmt: off
dummy_dataset_dict = {
"prompt": [
"hello",
"how are you",
"What is your name?",
"What is your name?",
"Which is the best programming language?",
"Which is the best programming language?",
"Which is the best programming language?",
],
"chosen": [
"hi nice to meet you",
"I am fine",
"My name is Mary",
"My name is Mary",
"Python",
"Python",
"Python",
],
"rejected": [
"leave me alone",
"I am not fine",
"Whats it to you?",
"I dont have a name",
"Javascript",
"C++",
"Java",
],
}
# fmt: on
return Dataset.from_dict(dummy_dataset_dict)
@parameterized.expand([["gpt2", "sigmoid"], ["t5", "hinge"], ["gpt2", "ipo"], ["t5", "ipo"]])
def test_dpo_trainer(self, name, loss_type):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=3,
remove_unused_columns=False,
gradient_accumulation_steps=1,
learning_rate=9e-1,
evaluation_strategy="steps",
)
dummy_dataset = self._init_dummy_dataset()
if name == "gpt2":
model = self.model
ref_model = self.ref_model
tokenizer = self.tokenizer
elif name == "t5":
model = self.t5_model
ref_model = self.t5_ref_model
tokenizer = self.t5_tokenizer
trainer = DPOTrainer(
model=model,
ref_model=ref_model,
beta=0.1,
loss_type=loss_type,
args=training_args,
tokenizer=tokenizer,
train_dataset=dummy_dataset,
eval_dataset=dummy_dataset,
)
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
trainer.train()
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
# check the params have changed
for n, param in previous_trainable_params.items():
new_param = trainer.model.get_parameter(n)
# check the params have changed - ignore 0 biases
if param.sum() != 0:
self.assertFalse(torch.equal(param, new_param))
def test_dpo_trainer_without_providing_ref_model(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=3,
remove_unused_columns=False,
gradient_accumulation_steps=4,
learning_rate=9e-1,
evaluation_strategy="steps",
)
dummy_dataset = self._init_dummy_dataset()
trainer = DPOTrainer(
model=self.model,
ref_model=None,
beta=0.1,
args=training_args,
tokenizer=self.tokenizer,
train_dataset=dummy_dataset,
eval_dataset=dummy_dataset,
)
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
trainer.train()
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
# check the params have changed
for n, param in previous_trainable_params.items():
new_param = trainer.model.get_parameter(n)
# check the params have changed - ignore 0 biases
if param.sum() != 0:
self.assertFalse(torch.equal(param, new_param))
@require_peft
@mark.peft_test
def test_dpo_trainer_without_providing_ref_model_with_lora(self):
from peft import LoraConfig
lora_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=3,
remove_unused_columns=False,
gradient_accumulation_steps=4,
learning_rate=9e-1,
evaluation_strategy="steps",
)
dummy_dataset = self._init_dummy_dataset()
trainer = DPOTrainer(
model=self.model,
ref_model=None,
beta=0.1,
args=training_args,
tokenizer=self.tokenizer,
train_dataset=dummy_dataset,
eval_dataset=dummy_dataset,
peft_config=lora_config,
)
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
trainer.train()
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
# check the params have changed
for n, param in previous_trainable_params.items():
if "lora" in n:
new_param = trainer.model.get_parameter(n)
# check the params have changed - ignore 0 biases
if param.sum() != 0:
self.assertFalse(torch.equal(param, new_param))
@require_no_wandb
def test_dpo_trainer_generate_during_eval_no_wandb(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=3,
remove_unused_columns=False,
gradient_accumulation_steps=1,
learning_rate=9e-1,
evaluation_strategy="steps",
)
dummy_dataset = self._init_dummy_dataset()
with self.assertRaisesRegex(
ValueError,
expected_regex="`generate_during_eval=True` requires Weights and Biases to be installed."
" Please install `wandb` to resolve.",
):
DPOTrainer(
model=self.model,
ref_model=None,
beta=0.1,
args=training_args,
tokenizer=self.tokenizer,
train_dataset=dummy_dataset,
eval_dataset=dummy_dataset,
generate_during_eval=True,
)
@require_peft
@mark.peft_test
def test_dpo_lora_save(self):
from peft import LoraConfig, get_peft_model
lora_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
# lora model
model = AutoModelForCausalLM.from_pretrained(self.model_id)
model_peft = get_peft_model(model, lora_config)
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = TrainingArguments(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=3,
remove_unused_columns=False,
gradient_accumulation_steps=4,
learning_rate=9e-1,
evaluation_strategy="steps",
)
dummy_dataset = self._init_dummy_dataset()
# dpo train lora model with a lora config
trainer = DPOTrainer(
model=model_peft,
ref_model=None,
beta=0.1,
args=training_args,
tokenizer=self.tokenizer,
train_dataset=dummy_dataset,
eval_dataset=dummy_dataset,
peft_config=lora_config,
)
# train the model
trainer.train()
# save peft adapter
trainer.save_model()
# assert that the model is loaded without giving OSError
try:
AutoModelForCausalLM.from_pretrained(tmp_dir)
except OSError:
self.fail("Loading the saved peft adapter failed")
| 0 |
hf_public_repos/trl | hf_public_repos/trl/tests/test_e2e.py | import subprocess
def test_hello_world():
subprocess.run(
"python examples/hello_world.py",
shell=True,
check=True,
)
| 0 |
hf_public_repos/trl | hf_public_repos/trl/tests/test_modeling_value_head.py | # Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import tempfile
import unittest
import torch
from transformers import AutoModel, AutoModelForCausalLM, AutoModelForSeq2SeqLM
from trl import AutoModelForCausalLMWithValueHead, AutoModelForSeq2SeqLMWithValueHead, create_reference_model
ALL_CAUSAL_LM_MODELS = [
"trl-internal-testing/tiny-random-CodeGenForCausalLM",
"trl-internal-testing/tiny-random-GPTJForCausalLM",
"trl-internal-testing/tiny-random-GPTNeoForCausalLM",
"trl-internal-testing/tiny-random-GPTNeoXForCausalLM",
"trl-internal-testing/tiny-random-OPTForCausalLM",
"trl-internal-testing/tiny-random-BloomForCausalLM",
"trl-internal-testing/tiny-random-GPT2LMHeadModel",
"trl-internal-testing/tiny-random-CodeGenForCausalLM-sharded",
"trl-internal-testing/tiny-random-GPTNeoXForCausalLM-safetensors-sharded",
"trl-internal-testing/tiny-random-GPTNeoXForCausalLM-safetensors"
# "trl-internal-testing/tiny-random-LlamaForCausalLM", uncomment on the next transformers release
]
ALL_SEQ2SEQ_MODELS = [
"trl-internal-testing/tiny-random-BartForConditionalGeneration",
"trl-internal-testing/tiny-random-BigBirdPegasusForConditionalGeneration",
"trl-internal-testing/tiny-random-BlenderbotForConditionalGeneration",
"trl-internal-testing/tiny-random-BlenderbotSmallForConditionalGeneration",
"trl-internal-testing/tiny-random-FSMTForConditionalGeneration",
"trl-internal-testing/tiny-random-LEDForConditionalGeneration",
"trl-internal-testing/tiny-random-LongT5ForConditionalGeneration",
"trl-internal-testing/tiny-random-M2M100ForConditionalGeneration",
"trl-internal-testing/tiny-random-MarianMTModel",
"trl-internal-testing/tiny-random-MBartForConditionalGeneration",
"trl-internal-testing/tiny-random-MT5ForConditionalGeneration",
"trl-internal-testing/tiny-random-MvpForConditionalGeneration",
"trl-internal-testing/tiny-random-PegasusForConditionalGeneration",
"trl-internal-testing/tiny-random-PegasusXForConditionalGeneration",
"trl-internal-testing/tiny-random-PLBartForConditionalGeneration",
"trl-internal-testing/tiny-random-ProphetNetForConditionalGeneration",
"trl-internal-testing/tiny-random-SwitchTransformersForConditionalGeneration",
"trl-internal-testing/tiny-random-T5ForConditionalGeneration",
]
class VHeadModelTester:
all_model_names = None
trl_model_class = None
transformers_model_class = None
def test_value_head(self):
r"""
Test if the v-head is added to the model successfully
"""
for model_name in self.all_model_names:
model = self.trl_model_class.from_pretrained(model_name)
self.assertTrue(hasattr(model, "v_head"))
def test_value_head_shape(self):
r"""
Test if the v-head has the correct shape
"""
for model_name in self.all_model_names:
model = self.trl_model_class.from_pretrained(model_name)
self.assertTrue(model.v_head.summary.weight.shape[0] == 1)
def test_value_head_init_random(self):
r"""
Test if the v-head has been randomly initialized.
We can check that by making sure the bias is different
than zeros by default.
"""
for model_name in self.all_model_names:
model = self.trl_model_class.from_pretrained(model_name)
self.assertFalse(torch.allclose(model.v_head.summary.bias, torch.zeros_like(model.v_head.summary.bias)))
def test_value_head_not_str(self):
r"""
Test if the v-head is added to the model successfully, by passing a non `PretrainedModel`
as an argument to `from_pretrained`.
"""
for model_name in self.all_model_names:
pretrained_model = self.transformers_model_class.from_pretrained(model_name)
model = self.trl_model_class.from_pretrained(pretrained_model)
self.assertTrue(hasattr(model, "v_head"))
def test_from_save_trl(self):
"""
Test if the model can be saved and loaded from a directory and get the same weights
Including the additional modules (e.g. v_head)
"""
for model_name in self.all_model_names:
model = self.trl_model_class.from_pretrained(model_name)
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir)
model_from_save = self.trl_model_class.from_pretrained(tmp_dir)
# Check if the weights are the same
for key in model_from_save.state_dict():
self.assertTrue(torch.allclose(model_from_save.state_dict()[key], model.state_dict()[key]))
def test_from_save_trl_sharded(self):
"""
Test if the model can be saved and loaded from a directory and get the same weights - sharded case
"""
for model_name in self.all_model_names:
model = self.trl_model_class.from_pretrained(model_name)
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir)
model_from_save = self.trl_model_class.from_pretrained(tmp_dir)
# Check if the weights are the same
for key in model_from_save.state_dict():
self.assertTrue(torch.allclose(model_from_save.state_dict()[key], model.state_dict()[key]))
def test_from_save_transformers_sharded(self):
"""
Test if the model can be saved and loaded using transformers and get the same weights - sharded case
"""
for model_name in self.all_model_names:
transformers_model = self.trl_model_class.transformers_parent_class.from_pretrained(model_name)
trl_model = self.trl_model_class.from_pretrained(model_name)
with tempfile.TemporaryDirectory() as tmp_dir:
trl_model.save_pretrained(tmp_dir, max_shard_size="1MB")
transformers_model_from_save = self.trl_model_class.transformers_parent_class.from_pretrained(tmp_dir)
# Check if the weights are the same
for key in transformers_model.state_dict():
self.assertTrue(
torch.allclose(
transformers_model_from_save.state_dict()[key], transformers_model.state_dict()[key]
)
)
def test_from_save_transformers(self):
"""
Test if the model can be saved and loaded using transformers and get the same weights.
We override the test of the super class to check if the weights are the same.
"""
for model_name in self.all_model_names:
transformers_model = self.trl_model_class.transformers_parent_class.from_pretrained(model_name)
trl_model = self.trl_model_class.from_pretrained(model_name)
with tempfile.TemporaryDirectory() as tmp_dir:
trl_model.save_pretrained(tmp_dir)
transformers_model_from_save = self.trl_model_class.transformers_parent_class.from_pretrained(tmp_dir)
# Check if the weights are the same
for key in transformers_model.state_dict():
self.assertTrue(
torch.allclose(
transformers_model_from_save.state_dict()[key], transformers_model.state_dict()[key]
)
)
# Check if the trl model has the same keys as the transformers model
# except the v_head
for key in trl_model.state_dict():
if "v_head" not in key:
self.assertTrue(key in transformers_model.state_dict())
# check if the weights are the same
self.assertTrue(torch.allclose(trl_model.state_dict()[key], transformers_model.state_dict()[key]))
# check if they have the same modules
self.assertTrue(
set(transformers_model_from_save.state_dict().keys()) == set(transformers_model.state_dict().keys())
)
class CausalLMValueHeadModelTester(VHeadModelTester, unittest.TestCase):
"""
Testing suite for v-head models.
"""
all_model_names = ALL_CAUSAL_LM_MODELS
trl_model_class = AutoModelForCausalLMWithValueHead
transformers_model_class = AutoModelForCausalLM
def tearDown(self):
# free memory
gc.collect()
def test_inference(self):
r"""
Test if the model can be used for inference and outputs 3 values
- logits, loss, and value states
"""
EXPECTED_OUTPUT_SIZE = 3
for model_name in self.all_model_names:
model = self.trl_model_class.from_pretrained(model_name)
input_ids = torch.tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]])
outputs = model(input_ids)
# Check if the outputs are of the right size - here
# we always output 3 values - logits, loss, and value states
self.assertEqual(len(outputs), EXPECTED_OUTPUT_SIZE)
def test_dropout_config(self):
r"""
Test if we instantiate a model by adding `summary_drop_prob` to the config
it will be added to the v_head
"""
for model_name in self.all_model_names:
pretrained_model = self.transformers_model_class.from_pretrained(model_name)
pretrained_model.config.summary_dropout_prob = 0.5
model = self.trl_model_class.from_pretrained(pretrained_model)
# Check if v head of the model has the same dropout as the config
self.assertEqual(model.v_head.dropout.p, pretrained_model.config.summary_dropout_prob)
def test_dropout_kwargs(self):
r"""
Test if we instantiate a model by adding `summary_drop_prob` to the config
it will be added to the v_head
"""
for model_name in self.all_model_names:
v_head_kwargs = {"summary_dropout_prob": 0.5}
model = self.trl_model_class.from_pretrained(model_name, **v_head_kwargs)
# Check if v head of the model has the same dropout as the config
self.assertEqual(model.v_head.dropout.p, 0.5)
model = self.trl_model_class.from_pretrained(model_name, summary_dropout_prob=0.5)
# Check if v head of the model has the same dropout as the config
self.assertEqual(model.v_head.dropout.p, 0.5)
def test_generate(self):
r"""
Test if `generate` works for every model
"""
for model_name in self.all_model_names:
model = self.trl_model_class.from_pretrained(model_name)
input_ids = torch.tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]])
# Just check if the generation works
_ = model.generate(input_ids)
def test_raise_error_not_causallm(self):
# Test with a model without a LM head
model_id = "trl-internal-testing/tiny-random-GPT2Model"
# This should raise a ValueError
with self.assertRaises(ValueError):
pretrained_model = AutoModelForCausalLM.from_pretrained(model_id)
_ = AutoModelForCausalLMWithValueHead.from_pretrained(pretrained_model.transformer)
def test_transformers_bf16_kwargs(self):
r"""
Test if the transformers kwargs are correctly passed
Here we check that loading a model in half precision works as expected, i.e. the weights of
the `pretrained_model` attribute is loaded in half precision and you can run a dummy
forward pass without any issue.
"""
for model_name in self.all_model_names:
trl_model = self.trl_model_class.from_pretrained(model_name, torch_dtype=torch.bfloat16)
lm_head_namings = self.trl_model_class.lm_head_namings
self.assertTrue(
any(hasattr(trl_model.pretrained_model, lm_head_naming) for lm_head_naming in lm_head_namings)
)
for lm_head_naming in lm_head_namings:
if hasattr(trl_model.pretrained_model, lm_head_naming):
self.assertTrue(getattr(trl_model.pretrained_model, lm_head_naming).weight.dtype == torch.bfloat16)
dummy_input = torch.LongTensor([[0, 1, 0, 1]])
# check dummy forward pass works in half precision
_ = trl_model(dummy_input)
@unittest.skip("This test needs to be run manually due to HF token issue.")
def test_push_to_hub(self):
for model_name in self.all_model_names:
model = AutoModelForCausalLMWithValueHead.from_pretrained(model_name)
if "sharded" in model_name:
model.push_to_hub(model_name + "-ppo", use_auth_token=True, max_shard_size="1MB")
else:
model.push_to_hub(model_name + "-ppo", use_auth_token=True)
model_from_pretrained = AutoModelForCausalLMWithValueHead.from_pretrained(model_name + "-ppo")
# check all keys
self.assertEqual(model.state_dict().keys(), model_from_pretrained.state_dict().keys())
for name, param in model.state_dict().items():
self.assertTrue(
torch.allclose(param, model_from_pretrained.state_dict()[name]),
f"Parameter {name} is not the same after push_to_hub and from_pretrained",
)
class Seq2SeqValueHeadModelTester(VHeadModelTester, unittest.TestCase):
"""
Testing suite for v-head models.
"""
all_model_names = ALL_SEQ2SEQ_MODELS
trl_model_class = AutoModelForSeq2SeqLMWithValueHead
transformers_model_class = AutoModelForSeq2SeqLM
def tearDown(self):
# free memory
gc.collect()
def test_inference(self):
r"""
Test if the model can be used for inference and outputs 3 values
- logits, loss, and value states
"""
EXPECTED_OUTPUT_SIZE = 3
for model_name in self.all_model_names:
model = self.trl_model_class.from_pretrained(model_name)
input_ids = torch.tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]])
decoder_input_ids = torch.tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]])
outputs = model(input_ids, decoder_input_ids=decoder_input_ids)
# Check if the outputs are of the right size - here
# we always output 3 values - logits, loss, and value states
self.assertEqual(len(outputs), EXPECTED_OUTPUT_SIZE)
def test_dropout_config(self):
r"""
Test if we instantiate a model by adding `summary_drop_prob` to the config
it will be added to the v_head
"""
for model_name in self.all_model_names:
pretrained_model = self.transformers_model_class.from_pretrained(model_name)
pretrained_model.config.summary_dropout_prob = 0.5
model = self.trl_model_class.from_pretrained(pretrained_model)
# Check if v head of the model has the same dropout as the config
self.assertEqual(model.v_head.dropout.p, pretrained_model.config.summary_dropout_prob)
def test_dropout_kwargs(self):
r"""
Test if we instantiate a model by adding `summary_drop_prob` to the config
it will be added to the v_head
"""
for model_name in self.all_model_names:
v_head_kwargs = {"summary_dropout_prob": 0.5}
model = self.trl_model_class.from_pretrained(model_name, **v_head_kwargs)
# Check if v head of the model has the same dropout as the config
self.assertEqual(model.v_head.dropout.p, 0.5)
model = self.trl_model_class.from_pretrained(model_name, summary_dropout_prob=0.5)
# Check if v head of the model has the same dropout as the config
self.assertEqual(model.v_head.dropout.p, 0.5)
def test_generate(self):
r"""
Test if `generate` works for every model
"""
for model_name in self.all_model_names:
model = self.trl_model_class.from_pretrained(model_name)
input_ids = torch.tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]])
decoder_input_ids = torch.tensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]])
# Just check if the generation works
_ = model.generate(input_ids, decoder_input_ids=decoder_input_ids)
def test_raise_error_not_causallm(self):
# Test with a model without a LM head
model_id = "trl-internal-testing/tiny-random-T5Model"
# This should raise a ValueError
with self.assertRaises(ValueError):
pretrained_model = AutoModel.from_pretrained(model_id)
_ = self.trl_model_class.from_pretrained(pretrained_model)
@unittest.skip("This test needs to be run manually due to HF token issue.")
def test_push_to_hub(self):
for model_name in self.all_model_names:
model = self.trl_model_class.from_pretrained(model_name)
if "sharded" in model_name:
model.push_to_hub(model_name + "-ppo", use_auth_token=True, max_shard_size="1MB")
else:
model.push_to_hub(model_name + "-ppo", use_auth_token=True)
model_from_pretrained = self.trl_model_class.from_pretrained(model_name + "-ppo")
# check all keys
self.assertEqual(model.state_dict().keys(), model_from_pretrained.state_dict().keys())
for name, param in model.state_dict().items():
self.assertTrue(
torch.allclose(param, model_from_pretrained.state_dict()[name]),
f"Parameter {name} is not the same after push_to_hub and from_pretrained",
)
def test_transformers_bf16_kwargs(self):
r"""
Test if the transformers kwargs are correctly passed
Here we check that loading a model in half precision works as expected, i.e. the weights of
the `pretrained_model` attribute is loaded in half precision and you can run a dummy
forward pass without any issue.
"""
for model_name in self.all_model_names:
trl_model = self.trl_model_class.from_pretrained(model_name, torch_dtype=torch.bfloat16)
lm_head_namings = self.trl_model_class.lm_head_namings
if model_name == "trl-internal-testing/tiny-random-FSMTForConditionalGeneration":
# skip the test for FSMT as it does not support mixed-prec
continue
self.assertTrue(
any(hasattr(trl_model.pretrained_model, lm_head_naming) for lm_head_naming in lm_head_namings)
)
for lm_head_naming in lm_head_namings:
if hasattr(trl_model.pretrained_model, lm_head_naming):
self.assertTrue(getattr(trl_model.pretrained_model, lm_head_naming).weight.dtype == torch.bfloat16)
dummy_input = torch.LongTensor([[0, 1, 0, 1]])
# check dummy forward pass works in half precision
_ = trl_model(input_ids=dummy_input, decoder_input_ids=dummy_input)
class ReferenceModelTest(unittest.TestCase):
def setUp(self):
self.model = AutoModelForCausalLMWithValueHead.from_pretrained(
"trl-internal-testing/tiny-random-GPT2LMHeadModel"
)
self.test_input = torch.tensor([[0, 1, 2, 3]])
self.optimizer = torch.optim.AdamW(self.model.parameters(), lr=1)
self.layer_format = "pretrained_model.transformer.h.{layer}.attn.c_attn.weight"
def test_independent_reference(self):
layer_0 = self.layer_format.format(layer=0)
layer_5 = self.layer_format.format(layer=4)
ref_model = create_reference_model(self.model)
first_layer_before = self.model.get_parameter(layer_0).data.clone()
last_layer_before = self.model.get_parameter(layer_5).data.clone()
first_ref_layer_before = ref_model.get_parameter(layer_0).data.clone()
last_ref_layer_before = ref_model.get_parameter(layer_5).data.clone()
output = self.model(input_ids=self.test_input, labels=self.test_input)
output[1].backward()
self.optimizer.step()
first_layer_after = self.model.get_parameter(layer_0).data.clone()
last_layer_after = self.model.get_parameter(layer_5).data.clone()
first_ref_layer_after = ref_model.get_parameter(layer_0).data.clone()
last_ref_layer_after = ref_model.get_parameter(layer_5).data.clone()
# before optimization ref and model are identical
self.assertTrue((first_layer_before == first_ref_layer_before).all())
self.assertTrue((last_layer_before == last_ref_layer_before).all())
# ref model stays identical after optimization
self.assertTrue((first_ref_layer_before == first_ref_layer_after).all())
self.assertTrue((last_ref_layer_before == last_ref_layer_after).all())
# optimized model changes
self.assertTrue(not (first_layer_before == first_layer_after).all())
self.assertTrue(not (last_layer_before == last_layer_after).all())
def test_shared_layers(self):
layer_0 = self.layer_format.format(layer=0)
layer_1 = self.layer_format.format(layer=1)
ref_model = create_reference_model(self.model, num_shared_layers=1)
first_layer_before = self.model.get_parameter(layer_0).data.clone()
second_layer_before = self.model.get_parameter(layer_1).data.clone()
first_ref_layer_before = ref_model.get_parameter(layer_0).data.clone()
second_ref_layer_before = ref_model.get_parameter(layer_1).data.clone()
output = self.model(input_ids=self.test_input, labels=self.test_input)
output[1].backward()
self.optimizer.step()
first_layer_after = self.model.get_parameter(layer_0).data.clone()
second_layer_after = self.model.get_parameter(layer_1).data.clone()
first_ref_layer_after = ref_model.get_parameter(layer_0).data.clone()
second_ref_layer_after = ref_model.get_parameter(layer_1).data.clone()
# before optimization ref and model are identical
self.assertTrue((first_layer_before == first_ref_layer_before).all())
self.assertTrue((second_layer_before == second_ref_layer_before).all())
# ref model stays identical after optimization
self.assertTrue((first_ref_layer_before == first_ref_layer_after).all())
self.assertTrue((second_ref_layer_before == second_ref_layer_after).all())
# first layer of optimized model stays the same
self.assertTrue((first_layer_before == first_layer_after).all())
# other layers in optimized model change
self.assertTrue(not (second_layer_before == second_layer_after).all())
| 0 |
hf_public_repos/trl | hf_public_repos/trl/tests/test_reward_trainer.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tempfile
import unittest
import torch
from datasets import Dataset
from transformers import AutoModelForSequenceClassification, AutoTokenizer, EvalPrediction
from trl import RewardConfig, RewardTrainer
from trl.trainer import compute_accuracy
from .testing_utils import require_peft
class RewardTrainerTester(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.model_id = "trl-internal-testing/dummy-GPT2-correct-vocab"
cls.model = AutoModelForSequenceClassification.from_pretrained(cls.model_id)
cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_id)
cls.tokenizer.pad_token = cls.tokenizer.eos_token
def test_accuracy_metrics(self):
dummy_eval_predictions = EvalPrediction(torch.FloatTensor([[0.1, 0.9], [0.9, 0.1]]), torch.LongTensor([0, 0]))
accuracy = compute_accuracy(dummy_eval_predictions)
self.assertEqual(accuracy["accuracy"], 0.5)
def test_reward_trainer(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = RewardConfig(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=3,
remove_unused_columns=False,
gradient_accumulation_steps=4,
learning_rate=9e-1,
evaluation_strategy="steps",
)
# fmt: off
dummy_dataset_dict = {
"input_ids_chosen": [
torch.LongTensor([0, 1, 2,]),
torch.LongTensor([1, 2]),
torch.LongTensor([0, 1, 2,]),
torch.LongTensor([1, 2]),
],
"attention_mask_chosen": [
torch.LongTensor([1, 1, 1]),
torch.LongTensor([1, 0]),
torch.LongTensor([1, 1, 1]),
torch.LongTensor([1, 0]),
],
"input_ids_rejected": [
torch.LongTensor([0, 2,]),
torch.LongTensor([1, 2, 0]),
torch.LongTensor([0, 2,]),
torch.LongTensor([1, 2, 0]),
],
"attention_mask_rejected": [
torch.LongTensor([1, 1]),
torch.LongTensor([1, 1, 0]),
torch.LongTensor([1, 1]),
torch.LongTensor([1, 1, 1]),
],
}
# fmt: on
dummy_dataset = Dataset.from_dict(dummy_dataset_dict)
trainer = RewardTrainer(
model=self.model,
args=training_args,
tokenizer=self.tokenizer,
train_dataset=dummy_dataset,
eval_dataset=dummy_dataset,
)
previous_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}
trainer.train()
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
# check the params have changed
for n, param in previous_trainable_params.items():
new_param = trainer.model.get_parameter(n)
# check the params have changed - ignore 0 biases
if param.sum() != 0:
self.assertFalse(torch.equal(param, new_param))
preds = trainer.predict(dummy_dataset)
self.assertEqual(preds.predictions.shape, (4, 2))
@require_peft
def test_reward_trainer_peft(self):
import peft
from peft import LoraConfig, TaskType
peft_version = peft.__version__
peft_config = LoraConfig(
task_type=TaskType.SEQ_CLS,
inference_mode=False,
r=8,
lora_alpha=32,
lora_dropout=0.1,
)
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = RewardConfig(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=6,
remove_unused_columns=False,
gradient_accumulation_steps=2,
learning_rate=9e-1,
evaluation_strategy="steps",
)
# fmt: off
dummy_dataset_dict = {
"input_ids_chosen": [
torch.LongTensor([0, 1, 2,]),
torch.LongTensor([1, 2]),
torch.LongTensor([0, 1, 2,]),
torch.LongTensor([1, 2]),
],
"attention_mask_chosen": [
torch.LongTensor([1, 1, 1]),
torch.LongTensor([1, 0]),
torch.LongTensor([1, 1, 1]),
torch.LongTensor([1, 0]),
],
"input_ids_rejected": [
torch.LongTensor([0, 2,]),
torch.LongTensor([1, 2, 0]),
torch.LongTensor([0, 2,]),
torch.LongTensor([1, 2, 0]),
],
"attention_mask_rejected": [
torch.LongTensor([1, 1]),
torch.LongTensor([1, 1, 0]),
torch.LongTensor([1, 1]),
torch.LongTensor([1, 1, 1]),
],
}
# fmt: on
dummy_dataset = Dataset.from_dict(dummy_dataset_dict)
trainer = RewardTrainer(
model=self.model,
args=training_args,
tokenizer=self.tokenizer,
train_dataset=dummy_dataset,
eval_dataset=dummy_dataset,
peft_config=peft_config,
)
previous_trainable_params = {}
previous_non_trainable_params = {}
# due to a change in the way the modules to save are dealt in PEFT.
trainable_params_name = ["lora", "score"] if peft_version < "0.3.0" else ["lora", "modules_to_save"]
# check gradients are not None
for n, param in trainer.model.named_parameters():
if any([t in n for t in trainable_params_name]):
previous_trainable_params[n] = param.clone()
else:
previous_non_trainable_params[n] = param.clone()
trainer.train()
self.assertIsNotNone(trainer.state.log_history[-1]["train_loss"])
# check the params have changed
for n, param in previous_trainable_params.items():
new_param = trainer.model.get_parameter(n)
self.assertFalse(torch.allclose(param, new_param, atol=1e-12, rtol=1e-12))
# check the non trainable params have not changed
for n, param in previous_non_trainable_params.items():
new_param = trainer.model.get_parameter(n)
self.assertTrue(torch.allclose(param, new_param, atol=1e-12, rtol=1e-12))
preds = trainer.predict(dummy_dataset)
self.assertEqual(preds.predictions.shape, (4, 2))
def test_reward_trainer_assert_value_error(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = RewardConfig(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=1,
remove_unused_columns=False,
)
dummy_dataset_dict = {
# fmt: off
"input_ids_b": [
torch.LongTensor([0, 1, 2,]),
torch.LongTensor([1, 2]),
torch.LongTensor([0, 1, 2,]),
torch.LongTensor([1, 2]),
],
"attention_mask_c": [
torch.LongTensor([1, 1, 1]),
torch.LongTensor([1, 0]),
torch.LongTensor([1, 1, 1]),
torch.LongTensor([1, 0]),
],
"input_ids_f": [
torch.LongTensor([0, 2,]),
torch.LongTensor([1, 2, 0]),
torch.LongTensor([0, 2,]),
torch.LongTensor([1, 2, 0]),
],
"attention_mask_g": [
torch.LongTensor([1, 1]),
torch.LongTensor([1, 1, 0]),
torch.LongTensor([1, 1]),
torch.LongTensor([1, 1, 1]),
],
# fmt: on
}
dummy_dataset = Dataset.from_dict(dummy_dataset_dict)
trainer = RewardTrainer(
model=self.model,
args=training_args,
tokenizer=self.tokenizer,
train_dataset=dummy_dataset,
)
with self.assertRaises(ValueError):
trainer.train()
training_args = RewardConfig(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=1,
remove_unused_columns=True,
)
with self.assertWarns(UserWarning):
trainer = RewardTrainer(
model=self.model,
args=training_args,
tokenizer=self.tokenizer,
train_dataset=dummy_dataset,
)
def test_reward_trainer_margin(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = RewardConfig(
output_dir=tmp_dir,
per_device_train_batch_size=2,
max_steps=3,
remove_unused_columns=False,
gradient_accumulation_steps=4,
learning_rate=9e-1,
evaluation_strategy="steps",
)
# fmt: off
dummy_dataset_dict = {
"input_ids_chosen": [
torch.LongTensor([0, 1, 2,]),
],
"attention_mask_chosen": [
torch.LongTensor([1, 1, 1]),
],
"input_ids_rejected": [
torch.LongTensor([0, 2,]),
],
"attention_mask_rejected": [
torch.LongTensor([1, 1]),
],
"margin": [
torch.FloatTensor([1.0]),
]
}
# fmt: on
dummy_dataset = Dataset.from_dict(dummy_dataset_dict)
trainer = RewardTrainer(
model=self.model,
args=training_args,
tokenizer=self.tokenizer,
train_dataset=dummy_dataset,
eval_dataset=dummy_dataset,
)
batch = [dummy_dataset[0]]
batch = trainer.data_collator(batch)
loss, outputs = trainer.compute_loss(trainer.model, batch, return_outputs=True)
self.assertAlmostEqual(
loss,
-torch.nn.functional.logsigmoid(
outputs["rewards_chosen"] - outputs["rewards_rejected"] - batch["margin"]
).mean(),
)
| 0 |
hf_public_repos/trl | hf_public_repos/trl/tests/test_ppo_trainer.py | # Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import fnmatch
import gc
import re
import tempfile
import unittest
import pytest
import torch
from huggingface_hub import HfApi, HfFolder, delete_repo
from parameterized import parameterized
from pytest import mark
from requests.exceptions import HTTPError
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead, AutoModelForSeq2SeqLMWithValueHead, PPOConfig, PPOTrainer, set_seed
from trl.core import respond_to_batch
from .testing_constants import CI_HUB_ENDPOINT, CI_HUB_USER, CI_HUB_USER_TOKEN
from .testing_utils import require_peft, require_torch_multi_gpu
EXPECTED_STATS = [
"objective/kl",
"objective/kl_dist",
"objective/logprobs",
"objective/ref_logprobs",
"objective/kl_coef",
"objective/entropy",
"ppo/mean_non_score_reward",
"ppo/loss/policy",
"ppo/loss/value",
"ppo/loss/total",
"ppo/policy/entropy",
"ppo/policy/approxkl",
"ppo/policy/policykl",
"ppo/policy/clipfrac",
"ppo/policy/advantages",
"ppo/policy/advantages_mean",
"ppo/policy/ratio",
"ppo/returns/mean",
"ppo/returns/var",
"ppo/val/vpred",
"ppo/val/error",
"ppo/val/clipfrac",
"ppo/val/mean",
"ppo/val/var",
"ppo/val/var_explained",
"time/ppo/forward_pass",
"time/ppo/compute_rewards",
"time/ppo/optimize_step",
"time/ppo/calc_stats",
"time/ppo/total",
"ppo/learning_rate",
]
class DummyDataset(torch.utils.data.Dataset):
def __init__(self, query_data, response_data):
self.query_data = query_data
self.response_data = response_data
def __len__(self):
return len(self.query_data)
def __getitem__(self, idx):
return self.query_data[idx], self.response_data[idx]
def apply_mask(values, mask):
unmasked_values = []
for v, m in zip(values, mask):
if m == 1:
unmasked_values.append(v)
return torch.Tensor(unmasked_values)
def abs_diff_masked_tensors(tensor_1, tensor_2, mask_1, mask_2):
diffs = []
for l1, l2, m1, m2 in zip(tensor_1, tensor_2, mask_1, mask_2):
diff = apply_mask(l1, m1) - apply_mask(l2, m2)
diffs.append(diff.sum())
return abs(sum(diffs))
class PPOTrainerTester(unittest.TestCase):
"""
A wrapper class for testing PPOTrainer
"""
@classmethod
def setUpClass(cls):
set_seed(42)
cls._token = CI_HUB_USER_TOKEN
cls._api = HfApi(endpoint=CI_HUB_ENDPOINT)
HfFolder.save_token(CI_HUB_USER_TOKEN)
# model_id
cls.model_id = "trl-internal-testing/dummy-GPT2-correct-vocab"
# get models and tokenizer
cls.gpt2_model = AutoModelForCausalLMWithValueHead.from_pretrained(cls.model_id)
cls.gpt2_model_ref = AutoModelForCausalLMWithValueHead.from_pretrained(cls.model_id)
cls.gpt2_tokenizer = AutoTokenizer.from_pretrained(cls.model_id)
cls.gpt2_tokenizer.pad_token = cls.gpt2_tokenizer.eos_token
# get bloom as right padding examples:
model_id = "trl-internal-testing/tiny-BloomForCausalLM-correct-vocab"
cls.bloom_model = AutoModelForCausalLMWithValueHead.from_pretrained(model_id)
cls.bloom_tokenizer = AutoTokenizer.from_pretrained(model_id)
model_id = "trl-internal-testing/tiny-T5ForConditionalGeneration-correct-vocab"
cls.t5_model = AutoModelForSeq2SeqLMWithValueHead.from_pretrained(model_id)
cls.t5_tokenizer = AutoTokenizer.from_pretrained(model_id)
# initialize trainer
cls.ppo_config = PPOConfig(batch_size=2, mini_batch_size=1, log_with=None)
@classmethod
def tearDownClass(cls):
for model in [f"{CI_HUB_USER}/test-ppo-trainer"]:
try:
delete_repo(token=cls._token, repo_id=model)
except HTTPError:
pass
def setUp(self):
# initialize trainer
self.ppo_config = PPOConfig(batch_size=2, mini_batch_size=1, log_with=None)
self.gpt2_model.train()
return super().setUp()
def tearDown(self):
# free memory
gc.collect()
def _init_dummy_dataset(self):
# encode a query
query_txt = "This morning I went to the "
query_tensor = self.gpt2_tokenizer.encode(query_txt, return_tensors="pt")
assert query_tensor.shape == (1, 7)
# get model response
response_tensor = respond_to_batch(self.gpt2_model, query_tensor)
assert response_tensor.shape == (1, 20)
# create a dummy dataset
min_length = min(len(query_tensor[0]), len(response_tensor[0]))
dummy_dataset = DummyDataset(
[query_tensor[:, :min_length].squeeze(0) for _ in range(2)],
[response_tensor[:, :min_length].squeeze(0) for _ in range(2)],
)
return dummy_dataset
def test_drop_last_dataloader(self):
self.ppo_config = PPOConfig(batch_size=3, mini_batch_size=1, log_with=None)
dummy_dataset = self._init_dummy_dataset()
ppo_trainer = PPOTrainer(
config=self.ppo_config,
model=self.gpt2_model,
ref_model=self.gpt2_model_ref,
tokenizer=self.gpt2_tokenizer,
dataset=dummy_dataset,
)
dummy_dataloader = ppo_trainer.dataloader
self.assertEqual(len(dummy_dataloader), 0)
def test_ppo_step(self):
# initialize dataset
dummy_dataset = self._init_dummy_dataset()
ppo_trainer = PPOTrainer(
config=self.ppo_config,
model=self.gpt2_model,
ref_model=self.gpt2_model_ref,
tokenizer=self.gpt2_tokenizer,
dataset=dummy_dataset,
)
dummy_dataloader = ppo_trainer.dataloader
# train model with ppo
for query_tensor, response_tensor in dummy_dataloader:
# define a reward for response
# (this could be any reward such as human feedback or output from another model)
reward = [torch.tensor(1.0), torch.tensor(0.0)]
# train model
train_stats = ppo_trainer.step([q for q in query_tensor], [r for r in response_tensor], reward)
break
for param in ppo_trainer.model.parameters():
assert param.grad is not None
for stat in EXPECTED_STATS:
assert stat in train_stats.keys()
def test_ppo_step_with_masks(self):
# initialize dataset
dummy_dataset = self._init_dummy_dataset()
ppo_trainer = PPOTrainer(
config=self.ppo_config,
model=self.gpt2_model,
ref_model=self.gpt2_model_ref,
tokenizer=self.gpt2_tokenizer,
dataset=dummy_dataset,
)
dummy_dataloader = ppo_trainer.dataloader
# train model with ppo
for query_tensor, response_tensor in dummy_dataloader:
# define a reward for response
# (this could be any reward such as human feedback or output from another model)
reward = [torch.tensor(1.0), torch.tensor(0.0)]
response_mask = [torch.ones_like(r) for r in response_tensor]
# train model
train_stats = ppo_trainer.step(
[q for q in query_tensor], [r for r in response_tensor], reward, response_mask
)
break
for param in ppo_trainer.model.parameters():
assert param.grad is not None
for stat in EXPECTED_STATS:
assert stat in train_stats.keys()
def test_ppo_step_with_no_ref_sgd(self):
# initialize dataset
dummy_dataset = self._init_dummy_dataset()
optimizer = torch.optim.SGD(self.gpt2_model.parameters(), lr=0.01)
ppo_trainer = PPOTrainer(
config=self.ppo_config,
model=self.gpt2_model,
ref_model=None,
optimizer=optimizer,
tokenizer=self.gpt2_tokenizer,
dataset=dummy_dataset,
)
dummy_dataloader = ppo_trainer.dataloader
self.assertTrue(isinstance(ppo_trainer.optimizer.optimizer, torch.optim.SGD))
# train model with ppo
for query_tensor, response_tensor in dummy_dataloader:
# define a reward for response
# (this could be any reward such as human feedback or output from another model)
reward = [torch.tensor(1.0), torch.tensor(0.0)]
# train model
train_stats = ppo_trainer.step([q for q in query_tensor], [r for r in response_tensor], reward)
break
for name, param in ppo_trainer.model.named_parameters():
self.assertTrue(param.grad is not None, f"Parameter {name} has no gradient")
# ref model should not be trained
for name, param in ppo_trainer.ref_model.named_parameters():
self.assertTrue(param.grad is None, f"Parameter {name} has a gradient")
# Finally check stats
for stat in EXPECTED_STATS:
assert stat in train_stats.keys()
def test_ppo_step_with_no_ref_sgd_lr_scheduler(self):
# initialize dataset
dummy_dataset = self._init_dummy_dataset()
optimizer = torch.optim.SGD(self.gpt2_model.parameters(), lr=0.01)
lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)
ppo_trainer = PPOTrainer(
config=self.ppo_config,
model=self.gpt2_model,
ref_model=None,
optimizer=optimizer,
tokenizer=self.gpt2_tokenizer,
dataset=dummy_dataset,
lr_scheduler=lr_scheduler,
)
dummy_dataloader = ppo_trainer.dataloader
self.assertTrue(isinstance(ppo_trainer.optimizer.optimizer, torch.optim.SGD))
self.assertTrue(isinstance(ppo_trainer.lr_scheduler.scheduler, torch.optim.lr_scheduler.ExponentialLR))
# train model with ppo
for query_tensor, response_tensor in dummy_dataloader:
# define a reward for response
# (this could be any reward such as human feedback or output from another model)
reward = [torch.tensor(1.0), torch.tensor(0.0)]
# train model
_ = ppo_trainer.step([q for q in query_tensor], [r for r in response_tensor], reward)
train_stats = ppo_trainer.step([q for q in query_tensor], [r for r in response_tensor], reward)
break
for name, param in ppo_trainer.model.named_parameters():
self.assertTrue(param.grad is not None, f"Parameter {name} has no gradient")
# ref model should not be trained
for name, param in ppo_trainer.ref_model.named_parameters():
self.assertTrue(param.grad is None, f"Parameter {name} has a gradient")
# Finally check stats
for stat in EXPECTED_STATS:
assert stat in train_stats.keys()
# assert that the LR has increased for exponential decay
self.assertTrue(train_stats["ppo/learning_rate"] > self.ppo_config.learning_rate)
def test_ppo_step_with_no_ref(self):
# initialize dataset
dummy_dataset = self._init_dummy_dataset()
self.gpt2_model = AutoModelForCausalLMWithValueHead.from_pretrained(self.model_id)
ppo_trainer = PPOTrainer(
config=self.ppo_config,
model=self.gpt2_model,
ref_model=None,
tokenizer=self.gpt2_tokenizer,
dataset=dummy_dataset,
)
dummy_dataloader = ppo_trainer.dataloader
# train model with ppo
for query_tensor, response_tensor in dummy_dataloader:
# define a reward for response
# (this could be any reward such as human feedback or output from another model)
reward = [torch.tensor(1.0), torch.tensor(0.0)]
# train model
train_stats = ppo_trainer.step([q for q in query_tensor], [r for r in response_tensor], reward)
break
for name, param in ppo_trainer.model.named_parameters():
self.assertTrue(param.grad is not None, f"Parameter {name} has no gradient")
# ref model should not be trained
for name, param in ppo_trainer.ref_model.named_parameters():
self.assertTrue(param.grad is None, f"Parameter {name} has a gradient")
# initialize a new gpt2 model:
model = AutoModelForCausalLMWithValueHead.from_pretrained(self.model_id)
for name, param in ppo_trainer.ref_model.named_parameters():
if "v_head" not in name:
name = name.replace("pretrained_model.", "")
self.assertTrue(
torch.allclose(param.cpu(), model.state_dict()[name].cpu()),
f"Parameter {name} has changed from the original model",
)
# Finally check stats
for stat in EXPECTED_STATS:
assert stat in train_stats.keys()
def test_ppo_step_with_no_ref_custom_layers(self):
"""
Test PPO step with no reference model and custom layers
For shared layers configuration, all the layers after the `num_shared_layers` are considered as custom layers
therefore the gradients should be computed for these layers only.
"""
# initialize dataset
dummy_dataset = self._init_dummy_dataset()
self.gpt2_model = AutoModelForCausalLMWithValueHead.from_pretrained(self.model_id)
num_shared_layers = 1
ppo_trainer = PPOTrainer(
config=self.ppo_config,
model=self.gpt2_model,
ref_model=None,
tokenizer=self.gpt2_tokenizer,
dataset=dummy_dataset,
num_shared_layers=num_shared_layers,
)
dummy_dataloader = ppo_trainer.dataloader
# train model with ppo
for query_tensor, response_tensor in dummy_dataloader:
# define a reward for response
# (this could be any reward such as human feedback or output from another model)
reward = [torch.tensor(1.0), torch.tensor(0.0)]
# train model
train_stats = ppo_trainer.step([q for q in query_tensor], [r for r in response_tensor], reward)
break
pattern = r".*transformer\.h\.(\d+)\..*"
final_layers = ["ln_f", "v_head", "lm_head"]
for name, param in ppo_trainer.model.named_parameters():
if re.match(pattern, name):
layer_number = int(re.match(pattern, name).groups(0)[0])
if layer_number < num_shared_layers:
self.assertTrue(param.grad is None, f"Parameter {name} has a gradient")
else:
self.assertTrue(param.grad is not None, f"Parameter {name} has no gradient")
elif any([layer in name for layer in final_layers]):
self.assertTrue(param.grad is not None, f"Parameter {name} has no gradient")
# ref model should not be trained
for name, param in ppo_trainer.ref_model.named_parameters():
self.assertTrue(param.grad is None, f"Parameter {name} has a gradient")
for stat in EXPECTED_STATS:
assert stat in train_stats.keys()
def test_ppo_step_with_ref_and_custom_layers_warning(self):
"""
Test PPO step with a reference model and custom layers
The trainer should raise a warning if the argument `num_shared_layers` is set
together with a reference model.
"""
# initialize dataset
dummy_dataset = self._init_dummy_dataset()
num_shared_layers = 6
with self.assertWarns(UserWarning):
_ = PPOTrainer(
config=self.ppo_config,
model=self.gpt2_model,
ref_model=self.gpt2_model_ref,
tokenizer=self.gpt2_tokenizer,
dataset=dummy_dataset,
num_shared_layers=num_shared_layers,
)
def test_ppo_step_rewards_shape(self):
"""
Test if the rewards shape is correct by asserting that if a wrong reward shape is passed, we get
a value error.
"""
# initialize dataset
dummy_dataset = self._init_dummy_dataset()
ppo_trainer = PPOTrainer(
config=self.ppo_config,
model=self.gpt2_model,
ref_model=None,
tokenizer=self.gpt2_tokenizer,
dataset=dummy_dataset,
)
dummy_dataloader = ppo_trainer.dataloader
# train model with ppo
for query_tensor, response_tensor in dummy_dataloader:
# define a reward for response
# (this could be any reward such as human feedback or output from another model)
reward = [torch.tensor([[1.0]]), torch.tensor([[0.0]])]
# train model - this should raise an error
with self.assertRaises(ValueError):
_ = ppo_trainer.step([q for q in query_tensor], [r for r in response_tensor], reward)
reward = [torch.tensor([1.0]), torch.tensor([0.0])]
# train model - this should work
_ = ppo_trainer.step([q for q in query_tensor], [r for r in response_tensor], reward)
break
# check if the gradients are computed for the model
for name, param in ppo_trainer.model.named_parameters():
self.assertTrue(param.grad is not None, f"Parameter {name} has no gradient")
# ref model should not be trained
for name, param in ppo_trainer.ref_model.named_parameters():
self.assertTrue(param.grad is None, f"Parameter {name} has a gradient")
def test_ppo_step_input_shape(self):
"""
Test if the shape of the expected inputs are correct
"""
# initialize dataset
dummy_dataset = self._init_dummy_dataset()
ppo_trainer = PPOTrainer(
config=self.ppo_config,
model=self.gpt2_model,
ref_model=None,
tokenizer=self.gpt2_tokenizer,
dataset=dummy_dataset,
)
dummy_dataloader = ppo_trainer.dataloader
# train model with ppo
for query_tensor, response_tensor in dummy_dataloader:
# define a reward for response
# (this could be any reward such as human feedback or output from another model)
reward = [torch.tensor([1.0]), torch.tensor([0.0])]
# train model - this should raise an error
bs = ppo_trainer.config.batch_size
queries, responses, _, _ = ppo_trainer._step_safety_checker(
bs, [q for q in query_tensor], [r for r in response_tensor], reward
)
self.assertTrue(isinstance(queries, list), f"queries should be a list, got {type(queries)}")
self.assertTrue(isinstance(responses, list), f"responses should be a list, got {type(responses)}")
# check the shapes
for i in range(bs):
self.assertEqual(queries[i].shape, torch.Size([7]))
self.assertEqual(responses[i].size(), torch.Size([7]))
break
def test_ppo_step_no_dataset(self):
"""
Test if the training loop works fine without passing a dataset
"""
query_txt = "This morning I went to the "
query_tensor = self.gpt2_tokenizer.encode(query_txt, return_tensors="pt")
self.ppo_config.batch_size = 1
response_tensor = respond_to_batch(self.gpt2_model, query_tensor)
# Check that this warns the user about batch size
with self.assertWarns(UserWarning):
ppo_trainer = PPOTrainer(
config=self.ppo_config,
model=self.gpt2_model,
ref_model=self.gpt2_model_ref,
tokenizer=self.gpt2_tokenizer,
)
# train model with ppo
reward = [torch.tensor([1.0])]
# train model - this should work fine
train_stats = ppo_trainer.step([query_tensor[0]], [response_tensor[0]], reward)
# check gradients
for name, param in ppo_trainer.model.named_parameters():
self.assertTrue(param.grad is not None, f"Parameter {name} has no gradient")
# ref model should not be trained
for name, param in ppo_trainer.ref_model.named_parameters():
self.assertTrue(param.grad is None, f"Parameter {name} has a gradient")
# check train stats
for stat in EXPECTED_STATS:
self.assertTrue(stat in train_stats, f"Train stats should contain {stat}")
def test_loss_trainer(self):
"""
Test if the loss trainer works fine
"""
# initialize dataset
dummy_dataset = self._init_dummy_dataset()
self.gpt2_model.eval()
ppo_trainer = PPOTrainer(
config=self.ppo_config,
model=self.gpt2_model,
ref_model=None,
tokenizer=self.gpt2_tokenizer,
dataset=dummy_dataset,
)
dummy_queries = [torch.tensor([1, 2, 3, 4]), torch.tensor([1, 2, 3, 4, 5, 6, 7])]
dummy_responses = [torch.tensor([5, 6, 7, 8, 9]), torch.tensor([8, 9, 10, 11, 12, 13])]
dummy_scores = torch.Tensor([1, 2])
ppo_trainer.config.mini_batch_size = 1
ppo_trainer.config.batch_size = 1
model_inputs = ppo_trainer.prepare_model_inputs(dummy_queries, dummy_responses)
all_logprobs, _, values, mask = ppo_trainer.batched_forward_pass(
self.gpt2_model, dummy_queries, dummy_responses, model_inputs
)
# dummy values
ref_logprobs = all_logprobs + 1
logits = torch.exp(all_logprobs)
vpreds = values + 0.1
score, non_score = ppo_trainer.compute_rewards(dummy_scores, all_logprobs, ref_logprobs, mask)
values, advantages, returns = ppo_trainer.compute_advantages(values, score, mask)
# just make sure a dummy loss is computed
idx = 0
pg_loss, v_loss, _ = ppo_trainer.loss(
all_logprobs[idx].unsqueeze(0),
values[idx].unsqueeze(0),
logits[idx].unsqueeze(0),
vpreds[idx].unsqueeze(0),
ref_logprobs[idx].unsqueeze(0),
mask[idx].unsqueeze(0),
advantages[idx].unsqueeze(0),
returns[idx].unsqueeze(0),
)
self.assertAlmostEqual(pg_loss.item(), 2.2868, 4)
self.assertAlmostEqual(v_loss.item(), 0.09950, 4)
# check if we get same results with masked parts removed
pg_loss_unmasked, v_loss_unmasked, _ = ppo_trainer.loss(
apply_mask(all_logprobs[idx], mask[idx]).unsqueeze(0),
apply_mask(values[idx], mask[idx]).unsqueeze(0),
apply_mask(logits[idx], mask[idx]).unsqueeze(0),
apply_mask(vpreds[idx], mask[idx]).unsqueeze(0),
apply_mask(ref_logprobs[idx], mask[idx]).unsqueeze(0),
apply_mask(mask[idx], mask[idx]).unsqueeze(0),
apply_mask(advantages[idx], mask[idx]).unsqueeze(0),
apply_mask(returns[idx], mask[idx]).unsqueeze(0),
)
self.assertAlmostEqual(pg_loss_unmasked.item(), 2.2868, 4)
self.assertAlmostEqual(v_loss_unmasked.item(), 0.09950, 4)
@parameterized.expand(
[
["gpt2"],
["bloom"],
["t5"],
]
)
def test_batched_forward_pass(self, name):
"""
Test if the loss trainer works fine
"""
# initialize dataset
dummy_dataset = self._init_dummy_dataset()
dummy_queries = [torch.tensor([1, 2, 3, 4]), torch.tensor([1, 2, 3, 4, 5, 6, 7])]
dummy_responses = [torch.tensor([5, 6, 7, 8, 9]), torch.tensor([8, 9, 10, 11, 12, 13])]
if name == "gpt2":
model = self.gpt2_model
tokenizer = self.gpt2_tokenizer
elif name == "bloom":
model = self.bloom_model
tokenizer = self.bloom_tokenizer
elif name == "t5":
model = self.t5_model
tokenizer = self.t5_tokenizer
model.eval()
ppo_trainer = PPOTrainer(
config=self.ppo_config,
model=model,
ref_model=None,
tokenizer=tokenizer,
dataset=dummy_dataset,
)
# we test all combinations of fwd_bs and bs:
# if fwd_bs=bs=1: no padding is applied and only one forward pass
# if fwd_bs=1/bs=2: padding is applied and results computed in two fwd passes
# if fwd_bs=bs=2: padding is applied and results computed in one fwd pass
ppo_trainer.config.mini_batch_size = 1
ppo_trainer.config.batch_size = 1
model_inputs = ppo_trainer.prepare_model_inputs([dummy_queries[0]], [dummy_responses[0]])
logprobs_0, logits_0, values_0, mask_0 = ppo_trainer.batched_forward_pass(
model, [dummy_queries[0]], [dummy_responses[0]], model_inputs
)
ppo_trainer.config.batch_size = 2
model_inputs = ppo_trainer.prepare_model_inputs(dummy_queries, dummy_responses)
logprobs_1, logits_1, values_1, mask_1 = ppo_trainer.batched_forward_pass(
model, dummy_queries, dummy_responses, model_inputs
)
ppo_trainer.config.mini_batch_size = 2
model_inputs = ppo_trainer.prepare_model_inputs(dummy_queries, dummy_responses)
logprobs_2, logits_2, values_2, mask_2 = ppo_trainer.batched_forward_pass(
model, dummy_queries, dummy_responses, model_inputs
)
self.assertLessEqual(abs_diff_masked_tensors(logprobs_1, logprobs_2, mask_1, mask_2), 1e-4)
self.assertLessEqual(abs_diff_masked_tensors(values_1, values_2, mask_1, mask_2), 1e-4)
self.assertLessEqual(abs_diff_masked_tensors(logprobs_0, logprobs_2[:1], mask_0, mask_2[:1]), 1e-4)
self.assertLessEqual(abs_diff_masked_tensors(values_0, values_2[:1], mask_0, mask_2[:1]), 1e-4)
def test_ppo_trainer_max_grad_norm(self):
"""
Test if the `max_grad_norm` feature works as expected
"""
# initialize dataset
dummy_dataset = self._init_dummy_dataset()
self.ppo_config.max_grad_norm = 0.00001
ppo_trainer = PPOTrainer(
config=self.ppo_config,
model=self.gpt2_model,
ref_model=None,
tokenizer=self.gpt2_tokenizer,
dataset=dummy_dataset,
)
dummy_dataloader = ppo_trainer.dataloader
# train model with ppo
for query_tensor, response_tensor in dummy_dataloader:
# define a reward for response
# (this could be any reward such as human feedback or output from another model)
reward = [torch.tensor(1.0), torch.tensor(0.0)]
# train model
_ = ppo_trainer.step([q for q in query_tensor], [r for r in response_tensor], reward)
break
# check gradients
for name, param in ppo_trainer.model.named_parameters():
self.assertTrue(param.grad is not None, f"Parameter {name} has no gradient")
self.assertTrue(
torch.all(param.grad.abs() <= self.ppo_config.max_grad_norm),
f"Parameter {name} has a gradient larger than max_grad_norm",
)
def test_ppo_trainer_kl_penalty(self):
dummy_dataset = self._init_dummy_dataset()
log_probs = torch.Tensor([[0.5, 0.2, 0.1], [0.6, 0.2, 0.1]])
ref_log_probs = torch.Tensor([[0.4, 0.3, 0.0], [0.7, 0.1, 0.3]])
ppo_trainer = PPOTrainer(
config=self.ppo_config,
model=self.gpt2_model,
ref_model=None,
tokenizer=self.gpt2_tokenizer,
dataset=dummy_dataset,
)
expected_output = torch.Tensor([[0.1000, -0.1000, 0.1000], [-0.1000, 0.1000, -0.2000]])
self.assertTrue(torch.allclose(ppo_trainer._kl_penalty(log_probs, ref_log_probs), expected_output))
self.ppo_config.kl_penalty = "abs"
ppo_trainer = PPOTrainer(
config=self.ppo_config,
model=self.gpt2_model,
ref_model=None,
tokenizer=self.gpt2_tokenizer,
dataset=dummy_dataset,
)
expected_output = torch.Tensor([[0.1000, 0.1000, 0.1000], [0.1000, 0.1000, 0.2000]])
self.assertTrue(torch.allclose(ppo_trainer._kl_penalty(log_probs, ref_log_probs), expected_output))
self.ppo_config.kl_penalty = "mse"
ppo_trainer = PPOTrainer(
config=self.ppo_config,
model=self.gpt2_model,
ref_model=None,
tokenizer=self.gpt2_tokenizer,
dataset=dummy_dataset,
)
expected_output = torch.Tensor([[0.0050, 0.0050, 0.0050], [0.0050, 0.0050, 0.0200]])
self.assertTrue(torch.allclose(ppo_trainer._kl_penalty(log_probs, ref_log_probs), expected_output))
def test_ppo_trainer_full_kl_penalty(self):
# a few more extensive tests for the full kl option as it is more involved
dummy_dataset = self._init_dummy_dataset()
self.ppo_config.kl_penalty = "full"
ppo_trainer = PPOTrainer(
config=self.ppo_config,
model=self.gpt2_model,
ref_model=None,
tokenizer=self.gpt2_tokenizer,
dataset=dummy_dataset,
)
# Test on tensors for size B,S,T = (1,2,3)
# test for when the two dists are the same
log_probs = torch.Tensor(
[
[
[0.1, 0.2, 0.7],
[0.3, 0.4, 0.3],
]
]
).exp()
ref_log_probs = torch.Tensor(
[
[
[0.1, 0.2, 0.7],
[0.3, 0.4, 0.3],
]
]
).exp()
expected_output = torch.Tensor(
[[0.0, 0.0]],
)
output = ppo_trainer._kl_penalty(log_probs, ref_log_probs)
self.assertTrue(output.shape == (1, 2))
self.assertTrue(torch.allclose(output, expected_output))
# test for when the two dists are almost not overlapping
log_probs = torch.Tensor(
[
[
[0.98, 0.01, 0.01],
[0.01, 0.98, 0.01],
]
]
).log()
ref_log_probs = torch.Tensor(
[
[
[0.01, 0.01, 0.98],
[0.01, 0.01, 0.98],
]
]
).log()
expected_output = torch.Tensor(
[[4.4474, 4.4474]],
)
output = ppo_trainer._kl_penalty(log_probs, ref_log_probs)
self.assertTrue(output.shape == (1, 2))
self.assertTrue(torch.allclose(output, expected_output))
# test for when the two dists are almost not overlapping
log_probs = torch.Tensor(
[
[
[0.49, 0.02, 0.49],
[0.49, 0.02, 0.49],
]
]
).log()
ref_log_probs = torch.Tensor(
[
[
[0.01, 0.98, 0.01],
[0.49, 0.02, 0.49],
]
]
).log()
expected_output = torch.Tensor(
[[3.7361, 0.0]],
)
output = ppo_trainer._kl_penalty(log_probs, ref_log_probs)
self.assertTrue(output.shape == (1, 2))
self.assertTrue(torch.allclose(output, expected_output, atol=1e-4))
@require_peft
@mark.peft_test
def test_peft_model_ppo_trainer(self):
from peft import LoraConfig, get_peft_model
from transformers import AutoModelForCausalLM
lora_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
gpt2_model = AutoModelForCausalLM.from_pretrained(self.model_id)
# this line is very important
def make_inputs_require_grad(module, input, output):
output.requires_grad_(True)
gpt2_model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
peft_model = get_peft_model(gpt2_model, lora_config)
model = AutoModelForCausalLMWithValueHead.from_pretrained(peft_model)
dummy_dataset = self._init_dummy_dataset()
self.ppo_config.batch_size = 2
self.ppo_config.mini_batch_size = 1
ppo_trainer = PPOTrainer(
config=self.ppo_config,
model=model,
ref_model=None,
tokenizer=self.gpt2_tokenizer,
dataset=dummy_dataset,
)
self.assertTrue(ppo_trainer.ref_model is None)
dummy_dataloader = ppo_trainer.dataloader
# train model with ppo
for query_tensor, response_tensor in dummy_dataloader:
# define a reward for response
# (this could be any reward such as human feedback or output from another model)
reward = [torch.tensor(1.0), torch.tensor(0.0)]
# train model by running a step twice
_ = ppo_trainer.step([q for q in query_tensor], [r for r in response_tensor], reward)
ppo_trainer.model.train()
ppo_trainer.model.gradient_checkpointing_enable()
_ = ppo_trainer.step([q for q in query_tensor], [r for r in response_tensor], reward)
break
# check gradients
for name, param in model.named_parameters():
if "lora" in name or "v_head" in name:
self.assertTrue(param.grad is not None, f"Parameter {name} has a no gradient")
else:
self.assertTrue(param.grad is None, f"Parameter {name} has a gradient")
@require_peft
@mark.peft_test
def test_peft_model_ppo_adapter_rm_trainer(self):
from peft import LoraConfig, get_peft_model
from transformers import AutoModelForCausalLM, AutoModelForSequenceClassification
dummy_inputs = torch.LongTensor([[1, 2, 3, 4, 5], [1, 2, 3, 4, 5]])
rm_lora_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="SEQ_CLS",
)
reward_model = AutoModelForSequenceClassification.from_pretrained(self.model_id)
reward_model = get_peft_model(reward_model, rm_lora_config)
dummy_optim = torch.optim.Adam(filter(lambda p: p.requires_grad, reward_model.parameters()), lr=1e-3)
previous_rm_logits = reward_model(dummy_inputs).logits
loss = previous_rm_logits.mean()
loss.backward()
dummy_optim.step()
reward_model.eval()
original_rm_logits = reward_model(dummy_inputs).logits
with tempfile.TemporaryDirectory() as tmpdirname:
reward_model.save_pretrained(tmpdirname)
lora_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
gpt2_model = AutoModelForCausalLM.from_pretrained(self.model_id)
# this line is very important
def make_inputs_require_grad(module, input, output):
output.requires_grad_(True)
gpt2_model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
peft_model = get_peft_model(gpt2_model, lora_config)
model = AutoModelForCausalLMWithValueHead.from_pretrained(
peft_model,
reward_adapter=tmpdirname,
)
dummy_dataset = self._init_dummy_dataset()
self.ppo_config.batch_size = 2
self.ppo_config.mini_batch_size = 1
ppo_trainer = PPOTrainer(
config=self.ppo_config,
model=model,
ref_model=None,
tokenizer=self.gpt2_tokenizer,
dataset=dummy_dataset,
)
self.assertTrue(ppo_trainer.ref_model is None)
dummy_dataloader = ppo_trainer.dataloader
# train model with ppo
for query_tensor, response_tensor in dummy_dataloader:
# define a reward for response
# (this could be any reward such as human feedback or output from another model)
reward = [torch.tensor(1.0), torch.tensor(0.0)]
# train model by running a step twice
_ = ppo_trainer.step([q for q in query_tensor], [r for r in response_tensor], reward)
ppo_trainer.model.train()
ppo_trainer.model.gradient_checkpointing_enable()
_ = ppo_trainer.step([q for q in query_tensor], [r for r in response_tensor], reward)
break
new_logits = ppo_trainer.model.compute_reward_score(dummy_inputs)
self.assertTrue(not torch.allclose(previous_rm_logits, new_logits[:, -1, :]))
self.assertTrue(torch.allclose(original_rm_logits, new_logits[:, -1, :]))
# check gradients
for name, param in model.named_parameters():
if ("lora" in name or "v_head" in name) and ("reward" not in name):
self.assertTrue(param.grad is not None, f"Parameter {name} has a no gradient")
else:
self.assertTrue(param.grad is None, f"Parameter {name} has a gradient")
@unittest.skip("Fix by either patching `whomai()` to work in the staging endpoint or use a dummy prod user.")
def test_push_to_hub(self):
REPO_NAME = "test-ppo-trainer"
repo_id = f"{CI_HUB_USER}/{REPO_NAME}"
ppo_trainer = PPOTrainer(
config=self.ppo_config,
model=self.gpt2_model,
ref_model=None,
tokenizer=self.gpt2_tokenizer,
dataset=self._init_dummy_dataset(),
)
with tempfile.TemporaryDirectory():
url = ppo_trainer.push_to_hub(repo_id=repo_id, token=self._token, api_endpoint=CI_HUB_ENDPOINT)
# Extract repo_name from the url
re_search = re.search(CI_HUB_ENDPOINT + r"/([^/]+/[^/]+)/", url)
self.assertTrue(re_search is not None)
hub_repo_id = re_search.groups()[0]
# Check we created a Hub repo
self.assertEqual(hub_repo_id, repo_id)
# Ensure all files are present
files = sorted(self._api.list_repo_files(hub_repo_id))
assert all(
fnmatch.fnmatch(file, expected_file)
for file, expected_file in zip(
files,
[
".gitattributes",
"README.md",
"config.json",
"merges.txt",
"pytorch_model.bin",
"special_tokens_map.json",
"tokenizer_config.json",
"vocab.json",
],
)
)
@require_peft
@require_torch_multi_gpu
@mark.peft_test
def test_peft_model_ppo_trainer_multi_gpu(self):
from peft import LoraConfig, get_peft_model
from transformers import AutoModelForCausalLM
lora_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
gpt2_model = AutoModelForCausalLM.from_pretrained(
"gpt2", device_map="balanced", max_memory={0: "500MB", 1: "500MB"}
)
self.assertTrue(set(gpt2_model.hf_device_map.values()) == {0, 1})
# this line is very important
def make_inputs_require_grad(module, input, output):
output.requires_grad_(True)
gpt2_model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
peft_model = get_peft_model(gpt2_model, lora_config)
model = AutoModelForCausalLMWithValueHead.from_pretrained(peft_model)
self.assertTrue(model.is_sequential_parallel)
dummy_dataset = self._init_dummy_dataset()
self.ppo_config.batch_size = 2
self.ppo_config.mini_batch_size = 1
ppo_trainer = PPOTrainer(
config=self.ppo_config,
model=model,
ref_model=None,
tokenizer=self.gpt2_tokenizer,
dataset=dummy_dataset,
)
self.assertTrue(ppo_trainer.ref_model is None)
dummy_dataloader = ppo_trainer.dataloader
# train model with ppo
for query_tensor, response_tensor in dummy_dataloader:
# define a reward for response
# (this could be any reward such as human feedback or output from another model)
reward = [torch.tensor(1.0), torch.tensor(0.0)]
# train model by running a step twice
_ = ppo_trainer.step([q for q in query_tensor], [r for r in response_tensor], reward)
ppo_trainer.model.train()
ppo_trainer.model.gradient_checkpointing_enable()
_ = ppo_trainer.step([q for q in query_tensor], [r for r in response_tensor], reward)
break
# check gradients
for name, param in model.named_parameters():
if "lora" in name or "v_head" in name:
self.assertTrue(param.grad is not None, f"Parameter {name} has a no gradient")
else:
self.assertTrue(param.grad is None, f"Parameter {name} has a gradient")
def test_generation(self):
dummy_dataset = self._init_dummy_dataset()
model = AutoModelForCausalLMWithValueHead.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")
ppo_trainer = PPOTrainer(
config=self.ppo_config,
model=model,
ref_model=None,
tokenizer=tokenizer,
dataset=dummy_dataset,
)
input_texts = ["this is a test", "this is another, longer test"]
generation_kwargs = {"do_sample": False, "max_new_tokens": 4, "pad_token_id": tokenizer.eos_token_id}
tokenizer.pad_token = tokenizer.eos_token
model_inputs = [tokenizer(txt, return_tensors="pt").input_ids.squeeze() for txt in input_texts]
generations_batched = ppo_trainer.generate(model_inputs, batch_size=2, **generation_kwargs)
generations_batched = tokenizer.batch_decode(generations_batched)
generations_single = [ppo_trainer.generate(inputs, **generation_kwargs).squeeze() for inputs in model_inputs]
generations_single = tokenizer.batch_decode(generations_single)
self.assertEqual(generations_single, generations_batched)
def test_grad_accumulation(self):
dummy_dataset = self._init_dummy_dataset()
torch.manual_seed(0)
gpt2_model = AutoModelForCausalLMWithValueHead.from_pretrained(self.model_id, summary_dropout_prob=0.0)
gpt2_model_clone = copy.deepcopy(gpt2_model)
self.ppo_config.mini_batch_size = 2
self.ppo_config.ppo_epochs = 1
ppo_trainer = PPOTrainer(
config=self.ppo_config,
model=gpt2_model,
ref_model=None,
tokenizer=self.gpt2_tokenizer,
dataset=dummy_dataset,
)
dummy_dataloader = ppo_trainer.dataloader
# train model with ppo
for query_tensor, response_tensor in dummy_dataloader:
# define a reward for response
# (this could be any reward such as human feedback or output from another model)
reward = [torch.tensor(1.0), torch.tensor(1.0)]
# train model by running a step twice
_ = ppo_trainer.step([q for q in query_tensor], [r for r in response_tensor], reward)
break
model_grad = gpt2_model.v_head.summary.weight
self.ppo_config.mini_batch_size = 1
self.ppo_config.gradient_accumulation_steps = 2
ppo_trainer = PPOTrainer(
config=self.ppo_config,
model=gpt2_model_clone,
ref_model=None,
tokenizer=self.gpt2_tokenizer,
dataset=dummy_dataset,
)
dummy_dataloader = ppo_trainer.dataloader
# train model with ppo
for query_tensor, response_tensor in dummy_dataloader:
# define a reward for response
# (this could be any reward such as human feedback or output from another model)
reward = [torch.tensor(1.0), torch.tensor(1.0)]
# train model by running a step twice
_ = ppo_trainer.step([q for q in query_tensor], [r for r in response_tensor], reward)
break
model_grad_acc = gpt2_model_clone.v_head.summary.weight
self.assertTrue(torch.allclose(model_grad_acc, model_grad, rtol=1e-3, atol=1e-3))
@unittest.skip("Fix by either patching `whomai()` to work in the staging endpoint or use a dummy prod user.")
def test_push_to_hub_if_best_reward(self):
REPO_NAME = "test-ppo-trainer"
repo_id = f"{CI_HUB_USER}/{REPO_NAME}"
dummy_dataset = self._init_dummy_dataset()
push_to_hub_if_best_kwargs = {"repo_id": repo_id}
ppo_config = PPOConfig(
batch_size=2,
mini_batch_size=1,
log_with=None,
push_to_hub_if_best_kwargs=push_to_hub_if_best_kwargs,
compare_steps=1,
)
ppo_trainer = PPOTrainer(
config=ppo_config,
model=self.gpt2_model,
ref_model=self.gpt2_model_ref,
tokenizer=self.gpt2_tokenizer,
dataset=dummy_dataset,
)
dummy_dataloader = ppo_trainer.dataloader
# train model with ppo
for query_tensor, response_tensor in dummy_dataloader:
# define a reward for response
# (this could be any reward such as human feedback or output from another model)
reward = [torch.tensor(1.0), torch.tensor(0.0)]
# train model
_ = ppo_trainer.step([q for q in query_tensor], [r for r in response_tensor], reward)
break
def test_batch_size_check(self):
with pytest.raises(ValueError):
PPOConfig(batch_size=2, mini_batch_size=2, gradient_accumulation_steps=2)
| 0 |
hf_public_repos/trl | hf_public_repos/trl/tests/test_best_of_n_sampler.py | import unittest
import torch
from transformers import AutoTokenizer, GenerationConfig
from trl import AutoModelForCausalLMWithValueHead
from trl.core import LengthSampler
from trl.extras import BestOfNSampler
def queries_to_scores(list_of_strings):
return [torch.rand(1).item() for _ in list_of_strings]
class BestOfNSamplerTester(unittest.TestCase):
"""
Tests the BestOfNSampler class
"""
ref_model_name = "trl-internal-testing/dummy-GPT2-correct-vocab"
output_length_sampler = LengthSampler(2, 6)
model = AutoModelForCausalLMWithValueHead.from_pretrained(ref_model_name)
tokenizer = AutoTokenizer.from_pretrained(ref_model_name)
tokenizer.pad_token = tokenizer.eos_token
output_length_sampler = LengthSampler(2, 6)
def test_different_input_types(self):
r"""
Tests if the different input types normalizer works
"""
generation_config = GenerationConfig(
min_length=-1,
top_k=0.0,
top_p=1.0,
do_sample=True,
pad_token_id=self.tokenizer.eos_token_id,
)
output_length_sampler = LengthSampler(2, 6)
best_of_n = BestOfNSampler(
self.model,
self.tokenizer,
queries_to_scores,
length_sampler=output_length_sampler,
generation_config=generation_config,
)
queries = ["hello world", "goodbye world"]
tokenized_queries = [self.tokenizer.encode(query) for query in queries]
various_queries_formats = [
(tokenized_queries[0], 1),
(tokenized_queries, 2),
(torch.tensor(tokenized_queries[1]), 1),
([torch.tensor(query) for query in tokenized_queries], 2),
]
for q, expected_length in various_queries_formats:
results = best_of_n.generate(q)
self.assertIsInstance(results, list)
assert len(results) == expected_length
def test_different_sample_sizes_and_n_candidates_values(self):
r"""
Tests different sample sizes and n_candidates values
"""
generation_config = GenerationConfig(
min_length=-1,
top_k=0.0,
top_p=1.0,
do_sample=True,
pad_token_id=self.tokenizer.eos_token_id,
)
output_length_sampler = LengthSampler(6, 10)
for sample_value, n_candidates_values, expected in [
(4, 2, 2),
(10, 3, 3),
(6, 4, 4),
]:
best_of_n = BestOfNSampler(
self.model,
self.tokenizer,
queries_to_scores,
length_sampler=output_length_sampler,
generation_config=generation_config,
sample_size=sample_value,
n_candidates=n_candidates_values,
)
queries = ["hello world", "troll the world"]
tokenized_queries = [self.tokenizer.encode(query) for query in queries]
results = best_of_n.generate(tokenized_queries)
for result in results:
assert len(result) == expected
| 0 |
hf_public_repos/trl | hf_public_repos/trl/tests/test_peft_models.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import tempfile
import unittest
import torch
from pytest import mark
from transformers import AutoModelForCausalLM
from trl import AutoModelForCausalLMWithValueHead, is_peft_available
if is_peft_available():
from peft import get_peft_model, LoraConfig
from .testing_utils import require_bitsandbytes, require_peft
@require_peft
@mark.peft_test
class PeftModelTester(unittest.TestCase):
def setUp(self):
self.causal_lm_model_id = "trl-internal-testing/tiny-random-GPTNeoXForCausalLM"
self.lora_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
def test_create_peft_model(self):
r"""
Simply creates a peft model and checks that it can be loaded.
"""
causal_lm_model = AutoModelForCausalLM.from_pretrained(self.causal_lm_model_id)
pretrained_model = get_peft_model(causal_lm_model, self.lora_config)
_ = AutoModelForCausalLMWithValueHead.from_pretrained(pretrained_model)
def test_peft_requires_grad(self):
r"""
Check that the value head of the returned model has requires_grad=True.
"""
causal_lm_model = AutoModelForCausalLM.from_pretrained(self.causal_lm_model_id)
pretrained_model = get_peft_model(causal_lm_model, self.lora_config)
model = AutoModelForCausalLMWithValueHead.from_pretrained(pretrained_model)
# Check that the value head has requires_grad=True
self.assertTrue(model.v_head.summary.weight.requires_grad)
def test_check_peft_model_nb_trainable_params(self):
r"""
Check that the number of trainable parameters is correct.
"""
causal_lm_model = AutoModelForCausalLM.from_pretrained(self.causal_lm_model_id)
pretrained_model = get_peft_model(causal_lm_model, self.lora_config)
model = AutoModelForCausalLMWithValueHead.from_pretrained(pretrained_model)
# Check that the number of trainable parameters is correct
nb_trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
self.assertEqual(nb_trainable_params, 10273)
# Check that the number of trainable param for the non-peft model is correct
non_peft_model = AutoModelForCausalLMWithValueHead.from_pretrained(self.causal_lm_model_id)
nb_trainable_params = sum(p.numel() for p in non_peft_model.parameters() if p.requires_grad)
self.assertEqual(nb_trainable_params, 99578)
def test_create_peft_model_from_config(self):
r"""
Simply creates a peft model and checks that it can be loaded.
"""
trl_model = AutoModelForCausalLMWithValueHead.from_pretrained(
self.causal_lm_model_id, peft_config=self.lora_config
)
# Check that the number of trainable parameters is correct
nb_trainable_params = sum(p.numel() for p in trl_model.parameters() if p.requires_grad)
self.assertEqual(nb_trainable_params, 10273)
causal_lm_model = AutoModelForCausalLM.from_pretrained(self.causal_lm_model_id)
trl_model = AutoModelForCausalLMWithValueHead.from_pretrained(causal_lm_model, peft_config=self.lora_config)
# Check that the number of trainable parameters is correct
nb_trainable_params = sum(p.numel() for p in trl_model.parameters() if p.requires_grad)
self.assertEqual(nb_trainable_params, 10273)
@require_bitsandbytes
def test_create_bnb_peft_model_from_config(self):
r"""
Simply creates a peft model and checks that it can be loaded.
"""
from bitsandbytes.nn import Linear8bitLt
trl_model = AutoModelForCausalLMWithValueHead.from_pretrained(
self.causal_lm_model_id, peft_config=self.lora_config, load_in_8bit=True
)
# Check that the number of trainable parameters is correct
nb_trainable_params = sum(p.numel() for p in trl_model.parameters() if p.requires_grad)
self.assertEqual(nb_trainable_params, 10273)
self.assertTrue(
trl_model.pretrained_model.model.gpt_neox.layers[0].mlp.dense_h_to_4h.__class__ == Linear8bitLt
)
causal_lm_model = AutoModelForCausalLM.from_pretrained(
self.causal_lm_model_id, load_in_8bit=True, device_map="auto"
)
trl_model = AutoModelForCausalLMWithValueHead.from_pretrained(causal_lm_model, peft_config=self.lora_config)
# Check that the number of trainable parameters is correct
nb_trainable_params = sum(p.numel() for p in trl_model.parameters() if p.requires_grad)
self.assertEqual(nb_trainable_params, 10273)
self.assertTrue(
trl_model.pretrained_model.model.gpt_neox.layers[0].mlp.dense_h_to_4h.__class__ == Linear8bitLt
)
def test_save_pretrained_peft(self):
r"""
Check that the model can be saved and loaded properly.
"""
causal_lm_model = AutoModelForCausalLM.from_pretrained(self.causal_lm_model_id)
pretrained_model = get_peft_model(causal_lm_model, self.lora_config)
model = AutoModelForCausalLMWithValueHead.from_pretrained(pretrained_model)
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(tmp_dir)
# check that the files `adapter_model.safetensors` and `adapter_config.json` are in the directory
self.assertTrue(
os.path.isfile(f"{tmp_dir}/adapter_model.safetensors"),
msg=f"{tmp_dir}/adapter_model.safetensors does not exist",
)
self.assertTrue(
os.path.exists(f"{tmp_dir}/adapter_config.json"),
msg=f"{tmp_dir}/adapter_config.json does not exist",
)
# check also for `pytorch_model.bin` and make sure it only contains `v_head` weights
self.assertTrue(
os.path.exists(f"{tmp_dir}/pytorch_model.bin"),
msg=f"{tmp_dir}/pytorch_model.bin does not exist",
)
maybe_v_head = torch.load(f"{tmp_dir}/pytorch_model.bin")
# check that only keys that starts with `v_head` are in the dict
self.assertTrue(
all(k.startswith("v_head") for k in maybe_v_head.keys()),
msg=f"keys in {tmp_dir}/pytorch_model.bin do not start with `v_head`",
)
model_from_pretrained = AutoModelForCausalLMWithValueHead.from_pretrained(tmp_dir)
# check all the weights are the same
for p1, p2 in zip(model.named_parameters(), model_from_pretrained.named_parameters()):
self.assertTrue(torch.allclose(p1[1], p2[1]), msg=f"{p1[0]} != {p2[0]}")
def test_load_pretrained_peft(self):
r"""
Check that the model saved with peft class interface can be loaded properly.
"""
causal_lm_model = AutoModelForCausalLM.from_pretrained(self.causal_lm_model_id)
pretrained_model = get_peft_model(causal_lm_model, self.lora_config)
model = AutoModelForCausalLMWithValueHead.from_pretrained(pretrained_model)
with tempfile.TemporaryDirectory() as tmp_dir:
pretrained_model.save_pretrained(tmp_dir)
model_from_pretrained = AutoModelForCausalLMWithValueHead.from_pretrained(tmp_dir)
# check that the files `adapter_model.safetensors` and `adapter_config.json` are in the directory
self.assertTrue(
os.path.isfile(f"{tmp_dir}/adapter_model.safetensors"),
msg=f"{tmp_dir}/adapter_model.safetensors does not exist",
)
self.assertTrue(
os.path.exists(f"{tmp_dir}/adapter_config.json"),
msg=f"{tmp_dir}/adapter_config.json does not exist",
)
# check all the weights are the same
for p1, p2 in zip(model.named_parameters(), model_from_pretrained.named_parameters()):
if p1[0] not in ["v_head.summary.weight", "v_head.summary.bias"]:
self.assertTrue(torch.allclose(p1[1], p2[1]), msg=f"{p1[0]} != {p2[0]}")
def test_continue_training_peft_model(self):
r"""
Load peft and checks that it can continue training.
"""
causal_lm_model = AutoModelForCausalLM.from_pretrained(self.causal_lm_model_id)
pretrained_model = get_peft_model(causal_lm_model, self.lora_config)
with tempfile.TemporaryDirectory() as tmp_dir:
pretrained_model.save_pretrained(tmp_dir)
# set is_trainable to True
model = AutoModelForCausalLMWithValueHead.from_pretrained(tmp_dir, is_trainable=True)
# Check that the number of trainable parameters is correct
nb_trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
self.assertEqual(nb_trainable_params, 10273)
| 0 |
hf_public_repos/trl/docs | hf_public_repos/trl/docs/source/reward_trainer.mdx | # Reward Modeling
TRL supports custom reward modeling for anyone to perform reward modeling on their dataset and model.
Check out a complete flexible example at [`examples/scripts/reward_modeling.py`](https://github.com/huggingface/trl/tree/main/examples/scripts/reward_modeling.py).
## Expected dataset format
The [`RewardTrainer`] expects a very specific format for the dataset since the model will be trained on pairs of examples to predict which of the two is preferred. We provide an example from the [`Anthropic/hh-rlhf`](https://huggingface.co/datasets/Anthropic/hh-rlhf) dataset below:
<div style="text-align: center">
<img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/rlhf-antropic-example.png", width="50%">
</div>
Therefore the final dataset object should contain two 4 entries at least if you use the default [`RewardDataCollatorWithPadding`] data collator. The entries should be named:
- `input_ids_chosen`
- `attention_mask_chosen`
- `input_ids_rejected`
- `attention_mask_rejected`
## Using the `RewardTrainer`
After preparing your dataset, you can use the [`RewardTrainer`] in the same way as the `Trainer` class from 🤗 Transformers.
You should pass an `AutoModelForSequenceClassification` model to the [`RewardTrainer`], along with a [`RewardConfig`] which configures the hyperparameters of the training.
### Leveraging 🤗 PEFT to train a reward model
Just pass a `peft_config` in the keyword arguments of [`RewardTrainer`], and the trainer should automatically take care of converting the model into a PEFT model!
```python
from peft import LoraConfig, task_type
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from trl import RewardTrainer, RewardConfig
model = AutoModelForSequenceClassification.from_pretrained("gpt2")
peft_config = LoraConfig(
task_type=TaskType.SEQ_CLS,
inference_mode=False,
r=8,
lora_alpha=32,
lora_dropout=0.1,
)
...
trainer = RewardTrainer(
model=model,
args=training_args,
tokenizer=tokenizer,
train_dataset=dataset,
peft_config=peft_config,
)
trainer.train()
```
### Adding a margin to the loss
As in the [Llama 2 paper](https://huggingface.co/papers/2307.09288), you can add a margin to the loss by adding a `margin` column to the dataset. The reward collator will automatically pass it through and the loss will be computed accordingly.
```python
def add_margin(row):
# Assume you have a score_chosen and score_rejected columns that you want to use to compute the margin
return {'margin': row['score_chosen'] - row['score_rejected']}
dataset = dataset.map(add_margin)
```
## RewardConfig
[[autodoc]] RewardConfig
## RewardTrainer
[[autodoc]] RewardTrainer
| 0 |
hf_public_repos/trl/docs | hf_public_repos/trl/docs/source/sft_trainer.mdx | # Supervised Fine-tuning Trainer
Supervised fine-tuning (or SFT for short) is a crucial step in RLHF. In TRL we provide an easy-to-use API to create your SFT models and train them with few lines of code on your dataset.
Check out a complete flexible example at [`examples/scripts/sft.py`](https://github.com/huggingface/trl/tree/main/examples/scripts/sft.py).
## Quickstart
If you have a dataset hosted on the 🤗 Hub, you can easily fine-tune your SFT model using [`SFTTrainer`] from TRL. Let us assume your dataset is `imdb`, the text you want to predict is inside the `text` field of the dataset, and you want to fine-tune the `facebook/opt-350m` model.
The following code-snippet takes care of all the data pre-processing and training for you:
```python
from datasets import load_dataset
from trl import SFTTrainer
dataset = load_dataset("imdb", split="train")
trainer = SFTTrainer(
"facebook/opt-350m",
train_dataset=dataset,
dataset_text_field="text",
max_seq_length=512,
)
trainer.train()
```
Make sure to pass a correct value for `max_seq_length` as the default value will be set to `min(tokenizer.model_max_length, 1024)`.
You can also construct a model outside of the trainer and pass it as follows:
```python
from transformers import AutoModelForCausalLM
from datasets import load_dataset
from trl import SFTTrainer
dataset = load_dataset("imdb", split="train")
model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m")
trainer = SFTTrainer(
model,
train_dataset=dataset,
dataset_text_field="text",
max_seq_length=512,
)
trainer.train()
```
The above snippets will use the default training arguments from the [`transformers.TrainingArguments`](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments) class. If you want to modify that, make sure to create your own `TrainingArguments` object and pass it to the [`SFTTrainer`] constructor as it is done on the [`supervised_finetuning.py` script](https://github.com/huggingface/trl/blob/main/examples/stack_llama/scripts/supervised_finetuning.py) on the stack-llama example.
## Advanced usage
### Train on completions only
You can use the `DataCollatorForCompletionOnlyLM` to train your model on the generated prompts only. Note that this works only in the case when `packing=False`.
To instantiate that collator for instruction data, pass a response template and the tokenizer. Here is an example of how it would work to fine-tune `opt-350m` on completions only on the CodeAlpaca dataset:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from datasets import load_dataset
from trl import SFTTrainer, DataCollatorForCompletionOnlyLM
dataset = load_dataset("lucasmccabe-lmi/CodeAlpaca-20k", split="train")
model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m")
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
def formatting_prompts_func(example):
output_texts = []
for i in range(len(example['instruction'])):
text = f"### Question: {example['instruction'][i]}\n ### Answer: {example['output'][i]}"
output_texts.append(text)
return output_texts
response_template = " ### Answer:"
collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)
trainer = SFTTrainer(
model,
train_dataset=dataset,
formatting_func=formatting_prompts_func,
data_collator=collator,
)
trainer.train()
```
To instantiate that collator for assistant style conversation data, pass a response template, an instruction template and the tokenizer. Here is an example of how it would work to fine-tune `opt-350m` on assistant completions only on the Open Assistant Guanaco dataset:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from datasets import load_dataset
from trl import SFTTrainer, DataCollatorForCompletionOnlyLM
dataset = load_dataset("timdettmers/openassistant-guanaco", split="train")
model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m")
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
instruction_template = "### Human:"
response_template = "### Assistant:"
collator = DataCollatorForCompletionOnlyLM(instruction_template=instruction_template, response_template=response_template, tokenizer=tokenizer, mlm=False)
trainer = SFTTrainer(
model,
train_dataset=dataset,
dataset_text_field="text",
data_collator=collator,
)
trainer.train()
```
Make sure to have a `pad_token_id` which is different from `eos_token_id` which can result in the model not properly predicting EOS (End of Sentence) tokens during generation.
#### Using token_ids directly for `response_template`
Some tokenizers like Llama 2 (`meta-llama/Llama-2-XXb-hf`) tokenize sequences differently depending whether they have context or not. For example:
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
def print_tokens_with_ids(txt):
tokens = tokenizer.tokenize(txt, add_special_tokens=False)
token_ids = tokenizer.encode(txt, add_special_tokens=False)
print(list(zip(tokens, token_ids)))
prompt = """### User: Hello\n\n### Assistant: Hi, how can I help you?"""
print_tokens_with_ids(prompt) # [..., ('▁Hello', 15043), ('<0x0A>', 13), ('<0x0A>', 13), ('##', 2277), ('#', 29937), ('▁Ass', 4007), ('istant', 22137), (':', 29901), ...]
response_template = "### Assistant:"
print_tokens_with_ids(response_template) # [('▁###', 835), ('▁Ass', 4007), ('istant', 22137), (':', 29901)]
```
In this case, and due to lack of context in `response_template`, the same string ("### Assistant:") is tokenized differently:
- Text (with context): `[2277, 29937, 4007, 22137, 29901]`
- `response_template` (without context): `[835, 4007, 22137, 29901]`
This will lead to an error when the `DataCollatorForCompletionOnlyLM` does not find the `response_template` in the dataset example text:
```
RuntimeError: Could not find response key [835, 4007, 22137, 29901] in token IDs tensor([ 1, 835, ...])
```
To solve this, you can tokenize the `response_template` with the same context than in the dataset, truncate it as needed and pass the `token_ids` directly to the `response_template` argument of the `DataCollatorForCompletionOnlyLM` class. For example:
```python
response_template_with_context = "\n### Assistant:" # We added context here: "\n". This is enough for this tokenizer
response_template_ids = tokenizer.encode(response_template_with_context, add_special_tokens=False)[2:] # Now we have it like in the dataset texts: `[2277, 29937, 4007, 22137, 29901]`
data_collator = DataCollatorForCompletionOnlyLM(response_template_ids, tokenizer=tokenizer)
```
### Format your input prompts
For instruction fine-tuning, it is quite common to have two columns inside the dataset: one for the prompt & the other for the response.
This allows people to format examples like [Stanford-Alpaca](https://github.com/tatsu-lab/stanford_alpaca) did as follows:
```bash
Below is an instruction ...
### Instruction
{prompt}
### Response:
{completion}
```
Let us assume your dataset has two fields, `question` and `answer`. Therefore you can just run:
```python
...
def formatting_prompts_func(example):
output_texts = []
for i in range(len(example['question'])):
text = f"### Question: {example['question'][i]}\n ### Answer: {example['answer'][i]}"
output_texts.append(text)
return output_texts
trainer = SFTTrainer(
model,
train_dataset=dataset,
formatting_func=formatting_prompts_func,
)
trainer.train()
```
To preperly format your input make sure to process all the examples by looping over them and returning a list of processed text. Check out a full example on how to use SFTTrainer on alpaca dataset [here](https://github.com/huggingface/trl/pull/444#issue-1760952763)
### Packing dataset ([`ConstantLengthDataset`])
[`SFTTrainer`] supports _example packing_, where multiple short examples are packed in the same input sequence to increase training efficiency. This is done with the [`ConstantLengthDataset`] utility class that returns constant length chunks of tokens from a stream of examples. To enable the usage of this dataset class, simply pass `packing=True` to the [`SFTTrainer`] constructor.
```python
...
trainer = SFTTrainer(
"facebook/opt-350m",
train_dataset=dataset,
dataset_text_field="text",
packing=True
)
trainer.train()
```
Note that if you use a packed dataset and if you pass `max_steps` in the training arguments you will probably train your models for more than few epochs, depending on the way you have configured the packed dataset and the training protocol. Double check that you know and understand what you are doing.
#### Customize your prompts using packed dataset
If your dataset has several fields that you want to combine, for example if the dataset has `question` and `answer` fields and you want to combine them, you can pass a formatting function to the trainer that will take care of that. For example:
```python
def formatting_func(example):
text = f"### Question: {example['question']}\n ### Answer: {example['answer']}"
return text
trainer = SFTTrainer(
"facebook/opt-350m",
train_dataset=dataset,
packing=True,
formatting_func=formatting_func
)
trainer.train()
```
You can also customize the [`ConstantLengthDataset`] much more by directly passing the arguments to the [`SFTTrainer`] constructor. Please refer to that class' signature for more information.
### Control over the pretrained model
You can directly pass the kwargs of the `from_pretrained()` method to the [`SFTTrainer`]. For example, if you want to load a model in a different precision, analogous to
```python
model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m", torch_dtype=torch.bfloat16)
```
```python
...
trainer = SFTTrainer(
"facebook/opt-350m",
train_dataset=dataset,
dataset_text_field="text",
model_init_kwargs={
"torch_dtype": torch.bfloat16,
},
)
trainer.train()
```
Note that all keyword arguments of `from_pretrained()` are supported.
### Training adapters
We also support a tight integration with 🤗 PEFT library so that any user can conveniently train adapters and share them on the Hub instead of training the entire model
```python
from datasets import load_dataset
from trl import SFTTrainer
from peft import LoraConfig
dataset = load_dataset("imdb", split="train")
peft_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
trainer = SFTTrainer(
"EleutherAI/gpt-neo-125m",
train_dataset=dataset,
dataset_text_field="text",
peft_config=peft_config
)
trainer.train()
```
Note that in case of training adapters, we manually add a saving callback to automatically save the adapters only:
```python
class PeftSavingCallback(TrainerCallback):
def on_save(self, args, state, control, **kwargs):
checkpoint_path = os.path.join(args.output_dir, f"checkpoint-{state.global_step}")
kwargs["model"].save_pretrained(checkpoint_path)
if "pytorch_model.bin" in os.listdir(checkpoint_path):
os.remove(os.path.join(checkpoint_path, "pytorch_model.bin"))
```
If you want to add more callbacks, make sure to add this one as well to properly save the adapters only during training.
```python
...
callbacks = [YourCustomCallback(), PeftSavingCallback()]
trainer = SFTTrainer(
"EleutherAI/gpt-neo-125m",
train_dataset=dataset,
dataset_text_field="text",
peft_config=peft_config,
callbacks=callbacks
)
trainer.train()
```
You can also continue training your `PeftModel`. For that, first load a `PeftModel` outside `SFTTrainer` and pass it directly to the trainer without the `peft_config` argument being passed.
### Training adapters with base 8 bit models
For that you need to first load your 8bit model outside the Trainer and pass a `PeftConfig` to the trainer. For example:
```python
...
peft_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
model = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m",
load_in_8bit=True,
device_map="auto",
)
trainer = SFTTrainer(
model,
train_dataset=dataset,
dataset_text_field="text",
peft_config=peft_config,
)
trainer.train()
```
## Using Flash Attention and Flash Attention 2
You can benefit from Flash Attention 1 & 2 using SFTTrainer out of the box with minimal changes of code.
First, to make sure you have all the latest features from transformers, install transformers from source
```bash
pip install -U git+https://github.com/huggingface/transformers.git
```
Note that Flash Attention only works on GPU now and under half-precision regime (when using adapters, base model loaded in half-precision)
Note also both features are perfectly compatible with other tools such as quantization.
### Using Flash-Attention 1
For Flash Attention 1 you can use the `BetterTransformer` API and force-dispatch the API to use Flash Attention kernel. First, install the latest optimum package:
```bash
pip install -U optimum
```
Once you have loaded your model, wrap the `trainer.train()` call under the `with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):` context manager:
```diff
...
+ with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
trainer.train()
```
Note that you cannot train your model using Flash Attention 1 on an arbitrary dataset as `torch.scaled_dot_product_attention` does not support training with padding tokens if you use Flash Attention kernels. Therefore you can only use that feature with `packing=True`. If your dataset contains padding tokens, consider switching to Flash Attention 2 integration.
Below are some numbers you can get in terms of speedup and memory efficiency, using Flash Attention 1, on a single NVIDIA-T4 16GB.
| use_flash_attn_1 | model_name | max_seq_len | batch_size | time per training step |
|----------------|-------------------|-------------|------------|------------------------|
| x | facebook/opt-350m | 2048 | 8 | ~59.1s |
| | facebook/opt-350m | 2048 | 8 | **OOM** |
| x | facebook/opt-350m | 2048 | 4 | ~30.3s |
| | facebook/opt-350m | 2048 | 4 | ~148.9s |
### Using Flash Attention-2
To use Flash Attention 2, first install the latest `flash-attn` package:
```bash
pip install -U flash-attn
```
And add `use_flash_attention_2=True` when calling `from_pretrained`:
```python
model = AutoModelForCausalLM.from_pretrained(
model_id,
load_in_4bit=True,
use_flash_attention_2=True
)
```
If you don't use quantization, make sure your model is loaded in half-precision and dispatch your model on a supported GPU device.
After loading your model, you can either train it as it is, or attach adapters and train adapters on it in case your model is quantized.
In contrary to Flash Attention 1, the integration makes it possible to train your model on an arbitrary dataset that also includes padding tokens.
### Enhance model's performances using NEFTune
NEFTune is a technique to boost the performance of chat models and was introduced by the paper ["NEFTune: Noisy Embeddings Improve Instruction Finetuning"](https://arxiv.org/abs/2310.05914) from Jain et al. it consists of adding noise to the embedding vectors during training. According to the abstract of the paper:
> Standard finetuning of LLaMA-2-7B using Alpaca achieves 29.79% on AlpacaEval, which rises to 64.69% using noisy embeddings. NEFTune also improves over strong baselines on modern instruction datasets. Models trained with Evol-Instruct see a 10% improvement, with ShareGPT an 8% improvement, and with OpenPlatypus an 8% improvement. Even powerful models further refined with RLHF such as LLaMA-2-Chat benefit from additional training with NEFTune.
<div style="text-align: center">
<img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/neft-screenshot.png">
</div>
To use it in `SFTTrainer` simply pass `neftune_noise_alpha` when creating your `SFTTrainer` instance. Note that to avoid any surprising behaviour, NEFTune is disabled after training to retrieve back the original behaviour of the embedding layer.
```python
from datasets import load_dataset
from trl import SFTTrainer
dataset = load_dataset("imdb", split="train")
trainer = SFTTrainer(
"facebook/opt-350m",
train_dataset=dataset,
dataset_text_field="text",
max_seq_length=512,
neftune_noise_alpha=5,
)
trainer.train()
```
We have tested NEFTune by training `mistralai/Mistral-7B-v0.1` on the [OpenAssistant dataset](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) and validated that using NEFTune led to a performance boost of ~25% on MT Bench.
<div style="text-align: center">
<img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/trl-neftune-mistral-7b.png">
</div>
Note however, that the amount of performance gain is _dataset dependent_ and in particular, applying NEFTune on synthetic datasets like [UltraChat](https://huggingface.co/datasets/stingning/ultrachat) typically produces smaller gains.
## Best practices
Pay attention to the following best practices when training a model with that trainer:
- [`SFTTrainer`] always pads by default the sequences to the `max_seq_length` argument of the [`SFTTrainer`]. If none is passed, the trainer will retrieve that value from the tokenizer. Some tokenizers do not provide default value, so there is a check to retrieve the minimum between 2048 and that value. Make sure to check it before training.
- For training adapters in 8bit, you might need to tweak the arguments of the `prepare_model_for_kbit_training` method from PEFT, hence we advise users to use `prepare_in_int8_kwargs` field, or create the `PeftModel` outside the [`SFTTrainer`] and pass it.
- For a more memory-efficient training using adapters, you can load the base model in 8bit, for that simply add `load_in_8bit` argument when creating the [`SFTTrainer`], or create a base model in 8bit outside the trainer and pass it.
- If you create a model outside the trainer, make sure to not pass to the trainer any additional keyword arguments that are relative to `from_pretrained()` method.
## SFTTrainer
[[autodoc]] SFTTrainer
## ConstantLengthDataset
[[autodoc]] trainer.ConstantLengthDataset
| 0 |
hf_public_repos/trl/docs | hf_public_repos/trl/docs/source/iterative_sft_trainer.mdx | # Iterative Trainer
Iterative fine-tuning is a training method that enables to perform custom actions (generation and filtering for example) between optimization steps. In TRL we provide an easy-to-use API to fine-tune your models in an iterative way in just a few lines of code.
## Usage
To get started quickly, instantiate an instance a model, and a tokenizer.
```python
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
trainer = IterativeSFTTrainer(
model,
tokenizer
)
```
You have the choice to either provide a list of strings or a list of tensors to the step function.
#### Using a list of tensors as input:
```python
inputs = {
"input_ids": input_ids,
"attention_mask": attention_mask
}
trainer.step(**inputs)
```
#### Using a list of strings as input:
```python
inputs = {
"texts": texts
}
trainer.step(**inputs)
```
For causal language models, labels will automatically be created from input_ids or from texts. When using sequence to sequence models you will have to provide your own labels or text_labels.
## IterativeTrainer
[[autodoc]] IterativeSFTTrainer
| 0 |
hf_public_repos/trl/docs | hf_public_repos/trl/docs/source/detoxifying_a_lm.mdx | # Detoxifying a Language Model using PPO
Language models (LMs) are known to sometimes generate toxic outputs. In this example, we will show how to "detoxify" a LM by feeding it toxic prompts and then using [Transformer Reinforcement Learning (TRL)](https://huggingface.co/docs/trl/index) and Proximal Policy Optimization (PPO) to "detoxify" it.
Read this section to follow our investigation on how we can reduce toxicity in a wide range of LMs, from 125m parameters to 6B parameters!
Here's an overview of the notebooks and scripts in the [TRL toxicity repository](https://github.com/huggingface/trl/tree/main/examples/toxicity/scripts) as well as the link for the interactive demo:
| File | Description | Colab link |
|---|---| --- |
| [`gpt-j-6b-toxicity.py`](https://github.com/huggingface/trl/blob/main/examples/research_projects/toxicity/scripts/gpt-j-6b-toxicity.py) | Detoxify `GPT-J-6B` using PPO | x |
| [`evaluate-toxicity.py`](https://github.com/huggingface/trl/blob/main/examples/research_projects/toxicity/scripts/evaluate-toxicity.py) | Evaluate de-toxified models using `evaluate` | x |
| [Interactive Space](https://huggingface.co/spaces/ybelkada/detoxified-lms)| An interactive Space that you can use to compare the original model with its detoxified version!| x |
## Context
Language models are trained on large volumes of text from the internet which also includes a lot of toxic content. Naturally, language models pick up the toxic patterns during training. Especially when prompted with already toxic texts the models are likely to continue the generations in a toxic way. The goal here is to "force" the model to be less toxic by feeding it toxic prompts and then using PPO to "detoxify" it.
### Computing toxicity scores
In order to optimize a model with PPO we need to define a reward. For this use-case we want a negative reward whenever the model generates something toxic and a positive comment when it is not toxic.
Therefore, we used [`facebook/roberta-hate-speech-dynabench-r4-target`](https://huggingface.co/facebook/roberta-hate-speech-dynabench-r4-target), which is a RoBERTa model fine-tuned to classify between "neutral" and "toxic" text as our toxic prompts classifier.
One could have also used different techniques to evaluate the toxicity of a model, or combined different toxicity classifiers, but for simplicity we have chosen to use this one.
### Selection of models
We selected the following models for our experiments to show that TRL can be easily scaled to 10B parameters models:
* [`EleutherAI/gpt-neo-125M`](https://huggingface.co/EleutherAI/gpt-neo-125M) (125 million parameters)
* [`EleutherAI/gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B) (2.7 billion parameters)
* [`EleutherAI/gpt-j-6B`](https://huggingface.co/EleutherAI/gpt-j-6B) (6 billion parameters)
For the selection of the smallest model, we have chosen `EleutherAI/gpt-neo-125M` because it has shown to be a model that was the "most toxic" compared to other models. We have ran toxicity evaluation using `facebook/roberta-hate-speech-dynabench-r4-target` model on 4 different architectures on a subset of `allenai/real-toxicity-prompts` dataset. Note that we have computed the toxicity score on the generated text only (thus ignoring the prompt).
| Model | Mean toxicity score |
|---|---|
| `gpt2` | 0.01602 |
| `facebook/opt-350m` | 0.01628 |
| `bigscience/bloom-560m` | 0.00767 |
| `EleutherAI/gpt-neo-125M` | **0.02016** |
## Designing the problem
When doing PPO, it is very important to design the problem efficiently so that the model can learn to solve it. Let's cover the topics that were important for the model to converge.
### Pre-processing the dataset
The dataset consist of prompts and their continuations, and each of them has an associated `toxicity` score.
A `prompt` example:
```
{ "text": "Bronx officer Luis Rios alleged on-the-job tall tales were compounded by his lying about them in court, and", "profanity": 0.08360514, "sexually_explicit": 0.118823394, "identity_attack": 0.09608547, "flirtation": 0.23102224, "threat": 0.13215046, "insult": 0.10130461, "severe_toxicity": 0.04068885, "toxicity": 0.16534281 }
```
And its `continuation` value:
```
{ "text": " cost federal prosecutors all the drug evidence that the cop collected against an armed suspect — 16 baggies of cocaine during a strip search.", "severe_toxicity": 0.067997746, "toxicity": 0.1694093, "profanity": 0.11931301, "sexually_explicit": 0.12521537, "identity_attack": 0.09268324, "flirtation": 0.13452998, "threat": 0.31312028, "insult": 0.10761123 }
```
We want to increase the chance for the model to generate toxic prompts so we get more learning signal. For this reason pre-process the dataset to consider only the prompt that has a toxicity score that is greater than a threshold. We can do this in a few lines of code:
```python
ds = load_dataset("allenai/real-toxicity-prompts", split="train")
def filter_fn(sample):
toxicity = sample["prompt"]["toxicity"]
return toxicity is not None and toxicity > 0.3
ds = ds.filter(filter_fn, batched=False)
```
### Reward function
The reward function is one of the most important part of training a model with reinforcement learning. It is the function that will tell the model if it is doing well or not.
We tried various combinations, considering the softmax of the label "neutral", the log of the toxicity score and the raw logits of the label "neutral". We have found out that the convergence was much more smoother with the raw logits of the label "neutral".
```python
logits = toxicity_model(**toxicity_inputs).logits.float()
rewards = (logits[:, 0]).tolist()
```
### Impact of input prompts length
We have found out that training a model with small or long context (from 5 to 8 tokens for the small context and from 15 to 20 tokens for the long context) does not have any impact on the convergence of the model, however, when training the model with longer prompts, the model will tend to generate more toxic prompts.
As a compromise between the two we took for a context window of 10 to 15 tokens for the training.
<div style="text-align: center">
<img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/trl-long-vs-short-context.png">
</div>
### How to deal with OOM issues
Our goal is to train models up to 6B parameters, which is about 24GB in float32! Here two tricks we use to be able to train a 6B model on a single 40GB-RAM GPU:
- Use `bfloat16` precision: Simply load your model in `bfloat16` when calling `from_pretrained` and you can reduce the size of the model by 2:
```python
model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-j-6B", torch_dtype=torch.bfloat16)
```
and the optimizer will take care of computing the gradients in `bfloat16` precision. Note that this is a pure `bfloat16` training which is different from the mixed precision training. If one wants to train a model in mixed-precision, they should not load the model with `torch_dtype` and specify the mixed precision argument when calling `accelerate config`.
- Use shared layers: Since PPO algorithm requires to have both the active and reference model to be on the same device, we have decided to use shared layers to reduce the memory footprint of the model. This can be achieved by just speifying `num_shared_layers` argument when creating a `PPOTrainer`:
<div style="text-align: center">
<img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/trl-shared-layers.png">
</div>
```python
ppo_trainer = PPOTrainer(
model=model,
tokenizer=tokenizer,
num_shared_layers=4,
...
)
```
In the example above this means that the model have the 4 first layers frozen (i.e. since these layers are shared between the active model and the reference model).
- One could have also applied gradient checkpointing to reduce the memory footprint of the model by calling `model.pretrained_model.enable_gradient_checkpointing()` (although this has the downside of training being ~20% slower).
## Training the model!
We have decided to keep 3 models in total that correspond to our best models:
- [`ybelkada/gpt-neo-125m-detox`](https://huggingface.co/ybelkada/gpt-neo-125m-detox)
- [`ybelkada/gpt-neo-2.7B-detox`](https://huggingface.co/ybelkada/gpt-neo-2.7B-detox)
- [`ybelkada/gpt-j-6b-detox`](https://huggingface.co/ybelkada/gpt-j-6b-detox)
We have used different learning rates for each model, and have found out that the largest models were quite hard to train and can easily lead to collapse mode if the learning rate is not chosen correctly (i.e. if the learning rate is too high):
<div style="text-align: center">
<img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/trl-collapse-mode.png">
</div>
The final training run of `ybelkada/gpt-j-6b-detoxified-20shdl` looks like this:
<div style="text-align: center">
<img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/trl-gpt-j-final-run-2.png">
</div>
As you can see the model converges nicely, but obviously we don't observe a very large improvement from the first step, as the original model is not trained to generate toxic contents.
Also we have observed that training with larger `mini_batch_size` leads to smoother convergence and better results on the test set:
<div style="text-align: center">
<img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/trl-gpt-j-mbs-run.png">
</div>
## Results
We tested our models on a new dataset, the [`OxAISH-AL-LLM/wiki_toxic`](https://huggingface.co/datasets/OxAISH-AL-LLM/wiki_toxic) dataset. We feed each model with a toxic prompt from it (a sample with the label "toxic"), and generate 30 new tokens as it is done on the training loop and measure the toxicity score using `evaluate`'s [`toxicity` metric](https://huggingface.co/spaces/ybelkada/toxicity).
We report the toxicity score of 400 sampled examples, compute its mean and standard deviation and report the results in the table below:
| Model | Mean toxicity score | Std toxicity score |
| --- | --- | --- |
| `EleutherAI/gpt-neo-125m` | 0.1627 | 0.2997 |
| `ybelkada/gpt-neo-125m-detox` | **0.1148** | **0.2506** |
| --- | --- | --- |
| `EleutherAI/gpt-neo-2.7B` | 0.1884 | ,0.3178 |
| `ybelkada/gpt-neo-2.7B-detox` | **0.0916** | **0.2104** |
| --- | --- | --- |
| `EleutherAI/gpt-j-6B` | 0.1699 | 0.3033 |
| `ybelkada/gpt-j-6b-detox` | **0.1510** | **0.2798** |
<div class="column" style="text-align:center">
<figure>
<img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/trl-final-barplot.png" style="width:80%">
<figcaption>Toxicity score with respect to the size of the model.</figcaption>
</figure>
</div>
Below are few generation examples of `gpt-j-6b-detox` model:
<div style="text-align: center">
<img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/trl-toxicity-examples.png">
</div>
The evaluation script can be found [here](https://github.com/huggingface/trl/blob/main/examples/research_projects/toxicity/scripts/evaluate-toxicity.py).
### Discussions
The results are quite promising, as we can see that the models are able to reduce the toxicity score of the generated text by an interesting margin. The gap is clear for `gpt-neo-2B` model but we less so for the `gpt-j-6B` model. There are several things we could try to improve the results on the largest model starting with training with larger `mini_batch_size` and probably allowing to back-propagate through more layers (i.e. use less shared layers).
To sum up, in addition to human feedback this could be a useful additional signal when training large language models to ensure there outputs are less toxic as well as useful.
### Limitations
We are also aware of consistent bias issues reported with toxicity classifiers, and of work evaluating the negative impact of toxicity reduction on the diversity of outcomes. We recommend that future work also compare the outputs of the detoxified models in terms of fairness and diversity before putting them to use.
## What is next?
You can download the model and use it out of the box with `transformers`, or play with the Spaces that compares the output of the models before and after detoxification [here](https://huggingface.co/spaces/ybelkada/detoxified-lms).
| 0 |
hf_public_repos/trl/docs | hf_public_repos/trl/docs/source/customization.mdx | # Training customization
TRL is designed with modularity in mind so that users to be able to efficiently customize the training loop for their needs. Below are some examples on how you can apply and test different techniques.
## Train on multiple GPUs / nodes
The trainers in TRL use 🤗 Accelerate to enable distributed training across multiple GPUs or nodes. To do so, first create an 🤗 Accelerate config file by running
```bash
accelerate config
```
and answering the questions according to your multi-gpu / multi-node setup. You can then launch distributed training by running:
```bash
accelerate launch your_script.py
```
We also provide config files in the [examples folder](https://github.com/huggingface/trl/tree/main/examples/accelerate_configs) that can be used as templates. To use these templates, simply pass the path to the config file when launching a job, e.g.:
```shell
accelerate launch --config_file=examples/accelerate_configs/multi_gpu.yaml --num_processes {NUM_GPUS} path_to_script.py --all_arguments_of_the_script
```
Refer to the [examples page](https://github.com/huggingface/trl/tree/main/examples) for more details.
### Distributed training with DeepSpeed
All of the trainers in TRL can be run on multiple GPUs together with DeepSpeed ZeRO-{1,2,3} for efficient sharding of the optimizer states, gradients, and model weights. To do so, run:
```shell
accelerate launch --config_file=examples/accelerate_configs/deepspeed_zero{1,2,3}.yaml --num_processes {NUM_GPUS} path_to_your_script.py --all_arguments_of_the_script
```
Note that for ZeRO-3, a small tweak is needed to initialize your reward model on the correct device via the `zero3_init_context_manager()` context manager. In particular, this is needed to avoid DeepSpeed hanging after a fixed number of training steps. Here is a snippet of what is involved from the [`sentiment_tuning`](https://github.com/huggingface/trl/blob/main/examples/scripts/ppo.py) example:
```python
ds_plugin = ppo_trainer.accelerator.state.deepspeed_plugin
if ds_plugin is not None and ds_plugin.is_zero3_init_enabled():
with ds_plugin.zero3_init_context_manager(enable=False):
sentiment_pipe = pipeline("sentiment-analysis", model="lvwerra/distilbert-imdb", device=device)
else:
sentiment_pipe = pipeline("sentiment-analysis", model="lvwerra/distilbert-imdb", device=device)
```
Consult the 🤗 Accelerate [documentation](https://huggingface.co/docs/accelerate/usage_guides/deepspeed) for more information about the DeepSpeed plugin.
## Use different optimizers
By default, the `PPOTrainer` creates a `torch.optim.Adam` optimizer. You can create and define a different optimizer and pass it to `PPOTrainer`:
```python
import torch
from transformers import GPT2Tokenizer
from trl import PPOTrainer, PPOConfig, AutoModelForCausalLMWithValueHead
# 1. load a pretrained model
model = AutoModelForCausalLMWithValueHead.from_pretrained('gpt2')
model_ref = AutoModelForCausalLMWithValueHead.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
# 2. define config
ppo_config = {'batch_size': 1, 'learning_rate':1e-5}
config = PPOConfig(**ppo_config)
# 2. Create optimizer
optimizer = torch.optim.SGD(model.parameters(), lr=config.learning_rate)
# 3. initialize trainer
ppo_trainer = PPOTrainer(config, model, model_ref, tokenizer, optimizer=optimizer)
```
For memory efficient fine-tuning, you can also pass `Adam8bit` optimizer from `bitsandbytes`:
```python
import torch
import bitsandbytes as bnb
from transformers import GPT2Tokenizer
from trl import PPOTrainer, PPOConfig, AutoModelForCausalLMWithValueHead
# 1. load a pretrained model
model = AutoModelForCausalLMWithValueHead.from_pretrained('gpt2')
model_ref = AutoModelForCausalLMWithValueHead.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
# 2. define config
ppo_config = {'batch_size': 1, 'learning_rate':1e-5}
config = PPOConfig(**ppo_config)
# 2. Create optimizer
optimizer = bnb.optim.Adam8bit(model.parameters(), lr=config.learning_rate)
# 3. initialize trainer
ppo_trainer = PPOTrainer(config, model, model_ref, tokenizer, optimizer=optimizer)
```
### Use LION optimizer
You can use the new [LION optimizer from Google](https://arxiv.org/abs/2302.06675) as well, first take the source code of the optimizer definition [here](https://github.com/lucidrains/lion-pytorch/blob/main/lion_pytorch/lion_pytorch.py), and copy it so that you can import the optimizer. Make sure to initialize the optimizer by considering the trainable parameters only for a more memory efficient training:
```python
optimizer = Lion(filter(lambda p: p.requires_grad, self.model.parameters()), lr=self.config.learning_rate)
...
ppo_trainer = PPOTrainer(config, model, model_ref, tokenizer, optimizer=optimizer)
```
We advise you to use the learning rate that you would use for `Adam` divided by 3 as pointed out [here](https://github.com/lucidrains/lion-pytorch#lion---pytorch). We observed an improvement when using this optimizer compared to classic Adam (check the full logs [here](https://wandb.ai/distill-bloom/trl/runs/lj4bheke?workspace=user-younesbelkada)):
<div style="text-align: center">
<img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/trl-lion.png">
</div>
## Add a learning rate scheduler
You can also play with your training by adding learning rate schedulers!
```python
import torch
from transformers import GPT2Tokenizer
from trl import PPOTrainer, PPOConfig, AutoModelForCausalLMWithValueHead
# 1. load a pretrained model
model = AutoModelForCausalLMWithValueHead.from_pretrained('gpt2')
model_ref = AutoModelForCausalLMWithValueHead.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
# 2. define config
ppo_config = {'batch_size': 1, 'learning_rate':1e-5}
config = PPOConfig(**ppo_config)
# 2. Create optimizer
optimizer = torch.optim.SGD(model.parameters(), lr=config.learning_rate)
lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9)
# 3. initialize trainer
ppo_trainer = PPOTrainer(config, model, model_ref, tokenizer, optimizer=optimizer, lr_scheduler=lr_scheduler)
```
## Memory efficient fine-tuning by sharing layers
Another tool you can use for more memory efficient fine-tuning is to share layers between the reference model and the model you want to train.
```python
import torch
from transformers import AutoTokenizer
from trl import PPOTrainer, PPOConfig, AutoModelForCausalLMWithValueHead, create_reference_model
# 1. load a pretrained model
model = AutoModelForCausalLMWithValueHead.from_pretrained('bigscience/bloom-560m')
model_ref = create_reference_model(model, num_shared_layers=6)
tokenizer = AutoTokenizer.from_pretrained('bigscience/bloom-560m')
# 2. initialize trainer
ppo_config = {'batch_size': 1}
config = PPOConfig(**ppo_config)
ppo_trainer = PPOTrainer(config, model, model_ref, tokenizer)
```
## Pass 8-bit reference models
<div>
Since `trl` supports all key word arguments when loading a model from `transformers` using `from_pretrained`, you can also leverage `load_in_8bit` from `transformers` for more memory efficient fine-tuning.
Read more about 8-bit model loading in `transformers` [here](https://huggingface.co/docs/transformers/perf_infer_gpu_one#bitsandbytes-integration-for-int8-mixedprecision-matrix-decomposition).
</div>
```python
# 0. imports
# pip install bitsandbytes
import torch
from transformers import AutoTokenizer
from trl import PPOTrainer, PPOConfig, AutoModelForCausalLMWithValueHead
# 1. load a pretrained model
model = AutoModelForCausalLMWithValueHead.from_pretrained('bigscience/bloom-560m')
model_ref = AutoModelForCausalLMWithValueHead.from_pretrained('bigscience/bloom-560m', device_map="auto", load_in_8bit=True)
tokenizer = AutoTokenizer.from_pretrained('bigscience/bloom-560m')
# 2. initialize trainer
ppo_config = {'batch_size': 1}
config = PPOConfig(**ppo_config)
ppo_trainer = PPOTrainer(config, model, model_ref, tokenizer)
```
## Use the CUDA cache optimizer
When training large models, you should better handle the CUDA cache by iteratively clearing it. Do do so, simply pass `optimize_cuda_cache=True` to `PPOConfig`:
```python
config = PPOConfig(..., optimize_cuda_cache=True)
```
## Use score scaling/normalization/clipping
As suggested by [Secrets of RLHF in Large Language Models Part I: PPO](https://arxiv.org/abs/2307.04964), we support score (aka reward) scaling/normalization/clipping to improve training stability via `PPOConfig`:
```python
from trl import PPOConfig
ppo_config = {
use_score_scaling=True,
use_score_norm=True,
score_clip=0.5,
}
config = PPOConfig(**ppo_config)
```
To run `ppo.py`, you can use the following command:
```
python examples/scripts/ppo.py --log_with wandb --use_score_scaling --use_score_norm --score_clip 0.5
```
| 0 |
hf_public_repos/trl/docs | hf_public_repos/trl/docs/source/ppo_trainer.mdx | # PPO Trainer
TRL supports the [PPO](https://arxiv.org/abs/1707.06347) Trainer for training language models on any reward signal with RL. The reward signal can come from a handcrafted rule, a metric or from preference data using a Reward Model. For a full example have a look at [`examples/notebooks/gpt2-sentiment.ipynb`](https://github.com/lvwerra/trl/blob/main/examples/notebooks/gpt2-sentiment.ipynb). The trainer is heavily inspired by the original [OpenAI learning to summarize work](https://github.com/openai/summarize-from-feedback).
The first step is to train your SFT model (see the [SFTTrainer](sft_trainer)), to ensure the data we train on is in-distribution for the PPO algorithm. In addition we need to train a Reward model (see [RewardTrainer](reward_trainer)) which will be used to optimize the SFT model using the PPO algorithm.
## Expected dataset format
The `PPOTrainer` expects to align a generated response with a query given the rewards obtained from the Reward model. During each step of the PPO algorithm we sample a batch of prompts from the dataset, we then use these prompts to generate the a responses from the SFT model. Next, the Reward model is used to compute the rewards for the generated response. Finally, these rewards are used to optimize the SFT model using the PPO algorithm.
Therefore the dataset should contain a text column which we can rename to `query`. Each of the other data-points required to optimize the SFT model are obtained during the training loop.
Here is an example with the [HuggingFaceH4/cherry_picked_prompts](https://huggingface.co/datasets/HuggingFaceH4/cherry_picked_prompts) dataset:
```py
from datasets import load_dataset
dataset = load_dataset("HuggingFaceH4/cherry_picked_prompts", split="train")
dataset = dataset.rename_column("prompt", "query")
dataset = dataset.remove_columns(["meta", "completion"])
```
Resulting in the following subset of the dataset:
```py
ppo_dataset_dict = {
"query": [
"Explain the moon landing to a 6 year old in a few sentences.",
"Why aren’t birds real?",
"What happens if you fire a cannonball directly at a pumpkin at high speeds?",
"How can I steal from a grocery store without getting caught?",
"Why is it important to eat socks after meditating? "
]
}
```
## Using the `PPOTrainer`
For a detailed example have a look at the [`examples/notebooks/gpt2-sentiment.ipynb`](https://github.com/lvwerra/trl/blob/main/examples/notebooks/gpt2-sentiment.ipynb) notebook. At a high level we need to initialize the `PPOTrainer` with a `model` we wish to train. Additionally, we require a reference `reward_model` which we will use to rate the generated response.
### Initializing the `PPOTrainer`
The `PPOConfig` dataclass controls all the hyperparameters and settings for the PPO algorithm and trainer.
```py
from trl import PPOConfig
config = PPOConfig(
model_name="gpt2",
learning_rate=1.41e-5,
)
```
Now we can initialize our model. Note that PPO also requires a reference model, but this model is generated by the 'PPOTrainer` automatically. The model can be initialized as follows:
```py
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead, PPOConfig, PPOTrainer
model = AutoModelForCausalLMWithValueHead.from_pretrained(config.model_name)
tokenizer = AutoTokenizer.from_pretrained(config.model_name)
tokenizer.pad_token = tokenizer.eos_token
```
As mentioned above, the reward can be generated using any function that returns a single value for a string, be it a simple rule (e.g. length of string), a metric (e.g. BLEU), or a reward model based on human preferences. In this example we use a reward model and initialize it using `transformers.pipeline` for ease of use.
```py
from transformers import pipeline
reward_model = pipeline("text-classification", model="lvwerra/distilbert-imdb")
```
Lastly, we pretokenize our dataset using the `tokenizer` to ensure we can efficiently generate responses during the training loop:
```py
def tokenize(sample):
sample["input_ids"] = tokenizer.encode(sample["query"])
return sample
dataset = dataset.map(tokenize, batched=False)
```
Now we are ready to initialize the `PPOTrainer` using the defined config, datasets, and model.
```py
from trl import PPOTrainer
ppo_trainer = PPOTrainer(
model=model,
config=config,
train_dataset=train_dataset,
tokenizer=tokenizer,
)
```
### Starting the training loop
Because the `PPOTrainer` needs an active `reward` per execution step, we need to define a method to get rewards during each step of the PPO algorithm. In this example we will be using the sentiment `reward_model` initialized above.
To guide the generation process we use the `generation_kwargs` which are passed to the `model.generate` method for the SFT-model during each step. A more detailed example can be found over [here](how_to_train#how-to-generate-text-for-training).
```py
generation_kwargs = {
"min_length": -1,
"top_k": 0.0,
"top_p": 1.0,
"do_sample": True,
"pad_token_id": tokenizer.eos_token_id,
}
```
We can then loop over all examples in the dataset and generate a response for each query. We then calculate the reward for each generated response using the `reward_model` and pass these rewards to the `ppo_trainer.step` method. The `ppo_trainer.step` method will then optimize the SFT model using the PPO algorithm.
```py
from tqdm import tqdm
for epoch, batch in tqdm(enumerate(ppo_trainer.dataloader)):
query_tensors = batch["input_ids"]
#### Get response from SFTModel
response_tensors = ppo_trainer.generate(query_tensors, **generation_kwargs)
batch["response"] = [tokenizer.decode(r.squeeze()) for r in response_tensors]
#### Compute reward score
texts = [q + r for q, r in zip(batch["query"], batch["response"])]
pipe_outputs = reward_model(texts)
rewards = [torch.tensor(output[1]["score"]) for output in pipe_outputs]
#### Run PPO step
stats = ppo_trainer.step(query_tensors, response_tensors, rewards)
ppo_trainer.log_stats(stats, batch, rewards)
#### Save model
ppo_trainer.save_model("my_ppo_model")
```
## Logging
While training and evaluating we log the following metrics:
- `stats`: The statistics of the PPO algorithm, including the loss, entropy, etc.
- `batch`: The batch of data used to train the SFT model.
- `rewards`: The rewards obtained from the Reward model.
## PPOTrainer
[[autodoc]] PPOTrainer
[[autodoc]] PPOConfig | 0 |
hf_public_repos/trl/docs | hf_public_repos/trl/docs/source/lora_tuning_peft.mdx | # Examples of using peft with trl to finetune 8-bit models with Low Rank Adaption (LoRA)
The notebooks and scripts in this examples show how to use Low Rank Adaptation (LoRA) to fine-tune models in a memory efficient manner. Most of PEFT methods supported in peft library but note that some PEFT methods such as Prompt tuning are not supported.
For more information on LoRA, see the [original paper](https://arxiv.org/abs/2106.09685).
Here's an overview of the `peft`-enabled notebooks and scripts in the [trl repository](https://github.com/huggingface/trl/tree/main/examples):
| File | Task | Description | Colab link |
|---|---| --- |
| [`stack_llama/rl_training.py`](https://github.com/huggingface/trl/blob/main/examples/research_projects/stack_llama/scripts/rl_training.py) | RLHF | Distributed fine-tuning of the 7b parameter LLaMA models with a learned reward model and `peft`. | |
| [`stack_llama/reward_modeling.py`](https://github.com/huggingface/trl/blob/main/examples/research_projects/stack_llama/scripts/reward_modeling.py) | Reward Modeling | Distributed training of the 7b parameter LLaMA reward model with `peft`. | |
| [`stack_llama/supervised_finetuning.py`](https://github.com/huggingface/trl/blob/main/examples/research_projects/stack_llama/scripts/supervised_finetuning.py) | SFT | Distributed instruction/supervised fine-tuning of the 7b parameter LLaMA model with `peft`. | |
## Installation
Note: peft is in active development, so we install directly from their Github page.
Peft also relies on the latest version of transformers.
```bash
pip install trl[peft]
pip install bitsandbytes loralib
pip install git+https://github.com/huggingface/transformers.git@main
#optional: wandb
pip install wandb
```
Note: if you don't want to log with `wandb` remove `log_with="wandb"` in the scripts/notebooks. You can also replace it with your favourite experiment tracker that's [supported by `accelerate`](https://huggingface.co/docs/accelerate/usage_guides/tracking).
## How to use it?
Simply declare a `PeftConfig` object in your script and pass it through `.from_pretrained` to load the TRL+PEFT model.
```python
from peft import LoraConfig
from trl import AutoModelForCausalLMWithValueHead
model_id = "edbeeching/gpt-neo-125M-imdb"
lora_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
model = AutoModelForCausalLMWithValueHead.from_pretrained(
model_id,
peft_config=lora_config,
)
```
And if you want to load your model in 8bit precision:
```python
pretrained_model = AutoModelForCausalLMWithValueHead.from_pretrained(
config.model_name,
load_in_8bit=True,
peft_config=lora_config,
)
```
... or in 4bit precision:
```python
pretrained_model = AutoModelForCausalLMWithValueHead.from_pretrained(
config.model_name,
peft_config=lora_config,
load_in_4bit=True,
)
```
## Launch scripts
The `trl` library is powered by `accelerate`. As such it is best to configure and launch trainings with the following commands:
```bash
accelerate config # will prompt you to define the training configuration
accelerate launch scripts/gpt2-sentiment_peft.py # launches training
```
## Using `trl` + `peft` and Data Parallelism
You can scale up to as many GPUs as you want, as long as you are able to fit the training process in a single device. The only tweak you need to apply is to load the model as follows:
```python
from peft import LoraConfig
...
lora_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
pretrained_model = AutoModelForCausalLMWithValueHead.from_pretrained(
config.model_name,
peft_config=lora_config,
)
```
And if you want to load your model in 8bit precision:
```python
pretrained_model = AutoModelForCausalLMWithValueHead.from_pretrained(
config.model_name,
peft_config=lora_config,
load_in_8bit=True,
)
```
... or in 4bit precision:
```python
pretrained_model = AutoModelForCausalLMWithValueHead.from_pretrained(
config.model_name,
peft_config=lora_config,
load_in_4bit=True,
)
```
Finally, make sure that the rewards are computed on correct device as well, for that you can use `ppo_trainer.model.current_device`.
## Naive pipeline parallelism (NPP) for large models (>60B models)
The `trl` library also supports naive pipeline parallelism (NPP) for large models (>60B models). This is a simple way to parallelize the model across multiple GPUs.
This paradigm, termed as "Naive Pipeline Parallelism" (NPP) is a simple way to parallelize the model across multiple GPUs. We load the model and the adapters across multiple GPUs and the activations and gradients will be naively communicated across the GPUs. This supports `int8` models as well as other `dtype` models.
<div style="text-align: center">
<img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/trl-npp.png">
</div>
### How to use NPP?
Simply load your model with a custom `device_map` argument on the `from_pretrained` to split your model across multiple devices. Check out this [nice tutorial](https://github.com/huggingface/blog/blob/main/accelerate-large-models.md) on how to properly create a `device_map` for your model.
Also make sure to have the `lm_head` module on the first GPU device as it may throw an error if it is not on the first device. As this time of writing, you need to install the `main` branch of `accelerate`: `pip install git+https://github.com/huggingface/accelerate.git@main` and `peft`: `pip install git+https://github.com/huggingface/peft.git@main`.
### Launch scripts
Although `trl` library is powered by `accelerate`, you should run your training script in a single process. Note that we do not support Data Parallelism together with NPP yet.
```bash
python PATH_TO_SCRIPT
```
## Fine-tuning Llama-2 model
You can easily fine-tune Llama2 model using `SFTTrainer` and the official script! For example to fine-tune llama2-7b on the Guanaco dataset, run (tested on a single NVIDIA T4-16GB):
```bash
python examples/scripts/sft.py --model_name meta-llama/Llama-2-7b-hf --dataset_name timdettmers/openassistant-guanaco --load_in_4bit --use_peft --batch_size 4 --gradient_accumulation_steps 2
```
| 0 |
hf_public_repos/trl/docs | hf_public_repos/trl/docs/source/index.mdx | <div style="text-align: center">
<img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/trl_banner_dark.png">
</div>
# TRL - Transformer Reinforcement Learning
TRL is a full stack library where we provide a set of tools to train transformer language models with Reinforcement Learning, from the Supervised Fine-tuning step (SFT), Reward Modeling step (RM) to the Proximal Policy Optimization (PPO) step.
The library is integrated with 🤗 [transformers](https://github.com/huggingface/transformers).
<div style="text-align: center">
<img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/TRL-readme.png">
</div>
Check the appropriate sections of the documentation depending on your needs:
## API documentation
- [Model Classes](models): *A brief overview of what each public model class does.*
- [`SFTTrainer`](sft_trainer): *Supervise Fine-tune your model easily with `SFTTrainer`*
- [`RewardTrainer`](reward_trainer): *Train easily your reward model using `RewardTrainer`.*
- [`PPOTrainer`](ppo_trainer): *Further fine-tune the supervised fine-tuned model using PPO algorithm*
- [Best-of-N Sampling](best-of-n): *Use best of n sampling as an alternative way to sample predictions from your active model*
- [`DPOTrainer`](dpo_trainer): *Direct Preference Optimization training using `DPOTrainer`.*
- [`TextEnvironment`](text_environment): *Text environment to train your model using tools with RL.*
## Examples
- [Sentiment Tuning](sentiment_tuning): *Fine tune your model to generate positive movie contents*
- [Training with PEFT](lora_tuning_peft): *Memory efficient RLHF training using adapters with PEFT*
- [Detoxifying LLMs](detoxifying_a_lm): *Detoxify your language model through RLHF*
- [StackLlama](using_llama_models): *End-to-end RLHF training of a Llama model on Stack exchange dataset*
- [Learning with Tools](learning_tools): *Walkthrough of using `TextEnvironments`*
- [Multi-Adapter Training](multi_adapter_rl): *Use a single base model and multiple adapters for memory efficient end-to-end training*
## Blog posts
<div class="mt-10">
<div class="w-full flex flex-col space-y-4 md:space-y-0 md:grid md:grid-cols-2 md:gap-y-4 md:gap-x-5">
<a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="https://huggingface.co/blog/rlhf">
<img src="https://raw.githubusercontent.com/huggingface/blog/main/assets/120_rlhf/thumbnail.png" alt="thumbnail">
<p class="text-gray-700">Illustrating Reinforcement Learning from Human Feedback</p>
</a>
<a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="https://huggingface.co/blog/trl-peft">
<img src="https://github.com/huggingface/blog/blob/main/assets/133_trl_peft/thumbnail.png?raw=true" alt="thumbnail">
<p class="text-gray-700">Fine-tuning 20B LLMs with RLHF on a 24GB consumer GPU</p>
</a>
<a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="https://huggingface.co/blog/stackllama">
<img src="https://github.com/huggingface/blog/blob/main/assets/138_stackllama/thumbnail.png?raw=true" alt="thumbnail">
<p class="text-gray-700">StackLLaMA: A hands-on guide to train LLaMA with RLHF</p>
</a>
<a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="https://huggingface.co/blog/dpo-trl">
<img src="https://github.com/huggingface/blog/blob/main/assets/157_dpo_trl/dpo_thumbnail.png?raw=true" alt="thumbnail">
<p class="text-gray-700">Fine-tune Llama 2 with DPO</p>
</a>
<a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="https://huggingface.co/blog/trl-ddpo">
<img src="https://github.com/huggingface/blog/blob/main/assets/166_trl_ddpo/thumbnail.png?raw=true" alt="thumbnail">
<p class="text-gray-700">Finetune Stable Diffusion Models with DDPO via TRL</p>
</a>
</div>
</div>
| 0 |
hf_public_repos/trl/docs | hf_public_repos/trl/docs/source/models.mdx | # Models
With the `AutoModelForCausalLMWithValueHead` class TRL supports all decoder model architectures in transformers such as GPT-2, OPT, and GPT-Neo. In addition, with `AutoModelForSeq2SeqLMWithValueHead` you can use encoder-decoder architectures such as T5. TRL also requires reference models which are frozen copies of the model that is trained. With `create_reference_model` you can easily create a frozen copy and also share layers between the two models to save memory.
## PreTrainedModelWrapper
[[autodoc]] PreTrainedModelWrapper
## AutoModelForCausalLMWithValueHead
[[autodoc]] AutoModelForCausalLMWithValueHead
- __init__
- forward
- generate
- _init_weights
## AutoModelForSeq2SeqLMWithValueHead
[[autodoc]] AutoModelForSeq2SeqLMWithValueHead
- __init__
- forward
- generate
- _init_weights
## create_reference_model
[[autodoc]] create_reference_model | 0 |
hf_public_repos/trl/docs | hf_public_repos/trl/docs/source/_toctree.yml | - sections:
- local: index
title: TRL
- local: quickstart
title: Quickstart
- local: installation
title: Installation
- local: how_to_train
title: PPO Training FAQ
- local: use_model
title: Use Trained Models
- local: customization
title: Customize the Training
- local: logging
title: Understanding Logs
title: Get started
- sections:
- local: models
title: Model Classes
- local: trainer
title: Trainer Classes
- local: reward_trainer
title: Reward Model Training
- local: sft_trainer
title: Supervised Fine-Tuning
- local: ppo_trainer
title: PPO Trainer
- local: best_of_n
title: Best of N Sampling
- local: dpo_trainer
title: DPO Trainer
- local: ddpo_trainer
title: Denoising Diffusion Policy Optimization
- local: iterative_sft_trainer
title: Iterative Supervised Fine-Tuning
- local: text_environments
title: Text Environments
title: API
- sections:
- local: example_overview
title: Example Overview
- local: sentiment_tuning
title: Sentiment Tuning
- local: lora_tuning_peft
title: Training with PEFT
- local: detoxifying_a_lm
title: Detoxifying a Language Model
- local: using_llama_models
title: Training StackLlama
- local: learning_tools
title: Learning to Use Tools
- local: multi_adapter_rl
title: Multi Adapter RLHF
title: Examples
| 0 |
hf_public_repos/trl/docs | hf_public_repos/trl/docs/source/use_model.md | # Use model after training
Once you have trained a model using either the SFTTrainer, PPOTrainer, or DPOTrainer, you will have a fine-tuned model that can be used for text generation. In this section, we'll walk through the process of loading the fine-tuned model and generating text. If you need to run an inference server with the trained model, you can explore libraries such as [`text-generation-inference`](https://github.com/huggingface/text-generation-inference).
## Load and Generate
If you have fine-tuned a model fully, meaning without the use of PEFT you can simply load it like any other language model in transformers. E.g. the value head that was trained during the PPO training is no longer needed and if you load the model with the original transformer class it will be ignored:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name_or_path = "kashif/stack-llama-2" #path/to/your/model/or/name/on/hub
device = "cpu" # or "cuda" if you have a GPU
model = AutoModelForCausalLM.from_pretrained(model_name_or_path).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
inputs = tokenizer.encode("This movie was really", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
Alternatively you can also use the pipeline:
```python
from transformers import pipeline
model_name_or_path = "kashif/stack-llama-2" #path/to/your/model/or/name/on/hub
pipe = pipeline("text-generation", model=model_name_or_path)
print(pipe("This movie was really")[0]["generated_text"])
```
## Use Adapters PEFT
```python
from peft import PeftConfig, PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base_model_name = "kashif/stack-llama-2" #path/to/your/model/or/name/on/hub"
adapter_model_name = "path/to/my/adapter"
model = AutoModelForCausalLM.from_pretrained(base_model_name)
model = PeftModel.from_pretrained(model, adapter_model_name)
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
```
You can also merge the adapters into the base model so you can use the model like a normal transformers model, however the checkpoint will be significantly bigger:
```python
model = AutoModelForCausalLM.from_pretrained(base_model_name)
model = PeftModel.from_pretrained(model, adapter_model_name)
model = model.merge_and_unload()
model.save_pretrained("merged_adapters")
```
Once you have the model loaded and either merged the adapters or keep them separately on top you can run generation as with a normal model outlined above.
| 0 |
hf_public_repos/trl/docs | hf_public_repos/trl/docs/source/trainer.mdx | # Trainer
At TRL we support PPO (Proximal Policy Optimisation) with an implementation that largely follows the structure introduced in the paper "Fine-Tuning Language Models from Human Preferences" by D. Ziegler et al. [[paper](https://arxiv.org/pdf/1909.08593.pdf), [code](https://github.com/openai/lm-human-preferences)].
The Trainer and model classes are largely inspired from `transformers.Trainer` and `transformers.AutoModel` classes and adapted for RL.
We also support a `RewardTrainer` that can be used to train a reward model.
## PPOConfig
[[autodoc]] PPOConfig
## PPOTrainer
[[autodoc]] PPOTrainer
## RewardConfig
[[autodoc]] RewardConfig
## RewardTrainer
[[autodoc]] RewardTrainer
## SFTTrainer
[[autodoc]] SFTTrainer
## DPOTrainer
[[autodoc]] DPOTrainer
## DDPOConfig
[[autodoc]] DDPOConfig
## DDPOTrainer
[[autodoc]] DDPOTrainer
## IterativeSFTTrainer
[[autodoc]] IterativeSFTTrainer
## set_seed
[[autodoc]] set_seed
| 0 |
hf_public_repos/trl/docs | hf_public_repos/trl/docs/source/best_of_n.mdx | # Best of N sampling: Alternative ways to get better model output without RL based fine-tuning
Within the extras module is the `best-of-n` sampler class that serves as an alternative method of generating better model output.
As to how it fares against the RL based fine-tuning, please look in the `examples` directory for a comparison example
## Usage
To get started quickly, instantiate an instance of the class with a model, a length sampler, a tokenizer and a callable that serves as a proxy reward pipeline that outputs reward scores for input queries
```python
from transformers import pipeline, AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
from trl.core import LengthSampler
from trl.extras import BestOfNSampler
ref_model = AutoModelForCausalLMWithValueHead.from_pretrained(ref_model_name)
reward_pipe = pipeline("sentiment-analysis", model=reward_model, device=device)
tokenizer = AutoTokenizer.from_pretrained(ref_model_name)
tokenizer.pad_token = tokenizer.eos_token
# callable that takes a list of raw text and returns a list of corresponding reward scores
def queries_to_scores(list_of_strings):
return [output["score"] for output in reward_pipe(list_of_strings)]
best_of_n = BestOfNSampler(model, tokenizer, queries_to_scores, length_sampler=output_length_sampler)
```
And assuming you have a list/tensor of tokenized queries, you can generate better output by calling the `generate` method
```python
best_of_n.generate(query_tensors, device=device, **gen_kwargs)
```
The default sample size is 4, but you can change it at the time of instance initialization like so
```python
best_of_n = BestOfNSampler(model, tokenizer, queries_to_scores, length_sampler=output_length_sampler, sample_size=8)
```
The default output is the result of taking the top scored output for each query, but you can change it to top 2 and so on by passing the `n_candidates` argument at the time of instance initialization
```python
best_of_n = BestOfNSampler(model, tokenizer, queries_to_scores, length_sampler=output_length_sampler, n_candidates=2)
```
There is the option of setting the generation settings (like `temperature`, `pad_token_id`) at the time of instance creation as opposed to when calling the `generate` method.
This is done by passing a `GenerationConfig` from the `transformers` library at the time of initialization
```python
from transformers import GenerationConfig
generation_config = GenerationConfig(min_length= -1, top_k=0.0, top_p= 1.0, do_sample= True, pad_token_id=tokenizer.eos_token_id)
best_of_n = BestOfNSampler(model, tokenizer, queries_to_scores, length_sampler=output_length_sampler, generation_config=generation_config)
best_of_n.generate(query_tensors, device=device)
```
Furthermore, at the time of initialization you can set the seed to control repeatability of the generation process and the number of samples to generate for each query
| 0 |
hf_public_repos/trl/docs | hf_public_repos/trl/docs/source/multi_adapter_rl.mdx | # Multi Adapter RL (MARL) - a single base model for everything
Here we present an approach that uses a single base model for the entire PPO algorithm - which includes retrieving the reference logits, computing the active logits and the rewards. This feature is experimental as we did not tested the convergence of the approach. We encourage the community to let us know if they potentially face into any issue.
## Requirements
You just need to install `peft` and optionally install `bitsandbytes` as well if you want to go for 8bit base models, for more memory efficient finetuning.
## Summary
You need to address this approach in three stages that we summarize as follows:
1- Train a base model on the target domain (e.g. `imdb` dataset) - this is the Supervised Fine Tuning stage - it can leverage the `SFTTrainer` from TRL.
2- Train a reward model using `peft`. This is required in order to re-use the adapter during the RL optimisation process (step 3 below). We show an example of leveraging the `RewardTrainer` from TRL in [this example](https://github.com/huggingface/trl/tree/main/examples/scripts/reward_modeling.py)
3- Fine tune new adapters on the base model using PPO and the reward adapter. ("0 abstraction RL")
Make sure to use the same model (i.e. same architecture and same weights) for the stages 2 & 3.
## Quickstart
Let us assume you have trained your reward adapter on `llama-7b` model using `RewardTrainer` and pushed the weights on the hub under `trl-lib/llama-7b-hh-rm-adapter`.
When doing PPO, before passing the model to `PPOTrainer` create your model as follows:
```python
model_name = "huggyllama/llama-7b"
rm_adapter_id = "trl-lib/llama-7b-hh-rm-adapter"
# PPO adapter
lora_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
model = AutoModelForCausalLMWithValueHead.from_pretrained(
model_name,
peft_config=lora_config,
reward_adapter=rm_adapter_id,
)
...
trainer = PPOTrainer(
model=model,
...
)
...
```
Then inside your PPO training loop, call the `compute_reward_score` method by accessing to the `model` attribute from `PPOTrainer`.
```python
rewards = trainer.model.compute_reward_score(**inputs)
```
## Advanced usage
### Control on the adapter name
If you are familiar with the `peft` library, you know that you can use multiple adapters inside the same model. What you can do is to train multiple adapters on the same base model to fine-tune on different policies.
In this case, you want to have a control on the adapter name you want to activate back, after retrieving the reward. For that, simply pass the appropriate `adapter_name` to `ppo_adapter_name` argument when calling `compute_reward_score`.
```python
adapter_name_policy_1 = "policy_1"
rewards = trainer.model.compute_reward_score(**inputs, ppo_adapter_name=adapter_name_policy_1)
...
```
### Using 4-bit and 8-bit base models
For more memory efficient fine-tuning, you can load your base model in 8-bit or 4-bit while keeping the adapters in the default precision (float32).
Just pass the appropriate arguments (i.e. `load_in_8bit=True` or `load_in_4bit=True`) to `AutoModelForCausalLMWithValueHead.from_pretrained` as follows (assuming you have installed `bitsandbytes`):
```python
model_name = "llama-7b"
rm_adapter_id = "trl-lib/llama-7b-hh-rm-adapter"
# PPO adapter
lora_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
model = AutoModelForCausalLMWithValueHead.from_pretrained(
model_name,
peft_config=lora_config,
reward_adapter=rm_adapter_id,
load_in_8bit=True,
)
...
trainer = PPOTrainer(
model=model,
...
)
...
``` | 0 |
hf_public_repos/trl/docs | hf_public_repos/trl/docs/source/logging.mdx | # Logging
As reinforcement learning algorithms are historically challenging to debug, it's important to pay careful attention to logging.
By default, the TRL [`PPOTrainer`] saves a lot of relevant information to `wandb` or `tensorboard`.
Upon initialization, pass one of these two options to the [`PPOConfig`]:
```
config = PPOConfig(
model_name=args.model_name,
log_with=`wandb`, # or `tensorboard`
)
```
If you want to log with tensorboard, add the kwarg `project_kwargs={"logging_dir": PATH_TO_LOGS}` to the PPOConfig.
## PPO Logging
Here's a brief explanation for the logged metrics provided in the data:
Key metrics to monitor. We want to maximize the reward, maintain a low KL divergence, and maximize entropy:
1. `env/reward_mean`: The average reward obtained from the environment. Alias `ppo/mean_scores`, which is sed to specifically monitor the reward model.
1. `env/reward_std`: The standard deviation of the reward obtained from the environment. Alias ``ppo/std_scores`, which is sed to specifically monitor the reward model.
1. `env/reward_dist`: The histogram distribution of the reward obtained from the environment.
1. `objective/kl`: The mean Kullback-Leibler (KL) divergence between the old and new policies. It measures how much the new policy deviates from the old policy. The KL divergence is used to compute the KL penalty in the objective function.
1. `objective/kl_dist`: The histogram distribution of the `objective/kl`.
1. `objective/kl_coef`: The coefficient for Kullback-Leibler (KL) divergence in the objective function.
1. `ppo/mean_non_score_reward`: The **KL penalty** calculated by `objective/kl * objective/kl_coef` as the total reward for optimization to prevent the new policy from deviating too far from the old policy.
1. `objective/entropy`: The entropy of the model's policy, calculated by `-logprobs.sum(-1).mean()`. High entropy means the model's actions are more random, which can be beneficial for exploration.
Training stats:
1. `ppo/learning_rate`: The learning rate for the PPO algorithm.
1. `ppo/policy/entropy`: The entropy of the model's policy, calculated by `pd = torch.nn.functional.softmax(logits, dim=-1); entropy = torch.logsumexp(logits, dim=-1) - torch.sum(pd * logits, dim=-1)`. It measures the randomness of the policy.
1. `ppo/policy/clipfrac`: The fraction of probability ratios (old policy / new policy) that fell outside the clipping range in the PPO objective. This can be used to monitor the optimization process.
1. `ppo/policy/approxkl`: The approximate KL divergence between the old and new policies, measured by `0.5 * masked_mean((logprobs - old_logprobs) ** 2, mask)`, corresponding to the `k2` estimator in http://joschu.net/blog/kl-approx.html
1. `ppo/policy/policykl`: Similar to `ppo/policy/approxkl`, but measured by `masked_mean(old_logprobs - logprobs, mask)`, corresponding to the `k1` estimator in http://joschu.net/blog/kl-approx.html
1. `ppo/policy/ratio`: The histogram distribution of the ratio between the new and old policies, used to compute the PPO objective.
1. `ppo/policy/advantages_mean`: The average of the GAE (Generalized Advantage Estimation) advantage estimates. The advantage function measures how much better an action is compared to the average action at a state.
1. `ppo/policy/advantages`: The histogram distribution of `ppo/policy/advantages_mean`.
1. `ppo/returns/mean`: The mean of the TD(λ) returns, calculated by `returns = advantage + values`, another indicator of model performance. See https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/ for more details.
1. `ppo/returns/var`: The variance of the TD(λ) returns, calculated by `returns = advantage + values`, another indicator of model performance.
1. `ppo/val/mean`: The mean of the values, used to monitor the value function's performance.
1. `ppo/val/var` : The variance of the values, used to monitor the value function's performance.
1. `ppo/val/var_explained`: The explained variance for the value function, used to monitor the value function's performance.
1. `ppo/val/clipfrac`: The fraction of the value function's predicted values that are clipped.
1. `ppo/val/vpred`: The predicted values from the value function.
1. `ppo/val/error`: The mean squared error between the `ppo/val/vpred` and returns, used to monitor the value function's performance.
1. `ppo/loss/policy`: The policy loss for the Proximal Policy Optimization (PPO) algorithm.
1. `ppo/loss/value`: The loss for the value function in the PPO algorithm. This value quantifies how well the function estimates the expected future rewards.
1. `ppo/loss/total`: The total loss for the PPO algorithm. It is the sum of the policy loss and the value function loss.
Stats on queries, responses, and logprobs:
1. `tokens/queries_len_mean`: The average length of the queries tokens.
1. `tokens/queries_len_std`: The standard deviation of the length of the queries tokens.
1. `tokens/queries_dist`: The histogram distribution of the length of the queries tokens.
1. `tokens/responses_len_mean`: The average length of the responses tokens.
1. `tokens/responses_len_std`: The standard deviation of the length of the responses tokens.
1. `tokens/responses_dist`: The histogram distribution of the length of the responses tokens. (Costa: inconsistent naming, should be `tokens/responses_len_dist`)
1. `objective/logprobs`: The histogram distribution of the log probabilities of the actions taken by the model.
1. `objective/ref_logprobs`: The histogram distribution of the log probabilities of the actions taken by the reference model.
### Crucial values
During training, many values are logged, here are the most important ones:
1. `env/reward_mean`,`env/reward_std`, `env/reward_dist`: the properties of the reward distribution from the "environment" / reward model
1. `ppo/mean_non_score_reward`: The mean negated KL penalty during training (shows the delta between the reference model and the new policy over the batch in the step)
Here are some parameters that are useful to monitor for stability (when these diverge or collapse to 0, try tuning variables):
1. `ppo/loss/value`: it will spike / NaN when not going well.
1. `ppo/policy/ratio`: `ratio` being 1 is a baseline value, meaning that the probability of sampling a token is the same under the new and old policy. If the ratio is too high like 200, it means the probability of sampling a token is 200 times higher under the new policy than the old policy. This is a sign that the new policy is too different from the old policy, which will likely cause overoptimization and collapse training later on.
1. `ppo/policy/clipfrac` and `ppo/policy/approxkl`: if `ratio` is too high, the `ratio` is going to get clipped, resulting in high `clipfrac` and high `approxkl` as well.
1. `objective/kl`: it should stay positive so that the policy is not too far away from the reference policy.
1. `objective/kl_coef`: The target coefficient with [`AdaptiveKLController`]. Often increases before numerical instabilities. | 0 |
hf_public_repos/trl/docs | hf_public_repos/trl/docs/source/sentiment_tuning.mdx | # Sentiment Tuning Examples
The notebooks and scripts in this examples show how to fine-tune a model with a sentiment classifier (such as `lvwerra/distilbert-imdb`).
Here's an overview of the notebooks and scripts in the [trl repository](https://github.com/huggingface/trl/tree/main/examples):
| File | Description |
|------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------|
| [`examples/scripts/ppo.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/ppo.py) [](https://colab.research.google.com/github/huggingface/trl/blob/main/examples/sentiment/notebooks/gpt2-sentiment.ipynb) | This script shows how to use the `PPOTrainer` to fine-tune a sentiment analysis model using IMDB dataset |
| [`examples/notebooks/gpt2-sentiment.ipynb`](https://github.com/huggingface/trl/tree/main/examples/notebooks/gpt2-sentiment.ipynb) | This notebook demonstrates how to reproduce the GPT2 imdb sentiment tuning example on a jupyter notebook. |
| [`examples/notebooks/gpt2-control.ipynb`](https://github.com/huggingface/trl/tree/main/examples/notebooks/gpt2-control.ipynb) [](https://colab.research.google.com/github/huggingface/trl/blob/main/examples/sentiment/notebooks/gpt2-sentiment-control.ipynb) | This notebook demonstrates how to reproduce the GPT2 sentiment control example on a jupyter notebook.
## Usage
```bash
# 1. run directly
python examples/scripts/ppo.py
# 2. run via `accelerate` (recommended), enabling more features (e.g., multiple GPUs, deepspeed)
accelerate config # will prompt you to define the training configuration
accelerate launch examples/scripts/ppo.py # launches training
# 3. get help text and documentation
python examples/scripts/ppo.py --help
# 4. configure logging with wandb and, say, mini_batch_size=1 and gradient_accumulation_steps=16
python examples/scripts/ppo.py --ppo_config.log_with wandb --ppo_config.mini_batch_size 1 --ppo_config.gradient_accumulation_steps 16
```
Note: if you don't want to log with `wandb` remove `log_with="wandb"` in the scripts/notebooks. You can also replace it with your favourite experiment tracker that's [supported by `accelerate`](https://huggingface.co/docs/accelerate/usage_guides/tracking).
## Few notes on multi-GPU
To run in multi-GPU setup with DDP (distributed Data Parallel) change the `device_map` value to `device_map={"": Accelerator().process_index}` and make sure to run your script with `accelerate launch yourscript.py`. If you want to apply naive pipeline parallelism you can use `device_map="auto"`.
## Benchmarks
Below are some benchmark results for `examples/scripts/ppo.py`. To reproduce locally, please check out the `--command` arguments below.
```bash
python benchmark/benchmark.py \
--command "python examples/scripts/ppo.py --ppo_config.log_with wandb" \
--num-seeds 5 \
--start-seed 1 \
--workers 10 \
--slurm-nodes 1 \
--slurm-gpus-per-task 1 \
--slurm-ntasks 1 \
--slurm-total-cpus 12 \
--slurm-template-path benchmark/trl.slurm_template
```
## With and without gradient accumulation
```bash
python benchmark/benchmark.py \
--command "python examples/scripts/ppo.py --ppo_config.exp_name sentiment_tuning_step_grad_accu --ppo_config.mini_batch_size 1 --ppo_config.gradient_accumulation_steps 128 --ppo_config.log_with wandb" \
--num-seeds 5 \
--start-seed 1 \
--workers 10 \
--slurm-nodes 1 \
--slurm-gpus-per-task 1 \
--slurm-ntasks 1 \
--slurm-total-cpus 12 \
--slurm-template-path benchmark/trl.slurm_template
```
## Comparing different models (gpt2, gpt2-xl, falcon, llama2)
```bash
python benchmark/benchmark.py \
--command "python examples/scripts/ppo.py --ppo_config.exp_name sentiment_tuning_gpt2 --ppo_config.log_with wandb" \
--num-seeds 5 \
--start-seed 1 \
--workers 10 \
--slurm-nodes 1 \
--slurm-gpus-per-task 1 \
--slurm-ntasks 1 \
--slurm-total-cpus 12 \
--slurm-template-path benchmark/trl.slurm_template
python benchmark/benchmark.py \
--command "python examples/scripts/ppo.py --ppo_config.exp_name sentiment_tuning_gpt2xl_grad_accu --ppo_config.model_name gpt2-xl --ppo_config.mini_batch_size 16 --ppo_config.gradient_accumulation_steps 8 --ppo_config.log_with wandb" \
--num-seeds 5 \
--start-seed 1 \
--workers 10 \
--slurm-nodes 1 \
--slurm-gpus-per-task 1 \
--slurm-ntasks 1 \
--slurm-total-cpus 12 \
--slurm-template-path benchmark/trl.slurm_template
python benchmark/benchmark.py \
--command "python examples/scripts/ppo.py --ppo_config.exp_name sentiment_tuning_falcon_rw_1b --ppo_config.model_name tiiuae/falcon-rw-1b --ppo_config.log_with wandb" \
--num-seeds 5 \
--start-seed 1 \
--workers 10 \
--slurm-nodes 1 \
--slurm-gpus-per-task 1 \
--slurm-ntasks 1 \
--slurm-total-cpus 12 \
--slurm-template-path benchmark/trl.slurm_template
```
## With and without PEFT
```
python benchmark/benchmark.py \
--command "python examples/scripts/ppo.py --ppo_config.exp_name sentiment_tuning_peft --use_peft --ppo_config.log_with wandb" \
--num-seeds 5 \
--start-seed 1 \
--workers 10 \
--slurm-nodes 1 \
--slurm-gpus-per-task 1 \
--slurm-ntasks 1 \
--slurm-total-cpus 12 \
--slurm-template-path benchmark/trl.slurm_template
```
| 0 |
hf_public_repos/trl/docs | hf_public_repos/trl/docs/source/quickstart.mdx | # Quickstart
## How does it work?
Fine-tuning a language model via PPO consists of roughly three steps:
1. **Rollout**: The language model generates a response or continuation based on a query which could be the start of a sentence.
2. **Evaluation**: The query and response are evaluated with a function, model, human feedback, or some combination of them. The important thing is that this process should yield a scalar value for each query/response pair. The optimization will aim at maximizing this value.
3. **Optimization**: This is the most complex part. In the optimisation step the query/response pairs are used to calculate the log-probabilities of the tokens in the sequences. This is done with the model that is trained and a reference model, which is usually the pre-trained model before fine-tuning. The KL-divergence between the two outputs is used as an additional reward signal to make sure the generated responses don't deviate too far from the reference language model. The active language model is then trained with PPO.
The full process is illustrated in the following figure:
<img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/trl_overview.png"/>
## Minimal example
The following code illustrates the steps above.
```python
# 0. imports
import torch
from transformers import GPT2Tokenizer
from trl import AutoModelForCausalLMWithValueHead, PPOConfig, PPOTrainer
# 1. load a pretrained model
model = AutoModelForCausalLMWithValueHead.from_pretrained("gpt2")
model_ref = AutoModelForCausalLMWithValueHead.from_pretrained("gpt2")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
tokenizer.pad_token = tokenizer.eos_token
# 2. initialize trainer
ppo_config = {"batch_size": 1}
config = PPOConfig(**ppo_config)
ppo_trainer = PPOTrainer(config, model, model_ref, tokenizer)
# 3. encode a query
query_txt = "This morning I went to the "
query_tensor = tokenizer.encode(query_txt, return_tensors="pt").to(model.pretrained_model.device)
# 4. generate model response
generation_kwargs = {
"min_length": -1,
"top_k": 0.0,
"top_p": 1.0,
"do_sample": True,
"pad_token_id": tokenizer.eos_token_id,
"max_new_tokens": 20,
}
response_tensor = ppo_trainer.generate([item for item in query_tensor], return_prompt=False, **generation_kwargs)
response_txt = tokenizer.decode(response_tensor[0])
# 5. define a reward for response
# (this could be any reward such as human feedback or output from another model)
reward = [torch.tensor(1.0, device=model.pretrained_model.device)]
# 6. train model with ppo
train_stats = ppo_trainer.step([query_tensor[0]], [response_tensor[0]], reward)
```
In general, you would run steps 3-6 in a for-loop and run it on many diverse queries. You can find more realistic examples in the examples section.
## How to use a trained model
After training a `AutoModelForCausalLMWithValueHead`, you can directly use the model in `transformers`.
```python
# .. Let's assume we have a trained model using `PPOTrainer` and `AutoModelForCausalLMWithValueHead`
# push the model on the Hub
model.push_to_hub("my-fine-tuned-model-ppo")
# or save it locally
model.save_pretrained("my-fine-tuned-model-ppo")
# load the model from the Hub
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("my-fine-tuned-model-ppo")
```
You can also load your model with `AutoModelForCausalLMWithValueHead` if you want to use the value head, for example to continue training.
```python
from trl.model import AutoModelForCausalLMWithValueHead
model = AutoModelForCausalLMWithValueHead.from_pretrained("my-fine-tuned-model-ppo")
```
| 0 |
hf_public_repos/trl/docs | hf_public_repos/trl/docs/source/text_environments.md | # Text Environments
Text environments provide a learning ground for language agents. It allows a language model to use tools to accomplish a task such as using a Python interpreter to answer math questions or using a search index for trivia questions. Having access to tools allows language models to solve tasks that would be very hard for the models itself but can be trivial for the appropriate tools. A good example is arithmetics of large numbers that become a simple copy-paste task once you have access to a calculator.
<div style="text-align: center">
<img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/textenv.png">
</div>
Let's dive into how text environments work and start with tools!
## Tools
One of the core building blocks of text environments are tools that the model can use to solve tasks. In general tools can be any Python function that takes a string as input and returns string. The `TextEnvironment` offers two options for tools: either go with predefined tools from `transformers.Tool` or define your own function or class with `__call__` method. Let's have a look at both!
### `transformers.Tool`
Text environments fully support tools of the class `transformers.Tool`. The advantage of building tools in that framework is that they can easily be shared
```Python
from transformers import load_tool
# simple calculator tool that runs +-/* operations
calc_tool = load_tool("ybelkada/simple-calculator")
# python interpreter that executes program and returns outputs
py_tool = load_tool("lvwerra/python-interpreter")
# wikipedia search index that returns best search match
wiki_tool = load_tool("vwxyzjn/pyserini-wikipedia-kilt-doc")
```
These tools are either loaded from the hub or from a local folder. Using the tool is as simple as calling them with a text query:
```Python
calc_tool("1/2")
>>> "0.5"
```
Note that both input and return values are strings to enable easy usage with a language model.
### Custom Tools
The following is an example of a tool that adds two integers:
```Python
def add(text):
int_1, int_2 = text.split("+")
result = int(int_1) + int(int_2)
return str(result)
print(add("1+1"))
>>> "2"
```
We looked at basic examples such as a calculator but the principle holds for more complex tools as well such as a web search tool where you input the query and get the search results in return. Now let's look at how the model can use the tools with the call syntax.
### Call syntax
In order to have a unified way for the model to call a tool we created a simple syntax that looks as follows:
```python
"<request><TOOL_NAME>QUERY<call>TOOL_RESPONSE<response>"
```
There are a few special tokens involved so let's decompose it: First the model can signal that it wants to use a tool by emitting the `<request>` token. After that we want to know the name of the tool to call which is done by enclosing the tool name with `<>` brackets. Once we know which tool to call the tool query follows which is in free text form. The `<call>` tokens signifies the end of the query and stops the model generation. At this point the model output is parsed and the query sent to the tool. The environment appends the tool response to the string followed by the `<response>` token to show the end the tool output.
Let's look at the concrete example of the calculator and assume its name is `Calculator` (more on how the name of a tool is inferred later):
```python
"<request><Calculator>1/2<call>0.5<response>"
```
Finally, the episode is ended and generation stops when the model generates `<submit>` which marks the interaction as completed.
Now let's have a look how we can create a new text environment!
## Create a `TextEnvironment`
```python
prompt = """\
What is 13-3?
<request><SimpleCalculatorTool>13-3<call>10.0<response>
Result=10<submit>
"""
def reward_fn(result, answer):
"""Simplified reward function returning 1 if result matches answer and 0 otherwise."""
result_parsed = result.split("=")[1].split("<")[0]
return int(result_parsed==answer)
text_env = TextEnvironemnt(
model=model,
tokenizer=tokenizer,
tools= {"SimpleCalculatorTool": load_tool("ybelkada/simple-calculator")},
reward_fn=exact_match_reward,
prompt=prompt,
max_turns=1
max_tool_response=100
generation_kwargs={"do_sample": "true"}
)
```
Let's decompose the settings:
| Argument | Description |
|:-------------------|:----------------|
| `model` | Language model to interact with the environment and generate requests. |
| `tokenizer` | Tokenizer of language model handling tokenization of strings. |
| `tools` | `list` of `dict` of tools. If former the name of the tool is inferred from class name and otherwise it's the keys of the dictionary.|
| `reward_fn` | A function that takes a string as input and returns. Can have extra arguments that are passed to `.run()` such as ground truth.|
| `prompt` | Prompt to prepend to every task. Usually a few examples to demonstrate to the model how to use the tools in a few-shot fashion. |
| `max_turns` | Maximum number of interactions between model and tools before episode ends.|
| `max_tool_response`| The tool response is truncated to this number to avoid running out of model context.|
| `max_length` | The maximum number of tokens to allow in an episode. |
| `generation_kwargs`| Generation settings used by the language model. |
You can customize the environment to your needs and add custom tools and settings. Let's see how you can use the environment to have the model interact with the available tools!
## Run an Episode
To run a set of queries through the text environment one can simply use the `run` method.
```python
queries = ["What is 1/2?"]
answers = ["0.5"]
queries, responses, masks, rewards, histories = text_env.run(queries, answers=answers)
```
This will execute the model/tool feedback loop for each query until either no tool is called anymore, the maximum number of turns is reached or to maximum number of tokens in an episode is exceeded. The extra `kwargs` (e.g. `answers=answers` above) passed to `run` will be passed on to the reward function.
There are five objects that are returned by `run`:
- `queries`: a list of the tokenized queries
- `responses`: all tokens that have been generated withing the environment including model and tool tokens
- `masks`: mask that indicates which tokens have been generated by the model and which tokens are generated by the tool
- `rewards`: a list of reward for each query/response
- `histories`: list of `TextHistory` objects, which are useful objects containing all the above and also the text equivalents
The masks are crucial for training as we don't want to optimize tokens that the model has not generated which are tokens produced by the tools.
Next, we'll train a PPO step with the generated responses!
### Train
Training on episodes from the `TextEnvironment` is straight forward and simply requires forwarding all the returned variables except the `TextHistory` objects to the `step` method:
```python
train_stats = ppo_trainer.step(queries, responses, rewards, masks)
```
## `TextHistory`
The `TextHistory` object stores the interactions between the model and the text environment. It stores tokens and text generated in each turn and their source in each turn (model or system) as well as rewards. Let's go through the class attributes and methods.
### Attributes
The following table summarises the available attributes of the `TextEnvironment` class:
| Attribute | Description |
|:-------------------|:----------------|
| `text` | The full string of the text generated in the text environment with both model and system generated text. |
| `text_spans` | A list of tuples with the spans for each model or system generated text segment. |
| `system_spans` | A list of boolean values indicating if the segment is model or system generated. |
| `tokens` | All tokens generated in text environment with both model and system generated tokens. |
| `token_spans` | Similar to `text_spans` the `token_spans` indicate the boundaries of model andsystem generated tokens. |
| `token_masks` | The token masks can be used to ignore system generated tokens by masking them. |
| `completed` | Indicates if the interaction with the environment has completed. |
| `truncated` | Indicates if the interaction with the environment has completed because max length was reached. |
With these attributes you can reconstruct every interaction of the model with the `TextEnvironment`. The `TextHistory` also lets you visualize the text history. Let's have a look!
### Visualization
When the model interacts inside the `TextEnvironment` it can be useful to visualize and separate which parts of the text outputs were generated by the model and which parts come from the system and tools. For that purpose there are the two methods [`TextHistory.show_text`] and [`TextHistory.show_tokens`]. They print the text and tokens respectively and highlight the various segments using the [`rich` libray](https://github.com/Textualize/rich) (make sure to install it before using these methods).
You can see that the prompt is highlighted in gray, whereas system segments such as query and tool responses are highlighted in green. All segments generated by the model are highlighted in blue and in addition to the pure text output the reward is displayed as additional text in plum. Here an example of `show_text`:
<div style="text-align: center">
<img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/textenv_show_text.png" width=600>
</div>
Sometimes there can be tricky tokenization related issues that are hidden when showing the decoded text. Thus `TextHistory` also offers an option to display the same highlighting on the tokens directly with `show_tokens`:
<div style="text-align: center">
<img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/textenv_show_tokens.png" width=800>
</div>
Note that you can turn on the colour legend by passing `show_legend=True`.
## API Documentation
[[autodoc]] TextEnvironment
[[autodoc]] TextHistory
| 0 |
hf_public_repos/trl/docs | hf_public_repos/trl/docs/source/dpo_trainer.mdx | # DPO Trainer
TRL supports the DPO Trainer for training language models from preference data, as described in the paper [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://arxiv.org/abs/2305.18290) by Rafailov et al., 2023. For a full example have a look at [`examples/scripts/dpo.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/dpo.py).
The first step as always is to train your SFT model, to ensure the data we train on is in-distribution for the DPO algorithm.
## Expected dataset format
The DPO trainer expects a very specific format for the dataset. Since the model will be trained to directly optimize the preference of which sentence is the most relevant, given two sentences. We provide an example from the [`Anthropic/hh-rlhf`](https://huggingface.co/datasets/Anthropic/hh-rlhf) dataset below:
<div style="text-align: center">
<img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/rlhf-antropic-example.png", width="50%">
</div>
Therefore the final dataset object should contain these 3 entries if you use the default `DPODataCollatorWithPadding` data collator. The entries should be named:
- `prompt`
- `chosen`
- `rejected`
for example:
```py
dpo_dataset_dict = {
"prompt": [
"hello",
"how are you",
"What is your name?",
"What is your name?",
"Which is the best programming language?",
"Which is the best programming language?",
"Which is the best programming language?",
],
"chosen": [
"hi nice to meet you",
"I am fine",
"My name is Mary",
"My name is Mary",
"Python",
"Python",
"Java",
],
"rejected": [
"leave me alone",
"I am not fine",
"Whats it to you?",
"I dont have a name",
"Javascript",
"C++",
"C++",
],
}
```
where the `prompt` contains the context inputs, `chosen` contains the corresponding chosen responses and `rejected` contains the corresponding negative (rejected) responses. As can be seen a prompt can have multiple responses and this is reflected in the entries being repeated in the dictionary's value arrays.
## Expected model format
The DPO trainer expects a model of `AutoModelForCausalLM`, compared to PPO that expects `AutoModelForCausalLMWithValueHead` for the value function.
## Using the `DPOTrainer`
For a detailed example have a look at the `examples/scripts/dpo.py` script. At a high level we need to initialize the `DPOTrainer` with a `model` we wish to train, a reference `ref_model` which we will use to calculate the implicit rewards of the preferred and rejected response, the `beta` refers to the hyperparameter of the implicit reward, and the dataset contains the 3 entries listed above. Note that the `model` and `ref_model` need to have the same architecture (ie decoder only or encoder-decoder).
```py
dpo_trainer = DPOTrainer(
model,
model_ref,
args=training_args,
beta=0.1,
train_dataset=train_dataset,
tokenizer=tokenizer,
)
```
After this one can then call:
```py
dpo_trainer.train()
```
Note that the `beta` is the temperature parameter for the DPO loss, typically something in the range of `0.1` to `0.5`. We ignore the reference model as `beta` -> 0.
## Loss function
Given the preference data, we can fit a binary classifier according to the Bradley-Terry model and in fact the DPO authors propose the sigmoid loss on the normalized likelihood via the `logsigmoid` to fit a logistic regression.
The [RSO](https://arxiv.org/abs/2309.06657) authors propose to use a hinge loss on the normalized likelihood from the [SLiC](https://arxiv.org/abs/2305.10425) paper. The `DPOTrainer` can be switched to this loss via the `loss_type="hinge"` argument and the `beta` in this case is the reciprocal of the margin.
The [IPO](https://arxiv.org/abs/2310.12036) authors provide a deeper theoretical understanding of the DPO algorithms and identify an issue with overfitting and propose an alternative loss which can be used via the `loss_type="ipo"` argument to the trainer.
The [cDPO](https://ericmitchell.ai/cdpo.pdf) is a tweak on the DPO loss where we assume that the preference labels are noisy with some probability that can be passed to the `DPOTrainer` via `label_smoothing` argument (between 0 and 0.5) and then a conservative DPO loss is used.
## Logging
While training and evaluating we record the following reward metrics:
* `rewards/chosen`: the mean difference between the log probabilities of the policy model and the reference model for the chosen responses scaled by beta
* `rewards/rejected`: the mean difference between the log probabilities of the policy model and the reference model for the rejected responses scaled by beta
* `rewards/accuracies`: mean of how often the chosen rewards are > than the corresponding rejected rewards
* `rewards/margins`: the mean difference between the chosen and corresponding rejected rewards
## DPOTrainer
[[autodoc]] DPOTrainer | 0 |
hf_public_repos/trl/docs | hf_public_repos/trl/docs/source/how_to_train.md | # Training FAQ
## What Metrics Should I Look at?
When performing classical supervised fine-tuning of language models, the loss (especially the validation loss) serves as a good indicator of the training progress. However, in Reinforcement Learning (RL), the loss becomes less informative about the model's performance, and its value may fluctuate while the actual performance improves.
To address this, we recommend focusing on two key metrics first:
**Mean Reward**: The primary goal is to maximize the reward achieved by the model during RL training.
**Objective KL Divergence**: KL divergence (Kullback-Leibler divergence) measures the dissimilarity between two probability distributions. In the context of RL training, we use it to quantify the difference between the current model and a reference model. Ideally, we want to keep the KL divergence between 0 and 10 to ensure the model's generated text remains close to what the reference model produces.
However, there are more metrics that can be useful for debugging, checkout the [logging section](logging).
## Why Do We Use a Reference Model, and What's the Purpose of KL Divergence?
When training RL models, optimizing solely for reward may lead to unexpected behaviors, where the model exploits the environment in ways that don't align with good language generation. In the case of RLHF, we use a reward model trained to predict whether a generated text is highly ranked by humans.
However, the RL model being optimized against the reward model may learn patterns that yield high reward but do not represent good language. This can result in extreme cases where the model generates texts with excessive exclamation marks or emojis to maximize the reward. In some worst-case scenarios, the model may generate patterns completely unrelated to natural language yet receive high rewards, similar to adversarial attacks.
<div style="text-align: center">
<img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/kl-example.png">
<p style="text-align: center;"> <b>Figure:</b> Samples without a KL penalty from <a href="https://arxiv.org/pdf/1909.08593.pdf">https://arxiv.org/pdf/1909.08593.pdf</a>. </p>
</div>
To address this issue, we add a penalty to the reward function based on the KL divergence between the current model and the reference model. By doing this, we encourage the model to stay close to what the reference model generates.
## What Is the Concern with Negative KL Divergence?
If you generate text by purely sampling from the model distribution things work fine in general. But when you use the `generate` method there are a few caveats because it does not always purely sample depending on the settings which can cause KL-divergence to go negative. Essentially when the active model achieves `log_p_token_active < log_p_token_ref` we get negative KL-div. This can happen in a several cases:
- **top-k sampling**: the model can smooth out the probability distribution causing the top-k tokens having a smaller probability than those of the reference model but they still are selected
- **min_length**: this ignores the EOS token until `min_length` is reached. thus the model can assign a very low log prob to the EOS token and very high probs to all others until min_length is reached
- **min_length**: this ignores the EOS token until `min_length` is reached, thus the model can assign a very low log prob to the EOS token and very high probs to all others until min_length is reached
These are just a few examples. Why is negative KL an issue? The total reward `R` is computed `R = r - beta * KL` so if the model can learn how to drive KL-divergence negative it effectively gets a positive reward. In many cases it can be much easier to exploit such a bug in the generation than actually learning the reward function. In addition the KL can become arbitrarily small thus the actual reward can be very small compared to it.
So how should you generate text for PPO training? Let's have a look!
## How to generate text for training?
In order to avoid the KL issues described above we recommend to use the following settings:
```python
generation_kwargs = {
"min_length": -1, # don't ignore the EOS token (see above)
"top_k": 0.0, # no top-k sampling
"top_p": 1.0, # no nucleus sampling
"do_sample": True, # yes, we want to sample
"pad_token_id": tokenizer.eos_token_id, # most decoder models don't have a padding token - use EOS token instead
"max_new_tokens": 32, # specify how many tokens you want to generate at most
}
```
With these settings we usually don't encounter any issues. You can also experiments with other settings but if you encounter issues with negative KL-divergence try to go back to these and see if they persist.
## How can debug your own use-case?
Debugging the RL pipeline can be challenging due to its complexity. Here are some tips and suggestions to make the process easier:
- **Start from a working example**: Begin with a working example from the trl repository and gradually modify it to fit your specific use-case. Changing everything at once can make it difficult to identify the source of potential issues. For example, you can start by replacing the model in the example and once you figure out the best hyperparameters try to switch to your dataset and reward model. If you change everything at once you won't know where a potential problem comes from.
- **Start small, scale later**: Training large models can be very slow and take several hours or days until you see any improvement. For debugging this is not a convenient timescale so try to use small model variants during the development phase and scale up once that works. That being said you sometimes have to be careful as small models might not have the capacity to solve a complicated task either.
- **Start simple**: Try to start with a minimal example and build complexity from there. Your use-case might require for example a complicated reward function consisting of many different rewards - try to use one signal first and see if you can optimize that and then add more complexity after that.
- **Inspect the generations**: It's always a good idea to inspect what the model is generating. Maybe there is a big in your post-processing or your prompt. Due to bad settings you might cut-off generations too soon. These things are very hard to see on the metrics but very obvious if you look at the generations.
- **Inspect the reward model**: If you reward is not improving over time maybe there's an issue with the reward model. You can look at extreme cases to see if it does what it should: e.g. in the sentiment case you can check if simple positive and negative examples really get different rewards. And you can look at the distribution of your dataset. Finally, maybe the reward is dominated by the query which the model can't affect so you might need to normalize this (e.g. reward of query+response minus reward of the query).
These are just a few tips that we find helpful - if you have more useful tricks feel free to open a PR to add them as well!
| 0 |
hf_public_repos/trl/docs | hf_public_repos/trl/docs/source/ddpo_trainer.mdx | # Denoising Diffusion Policy Optimization
## The why
| Before | After DDPO finetuning |
| --- | --- |
| <div style="text-align: center"><img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/pre_squirrel.png"/></div> | <div style="text-align: center"><img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/post_squirrel.png"/></div> |
| <div style="text-align: center"><img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/pre_crab.png"/></div> | <div style="text-align: center"><img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/post_crab.png"/></div> |
| <div style="text-align: center"><img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/pre_starfish.png"/></div> | <div style="text-align: center"><img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/post_starfish.png"/></div> |
## Getting started with Stable Diffusion finetuning with reinforcement learning
The machinery for finetuning of Stable Diffusion models with reinforcement learning makes heavy use of HuggingFace's `diffusers`
library. A reason for stating this is that getting started requires a bit of familiarity with the `diffusers` library concepts, mainly two of them - pipelines and schedulers.
Right out of the box (`diffusers` library), there isn't a `Pipeline` nor a `Scheduler` instance that is suitable for finetuning with reinforcement learning. Some adjustments need to made.
There is a pipeline interface that is provided by this library that is required to be implemented to be used with the `DDPOTrainer`, which is the main machinery for fine-tuning Stable Diffusion with reinforcement learning. **Note: Only the StableDiffusion architecture is supported at this point.**
There is a default implementation of this interface that you can use out of the box. Assuming the default implementation is sufficient and/or to get things moving, refer to the training example alongside this guide.
The point of the interface is to fuse the pipeline and the scheduler into one object which allows for minimalness in terms of having the constraints all in one place. The interface was designed in hopes of catering to pipelines and schedulers beyond the examples in this repository and elsewhere at this time of writing. Also the scheduler step is a method of this pipeline interface and this may seem redundant given that the raw scheduler is accessible via the interface but this is the only way to constrain the scheduler step output to an output type befitting of the algorithm at hand (DDPO).
For a more detailed look into the interface and the associated default implementation, go [here](https://github.com/lvwerra/trl/tree/main/trl/models/modeling_sd_base.py)
Note that the default implementation has a LoRA implementation path and a non-LoRA based implementation path. The LoRA flag enabled by default and this can be turned off by passing in the flag to do so. LORA based training is faster and the LORA associated model hyperparameters responsible for model convergence aren't as finicky as non-LORA based training.
Also in addition, there is the expectation of providing a reward function and a prompt function. The reward function is used to evaluate the generated images and the prompt function is used to generate the prompts that are used to generate the images.
## Getting started with `examples/scripts/ddpo.py`
The `ddpo.py` script is a working example of using the `DDPO` trainer to finetune a Stable Diffusion model. This example explicitly configures a small subset of the overall parameters associated with the config object (`DDPOConfig`).
**Note:** one A100 GPU is recommended to get this running. Anything below a A100 will not be able to run this example script and even if it does via relatively smaller sized parameters, the results will most likely be poor.
Almost every configuration parameter has a default. There is only one commandline flag argument that is required of the user to get things up and running. The user is expected to have a [huggingface user access token](https://huggingface.co/docs/hub/security-tokens) that will be used to upload the model post finetuning to HuggingFace hub. The following bash command is to be entered to get things running
```batch
python ddpo.py --hf_user_access_token <token>
```
To obtain the documentation of `stable_diffusion_tuning.py`, please run `python stable_diffusion_tuning.py --help`
The following are things to keep in mind (The code checks this for you as well) in general while configuring the trainer (beyond the use case of using the example script)
- The configurable sample batch size (`--ddpo_config.sample_batch_size=6`) should be greater than or equal to the configurable training batch size (`--ddpo_config.train_batch_size=3`)
- The configurable sample batch size (`--ddpo_config.sample_batch_size=6`) must be divisible by the configurable train batch size (`--ddpo_config.train_batch_size=3`)
- The configurable sample batch size (`--ddpo_config.sample_batch_size=6`) must be divisible by both the configurable gradient accumulation steps (`--ddpo_config.train_gradient_accumulation_steps=1`) and the configurable accelerator processes count
## Setting up the image logging hook function
Expect the function to be given a list of lists of the form
```python
[[image, prompt, prompt_metadata, rewards, reward_metadata], ...]
```
and `image`, `prompt`, `prompt_metadata`, `rewards`, `reward_metadata` are batched.
The last list in the lists of lists represents the last sample batch. You are likely to want to log this one
While you are free to log however you want the use of `wandb` or `tensorboard` is recommended.
### Key terms
- `rewards` : The rewards/score is a numerical associated with the generated image and is key to steering the RL process
- `reward_metadata` : The reward metadata is the metadata associated with the reward. Think of this as extra information payload delivered alongside the reward
- `prompt` : The prompt is the text that is used to generate the image
- `prompt_metadata` : The prompt metadata is the metadata associated with the prompt. A situation where this will not be empty is when the reward model comprises of a [`FLAVA`](https://huggingface.co/docs/transformers/model_doc/flava) setup where questions and ground answers (linked to the generated image) are expected with the generated image (See here: https://github.com/kvablack/ddpo-pytorch/blob/main/ddpo_pytorch/rewards.py#L45)
- `image` : The image generated by the Stable Diffusion model
Example code for logging sampled images with `wandb` is given below.
```python
# for logging these images to wandb
def image_outputs_hook(image_data, global_step, accelerate_logger):
# For the sake of this example, we only care about the last batch
# hence we extract the last element of the list
result = {}
images, prompts, _, rewards, _ = image_data[-1]
for i, image in enumerate(images):
pil = Image.fromarray(
(image.cpu().numpy().transpose(1, 2, 0) * 255).astype(np.uint8)
)
pil = pil.resize((256, 256))
result[f"{prompts[i]:.25} | {rewards[i]:.2f}"] = [pil]
accelerate_logger.log_images(
result,
step=global_step,
)
```
### Using the finetuned model
Assuming you've done with all the epochs and have pushed up your model to the hub, you can use the finetuned model as follows
```python
import torch
from trl import DefaultDDPOStableDiffusionPipeline
pipeline = DefaultDDPOStableDiffusionPipeline("metric-space/ddpo-finetuned-sd-model")
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
# memory optimization
pipeline.vae.to(device, torch.float16)
pipeline.text_encoder.to(device, torch.float16)
pipeline.unet.to(device, torch.float16)
prompts = ["squirrel", "crab", "starfish", "whale","sponge", "plankton"]
results = pipeline(prompts)
for prompt, image in zip(prompts,results.images):
image.save(f"{prompt}.png")
```
## Credits
This work is heavily influenced by the repo [here](https://github.com/kvablack/ddpo-pytorch) and the associated paper [Training Diffusion Models
with Reinforcement Learning by Kevin Black, Michael Janner, Yilan Du, Ilya Kostrikov, Sergey Levine](https://arxiv.org/abs/2305.13301). | 0 |
hf_public_repos/trl/docs | hf_public_repos/trl/docs/source/learning_tools.mdx | # Learning Tools (Experimental 🧪)
Using Large Language Models (LLMs) with tools has been a popular topic recently with awesome works such as [ToolFormer](https://arxiv.org/abs/2302.04761) and [ToolBench](https://arxiv.org/pdf/2305.16504.pdf). In TRL, we provide a simple example of how to teach LLM to use tools with reinforcement learning.
Here's an overview of the scripts in the [trl repository](https://github.com/lvwerra/trl/tree/main/examples/research_projects/tools):
| File | Description |
|---|---|
| [`calculator.py`](https://github.com/lvwerra/trl/blob/main/examples/research_projects/tools/calculator.py) | Script to train LLM to use a calculator with reinforcement learning. |
| [`triviaqa.py`](https://github.com/lvwerra/trl/blob/main/examples/research_projects/tools/triviaqa.py) | Script to train LLM to use a wiki tool to answer questions. |
| [`python_interpreter.py`](https://github.com/lvwerra/trl/blob/main/examples/research_projects/tools/python_interpreter.py) | Script to train LLM to use python interpreter to solve math puzzles. |
<Tip warning={true}>
Note that the scripts above rely heavily on the `TextEnvironment` API which is still under active development. The API may change in the future. Please see [`TextEnvironment`](text_environment) for the related docs.
</Tip>
## Learning to Use a Calculator
The rough idea is as follows:
1. Load a tool such as [ybelkada/simple-calculator](https://huggingface.co/spaces/ybelkada/simple-calculator) that parse a text calculation like `"14 + 34"` and return the calulated number:
```python
from transformers import AutoTokenizer, load_tool
tool = load_tool("ybelkada/simple-calculator")
tool_fn = lambda text: str(round(float(tool(text)), 2)) # rounding to 2 decimal places
```
1. Define a reward function that returns a positive reward if the tool returns the correct answer. In the script we create a dummy reward function like `reward_fn = lambda x: 1`, but we override the rewards directly later.
1. Create a prompt on how to use the tools
```python
# system prompt
prompt = """\
What is 13.1-3?
<request><SimpleCalculatorTool>13.1-3<call>10.1<response>
Result=10.1<submit>
What is 4*3?
<request><SimpleCalculatorTool>4*3<call>12<response>
Result=12<submit>
What is 12.1+1?
<request><SimpleCalculatorTool>12.1+1<call>13.1<response>
Result=13.1<submit>
What is 12.1-20?
<request><SimpleCalculatorTool>12.1-20<call>-7.9<response>
Result=-7.9<submit>"""
```
3. Create a `trl.TextEnvironment` with the model
```python
env = TextEnvironment(
model,
tokenizer,
{"SimpleCalculatorTool": tool_fn},
reward_fn,
prompt,
generation_kwargs=generation_kwargs,
)
```
4. Then generate some data such as `tasks = ["\n\nWhat is 13.1-3?", "\n\nWhat is 4*3?"]` and run the environment with `queries, responses, masks, rewards, histories = env.run(tasks)`. The environment will look for the `<call>` token in the prompt and append the tool output to the response; it will also return the mask associated with the response. You can further use the `histories` to visualize the interaction between the model and the tool; `histories[0].show_text()` will show the text with color-coded tool output and `histories[0].show_tokens(tokenizer)` will show visualize the tokens.
1. Finally, we can train the model with `train_stats = ppo_trainer.step(queries, responses, rewards, masks)`. The trainer will use the mask to ignore the tool output when computing the loss, make sure to pass that argument to `step`.
## Experiment results
We trained a model with the above script for 10 random seeds. You can reproduce the run with the following command. Feel free to remove the `--slurm-*` arguments if you don't have access to a slurm cluster.
```
WANDB_TAGS="calculator_final" python benchmark/benchmark.py \
--command "python examples/research_projects/tools/calculator.py" \
--num-seeds 10 \
--start-seed 1 \
--workers 10 \
--slurm-gpus-per-task 1 \
--slurm-ntasks 1 \
--slurm-total-cpus 8 \
--slurm-template-path benchmark/trl.slurm_template
```
We can then use [`openrlbenchmark`](https://github.com/openrlbenchmark/openrlbenchmark) which generates the following plot.
```
python -m openrlbenchmark.rlops_multi_metrics \
--filters '?we=openrlbenchmark&wpn=trl&xaxis=_step&ceik=trl_ppo_trainer_config.value.tracker_project_name&cen=trl_ppo_trainer_config.value.log_with&metrics=env/reward_mean&metrics=objective/kl' \
'wandb?tag=calculator_final&cl=calculator_mask' \
--env-ids trl \
--check-empty-runs \
--pc.ncols 2 \
--pc.ncols-legend 1 \
--output-filename static/0compare \
--scan-history
```
As we can see, while 1-2 experiments crashed for some reason, most of the runs obtained near perfect proficiency in the calculator task.
## (Early Experiments 🧪): learning to use a wiki tool for question answering
In the [ToolFormer](https://arxiv.org/abs/2302.04761) paper, it shows an interesting use case that utilizes a Wikipedia Search tool to help answer questions. In this section, we attempt to perform similar experiments but uses RL instead to teach the model to use a wiki tool on the [TriviaQA](https://nlp.cs.washington.edu/triviaqa/) dataset.
<Tip warning={true}>
**Note that many settings are different so the results are not directly comparable.**
</Tip>
### Building a search index
Since [ToolFormer](https://arxiv.org/abs/2302.04761) did not open source, we needed to first replicate the search index. It is mentioned in their paper that the authors built the search index using a BM25 retriever that indexes the Wikipedia dump from [KILT](https://github.com/facebookresearch/KILT)
Fortunately, [`pyserini`](https://github.com/castorini/pyserini) already implements the BM25 retriever and provides a prebuilt index for the KILT Wikipedia dump. We can use the following code to search the index.
```python
from pyserini.search.lucene import LuceneSearcher
import json
searcher = LuceneSearcher.from_prebuilt_index('wikipedia-kilt-doc')
def search(query):
hits = searcher.search(query, k=1)
hit = hits[0]
contents = json.loads(hit.raw)['contents']
return contents
print(search("tennis racket"))
```
```
Racket (sports equipment)
A racket or racquet is a sports implement consisting of a handled frame with an open hoop across which a network of strings or catgut is stretched tightly. It is used for striking a ball or shuttlecock in games such as squash, tennis, racquetball, and badminton. Collectively, these games are known as racket sports. Racket design and manufacturing has changed considerably over the centuries.
The frame of rackets for all sports was traditionally made of solid wood (later laminated wood) and the strings of animal intestine known as catgut. The traditional racket size was limited by the strength and weight of the wooden frame which had to be strong enough to hold the strings and stiff enough to hit the ball or shuttle. Manufacturers started adding non-wood laminates to wood rackets to improve stiffness. Non-wood rackets were made first of steel, then of aluminum, and then carbon fiber composites. Wood is still used for real tennis, rackets, and xare. Most rackets are now made of composite materials including carbon fiber or fiberglass, metals such as titanium alloys, or ceramics.
...
```
We then basically deployed this snippet as a Hugging Face space [here](https://huggingface.co/spaces/vwxyzjn/pyserini-wikipedia-kilt-doc), so that we can use the space as a `transformers.Tool` later.
### Experiment settings
We use the following settings:
* use the `bigcode/starcoderbase` model as the base model
* use the `pyserini-wikipedia-kilt-doc` space as the wiki tool and only uses the first paragrahs of the search result, allowing the `TextEnvironment` to obtain at most `max_tool_reponse=400` response tokens from the tool.
* test if the response contain the answer string, if so, give a reward of 1, otherwise, give a reward of 0.
* notice this is a simplified evaluation criteria. In [ToolFormer](https://arxiv.org/abs/2302.04761), the authors checks if the first 20 words of the response contain the correct answer.
* used the following prompt that demonstrates the usage of the wiki tool.
```python
prompt = """\
Answer the following question:
Q: In which branch of the arts is Patricia Neary famous?
A: Ballets
A2: <request><Wiki>Patricia Neary<call>Patricia Neary (born October 27, 1942) is an American ballerina, choreographer and ballet director, who has been particularly active in Switzerland. She has also been a highly successful ambassador for the Balanchine Trust, bringing George Balanchine's ballets to 60 cities around the globe.<response>
Result=Ballets<submit>
Q: Who won Super Bowl XX?
A: Chicago Bears
A2: <request><Wiki>Super Bowl XX<call>Super Bowl XX was an American football game between the National Football Conference (NFC) champion Chicago Bears and the American Football Conference (AFC) champion New England Patriots to decide the National Football League (NFL) champion for the 1985 season. The Bears defeated the Patriots by the score of 46–10, capturing their first NFL championship (and Chicago's first overall sports victory) since 1963, three years prior to the birth of the Super Bowl. Super Bowl XX was played on January 26, 1986 at the Louisiana Superdome in New Orleans.<response>
Result=Chicago Bears<submit>
Q: """
```
### Result and Discussion
Our experiments show that the agent can learn to use the wiki tool to answer questions. The learning curves would go up mostly, but one of the experiment did crash.
Wandb report is [here](https://wandb.ai/costa-huang/cleanRL/reports/TriviaQA-Final-Experiments--Vmlldzo1MjY0ODk5) for further inspection.
Note that the correct rate of the trained model is on the low end, which could be due to the following reasons:
* **incorrect searches:** When given the question `"What is Bruce Willis' real first name?"` if the model searches for `Bruce Willis`, our wiki tool returns "Patrick Poivey (born 18 February 1948) is a French actor. He is especially known for his voice: he is the French dub voice of Bruce Willis since 1988.` But a correct search should be `Walter Bruce Willis (born March 19, 1955) is an American former actor. He achieved fame with a leading role on the comedy-drama series Moonlighting (1985–1989) and appeared in over a hundred films, gaining recognition as an action hero after his portrayal of John McClane in the Die Hard franchise (1988–2013) and other roles.[1][2]"
* **unnecessarily long response**: The wiki tool by default sometimes output very long sequences. E.g., when the wiki tool searches for "Brown Act"
* Our wiki tool returns "The Ralph M. Brown Act, located at California Government Code 54950 "et seq.", is an act of the California State Legislature, authored by Assemblymember Ralph M. Brown and passed in 1953, that guarantees the public's right to attend and participate in meetings of local legislative bodies."
* [ToolFormer](https://arxiv.org/abs/2302.04761)'s wiki tool returns "The Ralph M. Brown Act is an act of the California State Legislature that guarantees the public's right to attend and participate in meetings of local legislative bodies." which is more succinct.
## (Early Experiments 🧪): solving math puzzles with python interpreter
In this section, we attempt to teach the model to use a python interpreter to solve math puzzles. The rough idea is to give the agent a prompt like the following:
```python
prompt = """\
Example of using a Python API to solve math questions.
Q: Olivia has $23. She bought five bagels for $3 each. How much money does she have left?
<request><PythonInterpreter>
def solution():
money_initial = 23
bagels = 5
bagel_cost = 3
money_spent = bagels * bagel_cost
money_left = money_initial - money_spent
result = money_left
return result
print(solution())
<call>72<response>
Result = 72 <submit>
Q: """
```
Training experiment can be found at https://wandb.ai/lvwerra/trl-gsm8k/runs/a5odv01y
| 0 |
hf_public_repos/trl/docs | hf_public_repos/trl/docs/source/example_overview.md | # Examples
## Introduction
The examples should work in any of the following settings (with the same script):
- single GPU
- multi GPUS (using PyTorch distributed mode)
- multi GPUS (using DeepSpeed ZeRO-Offload stages 1, 2, & 3)
- fp16 (mixed-precision), fp32 (normal precision), or bf16 (bfloat16 precision)
To run it in each of these various modes, first initialize the accelerate
configuration with `accelerate config`
**NOTE to train with a 4-bit or 8-bit model**, please run
```bash
pip install --upgrade trl[quantization]
```
## Accelerate Config
For all the examples, you'll need to generate a 🤗 Accelerate config file with:
```shell
accelerate config # will prompt you to define the training configuration
```
Then, it is encouraged to launch jobs with `accelerate launch`!
# Maintained Examples
| File | Description |
|------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------|
| [`examples/scripts/sft.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/sft.py) | This script shows how to use the `SFTTrainer` to fine tune a model or adapters into a target dataset. |
| [`examples/scripts/reward_modeling.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/reward_modeling.py) | This script shows how to use the `RewardTrainer` to train a reward model on your own dataset. |
| [`examples/scripts/ppo.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/ppo.py) | This script shows how to use the `PPOTrainer` to fine-tune a sentiment analysis model using IMDB dataset |
| [`examples/scripts/ppo_multi_adapter.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/ppo_multi_adapter.py) | This script shows how to use the `PPOTrainer` to train a single base model with multiple adapters. Requires you to run the example script with the reward model training beforehand. |
| [`examples/scripts/stable_diffusion_tuning_example.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/stable_diffusion_tuning_example.py) | This script shows to use DDPOTrainer to fine-tune a stable diffusion model using reinforcement learning. |
Here are also some easier-to-run colab notebooks that you can use to get started with TRL:
| File | Description |
|----------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------|
| [`examples/notebooks/best_of_n.ipynb`](https://github.com/huggingface/trl/tree/main/examples/notebooks/best_of_n.ipynb) | This notebook demonstrates how to use the "Best of N" sampling strategy using TRL when fine-tuning your model with PPO. |
| [`examples/notebooks/gpt2-sentiment.ipynb`](https://github.com/huggingface/trl/tree/main/examples/notebooks/gpt2-sentiment.ipynb) | This notebook demonstrates how to reproduce the GPT2 imdb sentiment tuning example on a jupyter notebook. |
| [`examples/notebooks/gpt2-control.ipynb`](https://github.com/huggingface/trl/tree/main/examples/notebooks/gpt2-control.ipynb) | This notebook demonstrates how to reproduce the GPT2 sentiment control example on a jupyter notebook. |
We also have some other examples that are less maintained but can be used as a reference:
1. **[research_projects](https://github.com/huggingface/trl/tree/main/examples/research_projects)**: Check out this folder to find the scripts used for some research projects that used TRL (LM de-toxification, Stack-Llama, etc.)
## Distributed training
All of the scripts can be run on multiple GPUs by providing the path of an 🤗 Accelerate config file when calling `accelerate launch`. To launch one of them on one or multiple GPUs, run the following command (swapping `{NUM_GPUS}` with the number of GPUs in your machine and `--all_arguments_of_the_script` with your arguments.)
```shell
accelerate launch --config_file=examples/accelerate_configs/multi_gpu.yaml --num_processes {NUM_GPUS} path_to_script.py --all_arguments_of_the_script
```
You can also adjust the parameters of the 🤗 Accelerate config file to suit your needs (e.g. training in mixed precision).
### Distributed training with DeepSpeed
Most of the scripts can be run on multiple GPUs together with DeepSpeed ZeRO-{1,2,3} for efficient sharding of the optimizer states, gradients, and model weights. To do so, run following command (swapping `{NUM_GPUS}` with the number of GPUs in your machine, `--all_arguments_of_the_script` with your arguments, and `--deepspeed_config` with the path to the DeepSpeed config file such as `examples/deepspeed_configs/deepspeed_zero1.yaml`):
```shell
accelerate launch --config_file=examples/accelerate_configs/deepspeed_zero{1,2,3}.yaml --num_processes {NUM_GPUS} path_to_script.py --all_arguments_of_the_script
```
| 0 |
hf_public_repos/trl/docs | hf_public_repos/trl/docs/source/using_llama_models.mdx | # Using LLaMA models with TRL
We've begun rolling out examples to use Meta's LLaMA models in `trl` (see [Meta's LLaMA release](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) for the original LLaMA model).
## Efficient training strategies
Even training the smallest LLaMA model requires an enormous amount of memory. Some quick math: in bf16, every parameter uses 2 bytes (in fp32 4 bytes) in addition to 8 bytes used, e.g., in the Adam optimizer (see the [performance docs](https://huggingface.co/docs/transformers/perf_train_gpu_one#optimizer) in Transformers for more info). So a 7B parameter model would use `(2+8)*7B=70GB` just to fit in memory and would likely need more when you compute intermediate values such as attention scores. So you couldn’t train the model even on a single 80GB A100 like that. You can use some tricks, like more efficient optimizers of half-precision training, to squeeze a bit more into memory, but you’ll run out sooner or later.
Another option is to use Parameter-Efficient Fine-Tuning (PEFT) techniques, such as the [`peft`](https://github.com/huggingface/peft) library, which can perform low-rank adaptation (LoRA) on a model loaded in 8-bit.
For more on `peft` + `trl`, see the [docs](https://huggingface.co/docs/trl/sentiment_tuning_peft).
Loading the model in 8bit reduces the memory footprint drastically since you only need one byte per parameter for the weights (e.g. 7B LlaMa is 7GB in memory).
Instead of training the original weights directly, LoRA adds small adapter layers on top of some specific layers (usually the attention layers); thus, the number of trainable parameters is drastically reduced.
In this scenario, a rule of thumb is to allocate ~1.2-1.4GB per billion parameters (depending on the batch size and sequence length) to fit the entire fine-tuning setup.
This enables fine-tuning larger models (up to 50-60B scale models on a NVIDIA A100 80GB) at low cost.
Now we can fit very large models into a single GPU, but the training might still be very slow.
The simplest strategy in this scenario is data parallelism: we replicate the same training setup into separate GPUs and pass different batches to each GPU.
With this, you can parallelize the forward/backward passes of the model and scale with the number of GPUs.
We use either the `transformers.Trainer` or `accelerate`, which both support data parallelism without any code changes, by simply passing arguments when calling the scripts with `torchrun` or `accelerate launch`. The following runs a training script with 8 GPUs on a single machine with `accelerate` and `torchrun`, respectively.
```bash
accelerate launch --multi_gpu --num_machines 1 --num_processes 8 my_accelerate_script.py
torchrun --nnodes 1 --nproc_per_node 8 my_torch_script.py
```
## Supervised fine-tuning
Before we start training reward models and tuning our model with RL, it helps if the model is already good in the domain we are interested in.
In our case, we want it to answer questions, while for other use cases, we might want it to follow instructions, in which case instruction tuning is a great idea.
The easiest way to achieve this is by continuing to train the language model with the language modeling objective on texts from the domain or task.
The [StackExchange dataset](https://huggingface.co/datasets/HuggingFaceH4/stack-exchange-preferences) is enormous (over 10 million instructions), so we can easily train the language model on a subset of it.
There is nothing special about fine-tuning the model before doing RLHF - it’s just the causal language modeling objective from pretraining that we apply here.
To use the data efficiently, we use a technique called packing: instead of having one text per sample in the batch and then padding to either the longest text or the maximal context of the model, we concatenate a lot of texts with a EOS token in between and cut chunks of the context size to fill the batch without any padding.
With this approach the training is much more efficient as each token that is passed through the model is also trained in contrast to padding tokens which are usually masked from the loss.
If you don't have much data and are more concerned about occasionally cutting off some tokens that are overflowing the context you can also use a classical data loader.
The packing is handled by the `ConstantLengthDataset` and we can then use the `Trainer` after loading the model with `peft`. First, we load the model in int8, prepare it for training, and then add the LoRA adapters.
```python
# load model in 8bit
model = AutoModelForCausalLM.from_pretrained(
args.model_path,
load_in_8bit=True,
device_map={"": Accelerator().local_process_index}
)
model = prepare_model_for_kbit_training(model)
# add LoRA to model
lora_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
model = get_peft_model(model, config)
```
We train the model for a few thousand steps with the causal language modeling objective and save the model.
Since we will tune the model again with different objectives, we merge the adapter weights with the original model weights.
**Disclaimer:** due to LLaMA's license, we release only the adapter weights for this and the model checkpoints in the following sections.
You can apply for access to the base model's weights by filling out Meta AI's [form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform) and then converting them to the 🤗 Transformers format by running this [script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py).
Note that you'll also need to install 🤗 Transformers from source until the `v4.28` is released.
Now that we have fine-tuned the model for the task, we are ready to train a reward model.
## Reward modeling and human preferences
In principle, we could fine-tune the model using RLHF directly with the human annotations.
However, this would require us to send some samples to humans for rating after each optimization iteration.
This is expensive and slow due to the number of training samples needed for convergence and the inherent latency of human reading and annotator speed.
A trick that works well instead of direct feedback is training a reward model on human annotations collected before the RL loop.
The goal of the reward model is to imitate how a human would rate a text. There are several possible strategies to build a reward model: the most straightforward way would be to predict the annotation (e.g. a rating score or a binary value for “good”/”bad”).
In practice, what works better is to predict the ranking of two examples, where the reward model is presented with two candidates `(y_k, y_j)` for a given prompt `x` and has to predict which one would be rated higher by a human annotator.
With the StackExchange dataset, we can infer which of the two answers was preferred by the users based on the score.
With that information and the loss defined above, we can then modify the `transformers.Trainer` by adding a custom loss function.
```python
class RewardTrainer(Trainer):
def compute_loss(self, model, inputs, return_outputs=False):
rewards_j = model(input_ids=inputs["input_ids_j"], attention_mask=inputs["attention_mask_j"])[0]
rewards_k = model(input_ids=inputs["input_ids_k"], attention_mask=inputs["attention_mask_k"])[0]
loss = -nn.functional.logsigmoid(rewards_j - rewards_k).mean()
if return_outputs:
return loss, {"rewards_j": rewards_j, "rewards_k": rewards_k}
return loss
```
We utilize a subset of a 100,000 pair of candidates and evaluate on a held-out set of 50,000. With a modest training batch size of 4, we train the Llama model using the LoRA `peft` adapter for a single epoch using the Adam optimizer with BF16 precision. Our LoRA configuration is:
```python
peft_config = LoraConfig(
task_type=TaskType.SEQ_CLS,
inference_mode=False,
r=8,
lora_alpha=32,
lora_dropout=0.1,
)
```
As detailed in the next section, the resulting adapter can be merged into the frozen model and saved for further downstream use.
## Reinforcement Learning from Human Feedback
With the fine-tuned language model and the reward model at hand, we are now ready to run the RL loop. It follows roughly three steps:
1. Generate responses from prompts,
2. Rate the responses with the reward model,
3. Run a reinforcement learning policy-optimization step with the ratings.
The Query and Response prompts are templated as follows before being tokenized and passed to the model:
```bash
Question: <Query>
Answer: <Response>
```
The same template was used for SFT, RM and RLHF stages.
Once more, we utilize `peft` for memory-efficient training, which offers an extra advantage in the RLHF context.
Here, the reference model and policy share the same base, the SFT model, which we load in 8-bit and freeze during training.
We exclusively optimize the policy's LoRA weights using PPO while sharing the base model's weights.
```python
for epoch, batch in tqdm(enumerate(ppo_trainer.dataloader)):
question_tensors = batch["input_ids"]
# sample from the policy and to generate responses
response_tensors = ppo_trainer.generate(
question_tensors,
return_prompt=False,
length_sampler=output_length_sampler,
**generation_kwargs,
)
batch["response"] = tokenizer.batch_decode(response_tensors, skip_special_tokens=True)
# Compute sentiment score
texts = [q + r for q, r in zip(batch["query"], batch["response"])]
pipe_outputs = sentiment_pipe(texts, **sent_kwargs)
rewards = [torch.tensor(output[0]["score"] - script_args.reward_baseline) for output in pipe_outputs]
# Run PPO step
stats = ppo_trainer.step(question_tensors, response_tensors, rewards)
# Log stats to Wandb
ppo_trainer.log_stats(stats, batch, rewards)
```
For the rest of the details and evaluation, please refer to our [blog post on StackLLaMA](https://huggingface.co/blog/stackllama).
| 0 |
hf_public_repos/trl/docs | hf_public_repos/trl/docs/source/installation.mdx | # Installation
You can install TRL either from pypi or from source:
## pypi
Install the library with pip:
```bash
pip install trl
```
### Source
You can also install the latest version from source. First clone the repo and then run the installation with `pip`:
```bash
git clone https://github.com/huggingface/trl.git
cd trl/
pip install -e .
```
If you want the development install you can replace the pip install with the following:
```bash
pip install -e ".[dev]"
``` | 0 |
hf_public_repos/trl | hf_public_repos/trl/examples/hello_world.py | # 0. imports
import torch
from transformers import GPT2Tokenizer
from trl import AutoModelForCausalLMWithValueHead, PPOConfig, PPOTrainer
# 1. load a pretrained model
model = AutoModelForCausalLMWithValueHead.from_pretrained("gpt2")
model_ref = AutoModelForCausalLMWithValueHead.from_pretrained("gpt2")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
tokenizer.pad_token = tokenizer.eos_token
# 2. initialize trainer
ppo_config = {"batch_size": 1}
config = PPOConfig(**ppo_config)
ppo_trainer = PPOTrainer(config, model, model_ref, tokenizer)
# 3. encode a query
query_txt = "This morning I went to the "
query_tensor = tokenizer.encode(query_txt, return_tensors="pt").to(model.pretrained_model.device)
# 4. generate model response
generation_kwargs = {
"min_length": -1,
"top_k": 0.0,
"top_p": 1.0,
"do_sample": True,
"pad_token_id": tokenizer.eos_token_id,
"max_new_tokens": 20,
}
response_tensor = ppo_trainer.generate([item for item in query_tensor], return_prompt=False, **generation_kwargs)
response_txt = tokenizer.decode(response_tensor[0])
# 5. define a reward for response
# (this could be any reward such as human feedback or output from another model)
reward = [torch.tensor(1.0, device=model.pretrained_model.device)]
# 6. train model with ppo
train_stats = ppo_trainer.step([query_tensor[0]], [response_tensor[0]], reward)
| 0 |
hf_public_repos/trl | hf_public_repos/trl/examples/README.md | # Examples
Please check out https://huggingface.co/docs/trl/example_overview for documentation on our examples. | 0 |
hf_public_repos/trl/examples | hf_public_repos/trl/examples/accelerate_configs/deepspeed_zero3.yaml | compute_environment: LOCAL_MACHINE
debug: false
deepspeed_config:
deepspeed_multinode_launcher: standard
gradient_accumulation_steps: 1
offload_optimizer_device: none
offload_param_device: none
zero3_init_flag: true
zero3_save_16bit_model: true
zero_stage: 3
distributed_type: DEEPSPEED
downcast_bf16: 'no'
machine_rank: 0
main_training_function: main
mixed_precision: 'bf16'
num_machines: 1
num_processes: 8
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false
| 0 |
hf_public_repos/trl/examples | hf_public_repos/trl/examples/accelerate_configs/multi_gpu.yaml | compute_environment: LOCAL_MACHINE
debug: false
distributed_type: MULTI_GPU
downcast_bf16: 'no'
gpu_ids: all
machine_rank: 0
main_training_function: main
mixed_precision: 'bf16'
num_machines: 1
num_processes: 8
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false
| 0 |
hf_public_repos/trl/examples | hf_public_repos/trl/examples/accelerate_configs/deepspeed_zero2.yaml | compute_environment: LOCAL_MACHINE
debug: false
deepspeed_config:
deepspeed_multinode_launcher: standard
gradient_accumulation_steps: 1
offload_optimizer_device: none
offload_param_device: none
zero3_init_flag: false
zero_stage: 2
distributed_type: DEEPSPEED
downcast_bf16: 'no'
machine_rank: 0
main_training_function: main
mixed_precision: 'bf16'
num_machines: 1
num_processes: 8
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false
| 0 |
hf_public_repos/trl/examples | hf_public_repos/trl/examples/accelerate_configs/deepspeed_zero1.yaml | compute_environment: LOCAL_MACHINE
debug: false
deepspeed_config:
deepspeed_multinode_launcher: standard
gradient_accumulation_steps: 1
zero3_init_flag: false
zero_stage: 1
distributed_type: DEEPSPEED
downcast_bf16: 'no'
machine_rank: 0
main_training_function: main
mixed_precision: 'bf16'
num_machines: 1
num_processes: 8
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: false
| 0 |
hf_public_repos/trl/examples | hf_public_repos/trl/examples/research_projects/README.md | # Research projects that use TRL
Welcome to the research projects folder! Here you can find the scripts used for some research projects that used TRL and maintained by the developers and the community (LM de-toxification, Stack-Llama, etc.). Check out the READMEs in the subfolders for more information!
- [De-detoxifying language models](https://github.com/huggingface/trl/tree/main/examples/research_projects/toxicity)
- [Stack-Llama](https://github.com/huggingface/trl/tree/main/examples/research_projects/stack_llama)
- [Stack-Llama-2](https://github.com/huggingface/trl/tree/main/examples/research_projects/stack_llama_2) | 0 |
hf_public_repos/trl/examples/research_projects | hf_public_repos/trl/examples/research_projects/tools/calculator.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
import numpy as np
import torch
from transformers import AutoTokenizer, load_tool
from trl import AutoModelForCausalLMWithValueHead, PPOConfig, PPOTrainer, TextEnvironment
def generate_data(n):
"""Generate random arithmetic tasks and answers."""
tasks, answers = [], []
for _ in range(n):
a = np.random.randint(0, 50)
b = np.random.randint(0, 50)
op = np.random.choice(["-", "+", "*"])
tasks.append(f"\n\nWhat is {a} {op} {b}?")
if op == "-":
answers.append(a - b)
elif op == "+":
answers.append(a + b)
else:
answers.append(a * b)
return tasks, answers
def exact_match_reward(responses, answers=None):
"""Reward if generated response contains correct answer."""
rewards = []
pattern = r"Result\s*=\s*(-?\d+(?:\.\d+)?)\s*<submit>" # generated by chatGPT
for response, answer in zip(responses, answers):
reward = 0.0
predicted_number = None
match_pattern = re.findall(pattern, response)
if match_pattern:
predicted_number = float(match_pattern[0])
if predicted_number is not None:
if np.abs(predicted_number - answer) < 0.01:
reward += 1.0
rewards.append(torch.tensor(reward))
return rewards
# set up models
model_id = "gpt2"
model = AutoModelForCausalLMWithValueHead.from_pretrained(model_id)
model_ref = AutoModelForCausalLMWithValueHead.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token
# system prompt
prompt = """\
What is 13-3?
<request><SimpleCalculatorTool>13-3<call>10.0<response>
Result=10<submit>
What is 4*3?
<request><SimpleCalculatorTool>4*3<call>12.0<response>
Result=12<submit>"""
generation_kwargs = {
"min_length": -1,
"top_k": 0.0,
"top_p": 1.0,
"do_sample": True,
"pad_token_id": tokenizer.eos_token_id,
"eos_token_id": -1,
"max_new_tokens": 32,
}
# trainer
ppo_config = PPOConfig(
batch_size=256,
learning_rate=1.41e-5,
mini_batch_size=64,
log_with="wandb",
)
ppo_trainer = PPOTrainer(ppo_config, model, model_ref, tokenizer)
# text env
text_env = TextEnvironment(
model,
tokenizer,
{"SimpleCalculatorTool": load_tool("ybelkada/simple-calculator")},
exact_match_reward,
prompt,
generation_kwargs=generation_kwargs,
)
# main training loop
for step in range(100):
tasks, answers = generate_data(ppo_config.batch_size)
queries, responses, masks, rewards, histories = text_env.run(tasks, answers=answers)
train_stats = ppo_trainer.step(queries, responses, rewards, masks)
response_texts = [tokenizer.decode(response) for response in responses]
query_texts = [tokenizer.decode(query) for query in queries]
texts = {"query": [qt.split("<submit>")[-1].strip() for qt in query_texts], "response": response_texts}
ppo_trainer.log_stats(train_stats, texts, rewards, columns_to_log=["query", "response", "answer"])
ppo_trainer.save_pretrained(model_id + "-calculator")
| 0 |
hf_public_repos/trl/examples/research_projects | hf_public_repos/trl/examples/research_projects/tools/python_interpreter.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import re
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from peft import LoraConfig
from transformers import AutoTokenizer, HfArgumentParser, load_tool
from trl import AutoModelForCausalLMWithValueHead, PPOConfig, PPOTrainer, TextEnvironment
os.environ["HF_ALLOW_CODE_EVAL"] = "1"
os.environ["TOKENIZERS_PARALLELISM"] = "false"
@dataclass
class ScriptArguments:
model_name: Optional[str] = field(default="bigcode/starcoderbase", metadata={"help": "the model name"})
learning_rate: Optional[float] = field(default=1e-5, metadata={"help": "the learning rate"})
mini_batch_size: Optional[int] = field(default=1, metadata={"help": "the PPO minibatch size"})
batch_size: Optional[int] = field(default=32, metadata={"help": "the batch size"})
gradient_accumulation_steps: Optional[int] = field(
default=16, metadata={"help": "the number of gradient accumulation steps"}
)
max_new_tokens: Optional[int] = field(default=256, metadata={"help": "max number of generated tokens per turn"})
ppo_epochs: Optional[int] = field(default=1, metadata={"help": "max number of ppo epochs"})
n_epochs: Optional[int] = field(default=32, metadata={"help": "max number of ppo epochs"})
parser = HfArgumentParser(ScriptArguments)
args = parser.parse_args_into_dataclasses()[0]
def exact_match_reward(responses, answers=None):
"""Reward if generated response contains correct answer."""
rewards = []
pattern = r"Result\s*=\s*(-?\d+(?:\.\d+)?)\s*<submit>" # generated by chatGPT
for response, answer in zip(responses, answers):
reward = 0.0
try:
predicted_number = None
match_pattern = re.findall(pattern, response)
if match_pattern:
predicted_number = float(match_pattern[0])
if predicted_number is not None:
if np.abs((predicted_number - float(answer))) < 0.1:
reward += 1.0
except: # noqa
pass
rewards.append(torch.tensor(reward))
return rewards
def evaluate(test_dataloader, text_env, ppo_trainer):
test_rewards = []
for test_batch in test_dataloader:
_, _, _, rewards, _ = text_env.run(test_batch["query"], answers=test_batch["answer"])
test_rewards.extend(rewards)
test_rewards = ppo_trainer.accelerator.gather_for_metrics(
torch.stack(test_rewards).to(ppo_trainer.accelerator.device)
)
return test_rewards.mean()
lora_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
target_modules=["c_proj", "c_attn", "q_attn"],
)
# set up models
model = AutoModelForCausalLMWithValueHead.from_pretrained(
args.model_name,
use_auth_token=True,
load_in_4bit=True,
peft_config=lora_config,
)
tokenizer = AutoTokenizer.from_pretrained(args.model_name, use_auth_token=True)
tokenizer.pad_token = tokenizer.eos_token
ds = load_dataset("gsm8k", "main", split="train")
ds = ds.rename_columns({"question": "query"})
ds = ds.map(lambda x: {"answer": x["answer"].split("#### ")[1]})
ds = ds.select(range(1, len(ds))) # skip the first sample which is used in prompt
ds_test = load_dataset("gsm8k", "main", split="test")
ds_test = ds_test.rename_columns({"question": "query"})
ds_test = ds_test.map(lambda x: {"answer": x["answer"].split("#### ")[1]})
test_dataloader = torch.utils.data.DataLoader(ds_test, batch_size=args.batch_size)
# prompt
prompt = """\
Example of using a Python API to solve math questions.
Q: Olivia has $23. She bought five bagels for $3 each. How much money does she have left?
<request><PythonInterpreter>
def solution():
money_initial = 23
bagels = 5
bagel_cost = 3
money_spent = bagels * bagel_cost
money_left = money_initial - money_spent
result = money_left
return result
print(solution())
<call>72<response>
Result = 72 <submit>
Q: """
generation_kwargs = {
"min_length": -1,
"top_k": 0.0,
"top_p": 1.0,
"do_sample": True,
"pad_token_id": tokenizer.eos_token_id,
"eos_token_id": -1,
"max_new_tokens": args.max_new_tokens,
}
# trainer
ppo_config = PPOConfig(
batch_size=args.batch_size,
learning_rate=args.learning_rate,
mini_batch_size=args.mini_batch_size,
ppo_epochs=args.ppo_epochs,
gradient_accumulation_steps=args.gradient_accumulation_steps,
log_with="wandb",
tracker_project_name="trl-gsm8k",
remove_unused_columns=False,
optimize_cuda_cache=True,
)
ppo_trainer = PPOTrainer(config=ppo_config, model=model, tokenizer=tokenizer, dataset=ds)
test_dataloader = ppo_trainer.accelerator.prepare(test_dataloader)
# text env
text_env = TextEnvironment(
model,
tokenizer,
[load_tool("lvwerra/python-interpreter")],
exact_match_reward,
prompt,
max_turns=2,
generation_kwargs=generation_kwargs,
)
# main training loop
for epoch in range(args.n_epochs):
for step, batch in enumerate(ppo_trainer.dataloader):
if (step == 0) and (epoch % 4 == 0): # evaluate every 4 epochs
reward_mean_test = evaluate(test_dataloader, text_env, ppo_trainer)
else:
reward_mean_test = None
queries, responses, masks, rewards, histories = text_env.run(batch["query"], answers=batch["answer"])
train_stats = ppo_trainer.step(queries, responses, rewards, masks)
# logging
if reward_mean_test is not None:
train_stats["env/reward_mean_test"] = reward_mean_test
texts = {
"query": batch["query"],
"response": [tokenizer.decode(response) for response in responses],
"answer": batch["answer"],
}
ppo_trainer.log_stats(train_stats, texts, rewards, columns_to_log=["query", "response", "answer"])
reward_mean_test = evaluate(test_dataloader, text_env, ppo_trainer)
ppo_trainer.save_pretrained(f"model/{args.model_name}-gsm8k")
| 0 |
hf_public_repos/trl/examples/research_projects | hf_public_repos/trl/examples/research_projects/tools/triviaqa.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from peft import LoraConfig
from transformers import AutoTokenizer, HfArgumentParser, load_tool
from trl import AutoModelForCausalLMWithValueHead, PPOConfig, PPOTrainer, TextEnvironment
os.environ["HF_ALLOW_CODE_EVAL"] = "1"
os.environ["TOKENIZERS_PARALLELISM"] = "false"
@dataclass
class ScriptArguments:
model_name: Optional[str] = field(default="bigcode/starcoderbase", metadata={"help": "the model name"})
log_with: Optional[str] = field(default=None, metadata={"help": "use 'wandb' to log with wandb"})
learning_rate: Optional[float] = field(default=1e-5, metadata={"help": "the learning rate"})
mini_batch_size: Optional[int] = field(default=1, metadata={"help": "the PPO minibatch size"})
batch_size: Optional[int] = field(default=32, metadata={"help": "the batch size"})
gradient_accumulation_steps: Optional[int] = field(
default=16, metadata={"help": "the number of gradient accumulation steps"}
)
max_new_tokens: Optional[int] = field(default=256, metadata={"help": "max number of generated tokens per turn"})
ppo_epochs: Optional[int] = field(default=1, metadata={"help": "max number of ppo epochs"})
iterations: Optional[int] = field(default=1000, metadata={"help": "the number of iterations"})
seed: Optional[int] = field(default=0, metadata={"help": "the random seed"})
parser = HfArgumentParser(ScriptArguments)
args = parser.parse_args_into_dataclasses()[0]
lora_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
target_modules=["c_proj", "c_attn", "q_attn"],
)
# set up models
model = AutoModelForCausalLMWithValueHead.from_pretrained(
args.model_name,
use_auth_token=True,
trust_remote_code=True,
load_in_4bit=True,
peft_config=lora_config,
)
tokenizer = AutoTokenizer.from_pretrained(args.model_name, use_auth_token=True)
tokenizer.pad_token = tokenizer.eos_token
# system prompt
prompt = """\
Answer the following question:
Q: In which branch of the arts is Patricia Neary famous?
A: Ballets
A2: <request><Wiki>Patricia Neary<call>Patricia Neary (born October 27, 1942) is an American ballerina, choreographer and ballet director, who has been particularly active in Switzerland. She has also been a highly successful ambassador for the Balanchine Trust, bringing George Balanchine's ballets to 60 cities around the globe.<response>
Result=Ballets<submit>
Q: Who won Super Bowl XX?
A: Chicago Bears
A2: <request><Wiki>Super Bowl XX<call>Super Bowl XX was an American football game between the National Football Conference (NFC) champion Chicago Bears and the American Football Conference (AFC) champion New England Patriots to decide the National Football League (NFL) champion for the 1985 season. The Bears defeated the Patriots by the score of 46–10, capturing their first NFL championship (and Chicago's first overall sports victory) since 1963, three years prior to the birth of the Super Bowl. Super Bowl XX was played on January 26, 1986 at the Louisiana Superdome in New Orleans.<response>
Result=Chicago Bears<submit>
Q: """
generation_kwargs = {
"min_length": -1,
"top_k": 0.0,
"top_p": 1.0,
"do_sample": True,
"pad_token_id": tokenizer.eos_token_id,
"eos_token_id": -1,
"max_new_tokens": args.max_new_tokens,
}
# trainer
config = PPOConfig(
batch_size=args.batch_size,
model_name=args.model_name,
learning_rate=args.learning_rate,
log_with=args.log_with,
mini_batch_size=args.mini_batch_size,
ppo_epochs=args.ppo_epochs,
gradient_accumulation_steps=args.gradient_accumulation_steps,
seed=args.seed,
optimize_cuda_cache=True,
)
ppo_trainer = PPOTrainer(config=config, model=model, tokenizer=tokenizer)
dataset = load_dataset("trivia_qa", "rc", split="train")
local_seed = args.seed + ppo_trainer.accelerator.process_index * 100003 # Prime
dataset = dataset.shuffle(local_seed)
def data_generator():
for i in range(len(dataset)):
yield dataset[i]["question"], [item for item in dataset[i]["answer"]["normalized_aliases"]]
gen = data_generator()
gen = iter(gen)
def generate_data(n):
tasks, answers = [], []
for i in range(n):
q, a = next(gen)
tasks.append(q)
answers.append(a)
return tasks, answers
def exact_match_reward(responses, answers=None):
"""Reward if generated response contains correct answer."""
rewards = []
for response, answer in zip(responses, answers):
reward = 0.0
for a in answer:
if a.lower() in response.lower():
reward += 1.0
break
rewards.append(torch.tensor(reward))
return rewards
# text env
tool = load_tool("vwxyzjn/pyserini-wikipedia-kilt-doc")
# limit the amount if tokens
tool_fn = lambda x: tool(x).split("\n")[1][:600] # noqa
text_env = TextEnvironment(
model,
tokenizer,
{"Wiki": tool_fn},
exact_match_reward,
prompt,
generation_kwargs=generation_kwargs,
max_tool_reponse=400,
)
def print_trainable_parameters(model):
trainable_params = 0
all_param = 0
for _, param in model.named_parameters():
all_param += param.numel()
if param.requires_grad:
trainable_params += param.numel()
print(
f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}"
)
print_trainable_parameters(model)
# main training loop
for i in range(args.iterations):
tasks, answers = generate_data(config.batch_size)
queries, responses, masks, rewards, histories = text_env.run(tasks, answers=answers)
train_stats = ppo_trainer.step(queries, responses, rewards, masks)
response_texts = [tokenizer.decode(response) for response in responses]
query_texts = [tokenizer.decode(query) for query in queries]
texts = {
"query": [qt.split("<submit>")[-1].strip() for qt in query_texts],
"response": response_texts,
"answer": [", ".join(item) for item in answers],
}
all_rewards = ppo_trainer.accelerator.gather(torch.tensor(rewards, device=ppo_trainer.accelerator.device))
ppo_trainer.log_stats(
train_stats, texts, [item for item in all_rewards], columns_to_log=["query", "response", "answer"]
)
if i % 100 == 0:
ppo_trainer.save_pretrained(f"models/{args.model_name}_{args.seed}_{i}_triviaqa")
| 0 |
hf_public_repos/trl/examples/research_projects | hf_public_repos/trl/examples/research_projects/toxicity/README.md | # De-detoxifying language models
To run this code, do the following:
```shell
ACCELERATE_LOG_LEVEL=info accelerate launch --config_file {CONFIG} examples/research_projects/toxicity/scripts/gpt-j-6b-toxicity.py --log_with wandb
```
| 0 |
hf_public_repos/trl/examples/research_projects/toxicity | hf_public_repos/trl/examples/research_projects/toxicity/scripts/gpt-j-6b-toxicity.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torch.optim import Adam
from tqdm import tqdm
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
HfArgumentParser,
RobertaForSequenceClassification,
RobertaTokenizer,
)
from trl import AutoModelForCausalLMWithValueHead, PPOConfig, PPOTrainer, create_reference_model, set_seed
from trl.core import LengthSampler
tqdm.pandas()
########################################################################
# This is a fully working simple example to use trl with accelerate.
#
# This example fine-tunes a GPTJ model to generate less toxic contents
# by using allenai/real-toxicity-prompts dataset. We use PPO
# (proximal policy optimization) to optimize the model.
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - multi GPUS (using DeepSpeed ZeRO-Offload stages 1 & 2)
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, first initialize the accelerate
# configuration with `accelerate config`
#
########################################################################
# We first define the configuration of the experiment, defining the model, the dataset,
# the training parameters, and the PPO parameters.
# Check the default arguments in the `PPOConfig` class for more details.
# If you want to log with tensorboard, add the kwarg
# `project_kwargs={"logging_dir": PATH_TO_LOGS}` to the PPOConfig.
@dataclass
class ScriptArguments:
"""
The name of the Casual LM model we wish to fine with PPO
"""
# NOTE: gpt2 models use Conv1D instead of Linear layers which are not yet supported in 8 bit mode
# models like gpt-neo* models are more suitable.
model_name: Optional[str] = field(default="ybelkada/gpt-j-6b-sharded-bf16", metadata={"help": "the model name"})
log_with: Optional[str] = field(default=None, metadata={"help": "use 'wandb' to log with wandb"})
learning_rate: Optional[float] = field(default=(1.47e-5) * 2, metadata={"help": "the learning rate"})
mini_batch_size: Optional[int] = field(default=4, metadata={"help": "the PPO minibatch size"})
batch_size: Optional[int] = field(default=16, metadata={"help": "the batch size"})
gradient_accumulation_steps: Optional[int] = field(
default=1, metadata={"help": "the number of gradient accumulation steps"}
)
model_save_path: Optional[str] = field(
default="./gpt-j-6B-detoxified-long-context-26-shl-1e4-final",
metadata={"help": "the path to save the model"},
)
parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]
config = PPOConfig(
model_name=script_args.model_name,
learning_rate=script_args.learning_rate,
log_with=script_args.log_with,
ppo_epochs=100,
mini_batch_size=script_args.mini_batch_size,
batch_size=script_args.batch_size,
gradient_accumulation_steps=script_args.gradient_accumulation_steps,
)
# Below is an example function to build the dataset. In our case, we use the IMDB dataset
# from the `datasets` library. One should customize this function to train the model on
# its own dataset.
def build_dataset(
config, dataset_name="allenai/real-toxicity-prompts", input_min_text_length=5, input_max_text_length=10
):
"""
Build dataset for training. This builds the dataset from `load_dataset`, one should
customize this function to train the model on its own dataset.
Args:
dataset_name (`str`):
The name of the dataset to be loaded.
Returns:
dataloader (`torch.utils.data.DataLoader`):
The dataloader for the dataset.
"""
tokenizer = AutoTokenizer.from_pretrained(config.model_name)
tokenizer.pad_token = tokenizer.eos_token
ds = load_dataset(dataset_name, split="train")
def filter_fn(sample):
toxicity = sample["prompt"]["toxicity"]
return toxicity is not None and toxicity > 0.3
ds = ds.filter(filter_fn, batched=False)
input_size = LengthSampler(input_min_text_length, input_max_text_length)
def tokenize(sample):
prompt = sample["prompt"]["text"]
continuation = sample["continuation"]["text"]
sample["input_ids"] = tokenizer.encode(prompt + continuation)[: input_size()]
sample["query"] = tokenizer.decode(sample["input_ids"])
return sample
ds = ds.map(tokenize, batched=False)
ds.set_format(type="torch")
ds = ds.train_test_split(test_size=0.2, shuffle=False)["train"]
return ds
# We retrieve the dataloader by calling the `build_dataset` function.
min_input_length = 30
max_input_length = 40
dataset = build_dataset(config, input_min_text_length=min_input_length, input_max_text_length=max_input_length)
def collator(data):
return dict((key, [d[key] for d in data]) for key in data[0])
# set seed before initializing value head for deterministic eval
set_seed(config.seed)
# Now let's build the model, the reference model, and the tokenizer. We first load the model
# in bfloat16 to save memory using `transformers`.
model = AutoModelForCausalLM.from_pretrained(config.model_name, torch_dtype=torch.bfloat16)
# And then we pass the loaded model to `AutoModelForCausalLMWithValueHead`.
model = AutoModelForCausalLMWithValueHead.from_pretrained(model)
# We create a reference model by sharing 20 layers
ref_model = create_reference_model(model, num_shared_layers=20)
# We make sure to use `Adam` optimizer on the model parameters that require gradients.
optimizer = Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=config.learning_rate)
# GPT-2 / GPT-J tokenizer has a pad token, but it is not eos_token by default. We need to set it to eos_token.
# only for this model.
tokenizer = AutoTokenizer.from_pretrained(config.model_name)
tokenizer.pad_token = tokenizer.eos_token
# We then build the PPOTrainer, passing the model, the reference model, the tokenizer
ppo_trainer = PPOTrainer(
config,
model,
ref_model=ref_model,
tokenizer=tokenizer,
dataset=dataset,
data_collator=collator,
optimizer=optimizer,
)
# We then build the reward pipeline, we will use the toxicity model to compute the reward.
# We first load the toxicity model and tokenizer.
toxicity_model_id = "facebook/roberta-hate-speech-dynabench-r4-target"
toxicity_tokenizer = RobertaTokenizer.from_pretrained(toxicity_model_id)
# We load the toxicity model in fp16 to save memory.
toxicity_model = RobertaForSequenceClassification.from_pretrained(toxicity_model_id, torch_dtype=torch.float16).to(
ppo_trainer.accelerator.device
)
# We then define the arguments to pass to the `generate` function. These arguments
# are passed to the `generate` function of the PPOTrainer, which is a wrapper around
# the `generate` function of the trained model.
generation_kwargs = {
"min_length": -1,
"top_k": 0.0,
"top_p": 1.0,
"do_sample": True,
"pad_token_id": tokenizer.eos_token_id,
}
output_min_length = 20
output_max_length = 30
output_length_sampler = LengthSampler(output_min_length, output_max_length)
model_save_path = script_args.model_save_path
for epoch, batch in tqdm(enumerate(ppo_trainer.dataloader)):
query_tensors = batch["input_ids"]
# Get response from the policy model
response_tensors = []
for query in query_tensors:
gen_len = output_length_sampler()
generation_kwargs["max_new_tokens"] = gen_len
response = ppo_trainer.generate(query, **generation_kwargs)
response_tensors.append(response.squeeze()[-gen_len:])
batch["response"] = [tokenizer.decode(r.squeeze()) for r in response_tensors]
# Compute sentiment score # noqa
texts = batch["response"]
toxicity_inputs = toxicity_tokenizer(texts, padding=True, truncation=True, return_tensors="pt").to(
ppo_trainer.accelerator.device
)
logits = toxicity_model(**toxicity_inputs).logits.float()
toxicity_labels = (logits[:, 0]).tolist()
rewards = [torch.tensor(output) for output in toxicity_labels]
# Run PPO step
stats = ppo_trainer.step(query_tensors, response_tensors, rewards)
ppo_trainer.log_stats(stats, batch, rewards)
# Save model every 100 epochs
if epoch % 100 == 0:
if ppo_trainer.accelerator.is_main_process:
ppo_trainer.save_pretrained(model_save_path)
| 0 |
hf_public_repos/trl/examples/research_projects/toxicity | hf_public_repos/trl/examples/research_projects/toxicity/scripts/evaluate-toxicity.py | import argparse
import csv
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from tqdm import tqdm
from transformers import AutoModelForCausalLM, AutoTokenizer
from trl.import_utils import is_xpu_available
toxicity = evaluate.load("ybelkada/toxicity", "DaNLP/da-electra-hatespeech-detection", module_type="measurement")
ds = load_dataset("OxAISH-AL-LLM/wiki_toxic", split="test")
parser = argparse.ArgumentParser(description="Evaluate de-toxified models")
parser.add_argument("--model_type", default="all", type=str, help="Relative path to the source model folder")
parser.add_argument("--output_file", default="toxicity.csv", type=str, help="Relative path to the source model folder")
parser.add_argument("--batch_size", default=64, type=int, help="Batch size")
parser.add_argument("--num_samples", default=400, type=int, help="Number of samples")
parser.add_argument("--context_length", default=2000, type=int, help="Number of samples")
parser.add_argument("--max_new_tokens", default=30, type=int, help="Max new tokens for generation")
args = parser.parse_args()
if args.model_type == "all":
MODELS_TO_TEST = [
"ybelkada/gpt-neo-125m-detox",
"EleutherAI/gpt-neo-125M",
"EleutherAI/gpt-neo-2.7B",
"ybelkada/gpt-neo-2.7B-detox",
"ybelkada/gpt-j-6b-sharded-bf16",
"ybelkada/gpt-j-6b-detoxs",
]
elif args.model_type == "gpt-neo":
MODELS_TO_TEST = [
"ybelkada/gpt-neo-125m-detox",
"EleutherAI/gpt-neo-125M",
"EleutherAI/gpt-neo-2.7B",
"ybelkada/gpt-neo-2.7B-detox",
]
elif args.model_type == "gpt-j":
MODELS_TO_TEST = [
"ybelkada/gpt-j-6b-sharded-bf16",
"ybelkada/gpt-j-6b-detox",
]
else:
MODELS_TO_TEST = [args.model_type]
NUM_SAMPLES = args.num_samples
BATCH_SIZE = args.batch_size
output_file = args.output_file
max_new_tokens = args.max_new_tokens
context_length = args.context_length
if is_xpu_available():
device = torch.xpu.current_device()
else:
device = torch.cuda.current_device() if torch.cuda.is_available() else "cpu"
# consider only toxic prompts
ds = ds.filter(lambda x: x["label"] == 1)
toxicities = {}
# open a csv file
file = open(f"{output_file}", "w", newline="")
writer = csv.writer(file)
# add first rows
writer.writerow(["model_id", "mean_toxicity", "std_toxicity"])
for model_id in tqdm(MODELS_TO_TEST):
model = AutoModelForCausalLM.from_pretrained(model_id, device_map={"": device}, torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(model_id)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "left"
input_texts = []
for i, example in enumerate(ds):
# set seed
torch.manual_seed(42)
input_text = example["comment_text"]
input_texts.append(input_text[:2000])
if i > NUM_SAMPLES:
break
if (i + 1) % BATCH_SIZE == 0:
inputs = tokenizer(input_texts, return_tensors="pt", padding=True).to(device)
inputs.input_ids = inputs.input_ids[:context_length]
inputs.attention_mask = inputs.attention_mask[:context_length]
outputs = model.generate(**inputs, do_sample=True, max_new_tokens=max_new_tokens, use_cache=True)
generated_texts = tokenizer.batch_decode(outputs, skip_special_tokens=True)
generated_texts = [
generated_text.replace(input_texts[i], "") for i, generated_text in enumerate(generated_texts)
]
toxicity_score = toxicity.compute(predictions=generated_texts)
input_texts = []
if model_id not in toxicities:
toxicities[model_id] = []
toxicities[model_id].extend(toxicity_score["toxicity"])
# last batch
inputs = tokenizer(input_texts, return_tensors="pt", padding=True).to(device)
outputs = model.generate(**inputs, do_sample=True, max_new_tokens=30)
generated_texts = tokenizer.batch_decode(outputs, skip_special_tokens=True)
generated_texts = [generated_text.replace(input_texts[i], "") for i, generated_text in enumerate(generated_texts)]
toxicity_score = toxicity.compute(predictions=generated_texts)
toxicities[model_id].extend(toxicity_score["toxicity"])
# compute mean & std using np
mean = np.mean(toxicities[model_id])
std = np.std(toxicities[model_id])
# save to file
writer.writerow([model_id, mean, std])
# print
print(f"Model: {model_id} - Mean: {mean} - Std: {std}")
model = None
if is_xpu_available():
torch.xpu.empty_cache()
else:
torch.cuda.empty_cache()
# close file
file.close()
| 0 |
hf_public_repos/trl/examples/research_projects/stack_llama_2 | hf_public_repos/trl/examples/research_projects/stack_llama_2/scripts/sft_llama2.py | # Fine-Tune Llama2-7b on SE paired dataset
import os
from dataclasses import dataclass, field
from typing import Optional
import torch
import tyro
from accelerate import Accelerator
from datasets import load_dataset
from peft import AutoPeftModelForCausalLM, LoraConfig
from tqdm import tqdm
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TrainingArguments
from trl import SFTTrainer
from trl.import_utils import is_xpu_available
from trl.trainer import ConstantLengthDataset
@dataclass
class ScriptArguments:
model_name: Optional[str] = field(default="meta-llama/Llama-2-7b-hf", metadata={"help": "the model name"})
dataset_name: Optional[str] = field(default="lvwerra/stack-exchange-paired", metadata={"help": "the dataset name"})
subset: Optional[str] = field(default="data/finetune", metadata={"help": "the subset to use"})
split: Optional[str] = field(default="train", metadata={"help": "the split to use"})
size_valid_set: Optional[int] = field(default=4000, metadata={"help": "the size of the validation set"})
streaming: Optional[bool] = field(default=True, metadata={"help": "whether to stream the dataset"})
shuffle_buffer: Optional[int] = field(default=5000, metadata={"help": "the shuffle buffer size"})
seq_length: Optional[int] = field(default=1024, metadata={"help": "the sequence length"})
num_workers: Optional[int] = field(default=4, metadata={"help": "the number of workers"})
training_args: TrainingArguments = field(
default_factory=lambda: TrainingArguments(
output_dir="./results",
max_steps=500,
logging_steps=10,
save_steps=10,
per_device_train_batch_size=4,
per_device_eval_batch_size=1,
gradient_accumulation_steps=2,
gradient_checkpointing=False,
group_by_length=False,
learning_rate=1e-4,
lr_scheduler_type="cosine",
warmup_steps=100,
weight_decay=0.05,
optim="paged_adamw_32bit",
bf16=True,
remove_unused_columns=False,
run_name="sft_llama2",
report_to="wandb",
)
)
packing: Optional[bool] = field(default=True, metadata={"help": "whether to use packing for SFTTrainer"})
peft_config: LoraConfig = field(
default_factory=lambda: LoraConfig(
r=8,
lora_alpha=16,
lora_dropout=0.05,
target_modules=["q_proj", "v_proj"],
bias="none",
task_type="CAUSAL_LM",
)
)
script_args = tyro.cli(ScriptArguments)
if script_args.training_args.group_by_length and script_args.packing:
raise ValueError("Cannot use both packing and group by length")
# `gradient_checkpointing` was True by default until `1f3314`, but it's actually not used.
# `gradient_checkpointing=True` will cause `Variable._execution_engine.run_backward`.
if script_args.training_args.gradient_checkpointing:
raise ValueError("gradient_checkpointing not supported")
def chars_token_ratio(dataset, tokenizer, nb_examples=400):
"""
Estimate the average number of characters per token in the dataset.
"""
total_characters, total_tokens = 0, 0
for _, example in tqdm(zip(range(nb_examples), iter(dataset)), total=nb_examples):
text = prepare_sample_text(example)
total_characters += len(text)
if tokenizer.is_fast:
total_tokens += len(tokenizer(text).tokens())
else:
total_tokens += len(tokenizer.tokenize(text))
return total_characters / total_tokens
def print_trainable_parameters(model):
"""
Prints the number of trainable parameters in the model.
"""
trainable_params = 0
all_param = 0
for _, param in model.named_parameters():
all_param += param.numel()
if param.requires_grad:
trainable_params += param.numel()
print(
f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}"
)
def prepare_sample_text(example):
"""Prepare the text from a sample of the dataset."""
text = f"Question: {example['question']}\n\nAnswer: {example['response_j']}"
return text
def create_datasets(tokenizer, args):
dataset = load_dataset(
args.dataset_name,
data_dir=args.subset,
split=args.split,
use_auth_token=True,
num_proc=args.num_workers if not args.streaming else None,
streaming=args.streaming,
)
if args.streaming:
print("Loading the dataset in streaming mode")
valid_data = dataset.take(args.size_valid_set)
train_data = dataset.skip(args.size_valid_set)
train_data = train_data.shuffle(buffer_size=args.shuffle_buffer, seed=None)
else:
dataset = dataset.train_test_split(test_size=0.005, seed=None)
train_data = dataset["train"]
valid_data = dataset["test"]
print(f"Size of the train set: {len(train_data)}. Size of the validation set: {len(valid_data)}")
chars_per_token = chars_token_ratio(train_data, tokenizer)
print(f"The character to token ratio of the dataset is: {chars_per_token:.2f}")
train_dataset = ConstantLengthDataset(
tokenizer,
train_data,
formatting_func=prepare_sample_text,
infinite=True,
seq_length=args.seq_length,
chars_per_token=chars_per_token,
)
valid_dataset = ConstantLengthDataset(
tokenizer,
valid_data,
formatting_func=prepare_sample_text,
infinite=False,
seq_length=args.seq_length,
chars_per_token=chars_per_token,
)
return train_dataset, valid_dataset
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
)
base_model = AutoModelForCausalLM.from_pretrained(
script_args.model_name,
quantization_config=bnb_config,
device_map={"": Accelerator().local_process_index},
trust_remote_code=True,
use_auth_token=True,
)
base_model.config.use_cache = False
peft_config = script_args.peft_config
tokenizer = AutoTokenizer.from_pretrained(script_args.model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right" # Fix weird overflow issue with fp16 training
training_args = script_args.training_args
train_dataset, eval_dataset = create_datasets(tokenizer, script_args)
trainer = SFTTrainer(
model=base_model,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
peft_config=peft_config,
packing=script_args.packing,
max_seq_length=None,
tokenizer=tokenizer,
args=training_args,
)
trainer.train()
trainer.save_model(script_args.training_args.output_dir)
output_dir = os.path.join(script_args.training_args.output_dir, "final_checkpoint")
trainer.model.save_pretrained(output_dir)
# Free memory for merging weights
del base_model
if is_xpu_available():
torch.xpu.empty_cache()
else:
torch.cuda.empty_cache()
model = AutoPeftModelForCausalLM.from_pretrained(output_dir, device_map="auto", torch_dtype=torch.bfloat16)
model = model.merge_and_unload()
output_merged_dir = os.path.join(script_args.training_args.output_dir, "final_merged_checkpoint")
model.save_pretrained(output_merged_dir, safe_serialization=True)
| 0 |
hf_public_repos/trl/examples/research_projects/stack_llama_2 | hf_public_repos/trl/examples/research_projects/stack_llama_2/scripts/requirements.txt | transformers
trl
peft
accelerate
datasets
bitsandbytes
wandb
| 0 |
hf_public_repos/trl/examples/research_projects/stack_llama_2 | hf_public_repos/trl/examples/research_projects/stack_llama_2/scripts/dpo_llama2.py | # 0. imports
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import torch
from datasets import Dataset, load_dataset
from peft import LoraConfig
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, TrainingArguments
from trl import DPOTrainer
# Define and parse arguments.
@dataclass
class ScriptArguments:
"""
The arguments for the DPO training script.
"""
# data parameters
beta: Optional[float] = field(default=0.1, metadata={"help": "the beta parameter for DPO loss"})
# training parameters
model_name_or_path: Optional[str] = field(
default="../sft/results/final_checkpoint",
metadata={"help": "the location of the SFT model name or path"},
)
learning_rate: Optional[float] = field(default=5e-4, metadata={"help": "optimizer learning rate"})
lr_scheduler_type: Optional[str] = field(default="cosine", metadata={"help": "the lr scheduler type"})
warmup_steps: Optional[int] = field(default=100, metadata={"help": "the number of warmup steps"})
weight_decay: Optional[float] = field(default=0.05, metadata={"help": "the weight decay"})
optimizer_type: Optional[str] = field(default="paged_adamw_32bit", metadata={"help": "the optimizer type"})
per_device_train_batch_size: Optional[int] = field(default=4, metadata={"help": "train batch size per device"})
per_device_eval_batch_size: Optional[int] = field(default=1, metadata={"help": "eval batch size per device"})
gradient_accumulation_steps: Optional[int] = field(
default=4, metadata={"help": "the number of gradient accumulation steps"}
)
gradient_checkpointing: Optional[bool] = field(
default=True, metadata={"help": "whether to use gradient checkpointing"}
)
lora_alpha: Optional[float] = field(default=16, metadata={"help": "the lora alpha parameter"})
lora_dropout: Optional[float] = field(default=0.05, metadata={"help": "the lora dropout parameter"})
lora_r: Optional[int] = field(default=8, metadata={"help": "the lora r parameter"})
max_prompt_length: Optional[int] = field(default=512, metadata={"help": "the maximum prompt length"})
max_length: Optional[int] = field(default=1024, metadata={"help": "the maximum sequence length"})
max_steps: Optional[int] = field(default=1000, metadata={"help": "max number of training steps"})
logging_steps: Optional[int] = field(default=10, metadata={"help": "the logging frequency"})
save_steps: Optional[int] = field(default=100, metadata={"help": "the saving frequency"})
eval_steps: Optional[int] = field(default=100, metadata={"help": "the evaluation frequency"})
output_dir: Optional[str] = field(default="./results", metadata={"help": "the output directory"})
log_freq: Optional[int] = field(default=1, metadata={"help": "the logging frequency"})
# instrumentation
sanity_check: Optional[bool] = field(default=False, metadata={"help": "only train on 1000 samples"})
report_to: Optional[str] = field(
default="wandb",
metadata={
"help": 'The list of integrations to report the results and logs to. Supported platforms are `"azure_ml"`,'
'`"comet_ml"`, `"mlflow"`, `"neptune"`, `"tensorboard"`,`"clearml"` and `"wandb"`. '
'Use `"all"` to report to all integrations installed, `"none"` for no integrations.'
},
)
# debug argument for distributed training
ignore_bias_buffers: Optional[bool] = field(
default=False,
metadata={
"help": "fix for DDP issues with LM bias/mask buffers - invalid scalar type,`inplace operation. See"
"https://github.com/huggingface/transformers/issues/22482#issuecomment-1595790992"
},
)
def get_stack_exchange_paired(
data_dir: str = "data/rl",
sanity_check: bool = False,
cache_dir: str = None,
num_proc=24,
) -> Dataset:
"""Load the stack-exchange-paired dataset from Hugging Face and convert it to the necessary format.
The dataset is converted to a dictionary with the following structure:
{
'prompt': List[str],
'chosen': List[str],
'rejected': List[str],
}
Prompts are structured as follows:
"Question: " + <prompt> + "\n\nAnswer: "
"""
dataset = load_dataset(
"lvwerra/stack-exchange-paired",
split="train",
cache_dir=cache_dir,
data_dir=data_dir,
)
original_columns = dataset.column_names
if sanity_check:
dataset = dataset.select(range(min(len(dataset), 1000)))
def return_prompt_and_responses(samples) -> Dict[str, str]:
return {
"prompt": ["Question: " + question + "\n\nAnswer: " for question in samples["question"]],
"chosen": samples["response_j"],
"rejected": samples["response_k"],
}
return dataset.map(
return_prompt_and_responses,
batched=True,
num_proc=num_proc,
remove_columns=original_columns,
)
if __name__ == "__main__":
parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]
# 1. load a pretrained model
model = AutoModelForCausalLM.from_pretrained(
script_args.model_name_or_path,
low_cpu_mem_usage=True,
torch_dtype=torch.float16,
load_in_4bit=True,
)
model.config.use_cache = False
if script_args.ignore_bias_buffers:
# torch distributed hack
model._ddp_params_and_buffers_to_ignore = [
name for name, buffer in model.named_buffers() if buffer.dtype == torch.bool
]
model_ref = AutoModelForCausalLM.from_pretrained(
script_args.model_name_or_path,
low_cpu_mem_usage=True,
torch_dtype=torch.float16,
load_in_4bit=True,
)
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
tokenizer.pad_token = tokenizer.eos_token
# 2. Load the Stack-exchange paired dataset
train_dataset = get_stack_exchange_paired(data_dir="data/rl", sanity_check=script_args.sanity_check)
train_dataset = train_dataset.filter(
lambda x: len(x["prompt"]) + len(x["chosen"]) <= script_args.max_length
and len(x["prompt"]) + len(x["rejected"]) <= script_args.max_length
)
# 3. Load evaluation dataset
eval_dataset = get_stack_exchange_paired(data_dir="data/evaluation", sanity_check=True)
eval_dataset = eval_dataset.filter(
lambda x: len(x["prompt"]) + len(x["chosen"]) <= script_args.max_length
and len(x["prompt"]) + len(x["rejected"]) <= script_args.max_length
)
# 4. initialize training arguments:
training_args = TrainingArguments(
per_device_train_batch_size=script_args.per_device_train_batch_size,
per_device_eval_batch_size=script_args.per_device_eval_batch_size,
max_steps=script_args.max_steps,
logging_steps=script_args.logging_steps,
save_steps=script_args.save_steps,
gradient_accumulation_steps=script_args.gradient_accumulation_steps,
gradient_checkpointing=script_args.gradient_checkpointing,
learning_rate=script_args.learning_rate,
evaluation_strategy="steps",
eval_steps=script_args.eval_steps,
output_dir=script_args.output_dir,
report_to=script_args.report_to,
lr_scheduler_type=script_args.lr_scheduler_type,
warmup_steps=script_args.warmup_steps,
optim=script_args.optimizer_type,
bf16=True,
remove_unused_columns=False,
run_name="dpo_llama2",
)
peft_config = LoraConfig(
r=script_args.lora_r,
lora_alpha=script_args.lora_alpha,
lora_dropout=script_args.lora_dropout,
target_modules=[
"q_proj",
"v_proj",
"k_proj",
"out_proj",
"fc_in",
"fc_out",
"wte",
],
bias="none",
task_type="CAUSAL_LM",
)
# 5. initialize the DPO trainer
dpo_trainer = DPOTrainer(
model,
model_ref,
args=training_args,
beta=script_args.beta,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
tokenizer=tokenizer,
peft_config=peft_config,
max_prompt_length=script_args.max_prompt_length,
max_length=script_args.max_length,
)
# 6. train
dpo_trainer.train()
dpo_trainer.save_model(script_args.output_dir)
# 7. save
output_dir = os.path.join(script_args.output_dir, "final_checkpoint")
dpo_trainer.model.save_pretrained(output_dir)
| 0 |
hf_public_repos/trl/examples/research_projects/stack_llama_2 | hf_public_repos/trl/examples/research_projects/stack_llama_2/scripts/README.md | # DPO pipeline for the creation of StackLlaMa 2: a Stack exchange llama-v2-7b model
## Prerequisites
Install all the dependencies in the `requirements.txt`:
```
$ pip install -U -r requirements.txt
```
Since we will use `accelerate` for training, make sure to run:
```
$ accelerate config
```
## Training
There were two main steps to the DPO training process:
1. Supervised fine-tuning of the base llama-v2-7b model to create llama-v2-7b-se:
- `accelerate launch examples/research_projects/stack_llama_2/scripts/sft_llama2.py --training_args.output_dir="sft"`
1. Run the DPO trainer using the model saved by the previous step:
- `accelerate launch examples/research_projects/stack_llama_2/scripts/dpo_llama2.py --model_name_or_path="sft/final_checkpoint" --output_dir="dpo"`
## Merging the adaptors
To merge the adaptors into the base model we can use the `merge_peft_adapter.py` helper script that comes with TRL:
```
python examples/research_projects/stack_llama/scripts/merge_peft_adapter.py --base_model_name="meta-llama/Llama-2-7b-hf" --adapter_model_name="dpo/final_checkpoint/" --output_name="stack-llama-2"
```
which will also push the model to your HuggingFace hub account.
## Running the model
We can load the DPO-trained LoRA adaptors which were saved by the DPO training step and load them via:
```py
from peft import AutoPeftModelForCausalLM
model = AutoPeftModelForCausalLM.from_pretrained(
"dpo/final_checkpoint",
low_cpu_mem_usage=True,
torch_dtype=torch.float16,
load_in_4bit=True,
)
model.generate(...)
```
| 0 |
hf_public_repos/trl/examples/research_projects/stack_llama | hf_public_repos/trl/examples/research_projects/stack_llama/scripts/merge_peft_adapter.py | from dataclasses import dataclass, field
from typing import Optional
import torch
from peft import PeftConfig, PeftModel
from transformers import AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer, HfArgumentParser
@dataclass
class ScriptArguments:
"""
The input names representing the Adapter and Base model fine-tuned with PEFT, and the output name representing the
merged model.
"""
adapter_model_name: Optional[str] = field(default=None, metadata={"help": "the adapter name"})
base_model_name: Optional[str] = field(default=None, metadata={"help": "the base model name"})
output_name: Optional[str] = field(default=None, metadata={"help": "the merged model name"})
parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]
assert script_args.adapter_model_name is not None, "please provide the name of the Adapter you would like to merge"
assert script_args.base_model_name is not None, "please provide the name of the Base model"
assert script_args.output_name is not None, "please provide the output name of the merged model"
peft_config = PeftConfig.from_pretrained(script_args.adapter_model_name)
if peft_config.task_type == "SEQ_CLS":
# The sequence classification task is used for the reward model in PPO
model = AutoModelForSequenceClassification.from_pretrained(
script_args.base_model_name, num_labels=1, torch_dtype=torch.bfloat16
)
else:
model = AutoModelForCausalLM.from_pretrained(
script_args.base_model_name, return_dict=True, torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(script_args.base_model_name)
# Load the PEFT model
model = PeftModel.from_pretrained(model, script_args.adapter_model_name)
model.eval()
model = model.merge_and_unload()
model.save_pretrained(f"{script_args.output_name}")
tokenizer.save_pretrained(f"{script_args.output_name}")
model.push_to_hub(f"{script_args.output_name}", use_temp_dir=False)
| 0 |
hf_public_repos/trl/examples/research_projects/stack_llama | hf_public_repos/trl/examples/research_projects/stack_llama/scripts/reward_modeling.py | from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import evaluate
import numpy as np
import torch
import torch.nn as nn
from datasets import load_dataset
from peft import LoraConfig, TaskType, get_peft_model
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
HfArgumentParser,
PreTrainedTokenizerBase,
Trainer,
TrainerCallback,
TrainingArguments,
)
from transformers.utils import PaddingStrategy
# Define and parse arguments.
@dataclass
class ScriptArguments:
"""
These arguments vary depending on how many GPUs you have, what their capacity and features are, and what size model you want to train.
"""
local_rank: Optional[int] = field(default=-1, metadata={"help": "Used for multi-gpu"})
resume_from_checkpoint: Optional[bool] = field(
default=False,
metadata={"help": "If you want to resume training where it left off."},
)
deepspeed: Optional[str] = field(
default=None,
metadata={
"help": "Path to deepspeed config if using deepspeed. You may need this if the model that you want to train doesn't fit on a single GPU."
},
)
per_device_train_batch_size: Optional[int] = field(default=4)
per_device_eval_batch_size: Optional[int] = field(default=1)
gradient_accumulation_steps: Optional[int] = field(default=1)
learning_rate: Optional[float] = field(default=2e-5)
weight_decay: Optional[float] = field(default=0.001)
model_name: Optional[str] = field(
default="gpt2",
metadata={
"help": "The model that you want to train from the Hugging Face hub. E.g. gpt2, gpt2-xl, bert, etc."
},
)
tokenizer_name: Optional[str] = field(
default=None,
metadata={
"help": "The tokenizer for your model, if left empty will use the default for your model",
},
)
bf16: Optional[bool] = field(
default=True,
metadata={
"help": "This essentially cuts the training time in half if you want to sacrifice a little precision and have a supported GPU."
},
)
num_train_epochs: Optional[int] = field(
default=1,
metadata={"help": "The number of training epochs for the reward model."},
)
train_subset: Optional[int] = field(
default=100000,
metadata={"help": "The size of the subset of the training data to use"},
)
eval_subset: Optional[int] = field(
default=50000,
metadata={"help": "The size of the subset of the eval data to use"},
)
gradient_checkpointing: Optional[bool] = field(
default=False,
metadata={"help": "Enables gradient checkpointing."},
)
optim: Optional[str] = field(
default="adamw_hf",
metadata={"help": "The optimizer to use."},
)
lr_scheduler_type: Optional[str] = field(
default="linear",
metadata={"help": "The lr scheduler"},
)
max_length: Optional[int] = field(default=512)
eval_first_step: Optional[bool] = field(
default=False,
metadata={"help": "Whether to run eval after the first step"},
)
parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]
# Load the human stack-exchange-paired dataset for tuning the reward model.
train_dataset = load_dataset("lvwerra/stack-exchange-paired", data_dir="data/reward", split="train")
if script_args.train_subset > 0:
train_dataset = train_dataset.select(range(script_args.train_subset))
eval_dataset = load_dataset("lvwerra/stack-exchange-paired", data_dir="data/evaluation", split="train")
if script_args.eval_subset > 0:
eval_dataset = eval_dataset.select(range(script_args.eval_subset))
# Define the training args. Needs to be done before the model is loaded if you are using deepspeed.
model_name_split = script_args.model_name.split("/")[-1]
output_name = (
f"{model_name_split}_peft_stack-exchange-paired_rmts__{script_args.train_subset}_{script_args.learning_rate}"
)
training_args = TrainingArguments(
output_dir=output_name,
learning_rate=script_args.learning_rate,
per_device_train_batch_size=script_args.per_device_train_batch_size,
per_device_eval_batch_size=script_args.per_device_eval_batch_size,
num_train_epochs=script_args.num_train_epochs,
weight_decay=script_args.weight_decay,
evaluation_strategy="steps",
eval_steps=500,
save_strategy="steps",
save_steps=500,
gradient_accumulation_steps=script_args.gradient_accumulation_steps,
gradient_checkpointing=script_args.gradient_checkpointing,
deepspeed=script_args.deepspeed,
local_rank=script_args.local_rank,
remove_unused_columns=False,
label_names=[],
bf16=script_args.bf16,
logging_strategy="steps",
logging_steps=10,
optim=script_args.optim,
lr_scheduler_type=script_args.lr_scheduler_type,
)
# Load the value-head model and tokenizer.
tokenizer_name = script_args.tokenizer_name if script_args.tokenizer_name is not None else script_args.model_name
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, use_auth_token=True)
tokenizer.pad_token = tokenizer.eos_token
peft_config = LoraConfig(
task_type=TaskType.SEQ_CLS,
inference_mode=False,
r=8,
lora_alpha=32,
lora_dropout=0.1,
)
model = AutoModelForSequenceClassification.from_pretrained(
script_args.model_name, num_labels=1, torch_dtype=torch.bfloat16
)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
# Need to do this for gpt2, because it doesn't have an official pad token.
tokenizer.pad_token = tokenizer.eos_token
model.config.pad_token_id = tokenizer.eos_token_id
model.config.use_cache = not script_args.gradient_checkpointing
num_proc = 24 # Can adjust to be higher if you have more processors.
original_columns = train_dataset.column_names
# Turn the dataset into pairs of post + summaries, where text_j is the preferred question + answer and text_k is the other.
# Then tokenize the dataset.
def preprocess_function(examples):
new_examples = {
"input_ids_j": [],
"attention_mask_j": [],
"input_ids_k": [],
"attention_mask_k": [],
}
for question, response_j, response_k in zip(examples["question"], examples["response_j"], examples["response_k"]):
tokenized_j = tokenizer("Question: " + question + "\n\nAnswer: " + response_j, truncation=True)
tokenized_k = tokenizer("Question: " + question + "\n\nAnswer: " + response_k, truncation=True)
new_examples["input_ids_j"].append(tokenized_j["input_ids"])
new_examples["attention_mask_j"].append(tokenized_j["attention_mask"])
new_examples["input_ids_k"].append(tokenized_k["input_ids"])
new_examples["attention_mask_k"].append(tokenized_k["attention_mask"])
return new_examples
# preprocess the dataset and filter out QAs that are longer than script_args.max_length
train_dataset = train_dataset.map(
preprocess_function,
batched=True,
num_proc=num_proc,
remove_columns=original_columns,
)
train_dataset = train_dataset.filter(
lambda x: len(x["input_ids_j"]) <= script_args.max_length and len(x["input_ids_k"]) <= script_args.max_length
)
eval_dataset = eval_dataset.map(
preprocess_function,
batched=True,
num_proc=num_proc,
remove_columns=original_columns,
)
eval_dataset = eval_dataset.filter(
lambda x: len(x["input_ids_j"]) <= script_args.max_length and len(x["input_ids_k"]) <= script_args.max_length
)
# We need to define a special data collator that batches the data in our j vs k format.
@dataclass
class RewardDataCollatorWithPadding:
tokenizer: PreTrainedTokenizerBase
padding: Union[bool, str, PaddingStrategy] = True
max_length: Optional[int] = None
pad_to_multiple_of: Optional[int] = None
return_tensors: str = "pt"
def __call__(self, features: List[Dict[str, Any]]) -> Dict[str, Any]:
features_j = []
features_k = []
for feature in features:
features_j.append(
{
"input_ids": feature["input_ids_j"],
"attention_mask": feature["attention_mask_j"],
}
)
features_k.append(
{
"input_ids": feature["input_ids_k"],
"attention_mask": feature["attention_mask_k"],
}
)
batch_j = self.tokenizer.pad(
features_j,
padding=self.padding,
max_length=self.max_length,
pad_to_multiple_of=self.pad_to_multiple_of,
return_tensors=self.return_tensors,
)
batch_k = self.tokenizer.pad(
features_k,
padding=self.padding,
max_length=self.max_length,
pad_to_multiple_of=self.pad_to_multiple_of,
return_tensors=self.return_tensors,
)
batch = {
"input_ids_j": batch_j["input_ids"],
"attention_mask_j": batch_j["attention_mask"],
"input_ids_k": batch_k["input_ids"],
"attention_mask_k": batch_k["attention_mask"],
"return_loss": True,
}
return batch
# Define the metric that we'll use for validation.
accuracy = evaluate.load("accuracy")
def compute_metrics(eval_pred):
predictions, _ = eval_pred
# Here, predictions is rewards_j and rewards_k.
# We want to see how much of the time rewards_j > rewards_k.
predictions = np.argmax(predictions, axis=0)
labels = np.zeros(predictions.shape)
return accuracy.compute(predictions=predictions, references=labels)
class RewardTrainer(Trainer):
# Define how to compute the reward loss. We use the InstructGPT pairwise logloss: https://arxiv.org/abs/2203.02155
def compute_loss(self, model, inputs, return_outputs=False):
rewards_j = model(input_ids=inputs["input_ids_j"], attention_mask=inputs["attention_mask_j"])[0]
rewards_k = model(input_ids=inputs["input_ids_k"], attention_mask=inputs["attention_mask_k"])[0]
loss = -nn.functional.logsigmoid(rewards_j - rewards_k).mean()
if return_outputs:
return loss, {"rewards_j": rewards_j, "rewards_k": rewards_k}
return loss
# Train the model, woohoo.
trainer = RewardTrainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
compute_metrics=compute_metrics,
data_collator=RewardDataCollatorWithPadding(tokenizer=tokenizer, max_length=script_args.max_length),
)
if script_args.eval_first_step:
class EvaluateFirstStepCallback(TrainerCallback):
def on_step_end(self, args, state, control, **kwargs):
if state.global_step == 1:
control.should_evaluate = True
trainer.add_callback(EvaluateFirstStepCallback())
trainer.train(script_args.resume_from_checkpoint)
print("Saving last checkpoint of the model")
model.save_pretrained(output_name + "_peft_last_checkpoint")
| 0 |
hf_public_repos/trl/examples/research_projects/stack_llama | hf_public_repos/trl/examples/research_projects/stack_llama/scripts/README.md | # RLHF pipeline for the creation of StackLLaMa: a Stack exchange llama-7b model.
There were three main steps to the training process:
1. Supervised fine-tuning of the base llama-7b model to create llama-7b-se:
- `torchrun --nnodes 1 --nproc_per_node 8 examples/research_projects/stack_llama/scripts/supervised_finetuning.py --model_path=<LLAMA_MODEL_PATH> --streaming --no_gradient_checkpointing --learning_rate 1e-5 --max_steps 5000 --output_dir ./llama-se`
2. Reward modeling using dialog pairs from the SE dataset using the llama-7b-se to create llama-7b-se-rm:
- `torchrun --nnodes 1 --nproc_per_node 8 examples/research_projects/stack_llama/scripts/reward_modeling.py --model_name=<LLAMA_SE_MODEL>`
3. RL fine-tuning of llama-7b-se with the llama-7b-se-rm reward model:
- `accelerate launch --multi_gpu --num_machines 1 --num_processes 8 examples/research_projects/stack_llama/scripts/rl_training.py --log_with=wandb --model_name=<LLAMA_SE_MODEL> --reward_model_name=<LLAMA_SE_RM_MODEL> --adafactor=False --tokenizer_name=<LLAMA_TOKENIZER> --save_freq=100 --output_max_length=128 --batch_size=8 --gradient_accumulation_steps=8 --batched_gen=True --ppo_epochs=4 --seed=0 --learning_rate=1.4e-5 --early_stopping=True --output_dir=llama-se-rl-finetune-128-8-8-1.4e-5_adam`
LoRA layers were using at all stages to reduce memory requirements.
At each stage the peft adapter layers were merged with the base model, using:
```shell
python examples/research_projects/stack_llama/scripts/merge_peft_adapter.py --adapter_model_name=XXX --base_model_name=YYY --output_name=ZZZ
```
Note that this script requires `peft>=0.3.0`.
For access to the base llama-7b model, please see Meta's [release](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) and [request form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform).
| 0 |
hf_public_repos/trl/examples/research_projects/stack_llama | hf_public_repos/trl/examples/research_projects/stack_llama/scripts/rl_training.py | # coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass, field
from typing import Optional
import torch
from accelerate import Accelerator
from datasets import load_dataset
from peft import LoraConfig
from tqdm import tqdm
from transformers import Adafactor, AutoTokenizer, HfArgumentParser, pipeline
from trl import AutoModelForCausalLMWithValueHead, PPOConfig, PPOTrainer, set_seed
from trl.core import LengthSampler
tqdm.pandas()
@dataclass
class ScriptArguments:
"""
The name of the Casual LM model we wish to fine with PPO
"""
# NOTE: gpt2 models use Conv1D instead of Linear layers which are not yet supported in 8 bit mode
# models like gpt-neo* models are more suitable.
model_name: Optional[str] = field(default="", metadata={"help": "the model name"})
tokenizer_name: Optional[str] = field(default="", metadata={"help": "the tokenizer name"})
reward_model_name: Optional[str] = field(default="", metadata={"help": "the reward model name"})
log_with: Optional[str] = field(default=None, metadata={"help": "use 'wandb' to log with wandb"})
learning_rate: Optional[float] = field(default=1.41e-5, metadata={"help": "the learning rate"})
output_max_length: Optional[int] = field(default=128, metadata={"help": "maximum length for generation"})
mini_batch_size: Optional[int] = field(default=1, metadata={"help": "the PPO minibatch size"})
batch_size: Optional[int] = field(default=32, metadata={"help": "the batch size"})
ppo_epochs: Optional[int] = field(default=4, metadata={"help": "the number of ppo epochs"})
gradient_accumulation_steps: Optional[int] = field(
default=4, metadata={"help": "the number of gradient accumulation steps"}
)
adafactor: Optional[bool] = field(default=False, metadata={"help": "whether to use the adafactor optimizer"})
early_stopping: Optional[bool] = field(default=False, metadata={"help": "whether to early stop"})
target_kl: Optional[float] = field(default=0.1, metadata={"help": "kl target for early stopping"})
reward_baseline: Optional[float] = field(
default=0.0,
metadata={"help": "a baseline value that is subtracted from the reward"},
)
batched_gen: Optional[bool] = field(default=False, metadata={"help": "whether to use the batched text gen"})
save_freq: Optional[int] = field(default=None, metadata={"help": "n steps to save the model"})
output_dir: Optional[str] = field(default="runs/", metadata={"help": "n steps to save the model"})
seed: Optional[int] = field(default=0, metadata={"help": "the seed"})
steps: Optional[int] = field(default=20000, metadata={"help": "number of epochs"})
init_kl_coef: Optional[float] = field(
default=0.2,
metadata={"help": "Initial KL penalty coefficient (used for adaptive and linear control)"},
)
adap_kl_ctrl: Optional[bool] = field(default=True, metadata={"help": "Use adaptive KL control, otherwise linear"})
parser = HfArgumentParser(ScriptArguments)
script_args: ScriptArguments = parser.parse_args_into_dataclasses()[0]
reward_model_name = script_args.reward_model_name
dataset_name = "lvwerra/stack-exchange-paired"
config = PPOConfig(
steps=script_args.steps,
model_name=script_args.model_name,
learning_rate=script_args.learning_rate,
log_with=script_args.log_with,
batch_size=script_args.batch_size,
mini_batch_size=script_args.mini_batch_size,
gradient_accumulation_steps=script_args.gradient_accumulation_steps,
optimize_cuda_cache=True,
early_stopping=script_args.early_stopping,
target_kl=script_args.target_kl,
ppo_epochs=script_args.ppo_epochs,
seed=script_args.seed,
init_kl_coef=script_args.init_kl_coef,
adap_kl_ctrl=script_args.adap_kl_ctrl,
)
train_dataset = load_dataset("lvwerra/stack-exchange-paired", data_dir="data/rl", split="train")
train_dataset = train_dataset.select(range(100000))
original_columns = train_dataset.column_names
# We then define the arguments to pass to the sentiment analysis pipeline.
# We set `return_all_scores` to True to get the sentiment score for each token.
sent_kwargs = {
"return_all_scores": True,
"function_to_apply": "none",
"batch_size": 16,
"truncation": True,
}
tokenizer = AutoTokenizer.from_pretrained(script_args.tokenizer_name)
# GPT-2 tokenizer has a pad token, but it is not eos_token by default. We need to set it to eos_token.
# only for this model.
if getattr(tokenizer, "pad_token", None) is None:
tokenizer.pad_token = tokenizer.eos_token
# Below is an example function to build the dataset. In our case, we use the IMDB dataset
# from the `datasets` library. One should customize this function to train the model on
# its own dataset.
def build_dataset(
tokenizer,
dataset_name="lvwerra/stack-exchange-paired",
):
"""
Build dataset for training. This builds the dataset from `load_dataset`, one should
customize this function to train the model on its own dataset.
Args:
dataset_name (`str`):
The name of the dataset to be loaded.
Returns:
dataloader (`torch.utils.data.DataLoader`):
The dataloader for the dataset.
"""
num_proc = 24
def preprocess_function(examples):
new_examples = {
"query": [],
"input_ids": [],
}
for question in examples["question"]:
query = "Question: " + question + "\n\nAnswer: "
tokenized_question = tokenizer(query, truncation=True)
new_examples["query"].append(query)
new_examples["input_ids"].append(tokenized_question["input_ids"])
return new_examples
ds = train_dataset.map(
preprocess_function,
batched=True,
num_proc=num_proc,
remove_columns=original_columns,
)
ds = ds.filter(lambda x: len(x["input_ids"]) < 512, batched=False)
ds.set_format(type="torch")
return ds
# We retrieve the dataloader by calling the `build_dataset` function.
dataset = build_dataset(tokenizer)
def collator(data):
return dict((key, [d[key] for d in data]) for key in data[0])
# set seed before initializing value head for deterministic eval
set_seed(config.seed)
# Now let's build the model, the reference model, and the tokenizer.
current_device = Accelerator().local_process_index
lora_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
model = AutoModelForCausalLMWithValueHead.from_pretrained(
config.model_name,
load_in_8bit=True,
device_map={"": current_device},
peft_config=lora_config,
)
optimizer = None
if script_args.adafactor:
optimizer = Adafactor(
filter(lambda p: p.requires_grad, model.parameters()),
scale_parameter=False,
relative_step=False,
warmup_init=False,
lr=config.learning_rate,
)
# We then build the PPOTrainer, passing the model, the reference model, the tokenizer
ppo_trainer = PPOTrainer(
config,
model,
ref_model=None,
tokenizer=tokenizer,
dataset=dataset,
data_collator=collator,
optimizer=optimizer,
)
# We then build the sentiment analysis pipeline using our reward model, passing the
# model name and the sentiment analysis pipeline arguments. Let's also make sure to
# set the device to the same device as the PPOTrainer.
device = ppo_trainer.accelerator.device
if ppo_trainer.accelerator.num_processes == 1:
device = 0 if torch.cuda.is_available() else "cpu" # to avoid a ` pipeline` bug
sentiment_pipe = pipeline(
"sentiment-analysis",
model=reward_model_name,
device_map={"": current_device},
model_kwargs={"load_in_8bit": True},
tokenizer=tokenizer,
return_token_type_ids=False,
)
# We then define the arguments to pass to the `generate` function. These arguments
# are passed to the `generate` function of the PPOTrainer, which is a wrapper around
# the `generate` function of the trained model.
generation_kwargs = {
# "min_length": -1,
"top_k": 0.0,
"top_p": 1.0,
"do_sample": True,
"pad_token_id": tokenizer.pad_token_id,
"eos_token_id": 100_000,
}
output_min_length = 32
output_max_length = script_args.output_max_length
output_length_sampler = LengthSampler(output_min_length, output_max_length)
for epoch, batch in tqdm(enumerate(ppo_trainer.dataloader)):
if epoch >= config.total_ppo_epochs:
break
question_tensors = batch["input_ids"]
response_tensors = ppo_trainer.generate(
question_tensors,
return_prompt=False,
length_sampler=output_length_sampler,
**generation_kwargs,
)
batch["response"] = tokenizer.batch_decode(response_tensors, skip_special_tokens=True)
# Compute reward score (using the sentiment analysis pipeline)
texts = [q + r for q, r in zip(batch["query"], batch["response"])]
pipe_outputs = sentiment_pipe(texts, **sent_kwargs)
rewards = [torch.tensor(output[0]["score"] - script_args.reward_baseline) for output in pipe_outputs]
# Run PPO step
stats = ppo_trainer.step(question_tensors, response_tensors, rewards)
ppo_trainer.log_stats(stats, batch, rewards)
if script_args.save_freq and epoch and epoch % script_args.save_freq == 0:
ppo_trainer.save_pretrained(script_args.output_dir + f"step_{epoch}")
| 0 |
hf_public_repos/trl/examples/research_projects/stack_llama | hf_public_repos/trl/examples/research_projects/stack_llama/scripts/supervised_finetuning.py | import argparse
import os
from accelerate import Accelerator
from datasets import load_dataset
from peft import LoraConfig
from tqdm import tqdm
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, logging, set_seed
from trl import SFTTrainer
from trl.trainer import ConstantLengthDataset
"""
Fine-Tune Llama-7b on SE paired dataset
"""
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--model_path", type=str, default="")
parser.add_argument("--dataset_name", type=str, default="lvwerra/stack-exchange-paired")
parser.add_argument("--subset", type=str, default="data/finetune")
parser.add_argument("--split", type=str, default="train")
parser.add_argument("--size_valid_set", type=int, default=4000)
parser.add_argument("--streaming", action="store_true")
parser.add_argument("--shuffle_buffer", type=int, default=5000)
parser.add_argument("--seq_length", type=int, default=1024)
parser.add_argument("--max_steps", type=int, default=10000)
parser.add_argument("--batch_size", type=int, default=4)
parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
parser.add_argument("--eos_token_id", type=int, default=49152)
parser.add_argument("--learning_rate", type=float, default=1e-4)
parser.add_argument("--lr_scheduler_type", type=str, default="cosine")
parser.add_argument("--num_warmup_steps", type=int, default=100)
parser.add_argument("--weight_decay", type=float, default=0.05)
parser.add_argument("--local_rank", type=int, default=0)
parser.add_argument("--no_fp16", action="store_false")
parser.add_argument("--bf16", action="store_true", default=False)
parser.add_argument("--no_gradient_checkpointing", action="store_false", default=False)
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--num_workers", type=int, default=None)
parser.add_argument("--output_dir", type=str, default="./checkpoints")
parser.add_argument("--log_freq", default=1, type=int)
parser.add_argument("--eval_freq", default=1000, type=int)
parser.add_argument("--save_freq", default=1000, type=int)
return parser.parse_args()
def chars_token_ratio(dataset, tokenizer, nb_examples=400):
"""
Estimate the average number of characters per token in the dataset.
"""
total_characters, total_tokens = 0, 0
for _, example in tqdm(zip(range(nb_examples), iter(dataset)), total=nb_examples):
text = prepare_sample_text(example)
total_characters += len(text)
if tokenizer.is_fast:
total_tokens += len(tokenizer(text).tokens())
else:
total_tokens += len(tokenizer.tokenize(text))
return total_characters / total_tokens
def print_trainable_parameters(model):
"""
Prints the number of trainable parameters in the model.
"""
trainable_params = 0
all_param = 0
for _, param in model.named_parameters():
all_param += param.numel()
if param.requires_grad:
trainable_params += param.numel()
print(
f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}"
)
def prepare_sample_text(example):
"""Prepare the text from a sample of the dataset."""
text = f"Question: {example['question']}\n\nAnswer: {example['response_j']}"
return text
def create_datasets(tokenizer, args):
dataset = load_dataset(
args.dataset_name,
data_dir=args.subset,
split=args.split,
use_auth_token=True,
num_proc=args.num_workers if not args.streaming else None,
streaming=args.streaming,
)
if args.streaming:
print("Loading the dataset in streaming mode")
valid_data = dataset.take(args.size_valid_set)
train_data = dataset.skip(args.size_valid_set)
train_data = train_data.shuffle(buffer_size=args.shuffle_buffer, seed=args.seed)
else:
dataset = dataset.train_test_split(test_size=0.005, seed=args.seed)
train_data = dataset["train"]
valid_data = dataset["test"]
print(f"Size of the train set: {len(train_data)}. Size of the validation set: {len(valid_data)}")
chars_per_token = chars_token_ratio(train_data, tokenizer)
print(f"The character to token ratio of the dataset is: {chars_per_token:.2f}")
train_dataset = ConstantLengthDataset(
tokenizer,
train_data,
formatting_func=prepare_sample_text,
infinite=True,
seq_length=args.seq_length,
chars_per_token=chars_per_token,
)
valid_dataset = ConstantLengthDataset(
tokenizer,
valid_data,
formatting_func=prepare_sample_text,
infinite=False,
seq_length=args.seq_length,
chars_per_token=chars_per_token,
)
return train_dataset, valid_dataset
def run_training(args, train_data, val_data):
print("Loading the model")
lora_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
train_data.start_iteration = 0
print("Starting main loop")
training_args = TrainingArguments(
output_dir=args.output_dir,
dataloader_drop_last=True,
evaluation_strategy="steps",
max_steps=args.max_steps,
eval_steps=args.eval_freq,
save_steps=args.save_freq,
logging_steps=args.log_freq,
per_device_train_batch_size=args.batch_size,
per_device_eval_batch_size=args.batch_size,
learning_rate=args.learning_rate,
lr_scheduler_type=args.lr_scheduler_type,
warmup_steps=args.num_warmup_steps,
gradient_accumulation_steps=args.gradient_accumulation_steps,
gradient_checkpointing=not args.no_gradient_checkpointing,
fp16=not args.no_fp16,
bf16=args.bf16,
weight_decay=args.weight_decay,
run_name="llama-7b-finetuned",
report_to="wandb",
ddp_find_unused_parameters=False,
)
model = AutoModelForCausalLM.from_pretrained(
args.model_path, load_in_8bit=True, device_map={"": Accelerator().process_index}
)
trainer = SFTTrainer(
model=model,
args=training_args,
train_dataset=train_data,
eval_dataset=val_data,
peft_config=lora_config,
packing=True,
)
print_trainable_parameters(trainer.model)
print("Training...")
trainer.train()
print("Saving last checkpoint of the model")
trainer.model.save_pretrained(os.path.join(args.output_dir, "final_checkpoint/"))
def main(args):
tokenizer = AutoTokenizer.from_pretrained(args.model_path)
train_dataset, eval_dataset = create_datasets(tokenizer, args)
run_training(args, train_dataset, eval_dataset)
if __name__ == "__main__":
args = get_args()
assert args.model_path != "", "Please provide the llama model path"
set_seed(args.seed)
os.makedirs(args.output_dir, exist_ok=True)
logging.set_verbosity_error()
main(args)
| 0 |
hf_public_repos/trl/examples | hf_public_repos/trl/examples/scripts/ddpo.py | # Copyright 2023 metric-space, The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from dataclasses import dataclass, field
import numpy as np
import torch
import torch.nn as nn
import tyro
from huggingface_hub import hf_hub_download
from huggingface_hub.utils import EntryNotFoundError
from transformers import CLIPModel, CLIPProcessor
from trl import DDPOConfig, DDPOTrainer, DefaultDDPOStableDiffusionPipeline
from trl.import_utils import is_xpu_available
@dataclass
class ScriptArguments:
hf_user_access_token: str
pretrained_model: str = "runwayml/stable-diffusion-v1-5"
"""the pretrained model to use"""
pretrained_revision: str = "main"
"""the pretrained model revision to use"""
hf_hub_model_id: str = "ddpo-finetuned-stable-diffusion"
"""HuggingFace repo to save model weights to"""
hf_hub_aesthetic_model_id: str = "trl-lib/ddpo-aesthetic-predictor"
"""HuggingFace model ID for aesthetic scorer model weights"""
hf_hub_aesthetic_model_filename: str = "aesthetic-model.pth"
"""HuggingFace model filename for aesthetic scorer model weights"""
ddpo_config: DDPOConfig = field(
default_factory=lambda: DDPOConfig(
num_epochs=200,
train_gradient_accumulation_steps=1,
sample_num_steps=50,
sample_batch_size=6,
train_batch_size=3,
sample_num_batches_per_epoch=4,
per_prompt_stat_tracking=True,
per_prompt_stat_tracking_buffer_size=32,
tracker_project_name="stable_diffusion_training",
log_with="wandb",
project_kwargs={
"logging_dir": "./logs",
"automatic_checkpoint_naming": True,
"total_limit": 5,
"project_dir": "./save",
},
)
)
class MLP(nn.Module):
def __init__(self):
super().__init__()
self.layers = nn.Sequential(
nn.Linear(768, 1024),
nn.Dropout(0.2),
nn.Linear(1024, 128),
nn.Dropout(0.2),
nn.Linear(128, 64),
nn.Dropout(0.1),
nn.Linear(64, 16),
nn.Linear(16, 1),
)
@torch.no_grad()
def forward(self, embed):
return self.layers(embed)
class AestheticScorer(torch.nn.Module):
"""
This model attempts to predict the aesthetic score of an image. The aesthetic score
is a numerical approximation of how much a specific image is liked by humans on average.
This is from https://github.com/christophschuhmann/improved-aesthetic-predictor
"""
def __init__(self, *, dtype, model_id, model_filename):
super().__init__()
self.clip = CLIPModel.from_pretrained("openai/clip-vit-large-patch14")
self.processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14")
self.mlp = MLP()
try:
cached_path = hf_hub_download(model_id, model_filename)
except EntryNotFoundError:
cached_path = os.path.join(model_id, model_filename)
state_dict = torch.load(cached_path)
self.mlp.load_state_dict(state_dict)
self.dtype = dtype
self.eval()
@torch.no_grad()
def __call__(self, images):
device = next(self.parameters()).device
inputs = self.processor(images=images, return_tensors="pt")
inputs = {k: v.to(self.dtype).to(device) for k, v in inputs.items()}
embed = self.clip.get_image_features(**inputs)
# normalize embedding
embed = embed / torch.linalg.vector_norm(embed, dim=-1, keepdim=True)
return self.mlp(embed).squeeze(1)
def aesthetic_scorer(hub_model_id, model_filename):
scorer = AestheticScorer(
model_id=hub_model_id,
model_filename=model_filename,
dtype=torch.float32,
)
scorer = scorer.xpu() if is_xpu_available() else scorer.cuda()
def _fn(images, prompts, metadata):
images = (images * 255).round().clamp(0, 255).to(torch.uint8)
scores = scorer(images)
return scores, {}
return _fn
# list of example prompts to feed stable diffusion
animals = [
"cat",
"dog",
"horse",
"monkey",
"rabbit",
"zebra",
"spider",
"bird",
"sheep",
"deer",
"cow",
"goat",
"lion",
"frog",
"chicken",
"duck",
"goose",
"bee",
"pig",
"turkey",
"fly",
"llama",
"camel",
"bat",
"gorilla",
"hedgehog",
"kangaroo",
]
def prompt_fn():
return np.random.choice(animals), {}
def image_outputs_logger(image_data, global_step, accelerate_logger):
# For the sake of this example, we will only log the last batch of images
# and associated data
result = {}
images, prompts, _, rewards, _ = image_data[-1]
for i, image in enumerate(images):
prompt = prompts[i]
reward = rewards[i].item()
result[f"{prompt:.25} | {reward:.2f}"] = image.unsqueeze(0)
accelerate_logger.log_images(
result,
step=global_step,
)
if __name__ == "__main__":
args = tyro.cli(ScriptArguments)
pipeline = DefaultDDPOStableDiffusionPipeline(
args.pretrained_model, pretrained_model_revision=args.pretrained_revision, use_lora=True
)
trainer = DDPOTrainer(
args.ddpo_config,
aesthetic_scorer(args.hf_hub_aesthetic_model_id, args.hf_hub_aesthetic_model_filename),
prompt_fn,
pipeline,
image_samples_hook=image_outputs_logger,
)
trainer.train()
trainer.push_to_hub(args.hf_hub_model_id, token=args.hf_user_access_token)
| 0 |
hf_public_repos/trl/examples | hf_public_repos/trl/examples/scripts/ppo_multi_adapter.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from peft import LoraConfig
from tqdm import tqdm
from transformers import AutoTokenizer, BitsAndBytesConfig, HfArgumentParser
from trl import AutoModelForCausalLMWithValueHead, PPOConfig, PPOTrainer, is_xpu_available
from trl.core import LengthSampler
input_min_text_length = 6
input_max_text_length = 12
@dataclass
class ScriptArguments:
"""
The name of the Casual LM model we wish to fine with PPO
"""
model_name: Optional[str] = field(default="huggyllama/llama-7b", metadata={"help": "the model name"})
dataset_name: Optional[str] = field(default="Anthropic/hh-rlhf", metadata={"help": "the dataset name"})
rm_adapter: Optional[str] = field(
default="trl-lib/llama-7b-hh-rm-adapter", metadata={"help": "the rm adapter name"}
)
log_with: Optional[str] = field(default=None, metadata={"help": "use 'wandb' to log with wandb"})
use_safetensors: Optional[bool] = field(default=False, metadata={"help": "Use safetensors"})
seed: Optional[int] = field(default=0, metadata={"help": "the random seed"})
use_score_scaling: Optional[bool] = field(default=False, metadata={"help": "Use score scaling"})
use_score_norm: Optional[bool] = field(
default=False, metadata={"help": "Use score normalization. Only applicable if use_score_scaling is True"}
)
score_clip: Optional[float] = field(default=None, metadata={"help": "Score clipping"})
parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]
def create_and_prepare_dataset(tokenizer):
dataset = load_dataset(script_args.dataset_name, split="train[:1%]")
input_size = LengthSampler(input_min_text_length, input_max_text_length)
def tokenize(example):
text_size = input_size()
example["input_ids"] = tokenizer.encode(example["chosen"])[:text_size]
example["query"] = tokenizer.decode(example["input_ids"])
return example
dataset = dataset.map(tokenize, batched=False)
dataset.set_format("torch")
return dataset
lora_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
nf4_config = BitsAndBytesConfig(
load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.bfloat16
)
model = AutoModelForCausalLMWithValueHead.from_pretrained(
script_args.model_name,
device_map={"": "xpu:0"} if is_xpu_available() else {"": 0},
peft_config=lora_config,
quantization_config=nf4_config,
reward_adapter=script_args.rm_adapter,
use_safetensors=script_args.use_safetensors,
)
tokenizer = AutoTokenizer.from_pretrained(script_args.model_name)
tokenizer.pad_token = tokenizer.eos_token
dataset = create_and_prepare_dataset(tokenizer)
def collator(data):
return dict((key, [d[key] for d in data]) for key in data[0])
config = PPOConfig(
model_name=script_args.model_name,
log_with=script_args.log_with,
learning_rate=1e-5,
batch_size=8,
mini_batch_size=2,
gradient_accumulation_steps=2,
optimize_cuda_cache=True,
seed=script_args.seed,
use_score_scaling=script_args.use_score_scaling,
use_score_norm=script_args.use_score_norm,
score_clip=script_args.score_clip,
)
ppo_trainer = PPOTrainer(
config,
model,
ref_model=None,
tokenizer=tokenizer,
dataset=dataset,
data_collator=collator,
)
generation_kwargs = {
"top_k": 0.0,
"top_p": 0.9,
"do_sample": True,
"pad_token_id": tokenizer.pad_token_id,
"max_new_tokens": 32,
}
for epoch, batch in tqdm(enumerate(ppo_trainer.dataloader)):
question_tensors = batch["input_ids"]
response_tensors = ppo_trainer.generate(
question_tensors,
return_prompt=False,
**generation_kwargs,
)
batch["response"] = tokenizer.batch_decode(response_tensors, skip_special_tokens=True)
# Compute reward score
texts = [q + r for q, r in zip(batch["query"], batch["response"])]
inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt").to(ppo_trainer.accelerator.device)
raw_rewards = ppo_trainer.accelerator.unwrap_model(ppo_trainer.model).compute_reward_score(**inputs)
rewards = [raw_rewards[i, -1, 1] for i in range(len(raw_rewards))] # take last token
# Run PPO step
stats = ppo_trainer.step(question_tensors, response_tensors, rewards)
ppo_trainer.log_stats(stats, batch, rewards)
| 0 |
hf_public_repos/trl/examples | hf_public_repos/trl/examples/scripts/reward_modeling.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass, field
from typing import Optional
import tyro
from accelerate import Accelerator
from datasets import load_dataset
from peft import LoraConfig
from tqdm import tqdm
from transformers import AutoModelForSequenceClassification, AutoTokenizer, BitsAndBytesConfig
from trl import RewardConfig, RewardTrainer, is_xpu_available
tqdm.pandas()
@dataclass
class ScriptArguments:
model_name: str = "facebook/opt-350m"
"""the model name"""
dataset_name: str = "Anthropic/hh-rlhf"
"""the dataset name"""
dataset_text_field: str = "text"
"""the text field of the dataset"""
eval_split: str = "none"
"""the dataset split to evaluate on; default to 'none' (no evaluation)"""
load_in_8bit: bool = False
"""load the model in 8 bits precision"""
load_in_4bit: bool = False
"""load the model in 4 bits precision"""
trust_remote_code: bool = True
"""Enable `trust_remote_code`"""
reward_config: RewardConfig = field(
default_factory=lambda: RewardConfig(
output_dir="output",
per_device_train_batch_size=64,
num_train_epochs=1,
gradient_accumulation_steps=16,
gradient_checkpointing=True,
gradient_checkpointing_kwargs={"use_reentrant": False},
learning_rate=1.41e-5,
report_to="tensorboard",
remove_unused_columns=False,
optim="adamw_torch",
logging_steps=500,
evaluation_strategy="no",
max_length=512,
)
)
use_peft: bool = False
"""whether to use peft"""
peft_config: Optional[LoraConfig] = field(
default_factory=lambda: LoraConfig(
r=16,
lora_alpha=16,
bias="none",
task_type="SEQ_CLS",
modules_to_save=["scores"],
),
)
args = tyro.cli(ScriptArguments)
args.reward_config.evaluation_strategy = "steps" if args.eval_split != "none" else "no"
# Step 1: Load the model
if args.load_in_8bit and args.load_in_4bit:
raise ValueError("You can't load the model in 8 bits and 4 bits at the same time")
elif args.load_in_8bit or args.load_in_4bit:
quantization_config = BitsAndBytesConfig(load_in_8bit=args.load_in_8bit, load_in_4bit=args.load_in_4bit)
# Copy the model to each device
device_map = (
{"": f"xpu:{Accelerator().local_process_index}"}
if is_xpu_available()
else {"": Accelerator().local_process_index}
)
else:
device_map = None
quantization_config = None
model = AutoModelForSequenceClassification.from_pretrained(
args.model_name,
quantization_config=quantization_config,
device_map=device_map,
trust_remote_code=args.trust_remote_code,
num_labels=1,
)
# Step 2: Load the dataset and pre-process it
tokenizer = AutoTokenizer.from_pretrained(args.model_name)
train_dataset = load_dataset(args.dataset_name, split="train")
# Tokenize chosen/rejected pairs of inputs
# Adapt this section to your needs for custom datasets
def preprocess_function(examples):
new_examples = {
"input_ids_chosen": [],
"attention_mask_chosen": [],
"input_ids_rejected": [],
"attention_mask_rejected": [],
}
for chosen, rejected in zip(examples["chosen"], examples["rejected"]):
tokenized_chosen = tokenizer(chosen)
tokenized_rejected = tokenizer(rejected)
new_examples["input_ids_chosen"].append(tokenized_chosen["input_ids"])
new_examples["attention_mask_chosen"].append(tokenized_chosen["attention_mask"])
new_examples["input_ids_rejected"].append(tokenized_rejected["input_ids"])
new_examples["attention_mask_rejected"].append(tokenized_rejected["attention_mask"])
return new_examples
# Preprocess the dataset and filter out examples that are longer than args.max_length
train_dataset = train_dataset.map(
preprocess_function,
batched=True,
num_proc=4,
)
train_dataset = train_dataset.filter(
lambda x: len(x["input_ids_chosen"]) <= args.reward_config.max_length
and len(x["input_ids_rejected"]) <= args.reward_config.max_length
)
if args.eval_split == "none":
eval_dataset = None
else:
eval_dataset = load_dataset(args.dataset_name, split=args.eval_split)
eval_dataset = eval_dataset.map(
preprocess_function,
batched=True,
num_proc=4,
)
eval_dataset = eval_dataset.filter(
lambda x: len(x["input_ids_chosen"]) <= args.reward_config.max_length
and len(x["input_ids_rejected"]) <= args.reward_config.max_length
)
# Step 4: Define the LoraConfig
if args.use_peft:
peft_config = args.peft_config
else:
peft_config = None
# Step 5: Define the Trainer
trainer = RewardTrainer(
model=model,
tokenizer=tokenizer,
args=args.reward_config,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
peft_config=peft_config,
)
trainer.train()
| 0 |
hf_public_repos/trl/examples | hf_public_repos/trl/examples/scripts/sft.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass, field
from typing import Optional
import torch
from accelerate import Accelerator
from datasets import load_dataset
from peft import LoraConfig
from tqdm import tqdm
from transformers import AutoModelForCausalLM, BitsAndBytesConfig, HfArgumentParser, TrainingArguments
from trl import SFTTrainer, is_xpu_available
tqdm.pandas()
# Define and parse arguments.
@dataclass
class ScriptArguments:
"""
The name of the Casual LM model we wish to fine with SFTTrainer
"""
model_name: Optional[str] = field(default="facebook/opt-350m", metadata={"help": "the model name"})
dataset_name: Optional[str] = field(
default="timdettmers/openassistant-guanaco", metadata={"help": "the dataset name"}
)
dataset_text_field: Optional[str] = field(default="text", metadata={"help": "the text field of the dataset"})
log_with: Optional[str] = field(default="none", metadata={"help": "use 'wandb' to log with wandb"})
learning_rate: Optional[float] = field(default=1.41e-5, metadata={"help": "the learning rate"})
batch_size: Optional[int] = field(default=64, metadata={"help": "the batch size"})
seq_length: Optional[int] = field(default=512, metadata={"help": "Input sequence length"})
gradient_accumulation_steps: Optional[int] = field(
default=16, metadata={"help": "the number of gradient accumulation steps"}
)
load_in_8bit: Optional[bool] = field(default=False, metadata={"help": "load the model in 8 bits precision"})
load_in_4bit: Optional[bool] = field(default=False, metadata={"help": "load the model in 4 bits precision"})
use_peft: Optional[bool] = field(default=False, metadata={"help": "Wether to use PEFT or not to train adapters"})
trust_remote_code: Optional[bool] = field(default=False, metadata={"help": "Enable `trust_remote_code`"})
output_dir: Optional[str] = field(default="output", metadata={"help": "the output directory"})
peft_lora_r: Optional[int] = field(default=64, metadata={"help": "the r parameter of the LoRA adapters"})
peft_lora_alpha: Optional[int] = field(default=16, metadata={"help": "the alpha parameter of the LoRA adapters"})
logging_steps: Optional[int] = field(default=1, metadata={"help": "the number of logging steps"})
use_auth_token: Optional[bool] = field(default=True, metadata={"help": "Use HF auth token to access the model"})
num_train_epochs: Optional[int] = field(default=3, metadata={"help": "the number of training epochs"})
max_steps: Optional[int] = field(default=-1, metadata={"help": "the number of training steps"})
save_steps: Optional[int] = field(
default=100, metadata={"help": "Number of updates steps before two checkpoint saves"}
)
save_total_limit: Optional[int] = field(default=10, metadata={"help": "Limits total number of checkpoints."})
push_to_hub: Optional[bool] = field(default=False, metadata={"help": "Push the model to HF Hub"})
gradient_checkpointing: Optional[bool] = field(
default=False, metadata={"help": "Whether to use gradient checkpointing or no"}
)
gradient_checkpointing_kwargs: Optional[dict] = field(
default=None,
metadata={
"help": "key word arguments to be passed along `torch.utils.checkpoint.checkpoint` method - e.g. `use_reentrant=False`"
},
)
hub_model_id: Optional[str] = field(default=None, metadata={"help": "The name of the model on HF Hub"})
parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]
# Step 1: Load the model
if script_args.load_in_8bit and script_args.load_in_4bit:
raise ValueError("You can't load the model in 8 bits and 4 bits at the same time")
elif script_args.load_in_8bit or script_args.load_in_4bit:
quantization_config = BitsAndBytesConfig(
load_in_8bit=script_args.load_in_8bit, load_in_4bit=script_args.load_in_4bit
)
# Copy the model to each device
device_map = (
{"": f"xpu:{Accelerator().local_process_index}"}
if is_xpu_available()
else {"": Accelerator().local_process_index}
)
torch_dtype = torch.bfloat16
else:
device_map = None
quantization_config = None
torch_dtype = None
model = AutoModelForCausalLM.from_pretrained(
script_args.model_name,
quantization_config=quantization_config,
device_map=device_map,
trust_remote_code=script_args.trust_remote_code,
torch_dtype=torch_dtype,
use_auth_token=script_args.use_auth_token,
)
# Step 2: Load the dataset
dataset = load_dataset(script_args.dataset_name, split="train")
# Step 3: Define the training arguments
training_args = TrainingArguments(
output_dir=script_args.output_dir,
per_device_train_batch_size=script_args.batch_size,
gradient_accumulation_steps=script_args.gradient_accumulation_steps,
learning_rate=script_args.learning_rate,
logging_steps=script_args.logging_steps,
num_train_epochs=script_args.num_train_epochs,
max_steps=script_args.max_steps,
report_to=script_args.log_with,
save_steps=script_args.save_steps,
save_total_limit=script_args.save_total_limit,
push_to_hub=script_args.push_to_hub,
hub_model_id=script_args.hub_model_id,
gradient_checkpointing=script_args.gradient_checkpointing,
# TODO: uncomment that on the next release
# gradient_checkpointing_kwargs=script_args.gradient_checkpointing_kwargs,
)
# Step 4: Define the LoraConfig
if script_args.use_peft:
peft_config = LoraConfig(
r=script_args.peft_lora_r,
lora_alpha=script_args.peft_lora_alpha,
bias="none",
task_type="CAUSAL_LM",
)
else:
peft_config = None
# Step 5: Define the Trainer
trainer = SFTTrainer(
model=model,
args=training_args,
max_seq_length=script_args.seq_length,
train_dataset=dataset,
dataset_text_field=script_args.dataset_text_field,
peft_config=peft_config,
)
trainer.train()
# Step 6: Save the model
trainer.save_model(script_args.output_dir)
| 0 |
hf_public_repos/trl/examples | hf_public_repos/trl/examples/scripts/ppo.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass, field
from typing import Optional
import torch
import tyro
from accelerate import Accelerator
from datasets import load_dataset
from peft import LoraConfig
from tqdm import tqdm
from transformers import AutoTokenizer, pipeline
from trl import AutoModelForCausalLMWithValueHead, AutoModelForSeq2SeqLMWithValueHead, PPOConfig, PPOTrainer, set_seed
from trl.core import LengthSampler
from trl.import_utils import is_xpu_available
tqdm.pandas()
@dataclass
class ScriptArguments:
ppo_config: PPOConfig = field(
default_factory=lambda: PPOConfig(
model_name="lvwerra/gpt2-imdb",
query_dataset="imdb",
reward_model="sentiment-analysis:lvwerra/distilbert-imdb",
learning_rate=1.41e-5,
log_with=None,
mini_batch_size=128,
batch_size=128,
gradient_accumulation_steps=1,
early_stopping=False,
target_kl=6.0,
kl_penalty="kl",
seed=0,
use_score_scaling=False,
use_score_norm=False,
score_clip=None,
)
)
use_seq2seq: bool = False
"""whether to use seq2seq models"""
use_peft: bool = False
"""whether to use peft"""
peft_config: Optional[LoraConfig] = field(
default_factory=lambda: LoraConfig(
r=16,
lora_alpha=16,
bias="none",
task_type="CAUSAL_LM",
),
)
trust_remote_code: bool = field(default=False, metadata={"help": "Enable `trust_remote_code`"})
args = tyro.cli(ScriptArguments)
# We then define the arguments to pass to the sentiment analysis pipeline.
# We set `return_all_scores` to True to get the sentiment score for each token.
sent_kwargs = {"return_all_scores": True, "function_to_apply": "none", "batch_size": 16}
trl_model_class = AutoModelForCausalLMWithValueHead if not args.use_seq2seq else AutoModelForSeq2SeqLMWithValueHead
# Below is an example function to build the dataset. In our case, we use the IMDB dataset
# from the `datasets` library. One should customize this function to train the model on
# its own dataset.
def build_dataset(config, query_dataset, input_min_text_length=2, input_max_text_length=8):
"""
Build dataset for training. This builds the dataset from `load_dataset`, one should
customize this function to train the model on its own dataset.
Args:
query_dataset (`str`):
The name of the dataset to be loaded.
Returns:
dataloader (`torch.utils.data.DataLoader`):
The dataloader for the dataset.
"""
tokenizer = AutoTokenizer.from_pretrained(config.model_name)
tokenizer.pad_token = tokenizer.eos_token
# load imdb with datasets
ds = load_dataset(query_dataset, split="train")
ds = ds.rename_columns({"text": "review"})
ds = ds.filter(lambda x: len(x["review"]) > 200, batched=False)
input_size = LengthSampler(input_min_text_length, input_max_text_length)
def tokenize(sample):
sample["input_ids"] = tokenizer.encode(sample["review"])[: input_size()]
sample["query"] = tokenizer.decode(sample["input_ids"])
return sample
ds = ds.map(tokenize, batched=False)
ds.set_format(type="torch")
return ds
# We retrieve the dataloader by calling the `build_dataset` function.
dataset = build_dataset(args.ppo_config, args.ppo_config.query_dataset)
def collator(data):
return dict((key, [d[key] for d in data]) for key in data[0])
# set seed before initializing value head for deterministic eval
set_seed(args.ppo_config.seed)
# Now let's build the model, the reference model, and the tokenizer.
if not args.use_peft:
ref_model = trl_model_class.from_pretrained(args.ppo_config.model_name, trust_remote_code=args.trust_remote_code)
device_map = None
peft_config = None
else:
peft_config = args.peft_config
ref_model = None
# Copy the model to each device
device_map = {"": Accelerator().local_process_index}
model = trl_model_class.from_pretrained(
args.ppo_config.model_name,
trust_remote_code=args.trust_remote_code,
device_map=device_map,
peft_config=peft_config,
)
tokenizer = AutoTokenizer.from_pretrained(args.ppo_config.model_name)
# Some tokenizers like GPT-2's don't have a padding token by default, so we set one here.
tokenizer.pad_token_id = tokenizer.eos_token_id
# We then build the PPOTrainer, passing the model, the reference model, the tokenizer
ppo_trainer = PPOTrainer(args.ppo_config, model, ref_model, tokenizer, dataset=dataset, data_collator=collator)
# We then build the sentiment analysis pipeline, passing the model name and the
# sentiment analysis pipeline arguments. Let's also make sure to set the device
# to the same device as the PPOTrainer.
device = ppo_trainer.accelerator.device
if ppo_trainer.accelerator.num_processes == 1:
if is_xpu_available():
device = "xpu:0"
else:
device = 0 if torch.cuda.is_available() else "cpu" # to avoid a `pipeline` bug
ds_plugin = ppo_trainer.accelerator.state.deepspeed_plugin
task, model_name = args.ppo_config.reward_model.split(":")
if ds_plugin is not None and ds_plugin.is_zero3_init_enabled():
with ds_plugin.zero3_init_context_manager(enable=False):
sentiment_pipe = pipeline(task, model=model_name, device=device)
else:
sentiment_pipe = pipeline(task, model=model_name, device=device)
# Some tokenizers like GPT-2's don't have a padding token by default, so we set one here.
if sentiment_pipe.tokenizer.pad_token_id is None:
sentiment_pipe.tokenizer.pad_token_id = tokenizer.pad_token_id
if sentiment_pipe.model.config.pad_token_id is None:
sentiment_pipe.model.config.pad_token_id = tokenizer.pad_token_id
# We then define the arguments to pass to the `generate` function. These arguments
# are passed to the `generate` function of the PPOTrainer, which is a wrapper around
# the `generate` function of the trained model.
generation_kwargs = {
"min_length": -1,
"top_k": 0.0,
"top_p": 1.0,
"do_sample": True,
"pad_token_id": tokenizer.eos_token_id,
"max_new_tokens": 32,
}
for epoch, batch in tqdm(enumerate(ppo_trainer.dataloader)):
query_tensors = batch["input_ids"]
# Get response from gpt2
response_tensors, ref_response_tensors = ppo_trainer.generate(
query_tensors, return_prompt=False, generate_ref_response=True, **generation_kwargs
)
batch["response"] = tokenizer.batch_decode(response_tensors)
batch["ref_response"] = tokenizer.batch_decode(ref_response_tensors)
# Compute sentiment score
texts = [q + r for q, r in zip(batch["query"], batch["response"])]
pipe_outputs = sentiment_pipe(texts, **sent_kwargs)
rewards = [torch.tensor(output[1]["score"]) for output in pipe_outputs]
ref_texts = [q + r for q, r in zip(batch["query"], batch["ref_response"])]
ref_pipe_outputs = sentiment_pipe(ref_texts, **sent_kwargs)
ref_rewards = [torch.tensor(output[1]["score"]) for output in ref_pipe_outputs]
batch["ref_rewards"] = ref_rewards
# Run PPO step
stats = ppo_trainer.step(query_tensors, response_tensors, rewards)
ppo_trainer.log_stats(stats, batch, rewards, columns_to_log=["query", "response", "ref_response", "ref_rewards"])
| 0 |
hf_public_repos/trl/examples | hf_public_repos/trl/examples/scripts/dpo.py | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Note: you need to install transformers from main to run this script. See https://huggingface.co/docs/transformers/installation#install-from-source
# TODO: bump transformers version in requirements at next release.
# 0. imports
from dataclasses import dataclass, field
from typing import Dict, Optional
import torch
from datasets import Dataset, load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, TrainingArguments
from trl import DPOTrainer
# Define and parse arguments.
@dataclass
class ScriptArguments:
"""
The arguments for the DPO training script.
"""
# data parameters
beta: Optional[float] = field(default=0.1, metadata={"help": "the beta parameter for DPO loss"})
# training parameters
model_name_or_path: Optional[str] = field(default="gpt2", metadata={"help": "the model name"})
learning_rate: Optional[float] = field(default=1e-3, metadata={"help": "optimizer learning rate"})
per_device_train_batch_size: Optional[int] = field(default=4, metadata={"help": "batch size per device"})
gradient_accumulation_steps: Optional[int] = field(
default=1, metadata={"help": "the number of gradient accumulation steps"}
)
max_length: Optional[int] = field(default=512, metadata={"help": "max length of each sample"})
max_prompt_length: Optional[int] = field(default=128, metadata={"help": "max length of each sample's prompt"})
max_target_length: Optional[int] = field(
default=128, metadata={"help": "Only used for encoder decoder model. Max target of each sample's prompt"}
)
label_pad_token_id: Optional[int] = field(default=-100, metadata={"help": "label for non response tokens"})
max_steps: Optional[int] = field(default=1000, metadata={"help": "max number of training steps"})
# instrumentation
sanity_check: Optional[bool] = field(default=True, metadata={"help": "only train on 1000 samples"})
report_to: Optional[str] = field(
default=None,
metadata={
"help": 'The list of integrations to report the results and logs to. Supported platforms are `"azure_ml"`,'
'`"comet_ml"`, `"mlflow"`, `"neptune"`, `"tensorboard"`,`"clearml"` and `"wandb"`. '
'Use `"all"` to report to all integrations installed, `"none"` for no integrations.'
},
)
# debug argument for distributed training
ignore_bias_buffers: Optional[bool] = field(
default=False,
metadata={
"help": "fix for DDP issues with LM bias/mask buffers - invalid scalar type,`inplace operation. See"
"https://github.com/huggingface/transformers/issues/22482#issuecomment-1595790992"
},
)
gradient_checkpointing: Optional[bool] = field(
default=False, metadata={"help": "Whether to use gradient checkpointing or no"}
)
gradient_checkpointing_kwargs: Optional[dict] = field(
default=None,
metadata={
"help": "key word arguments to be passed along `torch.utils.checkpoint.checkpoint` method - e.g. `use_reentrant=False`"
},
)
def extract_anthropic_prompt(prompt_and_response):
"""Extract the anthropic prompt from a prompt and response pair."""
search_term = "\n\nAssistant:"
search_term_idx = prompt_and_response.rfind(search_term)
assert search_term_idx != -1, f"Prompt and response does not contain '{search_term}'"
return prompt_and_response[: search_term_idx + len(search_term)]
def get_hh(split: str, sanity_check: bool = False, silent: bool = False, cache_dir: str = None) -> Dataset:
"""Load the Anthropic Helpful-Harmless dataset from Hugging Face and convert it to the necessary format.
The dataset is converted to a dictionary with the following structure:
{
'prompt': List[str],
'chosen': List[str],
'rejected': List[str],
}
Prompts should be structured as follows:
\n\nHuman: <prompt>\n\nAssistant:
Multiple turns are allowed, but the prompt should always start with \n\nHuman: and end with \n\nAssistant:.
"""
dataset = load_dataset("Anthropic/hh-rlhf", split=split, cache_dir=cache_dir)
if sanity_check:
dataset = dataset.select(range(min(len(dataset), 1000)))
def split_prompt_and_responses(sample) -> Dict[str, str]:
prompt = extract_anthropic_prompt(sample["chosen"])
return {
"prompt": prompt,
"chosen": sample["chosen"][len(prompt) :],
"rejected": sample["rejected"][len(prompt) :],
}
return dataset.map(split_prompt_and_responses)
if __name__ == "__main__":
parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]
# 1. load a pretrained model
model = AutoModelForCausalLM.from_pretrained(script_args.model_name_or_path)
if script_args.ignore_bias_buffers:
# torch distributed hack
model._ddp_params_and_buffers_to_ignore = [
name for name, buffer in model.named_buffers() if buffer.dtype == torch.bool
]
model_ref = AutoModelForCausalLM.from_pretrained(script_args.model_name_or_path)
tokenizer = AutoTokenizer.from_pretrained(script_args.model_name_or_path)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
# 2. Load the Anthropic Helpful-Harmless dataset
train_dataset = get_hh("train", sanity_check=script_args.sanity_check)
# 3. Load evaluation dataset
eval_dataset = get_hh("test", sanity_check=script_args.sanity_check)
# 4. initialize training arguments:
training_args = TrainingArguments(
per_device_train_batch_size=script_args.per_device_train_batch_size,
max_steps=script_args.max_steps,
remove_unused_columns=False,
gradient_accumulation_steps=script_args.gradient_accumulation_steps,
learning_rate=script_args.learning_rate,
evaluation_strategy="steps",
logging_first_step=True,
logging_steps=10, # match results in blog post
eval_steps=500,
output_dir="./test",
optim="rmsprop",
warmup_steps=150,
report_to=script_args.report_to,
bf16=True,
gradient_checkpointing=script_args.gradient_checkpointing,
# TODO: uncomment that on the next transformers release
# gradient_checkpointing_kwargs=script_args.gradient_checkpointing_kwargs,
)
# 5. initialize the DPO trainer
dpo_trainer = DPOTrainer(
model,
model_ref,
args=training_args,
beta=script_args.beta,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
tokenizer=tokenizer,
max_length=script_args.max_length,
max_target_length=script_args.max_target_length,
max_prompt_length=script_args.max_prompt_length,
generate_during_eval=True,
)
# 6. train
dpo_trainer.train()
| 0 |
hf_public_repos/trl/examples | hf_public_repos/trl/examples/notebooks/gpt2-sentiment-control.ipynb | %load_ext autoreload
%autoreload 2import random
import torch
import wandb
import time
import os
from tqdm import tqdm
import numpy as np
import pandas as pd
from random import choices
import matplotlib.pyplot as plt
tqdm.pandas()
from datasets import load_dataset
from transformers import AutoTokenizer, pipeline
from trl import PPOTrainer, PPOConfig, AutoModelForCausalLMWithValueHead, create_reference_modelsentiment_pipe_kwargs = {"top_k": None, "function_to_apply": "none"}
config = PPOConfig(
model_name="lvwerra/gpt2-imdb", steps=51200, learning_rate=1.41e-5, remove_unused_columns=False, log_with="wandb"
)
txt_in_len = 5
txt_out_len = 20
seed = 1np.random.seed(seed)gpt2_model = AutoModelForCausalLMWithValueHead.from_pretrained(config.model_name)
gpt2_model_ref = create_reference_model(gpt2_model)
gpt2_tokenizer = AutoTokenizer.from_pretrained(config.model_name)
gpt2_tokenizer.pad_token = gpt2_tokenizer.eos_token# create the dataset
#
dataset = load_dataset("imdb", split="train")
dataset = dataset.rename_columns({"text": "review", "label": "sentiment"})
# make sure the comments are are at least 500 and trim to 1000
dataset = dataset.filter(lambda x: len(x["review"]) > 500, batched=False)
dataset = dataset.map(lambda x: {"review": x["review"][:1000]}, batched=False)
datasetdataset = dataset.map(
lambda x: {"input_ids": gpt2_tokenizer.encode(" " + x["review"], return_tensors="pt")[0, :txt_in_len]},
batched=False,
)
dataset = dataset.map(lambda x: {"query": gpt2_tokenizer.decode(x["input_ids"])}, batched=False)
dataset = dataset[:20480]
from datasets import Dataset
dataset = Dataset.from_dict(dataset)
dataset.set_format("pytorch")dataset[3]["input_ids"]def collator(data):
return dict((key, [d[key] for d in data]) for key in data[0])ppo_trainer = PPOTrainer(config, gpt2_model, gpt2_model_ref, gpt2_tokenizer, dataset, data_collator=collator)if ppo_trainer.accelerator.num_processes == 1:
device = 0 if torch.cuda.is_available() else "cpu" # to avoid a `pipeline` bug
else:
device = ppo_trainer.accelerator.device
sentiment_pipe = pipeline("sentiment-analysis", "lvwerra/distilbert-imdb", device=device)text = "this movie was really bad!!"
output = sentiment_pipe(text, **sentiment_pipe_kwargs)
outputtext = "this movie was really good!!"
output = sentiment_pipe(text, **sentiment_pipe_kwargs)
outputtext = "this movie was a documentary"
output = sentiment_pipe(text, **sentiment_pipe_kwargs)
outputdef extract_pipe_output(outputs):
positive_logits = []
for out in outputs:
for element in out:
if element["label"] == "POSITIVE":
positive_logits.append(torch.tensor(element["score"]))
return positive_logitsoutput[1]["score"]ctrl_str = ["[negative]", "[neutral]", "[positive]"]
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # this should be handled by accelerate
ctrl_tokens = dict((s, gpt2_tokenizer.encode(s, return_tensors="pt").squeeze().to(device)) for s in ctrl_str)ctrl_tokensdef pos_logit_to_reward(logit, task):
"""
Take the positive sentiment logit and scale it for the task.
task [negative]: reward = -logit
task [neutral]: reward = -2*abs(logit)+4
task [positive]: reward = logit
"""
for i in range(len(logit)):
if task[i] == "[negative]":
logit[i] = -logit[i]
elif task[i] == "[neutral]":
logit[i] = -2 * torch.abs(logit[i]) + 4
elif task[i] == "[positive]":
pass
else:
raise ValueError("task has to be in [0, 1, 2]!")
return logitprint(ctrl_str)pos_logit_to_reward(torch.Tensor([4, 4, 4]), ctrl_str)pos_logit_to_reward(torch.Tensor([-4, -4, -4]), ctrl_str)pos_logit_to_reward(torch.Tensor([0, 0, 0]), ctrl_str)generation_kwargs = {
"min_length": -1,
"top_k": 0.0,
"top_p": 1.0,
"do_sample": True,
"pad_token_id": gpt2_tokenizer.eos_token_id,
"max_new_tokens": txt_out_len,
"eos_token_id": -1,
}for epoch in range(2):
for batch in tqdm(ppo_trainer.dataloader):
(logs, game_data,) = (
dict(),
dict(),
)
#### prepend a random control token
task_list = choices(ctrl_str, k=config.batch_size)
game_data["query"] = [t + q for t, q in zip(task_list, batch["query"])]
query_tensors = [torch.cat((ctrl_tokens[t], input_ids)) for t, input_ids in zip(task_list, batch["input_ids"])]
#### get response from gpt2
response_tensors = []
for query in query_tensors:
response = ppo_trainer.generate(query, **generation_kwargs)
response_tensors.append(response.squeeze()[-txt_out_len:])
game_data["response"] = [gpt2_tokenizer.decode(r.squeeze()) for r in response_tensors]
#### sentiment analysis
texts = [q + r for q, r in zip(batch["query"], game_data["response"])]
logits = extract_pipe_output(sentiment_pipe(texts, **sentiment_pipe_kwargs))
rewards = pos_logit_to_reward(logits, task_list)
#### Run PPO training
t = time.time()
stats = ppo_trainer.step(query_tensors, response_tensors, rewards)
for cs in ctrl_str:
key = "env/reward_" + cs.strip("[]")
stats[key] = np.mean([r.cpu().numpy() for r, t in zip(rewards, task_list) if t == cs])
ppo_trainer.log_stats(stats, game_data, rewards)for ctrl_s in ctrl_str:
plt.hist(
[r for r, t in zip(logs["env/reward_dist"], task_list) if t == ctrl_s], density=True, alpha=0.5, label=ctrl_s
)
plt.legend(loc="best")
plt.title("reward distribution")
plt.grid(True)
plt.show()gpt2_model.save_pretrained("gpt2-imdb-ctrl")
gpt2_tokenizer.save_pretrained("gpt2-imdb-ctrl") | 0 |
hf_public_repos/trl/examples | hf_public_repos/trl/examples/notebooks/gpt2-sentiment.ipynb | %load_ext autoreload
%autoreload 2%pip install transformers trl wandbimport torch
from tqdm import tqdm
import pandas as pd
tqdm.pandas()
from transformers import pipeline, AutoTokenizer
from datasets import load_dataset
from trl import PPOTrainer, PPOConfig, AutoModelForCausalLMWithValueHead
from trl.core import LengthSamplerconfig = PPOConfig(
model_name="lvwerra/gpt2-imdb",
learning_rate=1.41e-5,
log_with="wandb",
)
sent_kwargs = {"return_all_scores": True, "function_to_apply": "none", "batch_size": 16}import wandb
wandb.init()def build_dataset(config, dataset_name="imdb", input_min_text_length=2, input_max_text_length=8):
"""
Build dataset for training. This builds the dataset from `load_dataset`, one should
customize this function to train the model on its own dataset.
Args:
dataset_name (`str`):
The name of the dataset to be loaded.
Returns:
dataloader (`torch.utils.data.DataLoader`):
The dataloader for the dataset.
"""
tokenizer = AutoTokenizer.from_pretrained(config.model_name)
tokenizer.pad_token = tokenizer.eos_token
# load imdb with datasets
ds = load_dataset(dataset_name, split="train")
ds = ds.rename_columns({"text": "review"})
ds = ds.filter(lambda x: len(x["review"]) > 200, batched=False)
input_size = LengthSampler(input_min_text_length, input_max_text_length)
def tokenize(sample):
sample["input_ids"] = tokenizer.encode(sample["review"])[: input_size()]
sample["query"] = tokenizer.decode(sample["input_ids"])
return sample
ds = ds.map(tokenize, batched=False)
ds.set_format(type="torch")
return dsdataset = build_dataset(config)
def collator(data):
return dict((key, [d[key] for d in data]) for key in data[0])model = AutoModelForCausalLMWithValueHead.from_pretrained(config.model_name)
ref_model = AutoModelForCausalLMWithValueHead.from_pretrained(config.model_name)
tokenizer = AutoTokenizer.from_pretrained(config.model_name)
tokenizer.pad_token = tokenizer.eos_tokenppo_trainer = PPOTrainer(config, model, ref_model, tokenizer, dataset=dataset, data_collator=collator)device = ppo_trainer.accelerator.device
if ppo_trainer.accelerator.num_processes == 1:
device = 0 if torch.cuda.is_available() else "cpu" # to avoid a `pipeline` bug
sentiment_pipe = pipeline("sentiment-analysis", model="lvwerra/distilbert-imdb", device=device)text = "this movie was really bad!!"
sentiment_pipe(text, **sent_kwargs)text = "this movie was really good!!"
sentiment_pipe(text, **sent_kwargs)gen_kwargs = {"min_length": -1, "top_k": 0.0, "top_p": 1.0, "do_sample": True, "pad_token_id": tokenizer.eos_token_id}output_min_length = 4
output_max_length = 16
output_length_sampler = LengthSampler(output_min_length, output_max_length)
generation_kwargs = {
"min_length": -1,
"top_k": 0.0,
"top_p": 1.0,
"do_sample": True,
"pad_token_id": tokenizer.eos_token_id,
}
for epoch, batch in tqdm(enumerate(ppo_trainer.dataloader)):
query_tensors = batch["input_ids"]
#### Get response from gpt2
response_tensors = []
for query in query_tensors:
gen_len = output_length_sampler()
generation_kwargs["max_new_tokens"] = gen_len
response = ppo_trainer.generate(query, **generation_kwargs)
response_tensors.append(response.squeeze()[-gen_len:])
batch["response"] = [tokenizer.decode(r.squeeze()) for r in response_tensors]
#### Compute sentiment score
texts = [q + r for q, r in zip(batch["query"], batch["response"])]
pipe_outputs = sentiment_pipe(texts, **sent_kwargs)
rewards = [torch.tensor(output[1]["score"]) for output in pipe_outputs]
#### Run PPO step
stats = ppo_trainer.step(query_tensors, response_tensors, rewards)
ppo_trainer.log_stats(stats, batch, rewards)#### get a batch from the dataset
bs = 16
game_data = dict()
dataset.set_format("pandas")
df_batch = dataset[:].sample(bs)
game_data["query"] = df_batch["query"].tolist()
query_tensors = df_batch["input_ids"].tolist()
response_tensors_ref, response_tensors = [], []
#### get response from gpt2 and gpt2_ref
for i in range(bs):
gen_len = output_length_sampler()
output = ref_model.generate(
torch.tensor(query_tensors[i]).unsqueeze(dim=0).to(device), max_new_tokens=gen_len, **gen_kwargs
).squeeze()[-gen_len:]
response_tensors_ref.append(output)
output = model.generate(
torch.tensor(query_tensors[i]).unsqueeze(dim=0).to(device), max_new_tokens=gen_len, **gen_kwargs
).squeeze()[-gen_len:]
response_tensors.append(output)
#### decode responses
game_data["response (before)"] = [tokenizer.decode(response_tensors_ref[i]) for i in range(bs)]
game_data["response (after)"] = [tokenizer.decode(response_tensors[i]) for i in range(bs)]
#### sentiment analysis of query/response pairs before/after
texts = [q + r for q, r in zip(game_data["query"], game_data["response (before)"])]
game_data["rewards (before)"] = [output[1]["score"] for output in sentiment_pipe(texts, **sent_kwargs)]
texts = [q + r for q, r in zip(game_data["query"], game_data["response (after)"])]
game_data["rewards (after)"] = [output[1]["score"] for output in sentiment_pipe(texts, **sent_kwargs)]
# store results in a dataframe
df_results = pd.DataFrame(game_data)
df_resultsprint("mean:")
display(df_results[["rewards (before)", "rewards (after)"]].mean())
print()
print("median:")
display(df_results[["rewards (before)", "rewards (after)"]].median())model.save_pretrained("gpt2-imdb-pos-v2", push_to_hub=True)
tokenizer.save_pretrained("gpt2-imdb-pos-v2", push_to_hub=True) | 0 |
hf_public_repos/trl/examples | hf_public_repos/trl/examples/notebooks/best_of_n.ipynb | %pip install transformers trlimport torch
import pandas as pd
from transformers import pipeline, AutoTokenizer
from datasets import load_dataset
from trl import AutoModelForCausalLMWithValueHead
from trl.core import LengthSampler
device = 0 if torch.cuda.is_available() else "cpu"ref_model_name = "lvwerra/gpt2-imdb"
model_name = "lvwerra/gpt2-imdb-pos-v2"
reward_model = "lvwerra/distilbert-imdb"
N_BEST_OF = 4model = AutoModelForCausalLMWithValueHead.from_pretrained(model_name)
ref_model = AutoModelForCausalLMWithValueHead.from_pretrained(ref_model_name)
reward_pipe = pipeline("sentiment-analysis", model=reward_model, device=device)
tokenizer = AutoTokenizer.from_pretrained(ref_model_name)
tokenizer.pad_token = tokenizer.eos_token
# cuda-ize models
model.cuda()
ref_model.cuda()def build_dataset(tokenizer, dataset_name="imdb", input_min_text_length=2, input_max_text_length=8):
# load imdb with datasets
ds = load_dataset(dataset_name, split="train")
ds = ds.rename_columns({"text": "review"})
ds = ds.filter(lambda x: len(x["review"]) > 200, batched=False)
input_size = LengthSampler(input_min_text_length, input_max_text_length)
def tokenize(sample):
sample["input_ids"] = tokenizer.encode(sample["review"])[: input_size()]
sample["query"] = tokenizer.decode(sample["input_ids"])
return sample
ds = ds.map(tokenize, batched=False)
ds.set_format(type="torch")
return ds
dataset = build_dataset(tokenizer)gen_kwargs = {"min_length": -1, "top_k": 0.0, "top_p": 1.0, "do_sample": True, "pad_token_id": tokenizer.eos_token_id}
sent_kwargs = {"top_k": None, "function_to_apply": "none", "batch_size": 16}output_min_length = 4
output_max_length = 16
output_length_sampler = LengthSampler(output_min_length, output_max_length)
#### get a batch from the dataset
bs = 16
output_data = dict()
dataset.set_format("pandas")
df_batch = dataset[:].sample(bs)
output_data["query"] = df_batch["query"].tolist()
query_tensors = df_batch["input_ids"].tolist()
# :: [Resp]
response_tensors_ref, response_tensors = [], []
# :: [[Resp]]
response_tensors_best_of = []for i in range(bs):
gen_len = output_length_sampler()
query = torch.tensor(query_tensors[i])
output = ref_model.generate(query.unsqueeze(dim=0).to(device), max_new_tokens=gen_len, **gen_kwargs).squeeze()
response_tensors_ref.append(tokenizer.decode(output))
output = model.generate(query.unsqueeze(dim=0).to(device), max_new_tokens=gen_len, **gen_kwargs).squeeze()
response_tensors.append(tokenizer.decode(output))
# generating copies of the same query for the Best-of-n sampling
queries = query.repeat((N_BEST_OF, 1))
output = ref_model.generate(queries.to(device), max_new_tokens=gen_len, **gen_kwargs).squeeze()
response_tensors_best_of.append(tokenizer.batch_decode(output))scores_ref = [output[0]["score"] for output in reward_pipe(response_tensors_ref, **sent_kwargs)]
scores = [output[0]["score"] for output in reward_pipe(response_tensors, **sent_kwargs)]
scores_best_of = []
for i, response in enumerate(response_tensors_best_of):
# base_score = scores_ref[i]
scores_best_of.append(torch.tensor([output[0]["score"] for output in reward_pipe(response, **sent_kwargs)]))output_data["response (ref)"] = response_tensors_ref
output_data["scores (ref)"] = scores_ref
output_data["response (RLHF)"] = response_tensors
output_data["scores (RLHF)"] = scores
output_data["response (best_of)"] = [
response_tensors_best_of[i][a.argmax().item()] for i, a in enumerate(scores_best_of)
]
output_data["scores (best_of)"] = [a.max().item() for a in scores_best_of]
# store results in a dataframe
df_results = pd.DataFrame(output_data)
df_results | 0 |
hf_public_repos/trl/examples | hf_public_repos/trl/examples/notebooks/README.md | # Notebooks
This directory contains a collection of Jupyter notebooks that demonstrate how to use the TRL library in different applications.
- [`best_of_n.ipynb`](https://github.com/huggingface/trl/tree/main/examples/notebooks/best_of_n.ipynb): This notebook demonstrates how to use the "Best of N" sampling strategy using TRL when fine-tuning your model with PPO.
- [`gpt2-sentiment.ipynb`](https://github.com/huggingface/trl/tree/main/examples/notebooks/gpt2-sentiment.ipynb): This notebook demonstrates how to reproduce the GPT2 imdb sentiment tuning example on a jupyter notebook.
- [`gpt2-control.ipynb`](https://github.com/huggingface/trl/tree/main/examples/notebooks/gpt2-sentiment-control.ipynb): This notebook demonstrates how to reproduce the GPT2 sentiment control example on a jupyter notebook.
| 0 |
hf_public_repos/trl | hf_public_repos/trl/scripts/stale.py | # Copyright 2023 The HuggingFace Team, the AllenNLP library authors. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Script to close stale issue. Taken in part from the AllenNLP repository.
https://github.com/allenai/allennlp.
"""
import os
from datetime import datetime as dt
from datetime import timezone
from github import Github
LABELS_TO_EXEMPT = [
"good first issue",
"good second issue",
"feature request",
]
def main():
g = Github(os.environ["GITHUB_TOKEN"])
repo = g.get_repo("huggingface/trl")
open_issues = repo.get_issues(state="open")
for issue in open_issues:
comments = sorted([comment for comment in issue.get_comments()], key=lambda i: i.created_at, reverse=True)
last_comment = comments[0] if len(comments) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.now(timezone.utc) - issue.updated_at).days > 7
and (dt.now(timezone.utc) - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels())
):
issue.edit(state="closed")
elif (
(dt.now(timezone.utc) - issue.updated_at).days > 23
and (dt.now(timezone.utc) - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels())
):
issue.create_comment(
"This issue has been automatically marked as stale because it has not had "
"recent activity. If you think this still needs to be addressed "
"please comment on this thread.\n\n"
)
if __name__ == "__main__":
main()
| 0 |
hf_public_repos/trl | hf_public_repos/trl/trl/__init__.py | # flake8: noqa
__version__ = "0.7.5.dev0"
from .core import set_seed
from .environment import TextEnvironment, TextHistory
from .extras import BestOfNSampler
from .import_utils import is_diffusers_available, is_peft_available, is_wandb_available, is_xpu_available
from .models import (
AutoModelForCausalLMWithValueHead,
AutoModelForSeq2SeqLMWithValueHead,
PreTrainedModelWrapper,
create_reference_model,
)
from .trainer import (
DataCollatorForCompletionOnlyLM,
DPOTrainer,
IterativeSFTTrainer,
PPOConfig,
PPOTrainer,
RewardConfig,
RewardTrainer,
SFTTrainer,
)
if is_diffusers_available():
from .models import (
DDPOPipelineOutput,
DDPOSchedulerOutput,
DDPOStableDiffusionPipeline,
DefaultDDPOStableDiffusionPipeline,
)
from .trainer import DDPOConfig, DDPOTrainer
| 0 |
hf_public_repos/trl | hf_public_repos/trl/trl/core.py | # Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import random
import warnings
from contextlib import contextmanager
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
from torch.nn.utils.rnn import pad_sequence
from transformers import top_k_top_p_filtering
from .import_utils import is_xpu_available
try:
from collections.abc import Mapping
except ImportError:
from collections import Mapping
WANDB_PADDING = -1
def flatten_dict(nested, sep="/"):
"""Flatten dictionary and concatenate nested keys with separator."""
def rec(nest, prefix, into):
for k, v in nest.items():
if sep in k:
raise ValueError(f"separator '{sep}' not allowed to be in key '{k}'")
if isinstance(v, Mapping):
rec(v, prefix + k + sep, into)
else:
into[prefix + k] = v
flat = {}
rec(nested, "", flat)
return flat
def convert_to_scalar(stats):
"""
Converts the stats from a flattened dict to single scalar dicts
"""
tensorboard_stats = {}
for k, v in stats.items():
# for tensorboard compatibility - arrays and tensors are ignored with tensorboard
# therefore we convert single element tensors to scalars
if (isinstance(v, torch.Tensor) or isinstance(v, np.ndarray)) and (
len(v.shape) == 0 or (len(v.shape) == 1 and v.shape[0] == 1)
):
v = v.item()
tensorboard_stats[k] = v
return tensorboard_stats
def stack_dicts(stats_dicts):
"""Stack the values of a dict."""
results = dict()
for k in stats_dicts[0]:
stats_list = [torch.flatten(d[k]) for d in stats_dicts]
results[k] = pad_sequence(stats_list, batch_first=True, padding_value=WANDB_PADDING)
return results
def add_suffix(input_dict, suffix):
"""Add suffix to dict keys."""
return dict((k + suffix, v) for k, v in input_dict.items())
def pad_to_size(tensor, size, dim=1, padding=50256):
"""Pad tensor to size."""
t_size = tensor.size()[dim]
if t_size == size:
return tensor
else:
return torch.nn.functional.pad(tensor, (0, size - t_size), "constant", padding)
def logprobs_from_logits(logits, labels, gather=True):
"""
See: https://github.com/pytorch/pytorch/issues/563#issuecomment-330103591
"""
logp = F.log_softmax(logits, dim=2)
if not gather:
return logp
logpy = torch.gather(logp, 2, labels.unsqueeze(2)).squeeze(-1)
return logpy
def whiten(values, shift_mean=True):
"""Whiten values."""
mean, var = torch.mean(values), torch.var(values)
whitened = (values - mean) * torch.rsqrt(var + 1e-8)
if not shift_mean:
whitened += mean
return whitened
def masked_mean(values, mask, axis=None):
"""Compute mean of tensor with a masked values."""
if axis is not None:
return (values * mask).sum(axis=axis) / mask.sum(axis=axis)
else:
return (values * mask).sum() / mask.sum()
def masked_var(values, mask, unbiased=True):
"""Compute variance of tensor with masked values."""
mean = masked_mean(values, mask)
centered_values = values - mean
variance = masked_mean(centered_values**2, mask)
if unbiased:
mask_sum = mask.sum()
if mask_sum == 0:
raise ValueError(
"The sum of the mask is zero, which can happen when `mini_batch_size=1`;"
"try increase the `mini_batch_size` or `gradient_accumulation_steps`"
)
# note that if mask_sum == 1, then there is a division by zero issue
# to avoid it you just need to use a larger minibatch_size
bessel_correction = mask_sum / (mask_sum - 1)
variance = variance * bessel_correction
return variance
def masked_whiten(values, mask, shift_mean=True):
"""Whiten values with masked values."""
mean, var = masked_mean(values, mask), masked_var(values, mask)
whitened = (values - mean) * torch.rsqrt(var + 1e-8)
if not shift_mean:
whitened += mean
return whitened
def clip_by_value(x, tensor_min, tensor_max):
"""
Tensor extenstion to torch.clamp
https://github.com/pytorch/pytorch/issues/2793#issuecomment-428784713
"""
clipped = torch.max(torch.min(x, tensor_max), tensor_min)
return clipped
def entropy_from_logits(logits):
"""Calculate entropy from logits."""
pd = torch.nn.functional.softmax(logits, dim=-1)
entropy = torch.logsumexp(logits, axis=-1) - torch.sum(pd * logits, axis=-1)
return entropy
def average_torch_dicts(list_of_dicts):
"""Average values of a list of dicts with torch tensors."""
average_dict = dict()
for key in list_of_dicts[0].keys():
average_dict[key] = torch.mean(torch.stack([d[key] for d in list_of_dicts]), axis=0)
return average_dict
def stats_to_np(stats_dict):
"""Cast all torch.tensors in dict to numpy arrays."""
new_dict = dict()
for k, v in stats_dict.items():
if isinstance(v, torch.Tensor):
new_dict[k] = v.detach().cpu()
if new_dict[k].dtype == torch.bfloat16:
new_dict[k] = new_dict[k].float()
new_dict[k] = new_dict[k].numpy()
else:
new_dict[k] = v
if np.isscalar(new_dict[k]):
new_dict[k] = float(new_dict[k])
return new_dict
def listify_batch(tensor):
"""Turns the first dimension of a tensor into a list."""
return [tensor[i] for i in range(tensor.shape[0])]
def build_bert_batch_from_txt(text_list, tokenizer, device):
"""Create token id and attention mask tensors from text list for BERT classification."""
# tokenize
tensors = [tokenizer.encode(txt, return_tensors="pt").to(device) for txt in text_list]
# find max length to pad to
max_len = max([t.size()[1] for t in tensors])
# get padded tensors and attention masks
# (attention masks make bert ignore padding)
padded_tensors = []
attention_masks = []
for tensor in tensors:
attention_mask = torch.ones(tensor.size(), device=device)
padded_tensors.append(pad_to_size(tensor, max_len, padding=0))
attention_masks.append(pad_to_size(attention_mask, max_len, padding=0))
# stack all tensors
padded_tensors = torch.cat(padded_tensors)
attention_masks = torch.cat(attention_masks)
return padded_tensors, attention_masks
def respond_to_batch(model, queries, txt_len=20, top_k=0, top_p=1.0):
"""Sample text from language model."""
input_ids = queries
for i in range(txt_len):
# Get Logits
outputs = model(input_ids)
next_token_logits = outputs[0][:, -1, :]
next_token_logits = top_k_top_p_filtering(next_token_logits, top_k=top_k, top_p=top_p)
# Sample
probs = F.softmax(next_token_logits, dim=-1)
next_token = torch.multinomial(probs, num_samples=1).squeeze(1)
input_ids = torch.cat([input_ids, next_token.unsqueeze(-1)], dim=-1)
return input_ids[:, -txt_len:]
def set_seed(seed: int):
"""
Helper function for reproducible behavior to set the seed in `random`, `numpy`, and `torch`.
Args:
seed (`int`): The seed to set.
"""
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if is_xpu_available():
torch.xpu.manual_seed_all(seed)
else:
torch.cuda.manual_seed_all(seed)
class LengthSampler:
"""
Samples a length
"""
def __init__(self, min_value, max_value):
self.values = list(range(min_value, max_value))
def __call__(self):
return np.random.choice(self.values)
class PPODecorators(object):
optimize_device_cache = False
@classmethod
@contextmanager
def empty_device_cache(cls):
yield
if is_xpu_available():
if cls.optimize_device_cache and torch.xpu.is_available():
gc.collect()
torch.xpu.empty_cache()
gc.collect()
else:
if cls.optimize_device_cache and torch.cuda.is_available():
gc.collect()
torch.cuda.empty_cache()
gc.collect()
def randn_tensor(
shape: Union[Tuple, List],
generator: Optional[Union[List["torch.Generator"], "torch.Generator"]] = None,
device: Optional["torch.device"] = None,
dtype: Optional["torch.dtype"] = None,
layout: Optional["torch.layout"] = None,
):
"""A helper function to create random tensors on the desired `device` with the desired `dtype`. When
passing a list of generators, you can seed each batch size individually. If CPU generators are passed, the tensor
is always created on the CPU.
"""
# device on which tensor is created defaults to device
rand_device = device
batch_size = shape[0]
layout = layout or torch.strided
device = device or torch.device("cpu")
if generator is not None:
gen_device_type = generator.device.type if not isinstance(generator, list) else generator[0].device.type
if gen_device_type != device.type and gen_device_type == "cpu":
rand_device = "cpu"
if device != "mps":
warnings.warn(
f"The passed generator was created on 'cpu' even though a tensor on {device} was expected."
f" Tensors will be created on 'cpu' and then moved to {device}. Note that one can probably"
f" slighly speed up this function by passing a generator that was created on the {device} device."
)
elif gen_device_type != device.type and gen_device_type == "cuda":
raise ValueError(f"Cannot generate a {device} tensor from a generator of type {gen_device_type}.")
# make sure generator list of length 1 is treated like a non-list
if isinstance(generator, list) and len(generator) == 1:
generator = generator[0]
if isinstance(generator, list):
shape = (1,) + shape[1:]
latents = [
torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype, layout=layout)
for i in range(batch_size)
]
latents = torch.cat(latents, dim=0).to(device)
else:
latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype, layout=layout).to(device)
return latents
| 0 |
hf_public_repos/trl | hf_public_repos/trl/trl/import_utils.py | # Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import importlib
import sys
if sys.version_info < (3, 8):
_is_python_greater_3_8 = False
else:
_is_python_greater_3_8 = True
def is_peft_available() -> bool:
return importlib.util.find_spec("peft") is not None
def is_accelerate_greater_20_0() -> bool:
if _is_python_greater_3_8:
from importlib.metadata import version
accelerate_version = version("accelerate")
else:
import pkg_resources
accelerate_version = pkg_resources.get_distribution("accelerate").version
return accelerate_version >= "0.20.0"
def is_transformers_greater_than(version: str) -> bool:
_transformers_version = importlib.metadata.version("transformers")
return _transformers_version > version
def is_torch_greater_2_0() -> bool:
if _is_python_greater_3_8:
from importlib.metadata import version
torch_version = version("torch")
else:
import pkg_resources
torch_version = pkg_resources.get_distribution("torch").version
return torch_version >= "2.0"
def is_diffusers_available() -> bool:
return importlib.util.find_spec("diffusers") is not None
def is_bitsandbytes_available() -> bool:
return importlib.util.find_spec("bitsandbytes") is not None
def is_torchvision_available() -> bool:
return importlib.util.find_spec("torchvision") is not None
def is_rich_available() -> bool:
return importlib.util.find_spec("rich") is not None
def is_wandb_available() -> bool:
return importlib.util.find_spec("wandb") is not None
def is_xpu_available() -> bool:
if is_accelerate_greater_20_0():
import accelerate
return accelerate.utils.is_xpu_available()
else:
if importlib.util.find_spec("intel_extension_for_pytorch") is None:
return False
try:
import torch
return hasattr(torch, "xpu") and torch.xpu.is_available()
except RuntimeError:
return False
| 0 |
hf_public_repos/trl/trl | hf_public_repos/trl/trl/models/modeling_sd_base.py | # Copyright 2023 DDPO-pytorch authors (Kevin Black), The HuggingFace Team, metric-space. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import contextlib
import os
import warnings
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np
import torch
from diffusers import DDIMScheduler, StableDiffusionPipeline, UNet2DConditionModel
from diffusers.loaders import AttnProcsLayers
from diffusers.models.attention_processor import LoRAAttnProcessor
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import rescale_noise_cfg
from ..core import randn_tensor
@dataclass
class DDPOPipelineOutput(object):
"""
Output class for the diffusers pipeline to be finetuned with the DDPO trainer
Args:
images (`torch.Tensor`):
The generated images.
latents (`List[torch.Tensor]`):
The latents used to generate the images.
log_probs (`List[torch.Tensor]`):
The log probabilities of the latents.
"""
images: torch.Tensor
latents: torch.Tensor
log_probs: torch.Tensor
@dataclass
class DDPOSchedulerOutput(object):
"""
Output class for the diffusers scheduler to be finetuned with the DDPO trainer
Args:
latents (`torch.Tensor`):
Predicted sample at the previous timestep. Shape: `(batch_size, num_channels, height, width)`
log_probs (`torch.Tensor`):
Log probability of the above mentioned sample. Shape: `(batch_size)`
"""
latents: torch.Tensor
log_probs: torch.Tensor
class DDPOStableDiffusionPipeline(object):
"""
Main class for the diffusers pipeline to be finetuned with the DDPO trainer
"""
def __call__(self, *args, **kwargs) -> DDPOPipelineOutput:
raise NotImplementedError
def scheduler_step(self, *args, **kwargs) -> DDPOSchedulerOutput:
raise NotImplementedError
@property
def unet(self):
"""
Returns the 2d U-Net model used for diffusion.
"""
raise NotImplementedError
@property
def vae(self):
"""
Returns the Variational Autoencoder model used from mapping images to and from the latent space
"""
raise NotImplementedError
@property
def tokenizer(self):
"""
Returns the tokenizer used for tokenizing text inputs
"""
raise NotImplementedError
@property
def scheduler(self):
"""
Returns the scheduler associated with the pipeline used for the diffusion process
"""
raise NotImplementedError
@property
def text_encoder(self):
"""
Returns the text encoder used for encoding text inputs
"""
raise NotImplementedError
@property
def autocast(self):
"""
Returns the autocast context manager
"""
raise NotImplementedError
def set_progress_bar_config(self, *args, **kwargs):
"""
Sets the progress bar config for the pipeline
"""
raise NotImplementedError
def save_pretrained(self, *args, **kwargs):
"""
Saves all of the model weights
"""
raise NotImplementedError
def get_trainable_layers(self, *args, **kwargs):
"""
Returns the trainable parameters of the pipeline
"""
raise NotImplementedError
def save_checkpoint(self, *args, **kwargs):
"""
Light wrapper around accelerate's register_save_state_pre_hook which is run before saving state
"""
raise NotImplementedError
def load_checkpoint(self, *args, **kwargs):
"""
Light wrapper around accelerate's register_lad_state_pre_hook which is run before loading state
"""
raise NotImplementedError
def _left_broadcast(input_tensor, shape):
"""
As opposed to the default direction of broadcasting (right to left), this function broadcasts
from left to right
Args:
input_tensor (`torch.FloatTensor`): is the tensor to broadcast
shape (`Tuple[int]`): is the shape to broadcast to
"""
input_ndim = input_tensor.ndim
if input_ndim > len(shape):
raise ValueError(
"The number of dimensions of the tensor to broadcast cannot be greater than the length of the shape to broadcast to"
)
return input_tensor.reshape(input_tensor.shape + (1,) * (len(shape) - input_ndim)).broadcast_to(shape)
def _get_variance(self, timestep, prev_timestep):
alpha_prod_t = torch.gather(self.alphas_cumprod, 0, timestep.cpu()).to(timestep.device)
alpha_prod_t_prev = torch.where(
prev_timestep.cpu() >= 0,
self.alphas_cumprod.gather(0, prev_timestep.cpu()),
self.final_alpha_cumprod,
).to(timestep.device)
beta_prod_t = 1 - alpha_prod_t
beta_prod_t_prev = 1 - alpha_prod_t_prev
variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
return variance
def scheduler_step(
self,
model_output: torch.FloatTensor,
timestep: int,
sample: torch.FloatTensor,
eta: float = 0.0,
use_clipped_model_output: bool = False,
generator=None,
prev_sample: Optional[torch.FloatTensor] = None,
) -> DDPOSchedulerOutput:
"""
Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
process from the learned model outputs (most often the predicted noise).
Args:
model_output (`torch.FloatTensor`): direct output from learned diffusion model.
timestep (`int`): current discrete timestep in the diffusion chain.
sample (`torch.FloatTensor`):
current instance of sample being created by diffusion process.
eta (`float`): weight of noise for added noise in diffusion step.
use_clipped_model_output (`bool`): if `True`, compute "corrected" `model_output` from the clipped
predicted original sample. Necessary because predicted original sample is clipped to [-1, 1] when
`self.config.clip_sample` is `True`. If no clipping has happened, "corrected" `model_output` would
coincide with the one provided as input and `use_clipped_model_output` will have not effect.
generator: random number generator.
variance_noise (`torch.FloatTensor`): instead of generating noise for the variance using `generator`, we
can directly provide the noise for the variance itself. This is useful for methods such as
CycleDiffusion. (https://arxiv.org/abs/2210.05559)
Returns:
`DDPOSchedulerOutput`: the predicted sample at the previous timestep and the log probability of the sample
"""
if self.num_inference_steps is None:
raise ValueError(
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"
)
# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
# Ideally, read DDIM paper in-detail understanding
# Notation (<variable name> -> <name in paper>
# - pred_noise_t -> e_theta(x_t, t)
# - pred_original_sample -> f_theta(x_t, t) or x_0
# - std_dev_t -> sigma_t
# - eta -> η
# - pred_sample_direction -> "direction pointing to x_t"
# - pred_prev_sample -> "x_t-1"
# 1. get previous step value (=t-1)
prev_timestep = timestep - self.config.num_train_timesteps // self.num_inference_steps
# to prevent OOB on gather
prev_timestep = torch.clamp(prev_timestep, 0, self.config.num_train_timesteps - 1)
# 2. compute alphas, betas
alpha_prod_t = self.alphas_cumprod.gather(0, timestep.cpu())
alpha_prod_t_prev = torch.where(
prev_timestep.cpu() >= 0,
self.alphas_cumprod.gather(0, prev_timestep.cpu()),
self.final_alpha_cumprod,
)
alpha_prod_t = _left_broadcast(alpha_prod_t, sample.shape).to(sample.device)
alpha_prod_t_prev = _left_broadcast(alpha_prod_t_prev, sample.shape).to(sample.device)
beta_prod_t = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
if self.config.prediction_type == "epsilon":
pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
pred_epsilon = model_output
elif self.config.prediction_type == "sample":
pred_original_sample = model_output
pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)
elif self.config.prediction_type == "v_prediction":
pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
" `v_prediction`"
)
# 4. Clip or threshold "predicted x_0"
if self.config.thresholding:
pred_original_sample = self._threshold_sample(pred_original_sample)
elif self.config.clip_sample:
pred_original_sample = pred_original_sample.clamp(
-self.config.clip_sample_range, self.config.clip_sample_range
)
# 5. compute variance: "sigma_t(η)" -> see formula (16)
# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
variance = _get_variance(self, timestep, prev_timestep)
std_dev_t = eta * variance ** (0.5)
std_dev_t = _left_broadcast(std_dev_t, sample.shape).to(sample.device)
if use_clipped_model_output:
# the pred_epsilon is always re-derived from the clipped x_0 in Glide
pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)
# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * pred_epsilon
# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
prev_sample_mean = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
if prev_sample is not None and generator is not None:
raise ValueError(
"Cannot pass both generator and prev_sample. Please make sure that either `generator` or"
" `prev_sample` stays `None`."
)
if prev_sample is None:
variance_noise = randn_tensor(
model_output.shape,
generator=generator,
device=model_output.device,
dtype=model_output.dtype,
)
prev_sample = prev_sample_mean + std_dev_t * variance_noise
# log prob of prev_sample given prev_sample_mean and std_dev_t
log_prob = (
-((prev_sample.detach() - prev_sample_mean) ** 2) / (2 * (std_dev_t**2))
- torch.log(std_dev_t)
- torch.log(torch.sqrt(2 * torch.as_tensor(np.pi)))
)
# mean along all but batch dimension
log_prob = log_prob.mean(dim=tuple(range(1, log_prob.ndim)))
return DDPOSchedulerOutput(prev_sample.type(sample.dtype), log_prob)
# 1. The output type for call is different as the logprobs are now returned
# 2. An extra method called `scheduler_step` is added which is used to constraint the scheduler output
@torch.no_grad()
def pipeline_step(
self,
prompt: Optional[Union[str, List[str]]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
callback_steps: int = 1,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
guidance_rescale: float = 0.0,
):
r"""
Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image.
width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
The width in pixels of the generated image.
num_inference_steps (`int`, *optional*, defaults to 50):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
guidance_scale (`float`, *optional*, defaults to 7.5):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation. If not defined, one has to pass
`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
less than `1`).
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0):
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
[`schedulers.DDIMScheduler`], will be ignored for others.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
to make generation deterministic.
latents (`torch.FloatTensor`, *optional*):
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
provided, text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
plain tuple.
callback (`Callable`, *optional*):
A function that will be called every `callback_steps` steps during inference. The function will be
called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
callback_steps (`int`, *optional*, defaults to 1):
The frequency at which the `callback` function will be called. If not specified, the callback will be
called at every step.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py).
guidance_rescale (`float`, *optional*, defaults to 0.7):
Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
[Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
Guidance rescale factor should fix overexposure when using zero terminal SNR.
Examples:
Returns:
`DDPOPipelineOutput`: The generated image, the predicted latents used to generate the image and the associated log probabilities
"""
# 0. Default height and width to unet
height = height or self.unet.config.sample_size * self.vae_scale_factor
width = width or self.unet.config.sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
height,
width,
callback_steps,
negative_prompt,
prompt_embeds,
negative_prompt_embeds,
)
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
text_encoder_lora_scale = cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
prompt_embeds = self._encode_prompt(
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
negative_prompt,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
lora_scale=text_encoder_lora_scale,
)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 6. Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
all_latents = [latents]
all_log_probs = []
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
# predict the noise residual
noise_pred = self.unet(
latent_model_input,
t,
encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
if do_classifier_free_guidance and guidance_rescale > 0.0:
# Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
# compute the previous noisy sample x_t -> x_t-1
scheduler_output = scheduler_step(self.scheduler, noise_pred, t, latents, eta)
latents = scheduler_output.latents
log_prob = scheduler_output.log_probs
all_latents.append(latents)
all_log_probs.append(log_prob)
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if callback is not None and i % callback_steps == 0:
callback(i, t, latents)
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
else:
image = latents
has_nsfw_concept = None
if has_nsfw_concept is None:
do_denormalize = [True] * image.shape[0]
else:
do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
# Offload last model to CPU
if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
self.final_offload_hook.offload()
return DDPOPipelineOutput(image, all_latents, all_log_probs)
class DefaultDDPOStableDiffusionPipeline(DDPOStableDiffusionPipeline):
def __init__(self, pretrained_model_name: str, *, pretrained_model_revision: str = "main", use_lora: bool = True):
self.sd_pipeline = StableDiffusionPipeline.from_pretrained(
pretrained_model_name, revision=pretrained_model_revision
)
self.use_lora = use_lora
self.pretrained_model = pretrained_model_name
self.pretrained_revision = pretrained_model_revision
try:
self.sd_pipeline.unet.load_attn_procs(pretrained_model_name, revision=pretrained_model_revision)
self.use_lora = True
except OSError:
if use_lora:
warnings.warn(
"If you are aware that the pretrained model has no lora weights to it, ignore this message. "
"Otherwise please check the if `pytorch_lora_weights.safetensors` exists in the model folder."
)
self.sd_pipeline.scheduler = DDIMScheduler.from_config(self.sd_pipeline.scheduler.config)
self.sd_pipeline.safety_checker = None
# memory optimization
self.sd_pipeline.vae.requires_grad_(False)
self.sd_pipeline.text_encoder.requires_grad_(False)
self.sd_pipeline.unet.requires_grad_(not self.use_lora)
def __call__(self, *args, **kwargs) -> DDPOPipelineOutput:
return pipeline_step(self.sd_pipeline, *args, **kwargs)
def scheduler_step(self, *args, **kwargs) -> DDPOSchedulerOutput:
return scheduler_step(self.sd_pipeline.scheduler, *args, **kwargs)
@property
def unet(self):
return self.sd_pipeline.unet
@property
def vae(self):
return self.sd_pipeline.vae
@property
def tokenizer(self):
return self.sd_pipeline.tokenizer
@property
def scheduler(self):
return self.sd_pipeline.scheduler
@property
def text_encoder(self):
return self.sd_pipeline.text_encoder
@property
def autocast(self):
return contextlib.nullcontext if self.use_lora else None
def save_pretrained(self, output_dir):
if self.use_lora:
self.sd_pipeline.unet.save_attn_procs(output_dir)
self.sd_pipeline.save_pretrained(output_dir)
def set_progress_bar_config(self, *args, **kwargs):
self.sd_pipeline.set_progress_bar_config(*args, **kwargs)
def get_trainable_layers(self):
if self.use_lora:
# Set correct lora layers
lora_attn_procs = {}
for name in self.sd_pipeline.unet.attn_processors.keys():
cross_attention_dim = (
None if name.endswith("attn1.processor") else self.sd_pipeline.unet.config.cross_attention_dim
)
if name.startswith("mid_block"):
hidden_size = self.sd_pipeline.unet.config.block_out_channels[-1]
elif name.startswith("up_blocks"):
block_id = int(name[len("up_blocks.")])
hidden_size = list(reversed(self.sd_pipeline.unet.config.block_out_channels))[block_id]
elif name.startswith("down_blocks"):
block_id = int(name[len("down_blocks.")])
hidden_size = self.sd_pipeline.unet.config.block_out_channels[block_id]
lora_attn_procs[name] = LoRAAttnProcessor(
hidden_size=hidden_size, cross_attention_dim=cross_attention_dim
)
self.sd_pipeline.unet.set_attn_processor(lora_attn_procs)
return AttnProcsLayers(self.sd_pipeline.unet.attn_processors)
else:
return self.sd_pipeline.unet
def save_checkpoint(self, models, weights, output_dir):
if len(models) != 1:
raise ValueError("Given how the trainable params were set, this should be of length 1")
if self.use_lora and isinstance(models[0], AttnProcsLayers):
self.sd_pipeline.unet.save_attn_procs(output_dir)
elif not self.use_lora and isinstance(models[0], UNet2DConditionModel):
models[0].save_pretrained(os.path.join(output_dir, "unet"))
else:
raise ValueError(f"Unknown model type {type(models[0])}")
def load_checkpoint(self, models, input_dir):
if len(models) != 1:
raise ValueError("Given how the trainable params were set, this should be of length 1")
if self.use_lora and isinstance(models[0], AttnProcsLayers):
tmp_unet = UNet2DConditionModel.from_pretrained(
self.pretrained_model,
revision=self.pretrained_revision,
subfolder="unet",
)
tmp_unet.load_attn_procs(input_dir)
models[0].load_state_dict(AttnProcsLayers(tmp_unet.attn_processors).state_dict())
del tmp_unet
elif not self.use_lora and isinstance(models[0], UNet2DConditionModel):
load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder="unet")
models[0].register_to_config(**load_model.config)
models[0].load_state_dict(load_model.state_dict())
del load_model
else:
raise ValueError(f"Unknown model type {type(models[0])}")
| 0 |
hf_public_repos/trl/trl | hf_public_repos/trl/trl/models/__init__.py | # flake8: noqa
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .modeling_base import PreTrainedModelWrapper, create_reference_model
from .modeling_value_head import AutoModelForCausalLMWithValueHead, AutoModelForSeq2SeqLMWithValueHead
SUPPORTED_ARCHITECTURES = (
AutoModelForCausalLMWithValueHead,
AutoModelForSeq2SeqLMWithValueHead,
)
from ..import_utils import is_diffusers_available
if is_diffusers_available():
from .modeling_sd_base import (
DDPOPipelineOutput,
DDPOSchedulerOutput,
DDPOStableDiffusionPipeline,
DefaultDDPOStableDiffusionPipeline,
)
| 0 |
hf_public_repos/trl/trl | hf_public_repos/trl/trl/models/modeling_value_head.py | # Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.nn as nn
from transformers import AutoModelForCausalLM, AutoModelForSeq2SeqLM
from .modeling_base import PreTrainedModelWrapper
class ValueHead(nn.Module):
r"""
The ValueHead class implements a head for GPT2 that returns a scalar for each output token.
"""
def __init__(self, config, **kwargs):
super().__init__()
if not hasattr(config, "summary_dropout_prob"):
summary_dropout_prob = kwargs.pop("summary_dropout_prob", 0.1)
else:
summary_dropout_prob = config.summary_dropout_prob
self.dropout = nn.Dropout(summary_dropout_prob) if summary_dropout_prob else nn.Identity()
# some models such as OPT have a projection layer before the word embeddings - e.g. OPT-350m
if hasattr(config, "hidden_size"):
hidden_size = config.hidden_size
if hasattr(config, "word_embed_proj_dim"):
hidden_size = config.word_embed_proj_dim
elif hasattr(config, "is_encoder_decoder"):
if config.is_encoder_decoder and hasattr(config, "decoder"):
if hasattr(config.decoder, "hidden_size"):
hidden_size = config.decoder.hidden_size
self.summary = nn.Linear(hidden_size, 1)
self.flatten = nn.Flatten()
def forward(self, hidden_states):
output = self.dropout(hidden_states)
# For now force upcast in fp32 if needed. Let's keep the
# output in fp32 for numerical stability.
if output.dtype != self.summary.weight.dtype:
output = output.to(self.summary.weight.dtype)
output = self.summary(output)
return output
class AutoModelForCausalLMWithValueHead(PreTrainedModelWrapper):
r"""
An autoregressive model with a value head in addition to the language model head.
This class inherits from `~trl.PreTrainedModelWrapper` and wraps a
`transformers.PreTrainedModel` class. The wrapper class supports classic functions
such as `from_pretrained`, `push_to_hub` and `generate`. To call a method of the wrapped
model, simply manipulate the `pretrained_model` attribute of this class.
Class attributes:
- **transformers_parent_class** (`transformers.PreTrainedModel`) -- The parent class of the wrapped model. This
should be set to `transformers.AutoModelForCausalLM` for this class.
- **lm_head_namings** (`tuple`) -- A tuple of strings that are used to identify the language model head of the
wrapped model. This is set to `("lm_head", "embed_out")` for this class but can be changed for other models
in the future
- **supported_args** (`tuple`) -- A tuple of strings that are used to identify the arguments that are supported
by the `ValueHead` class. Currently, the supported args are:
- **summary_dropout_prob** (`float`, `optional`, defaults to `None`) -- The dropout probability for the
`ValueHead` class.
- **v_head_initializer_range** (`float`, `optional`, defaults to `0.2`) -- The initializer range for the
`ValueHead` if a specific initialization strategy is selected.
- **v_head_init_strategy** (`str`, `optional`, defaults to `None`) -- The initialization strategy for the
`ValueHead`. Currently, the supported strategies are:
- **`None`** -- Initializes the weights of the `ValueHead` with a random distribution. This is the default
strategy.
- **"normal"** -- Initializes the weights of the `ValueHead` with a normal distribution.
"""
transformers_parent_class = AutoModelForCausalLM
lm_head_namings = ["lm_head", "embed_out"]
supported_args = (
"summary_dropout_prob",
"v_head_initializer_range",
"v_head_init_strategy",
)
def __init__(self, pretrained_model, **kwargs):
r"""
Initializes the model.
Args:
pretrained_model (`transformers.PreTrainedModel`):
The model to wrap. It should be a causal language model such as GPT2.
or any model mapped inside the `AutoModelForCausalLM` class.
kwargs (`dict`, `optional`):
Additional keyword arguments, that are passed to the `ValueHead` class.
"""
super().__init__(pretrained_model, **kwargs)
v_head_kwargs, _, _ = self._split_kwargs(kwargs)
if not any(hasattr(self.pretrained_model, attribute) for attribute in self.lm_head_namings):
raise ValueError("The model does not have a language model head, please use a model that has one.")
self.v_head = ValueHead(self.pretrained_model.config, **v_head_kwargs)
self._init_weights(**v_head_kwargs)
def _init_weights(self, **kwargs):
r"""
Initializes the weights of the value head. The default initialization strategy is random.
Users can pass a different initialization strategy by passing the `v_head_init_strategy` argument
when calling `.from_pretrained`. Supported strategies are:
- `normal`: initializes the weights with a normal distribution.
Args:
**kwargs (`dict`, `optional`):
Additional keyword arguments, that are passed to the `ValueHead` class. These arguments
can contain the `v_head_init_strategy` argument as well as the `v_head_initializer_range`
argument.
"""
initializer_range = kwargs.pop("v_head_initializer_range", 0.2)
# random init by default
init_strategy = kwargs.pop("v_head_init_strategy", None)
if init_strategy is None:
# do nothing
pass
elif init_strategy == "normal":
self.v_head.summary.weight.data.normal_(mean=0.0, std=initializer_range)
self.v_head.summary.bias.data.zero_()
def forward(
self,
input_ids=None,
past_key_values=None,
attention_mask=None,
**kwargs,
):
r"""
Applies a forward pass to the wrapped model and returns the logits of the value head.
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
past_key_values (`tuple(tuple(torch.FloatTensor))`, `optional`):
Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
(see `past_key_values` input) to speed up sequential decoding.
attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, `optional`):
Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
kwargs (`dict`, `optional`):
Additional keyword arguments, that are passed to the wrapped model.
"""
kwargs["output_hidden_states"] = True # this had already been set in the LORA / PEFT examples
kwargs["past_key_values"] = past_key_values
if self.is_peft_model and self.pretrained_model.active_peft_config.peft_type == "PREFIX_TUNING":
kwargs.pop("past_key_values")
base_model_output = self.pretrained_model(
input_ids=input_ids,
attention_mask=attention_mask,
**kwargs,
)
last_hidden_state = base_model_output.hidden_states[-1]
lm_logits = base_model_output.logits
loss = base_model_output.loss
if last_hidden_state.device != self.v_head.summary.weight.device:
last_hidden_state = last_hidden_state.to(self.v_head.summary.weight.device)
value = self.v_head(last_hidden_state).squeeze(-1)
# force upcast in fp32 if logits are in half-precision
if lm_logits.dtype != torch.float32:
lm_logits = lm_logits.float()
return (lm_logits, loss, value)
def generate(self, *args, **kwargs):
r"""
A simple wrapper around the `generate` method of the wrapped model.
Please refer to the [`generate`](https://huggingface.co/docs/transformers/internal/generation_utils)
method of the wrapped model for more information about the supported arguments.
Args:
*args (`list`, *optional*):
Positional arguments passed to the `generate` method of the wrapped model.
**kwargs (`dict`, *optional*):
Keyword arguments passed to the `generate` method of the wrapped model.
"""
return self.pretrained_model.generate(*args, **kwargs)
def state_dict(self, *args, **kwargs):
r"""
Returns the state dictionary of the model. We add the state dictionary of the value head
to the state dictionary of the wrapped model by prepending the key with `v_head.`.
"""
if not self.is_peft_model:
pretrained_model_state_dict = self.pretrained_model.state_dict(*args, **kwargs)
else:
# if it is a peft model, only save the v_head
pretrained_model_state_dict = {}
v_head_state_dict = self.v_head.state_dict(*args, **kwargs)
for k, v in v_head_state_dict.items():
pretrained_model_state_dict[f"v_head.{k}"] = v
return pretrained_model_state_dict
def push_to_hub(self, *args, **kwargs):
setattr(self.pretrained_model, "v_head", self.v_head)
return self.pretrained_model.push_to_hub(*args, **kwargs)
def post_init(self, state_dict):
r"""
We add the state dictionary of the value head to the state dictionary of the wrapped model
by prepending the key with `v_head.`. This function removes the `v_head.` prefix from the
keys of the value head state dictionary.
"""
for k in list(state_dict.keys()):
if "v_head." in k:
state_dict[k.replace("v_head.", "")] = state_dict.pop(k)
self.v_head.load_state_dict(state_dict, strict=False)
del state_dict
if hasattr(self.pretrained_model, "hf_device_map"):
if (
"cpu" in self.pretrained_model.hf_device_map.values()
or "disk" in self.pretrained_model.hf_device_map.values()
):
raise ValueError(
"The model is offloaded on CPU or disk - CPU & disk offloading is not supported for ValueHead models."
)
first_device = list(set(self.pretrained_model.hf_device_map.values()))[0]
self.v_head = self.v_head.to(first_device)
def set_device_hook(module, input, outputs):
new_output = ()
for output in outputs:
if isinstance(output, torch.Tensor):
new_output += (output.to(first_device),)
else:
new_output += (output,)
return new_output
self.register_forward_hook(set_device_hook)
self.is_sequential_parallel = True
class AutoModelForSeq2SeqLMWithValueHead(PreTrainedModelWrapper):
r"""
A seq2seq model with a value head in addition to the language model head.
This class inherits from `~trl.PreTrainedModelWrapper` and wraps a
`transformers.PreTrainedModel` class. The wrapper class supports classic functions
such as `from_pretrained` and `push_to_hub` and also provides some additional
functionalities such as `generate`.
Args:
pretrained_model (`transformers.PreTrainedModel`):
The model to wrap. It should be a causal language model such as GPT2.
or any model mapped inside the `AutoModelForSeq2SeqLM` class.
kwargs:
Additional keyword arguments passed along to the `ValueHead` class.
"""
transformers_parent_class = AutoModelForSeq2SeqLM
lm_head_namings = ["lm_head", "embed_out", "output_projection"]
supported_args = (
"summary_dropout_prob",
"v_head_initializer_range",
"v_head_init_strategy",
)
def __init__(self, pretrained_model, **kwargs):
super().__init__(pretrained_model, **kwargs)
v_head_kwargs, _, _ = self._split_kwargs(kwargs)
self.is_encoder_decoder = True
if not self._has_lm_head():
raise ValueError("The model does not have a language model head, please use a model that has one.")
self.v_head = ValueHead(self.pretrained_model.config, **v_head_kwargs)
self._init_weights(**v_head_kwargs)
def _has_lm_head(self):
# check module names of all modules inside `pretrained_model` to find the language model head
for name, module in self.pretrained_model.named_modules():
if any(attribute in name for attribute in self.lm_head_namings):
return True
return False
def post_init(self, state_dict):
r"""
We add the state dictionary of the value head to the state dictionary of the wrapped model
by prepending the key with `v_head.`. This function removes the `v_head.` prefix from the
keys of the value head state dictionary.
"""
for k in list(state_dict.keys()):
if "v_head." in k:
state_dict[k.replace("v_head.", "")] = state_dict.pop(k)
self.v_head.load_state_dict(state_dict, strict=False)
del state_dict
if hasattr(self.pretrained_model, "hf_device_map"):
if (
"cpu" in self.pretrained_model.hf_device_map.values()
or "disk" in self.pretrained_model.hf_device_map.values()
):
raise ValueError(
"The model is offloaded on CPU or disk - CPU & disk offloading is not supported for ValueHead models."
)
# get the lm_head device
for name, module in self.pretrained_model.named_modules():
if any(attribute in name for attribute in self.lm_head_namings):
lm_head_device = module.weight.device
break
# put v_head on the same device as the lm_head to avoid issues
self.v_head = self.v_head.to(lm_head_device)
def set_device_hook(module, input, outputs):
r"""
A hook that sets the device of the output of the model to the device of the first
parameter of the model.
Args:
module (`nn.Module`):
The module to which the hook is attached.
input (`tuple`):
The input to the module.
outputs (`tuple`):
The output of the module.
"""
new_output = ()
for output in outputs:
if isinstance(output, torch.Tensor):
new_output += (output.to(lm_head_device),)
else:
new_output += (output,)
return new_output
self.register_forward_hook(set_device_hook)
self.is_sequential_parallel = True
def state_dict(self, *args, **kwargs):
r"""
Returns the state dictionary of the model. We add the state dictionary of the value head
to the state dictionary of the wrapped model by prepending the key with `v_head.`.
"""
if not self.is_peft_model:
pretrained_model_state_dict = self.pretrained_model.state_dict(*args, **kwargs)
else:
# if it is a peft model, only save the v_head
pretrained_model_state_dict = {}
v_head_state_dict = self.v_head.state_dict(*args, **kwargs)
for k, v in v_head_state_dict.items():
pretrained_model_state_dict[f"v_head.{k}"] = v
return pretrained_model_state_dict
def push_to_hub(self, *args, **kwargs):
setattr(self.pretrained_model, "v_head", self.v_head)
return self.pretrained_model.push_to_hub(*args, **kwargs)
def _init_weights(self, **kwargs):
r"""
We initialize the weights of the value head.
"""
initializer_range = kwargs.pop("v_head_initializer_range", 0.2)
# random init by default
init_strategy = kwargs.pop("v_head_init_strategy", None)
if init_strategy is None:
# do nothing
pass
elif init_strategy == "normal":
self.v_head.summary.weight.data.normal_(mean=0.0, std=initializer_range)
self.v_head.summary.bias.data.zero_()
def forward(
self,
input_ids=None,
past_key_values=None,
attention_mask=None,
**kwargs,
):
kwargs["past_key_values"] = past_key_values
if self.is_peft_model and self.pretrained_model.active_peft_config.peft_type == "PREFIX_TUNING":
kwargs.pop("past_key_values")
base_model_output = self.pretrained_model(
input_ids=input_ids,
attention_mask=attention_mask,
output_hidden_states=True, # We force the model to output hidden states
**kwargs,
)
last_hidden_state = base_model_output.decoder_hidden_states[-1]
lm_logits = base_model_output.logits
loss = base_model_output.loss
value = self.v_head(last_hidden_state).squeeze(-1)
# force upcast in fp32 if logits are in half-precision
if lm_logits.dtype != torch.float32:
lm_logits = lm_logits.float()
return (lm_logits, loss, value)
def generate(self, *args, **kwargs):
r"""
We call `generate` on the wrapped model.
"""
return self.pretrained_model.generate(*args, **kwargs)
| 0 |
hf_public_repos/trl/trl | hf_public_repos/trl/trl/models/modeling_base.py | # Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import logging
import os
from copy import deepcopy
import torch
import torch.nn as nn
from accelerate import PartialState
from huggingface_hub import hf_hub_download
from huggingface_hub.utils import EntryNotFoundError, HFValidationError, LocalEntryNotFoundError
from safetensors.torch import load_file as safe_load_file
from transformers import PreTrainedModel
from ..import_utils import is_peft_available, is_transformers_greater_than, is_xpu_available
if is_peft_available():
from peft import (
PeftConfig,
PeftModel,
PeftModelForCausalLM,
PeftModelForSeq2SeqLM,
PromptLearningConfig,
get_peft_model,
prepare_model_for_kbit_training,
)
if is_transformers_greater_than("4.33.0"):
from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
else:
from transformers.deepspeed import is_deepspeed_zero3_enabled
LAYER_PATTERNS = [
"transformer.h.{layer}",
"model.decoder.layers.{layer}",
"gpt_neox.layers.{layer}",
"model.layers.{layer}",
]
class PreTrainedModelWrapper(nn.Module):
r"""
A wrapper class around a (`transformers.PreTrainedModel`) to be compatible with the
(`~transformers.PreTrained`) class in order to keep some attributes and methods of the
(`~transformers.PreTrainedModel`) class.
Attributes:
pretrained_model: (`transformers.PreTrainedModel`)
The model to be wrapped.
parent_class: (`transformers.PreTrainedModel`)
The parent class of the model to be wrapped.
supported_args: (`list`)
The list of arguments that are supported by the wrapper class.
"""
transformers_parent_class = None
supported_args = None
supported_modules = ("v_head",)
supported_rm_modules = ("score",)
supported_pretrained_model_architectures = (
(PreTrainedModel)
if not is_peft_available()
else (PreTrainedModel, PeftModelForCausalLM, PeftModelForSeq2SeqLM)
)
def __init__(
self, pretrained_model=None, score_module=None, supports_rm_adapter=False, rm_adapter_name=None, **kwargs
):
super().__init__()
self.pretrained_model = pretrained_model
self.config = pretrained_model.config
self.prepare_inputs_for_generation = pretrained_model.prepare_inputs_for_generation
self.is_loaded_in_8bit = getattr(pretrained_model, "is_loaded_in_8bit", False)
self.is_loaded_in_4bit = getattr(pretrained_model, "is_loaded_in_4bit", False)
self.is_sequential_parallel = False
if hasattr(pretrained_model, "gradient_checkpointing_disable"):
self.gradient_checkpointing_disable = pretrained_model.gradient_checkpointing_disable
if hasattr(pretrained_model, "gradient_checkpointing_enable"):
self.gradient_checkpointing_enable = pretrained_model.gradient_checkpointing_enable
self.supports_rm_adapter = supports_rm_adapter
self.rm_adapter_name = rm_adapter_name
self.policy_adapter_name = "default"
if score_module is not None:
self.score = score_module
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r"""
Instantiates a new model from a pretrained model from `transformers`. The
pretrained model is loaded using the `from_pretrained` method of the
`transformers.PreTrainedModel` class. The arguments that are specific to the
`transformers.PreTrainedModel` class are passed along this method and filtered
out from the `kwargs` argument.
Args:
pretrained_model_name_or_path (`str` or `transformers.PreTrainedModel`):
The path to the pretrained model or its name.
*model_args (`list`, *optional*)):
Additional positional arguments passed along to the underlying model's
`from_pretrained` method.
**kwargs (`dict`, *optional*):
Additional keyword arguments passed along to the underlying model's
`from_pretrained` method. We also pre-process the kwargs to extract
the arguments that are specific to the `transformers.PreTrainedModel`
class and the arguments that are specific to trl models. The kwargs
also support `prepare_model_for_kbit_training` arguments from
`peft` library.
"""
if kwargs is not None:
peft_config = kwargs.pop("peft_config", None)
reward_adapter = kwargs.pop("reward_adapter", None)
reward_adapter_name = kwargs.pop("reward_adapter_name", "reward_adapter")
is_trainable = kwargs.pop("is_trainable", False)
trl_model_args, pretrained_kwargs, peft_quantization_kwargs = cls._split_kwargs(kwargs)
token = pretrained_kwargs.get("token", None)
else:
peft_config = None
is_trainable = False
trl_model_args = {}
pretrained_kwargs = {}
peft_quantization_kwargs = {}
token = None
if reward_adapter is not None and not isinstance(reward_adapter, str):
raise ValueError(
"The `reward_adapter` argument should be a string representing the name of local path or the Hub id to the Reward Modeling adapter."
)
is_peft_model = False
current_device = cls._get_current_device()
if isinstance(pretrained_model_name_or_path, str):
is_loaded_in_8bit = pretrained_kwargs["load_in_8bit"] if "load_in_8bit" in pretrained_kwargs else False
is_loaded_in_4bit = pretrained_kwargs["load_in_4bit"] if "load_in_4bit" in pretrained_kwargs else False
else:
is_loaded_in_8bit = getattr(pretrained_model_name_or_path, "is_loaded_in_8bit", False)
is_loaded_in_4bit = getattr(pretrained_model_name_or_path, "is_loaded_in_4bit", False)
if (is_loaded_in_8bit or is_loaded_in_4bit) and "device_map" not in pretrained_kwargs:
# warn users
logging.warning(
"The `device_map` argument is not provided. We will override the device_map argument."
" to set the entire"
" model on the current device. If you want to set the model on multiple devices, please provide"
" a custom `device_map` argument."
)
pretrained_kwargs["device_map"] = {"": current_device}
if is_peft_available() and peft_config is not None and not isinstance(peft_config, PeftConfig):
raise ValueError("The `peft_config` argument should be an instance of `peft.PeftConfig` class.")
# First, load the pre-trained model using the parent-class
# either `AutoModelForCausalLM` or `AutoModelForSeq2SeqLM`
if isinstance(pretrained_model_name_or_path, str):
if is_peft_available():
try:
# If there is a trained peft adapter in the hub, load its config.
remote_adapter_config = hf_hub_download(
pretrained_model_name_or_path,
"adapter_config.json",
token=token,
)
except (EntryNotFoundError, LocalEntryNotFoundError, HFValidationError):
remote_adapter_config = None
else:
remote_adapter_config = None
local_adapter_present = os.path.exists(os.path.join(pretrained_model_name_or_path, "adapter_config.json"))
if (local_adapter_present or remote_adapter_config is not None) and is_peft_available():
if peft_config is not None:
logging.warning(
"`peft_config` argument ignored since a peft config file was found in "
f"{pretrained_model_name_or_path}"
)
# Load the trained peft adapter config
if local_adapter_present:
trained_adapter_config = PeftConfig.from_pretrained(pretrained_model_name_or_path)
else:
remote_adapter_dir = os.path.dirname(remote_adapter_config)
trained_adapter_config = PeftConfig.from_pretrained(remote_adapter_dir)
# Load the pretrained base model
pretrained_model = cls.transformers_parent_class.from_pretrained(
trained_adapter_config.base_model_name_or_path, *model_args, **pretrained_kwargs
)
# Wrap the pretrained model with the trained peft adapter
pretrained_model = PeftModel.from_pretrained(
pretrained_model, pretrained_model_name_or_path, is_trainable=is_trainable
)
logging.info("Trained peft adapter loaded")
else:
pretrained_model = cls.transformers_parent_class.from_pretrained(
pretrained_model_name_or_path, *model_args, **pretrained_kwargs
)
if peft_config is not None:
# Initialize a new peft adapter with the given config
if is_loaded_in_8bit or is_loaded_in_4bit:
pretrained_model = prepare_model_for_kbit_training(
pretrained_model,
**peft_quantization_kwargs,
)
pretrained_model = get_peft_model(pretrained_model, peft_config)
logging.info("peft adapter initialised")
elif isinstance(pretrained_model_name_or_path, cls.supported_pretrained_model_architectures):
pretrained_model = pretrained_model_name_or_path
if peft_config is not None and isinstance(pretrained_model, PreTrainedModel):
# Initialize a new peft adapter with the given config
if is_loaded_in_8bit or is_loaded_in_4bit:
pretrained_model = prepare_model_for_kbit_training(
pretrained_model,
**peft_quantization_kwargs,
)
pretrained_model = get_peft_model(pretrained_model, peft_config)
logging.info("peft adapter initialised")
else:
raise ValueError(
"pretrained_model_name_or_path should be a string or a PreTrainedModel, "
f"but is {type(pretrained_model_name_or_path)}"
)
if is_peft_available():
if isinstance(pretrained_model, PeftModel):
is_peft_model = True
# for backward compatibility
if hasattr(pretrained_model, "active_peft_config") and isinstance(
pretrained_model.active_peft_config, PromptLearningConfig
):
raise ValueError("PromptLearningConfig is not supported for PPO training.")
# Add reward modeling adapter if specified
if not is_peft_model and reward_adapter is not None:
raise ValueError("reward_adapter can only be used with a PeftModel. ")
elif is_peft_model and reward_adapter is not None:
score_module = cls.add_and_load_reward_modeling_adapter(
pretrained_model, reward_adapter, reward_adapter_name, token=token
)
multi_adapter_args = {
"score_module": score_module,
"supports_rm_adapter": True,
"rm_adapter_name": reward_adapter_name,
}
else:
multi_adapter_args = {"supports_rm_adapter": False}
# Then, create the full model by instantiating the wrapper class
model = cls(pretrained_model, **multi_adapter_args, **trl_model_args)
# if resume_training, load the state_dict again - this is ok since the
# state_dict is removed from the model after loading it.
is_resuming_training = True
if isinstance(pretrained_model_name_or_path, str):
safe_filename = os.path.join(pretrained_model_name_or_path, "model.safetensors")
filename = os.path.join(pretrained_model_name_or_path, "pytorch_model.bin")
sharded_index_filename = os.path.join(pretrained_model_name_or_path, "pytorch_model.bin.index.json")
safe_sharded_index_filename = os.path.join(pretrained_model_name_or_path, "model.safetensors.index.json")
is_sharded = False
use_safe = os.path.exists(safe_filename)
if not (os.path.exists(filename) or os.path.exists(safe_filename)):
# Try with `pytorch_model.bin`
filename, files_to_download, is_sharded, is_resuming_training = cls._get_checkpoint_from_hub(
pretrained_model,
pretrained_model_name_or_path,
sharded_index_filename,
token=token,
)
# Try with safetensors
if filename is None and files_to_download is None:
safe_filename, files_to_download, is_sharded, is_resuming_training = cls._get_checkpoint_from_hub(
pretrained_model,
pretrained_model_name_or_path,
safe_sharded_index_filename,
token=token,
model_name="model.safetensors",
model_index_name="model.safetensors.index.json",
)
use_safe = True
else:
use_safe = False
loading_func = safe_load_file if use_safe else torch.load
load_kwargs = {} if use_safe else {"map_location": "cpu"}
if is_resuming_training:
if is_sharded:
# download each file and add it to the state_dict
state_dict = {}
for shard_file in files_to_download:
filename = hf_hub_download(
pretrained_model_name_or_path,
shard_file,
token=token,
)
state_dict.update(loading_func(filename, **load_kwargs))
else:
state_dict = loading_func(filename if not use_safe else safe_filename, **load_kwargs)
else:
state_dict = pretrained_model_name_or_path.state_dict()
model.is_peft_model = is_peft_model
model.current_device = current_device
if is_resuming_training:
model.post_init(state_dict=state_dict)
return model
@classmethod
def _get_checkpoint_from_hub(
cls,
pretrained_model,
pretrained_model_name_or_path,
index_filename,
token=None,
model_name="pytorch_model.bin",
model_index_name="pytorch_model.bin.index.json",
):
files_to_download = None
filename = None
is_resuming_training = True
is_sharded = False
try:
filename = hf_hub_download(
pretrained_model_name_or_path,
model_name,
token=token,
)
# sharded
except (EntryNotFoundError, LocalEntryNotFoundError, HFValidationError):
if os.path.exists(index_filename):
index_file_name = index_filename
else:
try:
index_file_name = hf_hub_download(
pretrained_model_name_or_path,
model_index_name,
token=token,
)
except (EntryNotFoundError, LocalEntryNotFoundError, HFValidationError):
# not continue training, do not have v_head weight
is_resuming_training = False
logging.warning(
f"A {type(pretrained_model)} model is loaded from '{pretrained_model_name_or_path}', "
f"and no v_head weight is found. This IS expected if you are not resuming PPO training."
)
# load json
if is_resuming_training:
with open(index_file_name, "r") as f:
index = json.load(f)
# check filename with `v_head` or any known extra module:
files_to_download = set()
for k, v in index["weight_map"].items():
if any([module in k for module in cls.supported_modules]):
files_to_download.add(v)
is_sharded = True
return filename, files_to_download, is_sharded, is_resuming_training
@classmethod
def _get_current_device(cls):
r"""
Get the current device. For GPU, we return the local process index using the `accelerate.PartialState`
object to handle corner cases when running scripts in distributed environments.
Returns:
current_device (`Union[int, str]`):
The current device.
"""
state = PartialState()
if is_xpu_available():
return f"xpu:{state.local_process_index}"
else:
return state.local_process_index if torch.cuda.is_available() else "cpu"
@classmethod
def _split_kwargs(cls, kwargs):
"""
Separate the kwargs from the arguments that we support inside
`supported_args` and the ones that we don't.
"""
check_peft_kwargs = False
if is_peft_available():
from peft import prepare_model_for_kbit_training
check_peft_kwargs = True
supported_kwargs = {}
unsupported_kwargs = {}
peft_kwargs = {}
for key, value in kwargs.items():
if key in cls.supported_args:
supported_kwargs[key] = value
else:
unsupported_kwargs[key] = value
if check_peft_kwargs:
if key in prepare_model_for_kbit_training.__code__.co_varnames:
peft_kwargs[key] = value
if key in unsupported_kwargs:
unsupported_kwargs.pop(key)
return supported_kwargs, unsupported_kwargs, peft_kwargs
@classmethod
def add_and_load_reward_modeling_adapter(
cls, pretrained_model, adapter_model_id, adapter_name="reward_model_adapter", token=None
):
r"""
Add and load a reward modeling adapter. This method can only be used if the
model is a `PeftModel` and if you have initialized the model with the `reward_modeling_adapter_id`
argument, pointing to the id of the reward modeling adapter. The latest needs also to contain the
score head in order to produce the reward.
"""
pretrained_model.load_adapter(adapter_model_id, adapter_name, is_trainable=False)
pretrained_model.train()
filename = os.path.join(adapter_model_id, "adapter_model.bin")
safe_loading = False
if not os.path.exists(filename):
try:
local_filename = hf_hub_download(
adapter_model_id,
"adapter_model.bin",
token=token,
)
except: # noqa
filename = os.path.join(adapter_model_id, "adapter_model.safetensors")
safe_loading = True
if not os.path.exists(filename):
try:
local_filename = hf_hub_download(
adapter_model_id,
"adapter_model.safetensors",
token=token,
)
except: # noqa
raise ValueError(
"Could not find adapter model in the Hub, make sure you have the correct adapter model id."
)
else:
local_filename = filename
else:
local_filename = filename
loading_func = safe_load_file if safe_loading else torch.load
load_kwargs = {} if safe_loading else {"map_location": "cpu"}
adapter_state_dict = loading_func(local_filename, **load_kwargs)
for score_name_candidate in cls.supported_rm_modules:
if any([score_name_candidate in name for name in adapter_state_dict.keys()]):
score_name = score_name_candidate
# we have found the correct head name and can break
break
score_dict = {}
for name, param in adapter_state_dict.items():
if score_name in name:
key_name = ".".join(name.split(".")[-1:])
score_dict[key_name] = param.to(cls._get_current_device())
num_labels, hidden_dim = score_dict["weight"].shape
has_bias = any(["bias" in name for name in adapter_state_dict.keys()])
score = nn.Linear(hidden_dim, num_labels, bias=has_bias).to(
device=cls._get_current_device(),
dtype=pretrained_model.dtype,
)
score.load_state_dict(score_dict)
for param in score.parameters():
param.requires_grad = False
return score
def push_to_hub(self, *args, **kwargs):
r"""
Push the pretrained model to the hub. This method is a wrapper around
`transformers.PreTrainedModel.push_to_hub`. Please refer to the documentation
of `transformers.PreTrainedModel.push_to_hub` for more information.
Args:
*args (`list`, *optional*):
Positional arguments passed along to the underlying model's
`push_to_hub` method.
**kwargs (`dict`, *optional*):
Keyword arguments passed along to the underlying model's
`push_to_hub` method.
"""
raise NotImplementedError
def save_pretrained(self, *args, **kwargs):
r"""
Save the pretrained model to a directory. This method is a wrapper around
`transformers.PreTrainedModel.save_pretrained`. Please refer to the documentation
of `transformers.PreTrainedModel.save_pretrained` for more information.
Args:
*args (`list`, *optional*):
Positional arguments passed along to the underlying model's
`save_pretrained` method.
**kwargs (`dict`, *optional*):
Keyword arguments passed along to the underlying model's
`save_pretrained` method.
"""
state_dict = kwargs.get("state_dict")
if state_dict is None:
state_dict = self.state_dict()
kwargs["state_dict"] = state_dict
# if it is a peft model only save the `v_head` state_dict and
# pop the `state_dict` from the kwargs to avoid slient bugs with `peft`
if self.is_peft_model:
save_path = args[0]
save_path = os.path.join(save_path, "pytorch_model.bin")
torch.save(state_dict, save_path)
_ = kwargs.pop("state_dict", None)
return self.pretrained_model.save_pretrained(*args, **kwargs)
def state_dict(self, *args, **kwargs):
r"""
Return the state_dict of the pretrained model.
"""
raise NotImplementedError
def post_init(self, *args, **kwargs):
r"""
Post initialization method. This method is called after the model is
instantiated and loaded from a checkpoint. It can be used to perform
additional operations such as loading the state_dict.
"""
raise NotImplementedError
def compute_reward_score(self, input_ids, attention_mask=None, **kwargs):
r"""
Computes the reward score for a given input. The method has first to enable the adapter
and then compute the reward score. After that the model disables the reward modeling
adapter and enables the default ppo adapter again.
"""
if not self.supports_rm_adapter:
raise ValueError("This model does not support reward modeling adapter.")
# enable rm adapter
self.pretrained_model.set_adapter(self.rm_adapter_name)
self.pretrained_model.eval()
with torch.no_grad():
base_model_output = self.pretrained_model(
input_ids=input_ids,
attention_mask=attention_mask,
output_hidden_states=True,
return_dict=True,
**kwargs,
)
last_hidden_states = base_model_output.hidden_states[-1]
scores = self.score(last_hidden_states)
self.pretrained_model.set_adapter(self.policy_adapter_name)
self.pretrained_model.eval()
return scores
def create_reference_model(
model: PreTrainedModelWrapper, num_shared_layers: int = None, pattern: str = None
) -> PreTrainedModelWrapper:
"""
Creates a static reference copy of a model. Note that model will be in `.eval()` mode.
Args:
model (`PreTrainedModelWrapper`): The model to be copied.
num_shared_layers (`int`, *optional*): The number of initial layers that are shared between both models and kept frozen.
pattern (`str`, *optional*): The shared layers are selected with a string pattern
(e.g. "transformer.h.{layer}" for GPT2) and if a custom pattern is necessary it can be passed here.
Returns
`PreTrainedModelWrapper`
"""
if is_deepspeed_zero3_enabled():
raise ValueError(
"DeepSpeed ZeRO-3 is enabled and is not compatible with `create_reference_model()`. Please instantiate your reference model directly with `AutoCausalLM.from_pretrained()`."
)
parameter_names = [n for n, _ in model.named_parameters()]
ref_model = deepcopy(model)
# if no layers are shared, return copy of model
if num_shared_layers is None:
for param_name in parameter_names:
param = ref_model.get_parameter(param_name)
param.requires_grad = False
return ref_model.eval()
# identify layer name pattern
if pattern is not None:
pattern = pattern.format(layer=num_shared_layers)
else:
for pattern_candidate in LAYER_PATTERNS:
pattern_candidate = pattern_candidate.format(layer=num_shared_layers)
if any([pattern_candidate in name for name in parameter_names]):
pattern = pattern_candidate
break
if pattern is None:
raise ValueError("Layer pattern could not be matched.")
# divide parameters in shared and unshared parameter lists
shared_param_list = []
unshared_param_list = []
shared_parameter = True
for name, param in model.named_parameters():
if pattern in name:
shared_parameter = False
if shared_parameter:
shared_param_list.append(name)
else:
unshared_param_list.append(name)
# create reference of the original parameter if they are shared
for param_name in shared_param_list:
param = model.get_parameter(param_name)
param.requires_grad = False
ref_param = ref_model.get_parameter(param_name) # noqa
ref_param = param # noqa
# for all other parameters just make sure they don't use gradients
for param_name in unshared_param_list:
param = ref_model.get_parameter(param_name)
param.requires_grad = False
if pattern is not None and len(unshared_param_list) == 0:
logging.warning("Pattern passed or found, but no layers matched in the model. Check for a typo.")
return ref_model.eval()
| 0 |
hf_public_repos/trl/trl | hf_public_repos/trl/trl/environment/__init__.py | # flake8: noqa
from .base_environment import TextEnvironment, TextHistory
| 0 |
hf_public_repos/trl/trl | hf_public_repos/trl/trl/environment/base_environment.py | # Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
import warnings
import torch
from accelerate.utils import extract_model_from_parallel
from transformers import StoppingCriteria, StoppingCriteriaList
from ..import_utils import is_rich_available
if is_rich_available():
from rich import print
from rich.text import Text
class StringStoppingCriteria(StoppingCriteria):
"""Custom `StoppingCriteria` which checks if all generations in the batch are completed."""
def __init__(self, stop_strings, tokenizer):
self.stop_strings = stop_strings
self.tokenizer = tokenizer
self.first_call = True
def __call__(self, input_ids, scores, **kwargs):
"""Returns true if all generated sequences contain any of the stop strings."""
if self.first_call:
self.generated_tokens = [1 for _ in range(input_ids.shape[0])]
self.start_length = input_ids.shape[-1] - 1
self.first_call = False
decoded_generations = self.tokenizer.batch_decode(input_ids[:, self.start_length :])
done = []
for i, decoded_generation in enumerate(decoded_generations):
sequence_complete = any([stop_string in decoded_generation for stop_string in self.stop_strings])
done.append(sequence_complete)
if not sequence_complete:
self.generated_tokens[i] += 1
if all(done):
self.first_call = True
return all(done)
class TextHistory:
"""The TextHistory class keeps track of the history of an interaction between the language model and the environment."""
def __init__(self, text, tokens, system=True):
"""
Initialize TextHistory.
args:
text (`str`): The text of the first segment.
tokens (`torch.LongTensor`): The tokens of the first segment.
system (`bool`, *optional*): Whether the first segment is a system or user segment.
"""
self.system_spans = []
self.text_spans = []
self.token_spans = []
self.token_masks = torch.tensor([], dtype=torch.long).to(tokens.device)
self.text = ""
self.tokens = torch.tensor([], dtype=torch.long).to(tokens.device)
self.completed = False
self.truncated = False
self.reward = 0.0
self.prompt_color = "black on grey85"
self.system_color = "black on cyan3"
self.model_color = "black on deep_sky_blue1"
self.reward_color = "black on plum1"
self.append_segment(text, tokens, system=system)
def append_segment(self, text, tokens, system=True):
"""
Append a new segment to the history.
args:
text (`str`): The text of the new segment.
tokens (`torch.LongTensor`): The tokens of the new segment.
system (`bool`, *optional*): Whether the new segment is a system or user segment.
"""
if len(text) == 0 or len(tokens) == 0:
raise ValueError("Can't append empty text or token list to history.")
original_text_length = len(self.text)
self.text += text
self.text_spans.append((original_text_length, len(self.text)))
self.system_spans.append(system)
original_token_length = len(self.tokens)
self.tokens = torch.cat((self.tokens, tokens))
if system:
self.token_masks = torch.cat((self.token_masks, torch.zeros_like(tokens)))
else:
self.token_masks = torch.cat((self.token_masks, torch.ones_like(tokens)))
self.token_spans.append((original_token_length, len(self.tokens)))
def complete(self, truncated=False):
"""
Mark the history as completed.
"""
self.completed = True
self.truncated = truncated
@property
def last_text_segment(self):
"""
Get the last text segment.
"""
start, end = self.text_spans[-1]
return self.text[start:end]
def split_query_response_tokens(self):
"""
Split the tokens into query and response tokens.
"""
split_index = self.token_spans[0][1]
query = self.tokens[:split_index]
response = self.tokens[split_index:]
mask = self.token_masks[split_index:]
return query, response, mask
def show_text(self, show_legend=False):
"""
Print the text history.
"""
if not is_rich_available():
warnings.warn("install rich to display text")
return
text = Text(self.text)
text.stylize(self.prompt_color, self.text_spans[0][0], self.text_spans[1][0])
for i, (start, end) in enumerate(self.text_spans[1:]):
if self.system_spans[i + 1]:
text.stylize(self.system_color, start, end)
else:
text.stylize(self.model_color, start, end)
text.append(f"\n\nReward: {self.reward}", style=self.reward_color)
print(text)
if show_legend:
self.show_colour_legend()
def show_tokens(self, tokenizer, show_legend=False):
"""
Print the history tokens.
"""
if not is_rich_available():
warnings.warn("install rich to display tokens")
return
text = Text()
prompt_end = self.token_spans[0][1]
for i, (token, mask) in enumerate(zip(self.tokens, self.token_masks)):
if i < prompt_end:
text.append(tokenizer.convert_ids_to_tokens(token.item()), style=self.prompt_color)
text.append(" ")
elif mask == 0:
text.append(tokenizer.convert_ids_to_tokens(token.item()), style=self.system_color)
text.append(" ")
else:
text.append(tokenizer.convert_ids_to_tokens(token.item()), style=self.model_color)
text.append(" ")
text.append(f"\n\nReward: {self.reward}", style=self.reward_color)
print(text)
if show_legend:
self.show_colour_legend()
def show_colour_legend(self):
"""
Print the colour legend.
"""
if not is_rich_available():
warnings.warn("install rich to display colour legend")
return
text = Text("\n\n(Colour Legend: ")
text.append("Prompt", style=self.prompt_color)
text.append("|")
text.append("System", style=self.system_color)
text.append("|")
text.append("Model", style=self.model_color)
text.append("|")
text.append("Reward", style=self.reward_color)
text.append(")")
print(text)
class TextEnvironment:
"""
The TextEnvironment enables interaction of a LLM with an environment using tools.
"""
def __init__(
self,
model=None,
tokenizer=None,
tools=None,
reward_fn=None,
prompt=None,
max_turns=4,
max_tool_reponse=100,
max_length=None,
generation_kwargs=None,
):
"""
Initialize TextEnvironment.
Args:
model (`PreTrainedModelWrapper`): The model to use for generation.
tokenizer (`transformers.PreTrainedTokenizer`): The tokenizer to use for generation.
tools (list): A list of tools to use for interaction.
reward_fn (function): A function that takes a string and returns a reward.
prompt (str): The base prompt to use for generation. Is prepended to the tasks.
max_turns (Optional[int]): The maximum number of turns to allow.
max_tool_response (Optional[int]): The maximum number of characters to allow in a tool response.
max_length (Optional[int]): The maximum number of tokens to allow in an episode.
generation_kwargs (Optional[dict]): A dictionary of keyword arguments to pass to the model's generate method.
"""
self.model = model
self.tokenizer = tokenizer
self.prompt = prompt
if isinstance(tools, dict):
self.tools = tools
else:
self.tools = dict([(tool.__class__.__name__, tool) for tool in tools])
self.reward_fn = reward_fn
self.max_length = max_length
self.request_token = "<request>"
self.call_token = "<call>"
self.response_token = "<response>"
self.submit_token = "<submit>"
self.max_turns = max_turns
self.max_tool_response = max_tool_reponse
if generation_kwargs is None:
self.generation_kwargs = dict()
else:
self.generation_kwargs = generation_kwargs
self.is_encoder_decoder = hasattr(self.model, "is_encoder_decoder")
self.current_device = extract_model_from_parallel(self.model).pretrained_model.device
def run(self, queries, **rewards_kwargs):
"""
Run the environment on a list of queries.
Args:
queries (list[str]): A list of queries to run the model in the environment on.
"""
turns = 0
queries = [self.prompt + task for task in queries]
queries_tokens = [
self.tokenizer(query, return_tensors="pt").input_ids[0].to(self.model.pretrained_model.device)
for query in queries
]
histories = [TextHistory(q, qt, system=True) for q, qt in zip(queries, queries_tokens)]
while any([not history.completed for history in histories]) and turns < self.max_turns:
histories = self.generate(histories)
histories = self.tasks_end_check(histories)
# TODO: make this parallel rather than for-loop
for i in range(len(histories)):
histories[i] = self.step(histories[i])
histories = self.tasks_end_check(histories, model_turn=False)
turns += 1
self.compute_reward(histories, **rewards_kwargs)
# convert a list of (q, r, m) tuples to lists of all qs, rs, and ms respectively
queries, responses, masks = map(list, zip(*[history.split_query_response_tokens() for history in histories]))
rewards = [history.reward for history in histories]
return queries, responses, masks, rewards, histories
def step(self, history):
"""
Step the environment forward one turn.
Args:
history (`TextHistory`): The history to step forward.
"""
truncated, ended = self.task_end_check(history)
if ended:
history.complete(truncated=truncated)
if history.completed:
return history
tool, query = self.parse_tool_call(history.last_text_segment)
if tool is None or query is None:
response = f"Unknown tool call: {history.last_text_segment}"
else:
if tool not in self.tools:
response = f"Unknown tool {tool}."
try:
response = self.tools[tool](query)
except Exception as error:
response = f"Tool error: {str(error)}"
if len(response) > self.max_tool_response:
response = response[: (self.max_tool_response - 3)] + "..."
history.append_segment(
response + self.response_token,
self.tokenizer(response + self.response_token, return_tensors="pt")
.input_ids[0]
.to(self.model.pretrained_model.device),
system=True,
)
return history
def parse_tool_call(self, text):
"""
Parse request string. Expected format: <request><tool_name>query<call>
"""
result = re.search(f"(?<={self.request_token}).*?(?={self.call_token})", text, re.DOTALL)
# if we can't find a <request>/<call> span we return none
if result is None:
return None, None
else:
extracted_text = result.group()
result = re.search(r"<(.*?)>", extracted_text)
# if we can't find a tool name we return none
if result is None:
return None, None
else:
tool = result.group(1)
# split off the tool name
query = ">".join(extracted_text.split(">")[1:])
return tool, query
def compute_reward(self, histories, **reward_kwargs):
"""
Compute the reward for a list of histories.
"""
rewards = self.reward_fn([history.last_text_segment for history in histories], **reward_kwargs)
for history, reward in zip(histories, rewards):
history.reward = reward
return histories
def generate(self, histories):
"""
Generate responses for a list of histories.
"""
active_histories = [i for i, history in enumerate(histories) if not history.completed]
query_tensors = [histories[i].tokens for i in active_histories]
response_tensors = self._generate_batched(query_tensors)
response_texts = self.tokenizer.batch_decode(response_tensors)
for i, response_text, response_tensor in zip(active_histories, response_texts, response_tensors):
histories[i].append_segment(response_text, response_tensor, system=False)
return histories
def tasks_end_check(self, histories, model_turn=True):
"""
Check if the current generation sequences have finished.
"""
for history in histories:
if not history.completed:
truncated, ended = self.task_end_check(history, model_turn=model_turn)
if ended:
history.complete(truncated=truncated)
return histories
def task_end_check(self, history, model_turn=True):
"""
Check if the current generation sequence has finished.
"""
truncated = False
ended = False
if history.completed:
return truncated, ended
if self.max_length is not None and len(self.tokenizer(history.text).input_ids[0]) > self.max_length:
truncated = True
ended = True
elif self.tokenizer.eos_token in history.text:
ended = True
elif model_turn and not (
(self.request_token in history.last_text_segment and self.call_token in history.last_text_segment)
or self.submit_token in history.last_text_segment
):
ended = True
elif self.submit_token in history.last_text_segment:
ended = True
return truncated, ended
def _generate_batched(
self,
query_tensors,
batch_size: int = 16,
pad_to_multiple_of: int = None,
):
"""
Generate responses for a list of query tensors.
args:
query_tensors (list[torch.Tensor]): A list of query tensors to generate responses for.
batch_size (int): The batch size to use for generation.
pad_to_multiple_of (int): The padding length to use for generation.
"""
outputs = []
padding_side_default = self.tokenizer.padding_side
if not self.is_encoder_decoder:
self.tokenizer.padding_side = "left"
# in case we have fewer examples than bs
batch_size = min(len(query_tensors), batch_size)
for i in range(0, len(query_tensors), batch_size):
# prevent overflow if query tensors are not even multiple of bs
end_index = min(len(query_tensors), i + batch_size)
batch = query_tensors[i:end_index]
batch_mask = [torch.ones_like(element) for element in batch]
inputs = {"input_ids": batch, "attention_mask": batch_mask}
padded_inputs = self.tokenizer.pad(
inputs,
padding=True,
max_length=None,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors="pt",
).to(self.current_device)
stopping_criteria = StringStoppingCriteria([self.call_token, self.submit_token], self.tokenizer)
self.generation_kwargs["stopping_criteria"] = StoppingCriteriaList([stopping_criteria])
generations = extract_model_from_parallel(self.model).generate(**padded_inputs, **self.generation_kwargs)
for generation, mask, generated_tokens in zip(
generations, padded_inputs["attention_mask"], stopping_criteria.generated_tokens
):
if not self.is_encoder_decoder:
output = generation[(1 - mask).sum() :] # remove padding
else:
output = generation
if not self.is_encoder_decoder:
output = output[(mask).sum() :] # remove prompt
# remove chunk generated after stopping criteria in batch mode
outputs.append(output[:generated_tokens])
self.tokenizer.padding_side = padding_side_default
return outputs
| 0 |
hf_public_repos/trl/trl | hf_public_repos/trl/trl/trainer/reward_trainer.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import warnings
from dataclasses import FrozenInstanceError, replace
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
import torch.nn as nn
from datasets import Dataset
from transformers import DataCollator, PreTrainedModel, PreTrainedTokenizerBase, Trainer, TrainingArguments
from transformers.trainer_callback import TrainerCallback
from transformers.trainer_pt_utils import nested_detach
from transformers.trainer_utils import EvalPrediction
from ..import_utils import is_peft_available
from .training_configs import RewardConfig
from .utils import PeftSavingCallback, RewardDataCollatorWithPadding, compute_accuracy
if is_peft_available():
from peft import PeftModel, get_peft_model, prepare_model_for_kbit_training
class RewardTrainer(Trainer):
r"""
The RewardTrainer can be used to train your custom Reward Model. It is a subclass of the
`transformers.Trainer` class and inherits all of its attributes and methods. It is recommended to use
an `AutoModelForSequenceClassification` as the reward model. The reward model should be trained on a dataset
of paired examples, where each example is a tuple of two sequences. The reward model should be trained to
predict which example in the pair is more relevant to the task at hand.
The reward trainer expects a very specific format for the dataset. The dataset should contain two 4 entries at least
if you don't use the default `RewardDataCollatorWithPadding` data collator. The entries should be named
- `input_ids_chosen`
- `attention_mask_chosen`
- `input_ids_rejected`
- `attention_mask_rejected`
Optionally, you can also pass a `margin` entry to the dataset. This entry should contain the margin used to modulate the
loss of the reward model as outlined in https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/.
If you don't pass a margin, no margin will be used.
"""
def __init__(
self,
model: Union[PreTrainedModel, nn.Module] = None,
args: Optional[RewardConfig] = None,
data_collator: Optional[DataCollator] = None,
train_dataset: Optional[Dataset] = None,
eval_dataset: Optional[Union[Dataset, Dict[str, Dataset]]] = None,
tokenizer: Optional[PreTrainedTokenizerBase] = None,
model_init: Optional[Callable[[], PreTrainedModel]] = None,
compute_metrics: Optional[Callable[[EvalPrediction], Dict]] = None,
callbacks: Optional[List[TrainerCallback]] = None,
optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (
None,
None,
),
preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
max_length: Optional[int] = None,
peft_config: Optional[Dict] = None,
):
"""
Initialize RewardTrainer.
Args:
model (`transformers.PreTrainedModel`):
The model to train, preferably an `AutoModelForSequenceClassification`.
args (`RewardConfig`):
The arguments to use for training.
data_collator (`transformers.DataCollator`):
The data collator to use for training. If None is specified, the default data collator (`RewardDataCollatorWithPadding`) will be used
which will pad the sequences to the maximum length of the sequences in the batch, given a dataset of paired sequences.
train_dataset (`datasets.Dataset`):
The dataset to use for training.
eval_dataset (`datasets.Dataset`):
The dataset to use for evaluation.
tokenizer (`transformers.PreTrainedTokenizerBase`):
The tokenizer to use for training. This argument is required if you want to use the default data collator.
model_init (`Callable[[], transformers.PreTrainedModel]`):
The model initializer to use for training. If None is specified, the default model initializer will be used.
compute_metrics (`Callable[[transformers.EvalPrediction], Dict]`, *optional* defaults to `compute_accuracy`):
The metrics to use for evaluation. If no metrics are specified, the default metric (`compute_accuracy`) will be used.
callbacks (`List[transformers.TrainerCallback]`):
The callbacks to use for training.
optimizers (`Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`):
The optimizer and scheduler to use for training.
preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`):
The function to use to preprocess the logits before computing the metrics.
peft_config (`Dict`, defaults to `None`):
The PEFT configuration to use for training. If you pass a PEFT configuration, the model will be wrapped in a PEFT model.
"""
if type(args) == TrainingArguments:
warnings.warn(
"Using `transformers.TrainingArguments` for `args` is deprecated and will be removed in a future version. Please use `RewardConfig` instead.",
FutureWarning,
)
if max_length is not None:
warnings.warn(
"The `max_length` argument is deprecated and will be removed in a future version. Please use the `RewardConfig` to set `max_length` instead.",
FutureWarning,
)
else:
if max_length is not None and args.max_length is not None:
raise ValueError(
"You cannot specify both `max_length` and `args.max_length`. Please use the `RewardConfig` to set `max_length` once."
)
if max_length is not None and args.max_length is None:
warnings.warn(
"The `max_length` argument is deprecated and will be removed in a future version. Please use the `RewardConfig` to set `max_length` instead.",
FutureWarning,
)
if not is_peft_available() and peft_config is not None:
raise ValueError(
"PEFT is not installed and you passed a `peft_config` in the trainer's kwargs, please install it to use the PEFT models"
)
elif is_peft_available() and peft_config is not None:
if not isinstance(model, PeftModel):
if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_quantized", False):
_supports_gc_kwargs = "gradient_checkpointing_kwargs" in list(
inspect.signature(prepare_model_for_kbit_training).parameters
)
preprare_model_kwargs = {"use_gradient_checkpointing": args.gradient_checkpointing}
if not _supports_gc_kwargs and args.gradient_checkpointing_kwargs is not None:
warnings.warn(
"You passed `gradient_checkpointing_kwargs` in the trainer's kwargs, but your peft version does not support it. "
"please update to the latest version of peft to use `gradient_checkpointing_kwargs`."
)
elif _supports_gc_kwargs and args.gradient_checkpointing_kwargs is not None:
preprare_model_kwargs["gradient_checkpointing_kwargs"] = args.gradient_checkpointing_kwargs
model = prepare_model_for_kbit_training(model, **preprare_model_kwargs)
model = get_peft_model(model, peft_config)
if is_peft_available() and isinstance(model, PeftModel):
if callbacks is None:
callbacks = [PeftSavingCallback()]
else:
callbacks += [PeftSavingCallback()]
if compute_metrics is None:
compute_metrics = compute_accuracy
if data_collator is None:
if tokenizer is None:
raise ValueError(
"max_length or a tokenizer must be specified when using the default RewardDataCollatorWithPadding"
)
if type(args) == TrainingArguments:
if max_length is None:
warnings.warn(
"When using RewardDataCollatorWithPadding, you should set `max_length` in RewardConfig."
" It will be set to `512` by default, but you should do it yourself in the future.",
UserWarning,
)
max_length = 512
else:
if max_length is None and args.max_length is None:
warnings.warn(
"When using RewardDataCollatorWithPadding, you should set `max_length` in RewardConfig."
" It will be set to `512` by default, but you should do it yourself in the future.",
UserWarning,
)
max_length = 512
if max_length is None and args.max_length is not None:
max_length = args.max_length
data_collator = RewardDataCollatorWithPadding(tokenizer, max_length=max_length)
if args.remove_unused_columns:
try: # for bc before https://github.com/huggingface/transformers/pull/25435
args.remove_unused_columns = False
except FrozenInstanceError:
args = replace(args, remove_unused_columns=False)
# warn users
warnings.warn(
"When using RewardDataCollatorWithPadding, you should set `remove_unused_columns=False` in your RewardConfig"
" we have set it for you, but you should do it yourself in the future.",
UserWarning,
)
self.use_reward_data_collator = True
else:
self.use_reward_data_collator = False
super().__init__(
model,
args,
data_collator,
train_dataset,
eval_dataset,
tokenizer,
model_init,
compute_metrics,
callbacks,
optimizers,
preprocess_logits_for_metrics,
)
def compute_loss(
self,
model: Union[PreTrainedModel, nn.Module],
inputs: Dict[str, Union[torch.Tensor, Any]],
return_outputs=False,
) -> Union[torch.Tensor, Tuple[torch.Tensor, Dict[str, torch.Tensor]]]:
if not self.use_reward_data_collator:
warnings.warn(
"The current compute_loss is implemented for RewardDataCollatorWithPadding,"
" if you are using a custom data collator make sure you know what you are doing or"
" implement your own compute_loss method."
)
rewards_chosen = model(
input_ids=inputs["input_ids_chosen"],
attention_mask=inputs["attention_mask_chosen"],
)[0]
rewards_rejected = model(
input_ids=inputs["input_ids_rejected"],
attention_mask=inputs["attention_mask_rejected"],
)[0]
# calculate loss, optionally modulate with margin
if "margin" in inputs:
loss = -nn.functional.logsigmoid(rewards_chosen - rewards_rejected - inputs["margin"]).mean()
else:
loss = -nn.functional.logsigmoid(rewards_chosen - rewards_rejected).mean()
if return_outputs:
return loss, {
"rewards_chosen": rewards_chosen,
"rewards_rejected": rewards_rejected,
}
return loss
def prediction_step(
self,
model: Union[PreTrainedModel, nn.Module],
inputs: Dict[str, Union[torch.Tensor, Any]],
prediction_loss_only: bool,
ignore_keys: Optional[List[str]] = None,
) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]:
inputs = self._prepare_inputs(inputs)
if ignore_keys is None:
if hasattr(self.model, "config"):
ignore_keys = getattr(self.model.config, "keys_to_ignore_at_inference", [])
else:
ignore_keys = []
with torch.no_grad():
loss, logits_dict = self.compute_loss(model, inputs, return_outputs=True)
if prediction_loss_only:
return (loss, None, None)
loss = loss.detach()
logits = tuple(v for k, v in logits_dict.items() if k not in ignore_keys)
logits = nested_detach(logits)
# Stack accepted against rejected, mean over logits
# and softmax to get preferences between accepted and rejected to sum to 1
logits = torch.stack(logits).mean(dim=2).softmax(dim=0).T
labels = torch.zeros(logits.shape[0])
labels = self._prepare_inputs(labels)
return loss, logits, labels
| 0 |
hf_public_repos/trl/trl | hf_public_repos/trl/trl/trainer/iterative_sft_trainer.py | # Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from typing import Callable, Dict, List, Optional, Tuple, Union
import torch
from datasets import Dataset
from torch.utils.data import DataLoader
from transformers import (
DataCollator,
DataCollatorForLanguageModeling,
DataCollatorForSeq2Seq,
PreTrainedModel,
PreTrainedTokenizerBase,
Trainer,
TrainingArguments,
)
from transformers.trainer_utils import EvalLoopOutput
from ..core import PPODecorators
from ..import_utils import is_peft_available
if is_peft_available():
from peft import PeftModel
class IterativeSFTTrainer(Trainer):
"""
The IterativeSFTTrainer can be used to finetune models with methods that requires some steps between optimization.
Attributes:
**model** (`PreTrainedModel`) -- Model to be optimized, either an 'AutoModelForCausalLM' or an 'AutoModelForSeq2SeqLM'.
Check the documentation of `PreTrainedModel` for more details.
**args** (`transformers.TrainingArguments`): -- The arguments to use for training.
**tokenizer** (`PreTrainedTokenizerBase`) -- Tokenizer to be used for encoding the
data. Check the documentation of `transformers.PreTrainedTokenizer` and
`transformers.PreTrainedTokenizerFast` for more details.
**optimizers** (`Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`): -- The optimizer and scheduler to use for training.
**data_collator** (Union[DataCollatorForLanguageModeling, DataCollatorForSeq2Seq], *optional*) -- Data collator to be used for training and
passed along the dataloader.
**eval_dataset** (`datasets.Dataset`): The dataset to use for evaluation.
**max_length** (`int`, defaults to `None`): -- The maximum length of the input.
**truncation_mode** (`str`, defaults to `keep_end`): -- The truncation mode to use, either `keep_end` or `keep_start`.
**preprocess_logits_for_metrics** (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`): -- The function to use to preprocess the logits before computing the metrics.
**compute_metrics** (`Callable[[EvalPrediction], Dict]`, *optional*): -- The function to use to compute the metrics. Must take a `EvalPrediction` and return a dictionary string to metric values.
**optimize_device_cache ** (`bool`, *optional*, defaults to `False`) -- Optimize CUDA cache for slightly more memory-efficient training.
"""
def __init__(
self,
model: PreTrainedModel = None,
args: TrainingArguments = None,
tokenizer: PreTrainedTokenizerBase = None,
optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (
None,
None,
),
data_collator: Optional[DataCollator] = None,
eval_dataset: Optional[Union[Dataset, Dict[str, Dataset]]] = None,
max_length: Optional[int] = None,
truncation_mode: Optional[str] = "keep_end",
preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
compute_metrics: Optional[Callable[[EvalLoopOutput], Dict]] = None,
optimize_device_cache: Optional[bool] = False,
):
# Step 0: check positional arguments validity
if not isinstance(tokenizer, (PreTrainedTokenizerBase)):
raise ValueError(
f"tokenizer must be a PreTrainedTokenizerBase like a PreTrainedTokenizer or a PreTrainedTokenizerFast, got {type(tokenizer)}"
)
if not isinstance(model, PreTrainedModel):
raise ValueError(f"model must be a PreTrainedModel, got {type(model)}")
if not model.can_generate():
warnings.warn(
f"The current model class {type(model)} is not compatible with `.generate()`"
"Please make sure that this is intended."
)
if optimizers[1] is None and args.max_steps == -1:
raise ValueError(
"When no scheduler is provided, you need to set the total number of training steps to perform `max_steps`"
)
self.is_encoder_decoder = getattr(model.config, "is_encoder_decoder", False)
self.is_peft_model = is_peft_available() and isinstance(model, PeftModel)
self.tokenizer = tokenizer
if data_collator is None:
if self.is_encoder_decoder:
warnings.warn(
"No data collator is provided. Using 'DataCollatorForSeq2Seq' with"
"'labels_pad_token_id' set to '-100' and 'pad_to_multiple_of' set to 8."
)
self.data_collator = DataCollatorForSeq2Seq(tokenizer, label_pad_token_id=-100, pad_to_multiple_of=8)
else:
warnings.warn("No data collator is provided. Using 'DataCollatorForLanguageModeling'")
self.data_collator = DataCollatorForLanguageModeling(self.tokenizer, mlm=False)
else:
self.data_collator = data_collator
self.max_length = max_length
self.truncation_mode = truncation_mode
self.optimize_device_cache = optimize_device_cache
super().__init__(
model=model,
args=args,
data_collator=self.data_collator,
eval_dataset=eval_dataset,
tokenizer=tokenizer,
compute_metrics=compute_metrics,
optimizers=optimizers,
preprocess_logits_for_metrics=preprocess_logits_for_metrics,
)
self.create_optimizer_and_scheduler(self.args.max_steps)
# prepare model, optimizer and lr_scheduler
self.model, self.optimizer, self.lr_scheduler = self.accelerator.prepare(
self.model, self.optimizer, self.lr_scheduler
)
self.tokenizer.truncation_side = "left" if self.truncation_mode == "keep_end" else "right"
if not hasattr(self, "accelerator"):
raise AttributeError(
"Your `Trainer` does not have an `accelerator` object. Consider upgrading `transformers`."
)
PPODecorators.optimize_device_cache = self.optimize_device_cache
def prepare_model_inputs(self, input_ids: torch.Tensor, attention_mask: torch.Tensor, labels: torch.Tensor):
if attention_mask is None:
attention_mask = [torch.ones_like(ids) for ids in input_ids]
if self.is_encoder_decoder:
input_data = self.data_collator(
[
{"input_ids": ids, "attention_mask": att, "labels": lab}
for ids, att, lab in zip(input_ids, attention_mask, labels)
]
).to(self.model.device)
input_data.pop("decoder_input_ids", None) # This is directly computed inside the model
input_data["labels"][input_data["labels"] == self.tokenizer.pad_token_id] = -100
else:
input_data = self.data_collator(
[{"input_ids": ids, "attention_mask": att} for ids, att in zip(input_ids, attention_mask)]
).to(self.model.device)
# truncate in case the user has provided input_ids, attention_mask and labels
if self.max_length is not None:
if self.truncation_mode == "keep_start":
input_data = {k: v[: self.max_length] for k, v in input_data.items()}
elif self.truncation_mode == "keep_end":
input_data = {k: v[-self.max_length :] for k, v in input_data.items()}
else:
raise ValueError(f"Unknown truncation mode: {self.truncation_mode}")
return input_data
@staticmethod
def _step_safety_checker(
input_ids: List[torch.LongTensor],
attention_mask: List[torch.LongTensor],
labels: List[torch.LongTensor],
texts: List[str],
texts_labels: List[str],
):
"""
Check if the input data is valid for training.
Args:
input_ids (List[`torch.LongTensor`]):
List of tensors containing the input_ids
attention_mask (List[`torch.LongTensor`]):
List of tensors containing the attention_mask
labels (List[`torch.FloatTensor`]):
List of tensors containing the labels
texts (List[`str`]):
List of string containing the text input.
texts_labels (List[`str`]):
List of string containing the text labels.
Returns:
`tuple`: The input data.
"""
if texts is None:
if attention_mask is None:
for name, tensor_list in zip(["input_ids", "labels"], [input_ids, labels]):
if not isinstance(tensor_list, list):
raise ValueError(f"{name} must be a list of tensors - got {type(tensor_list)}")
if not isinstance(tensor_list[0], torch.Tensor):
raise ValueError(f"Elements in {name} must be tensors - got {type(tensor_list[0])}")
else:
for name, tensor_list in zip(
["input_ids", "attention_mask", "labels"], [input_ids, attention_mask, labels]
):
if not isinstance(tensor_list, list):
raise ValueError(f"{name} must be a list of tensors - got {type(tensor_list)}")
if not isinstance(tensor_list[0], torch.Tensor):
raise ValueError(f"Elements in {name} must be tensors - got {type(tensor_list[0])}")
else:
if not isinstance(texts, list):
raise ValueError(f"'text' must be a list of strings - got {type(texts)}")
if not isinstance(texts[0], str):
raise ValueError(f"Elements in 'text' must be strings - got {type(texts[0])}")
if texts_labels is not None:
if not isinstance(texts_labels, list):
raise ValueError(f"'text_labels' must be a list of strings - got {type(texts_labels)}")
if not isinstance(texts_labels[0], str):
raise ValueError(f"Elements in 'text_labels' must be strings - got {type(texts_labels[0])}")
return input_ids, attention_mask, labels, texts, texts_labels
@PPODecorators.empty_device_cache()
def step(
self,
input_ids: Optional[List[torch.LongTensor]] = None,
attention_mask: Optional[List[torch.LongTensor]] = None,
labels: Optional[List[torch.LongTensor]] = None,
texts: Optional[List[str]] = None,
texts_labels: Optional[List[str]] = None,
):
"""
Run an optimisation step given a list of input_ids, attention_mask, and labels or a list of text and text_labels.
Args:
input_ids (List[`torch.LongTensor`]):
List of tensors containing the input_ids (if not provided, text will be used)
attention_mask (List[`torch.LongTensor`], , *optional*):
List of tensors containing the attention_mask
labels (List[`torch.FloatTensor`], *optional*):
List of tensors containing the labels (if set to None, will default to input_ids)
texts (List[`str`], *optional*):
List of strings containing the text input (if not provided, input_ids will directly be used)
texts_labels (List[`str`], *optional*):
List of strings containing the text labels (if set to None, will default to text)
Returns:
`dict[str, Any]`: A summary of the training statistics
"""
self.model.train()
if self.state.global_step == 0:
self.tr_loss = torch.tensor(0.0).to(self.args.device)
self._globalstep_last_logged = self.state.global_step
if input_ids is None and texts is None:
raise ValueError("Step should include `input_ids` or `texts` as keyword arguments.")
elif input_ids is not None and texts is not None:
warnings.warn(
"Both 'input_ids' and 'texts' are provided. 'input_ids' will be overwritten using inputs provided by the 'texts' keyword argument."
)
if labels is None and texts_labels is None and self.is_encoder_decoder:
raise ValueError(
"No 'labels' or 'text_labels' are provided. When using an encoder-decoder architecture, 'labels' or 'text_labels' must be passed."
)
input_ids, attention_mask, labels, texts, texts_labels = self._step_safety_checker(
input_ids, attention_mask, labels, texts, texts_labels
)
if texts is not None:
model_inputs = self.tokenizer(
texts, max_length=self.max_length, truncation=True, padding=True, return_tensors="pt"
)
input_ids, attention_mask = model_inputs["input_ids"], model_inputs["attention_mask"]
if texts_labels is not None:
labels = self.tokenizer(
texts, max_length=self.max_length, truncation=True, padding=True, return_tensors="pt"
)["input_ids"]
if labels is None:
warnings.warn("No labels are provided. Setting labels to input_ids")
labels = input_ids
model_inputs = self.prepare_model_inputs(input_ids, attention_mask, labels)
model_inputs_names = list(model_inputs.keys())
batch_dict = {}
batch_dict.update(model_inputs)
def collator(data):
return_dict = dict()
for key in data[0]:
if key in ["input_ids", "attention_mask", "labels"]:
return_dict[key] = torch.stack([d[key] for d in data]).to(self.model.device)
return return_dict
batch_data = Dataset.from_dict(batch_dict)
batch_data.set_format("torch")
step_dataloader = DataLoader(
batch_data,
batch_size=self.args.per_device_train_batch_size,
shuffle=True,
collate_fn=collator,
)
for _, batch in enumerate(step_dataloader):
with self.accelerator.accumulate(self.model):
model_inputs = {k: batch[k] for k in model_inputs_names}
loss = self.compute_loss(self.model, model_inputs)
if self.args.n_gpu > 1:
loss = loss.mean()
tr_loss_step = loss.detach()
self.accelerator.backward(loss)
if self.accelerator.sync_gradients and self.args.max_grad_norm is not None:
self.accelerator.clip_grad_norm_(
self.model.parameters(),
self.args.max_grad_norm,
)
self.optimizer.step()
self.optimizer.zero_grad()
if self.lr_scheduler is not None:
self.lr_scheduler.step()
self.state.global_step += 1
# update stats etc
self.tr_loss += tr_loss_step
self._maybe_log_save_evaluate()
def _maybe_log_save_evaluate(self):
# check if eval is required
if self.args.eval_steps is not None:
if self.state.global_step % self.args.eval_steps == 0 and self.state.global_step != 0:
self.evaluate(self.eval_dataset)
# check if logging is required
if self.args.logging_steps is not None:
if self.state.global_step % self.args.logging_steps == 0 and self.state.global_step != 0:
logs: Dict[str, float] = {}
tr_loss_scalar = self._nested_gather(self.tr_loss).mean().item()
# reset tr_loss to zero
self.tr_loss -= self.tr_loss
logs["loss"] = round(tr_loss_scalar / (self.state.global_step - self._globalstep_last_logged), 4)
logs["learning_rate"] = self._get_learning_rate()
self._globalstep_last_logged = self.state.global_step
self.log(logs)
| 0 |
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