Various fixes for CI, save_only_model for RL, prevent packing multiprocessing deadlocks (#2661)

* lean mistral ft tests, remove e2e torch 2.4.1 test

* make sure to pass save_only_model for RL

* more tests to make ci leaner, add cleanup to modal ci

* fix module for import in e2e tests

* use mp spawn to prevent deadlocks with packing

* make sure cleanup shell script is executable when cloned out
This commit is contained in:
Wing Lian
2025-05-12 10:51:18 -04:00
parent 27e3329273
commit cf69de2eb9
13 changed files with 190 additions and 93 deletions

0
cicd/__init__.py Normal file
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@@ -18,7 +18,7 @@ pytest -v --durations=10 \
--cov-append
# Run patched tests excluding lora kernels with coverage append
pytest -v --durations=10 \
pytest --full-trace -vvv --durations=10 \
--ignore=tests/e2e/patched/lora_kernels \
/workspace/axolotl/tests/e2e/patched \
--cov=axolotl \

19
cicd/cleanup.py Normal file
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@@ -0,0 +1,19 @@
"""Modal app to run axolotl GPU cleanup"""
from .single_gpu import VOLUME_CONFIG, app, cicd_image, run_cmd
@app.function(
image=cicd_image,
timeout=60 * 60,
cpu=8.0,
memory=131072,
volumes=VOLUME_CONFIG,
)
def cleanup():
run_cmd("./cicd/cleanup.sh", "/workspace/axolotl")
@app.local_entrypoint()
def main():
cleanup.remote()

6
cicd/cleanup.sh Executable file
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@@ -0,0 +1,6 @@
#!/bin/bash
set -e
# cleanup old cache files for datasets processing and intermediate mappings
find /workspace/data/huggingface-cache/hub/datasets -name "cache-*" -type f -mtime +1 -exec rm {} \;
find /workspace/data/huggingface-cache/hub/datasets -name "*.lock" -type f -mtime +1 -exec rm {} \;

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@@ -1,69 +1,6 @@
"""Modal app to run axolotl GPU tests"""
# pylint: disable=duplicate-code
import os
import pathlib
import tempfile
import jinja2
import modal
from jinja2 import select_autoescape
from modal import App, Image
cicd_path = pathlib.Path(__file__).parent.resolve()
template_loader = jinja2.FileSystemLoader(searchpath=cicd_path)
template_env = jinja2.Environment(
loader=template_loader, autoescape=select_autoescape()
)
df_template = template_env.get_template("Dockerfile.jinja")
df_args = {
"AXOLOTL_EXTRAS": os.environ.get("AXOLOTL_EXTRAS", ""),
"AXOLOTL_ARGS": os.environ.get("AXOLOTL_ARGS", ""),
"PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION", "2.4.1"),
"BASE_TAG": os.environ.get("BASE_TAG", "main-base-py3.11-cu121-2.4.1"),
"CUDA": os.environ.get("CUDA", "121"),
"GITHUB_REF": os.environ.get("GITHUB_REF", "refs/heads/main"),
"GITHUB_SHA": os.environ.get("GITHUB_SHA", ""),
"NIGHTLY_BUILD": os.environ.get("NIGHTLY_BUILD", ""),
"CODECOV_TOKEN": os.environ.get("CODECOV_TOKEN", ""),
"HF_HOME": "/workspace/data/huggingface-cache/hub",
}
dockerfile_contents = df_template.render(**df_args)
temp_dir = tempfile.mkdtemp()
with open(pathlib.Path(temp_dir) / "Dockerfile", "w", encoding="utf-8") as f:
f.write(dockerfile_contents)
cicd_image = Image.from_dockerfile(
pathlib.Path(temp_dir) / "Dockerfile",
context_mount=None,
force_build=True,
gpu="A10G",
).env(df_args)
app = App("Axolotl CI/CD", secrets=[])
hf_cache_volume = modal.Volume.from_name(
"axolotl-ci-hf-hub-cache", create_if_missing=True
)
VOLUME_CONFIG = {
"/workspace/data/huggingface-cache/hub": hf_cache_volume,
}
N_GPUS = int(os.environ.get("N_GPUS", 1))
GPU_CONFIG = modal.gpu.L40S(count=N_GPUS)
def run_cmd(cmd: str, run_folder: str):
import subprocess # nosec
# Propagate errors from subprocess.
if exit_code := subprocess.call(cmd.split(), cwd=run_folder): # nosec
exit(exit_code) # pylint: disable=consider-using-sys-exit
from .single_gpu import GPU_CONFIG, VOLUME_CONFIG, app, cicd_image, run_cmd
@app.function(

66
cicd/single_gpu.py Normal file
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@@ -0,0 +1,66 @@
"""Modal app to run axolotl GPU tests"""
# pylint: disable=duplicate-code
import os
import pathlib
import tempfile
import jinja2
import modal
from jinja2 import select_autoescape
from modal import App, Image
cicd_path = pathlib.Path(__file__).parent.resolve()
template_loader = jinja2.FileSystemLoader(searchpath=cicd_path)
template_env = jinja2.Environment(
loader=template_loader, autoescape=select_autoescape()
)
df_template = template_env.get_template("Dockerfile.jinja")
df_args = {
"AXOLOTL_EXTRAS": os.environ.get("AXOLOTL_EXTRAS", ""),
"AXOLOTL_ARGS": os.environ.get("AXOLOTL_ARGS", ""),
"PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION", "2.4.1"),
"BASE_TAG": os.environ.get("BASE_TAG", "main-base-py3.11-cu121-2.4.1"),
"CUDA": os.environ.get("CUDA", "121"),
"GITHUB_REF": os.environ.get("GITHUB_REF", "refs/heads/main"),
"GITHUB_SHA": os.environ.get("GITHUB_SHA", ""),
"NIGHTLY_BUILD": os.environ.get("NIGHTLY_BUILD", ""),
"CODECOV_TOKEN": os.environ.get("CODECOV_TOKEN", ""),
"HF_HOME": "/workspace/data/huggingface-cache/hub",
}
dockerfile_contents = df_template.render(**df_args)
temp_dir = tempfile.mkdtemp()
with open(pathlib.Path(temp_dir) / "Dockerfile", "w", encoding="utf-8") as f:
f.write(dockerfile_contents)
cicd_image = Image.from_dockerfile(
pathlib.Path(temp_dir) / "Dockerfile",
context_mount=None,
force_build=True,
gpu="A10G",
).env(df_args)
app = App("Axolotl CI/CD", secrets=[])
hf_cache_volume = modal.Volume.from_name(
"axolotl-ci-hf-hub-cache", create_if_missing=True
)
VOLUME_CONFIG = {
"/workspace/data/huggingface-cache/hub": hf_cache_volume,
}
N_GPUS = int(os.environ.get("N_GPUS", 1))
GPU_CONFIG = modal.gpu.L40S(count=N_GPUS)
def run_cmd(cmd: str, run_folder: str):
import subprocess # nosec
# Propagate errors from subprocess.
if exit_code := subprocess.call(cmd.split(), cwd=run_folder): # nosec
exit(exit_code) # pylint: disable=consider-using-sys-exit