3d8425fa91
* LoRA + activation fn Triton kernels: initial commit * implementing optims * finalizing MLP LoRA kernels and progress on QKV / W kernels * updates * O projection optim * adding monkey patching logic * doc strings, typing, pre-commit fixes * updates * adding lora 8b kernels example * working on fsdp support * tests and fixes * small fixes, getting tests to pass, adding doc strings * integration tests for LoRA patching * config.qmd * remove unneeded pytest fixture * fix * review comments first pass * improving tests, attention class agnostic patching * adding support for more archs * wip SiLU / GELU impls * improved testing, small updates, etc. * slightly updating docs * rebase * fixing test_attention_patching_integration * additional review comments, fixing test in CI (hopefully) * isolating problematic patching test * relaxing allclose threshold to reduce flakiness * fixing accidental change * adding model arch agnostic attention class fetching * removing unused activations
82 lines
2.2 KiB
Python
82 lines
2.2 KiB
Python
"""Modal app to run axolotl GPU tests"""
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# pylint: disable=duplicate-code
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import os
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import pathlib
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import tempfile
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import jinja2
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import modal
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from jinja2 import select_autoescape
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from modal import App, Image
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cicd_path = pathlib.Path(__file__).parent.resolve()
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template_loader = jinja2.FileSystemLoader(searchpath=cicd_path)
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template_env = jinja2.Environment(
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loader=template_loader, autoescape=select_autoescape()
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)
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df_template = template_env.get_template("Dockerfile.jinja")
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df_args = {
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"AXOLOTL_EXTRAS": os.environ.get("AXOLOTL_EXTRAS", ""),
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"AXOLOTL_ARGS": os.environ.get("AXOLOTL_ARGS", ""),
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"PYTORCH_VERSION": os.environ.get("PYTORCH_VERSION", "2.4.1"),
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"BASE_TAG": os.environ.get("BASE_TAG", "main-base-py3.11-cu121-2.4.1"),
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"CUDA": os.environ.get("CUDA", "121"),
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"GITHUB_REF": os.environ.get("GITHUB_REF", "refs/heads/main"),
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"GITHUB_SHA": os.environ.get("GITHUB_SHA", ""),
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"NIGHTLY_BUILD": os.environ.get("NIGHTLY_BUILD", ""),
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"HF_HOME": "/workspace/data/huggingface-cache/hub",
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}
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dockerfile_contents = df_template.render(**df_args)
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temp_dir = tempfile.mkdtemp()
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with open(pathlib.Path(temp_dir) / "Dockerfile", "w", encoding="utf-8") as f:
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f.write(dockerfile_contents)
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cicd_image = Image.from_dockerfile(
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pathlib.Path(temp_dir) / "Dockerfile",
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context_mount=None,
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force_build=True,
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gpu="A10G",
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).env(df_args)
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app = App("Axolotl CI/CD", secrets=[])
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hf_cache_volume = modal.Volume.from_name(
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"axolotl-ci-hf-hub-cache", create_if_missing=True
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)
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VOLUME_CONFIG = {
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"/workspace/data/huggingface-cache/hub": hf_cache_volume,
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}
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N_GPUS = int(os.environ.get("N_GPUS", 1))
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GPU_CONFIG = modal.gpu.L40S(count=N_GPUS)
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def run_cmd(cmd: str, run_folder: str):
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import subprocess # nosec
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# Propagate errors from subprocess.
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if exit_code := subprocess.call(cmd.split(), cwd=run_folder): # nosec
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exit(exit_code) # pylint: disable=consider-using-sys-exit
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@app.function(
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image=cicd_image,
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gpu=GPU_CONFIG,
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timeout=60 * 60,
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cpu=8.0,
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memory=131072,
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volumes=VOLUME_CONFIG,
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)
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def cicd_pytest():
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run_cmd("./cicd/cicd.sh", "/workspace/axolotl")
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@app.local_entrypoint()
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def main():
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cicd_pytest.remote()
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