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#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
"""
This script is adapted from the torchvision one.
"""
import os.path
import jinja2
import yaml
from packaging import version
# The CUDA versions which have pytorch conda packages available for linux for each
# version of pytorch.
CONDA_CUDA_VERSIONS = {
"1.12.0": ["cu113", "cu116"],
"1.12.1": ["cu113", "cu116"],
"1.13.0": ["cu116", "cu117"],
"1.13.1": ["cu116", "cu117"],
"2.0.0": ["cu117", "cu118"],
"2.0.1": ["cu117", "cu118"],
"2.1.0": ["cu118", "cu121"],
"2.1.1": ["cu118", "cu121"],
"2.1.2": ["cu118", "cu121"],
"2.2.0": ["cu118", "cu121"],
"2.2.2": ["cu118", "cu121"],
"2.3.1": ["cu118", "cu121"],
}
def conda_docker_image_for_cuda(cuda_version):
if len(cuda_version) != 5:
raise ValueError("Unknown cuda version")
return "pytorch/conda-builder:cuda" + cuda_version[2:]
def pytorch_versions_for_python(python_version):
if python_version in ["3.8", "3.9"]:
return list(CONDA_CUDA_VERSIONS)
if python_version == "3.10":
return [
i
for i in CONDA_CUDA_VERSIONS
if version.Version(i) >= version.Version("1.11.0")
]
if python_version == "3.11":
return [
i
for i in CONDA_CUDA_VERSIONS
if version.Version(i) >= version.Version("2.1.0")
]
if python_version == "3.12":
return [
i
for i in CONDA_CUDA_VERSIONS
if version.Version(i) >= version.Version("2.2.0")
]
def workflows(prefix="", filter_branch=None, upload=False, indentation=6):
w = []
for btype in ["conda"]:
for python_version in ["3.8", "3.9", "3.10", "3.11", "3.12"]:
for pytorch_version in pytorch_versions_for_python(python_version):
for cu_version in CONDA_CUDA_VERSIONS[pytorch_version]:
w += workflow_pair(
btype=btype,
python_version=python_version,
pytorch_version=pytorch_version,
cu_version=cu_version,
prefix=prefix,
upload=upload,
filter_branch=filter_branch,
)
return indent(indentation, w)
def workflow_pair(
*,
btype,
python_version,
pytorch_version,
cu_version,
prefix="",
upload=False,
filter_branch,
):
w = []
py = python_version.replace(".", "")
pyt = pytorch_version.replace(".", "")
base_workflow_name = f"{prefix}linux_{btype}_py{py}_{cu_version}_pyt{pyt}"
w.append(
generate_base_workflow(
base_workflow_name=base_workflow_name,
python_version=python_version,
pytorch_version=pytorch_version,
cu_version=cu_version,
btype=btype,
filter_branch=filter_branch,
)
)
if upload:
w.append(
generate_upload_workflow(
base_workflow_name=base_workflow_name,
btype=btype,
cu_version=cu_version,
filter_branch=filter_branch,
)
)
return w
def generate_base_workflow(
*,
base_workflow_name,
python_version,
cu_version,
pytorch_version,
btype,
filter_branch=None,
):
d = {
"name": base_workflow_name,
"python_version": python_version,
"cu_version": cu_version,
"pytorch_version": pytorch_version,
"context": "DOCKERHUB_TOKEN",
}
conda_docker_image = conda_docker_image_for_cuda(cu_version)
if conda_docker_image is not None:
d["conda_docker_image"] = conda_docker_image
if filter_branch is not None:
d["filters"] = {"branches": {"only": filter_branch}}
return {f"binary_linux_{btype}": d}
def generate_upload_workflow(*, base_workflow_name, btype, cu_version, filter_branch):
d = {
"name": f"{base_workflow_name}_upload",
"context": "org-member",
"requires": [base_workflow_name],
}
if btype == "wheel":
d["subfolder"] = cu_version + "/"
if filter_branch is not None:
d["filters"] = {"branches": {"only": filter_branch}}
return {f"binary_{btype}_upload": d}
def indent(indentation, data_list):
if len(data_list) == 0:
return ""
return ("\n" + " " * indentation).join(
yaml.dump(data_list, default_flow_style=False).splitlines()
)
if __name__ == "__main__":
d = os.path.dirname(__file__)
env = jinja2.Environment(
loader=jinja2.FileSystemLoader(d),
lstrip_blocks=True,
autoescape=False,
keep_trailing_newline=True,
)
with open(os.path.join(d, "config.yml"), "w") as f:
f.write(env.get_template("config.in.yml").render(workflows=workflows))
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