diff --git a/.github/workflows/validate-nightly-binaries.yml b/.github/workflows/validate-nightly-binaries.yml
index 13e41514d..8862009d7 100644
--- a/.github/workflows/validate-nightly-binaries.yml
+++ b/.github/workflows/validate-nightly-binaries.yml
@@ -25,7 +25,6 @@ on:
- .github/workflows/validate-macos-binaries.yml
- .github/workflows/validate-macos-arm64-binaries.yml
- test/smoke_test/*
-
jobs:
nightly:
uses: ./.github/workflows/validate-binaries.yml
diff --git a/.github/workflows/validate-windows-binaries.yml b/.github/workflows/validate-windows-binaries.yml
index 4d93e5db8..d01ebbbff 100644
--- a/.github/workflows/validate-windows-binaries.yml
+++ b/.github/workflows/validate-windows-binaries.yml
@@ -49,6 +49,7 @@ jobs:
repository: "pytorch/builder"
ref: ${{ inputs.ref || github.ref }}
job-name: ${{ matrix.build_name }}
+ timeout: 60
script: |
set -ex
export ENV_NAME="conda-env-${{ github.run_id }}"
diff --git a/test/smoke_test/smoke_test.py b/test/smoke_test/smoke_test.py
index a8de58bd6..e72d3c670 100644
--- a/test/smoke_test/smoke_test.py
+++ b/test/smoke_test/smoke_test.py
@@ -5,6 +5,8 @@
import argparse
import torch
import platform
+import importlib
+import subprocess
gpu_arch_ver = os.getenv("GPU_ARCH_VER")
gpu_arch_type = os.getenv("GPU_ARCH_TYPE")
@@ -14,6 +16,21 @@
SCRIPT_DIR = Path(__file__).parent
NIGHTLY_ALLOWED_DELTA = 3
+MODULES = [
+ {
+ "name": "torchvision",
+ "repo": "https://github.com/pytorch/vision.git",
+ "smoke_test": "python ./vision/test/smoke_test.py",
+ "extension": "extension",
+ },
+ {
+ "name": "torchaudio",
+ "repo": "https://github.com/pytorch/audio.git",
+ "smoke_test": "python ./audio/test/smoke_test/smoke_test.py --no-ffmpeg",
+ "extension": "_extension",
+ },
+]
+
def check_nightly_binaries_date(package: str) -> None:
from datetime import datetime, timedelta
format_dt = '%Y%m%d'
@@ -27,33 +44,16 @@ def check_nightly_binaries_date(package: str) -> None:
)
if(package == "all"):
- import torchaudio
- import torchvision
- ta_str = torchaudio.__version__
- tv_str = torchvision.__version__
- date_ta_str = re.findall("dev\d+", torchaudio.__version__)
- date_tv_str = re.findall("dev\d+", torchvision.__version__)
- date_ta_delta = datetime.now() - datetime.strptime(date_ta_str[0][3:], format_dt)
- date_tv_delta = datetime.now() - datetime.strptime(date_tv_str[0][3:], format_dt)
-
- # check that the above three lists are equal and none of them is empty
- if date_ta_delta.days > NIGHTLY_ALLOWED_DELTA or date_tv_delta.days > NIGHTLY_ALLOWED_DELTA:
- raise RuntimeError(
- f"Expected torchaudio, torchvision to be less then {NIGHTLY_ALLOWED_DELTA} days. But they are from {date_ta_str}, {date_tv_str} respectively"
- )
-
-def check_cuda_version(version: str, dlibary: str):
- version = torch.ops.torchaudio.cuda_version()
- if version is not None and torch.version.cuda is not None:
- version_str = str(version)
- ta_version = f"{version_str[:-3]}.{version_str[-2]}"
- t_version = torch.version.cuda.split(".")
- t_version = f"{t_version[0]}.{t_version[1]}"
- if ta_version != t_version:
- raise RuntimeError(
- "Detected that PyTorch and {dlibary} were compiled with different CUDA versions. "
- f"PyTorch has CUDA version {t_version} whereas {dlibary} has CUDA version {ta_version}. "
- )
+ for module in MODULES:
+ imported_module = importlib.import_module(module["name"])
+ module_version = imported_module.__version__
+ date_m_str = re.findall("dev\d+", module_version)
+ date_m_delta = datetime.now() - datetime.strptime(date_m_str[0][3:], format_dt)
+ print(f"Nightly date check for {module['name']} version {module_version}")
+ if date_m_delta.days > NIGHTLY_ALLOWED_DELTA:
+ raise RuntimeError(
+ f"Expected {module['name']} to be less then {NIGHTLY_ALLOWED_DELTA} days. But its {date_m_delta}"
+ )
def smoke_test_cuda(package: str) -> None:
if not torch.cuda.is_available() and is_cuda_system:
@@ -69,12 +69,16 @@ def smoke_test_cuda(package: str) -> None:
print(f"cuDNN enabled? {torch.backends.cudnn.enabled}")
if(package == 'all' and is_cuda_system):
- import torchaudio
- import torchvision
- print(f"torchvision cuda: {torch.ops.torchvision._cuda_version()}")
- print(f"torchaudio cuda: {torch.ops.torchaudio.cuda_version()}")
- check_cuda_version(torch.ops.torchvision._cuda_version(), "TorchVision")
- check_cuda_version(torch.ops.torchaudio.cuda_version(), "TorchAudio")
+ for module in MODULES:
+ imported_module = importlib.import_module(module["name"])
+ # TBD for vision move extension module to private so it will
+ # be _extention.
+ version = "N/A"
+ if module["extension"] == "extension":
+ version = imported_module.extension._check_cuda_version()
+ else:
+ version = imported_module._extension._check_cuda_version()
+ print(f"{module['name']} CUDA: {version}")
def smoke_test_conv2d() -> None:
@@ -97,67 +101,20 @@ def smoke_test_conv2d() -> None:
out = conv(x)
-def smoke_test_torchvision() -> None:
- print(
- "Is torchvision useable?",
- all(
- x is not None
- for x in [torch.ops.image.decode_png, torch.ops.torchvision.roi_align]
- ),
- )
-
-
-def smoke_test_torchvision_read_decode() -> None:
- from torchvision.io import read_image
-
- img_jpg = read_image(str(SCRIPT_DIR / "assets" / "rgb_pytorch.jpg"))
- if img_jpg.ndim != 3 or img_jpg.numel() < 100:
- raise RuntimeError(f"Unexpected shape of img_jpg: {img_jpg.shape}")
- img_png = read_image(str(SCRIPT_DIR / "assets" / "rgb_pytorch.png"))
- if img_png.ndim != 3 or img_png.numel() < 100:
- raise RuntimeError(f"Unexpected shape of img_png: {img_png.shape}")
-
-
-def smoke_test_torchvision_resnet50_classify(device: str = "cpu") -> None:
- from torchvision.io import read_image
- from torchvision.models import resnet50, ResNet50_Weights
-
- img = read_image(str(SCRIPT_DIR / "assets" / "dog2.jpg")).to(device)
-
- # Step 1: Initialize model with the best available weights
- weights = ResNet50_Weights.DEFAULT
- model = resnet50(weights=weights).to(device)
- model.eval()
-
- # Step 2: Initialize the inference transforms
- preprocess = weights.transforms()
-
- # Step 3: Apply inference preprocessing transforms
- batch = preprocess(img).unsqueeze(0)
-
- # Step 4: Use the model and print the predicted category
- prediction = model(batch).squeeze(0).softmax(0)
- class_id = prediction.argmax().item()
- score = prediction[class_id].item()
- category_name = weights.meta["categories"][class_id]
- expected_category = "German shepherd"
- print(f"{category_name}: {100 * score:.1f}%")
- if category_name != expected_category:
- raise RuntimeError(
- f"Failed ResNet50 classify {category_name} Expected: {expected_category}"
- )
-
-
-def smoke_test_torchaudio() -> None:
- import torchaudio
- import torchaudio.compliance.kaldi # noqa: F401
- import torchaudio.datasets # noqa: F401
- import torchaudio.functional # noqa: F401
- import torchaudio.models # noqa: F401
- import torchaudio.pipelines # noqa: F401
- import torchaudio.sox_effects # noqa: F401
- import torchaudio.transforms # noqa: F401
- import torchaudio.utils # noqa: F401
+def smoke_test_modules():
+ for module in MODULES:
+ if module["repo"]:
+ subprocess.check_output(f"git clone --depth 1 {module['repo']}", stderr=subprocess.STDOUT, shell=True)
+ try:
+ output = subprocess.check_output(
+ module["smoke_test"], stderr=subprocess.STDOUT, shell=True,
+ universal_newlines=True)
+ except subprocess.CalledProcessError as exc:
+ raise RuntimeError(
+ f"Module {module['name']} FAIL: {exc.returncode} Output: {exc.output}"
+ )
+ else:
+ print("Output: \n{}\n".format(output))
def main() -> None:
@@ -171,25 +128,16 @@ def main() -> None:
)
options = parser.parse_args()
print(f"torch: {torch.__version__}")
-
smoke_test_cuda(options.package)
smoke_test_conv2d()
+ if options.package == "all":
+ smoke_test_modules()
+
# only makes sense to check nightly package where dates are known
if installation_str.find("nightly") != -1:
check_nightly_binaries_date(options.package)
- if options.package == "all":
- import torchaudio
- import torchvision
- print(f"torchvision: {torchvision.__version__}")
- print(f"torchaudio: {torchaudio.__version__}")
- smoke_test_torchaudio()
- smoke_test_torchvision()
- smoke_test_torchvision_read_decode()
- smoke_test_torchvision_resnet50_classify()
- if torch.cuda.is_available():
- smoke_test_torchvision_resnet50_classify("cuda")
if __name__ == "__main__":
main()