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Replace CUDA 11.1 Linux CI with CUDA 11.2 (#51905)

Browse Source 
Summary:
Adding 11.2 to CI with BUILD_SPLIT_CUDA enabled.

Disabled the following tests as they were failing in test_optim.py:
test_adadelta
test_adam
test_adamw
test_multi_tensor_optimizers
test_rmsprop

(Issue tracking that is here: https://github.com/pytorch/pytorch/issues/51992)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/51905

Reviewed By: VitalyFedyunin

Differential Revision: D26368575

Pulled By: janeyx99

fbshipit-source-id: 31612c7d04d51afb3f18956e43dc7f7db8a91749
This commit is contained in:
Jane Xu 2021-02-10 11:41:47 -08:00 committed by Facebook GitHub Bot
parent 9b8d414a9c
commit a1b8f3d4b6
6 changed files with 38 additions and 34 deletions
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2
.circleci/cimodel/data/pytorch_build_data.py
View File

@ -57,7 +57,7 @@ CONFIG_TREE_DATA = [
]),
]),
]),
("11.1", [
("11.2", [
("3.8", [
X(True),
("libtorch", [

6
.circleci/cimodel/data/simple/docker_definitions.py
View File

@ -6,8 +6,8 @@ from cimodel.data.simple.util.branch_filters import gen_filter_dict, RC_PATTERN
# TODO: make this generated from a matrix rather than just a static list
IMAGE_NAMES = [
"pytorch-linux-bionic-cuda11.1-cudnn8-py3.6-gcc9",
"pytorch-linux-bionic-cuda11.1-cudnn8-py3.8-gcc9",
"pytorch-linux-bionic-cuda11.2-cudnn8-py3.6-gcc9",
"pytorch-linux-bionic-cuda11.2-cudnn8-py3.8-gcc9",
"pytorch-linux-bionic-cuda11.0-cudnn8-py3.6-gcc9",
"pytorch-linux-bionic-cuda11.0-cudnn8-py3.8-gcc9",
"pytorch-linux-bionic-cuda10.2-cudnn7-py3.8-gcc9",
@ -18,7 +18,7 @@ IMAGE_NAMES = [
"pytorch-linux-xenial-cuda10.1-cudnn7-py3-gcc7",
"pytorch-linux-xenial-cuda10.2-cudnn7-py3-gcc7",
"pytorch-linux-xenial-cuda11.0-cudnn8-py3-gcc7",
"pytorch-linux-xenial-cuda11.1-cudnn8-py3-gcc7",
"pytorch-linux-xenial-cuda11.2-cudnn8-py3-gcc7",
"pytorch-linux-xenial-cuda9.2-cudnn7-py3-gcc5.4",
"pytorch-linux-xenial-cuda9.2-cudnn7-py3-gcc7",
"pytorch-linux-xenial-py3-clang5-android-ndk-r19c",

36
.circleci/config.yml
View File

@ -6546,11 +6546,11 @@ workflows:
build:
jobs:
- docker_build_job:
name: "docker-pytorch-linux-bionic-cuda11.1-cudnn8-py3.6-gcc9"
image_name: "pytorch-linux-bionic-cuda11.1-cudnn8-py3.6-gcc9"
name: "docker-pytorch-linux-bionic-cuda11.2-cudnn8-py3.6-gcc9"
image_name: "pytorch-linux-bionic-cuda11.2-cudnn8-py3.6-gcc9"
- docker_build_job:
name: "docker-pytorch-linux-bionic-cuda11.1-cudnn8-py3.8-gcc9"
image_name: "pytorch-linux-bionic-cuda11.1-cudnn8-py3.8-gcc9"
name: "docker-pytorch-linux-bionic-cuda11.2-cudnn8-py3.8-gcc9"
image_name: "pytorch-linux-bionic-cuda11.2-cudnn8-py3.8-gcc9"
- docker_build_job:
name: "docker-pytorch-linux-bionic-cuda11.0-cudnn8-py3.6-gcc9"
image_name: "pytorch-linux-bionic-cuda11.0-cudnn8-py3.6-gcc9"
@ -6582,8 +6582,8 @@ workflows:
name: "docker-pytorch-linux-xenial-cuda11.0-cudnn8-py3-gcc7"
image_name: "pytorch-linux-xenial-cuda11.0-cudnn8-py3-gcc7"
- docker_build_job:
name: "docker-pytorch-linux-xenial-cuda11.1-cudnn8-py3-gcc7"
image_name: "pytorch-linux-xenial-cuda11.1-cudnn8-py3-gcc7"
name: "docker-pytorch-linux-xenial-cuda11.2-cudnn8-py3-gcc7"
image_name: "pytorch-linux-xenial-cuda11.2-cudnn8-py3-gcc7"
- docker_build_job:
name: "docker-pytorch-linux-xenial-cuda9.2-cudnn7-py3-gcc5.4"
image_name: "pytorch-linux-xenial-cuda9.2-cudnn7-py3-gcc5.4"
@ -6961,37 +6961,37 @@ workflows:
build_environment: "pytorch-libtorch-linux-xenial-cuda10.2-cudnn7-py3-gcc7-build"
docker_image: "308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-xenial-cuda10.2-cudnn7-py3-gcc7"
- pytorch_linux_build:
name: pytorch_linux_xenial_cuda11_1_cudnn8_py3_gcc7_build
name: pytorch_linux_xenial_cuda11_2_cudnn8_py3_gcc7_build
requires:
- "docker-pytorch-linux-xenial-cuda11.1-cudnn8-py3-gcc7"
- "docker-pytorch-linux-xenial-cuda11.2-cudnn8-py3-gcc7"
filters:
branches:
only:
- master
- /ci-all\/.*/
- /release\/.*/
build_environment: "pytorch-linux-xenial-cuda11.1-cudnn8-py3-gcc7-build"
docker_image: "308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-xenial-cuda11.1-cudnn8-py3-gcc7"
build_environment: "pytorch-linux-xenial-cuda11.2-cudnn8-py3-gcc7-build"
docker_image: "308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-xenial-cuda11.2-cudnn8-py3-gcc7"
- pytorch_linux_test:
name: pytorch_linux_xenial_cuda11_1_cudnn8_py3_gcc7_test
name: pytorch_linux_xenial_cuda11_2_cudnn8_py3_gcc7_test
requires:
- pytorch_linux_xenial_cuda11_1_cudnn8_py3_gcc7_build
- pytorch_linux_xenial_cuda11_2_cudnn8_py3_gcc7_build
filters:
branches:
only:
- master
- /ci-all\/.*/
- /release\/.*/
build_environment: "pytorch-linux-xenial-cuda11.1-cudnn8-py3-gcc7-test"
docker_image: "308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-xenial-cuda11.1-cudnn8-py3-gcc7"
build_environment: "pytorch-linux-xenial-cuda11.2-cudnn8-py3-gcc7-test"
docker_image: "308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-xenial-cuda11.2-cudnn8-py3-gcc7"
use_cuda_docker_runtime: "1"
resource_class: gpu.medium
- pytorch_linux_build:
name: pytorch_libtorch_linux_xenial_cuda11_1_cudnn8_py3_gcc7_build
name: pytorch_libtorch_linux_xenial_cuda11_2_cudnn8_py3_gcc7_build
requires:
- "docker-pytorch-linux-xenial-cuda11.1-cudnn8-py3-gcc7"
build_environment: "pytorch-libtorch-linux-xenial-cuda11.1-cudnn8-py3-gcc7-build"
docker_image: "308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-xenial-cuda11.1-cudnn8-py3-gcc7"
- "docker-pytorch-linux-xenial-cuda11.2-cudnn8-py3-gcc7"
build_environment: "pytorch-libtorch-linux-xenial-cuda11.2-cudnn8-py3-gcc7-build"
docker_image: "308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-xenial-cuda11.2-cudnn8-py3-gcc7"
- pytorch_linux_build:
name: pytorch_linux_bionic_py3_6_clang9_build
requires:

12
.circleci/docker/build.sh
View File

@ -158,8 +158,8 @@ case "$image" in
VISION=yes
KATEX=yes
;;
pytorch-linux-xenial-cuda11.1-cudnn8-py3-gcc7)
CUDA_VERSION=11.1
pytorch-linux-xenial-cuda11.2-cudnn8-py3-gcc7)
CUDA_VERSION=11.2.0 # Deviating from major.minor to conform to nvidia's Docker image names
CUDNN_VERSION=8
ANACONDA_PYTHON_VERSION=3.6
GCC_VERSION=7
@ -254,8 +254,8 @@ case "$image" in
VISION=yes
KATEX=yes
;;
pytorch-linux-bionic-cuda11.1-cudnn8-py3.6-gcc9)
CUDA_VERSION=11.1
pytorch-linux-bionic-cuda11.2-cudnn8-py3.6-gcc9)
CUDA_VERSION=11.2.0
CUDNN_VERSION=8
ANACONDA_PYTHON_VERSION=3.6
GCC_VERSION=9
@ -264,8 +264,8 @@ case "$image" in
VISION=yes
KATEX=yes
;;
pytorch-linux-bionic-cuda11.1-cudnn8-py3.8-gcc9)
CUDA_VERSION=11.1
pytorch-linux-bionic-cuda11.2-cudnn8-py3.8-gcc9)
CUDA_VERSION=11.2.0
CUDNN_VERSION=8
ANACONDA_PYTHON_VERSION=3.8
GCC_VERSION=9

2
.jenkins/pytorch/test.sh
View File

@ -22,7 +22,7 @@ if [[ "$BUILD_ENVIRONMENT" == *coverage* ]]; then
export PYTORCH_COLLECT_COVERAGE=1
fi
if [[ "$BUILD_ENVIRONMENT" == *cuda11.1* ]]; then
if [[ "$BUILD_ENVIRONMENT" == *cuda11* ]]; then
export BUILD_SPLIT_CUDA=ON
fi

6
test/test_optim.py
View File

@ -306,6 +306,7 @@ class TestOptim(TestCase):
)
@skipIfRocm
@unittest.skipIf(True, "test does not pass for CUDA 11.2")
def test_multi_tensor_optimizers(self):
if not torch.cuda.is_available():
return
@ -379,7 +380,7 @@ class TestOptim(TestCase):
for p1, p2 in zip(res[0], res[1]):
self.assertEqual(p1, p2)
@unittest.skipIf(True, "test does not pass for CUDA 11.2")
def test_adam(self):
for optimizer in [optim.Adam, optim_mt.Adam]:
self._test_basic_cases(
@ -425,6 +426,7 @@ class TestOptim(TestCase):
with self.assertRaisesRegex(ValueError, "Invalid weight_decay value: -1"):
optimizer(None, lr=1e-2, weight_decay=-1)
@unittest.skipIf(True, "test does not pass for CUDA 11.2")
def test_adamw(self):
for optimizer in [optim.AdamW, optim_mt.AdamW]:
self._test_basic_cases(
@ -459,6 +461,7 @@ class TestOptim(TestCase):
# ROCm precision is too low to pass this test
@skipIfRocm
@unittest.skipIf(True, "test does not pass for CUDA 11.2")
def test_adadelta(self):
for optimizer in [optim.Adadelta, optim_mt.Adadelta]:
self._test_basic_cases(
@ -535,6 +538,7 @@ class TestOptim(TestCase):
with self.assertRaisesRegex(ValueError, "Invalid beta parameter at index 1: 1.0"):
optimizer(None, lr=1e-2, betas=(0.0, 1.0))
@unittest.skipIf(True, "test does not pass for CUDA 11.2")
def test_rmsprop(self):
for optimizer in [optim.RMSprop, optim_mt.RMSprop]:
self._test_basic_cases(