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PyTorch 1.8 builds

Browse Source 
Summary: Nightly builds to support PyTorch 1.8.0 and PyTorch 1.8.1.

Reviewed By: patricklabatut

Differential Revision: D29098081

fbshipit-source-id: fc6b36e919892ea41979a03e64a6fc8003528b78
This commit is contained in:
Jeremy Reizenstein 2021-06-14 10:26:44 -07:00 committed by Facebook GitHub Bot
parent 780e231536
commit 9de627e01b
7 changed files with 186 additions and 28 deletions
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7
.circleci/config.in.yml
View File

@ -191,6 +191,7 @@ jobs:
docker pull $DOCKER_IMAGE
- run:
name: Build and run tests
no_output_timeout: 20m
command: |
set -e
@ -245,6 +246,12 @@ workflows:
python_version: "3.7"
pytorch_version: '1.7.0'
cu_version: "cu110"
- binary_linux_conda_cuda:
name: testrun_conda_cuda_py39_cu111_pyt181
context: DOCKERHUB_TOKEN
python_version: "3.9"
pytorch_version: '1.8.1'
cu_version: "cu111"
- binary_macos_wheel:
cu_version: cpu
name: macos_wheel_py36_cpu

151
.circleci/config.yml
View File

@ -191,6 +191,7 @@ jobs:
docker pull $DOCKER_IMAGE
- run:
name: Build and run tests
no_output_timeout: 20m
command: |
set -e
@ -328,6 +329,42 @@ workflows:
name: linux_conda_py36_cu110_pyt171
python_version: '3.6'
pytorch_version: 1.7.1
- binary_linux_conda:
context: DOCKERHUB_TOKEN
cu_version: cu101
name: linux_conda_py36_cu101_pyt180
python_version: '3.6'
pytorch_version: 1.8.0
- binary_linux_conda:
context: DOCKERHUB_TOKEN
cu_version: cu102
name: linux_conda_py36_cu102_pyt180
python_version: '3.6'
pytorch_version: 1.8.0
- binary_linux_conda:
context: DOCKERHUB_TOKEN
cu_version: cu111
name: linux_conda_py36_cu111_pyt180
python_version: '3.6'
pytorch_version: 1.8.0
- binary_linux_conda:
context: DOCKERHUB_TOKEN
cu_version: cu101
name: linux_conda_py36_cu101_pyt181
python_version: '3.6'
pytorch_version: 1.8.1
- binary_linux_conda:
context: DOCKERHUB_TOKEN
cu_version: cu102
name: linux_conda_py36_cu102_pyt181
python_version: '3.6'
pytorch_version: 1.8.1
- binary_linux_conda:
context: DOCKERHUB_TOKEN
cu_version: cu111
name: linux_conda_py36_cu111_pyt181
python_version: '3.6'
pytorch_version: 1.8.1
- binary_linux_conda:
context: DOCKERHUB_TOKEN
cu_version: cu92
@ -430,6 +467,42 @@ workflows:
name: linux_conda_py37_cu110_pyt171
python_version: '3.7'
pytorch_version: 1.7.1
- binary_linux_conda:
context: DOCKERHUB_TOKEN
cu_version: cu101
name: linux_conda_py37_cu101_pyt180
python_version: '3.7'
pytorch_version: 1.8.0
- binary_linux_conda:
context: DOCKERHUB_TOKEN
cu_version: cu102
name: linux_conda_py37_cu102_pyt180
python_version: '3.7'
pytorch_version: 1.8.0
- binary_linux_conda:
context: DOCKERHUB_TOKEN
cu_version: cu111
name: linux_conda_py37_cu111_pyt180
python_version: '3.7'
pytorch_version: 1.8.0
- binary_linux_conda:
context: DOCKERHUB_TOKEN
cu_version: cu101
name: linux_conda_py37_cu101_pyt181
python_version: '3.7'
pytorch_version: 1.8.1
- binary_linux_conda:
context: DOCKERHUB_TOKEN
cu_version: cu102
name: linux_conda_py37_cu102_pyt181
python_version: '3.7'
pytorch_version: 1.8.1
- binary_linux_conda:
context: DOCKERHUB_TOKEN
cu_version: cu111
name: linux_conda_py37_cu111_pyt181
python_version: '3.7'
pytorch_version: 1.8.1
- binary_linux_conda:
context: DOCKERHUB_TOKEN
cu_version: cu92
@ -532,6 +605,42 @@ workflows:
name: linux_conda_py38_cu110_pyt171
python_version: '3.8'
pytorch_version: 1.7.1
- binary_linux_conda:
context: DOCKERHUB_TOKEN
cu_version: cu101
name: linux_conda_py38_cu101_pyt180
python_version: '3.8'
pytorch_version: 1.8.0
- binary_linux_conda:
context: DOCKERHUB_TOKEN
cu_version: cu102
name: linux_conda_py38_cu102_pyt180
python_version: '3.8'
pytorch_version: 1.8.0
- binary_linux_conda:
context: DOCKERHUB_TOKEN
cu_version: cu111
name: linux_conda_py38_cu111_pyt180
python_version: '3.8'
pytorch_version: 1.8.0
- binary_linux_conda:
context: DOCKERHUB_TOKEN
cu_version: cu101
name: linux_conda_py38_cu101_pyt181
python_version: '3.8'
pytorch_version: 1.8.1
- binary_linux_conda:
context: DOCKERHUB_TOKEN
cu_version: cu102
name: linux_conda_py38_cu102_pyt181
python_version: '3.8'
pytorch_version: 1.8.1
- binary_linux_conda:
context: DOCKERHUB_TOKEN
cu_version: cu111
name: linux_conda_py38_cu111_pyt181
python_version: '3.8'
pytorch_version: 1.8.1
- binary_linux_conda:
context: DOCKERHUB_TOKEN
cu_version: cu101
@ -550,6 +659,42 @@ workflows:
name: linux_conda_py39_cu110_pyt171
python_version: '3.9'
pytorch_version: 1.7.1
- binary_linux_conda:
context: DOCKERHUB_TOKEN
cu_version: cu101
name: linux_conda_py39_cu101_pyt180
python_version: '3.9'
pytorch_version: 1.8.0
- binary_linux_conda:
context: DOCKERHUB_TOKEN
cu_version: cu102
name: linux_conda_py39_cu102_pyt180
python_version: '3.9'
pytorch_version: 1.8.0
- binary_linux_conda:
context: DOCKERHUB_TOKEN
cu_version: cu111
name: linux_conda_py39_cu111_pyt180
python_version: '3.9'
pytorch_version: 1.8.0
- binary_linux_conda:
context: DOCKERHUB_TOKEN
cu_version: cu101
name: linux_conda_py39_cu101_pyt181
python_version: '3.9'
pytorch_version: 1.8.1
- binary_linux_conda:
context: DOCKERHUB_TOKEN
cu_version: cu102
name: linux_conda_py39_cu102_pyt181
python_version: '3.9'
pytorch_version: 1.8.1
- binary_linux_conda:
context: DOCKERHUB_TOKEN
cu_version: cu111
name: linux_conda_py39_cu111_pyt181
python_version: '3.9'
pytorch_version: 1.8.1
- binary_linux_conda_cuda:
name: testrun_conda_cuda_py36_cu101_pyt14
context: DOCKERHUB_TOKEN
@ -568,6 +713,12 @@ workflows:
python_version: "3.7"
pytorch_version: '1.7.0'
cu_version: "cu110"
- binary_linux_conda_cuda:
name: testrun_conda_cuda_py39_cu111_pyt181
context: DOCKERHUB_TOKEN
python_version: "3.9"
pytorch_version: '1.8.1'
cu_version: "cu111"
- binary_macos_wheel:
cu_version: cpu
name: macos_wheel_py36_cpu

2
.circleci/regenerate.py
View File

@ -21,6 +21,8 @@ CONDA_CUDA_VERSIONS = {
"1.6.0": ["cu92", "cu101", "cu102"],
"1.7.0": ["cu101", "cu102", "cu110"],
"1.7.1": ["cu101", "cu102", "cu110"],
"1.8.0": ["cu101", "cu102", "cu111"],
"1.8.1": ["cu101", "cu102", "cu111"],
}

2
packaging/build_conda.sh
View File

@ -26,4 +26,4 @@ then
fi
# shellcheck disable=SC2086
conda build $CONDA_CHANNEL_FLAGS ${TEST_FLAG:-} -c bottler -c defaults -c fvcore -c iopath -c conda-forge --no-anaconda-upload --python "$PYTHON_VERSION" packaging/pytorch3d
conda build $CONDA_CHANNEL_FLAGS ${TEST_FLAG:-} -c bottler -c fvcore -c iopath -c conda-forge --no-anaconda-upload --python "$PYTHON_VERSION" packaging/pytorch3d

11
packaging/linux_wheels/inside.sh
View File

@ -26,8 +26,10 @@ declare -A CONDA_CUDA_VERSIONS=(
# ["1.5.0"]="cu101 cu102"
# ["1.5.1"]="cu101 cu102"
# ["1.6.0"]="cu101 cu102"
["1.7.0"]="cu101 cu102 cu110"
["1.7.1"]="cu101 cu102 cu110"
# ["1.7.0"]="cu101 cu102 cu110"
# ["1.7.1"]="cu101 cu102 cu110"
["1.8.0"]="cu101 cu102 cu111"
["1.8.1"]="cu101 cu102 cu111"
)
@ -52,6 +54,11 @@ do
for cu_version in ${CONDA_CUDA_VERSIONS[$pytorch_version]}
do
case "$cu_version" in
cu111)
export CUDA_HOME=/usr/local/cuda-11.1/
export CUDA_TAG=11.1
export NVCC_FLAGS="-gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_70,code=sm_70 -gencode=arch=compute_75,code=sm_75 -gencode=arch=compute_80,code=sm_80 -gencode=arch=compute_86,code=sm_86 -gencode=arch=compute_50,code=compute_50"
;;
cu110)
export CUDA_HOME=/usr/local/cuda-11.0/
export CUDA_TAG=11.0

17
packaging/pkg_helpers.bash
View File

@ -51,6 +51,17 @@ setup_cuda() {
# Now work out the CUDA settings
case "$CU_VERSION" in
cu111)
if [[ "$OSTYPE" == "msys" ]]; then
export CUDA_HOME="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.1"
else
export CUDA_HOME=/usr/local/cuda-11.1/
fi
export FORCE_CUDA=1
# Hard-coding gencode flags is temporary situation until
# https://github.com/pytorch/pytorch/pull/23408 lands
export NVCC_FLAGS="-gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_70,code=sm_70 -gencode=arch=compute_75,code=sm_75 -gencode=arch=compute_80,code=sm_80 -gencode=arch=compute_86,code=sm_86 -gencode=arch=compute_50,code=compute_50"
;;
cu110)
if [[ "$OSTYPE" == "msys" ]]; then
export CUDA_HOME="C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.0"
@ -236,7 +247,7 @@ setup_conda_pytorch_constraint() {
exit 1
fi
else
export CONDA_CHANNEL_FLAGS="-c pytorch -c pytorch-nightly"
export CONDA_CHANNEL_FLAGS="-c pytorch"
fi
if [[ "$CU_VERSION" == cpu ]]; then
export CONDA_PYTORCH_BUILD_CONSTRAINT="- pytorch==$PYTORCH_VERSION${PYTORCH_VERSION_SUFFIX}"
@ -256,6 +267,10 @@ setup_conda_cudatoolkit_constraint() {
export CONDA_CUDATOOLKIT_CONSTRAINT=""
else
case "$CU_VERSION" in
cu111)
export CONDA_CUDATOOLKIT_CONSTRAINT="- cudatoolkit >=11.1,<11.2 # [not osx]"
#export CONDA_CUB_CONSTRAINT="- nvidiacub"
;;
cu110)
export CONDA_CUDATOOLKIT_CONSTRAINT="- cudatoolkit >=11.0,<11.1 # [not osx]"
# Even though cudatoolkit 11.0 provides CUB we need our own, to control the

24
packaging/pytorch3d/conda_build_config.yaml
View File

@ -1,24 +0,0 @@
blas_impl:
- mkl # [x86_64]
c_compiler:
- vs2017 # [win]
cxx_compiler:
- vs2017 # [win]
python:
- 3.5
- 3.6
# This differs from target_platform in that it determines what subdir the compiler
# will target, not what subdir the compiler package will be itself.
# For example, we need a win-64 vs2008_win-32 package, so that we compile win-32
# code on win-64 miniconda.
cross_compiler_target_platform:
- win-64 # [win]
target_platform:
- win-64 # [win]
vc:
- 14
zip_keys:
- # [win]
- vc # [win]
- c_compiler # [win]
- cxx_compiler # [win]