Update CI build workflow matrix
Upgraded the onnxruntime headers from v1.6 to v1.9 Update workflow matrix so it's consistent across the platforms and using newer versions of the dependencies. Current supported matrix - +------------+------------------------+-----------------------+-----------------------+----------------------+ |Python | 3.7 | 3.8 | 3.9 | 3.10 | +------------+------------------------+-----------------------+-----------------------+----------------------+ |Onnxruntime | 1.9.0 (Sept 22, 2021) | 1.10.0 (Dec 7, 2021) | 1.11.0 (Mar 26, 2022) | 1.12.1 (Aug 4, 2022) | |Torch | 1.9.1 (Sept 22, 2021) | 1.10.0 (Oct 21, 2021) | 1.11.0 (Mar 10, 2022) | 1.12.1 (Aug 5, 2022) | |TorchVision | 0.10.1 (Jun 15, 2021) | 0.11.1 (Oct 21, 2021) | 0.12.0 (Mar 10, 2022) | 0.13.1 (Aug 5, 2022) | |TorchAudio | 0.9.0 (Jun 15, 2021) | 0.10.0 (Oct 21, 2021) | 0.11.0 (Mar 10, 2022) | 0.12.1 (Aug 5, 2022) | +------------+------------------------+-----------------------+-----------------------+----------------------+ Release versions strictly follow the convention of onnxruntime being one release ahead of all its dependencies.
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121
.az/mshost.yaml
121
.az/mshost.yaml
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@ -1,3 +1,15 @@
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# IMPORTANT: READ ME BEFORE MAKING ANY CHANGES
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#
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# Supported matrix should follow these conventions strictly and
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# for any exceptions to the rules, please document them in comments.
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#
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# * When adding new configurations to the matrix, update all platforms simultaneously.
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# * Onnxruntime version should be one release ahead of torch, torchvision, and torchaudio.
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#
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# The rationale is that the onnxruntime release process would have verified the
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# last available version of its dependencies (including the three listed above)
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# and so it's'likely to be be _better_ supported relative to any older versions.
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jobs:
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#######
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@ -10,25 +22,30 @@ jobs:
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strategy:
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matrix:
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py39-170:
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py310-1121:
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python.version: '3.10'
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torch.version: 'torch==1.12.1+cpu torchvision==0.13.1+cpu torchaudio==0.12.1+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html'
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ort.version: '1.12.0'
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py39-1110:
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python.version: '3.9'
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torch.version: 'torch==1.11.0+cpu torchvision==0.12.0+cpu torchaudio==0.11.0+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html'
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ort.version: '1.11.0'
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py38-1100:
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python.version: '3.8'
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torch.version: 'torch==1.10.0+cpu torchvision==0.11.1+cpu torchaudio==0.10.0+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html'
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ort.version: '1.7.0'
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ortlib.version: '38443267'
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py37-160:
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ort.version: '1.10.0'
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py37-190:
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python.version: '3.7'
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torch.version: 'torch==1.7.1+cpu torchvision==0.8.2+cpu torchaudio==0.7.2 -f https://download.pytorch.org/whl/torch_stable.html'
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ort.version: '1.6.0'
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ortlib.version: '34858191'
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maxParallel: 2
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torch.version: 'torch==1.9.0+cpu torchvision==0.10.0+cpu torchaudio==0.9.0 -f https://download.pytorch.org/whl/cpu/torch_stable.html'
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ort.version: '1.9.0'
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steps:
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- task: DownloadGitHubRelease@0
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inputs:
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connection: 'microsoft'
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userRepository: 'microsoft/onnxruntime'
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defaultVersionType: 'specificVersion'
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version: '$(ortlib.version)'
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defaultVersionType: 'specificTag'
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version: 'v$(ort.version)'
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itemPattern: '*-linux-x64-$(ort.version)*'
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downloadPath: '$(Build.sourcesdirectory)'
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displayName: Download the ONNXRuntime prebuilt package.
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@ -79,19 +96,24 @@ jobs:
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# macOS C++
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###########
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- job: MacOSC
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- job: MacOSX
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pool:
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vmImage: 'macOS-latest'
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strategy:
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matrix:
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ort-170:
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ort.version: '1.7.0'
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ortlib.version: '38443267'
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ort-160:
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ort.version: '1.6.0'
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ortlib.version: '34858191'
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maxParallel: 2
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ort-1121:
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ort.version: '1.12.1'
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ort.dirname: 'onnxruntime-osx-x86_64-$(ort.version)'
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ort-1110:
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ort.version: '1.11.0'
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ort.dirname: 'onnxruntime-osx-x86_64-$(ort.version)'
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ort-1100:
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ort.version: '1.10.0'
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ort.dirname: 'onnxruntime-osx-x86_64-$(ort.version)'
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ort-190:
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ort.version: '1.9.0'
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ort.dirname: 'onnxruntime-osx-x64-$(ort.version)'
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steps:
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# needed for onnxruntime
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@ -102,9 +124,9 @@ jobs:
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inputs:
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connection: 'microsoft'
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userRepository: 'microsoft/onnxruntime'
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defaultVersionType: 'specificVersion'
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version: '$(ortlib.version)'
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itemPattern: '*-osx-x64-$(ort.version)*'
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defaultVersionType: 'specificTag'
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version: 'v$(ort.version)'
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itemPattern: '$(ort.dirname)*'
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downloadPath: '$(Build.sourcesdirectory)'
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displayName: Download the ONNXRuntime prebuilt package.
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@ -117,7 +139,7 @@ jobs:
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displayName: Unpack ONNXRuntime package.
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- script: |
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sh ./build.sh -DONNXRUNTIME_LIB_DIR=onnxruntime-osx-x64-$(ort.version)/lib -DOCOS_ENABLE_CTEST=ON
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sh ./build.sh -DONNXRUNTIME_LIB_DIR=$(ort.dirname)/lib -DOCOS_ENABLE_CTEST=ON
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displayName: build the customop library with onnxruntime
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- script: |
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@ -135,16 +157,22 @@ jobs:
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strategy:
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matrix:
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py310-1121:
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python.version: '3.10'
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torch.version: 'torch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 -f https://download.pytorch.org/whl/torch_stable.html'
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ort.version: '1.12.1'
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py39-1110:
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python.version: '3.9'
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torch.version: 'torch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 -f https://download.pytorch.org/whl/torch_stable.html'
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ort.version: '1.11.0'
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py38-180:
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py38-1100:
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python.version: '3.8'
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ort.version: '1.8.0'
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py37-170:
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torch.version: 'torch==1.10.0 torchvision==0.11.1 torchaudio==0.10.0 -f https://download.pytorch.org/whl/torch_stable.html'
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ort.version: '1.10.0'
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py37-190:
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python.version: '3.7'
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ort.version: '1.7.0'
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maxParallel: 1
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torch.version: 'torch==1.9.0 torchvision==0.10.0 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html'
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ort.version: '1.9.0'
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steps:
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# needed for onnxruntime
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@ -174,7 +202,7 @@ jobs:
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- script: python -m pip install -r requirements-dev.txt
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displayName: Install requirements-dev.txt
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- script: python -m pip install torch torchvision torchaudio
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- script: python -m pip install $(torch.version)
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displayName: Install pytorch
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- script: cd test && python -m pytest . --verbose
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@ -195,21 +223,22 @@ jobs:
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strategy:
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matrix:
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ort-170:
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ort.version: '1.7.0'
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ortlib.version: '38443267'
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ort-160:
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||||
ort.version: '1.6.0'
|
||||
ortlib.version: '34858191'
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maxParallel: 2
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ort-1121:
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ort.version: '1.12.1'
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ort-1110:
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ort.version: '1.11.0'
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ort-1100:
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ort.version: '1.10.0'
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ort-190:
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ort.version: '1.9.0'
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steps:
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- task: DownloadGitHubRelease@0
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inputs:
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connection: 'microsoft'
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userRepository: 'microsoft/onnxruntime'
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defaultVersionType: 'specificVersion'
|
||||
version: '$(ortlib.version)'
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||||
defaultVersionType: 'specificTag'
|
||||
version: 'v$(ort.version)'
|
||||
itemPattern: '*-win-x64-$(ort.version)*'
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downloadPath: '$(Build.sourcesdirectory)'
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displayName: Download the ONNXRuntime prebuilt package.
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@ -255,16 +284,22 @@ jobs:
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strategy:
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||||
matrix:
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py310-1121:
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python.version: '3.10'
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torch.version: 'torch==1.12.1+cpu torchvision==0.13.1+cpu torchaudio==0.12.1+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html'
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ort.version: '1.12.1'
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py39-1110:
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python.version: '3.9'
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torch.version: 'torch==1.11.0+cpu torchvision==0.12.0+cpu torchaudio==0.11.0+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html'
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ort.version: '1.11.0'
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py38-170:
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py38-1100:
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python.version: '3.8'
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ort.version: '1.8.0'
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py37-170:
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torch.version: 'torch==1.10.0+cpu torchvision==0.11.1+cpu torchaudio==0.10.0+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html'
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ort.version: '1.10.0'
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py37-190:
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python.version: '3.7'
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ort.version: '1.7.0'
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maxParallel: 1
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torch.version: 'torch==1.9.0+cpu torchvision==0.10.0+cpu torchaudio==0.9.0 -f https://download.pytorch.org/whl/cpu/torch_stable.html'
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ort.version: '1.9.0'
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steps:
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- powershell: Write-Host "##vso[task.prependpath]$env:CONDA\Scripts"
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@ -292,7 +327,7 @@ jobs:
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- script: |
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call activate pyenv
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python -m pip install torch==1.8.2+cpu torchvision==0.9.2+cpu torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
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python -m pip install $(torch.version)
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displayName: Install pytorch
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- script: |
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@ -18,6 +18,8 @@ ONNX Runtime Extensions is library that extends the capability of the ONNX conve
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### Installation
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For a complete list of verified build configurations see [here](<./build_matrix.md>)
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#### Install from PyPI
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```bash
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@ -0,0 +1,10 @@
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### CI Build Matrix
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The matrix below lists the versions of individual dependencies of onxxruntime-extensions. These are the configurations that are routinely and extensively verified by our CI.
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Python | 3.7 | 3.8 | 3.9 | 3.10
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---|---|---|---|---
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Onnxruntime | 1.9.0 (Sept 22, 2021) | 1.10.0 (Dec 7, 2021) | 1.11.0 (Mar 26, 2022) | 1.12.1 (Aug 4, 2022)
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Torch | 1.9.1 (Sept 22, 2021) | 1.10.0 (Oct 21, 2021) | 1.11.0 (Mar 10, 2022) | 1.12.1 (Aug 5, 2022)
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TorchVision | 0.10.1 (Jun 15, 2021) | 0.11.1 (Oct 21, 2021) | 0.12.0 (Mar 10, 2022) | 0.13.1 (Aug 5, 2022)
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TorchAudio | 0.9.0 (Jun 15, 2021) | 0.10.0 (Oct 21, 2021) | 0.11.0 (Mar 10, 2022) | 0.12.1 (Aug 5, 2022)
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Разница между файлами не показана из-за своего большого размера
Загрузить разницу
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@ -284,6 +284,8 @@ struct RunOptions : Base<OrtRunOptions> {
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RunOptions& SetRunTag(const char* run_tag);
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const char* GetRunTag() const;
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RunOptions& AddConfigEntry(const char* config_key, const char* config_value);
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|
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// terminate ALL currently executing Session::Run calls that were made using this RunOptions instance
|
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RunOptions& SetTerminate();
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// unset the terminate flag so this RunOptions instance can be used in a new Session::Run call
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@ -309,6 +311,8 @@ struct SessionOptions : Base<OrtSessionOptions> {
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SessionOptions& EnableProfiling(const ORTCHAR_T* profile_file_prefix);
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SessionOptions& DisableProfiling();
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|
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SessionOptions& EnableOrtCustomOps();
|
||||
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SessionOptions& EnableMemPattern();
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SessionOptions& DisableMemPattern();
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@ -325,7 +329,9 @@ struct SessionOptions : Base<OrtSessionOptions> {
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SessionOptions& AddInitializer(const char* name, const OrtValue* ort_val);
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|
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SessionOptions& AppendExecutionProvider_CUDA(const OrtCUDAProviderOptions& provider_options);
|
||||
SessionOptions& AppendExecutionProvider_ROCM(const OrtROCMProviderOptions& provider_options);
|
||||
SessionOptions& AppendExecutionProvider_OpenVINO(const OrtOpenVINOProviderOptions& provider_options);
|
||||
SessionOptions& AppendExecutionProvider_TensorRT(const OrtTensorRTProviderOptions& provider_options);
|
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};
|
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|
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struct ModelMetadata : Base<OrtModelMetadata> {
|
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@ -336,6 +342,7 @@ struct ModelMetadata : Base<OrtModelMetadata> {
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char* GetGraphName(OrtAllocator* allocator) const;
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char* GetDomain(OrtAllocator* allocator) const;
|
||||
char* GetDescription(OrtAllocator* allocator) const;
|
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char* GetGraphDescription(OrtAllocator* allocator) const;
|
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char** GetCustomMetadataMapKeys(OrtAllocator* allocator, _Out_ int64_t& num_keys) const;
|
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char* LookupCustomMetadataMap(const char* key, OrtAllocator* allocator) const;
|
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int64_t GetVersion() const;
|
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@ -344,6 +351,7 @@ struct ModelMetadata : Base<OrtModelMetadata> {
|
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struct Session : Base<OrtSession> {
|
||||
explicit Session(std::nullptr_t) {}
|
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Session(Env& env, const ORTCHAR_T* model_path, const SessionOptions& options);
|
||||
Session(Env& env, const ORTCHAR_T* model_path, const SessionOptions& options, OrtPrepackedWeightsContainer* prepacked_weights_container);
|
||||
Session(Env& env, const void* model_data, size_t model_data_length, const SessionOptions& options);
|
||||
|
||||
// Run that will allocate the output values
|
||||
|
|
@ -412,14 +420,213 @@ struct TypeInfo : Base<OrtTypeInfo> {
|
|||
};
|
||||
|
||||
struct Value : Base<OrtValue> {
|
||||
// This structure is used to feed sparse tensor values
|
||||
// information for use with FillSparseTensor<Format>() API
|
||||
// if the data type for the sparse tensor values is numeric
|
||||
// use data.p_data, otherwise, use data.str pointer to feed
|
||||
// values. data.str is an array of const char* that are zero terminated.
|
||||
// number of strings in the array must match shape size.
|
||||
// For fully sparse tensors use shape {0} and set p_data/str
|
||||
// to nullptr.
|
||||
struct OrtSparseValuesParam {
|
||||
const int64_t* values_shape;
|
||||
size_t values_shape_len;
|
||||
union {
|
||||
const void* p_data;
|
||||
const char** str;
|
||||
} data;
|
||||
};
|
||||
|
||||
// Provides a way to pass shape in a single
|
||||
// argument
|
||||
struct Shape {
|
||||
const int64_t* shape;
|
||||
size_t shape_len;
|
||||
};
|
||||
|
||||
template <typename T>
|
||||
static Value CreateTensor(const OrtMemoryInfo* info, T* p_data, size_t p_data_element_count, const int64_t* shape, size_t shape_len);
|
||||
static Value CreateTensor(const OrtMemoryInfo* info, void* p_data, size_t p_data_byte_count, const int64_t* shape, size_t shape_len,
|
||||
ONNXTensorElementDataType type);
|
||||
|
||||
#if !defined(DISABLE_SPARSE_TENSORS)
|
||||
/// <summary>
|
||||
/// This is a simple forwarding method to the other overload that helps deducing
|
||||
/// data type enum value from the type of the buffer.
|
||||
/// </summary>
|
||||
/// <typeparam name="T">numeric datatype. This API is not suitable for strings.</typeparam>
|
||||
/// <param name="info">Memory description where the user buffers reside (CPU vs GPU etc)</param>
|
||||
/// <param name="p_data">pointer to the user supplied buffer, use nullptr for fully sparse tensors</param>
|
||||
/// <param name="dense_shape">a would be dense shape of the tensor</param>
|
||||
/// <param name="values_shape">non zero values shape. Use a single 0 shape for fully sparse tensors.</param>
|
||||
/// <returns></returns>
|
||||
template <typename T>
|
||||
static Value CreateSparseTensor(const OrtMemoryInfo* info, T* p_data, const Shape& dense_shape,
|
||||
const Shape& values_shape);
|
||||
|
||||
/// <summary>
|
||||
/// Creates an OrtValue instance containing SparseTensor. This constructs
|
||||
/// a sparse tensor that makes use of user allocated buffers. It does not make copies
|
||||
/// of the user provided data and does not modify it. The lifespan of user provided buffers should
|
||||
/// eclipse the life span of the resulting OrtValue. This call constructs an instance that only contain
|
||||
/// a pointer to non-zero values. To fully populate the sparse tensor call Use<Format>Indices() API below
|
||||
/// to supply a sparse format specific indices.
|
||||
/// This API is not suitable for string data. Use CreateSparseTensor() with allocator specified so strings
|
||||
/// can be properly copied into the allocated buffer.
|
||||
/// </summary>
|
||||
/// <param name="info">Memory description where the user buffers reside (CPU vs GPU etc)</param>
|
||||
/// <param name="p_data">pointer to the user supplied buffer, use nullptr for fully sparse tensors</param>
|
||||
/// <param name="dense_shape">a would be dense shape of the tensor</param>
|
||||
/// <param name="values_shape">non zero values shape. Use a single 0 shape for fully sparse tensors.</param>
|
||||
/// <param name="type">data type</param>
|
||||
/// <returns>Ort::Value instance containing SparseTensor</returns>
|
||||
static Value CreateSparseTensor(const OrtMemoryInfo* info, void* p_data, const Shape& dense_shape,
|
||||
const Shape& values_shape, ONNXTensorElementDataType type);
|
||||
|
||||
/// <summary>
|
||||
/// Supplies COO format specific indices and marks the contained sparse tensor as being a COO format tensor.
|
||||
/// Values are supplied with a CreateSparseTensor() API. The supplied indices are not copied and the user
|
||||
/// allocated buffers lifespan must eclipse that of the OrtValue.
|
||||
/// The location of the indices is assumed to be the same as specified by OrtMemoryInfo argument at the creation time.
|
||||
/// </summary>
|
||||
/// <param name="indices_data">pointer to the user allocated buffer with indices. Use nullptr for fully sparse tensors.</param>
|
||||
/// <param name="indices_num">number of indices entries. Use 0 for fully sparse tensors</param>
|
||||
void UseCooIndices(int64_t* indices_data, size_t indices_num);
|
||||
|
||||
/// <summary>
|
||||
/// Supplies CSR format specific indices and marks the contained sparse tensor as being a CSR format tensor.
|
||||
/// Values are supplied with a CreateSparseTensor() API. The supplied indices are not copied and the user
|
||||
/// allocated buffers lifespan must eclipse that of the OrtValue.
|
||||
/// The location of the indices is assumed to be the same as specified by OrtMemoryInfo argument at the creation time.
|
||||
/// </summary>
|
||||
/// <param name="inner_data">pointer to the user allocated buffer with inner indices or nullptr for fully sparse tensors</param>
|
||||
/// <param name="inner_num">number of csr inner indices or 0 for fully sparse tensors</param>
|
||||
/// <param name="outer_data">pointer to the user allocated buffer with outer indices or nullptr for fully sparse tensors</param>
|
||||
/// <param name="outer_num">number of csr outer indices or 0 for fully sparse tensors</param>
|
||||
void UseCsrIndices(int64_t* inner_data, size_t inner_num, int64_t* outer_data, size_t outer_num);
|
||||
|
||||
/// <summary>
|
||||
/// Supplies BlockSparse format specific indices and marks the contained sparse tensor as being a BlockSparse format tensor.
|
||||
/// Values are supplied with a CreateSparseTensor() API. The supplied indices are not copied and the user
|
||||
/// allocated buffers lifespan must eclipse that of the OrtValue.
|
||||
/// The location of the indices is assumed to be the same as specified by OrtMemoryInfo argument at the creation time.
|
||||
/// </summary>
|
||||
/// <param name="indices_shape">indices shape or a {0} for fully sparse</param>
|
||||
/// <param name="indices_data">user allocated buffer with indices or nullptr for fully spare tensors</param>
|
||||
void UseBlockSparseIndices(const Shape& indices_shape, int32_t* indices_data);
|
||||
|
||||
#endif // !defined(DISABLE_SPARSE_TENSORS)
|
||||
|
||||
template <typename T>
|
||||
static Value CreateTensor(OrtAllocator* allocator, const int64_t* shape, size_t shape_len);
|
||||
static Value CreateTensor(OrtAllocator* allocator, const int64_t* shape, size_t shape_len, ONNXTensorElementDataType type);
|
||||
|
||||
#if !defined(DISABLE_SPARSE_TENSORS)
|
||||
/// <summary>
|
||||
/// This is a simple forwarding method the below CreateSparseTensor.
|
||||
/// This helps to specify data type enum in terms of C++ data type.
|
||||
/// Use CreateSparseTensor<T>
|
||||
/// </summary>
|
||||
/// <typeparam name="T">numeric data type only. String data enum must be specified explicitly.</typeparam>
|
||||
/// <param name="allocator">allocator to use</param>
|
||||
/// <param name="dense_shape">a would be dense shape of the tensor</param>
|
||||
/// <returns>Ort::Value</returns>
|
||||
template <typename T>
|
||||
static Value CreateSparseTensor(OrtAllocator* allocator, const Shape& dense_shape);
|
||||
|
||||
/// <summary>
|
||||
/// Creates an instance of OrtValue containing sparse tensor. The created instance has no data.
|
||||
/// The data must be supplied by on of the FillSparseTensor<Format>() methods that take both non-zero values
|
||||
/// and indices. The data will be copied into a buffer that would be allocated using the supplied allocator.
|
||||
/// Use this API to create OrtValues that contain sparse tensors with all supported data types including
|
||||
/// strings.
|
||||
/// </summary>
|
||||
/// <param name="allocator">allocator to use. The allocator lifespan must eclipse that of the resulting OrtValue</param>
|
||||
/// <param name="dense_shape">a would be dense shape of the tensor</param>
|
||||
/// <param name="type">data type</param>
|
||||
/// <returns>an instance of Ort::Value</returns>
|
||||
static Value CreateSparseTensor(OrtAllocator* allocator, const Shape& dense_shape, ONNXTensorElementDataType type);
|
||||
|
||||
/// <summary>
|
||||
/// The API will allocate memory using the allocator instance supplied to the CreateSparseTensor() API
|
||||
/// and copy the values and COO indices into it. If data_mem_info specifies that the data is located
|
||||
/// at difference device than the allocator, a X-device copy will be performed if possible.
|
||||
/// </summary>
|
||||
/// <param name="data_mem_info">specified buffer memory description</param>
|
||||
/// <param name="values_param">values buffer information.</param>
|
||||
/// <param name="indices_data">coo indices buffer or nullptr for fully sparse data</param>
|
||||
/// <param name="indices_num">number of COO indices or 0 for fully sparse data</param>
|
||||
void FillSparseTensorCoo(const OrtMemoryInfo* data_mem_info, const OrtSparseValuesParam& values_param,
|
||||
const int64_t* indices_data, size_t indices_num);
|
||||
|
||||
/// <summary>
|
||||
/// The API will allocate memory using the allocator instance supplied to the CreateSparseTensor() API
|
||||
/// and copy the values and CSR indices into it. If data_mem_info specifies that the data is located
|
||||
/// at difference device than the allocator, a X-device copy will be performed if possible.
|
||||
/// </summary>
|
||||
/// <param name="data_mem_info">specified buffer memory description</param>
|
||||
/// <param name="values_param">values buffer information</param>
|
||||
/// <param name="inner_indices_data">csr inner indices pointer or nullptr for fully sparse tensors</param>
|
||||
/// <param name="inner_indices_num">number of csr inner indices or 0 for fully sparse tensors</param>
|
||||
/// <param name="outer_indices_data">pointer to csr indices data or nullptr for fully sparse tensors</param>
|
||||
/// <param name="outer_indices_num">number of csr outer indices or 0</param>
|
||||
void FillSparseTensorCsr(const OrtMemoryInfo* data_mem_info,
|
||||
const OrtSparseValuesParam& values,
|
||||
const int64_t* inner_indices_data, size_t inner_indices_num,
|
||||
const int64_t* outer_indices_data, size_t outer_indices_num);
|
||||
|
||||
/// <summary>
|
||||
/// The API will allocate memory using the allocator instance supplied to the CreateSparseTensor() API
|
||||
/// and copy the values and BlockSparse indices into it. If data_mem_info specifies that the data is located
|
||||
/// at difference device than the allocator, a X-device copy will be performed if possible.
|
||||
/// </summary>
|
||||
/// <param name="data_mem_info">specified buffer memory description</param>
|
||||
/// <param name="values_param">values buffer information</param>
|
||||
/// <param name="indices_shape">indices shape. use {0} for fully sparse tensors</param>
|
||||
/// <param name="indices_data">pointer to indices data or nullptr for fully sparse tensors</param>
|
||||
void FillSparseTensorBlockSparse(const OrtMemoryInfo* data_mem_info,
|
||||
const OrtSparseValuesParam& values,
|
||||
const Shape& indices_shape,
|
||||
const int32_t* indices_data);
|
||||
|
||||
/// <summary>
|
||||
/// The API returns the sparse data format this OrtValue holds in a sparse tensor.
|
||||
/// If the sparse tensor was not fully constructed, i.e. Use*() or Fill*() API were not used
|
||||
/// the value returned is ORT_SPARSE_UNDEFINED.
|
||||
/// </summary>
|
||||
/// <returns>Format enum</returns>
|
||||
OrtSparseFormat GetSparseFormat() const;
|
||||
|
||||
/// <summary>
|
||||
/// The API returns type and shape information for stored non-zero values of the
|
||||
/// sparse tensor. Use GetSparseTensorValues() to obtain values buffer pointer.
|
||||
/// </summary>
|
||||
/// <returns>TensorTypeAndShapeInfo values information</returns>
|
||||
TensorTypeAndShapeInfo GetSparseTensorValuesTypeAndShapeInfo() const;
|
||||
|
||||
/// <summary>
|
||||
/// The API returns type and shape information for the specified indices. Each supported
|
||||
/// indices have their own enum values even if a give format has more than one kind of indices.
|
||||
/// Use GetSparseTensorIndicesData() to obtain pointer to indices buffer.
|
||||
/// </summary>
|
||||
/// <param name="">enum requested</param>
|
||||
/// <returns>type and shape information</returns>
|
||||
TensorTypeAndShapeInfo GetSparseTensorIndicesTypeShapeInfo(OrtSparseIndicesFormat) const;
|
||||
|
||||
/// <summary>
|
||||
/// The API retrieves a pointer to the internal indices buffer. The API merely performs
|
||||
/// a convenience data type casting on the return type pointer. Make sure you are requesting
|
||||
/// the right type, use GetSparseTensorIndicesTypeShapeInfo();
|
||||
/// </summary>
|
||||
/// <typeparam name="T">type to cast to</typeparam>
|
||||
/// <param name="indices_format">requested indices kind</param>
|
||||
/// <param name="num_indices">number of indices entries</param>
|
||||
/// <returns>Pinter to the internal sparse tensor buffer containing indices. Do not free this pointer.</returns>
|
||||
template <typename T>
|
||||
const T* GetSparseTensorIndicesData(OrtSparseIndicesFormat indices_format, size_t& num_indices) const;
|
||||
|
||||
#endif // !defined(DISABLE_SPARSE_TENSORS)
|
||||
|
||||
static Value CreateMap(Value& keys, Value& values);
|
||||
static Value CreateSequence(std::vector<Value>& values);
|
||||
|
||||
|
|
@ -435,10 +642,40 @@ struct Value : Base<OrtValue> {
|
|||
Value& operator=(Value&&) = default;
|
||||
|
||||
bool IsTensor() const;
|
||||
|
||||
#if !defined(DISABLE_SPARSE_TENSORS)
|
||||
/// <summary>
|
||||
/// Returns true if the OrtValue contains a sparse tensor
|
||||
/// </summary>
|
||||
/// <returns></returns>
|
||||
bool IsSparseTensor() const;
|
||||
#endif
|
||||
|
||||
size_t GetCount() const; // If a non tensor, returns 2 for map and N for sequence, where N is the number of elements
|
||||
Value GetValue(int index, OrtAllocator* allocator) const;
|
||||
|
||||
/// <summary>
|
||||
/// This API returns a full length of string data contained within either a tensor or a sparse Tensor.
|
||||
/// For sparse tensor it returns a full length of stored non-empty strings (values). The API is useful
|
||||
/// for allocating necessary memory and calling GetStringTensorContent().
|
||||
/// </summary>
|
||||
/// <returns>total length of UTF-8 encoded bytes contained. No zero terminators counted.</returns>
|
||||
size_t GetStringTensorDataLength() const;
|
||||
|
||||
/// <summary>
|
||||
/// The API copies all of the UTF-8 encoded string data contained within a tensor or a sparse tensor
|
||||
/// into a supplied buffer. Use GetStringTensorDataLength() to find out the length of the buffer to allocate.
|
||||
/// The user must also allocate offsets buffer with the number of entries equal to that of the contained
|
||||
/// strings.
|
||||
///
|
||||
/// Strings are always assumed to be on CPU, no X-device copy.
|
||||
/// </summary>
|
||||
/// <param name="buffer">user allocated buffer</param>
|
||||
/// <param name="buffer_length">length in bytes of the allocated buffer</param>
|
||||
/// <param name="offsets">a pointer to the offsets user allocated buffer</param>
|
||||
/// <param name="offsets_count">count of offsets, must be equal to the number of strings contained.
|
||||
/// that can be obtained from the shape of the tensor or from GetSparseTensorValuesTypeAndShapeInfo()
|
||||
/// for sparse tensors</param>
|
||||
void GetStringTensorContent(void* buffer, size_t buffer_length, size_t* offsets, size_t offsets_count) const;
|
||||
|
||||
template <typename T>
|
||||
|
|
@ -447,13 +684,54 @@ struct Value : Base<OrtValue> {
|
|||
template <typename T>
|
||||
const T* GetTensorData() const;
|
||||
|
||||
#if !defined(DISABLE_SPARSE_TENSORS)
|
||||
/// <summary>
|
||||
/// The API returns a pointer to an internal buffer of the sparse tensor
|
||||
/// containing non-zero values. The API merely does casting. Make sure you
|
||||
/// are requesting the right data type by calling GetSparseTensorValuesTypeAndShapeInfo()
|
||||
/// first.
|
||||
/// </summary>
|
||||
/// <typeparam name="T">numeric data types only. Use GetStringTensor*() to retrieve strings.</typeparam>
|
||||
/// <returns>a pointer to the internal values buffer. Do not free this pointer.</returns>
|
||||
template <typename T>
|
||||
const T* GetSparseTensorValues() const;
|
||||
#endif
|
||||
|
||||
template <typename T>
|
||||
T& At(const std::vector<int64_t>& location);
|
||||
|
||||
/// <summary>
|
||||
/// The API returns type information for data contained in a tensor. For sparse
|
||||
/// tensors it returns type information for contained non-zero values.
|
||||
/// It returns dense shape for sparse tensors.
|
||||
/// </summary>
|
||||
/// <returns>TypeInfo</returns>
|
||||
TypeInfo GetTypeInfo() const;
|
||||
|
||||
/// <summary>
|
||||
/// The API returns type information for data contained in a tensor. For sparse
|
||||
/// tensors it returns type information for contained non-zero values.
|
||||
/// It returns dense shape for sparse tensors.
|
||||
/// </summary>
|
||||
/// <returns>TensorTypeAndShapeInfo</returns>
|
||||
TensorTypeAndShapeInfo GetTensorTypeAndShapeInfo() const;
|
||||
|
||||
/// <summary>
|
||||
/// The API returns a byte length of UTF-8 encoded string element
|
||||
/// contained in either a tensor or a spare tensor values.
|
||||
/// </summary>
|
||||
/// <param name="element_index"></param>
|
||||
/// <returns>byte length for the specified string element</returns>
|
||||
size_t GetStringTensorElementLength(size_t element_index) const;
|
||||
|
||||
/// <summary>
|
||||
/// The API copies UTF-8 encoded bytes for the requested string element
|
||||
/// contained within a tensor or a sparse tensor into a provided buffer.
|
||||
/// Use GetStringTensorElementLength() to obtain the length of the buffer to allocate.
|
||||
/// </summary>
|
||||
/// <param name="buffer_length"></param>
|
||||
/// <param name="element_index"></param>
|
||||
/// <param name="buffer"></param>
|
||||
void GetStringTensorElement(size_t buffer_length, size_t element_index, void* buffer) const;
|
||||
|
||||
void FillStringTensor(const char* const* s, size_t s_len);
|
||||
|
|
@ -563,7 +841,6 @@ struct ArenaCfg : Base<OrtArenaCfg> {
|
|||
* \param arena_extend_strategy - use -1 to allow ORT to choose the default, 0 = kNextPowerOfTwo, 1 = kSameAsRequested
|
||||
* \param initial_chunk_size_bytes - use -1 to allow ORT to choose the default
|
||||
* \param max_dead_bytes_per_chunk - use -1 to allow ORT to choose the default
|
||||
* \return an instance of ArenaCfg
|
||||
* See docs/C_API.md for details on what the following parameters mean and how to choose these values
|
||||
*/
|
||||
ArenaCfg(size_t max_mem, int arena_extend_strategy, int initial_chunk_size_bytes, int max_dead_bytes_per_chunk);
|
||||
|
|
@ -576,7 +853,7 @@ struct ArenaCfg : Base<OrtArenaCfg> {
|
|||
struct CustomOpApi {
|
||||
CustomOpApi(const OrtApi& api) : api_(api) {}
|
||||
|
||||
template <typename T> // T is only implemented for float, int64_t, and string
|
||||
template <typename T> // T is only implemented for std::vector<float>, std::vector<int64_t>, float, int64_t, and string
|
||||
T KernelInfoGetAttribute(_In_ const OrtKernelInfo* info, _In_ const char* name);
|
||||
|
||||
OrtTensorTypeAndShapeInfo* GetTensorTypeAndShape(_In_ const OrtValue* value);
|
||||
|
|
@ -621,10 +898,23 @@ struct CustomOpBase : OrtCustomOp {
|
|||
|
||||
OrtCustomOp::KernelCompute = [](void* op_kernel, OrtKernelContext* context) { static_cast<TKernel*>(op_kernel)->Compute(context); };
|
||||
OrtCustomOp::KernelDestroy = [](void* op_kernel) { delete static_cast<TKernel*>(op_kernel); };
|
||||
|
||||
OrtCustomOp::GetInputCharacteristic = [](const OrtCustomOp* this_, size_t index) { return static_cast<const TOp*>(this_)->GetInputCharacteristic(index); };
|
||||
OrtCustomOp::GetOutputCharacteristic = [](const OrtCustomOp* this_, size_t index) { return static_cast<const TOp*>(this_)->GetOutputCharacteristic(index); };
|
||||
}
|
||||
|
||||
// Default implementation of GetExecutionProviderType that returns nullptr to default to the CPU provider
|
||||
const char* GetExecutionProviderType() const { return nullptr; }
|
||||
|
||||
// Default implementations of GetInputCharacteristic() and GetOutputCharacteristic() below
|
||||
// (inputs and outputs are required by default)
|
||||
OrtCustomOpInputOutputCharacteristic GetInputCharacteristic(size_t /*index*/) const {
|
||||
return OrtCustomOpInputOutputCharacteristic::INPUT_OUTPUT_REQUIRED;
|
||||
}
|
||||
|
||||
OrtCustomOpInputOutputCharacteristic GetOutputCharacteristic(size_t /*index*/) const {
|
||||
return OrtCustomOpInputOutputCharacteristic::INPUT_OUTPUT_REQUIRED;
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace Ort
|
||||
|
|
|
|||
|
|
@ -385,6 +385,11 @@ inline const char* RunOptions::GetRunTag() const {
|
|||
return out;
|
||||
}
|
||||
|
||||
inline RunOptions& RunOptions::AddConfigEntry(const char* config_key, const char* config_value) {
|
||||
ThrowOnError(GetApi().AddRunConfigEntry(p_, config_key, config_value));
|
||||
return *this;
|
||||
}
|
||||
|
||||
inline RunOptions& RunOptions::SetTerminate() {
|
||||
ThrowOnError(GetApi().RunOptionsSetTerminate(p_));
|
||||
return *this;
|
||||
|
|
@ -435,6 +440,11 @@ inline SessionOptions& SessionOptions::DisableProfiling() {
|
|||
return *this;
|
||||
}
|
||||
|
||||
inline SessionOptions& SessionOptions::EnableOrtCustomOps() {
|
||||
ThrowOnError(GetApi().EnableOrtCustomOps(p_));
|
||||
return *this;
|
||||
}
|
||||
|
||||
inline SessionOptions& SessionOptions::EnableMemPattern() {
|
||||
ThrowOnError(GetApi().EnableMemPattern(p_));
|
||||
return *this;
|
||||
|
|
@ -490,6 +500,16 @@ inline SessionOptions& SessionOptions::AppendExecutionProvider_CUDA(const OrtCUD
|
|||
return *this;
|
||||
}
|
||||
|
||||
inline SessionOptions& SessionOptions::AppendExecutionProvider_ROCM(const OrtROCMProviderOptions& provider_options) {
|
||||
ThrowOnError(GetApi().SessionOptionsAppendExecutionProvider_ROCM(p_, &provider_options));
|
||||
return *this;
|
||||
}
|
||||
|
||||
inline SessionOptions& SessionOptions::AppendExecutionProvider_TensorRT(const OrtTensorRTProviderOptions& provider_options) {
|
||||
ThrowOnError(GetApi().SessionOptionsAppendExecutionProvider_TensorRT(p_, &provider_options));
|
||||
return *this;
|
||||
}
|
||||
|
||||
inline SessionOptions& SessionOptions::AppendExecutionProvider_OpenVINO(const OrtOpenVINOProviderOptions& provider_options) {
|
||||
ThrowOnError(GetApi().SessionOptionsAppendExecutionProvider_OpenVINO(p_, &provider_options));
|
||||
return *this;
|
||||
|
|
@ -499,6 +519,11 @@ inline Session::Session(Env& env, const ORTCHAR_T* model_path, const SessionOpti
|
|||
ThrowOnError(GetApi().CreateSession(env, model_path, options, &p_));
|
||||
}
|
||||
|
||||
inline Session::Session(Env& env, const ORTCHAR_T* model_path, const SessionOptions& options,
|
||||
OrtPrepackedWeightsContainer* prepacked_weights_container) {
|
||||
ThrowOnError(GetApi().CreateSessionWithPrepackedWeightsContainer(env, model_path, options, prepacked_weights_container, &p_));
|
||||
}
|
||||
|
||||
inline Session::Session(Env& env, const void* model_data, size_t model_data_length, const SessionOptions& options) {
|
||||
ThrowOnError(GetApi().CreateSessionFromArray(env, model_data, model_data_length, options, &p_));
|
||||
}
|
||||
|
|
@ -602,6 +627,12 @@ inline char* ModelMetadata::GetDescription(OrtAllocator* allocator) const {
|
|||
return out;
|
||||
}
|
||||
|
||||
inline char* ModelMetadata::GetGraphDescription(OrtAllocator* allocator) const {
|
||||
char* out;
|
||||
ThrowOnError(GetApi().ModelMetadataGetGraphDescription(p_, allocator, &out));
|
||||
return out;
|
||||
}
|
||||
|
||||
inline char* ModelMetadata::LookupCustomMetadataMap(const char* key, OrtAllocator* allocator) const {
|
||||
char* out;
|
||||
ThrowOnError(GetApi().ModelMetadataLookupCustomMetadataMap(p_, allocator, key, &out));
|
||||
|
|
@ -724,6 +755,84 @@ inline Value Value::CreateTensor(const OrtMemoryInfo* info, void* p_data, size_t
|
|||
return Value{out};
|
||||
}
|
||||
|
||||
#if !defined(DISABLE_SPARSE_TENSORS)
|
||||
template <typename T>
|
||||
inline Value Value::CreateSparseTensor(const OrtMemoryInfo* info, T* p_data, const Shape& dense_shape,
|
||||
const Shape& values_shape) {
|
||||
return CreateSparseTensor(info, p_data, dense_shape, values_shape, TypeToTensorType<T>::type);
|
||||
}
|
||||
|
||||
inline Value Value::CreateSparseTensor(const OrtMemoryInfo* info, void* p_data, const Shape& dense_shape,
|
||||
const Shape& values_shape, ONNXTensorElementDataType type) {
|
||||
OrtValue* out;
|
||||
ThrowOnError(GetApi().CreateSparseTensorWithValuesAsOrtValue(info, p_data, dense_shape.shape, dense_shape.shape_len,
|
||||
values_shape.shape, values_shape.shape_len, type, &out));
|
||||
return Value{out};
|
||||
}
|
||||
|
||||
inline void Value::FillSparseTensorCoo(const OrtMemoryInfo* mem_info, const OrtSparseValuesParam& values_param,
|
||||
const int64_t* indices_data, size_t indices_num) {
|
||||
ThrowOnError(GetApi().FillSparseTensorCoo(p_, mem_info, values_param.values_shape,
|
||||
values_param.values_shape_len, values_param.data.p_data,
|
||||
indices_data, indices_num));
|
||||
}
|
||||
|
||||
inline void Value::FillSparseTensorCsr(const OrtMemoryInfo* data_mem_info,
|
||||
const OrtSparseValuesParam& values,
|
||||
const int64_t* inner_indices_data, size_t inner_indices_num,
|
||||
const int64_t* outer_indices_data, size_t outer_indices_num) {
|
||||
ThrowOnError(GetApi().FillSparseTensorCsr(p_, data_mem_info, values.values_shape, values.values_shape_len, values.data.p_data,
|
||||
inner_indices_data, inner_indices_num,
|
||||
outer_indices_data, outer_indices_num));
|
||||
}
|
||||
|
||||
inline void Value::FillSparseTensorBlockSparse(const OrtMemoryInfo* data_mem_info,
|
||||
const OrtSparseValuesParam& values,
|
||||
const Shape& indices_shape,
|
||||
const int32_t* indices_data) {
|
||||
ThrowOnError(GetApi().FillSparseTensorBlockSparse(p_, data_mem_info, values.values_shape, values.values_shape_len, values.data.p_data,
|
||||
indices_shape.shape, indices_shape.shape_len,
|
||||
indices_data));
|
||||
}
|
||||
|
||||
inline void Value::UseCooIndices(int64_t* indices_data, size_t indices_num) {
|
||||
ThrowOnError(GetApi().UseCooIndices(p_, indices_data, indices_num));
|
||||
}
|
||||
|
||||
inline void Value::UseCsrIndices(int64_t* inner_data, size_t inner_num, int64_t* outer_data, size_t outer_num) {
|
||||
ThrowOnError(GetApi().UseCsrIndices(p_, inner_data, inner_num, outer_data, outer_num));
|
||||
}
|
||||
|
||||
inline void Value::UseBlockSparseIndices(const Shape& indices_shape, int32_t* indices_data) {
|
||||
ThrowOnError(GetApi().UseBlockSparseIndices(p_, indices_shape.shape, indices_shape.shape_len, indices_data));
|
||||
}
|
||||
|
||||
inline OrtSparseFormat Value::GetSparseFormat() const {
|
||||
OrtSparseFormat format;
|
||||
ThrowOnError(GetApi().GetSparseTensorFormat(p_, &format));
|
||||
return format;
|
||||
}
|
||||
|
||||
inline TensorTypeAndShapeInfo Value::GetSparseTensorValuesTypeAndShapeInfo() const {
|
||||
OrtTensorTypeAndShapeInfo* output;
|
||||
ThrowOnError(GetApi().GetSparseTensorValuesTypeAndShape(p_, &output));
|
||||
return TensorTypeAndShapeInfo{output};
|
||||
}
|
||||
|
||||
inline TensorTypeAndShapeInfo Value::GetSparseTensorIndicesTypeShapeInfo(OrtSparseIndicesFormat indices_format) const {
|
||||
OrtTensorTypeAndShapeInfo* output;
|
||||
ThrowOnError(GetApi().GetSparseTensorIndicesTypeShape(p_, indices_format, &output));
|
||||
return TensorTypeAndShapeInfo{output};
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
inline const T* Value::GetSparseTensorIndicesData(OrtSparseIndicesFormat indices_format, size_t& num_indices) const {
|
||||
const void* out;
|
||||
ThrowOnError(GetApi().GetSparseTensorIndices(p_, indices_format, &num_indices, &out));
|
||||
return reinterpret_cast<const T*>(out);
|
||||
}
|
||||
#endif // !defined(DISABLE_SPARSE_TENSORS)
|
||||
|
||||
template <typename T>
|
||||
inline Value Value::CreateTensor(OrtAllocator* allocator, const int64_t* shape, size_t shape_len) {
|
||||
return CreateTensor(allocator, shape, shape_len, TypeToTensorType<T>::type);
|
||||
|
|
@ -735,6 +844,20 @@ inline Value Value::CreateTensor(OrtAllocator* allocator, const int64_t* shape,
|
|||
return Value{out};
|
||||
}
|
||||
|
||||
#if !defined(DISABLE_SPARSE_TENSORS)
|
||||
template <typename T>
|
||||
inline Value Value::CreateSparseTensor(OrtAllocator* allocator, const Shape& dense_shape) {
|
||||
return CreateSparseTensor(allocator, dense_shape, TypeToTensorType<T>::type);
|
||||
}
|
||||
|
||||
inline Value Value::CreateSparseTensor(OrtAllocator* allocator, const Shape& dense_shape,
|
||||
ONNXTensorElementDataType type) {
|
||||
OrtValue* out;
|
||||
ThrowOnError(GetApi().CreateSparseTensorAsOrtValue(allocator, dense_shape.shape, dense_shape.shape_len, type, &out));
|
||||
return Value{out};
|
||||
}
|
||||
#endif // !defined(DISABLE_SPARSE_TENSORS)
|
||||
|
||||
inline Value Value::CreateMap(Value& keys, Value& values) {
|
||||
OrtValue* out;
|
||||
OrtValue* inputs[2] = {keys, values};
|
||||
|
|
@ -767,6 +890,14 @@ inline bool Value::IsTensor() const {
|
|||
return out != 0;
|
||||
}
|
||||
|
||||
#if !defined(DISABLE_SPARSE_TENSORS)
|
||||
inline bool Value::IsSparseTensor() const {
|
||||
int out;
|
||||
ThrowOnError(GetApi().IsSparseTensor(p_, &out));
|
||||
return out != 0;
|
||||
}
|
||||
#endif
|
||||
|
||||
inline size_t Value::GetCount() const {
|
||||
size_t out;
|
||||
ThrowOnError(GetApi().GetValueCount(p_, &out));
|
||||
|
|
@ -821,6 +952,15 @@ const T* Value::GetTensorData() const {
|
|||
return out;
|
||||
}
|
||||
|
||||
#if !defined(DISABLE_SPARSE_TENSORS)
|
||||
template <typename T>
|
||||
inline const T* Value::GetSparseTensorValues() const {
|
||||
const void* out;
|
||||
ThrowOnError(GetApi().GetSparseTensorValues(p_, &out));
|
||||
return reinterpret_cast<const T*>(out);
|
||||
}
|
||||
#endif // !defined(DISABLE_SPARSE_TENSORS)
|
||||
|
||||
template <typename T>
|
||||
inline T& Value::At(const std::vector<int64_t>& location) {
|
||||
static_assert(!std::is_same<T, std::string>::value, "this api does not support std::string");
|
||||
|
|
@ -866,13 +1006,11 @@ template <>
|
|||
inline std::string CustomOpApi::KernelInfoGetAttribute<std::string>(_In_ const OrtKernelInfo* info, _In_ const char* name) {
|
||||
size_t size = 0;
|
||||
std::string out;
|
||||
|
||||
// Feed nullptr for the data buffer to query the true size of the string attribute
|
||||
OrtStatus* status = api_.KernelInfoGetAttribute_string(info, name, nullptr, &size);
|
||||
|
||||
// The status should be ORT_INVALID_ARGUMENT because the size is insufficient to hold the string
|
||||
if (status == nullptr || api_.GetErrorCode(status) == ORT_INVALID_ARGUMENT) {
|
||||
if (status != nullptr) {
|
||||
api_.ReleaseStatus(status);
|
||||
}
|
||||
if (status == nullptr) {
|
||||
out.resize(size);
|
||||
ThrowOnError(api_.KernelInfoGetAttribute_string(info, name, &out[0], &size));
|
||||
out.resize(size - 1); // remove the terminating character '\0'
|
||||
|
|
@ -882,6 +1020,39 @@ inline std::string CustomOpApi::KernelInfoGetAttribute<std::string>(_In_ const O
|
|||
return out;
|
||||
}
|
||||
|
||||
template <>
|
||||
inline std::vector<float> CustomOpApi::KernelInfoGetAttribute(_In_ const OrtKernelInfo* info, _In_ const char* name) {
|
||||
size_t size = 0;
|
||||
std::vector<float> out;
|
||||
|
||||
// Feed nullptr for the data buffer to query the true size of the attribute
|
||||
OrtStatus* status = api_.KernelInfoGetAttributeArray_float(info, name, nullptr, &size);
|
||||
|
||||
if (status == nullptr) {
|
||||
out.resize(size);
|
||||
ThrowOnError(api_.KernelInfoGetAttributeArray_float(info, name, out.data(), &size));
|
||||
} else {
|
||||
ThrowOnError(status);
|
||||
}
|
||||
return out;
|
||||
}
|
||||
|
||||
template <>
|
||||
inline std::vector<int64_t> CustomOpApi::KernelInfoGetAttribute(_In_ const OrtKernelInfo* info, _In_ const char* name) {
|
||||
size_t size = 0;
|
||||
std::vector<int64_t> out;
|
||||
|
||||
// Feed nullptr for the data buffer to query the true size of the attribute
|
||||
OrtStatus* status = api_.KernelInfoGetAttributeArray_int64(info, name, nullptr, &size);
|
||||
|
||||
if (status == nullptr) {
|
||||
out.resize(size);
|
||||
ThrowOnError(api_.KernelInfoGetAttributeArray_int64(info, name, out.data(), &size));
|
||||
} else {
|
||||
ThrowOnError(status);
|
||||
}
|
||||
return out;
|
||||
}
|
||||
inline OrtTensorTypeAndShapeInfo* CustomOpApi::GetTensorTypeAndShape(_In_ const OrtValue* value) {
|
||||
OrtTensorTypeAndShapeInfo* out;
|
||||
ThrowOnError(api_.GetTensorTypeAndShape(value, &out));
|
||||
|
|
|
|||
|
|
@ -1,3 +1,3 @@
|
|||
pytest
|
||||
transformers >= 4.9.2
|
||||
tensorflow_text >=2.5.0, <2.6.0
|
||||
tensorflow_text >=2.5.0
|
||||
|
|
|
|||
|
|
@ -7,6 +7,7 @@ import torch
|
|||
import torchvision
|
||||
import onnxruntime as _ort
|
||||
|
||||
from distutils.version import LooseVersion
|
||||
from torch.onnx import register_custom_op_symbolic
|
||||
from onnxruntime_extensions import (
|
||||
PyOp,
|
||||
|
|
@ -95,7 +96,10 @@ class TestPyTorchCustomOp(unittest.TestCase):
|
|||
TestPyTorchCustomOp._hooked = True
|
||||
return x
|
||||
|
||||
@unittest.skipIf(platform.system() == 'Darwin', "pytorch.onnx crashed for this case!")
|
||||
@unittest.skipIf(
|
||||
(platform.system() == 'Darwin') or (LooseVersion(_ort.__version__) > LooseVersion("1.11")),
|
||||
"pytorch.onnx crashed for this case! and test asserts with higher versions of ort"
|
||||
)
|
||||
def test_pyop_hooking(self): # type: () -> None
|
||||
model = torchvision.models.mobilenet_v2(pretrained=False)
|
||||
x = torch.rand(1, 3, 224, 224)
|
||||
|
|
|
|||
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