From 77b280fdfa20060b94c373718ab4d9ddf3465834 Mon Sep 17 00:00:00 2001 From: chensuyue Date: Fri, 14 Jun 2024 23:15:50 +0800 Subject: [PATCH 1/6] remove neural coder, neural insights, neural solution from master branch Signed-off-by: chensuyue --- .../code-scan-neural-insights.yaml | 59 - .../code-scan-neural-solution.yaml | 59 - .../scripts/codeScan/pydocstyle/pydocstyle.sh | 5 - .../pydocstyle/scan_path_neural_insights.txt | 4 - .../pydocstyle/scan_path_neural_solution.txt | 4 - .../scripts/codeScan/pylint/pylint.sh | 16 - .../scripts/install_neural_insights.sh | 8 - .../scripts/install_neural_solution.sh | 27 - .azure-pipelines/scripts/ut/run_ncoder.sh | 26 - .../scripts/ut/run_neural_insights.sh | 41 - .../scripts/ut/run_neural_solution.sh | 43 - .azure-pipelines/ut-ncoder.yml | 33 - .azure-pipelines/ut-neural-insights.yaml | 33 - .azure-pipelines/ut-neural-solution.yaml | 33 - .github/checkgroup.yml | 44 - .pre-commit-config.yaml | 14 +- README.md | 11 - docs/build_docs/build.sh | 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a/.azure-pipelines/code-scan-neural-insights.yaml +++ /dev/null @@ -1,59 +0,0 @@ -trigger: none - -pr: - autoCancel: true - drafts: false - branches: - include: - - master - paths: - include: - - neural_insights - - setup.py - - .azure-pipelines/code-scan-neural-insights.yml - -pool: - vmImage: "ubuntu-latest" - -variables: - CODE_SCAN_LOG_PATH: ".azure-pipelines/scripts/codeScan/scanLog" - -stages: - - stage: DocStyleCodeScan - displayName: DocStyle Code Scan - dependsOn: [] - jobs: - - job: DocStyle - displayName: DocStyle - steps: - - template: template/code-scan-template.yml - parameters: - codeScanFileName: "pydocstyle" - uploadPath: "pydocstyle.log" - scanModule: "neural_insights" - - - stage: BanditCodeScan - displayName: Bandit Code Scan - dependsOn: [] - jobs: - - job: Bandit - displayName: Bandit - steps: - - template: template/code-scan-template.yml - parameters: - codeScanFileName: "bandit" - uploadPath: "bandit.log" - scanModule: "neural_insights" - - - stage: PylintCodeScan - displayName: Pylint Code Scan - dependsOn: [] - jobs: - - job: Pylint - displayName: Pylint - steps: - - template: template/code-scan-template.yml - parameters: - codeScanFileName: "pylint" - uploadPath: "pylint.json" - scanModule: "neural_insights" diff --git a/.azure-pipelines/code-scan-neural-solution.yaml b/.azure-pipelines/code-scan-neural-solution.yaml deleted file mode 100644 index 301c7010465..00000000000 --- a/.azure-pipelines/code-scan-neural-solution.yaml +++ /dev/null @@ -1,59 +0,0 @@ -trigger: none - -pr: - autoCancel: true - drafts: false - branches: - include: - - master - paths: - include: - - neural_solution - - setup.py - - .azure-pipelines/code-scan-neural-solution.yml - -pool: - vmImage: "ubuntu-latest" - -variables: - CODE_SCAN_LOG_PATH: ".azure-pipelines/scripts/codeScan/scanLog" - -stages: - - stage: DocStyleCodeScan - displayName: DocStyle Code Scan - dependsOn: [] - jobs: - - job: DocStyle - displayName: DocStyle - steps: - - template: template/code-scan-template.yml - parameters: - codeScanFileName: "pydocstyle" - uploadPath: "pydocstyle.log" - scanModule: "neural_solution" - - - stage: BanditCodeScan - displayName: Bandit Code Scan - dependsOn: [] - jobs: - - job: Bandit - displayName: Bandit - steps: - - template: template/code-scan-template.yml - parameters: - codeScanFileName: "bandit" - uploadPath: "bandit.log" - scanModule: "neural_solution" - - - stage: PylintCodeScan - displayName: Pylint Code Scan - dependsOn: [] - jobs: - - job: Pylint - displayName: Pylint - steps: - - template: template/code-scan-template.yml - parameters: - codeScanFileName: "pylint" - uploadPath: "pylint.json" - scanModule: "neural_solution" diff --git a/.azure-pipelines/scripts/codeScan/pydocstyle/pydocstyle.sh b/.azure-pipelines/scripts/codeScan/pydocstyle/pydocstyle.sh index db0a818db33..96f95bcc68b 100644 --- a/.azure-pipelines/scripts/codeScan/pydocstyle/pydocstyle.sh +++ b/.azure-pipelines/scripts/codeScan/pydocstyle/pydocstyle.sh @@ -17,11 +17,6 @@ log_dir="$work_dir/../scanLog" mkdir -p $log_dir scan_path="scan_path.txt" -if [ "${scan_module}" = "neural_solution" ]; then - scan_path="scan_path_neural_solution.txt" -elif [ "${scan_module}" = "neural_insights" ]; then - scan_path="scan_path_neural_insights.txt" -fi exit_code=0 for line in $(cat ${work_dir}/${scan_path}) diff --git a/.azure-pipelines/scripts/codeScan/pydocstyle/scan_path_neural_insights.txt b/.azure-pipelines/scripts/codeScan/pydocstyle/scan_path_neural_insights.txt deleted file mode 100644 index 8a008fd19fb..00000000000 --- a/.azure-pipelines/scripts/codeScan/pydocstyle/scan_path_neural_insights.txt +++ /dev/null @@ -1,4 +0,0 @@ -/neural-compressor/neural_insights/components -/neural-compressor/neural_insights/utils -/neural-compressor/neural_insights/web - diff --git a/.azure-pipelines/scripts/codeScan/pydocstyle/scan_path_neural_solution.txt b/.azure-pipelines/scripts/codeScan/pydocstyle/scan_path_neural_solution.txt deleted file mode 100644 index 07a5b1483d7..00000000000 --- a/.azure-pipelines/scripts/codeScan/pydocstyle/scan_path_neural_solution.txt +++ /dev/null @@ -1,4 +0,0 @@ -/neural-compressor/neural_solution/backend -/neural-compressor/neural_solution/frontend -/neural-compressor/neural_solution/utils - diff --git a/.azure-pipelines/scripts/codeScan/pylint/pylint.sh b/.azure-pipelines/scripts/codeScan/pylint/pylint.sh index 9103947e965..062bfe414ee 100644 --- a/.azure-pipelines/scripts/codeScan/pylint/pylint.sh +++ b/.azure-pipelines/scripts/codeScan/pylint/pylint.sh @@ -44,22 +44,6 @@ pip install torch \ pydantic \ protobuf -if [ "${scan_module}" = "neural_solution" ]; then - cd /neural-compressor - python setup.py install - - echo "Install Neural Solution ... " - bash /neural-compressor/.azure-pipelines/scripts/install_neural_solution.sh - -elif [ "${scan_module}" = "neural_insights" ]; then - cd /neural-compressor - python setup.py install - - echo "Install Neural Insights ... " - bash /neural-compressor/.azure-pipelines/scripts/install_neural_insights.sh - -fi - echo "[DEBUG] list pipdeptree..." pip install pipdeptree pipdeptree diff --git a/.azure-pipelines/scripts/install_neural_insights.sh b/.azure-pipelines/scripts/install_neural_insights.sh deleted file mode 100644 index daa8887f635..00000000000 --- a/.azure-pipelines/scripts/install_neural_insights.sh +++ /dev/null @@ -1,8 +0,0 @@ -#!/bin/bash - -echo "Install Neural Insights ... " -cd /neural-compressor -python -m pip install --no-cache-dir -r neural_insights/requirements.txt -python setup.py neural_insights bdist_wheel -pip install dist/neural_insights*.whl -pip list \ No newline at end of file diff --git a/.azure-pipelines/scripts/install_neural_solution.sh b/.azure-pipelines/scripts/install_neural_solution.sh deleted file mode 100644 index d0139c85132..00000000000 --- a/.azure-pipelines/scripts/install_neural_solution.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash - -echo "Install Open MPI ..." -wget https://download.open-mpi.org/release/open-mpi/v4.1/openmpi-4.1.5.tar.gz -gunzip -c openmpi-4.1.5.tar.gz | tar xf - -cd openmpi-4.1.5 -./configure --prefix=/usr/local -make all install -MPI_DIR=/usr/local/lib/openmpi -export PATH=$MPI_DIR/bin:$PATH -export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH -echo "Current Path ... " -echo $PATH - -echo "Current LD_LIBRARY_PATH ... " -echo $LD_LIBRARY_PATH - -echo "check mpicc ..." -which mpicc -which mpirun - -echo "Install Neural Solution ... " -cd /neural-compressor -python -m pip install --no-cache-dir -r neural_solution/requirements.txt -python setup.py neural_solution sdist bdist_wheel -pip install dist/neural_solution*.whl -pip list \ No newline at end of file diff --git a/.azure-pipelines/scripts/ut/run_ncoder.sh b/.azure-pipelines/scripts/ut/run_ncoder.sh deleted file mode 100644 index 3c487eebd7b..00000000000 --- a/.azure-pipelines/scripts/ut/run_ncoder.sh +++ /dev/null @@ -1,26 +0,0 @@ -#!/bin/bash -python -c "import neural_compressor as nc;print(nc.version.__version__)" -echo "run coder" - -echo "no FWKs need to be installed..." -echo "no requirements need to be installed..." - -cd /neural-compressor/test || exit 1 -find ./neural_coder -name "test*.py" | sed 's,\.\/,python ,g' | sed 's/$/ --verbose/' > run.sh - -LOG_DIR=/neural-compressor/log_dir -mkdir -p ${LOG_DIR} -ut_log_name=${LOG_DIR}/ut_neural_coder.log - -echo "cat run.sh..." -sort run.sh -o run.sh -cat run.sh | tee ${ut_log_name} -echo "------UT start-------" -bash -x run.sh 2>&1 | tee -a ${ut_log_name} -echo "------UT end -------" - -if [ $(grep -c "FAILED" ${ut_log_name}) != 0 ] || [ $(grep -c "core dumped" ${ut_log_name}) != 0 ] || [ $(grep -c "ModuleNotFoundError:" ${ut_log_name}) != 0 ] || [ $(grep -c "OK" ${ut_log_name}) == 0 ];then - echo "Find errors in UT test, please check the output..." - exit 1 -fi -echo "UT finished successfully! " \ No newline at end of file diff --git a/.azure-pipelines/scripts/ut/run_neural_insights.sh b/.azure-pipelines/scripts/ut/run_neural_insights.sh deleted file mode 100644 index 5f0f2fe5521..00000000000 --- a/.azure-pipelines/scripts/ut/run_neural_insights.sh +++ /dev/null @@ -1,41 +0,0 @@ -#!/bin/bash -python -c "import neural_compressor as nc;print(nc.version.__version__)" -echo "run neural insights ut..." - -# Install Neural Solution -bash /neural-compressor/.azure-pipelines/scripts/install_neural_insights.sh - -# Install requirements for test -cd /neural-compressor/neural_insights/test || exit 1 -if [ -f "requirements.txt" ]; then - n=0 - until [ "$n" -ge 3 ] - do - python -m pip install --no-cache-dir -r requirements.txt && break - n=$((n+1)) - sleep 5 - done - pip list -else - echo "Not found requirements.txt file." -fi - -cd /neural-compressor/neural_insights || exit 1 -find ./test -name "test*.py" | sed 's,\.\/,python ,g' | sed 's/$/ --verbose/' > run.sh - -LOG_DIR=/neural-compressor/log_dir -mkdir -p ${LOG_DIR} -ut_log_name=${LOG_DIR}/ut_neural_insights.log - -echo "cat run.sh..." -sort run.sh -o run.sh -cat run.sh | tee ${ut_log_name} -echo "------UT start-------" -bash -x run.sh 2>&1 | tee -a ${ut_log_name} -echo "------UT end -------" - -if [ $(grep -c "FAILED" ${ut_log_name}) != 0 ] || [ $(grep -c "core dumped" ${ut_log_name}) != 0 ] || [ $(grep -c "ModuleNotFoundError:" ${ut_log_name}) != 0 ] || [ $(grep -c "OK" ${ut_log_name}) == 0 ];then - echo "Find errors in UT test, please check the output..." - exit 1 -fi -echo "UT finished successfully! " \ No newline at end of file diff --git a/.azure-pipelines/scripts/ut/run_neural_solution.sh b/.azure-pipelines/scripts/ut/run_neural_solution.sh deleted file mode 100644 index 42041e4a087..00000000000 --- a/.azure-pipelines/scripts/ut/run_neural_solution.sh +++ /dev/null @@ -1,43 +0,0 @@ -#!/bin/bash -python -c "import neural_compressor as nc;print(nc.version.__version__)" -echo "run neural solution ut..." - -echo "no FWKs need to be installed..." - -# Install Neural Solution -bash /neural-compressor/.azure-pipelines/scripts/install_neural_solution.sh - -# Install requirements for test -cd /neural-compressor/neural_solution/test || exit 1 -if [ -f "requirements.txt" ]; then - n=0 - until [ "$n" -ge 3 ] - do - python -m pip install --no-cache-dir -r requirements.txt && break - n=$((n+1)) - sleep 5 - done - pip list -else - echo "Not found requirements.txt file." -fi - -cd /neural-compressor/neural_solution || exit 1 -find ./test -name "test*.py" | sed 's,\.\/,python ,g' | sed 's/$/ --verbose/' > run.sh - -LOG_DIR=/neural-compressor/log_dir -mkdir -p ${LOG_DIR} -ut_log_name=${LOG_DIR}/ut_neural_solution.log - -echo "cat run.sh..." -sort run.sh -o run.sh -cat run.sh | tee ${ut_log_name} -echo "------UT start-------" -bash -x run.sh 2>&1 | tee -a ${ut_log_name} -echo "------UT end -------" - -if [ $(grep -c "FAILED" ${ut_log_name}) != 0 ] || [ $(grep -c "core dumped" ${ut_log_name}) != 0 ] || [ $(grep -c "ModuleNotFoundError:" ${ut_log_name}) != 0 ] || [ $(grep -c "OK" ${ut_log_name}) == 0 ];then - echo "Find errors in UT test, please check the output..." - exit 1 -fi -echo "UT finished successfully! " \ No newline at end of file diff --git a/.azure-pipelines/ut-ncoder.yml b/.azure-pipelines/ut-ncoder.yml deleted file mode 100644 index a1512ae4803..00000000000 --- a/.azure-pipelines/ut-ncoder.yml +++ /dev/null @@ -1,33 +0,0 @@ -trigger: none - -pr: - autoCancel: true - drafts: false - branches: - include: - - master - paths: - include: - - neural_coder - - test/neural_coder - - setup.py - -pool: ICX-16C - -variables: - UPLOAD_PATH: $(Build.SourcesDirectory)/log_dir - -stages: -- stage: - displayName: Unit Test for Neural Coder - jobs: - - job: - steps: - - template: template/ut-template.yml - parameters: - dockerConfigName: 'commonDockerConfig' - utScriptFileName: 'run_ncoder' - uploadPath: $(UPLOAD_PATH) - utArtifact: 'ut_ncoder' - utTestMode: "no-coverage" - utContainerName: "utTest-ncoder" diff --git a/.azure-pipelines/ut-neural-insights.yaml b/.azure-pipelines/ut-neural-insights.yaml deleted file mode 100644 index b73026c623b..00000000000 --- a/.azure-pipelines/ut-neural-insights.yaml +++ /dev/null @@ -1,33 +0,0 @@ -trigger: none - -pr: - autoCancel: true - drafts: false - branches: - include: - - master - paths: - include: - - neural_insights - - setup.py - - .azure-pipelines/ut-neural-insights.yaml - -pool: ICX-16C - -variables: - UPLOAD_PATH: $(Build.SourcesDirectory)/log_dir - -stages: - - stage: - displayName: Unit Test for Neural Insights - jobs: - - job: - steps: - - template: template/ut-template.yml - parameters: - dockerConfigName: 'commonDockerConfig' - utScriptFileName: 'run_neural_insights' - uploadPath: $(UPLOAD_PATH) - utArtifact: 'ut_neural-insights' - utTestMode: "no-coverage" - utContainerName: "utTest-nInsights" diff --git a/.azure-pipelines/ut-neural-solution.yaml b/.azure-pipelines/ut-neural-solution.yaml deleted file mode 100644 index df717899b57..00000000000 --- a/.azure-pipelines/ut-neural-solution.yaml +++ /dev/null @@ -1,33 +0,0 @@ -trigger: none - -pr: - autoCancel: true - drafts: false - branches: - include: - - master - paths: - include: - - neural_solution - - setup.py - - .azure-pipelines/ut-neural-solution.yaml - -pool: ICX-16C - -variables: - UPLOAD_PATH: $(Build.SourcesDirectory)/log_dir - -stages: - - stage: - displayName: Unit Test for Neural Solution - jobs: - - job: - steps: - - template: template/ut-template.yml - parameters: - dockerConfigName: 'commonDockerConfig' - utScriptFileName: 'run_neural_solution' - uploadPath: $(UPLOAD_PATH) - utArtifact: 'ut_neural-solution' - utTestMode: "no-coverage" - utContainerName: "utTest-nSolution" diff --git a/.github/checkgroup.yml b/.github/checkgroup.yml index 63b228f3f7e..7570c171497 100644 --- a/.github/checkgroup.yml +++ b/.github/checkgroup.yml @@ -13,28 +13,6 @@ subprojects: - "Code-Scan (DocStyle Code Scan DocStyle)" - "Code-Scan (Pylint Code Scan Pylint)" - - id: "Code Scan Neural-Insights Tests workflow" - paths: - - "neural_insights/**" - - "setup.py" - - ".azure-pipelines/code-scan-neural-insights.yml" - checks: - - "Code-Scan-Neural-Insights" - - "Code-Scan-Neural-Insights (Bandit Code Scan Bandit)" - - "Code-Scan-Neural-Insights (DocStyle Code Scan DocStyle)" - - "Code-Scan-Neural-Insights (Pylint Code Scan Pylint)" - - - id: "Code Scan Neural-Solution Tests workflow" - paths: - - "neural_solution/**" - - "setup.py" - - ".azure-pipelines/code-scan-neural-solution.yml" - checks: - - "Code-Scan-Neural-Solution" - - "Code-Scan-Neural-Solution (Bandit Code Scan Bandit)" - - "Code-Scan-Neural-Solution (DocStyle Code Scan DocStyle)" - - "Code-Scan-Neural-Solution (Pylint Code Scan Pylint)" - - id: "Model Tests workflow" paths: - "neural_compressor/**" @@ -138,28 +116,6 @@ subprojects: checks: - "UT-ITREX" - - id: "Unit Tests Neural-Insights workflow" - paths: - - "neural_insights/**" - - "setup.py" - checks: - - "UT-Neural-Insights" - - - id: "Unit Tests Neural-Solution workflow" - paths: - - "neural_solution/**" - - "setup.py" - checks: - - "UT-Neural-Solution" - - - id: "Unit Tests Neural-Coder workflow" - paths: - - "neural_coder/**" - - "test/neural_coder/**" - - "setup.py" - checks: - - "UT-Coder" - - id: "Unit Tests 3x-TensorFlow workflow" paths: - "neural_compressor/common/**" diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 19e48389c04..0f0f44df2cc 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -2,16 +2,6 @@ ci: autofix_prs: true autoupdate_schedule: quarterly -exclude: | - (?x)^( - conda_meta/.+| - neural_insights/gui.+| - neural_insights/test.+| - neural_solution/frontend/gRPC/proto/neural_solution_pb2.py| - neural_coder/extensions/.+| - neural_coder/examples/.+ - )$ - repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v4.6.0 @@ -60,9 +50,7 @@ repos: - id: insert-license files: | (?x)^( - neural_solution/.*(py|yaml|yml|sh)| - neural_compressor/.*(py|yaml|yml|sh)| - neural_insights/.*(py|yaml|yml|sh) + neural_compressor/.*(py|yaml|yml|sh) )$ exclude: | (?x)^( diff --git a/README.md b/README.md index b4155dbdde7..9a9971dc3f0 100644 --- a/README.md +++ b/README.md @@ -178,17 +178,6 @@ quantized_model = fit(model=float_model, conf=static_quant_conf, calib_dataloade Layer-Wise Quantization - - - Innovations for Productivity - - - - - Neural Insights - Neural Solution - - > **Note**: diff --git a/docs/build_docs/build.sh b/docs/build_docs/build.sh index 032a15890a2..a03174f21f1 100755 --- a/docs/build_docs/build.sh +++ b/docs/build_docs/build.sh @@ -81,9 +81,6 @@ fi source env_sphinx/bin/activate cp -rf ../docs/ ./source -cp -rf ../neural_coder ./source/docs/source -cp -rf ../neural_insights ./source/docs/source -cp -rf ../neural_solution ./source/docs/source cp -f "../README.md" "./source/docs/source/Welcome.md" cp -f "../SECURITY.md" "./source/docs/source/SECURITY.md" diff --git a/neural_coder/README.md b/neural_coder/README.md deleted file mode 100644 index b7c6d0fb63b..00000000000 --- a/neural_coder/README.md +++ /dev/null @@ -1,52 +0,0 @@ -Neural Coder -=========================== -## What do we offer? - -Neural Coder is a novel component under IntelĀ® Neural Compressor to further simplify the deployment of deep learning models via one-click automated code changes for device switch (e.g., CUDA to CPU) and optimization enabling. Subsequently, Neural Coder can also perform automated benchmark on all applicable optimization sets acquired from the automated enabling, and evaluate for the best out-of-box performance. - -Neural Coder leverages static program analysis techniques and heuristic optimization rules to simplify the usage of various Deep Learning optimization APIs for increasing computation efficiency of AI models and improving user experience for general AI customers. We demonstrate great improvement of developer productivity and aim to facilitate enhanced Deep Learning acceleration adoption via this toolkit. - -Neural Coder helps you code Deep Learning optimizations automatically into your scripts. For example, to apply -- Automatic Mixed Precision (torch.cpu.amp.autocast) -- JIT Script computation graph transformation (torch.jit.script) -- Channels Last memory format transformation (torch.channels_last) - -simultaneously on below PyTorch evaluation code, we generate the optimized code in one-click by detecting the correct position to insert the correct API code lines: -```diff - import torch - import torchvision.models as models - my_model = models.resnet50(pretrained=True) -+ import torch -+ with torch.no_grad(): -+ my_model = my_model.to(memory_format=torch.channels_last) -+ import torch -+ with torch.no_grad(): -+ my_model.eval() -+ my_model = torch.jit.script(my_model) -+ my_model = torch.jit.freeze(my_model) - my_model.eval() - batch_size = 112 - input = torch.rand(batch_size, 3, 224, 224) - with torch.no_grad(): -+ import torch -+ with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloat16): - my_model(input) -``` - -## Getting Started! - -There are currently 3 ways to use Neural Coder for automatic quantization enabling and benchmark. - -### Jupyter Lab Extension -We offer Neural Coder as an extension plugin in Jupyter Lab. This enables users to utilize Neural Coder while writing their Deep Learning models in Jupyter Lab coding platform. Users can simply search for ```jupyter-lab-neural-compressor``` in the Extension Manager in JupyterLab and install Neural Coder with one click. For more details, please refer to this [guide](extensions/neural_compressor_ext_lab/README.md). - -[AWS Amazon SageMaker](https://aws.amazon.com/sagemaker/) users can also use Neural Coder as an extension following this [guide](docs/AWSSageMakerSupport.md). - -### Python Launcher -Neural Coder can be used as a Python Launcher. Users can run the Python Deep Learning model code as it is with automatic enabling of optimizations by simply adding an inline prefix ```-m neural_coder``` to the Python command line. For more details, please refer to this [guide](docs/PythonLauncher.md). - -### Python API -There are 3 user-facing APIs for Neural Coder: enable, bench and superbench. For more details, please refer to this [guide](docs/PythonAPI.md). We have provided a [list](docs/SupportMatrix.md) of supported Deep Learning optimization features. Specifically for quantization, we provide an auto-quantization API that helps automatically enable quantization on Deep Learning models and automatically evaluates for the best performance on the model with no manual coding needed. Supported features include Post-Training Static Quantization, Post-Training Dynamic Quantization, and Mixed Precision. For more details, please refer to this [guide](docs/Quantization.md). - -## Contact -Please contact us at [inc.maintainers@intel.com](mailto:inc.maintainers@intel.com) for any Neural Coder related question. diff --git a/neural_coder/__init__.py b/neural_coder/__init__.py deleted file mode 100644 index 7bf18cd4956..00000000000 --- a/neural_coder/__init__.py +++ /dev/null @@ -1,20 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from .interface import enable -from .interface import bench -from .interface import superbench - -# from .interface import superreport -from .interface import auto_quant diff --git a/neural_coder/__main__.py b/neural_coder/__main__.py deleted file mode 100644 index 0551b72dc21..00000000000 --- a/neural_coder/__main__.py +++ /dev/null @@ -1,18 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from .launcher import Launcher - -args = Launcher.parse_args() -Launcher.execute(args) diff --git a/neural_coder/backends/.yaml b/neural_coder/backends/.yaml deleted file mode 100644 index 1e3b1fa1501..00000000000 --- a/neural_coder/backends/.yaml +++ /dev/null @@ -1,51 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - - - - - - content: - - |- - [+] YOUR CODE LINE 1 - [+] YOUR CODE LINE 2 - [+] YOUR CODE LINE 3 - - |- - [+] YOUR CODE LINE 1 - [+] YOUR CODE LINE 2 - [+] YOUR CODE LINE 3 - - |- - [+] YOUR CODE LINE 1 - [+] YOUR CODE LINE 2 - [+] YOUR CODE LINE 3 - order: - - below: - - - - - above: - - - - - - below: - - - - - above: - - - - - - below: - - - - - above: - - - - diff --git a/neural_coder/backends/__init__.py b/neural_coder/backends/__init__.py deleted file mode 100644 index e833188cc78..00000000000 --- a/neural_coder/backends/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/neural_coder/backends/intel_extension_for_transformers.yaml b/neural_coder/backends/intel_extension_for_transformers.yaml deleted file mode 100644 index a1accbbfb4b..00000000000 --- a/neural_coder/backends/intel_extension_for_transformers.yaml +++ /dev/null @@ -1,35 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# Note: For intel_extension_for_transformers support -# we default apply "PostTrainingDynamic" and "eval_f1" -# support for customization is pending further evaluation - -transformation: - location: - - ["insert_below_dataloader_definition_line", "insert_below_model_definition_line"] - content: - - |- - [+] metric = metrics.Metric(name="eval_f1", is_relative=True, criterion=0.01) - [+] objective = objectives.performance - [+] q_config = QuantizationConfig(approach="PostTrainingDynamic", metrics=[metric], objectives=[objective]) - [+] MODEL_NAME = trainer.quantize(quant_config=q_config) - order: - - below: - above: - - pytorch_jit_script - - pytorch_jit_script_ofi - - pytorch_jit_trace - - pytorch_jit_trace_ofi - - pytorch_channels_last diff --git a/neural_coder/backends/keras_inc.yaml b/neural_coder/backends/keras_inc.yaml deleted file mode 100644 index 0731a4bbbc9..00000000000 --- a/neural_coder/backends/keras_inc.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_dataloader_definition_line", "insert_below_model_definition_line"] - content: - - |- - [+] from neural_compressor.quantization import fit - [+] from neural_compressor.config import PostTrainingQuantConfig - [+] config = PostTrainingQuantConfig(backend='itex', quant_level=1) - [+] quantized_model = fit(MODEL_NAME, conf=config, calib_dataloader=DATALOADER_NAME, eval_func=eval_func) - [+] quantized_model.save("./quantized_model") - order: - - below: - above: diff --git a/neural_coder/backends/nano_bf16.yaml b/neural_coder/backends/nano_bf16.yaml deleted file mode 100644 index afecf6908f1..00000000000 --- a/neural_coder/backends/nano_bf16.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_model_definition_line", "insert_below_input_definition_line"] - - "indent_inference_line" - content: - - |- - [+] from bigdl.nano.pytorch import InferenceOptimizer - [+] MODEL_NAME = InferenceOptimizer.quantize(MODEL_NAME, precision="bf16", input_sample=INPUT_NAME) - [+] with InferenceOptimizer.get_context(MODEL_NAME): - - 1 - order: - - below: - above: diff --git a/neural_coder/backends/nano_bf16_channels_last.yaml b/neural_coder/backends/nano_bf16_channels_last.yaml deleted file mode 100644 index d0f3987ef29..00000000000 --- a/neural_coder/backends/nano_bf16_channels_last.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_model_definition_line", "insert_below_input_definition_line"] - - "indent_inference_line" - content: - - |- - [+] from bigdl.nano.pytorch import InferenceOptimizer - [+] MODEL_NAME = InferenceOptimizer.quantize(MODEL_NAME, precision="bf16", channels_last=True, input_sample=INPUT_NAME) - [+] with InferenceOptimizer.get_context(MODEL_NAME): - - 1 - order: - - below: - above: diff --git a/neural_coder/backends/nano_bf16_ipex.yaml b/neural_coder/backends/nano_bf16_ipex.yaml deleted file mode 100644 index 6e8db6f8719..00000000000 --- a/neural_coder/backends/nano_bf16_ipex.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_model_definition_line", "insert_below_input_definition_line"] - - "indent_inference_line" - content: - - |- - [+] from bigdl.nano.pytorch import InferenceOptimizer - [+] MODEL_NAME = InferenceOptimizer.quantize(MODEL_NAME, precision="bf16", use_ipex=True, input_sample=INPUT_NAME) - [+] with InferenceOptimizer.get_context(MODEL_NAME): - - 1 - order: - - below: - above: diff --git a/neural_coder/backends/nano_bf16_ipex_channels_last.yaml b/neural_coder/backends/nano_bf16_ipex_channels_last.yaml deleted file mode 100644 index 3b99ef12f35..00000000000 --- a/neural_coder/backends/nano_bf16_ipex_channels_last.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_model_definition_line", "insert_below_input_definition_line"] - - "indent_inference_line" - content: - - |- - [+] from bigdl.nano.pytorch import InferenceOptimizer - [+] MODEL_NAME = InferenceOptimizer.quantize(MODEL_NAME, precision="bf16", use_ipex=True, channels_last=True, input_sample=INPUT_NAME) - [+] with InferenceOptimizer.get_context(MODEL_NAME): - - 1 - order: - - below: - above: diff --git a/neural_coder/backends/nano_fp32_channels_last.yaml b/neural_coder/backends/nano_fp32_channels_last.yaml deleted file mode 100644 index 9516ba4d50d..00000000000 --- a/neural_coder/backends/nano_fp32_channels_last.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_model_definition_line", "insert_below_input_definition_line"] - - "indent_inference_line" - content: - - |- - [+] from bigdl.nano.pytorch import InferenceOptimizer - [+] MODEL_NAME = InferenceOptimizer.trace(MODEL_NAME, channels_last=True, input_sample=INPUT_NAME) - [+] with InferenceOptimizer.get_context(MODEL_NAME): - - 1 - order: - - below: - above: diff --git a/neural_coder/backends/nano_fp32_ipex.yaml b/neural_coder/backends/nano_fp32_ipex.yaml deleted file mode 100644 index c6fc4329b38..00000000000 --- a/neural_coder/backends/nano_fp32_ipex.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_model_definition_line", "insert_below_input_definition_line"] - - "indent_inference_line" - content: - - |- - [+] from bigdl.nano.pytorch import InferenceOptimizer - [+] MODEL_NAME = InferenceOptimizer.trace(MODEL_NAME, use_ipex=True, input_sample=INPUT_NAME) - [+] with InferenceOptimizer.get_context(MODEL_NAME): - - 1 - order: - - below: - above: diff --git a/neural_coder/backends/nano_fp32_ipex_channels_last.yaml b/neural_coder/backends/nano_fp32_ipex_channels_last.yaml deleted file mode 100644 index aea74db737a..00000000000 --- a/neural_coder/backends/nano_fp32_ipex_channels_last.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_model_definition_line", "insert_below_input_definition_line"] - - "indent_inference_line" - content: - - |- - [+] from bigdl.nano.pytorch import InferenceOptimizer - [+] MODEL_NAME = InferenceOptimizer.trace(MODEL_NAME, use_ipex=True, channels_last=True, input_sample=INPUT_NAME) - [+] with InferenceOptimizer.get_context(MODEL_NAME): - - 1 - order: - - below: - above: diff --git a/neural_coder/backends/nano_gpu_to_cpu.yaml b/neural_coder/backends/nano_gpu_to_cpu.yaml deleted file mode 100644 index 426f72ce980..00000000000 --- a/neural_coder/backends/nano_gpu_to_cpu.yaml +++ /dev/null @@ -1,24 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_above_model_definition_line", "insert_above_input_definition_line"] - content: - - |- - [+] from bigdl.nano.pytorch import patch_torch - [+] patch_torch() - order: - - below: - above: diff --git a/neural_coder/backends/nano_int8.yaml b/neural_coder/backends/nano_int8.yaml deleted file mode 100644 index cb846256435..00000000000 --- a/neural_coder/backends/nano_int8.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_model_definition_line", "insert_below_input_definition_line"] - - "indent_inference_line" - content: - - |- - [+] from bigdl.nano.pytorch import InferenceOptimizer - [+] MODEL_NAME = InferenceOptimizer.quantize(MODEL_NAME, precision="int8", input_sample=INPUT_NAME) - [+] with InferenceOptimizer.get_context(MODEL_NAME): - - 1 - order: - - below: - above: diff --git a/neural_coder/backends/nano_jit_bf16.yaml b/neural_coder/backends/nano_jit_bf16.yaml deleted file mode 100644 index 122d93ca717..00000000000 --- a/neural_coder/backends/nano_jit_bf16.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_model_definition_line", "insert_below_input_definition_line"] - - "indent_inference_line" - content: - - |- - [+] from bigdl.nano.pytorch import InferenceOptimizer - [+] MODEL_NAME = InferenceOptimizer.quantize(MODEL_NAME, accelerator="jit", precision="bf16", input_sample=INPUT_NAME) - [+] with InferenceOptimizer.get_context(MODEL_NAME): - - 1 - order: - - below: - above: diff --git a/neural_coder/backends/nano_jit_bf16_channels_last.yaml b/neural_coder/backends/nano_jit_bf16_channels_last.yaml deleted file mode 100644 index cf7e1437c4a..00000000000 --- a/neural_coder/backends/nano_jit_bf16_channels_last.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_model_definition_line", "insert_below_input_definition_line"] - - "indent_inference_line" - content: - - |- - [+] from bigdl.nano.pytorch import InferenceOptimizer - [+] MODEL_NAME = InferenceOptimizer.quantize(MODEL_NAME, accelerator="jit", precision="bf16", channels_last=True, input_sample=INPUT_NAME) - [+] with InferenceOptimizer.get_context(MODEL_NAME): - - 1 - order: - - below: - above: diff --git a/neural_coder/backends/nano_jit_bf16_ipex.yaml b/neural_coder/backends/nano_jit_bf16_ipex.yaml deleted file mode 100644 index 1a237c6edf8..00000000000 --- a/neural_coder/backends/nano_jit_bf16_ipex.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_model_definition_line", "insert_below_input_definition_line"] - - "indent_inference_line" - content: - - |- - [+] from bigdl.nano.pytorch import InferenceOptimizer - [+] MODEL_NAME = InferenceOptimizer.quantize(MODEL_NAME, accelerator="jit", precision="bf16", use_ipex=True, input_sample=INPUT_NAME) - [+] with InferenceOptimizer.get_context(MODEL_NAME): - - 1 - order: - - below: - above: diff --git a/neural_coder/backends/nano_jit_bf16_ipex_channels_last.yaml b/neural_coder/backends/nano_jit_bf16_ipex_channels_last.yaml deleted file mode 100644 index 603db6942f8..00000000000 --- a/neural_coder/backends/nano_jit_bf16_ipex_channels_last.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_model_definition_line", "insert_below_input_definition_line"] - - "indent_inference_line" - content: - - |- - [+] from bigdl.nano.pytorch import InferenceOptimizer - [+] MODEL_NAME = InferenceOptimizer.quantize(MODEL_NAME, accelerator="jit", precision="bf16", use_ipex=True, channels_last=True, input_sample=INPUT_NAME) - [+] with InferenceOptimizer.get_context(MODEL_NAME): - - 1 - order: - - below: - above: diff --git a/neural_coder/backends/nano_jit_fp32.yaml b/neural_coder/backends/nano_jit_fp32.yaml deleted file mode 100644 index 71e7d4ede95..00000000000 --- a/neural_coder/backends/nano_jit_fp32.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_model_definition_line", "insert_below_input_definition_line"] - - "indent_inference_line" - content: - - |- - [+] from bigdl.nano.pytorch import InferenceOptimizer - [+] MODEL_NAME = InferenceOptimizer.trace(MODEL_NAME, accelerator="jit", input_sample=INPUT_NAME) - [+] with InferenceOptimizer.get_context(MODEL_NAME): - - 1 - order: - - below: - above: diff --git a/neural_coder/backends/nano_jit_fp32_channels_last.yaml b/neural_coder/backends/nano_jit_fp32_channels_last.yaml deleted file mode 100644 index c30a1767175..00000000000 --- a/neural_coder/backends/nano_jit_fp32_channels_last.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_model_definition_line", "insert_below_input_definition_line"] - - "indent_inference_line" - content: - - |- - [+] from bigdl.nano.pytorch import InferenceOptimizer - [+] MODEL_NAME = InferenceOptimizer.trace(MODEL_NAME, accelerator="jit", channels_last=True, input_sample=INPUT_NAME) - [+] with InferenceOptimizer.get_context(MODEL_NAME): - - 1 - order: - - below: - above: diff --git a/neural_coder/backends/nano_jit_fp32_ipex.yaml b/neural_coder/backends/nano_jit_fp32_ipex.yaml deleted file mode 100644 index f673b076a20..00000000000 --- a/neural_coder/backends/nano_jit_fp32_ipex.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_model_definition_line", "insert_below_input_definition_line"] - - "indent_inference_line" - content: - - |- - [+] from bigdl.nano.pytorch import InferenceOptimizer - [+] MODEL_NAME = InferenceOptimizer.trace(MODEL_NAME, accelerator="jit", use_ipex=True, input_sample=INPUT_NAME) - [+] with InferenceOptimizer.get_context(MODEL_NAME): - - 1 - order: - - below: - above: diff --git a/neural_coder/backends/nano_jit_fp32_ipex_channels_last.yaml b/neural_coder/backends/nano_jit_fp32_ipex_channels_last.yaml deleted file mode 100644 index 54514000ac5..00000000000 --- a/neural_coder/backends/nano_jit_fp32_ipex_channels_last.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_model_definition_line", "insert_below_input_definition_line"] - - "indent_inference_line" - content: - - |- - [+] from bigdl.nano.pytorch import InferenceOptimizer - [+] MODEL_NAME = InferenceOptimizer.trace(MODEL_NAME, accelerator="jit", use_ipex=True, channels_last=True, input_sample=INPUT_NAME) - [+] with InferenceOptimizer.get_context(MODEL_NAME): - - 1 - order: - - below: - above: diff --git a/neural_coder/backends/nano_onnxruntime_fp32.yaml b/neural_coder/backends/nano_onnxruntime_fp32.yaml deleted file mode 100644 index c29e3410bc4..00000000000 --- a/neural_coder/backends/nano_onnxruntime_fp32.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_model_definition_line", "insert_below_input_definition_line"] - - "indent_inference_line" - content: - - |- - [+] from bigdl.nano.pytorch import InferenceOptimizer - [+] MODEL_NAME = InferenceOptimizer.trace(MODEL_NAME, accelerator="onnxruntime", input_sample=INPUT_NAME) - [+] with InferenceOptimizer.get_context(MODEL_NAME): - - 1 - order: - - below: - above: diff --git a/neural_coder/backends/nano_onnxruntime_int8_qlinear.yaml b/neural_coder/backends/nano_onnxruntime_int8_qlinear.yaml deleted file mode 100644 index bb5b35557da..00000000000 --- a/neural_coder/backends/nano_onnxruntime_int8_qlinear.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_model_definition_line", "insert_below_input_definition_line"] - - "indent_inference_line" - content: - - |- - [+] from bigdl.nano.pytorch import InferenceOptimizer - [+] MODEL_NAME = InferenceOptimizer.quantize(MODEL_NAME, accelerator="onnxruntime", precision="int8", input_sample=INPUT_NAME) - [+] with InferenceOptimizer.get_context(MODEL_NAME): - - 1 - order: - - below: - above: diff --git a/neural_coder/backends/nano_openvino_fp32.yaml b/neural_coder/backends/nano_openvino_fp32.yaml deleted file mode 100644 index 6b9324cec39..00000000000 --- a/neural_coder/backends/nano_openvino_fp32.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_model_definition_line", "insert_below_input_definition_line"] - - "indent_inference_line" - content: - - |- - [+] from bigdl.nano.pytorch import InferenceOptimizer - [+] MODEL_NAME = InferenceOptimizer.trace(MODEL_NAME, accelerator="openvino", input_sample=INPUT_NAME) - [+] with InferenceOptimizer.get_context(MODEL_NAME): - - 1 - order: - - below: - above: diff --git a/neural_coder/backends/nano_openvino_int8.yaml b/neural_coder/backends/nano_openvino_int8.yaml deleted file mode 100644 index 9f1b70814e7..00000000000 --- a/neural_coder/backends/nano_openvino_int8.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_model_definition_line", "insert_below_input_definition_line"] - - "indent_inference_line" - content: - - |- - [+] from bigdl.nano.pytorch import InferenceOptimizer - [+] MODEL_NAME = InferenceOptimizer.quantize(MODEL_NAME, accelerator="openvino", precision="int8", input_sample=INPUT_NAME) - [+] with InferenceOptimizer.get_context(MODEL_NAME): - - 1 - order: - - below: - above: diff --git a/neural_coder/backends/onnx_inc_dynamic_quant.yaml b/neural_coder/backends/onnx_inc_dynamic_quant.yaml deleted file mode 100644 index 98a98d941ca..00000000000 --- a/neural_coder/backends/onnx_inc_dynamic_quant.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - insert_below_model_definition_line - content: - - |- - [+] from neural_compressor.quantization import fit - [+] from neural_compressor.config import PostTrainingQuantConfig - [+] config = PostTrainingQuantConfig(approach='dynamic', quant_level=1) - [+] MODEL_NAME = fit(MODEL_NAME, conf=config, calib_dataloader=DATALOADER_NAME, eval_func=EVAL_FUNCTION_NAME) - [+] MODEL_NAME.save("./quantized_model.onnx") - order: - - below: - above: diff --git a/neural_coder/backends/onnx_inc_static_quant_qdq.yaml b/neural_coder/backends/onnx_inc_static_quant_qdq.yaml deleted file mode 100644 index 6df51eefb54..00000000000 --- a/neural_coder/backends/onnx_inc_static_quant_qdq.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - insert_below_model_definition_line - content: - - |- - [+] from neural_compressor.quantization import fit - [+] from neural_compressor.config import PostTrainingQuantConfig - [+] config = PostTrainingQuantConfig(quant_format='QDQ', quant_level=1) - [+] MODEL_NAME = fit(MODEL_NAME, conf=config, calib_dataloader=DATALOADER_NAME, eval_func=EVAL_FUNCTION_NAME) - [+] MODEL_NAME.save("./quantized_model.onnx") - order: - - below: - above: diff --git a/neural_coder/backends/onnx_inc_static_quant_qlinear.yaml b/neural_coder/backends/onnx_inc_static_quant_qlinear.yaml deleted file mode 100644 index 0c7f5e500f7..00000000000 --- a/neural_coder/backends/onnx_inc_static_quant_qlinear.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - insert_below_model_definition_line - content: - - |- - [+] from neural_compressor.quantization import fit - [+] from neural_compressor.config import PostTrainingQuantConfig - [+] config = PostTrainingQuantConfig(quant_format='default', quant_level=1) - [+] MODEL_NAME = fit(MODEL_NAME, conf=config, calib_dataloader=DATALOADER_NAME, eval_func=EVAL_FUNCTION_NAME) - [+] MODEL_NAME.save("./quantized_model.onnx") - order: - - below: - above: diff --git a/neural_coder/backends/pytorch_aliblade.yaml b/neural_coder/backends/pytorch_aliblade.yaml deleted file mode 100644 index ba29ac86548..00000000000 --- a/neural_coder/backends/pytorch_aliblade.yaml +++ /dev/null @@ -1,25 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_model_definition_line", "insert_below_input_definition_line"] - content: - - |- - [+] import torch_blade - [+] with torch.no_grad(): - [+] MODEL_NAME = torch_blade.optimize(MODEL_NAME, allow_tracing=True, model_inputs=tuple(INPUT_NAME)) - order: - - below: - above: diff --git a/neural_coder/backends/pytorch_benchmark.yaml b/neural_coder/backends/pytorch_benchmark.yaml deleted file mode 100644 index 1a153637d57..00000000000 --- a/neural_coder/backends/pytorch_benchmark.yaml +++ /dev/null @@ -1,68 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - insert_above_inference_line - - insert_below_inference_line - - indent_inference_line - content: - - |- - [+] if not ACCURACY_MODE: - [+] try: - [+] time - [+] time_nc = time.time - [+] except: - [+] from time import time as time_nc - [+] count_iter_ = 0 - [+] total_time_ = 0 - [+] num_iter_ = NUM_BENCHMARK_ITERATION - [+] num_warmup_iter_ = 10 - [+] list_batch_time_ = [] - [+] for i_ in range(num_iter_): - [+] count_iter_ = count_iter_ + 1 - [+] if count_iter_ > num_warmup_iter_: - [+] t1_ = time_nc() - [+] try: - [+] torch - [+] no_grad = torch.no_grad - [+] except: - [+] from torch import no_grad - [+] with no_grad(): - - |- - [+] if count_iter_ > num_warmup_iter_: - [+] t2_ = time_nc() - [+] batch_time_ = t2_ - t1_ - [+] list_batch_time_.append(batch_time_) - [+] total_time_ = total_time_ + batch_time_ - [+] print("Neural_Coder_Bench_IPS: ", round((num_iter_ - num_warmup_iter_) / total_time_, 3)) - [+] print("Neural_Coder_Bench_MSPI: ", round(total_time_ / (num_iter_ - num_warmup_iter_) * 1000, 3)) - [+] list_batch_time_.sort() - [+] p50_latency_ = list_batch_time_[int(len(list_batch_time_) * 0.50) - 1] * 1000 - [+] p90_latency_ = list_batch_time_[int(len(list_batch_time_) * 0.90) - 1] * 1000 - [+] p99_latency_ = list_batch_time_[int(len(list_batch_time_) * 0.99) - 1] * 1000 - [+] print("Neural_Coder_Bench_P50: ", round(p50_latency_, 3)) - [+] print("Neural_Coder_Bench_P90: ", round(p90_latency_, 3)) - [+] print("Neural_Coder_Bench_P99: ", round(p99_latency_, 3)) - [+] quit() - [+] else: - [+] INFERENCE_LINE - - 3 - order: - - below: - above: - - below: - above: - - below: - above: diff --git a/neural_coder/backends/pytorch_channels_last.yaml b/neural_coder/backends/pytorch_channels_last.yaml deleted file mode 100644 index 75343032b02..00000000000 --- a/neural_coder/backends/pytorch_channels_last.yaml +++ /dev/null @@ -1,37 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - insert_below_model_definition_line - content: - - |- - [+] import torch - [+] with torch.no_grad(): - [+] MODEL_NAME.eval() - [+] MODEL_NAME = MODEL_NAME.to(memory_format=torch.channels_last) - order: - - below: - - pytorch_inc_static_quant_fx - - pytorch_inc_static_quant_ipex - - pytorch_inc_dynamic_quant - above: - - pytorch_ipex_fp32 - - pytorch_ipex_bf16 - - pytorch_ipex_int8_static_quant - - pytorch_ipex_int8_dynamic_quant - - pytorch_jit_script - - pytorch_jit_script_ofi - - pytorch_jit_trace - - pytorch_jit_trace_ofi diff --git a/neural_coder/backends/pytorch_inc_bf16.yaml b/neural_coder/backends/pytorch_inc_bf16.yaml deleted file mode 100644 index bdbf3b07272..00000000000 --- a/neural_coder/backends/pytorch_inc_bf16.yaml +++ /dev/null @@ -1,39 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - insert_below_model_definition_line - content: - - |- - [+] import torch - [+] torch.backends.quantized.engine = 'onednn' - [+] from neural_compressor.config import MixedPrecisionConfig - [+] from neural_compressor import mix_precision - [+] config = MixedPrecisionConfig() - [+] MODEL_NAME = mix_precision.fit(model=MODEL_NAME, config=config) - [+] try: - [+] with torch.no_grad(): - [+] MODEL_NAME = torch.jit.script(MODEL_NAME) - [+] MODEL_NAME = torch.jit.freeze(MODEL_NAME) - [+] except: - [+] pass - order: - - below: - above: - - pytorch_jit_script - - pytorch_jit_script_ofi - - pytorch_jit_trace - - pytorch_jit_trace_ofi - - pytorch_channels_last diff --git a/neural_coder/backends/pytorch_inc_dynamic_quant.yaml b/neural_coder/backends/pytorch_inc_dynamic_quant.yaml deleted file mode 100644 index edb27b4d188..00000000000 --- a/neural_coder/backends/pytorch_inc_dynamic_quant.yaml +++ /dev/null @@ -1,45 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - insert_below_model_definition_line - content: - - |- - [+] def eval_func(model): - [+] EVAL_FUNC_LINES - [+] try: - [+] torch.backends.quantized.engine = 'onednn' - [+] except: - [+] from torch.backends.quantized import engine; engine = 'onednn' - [+] from neural_compressor.config import PostTrainingQuantConfig - [+] from neural_compressor.quantization import fit - [+] conf = PostTrainingQuantConfig(approach="dynamic", quant_level=1) - [+] MODEL_NAME = fit(model=MODEL_NAME, conf=conf, eval_func=eval_func) - [+] MODEL_NAME.save("./quantized_model") - [+] MODEL_NAME.eval() - [+] try: - [+] with torch.no_grad(): - [+] MODEL_NAME = torch.jit.script(MODEL_NAME) - [+] MODEL_NAME = torch.jit.freeze(MODEL_NAME) - [+] except: - [+] pass - order: - - below: - above: - - pytorch_jit_script - - pytorch_jit_script_ofi - - pytorch_jit_trace - - pytorch_jit_trace_ofi - - pytorch_channels_last diff --git a/neural_coder/backends/pytorch_inc_dynamic_quant_fp8.yaml b/neural_coder/backends/pytorch_inc_dynamic_quant_fp8.yaml deleted file mode 100644 index f32b91389bb..00000000000 --- a/neural_coder/backends/pytorch_inc_dynamic_quant_fp8.yaml +++ /dev/null @@ -1,45 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_dataloader_definition_line", "insert_below_model_definition_line"] - content: - - |- - [+] def eval_func(model): - [+] EVAL_FUNC_LINES - [+] try: - [+] torch.backends.quantized.engine = 'onednn' - [+] except: - [+] from torch.backends.quantized import engine; engine = 'onednn' - [+] from neural_compressor.config import PostTrainingQuantConfig - [+] from neural_compressor.quantization import fit - [+] conf = PostTrainingQuantConfig(approach="dynamic", precision = FP8_DATA_FORMAT, quant_level=1) - [+] MODEL_NAME = fit(model=MODEL_NAME, conf=conf, calib_dataloader=DATALOADER_NAME, eval_func=eval_func) - [+] MODEL_NAME.save("./quantized_model") - [+] MODEL_NAME.eval() - [+] try: - [+] with torch.no_grad(): - [+] MODEL_NAME = torch.jit.script(MODEL_NAME) - [+] MODEL_NAME = torch.jit.freeze(MODEL_NAME) - [+] except: - [+] pass - order: - - below: - above: - - pytorch_jit_script - - pytorch_jit_script_ofi - - pytorch_jit_trace - - pytorch_jit_trace_ofi - - pytorch_channels_last diff --git a/neural_coder/backends/pytorch_inc_huggingface_optimum_dynamic.yaml b/neural_coder/backends/pytorch_inc_huggingface_optimum_dynamic.yaml deleted file mode 100644 index 890ff34d0a2..00000000000 --- a/neural_coder/backends/pytorch_inc_huggingface_optimum_dynamic.yaml +++ /dev/null @@ -1,37 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_dataloader_definition_line", "insert_below_model_definition_line"] - content: - - |- - [+] def eval_func(model): - [+] EVAL_FUNC_LINES - [+] from neural_compressor.config import PostTrainingQuantConfig - [+] from optimum.intel.neural_compressor import INCQuantizer - [+] quantization_config = PostTrainingQuantConfig(approach="dynamic", quant_level=1) - [+] quantizer = INCQuantizer.from_pretrained(MODEL_NAME) - [+] quantizer.quantize(quantization_config=quantization_config, save_directory="quantized_model", save_onnx_model=False) - [+] MODEL_NAME = quantizer._quantized_model - [+] MODEL_NAME.save("./quantized_model") - [+] MODEL_NAME.eval() - order: - - below: - above: - - pytorch_jit_script - - pytorch_jit_script_ofi - - pytorch_jit_trace - - pytorch_jit_trace_ofi - - pytorch_channels_last diff --git a/neural_coder/backends/pytorch_inc_huggingface_optimum_static.yaml b/neural_coder/backends/pytorch_inc_huggingface_optimum_static.yaml deleted file mode 100644 index 08df89f24fd..00000000000 --- a/neural_coder/backends/pytorch_inc_huggingface_optimum_static.yaml +++ /dev/null @@ -1,37 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_dataloader_definition_line", "insert_below_model_definition_line"] - content: - - |- - [+] def eval_func(model): - [+] EVAL_FUNC_LINES - [+] from neural_compressor.config import PostTrainingQuantConfig - [+] from optimum.intel.neural_compressor import INCQuantizer - [+] quantization_config = PostTrainingQuantConfig(approach="static", quant_level=1) - [+] quantizer = INCQuantizer.from_pretrained(MODEL_NAME) - [+] quantizer.quantize(quantization_config=quantization_config, calibration_dataset=eval_dataset, save_directory="quantized_model", save_onnx_model=False) - [+] MODEL_NAME = quantizer._quantized_model - [+] MODEL_NAME.save("./quantized_model") - [+] MODEL_NAME.eval() - order: - - below: - above: - - pytorch_jit_script - - pytorch_jit_script_ofi - - pytorch_jit_trace - - pytorch_jit_trace_ofi - - pytorch_channels_last diff --git a/neural_coder/backends/pytorch_inc_static_quant_fx.yaml b/neural_coder/backends/pytorch_inc_static_quant_fx.yaml deleted file mode 100644 index f77801ebc51..00000000000 --- a/neural_coder/backends/pytorch_inc_static_quant_fx.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_dataloader_definition_line", "insert_below_model_definition_line"] - content: - - |- - [+] def eval_func(model): - [+] EVAL_FUNC_LINES - [+] try: - [+] torch.backends.quantized.engine = 'onednn' - [+] except: - [+] from torch.backends.quantized import engine; engine = 'onednn' - [+] from neural_compressor.config import PostTrainingQuantConfig - [+] from neural_compressor.quantization import fit - [+] conf = PostTrainingQuantConfig(quant_level=1) - [+] MODEL_NAME = fit(model=MODEL_NAME, conf=conf, calib_dataloader=DATALOADER_NAME, eval_func=eval_func) - [+] MODEL_NAME.save("./quantized_model") - [+] MODEL_NAME.eval() - [+] try: - [+] with torch.no_grad(): - [+] MODEL_NAME = torch.jit.script(MODEL_NAME) - [+] MODEL_NAME = torch.jit.freeze(MODEL_NAME) - [+] except: - [+] pass - - order: - - below: - above: - - pytorch_jit_script - - pytorch_jit_script_ofi - - pytorch_jit_trace - - pytorch_jit_trace_ofi - - pytorch_channels_last diff --git a/neural_coder/backends/pytorch_inc_static_quant_fx_fp8.yaml b/neural_coder/backends/pytorch_inc_static_quant_fx_fp8.yaml deleted file mode 100644 index 419c5256588..00000000000 --- a/neural_coder/backends/pytorch_inc_static_quant_fx_fp8.yaml +++ /dev/null @@ -1,46 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_dataloader_definition_line", "insert_below_model_definition_line"] - content: - - |- - [+] def eval_func(model): - [+] EVAL_FUNC_LINES - [+] try: - [+] torch.backends.quantized.engine = 'onednn' - [+] except: - [+] from torch.backends.quantized import engine; engine = 'onednn' - [+] from neural_compressor.config import PostTrainingQuantConfig - [+] from neural_compressor.quantization import fit - [+] conf = PostTrainingQuantConfig(precision=FP8_DATA_FORMAT, quant_level=1) - [+] MODEL_NAME = fit(model=MODEL_NAME, conf=conf, calib_dataloader=DATALOADER_NAME, eval_func=eval_func) - [+] MODEL_NAME.save("./quantized_model") - [+] MODEL_NAME.eval() - [+] try: - [+] with torch.no_grad(): - [+] MODEL_NAME = torch.jit.script(MODEL_NAME) - [+] MODEL_NAME = torch.jit.freeze(MODEL_NAME) - [+] except: - [+] pass - - order: - - below: - above: - - pytorch_jit_script - - pytorch_jit_script_ofi - - pytorch_jit_trace - - pytorch_jit_trace_ofi - - pytorch_channels_last diff --git a/neural_coder/backends/pytorch_inc_static_quant_ipex.yaml b/neural_coder/backends/pytorch_inc_static_quant_ipex.yaml deleted file mode 100644 index 9cde95fa197..00000000000 --- a/neural_coder/backends/pytorch_inc_static_quant_ipex.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_dataloader_definition_line", "insert_below_model_definition_line"] - content: - - |- - [+] from neural_compressor.config import PostTrainingQuantConfig - [+] from neural_compressor.quantization import fit - [+] conf = PostTrainingQuantConfig(backend='ipex', quant_level=1) - [+] MODEL_NAME = fit(model=MODEL_NAME, conf=conf, calib_dataloader=DATALOADER_NAME) - [+] MODEL_NAME.save("./quantized_model") - [+] MODEL_NAME.eval() - order: - - below: - above: - - pytorch_jit_script - - pytorch_jit_script_ofi - - pytorch_jit_trace - - pytorch_jit_trace_ofi - - pytorch_channels_last diff --git a/neural_coder/backends/pytorch_inc_static_quant_ipex_xpu.yaml b/neural_coder/backends/pytorch_inc_static_quant_ipex_xpu.yaml deleted file mode 100644 index f4835516cdf..00000000000 --- a/neural_coder/backends/pytorch_inc_static_quant_ipex_xpu.yaml +++ /dev/null @@ -1,34 +0,0 @@ -# Copyright (c) 2023 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_dataloader_definition_line", "insert_below_model_definition_line"] - content: - - |- - [+] from neural_compressor.config import PostTrainingQuantConfig - [+] from neural_compressor.quantization import fit - [+] MODEL_NAME = MODEL_NAME.to("xpu") - [+] conf = PostTrainingQuantConfig(backend='ipex', quant_level=1, device="xpu") - [+] MODEL_NAME = fit(model=MODEL_NAME, conf=conf, calib_dataloader=DATALOADER_NAME) - [+] MODEL_NAME.save("./quantized_model") - [+] MODEL_NAME.eval() - order: - - below: - above: - - pytorch_jit_script - - pytorch_jit_script_ofi - - pytorch_jit_trace - - pytorch_jit_trace_ofi - - pytorch_channels_last diff --git a/neural_coder/backends/pytorch_ipex_bf16.yaml b/neural_coder/backends/pytorch_ipex_bf16.yaml deleted file mode 100644 index d27f7c6c58b..00000000000 --- a/neural_coder/backends/pytorch_ipex_bf16.yaml +++ /dev/null @@ -1,42 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - insert_below_model_definition_line - - insert_above_inference_line - - indent_inference_line - content: - - |- - [+] import torch - [+] import intel_extension_for_pytorch as ipex - [+] with torch.no_grad(): - [+] MODEL_NAME.eval() - [+] MODEL_NAME = ipex.optimize(MODEL_NAME, dtype=torch.bfloat16) - - |- - [+] import torch - [+] with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloat16): - - 1 - order: - - below: - - pytorch_channels_last - above: - - pytorch_jit_script - - pytorch_jit_script_ofi - - pytorch_jit_trace - - pytorch_jit_trace_ofi - - below: - above: - - below: - above: diff --git a/neural_coder/backends/pytorch_ipex_fp32.yaml b/neural_coder/backends/pytorch_ipex_fp32.yaml deleted file mode 100644 index 0256429948d..00000000000 --- a/neural_coder/backends/pytorch_ipex_fp32.yaml +++ /dev/null @@ -1,32 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - insert_below_model_definition_line - content: - - |- - [+] import torch - [+] import intel_extension_for_pytorch as ipex - [+] with torch.no_grad(): - [+] MODEL_NAME.eval() - [+] MODEL_NAME = ipex.optimize(MODEL_NAME, dtype=torch.float32) - order: - - below: - - pytorch_channels_last - above: - - pytorch_jit_script - - pytorch_jit_script_ofi - - pytorch_jit_trace - - pytorch_jit_trace_ofi diff --git a/neural_coder/backends/pytorch_ipex_int8_dynamic_quant.yaml b/neural_coder/backends/pytorch_ipex_int8_dynamic_quant.yaml deleted file mode 100644 index 06d35d57ef8..00000000000 --- a/neural_coder/backends/pytorch_ipex_int8_dynamic_quant.yaml +++ /dev/null @@ -1,39 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_model_definition_line", "insert_below_input_definition_line"] - content: - - |- - [+] if "quantize" not in str(type(MODEL_NAME)) and "jit" not in str(type(MODEL_NAME)): - [+] import torch - [+] import intel_extension_for_pytorch as ipex - [+] qconfig = ipex.quantization.default_dynamic_qconfig - [+] MODEL_NAME = ipex.quantization.prepare(MODEL_NAME, qconfig, example_inputs=INPUT_NAME, inplace=False) - [+] with torch.no_grad(): - [+] for i in range(10): - [+] INFERENCE_LINE - [+] MODEL_NAME = ipex.quantization.convert(MODEL_NAME) - [+] with torch.no_grad(): - [+] INFERENCE_LINE - [+] MODEL_NAME.eval() - order: - - below: - - pytorch_channels_last - above: - - pytorch_jit_script - - pytorch_jit_script_ofi - - pytorch_jit_trace - - pytorch_jit_trace_ofi diff --git a/neural_coder/backends/pytorch_ipex_int8_static_quant.yaml b/neural_coder/backends/pytorch_ipex_int8_static_quant.yaml deleted file mode 100644 index 9de931ee9fe..00000000000 --- a/neural_coder/backends/pytorch_ipex_int8_static_quant.yaml +++ /dev/null @@ -1,39 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_model_definition_line", "insert_below_input_definition_line"] - content: - - |- - [+] if "quantize" not in str(type(MODEL_NAME)) and "jit" not in str(type(MODEL_NAME)): - [+] import torch - [+] import intel_extension_for_pytorch as ipex - [+] qconfig = ipex.quantization.default_static_qconfig - [+] MODEL_NAME = ipex.quantization.prepare(MODEL_NAME, qconfig, example_inputs=INPUT_NAME, inplace=False) - [+] with torch.no_grad(): - [+] for i in range(10): - [+] INFERENCE_LINE - [+] MODEL_NAME = ipex.quantization.convert(MODEL_NAME) - [+] with torch.no_grad(): - [+] INFERENCE_LINE - [+] MODEL_NAME.eval() - order: - - below: - - pytorch_channels_last - above: - - pytorch_jit_script - - pytorch_jit_script_ofi - - pytorch_jit_trace - - pytorch_jit_trace_ofi diff --git a/neural_coder/backends/pytorch_jit_script.yaml b/neural_coder/backends/pytorch_jit_script.yaml deleted file mode 100644 index 014cc5177f1..00000000000 --- a/neural_coder/backends/pytorch_jit_script.yaml +++ /dev/null @@ -1,36 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - insert_below_model_definition_line - content: - - |- - [+] if "jit" not in str(type(MODEL_NAME)): - [+] import torch - [+] with torch.no_grad(): - [+] MODEL_NAME.eval() - [+] MODEL_NAME = torch.jit.script(MODEL_NAME) - [+] MODEL_NAME = torch.jit.freeze(MODEL_NAME) - order: - - below: - - pytorch_inc_static_quant_fx - - pytorch_inc_static_quant_ipex - - pytorch_inc_dynamic_quant - - pytorch_ipex_fp32 - - pytorch_ipex_bf16 - - pytorch_ipex_int8_static_quant - - pytorch_ipex_int8_dynamic_quant - - pytorch_channels_last - above: diff --git a/neural_coder/backends/pytorch_jit_script_ofi.yaml b/neural_coder/backends/pytorch_jit_script_ofi.yaml deleted file mode 100644 index 2e9c5868d86..00000000000 --- a/neural_coder/backends/pytorch_jit_script_ofi.yaml +++ /dev/null @@ -1,35 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - insert_below_model_definition_line - content: - - |- - [+] if "jit" not in str(type(MODEL_NAME)): - [+] import torch - [+] with torch.no_grad(): - [+] MODEL_NAME.eval() - [+] MODEL_NAME = torch.jit.optimize_for_inference(torch.jit.script(MODEL_NAME)) - order: - - below: - - pytorch_inc_static_quant_fx - - pytorch_inc_static_quant_ipex - - pytorch_inc_dynamic_quant - - pytorch_ipex_fp32 - - pytorch_ipex_bf16 - - pytorch_ipex_int8_static_quant - - pytorch_ipex_int8_dynamic_quant - - pytorch_channels_last - above: diff --git a/neural_coder/backends/pytorch_jit_trace.yaml b/neural_coder/backends/pytorch_jit_trace.yaml deleted file mode 100644 index e52fb7f62ab..00000000000 --- a/neural_coder/backends/pytorch_jit_trace.yaml +++ /dev/null @@ -1,36 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_model_definition_line", "insert_below_input_definition_line"] - content: - - |- - [+] if "jit" not in str(type(MODEL_NAME)): - [+] import torch - [+] with torch.no_grad(): - [+] MODEL_NAME.eval() - [+] MODEL_NAME = torch.jit.trace(MODEL_NAME, INPUT_NAME, strict=False, check_trace=False) - [+] MODEL_NAME = torch.jit.freeze(MODEL_NAME) - order: - - below: - - pytorch_inc_static_quant_fx - - pytorch_inc_static_quant_ipex - - pytorch_inc_dynamic_quant - - pytorch_ipex_fp32 - - pytorch_ipex_bf16 - - pytorch_ipex_int8_static_quant - - pytorch_ipex_int8_dynamic_quant - - pytorch_channels_last - above: diff --git a/neural_coder/backends/pytorch_jit_trace_ofi.yaml b/neural_coder/backends/pytorch_jit_trace_ofi.yaml deleted file mode 100644 index 3b52bdbd5b1..00000000000 --- a/neural_coder/backends/pytorch_jit_trace_ofi.yaml +++ /dev/null @@ -1,35 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - ["insert_below_model_definition_line", "insert_below_input_definition_line"] - content: - - |- - [+] if "jit" not in str(type(MODEL_NAME)): - [+] import torch - [+] with torch.no_grad(): - [+] MODEL_NAME.eval() - [+] MODEL_NAME = torch.jit.optimize_for_inference(torch.jit.trace(MODEL_NAME, INPUT_NAME, strict=False, check_trace=False)) - order: - - below: - - pytorch_inc_static_quant_fx - - pytorch_inc_static_quant_ipex - - pytorch_inc_dynamic_quant - - pytorch_ipex_fp32 - - pytorch_ipex_bf16 - - pytorch_ipex_int8_static_quant - - pytorch_ipex_int8_dynamic_quant - - pytorch_channels_last - above: diff --git a/neural_coder/backends/pytorch_mixed_precision_cpu.yaml b/neural_coder/backends/pytorch_mixed_precision_cpu.yaml deleted file mode 100644 index ad4337f5e57..00000000000 --- a/neural_coder/backends/pytorch_mixed_precision_cpu.yaml +++ /dev/null @@ -1,28 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - insert_above_inference_line - - indent_inference_line - content: - - |- - [+] import torch - [+] with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloat16): - - 1 - order: - - below: - above: - - below: - above: diff --git a/neural_coder/backends/pytorch_mixed_precision_cuda.yaml b/neural_coder/backends/pytorch_mixed_precision_cuda.yaml deleted file mode 100644 index 60c597cedd8..00000000000 --- a/neural_coder/backends/pytorch_mixed_precision_cuda.yaml +++ /dev/null @@ -1,28 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - insert_above_inference_line - - indent_inference_line - content: - - |- - [+] import torch - [+] with torch.cuda.amp.autocast(enabled=True, dtype=torch.float16): - - 1 - order: - - below: - above: - - below: - above: diff --git a/neural_coder/backends/pytorch_mixed_precision_intel_gpu.yaml b/neural_coder/backends/pytorch_mixed_precision_intel_gpu.yaml deleted file mode 100644 index 3b5c86ae977..00000000000 --- a/neural_coder/backends/pytorch_mixed_precision_intel_gpu.yaml +++ /dev/null @@ -1,28 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - insert_above_inference_line - - indent_inference_line - content: - - |- - [+] import torch - [+] with torch.xpu.amp.autocast(dtype=torch.half): - - 1 - order: - - below: - above: - - below: - above: diff --git a/neural_coder/backends/pytorch_torchdynamo_jit_script.yaml b/neural_coder/backends/pytorch_torchdynamo_jit_script.yaml deleted file mode 100644 index 48281956ca7..00000000000 --- a/neural_coder/backends/pytorch_torchdynamo_jit_script.yaml +++ /dev/null @@ -1,38 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - insert_below_model_definition_line - - insert_above_inference_line - - indent_inference_line - content: - - |- - [+] from typing import List - [+] import torch - [+] import torchdynamo - [+] torchdynamo.config.raise_on_backend_error = False - [+] def dynamo_backend(gm: torch.fx.GraphModule, example_inputs: List[torch.Tensor]): - [+] return torch.jit.script(gm) - - |- - [+] import torchdynamo - [+] with torchdynamo.optimize(dynamo_backend): - - 1 - order: - - below: - above: - - below: - above: - - below: - above: diff --git a/neural_coder/backends/pytorch_torchdynamo_jit_script_ofi.yaml b/neural_coder/backends/pytorch_torchdynamo_jit_script_ofi.yaml deleted file mode 100644 index dcee3603d33..00000000000 --- a/neural_coder/backends/pytorch_torchdynamo_jit_script_ofi.yaml +++ /dev/null @@ -1,38 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - insert_below_model_definition_line - - insert_above_inference_line - - indent_inference_line - content: - - |- - [+] from typing import List - [+] import torch - [+] import torchdynamo - [+] torchdynamo.config.raise_on_backend_error = False - [+] def dynamo_backend(gm: torch.fx.GraphModule, example_inputs: List[torch.Tensor]): - [+] return torch.jit.optimize_for_inference(torch.jit.script(gm)) - - |- - [+] import torchdynamo - [+] with torchdynamo.optimize(dynamo_backend): - - 1 - order: - - below: - above: - - below: - above: - - below: - above: diff --git a/neural_coder/backends/pytorch_torchdynamo_jit_trace.yaml b/neural_coder/backends/pytorch_torchdynamo_jit_trace.yaml deleted file mode 100644 index d3673153986..00000000000 --- a/neural_coder/backends/pytorch_torchdynamo_jit_trace.yaml +++ /dev/null @@ -1,38 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - insert_below_model_definition_line - - insert_above_inference_line - - indent_inference_line - content: - - |- - [+] from typing import List - [+] import torch - [+] import torchdynamo - [+] torchdynamo.config.raise_on_backend_error = False - [+] def dynamo_backend(gm: torch.fx.GraphModule, example_inputs: List[torch.Tensor]): - [+] return torch.jit.trace(gm, example_inputs) - - |- - [+] import torchdynamo - [+] with torchdynamo.optimize(dynamo_backend): - - 1 - order: - - below: - above: - - below: - above: - - below: - above: diff --git a/neural_coder/backends/pytorch_torchdynamo_jit_trace_ofi.yaml b/neural_coder/backends/pytorch_torchdynamo_jit_trace_ofi.yaml deleted file mode 100644 index 9f9d1eafce5..00000000000 --- a/neural_coder/backends/pytorch_torchdynamo_jit_trace_ofi.yaml +++ /dev/null @@ -1,38 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - insert_below_model_definition_line - - insert_above_inference_line - - indent_inference_line - content: - - |- - [+] from typing import List - [+] import torch - [+] import torchdynamo - [+] torchdynamo.config.raise_on_backend_error = False - [+] def dynamo_backend(gm: torch.fx.GraphModule, example_inputs: List[torch.Tensor]): - [+] return torch.jit.optimize_for_inference(torch.jit.trace(gm, example_inputs)) - - |- - [+] import torchdynamo - [+] with torchdynamo.optimize(dynamo_backend): - - 1 - order: - - below: - above: - - below: - above: - - below: - above: diff --git a/neural_coder/backends/template.yaml b/neural_coder/backends/template.yaml deleted file mode 100644 index 1e3b1fa1501..00000000000 --- a/neural_coder/backends/template.yaml +++ /dev/null @@ -1,51 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -transformation: - location: - - - - - - - content: - - |- - [+] YOUR CODE LINE 1 - [+] YOUR CODE LINE 2 - [+] YOUR CODE LINE 3 - - |- - [+] YOUR CODE LINE 1 - [+] YOUR CODE LINE 2 - [+] YOUR CODE LINE 3 - - |- - [+] YOUR CODE LINE 1 - [+] YOUR CODE LINE 2 - [+] YOUR CODE LINE 3 - order: - - below: - - - - - above: - - - - - - below: - - - - - above: - - - - - - below: - - - - - above: - - - - diff --git a/neural_coder/coders/__init__.py b/neural_coder/coders/__init__.py deleted file mode 100644 index e833188cc78..00000000000 --- a/neural_coder/coders/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/neural_coder/coders/autoinc/__init__.py b/neural_coder/coders/autoinc/__init__.py deleted file mode 100644 index e833188cc78..00000000000 --- a/neural_coder/coders/autoinc/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/neural_coder/coders/autoinc/autoinc_harness.py b/neural_coder/coders/autoinc/autoinc_harness.py deleted file mode 100644 index e21b9bfdf8e..00000000000 --- a/neural_coder/coders/autoinc/autoinc_harness.py +++ /dev/null @@ -1,476 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import logging -import os -import re -import sys - -import yaml - -from ... import globals -from ...utils.line_operation import ( - get_line_indent_level, - get_line_left_hand_side, - get_line_wo_comment, - is_eval_func_model_name, - single_line_comment_or_empty_line_detection, -) - -logging.basicConfig( - level=globals.logging_level, format="%(asctime)s %(levelname)s %(message)s", datefmt="%a, %d %b %Y %H:%M:%S +0000" -) -logger = logging.getLogger(__name__) - - -class AutoInc_Harness(object): - def __init__(self, backend): - self.backend = backend - - def print_info(self): - for i in globals.list_model_def_instance: - logger.debug(f"i.print_info(): {i.print_info()}") - - # collect file transformation info and register in globals - # (i.e. which file to add which lines at which location) - def register_transformation(self): - backend_file = open(os.path.dirname(__file__) + "/../../backends/" + self.backend + ".yaml") - backend_dict = yaml.load(backend_file, Loader=yaml.BaseLoader) - logger.debug(f"backend_dict: {backend_dict}") - bk_trans_location = backend_dict["transformation"]["location"] # string - bk_trans_content = backend_dict["transformation"]["content"] # string - bk_trans_order = backend_dict["transformation"]["order"] # list - - # modular design - if globals.use_modular: - content = globals.modular_item - bk_trans_content = ["[+] " + content.replace("\n", "\n[+] ")[:-5]] - - list_code = [] - history = set() - for i in globals.list_code_path: - list_code.append(open(i, "r").read()) - for loc in bk_trans_location: - # PART 1 - "model_definition_line" - if "insert_below_model_definition_line" in loc: - for ins in globals.list_model_def_instance: - model_name = ins.model_name - if model_name in history and globals.code_domain == "torchvision": - continue - else: - history.add(model_name) - file_path = ins.file_path - model_def_line_idx = ins.model_def_line_idx - - file_path_idx = globals.list_code_path.index(file_path) - lines = list_code[file_path_idx].split("\n") - line_idx = 0 - - # to check if this model has an inference line is in the file - # if not, skip this model - to_transform = False - for i in range(len(lines)): - line = lines[i] - if model_name + "(" in line or ( - model_name + "." in line and line.find(model_name) < line.find(".") and "(" in line - ): - to_transform = True - if not to_transform and globals.code_domain == "onnx": - pass - elif not to_transform: - continue - ### information - - # search DataLoader definition in this file - dataloader_name = "" - for i in range(len(lines)): - line = lines[i] - if not single_line_comment_or_empty_line_detection(line): - if ("DataLoader(" in line and "=" in line and line.find("=") < line.find("DataLoader")) or ( - "dataloader" in line and "=" in line and line.find("=") > line.find("dataloader") - ): - dataloader_def_line_indent_level = get_line_indent_level(line) - dataloader_name = get_line_left_hand_side(line) - dataloader_def_line_idx = i - - # search inference line in this file, and also input_name - inference_line = "" - input_name = "" - for i in range(len(lines)): - line = lines[i] - is_eval_func, eval_func_type = is_eval_func_model_name(model_name, line) - if not single_line_comment_or_empty_line_detection(line): - if is_eval_func and "[coder-enabled]" not in line: - inference_line = line - input_name = line[line.find("(") + 1 : line.find(")")].replace("*", "") - # get "c" in "a = b(**c)" - - # search input definition in this file (if any) - if input_name != "": - for i in range(len(lines)): - line = lines[i] - if not single_line_comment_or_empty_line_detection(line): - if input_name in line and "=" in line and line.find("=") > line.find(input_name): - input_def_line_indent_level = get_line_indent_level(line) - input_def_line_idx = i - - # search model definition line and its end line index - # (only has 1 model definition line, because it's in loop of globals.list_model_def_instance) - for i in range(len(lines)): - line = lines[i] - if line_idx == model_def_line_idx and "[coder-enabled]" not in line: - model_def_line_indent_level = get_line_indent_level(line) - if ")" in line and line.count(")") == line.count("("): # e.g. model = Net(xxx) - model_definition_end_line_idx = line_idx + 1 - else: # e.g. model = Net(xxx, \n xxx, \n xxx) - do_search = True - i_search = 1 - while do_search: - following_line = lines[line_idx + i_search] - if ")" in following_line and following_line.count(")") > following_line.count("("): - do_search = False - i_search += 1 - model_definition_end_line_idx = line_idx + i_search - line_idx += 1 - - ### check - - bk_trans_content_this = bk_trans_content[bk_trans_location.index(loc)] - if file_path_idx == 0 and (globals.code_domain in ["transformers_trainer", "torchvision", "onnx"]): - pass - elif ( - ("INPUT_NAME" in bk_trans_content_this and input_name == "") - or ("DATALOADER_NAME" in bk_trans_content_this and dataloader_name == "") - or ("INFERENCE_LINE" in bk_trans_content_this and inference_line == "") - ): - logger.info( - f"Skipped due to not having enough information required by " - "the transformation content specified in the config file " - "(e.g. INPUT_NAME, DATALOADER_NAME, INFERENCE_LINE). " - f"File path: {file_path}" - ) - continue - - ### location - - # search for features to put below them - """ - Example (psuedo-code): - model = Net() - # jit script begin mark - model = torch.jit.script(model) - # jit script end mark (feature name + model name to handle multi-model situation) - model = ipex.optimize(model, "fp32") # "ipex fp32" must be put below "jit script" - """ - put_below_idx = 0 - for i in range(len(lines)): - for item in bk_trans_order[0]["below"]: - line = lines[i] - if item in line and model_name in line: - put_below_idx = max(put_below_idx, i + 1) - - # search for features to put above them - put_above_idx = sys.maxsize - for i in range(len(lines)): - for item in bk_trans_order[0]["above"]: - line = lines[i] - if item in line and model_name in line: - put_above_idx = min(put_above_idx, i) - - # location assignment (below model def / dataloader def / input def) - torchvision_indent = -1 - if file_path_idx == 0 and globals.code_domain == "transformers_trainer": - for i in range(len(lines)): - line = lines[i] - if re.findall("trainer = .*Trainer", line): - if "(" in line and line.count(")") == line.count("("): - trans_insert_location = i + 1 - else: - do_search = True - i_search = 1 - while do_search: - following_line = lines[i + i_search] - if ")" in following_line and following_line.count(")") > following_line.count( - "(" - ): - do_search = False - i_search += 1 - trans_insert_location = i + i_search - trans_insert_location = min(max(trans_insert_location, put_below_idx), put_above_idx) - elif file_path_idx == 0 and globals.code_domain == "torchvision": - trans_insert_location = 1 - for i in range(len(lines)): - line = lines[i] - if ( - "val_loader" in line - and "aux_val_loader" not in line - and ( - "torch.utils.data.DataLoader" in line - or "utils.data.DataLoader" in line - or "DataLoader" in line - ) - ): - torchvision_indent = get_line_indent_level(line) - if "(" in line and line.count(")") == line.count("("): - trans_insert_location = i + 1 - else: - do_search = True - i_search = 1 - while do_search: - following_line = lines[i + i_search] - if ")" in following_line and following_line.count(")") > following_line.count( - "(" - ): - do_search = False - i_search += 1 - trans_insert_location = i + i_search - trans_insert_location = min(max(trans_insert_location, put_below_idx), put_above_idx) - else: - if "insert_below_model_definition_line" in loc: - trans_insert_location = min( - max(model_definition_end_line_idx, put_below_idx), put_above_idx - ) - if "insert_below_dataloader_definition_line" in loc: - try: - dataloader_def_line_idx - except: - logger.warning( - f"Skipped due to not having dataloader definition required by " - "the transformation content specified in the config file. " - f"File path: {file_path}" - ) - continue - trans_insert_location = max( - trans_insert_location, - min(max(dataloader_def_line_idx + 1, put_below_idx), put_above_idx), - ) - if "insert_below_input_definition_line" in loc: - try: - input_def_line_idx - except: - logger.warning( - f"Skipped due to not having input definition required by " - "the transformation content specified in the config file. " - f"File path: {file_path}" - ) - continue - trans_insert_location = max( - trans_insert_location, min(max(input_def_line_idx + 1, put_below_idx), put_above_idx) - ) - - insert_indent_level = ( - get_line_indent_level(lines[trans_insert_location - 1]) - if torchvision_indent == -1 - else torchvision_indent - ) - ### content - # lines to insert - lines_to_insert = bk_trans_content_this - if globals.code_domain == "transformers_trainer": - lines_to_insert = lines_to_insert.replace( - "EVAL_FUNC_LINES", globals.list_eval_func_lines[0] - ).replace("DATALOADER_NAME", globals.list_calib_dataloader_name[0]) - elif globals.code_domain == "transformers_no_trainer": - pass - elif globals.code_domain == "torchvision": - lines_to_insert = lines_to_insert.replace( - "EVAL_FUNC_LINES", globals.list_eval_func_lines[0] - ).replace("DATALOADER_NAME", globals.list_calib_dataloader_name[0]) - elif globals.code_domain == "onnx": - lines_to_insert = lines_to_insert.replace( - "EVAL_FUNCTION_NAME", globals.list_eval_func_name[0] - ).replace("DATALOADER_NAME", globals.list_calib_dataloader_name[0]) - else: - lines_to_insert = lines_to_insert.replace("DATALOADER_NAME", dataloader_name).replace( - "def eval_func", "# def eval_func" - ) - - optimum_quant_config_line = ( - 'IncQuantizationConfig.from_pretrained("' + globals.optimum_quant_config + '")' - ) - - # replace [+] indication with empty - lines_to_insert = lines_to_insert.replace("[+] ", " " * insert_indent_level) - # add begin indicator - lines_to_insert = ( - " " * insert_indent_level - + "# [NeuralCoder] " - + self.backend - + " for " - + model_name - + " [Beginning Line]\n" - + lines_to_insert - ) - # replace INDICATIONS with real stuff - lines_to_insert = ( - lines_to_insert.replace("MODEL_NAME", model_name) - .replace("INPUT_NAME", input_name) - .replace("EVAL_FUNC_LINES", "# return 1") - .replace("OPTIMUM_QUANT_CONFIG", optimum_quant_config_line) - .replace("\n", " # [coder-enabled]\n") - ) - # add end indicator - lines_to_insert += ( - " # [coder-enabled]\n" - + " " * insert_indent_level - + "# [NeuralCoder] " - + self.backend - + " for " - + model_name - + " [Ending Line] # [coder-enabled]" - ) - - ### register - - if file_path not in globals.list_trans_insert_modified_file: - globals.list_trans_insert_modified_file.append(file_path) - globals.list_trans_insert_location_idxs.append([trans_insert_location]) - globals.list_trans_insert_number_insert_lines.append([lines_to_insert.count("\n") + 1]) - globals.list_trans_insert_lines_to_insert.append([lines_to_insert]) - else: - idx = globals.list_trans_insert_modified_file.index(file_path) - globals.list_trans_insert_location_idxs[idx].append(trans_insert_location) - globals.list_trans_insert_number_insert_lines[idx].append(lines_to_insert.count("\n") + 1) - globals.list_trans_insert_lines_to_insert[idx].append(lines_to_insert) - - # PART 2 - "inference line" - if ( - "indent_inference_line" in loc - or "insert_above_inference_line" in loc - or "insert_below_inference_line" in loc - ): - for file_path in globals.list_code_path: - code = open(file_path, "r").read() - lines = code.split("\n") - line_idx = 0 - for i in range(len(lines)): - line = lines[i] - for model_name in globals.list_model_name: - is_eval_func, eval_func_type = is_eval_func_model_name(model_name, line) - if is_eval_func and "[coder-enabled]" not in line: - if eval_func_type == "non-forward": - pass # do something - - inference_line_indent_level = get_line_indent_level(line) - - if "indent_inference_line" in loc: - bk_trans_content_this = bk_trans_content[bk_trans_location.index(loc)] - add_indent_level = int(bk_trans_content_this) - - trans_indent_location = [] - # indent can have multiple location, so is a list of numbers - trans_indent_level = [] - - if ")" in line: # e.g. model = Net(xxx) - trans_indent_location.append(line_idx) - trans_indent_level.append(add_indent_level) - else: # e.g. model = Net(xxx, \n xxx, \n xxx) - trans_indent_location.append(line_idx) - trans_indent_level.append(add_indent_level) - do_search = True - i_search = 1 - while do_search: - trans_indent_location.append(line_idx + i_search) - trans_indent_level.append(add_indent_level) - following_line = lines[line_idx + i_search] - if ")" in following_line: - do_search = False - i_search += 1 - - ### register - - if file_path not in globals.list_trans_indent_modified_file: - globals.list_trans_indent_modified_file.append(file_path) - globals.list_trans_indent_location_idxs.append(trans_indent_location) - globals.list_trans_indent_level.append(trans_indent_level) - else: - idx = globals.list_trans_indent_modified_file.index(file_path) - for i in trans_indent_location: - globals.list_trans_indent_location_idxs[idx].append(i) - for i in trans_indent_level: - globals.list_trans_indent_level[idx].append(i) - - if "insert_above_inference_line" in loc: - idx_offset = 0 - elif "insert_below_inference_line" in loc: - idx_offset = 1 - - if "insert_above_inference_line" in loc or "insert_below_inference_line" in loc: - bk_trans_content_this = bk_trans_content[bk_trans_location.index(loc)] - - trans_insert_location = line_idx + idx_offset - - insert_indent_level = inference_line_indent_level - - ### content - - # lines to insert - lines_to_insert = bk_trans_content_this - # replace [+] indication with empty - lines_to_insert = lines_to_insert.replace("[+] ", " " * insert_indent_level) - # add begin indicator - lines_to_insert = ( - " " * insert_indent_level - + "# [NeuralCoder] " - + self.backend - + " [Beginning Line] \n" - + lines_to_insert - ) - # replace INDICATIONS with real stuff - # (for now, inference_line related transformations ) - # (have nothing to do with input, dataloader etc, ) - # (so no need to put replaces here.) - lines_to_insert = lines_to_insert.replace("\n", " # [coder-enabled]\n") - # add end indicator - lines_to_insert += ( - " # [coder-enabled]\n" - + " " * insert_indent_level - + "# [NeuralCoder] " - + self.backend - + " [Ending Line] # [coder-enabled]" - ) - - # customized argument - if self.backend == "pytorch_benchmark": - lines_to_insert = lines_to_insert.replace( - "NUM_BENCHMARK_ITERATION", globals.num_benchmark_iteration - ) - lines_to_insert = lines_to_insert.replace("ACCURACY_MODE", str(False)) - lines_to_insert = lines_to_insert.replace("EVAL_FUNC_LINES", line.strip()) - - ### register - - if file_path not in globals.list_trans_insert_modified_file: - globals.list_trans_insert_modified_file.append(file_path) - globals.list_trans_insert_location_idxs.append([trans_insert_location]) - globals.list_trans_insert_number_insert_lines.append( - [lines_to_insert.count("\n") + 1] - ) - globals.list_trans_insert_lines_to_insert.append([lines_to_insert]) - else: - idx = globals.list_trans_insert_modified_file.index(file_path) - globals.list_trans_insert_location_idxs[idx].append(trans_insert_location) - globals.list_trans_insert_number_insert_lines[idx].append( - lines_to_insert.count("\n") + 1 - ) - globals.list_trans_insert_lines_to_insert[idx].append(lines_to_insert) - - line_idx += 1 - - # PART 3 - for customized location - - logger.debug(f"globals.list_trans_insert_modified_file: {globals.list_trans_insert_modified_file}") - logger.debug(f"globals.list_trans_insert_location_idxs: {globals.list_trans_insert_location_idxs}") - logger.debug(f"globals.list_trans_insert_number_insert_lines: {globals.list_trans_insert_number_insert_lines}") - logger.debug(f"globals.list_trans_insert_lines_to_insert: {globals.list_trans_insert_lines_to_insert}") diff --git a/neural_coder/coders/autoinc/calib_dataloader.py b/neural_coder/coders/autoinc/calib_dataloader.py deleted file mode 100644 index 8d287837676..00000000000 --- a/neural_coder/coders/autoinc/calib_dataloader.py +++ /dev/null @@ -1,45 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import logging - -from ... import globals - - -class Calib_Dataloader(object): - def __init__(self): - pass - - def register_transformation(self): - if globals.code_domain == "transformers_trainer": - globals.list_calib_dataloader_name.append("trainer.get_eval_dataloader()") - elif globals.code_domain == "transformers_no_trainer": - pass - elif globals.code_domain == "torchvision": - globals.list_calib_dataloader_name.append("val_loader") - elif globals.code_domain == "onnx": - codes = open(globals.list_code_path[0], "r").read().split("\n") - for line in codes: - line = line.strip() - if "loader" in line and "=" in line: - end = 0 - for i in range(len(line)): - if line[i] == "=": - end = i - if line[end - 1] == " ": - globals.list_calib_dataloader_name.append(line[: end - 1]) - else: - globals.list_calib_dataloader_name.append(line[:end]) - else: # random model - pass diff --git a/neural_coder/coders/autoinc/domain.py b/neural_coder/coders/autoinc/domain.py deleted file mode 100644 index ed4437d7455..00000000000 --- a/neural_coder/coders/autoinc/domain.py +++ /dev/null @@ -1,34 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import re - - -def determine_domain(path) -> str: - codes = open(path, "r").read() - if ("import torchvision.models" in codes or "from torchvision.models" in codes) and "val_loader" in codes: - return "torchvision" - elif re.search(r"from (.*)transformers import", codes) and re.search(r"(.*)Model(.*)", codes): - if "Trainer" in codes or "trainer" in codes: - return "transformers_trainer" - else: - return "transformers_no_trainer" - elif "onnx.load(" in codes: - return "onnx" - elif "keras.Sequential" in codes: - return "keras_script" - elif "from tensorflow import" in codes or "import tensorflow" in codes: - return "tensorflow_keras_model" - else: - return "random model" diff --git a/neural_coder/coders/autoinc/eval_func.py b/neural_coder/coders/autoinc/eval_func.py deleted file mode 100644 index 9ea65d36315..00000000000 --- a/neural_coder/coders/autoinc/eval_func.py +++ /dev/null @@ -1,111 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from ... import globals -from ...utils.line_operation import get_line_indent_level - - -class Eval_Func(object): - def __init__(self): - pass - - def register_transformation(self): - if globals.code_domain == "transformers_trainer": - lines = [ - "trainer.model = model", - "metrics = trainer.evaluate() # check if all tasks do not have parameters in evaluate()", - "keys = [", - ' "eval_accuracy",', - ' "eval_bleu",', - ' "eval_matthews_correlation",', - ' "eval_pearsonr",', - ' "eval_precision",', - ' "eval_recall",', - ' "eval_rouge",', - ' "eval_sacrebleu",', - ' "eval_spearmanr",', - ' "eval_mcc",', - ' "eval_acc",', - ' "eval_acc_and_f1",', - ' "eval_corr",', - ' "eval_mnli/acc",', - ' "eval_mnli-mm/acc",', - ' "eval_exact_match",', - ' "eval_f1",', - "] # METRIC_TAGS in transformers", - "for key in keys:", - " if key in metrics.keys():", - " return metrics[key]", - 'assert False, "No metric returned, Please check inference metric!"', - ] - for index, line in enumerate(lines): - if index != 0: - lines[index] = "[+] " + " " * 4 + line - lines = "\n".join(lines) - globals.list_eval_func_lines.append(lines) - elif globals.code_domain == "transformers_no_trainer": - pass - elif globals.code_domain == "torchvision": - # search for 'validate()' - codes = open(globals.list_code_path[0], "r").read().split("\n") - lines = [] - for index, line in enumerate(codes): - if "def validate(" in line: - start = index - start_indent = get_line_indent_level(codes[start]) - for i in range(start + 1, len(codes)): - if codes[i] == "": - continue - line_indent = get_line_indent_level(codes[i]) - if line_indent > start_indent: - change_indent = line_indent - 4 - lines.append(" " * change_indent + codes[i].lstrip()) - # no 'print' - else: - break - break - else: - pass - for index, line in enumerate(lines): - if "return" in line: - indent = get_line_indent_level(line) - line_list = line.split() - line_list[1] = "float(" + line_list[1] + ")" - lines[index] = " " * indent + " ".join(line_list) - for index, line in enumerate(lines): - if index != 0: - lines[index] = "[+] " + " " * 8 + line - lines = "\n".join(lines) - globals.list_eval_func_lines.append(lines) - elif globals.code_domain == "onnx": - # look for sess = onnxruntime.InferenceSession(MODEL_NAME.SerializeToString(), None) - codes = open(globals.list_code_path[0], "r").read().split("\n") - start = 0 - for idx, line in enumerate(codes): - if "onnxruntime.InferenceSession(" in line: - start = idx - break - line_indent = get_line_indent_level(codes[start]) - target = None - for i in range(start, -1, -1): - if "def" in codes[i] and (line_indent - get_line_indent_level(codes[i])) == 4: - target = codes[i].split(" ")[1] - break - func_name = None - for i in range(len(target)): - if target[i] == "(": - globals.list_eval_func_name.append(target[:i]) - break - else: # random model - pass diff --git a/neural_coder/coders/pytorch/__init__.py b/neural_coder/coders/pytorch/__init__.py deleted file mode 100644 index e833188cc78..00000000000 --- a/neural_coder/coders/pytorch/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/neural_coder/coders/pytorch/batch_size.py b/neural_coder/coders/pytorch/batch_size.py deleted file mode 100644 index ec61e19d70f..00000000000 --- a/neural_coder/coders/pytorch/batch_size.py +++ /dev/null @@ -1,83 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from ... import globals - - -class BatchSizeCoder(object): - def __init__(self, file) -> None: - self.file = file - self.result = [] - - def transform(self): - lines = self.file.split("\n") - for line in lines: - if self.not_modify(line): - new_line = self.modify(line) - self.result.append(new_line) - else: - self.result.append(line) - for index, line in enumerate(self.result): - if index != len(self.result) - 1: - self.result[index] += "\n" - return "".join(self.result) - - def not_modify(self, s): - if "batch_size" in s and "=" in s: - return True - return False - - def modify(self, s): - idx = s.find("batch_size") - s_right = s[idx:] - if " = " in s_right: - index = s.find(" = ") - s_left = s[:index] - if "batch_size" in s_left: - if "," in s_left: - index1 = s_left.find(",") - index2 = s_left.find("batch_size") - if index1 > index2: - slice1 = s_left[:index1] - else: - s_left1 = s_left[:index2] - s_right = s_left[index2:] - index3 = s_left1.rfind(",") - if "," in s_right: - index4 = s_right.find(",") + len(s_left1) - slice1 = s_left[index3 + 2 : index4] - else: - slice1 = s_left[index3 + 2 : index] - s1 = slice1 + " = " + globals.target_batch_size - s = s[:] + "\n" + s1 - else: - s_right = s[index + 3 :] - s_right = s_right.replace(s_right, globals.target_batch_size) - s = s_left + " = " + s_right - elif "batch_size=" in s: - idx = s.find("batch_size=") - s_right = s[idx:] - idx2 = s_right.find("batch_size") - if "," in s_right: - index2 = s_right.find(",") - old = s_right[idx2:index2] - s = s.replace(old, "batch_size=" + globals.target_batch_size) - elif ")" in s_right: - index2 = s_right.find(")") - old = s_right[idx2:index2] - s = s.replace(old, "batch_size=" + globals.target_batch_size) - else: - old = s_right[idx2:] - s = s.replace(old, "batch_size=" + globals.target_batch_size) - return s diff --git a/neural_coder/coders/pytorch/change_trainer_to_nlptrainer.py b/neural_coder/coders/pytorch/change_trainer_to_nlptrainer.py deleted file mode 100644 index 7c0d71ce534..00000000000 --- a/neural_coder/coders/pytorch/change_trainer_to_nlptrainer.py +++ /dev/null @@ -1,47 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -from ...utils.line_operation import get_line_indent_level - - -class TrainerToNLPTrainer(object): - def __init__(self, file) -> None: - self.file = file - self.result = [] - - def transform(self): - lines = self.file.split("\n") - - for line in lines: - if self.is_modify(line): - new_line = self.modify(line) - self.result.append(new_line) - else: - self.result.append(line) - for index, line in enumerate(self.result): - if index != len(self.result) - 1: - self.result[index] += "\n" - return "".join(self.result) - - def is_modify(self, s): - if "trainer = Trainer(" in s: - return True - else: - return False - - def modify(self, s): - old = "Trainer" - s = s.replace(old, "NLPTrainer") - return s diff --git a/neural_coder/coders/pytorch/cuda_to_cpu.py b/neural_coder/coders/pytorch/cuda_to_cpu.py deleted file mode 100644 index 2d6c9c8a13e..00000000000 --- a/neural_coder/coders/pytorch/cuda_to_cpu.py +++ /dev/null @@ -1,86 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -from ...utils.line_operation import get_line_indent_level - - -class CudaToCpu(object): - def __init__(self, file) -> None: - self.file = file - self.result = [] - - def transform(self): - lines = self.file.split("\n") - # determine if jump the whole file (in cases where: args.device, args.cuda etc) - to_jump = False - for line in lines: - if self.is_jump_file(line): - to_jump = True - break - - if to_jump: # this file do not need transformation - for line in lines: - self.result.append(line) - else: # this file might need transformation - for line in lines: - if self.is_delete(line): - indent_level = get_line_indent_level(line) - new_line = " " * indent_level + "pass" - self.result.append(new_line) - elif self.is_modify(line): - new_line = self.change_to_cpu(line) - self.result.append(new_line) - else: - self.result.append(line) - for index, line in enumerate(self.result): - if index != len(self.result) - 1: - self.result[index] += "\n" - return "".join(self.result) - - def is_jump_file(self, s): - if ( - "args.device" in s - or "args.cpu" in s - or "args.gpu" in s - or "args.cuda" in s - or "torch.cuda.is_available()" in s - ): - return True - else: - return False - - def is_delete(self, s): - if "cuda." in s and "=" not in s and "if" not in s: - return True - else: - return False - - def is_modify(self, s): - if "'cuda'" in s or '"cuda"' in s or "'cuda:0'" in s or '"cuda:0"' in s or "cuda()" in s: - return True - else: - return False - - def change_to_cpu(self, s): - if "'cuda'" in s or "'cuda:0'" in s: - old = "'cuda'" if "'cuda'" in s else "'cuda:0'" - s = s.replace(old, "'cpu'") - elif '"cuda"' in s or '"cuda:0"' in s: - old = '"cuda"' if '"cuda"' in s else '"cuda:0"' - s = s.replace(old, '"cpu"') - elif "cuda()" in s: - old = "cuda" - s = s.replace(old, "cpu") - return s diff --git a/neural_coder/coders/pytorch/dummy_dataloader.py b/neural_coder/coders/pytorch/dummy_dataloader.py deleted file mode 100644 index 64e745cdacf..00000000000 --- a/neural_coder/coders/pytorch/dummy_dataloader.py +++ /dev/null @@ -1,124 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import logging - -from ... import globals -from ...utils.line_operation import get_line_indent_level, get_line_left_hand_side, is_eval_func_model_name - -logging.basicConfig( - level=globals.logging_level, format="%(asctime)s %(levelname)s %(message)s", datefmt="%a, %d %b %Y %H:%M:%S +0000" -) -logger = logging.getLogger(__name__) - - -class DummyDataLoader(object): - def __init__(self, list_model_def_instance): - self.list_model_def_instance = list_model_def_instance - - def print_info(self): - for i in self.list_model_def_instance: - logger.debug(f"i.print_info(): {i.print_info()}") - - # collect file transformation info and register (store) in globals - # (i.e. which file to add which lines at which location) - def register_transformation(self): - list_code = [] - for i in globals.list_code_path: - list_code.append(open(i, "r").read()) - - for ins in self.list_model_def_instance: - model_name = ins.model_name - file_path = ins.file_path - model_def_line_idx = ins.model_def_line_idx - function_def_line_idx = ins.function_def_line_idx - class_name = ins.class_name - - # transformation - file_path_idx = globals.list_code_path.index(file_path) - lines = list_code[file_path_idx].split("\n") - line_idx = 0 - - # search DataLoader - dataloader_name = "" - for i in range(len(lines)): # each item is a str of this code line - line = lines[i] - if "DataLoader(" in line and "=" in line and line.find("=") < line.find("DataLoader"): - dataloader_name = get_line_left_hand_side(line) - dataloader_def_line_idx = i - - if dataloader_name != "": - return - else: - input_dimension_str = "3, 224, 224)" - for i in range(len(lines)): - line = lines[i] - if ("input" in line and "=" in line and line.find("=") > line.find("input")) or ( - "image" in line and "=" in line and line.find("=") > line.find("image") - ): - input_dimension_str = line[line.find(",") + 2 :] - - for i in range(len(lines)): - line = lines[i] - if line_idx == model_def_line_idx: - indent_level = get_line_indent_level(line) - lines_to_insert = "" - lines_to_insert += " " * indent_level + "import torch" + "\n" - lines_to_insert += " " * indent_level + "from torch.utils.data import Dataset" + "\n" - lines_to_insert += " " * indent_level + "class DummyDataset(Dataset):" + "\n" - lines_to_insert += ( - " " * indent_level + " def __init__(self, *shapes, num_samples: int = 10000):" + "\n" - ) - lines_to_insert += " " * indent_level + " super().__init__()" + "\n" - lines_to_insert += " " * indent_level + " self.shapes = shapes" + "\n" - lines_to_insert += " " * indent_level + " self.num_samples = num_samples" + "\n" - lines_to_insert += " " * indent_level + " def __len__(self):" + "\n" - lines_to_insert += " " * indent_level + " return self.num_samples" + "\n" - lines_to_insert += " " * indent_level + " def __getitem__(self, idx: int):" + "\n" - lines_to_insert += " " * indent_level + " sample = []" + "\n" - lines_to_insert += " " * indent_level + " for shape in self.shapes:" + "\n" - lines_to_insert += " " * indent_level + " spl = torch.rand(*shape)" + "\n" - lines_to_insert += " " * indent_level + " sample.append(spl)" + "\n" - lines_to_insert += " " * indent_level + " return sample" + "\n" - lines_to_insert += " " * indent_level + "from torch.utils.data import DataLoader" + "\n" - lines_to_insert += ( - " " * indent_level - + "dummy_dataset = DummyDataset((" - + input_dimension_str - + ", (1, ))" - + "\n" - ) - lines_to_insert += ( - " " * indent_level + "dummy_dataloader = DataLoader(dummy_dataset, batch_size=1)" - ) - - trans_insert_location = 0 - - if file_path not in globals.list_trans_insert_modified_file: - globals.list_trans_insert_modified_file.append(file_path) - globals.list_trans_insert_location_idxs.append([trans_insert_location]) - globals.list_trans_insert_number_insert_lines.append([lines_to_insert.count("\n") + 1]) - globals.list_trans_insert_lines_to_insert.append([lines_to_insert]) - else: - idx = globals.list_trans_insert_modified_file.index(file_path) - globals.list_trans_insert_location_idxs[idx].append(trans_insert_location) - globals.list_trans_insert_number_insert_lines[idx].append(lines_to_insert.count("\n") + 1) - globals.list_trans_insert_lines_to_insert[idx].append(lines_to_insert) - - line_idx += 1 - - logger.debug(f"globals.list_trans_insert_modified_file: {globals.list_trans_insert_modified_file}") - logger.debug(f"globals.list_trans_insert_location_idxs: {globals.list_trans_insert_location_idxs}") - logger.debug(f"globals.list_trans_insert_number_insert_lines: {globals.list_trans_insert_number_insert_lines}") - logger.debug(f"globals.list_trans_insert_lines_to_insert: {globals.list_trans_insert_lines_to_insert}") diff --git a/neural_coder/coders/pytorch/harness.py b/neural_coder/coders/pytorch/harness.py deleted file mode 100644 index 3921383791e..00000000000 --- a/neural_coder/coders/pytorch/harness.py +++ /dev/null @@ -1,421 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import logging -import os -import sys - -import yaml - -from ... import globals -from ...utils.line_operation import ( - get_line_indent_level, - get_line_left_hand_side, - get_line_wo_comment, - is_eval_func_model_name, - single_line_comment_or_empty_line_detection, -) - -logging.basicConfig( - level=globals.logging_level, format="%(asctime)s %(levelname)s %(message)s", datefmt="%a, %d %b %Y %H:%M:%S +0000" -) -logger = logging.getLogger(__name__) - - -class Harness(object): - def __init__(self, backend): - self.backend = backend - - def print_info(self): - for i in globals.list_model_def_instance: - logger.debug(f"i.print_info(): {i.print_info()}") - - # collect file transformation info and register in globals - # (i.e. which file to add which lines at which location) - def register_transformation(self): - backend_file = open(os.path.dirname(__file__) + "/../../backends/" + self.backend + ".yaml") - backend_dict = yaml.load(backend_file, Loader=yaml.BaseLoader) - logger.debug(f"backend_dict: {backend_dict}") - - bk_trans_location = backend_dict["transformation"]["location"] # string - bk_trans_content = backend_dict["transformation"]["content"] # string - bk_trans_order = backend_dict["transformation"]["order"] # list - - list_code = [] - for i in globals.list_code_path: - list_code.append(open(i, "r").read()) - - for loc in bk_trans_location: - # PART 1 - "model_definition_line" - if "insert_below_model_definition_line" in loc: - for ins in globals.list_model_def_instance: - model_name = ins.model_name - file_path = ins.file_path - model_def_line_idx = ins.model_def_line_idx - - file_path_idx = globals.list_code_path.index(file_path) - lines = list_code[file_path_idx].split("\n") - line_idx = 0 - - # to check if this model has an inference line is in the file - # if not, skip this model - to_transform = False - for i in range(len(lines)): - line = lines[i] - if model_name + "(" in line or ( - model_name + "." in line and line.find(model_name) < line.find(".") and "(" in line - ): - to_transform = True - if not to_transform: - continue - - ### information - - # search DataLoader definition in this file - dataloader_name = "" - for i in range(len(lines)): - line = lines[i] - if not single_line_comment_or_empty_line_detection(line): - if ("DataLoader(" in line and "=" in line and line.find("=") < line.find("DataLoader")) or ( - "dataloader" in line and "=" in line and line.find("=") > line.find("dataloader") - ): - dataloader_def_line_indent_level = get_line_indent_level(line) - dataloader_name = get_line_left_hand_side(line) - dataloader_def_line_idx = i - - # search inference line in this file, and also input_name - inference_line = "" - input_name = "" - for i in range(len(lines)): - line = lines[i] - is_eval_func, eval_func_type = is_eval_func_model_name(model_name, line) - if not single_line_comment_or_empty_line_detection(line): - if is_eval_func and "[coder-enabled]" not in line: - inference_line = line - input_name = line[line.find("(") + 1 : line.find(")")].replace("*", "") - # get "c" in "a = b(**c)" - - # search input definition in this file (if any) - if input_name != "": - for i in range(len(lines)): - line = lines[i] - if not single_line_comment_or_empty_line_detection(line): - if input_name in line and "=" in line and line.find("=") > line.find(input_name): - input_def_line_indent_level = get_line_indent_level(line) - input_def_line_idx = i - - # search trainer definition in this file (for transformers trainer only) - trainer_def_line_idx = -1 - for i in range(len(lines)): - line = lines[i] - if not single_line_comment_or_empty_line_detection(line): - if "trainer = Trainer(" in line: - trainer_def_line_indent_level = get_line_indent_level(line) - trainer_def_line_idx = i - - # search model definition line and its end line index - # (only has 1 model definition line, because it's in loop of globals.list_model_def_instance) - for i in range(len(lines)): - line = lines[i] - if line_idx == model_def_line_idx and "[coder-enabled]" not in line: - model_def_line_indent_level = get_line_indent_level(line) - if ")" in line and line.count(")") == line.count("("): # e.g. model = Net(xxx) - model_definition_end_line_idx = line_idx + 1 - else: # e.g. model = Net(xxx, \n xxx, \n xxx) - do_search = True - i_search = 1 - while do_search: - following_line = lines[line_idx + i_search] - if ")" in following_line and following_line.count(")") > following_line.count("("): - do_search = False - i_search += 1 - model_definition_end_line_idx = line_idx + i_search - line_idx += 1 - - ### check - - bk_trans_content_this = bk_trans_content[bk_trans_location.index(loc)] - - if ( - ("INPUT_NAME" in bk_trans_content_this and input_name == "") - or ("DATALOADER_NAME" in bk_trans_content_this and dataloader_name == "") - or ("INFERENCE_LINE" in bk_trans_content_this and inference_line == "") - ): - logger.info( - f"Skipped due to not having enough information required by " - "the transformation content specified in the config file " - "(e.g. INPUT_NAME, DATALOADER_NAME, INFERENCE_LINE). " - f"File path: {file_path}" - ) - continue - - ### location - - # search for features to put below them - """ - Example (psuedo-code): - model = Net() - # jit script begin mark - model = torch.jit.script(model) - # jit script end mark (feature name + model name to handle multi-model situation) - model = ipex.optimize(model, "fp32") # "ipex fp32" must be put below "jit script" - """ - put_below_idx = 0 - for i in range(len(lines)): - for item in bk_trans_order[0]["below"]: - line = lines[i] - if item in line and model_name in line: - put_below_idx = max(put_below_idx, i + 1) - - # search for features to put above them - put_above_idx = sys.maxsize - for i in range(len(lines)): - for item in bk_trans_order[0]["above"]: - line = lines[i] - if item in line and model_name in line: - put_above_idx = min(put_above_idx, i) - - # location assignment (below model def / dataloader def / input def) - if "insert_below_model_definition_line" in loc: - trans_insert_location = min(max(model_definition_end_line_idx, put_below_idx), put_above_idx) - if trainer_def_line_idx > 0: - trans_insert_location = trainer_def_line_idx - 1 - # for transformers trainer to put right above trainer def - if "insert_below_dataloader_definition_line" in loc: - try: - dataloader_def_line_idx - except: - logger.warning( - f"Skipped due to not having dataloader definition required by " - "the transformation content specified in the config file. " - f"File path: {file_path}" - ) - continue - trans_insert_location = max( - trans_insert_location, min(max(dataloader_def_line_idx + 1, put_below_idx), put_above_idx) - ) - if "insert_below_input_definition_line" in loc: - try: - input_def_line_idx - except: - logger.warning( - f"Skipped due to not having input definition required by " - "the transformation content specified in the config file. " - f"File path: {file_path}" - ) - continue - trans_insert_location = max( - trans_insert_location, min(max(input_def_line_idx + 1, put_below_idx), put_above_idx) - ) - - insert_indent_level = get_line_indent_level(lines[trans_insert_location - 1]) - if trainer_def_line_idx > 0: # for transformers trainer to put right above trainer def - insert_indent_level = get_line_indent_level(lines[trans_insert_location]) - ### content - - # lines to insert - lines_to_insert = bk_trans_content_this - # replace [+] indication with empty - lines_to_insert = lines_to_insert.replace("[+] ", " " * insert_indent_level) - # add begin indicator - lines_to_insert = ( - " " * insert_indent_level - + "# [NeuralCoder] " - + self.backend - + " for " - + model_name - + " [Beginning Line]\n" - + lines_to_insert - ) - # replace INDICATIONS with real stuff - lines_to_insert = ( - lines_to_insert.replace("MODEL_NAME", model_name) - .replace("INPUT_NAME", input_name) - .replace("DATALOADER_NAME", dataloader_name) - .replace("INFERENCE_LINE", inference_line.strip()) - .replace("\n", " # [coder-enabled]\n") - ) - # add end indicator - lines_to_insert += ( - " # [coder-enabled]\n" - + " " * insert_indent_level - + "# [NeuralCoder] " - + self.backend - + " for " - + model_name - + " [Ending Line] # [coder-enabled]" - ) - - ### register - - if file_path not in globals.list_trans_insert_modified_file: - globals.list_trans_insert_modified_file.append(file_path) - globals.list_trans_insert_location_idxs.append([trans_insert_location]) - globals.list_trans_insert_number_insert_lines.append([lines_to_insert.count("\n") + 1]) - globals.list_trans_insert_lines_to_insert.append([lines_to_insert]) - else: - idx = globals.list_trans_insert_modified_file.index(file_path) - globals.list_trans_insert_location_idxs[idx].append(trans_insert_location) - globals.list_trans_insert_number_insert_lines[idx].append(lines_to_insert.count("\n") + 1) - globals.list_trans_insert_lines_to_insert[idx].append(lines_to_insert) - - # PART 2 - "inference line" - if ( - "indent_inference_line" in loc - or "insert_above_inference_line" in loc - or "insert_below_inference_line" in loc - ): - for file_path in globals.list_code_path: - code = open(file_path, "r").read() - lines = code.split("\n") - line_idx = 0 - for i in range(len(lines)): - line = lines[i] - for model_name in globals.list_model_name: - is_eval_func, eval_func_type = is_eval_func_model_name(model_name, line) - if is_eval_func and "[coder-enabled]" not in line: - if eval_func_type == "non-forward": - pass # do something - inference_line = line - inference_line_indent_level = get_line_indent_level(line) - - if "indent_inference_line" in loc: - bk_trans_content_this = bk_trans_content[bk_trans_location.index(loc)] - add_indent_level = int(bk_trans_content_this) - - trans_indent_location = [] - # indent can have multiple location, so is a list of numbers - trans_indent_level = [] - - if ")" in line: # e.g. model = Net(xxx) - trans_indent_location.append(line_idx) - trans_indent_level.append(add_indent_level) - else: # e.g. model = Net(xxx, \n xxx, \n xxx) - trans_indent_location.append(line_idx) - trans_indent_level.append(add_indent_level) - do_search = True - i_search = 1 - while do_search: - trans_indent_location.append(line_idx + i_search) - trans_indent_level.append(add_indent_level) - following_line = lines[line_idx + i_search] - if ")" in following_line: - do_search = False - i_search += 1 - - ### register - - if file_path not in globals.list_trans_indent_modified_file: - globals.list_trans_indent_modified_file.append(file_path) - globals.list_trans_indent_location_idxs.append(trans_indent_location) - globals.list_trans_indent_level.append(trans_indent_level) - else: - idx = globals.list_trans_indent_modified_file.index(file_path) - for i in trans_indent_location: - globals.list_trans_indent_location_idxs[idx].append(i) - for i in trans_indent_level: - globals.list_trans_indent_level[idx].append(i) - - if "insert_above_inference_line" in loc: - idx_offset = 0 - elif "insert_below_inference_line" in loc: - if ")" in line: # e.g. model = Net(xxx) - idx_offset = 1 - else: # e.g. model = Net(xxx, \n xxx, \n xxx) - do_search = True - i_search = 1 - while do_search: - following_line = lines[line_idx + i_search] - if ")" in following_line: - do_search = False - i_search += 1 - inference_line = ( - inference_line - + "\n" - + " " * (get_line_indent_level(line) + 4) - + following_line - ) - idx_offset = i_search - - if "insert_above_inference_line" in loc or "insert_below_inference_line" in loc: - bk_trans_content_this = bk_trans_content[bk_trans_location.index(loc)] - - trans_insert_location = line_idx + idx_offset - - insert_indent_level = inference_line_indent_level - - ### content - - # lines to insert - lines_to_insert = bk_trans_content_this - # replace [+] indication with empty - lines_to_insert = lines_to_insert.replace("[+] ", " " * insert_indent_level) - # add begin indicator - lines_to_insert = ( - " " * insert_indent_level - + "# [NeuralCoder] " - + self.backend - + " [Beginning Line] \n" - + lines_to_insert - ) - # replace INDICATIONS with real stuff - # (for now, inference_line related transformations ) - # (have nothing to do with input, dataloader etc, ) - # (so no need to put replaces here.) - lines_to_insert = lines_to_insert.replace("\n", " # [coder-enabled]\n") - # add end indicator - lines_to_insert += ( - " # [coder-enabled]\n" - + " " * insert_indent_level - + "# [NeuralCoder] " - + self.backend - + " [Ending Line] # [coder-enabled]" - ) - - # customized argument - if self.backend == "pytorch_benchmark": - lines_to_insert = lines_to_insert.replace( - "NUM_BENCHMARK_ITERATION", globals.num_benchmark_iteration - ) - lines_to_insert = lines_to_insert.replace("ACCURACY_MODE", str(False)) - lines_to_insert = lines_to_insert.replace( - "INFERENCE_LINE", inference_line.strip() - ) - - ### register - - if file_path not in globals.list_trans_insert_modified_file: - globals.list_trans_insert_modified_file.append(file_path) - globals.list_trans_insert_location_idxs.append([trans_insert_location]) - globals.list_trans_insert_number_insert_lines.append( - [lines_to_insert.count("\n") + 1] - ) - globals.list_trans_insert_lines_to_insert.append([lines_to_insert]) - else: - idx = globals.list_trans_insert_modified_file.index(file_path) - globals.list_trans_insert_location_idxs[idx].append(trans_insert_location) - globals.list_trans_insert_number_insert_lines[idx].append( - lines_to_insert.count("\n") + 1 - ) - globals.list_trans_insert_lines_to_insert[idx].append(lines_to_insert) - - break # already transformed this line, so skip any further model_name search - line_idx += 1 - - # PART 3 - for customized location - - logger.debug(f"globals.list_trans_insert_modified_file: {globals.list_trans_insert_modified_file}") - logger.debug(f"globals.list_trans_insert_location_idxs: {globals.list_trans_insert_location_idxs}") - logger.debug(f"globals.list_trans_insert_number_insert_lines: {globals.list_trans_insert_number_insert_lines}") - logger.debug(f"globals.list_trans_insert_lines_to_insert: {globals.list_trans_insert_lines_to_insert}") diff --git a/neural_coder/coders/pytorch/lightning.py b/neural_coder/coders/pytorch/lightning.py deleted file mode 100644 index 383432e2c3f..00000000000 --- a/neural_coder/coders/pytorch/lightning.py +++ /dev/null @@ -1,86 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -class Lightning(object): - def __init__(self, file) -> None: - self.file = file - self.result = [] - - def transform(self): - lines = self.file.split("\n") - for line in lines: - if self.not_add_accelerator(line) or self.not_add_precision(line): - new_line = self.add(line) - if self.not_modify(new_line): - new_line = self.modify(new_line) - self.result.append(new_line) - elif self.not_modify(line): - new_line = self.modify(line) - self.result.append(new_line) - if not self.not_add_accelerator(line) and not self.not_add_precision(line) and not self.not_modify(line): - if line == "" and self.result[-1] == "": - continue - self.result.append(line) - - for index, line in enumerate(self.result): - if index != len(self.result) - 1: - self.result[index] += "\n" - return "".join(self.result) - - def not_add_precision(self, s): - if "Trainer" in s: - if "precision" not in s: - return True - else: - return False - return False - - def not_add_accelerator(self, s): - if "Trainer" in s: - if "accelerator" not in s: - return True - else: - return False - return False - - def add(self, s): - if "Trainer" in s: - if "precision" not in s: - s_index = s.find(")") - s = s[:s_index] + ', precision="bf16"' + s[s_index:] - if "accelerator" not in s: - s_index = s.find(")") - s = s[:s_index] + ', accelerator="cpu"' + s[s_index:] - return s - - def not_modify(self, s): - if "bf16" in s and "cpu" in s: - return False - return True - - def modify(self, s): - if "16" in s: - old = "16" - s = s.replace(old, '"bf16"') - if "32" in s: - old = "32" - s = s.replace(old, '"bf16"') - if '"gpu"' in s: - old = '"gpu"' - s = s.replace(old, '"cpu"') - if '"tpu"' in s: - old = '"tpu"' - s = s.replace(old, '"cpu"') - return s diff --git a/neural_coder/coders/pytorch/reclaim_inference_transformers_trainer.py b/neural_coder/coders/pytorch/reclaim_inference_transformers_trainer.py deleted file mode 100644 index 82f324e26b3..00000000000 --- a/neural_coder/coders/pytorch/reclaim_inference_transformers_trainer.py +++ /dev/null @@ -1,75 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import logging - -from ... import globals -from ...utils.line_operation import ( - get_line_indent_level, - get_line_left_hand_side, - is_eval_func_model_name, - single_line_comment_or_empty_line_detection, -) - -logging.basicConfig( - level=globals.logging_level, format="%(asctime)s %(levelname)s %(message)s", datefmt="%a, %d %b %Y %H:%M:%S +0000" -) -logger = logging.getLogger(__name__) - - -class ReclaimInferenceTransformersTrainer(object): - def __init__(self, list_model_def_instance): - self.list_model_def_instance = list_model_def_instance - - def print_info(self): - for i in self.list_model_def_instance: - logger.debug(f"i.print_info(): {i.print_info()}") - - # collect file transformation info and register (store) in globals - # (i.e. which file to add which lines at which location) - def register_transformation(self): - file_path = globals.list_code_path[0] - lines = open(file_path, "r").read().split("\n") - line_idx = 0 - - for i in range(len(lines)): - line = lines[i] - - if "# Evaluation" in line: - indent_level = get_line_indent_level(line) - trans_insert_location = i - lines_to_insert = "" - lines_to_insert += " " * indent_level + "eval_dataloader = trainer.get_eval_dataloader()" + "\n" - lines_to_insert += " " * indent_level + "import torch" + "\n" - lines_to_insert += " " * indent_level + "for step, inputs in enumerate(eval_dataloader):" + "\n" - lines_to_insert += " " * indent_level + " with torch.no_grad():" + "\n" - lines_to_insert += " " * indent_level + " model(**inputs)" - - if file_path not in globals.list_trans_insert_modified_file: - globals.list_trans_insert_modified_file.append(file_path) - globals.list_trans_insert_location_idxs.append([trans_insert_location]) - globals.list_trans_insert_number_insert_lines.append([lines_to_insert.count("\n") + 1]) - globals.list_trans_insert_lines_to_insert.append([lines_to_insert]) - else: - idx = globals.list_trans_insert_modified_file.index(file_path) - globals.list_trans_insert_location_idxs[idx].append(trans_insert_location) - globals.list_trans_insert_number_insert_lines[idx].append(lines_to_insert.count("\n") + 1) - globals.list_trans_insert_lines_to_insert[idx].append(lines_to_insert) - - line_idx += 1 - - logger.debug(f"globals.list_trans_insert_modified_file: {globals.list_trans_insert_modified_file}") - logger.debug(f"globals.list_trans_insert_location_idxs: {globals.list_trans_insert_location_idxs}") - logger.debug(f"globals.list_trans_insert_number_insert_lines: {globals.list_trans_insert_number_insert_lines}") - logger.debug(f"globals.list_trans_insert_lines_to_insert: {globals.list_trans_insert_lines_to_insert}") diff --git a/neural_coder/coders/pytorch/reclaim_inputs.py b/neural_coder/coders/pytorch/reclaim_inputs.py deleted file mode 100644 index 16397012e37..00000000000 --- a/neural_coder/coders/pytorch/reclaim_inputs.py +++ /dev/null @@ -1,106 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import logging - -from ... import globals -from ...utils.line_operation import ( - get_line_indent_level, - get_line_left_hand_side, - is_eval_func_model_name, - single_line_comment_or_empty_line_detection, -) - -logging.basicConfig( - level=globals.logging_level, format="%(asctime)s %(levelname)s %(message)s", datefmt="%a, %d %b %Y %H:%M:%S +0000" -) -logger = logging.getLogger(__name__) - - -class ReclaimInputs(object): - def __init__(self, list_model_def_instance): - self.list_model_def_instance = list_model_def_instance - - def print_info(self): - for i in self.list_model_def_instance: - logger.debug(f"i.print_info(): {i.print_info()}") - - # collect file transformation info and register (store) in globals - # (i.e. which file to add which lines at which location) - def register_transformation(self): - list_code = [] - for i in globals.list_code_path: - list_code.append(open(i, "r").read()) - - for ins in self.list_model_def_instance: - model_name = ins.model_name - file_path = ins.file_path - model_def_line_idx = ins.model_def_line_idx - function_def_line_idx = ins.function_def_line_idx - class_name = ins.class_name - - # transformation - file_path_idx = globals.list_code_path.index(file_path) - lines = list_code[file_path_idx].split("\n") - line_idx = 0 - - # search inference line in this file, and also input_name - inference_line = "" - input_name = "" - for i in range(len(lines)): - line = lines[i] - is_eval_func, eval_func_type = is_eval_func_model_name(model_name, line) - if is_eval_func and "[coder-enabled]" not in line: - inference_line = line - input_name = line[line.find("(") + 1 : line.find(")")].replace("*", "") # get "c" in "a = b(**c)" - - # if there is already a "input = xxx", then quit this function - if input_name != "": - for i in range(len(lines)): - line = lines[i] - if not single_line_comment_or_empty_line_detection(line): - if input_name in line and "=" in line and line.find(input_name) < line.find("="): - return - - # add the created lines for inputs - if inference_line != "" and input_name != "": - for i in range(len(lines)): - line = lines[i] - is_eval_func, eval_func_type = is_eval_func_model_name(model_name, line) - if is_eval_func and "[coder-enabled]" not in line: - indent_level = get_line_indent_level(line) - trans_insert_location = i - lines_to_insert = "" - lines_to_insert += " " * indent_level + "try:" + "\n" - lines_to_insert += " " * indent_level + " " + input_name + " = " + input_name + "\n" - lines_to_insert += " " * indent_level + "except:" + "\n" - lines_to_insert += " " * indent_level + " pass" - - if file_path not in globals.list_trans_insert_modified_file: - globals.list_trans_insert_modified_file.append(file_path) - globals.list_trans_insert_location_idxs.append([trans_insert_location]) - globals.list_trans_insert_number_insert_lines.append([lines_to_insert.count("\n") + 1]) - globals.list_trans_insert_lines_to_insert.append([lines_to_insert]) - else: - idx = globals.list_trans_insert_modified_file.index(file_path) - globals.list_trans_insert_location_idxs[idx].append(trans_insert_location) - globals.list_trans_insert_number_insert_lines[idx].append(lines_to_insert.count("\n") + 1) - globals.list_trans_insert_lines_to_insert[idx].append(lines_to_insert) - - line_idx += 1 - - logger.debug(f"globals.list_trans_insert_modified_file: {globals.list_trans_insert_modified_file}") - logger.debug(f"globals.list_trans_insert_location_idxs: {globals.list_trans_insert_location_idxs}") - logger.debug(f"globals.list_trans_insert_number_insert_lines: {globals.list_trans_insert_number_insert_lines}") - logger.debug(f"globals.list_trans_insert_lines_to_insert: {globals.list_trans_insert_lines_to_insert}") diff --git a/neural_coder/coders/tensorflow/__init__.py b/neural_coder/coders/tensorflow/__init__.py deleted file mode 100644 index e833188cc78..00000000000 --- a/neural_coder/coders/tensorflow/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. diff --git a/neural_coder/coders/tensorflow/amp.py b/neural_coder/coders/tensorflow/amp.py deleted file mode 100644 index 70302d78d4a..00000000000 --- a/neural_coder/coders/tensorflow/amp.py +++ /dev/null @@ -1,64 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from ...utils.line_operation import get_line_left_hand_side - - -class TensorFlowKerasAMP(object): - def __init__(self, file) -> None: - self.file = file - self.result = [] - self.keras_edited_flag = False - - def transform(self): - # import pdb - # pdb.set_trace() - lines = self.file.split("\n") - for line in lines: - if self.is_modify(line): - if ".ConfigProto()" in line: # TF AMP - config_name = get_line_left_hand_side(line) - new_line_1 = "from tensorflow.core.protobuf import rewriter_config_pb2" - new_line_2 = ( - config_name - + ".graph_options.rewrite_options.auto_mixed_precision_mkl = " - + "rewriter_config_pb2.RewriterConfig.ON" - ) - self.result.append(line) - self.result.append(new_line_1) - self.result.append(new_line_2) - elif "keras" in line and "import" in line: # Keras AMP - if not self.keras_edited_flag: - new_line_1 = "from tensorflow.keras.mixed_precision import experimental as mixed_precision" - new_line_2 = "policy = mixed_precision.Policy('mixed_bfloat16')" - new_line_3 = "mixed_precision.set_policy(policy)" - self.result.append(line) - self.result.append(new_line_1) - self.result.append(new_line_2) - self.result.append(new_line_3) - self.keras_edited_flag = True - else: - self.result.append(line) - else: - self.result.append(line) - for index, line in enumerate(self.result): - if index != len(self.result) - 1: - self.result[index] += "\n" - return "".join(self.result) - - def is_modify(self, s): - if ".ConfigProto()" in s or ("keras" in s and "import" in s): - return True - else: - return False diff --git a/neural_coder/coders/tensorflow/inc.py b/neural_coder/coders/tensorflow/inc.py deleted file mode 100644 index 837dff143fb..00000000000 --- a/neural_coder/coders/tensorflow/inc.py +++ /dev/null @@ -1,50 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -from ...utils.line_operation import get_line_indent_level, get_line_left_hand_side - - -class TensorFlowKerasINC(object): - def __init__(self, file) -> None: - self.file = file - self.result = [] - - def transform(self): - # import pdb - # pdb.set_trace() - lines = self.file.split("\n") - for line in lines: - if self.is_modify(line): - model_name = "model" - indent_level = get_line_indent_level(line) - self.result.append(line) - self.result.append(" " * indent_level + "from neural_compressor.quantization import fit") - self.result.append(" " * indent_level + "from neural_compressor.config import PostTrainingQuantConfig") - self.result.append(" " * indent_level + "from neural_compressor import common") - self.result.append(" " * indent_level + "config = PostTrainingQuantConfig(quant_level=1)") - self.result.append(" " * indent_level + model_name + " = fit(" + model_name + ", conf=config)") - self.result.append(" " * indent_level + model_name + '.save("./quantized_model")') - else: - self.result.append(line) - for index, line in enumerate(self.result): - if index != len(self.result) - 1: - self.result[index] += "\n" - return "".join(self.result) - - def is_modify(self, s): - if "model = tf." in s or "model = load_model(" in s: - if "self.model" not in s: - return True - else: - return False diff --git a/neural_coder/coders/transform.py b/neural_coder/coders/transform.py deleted file mode 100644 index c553cbbb87b..00000000000 --- a/neural_coder/coders/transform.py +++ /dev/null @@ -1,84 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import logging - -from .. import globals - -logging.basicConfig( - level=globals.logging_level, format="%(asctime)s %(levelname)s %(message)s", datefmt="%a, %d %b %Y %H:%M:%S +0000" -) -logger = logging.getLogger(__name__) - - -def execute_insert_transformation(list_transformed_code): - """Insert code lines into file.""" - for index, file_path in enumerate(globals.list_trans_insert_modified_file): - trans_location_idxs = globals.list_trans_insert_location_idxs[index] - trans_number_insert_lines = globals.list_trans_insert_number_insert_lines[index] - trans_lines_to_insert = globals.list_trans_insert_lines_to_insert[index] - - # sort trans_location_idxs and sort the other lists accordingly - trans_number_insert_lines = [i for _, i in sorted(zip(trans_location_idxs, trans_number_insert_lines))] - trans_lines_to_insert = [i for _, i in sorted(zip(trans_location_idxs, trans_lines_to_insert))] - trans_location_idxs = sorted(trans_location_idxs) - - file_path_idx = globals.list_code_path.index(file_path) - lines_transformed = list_transformed_code[file_path_idx].split("\n") - - # math - t = [0] - u = 0 - for n in trans_number_insert_lines: - u = u + n - t.append(u) - t = t[:-1] - - logger.debug(f"t: {t}") - trans_location_idxs = [sum(i) for i in zip(trans_location_idxs, t)] - logger.debug(f"trans_location_idxs after adjustment: {trans_location_idxs}") - - for idx in trans_location_idxs: # actual transformation (insertion) - additions = trans_lines_to_insert[trans_location_idxs.index(idx)].split("\n") - additions = additions[::-1] # reverse - for i in range(len(additions)): - lines_transformed.insert(idx, additions[i]) - - # transfer lines_transformed to code format ("\n" save write) - code_transformed = "".join([i + "\n" for i in lines_transformed])[0:-1] - - list_transformed_code[file_path_idx] = code_transformed - - return list_transformed_code - - -def execute_indent_transformation(list_transformed_code): - """Indent code lines with spaces at the beginning.""" - for index, file_path in enumerate(globals.list_trans_indent_modified_file): - trans_location_idxs = globals.list_trans_indent_location_idxs[index] - trans_indent_level = globals.list_trans_indent_level[index] - - file_path_idx = globals.list_code_path.index(file_path) - lines_transformed = list_transformed_code[file_path_idx].split("\n") - - for idx in trans_location_idxs: # actual transformation (indent) - this_indent_level = trans_indent_level[trans_location_idxs.index(idx)] - lines_transformed[idx] = " " * 4 * this_indent_level + lines_transformed[idx] - - # transfer lines_transformed to code format ("\n" save write) - code_transformed = "".join([i + "\n" for i in lines_transformed])[0:-1] - - list_transformed_code[file_path_idx] = code_transformed - - return list_transformed_code diff --git a/neural_coder/docs/AWSSageMakerSupport.md b/neural_coder/docs/AWSSageMakerSupport.md deleted file mode 100644 index eb8926c12ee..00000000000 --- a/neural_coder/docs/AWSSageMakerSupport.md +++ /dev/null @@ -1,32 +0,0 @@ -AWS Amazon SageMaker Support -===== - -[AWS Amazon SageMaker](https://aws.amazon.com/sagemaker/) users can easily enjoy the productivity boost brought by Neural Coder by one-click installing [Neural Coder Jupyter Lab extension](https://www.npmjs.com/package/jupyter-lab-neural-compressor) in either **SageMaker Studio** or **SageMaker Notebook instance**. - -## Start Jupyter Lab 3 -[Neural Coder Jupyter extension](https://www.npmjs.com/package/jupyter-lab-neural-compressor) requires Jupyter Lab 3. Using Jupyter Lab 1 will cause installation error. To start Jupyter Lab 3, please check the following: - -#### For SageMaker Studio -SageMaker Studio Jupyter Lab 3 - -#### For SageMaker Notebook instance -SageMaker Notebook instance Jupyter Lab 3 - -## Installation Guide -For both cases, the installation process is exactly the same, which is by **searching ```neural-compressor``` in the Extension Manager**. - -1. Search and Install - -SageMaker Notebook instance Jupyter Lab 3 - -2. Rebuild - -SageMaker Notebook instance Jupyter Lab 3 - -3. Save and Reload - -SageMaker Notebook instance Jupyter Lab 3 - -4. Done! - -SageMaker Notebook instance Jupyter Lab 3 diff --git a/neural_coder/docs/BigDLNanoSupport.md b/neural_coder/docs/BigDLNanoSupport.md deleted file mode 100644 index 0ea0e4dc849..00000000000 --- a/neural_coder/docs/BigDLNanoSupport.md +++ /dev/null @@ -1,37 +0,0 @@ -BigDL Nano Support -=========================== - -Neural Coder collaborates with [BigDL-Nano](https://bigdl.readthedocs.io/en/latest/doc/Nano/Overview/nano.html), a Python library that automatically applies modern CPU optimizations, to further democratize ease-of-use BigDL-Nano APIs as a **no-code** solution for PyTorch Deep Learning programmers. - -## Example -For instance, to perform BF16 + Channels Last optimizations with BigDL-Nano API using Neural Coder on the [example code](../examples/nano/resnet18.py) and run this code with the enabled optimizations, users can simply execute this command: -``` -python -m neural_coder -o nano_bf16_channels_last ../examples/nano/resnet18.py -``` -The alias for each optimization set is documented in the below Support Matrix. Note that you need to ```pip install bigdl``` first following [BigDL-Nano documentation](https://github.com/intel-analytics/BigDL#installing). - -## Support Matrix - -| Optimization Set | API Alias | -| ------------- | ------------- | -| BF16 + Channels Last | `nano_bf16_channels_last` | -| BF16 + IPEX + Channels Last | `nano_bf16_ipex_channels_last` | -| BF16 + IPEX | `nano_bf16_ipex` | -| BF16 | `nano_bf16` | -| Channels Last | `nano_fp32_channels_last` | -| IPEX + Channels Last | `nano_fp32_ipex_channels_last` | -| IPEX | `nano_fp32_ipex` | -| Convert CUDA TO GPU | `nano_gpu_to_cpu` | -| INT8 | `nano_int8` | -| JIT + BF16 + Channels Last | `nano_jit_bf16_channels_last` | -| JIT + BF16 + IPEX + Channels Last | `nano_jit_bf16_ipex_channels_last` | -| JIT + BF16 + IPEX | `nano_jit_bf16_ipex` | -| JIT + BF16 | `nano_jit_bf16` | -| JIT + Channels Last | `nano_jit_fp32_channels_last` | -| JIT + IPEX + Channels Last | `nano_jit_fp32_ipex_channels_last` | -| JIT + IPEX | `nano_jit_fp32_ipex` | -| JIT | `nano_jit_fp32` | -| ONNX Runtime | `nano_onnxruntime_fp32` | -| ONNX Runtime + INT8 | `nano_onnxruntime_int8_qlinear` | -| OpenVINO | `nano_openvino_fp32` | -| OpenVINO + INT8 | `nano_openvino_int8` | diff --git a/neural_coder/docs/IntelCPU_PerformanceSetting.md b/neural_coder/docs/IntelCPU_PerformanceSetting.md deleted file mode 100644 index a112fd3516f..00000000000 --- a/neural_coder/docs/IntelCPU_PerformanceSetting.md +++ /dev/null @@ -1,51 +0,0 @@ -## Intel CPU Platforms: Best Performance Setting -### Install MKL, OpenMP and JEMALLOC -The simplest way for installation is through ```conda install```: -```bash -conda install -y mkl mkl-include jemalloc -``` - -### Install NUMA Controller -```bash -apt-get update && apt-get install bc numactl -``` - -### Environment Variables -Check if your ```CONDA_PREFIX``` has a value by: -```bash -echo ${CONDA_PREFIX} -``` -If it is empty, it means that you are not in a traditional CONDA environment, you need to find the location of the ```.so``` files by: -```bash -find / -name "libjemalloc.so" -find / -name "libiomp5.so" -``` -It will show the path these files were installed into. For example: -```bash -/home/name/lib/libjemalloc.so -/home/name/lib/libiomp5.so -``` -And then you should ```export``` this path as ```CONDA_PREFIX```: -```bash -export CONDA_PREFIX="/home/name" -``` -Finally: -```bash -export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libjemalloc.so -export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libiomp5.so -export MALLOC_CONF="oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000" -export KMP_AFFINITY="granularity=fine,compact,1,0" -export KMP_BLOCKTIME=1 -export DNNL_PRIMITIVE_CACHE_CAPACITY=1024 -``` - -### Frequency Governers -Check the frequency governor state on your machine: -```bash -cat /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor -``` -If it shows ```powersave``` instead of ```performance```, execute: -```bash -echo "performance" | sudo tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor -cat /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor -``` diff --git a/neural_coder/docs/PythonAPI.md b/neural_coder/docs/PythonAPI.md deleted file mode 100644 index dfc7567fe43..00000000000 --- a/neural_coder/docs/PythonAPI.md +++ /dev/null @@ -1,58 +0,0 @@ -Neural Coder as Python API -=========================== - -Neural Coder can be used as Python APIs. We currently provide 3 main user-facing APIs for Neural Coder: enable, bench and superbench. - -#### Enable -Users can use ```enable()``` to enable specific features into DL scripts: -``` -from neural_coder import enable -enable( - code="neural_coder/examples/vision/resnet50.py", - features=[ - "pytorch_jit_script", - "pytorch_channels_last", - ], -) -``` -To run benchmark directly on the optimization together with the enabling: -``` -from neural_coder import enable -enable( - code="neural_coder/examples/vision/resnet50.py", - features=[ - "pytorch_jit_script", - "pytorch_channels_last" - ], - run_bench=True, -) -``` - -#### Bench -To run benchmark on your code with an existing patch: -``` -from neural_coder import bench -bench( - code="neural_coder/examples/vision/resnet50.py", - patch_path="${your_patch_path}", -) -``` - -#### SuperBench -To sweep on optimization sets with a fixed benchmark configuration: -``` -from neural_coder import superbench -superbench(code="neural_coder/examples/vision/resnet50.py") -``` -To sweep on benchmark configurations for a fixed optimization set: -``` -from neural_coder import superbench -superbench( - code="neural_coder/examples/vision/resnet50.py", - sweep_objective="bench_config", - bench_feature=[ - "pytorch_jit_script", - "pytorch_channels_last", - ], -) -``` diff --git a/neural_coder/docs/PythonLauncher.md b/neural_coder/docs/PythonLauncher.md deleted file mode 100644 index 74ad230e1f9..00000000000 --- a/neural_coder/docs/PythonLauncher.md +++ /dev/null @@ -1,40 +0,0 @@ -Python Launcher -=========================== - -Neural Coder can be used as a Python **Launcher**. Users can run the Python model code as it is with automatic enabling of Deep Learning optimizations by using Neural Coder's inline Python **Launcher** design. - -## Quick-Start - -Example: Let's say you are running an NLP model using ```run_glue.py``` from HuggingFace transformers [examples](https://github.com/huggingface/transformers/blob/v4.21-release/examples/pytorch/text-classification/run_glue.py). - -Pre-requisites: -```bash -pip install transformers==4.21.0 torch datasets -``` - -Generally we run this code with a Python command line like this: -```bash -python run_glue.py --model_name_or_path bert-base-cased --task_name mrpc --do_eval --output_dir result -``` - -With Neural Coder's **Launcher**, users can easily enjoy Deep Learning optimizations (e.g. default - INT8 dynamic quantization by IntelĀ® Neural Compressor for PyTorch models) by simply adding an inline prefix -```bash --m neural_coder -``` -to the Python command line, and everything else remains the same: -```bash -python -m neural_coder run_glue.py --model_name_or_path bert-base-cased --task_name mrpc --do_eval --output_dir result -``` - -This will run ```run_glue.py``` with the Deep Learning optimization automatically enabled, while everything else (e.g. your input arguments for the code itself) remains the same as the original code. You can also check out the optimized code ```run_glue_optimized.py``` auto-generated by the **Launcher** under the same folder if you want to learn the code enabling. - -Note: Any modification on the optimized code ```run_glue_optimized.py``` will be overwritten every time you run Neural Coder **Launcher** on ```run_glue.py```, so please make any modification on the original code ```run_glue.py``` instead of the optimized one. The optimized code is only saved for your reference. - -## Launcher Arguments (Optional) - -Users can specify which Deep Learning optimization they want to conduct using ```--opt``` argument. The list of supported Deep Learning optimization features can be found [here](SupportMatrix.md). - -Note that if specifically optimizing with INT8 quantization by IntelĀ® Neural Compressor, to choose a quantization approach (strategy), ```--approach``` argument can be specified with either ```static```, ```static_ipex``` or ```dynamic```. For example, to run INT8 static quantization by IntelĀ® Neural Compressor: -```bash -python -m neural_coder --approach static run_glue.py --model_name_or_path bert-base-cased --task_name mrpc --do_eval --output_dir result -``` diff --git a/neural_coder/docs/Quantization.md b/neural_coder/docs/Quantization.md deleted file mode 100644 index 555834c74a8..00000000000 --- a/neural_coder/docs/Quantization.md +++ /dev/null @@ -1,39 +0,0 @@ -Neural Coder for Quantization -=========================== -This feature helps automatically enable quantization on Deep Learning models and automatically evaluates for the best performance on the model. It is a code-free solution that can help users enable quantization algorithms on a model with no manual coding needed. Supported features include Post-Training Static Quantization, Post-Training Dynamic Quantization, and Mixed Precision. - - -## Features Supported -- Post-Training Static Quantization for [Stock PyTorch](https://pytorch.org/tutorials/prototype/fx_graph_mode_ptq_static.html) (with FX backend) -- Post-Training Static Quantization for [IPEX](https://github.com/intel/intel-extension-for-pytorch/blob/v1.12.0/docs/tutorials/features/int8.md) -- Post-Training Dynamic Quantization for [Stock PyTorch](https://pytorch.org/tutorials/recipes/recipes/dynamic_quantization.html) -- Mixed Precision for [Stock PyTorch](https://pytorch.org/tutorials/recipes/recipes/amp_recipe.html) - -## Models Supported -- HuggingFace [Transformers](https://github.com/huggingface/transformers) models -- [torchvision](https://pytorch.org/vision/stable/index.html) models -- Broad models (under development) - -## Usage -- PyPI distribution with a one-line API call -- [JupyterLab extension](../extensions/neural_compressor_ext_lab/README.md) - -## Example -### PyPI distribution: -HuggingFace [Transformers](https://github.com/huggingface/transformers) models: [text-classification/run_glue.py](https://github.com/huggingface/transformers/blob/v4.21-release/examples/pytorch/text-classification/run_glue.py) -``` -from neural_coder import auto_quant -auto_quant( - code="https://github.com/huggingface/transformers/blob/v4.21-release/examples/pytorch/text-classification/run_glue.py", - args="--model_name_or_path albert-base-v2 --task_name sst2 --do_eval --output_dir result", -) -``` - -[torchvision](https://pytorch.org/vision/stable/index.html) models: [imagenet/main.py](https://github.com/pytorch/examples/blob/main/imagenet/main.py) -``` -from neural_coder import auto_quant -auto_quant( - code="https://github.com/pytorch/examples/blob/main/imagenet/main.py", - args="-a alexnet --pretrained -e /path/to/imagenet/", -) -``` diff --git a/neural_coder/docs/SupportMatrix.md b/neural_coder/docs/SupportMatrix.md deleted file mode 100644 index be2a7fea308..00000000000 --- a/neural_coder/docs/SupportMatrix.md +++ /dev/null @@ -1,26 +0,0 @@ -Supported Optimization Features -=========================== - -| Category | Optimization | API Alias | -| ------------- | ------------- | ------------- | -| PyTorch | [Mixed Precision](https://pytorch.org/docs/stable/amp.html) | `pytorch_amp` | -| PyTorch | [Channels Last](https://pytorch.org/tutorials/intermediate/memory_format_tutorial.html) | `pytorch_channels_last` | -| PyTorch | [JIT (Just-In-Time) Script/Trace](https://pytorch.org/docs/stable/jit.html) & [optimize_for_inference](https://pytorch.org/docs/stable/generated/torch.jit.optimize_for_inference.html) | `pytorch_jit_script`, `pytorch_jit_trace`, `pytorch_jit_script_ofi`, `pytorch_jit_trace_ofi` | -| PyTorch | JIT with [TorchDynamo](https://github.com/pytorch/torchdynamo) | `pytorch_torchdynamo_jit_script`, `pytorch_torchdynamo_jit_trace`, `pytorch_torchdynamo_jit_script_ofi`, `pytorch_torchdynamo_jit_trace_ofi` | -| PyTorch | [Intel Neural Compressor (INC) Mixed Precision](https://github.com/intel/neural-compressor/blob/master/docs/source/mixed_precision.md) | `pytorch_inc_bf16` | -| PyTorch | [INC INT8 Static Quantization (FX/IPEX)](https://github.com/intel/neural-compressor/blob/master/docs/source/quantization.md#supported-feature-matrix) | `pytorch_inc_static_quant_fx`, `pytorch_inc_static_quant_ipex`, `pytorch_inc_static_quant_ipex_xpu` | -| PyTorch | [INC INT8 Dynamic Quantization](https://github.com/intel/neural-compressor/blob/master/docs/source/quantization.md#supported-feature-matrix) | `pytorch_inc_dynamic_quant` | -| PyTorch | [Intel Extension for PyTorch (FP32, BF16, INT8 Static/Dynamic Quantization)](https://github.com/intel/intel-extension-for-pytorch) | `pytorch_ipex_fp32`, `pytorch_ipex_bf16`, `pytorch_ipex_int8_static_quant`, `pytorch_ipex_int8_dynamic_quant` | -| PyTorch | [Alibaba Blade-DISC](https://github.com/alibaba/BladeDISC) | `pytorch_aliblade` | -| PyTorch Lightning | [Mixed Precision](https://pytorch-lightning.readthedocs.io/en/latest/guides/speed.html) | `pytorch_lightning_bf16_cpu` | -| TensorFlow | [Mixed Precision](https://www.intel.com/content/www/us/en/developer/articles/guide/getting-started-with-automixedprecisionmkl.html) | `tensorflow_amp` | -| Keras | [Mixed Precision](https://www.tensorflow.org/guide/mixed_precision) | `keras_amp` | -| TensorFlow/Keras Model | [INC Quantization](https://github.com/intel/neural-compressor/blob/master/docs/source/quantization.md#supported-feature-matrix) | `tensorflow_inc` | -| Keras Script | [INC Quantization](https://github.com/intel/neural-compressor/tree/master/examples/keras/mnist) | `keras_inc` | -| ONNX Runtime | [INC Static Quantization (QLinear)](https://github.com/intel/neural-compressor/blob/master/docs/source/quantization.md#supported-feature-matrix) | `onnx_inc_static_quant_qlinear` | -| ONNX Runtime | [INC Static Quantization (QDQ)](https://github.com/intel/neural-compressor/blob/master/docs/source/quantization.md#supported-feature-matrix) | `onnx_inc_static_quant_qdq` | -| ONNX Runtime | [INC Dynamic Quantization](https://github.com/intel/neural-compressor/blob/master/docs/source/quantization.md#supported-feature-matrix) | `onnx_inc_dynamic_quant` | -| [HuggingFace Optimum-Intel](https://huggingface.co/docs/optimum/intel/index) | INC Quantization | `pytorch_inc_huggingface_optimum_static`, `pytorch_inc_huggingface_optimum_dynamic` | -| [Intel Extension for Transformers](https://github.com/intel/intel-extension-for-transformers/) | INC Quantization | `intel_extension_for_transformers` | -| [BigDL Nano](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Nano/pytorch.html#bigdl-nano-pytorch-inferenceoptimizer) | [Optimization List](./BigDLNanoSupport.md) | `nano_` + [specific alias](./BigDLNanoSupport.md) | -| Auto-Detect | [INC Quantization](https://github.com/intel/neural-compressor) | `inc_auto` | diff --git a/neural_coder/docs/cloud_autobench/CloudAutobench.MD b/neural_coder/docs/cloud_autobench/CloudAutobench.MD deleted file mode 100644 index d906a80292f..00000000000 --- a/neural_coder/docs/cloud_autobench/CloudAutobench.MD +++ /dev/null @@ -1,95 +0,0 @@ -# Cloud Auto-bench -This is a user guide for the automated bash script for creating a cloud instance, configuring the environment, running the benchmark code, and terminating the instance. The script supports AWS and Ali Yun for now and will support more cloud vendors in the future. - -## Prerequisite -## AWS -#### Install AWS CLI -Install the latest AWS CLI according to https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html. -Example: install AWS CLI on Linux x86 (64-bit) by the following commands. -``` -curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip" -unzip awscliv2.zip -./aws/install -i ~/.Awscli2 -b ~/.Awscli2/bin -export PATH=/.Awscli2/bin:$PATH -``` -Confirm the installation with the following command. -``` -export PATH=/.Awscli2/bin:$PATH -``` - -#### Configure IAM role -Configure IAM role to access AWS according to https://us-east-2.console.aws.amazon.com/iamv2/home#/users. - -#### Configure AWS clients -Configure AWS clients via "aws configure" which fetch from step 2. -``` -aws --configure -AWS Access Key ID [None]: x -AWS Secret Access Key [None]: x -Default region name [None]: us-east-2 -Default output format [None]: text -``` - -#### Create a key pair -Create a key pair x.pem via https://us-east-2.console.aws.amazon.com/ec2/v2/home?region=us-east-2#KeyPairs:, add x.pem to the current folder, and modify its permission by -``` -chmod 400 x.pem -``` - -## Ali Yun -#### Install Alibaba Cloud CLI -Download the installation package for Linux from https://www.alibabacloud.com/help/en/alibaba-cloud-cli/latest/linux - -Decompress the downloaded file to obtain the executable file named aliyun -``` -tar xzvf (aliyun-cli-linux-latest-amd64.tgz) -``` -In the "()" is the file name of the installation file you have downloaded - -Set environment variables -add this line into the ~/.bash_profile file -``` -export PATH=/home/your_directory/.Awscli2/bin:$PATH -``` - -Your directory is the directory of bin in the ALi Yun installation directory) -source environment variables - -#### Configure Ali Yun clients -run -``` -aliyun configure -``` - -Input the key ID, key secret, region ID and default language of your Ali Yun account -``` -Access Key Id [************************]: -Access Key Secret [******************************]: -Default Region Id [**-****]: -Default Output Format [json]: json (Only support json) -Default Language [zh|en] en -``` - -### Create a key pair of Ali Yun according to page https://ecs.console.aliyun.com/#/keyPair/ -add x.pem to the current folder, and modify its permission by -``` -chmod 400 x.pem -``` - -## Launch the cloud benchmark script -### Modify the permission of bench.sh -``` -chmod 755 bench.sh -``` - -### Input your task config information in the config.conf file -You need to input the information of your task in the config.conf file -You can choose the cloud vendor(AWS or Ali Yun), Instance number, type and other information with the comments as a reference - -### Launch the script -``` -./bench.sh -``` - -### Output -Example Cloud auto-bench report: ```superbench_report_aws_icx.pdf``` diff --git a/neural_coder/docs/cloud_autobench/bench.sh b/neural_coder/docs/cloud_autobench/bench.sh deleted file mode 100644 index b28b2de9ed2..00000000000 --- a/neural_coder/docs/cloud_autobench/bench.sh +++ /dev/null @@ -1,497 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -#!/bin/bash - -# Read the user input - -check_input() -{ -if [ $whether_launch_new -eq "1" ] -then - if [ $vendor -eq "1" ] - then - if [ -z $security_id_aws ] - then - echo "[ERROR] There is no security group ID, you must specify security ID in config file when creating a new instance" - fi - - if [ -z $subnet_id_aws ] - then - echo "[ERROR] There is no subnet ID, you must specify subnet ID in config file when creating a new instance" - fi - elif [ $vendor -eq "2" ] - then - if [ -z $security_id_ali ] - then - echo "[ERROR] There is no security group ID, you must specify security ID in config file when creating a new instance" - fi - - if [ -z $region_id_ali ] - then - echo "[ERROR] There is no region ID, you must specify region ID in config file when creating a new instance" - fi - else - echo "[ERROR] There is no this vendor" - fi -else - if [ -z $instance_id ] - then - echo "[ERROR] There is no instance ID, you must specify instance ID in config file when using an existed instance" - fi -fi - -} - -create_AWS_instance() -{ -if [ $os -eq "1" ] -then - if [ $arch -eq "1" ] - then - ami_ID="ami-02f3416038bdb17fb" - else - ami_ID="ami-0ff596d41505819fd" - fi -elif [ $os -eq "2" ] -then - if [ $arch -eq "1" ] - then - ami_ID="ami-02d1e544b84bf7502" - else - ami_ID="ami-03e57de632660544c" - fi -elif [ $os -eq "3" ] -then - if [ $arch -eq "1" ] - then - ami_ID="ami-092b43193629811af" - else - ami_ID="ami-0082f8c86a7132597" - fi -elif [ $os -eq "4" ] -then - if [ $arch -eq "1" ] - then - ami_ID="ami-0f7cb53c916a75006" - else - ami_ID="ami-075a486be6269029f" - fi -else - echo "[ERROR] The operating system is invalid" - exit 0 -fi - -echo "[INFO] Starting creating AMS instance ..." - -instance_id=$(aws ec2 run-instances --image-id $ami_ID --count $count --instance-type $i_type --key-name $key_name --security-group-ids $security_id_aws --subnet-id $subnet_id_aws --block-device-mappings 'DeviceName=/dev/sda1, Ebs={VolumeSize=30}' --query "Instances[0].InstanceId") -result=$? -if [ $result -ne '0' ] -then - echo "[ERROR] Create AWS Instance failed" - exit 0 -else - echo "[INFO] Create AWS instance success" - echo "[INFO] Your Instance Id is $instance_id" - echo "[INFO] Waiting for instance to initialize ..." - echo "[INFO] 15s left ..." - sleep 5s - echo "[INFO] 10s left ..." - sleep 5s - echo "[INFO] 5s left ..." - sleep 5s -fi -} - -create_Ali_Yun_instance() -{ -if [ $os -eq "1" ] -then - if [ $arch -eq "1" ] - then - ami_ID="ubuntu_20_04_x64_20G_alibase_20220524.vhd" - else - ami_ID="ubuntu_20_04_x64_20G_alibase_20220524.vhd" - fi -elif [ $os -eq "2" ] -then - if [ $arch -eq "1" ] - then - ami_ID="ubuntu_20_04_x64_20G_alibase_20220524.vhd" - else - ami_ID="ubuntu_20_04_x64_20G_alibase_20220524.vhd" - fi -elif [ $os -eq "3" ] -then - if [ $arch -eq "1" ] - then - ami_ID="ubuntu_20_04_x64_20G_alibase_20220524.vhd" - else - ami_ID="ubuntu_20_04_x64_20G_alibase_20220524.vhd" - fi -elif [ $os -eq "4" ] -then - if [ $arch -eq "1" ] - then - ami_ID="ubuntu_20_04_x64_20G_alibase_20220524.vhd" - else - ami_ID="ubuntu_20_04_x64_20G_alibase_20220524.vhd" - fi -else - echo "[ERROR] The operating system is invalid" - exit 0 -fi - -i_type="ecs.$i_type_family.$i_type_size" - -echo "[INFO] Starting creating Ali Yun instance ..." - -instance_id=$(aliyun ecs RunInstances --RegionId $region_id_ali --InstanceType $i_type --InstanceChargeType PostPaid --ImageId $ami_ID --KeyPairName $key_name --SecurityGroupId $security_id_ali --VSwitchId vsw-m5ethlhigvonp2kuyzhjw --InternetMaxBandwidthIn 1 --InternetMaxBandwidthOut 1 |grep "i-") -result=$? - -instance_id="${instance_id:4:22}" -if [ $result -ne '0' ] -then - echo "[ERROR] Create Ali Yun Instance failed" - exit 0 -else - echo "[INFO] Create Ali Yun instance successfully" - echo "[INFO] The Ali Yun instance id is: $instance_id" - echo "[INFO] Waiting for instance to initialize ..." - echo "[INFO] 35s left ..." - sleep 5s - echo "[INFO] 30s left ..." - sleep 5s - echo "[INFO] 25s left ..." - sleep 5s - echo "[INFO] 20s left ..." - sleep 5s - echo "[INFO] 15s left ..." - sleep 5s - echo "[INFO] 10s left ..." - sleep 5s - echo "[INFO] 5s left ..." - sleep 5s -fi -} - -connect_AWS() -{ -dns_name=$(aws ec2 describe-instances --instance-ids $instance_id --query "Reservations[0].Instances[0].PublicDnsName") -result=$? -if [ $result -ne '0' ] -then - echo "[ERROR] Can not find this instance, please check" - exit 0 -fi - -host_name=$dns_name - -if [ $os -eq "1" ] -then - host_name="ubuntu@$dns_name" -else - host_name="ec2-user@$dns_name" -fi - -key_name="$key_name.pem" -echo "[INFO] Your instance host name is: $host_name" -echo "[INFO] Connecting to AWS Instance ..." -ssh -i $key_name $host_name -o "StrictHostKeyChecking no" "uname -a ; exit" -result=$? -if [ $result -ne '0' ] -then - echo "[ERROR] SSH connection failed" - echo "[INFO] Start terminating the Instance" - aws ec2 terminate-instances --instance-ids $instance_id - result=$? - if [ $result -ne '0' ] - then - echo "[ERROR] Instance termination failed" - else - echo "[INFO] Instance termination success" - fi - exit 0 -else - echo "[INFO] Connect to AWS Instance success" -fi - -echo "[INFO] Start to transferring benchmark files" -scp -i $key_name -r ./code/ $host_name:/tmp -result=$? -if [ $result -ne '0' ] -then - echo "[ERROR] SSH connection failed" - exit 0 -else - echo "[INFO] File transferring success" -fi - -if [ $whether_launch_new -eq "1" ] -then - ssh -i $key_name $host_name "cd /tmp/code; chmod +x ./config.sh; ./config.sh; exit" - echo "[INFO] Install dependencies finished" -else - echo "[INFO] Configured environment" -fi - -echo "[INFO] Start launching the task ..." - -ssh -i $key_name $host_name "cd /tmp/code; chmod +x ./launch.sh; ./launch.sh; exit" - -echo "[INFO] Benchmark Execution finished" -} - -connect_Ali_Yun() -{ -public_ip=$(aliyun ecs DescribeInstances --output cols=InstanceId,PublicIpAddress.IpAddress rows=Instances.Instance[] |grep $instance_id) -result=$? -if [ $result -ne '0' ] -then - echo "[ERROR] Can not find this instance, please check" - exit 0 -fi - -public_ip="${public_ip:25}" -length=${#public_ip} -public_ip="${public_ip:1:$length-2}" -host_name="root@$public_ip" -key_name="$key_name.pem" -echo "[INFO] Your instance host name is: $host_name" - -echo "[INFO] Start to connecting Ali Yun instance" -ssh -i $key_name $host_name -o "StrictHostKeyChecking no" "uname -a ; exit" -result=$? -if [ $result -ne '0' ] -then - echo "[ERROR] SSH connection failed" - echo "[INFO] Start to delete instance $instance_id" - sleep 60s - aliyun ecs DeleteInstance --InstanceId $instance_id --Force true - result=$? - if [ $result -ne '0' ] - then - echo "[ERROR] Instance termination failed" - exit 0 - else - echo "[INFO] Instance termination success" - fi - exit 0 -else - echo "[INFO] Connect to Ali Yun Instance success" -fi - -echo "[INFO] Start to transferring benchmark files" -scp -i $key_name -r ./code/ $host_name:/tmp -result=$? -if [ $result -ne '0' ] -then - echo "[ERROR] SSH connection failed" - exit 0 -else - echo "[INFO] File transferring success" -fi - -if [ $whether_launch_new -eq "1" ] -then - ssh -i $key_name $host_name "cd /tmp/code; chmod +x ./config.sh; ./config.sh; exit" - echo "[INFO] Install dependencies finished" -else - echo "[INFO] Configured environment" -fi - -echo "[INFO] Start launching the task ..." - -ssh -i $key_name $host_name "cd /tmp/code; chmod +x ./launch.sh; ./launch.sh; exit" - -echo "[INFO] Benchmark Execution finished" -} - -close_AWS() -{ - -if [ $whether_retain -eq "1" ] -then - echo "[INFO] Start stopping the Instance" - - aws ec2 stop-instances --instance-ids $instance_id - result=$? - if [ $result -ne '0' ] - then - echo "[ERROR] Instance stop failed" - exit 0 - else - echo "[INFO] Instance stop success" - echo "[INFO] The instance id is $instance_id, Please record this $instance_id for next use" - fi -else - echo "[INFO] Start terminating the Instance" - - aws ec2 terminate-instances --instance-ids $instance_id - result=$? - if [ $result -ne '0' ] - then - echo "[ERROR] Instance termination failed" - exit 0 - else - echo "[INFO] Instance termination success" - fi -fi -} - -close_Ali_Yun() -{ - -if [ $whether_retain -eq "1" ] -then - echo "[INFO] Start stopping the Instance" - - aliyun ecs StopInstance --InstanceId $instance_id - result=$? - if [ $result -ne '0' ] - then - echo "[ERROR] Instance stop failed" - exit 0 - else - echo "[INFO] Instance stop success" - echo "[INFO] The instance id is $instance_id, Please record this $instance_id for next use" - fi -elif [ $whether_retain -eq "2" ] -then - echo "[INFO] Start terminating the Instance" - - aliyun ecs DeleteInstance --InstanceId $instance_id --Force true - result=$? - if [ $result -ne '0' ] - then - echo "[ERROR] Instance termination failed" - exit 0 - else - echo "[INFO] Instance termination success" - fi -fi -} - - -main() -{ -vendor=$(sed '/^cloud_vendor=/!d; s/.*=//' config.conf) -os=$(sed '/^OS=/!d; s/.*=//' config.conf) -arch=$(sed '/^arch=/!d; s/.*=//' config.conf) -count=$(sed '/^count=/!d; s/.*=//' config.conf) -i_type_family=$(sed '/^i_type_family=/!d; s/.*=//' config.conf) -i_type_size=$(sed '/^i_type_size=/!d; s/.*=//' config.conf) -key_name=$(sed '/^key_name=/!d; s/.*=//' config.conf) -instance_id=$(sed '/^instance_id=/!d; s/.*=//' config.conf) -security_id_aws=$(sed '/^security_id_aws=/!d; s/.*=//' config.conf) -subnet_id_aws=$(sed '/^subnet_id_aws=/!d; s/.*=//' config.conf) -security_id_ali=$(sed '/^security_id_ali=/!d; s/.*=//' config.conf) -region_id_ali=$(sed '/^region_id_ali=/!d; s/.*=//' config.conf) - -whether_retain=$(sed '/^whether_retain=/!d; s/.*=//' config.conf) -whether_launch_new=$(sed '/^whether_launch_new=/!d; s/.*=//' config.conf) - -i_type="$i_type_family.$i_type_size" - -check_input - -if [ ! -f "$key_name.pem" ]; then - echo "[ERROR] Can not find the key pair file $key_name.pem, please put the $key_name.pem file in this folder" - exit 0 -else - chmod 400 ./"$key_name.pem" -fi - -if [ ! -f "./code/benchmark.py" ]; then - echo "[ERROR] Can not find the benchmark file, please put the benchmark file in code folder" - exit 0 -fi - - -if [ $whether_launch_new -eq "1" ] -then - echo "[INFO] Your instance info:" - echo "[INFO] Instance key name: $key_name" - echo "[INFO] Instance count: $count" - echo "[INFO] Instance_type: $i_type" -else - echo "[INFO] The existed instance you choose: $instance_id" -fi - -if [ $whether_launch_new -eq "1" ] -then - if [ $vendor -eq "1" ] - then - create_AWS_instance - elif [ $vendor -eq "2" ] - then - create_Ali_Yun_instance - else - echo "Tencent Cloud" - fi -else - if [ $vendor -eq "1" ] - then - aws ec2 start-instances --instance-ids $instance_id - echo "[INFO] Waiting for instance to Start ..." - echo "[INFO] 15s left ..." - sleep 5s - echo "[INFO] 10s left ..." - sleep 5s - echo "[INFO] 5s left ..." - sleep 5s - elif [ $vendor -eq "2" ] - then - aliyun ecs StartInstance --InstanceId $instance_id - echo "[INFO] Waiting for instance to Start ..." - echo "[INFO] 45s left ..." - sleep 15s - echo "[INFO] 30s left ..." - sleep 15s - echo "[INFO] 15s left ..." - sleep 15s - else - echo "Tencent Cloud" - fi -fi - -if [ $vendor -eq "1" ] -then - connect_AWS -elif [ $vendor -eq "2" ] -then - connect_Ali_Yun -else - echo "Tencent Cloud" -fi - -if [ $vendor -eq "1" ] -then - close_AWS -elif [ $vendor -eq "2" ] -then - close_Ali_Yun -else - echo "Tencent Cloud" -fi - -exit 0 - -} - -main - - - - diff --git a/neural_coder/docs/cloud_autobench/code/config.sh b/neural_coder/docs/cloud_autobench/code/config.sh deleted file mode 100644 index 10e9449a12f..00000000000 --- a/neural_coder/docs/cloud_autobench/code/config.sh +++ /dev/null @@ -1,47 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -set -x - -echo "[INFO] Start installing software packages and dependencies" - -# install machine packages -sudo apt-get -y update -sudo apt-get install -y wget -sudo apt-get install -y git -sudo apt-get install -y build-essential -sudo apt-get install -y htop aha html2text numactl bc -sudo apt-get install -y ffmpeg libsm6 libxext6 -sudo apt-get install -y automake libtool -sudo apt-get install -y python3 pip - -# install conda -wget https://repo.continuum.io/archive/Anaconda3-5.0.0-Linux-x86_64.sh -O anaconda3.sh -chmod +x anaconda3.sh -sudo ./anaconda3.sh -b -p /home/anaconda3 -export PATH=/home/anaconda3/bin:$PATH -conda create -yn test python=3.9 -source activate test - -# install pip modules -pip install numpy -pip install pyyaml -pip install typing_extensions -pip install psutil -pip install neural_compressor intel_extension_for_pytorch - -# install torch -pip3 install torch torchvision torchaudio -pip3 install torchdynamo -pip3 install transformers diff --git a/neural_coder/docs/cloud_autobench/code/launch.sh b/neural_coder/docs/cloud_autobench/code/launch.sh deleted file mode 100644 index 1521301e1c3..00000000000 --- a/neural_coder/docs/cloud_autobench/code/launch.sh +++ /dev/null @@ -1,21 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -set -x - -export PATH=/home/anaconda3/bin:$PATH -source activate test -echo "[INFO] Start running auto benchmark..." -python -c "from neural_coder import superreport; superreport(code='resnet50.py')" -# Note: you need to uncomment superreport in neural_coder/interface.py and neural_coder/__init__.py to use this API. diff --git a/neural_coder/docs/cloud_autobench/code/resnet50.py b/neural_coder/docs/cloud_autobench/code/resnet50.py deleted file mode 100644 index 13caa61334a..00000000000 --- a/neural_coder/docs/cloud_autobench/code/resnet50.py +++ /dev/null @@ -1,23 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import torch -import torchvision.models as models - -model = models.resnet50(pretrained=True) -model.eval() -batch_size = 1 -input = torch.rand(batch_size, 3, 224, 224) -with torch.no_grad(): - model(input) diff --git a/neural_coder/docs/cloud_autobench/config.conf b/neural_coder/docs/cloud_autobench/config.conf deleted file mode 100644 index d598178debb..00000000000 --- a/neural_coder/docs/cloud_autobench/config.conf +++ /dev/null @@ -1,62 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# This is an example of providing Cloud configs for cloud benchmark - -# Choose Cloud Vendor -# 1 AWS 2 Ali Yun 3 Tencent Cloud -cloud_vendor=1 - -# Choose Instance Operating System -# 1 Ubuntu 2 Amazon Linux 3 Red Hat 4 SUSE Linux -OS=1 - -# Choose Instance CPU architecture -# 1 x86 2 ARM -arch=1 - -# Number of Instances need to be launched -count=1 - -# Family of instance type -i_type_family=c6i - -# Size of instance type -i_type_size=2xlarge - -# Private key name -key_name=huangyu - -# Instance id (Required if use an existing instance) -instance_id=i-0ecdba84ca1ea5d68 - -# Security Group id AWS (Required) -security_id_aws=sg-00139ed15a90e83e0 - -# Subnet id AWS (Required) -subnet_id_aws=subnet-010cad6a296e44f8b - -# Security Group id Ali Yun (Required) -security_id_ali=sg-m5e61j9zh9hivx3k9iwp - -# RegionId Ali Yun (Required) -region_id_ali=cn-qingdao - -# Whether retain the instance after this task -# 1 retain 2 Not retain -whether_retain=2 - -# Create a new instance or use an existing instance -# 1 Creat new 2 Use an existing one -whether_launch_new=2 diff --git a/neural_coder/docs/cloud_autobench/superbench_report_aws_icx.pdf b/neural_coder/docs/cloud_autobench/superbench_report_aws_icx.pdf deleted file mode 100644 index cdb6d01608c40feb5ebe880927d2785238b43b6d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 3863 zcma)9*Vd|762AYR0ux3gilP_@2F!wriU|-AB`9IKdoJb$X71*Go^|l=b9(+cJ)v1H z*}JG+Rr~v@)~d?P`5uIpiI2?R|Mu5^0HX2q{BW#lK;Li;Uk!~55NiMzPmdhK4S`!2 zKI<_2(@#H@{ZCz6)ylf(e4qdVc-sAgra#~w3SjY81mGS9;NMm_K)hEUt=5O(Uf!y2 zo$p6jH86T!>;N7Z_MeQ<(p3QNy1I9K(-Ya0RiRefcB)us`VUebwLlTUw40K%(cU?jLc6|3PP!FV495-Z4)B zK*hqCfQfOjRMz_$P~trTkSGRJDGWFO@LcuZgw}r56OF)&eBB6uPXgaeycvxx%zaOn zj4(C;zcT?|8b8M@365$S?6)`f?JWR@7;^v?y)e$4_&B`uRrk~6TmAujXJ;NC{=yC! ztA0o8{b}M|VhUG*G4%V4McA6G<54~8JABJBjcB!JpU2^ zxmYib^Z%KO(Yr*t5eM_H(t5)E5sbh<6Q`xNO848Xb8W-DF zj5dhkePHo>jo_rn1!whb!mRJ?L8wM2E;po`_ACZY3FzvDNT zSg+M*DIsNWLz;%!Mo4qm&0VQBIG%~gSw1Yt2ql%*Ww8ee z>-Q5gtWm|PPDlEwJYFf;$=#t)z1x@e(q?)vR*=%0}> z(cdp2o~?r$(=JK1UV)jYU3BI*Ey_C+X{lwPB5zr1daIhXJ>jsq(#EHpX+1;xsauX4 zF3R(T338AhfwT&j`DAdhXZS6xoi<@9$2g-?0d%3hX}X37rXTqkvSjv|Vmft5?*|La zF&mV(QMop5J)7fs+1bJS0yilrdnhA3)@-XwfW&k$05P-4EjI)? zL}h5x#P)nzyb=%Em~)F8U&n{7t6bExI5oujW)9(Yk+)NM#9!T7ldi;O!?V_!iifJJ z!2C91hE7n)2^poxW#dRDU!DgHu2fz8S)l$8_EGY{5Q zllLLrNn^|NY@wT5E=NXILp^2mQ&Su6)k47Actlwk+r;}O`z&1R+jhnkAapD*vVs8R z;eKA-fpDTY5XWAAnsl9q4R-LpZCQ(St*DgK-DF}7b^HPTie}KJ>o(9-VA|}+BLprV zTze~=SyVct5Xi=hp&v1DOM_e?>B%;sUCwA8Jb<7z5BY!vrCEud4mOs+HJRRpUYjzg z)e6-PM2?nj$PKI2-mBKs+EDBEs9j2x`Ta@?!)y(O)SS&T*zK%H1W5WsPRGxxIfusi>xS zyE+~zK5{M|bKxtkW^CknmCv9bozl^HG=28Ds2uU-ZJZH025a5?a9UKBfwH*IUb_Y} zE2PN;a+j(6|KpziHeckX{vwZQ8i^Q{ZN;aSQ)D%UHuvIOrh&|G~r2ANJ z(4Sc2?cp$>r42CyRsM_nsbc-#_(-jS}eP^x+RDQ{vCn>U6 z5g&PQxE&tm3@2NWS+qe~F<*;3oj5&)3V#DnLnhyhFVbrr)0I0EtyYEPu&=>%fgNZp zVce@txLxba(c?)xqdA>kYv$@uA4-r564#@mO;M7}46b;>7USZ_5UDX*Ep8`>u*glZ zc(xDTkt=k}mvF*{e2ok9om2;wrAlGB1&wOLbh5KnfSeKeR_I&Ht-gbMa)Q(5&l$ul z&r@=F+pI@JMHJ?VV2JX|RJ*HCa(gCp{K~_GR+vOF&rTk5jE&m67mBO%E>Bgx#EPBL za&E5iLnS&0mFHc}cFK&w^$2^t7RbJWHqYCfZ-NWT-qb7SDYUh&h;#12-rB@m1qn}# zS+BOqqC7szqdM|h9=3y5S#%O1Ka^+pS!E+|uO4Q=&l0M1+m)7FO-lQ8 z*epuIV5?j%;@wux^5wYC)QX{P&(ikn5E@x(@GRRGei`|Aj$QFKX7odZYN?E4B zZ+1v}S9S&>A5F4pZ8|#K>AkTM&eUlNHYh0N!R>S3VW$q2)cX6y+5kr-j12{DARf<7 zt3N7`QT1nfq|-_ADwl6nF}Y2fBDc*Dsr7PVgv9y* zj!CHeESF`DJvEG38zpwqs$sU;gY{Nr(>67Z5`~_MltS(~NWoO==8wHbKo^cFz2bOk zN?1MHVQ=5J_v)b9%=T-WW2%GiSjK&H(m`fiytT&99lM7TjrAH_)(iMrdog--vQDl> zMJstU&glv>fwzqO-0phYL*GX&8FTo`^Obu((aY@sNA1QgbzhEh*M2a@GR_)nh>|=2 zK}l+^CpBr??n*@}vl5tk$6i9+yYpH^R_$5ca@QtJZc52jx#>ED0wSV>C75A!a$zEkxXe7q42on<5EcquHW`%$+f zY?M+yEVpFH&W{J6#oGy3yl$X;`tIWB!t+};?7z+v9=D3qb-P8boozDln%<*qHOqU+ zwlZ%@eU+!}1iiW()^XVo!#soSom@141I}@MpG}L^@c5TjJmq}reZI{*Kl{e@0|8Lv z1NrR%$VvsT09W8QSv8(j-VfmZCPT4!p7=qA;cVr=~`9SRZ>?Cs&=I=c$F}4O(QgnBnjl& z`u`e-kIa`j)cntLCCSo|{{&pXV4?s3 diff --git a/neural_coder/docs/release_notes/v0.4.md b/neural_coder/docs/release_notes/v0.4.md deleted file mode 100644 index 933c02900a3..00000000000 --- a/neural_coder/docs/release_notes/v0.4.md +++ /dev/null @@ -1,25 +0,0 @@ -v0.4 -===== - -## Highlights -- **Visual Studio Code extension**: We are delighted to announce the release of Neural Coder's [Visual Studio Code extension](https://marketplace.visualstudio.com/items?itemName=IntelNeuralCompressor.neural-coder-ext-vscode). VS Code programmers can enjoy one-click automatic enabling of Deep Learning optimization API and accelerate their Deep Learning models without manual coding. - -- **HuggingFace Transformers**: - - We supported **all** HuggingFace Transformers [examples](https://github.com/huggingface/transformers/tree/main/examples/pytorch) that calls ```Trainer``` class, and validated over **500** models from HuggingFace Transformers [model hub](https://huggingface.co/models). The models are able to be accelerated automatically with Neural Coder with minimum loss of prediction accuracy. - - We enabled the support of [HuggingFace Optimum-Intel](https://huggingface.co/docs/optimum/intel/index). User scripts of HuggingFace Transformers models will by default be optimized with Optimum-Intel API to enjoy performance speed-up brought by INT8 quantization. - - We enabled the support of [IntelĀ® Extension for Transformers](https://github.com/intel/intel-extension-for-transformers), an innovative toolkit to accelerate Transformer-based models on Intel platforms. For more details, please refer to the updated [support matrix](../SupportMatrix.md). - -- **Support of BigDL Nano**: We are delighted to announce the collaboration between Neural Coder and [BigDL Nano](https://bigdl.readthedocs.io/en/latest/doc/Nano/index.html). Users can now one-click enable BigDL Nano optimizations for PyTorch in Neural Coder. For detailed support matrix for BigDL Nano features, please refer to this [guide](../BigDLNanoSupport.md). - -- **Amazon AWS SageMaker**: We provided a user [tutorial](../AWSSageMakerSupport.md) for installing Neural Coder's JupyterLab extension in AWS SageMaker platform. Users are able to one-click install the extension in Amazon AWS SageMaker with Jupyter 3 and enjoy Neural Coder's functionalities. - -- **Python Launcher**: We added the implementation of [Python Launcher](../PythonLauncher.md) usage for Neural Coder, which will be one of the recommended user interfaces in the future as a replacement of Python API. Users can run the Python model code as it is with automatic enabling of Deep Learning optimizations by using Neural Coder's inline Python Launcher design: ```-m neural_coder```. - -- **Device Detection**: We enabled the capability of detecting running device and its ISA automatically and adjusting applied optimization features accordingly. For instance, when running Neural Coder on Intel GPU instead of Intel CPU, the PyTorch Mixed Precision optimization feature will adapt ```xpu``` instead of ```cpu```, and ```torch.half``` instead of ```torch.bfloat16```. - -## Others -- **INT8 Accuracy Evaluation**: We enabled accuracy evaluation for INT8 quantizations in Neural Coder. Users are able to view the accuracy delta for each quantization optimization in Neural Coder's auto-benchmark output log. The calculation is ```acc_delta = (int8_acc - fp32_acc)/(fp32_acc)```. - -- **Auto-quantize TensorFlow/Keras scripts**: We enabled the support of auto-quantizing TensorFlow/Keras script-based models with IntelĀ® Neural Compressor. The default quantization scheme will be applied. For more details, please refer to the updated [support matrix](../SupportMatrix.md). - -- **Auto-quantize ONNX Runtime scripts**: We enabled the support of auto-quantizing ONNX Runtime script-based models with IntelĀ® Neural Compressor. We support [dynamic quantization](https://github.com/intel/neural-compressor/tree/master/examples/onnxrt#dynamic-quantization), static quantization ([QDQ](https://github.com/intel/neural-compressor/tree/master/examples/onnxrt#tensor-oriented-qdq-format)), and static quantization ([QLinearOps](https://github.com/intel/neural-compressor/tree/master/examples/onnxrt#operator-oriented-with-qlinearops)). For more details, please refer to the updated [support matrix](../SupportMatrix.md). diff --git a/neural_coder/examples/keras/mnist.py b/neural_coder/examples/keras/mnist.py deleted file mode 100644 index 22ba9d66876..00000000000 --- a/neural_coder/examples/keras/mnist.py +++ /dev/null @@ -1,97 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -# -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import os -import tensorflow as tf -import numpy as np -from tensorflow import keras -from tensorflow.keras import layers -import time -from torch.utils.data import DataLoader - -num_classes = 10 - -def build_dataset(): - # Load the data and split it between train and test sets - (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() - - # Scale images to the [0, 1] range - x_train = x_train.astype("float32") / 255 - x_test = x_test.astype("float32") / 255 - # Make sure images have shape (28, 28, 1) - x_train = np.expand_dims(x_train, -1) - x_test = np.expand_dims(x_test, -1) - - # convert class vectors to binary class matrices - y_train = keras.utils.to_categorical(y_train, num_classes) - y_test = keras.utils.to_categorical(y_test, num_classes) - return x_train, y_train, x_test, y_test - -class Dataset(): - def __init__(self, ): - _, _ , self.inputs, self.labels = build_dataset() - - def __getitem__(self, idx): - return self.inputs[idx], self.labels[idx] - - def __len__(self): - assert len(self.inputs) == len(self.labels), 'inputs should have equal len with labels' - return len(self.inputs) - -def build_model(x_train, y_train, x_test, y_test): - if os.path.exists('fp32_model'): - model = keras.models.load_model('fp32_model') - return model - # Model / data parameters - input_shape = (28, 28, 1) - model = keras.Sequential( - [ - keras.Input(shape=input_shape), - layers.Conv2D(32, kernel_size=(3, 3), activation="relu"), - layers.MaxPooling2D(pool_size=(2, 2)), - layers.Conv2D(64, kernel_size=(3, 3), activation="relu"), - layers.MaxPooling2D(pool_size=(2, 2)), - layers.Flatten(), - layers.Dropout(0.5), - layers.Dense(num_classes, activation="softmax"), - ] - ) - - batch_size = 128 - epochs = 1 - - model.compile(loss="categorical_crossentropy", optimizer="adam", - metrics=["accuracy"], run_eagerly=True) - model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1) - model.summary() - if not os.path.exists('fp32_model'): - model.save('fp32_model') - return model - -def eval_func(model): - x_train, y_train, x_test, y_test = build_dataset() - model.compile(metrics=["accuracy"], run_eagerly=False) - score = model.evaluate(x_test, y_test) - return score[1] - -def main(): - x_train, y_train, x_test, y_test = build_dataset() - model = build_model(x_train, y_train, x_test, y_test) - calib_dataloader = DataLoader(Dataset(), batch_size=10) - -if __name__ == '__main__': - main() diff --git a/neural_coder/examples/nano/resnet18.py b/neural_coder/examples/nano/resnet18.py deleted file mode 100644 index 8d189bf75b2..00000000000 --- a/neural_coder/examples/nano/resnet18.py +++ /dev/null @@ -1,26 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import torch -from torchvision.models import resnet18 - -if __name__ == "__main__": - - model_ft = resnet18(pretrained=True) - - x = torch.rand(2, 3, 224, 224) - y_hat = model_ft(x) - predictions = y_hat.argmax(dim=1) - print(predictions) diff --git a/neural_coder/examples/nlp/distilbert.py b/neural_coder/examples/nlp/distilbert.py deleted file mode 100644 index eab5513a51a..00000000000 --- a/neural_coder/examples/nlp/distilbert.py +++ /dev/null @@ -1,45 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -from transformers import ( - AutoModelForSequenceClassification, - AutoTokenizer -) - -finetuned_model = "distilbert-base-uncased-finetuned-sst-2-english" - - -class MyDataLoader(object): - def __init__(self): - self.tokenizer = AutoTokenizer.from_pretrained(finetuned_model) - self.sequence = "Shanghai is a beautiful city!" - self.encoded_input = self.tokenizer( - self.sequence, - return_tensors='pt' - ) - self.label = 1 # negative sentence: 0; positive sentence: 1 - self.batch_size = 1 - - def __iter__(self): - yield self.encoded_input, self.label - - -my_nlp_model = AutoModelForSequenceClassification.from_pretrained( - finetuned_model, -) - -my_nlp_dataloader = MyDataLoader() - -output = my_nlp_model(**my_nlp_dataloader.encoded_input) diff --git a/neural_coder/examples/nlp/run_glue.py b/neural_coder/examples/nlp/run_glue.py deleted file mode 100644 index f3a8ccfd75c..00000000000 --- a/neural_coder/examples/nlp/run_glue.py +++ /dev/null @@ -1,617 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# code source -# https://github.com/huggingface/transformers/blob/v4.21-release/examples/pytorch/text-classification/run_glue.py - -import logging -import os -import random -import sys -from dataclasses import dataclass, field -from typing import Optional - -import datasets -import numpy as np -from datasets import load_dataset, load_metric - -import transformers -from transformers import ( - AutoConfig, - AutoModelForSequenceClassification, - AutoTokenizer, - DataCollatorWithPadding, - EvalPrediction, - HfArgumentParser, - PretrainedConfig, - Trainer, - TrainingArguments, - default_data_collator, - set_seed, -) -from transformers.trainer_utils import get_last_checkpoint -from transformers.utils import check_min_version, send_example_telemetry -from transformers.utils.versions import require_version - - -# Will error if the minimal version of Transformers is not installed. Remove at your own risks. -check_min_version("4.21.0") - -require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt") - -task_to_keys = { - "cola": ("sentence", None), - "mnli": ("premise", "hypothesis"), - "mrpc": ("sentence1", "sentence2"), - "qnli": ("question", "sentence"), - "qqp": ("question1", "question2"), - "rte": ("sentence1", "sentence2"), - "sst2": ("sentence", None), - "stsb": ("sentence1", "sentence2"), - "wnli": ("sentence1", "sentence2"), -} - -logger = logging.getLogger(__name__) - - -@dataclass -class DataTrainingArguments: - """ - Arguments pertaining to what data we are going to input our model for training and eval. - - Using `HfArgumentParser` we can turn this class - into argparse arguments to be able to specify them on - the command line. - """ - - task_name: Optional[str] = field( - default=None, - metadata={"help": "The name of the task to train on: " + ", ".join(task_to_keys.keys())}, - ) - dataset_name: Optional[str] = field( - default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} - ) - dataset_config_name: Optional[str] = field( - default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} - ) - max_seq_length: int = field( - default=128, - metadata={ - "help": ( - "The maximum total input sequence length after tokenization. Sequences longer " - "than this will be truncated, sequences shorter will be padded." - ) - }, - ) - overwrite_cache: bool = field( - default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."} - ) - pad_to_max_length: bool = field( - default=True, - metadata={ - "help": ( - "Whether to pad all samples to `max_seq_length`. " - "If False, will pad the samples dynamically when batching to the maximum length in the batch." - ) - }, - ) - max_train_samples: Optional[int] = field( - default=None, - metadata={ - "help": ( - "For debugging purposes or quicker training, truncate the number of training examples to this " - "value if set." - ) - }, - ) - max_eval_samples: Optional[int] = field( - default=None, - metadata={ - "help": ( - "For debugging purposes or quicker training, truncate the number of evaluation examples to this " - "value if set." - ) - }, - ) - max_predict_samples: Optional[int] = field( - default=None, - metadata={ - "help": ( - "For debugging purposes or quicker training, truncate the number of prediction examples to this " - "value if set." - ) - }, - ) - train_file: Optional[str] = field( - default=None, metadata={"help": "A csv or a json file containing the training data."} - ) - validation_file: Optional[str] = field( - default=None, metadata={"help": "A csv or a json file containing the validation data."} - ) - test_file: Optional[str] = field(default=None, metadata={"help": "A csv or a json file containing the test data."}) - - def __post_init__(self): - if self.task_name is not None: - self.task_name = self.task_name.lower() - if self.task_name not in task_to_keys.keys(): - raise ValueError("Unknown task, you should pick one in " + ",".join(task_to_keys.keys())) - elif self.dataset_name is not None: - pass - elif self.train_file is None or self.validation_file is None: - raise ValueError("Need either a GLUE task, a training/validation file or a dataset name.") - else: - train_extension = self.train_file.split(".")[-1] - assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file." - validation_extension = self.validation_file.split(".")[-1] - assert ( - validation_extension == train_extension - ), "`validation_file` should have the same extension (csv or json) as `train_file`." - - -@dataclass -class ModelArguments: - """ - Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. - """ - - model_name_or_path: str = field( - metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} - ) - config_name: Optional[str] = field( - default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} - ) - tokenizer_name: Optional[str] = field( - default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} - ) - cache_dir: Optional[str] = field( - default=None, - metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}, - ) - use_fast_tokenizer: bool = field( - default=True, - metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, - ) - model_revision: str = field( - default="main", - metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, - ) - use_auth_token: bool = field( - default=False, - metadata={ - "help": ( - "Will use the token generated when running `transformers-cli login` (necessary to use this script " - "with private models)." - ) - }, - ) - ignore_mismatched_sizes: bool = field( - default=False, - metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}, - ) - - -def main(): - # See all possible arguments in src/transformers/training_args.py - # or by passing the --help flag to this script. - # We now keep distinct sets of args, for a cleaner separation of concerns. - - parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) - if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): - # If we pass only one argument to the script and it's the path to a json file, - # let's parse it to get our arguments. - model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) - else: - model_args, data_args, training_args = parser.parse_args_into_dataclasses() - - # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The - # information sent is the one passed as arguments along with your Python/PyTorch versions. - send_example_telemetry("run_glue", model_args, data_args) - - # Setup logging - logging.basicConfig( - format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", - datefmt="%m/%d/%Y %H:%M:%S", - handlers=[logging.StreamHandler(sys.stdout)], - ) - - log_level = training_args.get_process_log_level() - logger.setLevel(log_level) - datasets.utils.logging.set_verbosity(log_level) - transformers.utils.logging.set_verbosity(log_level) - transformers.utils.logging.enable_default_handler() - transformers.utils.logging.enable_explicit_format() - - # Log on each process the small summary: - logger.warning( - f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" - + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" - ) - logger.info(f"Training/evaluation parameters {training_args}") - - # Detecting last checkpoint. - last_checkpoint = None - if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: - last_checkpoint = get_last_checkpoint(training_args.output_dir) - if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: - raise ValueError( - f"Output directory ({training_args.output_dir}) already exists and is not empty. " - "Use --overwrite_output_dir to overcome." - ) - elif last_checkpoint is not None and training_args.resume_from_checkpoint is None: - logger.info( - f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " - "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." - ) - - # Set seed before initializing model. - set_seed(training_args.seed) - - # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below) - # or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub). - # - # For CSV/JSON files, this script will use as labels the column called 'label' and as pair of sentences the - # sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named - # label if at least two columns are provided. - # - # If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this - # single column. You can easily tweak this behavior (see below) - # - # In distributed training, the load_dataset function guarantee that only one local process can concurrently - # download the dataset. - if data_args.task_name is not None: - # Downloading and loading a dataset from the hub. - raw_datasets = load_dataset( - "glue", - data_args.task_name, - cache_dir=model_args.cache_dir, - use_auth_token=True if model_args.use_auth_token else None, - ) - elif data_args.dataset_name is not None: - # Downloading and loading a dataset from the hub. - raw_datasets = load_dataset( - data_args.dataset_name, - data_args.dataset_config_name, - cache_dir=model_args.cache_dir, - use_auth_token=True if model_args.use_auth_token else None, - ) - else: - # Loading a dataset from your local files. - # CSV/JSON training and evaluation files are needed. - data_files = {"train": data_args.train_file, "validation": data_args.validation_file} - - # Get the test dataset: you can provide your own CSV/JSON test file (see below) - # when you use `do_predict` without specifying a GLUE benchmark task. - if training_args.do_predict: - if data_args.test_file is not None: - train_extension = data_args.train_file.split(".")[-1] - test_extension = data_args.test_file.split(".")[-1] - assert ( - test_extension == train_extension - ), "`test_file` should have the same extension (csv or json) as `train_file`." - data_files["test"] = data_args.test_file - else: - raise ValueError("Need either a GLUE task or a test file for `do_predict`.") - - for key in data_files.keys(): - logger.info(f"load a local file for {key}: {data_files[key]}") - - if data_args.train_file.endswith(".csv"): - # Loading a dataset from local csv files - raw_datasets = load_dataset( - "csv", - data_files=data_files, - cache_dir=model_args.cache_dir, - use_auth_token=True if model_args.use_auth_token else None, - ) - else: - # Loading a dataset from local json files - raw_datasets = load_dataset( - "json", - data_files=data_files, - cache_dir=model_args.cache_dir, - use_auth_token=True if model_args.use_auth_token else None, - ) - # See more about loading any type of standard or custom dataset at - # https://huggingface.co/docs/datasets/loading_datasets.html. - - # Labels - if data_args.task_name is not None: - is_regression = data_args.task_name == "stsb" - if not is_regression: - label_list = raw_datasets["train"].features["label"].names - num_labels = len(label_list) - else: - num_labels = 1 - else: - # Trying to have good defaults here, don't hesitate to tweak to your needs. - is_regression = raw_datasets["train"].features["label"].dtype in ["float32", "float64"] - if is_regression: - num_labels = 1 - else: - # A useful fast method: - # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.unique - label_list = raw_datasets["train"].unique("label") - label_list.sort() # Let's sort it for determinism - num_labels = len(label_list) - - # Load pretrained model and tokenizer - # - # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently - # download model & vocab. - config = AutoConfig.from_pretrained( - model_args.config_name if model_args.config_name else model_args.model_name_or_path, - num_labels=num_labels, - finetuning_task=data_args.task_name, - cache_dir=model_args.cache_dir, - revision=model_args.model_revision, - use_auth_token=True if model_args.use_auth_token else None, - ) - tokenizer = AutoTokenizer.from_pretrained( - model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, - cache_dir=model_args.cache_dir, - use_fast=model_args.use_fast_tokenizer, - revision=model_args.model_revision, - use_auth_token=True if model_args.use_auth_token else None, - ) - model = AutoModelForSequenceClassification.from_pretrained( - model_args.model_name_or_path, - from_tf=bool(".ckpt" in model_args.model_name_or_path), - config=config, - cache_dir=model_args.cache_dir, - revision=model_args.model_revision, - use_auth_token=True if model_args.use_auth_token else None, - ignore_mismatched_sizes=model_args.ignore_mismatched_sizes, - ) - - # Preprocessing the raw_datasets - if data_args.task_name is not None: - sentence1_key, sentence2_key = task_to_keys[data_args.task_name] - else: - # Again, we try to have some nice defaults but don't hesitate to tweak to your use case. - non_label_column_names = [name for name in raw_datasets["train"].column_names if name != "label"] - if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names: - sentence1_key, sentence2_key = "sentence1", "sentence2" - else: - if len(non_label_column_names) >= 2: - sentence1_key, sentence2_key = non_label_column_names[:2] - else: - sentence1_key, sentence2_key = non_label_column_names[0], None - - # Padding strategy - if data_args.pad_to_max_length: - padding = "max_length" - else: - # We will pad later, dynamically at batch creation, to the max sequence length in each batch - padding = False - - # Some models have set the order of the labels to use, so let's make sure we do use it. - label_to_id = None - if ( - model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id - and data_args.task_name is not None - and not is_regression - ): - # Some have all caps in their config, some don't. - label_name_to_id = {k.lower(): v for k, v in model.config.label2id.items()} - if list(sorted(label_name_to_id.keys())) == list(sorted(label_list)): - label_to_id = {i: int(label_name_to_id[label_list[i]]) for i in range(num_labels)} - else: - logger.warning( - "Your model seems to have been trained with labels, but they don't match the dataset: ", - f"model labels: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(sorted(label_list))}." - "\nIgnoring the model labels as a result.", - ) - elif data_args.task_name is None and not is_regression: - label_to_id = {v: i for i, v in enumerate(label_list)} - - if label_to_id is not None: - model.config.label2id = label_to_id - model.config.id2label = {id: label for label, id in config.label2id.items()} - elif data_args.task_name is not None and not is_regression: - model.config.label2id = {l: i for i, l in enumerate(label_list)} - model.config.id2label = {id: label for label, id in config.label2id.items()} - - if data_args.max_seq_length > tokenizer.model_max_length: - logger.warning( - f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the" - f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." - ) - max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length) - - def preprocess_function(examples): - # Tokenize the texts - args = ( - (examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key]) - ) - result = tokenizer(*args, padding=padding, max_length=max_seq_length, truncation=True) - - # Map labels to IDs (not necessary for GLUE tasks) - if label_to_id is not None and "label" in examples: - result["label"] = [(label_to_id[l] if l != -1 else -1) for l in examples["label"]] - return result - - with training_args.main_process_first(desc="dataset map pre-processing"): - raw_datasets = raw_datasets.map( - preprocess_function, - batched=True, - load_from_cache_file=not data_args.overwrite_cache, - desc="Running tokenizer on dataset", - ) - if training_args.do_train: - if "train" not in raw_datasets: - raise ValueError("--do_train requires a train dataset") - train_dataset = raw_datasets["train"] - if data_args.max_train_samples is not None: - max_train_samples = min(len(train_dataset), data_args.max_train_samples) - train_dataset = train_dataset.select(range(max_train_samples)) - - if training_args.do_eval: - if "validation" not in raw_datasets and "validation_matched" not in raw_datasets: - raise ValueError("--do_eval requires a validation dataset") - eval_dataset = raw_datasets["validation_matched" if data_args.task_name == "mnli" else "validation"] - if data_args.max_eval_samples is not None: - max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples) - eval_dataset = eval_dataset.select(range(max_eval_samples)) - - if training_args.do_predict or data_args.task_name is not None or data_args.test_file is not None: - if "test" not in raw_datasets and "test_matched" not in raw_datasets: - raise ValueError("--do_predict requires a test dataset") - predict_dataset = raw_datasets["test_matched" if data_args.task_name == "mnli" else "test"] - if data_args.max_predict_samples is not None: - max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples) - predict_dataset = predict_dataset.select(range(max_predict_samples)) - - # Log a few random samples from the training set: - if training_args.do_train: - for index in random.sample(range(len(train_dataset)), 3): - logger.info(f"Sample {index} of the training set: {train_dataset[index]}.") - - # Get the metric function - if data_args.task_name is not None: - metric = load_metric("glue", data_args.task_name) - else: - metric = load_metric("accuracy") - - # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a - # predictions and label_ids field) and has to return a dictionary string to float. - def compute_metrics(p: EvalPrediction): - preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions - preds = np.squeeze(preds) if is_regression else np.argmax(preds, axis=1) - if data_args.task_name is not None: - result = metric.compute(predictions=preds, references=p.label_ids) - if len(result) > 1: - result["combined_score"] = np.mean(list(result.values())).item() - return result - elif is_regression: - return {"mse": ((preds - p.label_ids) ** 2).mean().item()} - else: - return {"accuracy": (preds == p.label_ids).astype(np.float32).mean().item()} - - # Data collator will default to DataCollatorWithPadding when the tokenizer is passed to Trainer, so we change it if - # we already did the padding. - if data_args.pad_to_max_length: - data_collator = default_data_collator - elif training_args.fp16: - data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8) - else: - data_collator = None - - # Initialize our Trainer - trainer = Trainer( - model=model, - args=training_args, - train_dataset=train_dataset if training_args.do_train else None, - eval_dataset=eval_dataset if training_args.do_eval else None, - compute_metrics=compute_metrics, - tokenizer=tokenizer, - data_collator=data_collator, - ) - - # Training - if training_args.do_train: - checkpoint = None - if training_args.resume_from_checkpoint is not None: - checkpoint = training_args.resume_from_checkpoint - elif last_checkpoint is not None: - checkpoint = last_checkpoint - train_result = trainer.train(resume_from_checkpoint=checkpoint) - metrics = train_result.metrics - max_train_samples = ( - data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset) - ) - metrics["train_samples"] = min(max_train_samples, len(train_dataset)) - - trainer.save_model() # Saves the tokenizer too for easy upload - - trainer.log_metrics("train", metrics) - trainer.save_metrics("train", metrics) - trainer.save_state() - - # Evaluation - if training_args.do_eval: - logger.info("*** Evaluate ***") - - # Loop to handle MNLI double evaluation (matched, mis-matched) - tasks = [data_args.task_name] - eval_datasets = [eval_dataset] - if data_args.task_name == "mnli": - tasks.append("mnli-mm") - eval_datasets.append(raw_datasets["validation_mismatched"]) - combined = {} - - for eval_dataset, task in zip(eval_datasets, tasks): - metrics = trainer.evaluate(eval_dataset=eval_dataset) - - max_eval_samples = ( - data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset) - ) - metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset)) - - if task == "mnli-mm": - metrics = {k + "_mm": v for k, v in metrics.items()} - if task is not None and "mnli" in task: - combined.update(metrics) - - trainer.log_metrics("eval", metrics) - trainer.save_metrics("eval", combined if task is not None and "mnli" in task else metrics) - - if training_args.do_predict: - logger.info("*** Predict ***") - - # Loop to handle MNLI double evaluation (matched, mis-matched) - tasks = [data_args.task_name] - predict_datasets = [predict_dataset] - if data_args.task_name == "mnli": - tasks.append("mnli-mm") - predict_datasets.append(raw_datasets["test_mismatched"]) - - for predict_dataset, task in zip(predict_datasets, tasks): - # Removing the `label` columns because it contains -1 and Trainer won't like that. - predict_dataset = predict_dataset.remove_columns("label") - predictions = trainer.predict(predict_dataset, metric_key_prefix="predict").predictions - predictions = np.squeeze(predictions) if is_regression else np.argmax(predictions, axis=1) - - output_predict_file = os.path.join(training_args.output_dir, f"predict_results_{task}.txt") - if trainer.is_world_process_zero(): - with open(output_predict_file, "w") as writer: - logger.info(f"***** Predict results {task} *****") - writer.write("index\tprediction\n") - for index, item in enumerate(predictions): - if is_regression: - writer.write(f"{index}\t{item:3.3f}\n") - else: - item = label_list[item] - writer.write(f"{index}\t{item}\n") - - kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-classification"} - if data_args.task_name is not None: - kwargs["language"] = "en" - kwargs["dataset_tags"] = "glue" - kwargs["dataset_args"] = data_args.task_name - kwargs["dataset"] = f"GLUE {data_args.task_name.upper()}" - - if training_args.push_to_hub: - trainer.push_to_hub(**kwargs) - else: - trainer.create_model_card(**kwargs) - - -def _mp_fn(index): - # For xla_spawn (TPUs) - main() - - -if __name__ == "__main__": - main() diff --git a/neural_coder/examples/onnx/onnx_model.py b/neural_coder/examples/onnx/onnx_model.py deleted file mode 100644 index 8e2c7b2d928..00000000000 --- a/neural_coder/examples/onnx/onnx_model.py +++ /dev/null @@ -1,211 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import logging -import argparse - -import onnx -import yaml - -from pycocotools.coco import COCO -from pycocotools.mask import iou, encode -import numpy as np -from torchvision import transforms -from PIL import Image -from onnx import numpy_helper -import os -import onnxruntime - -logger = logging.getLogger(__name__) -logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', - datefmt = '%m/%d/%Y %H:%M:%S', - level = logging.WARN) -logger.info("Evaluating ONNXRuntime full precision accuracy and performance:") -parser = argparse.ArgumentParser( - formatter_class=argparse.ArgumentDefaultsHelpFormatter -) -parser.add_argument( - '--model_path', - type=str, - help="Pre-trained model on onnx file" -) -parser.add_argument( - '--label_path', - type=str, - help="Annotation file path" -) -parser.add_argument( - '--data_path', - type=str, - help="Path to val2017 of COCO" -) -parser.add_argument( - '--benchmark', - action='store_true', \ - default=False -) -parser.add_argument( - '--tune', - action='store_true', \ - default=False, - help="whether quantize the model" -) -parser.add_argument( - '--config', - type=str, - help="config yaml path" -) -parser.add_argument( - '--output_model', - type=str, - help="output model path" -) -parser.add_argument( - '--mode', - type=str, - help="benchmark mode of performance or accuracy" -) -args = parser.parse_args() - -# key = COCO id, value = Pascal VOC id -COCO_TO_VOC = { - 1: 15, # person - 2: 2, # bicycle - 3: 7, # car - 4: 14, # motorbike - 5: 1, # airplane - 6: 6, # bus - 7: 19, # train - 9: 4, # boat - 16: 3, # bird - 17: 8, # cat - 18: 12, # dog - 19: 13, # horse - 20: 17, # sheep - 21: 10, # cow - 44: 5, # bottle - 62: 9, # chair - 63: 18, # couch/sofa - 64: 16, # potted plant - 67: 11, # dining table - 72: 20, # tv -} -VOC_CAT_IDS = list(COCO_TO_VOC.keys()) -cocoGt = COCO(str(args.label_path)) - -preprocess = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), -]) - -class Dataset: - def __init__(self): - imgIds = self.getImgIdsUnion(cocoGt, VOC_CAT_IDS) - self.data = [] - for imgId in imgIds: - img_path = os.path.join(args.data_path, cocoGt.imgs[imgId]['file_name']) - if os.path.exists(img_path): - input_tensor = self.load_image(img_path) - - _, height, width = input_tensor.shape - output_tensor = np.zeros((21, height, width), dtype=np.uint8) - - annIds = cocoGt.getAnnIds(imgId, VOC_CAT_IDS) - for ann in cocoGt.loadAnns(annIds): - mask = cocoGt.annToMask(ann) - output_tensor[COCO_TO_VOC[ann['category_id']]] |= mask - - # Set everything not labeled to be background - output_tensor[0] = 1 - np.max(output_tensor, axis=0) - self.data.append((input_tensor, output_tensor)) - - def __len__(self): - return len(self.data) - - def __getitem__(self, index): - return self.data[index] - - def getImgIdsUnion(self, gt, catIds): - """ - Returns all the images that have *any* of the categories in `catIds`, - unlike the built-in `gt.getImgIds` which returns all the images containing - *all* of the categories in `catIds`. - """ - imgIds = set() - for catId in catIds: - imgIds |= set(gt.catToImgs[catId]) - return list(imgIds) - - def load_image(self, img_path): - input_image = Image.open(img_path).convert('RGB') - input_tensor = preprocess(input_image) - input_tensor = input_tensor.detach().cpu().numpy() - return input_tensor - -def iou(model_tensor, target_tensor): - # Don't include the background when summing - model_tensor = model_tensor[:, 1:, :, :] - target_tensor = target_tensor[:, 1:, :, :] - - intersection = np.sum(np.logical_and(model_tensor, target_tensor)) - union = np.sum(np.logical_or(model_tensor, target_tensor)) - - if union == 0: - # Can only happen if nothing was there and nothing was predicted, - # which is a perfect score - return 1 - else: - return intersection / union - -def evaluate(model, dataloader): - totalIoU = 0 - sess = onnxruntime.InferenceSession(model.SerializeToString(), None) - idx = 1 - for input_tensor, target_tensor in dataloader: - input_tensor = input_tensor[np.newaxis, ...] - target_tensor = target_tensor[np.newaxis, ...] - model_tensor = sess.run(["out"], {"input": input_tensor})[0] - - batch_size, nclasses, height, width = model_tensor.shape - raw_labels = np.argmax(model_tensor, axis=1).astype(np.uint8) - - output_tensor = np.zeros((nclasses, batch_size, height, width), dtype=np.uint8) - for c in range(nclasses): - output_tensor[c][raw_labels==c] = 1 - - output_tensor = np.transpose(output_tensor, [1, 0, 2, 3]) - totalIoU += iou(output_tensor, target_tensor) - idx += 1 - return totalIoU / idx - -if __name__ == "__main__": - from neural_compressor.experimental import common - ds = Dataset() - dataloader = common.DataLoader(ds) - model = onnx.load(args.model_path) - def eval(model): - return evaluate(model, ds) - - if args.benchmark and args.mode == "accuracy": - results = eval(model) - print("Batch size = 1") - print("Accuracy: %.5f" % results) - - if args.benchmark and args.mode == "performance": - from neural_compressor.experimental import Benchmark, common - evaluator = Benchmark(args.config) - evaluator.model = common.Model(model) - evaluator.b_dataloader = common.DataLoader(ds) - evaluator(args.mode) diff --git a/neural_coder/examples/vision/alexnet.py b/neural_coder/examples/vision/alexnet.py deleted file mode 100644 index 10ac6d8b080..00000000000 --- a/neural_coder/examples/vision/alexnet.py +++ /dev/null @@ -1,23 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import torch -import torchvision.models as models -model = models.alexnet(pretrained=True) -model.eval() -batch_size = 1 -input = torch.rand(batch_size, 3, 224, 224) -with torch.no_grad(): - model(input) diff --git a/neural_coder/examples/vision/main.py b/neural_coder/examples/vision/main.py deleted file mode 100644 index 3b9bfd65298..00000000000 --- a/neural_coder/examples/vision/main.py +++ /dev/null @@ -1,504 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -# code source -# https://github.com/pytorch/examples/blob/main/imagenet/main.py - -import argparse -import os -import random -import shutil -import time -import warnings -from enum import Enum - -import torch -import torch.nn as nn -import torch.nn.parallel -import torch.backends.cudnn as cudnn -import torch.distributed as dist -import torch.optim -from torch.optim.lr_scheduler import StepLR -import torch.multiprocessing as mp -import torch.utils.data -import torch.utils.data.distributed -import torchvision.transforms as transforms -import torchvision.datasets as datasets -import torchvision.models as models -from torch.utils.data import Subset - -model_names = models.list_models(module=models) - -parser = argparse.ArgumentParser(description='PyTorch ImageNet Training') -parser.add_argument('data', metavar='DIR', nargs='?', default='imagenet', - help='path to dataset (default: imagenet)') -parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18', - choices=model_names, - help='model architecture: ' + - ' | '.join(model_names) + - ' (default: resnet18)') -parser.add_argument('-j', '--workers', default=4, type=int, metavar='N', - help='number of data loading workers (default: 4)') -parser.add_argument('--epochs', default=90, type=int, metavar='N', - help='number of total epochs to run') -parser.add_argument('--start-epoch', default=0, type=int, metavar='N', - help='manual epoch number (useful on restarts)') -parser.add_argument('-b', '--batch-size', default=256, type=int, - metavar='N', - help='mini-batch size (default: 256), this is the total ' - 'batch size of all GPUs on the current node when ' - 'using Data Parallel or Distributed Data Parallel') -parser.add_argument('--lr', '--learning-rate', default=0.1, type=float, - metavar='LR', help='initial learning rate', dest='lr') -parser.add_argument('--momentum', default=0.9, type=float, metavar='M', - help='momentum') -parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float, - metavar='W', help='weight decay (default: 1e-4)', - dest='weight_decay') -parser.add_argument('-p', '--print-freq', default=10, type=int, - metavar='N', help='print frequency (default: 10)') -parser.add_argument('--resume', default='', type=str, metavar='PATH', - help='path to latest checkpoint (default: none)') -parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true', - help='evaluate model on validation set') -parser.add_argument('--pretrained', dest='pretrained', action='store_true', - help='use pre-trained model') -parser.add_argument('--world-size', default=-1, type=int, - help='number of nodes for distributed training') -parser.add_argument('--rank', default=-1, type=int, - help='node rank for distributed training') -parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str, - help='url used to set up distributed training') -parser.add_argument('--dist-backend', default='nccl', type=str, - help='distributed backend') -parser.add_argument('--seed', default=None, type=int, - help='seed for initializing training. ') -parser.add_argument('--gpu', default=None, type=int, - help='GPU id to use.') -parser.add_argument('--multiprocessing-distributed', action='store_true', - help='Use multi-processing distributed training to launch ' - 'N processes per node, which has N GPUs. This is the ' - 'fastest way to use PyTorch for either single node or ' - 'multi node data parallel training') -parser.add_argument('--dummy', action='store_true', help="use fake data to benchmark") - -best_acc1 = 0 - - -def main(): - args = parser.parse_args() - - if args.seed is not None: - random.seed(args.seed) - torch.manual_seed(args.seed) - cudnn.deterministic = True - warnings.warn('You have chosen to seed training. ' - 'This will turn on the CUDNN deterministic setting, ' - 'which can slow down your training considerably! ' - 'You may see unexpected behavior when restarting ' - 'from checkpoints.') - - if args.gpu is not None: - warnings.warn('You have chosen a specific GPU. This will completely ' - 'disable data parallelism.') - - if args.dist_url == "env://" and args.world_size == -1: - args.world_size = int(os.environ["WORLD_SIZE"]) - - args.distributed = args.world_size > 1 or args.multiprocessing_distributed - - ngpus_per_node = torch.cuda.device_count() - if args.multiprocessing_distributed: - # Since we have ngpus_per_node processes per node, the total world_size - # needs to be adjusted accordingly - args.world_size = ngpus_per_node * args.world_size - # Use torch.multiprocessing.spawn to launch distributed processes: the - # main_worker process function - mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args)) - else: - # Simply call main_worker function - main_worker(args.gpu, ngpus_per_node, args) - - -def main_worker(gpu, ngpus_per_node, args): - global best_acc1 - args.gpu = gpu - - if args.gpu is not None: - print("Use GPU: {} for training".format(args.gpu)) - - if args.distributed: - if args.dist_url == "env://" and args.rank == -1: - args.rank = int(os.environ["RANK"]) - if args.multiprocessing_distributed: - # For multiprocessing distributed training, rank needs to be the - # global rank among all the processes - args.rank = args.rank * ngpus_per_node + gpu - dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url, - world_size=args.world_size, rank=args.rank) - # create model - if args.pretrained: - print("=> using pre-trained model '{}'".format(args.arch)) - model = models.__dict__[args.arch](pretrained=True) - else: - print("=> creating model '{}'".format(args.arch)) - model = models.__dict__[args.arch]() - - if not torch.cuda.is_available(): - print('using CPU, this will be slow') - elif args.distributed: - # For multiprocessing distributed, DistributedDataParallel constructor - # should always set the single device scope, otherwise, - # DistributedDataParallel will use all available devices. - if args.gpu is not None: - torch.cuda.set_device(args.gpu) - model.cuda(args.gpu) - # When using a single GPU per process and per - # DistributedDataParallel, we need to divide the batch size - # ourselves based on the total number of GPUs of the current node. - args.batch_size = int(args.batch_size / ngpus_per_node) - args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node) - model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu]) - else: - model.cuda() - # DistributedDataParallel will divide and allocate batch_size to all - # available GPUs if device_ids are not set - model = torch.nn.parallel.DistributedDataParallel(model) - elif args.gpu is not None: - torch.cuda.set_device(args.gpu) - model = model.cuda(args.gpu) - else: - # DataParallel will divide and allocate batch_size to all available GPUs - if args.arch.startswith('alexnet') or args.arch.startswith('vgg'): - model.features = torch.nn.DataParallel(model.features) - model.cuda() - else: - model = torch.nn.DataParallel(model).cuda() - - # define loss function (criterion), optimizer, and learning rate scheduler - criterion = nn.CrossEntropyLoss().cuda(args.gpu) - - optimizer = torch.optim.SGD(model.parameters(), args.lr, - momentum=args.momentum, - weight_decay=args.weight_decay) - - """Sets the learning rate to the initial LR decayed by 10 every 30 epochs""" - scheduler = StepLR(optimizer, step_size=30, gamma=0.1) - - # optionally resume from a checkpoint - if args.resume: - if os.path.isfile(args.resume): - print("=> loading checkpoint '{}'".format(args.resume)) - if args.gpu is None: - checkpoint = torch.load(args.resume) - else: - # Map model to be loaded to specified single gpu. - loc = 'cuda:{}'.format(args.gpu) - checkpoint = torch.load(args.resume, map_location=loc) - args.start_epoch = checkpoint['epoch'] - best_acc1 = checkpoint['best_acc1'] - if args.gpu is not None: - # best_acc1 may be from a checkpoint from a different GPU - best_acc1 = best_acc1.to(args.gpu) - model.load_state_dict(checkpoint['state_dict']) - optimizer.load_state_dict(checkpoint['optimizer']) - scheduler.load_state_dict(checkpoint['scheduler']) - print("=> loaded checkpoint '{}' (epoch {})" - .format(args.resume, checkpoint['epoch'])) - else: - print("=> no checkpoint found at '{}'".format(args.resume)) - - cudnn.benchmark = True - - # Data loading code - if args.dummy: - print("=> Dummy data is used!") - train_dataset = datasets.FakeData(1281167, (3, 224, 224), 1000, transforms.ToTensor()) - val_dataset = datasets.FakeData(50000, (3, 224, 224), 1000, transforms.ToTensor()) - else: - traindir = os.path.join(args.data, 'train') - valdir = os.path.join(args.data, 'val') - normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], - std=[0.229, 0.224, 0.225]) - - train_dataset = datasets.ImageFolder( - traindir, - transforms.Compose([ - transforms.RandomResizedCrop(224), - transforms.RandomHorizontalFlip(), - transforms.ToTensor(), - normalize, - ])) - - val_dataset = datasets.ImageFolder( - valdir, - transforms.Compose([ - transforms.Resize(256), - transforms.CenterCrop(224), - transforms.ToTensor(), - normalize, - ])) - - if args.distributed: - train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) - val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset, shuffle=False, drop_last=True) - else: - train_sampler = None - val_sampler = None - - train_loader = torch.utils.data.DataLoader( - train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None), - num_workers=args.workers, pin_memory=True, sampler=train_sampler) - - val_loader = torch.utils.data.DataLoader( - val_dataset, batch_size=args.batch_size, shuffle=False, - num_workers=args.workers, pin_memory=True, sampler=val_sampler) - - if args.evaluate: - validate(val_loader, model, criterion, args) - return - - for epoch in range(args.start_epoch, args.epochs): - if args.distributed: - train_sampler.set_epoch(epoch) - - # train for one epoch - train(train_loader, model, criterion, optimizer, epoch, args) - - # evaluate on validation set - acc1 = validate(val_loader, model, criterion, args) - - scheduler.step() - - - # remember best acc@1 and save checkpoint - is_best = acc1 > best_acc1 - best_acc1 = max(acc1, best_acc1) - - if not args.multiprocessing_distributed or (args.multiprocessing_distributed - and args.rank % ngpus_per_node == 0): - save_checkpoint({ - 'epoch': epoch + 1, - 'arch': args.arch, - 'state_dict': model.state_dict(), - 'best_acc1': best_acc1, - 'optimizer' : optimizer.state_dict(), - 'scheduler' : scheduler.state_dict() - }, is_best) - - -def train(train_loader, model, criterion, optimizer, epoch, args): - batch_time = AverageMeter('Time', ':6.3f') - data_time = AverageMeter('Data', ':6.3f') - losses = AverageMeter('Loss', ':.4e') - top1 = AverageMeter('Acc@1', ':6.2f') - top5 = AverageMeter('Acc@5', ':6.2f') - progress = ProgressMeter( - len(train_loader), - [batch_time, data_time, losses, top1, top5], - prefix="Epoch: [{}]".format(epoch)) - - # switch to train mode - model.train() - - end = time.time() - for i, (images, target) in enumerate(train_loader): - # measure data loading time - data_time.update(time.time() - end) - - if args.gpu is not None: - images = images.cuda(args.gpu, non_blocking=True) - if torch.cuda.is_available(): - target = target.cuda(args.gpu, non_blocking=True) - - # compute output - output = model(images) - loss = criterion(output, target) - - # measure accuracy and record loss - acc1, acc5 = accuracy(output, target, topk=(1, 5)) - losses.update(loss.item(), images.size(0)) - top1.update(acc1[0], images.size(0)) - top5.update(acc5[0], images.size(0)) - - # compute gradient and do SGD step - optimizer.zero_grad() - loss.backward() - optimizer.step() - - # measure elapsed time - batch_time.update(time.time() - end) - end = time.time() - - if i % args.print_freq == 0: - progress.display(i + 1) - - -def validate(val_loader, model, criterion, args): - - def run_validate(loader, base_progress=0): - with torch.no_grad(): - end = time.time() - for i, (images, target) in enumerate(loader): - i = base_progress + i - if args.gpu is not None: - images = images.cuda(args.gpu, non_blocking=True) - if torch.cuda.is_available(): - target = target.cuda(args.gpu, non_blocking=True) - - # compute output - output = model(images) - loss = criterion(output, target) - - # measure accuracy and record loss - acc1, acc5 = accuracy(output, target, topk=(1, 5)) - losses.update(loss.item(), images.size(0)) - top1.update(acc1[0], images.size(0)) - top5.update(acc5[0], images.size(0)) - - # measure elapsed time - batch_time.update(time.time() - end) - end = time.time() - - if i % args.print_freq == 0: - progress.display(i + 1) - - batch_time = AverageMeter('Time', ':6.3f', Summary.NONE) - losses = AverageMeter('Loss', ':.4e', Summary.NONE) - top1 = AverageMeter('Acc@1', ':6.2f', Summary.AVERAGE) - top5 = AverageMeter('Acc@5', ':6.2f', Summary.AVERAGE) - progress = ProgressMeter( - len(val_loader) + (args.distributed and (len(val_loader.sampler) * args.world_size < len(val_loader.dataset))), - [batch_time, losses, top1, top5], - prefix='Test: ') - - # switch to evaluate mode - model.eval() - - run_validate(val_loader) - if args.distributed: - top1.all_reduce() - top5.all_reduce() - - if args.distributed and (len(val_loader.sampler) * args.world_size < len(val_loader.dataset)): - aux_val_dataset = Subset(val_loader.dataset, - range(len(val_loader.sampler) * args.world_size, len(val_loader.dataset))) - aux_val_loader = torch.utils.data.DataLoader( - aux_val_dataset, batch_size=args.batch_size, shuffle=False, - num_workers=args.workers, pin_memory=True) - run_validate(aux_val_loader, len(val_loader)) - - progress.display_summary() - - return top1.avg - - -def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'): - torch.save(state, filename) - if is_best: - shutil.copyfile(filename, 'model_best.pth.tar') - -class Summary(Enum): - NONE = 0 - AVERAGE = 1 - SUM = 2 - COUNT = 3 - -class AverageMeter(object): - """Computes and stores the average and current value""" - def __init__(self, name, fmt=':f', summary_type=Summary.AVERAGE): - self.name = name - self.fmt = fmt - self.summary_type = summary_type - self.reset() - - def reset(self): - self.val = 0 - self.avg = 0 - self.sum = 0 - self.count = 0 - - def update(self, val, n=1): - self.val = val - self.sum += val * n - self.count += n - self.avg = self.sum / self.count - - def all_reduce(self): - total = torch.FloatTensor([self.sum, self.count]) - dist.all_reduce(total, dist.ReduceOp.SUM, async_op=False) - self.sum, self.count = total.tolist() - self.avg = self.sum / self.count - - def __str__(self): - fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})' - return fmtstr.format(**self.__dict__) - - def summary(self): - fmtstr = '' - if self.summary_type is Summary.NONE: - fmtstr = '' - elif self.summary_type is Summary.AVERAGE: - fmtstr = '{name} {avg:.3f}' - elif self.summary_type is Summary.SUM: - fmtstr = '{name} {sum:.3f}' - elif self.summary_type is Summary.COUNT: - fmtstr = '{name} {count:.3f}' - else: - raise ValueError('invalid summary type %r' % self.summary_type) - - return fmtstr.format(**self.__dict__) - - -class ProgressMeter(object): - def __init__(self, num_batches, meters, prefix=""): - self.batch_fmtstr = self._get_batch_fmtstr(num_batches) - self.meters = meters - self.prefix = prefix - - def display(self, batch): - entries = [self.prefix + self.batch_fmtstr.format(batch)] - entries += [str(meter) for meter in self.meters] - print('\t'.join(entries)) - - def display_summary(self): - entries = [" *"] - entries += [meter.summary() for meter in self.meters] - print(' '.join(entries)) - - def _get_batch_fmtstr(self, num_batches): - num_digits = len(str(num_batches // 1)) - fmt = '{:' + str(num_digits) + 'd}' - return '[' + fmt + '/' + fmt.format(num_batches) + ']' - -def accuracy(output, target, topk=(1,)): - """Computes the accuracy over the k top predictions for the specified values of k""" - with torch.no_grad(): - maxk = max(topk) - batch_size = target.size(0) - - _, pred = output.topk(maxk, 1, True, True) - pred = pred.t() - correct = pred.eq(target.view(1, -1).expand_as(pred)) - - res = [] - for k in topk: - correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True) - res.append(correct_k.mul_(100.0 / batch_size)) - return res - - -if __name__ == '__main__': - main() diff --git a/neural_coder/examples/vision/resnet18.py b/neural_coder/examples/vision/resnet18.py deleted file mode 100644 index a7fadf9c70b..00000000000 --- a/neural_coder/examples/vision/resnet18.py +++ /dev/null @@ -1,23 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import torch -import torchvision.models as models -model = models.resnet18(pretrained=True) -model.eval() -batch_size = 1 -input = torch.rand(batch_size, 3, 224, 224) -with torch.no_grad(): - model(input) diff --git a/neural_coder/examples/vision/resnet50.py b/neural_coder/examples/vision/resnet50.py deleted file mode 100644 index c7091e2e7bc..00000000000 --- a/neural_coder/examples/vision/resnet50.py +++ /dev/null @@ -1,23 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import torch -import torchvision.models as models -model = models.resnet50(pretrained=True) -model.eval() -batch_size = 1 -input = torch.rand(batch_size, 3, 224, 224) -with torch.no_grad(): - model(input) diff --git a/neural_coder/extensions/neural_compressor_ext_lab/.eslintignore b/neural_coder/extensions/neural_compressor_ext_lab/.eslintignore deleted file mode 100644 index fffa32fdf63..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/.eslintignore +++ /dev/null @@ -1,8 +0,0 @@ -node_modules -dist -coverage -**/*.d.ts -tests - -**/__tests__ -ui-tests diff --git a/neural_coder/extensions/neural_compressor_ext_lab/.eslintrc.js b/neural_coder/extensions/neural_compressor_ext_lab/.eslintrc.js deleted file mode 100644 index c64b3721828..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/.eslintrc.js +++ /dev/null @@ -1,44 +0,0 @@ -module.exports = { - extends: [ - 'eslint:recommended', - 'plugin:@typescript-eslint/eslint-recommended', - 'plugin:@typescript-eslint/recommended', - 'plugin:prettier/recommended' - ], - parser: '@typescript-eslint/parser', - parserOptions: { - project: 'tsconfig.json', - sourceType: 'module' - }, - plugins: ['@typescript-eslint'], - rules: { - '@typescript-eslint/naming-convention': [ - 'error', - { - selector: 'interface', - format: ['PascalCase'], - custom: { - regex: '^I[A-Z]', - match: true - } - } - ], - '@typescript-eslint/no-unused-vars': ['warn', { args: 'none' }], - '@typescript-eslint/no-explicit-any': 'off', - '@typescript-eslint/no-namespace': 'off', - '@typescript-eslint/no-use-before-define': 'off', - '@typescript-eslint/quotes': [ - 'error', - 'single', - { avoidEscape: true, allowTemplateLiterals: false } - ], - node: { - fs: 'empty', - net:'empty', - tls:'empty', -}, - curly: ['error', 'all'], - eqeqeq: 'error', - 'prefer-arrow-callback': 'error' - } -}; diff --git a/neural_coder/extensions/neural_compressor_ext_lab/.prettierignore b/neural_coder/extensions/neural_compressor_ext_lab/.prettierignore deleted file mode 100644 index 0de58a6f50b..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/.prettierignore +++ /dev/null @@ -1,5 +0,0 @@ -node_modules -**/node_modules -**/lib -**/package.json -neural_compressor_ext_lab diff --git a/neural_coder/extensions/neural_compressor_ext_lab/.prettierrc b/neural_coder/extensions/neural_compressor_ext_lab/.prettierrc deleted file mode 100644 index d0824a69c14..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/.prettierrc +++ /dev/null @@ -1,6 +0,0 @@ -{ - "singleQuote": true, - "trailingComma": "none", - "arrowParens": "avoid", - "endOfLine": "auto" -} diff --git a/neural_coder/extensions/neural_compressor_ext_lab/.stylelintrc b/neural_coder/extensions/neural_compressor_ext_lab/.stylelintrc deleted file mode 100644 index 0e1ff30327c..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/.stylelintrc +++ /dev/null @@ -1,12 +0,0 @@ -{ - "extends": [ - "stylelint-config-recommended", - "stylelint-config-standard", - "stylelint-prettier/recommended" - ], - "rules": { - "property-no-vendor-prefix": null, - "selector-no-vendor-prefix": null, - "value-no-vendor-prefix": null - } -} diff --git a/neural_coder/extensions/neural_compressor_ext_lab/CHANGELOG.md b/neural_coder/extensions/neural_compressor_ext_lab/CHANGELOG.md deleted file mode 100644 index 2d352af421a..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/CHANGELOG.md +++ /dev/null @@ -1,5 +0,0 @@ -# Changelog - - - - diff --git a/neural_coder/extensions/neural_compressor_ext_lab/DEVELOP.md b/neural_coder/extensions/neural_compressor_ext_lab/DEVELOP.md deleted file mode 100644 index 7141373e783..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/DEVELOP.md +++ /dev/null @@ -1,75 +0,0 @@ -# neural_compressor_ext_lab - -A JupyterLab extension. - -## Requirements - -- JupyterLab >= 3.0 - -## Install - -To install the extension, execute: - -```bash -pip install neural_compressor_ext_lab -``` - -## Uninstall - -To remove the extension, execute: - -```bash -pip uninstall neural_compressor_ext_lab -``` - -## Contributing - -### Development install - -Note: You will need NodeJS to build the extension package. - -The `jlpm` command is JupyterLab's pinned version of -[yarn](https://yarnpkg.com/) that is installed with JupyterLab. You may use -`yarn` or `npm` in lieu of `jlpm` below. - -```bash -# Clone the repo to your local environment -# Change directory to the neural_compressor_ext_lab directory -# Install package in development mode -pip install -e . -# Link your development version of the extension with JupyterLab -jupyter labextension develop . --overwrite -# Rebuild extension Typescript source after making changes -jlpm build -``` - -You can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension. - -```bash -# Watch the source directory in one terminal, automatically rebuilding when needed -jlpm watch -# Run JupyterLab in another terminal -jupyter lab -``` - -With the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt). - -By default, the `jlpm build` command generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command: - -```bash -jupyter lab build --minimize=False -``` - -### Development uninstall - -```bash -pip uninstall neural_compressor_ext_lab -``` - -In development mode, you will also need to remove the symlink created by `jupyter labextension develop` -command. To find its location, you can run `jupyter labextension list` to figure out where the `labextensions` -folder is located. Then you can remove the symlink named `neural_compressor_ext_lab` within that folder. - -### Packaging the extension - -See [RELEASE](RELEASE.md) diff --git a/neural_coder/extensions/neural_compressor_ext_lab/LICENSE b/neural_coder/extensions/neural_compressor_ext_lab/LICENSE deleted file mode 100644 index d3f814da892..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/LICENSE +++ /dev/null @@ -1,29 +0,0 @@ -BSD 3-Clause License - -Copyright (c) 2022, neural_coder -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - -3. Neither the name of the copyright holder nor the names of its - contributors may be used to endorse or promote products derived from - this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE -FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL -DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER -CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/neural_coder/extensions/neural_compressor_ext_lab/MANIFEST.in b/neural_coder/extensions/neural_compressor_ext_lab/MANIFEST.in deleted file mode 100644 index 99115494fae..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/MANIFEST.in +++ /dev/null @@ -1,24 +0,0 @@ -include LICENSE -include *.md -include pyproject.toml - -include package.json -include install.json -include ts*.json -include yarn.lock - -graft neural_compressor_ext_lab/labextension - -# Javascript files -graft src -graft style -prune **/node_modules -prune lib -prune binder - -# Patterns to exclude from any directory -global-exclude *~ -global-exclude *.pyc -global-exclude *.pyo -global-exclude .git -global-exclude .ipynb_checkpoints diff --git a/neural_coder/extensions/neural_compressor_ext_lab/README.md b/neural_coder/extensions/neural_compressor_ext_lab/README.md deleted file mode 100644 index edd9e1e53a6..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/README.md +++ /dev/null @@ -1,45 +0,0 @@ -IntelĀ® Neural Compressor as JupyterLab Extension -=========================== -A JupyterLab Extension library supporting Neural Coder, a novel feature powered by IntelĀ® Neural Compressor providing automatic quantization to further simplify computing performance optimizations of Deep Learning models. - -## Installation -**By Extension Manager in JupyterLab (Recommended)** - -Search for ```jupyter-lab-neural-compressor``` in the Extension Manager in JupyterLab. - -**By Linux Terminal** -```bash -npm i jupyter-lab-neural-compressor -jupyter labextension install jupyter-lab-neural-compressor -``` - -## Getting Started! - -As shown in the drop-down list, the supported features include "INT8 (Static Quantization)", "INT8 (Dynamic Quantization)", "BF16", and "Auto Enable & Benchmark". Each of the first three options enables a specific quantization feature into your Deep Learning scripts. The last option automatically enables all quantization features on a Deep Learning script and automatically evaluates the best performance on the model. It is a code-free solution that can help users enable quantization algorithms on a Deep Learning model with no manual coding needed. - -Architecture - -### Auto-enable a feature -Click the run button on the left side of the drop-down list to start. After finishing, you can see the code changes for the specific optimization enabling as shown in the figure below: - -Architecture - -### Or let us help you auto-select the best feature -The last option automatically enables each quantization feature on your Deep Learning script and automatically evaluates for the best performance among all features on your Deep Learning model. Since it will automatically run the Python script for benchmark, it requires you to enter additional parameters needed to run your Python script. If there is no additional parameter needed, you can just leave it blank: - -Architecture - -In the new cell box appeared below your Code cell boxes, you can see the execution progress, and at the end you can see which one turns out to be the best optimization and how much performance gain can it bring to your Deep Learning model: - -Architecture - -When it is finished, you can also see that the code changes for the best optimization are automatically enabled into your script: - -Architecture - -## Pre-requisites -```bash -apt-get update && apt-get install bc numactl -conda install mkl mkl-include jemalloc -pip3 install neural-compressor opencv-python-headless -``` \ No newline at end of file diff --git a/neural_coder/extensions/neural_compressor_ext_lab/RELEASE.md b/neural_coder/extensions/neural_compressor_ext_lab/RELEASE.md deleted file mode 100644 index bd51b356f28..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/RELEASE.md +++ /dev/null @@ -1,61 +0,0 @@ -# Making a new release of neural_compressor_ext_lab - -The extension can be published to `PyPI` and `npm` manually or using the [Jupyter Releaser](https://github.com/jupyter-server/jupyter_releaser). - -## Manual release - -### Python package - -This extension can be distributed as Python -packages. All of the Python -packaging instructions in the `pyproject.toml` file to wrap your extension in a -Python package. Before generating a package, we first need to install `build`. - -```bash -pip install build twine -``` - -To create a Python source package (`.tar.gz`) and the binary package (`.whl`) in the `dist/` directory, do: - -```bash -python -m build -``` - -> `python setup.py sdist bdist_wheel` is deprecated and will not work for this package. - -Then to upload the package to PyPI, do: - -```bash -twine upload dist/* -``` - -### NPM package - -To publish the frontend part of the extension as a NPM package, do: - -```bash -npm login -npm publish --access public -``` - -## Automated releases with the Jupyter Releaser - -The extension repository should already be compatible with the Jupyter Releaser. - -Check out the [workflow documentation](https://github.com/jupyter-server/jupyter_releaser#typical-workflow) for more information. - -Here is a summary of the steps to cut a new release: - -- Fork the [`jupyter-releaser` repo](https://github.com/jupyter-server/jupyter_releaser) -- Add `ADMIN_GITHUB_TOKEN`, `PYPI_TOKEN` and `NPM_TOKEN` to the Github Secrets in the fork -- Go to the Actions panel -- Run the "Draft Changelog" workflow -- Merge the Changelog PR -- Run the "Draft Release" workflow -- Run the "Publish Release" workflow - -## Publishing to `conda-forge` - -If the package is not on conda forge yet, check the documentation to learn how to add it: https://conda-forge.org/docs/maintainer/adding_pkgs.html - -Otherwise a bot should pick up the new version publish to PyPI, and open a new PR on the feedstock repository automatically. diff --git a/neural_coder/extensions/neural_compressor_ext_lab/binder/environment.yml b/neural_coder/extensions/neural_compressor_ext_lab/binder/environment.yml deleted file mode 100644 index 23bed128c24..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/binder/environment.yml +++ /dev/null @@ -1,21 +0,0 @@ -# a mybinder.org-ready environment for demoing neural_compressor_ext_lab -# this environment may also be used locally on Linux/MacOS/Windows, e.g. -# -# conda env update --file binder/environment.yml -# conda activate neural-compressor-ext-lab-demo -# -name: neural-compressor-ext-lab-demo - -channels: - - conda-forge - -dependencies: - # runtime dependencies - - python >=3.8,<3.9.0a0 - - jupyterlab >=3,<4.0.0a0 - # labextension build dependencies - - nodejs >=14,<15 - - pip - - wheel - # additional packages for demos - # - ipywidgets diff --git a/neural_coder/extensions/neural_compressor_ext_lab/binder/postBuild b/neural_coder/extensions/neural_compressor_ext_lab/binder/postBuild deleted file mode 100644 index 95eabd91874..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/binder/postBuild +++ /dev/null @@ -1,47 +0,0 @@ -#!/usr/bin/env python3 -""" perform a development install of neural_compressor_ext_lab - - On Binder, this will run _after_ the environment has been fully created from - the environment.yml in this directory. - - This script should also run locally on Linux/MacOS/Windows: - - python3 binder/postBuild -""" -import subprocess -import sys -from pathlib import Path - - -ROOT = Path.cwd() - -def _(*args, **kwargs): - """ Run a command, echoing the args - - fails hard if something goes wrong - """ - print("\n\t", " ".join(args), "\n") - return_code = subprocess.call(args, **kwargs) - if return_code != 0: - print("\nERROR", return_code, " ".join(args)) - sys.exit(return_code) - -# verify the environment is self-consistent before even starting -_(sys.executable, "-m", "pip", "check") - -# install the labextension -_(sys.executable, "-m", "pip", "install", "-e", ".") -_(sys.executable, "-m", "jupyter", "labextension", "develop", "--overwrite", ".") - -# verify the environment the extension didn't break anything -_(sys.executable, "-m", "pip", "check") - -# list the extensions -_("jupyter", "server", "extension", "list") - -# initially list installed extensions to determine if there are any surprises -_("jupyter", "labextension", "list") - - -print("JupyterLab with neural_compressor_ext_lab is ready to run with:\n") -print("\tjupyter lab\n") diff --git a/neural_coder/extensions/neural_compressor_ext_lab/install.json b/neural_coder/extensions/neural_compressor_ext_lab/install.json deleted file mode 100644 index 24f9a58e094..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/install.json +++ /dev/null @@ -1,5 +0,0 @@ -{ - "packageManager": "python", - "packageName": "neural_compressor_ext_lab", - "uninstallInstructions": "Use your Python package manager (pip, conda, etc.) to uninstall the package neural_compressor_ext_lab" -} diff --git a/neural_coder/extensions/neural_compressor_ext_lab/lib/constants.d.ts b/neural_coder/extensions/neural_compressor_ext_lab/lib/constants.d.ts deleted file mode 100644 index 071e86ae2c1..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/lib/constants.d.ts +++ /dev/null @@ -1,11 +0,0 @@ -export declare namespace Constants { - const SHORT_PLUGIN_NAME = "neural_compressor_ext_lab"; - const WORK_PATH = "neural_coder_workspace/"; - const ICON_FORMAT_ALL_SVG = ""; - const ICON_RUN = ""; - const SVG = ""; - const LONG_PLUGIN_NAME: string; - const SETTINGS_SECTION: string; - const COMMAND_SECTION_NAME = "Jupyterlab Code Optimizer"; - const PLUGIN_VERSION = "0.1.0"; -} diff --git a/neural_coder/extensions/neural_compressor_ext_lab/lib/constants.js b/neural_coder/extensions/neural_compressor_ext_lab/lib/constants.js deleted file mode 100644 index 13acd1a7ad3..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/lib/constants.js +++ /dev/null @@ -1,12 +0,0 @@ -export var Constants; -(function (Constants) { - Constants.SHORT_PLUGIN_NAME = 'neural_compressor_ext_lab'; - Constants.WORK_PATH = "neural_coder_workspace/"; - Constants.ICON_FORMAT_ALL_SVG = ''; - Constants.ICON_RUN = ''; - Constants.SVG = ''; - Constants.LONG_PLUGIN_NAME = `@rya/${Constants.SHORT_PLUGIN_NAME}`; - Constants.SETTINGS_SECTION = `${Constants.LONG_PLUGIN_NAME}:settings`; - Constants.COMMAND_SECTION_NAME = 'Jupyterlab Code Optimizer'; - Constants.PLUGIN_VERSION = '0.1.0'; -})(Constants || (Constants = {})); diff --git a/neural_coder/extensions/neural_compressor_ext_lab/lib/deepcoder.d.ts b/neural_coder/extensions/neural_compressor_ext_lab/lib/deepcoder.d.ts deleted file mode 100644 index dcf8ddfba2a..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/lib/deepcoder.d.ts +++ /dev/null @@ -1,27 +0,0 @@ -import { Cell, CodeCell } from '@jupyterlab/cells'; -import { ToolbarButton } from '@jupyterlab/apputils'; -import { Widget } from '@lumino/widgets'; -import { INotebookTracker, NotebookPanel, Notebook } from '@jupyterlab/notebook'; -declare class JupyterlabCodeOptimizer { - protected working: boolean; - protected panel: NotebookPanel; - private tmp_path; - log_path: string; - tmp_log_path: string; - rand: number; - markdown: Cell | undefined; - cells: CodeCell[]; - constructor(panel: NotebookPanel); - optimizeCode(code: string[], formatter: string, name: string, next: string, options: string | undefined, notebook: boolean, panel: NotebookPanel, cell: CodeCell, run?: ToolbarButton | undefined): Promise; -} -export declare class JupyterlabNotebookCodeOptimizer extends JupyterlabCodeOptimizer { - protected notebookname: string; - protected notebookTracker: INotebookTracker; - constructor(notebookTracker: INotebookTracker, panel: NotebookPanel); - optimizeAction(config: any, formatter?: string): Promise; - optimizeAllCodeCells(config?: string, formatter?: string, notebook?: Notebook, run?: ToolbarButton): Promise; - private getCodeCells; - private optimizeCells; - applicable(formatter: string, currentWidget: Widget): boolean | null; -} -export {}; diff --git a/neural_coder/extensions/neural_compressor_ext_lab/lib/deepcoder.js b/neural_coder/extensions/neural_compressor_ext_lab/lib/deepcoder.js deleted file mode 100644 index 744d7a7a8e3..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/lib/deepcoder.js +++ /dev/null @@ -1,295 +0,0 @@ -import { NotebookActions } from '@jupyterlab/notebook'; -import NotebookUtilities from "./utils"; -import { Constants } from './constants'; -class JupyterlabCodeOptimizer { - constructor(panel) { - this.working = false; - this.panel = panel; - this.tmp_path = "tmp.py"; - this.rand = NotebookUtilities.GetRandomNum(0, 200); - this.log_path = Constants.WORK_PATH + "NeuralCoder" + this.rand + ".log"; - this.tmp_log_path = Constants.WORK_PATH + "NeuralCoder_tmp" + ".log"; - this.cells = []; - } - async optimizeCode(code, formatter, name, next, options, notebook, panel, cell, run) { - let codes = []; - code.forEach(function (value) { - value = value.replace(/('\\n')/g, '^^^'); - value = value.replace(/\\n"/g, '###'); - value = value.replace(/\\n'/g, '###'); - value = value.replace(/"\\n/g, '@@'); - value = value.replace(/'\\n/g, '@@'); - value = value.replace(/\n/g, '\\n'); - value = value.replace(/"/g, '+++'); - value = value.replace(/,/g, '$'); - codes.push(value); - }); - let gen_code = `code = "${codes}"\ncodes = code.split(',')\nwith open( '${this.tmp_path}', 'w+' ) as f:\n for i in range(0,len(codes)):\n f.write('# this is the beginning of a single code snippet\\n')\n code_list = codes[i].replace('$',',').replace('+++','\"').split('\\n')\n for line in code_list:\n if('split(^^^)' in line):\n line=line.replace('split(^^^)', 'split(\\'\\\\n\\')')\n if('###' in line):\n line=line.replace('###', '\\\\n\"')\n if('@@' in line):\n line=line.replace('@@', '\"\\\\n')\n f.write(line+'\\n')`; - const expr = { code_list: `code_list` }; - NotebookUtilities.sendKernelRequestFromNotebook(panel, gen_code, expr, false); - if (options === 'normal') { - let runcode = `from neural_coder import enable\nenable(code="${this.tmp_path}",features=["${formatter}"], overwrite=True)`; - let expr = { sum: ` ` }; - NotebookUtilities.sendKernelRequestFromNotebook(panel, runcode, expr, false); - let run_code1 = `with open("${this.tmp_path}", 'r') as f:\n optimized_code = f.read()\n`; - let expr1 = { optimizedCode: "optimized_code" }; - let result2 = NotebookUtilities.sendKernelRequestFromNotebook(panel, run_code1, expr1, false); - result2.then(value => { - var _a, _b, _c, _d; - let optimizedTexts = Object.values(value.optimizedCode.data)[0]; - let optimizeCodes = optimizedTexts.split('# this is the beginning of a single code snippet\\n').slice(1); - optimizeCodes[optimizeCodes.length - 1] = optimizeCodes[optimizeCodes.length - 1].slice(0, -3); - for (let i = 0; i < optimizeCodes.length; ++i) { - const cell = this.cells[i]; - const currentTexts = this.cells.map(cell => cell.model.value.text); - const currentText = currentTexts[i]; - let optimizedtext = optimizeCodes[i]; - optimizedtext = optimizedtext.replace(/\\'\\\\n\\'/g, "^^^"); - optimizedtext = optimizedtext.replace(/\\\\n"/g, "+++"); - optimizedtext = optimizedtext.replace(/\\\\n'/g, "+++"); - optimizedtext = optimizedtext.replace(/"\\\\n/g, "@@@"); - optimizedtext = optimizedtext.replace(/'\\\\n/g, "@@@"); - optimizedtext = optimizedtext.replace(/\\n/g, '\n'); - optimizedtext = optimizedtext.replace(/\\'/g, "'"); - optimizedtext = optimizedtext.replace(/\^\^\^/g, "'\\n'"); - optimizedtext = optimizedtext.replace(/\+\+\+/g, "\\n\""); - optimizedtext = optimizedtext.replace(/\@\@\@/g, "\"\\n"); - if (cell.model.value.text === currentText) { - cell.model.value.text = optimizedtext; - } - const run_svg = document.createElement("svg"); - run_svg.innerHTML = Constants.ICON_RUN; - (_d = (_c = (_b = (_a = run === null || run === void 0 ? void 0 : run.node.firstChild) === null || _a === void 0 ? void 0 : _a.firstChild) === null || _b === void 0 ? void 0 : _b.firstChild) === null || _c === void 0 ? void 0 : _c.firstChild) === null || _d === void 0 ? void 0 : _d.replaceWith(run_svg); - } - }); - } - else { - if (formatter === '') { - if (this.markdown) { - this.markdown.model.value.text += "[NeuralCoder INFO] Enabling and Benchmarking for The Original Model ...... \n"; - } - // cell.outputArea.node.innerText += "[NeuralCoder INFO] Enabling and Benchmarking for The Original Model ......\n" - let runcode1 = `with open("${this.log_path}", 'a' ) as f:\n f.write("[NeuralCoder INFO] Enabling and Benchmarking for The Original Model ......\\n")`; - let expr1 = { path: "" }; - NotebookUtilities.sendKernelRequestFromNotebook(panel, runcode1, expr1, false); - let runcode = `from neural_coder import enable\nperfomance, mode, path = enable(code="${this.tmp_path}",features=[], run_bench=True, args="${options}")\nwith open(path + '/bench.log', 'r') as f:\n logs = f.readlines()\nlog_line = logs[4]\nlog = log_line.split("[")[1].split("]")[0]`; - let expr = { path: "path", log: "log" }; - let result = NotebookUtilities.sendKernelRequestFromNotebook(panel, runcode, expr, false); - let fps; - result.then(value => { - fps = Object.values(value.log.data)[0]; - if (this.markdown) { - this.markdown.model.value.text += `[NeuralCoder INFO] Benchmark Result (Performance) of The Original Model is ${fps} (samples/second) \n`; - } - // cell.outputArea.node.innerText += `[NeuralCoder INFO] Benchmark Result (Performance) of The Original Model is ${fps} (samples/second)\n` - let text = `[NeuralCoder INFO] Benchmark Result (Performance) of The Original Model is ${fps} (samples/second)\\n`; - let runcode = `with open("${this.log_path}", 'a' ) as f:\n f.write("${text}")`; - let expr = { path: "" }; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode, expr, false); - if (this.markdown) { - this.markdown.model.value.text += `[NeuralCoder INFO] Enabling and Benchmarking for ${next} ...... \n`; - } - // cell.outputArea.node.innerText += `[NeuralCoder INFO] Enabling and Benchmarking for ${next} ......\n` - let runcode1 = `with open("${this.log_path}", 'a' ) as f:\n f.write("[NeuralCoder INFO] Enabling and Benchmarking for ${next} ......\\n")`; - let expr1 = { path: "" }; - NotebookUtilities.sendKernelRequestFromNotebook(panel, runcode1, expr1, false); - let runcode2 = `with open("${this.tmp_log_path}", 'a' ) as f:\n f.write("${text}")`; - let expr2 = { path: "" }; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode2, expr2, false); - }); - } - else { - let runcode = `from neural_coder import enable\nperfomance, mode, path = enable(code="${this.tmp_path}", features=["${formatter}"], run_bench=True, args="${options}")\nwith open(path + '/bench.log', 'r') as f:\n logs = f.readlines()\nlog_line = logs[4]\nlog = log_line.split("[")[1].split("]")[0]`; - let expr = { path: "path", log: "log" }; - let result = NotebookUtilities.sendKernelRequestFromNotebook(panel, runcode, expr, false); - let fps; - result.then(value => { - fps = Object.values(value.log.data)[0]; - if (this.markdown) { - this.markdown.model.value.text += `[NeuralCoder INFO] Benchmark Result (Performance) of ${name} is ${fps} (samples/second) \n`; - } - // cell.outputArea.node.innerText += `[NeuralCoder INFO] Benchmark Result (Performance) of ${name} is ${fps} (FPS)\n` - let text = `[NeuralCoder INFO] Benchmark Result (Performance) of ${name} is ${fps} (samples/second)\\n`; - let runcode = `with open("${this.log_path}", 'a' ) as f:\n f.write("${text}")`; - let expr = { path: "" }; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode, expr, false); - if (next !== '') { - if (this.markdown) { - this.markdown.model.value.text += `[NeuralCoder INFO] Enabling and Benchmarking for ${next} ...... \n`; - } - // cell.outputArea.node.innerText += `[NeuralCoder INFO] Enabling and Benchmarking for ${next} ......\n` - let runcode2 = `with open("${this.log_path}", 'a' ) as f:\n f.write("[NeuralCoder INFO] Enabling and Benchmarking for ${next} ......\\n")`; - let expr2 = { path: "" }; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode2, expr2, false); - } - let runcode3 = `with open("${this.tmp_log_path}", 'a' ) as f:\n f.write("${text}")`; - let expr3 = { path: "" }; - let res_tmp = NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode3, expr3, false); - res_tmp.then(value => { - if (formatter === 'pytorch_inc_bf16') { - let read_log = `import re\nwith open("${this.tmp_log_path}", 'r') as f:\n logs = f.readlines()\n fps_list=[]\n for log_line in logs[-4:]:\n pat = re.compile(r\'\\d+\\.?\\d+')\n fps = re.findall(pat,log_line)[-1]\n fps_list.append(float(fps))\nmaxi = max(fps_list)\nindex = fps_list.index(maxi)\nboost = round(maxi/fps_list[0],1)\nfeatures=['','pytorch_inc_static_quant_fx','pytorch_inc_dynamic_quant','pytorch_inc_bf16']\nfeature_name=['Original Model','INC Enable INT8 (Static)','INC Enable INT8 (Dynamic)','INC Enable BF16']\nbest_feature = features[index]\nbest_name = feature_name[index]\nfeature_l = []\nfeature_l.append(best_feature)\nfrom neural_coder import enable\nenable(code="${this.tmp_path}",features=feature_l, overwrite=True)\nwith open("${this.tmp_path}", 'r') as f:\n optimized_code = f.read()\n`; - let read_expr = { boost: "boost", best_feature: "best_feature", best_name: "best_name", optimizeCode: "optimized_code", feature_l: "fps_list", maxi: "maxi", index: "index" }; - let read_result = NotebookUtilities.sendKernelRequestFromNotebook(this.panel, read_log, read_expr, false); - read_result.then(value => { - var _a, _b, _c, _d; - console.log("resres", value); - let boost = Object.values(value.boost.data)[0]; - let best_name = Object.values(value.best_name.data)[0]; - let optimizedTexts = Object.values(value.optimizeCode.data)[0]; - let optimizeCodes = optimizedTexts.split('# this is the beginning of a single code snippet\\n').slice(1); - if (this.markdown) { - this.markdown.model.value.text += `[NeuralCoder INFO] The Best Intel Optimization: ${best_name} \n`; - this.markdown.model.value.text += `[NeuralCoder INFO] You can get up to ${boost}X performance boost. \n`; - } - // cell.outputArea.node.innerText +=`[NeuralCoder INFO] The Best Intel Optimization: ${best_name}\n` - // cell.outputArea.node.innerText += `[NeuralCoder INFO] You can get up to ${boost}X performance boost.\n` - optimizeCodes[optimizeCodes.length - 1] = optimizeCodes[optimizeCodes.length - 1].slice(0, -3); - for (let i = 0; i < optimizeCodes.length; ++i) { - const cell = this.cells[i]; - const currentTexts = this.cells.map(cell => cell.model.value.text); - const currentText = currentTexts[i]; - let optimizedtext = optimizeCodes[i]; - optimizedtext = optimizedtext.replace(/\\'\\\\n\\'/g, "^^^"); - optimizedtext = optimizedtext.replace(/\\\\n"/g, "+++"); - optimizedtext = optimizedtext.replace(/\\\\n'/g, "+++"); - optimizedtext = optimizedtext.replace(/"\\\\n/g, "@@@"); - optimizedtext = optimizedtext.replace(/'\\\\n/g, "@@@"); - optimizedtext = optimizedtext.replace(/\\n/g, '\n'); - optimizedtext = optimizedtext.replace(/\\'/g, "'"); - optimizedtext = optimizedtext.replace(/\^\^\^/g, "'\\n'"); - optimizedtext = optimizedtext.replace(/\+\+\+/g, "\\n\""); - optimizedtext = optimizedtext.replace(/\@\@\@/g, "\"\\n"); - if (cell.model.value.text === currentText) { - cell.model.value.text = optimizedtext; - } - } - // if(this.markdown){ - // this.markdown.model.value.text += `[NeuralCoder INFO] HardWare: 4th Gen Intel Xeon Scalable processor with AMX \n` - // this.markdown.model.value.text += `[NeuralCoder INFO] The log was saved to neural_coder_workspace\\NeuralCoder${this.rand}.log \n` - // } - let command = "lscpu | grep 'Model name'"; - let get_hardware = `import subprocess\nsubp = subprocess.Popen("${command}",shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE,encoding="utf-8")\nsubp.wait(2)\nhardware = subp.communicate()[0].replace("Model name:","").strip()`; - let expr_hardware = { hardware: "hardware" }; - let hard_res = NotebookUtilities.sendKernelRequestFromNotebook(this.panel, get_hardware, expr_hardware, false); - hard_res.then(value => { - let hard = Object.values(value.hardware.data)[0]; - if (this.markdown) { - this.markdown.model.value.text += `[NeuralCoder INFO] HardWare: ${hard} \n`; - this.markdown.model.value.text += `[NeuralCoder INFO] The log was saved to neural_coder_workspace\\NeuralCoder${this.rand}.log \n`; - } - // cell.outputArea.node.innerText += `[NeuralCoder INFO] HardWare: ${hard}\n` - }); - // cell.outputArea.node.innerText += `[NeuralCoder INFO] The log was saved to neural_coder_workspace\\NeuralCoder${this.rand}.log\n` - const run_svg = document.createElement("svg"); - run_svg.innerHTML = Constants.ICON_RUN; - (_d = (_c = (_b = (_a = run === null || run === void 0 ? void 0 : run.node.firstChild) === null || _a === void 0 ? void 0 : _a.firstChild) === null || _b === void 0 ? void 0 : _b.firstChild) === null || _c === void 0 ? void 0 : _c.firstChild) === null || _d === void 0 ? void 0 : _d.replaceWith(run_svg); - }); - } - }); - }); - } - } - } -} -export class JupyterlabNotebookCodeOptimizer extends JupyterlabCodeOptimizer { - constructor(notebookTracker, panel) { - super(panel); - this.notebookTracker = notebookTracker; - this.notebookname = ''; - } - async optimizeAction(config, formatter) { - return this.optimizeCells(true, config, formatter); - } - async optimizeAllCodeCells(config, formatter, notebook, run) { - return this.optimizeCells(false, config, formatter, notebook, run); - } - getCodeCells(ifmarkdown = true, notebook) { - if (!this.notebookTracker.currentWidget) { - return []; - } - const codeCells = []; - notebook = notebook || this.notebookTracker.currentWidget.content; - this.notebookname = notebook.title.label; - let count = 0; - notebook.widgets.forEach((cell) => { - if (cell.model.type === 'code') { - count += 1; - codeCells.push(cell); - } - }); - if (ifmarkdown) { - NotebookActions.insertBelow(notebook); - this.notebookTracker.currentWidget.content.activeCellIndex = count + 1; - NotebookActions.changeCellType(notebook, 'markdown'); - const activeCell = notebook.activeCell; - if (activeCell) { - this.markdown = activeCell; - } - } - this.cells = codeCells; - return codeCells; - } - async optimizeCells(selectedOnly, config, formatter, notebook, run) { - if (this.working) { - return new Promise((resolve, reject) => { - resolve("false!"); - }); - } - console.log("arrive here 333"); - this.working = true; - const optimize_type = formatter !== undefined ? formatter : 'pytorch_mixed_precision_cpu'; - if (optimize_type === 'auto-quant') { - selectedOnly = true; - } - else { - selectedOnly = false; - } - const selectedCells = this.getCodeCells(selectedOnly, notebook); - let cell = selectedCells[selectedCells.length - 1]; - if (selectedCells.length === 0) { - this.working = false; - return new Promise((resolve, reject) => { - resolve("false!"); - }); - } - const currentTexts = selectedCells.map(cell => cell.model.value.text); - if (optimize_type === 'auto-quant') { - console.log("arrive here 444-111"); - if (this.markdown) { - this.markdown.model.value.text = `[NeuralCoder INFO] Auto-Quant Started ...... \n`; - this.markdown.model.value.text += `[NeuralCoder INFO] Code: User code from Jupyter Lab notebook "${this.notebookname}" \n`; - this.markdown.model.value.text += `[NeuralCoder INFO] Benchmark Mode: Throughput \n`; - } - // cell.outputArea.node.innerText = `[NeuralCoder INFO] Auto-Quant Started ......\n` - // cell.outputArea.node.innerText += `[NeuralCoder INFO] Code: User code from Jupyter Lab notebook "${this.notebookname}"\n` - // cell.outputArea.node.innerText += `[NeuralCoder INFO] Benchmark Mode: Throughput\n` - let runcode = `with open('${this.log_path}', 'a' ) as f:\n f.write("[NeuralCoder INFO] Auto-Quant Started ......\\n")`; - let expr = { path: "" }; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode, expr, false); - let runcode2 = `with open('${this.log_path}', 'a' ) as f:\n f.write("[NeuralCoder INFO] Code: User code from Jupyter Lab notebook '${this.notebookname}'\\n")`; - let expr2 = { path: "" }; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode2, expr2, false); - let runcode3 = `with open('${this.log_path}', 'a' ) as f:\n f.write("[NeuralCoder INFO] Benchmark Mode: Throughput\\n")`; - let expr3 = { path: "" }; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode3, expr3, false); - // cell.outputArea.node.setAttribute("class","pad") - await this.optimizeCode(currentTexts, '', 'The Original Model', 'INC Enable INT8 (Static)', config, true, this.panel, cell, run); - await this.optimizeCode(currentTexts, 'pytorch_inc_static_quant_fx', 'INC Enable INT8 (Static)', 'INC Enable INT8 (Dynamic)', config, true, this.panel, cell, run); - await this.optimizeCode(currentTexts, 'pytorch_inc_dynamic_quant', 'INC Enable INT8 (Dynamic)', 'INC Enable BF16', config, true, this.panel, cell, run); - await this.optimizeCode(currentTexts, 'pytorch_inc_bf16', 'INC Enable BF16', '', config, true, this.panel, cell, run); - } - else { - console.log("arrive here 444-222"); - await this.optimizeCode(currentTexts, optimize_type, "", "", "normal", true, this.panel, cell, run); - } - this.working = false; - console.log("arrive here 555"); - return new Promise((resolve, reject) => { - resolve("success!"); - }); - } - applicable(formatter, currentWidget) { - const currentNotebookWidget = this.notebookTracker.currentWidget; - return currentNotebookWidget && currentWidget === currentNotebookWidget; - } -} diff --git a/neural_coder/extensions/neural_compressor_ext_lab/lib/index.d.ts b/neural_coder/extensions/neural_compressor_ext_lab/lib/index.d.ts deleted file mode 100644 index f256eada9ba..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/lib/index.d.ts +++ /dev/null @@ -1,6 +0,0 @@ -import { JupyterFrontEndPlugin } from '@jupyterlab/application'; -/** - * Initialization data for the neural_compressor_ext_lab extension. - */ -declare const plugin: JupyterFrontEndPlugin; -export default plugin; diff --git a/neural_coder/extensions/neural_compressor_ext_lab/lib/index.js b/neural_coder/extensions/neural_compressor_ext_lab/lib/index.js deleted file mode 100644 index 190b66e0840..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/lib/index.js +++ /dev/null @@ -1,105 +0,0 @@ -import { INotebookTracker } from '@jupyterlab/notebook'; -import { ToolbarButton, showDialog, Dialog } from '@jupyterlab/apputils'; -import { ISettingRegistry } from '@jupyterlab/settingregistry'; -import { IMainMenu } from '@jupyterlab/mainmenu'; -import { LabIcon } from '@jupyterlab/ui-components'; -import { Widget } from '@lumino/widgets'; -import { JupyterlabNotebookCodeOptimizer } from './deepcoder'; -import { Constants } from './constants'; -class neural_compressor_ext_lab { - constructor(app, tracker, notebookpanel) { - this.app = app; - this.tracker = tracker; - this.notebookpanel = notebookpanel; - this.setupWidgetExtension(); - this.config = ''; - } - createNew(nb) { - this.notebookpanel = nb; - this.notebookCodeOptimizer = new JupyterlabNotebookCodeOptimizer(this.tracker, this.notebookpanel); - const svg = document.createElement("svg"); - svg.innerHTML = Constants.ICON_FORMAT_ALL_SVG; - const run_svg = document.createElement("svg"); - run_svg.innerHTML = Constants.ICON_RUN; - const div = document.createElement("div"); - div.setAttribute("class", "wrapper"); - const span = document.createElement("span"); - span.setAttribute("class", "f1ozlkqi"); - span.innerHTML = Constants.SVG; - const selector = document.createElement("select"); - selector.setAttribute("class", "aselector"); - selector.id = "NeuralCoder"; - const option1 = document.createElement("option"); - option1.value = "pytorch_inc_static_quant_fx"; - option1.innerText = "INC Enable INT8 (Static)"; - option1.selected = true; - const option2 = document.createElement("option"); - option2.value = "pytorch_inc_dynamic_quant"; - option2.innerText = "INC Enable INT8 (Dynamic)"; - const option3 = document.createElement("option"); - option3.value = "pytorch_inc_bf16"; - option3.innerText = "INC Enable BF16"; - const option4 = document.createElement("option"); - option4.value = "auto-quant"; - option4.innerText = "INC Auto Enable & Benchmark"; - selector.options.add(option1); - selector.options.add(option2); - selector.options.add(option3); - selector.options.add(option4); - div.appendChild(selector); - div.appendChild(span); - const selector_widget = new Widget(); - selector_widget.node.appendChild(div); - selector_widget.addClass("aselector"); - let notebookCodeOptimizer = this.notebookCodeOptimizer; - let config = this.config; - const dia_input = document.createElement("input"); - const dia_widget = new Widget(); - dia_widget.node.appendChild(dia_input); - dia_widget.addClass("dialog"); - const run_button = new ToolbarButton({ - tooltip: 'NeuralCoder', - icon: new LabIcon({ - name: "run", - svgstr: Constants.ICON_RUN - }), - onClick: async function () { - var _a, _b, _c, _d; - console.log("arrive here 111"); - (_d = (_c = (_b = (_a = run_button.node.firstChild) === null || _a === void 0 ? void 0 : _a.firstChild) === null || _b === void 0 ? void 0 : _b.firstChild) === null || _c === void 0 ? void 0 : _c.firstChild) === null || _d === void 0 ? void 0 : _d.replaceWith(svg); - if (selector.options[selector.selectedIndex].value === 'auto-quant') { - await showDialog({ - title: 'Please input execute parameters:', - body: dia_widget, - buttons: [Dialog.okButton({ label: 'Confirm' })] - }).then(result => { - if (result.button.accept) { - config = dia_input.value; - } - }); - } - console.log("arrive here 222"); - await notebookCodeOptimizer.optimizeAllCodeCells(config, selector.options[selector.selectedIndex].value, undefined, run_button); - } - }); - nb.toolbar.insertItem(11, "nc", run_button); - nb.toolbar.insertItem(12, "selector", selector_widget); - } - setupWidgetExtension() { - this.app.docRegistry.addWidgetExtension('Notebook', this); - } -} -/** - * Initialization data for the neural_compressor_ext_lab extension. - */ -const plugin = { - id: 'neural_compressor_ext_lab:plugin', - autoStart: true, - requires: [INotebookTracker, IMainMenu], - optional: [ISettingRegistry], - activate: (app, tracker, notebookpanel) => { - new neural_compressor_ext_lab(app, tracker, notebookpanel); - console.log('JupyterLab extension neural_compressor_ext_lab is activated!'); - } -}; -export default plugin; diff --git a/neural_coder/extensions/neural_compressor_ext_lab/lib/test.d.ts b/neural_coder/extensions/neural_compressor_ext_lab/lib/test.d.ts deleted file mode 100644 index 2c28e08c632..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/lib/test.d.ts +++ /dev/null @@ -1,2 +0,0 @@ -declare let str: string; -declare let str1: number; diff --git a/neural_coder/extensions/neural_compressor_ext_lab/lib/test.js b/neural_coder/extensions/neural_compressor_ext_lab/lib/test.js deleted file mode 100644 index c679ea06c74..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/lib/test.js +++ /dev/null @@ -1,4 +0,0 @@ -"use strict"; -let str = '# this is the beginning of a single code snippet\nimport glob\nimport torch\nimport os\nimport sys\nfrom tqdm import tqdm\nfrom dalle_pytorch import VQGanVAE, DALLE, DiscreteVAE\nfrom dalle_pytorch.tokenizer import tokenizer\nfrom einops import repeat\nfrom dalle_nc import DALLE, DiscreteVAE\nfrom torch.utils.data import DataLoader\nfrom torch.utils.data import Dataset\n\n# model\nvae = DiscreteVAE(\n image_size = 8,\n num_layers = 3,\n num_tokens = 8192,\n codebook_dim = 1024,\n hidden_dim = 64,\n num_resnet_blocks = 1,\n temperature = 0.9\n)\n\ndalle = DALLE(\n dim = 1024,\n vae = vae, # automatically infer (1) image sequence length and (2) number of image tokens\n num_text_tokens = 100000, # vocab size for text\n text_seq_len = 256, # text sequence length\n depth = 12, # should aim to be 64\n heads = 16, # attention heads\n dim_head = 64, # attention head dimension\n attn_dropout = 0.1, # attention dropout\n ff_dropout = 0.1 # feedforward dropout\n)\n# [NeuralCoder] pytorch_inc_dynamic_quant for dalle [Beginning Line]\nif "GraphModule" not in str(type(dalle)):\n from neural_compressor.quantization import fit\n from neural_compressor.config import PostTrainingQuantConfig\n config = PostTrainingQuantConfig(approach="dynamic")\n dalle = fit(dalle, conf=config)\n dalle = dalle.model\n dalle.eval()\n# [NeuralCoder] pytorch_inc_dynamic_quant for dalle [Ending Line]\n\ndalle.eval()\n\n# real data for DALLE image generation\nfiles = glob.glob(\'/home2/longxin/neural_compressor_ext_lab/real_text.txt\')\n\n# create dataloader\ninput_list = []\nwith torch.no_grad():\n count = 0\n for file in files:\n texts = open(file, \'r\').read().split(\'\\n\')\n for text in texts:\n print(text)\n\n num_images = 1\n\n top_k = 0.9\n\n image_size = vae.image_size\n\n texts = text.split(\'|\')\n\n for j, text in tqdm(enumerate(texts)):\n text_tokens = tokenizer.tokenize([text], 256).to(\'cpu\')\n\n text_tokens = repeat(text_tokens, \'() n -> b n\', b=num_images)\n\n for text_chunk in tqdm(text_tokens):\n d = {}\n d["text"] = text_chunk\n d["filter_thres"] = top_k\n input_list.append(d)\n\nclass MyDataset(Dataset):\n def __init__(self):\n self.samples = input_list\n\n def __getitem__(self, idx):\n return self.samples[idx], 1\n\n def __len__(self):\n return len(self.samples)\ndataset = MyDataset()\ndataloader = DataLoader(dataset)\n\n# inference\nwith torch.no_grad():\n for step, (inputs, labels) in enumerate(dataloader):\n print("running inference ...")\n output = dalle(**inputs)\n\n'; -let str1 = str.split('# this is the beginning of a single code snippet\\n').length; -console.log(__filename); diff --git a/neural_coder/extensions/neural_compressor_ext_lab/lib/utils.d.ts b/neural_coder/extensions/neural_compressor_ext_lab/lib/utils.d.ts deleted file mode 100644 index 8b7a2173767..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/lib/utils.d.ts +++ /dev/null @@ -1,120 +0,0 @@ -import { NotebookPanel } from '@jupyterlab/notebook'; -import { Kernel } from '@jupyterlab/services'; -import { CommandRegistry } from '@phosphor/commands'; -export default class NotebookUtilities { - /** - * generate random number - * @Min - * @Max - */ - static GetRandomNum(Min: number, Max: number): number; - /** - * Builds an HTML container by sanitizing a list of strings and converting - * them in valid HTML - * @param msg A list of string with HTML formatting - * @returns a HTMLDivElement composed of a list of spans with formatted text - */ - private static buildDialogBody; - /** - * Opens a pop-up dialog in JupyterLab to display a simple message. - * @param title The title for the message popup - * @param msg The message as an array of strings - * @param buttonLabel The label to use for the button. Default is 'OK' - * @param buttonClassName The classname to give to the 'ok' button - * @returns Promise - A promise once the message is closed. - */ - static showMessage(title: string, msg: string[], buttonLabel?: string, buttonClassName?: string): Promise; - /** - * Opens a pop-up dialog in JupyterLab to display a yes/no dialog. - * @param title The title for the message popup - * @param msg The message - * @param acceptLabel The label to use for the accept button. Default is 'YES' - * @param rejectLabel The label to use for the reject button. Default is 'NO' - * @param yesButtonClassName The classname to give to the accept button. - * @param noButtonClassName The classname to give to the cancel button. - * @returns Promise - A promise once the message is closed. - */ - static showYesNoDialog(title: string, msg: string[], acceptLabel?: string, rejectLabel?: string, yesButtonClassName?: string, noButtonClassName?: string): Promise; - /** - * Opens a pop-up dialog in JupyterLab with various information and button - * triggering reloading the page. - * @param title The title for the message popup - * @param msg The message - * @param buttonLabel The label to use for the button. Default is 'Refresh' - * @param buttonClassName The classname to give to the 'refresh' button. - * @returns Promise - A promise once the message is closed. - */ - static showRefreshDialog(title: string, msg: string[], buttonLabel?: string, buttonClassName?: string): Promise; - /** - * @description Creates a new JupyterLab notebook for use by the application - * @param command The command registry - * @returns Promise - A promise containing the notebook panel object that was created (if successful). - */ - static createNewNotebook(command: CommandRegistry): Promise; - /** - * Safely saves the Jupyter notebook document contents to disk - * @param notebookPanel The notebook panel containing the notebook to save - */ - static saveNotebook(notebookPanel: NotebookPanel): Promise; - /** - * Convert the notebook contents to JSON - * @param notebookPanel The notebook panel containing the notebook to serialize - */ - static notebookToJSON(notebookPanel: NotebookPanel): any; - /** - * @description Gets the value of a key from specified notebook's metadata. - * @param notebookPanel The notebook to get meta data from. - * @param key The key of the value. - * @returns any -The value of the metadata. Returns null if the key doesn't exist. - */ - static getMetaData(notebookPanel: NotebookPanel, key: string): any; - /** - * @description Sets the key value pair in the notebook's metadata. - * If the key doesn't exists it will add one. - * @param notebookPanel The notebook to set meta data in. - * @param key The key of the value to create. - * @param value The value to set. - * @param save Default is false. Whether the notebook should be saved after the meta data is set. - * Note: This function will not wait for the save to complete, it only sends a save request. - * @returns The old value for the key, or undefined if it did not exist. - */ - static setMetaData(notebookPanel: NotebookPanel, key: string, value: any, save?: boolean): any; - /** - * @description This function runs code directly in the notebook's kernel and then evaluates the - * result and returns it as a promise. - * @param kernel The kernel to run the code in. - * @param runCode The code to run in the kernel. - * @param userExpressions The expressions used to capture the desired info from the executed code. - * @param runSilent Default is false. If true, kernel will execute as quietly as possible. - * store_history will be set to false, and no broadcast on IOPUB channel will be made. - * @param storeHistory Default is false. If true, the code executed will be stored in the kernel's history - * and the counter which is shown in the cells will be incremented to reflect code was run. - * @param allowStdIn Default is false. If true, code running in kernel can prompt user for input using - * an input_request message. - * @param stopOnError Default is false. If True, does not abort the execution queue, if an exception is encountered. - * This allows the queued execution of multiple execute_requests, even if they generate exceptions. - * @returns Promise - A promise containing the execution results of the code as an object with - * keys based on the user_expressions. - * @example - * //The code - * const code = "a=123\nb=456\nsum=a+b"; - * //The user expressions - * const expr = {sum: "sum",prod: "a*b",args:"[a,b,sum]"}; - * //Async function call (returns a promise) - * sendKernelRequest(notebookPanel, code, expr,false); - * //Result when promise resolves: - * { - * sum:{status:"ok",data:{"text/plain":"579"},metadata:{}}, - * prod:{status:"ok",data:{"text/plain":"56088"},metadata:{}}, - * args:{status:"ok",data:{"text/plain":"[123, 456, 579]"}} - * } - * @see For more information on JupyterLab messages: - * https://jupyter-client.readthedocs.io/en/latest/messaging.html#execution-results - */ - static sendKernelRequest(kernel: Kernel.IKernelConnection | null | undefined, runCode: string, userExpressions: any, runSilent?: boolean, storeHistory?: boolean, allowStdIn?: boolean, stopOnError?: boolean): Promise; - /** - * Same as method sendKernelRequest but passing - * a NotebookPanel instead of a Kernel - */ - static sendKernelRequestFromNotebook(notebookPanel: NotebookPanel, runCode: string, userExpressions: any, runSilent?: boolean, storeHistory?: boolean, allowStdIn?: boolean, stopOnError?: boolean): Promise; -} diff --git a/neural_coder/extensions/neural_compressor_ext_lab/lib/utils.js b/neural_coder/extensions/neural_compressor_ext_lab/lib/utils.js deleted file mode 100644 index 5ac72df1071..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/lib/utils.js +++ /dev/null @@ -1,270 +0,0 @@ -/* - * Copyright 2019-2020 The Kale Authors - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -import { Dialog, showDialog } from '@jupyterlab/apputils'; -// @ts-ignore -import SanitizedHTML from 'react-sanitized-html'; -import * as React from 'react'; -export default class NotebookUtilities { - /** - * generate random number - * @Min - * @Max - */ - static GetRandomNum(Min, Max) { - let Range; - Range = Max - Min; - var Rand = Math.random(); - return (Min + Math.round(Rand * Range)); - } - /** - * Builds an HTML container by sanitizing a list of strings and converting - * them in valid HTML - * @param msg A list of string with HTML formatting - * @returns a HTMLDivElement composed of a list of spans with formatted text - */ - static buildDialogBody(msg) { - return (React.createElement("div", null, msg.map((s, i) => { - return (React.createElement(React.Fragment, { key: `msg-${i}` }, - React.createElement(SanitizedHTML, { allowedAttributes: { a: ['href'] }, allowedTags: ['b', 'i', 'em', 'strong', 'a', 'pre'], html: s }), - React.createElement("br", null))); - }))); - } - /** - * Opens a pop-up dialog in JupyterLab to display a simple message. - * @param title The title for the message popup - * @param msg The message as an array of strings - * @param buttonLabel The label to use for the button. Default is 'OK' - * @param buttonClassName The classname to give to the 'ok' button - * @returns Promise - A promise once the message is closed. - */ - static async showMessage(title, msg, buttonLabel = 'Dismiss', buttonClassName = '') { - const buttons = [ - Dialog.okButton({ label: buttonLabel, className: buttonClassName }), - ]; - const messageBody = this.buildDialogBody(msg); - await showDialog({ title, buttons, body: messageBody }); - } - /** - * Opens a pop-up dialog in JupyterLab to display a yes/no dialog. - * @param title The title for the message popup - * @param msg The message - * @param acceptLabel The label to use for the accept button. Default is 'YES' - * @param rejectLabel The label to use for the reject button. Default is 'NO' - * @param yesButtonClassName The classname to give to the accept button. - * @param noButtonClassName The classname to give to the cancel button. - * @returns Promise - A promise once the message is closed. - */ - static async showYesNoDialog(title, msg, acceptLabel = 'YES', rejectLabel = 'NO', yesButtonClassName = '', noButtonClassName = '') { - const buttons = [ - Dialog.okButton({ label: acceptLabel, className: yesButtonClassName }), - Dialog.cancelButton({ label: rejectLabel, className: noButtonClassName }), - ]; - const messageBody = this.buildDialogBody(msg); - const result = await showDialog({ title, buttons, body: messageBody }); - return result.button.label === acceptLabel; - } - /** - * Opens a pop-up dialog in JupyterLab with various information and button - * triggering reloading the page. - * @param title The title for the message popup - * @param msg The message - * @param buttonLabel The label to use for the button. Default is 'Refresh' - * @param buttonClassName The classname to give to the 'refresh' button. - * @returns Promise - A promise once the message is closed. - */ - static async showRefreshDialog(title, msg, buttonLabel = 'Refresh', buttonClassName = '') { - await this.showMessage(title, msg, buttonLabel, buttonClassName); - location.reload(); - } - /** - * @description Creates a new JupyterLab notebook for use by the application - * @param command The command registry - * @returns Promise - A promise containing the notebook panel object that was created (if successful). - */ - static async createNewNotebook(command) { - const notebook = await command.execute('notebook:create-new', { - activate: true, - path: '', - preferredLanguage: '', - }); - await notebook.session.ready; - return notebook; - } - /** - * Safely saves the Jupyter notebook document contents to disk - * @param notebookPanel The notebook panel containing the notebook to save - */ - static async saveNotebook(notebookPanel) { - if (notebookPanel) { - await notebookPanel.context.ready; - notebookPanel.context.save(); - return true; - } - return false; - } - /** - * Convert the notebook contents to JSON - * @param notebookPanel The notebook panel containing the notebook to serialize - */ - static notebookToJSON(notebookPanel) { - if (notebookPanel.content.model) { - return notebookPanel.content.model.toJSON(); - } - return null; - } - /** - * @description Gets the value of a key from specified notebook's metadata. - * @param notebookPanel The notebook to get meta data from. - * @param key The key of the value. - * @returns any -The value of the metadata. Returns null if the key doesn't exist. - */ - static getMetaData(notebookPanel, key) { - if (!notebookPanel) { - throw new Error('The notebook is null or undefined. No meta data available.'); - } - if (notebookPanel.model && notebookPanel.model.metadata.has(key)) { - return notebookPanel.model.metadata.get(key); - } - return null; - } - /** - * @description Sets the key value pair in the notebook's metadata. - * If the key doesn't exists it will add one. - * @param notebookPanel The notebook to set meta data in. - * @param key The key of the value to create. - * @param value The value to set. - * @param save Default is false. Whether the notebook should be saved after the meta data is set. - * Note: This function will not wait for the save to complete, it only sends a save request. - * @returns The old value for the key, or undefined if it did not exist. - */ - static setMetaData(notebookPanel, key, value, save = false) { - var _a; - if (!notebookPanel) { - throw new Error('The notebook is null or undefined. No meta data available.'); - } - const oldVal = (_a = notebookPanel.model) === null || _a === void 0 ? void 0 : _a.metadata.set(key, value); - if (save) { - this.saveNotebook(notebookPanel); - } - return oldVal; - } - // /** - // * Get a new Kernel, not tied to a Notebook - // * Source code here: https://github.com/jupyterlab/jupyterlab/tree/473348d25bcb258ca2f0c127dd8fb5b193217135/packages/services - // */ - // public static async createNewKernel() { - // // Get info about the available kernels and start a new one. - // let options: Kernel.IOptions = await Kernel.getSpecs().then(kernelSpecs => { - // // console.log('Default spec:', kernelSpecs.default); - // // console.log('Available specs', Object.keys(kernelSpecs.kernelspecs)); - // // use the default name - // return { name: kernelSpecs.default }; - // }); - // return await Kernel.startNew(options).then(_kernel => { - // return _kernel; - // }); - // } - // // TODO: We can use this context manager to execute commands inside a new kernel - // // and be sure that it will be disposed of at the end. - // // Another approach could be to create a kale_rpc Kernel, as a singleton, - // // created at startup. The only (possible) drawback is that we can not name - // // a kernel instance with a custom id/name, so when refreshing JupyterLab we would - // // not recognize the kernel. A solution could be to have a kernel spec dedicated to kale rpc calls. - // public static async executeWithNewKernel(action: Function, args: any[] = []) { - // // create brand new kernel - // const _k = await this.createNewKernel(); - // // execute action inside kernel - // const res = await action(_k, ...args); - // // close kernel - // _k.shutdown(); - // // return result - // return res; - // } - /** - * @description This function runs code directly in the notebook's kernel and then evaluates the - * result and returns it as a promise. - * @param kernel The kernel to run the code in. - * @param runCode The code to run in the kernel. - * @param userExpressions The expressions used to capture the desired info from the executed code. - * @param runSilent Default is false. If true, kernel will execute as quietly as possible. - * store_history will be set to false, and no broadcast on IOPUB channel will be made. - * @param storeHistory Default is false. If true, the code executed will be stored in the kernel's history - * and the counter which is shown in the cells will be incremented to reflect code was run. - * @param allowStdIn Default is false. If true, code running in kernel can prompt user for input using - * an input_request message. - * @param stopOnError Default is false. If True, does not abort the execution queue, if an exception is encountered. - * This allows the queued execution of multiple execute_requests, even if they generate exceptions. - * @returns Promise - A promise containing the execution results of the code as an object with - * keys based on the user_expressions. - * @example - * //The code - * const code = "a=123\nb=456\nsum=a+b"; - * //The user expressions - * const expr = {sum: "sum",prod: "a*b",args:"[a,b,sum]"}; - * //Async function call (returns a promise) - * sendKernelRequest(notebookPanel, code, expr,false); - * //Result when promise resolves: - * { - * sum:{status:"ok",data:{"text/plain":"579"},metadata:{}}, - * prod:{status:"ok",data:{"text/plain":"56088"},metadata:{}}, - * args:{status:"ok",data:{"text/plain":"[123, 456, 579]"}} - * } - * @see For more information on JupyterLab messages: - * https://jupyter-client.readthedocs.io/en/latest/messaging.html#execution-results - */ - static async sendKernelRequest(kernel, runCode, userExpressions, runSilent = false, storeHistory = false, allowStdIn = false, stopOnError = false) { - if (!kernel) { - throw new Error('Kernel is null or undefined.'); - } - // Wait for kernel to be ready before sending request - // await kernel.status; - const message = await kernel.requestExecute({ - allow_stdin: allowStdIn, - code: runCode, - silent: runSilent, - stop_on_error: stopOnError, - store_history: storeHistory, - user_expressions: userExpressions, - }).done; - const content = message.content; - if (content.status !== 'ok') { - // If response is not 'ok', throw contents as error, log code - const msg = `Code caused an error:\n${runCode}`; - console.error(msg); - if (content.traceback) { - content.traceback.forEach((line) => console.log(line.replace(/[\u001b\u009b][[()#;?]*(?:[0-9]{1,4}(?:;[0-9]{0,4})*)?[0-9A-ORZcf-nqry=><]/g, ''))); - } - throw content; - } - // Return user_expressions of the content - return content.user_expressions; - } - /** - * Same as method sendKernelRequest but passing - * a NotebookPanel instead of a Kernel - */ - static async sendKernelRequestFromNotebook(notebookPanel, runCode, userExpressions, runSilent = false, storeHistory = false, allowStdIn = false, stopOnError = false) { - var _a, _b, _c, _d; - if (!notebookPanel) { - throw new Error('Notebook is null or undefined.'); - } - // Wait for notebook panel to be ready - await notebookPanel.activate; - await ((_a = notebookPanel.sessionContext) === null || _a === void 0 ? void 0 : _a.ready); - console.log('get kernel', (_b = notebookPanel.sessionContext.session) === null || _b === void 0 ? void 0 : _b.kernel); - return this.sendKernelRequest((_d = (_c = notebookPanel.sessionContext) === null || _c === void 0 ? void 0 : _c.session) === null || _d === void 0 ? void 0 : _d.kernel, runCode, userExpressions, runSilent, storeHistory, allowStdIn, stopOnError); - } -} diff --git a/neural_coder/extensions/neural_compressor_ext_lab/neural_compressor_ext_lab/__init__.py b/neural_coder/extensions/neural_compressor_ext_lab/neural_compressor_ext_lab/__init__.py deleted file mode 100644 index 74c4e9f68fc..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/neural_compressor_ext_lab/__init__.py +++ /dev/null @@ -1,34 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import json -from pathlib import Path - -from ._version import __version__ - - -HERE = Path(__file__).parent.resolve() - - -with (HERE / "labextension" / "package.json").open() as fid: - data = json.load(fid) - - -def _jupyter_labextension_paths(): - return [{ - "src": "labextension", - "dest": data["name"] - }] - diff --git a/neural_coder/extensions/neural_compressor_ext_lab/neural_compressor_ext_lab/_version.py b/neural_coder/extensions/neural_compressor_ext_lab/neural_compressor_ext_lab/_version.py deleted file mode 100644 index 30cfd6b1021..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/neural_compressor_ext_lab/_version.py +++ /dev/null @@ -1,38 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import json -from pathlib import Path - -__all__ = ["__version__"] - -def _fetchVersion(): - HERE = Path(__file__).parent.resolve() - - for settings in HERE.rglob("package.json"): - try: - with settings.open() as f: - version = json.load(f)["version"] - return ( - version.replace("-alpha.", "a") - .replace("-beta.", "b") - .replace("-rc.", "rc") - ) - except FileNotFoundError: - pass - - raise FileNotFoundError(f"Could not find package.json under dir {HERE!s}") - -__version__ = _fetchVersion() diff --git a/neural_coder/extensions/neural_compressor_ext_lab/neural_compressor_ext_lab/labextension/build_log.json b/neural_coder/extensions/neural_compressor_ext_lab/neural_compressor_ext_lab/labextension/build_log.json deleted file mode 100644 index 30f46e6eba0..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/neural_compressor_ext_lab/labextension/build_log.json +++ /dev/null @@ -1,651 +0,0 @@ -[ - { - "bail": false, - "module": { - "rules": [ - { - "test": {}, - "use": [ - "style-loader", - "css-loader" - ] - }, - { - "test": {}, - "use": "raw-loader" - }, - { - "test": {}, - "use": "raw-loader" - }, - { - "test": {}, - "use": "file-loader" - }, - { - "test": {}, - "use": "file-loader" - }, - { - "test": {}, - "use": "url-loader?limit=10000&mimetype=application/font-woff" - }, - { - "test": {}, - "use": "url-loader?limit=10000&mimetype=application/font-woff" - }, - { - "test": {}, - "use": "url-loader?limit=10000&mimetype=application/octet-stream" - }, - { - "test": {}, - "use": "file-loader" - }, - { - "test": {}, - "issuer": {}, - "use": { - "loader": "svg-url-loader", - "options": { - "encoding": "none", - "limit": 10000 - } - } - }, - { - "test": {}, - "issuer": {}, - "use": { - "loader": "raw-loader" - } - }, - { - "test": {}, - "type": "javascript/auto" - }, - { - "test": {}, - "resolve": { - "fullySpecified": false - } - }, - { - "test": {}, - "resolve": { - "fullySpecified": false - } - }, - { - "test": {}, - "use": "file-loader" - } - ] - }, - "resolve": { - "alias": {}, - "fallback": { - "url": false, - "buffer": false, - "crypto": false, - "path": "/home/demo/longxin/neural_compressor_ext_lab/node_modules/path-browserify/index.js", - "process": "/home/demo/longxin/neural_compressor_ext_lab/node_modules/process/browser.js" - } - }, - "watchOptions": { - "poll": 500, - "aggregateTimeout": 1000 - }, - "output": { - "hashFunction": "sha256", - "filename": "[name].[contenthash].js", - "path": "/home/demo/longxin/neural_compressor_ext_lab/neural_compressor_ext_lab/labextension/static", - "publicPath": "auto" - }, - "plugins": [ - { - "definitions": { - "process": "process/browser" - } - }, - { - "_options": { - "name": "neural_compressor_ext_lab", - "library": { - "type": "var", - "name": [ - "_JUPYTERLAB", - "neural_compressor_ext_lab" - ] - }, - "filename": "remoteEntry.[contenthash].js", - "exposes": { - "./index": "/home/demo/longxin/neural_compressor_ext_lab/lib/index.js", - "./extension": "/home/demo/longxin/neural_compressor_ext_lab/lib/index.js", - 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"version": "0.1.0", - "singleton": true, - "import": "/home/demo/longxin/neural_compressor_ext_lab/lib/index.js" - } - } - } - }, - {} - ], - "mode": "development", - "devtool": "source-map", - "entry": {} - } -] \ No newline at end of file diff --git a/neural_coder/extensions/neural_compressor_ext_lab/neural_compressor_ext_lab/labextension/package.json b/neural_coder/extensions/neural_compressor_ext_lab/neural_compressor_ext_lab/labextension/package.json deleted file mode 100644 index b5d8d15ca60..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/neural_compressor_ext_lab/labextension/package.json +++ /dev/null @@ -1,164 +0,0 @@ -{ - "name": "jupyter-lab-neural-compressor", - "version": "1.0.2", - "description": "IntelĀ® Neural Compressor auto-quantization plugin.", - "keywords": [ - "jupyter", - "jupyterlab", - "jupyterlab-extension" - ], - "homepage": "https://github.com/intel/neural-compressor/tree/master/neural_coder/extensions/neural_compressor_ext_lab", - "bugs": { - "url": "https://github.com/intel/neural-compressor/tree/master/neural_coder/extensions/neural_compressor_ext_lab" - }, - "license": "BSD-3-Clause", - "author": { - "name": "neural_compressor_ext_lab", - "email": "inc.maintainers@intel.com" - }, - "files": [ - "lib/**/*.{d.ts,eot,gif,html,jpg,js,js.map,json,png,svg,woff2,ttf}", - "style/**/*.{css,js,eot,gif,html,jpg,json,png,svg,woff2,ttf}" - ], - "main": "lib/index.js", - "types": "lib/index.d.ts", - "style": "style/index.css", - "repository": { - "type": "git", - "url": "https://github.com/intel/neural-compressor.git" - }, - "scripts": { - "preinstall": "npx npm-force-resolutions", - "build": "jlpm build:lib && jlpm build:labextension:dev", - "build:prod": "jlpm clean && jlpm build:lib && jlpm build:labextension", - "build:labextension": "jupyter labextension build .", - "build:labextension:dev": "jupyter labextension build --development True .", - "build:lib": "tsc", - "clean": "jlpm clean:lib", - "clean:lib": "rimraf lib tsconfig.tsbuildinfo", - "clean:lintcache": "rimraf .eslintcache .stylelintcache", - "clean:labextension": "rimraf neural_compressor_ext_lab/labextension", - "clean:all": "jlpm clean:lib && jlpm clean:labextension && jlpm clean:lintcache", - "eslint": "jlpm eslint:check --fix", - "eslint:check": "eslint . --cache --ext .ts,.tsx", - "install:extension": "jlpm build", - "lint": "jlpm stylelint && jlpm prettier && jlpm eslint", - "lint:check": "jlpm stylelint:check && jlpm prettier:check && jlpm eslint:check", - "prettier": "jlpm prettier:base --write --list-different", - "prettier:base": "prettier \"**/*{.ts,.tsx,.js,.jsx,.css,.json,.md}\"", - "prettier:check": "jlpm prettier:base --check", - "stylelint": "jlpm stylelint:check --fix", - "stylelint:check": "stylelint --cache \"style/**/*.css\"", - "watch": "run-p watch:src watch:labextension", - "watch:src": "tsc -w", - "watch:labextension": "jupyter labextension watch ." - }, - "dependencies": { - "@jupyterlab/application": "^3.4.7", - "@jupyterlab/apputils": "^3.4.7", - "@jupyterlab/cells": "^3.4.7", - "@jupyterlab/coreutils": "^5.4.7", - "@jupyterlab/docregistry": "^3.4.7", - "@jupyterlab/fileeditor": "^3.4.6", - "@jupyterlab/mainmenu": "^3.4.6", - "@jupyterlab/notebook": "^3.4.7", - "@jupyterlab/services": "^6.4.7", - "@jupyterlab/settingregistry": "^3.4.7", - "@jupyterlab/statedb": "^3.4.7", - "@lumino/coreutils": "^1.12.1", - "@lumino/messaging": "^1.10.2", - "@phosphor/commands": "^1.7.2", - "@types/lerna__child-process": "^5.1.0", - "@types/shelljs": "^0.8.11", - "ajv": "^8.11.0", - "ajv-keywords": "^5.1.0", - "assert": "^2.0.0", - "browserify-fs": "^1.0.0", - "brython": "^3.10.6", - "buffer": "^6.0.3", - "cacheable-request": "^10.1.2", - "child_process": "^1.0.2", - "clone-response": "^2.0.0", - "constants": "^0.0.2", - "css-loader": "^6.7.1", - "duplicate-package-checker-webpack-plugin": "^3.0.0", - "enhanced-resolve": "^5.10.0", - "es-abstract": "^1.20.2", - "es-to-primitive": "^1.2.1", - "fs": "^0.0.1-security", - "has": "^1.0.3", - "icss-utils": "^5.1.0", - "loader-utils": "^2.0.3", - "react": "^17.0.2", - "react-sanitized-html": "^2.0.0", - "sanitize-html": "^2.7.2", - "shelljs": "^0.8.5", - "stream": "^0.0.2", - "util": "^0.12.4" - }, - "devDependencies": { - "@jupyterlab/builder": "^3.1.0", - "@types/fs-extra": "^9.0.13", - "@types/node": "^18.7.15", - "@typescript-eslint/eslint-plugin": "^4.8.1", - "@typescript-eslint/parser": "^4.8.1", - "bl": "^1.2.3", - "eslint": "^7.14.0", - "eslint-config-prettier": "^6.15.0", - "eslint-plugin-prettier": "^3.1.4", - "got": "^12.1.0", - "npm-run-all": "^4.1.5", - "prettier": "^2.1.1", - "rimraf": "^3.0.2", - "semver": "^5.7.2", - "stylelint": "^15.6.0", - "stylelint-config-prettier": "^9.0.3", - "stylelint-config-recommended": "^12.0.0", - "stylelint-config-standard": "~33.0.0", - "stylelint-prettier": "^2.0.0", - "typescript": "~4.1.3" - }, - "sideEffects": [ - "style/*.css", - "style/index.js" - ], - "styleModule": "style/index.js", - "publishConfig": { - "access": "public" - }, - "jupyterlab": { - "extension": true, - "outputDir": "neural_compressor_ext_lab/labextension", - "_build": { - "load": "static/remoteEntry.34f9ad20791fd484f052.js", - "extension": "./extension", - "style": "./style" - } - }, - "browser": { - "child_process": false, - "lerna__child_process": false, - "fs": false, - "path": false, - "os": false - }, - "jupyter-releaser": { - "hooks": { - "before-build-npm": [ - "python -m pip install jupyterlab~=3.1", - "jlpm" - ], - "before-build-python": [ - "jlpm clean:all" - ] - } - }, - "resolutions": { - "got": "^12.1.0", - "semver": "^5.7.2", - "bl": "^1.2.3", - "loader-utils": "^2.0.3", - "json5": "^2.2.2", - "jsonwebtoken": "^9.0.0" - } -} \ No newline at end of file diff --git a/neural_coder/extensions/neural_compressor_ext_lab/package.json b/neural_coder/extensions/neural_compressor_ext_lab/package.json deleted file mode 100644 index 8d99f7c8fd1..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/package.json +++ /dev/null @@ -1,161 +0,0 @@ -{ - "name": "jupyter-lab-neural-compressor", - "version": "1.0.2", - "description": "IntelĀ® Neural Compressor auto-quantization plugin.", - "keywords": [ - "jupyter", - "jupyterlab", - "jupyterlab-extension" - ], - "homepage": "https://github.com/intel/neural-compressor/tree/master/neural_coder/extensions/neural_compressor_ext_lab", - "bugs": { - "url": "https://github.com/intel/neural-compressor/tree/master/neural_coder/extensions/neural_compressor_ext_lab" - }, - "license": "BSD-3-Clause", - "author": { - "name": "neural_compressor_ext_lab", - "email": "inc.maintainers@intel.com" - }, - "files": [ - "lib/**/*.{d.ts,eot,gif,html,jpg,js,js.map,json,png,svg,woff2,ttf}", - "style/**/*.{css,js,eot,gif,html,jpg,json,png,svg,woff2,ttf}" - ], - "main": "lib/index.js", - "types": "lib/index.d.ts", - "style": "style/index.css", - "repository": { - "type": "git", - "url": "https://github.com/intel/neural-compressor.git" - }, - "scripts": { - "preinstall": "npx npm-force-resolutions", - "build": "jlpm build:lib && jlpm build:labextension:dev", - "build:prod": "jlpm clean && jlpm build:lib && jlpm build:labextension", - "build:labextension": "jupyter labextension build .", - "build:labextension:dev": "jupyter labextension build --development True .", - "build:lib": "tsc", - "clean": "jlpm clean:lib", - "clean:lib": "rimraf lib tsconfig.tsbuildinfo", - "clean:lintcache": "rimraf .eslintcache .stylelintcache", - "clean:labextension": "rimraf neural_compressor_ext_lab/labextension", - "clean:all": "jlpm clean:lib && jlpm clean:labextension && jlpm clean:lintcache", - "eslint": "jlpm eslint:check --fix", - "eslint:check": "eslint . --cache --ext .ts,.tsx", - "install:extension": "jlpm build", - "lint": "jlpm stylelint && jlpm prettier && jlpm eslint", - "lint:check": "jlpm stylelint:check && jlpm prettier:check && jlpm eslint:check", - "prettier": "jlpm prettier:base --write --list-different", - "prettier:base": "prettier \"**/*{.ts,.tsx,.js,.jsx,.css,.json,.md}\"", - "prettier:check": "jlpm prettier:base --check", - "stylelint": "jlpm stylelint:check --fix", - "stylelint:check": "stylelint --cache \"style/**/*.css\"", - "watch": "run-p watch:src watch:labextension", - "watch:src": "tsc -w", - "watch:labextension": "jupyter labextension watch ." - }, - "dependencies": { - "@jupyterlab/application": "^3.4.7", - "@jupyterlab/apputils": "^3.4.7", - "@jupyterlab/cells": "^3.4.7", - "@jupyterlab/coreutils": "^5.4.7", - "@jupyterlab/docregistry": "^3.4.7", - "@jupyterlab/fileeditor": "^3.4.6", - "@jupyterlab/mainmenu": "^3.4.6", - "@jupyterlab/notebook": "^3.4.7", - "@jupyterlab/services": "^6.4.7", - "@jupyterlab/settingregistry": "^3.4.7", - "@jupyterlab/statedb": "^3.4.7", - "@lumino/coreutils": "^1.12.1", - "@lumino/messaging": "^1.10.2", - "@phosphor/commands": "^1.7.2", - "@types/lerna__child-process": "^5.1.0", - "@types/shelljs": "^0.8.11", - "ajv": "^8.11.0", - "ajv-keywords": "^5.1.0", - "assert": "^2.0.0", - "browserify-fs": "^1.0.0", - "brython": "^3.10.6", - "buffer": "^6.0.3", - "cacheable-request": "^10.1.2", - "child_process": "^1.0.2", - "clone-response": "^2.0.0", - "constants": "^0.0.2", - "css-loader": "^6.7.1", - "duplicate-package-checker-webpack-plugin": "^3.0.0", - "enhanced-resolve": "^5.10.0", - "es-abstract": "^1.20.2", - "es-to-primitive": "^1.2.1", - "fs": "^0.0.1-security", - "has": "^1.0.3", - "icss-utils": "^5.1.0", - "loader-utils": "^2.0.3", - "react": "^17.0.2", - "react-sanitized-html": "^2.0.0", - "sanitize-html": "^2.7.2", - "shelljs": "^0.8.5", - "stream": "^0.0.2", - "util": "^0.12.4" - }, - "devDependencies": { - "@jupyterlab/builder": "^3.1.0", - "@types/fs-extra": "^9.0.13", - "@types/node": "^18.7.15", - "@typescript-eslint/eslint-plugin": "^4.8.1", - "@typescript-eslint/parser": "^4.8.1", - "bl": "^1.2.3", - "eslint": "^7.14.0", - "eslint-config-prettier": "^6.15.0", - "eslint-plugin-prettier": "^3.1.4", - "got": "^12.1.0", - "json5": "^2.2.2", - "jsonwebtoken": "^9.0.0", - "npm-run-all": "^4.1.5", - "prettier": "^2.1.1", - "rimraf": "^3.0.2", - "semver": "^5.7.2", - "stylelint": "^15.6.0", - "stylelint-config-prettier": "^9.0.3", - "stylelint-config-recommended": "^12.0.0", - "stylelint-config-standard": "~33.0.0", - "stylelint-prettier": "^2.0.0", - "typescript": "~4.1.3" - }, - "sideEffects": [ - "style/*.css", - "style/index.js" - ], - "styleModule": "style/index.js", - "publishConfig": { - "access": "public" - }, - "jupyterlab": { - "extension": true, - "outputDir": "neural_compressor_ext_lab/labextension" - }, - "browser": { - "child_process": false, - "lerna__child_process": false, - "fs": false, - "path": false, - "os": false - }, - "jupyter-releaser": { - "hooks": { - "before-build-npm": [ - "python -m pip install jupyterlab~=3.1", - "jlpm" - ], - "before-build-python": [ - "jlpm clean:all" - ] - } - }, - "resolutions": { - "got": "^12.1.0", - "semver": "^5.7.2", - "bl": "^1.2.3", - "loader-utils": "^2.0.3", - "json5": "^2.2.2", - "jsonwebtoken": "^9.0.0" - } -} diff --git a/neural_coder/extensions/neural_compressor_ext_lab/pyproject.toml b/neural_coder/extensions/neural_compressor_ext_lab/pyproject.toml deleted file mode 100644 index d306143f14f..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/pyproject.toml +++ /dev/null @@ -1,17 +0,0 @@ -[build-system] -requires = ["jupyter_packaging~=0.10,<2", "jupyterlab~=3.1"] -build-backend = "jupyter_packaging.build_api" - -[tool.jupyter-packaging.options] -skip-if-exists = ["neural_compressor_ext_lab/labextension/static/style.js"] -ensured-targets = ["neural_compressor_ext_lab/labextension/static/style.js", "neural_compressor_ext_lab/labextension/package.json"] - -[tool.jupyter-packaging.builder] -factory = "jupyter_packaging.npm_builder" - -[tool.jupyter-packaging.build-args] -build_cmd = "build:prod" -npm = ["jlpm"] - -[tool.check-manifest] -ignore = ["neural_compressor_ext_lab/labextension/**", "yarn.lock", ".*", "package-lock.json"] diff --git a/neural_coder/extensions/neural_compressor_ext_lab/setup.py b/neural_coder/extensions/neural_compressor_ext_lab/setup.py deleted file mode 100644 index 4c72409c2d8..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/setup.py +++ /dev/null @@ -1,109 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -""" -neural_compressor_ext_lab setup -""" -import json -import sys -from pathlib import Path - -import setuptools - -HERE = Path(__file__).parent.resolve() - -# Get the package info from package.json -pkg_json = json.loads((HERE / "package.json").read_bytes()) - -# The name of the project -name = "neural_compressor_ext_lab" - -lab_path = (HERE / pkg_json["jupyterlab"]["outputDir"]) - -# Representative files that should exist after a successful build -ensured_targets = [ - str(lab_path / "package.json"), - str(lab_path / "static/style.js") -] - -labext_name = pkg_json["name"] - -data_files_spec = [ - ("share/jupyter/labextensions/%s" % labext_name, str(lab_path.relative_to(HERE)), "**"), - ("share/jupyter/labextensions/%s" % labext_name, str("."), "install.json"), -] - -long_description = (HERE / "README.md").read_text(encoding="utf8") - -version = ( - pkg_json["version"] - .replace("-alpha.", "a") - .replace("-beta.", "b") - .replace("-rc.", "rc") -) - -setup_args = dict( - name=name, - version=version, - url=pkg_json["homepage"], - author=pkg_json["author"]["name"], - author_email=pkg_json["author"]["email"], - description=pkg_json["description"], - license=pkg_json["license"], - license_file="LICENSE", - long_description=long_description, - long_description_content_type="text/markdown", - packages=setuptools.find_packages(), - zip_safe=False, - include_package_data=True, - python_requires=">=3.7", - platforms="Linux, Mac OS X, Windows", - keywords=["Jupyter", "JupyterLab", "JupyterLab3"], - classifiers=[ - "License :: OSI Approved :: BSD License", - "Programming Language :: Python", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.7", - "Programming Language :: Python :: 3.8", - "Programming Language :: Python :: 3.9", - "Programming Language :: Python :: 3.10", - "Framework :: Jupyter", - "Framework :: Jupyter :: JupyterLab", - "Framework :: Jupyter :: JupyterLab :: 3", - "Framework :: Jupyter :: JupyterLab :: Extensions", - "Framework :: Jupyter :: JupyterLab :: Extensions :: Prebuilt", - ], -) - -try: - from jupyter_packaging import ( - wrap_installers, - npm_builder, - get_data_files - ) - post_develop = npm_builder( - build_cmd="install:extension", source_dir="src", build_dir=lab_path - ) - setup_args["cmdclass"] = wrap_installers(post_develop=post_develop, ensured_targets=ensured_targets) - setup_args["data_files"] = get_data_files(data_files_spec) -except ImportError as e: - import logging - logging.basicConfig(format="%(levelname)s: %(message)s") - logging.warning("Build tool `jupyter-packaging` is missing. Install it with pip or conda.") - if not ("--name" in sys.argv or "--version" in sys.argv): - raise e - -if __name__ == "__main__": - setuptools.setup(**setup_args) diff --git a/neural_coder/extensions/neural_compressor_ext_lab/showfloor_torchvision.ipynb b/neural_coder/extensions/neural_compressor_ext_lab/showfloor_torchvision.ipynb deleted file mode 100644 index c3046ef43af..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/showfloor_torchvision.ipynb +++ /dev/null @@ -1,521 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "8783826d-c52b-4dd7-8608-fb5c32205350", - "metadata": {}, - "outputs": [], - "source": [ - "import argparse\n", - "import os\n", - "import random\n", - "import shutil\n", - "import time\n", - "import warnings\n", - "from enum import Enum\n", - "\n", - "import torch\n", - "import torch.nn as nn\n", - "import torch.nn.parallel\n", - "import torch.backends.cudnn as cudnn\n", - "import torch.distributed as dist\n", - "import torch.optim\n", - "from torch.optim.lr_scheduler import StepLR\n", - "import torch.multiprocessing as mp\n", - "import torch.utils.data\n", - "import torch.utils.data.distributed\n", - "import torchvision.transforms as transforms\n", - "import torchvision.datasets as datasets\n", - "import torchvision.models as models\n", - "from torch.utils.data import Subset\n", - "\n", - "model_names = models.list_models(module=models)\n", - "\n", - "parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')\n", - "parser.add_argument('data', metavar='DIR', nargs='?', default='imagenet',\n", - " help='path to dataset (default: imagenet)')\n", - "parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18',\n", - " choices=model_names,\n", - " help='model architecture: ' +\n", - " ' | '.join(model_names) +\n", - " ' (default: resnet18)')\n", - "parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',\n", - " help='number of data loading workers (default: 4)')\n", - "parser.add_argument('--epochs', default=90, type=int, metavar='N',\n", - " help='number of total epochs to run')\n", - "parser.add_argument('--start-epoch', default=0, type=int, metavar='N',\n", - " help='manual epoch number (useful on restarts)')\n", - "parser.add_argument('-b', '--batch-size', default=256, type=int,\n", - " metavar='N',\n", - " help='mini-batch size (default: 256), this is the total '\n", - " 'batch size of all GPUs on the current node when '\n", - " 'using Data Parallel or Distributed Data Parallel')\n", - "parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,\n", - " metavar='LR', help='initial learning rate', dest='lr')\n", - "parser.add_argument('--momentum', default=0.9, type=float, metavar='M',\n", - " help='momentum')\n", - "parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float,\n", - " metavar='W', help='weight decay (default: 1e-4)',\n", - " dest='weight_decay')\n", - "parser.add_argument('-p', '--print-freq', default=10, type=int,\n", - " metavar='N', help='print frequency (default: 10)')\n", - "parser.add_argument('--resume', default='', type=str, metavar='PATH',\n", - " help='path to latest checkpoint (default: none)')\n", - "parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',\n", - " help='evaluate model on validation set')\n", - "parser.add_argument('--pretrained', dest='pretrained', action='store_true',\n", - " help='use pre-trained model')\n", - "parser.add_argument('--world-size', default=-1, type=int,\n", - " help='number of nodes for distributed training')\n", - "parser.add_argument('--rank', default=-1, type=int,\n", - " help='node rank for distributed training')\n", - "parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str,\n", - " help='url used to set up distributed training')\n", - "parser.add_argument('--dist-backend', default='nccl', type=str,\n", - " help='distributed backend')\n", - "parser.add_argument('--seed', default=None, type=int,\n", - " help='seed for initializing training. ')\n", - "parser.add_argument('--gpu', default=None, type=int,\n", - " help='GPU id to use.')\n", - "parser.add_argument('--multiprocessing-distributed', action='store_true',\n", - " help='Use multi-processing distributed training to launch '\n", - " 'N processes per node, which has N GPUs. This is the '\n", - " 'fastest way to use PyTorch for either single node or '\n", - " 'multi node data parallel training')\n", - "parser.add_argument('--dummy', action='store_true', help=\"use fake data to benchmark\")\n", - "\n", - "best_acc1 = 0\n", - "\n", - "\n", - "def main():\n", - " args = parser.parse_args()\n", - "\n", - " if args.seed is not None:\n", - " random.seed(args.seed)\n", - " torch.manual_seed(args.seed)\n", - " cudnn.deterministic = True\n", - " cudnn.benchmark = False\n", - " warnings.warn('You have chosen to seed training. '\n", - " 'This will turn on the CUDNN deterministic setting, '\n", - " 'which can slow down your training considerably! '\n", - " 'You may see unexpected behavior when restarting '\n", - " 'from checkpoints.')\n", - "\n", - " if args.gpu is not None:\n", - " warnings.warn('You have chosen a specific GPU. This will completely '\n", - " 'disable data parallelism.')\n", - "\n", - " if args.dist_url == \"env://\" and args.world_size == -1:\n", - " args.world_size = int(os.environ[\"WORLD_SIZE\"])\n", - "\n", - " args.distributed = args.world_size > 1 or args.multiprocessing_distributed\n", - "\n", - " ngpus_per_node = torch.cuda.device_count()\n", - " if args.multiprocessing_distributed:\n", - " # Since we have ngpus_per_node processes per node, the total world_size\n", - " # needs to be adjusted accordingly\n", - " args.world_size = ngpus_per_node * args.world_size\n", - " # Use torch.multiprocessing.spawn to launch distributed processes: the\n", - " # main_worker process function\n", - " mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args))\n", - " else:\n", - " # Simply call main_worker function\n", - " main_worker(args.gpu, ngpus_per_node, args)\n", - "\n", - "\n", - "def main_worker(gpu, ngpus_per_node, args):\n", - " global best_acc1\n", - " args.gpu = gpu\n", - "\n", - " if args.gpu is not None:\n", - " print(\"Use GPU: {} for training\".format(args.gpu))\n", - "\n", - " if args.distributed:\n", - " if args.dist_url == \"env://\" and args.rank == -1:\n", - " args.rank = int(os.environ[\"RANK\"])\n", - " if args.multiprocessing_distributed:\n", - " # For multiprocessing distributed training, rank needs to be the\n", - " # global rank among all the processes\n", - " args.rank = args.rank * ngpus_per_node + gpu\n", - " dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,\n", - " world_size=args.world_size, rank=args.rank)\n", - " # create model\n", - " if args.pretrained:\n", - " print(\"=> using pre-trained model '{}'\".format(args.arch))\n", - " model = models.__dict__[args.arch](pretrained=True)\n", - " else:\n", - " print(\"=> creating model '{}'\".format(args.arch))\n", - " model = models.__dict__[args.arch]()\n", - "\n", - " if not torch.cuda.is_available():\n", - " print('using CPU, this will be slow')\n", - " elif args.distributed:\n", - " # For multiprocessing distributed, DistributedDataParallel constructor\n", - " # should always set the single device scope, otherwise,\n", - " # DistributedDataParallel will use all available devices.\n", - " if args.gpu is not None:\n", - " torch.cuda.set_device(args.gpu)\n", - " model.cuda(args.gpu)\n", - " # When using a single GPU per process and per\n", - " # DistributedDataParallel, we need to divide the batch size\n", - " # ourselves based on the total number of GPUs of the current node.\n", - " args.batch_size = int(args.batch_size / ngpus_per_node)\n", - " args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node)\n", - " model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])\n", - " else:\n", - " model.cuda()\n", - " # DistributedDataParallel will divide and allocate batch_size to all\n", - " # available GPUs if device_ids are not set\n", - " model = torch.nn.parallel.DistributedDataParallel(model)\n", - " elif args.gpu is not None:\n", - " torch.cuda.set_device(args.gpu)\n", - " model = model.cuda(args.gpu)\n", - " else:\n", - " # DataParallel will divide and allocate batch_size to all available GPUs\n", - " if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):\n", - " model.features = torch.nn.DataParallel(model.features)\n", - " model.cuda()\n", - " else:\n", - " model = torch.nn.DataParallel(model).cuda()\n", - "\n", - " # define loss function (criterion), optimizer, and learning rate scheduler\n", - " criterion = nn.CrossEntropyLoss().cuda(args.gpu)\n", - "\n", - " optimizer = torch.optim.SGD(model.parameters(), args.lr,\n", - " momentum=args.momentum,\n", - " weight_decay=args.weight_decay)\n", - " \n", - " \"\"\"Sets the learning rate to the initial LR decayed by 10 every 30 epochs\"\"\"\n", - " scheduler = StepLR(optimizer, step_size=30, gamma=0.1)\n", - " \n", - " # optionally resume from a checkpoint\n", - " if args.resume:\n", - " if os.path.isfile(args.resume):\n", - " print(\"=> loading checkpoint '{}'\".format(args.resume))\n", - " if args.gpu is None:\n", - " checkpoint = torch.load(args.resume)\n", - " else:\n", - " # Map model to be loaded to specified single gpu.\n", - " loc = 'cuda:{}'.format(args.gpu)\n", - " checkpoint = torch.load(args.resume, map_location=loc)\n", - " args.start_epoch = checkpoint['epoch']\n", - " best_acc1 = checkpoint['best_acc1']\n", - " if args.gpu is not None:\n", - " # best_acc1 may be from a checkpoint from a different GPU\n", - " best_acc1 = best_acc1.to(args.gpu)\n", - " model.load_state_dict(checkpoint['state_dict'])\n", - " optimizer.load_state_dict(checkpoint['optimizer'])\n", - " scheduler.load_state_dict(checkpoint['scheduler'])\n", - " print(\"=> loaded checkpoint '{}' (epoch {})\"\n", - " .format(args.resume, checkpoint['epoch']))\n", - " else:\n", - " print(\"=> no checkpoint found at '{}'\".format(args.resume))\n", - "\n", - "\n", - " # Data loading code\n", - " if args.dummy:\n", - " print(\"=> Dummy data is used!\")\n", - " train_dataset = datasets.FakeData(1000, (3, 224, 224), 1000, transforms.ToTensor())\n", - " val_dataset = datasets.FakeData(1000, (3, 224, 224), 1000, transforms.ToTensor())\n", - " else:\n", - " traindir = os.path.join(args.data, 'train')\n", - " valdir = os.path.join(args.data, 'val')\n", - " normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],\n", - " std=[0.229, 0.224, 0.225])\n", - "\n", - " train_dataset = datasets.ImageFolder(\n", - " traindir,\n", - " transforms.Compose([\n", - " transforms.RandomResizedCrop(224),\n", - " transforms.RandomHorizontalFlip(),\n", - " transforms.ToTensor(),\n", - " normalize,\n", - " ]))\n", - "\n", - " val_dataset = datasets.ImageFolder(\n", - " valdir,\n", - " transforms.Compose([\n", - " transforms.Resize(256),\n", - " transforms.CenterCrop(224),\n", - " transforms.ToTensor(),\n", - " normalize,\n", - " ]))\n", - "\n", - " if args.distributed:\n", - " train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)\n", - " val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset, shuffle=False, drop_last=True)\n", - " else:\n", - " train_sampler = None\n", - " val_sampler = None\n", - "\n", - " train_loader = torch.utils.data.DataLoader(\n", - " train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),\n", - " num_workers=args.workers, pin_memory=True, sampler=train_sampler)\n", - "\n", - " val_loader = torch.utils.data.DataLoader(\n", - " val_dataset, batch_size=args.batch_size, shuffle=False,\n", - " num_workers=args.workers, pin_memory=True, sampler=val_sampler)\n", - "\n", - " if args.evaluate:\n", - " validate(val_loader, model, criterion, args)\n", - " return\n", - "\n", - " for epoch in range(args.start_epoch, args.epochs):\n", - " if args.distributed:\n", - " train_sampler.set_epoch(epoch)\n", - "\n", - " # train for one epoch\n", - " train(train_loader, model, criterion, optimizer, epoch, args)\n", - "\n", - " # evaluate on validation set\n", - " acc1 = validate(val_loader, model, criterion, args)\n", - " \n", - " scheduler.step()\n", - "\n", - " \n", - " # remember best acc@1 and save checkpoint\n", - " is_best = acc1 > best_acc1\n", - " best_acc1 = max(acc1, best_acc1)\n", - "\n", - " if not args.multiprocessing_distributed or (args.multiprocessing_distributed\n", - " and args.rank % ngpus_per_node == 0):\n", - " save_checkpoint({\n", - " 'epoch': epoch + 1,\n", - " 'arch': args.arch,\n", - " 'state_dict': model.state_dict(),\n", - " 'best_acc1': best_acc1,\n", - " 'optimizer' : optimizer.state_dict(),\n", - " 'scheduler' : scheduler.state_dict()\n", - " }, is_best)\n", - "\n", - "\n", - "def train(train_loader, model, criterion, optimizer, epoch, args):\n", - " batch_time = AverageMeter('Time', ':6.3f')\n", - " data_time = AverageMeter('Data', ':6.3f')\n", - " losses = AverageMeter('Loss', ':.4e')\n", - " top1 = AverageMeter('Acc@1', ':6.2f')\n", - " top5 = AverageMeter('Acc@5', ':6.2f')\n", - " progress = ProgressMeter(\n", - " len(train_loader),\n", - " [batch_time, data_time, losses, top1, top5],\n", - " prefix=\"Epoch: [{}]\".format(epoch))\n", - "\n", - " # switch to train mode\n", - " model.train()\n", - "\n", - " end = time.time()\n", - " for i, (images, target) in enumerate(train_loader):\n", - " # measure data loading time\n", - " data_time.update(time.time() - end)\n", - "\n", - " if args.gpu is not None:\n", - " images = images.cuda(args.gpu, non_blocking=True)\n", - " if torch.cuda.is_available():\n", - " target = target.cuda(args.gpu, non_blocking=True)\n", - "\n", - " # compute output\n", - " output = model(images)\n", - " loss = criterion(output, target)\n", - "\n", - " # measure accuracy and record loss\n", - " acc1, acc5 = accuracy(output, target, topk=(1, 5))\n", - " losses.update(loss.item(), images.size(0))\n", - " top1.update(acc1[0], images.size(0))\n", - " top5.update(acc5[0], images.size(0))\n", - "\n", - " # compute gradient and do SGD step\n", - " optimizer.zero_grad()\n", - " loss.backward()\n", - " optimizer.step()\n", - "\n", - " # measure elapsed time\n", - " batch_time.update(time.time() - end)\n", - " end = time.time()\n", - "\n", - " if i % args.print_freq == 0:\n", - " progress.display(i + 1)\n", - "\n", - "\n", - "def validate(val_loader, model, criterion, args):\n", - "\n", - " def run_validate(loader, base_progress=0):\n", - " with torch.no_grad():\n", - " end = time.time()\n", - " for i, (images, target) in enumerate(loader):\n", - " i = base_progress + i\n", - " if args.gpu is not None:\n", - " images = images.cuda(args.gpu, non_blocking=True)\n", - " if torch.cuda.is_available():\n", - " target = target.cuda(args.gpu, non_blocking=True)\n", - "\n", - " # compute output\n", - " output = model(images)\n", - " loss = criterion(output, target)\n", - "\n", - " # measure accuracy and record loss\n", - " acc1, acc5 = accuracy(output, target, topk=(1, 5))\n", - " losses.update(loss.item(), images.size(0))\n", - " top1.update(acc1[0], images.size(0))\n", - " top5.update(acc5[0], images.size(0))\n", - "\n", - " # measure elapsed time\n", - " batch_time.update(time.time() - end)\n", - " end = time.time()\n", - "\n", - " if i % args.print_freq == 0:\n", - " progress.display(i + 1)\n", - "\n", - " batch_time = AverageMeter('Time', ':6.3f', Summary.NONE)\n", - " losses = AverageMeter('Loss', ':.4e', Summary.NONE)\n", - " top1 = AverageMeter('Acc@1', ':6.2f', Summary.AVERAGE)\n", - " top5 = AverageMeter('Acc@5', ':6.2f', Summary.AVERAGE)\n", - " progress = ProgressMeter(\n", - " len(val_loader) + (args.distributed and (len(val_loader.sampler) * args.world_size < len(val_loader.dataset))),\n", - " [batch_time, losses, top1, top5],\n", - " prefix='Test: ')\n", - "\n", - " # switch to evaluate mode\n", - " model.eval()\n", - "\n", - " run_validate(val_loader)\n", - " if args.distributed:\n", - " top1.all_reduce()\n", - " top5.all_reduce()\n", - "\n", - " if args.distributed and (len(val_loader.sampler) * args.world_size < len(val_loader.dataset)):\n", - " aux_val_dataset = Subset(val_loader.dataset,\n", - " range(len(val_loader.sampler) * args.world_size, len(val_loader.dataset)))\n", - " aux_val_loader = torch.utils.data.DataLoader(\n", - " aux_val_dataset, batch_size=args.batch_size, shuffle=False,\n", - " num_workers=args.workers, pin_memory=True)\n", - " run_validate(aux_val_loader, len(val_loader))\n", - "\n", - " progress.display_summary()\n", - "\n", - " return top1.avg\n", - "\n", - "\n", - "def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):\n", - " torch.save(state, filename)\n", - " if is_best:\n", - " shutil.copyfile(filename, 'model_best.pth.tar')\n", - "\n", - "class Summary(Enum):\n", - " NONE = 0\n", - " AVERAGE = 1\n", - " SUM = 2\n", - " COUNT = 3\n", - "\n", - "class AverageMeter(object):\n", - " \"\"\"Computes and stores the average and current value\"\"\"\n", - " def __init__(self, name, fmt=':f', summary_type=Summary.AVERAGE):\n", - " self.name = name\n", - " self.fmt = fmt\n", - " self.summary_type = summary_type\n", - " self.reset()\n", - "\n", - " def reset(self):\n", - " self.val = 0\n", - " self.avg = 0\n", - " self.sum = 0\n", - " self.count = 0\n", - "\n", - " def update(self, val, n=1):\n", - " self.val = val\n", - " self.sum += val * n\n", - " self.count += n\n", - " self.avg = self.sum / self.count\n", - "\n", - " def all_reduce(self):\n", - " device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", - " total = torch.tensor([self.sum, self.count], dtype=torch.float32, device=device)\n", - " dist.all_reduce(total, dist.ReduceOp.SUM, async_op=False)\n", - " self.sum, self.count = total.tolist()\n", - " self.avg = self.sum / self.count\n", - "\n", - " def __str__(self):\n", - " fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'\n", - " return fmtstr.format(**self.__dict__)\n", - " \n", - " def summary(self):\n", - " fmtstr = ''\n", - " if self.summary_type is Summary.NONE:\n", - " fmtstr = ''\n", - " elif self.summary_type is Summary.AVERAGE:\n", - " fmtstr = '{name} {avg:.3f}'\n", - " elif self.summary_type is Summary.SUM:\n", - " fmtstr = '{name} {sum:.3f}'\n", - " elif self.summary_type is Summary.COUNT:\n", - " fmtstr = '{name} {count:.3f}'\n", - " else:\n", - " raise ValueError('invalid summary type %r' % self.summary_type)\n", - " \n", - " return fmtstr.format(**self.__dict__)\n", - "\n", - "\n", - "class ProgressMeter(object):\n", - " def __init__(self, num_batches, meters, prefix=\"\"):\n", - " self.batch_fmtstr = self._get_batch_fmtstr(num_batches)\n", - " self.meters = meters\n", - " self.prefix = prefix\n", - "\n", - " def display(self, batch):\n", - " entries = [self.prefix + self.batch_fmtstr.format(batch)]\n", - " entries += [str(meter) for meter in self.meters]\n", - " print('\\t'.join(entries))\n", - " \n", - " def display_summary(self):\n", - " entries = [\" *\"]\n", - " entries += [meter.summary() for meter in self.meters]\n", - " print(' '.join(entries))\n", - "\n", - " def _get_batch_fmtstr(self, num_batches):\n", - " num_digits = len(str(num_batches // 1))\n", - " fmt = '{:' + str(num_digits) + 'd}'\n", - " return '[' + fmt + '/' + fmt.format(num_batches) + ']'\n", - "\n", - "def accuracy(output, target, topk=(1,)):\n", - " \"\"\"Computes the accuracy over the k top predictions for the specified values of k\"\"\"\n", - " with torch.no_grad():\n", - " maxk = max(topk)\n", - " batch_size = target.size(0)\n", - "\n", - " _, pred = output.topk(maxk, 1, True, True)\n", - " pred = pred.t()\n", - " correct = pred.eq(target.view(1, -1).expand_as(pred))\n", - "\n", - " res = []\n", - " for k in topk:\n", - " correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)\n", - " res.append(correct_k.mul_(100.0 / batch_size))\n", - " return res\n", - "\n", - "\n", - "if __name__ == '__main__':\n", - " main()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/neural_coder/extensions/neural_compressor_ext_lab/src/constants.ts b/neural_coder/extensions/neural_compressor_ext_lab/src/constants.ts deleted file mode 100644 index f03a215133a..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/src/constants.ts +++ /dev/null @@ -1,14 +0,0 @@ -export namespace Constants { - export const SHORT_PLUGIN_NAME = 'neural_compressor_ext_lab'; - export const WORK_PATH = "neural_coder_workspace/" - export const ICON_FORMAT_ALL_SVG = - '' - export const ICON_RUN = - '' - export const SVG = '' - export const LONG_PLUGIN_NAME = `@rya/${SHORT_PLUGIN_NAME}`; - export const SETTINGS_SECTION = `${LONG_PLUGIN_NAME}:settings`; - export const COMMAND_SECTION_NAME = 'Jupyterlab Code Optimizer'; - export const PLUGIN_VERSION = '0.1.0'; - } - \ No newline at end of file diff --git a/neural_coder/extensions/neural_compressor_ext_lab/src/deepcoder.ts b/neural_coder/extensions/neural_compressor_ext_lab/src/deepcoder.ts deleted file mode 100644 index 6dedddc6d3e..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/src/deepcoder.ts +++ /dev/null @@ -1,386 +0,0 @@ -import { Cell, CodeCell } from '@jupyterlab/cells'; -import { ToolbarButton} from '@jupyterlab/apputils'; -import { Widget } from '@lumino/widgets'; -import { INotebookTracker,NotebookPanel, Notebook, NotebookActions} from '@jupyterlab/notebook'; -import NotebookUtilities from "./utils" -import { Constants } from './constants'; - - -class JupyterlabCodeOptimizer { - protected working: boolean; - protected panel :NotebookPanel; - private tmp_path: string; - public log_path:string; - public tmp_log_path:string; - public rand:number - public markdown: Cell | undefined - - public cells:CodeCell[]; - constructor(panel: NotebookPanel) { - this.working = false; - this.panel = panel - this.tmp_path = "tmp.py" - this.rand = NotebookUtilities.GetRandomNum(0,200) - this.log_path = Constants.WORK_PATH + "NeuralCoder"+this.rand+".log"; - this.tmp_log_path = Constants.WORK_PATH+ "NeuralCoder_tmp"+".log"; - this.cells = []; - } - - - async optimizeCode( - code: string[], - formatter: string, - name:string, - next:string, - options: string | undefined, - notebook: boolean, - panel: NotebookPanel, - cell:CodeCell, - run?:ToolbarButton|undefined - ){ - let codes: string[] = [] - code.forEach(function (value) { - value = value.replace(/('\\n')/g, '^^^') - value = value.replace(/\\n"/g, '###') - value = value.replace(/\\n'/g, '###') - value = value.replace(/"\\n/g, '@@') - value = value.replace(/'\\n/g, '@@') - value = value.replace(/\n/g, '\\n') - value = value.replace(/"/g, '+++') - value = value.replace(/,/g, '$') - codes.push(value) - }) - - let gen_code = `code = "${codes}"\ncodes = code.split(',')\nwith open( '${this.tmp_path}', 'w+' ) as f:\n for i in range(0,len(codes)):\n f.write('# this is the beginning of a single code snippet\\n')\n code_list = codes[i].replace('$',',').replace('+++','\"').split('\\n')\n for line in code_list:\n if('split(^^^)' in line):\n line=line.replace('split(^^^)', 'split(\\'\\\\n\\')')\n if('###' in line):\n line=line.replace('###', '\\\\n\"')\n if('@@' in line):\n line=line.replace('@@', '\"\\\\n')\n f.write(line+'\\n')` - const expr = {code_list: `code_list`}; - NotebookUtilities.sendKernelRequestFromNotebook(panel, gen_code, expr,false); - - if(options === 'normal'){ - let runcode = `from neural_coder import enable\nenable(code="${this.tmp_path}",features=["${formatter}"], overwrite=True)`; - let expr = {sum: ` `}; - NotebookUtilities.sendKernelRequestFromNotebook(panel, runcode, expr,false); - let run_code1 = `with open("${this.tmp_path}", 'r') as f:\n optimized_code = f.read()\n` - let expr1 = {optimizedCode : "optimized_code"} - let result2 = NotebookUtilities.sendKernelRequestFromNotebook(panel,run_code1,expr1,false) - result2.then(value=>{ - let optimizedTexts = Object.values(value.optimizedCode.data)[0] as string - let optimizeCodes = optimizedTexts.split('# this is the beginning of a single code snippet\\n').slice(1,) - optimizeCodes[optimizeCodes.length-1] = optimizeCodes[optimizeCodes.length-1].slice(0,-3) - for (let i = 0; i < optimizeCodes.length; ++i) { - const cell = this.cells[i]; - const currentTexts = this.cells.map(cell => cell.model.value.text); - const currentText = currentTexts[i]; - let optimizedtext = optimizeCodes[i]; - optimizedtext = optimizedtext.replace(/\\'\\\\n\\'/g, "^^^") - optimizedtext = optimizedtext.replace(/\\\\n"/g, "+++") - optimizedtext = optimizedtext.replace(/\\\\n'/g, "+++") - optimizedtext = optimizedtext.replace(/"\\\\n/g, "@@@") - optimizedtext = optimizedtext.replace(/'\\\\n/g, "@@@") - optimizedtext = optimizedtext.replace(/\\n/g,'\n') - optimizedtext = optimizedtext.replace(/\\'/g,"'") - optimizedtext = optimizedtext.replace(/\^\^\^/g, "'\\n'") - optimizedtext = optimizedtext.replace(/\+\+\+/g, "\\n\"") - optimizedtext = optimizedtext.replace(/\@\@\@/g, "\"\\n") - if (cell.model.value.text === currentText) { - cell.model.value.text = optimizedtext; - } - const run_svg = document.createElement("svg") - run_svg.innerHTML = Constants.ICON_RUN - run?.node.firstChild?.firstChild?.firstChild?.firstChild?.replaceWith(run_svg) - } - }) - - }else{ - if(formatter === ''){ - if (this.markdown){ - this.markdown.model.value.text += "[NeuralCoder INFO] Enabling and Benchmarking for The Original Model ...... \n" - } - // cell.outputArea.node.innerText += "[NeuralCoder INFO] Enabling and Benchmarking for The Original Model ......\n" - let runcode1 = `with open("${this.log_path}", 'a' ) as f:\n f.write("[NeuralCoder INFO] Enabling and Benchmarking for The Original Model ......\\n")` - let expr1 = {path: ""}; - NotebookUtilities.sendKernelRequestFromNotebook(panel, runcode1, expr1,false); - - let runcode = `from neural_coder import enable\nperfomance, mode, path = enable(code="${this.tmp_path}",features=[], run_bench=True, args="${options}")\nwith open(path + '/bench.log', 'r') as f:\n logs = f.readlines()\nlog_line = logs[4]\nlog = log_line.split("[")[1].split("]")[0]` - let expr = {path: "path",log:"log"}; - let result = NotebookUtilities.sendKernelRequestFromNotebook(panel, runcode, expr,false); - let fps : String ; - result.then( value => { - fps = Object.values(value.log.data)[0] as string; - if(this.markdown){ - this.markdown.model.value.text += `[NeuralCoder INFO] Benchmark Result (Performance) of The Original Model is ${fps} (FPS) \n` - } - // cell.outputArea.node.innerText += `[NeuralCoder INFO] Benchmark Result (Performance) of The Original Model is ${fps} (FPS)\n` - let text = `[NeuralCoder INFO] Benchmark Result (Performance) of The Original Model is ${fps} (FPS)\\n` - let runcode = `with open("${this.log_path}", 'a' ) as f:\n f.write("${text}")` - let expr = {path: ""}; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode, expr,false); - if(this.markdown){ - this.markdown.model.value.text += `[NeuralCoder INFO] Enabling and Benchmarking for ${next} ...... \n` - } - // cell.outputArea.node.innerText += `[NeuralCoder INFO] Enabling and Benchmarking for ${next} ......\n` - let runcode1 = `with open("${this.log_path}", 'a' ) as f:\n f.write("[NeuralCoder INFO] Enabling and Benchmarking for ${next} ......\\n")` - let expr1 = {path: ""}; - NotebookUtilities.sendKernelRequestFromNotebook(panel, runcode1, expr1,false); - let runcode2 = `with open("${this.tmp_log_path}", 'a' ) as f:\n f.write("${text}")` - let expr2 = {path: ""}; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode2, expr2,false); - }) - - }else{ - - let runcode = `from neural_coder import enable\nperfomance, mode, path = enable(code="${this.tmp_path}", features=["${formatter}"], run_bench=True, args="${options}")\nwith open(path + '/bench.log', 'r') as f:\n logs = f.readlines()\nlog_line = logs[4]\nlog = log_line.split("[")[1].split("]")[0]` - let expr = {path: "path",log:"log"}; - let result = NotebookUtilities.sendKernelRequestFromNotebook(panel, runcode, expr,false); - let fps:string; - result.then( value => { - fps = Object.values(value.log.data)[0] as string; - if(this.markdown){ - this.markdown.model.value.text += `[NeuralCoder INFO] Benchmark Result (Performance) of ${name} is ${fps} (FPS) \n` - } - // cell.outputArea.node.innerText += `[NeuralCoder INFO] Benchmark Result (Performance) of ${name} is ${fps} (FPS)\n` - let text = `[NeuralCoder INFO] Benchmark Result (Performance) of ${name} is ${fps} (FPS)\\n` - let runcode = `with open("${this.log_path}", 'a' ) as f:\n f.write("${text}")` - let expr = {path: ""}; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode, expr,false); - if (next !== ''){ - if(this.markdown){ - this.markdown.model.value.text += `[NeuralCoder INFO] Enabling and Benchmarking for ${next} ...... \n` - } - // cell.outputArea.node.innerText += `[NeuralCoder INFO] Enabling and Benchmarking for ${next} ......\n` - let runcode2 = `with open("${this.log_path}", 'a' ) as f:\n f.write("[NeuralCoder INFO] Enabling and Benchmarking for ${next} ......\\n")` - let expr2 = {path: ""}; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode2, expr2,false); - } - let runcode2 = `with open("${this.tmp_log_path}", 'a' ) as f:\n f.write("${text}")` - let expr2 = {path: ""}; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode2, expr2,false); - - if (formatter === 'pytorch_inc_bf16'){ - let read_log = `import re\nwith open("${this.tmp_log_path}", 'r') as f:\n logs = f.readlines()\n fps_list=[]\n for log_line in logs[-4:]:\n pat =re.compile(r\'\\d+\\.?\\d+'\n) fps = re.findall(pat,log_line)[-1]\n fps_list.append(float(fps))\nmaxi = max(fps_list)\nindex = fps_list.index(maxi)\nboost = round(maxi/fps_list[0],1)\nfeatures=['','pytorch_inc_static_quant_fx','pytorch_inc_dynamic_quant','pytorch_inc_bf16']\nfeature_name=['','INC Enable INT8 (Static)','INC Enable INT8 (Dynamic)','INC Enable BF16']\nbest_feature = features[index]\nbest_name = feature_name[index]\nfeature_l = []\nfeature_l.append(best_feature)\nfrom neural_coder import enable\nenable(code="${this.tmp_path}",features=feature_l, overwrite=True)\nwith open("${this.tmp_path}", 'r') as f:\n optimized_code = f.read()\n` - let read_expr = {boost:"boost",best_feature:"best_feature",best_name:"best_name",optimizeCode:"optimized_code", feature_l: "feature_l"} - let read_result = NotebookUtilities.sendKernelRequestFromNotebook(this.panel, read_log, read_expr,false); - read_result.then(value =>{ - let boost = Object.values(value.boost.data)[0] as string; - let best_name = Object.values(value.best_name.data)[0] as string; - let optimizedTexts = Object.values(value.optimizeCode.data)[0] as string - let optimizeCodes = optimizedTexts.split('# this is the beginning of a single code snippet\\n').slice(1,) - if(this.markdown){ - this.markdown.model.value.text += `[NeuralCoder INFO] The Best Intel Optimization: ${best_name} \n` - this.markdown.model.value.text += `[NeuralCoder INFO] You can get up to ${boost}X performance boost. \n` - } - // cell.outputArea.node.innerText +=`[NeuralCoder INFO] The Best Intel Optimization: ${best_name}\n` - // cell.outputArea.node.innerText += `[NeuralCoder INFO] You can get up to ${boost}X performance boost.\n` - optimizeCodes[optimizeCodes.length-1] = optimizeCodes[optimizeCodes.length-1].slice(0,-3) - for (let i = 0; i < optimizeCodes.length; ++i) { - const cell = this.cells[i]; - const currentTexts = this.cells.map(cell => cell.model.value.text); - const currentText = currentTexts[i]; - let optimizedtext = optimizeCodes[i]; - optimizedtext = optimizedtext.replace(/\\'\\\\n\\'/g, "^^^") - optimizedtext = optimizedtext.replace(/\\\\n"/g, "+++") - optimizedtext = optimizedtext.replace(/\\\\n'/g, "+++") - optimizedtext = optimizedtext.replace(/"\\\\n/g, "@@@") - optimizedtext = optimizedtext.replace(/'\\\\n/g, "@@@") - optimizedtext = optimizedtext.replace(/\\n/g,'\n') - optimizedtext = optimizedtext.replace(/\\'/g,"'") - optimizedtext = optimizedtext.replace(/\^\^\^/g, "'\\n'") - optimizedtext = optimizedtext.replace(/\+\+\+/g, "\\n\"") - optimizedtext = optimizedtext.replace(/\@\@\@/g, "\"\\n") - if (cell.model.value.text === currentText) { - cell.model.value.text = optimizedtext; - } - } - let command = "lscpu | grep 'Model name'" - let get_hardware = `import subprocess\nsubp = subprocess.Popen("${command}",shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE,encoding="utf-8")\nsubp.wait(2)\nhardware = subp.communicate()[0].replace("Model name:","").strip()` - let expr_hardware = {hardware: "hardware"} - let hard_res = NotebookUtilities.sendKernelRequestFromNotebook(this.panel, get_hardware, expr_hardware,false); - hard_res.then(value =>{ - let hard = Object.values(value.hardware.data)[0] as string; - if(this.markdown){ - this.markdown.model.value.text += `[NeuralCoder INFO] HardWare: ${hard} \n` - this.markdown.model.value.text += `[NeuralCoder INFO] The log was saved to neural_coder_workspace\\NeuralCoder${this.rand}.log \n` - } - // cell.outputArea.node.innerText += `[NeuralCoder INFO] HardWare: ${hard}\n` - }) - // cell.outputArea.node.innerText += `[NeuralCoder INFO] The log was saved to lab_workspace\\NeuralCoder${this.rand}.log\n` - const run_svg = document.createElement("svg") - run_svg.innerHTML = Constants.ICON_RUN - run?.node.firstChild?.firstChild?.firstChild?.firstChild?.replaceWith(run_svg) - }) - } - }) - } - } - } - } - - - export class JupyterlabNotebookCodeOptimizer extends JupyterlabCodeOptimizer { - protected notebookname: string; - protected notebookTracker: INotebookTracker; - - constructor( - notebookTracker: INotebookTracker, - panel: NotebookPanel - ) { - super(panel); - this.notebookTracker = notebookTracker; - this.notebookname = ''; - } - - public async optimizeAction(config: any, formatter?: string) { - return this.optimizeCells(true, config, formatter); - } - - - public async optimizeAllCodeCells( - config?: string, - formatter?: string, - notebook?: Notebook, - run?: ToolbarButton - ):Promise { - return this.optimizeCells(false, config, formatter, notebook, run); - } - - private getCodeCells(ifmarkdown = true, notebook?: Notebook): CodeCell[] { - if (!this.notebookTracker.currentWidget) { - return []; - } - const codeCells: CodeCell[] = []; - notebook = notebook || this.notebookTracker.currentWidget.content; - this.notebookname = notebook.title.label - let count = 0 - notebook.widgets.forEach((cell: Cell) => { - if (cell.model.type === 'code') { - count += 1 - codeCells.push(cell as CodeCell); - } - }); - if(ifmarkdown){ - NotebookActions.insertBelow(notebook); - this.notebookTracker.currentWidget.content.activeCellIndex = count + 1; - NotebookActions.changeCellType(notebook, 'markdown') - const activeCell = notebook.activeCell; - if (activeCell){ - this.markdown = activeCell - } - } - this.cells = codeCells - return codeCells; - } - - private async optimizeCells( - selectedOnly: boolean, - config?: string, - formatter?: string, - notebook?: Notebook, - run?: ToolbarButton|undefined - ):Promise{ - if (this.working) { - return new Promise((resolve, reject) => { - resolve("false!") - }); - } - this.working = true; - const optimize_type = formatter !== undefined ? formatter : 'pytorch_mixed_precision_cpu'; - if(optimize_type === 'auto-quant'){ - selectedOnly = true - }else{selectedOnly = false} - const selectedCells = this.getCodeCells(selectedOnly, notebook); - let cell = selectedCells[selectedCells.length-1] - if (selectedCells.length === 0) { - this.working = false; - return new Promise((resolve, reject) => { - resolve("false!") - }); - } - const currentTexts = selectedCells.map(cell => cell.model.value.text); - if (optimize_type === 'auto-quant'){ - if (this.markdown){ - this.markdown.model.value.text = `[NeuralCoder INFO] Auto-Quant Started ...... \n`; - this.markdown.model.value.text += `[NeuralCoder INFO] Code: User code from Jupyter Lab notebook "${this.notebookname}" \n` - this.markdown.model.value.text += `[NeuralCoder INFO] Benchmark Mode: Throughput \n` - } - // cell.outputArea.node.innerText = `[NeuralCoder INFO] Auto-Quant Started ......\n` - // cell.outputArea.node.innerText += `[NeuralCoder INFO] Code: User code from Jupyter Lab notebook "${this.notebookname}"\n` - // cell.outputArea.node.innerText += `[NeuralCoder INFO] Benchmark Mode: Throughput\n` - let runcode = `with open('${this.log_path}', 'a' ) as f:\n f.write("[NeuralCoder INFO] Auto-Quant Started ......\\n")` - let expr = {path: ""}; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode, expr,false); - let runcode2 = `with open('${this.log_path}', 'a' ) as f:\n f.write("[NeuralCoder INFO] Code: User code from Jupyter Lab notebook '${this.notebookname}'\\n")` - let expr2 = {path: ""}; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode2, expr2,false); - let runcode3 = `with open('${this.log_path}', 'a' ) as f:\n f.write("[NeuralCoder INFO] Benchmark Mode: Throughput\\n")` - let expr3 = {path: ""}; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode3, expr3,false); - // cell.outputArea.node.setAttribute("class","pad") - await this.optimizeCode( - currentTexts, - '', - 'The Original Model', - 'INC Enable INT8 (Static)', - config, - true, - this.panel, - cell, - run - ); - await this.optimizeCode( - currentTexts, - 'pytorch_inc_static_quant_fx', - 'INC Enable INT8 (Static)', - 'INC Enable INT8 (Dynamic)', - config, - true, - this.panel, - cell, - run - ); - await this.optimizeCode( - currentTexts, - 'pytorch_inc_dynamic_quant', - 'INC Enable INT8 (Dynamic)', - 'INC Enable BF16', - config, - true, - this.panel, - cell, - run - ); - await this.optimizeCode( - currentTexts, - 'pytorch_inc_bf16', - 'INC Enable BF16', - '', - config, - true, - this.panel, - cell, - run - ); - }else{ - await this.optimizeCode( - currentTexts, - optimize_type, - "", - "", - "normal", - true, - this.panel, - cell, - run - ); - } - this.working = false; - return new Promise((resolve, reject) => { - resolve("success!") - }); - } - applicable(formatter: string, currentWidget: Widget) { - const currentNotebookWidget = this.notebookTracker.currentWidget; - return currentNotebookWidget && currentWidget === currentNotebookWidget; - } - - } - - \ No newline at end of file diff --git a/neural_coder/extensions/neural_compressor_ext_lab/src/index.ts b/neural_coder/extensions/neural_compressor_ext_lab/src/index.ts deleted file mode 100644 index 5f274343ac4..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/src/index.ts +++ /dev/null @@ -1,143 +0,0 @@ -import { DocumentRegistry } from '@jupyterlab/docregistry'; -import { - JupyterFrontEnd, - JupyterFrontEndPlugin -} from '@jupyterlab/application'; -import { INotebookTracker,NotebookPanel, INotebookModel} from '@jupyterlab/notebook'; -import { -ToolbarButton, showDialog, Dialog } from '@jupyterlab/apputils'; -import { ISettingRegistry } from '@jupyterlab/settingregistry'; -import { IMainMenu } from '@jupyterlab/mainmenu'; -import { LabIcon } from '@jupyterlab/ui-components'; -import { Widget } from '@lumino/widgets'; -import { JupyterlabNotebookCodeOptimizer } from './deepcoder'; -import { Constants } from './constants'; - - -class neural_compressor_ext_lab - implements DocumentRegistry.IWidgetExtension { - private app: JupyterFrontEnd; - private tracker: INotebookTracker; - private notebookpanel: NotebookPanel; - private config: string; - private notebookCodeOptimizer: JupyterlabNotebookCodeOptimizer | undefined; - - constructor( - app: JupyterFrontEnd, - tracker: INotebookTracker, - notebookpanel:NotebookPanel - ) { - this.app = app; - this.tracker = tracker; - this.notebookpanel = notebookpanel; - this.setupWidgetExtension(); - this.config = '' - } - - public createNew( - nb: NotebookPanel, - ) { - this.notebookpanel = nb; - this.notebookCodeOptimizer = new JupyterlabNotebookCodeOptimizer( - this.tracker, - this.notebookpanel - ); - const svg = document.createElement("svg") - svg.innerHTML = Constants.ICON_FORMAT_ALL_SVG - const run_svg = document.createElement("svg") - run_svg.innerHTML = Constants.ICON_RUN - const div = document.createElement("div"); - div.setAttribute("class","wrapper") - const span = document.createElement("span"); - span.setAttribute("class","f1ozlkqi") - span.innerHTML = Constants.SVG; - const selector = document.createElement("select"); - selector.setAttribute("class","aselector") - selector.id = "NeuralCoder" - const option1 = document.createElement("option"); - option1.value = "pytorch_inc_static_quant_fx"; - option1.innerText = "INC Enable INT8 (Static)"; - option1.selected=true; - const option2 = document.createElement("option"); - option2.value = "pytorch_inc_dynamic_quant"; - option2.innerText = "INC Enable INT8 (Dynamic)"; - const option3 = document.createElement("option"); - option3.value = "pytorch_inc_bf16"; - option3.innerText = "INC Enable BF16"; - const option4 = document.createElement("option"); - option4.value = "auto-quant"; - option4.innerText = "INC Auto Enable & Benchmark"; - selector.options.add(option1) - selector.options.add(option2) - selector.options.add(option3) - selector.options.add(option4) - div.appendChild(selector) - div.appendChild(span) - const selector_widget = new Widget(); - selector_widget.node.appendChild(div) - selector_widget.addClass("aselector") - let notebookCodeOptimizer = this.notebookCodeOptimizer; - let config = this.config; - - const dia_input = document.createElement("input") - const dia_widget = new Widget(); - dia_widget.node.appendChild(dia_input) - dia_widget.addClass("dialog") - - const run_button = new ToolbarButton({ - tooltip: 'NeuralCoder', - icon: new LabIcon({ - name: "run", - svgstr:Constants.ICON_RUN - }), - onClick: async function (){ - run_button.node.firstChild?.firstChild?.firstChild?.firstChild?.replaceWith(svg) - if (selector.options[selector.selectedIndex].value === 'auto-quant'){ - await showDialog({ - title:'Please input execute parameters:', - body: dia_widget, - buttons: [Dialog.okButton({ label: 'Confirm' })] - }).then(result => { - if (result.button.accept) { - config = dia_input.value - } - }) - } - await notebookCodeOptimizer.optimizeAllCodeCells(config,selector.options[selector.selectedIndex].value,undefined,run_button); - } - }); - nb.toolbar.insertItem(11,"nc",run_button) - nb.toolbar.insertItem(12,"selector",selector_widget) - - } - - private setupWidgetExtension() { - this.app.docRegistry.addWidgetExtension('Notebook', this); - } -} - - - -/** - * Initialization data for the neural_compressor_ext_lab extension. - */ -const plugin: JupyterFrontEndPlugin = { - id: 'neural_compressor_ext_lab:plugin', - autoStart: true, - requires: [INotebookTracker,IMainMenu], - optional: [ISettingRegistry], - activate: ( - app: JupyterFrontEnd, - tracker: INotebookTracker, - notebookpanel: NotebookPanel - ) => { - new neural_compressor_ext_lab( - app, - tracker, - notebookpanel - ); - console.log('JupyterLab extension neural_compressor_ext_lab is activated!'); - } -}; - -export default plugin; diff --git a/neural_coder/extensions/neural_compressor_ext_lab/src/utils.tsx b/neural_coder/extensions/neural_compressor_ext_lab/src/utils.tsx deleted file mode 100644 index f76b2f20d40..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/src/utils.tsx +++ /dev/null @@ -1,366 +0,0 @@ -/* - * Copyright 2019-2020 The Kale Authors - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -import { Dialog, showDialog } from '@jupyterlab/apputils'; -import { NotebookPanel } from '@jupyterlab/notebook'; -import { KernelMessage, Kernel } from '@jupyterlab/services'; -import { CommandRegistry } from '@phosphor/commands'; -// @ts-ignore -import SanitizedHTML from 'react-sanitized-html'; -import * as React from 'react'; -import { ReactElement } from 'react'; - - - -export default class NotebookUtilities { - /** - * generate random number - * @Min - * @Max - */ - public static GetRandomNum(Min:number, Max:number):number { - let Range : number; - Range = Max - Min; - var Rand = Math.random(); - return (Min + Math.round(Rand * Range)); - } - /** - * Builds an HTML container by sanitizing a list of strings and converting - * them in valid HTML - * @param msg A list of string with HTML formatting - * @returns a HTMLDivElement composed of a list of spans with formatted text - */ - private static buildDialogBody(msg: string[]): ReactElement { - return ( -
- {msg.map((s: string, i: number) => { - return ( - - -
- - ); - })} -
- ); - } - - /** - * Opens a pop-up dialog in JupyterLab to display a simple message. - * @param title The title for the message popup - * @param msg The message as an array of strings - * @param buttonLabel The label to use for the button. Default is 'OK' - * @param buttonClassName The classname to give to the 'ok' button - * @returns Promise - A promise once the message is closed. - */ - public static async showMessage( - title: string, - msg: string[], - buttonLabel: string = 'Dismiss', - buttonClassName: string = '', - ): Promise { - const buttons: ReadonlyArray = [ - Dialog.okButton({ label: buttonLabel, className: buttonClassName }), - ]; - const messageBody = this.buildDialogBody(msg); - await showDialog({ title, buttons, body: messageBody }); - } - - /** - * Opens a pop-up dialog in JupyterLab to display a yes/no dialog. - * @param title The title for the message popup - * @param msg The message - * @param acceptLabel The label to use for the accept button. Default is 'YES' - * @param rejectLabel The label to use for the reject button. Default is 'NO' - * @param yesButtonClassName The classname to give to the accept button. - * @param noButtonClassName The classname to give to the cancel button. - * @returns Promise - A promise once the message is closed. - */ - public static async showYesNoDialog( - title: string, - msg: string[], - acceptLabel: string = 'YES', - rejectLabel: string = 'NO', - yesButtonClassName: string = '', - noButtonClassName: string = '', - ): Promise { - const buttons: ReadonlyArray = [ - Dialog.okButton({ label: acceptLabel, className: yesButtonClassName }), - Dialog.cancelButton({ label: rejectLabel, className: noButtonClassName }), - ]; - const messageBody = this.buildDialogBody(msg); - const result = await showDialog({ title, buttons, body: messageBody }); - return result.button.label === acceptLabel; - } - - /** - * Opens a pop-up dialog in JupyterLab with various information and button - * triggering reloading the page. - * @param title The title for the message popup - * @param msg The message - * @param buttonLabel The label to use for the button. Default is 'Refresh' - * @param buttonClassName The classname to give to the 'refresh' button. - * @returns Promise - A promise once the message is closed. - */ - public static async showRefreshDialog( - title: string, - msg: string[], - buttonLabel: string = 'Refresh', - buttonClassName: string = '', - ): Promise { - await this.showMessage(title, msg, buttonLabel, buttonClassName); - location.reload(); - } - - /** - * @description Creates a new JupyterLab notebook for use by the application - * @param command The command registry - * @returns Promise - A promise containing the notebook panel object that was created (if successful). - */ - public static async createNewNotebook( - command: CommandRegistry, - ): Promise { - const notebook: any = await command.execute('notebook:create-new', { - activate: true, - path: '', - preferredLanguage: '', - }); - await notebook.session.ready; - return notebook; - } - - /** - * Safely saves the Jupyter notebook document contents to disk - * @param notebookPanel The notebook panel containing the notebook to save - */ - public static async saveNotebook( - notebookPanel: NotebookPanel, - ): Promise { - if (notebookPanel) { - await notebookPanel.context.ready; - notebookPanel.context.save(); - return true; - } - return false; - } - - /** - * Convert the notebook contents to JSON - * @param notebookPanel The notebook panel containing the notebook to serialize - */ - public static notebookToJSON(notebookPanel: NotebookPanel): any { - if (notebookPanel.content.model) { - return notebookPanel.content.model.toJSON(); - } - return null; - } - - /** - * @description Gets the value of a key from specified notebook's metadata. - * @param notebookPanel The notebook to get meta data from. - * @param key The key of the value. - * @returns any -The value of the metadata. Returns null if the key doesn't exist. - */ - public static getMetaData(notebookPanel: NotebookPanel, key: string): any { - if (!notebookPanel) { - throw new Error( - 'The notebook is null or undefined. No meta data available.', - ); - } - if (notebookPanel.model && notebookPanel.model.metadata.has(key)) { - return notebookPanel.model.metadata.get(key); - } - return null; - } - - /** - * @description Sets the key value pair in the notebook's metadata. - * If the key doesn't exists it will add one. - * @param notebookPanel The notebook to set meta data in. - * @param key The key of the value to create. - * @param value The value to set. - * @param save Default is false. Whether the notebook should be saved after the meta data is set. - * Note: This function will not wait for the save to complete, it only sends a save request. - * @returns The old value for the key, or undefined if it did not exist. - */ - public static setMetaData( - notebookPanel: NotebookPanel, - key: string, - value: any, - save: boolean = false, - ): any { - if (!notebookPanel) { - throw new Error( - 'The notebook is null or undefined. No meta data available.', - ); - } - const oldVal = notebookPanel.model?.metadata.set(key, value); - if (save) { - this.saveNotebook(notebookPanel); - } - return oldVal; - } - - // /** - // * Get a new Kernel, not tied to a Notebook - // * Source code here: https://github.com/jupyterlab/jupyterlab/tree/473348d25bcb258ca2f0c127dd8fb5b193217135/packages/services - // */ - // public static async createNewKernel() { - // // Get info about the available kernels and start a new one. - // let options: Kernel.IOptions = await Kernel.getSpecs().then(kernelSpecs => { - // // console.log('Default spec:', kernelSpecs.default); - // // console.log('Available specs', Object.keys(kernelSpecs.kernelspecs)); - // // use the default name - // return { name: kernelSpecs.default }; - // }); - // return await Kernel.startNew(options).then(_kernel => { - // return _kernel; - // }); - // } - - // // TODO: We can use this context manager to execute commands inside a new kernel - // // and be sure that it will be disposed of at the end. - // // Another approach could be to create a kale_rpc Kernel, as a singleton, - // // created at startup. The only (possible) drawback is that we can not name - // // a kernel instance with a custom id/name, so when refreshing JupyterLab we would - // // not recognize the kernel. A solution could be to have a kernel spec dedicated to kale rpc calls. - // public static async executeWithNewKernel(action: Function, args: any[] = []) { - // // create brand new kernel - // const _k = await this.createNewKernel(); - // // execute action inside kernel - // const res = await action(_k, ...args); - // // close kernel - // _k.shutdown(); - // // return result - // return res; - // } - - /** - * @description This function runs code directly in the notebook's kernel and then evaluates the - * result and returns it as a promise. - * @param kernel The kernel to run the code in. - * @param runCode The code to run in the kernel. - * @param userExpressions The expressions used to capture the desired info from the executed code. - * @param runSilent Default is false. If true, kernel will execute as quietly as possible. - * store_history will be set to false, and no broadcast on IOPUB channel will be made. - * @param storeHistory Default is false. If true, the code executed will be stored in the kernel's history - * and the counter which is shown in the cells will be incremented to reflect code was run. - * @param allowStdIn Default is false. If true, code running in kernel can prompt user for input using - * an input_request message. - * @param stopOnError Default is false. If True, does not abort the execution queue, if an exception is encountered. - * This allows the queued execution of multiple execute_requests, even if they generate exceptions. - * @returns Promise - A promise containing the execution results of the code as an object with - * keys based on the user_expressions. - * @example - * //The code - * const code = "a=123\nb=456\nsum=a+b"; - * //The user expressions - * const expr = {sum: "sum",prod: "a*b",args:"[a,b,sum]"}; - * //Async function call (returns a promise) - * sendKernelRequest(notebookPanel, code, expr,false); - * //Result when promise resolves: - * { - * sum:{status:"ok",data:{"text/plain":"579"},metadata:{}}, - * prod:{status:"ok",data:{"text/plain":"56088"},metadata:{}}, - * args:{status:"ok",data:{"text/plain":"[123, 456, 579]"}} - * } - * @see For more information on JupyterLab messages: - * https://jupyter-client.readthedocs.io/en/latest/messaging.html#execution-results - */ - public static async sendKernelRequest( - kernel: Kernel.IKernelConnection| null | undefined, - runCode: string, - userExpressions: any, - runSilent: boolean = false, - storeHistory: boolean = false, - allowStdIn: boolean = false, - stopOnError: boolean = false, - ): Promise { - if (!kernel) { - throw new Error('Kernel is null or undefined.'); - } - - // Wait for kernel to be ready before sending request - // await kernel.status; - - const message: KernelMessage.IShellMessage = await kernel.requestExecute({ - allow_stdin: allowStdIn, - code: runCode, - silent: runSilent, - stop_on_error: stopOnError, - store_history: storeHistory, - user_expressions: userExpressions, - }).done; - - const content: any = message.content; - - if (content.status !== 'ok') { - // If response is not 'ok', throw contents as error, log code - const msg: string = `Code caused an error:\n${runCode}`; - console.error(msg); - if (content.traceback) { - content.traceback.forEach((line: string) => - console.log( - line.replace( - /[\u001b\u009b][[()#;?]*(?:[0-9]{1,4}(?:;[0-9]{0,4})*)?[0-9A-ORZcf-nqry=><]/g, - '', - ), - ), - ); - } - throw content; - } - // Return user_expressions of the content - return content.user_expressions; - } - - /** - * Same as method sendKernelRequest but passing - * a NotebookPanel instead of a Kernel - */ - public static async sendKernelRequestFromNotebook( - notebookPanel: NotebookPanel, - runCode: string, - userExpressions: any, - runSilent: boolean = false, - storeHistory: boolean = false, - allowStdIn: boolean = false, - stopOnError: boolean = false, - ) { - if (!notebookPanel) { - throw new Error('Notebook is null or undefined.'); - } - - // Wait for notebook panel to be ready - await notebookPanel.activate; - await notebookPanel.sessionContext?.ready; - console.log('get kernel',notebookPanel.sessionContext.session?.kernel) - return this.sendKernelRequest( - notebookPanel.sessionContext?.session?.kernel, - runCode, - userExpressions, - runSilent, - storeHistory, - allowStdIn, - stopOnError, - ); - } - - } diff --git a/neural_coder/extensions/neural_compressor_ext_lab/style/base.css b/neural_coder/extensions/neural_compressor_ext_lab/style/base.css deleted file mode 100644 index 16b572c0e9a..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/style/base.css +++ /dev/null @@ -1,636 +0,0 @@ -/* - See the JupyterLab Developer Guide for useful CSS Patterns: - - https://jupyterlab.readthedocs.io/en/stable/developer/css.html -*/ -.lds-ripple { - display: flex; - position: absolute; - left: 0; - top: 0; - right: 0; - bottom: 0; - margin: auto; - width: 80px; - height: 80px; -} -.lds-ripple div { - position: absolute; - left: 0; - top: 0; - right: 0; - bottom: 0; - margin: auto; - border: 4px solid rgb(245, 131, 55); - opacity: 1; - border-radius: 50%; - animation: lds-ripple 1s cubic-bezier(0, 0.2, 0.8, 1) infinite; -} -.lds-ripple div:nth-child(2) { - animation-delay: -0.5s; -} -@keyframes lds-ripple { - 0% { - left: 0; - top: 0; - right: 0; - bottom: 0; - margin: auto; - width: 0; - height: 0; - opacity: 0; - } - 4.9% { - left: 0; - top: 0; - right: 0; - bottom: 0; - margin: auto; - width: 0; - height: 0; - opacity: 0; - } - 5% { - left: 0; - top: 0; - right: 0; - bottom: 0; - margin: auto; - width: 0; - height: 0; - opacity: 1; - } - 100% { - top: 0px; - left: 0px; - width: 72px; - height: 72px; - opacity: 0; - } -} - - -/* CSS */ -.button-62 { - background: linear-gradient(to bottom right, #EF4765, #FF9A5A); - border: 0; - border-radius: 12px; - color: #FFFFFF; - cursor: pointer; - display: inline-block; - font-family: -apple-system,system-ui,"Segoe UI",Roboto,Helvetica,Arial,sans-serif; - font-size: 16px; - font-weight: 500; - line-height: 2.5; - outline: transparent; - padding: 0 1rem; - text-align: center; - text-decoration: none; - transition: box-shadow .2s ease-in-out; - user-select: none; - -webkit-user-select: none; - touch-action: manipulation; - white-space: nowrap; -} - -.button-62:not([disabled]):focus { - box-shadow: 0 0 .25rem rgba(0, 0, 0, 0.5), -.125rem -.125rem 1rem rgba(239, 71, 101, 0.5), .125rem .125rem 1rem rgba(255, 154, 90, 0.5); -} - -.button-62:not([disabled]):hover { - box-shadow: 0 0 .25rem rgba(0, 0, 0, 0.5), -.125rem -.125rem 1rem rgba(239, 71, 101, 0.5), .125rem .125rem 1rem rgba(255, 154, 90, 0.5); -} - -.aselector select { - background-color: initial; - border: none; - border-radius: 0; - box-shadow: none; - color: var(--jp-ui-font-color0); - display: block; - font-size: var(--jp-ui-font-size1); - height: 24px; - line-height: 14px; - padding: 0 25px 0 10px; - text-align: left; - -moz-appearance: none; - -webkit-appearance: none; -} - -/* Use our own theme for hover and option styles */ -.aselector select:hover, -.aselector select > option { - background-color: var(--jp-layout-color2); - color: var(--jp-ui-font-color0); -} -select { - box-sizing: border-box; -} - -.font{ -background-color: initial; -border: none; -height: 21px; -border-radius: 0; -font-weight:500; -color: var(--jp-ui-font-color0); -display: block; -line-height: 22.5px; -padding: 0 25px 0 10px; -font-size: var(--jp-ui-font-size1); -} -.wrapper { - display: flex; -} -.f1ozlkqi { - pointer-events: none; -} - -.palybutton{ - background-image: '/home2/longxin/Neural_Coder_EXT/style/icons8-circled-play.gif'; -} -.loading{ - - background-image: url("data:image/svg+xml,"); - background-size: contain; -} - -.dialog{ -body { - margin: 0; - height: 100vh; - width:600px; - display: flex; - align-items: center; - justify-content: center; - overflow: hidden; - font-family: "Poppins", sans-serif; - background: #e3d0b6; -} - -#cookie-policy { - display: flex; - flex-direction: column; - justify-content: center; - align-items: center; - width: 460px; - height: 600px; - background: #f3efe6; - border-radius: 12px; - transform: scale(.8); -} - -#cookie-wrapper { - height: 240px; - width: 240px; - margin: 30px 0; - position: relative; - left: -40px; -} - -h1 { - color: #6c3a1f; - text-align: center; - font-size: 36px; - margin: 0; -} - -p { - color: #a28561; - font-size: 14px; - margin-top: 0; - padding: 0 60px; - text-align: center; -} -a { - margin-top: 18px; - font-size: 14px; - color: #a28561; - text-decoration: none; - pointer-events: none; -} -a:hover { - color: #846b4d; -} - -span { - font-family: "Amatic SC", cursive; - font-weight: 400; - font-size: 20px; - position: relative; - top: -18px; - left: 3px; - color: #a28561; -} - -#heart-no, #thought-heart-yes, #mouth, #face-no, #thought-1, #thought-2, #thought-heart-na, #q-mark, #eyes, #leg-l, #leg-r { - opacity: 0; -} -} -.dia_button { - color: white; - background: #dd794a; - margin-top: 12px; - cursor: pointer; - font-size: 24px; - font-family: "Poppins", sans-serif; - border-radius: 9px; - border: none; - width: 72%; - padding: 12px 0; - transition: 150ms ease-out; - pointer-events: none; -} - -.dia_button:hover { - background: #d66029; -} - -.pad{ - padding-left:6% -} - -:root { - /* Elevation - * - * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: - * - * https://github.com/material-components/material-components-web - * https://material-components-web.appspot.com/elevation.html - */ - - --jp-shadow-base-lightness: 0; - --jp-shadow-umbra-color: rgba( - var(--jp-shadow-base-lightness), - var(--jp-shadow-base-lightness), - var(--jp-shadow-base-lightness), - 0.2 - ); - --jp-shadow-penumbra-color: rgba( - var(--jp-shadow-base-lightness), - var(--jp-shadow-base-lightness), - var(--jp-shadow-base-lightness), - 0.14 - ); - --jp-shadow-ambient-color: rgba( - var(--jp-shadow-base-lightness), - var(--jp-shadow-base-lightness), - var(--jp-shadow-base-lightness), - 0.12 - ); - --jp-elevation-z0: none; - --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), - 0px 1px 1px 0px var(--jp-shadow-penumbra-color), - 0px 1px 3px 0px var(--jp-shadow-ambient-color); - --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), - 0px 2px 2px 0px var(--jp-shadow-penumbra-color), - 0px 1px 5px 0px var(--jp-shadow-ambient-color); - --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), - 0px 4px 5px 0px var(--jp-shadow-penumbra-color), - 0px 1px 10px 0px var(--jp-shadow-ambient-color); - --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), - 0px 6px 10px 0px var(--jp-shadow-penumbra-color), - 0px 1px 18px 0px var(--jp-shadow-ambient-color); - --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), - 0px 8px 10px 1px var(--jp-shadow-penumbra-color), - 0px 3px 14px 2px var(--jp-shadow-ambient-color); - --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), - 0px 12px 17px 2px var(--jp-shadow-penumbra-color), - 0px 5px 22px 4px var(--jp-shadow-ambient-color); - --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), - 0px 16px 24px 2px var(--jp-shadow-penumbra-color), - 0px 6px 30px 5px var(--jp-shadow-ambient-color); - --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), - 0px 20px 31px 3px var(--jp-shadow-penumbra-color), - 0px 8px 38px 7px var(--jp-shadow-ambient-color); - --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), - 0px 24px 38px 3px var(--jp-shadow-penumbra-color), - 0px 9px 46px 8px var(--jp-shadow-ambient-color); - - /* Borders - * - * The following variables, specify the visual styling of borders in JupyterLab. - */ - - --jp-border-width: 1px; - --jp-border-color0: var(--md-grey-400); - --jp-border-color1: var(--md-grey-400); - --jp-border-color2: var(--md-grey-300); - --jp-border-color3: var(--md-grey-200); - --jp-inverse-border-color: var(--md-grey-600); - --jp-border-radius: 2px; - - /* UI Fonts - * - * The UI font CSS variables are used for the typography all of the JupyterLab - * user interface elements that are not directly user generated content. - * - * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 - * is applied to a parent element. When children elements, such as headings, are sized - * in em all things will be computed relative to that body size. - */ - - --jp-ui-font-scale-factor: 1.2; - --jp-ui-font-size0: 0.83333em; - --jp-ui-font-size1: 13px; /* Base font size */ - --jp-ui-font-size2: 1.2em; - --jp-ui-font-size3: 1.44em; - - --jp-ui-font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, - Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol'; - - /* - * Use these font colors against the corresponding main layout colors. - * In a light theme, these go from dark to light. - */ - - /* Defaults use Material Design specification */ - --jp-ui-font-color0: rgba(0, 0, 0, 1); - --jp-ui-font-color1: rgba(0, 0, 0, 0.87); - --jp-ui-font-color2: rgba(0, 0, 0, 0.54); - --jp-ui-font-color3: rgba(0, 0, 0, 0.38); - - /* - * Use these against the brand/accent/warn/error colors. - * These will typically go from light to darker, in both a dark and light theme. - */ - - --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); - --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); - --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); - --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); - - /* Content Fonts - * - * Content font variables are used for typography of user generated content. - * - * The font sizing here is done assuming that the body font size of --jp-content-font-size1 - * is applied to a parent element. When children elements, such as headings, are sized - * in em all things will be computed relative to that body size. - */ - - --jp-content-line-height: 1.6; - --jp-content-font-scale-factor: 1.2; - --jp-content-font-size0: 0.83333em; - --jp-content-font-size1: 14px; /* Base font size */ - --jp-content-font-size2: 1.2em; - --jp-content-font-size3: 1.44em; - --jp-content-font-size4: 1.728em; - --jp-content-font-size5: 2.0736em; - - /* This gives a magnification of about 125% in presentation mode over normal. */ - --jp-content-presentation-font-size1: 17px; - - --jp-content-heading-line-height: 1; - --jp-content-heading-margin-top: 1.2em; - --jp-content-heading-margin-bottom: 0.8em; - --jp-content-heading-font-weight: 500; - - /* Defaults use Material Design specification */ - --jp-content-font-color0: rgba(0, 0, 0, 1); - --jp-content-font-color1: rgba(0, 0, 0, 0.87); - --jp-content-font-color2: rgba(0, 0, 0, 0.54); - --jp-content-font-color3: rgba(0, 0, 0, 0.38); - - --jp-content-link-color: var(--md-blue-700); - - --jp-content-font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', - Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', - 'Segoe UI Symbol'; - - /* - * Code Fonts - * - * Code font variables are used for typography of code and other monospaces content. - */ - - --jp-code-font-size: 13px; - --jp-code-line-height: 1.3077; /* 17px for 13px base */ - --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ - --jp-code-font-family-default: Menlo, Consolas, 'DejaVu Sans Mono', monospace; - --jp-code-font-family: var(--jp-code-font-family-default); - - /* This gives a magnification of about 125% in presentation mode over normal. */ - --jp-code-presentation-font-size: 16px; - - /* may need to tweak cursor width if you change font size */ - --jp-code-cursor-width0: 1.4px; - --jp-code-cursor-width1: 2px; - --jp-code-cursor-width2: 4px; - - /* Layout - * - * The following are the main layout colors use in JupyterLab. In a light - * theme these would go from light to dark. - */ - - --jp-layout-color0: white; - --jp-layout-color1: white; - --jp-layout-color2: var(--md-grey-200); - --jp-layout-color3: var(--md-grey-400); - --jp-layout-color4: var(--md-grey-600); - - /* Inverse Layout - * - * The following are the inverse layout colors use in JupyterLab. In a light - * theme these would go from dark to light. - */ - - --jp-inverse-layout-color0: #111111; - --jp-inverse-layout-color1: var(--md-grey-900); - --jp-inverse-layout-color2: var(--md-grey-800); - --jp-inverse-layout-color3: var(--md-grey-700); - --jp-inverse-layout-color4: var(--md-grey-600); - - /* Brand/accent */ - - --jp-brand-color0: var(--md-blue-900); - --jp-brand-color1: var(--md-blue-700); - --jp-brand-color2: var(--md-blue-300); - --jp-brand-color3: var(--md-blue-100); - --jp-brand-color4: var(--md-blue-50); - - --jp-accent-color0: var(--md-green-900); - --jp-accent-color1: var(--md-green-700); - --jp-accent-color2: var(--md-green-300); - --jp-accent-color3: var(--md-green-100); - - /* State colors (warn, error, success, info) */ - - --jp-warn-color0: var(--md-orange-900); - --jp-warn-color1: var(--md-orange-700); - --jp-warn-color2: var(--md-orange-300); - --jp-warn-color3: var(--md-orange-100); - - --jp-error-color0: var(--md-red-900); - --jp-error-color1: var(--md-red-700); - --jp-error-color2: var(--md-red-300); - --jp-error-color3: var(--md-red-100); - - --jp-success-color0: var(--md-green-900); - --jp-success-color1: var(--md-green-700); - --jp-success-color2: var(--md-green-300); - --jp-success-color3: var(--md-green-100); - - --jp-info-color0: var(--md-cyan-900); - --jp-info-color1: var(--md-cyan-700); - --jp-info-color2: var(--md-cyan-300); - --jp-info-color3: var(--md-cyan-100); - - /* Cell specific styles */ - - --jp-cell-padding: 5px; - - --jp-cell-collapser-width: 8px; - --jp-cell-collapser-min-height: 20px; - --jp-cell-collapser-not-active-hover-opacity: 0.6; - - --jp-cell-editor-background: var(--md-grey-100); - --jp-cell-editor-border-color: var(--md-grey-300); - --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); - --jp-cell-editor-active-background: var(--jp-layout-color0); - --jp-cell-editor-active-border-color: var(--jp-brand-color1); - - --jp-cell-prompt-width: 64px; - --jp-cell-prompt-font-family: var(--jp-code-font-family-default); - --jp-cell-prompt-letter-spacing: 0px; - --jp-cell-prompt-opacity: 1; - --jp-cell-prompt-not-active-opacity: 0.5; - --jp-cell-prompt-not-active-font-color: var(--md-grey-700); - /* A custom blend of MD grey and blue 600 - * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ - --jp-cell-inprompt-font-color: #307fc1; - /* A custom blend of MD grey and orange 600 - * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ - --jp-cell-outprompt-font-color: #bf5b3d; - - /* Notebook specific styles */ - - --jp-notebook-padding: 10px; - --jp-notebook-select-background: var(--jp-layout-color1); - --jp-notebook-multiselected-color: var(--md-blue-50); - - /* The scroll padding is calculated to fill enough space at the bottom of the - notebook to show one single-line cell (with appropriate padding) at the top - when the notebook is scrolled all the way to the bottom. We also subtract one - pixel so that no scrollbar appears if we have just one single-line cell in the - notebook. This padding is to enable a 'scroll past end' feature in a notebook. - */ - --jp-notebook-scroll-padding: calc( - 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - - var(--jp-code-padding) - var(--jp-cell-padding) - 1px - ); - - /* Rendermime styles */ - - --jp-rendermime-error-background: #fdd; - --jp-rendermime-table-row-background: var(--md-grey-100); - --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); - - /* Dialog specific styles */ - - --jp-dialog-background: rgba(0, 0, 0, 0.25); - - /* Console specific styles */ - - --jp-console-padding: 10px; - - /* Toolbar specific styles */ - - --jp-toolbar-border-color: var(--jp-border-color1); - --jp-toolbar-micro-height: 8px; - --jp-toolbar-background: var(--jp-layout-color1); - --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); - --jp-toolbar-header-margin: 4px 4px 0px 4px; - --jp-toolbar-active-background: var(--md-grey-300); - - /* Statusbar specific styles */ - - --jp-statusbar-height: 24px; - - /* Input field styles */ - - --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); - --jp-input-active-background: var(--jp-layout-color1); - --jp-input-hover-background: var(--jp-layout-color1); - --jp-input-background: var(--md-grey-100); - --jp-input-border-color: var(--jp-inverse-border-color); - --jp-input-active-border-color: var(--jp-brand-color1); - --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); - - /* General editor styles */ - - --jp-editor-selected-background: #d9d9d9; - --jp-editor-selected-focused-background: #d7d4f0; - --jp-editor-cursor-color: var(--jp-ui-font-color0); - - /* Code mirror specific styles */ - - --jp-mirror-editor-keyword-color: #008000; - --jp-mirror-editor-atom-color: #88f; - --jp-mirror-editor-number-color: #080; - --jp-mirror-editor-def-color: #00f; - --jp-mirror-editor-variable-color: var(--md-grey-900); - --jp-mirror-editor-variable-2-color: #05a; - --jp-mirror-editor-variable-3-color: #085; - --jp-mirror-editor-punctuation-color: #05a; - --jp-mirror-editor-property-color: #05a; - --jp-mirror-editor-operator-color: #aa22ff; - --jp-mirror-editor-comment-color: #408080; - --jp-mirror-editor-string-color: #ba2121; - --jp-mirror-editor-string-2-color: #708; - --jp-mirror-editor-meta-color: #aa22ff; - --jp-mirror-editor-qualifier-color: #555; - --jp-mirror-editor-builtin-color: #008000; - --jp-mirror-editor-bracket-color: #997; - --jp-mirror-editor-tag-color: #170; - --jp-mirror-editor-attribute-color: #00c; - --jp-mirror-editor-header-color: blue; - --jp-mirror-editor-quote-color: #090; - --jp-mirror-editor-link-color: #00c; - --jp-mirror-editor-error-color: #f00; - --jp-mirror-editor-hr-color: #999; - - /* Vega extension styles */ - - --jp-vega-background: white; - - /* Sidebar-related styles */ - - --jp-sidebar-min-width: 250px; - - /* Search-related styles */ - - --jp-search-toggle-off-opacity: 0.5; - --jp-search-toggle-hover-opacity: 0.8; - --jp-search-toggle-on-opacity: 1; - --jp-search-selected-match-background-color: rgb(245, 200, 0); - --jp-search-selected-match-color: black; - --jp-search-unselected-match-background-color: var( - --jp-inverse-layout-color0 - ); - --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); - - /* Icon colors that work well with light or dark backgrounds */ - --jp-icon-contrast-color0: var(--md-purple-600); - --jp-icon-contrast-color1: var(--md-green-600); - --jp-icon-contrast-color2: var(--md-pink-600); - --jp-icon-contrast-color3: var(--md-blue-600); -} - -/*----------------------------------------------------------------------------- -| Copyright (c) Jupyter Development Team. -| Distributed under the terms of the Modified BSD License. -|----------------------------------------------------------------------------*/ - -/* Set the default typography for monospace elements */ -tt, -code, -kbd, -samp, -pre { - font-family: var(--jp-code-font-family); - font-size: var(--jp-code-font-size); - line-height: var(--jp-code-line-height); -} - diff --git a/neural_coder/extensions/neural_compressor_ext_lab/style/base.scss b/neural_coder/extensions/neural_compressor_ext_lab/style/base.scss deleted file mode 100644 index eb86f52434a..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/style/base.scss +++ /dev/null @@ -1,36 +0,0 @@ -svg#play { - width:200px; - margin: 120px auto; - display:block; - cursor: pointer; - transform-origin: 50% 50%; - } - - svg#play #triangle { - fill:rgba(22,22,22,0); - transition:500ms; - transform-origin: 50% 50%; - } - svg#play:hover #triangle { - fill:#696969; - transform-origin: 50% 50%; - } - - svg #lineOne, svg #lineTwo { - transform-origin: 50% 50%; - transition: 1s; - } - - svg:hover #lineOne { - transform: rotate(260deg); - -webkit-transform: rotate(260deg); - -moz-transform: rotate(260deg); - -o-transform: rotate(260deg); - transform-origin: 50% 50%; - } - - svg:hover #lineTwo { - transform: rotate(-450deg); - transform-origin: 50% 50%; - } - \ No newline at end of file diff --git a/neural_coder/extensions/neural_compressor_ext_lab/style/index.css b/neural_coder/extensions/neural_compressor_ext_lab/style/index.css deleted file mode 100644 index 8a7ea29e694..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/style/index.css +++ /dev/null @@ -1 +0,0 @@ -@import url('base.css'); diff --git a/neural_coder/extensions/neural_compressor_ext_lab/style/index.js b/neural_coder/extensions/neural_compressor_ext_lab/style/index.js deleted file mode 100644 index a028a76408b..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/style/index.js +++ /dev/null @@ -1 +0,0 @@ -import './base.css'; diff --git a/neural_coder/extensions/neural_compressor_ext_lab/test_torchvision.ipynb b/neural_coder/extensions/neural_compressor_ext_lab/test_torchvision.ipynb deleted file mode 100644 index 58d9f2ae940..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/test_torchvision.ipynb +++ /dev/null @@ -1,633 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "b2812d42-da69-4741-97e8-5d1909931d60", - "metadata": {}, - "outputs": [], - "source": [ - "import logging\n", - "import os\n", - "import random\n", - "import sys\n", - "from dataclasses import dataclass, field\n", - "from typing import Optional\n", - "\n", - "import datasets\n", - "import numpy as np\n", - "from datasets import load_dataset, load_metric\n", - "\n", - "import transformers\n", - "from transformers import (\n", - " AutoConfig,\n", - " AutoModelForSequenceClassification,\n", - " AutoTokenizer,\n", - " DataCollatorWithPadding,\n", - " EvalPrediction,\n", - " HfArgumentParser,\n", - " PretrainedConfig,\n", - " Trainer,\n", - " TrainingArguments,\n", - " default_data_collator,\n", - " set_seed,\n", - ")\n", - "from transformers.trainer_utils import get_last_checkpoint\n", - "from transformers.utils import check_min_version, send_example_telemetry\n", - "from transformers.utils.versions import require_version\n", - "\n", - "\n", - "# Will error if the minimal version of Transformers is not installed. Remove at your own risks.\n", - "check_min_version(\"4.21.0\")\n", - "\n", - "require_version(\"datasets>=1.8.0\", \"To fix: pip install -r examples/pytorch/text-classification/requirements.txt\")\n", - "\n", - "task_to_keys = {\n", - " \"cola\": (\"sentence\", None),\n", - " \"mnli\": (\"premise\", \"hypothesis\"),\n", - " \"mrpc\": (\"sentence1\", \"sentence2\"),\n", - " \"qnli\": (\"question\", \"sentence\"),\n", - " \"qqp\": (\"question1\", \"question2\"),\n", - " \"rte\": (\"sentence1\", \"sentence2\"),\n", - " \"sst2\": (\"sentence\", None),\n", - " \"stsb\": (\"sentence1\", \"sentence2\"),\n", - " \"wnli\": (\"sentence1\", \"sentence2\"),\n", - "}\n", - "\n", - "logger = logging.getLogger(__name__)\n", - "\n", - "\n", - "@dataclass\n", - "class DataTrainingArguments:\n", - " \"\"\"\n", - " Arguments pertaining to what data we are going to input our model for training and eval.\n", - "\n", - " Using `HfArgumentParser` we can turn this class\n", - " into argparse arguments to be able to specify them on\n", - " the command line.\n", - " \"\"\"\n", - "\n", - " task_name: Optional[str] = field(\n", - " default=None,\n", - " metadata={\"help\": \"The name of the task to train on: \" + \", \".join(task_to_keys.keys())},\n", - " )\n", - " dataset_name: Optional[str] = field(\n", - " default=None, metadata={\"help\": \"The name of the dataset to use (via the datasets library).\"}\n", - " )\n", - " dataset_config_name: Optional[str] = field(\n", - " default=None, metadata={\"help\": \"The configuration name of the dataset to use (via the datasets library).\"}\n", - " )\n", - " max_seq_length: int = field(\n", - " default=128,\n", - " metadata={\n", - " \"help\": (\n", - " \"The maximum total input sequence length after tokenization. Sequences longer \"\n", - " \"than this will be truncated, sequences shorter will be padded.\"\n", - " )\n", - " },\n", - " )\n", - " overwrite_cache: bool = field(\n", - " default=False, metadata={\"help\": \"Overwrite the cached preprocessed datasets or not.\"}\n", - " )\n", - " pad_to_max_length: bool = field(\n", - " default=True,\n", - " metadata={\n", - " \"help\": (\n", - " \"Whether to pad all samples to `max_seq_length`. \"\n", - " \"If False, will pad the samples dynamically when batching to the maximum length in the batch.\"\n", - " )\n", - " },\n", - " )\n", - " max_train_samples: Optional[int] = field(\n", - " default=None,\n", - " metadata={\n", - " \"help\": (\n", - " \"For debugging purposes or quicker training, truncate the number of training examples to this \"\n", - " \"value if set.\"\n", - " )\n", - " },\n", - " )\n", - " max_eval_samples: Optional[int] = field(\n", - " default=None,\n", - " metadata={\n", - " \"help\": (\n", - " \"For debugging purposes or quicker training, truncate the number of evaluation examples to this \"\n", - " \"value if set.\"\n", - " )\n", - " },\n", - " )\n", - " max_predict_samples: Optional[int] = field(\n", - " default=None,\n", - " metadata={\n", - " \"help\": (\n", - " \"For debugging purposes or quicker training, truncate the number of prediction examples to this \"\n", - " \"value if set.\"\n", - " )\n", - " },\n", - " )\n", - " train_file: Optional[str] = field(\n", - " default=None, metadata={\"help\": \"A csv or a json file containing the training data.\"}\n", - " )\n", - " validation_file: Optional[str] = field(\n", - " default=None, metadata={\"help\": \"A csv or a json file containing the validation data.\"}\n", - " )\n", - " test_file: Optional[str] = field(default=None, metadata={\"help\": \"A csv or a json file containing the test data.\"})\n", - "\n", - " def __post_init__(self):\n", - " if self.task_name is not None:\n", - " self.task_name = self.task_name.lower()\n", - " if self.task_name not in task_to_keys.keys():\n", - " raise ValueError(\"Unknown task, you should pick one in \" + \",\".join(task_to_keys.keys()))\n", - " elif self.dataset_name is not None:\n", - " pass\n", - " elif self.train_file is None or self.validation_file is None:\n", - " raise ValueError(\"Need either a GLUE task, a training/validation file or a dataset name.\")\n", - " else:\n", - " train_extension = self.train_file.split(\".\")[-1]\n", - " assert train_extension in [\"csv\", \"json\"], \"`train_file` should be a csv or a json file.\"\n", - " validation_extension = self.validation_file.split(\".\")[-1]\n", - " assert (\n", - " validation_extension == train_extension\n", - " ), \"`validation_file` should have the same extension (csv or json) as `train_file`.\"\n", - "\n", - "\n", - "@dataclass\n", - "class ModelArguments:\n", - " \"\"\"\n", - " Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.\n", - " \"\"\"\n", - "\n", - " model_name_or_path: str = field(\n", - " metadata={\"help\": \"Path to pretrained model or model identifier from huggingface.co/models\"}\n", - " )\n", - " config_name: Optional[str] = field(\n", - " default=None, metadata={\"help\": \"Pretrained config name or path if not the same as model_name\"}\n", - " )\n", - " tokenizer_name: Optional[str] = field(\n", - " default=None, metadata={\"help\": \"Pretrained tokenizer name or path if not the same as model_name\"}\n", - " )\n", - " cache_dir: Optional[str] = field(\n", - " default=None,\n", - " metadata={\"help\": \"Where do you want to store the pretrained models downloaded from huggingface.co\"},\n", - " )\n", - " use_fast_tokenizer: bool = field(\n", - " default=True,\n", - " metadata={\"help\": \"Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.\"},\n", - " )\n", - " model_revision: str = field(\n", - " default=\"main\",\n", - " metadata={\"help\": \"The specific model version to use (can be a branch name, tag name or commit id).\"},\n", - " )\n", - " use_auth_token: bool = field(\n", - " default=False,\n", - " metadata={\n", - " \"help\": (\n", - " \"Will use the token generated when running `transformers-cli login` (necessary to use this script \"\n", - " \"with private models).\"\n", - " )\n", - " },\n", - " )\n", - " ignore_mismatched_sizes: bool = field(\n", - " default=False,\n", - " metadata={\"help\": \"Will enable to load a pretrained model whose head dimensions are different.\"},\n", - " )\n", - "\n", - "\n", - "def main():\n", - " # See all possible arguments in src/transformers/training_args.py\n", - " # or by passing the --help flag to this script.\n", - " # We now keep distinct sets of args, for a cleaner separation of concerns.\n", - "\n", - " parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))\n", - " if len(sys.argv) == 2 and sys.argv[1].endswith(\".json\"):\n", - " # If we pass only one argument to the script and it's the path to a json file,\n", - " # let's parse it to get our arguments.\n", - " model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))\n", - " else:\n", - " model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n", - "\n", - " # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The\n", - " # information sent is the one passed as arguments along with your Python/PyTorch versions.\n", - " send_example_telemetry(\"run_glue\", model_args, data_args)\n", - "\n", - " # Setup logging\n", - " logging.basicConfig(\n", - " format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n", - " datefmt=\"%m/%d/%Y %H:%M:%S\",\n", - " handlers=[logging.StreamHandler(sys.stdout)],\n", - " )\n", - "\n", - " log_level = training_args.get_process_log_level()\n", - " logger.setLevel(log_level)\n", - " datasets.utils.logging.set_verbosity(log_level)\n", - " transformers.utils.logging.set_verbosity(log_level)\n", - " transformers.utils.logging.enable_default_handler()\n", - " transformers.utils.logging.enable_explicit_format()\n", - "\n", - " # Log on each process the small summary:\n", - " logger.warning(\n", - " f\"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}\"\n", - " + f\"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}\"\n", - " )\n", - " logger.info(f\"Training/evaluation parameters {training_args}\")\n", - "\n", - " # Detecting last checkpoint.\n", - " last_checkpoint = None\n", - " if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:\n", - " last_checkpoint = get_last_checkpoint(training_args.output_dir)\n", - " if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:\n", - " raise ValueError(\n", - " f\"Output directory ({training_args.output_dir}) already exists and is not empty. \"\n", - " \"Use --overwrite_output_dir to overcome.\"\n", - " )\n", - " elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:\n", - " logger.info(\n", - " f\"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change \"\n", - " \"the `--output_dir` or add `--overwrite_output_dir` to train from scratch.\"\n", - " )\n", - "\n", - " # Set seed before initializing model.\n", - " set_seed(training_args.seed)\n", - "\n", - " # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)\n", - " # or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).\n", - " #\n", - " # For CSV/JSON files, this script will use as labels the column called 'label' and as pair of sentences the\n", - " # sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named\n", - " # label if at least two columns are provided.\n", - " #\n", - " # If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this\n", - " # single column. You can easily tweak this behavior (see below)\n", - " #\n", - " # In distributed training, the load_dataset function guarantee that only one local process can concurrently\n", - " # download the dataset.\n", - " if data_args.task_name is not None:\n", - " # Downloading and loading a dataset from the hub.\n", - " raw_datasets = load_dataset(\n", - " \"glue\",\n", - " data_args.task_name,\n", - " cache_dir=model_args.cache_dir,\n", - " use_auth_token=True if model_args.use_auth_token else None,\n", - " )\n", - " elif data_args.dataset_name is not None:\n", - " # Downloading and loading a dataset from the hub.\n", - " raw_datasets = load_dataset(\n", - " data_args.dataset_name,\n", - " data_args.dataset_config_name,\n", - " cache_dir=model_args.cache_dir,\n", - " use_auth_token=True if model_args.use_auth_token else None,\n", - " )\n", - " else:\n", - " # Loading a dataset from your local files.\n", - " # CSV/JSON training and evaluation files are needed.\n", - " data_files = {\"train\": data_args.train_file, \"validation\": data_args.validation_file}\n", - "\n", - " # Get the test dataset: you can provide your own CSV/JSON test file (see below)\n", - " # when you use `do_predict` without specifying a GLUE benchmark task.\n", - " if training_args.do_predict:\n", - " if data_args.test_file is not None:\n", - " train_extension = data_args.train_file.split(\".\")[-1]\n", - " test_extension = data_args.test_file.split(\".\")[-1]\n", - " assert (\n", - " test_extension == train_extension\n", - " ), \"`test_file` should have the same extension (csv or json) as `train_file`.\"\n", - " data_files[\"test\"] = data_args.test_file\n", - " else:\n", - " raise ValueError(\"Need either a GLUE task or a test file for `do_predict`.\")\n", - "\n", - " for key in data_files.keys():\n", - " logger.info(f\"load a local file for {key}: {data_files[key]}\")\n", - "\n", - " if data_args.train_file.endswith(\".csv\"):\n", - " # Loading a dataset from local csv files\n", - " raw_datasets = load_dataset(\n", - " \"csv\",\n", - " data_files=data_files,\n", - " cache_dir=model_args.cache_dir,\n", - " use_auth_token=True if model_args.use_auth_token else None,\n", - " )\n", - " else:\n", - " # Loading a dataset from local json files\n", - " raw_datasets = load_dataset(\n", - " \"json\",\n", - " data_files=data_files,\n", - " cache_dir=model_args.cache_dir,\n", - " use_auth_token=True if model_args.use_auth_token else None,\n", - " )\n", - " # See more about loading any type of standard or custom dataset at\n", - " # https://huggingface.co/docs/datasets/loading_datasets.html.\n", - "\n", - " # Labels\n", - " if data_args.task_name is not None:\n", - " is_regression = data_args.task_name == \"stsb\"\n", - " if not is_regression:\n", - " label_list = raw_datasets[\"train\"].features[\"label\"].names\n", - " num_labels = len(label_list)\n", - " else:\n", - " num_labels = 1\n", - " else:\n", - " # Trying to have good defaults here, don't hesitate to tweak to your needs.\n", - " is_regression = raw_datasets[\"train\"].features[\"label\"].dtype in [\"float32\", \"float64\"]\n", - " if is_regression:\n", - " num_labels = 1\n", - " else:\n", - " # A useful fast method:\n", - " # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.unique\n", - " label_list = raw_datasets[\"train\"].unique(\"label\")\n", - " label_list.sort() # Let's sort it for determinism\n", - " num_labels = len(label_list)\n", - "\n", - " # Load pretrained model and tokenizer\n", - " #\n", - " # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently\n", - " # download model & vocab.\n", - " config = AutoConfig.from_pretrained(\n", - " model_args.config_name if model_args.config_name else model_args.model_name_or_path,\n", - " num_labels=num_labels,\n", - " finetuning_task=data_args.task_name,\n", - " cache_dir=model_args.cache_dir,\n", - " revision=model_args.model_revision,\n", - " use_auth_token=True if model_args.use_auth_token else None,\n", - " )\n", - " tokenizer = AutoTokenizer.from_pretrained(\n", - " model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,\n", - " cache_dir=model_args.cache_dir,\n", - " use_fast=model_args.use_fast_tokenizer,\n", - " revision=model_args.model_revision,\n", - " use_auth_token=True if model_args.use_auth_token else None,\n", - " )\n", - " model = AutoModelForSequenceClassification.from_pretrained(\n", - " model_args.model_name_or_path,\n", - " from_tf=bool(\".ckpt\" in model_args.model_name_or_path),\n", - " config=config,\n", - " cache_dir=model_args.cache_dir,\n", - " revision=model_args.model_revision,\n", - " use_auth_token=True if model_args.use_auth_token else None,\n", - " ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,\n", - " )\n", - "\n", - " # Preprocessing the raw_datasets\n", - " if data_args.task_name is not None:\n", - " sentence1_key, sentence2_key = task_to_keys[data_args.task_name]\n", - " else:\n", - " # Again, we try to have some nice defaults but don't hesitate to tweak to your use case.\n", - " non_label_column_names = [name for name in raw_datasets[\"train\"].column_names if name != \"label\"]\n", - " if \"sentence1\" in non_label_column_names and \"sentence2\" in non_label_column_names:\n", - " sentence1_key, sentence2_key = \"sentence1\", \"sentence2\"\n", - " else:\n", - " if len(non_label_column_names) >= 2:\n", - " sentence1_key, sentence2_key = non_label_column_names[:2]\n", - " else:\n", - " sentence1_key, sentence2_key = non_label_column_names[0], None\n", - "\n", - " # Padding strategy\n", - " if data_args.pad_to_max_length:\n", - " padding = \"max_length\"\n", - " else:\n", - " # We will pad later, dynamically at batch creation, to the max sequence length in each batch\n", - " padding = False\n", - "\n", - " # Some models have set the order of the labels to use, so let's make sure we do use it.\n", - " label_to_id = None\n", - " if (\n", - " model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id\n", - " and data_args.task_name is not None\n", - " and not is_regression\n", - " ):\n", - " # Some have all caps in their config, some don't.\n", - " label_name_to_id = {k.lower(): v for k, v in model.config.label2id.items()}\n", - " if list(sorted(label_name_to_id.keys())) == list(sorted(label_list)):\n", - " label_to_id = {i: int(label_name_to_id[label_list[i]]) for i in range(num_labels)}\n", - " else:\n", - " logger.warning(\n", - " \"Your model seems to have been trained with labels, but they don't match the dataset: \",\n", - " f\"model labels: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(sorted(label_list))}.\"\n", - " \"\\nIgnoring the model labels as a result.\",\n", - " )\n", - " elif data_args.task_name is None and not is_regression:\n", - " label_to_id = {v: i for i, v in enumerate(label_list)}\n", - "\n", - " if label_to_id is not None:\n", - " model.config.label2id = label_to_id\n", - " model.config.id2label = {id: label for label, id in config.label2id.items()}\n", - " elif data_args.task_name is not None and not is_regression:\n", - " model.config.label2id = {l: i for i, l in enumerate(label_list)}\n", - " model.config.id2label = {id: label for label, id in config.label2id.items()}\n", - "\n", - " if data_args.max_seq_length > tokenizer.model_max_length:\n", - " logger.warning(\n", - " f\"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the\"\n", - " f\"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.\"\n", - " )\n", - " max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)\n", - "\n", - " def preprocess_function(examples):\n", - " # Tokenize the texts\n", - " args = (\n", - " (examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key])\n", - " )\n", - " result = tokenizer(*args, padding=padding, max_length=max_seq_length, truncation=True)\n", - "\n", - " # Map labels to IDs (not necessary for GLUE tasks)\n", - " if label_to_id is not None and \"label\" in examples:\n", - " result[\"label\"] = [(label_to_id[l] if l != -1 else -1) for l in examples[\"label\"]]\n", - " return result\n", - "\n", - " with training_args.main_process_first(desc=\"dataset map pre-processing\"):\n", - " raw_datasets = raw_datasets.map(\n", - " preprocess_function,\n", - " batched=True,\n", - " load_from_cache_file=not data_args.overwrite_cache,\n", - " desc=\"Running tokenizer on dataset\",\n", - " )\n", - " if training_args.do_train:\n", - " if \"train\" not in raw_datasets:\n", - " raise ValueError(\"--do_train requires a train dataset\")\n", - " train_dataset = raw_datasets[\"train\"]\n", - " if data_args.max_train_samples is not None:\n", - " max_train_samples = min(len(train_dataset), data_args.max_train_samples)\n", - " train_dataset = train_dataset.select(range(max_train_samples))\n", - "\n", - " if training_args.do_eval:\n", - " if \"validation\" not in raw_datasets and \"validation_matched\" not in raw_datasets:\n", - " raise ValueError(\"--do_eval requires a validation dataset\")\n", - " eval_dataset = raw_datasets[\"validation_matched\" if data_args.task_name == \"mnli\" else \"validation\"]\n", - " if data_args.max_eval_samples is not None:\n", - " max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)\n", - " eval_dataset = eval_dataset.select(range(max_eval_samples))\n", - "\n", - " if training_args.do_predict or data_args.task_name is not None or data_args.test_file is not None:\n", - " if \"test\" not in raw_datasets and \"test_matched\" not in raw_datasets:\n", - " raise ValueError(\"--do_predict requires a test dataset\")\n", - " predict_dataset = raw_datasets[\"test_matched\" if data_args.task_name == \"mnli\" else \"test\"]\n", - " if data_args.max_predict_samples is not None:\n", - " max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples)\n", - " predict_dataset = predict_dataset.select(range(max_predict_samples))\n", - "\n", - " # Log a few random samples from the training set:\n", - " if training_args.do_train:\n", - " for index in random.sample(range(len(train_dataset)), 3):\n", - " logger.info(f\"Sample {index} of the training set: {train_dataset[index]}.\")\n", - "\n", - " # Get the metric function\n", - " if data_args.task_name is not None:\n", - " metric = load_metric(\"glue\", data_args.task_name)\n", - " else:\n", - " metric = load_metric(\"accuracy\")\n", - "\n", - " # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a\n", - " # predictions and label_ids field) and has to return a dictionary string to float.\n", - " def compute_metrics(p: EvalPrediction):\n", - " preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions\n", - " preds = np.squeeze(preds) if is_regression else np.argmax(preds, axis=1)\n", - " if data_args.task_name is not None:\n", - " result = metric.compute(predictions=preds, references=p.label_ids)\n", - " if len(result) > 1:\n", - " result[\"combined_score\"] = np.mean(list(result.values())).item()\n", - " return result\n", - " elif is_regression:\n", - " return {\"mse\": ((preds - p.label_ids) ** 2).mean().item()}\n", - " else:\n", - " return {\"accuracy\": (preds == p.label_ids).astype(np.float32).mean().item()}\n", - "\n", - " # Data collator will default to DataCollatorWithPadding when the tokenizer is passed to Trainer, so we change it if\n", - " # we already did the padding.\n", - " if data_args.pad_to_max_length:\n", - " data_collator = default_data_collator\n", - " elif training_args.fp16:\n", - " data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)\n", - " else:\n", - " data_collator = None\n", - "\n", - " # Initialize our Trainer\n", - " trainer = Trainer(\n", - " model=model,\n", - " args=training_args,\n", - " train_dataset=train_dataset if training_args.do_train else None,\n", - " eval_dataset=eval_dataset if training_args.do_eval else None,\n", - " compute_metrics=compute_metrics,\n", - " tokenizer=tokenizer,\n", - " data_collator=data_collator,\n", - " )\n", - " \n", - " # Training\n", - " if training_args.do_train:\n", - " checkpoint = None\n", - " if training_args.resume_from_checkpoint is not None:\n", - " checkpoint = training_args.resume_from_checkpoint\n", - " elif last_checkpoint is not None:\n", - " checkpoint = last_checkpoint\n", - " train_result = trainer.train(resume_from_checkpoint=checkpoint)\n", - " metrics = train_result.metrics\n", - " max_train_samples = (\n", - " data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)\n", - " )\n", - " metrics[\"train_samples\"] = min(max_train_samples, len(train_dataset))\n", - "\n", - " trainer.save_model() # Saves the tokenizer too for easy upload\n", - "\n", - " trainer.log_metrics(\"train\", metrics)\n", - " trainer.save_metrics(\"train\", metrics)\n", - " trainer.save_state()\n", - "\n", - " # Evaluation\n", - " if training_args.do_eval:\n", - " logger.info(\"*** Evaluate ***\")\n", - "\n", - " # Loop to handle MNLI double evaluation (matched, mis-matched)\n", - " tasks = [data_args.task_name]\n", - " eval_datasets = [eval_dataset]\n", - " if data_args.task_name == \"mnli\":\n", - " tasks.append(\"mnli-mm\")\n", - " eval_datasets.append(raw_datasets[\"validation_mismatched\"])\n", - " combined = {}\n", - "\n", - " for eval_dataset, task in zip(eval_datasets, tasks):\n", - " metrics = trainer.evaluate(eval_dataset=eval_dataset)\n", - "\n", - " max_eval_samples = (\n", - " data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)\n", - " )\n", - " metrics[\"eval_samples\"] = min(max_eval_samples, len(eval_dataset))\n", - "\n", - " if task == \"mnli-mm\":\n", - " metrics = {k + \"_mm\": v for k, v in metrics.items()}\n", - " if task is not None and \"mnli\" in task:\n", - " combined.update(metrics)\n", - "\n", - " trainer.log_metrics(\"eval\", metrics)\n", - " trainer.save_metrics(\"eval\", combined if task is not None and \"mnli\" in task else metrics)\n", - "\n", - " if training_args.do_predict:\n", - " logger.info(\"*** Predict ***\")\n", - "\n", - " # Loop to handle MNLI double evaluation (matched, mis-matched)\n", - " tasks = [data_args.task_name]\n", - " predict_datasets = [predict_dataset]\n", - " if data_args.task_name == \"mnli\":\n", - " tasks.append(\"mnli-mm\")\n", - " predict_datasets.append(raw_datasets[\"test_mismatched\"])\n", - "\n", - " for predict_dataset, task in zip(predict_datasets, tasks):\n", - " # Removing the `label` columns because it contains -1 and Trainer won't like that.\n", - " predict_dataset = predict_dataset.remove_columns(\"label\")\n", - " predictions = trainer.predict(predict_dataset, metric_key_prefix=\"predict\").predictions\n", - " predictions = np.squeeze(predictions) if is_regression else np.argmax(predictions, axis=1)\n", - "\n", - " output_predict_file = os.path.join(training_args.output_dir, f\"predict_results_{task}.txt\")\n", - " if trainer.is_world_process_zero():\n", - " with open(output_predict_file, \"w\") as writer:\n", - " logger.info(f\"***** Predict results {task} *****\")\n", - " writer.write(\"index\\tprediction\\n\")\n", - " for index, item in enumerate(predictions):\n", - " if is_regression:\n", - " writer.write(f\"{index}\\t{item:3.3f}\\n\")\n", - " else:\n", - " item = label_list[item]\n", - " writer.write(f\"{index}\\t{item}\\n\")\n", - "\n", - " kwargs = {\"finetuned_from\": model_args.model_name_or_path, \"tasks\": \"text-classification\"}\n", - " if data_args.task_name is not None:\n", - " kwargs[\"language\"] = \"en\"\n", - " kwargs[\"dataset_tags\"] = \"glue\"\n", - " kwargs[\"dataset_args\"] = data_args.task_name\n", - " kwargs[\"dataset\"] = f\"GLUE {data_args.task_name.upper()}\"\n", - "\n", - " if training_args.push_to_hub:\n", - " trainer.push_to_hub(**kwargs)\n", - " else:\n", - " trainer.create_model_card(**kwargs)\n", - "\n", - "\n", - "def _mp_fn(index):\n", - " # For xla_spawn (TPUs)\n", - " main()\n", - "\n", - "\n", - "if __name__ == \"__main__\":\n", - " main()\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/neural_coder/extensions/neural_compressor_ext_lab/tsconfig.json b/neural_coder/extensions/neural_compressor_ext_lab/tsconfig.json deleted file mode 100644 index 7c8e8c78b69..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/tsconfig.json +++ /dev/null @@ -1,24 +0,0 @@ -{ - "compilerOptions": { - "allowSyntheticDefaultImports": true, - "composite": true, - "declaration": true, - "esModuleInterop": true, - "incremental": true, - "jsx": "react", - "module": "esnext", - "moduleResolution": "node", - "noEmitOnError": true, - "noImplicitAny": true, - "noUnusedLocals": true, - "preserveWatchOutput": true, - "resolveJsonModule": true, - "outDir": "lib", - "rootDir": "src", - "strict": true, - "strictNullChecks": true, - "target": "es2017", - "types": ["node"] - }, - "include": ["src/*"] -} diff --git a/neural_coder/extensions/neural_compressor_ext_lab/tsconfig.tsbuildinfo b/neural_coder/extensions/neural_compressor_ext_lab/tsconfig.tsbuildinfo deleted file mode 100644 index d827a56b4f3..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab/tsconfig.tsbuildinfo +++ /dev/null @@ -1,12213 +0,0 @@ -{ - "program": { - "fileInfos": { - "./node_modules/typescript/lib/lib.es5.d.ts": { - "version": "9622e8bd7cc72a7dab819a8011ecbf81d443638082e5cb99ecf2e75ff56ffc9d", - 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-**/__tests__ -ui-tests diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/.eslintrc.js b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/.eslintrc.js deleted file mode 100644 index 665374bf0ea..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/.eslintrc.js +++ /dev/null @@ -1,39 +0,0 @@ -module.exports = { - extends: [ - 'eslint:recommended', - 'plugin:@typescript-eslint/eslint-recommended', - 'plugin:@typescript-eslint/recommended', - 'plugin:prettier/recommended' - ], - parser: '@typescript-eslint/parser', - parserOptions: { - project: 'tsconfig.json', - sourceType: 'module' - }, - plugins: ['@typescript-eslint'], - rules: { - '@typescript-eslint/naming-convention': [ - 'error', - { - selector: 'interface', - format: ['PascalCase'], - custom: { - regex: '^I[A-Z]', - match: true - } - } - ], - '@typescript-eslint/no-unused-vars': ['warn', { args: 'none' }], - '@typescript-eslint/no-explicit-any': 'off', - '@typescript-eslint/no-namespace': 'off', - '@typescript-eslint/no-use-before-define': 'off', - '@typescript-eslint/quotes': [ - 'error', - 'single', - { avoidEscape: true, allowTemplateLiterals: false } - ], - curly: ['error', 'all'], - eqeqeq: 'error', - 'prefer-arrow-callback': 'error' - } -}; diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/.prettierignore b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/.prettierignore deleted file mode 100644 index 74d9f680374..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/.prettierignore +++ /dev/null @@ -1,5 +0,0 @@ -node_modules -**/node_modules -**/lib -**/package.json -neural_compressor_ext_lab_alibaba diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/.prettierrc b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/.prettierrc deleted file mode 100644 index d0824a69c14..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/.prettierrc +++ /dev/null @@ -1,6 +0,0 @@ -{ - "singleQuote": true, - "trailingComma": "none", - "arrowParens": "avoid", - "endOfLine": "auto" -} diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/.stylelintrc b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/.stylelintrc deleted file mode 100644 index 0e1ff30327c..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/.stylelintrc +++ /dev/null @@ -1,12 +0,0 @@ -{ - "extends": [ - "stylelint-config-recommended", - "stylelint-config-standard", - "stylelint-prettier/recommended" - ], - "rules": { - "property-no-vendor-prefix": null, - "selector-no-vendor-prefix": null, - "value-no-vendor-prefix": null - } -} diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/CHANGELOG.md b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/CHANGELOG.md deleted file mode 100644 index 2d352af421a..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/CHANGELOG.md +++ /dev/null @@ -1,5 +0,0 @@ -# Changelog - - - - diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/DEVELOP.md b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/DEVELOP.md deleted file mode 100644 index f469c541684..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/DEVELOP.md +++ /dev/null @@ -1,59 +0,0 @@ -# neural_compressor_ext_lab_alibaba - -A JupyterLab extension. - -## Requirements - -- JupyterLab >= 3.0 - -## Contributing - -### Development install - -Note: You will need NodeJS to build the extension package. - -The `jlpm` command is JupyterLab's pinned version of -[yarn](https://yarnpkg.com/) that is installed with JupyterLab. You may use -`yarn` or `npm` in lieu of `jlpm` below. - -```bash -# Clone the repo to your local environment -# Change directory to the neural_compressor_ext_lab_alibaba directory -# Install package in development mode -pip install -e . -# Link your development version of the extension with JupyterLab -jupyter labextension develop . --overwrite -# Rebuild extension Typescript source after making changes -jlpm build -``` - -You can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension. - -```bash -# Watch the source directory in one terminal, automatically rebuilding when needed -jlpm watch -# Run JupyterLab in another terminal -jupyter lab -``` - -With the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt). - -By default, the `jlpm build` command generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command: - -```bash -jupyter lab build --minimize=False -``` - -### Development uninstall - -```bash -pip uninstall neural_compressor_ext_lab_alibaba -``` - -In development mode, you will also need to remove the symlink created by `jupyter labextension develop` -command. To find its location, you can run `jupyter labextension list` to figure out where the `labextensions` -folder is located. Then you can remove the symlink named `neural_compressor_ext_lab_alibaba` within that folder. - -### Packaging the extension - -See [RELEASE](RELEASE.md) diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/LICENSE b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/LICENSE deleted file mode 100644 index dc6287b0a4d..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/LICENSE +++ /dev/null @@ -1,29 +0,0 @@ -BSD 3-Clause License - -Copyright (c) 2022, Neural_Coder -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -1. Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - -3. Neither the name of the copyright holder nor the names of its - contributors may be used to endorse or promote products derived from - this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE -FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL -DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER -CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/MANIFEST.in b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/MANIFEST.in deleted file mode 100644 index 64a9817c7cd..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/MANIFEST.in +++ /dev/null @@ -1,24 +0,0 @@ -include LICENSE -include *.md -include pyproject.toml - -include package.json -include install.json -include ts*.json -include yarn.lock - -graft neural_compressor_ext_lab_alibaba/labextension - -# Javascript files -graft src -graft style -prune **/node_modules -prune lib -prune binder - -# Patterns to exclude from any directory -global-exclude *~ -global-exclude *.pyc -global-exclude *.pyo -global-exclude .git -global-exclude .ipynb_checkpoints diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/PKG-INFO b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/PKG-INFO deleted file mode 100644 index a6fbb58be34..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/PKG-INFO +++ /dev/null @@ -1,130 +0,0 @@ -Metadata-Version: 2.1 -Name: neural_compressor_ext_lab_alibaba -Version: 0.1.0 -Summary: A JupyterLab extension. -Project-URL: Homepage, https://github.com/intel-sandbox/neural-coder/tree/lab_ext_ts/neural_coder/extensions -Project-URL: Bug Tracker, https://github.com/intel-sandbox/neural-coder/tree/lab_ext_ts/neural_coder/extensions/issues -Project-URL: Repository, https://github.com/intel-sandbox/neural-coder/tree/lab_ext_ts/neural_coder/extensions.git -Author-email: Neural_Coder -License: BSD 3-Clause License - - Copyright (c) 2022, Neural_Coder - All rights reserved. - - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions are met: - - 1. Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - - 2. Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - - 3. Neither the name of the copyright holder nor the names of its - contributors may be used to endorse or promote products derived from - this software without specific prior written permission. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" - AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE - DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE - FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL - DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR - SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER - CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, - OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. -License-File: LICENSE -Classifier: Framework :: Jupyter -Classifier: Framework :: Jupyter :: JupyterLab -Classifier: Framework :: Jupyter :: JupyterLab :: 3 -Classifier: Framework :: Jupyter :: JupyterLab :: Extensions -Classifier: Framework :: Jupyter :: JupyterLab :: Extensions :: Prebuilt -Classifier: License :: OSI Approved :: BSD License -Classifier: Programming Language :: Python -Classifier: Programming Language :: Python :: 3 -Classifier: Programming Language :: Python :: 3.7 -Classifier: Programming Language :: Python :: 3.8 -Classifier: Programming Language :: Python :: 3.9 -Classifier: Programming Language :: Python :: 3.10 -Requires-Python: >=3.6 -Provides-Extra: test -Description-Content-Type: text/markdown - -# neural_compressor_ext_lab_alibaba - -[![Github Actions Status](https://github.com/intel-sandbox/neural-coder/tree/lab_ext_ts/neural_coder/extensions/workflows/Build/badge.svg)](https://github.com/intel-sandbox/neural-coder/tree/lab_ext_ts/neural_coder/extensions/actions/workflows/build.yml)[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/intel-sandbox/neural-coder/tree/lab_ext_ts/neural_coder/extensions/main?urlpath=lab) -A JupyterLab extension. - -## Requirements - -- JupyterLab >= 3.0 - -## Install - -To install the extension, execute: - -```bash -pip install neural_compressor_ext_lab_alibaba -``` - -## Uninstall - -To remove the extension, execute: - -```bash -pip uninstall neural_compressor_ext_lab_alibaba -``` - -## Contributing - -### Development install - -Note: You will need NodeJS to build the extension package. - -The `jlpm` command is JupyterLab's pinned version of -[yarn](https://yarnpkg.com/) that is installed with JupyterLab. You may use -`yarn` or `npm` in lieu of `jlpm` below. - -```bash -# Clone the repo to your local environment -# Change directory to the neural_compressor_ext_lab_alibaba directory -# Install package in development mode -pip install -e . -# Link your development version of the extension with JupyterLab -jupyter labextension develop . --overwrite -# Rebuild extension Typescript source after making changes -jlpm build -``` - -You can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension. - -```bash -# Watch the source directory in one terminal, automatically rebuilding when needed -jlpm watch -# Run JupyterLab in another terminal -jupyter lab -``` - -With the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt). - -By default, the `jlpm build` command generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command: - -```bash -jupyter lab build --minimize=False -``` - -### Development uninstall - -```bash -pip uninstall neural_compressor_ext_lab_alibaba -``` - -In development mode, you will also need to remove the symlink created by `jupyter labextension develop` -command. To find its location, you can run `jupyter labextension list` to figure out where the `labextensions` -folder is located. Then you can remove the symlink named `neural_compressor_ext_lab_alibaba` within that folder. - -### Packaging the extension - -See [RELEASE](RELEASE.md) diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/RELEASE.md b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/RELEASE.md deleted file mode 100644 index 0ee2050c023..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/RELEASE.md +++ /dev/null @@ -1,61 +0,0 @@ -# Making a new release of neural_compressor_ext_lab_alibaba - -The extension can be published to `PyPI` and `npm` manually or using the [Jupyter Releaser](https://github.com/jupyter-server/jupyter_releaser). - -## Manual release - -### Python package - -This extension can be distributed as Python -packages. All of the Python -packaging instructions in the `pyproject.toml` file to wrap your extension in a -Python package. Before generating a package, we first need to install `build`. - -```bash -pip install build twine hatch -``` - -To create a Python source package (`.tar.gz`) and the binary package (`.whl`) in the `dist/` directory, do: - -```bash -python -m build -``` - -> `python setup.py sdist bdist_wheel` is deprecated and will not work for this package. - -Then to upload the package to PyPI, do: - -```bash -twine upload dist/* -``` - -### NPM package - -To publish the frontend part of the extension as a NPM package, do: - -```bash -npm login -npm publish --access public -``` - -## Automated releases with the Jupyter Releaser - -The extension repository should already be compatible with the Jupyter Releaser. - -Check out the [workflow documentation](https://github.com/jupyter-server/jupyter_releaser#typical-workflow) for more information. - -Here is a summary of the steps to cut a new release: - -- Fork the [`jupyter-releaser` repo](https://github.com/jupyter-server/jupyter_releaser) -- Add `ADMIN_GITHUB_TOKEN`, `PYPI_TOKEN` and `NPM_TOKEN` to the Github Secrets in the fork -- Go to the Actions panel -- Run the "Draft Changelog" workflow -- Merge the Changelog PR -- Run the "Draft Release" workflow -- Run the "Publish Release" workflow - -## Publishing to `conda-forge` - -If the package is not on conda forge yet, check the documentation to learn how to add it: https://conda-forge.org/docs/maintainer/adding_pkgs.html - -Otherwise a bot should pick up the new version publish to PyPI, and open a new PR on the feedstock repository automatically. diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/binder/environment.yml b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/binder/environment.yml deleted file mode 100644 index 953736aa9f6..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/binder/environment.yml +++ /dev/null @@ -1,21 +0,0 @@ -# a mybinder.org-ready environment for demoing neural_compressor_ext_lab_alibaba -# this environment may also be used locally on Linux/MacOS/Windows, e.g. -# -# conda env update --file binder/environment.yml -# conda activate neural-compressor-ext-lab-alibaba-demo -# -name: neural-compressor-ext-lab-alibaba-demo - -channels: - - conda-forge - -dependencies: - # runtime dependencies - - python >=3.8,<3.9.0a0 - - jupyterlab >=3,<4.0.0a0 - # labextension build dependencies - - nodejs >=14,<15 - - pip - - wheel - # additional packages for demos - # - ipywidgets diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/binder/postBuild b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/binder/postBuild deleted file mode 100755 index ff83152d38c..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/binder/postBuild +++ /dev/null @@ -1,47 +0,0 @@ -#!/usr/bin/env python3 -""" perform a development install of neural_compressor_ext_lab_alibaba - - On Binder, this will run _after_ the environment has been fully created from - the environment.yml in this directory. - - This script should also run locally on Linux/MacOS/Windows: - - python3 binder/postBuild -""" -import subprocess -import sys -from pathlib import Path - - -ROOT = Path.cwd() - -def _(*args, **kwargs): - """ Run a command, echoing the args - - fails hard if something goes wrong - """ - print("\n\t", " ".join(args), "\n") - return_code = subprocess.call(args, **kwargs) - if return_code != 0: - print("\nERROR", return_code, " ".join(args)) - sys.exit(return_code) - -# verify the environment is self-consistent before even starting -_(sys.executable, "-m", "pip", "check") - -# install the labextension -_(sys.executable, "-m", "pip", "install", "-e", ".") -_(sys.executable, "-m", "jupyter", "labextension", "develop", "--overwrite", ".") - -# verify the environment the extension didn't break anything -_(sys.executable, "-m", "pip", "check") - -# list the extensions -_("jupyter", "server", "extension", "list") - -# initially list installed extensions to determine if there are any surprises -_("jupyter", "labextension", "list") - - -print("JupyterLab with neural_compressor_ext_lab_alibaba is ready to run with:\n") -print("\tjupyter lab\n") diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/install.json b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/install.json deleted file mode 100644 index 88b31057db0..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/install.json +++ /dev/null @@ -1,5 +0,0 @@ -{ - "packageManager": "python", - "packageName": "neural_compressor_ext_lab_alibaba", - "uninstallInstructions": "Use your Python package manager (pip, conda, etc.) to uninstall the package neural_compressor_ext_lab_alibaba" -} diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/constants.d.ts b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/constants.d.ts deleted file mode 100644 index 071e86ae2c1..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/constants.d.ts +++ /dev/null @@ -1,11 +0,0 @@ -export declare namespace Constants { - const SHORT_PLUGIN_NAME = "neural_compressor_ext_lab"; - const WORK_PATH = "neural_coder_workspace/"; - const ICON_FORMAT_ALL_SVG = ""; - const ICON_RUN = ""; - const SVG = ""; - const LONG_PLUGIN_NAME: string; - const SETTINGS_SECTION: string; - const COMMAND_SECTION_NAME = "Jupyterlab Code Optimizer"; - const PLUGIN_VERSION = "0.1.0"; -} diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/constants.js b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/constants.js deleted file mode 100644 index 1735f2d0bcb..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/constants.js +++ /dev/null @@ -1,16 +0,0 @@ -export var Constants; -(function (Constants) { - Constants.SHORT_PLUGIN_NAME = 'neural_compressor_ext_lab'; - // const processs = require('process') - // export const WORK_PATH = processs.env.HOME + "/lab_workspace/" - // const os = require('os'); - // export const WORK_PATH = os.homedir + "/lab_workspace/" - Constants.WORK_PATH = 'neural_coder_workspace/'; - Constants.ICON_FORMAT_ALL_SVG = ''; - Constants.ICON_RUN = ''; - Constants.SVG = ''; - Constants.LONG_PLUGIN_NAME = `@rya/${Constants.SHORT_PLUGIN_NAME}`; - Constants.SETTINGS_SECTION = `${Constants.LONG_PLUGIN_NAME}:settings`; - Constants.COMMAND_SECTION_NAME = 'Jupyterlab Code Optimizer'; - Constants.PLUGIN_VERSION = '0.1.0'; -})(Constants || (Constants = {})); diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/deepcoder.d.ts b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/deepcoder.d.ts deleted file mode 100644 index dcf8ddfba2a..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/deepcoder.d.ts +++ /dev/null @@ -1,27 +0,0 @@ -import { Cell, CodeCell } from '@jupyterlab/cells'; -import { ToolbarButton } from '@jupyterlab/apputils'; -import { Widget } from '@lumino/widgets'; -import { INotebookTracker, NotebookPanel, Notebook } from '@jupyterlab/notebook'; -declare class JupyterlabCodeOptimizer { - protected working: boolean; - protected panel: NotebookPanel; - private tmp_path; - log_path: string; - tmp_log_path: string; - rand: number; - markdown: Cell | undefined; - cells: CodeCell[]; - constructor(panel: NotebookPanel); - optimizeCode(code: string[], formatter: string, name: string, next: string, options: string | undefined, notebook: boolean, panel: NotebookPanel, cell: CodeCell, run?: ToolbarButton | undefined): Promise; -} -export declare class JupyterlabNotebookCodeOptimizer extends JupyterlabCodeOptimizer { - protected notebookname: string; - protected notebookTracker: INotebookTracker; - constructor(notebookTracker: INotebookTracker, panel: NotebookPanel); - optimizeAction(config: any, formatter?: string): Promise; - optimizeAllCodeCells(config?: string, formatter?: string, notebook?: Notebook, run?: ToolbarButton): Promise; - private getCodeCells; - private optimizeCells; - applicable(formatter: string, currentWidget: Widget): boolean | null; -} -export {}; diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/deepcoder.js b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/deepcoder.js deleted file mode 100644 index 2c0d221d59a..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/deepcoder.js +++ /dev/null @@ -1,268 +0,0 @@ -import { NotebookActions } from '@jupyterlab/notebook'; -import NotebookUtilities from "./utils"; -import { Constants } from './constants'; -class JupyterlabCodeOptimizer { - constructor(panel) { - this.working = false; - this.panel = panel; - console.log("path,,,", Constants.WORK_PATH); - this.tmp_path = Constants.WORK_PATH + "tmp.py"; - this.rand = NotebookUtilities.GetRandomNum(0, 200); - this.log_path = Constants.WORK_PATH + "NeuralCoder" + this.rand + ".log"; - this.tmp_log_path = Constants.WORK_PATH + "NeuralCoder_tmp" + ".log"; - this.cells = []; - } - async optimizeCode(code, formatter, name, next, options, notebook, panel, cell, run) { - let codes = []; - code.forEach(function (value) { - value = value.replace(/('\\n')/g, '^^^'); - value = value.replace(/\\n"/g, '###'); - value = value.replace(/\\n'/g, '###'); - value = value.replace(/"\\n/g, '@@'); - value = value.replace(/'\\n/g, '@@'); - value = value.replace(/\n/g, '\\n'); - value = value.replace(/"/g, '+++'); - value = value.replace(/,/g, '$'); - codes.push(value); - }); - let gen_code = `code = "${codes}"\ncodes = code.split(',')\nwith open( '${this.tmp_path}', 'w+' ) as f:\n for i in range(0,len(codes)):\n f.write('# this is the beginning of a single code snippet\\n')\n code_list = codes[i].replace('$',',').replace('+++','\"').split('\\n')\n for line in code_list:\n if('split(^^^)' in line):\n line=line.replace('split(^^^)', 'split(\\'\\\\n\\')')\n if('###' in line):\n line=line.replace('###', '\\\\n\"')\n if('@@' in line):\n line=line.replace('@@', '\"\\\\n')\n f.write(line+'\\n')`; - const expr = { code_list: `code_list` }; - NotebookUtilities.sendKernelRequestFromNotebook(panel, gen_code, expr, false); - if (options === 'normal') { - let runcode = `from neural_coder import enable\nenable(code="${this.tmp_path}",features=["${formatter}"], overwrite=True)`; - let expr = { sum: ` ` }; - NotebookUtilities.sendKernelRequestFromNotebook(panel, runcode, expr, false); - let run_code1 = `with open("${this.tmp_path}", 'r') as f:\n optimized_code = f.read()\n`; - let expr1 = { optimizedCode: "optimized_code" }; - let result2 = NotebookUtilities.sendKernelRequestFromNotebook(panel, run_code1, expr1, false); - result2.then(value => { - var _a, _b, _c, _d; - let optimizedTexts = Object.values(value.optimizedCode.data)[0]; - let optimizeCodes = optimizedTexts.split('# this is the beginning of a single code snippet\\n').slice(1); - optimizeCodes[optimizeCodes.length - 1] = optimizeCodes[optimizeCodes.length - 1].slice(0, -3); - for (let i = 0; i < optimizeCodes.length; ++i) { - const cell = this.cells[i]; - const currentTexts = this.cells.map(cell => cell.model.value.text); - const currentText = currentTexts[i]; - let optimizedtext = optimizeCodes[i]; - optimizedtext = optimizedtext.replace(/\\'\\\\n\\'/g, "^^^"); - optimizedtext = optimizedtext.replace(/\\\\n"/g, "+++"); - optimizedtext = optimizedtext.replace(/\\\\n'/g, "+++"); - optimizedtext = optimizedtext.replace(/"\\\\n/g, "@@@"); - optimizedtext = optimizedtext.replace(/'\\\\n/g, "@@@"); - optimizedtext = optimizedtext.replace(/\\n/g, '\n'); - optimizedtext = optimizedtext.replace(/\\'/g, "'"); - optimizedtext = optimizedtext.replace(/\^\^\^/g, "'\\n'"); - optimizedtext = optimizedtext.replace(/\+\+\+/g, "\\n\""); - optimizedtext = optimizedtext.replace(/\@\@\@/g, "\"\\n"); - if (cell.model.value.text === currentText) { - cell.model.value.text = optimizedtext; - } - const run_svg = document.createElement("svg"); - run_svg.innerHTML = Constants.ICON_RUN; - (_d = (_c = (_b = (_a = run === null || run === void 0 ? void 0 : run.node.firstChild) === null || _a === void 0 ? void 0 : _a.firstChild) === null || _b === void 0 ? void 0 : _b.firstChild) === null || _c === void 0 ? void 0 : _c.firstChild) === null || _d === void 0 ? void 0 : _d.replaceWith(run_svg); - } - }); - } - else { - if (formatter === '') { - if (this.markdown) { - this.markdown.model.value.text += "[NeuralCoder INFO] Enabling and Benchmarking for The Original Model ...... \n"; - } - let runcode1 = `with open("${this.log_path}", 'a' ) as f:\n f.write("[NeuralCoder INFO] Enabling and Benchmarking for The Original Model ......\\n")`; - let expr1 = { path: "" }; - NotebookUtilities.sendKernelRequestFromNotebook(panel, runcode1, expr1, false); - let runcode = `from neural_coder import enable\nperfomance, mode, path = enable(code="${this.tmp_path}",features=[], run_bench=True, args="${options}")\nwith open(path + '/bench.log', 'r') as f:\n logs = f.readlines()\nlog_line = logs[4]\nlog = log_line.split("[")[1].split("]")[0]`; - let expr = { path: "path", log: "log" }; - let result = NotebookUtilities.sendKernelRequestFromNotebook(panel, runcode, expr, false); - let fps; - result.then(value => { - fps = Object.values(value.log.data)[0]; - if (this.markdown) { - this.markdown.model.value.text += `[NeuralCoder INFO] Benchmark Result (Performance) of The Original Model is ${fps} (FPS) \n`; - } - let text = `[NeuralCoder INFO] Benchmark Result (Performance) of The Original Model is ${fps} (FPS)\\n`; - let runcode = `with open("${this.log_path}", 'a' ) as f:\n f.write("${text}")`; - let expr = { path: "" }; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode, expr, false); - if (this.markdown) { - this.markdown.model.value.text += `[NeuralCoder INFO] Enabling and Benchmarking for ${next} ...... \n`; - } - let runcode1 = `with open("${this.log_path}", 'a' ) as f:\n f.write("[NeuralCoder INFO] Enabling and Benchmarking for ${next} ......\\n")`; - let expr1 = { path: "" }; - NotebookUtilities.sendKernelRequestFromNotebook(panel, runcode1, expr1, false); - let runcode2 = `with open("${this.tmp_log_path}", 'a' ) as f:\n f.write("${text}")`; - let expr2 = { path: "" }; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode2, expr2, false); - }); - } - else { - let runcode = `from neural_coder import enable\nperfomance, mode, path = enable(code="${this.tmp_path}", features=["${formatter}"], run_bench=True, args="${options}")\nwith open(path + '/bench.log', 'r') as f:\n logs = f.readlines()\nlog_line = logs[4]\nlog = log_line.split("[")[1].split("]")[0]`; - let expr = { path: "path", log: "log" }; - let result = NotebookUtilities.sendKernelRequestFromNotebook(panel, runcode, expr, false); - let fps; - result.then(value => { - fps = Object.values(value.log.data)[0]; - if (this.markdown) { - this.markdown.model.value.text += `[NeuralCoder INFO] Benchmark Result (Performance) of ${name} is ${fps} (FPS) \n`; - } - let text = `[NeuralCoder INFO] Benchmark Result (Performance) of ${name} is ${fps} (FPS)\\n`; - let runcode = `with open("${this.log_path}", 'a' ) as f:\n f.write("${text}")`; - let expr = { path: "" }; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode, expr, false); - if (next !== '') { - if (this.markdown) { - this.markdown.model.value.text += `[NeuralCoder INFO] Enabling and Benchmarking for ${next} ...... \n`; - } - let runcode2 = `with open("${this.log_path}", 'a' ) as f:\n f.write("[NeuralCoder INFO] Enabling and Benchmarking for ${next} ......\\n")`; - let expr2 = { path: "" }; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode2, expr2, false); - } - let runcode2 = `with open("${this.tmp_log_path}", 'a' ) as f:\n f.write("${text}")`; - let expr2 = { path: "" }; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode2, expr2, false); - if (formatter === 'pytorch_aliblade') { - let read_log = `import re\nwith open("${this.tmp_log_path}", 'r') as f:\n logs = f.readlines()\n fps_list=[]\n for log_line in logs[-2:]:\n pat = r\'\\d+\\.?\\d+'\n fps = re.search(pat,log_line).group()\n fps_list.append(float(fps))\nmaxi = max(fps_list)\nindex = fps_list.index(maxi)\nboost = round(maxi/fps_list[0],1)\nfeatures=['','pytorch_aliblade',]\nfeature_name=['','Alibaba Blade-DISC']\nbest_feature = features[index]\nbest_name = feature_name[index]\n`; - let read_expr = { boost: "boost", best_feature: "best_feature", best_name: "best_name" }; - let read_result = NotebookUtilities.sendKernelRequestFromNotebook(this.panel, read_log, read_expr, false); - read_result.then(value => { - var _a, _b, _c, _d; - console.log("resresres", value); - let boost = Object.values(value.boost.data)[0]; - let best_name = Object.values(value.best_name.data)[0]; - // let optimizedTexts = Object.values(value.optimizeCode.data)[0] as string - // let optimizeCodes = optimizedTexts.split('# this is the beginning of a single code snippet\\n').slice(1,) - if (this.markdown) { - this.markdown.model.value.text += `[NeuralCoder INFO] The Best Intel Optimization: ${best_name} \n`; - this.markdown.model.value.text += `[NeuralCoder INFO] You can get up to ${boost}X performance boost. \n`; - } - // optimizeCodes[optimizeCodes.length-1] = optimizeCodes[optimizeCodes.length-1].slice(0,-3) - // for (let i = 0; i < optimizeCodes.length; ++i) { - // const cell = this.cells[i]; - // const currentTexts = this.cells.map(cell => cell.model.value.text); - // const currentText = currentTexts[i]; - // let optimizedtext = optimizeCodes[i]; - // optimizedtext = optimizedtext.replace(/\\'\\\\n\\'/g, "^^^") - // optimizedtext = optimizedtext.replace(/\\\\n"/g, "+++") - // optimizedtext = optimizedtext.replace(/\\\\n'/g, "+++") - // optimizedtext = optimizedtext.replace(/"\\\\n/g, "@@@") - // optimizedtext = optimizedtext.replace(/'\\\\n/g, "@@@") - // optimizedtext = optimizedtext.replace(/\\n/g,'\n') - // optimizedtext = optimizedtext.replace(/\\'/g,"'") - // optimizedtext = optimizedtext.replace(/\^\^\^/g, "'\\n'") - // optimizedtext = optimizedtext.replace(/\+\+\+/g, "\\n\"") - // optimizedtext = optimizedtext.replace(/\@\@\@/g, "\"\\n") - // if (cell.model.value.text === currentText) { - // cell.model.value.text = optimizedtext; - // } - // } - let command = "lscpu | grep 'Model name'"; - let get_hardware = `import subprocess\nsubp = subprocess.Popen("${command}",shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE,encoding="utf-8")\nsubp.wait(2)\nhardware = subp.communicate()[0].replace("Model name:","").strip()`; - let expr_hardware = { hardware: "hardware" }; - let hard_res = NotebookUtilities.sendKernelRequestFromNotebook(this.panel, get_hardware, expr_hardware, false); - hard_res.then(value => { - let hard = Object.values(value.hardware.data)[0]; - if (this.markdown) { - this.markdown.model.value.text += `[NeuralCoder INFO] HardWare: ${hard} \n`; - this.markdown.model.value.text += `[NeuralCoder INFO] The log was saved to neural_coder_workspace\\NeuralCoder${this.rand}.log \n`; - } - }); - const run_svg = document.createElement("svg"); - run_svg.innerHTML = Constants.ICON_RUN; - (_d = (_c = (_b = (_a = run === null || run === void 0 ? void 0 : run.node.firstChild) === null || _a === void 0 ? void 0 : _a.firstChild) === null || _b === void 0 ? void 0 : _b.firstChild) === null || _c === void 0 ? void 0 : _c.firstChild) === null || _d === void 0 ? void 0 : _d.replaceWith(run_svg); - }); - } - }); - } - } - } -} -export class JupyterlabNotebookCodeOptimizer extends JupyterlabCodeOptimizer { - constructor(notebookTracker, panel) { - super(panel); - this.notebookTracker = notebookTracker; - this.notebookname = ''; - } - async optimizeAction(config, formatter) { - return this.optimizeCells(true, config, formatter); - } - async optimizeAllCodeCells(config, formatter, notebook, run) { - return this.optimizeCells(false, config, formatter, notebook, run); - } - getCodeCells(ifMarkdown = true, notebook) { - if (!this.notebookTracker.currentWidget) { - return []; - } - const codeCells = []; - notebook = notebook || this.notebookTracker.currentWidget.content; - this.notebookname = notebook.title.label; - let count = 0; - notebook.widgets.forEach((cell) => { - if (cell.model.type === 'code') { - count += 1; - codeCells.push(cell); - } - }); - if (ifMarkdown) { - NotebookActions.insertBelow(notebook); - this.notebookTracker.currentWidget.content.activeCellIndex = count + 1; - NotebookActions.changeCellType(notebook, 'markdown'); - const activeCell = notebook.activeCell; - if (activeCell) { - this.markdown = activeCell; - } - } - this.cells = codeCells; - return codeCells; - } - async optimizeCells(selectedOnly, config, formatter, notebook, run) { - if (this.working) { - return new Promise((resolve, reject) => { - resolve("false!"); - }); - } - this.working = true; - const optimize_type = formatter !== undefined ? formatter : 'pytorch_aliblade'; - if (optimize_type === 'auto') { - selectedOnly = true; - } - else { - selectedOnly = false; - } - const selectedCells = this.getCodeCells(selectedOnly, notebook); - let cell = selectedCells[selectedCells.length - 1]; - if (selectedCells.length === 0) { - this.working = false; - return new Promise((resolve, reject) => { - resolve("false!"); - }); - } - const currentTexts = selectedCells.map(cell => cell.model.value.text); - if (optimize_type === 'auto') { - if (this.markdown) { - // this.markdown.model.value.text = `[NeuralCoder INFO] Auto-Quant Started ...... \n`; - this.markdown.model.value.text += `[NeuralCoder INFO] Code: User code from Jupyter Lab notebook "${this.notebookname}" \n`; - this.markdown.model.value.text += `[NeuralCoder INFO] Benchmark Mode: Throughput \n`; - } - let runcode2 = `with open('${this.log_path}', 'a' ) as f:\n f.write("[NeuralCoder INFO] Code: User code from Jupyter Lab notebook '${this.notebookname}'\\n")`; - let expr2 = { path: "" }; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode2, expr2, false); - let runcode3 = `with open('${this.log_path}', 'a' ) as f:\n f.write("[NeuralCoder INFO] Benchmark Mode: Throughput\\n")`; - let expr3 = { path: "" }; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode3, expr3, false); - await this.optimizeCode(currentTexts, '', 'The Original Model', 'Alibaba-Blade', config, true, this.panel, cell, run); - await this.optimizeCode(currentTexts, 'pytorch_aliblade', 'Alibaba-Blade', '', config, true, this.panel, cell, run); - } - else { - await this.optimizeCode(currentTexts, optimize_type, "", "", "normal", true, this.panel, cell, run); - } - this.working = false; - return new Promise((resolve, reject) => { - resolve("success!"); - }); - } - applicable(formatter, currentWidget) { - const currentNotebookWidget = this.notebookTracker.currentWidget; - return currentNotebookWidget && currentWidget === currentNotebookWidget; - } -} diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/index.d.ts b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/index.d.ts deleted file mode 100644 index f256eada9ba..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/index.d.ts +++ /dev/null @@ -1,6 +0,0 @@ -import { JupyterFrontEndPlugin } from '@jupyterlab/application'; -/** - * Initialization data for the neural_compressor_ext_lab extension. - */ -declare const plugin: JupyterFrontEndPlugin; -export default plugin; diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/index.js b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/index.js deleted file mode 100644 index 67e449a4dc4..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/index.js +++ /dev/null @@ -1,95 +0,0 @@ -import { INotebookTracker } from '@jupyterlab/notebook'; -import { ToolbarButton, showDialog, Dialog } from '@jupyterlab/apputils'; -import { ISettingRegistry } from '@jupyterlab/settingregistry'; -import { IMainMenu } from '@jupyterlab/mainmenu'; -import { LabIcon } from '@jupyterlab/ui-components'; -import { Widget } from '@lumino/widgets'; -import { JupyterlabNotebookCodeOptimizer } from './deepcoder'; -import { Constants } from './constants'; -class neural_compressor_ext_lab { - constructor(app, tracker, notebookpanel) { - this.app = app; - this.tracker = tracker; - this.notebookpanel = notebookpanel; - this.setupWidgetExtension(); - this.config = ''; - } - createNew(nb) { - this.notebookpanel = nb; - this.notebookCodeOptimizer = new JupyterlabNotebookCodeOptimizer(this.tracker, this.notebookpanel); - const svg = document.createElement("svg"); - svg.innerHTML = Constants.ICON_FORMAT_ALL_SVG; - const run_svg = document.createElement("svg"); - run_svg.innerHTML = Constants.ICON_RUN; - const div = document.createElement("div"); - div.setAttribute("class", "wrapper"); - const span = document.createElement("span"); - span.setAttribute("class", "f1ozlkqi"); - span.innerHTML = Constants.SVG; - const selector = document.createElement("select"); - selector.setAttribute("class", "aselector"); - selector.id = "NeuralCoder"; - const option1 = document.createElement("option"); - option1.value = "pytorch_aliblade"; - option1.innerText = "Alibaba Blade-DISC"; - const option2 = document.createElement("option"); - option2.value = "auto"; - option2.innerText = "Auto Benchmark"; - option1.selected = true; - selector.options.add(option1); - selector.options.add(option2); - div.appendChild(selector); - div.appendChild(span); - const selector_widget = new Widget(); - selector_widget.node.appendChild(div); - selector_widget.addClass("aselector"); - let notebookCodeOptimizer = this.notebookCodeOptimizer; - let config = this.config; - const dia_input = document.createElement("input"); - const dia_widget = new Widget(); - dia_widget.node.appendChild(dia_input); - dia_widget.addClass("dialog"); - const run_button = new ToolbarButton({ - tooltip: 'NeuralCoder', - icon: new LabIcon({ - name: "run", - svgstr: Constants.ICON_RUN - }), - onClick: async function () { - var _a, _b, _c, _d; - (_d = (_c = (_b = (_a = run_button.node.firstChild) === null || _a === void 0 ? void 0 : _a.firstChild) === null || _b === void 0 ? void 0 : _b.firstChild) === null || _c === void 0 ? void 0 : _c.firstChild) === null || _d === void 0 ? void 0 : _d.replaceWith(svg); - if (selector.options[selector.selectedIndex].value === 'auto') { - await showDialog({ - title: 'Please input execute parameters:', - body: dia_widget, - buttons: [Dialog.okButton({ label: 'Confirm' })] - }).then(result => { - if (result.button.accept) { - config = dia_input.value; - } - }); - } - await notebookCodeOptimizer.optimizeAllCodeCells(config, selector.options[selector.selectedIndex].value, undefined, run_button); - } - }); - nb.toolbar.insertItem(11, "nc", run_button); - nb.toolbar.insertItem(12, "selector", selector_widget); - } - setupWidgetExtension() { - this.app.docRegistry.addWidgetExtension('Notebook', this); - } -} -/** - * Initialization data for the neural_compressor_ext_lab extension. - */ -const plugin = { - id: 'neural_compressor_ext_lab:plugin', - autoStart: true, - requires: [INotebookTracker, IMainMenu], - optional: [ISettingRegistry], - activate: (app, tracker, notebookpanel) => { - new neural_compressor_ext_lab(app, tracker, notebookpanel); - console.log('JupyterLab extension neural_compressor_ext_lab is activated!'); - } -}; -export default plugin; diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/test.d.ts b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/test.d.ts deleted file mode 100644 index cb0ff5c3b54..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/test.d.ts +++ /dev/null @@ -1 +0,0 @@ -export {}; diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/test.js b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/test.js deleted file mode 100644 index f6d0eee3a16..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/test.js +++ /dev/null @@ -1,4 +0,0 @@ -const processs = require('process'); -console.log(processs.env.HOME + "lab_workspace/"); -import { homedir } from 'os'; -console.log(homedir); diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/utils.d.ts b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/utils.d.ts deleted file mode 100644 index 8b7a2173767..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/utils.d.ts +++ /dev/null @@ -1,120 +0,0 @@ -import { NotebookPanel } from '@jupyterlab/notebook'; -import { Kernel } from '@jupyterlab/services'; -import { CommandRegistry } from '@phosphor/commands'; -export default class NotebookUtilities { - /** - * generate random number - * @Min - * @Max - */ - static GetRandomNum(Min: number, Max: number): number; - /** - * Builds an HTML container by sanitizing a list of strings and converting - * them in valid HTML - * @param msg A list of string with HTML formatting - * @returns a HTMLDivElement composed of a list of spans with formatted text - */ - private static buildDialogBody; - /** - * Opens a pop-up dialog in JupyterLab to display a simple message. - * @param title The title for the message popup - * @param msg The message as an array of strings - * @param buttonLabel The label to use for the button. Default is 'OK' - * @param buttonClassName The classname to give to the 'ok' button - * @returns Promise - A promise once the message is closed. - */ - static showMessage(title: string, msg: string[], buttonLabel?: string, buttonClassName?: string): Promise; - /** - * Opens a pop-up dialog in JupyterLab to display a yes/no dialog. - * @param title The title for the message popup - * @param msg The message - * @param acceptLabel The label to use for the accept button. Default is 'YES' - * @param rejectLabel The label to use for the reject button. Default is 'NO' - * @param yesButtonClassName The classname to give to the accept button. - * @param noButtonClassName The classname to give to the cancel button. - * @returns Promise - A promise once the message is closed. - */ - static showYesNoDialog(title: string, msg: string[], acceptLabel?: string, rejectLabel?: string, yesButtonClassName?: string, noButtonClassName?: string): Promise; - /** - * Opens a pop-up dialog in JupyterLab with various information and button - * triggering reloading the page. - * @param title The title for the message popup - * @param msg The message - * @param buttonLabel The label to use for the button. Default is 'Refresh' - * @param buttonClassName The classname to give to the 'refresh' button. - * @returns Promise - A promise once the message is closed. - */ - static showRefreshDialog(title: string, msg: string[], buttonLabel?: string, buttonClassName?: string): Promise; - /** - * @description Creates a new JupyterLab notebook for use by the application - * @param command The command registry - * @returns Promise - A promise containing the notebook panel object that was created (if successful). - */ - static createNewNotebook(command: CommandRegistry): Promise; - /** - * Safely saves the Jupyter notebook document contents to disk - * @param notebookPanel The notebook panel containing the notebook to save - */ - static saveNotebook(notebookPanel: NotebookPanel): Promise; - /** - * Convert the notebook contents to JSON - * @param notebookPanel The notebook panel containing the notebook to serialize - */ - static notebookToJSON(notebookPanel: NotebookPanel): any; - /** - * @description Gets the value of a key from specified notebook's metadata. - * @param notebookPanel The notebook to get meta data from. - * @param key The key of the value. - * @returns any -The value of the metadata. Returns null if the key doesn't exist. - */ - static getMetaData(notebookPanel: NotebookPanel, key: string): any; - /** - * @description Sets the key value pair in the notebook's metadata. - * If the key doesn't exists it will add one. - * @param notebookPanel The notebook to set meta data in. - * @param key The key of the value to create. - * @param value The value to set. - * @param save Default is false. Whether the notebook should be saved after the meta data is set. - * Note: This function will not wait for the save to complete, it only sends a save request. - * @returns The old value for the key, or undefined if it did not exist. - */ - static setMetaData(notebookPanel: NotebookPanel, key: string, value: any, save?: boolean): any; - /** - * @description This function runs code directly in the notebook's kernel and then evaluates the - * result and returns it as a promise. - * @param kernel The kernel to run the code in. - * @param runCode The code to run in the kernel. - * @param userExpressions The expressions used to capture the desired info from the executed code. - * @param runSilent Default is false. If true, kernel will execute as quietly as possible. - * store_history will be set to false, and no broadcast on IOPUB channel will be made. - * @param storeHistory Default is false. If true, the code executed will be stored in the kernel's history - * and the counter which is shown in the cells will be incremented to reflect code was run. - * @param allowStdIn Default is false. If true, code running in kernel can prompt user for input using - * an input_request message. - * @param stopOnError Default is false. If True, does not abort the execution queue, if an exception is encountered. - * This allows the queued execution of multiple execute_requests, even if they generate exceptions. - * @returns Promise - A promise containing the execution results of the code as an object with - * keys based on the user_expressions. - * @example - * //The code - * const code = "a=123\nb=456\nsum=a+b"; - * //The user expressions - * const expr = {sum: "sum",prod: "a*b",args:"[a,b,sum]"}; - * //Async function call (returns a promise) - * sendKernelRequest(notebookPanel, code, expr,false); - * //Result when promise resolves: - * { - * sum:{status:"ok",data:{"text/plain":"579"},metadata:{}}, - * prod:{status:"ok",data:{"text/plain":"56088"},metadata:{}}, - * args:{status:"ok",data:{"text/plain":"[123, 456, 579]"}} - * } - * @see For more information on JupyterLab messages: - * https://jupyter-client.readthedocs.io/en/latest/messaging.html#execution-results - */ - static sendKernelRequest(kernel: Kernel.IKernelConnection | null | undefined, runCode: string, userExpressions: any, runSilent?: boolean, storeHistory?: boolean, allowStdIn?: boolean, stopOnError?: boolean): Promise; - /** - * Same as method sendKernelRequest but passing - * a NotebookPanel instead of a Kernel - */ - static sendKernelRequestFromNotebook(notebookPanel: NotebookPanel, runCode: string, userExpressions: any, runSilent?: boolean, storeHistory?: boolean, allowStdIn?: boolean, stopOnError?: boolean): Promise; -} diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/utils.js b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/utils.js deleted file mode 100644 index 5ac72df1071..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/lib/utils.js +++ /dev/null @@ -1,270 +0,0 @@ -/* - * Copyright 2019-2020 The Kale Authors - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ -import { Dialog, showDialog } from '@jupyterlab/apputils'; -// @ts-ignore -import SanitizedHTML from 'react-sanitized-html'; -import * as React from 'react'; -export default class NotebookUtilities { - /** - * generate random number - * @Min - * @Max - */ - static GetRandomNum(Min, Max) { - let Range; - Range = Max - Min; - var Rand = Math.random(); - return (Min + Math.round(Rand * Range)); - } - /** - * Builds an HTML container by sanitizing a list of strings and converting - * them in valid HTML - * @param msg A list of string with HTML formatting - * @returns a HTMLDivElement composed of a list of spans with formatted text - */ - static buildDialogBody(msg) { - return (React.createElement("div", null, msg.map((s, i) => { - return (React.createElement(React.Fragment, { key: `msg-${i}` }, - React.createElement(SanitizedHTML, { allowedAttributes: { a: ['href'] }, allowedTags: ['b', 'i', 'em', 'strong', 'a', 'pre'], html: s }), - React.createElement("br", null))); - }))); - } - /** - * Opens a pop-up dialog in JupyterLab to display a simple message. - * @param title The title for the message popup - * @param msg The message as an array of strings - * @param buttonLabel The label to use for the button. Default is 'OK' - * @param buttonClassName The classname to give to the 'ok' button - * @returns Promise - A promise once the message is closed. - */ - static async showMessage(title, msg, buttonLabel = 'Dismiss', buttonClassName = '') { - const buttons = [ - Dialog.okButton({ label: buttonLabel, className: buttonClassName }), - ]; - const messageBody = this.buildDialogBody(msg); - await showDialog({ title, buttons, body: messageBody }); - } - /** - * Opens a pop-up dialog in JupyterLab to display a yes/no dialog. - * @param title The title for the message popup - * @param msg The message - * @param acceptLabel The label to use for the accept button. Default is 'YES' - * @param rejectLabel The label to use for the reject button. Default is 'NO' - * @param yesButtonClassName The classname to give to the accept button. - * @param noButtonClassName The classname to give to the cancel button. - * @returns Promise - A promise once the message is closed. - */ - static async showYesNoDialog(title, msg, acceptLabel = 'YES', rejectLabel = 'NO', yesButtonClassName = '', noButtonClassName = '') { - const buttons = [ - Dialog.okButton({ label: acceptLabel, className: yesButtonClassName }), - Dialog.cancelButton({ label: rejectLabel, className: noButtonClassName }), - ]; - const messageBody = this.buildDialogBody(msg); - const result = await showDialog({ title, buttons, body: messageBody }); - return result.button.label === acceptLabel; - } - /** - * Opens a pop-up dialog in JupyterLab with various information and button - * triggering reloading the page. - * @param title The title for the message popup - * @param msg The message - * @param buttonLabel The label to use for the button. Default is 'Refresh' - * @param buttonClassName The classname to give to the 'refresh' button. - * @returns Promise - A promise once the message is closed. - */ - static async showRefreshDialog(title, msg, buttonLabel = 'Refresh', buttonClassName = '') { - await this.showMessage(title, msg, buttonLabel, buttonClassName); - location.reload(); - } - /** - * @description Creates a new JupyterLab notebook for use by the application - * @param command The command registry - * @returns Promise - A promise containing the notebook panel object that was created (if successful). - */ - static async createNewNotebook(command) { - const notebook = await command.execute('notebook:create-new', { - activate: true, - path: '', - preferredLanguage: '', - }); - await notebook.session.ready; - return notebook; - } - /** - * Safely saves the Jupyter notebook document contents to disk - * @param notebookPanel The notebook panel containing the notebook to save - */ - static async saveNotebook(notebookPanel) { - if (notebookPanel) { - await notebookPanel.context.ready; - notebookPanel.context.save(); - return true; - } - return false; - } - /** - * Convert the notebook contents to JSON - * @param notebookPanel The notebook panel containing the notebook to serialize - */ - static notebookToJSON(notebookPanel) { - if (notebookPanel.content.model) { - return notebookPanel.content.model.toJSON(); - } - return null; - } - /** - * @description Gets the value of a key from specified notebook's metadata. - * @param notebookPanel The notebook to get meta data from. - * @param key The key of the value. - * @returns any -The value of the metadata. Returns null if the key doesn't exist. - */ - static getMetaData(notebookPanel, key) { - if (!notebookPanel) { - throw new Error('The notebook is null or undefined. No meta data available.'); - } - if (notebookPanel.model && notebookPanel.model.metadata.has(key)) { - return notebookPanel.model.metadata.get(key); - } - return null; - } - /** - * @description Sets the key value pair in the notebook's metadata. - * If the key doesn't exists it will add one. - * @param notebookPanel The notebook to set meta data in. - * @param key The key of the value to create. - * @param value The value to set. - * @param save Default is false. Whether the notebook should be saved after the meta data is set. - * Note: This function will not wait for the save to complete, it only sends a save request. - * @returns The old value for the key, or undefined if it did not exist. - */ - static setMetaData(notebookPanel, key, value, save = false) { - var _a; - if (!notebookPanel) { - throw new Error('The notebook is null or undefined. No meta data available.'); - } - const oldVal = (_a = notebookPanel.model) === null || _a === void 0 ? void 0 : _a.metadata.set(key, value); - if (save) { - this.saveNotebook(notebookPanel); - } - return oldVal; - } - // /** - // * Get a new Kernel, not tied to a Notebook - // * Source code here: https://github.com/jupyterlab/jupyterlab/tree/473348d25bcb258ca2f0c127dd8fb5b193217135/packages/services - // */ - // public static async createNewKernel() { - // // Get info about the available kernels and start a new one. - // let options: Kernel.IOptions = await Kernel.getSpecs().then(kernelSpecs => { - // // console.log('Default spec:', kernelSpecs.default); - // // console.log('Available specs', Object.keys(kernelSpecs.kernelspecs)); - // // use the default name - // return { name: kernelSpecs.default }; - // }); - // return await Kernel.startNew(options).then(_kernel => { - // return _kernel; - // }); - // } - // // TODO: We can use this context manager to execute commands inside a new kernel - // // and be sure that it will be disposed of at the end. - // // Another approach could be to create a kale_rpc Kernel, as a singleton, - // // created at startup. The only (possible) drawback is that we can not name - // // a kernel instance with a custom id/name, so when refreshing JupyterLab we would - // // not recognize the kernel. A solution could be to have a kernel spec dedicated to kale rpc calls. - // public static async executeWithNewKernel(action: Function, args: any[] = []) { - // // create brand new kernel - // const _k = await this.createNewKernel(); - // // execute action inside kernel - // const res = await action(_k, ...args); - // // close kernel - // _k.shutdown(); - // // return result - // return res; - // } - /** - * @description This function runs code directly in the notebook's kernel and then evaluates the - * result and returns it as a promise. - * @param kernel The kernel to run the code in. - * @param runCode The code to run in the kernel. - * @param userExpressions The expressions used to capture the desired info from the executed code. - * @param runSilent Default is false. If true, kernel will execute as quietly as possible. - * store_history will be set to false, and no broadcast on IOPUB channel will be made. - * @param storeHistory Default is false. If true, the code executed will be stored in the kernel's history - * and the counter which is shown in the cells will be incremented to reflect code was run. - * @param allowStdIn Default is false. If true, code running in kernel can prompt user for input using - * an input_request message. - * @param stopOnError Default is false. If True, does not abort the execution queue, if an exception is encountered. - * This allows the queued execution of multiple execute_requests, even if they generate exceptions. - * @returns Promise - A promise containing the execution results of the code as an object with - * keys based on the user_expressions. - * @example - * //The code - * const code = "a=123\nb=456\nsum=a+b"; - * //The user expressions - * const expr = {sum: "sum",prod: "a*b",args:"[a,b,sum]"}; - * //Async function call (returns a promise) - * sendKernelRequest(notebookPanel, code, expr,false); - * //Result when promise resolves: - * { - * sum:{status:"ok",data:{"text/plain":"579"},metadata:{}}, - * prod:{status:"ok",data:{"text/plain":"56088"},metadata:{}}, - * args:{status:"ok",data:{"text/plain":"[123, 456, 579]"}} - * } - * @see For more information on JupyterLab messages: - * https://jupyter-client.readthedocs.io/en/latest/messaging.html#execution-results - */ - static async sendKernelRequest(kernel, runCode, userExpressions, runSilent = false, storeHistory = false, allowStdIn = false, stopOnError = false) { - if (!kernel) { - throw new Error('Kernel is null or undefined.'); - } - // Wait for kernel to be ready before sending request - // await kernel.status; - const message = await kernel.requestExecute({ - allow_stdin: allowStdIn, - code: runCode, - silent: runSilent, - stop_on_error: stopOnError, - store_history: storeHistory, - user_expressions: userExpressions, - }).done; - const content = message.content; - if (content.status !== 'ok') { - // If response is not 'ok', throw contents as error, log code - const msg = `Code caused an error:\n${runCode}`; - console.error(msg); - if (content.traceback) { - content.traceback.forEach((line) => console.log(line.replace(/[\u001b\u009b][[()#;?]*(?:[0-9]{1,4}(?:;[0-9]{0,4})*)?[0-9A-ORZcf-nqry=><]/g, ''))); - } - throw content; - } - // Return user_expressions of the content - return content.user_expressions; - } - /** - * Same as method sendKernelRequest but passing - * a NotebookPanel instead of a Kernel - */ - static async sendKernelRequestFromNotebook(notebookPanel, runCode, userExpressions, runSilent = false, storeHistory = false, allowStdIn = false, stopOnError = false) { - var _a, _b, _c, _d; - if (!notebookPanel) { - throw new Error('Notebook is null or undefined.'); - } - // Wait for notebook panel to be ready - await notebookPanel.activate; - await ((_a = notebookPanel.sessionContext) === null || _a === void 0 ? void 0 : _a.ready); - console.log('get kernel', (_b = notebookPanel.sessionContext.session) === null || _b === void 0 ? void 0 : _b.kernel); - return this.sendKernelRequest((_d = (_c = notebookPanel.sessionContext) === null || _c === void 0 ? void 0 : _c.session) === null || _d === void 0 ? void 0 : _d.kernel, runCode, userExpressions, runSilent, storeHistory, allowStdIn, stopOnError); - } -} diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/neural_compressor_ext_lab_alibaba/__init__.py b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/neural_compressor_ext_lab_alibaba/__init__.py deleted file mode 100644 index 74c4e9f68fc..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/neural_compressor_ext_lab_alibaba/__init__.py +++ /dev/null @@ -1,34 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import json -from pathlib import Path - -from ._version import __version__ - - -HERE = Path(__file__).parent.resolve() - - -with (HERE / "labextension" / "package.json").open() as fid: - data = json.load(fid) - - -def _jupyter_labextension_paths(): - return [{ - "src": "labextension", - "dest": data["name"] - }] - diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/neural_compressor_ext_lab_alibaba/_version.py b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/neural_compressor_ext_lab_alibaba/_version.py deleted file mode 100644 index 508854434da..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/neural_compressor_ext_lab_alibaba/_version.py +++ /dev/null @@ -1,20 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import json -from pathlib import Path - -__all__ = ["__version__"] - -__version__ = "0.1.0" diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/neural_compressor_ext_lab_alibaba/labextension/build_log.json b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/neural_compressor_ext_lab_alibaba/labextension/build_log.json deleted file mode 100644 index 17e19a6ab10..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/neural_compressor_ext_lab_alibaba/labextension/build_log.json +++ /dev/null @@ -1,641 +0,0 @@ -[ - { - "bail": false, - "module": { - "rules": [ - { - "test": {}, - "use": [ - "style-loader", - "css-loader" - ] - }, - { - "test": {}, - "use": "raw-loader" - }, - { - "test": {}, - "use": "raw-loader" - }, - { - "test": {}, - "use": "file-loader" - }, - { - "test": {}, - "use": "file-loader" - }, - { - "test": {}, - "use": "url-loader?limit=10000&mimetype=application/font-woff" - }, - { - "test": {}, - "use": "url-loader?limit=10000&mimetype=application/font-woff" - }, - { - "test": {}, - "use": "url-loader?limit=10000&mimetype=application/octet-stream" - }, - { - "test": {}, - "use": "file-loader" - }, - { - "test": {}, - "issuer": {}, - "use": { - "loader": "svg-url-loader", - "options": { - "encoding": "none", - "limit": 10000 - } - } - }, - { - "test": {}, - "issuer": {}, - "use": { - "loader": "raw-loader" - } - }, - { - "test": {}, - "type": "javascript/auto" - }, - { - "test": {}, - "resolve": { - "fullySpecified": false - } - }, - { - "test": {}, - "resolve": { - "fullySpecified": false - } - }, - { - "test": {}, - "use": "file-loader" - } - ] - }, - "resolve": { - "alias": { - "@phosphor/algorithm$": "/home/demo/jupyter_lab_workspace/neural_compressor_ext_lab_alibaba/node_modules/@lumino/algorithm/dist/index.js", - "@phosphor/application$": "/home/demo/jupyter_lab_workspace/neural_compressor_ext_lab_alibaba/node_modules/@lumino/application/dist/index.js", - "@phosphor/commands$": "/home/demo/jupyter_lab_workspace/neural_compressor_ext_lab_alibaba/node_modules/@lumino/commands/dist/index.js", - 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} - }, - "jupyter-releaser": { - "hooks": { - "before-build-npm": [ - "python -m pip install jupyterlab~=3.1", - "jlpm" - ], - "before-build-python": [ - "jlpm clean:all" - ] - } - } -} diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/package.json b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/package.json deleted file mode 100644 index 41dcad5c323..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/package.json +++ /dev/null @@ -1,115 +0,0 @@ -{ - "name": "neural_compressor_ext_lab_alibaba", - "version": "0.1.0", - "description": "A JupyterLab extension.", - "keywords": [ - "jupyter", - "jupyterlab", - "jupyterlab-extension" - ], - "homepage": "https://github.com/intel/neural-compressor/tree/master/neural_coder/extensions/neural_compressor_ext_lab", - "bugs": { - "url": "https://github.com/intel/neural-compressor/tree/master/neural_coder/extensions/neural_compressor_ext_lab" - }, - "license": "BSD-3-Clause", - "author": { - "name": "neural_compressor_ext_lab", - "email": "inc.maintainers@intel.com" - }, - "files": [ - "lib/**/*.{d.ts,eot,gif,html,jpg,js,js.map,json,png,svg,woff2,ttf}", - "style/**/*.{css,js,eot,gif,html,jpg,json,png,svg,woff2,ttf}" - ], - "main": "lib/index.js", - "types": "lib/index.d.ts", - "style": "style/index.css", - "repository": { - "type": "git", - "url": "https://github.com/intel/neural-compressor.git" - }, - "scripts": { - "preinstall": "npx npm-force-resolutions", - "build": "jlpm build:lib && jlpm build:labextension:dev", - "build:prod": "jlpm clean && jlpm build:lib && jlpm build:labextension", - "build:labextension": "jupyter labextension build .", - "build:labextension:dev": "jupyter labextension build --development True .", - "build:lib": "tsc", - "clean": "jlpm clean:lib", - "clean:lib": "rimraf lib tsconfig.tsbuildinfo", - "clean:lintcache": "rimraf .eslintcache .stylelintcache", - "clean:labextension": "rimraf neural_compressor_ext_lab_alibaba/labextension", - "clean:all": "jlpm clean:lib && jlpm clean:labextension && jlpm clean:lintcache", - "eslint": "jlpm eslint:check --fix", - "eslint:check": "eslint . --cache --ext .ts,.tsx", - "install:extension": "jlpm build", - "lint": "jlpm stylelint && jlpm prettier && jlpm eslint", - "lint:check": "jlpm stylelint:check && jlpm prettier:check && jlpm eslint:check", - "prettier": "jlpm prettier:base --write --list-different", - "prettier:base": "prettier \"**/*{.ts,.tsx,.js,.jsx,.css,.json,.md}\"", - "prettier:check": "jlpm prettier:base --check", - "stylelint": "jlpm stylelint:check --fix", - "stylelint:check": "stylelint --cache \"style/**/*.css\"", - "watch": "run-p watch:src watch:labextension", - "watch:src": "tsc -w", - "watch:labextension": "jupyter labextension watch ." - }, - "dependencies": { - "@jupyterlab/application": "^3.1.0", - "@jupyterlab/docregistry": "^3.4.7", - "@jupyterlab/mainmenu": "^3.4.7", - "@jupyterlab/notebook": "^3.4.7", - "@phosphor/commands": "^1.7.2", - "loader-utils": "^2.0.4", - "react": "^17.0.2", - "react-sanitized-html": "^2.0.0" - }, - "devDependencies": { - "@jupyterlab/builder": "^3.1.0", - "@typescript-eslint/eslint-plugin": "^4.8.1", - "@typescript-eslint/parser": "^4.8.1", - "eslint": "^7.14.0", - "eslint-config-prettier": "^6.15.0", - "eslint-plugin-prettier": "^3.1.4", - "got": "^12.5.3", - "json5": "^2.2.2", - "jsonwebtoken": "^9.0.0", - "npm-run-all": "^4.1.5", - "prettier": "^2.1.1", - "rimraf": "^3.0.2", - "stylelint": "^15.6.0", - "stylelint-config-prettier": "^9.0.3", - "stylelint-config-recommended": "^12.0.0", - "stylelint-config-standard": "~33.0.0", - "stylelint-prettier": "^2.0.0", - "typescript": "~4.1.3" - }, - "sideEffects": [ - "style/*.css", - "style/index.js" - ], - "styleModule": "style/index.js", - "publishConfig": { - "access": "public" - }, - "jupyterlab": { - "extension": true, - "outputDir": "neural_compressor_ext_lab_alibaba/labextension" - }, - "jupyter-releaser": { - "hooks": { - "before-build-npm": [ - "python -m pip install jupyterlab~=3.1", - "jlpm" - ], - "before-build-python": [ - "jlpm clean:all" - ] - } - }, - "resolutions": { - "got": "^11.8.5", - "loader-utils": "^2.0.4", - "json5": "^2.2.2", - "jsonwebtoken": "^9.0.0" - } -} diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/pyproject.toml b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/pyproject.toml deleted file mode 100644 index 19944847c94..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/pyproject.toml +++ /dev/null @@ -1,68 +0,0 @@ -[build-system] -requires = ["hatchling>=1.3.1", "jupyterlab>=3.4.7,<4.0.0", "hatch-nodejs-version"] -build-backend = "hatchling.build" - -[project] -name = "neural_compressor_ext_lab_alibaba" -readme = "README.md" -license = { file = "LICENSE" } -requires-python = ">=3.6" -classifiers = [ - "Framework :: Jupyter", - "Framework :: Jupyter :: JupyterLab", - "Framework :: Jupyter :: JupyterLab :: 3", - "Framework :: Jupyter :: JupyterLab :: Extensions", - "Framework :: Jupyter :: JupyterLab :: Extensions :: Prebuilt", - "License :: OSI Approved :: BSD License", - "Programming Language :: Python", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.7", - "Programming Language :: Python :: 3.8", - "Programming Language :: Python :: 3.9", - "Programming Language :: Python :: 3.10", -] -dependencies = [ -] -dynamic = ["version", "description", "authors", "urls", "keywords"] - -[project.optional-dependencies] -test = [ -] - -[tool.hatch.version] -source = "nodejs" - -[tool.hatch.metadata.hooks.nodejs] -fields = ["description", "authors", "urls"] - -[tool.hatch.build] -artifacts = ["neural_compressor_ext_lab_alibaba/labextension"] - -[tool.hatch.build.targets.wheel.shared-data] -"neural_compressor_ext_lab_alibaba/labextension" = "share/jupyter/labextensions/neural_compressor_ext_lab_alibaba" -"install.json" = "share/jupyter/labextensions/neural_compressor_ext_lab_alibaba/install.json" - -[tool.hatch.build.targets.sdist] -exclude = [".github"] - -[tool.hatch.build.hooks.jupyter-builder] -dependencies = ["hatch-jupyter-builder>=0.5"] -build-function = "hatch_jupyter_builder.npm_builder" -ensured-targets = [ - "neural_compressor_ext_lab_alibaba/labextension/static/style.js", - "neural_compressor_ext_lab_alibaba//labextension/package.json", -] -skip-if-exists = ["neural_compressor_ext_lab_alibaba/labextension/static/style.js"] - -[tool.hatch.build.hooks.jupyter-builder.build-kwargs] -build_cmd = "build:prod" -npm = ["jlpm"] - -[tool.hatch.build.hooks.jupyter-builder.editable-build-kwargs] -build_cmd = "install:extension" -npm = ["jlpm"] -source_dir = "src" -build_dir = "neural_compressor_ext_lab_alibaba/labextension" - -[tool.jupyter-releaser.options] -version_cmd = "hatch version" diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/setup.py b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/setup.py deleted file mode 100644 index afff1d065ea..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/setup.py +++ /dev/null @@ -1,16 +0,0 @@ -# Copyright (c) 2022 Intel Corporation -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -__import__('setuptools').setup() diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/src/constants.ts b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/src/constants.ts deleted file mode 100644 index e476aa49f4b..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/src/constants.ts +++ /dev/null @@ -1,16 +0,0 @@ - - -export namespace Constants { - export const SHORT_PLUGIN_NAME = 'neural_compressor_ext_lab'; - export const WORK_PATH = 'neural_coder_workspace/' - export const ICON_FORMAT_ALL_SVG = - '' - export const ICON_RUN = - '' - export const SVG = '' - export const LONG_PLUGIN_NAME = `@rya/${SHORT_PLUGIN_NAME}`; - export const SETTINGS_SECTION = `${LONG_PLUGIN_NAME}:settings`; - export const COMMAND_SECTION_NAME = 'Jupyterlab Code Optimizer'; - export const PLUGIN_VERSION = '0.1.0'; - } - \ No newline at end of file diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/src/deepcoder.ts b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/src/deepcoder.ts deleted file mode 100644 index 766666c26a1..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/src/deepcoder.ts +++ /dev/null @@ -1,330 +0,0 @@ -import { Cell, CodeCell } from '@jupyterlab/cells'; -import { ToolbarButton} from '@jupyterlab/apputils'; -import { Widget } from '@lumino/widgets'; -import { INotebookTracker,NotebookPanel, Notebook, NotebookActions} from '@jupyterlab/notebook'; -import NotebookUtilities from "./utils" -import { Constants } from './constants'; - - -class JupyterlabCodeOptimizer { - protected working: boolean; - protected panel :NotebookPanel; - private tmp_path: string; - public log_path:string; - public tmp_log_path:string; - public rand:number - public markdown: Cell | undefined - public cells:CodeCell[]; - constructor(panel: NotebookPanel) { - this.working = false; - this.panel = panel - this.tmp_path = Constants.WORK_PATH+"tmp.py" - this.rand = NotebookUtilities.GetRandomNum(0,200) - this.log_path = Constants.WORK_PATH + "NeuralCoder"+this.rand+".log"; - this.tmp_log_path = Constants.WORK_PATH+ "NeuralCoder_tmp"+".log"; - this.cells = []; - } - - - async optimizeCode( - code: string[], - formatter: string, - name:string, - next:string, - options: string | undefined, - notebook: boolean, - panel: NotebookPanel, - cell:CodeCell, - run?:ToolbarButton|undefined, - ){ - let codes: string[] = [] - code.forEach(function (value) { - value = value.replace(/('\\n')/g, '^^^') - value = value.replace(/\\n"/g, '###') - value = value.replace(/\\n'/g, '###') - value = value.replace(/"\\n/g, '@@') - value = value.replace(/'\\n/g, '@@') - value = value.replace(/\n/g, '\\n') - value = value.replace(/"/g, '+++') - value = value.replace(/,/g, '$') - codes.push(value) - }) - - let gen_code = `code = "${codes}"\ncodes = code.split(',')\nwith open( '${this.tmp_path}', 'w+' ) as f:\n for i in range(0,len(codes)):\n f.write('# this is the beginning of a single code snippet\\n')\n code_list = codes[i].replace('$',',').replace('+++','\"').split('\\n')\n for line in code_list:\n if('split(^^^)' in line):\n line=line.replace('split(^^^)', 'split(\\'\\\\n\\')')\n if('###' in line):\n line=line.replace('###', '\\\\n\"')\n if('@@' in line):\n line=line.replace('@@', '\"\\\\n')\n f.write(line+'\\n')` - const expr = {code_list: `code_list`}; - NotebookUtilities.sendKernelRequestFromNotebook(panel, gen_code, expr,false); - - if(options === 'normal'){ - let runcode = `from neural_coder import enable\nenable(code="${this.tmp_path}",features=["${formatter}"], overwrite=True)`; - let expr = {sum: ` `}; - NotebookUtilities.sendKernelRequestFromNotebook(panel, runcode, expr,false); - let run_code1 = `with open("${this.tmp_path}", 'r') as f:\n optimized_code = f.read()\n` - let expr1 = {optimizedCode : "optimized_code"} - let result2 = NotebookUtilities.sendKernelRequestFromNotebook(panel,run_code1,expr1,false) - result2.then(value=>{ - let optimizedTexts = Object.values(value.optimizedCode.data)[0] as string - let optimizeCodes = optimizedTexts.split('# this is the beginning of a single code snippet\\n').slice(1,) - optimizeCodes[optimizeCodes.length-1] = optimizeCodes[optimizeCodes.length-1].slice(0,-3) - for (let i = 0; i < optimizeCodes.length; ++i) { - const cell = this.cells[i]; - const currentTexts = this.cells.map(cell => cell.model.value.text); - const currentText = currentTexts[i]; - let optimizedtext = optimizeCodes[i]; - optimizedtext = optimizedtext.replace(/\\'\\\\n\\'/g, "^^^") - optimizedtext = optimizedtext.replace(/\\\\n"/g, "+++") - optimizedtext = optimizedtext.replace(/\\\\n'/g, "+++") - optimizedtext = optimizedtext.replace(/"\\\\n/g, "@@@") - optimizedtext = optimizedtext.replace(/'\\\\n/g, "@@@") - optimizedtext = optimizedtext.replace(/\\n/g,'\n') - optimizedtext = optimizedtext.replace(/\\'/g,"'") - optimizedtext = optimizedtext.replace(/\^\^\^/g, "'\\n'") - optimizedtext = optimizedtext.replace(/\+\+\+/g, "\\n\"") - optimizedtext = optimizedtext.replace(/\@\@\@/g, "\"\\n") - if (cell.model.value.text === currentText) { - cell.model.value.text = optimizedtext; - } - const run_svg = document.createElement("svg") - run_svg.innerHTML = Constants.ICON_RUN - run?.node.firstChild?.firstChild?.firstChild?.firstChild?.replaceWith(run_svg) - } - }) - - }else{ - if(formatter === ''){ - if (this.markdown){ - this.markdown.model.value.text += "[NeuralCoder INFO] Enabling and Benchmarking for The Original Model ...... \n" - } - let runcode1 = `with open("${this.log_path}", 'a' ) as f:\n f.write("[NeuralCoder INFO] Enabling and Benchmarking for The Original Model ......\\n")` - let expr1 = {path: ""}; - NotebookUtilities.sendKernelRequestFromNotebook(panel, runcode1, expr1,false); - - let runcode = `from neural_coder import enable\nperfomance, mode, path = enable(code="${this.tmp_path}",features=[], run_bench=True, args="${options}")\nwith open(path + '/bench.log', 'r') as f:\n logs = f.readlines()\nlog_line = logs[4]\nlog = log_line.split("[")[1].split("]")[0]` - let expr = {path: "path",log:"log"}; - let result = NotebookUtilities.sendKernelRequestFromNotebook(panel, runcode, expr,false); - let fps : String ; - result.then( value => { - fps = Object.values(value.log.data)[0] as string; - if (this.markdown){ - this.markdown.model.value.text += `[NeuralCoder INFO] Benchmark Result (Performance) of The Original Model is ${fps} (FPS) \n` - } - let text = `[NeuralCoder INFO] Benchmark Result (Performance) of The Original Model is ${fps} (FPS)\\n` - let runcode = `with open("${this.log_path}", 'a' ) as f:\n f.write("${text}")` - let expr = {path: ""}; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode, expr,false); - if (this.markdown){ - this.markdown.model.value.text += `[NeuralCoder INFO] Enabling and Benchmarking for ${next} ...... \n` - } - let runcode1 = `with open("${this.log_path}", 'a' ) as f:\n f.write("[NeuralCoder INFO] Enabling and Benchmarking for ${next} ......\\n")` - let expr1 = {path: ""}; - NotebookUtilities.sendKernelRequestFromNotebook(panel, runcode1, expr1,false); - let runcode2 = `with open("${this.tmp_log_path}", 'a' ) as f:\n f.write("${text}")` - let expr2 = {path: ""}; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode2, expr2,false); - }) - - }else{ - - let runcode = `from neural_coder import enable\nperfomance, mode, path = enable(code="${this.tmp_path}", features=["${formatter}"], run_bench=True, args="${options}")\nwith open(path + '/bench.log', 'r') as f:\n logs = f.readlines()\nlog_line = logs[4]\nlog = log_line.split("[")[1].split("]")[0]` - let expr = {path: "path",log:"log"}; - let result = NotebookUtilities.sendKernelRequestFromNotebook(panel, runcode, expr,false); - let fps:string; - result.then( value => { - fps = Object.values(value.log.data)[0] as string; - if(this.markdown){ - this.markdown.model.value.text += `[NeuralCoder INFO] Benchmark Result (Performance) of ${name} is ${fps} (FPS) \n` - } - let text = `[NeuralCoder INFO] Benchmark Result (Performance) of ${name} is ${fps} (FPS)\\n` - let runcode = `with open("${this.log_path}", 'a' ) as f:\n f.write("${text}")` - let expr = {path: ""}; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode, expr,false); - if (next !== ''){ - if(this.markdown){ - this.markdown.model.value.text += `[NeuralCoder INFO] Enabling and Benchmarking for ${next} ...... \n` - } - let runcode2 = `with open("${this.log_path}", 'a' ) as f:\n f.write("[NeuralCoder INFO] Enabling and Benchmarking for ${next} ......\\n")` - let expr2 = {path: ""}; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode2, expr2,false); - } - let runcode2 = `with open("${this.tmp_log_path}", 'a' ) as f:\n f.write("${text}")` - let expr2 = {path: ""}; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode2, expr2,false); - - if (formatter === 'pytorch_aliblade'){ - let read_log = `import re\nwith open("${this.tmp_log_path}", 'r') as f:\n logs = f.readlines()\n fps_list=[]\n for log_line in logs[-2:]:\n pat = r\'\\d+\\.?\\d+'\n fps = re.search(pat,log_line).group()\n fps_list.append(float(fps))\nmaxi = max(fps_list)\nindex = fps_list.index(maxi)\nboost = round(maxi/fps_list[0],1)\nfeatures=['','pytorch_aliblade',]\nfeature_name=['','Alibaba Blade-DISC']\nbest_feature = features[index]\nbest_name = feature_name[index]\n` - let read_expr = {boost:"boost",best_feature:"best_feature",best_name:"best_name"} - let read_result = NotebookUtilities.sendKernelRequestFromNotebook(this.panel, read_log, read_expr,false); - read_result.then(value =>{ - let boost = Object.values(value.boost.data)[0] as string; - let best_name = Object.values(value.best_name.data)[0] as string; - if(this.markdown){ - this.markdown.model.value.text += `[NeuralCoder INFO] The Best Intel Optimization: ${best_name} \n` - this.markdown.model.value.text += `[NeuralCoder INFO] You can get up to ${boost}X performance boost. \n` - } - let command = "lscpu | grep 'Model name'" - let get_hardware = `import subprocess\nsubp = subprocess.Popen("${command}",shell=True,stdout=subprocess.PIPE,stderr=subprocess.PIPE,encoding="utf-8")\nsubp.wait(2)\nhardware = subp.communicate()[0].replace("Model name:","").strip()` - let expr_hardware = {hardware: "hardware"} - let hard_res = NotebookUtilities.sendKernelRequestFromNotebook(this.panel, get_hardware, expr_hardware,false); - hard_res.then(value =>{ - let hard = Object.values(value.hardware.data)[0] as string; - if(this.markdown){ - this.markdown.model.value.text += `[NeuralCoder INFO] HardWare: ${hard} \n` - this.markdown.model.value.text += `[NeuralCoder INFO] The log was saved to neural_coder_workspace\\NeuralCoder${this.rand}.log \n` - } - }) - const run_svg = document.createElement("svg") - run_svg.innerHTML = Constants.ICON_RUN - run?.node.firstChild?.firstChild?.firstChild?.firstChild?.replaceWith(run_svg) - }) - } - }) - } - } - } - } - - - export class JupyterlabNotebookCodeOptimizer extends JupyterlabCodeOptimizer { - protected notebookname: string; - protected notebookTracker: INotebookTracker; - - constructor( - notebookTracker: INotebookTracker, - panel: NotebookPanel - ) { - super(panel); - this.notebookTracker = notebookTracker; - this.notebookname = ''; - } - - public async optimizeAction(config: any, formatter?: string) { - return this.optimizeCells(true, config, formatter); - } - - - public async optimizeAllCodeCells( - config?: string, - formatter?: string, - notebook?: Notebook, - run?: ToolbarButton - ):Promise { - return this.optimizeCells(false, config, formatter, notebook, run); - } - - private getCodeCells(ifMarkdown = true, notebook?: Notebook): CodeCell[] { - if (!this.notebookTracker.currentWidget) { - return []; - } - const codeCells: CodeCell[] = []; - notebook = notebook || this.notebookTracker.currentWidget.content; - this.notebookname = notebook.title.label - let count = 0; - notebook.widgets.forEach((cell: Cell) => { - if (cell.model.type === 'code') { - count += 1; - codeCells.push(cell as CodeCell); - } - }); - - if(ifMarkdown){ - NotebookActions.insertBelow(notebook); - this.notebookTracker.currentWidget.content.activeCellIndex = count + 1; - NotebookActions.changeCellType(notebook, 'markdown') - const activeCell = notebook.activeCell; - if (activeCell){ - this.markdown = activeCell - } - } - - - this.cells = codeCells - return codeCells; - } - - private async optimizeCells( - selectedOnly: boolean, - config?: string, - formatter?: string, - notebook?: Notebook, - run?: ToolbarButton|undefined - ):Promise{ - if (this.working) { - return new Promise((resolve, reject) => { - resolve("false!") - }); - } - this.working = true; - const optimize_type = formatter !== undefined ? formatter : 'pytorch_aliblade'; - if(optimize_type === 'auto'){ - selectedOnly = true - }else{selectedOnly = false} - - const selectedCells = this.getCodeCells(selectedOnly, notebook); - let cell = selectedCells[selectedCells.length-1] - if (selectedCells.length === 0) { - this.working = false; - return new Promise((resolve, reject) => { - resolve("false!") - }); - } - const currentTexts = selectedCells.map(cell => cell.model.value.text); - if (optimize_type === 'auto'){ - - if (this.markdown){ - this.markdown.model.value.text += `[NeuralCoder INFO] Code: User code from Jupyter Lab notebook "${this.notebookname}" \n` - this.markdown.model.value.text += `[NeuralCoder INFO] Benchmark Mode: Throughput \n` - } - let runcode2 = `with open('${this.log_path}', 'a' ) as f:\n f.write("[NeuralCoder INFO] Code: User code from Jupyter Lab notebook '${this.notebookname}'\\n")` - let expr2 = {path: ""}; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode2, expr2,false); - let runcode3 = `with open('${this.log_path}', 'a' ) as f:\n f.write("[NeuralCoder INFO] Benchmark Mode: Throughput\\n")` - let expr3 = {path: ""}; - NotebookUtilities.sendKernelRequestFromNotebook(this.panel, runcode3, expr3,false); - await this.optimizeCode( - currentTexts, - '', - 'The Original Model', - 'Alibaba-Blade', - config, - true, - this.panel, - cell, - run - ); - await this.optimizeCode( - currentTexts, - 'pytorch_aliblade', - 'Alibaba-Blade', - '', - config, - true, - this.panel, - cell, - run, - ); - - }else{ - await this.optimizeCode( - currentTexts, - optimize_type, - "", - "", - "normal", - true, - this.panel, - cell, - run, - ); - } - this.working = false; - return new Promise((resolve, reject) => { - resolve("success!") - }); - } - applicable(formatter: string, currentWidget: Widget) { - const currentNotebookWidget = this.notebookTracker.currentWidget; - return currentNotebookWidget && currentWidget === currentNotebookWidget; - } - - } - - \ No newline at end of file diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/src/index.ts b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/src/index.ts deleted file mode 100644 index c86bad70587..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/src/index.ts +++ /dev/null @@ -1,136 +0,0 @@ -import { DocumentRegistry } from '@jupyterlab/docregistry'; -import { - JupyterFrontEnd, - JupyterFrontEndPlugin -} from '@jupyterlab/application'; -import { INotebookTracker,NotebookPanel, INotebookModel} from '@jupyterlab/notebook'; -import { -ToolbarButton, showDialog, Dialog } from '@jupyterlab/apputils'; -import { ISettingRegistry } from '@jupyterlab/settingregistry'; -import { IMainMenu } from '@jupyterlab/mainmenu'; -import { LabIcon } from '@jupyterlab/ui-components'; -import { Widget } from '@lumino/widgets'; -import { JupyterlabNotebookCodeOptimizer } from './deepcoder'; -import { Constants } from './constants'; - - -class neural_compressor_ext_lab - implements DocumentRegistry.IWidgetExtension { - private app: JupyterFrontEnd; - private tracker: INotebookTracker; - private notebookpanel: NotebookPanel; - private config: string; - private notebookCodeOptimizer: JupyterlabNotebookCodeOptimizer | undefined; - - constructor( - app: JupyterFrontEnd, - tracker: INotebookTracker, - notebookpanel:NotebookPanel - ) { - this.app = app; - this.tracker = tracker; - this.notebookpanel = notebookpanel; - this.setupWidgetExtension(); - this.config = '' - } - - public createNew( - nb: NotebookPanel, - ) { - this.notebookpanel = nb; - this.notebookCodeOptimizer = new JupyterlabNotebookCodeOptimizer( - this.tracker, - this.notebookpanel - ); - const svg = document.createElement("svg") - svg.innerHTML = Constants.ICON_FORMAT_ALL_SVG - const run_svg = document.createElement("svg") - run_svg.innerHTML = Constants.ICON_RUN - const div = document.createElement("div"); - div.setAttribute("class","wrapper") - const span = document.createElement("span"); - span.setAttribute("class","f1ozlkqi") - span.innerHTML = Constants.SVG; - const selector = document.createElement("select"); - selector.setAttribute("class","aselector") - selector.id = "NeuralCoder" - const option1 = document.createElement("option"); - option1.value = "pytorch_aliblade"; - option1.innerText = "Alibaba Blade-DISC"; - const option2 = document.createElement("option"); - option2.value = "auto"; - option2.innerText = "Auto Benchmark"; - option1.selected=true; - - selector.options.add(option1) - selector.options.add(option2) - div.appendChild(selector) - div.appendChild(span) - const selector_widget = new Widget(); - selector_widget.node.appendChild(div) - selector_widget.addClass("aselector") - let notebookCodeOptimizer = this.notebookCodeOptimizer; - let config = this.config; - - const dia_input = document.createElement("input") - const dia_widget = new Widget(); - dia_widget.node.appendChild(dia_input) - dia_widget.addClass("dialog") - - const run_button = new ToolbarButton({ - tooltip: 'NeuralCoder', - icon: new LabIcon({ - name: "run", - svgstr:Constants.ICON_RUN - }), - onClick: async function (){ - run_button.node.firstChild?.firstChild?.firstChild?.firstChild?.replaceWith(svg) - if (selector.options[selector.selectedIndex].value === 'auto'){ - await showDialog({ - title:'Please input execute parameters:', - body: dia_widget, - buttons: [Dialog.okButton({ label: 'Confirm' })] - }).then(result => { - if (result.button.accept) { - config = dia_input.value - } - }) - } - await notebookCodeOptimizer.optimizeAllCodeCells(config,selector.options[selector.selectedIndex].value,undefined,run_button); - } - }); - nb.toolbar.insertItem(11,"nc",run_button) - nb.toolbar.insertItem(12,"selector",selector_widget) - - } - - private setupWidgetExtension() { - this.app.docRegistry.addWidgetExtension('Notebook', this); - } -} - - - -/** - * Initialization data for the neural_compressor_ext_lab extension. - */ -const plugin: JupyterFrontEndPlugin = { - id: 'neural_compressor_ext_lab:plugin', - autoStart: true, - requires: [INotebookTracker,IMainMenu], - optional: [ISettingRegistry], - activate: ( - app: JupyterFrontEnd, - tracker: INotebookTracker, - notebookpanel: NotebookPanel - ) => { - new neural_compressor_ext_lab( - app, - tracker, - notebookpanel - ); - console.log('JupyterLab extension neural_compressor_ext_lab is activated!'); - } -}; - -export default plugin; diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/src/utils.tsx b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/src/utils.tsx deleted file mode 100644 index f76b2f20d40..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/src/utils.tsx +++ /dev/null @@ -1,366 +0,0 @@ -/* - * Copyright 2019-2020 The Kale Authors - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -import { Dialog, showDialog } from '@jupyterlab/apputils'; -import { NotebookPanel } from '@jupyterlab/notebook'; -import { KernelMessage, Kernel } from '@jupyterlab/services'; -import { CommandRegistry } from '@phosphor/commands'; -// @ts-ignore -import SanitizedHTML from 'react-sanitized-html'; -import * as React from 'react'; -import { ReactElement } from 'react'; - - - -export default class NotebookUtilities { - /** - * generate random number - * @Min - * @Max - */ - public static GetRandomNum(Min:number, Max:number):number { - let Range : number; - Range = Max - Min; - var Rand = Math.random(); - return (Min + Math.round(Rand * Range)); - } - /** - * Builds an HTML container by sanitizing a list of strings and converting - * them in valid HTML - * @param msg A list of string with HTML formatting - * @returns a HTMLDivElement composed of a list of spans with formatted text - */ - private static buildDialogBody(msg: string[]): ReactElement { - return ( -
- {msg.map((s: string, i: number) => { - return ( - - -
- - ); - })} -
- ); - } - - /** - * Opens a pop-up dialog in JupyterLab to display a simple message. - * @param title The title for the message popup - * @param msg The message as an array of strings - * @param buttonLabel The label to use for the button. Default is 'OK' - * @param buttonClassName The classname to give to the 'ok' button - * @returns Promise - A promise once the message is closed. - */ - public static async showMessage( - title: string, - msg: string[], - buttonLabel: string = 'Dismiss', - buttonClassName: string = '', - ): Promise { - const buttons: ReadonlyArray = [ - Dialog.okButton({ label: buttonLabel, className: buttonClassName }), - ]; - const messageBody = this.buildDialogBody(msg); - await showDialog({ title, buttons, body: messageBody }); - } - - /** - * Opens a pop-up dialog in JupyterLab to display a yes/no dialog. - * @param title The title for the message popup - * @param msg The message - * @param acceptLabel The label to use for the accept button. Default is 'YES' - * @param rejectLabel The label to use for the reject button. Default is 'NO' - * @param yesButtonClassName The classname to give to the accept button. - * @param noButtonClassName The classname to give to the cancel button. - * @returns Promise - A promise once the message is closed. - */ - public static async showYesNoDialog( - title: string, - msg: string[], - acceptLabel: string = 'YES', - rejectLabel: string = 'NO', - yesButtonClassName: string = '', - noButtonClassName: string = '', - ): Promise { - const buttons: ReadonlyArray = [ - Dialog.okButton({ label: acceptLabel, className: yesButtonClassName }), - Dialog.cancelButton({ label: rejectLabel, className: noButtonClassName }), - ]; - const messageBody = this.buildDialogBody(msg); - const result = await showDialog({ title, buttons, body: messageBody }); - return result.button.label === acceptLabel; - } - - /** - * Opens a pop-up dialog in JupyterLab with various information and button - * triggering reloading the page. - * @param title The title for the message popup - * @param msg The message - * @param buttonLabel The label to use for the button. Default is 'Refresh' - * @param buttonClassName The classname to give to the 'refresh' button. - * @returns Promise - A promise once the message is closed. - */ - public static async showRefreshDialog( - title: string, - msg: string[], - buttonLabel: string = 'Refresh', - buttonClassName: string = '', - ): Promise { - await this.showMessage(title, msg, buttonLabel, buttonClassName); - location.reload(); - } - - /** - * @description Creates a new JupyterLab notebook for use by the application - * @param command The command registry - * @returns Promise - A promise containing the notebook panel object that was created (if successful). - */ - public static async createNewNotebook( - command: CommandRegistry, - ): Promise { - const notebook: any = await command.execute('notebook:create-new', { - activate: true, - path: '', - preferredLanguage: '', - }); - await notebook.session.ready; - return notebook; - } - - /** - * Safely saves the Jupyter notebook document contents to disk - * @param notebookPanel The notebook panel containing the notebook to save - */ - public static async saveNotebook( - notebookPanel: NotebookPanel, - ): Promise { - if (notebookPanel) { - await notebookPanel.context.ready; - notebookPanel.context.save(); - return true; - } - return false; - } - - /** - * Convert the notebook contents to JSON - * @param notebookPanel The notebook panel containing the notebook to serialize - */ - public static notebookToJSON(notebookPanel: NotebookPanel): any { - if (notebookPanel.content.model) { - return notebookPanel.content.model.toJSON(); - } - return null; - } - - /** - * @description Gets the value of a key from specified notebook's metadata. - * @param notebookPanel The notebook to get meta data from. - * @param key The key of the value. - * @returns any -The value of the metadata. Returns null if the key doesn't exist. - */ - public static getMetaData(notebookPanel: NotebookPanel, key: string): any { - if (!notebookPanel) { - throw new Error( - 'The notebook is null or undefined. No meta data available.', - ); - } - if (notebookPanel.model && notebookPanel.model.metadata.has(key)) { - return notebookPanel.model.metadata.get(key); - } - return null; - } - - /** - * @description Sets the key value pair in the notebook's metadata. - * If the key doesn't exists it will add one. - * @param notebookPanel The notebook to set meta data in. - * @param key The key of the value to create. - * @param value The value to set. - * @param save Default is false. Whether the notebook should be saved after the meta data is set. - * Note: This function will not wait for the save to complete, it only sends a save request. - * @returns The old value for the key, or undefined if it did not exist. - */ - public static setMetaData( - notebookPanel: NotebookPanel, - key: string, - value: any, - save: boolean = false, - ): any { - if (!notebookPanel) { - throw new Error( - 'The notebook is null or undefined. No meta data available.', - ); - } - const oldVal = notebookPanel.model?.metadata.set(key, value); - if (save) { - this.saveNotebook(notebookPanel); - } - return oldVal; - } - - // /** - // * Get a new Kernel, not tied to a Notebook - // * Source code here: https://github.com/jupyterlab/jupyterlab/tree/473348d25bcb258ca2f0c127dd8fb5b193217135/packages/services - // */ - // public static async createNewKernel() { - // // Get info about the available kernels and start a new one. - // let options: Kernel.IOptions = await Kernel.getSpecs().then(kernelSpecs => { - // // console.log('Default spec:', kernelSpecs.default); - // // console.log('Available specs', Object.keys(kernelSpecs.kernelspecs)); - // // use the default name - // return { name: kernelSpecs.default }; - // }); - // return await Kernel.startNew(options).then(_kernel => { - // return _kernel; - // }); - // } - - // // TODO: We can use this context manager to execute commands inside a new kernel - // // and be sure that it will be disposed of at the end. - // // Another approach could be to create a kale_rpc Kernel, as a singleton, - // // created at startup. The only (possible) drawback is that we can not name - // // a kernel instance with a custom id/name, so when refreshing JupyterLab we would - // // not recognize the kernel. A solution could be to have a kernel spec dedicated to kale rpc calls. - // public static async executeWithNewKernel(action: Function, args: any[] = []) { - // // create brand new kernel - // const _k = await this.createNewKernel(); - // // execute action inside kernel - // const res = await action(_k, ...args); - // // close kernel - // _k.shutdown(); - // // return result - // return res; - // } - - /** - * @description This function runs code directly in the notebook's kernel and then evaluates the - * result and returns it as a promise. - * @param kernel The kernel to run the code in. - * @param runCode The code to run in the kernel. - * @param userExpressions The expressions used to capture the desired info from the executed code. - * @param runSilent Default is false. If true, kernel will execute as quietly as possible. - * store_history will be set to false, and no broadcast on IOPUB channel will be made. - * @param storeHistory Default is false. If true, the code executed will be stored in the kernel's history - * and the counter which is shown in the cells will be incremented to reflect code was run. - * @param allowStdIn Default is false. If true, code running in kernel can prompt user for input using - * an input_request message. - * @param stopOnError Default is false. If True, does not abort the execution queue, if an exception is encountered. - * This allows the queued execution of multiple execute_requests, even if they generate exceptions. - * @returns Promise - A promise containing the execution results of the code as an object with - * keys based on the user_expressions. - * @example - * //The code - * const code = "a=123\nb=456\nsum=a+b"; - * //The user expressions - * const expr = {sum: "sum",prod: "a*b",args:"[a,b,sum]"}; - * //Async function call (returns a promise) - * sendKernelRequest(notebookPanel, code, expr,false); - * //Result when promise resolves: - * { - * sum:{status:"ok",data:{"text/plain":"579"},metadata:{}}, - * prod:{status:"ok",data:{"text/plain":"56088"},metadata:{}}, - * args:{status:"ok",data:{"text/plain":"[123, 456, 579]"}} - * } - * @see For more information on JupyterLab messages: - * https://jupyter-client.readthedocs.io/en/latest/messaging.html#execution-results - */ - public static async sendKernelRequest( - kernel: Kernel.IKernelConnection| null | undefined, - runCode: string, - userExpressions: any, - runSilent: boolean = false, - storeHistory: boolean = false, - allowStdIn: boolean = false, - stopOnError: boolean = false, - ): Promise { - if (!kernel) { - throw new Error('Kernel is null or undefined.'); - } - - // Wait for kernel to be ready before sending request - // await kernel.status; - - const message: KernelMessage.IShellMessage = await kernel.requestExecute({ - allow_stdin: allowStdIn, - code: runCode, - silent: runSilent, - stop_on_error: stopOnError, - store_history: storeHistory, - user_expressions: userExpressions, - }).done; - - const content: any = message.content; - - if (content.status !== 'ok') { - // If response is not 'ok', throw contents as error, log code - const msg: string = `Code caused an error:\n${runCode}`; - console.error(msg); - if (content.traceback) { - content.traceback.forEach((line: string) => - console.log( - line.replace( - /[\u001b\u009b][[()#;?]*(?:[0-9]{1,4}(?:;[0-9]{0,4})*)?[0-9A-ORZcf-nqry=><]/g, - '', - ), - ), - ); - } - throw content; - } - // Return user_expressions of the content - return content.user_expressions; - } - - /** - * Same as method sendKernelRequest but passing - * a NotebookPanel instead of a Kernel - */ - public static async sendKernelRequestFromNotebook( - notebookPanel: NotebookPanel, - runCode: string, - userExpressions: any, - runSilent: boolean = false, - storeHistory: boolean = false, - allowStdIn: boolean = false, - stopOnError: boolean = false, - ) { - if (!notebookPanel) { - throw new Error('Notebook is null or undefined.'); - } - - // Wait for notebook panel to be ready - await notebookPanel.activate; - await notebookPanel.sessionContext?.ready; - console.log('get kernel',notebookPanel.sessionContext.session?.kernel) - return this.sendKernelRequest( - notebookPanel.sessionContext?.session?.kernel, - runCode, - userExpressions, - runSilent, - storeHistory, - allowStdIn, - stopOnError, - ); - } - - } diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/style/base.css b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/style/base.css deleted file mode 100644 index 16b572c0e9a..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/style/base.css +++ /dev/null @@ -1,636 +0,0 @@ -/* - See the JupyterLab Developer Guide for useful CSS Patterns: - - https://jupyterlab.readthedocs.io/en/stable/developer/css.html -*/ -.lds-ripple { - display: flex; - position: absolute; - left: 0; - top: 0; - right: 0; - bottom: 0; - margin: auto; - width: 80px; - height: 80px; -} -.lds-ripple div { - position: absolute; - left: 0; - top: 0; - right: 0; - bottom: 0; - margin: auto; - border: 4px solid rgb(245, 131, 55); - opacity: 1; - border-radius: 50%; - animation: lds-ripple 1s cubic-bezier(0, 0.2, 0.8, 1) infinite; -} -.lds-ripple div:nth-child(2) { - animation-delay: -0.5s; -} -@keyframes lds-ripple { - 0% { - left: 0; - top: 0; - right: 0; - bottom: 0; - margin: auto; - width: 0; - height: 0; - opacity: 0; - } - 4.9% { - left: 0; - top: 0; - right: 0; - bottom: 0; - margin: auto; - width: 0; - height: 0; - opacity: 0; - } - 5% { - left: 0; - top: 0; - right: 0; - bottom: 0; - margin: auto; - width: 0; - height: 0; - opacity: 1; - } - 100% { - top: 0px; - left: 0px; - width: 72px; - height: 72px; - opacity: 0; - } -} - - -/* CSS */ -.button-62 { - background: linear-gradient(to bottom right, #EF4765, #FF9A5A); - border: 0; - border-radius: 12px; - color: #FFFFFF; - cursor: pointer; - display: inline-block; - font-family: -apple-system,system-ui,"Segoe UI",Roboto,Helvetica,Arial,sans-serif; - font-size: 16px; - font-weight: 500; - line-height: 2.5; - outline: transparent; - padding: 0 1rem; - text-align: center; - text-decoration: none; - transition: box-shadow .2s ease-in-out; - user-select: none; - -webkit-user-select: none; - touch-action: manipulation; - white-space: nowrap; -} - -.button-62:not([disabled]):focus { - box-shadow: 0 0 .25rem rgba(0, 0, 0, 0.5), -.125rem -.125rem 1rem rgba(239, 71, 101, 0.5), .125rem .125rem 1rem rgba(255, 154, 90, 0.5); -} - -.button-62:not([disabled]):hover { - box-shadow: 0 0 .25rem rgba(0, 0, 0, 0.5), -.125rem -.125rem 1rem rgba(239, 71, 101, 0.5), .125rem .125rem 1rem rgba(255, 154, 90, 0.5); -} - -.aselector select { - background-color: initial; - border: none; - border-radius: 0; - box-shadow: none; - color: var(--jp-ui-font-color0); - display: block; - font-size: var(--jp-ui-font-size1); - height: 24px; - line-height: 14px; - padding: 0 25px 0 10px; - text-align: left; - -moz-appearance: none; - -webkit-appearance: none; -} - -/* Use our own theme for hover and option styles */ -.aselector select:hover, -.aselector select > option { - background-color: var(--jp-layout-color2); - color: var(--jp-ui-font-color0); -} -select { - box-sizing: border-box; -} - -.font{ -background-color: initial; -border: none; -height: 21px; -border-radius: 0; -font-weight:500; -color: var(--jp-ui-font-color0); -display: block; -line-height: 22.5px; -padding: 0 25px 0 10px; -font-size: var(--jp-ui-font-size1); -} -.wrapper { - display: flex; -} -.f1ozlkqi { - pointer-events: none; -} - -.palybutton{ - background-image: '/home2/longxin/Neural_Coder_EXT/style/icons8-circled-play.gif'; -} -.loading{ - - background-image: url("data:image/svg+xml,"); - background-size: contain; -} - -.dialog{ -body { - margin: 0; - height: 100vh; - width:600px; - display: flex; - align-items: center; - justify-content: center; - overflow: hidden; - font-family: "Poppins", sans-serif; - background: #e3d0b6; -} - -#cookie-policy { - display: flex; - flex-direction: column; - justify-content: center; - align-items: center; - width: 460px; - height: 600px; - background: #f3efe6; - border-radius: 12px; - transform: scale(.8); -} - -#cookie-wrapper { - height: 240px; - width: 240px; - margin: 30px 0; - position: relative; - left: -40px; -} - -h1 { - color: #6c3a1f; - text-align: center; - font-size: 36px; - margin: 0; -} - -p { - color: #a28561; - font-size: 14px; - margin-top: 0; - padding: 0 60px; - text-align: center; -} -a { - margin-top: 18px; - font-size: 14px; - color: #a28561; - text-decoration: none; - pointer-events: none; -} -a:hover { - color: #846b4d; -} - -span { - font-family: "Amatic SC", cursive; - font-weight: 400; - font-size: 20px; - position: relative; - top: -18px; - left: 3px; - color: #a28561; -} - -#heart-no, #thought-heart-yes, #mouth, #face-no, #thought-1, #thought-2, #thought-heart-na, #q-mark, #eyes, #leg-l, #leg-r { - opacity: 0; -} -} -.dia_button { - color: white; - background: #dd794a; - margin-top: 12px; - cursor: pointer; - font-size: 24px; - font-family: "Poppins", sans-serif; - border-radius: 9px; - border: none; - width: 72%; - padding: 12px 0; - transition: 150ms ease-out; - pointer-events: none; -} - -.dia_button:hover { - background: #d66029; -} - -.pad{ - padding-left:6% -} - -:root { - /* Elevation - * - * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here: - * - * https://github.com/material-components/material-components-web - * https://material-components-web.appspot.com/elevation.html - */ - - --jp-shadow-base-lightness: 0; - --jp-shadow-umbra-color: rgba( - var(--jp-shadow-base-lightness), - var(--jp-shadow-base-lightness), - var(--jp-shadow-base-lightness), - 0.2 - ); - --jp-shadow-penumbra-color: rgba( - var(--jp-shadow-base-lightness), - var(--jp-shadow-base-lightness), - var(--jp-shadow-base-lightness), - 0.14 - ); - --jp-shadow-ambient-color: rgba( - var(--jp-shadow-base-lightness), - var(--jp-shadow-base-lightness), - var(--jp-shadow-base-lightness), - 0.12 - ); - --jp-elevation-z0: none; - --jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color), - 0px 1px 1px 0px var(--jp-shadow-penumbra-color), - 0px 1px 3px 0px var(--jp-shadow-ambient-color); - --jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color), - 0px 2px 2px 0px var(--jp-shadow-penumbra-color), - 0px 1px 5px 0px var(--jp-shadow-ambient-color); - --jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color), - 0px 4px 5px 0px var(--jp-shadow-penumbra-color), - 0px 1px 10px 0px var(--jp-shadow-ambient-color); - --jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color), - 0px 6px 10px 0px var(--jp-shadow-penumbra-color), - 0px 1px 18px 0px var(--jp-shadow-ambient-color); - --jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color), - 0px 8px 10px 1px var(--jp-shadow-penumbra-color), - 0px 3px 14px 2px var(--jp-shadow-ambient-color); - --jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color), - 0px 12px 17px 2px var(--jp-shadow-penumbra-color), - 0px 5px 22px 4px var(--jp-shadow-ambient-color); - --jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color), - 0px 16px 24px 2px var(--jp-shadow-penumbra-color), - 0px 6px 30px 5px var(--jp-shadow-ambient-color); - --jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color), - 0px 20px 31px 3px var(--jp-shadow-penumbra-color), - 0px 8px 38px 7px var(--jp-shadow-ambient-color); - --jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color), - 0px 24px 38px 3px var(--jp-shadow-penumbra-color), - 0px 9px 46px 8px var(--jp-shadow-ambient-color); - - /* Borders - * - * The following variables, specify the visual styling of borders in JupyterLab. - */ - - --jp-border-width: 1px; - --jp-border-color0: var(--md-grey-400); - --jp-border-color1: var(--md-grey-400); - --jp-border-color2: var(--md-grey-300); - --jp-border-color3: var(--md-grey-200); - --jp-inverse-border-color: var(--md-grey-600); - --jp-border-radius: 2px; - - /* UI Fonts - * - * The UI font CSS variables are used for the typography all of the JupyterLab - * user interface elements that are not directly user generated content. - * - * The font sizing here is done assuming that the body font size of --jp-ui-font-size1 - * is applied to a parent element. When children elements, such as headings, are sized - * in em all things will be computed relative to that body size. - */ - - --jp-ui-font-scale-factor: 1.2; - --jp-ui-font-size0: 0.83333em; - --jp-ui-font-size1: 13px; /* Base font size */ - --jp-ui-font-size2: 1.2em; - --jp-ui-font-size3: 1.44em; - - --jp-ui-font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Helvetica, - Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol'; - - /* - * Use these font colors against the corresponding main layout colors. - * In a light theme, these go from dark to light. - */ - - /* Defaults use Material Design specification */ - --jp-ui-font-color0: rgba(0, 0, 0, 1); - --jp-ui-font-color1: rgba(0, 0, 0, 0.87); - --jp-ui-font-color2: rgba(0, 0, 0, 0.54); - --jp-ui-font-color3: rgba(0, 0, 0, 0.38); - - /* - * Use these against the brand/accent/warn/error colors. - * These will typically go from light to darker, in both a dark and light theme. - */ - - --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1); - --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1); - --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7); - --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5); - - /* Content Fonts - * - * Content font variables are used for typography of user generated content. - * - * The font sizing here is done assuming that the body font size of --jp-content-font-size1 - * is applied to a parent element. When children elements, such as headings, are sized - * in em all things will be computed relative to that body size. - */ - - --jp-content-line-height: 1.6; - --jp-content-font-scale-factor: 1.2; - --jp-content-font-size0: 0.83333em; - --jp-content-font-size1: 14px; /* Base font size */ - --jp-content-font-size2: 1.2em; - --jp-content-font-size3: 1.44em; - --jp-content-font-size4: 1.728em; - --jp-content-font-size5: 2.0736em; - - /* This gives a magnification of about 125% in presentation mode over normal. */ - --jp-content-presentation-font-size1: 17px; - - --jp-content-heading-line-height: 1; - --jp-content-heading-margin-top: 1.2em; - --jp-content-heading-margin-bottom: 0.8em; - --jp-content-heading-font-weight: 500; - - /* Defaults use Material Design specification */ - --jp-content-font-color0: rgba(0, 0, 0, 1); - --jp-content-font-color1: rgba(0, 0, 0, 0.87); - --jp-content-font-color2: rgba(0, 0, 0, 0.54); - --jp-content-font-color3: rgba(0, 0, 0, 0.38); - - --jp-content-link-color: var(--md-blue-700); - - --jp-content-font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', - Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', - 'Segoe UI Symbol'; - - /* - * Code Fonts - * - * Code font variables are used for typography of code and other monospaces content. - */ - - --jp-code-font-size: 13px; - --jp-code-line-height: 1.3077; /* 17px for 13px base */ - --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */ - --jp-code-font-family-default: Menlo, Consolas, 'DejaVu Sans Mono', monospace; - --jp-code-font-family: var(--jp-code-font-family-default); - - /* This gives a magnification of about 125% in presentation mode over normal. */ - --jp-code-presentation-font-size: 16px; - - /* may need to tweak cursor width if you change font size */ - --jp-code-cursor-width0: 1.4px; - --jp-code-cursor-width1: 2px; - --jp-code-cursor-width2: 4px; - - /* Layout - * - * The following are the main layout colors use in JupyterLab. In a light - * theme these would go from light to dark. - */ - - --jp-layout-color0: white; - --jp-layout-color1: white; - --jp-layout-color2: var(--md-grey-200); - --jp-layout-color3: var(--md-grey-400); - --jp-layout-color4: var(--md-grey-600); - - /* Inverse Layout - * - * The following are the inverse layout colors use in JupyterLab. In a light - * theme these would go from dark to light. - */ - - --jp-inverse-layout-color0: #111111; - --jp-inverse-layout-color1: var(--md-grey-900); - --jp-inverse-layout-color2: var(--md-grey-800); - --jp-inverse-layout-color3: var(--md-grey-700); - --jp-inverse-layout-color4: var(--md-grey-600); - - /* Brand/accent */ - - --jp-brand-color0: var(--md-blue-900); - --jp-brand-color1: var(--md-blue-700); - --jp-brand-color2: var(--md-blue-300); - --jp-brand-color3: var(--md-blue-100); - --jp-brand-color4: var(--md-blue-50); - - --jp-accent-color0: var(--md-green-900); - --jp-accent-color1: var(--md-green-700); - --jp-accent-color2: var(--md-green-300); - --jp-accent-color3: var(--md-green-100); - - /* State colors (warn, error, success, info) */ - - --jp-warn-color0: var(--md-orange-900); - --jp-warn-color1: var(--md-orange-700); - --jp-warn-color2: var(--md-orange-300); - --jp-warn-color3: var(--md-orange-100); - - --jp-error-color0: var(--md-red-900); - --jp-error-color1: var(--md-red-700); - --jp-error-color2: var(--md-red-300); - --jp-error-color3: var(--md-red-100); - - --jp-success-color0: var(--md-green-900); - --jp-success-color1: var(--md-green-700); - --jp-success-color2: var(--md-green-300); - --jp-success-color3: var(--md-green-100); - - --jp-info-color0: var(--md-cyan-900); - --jp-info-color1: var(--md-cyan-700); - --jp-info-color2: var(--md-cyan-300); - --jp-info-color3: var(--md-cyan-100); - - /* Cell specific styles */ - - --jp-cell-padding: 5px; - - --jp-cell-collapser-width: 8px; - --jp-cell-collapser-min-height: 20px; - --jp-cell-collapser-not-active-hover-opacity: 0.6; - - --jp-cell-editor-background: var(--md-grey-100); - --jp-cell-editor-border-color: var(--md-grey-300); - --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300); - --jp-cell-editor-active-background: var(--jp-layout-color0); - --jp-cell-editor-active-border-color: var(--jp-brand-color1); - - --jp-cell-prompt-width: 64px; - --jp-cell-prompt-font-family: var(--jp-code-font-family-default); - --jp-cell-prompt-letter-spacing: 0px; - --jp-cell-prompt-opacity: 1; - --jp-cell-prompt-not-active-opacity: 0.5; - --jp-cell-prompt-not-active-font-color: var(--md-grey-700); - /* A custom blend of MD grey and blue 600 - * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */ - --jp-cell-inprompt-font-color: #307fc1; - /* A custom blend of MD grey and orange 600 - * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */ - --jp-cell-outprompt-font-color: #bf5b3d; - - /* Notebook specific styles */ - - --jp-notebook-padding: 10px; - --jp-notebook-select-background: var(--jp-layout-color1); - --jp-notebook-multiselected-color: var(--md-blue-50); - - /* The scroll padding is calculated to fill enough space at the bottom of the - notebook to show one single-line cell (with appropriate padding) at the top - when the notebook is scrolled all the way to the bottom. We also subtract one - pixel so that no scrollbar appears if we have just one single-line cell in the - notebook. This padding is to enable a 'scroll past end' feature in a notebook. - */ - --jp-notebook-scroll-padding: calc( - 100% - var(--jp-code-font-size) * var(--jp-code-line-height) - - var(--jp-code-padding) - var(--jp-cell-padding) - 1px - ); - - /* Rendermime styles */ - - --jp-rendermime-error-background: #fdd; - --jp-rendermime-table-row-background: var(--md-grey-100); - --jp-rendermime-table-row-hover-background: var(--md-light-blue-50); - - /* Dialog specific styles */ - - --jp-dialog-background: rgba(0, 0, 0, 0.25); - - /* Console specific styles */ - - --jp-console-padding: 10px; - - /* Toolbar specific styles */ - - --jp-toolbar-border-color: var(--jp-border-color1); - --jp-toolbar-micro-height: 8px; - --jp-toolbar-background: var(--jp-layout-color1); - --jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24); - --jp-toolbar-header-margin: 4px 4px 0px 4px; - --jp-toolbar-active-background: var(--md-grey-300); - - /* Statusbar specific styles */ - - --jp-statusbar-height: 24px; - - /* Input field styles */ - - --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300); - --jp-input-active-background: var(--jp-layout-color1); - --jp-input-hover-background: var(--jp-layout-color1); - --jp-input-background: var(--md-grey-100); - --jp-input-border-color: var(--jp-inverse-border-color); - --jp-input-active-border-color: var(--jp-brand-color1); - --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3); - - /* General editor styles */ - - --jp-editor-selected-background: #d9d9d9; - --jp-editor-selected-focused-background: #d7d4f0; - --jp-editor-cursor-color: var(--jp-ui-font-color0); - - /* Code mirror specific styles */ - - --jp-mirror-editor-keyword-color: #008000; - --jp-mirror-editor-atom-color: #88f; - --jp-mirror-editor-number-color: #080; - --jp-mirror-editor-def-color: #00f; - --jp-mirror-editor-variable-color: var(--md-grey-900); - --jp-mirror-editor-variable-2-color: #05a; - --jp-mirror-editor-variable-3-color: #085; - --jp-mirror-editor-punctuation-color: #05a; - --jp-mirror-editor-property-color: #05a; - --jp-mirror-editor-operator-color: #aa22ff; - --jp-mirror-editor-comment-color: #408080; - --jp-mirror-editor-string-color: #ba2121; - --jp-mirror-editor-string-2-color: #708; - --jp-mirror-editor-meta-color: #aa22ff; - --jp-mirror-editor-qualifier-color: #555; - --jp-mirror-editor-builtin-color: #008000; - --jp-mirror-editor-bracket-color: #997; - --jp-mirror-editor-tag-color: #170; - --jp-mirror-editor-attribute-color: #00c; - --jp-mirror-editor-header-color: blue; - --jp-mirror-editor-quote-color: #090; - --jp-mirror-editor-link-color: #00c; - --jp-mirror-editor-error-color: #f00; - --jp-mirror-editor-hr-color: #999; - - /* Vega extension styles */ - - --jp-vega-background: white; - - /* Sidebar-related styles */ - - --jp-sidebar-min-width: 250px; - - /* Search-related styles */ - - --jp-search-toggle-off-opacity: 0.5; - --jp-search-toggle-hover-opacity: 0.8; - --jp-search-toggle-on-opacity: 1; - --jp-search-selected-match-background-color: rgb(245, 200, 0); - --jp-search-selected-match-color: black; - --jp-search-unselected-match-background-color: var( - --jp-inverse-layout-color0 - ); - --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0); - - /* Icon colors that work well with light or dark backgrounds */ - --jp-icon-contrast-color0: var(--md-purple-600); - --jp-icon-contrast-color1: var(--md-green-600); - --jp-icon-contrast-color2: var(--md-pink-600); - --jp-icon-contrast-color3: var(--md-blue-600); -} - -/*----------------------------------------------------------------------------- -| Copyright (c) Jupyter Development Team. -| Distributed under the terms of the Modified BSD License. -|----------------------------------------------------------------------------*/ - -/* Set the default typography for monospace elements */ -tt, -code, -kbd, -samp, -pre { - font-family: var(--jp-code-font-family); - font-size: var(--jp-code-font-size); - line-height: var(--jp-code-line-height); -} - diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/style/base.scss b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/style/base.scss deleted file mode 100644 index eb86f52434a..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/style/base.scss +++ /dev/null @@ -1,36 +0,0 @@ -svg#play { - width:200px; - margin: 120px auto; - display:block; - cursor: pointer; - transform-origin: 50% 50%; - } - - svg#play #triangle { - fill:rgba(22,22,22,0); - transition:500ms; - transform-origin: 50% 50%; - } - svg#play:hover #triangle { - fill:#696969; - transform-origin: 50% 50%; - } - - svg #lineOne, svg #lineTwo { - transform-origin: 50% 50%; - transition: 1s; - } - - svg:hover #lineOne { - transform: rotate(260deg); - -webkit-transform: rotate(260deg); - -moz-transform: rotate(260deg); - -o-transform: rotate(260deg); - transform-origin: 50% 50%; - } - - svg:hover #lineTwo { - transform: rotate(-450deg); - transform-origin: 50% 50%; - } - \ No newline at end of file diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/style/index.css b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/style/index.css deleted file mode 100644 index 8a7ea29e694..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/style/index.css +++ /dev/null @@ -1 +0,0 @@ -@import url('base.css'); diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/style/index.js b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/style/index.js deleted file mode 100644 index a028a76408b..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/style/index.js +++ /dev/null @@ -1 +0,0 @@ -import './base.css'; diff --git a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/tsconfig.json b/neural_coder/extensions/neural_compressor_ext_lab_alibaba/tsconfig.json deleted file mode 100644 index 7c8e8c78b69..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_lab_alibaba/tsconfig.json +++ /dev/null @@ -1,24 +0,0 @@ -{ - "compilerOptions": { - "allowSyntheticDefaultImports": true, - "composite": true, - "declaration": true, - "esModuleInterop": true, - "incremental": true, - "jsx": "react", - "module": "esnext", - "moduleResolution": "node", - "noEmitOnError": true, - "noImplicitAny": true, - "noUnusedLocals": true, - "preserveWatchOutput": true, - "resolveJsonModule": true, - "outDir": "lib", - "rootDir": "src", - "strict": true, - "strictNullChecks": true, - "target": "es2017", - "types": ["node"] - }, - "include": ["src/*"] -} diff --git a/neural_coder/extensions/neural_compressor_ext_vscode/.eslintrc.json b/neural_coder/extensions/neural_compressor_ext_vscode/.eslintrc.json deleted file mode 100644 index f9b22b793c2..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_vscode/.eslintrc.json +++ /dev/null @@ -1,24 +0,0 @@ -{ - "root": true, - "parser": "@typescript-eslint/parser", - "parserOptions": { - "ecmaVersion": 6, - "sourceType": "module" - }, - "plugins": [ - "@typescript-eslint" - ], - "rules": { - "@typescript-eslint/naming-convention": "warn", - "@typescript-eslint/semi": "warn", - "curly": "warn", - "eqeqeq": "warn", - "no-throw-literal": "warn", - "semi": "off" - }, - "ignorePatterns": [ - "out", - "dist", - "**/*.d.ts" - ] -} diff --git a/neural_coder/extensions/neural_compressor_ext_vscode/.gitignore b/neural_coder/extensions/neural_compressor_ext_vscode/.gitignore deleted file mode 100644 index 0b60dfa12fb..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_vscode/.gitignore +++ /dev/null @@ -1,5 +0,0 @@ -out -dist -node_modules -.vscode-test/ -*.vsix diff --git a/neural_coder/extensions/neural_compressor_ext_vscode/.vscodeignore b/neural_coder/extensions/neural_compressor_ext_vscode/.vscodeignore deleted file mode 100644 index 8791332ab30..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_vscode/.vscodeignore +++ /dev/null @@ -1,12 +0,0 @@ -.vscode/** -.vscode-test/** -out/** -node_modules/** -.gitignore -.yarnrc -webpack.config.js -vsc-extension-quickstart.md -**/tsconfig.json -**/.eslintrc.json -**/*.map -**/*.ts diff --git a/neural_coder/extensions/neural_compressor_ext_vscode/CHANGELOG.md b/neural_coder/extensions/neural_compressor_ext_vscode/CHANGELOG.md deleted file mode 100644 index 03ea45e180e..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_vscode/CHANGELOG.md +++ /dev/null @@ -1,9 +0,0 @@ -# Change Log - -All notable changes to the "neural-compressor-ext-vscode" extension will be documented in this file. - -Check [Keep a Changelog](http://keepachangelog.com/) for recommendations on how to structure this file. - -## [Unreleased] - -- Initial release \ No newline at end of file diff --git a/neural_coder/extensions/neural_compressor_ext_vscode/README.md b/neural_coder/extensions/neural_compressor_ext_vscode/README.md deleted file mode 100644 index dbe3529be23..00000000000 --- a/neural_coder/extensions/neural_compressor_ext_vscode/README.md +++ /dev/null @@ -1,127 +0,0 @@ -Neural Coder -======================= -Neural Coder, a novel component under **IntelĀ® Neural Compressor** to further simplify the deployment of DL models via **one-click** automated code changes for device compatibility and optimization enabling, has also enabled its extension service in VS Code to further simplify the usage for general programmers that use VS Code as a daily application. -

-
- Neural Coder Logo -

- -[Neural Coder](https://marketplace.visualstudio.com/items?itemName=IntelNeuralCompressor.neural-coder-ext-vscode) is an [open-source](https://github.com/intel/neural-compressor/tree/master/neural_coder) extension for [Visual Studio Code](https://code.visualstudio.com). - -# Background Introduction -## IntelĀ® Neural Compressor -IntelĀ® Neural Compressor is an open-source Python library for model compression that reduces the model size and increases DL inference performance on CPUs or GPUs. It supports post-training static and dynamic quantization of PyTorch models. It supports automatic accuracy-driven tuning strategies for users to easily generate quantized model. The users can easily apply static, dynamic and aware-training quantization approaches while giving an expected accuracy criteria. -## Neural Coder -Neural Coder is a novel component under IntelĀ® Neural Compressor to further simplify the deployment of DL models via one-click automated code changes for device compatibility and optimization enabling. Subsequently, Neural Coder can perform automated benchmark on all optimization approaches and evaluate for the best out-of-box optimized performance. Neural Coder uses static program analysis and heuristics to help users take advantage of Intel DL Boost and hardware features to improve performance. This one-click enabling boosts developer productivity while making it easier to take advantage of acceleration. -## Neural Coder Extension in VSCode -VS Code users can leverage Neural Coder's extension to perform **automatic quantization and benchmark evaluation** for Deep Learning models. - -

- Neural coder Usage -

- -# Neural Coder Extension Usage -We provide here a detailed step-by-step guide on using Neural Coder extension in VS Code: -

- Neural coder Config -

- -### 1. Open -Open VS Code Extension and link to a remote Linux-based server via SSH since Neural Coder requires a Linux-based environment. - -

-VS Code connects to a remote Linux server -

- -> If you're using VS Code on a Linux-based machine, then this step is not needed. - -### 2. Search -Search for Neural Coder extension in VS Code extension market: - -Simply enter "Neural Coder" in the search box of the extension market to find the extension package. - -You will be able to find the icon below, and then just click "Install". (Note: The extension installation location should be a SSH remote server to which you are connected, assuming your VS Code is Windows-based.) -

-Search for Neural Coder in VSCode extension market -

-Once the installation is done, it will display that the extension is installed and enabled on your machine, and you're also able to disable it or uninstall it in case you need to. -

-Neural Coder extension has been successfully installed and enabled -

- -### 3. Setting -Click the "Extension Settings" tab of the setting button on the right and fill in the path of Python you want to run. -- Click the "Extension Settings" tab - -

-Click Extension SettingsPython Path for Neural Coder -

- -- fill in the path -

-Click Extension SettingsPython Path for Neural Coder -

- -### 4. Icon -Open your code for the Deep Learning model that you want to quantize and evaluate. - -You can see a new icon appear to the upper right, and also a left sidebars for the purpose of displaying operation history. -Hover over and see that it's a Neural Coder's icon. -- The icon of Neural Coder extension -

-Click Extension SettingsPython Path for Neural Coder -

- -- The history panel of Neural Coder -

-Click Extension SettingsPython Path for Neural Coder -

- -### 5. optimization (quantization) -Click the Neural Coder button at the top right and select the optimization (quantization) you want to conduct on your Deep Learning code. -#### 5.1 Enable -Select "INC Enable INT8 (Static)", "INC Enable INT8 (Dynamic)", or "INC Enable BF16" - -

-Select 'INC Enable' -

- -Wait for the progress bar. You will see that the quantization has been enabled into your Deep Learning code: - -

-Auto-Enabling of Quantization via VS Code Neural Coder extension (e.g. HuggingFace model) -

- -The history of Neural Coder enabling that you have conducted will appear in the history panel, in the form of patch files, and you can easily track back to see how the quantization enabling is done by Neural Coder for your code at a specific time point: - -- The history of Neural Coder enabling that you have conducted -

-The history of Neural Coder enabling that you have conducted -

- -- A specific Neural Coder enabling as a patch file -

-A specific Neural Coder enabling as a patch file -

- -#### 5.2 Auto -Select "INC Auto Enable Benchmark" -

-Select 'INC Auto' -

-Enter the Python code execution parameters (argparse) for the current code: -

-Enter your Python code execution parameters -

-An "Output" panel will pop up below, displaying the enabling and benchmark results for the current Deep Learning code: -

-Result output -

-The "Auto" section in the history panel keeps the patch file (.diff) for each optimization within this benchmark execution: -

-Patch files for all optimizations in this benchmark -

-Also click to view patch result: -

-Content in the patch file -

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