Releases: intel/neural-compressor
Intel® Neural Compressor v1.8.1 Release
Features
- Knowledge distillation
- Supported knowledge distillation on TensorFlow
- Pruning
- Support Multi-node training on TensorFlow
- Acceleration library
- Supported Hugging Face minilm_l6_h384_uncased_sst2, bert_base_cased_mrpc, and bert_base_nli_mean_tokens_stsb models
Validated Configurations
- Python 3.6 & 3.7 & 3.8 & 3.9
- Centos 8.3 & Ubuntu 18.04
- TensorFlow 2.6.2 & 2.7
- Intel TensorFlow 2.4.0, 2.5.0 and 1.15.0 UP3
- PyTorch 1.8.0+cpu, 1.9.0+cpu, IPEX 1.8.0
- MxNet 1.6.0, 1.7.0, 1.8.0
- ONNX Runtime 1.6.0, 1.7.0, 1.8.0
Distribution:
Channel | Links | Install Command | |
---|---|---|---|
Source | Github | https://github.com/intel/neural-compressor.git | $ git clone https://github.com/intel/neural-compressor.git |
Binary | Pip | https://pypi.org/project/neural-compressor | $ pip install neural-compressor |
Binary | Conda | https://anaconda.org/intel/neural-compressor | $ conda install neural-compressor -c conda-forge -c intel |
Contact:
Please feel free to contact inc.maintainers@intel.com, if you get any questions.
Intel® Neural Compressor v1.8 Release
Features
- Knowledge distillation
- Implemented the algorithms of paper “Pruning Once For All” accepted by NeurIPS 2021 ENLSP workshop
- Supported optimization pipelines (knowledge distillation & quantization-aware training) on PyTorch
- Quantization
- Added the support of ONNX RT 1.7
- Added the support of TensorFlow 2.6.2 and 2.7
- Added the support of PyTorch 1.10
- Pruning
- Supported magnitude pruning on TensorFlow
- Acceleration library
- Supported Hugging Face top 10 downloaded NLP models
Productivity
- Added performance profiling feature to INC UI service.
- Improved ease-of-use user interface for quantization with few clicks
Ecosystem
- Added notebook of using HuggingFace optimization library (Optimum) to Transformers
- Enabled top 20 downloaded Hugging Face NLP models with Optimum
- Upstreamed more INC quantized models to ONNX Model Zoo
Validated Configurations
- Python 3.6 & 3.7 & 3.8 & 3.9
- Centos 8.3 & Ubuntu 18.04
- TensorFlow 2.6.2 & 2.7
- Intel TensorFlow 2.4.0, 2.5.0 and 1.15.0 UP3
- PyTorch 1.8.0+cpu, 1.9.0+cpu, IPEX 1.8.0
- MxNet 1.6.0, 1.7.0, 1.8.0
- ONNX Runtime 1.6.0, 1.7.0, 1.8.0
Distribution:
Channel | Links | Install Command | |
---|---|---|---|
Source | Github | https://github.com/intel/neural-compressor.git | $ git clone https://github.com/intel/neural-compressor.git |
Binary | Pip | https://pypi.org/project/neural-compressor | $ pip install neural-compressor |
Binary | Conda | https://anaconda.org/intel/neural-compressor | $ conda install neural-compressor -c conda-forge -c intel |
Contact:
Please feel free to contact inc.maintainers@intel.com, if you get any questions.
Intel® Neural Compressor v1.7.1 Release
Intel® Neural Compressor(formerly known as Intel® Low Precision Optimization Tool) v1.7 release is featured by:
Features
- Acceleration library
- Support unified buffer memory allocation policy
Ecosystem
- Upstreamed INC quantized models (alexnet/caffenet/googlenet/squeezenet) to ONNX Model Zoo
Documentation
- Performance and accuracy data update
Validated Configurations
- Python 3.6 & 3.7 & 3.8 & 3.9
- Centos 8.3 & Ubuntu 18.04
- TensorFlow 2.6.0
- Intel TensorFlow 2.4.0, 2.5.0 and 1.15.0 UP3
- PyTorch 1.8.0+cpu, 1.9.0+cpu, IPEX 1.8.0
- MxNet 1.6.0, 1.7.0, 1.8.0
- ONNX Runtime 1.6.0, 1.7.0, 1.8.0
Distribution:
Channel | Links | Install Command | |
---|---|---|---|
Source | Github | https://github.com/intel/neural-compressor.git | $ git clone https://github.com/intel/neural-compressor.git |
Binary | Pip | https://pypi.org/project/neural-compressor | $ pip install neural-compressor |
Binary | Conda | https://anaconda.org/intel/neural-compressor | $ conda install neural-compressor -c conda-forge -c intel |
Contact:
Please feel free to contact INC Maintainers, if you get any questions.
Intel® Neural Compressor v1.7 Release
Intel® Neural Compressor(formerly known as Intel® Low Precision Optimization Tool) v1.7 release is featured by:
Features
- Quantization
- Improved quantization accuracy in SSD-Reset34 and MobileNet v3 on TensorFlow
- Pruning
- Supported magnitude pruning on TensorFlow
- Knowledge distillation
- Supported knowledge distillation on PyTorch
- Multi-node support
- Supported multi-node pruning with distributed dataloader on PyTorch
- Supported multi-node inference for benchmark on PyTorch
- Acceleration library
- Added a domain-specific acceleration library for NLP models
Productivity
- Supported the configuration-free (pure Python) quantization
- Improved ease-of-use user interface for quantization with few clicks
Ecosystem
- Integrated into HuggingFace optimization library (Optimum)
- Upstreamed INC quantized models (RN50, VGG16) to ONNX Model Zoo
Documentation
- Add tutorial and examples for knowledge distillation
- Add tutorial and examples for multi-node training
- Add tutorial and examples for acceleration library
Validated Configurations
- Python 3.6 & 3.7 & 3.8 & 3.9
- Centos 8.3 & Ubuntu 18.04
- TensorFlow 2.6.0
- Intel TensorFlow 2.4.0, 2.5.0 and 1.15.0 UP3
- PyTorch 1.8.0+cpu, 1.9.0+cpu, IPEX 1.8.0
- MxNet 1.6.0, 1.7.0, 1.8.0
- ONNX Runtime 1.6.0, 1.7.0, 1.8.0
Distribution:
Channel | Links | Install Command | |
---|---|---|---|
Source | Github | https://github.com/intel/neural-compressor.git | $ git clone https://github.com/intel/neural-compressor.git |
Binary | Pip | https://pypi.org/project/neural-compressor | $ pip install neural-compressor |
Binary | Conda | https://anaconda.org/intel/neural-compressor | $ conda install neural-compressor -c conda-forge -c intel |
Contact:
Please feel free to contact lpot.maintainers@intel.com, if you get any questions.
Intel® Low Precision Optimization Tool v1.6 Release
Intel® Low Precision Optimization Tool v1.6 release is featured by:
Pruning:
- Support pruning and post-training quantization pipeline on PyTorch
- Support pruning during quantization-aware training on PyTorch
Quantization:
- Support post-training quantization on TensorFlow 2.6.0, PyTorch 1.9.0, IPEX 1.8.0, and MXNet 1.8.0
- Support quantization-aware training on TensorFlow 2.x (Keras API)
User Experience:
- Improve quantization productivity with new UI
- Support quantized model recovery from tuning history
New Models:
- Support ResNet50 on ONNX model zoo
Documentation:
- Add pruned models
- Add quantized MLPerf models
Validated Configurations:
- Python 3.6 & 3.7 & 3.8 & 3.9
- Centos 8.3 & Ubuntu 18.04
- TensorFlow 2.6.0
- Intel TensorFlow 2.4.0, 2.5.0 and 1.15.0 UP3
- PyTorch 1.8.0+cpu, 1.9.0+cpu, IPEX 1.8.0
- MxNet 1.6.0, 1.7.0, 1.8.0
- ONNX Runtime 1.6.0, 1.7.0, 1.8.0
Distribution:
Channel | Links | Install Command | |
---|---|---|---|
Source | Github | https://github.com/intel/lpot.git | $ git clone https://github.com/intel/lpot.git |
Binary | Pip | https://pypi.org/project/lpot | $ pip install lpot |
Binary | Conda | https://anaconda.org/intel/lpot | $ conda install lpot -c conda-forge -c intel |
Contact:
Please feel free to contact lpot.maintainers@intel.com, if you get any questions.
Intel® Low Precision Optimization Tool v1.5.1 Release
Intel® Low Precision Optimization Tool v1.5.1 release is featured by:
- Gradient-sensitivity pruning for CNN model
- Static quantization support for ONNX NLP model
- Dynamic seq length support in NLP dataloader
- Enrich quantization statistics
Validated Configurations:
- Python 3.6 & 3.7 & 3.8 & 3.9
- Centos 8.3 & Ubuntu 18.04
- Intel TensorFlow 1.15.2, 2.1.0, 2.2.0, 2.3.0, 2.4.0, 2.5.0 and 1.15.0 UP1 & UP2 & UP3
- PyTorch 1.5.0+cpu, 1.6.0+cpu, 1.8.0+cpu, ipex
- MxNet 1.6.0, 1.7.0
- ONNX Runtime 1.6.0, 1.7.0, 1.8.0
Distribution:
Channel | Links | Install Command | |
---|---|---|---|
Source | Github | https://github.com/intel/lpot.git | $ git clone https://github.com/intel/lpot.git |
Binary | Pip | https://pypi.org/project/lpot | $ pip install lpot |
Binary | Conda | https://anaconda.org/intel/lpot | $ conda install lpot -c conda-forge -c intel |
Contact:
Please feel free to contact lpot.maintainers@intel.com, if you get any questions.
Intel® Low Precision Optimization Tool v1.5 Release
Intel® Low Precision Optimization Tool v1.5 release is featured by:
- Add pattern-lock sparsity algorithm for NLP fine-tuning tasks
- Up to 70% unstructured sparsity and 50% structured sparsity with <2% accuracy loss on 5 Bert finetuning tasks
- Add NLP head pruning algorithm for HuggingFace models
- Performance speedup up to 3.0X within 1.5% accuracy loss on HuggingFace BERT SST-2
- Support model optimization pipeline
- Integrate SigOPT with multi-metrics optimization
- Complementary as basic strategy to speed up the tuning
- Support TensorFlow 2.5, PyTorch 1.8, and ONNX Runtime 1.8
Validated Configurations:
- Python 3.6 & 3.7 & 3.8 & 3.9
- Centos 8.3 & Ubuntu 18.04
- Intel TensorFlow 1.15.2, 2.1.0, 2.2.0, 2.3.0, 2.4.0, 2.5.0 and 1.15.0 UP1 & UP2 & UP3
- PyTorch 1.5.0+cpu, 1.6.0+cpu, 1.8.0+cpu, ipex
- MxNet 1.6.0, 1.7.0
- ONNX Runtime 1.6.0, 1.7.0, 1.8.0
Distribution:
Channel | Links | Install Command | |
---|---|---|---|
Source | Github | https://github.com/intel/lpot.git | $ git clone https://github.com/intel/lpot.git |
Binary | Pip | https://pypi.org/project/lpot | $ pip install lpot |
Binary | Conda | https://anaconda.org/intel/lpot | $ conda install lpot -c conda-forge -c intel |
Contact:
Please feel free to contact lpot.maintainers@intel.com, if you get any questions.
Intel® Low Precision Optimization Tool v1.4.1 Release
Intel® Low Precision Optimization Tool v1.4.1 release is featured by:
- Support TensorFlow 2.5.0
- Support PyTorch 1.8.0
- Support TensorFlow Object Detection YOLO-V3 model
Validated Configurations:
- Python 3.6 & 3.7 & 3.8
- Centos 7 & Ubuntu 18.04
- Intel TensorFlow 1.15.2, 2.1.0, 2.2.0, 2.3.0, 2.4.0, 2.5.0 and 1.15.0 UP1 & UP2
- PyTorch 1.5.0+cpu, 1.6.0+cpu, ipex
- MxNet 1.7.0
- ONNX Runtime 1.6.0, 1.7.0
Distribution:
Channel | Links | Install Command | |
---|---|---|---|
Source | Github | https://github.com/intel/lpot.git | $ git clone https://github.com/intel/lpot.git |
Binary | Pip | https://pypi.org/project/lpot | $ pip install lpot |
Binary | Conda | https://anaconda.org/intel/lpot | $ conda install lpot -c conda-forge -c intel |
Contact:
Please feel free to contact lpot.maintainers@intel.com, if you get any questions.
Intel® Low Precision Optimization Tool v1.4 Release
Intel® Low Precision Optimization Tool v1.4 release is featured by:
Quantization
- PyTorch FX-based quantization support
- TensorFlow & ONNX RT quantization enhancement
Pruning
- Pruning/sparsity API refinement
- Magnitude-based pruning on PyTorch
Model Zoo
- INT8 key models updated (BERT on TensorFlow, DLRM on PyTorch, etc.)
- 20+ HuggingFace model quantization
User Experience
- More comprehensive logging message
- UI enhancement with FP32 optimization, auto-mixed precision (BF16/FP32), and graph visualization
- Online document: https://intel.github.io/lpot
Extended Capabilities
- Model conversion from QAT to Intel Optimized TensorFlow model
Validated Configurations:
- Python 3.6 & 3.7 & 3.8
- Centos 7 & Ubuntu 18.04
- Intel TensorFlow 1.15.2, 2.1.0, 2.2.0, 2.3.0, 2.4.0 and 1.15.0 UP1 & UP2
- PyTorch 1.5.0+cpu, 1.6.0+cpu, ipex
- MxNet 1.7.0
- ONNX Runtime 1.6.0, 1.7.0
Distribution:
Channel | Links | Install Command | |
---|---|---|---|
Source | Github | https://github.com/intel/lpot.git | $ git clone https://github.com/intel/lpot.git |
Binary | Pip | https://pypi.org/project/lpot | $ pip install lpot |
Binary | Conda | https://anaconda.org/intel/lpot | $ conda install lpot -c conda-forge -c intel |
Contact:
Please feel free to contact lpot.maintainers@intel.com, if you get any questions.
Intel® Low Precision Optimization Tool v1.3.1 Release
Intel® Low Precision Optimization Tool v1.3 release is featured by:
- Improve graph optimization without explicit input/output setting
Validated Configurations:
- Python 3.6 & 3.7 & 3.8
- Centos 7 & Ubuntu 18.04
- Intel TensorFlow 1.15.2, 2.1.0, 2.2.0, 2.3.0, 2.4.0 and 1.15.0 UP1 & UP2
- PyTorch 1.5.0+cpu, 1.6.0+cpu, ipex
- MxNet 1.7.0
- ONNX Runtime 1.6.0, 1.7.0
Distribution:
Channel | Links | Install Command | |
---|---|---|---|
Source | Github | https://github.com/intel/lpot.git | $ git clone https://github.com/intel/lpot.git |
Binary | Pip | https://pypi.org/project/lpot | $ pip install lpot |
Binary | Conda | https://anaconda.org/intel/lpot | $ conda install lpot -c conda-forge -c intel |
Contact:
Please feel free to contact lpot.maintainers@intel.com, if you get any questions.