v2.4.0

Target version updates:

• Bump target framework versions to PyTorch 1.13.1, TensorFlow 2.8.x, ONNX 1.12, ONNXRuntime 1.13.1
• Increased target HuggingFace transformers version for the integration patch to 4.23.1

Features:

• Official release of the ONNX framework support.
  NNCF may now be used for post-training quantization (PTQ) on ONNX models.
  Added an example script demonstrating the ONNX post-training quantization on MobileNetV2.
• Preview release of OpenVINO framework support.
  NNCF may now be used for post-training quantization on OpenVINO models. Added an example script demonstrating the OpenVINO post-training quantization on MobileNetV2.
  pip install nncf[openvino] will install NNCF with the required OV framework dependencies.
• Common post-training quantization API across the supported framework model formats (PyTorch, TensorFlow, ONNX, OpenVINO IR) via the nncf.quantize(...) function.
  The parameter set of the function is the same for all frameworks - actual framework-specific implementations are being dispatched based on the type of the model object argument.
• (PyTorch, TensorFlow) Improved the adaptive compression training functionality to reduce effective training time.
• (ONNX) Post-processing nodes are now automatically excluded from quantization.
• (PyTorch - Experimental) Joint Pruning, Quantization and Distillation for Transformers enabled for certain models from HuggingFace transformers repo.
  See description of the movement pruning involved in the JPQD for details.

Bugfixes:

• Fixed a division by zero if every operation is added to ignored scope
• Improved logging output, cutting down on the number of messages being output to the standard logging.INFO log level.
• Fixed FLOPS calculation for linear filters - this impacts existing models that were pruned with a FLOPS target.
• "chunk" and "split" ops are correctly handled during pruning.
• Linear layers may now be pruned by input and output independently.
• Matmul-like operations and subsequent arithmetic operations are now treated as a fused pattern.
• (PyTorch) Fixed a rare condition with accumulator overflow in CUDA quantization kernels, which led to CUDA runtime errors and NaN values appearing in quantized tensors and
• (PyTorch) transformers integration patch now allows to export to ONNX during training, and not only at the end of it.
• (PyTorch) torch.nn.utils.weight_norm weights are now detected correctly.
• (PyTorch) Exporting a model with sparsity or pruning no longer leads to weights in the original model object in-memory to be hard-set to 0.
• (PyTorch - Experimental) improved automatic search of blocks to skip within the NAS algorithm – overlapping blocks are correctly filtered.
• (PyTorch, TensorFlow) Various bugs and issues with compression training were fixed.
• (TensorFlow) Fixed an error with "num_bn_adaptation_samples": 0 in config leading to a TypeError during quantization algo initialization.
• (ONNX) Temporary model file is no longer saved on disk.
• (ONNX) Depthwise convolutions are now quantizable in per-channel mode.
• (ONNX) Improved the working time of PTQ by optimizing the calls to ONNX shape inferencing.

Breaking changes:

• Fused patterns will be excluded from quantization via ignored_scopes only if the top-most node in data flow order matches against ignored_scopes
• NNCF config's "ignored_scopes" and "target_scopes" are now strictly checked to be matching against at least one node in the model graph instead of silently ignoring the unmatched entries.
• Calling setup.py directly to install NNCF is deprecated and no longer guaranteed to work.
• Importing NNCF logger as from nncf.common.utils.logger import logger as nncf_logger is deprecated - use from nncf import nncf_logger instead.
• pruning_rate is renamed to pruning_level in pruning compression controllers.
• (ONNX) Removed CompressionBuilder. Excluded examples of NNCF for ONNX with CompressionBuilder API