Nncf

Post-training Quantization:

Bugfixes:
- (Common) Fixed issue with `nncf.compress_weights()` to avoid overflows on 32-bit Windows systems.
- (Common) Fixed performance issue with `nncf.compress_weights()` on LLama models.
- (Common) Fixed `nncf.quantize_with_accuracy_control` pipeline with `tune_hyperparams=True` enabled option.
- (OpenVINO) Fixed issue for stateful LLM models and added state restoring after the inference for it.
- (PyTorch) Fixed issue with `nncf.compress_weights()` for LLM models with the executing `is_floating_point` with tracing.

Post-training Quantization:

Breaking changes:
- `nncf.quantize` signature has been changed to add `mode: Optional[nncf.QuantizationMode] = None` as its 3-rd argument, between the original `calibration_dataset` and `preset` arguments.
- (Common) `nncf.common.quantization.structs.QuantizationMode` has been renamed to `nncf.common.quantization.structs.QuantizationScheme`
General:
- (OpenVINO) Changed default OpenVINO opset from 9 to 13.
Features:
- (OpenVINO) Added 4-bit data-aware weights compression. For that `dataset` optional parameter has been added to `nncf.compress_weights()` and can be used to minimize accuracy degradation of compressed models (note that this option increases the compression time).
- (PyTorch) Added support for PyTorch models with shared weights and custom PyTorch modules in `nncf.compress_weights()`. The weights compression algorithm for PyTorch models is now based on tracing the model graph. The `dataset` parameter is now required in `nncf.compress_weights()` for the compression of PyTorch models.
- (Common) Renamed the `nncf.CompressWeightsMode.INT8` to `nncf.CompressWeightsMode.INT8_ASYM` and introduce `nncf.CompressWeightsMode.INT8_SYM` that can be efficiently used with dynamic 8-bit quantization of activations.
The original `nncf.CompressWeightsMode.INT8` enum value is now deprecated.
- (OpenVINO) Added support for quantizing the ScaledDotProductAttention operation from OpenVINO opset 13.
- (OpenVINO) Added FP8 quantization support via `nncf.QuantizationMode.FP8_E4M3` and `nncf.QuantizationMode.FP8_E5M2` enum values, invoked via passing one of these values as an optional `mode` argument to `nncf.quantize`. Currently, OpenVINO supports inference of FP8-quantized models in reference mode with no performance benefits and can be used for accuracy projections.
- (Common) Post-training Quantization with Accuracy Control - `nncf.quantize_with_accuracy_control()` has been extended by `restore_mode` optional parameter to revert weights to int8 instead of the original precision.
This parameter helps to reduce the size of the quantized model and improves its performance.
By default, it's disabled and model weights are reverted to the original precision in `nncf.quantize_with_accuracy_control()`.
- (Common) Added an `all_layers: Optional[bool] = None` argument to `nncf.compress_weights` to indicate whether embeddings and last layers of the model should be compressed to a primary precision. This is relevant to 4-bit quantization only.
- (Common) Added a `sensitivity_metric: Optional[nncf.parameters.SensitivityMetric] = None` argument to `nncf.compress_weights` for finer control over the sensitivity metric for assigning quantization precision to layers.
Defaults to weight quantization error if a dataset is not provided for weight compression and to maximum variance of the layers' inputs multiplied by inverted 8-bit quantization noise if a dataset is provided.
By default, the backup precision is assigned for the embeddings and last layers.
Fixes:
- (OpenVINO) Models with embeddings (e.g. `gpt-2`, `stable-diffusion-v1-5`, `stable-diffusion-v2-1`, `opt-6.7b`, `falcon-7b`, `bloomz-7b1`) are now more accurately quantized.
- (PyTorch) `nncf.strip(..., do_copy=True)` now actually returns a deepcopy (stripped) of the model object.
- (PyTorch) Post-hooks can now be set up on operations that return `torch.return_type` (such as `torch.max`).
- (PyTorch) Improved dynamic graph tracing for various tensor operations from `torch` namespace.
- (PyTorch) More robust handling of models with disjoint traced graphs when applying PTQ.
Improvements:
- Reformatted the tutorials section in the top-level `README.md` for better readability.
Deprecations/Removals:
- (Common) The original `nncf.CompressWeightsMode.INT8` enum value is now deprecated.
- (PyTorch) The Git patch for integration with HuggingFace `transformers` repository is marked as deprecated and will be removed in a future release.
Developers are advised to use [optimum-intel](https://github.com/huggingface/optimum-intel) instead.
- Dockerfiles in the NNCF Git repository are deprecated and will be removed in a future release.

Post-training Quantization:

Features:
- (OpenVINO) Added support for data-free 4-bit weights compression through NF4 and INT4 data types (`compress_weights(…)` pipeline).
- (OpenVINO) Added support for [IF operation](https://docs.openvino.ai/latest/openvino_docs_ops_infrastructure_If_8.html) quantization.
- (OpenVINO) Added `dump_intermediate_model` parameter support for AccuracyAwareAlgorithm (`quantize_with_accuracy_control(…)` pipeline).
- (OpenVINO) Added support for SmoothQuant and ChannelAlignment algorithms for HyperparameterTuner algorithm (`quantize_with_tune_hyperparams(…)` pipeline).
- (PyTorch) Post-training Quantization is now supported with `quantize(…)` pipeline and the common implementation of quantization algorithms. Deprecated `create_compressed_model()` method for Post-training Quantization.
- Added new types (AvgPool, GroupNorm, LayerNorm) to the ignored scope for `ModelType.Transformer` scheme.
- `QuantizationPreset.Mixed` was set as the default for `ModelType.Transformer` scheme.
Fixes:
- (OpenVINO, ONNX, PyTorch) Aligned/added patterns between backends (SE block, MVN layer, multiple activations, etc.) to restore performance/metrics.
- Fixed patterns for `ModelType.Transformer` to align with the [quantization scheme](https://docs.openvino.ai/latest/openvino_docs_OV_UG_lpt.html).
Improvements:
- Improved UX with the new progress bar for pipeline, new exceptions, and .dot graph visualization updates.
- (OpenVINO) Optimized WeightsCompression algorithm (`compress_weights(…)` pipeline) execution time for LLM's quantization, added ignored scope support.
- (OpenVINO) Optimized AccuracyAwareQuantization algorithm execution time with multi-threaded approach while calculating ranking score (`quantize_with_accuracy_control(…)` pipeline).
- (OpenVINO) Added [extract_ov_subgraph tool](tools/extract_ov_subgraph.py) for large IR subgraph extraction.
- (ONNX) Optimized quantization pipeline (up to 1.15x speed up).
Tutorials:
- [Post-Training Optimization of BLIP Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/233-blip-visual-language-processing)
- [Post-Training Optimization of DeepFloyd IF Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/238-deepfloyd-if)
- [Post-Training Optimization of Grammatical Error Correction Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/214-grammar-correction)
- [Post-Training Optimization of Dolly 2.0 Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/240-dolly-2-instruction-following)
- [Post-Training Optimization of Massively Multilingual Speech Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/255-mms-massively-multilingual-speech)
- [Post-Training Optimization of OneFormer Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/249-oneformer-segmentation)
- [Post-Training Optimization of InstructPix2Pix Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/231-instruct-pix2pix-image-editing)
- [Post-Training Optimization of LLaVA Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/257-llava-multimodal-chatbot)
- [Post-Training Optimization of Latent Consistency Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/263-latent-consistency-models-image-generation)
- [Post-Training Optimization of Distil-Whisper Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/267-distil-whisper-asr)
- [Post-Training Optimization of FastSAM Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/261-fast-segment-anything)
Known issues:
- (ONNX) `quantize(...)` method can generate inaccurate int8 results for models with the BatchNormalization layer that contains biases. To get the best accuracy, use the `do_constant_folding=True` option during export from PyTorch to ONNX.

Compression-aware training:

Fixes:
- (PyTorch) Fixed Hessian trace calculation to solve [2155](https://github.com/openvinotoolkit/nncf/issues/2155) issue.
Requirements:
- Updated PyTorch version (2.1.0).
- Updated numpy version (<1.27).
Deprecations/Removals:
- (PyTorch) Removed legacy external quantizer storage names.
- (PyTorch) Removed torch < 2.0 version support.

Post-training Quantization:

Features:
- Added `CPU_SPR` device type support.
- Added quantizers scales unification.
- Added quantization scheme for ReduceSum operation.
- Added new types (ReduceL2, ReduceSum, Maximum) to the ignored scope for `ModelType.Transformer`.
- (OpenVINO) Added SmoothQuant algorithm.
- (OpenVINO) Added ChannelAlignment algorithm.
- (OpenVINO) Added HyperparameterTuner algorithm.
- (PyTorch) Added FastBiasCorrection algorithm support.
- (OpenVINO, ONNX) Added embedding weights quantization.
- (OpenVINO, PyTorch) Added new `compress_weights` method that provides data-free [INT8 weights compression](docs/compression_algorithms/CompressWeights.md).
Fixes:
- Fixed detection of decomposed post-processing in models.
- Multiple fixes (new patterns, bugfixes, etc.) to solve [1936](https://github.com/openvinotoolkit/nncf/issues/1936) issue.
- Fixed model reshaping while quantization to keep original model shape.
- (OpenVINO) Added support for sequential models quanitzation.
- (OpenVINO) Fixed in-place statistics cast to support empty dimensions.
- (OpenVINO, ONNX) Fixed quantization of the MatMul operation with weights rank > 2.
- (OpenVINO, ONNX) Fixed BiasCorrection algorithm to enable [CLIP model quantization](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/228-clip-zero-shot-image-classification).
Improvements:
- Optimized `quantize(…)` pipeline (up to 4.3x speed up in total).
- Optimized `quantize_with_accuracy_control(…)` pipelilne (up to 8x speed up for [122-quantizing-model-with-accuracy-control](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/122-quantizing-model-with-accuracy-control) notebook).
- Optimized general statistics collection (up to 1.2x speed up for ONNX backend).
- Ignored patterns separated from Fused patterns scheme (with multiple patterns addition).
Tutorials:
- [Post-Training Optimization of Segment Anything Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/237-segment-anything).
- [Post-Training Optimization of CLIP Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/228-clip-zero-shot-image-classification).
- [Post-Training Optimization of ImageBind Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/239-image-bind).
- [Post-Training Optimization of Whisper Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/227-whisper-subtitles-generation).
- [Post-Training Optimization with accuracy control](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/122-quantizing-model-with-accuracy-control).

Compression-aware training:

Features:
- Added shape pruning processor for BootstrapNAS algorithm.
- Added KD loss for BootstrapNAS algorithm.
- Added `validate_scopes` parameter for NNCF configuration.
- (PyTorch) Added PyTorch 2.0 support.
- (PyTorch) Added `.strip()` option to API.
- (PyTorch) Enabled bfloat data type for quantization kernels.
- (PyTorch) Quantized models can now be `torch.jit.trace`d without calling `.strip()`.
- (PyTorch) Added support for overridden `forward` instance attribute on model objects passed into `create_compressed_model`.
- (Tensorflow) Added Tensorflow 2.12 support.
Fixes:
- (PyTorch) Fixed padding adjustment issue in the elastic kernel to work with the different active kernel sizes.
- (PyTorch) Fixed the torch graph tracing in the case the tensors belonging to parallel edges are interleaved in the order of the tensor argument.
- (PyTorch) Fixed recurrent nodes matching (LSTM, GRU cells) condition with the strict rule to avoid adding not necessary nodes to the ignored scope.
- (PyTorch) Fixed `torch.jit.script` wrapper so that user-side handling exceptions during `torch.jit.script` invocation do not cause NNCF to be permanently disabled.
- (PyTorch, Tensorflow) Adjusted quantizer propagation algorithm to check if quantizer propagation will result in output quantization.
- (PyTorch) Added redefined `__class__` method for ProxyModule that avoids causing error while calling `.super()` in forward method.
Deprecations/Removals:
- (PyTorch) Removed deprecated `NNCFNetwork.__getattr__`, `NNCFNetwork.get_nncf_wrapped_model` methods.
Requirements:
- Updated PyTorch version (2.0.1).
- Updated Tensorflow version (2.12.0).

Post-training Quantization:

Features:
- Official release of OpenVINO framework support.
- Ported NNCF OpenVINO backend to use the [nGraph](https://docs.openvino.ai/2021.3/openvino_docs_nGraph_DG_Introduction.html) representation of OpenVINO models.
- Changed dependecies of NNCF OpenVINO backend. It now depends on `openvino` package and not on the `openvino-dev` package.
- Added GRU/LSTM quantization support.
- Added quantizer scales unification.
- Added support for models with 3D and 5D Depthwise convolution.
- Added FP16 OpenVINO models support.
- Added `"overflow_fix"` parameter (for `quantize(...)` & `quantize_with_accuracy_control(...)` methods) support & functionality. It improves accuracy for optimized model for affected devices. More details in [Quantization section](docs/compression_algorithms/Quantization.md).
- (OpenVINO) Added support for in-place statistics collection (reduce memory footprint during optimization).
- (OpenVINO) Added Quantization with accuracy control algorithm.
- (OpenVINO) Added YOLOv8 examples for [`quantize(...)`](examples/post_training_quantization/openvino/yolov8) & [`quantize_with_accuracy_control(...)`](examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control) methods.
- (PyTorch) Added min-max quantization algorithm as experimental.

Fixes:
- Fixed `ignored_scope` attribute behaviour for weights. Now, the weighted layers excludes from optimization scope correctly.
- (ONNX) Checking correct ONNX opset version via the `nncf.quantize(...)`. Now, models with opset < 13 are optimized correctly in per-tensor quantization.

Improvements:
- Added improvements for statistic collection process (collect weights statistics only once).
- (PyTorch, OpenVINO, ONNX) Introduced unified quantizer parameters calculation.

Known issues:
- `quantize(...)` method can generate inaccurate int8 results for models with the *DenseNet-like* architecture. Use `quantize_with_accuracy_control(...)` in such case.
- `quantize(...)` method can hang on models with *transformer* architecture when `fast_bias_correction` optional parameter is set to *False*. Don't set it to *False* or use `quantize_with_accuracy_control(...)` in such case.
- `quantize(...)` method can generate inaccurate int8 results for models with the *MobileNet-like* architecture on non-VNNI machines.

Compression-aware training:

New Features:
- Introduced automated structured pruning algorithm for JPQD with support for BERT, Wave2VecV2, Swin, ViT, DistilBERT, CLIP, and MobileBERT models.
- Added `nncf.common.utils.patcher.Patcher` - this class can be used to patch methods on live PyTorch model objects with wrappers such as `nncf.torch.dynamic_graph.context.no_nncf_trace` when doing so in the model code is not possible (e.g. if the model comes from an external library package).
- Compression controllers of the `nncf.api.compression.CompressionAlgorithmController` class now have a `.strip()` method that will return the compressed model object with as many custom NNCF additions removed as possible while preserving the functioning of the model object as a compressed model.

Fixes:
- Fixed statistics computation for pruned layers.
- (PyTorch) Fixed traced tensors to implement the YOLOv8 from Ultralytics.

Improvements:
- Extension of attributes (`transpose/permute/getitem`) for pruning node selector.
- NNCFNetwork was refactored from a wrapper-approach to a mixin-like approach.
- Added average pool 3d-like ops to pruning mask.
- Added Conv3d for overflow fix.
- `nncf.set_log_file(...)` can now be used to set location of the NNCF log file.
- (PyTorch) Added support for pruning of `torch.nn.functional.pad` operation.
- (PyTorch) Added `torch.baddbmm` as an alias for the matmul metatype for quantization purposes.
- (PyTorch) Added config file for ResNet18 accuracy-aware pruning + quantization on CIFAR10.
- (PyTorch) Fixed JIT-traceable PyTorch models with internal patching.
- (PyTorch) Added `__matmul__` magic functions to the list of patched ops (for SwinTransformer by Microsoft).

Requirements:
- Updated ONNX version (1.13)
- Updated Tensorflow version (2.11)

General changes:
- Added Windows support for NNCF.

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](examples/post_training_quantization/onnx/mobilenet_v2) 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](nncf/experimental/torch/sparsity/movement/MovementSparsity.md) 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