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Bump target framework versions to PyTorch 1.9.1 and TensorFlow 2.4.3
Increased target HuggingFace transformers version for the integration patch to 4.9.1
Bugfixes:
Fixed statistic collection for the algo mixing scenario
Increased pruning algorithm robustness in cases of a disconnected NNCF graph
Fixed the fatality of NNCF graph PNG rendering failures
Fixed README command lines
(PyTorch) Fixed a bug with quantizing shared weights multiple times
(PyTorch) Fixed knowledge distillation failures in CPU-only and DataParallel scenarios
(PyTorch) Fixed sparsity application for torch.nn.Embedding and EmbeddingBag modules
(PyTorch) Added GroupNorm + ReLU as a fusable pattern
(TensorFlow) Fixed gamma fusion handling for pruning TF BatchNorm
(PyTorch) Fixed pruning for models where operations have multiple convolution predecessors
(PyTorch) Fixed NNCFNetwork wrapper so that self in the calls to the wrapped model refer to the wrapper NNCFNetwork object and not to the wrapped model
(PyTorch) Fixed tracing of view operations to handle shape arguments with the torch.Tensor type
(PyTorch) Added matmul ops to be considered for fusing
(PyTorch, TensorFlow) Fixed tensorboard logging for accuracy-aware scenarios
(PyTorch, TensorFlow) Fixed FLOPS calculation for grouped convolutions
(PyTorch) Fixed knowledge distillation failures for tensors of unsupported shapes - will ignore output tensors with unsupported shapes now instead of crashing.
