Tags: youngboy52/nncf
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Release v1.5.0 of NNCF to master (openvinotoolkit#254) * Allow sharing activation quantizers in different graph points (openvinotoolkit#67) * Update version and docs on develop (openvinotoolkit#77) * Update 3rd party integration patches (openvinotoolkit#79) * Doc updates (openvinotoolkit#84) * Add info on export to Usage.md * Fix third party headers * Fix import in transformers patch (openvinotoolkit#85) * Fix percentile per-channel init (openvinotoolkit#86) Fixes: openvinotoolkit#83 * Omit nodes called during debugging from entering NNCF graph (openvinotoolkit#87) * Enable custom range initializers for overriden scopes in schema (openvinotoolkit#89) * Enable custom quantization configs and initializers for overriden scopes in schema * code style * remove range config duplication * obsolete import * Fix model saving in transformers patch (openvinotoolkit#91) * Patch TracedTensor's __repr__ method instead of torch.Tensor's (openvinotoolkit#92) * Fix mmdetection patch (openvinotoolkit#93) * Update mmdetection patch to v2.3.0 (openvinotoolkit#95) * Allow registering user modules as NNCF modules for weight quantization (openvinotoolkit#99) * Assign latest tensor shape during ForwardTraceOnly() (openvinotoolkit#96) * Enable GPT2 ops (openvinotoolkit#98) * Fix HW config scenario with ops missing in HW config definition (openvinotoolkit#94) * Fix input quantization in case of embeddings (openvinotoolkit#97) * Added sanity tests for third party integration (openvinotoolkit#45) * Expose quantizer linking through config (openvinotoolkit#100) * Add citing section to frontpage README (openvinotoolkit#103) * Fix bad rebase in asymmetric quantization ONNX export (openvinotoolkit#104) * Use default quantizer configuration for op weights not specified in HW config (openvinotoolkit#105) * Update transformers to v3.0.2 (openvinotoolkit#107) * Fix symmetric quantizer per-channel init for max values close to 0 (openvinotoolkit#109) * Add unified scales in HW config operation (via quantizer linking) (openvinotoolkit#108) * Add quantization metric (openvinotoolkit#33) * Make HW config parsing conform to the implicit rules (openvinotoolkit#111) (except for the "any supported quantization for the ops in config without specified quantizations", because they need config wildcarding, to be implemented as a follow-up) * Fix MobileNetV2 INT8 config (openvinotoolkit#113) * Use sequential sampling for evaluation across example scripts (openvinotoolkit#114) Hopefully this will make nightly compression training "eval" tests more stable. * Fix third_party_sanity tests (openvinotoolkit#115) * Properly handle ops in HW config without quantization configs associated (openvinotoolkit#119) These get associated with a "wildcard" propagating quantizer, which will either get merged with any other quantizer during propagation, or get assigned a default quantization config. * Make criterion optional in signature of register_default_init_args() (openvinotoolkit#121) * make optional criterion in signature of register_default_init_args() * update README.md as Vasiliy asked * Add Googlenet with pruning configs (openvinotoolkit#122) * Fix pretrained (openvinotoolkit#125) * Mark Convs as non-depthwise for 1 input channel case (openvinotoolkit#126) * Add non-RELU activations to fusable patterns (openvinotoolkit#124) * Fixed Pylint warnings (openvinotoolkit#129) * Fix bug with CompositeCompressionAlgorithmController export_model() signature (openvinotoolkit#132) * Add per layer initialization of ranges. (openvinotoolkit#116) * Add prepare_for_export() to commit pre export for CompressionAlgortihmController; Update for CompositeCompressionAlgorithmController (openvinotoolkit#138) * Fix PyLint. (openvinotoolkit#139) * Introduced compression ratio parameter for Mixed Precision init (openvinotoolkit#133) * Introduced compression ratio parameter for Mixed Precision init It's used for choosing optimal mixed precision configuration for a given ratio. Compression ratio of mixed precision quantization is calculated by relation to fully INT8 one. Total compression for the model is sum of compression for each quantized layer, which is multiplication the layer's (Conv, Deconv, Linear) FLOPS and number of bits for its quantization. The ratio is used for estimation of performance boost for quantized model It's a better proxy for amount of calculation then number of parameters multiplied by bitwidth * Added link to the full configuration file with template usage * disclaimer about model specific params in template * corrected articles, contractions, mixed precision-> mixed-precision * Fix bug with NoCompressionAlgorithmController (openvinotoolkit#150) * Set data loading workers to 0 across tests to force single process (openvinotoolkit#162) * Set data loading workers to 0 across tests to force single process Could fix the consequences of pytorch/pytorch#39570 * Remove more-itertools dependency * Specify NNCF import order in docs (openvinotoolkit#161) * Specify NNCF import order in docs * Fix frontpage integration instructions * Bump mmdetection version to 2.4.0 (openvinotoolkit#166) * Fix command line creation for test_compression_training (openvinotoolkit#167) * Improve eval test code (openvinotoolkit#160) * Fix bug with different torch devices in get_scale_zp_from_input_low_input_high (openvinotoolkit#158) * Fix third_party_sanity and eval test bugs (openvinotoolkit#169) * Fix mmdetection dataset search path for SSD (openvinotoolkit#176) * Test stability (openvinotoolkit#179) * Increase eval threshold for test_compression_training cases CUDA computation seems to inherently cause differences of at least 0.01% in accuracy metric computation between the train and eval runs * Reduce batch size for SSD512 eval CI runs (avoid OOM) * Renamings (openvinotoolkit#178) * Fixed disabling gradients of quantizers for HAWQ (openvinotoolkit#184) * Corrected default values in range initializers (openvinotoolkit#183) - Right minimal and maximum values for mean_min_max doesn't skip check for not collected statistics and prevents from initializing by inf values. - Percentile init doesn't crash by default * Refactor imports in setup.py (openvinotoolkit#182) Important for CI * Fix security issues with imports (openvinotoolkit#185) * Fix paths to COCO in mmdetection third party sanity tests (openvinotoolkit#186) * Build graphs within the torch.no_grad() context (openvinotoolkit#187) Should reduce memory usage during create_compressed_model * Fix security issues directly in code (openvinotoolkit#189) * Return zero-valued torch.Tensor in CompressionLoss by default instead of int (openvinotoolkit#190) * Make default install support non-GPU cases (openvinotoolkit#193) * Fixed backward compatibility test (openvinotoolkit#195) * Improve quantizer setup for hanging batchnorm nodes (openvinotoolkit#192) * Do not merge subgraphs if subgraph has more than one output node * Mark BatchNorm as INPUTS_QUANTIZABLE by default Will manifest itself in case there is a batch norm operation that was not merged to any previous op, i.e. should accept quantized input instead of FP32 * Fix export for nodes with metatypes not redefined by pruning algo (openvinotoolkit#171) * Add more security fixes (openvinotoolkit#197) * Removed double logging to stdout (openvinotoolkit#198) * ignore frozen layers during filter pruning (openvinotoolkit#200) * Use latest matplotlib version (openvinotoolkit#206) * Use propagation based mode by default (openvinotoolkit#181) * Set propagation_based mode by default. * Fix compressed graphs. * Fix quantize inputs option. * Add operator metatypes for 'sigmoid' and 'add' operator (openvinotoolkit#209) * Add operator metatypes for 'sigmoid' and 'add' operator * remove trailing spaces Co-authored-by: Chua, Vui Seng <vui.seng.chua@intel.com> * Introduced `enabled` parameter for Quantizers (openvinotoolkit#194) Also: * corrected script to add new quantization parameters to checkpoints * added warning on exporting disabled quantizations * print statistics about enabled quantizers by default * Update documentation (openvinotoolkit#219) * Update documentation. * Update docs. Add dependencies for param to json schema. * To fix cpu_only part (openvinotoolkit#221) * To update cpu_only part dockerfile; fix issue with setup.py install with --cpy-only opt; fix README.md * apply remarks * Fix register_operator (openvinotoolkit#224) * Add per-layer sparsity. (openvinotoolkit#127) * Do not call _quantize_inputs for propagation based mode (openvinotoolkit#229) * Consistent bitwidth for activations and weight in propagation mode (openvinotoolkit#191) * Added sota eval tests via AC (openvinotoolkit#142) * Refactored HAWQ: split functionality into separate files (openvinotoolkit#232) * Allow quantizing modules that share their weights for multiple operations (openvinotoolkit#235) * Filter quantizers that directly act upon integer inputs (openvinotoolkit#228) * Add support sparsity freeze epoch for magnitude sparsity. (openvinotoolkit#218) * Liberal bitwidth assignment mode by default on precision initialization (openvinotoolkit#222) * Fix AdaptiveSparsityScheduler. (openvinotoolkit#236) * Fix threesigma init (openvinotoolkit#240) * Build extensions in a temporary folder (openvinotoolkit#239) * Criterion generalization for HAWQ algorithm (openvinotoolkit#230) * Criterion generalization for HAWQ algorithm * scope_node -> node_scope * Documentation update * Described in docs when to use additional parameter 'criterion_fn' * fix quantization range initialization in case of 1 scale channel (openvinotoolkit#241) fix quantization range initialization in case of 1 scale channel to avoid initialization only by single slice of data (data[0]) and ignoring the other (data[1], data[2],.....) * Patch Semantic Segmentation Application to export onnx and test with resume flag (openvinotoolkit#244) Co-authored-by: Chua, Vui Seng <vui.seng.chua@intel.com> * Add DW-conv to input quantizable op. (openvinotoolkit#220) * Fixed skip Openvino tests and preinstall (openvinotoolkit#246) * Corrected handling of barrier on the graph traverse (openvinotoolkit#249) * Extend input handling flexibility (openvinotoolkit#242) * Handle inputs better using input_infos * Update nncf/model_creation.py * Corrected handling Inception outputs in classification sample (openvinotoolkit#251) * Change quantization levels for SymmetricQuantizer from 255 to 256 (openvinotoolkit#225) * Change quantization levels for SymmetricQuantizer from 255 to 256 * Update test_functions with new level * Fix bug with weights range, Make formulas dependent only from one value - levels, thereby reducing the chance to make a mistake * Fix PyLint * Update HW configs with new quantization level_low * Fix bug with float type * Change type() to isinstance() * Change return values order in calculate_level_ranges * Fix bug with export to Q/DQ (openvinotoolkit#248) * Fix bug with export to Q/DQ Add hack of export processing for our old checkpoints Add Exception raising for exporting per-channel Q/DQ layers, as PyTorch ONNX exporting supports only per-tensor. * Fix Pylint * Update layers.py * Fix bug in AssymetricQuantizer export; Add tests * Fix pylint * Fix bug in AssymetricQuantizer export; Add tests * Fix pylint Co-authored-by: Vasily Shamporov <vasily.shamporov@intel.com> * Update results and links to the checkpoints (openvinotoolkit#253) * Update documentation for release v1.5.0 (openvinotoolkit#252) * Update documentation for release v1.5.0 * Corrected HAWQ documentation * Add per-range initialization notes Co-authored-by: Lyalyushkin Nikolay <nikolay.lyalyushkin@intel.com> * Add Mask-RCNN-R50FPN-INT8 config for mmdetection (openvinotoolkit#174) * rebase * add third-party sanity tests for Mask-RCNN IS model * add Mask-RCNN accuracy results to tables * fix link in README * add instance segmentation ref to README * fix voc path * fix retinanet config * Update version.py Co-authored-by: Ivan Lazarevich <ivan.lazarevich@intel.com> Co-authored-by: Pave Finashov <66466565+pfinashx@users.noreply.github.com> Co-authored-by: Anastasia Senina <Anastasia.Senina@intel.com> Co-authored-by: Aleksei Kashapov <aleksei.kashapov@intel.com> Co-authored-by: Maria Kaglinskaya <maria.kaglinskaya@intel.com> Co-authored-by: Lyalyushkin Nikolay <nikolay.lyalyushkin@intel.com> Co-authored-by: vuiseng9 <vuiseng9@gmail.com> Co-authored-by: Chua, Vui Seng <vui.seng.chua@intel.com> Co-authored-by: Fyodor Kutsepin (aka Oddy O) <fedorx.kutsepin@intel.com> Co-authored-by: krodyush <konstantin.rodyushkin@intel.com>