Release v0.12.0

Breaking Changes

• TruncIntQuant, TruncAvgPool, Trunc QONNX Op changes #1042

Highlights

• New PTQ algorithms:
  • AWQ #1213
  • MagR #1214
  • QuaRot #1061
  • SpinQuant #1155
  • AutoRound #1064
  • SVDQuant #1210
• New datatype support
  • Hierarchical scales #1038
• Initial torch.compile support #1206
  • User guide here
• YAML-based experiments #1116
• Benchmarking scripts for LLM example #1166
• New operator support
  • Better SDPA quantization support #1090

What's Changed

• Feat (examples/generative): block-based optimization for GPTQ by @Giuseppe5 in #1046
• Fix (learned_round): disable return QuantTensor during float inference by @pablomlago in #1059
• Bump onnx from 1.15 to 1.17.0 in /requirements by @dependabot in #1069
• Fix (minifloat): correct minifloat computation and tests by @Giuseppe5 in #1067
• Feat (ptq): adding accumulator-aware extensions to GPxQ by @i-colbert in #1060
• Feat: add contributing guidelines by @Giuseppe5 in #1075
• Feat (float): adding new attributes to proxy and quant tensor by @i-colbert in #1072
• Feat (accelerate): improved accelerate compatibility by @Giuseppe5 in #1065
• Fix Transformers tests by @Giuseppe5 in #1081
• Fix (data): updating wikitext2 data utility by @i-colbert in #1080
• Fix (groupwise): correct log, groupdim, and scale computation by @Giuseppe5 in #1071
• Test (mx): add reference impl for MXFloat by @Giuseppe5 in #1068
• Fix (examples/generative): Fixed argument order for quantize_model by @nickfraser in #1084
• Feat (export): qonnx minifloat export by @Giuseppe5 in #1070
• Feat (core): use runtime parameter for scale by @Giuseppe5 in #1037
• Fix (per_group): fixing the per_group sym quantizer by @i-colbert in #1089
• Rotation based equalization by @Giuseppe5 in #1061
• Fix (examples/llm): fix for main and README by @Giuseppe5 in #1092
• Fix: correct output scale compute by @Giuseppe5 in #1077
• Fix (ptq/rotation): fix for rotation implementation (#1095) by @Giuseppe5 in #1095
• Fix (scaling)!: clamp to avoid inf/nan in forward/backward by @Giuseppe5 in #1097
• Setup: bump python & torch version by @Giuseppe5 in #1098
• Feat: Per-Row po2 float ocp by @Giuseppe5 in #1102
• Fix LLM tests by @pablomlago in #1088
• Feat (brevitas_examples/llm): remove dependencies from optimum-amd by @Giuseppe5 in #1094
• Feat auto round by @pablomlago in #1064
• Fix (hadamard): remove hadamard loading warning by @Giuseppe5 in #1108
• Hierarchical scales by @Giuseppe5 in #1038
• Improvements to learned round by @Giuseppe5 in #1107
• Feat (brevitas_examples/llm): update README by @Giuseppe5 in #1109
• Fix (gpxq): tensor unpacking and Cholesky stabilization by @i-colbert in #1111
• Feat (llm): adding more quantizers by @i-colbert in #1113
• Feat (llm/learned_round): fast block update by @Giuseppe5 in #1110
• Fix SignSGD docstring by @pablomlago in #1115
• Feat (nn/sdpa): quantization of scaled dot-product attention by @nickfraser in #1090
• Fix (brevitas_examples/llm): scaling_min_val for fp32 by @Giuseppe5 in #1117
• Feat (scaling): no tracked_parameter_list with individual quantizer by @Giuseppe5 in #1112
• Feat (brevitas_examples/llm): select act_eq alpha by @Giuseppe5 in #1121
• Fix llm tests transformers by @pablomlago in #1118
• Fix (float/clamp): Bugfix when unsigned by @nickfraser in #1132
• Feat (brevitas_examples/llm): inference_mode support by @Giuseppe5 in #1129
• Feat (brevitas_examples/llm): correct scale init with CPU offloading by @Giuseppe5 in #1124
• Feat (brevitas_examples/sdxl): inference_mode + compile by @Giuseppe5 in #1133
• Feat (proxy): flag to enable/disable QT return by @Giuseppe5 in #1083
• Feat (examples/llm): Specify experiments via YAML files by @nickfraser in #1116
• test (core/float): Enhanced testing of minifloat formats by @nickfraser in #1136
• Eval harness by @Giuseppe5 in #1131
• Fix: pytree warning by @i-colbert in #1144
• Fix LLM entry point by @i-colbert in #1145
• Fix (scaling/standalone): better switch from runtime stats to param by @Giuseppe5 in #1099
• Fix (proxy): fix groupwise scale/zp caching by @Giuseppe5 in #1137
• Fix (export/inference_mode): correct rounding function by @Giuseppe5 in #1146
• Setup: pin transformers version by @Giuseppe5 in #1150
• Feat (mx): unpadding during dequantization by @Giuseppe5 in #1134
• Feat (brevitas_examples/llm): load from checkpoint by @Giuseppe5 in #1151
• Feat (rotation): equalize across SDPA by @Giuseppe5 in #1149
• Feat (quantization): torch_function based quantization by @Giuseppe5 in #1147
• Setup: bump torch version for LLM tests by @Giuseppe5 in #1154
• Feat (equalize): enable parametrized rotations by @pablomlago in #1148
• Feat (optim): add Cailey SGD optimizer by @pablomlago in #1153
• Setup: update pre-commit python version by @Giuseppe5 in #1158
• Fix (brevitas_examples/llm): remove unecessary checkpointing by @Giuseppe5 in #1161
• Feat (zero_point): dynamic groupwise zero point by @Giuseppe5 in #1160
• New rotation by @Giuseppe5 in #1159
• Fix (brevitas_examples/llm): equalized module + fx compatibility by @Giuseppe5 in #1164
• Fix (runtime_act): fix negative group_dim handling by @Giuseppe5 in #1157
• Fix (a2q): missing restrict_pre_scaling_impl definition by @Giuseppe5 in #1167
• Feat (equalize): enable rotation matrix optimization by @pablomlago in #1155
• Add FP16 support to ptq_evaluate.py and update README argument list by @hkayann in #1174
• Feat (brevitas_examples/llm): separate KV Cache quantization by @Giuseppe5 in #1165
• Feat (hadamard): support region expansion by @Giuseppe5 in #1178
• Feat (llm): benchmark for llm entrypoint by @pablomlago in #1166
• fix (docs/faq): remove reference to gitter, switch affine quantization to be an example by @nickfraser in #1183
• Fix (brevitas_examples/sdxl): correct import for inference_mode by @Giuseppe5 in #1185
• Feat (gpfq): optimizing with lower diagonal matrix formulation by @i-colbert in #1172
• Feat (brevitas_examples/llm): better dtype selection by @Giuseppe5 in #1186
• Fix (brevitas_examples/sdxl): faster sdxl inference by @Giuseppe5 in #1188
• fix (examples/benchmark): Fix when run_results.yaml does not exist. by @nickfraser in #1189
• Feat (example/common): Added groupwise, float scaled OCP option by @nickfraser in #1190
• Fix (examples/llm): default dtype from None to float16 by @pablomlago in #1191
• Fix (utils/torch_utils): ensure gradient propagation through pad_to_dim by @pablomlago in #1194
• Fix (examples/llm): prevent layernorm_to_rmsnorm option when fused_no_fx by @pablomlago in #1192
• Feat (brevitas_examples/sdxl): update mlperf by @Giuseppe5 in https://github.com/Xilinx/bre...

Release v0.11.0

Breaking Changes

• Remove ONNX QOp export (#917)
• QuantTensor cannot have empty metadata fields (e.g., scale, bitwidth, etc.) (#819)
• Bias quantization now requires the specification of bit-width (#839)
• QuantLayers do not expose quant_metadata directly. This is delegated to the proxies (#883)
• QuantDropout has been removed (#861)
• QuantMaxPool has been removed (#858)

Highlights

• Support for OCP/FNUZ FP8 quantization

  • Compatibility with QAT/PTQ, including all current PTQ algorithms implemented (GPTQ, LearnedRound, GPFQ, etc.)
  • Possibility to fully customize the minifloat configuration (i.e., select mantissa/exponent bit-width, exponent bias, etc.)
  • Support for ONNX QDQ export

• Support for OCP MX Quantization

  • Compatibility with QAT/PTQ, including all current PTQ algorithms implemented (GPTQ, LearnedRound, GPFQ, etc.)
  • Possibility to fully customize the minifloat configuration (i.e., select mantissa/exponent bit-width, exponent bias, group size, etc.)

• New QuantTensor supports:

  • FloatQuantTensor: supports OCP FP formats and general minifloat quantization
  • GroupwiseQuantTensor: supports for OCP MX formats and general groupwise int/minifloat quantization

• Support for Channel splitting

• Support for HQO optimization for zero point

• Support for HQO optimization for scale (prototype)

• Improved SDXL entrypoint under brevitas_examples

• Improved LLM entrypoint under brevitas_examples

  • Compatibility with accelerate

• Prototype support for torch.compile:

  • Check PR #1006 for an example on how to use it

What's Changed

For a more comprehensive list of changes and fix, check the list below:

• Enhance: Importing quantized models after bias correction by @costigt-dev in #868
• Fix QCDQDecoupledWeightQuantProxyHandlerMixin return args by @costigt-dev in #870
• Fix - Speech to text: Create an empty json file by @costigt-dev in #871
• Feat (scaling/standalone): flag to retrieve full state dict by @Giuseppe5 in #874
• Notebooks: makes notebooks deterministic and prints output of asserts by @fabianandresgrob in #847
• Fix (proxy): revert value tracer change by @Giuseppe5 in #888
• Fix (proxy): fix for attributes retrieval by @Giuseppe5 in #880
• Feat (notebook): add example for dynamic quantization to ONNX export by @fabianandresgrob in #877
• Fix (gpxq): handling empty tensors with GPxQ and adding unit tests by @i-colbert in #892
• Fix (ptq): expose uint_sym_act flag and fix issue with minifloat sign by @fabianandresgrob in #898
• Feat (minifloat): add support for user specified minifloat format by @fabianandresgrob in #821
• Feat: Add QuantConv3d and QuantConv3dTranspose by @costigt-dev in #805
• Add tutorial examples of per-channel quantization by @OscarSavolainenDR in #867
• Fix (tests): revert pytest pin by @Giuseppe5 in #903
• Remove: Remove original_cat workaround by @costigt-dev in #902
• Infra: Update issue template by @nickfraser in #893
• Pull Request Template by @capnramses in #885
• Fix (core): add return in state_dict by @Giuseppe5 in #910
• Fix (quant_tensor): fix typing and remove unused checks by @Giuseppe5 in #913
• Fix (nn): removed unused caching in adaptive avgpool2d by @Giuseppe5 in #911
• Fix (quant_tensor): remove unused checks by @Giuseppe5 in #918
• Setup: pin ONNX to 1.15 due to ORT incompatibility by @Giuseppe5 in #924
• Feat (examples): add support for Stable Diffusion XL by @Giuseppe5 in #909
• Assert all ptq-common bit widths are positive integers by @OscarSavolainenDR in #931
• Enhance: Quant Tensor Test by @costigt-dev in #894
• Fix (examples/stable_diffusion): README formatting and clarification by @Giuseppe5 in #932
• Fix (examples/ptq): fix for bitwidth check by @Giuseppe5 in #934
• Feat: functionalize QuantTensor by @Giuseppe5 in #878
• Feat (minifloat): cleanup minifloat impl by @Giuseppe5 in #922
• Fix tests in dev by @Giuseppe5 in #939
• Feat (proxy): scale computation delegated to bias proxy by @Giuseppe5 in #938
• Fix (gpxq): adding input quant to process input by @i-colbert in #943
• Fix (quant): propagate device and dtype in subinjector by @Giuseppe5 in #942
• Fix (gpxq): correct variable name by @Giuseppe5 in #944
• Fix (quant_tensor): fix AvgPool functional implementation by @Giuseppe5 in #945
• Feat (quant_tensor): support for dim() and ndim by @Giuseppe5 in #947
• Fix (graph/standardize): correct check for Mean to AvgPool by @Giuseppe5 in #948
• Feat (graph/standardize): default keepdim value by @Giuseppe5 in #950
• Fix bullet formatting in getting started guide by @timkpaine in #952
• Fix (quant/float): correct scaling_impl and float_scaling_impl by @Giuseppe5 in #953
• Fix/remove-numel - Remove numel is zero check from context manager exit method by @costigt-dev in #920
• Feat (examples/ptq): support for dynamic act quant by @Giuseppe5 in #935
• Feat (quant_tensor): support for FloatQuantTensor by @Giuseppe5 in #919
• Fix (examples/llm): Add all rewriters to the list by @nickfraser in #956
• Fix (core/quant/float): use eps to avoid log(0) by @Giuseppe5 in #957
• Fix (test/actions): Excluded torch==1.9.1, platform=macos-latest tests by @nickfraser in #960
• Adding FP8 weight export by @costigt-dev in #907
• Fix (llm): fix device issue for eval when not using default device by @fabianandresgrob in #949
• Fix (GPFQ): using random projection for speed up/less memory usage by @fabianandresgrob in #964
• Fix (calibrate/minifloat): fix for act calibration by @Giuseppe5 in #966
• Fix (quant/float): restore fix for log(0) by @Giuseppe5 in #968
• Setup: pin numpy version by @Giuseppe5 in #974
• Feat (minifloat): support for FNUZ variants by @Giuseppe5 in #973
• Fix (core/float): add default for float_scaling_impl by @Giuseppe5 in #972
• Feat (graph/equalize): upcast during equalization computation by @Giuseppe5 in #970
• Generative improv by @Giuseppe5 in #965
• Fix (requirements/setuptools): Set maximum requirement for setuptools by @nickfraser in #963
• Fix: Typo fix on SDXL command line args by @nickfraser in #976
• Fix (graph/bias_correction): Fix when layer parameters are offloaded to accelerate by @nickfraser in #962
• Fix (ptq/bias_correction): remove unnecessary forward pass by @Giuseppe5 in #980
• Fix (export/qonnx): Fixed symbolic kwargs order. by @nickfraser in #988
• Various SDXL quantization fixes by @nickfraser in #977
• Fix (brevitas_examples/sdxl): Various fixes by @Giuseppe5 in #991
• Feat (proxy/parameter_quant): cache quant weights by @Giuseppe5 in #990
• Docs: Added 0.10.3 release note to README. by @nickfraser in #993
• Added some preliminary unit tests to the CNNs 'quantize_model' by @OscarSavolainenDR in #927
• Feat (tests): extended minifloat unit tests by @alexredd99 in #979
• Fix (proxy/runtime_quant): correct handling of mixed type quantization by @Giuseppe5 in #985
• docs (readme): Fixed GH actions badges by @nickfraser in #996
• Feat: Update LLM entry-point ...

Release v0.10.3

What's Changed

• Backport: Fix (export/qonnx): Fixed symbolic kwargs order. (#988) by @nickfraser in #992
• numpy version, onnx version and maximum setuptools version set

Full Changelog: v0.10.2...v0.10.3

Release v0.10.2

What's Changed

• Fix (QuantLayer): make bias for QuantLayer optional by @fabianandresgrob in #846
• Fix (examples/llm): set group_size only for groupwise quantization by @nickfraser in #853
• Fix (gpfq): updating input processing and L1-norm constraints for GPFA2Q by @i-colbert in #852
• ImageNet PTQ example fix by @Giuseppe5 in #863
• feat (gen/quantize): Added device flag to quantize_model by @nickfraser in #860
• Docs: update README for 0.10.2 release by @Giuseppe5 in #865

Full Changelog: v0.10.1...v0.10.2

Release v0.10.1

Highlights

• A2Q+ support paper
• A2Q+ examples with CIFAR10 and Super Resolution
• Support for concatenation equalization for weights and activations
• Support for GPFQ + A2Q L1 Norm bound
• Possibility to explicitly export Q node for weights in QCDQ export
• Support for float16 and bfloat16 for QCDQ export
• Support for Dynamic Activation Quantization for ONNX QDQ export
• Support for channel-splitting paper
• (Beta) Better compatibility with Huggingface accelerate and optimum
• (Beta) Improved support and testing for minifloat quantization

What's Changed

• Fix (examples/generative): set weight_bit_width in weight_quant by @Giuseppe5 in #783
• Feat (graph/equalize): improvements for llm equalization by @Giuseppe5 in #784
• [graph] Fix typo in class name by @nickfraser in #765
• Fix (graph/equalize): refactor for act equalization by @Giuseppe5 in #787
• [quant_tensor] Updates __truediv__ behaviour to match "standard fixed point rules" by @nickfraser in #769
• Feat (export): (b)float16 support for qcdq export by @Giuseppe5 in #776
• Feat (ptq): Adding A2Q Upper Bound clipping to GPFQ by @fabianandresgrob in #734
• Extended equalization by @Giuseppe5 in #778
• Better Bfloat16 support by @Giuseppe5 in #777
• Fix (stats): add return statement in state_dict by @Giuseppe5 in #792
• Fix (equalize): improved cat eq checks by @Giuseppe5 in #793
• Fix (export): add CastMixin by @Giuseppe5 in #794
• Dynamic Act Quant support by @Giuseppe5 in #796
• Fix (examples/quantizers): correct dynamic zero point handling by @Giuseppe5 in #806
• Feat (a2q+): improving accumulator-aware weight quantization by @i-colbert in #797
• Feat (a2q+): adding new super resolution models to brevitas_examples by @i-colbert in #811
• Feat (Channel-Splitting): sets up first skeleton for channel-splitting by @fabianandresgrob in #772
• Feat: support for optimum by @Giuseppe5 in #826
• Fix (tests): adding tests for FloatQuant by @fabianandresgrob in #815
• Fix (export): correct q node export by @Giuseppe5 in #829
• Fix (examples/llm): correct groupwise export by @Giuseppe5 in #832
• Fix (examples/super_res): updating README by @i-colbert in #828
• Fix (examples/export): improved export by @Giuseppe5 in #838
• Fix (graph/equalize): cleanup and device management by @Giuseppe5 in #840
• Feat (examples/a2q): adding CIFAR10 example by @i-colbert in #813
• Fix (export): check for Per Group quantization by @Giuseppe5 in #848

Full Changelog: v0.10.0...v0.10.1

A2Q+ CIFAR10 model release

This release contains training code and pre-trained weights to demonstrate accumulator-aware quantization (A2Q) on an image classification task. Code is also provided to demonstrate Euclidean projection-based weight initialization (EP-init) as proposed in our paper "A2Q+: Improving Accumulator-Aware Weight Quantization".

Find the associated docs at https://github.com/Xilinx/brevitas/tree/a2q_cifar10_r1/src/brevitas_examples/imagenet_classification/a2q.

A2Q+ model release

A2Q+ Super Resolution Experiments with Brevitas

This release contains training code and pre-trained weights to demonstrate accumulator-aware quantization (A2Q+) as proposed in our paper "A2Q+: Improving Accumulator-Aware Weight Quantization" on a super resolution task.

Find the associated docs at https://github.com/Xilinx/brevitas/tree/super_res_r2/src/brevitas_examples/super_resolution.

Release v0.10.0

Highlights

• Support for PyTorch up to version 2.1 .
• Support for GPTQ PTQ algorithm.
• Support for GPFQ PTQ algorithm.
• Support for SmoothQuant / activation equalization PTQ algorithm.
• Support for MSE based scale and zero-point for weights and activations.
• Support for row-wise scaling at the input of QuantLinear.
• Support for quantization of a slice of a weight tensor.
• End-to-end support for learned rounding in ImageNet PTQ.
• End-to-end example training scripts for A2Q (low precision accumulation) over superresolution.
• Experimental support for minifloats (eXmY quantization).
• Experimental LLM PTQ flow with support for weight-only and weight+activation quantization, together with GPTQ, AWQ and SmoothQuant.
• Experimental Stable Diffusion PTQ flow with support for weight-only quantization.
• Deprecated FINN ONNX export flow.
• Update custom value_trace FX tracer to latest FX.
• New custom variant of make_fx tracer with support for custom torch.library ops through @Wrap annotation.

What's Changed

• Feat (nn): cache modules that require subtensor slicing by @volcacius in #628
• Feat: support slicing for gptq by @Giuseppe5 in #626
• Feat: add support to row wise input quantization to QuantLinear by @volcacius in #625
• Fix (nn): disable weight tensor slicing syntax by @volcacius in #633
• Feat (core): add SliceTensor util for sub-weight quant by @volcacius in #634
• Fix (core): add missing dtype and device by @Giuseppe5 in #635
• Feat (ptq): activation equalization support by @Giuseppe5 in #541
• Feat (fx): value_trace improvements by @volcacius in #636
• Fix (core/utils): jit ignore eager mode tensor slicing impl by @volcacius in #637
• Fix (weight_eq): fix for llm equalization by @Giuseppe5 in #638
• Add missing license by @Giuseppe5 in #640
• Feat (ptq): act equalization support for vision by @Giuseppe5 in #643
• Fix (tracer): support for index and no-tracer ops by @Giuseppe5 in #644
• Setup: pin version of inflect for compatibility by @Giuseppe5 in #647
• Activation eq extension by @Giuseppe5 in #642
• Fix (core): correct forward in ParameterFromStatsFromParameter by @Giuseppe5 in #650
• Feat (zero_point): grid search for mse zp by @Giuseppe5 in #651
• Fix (weight_eq): correct handling of layernorm/batchnorm as sink by @Giuseppe5 in #646
• Feat (nn): set dim names in QuantMHA Linear by @volcacius in #629
• Fix (act_quant): flag to enable/disable stats collection by @Giuseppe5 in #641
• Feat (core): add keepdim to min/max/percentile stats by @volcacius in #657
• Fix (ptq): conflicts between gptq and equalization by @volcacius in #656
• Fix (nn): state_dict load for unpacked in_proj in MHA by @volcacius in #654
• Feat (ptq): learned round support in evaluate/benchmark by @Giuseppe5 in #639
• Feat (nn): avoid computing output scale/zp when not needed by @volcacius in #655
• Fix (QuantTensor): pixel_shuffle and unshuffle handler by @volcacius in #663
• Setup: fix installation of libgomp1 by @Giuseppe5 in #662
• Fix (quantize): fix and improvements for fx quantize by @Giuseppe5 in #661
• Fix (resnet18): fixing default weight quantizer for linear layer by @i-colbert in #660
• Fix(gptq): fix for quant convtranspose1d/2d and conv1d by @Giuseppe5 in #665
• Refactor of ptq_common by @Giuseppe5 in #649
• Examples: initial support for LLMs PTQ by @volcacius in #658
• Fix (weight_eq): mantain order of regions by @Giuseppe5 in #667
• Feat (core): simplify binary_sign impl by @volcacius in #672
• Feat (core): add permute_dims to all reshape fns by @volcacius in #671
• Feat (graph/equalize): clean up scale invariant ops by @volcacius in #669
• Misc: fix pre-commit by @volcacius in #676
• Misc: fix another pre-commit by @volcacius in #677
• Feat (examples/llm): initial support for loading AWQ results by @volcacius in #673
• Fix (espcn): updating links to use new tags by @i-colbert in #678
• Fix (ptq): fix for act quantizers by @Giuseppe5 in #675
• Fix (ptq): fix for residual with mha by @Giuseppe5 in #681
• Fix (fx): fix fx quantize for conv->bn by @Giuseppe5 in #680
• Feat (gptq): add option to return output from forward by @Giuseppe5 in #684
• Fix (a2q): correcting post-rounding scaling initialization by @i-colbert in #659
• Feat (quant): initial support for fp8 variants by @volcacius in #686
• Fix (gptq): fix for depthwise act_order by @Giuseppe5 in #688
• Feat (core): support for stochastic round by @volcacius in #689
• Fix (gptq): Caching quant_inp values for quant_weight by @i-colbert in #653
• Feat (gptq): support for groupwise conv by @Giuseppe5 in #690
• Fix (gptq): typo in variable name by @Giuseppe5 in #691
• Rename brevitas quant custom op by @jinchen62 in #693
• Change tolerance for fp16 by @jinchen62 in #694
• Fix (docs): Updating references to A2Q paper by @i-colbert in #698
• Feat (examples/llm): add first/last layer support by @volcacius in #699
• Feat (examples/llm): add packed 3/5/6b export by @volcacius in #700
• Fix (examples/llm): padding for packed 3/5/6b by @volcacius in #701
• Fix (gptq): linalg import fix by @Giuseppe5 in #705
• Examples (a2q): updating and extending ESPCN demo by @i-colbert in #706
• Examples (a2q): adding links for pretrained models by @i-colbert in #707
• Fix (nn): add missing support for padding_mode by @volcacius in #709
• Feat (examples/llm): add custom float support by @volcacius in #708
• GPFQ by @Giuseppe5 in #666
• Feat (ptq): support for float bias by @Giuseppe5 in #713
• Feat (ptq): flag to disable/enable signed activations by @Giuseppe5 in #714
• Support for minifloat benchmark by @Giuseppe5 in #712
• adding quant_format, mantissa, and exponent options to evaluate script by @fabianandresgrob in #717
• Fix (fx): import backport on 2.1 by @volcacius in #732
• Fix (ptq): correct bitwidth for layerwise int benchmark by @Giuseppe5 in #737
• Fix (ptq): fix for ptq_common by @Giuseppe5 in #739
• Fix (examples): adding bias_quant to final linear layer in resnet18 by @i-colbert in #720
• Fix (base): Updating A2Q defaults by @i-colbert in #718
• Fix (core): arithmetic of zero-point with positive only values by @volcacius in #670
• Fix (nn): QuantConv group calculation by @i-colbert in #703
• Feat (QuantTensor): QuantTensor x Tensor elementary ops dequantize to Tensor by @volcacius in #668
• Feat (examples): initial Stable Diffusion support by @volcacius in #715
• changes class_implementation to init_class in gpxq_mode by @fabianandresgrob in #754
• Fix errors in test by @Giuseppe5 in #716
• Fix (notebook): increase atol for asserts by @Giuseppe5 in #759
• Gpfq/act order by @fabianandresgrob in #729
• Fix (backport): op decomp in make_fx backport by @volcacius in #763
• Feat (export): deprecate FINN ONNX export by @Giuseppe5 in https://github.com/Xilinx/brevitas/p...

A2Q model release

Integer-Quantized Super Resolution Experiments with Brevitas

This release contains scripts demonstrating how to train integer-quantized super resolution models using Brevitas.
Code is also provided to demonstrate accumulator-aware quantization (A2Q) as proposed in our ICCV 2023 paper "A2Q: Accumulator-Aware Quantization with Guaranteed Overflow Avoidance".

Find the associated docs at https://github.com/Xilinx/brevitas/tree/super_res_r1/src/brevitas_examples/super_resolution .

Release v0.9.1

What's Changed

• Setup: add requirements-dev with pre-commit by @Giuseppe5 in #581
• CI update by @Giuseppe5 in #570
• Fix (brevitas_examples/bnn_pynq): missing 4b resnet18 link and hash fn by @volcacius in #583
• Docs: update READMEs by @Giuseppe5 in #584

Full Changelog: v0.9.0...v0.9.1