v0.4.0
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Docs | 08.03.2020 |
This is a major release for SOL coming with a series of new features, i.e. ONNX support, RNNs (for SX-Aurora only for now), AdaptivePooling, improved performance, better accuracy for BatchNorms and MeanReductions and many more.
Breaking Changes
- We no longer distribute the SOL images via GitLab. Please follow the installation steps described here.
- SOL API no longer requires to explicitly include the correct interface, i.e.
import sol.pytorch as sol. Instead just import sol and SOL will automatically detect the type of your model.
- The API for
sol.deploy(...) has been simplified, please checkout the Deployment documentation.
Closed Issues
- #2 Recurrent Neural Networks (RNNs)
- #9 Adaptive[Avg/Max]Pooling only works if it can be transformed into a normal Pooling
- #49 [DFP] Can't use reduction for MaxPooling
- #53 ONNX Support
- #66 [Docs] add ONNX docs
- #68 [DFP] missing gradient: MOD
- #86 [PIP] Solve name clash with public PYPI repo
- #92 [DFP] IDX not used in code generation
- #95 [PyTorch] Upgrade to 1.7.0
- #98 [DFP] SoftMax might produce uncompileable code
- #103 [DFP] Multi-Value Reductions
- #107 [GCC] Error compiling with GCC v4.8.5
- #109 [DFP] wrong gradient for SoftPlus
- #116 [PyTorch] Arange producing wrong results if non-integer values used
- #118 [Python] using dict for input/output causes randomized SOL hashes
- #119 [PyTorch] Double check API calls, if they have changed in last upgrade
- #121 [PyTorch] Missing Tests
- #122 [PyTorch] HugginFace BERT stopped working
- #124 [HuggingFace] Bert dimension mismatch
- #125 [Core] Check for Memleaks
- #126 [DL4J] Upgrade to new JSON format
- #127 [DL4J] Upgrade to new DType System
- #128 [Frontends] Make ```autotuning``` an additional parameter of the sol.optimize call!
- #129 [DFP] Optimize IDX usage
- #130 [DFP] wrong initial value for reduction accumulators
- #132 [PyTorch] Testcase Arange fails
- #133 [PyTorch] AddCDIV AddCMul missing
- #134 [DFP] Wrong gradient for PReLU-Weight
- #136 [PyTorch] Min/Max returned indicies do not match the PyTorch indicies format.
- #137 [PyTorch] can't use named tuples in output
- #140 [VE] Min/Max Reduce or Pooling, that need to use reduction within the kernel, produce wrong results during backward pass
- #141 [Performance] MaxPooling Backward Pass
- #142 [DFP] Improve Pooling Backward Performance
- #143 [DFP] Improve Pooling Fwd Performance
- #144 [PyTorch] Upgrade to 1.7.1
- #145 [DFP] Remove Inner
- #146 [VE] Fix Updating of VBS
- #147 [CUDNN] report Version and warn if version is < 7.6.0
- #149 [CUDA] Exclude Half Precision API from GPUs below Maxwell
- #150 [ISPC] Upgrade to 1.15.0
- #151 [DFP] Segfault when optimizing BERT
- #152 [VEDNN] Evaluate new LLVM-VE
- #153 [DNNL] upgrade to 1.8
- #154 [SQLITE] Update to 3.34.0
- #155 [Python] Add debug option to run ```python -m sol``` to check if SOL works correctly
- #156 [PyTorch] return_indices of MaxPooling does not match PyTorch value range
- #158 [DNNL] Update to v1.8.1
- #159 [PyTorch] SOL's behavior of inplace methods, i.e. neg_ is not identical
- #160 [PyTorch] SOL segfaults during execution when model uses the same tensor for multiple outputs
- #164 [PyTorch] verify that torch.nn.Conv1d is working
- #170 [SQLITE] Update to 3.34.1
- #172 [VEBLAS] RNN
- #178 [DFP] does not zero initialize gradient in backwardpass for narrows
- #180 [Python] Single Python Wrapper for all frameworks
- #185 [DFP] Missing LXXX_idx in BWD Filter pass for Conv
- #189 [DFP] Problem in Embedding BWD
- #190 [Deploy] Fix Deployment
- #192 [HLIR] add sol_layer_input(network, layer, IType, LayerOutput)
- #194 [HLIR] Make all Cat inputs to IType::Input/X instead of IType::None/0
- #215 [Debug] Increase font size in memory consumption graphs
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