MIOpen v1.8.0

Notes:

• This release contains full 3-D convolution support and int8 support for inference.
• Additionally, there are major updates in the performance database for major models including those found in Torchvision.
• An assortment of bugs have been resolved in this release.

Changes:

• Fixed various issues in assembly kernels
• Fixed issue #92 and #79 for miopenOpTensor
• Fixed issue #88 for bzip2
• Fixed issue #77 algorithm mismatch
• Added Winograd support for fp32 backwards weights
• Added pooling inclusive mode
• Added tuning for direct group convolution algorithms
• Added additional kernel support for group convolutions
• Added API for 3-D convolutions
• Added support for int8 inference convolutions
• Added integer selection for pooling indexing
• Added minimum dependencies support
• Added RNN fp16 support on the MIOpen-HIP backend
• Added 1x1 convolution + bias + activation fusions
• Added workaround for issue #84 GPU memory access fault
• Added performance tuning for direct backwards weights
• Improved performance database coverage
• Improved internal quality by reducing redunant code
• Improved build instructions in README.md
• Improved performance database coverage for fusions
• Updated Docker components and requirements

Known Issues:

• RNNs do not support fp16 on the MIOpen-OpenCL backend
• OpenCL backend does not support GEMM convolutions in fp16

MIOpen v1.7.1

Notes:

• This release contains minor bug fixes and performance improvements.

Changes:

• Fixed corrupt and obsolete performance database entries
• Fixed issue #70
• Fixed issue #72
• Fixed issue #77
• Removed default dependency of RNNs on rocBLAS
• Added a workaround for softmax fp16 correctness issue
• Added check to only make MIOpen with static boost libraries
• Improved performance database coverage

Known Issues:

• RNNs do not support fp16
• OpenCL backend does not support GEMM convolutions in fp16
• Layer fusions for convolution 1x1 fp16 are not supported
• Layer fusions for large image 1x1 convolutions may cause an exception instead of a warning during compile phase if plan is not supported

MIOpen v1.7.0

Notes:

• This release contains general bug fixes and an updated performance database
• Group convolutions backwards weights performance has been improved
• Logging across the library has been improved
• Performance database has been updated

Changes:

• Fixed logging issues with group convolution and pooling
• Fixed sphinx version issue in document generation
• Fixed issues with corrupt entries in performance database
• Removed external dependency on libSSL and libCrypto
• Added support for large image backwards weights in direct convolution
• Added fp16 support for RNNs on the HIP backend
• Improved performance database coverage

Known Issues:

• RNNs do not support fp16
• OpenCL backend does not support GEMM convolutions in fp16
• Layer fusions for convolution 1x1 fp16 are not supported
• Layer fusions for large image 1x1 convolutions may cause an exception instead of a warning during compile phase if plan is not supported

MIOpen v1.6.0

Notes:

• Training in fp16 (half precision) including mixed-precision is now fully supported
• Batch Normalization in fp16 (half precision) including mixed-precision are now available
• Performance improvements for 3x3 and 1x1 single-precision convolutions
• Layer fusions for BatchNorm+Activation are now available
• Layer fusions with convolutions now support varying strides and padding configurations

Changes:

• rocBLAS is now used as the default BLAS library for the HIP backend (minimum version 14.3.0)
• Fixed various bugs in convolution kernels
• Fixed issues with bad references in layer fusion
• Fixed gfx803 assembily issues
• Added support fp16 Winograd convolutions
• Added support for fp16 pooling
• Improved error reporting for convolutions and layer fusions
• Improved documentation

Known Issues:

• RNNs do not support fp16
• OpenCL backend does not have full fp16 support
• Layer fusions for convolution 1x1 fp16 are not supported

MIOpen v1.5.0

Notes:

• A new kernel fusion API is now available for inference for convolution, bias,
  batch normalization, and activations.
• This release includes new features and bug fixes
• Group and Depthwise convolutions are now available
• 3D Batch Normalization has been implemented for fully packed tensors
• Dilation for convolutions have been implemented

Changes:

• Fixed bugs in direct convolutions
• Fixed issue with paths when $HOME variable is not set
• Fixed padding issues with 1x1 convolutions
• Added incremental support for fp16
• Added fused kernels for Winograd and direct with bias and activations
• Added a getting started guide for kernel fusion.
• Added group and depthwise API for convolutions
• Added 3-D batch normalization support with 5-D tensors
• Improved max pooling performance
• Improved debug and error reporting information
• Improved documentation for convolutions

Known Issues:

• RNNs do not support fp16
• Training with CNNs does not support fp16

MIOpen v1.4.2

Notes:

• This release is a hot-fix to enable ICNet and PSPNet

Known Issues:

• RNNs do not support fp16
• Training with CNNs does not support fp16
• Users may encounter a warning that their performance database is out of date. The performance database can be updated by setting the environment variable for just the initial run of an application: MIOPEN_FIND_ENFORCE=search
  For more information on the performance database, see: https://rocmsoftwareplatform.github.io/MIOpen/doc/html/perfdatabase.html#

MIOpen v1.4.1

Notes:

• This release includes a bug fix for 3x3 convolutions
• Updated README file configuration instructions

Known Issues:

• RNNs do not support fp16
• Training with CNNs does not support fp16
• Users may encounter a warning that their performance database is out of date. The performance database can be updated by setting the environment variable for just the initial run of an application: MIOPEN_FIND_ENFORCE=search
  For more information on the performance database, see: https://rocmsoftwareplatform.github.io/MIOpen/doc/html/perfdatabase.html#

MIOpen v1.4.0

Notes:

• This release includes a number of performance improvements and bug fixes
• New features have been added to convolutions for auto-tuning kernels
• Activations now have new modes available
• Documentation has been updated and corrected

Changes:

• Fixed documentation errors
• Fixed bug in activations with pass-through mode
• Fixed performance database locking issues
• Fixed Winograd kernel behavior for stride 2 backwards data
• Fixed a bug in OpTensor layer
• Fixed a timing issue with batch normalization inline assembly
• Fixed issue with an unnecessary binary creation in assembly bug detection
• Fixed issue with disk program cache directory not being created
• Fixed a bug with convolution+bias
• Added to performance database functionality
• Added leaky-ReLU, clipped, and exponential-ReLU modes to activation
• Added documentation for performance database usage
• Added support for 1x1 convolutions with non-zero padding
• Added API for printing status codes as strings
• Added auto-tuning feature for convolutions
• Improved LSTM and GRU backwards pass performance
• Improved debug and error reporting information
• Improved performance of batch normalization spatial mode
• Improved find stage for convolutions
• Improved readability for user database file

Known Issues:

• RNNs do not support fp16
• Training with CNNs does not support fp16

MIOpen v1.3.0

Notes:

• Performance improvements for RNNs
• Performance improvements for convolutions using 1x1 filters
• Performance improvement for Batch Normalization
• This release adds preliminary fp16 support for Inference using CNNs
• Bug fixes for various components of MIOpen

Changes:

• Added 2 new API for RNNs: miopenGetRNNLayerParamOffset and miopenGetRNNLayerBiasOffset
• Added support for uninitialized hidden states and nullptr outputs in RNNs
• Added support for Set and Scale operations for strided tensors with dimensions 1 to 5
• Added multi-thread and multi-process support for the performance database
• Improved performance for OpTensor
• Fixed bug in convolutions for backward bias
• Fixed logic issues in get and set layer functions and related w_supertensor test
• Fixed hang in batch norm with batch sizes greater than 256

Known Issues:

• RNNs do not support fp16
• Training with CNNs does not support fp16

MIOpen v1.2.1

Notes:

This release adds support for ROCm 1.7.1.