Releases: ROCm/MIOpen
Releases · ROCm/MIOpen
MIOpen v2.0.0
Notes:
- This release contains several new features including an immediate mode for selecting convolutions, bfloat16 support, new layers, modes, and algorithms.
- MIOpenDriver, a tool for benchmarking and developing kernels is now shipped with MIOpen.
- BFloat16 now supported in HIP requires an updated rocBLAS as a GEMM backend.
- Immediate mode API now provides the ability to quickly obtain a convolution kernel.
- MIOpen now contains HIP source kernels and implements the ImplicitGEMM kernels. This is a new feature and is currently disabled by default. Use the environmental variable "MIOPEN_DEBUG_CONV_IMPLICIT_GEMM=1" to activation this feature. ImplicitGEMM requires an up to date HIP version of at least 1.5.9211.
- A new "loss" catagory of layers has been added, of which, CTC loss is the first. See the API reference for more details.
- 2.0 is the last release of active support for gfx803 architectures. In future releases, MIOpen will not actively debug and develop new features specifically for gfx803.
- System Find-Db in memory cache is disabled by default. Please see build instructions to enable this feature.
Changes:
- Added support for bfloat16 datatype in convolutions
- Added softmax channel mode and new softmax version 2 API
- Added fast / accurate / log softmax algorithms
- Added new implicit GEMM convolution algorithm for forward and backwards data passes, disabled by default
- Added int32 datatype support for output tensors in int8 convolutions
- Added immediate mode for finding the best convolution kernel for a given configuration
- Added a Find-Db infrastructure which stashes results of find on a user's system
- Added a shipped System Find-Db containing offline run Find() results
- Added an additional, faster batch norm assembly kernel for fp16
- Added CTC loss layer
- Added MIOpenDriver as a default component in MIOpen's build #34
- Fixed C compatability for boolean types in C API #103
- Fixed incorrect calculation in per-activation batch norm backwards pass #104
- Fixed bug #95 with asm batch norm ISA
- Fixed IsApplicable bug in Conv3x3Asm for group convolutions
- Improved performance of 1x1 stride 2 fp32 convolutions in the forward and backwards data passes
- Improved 3-D convolution stability
- Improved applicability of direct convolution backwards weights for 2x2, 5x10, and 5x20 filter sizes
- Improved maintainability in kernels and cpp code
- Updated rocBLAS minimum version to branch master-rocm-2.6
MIOpen v1.8.1
Notes:
- This release contains minor bug fixes and additional performance database improvements.
Changes:
- Fixed accuracy issue with backwards weights
- Fixed issue with name parsing for newer architectures
- Added narrow workaround for 5x10 and 5x20 filter performance regression
- Improved support in performance database for Radeon VII
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