Update release information and version number. (#319)

RELEASE.md

@@ -0,0 +1,92 @@
+# Release 0.2.0
+
+# Performance Optimization
+
+## GPU stitch fusion
+
+Make use of GPU shared memory to fuse reduce operator with its consumers into one kernel.
+It helps to accommodate complex memory-intensive computations (e.g., LayerNorm, SoftMax) into one kernel,
+reducing off-chip memory traffics and overhead of kernel scheduling and launching.
+It implements partial functions described in paper [AStitch](https://dl.acm.org/doi/abs/10.1145/3503222.3507723).
+It is currently under refactoring to enhance the robustness, for which it is not enabled by default.
+Users of BladeDISC can enable it by setting the environment variable `DISC_ENABLE_STITCH=true`.
+
+Note that we have already released the CPU stitch optimization when we open-source the BladeDISC project, which is enabled by default.
+Refer to the [materials](https://bladedisc.oss-cn-hangzhou.aliyuncs.com/docs/performance-optimization-practice.pdf) for more information about CPU stitch technique details.
+
+## GEMM merging
+
+Support two types of GEMM merging optimization.
+One is to merge two GEMMs sharing the same operand into a single GEMM.
+The other one is to merge two GEMMs with the same shape into a batched GEMM.
+The GEMM merging optimization helps to increase hardware utilization and to reduce kernel launch overhead.
+
+## CPU GEMM/Convolution weight pre-packing optimization
+
+Support weight pre-packing optimization for convolution (calling onednn library) and GEMM (calling mkl/onednn/acl libraries) operations.
+
+## Convolution layout optimization and transpose elimination
+
+Support to transform the layout of convolution operator to the friendliest format on the specific device (i.e., either CPU or GPU).
+Most of the introduced transpose operators can be eliminated in a following transpose-simplifier pass.
+
+## Other optimizations
+* Optimize the schedule selection strategy for reduce operator on GPU to enhance thread-level-parallelism.
+* Algebraic simplification for operators like power.
+* Support to fuse splat constant operator with its consumers, reducing memory access overhead.
+Refer to [issue](https://github.com/alibaba/BladeDISC/issues/113).
+
+# Function Enhancement
+
+## CPU end-to-end optimization
+
+Support end-to-end optimization for X86 and AArch64 CPUs.
+
+## TorchBlade/TensorFlowBlade clustering and optimizing with TensorRT
+
+According to the supported operators of TensorRT, cluster sub-graphs and apply TensorRT optimization for both TensorFlow and PyTorch models.
+
+## Accelerating PyTorch Training
+
+Release PoC version for accelerating PyTorch training via Disc + Lazy Tensor Core,
+referring to the related [issue](https://github.com/alibaba/BladeDISC/issues/156) and [design doc](https://github.com/alibaba/BladeDISC/blob/main/docs/design/ltc_disc.md).
+
+## Shape analysis and simplifier enhancement
+
+Enhance the shape equality analysis according to the dimension values.
+Add the function to analyze the collapse and expand relationship between dimensions,
+which helps to identify the dimension mapping between input and output values of reshape operator.
+This is the basic function to support GPU stitch fusion.
+
+## Codegen support for int8 datatype
+
+Support int8 datatype for the code generation of memory-intensive operators (e.g., element-wise, reduce operators).
+
+# Toolchain Support and Process Optimization
+
+## Replay tool
+Support to dump clusters and the corresponding input data, based on which developers can replay the execution.
+It is effective to help debugging and tuning.
+Refer to [issue](https://github.com/alibaba/BladeDISC/issues/76).
+
+## CI optimization
+Enhance the CI process of BladeDISC repo, which helps the people from community to contribute to BladeDISC more conveniently and efficiently.
+
+## TorchBlade bazel build
+Migrate TorchBlade's compilation toolchain from the original CMake to bazel, enhancing maintainability.
+
+# Other
+
+## Example preparation
+
+Prepare a set of commonly used models as the examples for BladeDISC.
+Compare the performance of BladeDISC with TensorRT, XLA and ONNX Runtime (ORT) upon the examples.
+
+## Community TF rebase
+
+Rebase to TensorFlow codebase for BladeDISC according to the newest community code.
+
+## Code maintenance
+
+Continuous bug fixing and code refactoring.
+

pytorch_blade/release_version.py

@@ -9,4 +9,4 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-__version__ = '0.0.0'
+__version__ = '0.2.0'

tao/python/blade_disc_tf/_version.py

@@ -9,4 +9,4 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-__version__ = '0.1.0'
+__version__ = '0.2.0'

tao/setup.py

@@ -26,7 +26,7 @@
 # Package meta-data.
 NAME_PREFIX = 'blade-disc'
 DESCRIPTION = 'TensorFlow wrapper for Blade DISC compiler.'
-URL = 'https://github.com/pai_disc/aicompiler'
+URL = 'https://https://github.com/alibaba/BladeDISC'
 EMAIL = '[email protected]'
 AUTHOR = 'Zhu Kai'
 REQUIRES_PYTHON = '>=3.6.0'

tensorflow_blade/version.py

@@ -9,4 +9,4 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-__version__ = "0.0.0"
+__version__ = "0.2.0"