Merge Navi changes for int8 unit tests and verification mlir issues (#2393)

Ensure unique module name for MLIR standalone ops (#2360)
Cherry Pick ASAN build excluding additional bin files -1839 #2370
Use an older version of numpy for compatibility with Python3.6 #2369
Add space after rate message
Fix wrong size check when axes not present for slice (#2270)
Updated Changelog to document latest release work (#2363)
Run simplify_qdq first before optimize_module (#2387)
fix unit tests and verification issues on navi
Set numpy to 1.21.6 and disable py3.6 test
Release notes & changelog updates (#2395)

CHANGELOG.md

-# Change Log for MIGraphX
+# Changelog for MIGraphX
 
-Full documentation for MIGraphX is available at [MIGraphX Documentation](https://rocmdocs.amd.com/projects/AMDMIGraphX/en/latest/).
+Full documentation for MIGraphX is available at
+[https://rocmdocs.amd.com/projects/AMDMIGraphX/en/latest/](https://rocmdocs.amd.com/projects/AMDMIGraphX/en/latest/).
 
 ## MIGraphX 2.8 for ROCm 6.0.0
-### Added
-- Support for MI300 GPUs
-- Support for TorchMIGraphX via PyTorch
-- Boosted overall performance by integrating rocMLIR 
-- INT8 support for ONNX Runtime
-- Support for ONNX version 1.14.1
-- Added operators Qlinearadd, QlinearGlobalAveragePool, Qlinearconv, Shrink, CastLike, and RandomUniform operators
-- Added an error message when gpu_targets is not set when compiling migraphx
-- Added parameter to set tolerances with migraphx-driver verify 
-- Added support for MXR files >4 GB 
-- Added MIGRAPHX_TRACE_MLIR flag
-- BETA added capability to use ROCm Composable Kernels via environment variable MIGRAPHX_ENABLE_CK=1
+
+### Additions
+
+* Support for MI300 GPUs
+* Support for TorchMIGraphX via PyTorch
+* Boosted overall performance by integrating rocMLIR
+* INT8 support for ONNX Runtime
+* Support for ONNX version 1.14.1
+* Added new operators: `Qlinearadd`, `QlinearGlobalAveragePool`, `Qlinearconv`, `Shrink`, `CastLike`,
+  and `RandomUniform`
+* Added an error message for when `gpu_targets` is not set during MIGraphX compilation
+* Added parameter to set tolerances with `migraphx-driver` verify
+* Added support for MXR files > 4 GB
+* Added `MIGRAPHX_TRACE_MLIR` flag
+* BETA added capability for using ROCm Composable Kernels via the `MIGRAPHX_ENABLE_CK=1`
+  environment variable
 
 ### Optimizations
-- Improved performance support for INT8
-- Improved time percision while benchmarking candidate kernels from CK or MLIR 
-- Remove contiguous from reshape parsing
-- Updated ConstantOfShape operator to support Dynamic Batch
-- Simplifies dynamic shapes related operators to their static versions if possible
-- Improved debugging tools for accuracy issues
-- Print warning about miopen_fusion while generating mxr 
-- General reduction in system memory usage during model compilation
-- Created additional fusion opportunities during model compilation
-- Improved debugging for matchers
-- Improved general debug messages 
-
-### Fixed
-- Fixed scatter operator for nonstandard shapes with some models from ONNX Model Zoo
-- Provided a compile option to improve accuracy of some models by disabling Fast-Math
-- Improved layernorm + pointwise fusion matching to ignore arguments order
-- Fixed accuracy issue with ROIAlign operator 
-- Fixed Trilu operator computation logic
-- Fixed support for the DETR model 
-
-### Changed
-- Changed migraphx version to 2.8
-- Extracted test packages as its own separate deb file when building migraphx from source
-
-### Removed
-- Removed building Python 2.7 bindings
 
+* Improved performance support for INT8
+* Improved time precision while benchmarking candidate kernels from CK or MLIR
+* Removed contiguous from reshape parsing
+* Updated the `ConstantOfShape` operator to support Dynamic Batch
+* Simplified dynamic shapes-related operators to their static versions, where possible
+* Improved debugging tools for accuracy issues
+* Included a print warning about `miopen_fusion` while generating `mxr`
+* General reduction in system memory usage during model compilation
+* Created additional fusion opportunities during model compilation
+* Improved debugging for matchers
+* Improved general debug messages
+
+### Fixes
+
+* Fixed scatter operator for nonstandard shapes with some models from ONNX Model Zoo
+* Provided a compile option to improve the accuracy of some models by disabling Fast-Math
+* Improved layernorm + pointwise fusion matching to ignore argument order
+* Fixed accuracy issue with `ROIAlign` operator
+* Fixed computation logic for the `Trilu` operator
+* Fixed support for the DETR model
+
+### Changes
+
+* Changed MIGraphX version to 2.8
+* Extracted the test packages into a separate deb file when building MIGraphX from source
+
+### Removals
+
+* Removed building Python 2.7 bindings
 
 ## MIGraphX 2.7 for ROCm 5.7.0
-### Added
-- Enabled hipRTC to not require dev packages for migraphx runtime and allow the ROCm install to be in a different directory than it was during build time
-- Add support for multi-target execution
-- Added Dynamic Batch support with C++/Python APIs
-- Add migraphx.create_argument to python API
-- Added dockerfile example for Ubuntu 22.04
-- Add TensorFlow supported ops in driver similar to exist onnx operator list
-- Add a MIGRAPHX_TRACE_MATCHES_FOR env variable to filter the matcher trace
-- Improved debugging by printing max,min,mean and stddev values for TRACE_EVAL = 2
-- use fast_math flag instead of ENV flag for GELU
-- Print message from driver if offload copy is set for compiled program
+
+### Additions
+
+* hipRTC no longer requires dev packages for MIGraphX runtime and allows the ROCm install to be in a
+   different directory than build time
+* Added support for multi-target execution
+* Added Dynamic Batch support with C++/Python APIs
+* Added `migraphx.create_argument` to Python API
+* Added dockerfile example for Ubuntu 22.04
+* Added TensorFlow supported ops in driver similar to exist onnx operator list
+* Added a MIGRAPHX_TRACE_MATCHES_FOR env variable to filter the matcher trace
+* Improved debugging by printing max,min,mean and stddev values for TRACE_EVAL = 2
+* You can now use the ` fast_math` flag instead of `ENV` for GELU
+* Print message from driver if offload copy is set for compiled program
+
 ### Optimizations
-- Optimized for ONNX Runtime 1.14.0
-- Improved compile times by only building for the GPU on the system
-- Improve performance of pointwise/reduction kernels when using NHWC layouts
-- Load specific version of the migraphx_py library
-- Annotate functions with the block size so the compiler can do a better job of optimizing 
-- Enable reshape on nonstandard shapes
-- Use half HIP APIs to compute max and min
-- Added support for broadcasted scalars to unsqueeze operator
-- Improved multiplies with dot operator
-- Handle broadcasts across dot and concat
-- Add verify namespace for better symbol resolution
-### Fixed
-- Resolved accuracy issues with FP16 resnet50
-- Update cpp generator to handle inf from  float
-- Fix assertion error during verify and make DCE work with tuples
-- Fix convert operation for NaNs
-- Fix shape typo in API test
-- Fix compile warnings for shadowing variable names
-- Add missing specialization for the `nullptr` for the hash function
-### Changed
-- Bumped version of half library to 5.6.0
-- Bumped CI to support rocm 5.6
-- Make building tests optional
-- replace np.bool with bool as per numpy request
-### Removed
-- Removed int8x4 rocBlas calls due to deprecation
-- removed std::reduce usage since not all OS' support it
 
+* Optimized for ONNX Runtime 1.14.0
+* Improved compile times by only building for the GPU on the system
+* Improved performance of pointwise/reduction kernels when using NHWC layouts
+* Loaded specific version of the `migraphx_py` library
+* Annotated functions with the block size so the compiler can do a better job of optimizing
+* Enabled reshape on nonstandard shapes
+* Used half HIP APIs to compute max and min
+* Added support for broadcasted scalars to unsqueeze operator
+* Improved multiplies with dot operator
+* Handled broadcasts across dot and concat
+* Added verify namespace for better symbol resolution
+
+### Fixes
+
+* Resolved accuracy issues with FP16 resnet50
+* Updated cpp generator to handle inf from float
+* Fixed assertion error during verify and made DCE work with tuples
+* Fixed convert operation for NaNs
+* Fixed shape typo in API test
+* Fixed compile warnings for shadowing variable names
+* Added missing specialization for the `nullptr` hash function
+
+### Changees
+
+* Bumped version of half library to 5.6.0
+* Bumped CI to support ROCm 5.6
+* Made building tests optional
+* Replaced `np.bool` with `bool` per NumPy request
+
+### Removals
+
+* Removed int8x4 rocBlas calls due to deprecation
+* Removed `std::reduce` usage because not all operating systems support it
 
 ## MIGraphX 2.5 for ROCm 5.5.0
-### Added
-- Y-Model feature to store tuning information with the optimized model
-- Added Python 3.10 bindings 
-- Accuracy checker tool based on ONNX Runtime
-- ONNX Operators parse_split, and Trilu 
-- Build support for ROCm MLIR
-- Added migraphx-driver flag to print optimizations in python (--python)
-- Added JIT implementation of the Gather and Pad operator which results in better handling of larger tensor sizes.
+
+### Additions
+
+* Y-Model feature will store tuning information with the optimized model
+* Added Python 3.10 bindings
+* Accuracy checker tool based on ONNX runtime
+* ONNX operators parse_split, and Trilu
+* Build support for ROCm MLIR
+* Added the `migraphx-driver` flag to print optimizations in Python (--python)
+* Added JIT implementation of the Gather and Pad operators, which results in better handling for
+  larger tensor sizes
+
 ### Optimizations
-- Improved performance of Transformer based models
-- Improved performance of the Pad, Concat, Gather, and Pointwise operators
-- Improved onnx/pb file loading speed
-- Added general optimize pass which runs several passes such as simplify_reshapes/algebra and DCE in loop.
-### Fixed
-- Improved parsing Tensorflow Protobuf files 
-- Resolved various accuracy issues with some onnx models
-- Resolved a gcc-12 issue with mivisionx
-- Improved support for larger sized models and batches
-- Use --offload-arch instead of --cuda-gpu-arch for the HIP compiler
-- Changes inside JIT to use float accumulator for large reduce ops of half type to avoid overflow.
-- Changes inside JIT to temporarily use cosine to compute sine function.
-### Changed
-- Changed version/location of 3rd party build dependencies to pick up fixes
+
+* Improved performance of Transformer-based models
+* Improved performance of the `Pad`, `Concat`, `Gather`, and `Pointwise` operators
+* Improved ONNX/pb file loading speed
+* Added a general optimize pass that runs several passes, such as `simplify_reshapes`, algebra, and DCE
+  in a loop
+
+### Fixes
+
+* Improved parsing for TensorFlow Protobuf files
+* Resolved various accuracy issues with some ONNX models
+* Resolved a gcc-12 issue with MIVisionX
+* Improved support for larger sized models and batches
+* Use `--offload-arch` instead of `--cuda-gpu-arch` for the HIP compiler
+* Changes inside JIT to use float accumulator for large reduce ops of half type to avoid overflow
+* Changes inside JIT to temporarily use cosine to compute sine function
+
+### Changes
+
+* Changed version and location of third-party build dependencies in order to pick up fixes

README.md

 # AMD MIGraphX
 
-AMD MIGraphX is AMD's graph inference engine that accelerates machine learning model inference. AMD MIGraphX can be used by
-installing binaries directly or building from source code.
+AMD MIGraphX is AMD's graph inference engine, which accelerates machine learning model inference.
+To use MIGraphX, you can install the binaries or build from source code. Refer to the following sections
+for Ubuntu installation instructions (we'll provide instructions for other Linux distributions in the future).
 
-In the following, instructions of how to build and install MIGraphX are described with Ubuntu as the OS
-(Instructions of installation on other Linux OSes will come later). Note that all the following instructions assume 
-ROCm has been installed successfully. ROCm installation instructions are explained in the [ROCm installation
-guide](https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation-Guide.html).
+```note
+You must [install ROCm](https://rocm.docs.amd.com/en/latest/deploy/linux/quick_start.html) before
+installing MIGraphX.
+```
 
 ## Installing from binaries
-With ROCm installed correctly, MIGraphX binaries can be installed on Ubuntu with the following command:
-```
+
+Install binaries using:
+
+```bash
 sudo apt update && sudo apt install -y migraphx
 ```
-then the header files and libs are installed under `/opt/rocm-<version>`, where `<version>` is the ROCm version.
+
+Header files and libraries are installed under `/opt/rocm-<version>`, where `<version>` is the ROCm
+version.
 
 ## Building from source
 
-There are three ways to build the MIGraphX sources. 
-* [Use the ROCm build tool](#use-the-rocm-build-tool-rbuild)
-    
-    This approach uses [rbuild](https://github.com/RadeonOpenCompute/rbuild) to install the prerequisites and
-build the libs with just one command. 
+You have three options for building from source:
 
-* [Use cmake](#use-cmake-to-build-migraphx)
-    
-    This approach uses a script to install the prerequisites, then use cmake to build the source.
-      
-* [Use docker](#use-docker)
-    
-    This approach builds a docker image with all prerequisites installed, then build the MIGraphX sources inside a docker container. 
+* [ROCm build tool](#use-the-rocm-build-tool-rbuild): Uses
+  [rbuild](https://github.com/RadeonOpenCompute/rbuild) to install prerequisites, then you can build
+  the libraries with a single command.
 
-In the following, we will first list the prerequisites required to build MIGraphX source code, then describe 
-each of the three approaches.
+* [CMake](#use-cmake-to-build-migraphx): Uses a script to install prerequisites, then you can use
+  CMake to build the source.
 
-### List of prerequisites
-The following is a list of prerequisites required to build MIGraphX source. 
+* [Docker](#use-docker): Builds a Docker image with all prerequisites installed, then you can build the
+  MIGraphX sources inside a Docker container.
 
-* [ROCm cmake modules](https://github.com/RadeonOpenCompute/rocm-cmake) **required**
+### Build prerequisites
+
+The following is a list of prerequisites for building MIGraphX.
+
+* [ROCm CMake modules](https://github.com/RadeonOpenCompute/rocm-cmake) **required**
 * [MIOpen](https://github.com/ROCmSoftwarePlatform/MIOpen) for running on the GPU
 * [rocBLAS](https://github.com/ROCmSoftwarePlatform/rocBLAS) for running on the GPU
 * [HIP](https://github.com/ROCm-Developer-Tools/HIP) for running on the GPU
-* [Protobuf](https://github.com/google/protobuf) for reading [onnx](https://github.com/onnx/onnx) files
-* [Half](http://half.sourceforge.net/) - IEEE 754-based half-precision floating point library
-* [pybind11](https://pybind11.readthedocs.io/en/stable/) - for python bindings
-* [JSON](https://github.com/nlohmann/json) - for model serialization to json string format
-* [MessagePack](https://msgpack.org/index.html) - for model serialization to binary format
-* [SQLite3](https://www.sqlite.org/index.html) - to create database of kernels' tuning information or execute queries on existing database
+* [Protobuf](https://github.com/google/protobuf) for reading [onnx](https://github.com/onnx/onnx)
+  files
+* [Half](http://half.sourceforge.net/), an IEEE 754-based half-precision floating point library
+* [pybind11](https://pybind11.readthedocs.io/en/stable/) for python bindings
+* [JSON](https://github.com/nlohmann/json) for model serialization to json string format
+* [MessagePack](https://msgpack.org/index.html) for model serialization to binary format
+* [SQLite3](https://www.sqlite.org/index.html) to create database of kernels' tuning information or run queries on existing database
 
-#### Use the ROCm build tool [rbuild](https://github.com/RadeonOpenCompute/rbuild).
+### Use the ROCm build tool [rbuild](https://github.com/RadeonOpenCompute/rbuild).
 
-In this approach, we use the [rbuild](https://github.com/RadeonOpenCompute/rbuild) build tool to
-build MIGraphX. The specific steps are as follows:
+1. Install `rocm-cmake`, `pip3`, `rocblas`, and `miopen-hip`:
 
-1) Install rocm-cmake, pip3, rocblas, and miopen-hip with the command
+    ```bash
+    sudo apt install -y rocm-cmake python3-pip rocblas miopen-hip
+    ```
 
-```
-sudo apt install -y rocm-cmake python3-pip rocblas miopen-hip
-```
+2. Install [rbuild](https://github.com/RadeonOpenCompute/rbuild) (sudo may be required):
 
-2) Install [rbuild](https://github.com/RadeonOpenCompute/rbuild) (sudo may be required here.)
+    ```bash
+    pip3 install https://github.com/RadeonOpenCompute/rbuild/archive/master.tar.gz
+    ```
 
-```
-pip3 install https://github.com/RadeonOpenCompute/rbuild/archive/master.tar.gz
-```
+3. Build MIGraphX source code:
 
-3) Build MIGraphX source code
+    ```bash
+    rbuild build -d depend -B build
+    ```
 
-```
-rbuild build -d depend -B build
+Once completed, all prerequisites are in the `depend` folder and MIGraphX is in the `build` directory.
+
+```note
+If you get an `rbuild: command not found` error, it's because `rbuild` is installed in `$HOME/.local/bin`,
+which is not in `PATH`. You can either export PATH as `export PATH=$HOME/.local/bin:$PATH` to add
+the folder to `PATH`, or add the option `--prefix /usr/local` in the pip3 command when installing `rbuild`.
 ```
 
-then all the prerequisites are in the folder `depend`, and MIGraphX is built in the `build` directory.
+### Use CMake to build MIGraphX
 
-Also note that you may meet the error of `rbuild: command not found`. It is because rbuild is installed 
-at `$HOME/.local/bin`, which is not in `PATH`. You can either export PATH as `export PATH=$HOME/.local/bin:$PATH` 
-to add the folder to `PATH` or add the option `--prefix /usr/local` in the pip3 command when installing rbuild.
+1. Install the prerequisites:
 
-#### Use cmake to build MIGraphX
+    ```bash
+    rbuild prepare -d depend
+    ```
 
-If using this approach, we need to install the prerequisites, configure the cmake, and then build the source.
+    This puts all the prerequisites are in `depend` the folder. They can be used in the `cmake`
+    configuration as `-DCMAKE_PREFIX_PATH=depend`.
 
-##### Installing the prerequisites
+    If you have sudo access, as an alternative to the `rbuild` command, you can install the prerequisites
+    in the same way as a Dockerfile, by calling `./tools/install_prereqs.sh`.
 
-For convenience, the prerequisites can be built automatically with rbuild as:
+    By default, all prerequisites are installed at the default location (`/usr/local`) and are accessible by all
+    users. For the default location, `sudo` is required to run the script. You can also specify a different
+    location using `./tools/install_prereqs.sh $custom_location`.
 
-```
-rbuild prepare -d depend
-```
+2. Go to the project folder and create a `build` directory:
 
-then all the prerequisites are in the folder `depend`, and they can be used in the `cmake` configuration
-as `-DCMAKE_PREFIX_PATH=depend`.
+    ```bash
+    mkdir build
+    cd build
+    ```
 
-If you have sudo access, as an alternative to the rbuild command, you can install the prerequisites just 
-like in the dockerfile by calling `./tools/install_prereqs.sh`.
+3. Configure CMake. If the prerequisites are installed at the default location `/usr/local`, use:
 
-(Note that this script is for Ubuntu. By default, all prerequisites are installed at the default location `/usr/local` 
-and are accessible by all users. For the default location, `sudo` is required to run the script.
-You can also specify a location at which the prerequisites are installed with `./tools/install_prereqs.sh $your_loc`.)
+    ```bash
+    CXX=/opt/rocm/llvm/bin/clang++ cmake ..
+    ```
 
-##### Building MIGraphX source and install libs
+    Otherwise, you need to set `-DCMAKE_PREFIX_PATH=$your_loc` to configure CMake.
 
-With the above prerequisites installed, we can build source as:
+4. Build MIGraphX source code:
 
-1) Go to the project folder and create a `build` directory:
+    ```cpp
+    make -j$(nproc)
+    ```
 
+    You can verify this using:
 
-```
-mkdir build
-cd build
-```
+    ```cpp
+    make -j$(nproc) check
+    ```
 
-2) Configure the cmake. If the prerequisites are installed at the default location `/usr/local`, the command is:
+5. Install MIGraphX libraries:
 
-```
-CXX=/opt/rocm/llvm/bin/clang++ cmake ..
-```
-Otherwise, you need to set `-DCMAKE_PREFIX_PATH=$your_loc` to configure the cmake. 
+    ```cpp
+    make install
+    ```
 
-3) Build MIGraphX source code
+### Use Docker
 
-```
-make -j$(nproc)
-```
+The easiest way to set up the development environment is to use Docker.
 
-Correctness can be verified as:
+1. With the Dockerfile, build a Docker image:
 
-```
-make -j$(nproc) check
-```
+    ```bash
+        docker build -t migraphx .
+    ```
 
-MIGraphX libs can be installed as:
+2. Enter the development environment using `docker run`:
 
-```
-make install
-```
+    ```bash
+        docker run --device='/dev/kfd' --device='/dev/dri' -v=`pwd`:/code/AMDMIGraphX -w /code/AMDMIGraphX --group-add video -it migraphx
+    ```
 
-#### Use docker
+3. In the Docker container, all required prerequisites are already installed, so you can go to the folder
+    `/code/AMDMIGraphX` and follow the steps (starting from 2) in the
+    [Use CMake to build MIGraphX](#use-cmake-to-build-migraphx).
 
-The easiest way to setup the development environment is to use docker. With the dockerfile, you can build a docker image as:
+## Using the MIGraphX Python module
 
-    docker build -t migraphx .
+To use MIGraphX's Python module, you can set `PYTHONPATH` or use the `.deb` package:
 
-Then to enter the developement environment use `docker run`:
+* Setting `PYTHONPATH`:
 
-    docker run --device='/dev/kfd' --device='/dev/dri' -v=`pwd`:/code/AMDMIGraphX -w /code/AMDMIGraphX --group-add video -it migraphx
+    ```bash
+    export PYTHONPATH=/opt/rocm/lib:$PYTHONPATH
+    ```
 
-In the docker container, all the required prerequisites are already installed, so users can just go to the folder 
-`/code/AMDMIGraphX` and follow the steps in the above [Build MIGraphX source and install
-libs](#building-migraphx-source-and-install-libs)
-section to build MIGraphX source.
+* Creating the `deb` package:
 
-### Using MIGraphX Python Module
-To use MIGraphX's Python module, please either set `PYTHONPATH` or use `.deb` package as explained below:
+    ```bash
+    make package
+    ```
 
-- Setting `PYTHONPATH` :
-```
-export PYTHONPATH=/opt/rocm/lib:$PYTHONPATH
-```
-- Creating and installing the package:
+    This provides the path for .deb package.
 
-To create deb package:
-```
-make package
-```
-This will provide the path of .deb package.
+    To install:
 
-To install:
-```
-dpkg -i <path_to_deb_file>
-```
+    ```bash
+    dpkg -i <path_to_deb_file>
+    ```
 
-### Calling MIGraphX APIs
-To use MIGraphX's C/C++ API in your cmake project, we need to set `CMAKE_PREFIX_PATH` to the MIGraphX
-installation location and then do 
-```
+## Calling MIGraphX APIs
+
+To use MIGraphX's C/C++ API in your CMake project, you must set `CMAKE_PREFIX_PATH` to the
+MIGraphX installation location and run:
+
+```bash
 find_package(migraphx)
 target_link_libraries(myApp migraphx::c)
 ```
-Where `myApp` is the cmake target in your project.
+
+Where `myApp` is the CMake target in your project.
 
 ## Building for development
 
-Using rbuild, the dependencies for development can be installed with:
+Using `rbuild`, you can install the dependencies for development with:
 
-```
+```bash
 rbuild develop
 ```
 
-This will install the dependencies for development into the `deps` directory and
-configure `cmake` to use those dependencies in the `build` directory. These
-directories can be changed by passing the `--deps-dir` and `--build-dir` flags
-to `rbuild` command:
+This installs development dependencies in the `deps` directory and configures `cmake` to use those
+dependencies in the `build` directory. You can change these directories by passing the `--deps-dir` and
+`--build-dir` flags to the `rbuild` command:
 
-```
+```bash
 rbuild develop --build-dir build_rocm_55 --deps-dir /home/user/deps_dir
 ```
 
@@ -223,12 +227,12 @@ Depending on your setup `sudo` may be required for the pip install.
 
 All the code is formatted using clang-format. To format a file, use:
 
-```
+```clang
 clang-format-10 -style=file -i <path-to-source-file>
 ```
 
 Also, githooks can be installed to format the code per-commit:
 
-```
+```bash
 ./.githooks/install
 ```

requirements.txt

@@ -29,4 +29,4 @@ pybind/pybind11@d159a563383d10c821ba7b2a71905d1207db6de4 --build
 msgpack/msgpack-c@cpp-3.3.0 -DMSGPACK_BUILD_TESTS=Off
 sqlite3@3.17 -DCMAKE_POSITION_INDEPENDENT_CODE=On
 ROCmSoftwarePlatform/composable_kernel@70eefcf4f263aa5c25f3c9ff0db8f6f199ef0fb9 -DCK_BUILD_JIT_LIB=On -DCMAKE_POSITION_INDEPENDENT_CODE=On
-ROCmSoftwarePlatform/rocMLIR@507bb94ce7873786486d296ec81d2eadaab49003 -DBUILD_FAT_LIBROCKCOMPILER=On
+ROCmSoftwarePlatform/rocMLIR@3700afd2564e21267a4d1fd8f1f80465f45daa93 -DBUILD_FAT_LIBROCKCOMPILER=On
\ No newline at end of file

src/quantization.cpp

@@ -147,8 +147,8 @@ void quantize_int8(program& prog,
 
     run_passes(prog,
                {quantize_int8_pass{ins_names, *int8_quant_params},
-                optimize_module{},
                 simplify_qdq{},
+                optimize_module{},
                 dead_code_elimination{}});
 }
 

test/py/CMakeLists.txt

@@ -40,7 +40,7 @@ function(add_py_venv_fixture FIXTURE_NAME VIRTUAL_ENV_DIR REQUIREMENTS_FILE)
         set(PYTHON_EXECUTABLE ${PYTHON_${PYTHON_VERSION}_EXECUTABLE})
 
         if(NOT TEST py_${PYTHON_VERSION}_${FIXTURE_NAME}_initialize_env)
-            if (NOT (${FIXTURE_NAME} STREQUAL "onnx" AND ${PYTHON_VERSION} STREQUAL ${PYTHON_VERSION_TO_DISABLE_ONNX}))
+            if (NOT (${PYTHON_VERSION} STREQUAL ${PYTHON_VERSION_TO_DISABLE_ONNX}))
                 add_test(NAME py_${PYTHON_VERSION}_${FIXTURE_NAME}_initialize_env COMMAND ${PYTHON_EXECUTABLE} -m venv ${VIRTUAL_ENV_DIR}/${PYTHON_VERSION} --clear)
                 set_tests_properties(py_${PYTHON_VERSION}_${FIXTURE_NAME}_initialize_env PROPERTIES FIXTURES_SETUP ${FIXTURE_NAME}_${PYTHON_VERSION}_INIT_VENV)
                 set(PYTHON_EXECUTABLE ${VIRTUAL_ENV_DIR}/${PYTHON_VERSION}/bin/python)
@@ -61,8 +61,12 @@ function(add_py_test NAME SCRIPT FIXTURE_NAME VENV_DIR)
             "PYTHONMALLOC=debug"
             "MALLOC_CHECK_=3"
         )
-        set(PYTHON_EXECUTABLE ${VENV_DIR}/${PYTHON_VERSION}/bin/python)
-        if(NOT (${FIXTURE_NAME} STREQUAL "onnx" AND ${PYTHON_VERSION} STREQUAL ${PYTHON_VERSION_TO_DISABLE_ONNX}))
+        if(MIGRAPHX_DISABLE_VIRTUAL_ENV)
+            set(PYTHON_EXECUTABLE ${PYTHON_${PYTHON_VERSION}_EXECUTABLE})
+        else()
+            set(PYTHON_EXECUTABLE ${VENV_DIR}/${PYTHON_VERSION}/bin/python)
+        endif()
+        if(NOT ${PYTHON_VERSION} STREQUAL ${PYTHON_VERSION_TO_DISABLE_ONNX})
             add_test(
                 NAME test_py_${PYTHON_VERSION}_${NAME}
                 COMMAND ${ENV_COMMAND} ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/${SCRIPT} ${ARGN})

test/py/requirements-onnx.txt

@@ -26,4 +26,4 @@ onnx==1.14.1
 protobuf==3.20.2
 numpy==1.21.6
 packaging==23.0
-pytest==6.0.1
+pytest==6.0.1
\ No newline at end of file

test/py/requirements.txt

@@ -22,4 +22,4 @@
 # THE SOFTWARE.
 #####################################################################################
 
-numpy==1.19.5
+numpy==1.21.6