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[NFC] Change ONNF based names to ONNX-MLIR (#32) 

* Rename onnf to onnx-mlir.

* Change workspace name.
This commit is contained in:
Gheorghe-Teodor Bercea 2020-03-17 09:16:33 -04:00 committed by GitHub
parent c25831094e
commit 1622b9f161
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GPG Key ID: 4AEE18F83AFDEB23
41 changed files with 204 additions and 204 deletions
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30
.buildbot/p9.sh
View File

@ -3,9 +3,9 @@
# Exit on error:
set -e
# Check for required env variables ONNF_DEP_DIR, LLVM_PROJECT_ROOT
if [[ -z "${ONNF_DEP_DIR}" ]]; then
echo "ONNF_DEP_DIR env var is missing."
# Check for required env variables ONNX_MLIR_DEP_DIR, LLVM_PROJECT_ROOT
if [[ -z "${ONNX_MLIR_DEP_DIR}" ]]; then
echo "ONNX_MLIR_DEP_DIR env var is missing."
exit 1
fi
@ -14,11 +14,11 @@ if [[ -z "${LLVM_PROJECT_ROOT}" ]]; then
exit 1
fi
# Set up env variables to expose onnf dependencies:
export PATH=$ONNF_DEP_DIR/bin:$PATH
export LD_LIBRARY_PATH=$ONNF_DEP_DIR/lib:$ONNF_DEP_DIR/lib64:
export CPATH=$ONNF_DEP_DIR/include:$CPATH
export PATH=$ONNF_DEP_DIR/bin:$PATH
# Set up env variables to expose onnx-mlir dependencies:
export PATH=$ONNX_MLIR_DEP_DIR/bin:$PATH
export LD_LIBRARY_PATH=$ONNX_MLIR_DEP_DIR/lib:$ONNX_MLIR_DEP_DIR/lib64:
export CPATH=$ONNX_MLIR_DEP_DIR/include:$CPATH
export PATH=$ONNX_MLIR_DEP_DIR/bin:$PATH
# Set up mock installation path within current workspace:
export INSTALL_PATH=$WORKSPACE/INSTALL_PATH
@ -28,18 +28,18 @@ export LD_LIBRARY_PATH=$INSTALL_PATH/lib:$INSTALL_PATH/lib64:$LD_LIBRARY_PATH
export CPATH=$INSTALL_PATH/include:$CPATH
# Create virtual environment specific to the current build instance:
conda create -n onnf_conda_workspace_"${BUILD_NUMBER}" python=3.7 numpy
source activate onnf_conda_workspace_"${BUILD_NUMBER}"
conda create -n onnx_mlir_conda_workspace_"${BUILD_NUMBER}" python=3.7 numpy
source activate onnx_mlir_conda_workspace_"${BUILD_NUMBER}"
# Create build directory and generate make files:
mkdir build && cd build
CC=$ONNF_DEP_DIR/bin/gcc \
CXX=$ONNF_DEP_DIR/bin/g++ \
BOOST_ROOT=$ONNF_DEP_DIR \
CC=$ONNX_MLIR_DEP_DIR/bin/gcc \
CXX=$ONNX_MLIR_DEP_DIR/bin/g++ \
BOOST_ROOT=$ONNX_MLIR_DEP_DIR \
LLVM_SRC=$LLVM_PROJECT_ROOT/llvm \
LLVM_BUILD=$LLVM_PROJECT_ROOT/build \
cmake3 -DONNF_ENABLE_MODEL_TEST_CPP=ON \
-DONNF_ENABLE_BENCHMARK=ON \
cmake3 -DONNX_MLIR_ENABLE_MODEL_TEST_CPP=ON \
-DONNX_MLIR_ENABLE_BENCHMARK=ON \
-DCMAKE_INSTALL_PREFIX="$INSTALL_PATH" \
..

8
.buildbot/z13.sh
View File

@ -34,10 +34,10 @@ cmake -DCMAKE_C_COMPILER=$CC \
-DCMAKE_CXX_COMPILER=$CXX \
-DCMAKE_VERBOSE_MAKEFILE=ON \
-DCMAKE_INSTALL_PREFIX=$INSTALL_PATH \
-DONNF_ENABLE_NODE_TEST_JAVA=ON \
-DONNF_ENABLE_NODE_TEST_JNI=ON \
-DONNF_ENABLE_NODE_TEST_CPP=OFF \
-DONNF_TARGET_ARCH=z13 ..
-DONNX_MLIR_ENABLE_NODE_TEST_JAVA=ON \
-DONNX_MLIR_ENABLE_NODE_TEST_JNI=ON \
-DONNX_MLIR_ENABLE_NODE_TEST_CPP=OFF \
-DONNX_MLIR_TARGET_ARCH=z13 ..
make -j "$(nproc)" install
ctest -j "$(nproc)"

20
.circleci/config.yml
View File

@ -10,11 +10,11 @@ jobs:
name: Installing GCC, CMake, Ninja, Protobuf
command: sudo apt-get update && sudo apt-get install -y gcc g++ cmake ninja-build protobuf-compiler
- checkout:
path: ONNF
path: onnx-mlir
- run:
name: Pull Submodules
command: |
cd ONNF
cd onnx-mlir
git submodule update --init --recursive
# Use cached mlir installation if possible.
- restore_cache:
@ -26,29 +26,29 @@ jobs:
# mlir-opt executable exists.
if [ ! -f llvm-project/build/bin/mlir-opt ]; then
export MAKEFLAGS=-j4
source ONNF/utils/install-mlir.sh
source onnx-mlir/utils/install-mlir.sh
fi
- save_cache:
key: V9-LLVM-PROJECT-{{ arch }}
paths:
- llvm-project
- run:
name: Install ONNF
command: source ONNF/utils/install-onnf.sh
name: Install ONNX MLIR
command: source onnx-mlir/utils/install-onnx-mlir.sh
- run:
name: Run End-To-End Tests
command: |
sudo pip install -q -e ./ONNF/third_party/onnx
cd ONNF/build
sudo pip install -q -e ./onnx-mlir/third_party/onnx
cd onnx-mlir/build
cmake --build . --target run-onnx-backend-test
- run:
name: Run DocCheck
command: cd ONNF/build && cmake --build . --target check-doc
command: cd onnx-mlir/build && cmake --build . --target check-doc
- run:
name: Ensure tablegen documentation is up-to-date
command: |
cd ONNF/build
cmake --build . --target onnf-doc
cd onnx-mlir/build
cmake --build . --target onnx-mlir-doc
# Check whether dialect documentation is up-to-date.
diff doc/Dialects ../doc/Dialects
- run:

6
CMakeLists.txt
View File

@ -6,13 +6,13 @@ endif()
# Require 3.3 and set policy CMP0057 for IN_LIST operator support
cmake_minimum_required(VERSION 3.3)
cmake_policy(SET CMP0057 NEW)
project(onnf)
project(onnx-mlir)
set(CMAKE_CXX_FLAGS_DEBUG "-g")
set(CMAKE_CXX_FLAGS_RELEASE "-O2 -DNDEBUG")
set(ONNF_SRC_ROOT "${CMAKE_CURRENT_SOURCE_DIR}")
set(ONNF_BIN_ROOT "${CMAKE_CURRENT_BINARY_DIR}")
set(ONNX_MLIR_SRC_ROOT "${CMAKE_CURRENT_SOURCE_DIR}")
set(ONNX_MLIR_BIN_ROOT "${CMAKE_CURRENT_BINARY_DIR}")
set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib)
set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/lib)
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)

24
MLIR.cmake
View File

@ -34,9 +34,9 @@ set(MLIR_SRC_INCLUDE_PATH ${LLVM_PROJ_SRC}/mlir/include)
set(MLIR_BIN_INCLUDE_PATH ${LLVM_PROJ_BUILD}/tools/mlir/include)
set(MLIR_TOOLS_DIR ${LLVM_PROJ_BUILD}/bin)
set(ONNF_TOOLS_DIR ${ONNF_BIN_ROOT}/bin)
set(ONNF_LIT_TEST_SRC_DIR ${CMAKE_SOURCE_DIR}/test/mlir)
set(ONNF_LIT_TEST_BUILD_DIR ${CMAKE_BINARY_DIR}/test/mlir)
set(ONNX_MLIR_TOOLS_DIR ${ONNX_MLIR_BIN_ROOT}/bin)
set(ONNX_MLIR_LIT_TEST_SRC_DIR ${CMAKE_SOURCE_DIR}/test/mlir)
set(ONNX_MLIR_LIT_TEST_BUILD_DIR ${CMAKE_BINARY_DIR}/test/mlir)
set(
MLIR_INCLUDE_PATHS
@ -182,12 +182,12 @@ function(whole_archive_link_mlir target)
whole_archive_link(${target} ${LLVM_PROJ_BUILD}/lib ${ARGN})
endfunction(whole_archive_link_mlir)
function(whole_archive_link_onnf target)
function(whole_archive_link_onnx_mlir target)
foreach(lib_target ${ARGN})
add_dependencies(${target} ${lib_target})
endforeach(lib_target)
whole_archive_link(${target} ${CMAKE_BINARY_DIR}/lib ${ARGN})
endfunction(whole_archive_link_onnf)
endfunction(whole_archive_link_onnx_mlir)
set(LLVM_CMAKE_DIR
"${LLVM_PROJ_BUILD}/lib/cmake/llvm"
@ -196,7 +196,7 @@ list(APPEND CMAKE_MODULE_PATH "${LLVM_CMAKE_DIR}")
include(AddLLVM)
include(TableGen)
function(onnf_tablegen ofn)
function(onnx_mlir_tablegen ofn)
tablegen(MLIR
${ARGV}
"-I${MLIR_SRC_INCLUDE_PATH}"
@ -214,14 +214,14 @@ set_property(TARGET mlir-tblgen
PROPERTY IMPORTED_LOCATION ${LLVM_PROJ_BUILD}/bin/mlir-tblgen)
set(MLIR_TABLEGEN_EXE mlir-tblgen)
# Add a dialect used by ONNF and copy the generated operation
# Add a dialect used by ONNX MLIR and copy the generated operation
# documentation to the desired places.
# c.f. https://github.com/llvm/llvm-project/blob/e298e216501abf38b44e690d2b28fc788ffc96cf/mlir/CMakeLists.txt#L11
function(add_onnf_dialect_doc dialect dialect_tablegen_file)
function(add_onnx_mlir_dialect_doc dialect dialect_tablegen_file)
# Generate Dialect Documentation
set(LLVM_TARGET_DEFINITIONS ${dialect_tablegen_file})
onnf_tablegen(${dialect}.md -gen-op-doc)
set(GEN_DOC_FILE ${ONNF_BIN_ROOT}/doc/Dialects/${dialect}.md)
onnx_mlir_tablegen(${dialect}.md -gen-op-doc)
set(GEN_DOC_FILE ${ONNX_MLIR_BIN_ROOT}/doc/Dialects/${dialect}.md)
add_custom_command(
OUTPUT ${GEN_DOC_FILE}
COMMAND ${CMAKE_COMMAND} -E copy
@ -229,7 +229,7 @@ function(add_onnf_dialect_doc dialect dialect_tablegen_file)
${GEN_DOC_FILE}
DEPENDS ${CMAKE_CURRENT_BINARY_DIR}/${dialect}.md)
add_custom_target(${dialect}DocGen DEPENDS ${GEN_DOC_FILE})
add_dependencies(onnf-doc ${dialect}DocGen)
add_dependencies(onnx-mlir-doc ${dialect}DocGen)
endfunction()
add_custom_target(onnf-doc)
add_custom_target(onnx-mlir-doc)

30
README.md
View File

@ -1,5 +1,5 @@
# ONNF
Open Neural Network Frontend : an ONNX frontend for MLIR.
# ONNX MLIR
The Open Neural Network Exchange implementation in MLIR.
[![CircleCI](https://circleci.com/gh/onnx/onnx-mlir/tree/master.svg?style=svg)](https://circleci.com/gh/onnx/onnx-mlir/tree/master)
@ -18,7 +18,7 @@ Firstly, install MLIR (as a part of LLVM-Project):
[same-as-file]: <> (utils/install-mlir.sh)
``` bash
git clone https://github.com/llvm/llvm-project.git
# Check out a specific branch that is known to work with ONNF.
# Check out a specific branch that is known to work with ONNX MLIR.
cd llvm-project && git checkout 076475713c236081a3247a53e9dbab9043c3eac2 && cd ..
mkdir llvm-project/build
cd llvm-project/build
@ -38,34 +38,34 @@ Two environment variables need to be set:
- LLVM_PROJ_SRC should point to the llvm-project src directory (e.g., llvm-project/).
- LLVM_PROJ_BUILD should point to the llvm-project build directory (e.g., llvm-project/build).
To build ONNF, use the following command:
To build ONNX-MLIR, use the following command:
[same-as-file]: <> ({"ref": "utils/install-onnf.sh", "skip-doc": 2})
[same-as-file]: <> ({"ref": "utils/install-onnx-mlir.sh", "skip-doc": 2})
```
git clone --recursive git@github.com:clang-ykt/ONNF.git
git clone --recursive git@github.com:onnx/onnx-mlir.git
# Export environment variables pointing to LLVM-Projects.
export LLVM_PROJ_SRC=$(pwd)/llvm-project/
export LLVM_PROJ_BUILD=$(pwd)/llvm-project/build
mkdir ONNF/build && cd ONNF/build
mkdir onnx-mlir/build && cd onnx-mlir/build
cmake ..
cmake --build . --target onnf
cmake --build . --target onnx-mlir
# Run FileCheck tests:
export LIT_OPTS=-v
cmake --build . --target check-mlir-lit
```
After the above commands succeed, an `onnf` executable should appear in the `bin` directory.
After the above commands succeed, an `onnx-mlir` executable should appear in the `bin` directory.
## Using ONNF
## Using ONNX MLIR
The usage of `onnf` is as such:
The usage of `onnx-mlir` is as such:
```
OVERVIEW: ONNF MLIR modular optimizer driver
OVERVIEW: ONNX MLIR modular optimizer driver
USAGE: onnf [options] <input file>
USAGE: onnx-mlir [options] <input file>
OPTIONS:
@ -75,7 +75,7 @@ Generic Options:
--help-list - Display list of available options (--help-list-hidden for more)
--version - Display the version of this program
ONNF Options:
ONNX MLIR Options:
These are frontend options.
Choose target to emit:
@ -89,7 +89,7 @@ These are frontend options.
For example, to lower an ONNX model (e.g., add.onnx) to ONNX dialect, use the following command:
```
./onnf --EmitONNXIR add.onnx
./onnx-mlir --EmitONNXIR add.onnx
```
The output should look like:
```

16
doc/ImportONNXDefs.md
View File

@ -1,23 +1,23 @@
# Import ONNX specifications into ONNF
# Import ONNX specifications into ONNX MLIR
The specifications of ONNX are defined under onnx/defs directory in ONNX projects.
There is a python script onnx/defs/gen_doc.py that automatically generate documents about operations in ONNX (docs/Operations.md).
ONNF modified this script to import ONNX specifications into ONNF. There are two files generated for ONNF with the modified gen_doc.py:
ONNX MLIR modified this script to import ONNX specifications into ONNX MLIR. There are two files generated for ONNX MLIR with the modified gen_doc.py:
1. src/dialect/onnx/onnxop.inc: Operation defintion for MLIR tablegen. Will be included in src/dialect/onnx/onnx.td
2. src/builder/op_build_table.inc: c code for ONNF frontend to import operation nodes from ONNX model. Will be included in src/builder/frontend_dialect_transformer.cpp
2. src/builder/op_build_table.inc: c code for ONNX MLIR frontend to import operation nodes from ONNX model. Will be included in src/builder/frontend_dialect_transformer.cpp
## How to use the script
1. Get ONNX. You can use ONNF/third_party/onnx
2. In your ONNX directory, copy the script docs/gen_doc.py in your ONNF to onnx/defs in ONNX
1. Get ONNX. You can use onnx-mlir/third_party/onnx
2. In your ONNX directory, copy the script docs/gen_doc.py in your ONNX MLIR to onnx/defs in ONNX
3. Run the script: python onnx/defs/gen_doc.py
4. Two files, onnxop.inc and op_buid_table.inc should be generated in current directory
5. copy the two file into your ONNF: cp onnxop.inc your_ONNF/src/dialect/onnx/onnxop.inc; cp op_build_table.inc your_ONNF/src/builder
6. go to your ONNF and build
5. copy the two file into your ONNX MLIR: cp onnxop.inc your_onnx-mlir/src/dialect/onnx/onnxop.inc; cp op_build_table.inc your_onnx-mlir/src/builder
6. go to your ONNX MLIR and build
## Consistency
The Operators.md generated by gen_doc.py is copied into doc. Please refer to this specification, not the one in onnx github, to make sure operators are consistent in version with onnxop.inc.
## Customization
In addition to following the ONNF specification, the modified gen_doc.py provides some mechanism for you to customize the output.
In addition to following the ONNX specification, the modified gen_doc.py provides some mechanism for you to customize the output.
Several tables are defined at the beginning of the script:
1. special_attr_defaults: gives attribute special default value.
2. special_op_handler: creates special import function in frontend_dialect_transformer.cpp. Currently special handler is used for operations with oprational arguments

74
src/CMakeLists.txt
View File

@ -15,10 +15,10 @@ add_library(compiler
pass/passes.hpp)
# Include root src directory.
target_include_directories(compiler PRIVATE ${ONNF_SRC_ROOT})
target_include_directories(compiler PRIVATE ${ONNX_MLIR_SRC_ROOT})
# Include tablegen generated header files.
target_include_directories(compiler PRIVATE ${ONNF_BIN_ROOT})
target_include_directories(compiler PRIVATE ${ONNX_MLIR_BIN_ROOT})
target_link_libraries(compiler
${CMAKE_THREAD_LIBS_INIT}
@ -27,55 +27,55 @@ target_link_libraries(compiler
curses)
set(LLVM_TARGET_DEFINITIONS pass/onnx_decompose.td)
onnf_tablegen(onnx_decompose.inc -gen-rewriters)
onnx_mlir_tablegen(onnx_decompose.inc -gen-rewriters)
add_public_tablegen_target(gen_onnx_decompose)
add_dependencies(compiler gen_onnx_decompose)
set(LLVM_TARGET_DEFINITIONS pass/shape_inference_interface.td)
onnf_tablegen(shape_inference.hpp.inc -gen-op-interface-decls)
onnf_tablegen(shape_inference.cpp.inc -gen-op-interface-defs)
onnx_mlir_tablegen(shape_inference.hpp.inc -gen-op-interface-decls)
onnx_mlir_tablegen(shape_inference.cpp.inc -gen-op-interface-defs)
add_public_tablegen_target(gen_shape_inference)
add_dependencies(compiler gen_shape_inference)
set(LLVM_TARGET_DEFINITIONS pass/onnx_combine.td)
onnf_tablegen(onnx_combine.inc -gen-rewriters)
onnx_mlir_tablegen(onnx_combine.inc -gen-rewriters)
add_public_tablegen_target(gen_onnx_combine)
add_dependencies(compiler gen_onnx_combine)
set(LLVM_TARGET_DEFINITIONS pass/onnx_rewrite.td)
onnf_tablegen(onnx_rewrite.inc -gen-rewriters)
onnx_mlir_tablegen(onnx_rewrite.inc -gen-rewriters)
add_public_tablegen_target(gen_onnx_rewrite)
add_dependencies(compiler gen_onnx_rewrite)
set(LLVM_TARGET_DEFINITIONS dialect/onnx/onnx.td)
onnf_tablegen(onnx.hpp.inc -gen-op-decls "-I${CMAKE_SOURCE_DIR}/compiler/pass")
onnf_tablegen(onnx.cpp.inc -gen-op-defs "-I${CMAKE_SOURCE_DIR}/compiler/pass")
onnx_mlir_tablegen(onnx.hpp.inc -gen-op-decls "-I${CMAKE_SOURCE_DIR}/compiler/pass")
onnx_mlir_tablegen(onnx.cpp.inc -gen-op-defs "-I${CMAKE_SOURCE_DIR}/compiler/pass")
set(GEN_DOC_FILE ${CMAKE_BINARY_DIR}/docs/Dialects/onnx.md)
add_public_tablegen_target(gen_onnx)
add_dependencies(compiler gen_onnx)
add_onnf_dialect_doc(onnx dialect/onnx/onnx.td)
add_onnx_mlir_dialect_doc(onnx dialect/onnx/onnx.td)
set(LLVM_TARGET_DEFINITIONS dialect/krnl/krnl_ops.td)
onnf_tablegen(krnl.hpp.inc -gen-op-decls)
onnf_tablegen(krnl.cpp.inc -gen-op-defs)
onnx_mlir_tablegen(krnl.hpp.inc -gen-op-decls)
onnx_mlir_tablegen(krnl.cpp.inc -gen-op-defs)
add_public_tablegen_target(gen_krnl_ops)
add_dependencies(compiler gen_krnl_ops)
add_library(onnf_onnx_decompose pass/onnx_decompose.cpp)
target_include_directories(onnf_onnx_decompose
PRIVATE ${ONNF_SRC_ROOT} ${ONNF_BIN_ROOT}
${ONNF_SRC_ROOT})
target_link_libraries(onnf_onnx_decompose ${MLIRLibs})
add_dependencies(onnf_onnx_decompose gen_krnl_ops)
add_library(onnx_mlir_onnx_decompose pass/onnx_decompose.cpp)
target_include_directories(onnx_mlir_onnx_decompose
PRIVATE ${ONNX_MLIR_SRC_ROOT} ${ONNX_MLIR_BIN_ROOT}
${ONNX_MLIR_SRC_ROOT})
target_link_libraries(onnx_mlir_onnx_decompose ${MLIRLibs})
add_dependencies(onnx_mlir_onnx_decompose gen_krnl_ops)
add_library(onnf_shape_inference pass/shape_inference_pass.cpp)
target_include_directories(onnf_shape_inference
PRIVATE ${ONNF_SRC_ROOT} ${ONNF_BIN_ROOT}
${ONNF_SRC_ROOT})
target_link_libraries(onnf_shape_inference ${MLIRLibs})
add_dependencies(onnf_shape_inference gen_krnl_ops)
add_library(onnx_mlir_shape_inference pass/shape_inference_pass.cpp)
target_include_directories(onnx_mlir_shape_inference
PRIVATE ${ONNX_MLIR_SRC_ROOT} ${ONNX_MLIR_BIN_ROOT}
${ONNX_MLIR_SRC_ROOT})
target_link_libraries(onnx_mlir_shape_inference ${MLIRLibs})
add_dependencies(onnx_mlir_shape_inference gen_krnl_ops)
add_library(onnf_lower_frontend
add_library(onnx_mlir_lower_frontend
conversion/onnx_to_krnl/onnx_to_krnl_common.cpp
conversion/onnx_to_krnl/onnx_to_krnl_common.hpp
conversion/onnx_to_krnl/math/elementwise.cpp
@ -93,25 +93,25 @@ add_library(onnf_lower_frontend
conversion/onnx_to_krnl/tensor/unsqueeze.cpp
conversion/onnx_to_krnl/tensor/constant.cpp
conversion/onnx_to_krnl/convert_onnx_to_krnl.cpp)
target_include_directories(onnf_lower_frontend
PRIVATE ${ONNF_SRC_ROOT} ${ONNF_BIN_ROOT}
${ONNF_SRC_ROOT})
target_link_libraries(onnf_lower_frontend ${MLIRLibs})
add_dependencies(onnf_lower_frontend gen_krnl_ops)
target_include_directories(onnx_mlir_lower_frontend
PRIVATE ${ONNX_MLIR_SRC_ROOT} ${ONNX_MLIR_BIN_ROOT}
${ONNX_MLIR_SRC_ROOT})
target_link_libraries(onnx_mlir_lower_frontend ${MLIRLibs})
add_dependencies(onnx_mlir_lower_frontend gen_krnl_ops)
add_subdirectory(transform)
add_subdirectory(tool)
add_subdirectory(builder)
add_subdirectory(runtime)
add_executable(onnf main.cpp)
add_executable(onnx-mlir main.cpp)
target_link_libraries(onnf builder ${MLIRLibs} onnf_transform onnf_onnx_decompose onnf_shape_inference onnf_lower_frontend)
whole_archive_link_mlir(onnf ${MLIRWholeArchiveLibs})
target_link_libraries(onnx-mlir builder ${MLIRLibs} onnx_mlir_transform onnx_mlir_onnx_decompose onnx_mlir_shape_inference onnx_mlir_lower_frontend)
whole_archive_link_mlir(onnx-mlir ${MLIRWholeArchiveLibs})
find_package(ZLIB REQUIRED)
target_link_libraries(onnf ${ZLIB_LIBRARIES})
target_link_libraries(onnx-mlir ${ZLIB_LIBRARIES})
target_include_directories(onnf PRIVATE ${CMAKE_SOURCE_DIR})
target_include_directories(onnf PRIVATE ${CMAKE_BINARY_DIR})
target_include_directories(onnx-mlir PRIVATE ${CMAKE_SOURCE_DIR})
target_include_directories(onnx-mlir PRIVATE ${CMAKE_BINARY_DIR})
install(TARGETS onnf DESTINATION bin)
install(TARGETS onnx-mlir DESTINATION bin)

22
src/builder/frontend_dialect_helper.cpp
View File

@ -10,7 +10,7 @@
#include "src/builder/frontend_dialect_helper.hpp"
namespace onnf {
namespace onnx_mlir {
void replaceAll(std::string &str, const std::string &from,
const std::string &to) {
@ -35,23 +35,23 @@ std::string legalize_name(std::string name) {
return name;
}
mlir::Value OnnxOnnfSymbolMapping::GetTensorByOnnxName(
mlir::Value OnnxMlirSymbolMapping::GetTensorByOnnxName(
const std::string &name) {
assert(onnx_name2onnf_tensor.find(legalize_name(name)) !=
onnx_name2onnf_tensor.end() &&
assert(onnx_name2onnx_mlir_tensor.find(legalize_name(name)) !=
onnx_name2onnx_mlir_tensor.end() &&
"Tensor not found");
return onnx_name2onnf_tensor.at(legalize_name(name));
return onnx_name2onnx_mlir_tensor.at(legalize_name(name));
}
void OnnxOnnfSymbolMapping::AddMapping(
void OnnxMlirSymbolMapping::AddMapping(
const std::string &name, mlir::Value tensor) {
assert(onnx_name2onnf_tensor.count(legalize_name(name)) == 0 &&
assert(onnx_name2onnx_mlir_tensor.count(legalize_name(name)) == 0 &&
"Tensor already exists.");
onnx_name2onnf_tensor.emplace(legalize_name(name), tensor);
onnx_name2onnx_mlir_tensor.emplace(legalize_name(name), tensor);
}
bool OnnxOnnfSymbolMapping::ContainKey(std::string name) {
return onnx_name2onnf_tensor.count(name) != 0;
bool OnnxMlirSymbolMapping::ContainKey(std::string name) {
return onnx_name2onnx_mlir_tensor.count(name) != 0;
}
template <typename T>
@ -181,4 +181,4 @@ mlir::Value InitializedTensorMapping::EmitInitializerForInputTensor(
loc, tensorType, nullptr, constantDenseAttribute);
}
} // namespace onnf
} // namespace onnx_mlir

10
src/builder/frontend_dialect_helper.hpp
View File

@ -34,18 +34,18 @@
#include "src/dialect/onnx/onnx_ops.hpp"
#include "onnx/onnx_pb.h"
namespace onnf {
namespace onnx_mlir {
void replaceAll(std::string &str, const std::string &from,
const std::string &to);
std::string legalize_name(std::string name);
struct OnnxOnnfSymbolMapping {
struct OnnxMlirSymbolMapping {
/*!
* Get MLIR tensor by onnx tensor name.
* @param name onnx tensor name.
* @return onnf tensor corresponding to `name`.
* @return onnx mlir tensor corresponding to `name`.
*/
mlir::Value GetTensorByOnnxName(const std::string &name);
@ -62,7 +62,7 @@ private:
/*!
* mapping from onnx tensor names to MLIR tensor.
*/
std::map<std::string, mlir::Value> onnx_name2onnf_tensor;
std::map<std::string, mlir::Value> onnx_name2onnx_mlir_tensor;
};
struct InitializedTensorMapping {
@ -98,4 +98,4 @@ private:
std::map<std::string, onnx::TensorProto> nameToInitializedTensor;
};
} // namespace onnf
} // namespace onnx_mlir

12
src/builder/frontend_dialect_transformer.cpp
View File

@ -21,7 +21,7 @@ namespace bstd = mpark;
#include "frontend_dialect_transformer.hpp"
namespace onnf {
namespace onnx_mlir {
namespace {
/*!
@ -47,13 +47,13 @@ private:
mlir::OpBuilder builder_;
mlir::Value none_;
// mapping between string name and symbol
OnnxOnnfSymbolMapping frontend_symbols_;
OnnxMlirSymbolMapping frontend_symbols_;
mlir::Location UnknownLoc() { return mlir::UnknownLoc::get(&context_); }
// Convert type to MLIR type.
// A complete list of types can be found in:
// <onnf-build-folder>/third_party/onnx/onnx/onnx.pb.h
// <onnx-mlir-build-folder>/third_party/onnx/onnx/onnx.pb.h
mlir::Type convertONNXTypeToMLIRType(onnx::TensorProto_DataType onnxType) {
switch (onnxType) {
case onnx::TensorProto_DataType::TensorProto_DataType_FLOAT16:
@ -473,9 +473,9 @@ private:
}
}; // FrontendGenImpl class
} // namespace
} // namespace onnf
} // namespace onnx_mlir
namespace onnf {
namespace onnx_mlir {
void ImportFrontendModelFile(std::string model_fname,
mlir::MLIRContext &context,
@ -489,4 +489,4 @@ void ImportFrontendModelFile(std::string model_fname,
FrontendGenImpl myONNXGen(context);
module = myONNXGen.ImportONNXModel(model);
}
} // namespace onnf
} // namespace onnx_mlir

8
src/builder/frontend_dialect_transformer.hpp
View File

@ -26,12 +26,12 @@ class OwningModuleRef;
} // namespace mlir
//===----------------------------------------------------------------------===//
// Import a model into one of ONNF's frontend models.
// Import a model into the ONNX MLIR dialect.
//===----------------------------------------------------------------------===//
namespace onnf {
namespace onnx_mlir {
/*!
* Import an ONNX model file into ONNF's ONNX Dialect.
* Import an ONNX model file into the ONNX Dialect.
* @param model_fname file name pointing to the onnx model protobuf.
* @return MLIR::module generated for the ONNX model.
*/
@ -43,4 +43,4 @@ void ImportFrontendModelFile(std::string model_fname,
* TODO: Import models into other extension dialects that cover the
* operations specific to other frameworks such as Tensorflow or Pytorch.
*/
} // namespace onnf
} // namespace onnx_mlir

4
src/dialect/krnl/krnl_helper.cpp
View File

@ -6,7 +6,7 @@
#include "krnl_helper.hpp"
namespace onnf {
namespace onnx_mlir {
using namespace mlir;
@ -120,7 +120,7 @@ void printBound(AffineMapAttr boundMap,
printDimAndSymbolList(
boundOperandsBeg, map.getNumDims(), map.getNumSymbols(), p);
}
} // namespace onnf
} // namespace onnx_mlir
namespace mlir {

4
src/dialect/krnl/krnl_helper.hpp
View File

@ -10,7 +10,7 @@
#include "mlir/IR/StandardTypes.h"
#include "mlir/Transforms/DialectConversion.h"
namespace onnf {
namespace onnx_mlir {
class KrnlDialectOperandParser {
public:
@ -59,7 +59,7 @@ void printDimAndSymbolList(mlir::Operation::operand_iterator &begin,
void printBound(mlir::AffineMapAttr boundMap,
mlir::Operation::operand_iterator &boundOperandsBeg, const char *prefix,
mlir::OpAsmPrinter &p);
} // namespace onnf
} // namespace onnx_mlir
namespace mlir {

6
src/dialect/krnl/krnl_ops.cpp
View File

@ -188,9 +188,9 @@ void print(OpAsmPrinter &p, KrnlIterateOp &op) {
p << " -> ";
p.printOperand(var);
p << " = ";
onnf::printBound((*boundItr++).cast<AffineMapAttr>(), operandItr, "max", p);
onnx_mlir::printBound((*boundItr++).cast<AffineMapAttr>(), operandItr, "max", p);
p << " to ";
onnf::printBound((*boundItr++).cast<AffineMapAttr>(), operandItr, "min", p);
onnx_mlir::printBound((*boundItr++).cast<AffineMapAttr>(), operandItr, "min", p);
delimiter = ", ";
}
@ -202,7 +202,7 @@ void print(OpAsmPrinter &p, KrnlIterateOp &op) {
ParseResult parseKrnlIterateOp(OpAsmParser &parser, OperationState &result) {
auto builder = parser.getBuilder();
auto context = builder.getContext();
onnf::KrnlDialectOperandParser operandParser(parser);
onnx_mlir::KrnlDialectOperandParser operandParser(parser);
// Parse optimized loops:
SmallVector<OpAsmParser::OperandType, 4> optimizedLoopRefs;

2
src/dialect/onnx/onnx.td
View File

@ -42,7 +42,7 @@ class ONNX_Op<string mnemonic, list<OpTrait> traits = []> :
//the tablegen code onnxop.in is generated with gen_doc.py
//clone and install onnx
// git clone --recursive https://github.com/onnx/onnx.git
// set up env for anaconda3 and for ONNF (BOOSTROOT, cmake, gcc ...)
// set up env for anaconda3 and for ONNX MLIR (BOOSTROOT, cmake, gcc ...)
// cd onnx
//install onnx
// CC=gcc CXX=g++ pip install -e .

2
src/dialect/onnx/onnx_ops.hpp
View File

@ -36,4 +36,4 @@ class ONNXOpsDialect : public Dialect {
} // end namespace mlir
namespace onnf {}
namespace onnx_mlir {}

14
src/main.cpp
View File

@ -36,11 +36,11 @@
void EmitLLVMBitCode(const mlir::OwningModuleRef &module);
using namespace std;
using namespace onnf;
using namespace onnx_mlir;
void LoadMLIR(string inputFilename, mlir::MLIRContext &context,
mlir::OwningModuleRef &module) {
// Handle '.mlir' input to the ONNF frontend.
// Handle '.mlir' input to the ONNX MLIR frontend.
// The mlir format indicates that one or more of the supported
// representations are used in the file.
llvm::ErrorOr<std::unique_ptr<llvm::MemoryBuffer>> fileOrErr =
@ -77,11 +77,11 @@ int main(int argc, char *argv[]) {
mlir::registerDialect<mlir::ONNXOpsDialect>();
mlir::registerDialect<mlir::KrnlOpsDialect>();
llvm::cl::OptionCategory OnnfOptions("ONNF Options",
llvm::cl::OptionCategory OnnxMlirOptions("ONNX MLIR Options",
"These are frontend options.");
llvm::cl::opt<string> inputFilename(
llvm::cl::Positional, llvm::cl::desc("<input file>"), llvm::cl::init("-"),
llvm::cl::cat(OnnfOptions));
llvm::cl::cat(OnnxMlirOptions));
enum EmissionTargetType {
EmitONNXIR,
@ -99,11 +99,11 @@ int main(int argc, char *argv[]) {
clEnumVal(EmitLLVMIR, "Lower model to LLVM IR (LLVM dialect)."),
clEnumVal(EmitLLVMBC, "Lower model to LLVM IR and emit (to file) "
"LLVM bitcode for model.")),
llvm::cl::init(EmitLLVMBC), llvm::cl::cat(OnnfOptions));
llvm::cl::init(EmitLLVMBC), llvm::cl::cat(OnnxMlirOptions));
llvm::cl::HideUnrelatedOptions(OnnfOptions);
llvm::cl::HideUnrelatedOptions(OnnxMlirOptions);
llvm::cl::ParseCommandLineOptions(argc, argv,
"ONNF MLIR modular optimizer driver\n");
"ONNX MLIR modular optimizer driver\n");
// Decide if the input file is an ONNX model or a model specified
// in MLIR. The extension of the file is the decider.

4
src/pass/passes.hpp
View File

@ -1,10 +1,10 @@
//===- passes.hpp - ONNF Passes Definition --------------------------------===//
//===- passes.hpp - ONNX MLIR Passes Definition ---------------------------===//
//
// Copyright 2019 The IBM Research Authors.
//
// =============================================================================
//
// This file exposes the entry points to create compiler passes for ONNF.
// This file exposes the entry points to create compiler passes for ONNX MLIR.
//
//===----------------------------------------------------------------------===//

8
src/runtime/CMakeLists.txt
View File

@ -3,8 +3,8 @@ add_library(cruntime
dyn_memref.h
data_type.h)
target_include_directories(cruntime
PRIVATE ${ONNF_SRC_ROOT} ${ONNF_BIN_ROOT}
${ONNF_SRC_ROOT})
PRIVATE ${ONNX_MLIR_SRC_ROOT} ${ONNX_MLIR_BIN_ROOT}
${ONNX_MLIR_SRC_ROOT})
pybind11_add_module(pyruntime
dyn_memref.cpp
@ -13,6 +13,6 @@ pybind11_add_module(pyruntime
runtime.hpp)
target_link_libraries(pyruntime PRIVATE ${CMAKE_DL_LIBS})
target_include_directories(pyruntime
PRIVATE ${ONNF_SRC_ROOT} ${ONNF_BIN_ROOT}
${ONNF_SRC_ROOT})
PRIVATE ${ONNX_MLIR_SRC_ROOT} ${ONNX_MLIR_BIN_ROOT}
${ONNX_MLIR_SRC_ROOT})
add_dependencies(pyruntime cruntime)

2
src/tool/CMakeLists.txt
View File

@ -1 +1 @@
add_subdirectory(onnf_opt)
add_subdirectory(onnx_mlir_opt)

9
src/tool/onnf_opt/CMakeLists.txt
View File

@ -1,9 +0,0 @@
add_executable(onnf-opt onnf_opt.cpp)
add_dependencies(onnf-opt gen_krnl_ops)
target_include_directories(onnf-opt PRIVATE ${ONNF_SRC_ROOT})
target_include_directories(onnf-opt PRIVATE ${ONNF_BIN_ROOT})
target_link_libraries(onnf-opt builder ${MLIRLibs} onnf_transform onnf_shape_inference onnf_lower_frontend curses)
whole_archive_link_mlir(onnf-opt ${MLIRWholeArchiveLibs})
whole_archive_link_onnf(onnf-opt compiler onnf_transform onnf_lower_frontend onnf_shape_inference)

9
src/tool/onnx_mlir_opt/CMakeLists.txt Normal file
View File

@ -0,0 +1,9 @@
add_executable(onnx-mlir-opt onnx_mlir_opt.cpp)
add_dependencies(onnx-mlir-opt gen_krnl_ops)
target_include_directories(onnx-mlir-opt PRIVATE ${ONNX_MLIR_SRC_ROOT})
target_include_directories(onnx-mlir-opt PRIVATE ${ONNX_MLIR_BIN_ROOT})
target_link_libraries(onnx-mlir-opt builder ${MLIRLibs} onnx_mlir_transform onnx_mlir_shape_inference onnx_mlir_lower_frontend curses)
whole_archive_link_mlir(onnx-mlir-opt ${MLIRWholeArchiveLibs})
whole_archive_link_onnx_mlir(onnx-mlir-opt compiler onnx_mlir_transform onnx_mlir_lower_frontend onnx_mlir_shape_inference)

6
src/tool/onnf_opt/onnf_opt.cpp → src/tool/onnx_mlir_opt/onnx_mlir_opt.cpp
View File

@ -1,4 +1,4 @@
//===--------------------- onnf_opt.cpp - MLIR Operations -----------------===//
//===---------------- onnx_mlir_opt.cpp - MLIR Operations -----------------===//
//
// Copyright 2019 The IBM Research Authors.
//
@ -19,7 +19,7 @@
#include "src/dialect/onnx/onnx_ops.hpp"
#include "src/pass/passes.hpp"
using namespace onnf;
using namespace onnx_mlir;
static llvm::cl::opt<std::string> input_filename(llvm::cl::Positional,
llvm::cl::desc("<input file>"),
@ -60,7 +60,7 @@ int main(int argc, char **argv) {
mlir::registerPassManagerCLOptions();
mlir::PassPipelineCLParser passPipeline("", "Compiler passes to run");
llvm::cl::ParseCommandLineOptions(argc, argv,
"ONNF MLIR modular optimizer driver\n");
"ONNX MLIR modular optimizer driver\n");
// Set up the input file.
std::string error_message;

12
src/transform/CMakeLists.txt
View File

@ -1,9 +1,9 @@
add_library(onnf_transform
add_library(onnx_mlir_transform
lower_krnl.cpp
lower_to_llvm.cpp)
target_include_directories(onnf_transform
PRIVATE ${ONNF_SRC_ROOT} ${ONNF_BIN_ROOT}
${ONNF_SRC_ROOT})
target_link_libraries(onnf_transform ${MLIRLibs})
add_dependencies(onnf_transform gen_krnl_ops)
target_include_directories(onnx_mlir_transform
PRIVATE ${ONNX_MLIR_SRC_ROOT} ${ONNX_MLIR_BIN_ROOT}
${ONNX_MLIR_SRC_ROOT})
target_link_libraries(onnx_mlir_transform ${MLIRLibs})
add_dependencies(onnx_mlir_transform gen_krnl_ops)

2
test/backend/CMakeLists.txt
View File

@ -6,5 +6,5 @@ add_custom_target(run-onnx-backend-test
COMMAND ${PYTHON_EXECUTABLE}
${CMAKE_CURRENT_BINARY_DIR}/test.py)
add_dependencies(run-onnx-backend-test onnf)
add_dependencies(run-onnx-backend-test onnx-mlir)
add_dependencies(run-onnx-backend-test pyruntime)

6
test/backend/test.py
View File

@ -16,12 +16,12 @@ import test_config
VERBOSE = bool(os.environ.get("VERBOSE"))
CXX = test_config.CXX_PATH
ONNF = os.path.join(test_config.ONNF_BUILD_PATH, "bin/onnf")
ONNX_MLIR = os.path.join(test_config.ONNX_MLIR_BUILD_PATH, "bin/onnx-mlir")
LLC = os.path.join(test_config.LLVM_PROJ_BUILD_PATH, "bin/llc")
# Make lib folder under build directory visible in PYTHONPATH
doc_check_base_dir = os.path.dirname(os.path.realpath(__file__))
RUNTIME_DIR = os.path.join(test_config.ONNF_BUILD_PATH, "lib")
RUNTIME_DIR = os.path.join(test_config.ONNX_MLIR_BUILD_PATH, "lib")
sys.path.append(RUNTIME_DIR)
from pyruntime import ExecutionSession
@ -39,7 +39,7 @@ class DummyBackend(onnx.backend.base.Backend):
# Save model to disk as temp_model.onnx.
onnx.save(model, "temp_model.onnx")
# Call frontend to process temp_model.onnx, bit code will be generated.
execute_commands([ONNF, "temp_model.onnx"])
execute_commands([ONNX_MLIR, "temp_model.onnx"])
# Call llc to generate object file from bitcode.
execute_commands(
[LLC, "-filetype=obj", "-relocation-model=pic", "model.bc"])

2
test/backend/test_config.py.in
View File

@ -1,3 +1,3 @@
ONNF_BUILD_PATH = "@CMAKE_BINARY_DIR@"
ONNX_MLIR_BUILD_PATH = "@CMAKE_BINARY_DIR@"
LLVM_PROJ_BUILD_PATH = "@LLVM_PROJ_BUILD@"
CXX_PATH = "@CMAKE_CXX_COMPILER@"

10
test/mlir/CMakeLists.txt
View File

@ -6,16 +6,16 @@ configure_lit_site_cfg(${CMAKE_CURRENT_SOURCE_DIR}/lit.site.cfg.py.in
MAIN_CONFIG
${CMAKE_CURRENT_SOURCE_DIR}/lit.cfg.py)
set(ONNF_MLIR_TEST_DEPENDS onnf-opt)
set(ONNX_MLIR_TEST_DEPENDS onnx-mlir-opt)
add_lit_testsuite(check-mlir-lit
"Running the ONNF MLIR regression tests"
"Running the ONNX MLIR regression tests"
${CMAKE_CURRENT_BINARY_DIR}
DEPENDS
${ONNF_MLIR_TEST_DEPENDS})
${ONNX_MLIR_TEST_DEPENDS})
set_target_properties(check-mlir-lit PROPERTIES FOLDER "Tests")
add_lit_testsuites(ONNF_MLIR
add_lit_testsuites(ONNX_MLIR
${CMAKE_CURRENT_SOURCE_DIR}
DEPENDS
${ONNF_MLIR_TEST_DEPS})
${ONNX_MLIR_TEST_DEPS})

4
test/mlir/krnl/ops.mlir
View File

@ -1,5 +1,5 @@
// RUN: onnf-opt %s -mlir-print-op-generic | FileCheck -check-prefix=GENERIC %s
// RUN: onnf-opt %s | FileCheck %s
// RUN: onnx-mlir-opt %s -mlir-print-op-generic | FileCheck -check-prefix=GENERIC %s
// RUN: onnx-mlir-opt %s | FileCheck %s
// GENERIC-DAG: #{{.*}} = affine_map<() -> (0)>
// GENERIC-DAG: #{{.*}} = affine_map<() -> (10)>

2
test/mlir/krnl/reshape.mlir
View File

@ -1,4 +1,4 @@
// RUN: onnf-opt --shape-inference --lower-frontend --lower-krnl --lower-all-llvm %s -split-input-file | FileCheck %s
// RUN: onnx-mlir-opt --shape-inference --lower-frontend --lower-krnl --lower-all-llvm %s -split-input-file | FileCheck %s
func @test_reshape(%arg0 : tensor<?x10xf32>, %arg1 : tensor<4xi32>) -> tensor<*xf32> {
%0 = "onnx.Reshape"(%arg0, %arg1) : (tensor<?x10xf32>, tensor<4xi32>) -> tensor<*xf32>

8
test/mlir/lit.cfg.py
View File

@ -17,10 +17,10 @@ config.name = 'Open Neural Network Frontend'
config.test_format = lit.formats.ShTest(not llvm_config.use_lit_shell)
# test_source_root: The root path where tests are located.
config.test_source_root = config.onnf_mlir_test_src_dir
config.test_source_root = config.onnx_mlir_test_src_dir
# test_exec_root: The root path where tests should be run.
config.test_exec_root = config.onnf_mlir_test_build_dir
config.test_exec_root = config.onnx_mlir_test_build_dir
llvm_config.use_default_substitutions()
@ -28,10 +28,10 @@ llvm_config.use_default_substitutions()
llvm_config.with_environment('PATH', config.llvm_tools_dir, append_path=True)
tool_dirs = [
config.onnf_mlir_tools_dir, config.mlir_tools_dir, config.llvm_tools_dir
config.onnx_mlir_tools_dir, config.mlir_tools_dir, config.llvm_tools_dir
]
tool_names = [
'onnf-opt', 'mlir-opt', 'mlir-translate'
'onnx-mlir-opt', 'mlir-opt', 'mlir-translate'
]
tools = [ToolSubst(s, unresolved='ignore') for s in tool_names]
llvm_config.add_tool_substitutions(tools, tool_dirs)

8
test/mlir/lit.site.cfg.py.in
View File

@ -6,11 +6,11 @@ config.mlir_obj_root = "@LLVM_PROJ_BUILD@"
config.mlir_tools_dir = "@MLIR_TOOLS_DIR@"
config.suffixes = ['.mlir']
config.onnf_mlir_tools_dir = "@ONNF_TOOLS_DIR@"
config.onnf_mlir_test_src_dir = "@ONNF_LIT_TEST_SRC_DIR@"
config.onnf_mlir_test_build_dir = "@ONNF_LIT_TEST_BUILD_DIR@"
config.onnx_mlir_tools_dir = "@ONNX_MLIR_TOOLS_DIR@"
config.onnx_mlir_test_src_dir = "@ONNX_MLIR_LIT_TEST_SRC_DIR@"
config.onnx_mlir_test_build_dir = "@ONNX_MLIR_LIT_TEST_BUILD_DIR@"
lit.llvm.initialize(lit_config, config)
# Let the main config do the real work.
lit_config.load_config(config, "@ONNF_LIT_TEST_SRC_DIR@/lit.cfg.py")
lit_config.load_config(config, "@ONNX_MLIR_LIT_TEST_SRC_DIR@/lit.cfg.py")

2
test/mlir/onnx/onnx_canonicalization.mlir
View File

@ -1,4 +1,4 @@
// RUN: onnf-opt --canonicalize %s -split-input-file | FileCheck %s
// RUN: onnx-mlir-opt --canonicalize %s -split-input-file | FileCheck %s
// CHECK-LABEL: func @test_matmul_add_fused(%{{.*}}: tensor<10x10xf32>, %{{.*}}: tensor<10x10xf32>, %{{.*}}: tensor<10x10xf32>) -> tensor<10x10xf32> {
func @test_matmul_add_fused(%a0: tensor<10x10xf32>, %a1: tensor<10x10xf32>, %a2: tensor<10x10xf32>) -> tensor<10x10xf32> {

2
test/mlir/onnx/onnx_decompose.mlir
View File

@ -1,4 +1,4 @@
// RUN: onnf-opt --decompose-onnx %s -split-input-file | FileCheck %s
// RUN: onnx-mlir-opt --decompose-onnx %s -split-input-file | FileCheck %s
// CHECK-LABEL: @test_reducel1(%{{.*}}: tensor<?x?x?xf32>) -> tensor<*xf32>
func @test_reducel1(%arg0 : tensor<?x?x?xf32>) -> tensor<*xf32> {

2
test/mlir/onnx/onnx_lowering.mlir
View File

@ -1,4 +1,4 @@
// RUN: onnf-opt --shape-inference --lower-frontend %s -split-input-file | FileCheck %s
// RUN: onnx-mlir-opt --shape-inference --lower-frontend %s -split-input-file | FileCheck %s
func @test_add(%arg0 : tensor<10x10xf32>, %arg1 : tensor<10x10xf32>) -> tensor<*xf32> {
%0 = "onnx.Add"(%arg0, %arg1) : (tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<*xf32>

2
test/mlir/onnx/onnx_lowering_with_dealloc.mlir
View File

@ -1,4 +1,4 @@
// RUN: onnf-opt --shape-inference --lower-frontend %s -split-input-file | FileCheck %s
// RUN: onnx-mlir-opt --shape-inference --lower-frontend %s -split-input-file | FileCheck %s
func @test_add_add(%arg0 : tensor<10x10xf32>, %arg1 : tensor<10x10xf32>) -> tensor<*xf32> {
%0 = "onnx.Add"(%arg0, %arg1) : (tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<*xf32>

2
test/mlir/onnx/onnx_shape_inference.mlir
View File

@ -1,4 +1,4 @@
// RUN: onnf-opt --shape-inference %s -split-input-file | FileCheck %s
// RUN: onnx-mlir-opt --shape-inference %s -split-input-file | FileCheck %s
//===----------------------------------------------------------------------===//
/// Test the default behavior of transpose when no information for the

2
test/mlir/onnx/onnx_shape_inference_maxpool.mlir
View File

@ -1,4 +1,4 @@
// RUN: onnf-opt --shape-inference %s -split-input-file | FileCheck %s
// RUN: onnx-mlir-opt --shape-inference %s -split-input-file | FileCheck %s
/// Test the default behavior of Max Pool with no padding (pad are set but shoudl be ignored)
func @test_default_maxpoolsingleout(%arg0 : tensor<5x5x32x32xf32>) -> tensor<*xf32> {

2
utils/install-mlir.sh
View File

@ -1,5 +1,5 @@
git clone https://github.com/llvm/llvm-project.git
# Check out a specific branch that is known to work with ONNF.
# Check out a specific branch that is known to work with ONNX MLIR.
cd llvm-project && git checkout 076475713c236081a3247a53e9dbab9043c3eac2 && cd ..
mkdir llvm-project/build
cd llvm-project/build

4
utils/install-onnf.sh → utils/install-onnx-mlir.sh
View File

@ -2,9 +2,9 @@
export LLVM_PROJ_SRC=$(pwd)/llvm-project/
export LLVM_PROJ_BUILD=$(pwd)/llvm-project/build
mkdir ONNF/build && cd ONNF/build
mkdir onnx-mlir/build && cd onnx-mlir/build
cmake ..
cmake --build . --target onnf
cmake --build . --target onnx-mlir
# Run FileCheck tests:
export LIT_OPTS=-v