MLIR入门系列系列学习笔记

目录

1 名字解释

这一定义包含3个关键元素：

2 代码演示

 2. 1 环境准备

2.2 编译llvm-project

2.3 测试解析

2.3.1 源程序

2.3.2 将源程序生成抽象语法树(AST)

 3 MLIR三要素

3.1 MLIRGen模块

3.2 Dialect模块

3.3 TableGen模块

3.3.1 定义一个和Toy Dialect的链接

3.3.2 创建一个Toy Dialect Operation的基类

3.3.3 创建Toy Dialect各种Operation的类

3.3.4 生成C++代码

---

学习链接：

[MLIR] 转换流程详解(以Toy接入为例) - 多一些不为什么的坚持 - 博客园 (cnblogs.com)

MLIR 文章视频汇总 - 知乎 (zhihu.com)

Getting Started - MLIR (llvm.org)

Getting Started with the LLVM System — LLVM 17.0.0git documentation

1 名字解释

项目	名字	概念	解释
1	MLIR	Mulit-Level Intermediate Representation	MLIR希望为各种DSL提供一种中间表达形式，将他们集成为一套生态系统，使用一种一致性强的方式编译到特定硬件平台的汇编语言上。利用这样的形式，MLIR就可以利用它模块化、可扩展的特点来解决IR之间相互配合的问题。
2	DSL	Domain Specific Language	针对某一领域，具有受限表达性的一种计算机程序设计语言。 常用于聚焦指定的领域或问题，这就要求 DSL 具备强大的表现力，同时在使用起来要简单。 见：DSL 领域特定语言_Impl_Sunny的博客-CSDN博客 这一定义包含3个关键元素： 1）语言性（language nature）：DSL是一种程序设计语言，因此它必须具备连贯的表达能力——不管是一个表达式还是多个表达式组合在一起。 2）受限的表达性（limited expressiveness）：通用程序设计语言提供广泛的能力：支持各种数据、控制，以及抽象结构。 这些能力很有用，但也会让语言难于学习和使用。DSL只支持特定领域所需要特性的最小集。使用DSL，无法构建一个完整的系统，相反，却可以解决系统某一方面的问题。 3）针对领域（domain focus）：只有在一个明确的小领域下，这种能力有限的语言才会有用。这个领域才使得这种语言值得使用。
3	Dialects		Dialects是将所有的IR放在了同一个命名空间中，分别对每个IR定义对应的产生式以及绑定相应的操作，从而生成一个MLIR的模型。整个的编译过程，从源语言生成AST，借助Dialects遍历AST，产生MLIR的表达式，此处可为多层IR通过Lowering Pass依次进行分析，最后经过MLIR分析器，生成目标语言。 transpose(a)的MLIR表达式由操作结果名称、Dialect命名空间、操作名、参数列表、输入参数类型、输出类型和操作在源文件中的位置组成。
4	AST	Abstract Syntax Tree	抽象语法树，源代码语法结构的一种抽象表示。它以树状的形式表现编程语言的语法结构，树上的每个节点都表示源代码中的一种结构。
5	ODS	Operation Definition Specification	Operation Definition Specification (ODS)框架是基于TableGen规范构造的。见：【从零开始学深度学习编译器】十六，MLIR ODS要点总结上篇 - 知乎
6		MLIR三要素	MLIRGen模块 -- 生产线的履带：遍历抽象语法树(AST)，据AST各节点的类型，递归调用子函数，子函数内部再根据不同情况，进行相应操作。 Dialect模块 -- 生产线的机械臂：负责定义各种操作和分析，同时还具备可扩展性。 TableGen模块 -- 生产线的零件：是一种声明性编程语言，用于描述MLIR中Operation的各种操作的类的定义，在源代码中它以.td文件的形式存在，在编译时会自动生成C++的相应文件，给Dialect模块文件提供支持。

2 代码演示

NVIDIA WSL环境搭建见：

NVIDIA GPU Accelerated Computing on WSL 2

CUDA Toolkit 12.1 Update 1 Downloads | NVIDIA Developer

在power shell中查看WSL2信息

PS C:\Users\Lenovo> wsl cat /proc/version
Linux version 5.15.90.1-microsoft-standard-WSL2 (oe-user@oe-host) (x86_64-msft-linux-gcc (GCC) 9.3.0, GNU ld (GNU Binutils) 2.34.0.20200220) #1 SMP Fri Jan 27 02:56:13 UTC 2023
PS C:\Users\Lenovo> wsl --list --verbose
  NAME      STATE           VERSION
* Ubuntu    Running         2

 2. 1 环境准备

$ uname -a
Linux LAPTOP-3SUHS40U 5.15.90.1-microsoft-standard-WSL2 #1 SMP Fri Jan 27 02:56:13 UTC 2023 x86_64 x86_64 x86_64 GNU/Linux

$ nvidia-smi.exe
Sun Apr 30 10:58:42 2023
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 457.49       Driver Version: 457.49       CUDA Version: 11.1     |
|-------------------------------+----------------------+----------------------+
| GPU  Name            TCC/WDDM | Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  GeForce RTX 2060   WDDM  | 00000000:01:00.0  On |                  N/A |
| N/A   47C    P8     7W /  N/A |    912MiB /  6144MiB |      5%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A      1784    C+G   ...y\ShellExperienceHost.exe    N/A      |
|    0   N/A  N/A      1884    C+G   Insufficient Permissions        N/A      |
|    0   N/A  N/A      8344    C+G   ...wekyb3d8bbwe\Video.UI.exe    N/A      |
+-----------------------------------------------------------------------------+

sudo apt-get install clang lld
sudo apt-get install cmake
sudo apt-get install re2c
git clone https://github.com/ninja-build/ninja.git
cd ninja/
git branch -r
git checkout release
cmake -Bbuild-cmake
cmake --build build-cmake
./build-cmake/ninja_test
cd build-cmake/
sudo make install
cd -

2.2 编译llvm-project

参见官方MLIR链接：Getting Started - MLIR (llvm.org)

参数介绍参见官方LLVM链接：Getting Started with the LLVM System — LLVM 17.0.0git documentation

git clone https://github.com/llvm/llvm-project.git
mkdir llvm-project/build
cd llvm-project/build
cmake -G Ninja ../llvm \
   -DLLVM_ENABLE_PROJECTS=mlir \
   -DLLVM_BUILD_EXAMPLES=ON \
   -DLLVM_TARGETS_TO_BUILD="Native;NVPTX;AMDGPU" \
   -DCMAKE_BUILD_TYPE=Release \
   -DLLVM_ENABLE_ASSERTIONS=ON
# Using clang and lld speeds up the build, we recommend adding:
#  -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DLLVM_ENABLE_LLD=ON
# CCache can drastically speed up further rebuilds, try adding:
#  -DLLVM_CCACHE_BUILD=ON
# Optionally, using ASAN/UBSAN can find bugs early in development, enable with:
# -DLLVM_USE_SANITIZER="Address;Undefined" 
# Optionally, enabling integration tests as well
# -DMLIR_INCLUDE_INTEGRATION_TESTS=ON
cmake --build . --target check-mlir

我使用的编译命令是：

cmake -G Ninja ../llvm \
   -DLLVM_ENABLE_PROJECTS=mlir \
   -DLLVM_BUILD_EXAMPLES=ON \
   -DLLVM_TARGETS_TO_BUILD="host" \
   -DCMAKE_BUILD_TYPE=Release \
   -DLLVM_ENABLE_ASSERTIONS=ON \
   -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DLLVM_ENABLE_LLD=ON \
   -DLLVM_CCACHE_BUILD=OFF \
   -DLLVM_USE_SANITIZER="Address;Undefined" \
   -DMLIR_INCLUDE_INTEGRATION_TESTS=ON

cmake --build . --target check-mlir

编译日志为：

llvm-project/build$ cmake -G Ninja ../llvm \
   -DLLVM_ENABLE_PROJECTS=mlir \
   -DLLVM_BUILD_EXAMPLES=ON \
   -DLLVM_TARGETS_TO_BUILD="host" \
   -DCMAKE_BUILD_TYPE=Release \
   -DLLVM_ENABLE_ASSERTIONS=ON \
   -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DLLVM_ENABLE_LLD=ON \
   -DLLVM_CCACHE_BUILD=OFF \
   -DLLVM_USE_SANITIZER="Address;Undefined" \
   -DMLIR_INCLUDE_INTEGRATION_TESTS=ON
-- The C compiler identification is Clang 14.0.0
-- The CXX compiler identification is Clang 14.0.0
-- The ASM compiler identification is Clang with GNU-like command-line
-- Found assembler: /usr/bin/clang
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Check for working C compiler: /usr/bin/clang - skipped
-- Detecting C compile features
-- Detecting C compile features - done
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Check for working CXX compiler: /usr/bin/clang++ - skipped
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- bolt project is disabled
-- clang project is disabled
-- clang-tools-extra project is disabled
-- compiler-rt project is disabled
-- cross-project-tests project is disabled
-- libc project is disabled
-- libclc project is disabled
-- lld project is disabled
-- lldb project is disabled
-- mlir project is enabled
-- openmp project is disabled
-- polly project is disabled
-- pstl project is disabled
-- flang project is disabled
-- Found Python3: /usr/bin/python3.10 (found suitable version "3.10.6", minimum required is "3.6") found components: Interpreter
-- Performing Test LLVM_LIBSTDCXX_MIN
-- Performing Test LLVM_LIBSTDCXX_MIN - Success
-- Performing Test LLVM_LIBSTDCXX_SOFT_ERROR
-- Performing Test LLVM_LIBSTDCXX_SOFT_ERROR - Success
-- Looking for dlfcn.h
-- Looking for dlfcn.h - found
-- Looking for errno.h
-- Looking for errno.h - found
-- Looking for fcntl.h
-- Looking for fcntl.h - found
-- Looking for link.h
-- Looking for link.h - found
-- Looking for malloc/malloc.h
-- Looking for malloc/malloc.h - not found
-- Looking for pthread.h
-- Looking for pthread.h - found
-- Looking for signal.h
-- Looking for signal.h - found
-- Looking for sys/ioctl.h
-- Looking for sys/ioctl.h - found
-- Looking for sys/mman.h
-- Looking for sys/mman.h - found
-- Looking for sys/param.h
-- Looking for sys/param.h - found
-- Looking for sys/resource.h
-- Looking for sys/resource.h - found
-- Looking for sys/stat.h
-- Looking for sys/stat.h - found
-- Looking for sys/time.h
-- Looking for sys/time.h - found
-- Looking for sys/types.h
-- Looking for sys/types.h - found
-- Looking for sysexits.h
-- Looking for sysexits.h - found
-- Looking for termios.h
-- Looking for termios.h - found
-- Looking for unistd.h
-- Looking for unistd.h - found
-- Looking for valgrind/valgrind.h
-- Looking for valgrind/valgrind.h - not found
-- Looking for fenv.h
-- Looking for fenv.h - found
-- Looking for FE_ALL_EXCEPT
-- Looking for FE_ALL_EXCEPT - found
-- Looking for FE_INEXACT
-- Looking for FE_INEXACT - found
-- Looking for mach/mach.h
-- Looking for mach/mach.h - not found
-- Looking for CrashReporterClient.h
-- Looking for CrashReporterClient.h - not found
-- Looking for linux/magic.h
-- Looking for linux/magic.h - found
-- Looking for pthread_create in pthread
-- Looking for pthread_create in pthread - found
-- Looking for pthread_rwlock_init in pthread
-- Looking for pthread_rwlock_init in pthread - found
-- Looking for pthread_mutex_lock in pthread
-- Looking for pthread_mutex_lock in pthread - found
-- Looking for dlopen in dl
-- Looking for dlopen in dl - found
-- Looking for clock_gettime in rt
-- Looking for clock_gettime in rt - found
-- Looking for pfm_initialize in pfm
-- Looking for pfm_initialize in pfm - not found
-- Looking for pthread.h
-- Looking for pthread.h - found
-- Performing Test CMAKE_HAVE_LIBC_PTHREAD
-- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Success
-- Found Threads: TRUE
-- Could NOT find ZLIB (missing: ZLIB_LIBRARY ZLIB_INCLUDE_DIR)
-- Found LibXml2: /usr/lib/x86_64-linux-gnu/libxml2.so (found version "2.9.13")
-- Looking for xmlReadMemory
-- Looking for xmlReadMemory - found
-- Performing Test Terminfo_LINKABLE
-- Performing Test Terminfo_LINKABLE - Success
-- Found Terminfo: /usr/lib/x86_64-linux-gnu/libtinfo.so
-- Looking for xar_open in xar
-- Looking for xar_open in xar - not found
-- Looking for arc4random
-- Looking for arc4random - not found
-- Looking for backtrace
-- Looking for backtrace - found
-- backtrace facility detected in default set of libraries
-- Found Backtrace: /usr/include
-- Performing Test C_SUPPORTS_WERROR_UNGUARDED_AVAILABILITY_NEW
-- Performing Test C_SUPPORTS_WERROR_UNGUARDED_AVAILABILITY_NEW - Success
-- Looking for __register_frame
-- Looking for __register_frame - found
-- Looking for __deregister_frame
-- Looking for __deregister_frame - found
-- Looking for __unw_add_dynamic_fde
-- Looking for __unw_add_dynamic_fde - not found
-- Looking for _Unwind_Backtrace
-- Looking for _Unwind_Backtrace - found
-- Looking for getpagesize
-- Looking for getpagesize - found
-- Looking for sysconf
-- Looking for sysconf - found
-- Looking for getrusage
-- Looking for getrusage - found
-- Looking for setrlimit
-- Looking for setrlimit - found
-- Looking for isatty
-- Looking for isatty - found
-- Looking for futimens
-- Looking for futimens - found
-- Looking for futimes
-- Looking for futimes - found
-- Looking for mallctl
-- Looking for mallctl - not found
-- Looking for mallinfo
-- Looking for mallinfo - found
-- Looking for mallinfo2
-- Looking for mallinfo2 - found
-- Looking for malloc_zone_statistics
-- Looking for malloc_zone_statistics - not found
-- Looking for getrlimit
-- Looking for getrlimit - found
-- Looking for posix_spawn
-- Looking for posix_spawn - found
-- Looking for pread
-- Looking for pread - found
-- Looking for sbrk
-- Looking for sbrk - found
-- Looking for strerror
-- Looking for strerror - found
-- Looking for strerror_r
-- Looking for strerror_r - found
-- Looking for strerror_s
-- Looking for strerror_s - not found
-- Looking for setenv
-- Looking for setenv - found
-- Performing Test HAVE_STRUCT_STAT_ST_MTIMESPEC_TV_NSEC
-- Performing Test HAVE_STRUCT_STAT_ST_MTIMESPEC_TV_NSEC - Failed
-- Performing Test HAVE_STRUCT_STAT_ST_MTIM_TV_NSEC
-- Performing Test HAVE_STRUCT_STAT_ST_MTIM_TV_NSEC - Success
-- Looking for __GLIBC__
-- Looking for __GLIBC__ - found
-- Looking for pthread_getname_np
-- Looking for pthread_getname_np - found
-- Looking for pthread_setname_np
-- Looking for pthread_setname_np - found
-- Looking for dlopen
-- Looking for dlopen - found
-- Looking for dladdr
-- Looking for dladdr - found
-- Looking for proc_pid_rusage
-- Looking for proc_pid_rusage - not found
-- Performing Test HAVE_CXX_ATOMICS_WITHOUT_LIB
-- Performing Test HAVE_CXX_ATOMICS_WITHOUT_LIB - Success
-- Performing Test HAVE_CXX_ATOMICS64_WITHOUT_LIB
-- Performing Test HAVE_CXX_ATOMICS64_WITHOUT_LIB - Success
-- Performing Test LLVM_HAS_ATOMICS
-- Performing Test LLVM_HAS_ATOMICS - Success
-- Performing Test SUPPORTS_VARIADIC_MACROS_FLAG
-- Performing Test SUPPORTS_VARIADIC_MACROS_FLAG - Success
-- Performing Test SUPPORTS_GNU_ZERO_VARIADIC_MACRO_ARGUMENTS_FLAG
-- Performing Test SUPPORTS_GNU_ZERO_VARIADIC_MACRO_ARGUMENTS_FLAG - Success
-- Native target architecture is X86
-- Threads enabled.
-- Doxygen disabled.
-- Ninja version: 1.11.1
-- Could NOT find OCaml (missing: OCAMLFIND OCAML_VERSION OCAML_STDLIB_PATH)
-- Could NOT find OCaml (missing: OCAMLFIND OCAML_VERSION OCAML_STDLIB_PATH)
-- OCaml bindings disabled.
-- Found Python module pygments
-- Found Python module pygments.lexers.c_cpp
-- Found Python module yaml
-- LLVM host triple: x86_64-unknown-linux-gnu
-- LLVM default target triple: x86_64-unknown-linux-gnu
-- Performing Test CXX_SUPPORTS_CUSTOM_LINKER
-- Performing Test CXX_SUPPORTS_CUSTOM_LINKER - Success
-- Performing Test C_SUPPORTS_FPIC
-- Performing Test C_SUPPORTS_FPIC - Success
-- Performing Test CXX_SUPPORTS_FPIC
-- Performing Test CXX_SUPPORTS_FPIC - Success
-- Building with -fPIC
-- Performing Test C_SUPPORTS_FNO_SEMANTIC_INTERPOSITION
-- Performing Test C_SUPPORTS_FNO_SEMANTIC_INTERPOSITION - Success
-- Performing Test CXX_SUPPORTS_FNO_SEMANTIC_INTERPOSITION
-- Performing Test CXX_SUPPORTS_FNO_SEMANTIC_INTERPOSITION - Success
-- Performing Test SUPPORTS_FVISIBILITY_INLINES_HIDDEN_FLAG
-- Performing Test SUPPORTS_FVISIBILITY_INLINES_HIDDEN_FLAG - Success
-- Performing Test C_SUPPORTS_WERROR_DATE_TIME
-- Performing Test C_SUPPORTS_WERROR_DATE_TIME - Success
-- Performing Test CXX_SUPPORTS_WERROR_DATE_TIME
-- Performing Test CXX_SUPPORTS_WERROR_DATE_TIME - Success
-- Performing Test CXX_SUPPORTS_WERROR_UNGUARDED_AVAILABILITY_NEW
-- Performing Test CXX_SUPPORTS_WERROR_UNGUARDED_AVAILABILITY_NEW - Success
-- Performing Test CXX_SUPPORTS_MISSING_FIELD_INITIALIZERS_FLAG
-- Performing Test CXX_SUPPORTS_MISSING_FIELD_INITIALIZERS_FLAG - Success
-- Performing Test C_SUPPORTS_CXX98_COMPAT_EXTRA_SEMI_FLAG
-- Performing Test C_SUPPORTS_CXX98_COMPAT_EXTRA_SEMI_FLAG - Success
-- Performing Test CXX_SUPPORTS_CXX98_COMPAT_EXTRA_SEMI_FLAG
-- Performing Test CXX_SUPPORTS_CXX98_COMPAT_EXTRA_SEMI_FLAG - Success
-- Performing Test C_SUPPORTS_IMPLICIT_FALLTHROUGH_FLAG
-- Performing Test C_SUPPORTS_IMPLICIT_FALLTHROUGH_FLAG - Success
-- Performing Test CXX_SUPPORTS_IMPLICIT_FALLTHROUGH_FLAG
-- Performing Test CXX_SUPPORTS_IMPLICIT_FALLTHROUGH_FLAG - Success
-- Performing Test C_SUPPORTS_COVERED_SWITCH_DEFAULT_FLAG
-- Performing Test C_SUPPORTS_COVERED_SWITCH_DEFAULT_FLAG - Success
-- Performing Test CXX_SUPPORTS_COVERED_SWITCH_DEFAULT_FLAG
-- Performing Test CXX_SUPPORTS_COVERED_SWITCH_DEFAULT_FLAG - Success
-- Performing Test CXX_SUPPORTS_CLASS_MEMACCESS_FLAG
-- Performing Test CXX_SUPPORTS_CLASS_MEMACCESS_FLAG - Failed
-- Performing Test CXX_SUPPORTS_NOEXCEPT_TYPE_FLAG
-- Performing Test CXX_SUPPORTS_NOEXCEPT_TYPE_FLAG - Success
-- Performing Test CXX_WONT_WARN_ON_FINAL_NONVIRTUALDTOR
-- Performing Test CXX_WONT_WARN_ON_FINAL_NONVIRTUALDTOR - Success
-- Performing Test CXX_SUPPORTS_SUGGEST_OVERRIDE_FLAG
-- Performing Test CXX_SUPPORTS_SUGGEST_OVERRIDE_FLAG - Success
-- Performing Test CXX_WSUGGEST_OVERRIDE_ALLOWS_ONLY_FINAL
-- Performing Test CXX_WSUGGEST_OVERRIDE_ALLOWS_ONLY_FINAL - Success
-- Performing Test C_WCOMMENT_ALLOWS_LINE_WRAP
-- Performing Test C_WCOMMENT_ALLOWS_LINE_WRAP - Success
-- Performing Test C_SUPPORTS_STRING_CONVERSION_FLAG
-- Performing Test C_SUPPORTS_STRING_CONVERSION_FLAG - Success
-- Performing Test CXX_SUPPORTS_STRING_CONVERSION_FLAG
-- Performing Test CXX_SUPPORTS_STRING_CONVERSION_FLAG - Success
-- Performing Test C_SUPPORTS_MISLEADING_INDENTATION_FLAG
-- Performing Test C_SUPPORTS_MISLEADING_INDENTATION_FLAG - Success
-- Performing Test CXX_SUPPORTS_MISLEADING_INDENTATION_FLAG
-- Performing Test CXX_SUPPORTS_MISLEADING_INDENTATION_FLAG - Success
-- Performing Test C_SUPPORTS_CTAD_MAYBE_UNSPPORTED_FLAG
-- Performing Test C_SUPPORTS_CTAD_MAYBE_UNSPPORTED_FLAG - Success
-- Performing Test CXX_SUPPORTS_CTAD_MAYBE_UNSPPORTED_FLAG
-- Performing Test CXX_SUPPORTS_CTAD_MAYBE_UNSPPORTED_FLAG - Success
-- Performing Test C_SUPPORTS_FNO_OMIT_FRAME_POINTER
-- Performing Test C_SUPPORTS_FNO_OMIT_FRAME_POINTER - Success
-- Performing Test CXX_SUPPORTS_FNO_OMIT_FRAME_POINTER
-- Performing Test CXX_SUPPORTS_FNO_OMIT_FRAME_POINTER - Success
-- Performing Test C_SUPPORTS_GLINE_TABLES_ONLY
-- Performing Test C_SUPPORTS_GLINE_TABLES_ONLY - Success
-- Performing Test CXX_SUPPORTS_GLINE_TABLES_ONLY
-- Performing Test CXX_SUPPORTS_GLINE_TABLES_ONLY - Success
-- Performing Test LINKER_SUPPORTS_COLOR_DIAGNOSTICS
-- Performing Test LINKER_SUPPORTS_COLOR_DIAGNOSTICS - Success
-- Performing Test C_SUPPORTS_FNO_FUNCTION_SECTIONS
-- Performing Test C_SUPPORTS_FNO_FUNCTION_SECTIONS - Success
-- Performing Test C_SUPPORTS_FFUNCTION_SECTIONS
-- Performing Test C_SUPPORTS_FFUNCTION_SECTIONS - Success
-- Performing Test CXX_SUPPORTS_FFUNCTION_SECTIONS
-- Performing Test CXX_SUPPORTS_FFUNCTION_SECTIONS - Success
-- Performing Test C_SUPPORTS_FDATA_SECTIONS
-- Performing Test C_SUPPORTS_FDATA_SECTIONS - Success
-- Performing Test CXX_SUPPORTS_FDATA_SECTIONS
-- Performing Test CXX_SUPPORTS_FDATA_SECTIONS - Success
-- Looking for os_signpost_interval_begin
-- Looking for os_signpost_interval_begin - not found
-- Linker detection: unknown
-- Performing Test HAS_WERROR_GLOBAL_CTORS
-- Performing Test HAS_WERROR_GLOBAL_CTORS - Success
-- Performing Test LLVM_HAS_NOGLOBAL_CTOR_MUTEX
-- Performing Test LLVM_HAS_NOGLOBAL_CTOR_MUTEX - Success
-- Looking for __x86_64__
-- Looking for __x86_64__ - found
-- Found Git: /usr/bin/git (found version "2.34.1")
-- Targeting X86
-- Performing Test C_SUPPORTS_WERROR_IMPLICIT_FUNCTION_DECLARATION
-- Performing Test C_SUPPORTS_WERROR_IMPLICIT_FUNCTION_DECLARATION - Success
-- Performing Test C_SUPPORTS_WERROR_MISMATCHED_TAGS
-- Performing Test C_SUPPORTS_WERROR_MISMATCHED_TAGS - Success
-- Performing Test C_SUPPORTS_WERROR_GLOBAL_CONSTRUCTOR
-- Performing Test C_SUPPORTS_WERROR_GLOBAL_CONSTRUCTOR - Success
-- Performing Test CXX_SUPPORTS_WERROR_GLOBAL_CONSTRUCTOR
-- Performing Test CXX_SUPPORTS_WERROR_GLOBAL_CONSTRUCTOR - Success
-- Performing Test COMPILER_SUPPORTS_WARNING_WEAK_VTABLES
-- Performing Test COMPILER_SUPPORTS_WARNING_WEAK_VTABLES - Success
-- Registering ExampleIRTransforms as a pass plugin (static build: OFF)
-- Registering Bye as a pass plugin (static build: OFF)
-- Failed to find LLVM FileCheck

-- git version: v0.0.0 normalized to 0.0.0
-- Version: 1.6.0
-- Looking for shm_open in rt
-- Looking for shm_open in rt - found
-- Performing Test HAVE_CXX_FLAG_STD_CXX11
-- Performing Test HAVE_CXX_FLAG_STD_CXX11 - Success
-- Performing Test HAVE_CXX_FLAG_WALL
-- Performing Test HAVE_CXX_FLAG_WALL - Success
-- Performing Test HAVE_CXX_FLAG_WEXTRA
-- Performing Test HAVE_CXX_FLAG_WEXTRA - Success
-- Performing Test HAVE_CXX_FLAG_WSHADOW
-- Performing Test HAVE_CXX_FLAG_WSHADOW - Success
-- Performing Test HAVE_CXX_FLAG_WSUGGEST_OVERRIDE
-- Performing Test HAVE_CXX_FLAG_WSUGGEST_OVERRIDE - Success
-- Performing Test HAVE_CXX_FLAG_PEDANTIC
-- Performing Test HAVE_CXX_FLAG_PEDANTIC - Success
-- Performing Test HAVE_CXX_FLAG_PEDANTIC_ERRORS
-- Performing Test HAVE_CXX_FLAG_PEDANTIC_ERRORS - Success
-- Performing Test HAVE_CXX_FLAG_WSHORTEN_64_TO_32
-- Performing Test HAVE_CXX_FLAG_WSHORTEN_64_TO_32 - Success
-- Performing Test HAVE_CXX_FLAG_FSTRICT_ALIASING
-- Performing Test HAVE_CXX_FLAG_FSTRICT_ALIASING - Success
-- Performing Test HAVE_CXX_FLAG_WNO_DEPRECATED_DECLARATIONS
-- Performing Test HAVE_CXX_FLAG_WNO_DEPRECATED_DECLARATIONS - Success
-- Performing Test HAVE_CXX_FLAG_FNO_EXCEPTIONS
-- Performing Test HAVE_CXX_FLAG_FNO_EXCEPTIONS - Success
-- Performing Test HAVE_CXX_FLAG_WSTRICT_ALIASING
-- Performing Test HAVE_CXX_FLAG_WSTRICT_ALIASING - Success
-- Performing Test HAVE_CXX_FLAG_WD654
-- Performing Test HAVE_CXX_FLAG_WD654 - Failed
-- Performing Test HAVE_CXX_FLAG_WTHREAD_SAFETY
-- Performing Test HAVE_CXX_FLAG_WTHREAD_SAFETY - Success
-- Performing Test HAVE_THREAD_SAFETY_ATTRIBUTES
-- Performing Test HAVE_THREAD_SAFETY_ATTRIBUTES
-- Performing Test HAVE_THREAD_SAFETY_ATTRIBUTES -- failed to compile
-- Performing Test HAVE_CXX_FLAG_COVERAGE
-- Performing Test HAVE_CXX_FLAG_COVERAGE - Success
-- Performing Test HAVE_GNU_POSIX_REGEX
-- Performing Test HAVE_GNU_POSIX_REGEX
-- Performing Test HAVE_GNU_POSIX_REGEX -- failed to compile
-- Performing Test HAVE_POSIX_REGEX
-- Performing Test HAVE_POSIX_REGEX
-- Performing Test HAVE_POSIX_REGEX -- success
-- Performing Test HAVE_STEADY_CLOCK
-- Performing Test HAVE_STEADY_CLOCK
-- Performing Test HAVE_STEADY_CLOCK -- success
-- Configuring done

-- Generating done
-- Build files have been written to: /mnt/e/opensource/llvm-project/build
$ cmake --build . --target check-mlir
[3488/3489] Running the MLIR regression tests

Testing Time: 234.88s
  Unsupported:  143
  Passed     : 1911

2.3 测试解析

整个MLIR的编译路径：（引用自：MLIR的惊鸿一瞥 - 知乎 (zhihu.com)）

2.3.1 源程序

位置：mlir\test\Examples\Toy\Ch2\codegen.toy

# RUN: toyc-ch2 %s -emit=mlir 2>&1 | FileCheck %s

# User defined generic function that operates on unknown shaped arguments
def multiply_transpose(a, b) {
  return transpose(a) * transpose(b);
}

def main() {
  var a<2, 3> = [[1, 2, 3], [4, 5, 6]];
  var b<2, 3> = [1, 2, 3, 4, 5, 6];
  var c = multiply_transpose(a, b);
  var d = multiply_transpose(b, a);
  print(d);
}

# CHECK-LABEL: toy.func @multiply_transpose(
# CHECK-SAME:                               [[VAL_0:%.*]]: tensor<*xf64>, [[VAL_1:%.*]]: tensor<*xf64>) -> tensor<*xf64>
# CHECK:         [[VAL_2:%.*]] = toy.transpose([[VAL_0]] : tensor<*xf64>) to tensor<*xf64>
# CHECK-NEXT:    [[VAL_3:%.*]] = toy.transpose([[VAL_1]] : tensor<*xf64>) to tensor<*xf64>
# CHECK-NEXT:    [[VAL_4:%.*]] = toy.mul [[VAL_2]], [[VAL_3]] :  tensor<*xf64>
# CHECK-NEXT:    toy.return [[VAL_4]] : tensor<*xf64>

# CHECK-LABEL: toy.func @main()
# CHECK-NEXT:    [[VAL_5:%.*]] = toy.constant dense<{{\[\[}}1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64>
# CHECK-NEXT:    [[VAL_6:%.*]] = toy.reshape([[VAL_5]] : tensor<2x3xf64>) to tensor<2x3xf64>
# CHECK-NEXT:    [[VAL_7:%.*]] = toy.constant dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00, 5.000000e+00, 6.000000e+00]> : tensor<6xf64>
# CHECK-NEXT:    [[VAL_8:%.*]] = toy.reshape([[VAL_7]] : tensor<6xf64>) to tensor<2x3xf64>
# CHECK-NEXT:    [[VAL_9:%.*]] = toy.generic_call @multiply_transpose([[VAL_6]], [[VAL_8]]) : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64>
# CHECK-NEXT:    [[VAL_10:%.*]] = toy.generic_call @multiply_transpose([[VAL_8]], [[VAL_6]]) : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64>
# CHECK-NEXT:    toy.print [[VAL_10]] : tensor<*xf64>
# CHECK-NEXT:    toy.return

2.3.2 将源程序生成抽象语法树(AST)

llvm-project/build$ bin/toyc-ch2 ../mlir/test/Examples/Toy/Ch2/codegen.toy -emit=ast
  Module:
    Function
      Proto 'multiply_transpose' @../mlir/test/Examples/Toy/Ch2/codegen.toy:4:1
      Params: [a, b]
      Block {
        Return
          BinOp: * @../mlir/test/Examples/Toy/Ch2/codegen.toy:5:25
            Call 'transpose' [ @../mlir/test/Examples/Toy/Ch2/codegen.toy:5:10
              var: a @../mlir/test/Examples/Toy/Ch2/codegen.toy:5:20
            ]
            Call 'transpose' [ @../mlir/test/Examples/Toy/Ch2/codegen.toy:5:25
              var: b @../mlir/test/Examples/Toy/Ch2/codegen.toy:5:35
            ]
      } // Block
    Function
      Proto 'main' @../mlir/test/Examples/Toy/Ch2/codegen.toy:8:1
      Params: []
      Block {
        VarDecl a<2, 3> @../mlir/test/Examples/Toy/Ch2/codegen.toy:9:3
          Literal: <2, 3>[ <3>[ 1.000000e+00, 2.000000e+00, 3.000000e+00], <3>[ 4.000000e+00, 5.000000e+00, 6.000000e+00]] @../mlir/test/Examples/Toy/Ch2/codegen.toy:9:17
        VarDecl b<2, 3> @../mlir/test/Examples/Toy/Ch2/codegen.toy:10:3
          Literal: <6>[ 1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00, 5.000000e+00, 6.000000e+00] @../mlir/test/Examples/Toy/Ch2/codegen.toy:10:17
        VarDecl c<> @../mlir/test/Examples/Toy/Ch2/codegen.toy:11:3
          Call 'multiply_transpose' [ @../mlir/test/Examples/Toy/Ch2/codegen.toy:11:11
            var: a @../mlir/test/Examples/Toy/Ch2/codegen.toy:11:30
            var: b @../mlir/test/Examples/Toy/Ch2/codegen.toy:11:33
          ]
        VarDecl d<> @../mlir/test/Examples/Toy/Ch2/codegen.toy:12:3
          Call 'multiply_transpose' [ @../mlir/test/Examples/Toy/Ch2/codegen.toy:12:11
            var: b @../mlir/test/Examples/Toy/Ch2/codegen.toy:12:30
            var: a @../mlir/test/Examples/Toy/Ch2/codegen.toy:12:33
          ]
        Print [ @../mlir/test/Examples/Toy/Ch2/codegen.toy:13:3
          var: d @../mlir/test/Examples/Toy/Ch2/codegen.toy:13:9
        ]
      } // Block

2.3.3 将抽象语法树(AST)生成MLIR表达式

llvm-project/build$  bin/toyc-ch2 ../mlir/test/Examples/Toy/Ch2/codegen.toy -emit=mlir -mlir-print-debuginfo
module {
  toy.func @multiply_transpose(%arg0: tensor<*xf64> loc("../mlir/test/Examples/Toy/Ch2/codegen.toy":4:1), %arg1: tensor<*xf64> loc("../mlir/test/Examples/Toy/Ch2/codegen.toy":4:1)) -> tensor<*xf64> {
    %0 = toy.transpose(%arg0 : tensor<*xf64>) to tensor<*xf64> loc("../mlir/test/Examples/Toy/Ch2/codegen.toy":5:10)
    %1 = toy.transpose(%arg1 : tensor<*xf64>) to tensor<*xf64> loc("../mlir/test/Examples/Toy/Ch2/codegen.toy":5:25)
    %2 = toy.mul %0, %1 : tensor<*xf64> loc("../mlir/test/Examples/Toy/Ch2/codegen.toy":5:25)
    toy.return %2 : tensor<*xf64> loc("../mlir/test/Examples/Toy/Ch2/codegen.toy":5:3)
  } loc("../mlir/test/Examples/Toy/Ch2/codegen.toy":4:1)
  toy.func @main() {
    %0 = toy.constant dense<[[1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64> loc("../mlir/test/Examples/Toy/Ch2/codegen.toy":9:17)
    %1 = toy.reshape(%0 : tensor<2x3xf64>) to tensor<2x3xf64> loc("../mlir/test/Examples/Toy/Ch2/codegen.toy":9:3)
    %2 = toy.constant dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00, 5.000000e+00, 6.000000e+00]> : tensor<6xf64> loc("../mlir/test/Examples/Toy/Ch2/codegen.toy":10:17)
    %3 = toy.reshape(%2 : tensor<6xf64>) to tensor<2x3xf64> loc("../mlir/test/Examples/Toy/Ch2/codegen.toy":10:3)
    %4 = toy.generic_call @multiply_transpose(%1, %3) : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64> loc("../mlir/test/Examples/Toy/Ch2/codegen.toy":11:11)
    %5 = toy.generic_call @multiply_transpose(%3, %1) : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64> loc("../mlir/test/Examples/Toy/Ch2/codegen.toy":12:11)
    toy.print %5 : tensor<*xf64> loc("../mlir/test/Examples/Toy/Ch2/codegen.toy":13:3)
    toy.return loc("../mlir/test/Examples/Toy/Ch2/codegen.toy":8:1)
  } loc("../mlir/test/Examples/Toy/Ch2/codegen.toy":8:1)
} loc(unknown)

MLIR表达式由操作结果名称、Dialect命名空间、操作名、参数列表、输入参数类型、输出类型和操作在源文件中的位置组成。以transpose为例：

 3 MLIR三要素

3.1 MLIRGen模块

MLIRGen根据AST各节点的类型，递归调用子函数，子函数内部再根据不同情况，进行相应操作。

  /// Emit a call expression. It emits specific operations for the `transpose`
  /// builtin. Other identifiers are assumed to be user-defined functions.
  mlir::Value mlirGen(CallExprAST &call) {
    llvm::StringRef callee = call.getCallee();
    auto location = loc(call.loc());

    // Codegen the operands first.
    SmallVector<mlir::Value, 4> operands;
    for (auto &expr : call.getArgs()) {
      auto arg = mlirGen(*expr);
      if (!arg)
        return nullptr;
      operands.push_back(arg);
    }

    // Builtin calls have their custom operation, meaning this is a
    // straightforward emission.
    if (callee == "transpose") {
      if (call.getArgs().size() != 1) {
        emitError(location, "MLIR codegen encountered an error: toy.transpose "
                            "does not accept multiple arguments");
        return nullptr;
      }
      return builder.create<TransposeOp>(location, operands[0]);
    }

    // Otherwise this is a call to a user-defined function. Calls to
    // user-defined functions are mapped to a custom call that takes the callee
    // name as an attribute.
    return builder.create<GenericCallOp>(location, callee, operands);
  }

3.2 Dialect模块

Dialect模块负责定义各种操作和分析，同时还具备可扩展性。对于transpose(a)来说，Dialect模块负责给这个转置操作添加相应的类型和操作数的值。

//===----------------------------------------------------------------------===//
// TransposeOp
//===----------------------------------------------------------------------===//

void TransposeOp::build(mlir::OpBuilder &builder, mlir::OperationState &state,
                        mlir::Value value) {
  state.addTypes(UnrankedTensorType::get(builder.getF64Type()));
  state.addOperands(value);
}

3.3 TableGen模块

Operation Definition Specification (ODS)框架是基于TableGen规范构造的。用于描述MLIR中Operation的类的定义，在源代码中它以.td文件的形式存在，在编译时会自动生成C++的相应文件，给Dialect模块文件提供支持。

如果我们使用手动编写的方式，在针对不同编译目标时，我们需要在一系列不同文件中编写一些相同的代码，这就造成了冗余的开发。而使用TableGen，我们只需要修改.td文件即可实现批量修改，也就解决了上述问题。

在Toy语言程序例子中，.td文件是由什么组成的，而TableGen又是怎么发挥的作用，按如下三步来解析。

3.3.1 定义一个和Toy Dialect的链接

在.td文件中定义一个TableGen和Dialect的链接，它负责把在Dialect中定义的所有Operation整合起来，mlir\examples\toy\Ch2\include\toy\Ops.td代码如下：

// Provide a definition of the 'toy' dialect in the ODS framework so that we
// can define our operations.
def Toy_Dialect : Dialect {
  let name = "toy";
  let cppNamespace = "::mlir::toy";
}

3.3.2 创建一个Toy Dialect Operation的基类

构造所有Dialect Operation的基类Toy_Op，所有的Operation类都将基于此类进行构造，mlir\examples\toy\Ch2\include\toy\Ops.td代码如下：

// Base class for toy dialect operations. This operation inherits from the base
// `Op` class in OpBase.td, and provides:
//   * The parent dialect of the operation.
//   * The mnemonic for the operation, or the name without the dialect prefix.
//   * A list of traits for the operation.
class Toy_Op<string mnemonic, list<Trait> traits = []> :
    Op<Toy_Dialect, mnemonic, traits>;

3.3.3 创建Toy Dialect各种Operation的类

所有定义的Operation的类都继承自上述基类，以mlir\examples\toy\Ch2\include\toy\Ops.td代码中TransposeOp为例，使用TableGen的规则 定义参数、值、builder、verifier等元素：

def TransposeOp : Toy_Op<"transpose"> {
  let summary = "transpose operation";

  let arguments = (ins F64Tensor:$input);
  let results = (outs F64Tensor);

  let assemblyFormat = [{
    `(` $input `:` type($input) `)` attr-dict `to` type(results)
  }];

  // Allow building a TransposeOp with from the input operand.
  let builders = [
    OpBuilder<(ins "Value":$input)>
  ];

  // Invoke a static verify method to verify this transpose operation.
  let hasVerifier = 1;
}

3.3.4 生成C++代码

在编写完TableGen描述之后，我们可以使用mlir-tblgen工具来生成C++代码，在编译时，TableGen将会发挥作用，把.td文件生成为C++的文件，而上述生成的代码将给Dialect模块提供支持。操作步骤如下：

llvm-project/build$ bin/mlir-tblgen -gen-op-defs ../mlir/examples/toy/Ch2/include/toy/Ops.td -I ../mlir/include/ > toy_chn2.cpp

生成的代码如下

/*===- TableGen'erated file -------------------------------------*- C++ -*-===*\
|*                                                                            *|
|* Op Definitions                                                             *|
|*                                                                            *|
|* Automatically generated file, do not edit!                                 *|
|*                                                                            *|
\*===----------------------------------------------------------------------===*/

#ifdef GET_OP_LIST
#undef GET_OP_LIST

::mlir::toy::AddOp,
::mlir::toy::ConstantOp,
::mlir::toy::FuncOp,
::mlir::toy::GenericCallOp,
::mlir::toy::MulOp,
::mlir::toy::PrintOp,
::mlir::toy::ReshapeOp,
::mlir::toy::ReturnOp,
::mlir::toy::TransposeOp
#endif  // GET_OP_LIST

#ifdef GET_OP_CLASSES
#undef GET_OP_CLASSES


//===----------------------------------------------------------------------===//
// Local Utility Method Definitions
//===----------------------------------------------------------------------===//

namespace mlir {
namespace toy {

static ::mlir::LogicalResult __mlir_ods_local_type_constraint_Ops0(
    ::mlir::Operation *op, ::mlir::Type type, ::llvm::StringRef valueKind,
    unsigned valueIndex) {
  if (!(((type.isa<::mlir::TensorType>())) && ([](::mlir::Type elementType) { return (elementType.isF64()); }(type.cast<::mlir::ShapedType>().getElementType())))) {
    return op->emitOpError(valueKind) << " #" << valueIndex
        << " must be tensor of 64-bit float values, but got " << type;
  }
  return ::mlir::success();
}

static ::mlir::LogicalResult __mlir_ods_local_type_constraint_Ops1(
    ::mlir::Operation *op, ::mlir::Type type, ::llvm::StringRef valueKind,
    unsigned valueIndex) {
  if (!((((type.isa<::mlir::RankedTensorType>())) && ((type.cast<::mlir::ShapedType>().hasStaticShape()))) && ([](::mlir::Type elementType) { return (elementType.isF64()); }(type.cast<::mlir::ShapedType>().getElementType())))) {
    return op->emitOpError(valueKind) << " #" << valueIndex
        << " must be statically shaped tensor of 64-bit float values, but got " << type;
  }
  return ::mlir::success();
}

static ::mlir::LogicalResult __mlir_ods_local_attr_constraint_Ops0(
    ::mlir::Operation *op, ::mlir::Attribute attr, ::llvm::StringRef attrName) {
  if (attr && !((attr.isa<::mlir::DenseFPElementsAttr>() &&attr.cast<::mlir::DenseElementsAttr>().getType().getElementType().isF64()))) {
    return op->emitOpError("attribute '") << attrName
        << "' failed to satisfy constraint: 64-bit float elements attribute";
  }
  return ::mlir::success();
}

static ::mlir::LogicalResult __mlir_ods_local_attr_constraint_Ops1(
    ::mlir::Operation *op, ::mlir::Attribute attr, ::llvm::StringRef attrName) {
  if (attr && !((attr.isa<::mlir::StringAttr>()))) {
    return op->emitOpError("attribute '") << attrName
        << "' failed to satisfy constraint: string attribute";
  }
  return ::mlir::success();
}

static ::mlir::LogicalResult __mlir_ods_local_attr_constraint_Ops2(
    ::mlir::Operation *op, ::mlir::Attribute attr, ::llvm::StringRef attrName) {
  if (attr && !(((attr.isa<::mlir::TypeAttr>())) && ((attr.cast<::mlir::TypeAttr>().getValue().isa<::mlir::FunctionType>())) && ((attr.cast<::mlir::TypeAttr>().getValue().isa<::mlir::FunctionType>())))) {
    return op->emitOpError("attribute '") << attrName
        << "' failed to satisfy constraint: type attribute of function type";
  }
  return ::mlir::success();
}

static ::mlir::LogicalResult __mlir_ods_local_attr_constraint_Ops3(
    ::mlir::Operation *op, ::mlir::Attribute attr, ::llvm::StringRef attrName) {
  if (attr && !(((attr.isa<::mlir::ArrayAttr>())) && (::llvm::all_of(attr.cast<::mlir::ArrayAttr>(), [&](::mlir::Attribute attr) { return attr && ((attr.isa<::mlir::DictionaryAttr>())); })))) {
    return op->emitOpError("attribute '") << attrName
        << "' failed to satisfy constraint: Array of dictionary attributes";
  }
  return ::mlir::success();
}

static ::mlir::LogicalResult __mlir_ods_local_attr_constraint_Ops4(
    ::mlir::Operation *op, ::mlir::Attribute attr, ::llvm::StringRef attrName) {
  if (attr && !((attr.isa<::mlir::FlatSymbolRefAttr>()))) {
    return op->emitOpError("attribute '") << attrName
        << "' failed to satisfy constraint: flat symbol reference attribute";
  }
  return ::mlir::success();
}

static ::mlir::LogicalResult __mlir_ods_local_region_constraint_Ops0(
    ::mlir::Operation *op, ::mlir::Region &region, ::llvm::StringRef regionName,
    unsigned regionIndex) {
  if (!((true))) {
    return op->emitOpError("region #") << regionIndex
        << (regionName.empty() ? " " : " ('" + regionName + "') ")
        << "failed to verify constraint: any region";
  }
  return ::mlir::success();
}
} // namespace toy
} // namespace mlir
namespace mlir {
namespace toy {

//===----------------------------------------------------------------------===//
// ::mlir::toy::AddOp definitions
//===----------------------------------------------------------------------===//

namespace detail {
AddOpGenericAdaptorBase::AddOpGenericAdaptorBase(::mlir::DictionaryAttr attrs, ::mlir::RegionRange regions) : odsAttrs(attrs), odsRegions(regions) {  if (odsAttrs)
    odsOpName.emplace("toy.add", odsAttrs.getContext());
}

std::pair<unsigned, unsigned> AddOpGenericAdaptorBase::getODSOperandIndexAndLength(unsigned index, unsigned odsOperandsSize) {
  return {index, 1};
}

::mlir::DictionaryAttr AddOpGenericAdaptorBase::getAttributes() {
  return odsAttrs;
}

} // namespace detail
AddOpAdaptor::AddOpAdaptor(AddOp op) : AddOpAdaptor(op->getOperands(), op->getAttrDictionary(), op->getRegions()) {}

::mlir::LogicalResult AddOpAdaptor::verify(::mlir::Location loc) {
  return ::mlir::success();
}

std::pair<unsigned, unsigned> AddOp::getODSOperandIndexAndLength(unsigned index) {
  return {index, 1};
}

::mlir::Operation::operand_range AddOp::getODSOperands(unsigned index) {
  auto valueRange = getODSOperandIndexAndLength(index);
  return {std::next(getOperation()->operand_begin(), valueRange.first),
           std::next(getOperation()->operand_begin(), valueRange.first + valueRange.second)};
}

::mlir::TypedValue<::mlir::TensorType> AddOp::getLhs() {
  return ::llvm::cast<::mlir::TypedValue<::mlir::TensorType>>(*getODSOperands(0).begin());
}

::mlir::TypedValue<::mlir::TensorType> AddOp::getRhs() {
  return ::llvm::cast<::mlir::TypedValue<::mlir::TensorType>>(*getODSOperands(1).begin());
}

::mlir::MutableOperandRange AddOp::getLhsMutable() {
  auto range = getODSOperandIndexAndLength(0);
  auto mutableRange = ::mlir::MutableOperandRange(getOperation(), range.first, range.second);
  return mutableRange;
}

::mlir::MutableOperandRange AddOp::getRhsMutable() {
  auto range = getODSOperandIndexAndLength(1);
  auto mutableRange = ::mlir::MutableOperandRange(getOperation(), range.first, range.second);
  return mutableRange;
}

std::pair<unsigned, unsigned> AddOp::getODSResultIndexAndLength(unsigned index) {
  return {index, 1};
}

::mlir::Operation::result_range AddOp::getODSResults(unsigned index) {
  auto valueRange = getODSResultIndexAndLength(index);
  return {std::next(getOperation()->result_begin(), valueRange.first),
           std::next(getOperation()->result_begin(), valueRange.first + valueRange.second)};
}

void AddOp::build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::Type resultType0, ::mlir::Value lhs, ::mlir::Value rhs) {
  odsState.addOperands(lhs);
  odsState.addOperands(rhs);
  odsState.addTypes(resultType0);
}

void AddOp::build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::TypeRange resultTypes, ::mlir::Value lhs, ::mlir::Value rhs) {
  odsState.addOperands(lhs);
  odsState.addOperands(rhs);
  assert(resultTypes.size() == 1u && "mismatched number of results");
  odsState.addTypes(resultTypes);
}

void AddOp::build(::mlir::OpBuilder &, ::mlir::OperationState &odsState, ::mlir::TypeRange resultTypes, ::mlir::ValueRange operands, ::llvm::ArrayRef<::mlir::NamedAttribute> attributes) {
  assert(operands.size() == 2u && "mismatched number of parameters");
  odsState.addOperands(operands);
  odsState.addAttributes(attributes);
  assert(resultTypes.size() == 1u && "mismatched number of return types");
  odsState.addTypes(resultTypes);
}

::mlir::LogicalResult AddOp::verifyInvariantsImpl() {
  {
    unsigned index = 0; (void)index;
    auto valueGroup0 = getODSOperands(0);

    for (auto v : valueGroup0) {
      if (::mlir::failed(__mlir_ods_local_type_constraint_Ops0(*this, v.getType(), "operand", index++)))
        return ::mlir::failure();
    }
    auto valueGroup1 = getODSOperands(1);

    for (auto v : valueGroup1) {
      if (::mlir::failed(__mlir_ods_local_type_constraint_Ops0(*this, v.getType(), "operand", index++)))
        return ::mlir::failure();
    }
  }
  {
    unsigned index = 0; (void)index;
    auto valueGroup0 = getODSResults(0);

    for (auto v : valueGroup0) {
      if (::mlir::failed(__mlir_ods_local_type_constraint_Ops0(*this, v.getType(), "result", index++)))
        return ::mlir::failure();
    }
  }
  return ::mlir::success();
}

::mlir::LogicalResult AddOp::verifyInvariants() {
  return verifyInvariantsImpl();
}

} // namespace toy
} // namespace mlir
MLIR_DEFINE_EXPLICIT_TYPE_ID(::mlir::toy::AddOp)

namespace mlir {
namespace toy {

//===----------------------------------------------------------------------===//
// ::mlir::toy::ConstantOp definitions
//===----------------------------------------------------------------------===//

namespace detail {
ConstantOpGenericAdaptorBase::ConstantOpGenericAdaptorBase(::mlir::DictionaryAttr attrs, ::mlir::RegionRange regions) : odsAttrs(attrs), odsRegions(regions) {  if (odsAttrs)
    odsOpName.emplace("toy.constant", odsAttrs.getContext());
}

std::pair<unsigned, unsigned> ConstantOpGenericAdaptorBase::getODSOperandIndexAndLength(unsigned index, unsigned odsOperandsSize) {
  return {index, 1};
}

::mlir::DictionaryAttr ConstantOpGenericAdaptorBase::getAttributes() {
  return odsAttrs;
}

::mlir::DenseElementsAttr ConstantOpGenericAdaptorBase::getValueAttr() {
  assert(odsAttrs && "no attributes when constructing adapter");
  auto attr = ::mlir::impl::getAttrFromSortedRange(odsAttrs.begin() + 0, odsAttrs.end() - 0, ConstantOp::getValueAttrName(*odsOpName)).cast<::mlir::DenseElementsAttr>();
  return attr;
}

::mlir::DenseElementsAttr ConstantOpGenericAdaptorBase::getValue() {
  auto attr = getValueAttr();
  return attr;
}

} // namespace detail
ConstantOpAdaptor::ConstantOpAdaptor(ConstantOp op) : ConstantOpAdaptor(op->getOperands(), op->getAttrDictionary(), op->getRegions()) {}

::mlir::LogicalResult ConstantOpAdaptor::verify(::mlir::Location loc) {
  auto namedAttrRange = odsAttrs;
  auto namedAttrIt = namedAttrRange.begin();
  ::mlir::Attribute tblgen_value;
  while (true) {
    if (namedAttrIt == namedAttrRange.end())
      return emitError(loc, "'toy.constant' op ""requires attribute 'value'");
    if (namedAttrIt->getName() == ConstantOp::getValueAttrName(*odsOpName)) {
      tblgen_value = namedAttrIt->getValue();
      break;
    }
    ++namedAttrIt;
  }

  if (tblgen_value && !((tblgen_value.isa<::mlir::DenseFPElementsAttr>() &&tblgen_value.cast<::mlir::DenseElementsAttr>().getType().getElementType().isF64())))
    return emitError(loc, "'toy.constant' op ""attribute 'value' failed to satisfy constraint: 64-bit float elements attribute");
  return ::mlir::success();
}

std::pair<unsigned, unsigned> ConstantOp::getODSOperandIndexAndLength(unsigned index) {
  return {index, 1};
}

::mlir::Operation::operand_range ConstantOp::getODSOperands(unsigned index) {
  auto valueRange = getODSOperandIndexAndLength(index);
  return {std::next(getOperation()->operand_begin(), valueRange.first),
           std::next(getOperation()->operand_begin(), valueRange.first + valueRange.second)};
}

std::pair<unsigned, unsigned> ConstantOp::getODSResultIndexAndLength(unsigned index) {
  return {index, 1};
}

::mlir::Operation::result_range ConstantOp::getODSResults(unsigned index) {
  auto valueRange = getODSResultIndexAndLength(index);
  return {std::next(getOperation()->result_begin(), valueRange.first),
           std::next(getOperation()->result_begin(), valueRange.first + valueRange.second)};
}

::mlir::DenseElementsAttr ConstantOp::getValueAttr() {
  return ::mlir::impl::getAttrFromSortedRange((*this)->getAttrs().begin() + 0, (*this)->getAttrs().end() - 0, getValueAttrName()).cast<::mlir::DenseElementsAttr>();
}

::mlir::DenseElementsAttr ConstantOp::getValue() {
  auto attr = getValueAttr();
  return attr;
}

void ConstantOp::setValueAttr(::mlir::DenseElementsAttr attr) {
  (*this)->setAttr(getValueAttrName(), attr);
}

void ConstantOp::build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, DenseElementsAttr value) {
      build(odsBuilder, odsState, value.getType(), value);
    
}

void ConstantOp::build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::Type resultType0, ::mlir::DenseElementsAttr value) {
  odsState.addAttribute(getValueAttrName(odsState.name), value);
  odsState.addTypes(resultType0);
}

void ConstantOp::build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::TypeRange resultTypes, ::mlir::DenseElementsAttr value) {
  odsState.addAttribute(getValueAttrName(odsState.name), value);
  assert(resultTypes.size() == 1u && "mismatched number of results");
  odsState.addTypes(resultTypes);
}

void ConstantOp::build(::mlir::OpBuilder &, ::mlir::OperationState &odsState, ::mlir::TypeRange resultTypes, ::mlir::ValueRange operands, ::llvm::ArrayRef<::mlir::NamedAttribute> attributes) {
  assert(operands.size() == 0u && "mismatched number of parameters");
  odsState.addOperands(operands);
  odsState.addAttributes(attributes);
  assert(resultTypes.size() == 1u && "mismatched number of return types");
  odsState.addTypes(resultTypes);
}

::mlir::LogicalResult ConstantOp::verifyInvariantsImpl() {
  auto namedAttrRange = (*this)->getAttrs();
  auto namedAttrIt = namedAttrRange.begin();
  ::mlir::Attribute tblgen_value;
  while (true) {
    if (namedAttrIt == namedAttrRange.end())
      return emitOpError("requires attribute 'value'");
    if (namedAttrIt->getName() == getValueAttrName()) {
      tblgen_value = namedAttrIt->getValue();
      break;
    }
    ++namedAttrIt;
  }

  if (::mlir::failed(__mlir_ods_local_attr_constraint_Ops0(*this, tblgen_value, "value")))
    return ::mlir::failure();
  {
    unsigned index = 0; (void)index;
    auto valueGroup0 = getODSResults(0);

    for (auto v : valueGroup0) {
      if (::mlir::failed(__mlir_ods_local_type_constraint_Ops0(*this, v.getType(), "result", index++)))
        return ::mlir::failure();
    }
  }
  return ::mlir::success();
}

::mlir::LogicalResult ConstantOp::verifyInvariants() {
  if(::mlir::succeeded(verifyInvariantsImpl()) && ::mlir::succeeded(verify()))
    return ::mlir::success();
  return ::mlir::failure();
}

void ConstantOp::getEffects(::llvm::SmallVectorImpl<::mlir::SideEffects::EffectInstance<::mlir::MemoryEffects::Effect>> &effects) {
}

} // namespace toy
} // namespace mlir
MLIR_DEFINE_EXPLICIT_TYPE_ID(::mlir::toy::ConstantOp)

namespace mlir {
namespace toy {

//===----------------------------------------------------------------------===//
// ::mlir::toy::FuncOp definitions
//===----------------------------------------------------------------------===//

namespace detail {
FuncOpGenericAdaptorBase::FuncOpGenericAdaptorBase(::mlir::DictionaryAttr attrs, ::mlir::RegionRange regions) : odsAttrs(attrs), odsRegions(regions) {  if (odsAttrs)
    odsOpName.emplace("toy.func", odsAttrs.getContext());
}

std::pair<unsigned, unsigned> FuncOpGenericAdaptorBase::getODSOperandIndexAndLength(unsigned index, unsigned odsOperandsSize) {
  return {index, 1};
}

::mlir::DictionaryAttr FuncOpGenericAdaptorBase::getAttributes() {
  return odsAttrs;
}

::mlir::StringAttr FuncOpGenericAdaptorBase::getSymNameAttr() {
  assert(odsAttrs && "no attributes when constructing adapter");
  auto attr = ::mlir::impl::getAttrFromSortedRange(odsAttrs.begin() + 1, odsAttrs.end() - 0, FuncOp::getSymNameAttrName(*odsOpName)).cast<::mlir::StringAttr>();
  return attr;
}

::llvm::StringRef FuncOpGenericAdaptorBase::getSymName() {
  auto attr = getSymNameAttr();
  return attr.getValue();
}

::mlir::TypeAttr FuncOpGenericAdaptorBase::getFunctionTypeAttr() {
  assert(odsAttrs && "no attributes when constructing adapter");
  auto attr = ::mlir::impl::getAttrFromSortedRange(odsAttrs.begin() + 0, odsAttrs.end() - 1, FuncOp::getFunctionTypeAttrName(*odsOpName)).cast<::mlir::TypeAttr>();
  return attr;
}

::mlir::FunctionType FuncOpGenericAdaptorBase::getFunctionType() {
  auto attr = getFunctionTypeAttr();
  return attr.getValue().cast<::mlir::FunctionType>();
}

::mlir::ArrayAttr FuncOpGenericAdaptorBase::getArgAttrsAttr() {
  assert(odsAttrs && "no attributes when constructing adapter");
  auto attr = ::mlir::impl::getAttrFromSortedRange(odsAttrs.begin() + 0, odsAttrs.end() - 2, FuncOp::getArgAttrsAttrName(*odsOpName)).dyn_cast_or_null<::mlir::ArrayAttr>();
  return attr;
}

::std::optional< ::mlir::ArrayAttr > FuncOpGenericAdaptorBase::getArgAttrs() {
  auto attr = getArgAttrsAttr();
  return attr ? ::std::optional< ::mlir::ArrayAttr >(attr) : (::std::nullopt);
}

::mlir::ArrayAttr FuncOpGenericAdaptorBase::getResAttrsAttr() {
  assert(odsAttrs && "no attributes when constructing adapter");
  auto attr = ::mlir::impl::getAttrFromSortedRange(odsAttrs.begin() + 1, odsAttrs.end() - 1, FuncOp::getResAttrsAttrName(*odsOpName)).dyn_cast_or_null<::mlir::ArrayAttr>();
  return attr;
}

::std::optional< ::mlir::ArrayAttr > FuncOpGenericAdaptorBase::getResAttrs() {
  auto attr = getResAttrsAttr();
  return attr ? ::std::optional< ::mlir::ArrayAttr >(attr) : (::std::nullopt);
}

::mlir::Region &FuncOpGenericAdaptorBase::getBody() {
  return *odsRegions[0];
}

::mlir::RegionRange FuncOpGenericAdaptorBase::getRegions() {
  return odsRegions;
}

} // namespace detail
FuncOpAdaptor::FuncOpAdaptor(FuncOp op) : FuncOpAdaptor(op->getOperands(), op->getAttrDictionary(), op->getRegions()) {}

::mlir::LogicalResult FuncOpAdaptor::verify(::mlir::Location loc) {
  auto namedAttrRange = odsAttrs;
  auto namedAttrIt = namedAttrRange.begin();
  ::mlir::Attribute tblgen_function_type;
  ::mlir::Attribute tblgen_arg_attrs;
  while (true) {
    if (namedAttrIt == namedAttrRange.end())
      return emitError(loc, "'toy.func' op ""requires attribute 'function_type'");
    if (namedAttrIt->getName() == FuncOp::getFunctionTypeAttrName(*odsOpName)) {
      tblgen_function_type = namedAttrIt->getValue();
      break;
    }
    else if (namedAttrIt->getName() == FuncOp::getArgAttrsAttrName(*odsOpName)) {
      tblgen_arg_attrs = namedAttrIt->getValue();
    }
    ++namedAttrIt;
  }
  ::mlir::Attribute tblgen_sym_name;
  ::mlir::Attribute tblgen_res_attrs;
  while (true) {
    if (namedAttrIt == namedAttrRange.end())
      return emitError(loc, "'toy.func' op ""requires attribute 'sym_name'");
    if (namedAttrIt->getName() == FuncOp::getSymNameAttrName(*odsOpName)) {
      tblgen_sym_name = namedAttrIt->getValue();
      break;
    }
    else if (namedAttrIt->getName() == FuncOp::getResAttrsAttrName(*odsOpName)) {
      tblgen_res_attrs = namedAttrIt->getValue();
    }
    ++namedAttrIt;
  }

  if (tblgen_sym_name && !((tblgen_sym_name.isa<::mlir::StringAttr>())))
    return emitError(loc, "'toy.func' op ""attribute 'sym_name' failed to satisfy constraint: string attribute");

  if (tblgen_function_type && !(((tblgen_function_type.isa<::mlir::TypeAttr>())) && ((tblgen_function_type.cast<::mlir::TypeAttr>().getValue().isa<::mlir::FunctionType>())) && ((tblgen_function_type.cast<::mlir::TypeAttr>().getValue().isa<::mlir::FunctionType>()))))
    return emitError(loc, "'toy.func' op ""attribute 'function_type' failed to satisfy constraint: type attribute of function type");

  if (tblgen_arg_attrs && !(((tblgen_arg_attrs.isa<::mlir::ArrayAttr>())) && (::llvm::all_of(tblgen_arg_attrs.cast<::mlir::ArrayAttr>(), [&](::mlir::Attribute attr) { return attr && ((attr.isa<::mlir::DictionaryAttr>())); }))))
    return emitError(loc, "'toy.func' op ""attribute 'arg_attrs' failed to satisfy constraint: Array of dictionary attributes");

  if (tblgen_res_attrs && !(((tblgen_res_attrs.isa<::mlir::ArrayAttr>())) && (::llvm::all_of(tblgen_res_attrs.cast<::mlir::ArrayAttr>(), [&](::mlir::Attribute attr) { return attr && ((attr.isa<::mlir::DictionaryAttr>())); }))))
    return emitError(loc, "'toy.func' op ""attribute 'res_attrs' failed to satisfy constraint: Array of dictionary attributes");
  return ::mlir::success();
}

std::pair<unsigned, unsigned> FuncOp::getODSOperandIndexAndLength(unsigned index) {
  return {index, 1};
}

::mlir::Operation::operand_range FuncOp::getODSOperands(unsigned index) {
  auto valueRange = getODSOperandIndexAndLength(index);
  return {std::next(getOperation()->operand_begin(), valueRange.first),
           std::next(getOperation()->operand_begin(), valueRange.first + valueRange.second)};
}

std::pair<unsigned, unsigned> FuncOp::getODSResultIndexAndLength(unsigned index) {
  return {index, 1};
}

::mlir::Operation::result_range FuncOp::getODSResults(unsigned index) {
  auto valueRange = getODSResultIndexAndLength(index);
  return {std::next(getOperation()->result_begin(), valueRange.first),
           std::next(getOperation()->result_begin(), valueRange.first + valueRange.second)};
}

::mlir::Region &FuncOp::getBody() {
  return (*this)->getRegion(0);
}

::mlir::StringAttr FuncOp::getSymNameAttr() {
  return ::mlir::impl::getAttrFromSortedRange((*this)->getAttrs().begin() + 1, (*this)->getAttrs().end() - 0, getSymNameAttrName()).cast<::mlir::StringAttr>();
}

::llvm::StringRef FuncOp::getSymName() {
  auto attr = getSymNameAttr();
  return attr.getValue();
}

::mlir::TypeAttr FuncOp::getFunctionTypeAttr() {
  return ::mlir::impl::getAttrFromSortedRange((*this)->getAttrs().begin() + 0, (*this)->getAttrs().end() - 1, getFunctionTypeAttrName()).cast<::mlir::TypeAttr>();
}

::mlir::FunctionType FuncOp::getFunctionType() {
  auto attr = getFunctionTypeAttr();
  return attr.getValue().cast<::mlir::FunctionType>();
}

::mlir::ArrayAttr FuncOp::getArgAttrsAttr() {
  return ::mlir::impl::getAttrFromSortedRange((*this)->getAttrs().begin() + 0, (*this)->getAttrs().end() - 2, getArgAttrsAttrName()).dyn_cast_or_null<::mlir::ArrayAttr>();
}

::std::optional< ::mlir::ArrayAttr > FuncOp::getArgAttrs() {
  auto attr = getArgAttrsAttr();
  return attr ? ::std::optional< ::mlir::ArrayAttr >(attr) : (::std::nullopt);
}

::mlir::ArrayAttr FuncOp::getResAttrsAttr() {
  return ::mlir::impl::getAttrFromSortedRange((*this)->getAttrs().begin() + 1, (*this)->getAttrs().end() - 1, getResAttrsAttrName()).dyn_cast_or_null<::mlir::ArrayAttr>();
}

::std::optional< ::mlir::ArrayAttr > FuncOp::getResAttrs() {
  auto attr = getResAttrsAttr();
  return attr ? ::std::optional< ::mlir::ArrayAttr >(attr) : (::std::nullopt);
}

void FuncOp::setSymNameAttr(::mlir::StringAttr attr) {
  (*this)->setAttr(getSymNameAttrName(), attr);
}

void FuncOp::setSymName(::llvm::StringRef attrValue) {
  (*this)->setAttr(getSymNameAttrName(), ::mlir::Builder((*this)->getContext()).getStringAttr(attrValue));
}

void FuncOp::setFunctionTypeAttr(::mlir::TypeAttr attr) {
  (*this)->setAttr(getFunctionTypeAttrName(), attr);
}

void FuncOp::setFunctionType(::mlir::FunctionType attrValue) {
  (*this)->setAttr(getFunctionTypeAttrName(), ::mlir::TypeAttr::get(attrValue));
}

void FuncOp::setArgAttrsAttr(::mlir::ArrayAttr attr) {
  (*this)->setAttr(getArgAttrsAttrName(), attr);
}

void FuncOp::setResAttrsAttr(::mlir::ArrayAttr attr) {
  (*this)->setAttr(getResAttrsAttrName(), attr);
}

::mlir::Attribute FuncOp::removeArgAttrsAttr() {
  return (*this)->removeAttr(getArgAttrsAttrName());
}

::mlir::Attribute FuncOp::removeResAttrsAttr() {
  return (*this)->removeAttr(getResAttrsAttrName());
}

::mlir::LogicalResult FuncOp::verifyInvariantsImpl() {
  auto namedAttrRange = (*this)->getAttrs();
  auto namedAttrIt = namedAttrRange.begin();
  ::mlir::Attribute tblgen_function_type;
  ::mlir::Attribute tblgen_arg_attrs;
  while (true) {
    if (namedAttrIt == namedAttrRange.end())
      return emitOpError("requires attribute 'function_type'");
    if (namedAttrIt->getName() == getFunctionTypeAttrName()) {
      tblgen_function_type = namedAttrIt->getValue();
      break;
    }
    else if (namedAttrIt->getName() == getArgAttrsAttrName()) {
      tblgen_arg_attrs = namedAttrIt->getValue();
    }
    ++namedAttrIt;
  }
  ::mlir::Attribute tblgen_sym_name;
  ::mlir::Attribute tblgen_res_attrs;
  while (true) {
    if (namedAttrIt == namedAttrRange.end())
      return emitOpError("requires attribute 'sym_name'");
    if (namedAttrIt->getName() == getSymNameAttrName()) {
      tblgen_sym_name = namedAttrIt->getValue();
      break;
    }
    else if (namedAttrIt->getName() == getResAttrsAttrName()) {
      tblgen_res_attrs = namedAttrIt->getValue();
    }
    ++namedAttrIt;
  }

  if (::mlir::failed(__mlir_ods_local_attr_constraint_Ops1(*this, tblgen_sym_name, "sym_name")))
    return ::mlir::failure();

  if (::mlir::failed(__mlir_ods_local_attr_constraint_Ops2(*this, tblgen_function_type, "function_type")))
    return ::mlir::failure();

  if (::mlir::failed(__mlir_ods_local_attr_constraint_Ops3(*this, tblgen_arg_attrs, "arg_attrs")))
    return ::mlir::failure();

  if (::mlir::failed(__mlir_ods_local_attr_constraint_Ops3(*this, tblgen_res_attrs, "res_attrs")))
    return ::mlir::failure();
  {
    unsigned index = 0; (void)index;

    for (auto &region : ::llvm::MutableArrayRef((*this)->getRegion(0)))
      if (::mlir::failed(__mlir_ods_local_region_constraint_Ops0(*this, region, "body", index++)))
        return ::mlir::failure();
  }
  return ::mlir::success();
}

::mlir::LogicalResult FuncOp::verifyInvariants() {
  return verifyInvariantsImpl();
}

} // namespace toy
} // namespace mlir
MLIR_DEFINE_EXPLICIT_TYPE_ID(::mlir::toy::FuncOp)

namespace mlir {
namespace toy {

//===----------------------------------------------------------------------===//
// ::mlir::toy::GenericCallOp definitions
//===----------------------------------------------------------------------===//

namespace detail {
GenericCallOpGenericAdaptorBase::GenericCallOpGenericAdaptorBase(::mlir::DictionaryAttr attrs, ::mlir::RegionRange regions) : odsAttrs(attrs), odsRegions(regions) {  if (odsAttrs)
    odsOpName.emplace("toy.generic_call", odsAttrs.getContext());
}

std::pair<unsigned, unsigned> GenericCallOpGenericAdaptorBase::getODSOperandIndexAndLength(unsigned index, unsigned odsOperandsSize) {
  bool isVariadic[] = {true};
  int prevVariadicCount = 0;
  for (unsigned i = 0; i < index; ++i)
    if (isVariadic[i]) ++prevVariadicCount;

  // Calculate how many dynamic values a static variadic operand corresponds to.
  // This assumes all static variadic operands have the same dynamic value count.
  int variadicSize = (odsOperandsSize - 0) / 1;
  // `index` passed in as the parameter is the static index which counts each
  // operand (variadic or not) as size 1. So here for each previous static variadic
  // operand, we need to offset by (variadicSize - 1) to get where the dynamic
  // value pack for this static operand starts.
  int start = index + (variadicSize - 1) * prevVariadicCount;
  int size = isVariadic[index] ? variadicSize : 1;
  return {start, size};
}

::mlir::DictionaryAttr GenericCallOpGenericAdaptorBase::getAttributes() {
  return odsAttrs;
}

::mlir::FlatSymbolRefAttr GenericCallOpGenericAdaptorBase::getCalleeAttr() {
  assert(odsAttrs && "no attributes when constructing adapter");
  auto attr = ::mlir::impl::getAttrFromSortedRange(odsAttrs.begin() + 0, odsAttrs.end() - 0, GenericCallOp::getCalleeAttrName(*odsOpName)).cast<::mlir::FlatSymbolRefAttr>();
  return attr;
}

::llvm::StringRef GenericCallOpGenericAdaptorBase::getCallee() {
  auto attr = getCalleeAttr();
  return attr.getValue();
}

} // namespace detail
GenericCallOpAdaptor::GenericCallOpAdaptor(GenericCallOp op) : GenericCallOpAdaptor(op->getOperands(), op->getAttrDictionary(), op->getRegions()) {}

::mlir::LogicalResult GenericCallOpAdaptor::verify(::mlir::Location loc) {
  auto namedAttrRange = odsAttrs;
  auto namedAttrIt = namedAttrRange.begin();
  ::mlir::Attribute tblgen_callee;
  while (true) {
    if (namedAttrIt == namedAttrRange.end())
      return emitError(loc, "'toy.generic_call' op ""requires attribute 'callee'");
    if (namedAttrIt->getName() == GenericCallOp::getCalleeAttrName(*odsOpName)) {
      tblgen_callee = namedAttrIt->getValue();
      break;
    }
    ++namedAttrIt;
  }

  if (tblgen_callee && !((tblgen_callee.isa<::mlir::FlatSymbolRefAttr>())))
    return emitError(loc, "'toy.generic_call' op ""attribute 'callee' failed to satisfy constraint: flat symbol reference attribute");
  return ::mlir::success();
}

std::pair<unsigned, unsigned> GenericCallOp::getODSOperandIndexAndLength(unsigned index) {
  bool isVariadic[] = {true};
  int prevVariadicCount = 0;
  for (unsigned i = 0; i < index; ++i)
    if (isVariadic[i]) ++prevVariadicCount;

  // Calculate how many dynamic values a static variadic operand corresponds to.
  // This assumes all static variadic operands have the same dynamic value count.
  int variadicSize = (getOperation()->getNumOperands() - 0) / 1;
  // `index` passed in as the parameter is the static index which counts each
  // operand (variadic or not) as size 1. So here for each previous static variadic
  // operand, we need to offset by (variadicSize - 1) to get where the dynamic
  // value pack for this static operand starts.
  int start = index + (variadicSize - 1) * prevVariadicCount;
  int size = isVariadic[index] ? variadicSize : 1;
  return {start, size};
}

::mlir::Operation::operand_range GenericCallOp::getODSOperands(unsigned index) {
  auto valueRange = getODSOperandIndexAndLength(index);
  return {std::next(getOperation()->operand_begin(), valueRange.first),
           std::next(getOperation()->operand_begin(), valueRange.first + valueRange.second)};
}

::mlir::Operation::operand_range GenericCallOp::getInputs() {
  return getODSOperands(0);
}

::mlir::MutableOperandRange GenericCallOp::getInputsMutable() {
  auto range = getODSOperandIndexAndLength(0);
  auto mutableRange = ::mlir::MutableOperandRange(getOperation(), range.first, range.second);
  return mutableRange;
}

std::pair<unsigned, unsigned> GenericCallOp::getODSResultIndexAndLength(unsigned index) {
  return {index, 1};
}

::mlir::Operation::result_range GenericCallOp::getODSResults(unsigned index) {
  auto valueRange = getODSResultIndexAndLength(index);
  return {std::next(getOperation()->result_begin(), valueRange.first),
           std::next(getOperation()->result_begin(), valueRange.first + valueRange.second)};
}

::mlir::FlatSymbolRefAttr GenericCallOp::getCalleeAttr() {
  return ::mlir::impl::getAttrFromSortedRange((*this)->getAttrs().begin() + 0, (*this)->getAttrs().end() - 0, getCalleeAttrName()).cast<::mlir::FlatSymbolRefAttr>();
}

::llvm::StringRef GenericCallOp::getCallee() {
  auto attr = getCalleeAttr();
  return attr.getValue();
}

void GenericCallOp::setCalleeAttr(::mlir::FlatSymbolRefAttr attr) {
  (*this)->setAttr(getCalleeAttrName(), attr);
}

void GenericCallOp::setCallee(::llvm::StringRef attrValue) {
  (*this)->setAttr(getCalleeAttrName(), ::mlir::SymbolRefAttr::get(::mlir::Builder((*this)->getContext()).getContext(), attrValue));
}

void GenericCallOp::build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::Type resultType0, ::mlir::FlatSymbolRefAttr callee, ::mlir::ValueRange inputs) {
  odsState.addOperands(inputs);
  odsState.addAttribute(getCalleeAttrName(odsState.name), callee);
  odsState.addTypes(resultType0);
}

void GenericCallOp::build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::TypeRange resultTypes, ::mlir::FlatSymbolRefAttr callee, ::mlir::ValueRange inputs) {
  odsState.addOperands(inputs);
  odsState.addAttribute(getCalleeAttrName(odsState.name), callee);
  assert(resultTypes.size() == 1u && "mismatched number of results");
  odsState.addTypes(resultTypes);
}

void GenericCallOp::build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::Type resultType0, ::llvm::StringRef callee, ::mlir::ValueRange inputs) {
  odsState.addOperands(inputs);
  odsState.addAttribute(getCalleeAttrName(odsState.name), ::mlir::SymbolRefAttr::get(odsBuilder.getContext(), callee));
  odsState.addTypes(resultType0);
}

void GenericCallOp::build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::TypeRange resultTypes, ::llvm::StringRef callee, ::mlir::ValueRange inputs) {
  odsState.addOperands(inputs);
  odsState.addAttribute(getCalleeAttrName(odsState.name), ::mlir::SymbolRefAttr::get(odsBuilder.getContext(), callee));
  assert(resultTypes.size() == 1u && "mismatched number of results");
  odsState.addTypes(resultTypes);
}

void GenericCallOp::build(::mlir::OpBuilder &, ::mlir::OperationState &odsState, ::mlir::TypeRange resultTypes, ::mlir::ValueRange operands, ::llvm::ArrayRef<::mlir::NamedAttribute> attributes) {
  odsState.addOperands(operands);
  odsState.addAttributes(attributes);
  assert(resultTypes.size() == 1u && "mismatched number of return types");
  odsState.addTypes(resultTypes);
}

::mlir::LogicalResult GenericCallOp::verifyInvariantsImpl() {
  auto namedAttrRange = (*this)->getAttrs();
  auto namedAttrIt = namedAttrRange.begin();
  ::mlir::Attribute tblgen_callee;
  while (true) {
    if (namedAttrIt == namedAttrRange.end())
      return emitOpError("requires attribute 'callee'");
    if (namedAttrIt->getName() == getCalleeAttrName()) {
      tblgen_callee = namedAttrIt->getValue();
      break;
    }
    ++namedAttrIt;
  }

  if (::mlir::failed(__mlir_ods_local_attr_constraint_Ops4(*this, tblgen_callee, "callee")))
    return ::mlir::failure();
  {
    unsigned index = 0; (void)index;
    auto valueGroup0 = getODSOperands(0);

    for (auto v : valueGroup0) {
      if (::mlir::failed(__mlir_ods_local_type_constraint_Ops0(*this, v.getType(), "operand", index++)))
        return ::mlir::failure();
    }
  }
  {
    unsigned index = 0; (void)index;
    auto valueGroup0 = getODSResults(0);

    for (auto v : valueGroup0) {
      if (::mlir::failed(__mlir_ods_local_type_constraint_Ops0(*this, v.getType(), "result", index++)))
        return ::mlir::failure();
    }
  }
  return ::mlir::success();
}

::mlir::LogicalResult GenericCallOp::verifyInvariants() {
  return verifyInvariantsImpl();
}

::mlir::ParseResult GenericCallOp::parse(::mlir::OpAsmParser &parser, ::mlir::OperationState &result) {
  ::mlir::FlatSymbolRefAttr calleeAttr;
  ::llvm::SmallVector<::mlir::OpAsmParser::UnresolvedOperand, 4> inputsOperands;
  ::llvm::SMLoc inputsOperandsLoc;
  (void)inputsOperandsLoc;
  ::llvm::ArrayRef<::mlir::Type> inputsTypes;
  ::llvm::ArrayRef<::mlir::Type> allResultTypes;

  if (parser.parseCustomAttributeWithFallback(calleeAttr, parser.getBuilder().getType<::mlir::NoneType>(), "callee",
          result.attributes)) {
    return ::mlir::failure();
  }
  if (parser.parseLParen())
    return ::mlir::failure();

  inputsOperandsLoc = parser.getCurrentLocation();
  if (parser.parseOperandList(inputsOperands))
    return ::mlir::failure();
  if (parser.parseRParen())
    return ::mlir::failure();
  if (parser.parseOptionalAttrDict(result.attributes))
    return ::mlir::failure();
  if (parser.parseColon())
    return ::mlir::failure();

  ::mlir::FunctionType inputs__allResult_functionType;
  if (parser.parseType(inputs__allResult_functionType))
    return ::mlir::failure();
  inputsTypes = inputs__allResult_functionType.getInputs();
  allResultTypes = inputs__allResult_functionType.getResults();
  result.addTypes(allResultTypes);
  if (parser.resolveOperands(inputsOperands, inputsTypes, inputsOperandsLoc, result.operands))
    return ::mlir::failure();
  return ::mlir::success();
}

void GenericCallOp::print(::mlir::OpAsmPrinter &_odsPrinter) {
  _odsPrinter << ' ';
  _odsPrinter.printAttributeWithoutType(getCalleeAttr());
  _odsPrinter << "(";
  _odsPrinter << getInputs();
  _odsPrinter << ")";
  ::llvm::SmallVector<::llvm::StringRef, 2> elidedAttrs;
  elidedAttrs.push_back("callee");
  _odsPrinter.printOptionalAttrDict((*this)->getAttrs(), elidedAttrs);
  _odsPrinter << ' ' << ":";
  _odsPrinter << ' ';
  _odsPrinter.printFunctionalType(getInputs().getTypes(), getOperation()->getResultTypes());
}

} // namespace toy
} // namespace mlir
MLIR_DEFINE_EXPLICIT_TYPE_ID(::mlir::toy::GenericCallOp)

namespace mlir {
namespace toy {

//===----------------------------------------------------------------------===//
// ::mlir::toy::MulOp definitions
//===----------------------------------------------------------------------===//

namespace detail {
MulOpGenericAdaptorBase::MulOpGenericAdaptorBase(::mlir::DictionaryAttr attrs, ::mlir::RegionRange regions) : odsAttrs(attrs), odsRegions(regions) {  if (odsAttrs)
    odsOpName.emplace("toy.mul", odsAttrs.getContext());
}

std::pair<unsigned, unsigned> MulOpGenericAdaptorBase::getODSOperandIndexAndLength(unsigned index, unsigned odsOperandsSize) {
  return {index, 1};
}

::mlir::DictionaryAttr MulOpGenericAdaptorBase::getAttributes() {
  return odsAttrs;
}

} // namespace detail
MulOpAdaptor::MulOpAdaptor(MulOp op) : MulOpAdaptor(op->getOperands(), op->getAttrDictionary(), op->getRegions()) {}

::mlir::LogicalResult MulOpAdaptor::verify(::mlir::Location loc) {
  return ::mlir::success();
}

std::pair<unsigned, unsigned> MulOp::getODSOperandIndexAndLength(unsigned index) {
  return {index, 1};
}

::mlir::Operation::operand_range MulOp::getODSOperands(unsigned index) {
  auto valueRange = getODSOperandIndexAndLength(index);
  return {std::next(getOperation()->operand_begin(), valueRange.first),
           std::next(getOperation()->operand_begin(), valueRange.first + valueRange.second)};
}

::mlir::TypedValue<::mlir::TensorType> MulOp::getLhs() {
  return ::llvm::cast<::mlir::TypedValue<::mlir::TensorType>>(*getODSOperands(0).begin());
}

::mlir::TypedValue<::mlir::TensorType> MulOp::getRhs() {
  return ::llvm::cast<::mlir::TypedValue<::mlir::TensorType>>(*getODSOperands(1).begin());
}

::mlir::MutableOperandRange MulOp::getLhsMutable() {
  auto range = getODSOperandIndexAndLength(0);
  auto mutableRange = ::mlir::MutableOperandRange(getOperation(), range.first, range.second);
  return mutableRange;
}

::mlir::MutableOperandRange MulOp::getRhsMutable() {
  auto range = getODSOperandIndexAndLength(1);
  auto mutableRange = ::mlir::MutableOperandRange(getOperation(), range.first, range.second);
  return mutableRange;
}

std::pair<unsigned, unsigned> MulOp::getODSResultIndexAndLength(unsigned index) {
  return {index, 1};
}

::mlir::Operation::result_range MulOp::getODSResults(unsigned index) {
  auto valueRange = getODSResultIndexAndLength(index);
  return {std::next(getOperation()->result_begin(), valueRange.first),
           std::next(getOperation()->result_begin(), valueRange.first + valueRange.second)};
}

void MulOp::build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::Type resultType0, ::mlir::Value lhs, ::mlir::Value rhs) {
  odsState.addOperands(lhs);
  odsState.addOperands(rhs);
  odsState.addTypes(resultType0);
}

void MulOp::build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::TypeRange resultTypes, ::mlir::Value lhs, ::mlir::Value rhs) {
  odsState.addOperands(lhs);
  odsState.addOperands(rhs);
  assert(resultTypes.size() == 1u && "mismatched number of results");
  odsState.addTypes(resultTypes);
}

void MulOp::build(::mlir::OpBuilder &, ::mlir::OperationState &odsState, ::mlir::TypeRange resultTypes, ::mlir::ValueRange operands, ::llvm::ArrayRef<::mlir::NamedAttribute> attributes) {
  assert(operands.size() == 2u && "mismatched number of parameters");
  odsState.addOperands(operands);
  odsState.addAttributes(attributes);
  assert(resultTypes.size() == 1u && "mismatched number of return types");
  odsState.addTypes(resultTypes);
}

::mlir::LogicalResult MulOp::verifyInvariantsImpl() {
  {
    unsigned index = 0; (void)index;
    auto valueGroup0 = getODSOperands(0);

    for (auto v : valueGroup0) {
      if (::mlir::failed(__mlir_ods_local_type_constraint_Ops0(*this, v.getType(), "operand", index++)))
        return ::mlir::failure();
    }
    auto valueGroup1 = getODSOperands(1);

    for (auto v : valueGroup1) {
      if (::mlir::failed(__mlir_ods_local_type_constraint_Ops0(*this, v.getType(), "operand", index++)))
        return ::mlir::failure();
    }
  }
  {
    unsigned index = 0; (void)index;
    auto valueGroup0 = getODSResults(0);

    for (auto v : valueGroup0) {
      if (::mlir::failed(__mlir_ods_local_type_constraint_Ops0(*this, v.getType(), "result", index++)))
        return ::mlir::failure();
    }
  }
  return ::mlir::success();
}

::mlir::LogicalResult MulOp::verifyInvariants() {
  return verifyInvariantsImpl();
}

} // namespace toy
} // namespace mlir
MLIR_DEFINE_EXPLICIT_TYPE_ID(::mlir::toy::MulOp)

namespace mlir {
namespace toy {

//===----------------------------------------------------------------------===//
// ::mlir::toy::PrintOp definitions
//===----------------------------------------------------------------------===//

namespace detail {
PrintOpGenericAdaptorBase::PrintOpGenericAdaptorBase(::mlir::DictionaryAttr attrs, ::mlir::RegionRange regions) : odsAttrs(attrs), odsRegions(regions) {  if (odsAttrs)
    odsOpName.emplace("toy.print", odsAttrs.getContext());
}

std::pair<unsigned, unsigned> PrintOpGenericAdaptorBase::getODSOperandIndexAndLength(unsigned index, unsigned odsOperandsSize) {
  return {index, 1};
}

::mlir::DictionaryAttr PrintOpGenericAdaptorBase::getAttributes() {
  return odsAttrs;
}

} // namespace detail
PrintOpAdaptor::PrintOpAdaptor(PrintOp op) : PrintOpAdaptor(op->getOperands(), op->getAttrDictionary(), op->getRegions()) {}

::mlir::LogicalResult PrintOpAdaptor::verify(::mlir::Location loc) {
  return ::mlir::success();
}

std::pair<unsigned, unsigned> PrintOp::getODSOperandIndexAndLength(unsigned index) {
  return {index, 1};
}

::mlir::Operation::operand_range PrintOp::getODSOperands(unsigned index) {
  auto valueRange = getODSOperandIndexAndLength(index);
  return {std::next(getOperation()->operand_begin(), valueRange.first),
           std::next(getOperation()->operand_begin(), valueRange.first + valueRange.second)};
}

::mlir::TypedValue<::mlir::TensorType> PrintOp::getInput() {
  return ::llvm::cast<::mlir::TypedValue<::mlir::TensorType>>(*getODSOperands(0).begin());
}

::mlir::MutableOperandRange PrintOp::getInputMutable() {
  auto range = getODSOperandIndexAndLength(0);
  auto mutableRange = ::mlir::MutableOperandRange(getOperation(), range.first, range.second);
  return mutableRange;
}

std::pair<unsigned, unsigned> PrintOp::getODSResultIndexAndLength(unsigned index) {
  return {index, 1};
}

::mlir::Operation::result_range PrintOp::getODSResults(unsigned index) {
  auto valueRange = getODSResultIndexAndLength(index);
  return {std::next(getOperation()->result_begin(), valueRange.first),
           std::next(getOperation()->result_begin(), valueRange.first + valueRange.second)};
}

void PrintOp::build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::Value input) {
  odsState.addOperands(input);
}

void PrintOp::build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::TypeRange resultTypes, ::mlir::Value input) {
  odsState.addOperands(input);
  assert(resultTypes.size() == 0u && "mismatched number of results");
  odsState.addTypes(resultTypes);
}

void PrintOp::build(::mlir::OpBuilder &, ::mlir::OperationState &odsState, ::mlir::TypeRange resultTypes, ::mlir::ValueRange operands, ::llvm::ArrayRef<::mlir::NamedAttribute> attributes) {
  assert(operands.size() == 1u && "mismatched number of parameters");
  odsState.addOperands(operands);
  odsState.addAttributes(attributes);
  assert(resultTypes.size() == 0u && "mismatched number of return types");
  odsState.addTypes(resultTypes);
}

::mlir::LogicalResult PrintOp::verifyInvariantsImpl() {
  {
    unsigned index = 0; (void)index;
    auto valueGroup0 = getODSOperands(0);

    for (auto v : valueGroup0) {
      if (::mlir::failed(__mlir_ods_local_type_constraint_Ops0(*this, v.getType(), "operand", index++)))
        return ::mlir::failure();
    }
  }
  return ::mlir::success();
}

::mlir::LogicalResult PrintOp::verifyInvariants() {
  return verifyInvariantsImpl();
}

::mlir::ParseResult PrintOp::parse(::mlir::OpAsmParser &parser, ::mlir::OperationState &result) {
  ::mlir::OpAsmParser::UnresolvedOperand inputRawOperands[1];
  ::llvm::ArrayRef<::mlir::OpAsmParser::UnresolvedOperand> inputOperands(inputRawOperands);  ::llvm::SMLoc inputOperandsLoc;
  (void)inputOperandsLoc;
  ::mlir::Type inputRawTypes[1];
  ::llvm::ArrayRef<::mlir::Type> inputTypes(inputRawTypes);

  inputOperandsLoc = parser.getCurrentLocation();
  if (parser.parseOperand(inputRawOperands[0]))
    return ::mlir::failure();
  if (parser.parseOptionalAttrDict(result.attributes))
    return ::mlir::failure();
  if (parser.parseColon())
    return ::mlir::failure();

  {
    ::mlir::TensorType type;
    if (parser.parseCustomTypeWithFallback(type))
      return ::mlir::failure();
    inputRawTypes[0] = type;
  }
  if (parser.resolveOperands(inputOperands, inputTypes, inputOperandsLoc, result.operands))
    return ::mlir::failure();
  return ::mlir::success();
}

void PrintOp::print(::mlir::OpAsmPrinter &_odsPrinter) {
  _odsPrinter << ' ';
  _odsPrinter << getInput();
  ::llvm::SmallVector<::llvm::StringRef, 2> elidedAttrs;
  _odsPrinter.printOptionalAttrDict((*this)->getAttrs(), elidedAttrs);
  _odsPrinter << ' ' << ":";
  _odsPrinter << ' ';
  {
    auto type = getInput().getType();
    if (auto validType = type.dyn_cast<::mlir::TensorType>())
      _odsPrinter.printStrippedAttrOrType(validType);
   else
     _odsPrinter << type;
  }
}

} // namespace toy
} // namespace mlir
MLIR_DEFINE_EXPLICIT_TYPE_ID(::mlir::toy::PrintOp)

namespace mlir {
namespace toy {

//===----------------------------------------------------------------------===//
// ::mlir::toy::ReshapeOp definitions
//===----------------------------------------------------------------------===//

namespace detail {
ReshapeOpGenericAdaptorBase::ReshapeOpGenericAdaptorBase(::mlir::DictionaryAttr attrs, ::mlir::RegionRange regions) : odsAttrs(attrs), odsRegions(regions) {  if (odsAttrs)
    odsOpName.emplace("toy.reshape", odsAttrs.getContext());
}

std::pair<unsigned, unsigned> ReshapeOpGenericAdaptorBase::getODSOperandIndexAndLength(unsigned index, unsigned odsOperandsSize) {
  return {index, 1};
}

::mlir::DictionaryAttr ReshapeOpGenericAdaptorBase::getAttributes() {
  return odsAttrs;
}

} // namespace detail
ReshapeOpAdaptor::ReshapeOpAdaptor(ReshapeOp op) : ReshapeOpAdaptor(op->getOperands(), op->getAttrDictionary(), op->getRegions()) {}

::mlir::LogicalResult ReshapeOpAdaptor::verify(::mlir::Location loc) {
  return ::mlir::success();
}

std::pair<unsigned, unsigned> ReshapeOp::getODSOperandIndexAndLength(unsigned index) {
  return {index, 1};
}

::mlir::Operation::operand_range ReshapeOp::getODSOperands(unsigned index) {
  auto valueRange = getODSOperandIndexAndLength(index);
  return {std::next(getOperation()->operand_begin(), valueRange.first),
           std::next(getOperation()->operand_begin(), valueRange.first + valueRange.second)};
}

::mlir::TypedValue<::mlir::TensorType> ReshapeOp::getInput() {
  return ::llvm::cast<::mlir::TypedValue<::mlir::TensorType>>(*getODSOperands(0).begin());
}

::mlir::MutableOperandRange ReshapeOp::getInputMutable() {
  auto range = getODSOperandIndexAndLength(0);
  auto mutableRange = ::mlir::MutableOperandRange(getOperation(), range.first, range.second);
  return mutableRange;
}

std::pair<unsigned, unsigned> ReshapeOp::getODSResultIndexAndLength(unsigned index) {
  return {index, 1};
}

::mlir::Operation::result_range ReshapeOp::getODSResults(unsigned index) {
  auto valueRange = getODSResultIndexAndLength(index);
  return {std::next(getOperation()->result_begin(), valueRange.first),
           std::next(getOperation()->result_begin(), valueRange.first + valueRange.second)};
}

void ReshapeOp::build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::Type resultType0, ::mlir::Value input) {
  odsState.addOperands(input);
  odsState.addTypes(resultType0);
}

void ReshapeOp::build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::TypeRange resultTypes, ::mlir::Value input) {
  odsState.addOperands(input);
  assert(resultTypes.size() == 1u && "mismatched number of results");
  odsState.addTypes(resultTypes);
}

void ReshapeOp::build(::mlir::OpBuilder &, ::mlir::OperationState &odsState, ::mlir::TypeRange resultTypes, ::mlir::ValueRange operands, ::llvm::ArrayRef<::mlir::NamedAttribute> attributes) {
  assert(operands.size() == 1u && "mismatched number of parameters");
  odsState.addOperands(operands);
  odsState.addAttributes(attributes);
  assert(resultTypes.size() == 1u && "mismatched number of return types");
  odsState.addTypes(resultTypes);
}

::mlir::LogicalResult ReshapeOp::verifyInvariantsImpl() {
  {
    unsigned index = 0; (void)index;
    auto valueGroup0 = getODSOperands(0);

    for (auto v : valueGroup0) {
      if (::mlir::failed(__mlir_ods_local_type_constraint_Ops0(*this, v.getType(), "operand", index++)))
        return ::mlir::failure();
    }
  }
  {
    unsigned index = 0; (void)index;
    auto valueGroup0 = getODSResults(0);

    for (auto v : valueGroup0) {
      if (::mlir::failed(__mlir_ods_local_type_constraint_Ops1(*this, v.getType(), "result", index++)))
        return ::mlir::failure();
    }
  }
  return ::mlir::success();
}

::mlir::LogicalResult ReshapeOp::verifyInvariants() {
  return verifyInvariantsImpl();
}

::mlir::ParseResult ReshapeOp::parse(::mlir::OpAsmParser &parser, ::mlir::OperationState &result) {
  ::mlir::OpAsmParser::UnresolvedOperand inputRawOperands[1];
  ::llvm::ArrayRef<::mlir::OpAsmParser::UnresolvedOperand> inputOperands(inputRawOperands);  ::llvm::SMLoc inputOperandsLoc;
  (void)inputOperandsLoc;
  ::mlir::Type inputRawTypes[1];
  ::llvm::ArrayRef<::mlir::Type> inputTypes(inputRawTypes);
  ::llvm::SmallVector<::mlir::Type, 1> allResultTypes;
  if (parser.parseLParen())
    return ::mlir::failure();

  inputOperandsLoc = parser.getCurrentLocation();
  if (parser.parseOperand(inputRawOperands[0]))
    return ::mlir::failure();
  if (parser.parseColon())
    return ::mlir::failure();

  {
    ::mlir::TensorType type;
    if (parser.parseCustomTypeWithFallback(type))
      return ::mlir::failure();
    inputRawTypes[0] = type;
  }
  if (parser.parseRParen())
    return ::mlir::failure();
  if (parser.parseOptionalAttrDict(result.attributes))
    return ::mlir::failure();
  if (parser.parseKeyword("to"))
    return ::mlir::failure();

  if (parser.parseTypeList(allResultTypes))
    return ::mlir::failure();
  result.addTypes(allResultTypes);
  if (parser.resolveOperands(inputOperands, inputTypes, inputOperandsLoc, result.operands))
    return ::mlir::failure();
  return ::mlir::success();
}

void ReshapeOp::print(::mlir::OpAsmPrinter &_odsPrinter) {
  _odsPrinter << "(";
  _odsPrinter << getInput();
  _odsPrinter << ' ' << ":";
  _odsPrinter << ' ';
  {
    auto type = getInput().getType();
    if (auto validType = type.dyn_cast<::mlir::TensorType>())
      _odsPrinter.printStrippedAttrOrType(validType);
   else
     _odsPrinter << type;
  }
  _odsPrinter << ")";
  ::llvm::SmallVector<::llvm::StringRef, 2> elidedAttrs;
  _odsPrinter.printOptionalAttrDict((*this)->getAttrs(), elidedAttrs);
  _odsPrinter << ' ' << "to";
  _odsPrinter << ' ';
  _odsPrinter << getOperation()->getResultTypes();
}

} // namespace toy
} // namespace mlir
MLIR_DEFINE_EXPLICIT_TYPE_ID(::mlir::toy::ReshapeOp)

namespace mlir {
namespace toy {

//===----------------------------------------------------------------------===//
// ::mlir::toy::ReturnOp definitions
//===----------------------------------------------------------------------===//

namespace detail {
ReturnOpGenericAdaptorBase::ReturnOpGenericAdaptorBase(::mlir::DictionaryAttr attrs, ::mlir::RegionRange regions) : odsAttrs(attrs), odsRegions(regions) {  if (odsAttrs)
    odsOpName.emplace("toy.return", odsAttrs.getContext());
}

std::pair<unsigned, unsigned> ReturnOpGenericAdaptorBase::getODSOperandIndexAndLength(unsigned index, unsigned odsOperandsSize) {
  bool isVariadic[] = {true};
  int prevVariadicCount = 0;
  for (unsigned i = 0; i < index; ++i)
    if (isVariadic[i]) ++prevVariadicCount;

  // Calculate how many dynamic values a static variadic operand corresponds to.
  // This assumes all static variadic operands have the same dynamic value count.
  int variadicSize = (odsOperandsSize - 0) / 1;
  // `index` passed in as the parameter is the static index which counts each
  // operand (variadic or not) as size 1. So here for each previous static variadic
  // operand, we need to offset by (variadicSize - 1) to get where the dynamic
  // value pack for this static operand starts.
  int start = index + (variadicSize - 1) * prevVariadicCount;
  int size = isVariadic[index] ? variadicSize : 1;
  return {start, size};
}

::mlir::DictionaryAttr ReturnOpGenericAdaptorBase::getAttributes() {
  return odsAttrs;
}

} // namespace detail
ReturnOpAdaptor::ReturnOpAdaptor(ReturnOp op) : ReturnOpAdaptor(op->getOperands(), op->getAttrDictionary(), op->getRegions()) {}

::mlir::LogicalResult ReturnOpAdaptor::verify(::mlir::Location loc) {
  return ::mlir::success();
}

std::pair<unsigned, unsigned> ReturnOp::getODSOperandIndexAndLength(unsigned index) {
  bool isVariadic[] = {true};
  int prevVariadicCount = 0;
  for (unsigned i = 0; i < index; ++i)
    if (isVariadic[i]) ++prevVariadicCount;

  // Calculate how many dynamic values a static variadic operand corresponds to.
  // This assumes all static variadic operands have the same dynamic value count.
  int variadicSize = (getOperation()->getNumOperands() - 0) / 1;
  // `index` passed in as the parameter is the static index which counts each
  // operand (variadic or not) as size 1. So here for each previous static variadic
  // operand, we need to offset by (variadicSize - 1) to get where the dynamic
  // value pack for this static operand starts.
  int start = index + (variadicSize - 1) * prevVariadicCount;
  int size = isVariadic[index] ? variadicSize : 1;
  return {start, size};
}

::mlir::Operation::operand_range ReturnOp::getODSOperands(unsigned index) {
  auto valueRange = getODSOperandIndexAndLength(index);
  return {std::next(getOperation()->operand_begin(), valueRange.first),
           std::next(getOperation()->operand_begin(), valueRange.first + valueRange.second)};
}

::mlir::Operation::operand_range ReturnOp::getInput() {
  return getODSOperands(0);
}

::mlir::MutableOperandRange ReturnOp::getInputMutable() {
  auto range = getODSOperandIndexAndLength(0);
  auto mutableRange = ::mlir::MutableOperandRange(getOperation(), range.first, range.second);
  return mutableRange;
}

std::pair<unsigned, unsigned> ReturnOp::getODSResultIndexAndLength(unsigned index) {
  return {index, 1};
}

::mlir::Operation::result_range ReturnOp::getODSResults(unsigned index) {
  auto valueRange = getODSResultIndexAndLength(index);
  return {std::next(getOperation()->result_begin(), valueRange.first),
           std::next(getOperation()->result_begin(), valueRange.first + valueRange.second)};
}

void ReturnOp::build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState) {
 build(odsBuilder, odsState, std::nullopt); 
}

void ReturnOp::build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::ValueRange input) {
  odsState.addOperands(input);
}

void ReturnOp::build(::mlir::OpBuilder &, ::mlir::OperationState &odsState, ::mlir::TypeRange resultTypes, ::mlir::ValueRange operands, ::llvm::ArrayRef<::mlir::NamedAttribute> attributes) {
  odsState.addOperands(operands);
  odsState.addAttributes(attributes);
  assert(resultTypes.size() == 0u && "mismatched number of return types");
  odsState.addTypes(resultTypes);
}

::mlir::LogicalResult ReturnOp::verifyInvariantsImpl() {
  {
    unsigned index = 0; (void)index;
    auto valueGroup0 = getODSOperands(0);

    for (auto v : valueGroup0) {
      if (::mlir::failed(__mlir_ods_local_type_constraint_Ops0(*this, v.getType(), "operand", index++)))
        return ::mlir::failure();
    }
  }
  return ::mlir::success();
}

::mlir::LogicalResult ReturnOp::verifyInvariants() {
  if(::mlir::succeeded(verifyInvariantsImpl()) && ::mlir::succeeded(verify()))
    return ::mlir::success();
  return ::mlir::failure();
}

::mlir::ParseResult ReturnOp::parse(::mlir::OpAsmParser &parser, ::mlir::OperationState &result) {
  ::llvm::SmallVector<::mlir::OpAsmParser::UnresolvedOperand, 4> inputOperands;
  ::llvm::SMLoc inputOperandsLoc;
  (void)inputOperandsLoc;
  ::llvm::SmallVector<::mlir::Type, 1> inputTypes;

  inputOperandsLoc = parser.getCurrentLocation();
  if (parser.parseOperandList(inputOperands))
    return ::mlir::failure();
  if (!inputOperands.empty()) {
  if (parser.parseColon())
    return ::mlir::failure();

  if (parser.parseTypeList(inputTypes))
    return ::mlir::failure();
  }
  if (parser.parseOptionalAttrDict(result.attributes))
    return ::mlir::failure();
  if (parser.resolveOperands(inputOperands, inputTypes, inputOperandsLoc, result.operands))
    return ::mlir::failure();
  return ::mlir::success();
}

void ReturnOp::print(::mlir::OpAsmPrinter &_odsPrinter) {
  if (!getInput().empty()) {
    _odsPrinter << ' ';
    _odsPrinter << getInput();
    _odsPrinter << ' ' << ":";
    _odsPrinter << ' ';
    _odsPrinter << getInput().getTypes();
  }
  ::llvm::SmallVector<::llvm::StringRef, 2> elidedAttrs;
  _odsPrinter.printOptionalAttrDict((*this)->getAttrs(), elidedAttrs);
}

void ReturnOp::getEffects(::llvm::SmallVectorImpl<::mlir::SideEffects::EffectInstance<::mlir::MemoryEffects::Effect>> &effects) {
}

} // namespace toy
} // namespace mlir
MLIR_DEFINE_EXPLICIT_TYPE_ID(::mlir::toy::ReturnOp)

namespace mlir {
namespace toy {

//===----------------------------------------------------------------------===//
// ::mlir::toy::TransposeOp definitions
//===----------------------------------------------------------------------===//

namespace detail {
TransposeOpGenericAdaptorBase::TransposeOpGenericAdaptorBase(::mlir::DictionaryAttr attrs, ::mlir::RegionRange regions) : odsAttrs(attrs), odsRegions(regions) {  if (odsAttrs)
    odsOpName.emplace("toy.transpose", odsAttrs.getContext());
}

std::pair<unsigned, unsigned> TransposeOpGenericAdaptorBase::getODSOperandIndexAndLength(unsigned index, unsigned odsOperandsSize) {
  return {index, 1};
}

::mlir::DictionaryAttr TransposeOpGenericAdaptorBase::getAttributes() {
  return odsAttrs;
}

} // namespace detail
TransposeOpAdaptor::TransposeOpAdaptor(TransposeOp op) : TransposeOpAdaptor(op->getOperands(), op->getAttrDictionary(), op->getRegions()) {}

::mlir::LogicalResult TransposeOpAdaptor::verify(::mlir::Location loc) {
  return ::mlir::success();
}

std::pair<unsigned, unsigned> TransposeOp::getODSOperandIndexAndLength(unsigned index) {
  return {index, 1};
}

::mlir::Operation::operand_range TransposeOp::getODSOperands(unsigned index) {
  auto valueRange = getODSOperandIndexAndLength(index);
  return {std::next(getOperation()->operand_begin(), valueRange.first),
           std::next(getOperation()->operand_begin(), valueRange.first + valueRange.second)};
}

::mlir::TypedValue<::mlir::TensorType> TransposeOp::getInput() {
  return ::llvm::cast<::mlir::TypedValue<::mlir::TensorType>>(*getODSOperands(0).begin());
}

::mlir::MutableOperandRange TransposeOp::getInputMutable() {
  auto range = getODSOperandIndexAndLength(0);
  auto mutableRange = ::mlir::MutableOperandRange(getOperation(), range.first, range.second);
  return mutableRange;
}

std::pair<unsigned, unsigned> TransposeOp::getODSResultIndexAndLength(unsigned index) {
  return {index, 1};
}

::mlir::Operation::result_range TransposeOp::getODSResults(unsigned index) {
  auto valueRange = getODSResultIndexAndLength(index);
  return {std::next(getOperation()->result_begin(), valueRange.first),
           std::next(getOperation()->result_begin(), valueRange.first + valueRange.second)};
}

void TransposeOp::build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::Type resultType0, ::mlir::Value input) {
  odsState.addOperands(input);
  odsState.addTypes(resultType0);
}

void TransposeOp::build(::mlir::OpBuilder &odsBuilder, ::mlir::OperationState &odsState, ::mlir::TypeRange resultTypes, ::mlir::Value input) {
  odsState.addOperands(input);
  assert(resultTypes.size() == 1u && "mismatched number of results");
  odsState.addTypes(resultTypes);
}

void TransposeOp::build(::mlir::OpBuilder &, ::mlir::OperationState &odsState, ::mlir::TypeRange resultTypes, ::mlir::ValueRange operands, ::llvm::ArrayRef<::mlir::NamedAttribute> attributes) {
  assert(operands.size() == 1u && "mismatched number of parameters");
  odsState.addOperands(operands);
  odsState.addAttributes(attributes);
  assert(resultTypes.size() == 1u && "mismatched number of return types");
  odsState.addTypes(resultTypes);
}

::mlir::LogicalResult TransposeOp::verifyInvariantsImpl() {
  {
    unsigned index = 0; (void)index;
    auto valueGroup0 = getODSOperands(0);

    for (auto v : valueGroup0) {
      if (::mlir::failed(__mlir_ods_local_type_constraint_Ops0(*this, v.getType(), "operand", index++)))
        return ::mlir::failure();
    }
  }
  {
    unsigned index = 0; (void)index;
    auto valueGroup0 = getODSResults(0);

    for (auto v : valueGroup0) {
      if (::mlir::failed(__mlir_ods_local_type_constraint_Ops0(*this, v.getType(), "result", index++)))
        return ::mlir::failure();
    }
  }
  return ::mlir::success();
}

::mlir::LogicalResult TransposeOp::verifyInvariants() {
  if(::mlir::succeeded(verifyInvariantsImpl()) && ::mlir::succeeded(verify()))
    return ::mlir::success();
  return ::mlir::failure();
}

::mlir::ParseResult TransposeOp::parse(::mlir::OpAsmParser &parser, ::mlir::OperationState &result) {
  ::mlir::OpAsmParser::UnresolvedOperand inputRawOperands[1];
  ::llvm::ArrayRef<::mlir::OpAsmParser::UnresolvedOperand> inputOperands(inputRawOperands);  ::llvm::SMLoc inputOperandsLoc;
  (void)inputOperandsLoc;
  ::mlir::Type inputRawTypes[1];
  ::llvm::ArrayRef<::mlir::Type> inputTypes(inputRawTypes);
  ::llvm::SmallVector<::mlir::Type, 1> allResultTypes;
  if (parser.parseLParen())
    return ::mlir::failure();

  inputOperandsLoc = parser.getCurrentLocation();
  if (parser.parseOperand(inputRawOperands[0]))
    return ::mlir::failure();
  if (parser.parseColon())
    return ::mlir::failure();

  {
    ::mlir::TensorType type;
    if (parser.parseCustomTypeWithFallback(type))
      return ::mlir::failure();
    inputRawTypes[0] = type;
  }
  if (parser.parseRParen())
    return ::mlir::failure();
  if (parser.parseOptionalAttrDict(result.attributes))
    return ::mlir::failure();
  if (parser.parseKeyword("to"))
    return ::mlir::failure();

  if (parser.parseTypeList(allResultTypes))
    return ::mlir::failure();
  result.addTypes(allResultTypes);
  if (parser.resolveOperands(inputOperands, inputTypes, inputOperandsLoc, result.operands))
    return ::mlir::failure();
  return ::mlir::success();
}

void TransposeOp::print(::mlir::OpAsmPrinter &_odsPrinter) {
  _odsPrinter << "(";
  _odsPrinter << getInput();
  _odsPrinter << ' ' << ":";
  _odsPrinter << ' ';
  {
    auto type = getInput().getType();
    if (auto validType = type.dyn_cast<::mlir::TensorType>())
      _odsPrinter.printStrippedAttrOrType(validType);
   else
     _odsPrinter << type;
  }
  _odsPrinter << ")";
  ::llvm::SmallVector<::llvm::StringRef, 2> elidedAttrs;
  _odsPrinter.printOptionalAttrDict((*this)->getAttrs(), elidedAttrs);
  _odsPrinter << ' ' << "to";
  _odsPrinter << ' ';
  _odsPrinter << getOperation()->getResultTypes();
}

} // namespace toy
} // namespace mlir
MLIR_DEFINE_EXPLICIT_TYPE_ID(::mlir::toy::TransposeOp)


#endif  // GET_OP_CLASSES