add kunlun squeeze kernel (#229)

Co-authored-by: Haojie Wang <haojie0429@gmail.com>

.github/workflows/build.yml

@@ -1,12 +1,11 @@
 name: Build and test cpu
 on:
   push:
-    branch: 'master'
     paths-ignore:
       - '**.md'
       - 'LICENSE'
   pull_request:
-    paths-ignore:
+    paths:
       - '**.md'
       - 'LICENSE'
 
@@ -15,10 +14,10 @@ env:
   protobuf-version: "3.21.12"
   python-version: "3.10"
 
-  resnet-download: https://github.com/onnx/models/raw/main/vision/classification/resnet/model/resnet18-v2-7.onnx
-  inception-download: https://media.githubusercontent.com/media/onnx/models/main/vision/classification/inception_and_googlenet/inception_v2/model/inception-v2-9.onnx
-  densenet-download: https://github.com/onnx/models/raw/main/vision/classification/densenet-121/model/densenet-12.onnx
-  efficientnet-download: https://github.com/onnx/models/raw/main/vision/classification/efficientnet-lite4/model/efficientnet-lite4-11.onnx
+  resnet-download: https://github.com/InfiniTensor/InfiniTensor/releases/download/test-models/resnet18-v2-7.onnx
+  inception-download: https://github.com/InfiniTensor/InfiniTensor/releases/download/test-models/inception-v2-9.onnx
+  densenet-download: https://github.com/InfiniTensor/InfiniTensor/releases/download/test-models/densenet-12.onnx
+  efficientnet-download: https://github.com/InfiniTensor/InfiniTensor/releases/download/test-models/efficientnet-lite4-11.onnx
 
 jobs:
   build:

.github/workflows/clang-format-check.yml

@@ -1,12 +1,11 @@
 name: clang-format Check
 on:
   push:
-    branch: 'master'
     paths-ignore:
       - '**.md'
       - 'LICENSE'
   pull_request:
-    paths-ignore:
+    paths:
       - '**.md'
       - 'LICENSE'
 

.gitignore

@@ -42,3 +42,5 @@ build_debug/
 
 # onnx model
 *.onnx
+*.pb
+*.npy

.gitmodules

@@ -11,5 +11,8 @@
 	path = 3rd-party/backward-cpp
 	url = git@github.com:bombela/backward-cpp.git
 [submodule "example"]
-	path = example
+	path = examples/NNmodel
 	url = git@github.com:wanghailu0717/NNmodel.git
+[submodule "examples/distributed/onnxsim_large_model"]
+	path = examples/distributed/onnxsim_large_model
+	url = git@github.com:luchangli03/onnxsim_large_model.git

CMakeLists.txt

@@ -1,20 +1,45 @@
-cmake_minimum_required(VERSION 3.17) # FindCUDAToolkit
-include(CMakeDependentOption)
-project(InfiniTensor C CXX)
-
 # Do not change these options in this file. Use cmake.config, cmake -DOPTION=VALUE, or ccmake to specify them.
 option(USE_CUDA "Support CUDA GPU" OFF)
 option(USE_BANG "Support BANG MLU" OFF)
+option(USE_KUNLUN "Support KUNLUN XPU" OFF)
 option(USE_INTELCPU "Support INTELCPU" OFF)
 option(USE_BACKTRACE "Print backtrace on exception and segmentation fault" ON)
 option(USE_PROTOBUF "Serialize and deserialize tensors" OFF)
+option(BUILD_NNET "Build nnet" OFF)
+option(BUILD_DIST "Build project for distributed running" OFF)
 option(BUILD_TEST "Build tests" OFF)
 
+if(USE_CUDA)
+    message("CMake 3.18 or higher is required for setting CUDAToolkit")
+    cmake_minimum_required(VERSION 3.18) # FindCUDAToolkit
+else()
+    cmake_minimum_required(VERSION 3.17)
+endif()
+
+include(CMakeDependentOption)
+project(InfiniTensor C CXX)
+
 cmake_dependent_option(BUILD_TEST_CORE "Build tests for core components" ON BUILD_TEST OFF)
 cmake_dependent_option(BUILD_TEST_PET "Build tests for PET" OFF BUILD_TEST OFF)
-cmake_dependent_option(BUILD_TEST_EINNET "Build tests for EINNET" OFF BUILD_TEST OFF)
 
 set(DEFAULT_BUILD_TYPE "RelWithDebInfo")
+# Build Type
+if(CMAKE_BUILD_TYPE STREQUAL "Debug")
+    message("Configuring for Debug build.")
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -O0")
+    add_compile_definitions(DEBUG_MODE)
+elseif(CMAKE_BUILD_TYPE STREQUAL "Release")
+    message("Configuring for Release build.")
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O2")
+    add_compile_definitions(NDEBUG)
+elseif(CMAKE_BUILD_TYPE STREQUAL "RelWithDebInfo")
+    message("Configuring for RelWithDebInfo build.")
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -O2")
+else()
+    message("Build type not specified. Configuring for RelWithDebInfo build.")
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -O2")
+endif()
+
 
 if(EXISTS ${CMAKE_CURRENT_BINARY_DIR}/config.cmake)
   message(STATUS "Using config.cmake in CMAKE_CURRENT_BINARY_DIR directory")
@@ -28,11 +53,13 @@ endif()
 
 set(CMAKE_CXX_STANDARD 17)
 set(CMAKE_CXX_EXTENSIONS OFF) # -std=gnu++11 when on, -std=c++11 when off
+add_compile_options(-Wno-error=unused-variable)
 
 find_package(
   Python
   COMPONENTS Interpreter Development
   REQUIRED)
+
 # OpenMP
 find_package(OpenMP)
 if(OpenMP_C_FOUND)
@@ -69,16 +96,17 @@ add_subdirectory(3rd-party/nlohmann_json_cmake_fetchcontent)
 include_directories(3rd-party/nlohmann_json_cmake_fetchcontent/single_include)
 
 # TVM backend
-if(BUILD_TEST_EINNET)
-  if (NOT TVM_INCLUDE_DIR OR NOT DMLC_INCLUDE_DIR OR NOT DLPACK_INCLUDE_DIR OR NOT DLPACK_INCLUDE_DIR)
-    message(FATAL_ERROR "TVM_INCLUDE_DIR, DMLC_INCLUDE_DIR, and DLPACK_INCLUDE_DIR must be set when BUILD_TEST_EINNET is ON")
-  endif()
+if(BUILD_NNET AND BUILD_TEST)
   # TVM and DMLC for invoking TVM packed functions
   include_directories(${TVM_INCLUDE_DIR})
   include_directories(${DMLC_INCLUDE_DIR})
   include_directories(${DLPACK_INCLUDE_DIR})
-  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DDMLC_USE_LOGGING_LIBRARY=\\\<${TVM_INCLUDE_DIR}/tvm/runtime/logging.h\\\> ")
-  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DINFINI_USE_TVM=1") # Enable TVM codegen kernels
+  if (TVM_INCLUDE_DIR AND DMLC_INCLUDE_DIR AND DLPACK_INCLUDE_DIR AND DLPACK_INCLUDE_DIR)
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DDMLC_USE_LOGGING_LIBRARY=\\\<${TVM_INCLUDE_DIR}/tvm/runtime/logging.h\\\> ")
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DINFINI_USE_TVM=1") # Enable TVM codegen kernels
+  else()
+    # message(FATAL_ERROR "TVM_INCLUDE_DIR, DMLC_INCLUDE_DIR, and DLPACK_INCLUDE_DIR must be set when BUILD_NNET AND BUILD_TEST is ON")
+  endif()
 endif()
 
 if(BUILD_TEST)
@@ -92,13 +120,21 @@ if(BUILD_TEST)
   include_directories(3rd-party/googletest/googletest/include)
 endif()
 
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -Wall -Werror -Wno-error=deprecated-declarations")
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -Wall -Werror -Wno-error=deprecated-declarations -Wno-error=pointer-arith")
 set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -UNDEBUG") # Enable assertion
 set(CMAKE_CXX_FLAGS_RELWITHDEBINFO "${CMAKE_CXX_FLAGS_RELWITHDEBINFO} -UNDEBUG") # Enable assertion
 
 
 # Source files
-file(GLOB_RECURSE SRC src/ffi/*.cc src/core/*.cc src/kernels/cpu/*.cc src/nnet/*.cc src/operators/*.cc src/utils/*.cc)
+file(GLOB_RECURSE SRC src/ffi/*.cc src/core/*.cc src/kernels/cpu/*.cc src/operators/*.cc src/utils/*.cc)
+
+if(BUILD_NNET)
+  add_compile_definitions(BUILD_NNET=1)
+  file(GLOB_RECURSE SRC_NNET src/nnet/*.cc)
+  list (APPEND SRC ${SRC_NNET})
+  # For locating resource files
+  set_source_files_properties(src/nnet/test.cc PROPERTIES COMPILE_OPTIONS "-DINFINI_PROJECT_HOME=${CMAKE_CURRENT_SOURCE_DIR}")
+endif()
 
 if(USE_CUDA)
   file(GLOB_RECURSE SRC_CUDA src/cuda/*.cc src/cuda/*.cu src/kernels/cuda/*.cc src/kernels/cuda/*.cu)
@@ -110,6 +146,11 @@ if(USE_BANG)
   list (APPEND SRC ${SRC_BANG})
 endif()
 
+if(USE_KUNLUN)
+  file(GLOB_RECURSE SRC_KUNLUN src/kunlun/*.cc src/kernels/kunlun/*.cc )
+  list (APPEND SRC ${SRC_KUNLUN})
+endif()
+
 if(USE_INTELCPU)
   file(GLOB_RECURSE SRC_INTELCPU src/intelcpu/*.cc src/kernels/intelcpu/*.cc )
   list (APPEND SRC ${SRC_INTELCPU})
@@ -124,7 +165,7 @@ endif()
 target_link_libraries(InfiniTensor pybind11::embed)
 
 # TVM backend
-if(BUILD_TEST_EINNET)
+if(BUILD_NNET AND BUILD_TEST AND TVM_LIB_DIR)
   target_link_libraries(InfiniTensor ${TVM_LIB_DIR}/libtvm.so)
 endif()
 
@@ -177,6 +218,13 @@ if(USE_CUDA)
   enable_language(CUDA)
   find_package(CUDAToolkit) # For nvrtc and cuda driver
   target_link_libraries(InfiniTensor cudnn CUDA::curand CUDA::cublas CUDA::nvrtc CUDA::cudart CUDA::cuda_driver)
+  if (BUILD_DIST)
+    message(STATUS "Add BUILD_DIST, use NCCL with CUDA")
+    list(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake)
+    find_package(NCCL REQUIRED)
+    add_compile_definitions(INFINI_USE_NCCL=1)
+    target_link_libraries(InfiniTensor nccl)
+  endif()
 endif()
 
 if(USE_BANG)
@@ -215,7 +263,50 @@ if(USE_BANG)
   # BangC Kernels
   ################################################################################
 
-  target_link_libraries(InfiniTensor ${CAMBRICON_CNNL} ${CAMBRICON_CNRT} ${CAMBRICON_CNDRV} stdc++)
+  if (BUILD_DIST)
+    find_library(CAMBRICON_CNCL libcncl.so "${NEUWARE_HOME}/lib64")
+    target_link_libraries(InfiniTensor ${CAMBRICON_CNCL} ${CAMBRICON_CNNL} ${CAMBRICON_CNRT} ${CAMBRICON_CNDRV} stdc++)
+    message(STATUS "Add BUILD_DIST, use CNCL with BANG")
+    add_compile_definitions(INFINI_USE_CNCL=1)
+  else()
+    target_link_libraries(InfiniTensor ${CAMBRICON_CNNL} ${CAMBRICON_CNRT} ${CAMBRICON_CNDRV} stdc++)
+  endif()
+endif()
+
+if(USE_KUNLUN)
+  add_compile_definitions(USE_KUNLUN=1)
+  if ((NOT DEFINED KUNLUN_HOME) AND (NOT DEFINED ENV{KUNLUN_HOME}))
+    message(FATAL_ERROR "KUNLUN_HOME is not defined from cmake or env")
+  elseif (DEFINED KUNLUN_HOME)
+          set(KUNLUN_HOME ${KUNLUN_HOME} CACHE STRING "KUNLUN_HOME directory for Kunlun development")
+  else()
+          set(KUNLUN_HOME $ENV{KUNLUN_HOME} CACHE STRING "KUNLUN_HOME directory for Kunlun development")
+  endif()
+  message(STATUS "KUNLUN_HOME: ${KUNLUN_HOME}")
+
+  include_directories("${KUNLUN_HOME}/include/")
+  find_library(KUNLUN_RT libxpurt.so "${KUNLUN_HOME}/lib64/")
+  find_library(KUNLUN_DNN libxpuapi.so "${KUNLUN_HOME}/lib64/")
+  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -lstdc++ -Wall -Werror")
+
+  if ((NOT DEFINED TARGET_CPU_ARCH) AND (NOT DEFINED ENV{TARGET_CPU_ARCH}))
+    execute_process(COMMAND uname -m OUTPUT_VARIABLE _uname_m OUTPUT_STRIP_TRAILING_WHITESPACE)
+    set(TARGET_CPU_ARCH "${_uname_m}" CACHE STRING "Target CPU ARCH")
+  elseif(DEFINED TARGET_CPU_ARCH)
+    set(TARGET_CPU_ARCH ${TARGET_CPU_ARCH} CACHE STRING "Target CPU ARCH")
+  else()
+    set(TARGET_CPU_ARCH $ENV{TARGET_CPU_ARCH} CACHE STRING "Target CPU ARCH")
+  endif()
+  message(STATUS "TARGET_CPU_ARCH: ${TARGET_CPU_ARCH}")
+
+  if (BUILD_DIST)
+    message(STATUS "Add BUILD_DIST, use XCCL with KUNLUN XPU")
+    list(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake)
+    find_package(XCCL REQUIRED)
+    add_compile_definitions(INFINI_USE_XCCL=1)
+    target_link_libraries(InfiniTensor ${XCCL_LIBRARIES})
+  endif()
+  target_link_libraries(InfiniTensor ${KUNLUN_RT} ${KUNLUN_DNN} stdc++)
 endif()
 
 # # Python bindings
@@ -242,11 +333,18 @@ if(BUILD_TEST)
   if(BUILD_TEST_CORE)
     build_test(test/core/*.cc)
     build_test(test/operators/*.cc)
+    build_test(test/kernels/nativecpu/*.cc)
     if (USE_CUDA)
       build_test(test/kernels/cuda/*.cc)
+      build_test(test/cuda/*.cc)
     endif()
     if (USE_BANG)
       build_test(test/kernels/bang/*.cc)
+      build_test(test/bang/*.cc)
+    endif()
+    if (USE_KUNLUN)
+      build_test(test/kernels/kunlun/*.cc)
+      build_test(test/kunlun/*.cc)
     endif()
     if (USE_INTELCPU)
       build_test(test/kernels/intelcpu/*.cc)
@@ -255,7 +353,7 @@ if(BUILD_TEST)
   if(BUILD_TEST_PET)
     build_test(test/pet/*.cc)
   endif()
-  if(BUILD_TEST_EINNET)
+  if(BUILD_NNET AND BUILD_TEST)
     build_test(test/nnet/test_*.cc)
 
     # Build expression reader

Makefile

@@ -3,16 +3,36 @@
 TYPE ?= Release
 CUDA ?= OFF
 BANG ?= OFF
+KUNLUN ?= OFF
 INTELCPU ?= off
 BACKTRACE ?= ON
 TEST ?= ON
+DIST ?= OFF
+NNET ?= OFF
+DIST ?= OFF
 FORMAT_ORIGIN ?=
+# Docker build options
+DOCKER_NAME ?= infinitensor
+DOCKER_IMAGE_NAME ?= infinitensor
+DOCKER_FILE ?= infinitensor_ubuntu_22.04.dockerfile
+DOCKER_RUN_OPTION ?=
+
+# CUDA option.
+ifeq ($(CUDA), ON)
+	DOCKER_IMAGE_NAME = infinitensor_cuda
+	DOCKER_NAME = infinitensor_cuda
+	DOCKER_FILE = infinitensor_ubuntu_22.04_CUDA.dockerfile
+	DOCKER_RUN_OPTION += --gpus all -it --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 -v `pwd`:`pwd` -w `pwd`
+endif
 
 CMAKE_OPT = -DCMAKE_BUILD_TYPE=$(TYPE)
 CMAKE_OPT += -DUSE_CUDA=$(CUDA)
 CMAKE_OPT += -DUSE_BANG=$(BANG)
+CMAKE_OPT += -DUSE_KUNLUN=$(KUNLUN)
 CMAKE_OPT += -DUSE_BACKTRACE=$(BACKTRACE)
 CMAKE_OPT += -DBUILD_TEST=$(TEST)
+CMAKE_OPT += -DBUILD_DIST=$(DIST)
+CMAKE_OPT += -DBUILD_NNET=$(NNET)
 
 ifeq ($(INTELCPU), ON)
 	CMAKE_OPT += -DUSE_INTELCPU=ON -DCMAKE_CXX_COMPILER=dpcpp
@@ -39,3 +59,19 @@ test-cpp:
 test-onnx:
 	@echo
 	python3 pyinfinitensor/tests/test_onnx.py
+
+test-api:
+	@echo
+	python3 pyinfinitensor/tests/test_api.py
+
+docker-build:
+	docker build -f scripts/dockerfile/$(DOCKER_FILE) -t $(DOCKER_NAME) .
+
+docker-run:
+	docker run -t --name $(DOCKER_IMAGE_NAME) -d $(DOCKER_NAME) $(DOCKER_RUN_OPTION)
+
+docker-start:
+	docker start $(DOCKER_IMAGE_NAME)
+
+docker-exec:
+	docker exec -it $(DOCKER_IMAGE_NAME) bash

README.md

@@ -33,13 +33,14 @@ There are several configurable CMake options, see the [CMakeLists.txt](/CMakeLis
 
 ## Roadmap
 
+- [RefactorGraph](https://github.com/InfiniTensor/RefactorGraph) is a newly designed AI framework that is set to replace the current main branch.
 - [EinNet](https://github.com/InfiniTensor/InfiniTensor/tree/NNET_e2e) is going to be merged into the main branch.
 - Integration of [PET](https://github.com/thu-pacman/PET), a tensor program optimizer supporting partially equivalent transformations.
 - Supported hardware
   - ✔ NVIDIA GPU
   - ✔ Cambricon MLU
+  - ✔ Kunlunxin XPU
   - ⬜ Ascend NPU
-  - ⬜ Kunlunxin XPU
 
 ## Contributor Guide
 

cmake/FindCNCL.cmake

@@ -0,0 +1,76 @@
+SET(CNCL_LIB_SEARCH_PATHS $ENV{NEUWARE_HOME}/lib64)
+SET(CNCL_INCLUDE_SEARCH_PATHS $ENV{NEUWARE_HOME}/include)
+
+set(CNCL_INCLUDE_DIR $ENV{NEUWARE_HOME}/include)
+set(CNCL_LIB_DIR $ENV{NEUWARE_HOME}/lib64)
+set(CNCL_VERSION $ENV{CNCL_VERSION} CACHE STRING "Version of CNCL to build with")
+
+if ($ENV{CNCL_ROOT_DIR})
+  message(WARNING "CNCL_ROOT_DIR is deprecated. Please set CNCL_ROOT instead.")
+endif()
+list(APPEND CNCL_ROOT $ENV{CNCL_ROOT_DIR} ${MLU_TOOLKIT_ROOT_DIR})
+# Compatible layer for CMake <3.12. CNCL_ROOT will be accounted in for searching paths and libraries for CMake >=3.12.
+list(APPEND CMAKE_PREFIX_PATH ${CNCL_ROOT})
+
+find_path(CNCL_INCLUDE_DIRS
+  NAMES cncl.h
+  HINTS ${CNCL_INCLUDE_DIR})
+
+if (USE_STATIC_CNCL)
+  MESSAGE(STATUS "USE_STATIC_CNCL is set. Linking with static CNCL library.")
+  SET(CNCL_LIBNAME "CNCL_static")
+  if (CNCL_VERSION)  # Prefer the versioned library if a specific CNCL version is specified
+    set(CMAKE_FIND_LIBRARY_SUFFIXES ".a.${CNCL_VERSION}" ${CMAKE_FIND_LIBRARY_SUFFIXES})
+  endif()
+else()
+  SET(CNCL_LIBNAME "cncl")
+  if (CNCL_VERSION)  # Prefer the versioned library if a specific CNCL version is specified
+    set(CMAKE_FIND_LIBRARY_SUFFIXES ".so.${CNCL_VERSION}" ${CMAKE_FIND_LIBRARY_SUFFIXES})
+  endif()
+endif()
+
+find_library(CNCL_LIBRARIES
+  NAMES ${CNCL_LIBNAME}
+  HINTS ${CNCL_LIB_DIR})
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(CNCL DEFAULT_MSG CNCL_INCLUDE_DIRS CNCL_LIBRARIES)
+
+if(CNCL_FOUND)  # obtaining CNCL version and some sanity checks
+  set (CNCL_HEADER_FILE "${CNCL_INCLUDE_DIRS}/cncl.h")
+  message (STATUS "Determining CNCL version from ${CNCL_HEADER_FILE}...")
+  set (OLD_CMAKE_REQUIRED_INCLUDES ${CMAKE_REQUIRED_INCLUDES})
+  list (APPEND CMAKE_REQUIRED_INCLUDES ${CNCL_INCLUDE_DIRS})
+  include(CheckCXXSymbolExists)
+  check_cxx_symbol_exists(CNCL_VERSION_CODE CNCL.h CNCL_VERSION_DEFINED)
+
+  if (CNCL_VERSION_DEFINED)
+    set(file "${PROJECT_BINARY_DIR}/detect_cncl_version.cc")
+    file(WRITE ${file} "
+      #include <iostream>
+      #include <cncl.h>
+      int main()
+      {
+        std::cout << CNCL_MAJOR << '.' << CNCL_MINOR << '.' << CNCL_PATCH << std::endl;
+        int x;
+        CNCLGetVersion(&x);
+        return x == CNCL_VERSION_CODE;
+      }
+")
+    try_run(CNCL_VERSION_MATCHED compile_result ${PROJECT_BINARY_DIR} ${file}
+          RUN_OUTPUT_VARIABLE CNCL_VERSION_FROM_HEADER
+          CMAKE_FLAGS  "-DINCLUDE_DIRECTORIES=${CNCL_INCLUDE_DIRS}"
+          LINK_LIBRARIES ${CNCL_LIBRARIES})
+    if (NOT CNCL_VERSION_MATCHED)
+      message(FATAL_ERROR "Found CNCL header version and library version do not match! \
+(include: ${CNCL_INCLUDE_DIRS}, library: ${CNCL_LIBRARIES}) Please set CNCL_INCLUDE_DIR and CNCL_LIB_DIR manually.")
+    endif()
+    message(STATUS "CNCL version: ${CNCL_VERSION_FROM_HEADER}")
+  else()
+    # message(STATUS "CNCL version < 2.3.5-5")
+  endif ()
+  set (CMAKE_REQUIRED_INCLUDES ${OLD_CMAKE_REQUIRED_INCLUDES})
+
+  message(STATUS "Found CNCL (include: ${CNCL_INCLUDE_DIRS}, library: ${CNCL_LIBRARIES})")
+  mark_as_advanced(CNCL_ROOT_DIR CNCL_INCLUDE_DIRS CNCL_LIBRARIES)
+endif()

cmake/FindNCCL.cmake

@@ -0,0 +1,165 @@
+# Copyright (c) 2021-2022, NVIDIA CORPORATION. All rights reserved.
+# 
+# From PyTorch:
+# 
+# Copyright (c) 2016-     Facebook, Inc            (Adam Paszke)
+# Copyright (c) 2014-     Facebook, Inc            (Soumith Chintala)
+# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
+# Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
+# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
+# Copyright (c) 2011-2013 NYU                      (Clement Farabet)
+# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
+# Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
+# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
+# 
+# From Caffe2:
+# 
+# Copyright (c) 2016-present, Facebook Inc. All rights reserved.
+# 
+# All contributions by Facebook:
+# Copyright (c) 2016 Facebook Inc.
+# 
+# All contributions by Google:
+# Copyright (c) 2015 Google Inc.
+# All rights reserved.
+# 
+# All contributions by Yangqing Jia:
+# Copyright (c) 2015 Yangqing Jia
+# All rights reserved.
+# 
+# All contributions by Kakao Brain:
+# Copyright 2019-2020 Kakao Brain
+# 
+# All contributions from Caffe:
+# Copyright(c) 2013, 2014, 2015, the respective contributors
+# All rights reserved.
+# 
+# All other contributions:
+# Copyright(c) 2015, 2016 the respective contributors
+# All rights reserved.
+# 
+# Caffe2 uses a copyright model similar to Caffe: each contributor holds
+# copyright over their contributions to Caffe2. The project versioning records
+# all such contribution and copyright details. If a contributor wants to further
+# mark their specific copyright on a particular contribution, they should
+# indicate their copyright solely in the commit message of the change when it is
+# committed.
+# 
+# All rights reserved.
+# 
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+# 
+# 1. Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+# 
+# 2. Redistributions in binary form must reproduce the above copyright
+#    notice, this list of conditions and the following disclaimer in the
+#    documentation and/or other materials provided with the distribution.
+# 
+# 3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC Laboratories America
+#    and IDIAP Research Institute nor the names of its contributors may be
+#    used to endorse or promote products derived from this software without
+#    specific prior written permission.
+# 
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
+# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+# POSSIBILITY OF SUCH DAMAGE.
+# 
+# Find the nccl libraries
+#
+# The following variables are optionally searched for defaults
+#  NCCL_ROOT: Base directory where all NCCL components are foundHong Xu, 1 year ago: • Let CMake handle NCCL detection instead of ou…
+#  NCCL_INCLUDE_DIR: Directory where NCCL header is foundPieter Noordhuis, 3 years ago: • Bump gloo
+#  NCCL_LIB_DIR: Directory where NCCL library is found
+#
+# The following are set after configuration is done:
+#  NCCL_FOUND
+#  NCCL_INCLUDE_DIRS
+#  NCCL_LIBRARIES
+#
+# The path hints include CUDA_TOOLKIT_ROOT_DIR seeing as some folks
+# install NCCL in the same location as the CUDA toolkit.
+# See https://github.com/caffe2/caffe2/issues/1601
+
+set(NCCL_INCLUDE_DIR $ENV{NCCL_INCLUDE_DIR} CACHE PATH "Folder contains NVIDIA NCCL headers")
+set(NCCL_LIB_DIR $ENV{NCCL_LIB_DIR} CACHE PATH "Folder contains NVIDIA NCCL libraries")
+set(NCCL_VERSION $ENV{NCCL_VERSION} CACHE STRING "Version of NCCL to build with")
+
+if ($ENV{NCCL_ROOT_DIR})
+  message(WARNING "NCCL_ROOT_DIR is deprecated. Please set NCCL_ROOT instead.")
+endif()
+list(APPEND NCCL_ROOT $ENV{NCCL_ROOT_DIR} ${CUDA_TOOLKIT_ROOT_DIR})
+# Compatible layer for CMake <3.12. NCCL_ROOT will be accounted in for searching paths and libraries for CMake >=3.12.
+list(APPEND CMAKE_PREFIX_PATH ${NCCL_ROOT})
+
+find_path(NCCL_INCLUDE_DIRS
+  NAMES nccl.h
+  HINTS ${NCCL_INCLUDE_DIR})
+
+if (USE_STATIC_NCCL)
+  MESSAGE(STATUS "USE_STATIC_NCCL is set. Linking with static NCCL library.")
+  SET(NCCL_LIBNAME "nccl_static")
+  if (NCCL_VERSION)  # Prefer the versioned library if a specific NCCL version is specified
+    set(CMAKE_FIND_LIBRARY_SUFFIXES ".a.${NCCL_VERSION}" ${CMAKE_FIND_LIBRARY_SUFFIXES})
+  endif()
+else()
+  SET(NCCL_LIBNAME "nccl")
+  if (NCCL_VERSION)  # Prefer the versioned library if a specific NCCL version is specified
+    set(CMAKE_FIND_LIBRARY_SUFFIXES ".so.${NCCL_VERSION}" ${CMAKE_FIND_LIBRARY_SUFFIXES})
+  endif()
+endif()
+
+find_library(NCCL_LIBRARIES
+  NAMES ${NCCL_LIBNAME}
+  HINTS ${NCCL_LIB_DIR})
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(NCCL DEFAULT_MSG NCCL_INCLUDE_DIRS NCCL_LIBRARIES)
+
+if(NCCL_FOUND)  # obtaining NCCL version and some sanity checks
+  set (NCCL_HEADER_FILE "${NCCL_INCLUDE_DIRS}/nccl.h")
+  message (STATUS "Determining NCCL version from ${NCCL_HEADER_FILE}...")
+  set (OLD_CMAKE_REQUIRED_INCLUDES ${CMAKE_REQUIRED_INCLUDES})
+  list (APPEND CMAKE_REQUIRED_INCLUDES ${NCCL_INCLUDE_DIRS})
+  include(CheckCXXSymbolExists)
+  check_cxx_symbol_exists(NCCL_VERSION_CODE nccl.h NCCL_VERSION_DEFINED)
+
+  if (NCCL_VERSION_DEFINED)
+    set(file "${PROJECT_BINARY_DIR}/detect_nccl_version.cc")
+    file(WRITE ${file} "
+      #include <iostream>
+      #include <nccl.h>
+      int main()
+      {
+        std::cout << NCCL_MAJOR << '.' << NCCL_MINOR << '.' << NCCL_PATCH << std::endl;
+        int x;
+        ncclGetVersion(&x);
+        return x == NCCL_VERSION_CODE;
+      }
+")
+    try_run(NCCL_VERSION_MATCHED compile_result ${PROJECT_BINARY_DIR} ${file}
+          RUN_OUTPUT_VARIABLE NCCL_VERSION_FROM_HEADER
+          CMAKE_FLAGS  "-DINCLUDE_DIRECTORIES=${NCCL_INCLUDE_DIRS}"
+          LINK_LIBRARIES ${NCCL_LIBRARIES})
+    if (NOT NCCL_VERSION_MATCHED)
+      message(FATAL_ERROR "Found NCCL header version and library version do not match! \
+(include: ${NCCL_INCLUDE_DIRS}, library: ${NCCL_LIBRARIES}) Please set NCCL_INCLUDE_DIR and NCCL_LIB_DIR manually.")
+    endif()
+    message(STATUS "NCCL version: ${NCCL_VERSION_FROM_HEADER}")
+  else()
+    # message(STATUS "NCCL version < 2.3.5-5")
+  endif ()
+  set (CMAKE_REQUIRED_INCLUDES ${OLD_CMAKE_REQUIRED_INCLUDES})
+
+  message(STATUS "Found NCCL (include: ${NCCL_INCLUDE_DIRS}, library: ${NCCL_LIBRARIES})")
+  mark_as_advanced(NCCL_ROOT_DIR NCCL_INCLUDE_DIRS NCCL_LIBRARIES)
+endif()

cmake/FindXCCL.cmake

@@ -0,0 +1,27 @@
+# Find the xccl libraries
+set(XCCL_INCLUDE_DIR $ENV{KUNLUN_HOME}/include CACHE PATH "Folder contains KUNLUN XCCL headers")
+set(XCCL_LIB_DIR $ENV{KUNLUN_HOME}  CACHE PATH "Folder contains KUNLUN XCCL libraries")
+
+list(APPEND CMAKE_PREFIX_PATH $ENV{KUNLUN_HOME})
+
+find_path(XCCL_INCLUDE_DIRS # ${XCCL_INCLUDE_DIR}
+  NAMES xpu/bkcl.h
+  HINTS XCCL_INCLUDE_DIR)
+
+find_library(XCCL_LIBRARIES # ${XCCL_LIB_DIR}
+  NAMES lib64/libbkcl.so
+  HINTS XCCL_LIB_DIR)
+
+message(STATUS "XCCL_INCLUDE_DIRS: ${XCCL_INCLUDE_DIRS}")
+message(STATUS "XCCL_LIBRARIES: ${XCCL_LIBRARIES}")
+
+include(FindPackageHandleStandardArgs)
+find_package_handle_standard_args(XCCL DEFAULT_MSG XCCL_INCLUDE_DIRS XCCL_LIBRARIES)
+
+if (XCCL_FOUND)
+  set (XCCL_HEADER_FILE "${XCCL_INCLUDE_DIRS}/xpu/bkcl.h")
+  message (STATUS "Determing XCCL version from ${XCCL_HEADER_FILE}...")
+  list (APPEND CMAKE_REQUIRED_INCLUDES ${XCCL_INCLUDE_DIRS})
+  message(STATUS "Found XCCL (include: ${XCCL_INCLUDE_DIRS}, library: ${XCCL_LIBRARIES})")
+  mark_as_advanced(XCCL_INCLUDE_DIRS XCCL_LIBRARIES)
+endif()

docs/INSTALL_GUIDE_CN.md

@@ -133,10 +133,40 @@
    make install-python BANG=ON
    ```
 
+   编译 CPU 部分，同时编译昆仑 XPU 部分：
+
+   ```bash
+   export KUNLUN_HOME=/path/to/your/kunlun_home
+   make install-python KUNLUN=ON
+   ```
+
 3. 使用方法
 
    安装成功后，您就可以使用本项目的 Python 接口进行编码并运行。具体使用方式可以参考项目样例代码 example/Resnet/resnet.py 以及用户使用手册
 
+## Docker
+
+本项目也提供了 Docker 的环境，您可以使用 `make docker-build` 或 `make docker-build CUDA=ON` 命令启动并编译 Dockerfile，您可以通过添加编译选项或者修改 Makefile 变量修改 docker image 名称或者所选的 Dockerfile 文件。
+ 
+由于在拉取 github repo 时需要将 ssh key 加入到 github profile 中，因此暂时注释掉拉取 repo 并编译项目的过程，由用户在进入 docker 后自己维护 ssh key（将 host 中的 ssh key 复制到 docker 中可能会遇到环境不一致的问题）。
+
+```shell
+# Build docker container.
+make docker-build
+# Run docker image.
+make docker-run
+# Execute docker image.
+make docker-exec
+```
+
+如果需要编译 CUDA 版，请使用如下命令：
+```shell
+# Build docker container.
+make docker-build CUDA=ON
+# Run docker image.
+make docker-run CUDA=ON
+```
+
 ## 技术支持
 
 如遇到问题，请联系我们技术支持团队

docs/SUPPORT_MATRIX_CN.md

@@ -2,6 +2,7 @@
 
 ## 目录
 
+
 - [环境支持](#环境支持)
 - [神经网络支持](#神经网络支持)
 - [技术支持](#技术支持)
@@ -19,10 +20,10 @@
 
 目前已经验证过的神经网络模型有
 
-- [x] [ResNet18-v2](https://github.com/onnx/models/blob/main/vision/classification/resnet/model/resnet18-v2-7.onnx)
-- [x] [DenseNet-121-12](https://github.com/onnx/models/blob/main/vision/classification/densenet-121/model/densenet-12.onnx)
-- [x] [Inception-2](https://github.com/onnx/models/blob/main/vision/classification/inception_and_googlenet/inception_v2/model/inception-v2-9.onnx)
-- [x] [EfficientNet-Lite4](https://github.com/onnx/models/blob/main/vision/classification/efficientnet-lite4/model/efficientnet-lite4-11.onnx)
+- [x] [ResNet18-v2](https://github.com/onnx/models/blob/main/validated/vision/classification/resnet/model/resnet18-v2-7.onnx)
+- [x] [DenseNet-121-12](https://github.com/onnx/models/blob/main/validated/vision/classification/densenet-121/model/densenet-12.onnx)
+- [x] [Inception-2](https://github.com/onnx/models/blob/main/validated/vision/classification/inception_and_googlenet/inception_v2/model/inception-v2-9.onnx)
+- [x] [EfficientNet-Lite4](https://github.com/onnx/models/blob/main/validated/vision/classification/efficientnet-lite4/model/efficientnet-lite4-11.onnx)
 
 ## 技术支持
 

docs/USER_GUIDE_CN.md

@@ -3,9 +3,10 @@
 ## 目录
 
 - [使用方法](#使用方法)
-- [python-前端应用指南](#python-前端应用指南)
-  - [导入-onnx-模型](#导入-onnx-模型)
-  - [导出-onnx-模型](#导出-onnx-模型)
+- [python 前端应用指南](#python-前端应用指南)
+  - [导入 onnx 模型](#导入-onnx-模型)
+  - [优化](#优化)
+  - [导出 onnx 模型](#导出-onnx-模型)
   - [执行推理](#执行推理)
   - [样例代码](#样例代码)
 - [技术支持](#技术支持)
@@ -13,7 +14,7 @@
 
 ## 使用方法
 
-项目管理功能已写到 [Makefile](Makefile)，支持下列功能：
+项目管理功能已写到 [Makefile](../Makefile)，支持下列功能：
 
 - 编译项目：`make`/`make build`
 - 清理生成文件：`make clean`
@@ -26,6 +27,7 @@
 - `TYPE`：编译模式（`debug`/`release`），默认值为 `release`
 - `CUDA`：是否编译 CUDA 后端，默认为 `OFF`，`ON` 打开
 - `BANG`：是否编译寒武纪后端，默认为 `OFF`，`ON` 打开
+- `KUNLUN`：是否编译昆仑后端，默认为 `OFF`，`ON` 打开
 - `BACKTRACE`：是否启用栈回溯，默认为 `ON`，`OFF` 关闭，建议调试时打开
 - `TEST`：是否编译 `googletest`，默认为 `ON`，`OFF` 关闭，只有 `test-cpp` 时必要
 
@@ -37,10 +39,10 @@
 
 支持的模型：
 
-- [x] [ResNet18-v2](https://github.com/onnx/models/blob/main/vision/classification/resnet/model/resnet18-v2-7.onnx)
-- [x] [DenseNet-121-12](https://github.com/onnx/models/blob/main/vision/classification/densenet-121/model/densenet-12.onnx)
-- [x] [Inception-2](https://github.com/onnx/models/blob/main/vision/classification/inception_and_googlenet/inception_v2/model/inception-v2-9.onnx)
-- [x] [EfficientNet-Lite4](https://github.com/onnx/models/blob/main/vision/classification/efficientnet-lite4/model/efficientnet-lite4-11.onnx)
+- [x] [ResNet18-v2](https://github.com/onnx/models/blob/main/validated/vision/classification/resnet/model/resnet18-v2-7.onnx)
+- [x] [DenseNet-121-12](https://github.com/onnx/models/blob/main/validated/vision/classification/densenet-121/model/densenet-12.onnx)
+- [x] [Inception-2](https://github.com/onnx/models/blob/main/validated/vision/classification/inception_and_googlenet/inception_v2/model/inception-v2-9.onnx)
+- [x] [EfficientNet-Lite4](https://github.com/onnx/models/blob/main/validated/vision/classification/efficientnet-lite4/model/efficientnet-lite4-11.onnx)
 
 ```python
 import onnx
@@ -95,7 +97,7 @@ for name, tensor in stub.inputs.items():
     print(name, tensor.shape(), tensor)
 ```
 
-对于 [resnet18-v2-7.onnx](https://github.com/onnx/models/blob/main/vision/classification/resnet/model/resnet18-v2-7.onnx)，会打印出：
+对于 [resnet18-v2-7.onnx](https://github.com/onnx/models/blob/main/validated/vision/classification/resnet/model/resnet18-v2-7.onnx)，会打印出：
 
 ```plaintext
 data [1, 3, 224, 224] <backend.Tensor object at 0x7efeb828e3b0>
@@ -136,7 +138,7 @@ for name, tensor in stub.outputs.items():
 
 ### 样例代码
 
-您可以参照[./example/Resnet/resnet.py](./example/ResNet/resnet.py)的样例代码进行了解，并尝试运行。在这个文件中，我们使用了 Pytorch 构建了 resnet 网络。您可以查阅该脚本使用方式：
+您可以参照[resnet.py](https://github.com/wanghailu0717/NNmodel/blob/main/ResNet/resnet.py)的样例代码进行了解，并尝试运行。在这个文件中，我们使用了 Pytorch 构建了 resnet 网络。您可以查阅该脚本使用方式：
 
 ```python
 python resnet.py -h

env.sh

@@ -35,4 +35,4 @@ export LD_LIBRARY_PATH="${NEUWARE_HOME}/lib64:${LD_LIBRARY_PATH}"
 # ├── tools
 # ├── version
 # └── XTDK
-export XPU_HOME=/usr/local/xpu
+export KUNLUN_HOME=/usr/local/xpu

examples/distributed/README.md

@@ -0,0 +1,39 @@
+# 分布式脚本
+
+## 英伟达平台运行方式
+
+#### 1. 运行pytorch模型并生成输入和标准输出，可选择导出onnx
+
+使用 `--export_onnx` 设置导出onnx的目录，默认为当前路径 `./`，不使用这个flag则只进行计算和生成输入输出。
+
+```bash
+python run_pytorch.py --model gpt2  --batch_size 1  --length 1 --export_onnx ./
+```
+
+会在当前目录下生成输入输出文件`test_inputs.npy` 和 `test_results.npy`，目前只支持单一输入输出。
+
+#### 2. 运行InfiniTensor分布式脚本
+
+```bash
+python cuda_launch.py --model "/XXX/XXX.onnx" --nproc_per_node 4 
+```
+
+## 寒武纪平台运行方式
+
+**将上述运行脚本 `run_pytorch.py` 以及 `cuda_launch.py` 针对寒武纪平台做了相应的适配，具体见 `run_pytorch_mlu.py` 以及 `bang_launch.py`。**
+
+#### 1. 运行pytorch模型并生成输入和标准输出，可选择导出onnx
+
+使用 `--export_onnx` 设置导出onnx的目录，默认为当前路径 `./`，不使用这个flag则只进行计算和生成输入输出。
+
+```bash
+python run_pytorch_mlu.py --model gpt2  --batch_size 1  --length 1 --export_onnx ./
+```
+
+会在当前目录下生成输入输出文件`test_inputs.npy` 和 `test_results.npy`，目前只支持单一输入输出。
+
+#### 2. 运行InfiniTensor分布式脚本
+
+```bash
+python bang_launch.py --model "/XXX/XXX.onnx" --nproc_per_node 4 
+```

examples/distributed/bang/bang_launch.py

@@ -0,0 +1,187 @@
+import sys
+sys.path.append('../')
+
+import argparse
+import os
+import time
+import multiprocessing as mp
+from pyinfinitensor.onnx import OnnxStub, backend
+import onnx
+from onnx.external_data_helper import convert_model_to_external_data
+from onnx.shape_inference import infer_shapes_path
+import numpy as np
+from parallel_opt import parallel_model
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="launch distributed infinitensor")
+    parser.add_argument("--num_nodes", type=int, default=1, help="number of nodes")
+    parser.add_argument(
+        "--nproc_per_node", type=int, default=1, help="number of processes per node"
+    )
+    parser.add_argument(
+        "--name", type=str, default="test", help="name of this instance."
+    )
+    parser.add_argument(
+        "--model", type=str, required=True, help="path to the ONNX model file."
+    )
+    parser.add_argument("--batch_size", type=int, default=1, help="batch size.")
+    parser.add_argument("--length", type=int, default=1, help="sequence length.")
+    parser.add_argument(
+        "--gen_std",
+        action="store_true",
+        help="whether to generate the standard results.",
+    )
+    parser.add_argument(
+        "--type", type=str, choices=["fp32", "fp16", "tf32"], default="fp32", help="data type"
+    )
+    args = parser.parse_args()
+    print("arg setting: ", args)
+    return (
+        args.num_nodes,
+        args.nproc_per_node,
+        args.name,
+        args.model,
+        args.batch_size,
+        args.length,
+        args.gen_std,
+        args.type,
+    )
+
+
+def run_model(model, runtime, world_size=1, rank=0, n=10, data_type="default"):
+    stub = OnnxStub(model, runtime, matmul_compute_type=data_type)
+    load_inputs(stub, world_size, rank)
+    # stub.tune()
+    stub.run()
+    # get outputs
+    outputs = next(stub.outputs.values().__iter__()).copyout_numpy()
+
+    # bench
+    for _ in range(n):
+        stub.run()
+    begin = time.time()
+    for _ in range(n * 2):
+        stub.run()
+    end = time.time()
+    avg_time = (end - begin) / (n * 2)
+    print(f"average time: {avg_time}")
+    return outputs
+
+def load_inputs(stub, world_size=1, rank=0):
+    for i, (name, tensor) in enumerate(stub.inputs.items()):
+        input = np.load(f"./data/input_{i}.npy")
+        if all(x == y for x,y in zip(input.shape,tensor.shape())):
+            tensor.copyin_numpy(input)
+        else:
+            tensor.copyin_numpy(np.hsplit(input, world_size)[rank])
+
+
+def run_and_compare(name, model, runtime, world_size=1, rank=0, data_type="default"):
+    results = np.load(f"./data/output.npy")
+    outputs = run_model(model, runtime, world_size, rank, data_type=data_type)
+    print("outputs abs mean:", abs(outputs).mean())
+    print("max abs diff:", abs(outputs - results).max())
+
+def start_worker(
+    name: str, world_size: int, rank: int, local_rank: int, model: onnx.ModelProto, data_type: str
+):
+    dist_name = name + "_dist"
+    model = parallel_model(model, world_size, rank)
+    extern_path = f"./{dist_name}_rank{rank}.pb"
+    if os.path.exists(extern_path):
+        os.remove(extern_path)
+    onnx.save_model(
+        model,
+        f"./{dist_name}_rank{rank}.onnx",
+        save_as_external_data=True,
+        location=extern_path,
+    )
+    #infer_shapes_path(f"./{dist_name}_rank{rank}.onnx")
+    runtime = backend.BangRuntime(local_rank)
+    # print("init comm")
+    runtime.init_comm(
+        dist_name,
+        world_size,
+        rank,
+    )
+    run_and_compare(name, model, runtime, world_size, rank, data_type)
+
+
+def start_single(name, model, data_type):
+    runtime = backend.BangRuntime(0)
+    run_and_compare(name, model, runtime, data_type=data_type)
+
+def generate_input_output(model):
+    os.makedirs(os.path.dirname("./data/"), exist_ok=True)
+    runtime = backend.BangRuntime(0)
+    stub = OnnxStub(model, runtime)
+    position_id = 0
+    for i, (name, tensor) in enumerate(stub.inputs.items()):
+        input = tensor.copyout_numpy()
+        if np.issubdtype(input.dtype, np.integer):
+            if input.size == 1:
+                # input = np.array([position_id])
+                input = np.random.randint(0,2,size=input.shape, dtype=input.dtype)
+            else:
+                input = np.random.randint(0,2,size=input.shape, dtype=input.dtype)
+        elif input.dtype == np.bool_:
+            input = np.random.randint(0,2,size=input.shape) > 0
+        else:
+            if i == 0:
+                input = np.ones(input.shape).astype(input.dtype)
+                position_id = input.shape[-1] - 1
+            else:
+                input = np.random.rand(*input.shape).astype(input.dtype)
+        tensor.copyin_numpy(input)
+        np.save(f"./data/input_{i}", input)
+    stub.run()
+    time.sleep(0.01)
+    output = next(stub.outputs.values().__iter__()).copyout_numpy()
+    if np.isnan(output).any():
+        print("Nan in output")
+    np.save(f"./data/output", output)
+
+
+def main():
+    nnodes, nproc_per_node, name, model_path, bs, length, gen_std, data_type = parse_args()
+    data_type = "default" if data_type == "fp32" else data_type
+    
+    model = onnx.load(model_path)
+
+    # generate standart output
+    if gen_std:
+        print(f"generate standard data for {name}.")
+        # a small vocabulary size to fit all LLM.
+        generate_input_output(model)
+        return
+
+    if nproc_per_node == 1:
+        # run single process.
+        # use standalone process to isolate bang.
+        print("run model by single MLU.")
+        # p = mp.Process(target=start_single, args=(name, model, data_type))
+        # p.start()
+        # p.join()
+        start_single(name, model, data_type)
+        return
+
+    # run distributed parallel.
+    world_size = nnodes * nproc_per_node
+    print(f"run model by {world_size} MLU in parallel.")
+    workers = [
+        mp.Process(
+            target=start_worker,
+            args=(name, world_size, rank, rank % nproc_per_node, model, data_type),
+        )
+        for rank in range(world_size)
+    ]
+
+    for w in workers:
+        w.start()
+
+    for w in workers:
+        w.join()
+
+
+if __name__ == "__main__":
+    main()

examples/distributed/bang/run_pytorch_mlu.py

@@ -0,0 +1,249 @@
+import argparse
+import torch
+import torch_mlu
+from transformers import BertModel, BertConfig
+from transformers import GPT2Model, GPT2Config
+from transformers import OPTModel, OPTConfig
+from transformers import AlbertModel, AlbertConfig
+from transformers import LlamaModel, LlamaConfig
+import time
+import numpy as np
+import onnx
+import sys
+import os
+from onnx.external_data_helper import convert_model_to_external_data
+from onnxsim import simplify
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Run pytorch gpt2/bert/opt and optionally export onnx.")
+    parser.add_argument(
+        "--model", type=str, choices=["gpt2", "bert", "opt", "llama", "albert"], required=True, help="model type"
+    )
+    parser.add_argument("--batch_size", type=int, default=1, help="batch size.")
+    parser.add_argument("--length", type=int, default=1, help="sequence length.")
+    parser.add_argument(
+        "--export_onnx",
+        type=str,
+        nargs="?",
+        default=None,
+        const="./",
+        help="whether and where to export onnx file",
+    )
+    parser.add_argument(
+        "--type", type=str, choices=["fp32", "fp16", "tf32"], required=True, help="model data type"
+    )
+    args = parser.parse_args()
+    print("arg setting: ", args)
+    return (
+        args.model,
+        args.batch_size,
+        args.length,
+        args.export_onnx,
+        args.type
+    )
+
+
+def get_model(modelname):
+    match modelname:
+        case "albert":
+            model = AlbertModel.from_pretrained("albert/albert-base-v2")
+            voc_size = AlbertConfig().vocab_size
+        case "bert":
+            model = BertModel.from_pretrained("bert-base-uncased", add_pooling_layer=False, hidden_act="gelu_new") # erf is not impl by infini
+            voc_size = BertConfig().vocab_size
+        case "gpt2":
+            model = GPT2Model.from_pretrained("GPT2")
+            voc_size = GPT2Config().vocab_size
+        case "opt":
+            model = OPTModel.from_pretrained("facebook/opt-125m")
+            voc_size = OPTConfig().vocab_size
+        case "llama":
+            model = LlamaModel.from_pretrained("meta-llama/Llama-2-7b-hf")
+            voc_size = LlamaConfig().vocab_size
+        case _:
+            raise KeyError(modelname)
+
+    model = model.eval()
+    return model, voc_size
+
+def run_pytorch(torch_model, voc_size, batchsize, len, dtype="fp32"):
+    data = np.random.randint(0, voc_size, (batchsize, len), dtype=np.int32)
+    os.makedirs(os.path.dirname("./data/"), exist_ok=True)
+    np.save("./data/input_0", data)
+    inputs = torch.from_numpy(data).to("mlu")
+    torch_model = torch_model.to("mlu")
+    if dtype == "fp16":
+        torch_model = torch_model.half()
+
+    n_iter = 20
+    with torch.no_grad():
+        for _ in range(10):
+            outputs = torch_model(inputs)
+    torch.mlu.synchronize()
+    begin = time.time()
+    with torch.no_grad():
+        for _ in range(n_iter):
+            torch.mlu.synchronize()
+            outputs = torch_model(inputs)
+            torch.mlu.synchronize()
+    torch.mlu.synchronize()
+    end = time.time()
+    
+    avg_time = (end - begin) / n_iter
+    outputs = outputs.last_hidden_state.to("cpu")
+    print("outputs abs mean:", abs(np.array(outputs)).mean())
+    print(f"average time: {avg_time}")
+    # torch.mlu.memory.empty_cache()
+    np.save("./data/output", np.array(outputs))
+    print("Save input & output into ./data.")
+
+
+def export_onnx(modelname, model, data, path, extern=False, dtype="fp32"):
+    data = data.to("mlu")
+    model = model.to("mlu")
+    if dtype == "fp16":
+        model = model.half()
+    torch.onnx.export(model, data, path, verbose=False, do_constant_folding=True)
+    if modelname != "llama":
+        # use onnxsim to simplify
+        onnx_model = onnx.load(path)
+        onnx_model, check = simplify(onnx_model, skipped_optimizers=['eliminate_duplicate_initializer'])
+        # onnx_model, check = simplify(onnx_model, skipped_optimizers=['fuse_qkv', 'eliminate_duplicate_initializer'])
+        assert check
+        add_value_info_for_constants(onnx_model)
+        onnx_model = onnx.shape_inference.infer_shapes(onnx_model)
+        if extern:
+            extern_path = path.replace('.onnx', '.pb')
+            if os.path.exists(extern_path):
+                os.remove(extern_path)
+            extern_path = extern_path.split("/")[-1]
+            convert_model_to_external_data(
+                onnx_model,
+                all_tensors_to_one_file=True,
+                location=extern_path,
+                size_threshold=1024,
+                convert_attribute=False,
+            )
+        onnx.save(onnx_model, path)
+    else:
+        # use third party tool to simplify llama
+        # reference: https://github.com/luchangli03/onnxsim_large_model/
+        sys.path.append("onnxsim_large_model")
+        from onnx_utils import set_onnx_input_shape
+        from compress_model import SIZE_1MB, compress_onnx_model, uncompress_onnx_model
+
+        in_model_path = path
+        out_model_path = path
+        if not out_model_path:
+            out_model_path = in_model_path[:-5] + ".sim.onnx"
+        if os.path.isdir(out_model_path):
+            out_model_path = os.path.join(out_model_path, os.path.basename(in_model_path))
+
+        onnx_model = onnx.load(in_model_path)
+        print(f"load model from {in_model_path} success")
+
+        size_th_bytes = 1024 * 1024
+
+        onnx_model, removed_inits = compress_onnx_model(onnx_model, size_th_bytes=size_th_bytes)
+        print(f"compress model success")
+
+        onnx_model = set_onnx_input_shape(onnx_model, "")
+
+        tensor_size_threshold = f"1024KB"
+        skipped_optimizers = []
+        skipped_optimizers.append("eliminate_duplicate_initializer")
+        onnx_model, check = simplify(onnx_model, skipped_optimizers=skipped_optimizers,
+                                    tensor_size_threshold=tensor_size_threshold)
+        if not check:
+            raise ValueError(f"simplify compressed model {in_model_path} failed")
+
+        print(f"simplify model success")
+
+        onnx_model = uncompress_onnx_model(onnx_model, removed_inits)
+        print(f"uncompress model success")
+
+        add_value_info_for_constants(onnx_model)
+
+        onnx.save(onnx_model, out_model_path, save_as_external_data=True)
+
+
+def add_value_info_for_constants(model : onnx.ModelProto):
+    """
+    Currently onnx.shape_inference doesn't use the shape of initializers, so add
+    that info explicitly as ValueInfoProtos.
+    Mutates the model.
+    Args:
+        model: The ModelProto to update.
+    """
+    # All (top-level) constants will have ValueInfos before IRv4 as they are all inputs
+    if model.ir_version < 4:
+        return
+
+    def add_const_value_infos_to_graph(graph : onnx.GraphProto):
+        inputs = {i.name for i in graph.input}
+        existing_info = {vi.name: vi for vi in graph.value_info}
+        for init in graph.initializer:
+            # Check it really is a constant, not an input
+            if init.name in inputs:
+                continue
+
+            # The details we want to add
+            elem_type = init.data_type
+            shape = init.dims
+
+            # Get existing or create new value info for this constant
+            vi = existing_info.get(init.name)
+            if vi is None:
+                vi = graph.value_info.add()
+                vi.name = init.name
+
+            # Even though it would be weird, we will not overwrite info even if it doesn't match
+            tt = vi.type.tensor_type
+            if tt.elem_type == onnx.TensorProto.UNDEFINED:
+                tt.elem_type = elem_type
+            if not tt.HasField("shape"):
+                # Ensure we set an empty list if the const is scalar (zero dims)
+                tt.shape.dim.extend([])
+                for dim in shape:
+                    tt.shape.dim.add().dim_value = dim
+
+        # Handle subgraphs
+        for node in graph.node:
+            for attr in node.attribute:
+                # Ref attrs refer to other attrs, so we don't need to do anything
+                if attr.ref_attr_name != "":
+                    continue
+
+                if attr.type == onnx.AttributeProto.GRAPH:
+                    add_const_value_infos_to_graph(attr.g)
+                if attr.type == onnx.AttributeProto.GRAPHS:
+                    for g in attr.graphs:
+                        add_const_value_infos_to_graph(g)
+
+
+    return add_const_value_infos_to_graph(model.graph)
+
+
+def main():
+    torch.backends.mlu.matmul.allow_tf32 = False
+    torch.backends.cnnl.allow_tf32 = False
+    modelname, batchsize, seqlen, export_path, dtype = parse_args()
+    if dtype == "tf32":
+        torch.backends.mlu.matmul.allow_tf32 = True
+    else:
+        os.environ["CAMBRICON_TF32_OVERRIDE"] = "0"
+
+    model, voc_size = get_model(modelname)
+    if export_path is not None:
+        filename = "{}_{}_{}_{}.onnx".format(modelname, batchsize, seqlen, dtype)
+        path = os.path.join(export_path, filename)
+        if not os.path.exists(path):
+            param = torch.zeros((batchsize, seqlen), dtype=torch.int)
+            export_onnx(modelname, model, param, path, True, dtype)
+        else:
+            print("Onnx path exists, skipping export.")
+
+    run_pytorch(model, voc_size, batchsize, seqlen, dtype)
+
+if __name__ == "__main__":
+    main()

examples/distributed/cuda/cuda_launch.py

@@ -0,0 +1,161 @@
+import argparse
+import os
+import time
+import multiprocessing as mp
+from pyinfinitensor.onnx import OnnxStub, backend
+import onnx
+from onnx.external_data_helper import convert_model_to_external_data
+from onnx.shape_inference import infer_shapes_path
+import numpy as np
+from parallel_opt import parallel_model
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="launch distributed infinitensor")
+    parser.add_argument("--num_nodes", type=int, default=1, help="number of nodes")
+    parser.add_argument(
+        "--nproc_per_node", type=int, default=1, help="number of processes per node"
+    )
+    parser.add_argument(
+        "--name", type=str, default="test", help="name of this instance."
+    )
+    parser.add_argument(
+        "--model", type=str, required=True, help="path to the ONNX model file."
+    )
+    parser.add_argument("--batch_size", type=int, default=1, help="batch size.")
+    parser.add_argument("--length", type=int, default=1, help="sequence length.")
+    parser.add_argument(
+        "--gen_std",
+        action="store_true",
+        help="whether to generate the standard results.",
+    )
+    parser.add_argument(
+        "--type", type=str, choices=["fp32", "fp16", "tf32"], default="fp32", help="data type"
+    )
+    args = parser.parse_args()
+    print("arg setting: ", args)
+    return (
+        args.num_nodes,
+        args.nproc_per_node,
+        args.name,
+        args.model,
+        args.batch_size,
+        args.length,
+        args.gen_std,
+        args.type,
+    )
+
+
+def run_model(model, runtime, inputs, n=10, data_type = "default"):
+    stub = OnnxStub(model, runtime, matmul_compute_type=data_type)
+    for tensor, input in zip(stub.inputs.values(), inputs, strict=False):
+        tensor.copyin_numpy(input)
+    # stub.tune()
+    stub.run()
+    # get outputs
+    outputs = next(stub.outputs.values().__iter__()).copyout_numpy()
+
+    # bench
+    for tensor, input in zip(stub.inputs.values(), inputs, strict=False):
+        tensor.copyin_numpy(input)
+    begin = time.time()
+    for _ in range(n):
+        stub.run()
+    end = time.time()
+    avg_time = (end - begin) / n
+    print(f"average time: {avg_time}")
+    return outputs
+
+
+def run_and_compare(name, model, runtime, data_type):
+    input_ids = np.load(f"{name}_inputs.npy")
+    position_ids = np.arange(input_ids.shape[-1])
+    results = np.load(f"{name}_results.npy")
+    outputs = run_model(model, runtime, (input_ids, position_ids), data_type=data_type)
+    print("outputs abs mean:", abs(outputs).mean())
+    print("max abs diff:", abs(outputs - results).max())
+
+
+def start_worker(
+    name: str, world_size: int, rank: int, local_rank: int, model: onnx.ModelProto, data_type: str
+):
+    dist_name = name + "_dist"
+    model = parallel_model(model, world_size, rank)
+    extern_path = f"./{dist_name}_rank{rank}.pb"
+    if os.path.exists(extern_path):
+        os.remove(extern_path)
+    onnx.save_model(
+        model,
+        f"./{dist_name}_rank{rank}.onnx",
+        save_as_external_data=True,
+        location=extern_path,
+    )
+    #infer_shapes_path(f"./{dist_name}_rank{rank}.onnx")
+    runtime = backend.CudaRuntime(local_rank)
+    # print("init comm")
+    runtime.init_comm(
+        dist_name,
+        world_size,
+        rank,
+    )
+    run_and_compare(name, model, runtime, data_type)
+
+
+def start_single(name, model, data_type):
+    runtime = backend.CudaRuntime(0)
+    run_and_compare(name, model, runtime, data_type)
+
+
+def gen_standard(name, model, voc_size, bs, len):
+    # generate standard results
+    input_ids = np.random.randint(0, voc_size, (bs, len))
+    position_ids = np.arange(len)
+    np.save(f"{name}_inputs", input_ids)
+    runtime = backend.CudaRuntime(0)
+    outputs = run_model(model, runtime, (input_ids, position_ids), 1)
+    print("outputs abs mean:", abs(outputs).mean())
+    np.save(f"{name}_results", outputs)
+
+
+def main():
+    nnodes, nproc_per_node, name, model_path, bs, length, gen_std, data_type = parse_args()
+    data_type = "default" if data_type == "fp32" else data_type
+    if data_type != "tf32":
+        os.environ["NVIDIA_TF32_OVERRIDE"] = "0"
+    model = onnx.load(model_path)
+
+    # generate standart output
+    if gen_std:
+        print(f"generate standard data for {name}.")
+        # a small vocabulary size to fit all LLM.
+        voc_size = 1000
+        gen_standard(name, model, voc_size, bs, length)
+        return
+
+    # run single process.
+    # use standalone process to isolate cuda.
+    print("run model by single GPU.")
+    p = mp.Process(target=start_single, args=(name, model, data_type))
+    p.start()
+    p.join()
+
+    # run distributed parallel.
+    world_size = nnodes * nproc_per_node
+    print(f"run model by {world_size} GPU in parallel.")
+    workers = [
+        mp.Process(
+            target=start_worker,
+            args=(name, world_size, rank, rank % nproc_per_node, model, data_type),
+        )
+        for rank in range(world_size)
+    ]
+
+    for w in workers:
+        w.start()
+
+    for w in workers:
+        w.join()
+
+
+if __name__ == "__main__":
+    main()

examples/distributed/cuda/launch_kvcache.py

@@ -0,0 +1,245 @@
+import argparse
+import os
+import time
+import multiprocessing as mp
+from pyinfinitensor.onnx import OnnxStub, backend
+import onnx
+from onnx.external_data_helper import convert_model_to_external_data
+import numpy as np
+from parallel_opt import parallel_model
+
+
+os.environ["NVIDIA_TF32_OVERRIDE"] = "0"
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="launch distributed infinitensor")
+    parser.add_argument("--num_nodes", type=int, default=1, help="number of nodes")
+    parser.add_argument(
+        "--nproc_per_node", type=int, default=1, help="number of processes per node"
+    )
+    parser.add_argument(
+        "--name", type=str, default="test", help="name of this instance."
+    )
+    parser.add_argument(
+        "--model1", type=str, required=True, help="path to the ONNX model file."
+    )
+    parser.add_argument(
+        "--model2", type=str, required=True, help="path to the ONNX model file."
+    )    
+    parser.add_argument("--batch_size", type=int, default=1, help="batch size.")
+    parser.add_argument("--length", type=int, default=1, help="sequence length.")
+    parser.add_argument(
+        "--gen_std",
+        action="store_true",
+        help="whether to generate the standard results.",
+    )
+    args = parser.parse_args()
+    print("arg setting: ", args)
+    return (
+        args.num_nodes,
+        args.nproc_per_node,
+        args.name,
+        args.model1,
+        args.model2,
+        args.batch_size,
+        args.length,
+        args.gen_std,
+    )
+
+
+def run_model(model1, model2, runtime1, runtime2, inputs1: np.array, inputs2: np.array, n=20):
+    ####################################
+    # run the first graph without kvcache
+    ####################################
+    stub1 = OnnxStub(model1, runtime1)
+    stub1.inputs['onnx::Reshape_0'].copyin_int32(inputs1.reshape(-1).tolist())
+    stub1.tune()
+    stub1.run()
+    kvcache_it1 = []
+    count = 0
+    for output in stub1.outputs.items().__iter__():
+        if count == 0:
+            logits_it1 = np.array(output[1].copyout_float(), dtype=np.float32)
+        else:
+            kvcache_it1.append(np.array(output[1].copyout_float(), dtype=np.float32))
+        count = count + 1
+        
+    # bench for stub1
+    next(stub1.inputs.items().__iter__())[1].copyin_int32(inputs1.reshape(-1).tolist())
+    begin = time.time()
+    for _ in range(n):
+        stub1.run()
+    end = time.time()
+    avg_time = (end - begin) / n
+    print(f"stub1 average time: {avg_time}")        
+        
+    ####################################
+    # run the second graph with kvcache
+    ####################################
+    i = 0
+    batchsize = 1
+    stub2 = OnnxStub(model2, runtime2)
+    past_kvcache_length = (i+2)*np.ones((batchsize, 1), dtype=np.int32)
+    # copyin input
+    stub2.inputs['onnx::Reshape_0'].copyin_int32(inputs2.reshape(-1).tolist())
+    stub2.inputs['input.3'].copyin_int32(past_kvcache_length.reshape(-1).tolist())
+    count = -1
+    for input in stub2.inputs.items().__iter__():
+        if count in range(24):
+            # print(count, input[0])
+            # print(np.dtype(kvcache_it1[count][0]), kvcache_it1[count].shape)
+            input[1].copyin_float(kvcache_it1[count].reshape(-1).tolist())
+        count = count + 1
+    stub2.tune()
+    stub2.run()
+    
+    # copyout output
+    count = 0
+    kvcache_it2 = []
+    for output in stub2.outputs.items().__iter__():
+        if count == 0:
+            logits_it2 = np.array(output[1].copyout_float(), dtype=np.float32)
+        else:
+            kvcache_it2.append(np.array(output[1].copyout_float(), dtype=np.float32))
+        count = count + 1     
+
+    # bench for stub2
+    # copyin input
+    stub2.inputs['onnx::Reshape_0'].copyin_int32(inputs2.reshape(-1).tolist())
+    stub2.inputs['input.3'].copyin_int32(past_kvcache_length.reshape(-1).tolist())
+    count = -1
+    for input in stub2.inputs.items().__iter__():
+        if count in range(24):
+            input[1].copyin_float(kvcache_it1[count].reshape(-1).tolist())
+        count = count + 1
+    begin = time.time()
+    for _ in range(n):
+        stub2.run()
+    end = time.time()
+    avg_time = (end - begin) / n
+    print(f"stub2 average time: {avg_time}")
+    return logits_it2
+
+
+def run_and_compare(name, model1, model2, runtime1, runtime2):
+    data1 = np.load(f"{name}_inputs1.npy")
+    data2 = np.load(f"{name}_inputs2.npy")
+    results = np.load(f"{name}_results.npy")
+    outputs = run_model(model1, model2, runtime1, runtime2, data1, data2)
+    print("outputs sum:", outputs.sum())
+    print("max abs diff:", abs(outputs - results).max())
+    print("max rel diff:", abs((outputs - results) / results).max())
+    # assert np.allclose(outputs, results, rtol=1e-3, atol=1e-6)
+
+
+def start_worker(
+    name: str, world_size: int, rank: int, local_rank: int, model1: onnx.ModelProto, model2: onnx.ModelProto
+):
+    dist_name = name + "_dist"
+    ####################################
+    # shard the first graph
+    ####################################
+    model1 = parallel_model(model1, world_size, rank)
+    extern_path = f"./{dist_name}_stub1_rank{rank}.pb"
+    if os.path.exists(extern_path):
+        os.remove(extern_path)
+    convert_model_to_external_data(
+        model1,
+        all_tensors_to_one_file=True,
+        location=extern_path,
+        size_threshold=1024,
+        convert_attribute=False,
+    )
+    onnx.save(model1, f"./{dist_name}_stub1_rank{rank}.onnx")
+    runtime1 = backend.CudaRuntime(local_rank)
+    runtime1.init_comm(
+        dist_name,
+        world_size,
+        rank,
+    )
+    
+    ####################################
+    # shard the second graph
+    ####################################    
+    model2 = parallel_model(model2, world_size, rank)
+    extern_path = f"./{dist_name}_stub2_rank{rank}.pb"
+    if os.path.exists(extern_path):
+        os.remove(extern_path)
+    convert_model_to_external_data(
+        model2,
+        all_tensors_to_one_file=True,
+        location=extern_path,
+        size_threshold=1024,
+        convert_attribute=False,
+    )    
+    onnx.save(model2, f"./{dist_name}_stub2_rank{rank}.onnx")
+    runtime2 = backend.CudaRuntime(local_rank)
+    # print("init comm")
+    runtime2.init_comm(
+        dist_name,
+        world_size,
+        rank,
+    )    
+    
+    # run the two graphs
+    run_and_compare(name, model1, model2, runtime1, runtime2)
+
+
+def start_single(name, model1, model2):
+    runtime1 = backend.CudaRuntime(0)
+    runtime2 = backend.CudaRuntime(0)
+    run_and_compare(name, model1, model2, runtime1, runtime2)
+
+
+def gen_standard(name, model1, model2, voc_size, bs, len):
+    # generate standard results
+    data1 = np.random.randint(0, voc_size, (bs, len), dtype=np.int32)
+    data2 = np.random.randint(0, voc_size, (bs, len), dtype=np.int32)
+    np.save(f"{name}_inputs1", data1)
+    np.save(f"{name}_inputs2", data2)
+    runtime1 = backend.CudaRuntime(0)
+    runtime2 = backend.CudaRuntime(0)
+    outputs = run_model(model1, model2, runtime1, runtime2, data1, data2, 1)
+    np.save(f"{name}_results", outputs)
+
+
+def main():
+    nnodes, nproc_per_node, name, model1_path, model2_path, bs, length, gen_std = parse_args()
+
+    model1 = onnx.load(model1_path)
+    model2 = onnx.load(model2_path)
+
+    # generate standart output
+    if gen_std:
+        print(f"generate standard data for {name}.")
+        # a small vocabulary size to fit all LLM.
+        voc_size = 1000
+        gen_standard(name, model1, model2, voc_size, bs, length)
+        return
+
+    # run single process.
+    # use standalone process to isolate cuda.
+    p = mp.Process(target=start_single, args=(name, model1, model2))
+    p.start()
+    p.join()
+
+    # run distributed parallel.
+    world_size = nnodes * nproc_per_node
+    workers = [
+        mp.Process(
+            target=start_worker,
+            args=(name, world_size, rank, rank % nproc_per_node, model1, model2),
+        )
+        for rank in range(world_size)
+    ]
+
+    for w in workers:
+        w.start()
+
+    for w in workers:
+        w.join()
+
+
+if __name__ == "__main__":
+    main()

examples/distributed/cuda/run_pytorch.py

@@ -0,0 +1,188 @@
+import argparse
+import torch
+from transformers import BertModel, BertConfig
+from transformers import GPT2Model, GPT2Config
+from transformers import OPTModel, OPTConfig
+import time
+import numpy as np
+import onnx
+import os
+from onnx.external_data_helper import convert_model_to_external_data
+from onnxsim import simplify
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Run pytorch gpt2/bert/opt and optionally export onnx.")
+    parser.add_argument(
+        "--model", type=str, choices=["gpt2", "bert", "opt"], required=True, help="model type"
+    )
+    parser.add_argument("--batch_size", type=int, default=1, help="batch size.")
+    parser.add_argument("--length", type=int, default=1, help="sequence length.")
+    parser.add_argument(
+        "--export_onnx",
+        type=str,
+        nargs="?",
+        default=None,
+        const="./",
+        help="whether and where to export onnx file",
+    )
+    parser.add_argument(
+        "--type", type=str, choices=["fp32", "fp16", "tf32"], default="fp32", help="data type"
+    )
+    args = parser.parse_args()
+    print("arg setting: ", args)
+    return (
+        args.model,
+        args.batch_size,
+        args.length,
+        args.export_onnx,
+        args.type,
+    )
+
+
+def get_model(modelname):
+    match modelname:
+        case "bert":
+            model = BertModel.from_pretrained("bert-base-uncased", add_pooling_layer=False, hidden_act="gelu_new") # erf is not impl by infini
+            voc_size = BertConfig().vocab_size
+        case "gpt2":
+            model = GPT2Model.from_pretrained("gpt2")
+            voc_size = GPT2Config().vocab_size
+        case "opt":
+            model = model = OPTModel.from_pretrained("./opt-125m")
+            voc_size = OPTConfig().vocab_size
+        case _:
+            raise KeyError(modelname)
+
+    model = model.eval()
+    return model, voc_size
+
+def run_pytorch(torch_model, voc_size, batchsize, len):
+    data = np.random.randint(0, voc_size, (batchsize, len), dtype=np.int32)
+    np.save("test_inputs", data)
+    inputs = torch.from_numpy(data).to("cuda")
+    torch_model = torch_model.to("cuda")
+
+    n_iter = 20
+    with torch.no_grad():
+        for _ in range(10):
+            outputs = torch_model(inputs)
+    torch.cuda.synchronize()
+    begin = time.time()
+    with torch.no_grad():
+        for _ in range(n_iter):
+            torch.cuda.synchronize()
+            outputs = torch_model(inputs)
+            # 
+            torch.cuda.synchronize()
+    torch.cuda.synchronize()
+    end = time.time()
+    
+    avg_time = (end - begin) / n_iter
+    outputs = outputs.last_hidden_state.to("cpu")
+    print("outputs abs mean:", abs(np.array(outputs)).mean())
+    print(f"average time: {avg_time}")
+    torch.cuda.memory.empty_cache()
+    np.save("test_results", np.array(outputs, dtype=np.float32))
+    print("Save input & output as test_inputs.npy and test_results.npy")
+
+
+def export_onnx(model, data, path, extern=False):
+    torch.onnx.export(model, data, path, verbose=False, do_constant_folding=True)
+    onnx_model = onnx.load(path)
+    onnx_model, check = simplify(onnx_model, skipped_optimizers=['eliminate_duplicate_initializer'])
+    #onnx_model, check = simplify(onnx_model, skipped_optimizers=['fuse_qkv', 'eliminate_duplicate_initializer'])
+    assert check
+    add_value_info_for_constants(onnx_model)
+    onnx_model = onnx.shape_inference.infer_shapes(onnx_model)
+    if extern:
+        extern_path = path.replace('.onnx', '.pb')
+        if os.path.exists(extern_path):
+            os.remove(extern_path)
+        convert_model_to_external_data(
+            onnx_model,
+            all_tensors_to_one_file=True,
+            location=extern_path,
+            size_threshold=1024,
+            convert_attribute=False,
+        )
+    onnx.save(onnx_model, path)
+
+def add_value_info_for_constants(model : onnx.ModelProto):
+    """
+    Currently onnx.shape_inference doesn't use the shape of initializers, so add
+    that info explicitly as ValueInfoProtos.
+    Mutates the model.
+    Args:
+        model: The ModelProto to update.
+    """
+    # All (top-level) constants will have ValueInfos before IRv4 as they are all inputs
+    if model.ir_version < 4:
+        return
+
+    def add_const_value_infos_to_graph(graph : onnx.GraphProto):
+        inputs = {i.name for i in graph.input}
+        existing_info = {vi.name: vi for vi in graph.value_info}
+        for init in graph.initializer:
+            # Check it really is a constant, not an input
+            if init.name in inputs:
+                continue
+
+            # The details we want to add
+            elem_type = init.data_type
+            shape = init.dims
+
+            # Get existing or create new value info for this constant
+            vi = existing_info.get(init.name)
+            if vi is None:
+                vi = graph.value_info.add()
+                vi.name = init.name
+
+            # Even though it would be weird, we will not overwrite info even if it doesn't match
+            tt = vi.type.tensor_type
+            if tt.elem_type == onnx.TensorProto.UNDEFINED:
+                tt.elem_type = elem_type
+            if not tt.HasField("shape"):
+                # Ensure we set an empty list if the const is scalar (zero dims)
+                tt.shape.dim.extend([])
+                for dim in shape:
+                    tt.shape.dim.add().dim_value = dim
+
+        # Handle subgraphs
+        for node in graph.node:
+            for attr in node.attribute:
+                # Ref attrs refer to other attrs, so we don't need to do anything
+                if attr.ref_attr_name != "":
+                    continue
+
+                if attr.type == onnx.AttributeProto.GRAPH:
+                    add_const_value_infos_to_graph(attr.g)
+                if attr.type == onnx.AttributeProto.GRAPHS:
+                    for g in attr.graphs:
+                        add_const_value_infos_to_graph(g)
+
+
+    return add_const_value_infos_to_graph(model.graph)
+
+
+def main():
+    torch.backends.cuda.matmul.allow_tf32 = False
+    torch.backends.cudnn.allow_tf32 = False
+    modelname, batchsize, seqlen, export_path, data_type = parse_args()
+    if data_type == "tf32":
+        torch.backends.cuda.matmul.allow_tf32 = True
+    else:
+        os.environ["NVIDIA_TF32_OVERRIDE"] = "0"
+
+    model, voc_size = get_model(modelname)
+    if export_path is not None:
+        filename = "{}_{}_{}.onnx".format(modelname, batchsize, seqlen)
+        path = os.path.join(export_path, filename)
+        param = torch.zeros((batchsize, seqlen), dtype=torch.int)
+        export_onnx(model, param, path, True)
+
+    if data_type == "fp16":
+        model = model.half()
+    run_pytorch(model, voc_size, batchsize, seqlen)
+
+if __name__ == "__main__":
+    main()

examples/distributed/kunlun/export_onnx.sh

@@ -0,0 +1,14 @@
+ export HF_ENDPOINT=https://hf-mirror.com
+
+models=("bert" "gpt2" "llama")
+batch_size=(1 32)
+seq_len=(100 500)
+nproc=(1 2 4)
+
+for model in "${models[@]}"; do
+    for bs in "${batch_size[@]}"; do
+        for len in "${seq_len[@]}"; do
+            python run_pytorch.py --model "$model" --batch_size "$bs" --length "$len" --export_onnx ../models/"$model" --export_only 
+        done
+    done
+done 

examples/distributed/kunlun/kunlun_launch.py

@@ -0,0 +1,280 @@
+import sys
+sys.path.append('../')
+
+import argparse
+import os
+import time
+import multiprocessing as mp
+from pyinfinitensor.onnx import OnnxStub, backend
+import onnx
+from onnx.external_data_helper import convert_model_to_external_data
+from onnx.shape_inference import infer_shapes_path
+import numpy as np
+from parallel_opt import parallel_model
+from functools import wraps
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="launch distributed infinitensor")
+    parser.add_argument("--num_nodes", type=int, default=1, help="number of nodes")
+    parser.add_argument(
+        "--nproc_per_node", type=int, default=2, help="number of processes per node"
+    )
+    parser.add_argument(
+        "--name", type=str, choices=["gpt2", "bert", "llama"], help="name of model."
+    )
+    parser.add_argument(
+        "--model", type=str, default="", help="path to the ONNX model file."
+    )
+    parser.add_argument(
+        "--gen_std",
+        default=False,
+        action="store_true",
+        help="whether to generate the standard results.",
+    )
+    parser.add_argument(
+        "--run_single",
+        default=False,
+        action="store_true",
+        help="whether run model with single process with standard inputs"
+    )
+    parser.add_argument(
+        "--input_dir",
+        default="./",
+        help="path to save model input data"
+    )
+    parser.add_argument(
+        "--result_dir",
+        default="./",
+        help="path to save model standard output"
+    )
+    parser.add_argument(
+        "--internal_model_dir",
+        default="./",
+        help="path to save internal onnx model for parallel run"
+    )
+    args = parser.parse_args()
+
+    # check path, mkdir if not exist
+    check_exists(args.input_dir)
+    check_exists(args.result_dir)
+    check_exists(args.internal_model_dir)
+
+    print("arg setting: ", args)
+    return (
+        args.num_nodes,
+        args.nproc_per_node,
+        args.name,
+        args.model,
+        args.gen_std,
+        args.run_single,
+        args.input_dir,
+        args.result_dir,
+        args.internal_model_dir
+    )
+
+
+"""
+utils function for this scripts
+"""
+def check_exists(path: str):
+    if not os.path.exists(path):
+        os.makedirs(path)
+
+def np_assert(base, test, rtol=1e-2, atol=1e-1):
+    # np.testing.assert_allclose(test, base, rtol, atol)
+    print("max abs diff:", abs(base - test).max())
+
+
+"""
+Perf wrapper, run function n times
+then average
+"""
+def perf_it(n):
+    def decorator(func):
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            # warmup
+            for _ in range(n):
+                func(*args, **kwargs)
+
+            t_total = 0
+            for _ in range(n):
+                t0 = time.time()
+                func(*args, **kwargs)
+                t1 = time.time()
+                t_total += t1 - t0
+            avg_time = (t_total) / n
+            print(f"Avg runtime of {n} time is {avg_time:.6f} seconds")
+            return avg_time
+        return wrapper
+    return decorator
+
+
+"""
+Run InfiniTensor model with Standard input
+check=True: check with standard output gen by pytorch
+perf=True: run n times to get avg time
+"""
+def run_model(task_name,
+              model,
+              runtime,
+              world_size=1,
+              rank=0,
+              n=10,
+              check=True,
+              perf=True):
+
+    stub = OnnxStub(model, runtime,
+                    use_naive_allocator=True \
+                    if task_name == "llama" else False)
+
+    # load in Onnx model inputs
+    def load_inputs(stub: OnnxStub):
+        # check exists
+        inputs = []
+        for i, (name, tensor) in enumerate(stub.inputs.items()):
+            input_path = os.path.join(input_dir, \
+                                f"{task_name}_input_{i}.npy")
+            print(input_path)
+            if os.path.exists(input_path):
+                input = np.load(input_path)
+            else :
+                raise KeyError(f"{i} th input of model not exists")
+            # check shape
+            if all(x == y for x,y in zip(input.shape, tensor.shape())):
+                tensor.copyin_numpy(input)
+            else:
+                tensor.copyin_numpy(np.hsplit(input, world_size)[rank])
+
+    load_inputs(stub)
+    # stub.tune()
+    stub.run()
+    time.sleep(0.01)
+    output = next(stub.outputs.values().__iter__()).copyout_numpy()
+
+    # check output results with standard output
+    if check:
+        st_output_path = os.path.join(result_dir, \
+                                f"{task_name}_output.npy")
+        assert os.path.exists(st_output_path) , \
+                    "standard output not exists"
+        st_output = np.load(st_output_path)
+        if np.isnan(output).any():
+            print("Nan in output")
+            exit()
+        np_assert(st_output, output)
+
+    # perf
+    if perf:
+        @perf_it(n)
+        def perf_infinitensor(stub: OnnxStub):
+            stub.run()
+        perf_infinitensor(stub)
+
+    return output
+
+
+"""
+Start a worker in Parallel
+"""
+def start_worker(name: str,
+           world_size: int,
+           rank: int,
+           local_rank: int,
+           model: onnx.ModelProto):
+
+    dist_name = name + "_dist"
+    # partial a onnx model to world_size part
+    model = parallel_model(model, world_size, rank)
+    onnx.save(model, os.path.join(internal_model_dir, \
+                                    f"{dist_name}_rank{rank}.onnx"), save_as_external_data=True)
+    runtime = backend.KUNLUNRuntime(local_rank)
+    # print("init comm")
+    runtime.init_comm(
+        dist_name,
+        world_size,
+        rank,
+    )
+    run_model(name, model, runtime, world_size, rank)
+
+
+"""
+generate standard input/output with
+sigle card run
+"""
+def gen_standard(task_name: str, model: onnx.ModelProto):
+    runtime = backend.KUNLUNRuntime(0)
+    stub = OnnxStub(model, runtime)
+    position_id = 0
+    # generate random input for model
+    for i, (name, tensor) in enumerate(stub.inputs.items()):
+        input = tensor.copyout_numpy()
+        if np.issubdtype(input.dtype, np.integer):
+            if input.size == 1:
+                input = np.random.randint(0,2,size=input.shape, dtype=input.dtype)
+            else:
+                input = np.random.randint(0,2,size=input.shape, dtype=input.dtype)
+        elif input.dtype == np.bool_:
+            input = np.random.randint(0,2,size=input.shape) > 0
+        else:
+            if i == 0:
+                input = np.ones(input.shape).astype(input.dtype)
+                position_id = input.shape[-1] - 1
+            else:
+                input = np.random.rand(*input.shape).astype(input.dtype)
+        tensor.copyin_numpy(input)
+        np.save(os.path.join(input_dir, \
+                    f"{task_name}_input_{i}.npy"), input)
+    stub.run()
+    # print(stub.outputs)
+    output = next(stub.outputs.values().__iter__()).copyout_numpy()
+    if np.isnan(output).any():
+        print("Nan in output")
+        exit()
+    np.save(os.path.join(result_dir, f"{task_name}_output.npy"), output)
+
+
+def main():
+
+    global input_dir, result_dir, internal_model_dir
+
+    nnodes, nproc_per_node, task_name, \
+        model_path, gen_std, run_single, \
+            input_dir, result_dir, internal_model_dir = parse_args()
+
+    # load input onnx model
+    model = onnx.load(model_path)
+
+    # generate standart output
+    if gen_std:
+        print("Generate inputs and outputs.")
+        gen_standard(task_name, model)
+        return
+
+    if run_single:
+        print("Run model by one GPU card.")
+        runtime = backend.KUNLUNRuntime(0)
+        run_model(task_name, model, runtime)
+        return
+
+    # run distributed parallel.
+    world_size = nnodes * nproc_per_node
+    print(f"Run model by {world_size} GPU in parallel.")
+    workers = [
+        mp.Process(
+            target=start_worker,
+            args=(task_name, world_size, rank, rank % nproc_per_node, model),
+        )
+        for rank in range(world_size)
+    ]
+
+    for w in workers:
+        w.start()
+
+    for w in workers:
+        w.join()
+
+
+if __name__ == "__main__":
+    main()

examples/distributed/kunlun/launch.sh

@@ -0,0 +1,36 @@
+export HF_ENDPOINT=https://hf-mirror.com
+
+# models=("bert" "gpt2" "llama")
+models=("bert" "gpt2")
+batch_size=(1 32)
+seq_len=(100 500)
+nproc=(1 2 4)
+
+results_dir="results"
+
+if [ -d "$results_dir" ]; then
+    echo "directory ./$results_dir exists"
+else
+    mkdir -p "$results_dir"
+    echo "mkdir $results_dir, logs saved there"
+fi
+
+
+for model in "${models[@]}"; do
+    for bs in "${batch_size[@]}"; do
+        for len in "${seq_len[@]}"; do
+            # run pytorch model
+            echo "Run pytorch $model with batch_size=$bs length=$len ."
+            python run_pytorch.py --model "$model" --batch_size "$bs" --length "$len" #> results/"$model"_"$bs"_"$len"_pytorch
+            for n in "${nproc[@]}"; do
+                # run infinitensor 
+                echo "Run $n parallel infinitensor "$model" with batch_size=$bs and length=$len ."
+                python kunlun_launch.py --name "$model" --model ../models/"$model"/"$model"_"$bs"_"$len".onnx --nproc_per_node=$n # >> results/"$model"_"$bs"_"$len"_infini 
+                # delete internal files
+                find ./ -type f -name "*.onnx" -delete
+                find ./ -type f -name "*.pb" -delete
+            done
+            find ./ -type f -name "*.npy" -delete
+        done
+    done
+done

examples/distributed/kunlun/llama_launch.sh

@@ -0,0 +1,35 @@
+export HF_ENDPOINT=https://hf-mirror.com
+
+# models=("bert" "gpt2" "llama")
+models=("llama")
+batch_size=(1 )
+seq_len=(100 500)
+nproc=(1 2 4)
+
+results_dir="results"
+
+if [ -d "$results_dir" ]; then
+    echo "directory ./$results_dir exists"
+else
+    mkdir -p "$results_dir"
+    echo "mkdir $results_dir, logs saved there"
+fi
+
+
+for model in "${models[@]}"; do
+    for bs in "${batch_size[@]}"; do
+        for len in "${seq_len[@]}"; do
+            echo "Run pytorch llama with batch_size="$bs" and length="$len""
+            python run_pytorch.py --model "$model" --batch_size "$bs" --length "$len"
+            for n in "${nproc[@]}"; do
+                    # run pytorch model
+                    echo "Run infinitensor llama with batch_size="$bs" and length="$len" and nproc="$n"."
+                    python kunlun_launch.py --name llama --model ../models/llama/llama_"$bs"_"$len"_fp32.onnx --nproc_per_node=$n
+                    # delete internal files
+                    find ./ -type f -name "*.onnx" -delete
+                    find ./ -type f -name "*0c" -delete
+            done
+            find ./ -type f -name "*.npy" -delete
+        done
+    done
+done

examples/distributed/kunlun/run_pytorch.py

@@ -0,0 +1,245 @@
+import argparse
+import torch
+from transformers import BertModel, BertConfig
+from transformers import GPT2Model, GPT2Config
+from transformers import OPTModel, OPTConfig
+from transformers import LlamaModel, LlamaConfig
+import time
+import numpy as np
+import onnx
+import os
+import sys
+from onnx.external_data_helper import convert_model_to_external_data
+from onnxsim import simplify
+
+torch.backends.cuda.matmul.allow_tf32 = False
+torch.backends.cudnn.allow_tf32 = False
+def parse_args():
+    parser = argparse.ArgumentParser(description="Run pytorch gpt2/bert/opt and optionally export onnx.")
+    parser.add_argument(
+        "--model", type=str, choices=["gpt2", "bert", "opt", "llama"], required=True, help="model type"
+    )
+    parser.add_argument("--batch_size", type=int, default=1, help="batch size.")
+    parser.add_argument("--length", type=int, default=1, help="sequence length.")
+    parser.add_argument(
+        "--export_onnx",
+        type=str,
+        nargs="?",
+        default=None,
+        const="./",
+        help="whether and where to export onnx file",
+    )
+    parser.add_argument(
+        "--input_dir",
+        type=str,
+        default="./",
+        help="path to save pytorch model input data"
+    )
+    parser.add_argument(
+        "--result_dir",
+        type=str,
+        default="./",
+        help="path to save pytorch model output data"
+    )
+    parser.add_argument(
+        "--export_only",
+        action="store_true"
+    )
+    args = parser.parse_args()
+    print("arg setting: ", args)
+    return (
+        args.model,
+        args.batch_size,
+        args.length,
+        args.export_onnx,
+        args.input_dir,
+        args.result_dir,
+        args.export_only
+    )
+
+
+def get_model(modelname):
+    if modelname == "bert":
+        model = BertModel.from_pretrained("bert-base-uncased", add_pooling_layer=False, hidden_act="gelu_new") # erf is not impl by infini
+        voc_size = BertConfig().vocab_size
+    elif modelname == "gpt2":
+        model = GPT2Model.from_pretrained("gpt2")
+        voc_size = GPT2Config().vocab_size
+    elif modelname == "opt":
+        model = OPTModel.from_pretrained("./opt-125m")
+        voc_size = OPTConfig().vocab_size
+    elif modelname == "llama":
+        model = LlamaModel.from_pretrained("meta-llama/Llama-2-7b-hf")
+        voc_size = LlamaConfig().vocab_size
+    else :
+        raise KeyError(modelname)
+
+    model = model.eval()
+    return model, voc_size
+
+def run_pytorch(torch_model, voc_size, batchsize, len, model_name):
+    data = np.random.randint(0, voc_size, (batchsize, len), dtype=np.int32)
+    np.save(os.path.join(input_dir, f"{model_name}_input_0.npy"), data)
+    inputs = torch.from_numpy(data).to("cuda")
+    torch_model = torch_model.to("cuda")
+
+    n_iter = 10
+    with torch.no_grad():
+        for _ in range(10):
+            outputs = torch_model(inputs)
+    torch.cuda.synchronize()
+    begin = time.time()
+    with torch.no_grad():
+        for _ in range(n_iter):
+            torch.cuda.synchronize()
+            outputs = torch_model(inputs)
+            #
+            torch.cuda.synchronize()
+    torch.cuda.synchronize()
+    end = time.time()
+
+    avg_time = (end - begin) / n_iter
+    outputs = outputs.last_hidden_state.to("cpu")
+    print("outputs abs mean:", abs(np.array(outputs)).mean())
+    print(f"average time: {avg_time}")
+    torch.cuda.memory.empty_cache()
+    np.save(os.path.join(result_dir, f"{model_name}_output.npy"), \
+                                        np.array(outputs))
+    print(f"Save input & output as {model_name}_input_0.npy and {model_name}_output.npy")
+
+
+def export_onnx(model_name, model, data, path, extern=False):
+    # torch.onnx.export(model, data, path, verbose=False, do_constant_folding=True)
+
+    if model_name != "llama":
+        onnx_model = onnx.load(path)
+        onnx_model, check = simplify(onnx_model,
+                                 skipped_optimizers=['fuse_qkv', 'eliminate_duplicate_initializer'])
+                                 # skipped_optimizers=['fuse_qkv'])
+        assert check
+        add_value_info_for_constants(onnx_model)
+        onnx_model = onnx.shape_inference.infer_shapes(onnx_model)
+        if extern:
+            extern_path = path.replace('.onnx', '.pb')
+            if os.path.exists(extern_path):
+                os.remove(extern_path)
+            convert_model_to_external_data(
+                onnx_model,
+                all_tensors_to_one_file=True,
+                location=extern_path.split("/")[-1],
+                size_threshold=1024,
+                convert_attribute=False,
+            )
+        onnx.save(onnx_model, path)
+    else:
+        sys.path.append("onnxsim_large_model")
+        from onnx_utils import set_onnx_input_shape
+        from compress_model import SIZE_1MB, compress_onnx_model, uncompress_onnx_model
+
+        in_model_path = path
+        out_model_path = in_model_path[:-5] + ".sim.onnx"
+
+        onnx_model = onnx.load(in_model_path)
+        print(f"load model from {in_model_path} success")
+
+        size_th_bytes = 1024 * 1024
+        onnx_model, removed_inits = compress_onnx_model(onnx_model, size_th_bytes=size_th_bytes)
+        print("compress model success")
+
+        onnx_model = set_onnx_input_shape(onnx_model, "")
+        tensor_size_threshold = f"1024KB"
+        skipped_optimizers = []
+        skipped_optimizers.append("eliminate_duplicate_initializer")
+        onnx_model, check = simplify(onnx_model, skipped_optimizers=skipped_optimizers,
+                                    tensor_size_threshold=tensor_size_threshold)
+        if not check:
+            raise ValueError(f"simplify compressed model {in_model_path} failed")
+
+        print(f"simplify model success")
+
+        onnx_model = uncompress_onnx_model(onnx_model, removed_inits)
+        print(f"uncompress model success")
+
+        add_value_info_for_constants(onnx_model)
+
+        onnx.save(onnx_model, out_model_path, save_as_external_data=True)
+
+
+def add_value_info_for_constants(model : onnx.ModelProto):
+    """
+    Currently onnx.shape_inference doesn't use the shape of initializers, so add
+    that info explicitly as ValueInfoProtos.
+    Mutates the model.
+    Args:
+        model: The ModelProto to update.
+    """
+    # All (top-level) constants will have ValueInfos before IRv4 as they are all inputs
+    if model.ir_version < 4:
+        return
+
+    def add_const_value_infos_to_graph(graph : onnx.GraphProto):
+        inputs = {i.name for i in graph.input}
+        existing_info = {vi.name: vi for vi in graph.value_info}
+        for init in graph.initializer:
+            # Check it really is a constant, not an input
+            if init.name in inputs:
+                continue
+
+            # The details we want to add
+            elem_type = init.data_type
+            shape = init.dims
+
+            # Get existing or create new value info for this constant
+            vi = existing_info.get(init.name)
+            if vi is None:
+                vi = graph.value_info.add()
+                vi.name = init.name
+
+            # Even though it would be weird, we will not overwrite info even if it doesn't match
+            tt = vi.type.tensor_type
+            if tt.elem_type == onnx.TensorProto.UNDEFINED:
+                tt.elem_type = elem_type
+            if not tt.HasField("shape"):
+                # Ensure we set an empty list if the const is scalar (zero dims)
+                tt.shape.dim.extend([])
+                for dim in shape:
+                    tt.shape.dim.add().dim_value = dim
+
+        # Handle subgraphs
+        for node in graph.node:
+            for attr in node.attribute:
+                # Ref attrs refer to other attrs, so we don't need to do anything
+                if attr.ref_attr_name != "":
+                    continue
+
+                if attr.type == onnx.AttributeProto.GRAPH:
+                    add_const_value_infos_to_graph(attr.g)
+                if attr.type == onnx.AttributeProto.GRAPHS:
+                    for g in attr.graphs:
+                        add_const_value_infos_to_graph(g)
+
+
+    return add_const_value_infos_to_graph(model.graph)
+
+
+def main():
+    global input_dir, result_dir
+
+    modelname, batchsize, seqlen, \
+        export_path, input_dir, result_dir, export_only = parse_args()
+
+    model, voc_size = get_model(modelname) # pytorch model
+
+    if export_path is not None:
+        os.makedirs(export_path, exist_ok=True)
+        filename = "{}_{}_{}.onnx".format(modelname, batchsize, seqlen)
+        path = os.path.join(export_path, filename)
+        param = torch.zeros((batchsize, seqlen), dtype=torch.int)
+        export_onnx(modelname, model, param, path, True) # export pytorch model to onnx model
+        if export_only:
+            return
+
+    run_pytorch(model, voc_size, batchsize, seqlen, modelname)
+
+if __name__ == "__main__":
+    main()

examples/distributed/onnxsim_large_model

@@ -0,0 +1 @@
+Subproject commit cbcf3fbf985a00494b0f136c92eaccd42031bf65

examples/distributed/parallel.py

@@ -0,0 +1,103 @@
+import onnx
+from onnx import (
+    ModelProto,
+    TensorProto,
+    NodeProto,
+    AttributeProto,
+)
+from onnx import helper, numpy_helper
+from typing import Dict, Any
+
+
+def parse_attribute(node: NodeProto, attrs: Dict[str, Any] = dict()) -> Dict[str, Any]:
+    for attr in node.attribute:
+        if attr.name in attrs:
+            if attr.type == AttributeProto.INT:
+                attrs[attr.name] = attr.i
+            elif attr.type == AttributeProto.INTS:
+                attrs[attr.name] = attr.ints
+            elif attr.type == AttributeProto.FLOAT:
+                attrs[attr.name] = attr.f
+            elif attr.type == AttributeProto.STRING:
+                attrs[attr.name] = attr.s
+            elif attr.type == AttributeProto.TENSOR:
+                attrs[attr.name] = attr.t
+            else:
+                assert False, "Unsupported Attribute Type: {}".format(attr.type)
+    return attrs
+
+
+def parallel_model(model: ModelProto, tp_world_size: int = 1, tp_rank: int = 0):
+    data = {init.name: init for init in model.graph.initializer}
+    nodes = list(model.graph.node)
+
+    def shard_tensor(tensor: TensorProto, dim: int):
+        array = numpy_helper.to_array(tensor)
+        if dim >= array.ndim:
+            dim = array.ndim - 1
+        assert array.shape[dim] % tp_world_size == 0
+        seg = array.shape[dim] // tp_world_size
+        array = array[tp_rank * seg : (tp_rank + 1) * seg]
+        return numpy_helper.from_array(array, name=tensor.name + f":sharded({dim})")
+
+    def shard_gemm(node: NodeProto):
+        attrs = parse_attribute(
+            node, {"alpha": 1.0, "beta": 1.0, "transA": 0, "transB": 0}
+        )
+        trans = [attrs["transA"], attrs["transB"]]
+        dim = 0
+        for i, (input, t) in enumerate(zip(node.input, trans)):
+            if input in data:
+                dim = i
+                sharded = shard_tensor(data[input], dim ^ t)
+                node.input[i] = sharded.name
+                data[input] = sharded
+        if len(node.input) > 2:
+            input = node.input[2]
+            sharded = shard_tensor(data[input], dim)
+            node.input[2] = sharded.name
+            data[input] = sharded
+
+        node.output[0] += f":sharded({dim})"
+        return dim
+
+    for i, node in enumerate(nodes):
+        if node.op_type == "Gemm":
+            output = node.output[0]
+            dim = shard_gemm(node)
+            gathered = [node.output[0] + f".{i}" for i in range(tp_world_size)]
+            # all_gather
+            nodes.insert(
+                i + 1,
+                helper.make_node(
+                    op_type="AllGather",
+                    inputs=[node.output[0]],
+                    outputs=gathered,
+                    name=node.name + "/allgather",
+                    # domain="infini", # shape inference fails for custom domain
+                ),
+            )
+            # concat
+            nodes.insert(
+                i + 2,
+                helper.make_node(
+                    op_type="Concat",
+                    inputs=gathered,
+                    outputs=[output],
+                    name=node.name + "/concat",
+                    axis=dim,
+                ),
+            )
+    graph = helper.make_graph(
+        nodes,
+        model.graph.name + f"_{tp_rank}",
+        model.graph.input,
+        model.graph.output,
+        data.values(),
+        doc_string=model.graph.doc_string,
+        value_info=model.graph.value_info,
+    )
+    model = helper.make_model(graph)
+
+    onnx.shape_inference.infer_shapes(model)
+    return model

examples/distributed/parallel_opt.py

@@ -0,0 +1,247 @@
+import onnx
+from onnx import ModelProto, NodeProto, TensorProto, ValueInfoProto
+from onnx import helper, numpy_helper
+from typing import Dict, List
+from placement import Placement, Replicate, Shard, _Partial
+import numpy as np
+
+
+def parallel_model(model: ModelProto, tp_world_size: int = 1, tp_rank: int = 0):
+    data = {init.name: init for init in model.graph.initializer}
+    vinfo = {info.name: info for info in model.graph.value_info}
+    vinfo.update({info.name: info for info in model.graph.input})
+    vinfo.update({info.name: info for info in model.graph.output})
+    output = {info.name: info for info in model.graph.output}
+    place: Dict[str, Placement] = {}
+    nodes: List[NodeProto] = []
+
+    def is_sharded(name: str):
+        return place[name].is_shard()
+
+    def shard_tensor(tensor: TensorProto, plc: Shard, groups: int = 1):
+        # print(f"shard {tensor.name} at dim {dim}")
+        assert plc.is_shard(), plc
+        ndim = len(tensor.dims)
+        if plc.dim < 0:
+            plc.dim += ndim
+        if tensor.dims[plc.dim] == 1:  # broadcast dim, no need to shard.
+            return tensor
+        array = numpy_helper.to_array(tensor)
+        assert array.shape[plc.dim] % tp_world_size == 0, array.shape[plc.dim]
+        dims = list(tensor.dims)
+        dims.insert(plc.dim, groups)
+        dims[plc.dim + 1] //= groups
+        array = array.reshape(dims)
+        seg = array.shape[plc.dim + 1] // tp_world_size
+        array = array.take(
+            indices=range(tp_rank * seg, (tp_rank + 1) * seg), axis=plc.dim + 1
+        )
+        dims = list(tensor.dims)
+        dims[plc.dim] //= tp_world_size
+        array = array.reshape(dims)
+        tensor = numpy_helper.from_array(array, name=tensor.name)
+        place[tensor.name] = plc
+        return tensor
+
+    def shard_gemm(node: NodeProto, groups: int = 1):
+        # print("gemm", node.name)
+        in_plc = place[node.input[0]]
+        w_plc = Shard(-1) if in_plc.is_replicate() else Shard(0)
+        transB = next((attr.i for attr in node.attribute if attr.name == "transB"), 0)
+        if transB:
+            w_plc.dim = ~w_plc.dim
+        input = node.input[1]
+        data[input] = shard_tensor(data[input], w_plc, groups)
+
+        output = node.output[0]
+        ndim = len(vinfo[output].type.tensor_type.shape.dim)
+        out_plc = Shard(ndim - 1) if in_plc.is_replicate() else _Partial()
+        place[node.output[0]] = out_plc
+
+    def shard_concat(node: NodeProto):
+        # hack for kvcache
+        in_plc = place[node.input[1]]
+        if in_plc.is_shard():
+            seq_len_dim = vinfo[node.input[0]].type.tensor_type.shape.dim.pop(1)
+            seq_len_dim.dim_value //= tp_world_size
+            vinfo[node.input[0]].type.tensor_type.shape.dim.insert(1, seq_len_dim)
+            place[node.input[0]] = in_plc
+            place[node.output[0]] = in_plc
+
+    def shard_binary(node: NodeProto, groups: int = 1):
+        # print("binary", node.name, node.input[0], place[node.input[0]])
+        a = node.input[0]
+        b = node.input[1]
+        if a in data:
+            a, b = b, a
+        place[node.output[0]] = place[a]
+        if is_sharded(a) and b in data and len(data[b].dims) == 1:  # broadcast
+            data[b] = shard_tensor(data[b], Shard(0), groups)
+
+    def shard_reshape(node: NodeProto):
+        # print("reshape", node.name, node.input[0], place[node.input[0]])
+        if not is_sharded(node.input[0]):
+            return
+        in_plc = place[node.input[0]]
+        s_dim = -1
+        in_dims = [d.dim_value for d in vinfo[node.input[0]].type.tensor_type.shape.dim]
+        tensor = data[node.input[1]]
+        out_dims = numpy_helper.to_array(tensor).copy()
+        if len(in_dims) == 3 and len(out_dims) == 4:
+            if in_plc.dim == 0:
+                s_dim = 1
+            elif in_plc.dim == 2:
+                s_dim = 2
+        if len(in_dims) == 4 and len(out_dims) == 3:
+            if in_plc.dim == 1:
+                s_dim = 0
+            elif in_plc.dim == 2:
+                s_dim = 2
+        if len(in_dims) == 2 and len(out_dims) == 3:
+            if in_plc.dim == 1:
+                s_dim = 2
+        if len(in_dims) == 4 and len(out_dims) == 2:
+            if in_plc.dim == 1:
+                s_dim = 0
+            elif in_plc.dim == 2:
+                s_dim = 1
+        if len(in_dims) == 3 and len(out_dims) == 2:
+            if in_plc.dim == 1:
+                s_dim = 0
+            elif in_plc.dim == 2:
+                s_dim = 1
+        assert s_dim != -1
+        assert out_dims[s_dim] % tp_world_size == 0, out_dims
+        out_dims[s_dim] //= tp_world_size
+        # if ONNX uses the same tensor for multiple Reshape Nodes, then rename it to distingush from others.
+        node.input[1] = node.output[0] + "_shape"
+        data[node.input[1]] = numpy_helper.from_array(out_dims, name=node.input[1])
+        place[node.output[0]] = Shard(s_dim)
+
+    def shard_split(node: NodeProto):
+        if not is_sharded(node.input[0]):
+            return
+        in_plc = place[node.input[0]]
+        split_tensor = data[node.input[1]]
+        split = numpy_helper.to_array(split_tensor).copy()
+        split //= tp_world_size
+        data[node.input[1]] = numpy_helper.from_array(split, name=node.input[1])
+        for output in node.output:
+            place[output] = in_plc
+
+    def shard_transpose(node: NodeProto):
+        plc = place[node.input[0]]
+        if plc.is_shard():
+            perm = next(attr.ints for attr in node.attribute if attr.name == "perm")
+            place[node.output[0]] = Shard(list(perm).index(plc.dim))
+
+    def shard_node(node: NodeProto):
+        if node.op_type in ["Relu", "Tanh", "Softmax", "Cast"]:
+            place[node.output[0]] = place[node.input[0]]
+        elif node.op_type in ["Where"]:
+            place[node.output[0]] = place[node.input[1]]
+        if node.op_type in {"Add", "Mul", "Div", "Max"}:
+            shard_binary(node)
+        elif node.op_type == "Reshape":
+            shard_reshape(node)
+        elif node.op_type == "Transpose":
+            shard_transpose(node)
+        elif node.op_type == "Split":
+            shard_split(node)
+        elif node.op_type == "MatMul":
+            assert (
+                place[node.input[0]] == place[node.input[1]]
+            ), f"{place[node.input[0]]} != {place[node.input[1]]}"
+            place[node.output[0]] = place[node.input[0]]
+        elif node.op_type == "Concat":
+            shard_concat(node)
+
+    def find_successor(op_type: str, idx: int, search_limit: int = 1):
+        for node in model.graph.node[idx + 1 : idx + 1 + search_limit]:
+            if node.op_type == op_type:
+                return node
+        return None
+
+    # all tensors are initially replicated.
+    for v in vinfo:
+        place[v] = Replicate()
+
+    for t in data:
+        place[t] = Replicate()
+
+    for index, node in enumerate(model.graph.node):
+        nodes.append(node)
+        # linear
+        if (node.op_type == "MatMul" or node.op_type == "Gemm") and any(
+            input in data for input in node.input
+        ):
+            # FIXME(constroy): the last MatMul should not be sharded as TP.
+            if (
+                node.output[0] in output
+                or (
+                    index + 1 < len(model.graph.node)
+                    and model.graph.node[index + 1].output[0]
+                )
+                in output
+            ):
+                continue
+            groups = 1
+            # If the Gemm or Matmul is followed by a split, then the inputs are concatinated by groups
+            split_node = find_successor("Split", index, search_limit=2)
+            if split_node is not None:
+                groups = len(split_node.output)
+            shard_gemm(node, groups)
+            plc = place[node.output[0]]
+            if plc.is_partial():
+                new_name = node.output[0] + f":{plc}"
+                place[new_name] = place[node.output[0]]
+                # insert all_reduce
+                nodes.append(
+                    helper.make_node(
+                        op_type="ReduceSum",
+                        inputs=[new_name],
+                        outputs=[node.output[0]],
+                        name=node.name + "/all_reduce",
+                        noop_with_empty_axes=1,
+                        communicator=0,  # hack to treat ReduceSum as AllReduceSum
+                    )
+                )
+                place[node.output[0]] = Replicate()
+                node.output[0] = new_name
+            if len(node.input) > 2:  # split bias to add
+                prev = nodes[-1]
+                new_name = prev.output[0] + "_no_bias"
+                place[new_name] = place[node.output[0]]
+                bias = helper.make_node(
+                    op_type="Add",
+                    inputs=[new_name, node.input[2]],
+                    outputs=[prev.output[0]],
+                    name=node.name + "/bias",
+                )
+                node.input.pop()
+                prev.output[0] = new_name
+                shard_binary(bias, groups)
+                nodes.append(bias)
+            continue
+        shard_node(node)
+
+    new_input = []
+    for info in model.graph.input:
+        new_input.append(vinfo[info.name])
+
+    graph = helper.make_graph(
+        nodes,
+        model.graph.name + f"_{tp_rank}",
+        new_input,
+        model.graph.output,
+        data.values(),
+        doc_string=model.graph.doc_string,
+        # value_info=vinfo.values(),
+    )
+    for output in graph.output:
+        tt = output.type.tensor_type
+        if tt.HasField("shape"):
+            tt.ClearField("shape")
+    model = helper.make_model(graph)
+    #model = onnx.shape_inference.infer_shapes(model)
+    return model

examples/distributed/placement.py

@@ -0,0 +1,64 @@
+from typing import Optional
+
+
+class Placement:
+    # base class Placement type
+
+    # convenient utils to check for placement types
+    def is_shard(self, dim: Optional[int] = None) -> bool:
+        if dim is not None and isinstance(self, Shard):
+            return self.dim == dim
+        else:
+            return isinstance(self, Shard)
+
+    def is_replicate(self) -> bool:
+        return isinstance(self, Replicate)
+
+    def is_partial(self) -> bool:
+        return isinstance(self, _Partial)
+
+
+class Replicate(Placement):
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, Replicate):
+            return False
+        return True
+
+    def __repr__(self) -> str:
+        """
+        machine readable representation of the Replicate placement
+        """
+        return "Replicate()"
+
+
+class Shard(Placement):
+    # shard placement, shard on a dim
+    def __init__(self, dim):
+        self.dim = dim
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, Shard):
+            return False
+        return self.dim == other.dim
+
+    def __repr__(self) -> str:
+        """
+        machine readable representation of the Shard placement
+        """
+        return f"Shard(dim={self.dim})"
+
+
+class _Partial(Placement):
+    def __init__(self, reduce_op: str = "sum"):
+        self.reduce_op: str = reduce_op
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, _Partial):
+            return False
+        return self.reduce_op == other.reduce_op
+
+    def __repr__(self) -> str:
+        """
+        machine readable representation of the Partial placement
+        """
+        return f"_Partial(reduce_op={self.reduce_op})"

examples/python/llama_kvcache_inference.py

@@ -0,0 +1,145 @@
+import os
+from pyinfinitensor.onnx import OnnxStub, backend
+import numpy as np
+import onnx
+import torch
+from transformers import LlamaModel, LlamaForCausalLM
+from tqdm import tqdm
+import onnx_graphsurgeon as gs
+from onnxsim import simplify
+import argparse
+
+parser = argparse.ArgumentParser(description='')
+parser.add_argument('--batchsize', dest='batchsize', type=int, default=1)
+parser.add_argument('--layer', dest='n_layers', type=int, default=2)
+parser.add_argument('--iter', dest='n_iter', type=int, default=1)
+parser.add_argument('--n_max_length', dest='n_max_length', type=int, default=1024)
+parser.add_argument('--pretrained_llama_path', dest='pretrained_llama_path', type=str, 
+                    default="/data0/shared/data/public/opensource_models/meta-llama/Llama-2-7b-hf/")
+parser.add_argument('--onnx_model_path', dest='onnx_model_path', type=str, 
+                    default="/data1/shared/llama")
+args = parser.parse_args()
+
+ONNX_MODEL_PATH = "{}/llama_bs{}_layer{}.onnx".format(args.onnx_model_path, args.batchsize, args.n_layers)
+ONNX_WEIGHT_PATH = "./llama_bs{}_layer{}.pb".format(args.batchsize, args.n_layers)
+
+def export_onnx(model: LlamaModel, ONNX_MODEL_PATH):
+    param = torch.zeros(
+        (args.batchsize, 1024), dtype=torch.long)
+    logits = model(param, past_key_values=None)
+    param_kvcache = torch.zeros((args.batchsize, 1), dtype=torch.long)
+
+    torch.onnx.export(model, (param_kvcache, {"past_key_values": logits.past_key_values,
+                                              "position_ids": param_kvcache}), ONNX_MODEL_PATH, verbose=False,
+                      do_constant_folding=True,)
+    onnx_model = onnx.load(ONNX_MODEL_PATH)
+    print("simplifing onnx model")
+    onnx_model, check = simplify(onnx_model, skipped_optimizers=[
+                                 'eliminate_duplicate_initializer'])
+    assert check
+    
+    onnx.save(onnx_model, ONNX_MODEL_PATH, save_as_external_data=True, location=ONNX_WEIGHT_PATH)
+    print("simlifing finished.")
+
+
+@gs.Graph.register()
+def replace_with_attention(self, inputs, outputs, inputs_added, outputs_removed):
+    for inp in inputs:
+        inp.outputs.clear()   
+    for out in outputs:
+        out.inputs.clear()
+    for inp in inputs_added:
+        inputs.append(inp)
+    for out in outputs_removed:
+        out.inputs.clear()
+    return self.layer(op="AttentionKVCache", inputs=inputs, outputs=outputs)
+
+
+def replace_onnx_with_attention_op():
+    graph = gs.import_onnx(
+        onnx.load(ONNX_MODEL_PATH))
+    tmap = graph.tensors()
+    for i in range(args.n_layers):
+        inputs = [
+            tmap["onnx::Concat_" + str((i+1)*2)],
+            tmap["onnx::Concat_" + str((i+1)*2+1)],
+            tmap["/model/layers." + str(i) + "/self_attn/Add_output_0"],
+            tmap["/model/layers." + str(i) + "/self_attn/Add_1_output_0"],
+            tmap["/model/layers." + str(i) + "/self_attn/Transpose_2_output_0"]]
+        outputs = [
+            tmap["/model/layers." + str(i) + "/self_attn/MatMul_1_output_0"]]
+
+        inputs_added = [graph.inputs[1]]
+        outputs_removed = []
+
+        graph.replace_with_attention(
+            inputs, outputs, inputs_added, outputs_removed)
+        
+    graph.outputs = [tmap[graph.outputs[0].name]]
+    graph.cleanup(True).toposort()
+    onnx.save(gs.export_onnx(graph), ONNX_MODEL_PATH, save_as_external_data=True)
+
+
+if __name__ == "__main__":
+    kvcache_torch = None
+    torch_model = LlamaForCausalLM.from_pretrained(
+        args.pretrained_llama_path, num_hidden_layers=int(args.n_layers)).eval()
+    
+    n_heads = torch_model.config.num_attention_heads
+    n_dims = torch_model.config.hidden_size // n_heads
+    
+    if not os.path.exists(ONNX_MODEL_PATH):
+        print("exporting onnx graph")
+        export_onnx(torch_model, ONNX_MODEL_PATH)
+        replace_onnx_with_attention_op()
+    else:
+        print("will use exsiting onnx graph")
+
+    onnx_model = onnx.load(ONNX_MODEL_PATH)
+    stub = OnnxStub(onnx_model, backend.cuda_runtime())
+
+    count_wrong = 0
+    for i in tqdm(range(0, args.n_max_length)):
+        query = np.random.randint(
+            torch_model.config.vocab_size, size=(args.batchsize, 1), dtype=np.int32)
+        position_id = i*np.ones((args.batchsize, 1), dtype=np.int32)
+
+        ####################################
+        # pytorch
+        ####################################
+        outputs_torch = torch_model(
+            torch.tensor(query), past_key_values=kvcache_torch)
+        logit_torch = outputs_torch['logits']
+        kvcache_torch = outputs_torch['past_key_values']
+
+        ####################################
+        # infinitensor
+        ####################################
+        # copyin input
+        (list(stub.inputs.items()))[0][1].copyin_int64(
+            query.reshape(-1).tolist())
+        (list(stub.inputs.items()))[1][1].copyin_int64(
+            position_id.reshape(-1).tolist())
+
+        stub.run()
+
+        ####################################
+        # validation
+        ####################################
+        # copyout output
+        logits_it = np.array((list(stub.outputs.items()))
+                                [0][1].copyout_float())
+        
+        try:
+            np.testing.assert_allclose(
+                logit_torch[:, -1, :].detach().cpu().numpy().flatten(), logits_it, rtol=1e-3, atol=1e-3)
+        except Exception as e: 
+            try:
+                np.testing.assert_allclose(
+                    np.argmax(logit_torch[:, -1, :].detach().cpu().numpy().flatten()), np.argmax(logits_it), rtol=1e-3, atol=1e-3)
+            except:
+                count_wrong = count_wrong + 1
+
+    result = "{}/{} failed.".format(count_wrong, args.n_max_length)
+    print(result)
+    del stub

examples/python/onnx_inference.py

@@ -0,0 +1,29 @@
+import sys
+import onnx
+import torch
+import numpy as np
+from pyinfinitensor.onnx import OnnxStub, backend
+
+if __name__ == '__main__':
+    args = sys.argv
+    if len(sys.argv) != 2:
+        print("Usage: python onnx_inference.py model_name.onnx")
+        exit()
+    model_path = sys.argv[1]
+    # print(model_path)
+
+    onnx_model = onnx.load(model_path)
+    onnx_input = onnx_model.graph.input[0]
+    input_shape = [[d.dim_value for d in _input.type.tensor_type.shape.dim]
+                   for _input in onnx_model.graph.input]
+    # Assume that there is only one input tensor
+    input_shape = input_shape[0]
+    # print(input_shape)
+    input_data = np.random.random(input_shape).astype(np.float32)
+
+    model = OnnxStub(onnx_model, backend.cuda_runtime())
+    next(iter(model.inputs.values())).copyin_numpy(input_data)
+    model.run()
+    outputs = next(iter(model.outputs.values())).copyout_numpy()
+    outputs = torch.tensor(outputs)
+    print(outputs.shape)

examples/python/paddle_densenet.py

@@ -0,0 +1,80 @@
+
+import paddle
+import paddle.vision.transforms as T
+from paddle.vision.datasets import Cifar10
+from pyinfinitensor.onnx import OnnxStub, backend
+import onnx
+import itertools
+
+def run_cifar_train_and_infer():
+    
+    paddle.device.set_device("gpu")
+
+    transform = T.Compose(
+        [
+            T.Resize(224),
+            T.ToTensor(),
+            T.Normalize(
+                mean=[0.5, 0.5, 0.5],
+                std=[0.5, 0.5, 0.5],
+                to_rgb=True,
+            ),
+        ]
+    )
+    
+    # 下载数据集并初始化 DataSet
+    train_dataset = paddle.vision.datasets.Cifar10(mode='train', transform=transform)
+    test_dataset = paddle.vision.datasets.Cifar10(mode='test', transform=transform)
+
+    # 模型组网并初始化网络
+    densenet = paddle.vision.models.DenseNet(num_classes=10)
+    model = paddle.Model(densenet)
+
+    # 模型训练的配置准备，准备损失函数，优化器和评价指标
+    model.prepare(paddle.optimizer.Adam(parameters=model.parameters()), 
+                paddle.nn.CrossEntropyLoss(),
+                paddle.metric.Accuracy())
+
+    # 模型训练
+    model.fit(train_dataset, epochs=5, batch_size=64, verbose=1)
+    # 模型评估
+    model.evaluate(test_dataset, batch_size=64, verbose=1)
+
+    # export to ONNX
+    save_path = 'onnx.save/densenet' # 需要保存的路径
+    x_spec = paddle.static.InputSpec([1, 3, 224, 224], 'float32', 'x') # 为模型指定输入的形状和数据类型，支持持 Tensor 或 InputSpec ，InputSpec 支持动态的 shape。
+    paddle.onnx.export(densenet, save_path, input_spec=[x_spec], opset_version=11)
+
+    # 加载onnx模型并放到Infinitensor中
+    model_path = save_path + ".onnx"
+    onnx_model = onnx.load(model_path)
+    gofusion_model = OnnxStub(onnx_model, backend.cuda_runtime())
+    model = gofusion_model
+    model.init()
+
+    # 启动推理
+    cifar10_test = Cifar10(
+        mode="test",
+        transform=transform,  # apply transform to every image
+        backend="cv2",  # use OpenCV as image transform backend
+    )
+    batch_size = 1
+    total_size = 0
+    total_acc = 0.0
+    for data in itertools.islice(iter(cifar10_test), 10000):
+        images, labels = data
+        next(model.inputs.items().__iter__())[1].copyin_float(images.reshape([3*224*224]).tolist())
+        model.run()
+        outputs = next(model.outputs.items().__iter__())[1].copyout_float()
+        outputs = paddle.to_tensor(outputs)
+        outputs = paddle.reshape(outputs, (1, 10))
+        labels = paddle.to_tensor(labels)
+        labels = paddle.reshape(labels, (1,1))
+        acc = paddle.metric.accuracy(outputs, labels)
+        total_acc += acc
+        total_size += batch_size
+    print("test acc: {}".format(total_acc.numpy() / total_size))
+
+
+if __name__ == "__main__":
+    run_cifar_train_and_infer()

examples/python/paddle_inception.py

@@ -0,0 +1,80 @@
+import paddle
+import paddle.vision.transforms as T
+from paddle.vision.datasets import Cifar10
+from pyinfinitensor.onnx import OnnxStub, backend
+import onnx
+import itertools
+
+def run_cifar_train_and_infer():
+    
+    paddle.device.set_device("gpu")
+
+    transform = T.Compose(
+        [
+            T.Resize(224),
+            T.ToTensor(),
+            T.Normalize(
+                mean=[0.5, 0.5, 0.5],
+                std=[0.5, 0.5, 0.5],
+                to_rgb=True,
+            ),
+        ]
+    )
+    
+    # 下载数据集并初始化 DataSet
+    train_dataset = paddle.vision.datasets.Cifar10(mode='train', transform=transform)
+    test_dataset = paddle.vision.datasets.Cifar10(mode='test', transform=transform)
+
+    # 模型组网并初始化网络
+    inception = paddle.vision.models.InceptionV3(num_classes=10)
+    model = paddle.Model(inception)
+
+    # 模型训练的配置准备，准备损失函数，优化器和评价指标
+    model.prepare(paddle.optimizer.Adam(parameters=model.parameters()), 
+                paddle.nn.CrossEntropyLoss(),
+                paddle.metric.Accuracy())
+
+    # 模型训练
+    model.fit(train_dataset, epochs=5, batch_size=64, verbose=1)
+    # 模型评估
+    model.evaluate(test_dataset, batch_size=64, verbose=1)
+
+    # export to ONNX
+    save_path = 'onnx.save/inception' # 需要保存的路径
+    x_spec = paddle.static.InputSpec([1, 3, 224, 224], 'float32', 'x') # 为模型指定输入的形状和数据类型，支持持 Tensor 或 InputSpec ，InputSpec 支持动态的 shape。
+    paddle.onnx.export(inception, save_path, input_spec=[x_spec], opset_version=11)
+
+    # 加载onnx模型并放到Infinitensor中
+    model_path = save_path + ".onnx"
+    onnx_model = onnx.load(model_path)
+    gofusion_model = OnnxStub(onnx_model, backend.cuda_runtime())
+    model = gofusion_model
+    model.init()
+
+    # 启动推理
+    cifar10_test = Cifar10(
+        mode="test",
+        transform=transform,  # apply transform to every image
+        backend="cv2",  # use OpenCV as image transform backend
+    )
+    batch_size = 1
+    total_size = 0
+    total_acc = 0.0
+    for data in itertools.islice(iter(cifar10_test), 10000):
+        images, labels = data
+        next(model.inputs.items().__iter__())[1].copyin_float(images.reshape([3*224*224]).tolist())
+        model.run()
+        outputs = next(model.outputs.items().__iter__())[1].copyout_float()
+        outputs = paddle.to_tensor(outputs)
+        outputs = paddle.reshape(outputs, (1, 10))
+        labels = paddle.to_tensor(labels)
+        labels = paddle.reshape(labels, (1,1))
+        acc = paddle.metric.accuracy(outputs, labels)
+        total_acc += acc
+        total_size += batch_size
+    print("test acc: {}".format(total_acc.numpy() / total_size))
+
+
+
+if __name__ == "__main__":
+    run_cifar_train_and_infer() 

examples/python/paddle_model_dev.md

@@ -0,0 +1,31 @@
+## Description
+
+This is a doc to tell you how to run paddle*.py in your machine. If your model run on other machines except Nvidia, you may need to make some change.
+
+## What do we do in paddle*.py files?
+
+1. Train model and evalute model with Cifar10 dataset
+
+2. Export paddle model to onnx model
+
+3. Load onnx model, infer with InfiniTensor and calculate the inference accuracy
+
+## Command
+
+1. Go to `/examples/python` folder 
+
+2. Run the following command
+   
+   1. ```
+      python paddle_resnet.py
+      python paddle_densenet.py
+      python paddle_inception.py
+      ```
+
+## What should I do if I use other device(MLU, XPU, NPU)?
+
+You need to change this code:
+
+```
+paddle.device.set_device("gpu") # Change gpu to mlu, xpu or npu
+```

examples/python/paddle_resnet.py

@@ -0,0 +1,81 @@
+
+import paddle
+import paddle.vision.transforms as T
+from paddle.vision.datasets import Cifar10
+from pyinfinitensor.onnx import OnnxStub, backend
+import onnx
+import itertools
+from paddle.vision.models.resnet import BasicBlock
+
+def run_cifar_train_and_infer():
+    
+    paddle.device.set_device("gpu")
+
+    transform = T.Compose(
+        [
+            T.Resize(224),
+            T.ToTensor(),
+            T.Normalize(
+                mean=[0.5, 0.5, 0.5],
+                std=[0.5, 0.5, 0.5],
+                to_rgb=True,
+            ),
+        ]
+    )
+    
+    # 下载数据集并初始化 DataSet
+    train_dataset = paddle.vision.datasets.Cifar10(mode='train', transform=transform)
+    test_dataset = paddle.vision.datasets.Cifar10(mode='test', transform=transform)
+
+    # 模型组网并初始化网络
+    resnet = paddle.vision.models.ResNet(BasicBlock, depth=18, num_classes=10)
+    model = paddle.Model(resnet)
+
+    # 模型训练的配置准备，准备损失函数，优化器和评价指标
+    model.prepare(paddle.optimizer.Adam(parameters=model.parameters()), 
+                paddle.nn.CrossEntropyLoss(),
+                paddle.metric.Accuracy())
+
+    # 模型训练
+    model.fit(train_dataset, epochs=5, batch_size=64, verbose=1)
+    # 模型评估
+    model.evaluate(test_dataset, batch_size=64, verbose=1)
+
+    # export to ONNX
+    save_path = 'onnx.save/resnet' # 需要保存的路径
+    x_spec = paddle.static.InputSpec([1, 3, 224, 224], 'float32', 'x') # 为模型指定输入的形状和数据类型，支持持 Tensor 或 InputSpec ，InputSpec 支持动态的 shape。
+    paddle.onnx.export(resnet, save_path, input_spec=[x_spec], opset_version=11)
+
+    # 加载onnx模型并放到Infinitensor中
+    model_path = save_path + ".onnx"
+    onnx_model = onnx.load(model_path)
+    gofusion_model = OnnxStub(onnx_model, backend.cuda_runtime())
+    model = gofusion_model
+    model.init()
+
+    # 启动推理
+    cifar10_test = Cifar10(
+        mode="test",
+        transform=transform,  # apply transform to every image
+        backend="cv2",  # use OpenCV as image transform backend
+    )
+    batch_size = 1
+    total_size = 0
+    total_acc = 0.0
+    for data in itertools.islice(iter(cifar10_test), 10000):
+        images, labels = data
+        next(model.inputs.items().__iter__())[1].copyin_float(images.reshape([3*224*224]).tolist())
+        model.run()
+        outputs = next(model.outputs.items().__iter__())[1].copyout_float()
+        outputs = paddle.to_tensor(outputs)
+        outputs = paddle.reshape(outputs, (1, 10))
+        labels = paddle.to_tensor(labels)
+        labels = paddle.reshape(labels, (1,1))
+        acc = paddle.metric.accuracy(outputs, labels)
+        total_acc += acc
+        total_size += batch_size
+    print("test acc: {}".format(total_acc.numpy() / total_size))
+
+
+if __name__ == "__main__":
+    run_cifar_train_and_infer()

examples/python/resnet_inference.py

@@ -0,0 +1,24 @@
+import sys
+import onnx
+import torch
+import numpy as np
+from pyinfinitensor.onnx import OnnxStub, backend
+import torchvision.models as models
+
+if __name__ == '__main__':
+    model_path = './resnet18.onnx'
+    tv_model = models.resnet50(weights=None)
+    input_shape = (1, 3, 224, 224)
+    param = torch.rand(input_shape)
+    torch.onnx.export(tv_model, param, model_path, verbose=False)
+
+    onnx_model = onnx.load(model_path)
+    model = OnnxStub(onnx_model, backend.cuda_runtime())
+    images = np.random.random(input_shape).astype(np.float32)
+    next(iter(model.inputs.values())).copyin_numpy(images)
+    model.run()
+    outputs = next(iter(model.outputs.values())).copyout_numpy()
+    outputs = torch.tensor(outputs)
+    outputs = torch.reshape(outputs, (1, 1000))
+    _, predicted = torch.max(outputs, 1)
+    print(predicted)

include/bang/bang_common.h

@@ -2,6 +2,10 @@
 #include "cnnl.h"
 #include "cnrt.h"
 #include "core/common.h"
+#include "core/data_type.h"
+#ifdef INFINI_USE_CNCL
+#include "cncl.h"
+#endif
 
 #define checkBangError(call)                                                   \
     {                                                                          \
@@ -27,4 +31,70 @@ namespace infini {
 
 using BangPtr = void *;
 
+inline cnnlDataType_t cnnlDataTypeConvert(DataType dataType) {
+    if (dataType == DataType::Float32) {
+        return CNNL_DTYPE_FLOAT;
+    }
+    if (dataType == DataType::Float16) {
+        return CNNL_DTYPE_HALF;
+    }
+    if (dataType == DataType::Double) {
+        return CNNL_DTYPE_DOUBLE;
+    }
+    if (dataType == DataType::Int8) {
+        return CNNL_DTYPE_INT8;
+    }
+    if (dataType == DataType::Int32) {
+        return CNNL_DTYPE_INT32;
+    }
+    if (dataType == DataType::UInt8) {
+        return CNNL_DTYPE_UINT8;
+    }
+    if (dataType == DataType::BFloat16) {
+        return CNNL_DTYPE_BFLOAT16;
+    }
+    if (dataType == DataType::Int64) {
+        return CNNL_DTYPE_INT64;
+    }
+    if (dataType == DataType::Bool) {
+        return CNNL_DTYPE_BOOL;
+    }
+    IT_TODO_HALT_MSG("Data type " + dataType.toString() +
+                     " not supported in CNNL.");
+}
+
+#ifdef INFINI_USE_CNCL
+inline cnclDataType_t cnclDataTypeConvert(DataType dataType) {
+    if (dataType == DataType::Float32) {
+        return cnclFloat32;
+    }
+    if (dataType == DataType::Float16) {
+        return cnclHalf;
+    }
+    if (dataType == DataType::Int8) {
+        return cnclInt8;
+    }
+    if (dataType == DataType::Int16) {
+        return cnclInt16;
+    }
+    if (dataType == DataType::Int32) {
+        return cnclInt32;
+    }
+    if (dataType == DataType::UInt8) {
+        return cnclUint8;
+    }
+    if (dataType == DataType::UInt16) {
+        return cnclUint16;
+    }
+    if (dataType == DataType::UInt32) {
+        return cnclUint32;
+    }
+    if (dataType == DataType::BFloat16) {
+        return cnclBfloat16;
+    }
+    IT_TODO_HALT_MSG("Data type " + dataType.toString() +
+                     " not supported in CNCL.");
+}
+#endif
+
 } // namespace infini

include/bang/bang_runtime.h

@@ -7,16 +7,19 @@ namespace infini {
 class BangRuntimeObj : public RuntimeObj {
   private:
     cnnlHandle_t cnnl;
+    cnrtQueue_t queue;
+    std::unique_ptr<CommunicatorObj> comm;
     BangPtr workspace;
     size_t workspaceSize;
+    mutable size_t cursor;
 
   public:
-    BangRuntimeObj() : RuntimeObj(Device::BANG) {
+    explicit BangRuntimeObj(int deviceId = 0)
+        : RuntimeObj(Device::BANG, deviceId) {
         cnInit(0);
         CNdev dev;
-        cnDeviceGet(&dev, 0);
+        cnDeviceGet(&dev, deviceId);
         checkBangError(cnrtSetDevice(dev));
-        cnrtQueue_t queue;
         checkBangError(cnrtQueueCreate(&queue));
 
         checkCnnlError(cnnlCreate(&cnnl));
@@ -24,10 +27,12 @@ class BangRuntimeObj : public RuntimeObj {
         // 10GB for Longformer
         // size_t longformerNum = 3lu * (1 << 30);
         workspaceSize = 7ll << 30; // 7 GB
+        cursor = 0;
         workspace = alloc(workspaceSize);
     }
     virtual ~BangRuntimeObj() {
         dealloc(workspace);
+        checkBangError(cnrtQueueDestroy(queue));
         checkCnnlError(cnnlDestroy(cnnl));
     }
     string toString() const override;
@@ -45,10 +50,15 @@ class BangRuntimeObj : public RuntimeObj {
     void dealloc(void *ptr) override { checkBangError(cnrtFree(ptr)); }
     cnnlHandle_t cnnlHandle() const { return cnnl; }
     BangPtr getWorkspace(size_t size) const {
-        IT_ASSERT(size <= workspaceSize);
-        return workspace;
+        IT_ASSERT((cursor + size) <= workspaceSize);
+        cursor += size;
+        void *temp = workspace;
+        temp += (cursor - size);
+        return temp;
     }
 
+    void resetWorkspace() const { cursor = 0; }
+
     void copyBlobFromCPU(void *dst, const void *src,
                          size_t bytes) const override {
         checkBangError(cnrtMemcpy(dst, const_cast<void *>(src), bytes,
@@ -66,6 +76,9 @@ class BangRuntimeObj : public RuntimeObj {
         checkBangError(cnrtMemcpy(dst, const_cast<void *>(src), bytes,
                                   CNRT_MEM_TRANS_DIR_PEER2PEER));
     }
+    void initComm(const string &name, int worldSize, int rank) final;
+    CommunicatorObj &getCommunicator() const override { return *comm; }
+    cnrtQueue_t getBangQueue() const { return queue; }
 
   private:
     void runWithoutSync(const Graph &graph, bool tune, bool profiling) const;

include/bang/cncl_communicator.h

@@ -0,0 +1,79 @@
+#pragma once
+#include "bang_common.h"
+#include "core/communicator.h"
+#include <chrono>
+#include <cncl.h>
+#include <cnrt.h>
+#include <cstdlib>
+#include <filesystem>
+#include <fstream>
+#include <mutex>
+#include <thread>
+
+namespace infini {
+
+class CnclCommunicatorObj final : public CommunicatorObj {
+  private:
+    cnclComm_t *comms;
+
+  public:
+    CnclCommunicatorObj(const string &name, int worldSize, int rank)
+        : CommunicatorObj(worldSize, rank) {
+        const std::string filePath("./" + name + "_cncl_id.bin");
+        cnclCliqueId clique_id;
+        if (rank == 0) {
+            CNCL_CHECK(cnclGetCliqueId(&clique_id));
+            std::ofstream ofs(filePath, std::ios::binary);
+            ofs.write((char *)&clique_id, sizeof(cnclCliqueId));
+
+        } else {
+            auto begin = std::chrono::steady_clock::now();
+            while (!std::filesystem::exists(filePath)) {
+                auto now = std::chrono::steady_clock::now();
+                _IT_ASSERT_2(now < begin + std::chrono::seconds(10),
+                             "time limit (10s) exceeded.");
+                std::this_thread::sleep_for(std::chrono::milliseconds(100));
+            }
+            std::ifstream ifs(filePath, std::ios::binary);
+            ifs.read((char *)&clique_id, sizeof(cnclCliqueId));
+        }
+
+        int num_comms = 1;
+        int *dev_list = new int[num_comms];
+        int *rank_list = new int[num_comms];
+        comms = new cnclComm_t[num_comms];
+        uint32_t num_dev = 0;
+        checkBangError(cnrtGetDeviceCount(&num_dev));
+
+        for (int i = 0; i < num_comms; i++) {
+            rank_list[i] = rank;
+            dev_list[i] = rank_list[i] % num_dev;
+        }
+
+        CNCL_CHECK(cnclInitComms(comms, num_comms, dev_list, rank_list,
+                                 worldSize, &clique_id));
+
+        if (rank == 0) {
+            std::filesystem::remove(filePath);
+        }
+
+        delete[] dev_list;
+        delete[] rank_list;
+    }
+
+    ~CnclCommunicatorObj() {
+        CNCL_CHECK(cnclDestroyComms(comms, 1));
+        delete[] comms;
+    }
+
+    // Get the actual cnclComm_t
+    cnclComm_t getCnclComm() { return comms[0]; }
+
+    virtual string toString() const final {
+        std::ostringstream oss;
+        oss << "CNCL communicator";
+        return oss.str();
+    }
+};
+
+} // namespace infini

include/core/common.h

@@ -40,12 +40,12 @@ using HashType = uint64_t; // compatible with std::hash
 
 // Assert: conditions should have no side effect
 #define _IT_ASSERT_2(condition, info)                                          \
-    (static_cast<bool>(condition)                                              \
-         ? void(0)                                                             \
-         : throw ::infini::Exception(                                          \
-               std::string("[") + __FILE__ + ":" + std::to_string(__LINE__) +  \
-               "] Assertion failed (" + #condition + "): " + info))
-#define _IT_ASSERT_1(condition) _IT_ASSERT_2(condition, "");
+    static_cast<bool>(condition)                                               \
+        ? void(0)                                                              \
+        : throw ::infini::Exception(                                           \
+              std::string("[") + __FILE__ + ":" + std::to_string(__LINE__) +   \
+              "] Assertion failed (" + #condition + "): " + info)
+#define _IT_ASSERT_1(condition) _IT_ASSERT_2(condition, "")
 #define IT_ASSERT(...) _VA_SELECT(_IT_ASSERT, __VA_ARGS__)
 
 #define IT_TODO_HALT() _IT_ASSERT_2(false, "Unimplemented")
@@ -61,21 +61,35 @@ template <typename T> auto enum_to_underlying(T e) {
 }
 
 template <typename T> std::string vecToString(const std::vector<T> &vec) {
-    std::string ret;
-    ret.append("[");
-    for (auto d : vec) {
-        ret.append(std::to_string(d));
-        ret.append(",");
+    std::stringstream ss;
+    ss << "[";
+    for (size_t i = 0; i < vec.size(); ++i) {
+        ss << vec.at(i);
+        if (i < vec.size() - 1) {
+            ss << ",";
+        }
     }
-    if (!vec.empty())
-        ret.pop_back();
-    ret.append("]");
-    return ret;
+    ss << "]";
+    return ss.str();
+}
+
+template <typename T> std::string vecToString(const T *st, size_t length) {
+    std::stringstream ss;
+    ss << "[";
+    size_t i = 0;
+    for (i = 0; i < length; i++) {
+        ss << *(st + i);
+        if (i < length - 1) {
+            ss << ",";
+        }
+    }
+    ss << "]";
+    return ss.str();
 }
 
 double timeit(
     const std::function<void()> &func,
-    const std::function<void(void)> &sync = []() {}, int warmupRounds = 200,
-    int timingRounds = 200);
+    const std::function<void(void)> &sync = []() {}, int warmupRounds = 10,
+    int timingRounds = 10);
 
 } // namespace infini

include/core/communicator.h

@@ -0,0 +1,22 @@
+#pragma once
+#include "object.h"
+#include "ref.h"
+
+namespace infini {
+
+// base class
+class CommunicatorObj : public Object {
+  protected:
+    int worldSize;
+    int rank;
+
+  public:
+    CommunicatorObj(int worldSize, int rank)
+        : worldSize(worldSize), rank(rank) {}
+
+    virtual ~CommunicatorObj() = default;
+    virtual int getWorldSize() const { return worldSize; }
+    virtual int getRank() const { return rank; }
+};
+
+} // namespace infini

include/core/data_type.h

@@ -1,3 +1,4 @@
+#pragma once
 #include "core/common.h"
 
 namespace infini {
@@ -69,23 +70,6 @@ class DataType {
     int getIndex() const { return index; }
 };
 
-// to be consistent with onnx
-// https://github.com/onnx/onnx/blob/aeb21329122b96df1d3ef33b500a35ca140b1431/onnx/onnx.proto#L484
-inline const DataType DataType::Undefine(0);
-inline const DataType DataType::Float32(1);
-inline const DataType DataType::UInt8(2);
-inline const DataType DataType::Int8(3);
-inline const DataType DataType::UInt16(4);
-inline const DataType DataType::Int16(5);
-inline const DataType DataType::Int32(6);
-inline const DataType DataType::Int64(7);
-inline const DataType DataType::String(8);
-inline const DataType DataType::Bool(9);
-inline const DataType DataType::Float16(10);
-inline const DataType DataType::Double(11);
-inline const DataType DataType::UInt32(12);
-inline const DataType DataType::UInt64(13);
-inline const DataType DataType::BFloat16(16);
 // Method definitions are out of the declaration due to GCC bug:
 // https://stackoverflow.com/questions/49707184/explicit-specialization-in-non-namespace-scope-does-not-compile-in-gcc
 template <> inline int DataType::get<float>() { return 0; }

include/core/graph.h

@@ -53,6 +53,7 @@ class GraphObj : public Object {
     const TensorVec &getTensors() const { return tensors; }
     const OpVec &getOperators() const { return ops; }
     OpVec getComputeOps() const;
+    Tensor getTensor(int) const;
 
     /**
      * Sort the nodes in topological order.
@@ -64,7 +65,13 @@ class GraphObj : public Object {
 
     void optimize();
 
-    void dataMalloc();
+    void shape_infer();
+
+    void dataMalloc(bool useNaiveAllocator = false, size_t memPoolSize = 0);
+
+    Tensor cloneKV(Tensor &tensor);
+
+    void freeHeap();
 
     /**
      * @brief Add an operator and create its outputs. Output tensor arguments
@@ -120,6 +127,11 @@ class GraphObj : public Object {
      * @brief If the nodes is sorted in topological order.
      */
     bool sorted;
+
+    /**
+     * @brief If the weight tensors are allocated.
+     */
+    bool weightAllocated = false;
 };
 
 } // namespace infini

include/core/graph_handler.h

@@ -5,6 +5,10 @@
 #include <cstdint>
 #include <iostream>
 
+#ifdef USE_CUDA
+#include "cuda/cuda_runtime.h"
+#endif
+
 namespace infini {
 
 class GraphHandlerObj {
@@ -26,28 +30,40 @@ class GraphHandlerObj {
                             int pw, int sh, int sw, int dh, int dw, int oph,
                             int opw);
     Tensor matmul(Tensor a, Tensor b, Tensor y, bool transA, bool transB,
-                  Tensor bias, ActType act);
+                  Tensor bias, ActType act,
+                  std::string matmul_compute_type = "default");
     Tensor batchNormalization(Tensor input, Tensor output, Tensor mean,
                               Tensor var, Tensor scale, Tensor bias,
                               float momentum, float eps, bool training);
+    Tensor layerNormalization(Tensor input, Tensor scale, Tensor output,
+                              Tensor bias, float eps, int axis, int stash_type);
+    Tensor rmsNorm(Tensor input, Tensor weight, Tensor output);
 
     Tensor maxPool(Tensor input, Tensor output, int kh, int kw, int dh, int dw,
-                   int ph, int pw, int sh, int sw);
+                   int ph, int pw, int sh, int sw, int ceilMode);
     Tensor avgPool(Tensor input, Tensor output, int kh, int kw, int dh, int dw,
-                   int ph, int pw, int sh, int sw);
+                   int ph, int pw, int sh, int sw, int ceilMode);
 
     Tensor add(Tensor a, Tensor b, Tensor c);
     Tensor sub(Tensor a, Tensor b, Tensor c);
     Tensor mul(Tensor a, Tensor b, Tensor c);
     Tensor div(Tensor a, Tensor b, Tensor c);
     Tensor pow(Tensor a, Tensor b, Tensor c);
+    Tensor min(Tensor a, Tensor b, Tensor c);
+    Tensor max(Tensor a, Tensor b, Tensor c);
 
     Tensor relu(Tensor x, Tensor y);
+    Tensor silu(Tensor x, Tensor y);
+    Tensor gelu(Tensor x, Tensor y);
     Tensor sigmoid(Tensor x, Tensor y);
+    Tensor hardSigmoid(Tensor x, Tensor y);
+    Tensor hardSwish(Tensor x, Tensor y);
     Tensor tanh(Tensor x, Tensor y);
+    Tensor erf(Tensor x, Tensor y);
     Tensor softmax(Tensor x, Tensor y, int axis);
     Tensor abs(Tensor x, Tensor y);
     Tensor sqrt(Tensor x, Tensor y);
+    Tensor neg(Tensor x, Tensor y);
     Tensor shape(Tensor x, Tensor y);
     Tensor identity(Tensor x, Tensor y);
     Tensor flatten(Tensor s, Tensor y, int axis);
@@ -56,18 +72,51 @@ class GraphHandlerObj {
                 std::optional<float> max);
     Tensor transpose(Tensor data, Tensor transposed, Shape perm);
     Tensor reshape(Tensor data, Tensor reshaped, Shape shape);
+    Tensor resize(Tensor input, Tensor output,
+                  const std::optional<vector<int>> &axes, Tensor sizes,
+                  Tensor scales, Tensor roi, vector<uint32_t> sizes_,
+                  vector<float> scales_, vector<float> roi_, string mode,
+                  string ratioPolicy, string nearestMode,
+                  string coordTransMode);
+    Tensor squeeze(Tensor input, Tensor output, Shape axes);
+    Tensor unsqueeze(Tensor input, Tensor output, Shape axes);
     Tensor concat(TensorVec inputs, Tensor output, int dim);
+    Tensor attentionKVCache(Tensor input_k_cache, Tensor input_v_cache,
+                            Tensor input_q, Tensor input_k, Tensor input_v,
+                            Tensor position_id, Tensor output_matmul);
+    Tensor RoPE(Tensor pos, Tensor input, Tensor output);
     TensorVec split(Tensor input, std::optional<TensorVec> outputs, int axis,
-                    int num_outputs);
+                    std::variant<int, vector<int>> numOrRatio);
     Tensor gather(Tensor data, Tensor indices, Tensor output, int axis);
+    Tensor gatherElements(Tensor data, Tensor indices, Tensor output, int axis);
     Tensor reduceMean(Tensor data, Tensor reduced,
                       const optional<vector<int>> &axes, bool keepdims);
+    Tensor reduceSum(Tensor data, Tensor reduced,
+                     const optional<vector<int>> &axes, bool keepdims);
     Tensor slice(Tensor input, Tensor output, const vector<int> &starts,
                  const vector<int> &ends, const optional<vector<int>> &axes,
                  const optional<vector<int>> &steps);
     Tensor pad(Tensor input, Tensor output, const vector<int> &pads,
                const optional<vector<int>> &axes);
     Tensor cast(Tensor input, Tensor output, int to);
+    Tensor expand(Tensor input, Tensor output, Shape dims);
+    Tensor where(Tensor inputX, Tensor inputY, Tensor condition, Tensor output);
+    std::vector<int> getDims(Tensor x) { return x->getDims(); }
+
+    Tensor allReduceSum(Tensor input, Tensor output);
+    Tensor allReduceProd(Tensor input, Tensor output);
+    Tensor allReduceMin(Tensor input, Tensor output);
+    Tensor allReduceMax(Tensor input, Tensor output);
+    Tensor allReduceAvg(Tensor input, Tensor output);
+    TensorVec allGather(Tensor input, std::optional<TensorVec> outputs, int n);
+    Tensor broadcast(Tensor input, Tensor output, int root);
+    Tensor send(Tensor input, int source, int destination, Tensor output);
+    Tensor recv(Tensor output, int source, int destination, Shape dims,
+                int outputType, Tensor input);
+    Tensor depthToSpace(Tensor input, Tensor output, int blocksize,
+                        std::string mode);
+    Tensor lrn(Tensor input, Tensor output, float alpha, float beta, float bias,
+               int size);
 
     //------ modifiers
 
@@ -75,15 +124,31 @@ class GraphHandlerObj {
 
     inline void optimize() { g->optimize(); }
 
+    inline void shape_infer() { g->shape_infer(); }
+
+    void change_shape(const vector<int> &shape, int tensorId);
     //------ runtime
 
-    inline void data_malloc() { g->dataMalloc(); }
+    inline void data_malloc(bool useNaiveAllocator = false,
+                            size_t memPoolSize = 0) {
+        g->dataMalloc(useNaiveAllocator, memPoolSize);
+    }
+
+    inline Tensor clone_KV(Tensor &tensor) { return g->cloneKV(tensor); }
+
+    inline void free_heap() { g->freeHeap(); }
 
     inline void tune() { g->getRuntime()->run(g, true); }
 
     inline void run() { g->getRuntime()->run(g); }
 
     inline double get_perf_time() { return g->getRuntime()->getPerfTime(g); }
+
+#ifdef USE_CUDA
+    inline void run_with_cudagraph() {
+        (as<CudaRuntimeObj>(g->getRuntime()))->runWithCudaGraph(g);
+    }
+#endif
 };
 
 } // namespace infini

include/core/kernel.h

@@ -2,10 +2,11 @@
 #include "core/common.h"
 #include "core/operator.h"
 #include "core/tensor.h"
+#include "utils/operator_utils.h"
 #include <functional>
 #include <nlohmann/json.hpp>
-using json = nlohmann::json;
 namespace infini {
+using json = nlohmann::json;
 
 class RuntimeObj; // Forward declaration for Kernel::compute
 
@@ -29,7 +30,6 @@ class Kernel {
   public:
     Kernel() {}
     virtual ~Kernel() {}
-
     /**
      * @param op The operator to be executed.
      * @param record The parameters for kernel execution. If extra parameters
@@ -102,11 +102,9 @@ class KernelRegistry {
     }
     Kernel *getKernel(const KernelAttrs &kernelAttrs) const {
         auto it = kernels.find(kernelAttrs);
-        IT_ASSERT(it != kernels.end(),
-                  "Kernel not found for key {" +
-                      to_string(enum_to_underlying(std::get<0>(kernelAttrs))) +
-                      ", " + std::to_string(std::get<1>(kernelAttrs)) + ", " +
-                      std::get<2>(kernelAttrs).toString() + "}");
+        IT_ASSERT(it != kernels.end(), "Kernel not found for key {" +
+                                           get_kernel_attrs_str(kernelAttrs) +
+                                           "}");
         return std::get<0>(it->second);
     }
     const KernelRecord &getKernelItem(const KernelAttrs &kernelAttrs) const {
@@ -131,15 +129,16 @@ class CpuKernelWithoutConfig : public Kernel {
 
 } // namespace infini
 
-#define _REGISTER_KERNEL_1(device, opType, dataType, kernel, name, cnt)        \
+#define _REGISTER_KERNEL_1(device, opType, kernel, name, cnt)                  \
     namespace infini {                                                         \
     static const bool _CAT(_register_kernel_, cnt) =                           \
-        KernelRegistry::getInstance().registerKernel(                          \
-            KernelAttrs{device, opType, dataType}, new kernel(), name);        \
+        KernelRegistry::getInstance().registerKernel(KernelAttrs{device,       \
+                                                                 opType},      \
+                                                     new kernel(), name);      \
     }
 
-#define REGISTER_KERNEL(device, opType, dataType, kernel, name)                \
-    _REGISTER_KERNEL_1(device, opType, dataType, kernel, name, __COUNTER__)
+#define REGISTER_KERNEL(device, opType, kernel, name)                          \
+    _REGISTER_KERNEL_1(device, opType, kernel, name, __COUNTER__)
 
 #define _REGISTER_CONSTRUCTOR_1(type, constructor, cnt)                        \
     namespace infini {                                                         \

include/core/lazy_allocator.h

@@ -20,14 +20,32 @@ class LazyAllocator {
 
     Runtime runtime;
 
-    size_t used;
+    size_t used = 0;
 
-    size_t peak;
+    size_t peak = 0;
+
+    size_t weightPeak = 0;
+
+    size_t heapPeak = 0;
 
     size_t alignment;
 
+    bool hasMemPool = false;
+
+    size_t memPoolSize = 0;
+
     // pointer to the memory actually allocated
-    void *ptr;
+    void *ptr = nullptr;
+
+    // pointer to the weight memory space
+    void *weightPtr = nullptr;
+
+    // memory pool ptr
+    void *memPoolPtr = nullptr;
+
+    // // a cache designed for a batch size that has already occurred
+    // std::unordered_map<size_t, std::unordered_map<TensorObj *, size_t>>
+    // batchsizeToTensorOffset;
 
     struct freeBlockInfo {
         size_t addr;
@@ -57,12 +75,24 @@ class LazyAllocator {
 
     virtual ~LazyAllocator();
 
+    void init();
+
+    void setMemPool(size_t memPoolSize);
+
+    bool getMemPoolStatus();
+
     // function: simulate memory allocation
     // arguments：
     //     size: size of memory block to be allocated
     // return: head address offset of the allocated memory block
     size_t alloc(size_t size);
 
+    size_t allocWeight(size_t size);
+
+    size_t heapAlloc(size_t size);
+
+    void freeHeap();
+
     // function: simulate memory free
     // arguments:
     //     addr: head address offset of memory block to be free
@@ -73,6 +103,14 @@ class LazyAllocator {
     // return: pointer to the head address of the allocated memory
     void *getPtr();
 
+    // void addCache(size_t batchsize, std::unordered_map<TensorObj *, size_t>);
+
+    // std::unordered_map<TensorObj *, size_t> getCache(size_t batchsize);
+
+    void *getWeightPtr();
+
+    void *getHeapPtr();
+
     void info();
 
   private:

include/core/op_type.h

@@ -21,10 +21,11 @@ struct OpType {
         Add,                // Binary
         And,                // Binary
         ArgMax,             //
-        Asin,               // Binary
-        Asinh,              // Binary
-        Atan,               // Binary
-        Atanh,              // Binary
+        Asin,               // Unary
+        Asinh,              // Unary
+        Atan,               // Unary
+        Atanh,              // Unary
+        AttentionKVCache,   // Fusion
         AveragePool,        // Pool
         BatchNormalization, //
         Bernoulli,          //
@@ -73,6 +74,7 @@ struct OpType {
         GatherElements,
         GatherND,
         Gemm,
+        Gelu,              // Unary
         GlobalAveragePool, // GlobalPool
         GlobalLpPool,      // GlobalPool
         GlobalMaxPool,     // GlobalPool
@@ -149,11 +151,14 @@ struct OpType {
         ReduceSum,       // Reduce
         ReduceSumSquare, // Reduce
         Relu,            // Unary
+        Silu,            // Unary
         Reshape,
         Resize,
         ReverseSequence,
         RoiAlign,
-        Round, // Unary
+        RoPE,    // Fusion
+        Round,   // Unary
+        RMSNorm, // Fusion
         STFT,
         Scan,
         Scatter,
@@ -221,6 +226,17 @@ struct OpType {
         FloorMod,
         Square,
         SquaredDifference,
+
+        // Communication Ops
+        AllReduceSum,
+        AllReduceProd,
+        AllReduceMin,
+        AllReduceMax,
+        AllReduceAvg,
+        AllGather,
+        Broadcast,
+        Send,
+        Recv,
     } type;
 
     constexpr OpType(decltype(type) t) : type(t) {}

include/core/operator.h

@@ -4,7 +4,7 @@
 #include "core/tensor.h"
 
 namespace infini {
-using KernelAttrs = std::tuple<Device, OpType::underlying_t, DataType>;
+using KernelAttrs = std::tuple<Device, OpType::underlying_t>;
 
 struct OpPerfKey {
     HashType hash;
@@ -55,8 +55,7 @@ class OperatorObj : public Object {
 
   public:
     OperatorObj(OpType opType, TensorVec inputs, TensorVec outputs);
-    virtual optional<vector<Shape>>
-    inferShape(const TensorVec &inputs) const = 0;
+    virtual optional<vector<Shape>> inferShape(const TensorVec &inputs) = 0;
     virtual vector<DataType> inferDataType(const TensorVec &inputs) const;
     /**
      * @brief Constructs outputs (if requried) and check whether the operator is
@@ -91,6 +90,7 @@ class OperatorObj : public Object {
     OpType getOpType() const { return type; }
     // HACK: set correct data type
     DataType getDType() const { return getInputs(0)->getDType(); }
+    DataType getOutDType() const { return getOutput()->getDType(); }
     virtual int numInputs() const = 0;
     virtual int numOutputs() const = 0;
 
@@ -105,7 +105,7 @@ class OperatorObj : public Object {
                            const TensorVec &newOutputs) const = 0;
 
   protected:
-    optional<vector<Shape>> inferShape() const;
+    optional<vector<Shape>> inferShape();
     vector<DataType> inferDataType() const;
 
   private:

include/core/perf_engine.h

@@ -2,8 +2,8 @@
 #include "core/graph.h"
 #include "core/kernel.h"
 #include <nlohmann/json_fwd.hpp>
-using json = nlohmann::json;
 namespace infini {
+using json = nlohmann::json;
 
 class PerfEngine {
   public:

include/core/runtime.h

@@ -1,5 +1,6 @@
 #pragma once
 #include "core/common.h"
+#include "core/communicator.h"
 #include "core/op_type.h"
 #include "core/ref.h"
 #include <memory>
@@ -14,6 +15,7 @@ class GraphObj;
 class GraphHandlerObj;
 class RuntimeObj;
 class BlobObj;
+template <typename T> class WorkspaceObj;
 
 using TensorBase = Ref<TensorBaseObj>;
 using Tensor = Ref<TensorObj>;
@@ -22,6 +24,7 @@ using Graph = Ref<GraphObj>;
 using GraphHandler = Ref<GraphHandlerObj>;
 using Runtime = Ref<RuntimeObj>;
 using Blob = Ref<BlobObj>;
+template <typename T> using Workspace = Ref<WorkspaceObj<T>>;
 
 using TensorVec = vector<Tensor>;
 using OpVec = vector<Operator>;
@@ -29,15 +32,17 @@ using OpLists = list<Operator>;
 
 using VType = uint32_t;
 
-enum class Device { CPU = 1, CUDA, BANG, INTELCPU };
+enum class Device { CPU = 1, CUDA, BANG, INTELCPU, KUNLUN };
 /***************** Forward declaration end *****************/
 
 class RuntimeObj : public std::enable_shared_from_this<RuntimeObj> {
   protected:
     Device device;
+    int deviceId;
 
   public:
-    RuntimeObj(Device device) : device(device) {}
+    explicit RuntimeObj(Device device, int deviceId = 0)
+        : device(device), deviceId(deviceId) {}
     RuntimeObj(RuntimeObj &other) = delete;
     RuntimeObj &operator=(RuntimeObj const &) = delete;
     virtual ~RuntimeObj() {}
@@ -69,6 +74,7 @@ class RuntimeObj : public std::enable_shared_from_this<RuntimeObj> {
     }
     bool isCuda() const { return device == Device::CUDA; }
     bool isBang() const { return device == Device::BANG; }
+    bool isKUNLUN() const { return device == Device::KUNLUN; }
     void copyBlob(const TensorObj *dst, const TensorObj *src) const;
     // TODO: unify these copy APIs
     virtual void copyBlobFromCPU(void *dst, const void *src,
@@ -77,6 +83,12 @@ class RuntimeObj : public std::enable_shared_from_this<RuntimeObj> {
                                size_t bytes) const = 0;
     virtual string toString() const = 0;
 
+    int getDeviceId() const { return deviceId; }
+
+    virtual void initComm(const string &name, int worldSize, int rank) = 0;
+
+    virtual CommunicatorObj &getCommunicator() const = 0;
+
   protected:
     void printProfilingData(double totTime,
                             const std::map<OpType, double> &opTime,
@@ -97,6 +109,9 @@ class CpuRuntimeObj : public RuntimeObj {
     void copyBlobToCPU(void *dst, const void *src, size_t bytes) const override;
     void copyBlobInsideRuntime(void *dst, const void *src,
                                size_t bytes) const override;
+    void initComm(const string &, int, int) override { IT_TODO_HALT(); }
+
+    CommunicatorObj &getCommunicator() const override { IT_TODO_HALT(); }
 };
 
 class NativeCpuRuntimeObj : public CpuRuntimeObj {

include/core/tensor.h

@@ -1,13 +1,17 @@
 #pragma once
 #include "core/tensor_base.h"
+#include "core/tensor_type.h"
 #include "utils/data_convert.h"
 #include <cmath>
 #include <cstring>
+#include <fstream>
 
 #if USE_CUDA
 #include "cuda/cuda_runtime.h"
 #endif
-
+#if USE_BANG
+#include "bang/bang_runtime.h"
+#endif
 namespace infini {
 
 // TODO: how to deal with this
@@ -19,13 +23,7 @@ class TensorObj : public TensorBaseObj {
     size_t _size; // Cache of Π(shape).
     Fuid fuid;    // Cloned tensors share the same id. Tensors constructed from
                   // scratch have a new id.
-
-    void copyin(const void *ptr, size_t size) {
-        runtime->copyBlobFromCPU(getRawDataPtr<void *>(), ptr, size);
-    }
-    void copyout(void *ptr, size_t size) const {
-        runtime->copyBlobToCPU(ptr, getRawDataPtr<void *>(), size);
-    }
+    TensorType tensorType = TensorType::others;
 
   public:
     TensorObj(Shape shape, DataType dtype, Runtime runtime);
@@ -36,19 +34,62 @@ class TensorObj : public TensorBaseObj {
     size_t getBytes() const { return _size * dtype.getSize(); }
 
     Shape getDims() const { return shape; }
+    void setShape(Shape shape_);
     size_t getRank() const { return shape.size(); }
-    vector<size_t> getStride() const;
+    Shape getStride() const;
     size_t getOffset(const vector<int> &ds) const;
     void dataMalloc();
     UidBaseType getFuid() const { return fuid; }
+    bool isWeight() const { return tensorType == TensorType::weight; }
+    bool isInput() const { return tensorType == TensorType::input; }
+    bool isOutput() const { return tensorType == TensorType::output; }
+    bool isOthers() const { return tensorType == TensorType::others; }
+    void setWeight() { tensorType = TensorType::weight; }
+    void setInput() {
+        if (!this->isWeight()) {
+            tensorType = TensorType::input;
+        }
+    }
+    void setOutput() {
+        if (!this->isWeight()) {
+            tensorType = TensorType::output;
+        }
+    }
+    string tensorTypeToString() const {
+        switch (tensorType) {
+        case TensorType::weight:
+            return "weight";
+            break;
+        case TensorType::input:
+            return "input";
+            break;
+        case TensorType::output:
+            return "output";
+            break;
+        case TensorType::others:
+            return "others";
+            break;
+
+        default:
+            return "unknown tensor type";
+            break;
+        }
+    }
 
     void load(std::string file_path);
     void save(std::string file_path);
 
+    void copyin(const void *ptr, size_t size) {
+        runtime->copyBlobFromCPU(getRawDataPtr<void *>(), ptr, size);
+    }
+    void copyout(void *ptr, size_t size) const {
+        runtime->copyBlobToCPU(ptr, getRawDataPtr<void *>(), size);
+    }
+
     // Copy elements from `data`.
     template <typename T> void copyin(const vector<T> &data) {
         IT_ASSERT(DataType::get<T>() == dtype.cpuTypeInt());
-        IT_ASSERT(data.size() >= _size);
+        IT_ASSERT(data.size() == _size);
         copyin(data.data(), getBytes());
     }
     // Copy all the elements to a vector.
@@ -103,6 +144,7 @@ class TensorObj : public TensorBaseObj {
     }
 
     void printData() const;
+    void dumpData(std::ofstream &ofs) const;
     bool equalData(const Tensor &rhs, double relativeError = 1e-6) const;
 
     template <typename T> bool equalData(const vector<T> &dataVector) {
@@ -151,19 +193,27 @@ class TensorObj : public TensorBaseObj {
     }
 
     template <typename T>
-    bool equalDataImpl(const T *a, const T *b, size_t size) const {
+    bool equalDataImpl(const T *a, const T *b, size_t size,
+                       double relativeError = 1e-6) const {
         for (size_t i = 0; i < size; ++i) {
             if constexpr (std::is_integral_v<T>) {
                 if (a[i] != b[i])
                     return false;
             } else if constexpr (std::is_floating_point_v<T>) {
-                if (fabs(a[i] - b[i]) / std::max(fabs(a[i]), fabs(b[i])) >
-                    1e-6) {
+                if (std::min(fabs(a[i]), fabs(b[i])) == 0. &&
+                    fabs(a[i] - b[i]) > relativeError) {
+                    printf("Error on %lu: %f %f\n", i, a[i], b[i]);
+                    return false;
+                } else if (std::min(fabs(a[i]), fabs(b[i])) != 0. &&
+                           fabs(a[i] - b[i]) /
+                                   std::max(fabs(a[i]), fabs(b[i])) >
+                               relativeError) {
                     printf("Error on %lu: %f %f\n", i, a[i], b[i]);
                     return false;
                 }
-            } else
+            } else {
                 static_assert(!sizeof(T), "Unsupported data type");
+            }
         }
         return true;
     }
@@ -198,8 +248,8 @@ class TensorObj : public TensorBaseObj {
     //         // std::cerr << "Init beginned " << std::endl;
     // #pragma omp parallel for
     //         for (size_t i = 0; i < iEnd; ++i)
-    //             data[i] = fastrand(random_seed[omp_get_thread_num() * 16]) %
-    //             10000;
+    //             data[i] = fastrand(random_seed[omp_get_thread_num() *
+    //             16]) % 10000;
     //         // std::cerr << "Init finished" << std::endl;
     //         computed = ComputedFull;
     //         return true;
@@ -244,8 +294,8 @@ class TensorObj : public TensorBaseObj {
     //         auto nDim = dims.size();
     //         auto nBroadcastDim = ds.size() - nDim;
     //         for (size_t i = 0; i < nDim; ++i)
-    //             if (ds[nBroadcastDim + i] < 0 || ds[nBroadcastDim + i] >=
-    //             dims[i])
+    //             if (ds[nBroadcastDim + i] < 0 || ds[nBroadcastDim +
+    //             i] >= dims[i])
     //                 return (size_t)-1;
     //         size_t idx = 0;
     //         for (size_t i = 0; i < nDim; ++i)
@@ -304,12 +354,14 @@ class TensorObj : public TensorBaseObj {
     //         return (g_seed >> 16) & 0x7FFF;
     //     }
 
-    //     std::vector<std::vector<int>> const *getSplittingPoints() const {
+    //     std::vector<std::vector<int>> const *getSplittingPoints()
+    //     const {
     //         assert(!splittingPoints.empty());
     //         return &splittingPoints;
     //     }
 
-    //     bool setSplittingPoints(std::vector<std::vector<int>> value) {
+    //     bool setSplittingPoints(std::vector<std::vector<int>> value)
+    //     {
     //         assert(!value.empty());
     //         splittingPoints = value;
     //         return true;

include/core/tensor_base.h

@@ -44,6 +44,7 @@ class TensorBaseObj : public Object {
     }
 
     DataType getDType() const { return dtype; }
+    int getDTypeIndex() const { return dtype.getIndex(); }
     Runtime getRuntime() const { return runtime; }
 
     //     std::pair<Operator *, int> getOutputOfWithIndex();

include/core/tensor_type.h

@@ -0,0 +1,7 @@
+#pragma once
+
+namespace infini {
+
+enum class TensorType { weight, input, output, others };
+
+} // namespace infini

include/core/workspace.h

@@ -0,0 +1,42 @@
+#pragma once
+#include "core/runtime.h"
+
+namespace infini {
+
+template <class T> class WorkspaceObj {
+  private:
+    T workspace;           // workspace pointer
+    size_t workspaceSize;  // Size of workspace
+    size_t workspaceAlloc; // currently use workspace size
+
+  public:
+    WorkspaceObj(T workspace_, size_t workspaceSize_)
+        : workspace(workspace_), workspaceSize(workspaceSize_) {
+        workspaceAlloc = 0;
+    }
+    virtual ~WorkspaceObj() {
+        // Dealloc workspace in RuntimeObj
+        // Set workspace = nullptr here
+        workspace = nullptr;
+    }
+    size_t getWorkspaceSize() const { return workspaceSize; }
+
+    T getWorkspace(size_t size) {
+        // Get unused workspace
+        IT_ASSERT(size + workspaceAlloc <= workspaceSize);
+        auto ret = (T)(static_cast<uint8_t *>(workspace) + workspaceAlloc);
+        workspaceAlloc += size;
+        return ret;
+    }
+    T getWorkspace() {
+        // Override getWorkspace in order to dealloc in runtime
+        return workspace;
+    }
+    void resetWorkspace() {
+        // Reset workspaceAlloc every time end kernel
+        workspaceAlloc = 0;
+    }
+    size_t getWorkspaceAlloc() const { return workspaceAlloc; }
+};
+
+} // namespace infini

include/cuda/cuda_attention_kvcache.h

@@ -0,0 +1,17 @@
+#pragma once
+#include "core/common.h"
+#include <cstdio>
+
+struct AttentionKVCacheMetadata {
+    int dimSize[4];
+    int stride[4];
+};
+
+namespace infini {
+void attention_kvcache_kernel(float *input_k_cache, float *input_v_cache,
+                              float *input_q, float *input_k, float *input_v,
+                              int *position_id, float *output_matmul,
+                              const AttentionKVCacheMetadata &compMeta,
+                              float *output_O_temp, float *output_sum_temp);
+
+} // namespace infini

include/cuda/cuda_common.h

@@ -5,17 +5,13 @@
 #include <cuda_profiler_api.h>
 #include <cudnn.h>
 #include <curand.h>
+#include <memory>
 
-// TODO: replace with Exception (IT_ASSERT)
 #define checkCudaError(call)                                                   \
-    {                                                                          \
-        auto err = call;                                                       \
-        if (cudaSuccess != err) {                                              \
-            fprintf(stderr, "Cuda error in %s:%i : %s.\n", __FILE__, __LINE__, \
-                    cudaGetErrorString(err));                                  \
-            exit(EXIT_FAILURE);                                                \
-        }                                                                      \
-    }
+    if (auto err = call; err != cudaSuccess)                                   \
+    throw ::infini::Exception(std::string("[") + __FILE__ + ":" +              \
+                              std::to_string(__LINE__) + "] CUDA error (" +    \
+                              #call + "): " + cudaGetErrorString(err))
 
 #define checkCUresult(call)                                                    \
     {                                                                          \
@@ -39,14 +35,10 @@
     }
 
 #define checkCudnnError(call)                                                  \
-    {                                                                          \
-        auto err = call;                                                       \
-        if (CUDNN_STATUS_SUCCESS != err) {                                     \
-            fprintf(stderr, "cuDNN error in %s:%i : %s.\n", __FILE__,          \
-                    __LINE__, cudnnGetErrorString(err));                       \
-            exit(EXIT_FAILURE);                                                \
-        }                                                                      \
-    }
+    if (auto err = call; err != CUDNN_STATUS_SUCCESS)                          \
+    throw ::infini::Exception(std::string("[") + __FILE__ + ":" +              \
+                              std::to_string(__LINE__) + "] cuDNN error (" +   \
+                              #call + "): " + cudnnGetErrorString(err))
 
 #define checkCurandError(call)                                                 \
     {                                                                          \
@@ -120,4 +112,20 @@ inline const char *curandGetErrorString(curandStatus_t error) {
 
 using CudaPtr = void *;
 
+class CUDAStream {
+  public:
+    CUDAStream(const CUDAStream &) = delete;
+    CUDAStream(CUDAStream &&) = delete;
+    void operator=(const CUDAStream &) = delete;
+    void operator=(CUDAStream &&) = delete;
+    static cudaStream_t getCurrentStream() { return _stream; }
+    static void Init() { CUDAStream::_stream = 0; };
+    static void createStream() { checkCudaError(cudaStreamCreate(&_stream)); }
+    static void destroyStream() { checkCudaError(cudaStreamDestroy(_stream)); }
+
+  private:
+    CUDAStream(){};
+    static cudaStream_t _stream;
+};
+
 } // namespace infini

include/cuda/cuda_element_wise.h

@@ -1,8 +1,20 @@
 #pragma once
 
 namespace infini {
-void div_kernel(float *a, float *b, float *c, int a0, int a1, int a2, int a3,
-                int b0, int b1, int b2, int b3, int c0, int c1, int c2, int c3);
-void pow_kernel(float *a, float *b, float *c, int a0, int a1, int a2, int a3,
-                int b0, int b1, int b2, int b3, int c0, int c1, int c2, int c3);
+void div_kernel(int dtypeIndex, void *a, void *b, void *c, int a0, int a1,
+                int a2, int a3, int b0, int b1, int b2, int b3, int c0, int c1,
+                int c2, int c3);
+void add_kernel(int dtypeIndex, void *a, void *b, void *c, int a0, int a1,
+                int a2, int a3, int b0, int b1, int b2, int b3, int c0, int c1,
+                int c2, int c3);
+void pow_kernel(int dtypeIndex, void *a, void *b, void *c, int a0, int a1,
+                int a2, int a3, int b0, int b1, int b2, int b3, int c0, int c1,
+                int c2, int c3);
+void less_kernel(int dtypeIndex, void *a, void *b, void *c, int a0, int a1,
+                 int a2, int a3, int b0, int b1, int b2, int b3, int c0, int c1,
+                 int c2, int c3);
+
+void div_const_kernel(int dType, void *a, void *b, void *c, size_t n);
+
+void pow_const_kernel(int dType, void *a, void *b, void *c, size_t n);
 }; // namespace infini

include/cuda/cuda_expand.h

@@ -0,0 +1,12 @@
+#pragma once
+
+#include "operators/unary.h"
+#include "utils/small_array.h"
+namespace infini {
+void expandKernel(int dType, void *input, void *output, int nDims,
+                  int outputsize, SmallArray inputShape,
+                  SmallArray outputShape);
+
+void expandRowKernel(int dType, void *input, void *output, int n_rows,
+                     int row_len);
+}; // namespace infini

include/cuda/cuda_layernorm.h

@@ -0,0 +1,17 @@
+#pragma once
+#include "operators/unary.h"
+
+namespace infini {
+void LaynormKernel(const float *input, const float *scale, const float eps,
+                   int size, int scaleSize, const int dimsize, const int stride,
+                   float *output, const float *bias, int biasSize);
+void LaynormKernel(const float *input, const float *scale, const float eps,
+                   int size, int scaleSize, const int dimsize, const int stride,
+                   float *output);
+void LaynormKernel(const half *input, const half *scale, const half eps,
+                   int size, int scaleSize, const int dimsize, const int stride,
+                   half *output, const half *bias, int biasSize);
+void LaynormKernel(const half *input, const half *scale, const half eps,
+                   int size, int scaleSize, const int dimsize, const int stride,
+                   half *output);
+}; // namespace infini

include/cuda/cuda_pad_slice.h

@@ -10,10 +10,11 @@ typedef struct {
     int wholeNDim[MAX_DIM];  // dim size after padding or before slicing
     int partNDim[MAX_DIM];   // dim size before padding or after slicing
     int partStride[MAX_DIM]; // stride before padding or after slicing
+    int DType;
 } TransMetaData;
 
 namespace infini {
-void pad_slice_kernel(float *partData, float *wholeData,
+void pad_slice_kernel(void *partData, void *wholeData,
                       const TransMetaData &metadata, int nDims, int num,
                       bool isPad);
 } // namespace infini

include/cuda/cuda_rmsnorm.h

@@ -0,0 +1,10 @@
+#pragma once
+
+#include "operators/rms_norm.h"
+
+namespace infini {
+
+void rmsnorm_kernel(int dType, void *input, void *weight, void *output,
+                    int num_tokens, int hidden_size);
+
+}; // namespace infini

include/cuda/cuda_rope.h

@@ -0,0 +1,12 @@
+#pragma once
+
+#include "operators/rope.h"
+#include "utils/small_array.h"
+
+namespace infini {
+
+void rope_kernel(int dType, int *pos, void *input, void *output, int size,
+                 int dim_model, int dim_head, int hidden_stride,
+                 int pos_stride);
+
+}; // namespace infini

include/cuda/cuda_runtime.h

@@ -1,6 +1,9 @@
 #pragma once
 #include "core/runtime.h"
 #include "cuda/cuda_common.h"
+#ifdef INFINI_USE_NCCL
+#include "cuda/nccl_communicator.h"
+#endif
 
 namespace infini {
 
@@ -8,21 +11,34 @@ class CudaRuntimeObj : public RuntimeObj {
   private:
     cudnnHandle_t cudnn;
     cublasHandle_t cublas;
+    std::unique_ptr<CommunicatorObj> comm;
     CudaPtr workspace;
     size_t workspaceSize;
+    bool isCudaGraphCreated;
+    cudaGraph_t cudaGraph;
+    cudaGraphExec_t cudaGraphInstance;
 
   public:
-    CudaRuntimeObj() : RuntimeObj(Device::CUDA) {
+    explicit CudaRuntimeObj(int deviceId = 0)
+        : RuntimeObj(Device::CUDA, deviceId) {
 
+        checkCudaError(cudaSetDevice(deviceId));
         checkCudnnError(cudnnCreate(&cudnn));
         checkCublasError(cublasCreate(&cublas));
         // 10GB for Longformer
         // size_t longformerNum = 3lu * (1 << 30);
         workspaceSize = 7ll << 30; // 7 GB
         workspace = alloc(workspaceSize);
+        isCudaGraphCreated = false;
+        CUDAStream::Init();
     }
     virtual ~CudaRuntimeObj() {
         try {
+            if (isCudaGraphCreated) {
+                checkCudaError(cudaGraphExecDestroy(cudaGraphInstance));
+                checkCudaError(cudaGraphDestroy(cudaGraph));
+                CUDAStream::destroyStream();
+            }
             dealloc(workspace);
             checkCudnnError(cudnnDestroy(cudnn));
             checkCublasError(cublasDestroy(cublas));
@@ -69,6 +85,13 @@ class CudaRuntimeObj : public RuntimeObj {
 
     void runWithoutSync(const Graph &graph) const;
 
+    void runWithCudaGraph(const Graph &graph);
+
+    // init communicator
+    void initComm(const string &name, int worldSize, int rank) final;
+
+    CommunicatorObj &getCommunicator() const final { return *comm; }
+
   private:
     void tune(const Graph &graph, bool profiling) const;
 };

include/cuda/cuda_softmax.h

@@ -0,0 +1,8 @@
+#pragma once
+#include "utils/small_array.h"
+namespace infini {
+void softmax_kernel(int num_blocks, float *input, float *output, int size,
+                    int dimsize, int stride);
+void softmax_kernel(int num_blocks, half *input, half *output, int size,
+                    int dimsize, int stride);
+} // namespace infini

include/cuda/cuda_split_concat.h

@@ -3,13 +3,13 @@
 #include <cstdio>
 
 const int BATCH_SIZE = 32; // parallel tensor number.
-const int DIM_MAX_SIZE = 4;
+const int DIM_MAX_SIZE = 8;
 
 // Concat operator acts like element tensors composing to one big tensor,and
 // split operator acts like one big tensor being composed by element
 // tensors.
-struct ElementTensorMetadata {
-    float *data[BATCH_SIZE];
+template <typename T> struct ElementTensorMetadata {
+    T *data[BATCH_SIZE];
     int dimBgNo[BATCH_SIZE]; // the dimention begin no of the element tensor in
                              // the composed tensor.
     int dimSize[BATCH_SIZE]; // the dimention size of the element tensor.
@@ -20,16 +20,17 @@ struct ElementTensorMetadata {
                    data[i], dimBgNo[i], dimSize[i], nElements[i]);
     }
 };
-
-struct ComposedTensorMetadata {
+template <typename T> struct ComposedTensorMetadata {
     int dimSize[DIM_MAX_SIZE];
     int stride[DIM_MAX_SIZE];
-    float *data;
+    T *data;
 };
 
 namespace infini {
-void split_concat_kernel(const ElementTensorMetadata &eleMeta,
-                         const ComposedTensorMetadata &compMeta, int dim,
+void split_concat_kernel(const ElementTensorMetadata<float> &eleMeta,
+                         const ComposedTensorMetadata<float> &compMeta, int dim,
+                         int batchSize, int nDims, bool isSplit);
+void split_concat_kernel(const ElementTensorMetadata<half> &eleMeta,
+                         const ComposedTensorMetadata<half> &compMeta, int dim,
                          int batchSize, int nDims, bool isSplit);
-
 } // namespace infini

include/cuda/cuda_transpose.h

@@ -0,0 +1,11 @@
+#pragma once
+
+#include "operators/transpose.h"
+#include "utils/small_array.h"
+
+namespace infini {
+
+void transpose_kernel(int dType, void *input, void *output, int nDims, int size,
+                      SmallArray strides, SmallArray outputShape);
+
+}; // namespace infini

include/cuda/cuda_unary.h

@@ -3,34 +3,22 @@
 #include "operators/unary.h"
 
 namespace infini {
-// TODO(constroy): num should be size_t.
-void softmax_kernel(float *input, float *output, int num);
-void relu_kernel(float *input, float *output, int num);
-void sigmoid_kernel(float *input, float *output, int num);
-void tanh_kernel(float *input, float *output, int num);
-void abs_kernel(float *input, float *output, int num);
-void sqrt_kernel(float *input, float *output, int num);
+template <typename T> void softmax_kernel(T *input, T *output, size_t num);
+template <typename T> void relu_kernel(T *input, T *output, size_t num);
+template <typename T> void silu_kernel(T *input, T *output, size_t num);
+template <typename T> void sigmoid_kernel(T *input, T *output, size_t num);
+template <typename T> void tanh_kernel(T *input, T *output, size_t num);
+template <typename T> void abs_kernel(T *input, T *output, size_t num);
+template <typename T> void sqrt_kernel(T *input, T *output, size_t num);
+template <typename T> void neg_kernel(T *input, T *output, size_t num);
+template <typename T> void gelu_kernel(T *input, T *output, size_t num);
+template <typename T> void erf_kernel(T *input, T *output, size_t num);
+template <typename T> void hard_sigmoid_kernel(T *input, T *output, size_t num);
+template <typename T> void hard_swish_kernel(T *input, T *output, size_t num);
 
-void unary_kernel(const Operator &_op) {
-    auto op = as<UnaryObj>(_op);
-    float *const inputData = (op->getInputs(0)->getRawDataPtr<float *>());
-    float *const outputData = (op->getOutput()->getRawDataPtr<float *>());
+template <typename INPUT, typename OUTPUT>
+void cast_kernel(INPUT *input, OUTPUT *output, size_t num);
 
-    size_t num = op->getOutput()->size();
-    if (op->getOpType() == OpType::Softmax)
-        softmax_kernel(inputData, outputData, num);
-    else if (op->getOpType() == OpType::Relu)
-        relu_kernel(inputData, outputData, num);
-    else if (op->getOpType() == OpType::Sigmoid)
-        sigmoid_kernel(inputData, outputData, num);
-    else if (op->getOpType() == OpType::Tanh)
-        tanh_kernel(inputData, outputData, num);
-    else if (op->getOpType() == OpType::Abs)
-        abs_kernel(inputData, outputData, num);
-    else if (op->getOpType() == OpType::Sqrt)
-        sqrt_kernel(inputData, outputData, num);
-    else
-        IT_TODO_HALT();
-}
+void unary_kernel(const Operator &_op);
 
 }; // namespace infini

include/cuda/cuda_utility.h

@@ -1,11 +1,29 @@
+#pragma once
 #include "core/tensor.h"
+#include "cuda/cuda_common.h"
 
 namespace infini {
 
 void cudaPrintFloat(float *x, int len);
 
-void cudaPrintTensor(const Tensor &tensor) {
-    cudaPrintFloat(tensor->getRawDataPtr<float *>(), tensor->size());
-}
+void cudaPrintTensor(const Tensor &tensor);
 
+cudnnDataType_t cudnnDataTypeConvert(DataType dataType);
+cudaDataType cublasDataTypeConvert(DataType);
+
+template <int index> struct DT_CUDA {};
+template <> struct DT_CUDA<0> { using t = bool; };
+template <> struct DT_CUDA<1> { using t = float; };
+template <> struct DT_CUDA<2> { using t = unsigned char; };
+template <> struct DT_CUDA<3> { using t = char; };
+template <> struct DT_CUDA<4> { using t = unsigned short; };
+template <> struct DT_CUDA<5> { using t = short; };
+template <> struct DT_CUDA<6> { using t = int; };
+template <> struct DT_CUDA<7> { using t = long long; };
+template <> struct DT_CUDA<9> { using t = bool; };
+template <> struct DT_CUDA<10> { using t = half; };
+template <> struct DT_CUDA<11> { using t = double; };
+template <> struct DT_CUDA<12> { using t = unsigned int; };
+template <> struct DT_CUDA<13> { using t = unsigned long long; };
+template <> struct DT_CUDA<16> { using t = nv_bfloat16; };
 } // namespace infini

include/cuda/cuda_where.h

@@ -0,0 +1,17 @@
+#pragma once
+#include "operators/unary.h"
+#include "utils/small_array.h"
+
+namespace infini {
+
+void whereKernel(const float *inputX, const float *inputY,
+                 const uint8_t *condition, float *output, int nDims,
+                 int outputsize, SmallArray inputXShape, SmallArray inputYShape,
+                 SmallArray conditionShape, SmallArray outputShape, int xSize,
+                 int ySize, int cSize);
+void whereKernel(const half *inputX, const half *inputY,
+                 const uint8_t *condition, half *output, int nDims,
+                 int outputsize, SmallArray inputXShape, SmallArray inputYShape,
+                 SmallArray conditionShape, SmallArray outputShape, int xSize,
+                 int ySize, int cSize);
+}; // namespace infini

include/cuda/gather.h

@@ -1,17 +1,61 @@
 #pragma once
-
-typedef struct {
-    int *indexValue;
-    int axis;
-    int inNDim;
-    int outNDim;
-    int idxNDim;
-    int outDim[4];
-    int idxDim[4];
-    int idxStride[4];
-    int inStride[4];
-} GatherMetaData;
+#include "core/data_type.h"
+#include "core/operator.h"
+#include "operators/gather.h"
 
 namespace infini {
-void gather_kernel(float *in, float *out, GatherMetaData metaData, int num);
+struct GatherMetaData {
+    // Pointer to indices
+    void *indexValue;
+    // Type of index values
+    DataType indexType;
+    // Type of input and output data
+    DataType dataType;
+    // Axis of the gather operation
+    int axis;
+    // Rank of input
+    int inNDim;
+    // Rank of output
+    int outNDim;
+    // Rank of indices
+    int idxNDim;
+    // Shape of output
+    int outDim[4];
+    // Shape of indices
+    int idxDim[4];
+    // Strides of indices
+    int idxStride[4];
+    // Strides of input
+    int inStride[4];
+};
+
+inline void initGatherMetaData(GatherMetaData &metaData,
+                               const Ref<OperatorObj> &_op) {
+    memset(&metaData, 0, sizeof(metaData));
+    auto op = as<GatherBaseObj>(_op);
+    Ref<TensorObj> in = op->getInputs(0);
+    Ref<TensorObj> index = op->getInputs(1);
+    Ref<TensorObj> out = op->getOutput();
+    metaData.indexValue = index->getRawDataPtr<void *>();
+    metaData.indexType = index->getDType();
+    metaData.dataType = in->getDType();
+    metaData.axis = op->getAxis();
+    metaData.inNDim = in->getRank();
+    metaData.outNDim = out->getRank();
+    metaData.idxNDim = index->getRank();
+    for (int i = 0; i < metaData.outNDim; ++i)
+        metaData.outDim[i] = out->getDims()[i];
+    for (int i = 0; i < metaData.idxNDim; ++i) {
+        metaData.idxDim[i] = index->getDims()[i];
+        metaData.idxStride[i] = index->getStride()[i];
+    }
+    for (int i = 0; i < metaData.inNDim; ++i) {
+        metaData.inStride[i] = in->getStride()[i];
+    }
 }
+template <typename T>
+void gather_kernel(T *in, T *out, GatherMetaData metaData, size_t num);
+
+void gather_elements_kernel(void *in, void *out, GatherMetaData metaData,
+                            size_t num);
+} // namespace infini

include/cuda/nccl_communicator.h

@@ -0,0 +1,70 @@
+#pragma once
+#include "core/communicator.h"
+#include <chrono>
+#include <cstdlib>
+#include <filesystem>
+#include <fstream>
+#include <nccl.h>
+#include <thread>
+
+#define checkNcclError(call)                                                   \
+    {                                                                          \
+        auto err = call;                                                       \
+        if (ncclSuccess != err) {                                              \
+            fprintf(stderr, "NCCL error in %s:%i : %s.\n", __FILE__, __LINE__, \
+                    ncclGetErrorString(err));                                  \
+            exit(EXIT_FAILURE);                                                \
+        }                                                                      \
+    }
+
+namespace infini {
+
+class NcclCommunicatorObj final : public CommunicatorObj {
+  private:
+    ncclComm_t comm;
+
+  public:
+    NcclCommunicatorObj(const string &name, int worldSize, int rank)
+        : CommunicatorObj(worldSize, rank) {
+        const std::string filePath("./" + name + "_nccl_id.bin");
+        ncclUniqueId commId;
+        if (rank == 0) {
+            checkNcclError(ncclGetUniqueId(&commId));
+            std::ofstream ofs(filePath, std::ios::binary);
+            ofs.write((char *)&commId, sizeof(ncclUniqueId));
+
+        } else {
+            auto begin = std::chrono::steady_clock::now();
+            while (!std::filesystem::exists(filePath)) {
+                auto now = std::chrono::steady_clock::now();
+                _IT_ASSERT_2(now < begin + std::chrono::seconds(10),
+                             "time limit (10s) exceeded.");
+                std::this_thread::sleep_for(std::chrono::milliseconds(100));
+            }
+            std::ifstream ifs(filePath, std::ios::binary);
+            ifs.read((char *)&commId, sizeof(ncclUniqueId));
+        }
+        checkNcclError(ncclCommInitRank(&comm, worldSize, commId, rank));
+        if (rank == 0) {
+            std::filesystem::remove(filePath);
+        }
+    }
+
+    // Get the actual ncclComm_t
+    ncclComm_t getNcclComm() { return comm; }
+
+    void finalize() { checkNcclError(ncclCommFinalize(comm)); }
+
+    ~NcclCommunicatorObj() final {
+        finalize();
+        checkNcclError(ncclCommDestroy(comm));
+    }
+
+    virtual string toString() const final {
+        std::ostringstream oss;
+        oss << "NCCL communicator";
+        return oss.str();
+    }
+};
+
+} // namespace infini

include/cuda/softmax.h

@@ -1,6 +0,0 @@
-#pragma once
-
-namespace infini {
-void softmax_kernel(int max_threadblock_size, int batch_size, float *x,
-                    float *y, int dim, int stride);
-}

include/kunlun/kunlun_act_type.h

@@ -0,0 +1,23 @@
+#include "core/op_type.h"
+#include "kunlun/kunlun_common.h"
+
+namespace infini {
+using KunlunActType = xdnn::Activation_t;
+KunlunActType parseActType(ActType act) {
+    switch (act) {
+    case ActType::None:
+        return KunlunActType::LINEAR;
+    case ActType::Tanh:
+        return KunlunActType::TANH;
+    case ActType::Sigmoid:
+        return KunlunActType::SIGMOID;
+    case ActType::Relu:
+        return KunlunActType::RELU6;
+    default:
+        fprintf(stderr, "Activation Type not support yet!\n");
+        break;
+    }
+    return KunlunActType::LINEAR;
+}
+
+}; // namespace infini

include/kunlun/kunlun_common.h

@@ -0,0 +1,22 @@
+#pragma once
+#include "core/common.h"
+#include "xpu/runtime_ex.h"
+#include "xpu/xdnn.h"
+
+namespace xdnn = baidu::xpu::api;
+
+#define checkKUNLUNError(call)                                                 \
+    {                                                                          \
+        auto err = call;                                                       \
+        if (XPU_SUCCESS != err) {                                              \
+            fprintf(stderr, "KUNLUN error in %s:%i : %s.\n", __FILE__,         \
+                    __LINE__, xpu_strerror(err));                              \
+            exit(EXIT_FAILURE);                                                \
+        }                                                                      \
+    }
+
+namespace infini {
+
+using KUNLUNPtr = void *;
+
+} // namespace infini

include/kunlun/kunlun_kernel_without_config.h

@@ -0,0 +1,24 @@
+#pragma once
+#include "core/kernel.h"
+#include "kunlun/kunlun_runtime.h"
+
+namespace infini {
+
+class KUNLUNKernelWithoutConfig : public Kernel {
+  public:
+    virtual void compute(const Operator &op, const PerfRecord &record,
+                         const RuntimeObj *context) const {
+        compute(op, context);
+    }
+    virtual void compute(const Operator &op,
+                         const RuntimeObj *context) const = 0;
+    // Premise: op is idempotent since it is called multiple times.
+    virtual PerfRecord tune(const Operator &op,
+                            const RuntimeObj *_context) const {
+        auto context = dynamic_cast<const KUNLUNRuntimeObj *>(_context);
+        return make_ref<PerfRecordObj>(timeit([&]() { compute(op, _context); },
+                                              [&]() { context->sync(); }));
+    }
+};
+
+} // namespace infini

include/kunlun/kunlun_runtime.h

@@ -0,0 +1,81 @@
+#pragma once
+#include "core/runtime.h"
+#include "core/workspace.h"
+#include "kunlun/kunlun_common.h"
+#ifdef INFINI_USE_XCCL
+#include "kunlun/xccl_communicator.h"
+#endif
+namespace infini {
+
+class KUNLUNRuntimeObj : public RuntimeObj {
+  private:
+    xdnn::Context *ctx;
+    std::unique_ptr<CommunicatorObj> comm;
+    // KUNLUNPtr workspace;
+    // size_t workspaceSize;
+    Workspace<KUNLUNPtr> workspace;
+
+  public:
+    KUNLUNRuntimeObj(int deviceId = 0) : RuntimeObj(Device::KUNLUN) {
+        xpu_set_device(deviceId);
+        ctx = xdnn::create_context();
+        // 10GB for Longformer
+        // size_t longformerNum = 3lu * (1 << 30);
+        size_t workspaceSize = 2llu << 30; // 2 GB
+        KUNLUNPtr wkspacePtr = alloc(workspaceSize);
+        workspace =
+            make_ref<WorkspaceObj<KUNLUNPtr>>(wkspacePtr, workspaceSize);
+    }
+    virtual ~KUNLUNRuntimeObj() {
+        KUNLUNPtr wkspacePtr = workspace->getWorkspace();
+        dealloc(wkspacePtr);
+        xdnn::destroy_context(ctx);
+    }
+    string toString() const override;
+
+    void run(const Graph &graph, bool tune = false,
+             bool profiling = false) const;
+    // double runEvaluation(const Graph &graph, int nWarmups,
+    //                      int nEvaluations) const;
+    void sync() const;
+
+    KUNLUNPtr alloc(size_t size) override {
+        void *ptr;
+        checkKUNLUNError(
+            xpu_malloc((void **)&ptr, size, XPUMemoryKind::XPU_MEM_HBM));
+        return ptr;
+    }
+    void dealloc(void *ptr) override { xpu_free(ptr); }
+
+    xdnn::Context *KUNLUNHandle() const { return ctx; }
+    // Get $size workspace by bytes
+    KUNLUNPtr getWorkspace(size_t size) const {
+        auto ret = workspace->getWorkspace(size);
+        return ret;
+    }
+    Workspace<KUNLUNPtr> getWorkspaceObj() const { return workspace; }
+
+    void copyBlobFromCPU(void *dst, const void *src,
+                         size_t bytes) const override {
+        xpu_memcpy(dst, const_cast<void *>(src), bytes,
+                   XPUMemcpyKind::XPU_HOST_TO_DEVICE);
+    }
+    void copyBlobToCPU(void *dst, const void *src,
+                       size_t bytes) const override {
+        xpu_memcpy(dst, const_cast<void *>(src), bytes,
+                   XPUMemcpyKind::XPU_DEVICE_TO_HOST);
+    }
+    void copyBlobInsideRuntime(void *dst, const void *src,
+                               size_t bytes) const override {
+        xpu_memcpy(dst, const_cast<void *>(src), bytes,
+                   XPUMemcpyKind::XPU_DEVICE_TO_DEVICE);
+    }
+    void initComm(const string &name, int worldSize, int rank) final;
+
+    CommunicatorObj &getCommunicator() const final { return *comm; }
+
+  private:
+    void runWithoutSync(const Graph &graph, bool tune, bool profiling) const;
+};
+
+} // namespace infini

include/kunlun/operator_timer.h

@@ -0,0 +1,10 @@
+#pragma once
+namespace infini {
+namespace opTimer {
+double getPerfConvXdnn(int n, int c, int h, int w, int f, int r, int s,
+                       int padh, int padw, int strideh, int stridew,
+                       int dilationh, int dilationw, int group,
+                       const char *name);
+double getPerfMatmulXdnn(int b, int m, int n, int k, const char *name);
+} // namespace opTimer
+} // namespace infini

include/kunlun/xccl_communicator.h

@@ -0,0 +1,60 @@
+#pragma once
+#include "core/communicator.h"
+#include "xpu/bkcl.h"
+#include <chrono>
+#include <filesystem>
+#include <fstream>
+#include <thread>
+
+#define checkXcclError(call)                                                   \
+    {                                                                          \
+        auto err = call;                                                       \
+        if (BKCL_SUCCESS != err) {                                             \
+            fprintf(stderr, "XCCL error in %s:%i.\n", __FILE__, __LINE__);     \
+            exit(EXIT_FAILURE);                                                \
+        }                                                                      \
+    }
+
+namespace infini {
+
+class XcclCommunicatorObj final : public CommunicatorObj {
+  private:
+    BKCLContext_t comm;
+
+  public:
+    XcclCommunicatorObj(const string &name, int worldSize, int rank)
+        : CommunicatorObj(worldSize, rank) {
+        const std::string filePath("./" + name + "_xccl_id.bin");
+        BKCLUniqueId commId;
+        if (rank == 0) {
+            checkXcclError(bkcl_get_unique_id(&commId));
+            std::ofstream ofs(filePath, std::ios::binary);
+            ofs.write((char *)&commId, sizeof(BKCLUniqueId));
+        } else {
+            auto begin = std::chrono::steady_clock::now();
+            while (!std::filesystem::exists(filePath)) {
+                auto now = std::chrono::steady_clock::now();
+                _IT_ASSERT_2(now < begin + std::chrono::seconds(100),
+                             "time limit (100s) exceeded.");
+                std::this_thread::sleep_for(std::chrono::milliseconds(100));
+            }
+            std::ifstream ifs(filePath, std::ios::binary);
+            ifs.read((char *)&commId, sizeof(BKCLUniqueId));
+        }
+        checkXcclError(bkcl_init_rank(&comm, rank, worldSize, &commId));
+        if (rank == 0) {
+            std::filesystem::remove(filePath);
+        }
+    }
+
+    BKCLContext_t getXcclComm() { return comm; }
+
+    ~XcclCommunicatorObj() final { checkXcclError(bkcl_destroy_context(comm)); }
+    virtual string toString() const final {
+        std::ostringstream oss;
+        oss << "XCCL communicator";
+        return oss.str();
+    }
+};
+
+} // namespace infini

include/nnet/test.h

@@ -24,7 +24,7 @@
 // clang-format on
 
 namespace nnet {
-int matchExprResult(Derivator &derivator, string fn);
-bool checkExprLogSame(string fnPrefix, int start, int end);
+int matchExprResult(Derivator &derivator, string pathRelativeToProjectHome);
+bool checkExprLogSame(string pathRelativeToProjectHome, int start, int end);
 bool checkExprsEquvivalence(VecExpr exprs);
 } // namespace nnet

include/operators/G2BMM.h

@@ -35,7 +35,7 @@ class G2BMMObj : public OperatorObj {
     OP_CLONE(G2BMMObj);
 
     std::string toString() const override;
-    optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
+    optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
 
     int numInputs() const override { return 2; }
     int numOutputs() const override { return 1; }

include/operators/GBMM.h

@@ -33,7 +33,7 @@ class GBMMObj : public OperatorObj {
     OP_CLONE(GBMMObj);
 
     std::string toString() const override;
-    optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
+    optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
 
     int numInputs() const override { return 2; }
     int numOutputs() const override { return 1; }

include/operators/activation_backward.h

@@ -7,7 +7,7 @@ class ActivationBackwardObj : public OperatorObj {
     ActivationBackwardObj(OpType type, GraphObj *graph, Tensor y, Tensor diff_y,
                           Tensor x, Tensor diff_x);
     OP_CLONE(ActivationBackwardObj);
-    optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
+    optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
 
     std::string toString() const override;
     int numInputs() const override { return 3; }

include/operators/all_gather.h

@@ -0,0 +1,44 @@
+#pragma once
+#include "core/operator.h"
+
+namespace infini {
+/**
+ * @brief The AllGather operation gathers N values from k ranks into
+ * an output of size k*N, and distributes that result to all ranks.
+ * The output is ordered by rank index.
+ *
+ * For more details:
+ * https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/usage/collectives.html#allgather
+ */
+class AllGatherObj : public OperatorObj {
+
+  public:
+    /**
+     * @brief Construct a new AllGather object
+     *
+     * @param graph The computation graph that this operator belongs to.
+     * @param input The input tensor from this rank.
+     * @param outputs A list of output tensors collected from all ranks.
+     * @param world_size Total number of ranks.
+     */
+    AllGatherObj(GraphObj *graph, Tensor input, std::optional<TensorVec>,
+                 int world_size);
+    OP_CLONE(AllGatherObj);
+
+    int numInputs() const override { return 1; }
+    int numOutputs() const override { return world_size; }
+    optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
+
+    std::string toString() const override;
+
+    int getWorldSize() const { return world_size; }
+
+  private:
+    vector<int> getWorkloadVector() const override;
+    vector<int> getOpAttrVector() const override;
+    vector<DataType> inferDataType(const TensorVec &inputs) const override;
+
+  protected:
+    int world_size;
+};
+} // namespace infini

include/operators/all_reduce.h

@@ -0,0 +1,75 @@
+#pragma once
+#include "core/operator.h"
+
+namespace infini {
+/**
+ * @brief The AllReduce operation is performing reductions on data (sum, min,
+ * max, avg, or div) across devices and writing the result in the
+ * receive buffers of every rank. For example, in an allreduce operation between
+ * k ranks and performing a sum, each rank will provide an array Vk of N values,
+ * and receive an identical arrays S of N values, where S[i] =
+ * V0[i]+V1[i]+…+Vk-1[i].
+ *
+ * For more details:
+ * https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/usage/collectives.html#allreduce
+ */
+class AllReduceBaseObj : public OperatorObj {
+
+  public:
+    /**
+     * @brief Construct a new AllReduce base object. Should be called by every
+     * child class constructor, but not directly.
+     *
+     * @param graph The computation graph that this operator belongs to.
+     * @param opType The operation type. This param is taken care of by child
+     * classes.
+     * @param input The input tensor from this rank.
+     * @param output The output tensor, same size as input.
+     */
+    AllReduceBaseObj(GraphObj *graph, OpType opType, Tensor input,
+                     Tensor output);
+    OP_CLONE(AllReduceBaseObj);
+
+    int numInputs() const override { return 1; }
+    int numOutputs() const override { return 1; }
+
+    optional<vector<Shape>> inferShape(const TensorVec &inputs) override {
+        return {{inputs[0]->getDims()}};
+    };
+
+    std::string toString() const override;
+
+  private:
+    vector<int> getWorkloadVector() const override;
+    vector<int> getOpAttrVector() const override;
+    vector<DataType> inferDataType(const TensorVec &inputs) const override {
+        return {inputs[0]->getDType()};
+    };
+};
+
+class AllReduceSumObj : public AllReduceBaseObj {
+  public:
+    AllReduceSumObj(GraphObj *graph, Tensor input, Tensor output);
+};
+
+class AllReduceProdObj : public AllReduceBaseObj {
+  public:
+    AllReduceProdObj(GraphObj *graph, Tensor input, Tensor output);
+};
+
+class AllReduceMinObj : public AllReduceBaseObj {
+  public:
+    AllReduceMinObj(GraphObj *graph, Tensor input, Tensor output);
+};
+
+class AllReduceMaxObj : public AllReduceBaseObj {
+  public:
+    AllReduceMaxObj(GraphObj *graph, Tensor input, Tensor output);
+};
+
+class AllReduceAvgObj : public AllReduceBaseObj {
+  public:
+    AllReduceAvgObj(GraphObj *graph, Tensor input, Tensor output);
+};
+
+} // namespace infini

include/operators/attention_kvcache.h

@@ -0,0 +1,43 @@
+#pragma once
+#include "core/operator.h"
+
+namespace infini {
+/**
+ * @brief Fused Attention with KVCache input operator. All the input and output
+ * tensors should have the same rank except for the position_id.
+ *
+ */
+class AttentionKVCacheObj : public OperatorObj {
+    int dim;
+
+  public:
+    /**
+     * @brief Construct a new AttentionKVCache object.
+     *
+     * @param graph The computation graph that this operator belongs to.
+     * @param input_k_cache The k_cache input tensor.
+     * @param input_v_cache The v_cache input tensor.
+     * @param input_q The query input tensor.
+     * @param input_k The key input tensor.
+     * @param input_v The value input tensor.
+     * @param position_id The positon id of the query,
+     * @param output_matmul The query output tensor.
+     */
+    AttentionKVCacheObj(GraphObj *graph, Tensor input_k_cache,
+                        Tensor input_v_cache, Tensor input_q, Tensor input_k,
+                        Tensor input_v, Tensor position_id,
+                        Tensor output_matmul);
+    OP_CLONE(AttentionKVCacheObj);
+
+    optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
+
+    std::string toString() const override;
+    int numInputs() const override { return 6; }
+    int numOutputs() const override { return 1; }
+    int getDim() const { return dim; }
+
+  private:
+    vector<int> getWorkloadVector() const override;
+    vector<int> getOpAttrVector() const override;
+};
+} // namespace infini

include/operators/batch_norm.h

@@ -34,7 +34,7 @@ class BatchNormObj : public OperatorObj {
                  Tensor var, Tensor scale, Tensor bias, float momentum = 0.9,
                  float eps = 1e-5, bool trainingMode = false);
     OP_CLONE(BatchNormObj);
-    optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
+    optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
     std::string toString() const override;
 
     // output size will be 3 when training

include/operators/broadcast.h

@@ -0,0 +1,49 @@
+#pragma once
+#include "core/operator.h"
+
+namespace infini {
+/**
+ * @brief The Broadcast operation copies an N-element buffer on the root rank to
+ * all ranks.
+ *
+ * For more details:
+ * https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/usage/collectives.html#broadcast
+ */
+class BroadcastObj : public OperatorObj {
+  public:
+    /**
+     * @brief Construct a new Broadcast object.
+     *
+     * @param graph The computation graph that this operator belongs to.
+     * @param input The input tensor. Only root needs to initialize it with
+     * data.
+     * @param output The output tensor, same size as input.
+     * @param root The root rank who performs the broadcast.
+     */
+    BroadcastObj(GraphObj *graph, Tensor input, Tensor output, int root);
+    OP_CLONE(BroadcastObj);
+
+    int numInputs() const override { return 1; }
+    int numOutputs() const override { return 1; }
+
+    optional<vector<Shape>> inferShape(const TensorVec &inputs) override {
+        return {{inputs[0]->getDims()}};
+    };
+
+    std::string toString() const override;
+
+    int getRoot() const { return root; }
+
+  private:
+    vector<int> getWorkloadVector() const override;
+    vector<int> getOpAttrVector() const override;
+    vector<DataType> inferDataType(const TensorVec &inputs) const override {
+        return {inputs[0]->getDType()};
+    };
+
+  protected:
+    // The rank who broadcasts data among this communication group
+    int root;
+};
+
+} // namespace infini

include/operators/concat.h

@@ -22,7 +22,7 @@ class ConcatObj : public OperatorObj {
     ConcatObj(GraphObj *graph, TensorVec inputs, Tensor output, int dim);
     OP_CLONE(ConcatObj);
 
-    optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
+    optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
 
     std::string toString() const override;
     int numInputs() const override { return inputs.size(); }

include/operators/conv.h

@@ -142,7 +142,7 @@ class ConvObj : public ConvBaseObj {
             ActType act = ActType::None);
     OP_CLONE(ConvObj);
 
-    optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
+    optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
     int getNumGroups() const override { return c / getChannelPerGroup(); }
 
   private:
@@ -164,7 +164,7 @@ class ConvBackwardFilterObj : public ConvBaseObj {
                           int sh = 1, int sw = 1, int dh = 1, int dw = 1,
                           Tensor bias = nullptr, ActType act = ActType::None);
 
-    optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
+    optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
     ActType getAct() const { return act; }
     int getNumGroups() const override { return c / getChannelPerGroup(); }
 
@@ -191,7 +191,7 @@ class ConvTransposed2dObj : public ConvBaseObj {
                         Tensor bias = nullptr, ActType act = ActType::None);
     OP_CLONE(ConvTransposed2dObj);
 
-    optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
+    optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
     int getNumGroups() const override { return group; }
     std::pair<int, int> getOutputPadding() const { return {oph, opw}; }
 
@@ -218,7 +218,7 @@ class ConvTransposed2dNHWCObj : public ConvBaseObj {
                             Tensor bias = nullptr, ActType act = ActType::None);
     OP_CLONE(ConvTransposed2dNHWCObj);
 
-    optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
+    optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
     int getNumGroups() const override { return group; }
 
   private:

include/operators/det.h

@@ -7,7 +7,7 @@ class DetObj : public OperatorObj {
     enum Mode { NormalDet = 0, LogDet };
     DetObj(GraphObj *graph, Tensor input, Tensor output, Mode mode);
     OP_CLONE(DetObj);
-    optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
+    optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
 
     std::string toString() const override;
     int numInputs() const override { return 1; }

include/operators/dropout.h

@@ -37,7 +37,7 @@ class DropoutObj : public OperatorObj {
     DropoutObj(GraphObj *graph, Tensor data, Tensor output, Tensor mask,
                float ratio, bool training_mode);
     OP_CLONE(DropoutObj);
-    optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
+    optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
 
     std::string toString() const override;
     int numInputs() const override { return 1; }

include/operators/element_wise.h

@@ -21,7 +21,7 @@ class ElementWiseObj : public OperatorObj {
      */
     ElementWiseObj(OpType type, GraphObj *graph, Tensor input0, Tensor input1,
                    Tensor output);
-    optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
+    optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
 
     std::string toString() const override;
     int numInputs() const override { return 2; }
@@ -38,7 +38,7 @@ class MSELossObj : public OperatorObj {
     MSELossObj(GraphObj *graph, Tensor input0, Tensor input1,
                Reduction reduction, Tensor output);
     OP_CLONE(MSELossObj);
-    optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
+    optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
 
     Reduction getReduction() const { return reductionMode; }
     std::string toString() const override;

include/operators/expand.h

@@ -0,0 +1,36 @@
+#pragma once
+#include "core/operator.h"
+
+namespace infini {
+/**
+ *  @brief Broadcast the input tensor following the given shape and the
+ * broadcast rule.
+ *
+ */
+class ExpandObj : public OperatorObj {
+    Shape dims;
+
+  public:
+    /**
+     * @brief Construct a new Expand object.
+     * @param graph The computation graph that this operator belongs to.
+     * @param input The input tensor.
+     * @param output The output tensor.
+     * @param dims The shape you want to expand to, following the broadcast
+     * rule.
+     */
+    ExpandObj(GraphObj *graph, Tensor input, Tensor output, Shape dims);
+    OP_CLONE(ExpandObj);
+    optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
+
+    std::string toString() const override;
+    int numInputs() const override { return 1; }
+    int numOutputs() const override { return 1; }
+    Shape getShape() const { return dims; }
+
+  private:
+    vector<int> getWorkloadVector() const override;
+    vector<int> getOpAttrVector() const override;
+};
+
+} // namespace infini

include/operators/extend.h

@@ -23,7 +23,7 @@ class ExtendObj : public OperatorObj {
     ExtendObj(GraphObj *graph, Tensor input, Tensor output, int dim,
               int num = 1);
     OP_CLONE(ExtendObj);
-    optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
+    optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
 
     std::string toString() const override;
     int numInputs() const override { return 1; }

include/operators/gather.h

@@ -3,14 +3,28 @@
 #include "core/operator.h"
 
 namespace infini {
+
+class GatherBaseObj : public OperatorObj {
+  protected:
+    int axis;
+
+  public:
+    GatherBaseObj(OpType opType, TensorVec inputs, TensorVec outputs, int axis)
+        : OperatorObj(opType, inputs, outputs), axis(axis) {}
+
+    virtual ~GatherBaseObj() {}
+    int numInputs() const override { return 2; }
+    int numOutputs() const override { return 1; }
+
+    int getAxis() const { return axis; }
+};
+
 /**
  * @brief Gather and concatenate given positions on a certain dimension of the
  * input tensor using an index tensor.
  *
  */
-class GatherObj : public OperatorObj {
-    int axis;
-
+class GatherObj : public GatherBaseObj {
   public:
     /**
      * @brief Construct a new Gather object.
@@ -25,10 +39,7 @@ class GatherObj : public OperatorObj {
               int axis);
     OP_CLONE(GatherObj);
     std::string toString() const override;
-    int numInputs() const override { return 2; }
-    int numOutputs() const override { return 1; }
-    optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
-    int getAxis() const { return axis; }
+    optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
     vector<DataType> inferDataType(const TensorVec &inputs) const override;
 
   private:
@@ -36,4 +47,33 @@ class GatherObj : public OperatorObj {
     vector<int> getWorkloadVector() const override;
     vector<int> getOpAttrVector() const override;
 };
+
+/**
+ * @brief GatherElements takes two inputs data and indices of the
+ * same rank r >= 1 and an optional attribute axis that identifies
+ * an axis of data.
+ *
+ */
+class GatherElementsObj : public GatherBaseObj {
+  public:
+    /**
+     * @brief Construct a new GatherElements object.
+     *
+     * @param graph The computation graph that this operator belongs to.
+     * @param input The input tensor.
+     * @param indices The index tensor.
+     * @param output The output tensor. Same shape as indices.
+     * @param axis The axis to gather on.
+     */
+    GatherElementsObj(GraphObj *graph, Tensor input, Tensor indices,
+                      Tensor output, int axis);
+    OP_CLONE(GatherElementsObj);
+    std::string toString() const override;
+    optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
+    vector<DataType> inferDataType(const TensorVec &inputs) const override;
+
+  private:
+    vector<int> getWorkloadVector() const override;
+    vector<int> getOpAttrVector() const override;
+};
 } // namespace infini

include/operators/layer_norm.h

@@ -0,0 +1,30 @@
+#pragma once
+#include "core/operator.h"
+
+namespace infini {
+class LayerNormObj : public OperatorObj {
+    float eps;
+    int axis, stash_type;
+
+  public:
+    LayerNormObj(GraphObj *graph, Tensor input, Tensor scale, Tensor output,
+                 Tensor bias = nullptr, float eps = 1e-5, int axis = -1,
+                 int stash_type = 1);
+    OP_CLONE(LayerNormObj);
+    optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
+    std::string toString() const override;
+
+    Tensor getBias() const { return inputs.size() > 2 ? inputs[2] : nullptr; }
+    int numInputs() const override { return inputs.size(); }
+    int numOutputs() const override { return outputs.size(); }
+    float getEps() const { return eps; }
+    int getAxis() const { return axis; }
+    int getStashType() const { return stash_type; }
+
+  private:
+    vector<int> getWorkloadVector() const override;
+    vector<int> getOpAttrVector() const override;
+
+    vector<DataType> inferDataType(const TensorVec &inputs) const override;
+};
+} // namespace infini

include/operators/lrn.h

@@ -0,0 +1,29 @@
+#pragma once
+#include "core/operator.h"
+
+namespace infini {
+class LRNObj : public OperatorObj {
+
+  public:
+    LRNObj(GraphObj *graph, Tensor inputX, Tensor inputY, float alpha,
+           float beta, float bias, int size);
+    OP_CLONE(LRNObj);
+
+    optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
+
+    std::string toString() const override;
+    int numInputs() const override { return inputs.size(); }
+    int numOutputs() const override { return 1; }
+    auto getAlphaBetaBias() const {
+        return tuple(alpha_value, beta_value, bias_value);
+    }
+    auto getSize() const { return size_value; }
+
+  private:
+    float alpha_value, beta_value, bias_value;
+    int size_value;
+    vector<int> getWorkloadVector() const override;
+    vector<int> getOpAttrVector() const override;
+};
+
+} // namespace infini

include/operators/matmul.h

@@ -17,6 +17,9 @@ class MatmulObj : public OperatorObj {
     // Auxiliary attributes which are not a part of operator attributes.
     int b, m, n, k;
 
+    // Specifies the data precision for the matrix multiply.
+    std::string computeType = "default";
+
   public:
     /**
      * @brief Matmul operator with batch broadcast and tensor transpose
@@ -38,14 +41,15 @@ class MatmulObj : public OperatorObj {
      * @param transB If matrix B should be transposed when computing.
      * @param bias The bias tensor.
      * @param act The activation function.
+     * @param computeType Specifies the data precision for the matrix multiply.
      */
     MatmulObj(GraphObj *graph, Tensor A, Tensor B, Tensor C,
               bool transA = false, bool transB = false, Tensor bias = nullptr,
-              ActType act = ActType::None);
+              ActType act = ActType::None, std::string computeType = "default");
     OP_CLONE(MatmulObj);
 
     std::string toString() const override;
-    optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
+    optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
 
     int numInputs() const override { return inputs.size(); }
     int numOutputs() const override { return 1; }
@@ -60,6 +64,7 @@ class MatmulObj : public OperatorObj {
     int getN() const { return n; }
     int getK() const { return k; }
     auto getBMNK() const { return tuple{b, m, n, k}; }
+    std::string getComputeType() const { return computeType; }
 
   private:
     vector<int> getWorkloadVector() const override;