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InfiniTensor
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Dev for 202303ddl (#66) 

* add activation operatiopn relu, tanh, sigmoid on mlu

* commit for format

* add activation backward operation

* add test for activation_backward

* add test

* add convbpfilter

* fix

* add transpsoe code and test

* add trigon function operation on mlu: sin,cos,tan,asin,sinh,asinh

* add copy operation on mlu

* add ceil operation and floor operation

* add operation clip

* add operation cnnl div, test and test for divdemo bangc kernel

* add divnonan operation and test

* add erf operation

* add exp operation

* add operation fill

* add log operation

* add log1p operation

* add l2loss operation

* add maximum and minimum operation

* add mseloss operation

* add negTensor operation

* add power operation

* add reciprocal operation

* add sqrt and rsqrt operation

* add transform operation

* add addn operation

* add muln operation

* cherrry pick some operation

* add floordiv operation and floordivtrunc operation

* add floormod operation

* add cumsum operation

* add det operation

* add pad operation

* format

* add concat operation

* format

* add split operation

* fix concat and split operation

* add round operation

* add pooling operation

* add square operation

* add squaredDifference operation

* code format fix

* add flip operation

* code format fix

* add hardtanh operation

* add logic operation

* add addcdiv and addcmul operation

* add arange operation

* add bitcompute operation

* add net test

* fmt

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* style: rename

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* fix: 用 NativeCpuRuntime 替换 CpuRuntime

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* fix code

* fix code

* fix code by review suggestion

* remove operation which is not the onnx operation

* fix format

* clang format

* refactor: tensor 的 print 加一层模板的 dataToString

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* fix: onnx 导出

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* feat: 增加计算图优化接口

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* add clip operation

* feat: 支持导入 clip

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* test: 导入导出测试加入 ci

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* fix batch norm

* feat: 增加 Shape 算子

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* feat: 支持导入 unsqueeze

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* fix: 修正 clip 接口

feat: 支持导入 transpose
Signed-off-by: YdrMaster <ydrml@hotmail.com>

* add broadcast operation

* fix elementwise-broadcast

* fix elementwise broadcast

* add broadcast for gpu elementsie

* feat: pad 支持 axes 负数

feat: 不支持的 padding 导出为独立的 pad 算子

feat: 支持导入 onnxsim 过的 inception
Signed-off-by: YdrMaster <ydrml@hotmail.com>

* fix: 修正池化的测试

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* feat: 导出 pads，支持 inception 导入导出，已加入 ci

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* feat: 支持 densenet 导入导出，并加入 ci

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* feat: 导入 squeeze

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* fix softmax

* feat: 导出 clip 和 transpose

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* feat: 支持 Conv 的 bias

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* fix: bias of conv

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* fix: bias of conv

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* feat: 导入 split

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* feat: 导出 split

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* fix: conv

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* fix: conv group

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* fix: matmul 的 bias 没有放在输入里，修正

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* fix exmaple

* fix: 改正 reduce_mean 导出

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* refactor: 修改 slice 实现与 onnx 一致

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* style: 不导出两个 runtime 函数

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* doc: 中文使用指南

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* doc: 补全指南

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* fix: 修复导入数据的问题

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* fmt

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* feat: 添加 Dropout 基本结构，但不支持两个输出是不同的类型

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* feat: 重新导出优化接口

feat: dropout 导入
Signed-off-by: YdrMaster <ydrml@hotmail.com>

* build: BANG 选项加入 Makefile

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* fxi code, change of test/kernels/bang/test* is use NativeCpuRuntime.
chaneg of include/bang/bang_runtime is for the cntoolkit upgrade.

* feat: 导出 bang runtime

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* add USE_BANG=1

* fix matmul

* fix reshape

* fix

* fix activation

* fix transpose

* format

* format

* update Makefile

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* feat: 支持导入导出 ConvTranspose

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* add prelu on mlu

* fix: ConvTranspose

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* feat: 支持导入导出 PRelu

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* add convtrans on mlu

* fmt

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* docs: 更新 README_CN.md

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* fix code by review suggestions

* style

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* fix: Softmax 的 axis 可以用默认值？感觉是 onnx 不标准

Signed-off-by: YdrMaster <ydrml@hotmail.com>

* fix cuda & intelcpu bugs after merging

---------

Signed-off-by: YdrMaster <ydrml@hotmail.com>
Co-authored-by: wanghailu <wanghailu0717@163.com>
Co-authored-by: wanghailu <wanghailu@qiyuanlab.com>
Co-authored-by: whjthu <haojie0429@gmail.com>
This commit is contained in:
YdrMaster 2023-04-18 15:10:33 +08:00 committed by GitHub
parent a1974aabcd
commit 26f0d13c26
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161 changed files with 6913 additions and 614 deletions
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56
.github/workflows/build.yml vendored
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@ -2,6 +2,9 @@ name: Build and test cpu
on:
push:
branch: 'master'
paths-ignore:
- '**.md'
- 'LICENSE'
pull_request:
paths-ignore:
- '**.md'
@ -11,8 +14,11 @@ env:
protobuf-download: https://github.com/protocolbuffers/protobuf/releases/download/v21.12/protobuf-cpp-3.21.12.tar.gz
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
resnet-file: 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
jobs:
build:
@ -31,28 +37,28 @@ jobs:
- name: Install libdw
run: sudo apt-get update && sudo apt-get install libdw-dev
- name: Cache protobuf
id: cache-protobuf
uses: actions/cache@v3
with:
path: protobuf-${{ env.protobuf-version }}
key: protobuf-${{ env.protobuf-version }}
# - name: Cache protobuf
# id: cache-protobuf
# uses: actions/cache@v3
# with:
# path: protobuf-${{ env.protobuf-version }}
# key: protobuf-${{ env.protobuf-version }}
- name: Download and compile protobuf
if: steps.cache-protobuf.outputs.cache-hit != 'true'
run: |
wget ${{ env.protobuf-download }}
tar xf protobuf-cpp-${{ env.protobuf-version }}.tar.gz
cd protobuf-${{ env.protobuf-version }}
./autogen.sh
./configure CFLAGS="-fPIC" CXXFLAGS="-fPIC"
make -j8
# - name: Download and compile protobuf
# if: steps.cache-protobuf.outputs.cache-hit != 'true'
# run: |
# wget ${{ env.protobuf-download }}
# tar xf protobuf-cpp-${{ env.protobuf-version }}.tar.gz
# cd protobuf-${{ env.protobuf-version }}
# ./autogen.sh
# ./configure CFLAGS="-fPIC" CXXFLAGS="-fPIC"
# make -j8
- name: Install protobuf
run: |
cd protobuf-${{ env.protobuf-version }}
sudo make install
sudo ldconfig
# - name: Install protobuf
# run: |
# cd protobuf-${{ env.protobuf-version }}
# sudo make install
# sudo ldconfig
- name: Build
run: make
@ -65,8 +71,12 @@ jobs:
python -m pip install --upgrade pip
make install-python
- name: Download test model
run: wget ${{ env.resnet-download }}
- name: Download test models
run: |
wget ${{ env.resnet-download }}
wget ${{ env.inception-download }}
wget ${{ env.densenet-download }}
wget ${{ env.efficientnet-download }}
- name: Test onnx frontend
run: make test-onnx

3
.github/workflows/clang-format-check.yml vendored
View File

@ -2,6 +2,9 @@ name: clang-format Check
on:
push:
branch: 'master'
paths-ignore:
- '**.md'
- 'LICENSE'
pull_request:
paths-ignore:
- '**.md'

3
.gitmodules vendored
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@ -10,3 +10,6 @@
[submodule "3rd-party/backward-cpp"]
path = 3rd-party/backward-cpp
url = git@github.com:bombela/backward-cpp.git
[submodule "example"]
path = example
url = git@github.com:wanghailu0717/NNmodel.git

8
CMakeLists.txt
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@ -7,8 +7,8 @@ option(USE_CUDA "Support CUDA GPU" OFF)
option(USE_BANG "Support BANG MLU" 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" ON)
option(BUILD_TEST "Build tests" ON)
option(USE_PROTOBUF "Serialize and deserialize tensors" OFF)
option(BUILD_TEST "Build tests" OFF)
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)
@ -151,6 +151,7 @@ if(USE_INTELCPU)
set(DNNL_CONFIGURATION "cpu_gomp")
find_package(dnnl CONFIG REQUIRED)
if(dnnl_FOUND)
add_compile_definitions(USE_MKL=1)
include_directories(BEFORE ${dnnl_DIR}/../../../cpu_gomp/include/)
link_directories(${dnnl_DIR}/../../../cpu_gomp/lib)
target_link_libraries(InfiniTensor dnnl)
@ -179,6 +180,7 @@ if(USE_CUDA)
endif()
if(USE_BANG)
add_compile_definitions(USE_BANG=1)
include_directories(src/kernels/mlu/include)
################################################################################
# Neuware Evironment
@ -212,10 +214,8 @@ if(USE_BANG)
################################################################################
# BangC Kernels
################################################################################
add_subdirectory(src/kernels/mlu)
target_link_libraries(InfiniTensor ${CAMBRICON_CNNL} ${CAMBRICON_CNRT} ${CAMBRICON_CNDRV} stdc++)
target_link_libraries(InfiniTensor bangops)
endif()
# # Python bindings

13
Makefile
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@ -1,14 +1,17 @@
.PHONY : build clean install-python test-cpp test-onnx
TYPE ?= release
CUDA ?= off
CUDA ?= OFF
BANG ?= OFF
INTELCPU ?= off
BACKTRACE ?= ON
TEST ?= ON
CMAKE_OPT = -DCMAKE_BUILD_TYPE=$(TYPE)
ifeq ($(CUDA), ON)
CMAKE_OPT += -DUSE_CUDA=ON
endif
CMAKE_OPT += -DUSE_CUDA=$(CUDA)
CMAKE_OPT += -DUSE_BANG=$(BANG)
CMAKE_OPT += -DUSE_BACKTRACE=$(BACKTRACE)
CMAKE_OPT += -DBUILD_TEST=$(TEST)
ifeq ($(INTELCPU), ON)
CMAKE_OPT += -DUSE_INTELCPU=ON -DCMAKE_CXX_COMPILER=dpcpp

4
README.md
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@ -24,7 +24,9 @@ cmake -DUSE_INTELCPU=ON -DCMAKE_CXX_COMPILER=dpcpp .. && make -j 12
---
> Sets env: `CUDA=ON` to enable cuda.
> - Sets env: `TEST=OFF` to accelerate compiling.
> - Sets env: `CUDA=ON` to enable cuda.
> - Sets env: `BANG=ON` to enable bang.
### CMake Options

221
README_CN.md Normal file
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@ -0,0 +1,221 @@
# 使用指南
## 目录
- [编译](#编译)
- [使用](#使用)
- [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)
- [执行推理](#执行推理)
- [测试](#测试)
## 编译
推荐使用 Ubuntu-22.04，本文以此环境为例。
1. 使用 apt 安装依赖
> 如果不使用 Ubuntu-22.04，部分软件版本可能不够高。
```bash
sudo apt-get install make cmake build-essential python-is-python3 python-dev-is-python3 python3-pip libdw-dev
```
2. 更新 pip 并换清华源
```bash
python -m pip install -i https://pypi.tuna.tsinghua.edu.cn/simple --upgrade pip
pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
```
3. 编译并安装 python 库
> 第一次执行会同时安装 python 依赖库，比较慢
仅编译 CPU 部分：
```bash
make install-python
```
编译 GPU 部分：
```bash
make install-python CUDA=ON
```
## 使用
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`
- 安装 python 库：`make install-python`
- 测试 c++ 后端：`make test-cpp`
- 测试 python 前端：`make test-onnx`
并使用下列环境变量传递选项参数：
- `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
- `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
## python 前端应用指南
`make install-python` 会将项目的 python 前端以 `pyinfinitensor` 为名字安装到系统目录，可以直接 `import pyinfinitensor` 来使用。现阶段，项目的主要用法是从 onnx 导入模型进行优化，然后可以再导出优化后的模型到 onnx，也可以直接运行推理。
### 导入 onnx 模型
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)
```python
import onnx
from pyinfinitensor.onnx import OnnxStub
from pyinfinitensor import backend
stub = OnnxStub(onnx.load("model_file"), backend.cpu_runtime())
```
[`onnx.load`](https://onnx.ai/onnx/api/serialization.html#load-a-model) 是 onnx 提供的加载函数，将 onnx 文件读取为保存在内存中的 onnx 模型。
`OnnxStub` 是 onnx 模型在项目中的表示，通过构造这个对象，将 onnx 模型导入到项目中。其构造器的第一个参数是 onnx 模型文件；第二个参数是模型运行的后端运行时，可以是 `backend.cpu_runtime()`、`backend.cuda_runtime()` 或 `backend.bang_runtime()`。
构造出的 stub 对象可以用于操作项目中的模型和运行时。
### 优化
TODO
### 导出 onnx 模型
优化后的模型可以导出成 onnx 文件提供给其他运行时。
```python
with open("optimized.onnx", "wb") as f:
f.write(stub.to_onnx("optimized").SerializeToString())
```
`stub.to_onnx(<name>)` 将模型转换为 onnx 模型对象，`<name>` 将填写到 onnx 模型的 `name` 字段。序列化到文件的代码见[官方示例](https://onnx.ai/onnx/intro/python.html#model-serialization)。
要可视化检查导出的模型文件，可以利用 [onnx 提供的功能](https://onnx.ai/onnx/api/shape_inference.html#infer-shapes)将所有的张量的形状推理出来再导出：
```python
from onnx.shape_inference import infer_shapes
with open("optimized.onnx", "wb") as f:
f.write(infer_shapes(stub.to_onnx("optimized")).SerializeToString())
```
然后用 [Netron](https://netron.app/) 绘制计算图。
### 执行推理
也可以使用项目的运行时执行推理。
第一步是将数据传入计算图。`OnnxStub.inputs` 是一个 `Dict[str, Tensor]`，保存着模型的所有输入的名字和对象。可以用 [`items()`](https://docs.python.org/zh-cn/3/library/stdtypes.html#dict.items) 来遍历。
这个代码片段显示了如何打印出模型所有输入张量的名字、形状和对象指针：
```python
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：
```plaintext
data [1, 3, 224, 224] <backend.Tensor object at 0x7efeb828e3b0>
```
当然，地址是随机的。这个输出表明需要输入一个名为 “data”，形为 1×3×224×224 的数据。通常来说，这表示一张 224×224 的 rgb 图片。而这个模型是一个 1000 分类的图像分类模型。
为了方便，这里我们向模型传入一个随机的数据。
```python
import numpy
stub.init()
for name, tensor in stub.inputs.items():
print(name, tensor.shape(), tensor)
input = numpy.random.random(tensor.shape()).astype(numpy.float32)
tensor.copyin_float(input.flatten().tolist())
```
`stub.init()` 为所有张量分配空间。空间是预分配的，所以不支持动态 size 的模型。
`tensor.copyin_float(<data>)` 向张量传入数据。其参数必须是一个 `List[float]`，即压平的数据。类似的函数还有 `copyin_int32(<data>)` 和 `copyin_int64(<data>)`
然后，调用 `stub.run()` 执行推理：
```python
stub.run()
```
最后，将结果拷贝出来，传入类似：
```python
stub.init()
for name, tensor in stub.outputs.items():
print(name, tensor.shape(), tensor)
print(tensor.copyout_float())
```
## 测试
除了单元测试 `make test-cpp` 和 `make test-onnx` 之外，还可以用其他方式来测试单个模型导入导出和优化的正确性。
这个脚本利用 onnxruntime 来测试导出的模型是否与导入的模型等价：
```python
import onnx
import numpy
import sys
from onnx import ModelProto, ValueInfoProto
from pyinfinitensor.onnx import OnnxStub
from pyinfinitensor import backend
from onnxruntime import InferenceSession
def infer(model: ModelProto, input) -> dict:
collection = set()
for node in model.graph.node:
for output in node.output:
collection.add(output)
model.graph.output.extend([ValueInfoProto(name=x) for x in collection])
session = InferenceSession(model.SerializeToString())
i = session.get_inputs()[0].name
return dict(
zip(
[x.name for x in session.get_outputs()],
[x.flatten() for x in session.run(None, {i: input})],
)
)
model0 = onnx.load(sys.argv[1])
model1 = OnnxStub(model0, backend.cpu_runtime()).to_onnx("new")
input_shape = [x.dim_value for x in model1.graph.input[0].type.tensor_type.shape.dim]
input = numpy.random.random(input_shape).astype(numpy.float32)
output0 = infer(model0, input)[model0.graph.output[0].name]
output1 = infer(model1, input)[model1.graph.output[0].name]
print("error =", sum((output1 - output0) ** 2) / len(output0))
```
要运行脚本，先安装 onnxruntime：
```bash
pip install onnxruntime
```
打印出的 `error = ...` 是两个模型输出张量的均方误差。对于不同的模型，这个误差最小为 0，最大不超过 1e-9。

1
example Submodule

@ -0,0 +1 @@
Subproject commit d6ac8c8c73bf83833a71b41e95820d4eb7741fa9

22
include/bang/bang_element_wise.h
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@ -1,22 +0,0 @@
#pragma once
#include "bang/bang_runtime.h"
#include "bang_div.h"
#include "operators/element_wise.h"
namespace infini {
void element_wise_kernel(const RuntimeObj *obj, const Operator &_op) {
auto op = as<ElementWiseObj>(_op);
float *const aData = (op->getInputs(0)->getRawDataPtr<float *>());
float *const bData = (op->getInputs(1)->getRawDataPtr<float *>());
float *const cData = (op->getOutput()->getRawDataPtr<float *>());
auto dim = op->getInputs(0)->getDims();
auto context = dynamic_cast<const BangRuntimeObj *>(obj);
int n = dim[0], c = dim[1], h = dim[2], w = dim[3];
if (op->getOpType() == OpType::Div)
div_kernel(context->cnnlHandle(), aData, bData, cData, n * c * h * w);
else
IT_TODO_HALT();
}
}; // namespace infini

10
include/core/data_type.h
View File

@ -52,4 +52,14 @@ template <> inline DataType DataType::get<int16_t>() { return Int16; }
template <> inline DataType DataType::get<int32_t>() { return Int32; }
template <> inline DataType DataType::get<int64_t>() { return Int64; }
template <int index> struct DT {};
template <> struct DT<0> { using t = float; };
template <> struct DT<1> { using t = uint32_t; };
template <> struct DT<2> { using t = uint8_t; };
template <> struct DT<3> { using t = int8_t; };
template <> struct DT<4> { using t = uint16_t; };
template <> struct DT<5> { using t = int16_t; };
template <> struct DT<6> { using t = int32_t; };
template <> struct DT<7> { using t = int64_t; };
} // namespace infini

2
include/core/graph.h
View File

@ -59,6 +59,8 @@ class GraphObj : public Object {
*/
bool topo_sort();
void optimize();
void dataMalloc();
/**

12
include/core/graph_handler.h
View File

@ -46,6 +46,9 @@ class GraphHandlerObj {
Tensor conv(Tensor input, Tensor weight, Tensor output, int ph, int pw,
int sh, int sw, int dh, int dw);
Tensor convTransposed2d(Tensor input, Tensor weight, Tensor output, int ph,
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 batchNorm(Tensor input, Tensor output, Tensor mean, Tensor var,
@ -68,10 +71,17 @@ class GraphHandlerObj {
Tensor tanh(Tensor x, Tensor y);
Tensor softmax(Tensor x, Tensor y, int axis);
Tensor abs(Tensor x, Tensor y);
Tensor shape(Tensor x, Tensor y);
Tensor identity(Tensor x, Tensor y);
Tensor flatten(Tensor s, Tensor y, int axis);
Tensor pRelu(Tensor x, Tensor slope, Tensor y);
Tensor clip(Tensor x, Tensor y, std::optional<float> min,
std::optional<float> max);
Tensor transpose(Tensor data, Tensor transposed, Shape perm);
Tensor reshape(Tensor data, Tensor reshaped, Shape shape);
Tensor concat(TensorVec inputs, Tensor output, int dim);
TensorVec split(Tensor input, std::optional<TensorVec> outputs, int axis,
int num_outputs);
Tensor gather(Tensor data, Tensor indices, Tensor output, int axis);
Tensor reduceMean(Tensor data, Tensor reduced,
const optional<vector<int>> &axes, bool keepdims);
@ -85,6 +95,8 @@ class GraphHandlerObj {
inline bool topo_sort() { return g->topo_sort(); }
inline void optimize() { g->optimize(); }
//------ runtime
inline void data_malloc() { g->dataMalloc(); }

123
include/core/operator.h
View File

@ -6,6 +6,8 @@ enum class OpType {
Unknown = 0,
// linear
Conv = 100,
ConvBackwardFilter,
ConvBackwardData,
Matmul,
ConvTrans,
ConvTransNHWC,
@ -34,10 +36,70 @@ enum class OpType {
Softmax,
Activation,
Relu,
ReluBackward,
PRelu,
Sigmoid,
SigmoidBackward,
Tanh,
TanhBackward,
Abs,
Sin,
Cos,
Tan,
ASin,
ACos,
ATan,
SinH,
CosH,
TanH,
ASinH,
ACosH,
ATanH,
Resize,
Arange,
Shape,
Copy,
Ceil,
Floor,
Clip,
Erf,
Exp,
Fill,
Log,
L2Loss,
Maximum,
Minimum,
MSELoss,
Neg,
Power,
Reciprocal,
Sqrt,
Rsqrt,
Cast,
FloorDiv,
FloorMod,
Det,
Round,
Square,
SquaredDifference,
Hardtanh,
Equal,
NotEqual,
GreaterThan,
GreaterEqual,
LessThan,
LessEqual,
And,
Or,
Xor,
Not,
BitAnd,
BitOr,
BitXor,
BitNot,
BitLeftShift,
BitRightShift,
Dropout,
//
MemBound = 300,
};
@ -55,6 +117,8 @@ class OpRegistry {
FOP(Unknown);
// linear
FOP(Conv);
FOP(ConvBackwardFilter);
FOP(ConvBackwardData);
FOP(Matmul);
FOP(ConvTrans);
FOP(G2BMM);
@ -76,15 +140,72 @@ class OpRegistry {
FOP(ReduceMean);
FOP(Reshape);
FOP(Identity);
FOP(Shape);
// element wise
FOP(BatchNorm);
FOP(Softmax);
FOP(Activation);
FOP(Relu);
FOP(ReluBackward);
FOP(PRelu);
FOP(Sigmoid);
FOP(SigmoidBackward);
FOP(Tanh);
FOP(TanhBackward);
FOP(Abs);
FOP(ConvTransNHWC);
FOP(Sin);
FOP(Cos);
FOP(Tan);
FOP(ASin);
FOP(ACos);
FOP(ATan);
FOP(SinH);
FOP(CosH);
FOP(TanH);
FOP(ASinH);
FOP(ACosH);
FOP(ATanH);
FOP(Copy);
FOP(Ceil);
FOP(Floor);
FOP(Clip);
FOP(Erf);
FOP(Exp);
FOP(Fill);
FOP(Log);
FOP(L2Loss);
FOP(Maximum);
FOP(Minimum);
FOP(MSELoss);
FOP(Neg);
FOP(Power);
FOP(Reciprocal);
FOP(Sqrt);
FOP(Rsqrt);
FOP(Cast);
FOP(FloorDiv);
FOP(FloorMod);
FOP(Det);
FOP(Round);
FOP(Square);
FOP(SquaredDifference);
FOP(Hardtanh);
FOP(Equal);
FOP(NotEqual);
FOP(GreaterThan);
FOP(GreaterEqual);
FOP(LessThan);
FOP(LessEqual);
FOP(And);
FOP(Or);
FOP(Xor);
FOP(Not);
FOP(BitAnd);
FOP(BitOr);
FOP(BitXor);
FOP(BitNot);
FOP(BitLeftShift);
FOP(BitRightShift);
//
FOP(MemBound);
default:

43
include/core/tensor.h
View File

@ -73,7 +73,7 @@ class TensorObj : public TensorBaseObj {
// FIXME: std::fucntion copies the generator instead of passing it by ref.
// Thus the internal state of generator cannot be updated.
void setData(
const std::function<void(void *, size_t, DataType)> &generator) const;
std::function<void(void *, size_t, DataType)> const &generator) const;
Tensor clone() const {
auto obj = make_ref<TensorObj>(*this);
obj->freeData();
@ -100,26 +100,53 @@ class TensorObj : public TensorBaseObj {
template <typename T> bool equalData(const vector<T> &dataVector) {
IT_ASSERT(DataType::get<T>() == dtype);
IT_ASSERT(size() == dataVector.size());
return equalDataImpl(getRawDataPtr<T *>(), dataVector.data(), size(),
1e-6);
return equalDataImpl(getRawDataPtr<T *>(), dataVector.data(), size());
}
size_t getOffsetByBroadcastOffset(size_t bcOffset, Shape bcShape) const;
private:
void printDataFloat(float *ptr) const;
void printDataUint32_t(uint32_t *ptr) const;
template <class T> string dataToString() const {
std::stringstream builder;
builder << "Tensor: " << guid << std::endl;
auto numDims = shape.size();
auto dimSzVec = vector<int>(numDims, 1);
auto ptr = data->getPtr<T *>();
dimSzVec[numDims - 1] = shape[numDims - 1];
for (int i = numDims - 1; i != 0; --i)
dimSzVec[i - 1] = dimSzVec[i] * shape[i - 1];
for (size_t i = 0, iEnd = size(); i < iEnd; ++i) {
for (size_t j = 0; j < numDims; ++j)
if (i % dimSzVec[j] == 0)
builder << "[";
builder << ptr[i];
for (size_t j = 0; j < numDims; ++j)
if ((int)i % dimSzVec[j] == dimSzVec[j] - 1)
builder << "]";
if (i != size() - 1)
builder << ", ";
auto column = (size_t)dimSzVec[numDims - 1];
if (i % column == column - 1)
builder << std::endl;
}
return builder.str();
}
template <typename T>
bool equalDataImpl(const T *a, const T *b, size_t size,
double relativeError) const {
bool equalDataImpl(const T *a, const T *b, size_t size) 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])) >
relativeError) {
1e-6) {
printf("Error on %lu: %f %f\n", i, a[i], b[i]);
return false;
}

9
include/cuda/cuda_clip.h Normal file
View File

@ -0,0 +1,9 @@
#pragma once
#include "operators/unary.h"
namespace infini {
void clip_kernel(float *input, float *output, int num, float minValue,
float maxValue);
}; // namespace infini

6
include/cuda/cuda_element_wise.h
View File

@ -1,6 +1,8 @@
#pragma once
namespace infini {
void div_kernel(float *a, float *b, float *c, int num);
void pow_kernel(float *a, float *b, float *c, int num);
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);
}; // namespace infini

2
include/nnet/dbg.h
View File

@ -231,7 +231,7 @@ template <typename... T> std::string type_list_to_string() {
result.pop_back();
}
return result;
}
} // namespace dbg
template <typename... T> std::string get_type_name(type_tag<std::tuple<T...>>) {
return "std::tuple<" + type_list_to_string<T...>() + ">";

32
include/operators/activation_backward.h Normal file
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@ -0,0 +1,32 @@
#pragma once
#include "core/operator.h"
namespace infini {
class ActivationBackwardObj : public OperatorObj {
public:
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;
std::string toString() const override;
int numInputs() const override { return 3; }
int numOutputs() const override { return 1; }
private:
vector<int> getWorkloadVector() const override;
vector<int> getOpAttrVector() const override;
};
#define DEFINE_ACTIVATION_BACKWARD_OBJ(prefix, type) \
class prefix##Obj : public ActivationBackwardObj { \
public: \
prefix##Obj(GraphObj *graph, Tensor y, Tensor diff_y, Tensor x, \
Tensor diff_x) \
: ActivationBackwardObj(type, graph, y, diff_y, x, diff_x) {} \
};
DEFINE_ACTIVATION_BACKWARD_OBJ(ReluBackward, OpType::ReluBackward)
DEFINE_ACTIVATION_BACKWARD_OBJ(SigmoidBackward, OpType::SigmoidBackward)
DEFINE_ACTIVATION_BACKWARD_OBJ(TanhBackward, OpType::TanhBackward)
}; // namespace infini

8
include/operators/batch_norm.h
View File

@ -9,7 +9,7 @@ namespace infini {
*/
class BatchNormObj : public OperatorObj {
float momentum, eps;
bool training;
bool trainingMode;
public:
/**
@ -28,11 +28,11 @@ class BatchNormObj : public OperatorObj {
* Default is 0.9.
* @param eps The epsilon value to use to avoid division by zero. Default is
* 1e-5.
* @param training Set to true when used for training.
* @param trainingMode Set to true when used for training.
*/
BatchNormObj(GraphObj *graph, Tensor input, Tensor output, Tensor mean,
Tensor var, Tensor scale, Tensor bias, float momentum = 0.9,
float eps = 1e-5, bool training = false);
float eps = 1e-5, bool trainingMode = false);
OP_CLONE(BatchNormObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
std::string toString() const override;
@ -42,7 +42,7 @@ class BatchNormObj : public OperatorObj {
int numOutputs() const override { return outputs.size(); }
float getMomentum() const { return momentum; }
float getEps() const { return eps; }
bool getTraining() const { return training; }
bool getTrainingMode() const { return trainingMode; }
private:
vector<int> getWorkloadVector() const override;

24
include/operators/conv.h
View File

@ -149,6 +149,29 @@ class ConvObj : public ConvBaseObj {
void setAuxilaryAttributes(PaddingMode mode) override;
};
class ConvBackwardFilterObj : public ConvBaseObj {
private:
ActType act;
public:
ConvBackwardFilterObj(GraphObj *graph, Tensor inputX, Tensor diffY,
Tensor diffW, int ph, int pw, int sh = 1, int sw = 1,
int dh = 1, int dw = 1, Tensor bias = nullptr,
ActType act = ActType::None);
// Constructors for setting padding mode
ConvBackwardFilterObj(GraphObj *graph, Tensor inputX, Tensor diffY,
Tensor diffW, PaddingMode mode = PaddingMode::Same,
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;
ActType getAct() const { return act; }
int getNumGroups() const override { return c / getChannelPerGroup(); }
private:
void setAuxilaryAttributes(PaddingMode mode) override;
};
class ConvTransposed2dObj : public ConvBaseObj {
private:
int oph, opw;
@ -170,6 +193,7 @@ class ConvTransposed2dObj : public ConvBaseObj {
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
int getNumGroups() const override { return group; }
std::pair<int, int> getOutputPadding() const { return {oph, opw}; }
private:
void setAuxilaryAttributes(PaddingMode mode) override;

22
include/operators/det.h Normal file
View File

@ -0,0 +1,22 @@
#pragma once
#include "core/operator.h"
namespace infini {
class DetObj : public OperatorObj {
public:
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;
std::string toString() const override;
int numInputs() const override { return 1; }
int numOutputs() const override { return 1; }
Mode getMode() const { return modeValue; }
private:
Mode modeValue;
vector<int> getWorkloadVector() const override;
vector<int> getOpAttrVector() const override;
};
}; // namespace infini

52
include/operators/dropout.h Normal file
View File

@ -0,0 +1,52 @@
#pragma once
#include "core/operator.h"
namespace infini {
/**
* @brief Copy a tensor along a centain dimension for multiple times.
*/
class DropoutObj : public OperatorObj {
float ratio;
// bool training_mode; // TODO must be false.
public:
/**
* @brief Dropout takes an input floating-point tensor, an input ratio
* (floating-point scalar) and an input training_mode (boolean scalar). It
* produces two tensor outputs, output (floating-point tensor) and mask
* (bool tensor). If training_mode is true then the output Y will be a
* random dropout; Note that this Dropout scales the masked input data by
* the following equation, so to convert the trained model into inference
* mode, the user can simply not pass training_mode input or set it to
* false.
*
* @param graph The computation graph that this operator belongs to.
* @param data The input tensor.
* @param output The output tensor.
* @param mask The mask tensor.
* @param ratio The ratio of random dropout, with value in [0, 1). If this
* input was not set, or if it was set to 0, the output would be a simple
* copy of the input. If it’s non-zero, output will be a random dropout of
* the scaled input, which is typically the case during training.
* @param training_mode If set to true then it indicates dropout is being
* used for training. It is an optional value hence unless specified
* explicitly, it is false. If it is false, ratio is ignored and the
* operation mimics inference mode where nothing will be dropped from the
* input data and if mask is requested as output it will contain all ones.
*/
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;
std::string toString() const override;
int numInputs() const override { return 1; }
int numOutputs() const override { return 2; }
float getRatio() const { return ratio; }
bool getTrainingMode() const { return false; }
private:
vector<int> getWorkloadVector() const override;
vector<int> getOpAttrVector() const override;
};
} // namespace infini

41
include/operators/element_wise.h
View File

@ -32,6 +32,25 @@ class ElementWiseObj : public OperatorObj {
vector<int> getOpAttrVector() const override;
};
class MSELossObj : public OperatorObj {
public:
enum Reduction { None = 0, Sum, Mean };
MSELossObj(GraphObj *graph, Tensor input0, Tensor input1,
Reduction reduction, Tensor output);
OP_CLONE(MSELossObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
Reduction getReduction() const { return reductionMode; }
std::string toString() const override;
int numInputs() const override { return 2; }
int numOutputs() const override { return 1; }
private:
Reduction reductionMode;
vector<int> getWorkloadVector() const override;
vector<int> getOpAttrVector() const override;
};
#define DEFINE_ELEMENT_WISE_OBJ(prefix, type) \
class prefix##Obj : public ElementWiseObj { \
public: \
@ -46,4 +65,26 @@ DEFINE_ELEMENT_WISE_OBJ(Sub, OpType::Sub)
DEFINE_ELEMENT_WISE_OBJ(Mul, OpType::Mul)
DEFINE_ELEMENT_WISE_OBJ(Div, OpType::Div)
DEFINE_ELEMENT_WISE_OBJ(Pow, OpType::Pow)
DEFINE_ELEMENT_WISE_OBJ(Maximum, OpType::Maximum)
DEFINE_ELEMENT_WISE_OBJ(Minimum, OpType::Minimum)
DEFINE_ELEMENT_WISE_OBJ(Power, OpType::Power)
DEFINE_ELEMENT_WISE_OBJ(FloorDiv, OpType::FloorDiv)
DEFINE_ELEMENT_WISE_OBJ(FloorMod, OpType::FloorMod)
DEFINE_ELEMENT_WISE_OBJ(SquaredDifference, OpType::SquaredDifference)
DEFINE_ELEMENT_WISE_OBJ(Equal, OpType::Equal)
DEFINE_ELEMENT_WISE_OBJ(NotEqual, OpType::NotEqual)
DEFINE_ELEMENT_WISE_OBJ(GreaterThan, OpType::GreaterThan)
DEFINE_ELEMENT_WISE_OBJ(GreaterEqual, OpType::GreaterEqual)
DEFINE_ELEMENT_WISE_OBJ(LessThan, OpType::LessThan)
DEFINE_ELEMENT_WISE_OBJ(LessEqual, OpType::LessEqual)
DEFINE_ELEMENT_WISE_OBJ(And, OpType::And)
DEFINE_ELEMENT_WISE_OBJ(Or, OpType::Or)
DEFINE_ELEMENT_WISE_OBJ(Xor, OpType::Xor)
DEFINE_ELEMENT_WISE_OBJ(Not, OpType::Not)
DEFINE_ELEMENT_WISE_OBJ(BitAnd, OpType::BitAnd)
DEFINE_ELEMENT_WISE_OBJ(BitOr, OpType::BitOr)
DEFINE_ELEMENT_WISE_OBJ(BitXor, OpType::BitXor)
DEFINE_ELEMENT_WISE_OBJ(BitNot, OpType::BitNot)
DEFINE_ELEMENT_WISE_OBJ(BitLeftShift, OpType::BitLeftShift)
DEFINE_ELEMENT_WISE_OBJ(BitRightShift, OpType::BitRightShift)
}; // namespace infini

4
include/operators/matmul.h
View File

@ -47,10 +47,10 @@ class MatmulObj : public OperatorObj {
std::string toString() const override;
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
int numInputs() const override { return 2; }
int numInputs() const override { return inputs.size(); }
int numOutputs() const override { return 1; }
Tensor getBias() const { return inputs[2]; }
Tensor getBias() const { return inputs.size() > 2 ? inputs[2] : nullptr; }
ActType getAct() const { return act; }
auto getBMNKTransAB() const { return tuple(b, m, n, k, transA, transB); }
bool getTransA() const { return transA; }

26
include/operators/slice.h
View File

@ -7,7 +7,8 @@ namespace infini {
*
*/
class SliceObj : public OperatorObj {
vector<int> starts, ends; // the start no. and end no. for all dims.
template <class T> struct range_t { T start, end, step; };
vector<range_t<int>> axes;
public:
/**
@ -33,9 +34,26 @@ class SliceObj : public OperatorObj {
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
std::string toString() const override;
int numInputs() const override { return 1; }
int numOutputs() const override { return 1; }
Shape getStart() const { return starts; }
inline int numInputs() const override { return 1; }
inline int numOutputs() const override { return 1; }
inline Shape getStarts() const {
Shape ans(axes.size());
std::transform(axes.begin(), axes.end(), ans.begin(),
[](auto x) { return x.start; });
return ans;
}
inline Shape getEnds() const {
Shape ans(axes.size());
std::transform(axes.begin(), axes.end(), ans.begin(),
[](auto x) { return x.end; });
return ans;
}
inline Shape getSteps() const {
Shape ans(axes.size());
std::transform(axes.begin(), axes.end(), ans.begin(),
[](auto x) { return x.step; });
return ans;
}
private:
vector<int> getWorkloadVector() const override;

22
include/operators/transpose.h Normal file
View File

@ -0,0 +1,22 @@
#pragma once
#include "core/operator.h"
namespace infini {
class TransposeObj : public OperatorObj {
public:
TransposeObj(GraphObj *graph, Tensor input, Tensor output,
vector<int> permute);
OP_CLONE(TransposeObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
std::string toString() const override;
int numInputs() const override { return 1; }
int numOutputs() const override { return 1; }
std::vector<int> getPermute() const { return transposePermute; }
private:
vector<int> transposePermute = {1, 1, 1, 1};
vector<int> getWorkloadVector() const override;
vector<int> getOpAttrVector() const override;
};
}; // namespace infini

262
include/operators/unary.h
View File

@ -28,6 +28,244 @@ class UnaryObj : public OperatorObj {
vector<int> getOpAttrVector() const override;
};
class ClipObj : public OperatorObj {
public:
ClipObj(GraphObj *graph, Tensor input, Tensor output,
std::optional<float> min, std::optional<float> max);
OP_CLONE(ClipObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
std::string toString() const override;
std::optional<float> getMin() const { return minValue; };
std::optional<float> getMax() const { return maxValue; };
int numInputs() const override { return 1; }
int numOutputs() const override { return 1; }
private:
std::optional<float> minValue, maxValue;
vector<int> getWorkloadVector() const override;
vector<int> getOpAttrVector() const override;
};
class HardtanhObj : public OperatorObj {
public:
HardtanhObj(GraphObj *graph, Tensor input, Tensor output, float min,
float max);
OP_CLONE(HardtanhObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
std::string toString() const override;
float getMin() const { return minValue; };
float getMax() const { return maxValue; };
int numInputs() const override { return 1; }
int numOutputs() const override { return 1; }
private:
float minValue, maxValue;
vector<int> getWorkloadVector() const override;
vector<int> getOpAttrVector() const override;
};
class FlipObj : public OperatorObj {
public:
FlipObj(GraphObj *graph, Tensor input, Tensor output, vector<int> axis);
OP_CLONE(FlipObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
std::string toString() const override;
vector<int> getAxis() const { return axisValue; };
int numInputs() const override { return 1; }
int numOutputs() const override { return 1; }
private:
vector<int> axisValue;
vector<int> getWorkloadVector() const override;
vector<int> getOpAttrVector() const override;
};
class FillObj : public OperatorObj {
public:
FillObj(GraphObj *graph, Tensor input, Tensor output, float value);
OP_CLONE(FillObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
std::string toString() const override;
float getValue() const { return setValue; };
int numInputs() const override { return 1; }
int numOutputs() const override { return 1; }
private:
float setValue;
vector<int> getWorkloadVector() const override;
vector<int> getOpAttrVector() const override;
};
class L2LossObj : public OperatorObj {
public:
L2LossObj(GraphObj *graph, Tensor input, Tensor output);
OP_CLONE(L2LossObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
std::string toString() const override;
int numInputs() const override { return 1; }
int numOutputs() const override { return 1; }
private:
vector<int> getWorkloadVector() const override;
vector<int> getOpAttrVector() const override;
};
class TransformObj : public OperatorObj {
public:
TransformObj(GraphObj *graph, Tensor input, Tensor output, float alpha,
float beta);
OP_CLONE(TransformObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
std::string toString() const override;
float getAlpha() const { return alphaValue; }
float getBeta() const { return betaValue; }
int numInputs() const override { return 1; }
int numOutputs() const override { return 1; }
private:
float alphaValue, betaValue;
vector<int> getWorkloadVector() const override;
vector<int> getOpAttrVector() const override;
};
class CastObj : public OperatorObj {
public:
enum CastType {
Float2Half = 0,
Float2Int64,
Float2Int32,
Float2Int16,
Float2Int8,
Int322Float,
Int322Int8,
Int322Int16,
Int162Float,
Int162Int32,
Int82Float,
Int82Int16,
Int82Int32,
Uint82Float,
Uint82Int32,
Uint82Int64,
Int322Int64,
Int642Int32,
Int642Uint32,
Int642Float,
Uint322Int64,
};
CastObj(GraphObj *graph, Tensor input, Tensor output, CastType type);
OP_CLONE(CastObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
vector<DataType> inferDataType(const TensorVec &inputs) const override;
std::string toString() const override;
CastType getType() const { return castType; }
DataType getOutputDataType() const;
int numInputs() const override { return 1; }
int numOutputs() const override { return 1; }
private:
CastType castType;
vector<int> getWorkloadVector() const override;
vector<int> getOpAttrVector() const override;
};
class CumsumObj : public OperatorObj {
public:
CumsumObj(GraphObj *graph, Tensor input, Tensor output, int axis,
bool exclusive, bool reverse);
OP_CLONE(CumsumObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
std::string toString() const override;
int getAxis() const { return axisValue; }
float getExclusive() const { return exclusiveValue; }
float getReverse() const { return reverseValue; }
int numInputs() const override { return 1; }
int numOutputs() const override { return 1; }
private:
int axisValue;
bool exclusiveValue, reverseValue;
vector<int> getWorkloadVector() const override;
vector<int> getOpAttrVector() const override;
};
class ArangeObj : public OperatorObj {
public:
ArangeObj(GraphObj *graph, float start, float step, int length,
Tensor output);
OP_CLONE(ArangeObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
std::string toString() const override;
int numInputs() const override { return 0; }
int numOutputs() const override { return 1; }
float getStartValue() { return startValue; }
float getStepValue() { return stepValue; }
int getLength() { return lengthValue; }
private:
float startValue, stepValue;
int lengthValue;
vector<int> getWorkloadVector() const override;
vector<int> getOpAttrVector() const override;
};
class ShapeObj : public OperatorObj {
public:
ShapeObj(GraphObj *graph, Tensor input, Tensor output);
OP_CLONE(ShapeObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
std::string toString() const override;
int numInputs() const override { return 1; }
int numOutputs() const override { return 1; }
};
class PReluObj : public OperatorObj {
public:
PReluObj(GraphObj *graph, Tensor input, Tensor alpha, Tensor output);
OP_CLONE(PReluObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
std::string toString() const override;
int numInputs() const override { return 2; }
int numOutputs() const override { return 1; }
private:
vector<int> getWorkloadVector() const override;
vector<int> getOpAttrVector() const override;
};
class LogObj : public OperatorObj {
public:
enum LogType {
LogE = 0,
Log2,
Log10,
};
LogObj(GraphObj *graph, Tensor input, Tensor output, LogType type);
OP_CLONE(LogObj);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
std::string toString() const override;
LogType getType() const { return logType; }
int numInputs() const override { return 1; }
int numOutputs() const override { return 1; }
private:
LogType logType;
vector<int> getWorkloadVector() const override;
vector<int> getOpAttrVector() const override;
};
#define DEFINE_UNARY_OBJ(prefix, type) \
class prefix##Obj : public UnaryObj { \
public: \
@ -42,4 +280,28 @@ DEFINE_UNARY_OBJ(Tanh, OpType::Tanh)
// DEFINE_UNARY_OBJ(Softmax, OpType::Softmax)
DEFINE_UNARY_OBJ(Abs, OpType::Abs)
DEFINE_UNARY_OBJ(Sin, OpType::Sin)
DEFINE_UNARY_OBJ(Cos, OpType::Cos)
DEFINE_UNARY_OBJ(Tan, OpType::Tan)
DEFINE_UNARY_OBJ(ASin, OpType::ASin)
DEFINE_UNARY_OBJ(ACos, OpType::ACos)
DEFINE_UNARY_OBJ(ATan, OpType::ATan)
DEFINE_UNARY_OBJ(SinH, OpType::SinH)
DEFINE_UNARY_OBJ(CosH, OpType::CosH)
DEFINE_UNARY_OBJ(TanH, OpType::TanH)
DEFINE_UNARY_OBJ(ASinH, OpType::ASinH)
DEFINE_UNARY_OBJ(ACosH, OpType::ACosH)
DEFINE_UNARY_OBJ(ATanH, OpType::ATanH)
DEFINE_UNARY_OBJ(Copy, OpType::Copy)
DEFINE_UNARY_OBJ(Ceil, OpType::Ceil)
DEFINE_UNARY_OBJ(Floor, OpType::Floor)
DEFINE_UNARY_OBJ(Erf, OpType::Erf)
DEFINE_UNARY_OBJ(Exp, OpType::Exp)
DEFINE_UNARY_OBJ(Neg, OpType::Neg)
DEFINE_UNARY_OBJ(Reciprocal, OpType::Reciprocal)
DEFINE_UNARY_OBJ(Sqrt, OpType::Sqrt)
DEFINE_UNARY_OBJ(Rsqrt, OpType::Rsqrt)
DEFINE_UNARY_OBJ(Round, OpType::Round)
DEFINE_UNARY_OBJ(Square, OpType::Square)
}; // namespace infini

2
pyinfinitensor/pyproject.toml
View File

@ -8,7 +8,7 @@ version = "0.0.0"
authors = [{ name = "YdrMaster", email = "ydrml@hotmail.com" }]
description = "Python frontend of InfiniTensor"
readme = "README.md"
requires-python = ">=3.8"
requires-python = ">=3.7"
keywords = ["optimizer"]
license = { text = "Apache" }
classifiers = ["Programming Language :: Python :: 3"]

414
pyinfinitensor/src/pyinfinitensor/onnx.py
View File

@ -22,12 +22,17 @@ from onnx.checker import (
check_tensor,
)
from onnx.shape_inference import infer_shapes
from onnx.numpy_helper import to_array
from typing import Dict, List, Any, Tuple, Sequence, Union, Optional
from functools import reduce
runtime = backend.runtime()
class OnnxStub:
"""
The Onnx model imported into infinitensor.
It can be generated from an Onnx model object.
"""
inputs: Dict[str, backend.Tensor] = {}
outputs: Dict[str, backend.Tensor] = {}
initializer: Dict[int, TensorProto] = {}
@ -53,6 +58,8 @@ class OnnxStub:
)
for initializer in model.graph.initializer:
dims = [d for d in initializer.dims]
tensors[initializer.name] = self.handler.tensor(dims, initializer.data_type)
data[initializer.name] = initializer
for node in model.graph.node:
@ -61,14 +68,81 @@ class OnnxStub:
node,
{
"dilations": [1, 1],
"pads": [0, 0],
"pads": [0, 0, 0, 0],
"strides": [1, 1],
},
)
(d, p, s) = (
attributes[name] for name in ["dilations", "pads", "strides"]
)
if p[0] != p[2] or p[1] != p[3]:
adapt = "{}-adapt".format(node.output[0])
tensors[adapt] = self.handler.pad(
tensors[node.input[0]], None, p, [-2, -1]
)
p = [0, 0, 0, 0]
else:
adapt = node.input[0]
if len(node.input) > 2:
bias = "{}-bias".format(node.output[0])
reshape = "{}-reshape".format(node.output[0])
tensors[bias] = self.handler.conv(
tensors[adapt],
tensors[node.input[1]],
None,
p[0],
p[1],
s[0],
s[1],
d[0],
d[1],
)
tensors[reshape] = self.handler.reshape(
tensors[node.input[2]],
None,
[
1,
reduce(
lambda acc, x: acc * x,
_search_shape(model, node.input[2]),
),
1,
1,
],
)
tensors[node.output[0]] = self.handler.add(
tensors[bias],
tensors[reshape],
tensors.get(node.output[0]),
)
else:
tensors[node.output[0]] = self.handler.conv(
tensors[adapt],
tensors[node.input[1]],
tensors.get(node.output[0]),
p[0],
p[1],
s[0],
s[1],
d[0],
d[1],
)
elif node.op_type == "ConvTranspose":
attributes = _parse_attribute(
node,
{
"dilations": [1, 1],
"pads": [0, 0],
"strides": [1, 1],
"output_padding": [0, 0],
},
)
(d, p, s, op) = (
attributes[name]
for name in ["dilations", "pads", "strides", "output_padding"]
)
tensors[node.output[0]] = self.handler.convTransposed2d(
tensors[node.input[0]],
tensors[node.input[1]],
tensors.get(node.output[0]),
@ -78,6 +152,8 @@ class OnnxStub:
s[1],
d[0],
d[1],
op[0],
op[1],
)
elif node.op_type == "MatMul":
tensors[node.output[0]] = self.handler.matmul(
@ -129,7 +205,7 @@ class OnnxStub:
{
"kernel_shape": None,
"dilations": [1, 1],
"pads": [0, 0],
"pads": [0, 0, 0, 0],
"strides": [1, 1],
},
)
@ -137,6 +213,24 @@ class OnnxStub:
attributes[name]
for name in ["kernel_shape", "dilations", "pads", "strides"]
)
if p[0] != p[2] or p[1] != p[3]:
adapt = "{}-adapt".format(node.output[0])
tensors[adapt] = self.handler.pad(
tensors.get(node.input[0]), None, p, [-2, -1]
)
tensors[node.output[0]] = self.handler.maxPool(
tensors[adapt],
tensors.get(node.output[0]),
k[0],
k[1],
d[0],
d[1],
0,
0,
s[0],
s[1],
)
else:
tensors[node.output[0]] = self.handler.maxPool(
tensors[node.input[0]],
tensors.get(node.output[0]),
@ -154,13 +248,31 @@ class OnnxStub:
node,
{
"kernel_shape": None,
"pads": [0, 0],
"pads": [0, 0, 0, 0],
"strides": [1, 1],
},
)
(k, p, s) = (
attributes[name] for name in ["kernel_shape", "pads", "strides"]
)
if p[0] != p[2] or p[1] != p[3]:
adapt = "{}-adapt".format(node.output[0])
tensors[adapt] = self.handler.pad(
tensors.get(node.input[0]), None, p, [-2, -1]
)
tensors[node.output[0]] = self.handler.avgPool(
tensors[adapt],
tensors.get(node.output[0]),
k[0],
k[1],
1,
1,
0,
0,
s[0],
s[1],
)
else:
tensors[node.output[0]] = self.handler.avgPool(
tensors[node.input[0]],
tensors.get(node.output[0]),
@ -174,19 +286,7 @@ class OnnxStub:
s[1],
)
elif node.op_type == "GlobalAveragePool":
shape = next(
(
value.type.tensor_type.shape
for value in model.graph.value_info
if value.name == node.input[0]
),
None,
) or next(
input.type.tensor_type.shape
for input in model.graph.input
if input.name == node.input[0]
)
[_, _, h, w] = _take_shape_dim(shape)
[_, _, h, w] = _search_shape(model, node.input[0])
tensors[node.output[0]] = self.handler.avgPool(
tensors[node.input[0]],
tensors.get(node.output[0]),
@ -248,58 +348,123 @@ class OnnxStub:
tensors[node.output[0]] = self.handler.softmax(
tensors[node.input[0]],
tensors.get(node.output[0]),
next((attr.i for attr in node.attribute if attr.name == "axis")),
next(
(attr.i for attr in node.attribute if attr.name == "axis"), -1
),
)
elif node.op_type == "Abs":
tensors[node.output[0]] = self.handler.abs(
tensors[node.input[0]],
tensors.get(node.output[0]),
)
elif node.op_type == "Shape":
tensors[node.output[0]] = self.handler.shape(
tensors[node.input[0]],
tensors.get(node.output[0]),
)
elif node.op_type == "Identity":
tensors[node.output[0]] = self.handler.identity(
tensors[node.input[0]],
tensors.get(node.output[0]),
)
elif node.op_type == "Flatten":
tensors[node.output[0]] = self.handler.flatten(
tensors[node.input[0]],
tensors.get(node.output[0]),
next((attr.i for attr in node.attribute if attr.name == "axis")),
)
elif node.op_type == "Reshape":
input_shape = next(
(
value.type.tensor_type.shape
for value in model.graph.value_info
if value.name == node.input[0]
),
None,
) or next(
input.type.tensor_type.shape
for input in model.graph.input
if input.name == node.input[0]
elif node.op_type == "PRelu":
tensors[node.output[0]] = self.handler.pRelu(
tensors[node.input[0]],
tensors[node.input[1]],
tensors.get(node.output[0]),
)
dims = _take_shape_dim(input_shape)
elif node.op_type == "Clip":
tensors[node.output[0]] = self.handler.clip(
tensors[node.input[0]],
tensors.get(node.output[0]),
next(_parse_data(data[node.input[1]]).__iter__(), None)
if len(node.input) > 1
else None,
next(_parse_data(data[node.input[2]]).__iter__(), None)
if len(node.input) > 2
else None,
)
elif node.op_type == "Transpose":
perm = next(
(attr.ints for attr in node.attribute if attr.name == "perm"), None
)
tensors[node.output[0]] = self.handler.transpose(
tensors[node.input[0]],
tensors.get(node.output[0]),
perm,
)
elif node.op_type == "Reshape":
dims = _search_shape(model, node.input[0])
size = reduce(lambda acc, x: acc * x, dims)
output_shape = [int(i) for i in data[node.input[1]].int64_data]
for i, x in enumerate(output_shape):
input_shape = _parse_data(data[node.input[1]])
for i, x in enumerate(input_shape):
if x == 0:
output_shape[i] = dims[i]
temp = reduce(lambda acc, x: acc * x, output_shape)
input_shape[i] = dims[i]
temp = reduce(lambda acc, x: acc * x, input_shape, 1)
if temp < 0:
output_shape[output_shape.index(-1)] = size // -temp
input_shape[input_shape.index(-1)] = size // -temp
tensors[node.output[0]] = self.handler.reshape(
tensors[node.input[0]],
tensors.get(node.output[0]),
input_shape,
)
elif node.op_type == "Squeeze":
input_shape = _search_shape(model, node.input[0])
axes = set(
[int(i) for i in data[node.input[1]].int64_data]
if len(node.input) > 1
else _parse_attribute(node, {"axes": None})["axes"]
)
assert all(input_shape[d] == 1 for d in axes)
output_shape = []
for i, x in enumerate(input_shape):
if i not in axes:
output_shape.append(x)
tensors[node.output[0]] = self.handler.reshape(
tensors[node.input[0]],
tensors.get(node.output[0]),
output_shape,
)
elif node.op_type == "Unsqueeze":
input_shape = _search_shape(model, node.input[0])
axes = (
[int(i) for i in data[node.input[1]].int64_data]
if len(node.input) > 1
else _parse_attribute(node, {"axes": None})["axes"]
)
for i in axes:
input_shape.insert(i, 1)
tensors[node.output[0]] = self.handler.reshape(
tensors[node.input[0]],
tensors.get(node.output[0]),
input_shape,
)
elif node.op_type == "Concat":
tensors[node.output[0]] = self.handler.concat(
[tensors[name] for name in node.input],
tensors.get(node.output[0]),
next((attr.i for attr in node.attribute if attr.name == "axis")),
)
elif node.op_type == "Split":
for name, tensor in zip(
node.output,
self.handler.split(
tensors[node.input[0]],
None,
next(
(attr.i for attr in node.attribute if attr.name == "axis"),
0,
),
len(node.output),
),
):
tensors[name] = tensor
elif node.op_type == "Gather":
tensors[node.output[0]] = self.handler.gather(
tensors[node.input[0]],
@ -331,6 +496,22 @@ class OnnxStub:
_parse_data(data[node.input[1]]),
_parse_data(data[node.input[3]]) if len(node.input) > 3 else None,
)
elif node.op_type == "Dropout":
for name, tensor in zip(
node.output,
self.handler.dropout(
tensors[node.input[0]],
tensors.get(node.output[0]),
tensors.get(node.output[1]) if len(node.output) > 1 else None,
_parse_data(data[node.input[1]])[0]
if len(node.input) > 1
else 0.5,
_parse_data(data[node.input[2]])[0]
if len(node.input) > 2
else False,
),
):
tensors[name] = tensor
else:
raise Exception('Unsupported operator "{}"'.format(node.op_type))
@ -344,11 +525,11 @@ class OnnxStub:
else:
self.initializer[obj.fuid()] = tensor
if tensor.data_type == TensorProto.INT32:
obj.copyin_int32([int(i) for i in tensor.int32_data])
obj.copyin_int32(_parse_data(tensor))
elif tensor.data_type == TensorProto.INT64:
obj.copyin_int64([int(i) for i in tensor.int64_data])
obj.copyin_int64(_parse_data(tensor))
elif tensor.data_type == TensorProto.FLOAT:
obj.copyin_float([int(i) for i in tensor.float_data])
obj.copyin_float(_parse_data(tensor))
else:
assert False, "Unsupported Tensor Type: {}".format(tensor.data_type)
@ -398,14 +579,15 @@ class OnnxStub:
self.count_in += 1
name = "input{}".format(self.count_in)
self.names[tensor] = name
if init != None:
init.name = name
self.initializers.append(init)
else:
shape = tensor.shape()
dtype = backend.tensor_dtype(tensor)
value_info = make_tensor_value_info(name, dtype, shape)
check_value_info(value_info)
self.inputs.append(value_info)
if init != None:
init.name = name
self.initializers.append(init)
return name
def push_data_input(
@ -417,11 +599,8 @@ class OnnxStub:
vals: Any,
) -> str:
name = "{}_{}".format(node_name, attr_name)
value_info = make_tensor_value_info(name, elem_type, shape)
tensor = make_tensor(name, elem_type, shape, vals)
check_value_info(value_info)
check_tensor(tensor)
self.inputs.append(value_info)
self.initializers.append(tensor)
return name
@ -459,20 +638,40 @@ class OnnxStub:
for (i, it) in enumerate(op.outputs())
]
if ty == backend.OpType.Conv:
ph, pw, sh, sw, dh, dw = backend.conv_attrs_of(op)
ph, pw, dh, dw, sh, sw = backend.conv_attrs_of(op)
ctx.push_node(
make_node(
ty.name,
inputs,
outputs,
name,
pads=[ph, pw, ph, pw],
strides=[sh, sw],
dilations=[dh, dw],
group=op.inputs()[0].shape()[1] // op.inputs()[1].shape()[1],
)
)
elif ty == backend.OpType.ConvTrans:
ph, pw, sh, sw, dh, dw, oph, opw = backend.conv_trans_attrs_of(op)
ctx.push_node(
make_node(
"ConvTranspose",
inputs,
outputs,
name,
pads=[ph, pw],
strides=[sh, sw],
dilations=[dh, dw],
output_padding=[oph, opw],
)
)
elif ty == backend.OpType.Matmul:
ctx.push_node(make_node("MatMul", inputs, outputs, name))
transA, transB = backend.matmul_attrs_of(op)
ctx.push_node(
make_node(
"Gemm", inputs, outputs, name, transA=transA, transB=transB
)
)
elif ty == backend.OpType.BatchNorm:
inputs = [inputs[i] for i in [0, 3, 4, 1, 2]]
momentum, eps, training = backend.batch_norm_attrs_of(op)
@ -496,7 +695,7 @@ class OnnxStub:
outputs,
name,
kernel_shape=[kh, kw],
pads=[ph, pw],
pads=[ph, pw, ph, pw],
dilations=[dh, dw],
strides=[sh, sw],
)
@ -510,7 +709,7 @@ class OnnxStub:
outputs,
name,
kernel_shape=[kh, kw],
pads=[ph, pw],
pads=[ph, pw, ph, pw],
strides=[sh, sw],
)
)
@ -526,17 +725,21 @@ class OnnxStub:
backend.OpType.Softmax,
backend.OpType.Abs,
backend.OpType.Identity,
backend.OpType.PRelu,
]:
ctx.push_node(make_node(ty.name, inputs, outputs, name))
elif ty == backend.OpType.Flatten:
raise Exception("TODO")
elif ty == backend.OpType.Transpose:
perm = backend.transpose_permute_of(op)
ctx.push_node(make_node(ty.name, inputs, outputs, name, perm=perm))
elif ty == backend.OpType.Reshape:
shape = backend.reshape_shape_of(op)
inputs.append(
ctx.push_data_input(
name,
"shape",
TensorProto.INT32,
TensorProto.INT64,
[len(shape)],
shape,
)
@ -545,29 +748,81 @@ class OnnxStub:
elif ty == backend.OpType.Concat:
axis = backend.concat_axis_of(op)
ctx.push_node(make_node(ty.name, inputs, outputs, name, axis=axis))
elif ty == backend.OpType.Split:
axis = backend.split_axis_of(op)
num_outputs = len(outputs)
split = op.inputs()[0].shape()[axis] // num_outputs
inputs.append(
ctx.push_data_input(
name,
"split",
TensorProto.INT64,
[len(outputs)],
[split for _ in range(0, num_outputs)],
)
)
ctx.push_node(
make_node(
ty.name,
inputs,
outputs,
name,
axis=axis,
)
)
elif ty == backend.OpType.Gather:
axis = backend.gather_axis_of(op)
ctx.push_node(make_node(ty.name, inputs, outputs, name, axis=axis))
elif ty == backend.OpType.ReduceMean:
axes = backend.reduce_mean_axes_of(op)
axes, keepdims = backend.reduce_mean_attrs_of(op)
inputs.append(
ctx.push_data_input(
name, "axes", TensorProto.INT32, [len(axes)], axes
name, "axes", TensorProto.INT64, [len(axes)], axes
)
)
ctx.push_node(make_node(ty.name, inputs, outputs, name, keepdims=1))
ctx.push_node(
make_node(ty.name, inputs, outputs, name, keepdims=keepdims)
)
elif ty == backend.OpType.Slice:
raise Exception("TODO")
elif ty == backend.OpType.Pad:
raise Exception("TODO")
pads = backend.pad_pads_of(op)
inputs.append(
ctx.push_data_input(
name, "pads", TensorProto.INT64, [len(pads)], pads
)
)
ctx.push_node(make_node(ty.name, inputs, outputs, name))
elif ty == backend.OpType.Clip:
min, max = backend.clip_attrs_of(op)
if min != None:
inputs.append(
ctx.push_data_input(name, "min", TensorProto.FLOAT, [], [min])
)
else:
raise Exception("Unsupported OpType {}".format(ty.name))
inputs.append(
ctx.push_data_input(name, "min", TensorProto.FLOAT, [], [])
)
if max != None:
inputs.append(
ctx.push_data_input(name, "max", TensorProto.FLOAT, [], [max])
)
else:
inputs.append(
ctx.push_data_input(name, "max", TensorProto.FLOAT, [], [])
)
ctx.push_node(make_node(ty.name, inputs, outputs, name))
else:
raise Exception("Unsupported OpType", ty)
return ctx.build(name)
def init(self) -> None:
self.handler.data_malloc()
def optimize(self) -> None:
self.handler.optimize()
def run(self) -> None:
self.handler.run()
@ -576,9 +831,39 @@ def from_onnx(model: ModelProto, runtime):
stub = OnnxStub(model, runtime)
return stub.inputs, stub.outputs, stub.handler
def run_onnx(model: ModelProto, runtime):
stub = OnnxStub(model, runtime)
stub.run()
def _search_shape(model: ModelProto, name: str) -> List[int]:
ans = (
next(
(
[
(d.dim_value if d.dim_value > 0 else 1)
for d in tensor.type.tensor_type.shape.dim
]
for tensor in model.graph.value_info
if tensor.name == name
),
None,
)
or next(
(
[
(d.dim_value if d.dim_value > 0 else 1)
for d in tensor.type.tensor_type.shape.dim
]
for tensor in model.graph.input
if tensor.name == name
),
None,
)
or next(
[int(d) for d in tensor.dims]
for tensor in model.graph.initializer
if tensor.name == name
)
)
return ans
def _parse_attribute(node: NodeProto, attrs: Dict[str, Any] = dict()) -> Dict[str, Any]:
for attr in node.attribute:
@ -598,15 +883,8 @@ def _parse_attribute(node: NodeProto, attrs: Dict[str, Any] = dict()) -> Dict[st
return attrs
def _parse_data(tensor: TensorProto) -> List[Union[int, float]]:
if tensor.data_type == TensorProto.INT32:
return [int(i) for i in tensor.int32_data]
elif tensor.data_type == TensorProto.INT64:
return [int(i) for i in tensor.int64_data]
elif tensor.data_type == TensorProto.FLOAT:
return [float(i) for i in tensor.float_data]
else:
assert False, "Unsupported Tensor Type: {}".format(tensor.data_type)
def _parse_data(tensor: TensorProto) -> List[Any]:
return to_array(tensor).flatten().tolist()
def _take_shape_dim(shape: TensorShapeProto) -> List[int]:

50
pyinfinitensor/tests/test_onnx.py
View File

@ -7,18 +7,20 @@ from onnx.helper import (
make_graph,
make_tensor_value_info,
)
from onnx.checker import check_model
from pyinfinitensor.onnx import from_onnx, backend, runtime, run_onnx
from onnx.checker import check_model, check_graph
from onnx.shape_inference import infer_shapes
from pyinfinitensor.onnx import from_onnx, OnnxStub, backend
def make_and_import_model(graph: onnx.GraphProto):
check_graph(graph)
model = make_model(graph)
check_model(model)
from_onnx(model, runtime)
from_onnx(model, backend.cpu_runtime())
class TestStringMethods(unittest.TestCase):
#def test_run(self):
# def test_run(self):
# model_file = next(
# (name for name in os.listdir() if name.endswith(".onnx")), None
# )
@ -31,16 +33,17 @@ class TestStringMethods(unittest.TestCase):
# run_onnx(onnx.load(model_file), runtime)
def test_load(self):
model_file = next(
(name for name in os.listdir() if name.endswith(".onnx")), None
)
if model_file != None:
for model_file in os.listdir():
if model_file.endswith(".onnx"):
print(
"model: {file}({size:.2f} MiB)".format(
file=model_file, size=os.path.getsize(model_file) / 1024 / 1024
)
)
from_onnx(onnx.load(model_file), runtime)
model = OnnxStub(onnx.load(model_file), backend.cpu_runtime()).to_onnx(
"new"
)
model = infer_shapes(model)
def test_tensor(self):
x = make_tensor_value_info("x", TensorProto.FLOAT, [1, 2, 3])
@ -55,7 +58,7 @@ class TestStringMethods(unittest.TestCase):
["i", "w"],
["o"],
"conv",
pads=[1, 1],
pads=[1, 1, 1, 1],
strides=[2, 1],
dilations=[1, 2],
)
@ -102,7 +105,7 @@ class TestStringMethods(unittest.TestCase):
["y"],
kernel_shape=[3, 3],
dilations=[1, 1],
pads=[0, 0],
pads=[0, 0, 0, 0],
strides=[2, 2],
name="maxPool",
)
@ -116,7 +119,7 @@ class TestStringMethods(unittest.TestCase):
["x"],
["y"],
kernel_shape=[3, 3],
pads=[0, 0],
pads=[0, 0, 0, 0],
strides=[2, 2],
name="avgPool",
)
@ -206,7 +209,7 @@ class TestStringMethods(unittest.TestCase):
def test_flatten(self):
x = make_tensor_value_info("x", TensorProto.FLOAT, [1, 3, 5, 7])
y = make_tensor_value_info("y", TensorProto.FLOAT, [1*3, 5 * 7])
y = make_tensor_value_info("y", TensorProto.FLOAT, [1 * 3, 5 * 7])
flatten = make_node("Flatten", ["x"], ["y"], axis=2, name="flatten")
# make_and_import_model(
make_graph([flatten], "flatten", [x], [y])
@ -254,22 +257,19 @@ class TestStringMethods(unittest.TestCase):
def test_slice(self):
data = make_tensor_value_info("data", TensorProto.UINT32, [10, 64, 162, 162])
output = make_tensor_value_info("output", TensorProto.UINT32, [1, 0, 99, 95])
starts = make_tensor_value_info("starts", TensorProto.INT64, [4])
starts_data = make_tensor("starts", TensorProto.INT64, [4], [2, 10, 1, 5])
ends = make_tensor_value_info("ends", TensorProto.INT64, [4])
ends_data = make_tensor("ends", TensorProto.INT64, [4], [3, 10, 100, 100])
output = make_tensor_value_info("output", TensorProto.UINT32, [1, 1, 99, 95])
starts = make_tensor("starts", TensorProto.INT64, [4], [2, 9, 1, 5])
ends = make_tensor("ends", TensorProto.INT64, [4], [3, 10, 100, 100])
slice = make_node("Slice", ["data", "starts", "ends"], ["output"], name="slice")
# FIXME 后端的实现是 axis:[start,end]，onnx 的实现是 axis:[start,end)
# make_and_import_model(
make_and_import_model(
make_graph(
[slice],
"slice",
[data, starts, ends],
[data],
[output],
[starts_data, ends_data],
[starts, ends],
)
)
# )
def test_pad(self):
data = make_tensor_value_info("data", TensorProto.UINT32, [1, 64, 162, 162])
@ -300,10 +300,10 @@ class TestStringMethods(unittest.TestCase):
graph = make_graph([matmul, add], "lr", [x, a, b], [y])
model = make_model(graph)
check_model(model)
from_onnx(model, runtime)
from_onnx(model, backend.cpu_runtime())
def test_frontend(self):
handler = backend.GraphHandler(runtime)
handler = backend.GraphHandler(backend.cpu_runtime())
a = handler.tensor([1, 2, 3], 12)
b = handler.tensor([1, 2, 3], 12)
c = handler.tensor([1, 2, 3], 12)

9
src/core/graph.cc
View File

@ -114,6 +114,15 @@ bool GraphObj::topo_sort() {
return this->sorted = true;
}
void GraphObj::optimize() {
for (auto &op : ops) {
switch (op->getOpType()) {
default:
break;
}
}
}
void GraphObj::dataMalloc() {
for (auto &tensor : tensors) {
tensor->dataMalloc();

64
src/core/graph_handler.cc
View File

@ -11,6 +11,8 @@
#include "operators/reshape.h"
#include "operators/slice.h"
#include "operators/softmax.h"
#include "operators/split.h"
#include "operators/transpose.h"
#include "operators/unary.h"
namespace infini {
@ -35,6 +37,24 @@ Tensor GraphHandlerObj::conv(Tensor input, Tensor weight, Tensor output, int ph,
}
}
Tensor GraphHandlerObj::convTransposed2d(Tensor input, Tensor weight,
Tensor output, int ph, int pw, int sh,
int sw, int dh, int dw, int oph,
int opw) {
if (output) {
g->addOpWithOutputs<ConvTransposed2dObj>(std::move(input),
std::move(weight), output, ph,
pw, sh, sw, dh, dw, oph, opw);
return output;
} else {
return g
->addOp<ConvTransposed2dObj>(std::move(input), std::move(weight),
output, ph, pw, sh, sw, dh, dw, oph,
opw)
->getOutput();
}
}
Tensor GraphHandlerObj::matmul(Tensor a, Tensor b, Tensor y, bool transA,
bool transB, Tensor bias, ActType act) {
if (y) {
@ -128,9 +148,31 @@ DEFINE_UNARY_METHOD(relu, Relu)
DEFINE_UNARY_METHOD(sigmoid, Sigmoid)
DEFINE_UNARY_METHOD(tanh, Tanh)
DEFINE_UNARY_METHOD(abs, Abs)
DEFINE_UNARY_METHOD(shape, Shape)
// see operators/reshape.h
DEFINE_UNARY_METHOD(identity, Identity)
Tensor GraphHandlerObj::pRelu(Tensor x, Tensor slope, Tensor y) {
if (y) {
g->addOpWithOutputs<PReluObj>(std::move(x), std::move(slope), y);
return y;
} else {
return g->addOp<PReluObj>(std::move(x), std::move(slope), y)
->getOutput();
}
}
Tensor GraphHandlerObj::clip(Tensor x, Tensor y, std::optional<float> min,
std::optional<float> max) {
if (y) {
g->addOpWithOutputs<ClipObj>(std::move(x), y, min, max);
return y;
} else {
return g->addOp<ClipObj>(std::move(x), y, min, max)->getOutput();
}
}
Tensor GraphHandlerObj::softmax(Tensor input, Tensor output, int axis) {
if (output) {
g->addOpWithOutputs<SoftmaxObj>(std::move(input), output, axis);
@ -151,6 +193,16 @@ Tensor GraphHandlerObj::flatten(Tensor input, Tensor output, int axis) {
}
}
Tensor GraphHandlerObj::transpose(Tensor data, Tensor transposed, Shape perm) {
if (transposed) {
g->addOpWithOutputs<TransposeObj>(std::move(data), transposed, perm);
return transposed;
} else {
return g->addOp<TransposeObj>(std::move(data), transposed, perm)
->getOutput();
}
}
Tensor GraphHandlerObj::reshape(Tensor data, Tensor reshaped, Shape shape) {
if (reshaped) {
g->addOpWithOutputs<ReshapeObj>(std::move(data), reshaped,
@ -171,6 +223,18 @@ Tensor GraphHandlerObj::concat(TensorVec inputs, Tensor output, int dim) {
}
}
TensorVec GraphHandlerObj::split(Tensor input, std::optional<TensorVec> outputs,
int axis, int num_outputs) {
if (outputs) {
g->addOpWithOutputs<SplitObj>(std::move(input), outputs, axis,
num_outputs);
return *outputs;
} else {
return g->addOp<SplitObj>(std::move(input), outputs, axis, num_outputs)
->getOutputs();
}
}
Tensor GraphHandlerObj::gather(Tensor data, Tensor indices, Tensor output,
int axis) {
if (output) {

121
src/core/tensor.cc
View File

@ -64,79 +64,24 @@ vector<size_t> TensorObj::getStride() const {
void TensorObj::printData() const {
IT_ASSERT(data != nullptr);
void *ptr = nullptr;
Blob buffer;
if (!runtime->isCpu()) {
buffer = NativeCpuRuntimeObj::getInstance()->allocBlob(getBytes());
runtime->copyBlobToCPU(buffer->getPtr<void *>(),
getRawDataPtr<void *>(), getBytes());
ptr = buffer->getPtr<void *>();
} else
ptr = data->getPtr<float *>();
if (dtype == DataType::Float32)
printDataFloat(static_cast<float *>(ptr));
else if (dtype == DataType::UInt32)
printDataUint32_t(static_cast<uint32_t *>(ptr));
else
if (!runtime->isCpu())
IT_TODO_HALT();
}
void TensorObj::printDataFloat(float *ptr) const {
std::cout << "Tensor: " << guid << std::endl;
auto numDims = shape.size();
auto dimSzVec = std::vector<int>(numDims, 1);
dimSzVec[numDims - 1] = shape[numDims - 1];
for (int i = numDims - 1; i != 0; --i)
dimSzVec[i - 1] = dimSzVec[i] * shape[i - 1];
for (size_t i = 0, iEnd = size(); i < iEnd; ++i) {
if (iEnd > 1000 && i > 20 && i < iEnd - 20) {
printf("... , ");
i = iEnd - 20;
continue;
}
for (size_t j = 0; j < numDims; ++j) {
if (i % dimSzVec[j] == 0) {
std::cout << "[";
}
}
printf("%.1f", ptr[i]);
for (size_t j = 0; j < numDims; ++j) {
if ((int)i % dimSzVec[j] == dimSzVec[j] - 1) {
std::cout << "]";
}
}
if (i != size() - 1)
std::cout << ", ";
if ((int)i % dimSzVec[numDims - 1] == dimSzVec[numDims - 1] - 1)
std::cout << std::endl;
}
}
#define TRY_PRINT(N) \
if (dtype == DataType(N)) \
std::cout << dataToString<DT<N>::t>() << std::endl;
void TensorObj::printDataUint32_t(uint32_t *ptr) const {
IT_ASSERT(data != nullptr);
std::cout << "Tensor: " << guid << std::endl;
auto numDims = shape.size();
auto dimSzVec = std::vector<int>(numDims, 1);
dimSzVec[numDims - 1] = shape[numDims - 1];
for (int i = numDims - 1; i != 0; --i)
dimSzVec[i - 1] = dimSzVec[i] * shape[i - 1];
for (size_t i = 0, iEnd = size(); i < iEnd; ++i) {
for (size_t j = 0; j < numDims; ++j) {
if (i % dimSzVec[j] == 0) {
std::cout << "[";
}
}
std::cout << ptr[i];
for (size_t j = 0; j < numDims; ++j) {
if ((int)i % dimSzVec[j] == dimSzVec[j] - 1) {
std::cout << "]";
}
}
if (i != size() - 1)
std::cout << ", ";
if ((int)i % dimSzVec[numDims - 1] == dimSzVec[numDims - 1] - 1)
std::cout << std::endl;
}
TRY_PRINT(0) // fmt: new line
else TRY_PRINT(1) //
else TRY_PRINT(2) //
else TRY_PRINT(3) //
else TRY_PRINT(4) //
else TRY_PRINT(5) //
else TRY_PRINT(6) //
else TRY_PRINT(7) //
else IT_TODO_HALT();
#undef TRY_PRINT
}
bool TensorObj::equalData(const Tensor &rhs, double relativeError) const {
@ -147,19 +92,27 @@ bool TensorObj::equalData(const Tensor &rhs, double relativeError) const {
IT_ASSERT(rhs->getRuntime()->isCpu());
if (size() != rhs->size())
return false;
if (getDType() == DataType::UInt32)
return equalDataImpl(getRawDataPtr<uint32_t *>(),
rhs->getRawDataPtr<uint32_t *>(), size(), 0);
else if (getDType() == DataType::Float32)
return equalDataImpl(getRawDataPtr<float *>(),
rhs->getRawDataPtr<float *>(), size(),
relativeError);
else
IT_TODO_HALT();
#define TEST_EQUAL(N) \
if (dtype == DataType(N)) \
return equalDataImpl(getRawDataPtr<DT<N>::t *>(), \
rhs->getRawDataPtr<DT<N>::t *>(), size());
TEST_EQUAL(0) // fmt: new line
else TEST_EQUAL(1) //
else TEST_EQUAL(2) //
else TEST_EQUAL(3) //
else TEST_EQUAL(4) //
else TEST_EQUAL(5) //
else TEST_EQUAL(6) //
else TEST_EQUAL(7) //
else IT_TODO_HALT();
#undef TEST_EQUAL
}
void TensorObj::dataMalloc() {
if (data == nullptr)
if (!data)
data = runtime->allocBlob(getBytes());
}
@ -201,9 +154,9 @@ Shape TensorObj::getPosByOffset(size_t offset, Shape dim) const {
size_t TensorObj::getOffsetByPos(Shape pos, Shape dim) const {
int n = dim.size();
size_t offset = pos.at(0);
for (auto i = 1; i < n; i++) {
for (auto i = 1; i < n; i++)
offset = offset * dim.at(i) + pos.at(i);
}
return offset;
}
@ -213,10 +166,10 @@ size_t TensorObj::getOffsetByBroadcastOffset(size_t bcOffset,
Shape pos = bcPos;
int n = shape.size();
for (auto i = 0; i < n; i++) {
for (auto i = 0; i < n; i++)
if (shape.at(i) == 1)
pos[i] = 0;
}
return getOffsetByPos(pos, shape);
}
}; // namespace infini

6
src/cuda/cuda_runtime.cc
View File

@ -1,6 +1,7 @@
#include "cuda/cuda_runtime.h"
#include "core/kernel.h"
#include "core/perf_engine.h"
#include "core/runtime.h"
#include "operators/conv.h"
#include "operators/matmul.h"
namespace infini {
@ -16,11 +17,12 @@ void CudaRuntimeObj::runWithoutSync(const Graph &graph) const {
auto perfKey = PerfEngine::Key{kernelAttrs, op->getOpPerfKey()};
auto perfData = perfEngine.getPerfData(perfKey);
// IT_ASSERT(perfData, "No perf data for OP " + op->toString());
if (perfData)
if (perfData) {
kernel->compute(op, perfData, this);
else
} else {
kernel->compute(op, this);
}
}
}
void CudaRuntimeObj::tune(const Graph &graph, bool profiling = false) const {

119
src/ffi/ffi_infinitensor.cc
View File

@ -3,15 +3,24 @@
#include "operators/concat.h"
#include "operators/conv.h"
#include "operators/gather.h"
#include "operators/matmul.h"
#include "operators/pad.h"
#include "operators/pooling.h"
#include "operators/reduce_mean.h"
#include "operators/reshape.h"
#include "operators/split.h"
#include "operators/transpose.h"
#include "operators/unary.h"
#include <algorithm>
#include <pybind11/stl.h>
#ifdef USE_CUDA
#include "cuda/cuda_runtime.h"
#include "cuda/operator_timer.h"
#endif
#ifdef USE_BANG
#include "bang/bang_runtime.h"
#endif
#ifdef USE_INTELCPU
#include "intelcpu/mkl_runtime.h"
#include "intelcpu/operator_timer.h"
@ -57,6 +66,7 @@ void export_values(py::module &m) {
.VALUE(OpType, G2BMM)
.VALUE(OpType, GBMM)
.VALUE(OpType, Pad)
.VALUE(OpType, Clip)
.VALUE(OpType, Slice)
.VALUE(OpType, Concat)
.VALUE(OpType, Split)
@ -78,11 +88,12 @@ void export_values(py::module &m) {
.VALUE(OpType, Softmax)
.VALUE(OpType, Activation)
.VALUE(OpType, Relu)
.VALUE(OpType, PRelu)
.VALUE(OpType, Sigmoid)
.VALUE(OpType, Tanh)
.VALUE(OpType, Abs)
.VALUE(OpType, Resize)
.VALUE(OpType, MemBound)
.VALUE(OpType, Dropout)
.export_values();
#undef VALUE
@ -112,6 +123,10 @@ static int tensor_dtype(Tensor t) {
static Ref<CudaRuntimeObj> cuda_runtime() { return make_ref<CudaRuntimeObj>(); }
#endif
#ifdef USE_BANG
static Ref<BangRuntimeObj> bang_runtime() { return make_ref<BangRuntimeObj>(); }
#endif
#ifdef USE_INTELCPU
static Ref<RuntimeObj> intelcpu_runtime() { return make_ref<MklRuntimeObj>(); }
#endif
@ -123,11 +138,27 @@ static std::tuple<int, int, int, int, int, int> conv_attrs_of(Operator op) {
conv->getDw(), conv->getSh(), conv->getSw());
}
static std::tuple<int, int, int, int, int, int, int, int>
conv_trans_attrs_of(Operator op) {
IT_ASSERT(op->getOpType() == OpType::ConvTrans);
auto conv = dynamic_cast<const ConvTransposed2dObj *>(op.get());
auto [oph, opw] = conv->getOutputPadding();
return std::make_tuple(conv->getPh(), conv->getPw(), conv->getDh(),
conv->getDw(), conv->getSh(), conv->getSw(), oph,
opw);
}
static std::tuple<bool, bool> matmul_attrs_of(Operator op) {
IT_ASSERT(op->getOpType() == OpType::Matmul);
auto matmul = dynamic_cast<const MatmulObj *>(op.get());
return std::make_tuple(matmul->getTransA(), matmul->getTransB());
}
static std::tuple<float, float, bool> batch_norm_attrs_of(Operator op) {
IT_ASSERT(op->getOpType() == OpType::BatchNorm);
auto batchnorm = dynamic_cast<const BatchNormObj *>(op.get());
return std::make_tuple(batchnorm->getMomentum(), batchnorm->getEps(),
batchnorm->getTraining());
batchnorm->getTrainingMode());
}
static std::tuple<int, int, int, int, int, int, int, int>
@ -140,45 +171,88 @@ pool_attrs_of(Operator op) {
pool->getSh(), pool->getSw());
}
static std::tuple<std::optional<float>, std::optional<float>>
clip_attrs_of(Operator op) {
IT_ASSERT(op->getOpType() == OpType::Clip);
auto clip = dynamic_cast<const ClipObj *>(op.get());
return std::make_tuple(clip->getMin(), clip->getMax());
}
static std::tuple<vector<int>, bool> reduce_mean_attrs_of(Operator op) {
IT_ASSERT(op->getOpType() == OpType::ReduceMean);
auto reduce_mean = dynamic_cast<const ReduceMeanObj *>(op.get());
auto &set = reduce_mean->getAxes();
return std::make_tuple(vector(set.begin(), set.end()),
reduce_mean->getKeepDims());
}
static int concat_axis_of(Operator op) {
IT_ASSERT(op->getOpType() == OpType::Concat);
return dynamic_cast<const ConcatObj *>(op.get())->getDim();
}
static int split_axis_of(Operator op) {
IT_ASSERT(op->getOpType() == OpType::Split);
return dynamic_cast<const SplitObj *>(op.get())->getDim();
}
static int gather_axis_of(Operator op) {
IT_ASSERT(op->getOpType() == OpType::Gather);
return dynamic_cast<const GatherObj *>(op.get())->getAxis();
}
static vector<int> reduce_mean_axes_of(Operator op) {
IT_ASSERT(op->getOpType() == OpType::ReduceMean);
auto &set = dynamic_cast<const ReduceMeanObj *>(op.get())->getAxes();
return vector(set.begin(), set.end());
static vector<int64_t> reshape_shape_of(Operator op) {
IT_ASSERT(op->getOpType() == OpType::Reshape);
auto shape = dynamic_cast<const ReshapeObj *>(op.get())->getShape();
vector<int64_t> ans(shape.size());
std::transform(shape.begin(), shape.end(), ans.begin(),
[](auto x) { return static_cast<int64_t>(x); });
return ans;
}
static Shape reshape_shape_of(Operator op) {
IT_ASSERT(op->getOpType() == OpType::Reshape);
return dynamic_cast<const ReshapeObj *>(op.get())->getShape();
static vector<int64_t> pad_pads_of(Operator op) {
IT_ASSERT(op->getOpType() == OpType::Pad);
auto shape = dynamic_cast<const PadObj *>(op.get())->getPads();
vector<int64_t> ans(shape.size());
std::transform(shape.begin(), shape.end(), ans.begin(),
[](auto x) { return static_cast<int64_t>(x); });
return ans;
}
static vector<int> transpose_permute_of(Operator op) {
IT_ASSERT(op->getOpType() == OpType::Transpose);
return dynamic_cast<const TransposeObj *>(op.get())->getPermute();
}
void export_functions(py::module &m) {
#define FUNCTION(NAME) def(#NAME, &NAME)
m.def("cpu_runtime", &NativeCpuRuntimeObj::getInstance)
#ifdef USE_CUDA
m.def("runtime", cuda_runtime)
#elif USE_INTELCPU
m.def("runtime", intelcpu_runtime)
#else
m.def("runtime", &NativeCpuRuntimeObj::getInstance)
.def("cuda_runtime", cuda_runtime)
#endif
#ifdef USE_INTELCPU
.def("intelcpu_runtime", intelcpu_runtime)
#endif
#ifdef USE_CUDA
.FUNCTION(cuda_runtime)
#endif
#ifdef USE_BANG
.FUNCTION(bang_runtime)
#endif
.FUNCTION(conv_attrs_of)
.FUNCTION(conv_trans_attrs_of)
.FUNCTION(matmul_attrs_of)
.FUNCTION(batch_norm_attrs_of)
.FUNCTION(pool_attrs_of)
.FUNCTION(clip_attrs_of)
.FUNCTION(reduce_mean_attrs_of)
.FUNCTION(tensor_dtype)
.FUNCTION(reshape_shape_of)
.FUNCTION(pad_pads_of)
.FUNCTION(transpose_permute_of)
.FUNCTION(concat_axis_of)
.FUNCTION(gather_axis_of)
.FUNCTION(reduce_mean_axes_of);
.FUNCTION(split_axis_of)
.FUNCTION(gather_axis_of);
#undef FUNCTION
}
@ -191,6 +265,10 @@ void init_graph_builder(py::module &m) {
#ifdef USE_CUDA
py::class_<CudaRuntimeObj, std::shared_ptr<CudaRuntimeObj>, RuntimeObj>(
m, "CudaRuntime");
#endif
#ifdef USE_BANG
py::class_<BangRuntimeObj, std::shared_ptr<BangRuntimeObj>, RuntimeObj>(
m, "BangRuntime");
#endif
py::class_<TensorObj, std::shared_ptr<TensorObj>>(m, "Tensor")
.def("fuid", &TensorObj::getFuid, policy::automatic)
@ -215,6 +293,7 @@ void init_graph_builder(py::module &m) {
.def(py::init<Runtime>())
.def("tensor", &Handler::tensor, policy::move)
.def("conv", &Handler::conv, policy::move)
.def("convTransposed2d", &Handler::convTransposed2d, policy::move)
.def("matmul", &Handler::matmul, policy::move)
.def("batchNorm", &Handler::batchNorm, policy::move)
.def("maxPool", &Handler::maxPool, policy::move)
@ -229,15 +308,21 @@ void init_graph_builder(py::module &m) {
.def("tanh", &Handler::tanh, policy::move)
.def("softmax", &Handler::softmax, policy::move)
.def("abs", &Handler::abs, policy::move)
.def("shape", &Handler::shape, policy::move)
.def("identity", &Handler::identity, policy::move)
.def("flatten", &Handler::flatten, policy::move)
.def("pRelu", &Handler::pRelu, policy::move)
.def("clip", &Handler::clip, policy::move)
.def("transpose", &Handler::transpose, policy::move)
.def("reshape", &Handler::reshape, policy::move)
.def("concat", &Handler::concat, policy::move)
.def("split", &Handler::split, policy::move)
.def("gather", &Handler::gather, policy::move)
.def("reduce_mean", &Handler::reduceMean, policy::move)
.def("slice", &Handler::slice, policy::move)
.def("pad", &Handler::pad, policy::move)
.def("topo_sort", &Handler::topo_sort, policy::automatic)
.def("optimize", &Handler::optimize, policy::automatic)
.def("operators", &Handler::operators, policy::move)
.def("data_malloc", &Handler::data_malloc, policy::automatic)
.def("run", &Handler::run, policy::automatic);

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#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
#include "operators/unary.h"
namespace infini {
class UnaryCnnl : public BangKernelWithoutConfig {
virtual cnnlActivationMode_t getOpType() const = 0;
virtual float getCoef() const = 0;
virtual tuple<float, float> getAlphBeta() const { return {1.f, 0.f}; }
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<UnaryObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
int len = dim.size();
int size = 1;
for (int i = 0; i < len; ++i) {
size *= dim[i];
}
int dim_array[1] = {size};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_ARRAY,
CNNL_DTYPE_FLOAT, 1, dim_array));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_ARRAY,
CNNL_DTYPE_FLOAT, 1, dim_array));
// get op descriptor
cnnlActivationDescriptor_t opDesc;
checkCnnlError(cnnlCreateActivationDescriptor(&opDesc));
checkCnnlError(cnnlSetActivationDescriptor(
opDesc, getOpType(), CNNL_NOT_PROPAGATE_NAN, getCoef()));
auto [alpha, beta] = getAlphBeta();
cnnlStatus_t stat =
cnnlActivationForward(context->cnnlHandle(), opDesc, &alpha, aDesc,
aData, &beta, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
checkCnnlError(cnnlDestroyActivationDescriptor(opDesc));
}
};
class RoundCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<UnaryObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
cnnlStatus_t stat =
cnnlRound(context->cnnlHandle(), aDesc, aData, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
};
class SquareCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<UnaryObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
cnnlStatus_t stat =
cnnlSquare(context->cnnlHandle(), aDesc, aData, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
};
class PReluCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<PReluObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const bData = (op->getInputs(1)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, bDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
int alpha_array[4] = {1, 1, 1, 1};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
checkCnnlError(cnnlCreateTensorDescriptor(&bDesc));
checkCnnlError(cnnlSetTensorDescriptor(
bDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, alpha_array));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
cnnlStatus_t stat = cnnlPrelu(context->cnnlHandle(), aDesc, aData,
bDesc, bData, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(bDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
};
class ReluCnnl : public UnaryCnnl {
cnnlActivationMode_t getOpType() const override {
return CNNL_ACTIVATION_RELU;
}
float getCoef() const override { return 0.0; }
};
class SigmoidCnnl : public UnaryCnnl {
cnnlActivationMode_t getOpType() const override {
return CNNL_ACTIVATION_SIGMOID;
}
float getCoef() const override { return 0.0; }
};
class TanhCnnl : public UnaryCnnl {
cnnlActivationMode_t getOpType() const override {
return CNNL_ACTIVATION_TANH;
}
float getCoef() const override { return 0.0; }
};
REGISTER_KERNEL(Device::BANG, OpType::Relu, DataType::Float32, ReluCnnl,
"Relu_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::PRelu, DataType::Float32, PReluCnnl,
"PRelu_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::Sigmoid, DataType::Float32, SigmoidCnnl,
"Sigmoid_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::Tanh, DataType::Float32, TanhCnnl,
"Tanh_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::Round, DataType::Float32, RoundCnnl,
"Round_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::Square, DataType::Float32, SquareCnnl,
"Square_cnnl_BANG_Float32");
}; // namespace infini

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#include "operators/activation_backward.h"
#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
namespace infini {
class ActivationBackwardCnnl : public BangKernelWithoutConfig {
virtual cnnlActivationMode_t getOpType() const = 0;
virtual float getCoef() const = 0;
virtual tuple<float, float> getAlphBeta() const { return {1.f, 0.f}; }
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<ActivationBackwardObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const yData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const diffYData = (op->getInputs(1)->getRawDataPtr<void *>());
void *const xData = (op->getInputs(2)->getRawDataPtr<void *>());
void *const diffXData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t yDesc, diffYDesc, xDesc, diffXDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&yDesc));
checkCnnlError(cnnlSetTensorDescriptor(yDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&diffYDesc));
checkCnnlError(cnnlSetTensorDescriptor(diffYDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&xDesc));
checkCnnlError(cnnlSetTensorDescriptor(xDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&diffXDesc));
checkCnnlError(cnnlSetTensorDescriptor(diffXDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get op descriptor
cnnlActivationDescriptor_t opDesc;
checkCnnlError(cnnlCreateActivationDescriptor(&opDesc));
checkCnnlError(cnnlSetActivationDescriptor(
opDesc, getOpType(), CNNL_NOT_PROPAGATE_NAN, getCoef()));
auto [alpha, beta] = getAlphBeta();
cnnlStatus_t stat = cnnlActivationBackward(
context->cnnlHandle(), opDesc, &alpha, yDesc, yData, diffYDesc,
diffYData, xDesc, xData, &beta, diffXDesc, diffXData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(yDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(diffYDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(xDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(diffXDesc));
checkCnnlError(cnnlDestroyActivationDescriptor(opDesc));
}
};
class ReluBackwardCnnl : public ActivationBackwardCnnl {
cnnlActivationMode_t getOpType() const override {
return CNNL_ACTIVATION_RELU;
}
float getCoef() const override { return 0.0; }
};
class SigmoidBackwardCnnl : public ActivationBackwardCnnl {
cnnlActivationMode_t getOpType() const override {
return CNNL_ACTIVATION_SIGMOID;
}
float getCoef() const override { return 0.0; }
};
class TanhBackwardCnnl : public ActivationBackwardCnnl {
cnnlActivationMode_t getOpType() const override {
return CNNL_ACTIVATION_TANH;
}
float getCoef() const override { return 0.0; }
};
REGISTER_KERNEL(Device::BANG, OpType::ReluBackward, DataType::Float32,
ReluBackwardCnnl, "ReluBackward_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::SigmoidBackward, DataType::Float32,
SigmoidBackwardCnnl, "SigmoidBackward_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::TanhBackward, DataType::Float32,
TanhBackwardCnnl, "TanhBackward_cnnl_BANG_Float32");
}; // namespace infini

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#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
#include "operators/unary.h"
namespace infini {
class CastCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<CastObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
cnnlCastDataType_t NlCastType;
CastObj::CastType type = op->getType();
switch (type) {
case CastObj::Float2Int64:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT64, 4, dim_array));
NlCastType = CNNL_CAST_FLOAT_TO_INT64;
break;
case CastObj::Float2Int32:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT32, 4, dim_array));
NlCastType = CNNL_CAST_FLOAT_TO_INT32;
break;
case CastObj::Float2Int16:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT16, 4, dim_array));
NlCastType = CNNL_CAST_FLOAT_TO_INT16;
break;
case CastObj::Float2Int8:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT8, 4, dim_array));
NlCastType = CNNL_CAST_FLOAT_TO_INT8;
break;
case CastObj::Int322Float:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT32, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, dim_array));
NlCastType = CNNL_CAST_INT32_TO_FLOAT;
break;
case CastObj::Int322Int8:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT32, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT8, 4, dim_array));
NlCastType = CNNL_CAST_INT32_TO_INT8;
break;
case CastObj::Int322Int16:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT32, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT16, 4, dim_array));
NlCastType = CNNL_CAST_INT32_TO_INT16;
break;
case CastObj::Int162Float:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT16, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, dim_array));
NlCastType = CNNL_CAST_INT16_TO_FLOAT;
break;
case CastObj::Int162Int32:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT16, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT32, 4, dim_array));
NlCastType = CNNL_CAST_INT16_TO_INT32;
break;
case CastObj::Int82Float:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT8, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, dim_array));
NlCastType = CNNL_CAST_INT8_TO_FLOAT;
break;
case CastObj::Int82Int16:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT8, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT16, 4, dim_array));
NlCastType = CNNL_CAST_INT8_TO_INT16;
break;
case CastObj::Int82Int32:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT8, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT32, 4, dim_array));
NlCastType = CNNL_CAST_INT8_TO_INT32;
break;
case CastObj::Uint82Float:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_UINT8, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, dim_array));
NlCastType = CNNL_CAST_UINT8_TO_FLOAT;
break;
case CastObj::Uint82Int32:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_UINT8, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT32, 4, dim_array));
NlCastType = CNNL_CAST_UINT8_TO_INT32;
break;
case CastObj::Uint82Int64:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_UINT8, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT64, 4, dim_array));
NlCastType = CNNL_CAST_UINT8_TO_INT64;
break;
case CastObj::Int322Int64:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT32, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT64, 4, dim_array));
NlCastType = CNNL_CAST_INT32_TO_INT64;
break;
case CastObj::Int642Int32:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT64, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT32, 4, dim_array));
NlCastType = CNNL_CAST_INT64_TO_INT32;
break;
case CastObj::Int642Uint32:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT64, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_UINT32, 4, dim_array));
NlCastType = CNNL_CAST_INT64_TO_UINT32;
break;
case CastObj::Int642Float:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT64, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, dim_array));
NlCastType = CNNL_CAST_INT64_TO_FLOAT;
break;
case CastObj::Uint322Int64:
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_UINT32, 4, dim_array));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_INT64, 4, dim_array));
NlCastType = CNNL_CAST_UINT32_TO_INT64;
break;
default:
IT_TODO_HALT();
}
cnnlStatus_t stat = cnnlCastDataType(context->cnnlHandle(), aDesc,
aData, NlCastType, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
};
REGISTER_KERNEL(Device::BANG, OpType::Cast, DataType::Float32, CastCnnl,
"Cast_cnnl_BANG_Float32");
}; // namespace infini

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#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
#include "operators/unary.h"
namespace infini {
class CeilCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<UnaryObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
cnnlStatus_t stat =
cnnlCeil(context->cnnlHandle(), aDesc, aData, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
};
REGISTER_KERNEL(Device::BANG, OpType::Ceil, DataType::Float32, CeilCnnl,
"Ceil_cnnl_BANG_Float32");
}; // namespace infini

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#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
#include "operators/unary.h"
namespace infini {
class ClipCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<ClipObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
float min = op->getMin().value();
float max = op->getMax().value();
cnnlTensorDescriptor_t aDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
cnnlStatus_t stat =
cnnlClip(context->cnnlHandle(), aDesc, aData, &min, &max, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
}
};
REGISTER_KERNEL(Device::BANG, OpType::Clip, DataType::Float32, ClipCnnl,
"Clip_cnnl_BANG_Float32");
}; // namespace infini

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#include "operators/concat.h"
#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
namespace infini {
class ConcatCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<ConcatObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
int num = op->numInputs();
int axis = op->getDim();
void *argv[num];
for (int i = 0; i < num; ++i) {
argv[i] = op->getInputs(i)->getRawDataPtr<void *>();
}
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t desc;
int dim_array[num][4];
for (int i = 0; i < num; ++i) {
auto dim = op->getInputs(i)->getDims();
if (dim.size() != 4) {
IT_TODO_HALT();
}
dim_array[i][0] = dim[0];
dim_array[i][1] = dim[1];
dim_array[i][2] = dim[2];
dim_array[i][3] = dim[3];
}
auto dim = op->getOutput()->getDims();
int dimout_array[4] = {dim[0], dim[1], dim[2], dim[3]};
checkCnnlError(cnnlCreateTensorDescriptor(&desc));
checkCnnlError(cnnlSetTensorDescriptor(
desc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, dimout_array));
cnnlTensorDescriptor_t descArray[num];
for (int i = 0; i < num; ++i) {
checkCnnlError(cnnlCreateTensorDescriptor(&descArray[i]));
checkCnnlError(
cnnlSetTensorDescriptor(descArray[i], CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array[i]));
}
size_t wsSize;
cnnlGetConcatWorkspaceSize(context->cnnlHandle(), num, &wsSize);
BangPtr wsData = context->getWorkspace(wsSize);
cnnlStatus_t stat =
cnnlConcat(context->cnnlHandle(), num, axis, descArray, argv,
wsData, wsSize, desc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
for (int i = 0; i < num; ++i) {
checkCnnlError(cnnlDestroyTensorDescriptor(descArray[i]));
}
checkCnnlError(cnnlDestroyTensorDescriptor(desc));
}
};
REGISTER_KERNEL(Device::BANG, OpType::Concat, DataType::Float32, ConcatCnnl,
"Concat_cnnl_BANG_Float32");
}; // namespace infini

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#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
#include "operators/conv.h"
namespace infini {
class ConvTransCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<ConvBaseObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
const auto [ph, pw, sh, sw, dh, dw] = op->getPadStrideDilation();
const auto [n, c, h, w, f, r, s] = op->getNCHWFRS();
const int cpg = op->getChannelPerGroup();
const int g = c / cpg;
int pad[4] = {ph, ph, pw, pw};
int stride[2] = {sh, sw};
int dilation[2] = {dh, dw};
cnnlConvolutionDescriptor_t convDesc;
checkCnnlError(cnnlCreateConvolutionDescriptor(&convDesc));
checkCnnlError(cnnlSetConvolutionDescriptor(
convDesc, 4, pad, stride, dilation, g, CNNL_DTYPE_FLOAT));
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const bData = (op->getInputs(1)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, bDesc, cDesc;
auto dimInputs0 = op->getInputs(0)->getDims();
auto dimInputs1 = op->getInputs(1)->getDims();
auto dimOutput = op->getOutput()->getDims();
if (dimInputs0.size() != 4)
IT_TODO_HALT();
if (dimInputs1.size() != 4)
IT_TODO_HALT();
if (dimOutput.size() != 4)
IT_TODO_HALT();
int inputs0[4] = {dimInputs0[0], dimInputs0[1], dimInputs0[2],
dimInputs0[3]};
int inputs1[4] = {dimInputs1[0], dimInputs1[1], dimInputs1[2],
dimInputs1[3]};
int output[4] = {dimOutput[0], dimOutput[1], dimOutput[2],
dimOutput[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, inputs0));
checkCnnlError(cnnlCreateTensorDescriptor(&bDesc));
checkCnnlError(cnnlSetTensorDescriptor(bDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, inputs1));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, output));
cnnlConvolutionBwdDataAlgo_t algo;
cnnlGetConvolutionBackwardDataAlgorithm(
context->cnnlHandle(), aDesc, bDesc, convDesc, cDesc,
CNNL_CONVOLUTION_BWD_DATA_FASTEST, &algo);
size_t wsSize;
cnnlGetConvolutionBackwardDataWorkspaceSize(context->cnnlHandle(),
aDesc, bDesc, convDesc,
cDesc, algo, &wsSize);
BangPtr wsData = context->getWorkspace(wsSize);
cnnlStatus_t stat = cnnlConvolutionBackwardData(
context->cnnlHandle(), NULL, aDesc, aData, bDesc, bData, convDesc,
algo, wsData, wsSize, NULL, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(bDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
checkCnnlError(cnnlDestroyConvolutionDescriptor(convDesc));
}
};
REGISTER_KERNEL(Device::BANG, OpType::ConvTrans, DataType::Float32,
ConvTransCnnl, "ConvTrans_cnnl_BANG_Float32");
}; // namespace infini

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#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
#include "operators/conv.h"
namespace infini {
class ConvBackwardFilterCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<ConvBackwardFilterObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
const auto [ph, pw, sh, sw, dh, dw] = op->getPadStrideDilation();
const auto [n, c, h, w, f, r, s] = op->getNCHWFRS();
const int cpg = op->getChannelPerGroup();
const int g = c / cpg;
int pad[4] = {ph, ph, pw, pw};
int stride[2] = {sh, sw};
int dilation[2] = {dh, dw};
cnnlConvolutionDescriptor_t convDesc;
checkCnnlError(cnnlCreateConvolutionDescriptor(&convDesc));
checkCnnlError(cnnlSetConvolutionDescriptor(
convDesc, 4, pad, stride, dilation, g, CNNL_DTYPE_FLOAT));
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const bData = (op->getInputs(1)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, bDesc, cDesc, aDescTrans, bDescTrans,
cDescTrans;
auto dimInputs0 = op->getInputs(0)->getDims();
auto dimInputs1 = op->getInputs(1)->getDims();
auto dimOutput = op->getOutput()->getDims();
if (dimInputs0.size() != 4)
IT_TODO_HALT();
if (dimInputs1.size() != 4)
IT_TODO_HALT();
if (dimOutput.size() != 4)
IT_TODO_HALT();
int inputs0Array[4] = {dimInputs0[0], dimInputs0[1], dimInputs0[2],
dimInputs0[3]};
int inputs1Array[4] = {dimInputs1[0], dimInputs1[1], dimInputs1[2],
dimInputs1[3]};
int outputArray[4] = {dimOutput[0], dimOutput[1], dimOutput[2],
dimOutput[3]};
int inputs0ArrayTrans[4] = {dimInputs0[0], dimInputs0[2], dimInputs0[3],
dimInputs0[1]};
int inputs1ArrayTrans[4] = {dimInputs1[0], dimInputs1[2], dimInputs1[3],
dimInputs1[1]};
int outputArrayTrans[4] = {dimOutput[0], dimOutput[2], dimOutput[3],
dimOutput[1]};
int transMode[4] = {0, 2, 3, 1};
cnnlTransposeDescriptor_t transDesc;
checkCnnlError(cnnlCreateTransposeDescriptor(&transDesc));
checkCnnlError(cnnlSetTransposeDescriptor(transDesc, 4, transMode));
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, inputs0Array));
checkCnnlError(cnnlCreateTensorDescriptor(&aDescTrans));
checkCnnlError(cnnlSetTensorDescriptor(aDescTrans, CNNL_LAYOUT_NHWC,
CNNL_DTYPE_FLOAT, 4,
inputs0ArrayTrans));
size_t wsTrans1Size = dimInputs0[0] * dimInputs0[1] * dimInputs0[2] *
dimInputs0[3] * sizeof(float);
BangPtr wsTrans1Data = context->getWorkspace(wsTrans1Size);
cnnlStatus_t stat =
cnnlTranspose(context->cnnlHandle(), transDesc, aDesc, aData,
aDescTrans, wsTrans1Data);
if (stat != CNNL_STATUS_SUCCESS)
return;
checkCnnlError(cnnlCreateTensorDescriptor(&bDesc));
checkCnnlError(cnnlSetTensorDescriptor(
bDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, inputs1Array));
checkCnnlError(cnnlCreateTensorDescriptor(&bDescTrans));
checkCnnlError(cnnlSetTensorDescriptor(bDescTrans, CNNL_LAYOUT_NHWC,
CNNL_DTYPE_FLOAT, 4,
inputs1ArrayTrans));
size_t wsTrans2Size = dimInputs1[0] * dimInputs1[1] * dimInputs1[2] *
dimInputs1[3] * sizeof(float);
BangPtr wsTrans2Data = context->getWorkspace(wsTrans2Size);
stat = cnnlTranspose(context->cnnlHandle(), transDesc, bDesc, bData,
bDescTrans, wsTrans2Data);
if (stat != CNNL_STATUS_SUCCESS)
return;
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, outputArray));
checkCnnlError(cnnlCreateTensorDescriptor(&cDescTrans));
checkCnnlError(cnnlSetTensorDescriptor(cDescTrans, CNNL_LAYOUT_NHWC,
CNNL_DTYPE_FLOAT, 4,
outputArrayTrans));
size_t wsTrans3Size = dimOutput[0] * dimOutput[1] * dimOutput[2] *
dimOutput[3] * sizeof(float);
BangPtr wsTrans3Data = context->getWorkspace(wsTrans3Size);
cnnlConvolutionBwdFilterAlgo_t algo;
cnnlGetConvolutionBackwardFilterAlgorithm(
context->cnnlHandle(), convDesc, aDescTrans, bDescTrans, cDescTrans,
CNNL_CONVOLUTION_BWD_FILTER_FASTEST, &algo);
size_t wsSize;
cnnlGetConvolutionBackwardFilterWorkspaceSize(
context->cnnlHandle(), aDescTrans, bDescTrans, cDescTrans, convDesc,
algo, &wsSize);
BangPtr wsData = context->getWorkspace(wsSize);
stat = cnnlConvolutionBackwardFilter(
context->cnnlHandle(), NULL, aDescTrans, wsTrans1Data, bDescTrans,
wsTrans2Data, convDesc, algo, wsData, wsSize, NULL, cDescTrans,
wsTrans3Data);
if (stat != CNNL_STATUS_SUCCESS)
return;
int transMode2[4] = {0, 3, 1, 2};
cnnlTransposeDescriptor_t transOutputDesc;
checkCnnlError(cnnlCreateTransposeDescriptor(&transOutputDesc));
checkCnnlError(
cnnlSetTransposeDescriptor(transOutputDesc, 4, transMode2));
stat = cnnlTranspose(context->cnnlHandle(), transOutputDesc, cDescTrans,
wsTrans3Data, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(bDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(aDescTrans));
checkCnnlError(cnnlDestroyTensorDescriptor(bDescTrans));
checkCnnlError(cnnlDestroyTensorDescriptor(cDescTrans));
checkCnnlError(cnnlDestroyTransposeDescriptor(transDesc));
checkCnnlError(cnnlDestroyTransposeDescriptor(transOutputDesc));
checkCnnlError(cnnlDestroyConvolutionDescriptor(convDesc));
}
};
REGISTER_KERNEL(Device::BANG, OpType::ConvBackwardFilter, DataType::Float32,
ConvBackwardFilterCnnl, "ConvBackwardFilter_cnnl_BANG_Float32");
}; // namespace infini

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#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
#include "operators/unary.h"
namespace infini {
class CopyCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<UnaryObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
cnnlStatus_t stat =
cnnlCopy(context->cnnlHandle(), aDesc, aData, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
};
REGISTER_KERNEL(Device::BANG, OpType::Copy, DataType::Float32, CopyCnnl,
"Copy_cnnl_BANG_Float32");
}; // namespace infini

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#include "operators/det.h"
#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
namespace infini {
class DetCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<DetObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
DetObj::Mode mode = op->getMode();
cnnlDetMode_t nlMode;
if (mode == DetObj::LogDet) {
nlMode = CNNL_DET_MODE_LOGDET;
} else {
nlMode = CNNL_DET_MODE_DET;
}
cnnlTensorDescriptor_t aDesc, cDesc;
auto dimin = op->getInputs(0)->getDims();
auto dimout = op->getOutput()->getDims();
if (dimin.size() != 4 || dimout.size() != 2)
IT_TODO_HALT();
int dimin_array[4] = {dimin[0], dimin[1], dimin[2], dimin[3]};
int dimout_array[2] = {dimout[0], dimout[1]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_ARRAY, CNNL_DTYPE_FLOAT, 4, dimin_array));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_ARRAY, CNNL_DTYPE_FLOAT, 2, dimout_array));
cnnlStatus_t stat =
cnnlDet(context->cnnlHandle(), nlMode, aDesc, aData, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
};
REGISTER_KERNEL(Device::BANG, OpType::Det, DataType::Float32, DetCnnl,
"Det_cnnl_BANG_Float32");
}; // namespace infini

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src/kernels/bang/element_wise.cc
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@ -1,5 +1,4 @@
#include "operators/element_wise.h"
#include "bang/bang_element_wise.h"
#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
@ -66,6 +65,514 @@ class ElementWiseCnnl : public BangKernelWithoutConfig {
}
};
class LogicOpCnnl : public BangKernelWithoutConfig {
virtual cnnlLogicOp_t getOpType() const = 0;
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<ElementWiseObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const bData = (op->getInputs(1)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, bDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
checkCnnlError(cnnlCreateTensorDescriptor(&bDesc));
checkCnnlError(cnnlSetTensorDescriptor(bDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
size_t wsSize;
cnnlGetLogicOpWorkspaceSize(context->cnnlHandle(), aDesc, bDesc, cDesc,
&wsSize);
BangPtr wsData = context->getWorkspace(wsSize);
cnnlStatus_t stat =
cnnlLogicOp(context->cnnlHandle(), getOpType(), aDesc, aData, bDesc,
bData, wsData, wsSize, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(bDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
};
class BitComputeCnnl : public BangKernelWithoutConfig {
virtual cnnlBitComputeOp_t getOpType() const = 0;
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<ElementWiseObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const bData = (op->getInputs(1)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, bDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT32, 4, dim_array));
checkCnnlError(cnnlCreateTensorDescriptor(&bDesc));
checkCnnlError(cnnlSetTensorDescriptor(bDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT32, 4, dim_array));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_INT32, 4, dim_array));
size_t wsSize;
cnnlGetBitComputeWorkspaceSize(context->cnnlHandle(), aDesc, bDesc,
cDesc, &wsSize);
BangPtr wsData = context->getWorkspace(wsSize);
cnnlStatus_t stat =
cnnlBitCompute_v2(context->cnnlHandle(), getOpType(), aDesc, aData,
bDesc, bData, cDesc, cData, wsData, wsSize);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(bDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
};
class DivCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<ElementWiseObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const bData = (op->getInputs(1)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, bDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
checkCnnlError(cnnlCreateTensorDescriptor(&bDesc));
checkCnnlError(cnnlSetTensorDescriptor(bDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
size_t wsSize;
cnnlGetDivWorkspaceSize(context->cnnlHandle(), aDesc, bDesc, cDesc,
&wsSize);
BangPtr wsData = context->getWorkspace(wsSize);
cnnlStatus_t stat = cnnlDiv_v2(
context->cnnlHandle(), CNNL_COMPUTATION_HIGH_PRECISION, aDesc,
aData, bDesc, bData, wsData, wsSize, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(bDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
};
class MaximumCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<ElementWiseObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const bData = (op->getInputs(1)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, bDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
checkCnnlError(cnnlCreateTensorDescriptor(&bDesc));
checkCnnlError(cnnlSetTensorDescriptor(bDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get op descriptor
size_t wsSize;
cnnlGetMaximumWorkspaceSize(context->cnnlHandle(), cDesc, &wsSize);
BangPtr wsData = context->getWorkspace(wsSize);
cnnlStatus_t stat =
cnnlMaximum(context->cnnlHandle(), aDesc, aData, bDesc, bData,
cDesc, cData, wsData, wsSize);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(bDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
};
class MinimumCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<ElementWiseObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const bData = (op->getInputs(1)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, bDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
checkCnnlError(cnnlCreateTensorDescriptor(&bDesc));
checkCnnlError(cnnlSetTensorDescriptor(bDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get op descriptor
size_t wsSize;
cnnlGetMinimumWorkspaceSize(context->cnnlHandle(), cDesc, &wsSize);
BangPtr wsData = context->getWorkspace(wsSize);
cnnlStatus_t stat =
cnnlMinimum(context->cnnlHandle(), aDesc, aData, bDesc, bData,
cDesc, cData, wsData, wsSize);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(bDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
};
class MSELossCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<MSELossObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const bData = (op->getInputs(1)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
MSELossObj::Reduction reduction = op->getReduction();
cnnlTensorDescriptor_t aDesc, bDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
int dim_out[4] = {1, 1, 1, 1};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
checkCnnlError(cnnlCreateTensorDescriptor(&bDesc));
checkCnnlError(cnnlSetTensorDescriptor(bDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
if (reduction == MSELossObj::None) {
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, dim_array));
} else {
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_NCHW, CNNL_DTYPE_FLOAT, 4, dim_out));
}
cnnlStatus_t stat;
if (reduction == MSELossObj::None) {
stat = cnnlMSELoss(context->cnnlHandle(), CNNL_MSE_LOSS_NONE, aDesc,
aData, bDesc, bData, cDesc, cData);
} else if (reduction == MSELossObj::Sum) {
stat = cnnlMSELoss(context->cnnlHandle(), CNNL_MSE_LOSS_SUM, aDesc,
aData, bDesc, bData, cDesc, cData);
} else {
stat = cnnlMSELoss(context->cnnlHandle(), CNNL_MSE_LOSS_MEAN, aDesc,
aData, bDesc, bData, cDesc, cData);
}
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(bDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
};
class PowerCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<ElementWiseObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const bData = (op->getInputs(1)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, bDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
checkCnnlError(cnnlCreateTensorDescriptor(&bDesc));
checkCnnlError(cnnlSetTensorDescriptor(bDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get op descriptor
size_t wsSize;
cnnlGetPowWorkspaceSize(context->cnnlHandle(), aDesc, bDesc, cDesc,
&wsSize);
BangPtr wsData = context->getWorkspace(wsSize);
cnnlStatus_t stat =
cnnlPow(context->cnnlHandle(), CNNL_COMPUTATION_HIGH_PRECISION,
aDesc, aData, bDesc, bData, wsData, wsSize, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(bDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
};
class FloorDivCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<ElementWiseObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const bData = (op->getInputs(1)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, bDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
checkCnnlError(cnnlCreateTensorDescriptor(&bDesc));
checkCnnlError(cnnlSetTensorDescriptor(bDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
size_t wsSize;
cnnlGetFloorDivWorkspaceSize(context->cnnlHandle(), aDesc, bDesc, cDesc,
&wsSize);
BangPtr wsData = context->getWorkspace(wsSize);
cnnlStatus_t stat = cnnlFloorDiv_v2(
context->cnnlHandle(), CNNL_COMPUTATION_HIGH_PRECISION, aDesc,
aData, bDesc, bData, cDesc, cData, wsData, wsSize);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(bDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
};
class FloorModCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<ElementWiseObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const bData = (op->getInputs(1)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, bDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
checkCnnlError(cnnlCreateTensorDescriptor(&bDesc));
checkCnnlError(cnnlSetTensorDescriptor(bDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
size_t wsSize;
cnnlGetFloorModWorkspaceSize(context->cnnlHandle(), aDesc, bDesc, cDesc,
&wsSize);
BangPtr wsData = context->getWorkspace(wsSize);
cnnlStatus_t stat =
cnnlFloorMod(context->cnnlHandle(), aDesc, aData, bDesc, bData,
cDesc, cData, wsData, wsSize);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(bDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
};
class SquaredDifferenceCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<ElementWiseObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const bData = (op->getInputs(1)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, bDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
checkCnnlError(cnnlCreateTensorDescriptor(&bDesc));
checkCnnlError(cnnlSetTensorDescriptor(bDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
size_t wsSize;
cnnlGetSquaredDifferenceWorkspaceSize(context->cnnlHandle(), aDesc,
bDesc, cDesc, &wsSize);
BangPtr wsData = context->getWorkspace(wsSize);
cnnlStatus_t stat =
cnnlSquaredDifference(context->cnnlHandle(), aDesc, aData, bDesc,
bData, cDesc, cData, wsData, wsSize);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(bDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
};
class AddCnnl : public ElementWiseCnnl {
cnnlOpTensorDesc_t getOpType() const override { return CNNL_OP_TENSOR_ADD; }
};
@ -81,12 +588,57 @@ class MulCnnl : public ElementWiseCnnl {
cnnlOpTensorDesc_t getOpType() const override { return CNNL_OP_TENSOR_MUL; }
};
class ElementWiseBang : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
element_wise_kernel(_context, _op);
}
class EqualCnnl : public LogicOpCnnl {
cnnlLogicOp_t getOpType() const override { return CNNL_LOGIC_OP_EQ; }
};
class NotEqualCnnl : public LogicOpCnnl {
cnnlLogicOp_t getOpType() const override { return CNNL_LOGIC_OP_NE; }
};
class GreaterThanCnnl : public LogicOpCnnl {
cnnlLogicOp_t getOpType() const override { return CNNL_LOGIC_OP_GT; }
};
class GreaterEqualCnnl : public LogicOpCnnl {
cnnlLogicOp_t getOpType() const override { return CNNL_LOGIC_OP_GE; }
};
class LessThanCnnl : public LogicOpCnnl {
cnnlLogicOp_t getOpType() const override { return CNNL_LOGIC_OP_LT; }
};
class LessEqualCnnl : public LogicOpCnnl {
cnnlLogicOp_t getOpType() const override { return CNNL_LOGIC_OP_LE; }
};
class AndCnnl : public LogicOpCnnl {
cnnlLogicOp_t getOpType() const override { return CNNL_LOGIC_OP_AND; }
};
class OrCnnl : public LogicOpCnnl {
cnnlLogicOp_t getOpType() const override { return CNNL_LOGIC_OP_OR; }
};
class XorCnnl : public LogicOpCnnl {
cnnlLogicOp_t getOpType() const override { return CNNL_LOGIC_OP_XOR; }
};
class NotCnnl : public LogicOpCnnl {
cnnlLogicOp_t getOpType() const override { return CNNL_LOGIC_OP_NOT; }
};
class BitAndCnnl : public BitComputeCnnl {
cnnlBitComputeOp_t getOpType() const override { return CNNL_CYCLE_BAND_OP; }
};
class BitOrCnnl : public BitComputeCnnl {
cnnlBitComputeOp_t getOpType() const override { return CNNL_CYCLE_BOR_OP; }
};
class BitXorCnnl : public BitComputeCnnl {
cnnlBitComputeOp_t getOpType() const override { return CNNL_CYCLE_BXOR_OP; }
};
class BitNotCnnl : public BitComputeCnnl {
cnnlBitComputeOp_t getOpType() const override { return CNNL_BNOT_OP; }
};
// class BitLeftShiftCnnl : public BitComputeCnnl {
// cnnlBitComputeOp_t getOpType() const override { return
// CNNL_BLEFT_SHIFT_OP_V2; }
// };
// class BitRightShiftCnnl : public BitComputeCnnl {
// cnnlBitComputeOp_t getOpType() const override { return
// CNNL_BLEFT_SHIFT_OP_V2; }
// };
REGISTER_KERNEL(Device::BANG, OpType::Add, DataType::Float32, AddCnnl,
"Add_cnnl_BANG_Float32");
@ -95,8 +647,56 @@ REGISTER_KERNEL(Device::BANG, OpType::Sub, DataType::Float32, SubCnnl,
REGISTER_KERNEL(Device::BANG, OpType::Mul, DataType::Float32, MulCnnl,
"Mul_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::Div, DataType::Float32, ElementWiseBang,
"Div_Bang_Float32");
REGISTER_KERNEL(Device::BANG, OpType::Div, DataType::Float32, DivCnnl,
"Div_cnnl_Float32");
REGISTER_KERNEL(Device::BANG, OpType::Maximum, DataType::Float32, MaximumCnnl,
"Maximum_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::Minimum, DataType::Float32, MinimumCnnl,
"Minimum_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::MSELoss, DataType::Float32, MSELossCnnl,
"MSELoss_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::Power, DataType::Float32, PowerCnnl,
"Power_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::FloorDiv, DataType::Float32, FloorDivCnnl,
"FloorDiv_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::FloorMod, DataType::Float32, FloorModCnnl,
"FloorMod_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::SquaredDifference, DataType::Float32,
SquaredDifferenceCnnl, "SquaredDifference_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::Equal, DataType::Float32, EqualCnnl,
"Equal_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::NotEqual, DataType::Float32, NotEqualCnnl,
"NotEqual_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::GreaterThan, DataType::Float32,
GreaterThanCnnl, "GreaterThan_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::GreaterEqual, DataType::Float32,
GreaterEqualCnnl, "GreaterEqual_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::LessThan, DataType::Float32, LessThanCnnl,
"LessThan_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::LessEqual, DataType::Float32,
LessEqualCnnl, "LessEqual_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::And, DataType::Float32, AndCnnl,
"And_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::Or, DataType::Float32, OrCnnl,
"Or_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::Xor, DataType::Float32, XorCnnl,
"Xor_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::Not, DataType::Float32, NotCnnl,
"Not_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::BitAnd, DataType::Float32, BitAndCnnl,
"BitAnd_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::BitOr, DataType::Float32, BitOrCnnl,
"BitOr_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::BitXor, DataType::Float32, BitXorCnnl,
"BitXor_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::BitNot, DataType::Float32, BitNotCnnl,
"BitNot_cnnl_BANG_Float32");
// REGISTER_KERNEL(Device::BANG, OpType::BitLeftShift, DataType::Float32,
// BitLeftShiftCnnl,
// "BitLeftShift_cnnl_BANG_Float32");
// REGISTER_KERNEL(Device::BANG, OpType::BitRightShift, DataType::Float32,
// BitRightShiftCnnl,
// "BitRightShift_cnnl_BANG_Float32");
// REGISTER_KERNEL(Device::BANG, OpType::Pow, DataType::Float32,
// ElementWiseBang,
// "Pow_Bang_Float32");

47
src/kernels/bang/erf.cc Normal file
View File

@ -0,0 +1,47 @@
#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
#include "operators/unary.h"
namespace infini {
class ErfCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<UnaryObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
cnnlStatus_t stat =
cnnlErf_v2(context->cnnlHandle(), CNNL_COMPUTATION_HIGH_PRECISION,
aDesc, aData, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
};
REGISTER_KERNEL(Device::BANG, OpType::Erf, DataType::Float32, ErfCnnl,
"Erf_cnnl_BANG_Float32");
}; // namespace infini

47
src/kernels/bang/exp.cc Normal file
View File

@ -0,0 +1,47 @@
#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
#include "operators/unary.h"
namespace infini {
class ExpCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<UnaryObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
cnnlStatus_t stat =
cnnlExp_v2(context->cnnlHandle(), CNNL_COMPUTATION_HIGH_PRECISION,
aDesc, aData, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
};
REGISTER_KERNEL(Device::BANG, OpType::Exp, DataType::Float32, ExpCnnl,
"Exp_cnnl_BANG_Float32");
}; // namespace infini

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#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
#include "operators/unary.h"
namespace infini {
class FillCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<FillObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
float value = op->getValue();
cnnlTensorDescriptor_t cDesc;
auto dim = op->getOutput()->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
cnnlStatus_t stat =
cnnlFill(context->cnnlHandle(), value, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
};
REGISTER_KERNEL(Device::BANG, OpType::Fill, DataType::Float32, FillCnnl,
"Fill_cnnl_BANG_Float32");
}; // namespace infini

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#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
#include "operators/unary.h"
namespace infini {
class FloorCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<UnaryObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
cnnlStatus_t stat =
cnnlFloor(context->cnnlHandle(), aDesc, aData, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
};
REGISTER_KERNEL(Device::BANG, OpType::Floor, DataType::Float32, FloorCnnl,
"Floor_cnnl_BANG_Float32");
}; // namespace infini

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#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
#include "operators/unary.h"
namespace infini {
class HardtanhCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<HardtanhObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
float min = op->getMin();
float max = op->getMax();
cnnlTensorDescriptor_t aDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
cnnlStatus_t stat = cnnlHardtanh(context->cnnlHandle(), aDesc, aData,
max, min, aDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
}
};
REGISTER_KERNEL(Device::BANG, OpType::Hardtanh, DataType::Float32, HardtanhCnnl,
"Hardtanh_cnnl_BANG_Float32");
}; // namespace infini

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#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
#include "operators/unary.h"
namespace infini {
class L2LossCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<L2LossObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
cnnlStatus_t stat =
cnnlL2Loss(context->cnnlHandle(), aDesc, aData, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
}
};
REGISTER_KERNEL(Device::BANG, OpType::L2Loss, DataType::Float32, L2LossCnnl,
"L2Loss_cnnl_BANG_Float32");
}; // namespace infini

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#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
#include "operators/unary.h"
namespace infini {
class LogCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<LogObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
auto type = op->getType();
cnnlLogBase_t base;
switch (type) {
case LogObj::Log2:
base = CNNL_LOG_2;
break;
case LogObj::LogE:
base = CNNL_LOG_E;
break;
case LogObj::Log10:
base = CNNL_LOG_10;
break;
default:
IT_TODO_HALT();
}
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
cnnlStatus_t stat =
cnnlLog_v2(context->cnnlHandle(), CNNL_COMPUTATION_HIGH_PRECISION,
base, aDesc, aData, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
};
REGISTER_KERNEL(Device::BANG, OpType::Log, DataType::Float32, LogCnnl,
"Log_cnnl_BANG_Float32");
}; // namespace infini

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@ -18,19 +18,27 @@ class MatmulCnnl : public BangKernelWithoutConfig {
auto dimInputs0 = op->getInputs(0)->getDims();
auto dimInputs1 = op->getInputs(1)->getDims();
auto dimOutput = op->getOutput()->getDims();
if (dimInputs0.size() != 3)
IT_TODO_HALT();
if (dimInputs1.size() != 3)
IT_TODO_HALT();
if (dimOutput.size() != 3)
IT_TODO_HALT();
int input0_batch_size = 1;
int input1_batch_size = 1;
int output_batch_size = 1;
for (size_t i = 0; i < dimInputs0.size() - 2; ++i) {
input0_batch_size *= dimInputs0[i];
input1_batch_size *= dimInputs1[i];
output_batch_size *= dimOutput[i];
}
bool transA = op->getTransA();
bool transB = op->getTransB();
int inputs0Array[3] = {dimInputs0[0], dimInputs0[1], dimInputs0[2]};
int inputs1Array[3] = {dimInputs1[0], dimInputs1[1], dimInputs1[2]};
int outputArray[3] = {dimOutput[0], dimOutput[1], dimOutput[2]};
int inputs0Array[3] = {input0_batch_size,
dimInputs0[dimInputs0.size() - 2],
dimInputs0[dimInputs0.size() - 1]};
int inputs1Array[3] = {input1_batch_size,
dimInputs1[dimInputs1.size() - 2],
dimInputs1[dimInputs1.size() - 1]};
int outputArray[3] = {output_batch_size,
dimOutput[dimOutput.size() - 2],
dimOutput[dimOutput.size() - 1]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));

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#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
#include "operators/unary.h"
namespace infini {
class NegTensorCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<UnaryObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
cnnlStatus_t stat =
cnnlNegTensor(context->cnnlHandle(), aDesc, aData, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
};
REGISTER_KERNEL(Device::BANG, OpType::Neg, DataType::Float32, NegTensorCnnl,
"Neg_cnnl_BANG_Float32");
}; // namespace infini

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#include "operators/pad.h"
#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
namespace infini {
class PadCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<PadObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getOutput()->getDims();
int dim_size = dim.size();
int dim_array[dim_size];
for (int i = 0; i < dim_size; ++i) {
dim_array[i] = dim[i];
}
int paddings[dim_size * 2];
std::vector<int> pads = op->getPads();
if (pads.size() == 2 && dim_size != 1) {
for (int i = 0; i < dim_size * 2; i += 2) {
paddings[i] = pads[0];
paddings[i + 1] = pads[1];
}
} else {
for (int i = 0; i < dim_size * 2; i += 2) {
paddings[i] = pads[i / 2];
paddings[i + 1] = pads[i / 2 + dim_size];
}
}
int dimout_array[dim_size];
for (int i = 0; i < dim_size; ++i) {
dimout_array[i] = dim[i] + paddings[2 * i] + paddings[2 * i + 1];
}
float paddingValue = 0.0;
// input
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_ARRAY, CNNL_DTYPE_FLOAT, dim_size, dim_array));
// output
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_ARRAY,
CNNL_DTYPE_FLOAT, dim_size,
dimout_array));
cnnlStatus_t stat = cnnlPad(context->cnnlHandle(), aDesc, aData,
paddings, &paddingValue, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
};
REGISTER_KERNEL(Device::BANG, OpType::Pad, DataType::Float32, PadCnnl,
"Pad_cnnl_BANG_Float32");
}; // namespace infini

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#include "operators/pooling.h"
#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
namespace infini {
class PoolingCnnl : public BangKernelWithoutConfig {
virtual cnnlPoolingMode_t getPoolingMode() const = 0;
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<PoolingObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const inData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const outData = (op->getOutput()->getRawDataPtr<void *>());
const auto [n, c, h, w, kh, kw] = op->getNCHWRS();
const auto [ph, pw, sh, sw, dh, dw] = op->getPadStrideDilation();
// get inputs
int inArray[4] = {n, c, h, w};
cnnlTensorDescriptor_t inDesc;
checkCnnlError(cnnlCreateTensorDescriptor(&inDesc));
checkCnnlError(cnnlSetTensorDescriptor(inDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, inArray));
// get maxpool descriptor
cnnlPoolingDescriptor_t poolingDesc;
checkCnnlError(cnnlCreatePoolingDescriptor(&poolingDesc));
checkCnnlError(cnnlSetPooling2dDescriptor_v2(
poolingDesc, getPoolingMode(), CNNL_NOT_PROPAGATE_NAN, kh, kw, ph,
ph, pw, pw, sh, sw, dh, dw, false));
// get outputs
auto outVec = op->getOutput()->getDims();
int outArray[4] = {outVec[0], outVec[1], outVec[2], outVec[3]};
cnnlTensorDescriptor_t outDesc;
checkCnnlError(cnnlCreateTensorDescriptor(&outDesc));
checkCnnlError(cnnlSetTensorDescriptor(outDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, outArray));
size_t wsSize;
cnnlGetPoolingWorkspaceSize(context->cnnlHandle(), getPoolingMode(),
outVec[3], outVec[2], &wsSize);
BangPtr wsData = context->getWorkspace(wsSize);
float alpha = 1.f, beta = 0.f;
checkCnnlError(cnnlPoolingForward(context->cnnlHandle(), poolingDesc,
&alpha, inDesc, inData, &beta,
outDesc, outData, wsData, wsSize));
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(inDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(outDesc));
checkCnnlError(cnnlDestroyPoolingDescriptor(poolingDesc));
}
};
class maxPoolCnnl : public PoolingCnnl {
cnnlPoolingMode_t getPoolingMode() const override {
return CNNL_POOLING_MAX;
}
};
class avgPoolCnnl : public PoolingCnnl {
cnnlPoolingMode_t getPoolingMode() const override {
return CNNL_POOLING_AVERAGE_COUNT_INCLUDE_PADDING;
}
};
REGISTER_KERNEL(Device::BANG, OpType::MaxPool, DataType::Float32, maxPoolCnnl,
"MaxPool_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::AvgPool, DataType::Float32, avgPoolCnnl,
"AvgPool_cnnl_BANG_Float32");
}; // namespace infini

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#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
#include "operators/unary.h"
namespace infini {
class ReciprocalCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<UnaryObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
cnnlStatus_t stat =
cnnlReciprocal(context->cnnlHandle(), aDesc, aData, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
};
REGISTER_KERNEL(Device::BANG, OpType::Reciprocal, DataType::Float32,
ReciprocalCnnl, "Reciprocal_cnnl_BANG_Float32");
}; // namespace infini

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#include "operators/reshape.h"
#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
namespace infini {
class CopyBang : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<ReshapeObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
auto inData = op->getInputs(0)->getRawDataPtr<void *>();
auto outData = op->getOutputs()[0]->getRawDataPtr<void *>();
cnnlTensorDescriptor_t aDesc;
auto dim = op->getInputs(0)->getDims();
int len = dim.size();
int size = 1;
for (int i = 0; i < len; ++i) {
size *= dim[i];
}
int dim_array[1] = {size};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_ARRAY,
CNNL_DTYPE_FLOAT, 1, dim_array));
cnnlStatus_t stat =
cnnlCopy(context->cnnlHandle(), aDesc, inData, aDesc, outData);
if (stat != CNNL_STATUS_SUCCESS)
return;
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
}
};
// reshape/flatten/identity all act as copying from input to output.
REGISTER_KERNEL(Device::BANG, OpType::Reshape, DataType::Float32, CopyBang,
"Reshape_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::Flatten, DataType::Float32, CopyBang,
"Flatten_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::Identity, DataType::Float32, CopyBang,
"Identity_BANG_Float32");
} // namespace infini

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#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
#include "operators/unary.h"
namespace infini {
class RsqrtCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<UnaryObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
cnnlStatus_t stat =
cnnlRsqrt_v2(context->cnnlHandle(), CNNL_COMPUTATION_HIGH_PRECISION,
aDesc, aData, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
};
REGISTER_KERNEL(Device::BANG, OpType::Rsqrt, DataType::Float32, RsqrtCnnl,
"Rsqrt_cnnl_BANG_Float32");
}; // namespace infini

69
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@ -0,0 +1,69 @@
#include "operators/split.h"
#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
namespace infini {
class SplitCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<SplitObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
int num = op->numOutputs();
int axis = op->getDim();
void *argv[num];
for (int i = 0; i < num; ++i) {
argv[i] = op->getOutput(i)->getRawDataPtr<void *>();
}
void *const inputData = (op->getInputs(0)->getRawDataPtr<void *>());
cnnlTensorDescriptor_t desc;
int dimout_array[num][4];
for (int i = 0; i < num; ++i) {
auto dim = op->getOutput(i)->getDims();
if (dim.size() != 4) {
IT_TODO_HALT();
}
dimout_array[i][0] = dim[0];
dimout_array[i][1] = dim[1];
dimout_array[i][2] = dim[2];
dimout_array[i][3] = dim[3];
}
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4) {
IT_TODO_HALT();
}
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
checkCnnlError(cnnlCreateTensorDescriptor(&desc));
checkCnnlError(cnnlSetTensorDescriptor(desc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
cnnlTensorDescriptor_t descArray[num];
for (int i = 0; i < num; ++i) {
checkCnnlError(cnnlCreateTensorDescriptor(&descArray[i]));
checkCnnlError(
cnnlSetTensorDescriptor(descArray[i], CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dimout_array[i]));
}
size_t wsSize;
cnnlGetSplitWorkspaceSize(context->cnnlHandle(), num, &wsSize);
BangPtr wsData = context->getWorkspace(wsSize);
cnnlStatus_t stat =
cnnlSplit(context->cnnlHandle(), num, axis, desc, inputData, wsData,
wsSize, descArray, argv);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
for (int i = 0; i < num; ++i) {
checkCnnlError(cnnlDestroyTensorDescriptor(descArray[i]));
}
checkCnnlError(cnnlDestroyTensorDescriptor(desc));
}
};
REGISTER_KERNEL(Device::BANG, OpType::Split, DataType::Float32, SplitCnnl,
"Split_cnnl_BANG_Float32");
}; // namespace infini

47
src/kernels/bang/sqrt.cc Normal file
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@ -0,0 +1,47 @@
#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
#include "operators/unary.h"
namespace infini {
class SqrtCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<UnaryObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
cnnlStatus_t stat =
cnnlSqrt_v2(context->cnnlHandle(), CNNL_COMPUTATION_HIGH_PRECISION,
aDesc, aData, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
}
};
REGISTER_KERNEL(Device::BANG, OpType::Sqrt, DataType::Float32, SqrtCnnl,
"Sqrt_cnnl_BANG_Float32");
}; // namespace infini

60
src/kernels/bang/transpose.cc Normal file
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@ -0,0 +1,60 @@
#include "operators/transpose.h"
#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
namespace infini {
class TransposeCnnl : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<TransposeObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, cDesc;
auto dimin = op->getInputs(0)->getDims();
auto dimout = op->getOutput()->getDims();
if (dimin.size() != 4 || dimout.size() != 4)
IT_TODO_HALT();
int dimin_array[4] = {dimin[0], dimin[1], dimin[2], dimin[3]};
int dimout_array[4] = {dimout[0], dimout[1], dimout[2], dimout[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(
aDesc, CNNL_LAYOUT_ARRAY, CNNL_DTYPE_FLOAT, 4, dimin_array));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(
cDesc, CNNL_LAYOUT_ARRAY, CNNL_DTYPE_FLOAT, 4, dimout_array));
// get op descriptor
auto permute = op->getPermute();
cnnlTransposeDescriptor_t opDesc;
checkCnnlError(cnnlCreateTransposeDescriptor(&opDesc));
checkCnnlError(cnnlSetTransposeDescriptor(opDesc, 4, permute.data()));
size_t wsSize;
cnnlGetTransposeWorkspaceSize(context->cnnlHandle(), aDesc, opDesc,
&wsSize);
BangPtr wsData = context->getWorkspace(wsSize);
cnnlStatus_t stat =
cnnlTranspose_v2(context->cnnlHandle(), opDesc, aDesc, aData, cDesc,
cData, wsData, wsSize);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
checkCnnlError(cnnlDestroyTransposeDescriptor(opDesc));
}
};
REGISTER_KERNEL(Device::BANG, OpType::Transpose, DataType::Float32,
TransposeCnnl, "Transpose_cnnl_BANG_Float32");
}; // namespace infini

184
src/kernels/bang/trigon.cc Normal file
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@ -0,0 +1,184 @@
#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
#include "operators/unary.h"
namespace infini {
class TrigonCnnl : public BangKernelWithoutConfig {
virtual cnnlTrigonFunctionMode_t getOpType() const = 0;
virtual cnnlComputationPreference_t getPrefer() const = 0;
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<UnaryObj>(_op);
auto context = dynamic_cast<const BangRuntimeObj *>(_context);
void *const aData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cnnlTensorDescriptor_t aDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
IT_TODO_HALT();
int dim_array[4] = {dim[0], dim[1], dim[2], dim[3]};
// get inputs
checkCnnlError(cnnlCreateTensorDescriptor(&aDesc));
checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get outputs
checkCnnlError(cnnlCreateTensorDescriptor(&cDesc));
checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
CNNL_DTYPE_FLOAT, 4, dim_array));
// get op descriptor
cnnlTrigonDescriptor_t opDesc;
checkCnnlError(cnnlCreateTrigonDescriptor(&opDesc));
checkCnnlError(cnnlSetTrigonDescriptor(opDesc, getOpType()));
cnnlStatus_t stat = cnnlTrigonForward(context->cnnlHandle(), opDesc,
aDesc, aData, cDesc, cData);
if (stat != CNNL_STATUS_SUCCESS)
return;
// Destories in BANG does not require sync. But cnnl does not state
// whether sync is required before destories.
checkCnnlError(cnnlDestroyTensorDescriptor(aDesc));
checkCnnlError(cnnlDestroyTensorDescriptor(cDesc));
checkCnnlError(cnnlDestroyTrigonDescriptor(opDesc));
}
};
class SinCnnl : public TrigonCnnl {
cnnlTrigonFunctionMode_t getOpType() const override {
return CNNL_TRIGON_SIN;
}
cnnlComputationPreference_t getPrefer() const override {
return CNNL_COMPUTATION_HIGH_PRECISION;
}
};
class CosCnnl : public TrigonCnnl {
cnnlTrigonFunctionMode_t getOpType() const override {
return CNNL_TRIGON_COS;
}
cnnlComputationPreference_t getPrefer() const override {
return CNNL_COMPUTATION_HIGH_PRECISION;
}
};
class TanCnnl : public TrigonCnnl {
cnnlTrigonFunctionMode_t getOpType() const override {
return CNNL_TRIGON_TAN;
}
cnnlComputationPreference_t getPrefer() const override {
return CNNL_COMPUTATION_HIGH_PRECISION;
}
};
class ASinCnnl : public TrigonCnnl {
cnnlTrigonFunctionMode_t getOpType() const override {
return CNNL_TRIGON_ASIN;
}
cnnlComputationPreference_t getPrefer() const override {
return CNNL_COMPUTATION_HIGH_PRECISION;
}
};
class ACosCnnl : public TrigonCnnl {
cnnlTrigonFunctionMode_t getOpType() const override {
return CNNL_TRIGON_ACOS;
}
cnnlComputationPreference_t getPrefer() const override {
return CNNL_COMPUTATION_HIGH_PRECISION;
}
};
class ATanCnnl : public TrigonCnnl {
cnnlTrigonFunctionMode_t getOpType() const override {
return CNNL_TRIGON_ATAN;
}
cnnlComputationPreference_t getPrefer() const override {
return CNNL_COMPUTATION_HIGH_PRECISION;
}
};
class SinHCnnl : public TrigonCnnl {
cnnlTrigonFunctionMode_t getOpType() const override {
return CNNL_TRIGON_SINH;
}
cnnlComputationPreference_t getPrefer() const override {
return CNNL_COMPUTATION_HIGH_PRECISION;
}
};
class CosHCnnl : public TrigonCnnl {
cnnlTrigonFunctionMode_t getOpType() const override {
return CNNL_TRIGON_COSH;
}
cnnlComputationPreference_t getPrefer() const override {
return CNNL_COMPUTATION_HIGH_PRECISION;
}
};
class TanHCnnl : public TrigonCnnl {
cnnlTrigonFunctionMode_t getOpType() const override {
return CNNL_TRIGON_TANH;
}
cnnlComputationPreference_t getPrefer() const override {
return CNNL_COMPUTATION_HIGH_PRECISION;
}
};
class ASinHCnnl : public TrigonCnnl {
cnnlTrigonFunctionMode_t getOpType() const override {
return CNNL_TRIGON_ASINH;
}
cnnlComputationPreference_t getPrefer() const override {
return CNNL_COMPUTATION_HIGH_PRECISION;
}
};
class ACosHCnnl : public TrigonCnnl {
cnnlTrigonFunctionMode_t getOpType() const override {
return CNNL_TRIGON_ACOSH;
}
cnnlComputationPreference_t getPrefer() const override {
return CNNL_COMPUTATION_HIGH_PRECISION;
}
};
class ATanHCnnl : public TrigonCnnl {
cnnlTrigonFunctionMode_t getOpType() const override {
return CNNL_TRIGON_ATANH;
}
cnnlComputationPreference_t getPrefer() const override {
return CNNL_COMPUTATION_HIGH_PRECISION;
}
};
REGISTER_KERNEL(Device::BANG, OpType::Sin, DataType::Float32, SinCnnl,
"Sin_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::Cos, DataType::Float32, CosCnnl,
"Cos_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::Tan, DataType::Float32, TanCnnl,
"Tan_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::ASin, DataType::Float32, ASinCnnl,
"ASin_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::ACos, DataType::Float32, ACosCnnl,
"ACos_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::ATan, DataType::Float32, ATanCnnl,
"ATan_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::SinH, DataType::Float32, SinHCnnl,
"SinH_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::CosH, DataType::Float32, CosHCnnl,
"CosH_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::TanH, DataType::Float32, TanHCnnl,
"TanH_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::ASinH, DataType::Float32, ASinHCnnl,
"ASinH_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::ACosH, DataType::Float32, ACosHCnnl,
"ACosH_cnnl_BANG_Float32");
REGISTER_KERNEL(Device::BANG, OpType::ATanH, DataType::Float32, ATanHCnnl,
"ATanH_cnnl_BANG_Float32");
}; // namespace infini

40
src/kernels/cpu/element_wise.cc
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@ -11,17 +11,37 @@ template <typename T> class NativeElementWise : public CpuKernelWithoutConfig {
T *inptr1 = op->getInputs(1)->getRawDataPtr<T *>();
T *outptr = op->getOutput()->getRawDataPtr<T *>();
auto outDim = op->getOutput()->getDims();
int a[4] = {1, 1, 1, 1};
int b[4] = {1, 1, 1, 1};
int c[4] = {1, 1, 1, 1};
auto a_input = op->getInputs(0)->getDims();
auto b_input = op->getInputs(1)->getDims();
auto c_output = op->getOutput()->getDims();
std::copy(a_input.begin(), a_input.end(), a + (4 - a_input.size()));
std::copy(b_input.begin(), b_input.end(), b + (4 - b_input.size()));
std::copy(c_output.begin(), c_output.end(), c + (4 - c_output.size()));
auto n = op->getOutput()->size();
for (size_t offset = 0; offset < n; offset++) {
// For now,we only process the same dims here, broardcast will be
// considered in the opt layer.
/*auto offset0 =
op->getInputs(0)->getOffsetByBroadcastOffset(offset, outDim);
auto offset1 =
op->getInputs(1)->getOffsetByBroadcastOffset(offset, outDim);
outptr[offset] = doCompute(inptr0[offset0], inptr1[offset1]);*/
outptr[offset] = doCompute(inptr0[offset], inptr1[offset]);
for (size_t i = 0; i < n; ++i) {
int c0_index = i / (c[1] * c[2] * c[3]);
int c1_index = (i % (c[1] * c[2] * c[3])) / (c[2] * c[3]);
int c2_index = ((i % (c[1] * c[2] * c[3])) % (c[2] * c[3])) / c[3];
int c3_index = ((i % (c[1] * c[2] * c[3])) % (c[2] * c[3])) % c[3];
int a0_index = c0_index % a[0];
int a1_index = c1_index % a[1];
int a2_index = c2_index % a[2];
int a3_index = c3_index % a[3];
int b0_index = c0_index % b[0];
int b1_index = c1_index % b[1];
int b2_index = c2_index % b[2];
int b3_index = c3_index % b[3];
outptr[i] = doCompute(
inptr0[a0_index * a[1] * a[2] * a[3] + a1_index * a[2] * a[3] +
a2_index * a[3] + a3_index],
inptr1[b0_index * b[1] * b[2] * b[3] + b1_index * b[2] * b[3] +
b2_index * b[3] + b3_index]);
}
}
};

21
src/kernels/cpu/unary.cc
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@ -56,6 +56,25 @@ template <typename T> class NaiveAbs : public NativeUnary<T> {
T doCompute(T val) const override { return val < 0 ? -val : val; }
};
template <typename T> class Clip : public CpuKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *context) const override {
auto op = as<ClipObj>(_op);
T *inptr = op->getInputs(0)->getRawDataPtr<T *>();
T *outptr = op->getOutput()->getRawDataPtr<T *>();
auto minValue = op->getMin();
auto maxValue = op->getMax();
auto n = op->getOutput()->size();
for (size_t offset = 0; offset < n; offset++) {
auto val = *inptr++;
*outptr++ = (minValue && val < *minValue) ? *minValue
: (maxValue && val > *maxValue) ? *maxValue
: val;
}
}
};
REGISTER_KERNEL(Device::CPU, OpType::Relu, DataType::UInt32,
NaiveRelu<uint32_t>, "reluNaive_CPU_uint32");
REGISTER_KERNEL(Device::CPU, OpType::Relu, DataType::Float32, NaiveRelu<float>,
@ -76,4 +95,6 @@ REGISTER_KERNEL(Device::CPU, OpType::Softmax, DataType::UInt32,
NaiveSoftmax<uint32_t>, "softmaxNaive_CPU_uint32");
REGISTER_KERNEL(Device::CPU, OpType::Softmax, DataType::Float32,
NaiveSoftmax<float>, "softmaxNaive_CPU_float32");
REGISTER_KERNEL(Device::CPU, OpType::Clip, DataType::Float32, Clip<float>,
"Clip_CPU_float32");
}; // namespace infini

9
src/kernels/cuda/batch_norm.cc
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@ -20,18 +20,17 @@ class BatchNormCudnn : public CudaKernelWithoutConfig {
auto dims = op->getInputs(0)->getDims();
// Only 4D and 5D tensors are supported by
// cudnnBatchNormalizationForwardInference
IT_ASSERT(dims.size() == 4 || dims.size() == 5);
IT_ASSERT(dims.size() == 4);
int dimArray[CUDNN_DIM_MAX], strideArray[CUDNN_DIM_MAX],
dimPArray[CUDNN_DIM_MAX], stridePArray[CUDNN_DIM_MAX];
int dimArray[4], strideArray[4], dimPArray[4], stridePArray[4];
for (size_t i = 0; i < dims.size(); ++i) {
dimArray[i] = dims[i];
strideArray[i] = op->getInputs(0)->getStride()[i];
dimPArray[i] = 1;
stridePArray[i] = 1;
}
dimPArray[1] = op->getInputs(0)->getDims()[1];
stridePArray[1] = op->getInputs(0)->getStride()[1];
dimPArray[1] = op->getInputs(1)->getDims()[0];
stridePArray[0] = op->getInputs(1)->getDims()[0];
// get inputs
cudnnTensorDescriptor_t inDesc;
checkCudnnError(cudnnCreateTensorDescriptor(&inDesc));

27
src/kernels/cuda/clip.cc Normal file
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@ -0,0 +1,27 @@
#include "cuda/cuda_clip.h"
#include "cuda/cuda_kernel_wihtout_config.h"
#include "cuda/cuda_runtime.h"
#include "operators/unary.h"
namespace infini {
class ClipCuda : public CudaKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<ClipObj>(_op);
void *const inputData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const outputData = (op->getOutput()->getRawDataPtr<void *>());
auto min = op->getMin();
auto max = op->getMax();
auto dim = op->getInputs(0)->getDims();
int num = dim[0] * dim[1] * dim[2] * dim[3];
clip_kernel((float *)inputData, (float *)outputData, num,
min ? *min : NAN, max ? *max : NAN);
}
};
REGISTER_KERNEL(Device::CUDA, OpType::Clip, DataType::Float32, ClipCuda,
"Clip_CUDA_Float32");
}; // namespace infini

32
src/kernels/cuda/clip.cu Normal file
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@ -0,0 +1,32 @@
#include "core/common.h"
#include "core/constants.h"
#include "cuda/cuda_common.h"
#include <math.h>
using infini::E_CONSTANT;
constexpr unsigned int num_threads() { return 32 * 4; }
constexpr int thread_work_size() { return 4; }
constexpr int block_work_size() { return thread_work_size() * num_threads(); }
__global__ void _clip_kernel(float *input, float *output, int n, float minValue,
float maxValue) {
int index = threadIdx.x + blockIdx.x * blockDim.x;
int stride = blockDim.x * gridDim.x;
for (int i = index; i < n; i += stride) {
output[i] = (!isnan(minValue) && input[i] < minValue)
? minValue
: (!isnan(maxValue) && input[i] > maxValue)
? maxValue : input[i];
}
}
namespace infini {
void clip_kernel(float *input, float *output, int num, float minValue,
float maxValue) {
int blocksize = block_work_size();
int gridsize = (num + block_work_size() - 1) / block_work_size();
_clip_kernel<<<blocksize, gridsize>>>(input, output, num, minValue,
maxValue);
}
}; // namespace infini

53
src/kernels/cuda/element_wise.cc
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@ -19,24 +19,37 @@ class ElementWiseCudnn : public CudaKernelWithoutConfig {
void *const cData = (op->getOutput()->getRawDataPtr<void *>());
cudnnTensorDescriptor_t aDesc, bDesc, cDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() != 4)
auto a_dim = op->getInputs(0)->getDims();
auto b_dim = op->getInputs(1)->getDims();
auto c_dim = op->getOutput()->getDims();
if (a_dim.size() > 4 || b_dim.size() > 4 || c_dim.size() > 4)
IT_TODO_HALT();
int n = dim[0], c = dim[1], h = dim[2], w = dim[3];
int a[4] = {1, 1, 1, 1};
int b[4] = {1, 1, 1, 1};
int c[4] = {1, 1, 1, 1};
std::copy(a_dim.begin(), a_dim.end(), a + (4 - a_dim.size()));
std::copy(b_dim.begin(), b_dim.end(), b + (4 - b_dim.size()));
std::copy(c_dim.begin(), c_dim.end(), c + (4 - c_dim.size()));
// get inputs
checkCudnnError(cudnnCreateTensorDescriptor(&aDesc));
checkCudnnError(cudnnSetTensor4dDescriptor(
aDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, n, c, h, w));
checkCudnnError(cudnnSetTensor4dDescriptor(aDesc, CUDNN_TENSOR_NCHW,
CUDNN_DATA_FLOAT, a[0], a[1],
a[2], a[3]));
checkCudnnError(cudnnCreateTensorDescriptor(&bDesc));
checkCudnnError(cudnnSetTensor4dDescriptor(
bDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, n, c, h, w));
checkCudnnError(cudnnSetTensor4dDescriptor(bDesc, CUDNN_TENSOR_NCHW,
CUDNN_DATA_FLOAT, b[0], b[1],
b[2], b[3]));
// get outputs
checkCudnnError(cudnnCreateTensorDescriptor(&cDesc));
checkCudnnError(cudnnSetTensor4dDescriptor(
cDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, n, c, h, w));
checkCudnnError(cudnnSetTensor4dDescriptor(cDesc, CUDNN_TENSOR_NCHW,
CUDNN_DATA_FLOAT, c[0], c[1],
c[2], c[3]));
// get op descriptor
cudnnOpTensorDescriptor_t opDesc;
@ -81,13 +94,27 @@ class ElementWiseCuda : public CudaKernelWithoutConfig {
float *const aData = (op->getInputs(0)->getRawDataPtr<float *>());
float *const bData = (op->getInputs(1)->getRawDataPtr<float *>());
float *const cData = (op->getOutput()->getRawDataPtr<float *>());
auto a_dim = op->getInputs(0)->getDims();
auto b_dim = op->getInputs(1)->getDims();
auto c_dim = op->getOutput()->getDims();
if (a_dim.size() > 4 || b_dim.size() > 4 || c_dim.size() > 4)
IT_TODO_HALT();
int a[4] = {1, 1, 1, 1};
int b[4] = {1, 1, 1, 1};
int c[4] = {1, 1, 1, 1};
std::copy(a_dim.begin(), a_dim.end(), a + (4 - a_dim.size()));
std::copy(b_dim.begin(), b_dim.end(), b + (4 - b_dim.size()));
std::copy(c_dim.begin(), c_dim.end(), c + (4 - c_dim.size()));
auto dim = op->getInputs(0)->getDims();
int n = dim[0], c = dim[1], h = dim[2], w = dim[3];
if (op->getOpType() == OpType::Div)
div_kernel(aData, bData, cData, n * c * h * w);
div_kernel(aData, bData, cData, a[0], a[1], a[2], a[3], b[0], b[1],
b[2], b[3], c[0], c[1], c[2], c[3]);
else if (op->getOpType() == OpType::Pow)
pow_kernel(aData, bData, cData, n * c * h * w);
pow_kernel(aData, bData, cData, a[0], a[1], a[2], a[3], b[0], b[1],
b[2], b[3], c[0], c[1], c[2], c[3]);
else
IT_TODO_HALT();
}

59
src/kernels/cuda/element_wise.cu
View File

@ -5,34 +5,75 @@ constexpr unsigned int num_threads() { return 32 * 4; }
constexpr int thread_work_size() { return 4; }
constexpr int block_work_size() { return thread_work_size() * num_threads(); }
__global__ void _div_kernel(float *x, float *y, float *z, int n) {
__global__ void _div_kernel(float *x, float *y, float *z, int a0, int a1, int a2, int a3,
int b0, int b1, int b2, int b3,
int c0, int c1, int c2, int c3) {
int index = threadIdx.x + blockIdx.x * blockDim.x;
int stride = blockDim.x * gridDim.x;
int n = c0 * c1 * c2 * c3;
for (int i = index; i < n; i += stride) {
z[i] = x[i] / y[i];
int c0_index = i/ (c1 * c2 * c3);
int c1_index = (i % (c1 * c2 * c3)) / (c2 * c3);
int c2_index = ((i % (c1 * c2 * c3)) % (c2 * c3)) / c3;
int c3_index = ((i % (c1 * c2 * c3)) % (c2 * c3)) % c3;
int a0_index = c0_index % a0;
int a1_index = c1_index % a1;
int a2_index = c2_index % a2;
int a3_index = c3_index % a3;
int b0_index = c0_index % b0;
int b1_index = c1_index % b1;
int b2_index = c2_index % b2;
int b3_index = c3_index % b3;
z[i] = x[a0_index*a1*a2*a3 + a1_index*a2*a3 + a2_index*a3 + a3_index] / y[b0_index*b1*b2*b3 + b1_index*b2*b3 + b2_index*b3 + b3_index];
}
}
__global__ void _pow_kernel(float *x, float *y, float *z, int n) {
__global__ void _pow_kernel(float *x, float *y, float *z, int a0, int a1, int a2, int a3,
int b0, int b1, int b2, int b3,
int c0, int c1, int c2, int c3) {
int index = threadIdx.x + blockIdx.x * blockDim.x;
int stride = blockDim.x * gridDim.x;
int n = c0 * c1 * c2 * c3;
for (int i = index; i < n; i += stride) {
z[i] = pow(x[i], y[i]);
int c0_index = i/ (c1 * c2 * c3);
int c1_index = (i % (c1 * c2 * c3)) / (c2 * c3);
int c2_index = ((i % (c1 * c2 * c3)) % (c2 * c3)) / c3;
int c3_index = ((i % (c1 * c2 * c3)) % (c2 * c3)) % c3;
int a0_index = c0_index % a0;
int a1_index = c1_index % a1;
int a2_index = c2_index % a2;
int a3_index = c3_index % a3;
int b0_index = c0_index % b0;
int b1_index = c1_index % b1;
int b2_index = c2_index % b2;
int b3_index = c3_index % b3;
z[i] = pow(x[a0_index*a1*a2*a3 + a1_index*a2*a3 + a2_index*a3 + a3_index], y[b0_index*b1*b2*b3 + b1_index*b2*b3 + b2_index*b3 + b3_index]);
}
}
namespace infini {
void div_kernel(float *a, float *b, float *c, int num) {
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) {
int blocksize = block_work_size();
int num = c0*c1*c2*c3;
int gridsize = (num + block_work_size() - 1) / block_work_size();
_div_kernel<<<blocksize, gridsize>>>(a, b, c, num);
_div_kernel<<<blocksize, gridsize>>>(a, b, c, a0, a1, a2, a3, b0, b1, b2, b3, c0, c1, c2, c3);
}
void pow_kernel(float *a, float *b, float *c, int num) {
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) {
int blocksize = block_work_size();
int num = c0*c1*c2*c3;
int gridsize = (num + block_work_size() - 1) / block_work_size();
_pow_kernel<<<blocksize, gridsize>>>(a, b, c, num);
_pow_kernel<<<blocksize, gridsize>>>(a, b, c, a0, a1, a2, a3, b0, b1, b2, b3, c0, c1, c2, c3);
}
}; // namespace infini

2
src/kernels/cuda/pad_slice.cc
View File

@ -34,7 +34,7 @@ class SliceCuda : private PadSliceCudaCompute, public CudaKernelWithoutConfig {
void compute(const Operator &op,
const RuntimeObj *_context) const override {
do_compute(op->getOutput(), op->getInputs(0),
as<SliceObj>(op)->getStart(), false);
as<SliceObj>(op)->getStarts(), false);
}
};

46
src/kernels/cuda/unary.cc
View File

@ -60,6 +60,52 @@ class ActivationCudnn : public CudaKernelWithoutConfig {
}
};
class SoftmaxCudnn : public CudaKernelWithoutConfig {
virtual cudnnSoftmaxAlgorithm_t getAlgorithmType() const = 0;
virtual cudnnSoftmaxMode_t getModeType() const = 0;
virtual tuple<float, float> getAlphBeta() const { return {1.f, 0.f}; }
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<UnaryObj>(_op);
auto context = dynamic_cast<const CudaRuntimeObj *>(_context);
void *const inputData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const outputData = (op->getOutput()->getRawDataPtr<void *>());
cudnnTensorDescriptor_t inputDesc, outputDesc;
auto dim = op->getInputs(0)->getDims();
if (dim.size() > 4)
IT_TODO_HALT();
int dim_array[4] = {1, 1, 1, 1};
memcpy(dim_array + (4 - dim.size()), dim.data(),
dim.size() * sizeof(int));
// get inputs
checkCudnnError(cudnnCreateTensorDescriptor(&inputDesc));
checkCudnnError(cudnnSetTensor4dDescriptor(
inputDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, dim_array[0],
dim_array[1], dim_array[2], dim_array[3]));
// get outputs
checkCudnnError(cudnnCreateTensorDescriptor(&outputDesc));
checkCudnnError(cudnnSetTensor4dDescriptor(
outputDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, dim_array[0],
dim_array[1], dim_array[2], dim_array[3]));
auto [alpha, beta] = getAlphBeta();
cudnnStatus_t stat = cudnnSoftmaxForward(
context->cudnnHandle(), getAlgorithmType(), getModeType(), &alpha,
inputDesc, inputData, &beta, outputDesc, outputData);
if (stat != CUDNN_STATUS_SUCCESS)
return;
// Destories in CUDA does not require sync. But cuDNN does not state
// whether sync is required before destories.
checkCudnnError(cudnnDestroyTensorDescriptor(inputDesc));
checkCudnnError(cudnnDestroyTensorDescriptor(outputDesc));
}
};
class ReluCudnn : public ActivationCudnn {
cudnnActivationMode_t getOpType() const override {
return CUDNN_ACTIVATION_RELU;

2
src/kernels/intelcpu/slice.cc
View File

@ -23,7 +23,7 @@ class MklSlice : public MklKernelWithoutConfig {
std::vector<dnnl_dim_t> sDims, offsets;
for (int i = 0; i < ndim; ++i) {
sDims.push_back(oDims.at(i));
offsets.push_back(op->getStart().at(i));
offsets.push_back(op->getStarts().at(i));
}
auto sliceMd = srcMd.submemory_desc(sDims, offsets);
auto sliceMemory =

46
src/kernels/mlu/CMakeLists.txt
View File

@ -1,46 +0,0 @@
cmake_minimum_required(VERSION 3.3)
project(bangops)
include_directories("${CMAKE_CURRENT_SOURCE_DIR}/include")
set(LIBRARY_OUTPUT_PATH "${CMAKE_BINARY_DIR}/lib")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Werror -fPIC -std=c++11 -pthread -pipe")
set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} ${CMAKE_CXX_FLAGS} -O3")
set(CMAKE_EXE_LINKER_FLAGS_RELEASE "${CMAKE_EXE_LINKER_FLAGS_RELEASE} -Wl,--gc-sections -fPIC")
# check `NEUWARE_HOME` env
message(${NEUWARE_HOME})
if(EXISTS ${NEUWARE_HOME})
include_directories("${NEUWARE_HOME}/include")
link_directories("${NEUWARE_HOME}/lib64")
link_directories("${NEUWARE_HOME}/lib")
set(NEUWARE_ROOT_DIR "${NEUWARE_HOME}")
else()
message(FATAL_ERROR "NEUWARE directory cannot be found, refer README.md to prepare NEUWARE_HOME environment.")
endif()
# setup cmake search path
set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH}
"${CMAKE_SOURCE_DIR}/cmake"
"${NEUWARE_HOME}/cmake"
"${NEUWARE_HOME}/cmake/modules"
)
# include FindBANG.cmake and check cncc
find_package(BANG)
if(NOT BANG_FOUND)
message(FATAL_ERROR "BANG cannot be found.")
elseif (NOT BANG_CNCC_EXECUTABLE)
message(FATAL_ERROR "cncc not found, please ensure cncc is in your PATH env or set variable BANG_CNCC_EXECUTABLE from cmake. Otherwise you should check path used by find_program(BANG_CNCC_EXECUTABLE) in FindBANG.cmake")
endif()
# setup cncc flags
set(BANG_CNCC_FLAGS "${BANG_CNCC_FLAGS} -fPIC -Wall -Werror -std=c++11 -pthread")
set(BANG_CNCC_FLAGS "${BANG_CNCC_FLAGS} -O3")
set(BANG_CNCC_FLAGS "${BANG_CNCC_FLAGS}" "--bang-mlu-arch=mtp_220"
"--bang-mlu-arch=mtp_270"
"--bang-mlu-arch=mtp_290"
"--bang-mlu-arch=mtp_372"
)
file(GLOB_RECURSE src_files ${src_files} "${CMAKE_CURRENT_SOURCE_DIR}/src/*.mlu")
bang_add_library(bangops SHARED ${src_files})

7
src/kernels/mlu/include/bang_div.h
View File

@ -1,7 +0,0 @@
#pragma once
#include "cnnl.h"
namespace infini {
void div_kernel(cnnlHandle_t handle, const float *input1, const float *input2,
float *output, const uint32_t num);
}; // namespace infini

7
src/kernels/mlu/include/div.h
View File

@ -1,7 +0,0 @@
#ifndef BANG_KERNELS_DIVOPERATION_DIV_H_
#define BANG_KERNELS_DIVOPERATION_DIV_H_
__mlu_global__ void MLUDivKernelUnion1(float *output, float *input1,
float *input2, uint32_t num);
#endif // BANG_KERNELS_DIVOPERATION_DIV_H_

24
src/kernels/mlu/src/div.mlu
View File

@ -1,24 +0,0 @@
#include "bang_div.h"
#include "div.h"
namespace infini {
void div_kernel(cnnlHandle_t handle,
const float *input1,
const float *input2,
float *output,
const uint32_t num) {
// 任务类型和调度方法
cnrtDim3_t k_dim;
cnrtFunctionType_t k_type;
cnrtQueue_t queue;
cnnlGetQueue(handle, &queue);
k_dim.x = 4;
k_dim.y = 8;
k_dim.z = 1;
k_type = CNRT_FUNC_TYPE_UNION1;
// launch 任务
MLUDivKernelUnion1<<<k_dim, k_type, queue>>>((float*)output,
(float*)input1,
(float*)input2,
num);
}
};

50
src/kernels/mlu/src/div_device.mlu
View File

@ -1,50 +0,0 @@
#include "div.h"
#define NRAM_USE_SIZE 102400
__nram__ char left[NRAM_USE_SIZE];
__nram__ char right[NRAM_USE_SIZE];
__nram__ char output[NRAM_USE_SIZE];
template<typename T>
__mlu_device__ void DivFunction(T* output1, T* input1, T* input2, size_t num) {
int use_nram_size = NRAM_USE_SIZE;
int deal_align = use_nram_size / sizeof(T);
int num_per_core = num / taskDim;
int num_rem = num % taskDim;
int easy = num_per_core;
int hard = num_per_core + (num_rem != 0 ? 1 : 0);
int my = taskId < num_rem ? hard : easy;
int start = (taskId < num_rem) ? (hard * taskId) : (hard * num_rem + (taskId - num_rem) * easy);
char* input1_start = (char*)input1 + start * sizeof(T);
char* input2_start = (char*)input2 + start * sizeof(T);
char* output_start = (char*)output1 + start * sizeof(T);
int my_repeat = my / deal_align;
int my_rem = my % deal_align;
for(int i = 0; i < my_repeat; ++i) {
__memcpy(left, input1_start, use_nram_size, GDRAM2NRAM);
__memcpy(right, input2_start, use_nram_size, GDRAM2NRAM);
__bang_active_recip((T*)right, (T*)right, deal_align);
__bang_mul((T*)output, (T*)left, (T*)right, deal_align);
__memcpy(output_start, output, use_nram_size, NRAM2GDRAM);
input1_start += use_nram_size;
input2_start += use_nram_size;
output_start += use_nram_size;
}
if(my_rem) {
__memcpy(left, input1_start, my_rem * sizeof(T), GDRAM2NRAM);
__memcpy(right, input2_start, my_rem * sizeof(T), GDRAM2NRAM);
__bang_active_recip((T*)right, (T*)right, deal_align);
__bang_mul((T*)output, (T*)left, (T*)right, deal_align);
__memcpy(output_start, output, my_rem * sizeof(T), NRAM2GDRAM);
}
}
__mlu_global__ void MLUDivKernelUnion1(float *output,
float *input1,
float *input2,
uint32_t num) {
DivFunction((float*)output, (float*)input1, (float*)input2, num);
}

37
src/operators/activation_backward.cc Normal file
View File

@ -0,0 +1,37 @@
#include "operators/activation_backward.h"
namespace infini {
ActivationBackwardObj::ActivationBackwardObj(OpType type, GraphObj *graph,
Tensor y, Tensor diff_y, Tensor x,
Tensor diff_x)
: OperatorObj(type, {y, diff_y, x}, {diff_x}) {
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>>
ActivationBackwardObj::inferShape(const TensorVec &inputs) const {
return {{inputs[0]->getDims()}};
}
std::string ActivationBackwardObj::toString() const {
std::ostringstream os;
os << OpRegistry::getOpName(type) << "[" << getGuid() << "]";
os << "(";
os << vecToString(inputs[0]->getDims()) << ",";
os << "input=" << inputs[0]->getGuid() << ",";
os << "output=" << outputs[0]->getGuid() << ")";
return os.str();
}
vector<int> ActivationBackwardObj::getWorkloadVector() const {
vector<int> ret{enum_to_underlying(type)};
const Shape shape = outputs[0]->getDims();
ret.insert(ret.end(), shape.begin(), shape.end());
return ret;
}
vector<int> ActivationBackwardObj::getOpAttrVector() const {
return {enum_to_underlying(type)};
}
}; // namespace infini

6
src/operators/batch_norm.cc
View File

@ -3,10 +3,10 @@
namespace infini {
BatchNormObj::BatchNormObj(GraphObj *graph, Tensor input, Tensor output,
Tensor mean, Tensor var, Tensor scale, Tensor bias,
float momentum, float eps, bool training)
float momentum, float eps, bool trainingMode)
: OperatorObj(OpType::BatchNorm, {input, mean, var, scale, bias}, {output}),
momentum(momentum), eps(eps), training(training) {
if (training)
momentum(momentum), eps(eps), trainingMode(trainingMode) {
if (trainingMode)
IT_TODO_HALT();
IT_ASSERT(checkValid(graph));

75
src/operators/conv.cc
View File

@ -178,6 +178,81 @@ void ConvTransposed2dObj::setAuxilaryAttributes(PaddingMode mode) {
}
}
void ConvBackwardFilterObj::setAuxilaryAttributes(PaddingMode mode) {
const Tensor &inputX = inputs[0];
const Tensor &diffY = inputs[1];
n = inputX->getDims()[0], c = inputX->getDims()[1],
h = inputX->getDims()[2], w = inputX->getDims()[3], f = diffY->getDims()[0],
r = diffY->getDims()[2], s = diffY->getDims()[3];
if (mode == PaddingMode::Same) {
int oh = h / sh;
int ow = w / sw;
ph = (h - oh * sh + (r - sh) * dh) / 2;
pw = (w - ow * sw + (s - sw) * dw) / 2;
} else if (mode == PaddingMode::Valid) {
ph = pw = 0;
}
}
ConvBackwardFilterObj::ConvBackwardFilterObj(GraphObj *graph, Tensor inputX,
Tensor diffY, Tensor diffW, int ph,
int pw, int sh, int sw, int dh,
int dw, Tensor bias, ActType act)
: ConvBaseObj(OpType::Conv, {inputX, diffY}, diffW, ph, pw, sh, sw, dh, dw,
inputX, diffY),
act(act) {
if (bias)
IT_TODO_HALT();
setAuxilaryAttributes(PaddingMode::Other);
IT_ASSERT(checkValid(graph));
}
ConvBackwardFilterObj::ConvBackwardFilterObj(GraphObj *graph, Tensor inputX,
Tensor diffY, Tensor diffW,
PaddingMode mode, int sh, int sw,
int dh, int dw, Tensor bias,
ActType act)
: ConvBaseObj(OpType::Conv, {inputX, diffY}, diffW, mode, sh, sw, dh, dw,
inputX, diffY),
act(act) {
if (bias)
IT_TODO_HALT();
setAuxilaryAttributes(mode);
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>>
ConvBackwardFilterObj::inferShape(const TensorVec &inputs) const {
const auto &inputX = inputs[0], &diffY = inputs[1];
auto n = inputX->getDims()[0];
auto h = inputX->getDims()[2];
auto w = inputX->getDims()[3];
auto f = diffY->getDims()[0];
auto r = diffY->getDims()[2];
auto s = diffY->getDims()[3];
int on = n, oc = f;
int oh = 0, ow = 0;
// For NCHW+FCRS layout, C of input is divisable by C of weight
if (inputX->getDims()[1] % diffY->getDims()[1] != 0)
return {};
// Set padding size
if (padding == PaddingMode::Other) {
oh = (h - (r - sh) * dh + ph * 2) / sh;
ow = (w - (s - sw) * dw + pw * 2) / sw;
} else if (padding == PaddingMode::Same) {
oh = h / sh;
ow = w / sw;
// ph = (h - oh * sh + (r - sh) * dh) / 2;
// pw = (w - ow * sw + (s - sw) * dw) / 2;
} else if (padding == PaddingMode::Valid) {
int ph = 0;
int pw = 0;
oh = (h - (r - sh) * dh + ph * 2) / sh;
ow = (w - (s - sw) * dw + pw * 2) / sw;
}
return {{{on, oc, oh, ow}}};
}
ConvTransposed2dNHWCObj::ConvTransposed2dNHWCObj(GraphObj *graph, Tensor input,
Tensor weight, Tensor output,
int ph, int pw, int sh, int sw,

43
src/operators/det.cc Normal file
View File

@ -0,0 +1,43 @@
#include "operators/det.h"
namespace infini {
DetObj::DetObj(GraphObj *graph, Tensor input, Tensor output, Mode mode)
: OperatorObj(OpType::Det, {input}, {output}), modeValue(mode) {
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> DetObj::inferShape(const TensorVec &inputs) const {
const auto A = inputs[0];
auto input = A->getDims();
int length = input.size();
if (length == 2) {
std::vector<int> output = {1};
return {{output}};
} else {
std::vector<int> output(input.begin(), input.end() - 2);
return {{output}};
}
}
std::string DetObj::toString() const {
std::ostringstream os;
os << OpRegistry::getOpName(type) << "[" << getGuid() << "]";
os << "(";
os << vecToString(inputs[0]->getDims()) << ",";
os << "input=" << inputs[0]->getGuid() << ",";
os << "output=" << outputs[0]->getGuid() << ")";
return os.str();
}
vector<int> DetObj::getWorkloadVector() const {
vector<int> ret{enum_to_underlying(type)};
const Shape shape = outputs[0]->getDims();
ret.insert(ret.end(), shape.begin(), shape.end());
return ret;
}
vector<int> DetObj::getOpAttrVector() const {
return {enum_to_underlying(type)};
}
}; // namespace infini

40
src/operators/dropout.cc Normal file
View File

@ -0,0 +1,40 @@
#include "operators/dropout.h"
namespace infini {
DropoutObj::DropoutObj(GraphObj *graph, Tensor data, Tensor output, Tensor mask,
float ratio, bool training_mode)
: OperatorObj(OpType::Dropout, {data}, {output, mask}), ratio(ratio) {
IT_ASSERT(0 <= ratio && ratio < 1);
IT_ASSERT(!training_mode);
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> DropoutObj::inferShape(const TensorVec &inputs) const {
auto shape = inputs[0]->getDims();
return {{shape, shape}};
}
std::string DropoutObj::toString() const {
std::ostringstream os;
os << "Dropout[" << getGuid() << "](" << vecToString(inputs[0]->getDims())
<< ", "
<< "ratio=" << ratio << ", "
<< "training_mode=false, "
<< "input=" << inputs[0]->getGuid() << ", "
<< "outputs=" << outputs[0]->getGuid() << ", " << outputs[1]->getGuid()
<< ")";
return os.str();
}
vector<int> DropoutObj::getWorkloadVector() const {
vector<int> ret = inputs[0]->getDims();
ret.emplace_back(static_cast<int>(ratio));
ret.emplace(ret.begin(), enum_to_underlying(type));
return ret;
}
vector<int> DropoutObj::getOpAttrVector() const {
return {enum_to_underlying(type), static_cast<int>(ratio), false};
}
} // namespace infini

78
src/operators/element_wise.cc
View File

@ -12,23 +12,29 @@ ElementWiseObj::inferShape(const TensorVec &inputs) const {
// For now,we only process the same dims here, broardcast will be considered
// in the opt layer.
const auto A = inputs[0], B = inputs[1];
if (A->getDims().size() != B->getDims().size() ||
A->getDims() != B->getDims())
return {};
int max_len = std::max(A->getDims().size(), B->getDims().size());
std::vector<int> A_(max_len, 1);
std::vector<int> B_(max_len, 1);
std::vector<int> res(max_len, 1);
memcpy(A_.data() + max_len - A->getDims().size(), A->getDims().data(),
A->getDims().size() * sizeof(int));
memcpy(B_.data() + max_len - B->getDims().size(), B->getDims().data(),
B->getDims().size() * sizeof(int));
// std::copy(A->getDims().begin(), A->getDims().end(), A_.begin() + (max_len
// - A->getDims().size())); std::copy(B->getDims().begin(),
// B->getDims().end(), B_.begin() + (max_len - B->getDims().size()));
// std::copy(A->getDims().rbegin(), A->getDims().rend(), A_.rbegin());
// std::copy(B->getDims().rbegin(), B->getDims().rend(), B_.rbegin());
return {{A->getDims()}};
/*
int n = A->getDims().size();
Shape shape;
for (int i = 0; i < n; i++) {
auto dimA = A->getDims().at(i);
auto dimB = B->getDims().at(i);
if (!(dimA == dimB || dimA == 1 || dimB == 1))
for (int i = 0; i < max_len; ++i) {
if (A_[i] == B_[i] || (A_[i] == 1 || B_[i] == 1)) {
res[i] = std::max(A_[i], B_[i]);
} else {
return {};
auto dimI = dimA > dimB ? dimA : dimB;
shape.emplace_back(dimI);
}
return {{shape}};*/
}
return {{res}};
}
std::string ElementWiseObj::toString() const {
@ -54,4 +60,48 @@ vector<int> ElementWiseObj::getOpAttrVector() const {
return {enum_to_underlying(type)};
}
MSELossObj::MSELossObj(GraphObj *graph, Tensor input0, Tensor input1,
Reduction reduction, Tensor output)
: OperatorObj(OpType::MSELoss, {input0, input1}, {output}),
reductionMode(reduction) {
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> MSELossObj::inferShape(const TensorVec &inputs) const {
const auto A = inputs[0], B = inputs[1];
if (A->getDims().size() != B->getDims().size() ||
A->getDims() != B->getDims())
return {};
if (reductionMode == None) {
return {{A->getDims()}};
} else {
Shape temp = {1};
return {{temp}};
}
}
std::string MSELossObj::toString() const {
std::ostringstream os;
os << OpRegistry::getOpName(type) << "[" << getGuid() << "]";
os << "(";
os << vecToString(inputs[0]->getDims()) << ",";
os << vecToString(inputs[1]->getDims()) << ",";
os << "input0=" << inputs[0]->getGuid() << ",";
os << "input1=" << inputs[1]->getGuid() << ",";
os << "output=" << outputs[0]->getGuid() << ")";
return os.str();
}
// use output dim or inputs dim?
vector<int> MSELossObj::getWorkloadVector() const {
vector<int> ret = outputs[0]->getDims();
ret.emplace(ret.begin(), enum_to_underlying(type));
return ret;
}
vector<int> MSELossObj::getOpAttrVector() const {
return {enum_to_underlying(type)};
}
}; // namespace infini

5
src/operators/matmul.cc
View File

@ -4,8 +4,9 @@ namespace infini {
MatmulObj::MatmulObj(GraphObj *graph, Tensor A, Tensor B, Tensor C, bool transA,
bool transB, [[maybe_unused]] Tensor bias, ActType act)
: OperatorObj(OpType::Matmul, {A, B}, {C}), transA(transA), transB(transB),
act(act) {
: OperatorObj(OpType::Matmul,
bias ? TensorVec{A, B, bias} : TensorVec{A, B}, {C}),
transA(transA), transB(transB), act(act), b(1) {
auto shape_a = A->getDims();
auto shape_b = B->getDims();
int dimA = shape_a.size(), dimB = shape_b.size();

3
src/operators/pad.cc
View File

@ -13,7 +13,8 @@ PadObj::PadObj(GraphObj *graph, Tensor input, Tensor output,
pads = vector<int>(nDims * 2, 0);
for (size_t i = 0; i < nAxis; ++i) {
auto j = (*axes)[i];
auto k = (*axes)[i];
auto j = k < 0 ? nDims + k : k;
pads[j] = _pads[i];
pads[j + nDims] = _pads[i + nAxis];
}

5
src/operators/reduce_mean.cc
View File

@ -37,8 +37,9 @@ ReduceMeanObj::inferShape(const TensorVec &inputs) const {
if (!isReduced(i))
ret.emplace_back(dims[i]);
}
if (ret.size() == (size_t)0)
ret.emplace_back(1);
if (ret.empty())
return {{{1}}};
else
return {{ret}};
}
}

126
src/operators/slice.cc
View File

@ -3,77 +3,103 @@
namespace infini {
SliceObj::SliceObj(GraphObj *graph, Tensor input, Tensor output,
const vector<int> &starts, const vector<int> &ends,
const optional<vector<int>> &axes,
const optional<vector<int>> &steps)
const optional<vector<int>> &_axes,
const optional<vector<int>> &_steps)
: OperatorObj(OpType::Slice, {input}, {output}) {
if (steps)
IT_TODO_HALT();
IT_ASSERT(starts.size() == ends.size());
auto shape = input->getDims(); // shape of input
map<size_t, size_t> axes;
vector<int> steps;
{
auto size = starts.size(); // size of starts
IT_ASSERT(size == ends.size()); // size of ends
if (!axes) {
this->starts = starts;
this->ends = ends;
} else {
auto nAxis = (*axes).size();
IT_ASSERT(starts.size() == nAxis);
if (_axes) {
IT_ASSERT(size == _axes->size());
// onnx doc: "Behavior is undefined if an axis is repeated."
IT_ASSERT(size == std::set(_axes->begin(), _axes->end()).size());
auto dims = input->getDims();
this->starts = vector<int>(dims.size(), 0);
this->ends.resize(dims.size());
std::transform(dims.begin(), dims.end(), this->ends.begin(),
[](auto x) { return x - 1; });
for (size_t j = 0; j < nAxis; ++j) {
auto i = (*axes)[j];
if (i < 0)
IT_TODO_HALT();
this->starts[i] = starts[j];
this->ends[i] = ends[j];
for (size_t i = 0; i < size; ++i) {
auto index = _axes->at(i);
if (index < 0)
index += shape.size();
axes[index] = i;
}
} else
for (size_t i = 0; i < size; ++i)
axes[i] = i;
if (_steps) {
IT_ASSERT(size == _steps->size());
// onnx doc: "‘steps’ cannot be 0."
IT_ASSERT(std::find(_steps->begin(), _steps->end(), 0) ==
_steps->end());
steps = *_steps;
} else {
steps.reserve(size);
for (size_t i = 0; i < size; ++i)
steps.push_back(1);
}
}
auto size = shape.size();
this->axes.reserve(size);
for (size_t i = 0; i < size; ++i)
if (auto _i = axes.find(i); _i != axes.end()) {
auto __i = _i->second;
auto start = starts[__i];
auto end = ends[__i];
this->axes.push_back({start >= 0 ? start : start + shape[__i],
end >= 0 ? end : end + shape[__i],
steps[__i]});
} else {
this->axes.push_back({0, shape[i], 1});
}
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> SliceObj::inferShape(const TensorVec &inputs) const {
auto dims = inputs[0]->getDims();
int nDims = dims.size();
if (nDims != (int)starts.size())
return {};
for (int i = 0; i < nDims; ++i) {
if (starts[i] < 0 || ends[i] >= dims[i] || starts[i] > ends[i])
return {};
dims[i] = ends[i] - starts[i] + 1;
Shape ans;
ans.reserve(axes.size());
for (const auto &range : axes) {
auto step = std::abs(range.step);
ans.push_back((range.end - range.start + step - 1) / step);
}
return {{dims}};
return {{ans}};
}
std::string SliceObj::toString() const {
std::ostringstream os;
os << "Slice"
<< "[" << getGuid() << "]";
os << "(";
os << vecToString(inputs[0]->getDims()) << ",";
os << "starts=" << vecToString(starts) << ",";
os << "ends=" << vecToString(ends) << ",";
os << "input=" << inputs[0]->getGuid() << ",";
os << "output=" << outputs[0]->getGuid() << ")";
os << "Slice[" << getGuid() << "][";
for (const auto &range : axes) {
os << range.start << ':' << range.step << ':' << range.end << ", ";
}
os << "]("
<< "input=" << inputs[0]->getGuid() << ", "
<< "output=" << outputs[0]->getGuid() << ")";
return os.str();
}
vector<int> SliceObj::getWorkloadVector() const {
vector<int> ret = inputs[0]->getDims();
ret.insert(ret.end(), starts.begin(), starts.end());
ret.insert(ret.end(), ends.begin(), ends.end());
ret.emplace(ret.begin(), enum_to_underlying(type));
return ret;
auto ans = getOpAttrVector();
{
auto i = inputs[0]->getDims();
ans.insert(ans.end(), i.begin(), i.end());
}
if (!outputs.empty()) {
auto o = outputs[0]->getDims();
ans.insert(ans.end(), o.begin(), o.end());
}
return ans;
}
vector<int> SliceObj::getOpAttrVector() const {
vector<int> ret = starts;
ret.insert(ret.end(), ends.begin(), ends.end());
ret.emplace(ret.begin(), enum_to_underlying(type));
return ret;
vector<int> ans{enum_to_underlying(type)};
for (const auto &range : axes) {
ans.push_back(range.start);
ans.push_back(range.end);
ans.push_back(range.step);
}
return ans;
}
} // namespace infini

50
src/operators/transpose.cc Normal file
View File

@ -0,0 +1,50 @@
#include "operators/transpose.h"
namespace infini {
TransposeObj::TransposeObj(GraphObj *graph, Tensor input, Tensor output,
vector<int> permute)
: OperatorObj(OpType::Transpose, {input}, {output}) {
if (permute.size() != 4) {
IT_TODO_HALT();
}
transposePermute[0] = permute[0];
transposePermute[1] = permute[1];
transposePermute[2] = permute[2];
transposePermute[3] = permute[3];
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>>
TransposeObj::inferShape(const TensorVec &inputs) const {
const auto A = inputs[0];
auto input = A->getDims();
auto output = input;
for (int i = 0; i < 4; ++i) {
output[i] = input[transposePermute[i]];
}
return {{output}};
}
std::string TransposeObj::toString() const {
std::ostringstream os;
os << OpRegistry::getOpName(type) << "[" << getGuid() << "]";
os << "(";
os << vecToString(inputs[0]->getDims()) << ",";
os << "input=" << inputs[0]->getGuid() << ",";
os << "output=" << outputs[0]->getGuid() << ")";
return os.str();
}
vector<int> TransposeObj::getWorkloadVector() const {
vector<int> ret{enum_to_underlying(type)};
const Shape shape = outputs[0]->getDims();
ret.insert(ret.end(), shape.begin(), shape.end());
return ret;
}
vector<int> TransposeObj::getOpAttrVector() const {
return {enum_to_underlying(type)};
}
}; // namespace infini

284
src/operators/unary.cc
View File

@ -32,4 +32,288 @@ vector<int> UnaryObj::getOpAttrVector() const {
return {enum_to_underlying(type)};
}
ClipObj::ClipObj(GraphObj *graph, Tensor input, Tensor output,
std::optional<float> min, std::optional<float> max)
: OperatorObj(OpType::Clip, {input}, {output}), minValue(min),
maxValue(max) {
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> ClipObj::inferShape(const TensorVec &inputs) const {
const auto A = inputs[0];
return {{A->getDims()}};
}
std::string ClipObj::toString() const {
std::ostringstream os;
os << OpRegistry::getOpName(type) << "[" << getGuid() << "]";
os << "(";
os << vecToString(inputs[0]->getDims()) << ",";
os << "input=" << inputs[0]->getGuid() << ",";
os << "output=" << outputs[0]->getGuid() << ")";
return os.str();
}
vector<int> ClipObj::getWorkloadVector() const {
vector<int> ret{enum_to_underlying(type)};
const Shape shape = outputs[0]->getDims();
ret.insert(ret.end(), shape.begin(), shape.end());
return ret;
}
vector<int> ClipObj::getOpAttrVector() const {
return {enum_to_underlying(type)};
}
HardtanhObj::HardtanhObj(GraphObj *graph, Tensor input, Tensor output,
float min, float max)
: OperatorObj(OpType::Hardtanh, {input}, {output}), minValue(min),
maxValue(max) {
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> HardtanhObj::inferShape(const TensorVec &inputs) const {
const auto A = inputs[0];
return {{A->getDims()}};
}
std::string HardtanhObj::toString() const {
std::ostringstream os;
os << OpRegistry::getOpName(type) << "[" << getGuid() << "]";
os << "(";
os << vecToString(inputs[0]->getDims()) << ",";
os << "input=" << inputs[0]->getGuid() << ",";
os << "output=" << outputs[0]->getGuid() << ")";
return os.str();
}
vector<int> HardtanhObj::getWorkloadVector() const {
vector<int> ret{enum_to_underlying(type)};
const Shape shape = outputs[0]->getDims();
ret.insert(ret.end(), shape.begin(), shape.end());
return ret;
}
vector<int> HardtanhObj::getOpAttrVector() const {
return {enum_to_underlying(type)};
}
FillObj::FillObj(GraphObj *graph, Tensor input, Tensor output, float value)
: OperatorObj(OpType::Fill, {input}, {output}), setValue(value) {
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> FillObj::inferShape(const TensorVec &inputs) const {
const auto A = inputs[0];
return {{A->getDims()}};
}
std::string FillObj::toString() const {
std::ostringstream os;
os << OpRegistry::getOpName(type) << "[" << getGuid() << "]";
os << "(";
os << "output=" << outputs[0]->getGuid() << ")";
return os.str();
}
vector<int> FillObj::getWorkloadVector() const {
vector<int> ret{enum_to_underlying(type)};
const Shape shape = outputs[0]->getDims();
ret.insert(ret.end(), shape.begin(), shape.end());
return ret;
}
vector<int> FillObj::getOpAttrVector() const {
return {enum_to_underlying(type)};
}
L2LossObj::L2LossObj(GraphObj *graph, Tensor input, Tensor output)
: OperatorObj(OpType::L2Loss, {input}, {output}) {
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> L2LossObj::inferShape(const TensorVec &inputs) const {
Shape temp = {1};
return {{temp}};
}
std::string L2LossObj::toString() const {
std::ostringstream os;
os << OpRegistry::getOpName(type) << "[" << getGuid() << "]";
os << "(";
os << "output=" << outputs[0]->getGuid() << ")";
return os.str();
}
vector<int> L2LossObj::getWorkloadVector() const {
vector<int> ret{enum_to_underlying(type)};
const Shape shape = outputs[0]->getDims();
ret.insert(ret.end(), shape.begin(), shape.end());
return ret;
}
vector<int> L2LossObj::getOpAttrVector() const {
return {enum_to_underlying(type)};
}
CastObj::CastObj(GraphObj *graph, Tensor input, Tensor output, CastType type)
: OperatorObj(OpType::Cast, {input}, {output}), castType(type) {
IT_ASSERT(checkValid(graph));
}
vector<DataType> CastObj::inferDataType(const TensorVec &inputs) const {
auto input_dataType = inputs[0]->getDType();
auto output_dataType = getOutputDataType();
for (const auto &tensor : inputs)
IT_ASSERT(input_dataType == tensor->getDType());
return vector(numOutputs(), output_dataType);
}
optional<vector<Shape>> CastObj::inferShape(const TensorVec &inputs) const {
const auto A = inputs[0];
return {{A->getDims()}};
}
std::string CastObj::toString() const {
std::ostringstream os;
os << OpRegistry::getOpName(type) << "[" << getGuid() << "]";
os << "(";
os << "output=" << outputs[0]->getGuid() << ")";
return os.str();
}
vector<int> CastObj::getWorkloadVector() const {
vector<int> ret{enum_to_underlying(type)};
const Shape shape = outputs[0]->getDims();
ret.insert(ret.end(), shape.begin(), shape.end());
return ret;
}
vector<int> CastObj::getOpAttrVector() const {
return {enum_to_underlying(type)};
}
DataType CastObj::getOutputDataType() const {
switch (castType) {
case CastObj::Float2Int64:
return DataType::Int64;
case CastObj::Float2Int32:
return DataType::Int32;
case CastObj::Float2Int16:
return DataType::Int16;
case CastObj::Float2Int8:
return DataType::Int8;
case CastObj::Int322Float:
return DataType::Float32;
case CastObj::Int322Int8:
return DataType::Int8;
case CastObj::Int322Int16:
return DataType::Int16;
case CastObj::Int162Float:
return DataType::Float32;
case CastObj::Int162Int32:
return DataType::Int32;
case CastObj::Int82Float:
return DataType::Float32;
case CastObj::Int82Int16:
return DataType::Int16;
case CastObj::Int82Int32:
return DataType::Int32;
case CastObj::Uint82Float:
return DataType::Float32;
case CastObj::Uint82Int32:
return DataType::Int32;
case CastObj::Uint82Int64:
return DataType::Int64;
case CastObj::Int322Int64:
return DataType::Int64;
case CastObj::Int642Int32:
return DataType::Int32;
case CastObj::Int642Uint32:
return DataType::UInt32;
case CastObj::Int642Float:
return DataType::Float32;
case CastObj::Uint322Int64:
return DataType::Int64;
default:
IT_TODO_HALT();
}
}
ShapeObj::ShapeObj(GraphObj *graph, Tensor input, Tensor output)
: OperatorObj(OpType::Shape, {input}, {output}) {
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> ShapeObj::inferShape(const TensorVec &inputs) const {
return {{{static_cast<int>(inputs[0]->getDims().size())}}};
}
std::string ShapeObj::toString() const {
std::ostringstream os;
os << OpRegistry::getOpName(type) << "[" << getGuid() << "]("
<< "output=" << outputs[0]->getGuid() << ")";
return os.str();
}
PReluObj::PReluObj(GraphObj *graph, Tensor input, Tensor alpha, Tensor output)
: OperatorObj(OpType::PRelu, {input, alpha}, {output}) {
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> PReluObj::inferShape(const TensorVec &inputs) const {
const auto A = inputs[0];
return {{A->getDims()}};
}
std::string PReluObj::toString() const {
std::ostringstream os;
os << OpRegistry::getOpName(type) << "[" << getGuid() << "]";
os << "(";
os << vecToString(inputs[0]->getDims()) << ",";
os << "input=" << inputs[0]->getGuid() << ",";
os << "output=" << outputs[0]->getGuid() << ")";
return os.str();
}
vector<int> PReluObj::getWorkloadVector() const {
vector<int> ret{enum_to_underlying(type)};
const Shape shape = outputs[0]->getDims();
ret.insert(ret.end(), shape.begin(), shape.end());
return ret;
}
vector<int> PReluObj::getOpAttrVector() const {
return {enum_to_underlying(type)};
}
LogObj::LogObj(GraphObj *graph, Tensor input, Tensor output, LogType type)
: OperatorObj(OpType::Log, {input}, {output}), logType(type) {
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> LogObj::inferShape(const TensorVec &inputs) const {
const auto A = inputs[0];
return {{A->getDims()}};
}
std::string LogObj::toString() const {
std::ostringstream os;
os << OpRegistry::getOpName(type) << "[" << getGuid() << "]";
os << "(";
os << "output=" << outputs[0]->getGuid() << ")";
return os.str();
}
vector<int> LogObj::getWorkloadVector() const {
vector<int> ret{enum_to_underlying(type)};
const Shape shape = outputs[0]->getDims();
ret.insert(ret.end(), shape.begin(), shape.end());
return ret;
}
vector<int> LogObj::getOpAttrVector() const {
return {enum_to_underlying(type)};
}
}; // namespace infini

6
test/core/test_graph_replace.cc
View File

@ -281,7 +281,7 @@ TEST(MatchGraph, multi_input_output) {
SubGraph subg0 = make_ref<SubGraphObj>(runtime, TensorVec{i0, i1});
{
auto slice = subg0->addOp<SliceObj>(i0, nullptr, vector<int>{0, 0},
vector<int>{55, 55},
vector<int>{56, 56},
vector<int>{2, 3}, std::nullopt);
auto relu0 = subg0->addOp<ReluObj>(slice->getOutput(0), nullptr);
Tensor w0 = subg0->addTensor(Shape{256, 64, 1, 1}, DataType::UInt32);
@ -303,7 +303,7 @@ TEST(MatchGraph, multi_input_output) {
SubGraph subg1 = make_ref<SubGraphObj>(runtime, TensorVec{i1, i0});
{
auto slice = subg1->addOp<SliceObj>(i0, nullptr, vector<int>{0, 0},
vector<int>{55, 55},
vector<int>{56, 56},
vector<int>{2, 3}, std::nullopt);
auto relu0 = subg1->addOp<ReluObj>(slice->getOutput(0), nullptr);
Tensor w0 = subg1->addTensor(Shape{256, 64, 1, 1}, DataType::UInt32);
@ -328,7 +328,7 @@ TEST(MatchGraph, multi_input_output) {
auto slice = subg2->addOp<SliceObj>(
extend->getOutput(0), nullptr, vector<int>{0, 0},
vector<int>{55, 55}, vector<int>{2, 3}, std::nullopt);
vector<int>{56, 56}, vector<int>{2, 3}, std::nullopt);
auto extend1 = subg2->addOp<ExtendObj>(i1, nullptr, 1, 3);
auto add = subg2->addOp<AddObj>(extend1->getOutput(0),

2
test/core/test_tensor_save.cc
View File

@ -7,6 +7,7 @@
namespace infini {
TEST(Prtotbuf, save_and_load) {
#ifdef TENSOR_PROTOBUF
Runtime runtime = NativeCpuRuntimeObj::getInstance();
Graph g = make_ref<GraphObj>(runtime);
Tensor i0 = g->addTensor({1, 3, 4}, DataType::Float32);
@ -28,6 +29,7 @@ TEST(Prtotbuf, save_and_load) {
u1->load("u.pb");
u1->printData();
EXPECT_TRUE(u1->equalData(u0));
#endif
}
} // namespace infini

56
test/kernels/bang/test_bang_activation_backward.cc Normal file
View File

@ -0,0 +1,56 @@
#include "bang/bang_runtime.h"
#include "core/graph.h"
#include "core/kernel.h"
#include "core/runtime.h"
#include "operators/activation_backward.h"
#include "operators/element_wise.h"
#include "operators/unary.h"
#include "test.h"
namespace infini {
template <class T, class D>
void testActivationBackward(
const std::function<void(void *, size_t, DataType)> &generator,
const Shape &shape) {
// Runtime
Runtime cpuRuntime = NativeCpuRuntimeObj::getInstance();
auto bangRuntime = make_ref<BangRuntimeObj>();
// Build input data on CPU
Tensor yCpu = make_ref<TensorObj>(shape, DataType::Float32, cpuRuntime);
Tensor diffYCpu = make_ref<TensorObj>(shape, DataType::Float32, cpuRuntime);
Tensor xCpu = make_ref<TensorObj>(shape, DataType::Float32, cpuRuntime);
yCpu->dataMalloc();
diffYCpu->dataMalloc();
xCpu->dataMalloc();
yCpu->setData(generator);
diffYCpu->setData(generator);
xCpu->setData(generator);
// GPU
Graph bangGraph = make_ref<GraphObj>(bangRuntime);
auto yGpu = bangGraph->cloneTensor(yCpu);
auto diffYGpu = bangGraph->cloneTensor(diffYCpu);
auto xGpu = bangGraph->cloneTensor(xCpu);
auto gpuOp = bangGraph->addOp<T>(yGpu, diffYGpu, xGpu, nullptr);
bangGraph->dataMalloc();
bangRuntime->run(bangGraph);
auto diffXGpu = gpuOp->getOutput();
EXPECT_TRUE(1);
}
TEST(cnnl_ActivationBackward, run) {
testActivationBackward<ReluBackwardObj, ReluObj>(IncrementalGenerator(),
Shape{1, 2, 2, 3});
testActivationBackward<SigmoidBackwardObj, SigmoidObj>(
IncrementalGenerator(), Shape{1, 2, 2, 3});
testActivationBackward<TanhBackwardObj, TanhObj>(IncrementalGenerator(),
Shape{1, 2, 2, 3});
}
} // namespace infini

51
test/kernels/bang/test_bang_bitcompute.cc Normal file
View File

@ -0,0 +1,51 @@
#include "bang/bang_runtime.h"
#include "core/graph.h"
#include "core/kernel.h"
#include "core/runtime.h"
#include "operators/element_wise.h"
#include "test.h"
namespace infini {
template <class T>
void testBitCompute(
const std::function<void(void *, size_t, DataType)> &generator,
const Shape &shape) {
// Runtime
Runtime cpuRuntime = NativeCpuRuntimeObj::getInstance();
auto bangRuntime = make_ref<BangRuntimeObj>();
// Build input data on CPU
Tensor inputCpu1 =
make_ref<TensorObj>(shape, DataType::Float32, cpuRuntime);
inputCpu1->dataMalloc();
inputCpu1->setData(generator);
Tensor inputCpu2 =
make_ref<TensorObj>(shape, DataType::Float32, cpuRuntime);
inputCpu2->dataMalloc();
inputCpu2->setData(generator);
// GPU
Graph bangGraph = make_ref<GraphObj>(bangRuntime);
auto inputGpu1 = bangGraph->cloneTensor(inputCpu1);
auto inputGpu2 = bangGraph->cloneTensor(inputCpu2);
auto gpuOp = bangGraph->addOp<T>(inputGpu1, inputGpu2, nullptr);
bangGraph->dataMalloc();
bangRuntime->run(bangGraph);
auto outputGpu = gpuOp->getOutput();
auto outputGpu2Cpu = outputGpu->clone(cpuRuntime);
inputCpu1->printData();
inputCpu2->printData();
outputGpu2Cpu->printData();
EXPECT_TRUE(1);
}
TEST(cnnl_BitCompute, run) {
testBitCompute<BitAndObj>(IncrementalGenerator(), Shape{1, 2, 2, 3});
testBitCompute<BitOrObj>(IncrementalGenerator(), Shape{1, 2, 2, 3});
testBitCompute<BitXorObj>(IncrementalGenerator(), Shape{1, 2, 2, 3});
testBitCompute<BitNotObj>(IncrementalGenerator(), Shape{1, 2, 2, 3});
}
} // namespace infini

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test/kernels/bang/test_bang_cast.cc Normal file
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#include "bang/bang_runtime.h"
#include "core/graph.h"
#include "core/kernel.h"
#include "core/runtime.h"
#include "operators/unary.h"
#include "test.h"
namespace infini {
template <class T>
void testCast(const std::function<void(void *, size_t, DataType)> &generator,
const Shape &shape) {
// Runtime
Runtime cpuRuntime = NativeCpuRuntimeObj::getInstance();
auto bangRuntime = make_ref<BangRuntimeObj>();
// Build input data on CPU
Tensor inputCpu = make_ref<TensorObj>(shape, DataType::Float32, cpuRuntime);
inputCpu->dataMalloc();
inputCpu->setData(generator);
// GPU
Graph bangGraph = make_ref<GraphObj>(bangRuntime);
auto inputGpu = bangGraph->cloneTensor(inputCpu);
auto gpuOp = bangGraph->addOp<T>(inputGpu, nullptr, CastObj::Float2Int32);
auto outputGpu = gpuOp->getOutput();
bangGraph->dataMalloc();
bangRuntime->run(bangGraph);
auto outputGpu2Cpu = outputGpu->clone(cpuRuntime);
inputCpu->printData();
outputGpu2Cpu->printData();
EXPECT_TRUE(1);
}
TEST(cnnl_Cast, run) {
testCast<CastObj>(IncrementalGenerator(), Shape{1, 2, 2, 3});
}
} // namespace infini

40
test/kernels/bang/test_bang_ceil.cc Normal file
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#include "bang/bang_runtime.h"
#include "core/graph.h"
#include "core/kernel.h"
#include "core/runtime.h"
#include "operators/unary.h"
#include "test.h"
namespace infini {
template <class T>
void testCeil(const std::function<void(void *, size_t, DataType)> &generator,
const Shape &shape) {
// Runtime
Runtime cpuRuntime = NativeCpuRuntimeObj::getInstance();
auto bangRuntime = make_ref<BangRuntimeObj>();
// Build input data on CPU
Tensor inputCpu = make_ref<TensorObj>(shape, DataType::Float32, cpuRuntime);
inputCpu->dataMalloc();
inputCpu->setData(generator);
// GPU
Graph bangGraph = make_ref<GraphObj>(bangRuntime);
auto inputGpu = bangGraph->cloneTensor(inputCpu);
auto gpuOp = bangGraph->addOp<T>(inputGpu, nullptr);
bangGraph->dataMalloc();
bangRuntime->run(bangGraph);
auto outputGpu = gpuOp->getOutput();
auto outputGpu2Cpu = outputGpu->clone(cpuRuntime);
inputCpu->printData();
outputGpu2Cpu->printData();
EXPECT_TRUE(1);
}
TEST(cnnl_Ceil, run) {
testCeil<CeilObj>(IncrementalGenerator(), Shape{1, 2, 2, 3});
}
} // namespace infini

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test/kernels/bang/test_bang_clip.cc Normal file
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#include "bang/bang_runtime.h"
#include "core/graph.h"
#include "core/kernel.h"
#include "core/runtime.h"
#include "operators/unary.h"
#include "test.h"
namespace infini {
template <class T>
void testClip(const std::function<void(void *, size_t, DataType)> &generator,
const Shape &shape) {
// Runtime
Runtime cpuRuntime = NativeCpuRuntimeObj::getInstance();
auto bangRuntime = make_ref<BangRuntimeObj>();
// Build input data on CPU
Tensor inputCpu = make_ref<TensorObj>(shape, DataType::Float32, cpuRuntime);
inputCpu->dataMalloc();
inputCpu->setData(generator);
// GPU
Graph bangGraph = make_ref<GraphObj>(bangRuntime);
auto inputGpu = bangGraph->cloneTensor(inputCpu);
float min = 1.0;
float max = 4.0;
auto gpuOp = bangGraph->addOp<T>(inputGpu, nullptr, min, max);
bangGraph->dataMalloc();
bangRuntime->run(bangGraph);
auto outputGpu = gpuOp->getOutput();
auto outputGpu2Cpu = outputGpu->clone(cpuRuntime);
inputCpu->printData();
outputGpu2Cpu->printData();
EXPECT_TRUE(1);
}
TEST(cnnl_Clip, run) {
testClip<ClipObj>(IncrementalGenerator(), Shape{1, 2, 2, 3});
}
} // namespace infini

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test/kernels/bang/test_bang_concat.cc Normal file
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#include "bang/bang_runtime.h"
#include "core/graph.h"
#include "core/kernel.h"
#include "core/runtime.h"
#include "operators/concat.h"
#include "test.h"
namespace infini {
template <class T>
void testConcat(const std::function<void(void *, size_t, DataType)> &generator,
const Shape &shape) {
// Runtime
Runtime cpuRuntime = NativeCpuRuntimeObj::getInstance();
auto bangRuntime = make_ref<BangRuntimeObj>();
// Build input data on CPU
Tensor inputCpu1 =
make_ref<TensorObj>(shape, DataType::Float32, cpuRuntime);
inputCpu1->dataMalloc();
inputCpu1->setData(generator);
Tensor inputCpu2 =
make_ref<TensorObj>(shape, DataType::Float32, cpuRuntime);
inputCpu2->dataMalloc();
inputCpu2->setData(generator);
// GPU
Graph bangGraph = make_ref<GraphObj>(bangRuntime);
auto inputGpu1 = bangGraph->cloneTensor(inputCpu1);
auto inputGpu2 = bangGraph->cloneTensor(inputCpu2);
auto gpuOp =
bangGraph->addOp<T>(TensorVec{inputGpu1, inputGpu2}, nullptr, 2);
bangGraph->dataMalloc();
bangRuntime->run(bangGraph);
auto outputGpu = gpuOp->getOutput();
auto outputGpu2Cpu = outputGpu->clone(cpuRuntime);
// Check
inputCpu1->print();
inputCpu1->printData();
inputCpu2->print();
inputCpu2->printData();
outputGpu2Cpu->print();
outputGpu2Cpu->printData();
EXPECT_TRUE(1);
}
TEST(cnnl_Concat, run) {
testConcat<ConcatObj>(IncrementalGenerator(), Shape{1, 2, 2, 3});
}
} // namespace infini

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test/kernels/bang/test_bang_copy.cc Normal file
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#include "bang/bang_runtime.h"
#include "core/graph.h"
#include "core/kernel.h"
#include "core/runtime.h"
#include "operators/unary.h"
#include "test.h"
namespace infini {
template <class T>
void testCopy(const std::function<void(void *, size_t, DataType)> &generator,
const Shape &shape) {
// Runtime
Runtime cpuRuntime = NativeCpuRuntimeObj::getInstance();
auto bangRuntime = make_ref<BangRuntimeObj>();
// Build input data on CPU
Tensor inputCpu = make_ref<TensorObj>(shape, DataType::Float32, cpuRuntime);
inputCpu->dataMalloc();
inputCpu->setData(generator);
// GPU
Graph bangGraph = make_ref<GraphObj>(bangRuntime);
auto inputGpu = bangGraph->cloneTensor(inputCpu);
auto gpuOp = bangGraph->addOp<T>(inputGpu, nullptr);
bangGraph->dataMalloc();
bangRuntime->run(bangGraph);
auto outputGpu = gpuOp->getOutput();
auto outputGpu2Cpu = outputGpu->clone(cpuRuntime);
inputCpu->printData();
outputGpu2Cpu->printData();
EXPECT_TRUE(outputGpu2Cpu->equalData(inputCpu));
}
TEST(cnnl_Copy, run) {
testCopy<CopyObj>(IncrementalGenerator(), Shape{1, 2, 2, 3});
}
} // namespace infini

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