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InfiniTensor
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fix bang_unary_fusion_kernl

This commit is contained in:
wanghailu 2023-07-21 10:54:33 +08:00
parent 93778f212e
commit 0fa4e8efe1
12 changed files with 143 additions and 44 deletions
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4
CMakeLists.txt
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@ -8,7 +8,7 @@ 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" OFF)
option(BUILD_TEST "Build tests" OFF)
option(BUILD_TEST "Build tests" ON)
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)
@ -181,7 +181,7 @@ endif()
if(USE_BANG)
add_compile_definitions(USE_BANG=1)
include_directories(src/kernels/mlu/include)
include_directories(src/kernels/bang_kernel/include)
################################################################################
# Neuware Evironment
################################################################################

26
include/bang/bang_unary_list.h Normal file
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@ -0,0 +1,26 @@
#pragma once
#include "bang/bang_runtime.h"
#include "bang_unarylist.h"
#include "operators/unary.h"
namespace infini {
// void unary_kernel(cnnlHandle_t handle,
// const float *input,
// float *output,
// const uint32_t num,
// const uint32_t op_num,
// int* list);
void bang_unary_kernel(const RuntimeObj* obj, const Operator &_op) {
auto op = as<UnaryKernelObj>(_op);
float *const aData = (op->getInputs(0)->getRawDataPtr<float *>());
float *const cData = (op->getOutput()->getRawDataPtr<float *>());
auto dim = op->getInputs(0)->getDims();
auto context = dynamic_cast<const BangRuntimeObj *>(obj);
auto list = op->getOpList();
int n = dim[0], c = dim[1], h = dim[2], w = dim[3];
unary_kernel_list(context->cnnlHandle(), aData, cData, n * c * h * w, list.size(), list.data());
}
}; // namespace infini

2
include/core/operator.h
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@ -100,6 +100,7 @@ enum class OpType {
BitLeftShift,
BitRightShift,
Dropout,
UnaryKernel,
//
MemBound = 300,
MemoryGraph,
@ -207,6 +208,7 @@ class OpRegistry {
FOP(BitNot);
FOP(BitLeftShift);
FOP(BitRightShift);
FOP(UnaryKernel);
//
FOP(MemBound);
default:

25
include/operators/unary.h
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@ -28,6 +28,31 @@ class UnaryObj : public OperatorObj {
vector<int> getOpAttrVector() const override;
};
class UnaryKernelObj : public OperatorObj {
public:
/**
* @brief Construct a new Unary object.
*
* @param type Operator type.
* @param graph The computation graph that this operator belongs to.
* @param input The input tensor.
* @param output The output tensor.
*/
UnaryKernelObj(GraphObj *graph, Tensor input, Tensor output, std::vector<int> op_list);
OP_CLONE(UnaryKernelObj);
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> getOpList() const { return opList; }
private:
std::vector<int> opList;
vector<int> getWorkloadVector() const override;
vector<int> getOpAttrVector() const override;
};
class ClipObj : public OperatorObj {
public:
ClipObj(GraphObj *graph, Tensor input, Tensor output,

17
src/kernels/bang/unary_kernel.cc Normal file
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@ -0,0 +1,17 @@
#include "bang/bang_kernel_without_config.h"
#include "bang/bang_runtime.h"
#include "bang/bang_unary_list.h"
#include "operators/unary.h"
namespace infini {
class UnaryKernel : public BangKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
bang_unary_kernel(_context, _op);
}
};
REGISTER_KERNEL(Device::BANG, OpType::UnaryKernel, DataType::Float32, UnaryKernel,
"Unary_BANG_Float32");
}; // namespace infini

19
src/kernels/bang_kernel/include/bang_unary.h
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@ -1,19 +0,0 @@
#pragma once
#include "cnnl.h"
namespace infini {
typedef enum {
Abs = 1,
Relu = 2,
Sigmoid = 3,
} UnaryOpType;
void unary_kernel(cnnlHandle_t handle, const float *input, float *output,
const uint32_t num, const uint32_t op_num,
UnaryOpType list[]);
__mlu_global__ void MLUUnaryKernelUnion1(float *output, float *input,
uint32_t num, uint32_t op_list);
}; // namespace infini

16
src/kernels/bang_kernel/include/bang_unarylist.h Normal file
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@ -0,0 +1,16 @@
#pragma once
#include "cnnl.h"
namespace infini {
typedef enum {
Abs = 1,
Relu = 2,
Sigmoid = 3,
} UnaryOpType;
void unary_kernel_list(cnnlHandle_t handle, const float *input, float *output,
const uint32_t num, const uint32_t op_num,
int* list);
}; // namespace infini

3
src/kernels/bang_kernel/include/unarylist.h Normal file
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@ -0,0 +1,3 @@
#pragma once
__mlu_global__ void MLUUnaryKernelUnion1(float *output, float *input,
uint32_t num, uint32_t op_list);

2
src/kernels/bang_kernel/src/bang_unary.mlu → src/kernels/bang_kernel/src/bang_unary_device.mlu
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@ -1,4 +1,4 @@
#include "bang_unary.h"
#include "unarylist.h"
#define NRAM_USE_SIZE 102400

15
src/kernels/bang_kernel/src/bang_unary.cpp → src/kernels/bang_kernel/src/bang_unarylist.mlu
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@ -1,12 +1,13 @@
#include "bang_unary.h"
#include "bang_unarylist.h"
#include "unarylist.h"
namespace infini {
void unary_kernel(cnnlHandle_t handle,
const float *input,
float *output,
const uint32_t num,
const uint32_t op_num,
UnaryOpType list[]) {
void unary_kernel_list(cnnlHandle_t handle,
const float *input,
float *output,
const uint32_t num,
const uint32_t op_num,
int* list) {
// 任务类型和调度方法
cnrtDim3_t k_dim;
cnrtFunctionType_t k_type;

32
src/operators/unary.cc
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@ -32,6 +32,38 @@ vector<int> UnaryObj::getOpAttrVector() const {
return {enum_to_underlying(type)};
}
UnaryKernelObj::UnaryKernelObj(GraphObj *graph, Tensor input, Tensor output, std::vector<int> op_list)
: OperatorObj(OpType::UnaryKernel, {input}, {output}), opList(op_list) {
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>> UnaryKernelObj::inferShape(const TensorVec &inputs) const {
const auto A = inputs[0];
return {{A->getDims()}};
}
std::string UnaryKernelObj::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> UnaryKernelObj::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> UnaryKernelObj::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),

26
test/kernels/bang/test_bang_unary.cc → test/kernels/bang/test_bang_unary_kernel.cc
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@ -9,8 +9,8 @@
namespace infini {
template <class T>
void testUnary(const std::function<void(void *, size_t, DataType)> &generator,
const Shape &shape) {
void testUnaryKernel(const std::function<void(void *, size_t, DataType)> &generator,
const Shape &shape) {
// Runtime
Runtime cpuRuntime = NativeCpuRuntimeObj::getInstance();
auto bangRuntime = make_ref<BangRuntimeObj>();
@ -23,25 +23,21 @@ void testUnary(const std::function<void(void *, size_t, DataType)> &generator,
// GPU
Graph bangGraph = make_ref<GraphObj>(bangRuntime);
auto inputGpu = bangGraph->cloneTensor(inputCpu);
auto gpuOp = bangGraph->addOp<T>(inputGpu, nullptr);
std::vector<int> op_list = {1,2,3};
auto gpuOp = bangGraph->addOp<T>(inputGpu, nullptr, op_list);
bangGraph->dataMalloc();
bangRuntime->run(bangGraph);
auto outputGpu = gpuOp->getOutput();
auto outputGpu2Cpu = outputGpu->clone(cpuRuntime);
// CPU
Graph cpuGraph = make_ref<GraphObj>(cpuRuntime);
auto cpuOp = cpuGraph->addOp<T>(inputCpu, nullptr);
cpuGraph->dataMalloc();
cpuRuntime->run(cpuGraph);
auto outputCpu = cpuOp->getOutput();
// Check
EXPECT_TRUE(outputCpu->equalData(outputGpu2Cpu));
inputCpu->printData();
outputGpu2Cpu->printData();
EXPECT_TRUE(1);
}
TEST(cnnl_Unary, run) {
testUnary<ReluObj>(IncrementalGenerator(), Shape{1, 2, 2, 3});
testUnary<SigmoidObj>(IncrementalGenerator(), Shape{1, 2, 2, 3});
testUnary<TanhObj>(IncrementalGenerator(), Shape{1, 2, 2, 3});
TEST(cnnl_unary_kernel, run) {
testUnaryKernel<UnaryKernelObj>(IncrementalGenerator(), Shape{1, 2, 2, 3});
}
} // namespace infini