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ADD: batch norm operator and cuda kernel. (#44) 

fix numInputs of batchNorm, add new line in file ending.

ADD: batch norm operator and cuda kernel.

add training

remove comments.

fix compile error.

add batch norm operator and cuda kernel.
This commit is contained in:
wendy12022 2022-10-15 16:29:28 +08:00 committed by GitHub
parent 1152adc94a
commit a4d6426589
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GPG Key ID: 4AEE18F83AFDEB23
74 changed files with 362 additions and 122 deletions
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28
include/operators/batch_norm.h Normal file
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@ -0,0 +1,28 @@
#pragma once
#include "core/operator.h"
namespace infini {
class BatchNormObj : public OperatorObj {
float momentum, eps;
bool training;
public:
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);
optional<vector<Shape>> inferShape(const TensorVec &inputs) const override;
std::string toString() const override;
// output size will be 3 when training
int numInputs() const override { return 5; }
int numOutputs() const override { return outputs.size(); }
float getEps() const { return eps; }
private:
vector<int> getWorkloadVector() const override;
vector<int> getOpAttrVector() const override;
vector<DataType> inferDataType(const TensorVec &inputs) const override;
};
} // namespace infini

8
include/utils/data_generator.h
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@ -38,14 +38,14 @@ class IncrementalGenerator : public DataGenerator {
void fill(float *data, size_t size) override { fill<float>(data, size); }
};
class OneGenerator : public DataGenerator {
template <int val> class ValGenerator : public DataGenerator {
public:
virtual ~OneGenerator() {}
virtual ~ValGenerator() {}
private:
template <typename T> void fill(T *data, size_t size) {
for (size_t i = 0; i < size; i++) {
data[i] = 1;
data[i] = val;
}
}
@ -54,4 +54,6 @@ class OneGenerator : public DataGenerator {
}
void fill(float *data, size_t size) override { fill<float>(data, size); }
};
typedef ValGenerator<1> OneGenerator;
typedef ValGenerator<0> ZeroGenerator;
} // namespace infini

64
src/kernels/cuda/batch_norm.cc Normal file
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@ -0,0 +1,64 @@
#include "operators/batch_norm.h"
#include "core/kernel.h"
#include "cuda/cuda_kernel_wihtout_config.h"
#include "cuda/cuda_runtime.h"
namespace infini {
class BatchNormCudnn : public CudaKernelWithoutConfig {
void compute(const Operator &_op,
const RuntimeObj *_context) const override {
auto op = as<BatchNormObj>(_op);
auto context = dynamic_cast<const CudaRuntimeObj *>(_context);
cudnnStatus_t stat;
void *const inData = (op->getInputs(0)->getRawDataPtr<void *>());
void *const outData = (op->getOutput()->getRawDataPtr<void *>());
void *const meanData = (op->getInputs(1)->getRawDataPtr<void *>());
void *const varData = (op->getInputs(2)->getRawDataPtr<void *>());
void *const scaleData = (op->getInputs(3)->getRawDataPtr<void *>());
void *const biasData = (op->getInputs(4)->getRawDataPtr<void *>());
auto dims = op->getInputs(0)->getDims();
if (dims.size() == 2)
IT_TODO_HALT();
// Only 4D and 5D tensors are supported by
// cudnnBatchNormalizationForwardInference
IT_ASSERT(dims.size() == 4 || dims.size() == 5);
int dimArray[CUDNN_DIM_MAX], strideArray[CUDNN_DIM_MAX],
dimPArray[CUDNN_DIM_MAX], stridePArray[CUDNN_DIM_MAX];
for (size_t i = 0; i < dims.size(); ++i) {
dimArray[i] = dims[i];
strideArray[i] = op->getInputs(0)->getStride()[i];
dimPArray[i] = op->getInputs(1)->getDims()[i];
stridePArray[i] = op->getInputs(1)->getStride()[i];
}
// get inputs
cudnnTensorDescriptor_t inDesc;
checkCudnnError(cudnnCreateTensorDescriptor(&inDesc));
checkCudnnError(cudnnSetTensorNdDescriptor(
inDesc, CUDNN_DATA_FLOAT, dims.size(), dimArray, strideArray));
// get bnScaleBiasMeanVarDesc
cudnnTensorDescriptor_t paraDesc;
checkCudnnError(cudnnCreateTensorDescriptor(&paraDesc));
checkCudnnError(cudnnSetTensorNdDescriptor(
paraDesc, CUDNN_DATA_FLOAT, dims.size(), dimPArray, stridePArray));
float alpha = 1.f, beta = 0.f;
// This mode is intended for use after convolutional layers
stat = cudnnBatchNormalizationForwardInference(
context->cudnnHandle(), CUDNN_BATCHNORM_SPATIAL, &alpha, &beta,
inDesc, inData, inDesc, outData, paraDesc, scaleData, biasData,
meanData, varData, op->getEps());
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(inDesc));
checkCudnnError(cudnnDestroyTensorDescriptor(paraDesc));
}
};
REGISTER_KERNEL(Device::CUDA, OpType::BatchNorm, DataType::Float32,
BatchNormCudnn, "BatchNorm_cuDNN_CUDA_Float32");
} // namespace infini

6
src/kernels/cuda/gather.cc
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@ -4,8 +4,9 @@
#include "cuda/gather.h"
namespace infini {
void initGatherMetaData(GatherMetaData &metaData, const Operator &_op) {
class GatherCuda : public CudaKernelWithoutConfig {
void initGatherMetaData(GatherMetaData &metaData,
const Operator &_op) const {
memset(&metaData, 0, sizeof(metaData));
auto op = as<GatherObj>(_op);
auto in = op->getInputs(0);
@ -27,7 +28,6 @@ void initGatherMetaData(GatherMetaData &metaData, const Operator &_op) {
}
}
class GatherCuda : public CudaKernelWithoutConfig {
void compute(const Operator &op,
const RuntimeObj *_context) const override {

1
src/kernels/cuda/gbmm_g2bmm.cu
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@ -14,4 +14,3 @@ void _sgbmml(float *__restrict__ q, float *__restrict__ k,
}
} // namespace infini

6
src/kernels/cuda/split_concat.cc
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@ -6,8 +6,9 @@
namespace infini {
class CudaCompute {
void initComposedTensorMetadata(ComposedTensorMetadata &metadata,
Tensor tensor) {
Tensor tensor) const {
int nDims = tensor->getDims().size();
auto strides = tensor->getStride();
IT_ASSERT(strides.size() == (size_t)nDims);
@ -20,7 +21,7 @@ void initComposedTensorMetadata(ComposedTensorMetadata &metadata,
void initElementTensorMetadata(ElementTensorMetadata &metadata,
TensorVec tensors, int idx, int dim,
int &dimBgIdx, int &batchCounter) {
int &dimBgIdx, int &batchCounter) const {
int nTensors = tensors.size();
for (; batchCounter < BATCH_SIZE && idx + batchCounter < nTensors;
++batchCounter) {
@ -34,7 +35,6 @@ void initElementTensorMetadata(ElementTensorMetadata &metadata,
}
}
class CudaCompute {
public:
void do_compute(Tensor composedTensor, TensorVec elementsTensor, int dim,
int nDims, bool isSplit) const {

72
src/operators/batch_norm.cc Normal file
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@ -0,0 +1,72 @@
#include "operators/batch_norm.h"
namespace infini {
BatchNormObj::BatchNormObj(GraphObj *graph, Tensor input, Tensor output,
Tensor mean, Tensor var, Tensor scale, Tensor bias,
float momentum, float eps, bool training)
: OperatorObj(OpType::BatchNorm, {input, mean, var, scale, bias}, {output}),
momentum(momentum), eps(eps), training(training) {
if (training)
IT_TODO_HALT();
IT_ASSERT(checkValid(graph));
}
optional<vector<Shape>>
BatchNormObj::inferShape(const TensorVec &inputs) const {
auto input = inputs[0];
auto mean = inputs[1];
auto var = inputs[2];
auto scale = inputs[3];
auto bias = inputs[4];
if (input->getDims().size() < 2)
return {};
Shape dims(input->getDims().size(), 1);
dims[1] = input->getDims()[1]; //
if (mean->getDims() != dims || var->getDims() != dims ||
scale->getDims() != dims || bias->getDims() != dims)
return {};
return {{input->getDims()}};
}
vector<DataType> BatchNormObj::inferDataType(const TensorVec &inputs) const {
IT_ASSERT(inputs.size() == 5);
auto index = inputs[1];
IT_ASSERT(inputs[1]->getDType() == DataType::Float32);
IT_ASSERT(inputs[2]->getDType() == DataType::Float32);
IT_ASSERT(inputs[3]->getDType() == DataType::Float32);
IT_ASSERT(inputs[4]->getDType() == DataType::Float32);
return {inputs[0]->getDType()};
}
std::string BatchNormObj::toString() const {
std::ostringstream os;
os << "BatchNorm[" << getGuid() << "]";
os << "(";
os << vecToString(inputs[0]->getDims()) << ",";
os << "momentum=" << momentum << ",";
os << "eps=" << eps << ",";
os << "input=" << inputs[0]->getGuid() << ",";
os << "mean=" << inputs[1]->getGuid() << ",";
os << "var=" << inputs[2]->getGuid() << ",";
os << "scale=" << inputs[3]->getGuid() << ",";
os << "bias=" << inputs[4]->getGuid() << ",";
os << "output=";
for (auto output : outputs)
os << output->getGuid() << ",";
return os.str();
}
// need eps and momentum?
vector<int> BatchNormObj::getWorkloadVector() const {
vector<int> ret = inputs[0]->getDims();
ret.emplace(ret.begin(), enum_to_underlying(type));
return ret;
}
// need eps and momentum?
vector<int> BatchNormObj::getOpAttrVector() const {
return {enum_to_underlying(type)};
}
} // namespace infini

54
test/kernels/cuda/test_cuda_batch_norm.cc Normal file
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@ -0,0 +1,54 @@
#include "core/graph.h"
#include "core/runtime.h"
#include "cuda/cuda_runtime.h"
#include "cuda/cuda_utility.h"
#include "operators/batch_norm.h"
#include "test.h"
namespace infini {
TEST(CUDA_BatchNorm, run) {
Runtime cpuRuntime = CpuRuntimeObj::getInstance();
auto cudaRuntime = make_ref<CudaRuntimeObj>();
// Build cpu graph
Graph gCpu = make_ref<GraphObj>(cpuRuntime);
auto iCpu = gCpu->addTensor(Shape{1, 3, 2, 2}, DataType::Float32);
auto meanCpu = gCpu->addTensor(Shape{1, 3, 1, 1}, DataType::Float32);
auto varCpu = gCpu->addTensor(Shape{1, 3, 1, 1}, DataType::Float32);
auto scaleCpu = gCpu->addTensor(Shape{1, 3, 1, 1}, DataType::Float32);
auto biasCpu = gCpu->addTensor(Shape{1, 3, 1, 1}, DataType::Float32);
// Build input data on CPU
gCpu->dataMalloc();
iCpu->setData(IncrementalGenerator());
meanCpu->copyData(vector<float>{1, 6, 9});
varCpu->copyData(vector<float>{4, 1, 9});
scaleCpu->setData(OneGenerator());
biasCpu->setData(ZeroGenerator());
// Build CUDA graph
Graph g = make_ref<GraphObj>(cudaRuntime);
auto i = g->cloneTensor(iCpu);
auto mean = g->cloneTensor(meanCpu);
auto var = g->cloneTensor(varCpu);
auto scale = g->cloneTensor(scaleCpu);
auto bias = g->cloneTensor(biasCpu);
auto op =
g->addOp<BatchNormObj>(i, nullptr, mean, var, scale, bias, 0.9, 0);
// allocate CUDA memory
g->dataMalloc();
// Execute on CUDA
cudaRuntime->run(g);
// clone CUDA output to CPU
auto o = op->getOutput();
auto ocpu = o->clone(cpuRuntime);
// check results on CPU
EXPECT_TRUE(ocpu->equalData(vector<float>{
-0.5, 0, 0.5, 1, -2, -1, 0, 1, -0.333333, 0, 0.333333, 0.666667}));
}
} // namespace infini

21
test/operators/test_batch_norm.cc Normal file
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@ -0,0 +1,21 @@
#include "core/graph.h"
#include "core/runtime.h"
#include "operators/batch_norm.h"
#include "test.h"
namespace infini {
TEST(BatchNorm, ShapeInference) {
Runtime cpuRuntime = CpuRuntimeObj::getInstance();
{
Graph g = make_ref<GraphObj>(cpuRuntime);
Tensor i = g->addTensor({1, 3, 2, 2}, DataType::UInt32);
Tensor mean = g->addTensor({1, 3, 1, 1}, DataType::Float32);
Tensor var = g->addTensor({1, 3, 1, 1}, DataType::Float32);
Tensor scaler = g->addTensor({1, 3, 1, 1}, DataType::Float32);
Tensor bias = g->addTensor({1, 3, 1, 1}, DataType::Float32);
auto op = g->addOp<BatchNormObj>(i, nullptr, mean, var, scaler, bias,
0.9, 1e-5);
EXPECT_EQ(op->getOutput()->getDims(), (Shape{1, 3, 2, 2}));
}
}
} // namespace infini