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!4786 add op_fused_batchnorm_int8

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Merge pull request !4786 from songhonglei413/roi
pull/4786/MERGE
mindspore-ci-bot 5 years ago committed by Gitee
parent a27e6f5769 3b9ca7780e
commit c7b7af6c3a
6 changed files with 184 additions and 12 deletions
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2
mindspore/lite/src/populate_parameter.cc
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@ -192,6 +192,7 @@ OpParameter *PopulateBatchNorm(const mindspore::lite::PrimitiveC *primitive) {
}
batch_norm_param->op_parameter_.type_ = primitive->Type();
batch_norm_param->epsilon_ = param->GetEpsilon();
batch_norm_param->fused_ = false;
return reinterpret_cast<OpParameter *>(batch_norm_param);
}
@ -648,6 +649,7 @@ OpParameter *PopulateFusedBatchNorm(const mindspore::lite::PrimitiveC *primitive
batch_norm_param->op_parameter_.type_ = primitive->Type();
auto param = dynamic_cast<const mindspore::lite::FusedBatchNorm *>(primitive);
batch_norm_param->epsilon_ = param->GetEpsilon();
batch_norm_param->fused_ = true;
return reinterpret_cast<OpParameter *>(batch_norm_param);
}

80
mindspore/lite/src/runtime/kernel/arm/int8/batchnorm_int8.cc
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@ -27,6 +27,7 @@ using mindspore::lite::KernelRegistrar;
using mindspore::lite::RET_ERROR;
using mindspore::lite::RET_OK;
using mindspore::schema::PrimitiveType_BatchNorm;
using mindspore::schema::PrimitiveType_FusedBatchNorm;
namespace mindspore::kernel {
BatchnormInt8CPUKernel::~BatchnormInt8CPUKernel() {
@ -82,22 +83,86 @@ int BatchnormInt8CPUKernel::InitConstTensor() {
return RET_OK;
}
int BatchnormInt8CPUKernel::InitFusedConstTensor() {
auto input = in_tensors_[0];
auto scale = in_tensors_[1];
auto offset = in_tensors_[2];
auto mean = in_tensors_[3];
auto variance = in_tensors_[4];
auto output = out_tensors_[0];
auto scale_ptr = reinterpret_cast<int8_t *>(scale->Data());
auto offset_ptr = reinterpret_cast<int8_t *>(offset->Data());
auto mean_ptr = reinterpret_cast<int8_t *>(mean->Data());
auto var_ptr = reinterpret_cast<int8_t *>(variance->Data());
alpha_addr_ = reinterpret_cast<float *>(malloc(mean->ElementsNum() * sizeof(float)));
if (alpha_addr_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
return RET_ERROR;
}
beta_addr_ = reinterpret_cast<float *>(malloc(variance->ElementsNum() * sizeof(float)));
if (beta_addr_ == nullptr) {
MS_LOG(ERROR) << "Malloc buffer failed.";
return RET_ERROR;
}
// compute alpha, beta;
// 0. tmp = (S6 * Sqrt(e + S5 * (q5 - Z5)));
// 1. A = S1 * S2 * (q2 - Z2) / tmp;
// 2. B = Z6 - (A1 * Z1) -((S3 * (q3 - Z3)) / S6 - S2 * S4 * (q2 - Z4) * (q4 - z4) / tmp;
auto eps = batchnorm_param_->epsilon_;
auto zp_in = input->GetQuantParams().front().zeroPoint;
auto zp_scale = scale->GetQuantParams().front().zeroPoint;
auto zp_offset = offset->GetQuantParams().front().zeroPoint;
auto zp_mean = mean->GetQuantParams().front().zeroPoint;
auto zp_var = variance->GetQuantParams().front().zeroPoint;
auto zp_out = output->GetQuantParams().front().zeroPoint;
auto s_in = input->GetQuantParams().front().scale;
auto s_scale = scale->GetQuantParams().front().scale;
auto s_offset = offset->GetQuantParams().front().scale;
auto s_mean = mean->GetQuantParams().front().scale;
auto s_var = variance->GetQuantParams().front().scale;
auto s_out = output->GetQuantParams().front().scale;
float mul_12 = s_in * s_scale;
float mul_24 = s_scale * s_mean;
float div_36 = s_offset / s_out;
for (int i = 0; i < batchnorm_param_->channel_; ++i) {
float tmp = s_out * sqrt(eps + s_var * (var_ptr[i] - zp_var));
float tmp_a = (mul_12 * (scale_ptr[i] - zp_scale)) / tmp;
float tmp_b = zp_out + div_36 * (offset_ptr[i] - zp_offset) - tmp_a * zp_in -
(mul_24 * (scale_ptr[i] - zp_scale) * (mean_ptr[i] - zp_mean)) / tmp;
alpha_addr_[i] = tmp_a;
beta_addr_[i] = tmp_b;
}
return RET_OK;
}
int BatchnormInt8CPUKernel::Init() {
auto input_shapes = in_tensors_[0]->shape();
auto n_dim = input_shapes.size();
batchnorm_param_->channel_ = input_shapes[n_dim - 1];
batchnorm_param_->unit_ = 1;
batchnorm_param_->units_ = 1;
for (int i = 0; i < n_dim - 1; i++) {
batchnorm_param_->unit_ *= input_shapes[i];
batchnorm_param_->units_ *= input_shapes[i];
}
batchnorm_param_->op_parameter_.thread_num_ =
MSMIN(batchnorm_param_->op_parameter_.thread_num_, batchnorm_param_->channel_);
auto ret = InitConstTensor();
if (ret != 0) {
MS_LOG(ERROR) << "Batchnorm fp32 InitConstTensor failed.";
return RET_ERROR;
batchnorm_param_->unit_ = UP_DIV(batchnorm_param_->units_, batchnorm_param_->op_parameter_.thread_num_);
if (batchnorm_param_->fused_) {
auto ret = InitFusedConstTensor();
if (ret != 0) {
MS_LOG(ERROR) << "FusedBatchnorm int8 InitFusedConstTensor failed.";
return RET_ERROR;
}
} else {
auto ret = InitConstTensor();
if (ret != 0) {
MS_LOG(ERROR) << "Batchnorm int8 InitConstTensor failed.";
return RET_ERROR;
}
}
return RET_OK;
}
@ -165,4 +230,5 @@ kernel::LiteKernel *CpuBatchnormInt8KernelCreator(const std::vector<lite::tensor
}
REG_KERNEL(kCPU, kNumberTypeInt8, PrimitiveType_BatchNorm, CpuBatchnormInt8KernelCreator)
REG_KERNEL(kCPU, kNumberTypeInt8, PrimitiveType_FusedBatchNorm, CpuBatchnormInt8KernelCreator)
} // namespace mindspore::kernel

1
mindspore/lite/src/runtime/kernel/arm/int8/batchnorm_int8.h
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@ -40,6 +40,7 @@ class BatchnormInt8CPUKernel : public LiteKernel {
int ReSize() override;
int Run() override;
int InitConstTensor();
int InitFusedConstTensor();
int DoExecute(int tid);
private:

2
mindspore/lite/src/runtime/kernel/arm/nnacl/batchnorm_parameter.h
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@ -23,7 +23,9 @@ typedef struct BatchNormParameter {
OpParameter op_parameter_;
float epsilon_;
int unit_;
int units_;
int channel_;
bool fused_;
} BatchNormParameter;
#endif // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_NNACL_BATCHNORM_PARAMETER_H_

6
mindspore/lite/src/runtime/kernel/arm/nnacl/int8/batchnorm_int8.c
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@ -20,8 +20,10 @@
void BatchNormInt8(int8_t *output_ptr, const int8_t *input_ptr, const float *alpha_ptr, const float *beta_ptr,
int task_id, BatchNormParameter *param) {
for (int c = task_id; c < param->channel_; c += param->op_parameter_.thread_num_) {
for (int u = 0; u < param->unit_; u++) {
int unit_st = task_id * param->unit_;
int unit_end = MSMIN((task_id + 1) * param->unit_, param->units_);
for (int u = unit_st; u < unit_end; u++) {
for (int c = 0; c < param->channel_; c++) {
int32_t output_tmp = round(input_ptr[u * param->channel_ + c] * alpha_ptr[c] + beta_ptr[c]);
output_tmp = output_tmp > 127 ? 127 : output_tmp;
output_tmp = output_tmp < -128 ? -128 : output_tmp;

105
mindspore/lite/test/ut/src/runtime/kernel/arm/int8/batchnorm_int8_test.cc
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@ -27,6 +27,104 @@ class TestBatchnormInt8 : public mindspore::CommonTest {
TestBatchnormInt8() {}
};
TEST_F(TestBatchnormInt8, FusedTest) {
std::vector<int8_t> in_data = {11, 41, 21, 51, 31, 61, -11, -41, -21, -51, -31, -61};
std::vector<int8_t> in_data1 = {4, 4};
std::vector<int8_t> in_data2 = {8, 33};
std::vector<int8_t> in_data3 = {35, 55};
std::vector<int8_t> in_data4 = {2, 3};
std::vector<lite::tensor::Tensor *> inputs_tensor;
std::vector<lite::tensor::Tensor *> outputs_tensor;
BatchNormParameter op_param;
op_param.op_parameter_.type_ = schema::PrimitiveType_FusedBatchNorm;
op_param.epsilon_ = 0.001f;
op_param.fused_ = true;
std::vector<int> shape = {1, 1, 6, 2};
lite::tensor::QuantArg input_quant_arg;
input_quant_arg.scale = 0.1;
input_quant_arg.zeroPoint = 1;
lite::tensor::QuantArg input_quant_arg_1;
input_quant_arg_1.scale = 0.5;
input_quant_arg_1.zeroPoint = 2;
lite::tensor::QuantArg input_quant_arg_2;
input_quant_arg_2.scale = 0.02;
input_quant_arg_2.zeroPoint = 3;
lite::tensor::QuantArg input_quant_arg_3;
input_quant_arg_3.scale = 0.5;
input_quant_arg_3.zeroPoint = 15;
lite::tensor::QuantArg input_quant_arg_4;
input_quant_arg_4.scale = 0.25;
input_quant_arg_4.zeroPoint = 1;
lite::tensor::QuantArg output_quant_arg;
output_quant_arg.scale = 0.8;
output_quant_arg.zeroPoint = 0;
lite::tensor::Tensor input0_tensor;
lite::tensor::Tensor input1_tensor;
lite::tensor::Tensor input2_tensor;
lite::tensor::Tensor input3_tensor;
lite::tensor::Tensor input4_tensor;
inputs_tensor.push_back(&input0_tensor);
inputs_tensor.push_back(&input1_tensor);
inputs_tensor.push_back(&input2_tensor);
inputs_tensor.push_back(&input3_tensor);
inputs_tensor.push_back(&input4_tensor);
input0_tensor.SetData(in_data.data());
input1_tensor.SetData(in_data1.data());
input2_tensor.SetData(in_data2.data());
input3_tensor.SetData(in_data3.data());
input4_tensor.SetData(in_data4.data());
input0_tensor.set_shape(shape);
input1_tensor.set_shape({2});
input2_tensor.set_shape({2});
input3_tensor.set_shape({2});
input4_tensor.set_shape({2});
input0_tensor.AddQuantParam(input_quant_arg);
input1_tensor.AddQuantParam(input_quant_arg_1);
input2_tensor.AddQuantParam(input_quant_arg_2);
input3_tensor.AddQuantParam(input_quant_arg_3);
input4_tensor.AddQuantParam(input_quant_arg_4);
std::vector<int8_t> output(12);
// std::vector<int8_t> corr_out = {-18, -22, -16, -21, -14, -19, -22, -34, -24, -35, -26, -36 };
std::vector<int8_t> corr_out = {-22, -28, -20, -26, -17, -24, -28, -42, -30, -44, -33, -46};
lite::tensor::Tensor output0_tensor;
outputs_tensor.push_back(&output0_tensor);
output0_tensor.SetData(output.data());
output0_tensor.set_shape(shape);
output0_tensor.AddQuantParam(output_quant_arg);
kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeInt8, schema::PrimitiveType_FusedBatchNorm};
auto creator = lite::KernelRegistry::GetInstance()->GetCreator(desc);
ASSERT_NE(creator, nullptr);
lite::Context ctx;
ctx.thread_num_ = 3;
kernel::LiteKernel *kernel =
creator(inputs_tensor, outputs_tensor, reinterpret_cast<OpParameter *>(&op_param), &ctx, desc, nullptr);
ASSERT_NE(kernel, nullptr);
auto output_tensor_shape = output0_tensor.shape();
kernel->Run();
printf("==================output data=================\n");
for (int i = 0; i < output0_tensor.ElementsNum(); i++) {
printf("%d, ", output[i]);
}
std::cout << std::endl;
CompareOutputData(output.data(), corr_out.data(), output0_tensor.ElementsNum(), 0.001);
input0_tensor.SetData(nullptr);
input1_tensor.SetData(nullptr);
input2_tensor.SetData(nullptr);
input3_tensor.SetData(nullptr);
input4_tensor.SetData(nullptr);
output0_tensor.SetData(nullptr);
MS_LOG(INFO) << "TestBathNormFp32 accuracy passed";
}
TEST_F(TestBatchnormInt8, BNTest) {
std::vector<int8_t> in_data = {11, 41, 21, 51, 31, 61, -11, -41, -21, -51, -31, -61};
std::vector<int8_t> in_data1 = {4, 14};
@ -37,6 +135,7 @@ TEST_F(TestBatchnormInt8, BNTest) {
BatchNormParameter op_param;
op_param.op_parameter_.type_ = schema::PrimitiveType_BatchNorm;
op_param.epsilon_ = 0.001f;
op_param.fused_ = false;
std::vector<int> shape = {1, 1, 6, 2};
@ -50,7 +149,7 @@ TEST_F(TestBatchnormInt8, BNTest) {
input_quant_arg_2.scale = 0.1;
input_quant_arg_2.zeroPoint = -1;
lite::tensor::QuantArg output_quant_arg;
output_quant_arg.scale = 1;
output_quant_arg.scale = 0.5;
output_quant_arg.zeroPoint = 0;
lite::tensor::Tensor input0_tensor;
@ -70,8 +169,7 @@ TEST_F(TestBatchnormInt8, BNTest) {
input2_tensor.AddQuantParam(input_quant_arg_2);
std::vector<int8_t> output(12);
// std::vector<int8_t> corr_out1 = {5, 17, 11, 22, 17, 27, -6, -23, -12, -28, -18, -33};
std::vector<int8_t> corr_out = {1, 2, 1, 2, 2, 3, -1, -2, -1, -3, -2, -3};
std::vector<int8_t> corr_out = {1, 3, 2, 4, 3, 5, -2, -5, -3, -6, -4, -7};
lite::tensor::Tensor output0_tensor;
outputs_tensor.push_back(&output0_tensor);
@ -87,6 +185,7 @@ TEST_F(TestBatchnormInt8, BNTest) {
kernel::LiteKernel *kernel =
creator(inputs_tensor, outputs_tensor, reinterpret_cast<OpParameter *>(&op_param), &ctx, desc, nullptr);
ASSERT_NE(kernel, nullptr);
auto output_tensor_shape = output0_tensor.shape();
kernel->Run();

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