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[core] Fix swish op and the output type of benchmark_bin #10500

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May 13, 2024
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[core] Fix swish op and the output type of benchmark_bin #10500

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90 changes: 54 additions & 36 deletions lite/api/tools/benchmark/benchmark.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -105,6 +105,47 @@ void RunImpl(std::shared_ptr<PaddlePredictor> predictor,
perf_data->set_post_process_time(timer.Stop());
}
#endif
template <typename T>
void outputIdxTensor(const Tensor* output_tensor,
std::stringstream& out_ss,
size_t tidx) {
auto out_shape = output_tensor->shape();
auto out_data = output_tensor->data<T>();
auto ele_num = lite::ShapeProduction(out_shape);
auto out_mean = lite::compute_mean<T>(out_data, ele_num);
auto out_std_dev =
lite::compute_standard_deviation<T>(out_data, ele_num, true, out_mean);

out_ss << "output shape(NCHW): " << lite::ShapePrint(out_shape) << std::endl;
out_ss << "output tensor " << tidx << " elem num: " << ele_num << std::endl;
out_ss << "output tensor " << tidx << " mean value: " << out_mean
<< std::endl;
out_ss << "output tensor " << tidx << " standard deviation: " << out_std_dev
<< std::endl;

if (FLAGS_show_output_elem) {
for (int i = 0; i < ele_num; ++i) {
out_ss << "out[" << tidx << "][" << i
<< "]:" << output_tensor->data<T>()[i] << std::endl;
}
}

// TODO(sprouteer): Only support float for now, add more types if needed.
if (!FLAGS_output_data_path.empty()) {
std::stringstream out_data;
auto output_path = lite::Split(FLAGS_output_data_path, ":");
if (output_path.size() <= tidx) {
std::cerr << "Fail to write output tensor to file, tensor_output_path "
"not matching output tensor number. "
<< std::endl;
} else {
for (int i = 0; i < ele_num; ++i) {
out_data << output_tensor->data<T>()[i] << std::endl;
}
StoreOutputTensor(out_data, output_path[tidx]);
}
}
}

void Run(const std::string& model_file,
const std::vector<std::vector<int64_t>>& input_shapes) {
Expand Down Expand Up @@ -225,42 +266,19 @@ void Run(const std::string& model_file,
for (size_t tidx = 0; tidx < output_tensor_num; ++tidx) {
std::unique_ptr<const Tensor> output_tensor = predictor->GetOutput(tidx);
out_ss << "\n--- output tensor " << tidx << " ---\n";
auto out_shape = output_tensor->shape();
auto out_data = output_tensor->data<float>();
auto ele_num = lite::ShapeProduction(out_shape);
auto out_mean = lite::compute_mean<float>(out_data, ele_num);
auto out_std_dev = lite::compute_standard_deviation<float>(
out_data, ele_num, true, out_mean);

out_ss << "output shape(NCHW): " << lite::ShapePrint(out_shape)
<< std::endl;
out_ss << "output tensor " << tidx << " elem num: " << ele_num << std::endl;
out_ss << "output tensor " << tidx << " mean value: " << out_mean
<< std::endl;
out_ss << "output tensor " << tidx << " standard deviation: " << out_std_dev
<< std::endl;

if (FLAGS_show_output_elem) {
for (int i = 0; i < ele_num; ++i) {
out_ss << "out[" << tidx << "][" << i
<< "]:" << output_tensor->data<float>()[i] << std::endl;
}
}

// TODO(sprouteer): Only support float for now, add more types if needed.
if (!FLAGS_output_data_path.empty()) {
std::stringstream out_data;
auto output_path = lite::Split(FLAGS_output_data_path, ":");
if (output_path.size() <= tidx) {
std::cerr << "Fail to write output tensor to file, tensor_output_path "
"not matching output tensor number. "
<< std::endl;
} else {
for (int i = 0; i < ele_num; ++i) {
out_data << output_tensor->data<float>()[i] << std::endl;
}
StoreOutputTensor(out_data, output_path[tidx]);
}
switch (output_tensor->precision()) {
case PRECISION(kFloat):
outputIdxTensor<float>(output_tensor.get(), out_ss, tidx);
break;
case PRECISION(kInt32):
outputIdxTensor<int32_t>(output_tensor.get(), out_ss, tidx);
break;
case PRECISION(kInt64):
outputIdxTensor<int64_t>(output_tensor.get(), out_ss, tidx);
break;
default:
LOG(FATAL) << "outputIdxTensor unsupported precision type: "
<< static_cast<int>(output_tensor->precision());
}
}

Expand Down
6 changes: 5 additions & 1 deletion lite/operators/activation_ops.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -51,7 +51,11 @@ bool ActivationOp::AttachImpl(const cpp::OpDesc& opdesc, lite::Scope* scope) {
scope->FindVar(prelu_alpha_name)->GetMutable<lite::Tensor>();
param_.active_type = lite_api::ActivationType::kPRelu;
} else if (opdesc.Type() == "swish") {
param_.Swish_beta = opdesc.GetAttr<float>("beta");
if (opdesc.HasAttr("beta")) {
param_.Swish_beta = opdesc.GetAttr<float>("beta");
} else {
param_.Swish_beta = 1.0f;
}
param_.active_type = lite_api::ActivationType::kSwish;
} else if (opdesc.Type() == "hard_sigmoid") {
param_.active_type = lite_api::ActivationType::kHardSigmoid;
Expand Down