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[QualcommQnn] support models #9378

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Aug 31, 2022
Merged

[QualcommQnn] support models #9378

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8c6a5f8
[QualcommQnn] support ocr models
zhupengyang Aug 25, 2022
f0feb16
test
zhupengyang Aug 26, 2022
ae7cfd9
test
zhupengyang Aug 30, 2022
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2 changes: 1 addition & 1 deletion lite/backends/nnadapter/nnadapter/src/optimizer/convert_datalayout_nchw_to_nhwc.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -553,7 +553,7 @@ void NCHW2NHWCDataLayoutConverter::ConvertActivation(
auto& output_operands = operation->output_operands;
auto input_count = input_operands.size();
auto output_count = output_operands.size();
NNADAPTER_CHECK_EQ(input_count, 1);
NNADAPTER_CHECK_GE(input_count, 1);
NNADAPTER_CHECK_EQ(output_count, 1);
auto input_operand = input_operands[0];
auto output_operand = output_operands[0];
Expand Down
10 changes: 5 additions & 5 deletions lite/kernels/nnadapter/converter/all.h
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Expand Up @@ -241,14 +241,14 @@ REGISTER_CONVERTER(gelu,
ConvertGelu,
"huawei_ascend_npu,huawei_kirin_npu,kunlunxin_xtcl,"
"cambricon_mlu,intel_openvino,qualcomm_qnn");
REGISTER_CONVERTER(
hard_sigmoid,
ConvertHardSigmoid,
"huawei_ascend_npu,huawei_kirin_npu,verisilicon_timvx,intel_openvino");
REGISTER_CONVERTER(hard_sigmoid,
ConvertHardSigmoid,
"huawei_ascend_npu,huawei_kirin_npu,verisilicon_timvx,intel_"
"openvino,qualcomm_qnn");
REGISTER_CONVERTER(hard_swish,
ConvertHardSwish,
"huawei_ascend_npu,huawei_kirin_npu,verisilicon_timvx,"
"nvidia_tensorrt,intel_openvino,eeasytech_npu");
"nvidia_tensorrt,intel_openvino,eeasytech_npu,qualcomm_qnn");
REGISTER_CONVERTER(arg_max,
ConvertArgMinMax,
"huawei_ascend_npu,huawei_kirin_npu,nvidia_tensorrt,"
Expand Down
3 changes: 3 additions & 0 deletions lite/tests/api/test_bisenet_fp32_v2_3_nnadapter.cc
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Expand Up @@ -47,6 +47,9 @@ TEST(bisenet, test_bisenet_fp32_v2_3_nnadapter) {
#elif defined(NNADAPTER_WITH_INTEL_OPENVINO)
nnadapter_device_names.emplace_back("intel_openvino");
// TODO(hong19860320) Fix core dump
// 1. Model (split to relu_8.tmp_0) core dump (android htp fp16)
// 2. Error is "A single op (1e7f00000017) requires 0x704800 bytes of TCM, which
// is greater than the TCM size of 0x400000!". It seems like shape is too large?
// #elif defined(NNADAPTER_WITH_QUALCOMM_QNN)
// nnadapter_device_names.emplace_back("qualcomm_qnn");
#else
Expand Down
15 changes: 10 additions & 5 deletions lite/tests/api/test_ch_ppocr_mobile_v2_0_det_fp32_v2_3_nnadapter.cc
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Expand Up @@ -30,6 +30,8 @@ namespace lite {

TEST(ch_ppocr_mobile_v2_0_det,
test_ch_ppocr_mobile_v2_0_det_fp32_v2_3_nnadapter) {
FLAGS_warmup = 1;
bool prepare_before_timing = true;
std::vector<std::string> nnadapter_device_names;
std::string nnadapter_context_properties;
std::vector<paddle::lite_api::Place> valid_places;
Expand All @@ -50,9 +52,13 @@ TEST(ch_ppocr_mobile_v2_0_det,
nnadapter_device_names.emplace_back("huawei_kirin_npu");
#elif defined(NNADAPTER_WITH_INTEL_OPENVINO)
nnadapter_device_names.emplace_back("intel_openvino");
// TODO(hong19860320) Fix timeout
// #elif defined(NNADAPTER_WITH_QUALCOMM_QNN)
// nnadapter_device_names.emplace_back("qualcomm_qnn");
#elif defined(NNADAPTER_WITH_QUALCOMM_QNN)
nnadapter_device_names.emplace_back("qualcomm_qnn");
// 1. Not support dynamic shape
// 2. Reduce execute time
FLAGS_iteration = 1;
FLAGS_warmup = 0;
prepare_before_timing = false;
#else
LOG(INFO) << "Unsupported NNAdapter device!";
return;
Expand Down Expand Up @@ -100,7 +106,6 @@ TEST(ch_ppocr_mobile_v2_0_det,
ReadRawData(raw_data_dir, input_names[i], input_shapes[i]));
}

FLAGS_warmup = 1;
for (int i = 0; i < FLAGS_warmup; ++i) {
fill_tensor(predictor, 0, raw_data[i].data(), input_shapes[i]);
predictor->Run();
Expand All @@ -110,7 +115,7 @@ TEST(ch_ppocr_mobile_v2_0_det,
std::vector<std::vector<float>> results;
for (size_t i = 0; i < raw_data.size(); ++i) {
fill_tensor(predictor, 0, raw_data[i].data(), input_shapes[i]);
predictor->Run();
if (prepare_before_timing) predictor->Run();

double start = GetCurrentUS();
predictor->Run();
Expand Down
24 changes: 19 additions & 5 deletions lite/tests/api/test_ch_ppocr_mobile_v2_0_rec_fp32_v2_3_nnadapter.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -30,6 +30,9 @@ namespace lite {

TEST(ch_ppocr_mobile_v2_0_rec,
test_ch_ppocr_mobile_v2_0_rec_fp32_v2_3_nnadapter) {
FLAGS_warmup = 1;
bool prepare_before_timing = true;
std::string nnadapter_subgraph_partition_config_buffer;
std::vector<std::string> nnadapter_device_names;
std::string nnadapter_context_properties;
std::vector<paddle::lite_api::Place> valid_places;
Expand All @@ -48,9 +51,19 @@ TEST(ch_ppocr_mobile_v2_0_rec,
nnadapter_context_properties = "HUAWEI_ASCEND_NPU_SELECTED_DEVICE_IDS=0";
#elif defined(NNADAPTER_WITH_INTEL_OPENVINO)
nnadapter_device_names.emplace_back("intel_openvino");
// TODO(hong19860320) Fix core dump
// #elif defined(NNADAPTER_WITH_QUALCOMM_QNN)
// nnadapter_device_names.emplace_back("qualcomm_qnn");
#elif defined(NNADAPTER_WITH_QUALCOMM_QNN)
nnadapter_device_names.emplace_back("qualcomm_qnn");
// 1. Not support dynamic shape
// 2. Reduce execute time
FLAGS_iteration = 1;
FLAGS_warmup = 0;
prepare_before_timing = false;
// TODO(zhupengyang): Last matmul is not supported on htp+fp16.
nnadapter_subgraph_partition_config_buffer =
"transpose2:lstm_0.tmp_0:transpose_2.tmp_0,transpose_2.tmp_1\n"
"matmul:transpose_2.tmp_0,ctc_fc_w_attr:ctc_fc.tmp_0\n"
"elementwise_add:ctc_fc.tmp_0,ctc_fc_b_attr:ctc_fc.tmp_1\n"
"softmax:ctc_fc.tmp_1:save_infer_model/scale_0.tmp_1";
#else
LOG(INFO) << "Unsupported NNAdapter device!";
return;
Expand All @@ -62,6 +75,8 @@ TEST(ch_ppocr_mobile_v2_0_rec,
cxx_config.set_valid_places(valid_places);
cxx_config.set_nnadapter_device_names(nnadapter_device_names);
cxx_config.set_nnadapter_context_properties(nnadapter_context_properties);
cxx_config.set_nnadapter_subgraph_partition_config_buffer(
nnadapter_subgraph_partition_config_buffer);
predictor = lite_api::CreatePaddlePredictor(cxx_config);
predictor->SaveOptimizedModel(FLAGS_model_dir,
paddle::lite_api::LiteModelType::kNaiveBuffer);
Expand Down Expand Up @@ -103,7 +118,6 @@ TEST(ch_ppocr_mobile_v2_0_rec,
ReadRawData(raw_data_dir, input_names[i], input_shapes[i]));
}

FLAGS_warmup = 1;
for (int i = 0; i < FLAGS_warmup; ++i) {
fill_tensor(predictor, 0, raw_data[i].data(), input_shapes[i]);
predictor->Run();
Expand All @@ -113,7 +127,7 @@ TEST(ch_ppocr_mobile_v2_0_rec,
std::vector<std::vector<float>> results;
for (size_t i = 0; i < raw_data.size(); ++i) {
fill_tensor(predictor, 0, raw_data[i].data(), input_shapes[i]);
predictor->Run();
if (prepare_before_timing) predictor->Run();

double start = GetCurrentUS();
predictor->Run();
Expand Down
15 changes: 10 additions & 5 deletions lite/tests/api/test_ch_ppocr_server_v2_0_det_fp32_v2_3_nnadapter.cc
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Expand Up @@ -30,6 +30,8 @@ namespace lite {

TEST(ch_ppocr_server_v2_0_det,
test_ch_ppocr_server_v2_0_det_fp32_v2_3_nnadapter) {
FLAGS_warmup = 1;
bool prepare_before_timing = true;
std::vector<std::string> nnadapter_device_names;
std::string nnadapter_context_properties;
std::vector<paddle::lite_api::Place> valid_places;
Expand All @@ -50,9 +52,13 @@ TEST(ch_ppocr_server_v2_0_det,
nnadapter_device_names.emplace_back("huawei_kirin_npu");
#elif defined(NNADAPTER_WITH_INTEL_OPENVINO)
nnadapter_device_names.emplace_back("intel_openvino");
// TODO(hong19860320) Fix timeout
// #elif defined(NNADAPTER_WITH_QUALCOMM_QNN)
// nnadapter_device_names.emplace_back("qualcomm_qnn");
#elif defined(NNADAPTER_WITH_QUALCOMM_QNN)
nnadapter_device_names.emplace_back("qualcomm_qnn");
// 1. Not support dynamic shape
// 2. Reduce execute time
FLAGS_iteration = 1;
FLAGS_warmup = 0;
prepare_before_timing = false;
#else
LOG(INFO) << "Unsupported NNAdapter device!";
return;
Expand Down Expand Up @@ -100,7 +106,6 @@ TEST(ch_ppocr_server_v2_0_det,
ReadRawData(raw_data_dir, input_names[i], input_shapes[i]));
}

FLAGS_warmup = 1;
for (int i = 0; i < FLAGS_warmup; ++i) {
fill_tensor(predictor, 0, raw_data[i].data(), input_shapes[i]);
predictor->Run();
Expand All @@ -110,7 +115,7 @@ TEST(ch_ppocr_server_v2_0_det,
std::vector<std::vector<float>> results;
for (size_t i = 0; i < raw_data.size(); ++i) {
fill_tensor(predictor, 0, raw_data[i].data(), input_shapes[i]);
predictor->Run();
if (prepare_before_timing) predictor->Run();

double start = GetCurrentUS();
predictor->Run();
Expand Down
24 changes: 19 additions & 5 deletions lite/tests/api/test_ch_ppocr_server_v2_0_rec_fp32_v2_3_nnadapter.cc
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Expand Up @@ -30,6 +30,9 @@ namespace lite {

TEST(ch_ppocr_server_v2_0_rec,
test_ch_ppocr_server_v2_0_rec_fp32_v2_3_nnadapter) {
FLAGS_warmup = 1;
bool prepare_before_timing = true;
std::string nnadapter_subgraph_partition_config_buffer;
std::vector<std::string> nnadapter_device_names;
std::string nnadapter_context_properties;
std::vector<paddle::lite_api::Place> valid_places;
Expand All @@ -48,9 +51,19 @@ TEST(ch_ppocr_server_v2_0_rec,
nnadapter_context_properties = "HUAWEI_ASCEND_NPU_SELECTED_DEVICE_IDS=0";
#elif defined(NNADAPTER_WITH_INTEL_OPENVINO)
nnadapter_device_names.emplace_back("intel_openvino");
// TODO(hong19860320) Fix core dump
// #elif defined(NNADAPTER_WITH_QUALCOMM_QNN)
// nnadapter_device_names.emplace_back("qualcomm_qnn");
#elif defined(NNADAPTER_WITH_QUALCOMM_QNN)
nnadapter_device_names.emplace_back("qualcomm_qnn");
// 1. Not support dynamic shape
// 2. Reduce execute time
FLAGS_iteration = 1;
FLAGS_warmup = 0;
prepare_before_timing = false;
// TODO(zhupengyang): Last matmul is not supported on htp+fp16.
nnadapter_subgraph_partition_config_buffer =
"transpose2:lstm_0.tmp_0:transpose_2.tmp_0,transpose_2.tmp_1\n"
"matmul:transpose_2.tmp_0,ctc_fc_w_attr:ctc_fc.tmp_0\n"
"elementwise_add:ctc_fc.tmp_0,ctc_fc_b_attr:ctc_fc.tmp_1\n"
"softmax:ctc_fc.tmp_1:save_infer_model/scale_0.tmp_1";
#else
LOG(INFO) << "Unsupported NNAdapter device!";
return;
Expand All @@ -62,6 +75,8 @@ TEST(ch_ppocr_server_v2_0_rec,
cxx_config.set_valid_places(valid_places);
cxx_config.set_nnadapter_device_names(nnadapter_device_names);
cxx_config.set_nnadapter_context_properties(nnadapter_context_properties);
cxx_config.set_nnadapter_subgraph_partition_config_buffer(
nnadapter_subgraph_partition_config_buffer);
predictor = lite_api::CreatePaddlePredictor(cxx_config);
predictor->SaveOptimizedModel(FLAGS_model_dir,
paddle::lite_api::LiteModelType::kNaiveBuffer);
Expand Down Expand Up @@ -96,7 +111,6 @@ TEST(ch_ppocr_server_v2_0_rec,
ReadRawData(raw_data_dir, input_names[i], input_shapes[i]));
}

FLAGS_warmup = 1;
for (int i = 0; i < FLAGS_warmup; ++i) {
fill_tensor(predictor, 0, raw_data[i].data(), input_shapes[i]);
predictor->Run();
Expand All @@ -106,7 +120,7 @@ TEST(ch_ppocr_server_v2_0_rec,
std::vector<std::vector<float>> results;
for (size_t i = 0; i < raw_data.size(); ++i) {
fill_tensor(predictor, 0, raw_data[i].data(), input_shapes[i]);
predictor->Run();
if (prepare_before_timing) predictor->Run();

double start = GetCurrentUS();
predictor->Run();
Expand Down
15 changes: 10 additions & 5 deletions lite/tests/api/test_ch_ppocr_v2_det_fp32_v2_3_nnadapter.cc
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Expand Up @@ -29,6 +29,8 @@ namespace paddle {
namespace lite {

TEST(ch_ppocr_v2_det, test_ch_ppocr_v2_det_fp32_v2_3_nnadapter) {
FLAGS_warmup = 1;
bool prepare_before_timing = true;
std::vector<std::string> nnadapter_device_names;
std::string nnadapter_context_properties;
std::vector<paddle::lite_api::Place> valid_places;
Expand All @@ -47,9 +49,13 @@ TEST(ch_ppocr_v2_det, test_ch_ppocr_v2_det_fp32_v2_3_nnadapter) {
nnadapter_context_properties = "HUAWEI_ASCEND_NPU_SELECTED_DEVICE_IDS=0";
#elif defined(NNADAPTER_WITH_INTEL_OPENVINO)
nnadapter_device_names.emplace_back("intel_openvino");
// TODO(hong19860320) Fix timeout
// #elif defined(NNADAPTER_WITH_QUALCOMM_QNN)
// nnadapter_device_names.emplace_back("qualcomm_qnn");
#elif defined(NNADAPTER_WITH_QUALCOMM_QNN)
nnadapter_device_names.emplace_back("qualcomm_qnn");
// 1. Not support dynamic shape
// 2. Reduce execute time
FLAGS_iteration = 1;
FLAGS_warmup = 0;
prepare_before_timing = false;
#else
LOG(INFO) << "Unsupported NNAdapter device!";
return;
Expand Down Expand Up @@ -97,7 +103,6 @@ TEST(ch_ppocr_v2_det, test_ch_ppocr_v2_det_fp32_v2_3_nnadapter) {
ReadRawData(raw_data_dir, input_names[i], input_shapes[i]));
}

FLAGS_warmup = 1;
for (int i = 0; i < FLAGS_warmup; ++i) {
fill_tensor(predictor, 0, raw_data[i].data(), input_shapes[i]);
predictor->Run();
Expand All @@ -107,7 +112,7 @@ TEST(ch_ppocr_v2_det, test_ch_ppocr_v2_det_fp32_v2_3_nnadapter) {
std::vector<std::vector<float>> results;
for (size_t i = 0; i < raw_data.size(); ++i) {
fill_tensor(predictor, 0, raw_data[i].data(), input_shapes[i]);
predictor->Run();
if (prepare_before_timing) predictor->Run();

double start = GetCurrentUS();
predictor->Run();
Expand Down
25 changes: 21 additions & 4 deletions lite/tests/api/test_ch_ppocr_v2_rec_fp32_v2_3_nnadapter.cc
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Expand Up @@ -29,6 +29,9 @@ namespace paddle {
namespace lite {

TEST(ch_ppocr_v2_rec, test_ch_ppocr_v2_rec_fp32_v2_3_nnadapter) {
FLAGS_warmup = 1;
bool prepare_before_timing = true;
std::string nnadapter_subgraph_partition_config_buffer;
std::vector<std::string> nnadapter_device_names;
std::string nnadapter_context_properties;
std::vector<paddle::lite_api::Place> valid_places;
Expand All @@ -47,9 +50,21 @@ TEST(ch_ppocr_v2_rec, test_ch_ppocr_v2_rec_fp32_v2_3_nnadapter) {
nnadapter_context_properties = "HUAWEI_ASCEND_NPU_SELECTED_DEVICE_IDS=0";
#elif defined(NNADAPTER_WITH_INTEL_OPENVINO)
nnadapter_device_names.emplace_back("intel_openvino");
// TODO(hong19860320) Fix core dump
// #elif defined(NNADAPTER_WITH_QUALCOMM_QNN)
// nnadapter_device_names.emplace_back("qualcomm_qnn");
#elif defined(NNADAPTER_WITH_QUALCOMM_QNN)
nnadapter_device_names.emplace_back("qualcomm_qnn");
// 1. Not support dynamic shape
// 2. Reduce execute time
FLAGS_iteration = 1;
FLAGS_warmup = 0;
prepare_before_timing = false;
// TODO(zhupengyang): Last matmul is not supported on htp+fp16.
nnadapter_subgraph_partition_config_buffer =
"transpose2:lstm_1.tmp_0:transpose_2.tmp_0,transpose_2.tmp_1\n"
"matmul:transpose_2.tmp_0,student_ctc_head_1_w_attrn"
"elementwise_add:student_ctc_head_1.tmp_0,student_ctc_head_1_b_attr\n"
"matmul:student_ctc_head_1.tmp_1,student_ctc_head_2_w_attr\n"
"elementwise_add:student_ctc_head_2.tmp_0,student_ctc_head_2_b_attr\n"
"softmax:student_ctc_head_2.tmp_1:save_infer_model/scale_0.tmp_1";
#else
LOG(INFO) << "Unsupported NNAdapter device!";
return;
Expand All @@ -61,6 +76,8 @@ TEST(ch_ppocr_v2_rec, test_ch_ppocr_v2_rec_fp32_v2_3_nnadapter) {
cxx_config.set_valid_places(valid_places);
cxx_config.set_nnadapter_device_names(nnadapter_device_names);
cxx_config.set_nnadapter_context_properties(nnadapter_context_properties);
cxx_config.set_nnadapter_subgraph_partition_config_buffer(
nnadapter_subgraph_partition_config_buffer);
predictor = lite_api::CreatePaddlePredictor(cxx_config);
predictor->SaveOptimizedModel(FLAGS_model_dir,
paddle::lite_api::LiteModelType::kNaiveBuffer);
Expand Down Expand Up @@ -105,7 +122,7 @@ TEST(ch_ppocr_v2_rec, test_ch_ppocr_v2_rec_fp32_v2_3_nnadapter) {
std::vector<std::vector<float>> results;
for (size_t i = 0; i < raw_data.size(); ++i) {
fill_tensor(predictor, 0, raw_data[i].data(), input_shapes[i]);
predictor->Run();
if (prepare_before_timing) predictor->Run();

double start = GetCurrentUS();
predictor->Run();
Expand Down
5 changes: 2 additions & 3 deletions lite/tests/api/test_darknet53_fp32_v2_3_nnadapter.cc
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Expand Up @@ -55,9 +55,8 @@ TEST(DarkNet, test_darknet53_fp32_v2_3_nnadapter) {
out_accuracy_threshold = 0.75f;
#elif defined(NNADAPTER_WITH_QUALCOMM_QNN)
nnadapter_device_names.emplace_back("qualcomm_qnn");
FLAGS_iteration = 1;
// TODO(hong19860320) Fix precision
out_accuracy_threshold = 0.f;
FLAGS_iteration = 5;
out_accuracy_threshold = 0.6f;
#else
LOG(INFO) << "Unsupported NNAdapter device!";
return;
Expand Down
12 changes: 9 additions & 3 deletions lite/tests/api/test_deepfm_fp32_v2_1_nnadapter.cc
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Expand Up @@ -28,6 +28,7 @@ namespace paddle {
namespace lite {

TEST(deepfm, test_deepfm_fp32_v2_1_nnadapter) {
std::string nnadapter_subgraph_partition_config_buffer;
std::vector<std::string> nnadapter_device_names;
std::string nnadapter_context_properties;
std::vector<paddle::lite_api::Place> valid_places;
Expand All @@ -46,9 +47,12 @@ TEST(deepfm, test_deepfm_fp32_v2_1_nnadapter) {
nnadapter_context_properties = "HUAWEI_ASCEND_NPU_SELECTED_DEVICE_IDS=0";
#elif defined(NNADAPTER_WITH_INTEL_OPENVINO)
nnadapter_device_names.emplace_back("intel_openvino");
// TODO(hong19860320) Support int64 datatype
// #elif defined(NNADAPTER_WITH_QUALCOMM_QNN)
// nnadapter_device_names.emplace_back("qualcomm_qnn");
#elif defined(NNADAPTER_WITH_QUALCOMM_QNN)
nnadapter_device_names.emplace_back("qualcomm_qnn");
// Not support int64 datatype
nnadapter_subgraph_partition_config_buffer =
"concat::concat_0.tmp_0\n"
"lookup_table_v2";
#else
LOG(INFO) << "Unsupported NNAdapter device!";
return;
Expand All @@ -60,6 +64,8 @@ TEST(deepfm, test_deepfm_fp32_v2_1_nnadapter) {
cxx_config.set_valid_places(valid_places);
cxx_config.set_nnadapter_device_names(nnadapter_device_names);
cxx_config.set_nnadapter_context_properties(nnadapter_context_properties);
cxx_config.set_nnadapter_subgraph_partition_config_buffer(
nnadapter_subgraph_partition_config_buffer);
predictor = lite_api::CreatePaddlePredictor(cxx_config);
predictor->SaveOptimizedModel(FLAGS_model_dir,
paddle::lite_api::LiteModelType::kNaiveBuffer);
Expand Down
5 changes: 1 addition & 4 deletions lite/tests/api/utility.h
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Expand Up @@ -32,10 +32,7 @@ T CalOutAccuracy(const std::vector<std::vector<T>>& out,
size_t right_count = 0;
size_t all_count = 0;
for (size_t i = 0; i < out.size(); i++) {
if (out[i].size() != ref_out[i].size()) {
LOG(FATAL) << "size error: out_size = " << out[i].size()
<< ", ref_out_size = " << ref_out[i].size() << ", i = " << i;
}
CHECK_EQ(out[i].size(), ref_out[i].size()) << "Size error, i: " << i;
for (size_t j = 0; j < out[i].size(); j++) {
if (std::abs(out[i][j] - ref_out[i][j]) < abs_error) {
right_count++;
Expand Down
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