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Add default logger for TRT EP (cont.)
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‎plugin_execution_providers/tensorrt/tensorrt_execution_provider.cc‎

Lines changed: 68 additions & 29 deletions
Original file line numberDiff line numberDiff line change
@@ -128,9 +128,13 @@ bool ApplyProfileShapesFromProviderOptions(std::vector<nvinfer1::IOptimizationPr
128128
std::unordered_map<std::string, std::vector<std::vector<int64_t>>>& profile_min_shapes,
129129
std::unordered_map<std::string, std::vector<std::vector<int64_t>>>& profile_max_shapes,
130130
std::unordered_map<std::string, std::vector<std::vector<int64_t>>>& profile_opt_shapes,
131-
ShapeRangesMap& input_explicit_shape_ranges) {
131+
ShapeRangesMap& input_explicit_shape_ranges,
132+
const OrtLogger* logger) {
132133
if (trt_profiles.size() == 0) {
133-
// LOGS_DEFAULT(WARNING) << "[TensorRT EP] Number of optimization profiles should be greater than 0, but it's 0.";
134+
std::string message = "[TensorRT EP] Number of optimization profiles should be greater than 0, but it's 0.";
135+
Ort::ThrowOnError(g_ort_api->Logger_LogMessage(logger,
136+
OrtLoggingLevel::ORT_LOGGING_LEVEL_WARNING,
137+
message.c_str(), ORT_FILE, __LINE__, __FUNCTION__));
134138
return false;
135139
}
136140

@@ -144,8 +148,11 @@ bool ApplyProfileShapesFromProviderOptions(std::vector<nvinfer1::IOptimizationPr
144148
input_explicit_shape_ranges[input_name] = inner_map;
145149
}
146150

147-
// LOGS_DEFAULT(VERBOSE) << "[TensorRT EP] Begin to apply profile shapes ...";
148-
// LOGS_DEFAULT(VERBOSE) << "[TensorRT EP] Input tensor name is '" << input_name << "', number of profiles found is " << trt_profiles.size();
151+
std::string message = "[TensorRT EP] Begin to apply profile shapes ...\n" +
152+
std::string("[TensorRT EP] Input tensor name is '") + input_name + std::string("', number of profiles found is ") + std::to_string(trt_profiles.size());
153+
Ort::ThrowOnError(g_ort_api->Logger_LogMessage(logger,
154+
OrtLoggingLevel::ORT_LOGGING_LEVEL_VERBOSE,
155+
message.c_str(), ORT_FILE, __LINE__, __FUNCTION__));
149156

150157
for (size_t i = 0; i < trt_profiles.size(); i++) {
151158
nvinfer1::Dims dims = input->getDimensions();
@@ -158,7 +165,10 @@ bool ApplyProfileShapesFromProviderOptions(std::vector<nvinfer1::IOptimizationPr
158165
int shape_size = nb_dims == 0 ? 1 : static_cast<int>(profile_min_shapes[input_name][i].size());
159166
std::vector<int32_t> shapes_min(shape_size), shapes_opt(shape_size), shapes_max(shape_size);
160167

161-
// LOGS_DEFAULT(VERBOSE) << "[TensorRT EP] shape size of this shape tensor is " << shape_size;
168+
std::string message = "[TensorRT EP] shape size of this shape tensor is " + std::to_string(shape_size);
169+
Ort::ThrowOnError(g_ort_api->Logger_LogMessage(logger,
170+
OrtLoggingLevel::ORT_LOGGING_LEVEL_VERBOSE,
171+
message.c_str(), ORT_FILE, __LINE__, __FUNCTION__));
162172

163173
for (int j = 0; j < shape_size; j++) {
164174
auto min_value = profile_min_shapes[input_name][i][j];
@@ -167,9 +177,12 @@ bool ApplyProfileShapesFromProviderOptions(std::vector<nvinfer1::IOptimizationPr
167177
shapes_min[j] = static_cast<int32_t>(min_value);
168178
shapes_max[j] = static_cast<int32_t>(max_value);
169179
shapes_opt[j] = static_cast<int32_t>(opt_value);
170-
// LOGS_DEFAULT(VERBOSE) << "[TensorRT EP] shapes_min.d[" << j << "] is " << shapes_min[j];
171-
// LOGS_DEFAULT(VERBOSE) << "[TensorRT EP] shapes_max.d[" << j << "] is " << shapes_max[j];
172-
// LOGS_DEFAULT(VERBOSE) << "[TensorRT EP] shapes_opt.d[" << j << "] is " << shapes_opt[j];
180+
std::string message = "[TensorRT EP] shapes_min.d[" + std::to_string(j) + std::string("] is ") + std::to_string(shapes_min[j]) + std::string("\n") +
181+
std::string("[TensorRT EP] shapes_max.d[") + std::to_string(j) + std::string("] is ") + std::to_string(shapes_max[j]) + std::string("\n") +
182+
std::string("[TensorRT EP] shapes_opt.d[") + std::to_string(j) + std::string("] is ") + std::to_string(shapes_opt[j]);
183+
Ort::ThrowOnError(g_ort_api->Logger_LogMessage(logger,
184+
OrtLoggingLevel::ORT_LOGGING_LEVEL_VERBOSE,
185+
message.c_str(), ORT_FILE, __LINE__, __FUNCTION__));
173186

174187
if (input_explicit_shape_ranges[input_name].find(j) == input_explicit_shape_ranges[input_name].end()) {
175188
std::vector<std::vector<int64_t>> profile_vector(trt_profiles.size());
@@ -191,7 +204,10 @@ bool ApplyProfileShapesFromProviderOptions(std::vector<nvinfer1::IOptimizationPr
191204
dims_max.nbDims = nb_dims;
192205
dims_opt.nbDims = nb_dims;
193206

194-
// LOGS_DEFAULT(VERBOSE) << "[TensorRT EP] number of dimension of this execution tensor is " << nb_dims;
207+
std::string message = "[TensorRT EP] number of dimension of this execution tensor is " + std::to_string(nb_dims);
208+
Ort::ThrowOnError(g_ort_api->Logger_LogMessage(logger,
209+
OrtLoggingLevel::ORT_LOGGING_LEVEL_VERBOSE,
210+
message.c_str(), ORT_FILE, __LINE__, __FUNCTION__));
195211

196212
for (int j = 0; j < nb_dims; j++) {
197213
if (dims.d[j] == -1) {
@@ -201,9 +217,13 @@ bool ApplyProfileShapesFromProviderOptions(std::vector<nvinfer1::IOptimizationPr
201217
dims_min.d[j] = static_cast<int32_t>(min_value);
202218
dims_max.d[j] = static_cast<int32_t>(max_value);
203219
dims_opt.d[j] = static_cast<int32_t>(opt_value);
204-
// LOGS_DEFAULT(VERBOSE) << "[TensorRT EP] dims_min.d[" << j << "] is " << dims_min.d[j];
205-
// LOGS_DEFAULT(VERBOSE) << "[TensorRT EP] dims_max.d[" << j << "] is " << dims_max.d[j];
206-
// LOGS_DEFAULT(VERBOSE) << "[TensorRT EP] dims_opt.d[" << j << "] is " << dims_opt.d[j];
220+
221+
std::string message = "[TensorRT EP] dims_min.d[" + std::to_string(j) + std::string("] is ") + std::to_string(dims_min.d[j]) + std::string("\n") +
222+
std::string("[TensorRT EP] dims_max.d[") + std::to_string(j) + std::string("] is ") + std::to_string(dims_max.d[j]) + std::string("\n") +
223+
std::string("[TensorRT EP] dims_opt.d[") + std::to_string(j) + std::string("] is ") + std::to_string(dims_opt.d[j]);
224+
Ort::ThrowOnError(g_ort_api->Logger_LogMessage(logger,
225+
OrtLoggingLevel::ORT_LOGGING_LEVEL_VERBOSE,
226+
message.c_str(), ORT_FILE, __LINE__, __FUNCTION__));
207227

208228
if (input_explicit_shape_ranges[input_name].find(j) == input_explicit_shape_ranges[input_name].end()) {
209229
std::vector<std::vector<int64_t>> profile_vector(trt_profiles.size());
@@ -1178,7 +1198,7 @@ OrtStatus* TensorrtExecutionProvider::CreateNodeComputeInfoFromGraph(OrtEp* this
11781198
if (has_explicit_profile) {
11791199
apply_explicit_profile =
11801200
ApplyProfileShapesFromProviderOptions(trt_profiles, input, profile_min_shapes_, profile_max_shapes_,
1181-
profile_opt_shapes_, input_explicit_shape_ranges);
1201+
profile_opt_shapes_, input_explicit_shape_ranges, &ep->logger_);
11821202
}
11831203

11841204
// If no explicit optimization profile is being applied, TRT EP will later set min/max/opt shape values based on
@@ -1270,8 +1290,10 @@ OrtStatus* TensorrtExecutionProvider::CreateNodeComputeInfoFromGraph(OrtEp* this
12701290
#pragma warning(pop)
12711291
#endif
12721292
int8_enable_ = false;
1273-
// LOGS_DEFAULT(WARNING)
1274-
// << "[TensorRT EP] ORT_TENSORRT_INT8_ENABLE is set, but platform doesn't support fast native int8";
1293+
std::string message = "[TensorRT EP] ORT_TENSORRT_INT8_ENABLE is set, but platform doesn't support fast native int8";
1294+
Ort::ThrowOnError(ep->ort_api.Logger_LogMessage(&ep->logger_,
1295+
OrtLoggingLevel::ORT_LOGGING_LEVEL_WARNING,
1296+
message.c_str(), ORT_FILE, __LINE__, __FUNCTION__));
12751297
}
12761298
}
12771299

@@ -1356,9 +1378,12 @@ OrtStatus* TensorrtExecutionProvider::CreateNodeComputeInfoFromGraph(OrtEp* this
13561378
#if NV_TENSORRT_MAJOR == 8 && NV_TENSORRT_MINOR == 5
13571379
if (build_heuristics_enable_) {
13581380
trt_config->setFlag(nvinfer1::BuilderFlag::kENABLE_TACTIC_HEURISTIC);
1359-
LOGS_DEFAULT(WARNING) << "[TensorRT EP] Builder heuristics are enabled."
1360-
<< " For TRT > 8.5, trt_build_heuristics_enable is deprecated, please set builder "
1361-
"optimization level as 2 to enable builder heuristics.";
1381+
std::string message = "[TensorRT EP] Builder heuristics are enabled." +
1382+
std::string(" For TRT > 8.5, trt_build_heuristics_enable is deprecated, please set builder ") +
1383+
std::string("optimization level as 2 to enable builder heuristics.");
1384+
Ort::ThrowOnError(ep->ort_api.Logger_LogMessage(&ep->logger_,
1385+
OrtLoggingLevel::ORT_LOGGING_LEVEL_WARNING,
1386+
message.c_str(), ORT_FILE, __LINE__, __FUNCTION__));
13621387
}
13631388
#elif NV_TENSORRT_MAJOR == 8 && NV_TENSORRT_MINOR > 5 || NV_TENSORRT_MAJOR > 8
13641389
// for TRT 8.6 onwards, heuristic-based tactic option is automatically enabled by setting builder optimization level 2
@@ -1399,10 +1424,16 @@ OrtStatus* TensorrtExecutionProvider::CreateNodeComputeInfoFromGraph(OrtEp* this
13991424
}
14001425
#else
14011426
if (builder_optimization_level_ != 3) {
1402-
LOGS_DEFAULT(WARNING) << "[TensorRT EP] Builder optimization level can only be used on TRT 8.6 onwards!";
1427+
std::string message = "[TensorRT EP] Builder optimization level can only be used on TRT 8.6 onwards!";
1428+
Ort::ThrowOnError(ep->ort_api.Logger_LogMessage(&ep->logger_,
1429+
OrtLoggingLevel::ORT_LOGGING_LEVEL_WARNING,
1430+
message.c_str(), ORT_FILE, __LINE__, __FUNCTION__));
14031431
}
14041432
if (auxiliary_streams_ >= 0) {
1405-
LOGS_DEFAULT(WARNING) << "[TensorRT EP] Auxiliary streams can only be set on TRT 8.6 onwards!";
1433+
std::string message = "[TensorRT EP] Auxiliary streams can only be set on TRT 8.6 onwards!";
1434+
Ort::ThrowOnError(ep->ort_api.Logger_LogMessage(&ep->logger_,
1435+
OrtLoggingLevel::ORT_LOGGING_LEVEL_WARNING,
1436+
message.c_str(), ORT_FILE, __LINE__, __FUNCTION__));
14061437
}
14071438
#endif
14081439

@@ -1419,7 +1450,10 @@ OrtStatus* TensorrtExecutionProvider::CreateNodeComputeInfoFromGraph(OrtEp* this
14191450
OrtLoggingLevel::ORT_LOGGING_LEVEL_VERBOSE,
14201451
message.c_str(), ORT_FILE, __LINE__, __FUNCTION__));
14211452
#else
1422-
LOGS_DEFAULT(WARNING) << "[TensorRT EP] weight-stripped engines can only be used on TRT 10.0 onwards!";
1453+
std::string message = "[TensorRT EP] weight-stripped engines can only be used on TRT 10.0 onwards!";
1454+
Ort::ThrowOnError(ep->ort_api.Logger_LogMessage(&ep->logger_,
1455+
OrtLoggingLevel::ORT_LOGGING_LEVEL_WARNING,
1456+
message.c_str(), ORT_FILE, __LINE__, __FUNCTION__));
14231457
#endif
14241458
}
14251459

@@ -1613,10 +1647,11 @@ OrtStatus* TensorrtExecutionProvider::CreateNodeComputeInfoFromGraph(OrtEp* this
16131647
}
16141648
if (detailed_build_log_) {
16151649
auto engine_build_stop = std::chrono::steady_clock::now();
1616-
// LOGS_DEFAULT(INFO)
1617-
// << "TensorRT engine build for " << trt_node_name_with_precision << " took: "
1618-
// << std::chrono::duration_cast<std::chrono::milliseconds>(engine_build_stop - engine_build_start).count()
1619-
// << "ms" << std::endl;
1650+
std::string message = "TensorRT engine build for " + trt_node_name_with_precision + std::string(" took: ") +
1651+
std::to_string(std::chrono::duration_cast<std::chrono::milliseconds>(engine_build_stop - engine_build_start).count()) + std::string("ms");
1652+
Ort::ThrowOnError(ep->ort_api.Logger_LogMessage(&ep->logger_,
1653+
OrtLoggingLevel::ORT_LOGGING_LEVEL_INFO,
1654+
message.c_str(), ORT_FILE, __LINE__, __FUNCTION__));
16201655
}
16211656
if (engine_cache_enable_) {
16221657
// Serialize engine profile if it has explicit profiles
@@ -1642,8 +1677,10 @@ OrtStatus* TensorrtExecutionProvider::CreateNodeComputeInfoFromGraph(OrtEp* this
16421677
OrtLoggingLevel::ORT_LOGGING_LEVEL_VERBOSE,
16431678
message.c_str(), ORT_FILE, __LINE__, __FUNCTION__));
16441679
} else {
1645-
// LOGS_DEFAULT(WARNING)
1646-
// << "[TensorRT EP] Engine cache encryption function is not found. No cache is written to disk";
1680+
std::string message = "[TensorRT EP] Engine cache encryption function is not found. No cache is written to disk";
1681+
Ort::ThrowOnError(ep->ort_api.Logger_LogMessage(&ep->logger_,
1682+
OrtLoggingLevel::ORT_LOGGING_LEVEL_WARNING,
1683+
message.c_str(), ORT_FILE, __LINE__, __FUNCTION__));
16471684
}
16481685
} else {
16491686
std::ofstream file(engine_cache_path, std::ios::binary | std::ios::out);
@@ -3013,8 +3050,10 @@ OrtStatus* TRTEpNodeComputeInfo::ComputeImpl(OrtNodeComputeInfo* this_ptr, void*
30133050
OrtLoggingLevel::ORT_LOGGING_LEVEL_VERBOSE,
30143051
message.c_str(), ORT_FILE, __LINE__, __FUNCTION__));
30153052
} else {
3016-
// LOGS_DEFAULT(WARNING)
3017-
// << "[TensorRT EP] Engine cache encryption function is not found. No cache is written to disk";
3053+
std::string message = "[TensorRT EP] Engine cache encryption function is not found. No cache is written to disk";
3054+
Ort::ThrowOnError(ep.ort_api.Logger_LogMessage(&ep.logger_,
3055+
OrtLoggingLevel::ORT_LOGGING_LEVEL_WARNING,
3056+
message.c_str(), ORT_FILE, __LINE__, __FUNCTION__));
30183057
}
30193058
} else {
30203059
std::ofstream file(engine_cache_path, std::ios::binary | std::ios::out);

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