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Merged
ankan-ban merged 1 commit into from
Apr 22, 2025
Merged

use setShapeValuesV2 #7

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7 changes: 0 additions & 7 deletions cmake/onnxruntime_providers_nv.cmake
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Original file line number Diff line number Diff line change
Expand Up @@ -30,13 +30,6 @@
endif()
set(CXX_VERSION_DEFINED TRUE)

# There is an issue when running "Debug build" NV EP with "Release build" TRT builtin parser on Windows.
# We enforce following workaround for now until the real fix.
if (WIN32 AND CMAKE_BUILD_TYPE STREQUAL "Debug")
set(onnxruntime_USE_TENSORRT_BUILTIN_PARSER OFF)
MESSAGE(STATUS "[Note] There is an issue when running \"Debug build\" TRT EP with \"Release build\" TRT built-in parser on Windows. This build will use tensorrt oss parser instead.")
endif()

find_path(TENSORRT_INCLUDE_DIR NvInfer.h
HINTS ${TENSORRT_ROOT}
PATH_SUFFIXES include)
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42 changes: 21 additions & 21 deletions onnxruntime/core/providers/nv_tensorrt_rtx/nv_execution_provider.cc
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Expand Up @@ -514,17 +514,17 @@ bool ApplyProfileShapesFromProviderOptions(std::vector<nvinfer1::IOptimizationPr
// Shape tensor
if (input->isShapeTensor()) {
int shape_size = nb_dims == 0 ? 1 : static_cast<int>(profile_min_shapes[input_name][i].size());
std::vector<int32_t> shapes_min(shape_size), shapes_opt(shape_size), shapes_max(shape_size);
std::vector<int64_t> shapes_min(shape_size), shapes_opt(shape_size), shapes_max(shape_size);

LOGS_DEFAULT(VERBOSE) << "[Nv EP] shape size of this shape tensor is " << shape_size;

for (int j = 0; j < shape_size; j++) {
auto min_value = profile_min_shapes[input_name][i][j];
auto max_value = profile_max_shapes[input_name][i][j];
auto opt_value = profile_opt_shapes[input_name][i][j];
shapes_min[j] = static_cast<int32_t>(min_value);
shapes_max[j] = static_cast<int32_t>(max_value);
shapes_opt[j] = static_cast<int32_t>(opt_value);
shapes_min[j] = static_cast<int64_t>(min_value);
shapes_max[j] = static_cast<int64_t>(max_value);
shapes_opt[j] = static_cast<int64_t>(opt_value);
LOGS_DEFAULT(VERBOSE) << "[Nv EP] shapes_min.d[" << j << "] is " << shapes_min[j];
LOGS_DEFAULT(VERBOSE) << "[Nv EP] shapes_max.d[" << j << "] is " << shapes_max[j];
LOGS_DEFAULT(VERBOSE) << "[Nv EP] shapes_opt.d[" << j << "] is " << shapes_opt[j];
Expand All @@ -538,9 +538,9 @@ bool ApplyProfileShapesFromProviderOptions(std::vector<nvinfer1::IOptimizationPr
input_explicit_shape_ranges[input_name][static_cast<int64_t>(j)][i].push_back(opt_value);
}

trt_profile->setShapeValues(input_name.c_str(), nvinfer1::OptProfileSelector::kMIN, &shapes_min[0], shape_size);
trt_profile->setShapeValues(input_name.c_str(), nvinfer1::OptProfileSelector::kMAX, &shapes_max[0], shape_size);
trt_profile->setShapeValues(input_name.c_str(), nvinfer1::OptProfileSelector::kOPT, &shapes_opt[0], shape_size);
trt_profile->setShapeValuesV2(input_name.c_str(), nvinfer1::OptProfileSelector::kMIN, &shapes_min[0], shape_size);
trt_profile->setShapeValuesV2(input_name.c_str(), nvinfer1::OptProfileSelector::kMAX, &shapes_max[0], shape_size);
trt_profile->setShapeValuesV2(input_name.c_str(), nvinfer1::OptProfileSelector::kOPT, &shapes_opt[0], shape_size);
}
// Execution tensor
else {
Expand Down Expand Up @@ -627,15 +627,15 @@ Status ApplyProfileShapesFromInputTensorValue(std::vector<nvinfer1::IOptimizatio
if (input->isShapeTensor()) {
// shape tensor
int shape_size = nb_dims == 0 ? 1 : static_cast<int>(tensor_shapes[0]);
std::vector<int32_t> shapes_min(shape_size), shapes_opt(shape_size), shapes_max(shape_size);
std::vector<int64_t> shapes_min(shape_size), shapes_opt(shape_size), shapes_max(shape_size);
for (int j = 0; j < shape_size; j++) {
shapes_min[j] = *(trt_profiles[0]->getShapeValues(input_name.c_str(), nvinfer1::OptProfileSelector::kMIN));
shapes_max[j] = *(trt_profiles[0]->getShapeValues(input_name.c_str(), nvinfer1::OptProfileSelector::kMAX));
shapes_opt[j] = *(trt_profiles[0]->getShapeValues(input_name.c_str(), nvinfer1::OptProfileSelector::kOPT));
shapes_min[j] = *(trt_profiles[0]->getShapeValuesV2(input_name.c_str(), nvinfer1::OptProfileSelector::kMIN));
shapes_max[j] = *(trt_profiles[0]->getShapeValuesV2(input_name.c_str(), nvinfer1::OptProfileSelector::kMAX));
shapes_opt[j] = *(trt_profiles[0]->getShapeValuesV2(input_name.c_str(), nvinfer1::OptProfileSelector::kOPT));
}
trt_profile->setShapeValues(input_name.c_str(), nvinfer1::OptProfileSelector::kMIN, &shapes_min[0], shape_size);
trt_profile->setShapeValues(input_name.c_str(), nvinfer1::OptProfileSelector::kMAX, &shapes_max[0], shape_size);
trt_profile->setShapeValues(input_name.c_str(), nvinfer1::OptProfileSelector::kOPT, &shapes_opt[0], shape_size);
trt_profile->setShapeValuesV2(input_name.c_str(), nvinfer1::OptProfileSelector::kMIN, &shapes_min[0], shape_size);
trt_profile->setShapeValuesV2(input_name.c_str(), nvinfer1::OptProfileSelector::kMAX, &shapes_max[0], shape_size);
trt_profile->setShapeValuesV2(input_name.c_str(), nvinfer1::OptProfileSelector::kOPT, &shapes_opt[0], shape_size);
} else {
// execution tensor
nvinfer1::Dims dims_min, dims_opt, dims_max;
Expand Down Expand Up @@ -692,15 +692,15 @@ Status ApplyProfileShapesFromInputTensorValue(std::vector<nvinfer1::IOptimizatio
}

// Update shape ranges
std::vector<int32_t> shapes_min(shape_size), shapes_opt(shape_size), shapes_max(shape_size);
std::vector<int64_t> shapes_min(shape_size), shapes_opt(shape_size), shapes_max(shape_size);
int shape_range_size = static_cast<int>(shape_ranges_per_input.size());
if (shape_size == shape_range_size) {
// If shape size matches, check/update shape range
for (int j = 0; j < shape_size; ++j) {
auto& shape_range = shape_ranges_per_input[j][0]; // only has one profile
shapes_min[j] = static_cast<int32_t>(shape_range[0]);
shapes_max[j] = static_cast<int32_t>(shape_range[1]);
shapes_opt[j] = static_cast<int32_t>(shape_range[2]);
shapes_min[j] = static_cast<int64_t>(shape_range[0]);
shapes_max[j] = static_cast<int64_t>(shape_range[1]);
shapes_opt[j] = static_cast<int64_t>(shape_range[2]);

const auto& tensor_shape_value = values[j];
// Update shape range lower bound
Expand Down Expand Up @@ -734,9 +734,9 @@ Status ApplyProfileShapesFromInputTensorValue(std::vector<nvinfer1::IOptimizatio
*engine_update = true;
}

trt_profile->setShapeValues(input_name.c_str(), nvinfer1::OptProfileSelector::kMIN, &shapes_min[0], shape_size);
trt_profile->setShapeValues(input_name.c_str(), nvinfer1::OptProfileSelector::kMAX, &shapes_max[0], shape_size);
trt_profile->setShapeValues(input_name.c_str(), nvinfer1::OptProfileSelector::kOPT, &shapes_opt[0], shape_size);
trt_profile->setShapeValuesV2(input_name.c_str(), nvinfer1::OptProfileSelector::kMIN, &shapes_min[0], shape_size);
trt_profile->setShapeValuesV2(input_name.c_str(), nvinfer1::OptProfileSelector::kMAX, &shapes_max[0], shape_size);
trt_profile->setShapeValuesV2(input_name.c_str(), nvinfer1::OptProfileSelector::kOPT, &shapes_opt[0], shape_size);
} else { // Execution tensor
nvinfer1::Dims dims_min(dims), dims_opt(dims), dims_max(dims);
for (int j = 0, end = nb_dims; j < end; ++j) {
Expand Down