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Fix explicit quantization conv2d error #58015

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merged 2 commits into from
Oct 13, 2023
Merged

Fix explicit quantization conv2d error #58015

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e04814e
Fix explicit quantization conv2d error
leo0519 Oct 11, 2023
ea7036c
Fix the cmake file error
leo0519 Oct 11, 2023
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78 changes: 35 additions & 43 deletions paddle/fluid/inference/tensorrt/convert/conv2d_op.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -43,45 +43,16 @@ void ConvertConv2d(TensorRTEngine* engine,
framework::OpDesc op_desc(op, nullptr);

auto* X = engine->GetITensor(op_desc.Input("Input").front());
bool enable_int8 = op_desc.HasAttr("enable_int8");

if (enable_int8) {
#if IS_TRT_VERSION_GE(5000)
float in_scale = PADDLE_GET_CONST(float, op_desc.GetAttr("Input_scale"));
engine->SetTensorDynamicRange(X, in_scale);
#endif
}

std::string filter_var_name = op_desc.Input("Filter").front();
auto* Y_v = scope.FindVar(filter_var_name);
phi::DenseTensor* Y_t = nullptr;
nvinfer1::ITensor* filter = nullptr;
int n_output;
int n_input;
int filter_h;
int filter_w;
std::string filter_var_name = op_desc.Input("Filter").front();
TensorRTEngine::Weight weight;
if (engine->use_explicit_quantization()) {
auto* filter = engine->GetITensor(filter_var_name);
PADDLE_ENFORCE_NOT_NULL(
filter,
platform::errors::NotFound("Can not find %s ITensor in engine",
filter_var_name));
auto filter_dims = filter->getDimensions();
PADDLE_ENFORCE_EQ(
filter_dims.nbDims,
4UL,
platform::errors::InvalidArgument(
"The conv2d filter's dims size should be 4, but got %d",
filter_dims.nbDims));
n_output = filter_dims.d[0];
n_input = filter_dims.d[1];
filter_h = filter_dims.d[2];
filter_w = filter_dims.d[3];
} else {
auto* Y_v = scope.FindVar(filter_var_name);
PADDLE_ENFORCE_NOT_NULL(
Y_v,
platform::errors::NotFound("Can not find %s presistale var in scope.",
filter_var_name));
auto* Y_t = Y_v->GetMutable<phi::DenseTensor>();
if (Y_v) {
Y_t = Y_v->GetMutable<phi::DenseTensor>();
PADDLE_ENFORCE_EQ(
Y_t->dims().size(),
4UL,
Expand All @@ -92,7 +63,27 @@ void ConvertConv2d(TensorRTEngine* engine,
n_input = Y_t->dims()[1];
filter_h = Y_t->dims()[2];
filter_w = Y_t->dims()[3];
weight = engine->GetTrtWeight(op_desc.Input("Filter").front(), *Y_t);
} else {
filter = engine->GetITensor(op_desc.Input("Filter").front());
PADDLE_ENFORCE_EQ(
filter->getDimensions().nbDims,
4UL,
platform::errors::InvalidArgument(
"The conv2d filter's dims size should be 4, but got %d",
filter->getDimensions().nbDims));
n_output = filter->getDimensions().d[0];
n_input = filter->getDimensions().d[1];
filter_h = filter->getDimensions().d[2];
filter_w = filter->getDimensions().d[3];
}

bool enable_int8 = op_desc.HasAttr("enable_int8");

if (enable_int8) {
#if IS_TRT_VERSION_GE(5000)
float in_scale = PADDLE_GET_CONST(float, op_desc.GetAttr("Input_scale"));
engine->SetTensorDynamicRange(X, in_scale);
#endif
}
const int groups = PADDLE_GET_CONST(int, op_desc.GetAttr("groups"));
const std::vector<int> dilations =
Expand Down Expand Up @@ -133,7 +124,10 @@ void ConvertConv2d(TensorRTEngine* engine,
nv_post_paddings.d[0] = paddings[1];
nv_post_paddings.d[1] = paddings[3];
}

TensorRTEngine::Weight weight(nvinfer1::DataType::kFLOAT, nullptr, 0);
if (Y_v) {
weight = engine->GetTrtWeight(op_desc.Input("Filter").front(), *Y_t);
}
TensorRTEngine::Weight bias;
bias.SetDataType(weight.get().type);
bias.SetCount(0);
Expand Down Expand Up @@ -167,7 +161,10 @@ void ConvertConv2d(TensorRTEngine* engine,
layer->setStrideNd(nv_strides);

layer->setPrePadding(nv_pre_paddings);
if (output_padding.size() > 0) {

if (!Y_v) layer->setInput(1, *filter);

if (!output_padding.empty()) {
nv_post_paddings.d[0] -= output_padding[0];
nv_post_paddings.d[1] -= output_padding[1];
}
Expand All @@ -186,11 +183,6 @@ void ConvertConv2d(TensorRTEngine* engine,
// set dilations
fset_dilation(layer, nv_dilations);

if (engine->use_explicit_quantization()) {
auto* filter_tensor = engine->GetITensor(op_desc.Input("Filter").front());
layer->setInput(1, *filter_tensor);
}

auto output_name = op_desc.Output("Output").front();
layer->setName((name + " (Output: " + output_name + ")").c_str());
layer->getOutput(0)->setName(output_name.c_str());
Expand Down
4 changes: 2 additions & 2 deletions paddle/fluid/inference/tensorrt/op_teller.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -324,12 +324,12 @@ struct SimpleOpTypeSetTeller : public Teller {
auto* block = desc.Block();
if (block) {
auto* filter_var_desc = block->FindVar(desc.Input("Filter")[0]);
if (!filter_var_desc->Persistable() && !use_explicit_quantization) {
if (!filter_var_desc->Persistable()) {
#if IS_TRT_VERSION_GE(8600)
#else
LOG(INFO)
<< "Trt below 8.6 not support conv2d's filter is a intermedoate "
"tensor in conv2d op, please upgarde your TenroRT.";
"tensor in conv2d op, please upgarde your TensorRT.";
return false;
#endif
}
Expand Down
10 changes: 6 additions & 4 deletions test/ir/inference/CMakeLists.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -181,10 +181,12 @@ if(WITH_GPU AND TENSORRT_FOUND)
set_tests_properties(test_trt_inference_predictor PROPERTIES TIMEOUT 60)
set_tests_properties(test_trt_inference_fp16_io PROPERTIES TIMEOUT 300)
set_tests_properties(test_trt_optimization_level PROPERTIES TIMEOUT 300)
set_tests_properties(test_trt_explicit_quantization_resnet PROPERTIES TIMEOUT
300)
set_tests_properties(test_trt_explicit_quantization_mobilenet
PROPERTIES TIMEOUT 300)
if(NOT WIN32)
set_tests_properties(test_trt_explicit_quantization_resnet
PROPERTIES TIMEOUT 300)
set_tests_properties(test_trt_explicit_quantization_mobilenet
PROPERTIES TIMEOUT 300)
endif()
if(WITH_MKLDNN)
set_tests_properties(test_save_optimized_model_pass PROPERTIES TIMEOUT 300)
endif()
Expand Down