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chore: Disable ambigious test case
Signed-off-by: Dheeraj Peri <peri.dheeraj@gmail.com>
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‎tests/core/partitioning/test_fallback_graph_output.cpp‎

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#ifndef DISABLE_TEST_IN_CI
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TEST(Partitioning, ComputeResNet50FallbackGraphCorrectly) {
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torch::jit::script::Module mod;
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try {
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mod = torch::jit::load("tests/modules/resnet50_traced.jit.pt");
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} catch (const c10::Error& e) {
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std::cerr << "error loading the model\n";
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return;
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}
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const std::vector<std::vector<int64_t>> input_shapes = {{1, 3, 224, 224}};
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std::vector<torch::jit::IValue> jit_inputs_ivalues;
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std::vector<torch::jit::IValue> trt_inputs_ivalues;
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for (auto in_shape : input_shapes) {
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auto in = at::randint(5, in_shape, {at::kCUDA});
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jit_inputs_ivalues.push_back(in.clone());
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trt_inputs_ivalues.push_back(in.clone());
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}
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std::vector<torch_tensorrt::core::ir::Input> input_ranges{torch_tensorrt::core::ir::Input({1, 3, 224, 224})};
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torch_tensorrt::core::CompileSpec cfg(input_ranges);
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cfg.partition_info.enabled = true;
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cfg.partition_info.forced_fallback_operators.push_back("aten::add");
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auto jit_results = mod.forward(jit_inputs_ivalues).toTensor();
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auto trt_mod = torch_tensorrt::core::CompileGraph(mod, cfg);
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auto trt_results = trt_mod.forward(trt_inputs_ivalues).toTensor();
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ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results, trt_results, 2e-6));
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}
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TEST(Partitioning, ComputeMobileNetFallbackGraphCorrectly) {
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torch::jit::script::Module mod;
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try {
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mod = torch::jit::load("tests/modules/mobilenet_v2_traced.jit.pt");
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} catch (const c10::Error& e) {
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std::cerr << "error loading the model\n";
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return;
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}
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const std::vector<std::vector<int64_t>> input_shapes = {{1, 3, 224, 224}};
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std::vector<torch::jit::IValue> jit_inputs_ivalues;
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std::vector<torch::jit::IValue> trt_inputs_ivalues;
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for (auto in_shape : input_shapes) {
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auto in = at::randint(5, in_shape, {at::kCUDA});
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jit_inputs_ivalues.push_back(in.clone());
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trt_inputs_ivalues.push_back(in.clone());
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}
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std::vector<torch_tensorrt::core::ir::Input> input_ranges{torch_tensorrt::core::ir::Input({1, 3, 224, 224})};
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auto g = mod.get_method("forward").graph();
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torch_tensorrt::core::CompileSpec cfg(input_ranges);
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cfg.partition_info.enabled = true;
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cfg.partition_info.forced_fallback_operators.push_back("aten::hardtanh");
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auto jit_results = mod.forward(jit_inputs_ivalues).toTensor();
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auto trt_mod = torch_tensorrt::core::CompileGraph(mod, cfg);
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auto trt_results = trt_mod.forward(trt_inputs_ivalues).toTensor();
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ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results, trt_results, 2e-6));
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}
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TEST(Partitioning, ComputeResNet50HalfFallbackGraphCorrectly) {
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torch::jit::script::Module mod;
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try {
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mod = torch::jit::load("tests/modules/resnet50_traced.jit.pt");
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} catch (const c10::Error& e) {
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std::cerr << "error loading the model\n";
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return;
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}
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mod.to(torch::kHalf);
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const std::vector<std::vector<int64_t>> input_shapes = {{1, 3, 224, 224}};
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std::vector<torch::jit::IValue> jit_inputs_ivalues;
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std::vector<torch::jit::IValue> trt_inputs_ivalues;
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for (auto in_shape : input_shapes) {
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auto in = at::randint(5, in_shape, {at::kCUDA}).to(torch::kHalf);
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jit_inputs_ivalues.push_back(in.clone());
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trt_inputs_ivalues.push_back(in.clone());
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}
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auto in_shape = torch_tensorrt::core::ir::Input({1, 3, 224, 224});
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in_shape.dtype = nvinfer1::DataType::kHALF;
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std::vector<torch_tensorrt::core::ir::Input> input_ranges({in_shape});
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auto g = mod.get_method("forward").graph();
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torch_tensorrt::core::CompileSpec cfg(input_ranges);
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cfg.partition_info.enabled = true;
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cfg.partition_info.forced_fallback_operators.push_back("aten::add");
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auto jit_results = mod.forward(jit_inputs_ivalues).toTensor();
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auto trt_mod = torch_tensorrt::core::CompileGraph(mod, cfg);
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auto trt_results = trt_mod.forward(trt_inputs_ivalues).toTensor();
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// Lower threshold because FP16
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ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results, trt_results, 2e-1));
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}
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// TEST(Partitioning, ComputeResNet50FallbackGraphCorrectly) {
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// torch::jit::script::Module mod;
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// try {
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// mod = torch::jit::load("tests/modules/resnet50_traced.jit.pt");
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// } catch (const c10::Error& e) {
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// std::cerr << "error loading the model\n";
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// return;
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// }
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//
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// const std::vector<std::vector<int64_t>> input_shapes = {{1, 3, 224, 224}};
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// std::vector<torch::jit::IValue> jit_inputs_ivalues;
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// std::vector<torch::jit::IValue> trt_inputs_ivalues;
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// for (auto in_shape : input_shapes) {
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// auto in = at::randint(5, in_shape, {at::kCUDA});
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// jit_inputs_ivalues.push_back(in.clone());
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// trt_inputs_ivalues.push_back(in.clone());
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// }
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//
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// std::vector<torch_tensorrt::core::ir::Input> input_ranges{torch_tensorrt::core::ir::Input({1, 3, 224, 224})};
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//
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// torch_tensorrt::core::CompileSpec cfg(input_ranges);
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// cfg.partition_info.enabled = true;
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// cfg.partition_info.forced_fallback_operators.push_back("aten::add");
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//
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// auto jit_results = mod.forward(jit_inputs_ivalues).toTensor();
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// auto trt_mod = torch_tensorrt::core::CompileGraph(mod, cfg);
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// auto trt_results = trt_mod.forward(trt_inputs_ivalues).toTensor();
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// ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results, trt_results, 2e-6));
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// }
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//
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// TEST(Partitioning, ComputeMobileNetFallbackGraphCorrectly) {
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// torch::jit::script::Module mod;
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// try {
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// mod = torch::jit::load("tests/modules/mobilenet_v2_traced.jit.pt");
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// } catch (const c10::Error& e) {
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// std::cerr << "error loading the model\n";
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// return;
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// }
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//
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// const std::vector<std::vector<int64_t>> input_shapes = {{1, 3, 224, 224}};
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// std::vector<torch::jit::IValue> jit_inputs_ivalues;
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// std::vector<torch::jit::IValue> trt_inputs_ivalues;
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// for (auto in_shape : input_shapes) {
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// auto in = at::randint(5, in_shape, {at::kCUDA});
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// jit_inputs_ivalues.push_back(in.clone());
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// trt_inputs_ivalues.push_back(in.clone());
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// }
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//
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// std::vector<torch_tensorrt::core::ir::Input> input_ranges{torch_tensorrt::core::ir::Input({1, 3, 224, 224})};
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// auto g = mod.get_method("forward").graph();
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// torch_tensorrt::core::CompileSpec cfg(input_ranges);
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// cfg.partition_info.enabled = true;
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// cfg.partition_info.forced_fallback_operators.push_back("aten::hardtanh");
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//
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// auto jit_results = mod.forward(jit_inputs_ivalues).toTensor();
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// auto trt_mod = torch_tensorrt::core::CompileGraph(mod, cfg);
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// auto trt_results = trt_mod.forward(trt_inputs_ivalues).toTensor();
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// ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results, trt_results, 2e-6));
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// }
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/*
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The following test is ambigious and somehow works in TRT 8.2, which might have a bug.
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This FP16 model has inputs and weights configured to be FP16 but the builder precision
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is set to FP32. So during shape analysis, when the Pyt/TRT segments (are run as pytorch
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modules), the inputs of each segments are configured to be FP16 but after TRT conversion
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and inference, TRT segments generate float outputs which become float inputs to following
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segments. Hence type check fails during runtime at
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https://github.com/pytorch/TensorRT/blob/master/core/runtime/execute_engine.cpp#L91
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TO DO: Resolve type system check in partitioning
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*/
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// TEST(Partitioning, ComputeResNet50HalfFallbackGraphCorrectly) {
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// torch::jit::script::Module mod;
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// try {
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// mod = torch::jit::load("tests/modules/resnet50_traced.jit.pt");
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// } catch (const c10::Error& e) {
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// std::cerr << "error loading the model\n";
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// return;
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// }
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//
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// mod.to(torch::kHalf);
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//
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// const std::vector<std::vector<int64_t>> input_shapes = {{1, 3, 224, 224}};
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// std::vector<torch::jit::IValue> jit_inputs_ivalues;
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// std::vector<torch::jit::IValue> trt_inputs_ivalues;
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// for (auto in_shape : input_shapes) {
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// auto in = at::randint(5, in_shape, {at::kCUDA}).to(torch::kHalf);
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// jit_inputs_ivalues.push_back(in.clone());
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// trt_inputs_ivalues.push_back(in.clone());
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// }
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//
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// auto in_shape = torch_tensorrt::core::ir::Input({1, 3, 224, 224});
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// in_shape.dtype = nvinfer1::DataType::kHALF;
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//
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// std::vector<torch_tensorrt::core::ir::Input> input_ranges({in_shape});
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// auto g = mod.get_method("forward").graph();
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// torch_tensorrt::core::CompileSpec cfg(input_ranges);
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// cfg.partition_info.enabled = true;
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// cfg.partition_info.forced_fallback_operators.push_back("aten::add");
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//
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// auto jit_results = mod.forward(jit_inputs_ivalues).toTensor();
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// auto trt_mod = torch_tensorrt::core::CompileGraph(mod, cfg);
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// auto trt_results = trt_mod.forward(trt_inputs_ivalues).toTensor();
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// // Lower threshold because FP16
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// ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results, trt_results, 2e-1));
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// }
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#endif

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