feat(//cpp/trtorchexec): TRTorch exec now supports checking correctness

of multiple outputs

Signed-off-by: Naren Dasan <[email protected]>
Signed-off-by: Naren Dasan <[email protected]>

Author: narendasan

Diff for: cpp/trtorchexec/main.cpp

@@ -55,14 +55,17 @@ int main(int argc, const char* argv[]) {
         dims.push_back(v);
     }
 
+    auto extra_info = trtorch::ExtraInfo(dims);
+    extra_info.workspace_size = 1 << 24;
+
     std::cout << "Checking operator support" << std::endl;
     if (!trtorch::CheckMethodOperatorSupport(mod, "forward")) {
         std::cerr << "Method is not currently supported by TRTorch" << std::endl;
         return -1;
     }
 
     std::cout << "Compiling graph to save as TRT engine (/tmp/engine_converted_from_jit.trt)" << std::endl;
-    auto engine = trtorch::ConvertGraphToTRTEngine(mod, "forward", dims);
+    auto engine = trtorch::ConvertGraphToTRTEngine(mod, "forward", extra_info);
     std::ofstream out("/tmp/engine_converted_from_jit.trt");
     out << engine;
     out.close();
@@ -75,14 +78,28 @@ int main(int argc, const char* argv[]) {
 
     torch::jit::IValue jit_results_ivalues = mod.forward(jit_inputs_ivalues);
     std::vector<at::Tensor> jit_results;
-    jit_results.push_back(jit_results_ivalues.toTensor());
+    if (jit_results_ivalues.isTensor()) {
+        jit_results.push_back(jit_results_ivalues.toTensor());
+    } else {
+        auto results = jit_results_ivalues.toTuple()->elements();
+        for (auto r : results) {
+            jit_results.push_back(r.toTensor());
+        }
+    }
 
     std::cout << "Compiling graph as module" << std::endl;
-    auto trt_mod = trtorch::CompileGraph(mod, dims);
+    auto trt_mod = trtorch::CompileGraph(mod, extra_info);
     std::cout << "Running TRT module" << std::endl;
     torch::jit::IValue trt_results_ivalues = trt_mod.forward(trt_inputs_ivalues);
     std::vector<at::Tensor> trt_results;
-    trt_results.push_back(trt_results_ivalues.toTensor());
+     if (trt_results_ivalues.isTensor()) {
+        trt_results.push_back(trt_results_ivalues.toTensor());
+    } else {
+        auto results = trt_results_ivalues.toTuple()->elements();
+        for (auto r : results) {
+            trt_results.push_back(r.toTensor());
+        }
+    }
 
     for (size_t i = 0; i < trt_results.size(); i++) {
         almostEqual(jit_results[i], trt_results[i].reshape_as(jit_results[i]));