New API to display if a method can be converted to TensorRT

core/compiler.cpp

@@ -64,6 +64,28 @@ void AddEngineToGraph(torch::jit::script::Module mod, std::shared_ptr<torch::jit
     return;
 }
 
+bool CheckMethodOperatorSupport(const torch::jit::script::Module& mod,
+                                std::string method_name) {
+    auto g = mod.get_method(method_name).graph();
+    // Go through PyTorch Lowering to simplify graph and extract weight parameters
+    auto graph_and_parameters = torch::jit::LowerGraph(*g, mod._ivalue());
+
+    g = graph_and_parameters.first;
+
+    // Go through TRTorch Lowering to reformat graph to be conversion friendly
+    // and also segment for accelerators and executors (TRT-DLA, TRT-GPU, PYT)
+    lowering::LowerGraph(g);
+
+    auto params = graph_and_parameters.second;
+    auto named_params = conversion::get_named_params(g->inputs(), params);
+    LOG_DEBUG(*g << "(CheckMethodOperatorSupport)\n");
+
+    // Is this necessary?
+    lowering::LowerBlock(g->block());
+
+    return conversion::VerifyConverterSupportForBlock(g->block());
+}
+
 std::string ConvertGraphToTRTEngine(const torch::jit::script::Module& mod,
                                     std::string method_name,
                                     conversion::ExtraInfo cfg) {
@@ -87,7 +109,6 @@ std::string ConvertGraphToTRTEngine(const torch::jit::script::Module& mod,
     return std::move(engine);
 }
 
-// TODO: Consider if there is a better way to deal with input size
 torch::jit::script::Module CompileGraph(const torch::jit::script::Module& mod,
                                         conversion::ExtraInfo cfg) {
     // TODO: Should be doing a functional transform but need PR #31978

core/compiler.h

@@ -6,9 +6,11 @@
 
 namespace trtorch {
 namespace core {
+bool CheckMethodOperatorSupport(const torch::jit::script::Module& mod, std::string method_name);
 
 std::string ConvertGraphToTRTEngine(const torch::jit::script::Module& mod,
                                     std::string method_name, conversion::ExtraInfo cfg);
+
 torch::jit::script::Module CompileGraph(const torch::jit::script::Module& module, conversion::ExtraInfo cfg);
 
 } // namespace core

core/conversion/conversion.cpp

@@ -11,15 +11,15 @@ namespace core {
 namespace conversion {
 
 // Defined in core/conversion/conversion_blacklist.cpp 
-bool isNodeConversionBlacklisted(torch::jit::Node* n);
+bool isNodeConversionBlacklisted(const torch::jit::Node* n);
 
 bool OpSupported(const torch::jit::Node* n) {
     bool evalable = evaluators::shouldEvalAtConversionTime(n);
     bool convertable = converters::node_is_convertable(n);
     return evalable || convertable;
 }
 
-c10::optional<torch::jit::IValue> EvaluateNode(ConversionCtx* ctx, torch::jit::Node* n, int level=0, int limit=10) {
+c10::optional<torch::jit::IValue> EvaluateNode(ConversionCtx* ctx, const torch::jit::Node* n, int level=0, int limit=10) {
     // Check to see if you can just go through and eval all of these AOT (saves the recursion)
     // Also probably a better way to deal with the two error cases;
     TRTORCH_CHECK(level < limit, "Failed to evaluate node: " << *n              \
@@ -55,7 +55,7 @@ c10::optional<torch::jit::IValue> EvaluateNode(ConversionCtx* ctx, torch::jit::N
     return eval;
 }
 
-bool AddLayer(ConversionCtx* ctx, torch::jit::Node* n) {
+bool AddLayer(ConversionCtx* ctx, const torch::jit::Node* n) {
     LOG_INFO(ctx->logger,
              "Adding Layer " << util::node_info(n) << " (ctx.AddLayer)");
     converters::args node_args;
@@ -114,11 +114,11 @@ bool AddLayer(ConversionCtx* ctx, torch::jit::Node* n) {
 }
 
 bool AddInputs(ConversionCtx* ctx,
-                at::ArrayRef<torch::jit::Value*> inputs,
+                at::ArrayRef<const torch::jit::Value*> inputs,
                 std::vector<InputRange>& input_dims) {
 
     auto type_lut = torch::jit::script::string_to_type_lut();
-    std::vector<torch::jit::Value*> input_tensors;
+    std::vector<const torch::jit::Value*> input_tensors;
     for (auto in : inputs) {
         // Disregarding inputs that are not tensors
         //
@@ -163,7 +163,7 @@ bool AddInputs(ConversionCtx* ctx,
     return true;
 }
 
-bool MarkOutputs(ConversionCtx* ctx, at::ArrayRef<torch::jit::Value*> outputs) {
+bool MarkOutputs(ConversionCtx* ctx, at::ArrayRef<const torch::jit::Value*> outputs) {
     for (auto out : outputs) {
         ctx->net->markOutput(*(ctx->value_tensor_map[out]));
         LOG_INFO(ctx->logger,
@@ -178,7 +178,7 @@ void AddParamsToCtxValueMap(ConversionCtx* ctx, GraphParams& params) {
     }
 }
 
-void ConvertBlockToNetDef(ConversionCtx* ctx, torch::jit::Block* b, ExtraInfo build_info, GraphParams& static_params) {
+void ConvertBlockToNetDef(ConversionCtx* ctx, const torch::jit::Block* b, ExtraInfo build_info, GraphParams& static_params) {
      LOG_INFO(ctx->logger, "Converting Block");
 
     auto inputs = b->inputs();
@@ -188,7 +188,6 @@ void ConvertBlockToNetDef(ConversionCtx* ctx, torch::jit::Block* b, ExtraInfo bu
     auto nodes = b->nodes();
 
     for (const auto n : nodes) {
-
         bool to_eval = evaluators::shouldEvalAtConversionTime(n);
         bool blacklisted = isNodeConversionBlacklisted(n);
         if (!to_eval && !blacklisted) {
@@ -220,13 +219,41 @@ void ConvertBlockToNetDef(ConversionCtx* ctx, torch::jit::Block* b, ExtraInfo bu
 // a serialized TensorRT engine that can be deserialized and run
 
 // Probably should consolidate these two functions 
-std::string ConvertBlockToEngine(torch::jit::Block* b, ExtraInfo build_info, GraphParams& static_params) {
+std::string ConvertBlockToEngine(const torch::jit::Block* b, ExtraInfo build_info, GraphParams& static_params) {
     ConversionCtx ctx(build_info.engine_settings);
     ConvertBlockToNetDef(&ctx, b, build_info, static_params);
     std::string engine = ctx.SerializeEngine();
     return engine;
 }
 
+bool VerifyConverterSupportForBlock(const torch::jit::Block* b) {
+    bool supported = true;
+    std::set<std::string> unsupported_ops;
+    for (const auto n : b->nodes()) {
+        if (!OpSupported(n)) {
+            auto schema = n->maybeSchema();
+            TRTORCH_CHECK(schema, "Unable to get schema for Node " << util::node_info(n) \
+                                    << " (conversion.AddLayer)");
+            std::stringstream ss;
+            ss << *schema;
+            unsupported_ops.insert(ss.str());
+            supported = false;
+        }
+    }
+
+    if (!supported) {
+        std::stringstream unsupported_msg;
+         unsupported_msg << "Method requested cannot be compiled by TRTorch.\nUnsupported operators listed below:" << std::endl;
+        for (auto s : unsupported_ops) {
+            unsupported_msg << "  -  " << s << std::endl;
+        }
+        unsupported_msg << "You can either implement converters for these ops in your application or file a bug" << std::endl;
+        unsupported_msg <<  "https://www.github.com/nvidia/TRTorch/issues" << std::endl;
+        LOG_ERROR(unsupported_msg.str());
+    }
+    return supported;
+}
+
 } // namespace conversion
 } // namespace core
 } // namespace trtorch

core/conversion/conversion.h

@@ -43,10 +43,12 @@ GraphParams get_named_params(c10::ArrayRef<torch::jit::Value*> inputs, std::vect
 
 // Converts a already lowered block (blocks with no sub blocks) to
 // a serialized TensorRT engine that can be deserialized and run
-std::string ConvertBlockToEngine(torch::jit::Block* b, ExtraInfo build_info, GraphParams& static_params);
+std::string ConvertBlockToEngine(const torch::jit::Block* b, ExtraInfo build_info, GraphParams& static_params);
 
 bool OpSupported(const torch::jit::Node* n);
 
+bool VerifyConverterSupportForBlock(const torch::jit::Block* b);
+
 } // namespace conversion
 } // namespace core
 } // namespace trtorch

core/conversion/conversion_blacklist.cpp

@@ -24,7 +24,7 @@ const std::unordered_set<std::string>& get_non_convertable_nodes() {
     return nonconvertable_nodes;
 }
 
-bool isNodeConversionBlacklisted(torch::jit::Node* n) {
+bool isNodeConversionBlacklisted(const torch::jit::Node* n) {
     auto kind = n->kind();
     auto convertableIt = get_non_convertable_nodes().find(kind.toQualString());
     if (convertableIt == get_non_convertable_nodes().end()) {

core/conversion/converters/NodeConverterRegistry.cpp

@@ -46,10 +46,6 @@ using ConverterLUT = std::unordered_map<torch::jit::Symbol, OpConverter>;
 class NodeConverterRegistry {
 public:
     bool RegisterConverter(torch::jit::FunctionSchema* signature, OpConverter& converter) {
-        // NOTE: This is useful for people developing extentions to the conversion registry as is
-        // If you are working on the core conversion library and the conversion registry
-        // itself, it might helpful to set -DDEBUG_MSGS when you compile so you can watch the
-        // registration of core converters during init, otherwise the messages will be masked
         LOG_DEBUG("Registering Converter for " << canonical_schema_string(*signature));
         auto sym = torch::jit::Symbol::fromQualString(signature->name());
         converter_lut_[sym] = std::move(converter);
@@ -70,13 +66,12 @@ class NodeConverterRegistry {
     bool Convertable(const torch::jit::Node* n) {
         auto schema = n->maybeSchema();
         if (schema) {
-            auto converter = GetConverter(schema);
-            if (converter) {
-                return true;
+            auto sym = torch::jit::Symbol::fromQualString(schema->name());
+            auto iter = converter_lut_.find(sym);
+            if (iter == converter_lut_.end()) {
+               return false;
             } else {
-                LOG_DEBUG("Node has no registered converter: " << util::node_info(n) \
-                          << " (NodeConverterRegistry.Convertable)\nSchema: " << *schema);
-                return false;
+                return true;
             }
         } else {
             LOG_DEBUG("Unable to get schema for Node " << util::node_info(n) \

core/conversion/evaluators/NodeEvaluatorRegistry.cpp

@@ -20,10 +20,6 @@ using EvaluatorLUT = std::unordered_map<torch::jit::NodeKind, NodeEvaluator>;
 class NodeEvaluatorRegistry {
 public:
     void RegisterEvaluator(torch::jit::NodeKind node_kind, NodeEvaluator& evaluator) {
-        // NOTE: This is useful for people developing extentions to the conversion registry as is
-        // If you are working on the core conversion library and the conversion registry
-        // itself, it might helpful to set -DDEBUG_MSGS when you compile so you can watch the
-        // registration of core converters during init, otherwise the messages will be masked
         LOG_DEBUG("Registering evaluator for " << node_kind.toQualString());
         evaluator_lut_[node_kind] = std::move(evaluator);
     }

cpp/api/include/trtorch/trtorch.h

@@ -215,6 +215,19 @@ TRTORCH_API std::string get_build_info();
  */
 TRTORCH_API void dump_build_info();
 
+/**
+ * @brief Check to see if a module is fully supported by the compiler
+ *
+ * @param module: torch::jit::script::Module - Existing TorchScript module 
+ * @param method_name: std::string - Name of method to compile
+ *
+ * Takes a module and a method name and checks if the method graph contains purely
+ * convertable operators 
+ * 
+ * Will print out a list of unsupported operators if the graph is unsupported
+ */ 
+TRTORCH_API bool CheckMethodOperatorSupport(const torch::jit::script::Module& module, std::string method_name);
+
 /**
  * @brief Compile a TorchScript module for NVIDIA GPUs using TensorRT
  *
@@ -239,5 +252,5 @@ TRTORCH_API torch::jit::script::Module CompileGraph(const torch::jit::script::Mo
  * and will convert selected method to a serialized TensorRT engine which can be run with
  * TensorRT
  */
-TRTORCH_API std::string ConvertGraphToTRTEngine(const torch::jit::script::Module& mod, std::string method_name, ExtraInfo info);
+TRTORCH_API std::string ConvertGraphToTRTEngine(const torch::jit::script::Module& module, std::string method_name, ExtraInfo info);
 } // namespace trtorch

cpp/api/src/trtorch.cpp

@@ -10,12 +10,17 @@ namespace trtorch {
 // Defined in extra_info.cpp
 core::conversion::ExtraInfo to_internal_extra_info(ExtraInfo external);
 
-std::string ConvertGraphToTRTEngine(const torch::jit::script::Module& mod,
+bool CheckMethodOperatorSupport(const torch::jit::script::Module& module,
+                                std::string method_name) {
+    return core::CheckMethodOperatorSupport(module, method_name);
+}
+
+std::string ConvertGraphToTRTEngine(const torch::jit::script::Module& module,
                                     std::string method_name, ExtraInfo info) {
     LOG_DEBUG(get_build_info());
     // Want to export a much simpler (non TRT header dependent) API so doing the
     // type conversion here
-    return std::move(core::ConvertGraphToTRTEngine(mod, method_name, to_internal_extra_info(info)));
+    return std::move(core::ConvertGraphToTRTEngine(module, method_name, to_internal_extra_info(info)));
 }
 
 torch::jit::script::Module CompileGraph(const torch::jit::script::Module& module, ExtraInfo info) {

cpp/trtorchexec/main.cpp

@@ -55,6 +55,11 @@ int main(int argc, const char* argv[]) {
         dims.push_back(v);
     }
 
+    if (!trtorch::CheckMethodOperatorSupport(mod, "forward")) {
+        std::cerr << "Method is not currently supported by TRTorch" << std::endl;
+        return -1;
+    }
+
     auto engine = trtorch::ConvertGraphToTRTEngine(mod, "forward", dims);
     std::ofstream out("/tmp/engine_converted_from_jit.trt");
     out << engine;
@@ -69,7 +74,6 @@ 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());
-
 
     auto trt_mod = trtorch::CompileGraph(mod, dims);
     torch::jit::IValue trt_results_ivalues = trt_mod.forward(trt_inputs_ivalues);