feat: Add option to specify int64 as an Input dtype

- Rework `Input` paradigm to be based on `at::ScalarType` as opposed to
the previous `nvinfer1::DataType`, allowing a larger representation
space of data types
- When paired with `truncate_long_and_double`, insert casts to ensure
Torch engines using Int64 tensors receive the correct types, and
TensorRT engines operating on those tensors receive downcasted Int32
versions thereof
- Add Torch block at the beginning of model graph to prepare types
of input tensors for forthcoming engines in sequence
- Automatically follow internal tensor types to abstract away the
different internal engines used (Torch/TensorRT) from the user
- Provide a framework for streamlined addition of other data types,
including `torch.double` as valid input types
- Improve error checking to ensure model compilation and behavior is as
documented. For example, disallow specification of Long type input if
the engine is required to be converted entirely to TRT

- Known Limitations:
- Specifying `dtype=torch.long` on an `Input` in an `input_signature` is
not supported currently and will throw an error before model compilation
when used with the Python API
- While Torch may output Int64 tensors from the overall model, Torch-TRT
currently can only output Int32 tensors for models using TRT, as there
is not a mechanism in place for differentiating intermediate blocks from
final/beginning blocks in the graph
- Torch-TRT will almost definitely alter the data type of the input
tensor, in-place, if `dtype=torch.long` is specified, and the returned
result will be of type `torch.int32`

core/compiler.cpp

@@ -187,7 +187,7 @@ partitioning::GraphAndMapping BuildHybridGraph(
   return partitioning::stitch(&partitioning_ctx, block);
 }
 
-void MapInputsAndDetermineDTypes(
+ir::TypeMap MapInputsAndDetermineDTypes(
     CompileSpec& cfg,
     std::shared_ptr<torch::jit::Graph>& g,
     ir::StaticParams& static_params,
@@ -197,6 +197,7 @@ void MapInputsAndDetermineDTypes(
   cfg.partitioning_info.collection_input_spec_map =
       ir::CollectionInputSpecMap(cfg.convert_info.collection_input_spec_map);
 
+  ir::TypeMap inferred_dtypes;
   auto collection_inputs = ir::get_collection_inputs(g, static_params);
   LOG_DEBUG(
       "In MapInputsAndDetermineDTypes, the g->inputs() size is "
@@ -218,13 +219,13 @@ void MapInputsAndDetermineDTypes(
         LOG_INFO(
             "Since input type is not explicitly defined, infering using first tensor calculation\n  Inferred input "
             << in->debugName() << " has type " << est_type_opt[i].value());
-        spec[i].dtype = util::ScalarTypeToTRTDataType(est_type_opt[i].value());
+        spec[i].dtype = est_type_opt[i].value();
       } else if (!est_type_opt[i] && !spec[i].dtype_is_user_defined) {
         // If we cannot calculate the type and the user did not define the type, then default to FP32
         LOG_WARNING(
             "Cannot infer input type from calcuations in graph for input "
             << in->debugName() << ". Assuming it is Float32. If not, specify input type explicity");
-        spec[i].dtype = nvinfer1::DataType::kFLOAT;
+        spec[i].dtype = at::kFloat;
       } else if (spec[i].dtype_is_user_defined && cfg.partitioning_info.enabled) {
         if (!est_type_opt[i]) {
           LOG_INFO("Cannot infer input tensor dtype in graph, compiler is going to use the user setting");
@@ -236,37 +237,35 @@ void MapInputsAndDetermineDTypes(
           auto warn_str = ss.str();
           LOG_WARNING(warn_str);
           // Overwrite type map with user settings
-          first_use_type_map[in][i] = {
-              util::TRTDataTypeToScalarType(cfg.convert_info.collection_input_spec_map.find(in)->second[i].dtype)};
-
-        } else {
-          if (util::TRTDataTypeToScalarType(cfg.convert_info.collection_input_spec_map.find(in)->second[i].dtype) !=
-              est_type_opt[i].value()) {
-            std::stringstream ss;
-            ss << "For input " << in->debugName() << ", found user specified input dtype as ";
-            ss << cfg.convert_info.collection_input_spec_map.find(in)->second[i].dtype;
-            ss << ", however when inspecting the graph, the input type expected was inferred to be ";
-            ss << est_type_opt[i].value() << std::endl;
-            ss << "The compiler is going to use the user setting "
-               << cfg.convert_info.collection_input_spec_map.find(in)->second[i].dtype;
-            ss << "\nThis conflict may cause an error at runtime due to partial compilation being enabled and therefore\n";
-            ss << "compatibility with PyTorch's data type convention is required.\n";
-            ss << "If you do indeed see errors at runtime either:\n";
-            ss << "- Remove the dtype spec for " << in->debugName() << std::endl;
-            ss << "- Disable partial compilation by setting require_full_compilation to True";
-            auto warn_str = ss.str();
-            LOG_WARNING(warn_str);
-            // Overwrite type map with user settings
-            first_use_type_map[in][i] = {
-                util::TRTDataTypeToScalarType(cfg.convert_info.collection_input_spec_map.find(in)->second[i].dtype)};
-          }
+          first_use_type_map[in][i] = {cfg.convert_info.collection_input_spec_map.find(in)->second[i].dtype};
+
+        } else if (cfg.convert_info.collection_input_spec_map.find(in)->second[i].dtype != est_type_opt[i].value()) {
+          std::stringstream ss;
+          ss << "For input " << in->debugName() << ", found user specified input dtype as ";
+          ss << cfg.convert_info.collection_input_spec_map.find(in)->second[i].dtype;
+          ss << ", however when inspecting the graph, the input type expected was inferred to be ";
+          ss << est_type_opt[i].value() << std::endl;
+          ss << "The compiler is going to use the user setting "
+             << cfg.convert_info.collection_input_spec_map.find(in)->second[i].dtype;
+          ss << "\nThis conflict may cause an error at runtime due to partial compilation being enabled and therefore\n";
+          ss << "compatibility with PyTorch's data type convention is required.\n";
+          ss << "If you do indeed see errors at runtime either:\n";
+          ss << "- Remove the dtype spec for " << in->debugName() << std::endl;
+          ss << "- Disable partial compilation by setting require_full_compilation to True";
+          auto warn_str = ss.str();
+          LOG_WARNING(warn_str);
+          // Overwrite type map with user settings
+          first_use_type_map[in][i] = {cfg.convert_info.collection_input_spec_map.find(in)->second[i].dtype};
         }
       } else {
         // The user defined the type so no changes are necessary
       }
+
+      // Insert entry for Value pointer and determined ScalarType
+      inferred_dtypes.insert({in, {spec[i].dtype}});
     }
   }
-  // }
+  return inferred_dtypes;
 }
 
 std::string ConvertGraphToTRTEngine(const torch::jit::script::Module& mod, std::string method_name, CompileSpec cfg) {
@@ -284,6 +283,15 @@ std::string ConvertGraphToTRTEngine(const torch::jit::script::Module& mod, std::
 
   MapInputsAndDetermineDTypes(cfg, g, static_params, first_use_types);
 
+  // Ensure none of the specified types are of acceptable input types incompatible with TRT
+  // Currently, only at::kLong is an acceptable, though TRT-incompatible type
+  for (auto value_to_dtypes : first_use_types) {
+    for (auto dtype : value_to_dtypes.second) {
+      TORCHTRT_CHECK(
+          !dtype || dtype.value() != at::kLong, "Cannot specify Int64 input for a model fully compiled in TRT");
+    }
+  }
+
   auto engine = conversion::ConvertBlockToEngine(g->block(), cfg.convert_info, static_params);
 
   return engine;
@@ -307,10 +315,24 @@ torch::jit::Module CompileGraph(const torch::jit::Module& mod, CompileSpec cfg)
       // Infer the type of an input from the weights of the calculation
       auto first_use_types = ir::get_block_first_calc_dtypes_opt_collection(g->block());
 
-      MapInputsAndDetermineDTypes(cfg, g, static_params, first_use_types);
+      // Extract map of IValue to DType
+      auto type_map = MapInputsAndDetermineDTypes(cfg, g, static_params, first_use_types);
+
+      // Check whether any of the input types are Long
+      bool user_requested_long = false;
+      for (auto dtype : type_map) {
+        user_requested_long |= dtype.second && (dtype.second.value() == at::kLong);
+      }
+
+      // Use dtype map to autocast Tensor-type inputs to Long dtype as necessary
+      if (cfg.partitioning_info.enabled && cfg.partitioning_info.truncate_long_and_double && user_requested_long) {
+        auto casts_inserted = lowering::AutocastLongInputs(g, type_map, cfg.lower_info.getGPUDeviceString());
+        user_requested_long &= (casts_inserted > 0);
+      }
+
       auto isBlockConvertible = conversion::VerifyConverterSupportForBlock(g->block(), true);
       auto outputIsCollection = conversion::OutputIsCollection(g->block());
-      if (cfg.partitioning_info.enabled &&
+      if (cfg.partitioning_info.enabled && !user_requested_long &&
           (cfg.lower_info.forced_fallback_modules.size() == 0 &&
            cfg.partitioning_info.forced_fallback_operators.size() == 0 && isBlockConvertible) &&
           !outputIsCollection) {
@@ -320,7 +342,7 @@ torch::jit::Module CompileGraph(const torch::jit::Module& mod, CompileSpec cfg)
       if (cfg.partitioning_info.enabled &&
           (!(cfg.lower_info.forced_fallback_modules.size() == 0 &&
              cfg.partitioning_info.forced_fallback_operators.size() == 0 && isBlockConvertible) ||
-           outputIsCollection)) {
+           outputIsCollection || user_requested_long)) {
         auto graph_and_mapping = BuildHybridGraph(new_mod, g->block(), cfg, static_params, first_use_types);
         new_g = graph_and_mapping.first;
         // renaming the input name of graph after fallback to ensure pytorch deserialize it correctly

core/conversion/conversion.cpp

@@ -183,7 +183,7 @@ void AddInputs(ConversionCtx* ctx, c10::ArrayRef<const torch::jit::Value*> input
         "Adding Input " << in->debugName() << " (named: " << name << "): " << spec
                         << " in engine (conversion.AddInputs)");
 
-    auto trt_in = ctx->net->addInput(name.c_str(), spec.dtype, spec.input_shape);
+    auto trt_in = ctx->net->addInput(name.c_str(), util::ScalarTypeToTRTDataType(spec.dtype), spec.input_shape);
     TORCHTRT_CHECK(trt_in, "Failed to add input node: " << in->debugName() << " (conversion.AddInputs)");
     trt_in->setAllowedFormats(1U << static_cast<int>(spec.format));
 

core/ir/Input.cpp

@@ -71,7 +71,7 @@ bool valid_input_dtype(nvinfer1::DataType dtype) {
 
 Input::Input(
     std::vector<int64_t> shape,
-    nvinfer1::DataType dtype,
+    at::ScalarType dtype,
     nvinfer1::TensorFormat format,
     bool dtype_is_user_defined) {
   if (shape.size() > 5) {
@@ -84,10 +84,10 @@ Input::Input(
   input_shape = util::toDims(shape);
   input_is_dynamic = false;
 
-  TORCHTRT_CHECK(valid_input_dtype(dtype), "Unsupported input data type: " << dtype);
+  TORCHTRT_CHECK(valid_input_dtype(util::ScalarTypeToTRTDataType(dtype)), "Unsupported input data type: " << dtype);
   this->dtype = dtype;
   TORCHTRT_CHECK(
-      valid_dtype_format_combo(dtype, format),
+      valid_dtype_format_combo(util::ScalarTypeToTRTDataType(dtype), format),
       "Unsupported combination of dtype and tensor format: ("
           << dtype << ", " << format
           << "), Torch-TensorRT only supports contiguous format (NCHW) except with input type Float32 where channel last (NHWC) is also supported");
@@ -99,7 +99,7 @@ Input::Input(
     std::vector<int64_t> min_shape,
     std::vector<int64_t> opt_shape,
     std::vector<int64_t> max_shape,
-    nvinfer1::DataType dtype,
+    at::ScalarType dtype,
     nvinfer1::TensorFormat format,
     bool dtype_is_user_defined) {
   if (min_shape.size() > 5 || opt_shape.size() > 5 || max_shape.size() > 5) {
@@ -137,10 +137,10 @@ Input::Input(
 
   input_shape = util::toDims(dyn_shape);
 
-  TORCHTRT_CHECK(valid_input_dtype(dtype), "Unsupported input data type: " << dtype);
+  TORCHTRT_CHECK(valid_input_dtype(util::ScalarTypeToTRTDataType(dtype)), "Unsupported input data type: " << dtype);
   this->dtype = dtype;
   TORCHTRT_CHECK(
-      valid_dtype_format_combo(dtype, format),
+      valid_dtype_format_combo(util::ScalarTypeToTRTDataType(dtype), format),
       "Unsupported combination of dtype and tensor format: ("
           << dtype << ", " << format
           << "), Torch-TensorRT only supports contiguous format (NCHW) except with input type Float32 where channel last (NHWC) is also supported");

core/ir/ir.h

@@ -29,16 +29,17 @@ struct Input : torch::CustomClassHolder {
   Input(){};
   Input(
       std::vector<int64_t> shape,
-      nvinfer1::DataType dtype = nvinfer1::DataType::kFLOAT,
+      at::ScalarType dtype = at::kFloat,
       nvinfer1::TensorFormat format = nvinfer1::TensorFormat::kLINEAR,
       bool dtype_is_user_defined = false);
   Input(
       std::vector<int64_t> min_shape,
       std::vector<int64_t> opt_shape,
       std::vector<int64_t> max_shape,
-      nvinfer1::DataType dtype = nvinfer1::DataType::kFLOAT,
+      at::ScalarType dtype = at::kFloat,
       nvinfer1::TensorFormat format = nvinfer1::TensorFormat::kLINEAR,
       bool dtype_is_used_defined = false);
+
   friend std::ostream& operator<<(std::ostream& os, const Input& input);
 
   bool input_is_dynamic = false;
@@ -47,7 +48,7 @@ struct Input : torch::CustomClassHolder {
   nvinfer1::Dims min;
   nvinfer1::Dims max;
   nvinfer1::Dims opt;
-  nvinfer1::DataType dtype;
+  at::ScalarType dtype;
   nvinfer1::TensorFormat format;
   int id;
 };

core/lowering/lowering.cpp

@@ -26,6 +26,63 @@ void LowerBlock(torch::jit::Block* b) {
   DropUnusedNodes(b);
 }
 
+int AutocastLongInputs(
+    std::shared_ptr<torch::jit::Graph>& g,
+    ir::TypeMap input_type_map,
+    std::string target_device_name) {
+  int num_autocasts = 0;
+  // For each graph input, determine if it can be autocasted
+  for (int i = 0; i < g->inputs().size(); i++) {
+    auto input = g->inputs()[i];
+
+    // Autocasted inputs must be Tensor-type
+    if (input->type()->isSubtypeOf(c10::TensorType::get())) {
+      auto dtype_input = input_type_map.find(input);
+
+      // Ensure the data type to be casted to exists in the type map
+      if (dtype_input == input_type_map.end() || !dtype_input->second) {
+        LOG_DEBUG("No inferred input dtype for tensor " << input->debugName() << ", skipping autocast");
+        continue;
+      }
+
+      auto dtype = dtype_input->second.value();
+      // Currently, we do not autocast inputs for which the determined type is not long
+      if (dtype != at::kLong) {
+        continue;
+      }
+
+      LOG_DEBUG("Inserting aten::to casting " << input->debugName() << " to dtype " << dtype);
+
+      // Generate cast node sending input tensors to the inferred or specified datatype (long)
+      auto const_type = g->insertConstant(dtype);
+      auto const_false = g->insertConstant(0);
+      const_false->setType(torch::jit::BoolType::get());
+      auto cuda = g->insertConstant(target_device_name);
+      cuda->setType(torch::jit::DeviceObjType::get());
+      auto none_val = g->insertNode(g->createNone())->output();
+      auto cast_node = g->create(torch::jit::aten::to, {input, cuda, const_type, const_false, const_false, none_val});
+
+      // Replace all uses of the original tensor with that of the casted tensor
+      g->prependNode(cast_node);
+      input->replaceAllUsesAfterNodeWith(cast_node, cast_node->outputs()[0]);
+
+      // Mark the cast node to run in PyTorch for ease of casting
+      LOG_GRAPH("Marking autocast node " << util::node_info(cast_node) << " to run in PyTorch");
+      cast_node->i_(c10::Symbol::attr("to_compile"), (int64_t) false);
+      num_autocasts++;
+    }
+  }
+
+  LOG_WARNING(
+      "Input tensors to this Torch-TRT engine may have their data types in-place modified "
+      << "if the type does not match the determined required type for TRT. To disable this "
+      << "automatic casting, specify an Input dtype other than Long");
+
+  LOG_GRAPH("Graph after Autocast: " << *g);
+
+  return num_autocasts;
+}
+
 void LowerGraph(std::shared_ptr<torch::jit::Graph>& g, std::vector<torch::jit::IValue>& params, LowerInfo lower_info) {
   torch::jit::EliminateRedundantGuards(g);
   torch::jit::RemoveListMutation(g);

core/lowering/lowering.h

@@ -27,6 +27,10 @@ struct LowerInfo {
 
 void LowerBlock(torch::jit::Block* b);
 void LowerGraph(std::shared_ptr<torch::jit::Graph>& g, LowerInfo lower_info);
+int AutocastLongInputs(
+    std::shared_ptr<torch::jit::Graph>& g,
+    ir::TypeMap input_type_map,
+    std::string target_device_name);
 torch::jit::Module LowerModule(
     const torch::jit::Module& mod,
     std::string method_name,

core/partitioning/segmentedblock/SegmentedBlock.cpp

@@ -62,13 +62,13 @@ std::vector<ir::Input> SegmentedBlock::construct_inputs_spec() const {
   if (min_shapes_.size() == opt_shapes_.size() && opt_shapes_.size() == max_shapes_.size()) {
     for (uint64_t i = 0; i < opt_shapes_.size(); i++) {
       auto in = ir::Input(min_shapes_[i], opt_shapes_[i], max_shapes_[i]);
-      in.dtype = util::ScalarTypeToTRTDataType(in_types_[i]);
+      in.dtype = in_types_[i];
       inputs.push_back(in);
     }
   } else {
     for (uint64_t i = 0; i < opt_shapes_.size(); i++) {
       auto in = ir::Input(opt_shapes_[i]);
-      in.dtype = util::ScalarTypeToTRTDataType(in_types_[i]);
+      in.dtype = in_types_[i];
       inputs.push_back(in);
     }
   }

core/partitioning/shape_analysis.cpp

@@ -266,10 +266,10 @@ void getSegmentsOutputByRunning(
             "Unable to process subgraph input type of at::kLong/at::kDouble, try to compile model with truncate_long_and_double enabled");
       } else if (partitioning_info.truncate_long_and_double && t == at::kLong) {
         cur_ivalue = cur_ivalue.toTensor().to(at::kInt);
-        LOG_WARNING("Truncating graph input type from at::kLong to at::kInt");
+        LOG_WARNING("Truncating intermediate graph input type from at::kLong to at::kInt");
       } else if (partitioning_info.truncate_long_and_double && t == at::kDouble) {
         cur_ivalue = cur_ivalue.toTensor().to(at::kFloat);
-        LOG_WARNING("Truncating graph input type from at::kDouble to at::kFloat");
+        LOG_WARNING("Truncating intermediate graph input type from at::kDouble to at::kFloat");
       }
       c10::optional<nvinfer1::DataType> dtype = util::optTypeMetaToTRTDataType(cur_ivalue.toTensor().dtype());
       if (dtype == c10::nullopt) {

core/util/trt_util.cpp

@@ -251,6 +251,7 @@ const std::unordered_map<at::ScalarType, nvinfer1::DataType>& get_at_trt_type_ma
       {at::kFloat, nvinfer1::DataType::kFLOAT},
       {at::kHalf, nvinfer1::DataType::kHALF},
       {at::kInt, nvinfer1::DataType::kINT32},
+      {at::kLong, nvinfer1::DataType::kINT32},
       {at::kChar, nvinfer1::DataType::kINT8},
       {at::kBool, nvinfer1::DataType::kBOOL}};
   return at_trt_type_map;

cpp/include/torch_tensorrt/torch_tensorrt.h

@@ -58,6 +58,8 @@ class DataType {
    * ex. torch_tensorrt::DataType type = DataType::kFloat;
    */
   enum Value : int8_t {
+    /// INT64
+    kLong,
     /// FP32
     kFloat,
     /// FP16

cpp/src/types.cpp

@@ -87,6 +87,25 @@ nvinfer1::DataType toTRTDataType(DataType value) {
   }
 }
 
+at::ScalarType toAtDataType(DataType value) {
+  switch (value) {
+    case DataType::kChar:
+      return at::kChar;
+    case DataType::kHalf:
+      return at::kHalf;
+    case DataType::kInt:
+      return at::kInt;
+    case DataType::kLong:
+      return at::kLong;
+    case DataType::kBool:
+      return at::kBool;
+    case DataType::kFloat:
+    case DataType::kUnknown:
+    default:
+      return at::kFloat;
+  }
+}
+
 nvinfer1::TensorFormat toTRTTensorFormat(TensorFormat value) {
   TORCHTRT_CHECK(!(value == TensorFormat::kUnknown), "Tensor format is unknown");
   switch (value) {
@@ -267,7 +286,7 @@ torch_tensorrt::core::ir::Input to_internal_input(Input& i) {
       i.min_shape,
       i.opt_shape,
       i.max_shape,
-      toTRTDataType(i.dtype),
+      toAtDataType(i.dtype),
       toTRTTensorFormat(i.format),
       !(i.dtype == DataType::kUnknown));
 }