Merge branch 'torch_tensorrt_rebrand' into docs_refactor

Author: peri044

core/compiler.cpp

@@ -297,46 +297,48 @@ void MapInputsAndDetermineDTypes(
   cfg.convert_info.inputs = std::move(ir::associate_specs_with_inputs(g, cfg.inputs, static_params));
 
   for (auto& in : g->inputs()) {
-    auto est_type_opt = first_use_type_map.find(in)->second;
-    ir::Input& spec = cfg.convert_info.inputs.find(in)->second;
-    if (est_type_opt && !spec.dtype_is_user_defined) {
-      // If we can calculate the type from the graph and the type was not defined by the user then use the calculated
-      // type
-      LOG_INFO(
-          "Since input type is not explicitly defined, infering using first tensor calculation\n  Found input "
-          << in->debugName() << " has type " << est_type_opt.value()
-          << ". If this is incorrect explicitly set dtype for input and file a bug");
-      spec.dtype = util::ScalarTypeToTRTDataType(est_type_opt.value());
-    } else if (!est_type_opt && !spec.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.dtype = nvinfer1::DataType::kFLOAT;
-    } else if (spec.dtype_is_user_defined && cfg.partition_info.enabled) {
-      if (!est_type_opt) {
-        LOG_INFO("Cannot infer input tensor dtype in graph, unable to verify user input dtype settings");
-      } else {
-        if (util::TRTDataTypeToScalarType(cfg.convert_info.inputs.find(in)->second.dtype) != est_type_opt.value()) {
-          std::stringstream ss;
-          ss << "For input " << in->debugName() << ", found user specified input dtype as ";
-          ss << cfg.convert_info.inputs.find(in)->second.dtype;
-          ss << ", however when inspecting the graph, the input type expected was inferred to be ";
-          ss << est_type_opt.value() << std::endl;
-          ss << "The compiler is going to use the user setting " << cfg.convert_info.inputs.find(in)->second.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] = {util::TRTDataTypeToScalarType(cfg.convert_info.inputs.find(in)->second.dtype)};
+    if (static_params.find(in) == static_params.end()) {
+      ir::Input& spec = cfg.convert_info.inputs.find(in)->second;
+      auto est_type_opt = first_use_type_map.find(in)->second;
+      if (est_type_opt && !spec.dtype_is_user_defined) {
+        // If we can calculate the type from the graph and the type was not defined by the user then use the calculated
+        // type
+        LOG_INFO(
+            "Since input type is not explicitly defined, infering using first tensor calculation\n  Found input "
+            << in->debugName() << " has type " << est_type_opt.value()
+            << ". If this is incorrect explicitly set dtype for input and file a bug");
+        spec.dtype = util::ScalarTypeToTRTDataType(est_type_opt.value());
+      } else if (!est_type_opt && !spec.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.dtype = nvinfer1::DataType::kFLOAT;
+      } else if (spec.dtype_is_user_defined && cfg.partition_info.enabled) {
+        if (!est_type_opt) {
+          LOG_INFO("Cannot infer input tensor dtype in graph, unable to verify user input dtype settings");
+        } else {
+          if (util::TRTDataTypeToScalarType(cfg.convert_info.inputs.find(in)->second.dtype) != est_type_opt.value()) {
+            std::stringstream ss;
+            ss << "For input " << in->debugName() << ", found user specified input dtype as ";
+            ss << cfg.convert_info.inputs.find(in)->second.dtype;
+            ss << ", however when inspecting the graph, the input type expected was inferred to be ";
+            ss << est_type_opt.value() << std::endl;
+            ss << "The compiler is going to use the user setting " << cfg.convert_info.inputs.find(in)->second.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] = {util::TRTDataTypeToScalarType(cfg.convert_info.inputs.find(in)->second.dtype)};
+          }
         }
+      } else {
+        // The user defined the type so no changes are necessary
       }
-    } else {
-      // The user defined the type so no changes are necessary
     }
   }
 }
@@ -375,7 +377,7 @@ std::string ConvertGraphToTRTEngine(const torch::jit::script::Module& mod, std::
 
   auto engine = conversion::ConvertBlockToEngine(g->block(), cfg.convert_info, static_params);
 
-  return std::move(engine);
+  return engine;
 }
 
 torch::jit::Module CompileGraph(const torch::jit::Module& mod, CompileSpec cfg) {

core/compiler.h

@@ -31,4 +31,4 @@ torch::jit::script::Module EmbedEngineInNewModule(const std::string& engine, run
 void set_device(const int gpu_id);
 
 } // namespace core
-} // namespace trtorch
+} // namespace torch_tensorrt

core/ir/StaticParams.cpp

@@ -20,7 +20,7 @@ StaticParams get_static_params(c10::ArrayRef<torch::jit::Value*> inputs, std::ve
   TRTORCH_CHECK(
       static_params.size() == params.size(),
       "Graph parameter parsing failed, mismatched number of static parameters and IValues")
-  return std::move(static_params);
+  return static_params;
 }
 
 } // namespace ir

core/ir/ir.cpp

@@ -24,7 +24,7 @@ InputSpecMap pair_input_vals_with_specs(std::vector<const torch::jit::Value*> va
     LOG_DEBUG("Paring " << i << ": " << vals[i]->debugName() << " : " << specs[i]);
     a.insert({vals[i], specs[i]});
   }
-  return std::move(a);
+  return a;
 }
 
 std::vector<const torch::jit::Value*> get_tensor_inputs(

core/partitioning/partitioning.cpp

@@ -133,7 +133,7 @@ std::vector<SegmentedBlock> segmentBlocksWithNonTensorInputs(SegmentedBlock& seg
     }
   }
 
-  return std::move(new_seg_blocks);
+  return new_seg_blocks;
 }
 
 void resolveNonTensorInputs(PartitionedGraph& segmented_blocks) { // , std::shared_ptr<torch::jit::Graph> g
@@ -385,7 +385,7 @@ PartitionedGraph segment_graph(torch::jit::Block* block, const PartitionInfo& pa
     finalize_block(segmented_blocks, SegmentedBlock::kTorch, in_prog_pyt_blk_nodes);
   }
 
-  return std::move(segmented_blocks);
+  return segmented_blocks;
 }
 
 PartitionedGraph Partition(

core/util/jit_util.cpp

@@ -1,12 +1,12 @@
-ARG BASE=21.06
+ARG BASE=21.09
 ARG BASE_IMG=nvcr.io/nvidia/pytorch:${BASE}-py3
 FROM ${BASE_IMG} as base
 
-FROM base as trtorch-builder-base
+FROM base as torch-tensorrt-builder-base
 
-# Removing any bazel or trtorch pre-installed from the base image
+# Removing any bazel or torch-tensorrt pre-installed from the base image
 
-RUN rm -rf /opt/pytorch/trtorch /usr/bin/bazel
+RUN rm -rf /opt/torch-tensorrt /usr/bin/bazel
 
 RUN apt-get update && apt-get install --no-install-recommends -y curl gnupg
 RUN curl -fsSL https://bazel.build/bazel-release.pub.gpg | gpg --dearmor > /etc/apt/trusted.gpg.d/bazel.gpg
@@ -20,30 +20,32 @@ RUN cp /usr/lib/x86_64-linux-gnu/libnvinfer.so /usr/lib/x86_64-linux-gnu/libnvin
 
 RUN apt-get update && apt-get install -y --no-install-recommends locales ninja-build && rm -rf /var/lib/apt/lists/* && locale-gen en_US.UTF-8
 
-FROM trtorch-builder-base as trtorch-builder
+FROM torch-tensorrt-builder-base as torch-tensorrt-builder
 
-COPY . /workspace/trtorch/src
-WORKDIR /workspace/trtorch/src
-RUN cp ./docker/WORKSPACE.cu.docker  WORKSPACE
+COPY . /workspace/torch_tensorrt/src
+WORKDIR /workspace/torch_tensorrt/src
+RUN cp ./docker/WORKSPACE.docker WORKSPACE
 
 # This script builds both libtrtorch bin/lib/include tarball and the Pythin wheel, in dist/
 RUN ./docker/dist-build.sh
 
-FROM base as trtorch
+FROM base as torch-tensorrt
 
 # copy source repo
-COPY . /workspace/trtorch
-COPY --from=trtorch-builder  /workspace/trtorch/src/dist/ .
-RUN patch -u /opt/conda/lib/python3.8/site-packages/pytorch_quantization/nn/modules/tensor_quantizer.py -i /workspace/trtorch/docker/qat.patch
+COPY . /workspace/torch_tensorrt
+COPY --from=torch-tensorrt-builder  /workspace/torch_tensorrt/src/dist/ .
+RUN patch -u /opt/conda/lib/python3.8/site-packages/pytorch_quantization/nn/modules/tensor_quantizer.py -i /workspace/torch_tensorrt/docker/qat.patch
 RUN conda init bash
 
 RUN pip3 install ipywidgets --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host=files.pythonhosted.org
 RUN jupyter nbextension enable --py widgetsnbextension
 
-RUN mkdir -p /opt/trtorch && tar xvf libtrtorch.tar.gz --strip-components 2 -C /opt/trtorch --exclude=LICENSE && pip3 install *.whl && rm -fr /workspace/trtorch/dist/*
+RUN pip3 install *.whl && rm -fr /workspace/torch_tensorrt/dist/* *.whl
 
-ENV LD_LIBRARY_PATH /opt/conda/lib/python3.8/site-packages/torch/lib:/opt/trtorch/lib:${LD_LIBRARY_PATH}
-ENV PATH /opt/trtorch/bin:${PATH}
+ENV LD_LIBRARY_PATH /opt/conda/lib/python3.8/site-packages/torch/lib:/opt/conda/lib/python3.8/site-packages/torch_tensorrt/lib:${LD_LIBRARY_PATH}
+ENV PATH /opt/conda/lib/python3.8/site-packages/torch_tensorrt/bin:${PATH}
+
+WORKDIR /workspace
+RUN mv /workspace/torch_tensorrt /opt/pytorch/torch_tensorrt
 
-WORKDIR /workspace/trtorch/
 CMD /bin/bash