diff --git a/.circleci/config.yml b/.circleci/config.yml index dcbc84cc9a..16dda8609f 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -435,6 +435,7 @@ commands: mkdir -p /tmp/artifacts/test_results cd tests/py pytest --junitxml=/tmp/artifacts/test_results/api/api_test_results.xml api/ + pytest --junitxml=/tmp/artifacts/test_results/models/models_test_results.xml models/ pytest --junitxml=/tmp/artifacts/test_results/integrations/integrations_test_results.xml integrations/ cd ~/project diff --git a/.github/workflows/docgen.yml b/.github/workflows/docgen.yml index 7b66b98be5..61af5bc5d9 100644 --- a/.github/workflows/docgen.yml +++ b/.github/workflows/docgen.yml @@ -31,7 +31,7 @@ jobs: - name: Set up Python 3.9.4 uses: actions/setup-python@v2 with: - python-version: 3.9.4 + python-version: 3.9.4 - uses: actions/checkout@v2 with: ref: ${{github.head_ref}} diff --git a/.github/workflows/linter.yml b/.github/workflows/linter.yml index 58c8440684..b56a233169 100644 --- a/.github/workflows/linter.yml +++ b/.github/workflows/linter.yml @@ -39,7 +39,7 @@ jobs: pip3 install -r $GITHUB_WORKSPACE/.github/scripts/requirements.txt pip3 install -r $GITHUB_WORKSPACE/requirements-dev.txt - name: Lint C++ - run: | + run: | cd $GITHUB_WORKSPACE python3 $GITHUB_WORKSPACE/.github/scripts/run_cpp_linter.py env: diff --git a/core/conversion/converters/impl/max.cpp b/core/conversion/converters/impl/max.cpp index 175cc75461..3ccf165bbe 100644 --- a/core/conversion/converters/impl/max.cpp +++ b/core/conversion/converters/impl/max.cpp @@ -13,47 +13,95 @@ namespace conversion { namespace converters { namespace impl { namespace { -auto max_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns().pattern( - {"aten::max.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)", - [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool { - auto self = args[0].ITensorOrFreeze(ctx); - auto dim = args[1].unwrapToInt(); - auto keep_dims = args[2].unwrapToBool(); - auto selfDim = util::toVec(self->getDimensions()); - if (dim < 0) { - dim = selfDim.size() + dim; - } - uint32_t shiftDim = 1 << dim; - auto TopKOperation = nvinfer1::TopKOperation::kMAX; - auto topk_layer = ctx->net->addTopK(*self, TopKOperation, 1, shiftDim); - TORCHTRT_CHECK(topk_layer, "Unable to create max layer from node: " << *n); - auto topk_dims = util::toVec(topk_layer->getOutput(0)->getDimensions()); - - nvinfer1::ITensor* out0 = nullptr; - nvinfer1::ITensor* out1 = nullptr; - if (!keep_dims) { - if (topk_dims[dim] == 1) { - auto squeeze_layer = ctx->net->addShuffle(*topk_layer->getOutput(0)); - squeeze_layer->setReshapeDimensions(util::squeezeDims(topk_layer->getOutput(0)->getDimensions(), dim)); - TORCHTRT_CHECK(squeeze_layer, "Unable to create squeeze_layer layer from node: " << *n); - out0 = ctx->AssociateValueAndTensor(n->outputs()[0], squeeze_layer->getOutput(0)); - - auto squeeze_layer_indices = ctx->net->addShuffle(*topk_layer->getOutput(1)); - squeeze_layer_indices->setReshapeDimensions( - util::squeezeDims(topk_layer->getOutput(1)->getDimensions(), dim)); - TORCHTRT_CHECK(squeeze_layer_indices, "Unable to create squeeze_layer_indices layer from node: " << *n); - out1 = ctx->AssociateValueAndTensor(n->outputs()[1], squeeze_layer_indices->getOutput(0)); - } - } else { - out0 = ctx->AssociateValueAndTensor(n->outputs()[0], topk_layer->getOutput(0)); - out1 = ctx->AssociateValueAndTensor(n->outputs()[1], topk_layer->getOutput(1)); - } - - LOG_DEBUG("Output tensor(0) shape: " << out0->getDimensions()); - LOG_DEBUG("Output tensor(1) shape: " << out1->getDimensions()); - - return true; - }}); + +bool min_max_dim(ConversionCtx* ctx, const torch::jit::Node* n, args& args, nvinfer1::TopKOperation topKOperation) { + auto self = args[0].ITensorOrFreeze(ctx); + auto dim = args[1].unwrapToInt(); + auto keep_dims = args[2].unwrapToBool(); + auto selfDim = util::toVec(self->getDimensions()); + if (dim < 0) { + dim = selfDim.size() + dim; + } + uint32_t reduce_axes_mask = 1 << dim; + auto topk_layer = ctx->net->addTopK(*self, topKOperation, 1, reduce_axes_mask); + TORCHTRT_CHECK(topk_layer, "Unable to create topk layer from node: " << *n); + auto topk_dims = util::toVec(topk_layer->getOutput(0)->getDimensions()); + + nvinfer1::ITensor* out0 = nullptr; + nvinfer1::ITensor* out1 = nullptr; + if (!keep_dims) { + TORCHTRT_CHECK(topk_dims[dim] == 1, "Unexpected size in squeeze dimension. Expected: 1 Actual: " << topk_dims[dim]); + auto squeeze_layer = ctx->net->addShuffle(*topk_layer->getOutput(0)); + squeeze_layer->setReshapeDimensions(util::squeezeDims(topk_layer->getOutput(0)->getDimensions(), dim)); + TORCHTRT_CHECK(squeeze_layer, "Unable to create squeeze_layer layer from node: " << *n); + out0 = ctx->AssociateValueAndTensor(n->outputs()[0], squeeze_layer->getOutput(0)); + + auto squeeze_layer_indices = ctx->net->addShuffle(*topk_layer->getOutput(1)); + squeeze_layer_indices->setReshapeDimensions(util::squeezeDims(topk_layer->getOutput(1)->getDimensions(), dim)); + TORCHTRT_CHECK(squeeze_layer_indices, "Unable to create squeeze_layer_indices layer from node: " << *n); + out1 = ctx->AssociateValueAndTensor(n->outputs()[1], squeeze_layer_indices->getOutput(0)); + } else { + out0 = ctx->AssociateValueAndTensor(n->outputs()[0], topk_layer->getOutput(0)); + out1 = ctx->AssociateValueAndTensor(n->outputs()[1], topk_layer->getOutput(1)); + } + + LOG_DEBUG("Output tensor(0) shape: " << out0->getDimensions()); + LOG_DEBUG("Output tensor(1) shape: " << out1->getDimensions()); + + return true; +} + +bool arg_min_max(ConversionCtx* ctx, const torch::jit::Node* n, args& args, nvinfer1::TopKOperation topKOperation) { + auto self = args[0].ITensorOrFreeze(ctx); + auto dim = args[1].unwrapToInt(); + auto keep_dims = args[2].unwrapToBool(); + auto selfDim = util::toVec(self->getDimensions()); + if (dim < 0) { + dim = selfDim.size() + dim; + } + uint32_t reduce_axes_mask = 1 << dim; + auto topk_layer = ctx->net->addTopK(*self, topKOperation, 1, reduce_axes_mask); + TORCHTRT_CHECK(topk_layer, "Unable to create topk layer from node: " << *n); + auto topk_dims = util::toVec(topk_layer->getOutput(0)->getDimensions()); + + nvinfer1::ITensor* out = nullptr; + if (!keep_dims) { + TORCHTRT_CHECK(topk_dims[dim] == 1, "Unexpected size in squeeze dimension. Expected: 1 Actual: " << topk_dims[dim]); + auto squeeze_layer_indices = ctx->net->addShuffle(*topk_layer->getOutput(1)); + squeeze_layer_indices->setReshapeDimensions(util::squeezeDims(topk_layer->getOutput(1)->getDimensions(), dim)); + TORCHTRT_CHECK(squeeze_layer_indices, "Unable to create squeeze_layer_indices layer from node: " << *n); + out = ctx->AssociateValueAndTensor(n->outputs()[0], squeeze_layer_indices->getOutput(0)); + } else { + out = ctx->AssociateValueAndTensor(n->outputs()[0], topk_layer->getOutput(1)); + } + + LOG_DEBUG("Output tensor shape: " << out->getDimensions()); + + return true; +} + +auto max_registrations TORCHTRT_UNUSED = + RegisterNodeConversionPatterns() + .pattern( + {"aten::max.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)", + [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool { + return min_max_dim(ctx, n, args, nvinfer1::TopKOperation::kMAX); + }}) + .pattern( + {"aten::min.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)", + [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool { + return min_max_dim(ctx, n, args, nvinfer1::TopKOperation::kMIN); + }}) + .pattern( + {"aten::argmax(Tensor self, int dim, bool keepdim=False) -> (Tensor)", + [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool { + return arg_min_max(ctx, n, args, nvinfer1::TopKOperation::kMAX); + }}) + .pattern( + {"aten::argmin(Tensor self, int dim, bool keepdim=False) -> (Tensor)", + [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool { + return arg_min_max(ctx, n, args, nvinfer1::TopKOperation::kMIN); + }}); } // namespace } // namespace impl } // namespace converters diff --git a/core/conversion/converters/impl/unary.cpp b/core/conversion/converters/impl/unary.cpp index fa4e88fa5e..c78602963c 100644 --- a/core/conversion/converters/impl/unary.cpp +++ b/core/conversion/converters/impl/unary.cpp @@ -49,6 +49,21 @@ auto abs_registration TORCHTRT_UNUSED = RegisterNodeConversionPatterns().pattern } }}); +auto reciprocal_registration TORCHTRT_UNUSED = RegisterNodeConversionPatterns().pattern( + {"aten::reciprocal(Tensor self) -> Tensor", [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool { + auto in = args[0].ITensorOrFreeze(ctx); + if (in->getType() == nvinfer1::DataType::kINT32) { + // pytorch implicitly casts to float for aten::reciprocal(int) + in = castITensor(ctx, in, nvinfer1::DataType::kFLOAT); + } + auto unary_layer = ctx->net->addUnary(*in, nvinfer1::UnaryOperation::kRECIP); + TORCHTRT_CHECK(unary_layer, "Unable to create recip layer from node: " << *n); + unary_layer->setName(util::node_info(n).c_str()); + auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], unary_layer->getOutput(0)); + LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions()); + return true; + }}); + #define convert(unary, trt_type) \ auto unary##_registrations TORCHTRT_UNUSED = RegisterNodeConversionPatterns().pattern( \ {"aten::" #unary "(Tensor self) -> Tensor", \ @@ -74,7 +89,6 @@ convert(sinh, kSINH); convert(tan, kTAN); convert(atan, kATAN); convert(floor, kFLOOR); -convert(reciprocal, kRECIP); convert(log, kLOG); convert(ceil, kCEIL); convert(sqrt, kSQRT); diff --git a/core/partitioning/shape_analysis.cpp b/core/partitioning/shape_analysis.cpp index 7a36529949..f940c87751 100644 --- a/core/partitioning/shape_analysis.cpp +++ b/core/partitioning/shape_analysis.cpp @@ -167,7 +167,7 @@ void getSegmentsOutputByRunning( } if (cur_ivalue.toTensor().sizes().size() == 0) { // handle Scalar types, which has sizes of [] - input_shapes.push_back(util::toVec(util::toDims(c10::List({1})))); + input_shapes.push_back(util::toVec(util::toDims(c10::List({1})))); } else { input_shapes.push_back(util::toVec(util::toDims(cur_ivalue.toTensor().sizes()))); } diff --git a/cpp/bin/torchtrtc/main.cpp b/cpp/bin/torchtrtc/main.cpp index f98ed848de..bc3d5d4af0 100644 --- a/cpp/bin/torchtrtc/main.cpp +++ b/cpp/bin/torchtrtc/main.cpp @@ -35,7 +35,7 @@ bool unload_library(void* custom_lib) { bool success = false; #if defined(_WIN32) // Returns status non-zero for success - success = FreeLibrary(custom_lib) ? true : false; + success = FreeLibrary((HMODULE)custom_lib) ? true : false; #else success = dlclose(custom_lib) ? false : true; #endif diff --git a/cpp/include/torch_tensorrt/torch_tensorrt.h b/cpp/include/torch_tensorrt/torch_tensorrt.h index 45497a13a3..80db25e8f9 100644 --- a/cpp/include/torch_tensorrt/torch_tensorrt.h +++ b/cpp/include/torch_tensorrt/torch_tensorrt.h @@ -365,7 +365,7 @@ class TensorFormat { * signifying a static input shape or a set of three input shapes representing * the min, optiminal and max input shapes allowed for the engine. */ -struct TORCHTRT_API Input : torch::CustomClassHolder { +struct Input : torch::CustomClassHolder { /// Minimum acceptable input size into the engine std::vector min_shape; /// Optimal input size into the engine (size optimized for given kernels accept any size in min max range) @@ -520,7 +520,7 @@ struct TORCHTRT_API Input : torch::CustomClassHolder { * * This struct can either hold a complex inputs of shape or a flattened one, */ -struct TORCHTRT_API GraphInputs { +struct GraphInputs { torch::jit::IValue input_signature; // nested Input, full input spec std::vector inputs; // flatten input spec }; @@ -592,14 +592,14 @@ struct CompileSpec { * * @param inputs */ - CompileSpec(std::vector inputs); + TORCHTRT_API CompileSpec(std::vector inputs); /** * @brief Construct a new Compile Spec object from IValue which represents the nesting of input tensors for a module. * * @param input_signature */ - CompileSpec(torch::jit::IValue input_signature); + TORCHTRT_API CompileSpec(torch::jit::IValue input_signature); // Defaults should reflect TensorRT defaults for BuilderConfig /** diff --git a/docs/_cpp_api/classtorch__tensorrt_1_1DataType.html b/docs/_cpp_api/classtorch__tensorrt_1_1DataType.html index c5baedfa0a..63c732bd65 100644 --- a/docs/_cpp_api/classtorch__tensorrt_1_1DataType.html +++ b/docs/_cpp_api/classtorch__tensorrt_1_1DataType.html @@ -199,7 +199,7 @@
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@@ -508,7 +508,7 @@ Value value; }; -struct TORCHTRT_API Input : torch::CustomClassHolder { +struct Input : torch::CustomClassHolder { std::vector<int64_t> min_shape; std::vector<int64_t> opt_shape; std::vector<int64_t> max_shape; @@ -558,7 +558,7 @@ bool input_is_dynamic; }; -struct TORCHTRT_API GraphInputs { +struct GraphInputs { torch::jit::IValue input_signature; // nested Input, full input spec std::vector<Input> inputs; // flatten input spec }; @@ -575,9 +575,9 @@ TORCHTRT_API CompileSpec(std::vector<c10::ArrayRef<int64_t>> fixed_sizes); - CompileSpec(std::vector<Input> inputs); + TORCHTRT_API CompileSpec(std::vector<Input> inputs); - CompileSpec(torch::jit::IValue input_signature); + TORCHTRT_API CompileSpec(torch::jit::IValue input_signature); // Defaults should reflect TensorRT defaults for BuilderConfig GraphInputs graph_inputs; diff --git a/docs/_cpp_api/structtorch__tensorrt_1_1Device.html b/docs/_cpp_api/structtorch__tensorrt_1_1Device.html index ca9d929ab5..05ebff4d28 100644 --- a/docs/_cpp_api/structtorch__tensorrt_1_1Device.html +++ b/docs/_cpp_api/structtorch__tensorrt_1_1Device.html @@ -199,7 +199,7 @@
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@@ -388,7 +388,7 @@

Struct Documentation
-CompileSpec(std::vector<Input> inputs)
+TORCHTRT_API CompileSpec(std::vector<Input> inputs)

Construct a new Compile Spec object from input ranges. Each entry in the vector represents a input and should be provided in call order.

Use this constructor to define inputs with dynamic shape, specific input types or tensor formats

@@ -400,7 +400,7 @@

Struct Documentation
-CompileSpec(torch::jit::IValue input_signature)
+TORCHTRT_API CompileSpec(torch::jit::IValue input_signature)

Construct a new Compile Spec object from IValue which represents the nesting of input tensors for a module.

Parameters
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Source code for torch_tensorrt.ptq

     if self.current_batch_idx + self.batch_size > len(self.data_loader.dataset):
         return None
 
-    batch = self.dataset_iterator.next()
+    batch = next(self.dataset_iterator)
     self.current_batch_idx += self.batch_size
     inputs_gpu = []
     if isinstance(batch, list):
@@ -401,6 +401,13 @@ 
Source code for torch_tensorrt.ptq
         return b""

 
 
+# deepcopy (which involves pickling) is performed on the compile_spec internally during compilation.
+# We register this __reduce__ function for pickler to identity the calibrator object returned by DataLoaderCalibrator during deepcopy.
+# This should be the object's local name relative to the module https://docs.python.org/3/library/pickle.html#object.__reduce__
+def __reduce__(self):
+    return self.__class__.__name__
+
+
 
[docs]class DataLoaderCalibrator(object):
     """
     Constructs a calibrator class in TensorRT and uses pytorch dataloader to load/preproces
@@ -459,24 +466,27 @@ 
Source code for torch_tensorrt.ptq
             "get_batch": get_cache_mode_batch if use_cache else get_batch,
             "read_calibration_cache": read_calibration_cache,
             "write_calibration_cache": write_calibration_cache,
+            "__reduce__": __reduce__,  # used when you deepcopy the DataLoaderCalibrator object
         }
 
         # Using type metaclass to construct calibrator class based on algorithm type
         if algo_type == CalibrationAlgo.ENTROPY_CALIBRATION:
             return type(
-                "DataLoaderCalibrator", (_C.IInt8EntropyCalibrator,), attribute_mapping
+                "Int8EntropyCalibrator", (_C.IInt8EntropyCalibrator,), attribute_mapping
             )()
         elif algo_type == CalibrationAlgo.ENTROPY_CALIBRATION_2:
             return type(
-                "DataLoaderCalibrator", (_C.IInt8MinMaxCalibrator,), attribute_mapping
+                "Int8EntropyCalibrator2",
+                (_C.IInt8EntropyCalibrator2,),
+                attribute_mapping,
             )()
         elif algo_type == CalibrationAlgo.LEGACY_CALIBRATION:
             return type(
-                "DataLoaderCalibrator", (_C.IInt8LegacyCalibrator,), attribute_mapping
+                "Int8LegacyCalibrator", (_C.IInt8LegacyCalibrator,), attribute_mapping
             )()
         elif algo_type == CalibrationAlgo.MINMAX_CALIBRATION:
             return type(
-                "DataLoaderCalibrator", (_C.IInt8MinMaxCalibrator,), attribute_mapping
+                "Int8MinMaxCalibrator", (_C.IInt8MinMaxCalibrator,), attribute_mapping
             )()
         else:
             log(
diff --git a/docs/_modules/torch_tensorrt/ts/_compile_spec.html b/docs/_modules/torch_tensorrt/ts/_compile_spec.html
index f873b32923..640aafdaca 100644
--- a/docs/_modules/torch_tensorrt/ts/_compile_spec.html
+++ b/docs/_modules/torch_tensorrt/ts/_compile_spec.html
@@ -196,7 +196,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
@@ -570,7 +570,7 @@ 
Source code for torch_tensorrt.ts._compile_spec
<
 
 
 def _parse_compile_spec(compile_spec_: Dict[str, Any]) -> _ts_C.CompileSpec:
-    # TODO: Remove deep copy once collections does not need partial compilation
+    # TODO: Use deepcopy to support partial compilation of collections
     compile_spec = deepcopy(compile_spec_)
     info = _ts_C.CompileSpec()
 
@@ -646,7 +646,7 @@ 
Source code for torch_tensorrt.ts._compile_spec
<
             compile_spec["enabled_precisions"]
         )
 
-    if "calibrator" in compile_spec:
+    if "calibrator" in compile_spec and compile_spec["calibrator"]:
         info.ptq_calibrator = compile_spec["calibrator"]
 
     if "sparse_weights" in compile_spec:
diff --git a/docs/_modules/torch_tensorrt/ts/_compiler.html b/docs/_modules/torch_tensorrt/ts/_compiler.html
index ba86b16c3d..b636c58d37 100644
--- a/docs/_modules/torch_tensorrt/ts/_compiler.html
+++ b/docs/_modules/torch_tensorrt/ts/_compiler.html
@@ -196,7 +196,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_torch_tensorrt.h.rst.txt b/docs/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_torch_tensorrt.h.rst.txt
index 7e00b9350a..2ba864f7cd 100644
--- a/docs/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_torch_tensorrt.h.rst.txt
+++ b/docs/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_torch_tensorrt.h.rst.txt
@@ -170,7 +170,7 @@ Program Listing for File torch_tensorrt.h
      Value value;
    };
    
-   struct TORCHTRT_API Input : torch::CustomClassHolder {
+   struct Input : torch::CustomClassHolder {
      std::vector min_shape;
      std::vector opt_shape;
      std::vector max_shape;
@@ -220,7 +220,7 @@ Program Listing for File torch_tensorrt.h
      bool input_is_dynamic;
    };
    
-   struct TORCHTRT_API GraphInputs {
+   struct GraphInputs {
      torch::jit::IValue input_signature; // nested Input, full input spec
      std::vector inputs; // flatten input spec
    };
@@ -237,9 +237,9 @@ Program Listing for File torch_tensorrt.h
    
      TORCHTRT_API CompileSpec(std::vector> fixed_sizes);
    
-     CompileSpec(std::vector inputs);
+     TORCHTRT_API CompileSpec(std::vector inputs);
    
-     CompileSpec(torch::jit::IValue input_signature);
+     TORCHTRT_API CompileSpec(torch::jit::IValue input_signature);
      // Defaults should reflect TensorRT defaults for BuilderConfig
    
      GraphInputs graph_inputs;
diff --git a/docs/_sources/getting_started/getting_started_with_windows.rst.txt b/docs/_sources/getting_started/getting_started_with_windows.rst.txt
new file mode 100644
index 0000000000..d5d3394855
--- /dev/null
+++ b/docs/_sources/getting_started/getting_started_with_windows.rst.txt
@@ -0,0 +1,78 @@
+.. _getting_started_windows:
+
+Building Torch-TensorRT on Windows
+====================================
+
+Torch-TensorRT has community support for Windows platform using CMake
+
+Prerequisite:
+
+* Microsoft Visual Studio
+* LibTorch
+* TensorRT
+* CUDA
+* cuDNN
+
+
+Build configuration
+-------------------
+
+* Open Microsoft Visual Studio
+* Open Torch-TensorRT source code folder
+* Open Manage configurations -> Edit JSON to open CMakeSettings.json file.
+* Configure the CMake build configurations. Following is an example configuration:
+
+.. code-block:: none
+
+    {
+      "configurations": [
+        {
+          "name": "x64-Debug",
+          "generator": "Ninja",
+          "configurationType": "Debug",
+          "inheritEnvironments": [ "msvc_x64_x64" ],
+          "buildRoot": "${projectDir}\\out\\build\\${name}",
+          "installRoot": "${projectDir}\\out\\install\\${name}",
+          "cmakeCommandArgs": "-S . -B out",
+          "buildCommandArgs": "cmake --build out",
+          "ctestCommandArgs": "",
+          "variables": [
+            {
+              "name": "CMAKE_MODULE_PATH",
+              "value": "$PWD\cmake\Modules",
+              "type": "FILEPATH"
+            },
+            {
+              "name": "Torch_DIR",
+              "value": "\share\cmake\Torch",
+              "type": "FILEPATH"
+            },
+            {
+              "name": "TensorRT_ROOT",
+              "value": "",
+              "type": "FILEPATH"
+            },
+            {
+              "name": "CMAKE_BUILD_TYPE",
+              "value": "Release",
+              "type": " STRING"
+            }
+          ]
+        }
+      ]  
+    }
+
+
+Compilation
+-----------
+
+* Click Build -> Build All or directly press Ctrl + Shift + B
+
+Note: After successful compilation, the build artifacts will be present at buildRoot path configured.
+
+Installation
+------------
+
+* Build -> Install Torch-TensorRT
+
+Note: After successful installation, the artifacts will be present at installRoot.
\ No newline at end of file
diff --git a/docs/cli/torchtrtc.html b/docs/cli/torchtrtc.html
index e16f9bd1f6..3eb5c95bed 100644
--- a/docs/cli/torchtrtc.html
+++ b/docs/cli/torchtrtc.html
@@ -199,7 +199,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/contributors/conversion.html b/docs/contributors/conversion.html
index c0c13ea1c6..a16bc6757d 100644
--- a/docs/contributors/conversion.html
+++ b/docs/contributors/conversion.html
@@ -199,7 +199,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/contributors/lowering.html b/docs/contributors/lowering.html
index 2d9a812750..b7ef6ff5f2 100644
--- a/docs/contributors/lowering.html
+++ b/docs/contributors/lowering.html
@@ -199,7 +199,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/contributors/partitioning.html b/docs/contributors/partitioning.html
index f524e76918..9ba9d118a8 100644
--- a/docs/contributors/partitioning.html
+++ b/docs/contributors/partitioning.html
@@ -199,7 +199,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/contributors/phases.html b/docs/contributors/phases.html
index 2a93a59124..56af2f093a 100644
--- a/docs/contributors/phases.html
+++ b/docs/contributors/phases.html
@@ -197,7 +197,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/contributors/runtime.html b/docs/contributors/runtime.html
index 08b4da78ee..3d716472ed 100644
--- a/docs/contributors/runtime.html
+++ b/docs/contributors/runtime.html
@@ -199,7 +199,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/contributors/system_overview.html b/docs/contributors/system_overview.html
index 0601b9307b..e4c49fa61b 100644
--- a/docs/contributors/system_overview.html
+++ b/docs/contributors/system_overview.html
@@ -199,7 +199,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/contributors/useful_links.html b/docs/contributors/useful_links.html
index 57cd2fe3cc..5c5565af99 100644
--- a/docs/contributors/useful_links.html
+++ b/docs/contributors/useful_links.html
@@ -199,7 +199,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/contributors/writing_converters.html b/docs/contributors/writing_converters.html
index 956ec8702e..28877f7466 100644
--- a/docs/contributors/writing_converters.html
+++ b/docs/contributors/writing_converters.html
@@ -199,7 +199,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/genindex.html b/docs/genindex.html
index 7af5bb2ab6..6c13b858c1 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -196,7 +196,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/getting_started/getting_started_with_cpp_api.html b/docs/getting_started/getting_started_with_cpp_api.html
index fcce40782e..1c7d35be3b 100644
--- a/docs/getting_started/getting_started_with_cpp_api.html
+++ b/docs/getting_started/getting_started_with_cpp_api.html
@@ -199,7 +199,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/getting_started/getting_started_with_python_api.html b/docs/getting_started/getting_started_with_python_api.html
index effd46eabc..d74eeb76b4 100644
--- a/docs/getting_started/getting_started_with_python_api.html
+++ b/docs/getting_started/getting_started_with_python_api.html
@@ -199,7 +199,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/getting_started/getting_started_with_windows.html b/docs/getting_started/getting_started_with_windows.html
new file mode 100644
index 0000000000..c35e9ac2b8
--- /dev/null
+++ b/docs/getting_started/getting_started_with_windows.html
@@ -0,0 +1,708 @@
+
+
+
+
+
+  
+
+  
+  
+
+  
+  
+  Building Torch-TensorRT on Windows — Torch-TensorRT master documentation
+  
+
+  
+  
+  
+  
+
+  
+
+  
+  
+    
+
+  
+
+  
+  
+  
+  
+  
+    
+    
+  
+  
+  
+  
+
+  
+  
+
+  
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  
+
+
+
+
+
+
+   
+
+    
+
+    

+      Table of Contents
+      
+    

+
+    
+
+    

+      

+        

+          
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

+
+  
    
+    
+      
  • 
+        
+          
+            Docs
+          
+         >
+      
    • 
+
+        
+      
  • Building Torch-TensorRT on Windows
    • 
+    
+    
+      
  • 
+        
+            
+            
+          
+        
+      
    • 
+    
+  

+
+  
+

+        

+
+        

+          Shortcuts
+        

+      

+
+      

+        

+
+        
+          
+          

+          
+            

+             

+              
+  

+
Building Torch-TensorRT on Windows

+
Torch-TensorRT has community support for Windows platform using CMake

+
Prerequisite:

+
    
+
  • Microsoft Visual Studio
    • 
+
  • LibTorch
    • 
+
  • TensorRT
    • 
+
  • CUDA
    • 
+
  • cuDNN
    • 
+

+

+
Build configuration

+
    
+
  • Open Microsoft Visual Studio
    • 
+
  • Open Torch-TensorRT source code folder
    • 
+
  • Open Manage configurations -> Edit JSON to open CMakeSettings.json file.
    • 
+
  • Configure the CMake build configurations. Following is an example configuration:
    • 
+

+
{
+  "configurations": [
+    {
+      "name": "x64-Debug",
+      "generator": "Ninja",
+      "configurationType": "Debug",
+      "inheritEnvironments": [ "msvc_x64_x64" ],
+      "buildRoot": "${projectDir}\\out\\build\\${name}",
+      "installRoot": "${projectDir}\\out\\install\\${name}",
+      "cmakeCommandArgs": "-S . -B out",
+      "buildCommandArgs": "cmake --build out",
+      "ctestCommandArgs": "",
+      "variables": [
+        {
+          "name": "CMAKE_MODULE_PATH",
+          "value": "$PWD\cmake\Modules",
+          "type": "FILEPATH"
+        },
+        {
+          "name": "Torch_DIR",
+          "value": "<Path to libtorch>\share\cmake\Torch",
+          "type": "FILEPATH"
+        },
+        {
+          "name": "TensorRT_ROOT",
+          "value": "<Path to TensorRT directory>",
+          "type": "FILEPATH"
+        },
+        {
+          "name": "CMAKE_BUILD_TYPE",
+          "value": "Release",
+          "type": " STRING"
+        }
+      ]
+    }
+  ]
+}
+

+

+

+

+
Compilation

+
    
+
  • Click Build -> Build All or directly press Ctrl + Shift + B
    • 
+

+
Note: After successful compilation, the build artifacts will be present at buildRoot path configured.

+

+

+
Installation

+
    
+
  • Build -> Install Torch-TensorRT
    • 
+

+
Note: After successful installation, the artifacts will be present at installRoot.

+

+

+
+
+             

+             
+            

+            

+  
+
+  
+
+    

+
+  
+
+  

+    

+        © Copyright 2021, NVIDIA Corporation.
+
+    

+  

+    
+      

+        Built with Sphinx using a theme provided by Read the Docs.
+      

+     
+
+

+
+          

+        

+
+        

+          
+        

+      

+    

+
+  
+
+
+  
+
+     
+       
+         
+         
+         
+         
+         
+         
+         
+     
+
+  
+
+  
+  
+  
+  
+
+   
+
+  
+
+  

+    

+      

+        

+          
Docs

+          
Access comprehensive developer documentation for PyTorch

+          View Docs
+        

+
+        

+          
Tutorials

+          
Get in-depth tutorials for beginners and advanced developers

+          View Tutorials
+        

+
+        

+          
Resources

+          
Find development resources and get your questions answered

+          View Resources
+        

+      

+    

+  

+
+  
+
+  

+  

+    
To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: Cookies Policy.

+    
+  

+

+
+  
+
+  
+
+  

+    

+      

+        

+          
+          
+        

+      

+    

+
+    

+      

+        
+      

+    

+  

+
+  
+
+  
+
+  
+
+
\ No newline at end of file
diff --git a/docs/getting_started/installation.html b/docs/getting_started/installation.html
index a51adc6deb..f15ad10b22 100644
--- a/docs/getting_started/installation.html
+++ b/docs/getting_started/installation.html
@@ -199,7 +199,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/index.html b/docs/index.html
index 81c151175f..640f27ce58 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -198,7 +198,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/indices/supported_ops.html b/docs/indices/supported_ops.html
index d230472422..da28244355 100644
--- a/docs/indices/supported_ops.html
+++ b/docs/indices/supported_ops.html
@@ -198,7 +198,7 @@
               
               
                 

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+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/objects.inv b/docs/objects.inv
index 71491c9fdf..53f8d22cd4 100644
Binary files a/docs/objects.inv and b/docs/objects.inv differ
diff --git a/docs/py-modindex.html b/docs/py-modindex.html
index 226e8ddf92..f1fbf929a6 100644
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+++ b/docs/py-modindex.html
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diff --git a/docs/py_api/fx.html b/docs/py_api/fx.html
index ef36006a65..f1815325f1 100644
--- a/docs/py_api/fx.html
+++ b/docs/py_api/fx.html
@@ -199,7 +199,7 @@
               
               
                 

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diff --git a/docs/py_api/logging.html b/docs/py_api/logging.html
index e7f157b23b..521d08972d 100644
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+++ b/docs/py_api/logging.html
@@ -199,7 +199,7 @@
               
               
                 

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+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/py_api/ptq.html b/docs/py_api/ptq.html
index e2a4f71ded..44c01e8bd6 100644
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+++ b/docs/py_api/ptq.html
@@ -199,7 +199,7 @@
               
               
                 

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diff --git a/docs/py_api/torch_tensorrt.html b/docs/py_api/torch_tensorrt.html
index c5428f6c8e..9d7aec6fdb 100644
--- a/docs/py_api/torch_tensorrt.html
+++ b/docs/py_api/torch_tensorrt.html
@@ -199,7 +199,7 @@
               
               
                 

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diff --git a/docs/py_api/ts.html b/docs/py_api/ts.html
index 02b8bbe6ed..a67ad2a27c 100644
--- a/docs/py_api/ts.html
+++ b/docs/py_api/ts.html
@@ -199,7 +199,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
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@@ -567,7 +567,7 @@ 
Functions
 

-torch_tensorrt.ts.TensorRTCompileSpec(inputs=[], input_signature=None, device=None, disable_tf32=False, sparse_weights=False, enabled_precisions={}, refit=False, debug=False, capability=<EngineCapability.default: 0>, num_avg_timing_iters=1, workspace_size=0, dla_sram_size=1048576, dla_local_dram_size=1073741824, dla_global_dram_size=536870912, truncate_long_and_double=False, calibrator=None) → <torch.ScriptClass object at 0x7f89ba6d5eb0>[source]

+torch_tensorrt.ts.TensorRTCompileSpec(inputs=[], input_signature=None, device=None, disable_tf32=False, sparse_weights=False, enabled_precisions={}, refit=False, debug=False, capability=<EngineCapability.default: 0>, num_avg_timing_iters=1, workspace_size=0, dla_sram_size=1048576, dla_local_dram_size=1073741824, dla_global_dram_size=536870912, truncate_long_and_double=False, calibrator=None) → <torch.ScriptClass object at 0x7f9bda9065f0>[source]
 
Utility to create a formated spec dictionary for using the PyTorch TensorRT backend

 

 
Keyword Arguments

diff --git a/docs/search.html b/docs/search.html
index c5405377d8..2d05d15033 100644
--- a/docs/search.html
+++ b/docs/search.html
@@ -196,7 +196,7 @@
               
               
                 

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diff --git a/docs/searchindex.js b/docs/searchindex.js
index 6125435bfd..9506669f2c 100644
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PyTorch","DLA"],titleterms:{"1":[77,86],"10":77,"11":77,"12":77,"13":77,"14":77,"15":77,"16":77,"17":77,"18":77,"19":77,"2":[77,78,86],"20":77,"3":[77,86],"4":77,"5":77,"6":77,"7":77,"8":77,"9":77,"class":[0,1,2,3,4,20,21,38,40,41,50,67,69,70],"enum":[16,17,18,21,38,39,50,69,70],"function":[18,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,50,59,67,70,71],"long":[77,79],A:75,And:75,But:76,By:[18,19],Or:54,The:[61,75],To:54,aarch64:64,abi:[57,64],acc:82,acceler:83,add:82,addmm:54,admonit:75,advic:60,ahead:65,an:79,api:[50,51,59,64,65],applic:84,arg:[60,74],automat:55,avail:59,awar:83,background:[57,60],base:[3,4,48,73],bert:83,binari:64,block:75,branch:54,build:[63,64,73,86],bullet:76,c:[50,59,61,64,65,83,84],can:76,caption:[76,79],center:75,ch:75,changelog:72,check_method_operator_support:31,choos:64,citat:[75,84],citrinet:83,cli:[64,65],client:86,cmake:64,code:[54,75],compil:[32,56,58,61,63,64,65,83],compilespec:49,compound:75,configur:[63,73],construct:57,content:[18,19,20,21,38,39,40,41,73,74,75,76,77,78],context:[60,73],contigu:54,contract:60,contributor:65,convers:[53,56,58,60],convert:[53,60,61,66,82],convert_method_to_trt_engin:37,cpp:[13,18,19,20,21,55],creat:[81,84],creativ:75,cudnn:64,current:66,custom:61,cxx11:64,data:74,datatyp:0,dead:54,debug:64,deep:83,deeper:76,defin:[5,6,7,8,9,10,11,12,19,50],definit:[18,19,20,21,76],demo:79,depend:64,deploi:[83,85],deseri:57,detect:83,develop:59,devic:[1,46],devicetyp:1,dimens:59,direct:75,directli:87,directori:[13,14,15,51],disk:81,distribut:64,dla:88,doctest:75,documen:65,document:[0,1,2,3,4,5,6,7,8,9,10,11,12,16,17,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,46,47,48,49,59,65,78,79],down:76,download:[75,80],dropout:54,dump_build_info:36,dynam:83,easier:59,efficentnet:83,element:78,elimin:54,eliminatecommonsubexpress:54,embed_engine_in_new_modul:33,emphas:75,engin:[57,82],enginecap:17,enumer:76,envior:64,evalu:[53,66],exampl:[75,77,83],execept:54,executor:57,expect:59,face:83,fallback:[54,55],field:76,figur:75,file:[15,18,19,20,21,42,43,44,45,50,51],flatten:54,footnot:75,format:57,freez:54,from:[64,87],frontend:[82,83],full:[50,51],fuse:54,fx2trt:82,fx:[67,82,83],gaurd:54,gener:74,get:65,get_build_info:34,get_is_colored_output_on:24,get_logging_prefix:22,get_reportable_log_level:23,giant:76,git:80,glossari:75,gpu:65,graph:[54,57],graphinput:47,grid:76,guarante:60,guid:82,h:[18,19,20,21,42,43,44,45,55],have:76,hierarchi:50,hlist:76,hole:76,hood:61,how:[73,82,84],html:73,hug:83,ien:75,imag:[75,76],includ:[14,18,19,20,21],incred:79,index:74,indic:65,infer:86,inherit:[3,4,48],inlin:75,input:48,instal:[63,64,80],int8:83,int8cachecalibr:3,int8calibr:4,ir:59,jetson:64,jit:65,languag:83,layer:59,learn:83,lenet:83,level:[16,73,75,76],librari:[64,85],libtorchtrt:85,like:76,line:75,linear:54,link:[59,75],list:[42,43,44,45,76],liter:75,local:64,log:[18,22,23,24,25,26,27,28,39,42,68],logsoftmax:54,loop:54,lower:[54,56,58],macro:[19,43],make_int8_cache_calibr:29,make_int8_calibr:30,markup:75,mask:83,math:75,menu:[77,79],meta:75,miss:82,mlm:83,mod:74,model:[82,83,86],modul:[54,61,81],namespac:[18,19,20,21,38,39,40,41,50],nativ:64,native_op:59,nav:77,nest:[1,46],node:53,notebook:83,number:[75,76],nvidia:65,object:83,one:76,op:[57,82],oper:[61,66],optim:86,optimz:54,option:[73,74,76],other:60,overview:58,own:84,packag:[64,85],page:73,paragraph:[75,78],paramet:74,partit:[55,56,58],partitoninfo:55,pass:54,pattern:54,peephol:54,phase:[53,54,55,56,57,58],plugin:85,post:84,pre:64,precompil:64,prerequisit:64,program:[42,43,44,45,85],project:73,ptq:[20,29,30,40,44,69,84],python:[59,62,64,65,81,84],pytorch:[59,65,82,83,87],quantiz:[83,84],queri:86,quickstart:61,quot:75,rabbit:76,read:59,redund:54,refer:75,regist:61,relationship:[1,3,4,46,48],releas:64,remov:54,replac:75,resnet50:83,respons:60,result:57,right:64,rubric:75,runtim:[56,57,58,85],save:81,second:76,section:78,segmentedblock:55,serial:57,serv:[83,86],server:86,set:86,set_devic:35,set_is_colored_output_on:27,set_logging_prefix:28,set_reportable_log_level:25,setup:64,shape:83,shape_analysi:55,sidebar:75,so:85,sometim:59,sourc:64,ssd:83,start:65,step:86,sticki:77,str:5,struct:[46,47,48,49,50],structur:78,subdirectori:[13,14],submenu:77,submodul:70,subsect:78,subsubmenu:77,subsubsect:78,support:66,system:58,tabl:[73,74,75,76,77,78],tarbal:64,target:75,templat:[3,4,29,30],tensorformat:2,tensorrt:[50,57,59,61,62,63,64,65,82,83,85,86,87],test_py_modul:74,text:75,theme:[73,79],thi:[76,79],through:66,time:65,titl:75,toc:73,topic:75,torch:[50,59,61,62,63,65,82,83,85,86,87],torch_tensorrt:[15,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,45,67,68,69,70,71],torch_tensorrt_major_vers:7,torch_tensorrt_minor_vers:8,torch_tensorrt_patch_vers:6,torch_tensorrt_vers:12,torchscript:[31,32,33,37,41,61,65,81],torchtrt_api:9,torchtrt_hidden:11,torchtrtc:[52,61],tracer:82,train:[83,84],transform:83,triton:86,ts:71,tupl:54,tutori:65,type:[3,4,46,48],under:61,unpack:54,unrol:54,unsupport:61,up:86,us:[54,59,61,62,64,83,87],user:82,version:57,via:80,wai:75,weight:60,what:60,wide:73,window:63,work:[61,81],write:60,xstr:10,your:[84,86]}})
\ No newline at end of file
diff --git a/docs/src/pytorch-sphinx-theme/docs/changelog.html b/docs/src/pytorch-sphinx-theme/docs/changelog.html
index 1c06b101c5..46c8448cfb 100644
--- a/docs/src/pytorch-sphinx-theme/docs/changelog.html
+++ b/docs/src/pytorch-sphinx-theme/docs/changelog.html
@@ -197,7 +197,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/src/pytorch-sphinx-theme/docs/configuring.html b/docs/src/pytorch-sphinx-theme/docs/configuring.html
index f1c5a3d2e0..28b617e03b 100644
--- a/docs/src/pytorch-sphinx-theme/docs/configuring.html
+++ b/docs/src/pytorch-sphinx-theme/docs/configuring.html
@@ -197,7 +197,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/api.html b/docs/src/pytorch-sphinx-theme/docs/demo/api.html
index af2ed1dfe0..552258fef0 100644
--- a/docs/src/pytorch-sphinx-theme/docs/demo/api.html
+++ b/docs/src/pytorch-sphinx-theme/docs/demo/api.html
@@ -197,7 +197,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/demo.html b/docs/src/pytorch-sphinx-theme/docs/demo/demo.html
index 9d344d63bc..d467f285b8 100644
--- a/docs/src/pytorch-sphinx-theme/docs/demo/demo.html
+++ b/docs/src/pytorch-sphinx-theme/docs/demo/demo.html
@@ -199,7 +199,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
@@ -544,7 +544,7 @@ 
3.4.4. 
 
3.4.5. Code Blocks

 
# parsed-literal test
-curl -O http://someurl/release-master (1.2.0a0+51a991e).tar-gz

+curl -O http://someurl/release-master (1.2.0a0+096fd41).tar-gz

 

 
Code Blocks can have captions.

 
{
diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/lists_tables.html b/docs/src/pytorch-sphinx-theme/docs/demo/lists_tables.html
index 5695156066..df35196c0b 100644
--- a/docs/src/pytorch-sphinx-theme/docs/demo/lists_tables.html
+++ b/docs/src/pytorch-sphinx-theme/docs/demo/lists_tables.html
@@ -197,7 +197,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/long.html b/docs/src/pytorch-sphinx-theme/docs/demo/long.html
index 6371569524..62c792ecf6 100644
--- a/docs/src/pytorch-sphinx-theme/docs/demo/long.html
+++ b/docs/src/pytorch-sphinx-theme/docs/demo/long.html
@@ -197,7 +197,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/src/pytorch-sphinx-theme/docs/demo/structure.html b/docs/src/pytorch-sphinx-theme/docs/demo/structure.html
index 40aff14ed5..9c551adf3a 100644
--- a/docs/src/pytorch-sphinx-theme/docs/demo/structure.html
+++ b/docs/src/pytorch-sphinx-theme/docs/demo/structure.html
@@ -197,7 +197,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/src/pytorch-sphinx-theme/docs/index.html b/docs/src/pytorch-sphinx-theme/docs/index.html
index b03537a762..7af568e2a1 100644
--- a/docs/src/pytorch-sphinx-theme/docs/index.html
+++ b/docs/src/pytorch-sphinx-theme/docs/index.html
@@ -197,7 +197,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/src/pytorch-sphinx-theme/docs/installing.html b/docs/src/pytorch-sphinx-theme/docs/installing.html
index 7ce3043701..816954b554 100644
--- a/docs/src/pytorch-sphinx-theme/docs/installing.html
+++ b/docs/src/pytorch-sphinx-theme/docs/installing.html
@@ -197,7 +197,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/tutorials/creating_torchscript_module_in_python.html b/docs/tutorials/creating_torchscript_module_in_python.html
index bdb3cf0426..f21fe1598d 100644
--- a/docs/tutorials/creating_torchscript_module_in_python.html
+++ b/docs/tutorials/creating_torchscript_module_in_python.html
@@ -199,7 +199,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/tutorials/getting_started_with_fx_path.html b/docs/tutorials/getting_started_with_fx_path.html
index 936f983a04..651f96f772 100644
--- a/docs/tutorials/getting_started_with_fx_path.html
+++ b/docs/tutorials/getting_started_with_fx_path.html
@@ -199,7 +199,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/tutorials/notebooks.html b/docs/tutorials/notebooks.html
index f4c8dbda7b..0152930ccc 100644
--- a/docs/tutorials/notebooks.html
+++ b/docs/tutorials/notebooks.html
@@ -199,7 +199,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/tutorials/ptq.html b/docs/tutorials/ptq.html
index 6c48abaf2b..ea05af3f48 100644
--- a/docs/tutorials/ptq.html
+++ b/docs/tutorials/ptq.html
@@ -199,7 +199,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/tutorials/runtime.html b/docs/tutorials/runtime.html
index 274b80dce3..837b96e43d 100644
--- a/docs/tutorials/runtime.html
+++ b/docs/tutorials/runtime.html
@@ -199,7 +199,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/tutorials/serving_torch_tensorrt_with_triton.html b/docs/tutorials/serving_torch_tensorrt_with_triton.html
index 732b18ef42..a93d70c666 100644
--- a/docs/tutorials/serving_torch_tensorrt_with_triton.html
+++ b/docs/tutorials/serving_torch_tensorrt_with_triton.html
@@ -199,7 +199,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/tutorials/use_from_pytorch.html b/docs/tutorials/use_from_pytorch.html
index e9e8e75342..4ee09bfc1f 100644
--- a/docs/tutorials/use_from_pytorch.html
+++ b/docs/tutorials/use_from_pytorch.html
@@ -199,7 +199,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docs/tutorials/using_dla.html b/docs/tutorials/using_dla.html
index 6252d51262..c3ca7e9e93 100644
--- a/docs/tutorials/using_dla.html
+++ b/docs/tutorials/using_dla.html
@@ -199,7 +199,7 @@
               
               
                 

-                  master (1.2.0a0+51a991e)
+                  master (1.2.0a0+096fd41)
                 

               
             
diff --git a/docsrc/getting_started/getting_started_with_windows.rst b/docsrc/getting_started/getting_started_with_windows.rst
new file mode 100644
index 0000000000..d5d3394855
--- /dev/null
+++ b/docsrc/getting_started/getting_started_with_windows.rst
@@ -0,0 +1,78 @@
+.. _getting_started_windows:
+
+Building Torch-TensorRT on Windows
+====================================
+
+Torch-TensorRT has community support for Windows platform using CMake
+
+Prerequisite:
+
+* Microsoft Visual Studio
+* LibTorch
+* TensorRT
+* CUDA
+* cuDNN
+
+
+Build configuration
+-------------------
+
+* Open Microsoft Visual Studio
+* Open Torch-TensorRT source code folder
+* Open Manage configurations -> Edit JSON to open CMakeSettings.json file.
+* Configure the CMake build configurations. Following is an example configuration:
+
+.. code-block:: none
+
+    {
+      "configurations": [
+        {
+          "name": "x64-Debug",
+          "generator": "Ninja",
+          "configurationType": "Debug",
+          "inheritEnvironments": [ "msvc_x64_x64" ],
+          "buildRoot": "${projectDir}\\out\\build\\${name}",
+          "installRoot": "${projectDir}\\out\\install\\${name}",
+          "cmakeCommandArgs": "-S . -B out",
+          "buildCommandArgs": "cmake --build out",
+          "ctestCommandArgs": "",
+          "variables": [
+            {
+              "name": "CMAKE_MODULE_PATH",
+              "value": "$PWD\cmake\Modules",
+              "type": "FILEPATH"
+            },
+            {
+              "name": "Torch_DIR",
+              "value": "\share\cmake\Torch",
+              "type": "FILEPATH"
+            },
+            {
+              "name": "TensorRT_ROOT",
+              "value": "",
+              "type": "FILEPATH"
+            },
+            {
+              "name": "CMAKE_BUILD_TYPE",
+              "value": "Release",
+              "type": " STRING"
+            }
+          ]
+        }
+      ]  
+    }
+
+
+Compilation
+-----------
+
+* Click Build -> Build All or directly press Ctrl + Shift + B
+
+Note: After successful compilation, the build artifacts will be present at buildRoot path configured.
+
+Installation
+------------
+
+* Build -> Install Torch-TensorRT
+
+Note: After successful installation, the artifacts will be present at installRoot.
\ No newline at end of file
diff --git a/examples/int8/training/vgg16/export_qat.py b/examples/int8/training/vgg16/export_qat.py
index af881c5642..72974d1f4d 100644
--- a/examples/int8/training/vgg16/export_qat.py
+++ b/examples/int8/training/vgg16/export_qat.py
@@ -81,7 +81,7 @@ def test(model, dataloader, crit):
 quant_nn.TensorQuantizer.use_fb_fake_quant = True
 with torch.no_grad():
     data = iter(testing_dataloader)
-    images, _ = data.next()
+    images, _ = next(data)
     jit_model = torch.jit.trace(model, images.to("cuda"))
     torch.jit.save(jit_model, "trained_vgg16_qat.jit.pt")
 
diff --git a/noxfile.py b/noxfile.py
index 41926b5ee1..eff8136fbb 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -30,6 +30,9 @@
 if USE_HOST_DEPS:
     print("Using dependencies from host python")
 
+# Set epochs to train VGG model for accuracy tests
+EPOCHS = 25
+
 SUPPORTED_PYTHON_VERSIONS = ["3.7", "3.8", "3.9", "3.10"]
 
 nox.options.sessions = [
@@ -63,31 +66,6 @@ def install_torch_trt(session):
         session.run("python", "setup.py", "develop")
 
 
-def download_datasets(session):
-    print(
-        "Downloading dataset to path",
-        os.path.join(TOP_DIR, "examples/int8/training/vgg16"),
-    )
-    session.chdir(os.path.join(TOP_DIR, "examples/int8/training/vgg16"))
-    session.run_always(
-        "wget", "https://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz", external=True
-    )
-    session.run_always("tar", "-xvzf", "cifar-10-binary.tar.gz", external=True)
-    session.run_always(
-        "mkdir",
-        "-p",
-        os.path.join(TOP_DIR, "tests/accuracy/datasets/data"),
-        external=True,
-    )
-    session.run_always(
-        "cp",
-        "-rpf",
-        os.path.join(TOP_DIR, "examples/int8/training/vgg16/cifar-10-batches-bin"),
-        os.path.join(TOP_DIR, "tests/accuracy/datasets/data/cidar-10-batches-bin"),
-        external=True,
-    )
-
-
 def train_model(session):
     session.chdir(os.path.join(TOP_DIR, "examples/int8/training/vgg16"))
     session.install("-r", "requirements.txt")
@@ -107,14 +85,14 @@ def train_model(session):
             "--ckpt-dir",
             "vgg16_ckpts",
             "--epochs",
-            "25",
+            str(EPOCHS),
             env={"PYTHONPATH": PYT_PATH},
         )
 
         session.run_always(
             "python",
             "export_ckpt.py",
-            "vgg16_ckpts/ckpt_epoch25.pth",
+            "vgg16_ckpts/ckpt_epoch" + str(EPOCHS) + ".pth",
             env={"PYTHONPATH": PYT_PATH},
         )
     else:
@@ -130,10 +108,12 @@ def train_model(session):
             "--ckpt-dir",
             "vgg16_ckpts",
             "--epochs",
-            "25",
+            str(EPOCHS),
         )
 
-        session.run_always("python", "export_ckpt.py", "vgg16_ckpts/ckpt_epoch25.pth")
+        session.run_always(
+            "python", "export_ckpt.py", "vgg16_ckpts/ckpt_epoch" + str(EPOCHS) + ".pth"
+        )
 
 
 def finetune_model(session):
@@ -156,9 +136,9 @@ def finetune_model(session):
             "--ckpt-dir",
             "vgg16_ckpts",
             "--start-from",
-            "25",
+            str(EPOCHS),
             "--epochs",
-            "26",
+            str(EPOCHS + 1),
             env={"PYTHONPATH": PYT_PATH},
         )
 
@@ -166,7 +146,7 @@ def finetune_model(session):
         session.run_always(
             "python",
             "export_qat.py",
-            "vgg16_ckpts/ckpt_epoch26.pth",
+            "vgg16_ckpts/ckpt_epoch" + str(EPOCHS + 1) + ".pth",
             env={"PYTHONPATH": PYT_PATH},
         )
     else:
@@ -182,13 +162,17 @@ def finetune_model(session):
             "--ckpt-dir",
             "vgg16_ckpts",
             "--start-from",
-            "25",
+            str(EPOCHS),
             "--epochs",
-            "26",
+            str(EPOCHS + 1),
         )
 
         # Export model
-        session.run_always("python", "export_qat.py", "vgg16_ckpts/ckpt_epoch26.pth")
+        session.run_always(
+            "python",
+            "export_qat.py",
+            "vgg16_ckpts/ckpt_epoch" + str(EPOCHS + 1) + ".pth",
+        )
 
 
 def cleanup(session):
@@ -219,6 +203,19 @@ def run_base_tests(session):
             session.run_always("pytest", test)
 
 
+def run_model_tests(session):
+    print("Running model tests")
+    session.chdir(os.path.join(TOP_DIR, "tests/py"))
+    tests = [
+        "models",
+    ]
+    for test in tests:
+        if USE_HOST_DEPS:
+            session.run_always("pytest", test, env={"PYTHONPATH": PYT_PATH})
+        else:
+            session.run_always("pytest", test)
+
+
 def run_accuracy_tests(session):
     print("Running accuracy tests")
     session.chdir(os.path.join(TOP_DIR, "tests/py"))
@@ -268,8 +265,8 @@ def run_trt_compatibility_tests(session):
     copy_model(session)
     session.chdir(os.path.join(TOP_DIR, "tests/py"))
     tests = [
-        "test_trt_intercompatibility.py",
-        "test_ptq_trt_calibrator.py",
+        "integrations/test_trt_intercompatibility.py",
+        # "ptq/test_ptq_trt_calibrator.py",
     ]
     for test in tests:
         if USE_HOST_DEPS:
@@ -282,7 +279,7 @@ def run_dla_tests(session):
     print("Running DLA tests")
     session.chdir(os.path.join(TOP_DIR, "tests/py"))
     tests = [
-        "test_api_dla.py",
+        "hw/test_api_dla.py",
     ]
     for test in tests:
         if USE_HOST_DEPS:
@@ -295,7 +292,7 @@ def run_multi_gpu_tests(session):
     print("Running multi GPU tests")
     session.chdir(os.path.join(TOP_DIR, "tests/py"))
     tests = [
-        "test_multi_gpu.py",
+        "hw/test_multi_gpu.py",
     ]
     for test in tests:
         if USE_HOST_DEPS:
@@ -322,13 +319,12 @@ def run_l0_dla_tests(session):
     cleanup(session)
 
 
-def run_l1_accuracy_tests(session):
+def run_l1_model_tests(session):
     if not USE_HOST_DEPS:
         install_deps(session)
         install_torch_trt(session)
-    download_datasets(session)
-    train_model(session)
-    run_accuracy_tests(session)
+    download_models(session)
+    run_model_tests(session)
     cleanup(session)
 
 
@@ -336,7 +332,6 @@ def run_l1_int8_accuracy_tests(session):
     if not USE_HOST_DEPS:
         install_deps(session)
         install_torch_trt(session)
-    download_datasets(session)
     train_model(session)
     finetune_model(session)
     run_int8_accuracy_tests(session)
@@ -347,9 +342,6 @@ def run_l2_trt_compatibility_tests(session):
     if not USE_HOST_DEPS:
         install_deps(session)
         install_torch_trt(session)
-    download_models(session)
-    download_datasets(session)
-    train_model(session)
     run_trt_compatibility_tests(session)
     cleanup(session)
 
@@ -376,9 +368,9 @@ def l0_dla_tests(session):
 
 
 @nox.session(python=SUPPORTED_PYTHON_VERSIONS, reuse_venv=True)
-def l1_accuracy_tests(session):
-    """Checking accuracy performance on various usecases"""
-    run_l1_accuracy_tests(session)
+def l1_model_tests(session):
+    """When a user needs to test the functionality of standard models compilation and results"""
+    run_l1_model_tests(session)
 
 
 @nox.session(python=SUPPORTED_PYTHON_VERSIONS, reuse_venv=True)
@@ -397,13 +389,3 @@ def l2_trt_compatibility_tests(session):
 def l2_multi_gpu_tests(session):
     """Makes sure that Torch-TensorRT can operate on multi-gpu systems"""
     run_l2_multi_gpu_tests(session)
-
-
-@nox.session(python=SUPPORTED_PYTHON_VERSIONS, reuse_venv=True)
-def download_test_models(session):
-    """Grab all the models needed for testing"""
-    try:
-        import torch
-    except ModuleNotFoundError:
-        install_deps(session)
-    download_models(session)
diff --git a/py/torch_tensorrt/fx/lower.py b/py/torch_tensorrt/fx/lower.py
index 59b59d580f..9a641e000e 100644
--- a/py/torch_tensorrt/fx/lower.py
+++ b/py/torch_tensorrt/fx/lower.py
@@ -86,7 +86,9 @@ def create(cls, lower_setting):
 
     def __call__(self, mod, input, split_name) -> TRTInterpreterResult:
         assert self.lower_setting.input_specs, "Can't find input specs for lowering!"
-        logger.info(f"{split_name=} {self.lower_setting.input_specs=}")
+        logger.info(
+            f"split_name={split_name}, input_specs={self.lower_setting.input_specs}"
+        )
 
         # Prepare algorithm selector and timing_cache for TRTInterpreter
         algo_selector = None
diff --git a/py/torch_tensorrt/ptq.py b/py/torch_tensorrt/ptq.py
index 326f35f942..f60dd74b52 100644
--- a/py/torch_tensorrt/ptq.py
+++ b/py/torch_tensorrt/ptq.py
@@ -28,7 +28,7 @@ def get_batch(self, names):
     if self.current_batch_idx + self.batch_size > len(self.data_loader.dataset):
         return None
 
-    batch = self.dataset_iterator.next()
+    batch = next(self.dataset_iterator)
     self.current_batch_idx += self.batch_size
     inputs_gpu = []
     if isinstance(batch, list):
@@ -56,6 +56,13 @@ def write_calibration_cache(self, cache):
         return b""
 
 
+# deepcopy (which involves pickling) is performed on the compile_spec internally during compilation.
+# We register this __reduce__ function for pickler to identity the calibrator object returned by DataLoaderCalibrator during deepcopy.
+# This should be the object's local name relative to the module https://docs.python.org/3/library/pickle.html#object.__reduce__
+def __reduce__(self):
+    return self.__class__.__name__
+
+
 class DataLoaderCalibrator(object):
     """
     Constructs a calibrator class in TensorRT and uses pytorch dataloader to load/preproces
@@ -114,24 +121,27 @@ def __new__(cls, *args, **kwargs):
             "get_batch": get_cache_mode_batch if use_cache else get_batch,
             "read_calibration_cache": read_calibration_cache,
             "write_calibration_cache": write_calibration_cache,
+            "__reduce__": __reduce__,  # used when you deepcopy the DataLoaderCalibrator object
         }
 
         # Using type metaclass to construct calibrator class based on algorithm type
         if algo_type == CalibrationAlgo.ENTROPY_CALIBRATION:
             return type(
-                "DataLoaderCalibrator", (_C.IInt8EntropyCalibrator,), attribute_mapping
+                "Int8EntropyCalibrator", (_C.IInt8EntropyCalibrator,), attribute_mapping
             )()
         elif algo_type == CalibrationAlgo.ENTROPY_CALIBRATION_2:
             return type(
-                "DataLoaderCalibrator", (_C.IInt8MinMaxCalibrator,), attribute_mapping
+                "Int8EntropyCalibrator2",
+                (_C.IInt8EntropyCalibrator2,),
+                attribute_mapping,
             )()
         elif algo_type == CalibrationAlgo.LEGACY_CALIBRATION:
             return type(
-                "DataLoaderCalibrator", (_C.IInt8LegacyCalibrator,), attribute_mapping
+                "Int8LegacyCalibrator", (_C.IInt8LegacyCalibrator,), attribute_mapping
             )()
         elif algo_type == CalibrationAlgo.MINMAX_CALIBRATION:
             return type(
-                "DataLoaderCalibrator", (_C.IInt8MinMaxCalibrator,), attribute_mapping
+                "Int8MinMaxCalibrator", (_C.IInt8MinMaxCalibrator,), attribute_mapping
             )()
         else:
             log(
diff --git a/py/torch_tensorrt/ts/_compile_spec.py b/py/torch_tensorrt/ts/_compile_spec.py
index 154b29dd7b..9616111caa 100644
--- a/py/torch_tensorrt/ts/_compile_spec.py
+++ b/py/torch_tensorrt/ts/_compile_spec.py
@@ -225,7 +225,7 @@ def _parse_input_signature(input_signature: Any):
 
 
 def _parse_compile_spec(compile_spec_: Dict[str, Any]) -> _ts_C.CompileSpec:
-    # TODO: Remove deep copy once collections does not need partial compilation
+    # TODO: Use deepcopy to support partial compilation of collections
     compile_spec = deepcopy(compile_spec_)
     info = _ts_C.CompileSpec()
 
@@ -301,7 +301,7 @@ def _parse_compile_spec(compile_spec_: Dict[str, Any]) -> _ts_C.CompileSpec:
             compile_spec["enabled_precisions"]
         )
 
-    if "calibrator" in compile_spec:
+    if "calibrator" in compile_spec and compile_spec["calibrator"]:
         info.ptq_calibrator = compile_spec["calibrator"]
 
     if "sparse_weights" in compile_spec:
diff --git a/tests/core/conversion/converters/BUILD b/tests/core/conversion/converters/BUILD
index 82bc2f7033..5246de4cf1 100644
--- a/tests/core/conversion/converters/BUILD
+++ b/tests/core/conversion/converters/BUILD
@@ -71,6 +71,10 @@ converter_test(
     name = "test_matrix_multiply",
 )
 
+converter_test(
+    name = "test_max",
+)
+
 converter_test(
     name = "test_normalize",
 )
@@ -156,6 +160,7 @@ test_suite(
         ":test_linear",
         ":test_lstm_cell",
         ":test_matrix_multiply",
+        ":test_max",
         ":test_normalize",
         ":test_pooling",
         ":test_reduce",
diff --git a/tests/core/conversion/converters/test_max.cpp b/tests/core/conversion/converters/test_max.cpp
new file mode 100644
index 0000000000..dfc2432c24
--- /dev/null
+++ b/tests/core/conversion/converters/test_max.cpp
@@ -0,0 +1,147 @@
+#include 
+#include "core/compiler.h"
+#include "gtest/gtest.h"
+#include "tests/util/util.h"
+#include "torch/csrc/jit/ir/irparser.h"
+
+TEST(Converters, ATenMaxDimConvertsCorrectly) {
+  const auto graph = R"IR(
+    graph(%x.1 : Tensor):
+      %2 : int = prim::Constant[value=0]()
+      %3 : bool = prim::Constant[value=0]()
+      %4 : Tensor, %5 : Tensor = aten::max(%x.1, %2, %3)
+      return (%4, %5))IR";
+
+  auto g = std::make_shared();
+  torch::jit::parseIR(graph, g.get());
+
+  auto in = at::rand({2, 3, 5, 5}, {at::kCUDA});
+
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in});
+
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in});
+
+  ASSERT_TRUE(
+      torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6));
+  ASSERT_TRUE(
+      torch_tensorrt::tests::util::almostEqual(jit_results[1], trt_results[1].reshape_as(jit_results[1]), 2e-6));
+}
+
+TEST(Converters, ATenMinDimConvertsCorrectly) {
+  const auto graph = R"IR(
+    graph(%x.1 : Tensor):
+      %2 : int = prim::Constant[value=0]()
+      %3 : bool = prim::Constant[value=0]()
+      %4 : Tensor, %5 : Tensor = aten::min(%x.1, %2, %3)
+      return (%4, %5))IR";
+
+  auto g = std::make_shared();
+  torch::jit::parseIR(graph, g.get());
+
+  auto in = at::rand({2, 3, 5, 5}, {at::kCUDA});
+
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in});
+
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in});
+
+  ASSERT_TRUE(
+      torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6));
+  ASSERT_TRUE(
+      torch_tensorrt::tests::util::almostEqual(jit_results[1], trt_results[1].reshape_as(jit_results[1]), 2e-6));
+}
+
+TEST(Converters, ATenArgMaxConvertsCorrectly) {
+  const auto graph = R"IR(
+    graph(%x.1 : Tensor):
+      %2 : int = prim::Constant[value=0]()
+      %3 : bool = prim::Constant[value=0]()
+      %4 : Tensor = aten::argmax(%x.1, %2, %3)
+      return (%4))IR";
+
+  auto g = std::make_shared();
+  torch::jit::parseIR(graph, g.get());
+
+  auto in = at::rand({2, 3, 5, 5}, {at::kCUDA});
+
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in});
+
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in});
+
+  ASSERT_TRUE(
+      torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6));
+}
+
+TEST(Converters, ATenArgMaxKeepdimConvertsCorrectly) {
+  const auto graph = R"IR(
+    graph(%x.1 : Tensor):
+      %2 : int = prim::Constant[value=1]()
+      %3 : bool = prim::Constant[value=1]()
+      %4 : Tensor = aten::argmax(%x.1, %2, %3)
+      return (%4))IR";
+
+  auto g = std::make_shared();
+  torch::jit::parseIR(graph, g.get());
+
+  auto in = at::rand({2, 3, 5, 5}, {at::kCUDA});
+
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in});
+
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in});
+
+  ASSERT_TRUE(
+      torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6));
+}
+
+TEST(Converters, ATenArgMinConvertsCorrectly) {
+  const auto graph = R"IR(
+    graph(%x.1 : Tensor):
+      %2 : int = prim::Constant[value=0]()
+      %3 : bool = prim::Constant[value=0]()
+      %4 : Tensor = aten::argmin(%x.1, %2, %3)
+      return (%4))IR";
+
+  auto g = std::make_shared();
+  torch::jit::parseIR(graph, g.get());
+
+  auto in = at::rand({2, 3, 5, 5}, {at::kCUDA});
+
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in});
+
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in});
+
+  ASSERT_TRUE(
+      torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6));
+}
+
+TEST(Converters, ATenArgMinKeepdimConvertsCorrectly) {
+  const auto graph = R"IR(
+    graph(%x.1 : Tensor):
+      %2 : int = prim::Constant[value=1]()
+      %3 : bool = prim::Constant[value=1]()
+      %4 : Tensor = aten::argmin(%x.1, %2, %3)
+      return (%4))IR";
+
+  auto g = std::make_shared();
+  torch::jit::parseIR(graph, g.get());
+
+  auto in = at::rand({2, 3, 5, 5}, {at::kCUDA});
+
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in});
+
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in});
+
+  ASSERT_TRUE(
+      torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6));
+}
diff --git a/tests/core/conversion/converters/test_topk.cpp b/tests/core/conversion/converters/test_topk.cpp
index 1885493737..c53d209c1f 100644
--- a/tests/core/conversion/converters/test_topk.cpp
+++ b/tests/core/conversion/converters/test_topk.cpp
@@ -30,28 +30,3 @@ TEST(Converters, ATenTopKConvertsCorrectly) {
   ASSERT_TRUE(
       torch_tensorrt::tests::util::almostEqual(jit_results[1], trt_results[1].reshape_as(jit_results[1]), 2e-6));
 }
-
-TEST(Converters, ATenMaxDimConvertsCorrectly) {
-  const auto graph = R"IR(
-    graph(%x.1 : Tensor):
-      %2 : int = prim::Constant[value=0]()
-      %3 : bool = prim::Constant[value=0]()
-      %4 : Tensor, %5 : Tensor = aten::max(%x.1, %2, %3)
-      return (%4, %5))IR";
-
-  auto g = std::make_shared();
-  torch::jit::parseIR(graph, g.get());
-
-  auto in = at::rand({2, 3, 5, 5}, {at::kCUDA});
-
-  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
-  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in});
-
-  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
-  auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in});
-
-  ASSERT_TRUE(
-      torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6));
-  ASSERT_TRUE(
-      torch_tensorrt::tests::util::almostEqual(jit_results[1], trt_results[1].reshape_as(jit_results[1]), 2e-6));
-}
diff --git a/tests/core/conversion/converters/test_unary.cpp b/tests/core/conversion/converters/test_unary.cpp
index 1d40c3c94b..06f092ff36 100644
--- a/tests/core/conversion/converters/test_unary.cpp
+++ b/tests/core/conversion/converters/test_unary.cpp
@@ -31,6 +31,22 @@ TEST(Converters, ATenAbsIntConvertsCorrectly) {
   ASSERT_TRUE(torch_tensorrt::tests::util::exactlyEqual(jit_results[0], trt_results[0]));
 }
 
+TEST(Converters, ATenReciprocalIntConvertsCorrectly) {
+  const auto graph = gen_test_graph("reciprocal");
+  auto g = std::make_shared();
+  torch::jit::parseIR(graph, g.get());
+
+  auto in = at::tensor({-1, 1, -2, 2, -3, 3}, {at::kCUDA}).to(torch::kInt32);
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in});
+
+  in = at::clone(in);
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in});
+
+  ASSERT_TRUE(torch_tensorrt::tests::util::exactlyEqual(jit_results[0], trt_results[0]));
+}
+
 #define test_unary(unary, name)                                                                  \
   TEST(Converters, ATen##name##ConvertsCorrectly) {                                              \
     const auto graph = gen_test_graph(#unary);                                                   \
diff --git a/tests/core/lowering/test_module_fallback_passes.cpp b/tests/core/lowering/test_module_fallback_passes.cpp
index f11882df8b..e6eb098079 100644
--- a/tests/core/lowering/test_module_fallback_passes.cpp
+++ b/tests/core/lowering/test_module_fallback_passes.cpp
@@ -124,5 +124,5 @@ TEST(Lowering, LowerAndPartitionSimpleModuleFallbackCorrectly) {
   }
 
   auto trt_results = trt_mod.forward(trt_inputs_ivalues).toTensor();
-  ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results, trt_results, 2e-6));
+  ASSERT_TRUE(torch_tensorrt::tests::util::cosineSimEqual(jit_results, trt_results, 0.99));
 }
diff --git a/tests/core/partitioning/test_fallback_graph_output.cpp b/tests/core/partitioning/test_fallback_graph_output.cpp
index 98fc4e6128..3da717074a 100644
--- a/tests/core/partitioning/test_fallback_graph_output.cpp
+++ b/tests/core/partitioning/test_fallback_graph_output.cpp
@@ -34,7 +34,7 @@ TEST(Partitioning, ComputeResNet50FallbackGraphCorrectly) {
   auto jit_results = mod.forward(jit_inputs_ivalues).toTensor();
   auto trt_mod = torch_tensorrt::core::CompileGraph(mod, cfg);
   auto trt_results = trt_mod.forward(trt_inputs_ivalues).toTensor();
-  ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results, trt_results, 2e-6));
+  ASSERT_TRUE(torch_tensorrt::tests::util::cosineSimEqual(jit_results, trt_results, 0.99));
 }
 
 TEST(Partitioning, ComputeMobileNetFallbackGraphCorrectly) {
@@ -64,6 +64,6 @@ TEST(Partitioning, ComputeMobileNetFallbackGraphCorrectly) {
   auto jit_results = mod.forward(jit_inputs_ivalues).toTensor();
   auto trt_mod = torch_tensorrt::core::CompileGraph(mod, cfg);
   auto trt_results = trt_mod.forward(trt_inputs_ivalues).toTensor();
-  ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results, trt_results, 2e-6));
+  ASSERT_TRUE(torch_tensorrt::tests::util::cosineSimEqual(jit_results, trt_results, 0.99));
 }
 #endif
diff --git a/tests/cpp/test_collections.cpp b/tests/cpp/test_collections.cpp
index d01665adcd..e3f0d91dfe 100644
--- a/tests/cpp/test_collections.cpp
+++ b/tests/cpp/test_collections.cpp
@@ -42,7 +42,7 @@ TEST(CppAPITests, TestCollectionStandardTensorInput) {
   auto trt_mod = torch_tensorrt::torchscript::compile(mod, compile_settings);
   auto trt_out = trt_mod.forward(inputs_);
 
-  ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(out.toTensor(), trt_out.toTensor(), 1e-5));
+  ASSERT_TRUE(torch_tensorrt::tests::util::cosineSimEqual(out.toTensor(), trt_out.toTensor(), 0.99));
 }
 
 TEST(CppAPITests, TestCollectionTupleInput) {
@@ -85,7 +85,7 @@ TEST(CppAPITests, TestCollectionTupleInput) {
   auto trt_mod = torch_tensorrt::torchscript::compile(mod, compile_settings);
   auto trt_out = trt_mod.forward(complex_inputs);
 
-  ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(out.toTensor(), trt_out.toTensor(), 1e-5));
+  ASSERT_TRUE(torch_tensorrt::tests::util::cosineSimEqual(out.toTensor(), trt_out.toTensor(), 0.99));
 }
 
 TEST(CppAPITests, TestCollectionListInput) {
@@ -144,7 +144,7 @@ TEST(CppAPITests, TestCollectionListInput) {
   LOG_DEBUG("Finish compile");
   auto trt_out = trt_mod.forward(complex_inputs);
 
-  ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(out.toTensor(), trt_out.toTensor(), 1e-5));
+  ASSERT_TRUE(torch_tensorrt::tests::util::cosineSimEqual(out.toTensor(), trt_out.toTensor(), 0.99));
 }
 
 TEST(CppAPITests, TestCollectionTupleInputOutput) {
@@ -317,4 +317,4 @@ TEST(CppAPITests, TestCollectionComplexModel) {
       out.toTuple()->elements()[0].toTensor(), trt_out.toTuple()->elements()[0].toTensor(), 1e-5));
   ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(
       out.toTuple()->elements()[1].toTensor(), trt_out.toTuple()->elements()[1].toTensor(), 1e-5));
-}
\ No newline at end of file
+}
diff --git a/tests/cpp/test_compiled_modules.cpp b/tests/cpp/test_compiled_modules.cpp
index 595dd7044f..3a81f0a531 100644
--- a/tests/cpp/test_compiled_modules.cpp
+++ b/tests/cpp/test_compiled_modules.cpp
@@ -42,7 +42,7 @@ TEST_P(CppAPITests, CompiledModuleIsClose) {
 
   for (size_t i = 0; i < trt_results.size(); i++) {
     ASSERT_TRUE(
-        torch_tensorrt::tests::util::almostEqual(jit_results[i], trt_results[i].reshape_as(jit_results[i]), threshold));
+        torch_tensorrt::tests::util::cosineSimEqual(jit_results[i], trt_results[i].reshape_as(jit_results[i]), 0.99));
   }
 }
 
@@ -52,11 +52,7 @@ INSTANTIATE_TEST_SUITE_P(
     CompiledModuleForwardIsCloseSuite,
     CppAPITests,
     testing::Values(
-        PathAndInput({"tests/modules/resnet18_traced.jit.pt", {{1, 3, 224, 224}}, {at::kFloat}, 2e-5}),
-        PathAndInput({"tests/modules/resnet50_traced.jit.pt", {{1, 3, 224, 224}}, {at::kFloat}, 2e-5}),
-        PathAndInput({"tests/modules/mobilenet_v2_traced.jit.pt", {{1, 3, 224, 224}}, {at::kFloat}, 2e-5}),
         PathAndInput({"tests/modules/resnet18_scripted.jit.pt", {{1, 3, 224, 224}}, {at::kFloat}, 2e-5}),
-        PathAndInput({"tests/modules/resnet50_scripted.jit.pt", {{1, 3, 224, 224}}, {at::kFloat}, 2e-5}),
         PathAndInput({"tests/modules/mobilenet_v2_scripted.jit.pt", {{1, 3, 224, 224}}, {at::kFloat}, 2e-5}),
         PathAndInput({"tests/modules/efficientnet_b0_scripted.jit.pt", {{1, 3, 224, 224}}, {at::kFloat}, 8e-3}),
         PathAndInput({"tests/modules/bert_base_uncased_traced.jit.pt", {{1, 14}, {1, 14}}, {at::kInt, at::kInt}, 8e-2}),
diff --git a/tests/cpp/test_module_fallback.cpp b/tests/cpp/test_module_fallback.cpp
index d1221cde4d..bfdfc46b04 100644
--- a/tests/cpp/test_module_fallback.cpp
+++ b/tests/cpp/test_module_fallback.cpp
@@ -30,7 +30,7 @@ TEST(CppAPITest, ResNetModuleFallbacksCorrectly) {
   auto jit_results = mod.forward(jit_inputs_ivalues).toTensor();
   auto trt_mod = torch_tensorrt::ts::compile(mod, cfg);
   auto trt_results = trt_mod.forward(trt_inputs_ivalues).toTensor();
-  ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results, trt_results, 2e-6));
+  ASSERT_TRUE(torch_tensorrt::tests::util::cosineSimEqual(jit_results, trt_results, 0.99));
 }
 
 TEST(CppAPITest, MobileNetModuleFallbacksCorrectlyWithOneEngine) {
@@ -69,6 +69,6 @@ TEST(CppAPITest, MobileNetModuleFallbacksCorrectlyWithOneEngine) {
   ASSERT_TRUE(trt_count == 1);
 
   auto trt_results = trt_mod.forward(trt_inputs_ivalues).toTensor();
-  ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results, trt_results, 2e-6));
+  ASSERT_TRUE(torch_tensorrt::tests::util::cosineSimEqual(jit_results, trt_results, 0.99));
 }
 #endif
diff --git a/tests/cpp/test_modules_as_engines.cpp b/tests/cpp/test_modules_as_engines.cpp
index 4437b1218c..11b7a54fb0 100644
--- a/tests/cpp/test_modules_as_engines.cpp
+++ b/tests/cpp/test_modules_as_engines.cpp
@@ -14,41 +14,8 @@ TEST_P(CppAPITests, ModuleAsEngineIsClose) {
   jit_results.push_back(jit_results_ivalues.toTensor());
   auto trt_results = torch_tensorrt::tests::util::RunModuleForwardAsEngine(mod, inputs);
 
-  ASSERT_TRUE(
-      torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), threshold));
-}
-
-TEST_P(CppAPITests, ModuleToEngineToModuleIsClose) {
-  std::vector inputs;
-  std::vector inputs_ivalues;
-  for (uint64_t i = 0; i < input_shapes.size(); i++) {
-    inputs.push_back(at::randint(5, input_shapes[i], {at::kCUDA}).to(input_types[i]));
-    inputs_ivalues.push_back(inputs[inputs.size() - 1].clone());
-  }
-
-  torch::jit::IValue jit_results_ivalues = torch_tensorrt::tests::util::RunModuleForward(mod, inputs_ivalues);
-  std::vector jit_results;
-  jit_results.push_back(jit_results_ivalues.toTensor());
-
-  std::vector> input_ranges;
-  for (auto in : inputs) {
-    input_ranges.push_back(in.sizes());
-  }
-
-  auto compile_spec = torch_tensorrt::ts::CompileSpec({input_ranges});
-  int device_id = 0;
-  cudaGetDevice(&device_id);
-  compile_spec.device.device_type = torch_tensorrt::Device::DeviceType::kGPU;
-  compile_spec.device.gpu_id = device_id;
-  auto engine = torch_tensorrt::ts::convert_method_to_trt_engine(mod, "forward", input_ranges);
-  auto trt_mod = torch_tensorrt::ts::embed_engine_in_new_module(engine, compile_spec.device);
-
-  torch::jit::IValue trt_results_ivalues = torch_tensorrt::tests::util::RunModuleForward(trt_mod, inputs_ivalues);
-  std::vector trt_results;
-  trt_results.push_back(trt_results_ivalues.toTensor());
-
-  ASSERT_TRUE(
-      torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), threshold));
+  ASSERT_TRUE(torch_tensorrt::tests::util::cosineSimEqual(
+      jit_results[0], trt_results[0].reshape_as(jit_results[0]), threshold));
 }
 
 #ifndef DISABLE_TEST_IN_CI
@@ -57,12 +24,8 @@ INSTANTIATE_TEST_SUITE_P(
     ModuleAsEngineForwardIsCloseSuite,
     CppAPITests,
     testing::Values(
-        PathAndInput({"tests/modules/resnet18_traced.jit.pt", {{1, 3, 224, 224}}, {at::kFloat}, 2e-5}),
-        PathAndInput({"tests/modules/resnet50_traced.jit.pt", {{1, 3, 224, 224}}, {at::kFloat}, 2e-5}),
-        PathAndInput({"tests/modules/mobilenet_v2_traced.jit.pt", {{1, 3, 224, 224}}, {at::kFloat}, 2e-5}),
-        PathAndInput({"tests/modules/resnet18_scripted.jit.pt", {{1, 3, 224, 224}}, {at::kFloat}, 2e-5}),
-        PathAndInput({"tests/modules/resnet50_scripted.jit.pt", {{1, 3, 224, 224}}, {at::kFloat}, 2e-5}),
-        PathAndInput({"tests/modules/mobilenet_v2_scripted.jit.pt", {{1, 3, 224, 224}}, {at::kFloat}, 2e-5}),
-        PathAndInput({"tests/modules/efficientnet_b0_scripted.jit.pt", {{1, 3, 224, 224}}, {at::kFloat}, 1e-4}),
-        PathAndInput({"tests/modules/vit_scripted.jit.pt", {{1, 3, 224, 224}}, {at::kFloat}, 8e-2})));
+        PathAndInput({"tests/modules/resnet18_scripted.jit.pt", {{1, 3, 224, 224}}, {at::kFloat}, 0.99}),
+        PathAndInput({"tests/modules/mobilenet_v2_scripted.jit.pt", {{1, 3, 224, 224}}, {at::kFloat}, 0.99}),
+        PathAndInput({"tests/modules/efficientnet_b0_scripted.jit.pt", {{1, 3, 224, 224}}, {at::kFloat}, 0.99}),
+        PathAndInput({"tests/modules/vit_scripted.jit.pt", {{1, 3, 224, 224}}, {at::kFloat}, 0.99})));
 #endif
diff --git a/tests/cpp/test_multi_gpu_serde.cpp b/tests/cpp/test_multi_gpu_serde.cpp
index 8672ae9517..0b3944125b 100644
--- a/tests/cpp/test_multi_gpu_serde.cpp
+++ b/tests/cpp/test_multi_gpu_serde.cpp
@@ -23,12 +23,12 @@ TEST_P(CppAPITests, CompiledModuleIsClose) {
   trt_results.push_back(trt_results_ivalues.toTensor());
 
   for (size_t i = 0; i < trt_results.size(); i++) {
-    ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(
-        jit_results[i], trt_results[i].reshape_as(jit_results[i]).to(torch::Device("cuda:0")), 2e-5));
+    ASSERT_TRUE(torch_tensorrt::tests::util::cosineSimEqual(
+        jit_results[i], trt_results[i].reshape_as(jit_results[i]).to(torch::Device("cuda:0")), threshold));
   }
 }
 
 INSTANTIATE_TEST_SUITE_P(
     CompiledModuleForwardIsCloseSuite,
     CppAPITests,
-    testing::Values(PathAndInput({"tests/modules/resnet18_traced.jit.pt", {{1, 3, 224, 224}}, {at::kFloat}, 2e-5})));
+    testing::Values(PathAndInput({"tests/modules/resnet18_traced.jit.pt", {{1, 3, 224, 224}}, {at::kFloat}, 0.99})));
diff --git a/tests/cpp/test_multiple_registered_engines.cpp b/tests/cpp/test_multiple_registered_engines.cpp
index 2746687f68..658f59ca74 100644
--- a/tests/cpp/test_multiple_registered_engines.cpp
+++ b/tests/cpp/test_multiple_registered_engines.cpp
@@ -10,7 +10,7 @@ TEST(CppAPITest, CanRunMultipleEngines) {
   torch::jit::script::Module mod1;
   torch::jit::script::Module mod2;
   try {
-    mod1 = torch::jit::load("tests/modules/resnet50_traced.jit.pt");
+    mod1 = torch::jit::load("tests/modules/resnet18_traced.jit.pt");
     mod2 = torch::jit::load("tests/modules/resnet18_traced.jit.pt");
   } catch (const c10::Error& e) {
     std::cerr << "error loading the model\n";
@@ -56,13 +56,13 @@ TEST(CppAPITest, CanRunMultipleEngines) {
   trt2_results.push_back(trt2_results_ivalues.toTensor());
 
   for (size_t i = 0; i < trt1_results.size(); i++) {
-    ASSERT_TRUE(
-        torch_tensorrt::tests::util::almostEqual(jit1_results[i], trt1_results[i].reshape_as(jit1_results[i]), 2e-5));
+    ASSERT_TRUE(torch_tensorrt::tests::util::cosineSimEqual(
+        jit1_results[i], trt1_results[i].reshape_as(jit1_results[i]), 0.99));
   }
 
   for (size_t i = 0; i < trt2_results.size(); i++) {
-    ASSERT_TRUE(
-        torch_tensorrt::tests::util::almostEqual(jit2_results[i], trt2_results[i].reshape_as(jit2_results[i]), 2e-5));
+    ASSERT_TRUE(torch_tensorrt::tests::util::cosineSimEqual(
+        jit2_results[i], trt2_results[i].reshape_as(jit2_results[i]), 0.99));
   }
 }
 #endif
diff --git a/tests/py/api/test_collections.py b/tests/py/api/test_collections.py
index dfae3f18c9..936a4d5c73 100644
--- a/tests/py/api/test_collections.py
+++ b/tests/py/api/test_collections.py
@@ -3,6 +3,7 @@
 import torch
 import torchvision.models as models
 import os
+from utils import cosine_similarity, COSINE_THRESHOLD
 
 
 def find_repo_root(max_depth=10):
@@ -40,12 +41,13 @@ def test_compile(self):
         }
 
         trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
-        same = (
-            (trt_mod(self.input, self.input) - self.model(self.input, self.input))
-            .abs()
-            .max()
+        cos_sim = cosine_similarity(
+            self.model(self.input, self.input), trt_mod(self.input, self.input)
+        )
+        self.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"standard_tensor_input_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
         )
-        self.assertTrue(same < 2e-2)
 
 
 class TestTupleInput(unittest.TestCase):
@@ -68,12 +70,13 @@ def test_compile(self):
         }
 
         trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
-        same = (
-            (trt_mod((self.input, self.input)) - self.model((self.input, self.input)))
-            .abs()
-            .max()
+        cos_sim = cosine_similarity(
+            self.model((self.input, self.input)), trt_mod((self.input, self.input))
+        )
+        self.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"tuple_input_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
         )
-        self.assertTrue(same < 2e-2)
 
 
 class TestListInput(unittest.TestCase):
@@ -94,12 +97,13 @@ def test_compile(self):
         }
 
         trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
-        same = (
-            (trt_mod([self.input, self.input]) - self.model([self.input, self.input]))
-            .abs()
-            .max()
+        cos_sim = cosine_similarity(
+            self.model([self.input, self.input]), trt_mod([self.input, self.input])
+        )
+        self.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"list_input_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
         )
-        self.assertTrue(same < 2e-2)
 
 
 class TestTupleInputOutput(unittest.TestCase):
@@ -124,8 +128,12 @@ def test_compile(self):
         trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
         trt_out = trt_mod((self.input, self.input))
         pyt_out = self.model((self.input, self.input))
-        results = [(t - p).abs().max() < 2e-2 for (t, p) in zip(trt_out, pyt_out)]
-        self.assertTrue(all(results))
+        for (t, p) in zip(trt_out, pyt_out):
+            cos_sim = cosine_similarity(t, p)
+            self.assertTrue(
+                cos_sim > COSINE_THRESHOLD,
+                msg=f"tuple_input_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+            )
 
 
 class TestListInputOutput(unittest.TestCase):
@@ -150,8 +158,13 @@ def test_compile(self):
         trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
         trt_out = trt_mod((self.input, self.input))
         pyt_out = self.model((self.input, self.input))
-        results = [(t - p).abs().max() < 2e-2 for (t, p) in zip(trt_out, pyt_out)]
-        self.assertTrue(all(results))
+
+        for (t, p) in zip(trt_out, pyt_out):
+            cos_sim = cosine_similarity(t, p)
+            self.assertTrue(
+                cos_sim > COSINE_THRESHOLD,
+                msg=f"list_input_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+            )
 
 
 class TestListInputTupleOutput(unittest.TestCase):
@@ -176,8 +189,12 @@ def test_compile(self):
         trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
         trt_out = trt_mod((self.input, self.input))
         pyt_out = self.model((self.input, self.input))
-        results = [(t - p).abs().max() < 2e-2 for (t, p) in zip(trt_out, pyt_out)]
-        self.assertTrue(all(results))
+        for (t, p) in zip(trt_out, pyt_out):
+            cos_sim = cosine_similarity(t, p)
+            self.assertTrue(
+                cos_sim > COSINE_THRESHOLD,
+                msg=f"list_input_tuple_output_scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+            )
 
 
 if __name__ == "__main__":
diff --git a/tests/py/api/test_e2e_behavior.py b/tests/py/api/test_e2e_behavior.py
index d1da3e0465..385fe916f4 100644
--- a/tests/py/api/test_e2e_behavior.py
+++ b/tests/py/api/test_e2e_behavior.py
@@ -6,102 +6,6 @@
 from typing import Dict
 
 
-class TestCompileHalf(unittest.TestCase):
-    def test_compile_script_half(self):
-        self.model = models.resnet18(pretrained=True).eval().to("cuda")
-        self.input = torch.randn((1, 3, 224, 224)).to("cuda")
-        self.scripted_model = torch.jit.script(self.model)
-        self.scripted_model.half()
-
-        compile_spec = {
-            "inputs": [torchtrt.Input(shape=self.input.shape, dtype=torch.half)],
-            "device": {
-                "device_type": torchtrt.DeviceType.GPU,
-                "gpu_id": 0,
-            },
-            "enabled_precisions": {torch.half},
-        }
-
-        trt_mod = torchtrt.ts.compile(self.scripted_model, **compile_spec)
-        same = (
-            (trt_mod(self.input.half()) - self.scripted_model(self.input.half()))
-            .abs()
-            .max()
-        )
-        torchtrt.logging.log(torchtrt.logging.Level.Debug, "Max diff: " + str(same))
-        self.assertTrue(same < 3e-2)
-
-    def test_compile_script_half_by_default(self):
-        self.model = models.resnet18(pretrained=True).eval().to("cuda")
-        self.input = torch.randn((1, 3, 224, 224)).to("cuda")
-        self.scripted_model = torch.jit.script(self.model)
-        self.scripted_model.half()
-
-        compile_spec = {
-            "inputs": [torchtrt.Input(shape=self.input.shape)],
-            "device": {
-                "device_type": torchtrt.DeviceType.GPU,
-                "gpu_id": 0,
-            },
-            "enabled_precisions": {torch.float, torch.half},
-        }
-
-        trt_mod = torchtrt.ts.compile(self.scripted_model, **compile_spec)
-        same = (
-            (trt_mod(self.input.half()) - self.scripted_model(self.input.half()))
-            .abs()
-            .max()
-        )
-        torchtrt.logging.log(torchtrt.logging.Level.Debug, "Max diff: " + str(same))
-        self.assertTrue(same < 3e-2)
-
-
-class TestFallbackToTorch(unittest.TestCase):
-    def test_fallback(self):
-        self.model = models.resnet18(pretrained=True).eval().to("cuda")
-        self.input = torch.randn((1, 3, 224, 224)).to("cuda")
-        self.scripted_model = torch.jit.script(self.model)
-
-        compile_spec = {
-            "inputs": [torchtrt.Input(self.input.shape)],
-            "device": {
-                "device_type": torchtrt.DeviceType.GPU,
-                "gpu_id": 0,
-                "allow_gpu_fallback": False,
-                "disable_tf32": False,
-            },
-            "require_full_compilation": False,
-            "torch_executed_ops": ["aten::max_pool2d"],
-            "min_block_size": 1,
-        }
-
-        trt_mod = torchtrt.ts.compile(self.scripted_model, **compile_spec)
-        same = (trt_mod(self.input) - self.scripted_model(self.input)).abs().max()
-        self.assertTrue(same < 2e-3)
-
-    def test_module_fallback(self):
-        self.model = models.resnet18(pretrained=True).eval().to("cuda")
-        self.input = torch.randn((1, 3, 224, 224)).to("cuda")
-        self.scripted_model = torch.jit.script(self.model)
-
-        compile_spec = {
-            "inputs": [torchtrt.Input(self.input.shape)],
-            "device": {
-                "device_type": torchtrt.DeviceType.GPU,
-                "gpu_id": 0,
-                "allow_gpu_fallback": False,
-                "disable_tf32": False,
-            },
-            "require_full_compilation": False,
-            "torch_executed_modules": ["torchvision.models.resnet.BasicBlock"],
-            "min_block_size": 1,
-        }
-
-        trt_mod = torchtrt.ts.compile(self.scripted_model, **compile_spec)
-        same = (trt_mod(self.input) - self.scripted_model(self.input)).abs().max()
-        self.assertTrue(same < 2e-3)
-
-
 class TestInputTypeDefaultsFP32Model(unittest.TestCase):
     def test_input_use_default_fp32(self):
         self.model = models.resnet18(pretrained=True).eval().to("cuda")
diff --git a/tests/py/api/test_embed_engines.py b/tests/py/api/test_embed_engines.py
new file mode 100644
index 0000000000..d21e139eca
--- /dev/null
+++ b/tests/py/api/test_embed_engines.py
@@ -0,0 +1,73 @@
+import unittest
+import torch_tensorrt as torchtrt
+import torch
+import torchvision.models as models
+import copy
+import timm
+from typing import Dict
+from utils import cosine_similarity, COSINE_THRESHOLD
+
+
+class TestModelToEngineToModel(unittest.TestCase):
+    def test_resnet50(self):
+        self.model = models.resnet50(pretrained=True).eval().to("cuda")
+        self.input = torch.randn((1, 3, 224, 224)).to("cuda")
+
+        compile_spec = {
+            "inputs": [
+                torchtrt.Input(
+                    self.input.shape, dtype=torch.float, format=torch.contiguous_format
+                )
+            ],
+            "device": {
+                "device_type": torchtrt.DeviceType.GPU,
+                "gpu_id": 0,
+            },
+            "enabled_precisions": {torch.float},
+        }
+
+        self.scripted_model = torch.jit.script(self.model)
+        trt_engine = torchtrt.ts.convert_method_to_trt_engine(
+            self.scripted_model, "forward", **compile_spec
+        )
+        trt_mod = torchtrt.ts.embed_engine_in_new_module(trt_engine)
+        cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
+        self.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"Resnet50 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+        )
+
+    def test_efficientnet_b0(self):
+        self.model = (
+            timm.create_model("efficientnet_b0", pretrained=True).eval().to("cuda")
+        )
+        self.input = torch.randn((1, 3, 224, 224)).to("cuda")
+
+        compile_spec = {
+            "inputs": [
+                torchtrt.Input(
+                    self.input.shape, dtype=torch.float, format=torch.contiguous_format
+                )
+            ],
+            "device": {
+                "device_type": torchtrt.DeviceType.GPU,
+                "gpu_id": 0,
+            },
+            "enabled_precisions": {torch.float},
+        }
+
+        self.scripted_model = torch.jit.script(self.model)
+        trt_engine = torchtrt.ts.convert_method_to_trt_engine(
+            self.scripted_model, "forward", **compile_spec
+        )
+        trt_mod = torchtrt.ts.embed_engine_in_new_module(trt_engine)
+
+        cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
+        self.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"EfficientNet-B0 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/py/api/test_module_fallback.py b/tests/py/api/test_module_fallback.py
new file mode 100644
index 0000000000..5eda2cdbfc
--- /dev/null
+++ b/tests/py/api/test_module_fallback.py
@@ -0,0 +1,62 @@
+import unittest
+import torch_tensorrt as torchtrt
+import torch
+import torchvision.models as models
+import copy
+from typing import Dict
+from utils import cosine_similarity, COSINE_THRESHOLD
+
+
+class TestModuleFallback(unittest.TestCase):
+    def test_fallback_resnet18(self):
+        self.model = models.resnet18(pretrained=True).eval().to("cuda")
+        self.input = torch.randn((1, 3, 224, 224)).to("cuda")
+        compile_spec = {
+            "inputs": [
+                torchtrt.Input(
+                    self.input.shape, dtype=torch.float, format=torch.contiguous_format
+                )
+            ],
+            "device": {
+                "device_type": torchtrt.DeviceType.GPU,
+                "gpu_id": 0,
+            },
+            "enabled_precisions": {torch.float},
+            "torch_executed_modules": ["torchvision.models.resnet.BasicBlock"],
+        }
+        trt_mod = torchtrt.compile(self.model, **compile_spec)
+        cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
+        self.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+        )
+
+    def test_fallback_mobilenet_v2(self):
+        self.model = models.mobilenet_v2(pretrained=True).eval().to("cuda")
+        self.input = torch.randn((1, 3, 224, 224)).to("cuda")
+        compile_spec = {
+            "inputs": [
+                torchtrt.Input(
+                    self.input.shape, dtype=torch.float, format=torch.contiguous_format
+                )
+            ],
+            "device": {
+                "device_type": torchtrt.DeviceType.GPU,
+                "gpu_id": 0,
+            },
+            "enabled_precisions": {torch.float},
+            "torch_executed_modules": [
+                "torchvision.models.mobilenetv2.ConvBNActivation"
+            ],
+            "min_block_size": 5,
+        }
+        trt_mod = torchtrt.compile(self.model, **compile_spec)
+        cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
+        self.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"Mobilenet V2 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/py/api/test_operator_fallback.py b/tests/py/api/test_operator_fallback.py
new file mode 100644
index 0000000000..302a663e24
--- /dev/null
+++ b/tests/py/api/test_operator_fallback.py
@@ -0,0 +1,59 @@
+import unittest
+import torch_tensorrt as torchtrt
+import torch
+import torchvision.models as models
+import copy
+from typing import Dict
+from utils import cosine_similarity, COSINE_THRESHOLD
+
+
+class TestFallbackModels(unittest.TestCase):
+    def test_fallback_resnet18(self):
+        self.model = models.resnet18(pretrained=True).eval().to("cuda")
+        self.input = torch.randn((1, 3, 224, 224)).to("cuda")
+        compile_spec = {
+            "inputs": [
+                torchtrt.Input(
+                    self.input.shape, dtype=torch.float, format=torch.contiguous_format
+                )
+            ],
+            "device": {
+                "device_type": torchtrt.DeviceType.GPU,
+                "gpu_id": 0,
+            },
+            "enabled_precisions": {torch.float},
+            "torch_executed_ops": ["aten::add"],
+        }
+        trt_mod = torchtrt.compile(self.model, **compile_spec)
+        cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
+        self.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+        )
+
+    def test_fallback_mobilenet_v2(self):
+        self.model = models.mobilenet_v2(pretrained=True).eval().to("cuda")
+        self.input = torch.randn((1, 3, 224, 224)).to("cuda")
+        compile_spec = {
+            "inputs": [
+                torchtrt.Input(
+                    self.input.shape, dtype=torch.float, format=torch.contiguous_format
+                )
+            ],
+            "device": {
+                "device_type": torchtrt.DeviceType.GPU,
+                "gpu_id": 0,
+            },
+            "enabled_precisions": {torch.float},
+            "torch_executed_ops": ["aten::hardtanh"],
+        }
+        trt_mod = torchtrt.compile(self.model, **compile_spec)
+        cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
+        self.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"Mobilenet V2 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/py/api/test_ts_backend.py b/tests/py/api/test_ts_backend.py
index d0654a8f75..e56ab4f902 100644
--- a/tests/py/api/test_ts_backend.py
+++ b/tests/py/api/test_ts_backend.py
@@ -4,6 +4,7 @@
 import torchvision.models as models
 import copy
 from typing import Dict
+from utils import cosine_similarity, COSINE_THRESHOLD
 
 
 class TestCompile(unittest.TestCase):
@@ -26,8 +27,11 @@ def test_compile_traced(self):
         }
 
         trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
-        same = (trt_mod(self.input) - self.traced_model(self.input)).abs().max()
-        self.assertTrue(same < 2e-2)
+        cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
+        self.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+        )
 
     def test_compile_script(self):
         self.model = models.vgg16(pretrained=True).eval().to("cuda")
@@ -40,8 +44,11 @@ def test_compile_script(self):
                 device=torchtrt.Device(gpu_id=0),
                 enabled_precisions={torch.float},
             )
-            same = (trt_mod(self.input) - self.scripted_model(self.input)).abs().max()
-            self.assertTrue(same < 2e-2)
+            cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
+            self.assertTrue(
+                cos_sim > COSINE_THRESHOLD,
+                msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+            )
 
     def test_compile_global(self):
         self.model = models.vgg16(pretrained=True).eval().to("cuda")
@@ -53,21 +60,11 @@ def test_compile_global(self):
             device=torchtrt.Device(gpu_id=0),
             enabled_precisions={torch.float},
         )
-        same = (trt_mod(self.input) - self.scripted_model(self.input)).abs().max()
-        self.assertTrue(same < 2e-2)
-
-    def test_compile_global_nn_mod(self):
-        self.model = models.vgg16(pretrained=True).eval().to("cuda")
-        self.input = torch.randn((1, 3, 224, 224)).to("cuda")
-        with torch.no_grad():
-            trt_mod = torchtrt.compile(
-                self.model,
-                inputs=[self.input],
-                device=torchtrt.Device(gpu_id=0),
-                enabled_precisions={torch.float},
-            )
-            same = (trt_mod(self.input) - self.model(self.input)).abs().max()
-            self.assertTrue(same < 2e-2)
+        cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
+        self.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+        )
 
     def test_from_torch_tensor(self):
         self.model = models.vgg16(pretrained=True).eval().to("cuda")
@@ -83,8 +80,11 @@ def test_from_torch_tensor(self):
         }
 
         trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
-        same = (trt_mod(self.input) - self.traced_model(self.input)).abs().max()
-        self.assertTrue(same < 2e-2)
+        cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
+        self.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+        )
 
     def test_device(self):
         self.model = models.vgg16(pretrained=True).eval().to("cuda")
@@ -97,8 +97,11 @@ def test_device(self):
         }
 
         trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
-        same = (trt_mod(self.input) - self.traced_model(self.input)).abs().max()
-        self.assertTrue(same < 2e-2)
+        cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
+        self.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+        )
 
     def test_default_device(self):
         self.model = models.vgg16(pretrained=True).eval().to("cuda")
@@ -107,51 +110,11 @@ def test_default_device(self):
         compile_spec = {"inputs": [self.input], "enabled_precisions": {torch.float}}
 
         trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
-        same = (trt_mod(self.input) - self.traced_model(self.input)).abs().max()
-        self.assertTrue(same < 2e-2)
-
-    def test_compile_script_from_dict(self):
-        self.model = models.vgg16(pretrained=True).eval().to("cuda")
-        self.input = torch.randn((1, 3, 224, 224)).to("cuda")
-        self.traced_model = torch.jit.trace(self.model, [self.input])
-        compile_spec = {
-            "inputs": [torchtrt.Input(shape=self.input.shape)],
-            "device": {
-                "device_type": torchtrt.DeviceType.GPU,
-                "gpu_id": 0,
-            },
-            "enabled_precisions": {torch.float},
-        }
-
-        trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
-        same = (trt_mod(self.input) - self.traced_model(self.input)).abs().max()
-        self.assertTrue(same < 2e-2)
-
-
-class TestPTtoTRTtoPT(unittest.TestCase):
-    def test_pt_to_trt_to_pt(self):
-        self.model = models.vgg16(pretrained=True).eval().to("cuda")
-        self.input = torch.randn((1, 3, 224, 224)).to("cuda")
-        self.ts_model = torch.jit.trace(self.model, [self.input])
-
-        compile_spec = {
-            "inputs": [torchtrt.Input(self.input.shape)],
-            "device": {
-                "device_type": torchtrt.DeviceType.GPU,
-                "gpu_id": 0,
-                "allow_gpu_fallback": False,
-                "disable_tf32": False,
-            },
-        }
-
-        trt_engine = torchtrt.ts.convert_method_to_trt_engine(
-            self.ts_model, "forward", **compile_spec
-        )
-        trt_mod = torchtrt.ts.embed_engine_in_new_module(
-            trt_engine, torchtrt.Device("cuda:0")
+        cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
+        self.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"VGG16 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
         )
-        same = (trt_mod(self.input) - self.ts_model(self.input)).abs().max()
-        self.assertTrue(same < 2e-3)
 
 
 class TestCheckMethodOpSupport(unittest.TestCase):
diff --git a/tests/py/api/utils.py b/tests/py/api/utils.py
new file mode 100644
index 0000000000..b1e6632ec3
--- /dev/null
+++ b/tests/py/api/utils.py
@@ -0,0 +1,15 @@
+import torch
+
+COSINE_THRESHOLD = 0.99
+
+
+def cosine_similarity(gt_tensor, pred_tensor):
+    gt_tensor = gt_tensor.flatten().to(torch.float32)
+    pred_tensor = pred_tensor.flatten().to(torch.float32)
+    if torch.sum(gt_tensor) == 0.0 or torch.sum(pred_tensor) == 0.0:
+        if torch.allclose(gt_tensor, pred_tensor, atol=1e-4, rtol=1e-4, equal_nan=True):
+            return 1.0
+    res = torch.nn.functional.cosine_similarity(gt_tensor, pred_tensor, dim=0, eps=1e-6)
+    res = res.cpu().detach().item()
+
+    return res
diff --git a/tests/py/hw/test_api_dla.py b/tests/py/hw/test_api_dla.py
index 57b149faa7..5328b92233 100644
--- a/tests/py/hw/test_api_dla.py
+++ b/tests/py/hw/test_api_dla.py
@@ -2,6 +2,7 @@
 import torch_tensorrt as torchtrt
 import torch
 import torchvision.models as models
+from utils import cosine_similarity, COSINE_THRESHOLD
 
 
 class ModelTestCaseOnDLA(unittest.TestCase):
@@ -39,8 +40,11 @@ def test_compile_traced(self):
         }
 
         trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
-        same = (trt_mod(self.input) - self.traced_model(self.input)).abs().max()
-        self.assertTrue(same < 2e-2)
+        cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
+        self.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"ModelTestCaseOnDLA traced TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+        )
 
     def test_compile_script(self):
         compile_spec = {
@@ -55,8 +59,11 @@ def test_compile_script(self):
         }
 
         trt_mod = torchtrt.ts.compile(self.scripted_model, **compile_spec)
-        same = (trt_mod(self.input) - self.scripted_model(self.input)).abs().max()
-        self.assertTrue(same < 2e-2)
+        cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
+        self.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"ModelTestCaseOnDLA scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+        )
 
 
 def test_suite():
diff --git a/tests/py/hw/test_multi_gpu.py b/tests/py/hw/test_multi_gpu.py
index c068cc71b0..b6fa3f220b 100644
--- a/tests/py/hw/test_multi_gpu.py
+++ b/tests/py/hw/test_multi_gpu.py
@@ -35,9 +35,12 @@ def test_compile_traced(self):
 
         trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
         torchtrt.set_device(self.target_gpu)
-        same = (trt_mod(self.input) - self.traced_model(self.input)).abs().max()
+        cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
         torchtrt.set_device(0)
-        self.assertTrue(same < 2e-3)
+        self.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"TestMultiGpuSwitching traced TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+        )
 
     def test_compile_script(self):
         torchtrt.set_device(0)
@@ -54,9 +57,12 @@ def test_compile_script(self):
 
         trt_mod = torchtrt.ts.compile(self.scripted_model, **compile_spec)
         torchtrt.set_device(self.target_gpu)
-        same = (trt_mod(self.input) - self.scripted_model(self.input)).abs().max()
+        cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
         torchtrt.set_device(0)
-        self.assertTrue(same < 2e-3)
+        self.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"TestMultiGpuSwitching scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+        )
 
 
 class TestMultiGpuSerializeDeserializeSwitching(ModelTestCase):
@@ -89,8 +95,11 @@ def test_compile_traced(self):
         trt_mod = torchtrt.ts.compile(self.traced_model, **compile_spec)
         # Changing the device ID deliberately. It should still run on correct device ID by context switching
         torchtrt.set_device(1)
-        same = (trt_mod(self.input) - self.traced_model(self.input)).abs().max()
-        self.assertTrue(same < 2e-3)
+        cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
+        self.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"TestMultiGpuSerializeDeserializeSwitching traced TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+        )
 
     def test_compile_script(self):
         torchtrt.set_device(0)
@@ -108,8 +117,11 @@ def test_compile_script(self):
         trt_mod = torchtrt.ts.compile(self.scripted_model, **compile_spec)
         # Changing the device ID deliberately. It should still run on correct device ID by context switching
         torchtrt.set_device(1)
-        same = (trt_mod(self.input) - self.scripted_model(self.input)).abs().max()
-        self.assertTrue(same < 2e-3)
+        cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
+        self.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"TestMultiGpuSerializeDeserializeSwitching scripted TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+        )
 
 
 def test_suite():
diff --git a/tests/py/hw/utils.py b/tests/py/hw/utils.py
new file mode 100644
index 0000000000..b1e6632ec3
--- /dev/null
+++ b/tests/py/hw/utils.py
@@ -0,0 +1,15 @@
+import torch
+
+COSINE_THRESHOLD = 0.99
+
+
+def cosine_similarity(gt_tensor, pred_tensor):
+    gt_tensor = gt_tensor.flatten().to(torch.float32)
+    pred_tensor = pred_tensor.flatten().to(torch.float32)
+    if torch.sum(gt_tensor) == 0.0 or torch.sum(pred_tensor) == 0.0:
+        if torch.allclose(gt_tensor, pred_tensor, atol=1e-4, rtol=1e-4, equal_nan=True):
+            return 1.0
+    res = torch.nn.functional.cosine_similarity(gt_tensor, pred_tensor, dim=0, eps=1e-6)
+    res = res.cpu().detach().item()
+
+    return res
diff --git a/tests/py/integrations/test_to_backend_api.py b/tests/py/integrations/test_to_backend_api.py
index 16d839b1b0..0f74a3af15 100644
--- a/tests/py/integrations/test_to_backend_api.py
+++ b/tests/py/integrations/test_to_backend_api.py
@@ -2,6 +2,7 @@
 import torch_tensorrt as torchtrt
 import torch
 import torchvision.models as models
+from utils import cosine_similarity, COSINE_THRESHOLD
 
 
 class TestToBackendLowering(unittest.TestCase):
@@ -31,10 +32,11 @@ def setUp(self):
 
     def test_to_backend_lowering(self):
         trt_mod = torch._C._jit_to_backend("tensorrt", self.scripted_model, self.spec)
-        same = (
-            (trt_mod.forward(self.input) - self.scripted_model(self.input)).abs().max()
+        cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
+        self.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"TestToBackendLowering TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
         )
-        self.assertTrue(same < 2e-3)
 
 
 if __name__ == "__main__":
diff --git a/tests/py/integrations/test_trt_intercompatibility.py b/tests/py/integrations/test_trt_intercompatibility.py
index 96b47b7ccc..b938e4a1ac 100644
--- a/tests/py/integrations/test_trt_intercompatibility.py
+++ b/tests/py/integrations/test_trt_intercompatibility.py
@@ -3,6 +3,7 @@
 import torch
 import torchvision.models as models
 import tensorrt as trt
+from utils import cosine_similarity, COSINE_THRESHOLD
 
 
 class TestPyTorchToTRTEngine(unittest.TestCase):
@@ -42,8 +43,11 @@ def test_pt_to_trt(self):
                         device="cuda:0"
                     ).cuda_stream,
                 )
-                same = (out - self.ts_model(self.input)).abs().max()
-                self.assertTrue(same < 2e-3)
+                cos_sim = cosine_similarity(self.model(self.input), out)
+                self.assertTrue(
+                    cos_sim > COSINE_THRESHOLD,
+                    msg=f"TestPyTorchToTRTEngine TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+                )
 
 
 if __name__ == "__main__":
diff --git a/tests/py/integrations/utils.py b/tests/py/integrations/utils.py
new file mode 100644
index 0000000000..b1e6632ec3
--- /dev/null
+++ b/tests/py/integrations/utils.py
@@ -0,0 +1,15 @@
+import torch
+
+COSINE_THRESHOLD = 0.99
+
+
+def cosine_similarity(gt_tensor, pred_tensor):
+    gt_tensor = gt_tensor.flatten().to(torch.float32)
+    pred_tensor = pred_tensor.flatten().to(torch.float32)
+    if torch.sum(gt_tensor) == 0.0 or torch.sum(pred_tensor) == 0.0:
+        if torch.allclose(gt_tensor, pred_tensor, atol=1e-4, rtol=1e-4, equal_nan=True):
+            return 1.0
+    res = torch.nn.functional.cosine_similarity(gt_tensor, pred_tensor, dim=0, eps=1e-6)
+    res = res.cpu().detach().item()
+
+    return res
diff --git a/tests/py/models/custom_models.py b/tests/py/models/custom_models.py
new file mode 100644
index 0000000000..a19b9ca81c
--- /dev/null
+++ b/tests/py/models/custom_models.py
@@ -0,0 +1,28 @@
+import torch
+from transformers import BertModel, BertTokenizer, BertConfig
+
+
+def BertModule():
+    model_name = "bert-base-uncased"
+    enc = BertTokenizer.from_pretrained(model_name)
+    text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
+    tokenized_text = enc.tokenize(text)
+    masked_index = 8
+    tokenized_text[masked_index] = "[MASK]"
+    indexed_tokens = enc.convert_tokens_to_ids(tokenized_text)
+    segments_ids = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]
+    tokens_tensor = torch.tensor([indexed_tokens])
+    segments_tensors = torch.tensor([segments_ids])
+    config = BertConfig(
+        vocab_size_or_config_json_file=32000,
+        hidden_size=768,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        intermediate_size=3072,
+        torchscript=True,
+    )
+    model = BertModel(config)
+    model.eval()
+    model = BertModel.from_pretrained(model_name, torchscript=True)
+    traced_model = torch.jit.trace(model, [tokens_tensor, segments_tensors])
+    return traced_model
diff --git a/tests/py/models/test_models.py b/tests/py/models/test_models.py
new file mode 100644
index 0000000000..6cc9759626
--- /dev/null
+++ b/tests/py/models/test_models.py
@@ -0,0 +1,153 @@
+import unittest
+import torch_tensorrt as torchtrt
+import torch
+import torchvision.models as models
+import copy
+import timm
+import custom_models as cm
+from typing import Dict
+from utils import cosine_similarity, COSINE_THRESHOLD
+
+
+class TestModels(unittest.TestCase):
+    def test_resnet18(self):
+        self.model = models.resnet18(pretrained=True).eval().to("cuda")
+        self.input = torch.randn((1, 3, 224, 224)).to("cuda")
+
+        compile_spec = {
+            "inputs": [
+                torchtrt.Input(
+                    self.input.shape, dtype=torch.float, format=torch.contiguous_format
+                )
+            ],
+            "device": {
+                "device_type": torchtrt.DeviceType.GPU,
+                "gpu_id": 0,
+            },
+            "enabled_precisions": {torch.float},
+        }
+
+        trt_mod = torchtrt.compile(self.model, **compile_spec)
+        cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
+        self.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"Resnet50 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+        )
+
+    def test_mobilenet_v2(self):
+        self.model = models.mobilenet_v2(pretrained=True).eval().to("cuda")
+        self.input = torch.randn((1, 3, 224, 224)).to("cuda")
+
+        compile_spec = {
+            "inputs": [
+                torchtrt.Input(
+                    self.input.shape, dtype=torch.float, format=torch.contiguous_format
+                )
+            ],
+            "device": {
+                "device_type": torchtrt.DeviceType.GPU,
+                "gpu_id": 0,
+            },
+            "enabled_precisions": {torch.float},
+        }
+
+        trt_mod = torchtrt.compile(self.model, **compile_spec)
+        cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
+        self.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"Mobilenet v2 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+        )
+
+    def test_efficientnet_b0(self):
+        self.model = (
+            timm.create_model("efficientnet_b0", pretrained=True).eval().to("cuda")
+        )
+        self.input = torch.randn((1, 3, 224, 224)).to("cuda")
+
+        compile_spec = {
+            "inputs": [
+                torchtrt.Input(
+                    self.input.shape, dtype=torch.float, format=torch.contiguous_format
+                )
+            ],
+            "device": {
+                "device_type": torchtrt.DeviceType.GPU,
+                "gpu_id": 0,
+            },
+            "enabled_precisions": {torch.float},
+        }
+
+        trt_mod = torchtrt.compile(self.model, **compile_spec)
+        cos_sim = cosine_similarity(self.model(self.input), trt_mod(self.input))
+        self.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"EfficientNet-B0 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+        )
+
+    def test_bert_base_uncased(self):
+        self.model = cm.BertModule().cuda()
+        self.input = torch.randint(0, 5, (1, 14), dtype=torch.int32).to("cuda")
+
+        compile_spec = {
+            "inputs": [
+                torchtrt.Input(
+                    self.input.shape,
+                    dtype=self.input.dtype,
+                    format=torch.contiguous_format,
+                ),
+                torchtrt.Input(
+                    self.input.shape,
+                    dtype=self.input.dtype,
+                    format=torch.contiguous_format,
+                ),
+            ],
+            "device": {
+                "device_type": torchtrt.DeviceType.GPU,
+                "gpu_id": 0,
+            },
+            "enabled_precisions": {torch.float},
+            "truncate_long_and_double": True,
+        }
+        with torchtrt.logging.errors():
+            trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
+
+        model_outputs = self.model(self.input, self.input)
+        trt_model_outputs = trt_mod(self.input, self.input)
+        for out, trt_out in zip(model_outputs, trt_model_outputs):
+            cos_sim = cosine_similarity(out, trt_out)
+            self.assertTrue(
+                cos_sim > COSINE_THRESHOLD,
+                msg=f"HF BERT base-uncased TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+            )
+
+    def test_resnet18_half(self):
+        self.model = models.resnet18(pretrained=True).eval().to("cuda")
+        self.input = torch.randn((1, 3, 224, 224)).to("cuda")
+        self.scripted_model = torch.jit.script(self.model)
+        self.scripted_model.half()
+
+        compile_spec = {
+            "inputs": [
+                torchtrt.Input(
+                    self.input.shape, dtype=torch.half, format=torch.contiguous_format
+                )
+            ],
+            "device": {
+                "device_type": torchtrt.DeviceType.GPU,
+                "gpu_id": 0,
+            },
+            "enabled_precisions": {torch.half},
+        }
+
+        trt_mod = torchtrt.compile(self.scripted_model, **compile_spec)
+        cos_sim = cosine_similarity(
+            self.model.half()(self.input.half()), trt_mod(self.input.half())
+        )
+        self.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"Resnet50 Half TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/py/models/test_multiple_registered_engines.py b/tests/py/models/test_multiple_registered_engines.py
new file mode 100644
index 0000000000..98f012597b
--- /dev/null
+++ b/tests/py/models/test_multiple_registered_engines.py
@@ -0,0 +1,52 @@
+import unittest
+import torch_tensorrt as torchtrt
+import torch
+import torchvision.models as models
+import copy
+import timm
+import custom_models as cm
+from typing import Dict
+from utils import cosine_similarity, COSINE_THRESHOLD
+
+
+class TestModelToEngineToModel(unittest.TestCase):
+    def test_multiple_engines(self):
+        self.resnet18 = models.resnet18(pretrained=True).eval().to("cuda")
+        self.resnet50 = models.resnet50(pretrained=True).eval().to("cuda")
+        self.input1 = torch.randn((1, 3, 224, 224)).to("cuda")
+        self.input2 = torch.randn((1, 3, 224, 224)).to("cuda")
+
+        compile_spec = {
+            "inputs": [
+                torchtrt.Input(
+                    self.input1.shape, dtype=torch.float, format=torch.contiguous_format
+                )
+            ],
+            "device": {
+                "device_type": torchtrt.DeviceType.GPU,
+                "gpu_id": 0,
+            },
+            "enabled_precisions": {torch.float},
+        }
+        rn18_trt_mod = torchtrt.compile(self.resnet18, **compile_spec)
+        rn50_trt_mod = torchtrt.compile(self.resnet50, **compile_spec)
+
+        cos_sim = cosine_similarity(
+            self.resnet18(self.input1), rn18_trt_mod(self.input1)
+        )
+        self.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"Resnet18 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+        )
+
+        cos_sim = cosine_similarity(
+            self.resnet50(self.input1), rn50_trt_mod(self.input1)
+        )
+        self.assertTrue(
+            cos_sim > COSINE_THRESHOLD,
+            msg=f"Resnet50 TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/py/models/utils.py b/tests/py/models/utils.py
new file mode 100644
index 0000000000..b1e6632ec3
--- /dev/null
+++ b/tests/py/models/utils.py
@@ -0,0 +1,15 @@
+import torch
+
+COSINE_THRESHOLD = 0.99
+
+
+def cosine_similarity(gt_tensor, pred_tensor):
+    gt_tensor = gt_tensor.flatten().to(torch.float32)
+    pred_tensor = pred_tensor.flatten().to(torch.float32)
+    if torch.sum(gt_tensor) == 0.0 or torch.sum(pred_tensor) == 0.0:
+        if torch.allclose(gt_tensor, pred_tensor, atol=1e-4, rtol=1e-4, equal_nan=True):
+            return 1.0
+    res = torch.nn.functional.cosine_similarity(gt_tensor, pred_tensor, dim=0, eps=1e-6)
+    res = res.cpu().detach().item()
+
+    return res
diff --git a/tests/py/ptq/test_ptq_dataloader_calibrator.py b/tests/py/ptq/test_ptq_dataloader_calibrator.py
index 2ee1fa5b08..79c19dadbf 100644
--- a/tests/py/ptq/test_ptq_dataloader_calibrator.py
+++ b/tests/py/ptq/test_ptq_dataloader_calibrator.py
@@ -81,9 +81,6 @@ def test_compile_script(self):
             device=torch.device("cuda:0"),
         )
 
-        fp32_test_acc = compute_accuracy(self.testing_dataloader, self.model)
-        log(Level.Info, "[Pyt FP32] Test Acc: {:.2f}%".format(100 * fp32_test_acc))
-
         compile_spec = {
             "inputs": [torchtrt.Input([1, 3, 32, 32])],
             "enabled_precisions": {torch.float, torch.int8},
@@ -96,8 +93,11 @@ def test_compile_script(self):
                 "allow_gpu_fallback": False,
             },
         }
-
         trt_mod = torchtrt.ts.compile(self.model, **compile_spec)
+
+        fp32_test_acc = compute_accuracy(self.testing_dataloader, self.model)
+        log(Level.Info, "[Pyt FP32] Test Acc: {:.2f}%".format(100 * fp32_test_acc))
+
         int8_test_acc = compute_accuracy(self.testing_dataloader, trt_mod)
         log(Level.Info, "[TRT INT8] Test Acc: {:.2f}%".format(100 * int8_test_acc))
         acc_diff = fp32_test_acc - int8_test_acc
diff --git a/tests/py/ptq/test_ptq_trt_calibrator.py b/tests/py/ptq/test_ptq_trt_calibrator.py
index bda117d3a5..93596c895d 100644
--- a/tests/py/ptq/test_ptq_trt_calibrator.py
+++ b/tests/py/ptq/test_ptq_trt_calibrator.py
@@ -75,7 +75,7 @@ def get_batch(self, names):
         ):
             return None
 
-        batch = self.dataset_iterator.next()
+        batch = next(self.dataset_iterator)
         self.current_batch_idx += self.batch_size
         # Treat the first element as input and others as targets.
         if isinstance(batch, list):
diff --git a/tests/py/utils.py b/tests/py/utils.py
new file mode 100644
index 0000000000..b1e6632ec3
--- /dev/null
+++ b/tests/py/utils.py
@@ -0,0 +1,15 @@
+import torch
+
+COSINE_THRESHOLD = 0.99
+
+
+def cosine_similarity(gt_tensor, pred_tensor):
+    gt_tensor = gt_tensor.flatten().to(torch.float32)
+    pred_tensor = pred_tensor.flatten().to(torch.float32)
+    if torch.sum(gt_tensor) == 0.0 or torch.sum(pred_tensor) == 0.0:
+        if torch.allclose(gt_tensor, pred_tensor, atol=1e-4, rtol=1e-4, equal_nan=True):
+            return 1.0
+    res = torch.nn.functional.cosine_similarity(gt_tensor, pred_tensor, dim=0, eps=1e-6)
+    res = res.cpu().detach().item()
+
+    return res
diff --git a/tests/util/util.cpp b/tests/util/util.cpp
index 13d0d18566..8359d31576 100644
--- a/tests/util/util.cpp
+++ b/tests/util/util.cpp
@@ -1,10 +1,23 @@
 #include "core/util/prelude.h"
 #include "torch/script.h"
+#include "torch/torch.h"
 
 namespace torch_tensorrt {
 namespace tests {
 namespace util {
 
+bool cosineSimEqual(const at::Tensor& computed_tensor, const at::Tensor& gt_tensor, float threshold = 0.99f) {
+  torch::Tensor cosine_sim = torch::nn::functional::cosine_similarity(
+      computed_tensor.flatten(), gt_tensor.flatten(), torch::nn::functional::CosineSimilarityFuncOptions().dim(0));
+  std::ostringstream ss;
+  ss << computed_tensor << std::endl << gt_tensor << std::endl;
+  LOG_GRAPH(ss.str());
+  LOG_GRAPH(std::string("Cosine Similarity score: ") + std::to_string(cosine_sim.item()));
+  LOG_GRAPH(std::string("Acceptable Threshold: ") + std::to_string(threshold));
+
+  return cosine_sim.item() >= threshold;
+}
+
 bool almostEqual(const at::Tensor& computed_tensor, const at::Tensor& gt_tensor, float atol = 1e-8, float rtol = 1e-5) {
   std::ostringstream ss;
   ss << computed_tensor << std::endl << gt_tensor << std::endl;
diff --git a/tests/util/util.h b/tests/util/util.h
index f39e2a5766..1ea62a16e0 100644
--- a/tests/util/util.h
+++ b/tests/util/util.h
@@ -11,6 +11,8 @@ namespace torch_tensorrt {
 namespace tests {
 namespace util {
 
+bool cosineSimEqual(const at::Tensor& computed_tensor, const at::Tensor& gt_tensor, float threshold);
+
 bool almostEqual(const at::Tensor& computed_tensor, const at::Tensor& gt_tensor, float atol = 1e-8, float rtol = 1e-5);
 
 bool exactlyEqual(const at::Tensor& a, const at::Tensor& b);
diff --git a/tools/perf/README.md b/tools/perf/README.md
index 4c4a58bfd0..45630b4f29 100644
--- a/tools/perf/README.md
+++ b/tools/perf/README.md
@@ -4,7 +4,9 @@ This is a comprehensive Python benchmark suite to run perf runs using different
 
 1. Torch
 2. Torch-TensorRT
-3. TensorRT
+3. FX-TRT
+4. TensorRT
+
 
 Note: Please note that for ONNX models, user can convert the ONNX model to TensorRT serialized engine and then use this package.
 
@@ -25,21 +27,35 @@ Benchmark scripts depends on following Python packages in addition to requiremen
 │   └── vgg16.yml
 ├── models
 ├── perf_run.py
+├── hub.py
+├── custom_models.py
+├── requirements.txt
+├── benchmark.sh
 └── README.md
 ```
 
-Please save your configuration files at config directory. Similarly, place your model files at models path.
+
+
+* `config` - Directory which contains sample yaml configuration files for VGG network.
+* `models` - Model directory
+* `perf_run.py` - Performance benchmarking script which supports torch, torch_tensorrt, fx2trt, tensorrt backends
+* `hub.py` - Script to download torchscript models for VGG16, Resnet50, EfficientNet-B0, VIT, HF-BERT
+* `custom_models.py` - Script which includes custom models other than torchvision and timm (eg: HF BERT)
+* `utils.py` - utility functions script
+* `benchmark.sh` - This is used for internal performance testing of VGG16, Resnet50, EfficientNet-B0, VIT, HF-BERT.
 
 ## Usage
 
+There are two ways you can run a performance benchmark.
+
+### Using YAML config files
+
 To run the benchmark for a given configuration file:
 
-```
+```python
 python perf_run.py --config=config/vgg16.yml
 ```
 
-## Configuration
-
 There are two sample configuration files added.
 
 * vgg16.yml demonstrates a configuration with all the supported backends (Torch, Torch-TensorRT, TensorRT)
@@ -48,23 +64,17 @@ There are two sample configuration files added.
 
 ### Supported fields
 
-| Name | Supported Values | Description |
-| --- | --- | --- |
-| backend | all, torch, torch_tensorrt, tensorrt | Supported backends for inference. |
-| input | - | Input binding names. Expected to list shapes of each input bindings |
-| model | - | Configure the model filename and name |
-| filename | - | Model file name to load from disk. |
-| name | - | Model name |
-| runtime | - | Runtime configurations |
-| device | 0 | Target device ID to run inference. Range depends on available GPUs |
-| precision | fp32, fp16 or half, int8 | Target precision to run inference. int8 cannot be used with 'all' backend |
-| calibration_cache | - | Calibration cache file expected for torch_tensorrt runtime in int8 precision |
-
-Note:
-1. Please note that torch runtime perf is not supported for int8 yet.
-2. Torchscript module filename should end with .jit.pt otherwise it will be treated as a TensorRT engine.
-
-
+| Name              | Supported Values                     | Description                                                  |
+| ----------------- | ------------------------------------ | ------------------------------------------------------------ |
+| backend           | all, torch, torch_tensorrt, tensorrt | Supported backends for inference.                            |
+| input             | -                                    | Input binding names. Expected to list shapes of each input bindings |
+| model             | -                                    | Configure the model filename and name                        |
+| filename          | -                                    | Model file name to load from disk.                           |
+| name              | -                                    | Model name                                                   |
+| runtime           | -                                    | Runtime configurations                                       |
+| device            | 0                                    | Target device ID to run inference. Range depends on available GPUs |
+| precision         | fp32, fp16 or half, int8             | Target precision to run inference. int8 cannot be used with 'all' backend |
+| calibration_cache | -                                    | Calibration cache file expected for torch_tensorrt runtime in int8 precision |
 
 Additional sample use case:
 
@@ -88,3 +98,41 @@ runtime:
     - fp32
     - fp16
 ```
+
+Note:
+
+1. Please note that measuring INT8 performance is only supported via a `calibration cache` file or QAT mode for `torch_tensorrt` backend.
+2. TensorRT engine filename should end with `.plan` otherwise it will be treated as Torchscript module.
+
+### Using CompileSpec options via CLI
+
+Here are the list of `CompileSpec` options that can be provided directly to compile the pytorch module
+
+* `--backends` : Comma separated string of backends. Eg: torch,torch_tensorrt, tensorrt or fx2trt
+* `--model` : Name of the model file (Can be a torchscript module or a tensorrt engine (ending in `.plan` extension)). If the backend is `fx2trt`, the input should be a Pytorch module (instead of a torchscript module) and the options for model are (`vgg16` | `resnet50` | `efficientnet_b0`)
+* `--inputs` : List of input shapes & dtypes. Eg: (1, 3, 224, 224)@fp32 for Resnet or (1, 128)@int32;(1, 128)@int32 for BERT
+* `--batch_size` : Batch size
+* `--precision` : Comma separated list of precisions to build TensorRT engine Eg: fp32,fp16
+* `--device` : Device ID
+* `--truncate` : Truncate long and double weights in the network in Torch-TensorRT
+* `--is_trt_engine` : Boolean flag to be enabled if the model file provided is a TensorRT engine.
+* `--report` : Path of the output file where performance summary is written.
+
+Eg:
+
+```
+  python perf_run.py --model ${MODELS_DIR}/vgg16_scripted.jit.pt \
+                     --precision fp32,fp16 --inputs="(1, 3, 224, 224)@fp32" \
+                     --batch_size 1 \
+                     --backends torch,torch_tensorrt,tensorrt \
+                     --report "vgg_perf_bs1.txt"
+```
+
+### Example models
+
+This tool benchmarks any pytorch model or torchscript module. As an example, we provide VGG16, Resnet50, EfficientNet-B0, VIT, HF-BERT models in `hub.py` that we internally test for performance.
+The torchscript modules for these models can be generated by running
+```
+python hub.py
+```
+You can refer to `benchmark.sh` on how we run/benchmark these models.
diff --git a/tools/perf/benchmark.sh b/tools/perf/benchmark.sh
new file mode 100644
index 0000000000..b84061025d
--- /dev/null
+++ b/tools/perf/benchmark.sh
@@ -0,0 +1,64 @@
+#!/bin/bash
+
+MODELS_DIR="models"
+
+# Download the Torchscript models
+python hub.py
+
+batch_sizes=(1 2 4 8 16 32 64 128 256)
+
+#Benchmark VGG16 model
+echo "Benchmarking VGG16 model"
+for bs in ${batch_sizes[@]}
+do
+  python perf_run.py --model ${MODELS_DIR}/vgg16_scripted.jit.pt \
+                     --precision fp32,fp16 --inputs="(${bs}, 3, 224, 224)" \
+                     --batch_size ${bs} \
+                     --backends torch,torch_tensorrt,tensorrt \
+                     --report "vgg_perf_bs${bs}.txt"
+done
+
+# Benchmark Resnet50 model
+echo "Benchmarking Resnet50 model"
+for bs in ${batch_sizes[@]}
+do
+  python perf_run.py --model ${MODELS_DIR}/resnet50_scripted.jit.pt \
+                     --precision fp32,fp16 --inputs="(${bs}, 3, 224, 224)" \
+                     --batch_size ${bs} \
+                     --backends torch,torch_tensorrt,tensorrt \
+                     --report "rn50_perf_bs${bs}.txt"
+done
+
+# Benchmark VIT model
+echo "Benchmarking VIT model"
+for bs in ${batch_sizes[@]}
+do
+  python perf_run.py --model ${MODELS_DIR}/vit_scripted.jit.pt \
+                     --precision fp32,fp16 --inputs="(${bs}, 3, 224, 224)" \
+                     --batch_size ${bs} \
+                     --backends torch,torch_tensorrt,tensorrt \
+                     --report "vit_perf_bs${bs}.txt"
+done
+
+# Benchmark EfficientNet-B0 model
+echo "Benchmarking EfficientNet-B0 model"
+for bs in ${batch_sizes[@]}
+do
+  python perf_run.py --model ${MODELS_DIR}/efficientnet_b0_scripted.jit.pt \
+                     --precision fp32,fp16 --inputs="(${bs}, 3, 224, 224)" \
+                     --batch_size ${bs} \
+                     --backends torch,torch_tensorrt,tensorrt \
+                     --report "eff_b0_perf_bs${bs}.txt"
+done
+
+# Benchmark BERT model
+echo "Benchmarking Huggingface BERT base model"
+for bs in ${batch_sizes[@]}
+do
+  python perf_run.py --model ${MODELS_DIR}/bert_base_uncased_traced.jit.pt \
+                     --precision fp32 --inputs="(${bs}, 128)@int32;(${bs}, 128)@int32" \
+                     --batch_size ${bs} \
+                     --backends torch,torch_tensorrt \
+                     --truncate \
+                     --report "bert_base_perf_bs${bs}.txt"
+done
diff --git a/tools/perf/config/vgg16.yml b/tools/perf/config/vgg16.yml
index 458dc1b1f6..d88d489458 100755
--- a/tools/perf/config/vgg16.yml
+++ b/tools/perf/config/vgg16.yml
@@ -8,8 +8,9 @@ input:
     - 224
     - 224
   num_inputs: 1
+  batch_size: 1
 model:
-  filename: models/vgg16_traced.jit.pt
+  filename: models/vgg16_scripted.jit.pt
   name: vgg16
 runtime:
   device: 0
diff --git a/tools/perf/custom_models.py b/tools/perf/custom_models.py
new file mode 100644
index 0000000000..a8b8a5dae0
--- /dev/null
+++ b/tools/perf/custom_models.py
@@ -0,0 +1,30 @@
+import torch
+import torch.nn as nn
+from transformers import BertModel, BertTokenizer, BertConfig
+import torch.nn.functional as F
+
+
+def BertModule():
+    model_name = "bert-base-uncased"
+    enc = BertTokenizer.from_pretrained(model_name)
+    text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
+    tokenized_text = enc.tokenize(text)
+    masked_index = 8
+    tokenized_text[masked_index] = "[MASK]"
+    indexed_tokens = enc.convert_tokens_to_ids(tokenized_text)
+    segments_ids = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]
+    tokens_tensor = torch.tensor([indexed_tokens])
+    segments_tensors = torch.tensor([segments_ids])
+    config = BertConfig(
+        vocab_size_or_config_json_file=32000,
+        hidden_size=768,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        intermediate_size=3072,
+        torchscript=True,
+    )
+    model = BertModel(config)
+    model.eval()
+    model = BertModel.from_pretrained(model_name, torchscript=True)
+    traced_model = torch.jit.trace(model, [tokens_tensor, segments_tensors])
+    return traced_model
diff --git a/tools/perf/hub.py b/tools/perf/hub.py
new file mode 100644
index 0000000000..e54734f8a1
--- /dev/null
+++ b/tools/perf/hub.py
@@ -0,0 +1,132 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.models as models
+import timm
+from transformers import BertModel, BertTokenizer, BertConfig
+import os
+import json
+import custom_models as cm
+
+torch.hub._validate_not_a_forked_repo = lambda a, b, c: True
+
+torch_version = torch.__version__
+
+# Detect case of no GPU before deserialization of models on GPU
+if not torch.cuda.is_available():
+    raise Exception(
+        "No GPU found. Please check if installed torch version is compatible with CUDA version"
+    )
+
+# Downloads all model files again if manifest file is not present
+MANIFEST_FILE = "model_manifest.json"
+
+BENCHMARK_MODELS = {
+    "vgg16": {"model": models.vgg16(weights=None), "path": "script"},
+    "resnet50": {"model": models.resnet50(weights=None), "path": "script"},
+    "efficientnet_b0": {
+        "model": timm.create_model("efficientnet_b0", pretrained=True),
+        "path": "script",
+    },
+    "vit": {
+        "model": timm.create_model("vit_base_patch16_224", pretrained=True),
+        "path": "script",
+    },
+    "bert_base_uncased": {"model": cm.BertModule(), "path": "trace"},
+}
+
+
+def get(n, m, manifest):
+    print("Downloading {}".format(n))
+    traced_filename = "models/" + n + "_traced.jit.pt"
+    script_filename = "models/" + n + "_scripted.jit.pt"
+    x = torch.ones((1, 3, 300, 300)).cuda()
+    if n == "bert-base-uncased":
+        traced_model = m["model"]
+        torch.jit.save(traced_model, traced_filename)
+        manifest.update({n: [traced_filename]})
+    else:
+        m["model"] = m["model"].eval().cuda()
+        if m["path"] == "both" or m["path"] == "trace":
+            trace_model = torch.jit.trace(m["model"], [x])
+            torch.jit.save(trace_model, traced_filename)
+            manifest.update({n: [traced_filename]})
+        if m["path"] == "both" or m["path"] == "script":
+            script_model = torch.jit.script(m["model"])
+            torch.jit.save(script_model, script_filename)
+            if n in manifest.keys():
+                files = list(manifest[n]) if type(manifest[n]) != list else manifest[n]
+                files.append(script_filename)
+                manifest.update({n: files})
+            else:
+                manifest.update({n: [script_filename]})
+    return manifest
+
+
+def download_models(version_matches, manifest):
+    # Download all models if torch version is different than model version
+    if not version_matches:
+        for n, m in BENCHMARK_MODELS.items():
+            manifest = get(n, m, manifest)
+    else:
+        for n, m in BENCHMARK_MODELS.items():
+            scripted_filename = "models/" + n + "_scripted.jit.pt"
+            traced_filename = "models/" + n + "_traced.jit.pt"
+            # Check if model file exists on disk
+            if (
+                (
+                    m["path"] == "both"
+                    and os.path.exists(scripted_filename)
+                    and os.path.exists(traced_filename)
+                )
+                or (m["path"] == "script" and os.path.exists(scripted_filename))
+                or (m["path"] == "trace" and os.path.exists(traced_filename))
+            ):
+                print("Skipping {} ".format(n))
+                continue
+            manifest = get(n, m, manifest)
+
+
+def main():
+    manifest = None
+    version_matches = False
+    manifest_exists = False
+
+    # Check if Manifest file exists or is empty
+    if not os.path.exists(MANIFEST_FILE) or os.stat(MANIFEST_FILE).st_size == 0:
+        manifest = {"version": torch_version}
+
+        # Creating an empty manifest file for overwriting post setup
+        os.system("touch {}".format(MANIFEST_FILE))
+    else:
+        manifest_exists = True
+
+        # Load manifest if already exists
+        with open(MANIFEST_FILE, "r") as f:
+            manifest = json.load(f)
+            if manifest["version"] == torch_version:
+                version_matches = True
+            else:
+                print(
+                    "Torch version: {} mismatches \
+                with manifest's version: {}. Re-downloading \
+                all models".format(
+                        torch_version, manifest["version"]
+                    )
+                )
+
+                # Overwrite the manifest version as current torch version
+                manifest["version"] = torch_version
+
+    download_models(version_matches, manifest)
+
+    # Write updated manifest file to disk
+    with open(MANIFEST_FILE, "r+") as f:
+        data = f.read()
+        f.seek(0)
+        record = json.dumps(manifest)
+        f.write(record)
+        f.truncate()
+
+
+main()
diff --git a/tools/perf/perf_run.py b/tools/perf/perf_run.py
index f0386f4e5a..fbdf3b6c40 100644
--- a/tools/perf/perf_run.py
+++ b/tools/perf/perf_run.py
@@ -15,7 +15,17 @@
 # Importing supported Backends
 import torch
 import torch_tensorrt as torchtrt
+from torch_tensorrt.fx.lower import compile
+from torch_tensorrt.fx.utils import LowerPrecision
+
 import tensorrt as trt
+from utils import (
+    parse_inputs,
+    parse_backends,
+    precision_to_dtype,
+    parse_precisions,
+    BENCHMARK_MODELS,
+)
 
 WARMUP_ITER = 10
 results = []
@@ -49,8 +59,8 @@ def get(self, key, default_value=None):
 
 
 # Runs inference using Torch backend
-def run_torch(model, input_tensors, params, precision):
-    print("Running Torch for precision: ", precision)
+def run_torch(model, input_tensors, params, precision, batch_size):
+    print("Running Torch for precision: ", precision, " batch_size : ", batch_size)
     iters = params.get("iterations", 20)
 
     # Warm up
@@ -69,19 +79,25 @@ def run_torch(model, input_tensors, params, precision):
             end_time = timeit.default_timer()
             meas_time = end_time - start_time
             timings.append(meas_time)
-            print("Iteration {}: {:.6f} s".format(i, end_time - start_time))
 
-    printStats("Torch", timings, precision)
+    recordStats("Torch", timings, precision, batch_size)
 
 
 # Runs inference using Torch-TensorRT backend
-def run_torch_tensorrt(model, input_tensors, params, precision):
-    print("Running Torch-TensorRT")
-
+def run_torch_tensorrt(
+    model, input_tensors, params, precision, truncate_long_and_double, batch_size
+):
+    print(
+        "Running Torch-TensorRT for precision: ",
+        precision,
+        " batch_size : ",
+        batch_size,
+    )
     # Compiling Torch-TensorRT model
     compile_settings = {
         "inputs": input_tensors,
         "enabled_precisions": {precision_to_dtype(precision)},
+        "truncate_long_and_double": truncate_long_and_double,
     }
 
     if precision == "int8":
@@ -106,9 +122,47 @@ def run_torch_tensorrt(model, input_tensors, params, precision):
             end_time = timeit.default_timer()
             meas_time = end_time - start_time
             timings.append(meas_time)
-            print("Iteration {}: {:.6f} s".format(i, end_time - start_time))
 
-    printStats("Torch-TensorRT", timings, precision)
+    recordStats("Torch-TensorRT", timings, precision, batch_size)
+
+
+# Runs inference using FX2TRT backend
+def run_fx2trt(model, input_tensors, params, precision, batch_size):
+    print("Running FX2TRT for precision: ", precision, " batch_size : ", batch_size)
+    if precision == "fp32":
+        precision = LowerPrecision.FP32
+    elif precision == "fp16":
+        precision = LowerPrecision.FP16
+        model.half()
+        input_tensors = [tensor.half() for tensor in input_tensors]
+    # Run lowering eager mode benchmark
+    model = compile(
+        model,
+        input_tensors,
+        max_batch_size=batch_size,
+        lower_precision=precision,
+        verbose_log=False,
+    )
+
+    iters = params.get("iterations", 20)
+    # Warm up
+    with torch.no_grad():
+        for _ in range(WARMUP_ITER):
+            features = model(*input_tensors)
+
+    torch.cuda.synchronize()
+
+    timings = []
+    with torch.no_grad():
+        for i in range(iters):
+            start_time = timeit.default_timer()
+            features = model(*input_tensors)
+            torch.cuda.synchronize()
+            end_time = timeit.default_timer()
+            meas_time = end_time - start_time
+            timings.append(meas_time)
+
+    recordStats("FX-TensorRT", timings, precision, batch_size)
 
 
 def torch_dtype_from_trt(dtype):
@@ -135,7 +189,15 @@ def torch_device_from_trt(device):
         return TypeError("%s is not supported by torch" % device)
 
 
-def run_tensorrt(model, input_tensors, params, precision, is_trt_engine=False):
+def run_tensorrt(
+    model,
+    input_tensors,
+    params,
+    precision,
+    truncate_long_and_double=False,
+    is_trt_engine=False,
+    batch_size=1,
+):
     engine = None
 
     # If the model file is a TensorRT engine then directly deserialize and run inference
@@ -144,10 +206,11 @@ def run_tensorrt(model, input_tensors, params, precision, is_trt_engine=False):
         compile_settings = {
             "inputs": input_tensors,
             "enabled_precisions": {precision_to_dtype(precision)},
+            "truncate_long_and_double": truncate_long_and_double,
         }
 
         print("Converting method to TensorRT engine...")
-        with torch.no_grad():
+        with torch.no_grad(), torchtrt.logging.errors():
             model = torchtrt.ts.convert_method_to_trt_engine(
                 model, "forward", **compile_settings
             )
@@ -156,17 +219,15 @@ def run_tensorrt(model, input_tensors, params, precision, is_trt_engine=False):
     with trt.Logger() as logger, trt.Runtime(logger) as runtime:
         engine = runtime.deserialize_cuda_engine(model)
 
-    print("Running TensorRT")
+    print("Running TensorRT for precision: ", precision, " batch_size : ", batch_size)
     iters = params.get("iterations", 20)
-    batch_size = params.get("batch", 1)
 
     # Compiling the bindings
     bindings = engine.num_bindings * [None]
-
     k = 0
     for idx, _ in enumerate(bindings):
         dtype = torch_dtype_from_trt(engine.get_binding_dtype(idx))
-        shape = (batch_size,) + tuple(engine.get_binding_shape(idx))
+        shape = tuple(engine.get_binding_shape(idx))
         device = torch_device_from_trt(engine.get_location(idx))
         if not engine.binding_is_input(idx):
             # Output bindings
@@ -180,29 +241,32 @@ def run_tensorrt(model, input_tensors, params, precision, is_trt_engine=False):
     timings = []
     with engine.create_execution_context() as context:
         for i in range(WARMUP_ITER):
-            context.execute_async(
-                batch_size, bindings, torch.cuda.current_stream().cuda_stream
-            )
+            context.execute_async_v2(bindings, torch.cuda.current_stream().cuda_stream)
             torch.cuda.synchronize()
 
         for i in range(iters):
             start_time = timeit.default_timer()
-            context.execute_async(
-                batch_size, bindings, torch.cuda.current_stream().cuda_stream
-            )
+            context.execute_async_v2(bindings, torch.cuda.current_stream().cuda_stream)
             torch.cuda.synchronize()
             end_time = timeit.default_timer()
             meas_time = end_time - start_time
             timings.append(meas_time)
-            print("Iterations {}: {:.6f} s".format(i, end_time - start_time))
 
-    printStats("TensorRT", timings, precision)
+    recordStats("TensorRT", timings, precision, batch_size)
 
 
 # Deploys inference run for different backend configurations
-def run(model, input_tensors, params, precision, is_trt_engine=False):
-    for backend in params.get("backend"):
-
+def run(
+    model,
+    backends,
+    input_tensors,
+    params,
+    precision,
+    truncate_long_and_double=False,
+    batch_size=1,
+    is_trt_engine=False,
+):
+    for backend in backends:
         if precision == "int8":
             if backend == "all" or backend == "torch":
                 print(
@@ -219,22 +283,55 @@ def run(model, input_tensors, params, precision, is_trt_engine=False):
                 return False
 
         if backend == "all":
-            run_torch(model, input_tensors, params, precision)
-            run_torch_tensorrt(model, input_tensors, params, precision)
-            run_tensorrt(model, input_tensors, params, precision, is_trt_engine)
+            run_torch(model, input_tensors, params, precision, batch_size)
+            run_torch_tensorrt(
+                model,
+                input_tensors,
+                params,
+                precision,
+                truncate_long_and_double,
+                batch_size,
+            )
+            run_tensorrt(
+                model,
+                input_tensors,
+                params,
+                precision,
+                truncate_long_and_double,
+                is_trt_engine,
+                batch_size,
+            )
 
         elif backend == "torch":
-            run_torch(model, input_tensors, params, precision)
+            run_torch(model, input_tensors, params, precision, batch_size)
 
         elif backend == "torch_tensorrt":
-            run_torch_tensorrt(model, input_tensors, params, precision)
+            run_torch_tensorrt(
+                model,
+                input_tensors,
+                params,
+                precision,
+                truncate_long_and_double,
+                batch_size,
+            )
+
+        elif backend == "fx2trt":
+            run_fx2trt(model, input_tensors, params, precision, batch_size)
 
         elif backend == "tensorrt":
-            run_tensorrt(model, input_tensors, params, precision, is_trt_engine)
+            run_tensorrt(
+                model,
+                input_tensors,
+                params,
+                precision,
+                truncate_long_and_double,
+                is_trt_engine,
+                batch_size,
+            )
 
 
 # Generate report
-def printStats(backend, timings, precision, batch_size=1):
+def recordStats(backend, timings, precision, batch_size=1):
     times = np.array(timings)
     steps = len(times)
     speeds = batch_size / times
@@ -245,43 +342,16 @@ def printStats(backend, timings, precision, batch_size=1):
     speed_mean = np.mean(speeds)
     speed_med = np.median(speeds)
 
-    msg = (
-        "\n%s =================================\n"
-        "batch size=%d, num iterations=%d\n"
-        "  Median FPS: %.1f, mean: %.1f\n"
-        "  Median latency: %.6f, mean: %.6f, 99th_p: %.6f, std_dev: %.6f\n"
-    ) % (
-        backend,
-        batch_size,
-        steps,
-        speed_med,
-        speed_mean,
-        time_med,
-        time_mean,
-        time_99th,
-        time_std,
-    )
-    print(msg)
-    meas = {
+    stats = {
         "Backend": backend,
-        "precision": precision,
+        "Precision": precision,
+        "Batch size": batch_size,
         "Median(FPS)": speed_med,
         "Mean(FPS)": speed_mean,
-        "Median-Latency(ms)": time_med,
-        "Mean-Latency(ms)": time_mean,
-        "99th_p": time_99th,
-        "std_dev": time_std,
+        "Median-Latency(ms)": time_med * 1000,
+        "Mean-Latency(ms)": time_mean * 1000,
     }
-    results.append(meas)
-
-
-def precision_to_dtype(pr):
-    if pr == "fp32":
-        return torch.float
-    elif pr == "fp16" or pr == "half":
-        return torch.half
-    else:
-        return torch.int8
+    results.append(stats)
 
 
 def load_model(params):
@@ -289,15 +359,21 @@ def load_model(params):
     is_trt_engine = False
     # Load torch model traced/scripted
     model_file = params.get("model").get("filename")
+    try:
+        model_name = params.get("model").get("name")
+    except:
+        model_name = model_file
 
-    if model_file.endswith(".jit.pt"):
-        model = torch.jit.load(model_file).cuda()
-    else:
+    print("Loading model: ", model_file)
+    if model_file.endswith(".plan"):
         is_trt_engine = True
         # Read the TensorRT engine file
         with open(model_file, "rb") as fin:
             model = fin.read()
-    return model, is_trt_engine
+    else:
+        model = torch.jit.load(model_file).cuda()
+
+    return model, model_name, is_trt_engine
 
 
 if __name__ == "__main__":
@@ -306,57 +382,147 @@ def load_model(params):
     )
     arg_parser.add_argument(
         "--config",
+        type=str,
         help="Load YAML based configuration file to run the inference. If this is used other params will be ignored",
     )
+    # The following options are manual user provided settings
+    arg_parser.add_argument(
+        "--backends",
+        type=str,
+        help="Comma separated string of backends. Eg: torch,torch_tensorrt,fx2trt,tensorrt",
+    )
+    arg_parser.add_argument("--model", type=str, help="Name of the model file")
+    arg_parser.add_argument(
+        "--inputs",
+        type=str,
+        help="List of input shapes. Eg: (1, 3, 224, 224)@fp32 for Resnet or (1, 128)@int32;(1, 128)@int32 for BERT",
+    )
+    arg_parser.add_argument(
+        "--batch_size", type=int, default=1, help="Batch size to build and run"
+    )
+    arg_parser.add_argument(
+        "--precision",
+        default="fp32",
+        type=str,
+        help="Comma separated list of precisions to build TensorRT engine Eg: fp32,fp16",
+    )
+    arg_parser.add_argument(
+        "--calibration_cache", type=str, help="Name of the calibration cache file"
+    )
+    arg_parser.add_argument("--device", type=int, help="device id")
+    arg_parser.add_argument(
+        "--truncate",
+        action="store_true",
+        help="Truncate long and double weights in the network  in Torch-TensorRT",
+    )
+    arg_parser.add_argument(
+        "--is_trt_engine",
+        action="store_true",
+        help="Boolean flag to determine if the user provided model is a TRT engine or not",
+    )
+    arg_parser.add_argument(
+        "--report",
+        type=str,
+        help="Path of the output file where performance summary is written.",
+    )
     args = arg_parser.parse_args()
 
-    parser = ConfigParser(args.config)
-    # Load YAML params
-    params = parser.read_config()
-    print("Loading model: ", params.get("model").get("filename"))
-
-    model = None
-
-    # Default device is set to 0. Configurable using yaml config file.
-    torch.cuda.set_device(params.get("runtime").get("device", 0))
-
-    # Load the model file from disk. If the loaded file is TensorRT engine then is_trt_engine is returned as True
-    model, is_trt_engine = load_model(params)
     cudnn.benchmark = True
-
     # Create random input tensor of certain size
     torch.manual_seed(12345)
+    model_name = "Model"
+    if args.config:
+        parser = ConfigParser(args.config)
+        # Load YAML params
+        params = parser.read_config()
+        model, model_name, is_trt_engine = load_model(params)
+
+        # Default device is set to 0. Configurable using yaml config file.
+        torch.cuda.set_device(params.get("runtime").get("device", 0))
+
+        num_input = params.get("input").get("num_inputs")
+        truncate_long_and_double = params.get("runtime").get(
+            "truncate_long_and_double", False
+        )
+        batch_size = params.get("input").get("batch_size", 1)
+        for precision in params.get("runtime").get("precision", "fp32"):
+            input_tensors = []
+            num_input = params.get("input").get("num_inputs", 1)
+            for i in range(num_input):
+                inp_tensor = params.get("input").get("input" + str(i))
+                input_tensors.append(
+                    torch.randint(
+                        0,
+                        2,
+                        tuple(d for d in inp_tensor),
+                        dtype=precision_to_dtype(precision),
+                    ).cuda()
+                )
 
-    num_input = params.get("input").get("num_inputs")
-    for precision in params.get("runtime").get("precision", "fp32"):
-        input_tensors = []
-        num_input = params.get("input").get("num_inputs", 1)
-        for i in range(num_input):
-            inp_tensor = params.get("input").get("input" + str(i))
-            input_tensors.append(
-                torch.randint(
-                    0,
-                    2,
-                    tuple(d for d in inp_tensor),
-                    dtype=precision_to_dtype(precision),
-                ).cuda()
-            )
+            if is_trt_engine:
+                print(
+                    "Warning, TensorRT engine file is configured. Please make sure the precision matches with the TRT engine for reliable results"
+                )
 
-        if is_trt_engine:
-            print(
-                "Warning, TensorRT engine file is configured. Please make sure the precision matches with the TRT engine for reliable results"
+            if not is_trt_engine and (precision == "fp16" or precision == "half"):
+                # If model is TensorRT serialized engine then model.half will report failure
+                model = model.half()
+
+            backends = params.get("backend")
+            # Run inference
+            status = run(
+                model,
+                backends,
+                input_tensors,
+                params,
+                precision,
+                truncate_long_and_double,
+                batch_size,
+                is_trt_engine,
+            )
+    else:
+        params = vars(args)
+        model_name = params["model"]
+        if os.path.exists(model_name):
+            print("Loading user provided model: ", model_name)
+            model = torch.jit.load(model_name).cuda().eval()
+        elif model_name in BENCHMARK_MODELS:
+            model = BENCHMARK_MODELS[model_name]["model"].eval().cuda()
+        else:
+            raise ValueError(
+                "Invalid model name. Please provide a torchscript model file or model name (among the following options vgg16|resnet50|efficientnet_b0|vit)"
             )
 
-        if not is_trt_engine and precision == "fp16" or precision == "half":
-            # If model is TensorRT serialized engine then model.half will report failure
-            model = model.half()
+        backends = parse_backends(params["backends"])
+        truncate_long_and_double = params["truncate"]
+        batch_size = params["batch_size"]
+        is_trt_engine = params["is_trt_engine"]
+        precisions = parse_precisions(params["precision"])
 
-        # Run inference
-        status = run(model, input_tensors, params, precision, is_trt_engine)
-        if status == False:
-            continue
+        for precision in precisions:
+            input_tensors = parse_inputs(
+                params["inputs"], precision_to_dtype(precision)
+            )
+            if not is_trt_engine and (precision == "fp16" or precision == "half"):
+                # If model is TensorRT serialized engine then model.half will report failure
+                model = model.half()
+            status = run(
+                model,
+                backends,
+                input_tensors,
+                params,
+                precision,
+                truncate_long_and_double,
+                batch_size,
+                is_trt_engine,
+            )
 
     # Generate report
-    print("Model Summary:")
+    print("Model Summary: ", model_name)
     summary = pd.DataFrame(results)
     print(summary)
+    if args.report:
+        with open(args.report, "w") as file:
+            file.write("Model Summary: " + model_name + "\n")
+            file.write(summary.to_string())
+        file.close()
diff --git a/tools/perf/utils.py b/tools/perf/utils.py
new file mode 100644
index 0000000000..3d63dcd4b7
--- /dev/null
+++ b/tools/perf/utils.py
@@ -0,0 +1,61 @@
+import torch
+import torch_tensorrt
+import custom_models as cm
+import torchvision.models as models
+import timm
+
+BENCHMARK_MODELS = {
+    "vgg16": {"model": models.vgg16(pretrained=True), "path": "script"},
+    "resnet50": {
+        "model": torch.hub.load("pytorch/vision:v0.9.0", "resnet50", pretrained=True),
+        "path": "script",
+    },
+    "efficientnet_b0": {
+        "model": timm.create_model("efficientnet_b0", pretrained=True),
+        "path": "script",
+    },
+    "vit": {
+        "model": timm.create_model("vit_base_patch16_224", pretrained=True),
+        "path": "script",
+    },
+    "bert_base_uncased": {"model": cm.BertModule(), "path": "trace"},
+}
+
+
+def precision_to_dtype(pr):
+    if pr == "fp32":
+        return torch.float
+    elif pr == "fp16" or pr == "half":
+        return torch.half
+    elif pr == "int32":
+        return torch.int32
+    elif pr == "bool":
+        return torch.bool
+    else:
+        return torch.float32
+
+
+def parse_inputs(user_inputs, dtype):
+    parsed_inputs = user_inputs.split(";")
+    torchtrt_inputs = []
+    for input in parsed_inputs:
+        input_shape = []
+        input_shape_and_dtype = input.split("@")
+        dtype = (
+            precision_to_dtype(input_shape_and_dtype[1])
+            if len(input_shape_and_dtype) == 2
+            else dtype
+        )
+        for input_dim in input_shape_and_dtype[0][1:-1].split(","):
+            input_shape.append(int(input_dim))
+        torchtrt_inputs.append(torch.randint(0, 5, input_shape, dtype=dtype).cuda())
+
+    return torchtrt_inputs
+
+
+def parse_backends(backends):
+    return backends.split(",")
+
+
+def parse_precisions(precisions):
+    return precisions.split(",")