refactor: Apply linting

Signed-off-by: Naren Dasan <[email protected]> Signed-off-by: Naren Dasan <[email protected]>
Xseventh · Jul 21, 2021 · 265f71e · 265f71e
1 parent aaddaf1
commit 265f71e
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Showing 15 changed files with 207 additions and 116 deletions.
diff --git a/core/conversion/conversion.cpp b/core/conversion/conversion.cpp
@@ -125,10 +125,7 @@ void AddLayer(ConversionCtx* ctx, const torch::jit::Node* n) {
                        << "please report this error to https://www.github.com/NVIDIA/TRTorch/issues");
 }
 
-void AddInputs(
-    ConversionCtx* ctx,
-    at::ArrayRef<const torch::jit::Value*> inputs,
-    std::vector<ir::Input>& input_specs) {
+void AddInputs(ConversionCtx* ctx, at::ArrayRef<const torch::jit::Value*> inputs, std::vector<ir::Input>& input_specs) {
   std::vector<const torch::jit::Value*> input_tensors;
   for (auto in : inputs) {
     // Disregarding inputs that are not tensors
@@ -164,7 +161,8 @@ void AddInputs(
     std::string name = std::string("input_") + std::to_string(ctx->num_inputs);
     LOG_INFO(
         ctx->logger,
-        "Adding Input " << in->debugName() << " (named: " << name << "): " << spec << " in engine (conversion.AddInputs)");
+        "Adding Input " << in->debugName() << " (named: " << name << "): " << spec
+                        << " in engine (conversion.AddInputs)");
 
     auto trt_in = ctx->net->addInput(name.c_str(), spec.dtype, spec.input_shape);
     TRTORCH_CHECK(trt_in, "Failed to add input node: " << in->debugName() << " (conversion.AddInputs)");

diff --git a/core/conversion/conversion.h b/core/conversion/conversion.h
@@ -14,8 +14,7 @@ namespace conversion {
 struct ConversionInfo {
   std::vector<ir::Input> inputs;
   BuilderSettings engine_settings;
-  ConversionInfo(std::vector<ir::Input> inputs)
-      : inputs(std::move(inputs)), engine_settings(BuilderSettings()) {}
+  ConversionInfo(std::vector<ir::Input> inputs) : inputs(std::move(inputs)), engine_settings(BuilderSettings()) {}
 };
 
 // TODO: REMOVE GRAPH AND PARAMS AND MOVE FULLY TO INLINED CONSTANTS

diff --git a/core/conversion/conversionctx/ConversionCtx.cpp b/core/conversion/conversionctx/ConversionCtx.cpp
@@ -60,7 +60,7 @@ ConversionCtx::ConversionCtx(BuilderSettings build_settings)
   LOG_DEBUG(build_settings);
   cfg = builder->createBuilderConfig();
 
-  for(auto p = settings.enabled_precisions.begin(); p != settings.enabled_precisions.end(); ++p) {
+  for (auto p = settings.enabled_precisions.begin(); p != settings.enabled_precisions.end(); ++p) {
     switch (*p) {
       case nvinfer1::DataType::kHALF:
         TRTORCH_CHECK(builder->platformHasFastFp16(), "Requested inference in FP16 but platform does not support FP16");
@@ -121,7 +121,9 @@ ConversionCtx::ConversionCtx(BuilderSettings build_settings)
         static_cast<int>(settings.device.dla_core) < nbDLACores,
         "Configured DLA Core ID: " << settings.device.dla_core
                                    << " not available. Total number of available DLA Cores: " << nbDLACores);
-    TRTORCH_CHECK(settings.enabled_precisions.find(nvinfer1::DataType::kFLOAT) == settings.enabled_precisions.end(), "DLA supports only fp16 or int8 precision");
+    TRTORCH_CHECK(
+        settings.enabled_precisions.find(nvinfer1::DataType::kFLOAT) == settings.enabled_precisions.end(),
+        "DLA supports only fp16 or int8 precision");
     cfg->setDLACore(settings.device.dla_core);
   }
 }

diff --git a/core/conversion/conversionctx/ConversionCtx.h b/core/conversion/conversionctx/ConversionCtx.h
@@ -2,8 +2,8 @@
 
 #include <map>
 #include <memory>
-#include <unordered_map>
 #include <set>
+#include <unordered_map>
 
 #include "NvInfer.h"
 #include "torch/csrc/jit/ir/ir.h"

diff --git a/core/conversion/converters/impl/activation.cpp b/core/conversion/converters/impl/activation.cpp
@@ -177,8 +177,9 @@ auto acthardtanh TRTORCH_UNUSED =
                     std::string pluginName = "CustomGeluPluginDynamic";
                     nvinfer1::PluginFieldCollection fc;
                     std::vector<nvinfer1::PluginField> f;
-                    //REVIEW is this right?
-                    int type_id = ctx->settings.enabled_precisions.find(nvinfer1::DataType::kHALF) == ctx->settings.enabled_precisions.end()
+                    // REVIEW is this right?
+                    int type_id = ctx->settings.enabled_precisions.find(nvinfer1::DataType::kHALF) ==
+                            ctx->settings.enabled_precisions.end()
                         ? 0
                         : 1; // Integer encoding the DataType (0: FP32, 1: FP16)
                     f.emplace_back(nvinfer1::PluginField("type_id", &type_id, nvinfer1::PluginFieldType::kINT32, 1));

diff --git a/core/ir/Input.cpp b/core/ir/Input.cpp
@@ -131,11 +131,20 @@ Input::Input(std::vector<int64_t> shape, nvinfer1::DataType dtype, nvinfer1::Ten
 
   TRTORCH_CHECK(valid_input_dtype(dtype), "Unsupported input data type: " << dtype);
   this->dtype = dtype;
-  TRTORCH_CHECK(valid_dtype_format_combo(dtype, format), "Unsupported combination of dtype and tensor format: (" << dtype << ", " << format << "), TRTorch only supports contiguous format (NCHW) except with input type Float32 where channel last (NHWC) is also supported");
+  TRTORCH_CHECK(
+      valid_dtype_format_combo(dtype, format),
+      "Unsupported combination of dtype and tensor format: ("
+          << dtype << ", " << format
+          << "), TRTorch only supports contiguous format (NCHW) except with input type Float32 where channel last (NHWC) is also supported");
   this->format = format;
 }
 
-Input::Input(std::vector<int64_t> min_shape, std::vector<int64_t> opt_shape, std::vector<int64_t> max_shape, nvinfer1::DataType dtype, nvinfer1::TensorFormat format) {
+Input::Input(
+    std::vector<int64_t> min_shape,
+    std::vector<int64_t> opt_shape,
+    std::vector<int64_t> max_shape,
+    nvinfer1::DataType dtype,
+    nvinfer1::TensorFormat format) {
   if (min_shape.size() > 5 || opt_shape.size() > 5 || max_shape.size() > 5) {
     LOG_WARNING("Verify that this dim size is accepted");
   }
@@ -174,15 +183,20 @@ Input::Input(std::vector<int64_t> min_shape, std::vector<int64_t> opt_shape, std
 
   TRTORCH_CHECK(valid_input_dtype(dtype), "Unsupported input data type: " << dtype);
   this->dtype = dtype;
-  TRTORCH_CHECK(valid_dtype_format_combo(dtype, format), "Unsupported combination of dtype and tensor format: (" << dtype << ", " << format << "), TRTorch only supports contiguous format (NCHW) except with input type Float32 where channel last (NHWC) is also supported");
+  TRTORCH_CHECK(
+      valid_dtype_format_combo(dtype, format),
+      "Unsupported combination of dtype and tensor format: ("
+          << dtype << ", " << format
+          << "), TRTorch only supports contiguous format (NCHW) except with input type Float32 where channel last (NHWC) is also supported");
   this->format = format;
 }
 
 std::ostream& operator<<(std::ostream& os, const Input& input) {
   if (!input.input_is_dynamic) {
     os << "Input(shape: " << input.input_shape << ", dtype: " << input.dtype << ", format: " << input.format << ')';
   } else {
-    os << "Input(shape: " << input.input_shape << ", min: " << input.min << ", opt: " << input.opt << ", max: " << input.max << ", dtype: " << input.dtype << ", format: " << input.format << ')';
+    os << "Input(shape: " << input.input_shape << ", min: " << input.min << ", opt: " << input.opt
+       << ", max: " << input.max << ", dtype: " << input.dtype << ", format: " << input.format << ')';
   }
   return os;
 }

diff --git a/core/ir/ir.h b/core/ir/ir.h
@@ -1,18 +1,26 @@
 #pragma once
 
-#include <vector>
 #include <iostream>
+#include <vector>
 #include "NvInfer.h"
 
 namespace trtorch {
 namespace core {
 namespace ir {
 
 struct Input {
-  //Input(std::vector<int64_t> shape);
-  //Input(std::vector<int64_t> min_shape, std::vector<int64_t> opt_shape, std::vector<int64_t> max_shape);
-  Input(std::vector<int64_t> shape, nvinfer1::DataType dtype=nvinfer1::DataType::kFLOAT, nvinfer1::TensorFormat format=nvinfer1::TensorFormat::kLINEAR);
-  Input(std::vector<int64_t> min_shape, std::vector<int64_t> opt_shape, std::vector<int64_t> max_shape, nvinfer1::DataType dtype=nvinfer1::DataType::kFLOAT, nvinfer1::TensorFormat format=nvinfer1::TensorFormat::kLINEAR);
+  // Input(std::vector<int64_t> shape);
+  // Input(std::vector<int64_t> min_shape, std::vector<int64_t> opt_shape, std::vector<int64_t> max_shape);
+  Input(
+      std::vector<int64_t> shape,
+      nvinfer1::DataType dtype = nvinfer1::DataType::kFLOAT,
+      nvinfer1::TensorFormat format = nvinfer1::TensorFormat::kLINEAR);
+  Input(
+      std::vector<int64_t> min_shape,
+      std::vector<int64_t> opt_shape,
+      std::vector<int64_t> max_shape,
+      nvinfer1::DataType dtype = nvinfer1::DataType::kFLOAT,
+      nvinfer1::TensorFormat format = nvinfer1::TensorFormat::kLINEAR);
   friend std::ostream& operator<<(std::ostream& os, const Input& input);
 
   bool input_is_dynamic = false;