Improve batch_norm fp16 accuracy (#70)

# Description

Use float types for compile-time calculations around batch_norm. Improves fp16 accuracy relative to pytorch.
Fixes # (issue)

## Type of change

Please delete options that are not relevant and/or add your own.

- Bug fix (non-breaking change which fixes an issue)
- New feature (non-breaking change which adds functionality)
- Breaking change (fix or feature that would cause existing functionality to not work as expected)
- This change requires a documentation update

# Checklist:

- [ ] My code follows the style guidelines of this project (You can use the linters)
- [ ] I have performed a self-review of my own code
- [ ] I have commented my code, particularly in hard-to-understand areas and hacks
- [ ] I have made corresponding changes to the documentation
- [ ] I have added tests to verify my fix or my feature
- [ ] New and existing unit tests pass locally with my changes
- [ ] I have added the relevant labels to my PR in so that relevant reviewers are notified

core/conversion/converters/impl/batch_norm.cpp

@@ -20,15 +20,28 @@ void _batch_norm(
     const torch::Tensor& mean,
     const torch::Tensor& var,
     const float eps) {
-  auto scale = gamma / torch::sqrt(var + eps);
-  auto bias = beta - mean * scale;
+  auto orig_dtype = var.dtype();
+  // perform compile-time weight calculations in float to improve accuracy
+  // resulting weights will be embedded as the original dtype
+  auto calculation_gamma = gamma;
+  auto calculation_beta = beta;
+  auto calculation_mean = mean;
+  auto calculation_var = var;
+  if (orig_dtype == torch::kHalf) {
+    calculation_gamma = calculation_gamma.to(torch::kFloat);
+    calculation_beta = calculation_beta.to(torch::kFloat);
+    calculation_mean = calculation_mean.to(torch::kFloat);
+    calculation_var = calculation_var.to(torch::kFloat);
+  }
+  auto scale = calculation_gamma / torch::sqrt(calculation_var + eps);
+  auto bias = calculation_beta - calculation_mean * scale;
   LOG_DEBUG("_batch_norm Tensor Scale : " << scale.sizes());
   LOG_DEBUG("_batch_norm Tensor bias : " << bias.sizes());
 
-  auto scale_weights = Weights(ctx, scale);
-  auto bias_weights = Weights(ctx, bias);
+  auto scale_weights = Weights(ctx, scale.to(orig_dtype));
+  auto bias_weights = Weights(ctx, bias.to(orig_dtype));
 
-  auto power = Weights(ctx, at::ones_like(scale));
+  auto power = Weights(ctx, at::ones_like(scale).to(orig_dtype));
   auto bn =
       ctx->net->addScaleNd(*input, nvinfer1::ScaleMode::kCHANNEL, bias_weights.data, scale_weights.data, power.data, 1);
   bn->setName(util::node_info(n).c_str());

tests/core/conversion/converters/test_batch_norm.cpp

@@ -137,3 +137,33 @@ TEST(Converters, ATenBatchNormShouldUnpackConvertsCorrectly) {
   ASSERT_TRUE(
       torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0].reshape_as(jit_results[0]), 2e-6));
 }
+
+TEST(Converters, ATenBatchNormHalfConvertsCorrectly) {
+  const auto graph = R"IR(
+      graph(%input : Tensor, %running_var : Half(32, strides=[1], requires_grad=0, device=cuda:0), %running_mean : Half(32, strides=[1], requires_grad=0, device=cuda:0)):
+        %5 : bool = prim::Constant[value=0]()
+        %4 : float = prim::Constant[value=0.01]()
+        %3 : float = prim::Constant[value=0.001]()
+        %2 : bool = prim::Constant[value=1]()
+        %8 : Tensor = aten::batch_norm(%input, %running_var, %running_mean, %running_mean, %running_var, %5, %4, %3, %2)
+        return (%8))IR";
+
+  auto g = std::make_shared<torch::jit::Graph>();
+  torch::jit::parseIR(graph, &*g);
+
+  auto in = at::randn({2, 32, 5, 5}, {at::kCUDA}).to(at::kHalf);
+  auto mean = at::ones({32}, {at::kCUDA}).to(at::kHalf);
+  auto var = at::zeros({32}, {at::kCUDA}).to(at::kHalf);
+
+  auto trt_in = at::clone(in);
+  auto trt_mean = at::clone(mean);
+  auto trt_var = at::clone(var);
+
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {mean, var});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in});
+
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {trt_mean, trt_var});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {trt_in}, {nvinfer1::DataType::kHALF});
+
+  ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0], 2e-6));
+}