Upstream native_batch_norm and native_batch_norm_backward shape infer…

…ence functions (#978) * Removed compute_shape_native_batch_norm * Removed compute_shape_native_batch_norm_backward
llvm · Jul 30, 2022 · 1510eae · 1510eae
1 parent 0cee0dc
commit 1510eae
Showing 1 changed file with 0 additions and 65 deletions.
diff --git a/python/torch_mlir/csrc/base_lazy_backend/shape_inference.cpp b/python/torch_mlir/csrc/base_lazy_backend/shape_inference.cpp
@@ -29,71 +29,6 @@ compute_shape_mul(const at::Tensor& self, const at::Scalar& other) {
   return {Shape(self.scalar_type(), self.sizes().vec())};
 }
 
-std::vector<torch::lazy::Shape> compute_shape_native_batch_norm(
-    const at::Tensor& input, const c10::optional<at::Tensor>& weight,
-    const c10::optional<at::Tensor>& bias,
-    const c10::optional<at::Tensor>& running_mean,
-    const c10::optional<at::Tensor>& running_var, bool training,
-    double momentum, double eps) {
-  std::vector<torch::lazy::Shape> shapes;
-  shapes.reserve(3);
-  shapes.emplace_back(input.scalar_type(), input.sizes().vec());
-
-  // A separate mean and var needs to be kept for each channel.
-  TORCH_CHECK(
-      input.sizes().size() >= 2,
-      "Input tensor must have at least batch and channel dimensions!");
-  int64_t num_features = input.sizes().vec()[1];
-
-  if (running_mean.has_value()) {
-    shapes.emplace_back(
-        running_mean.value().scalar_type(), running_mean.value().sizes().vec());
-  } else {
-    shapes.emplace_back(
-        at::get_default_dtype_as_scalartype(),
-        std::vector<int64_t>{num_features});
-  }
-
-  if (running_var.has_value()) {
-    shapes.emplace_back(
-        running_var.value().scalar_type(), running_var.value().sizes().vec());
-  } else {
-    shapes.emplace_back(
-        at::get_default_dtype_as_scalartype(),
-        std::vector<int64_t>{num_features});
-  }
-  return shapes;
-}
-
-std::vector<torch::lazy::Shape> compute_shape_native_batch_norm_backward(
-    const at::Tensor& grad_out, const at::Tensor& input,
-    const c10::optional<at::Tensor>& weight,
-    const c10::optional<at::Tensor>& running_mean,
-    const c10::optional<at::Tensor>& running_var,
-    const c10::optional<at::Tensor>& save_mean,
-    const c10::optional<at::Tensor>& save_invstd, bool train, double eps,
-    ::std::array<bool, 3> output_mask) {
-  std::vector<torch::lazy::Shape> shapes;
-  shapes.reserve(3);
-  shapes.emplace_back(input.scalar_type(), input.sizes().vec());
-
-  // A separate mean and var needs to be kept for each channel.
-  TORCH_CHECK(
-      input.sizes().size() >= 2,
-      "Input tensor must have at least batch and channel dimensions!");
-  int64_t num_features = input.sizes().vec()[1];
-
-  // `weight` and `bias` are vectors of length C (number of channels)`
-  shapes.emplace_back(
-      at::get_default_dtype_as_scalartype(),
-      std::vector<int64_t>{num_features});
-  shapes.emplace_back(
-      at::get_default_dtype_as_scalartype(),
-      std::vector<int64_t>{num_features});
-
-  return shapes;
-}
-
 std::vector<torch::lazy::Shape> compute_shape_new_empty(const at::Tensor & self, at::IntArrayRef size, c10::optional<at::ScalarType> dtype, c10::optional<at::Layout> layout, c10::optional<at::Device> device, c10::optional<bool> pin_memory) {
   if (dtype.has_value()) {
     return {Shape(*dtype, size)};