Add aten::huber_loss/backward and their variants

src/ATen/native/xpu/Loss.cpp

@@ -1,14 +1,24 @@
 #include <ATen/ATen.h>
 #include <ATen/core/Reduction.h>
 #include <ATen/core/Tensor.h>
-#include <ATen/xpu/XPUNativeFunctions.h>
-
 #include <ATen/native/xpu/sycl/BinaryMiscOpsKernels.h>
 #include <ATen/native/xpu/sycl/PointwiseOpsKernels.h>
+#include <ATen/xpu/XPUNativeFunctions.h>
 #include <comm/RegisterUtils.h>
 
 namespace at {
 
+static inline at::Tensor apply_loss_reduction(
+    const at::Tensor& unreduced,
+    int64_t reduction) {
+  if (reduction == at::Reduction::Mean) {
+    return unreduced.mean();
+  } else if (reduction == at::Reduction::Sum) {
+    return unreduced.sum();
+  }
+  return unreduced;
+}
+
 Tensor& XPUNativeFunctions::mse_loss_out(
     const Tensor& input,
     const Tensor& target,
@@ -69,4 +79,53 @@ Tensor& XPUNativeFunctions::mse_loss_backward_out(
   return grad_input;
 }
 
+Tensor XPUNativeFunctions::huber_loss(
+    const Tensor& input,
+    const Tensor& target,
+    int64_t reduction,
+    double delta) {
+  TORCH_CHECK(
+      delta > 0, "huber_loss does not support non-positive values for delta.")
+  Tensor loss = at::empty_like(input);
+  auto iter = TensorIterator::borrowing_binary_op(loss, input, target);
+  native::xpu::huber_kernel(iter, delta);
+  return apply_loss_reduction(loss, reduction);
+}
+
+Tensor& XPUNativeFunctions::huber_loss_out(
+    const Tensor& input,
+    const Tensor& target,
+    int64_t reduction,
+    double delta,
+    Tensor& result) {
+  TORCH_CHECK(
+      delta > 0, "huber_loss does not support non-positive values for delta.")
+  auto iter = TensorIterator::borrowing_binary_op(result, input, target);
+  native::xpu::huber_kernel(iter, delta);
+  if (reduction != Reduction::None) {
+    auto reduced = apply_loss_reduction(result, reduction);
+    result.resize_({});
+    result.copy_(reduced);
+  }
+  return result;
+}
+
+Tensor& XPUNativeFunctions::huber_loss_backward_out(
+    const Tensor& grad_output,
+    const Tensor& input,
+    const Tensor& target,
+    int64_t reduction,
+    double delta,
+    Tensor& grad_input) {
+  auto norm = (reduction == Reduction::Mean) ? (1. / input.numel()) : 1.;
+  auto iter = at::TensorIteratorConfig()
+                  .add_output(grad_input)
+                  .add_const_input(input)
+                  .add_const_input(target)
+                  .add_const_input(grad_output)
+                  .build();
+  native::xpu::huber_backward_kernel(iter, norm, delta);
+  return grad_input;
+}
+
 } // namespace at

src/ATen/native/xpu/XPUFallback.template

@@ -218,8 +218,6 @@ TORCH_LIBRARY_IMPL(aten, XPU, m) {
     "hardshrink.out",
     "heaviside.out",
     "histc",
-    "huber_loss",
-    "huber_loss_backward.out",
     "i0.out",
     "igammac.out",
     "igamma.out",

src/ATen/native/xpu/sycl/BinaryMiscOpsKernels.cpp

@@ -23,4 +23,25 @@ void mse_kernel(TensorIteratorBase& iter) {
       [&]() { gpu_kernel(iter, MSEFunctor<scalar_t>()); });
 }
 
+template <typename scalar_t>
+struct HuberFunctor {
+  scalar_t operator()(scalar_t a, scalar_t b) const {
+    auto z = std::abs(a - b);
+    return z < delta_val_ ? scalar_t(0.5) * z * z
+                          : delta_val_ * (z - scalar_t(0.5) * delta_val_);
+  }
+  HuberFunctor(scalar_t delta_val) : delta_val_(delta_val) {}
+
+ private:
+  scalar_t delta_val_;
+};
+
+void huber_kernel(TensorIterator& iter, double delta) {
+  AT_DISPATCH_FLOATING_TYPES_AND2(
+      kBFloat16, kHalf, iter.dtype(), "huber_xpu", [&iter, delta] {
+        scalar_t delta_val(delta);
+        gpu_kernel(iter, HuberFunctor<scalar_t>(delta_val));
+      });
+}
+
 } // namespace at::native::xpu

src/ATen/native/xpu/sycl/BinaryMiscOpsKernels.h

@@ -6,4 +6,6 @@ namespace at::native::xpu {
 
 void mse_kernel(TensorIteratorBase& iter);
 
+void huber_kernel(TensorIterator& iter, double delta);
+
 } // namespace at::native::xpu

src/ATen/native/xpu/sycl/PointwiseOpsKernels.cpp

@@ -125,4 +125,41 @@ void mse_backward_kernel(TensorIterator& iter, const Scalar& value) {
       });
 }
 
+template <typename scalar_t>
+struct HuberBackwardFunctor {
+  scalar_t operator()(scalar_t input, scalar_t target, scalar_t grad_output)
+      const {
+    const auto x = input - target;
+    if (x < -delta_val_) {
+      return -norm_val_ * grad_output * delta_val_;
+    } else if (x > delta_val_) {
+      return norm_val_ * grad_output * delta_val_;
+    } else {
+      return norm_val_ * x * grad_output;
+    }
+  }
+  HuberBackwardFunctor(scalar_t norm_val, scalar_t delta_val)
+      : norm_val_(norm_val), delta_val_(delta_val) {}
+
+ private:
+  scalar_t norm_val_;
+  scalar_t delta_val_;
+};
+
+void huber_backward_kernel(
+    TensorIterator& iter,
+    const Scalar& norm,
+    double delta) {
+  AT_DISPATCH_FLOATING_TYPES_AND2(
+      kBFloat16,
+      kHalf,
+      iter.dtype(),
+      "huber_backward_xpu",
+      [&iter, &norm, delta] {
+        auto norm_val = norm.to<scalar_t>();
+        scalar_t delta_val(delta);
+        gpu_kernel(iter, HuberBackwardFunctor<scalar_t>(norm_val, delta_val));
+      });
+}
+
 } // namespace at::native::xpu

src/ATen/native/xpu/sycl/PointwiseOpsKernels.h

@@ -10,4 +10,9 @@ void addcdiv_kernel(TensorIterator& iter, Scalar value);
 
 void mse_backward_kernel(TensorIterator& iter, const Scalar& value);
 
+void huber_backward_kernel(
+    TensorIterator& iter,
+    const Scalar& norm,
+    double delta);
+
 } // namespace at::native::xpu

test/xpu/extended/run_test_with_skip.py

@@ -107,6 +107,7 @@
     "test_compare_cpu_nn_functional_batch_norm_xpu_bfloat16",
     "test_compare_cpu__batch_norm_with_update_xpu_bfloat16",
     "test_compare_cpu__batch_norm_with_update_xpu_float16",
+    "test_compare_cpu_nn_functional_huber_loss_xpu_bfloat16",
 
     # Not implemented operators, aten::upsample_linear1d, aten::upsample_bilinear2d,
     # aten::upsample_trilinear3d

test/xpu/xpu_test_utils.py

@@ -137,6 +137,7 @@
     "nn.functional.upsample_nearest",
     # "nn.functional.nll_loss", # Lack of XPU implementation of aten::nll_loss2d_forward. Will retrieve the case, only if the op is implemented.
     "nn.functional.mse_loss",
+    "nn.functional.huber_loss",
     "sigmoid",
     "sgn",
     "nn.functional.embedding_bag",

yaml/xpu_functions.yaml

@@ -408,6 +408,9 @@ supported:
   - nll_loss_forward
   - nll_loss_backward.grad_input
   - nll_loss_backward
+  - huber_loss
+  - huber_loss.out
+  - huber_loss_backward.out
   - batch_norm_stats
   - batch_norm_elemt
   - batch_norm_elemt.out