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remove useless comments
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@ZhiweiYan-96
ZhiweiYan-96 committed Jun 19, 2024
1 parent b9a5681 commit 9d4af92
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Showing 20 changed files with 2 additions and 2,940 deletions.
160 changes: 0 additions & 160 deletions src/ATen/native/xpu/Activation.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -7,17 +7,13 @@
#include <ATen/xpu/ops/gelu_backward_native.h>
#include <ATen/xpu/ops/gelu_native.h>

// #include <ATen/xpu/XPUNativeFunctions.h>
#include <ATen/native/xpu/sycl/ActivationGeluKernel.h>
#include <ATen/native/xpu/sycl/ActivationThresholdKernel.h>

namespace at {

namespace native {
REGISTER_XPU_DISPATCH(threshold_stub, xpu::threshold_kernel);
// REGISTER_XPU_DISPATCH(GeluKernel, xpu::gelu_kernel);
// REGISTER_XPU_DISPATCH(GeluBackwardKernel, xpu::gelu_backward_kernel);

TORCH_IMPL_FUNC(gelu_backward_out_xpu)
(const Tensor& /*grad*/,
const Tensor& /*self*/,
Expand All @@ -34,160 +30,4 @@ TORCH_IMPL_FUNC(gelu_out_xpu)
}

} // namespace native

// Tensor XPUNativeFunctions::relu(const Tensor& self) {
// TORCH_CHECK(
// self.scalar_type() != at::kBool, "Boolean inputs not supported for
// relu");
// return at::clamp_min(self, 0);
// }

// Tensor& XPUNativeFunctions::relu_(Tensor& self) {
// TORCH_CHECK(
// self.scalar_type() != at::kBool, "Boolean inputs not supported for
// relu");
// return at::clamp_min_(self, 0);
// }

// Tensor& XPUNativeFunctions::relu_out(const Tensor& self, Tensor& out) {
// TORCH_CHECK(
// self.scalar_type() != at::kBool, "Boolean inputs not supported for
// relu");
// return at::clamp_min_out(out, self, 0);
// }

// TensorIterator threshold_meta(
// const Tensor& self,
// const Scalar& threshold,
// const Scalar& value,
// Tensor& out) {
// TensorIterator iter;
// iter.build(TensorIteratorConfig()
// .set_check_mem_overlap(
// false) // threshold is idempotent, so overlap is okay
// .add_output(out)
// .add_const_input(self)
// .add_const_input(self) // other
// .allow_cpu_scalars(true)
// .promote_inputs_to_common_dtype(true)
// .cast_common_dtype_to_outputs(true)
// .enforce_safe_casting_to_output(true));
// return iter;
// }

// Tensor XPUNativeFunctions::threshold(
// const Tensor& self,
// const Scalar& threshold,
// const Scalar& value) {
// Tensor out;
// auto iter = threshold_meta(self, threshold, value, out);
// native::xpu::threshold_kernel(iter, threshold, value);
// return iter.output();
// }

// Tensor& XPUNativeFunctions::threshold_(
// Tensor& self,
// const Scalar& threshold,
// const Scalar& value) {
// auto iter = threshold_meta(self, threshold, value, self);
// native::xpu::threshold_kernel(iter, threshold, value);
// return self;
// }

// Tensor& XPUNativeFunctions::threshold_out(
// const Tensor& self,
// const Scalar& threshold,
// const Scalar& value,
// Tensor& out) {
// auto iter = threshold_meta(self, threshold, value, out);
// native::xpu::threshold_kernel(iter, threshold, value);
// return out;
// }

// TensorIterator threshold_backward_meta(
// const Tensor& grad,
// const Tensor& self,
// const Scalar& threshold,
// Tensor& gradInput) {
// TensorIterator iter;
// iter.build(TensorIteratorConfig()
// .set_check_mem_overlap(
// false) // threshold is idempotent, so overlap is okay
// .add_output(gradInput)
// .add_input(self)
// .add_input(grad) // other
// .allow_cpu_scalars(true)
// .promote_inputs_to_common_dtype(true)
// .cast_common_dtype_to_outputs(true)
// .enforce_safe_casting_to_output(true));
// return iter;
// }

// Tensor XPUNativeFunctions::threshold_backward(
// const Tensor& grad_output,
// const Tensor& self,
// const Scalar& threshold) {
// Tensor grad_input;
// auto iter = threshold_backward_meta(grad_output, self, threshold,
// grad_input); native::xpu::threshold_kernel(iter, threshold, 0); return
// iter.output();
// }

// Tensor& XPUNativeFunctions::threshold_backward_out(
// const Tensor& grad_output,
// const Tensor& self,
// const Scalar& threshold,
// Tensor& grad_input) {
// auto iter = threshold_backward_meta(grad_output, self, threshold,
// grad_input); native::xpu::threshold_kernel(iter, threshold, 0); return
// grad_input;
// }

// Tensor XPUNativeFunctions::gelu(
// const Tensor& self,
// c10::string_view approximate) {
// Tensor out;
// auto iter = TensorIterator::unary_op(out, self);
// native::xpu::gelu_kernel(iter, approximate);
// return iter.output();
// }

// Tensor& XPUNativeFunctions::gelu_(Tensor& self, c10::string_view approximate)
// {
// auto iter = TensorIterator::unary_op(self, self);
// native::xpu::gelu_kernel(iter, approximate);
// return self;
// }

// Tensor& XPUNativeFunctions::gelu_out(
// const Tensor& self,
// c10::string_view approximate,
// Tensor& out) {
// auto iter = TensorIterator::unary_op(out, self);
// native::xpu::gelu_kernel(iter, approximate);
// return out;
// }

// Tensor XPUNativeFunctions::gelu_backward(
// const Tensor& grad_output,
// const Tensor& self,
// c10::string_view approximate) {
// Tensor grad_input;
// auto iter =
// TensorIterator::borrowing_binary_op(grad_input, grad_output, self);
// native::xpu::gelu_backward_kernel(iter, approximate);
// return iter.output();
// }

// Tensor& XPUNativeFunctions::gelu_backward_out(
// const Tensor& grad_output,
// const Tensor& self,
// c10::string_view approximate,
// Tensor& grad_input) {
// auto iter =
// TensorIterator::borrowing_binary_op(grad_input, grad_output, self);
// native::xpu::gelu_backward_kernel(iter, approximate);
// return grad_input;
// }

} // namespace at
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