// license that can be found in the LICENSE file.

//go:build freebsd

package runtime

import (
	"internal/runtime/atomic"
	"unsafe"
)

const _VDSO_TH_NUM = 4 // defined in <sys/vdso.h> #ifdef _KERNEL

var timekeepSharedPage *vdsoTimekeep

//go:nosplit
func (bt *bintime) Add(bt2 *bintime) {
	u := bt.frac
	bt.frac += bt2.frac
	if u > bt.frac {
		bt.sec++
	}
	bt.sec += bt2.sec
}

  auto add = Add(root.WithKernelLabel("AddWithKernelLabel"), 1.0f, 2.0f);
  TF_EXPECT_OK(root.status());
  AttrSlice attrs = add.z.op().node()->attrs();
  const auto* kernel_attr = attrs.Find("_kernel");
  ASSERT_TRUE(kernel_attr);
  TF_EXPECT_OK(AttrValueHasType(*kernel_attr, "string"));
  EXPECT_EQ(kernel_attr->s(), "AddWithKernelLabel");
}

TEST(CCOpTest, ColocateWith) {

  *kernel = std::make_unique<XlaLocalLaunchBase>(
      &construction, constant_arg_indices, resource_arg_indices, function,
      /*has_ref_vars=*/false);
  return s;
}

Status XlaKernelCreator::CreateKernel(
    FunctionLibraryRuntime* flr,
    const std::shared_ptr<const NodeProperties>& props,
    std::unique_ptr<OpKernel>* kernel) const {

  std::unique_ptr<OpKernel> kernel =
      CreateOpKernel(DeviceType(DEVICE_CPU), nullptr, nullptr, def, 1, &status);
  ASSERT_TRUE(status.ok()) << status.ToString();
  OpKernelContext::Params params;
  DummyDevice dummy_device(nullptr);
  params.device = &dummy_device;
  params.op_kernel = kernel.get();
  AllocatorAttributes alloc_attrs;
  params.output_attr_array = &alloc_attrs;

  std::unique_ptr<OpKernel> kernel =
      CreateOpKernel(DeviceType(DEVICE_CPU), nullptr, nullptr, def, 1, &status);
  ASSERT_TRUE(status.ok()) << status.ToString();

  OpKernelContext::Params params;
  DummyDevice dummy_device(nullptr);
  params.device = &dummy_device;
  params.op_kernel = kernel.get();
  gtl::InlinedVector<TensorValue, 4> inputs;

  }
  ~Params() {
    TF_DeleteStatus(status);
    TF_DeleteTensor(tags);
    TF_DeleteTensor(values);
  }
};

// dummy functions used for kernel registration
void* ScalarSummaryOp_Create(TF_OpKernelConstruction* ctx) { return nullptr; }

void ScalarSummaryOp_Delete(void* kernel) {}

// Helper functions for compute method
bool IsSameSize(TF_Tensor* tensor1, TF_Tensor* tensor2);

// It does not have corresponding OpDef because it is never present
// in the GraphDef.
// Currently, it is used by eager runtime. FunctionLibraryRuntime creates
// this kernel when asked to create a kernel for an XLA-compiled function.
//
// `has_ref_vars`: whether the input computation can have reference variables.
// TODO(cheshire): instead derive this information from the input graph.

 protected:
  // Adds the kernel spec with the custom scale function for the kernel.
  LogicalResult RegisterKernel(llvm::StringRef kernel,
                               const KernelSpecs::Signature& signature,
                               const ScaleFn& fn, const ScaleDecomposeFn& dfn);

  // Adds the kernel spec with the scale constraint type for the kernel.
  LogicalResult RegisterKernel(llvm::StringRef kernel,

        These ops can also be composite ops.

*   (Performance) User defines a custom kernel for a regular structure (i.e.
    LSTM), but it is hard to add the logic to fuse the individual ops to target
    this kernel in the inference graph.

    *   *Solution*: The user should define a new TF op, which corresponds to the
        fused kernel, with composition, and use this op to build the model for

		// When this happens, we have two options. If the Linux kernel is new
		// enough (4.11+), we can read the arm64 registers directly which'll
		// trap into the kernel and then return back to userspace.
		//
		// But on older kernels, such as Linux 4.4.180 as used on many Synology
		// devices, calling readARM64Registers (specifically getisar0) will
		// cause a SIGILL and we'll die. So for older kernels, parse /proc/cpuinfo
		// instead.
		//