```python
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline

_cuda_source = r"""
#include <torch/extension.h>
#include <ATen/AccumulateType.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <c10/cuda/CUDAException.h>
#include <cuda_runtime.h>
#include <algorithm>

template <typename T>
__device__ __forceinline__ T activate(T x) {
    if (x <= T(0)) return T(0);
    if (x >= T(3)) return x;
    return (x * (x + T(3))) / T(6);
}

__global__ void activate_float4(float* data, int64_t n) {
    const int64_t index =
        static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
    const int64_t stride =
        static_cast<int64_t>(gridDim.x) * blockDim.x;

    float4* vectors = reinterpret_cast<float4*>(data);
    const int64_t vector_count = n / 4;

    for (int64_t i = index; i < vector_count; i += stride) {
        float4 v = vectors[i];
        v.x = activate(v.x);
        v.y = activate(v.y);
        v.z = activate(v.z);
        v.w = activate(v.w);
        vectors[i] = v;
    }

    for (int64_t i = vector_count * 4 + index; i < n; i += stride) {
        data[i] = activate(data[i]);
    }
}

template <typename scalar_t>
__global__ void activate_scalar(scalar_t* data, int64_t n) {
    using acc_t = at::acc_type<scalar_t, true>;
    const int64_t stride =
        static_cast<int64_t>(gridDim.x) * blockDim.x;

    for (int64_t i =
             static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
         i < n;
         i += stride) {
        data[i] = static_cast<scalar_t>(
            activate(static_cast<acc_t>(data[i])));
    }
}

torch::Tensor fused_activation_(torch::Tensor x) {
    TORCH_CHECK(x.is_cuda(), "Expected a CUDA tensor");
    TORCH_CHECK(x.is_non_overlapping_and_dense(),
                "Expected a dense tensor");

    const c10::cuda::CUDAGuard guard(x.device());
    const int64_t n = x.numel();
    if (n == 0) return x;

    constexpr int threads = 256;
    const auto stream = at::cuda::getCurrentCUDAStream(x.get_device());

    if (x.scalar_type() == at::kFloat) {
        const int64_t work = (n + 3) / 4;
        const int blocks = static_cast<int>(
            std::min<int64_t>((work + threads - 1) / threads, 4096));
        activate_float4<<<blocks, threads, 0, stream>>>(
            x.data_ptr<float>(), n);
    } else {
        const int blocks = static_cast<int>(
            std::min<int64_t>((n + threads - 1) / threads, 4096));
        AT_DISPATCH_FLOATING_TYPES_AND2(
            at::ScalarType::Half,
            at::ScalarType::BFloat16,
            x.scalar_type(),
            "fused_hardswish_relu",
            [&] {
                activate_scalar<scalar_t><<<blocks, threads, 0, stream>>>(
                    x.data_ptr<scalar_t>(), n);
            });
    }

    C10_CUDA_KERNEL_LAUNCH_CHECK();
    return x;
}
"""

_fused_activation = load_inline(
    name="fused_hardswish_relu_cuda_v1",
    cpp_sources="torch::Tensor fused_activation_(torch::Tensor x);",
    cuda_sources=_cuda_source,
    functions=["fused_activation_"],
    extra_cflags=["-O3"],
    extra_cuda_cflags=["-O3"],
    verbose=False,
)


class ModelNew(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size):
        super().__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)

    def forward(self, x):
        x = self.conv(x)
        if not x.is_cuda or (torch.is_grad_enabled() and x.requires_grad):
            return torch.relu(torch.nn.functional.hardswish(x))
        return _fused_activation.fused_activation_(x)
```