```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/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <c10/cuda/CUDAException.h>
#include <cuda_runtime.h>
#include <algorithm>
#include <cstdint>

__global__ void scale_float4(
    const float* __restrict__ input,
    float* __restrict__ output,
    int64_t n,
    float scalar)
{
    const int64_t tid =
        static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
    const int64_t stride =
        static_cast<int64_t>(gridDim.x) * blockDim.x;
    const int64_t vectors = n / 4;

    const float4* input4 = reinterpret_cast<const float4*>(input);
    float4* output4 = reinterpret_cast<float4*>(output);

    for (int64_t i = tid; i < vectors; i += stride) {
        float4 value = input4[i];
        value.x *= scalar;
        value.y *= scalar;
        value.z *= scalar;
        value.w *= scalar;
        output4[i] = value;
    }

    const int64_t tail = vectors * 4 + tid;
    if (tail < n) {
        output[tail] = input[tail] * scalar;
    }
}

__global__ void scale_scalar(
    const float* __restrict__ input,
    float* __restrict__ output,
    int64_t n,
    float scalar)
{
    const int64_t tid =
        static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
    const int64_t stride =
        static_cast<int64_t>(gridDim.x) * blockDim.x;

    for (int64_t i = tid; i < n; i += stride) {
        output[i] = input[i] * scalar;
    }
}

torch::Tensor scale_cuda(torch::Tensor input, double scalar)
{
    TORCH_CHECK(input.is_cuda(), "Expected a CUDA tensor");
    TORCH_CHECK(input.scalar_type() == at::kFloat, "Expected float32");
    TORCH_CHECK(input.is_contiguous(), "Expected contiguous input");

    const c10::cuda::CUDAGuard device_guard(input.device());
    auto output = torch::empty(input.sizes(), input.options());
    const int64_t n = input.numel();
    if (n == 0) {
        return output;
    }

    constexpr int threads = 256;
    const float* source = input.data_ptr<float>();
    float* destination = output.data_ptr<float>();
    const bool aligned =
        (reinterpret_cast<uintptr_t>(source) % alignof(float4)) == 0;

    const int64_t work = aligned ? (n + 3) / 4 : n;
    const int blocks = static_cast<int>(
        std::min<int64_t>((work + threads - 1) / threads, 65535));
    const cudaStream_t stream =
        at::cuda::getCurrentCUDAStream(input.get_device());

    if (aligned) {
        scale_float4<<<blocks, threads, 0, stream>>>(
            source, destination, n, static_cast<float>(scalar));
    } else {
        scale_scalar<<<blocks, threads, 0, stream>>>(
            source, destination, n, static_cast<float>(scalar));
    }

    C10_CUDA_KERNEL_LAUNCH_CHECK();
    return output;
}
"""

_scale_extension = load_inline(
    name="matrix_scalar_scale_float4",
    cpp_sources="torch::Tensor scale_cuda(torch::Tensor input, double scalar);",
    cuda_sources=_cuda_source,
    functions=["scale_cuda"],
    extra_cflags=["-O3"],
    extra_cuda_cflags=["-O3"],
    verbose=False,
)


class ModelNew(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, A: torch.Tensor, s: float) -> torch.Tensor:
        if (
            A.is_cuda
            and A.dtype == torch.float32
            and A.is_contiguous()
            and not (torch.is_grad_enabled() and A.requires_grad)
        ):
            return _scale_extension.scale_cuda(A, s)
        return A * s
```