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

_cpp_source = r"""
#include <torch/extension.h>
torch::Tensor diagonal_mul_cuda(torch::Tensor A, torch::Tensor B);
"""

_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>
#include <cstdint>

__global__ void diagonal_mul_float4(
    const float* __restrict__ A,
    const float4* __restrict__ B,
    float4* __restrict__ C,
    int64_t count,
    int64_t vectors_per_row)
{
    for (int64_t i = int64_t(blockIdx.x) * blockDim.x + threadIdx.x;
         i < count;
         i += int64_t(blockDim.x) * gridDim.x) {
        const float a = A[i / vectors_per_row];
        const float4 b = B[i];
        C[i] = make_float4(a * b.x, a * b.y, a * b.z, a * b.w);
    }
}

template <typename scalar_t>
__global__ void diagonal_mul_scalar(
    const scalar_t* __restrict__ A,
    const scalar_t* __restrict__ B,
    scalar_t* __restrict__ C,
    int64_t count,
    int64_t columns)
{
    using acc_t = at::acc_type<scalar_t, true>;
    for (int64_t i = int64_t(blockIdx.x) * blockDim.x + threadIdx.x;
         i < count;
         i += int64_t(blockDim.x) * gridDim.x) {
        C[i] = static_cast<scalar_t>(
            static_cast<acc_t>(A[i / columns]) *
            static_cast<acc_t>(B[i]));
    }
}

torch::Tensor diagonal_mul_cuda(torch::Tensor A, torch::Tensor B) {
    TORCH_CHECK(A.is_cuda() && B.is_cuda(), "Inputs must be CUDA tensors");
    TORCH_CHECK(A.device() == B.device(), "Inputs must share a device");
    TORCH_CHECK(A.scalar_type() == B.scalar_type(),
                "Inputs must share a dtype");
    TORCH_CHECK(A.dim() == 1 && B.dim() == 2 && A.size(0) == B.size(0),
                "Expected A: (N,), B: (N, M)");

    const c10::cuda::CUDAGuard guard(B.device());
    A = A.contiguous();
    B = B.contiguous();
    auto C = torch::empty(B.sizes(), B.options());

    const int64_t count = B.numel();
    if (count == 0) return C;

    constexpr int threads = 256;
    const int64_t columns = B.size(1);
    const auto stream = at::cuda::getCurrentCUDAStream();

    if (B.scalar_type() == at::kFloat &&
        columns % 4 == 0 &&
        reinterpret_cast<uintptr_t>(B.data_ptr<float>()) % 16 == 0 &&
        reinterpret_cast<uintptr_t>(C.data_ptr<float>()) % 16 == 0) {
        const int64_t vectors = count / 4;
        const int blocks = static_cast<int>(
            std::min<int64_t>((vectors + threads - 1) / threads, 65535));
        diagonal_mul_float4<<<blocks, threads, 0, stream>>>(
            A.data_ptr<float>(),
            reinterpret_cast<const float4*>(B.data_ptr<float>()),
            reinterpret_cast<float4*>(C.data_ptr<float>()),
            vectors, columns / 4);
    } else {
        const int blocks = static_cast<int>(
            std::min<int64_t>((count + threads - 1) / threads, 65535));
        AT_DISPATCH_FLOATING_TYPES_AND2(
            at::ScalarType::Half, at::ScalarType::BFloat16,
            B.scalar_type(), "diagonal_mul_cuda", [&] {
                diagonal_mul_scalar<scalar_t>
                    <<<blocks, threads, 0, stream>>>(
                        A.data_ptr<scalar_t>(),
                        B.data_ptr<scalar_t>(),
                        C.data_ptr<scalar_t>(),
                        count, columns);
            });
    }

    C10_CUDA_KERNEL_LAUNCH_CHECK();
    return C;
}
"""

_diagonal_mul = load_inline(
    name="diagonal_row_scale_cuda",
    cpp_sources=_cpp_source,
    cuda_sources=_cuda_source,
    functions=["diagonal_mul_cuda"],
    extra_cflags=["-O3"],
    extra_cuda_cflags=["-O3"],
    verbose=False,
)


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

    def forward(self, A, B):
        return _diagonal_mul.diagonal_mul_cuda(A, B)
```