You write custom CUDA kernels to replace the pytorch operators in the given architecture to get speedups. 

    You have complete freedom to choose the set of operators you want to replace. You may make the decision to replace some operators with custom CUDA kernels and leave others unchanged. You may replace multiple operators with custom implementations, consider operator fusion opportunities (combining multiple operators into a single kernel, for example, combining matmul+relu), or algorithmic changes (such as online softmax). You are only limited by your imagination.


        Here's an example to show you the syntax of inline embedding custom CUDA operators in torch: The example given architecture is: 

        ``` 

        # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import torch
import torch.nn as nn
import torch.nn.functional as F


class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()

    def forward(self, a, b):
        return a + b


def get_inputs():
    # randomly generate input tensors based on the model architecture
    a = torch.randn(1, 128).cuda()
    b = torch.randn(1, 128).cuda()
    return [a, b]


def get_init_inputs():
    # randomly generate tensors required for initialization based on the model architecture
    return []

        ``` 

        The example new arch with custom CUDA kernels looks like this:
        ```
        # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.cpp_extension import load_inline

# Define the custom CUDA kernel for element-wise addition
elementwise_add_source = """
#include <torch/extension.h>
#include <cuda_runtime.h>

__global__ void elementwise_add_kernel(const float* a, const float* b, float* out, int size) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < size) {
        out[idx] = a[idx] + b[idx];
    }
}

torch::Tensor elementwise_add_cuda(torch::Tensor a, torch::Tensor b) {
    auto size = a.numel();
    auto out = torch::zeros_like(a);

    const int block_size = 256;
    const int num_blocks = (size + block_size - 1) / block_size;

    elementwise_add_kernel<<<num_blocks, block_size>>>(a.data_ptr<float>(), b.data_ptr<float>(), out.data_ptr<float>(), size);

    return out;
}
"""

elementwise_add_cpp_source = (
    "torch::Tensor elementwise_add_cuda(torch::Tensor a, torch::Tensor b);"
)

# Compile the inline CUDA code for element-wise addition
elementwise_add = load_inline(
    name="elementwise_add",
    cpp_sources=elementwise_add_cpp_source,
    cuda_sources=elementwise_add_source,
    functions=["elementwise_add_cuda"],
    verbose=True,
    extra_cflags=[""],
    extra_ldflags=[""],
)


class ModelNew(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.elementwise_add = elementwise_add

    def forward(self, a, b):
        return self.elementwise_add.elementwise_add_cuda(a, b)

        ``` 

        
    You are given the following architecture: 

    ```
    # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Simple model that performs a matrix multiplication of a diagonal matrix with another matrix.
    C = diag(A) * B
    """
    def __init__(self):
        super(Model, self).__init__()
    
    def forward(self, A, B):
        """
        Performs the matrix multiplication.

        Args:
            A (torch.Tensor): A 1D tensor representing the diagonal of the diagonal matrix. Shape: (N,).
            B (torch.Tensor): A 2D tensor representing the second matrix. Shape: (N, M).

        Returns:
            torch.Tensor: The result of the matrix multiplication. Shape: (N, M).
        """
        return torch.diag(A) @ B

M = 4096
N = 4096

def get_inputs():
    A = torch.rand(N)
    B = torch.rand(N, M)
    return [A, B]

def get_init_inputs():
    return []  # No special initialization inputs needed
    ```
    
Optimize the architecture named Model with custom CUDA operators! Name your optimized output architecture ModelNew. Output the new code in codeblocks. Please generate real code, NOT pseudocode, make sure the code compiles and is fully functional. Just output the new model code, no other text, and NO testing code! 



This is turn 2 of 6 for this task. Your previous solution (turn 1) was:
```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)
```
Result of evaluating it:
The solution is correct on the test inputs. Mean runtime 0.113 ms; the PyTorch reference (with TF32 enabled) takes 1.08 ms; speedup 9.558x.
Profiler, top device kernels of one forward pass of your solution:
| device kernel | total device time (us) | calls |
|---|---|---|
| diagonal_mul_float4(float const*, float4 const*, float4*, long, long) | 99.8 | 1 |

Write an improved complete ModelNew: fix any error, and if the solution is already correct, make it faster. Output the new code in one ```python codeblock. Just the model code, no other text, and NO testing code.
