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
# 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
from torch.utils.cpp_extension import load_inline

# ---------------------------------------------------------------------------
# Custom fused CUDA kernel for C = diag(A) @ B.
#
# Key insight: (diag(A) @ B)[i, j] = A[i] * B[i, j], i.e. the operation is
# simply a per-row scaling of B. The original implementation materializes an
# (N, N) diagonal matrix (O(N^2) memory) and runs an O(N^2 * M) GEMM. This
# kernel computes the result directly in O(N * M) time with no extra memory,
# using vectorized 128-bit (float4) loads/stores when possible.
# ---------------------------------------------------------------------------

diag_matmul_cuda_source = r"""
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda_runtime.h>
#include <algorithm>
#include <cstdint>

// Vectorized path (requires M % 4 == 0 so every row is 16-byte aligned).
__global__ void diag_matmul_vec4_kernel(const float* __restrict__ A,
                                        const float* __restrict__ B,
                                        float* __restrict__ C,
                                        int N, int Mv /* = M / 4 */) {
    for (int row = blockIdx.y; row < N; row += gridDim.y) {
        const float a = A[row];
        const float4* Brow = reinterpret_cast<const float4*>(B) + (int64_t)row * Mv;
        float4* Crow = reinterpret_cast<float4*>(C) + (int64_t)row * Mv;
        for (int i = blockIdx.x * blockDim.x + threadIdx.x;
             i < Mv;
             i += blockDim.x * gridDim.x) {
            const float4 b = Brow[i];
            float4 c;
            c.x = a * b.x;
            c.y = a * b.y;
            c.z = a * b.z;
            c.w = a * b.w;
            Crow[i] = c;
        }
    }
}

// Generic scalar path (any M).
__global__ void diag_matmul_scalar_kernel(const float* __restrict__ A,
                                          const float* __restrict__ B,
                                          float* __restrict__ C,
                                          int N, int M) {
    for (int row = blockIdx.y; row < N; row += gridDim.y) {
        const float a = A[row];
        const int64_t base = (int64_t)row * M;
        for (int col = blockIdx.x * blockDim.x + threadIdx.x;
             col < M;
             col += blockDim.x * gridDim.x) {
            C[base + col] = a * B[base + col];
        }
    }
}

torch::Tensor diag_matmul_cuda(torch::Tensor A, torch::Tensor B) {
    TORCH_CHECK(A.is_cuda() && B.is_cuda(), "A and B must be CUDA tensors");
    TORCH_CHECK(A.dim() == 1, "A must be a 1D tensor");
    TORCH_CHECK(B.dim() == 2, "B must be a 2D tensor");
    TORCH_CHECK(A.size(0) == B.size(0), "A must have one element per row of B");
    TORCH_CHECK(A.scalar_type() == at::kFloat && B.scalar_type() == at::kFloat,
                "Only float32 tensors are supported");
    TORCH_CHECK(A.size(0) <= 2147483647LL && B.size(1) <= 2147483647LL,
                "Tensor dimensions too large");

    auto A_c = A.contiguous();
    auto B_c = B.contiguous();
    auto C = torch::empty_like(B_c);

    const int N = (int)A_c.size(0);
    const int M = (int)B_c.size(1);
    if (N == 0 || M == 0) {
        return C;
    }

    const int threads = 256;
    const float* A_ptr = A_c.data_ptr<float>();
    const float* B_ptr = B_c.data_ptr<float>();
    float* C_ptr = C.data_ptr<float>();
    cudaStream_t stream = at::cuda::getCurrentCUDAStream();

    if ((M & 3) == 0) {
        const int Mv = M >> 2;
        const int blocks_x = std::max(1, std::min((Mv + threads - 1) / threads, 1024));
        const int blocks_y = std::max(1, std::min(N, 65535));
        dim3 grid(blocks_x, blocks_y);
        diag_matmul_vec4_kernel<<<grid, threads, 0, stream>>>(A_ptr, B_ptr, C_ptr, N, Mv);
    } else {
        const int blocks_x = std::max(1, std::min((M + threads - 1) / threads, 1024));
        const int blocks_y = std::max(1, std::min(N, 65535));
        dim3 grid(blocks_x, blocks_y);
        diag_matmul_scalar_kernel<<<grid, threads, 0, stream>>>(A_ptr, B_ptr, C_ptr, N, M);
    }
    return C;
}
"""

diag_matmul_cpp_source = "torch::Tensor diag_matmul_cuda(torch::Tensor A, torch::Tensor B);"

# Compile the inline CUDA kernel.
diag_matmul = load_inline(
    name="diag_matmul_fused",
    cpp_sources=diag_matmul_cpp_source,
    cuda_sources=diag_matmul_cuda_source,
    functions=["diag_matmul_cuda"],
    verbose=False,
    extra_cuda_cflags=["-O3"],
)


class ModelNew(nn.Module):
    """
    Optimized version of Model that computes C = diag(A) @ B.

    Instead of building an (N, N) diagonal matrix and performing an
    O(N^2 * M) matrix multiplication, this module uses a fused custom CUDA
    kernel that directly evaluates C[i, j] = A[i] * B[i, j] in O(N * M),
    which is purely memory-bound and avoids materializing the diagonal
    matrix entirely.
    """

    def __init__(self):
        super(ModelNew, self).__init__()
        self.diag_matmul = diag_matmul

    def forward(self, A, B):
        if (A.is_cuda and B.is_cuda
                and A.dtype == torch.float32 and B.dtype == torch.float32
                and A.dim() == 1 and B.dim() == 2):
            return self.diag_matmul.diag_matmul_cuda(A, B)
        # Generic fallback (mathematically identical to torch.diag(A) @ B).
        return A.unsqueeze(-1) * B
```
Result of evaluating it:
The solution is correct on the test inputs. Mean runtime 0.128 ms; the PyTorch reference (with TF32 enabled) takes 1.08 ms; speedup 8.438x.
Profiler, top device kernels of one forward pass of your solution:
| device kernel | total device time (us) | calls |
|---|---|---|
| diag_matmul_vec4_kernel(float const*, float const*, float*, int, int) | 100.1 | 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.
