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 single matrix multiplication (C = A * B)
    """
    def __init__(self):
        super(Model, self).__init__()
    
    def forward(self, A: torch.Tensor, B: torch.Tensor) -> torch.Tensor:
        """
        Performs matrix multiplication.

        Args:
            A: Input tensor of shape (M, K).
            B: Input tensor of shape (K, N).

        Returns:
            Output tensor of shape (M, N).
        """
        return torch.matmul(A, B)

M = 1024 * 2
K = 4096 * 2
N = 2048 * 2

def get_inputs():
    A = torch.rand(M, K)
    B = torch.rand(K, N)
    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 3 of 6 for this task. Your previous solution (turn 2) 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 fast_gemm(torch::Tensor a, torch::Tensor b);
"""

_cuda_source = r"""
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <c10/cuda/CUDAException.h>
#include <cuda_fp16.h>
#include <cublas_v2.h>
#include <algorithm>
#include <limits>

__global__ void convert_inputs(
    const float* __restrict__ a,
    const float* __restrict__ b,
    __half* __restrict__ ah,
    __half* __restrict__ bh,
    int64_t na,
    int64_t nb)
{
    const int64_t av = na / 4;
    const int64_t bv = nb / 4;
    const int64_t stride = int64_t(blockDim.x) * gridDim.x;

    for (int64_t i = int64_t(blockIdx.x) * blockDim.x + threadIdx.x;
         i < av + bv; i += stride) {
        const bool use_a = i < av;
        const int64_t j = use_a ? i : i - av;
        const float4 x = reinterpret_cast<const float4*>(
            use_a ? a : b)[j];
        __half2* dst = reinterpret_cast<__half2*>(use_a ? ah : bh);
        dst[2 * j] = __floats2half2_rn(x.x, x.y);
        dst[2 * j + 1] = __floats2half2_rn(x.z, x.w);
    }

    if (blockIdx.x == 0 && threadIdx.x < 4) {
        const int64_t ai = av * 4 + threadIdx.x;
        const int64_t bi = bv * 4 + threadIdx.x;
        if (ai < na) ah[ai] = __float2half_rn(a[ai]);
        if (bi < nb) bh[bi] = __float2half_rn(b[bi]);
    }
}

struct BlasStateGuard {
    cublasHandle_t handle;
    cublasMath_t math;
    cublasPointerMode_t pointer;

    explicit BlasStateGuard(cublasHandle_t h) : handle(h) {
        TORCH_CHECK(cublasGetMathMode(h, &math) == CUBLAS_STATUS_SUCCESS,
                    "cublasGetMathMode failed");
        TORCH_CHECK(cublasGetPointerMode(h, &pointer) == CUBLAS_STATUS_SUCCESS,
                    "cublasGetPointerMode failed");
    }

    ~BlasStateGuard() {
        cublasSetMathMode(handle, math);
        cublasSetPointerMode(handle, pointer);
    }
};

torch::Tensor fast_gemm(torch::Tensor a, torch::Tensor b) {
    TORCH_CHECK(a.is_cuda() && b.is_cuda(), "CUDA tensors required");
    TORCH_CHECK(a.device() == b.device(), "Devices must match");
    TORCH_CHECK(a.scalar_type() == at::kFloat &&
                b.scalar_type() == at::kFloat, "Float32 tensors required");
    TORCH_CHECK(a.dim() == 2 && b.dim() == 2 &&
                a.size(1) == b.size(0), "Invalid matrix dimensions");
    TORCH_CHECK(a.is_contiguous() && b.is_contiguous(),
                "Contiguous tensors required");

    c10::cuda::CUDAGuard guard(a.device());

    const int64_t m64 = a.size(0);
    const int64_t k64 = a.size(1);
    const int64_t n64 = b.size(1);
    TORCH_CHECK(m64 <= std::numeric_limits<int>::max() &&
                k64 <= std::numeric_limits<int>::max() &&
                n64 <= std::numeric_limits<int>::max(),
                "Matrix dimensions exceed cuBLAS limits");

    auto out = torch::empty({m64, n64}, a.options());
    if (m64 == 0 || n64 == 0) return out;

    cudaStream_t stream = at::cuda::getCurrentCUDAStream();
    if (k64 == 0) {
        C10_CUDA_CHECK(cudaMemsetAsync(
            out.data_ptr<float>(), 0, out.numel() * sizeof(float), stream));
        return out;
    }

    auto ah = torch::empty(a.sizes(), a.options().dtype(at::kHalf));
    auto bh = torch::empty(b.sizes(), b.options().dtype(at::kHalf));

    const int64_t vectors = a.numel() / 4 + b.numel() / 4;
    const int blocks = static_cast<int>(
        std::max<int64_t>(1, std::min<int64_t>((vectors + 255) / 256, 4096)));

    convert_inputs<<<blocks, 256, 0, stream>>>(
        a.data_ptr<float>(), b.data_ptr<float>(),
        reinterpret_cast<__half*>(ah.data_ptr<at::Half>()),
        reinterpret_cast<__half*>(bh.data_ptr<at::Half>()),
        a.numel(), b.numel());
    C10_CUDA_KERNEL_LAUNCH_CHECK();

    cublasHandle_t handle = at::cuda::getCurrentCUDABlasHandle();
    BlasStateGuard state(handle);
    TORCH_CHECK(cublasSetPointerMode(handle, CUBLAS_POINTER_MODE_HOST) ==
                CUBLAS_STATUS_SUCCESS, "cublasSetPointerMode failed");
    TORCH_CHECK(cublasSetMathMode(
                    handle,
                    static_cast<cublasMath_t>(
                        CUBLAS_DEFAULT_MATH |
                        CUBLAS_MATH_DISALLOW_REDUCED_PRECISION_REDUCTION)) ==
                CUBLAS_STATUS_SUCCESS, "cublasSetMathMode failed");

    const float alpha = 1.0f;
    const float beta = 0.0f;
    const int m = static_cast<int>(m64);
    const int n = static_cast<int>(n64);
    const int k = static_cast<int>(k64);

    // FP16 tensor-core multiplication with FP32 accumulation and output.
    const cublasStatus_t status = cublasGemmEx(
        handle, CUBLAS_OP_N, CUBLAS_OP_N,
        n, m, k,
        &alpha,
        bh.data_ptr<at::Half>(), CUDA_R_16F, n,
        ah.data_ptr<at::Half>(), CUDA_R_16F, k,
        &beta,
        out.data_ptr<float>(), CUDA_R_32F, n,
        CUBLAS_COMPUTE_32F,
        CUBLAS_GEMM_DEFAULT_TENSOR_OP);
    TORCH_CHECK(status == CUBLAS_STATUS_SUCCESS,
                "cuBLAS GEMM failed: ", static_cast<int>(status));
    return out;
}
"""

_extension = load_inline(
    name="vectorized_fp16_fp32_gemm_v2",
    cpp_sources=_cpp_source,
    cuda_sources=_cuda_source,
    functions=["fast_gemm"],
    extra_cflags=["-O3"],
    extra_cuda_cflags=["-O3"],
    extra_ldflags=["-lcublas"],
    verbose=False,
)


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

    def forward(self, A: torch.Tensor, B: torch.Tensor) -> torch.Tensor:
        if (
            not A.is_cuda
            or not B.is_cuda
            or A.dtype != torch.float32
            or B.dtype != torch.float32
            or A.ndim != 2
            or B.ndim != 2
            or (torch.is_grad_enabled() and (A.requires_grad or B.requires_grad))
        ):
            return torch.matmul(A, B)
        return _extension.fast_gemm(A.contiguous(), B.contiguous())
```
Result of evaluating it:
The solution is correct on the test inputs. Mean runtime 0.744 ms; the PyTorch reference (with TF32 enabled) takes 1.15 ms; speedup 1.546x.
Profiler, top device kernels of one forward pass of your solution:
| device kernel | total device time (us) | calls |
|---|---|---|
| void cutlass::Kernel2<cutlass_80_tensorop_s16816gemm_f16_128x128_32x4_nn_align8>(cutlass_8 | 505.1 | 1 |
| convert_inputs(float const*, float const*, __half*, __half*, long, long) | 224.0 | 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.
