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 Gemm, multiplies the result, and applies LeakyReLU.
    """
    def __init__(self, in_features, out_features, multiplier, negative_slope):
        super(Model, self).__init__()
        self.gemm = nn.Linear(in_features, out_features)
        self.multiplier = multiplier
        self.leaky_relu = nn.LeakyReLU(negative_slope)

    def forward(self, x):
        x = self.gemm(x)
        x = x * self.multiplier
        x = self.leaky_relu(x)
        return x

batch_size = 1024
in_features  = 8192  
out_features = 8192
multiplier = 2.0
negative_slope = 0.1

def get_inputs():
    return [torch.rand(batch_size, in_features)]

def get_init_inputs():
    return [in_features, out_features, multiplier, negative_slope]
    ```
    
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 gemm_scale_leaky_cuda(
    torch::Tensor x,
    torch::Tensor weight,
    torch::Tensor bias,
    double multiplier,
    double negative_slope);
"""

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

__device__ __forceinline__ float activate(
    float x, float bias, float multiplier, float slope) {
    float y = __fmul_rn(__fadd_rn(x, bias), multiplier);
    return y > 0.0f ? y : __fmul_rn(y, slope);
}

__global__ void epilogue_vec4(
    float4* __restrict__ output,
    const float4* __restrict__ bias,
    int64_t count,
    int channels4,
    float multiplier,
    float slope) {
    int64_t i = static_cast<int64_t>(blockIdx.x) * blockDim.x
              + threadIdx.x;
    if (i >= count) return;

    float4 v = output[i];
    float4 b = bias[i % channels4];
    v.x = activate(v.x, b.x, multiplier, slope);
    v.y = activate(v.y, b.y, multiplier, slope);
    v.z = activate(v.z, b.z, multiplier, slope);
    v.w = activate(v.w, b.w, multiplier, slope);
    output[i] = v;
}

__global__ void epilogue_scalar(
    float* __restrict__ output,
    const float* __restrict__ bias,
    int64_t count,
    int channels,
    float multiplier,
    float slope) {
    int64_t i = static_cast<int64_t>(blockIdx.x) * blockDim.x
              + threadIdx.x;
    if (i < count) {
        output[i] = activate(
            output[i], bias[i % channels], multiplier, slope);
    }
}

torch::Tensor gemm_scale_leaky_cuda(
    torch::Tensor x,
    torch::Tensor weight,
    torch::Tensor bias,
    double multiplier,
    double negative_slope) {
    TORCH_CHECK(x.is_cuda() && weight.is_cuda() && bias.is_cuda(),
                "Expected CUDA tensors");
    TORCH_CHECK(x.device() == weight.device() &&
                x.device() == bias.device(),
                "All tensors must use the same device");
    TORCH_CHECK(x.scalar_type() == at::kFloat &&
                weight.scalar_type() == at::kFloat &&
                bias.scalar_type() == at::kFloat,
                "Expected float32 tensors");
    TORCH_CHECK(x.dim() >= 1 && weight.dim() == 2 && bias.dim() == 1,
                "Invalid tensor dimensions");
    TORCH_CHECK(x.is_contiguous() && weight.is_contiguous() &&
                bias.is_contiguous(), "Expected contiguous tensors");

    const int64_t k64 = weight.size(1);
    const int64_t n64 = weight.size(0);
    TORCH_CHECK(k64 > 0 && x.size(-1) == k64 &&
                bias.numel() == n64, "Invalid feature dimensions");
    const int64_t m64 = x.numel() / k64;
    TORCH_CHECK(m64 <= INT_MAX && n64 <= INT_MAX && k64 <= INT_MAX,
                "GEMM dimensions exceed supported limits");

    const c10::cuda::CUDAGuard guard(x.device());
    auto sizes = x.sizes().vec();
    sizes.back() = n64;
    auto output = torch::empty(sizes, x.options());
    if (output.numel() == 0) return output;

    const int m = static_cast<int>(m64);
    const int n = static_cast<int>(n64);
    const int k = static_cast<int>(k64);

    auto stream = at::cuda::getCurrentCUDAStream();
    auto handle = at::cuda::getCurrentCUDABlasHandle();

    const float alpha = 1.0f;
    const float beta = 0.0f;

    // Column-major interpretation computes output.T = weight @ x.T.
    // Explicit TF32 compute avoids depending on PyTorch's global setting.
    cublasStatus_t status = cublasGemmEx(
        handle,
        CUBLAS_OP_T,
        CUBLAS_OP_N,
        n, m, k,
        &alpha,
        weight.data_ptr<float>(), CUDA_R_32F, k,
        x.data_ptr<float>(), CUDA_R_32F, k,
        &beta,
        output.data_ptr<float>(), CUDA_R_32F, n,
        CUBLAS_COMPUTE_32F_FAST_TF32,
        CUBLAS_GEMM_DEFAULT_TENSOR_OP);

    TORCH_CHECK(status == CUBLAS_STATUS_SUCCESS,
                "cublasGemmEx failed: ", static_cast<int>(status));

    constexpr int threads = 256;
    const int64_t count = output.numel();
    if (n % 4 == 0 &&
        reinterpret_cast<uintptr_t>(bias.data_ptr<float>()) % 16 == 0) {
        const int64_t vectors = count / 4;
        epilogue_vec4<<<
            static_cast<unsigned int>((vectors + threads - 1) / threads),
            threads, 0, stream.stream()>>>(
                reinterpret_cast<float4*>(output.data_ptr<float>()),
                reinterpret_cast<const float4*>(bias.data_ptr<float>()),
                vectors, n / 4,
                static_cast<float>(multiplier),
                static_cast<float>(negative_slope));
    } else {
        epilogue_scalar<<<
            static_cast<unsigned int>((count + threads - 1) / threads),
            threads, 0, stream.stream()>>>(
                output.data_ptr<float>(), bias.data_ptr<float>(),
                count, n,
                static_cast<float>(multiplier),
                static_cast<float>(negative_slope));
    }
    C10_CUDA_KERNEL_LAUNCH_CHECK();
    return output;
}
"""

_ops = load_inline(
    name="tf32_gemm_inplace_scale_leaky_v2",
    cpp_sources=_cpp_source,
    cuda_sources=_cuda_source,
    functions=["gemm_scale_leaky_cuda"],
    extra_cflags=["-O3"],
    extra_cuda_cflags=["-O3"],
    extra_ldflags=["-lcublas"],
    verbose=False,
)


class ModelNew(nn.Module):
    def __init__(self, in_features, out_features, multiplier, negative_slope):
        super().__init__()
        self.gemm = nn.Linear(in_features, out_features)
        self.multiplier = multiplier
        self.leaky_relu = nn.LeakyReLU(negative_slope)

    def forward(self, x):
        if (
            x.is_cuda
            and x.dtype == torch.float32
            and self.gemm.weight.dtype == torch.float32
            and self.gemm.bias.dtype == torch.float32
            and not torch.is_autocast_enabled()
            and self.gemm.in_features > 0
        ):
            return _ops.gemm_scale_leaky_cuda(
                x.contiguous(),
                self.gemm.weight.contiguous(),
                self.gemm.bias.contiguous(),
                float(self.multiplier),
                float(self.leaky_relu.negative_slope),
            )
        return self.leaky_relu(self.gemm(x) * self.multiplier)
```
Result of evaluating it:
The solution is correct on the test inputs. Mean runtime 1.17 ms; the PyTorch reference (with TF32 enabled) takes 1.22 ms; speedup 1.043x.
Profiler, top device kernels of one forward pass of your solution:
| device kernel | total device time (us) | calls |
|---|---|---|
| void cutlass::Kernel2<cutlass_80_tensorop_s1688gemm_256x128_32x3_tn_align4>(cutlass_80_ten | 1071.0 | 1 |
| epilogue_vec4(float4*, float4 const*, long, int, float, float) | 36.9 | 1 |
| Memset (Device) | 1.2 | 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.
