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, adds a value, applies Swish, Tanh, GELU, and Hardtanh activation functions.
    """
    def __init__(self, in_features, out_features, add_value_shape):
        super(Model, self).__init__()
        self.matmul = nn.Linear(in_features, out_features)
        self.add_value = nn.Parameter(torch.randn(add_value_shape)) 

    def forward(self, x):
        x = self.matmul(x)
        x = x + self.add_value
        x = torch.sigmoid(x) * x # Swish
        x = torch.tanh(x)
        x = torch.nn.functional.gelu(x) # GELU
        x = torch.nn.functional.hardtanh(x, min_val=-1, max_val=1) # Hardtanh
        return x

batch_size = 1024
in_features = 8192
out_features = 8192
add_value_shape = (out_features,)

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

def get_init_inputs():
    return [in_features, out_features, add_value_shape]
    ```
    
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
import torch.nn.functional as F
from torch.utils.cpp_extension import load_inline

_cpp_source = r"""
#include <torch/extension.h>
torch::Tensor fused_activations_cuda(torch::Tensor x, torch::Tensor add);
"""

_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>

template <typename T>
__device__ __forceinline__ T activate(T x);

template <>
__device__ __forceinline__ float activate<float>(float x) {
    x = x / (1.0f + expf(-x));
    x = tanhf(x);
    x = (0.5f * x) *
        (1.0f + erff(x * 0.70710678118654752440f));
    return x < -1.0f ? -1.0f : (x > 1.0f ? 1.0f : x);
}

template <>
__device__ __forceinline__ double activate<double>(double x) {
    x = x / (1.0 + exp(-x));
    x = tanh(x);
    x = (0.5 * x) *
        (1.0 + erf(x * 0.70710678118654752440));
    return x < -1.0 ? -1.0 : (x > 1.0 ? 1.0 : x);
}

__global__ void fused_float4_kernel(
    const float4* __restrict__ x,
    const float4* __restrict__ add,
    float4* __restrict__ out,
    int64_t count,
    int64_t columns
) {
    for (int64_t i = static_cast<int64_t>(blockIdx.x) * blockDim.x
                     + threadIdx.x;
         i < count;
         i += static_cast<int64_t>(blockDim.x) * gridDim.x) {
        float4 v = x[i];
        const float4 a = add[i % columns];
        v.x = activate<float>(v.x + a.x);
        v.y = activate<float>(v.y + a.y);
        v.z = activate<float>(v.z + a.z);
        v.w = activate<float>(v.w + a.w);
        out[i] = v;
    }
}

template <typename scalar_t>
__global__ void fused_scalar_kernel(
    const scalar_t* __restrict__ x,
    const scalar_t* __restrict__ add,
    scalar_t* __restrict__ out,
    int64_t count,
    int64_t columns
) {
    using acc_t = at::acc_type<scalar_t, true>;
    for (int64_t i = static_cast<int64_t>(blockIdx.x) * blockDim.x
                     + threadIdx.x;
         i < count;
         i += static_cast<int64_t>(blockDim.x) * gridDim.x) {
        acc_t v = static_cast<acc_t>(x[i])
                + static_cast<acc_t>(add[i % columns]);
        out[i] = static_cast<scalar_t>(activate<acc_t>(v));
    }
}

torch::Tensor fused_activations_cuda(
    torch::Tensor x,
    torch::Tensor add
) {
    TORCH_CHECK(x.is_cuda() && add.is_cuda(), "Expected CUDA tensors");
    TORCH_CHECK(x.device() == add.device(), "Device mismatch");
    TORCH_CHECK(x.scalar_type() == add.scalar_type(), "Dtype mismatch");
    TORCH_CHECK(x.is_contiguous() && add.is_contiguous(),
                "Expected contiguous tensors");
    TORCH_CHECK(x.dim() >= 1 && add.numel() == x.size(-1),
                "Expected one addition value per output feature");

    const c10::cuda::CUDAGuard guard(x.device());
    auto out = torch::empty_like(x);
    const int64_t count = x.numel();
    if (count == 0) {
        return out;
    }

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

    if (x.scalar_type() == at::kFloat && columns % 4 == 0 &&
        reinterpret_cast<uintptr_t>(x.data_ptr()) % 16 == 0 &&
        reinterpret_cast<uintptr_t>(add.data_ptr()) % 16 == 0) {
        const int64_t vectors = count / 4;
        const int blocks = static_cast<int>(
            std::min<int64_t>((vectors + threads - 1) / threads, 4096));
        fused_float4_kernel<<<blocks, threads, 0, stream>>>(
            reinterpret_cast<const float4*>(x.data_ptr<float>()),
            reinterpret_cast<const float4*>(add.data_ptr<float>()),
            reinterpret_cast<float4*>(out.data_ptr<float>()),
            vectors,
            columns / 4);
    } else {
        const int blocks = static_cast<int>(
            std::min<int64_t>((count + threads - 1) / threads, 4096));
        AT_DISPATCH_FLOATING_TYPES(
            x.scalar_type(), "fused_activations_cuda", [&] {
                fused_scalar_kernel<scalar_t>
                    <<<blocks, threads, 0, stream>>>(
                        x.data_ptr<scalar_t>(),
                        add.data_ptr<scalar_t>(),
                        out.data_ptr<scalar_t>(),
                        count,
                        columns);
            });
    }

    C10_CUDA_KERNEL_LAUNCH_CHECK();
    return out;
}
"""

_fused_ops = load_inline(
    name="linear_swish_tanh_gelu_hardtanh_fused",
    cpp_sources=_cpp_source,
    cuda_sources=_cuda_source,
    functions=["fused_activations_cuda"],
    extra_cflags=["-O3"],
    extra_cuda_cflags=["-O3"],
    verbose=False,
)


class ModelNew(nn.Module):
    def __init__(self, in_features, out_features, add_value_shape):
        super().__init__()
        self.matmul = nn.Linear(in_features, out_features)
        self.add_value = nn.Parameter(torch.randn(add_value_shape))

    def forward(self, x):
        x = self.matmul(x)

        if (
            x.is_cuda
            and x.dtype in (torch.float32, torch.float64)
            and self.add_value.dtype == x.dtype
            and self.add_value.ndim == 1
            and self.add_value.numel() == x.shape[-1]
        ):
            return _fused_ops.fused_activations_cuda(
                x.contiguous(), self.add_value.contiguous()
            )

        x = x + self.add_value
        x = torch.sigmoid(x) * x
        x = torch.tanh(x)
        x = F.gelu(x)
        return F.hardtanh(x, min_val=-1, max_val=1)
```
Result of evaluating it:
The solution is correct on the test inputs. Mean runtime 7.91 ms; the PyTorch reference (with TF32 enabled) takes 1.45 ms; speedup 0.183x.
Profiler, top device kernels of one forward pass of your solution:
| device kernel | total device time (us) | calls |
|---|---|---|
| ampere_sgemm_128x32_tn | 7862.9 | 1 |
| fused_float4_kernel(float4 const*, float4 const*, float4*, long, long) | 53.1 | 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.
