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):
    """
    Model that performs a convolution, subtracts two values, applies Mish activation.
    """
    def __init__(self, in_channels, out_channels, kernel_size, subtract_value_1, subtract_value_2):
        super(Model, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)
        self.subtract_value_1 = subtract_value_1
        self.subtract_value_2 = subtract_value_2

    def forward(self, x):
        x = self.conv(x)
        x = x - self.subtract_value_1
        x = x - self.subtract_value_2
        x = torch.nn.functional.mish(x)
        return x

batch_size = 128
in_channels = 8
out_channels = 64
height, width = 256, 256
kernel_size = 3
subtract_value_1 = 0.5
subtract_value_2 = 0.2

def get_inputs():
    return [torch.rand(batch_size, in_channels, height, width)]

def get_init_inputs():
    return [in_channels, out_channels, kernel_size, subtract_value_1, subtract_value_2]
    ```
    
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 6 of 6 for this task. Your previous solution (turn 5) 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 channels_last_input(torch::Tensor x);
torch::Tensor fused_conv_mish(
    torch::Tensor x, torch::Tensor weight, torch::Tensor bias,
    double s1, double s2);
torch::Tensor fused_bias_mish(
    torch::Tensor x, torch::Tensor bias, double s1, double s2);
"""

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

namespace wmma = nvcuda::wmma;

__global__ void nchw8_to_nhwc(
    const float* __restrict__ src,
    float* __restrict__ dst,
    int64_t spatial,
    int64_t tiles_per_image
) {
    __shared__ float tile[8][516];

    const int tid = threadIdx.x;
    const int channel = tid >> 5;
    const int lane = tid & 31;
    const int64_t batch = blockIdx.x / tiles_per_image;
    const int64_t first_pixel = (blockIdx.x % tiles_per_image) * 512;

    #pragma unroll
    for (int j = 0; j < 4; ++j) {
        const int p = lane * 4 + j * 128;
        const float4 v = *reinterpret_cast<const float4*>(
            src + (batch * 8 + channel) * spatial + first_pixel + p);
        tile[channel][p] = v.x;
        tile[channel][p + 1] = v.y;
        tile[channel][p + 2] = v.z;
        tile[channel][p + 3] = v.w;
    }

    __syncthreads();

    const int c = (tid & 1) * 4;
    #pragma unroll
    for (int j = 0; j < 4; ++j) {
        const int p = (tid >> 1) + j * 128;
        const float4 v = make_float4(
            tile[c][p], tile[c + 1][p],
            tile[c + 2][p], tile[c + 3][p]);
        *reinterpret_cast<float4*>(
            dst + (batch * spatial + first_pixel + p) * 8 + c) = v;
    }
}

__device__ __forceinline__ float activate(
    float x, float bias, float s1, float s2
) {
    x = x + bias;
    x = x - s1;
    x = x - s2;
    if (x > 20.0f)
        return x;

    const float e = __expf(x);
    const float q = e * (e + 2.0f);
    return x * __fdividef(q, q + 2.0f);
}

__device__ __forceinline__ float4 activate4(
    float4 x, float4 b, float s1, float s2
) {
    x.x = activate(x.x, b.x, s1, s2);
    x.y = activate(x.y, b.y, s1, s2);
    x.z = activate(x.z, b.z, s1, s2);
    x.w = activate(x.w, b.w, s1, s2);
    return x;
}

__global__ void pack_weights(
    const float* __restrict__ weight,
    float* __restrict__ packed
) {
#if __CUDA_ARCH__ >= 800
    const int i = blockIdx.x * blockDim.x + threadIdx.x;
    if (i < 64 * 80) {
        const int c = i / 80;
        const int k = i % 80;
        const float v = k < 72 ? weight[c * 72 + k] : 0.0f;
        const float hi = __float_to_tf32(v);
        packed[i] = hi;
        packed[64 * 80 + i] = __float_to_tf32(v - hi);
    }
#endif
}

__global__ __launch_bounds__(256) void conv8_64_mish(
    const float* __restrict__ input,
    const float* __restrict__ packed_weight,
    const float4* __restrict__ bias,
    float4* __restrict__ output,
    float s1,
    float s2
) {
#if __CUDA_ARCH__ >= 800
    constexpr int M = 64;
    constexpr int LD = 80;
    constexpr int Spatial = 254 * 254;

    __shared__ __align__(32) float storage[2 * M * LD];
    float* const ah = storage;
    float* const al = storage + M * LD;

    const int tid = threadIdx.x;
    const int first = blockIdx.x * M;
    const int batch = blockIdx.y;

    // Materialize this output tile's convolution patches directly in shared
    // memory, splitting each float into two TF32 components.
    for (int i = tid; i < M * 18; i += 256) {
        const int row = i / 18;
        const int kv = i - row * 18;
        const int pixel = first + row;
        float4 v = make_float4(0.f, 0.f, 0.f, 0.f);

        if (pixel < Spatial) {
            const int oh = pixel / 254;
            const int ow = pixel - oh * 254;
            const int position = kv >> 1;
            const int kh = position / 3;
            const int kw = position - kh * 3;
            const int c = (kv & 1) * 4;

            const int64_t offset =
                ((int64_t(batch) * 256 + oh + kh) * 256 + ow + kw) * 8 + c;
            v = *reinterpret_cast<const float4*>(input + offset);
        }

        const float4 hi = make_float4(
            __float_to_tf32(v.x), __float_to_tf32(v.y),
            __float_to_tf32(v.z), __float_to_tf32(v.w));
        const float4 lo = make_float4(
            __float_to_tf32(v.x - hi.x),
            __float_to_tf32(v.y - hi.y),
            __float_to_tf32(v.z - hi.z),
            __float_to_tf32(v.w - hi.w));

        *reinterpret_cast<float4*>(ah + row * LD + kv * 4) = hi;
        *reinterpret_cast<float4*>(al + row * LD + kv * 4) = lo;
    }

    __syncthreads();

    const int warp = tid >> 5;
    const int row = (warp >> 1) * 16;
    const int col = (warp & 1) * 32;

    wmma::fragment<wmma::accumulator, 16, 16, 8, float> acc0, acc1;
    wmma::fill_fragment(acc0, 0.0f);
    wmma::fill_fragment(acc1, 0.0f);

    const float* const bh = packed_weight;
    const float* const bl = packed_weight + 64 * LD;

    #pragma unroll
    for (int k = 0; k < 72; k += 8) {
        wmma::fragment<
            wmma::matrix_a, 16, 16, 8,
            wmma::precision::tf32, wmma::row_major> a_hi, a_lo;
        wmma::fragment<
            wmma::matrix_b, 16, 16, 8,
            wmma::precision::tf32, wmma::col_major> b_hi, b_lo;

        wmma::load_matrix_sync(a_hi, ah + row * LD + k, LD);
        wmma::load_matrix_sync(a_lo, al + row * LD + k, LD);

        wmma::load_matrix_sync(b_hi, bh + col * LD + k, LD);
        wmma::load_matrix_sync(b_lo, bl + col * LD + k, LD);
        wmma::mma_sync(acc0, a_hi, b_hi, acc0);
        wmma::mma_sync(acc0, a_lo, b_hi, acc0);
        wmma::mma_sync(acc0, a_hi, b_lo, acc0);

        wmma::load_matrix_sync(b_hi, bh + (col + 16) * LD + k, LD);
        wmma::load_matrix_sync(b_lo, bl + (col + 16) * LD + k, LD);
        wmma::mma_sync(acc1, a_hi, b_hi, acc1);
        wmma::mma_sync(acc1, a_lo, b_hi, acc1);
        wmma::mma_sync(acc1, a_hi, b_lo, acc1);
    }

    __syncthreads();

    wmma::store_matrix_sync(
        storage + row * 64 + col, acc0, 64, wmma::mem_row_major);
    wmma::store_matrix_sync(
        storage + row * 64 + col + 16, acc1, 64, wmma::mem_row_major);

    __syncthreads();

    const float4 b = bias[tid & 15];

    #pragma unroll
    for (int j = 0; j < 4; ++j) {
        const int i = tid + j * 256;
        const int pixel = first + (i >> 4);
        if (pixel < Spatial) {
            float4 v = reinterpret_cast<const float4*>(storage)[i];
            v = activate4(v, b, s1, s2);
            output[(int64_t(batch) * Spatial + pixel) * 16 + (i & 15)] = v;
        }
    }
#endif
}

__global__ void bias_mish64(
    float4* __restrict__ x,
    const float4* __restrict__ bias,
    int64_t vectors,
    float s1,
    float s2
) {
    const float4 b = bias[threadIdx.x & 15];
    const int64_t step = int64_t(gridDim.x) * 1024;

    for (int64_t base = int64_t(blockIdx.x) * 1024;
         base < vectors; base += step) {
        const int64_t i = base + threadIdx.x;

        if (base + 1024 <= vectors) {
            float4 a0 = x[i];
            float4 a1 = x[i + 256];
            float4 a2 = x[i + 512];
            float4 a3 = x[i + 768];

            a0 = activate4(a0, b, s1, s2);
            a1 = activate4(a1, b, s1, s2);
            a2 = activate4(a2, b, s1, s2);
            a3 = activate4(a3, b, s1, s2);

            x[i] = a0;
            x[i + 256] = a1;
            x[i + 512] = a2;
            x[i + 768] = a3;
        } else {
            #pragma unroll
            for (int j = 0; j < 4; ++j) {
                const int64_t k = i + j * 256;
                if (k < vectors)
                    x[k] = activate4(x[k], b, s1, s2);
            }
        }
    }
}

__global__ void bias_mish_scalar(
    float* __restrict__ x,
    const float* __restrict__ bias,
    int64_t count,
    int channels,
    float s1,
    float s2
) {
    for (int64_t i = int64_t(blockIdx.x) * blockDim.x + threadIdx.x;
         i < count; i += int64_t(blockDim.x) * gridDim.x) {
        x[i] = activate(x[i], bias[i % channels], s1, s2);
    }
}

torch::Tensor channels_last_input(torch::Tensor x) {
    TORCH_CHECK(x.is_cuda() && x.dim() == 4, "Expected CUDA NCHW tensor");

    if (x.is_contiguous(at::MemoryFormat::ChannelsLast))
        return x;

    const c10::cuda::CUDAGuard guard(x.device());
    const int64_t spatial = x.size(2) * x.size(3);

    if (x.scalar_type() != at::kFloat ||
        x.size(1) != 8 ||
        !x.is_contiguous() ||
        spatial == 0 ||
        spatial % 512 != 0 ||
        reinterpret_cast<uintptr_t>(x.data_ptr()) % 16 != 0) {
        return x.contiguous(at::MemoryFormat::ChannelsLast);
    }

    auto output = torch::empty(
        x.sizes(), x.options().memory_format(at::MemoryFormat::ChannelsLast));

    const int64_t tiles = spatial / 512;
    const int64_t blocks = x.size(0) * tiles;

    if (blocks != 0) {
        nchw8_to_nhwc<<<blocks, 256, 0, at::cuda::getCurrentCUDAStream()>>>(
            x.data_ptr<float>(), output.data_ptr<float>(), spatial, tiles);
        C10_CUDA_KERNEL_LAUNCH_CHECK();
    }
    return output;
}

torch::Tensor fused_conv_mish(
    torch::Tensor x, torch::Tensor weight, torch::Tensor bias,
    double s1, double s2
) {
    const c10::cuda::CUDAGuard guard(x.device());

    TORCH_CHECK(
        x.is_cuda() && weight.is_cuda() && bias.is_cuda() &&
        x.device() == weight.device() && x.device() == bias.device(),
        "Expected tensors on the same CUDA 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() == 4 && x.size(1) == 8 &&
        x.size(2) == 256 && x.size(3) == 256 &&
        x.is_contiguous(at::MemoryFormat::ChannelsLast),
        "Unsupported input");
    TORCH_CHECK(
        weight.dim() == 4 && weight.size(0) == 64 &&
        weight.size(1) == 8 && weight.size(2) == 3 &&
        weight.size(3) == 3 &&
        weight.is_contiguous(at::MemoryFormat::ChannelsLast) &&
        bias.is_contiguous() && bias.numel() == 64,
        "Unsupported weights");

    auto output = torch::empty(
        {x.size(0), 64, 254, 254},
        x.options().memory_format(at::MemoryFormat::ChannelsLast));

    if (x.size(0) == 0)
        return output;

    auto packed = torch::empty({2, 64, 80}, weight.options());
    const auto stream = at::cuda::getCurrentCUDAStream();

    pack_weights<<<20, 256, 0, stream>>>(
        weight.data_ptr<float>(), packed.data_ptr<float>());
    C10_CUDA_KERNEL_LAUNCH_CHECK();

    const dim3 grid((254 * 254 + 63) / 64, x.size(0));
    conv8_64_mish<<<grid, 256, 0, stream>>>(
        x.data_ptr<float>(),
        packed.data_ptr<float>(),
        reinterpret_cast<const float4*>(bias.data_ptr<float>()),
        reinterpret_cast<float4*>(output.data_ptr<float>()),
        static_cast<float>(s1), static_cast<float>(s2));
    C10_CUDA_KERNEL_LAUNCH_CHECK();

    return output;
}

torch::Tensor fused_bias_mish(
    torch::Tensor x, torch::Tensor bias, double s1, double s2
) {
    const c10::cuda::CUDAGuard guard(x.device());

    TORCH_CHECK(
        x.is_cuda() && bias.is_cuda() && x.device() == bias.device(),
        "Expected tensors on the same CUDA device");
    TORCH_CHECK(
        x.scalar_type() == at::kFloat &&
        bias.scalar_type() == at::kFloat &&
        x.dim() == 4 &&
        x.is_contiguous(at::MemoryFormat::ChannelsLast) &&
        bias.is_contiguous() && bias.numel() == x.size(1),
        "Invalid tensors");

    const int64_t count = x.numel();
    if (count == 0)
        return x;

    const auto stream = at::cuda::getCurrentCUDAStream();
    const bool aligned =
        reinterpret_cast<uintptr_t>(x.data_ptr()) % 16 == 0 &&
        reinterpret_cast<uintptr_t>(bias.data_ptr()) % 16 == 0;

    if (x.size(1) == 64 && aligned) {
        const int64_t vectors = count / 4;
        const int blocks = static_cast<int>(
            std::min<int64_t>((vectors + 1023) / 1024, 2048));
        bias_mish64<<<blocks, 256, 0, stream>>>(
            reinterpret_cast<float4*>(x.data_ptr<float>()),
            reinterpret_cast<const float4*>(bias.data_ptr<float>()),
            vectors, static_cast<float>(s1), static_cast<float>(s2));
    } else {
        const int blocks = static_cast<int>(
            std::min<int64_t>((count + 255) / 256, 2048));
        bias_mish_scalar<<<blocks, 256, 0, stream>>>(
            x.data_ptr<float>(), bias.data_ptr<float>(), count,
            static_cast<int>(x.size(1)),
            static_cast<float>(s1), static_cast<float>(s2));
    }

    C10_CUDA_KERNEL_LAUNCH_CHECK();
    return x;
}
"""

_ops = load_inline(
    name="fused_conv8_64_mish_tf32x3_v5",
    cpp_sources=_cpp_source,
    cuda_sources=_cuda_source,
    functions=[
        "channels_last_input",
        "fused_conv_mish",
        "fused_bias_mish",
    ],
    extra_cflags=["-O3"],
    extra_cuda_cflags=["-O3", "--fmad=false"],
    verbose=False,
)


class ModelNew(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        subtract_value_1,
        subtract_value_2,
    ):
        super().__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)
        self.conv.to(memory_format=torch.channels_last)
        self.subtract_value_1 = subtract_value_1
        self.subtract_value_2 = subtract_value_2

    def forward(self, x):
        if (
            not x.is_cuda
            or x.dtype != torch.float32
            or self.conv.weight.dtype != torch.float32
            or self.conv.bias is None
            or self.conv.bias.dtype != torch.float32
            or torch.is_autocast_enabled()
        ):
            return F.mish(
                (self.conv(x) - self.subtract_value_1)
                - self.subtract_value_2
            )

        x = _ops.channels_last_input(x)
        weight = self.conv.weight.contiguous(
            memory_format=torch.channels_last
        )
        bias = self.conv.bias.contiguous()

        if (
            x.shape[1:] == (8, 256, 256)
            and x.shape[0] <= 65535
            and weight.shape == (64, 8, 3, 3)
            and self.conv.stride == (1, 1)
            and self.conv.padding == (0, 0)
            and self.conv.dilation == (1, 1)
            and self.conv.groups == 1
            and torch.cuda.get_device_capability(x.device)[0] >= 8
        ):
            return _ops.fused_conv_mish(
                x,
                weight,
                bias,
                self.subtract_value_1,
                self.subtract_value_2,
            )

        y = F.conv2d(
            x,
            weight,
            None,
            self.conv.stride,
            self.conv.padding,
            self.conv.dilation,
            self.conv.groups,
        ).contiguous(memory_format=torch.channels_last)

        return _ops.fused_bias_mish(
            y,
            bias,
            self.subtract_value_1,
            self.subtract_value_2,
        )
```
Result of evaluating it:
The solution failed to compile. Error output:
```
RuntimeError: Error building extension 'fused_conv8_64_mish_tf32x3_v5': [1/3] c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=fused_conv8_64_mish_tf32x3_v5 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /opt/kbv/v/lib/python3.12/site-packages/torch/include -isystem /opt/kbv/v/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -isystem /opt/kbv/v/lib/python3.12/site-packages/nvidia/cu13/include -isystem /usr/include/python3.12 -fPIC -std=c++20 -O3 -c /srv/jobs/1791728917392428855-3456539-0/build/fused_conv8_64_mish_tf32x3_v5/main.cpp -o main.o 
[2/3] /opt/kbv/v/lib/python3.12/site-packages/nvidia/cu13/bin/nvcc -MD -MF cuda.cuda.o.d -DTORCH_EXTENSION_NAME=fused_conv8_64_mish_tf32x3_v5 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /opt/kbv/v/lib/python3.12/site-packages/torch/include -isystem /opt/kbv/v/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -isystem /opt/kbv/v/lib/python3.12/site-packages/nvidia/cu13/include -isystem /usr/include/python3.12 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_80,code=sm_80 --compiler-options '-fPIC' -O3 --fmad=false -std=c++20 -c /srv/jobs/1791728917392428855-3456539-0/build/fused_conv8_64_mish_tf32x3_v5/cuda.cu -o cuda.cuda.o 
FAILED: [code=1] cuda.cuda.o 
/opt/kbv/v/lib/python3.12/site-packages/nvidia/cu13/bin/nvcc -MD -MF cuda.cuda.o.d -DTORCH_EXTENSION_NAME=fused_conv8_64_mish_tf32x3_v5 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /opt/kbv/v/lib/python3.12/site-packages/torch/include -isystem /opt/kbv/v/lib/python3.12/site-packages/torch/include/torch/csrc/api/include -isystem /opt/kbv/v/lib/python3.12/site-packages/nvidia/cu13/include -isystem /usr/include/python3.12 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_80,code=sm_80 --compiler-options '-fPIC' -O3 --fmad=false -std=c++20 -c /srv/jobs/1791728917392428855-3456539-0/build/fused_conv8_64_mish_tf32x3_v5/cuda.cu -o cuda.cuda.o 
/srv/jobs/1791728917392428855-3456539-0/build/fused_conv8_64_mish_tf32x3_v5/cuda.cu(89): error: identifier "__float_to_tf32" is undefined
          const float hi = __float_to_tf32(v);
                           ^

/srv/jobs/1791728917392428855-3456539-0/build/fused_conv8_64_mish_tf32x3_v5/cuda.cu(139): error: identifier "__float_to_tf32" is undefined
              __float_to_tf32(v.x), __float_to_tf32(v.y),
              ^

2 errors detected in the compilation of "/srv/jobs/1791728917392428855-3456539-0/build/fused_conv8_64_mish_tf32x3_v5/cuda.cu".
ninja: build stopped: subcommand failed.

```

The fastest correct solution so far (3.641x speedup) is:
```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 channels_last_input(torch::Tensor x);
torch::Tensor fused_bias_mish(
    torch::Tensor x, torch::Tensor bias, double s1, double s2);
"""

_cuda_source = r"""
#include <torch/extension.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 nchw8_to_nhwc(
    const float* __restrict__ src,
    float* __restrict__ dst,
    int64_t spatial,
    int64_t tiles_per_image
) {
    __shared__ float tile[8][516];

    const int tid = threadIdx.x;
    const int channel = tid >> 5;
    const int lane = tid & 31;
    const int64_t batch = blockIdx.x / tiles_per_image;
    const int64_t tile_id = blockIdx.x % tiles_per_image;
    const int64_t first_pixel = tile_id * 512;

    #pragma unroll
    for (int j = 0; j < 4; ++j) {
        const int p = lane * 4 + j * 128;
        const int64_t input_offset =
            (batch * 8 + channel) * spatial + first_pixel + p;
        const float4 v =
            *reinterpret_cast<const float4*>(src + input_offset);

        tile[channel][p] = v.x;
        tile[channel][p + 1] = v.y;
        tile[channel][p + 2] = v.z;
        tile[channel][p + 3] = v.w;
    }

    __syncthreads();

    const int output_channel = (tid & 1) * 4;

    #pragma unroll
    for (int j = 0; j < 4; ++j) {
        const int p = (tid >> 1) + j * 128;
        float4 v;
        v.x = tile[output_channel][p];
        v.y = tile[output_channel + 1][p];
        v.z = tile[output_channel + 2][p];
        v.w = tile[output_channel + 3][p];

        const int64_t output_offset =
            (batch * spatial + first_pixel + p) * 8 + output_channel;
        *reinterpret_cast<float4*>(dst + output_offset) = v;
    }
}

__device__ __forceinline__ float activate(
    float x, float bias, float s1, float s2
) {
    x = x + bias;
    x = x - s1;
    x = x - s2;

    if (x > 20.0f) return x;
    const float e = __expf(x);
    const float q = e * (e + 2.0f);
    return x * __fdividef(q, q + 2.0f);
}

__device__ __forceinline__ float4 activate4(
    float4 x, const float4 bias, float s1, float s2
) {
    x.x = activate(x.x, bias.x, s1, s2);
    x.y = activate(x.y, bias.y, s1, s2);
    x.z = activate(x.z, bias.z, s1, s2);
    x.w = activate(x.w, bias.w, s1, s2);
    return x;
}

__global__ void bias_mish64(
    float4* __restrict__ x,
    const float4* __restrict__ bias,
    int64_t vectors,
    float s1,
    float s2
) {
    constexpr int Threads = 256;
    constexpr int Tile = Threads * 4;

    const float4 b = bias[threadIdx.x & 15];
    const int64_t step = int64_t(gridDim.x) * Tile;

    for (int64_t base = int64_t(blockIdx.x) * Tile;
         base < vectors; base += step) {
        const int64_t i = base + threadIdx.x;

        if (base + Tile <= vectors) {
            float4 a0 = x[i];
            float4 a1 = x[i + Threads];
            float4 a2 = x[i + 2 * Threads];
            float4 a3 = x[i + 3 * Threads];

            a0 = activate4(a0, b, s1, s2);
            a1 = activate4(a1, b, s1, s2);
            a2 = activate4(a2, b, s1, s2);
            a3 = activate4(a3, b, s1, s2);

            x[i] = a0;
            x[i + Threads] = a1;
            x[i + 2 * Threads] = a2;
            x[i + 3 * Threads] = a3;
        } else {
            #pragma unroll
            for (int j = 0; j < 4; ++j) {
                const int64_t k = i + j * Threads;
                if (k < vectors)
                    x[k] = activate4(x[k], b, s1, s2);
            }
        }
    }
}

__global__ void bias_mish_vector(
    float4* __restrict__ x,
    const float4* __restrict__ bias,
    int64_t vectors,
    int channel_vectors,
    float s1,
    float s2
) {
    const int64_t stride = int64_t(blockDim.x) * gridDim.x;

    for (int64_t i = int64_t(blockIdx.x) * blockDim.x + threadIdx.x;
         i < vectors; i += stride) {
        const float4 b = bias[i % channel_vectors];
        x[i] = activate4(x[i], b, s1, s2);
    }
}

__global__ void bias_mish_scalar(
    float* __restrict__ x,
    const float* __restrict__ bias,
    int64_t count,
    int channels,
    float s1,
    float s2
) {
    const int64_t stride = int64_t(blockDim.x) * gridDim.x;

    for (int64_t i = int64_t(blockIdx.x) * blockDim.x + threadIdx.x;
         i < count; i += stride) {
        x[i] = activate(x[i], bias[i % channels], s1, s2);
    }
}

torch::Tensor channels_last_input(torch::Tensor x) {
    TORCH_CHECK(x.is_cuda() && x.dim() == 4, "Expected CUDA NCHW tensor");

    if (x.is_contiguous(at::MemoryFormat::ChannelsLast))
        return x;

    const c10::cuda::CUDAGuard guard(x.device());
    const int64_t spatial = x.size(2) * x.size(3);

    if (x.scalar_type() != at::kFloat ||
        x.size(1) != 8 ||
        !x.is_contiguous() ||
        spatial == 0 ||
        spatial % 512 != 0 ||
        reinterpret_cast<uintptr_t>(x.data_ptr()) % 16 != 0) {
        return x.contiguous(at::MemoryFormat::ChannelsLast);
    }

    auto output = torch::empty(
        x.sizes(), x.options().memory_format(at::MemoryFormat::ChannelsLast));

    const int64_t tiles = spatial / 512;
    const int64_t blocks = x.size(0) * tiles;

    if (blocks != 0) {
        const auto stream = at::cuda::getCurrentCUDAStream();
        nchw8_to_nhwc<<<blocks, 256, 0, stream>>>(
            x.data_ptr<float>(), output.data_ptr<float>(), spatial, tiles);
        C10_CUDA_KERNEL_LAUNCH_CHECK();
    }

    return output;
}

torch::Tensor fused_bias_mish(
    torch::Tensor x, torch::Tensor bias, double s1, double s2
) {
    TORCH_CHECK(x.is_cuda() && bias.is_cuda(), "Expected CUDA tensors");
    TORCH_CHECK(
        x.scalar_type() == at::kFloat &&
        bias.scalar_type() == at::kFloat, "Expected float32 tensors");
    TORCH_CHECK(
        x.dim() == 4 && x.is_contiguous(at::MemoryFormat::ChannelsLast),
        "Expected channels-last output");
    TORCH_CHECK(x.device() == bias.device(), "Device mismatch");
    TORCH_CHECK(
        bias.is_contiguous() && bias.numel() == x.size(1),
        "Invalid bias");

    const c10::cuda::CUDAGuard guard(x.device());
    const int64_t count = x.numel();
    if (count == 0) return x;

    const int channels = static_cast<int>(x.size(1));
    constexpr int threads = 256;
    const auto stream = at::cuda::getCurrentCUDAStream();

    const bool aligned =
        reinterpret_cast<uintptr_t>(x.data_ptr()) % 16 == 0 &&
        reinterpret_cast<uintptr_t>(bias.data_ptr()) % 16 == 0;

    if (channels == 64 && aligned) {
        const int64_t vectors = count / 4;
        const int blocks = static_cast<int>(
            std::min<int64_t>((vectors + 1023) / 1024, 2048));

        bias_mish64<<<blocks, threads, 0, stream>>>(
            reinterpret_cast<float4*>(x.data_ptr<float>()),
            reinterpret_cast<const float4*>(bias.data_ptr<float>()),
            vectors, static_cast<float>(s1), static_cast<float>(s2));
    } else if (channels % 4 == 0 && aligned) {
        const int64_t vectors = count / 4;
        const int blocks = static_cast<int>(
            std::min<int64_t>((vectors + threads - 1) / threads, 2048));

        bias_mish_vector<<<blocks, threads, 0, stream>>>(
            reinterpret_cast<float4*>(x.data_ptr<float>()),
            reinterpret_cast<const float4*>(bias.data_ptr<float>()),
            vectors, channels / 4,
            static_cast<float>(s1), static_cast<float>(s2));
    } else {
        const int blocks = static_cast<int>(
            std::min<int64_t>((count + threads - 1) / threads, 2048));

        bias_mish_scalar<<<blocks, threads, 0, stream>>>(
            x.data_ptr<float>(), bias.data_ptr<float>(),
            count, channels, static_cast<float>(s1), static_cast<float>(s2));
    }

    C10_CUDA_KERNEL_LAUNCH_CHECK();
    return x;
}
"""

_ops = load_inline(
    name="conv_bias_mish_nhwc_v4",
    cpp_sources=_cpp_source,
    cuda_sources=_cuda_source,
    functions=["channels_last_input", "fused_bias_mish"],
    extra_cflags=["-O3"],
    extra_cuda_cflags=["-O3", "--fmad=false"],
    verbose=False,
)


class ModelNew(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        subtract_value_1,
        subtract_value_2,
    ):
        super().__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)
        self.conv.to(memory_format=torch.channels_last)
        self.subtract_value_1 = subtract_value_1
        self.subtract_value_2 = subtract_value_2

    def forward(self, x):
        if (
            not x.is_cuda
            or x.dtype != torch.float32
            or self.conv.weight.dtype != torch.float32
        ):
            return F.mish(
                (self.conv(x) - self.subtract_value_1)
                - self.subtract_value_2
            )

        x = _ops.channels_last_input(x)
        weight = self.conv.weight.contiguous(
            memory_format=torch.channels_last
        )
        y = F.conv2d(
            x,
            weight,
            None,
            self.conv.stride,
            self.conv.padding,
            self.conv.dilation,
            self.conv.groups,
        )

        if y.dtype != torch.float32:
            y = y + self.conv.bias.to(y.dtype).view(1, -1, 1, 1)
            return F.mish(
                (y - self.subtract_value_1) - self.subtract_value_2
            )

        return _ops.fused_bias_mish(
            y,
            self.conv.bias,
            self.subtract_value_1,
            self.subtract_value_2,
        )
```

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.
