```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
import torch.nn.functional as F
from torch.utils.cpp_extension import load_inline

# Enable cuDNN autotuning so the (dominant) convolution picks the fastest algorithm.
torch.backends.cudnn.benchmark = True

# Fused CUDA kernel: (x - s1) - s2 followed by Mish activation, in a single pass.
# Uses float4 vectorized loads/stores to saturate memory bandwidth.
sub_mish_cuda_source = """
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda_runtime.h>

__device__ __forceinline__ float mish_fn(float x) {
    // mish(x) = x * tanh(softplus(x))
    // Equivalent rational form: tanh(log(1+e^x)) = (e^2x + 2e^x) / (e^2x + 2e^x + 2)
    if (x > 20.0f) {
        return x;  // tanh(softplus(x)) == 1.0f to float precision
    }
    float e = expf(x);
    float n = e * e + 2.0f * e;
    return x * (n / (n + 2.0f));
}

__global__ void sub_sub_mish_vec4_kernel(const float4* __restrict__ in,
                                         float4* __restrict__ out,
                                         float s1, float s2,
                                         long long n4) {
    long long i = (long long)blockIdx.x * blockDim.x + threadIdx.x;
    if (i < n4) {
        float4 v = in[i];
        v.x = mish_fn((v.x - s1) - s2);
        v.y = mish_fn((v.y - s1) - s2);
        v.z = mish_fn((v.z - s1) - s2);
        v.w = mish_fn((v.w - s1) - s2);
        out[i] = v;
    }
}

__global__ void sub_sub_mish_scalar_kernel(const float* __restrict__ in,
                                           float* __restrict__ out,
                                           float s1, float s2,
                                           long long n) {
    long long i = (long long)blockIdx.x * blockDim.x + threadIdx.x;
    if (i < n) {
        out[i] = mish_fn((in[i] - s1) - s2);
    }
}

torch::Tensor sub_mish_cuda(torch::Tensor x, double s1, double s2) {
    TORCH_CHECK(x.is_cuda(), "input must be a CUDA tensor");
    TORCH_CHECK(x.scalar_type() == at::kFloat, "input must be float32");

    auto xc = x.contiguous();
    auto out = at::empty_like(xc);
    long long n = (long long)xc.numel();
    if (n == 0) {
        return out;
    }

    float f1 = (float)s1;
    float f2 = (float)s2;
    const int block = 256;
    cudaStream_t stream = at::cuda::getCurrentCUDAStream();

    if ((n & 3) == 0) {
        long long n4 = n >> 2;
        long long blocks = (n4 + block - 1) / block;
        sub_sub_mish_vec4_kernel<<<(unsigned int)blocks, block, 0, stream>>>(
            reinterpret_cast<const float4*>(xc.data_ptr<float>()),
            reinterpret_cast<float4*>(out.data_ptr<float>()),
            f1, f2, n4);
    } else {
        long long blocks = (n + block - 1) / block;
        sub_sub_mish_scalar_kernel<<<(unsigned int)blocks, block, 0, stream>>>(
            xc.data_ptr<float>(), out.data_ptr<float>(), f1, f2, n);
    }
    return out;
}
"""

sub_mish_cpp_source = (
    "torch::Tensor sub_mish_cuda(torch::Tensor x, double s1, double s2);"
)

# Compile the fused subtract + Mish CUDA kernel.
sub_mish = load_inline(
    name="sub_sub_mish_fused",
    cpp_sources=sub_mish_cpp_source,
    cuda_sources=sub_mish_cuda_source,
    functions=["sub_mish_cuda"],
    verbose=False,
    extra_cuda_cflags=["-O3"],
)


class ModelNew(nn.Module):
    """
    Optimized model: cuDNN convolution followed by a single fused CUDA kernel
    performing (x - s1 - s2) and the Mish activation in one memory pass.
    """
    def __init__(self, in_channels, out_channels, kernel_size, subtract_value_1, subtract_value_2):
        super(ModelNew, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size)
        self.subtract_value_1 = float(subtract_value_1)
        self.subtract_value_2 = float(subtract_value_2)

    def forward(self, x):
        x = self.conv(x)
        if x.is_cuda and x.dtype == torch.float32:
            x = sub_mish.sub_mish_cuda(x, self.subtract_value_1, self.subtract_value_2)
        else:
            x = x - self.subtract_value_1
            x = x - self.subtract_value_2
            x = F.mish(x)
        return x
```