```python
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline

_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>

__device__ __forceinline__ float activate(
    float x, float subtract_value, float multiply_value) {
    float y = (x - subtract_value) * multiply_value;
    return y < 0.0f ? 0.0f : y;
}

__global__ void fused_activation_kernel(
    const float* __restrict__ input,
    float* __restrict__ output,
    int64_t n,
    float subtract_value,
    float multiply_value) {
    const int64_t i =
        static_cast<int64_t>(blockIdx.x) * blockDim.x + threadIdx.x;
    const int64_t vector_count = n / 4;

    if (i < vector_count) {
        float4 v = reinterpret_cast<const float4*>(input)[i];
        v.x = activate(v.x, subtract_value, multiply_value);
        v.y = activate(v.y, subtract_value, multiply_value);
        v.z = activate(v.z, subtract_value, multiply_value);
        v.w = activate(v.w, subtract_value, multiply_value);
        reinterpret_cast<float4*>(output)[i] = v;
    }

    const int64_t tail = vector_count * 4 + i;
    if (tail < n) {
        output[tail] =
            activate(input[tail], subtract_value, multiply_value);
    }
}

torch::Tensor fused_activation_cuda(
    torch::Tensor input,
    double subtract_value,
    double multiply_value) {
    TORCH_CHECK(input.is_cuda(), "Expected a CUDA tensor");
    TORCH_CHECK(input.scalar_type() == at::kFloat, "Expected float32");
    TORCH_CHECK(input.is_contiguous(), "Expected contiguous input");

    const c10::cuda::CUDAGuard device_guard(input.device());
    auto output = torch::empty_like(input);
    const int64_t n = input.numel();

    if (n != 0) {
        constexpr int threads = 256;
        const int64_t work = (n + 3) / 4;
        const int blocks = static_cast<int>((work + threads - 1) / threads);

        fused_activation_kernel<<<
            blocks, threads, 0,
            at::cuda::getCurrentCUDAStream(input.get_device())
        >>>(
            input.data_ptr<float>(),
            output.data_ptr<float>(),
            n,
            static_cast<float>(subtract_value),
            static_cast<float>(multiply_value)
        );
        C10_CUDA_KERNEL_LAUNCH_CHECK();
    }

    return output;
}
"""

_extension = load_inline(
    name="linear_subtract_multiply_relu_cuda",
    cpp_sources=r"""
        torch::Tensor fused_activation_cuda(
            torch::Tensor input,
            double subtract_value,
            double multiply_value);
    """,
    cuda_sources=_cuda_source,
    functions=["fused_activation_cuda"],
    extra_cflags=["-O3"],
    extra_cuda_cflags=["-O3"],
    verbose=False,
)


class _FusedActivation(torch.autograd.Function):
    @staticmethod
    def forward(ctx, x, subtract_value, multiply_value):
        output = _extension.fused_activation_cuda(
            x, subtract_value, multiply_value
        )
        ctx.multiply_value = multiply_value
        ctx.save_for_backward(output)
        return output

    @staticmethod
    def backward(ctx, grad_output):
        (output,) = ctx.saved_tensors
        grad = grad_output.masked_fill(output <= 0, 0)
        return grad * ctx.multiply_value, None, None


class ModelNew(nn.Module):
    def __init__(
        self, in_features, out_features, subtract_value, multiply_value
    ):
        super().__init__()
        self.linear = nn.Linear(in_features, out_features)
        self.subtract_value = float(subtract_value)
        self.multiply_value = float(multiply_value)

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

        if x.is_cuda and x.dtype == torch.float32 and x.is_contiguous():
            if torch.is_grad_enabled() and x.requires_grad:
                return _FusedActivation.apply(
                    x, self.subtract_value, self.multiply_value
                )
            return _extension.fused_activation_cuda(
                x, self.subtract_value, self.multiply_value
            )

        return torch.relu(
            (x - self.subtract_value) * self.multiply_value
        )
```