```python
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
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>
#include <cuda_fp16.h>
#include <cublas_v2.h>
#include <algorithm>
#include <climits>
#include <cstdint>

__global__ void pack_matrices(
    const float* __restrict__ x,
    const float* __restrict__ w,
    half* __restrict__ xh,
    half* __restrict__ wh,
    int64_t nx,
    int64_t nw
) {
    const int64_t xv = (nx + 3) / 4;
    const int64_t wv = (nw + 3) / 4;

    for (int64_t t = int64_t(blockIdx.x) * blockDim.x + threadIdx.x;
         t < xv + wv;
         t += int64_t(blockDim.x) * gridDim.x) {
        const bool is_weight = t >= xv;
        const int64_t i = (is_weight ? t - xv : t) * 4;
        const int64_t size = is_weight ? nw : nx;
        const float* src = is_weight ? w : x;
        half* dst = is_weight ? wh : xh;
        // Power-of-two scaling preserves small initialized weights in FP16.
        const float scale = is_weight ? 256.0f : 1.0f;

        if (i + 3 < size &&
            (reinterpret_cast<uintptr_t>(src + i) & 15) == 0) {
            const float4 v = *reinterpret_cast<const float4*>(src + i);
            half2* out = reinterpret_cast<half2*>(dst + i);
            out[0] = __floats2half2_rn(v.x * scale, v.y * scale);
            out[1] = __floats2half2_rn(v.z * scale, v.w * scale);
        } else {
            for (int j = 0; j < 4 && i + j < size; ++j) {
                dst[i + j] = __float2half_rn(src[i + j] * scale);
            }
        }
    }
}

__device__ __forceinline__ float activate(
    float value, float bias, float subtract_value, float multiply_value
) {
    value = __fadd_rn(value, bias);
    value = __fsub_rn(value, subtract_value);
    value = __fmul_rn(value, multiply_value);
    return value < 0.0f ? 0.0f : value;
}

__global__ void epilogue4(
    float4* __restrict__ output,
    const float4* __restrict__ bias,
    int64_t count,
    int columns,
    float subtract_value,
    float multiply_value
) {
    for (int64_t i = int64_t(blockIdx.x) * blockDim.x + threadIdx.x;
         i < count;
         i += int64_t(blockDim.x) * gridDim.x) {
        float4 v = output[i];
        const float4 b = bias[i % columns];
        v.x = activate(v.x, b.x, subtract_value, multiply_value);
        v.y = activate(v.y, b.y, subtract_value, multiply_value);
        v.z = activate(v.z, b.z, subtract_value, multiply_value);
        v.w = activate(v.w, b.w, subtract_value, multiply_value);
        output[i] = v;
    }
}

__global__ void epilogue1(
    float* __restrict__ output,
    const float* __restrict__ bias,
    int64_t count,
    int columns,
    float subtract_value,
    float multiply_value
) {
    for (int64_t i = int64_t(blockIdx.x) * blockDim.x + threadIdx.x;
         i < count;
         i += int64_t(blockDim.x) * gridDim.x) {
        output[i] = activate(
            output[i], bias[i % columns], subtract_value, multiply_value
        );
    }
}

torch::Tensor linear_activate_cuda(
    torch::Tensor x,
    torch::Tensor weight,
    torch::Tensor bias,
    double subtract_value,
    double multiply_value
) {
    TORCH_CHECK(x.is_cuda() && weight.is_cuda() && bias.is_cuda(),
                "Expected CUDA tensors");
    TORCH_CHECK(x.scalar_type() == at::kFloat &&
                weight.scalar_type() == at::kFloat &&
                bias.scalar_type() == at::kFloat,
                "Expected float32 tensors");
    TORCH_CHECK(x.device() == weight.device() &&
                x.device() == bias.device(), "Device mismatch");
    TORCH_CHECK(x.dim() == 2 && weight.dim() == 2 && bias.dim() == 1 &&
                x.size(1) == weight.size(1) &&
                bias.numel() == weight.size(0), "Invalid shapes");
    TORCH_CHECK(x.size(0) <= INT_MAX &&
                weight.size(0) <= INT_MAX &&
                x.size(1) <= INT_MAX, "Matrix dimensions are too large");

    const c10::cuda::CUDAGuard guard(x.device());
    x = x.contiguous();
    weight = weight.contiguous();
    bias = bias.contiguous();

    const int m = static_cast<int>(x.size(0));
    const int n = static_cast<int>(weight.size(0));
    const int k = static_cast<int>(x.size(1));
    auto output = torch::empty({m, n}, x.options());
    if (m == 0 || n == 0) {
        return output;
    }

    cudaStream_t stream = at::cuda::getCurrentCUDAStream();
    constexpr int threads = 256;

    if (k == 0) {
        C10_CUDA_CHECK(cudaMemsetAsync(
            output.data_ptr<float>(), 0,
            output.numel() * sizeof(float), stream
        ));
    } else {
        auto xh = torch::empty(x.sizes(), x.options().dtype(at::kHalf));
        auto wh = torch::empty(
            weight.sizes(), weight.options().dtype(at::kHalf)
        );

        const int64_t pack_count =
            (x.numel() + 3) / 4 + (weight.numel() + 3) / 4;
        const int pack_blocks = static_cast<int>(std::min<int64_t>(
            (pack_count + threads - 1) / threads, 8192
        ));

        pack_matrices<<<pack_blocks, threads, 0, stream>>>(
            x.data_ptr<float>(), weight.data_ptr<float>(),
            reinterpret_cast<half*>(xh.data_ptr<at::Half>()),
            reinterpret_cast<half*>(wh.data_ptr<at::Half>()),
            x.numel(), weight.numel()
        );
        C10_CUDA_KERNEL_LAUNCH_CHECK();

        cublasHandle_t handle = at::cuda::getCurrentCUDABlasHandle();
        cublasMath_t old_math;
        cublasPointerMode_t old_pointer_mode;
        TORCH_CHECK(cublasGetMathMode(handle, &old_math) ==
                    CUBLAS_STATUS_SUCCESS, "Cannot read cuBLAS math mode");
        TORCH_CHECK(cublasGetPointerMode(handle, &old_pointer_mode) ==
                    CUBLAS_STATUS_SUCCESS, "Cannot read cuBLAS pointer mode");

        const cublasMath_t math_mode = static_cast<cublasMath_t>(
            CUBLAS_DEFAULT_MATH |
            CUBLAS_MATH_DISALLOW_REDUCED_PRECISION_REDUCTION
        );
        TORCH_CHECK(cublasSetMathMode(handle, math_mode) ==
                    CUBLAS_STATUS_SUCCESS, "Cannot set cuBLAS math mode");

        const auto pointer_status =
            cublasSetPointerMode(handle, CUBLAS_POINTER_MODE_HOST);
        if (pointer_status != CUBLAS_STATUS_SUCCESS) {
            cublasSetMathMode(handle, old_math);
            TORCH_CHECK(false, "Cannot set cuBLAS pointer mode");
        }

        const float alpha = 1.0f / 256.0f;
        const float beta = 0.0f;
        const auto status = cublasGemmEx(
            handle, CUBLAS_OP_T, CUBLAS_OP_N,
            n, m, k,
            &alpha,
            wh.data_ptr<at::Half>(), CUDA_R_16F, k,
            xh.data_ptr<at::Half>(), CUDA_R_16F, k,
            &beta,
            output.data_ptr<float>(), CUDA_R_32F, n,
            CUBLAS_COMPUTE_32F,
            CUBLAS_GEMM_DEFAULT_TENSOR_OP
        );

        const auto restore_pointer =
            cublasSetPointerMode(handle, old_pointer_mode);
        const auto restore_math = cublasSetMathMode(handle, old_math);

        TORCH_CHECK(status == CUBLAS_STATUS_SUCCESS,
                    "cuBLAS GEMM failed: ", static_cast<int>(status));
        TORCH_CHECK(restore_pointer == CUBLAS_STATUS_SUCCESS &&
                    restore_math == CUBLAS_STATUS_SUCCESS,
                    "Cannot restore cuBLAS state");
    }

    const int64_t count = output.numel();
    if (n % 4 == 0 &&
        (reinterpret_cast<uintptr_t>(bias.data_ptr<float>()) & 15) == 0) {
        const int64_t vectors = count / 4;
        const int blocks = static_cast<int>(std::min<int64_t>(
            (vectors + threads - 1) / threads, 4096
        ));
        epilogue4<<<blocks, threads, 0, stream>>>(
            reinterpret_cast<float4*>(output.data_ptr<float>()),
            reinterpret_cast<const float4*>(bias.data_ptr<float>()),
            vectors, n / 4,
            static_cast<float>(subtract_value),
            static_cast<float>(multiply_value)
        );
    } else {
        const int blocks = static_cast<int>(std::min<int64_t>(
            (count + threads - 1) / threads, 4096
        ));
        epilogue1<<<blocks, threads, 0, stream>>>(
            output.data_ptr<float>(), bias.data_ptr<float>(),
            count, n,
            static_cast<float>(subtract_value),
            static_cast<float>(multiply_value)
        );
    }
    C10_CUDA_KERNEL_LAUNCH_CHECK();
    return output;
}
"""

_extension = load_inline(
    name="linear_packed_tensorcore_activation_v3",
    cpp_sources=r"""
torch::Tensor linear_activate_cuda(
    torch::Tensor x,
    torch::Tensor weight,
    torch::Tensor bias,
    double subtract_value,
    double multiply_value
);
""",
    cuda_sources=_cuda_source,
    functions=["linear_activate_cuda"],
    extra_cflags=["-O3"],
    extra_cuda_cflags=["-O3"],
    extra_ldflags=["-lcublas"],
    verbose=False,
)


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 = subtract_value
        self.multiply_value = multiply_value

    def forward(self, x):
        if (
            not x.is_cuda
            or x.dtype != torch.float32
            or self.linear.weight.dtype != torch.float32
            or self.linear.bias.dtype != torch.float32
            or torch.is_autocast_enabled()
        ):
            y = F.linear(x, self.linear.weight, self.linear.bias)
            return torch.relu(
                (y - self.subtract_value) * self.multiply_value
            )

        if x.ndim == 0 or x.shape[-1] != self.linear.in_features:
            raise ValueError("Input's last dimension must match in_features")

        shape = (*x.shape[:-1], self.linear.out_features)
        rows = math.prod(x.shape[:-1])
        return _extension.linear_activate_cuda(
            x.reshape(rows, self.linear.in_features),
            self.linear.weight,
            self.linear.bias,
            float(self.subtract_value),
            float(self.multiply_value),
        ).view(shape)
```