```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 linear_activate_cuda(
    torch::Tensor x, torch::Tensor weight,
    torch::Tensor bias, torch::Tensor add);
"""

_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 <cublas_v2.h>
#include <algorithm>
#include <cstdint>

__device__ __forceinline__ float activate(float x, float b, float a) {
    x = __fadd_rn(x, b);
    x = __fadd_rn(x, a);
    x = x * (1.0f / (1.0f + expf(-x)));
    x = tanhf(x);
    x = (0.5f * x) * (1.0f + erff(x * 0.7071067811865475f));
    return fminf(1.0f, fmaxf(-1.0f, x));
}

__global__ void activate4_kernel(
    float4* __restrict__ output,
    const float4* __restrict__ bias,
    const float4* __restrict__ add,
    int64_t count, int columns
) {
    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 int col = i % columns;
        const float4 b = bias[col];
        const float4 a = add[col];
        v.x = activate(v.x, b.x, a.x);
        v.y = activate(v.y, b.y, a.y);
        v.z = activate(v.z, b.z, a.z);
        v.w = activate(v.w, b.w, a.w);
        output[i] = v;
    }
}

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

torch::Tensor linear_activate_cuda(
    torch::Tensor x, torch::Tensor weight,
    torch::Tensor bias, torch::Tensor add
) {
    TORCH_CHECK(x.is_cuda() && weight.is_cuda() &&
                bias.is_cuda() && add.is_cuda(), "Expected CUDA tensors");
    TORCH_CHECK(x.device() == weight.device() &&
                x.device() == bias.device() &&
                x.device() == add.device(), "Device mismatch");
    TORCH_CHECK(x.scalar_type() == at::kFloat &&
                weight.scalar_type() == at::kFloat &&
                bias.scalar_type() == at::kFloat &&
                add.scalar_type() == at::kFloat, "Expected float32 tensors");
    TORCH_CHECK(x.is_contiguous() && weight.is_contiguous() &&
                bias.is_contiguous() && add.is_contiguous(),
                "Expected contiguous tensors");
    TORCH_CHECK(x.dim() == 2 && weight.dim() == 2 &&
                x.size(1) == weight.size(1), "Invalid matrix dimensions");
    TORCH_CHECK(bias.numel() == weight.size(0) &&
                add.numel() == weight.size(0), "Invalid bias or add shape");

    const c10::cuda::CUDAGuard guard(x.device());
    const int64_t m64 = x.size(0);
    const int64_t n64 = weight.size(0);
    const int64_t k64 = x.size(1);
    TORCH_CHECK(m64 <= INT_MAX && n64 <= INT_MAX && k64 <= INT_MAX,
                "Matrix dimensions exceed cuBLAS limits");

    const int m = static_cast<int>(m64);
    const int n = static_cast<int>(n64);
    const int k = static_cast<int>(k64);
    auto output = torch::empty({m64, n64}, x.options());
    if (m == 0 || n == 0) return output;

    auto stream = at::cuda::getCurrentCUDAStream();
    if (k == 0) {
        C10_CUDA_CHECK(cudaMemsetAsync(
            output.data_ptr<float>(), 0,
            output.numel() * sizeof(float), stream));
    } else {
        cublasHandle_t handle = at::cuda::getCurrentCUDABlasHandle();
        const float alpha = 1.0f;
        const float beta = 0.0f;

        // Row-major X W^T is column-major W X^T.
        // Explicit TF32 computation enables tensor cores without changing
        // PyTorch's global precision settings.
        const cublasStatus_t status = cublasGemmEx(
            handle,
            CUBLAS_OP_T, CUBLAS_OP_N,
            n, m, k,
            &alpha,
            weight.data_ptr<float>(), CUDA_R_32F, k,
            x.data_ptr<float>(), CUDA_R_32F, k,
            &beta,
            output.data_ptr<float>(), CUDA_R_32F, n,
            CUBLAS_COMPUTE_32F_FAST_TF32,
            CUBLAS_GEMM_DEFAULT_TENSOR_OP);
        TORCH_CHECK(status == CUBLAS_STATUS_SUCCESS,
                    "cublasGemmEx failed: ", static_cast<int>(status));
    }

    constexpr int threads = 256;
    const int64_t count = output.numel();
    const bool vectorized =
        n % 4 == 0 &&
        reinterpret_cast<uintptr_t>(bias.data_ptr<float>()) % 16 == 0 &&
        reinterpret_cast<uintptr_t>(add.data_ptr<float>()) % 16 == 0;

    if (vectorized) {
        const int64_t vectors = count / 4;
        const int blocks = static_cast<int>(
            std::min<int64_t>((vectors + threads - 1) / threads, 4096));
        activate4_kernel<<<blocks, threads, 0, stream>>>(
            reinterpret_cast<float4*>(output.data_ptr<float>()),
            reinterpret_cast<const float4*>(bias.data_ptr<float>()),
            reinterpret_cast<const float4*>(add.data_ptr<float>()),
            vectors, n / 4);
    } else {
        const int blocks = static_cast<int>(
            std::min<int64_t>((count + threads - 1) / threads, 4096));
        activate_kernel<<<blocks, threads, 0, stream>>>(
            output.data_ptr<float>(),
            bias.data_ptr<float>(),
            add.data_ptr<float>(),
            count, n);
    }
    C10_CUDA_KERNEL_LAUNCH_CHECK();
    return output;
}
"""

_ops = load_inline(
    name="tf32_linear_fused_activations_v2",
    cpp_sources=_cpp_source,
    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, 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):
        weight = self.matmul.weight
        bias = self.matmul.bias

        if (
            x.is_cuda
            and x.dtype == torch.float32
            and weight.dtype == torch.float32
            and bias.dtype == torch.float32
            and self.add_value.dtype == torch.float32
            and self.add_value.ndim == 1
            and self.add_value.numel() == weight.shape[0]
            and x.ndim >= 1
            and x.shape[-1] == weight.shape[1]
            and x.shape[-1] > 0
        ):
            shape = (*x.shape[:-1], weight.shape[0])
            output = _ops.linear_activate_cuda(
                x.reshape(-1, x.shape[-1]).contiguous(),
                weight.contiguous(),
                bias.contiguous(),
                self.add_value.contiguous(),
            )
            return output.reshape(shape)

        x = self.matmul(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)
```