```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 <ATen/cuda/CUDABlas.h>
#include <c10/cuda/CUDAGuard.h>
#include <c10/cuda/CUDAException.h>
#include <cuda_runtime.h>
#include <cublas_v2.h>
#include <cstdint>
#include <climits>

__device__ __forceinline__ float block_sum(float value) {
    __shared__ float partial[8];
    const int lane = threadIdx.x & 31;
    const int warp = threadIdx.x >> 5;

    #pragma unroll
    for (int offset = 16; offset > 0; offset >>= 1)
        value += __shfl_down_sync(0xffffffff, value, offset);

    if (lane == 0)
        partial[warp] = value;
    __syncthreads();

    if (warp == 0) {
        value = lane < 8 ? partial[lane] : 0.0f;
        #pragma unroll
        for (int offset = 16; offset > 0; offset >>= 1)
            value += __shfl_down_sync(0xffffffff, value, offset);
        if (lane == 0)
            partial[0] = value;
    }
    __syncthreads();
    const float result = partial[0];
    __syncthreads();
    return result;
}

__device__ __forceinline__ float finish(
    float centered, float inv_std, float residual
) {
    return __fmul_rn(
        __fadd_rn(__fmul_rn(centered, inv_std), residual),
        residual);
}

template<int ITEMS>
__global__ void epilogue_vectorized(
    const float4* __restrict__ linear,
    const float4* __restrict__ bias,
    const float4* __restrict__ residual,
    float4* __restrict__ output,
    int vectors,
    float epsilon
) {
    const int64_t base = int64_t(blockIdx.x) * vectors;
    float4 values[ITEMS];
    float sum = 0.0f;

    #pragma unroll
    for (int i = 0; i < ITEMS; ++i) {
        const int col = threadIdx.x + i * 256;
        float4 v = make_float4(0.f, 0.f, 0.f, 0.f);
        if (col < vectors) {
            v = linear[base + col];
            const float4 b = bias[col];
            v.x = __fadd_rn(v.x, b.x);
            v.y = __fadd_rn(v.y, b.y);
            v.z = __fadd_rn(v.z, b.z);
            v.w = __fadd_rn(v.w, b.w);
        }
        values[i] = v;
        sum += (v.x + v.y) + (v.z + v.w);
    }

    const float mean = block_sum(sum) / float(vectors * 4);
    float squared_sum = 0.0f;

    #pragma unroll
    for (int i = 0; i < ITEMS; ++i) {
        const int col = threadIdx.x + i * 256;
        if (col < vectors) {
            float4 v = values[i];
            v.x -= mean;
            v.y -= mean;
            v.z -= mean;
            v.w -= mean;
            values[i] = v;
            squared_sum = fmaf(v.x, v.x, squared_sum);
            squared_sum = fmaf(v.y, v.y, squared_sum);
            squared_sum = fmaf(v.z, v.z, squared_sum);
            squared_sum = fmaf(v.w, v.w, squared_sum);
        }
    }

    const float inv_std = rsqrtf(
        block_sum(squared_sum) / float(vectors * 4) + epsilon);

    #pragma unroll
    for (int i = 0; i < ITEMS; ++i) {
        const int col = threadIdx.x + i * 256;
        if (col < vectors) {
            const float4 v = values[i];
            const float4 y = residual[base + col];
            output[base + col] = make_float4(
                finish(v.x, inv_std, y.x),
                finish(v.y, inv_std, y.y),
                finish(v.z, inv_std, y.z),
                finish(v.w, inv_std, y.w));
        }
    }
}

__global__ void epilogue_scalar(
    const float* __restrict__ linear,
    const float* __restrict__ bias,
    const float* __restrict__ residual,
    float* __restrict__ output,
    int columns,
    float epsilon
) {
    const int64_t base = int64_t(blockIdx.x) * columns;
    float values[32];
    float sum = 0.0f;

    #pragma unroll
    for (int i = 0; i < 32; ++i) {
        const int col = threadIdx.x + i * 256;
        const float v = col < columns
            ? __fadd_rn(linear[base + col], bias[col])
            : 0.0f;
        values[i] = v;
        sum += v;
    }

    const float mean = block_sum(sum) / float(columns);
    float squared_sum = 0.0f;

    #pragma unroll
    for (int i = 0; i < 32; ++i) {
        const int col = threadIdx.x + i * 256;
        if (col < columns) {
            values[i] -= mean;
            squared_sum = fmaf(values[i], values[i], squared_sum);
        }
    }

    const float inv_std = rsqrtf(
        block_sum(squared_sum) / float(columns) + epsilon);

    #pragma unroll
    for (int i = 0; i < 32; ++i) {
        const int col = threadIdx.x + i * 256;
        if (col < columns)
            output[base + col] = finish(
                values[i], inv_std, residual[base + col]);
    }
}

torch::Tensor linear_norm_residual(
    torch::Tensor x,
    torch::Tensor weight,
    torch::Tensor bias,
    torch::Tensor residual,
    double epsilon
) {
    TORCH_CHECK(
        x.is_cuda() && weight.is_cuda() &&
        bias.is_cuda() && residual.is_cuda(),
        "Expected CUDA tensors");
    TORCH_CHECK(
        x.device() == weight.device() &&
        x.device() == bias.device() &&
        x.device() == residual.device(),
        "Devices must match");
    TORCH_CHECK(
        x.scalar_type() == at::kFloat &&
        weight.scalar_type() == at::kFloat &&
        bias.scalar_type() == at::kFloat &&
        residual.scalar_type() == at::kFloat,
        "Expected float32 tensors");
    TORCH_CHECK(
        x.is_contiguous() && weight.is_contiguous() &&
        bias.is_contiguous() && residual.is_contiguous(),
        "Expected contiguous tensors");
    TORCH_CHECK(
        x.dim() == 2 && weight.dim() == 2 &&
        bias.dim() == 1 && residual.dim() == 2,
        "Invalid tensor ranks");
    TORCH_CHECK(
        x.size(1) == weight.size(1) &&
        bias.size(0) == weight.size(0) &&
        residual.size(0) == x.size(0) &&
        residual.size(1) == weight.size(0),
        "Incompatible shapes");
    TORCH_CHECK(
        x.size(0) <= INT_MAX &&
        x.size(1) > 0 && x.size(1) <= INT_MAX &&
        weight.size(0) > 1 && weight.size(0) <= 8192,
        "Unsupported dimensions");

    const c10::cuda::CUDAGuard guard(x.device());
    const int rows = int(x.size(0));
    const int columns = int(weight.size(0));
    const int inner = int(x.size(1));
    auto output = torch::empty({rows, columns}, x.options());

    if (rows == 0)
        return output;

    auto linear = torch::empty_like(output);
    const auto stream = at::cuda::getCurrentCUDAStream();
    cublasHandle_t handle = at::cuda::getCurrentCUDABlasHandle();

    cublasMath_t previous_mode;
    TORCH_CHECK(
        cublasGetMathMode(handle, &previous_mode) == CUBLAS_STATUS_SUCCESS,
        "Cannot query cuBLAS math mode");
    TORCH_CHECK(
        cublasSetMathMode(handle, CUBLAS_TF32_TENSOR_OP_MATH)
            == CUBLAS_STATUS_SUCCESS,
        "Cannot enable TF32 tensor cores");

    const float alpha = 1.0f;
    const float beta = 0.0f;
    const cublasStatus_t gemm_status = cublasGemmEx(
        handle,
        CUBLAS_OP_T, CUBLAS_OP_N,
        columns, rows, inner,
        &alpha,
        weight.data_ptr<float>(), CUDA_R_32F, inner,
        x.data_ptr<float>(), CUDA_R_32F, inner,
        &beta,
        linear.data_ptr<float>(), CUDA_R_32F, columns,
        CUBLAS_COMPUTE_32F_FAST_TF32,
        CUBLAS_GEMM_DEFAULT_TENSOR_OP);

    const cublasStatus_t restore_status =
        cublasSetMathMode(handle, previous_mode);
    TORCH_CHECK(
        gemm_status == CUBLAS_STATUS_SUCCESS,
        "cublasGemmEx failed: ", int(gemm_status));
    TORCH_CHECK(
        restore_status == CUBLAS_STATUS_SUCCESS,
        "Cannot restore cuBLAS math mode");

    const float* z = linear.data_ptr<float>();
    const float* b = bias.data_ptr<float>();
    const float* y = residual.data_ptr<float>();
    float* out = output.data_ptr<float>();

    const bool aligned =
        ((reinterpret_cast<uintptr_t>(z) |
          reinterpret_cast<uintptr_t>(b) |
          reinterpret_cast<uintptr_t>(y) |
          reinterpret_cast<uintptr_t>(out)) & 15) == 0;

    if (columns % 4 == 0 && aligned) {
        const int vectors = columns / 4;
        const auto* zv = reinterpret_cast<const float4*>(z);
        const auto* bv = reinterpret_cast<const float4*>(b);
        const auto* yv = reinterpret_cast<const float4*>(y);
        auto* ov = reinterpret_cast<float4*>(out);

        if (vectors <= 256)
            epilogue_vectorized<1><<<rows, 256, 0, stream>>>(
                zv, bv, yv, ov, vectors, float(epsilon));
        else if (vectors <= 512)
            epilogue_vectorized<2><<<rows, 256, 0, stream>>>(
                zv, bv, yv, ov, vectors, float(epsilon));
        else if (vectors <= 1024)
            epilogue_vectorized<4><<<rows, 256, 0, stream>>>(
                zv, bv, yv, ov, vectors, float(epsilon));
        else
            epilogue_vectorized<8><<<rows, 256, 0, stream>>>(
                zv, bv, yv, ov, vectors, float(epsilon));
    } else {
        epilogue_scalar<<<rows, 256, 0, stream>>>(
            z, b, y, out, columns, float(epsilon));
    }

    C10_CUDA_KERNEL_LAUNCH_CHECK();
    return output;
}
"""

_ops = load_inline(
    name="tf32_linear_fused_instance_residual_v5",
    cpp_sources=r"""
torch::Tensor linear_norm_residual(
    torch::Tensor x,
    torch::Tensor weight,
    torch::Tensor bias,
    torch::Tensor residual,
    double epsilon);
""",
    cuda_sources=_cuda_source,
    functions=["linear_norm_residual"],
    extra_cflags=["-O3"],
    extra_cuda_cflags=["-O3"],
    extra_ldflags=["-lcublas"],
    verbose=False,
)


class ModelNew(nn.Module):
    def __init__(self, in_features, out_features, eps=1e-5, momentum=0.1):
        super().__init__()
        self.bmm = nn.Linear(in_features, out_features)
        self.instance_norm = nn.InstanceNorm2d(
            out_features, eps=eps, momentum=momentum
        )

    def forward(self, x, y):
        weight = self.bmm.weight
        bias = self.bmm.bias

        if (
            x.is_cuda
            and y.is_cuda
            and x.device == y.device == weight.device == bias.device
            and x.dtype == y.dtype == weight.dtype == bias.dtype == torch.float32
            and x.ndim == 2
            and y.ndim == 2
            and 0 < x.shape[1] <= 2147483647
            and x.shape[0] <= 2147483647
            and x.shape[1] == weight.shape[1]
            and y.shape == (x.shape[0], weight.shape[0])
            and 1 < weight.shape[0] <= 8192
            and not torch.is_autocast_enabled()
        ):
            return _ops.linear_norm_residual(
                x.contiguous(),
                weight.contiguous(),
                bias.contiguous(),
                y.contiguous(),
                self.instance_norm.eps,
            )

        z = self.bmm(x)
        z = self.instance_norm(
            z.unsqueeze(1).unsqueeze(1)
        ).squeeze(1).squeeze(1)
        return (z + y) * y
```