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

_cpp_source = r"""
#include <torch/extension.h>

torch::Tensor fused_groupnorm_swish_cuda(
    torch::Tensor x,
    torch::Tensor gamma,
    torch::Tensor beta,
    torch::Tensor multiplier,
    int64_t groups,
    double eps);
"""

_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 warp_sum(float value) {
    #pragma unroll
    for (int offset = 16; offset > 0; offset >>= 1)
        value += __shfl_down_sync(0xffffffffu, value, offset);
    return __shfl_sync(0xffffffffu, value, 0);
}

__device__ __forceinline__ float swish(float value) {
    return value / (1.0f + __expf(-value));
}

template<bool GROUP32>
__global__ void fused_kernel(
    const float* __restrict__ x,
    const float* __restrict__ gamma,
    const float* __restrict__ beta,
    const float* __restrict__ multiplier,
    float* __restrict__ output,
    int64_t total_groups,
    int groups_per_row,
    int runtime_group_size,
    float eps) {

    constexpr int WARPS_PER_BLOCK = 8;
    const int lane = threadIdx.x & 31;
    const int64_t group =
        static_cast<int64_t>(blockIdx.x) * WARPS_PER_BLOCK +
        (threadIdx.x >> 5);

    if (group >= total_groups)
        return;

    const int group_size = GROUP32 ? 32 : runtime_group_size;
    const int channel_base = (group % groups_per_row) * group_size;
    const int64_t input_base = group * group_size;

    if (GROUP32) {
        const float value = x[input_base + lane];
        const float mean = warp_sum(value) * (1.0f / 32.0f);
        const float centered = value - mean;
        const float variance =
            warp_sum(centered * centered) * (1.0f / 32.0f);
        const float inverse_std = rsqrtf(variance + eps);

        const int channel = channel_base + lane;
        const float normalized =
            (centered * inverse_std) * gamma[channel] + beta[channel];
        const float multiplied = swish(normalized) * multiplier[channel];
        output[input_base + lane] = swish(multiplied);
    } else {
        float sum = 0.0f;
        for (int j = lane; j < group_size; j += 32)
            sum += x[input_base + j];

        const float mean = warp_sum(sum) / group_size;

        float squared_sum = 0.0f;
        for (int j = lane; j < group_size; j += 32) {
            const float centered = x[input_base + j] - mean;
            squared_sum += centered * centered;
        }

        const float variance = warp_sum(squared_sum) / group_size;
        const float inverse_std = rsqrtf(variance + eps);

        for (int j = lane; j < group_size; j += 32) {
            const int channel = channel_base + j;
            const float centered = x[input_base + j] - mean;
            const float normalized =
                (centered * inverse_std) * gamma[channel] + beta[channel];
            const float multiplied = swish(normalized) * multiplier[channel];
            output[input_base + j] = swish(multiplied);
        }
    }
}

torch::Tensor fused_groupnorm_swish_cuda(
    torch::Tensor x,
    torch::Tensor gamma,
    torch::Tensor beta,
    torch::Tensor multiplier,
    int64_t groups,
    double eps) {

    const c10::cuda::CUDAGuard device_guard(x.device());

    x = x.contiguous();
    gamma = gamma.contiguous();
    beta = beta.contiguous();
    multiplier = multiplier.contiguous();

    auto output = torch::empty_like(x);
    const int64_t total_groups = x.size(0) * groups;
    if (total_groups == 0)
        return output;

    const int group_size = static_cast<int>(x.size(1) / groups);
    const int blocks = static_cast<int>((total_groups + 7) / 8);
    const auto stream = at::cuda::getCurrentCUDAStream();

    if (group_size == 32) {
        fused_kernel<true><<<blocks, 256, 0, stream>>>(
            x.data_ptr<float>(),
            gamma.data_ptr<float>(),
            beta.data_ptr<float>(),
            multiplier.data_ptr<float>(),
            output.data_ptr<float>(),
            total_groups,
            static_cast<int>(groups),
            group_size,
            static_cast<float>(eps));
    } else {
        fused_kernel<false><<<blocks, 256, 0, stream>>>(
            x.data_ptr<float>(),
            gamma.data_ptr<float>(),
            beta.data_ptr<float>(),
            multiplier.data_ptr<float>(),
            output.data_ptr<float>(),
            total_groups,
            static_cast<int>(groups),
            group_size,
            static_cast<float>(eps));
    }

    C10_CUDA_KERNEL_LAUNCH_CHECK();
    return output;
}
"""

_fused_ops = load_inline(
    name="fused_groupnorm_double_swish_v1",
    cpp_sources=_cpp_source,
    cuda_sources=_cuda_source,
    functions=["fused_groupnorm_swish_cuda"],
    extra_cflags=["-O3"],
    extra_cuda_cflags=["-O3"],
    verbose=False,
)


class ModelNew(nn.Module):
    def __init__(
        self, in_features, out_features, num_groups, multiply_weight_shape
    ):
        super().__init__()
        self.gemm = nn.Linear(in_features, out_features)
        self.group_norm = nn.GroupNorm(num_groups, out_features)
        self.multiply_weight = nn.Parameter(torch.randn(multiply_weight_shape))

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

        if (
            x.is_cuda
            and x.dtype == torch.float32
            and x.ndim == 2
            and self.group_norm.weight.dtype == torch.float32
            and self.group_norm.bias.dtype == torch.float32
            and self.multiply_weight.dtype == torch.float32
            and tuple(self.multiply_weight.shape) == (x.shape[1],)
        ):
            return _fused_ops.fused_groupnorm_swish_cuda(
                x,
                self.group_norm.weight,
                self.group_norm.bias,
                self.multiply_weight,
                self.group_norm.num_groups,
                self.group_norm.eps,
            )

        x = self.group_norm(x)
        x = x * torch.sigmoid(x)
        x = x * self.multiply_weight
        return x * torch.sigmoid(x)
```