```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>
#include <vector>

std::vector<torch::Tensor> prepare_conv_cuda(
    torch::Tensor x, torch::Tensor weight, int64_t groups);
"""

_cuda_source = r"""
#include <torch/extension.h>
#include <ATen/Dispatch.h>
#include <c10/cuda/CUDAGuard.h>
#include <c10/cuda/CUDAStream.h>
#include <c10/cuda/CUDAException.h>
#include <cuda_runtime.h>
#include <vector>

// Coalesced NCHW -> channels-last conversion.
template <typename scalar_t>
__global__ void pack_input(
    const scalar_t* __restrict__ src,
    scalar_t* __restrict__ dst,
    int channels,
    int64_t spatial)
{
    __shared__ scalar_t tile[32][33];

    const int tx = threadIdx.x;
    const int ty = threadIdx.y;
    const int batch = blockIdx.z;
    const int channel_base = blockIdx.y * 32;
    const int64_t spatial_base = int64_t(blockIdx.x) * 32;

    #pragma unroll
    for (int j = 0; j < 32; j += 8) {
        const int c = channel_base + ty + j;
        const int64_t s = spatial_base + tx;
        if (c < channels && s < spatial) {
            tile[ty + j][tx] =
                src[(int64_t(batch) * channels + c) * spatial + s];
        }
    }

    __syncthreads();

    #pragma unroll
    for (int j = 0; j < 32; j += 8) {
        const int c = channel_base + tx;
        const int64_t s = spatial_base + ty + j;
        if (c < channels && s < spatial) {
            dst[(int64_t(batch) * spatial + s) * channels + c] =
                tile[tx][ty + j];
        }
    }
}

// Convert transposed-convolution weights to spatially reversed,
// channels-last forward-convolution weights.
template <typename scalar_t>
__global__ void pack_weight(
    const scalar_t* __restrict__ src,
    scalar_t* __restrict__ dst,
    int64_t count,
    int input_per_group,
    int output_per_group,
    int kh,
    int kw)
{
    for (int64_t index =
             int64_t(blockIdx.x) * blockDim.x + threadIdx.x;
         index < count;
         index += int64_t(blockDim.x) * gridDim.x) {
        int64_t q = index;
        const int ic = q % input_per_group;
        q /= input_per_group;
        const int kx = q % kw;
        q /= kw;
        const int ky = q % kh;
        q /= kh;
        const int oc = int(q);

        const int group = oc / output_per_group;
        const int local_oc = oc % output_per_group;
        const int global_ic = group * input_per_group + ic;

        const int64_t source_index =
            ((int64_t(global_ic) * output_per_group + local_oc) * kh
              + (kh - 1 - ky)) * kw + (kw - 1 - kx);

        dst[index] = src[source_index];
    }
}

std::vector<torch::Tensor> prepare_conv_cuda(
    torch::Tensor x,
    torch::Tensor weight,
    int64_t groups)
{
    TORCH_CHECK(x.is_cuda() && weight.is_cuda(),
                "Expected CUDA tensors");
    TORCH_CHECK(x.device() == weight.device(),
                "Input and weight must share a device");
    TORCH_CHECK(x.scalar_type() == weight.scalar_type(),
                "Input and weight must share a dtype");
    TORCH_CHECK(x.dim() == 4 && weight.dim() == 4,
                "Expected four-dimensional tensors");
    TORCH_CHECK(weight.is_contiguous(), "Expected contiguous weights");
    TORCH_CHECK(groups > 0 && weight.size(0) % groups == 0,
                "Invalid groups");

    const c10::cuda::CUDAGuard guard(x.device());
    const auto stream = c10::cuda::getCurrentCUDAStream();

    const int channels = int(x.size(1));
    const int64_t spatial = x.size(2) * x.size(3);
    const int input_per_group = int(weight.size(0) / groups);
    const int output_per_group = int(weight.size(1));
    const int output_channels = int(output_per_group * groups);
    const int kh = int(weight.size(2));
    const int kw = int(weight.size(3));

    torch::Tensor packed_x;
    if (x.is_contiguous(at::MemoryFormat::ChannelsLast)) {
        packed_x = x;
    } else {
        TORCH_CHECK(x.is_contiguous(), "Expected contiguous input");
        packed_x = torch::empty(
            x.sizes(),
            x.options().memory_format(at::MemoryFormat::ChannelsLast));

        const dim3 threads(32, 8);
        const dim3 blocks(
            unsigned((spatial + 31) / 32),
            unsigned((channels + 31) / 32),
            unsigned(x.size(0)));

        AT_DISPATCH_FLOATING_TYPES_AND2(
            at::ScalarType::Half, at::ScalarType::BFloat16,
            x.scalar_type(), "pack_conv_input", [&] {
                pack_input<scalar_t><<<blocks, threads, 0, stream.stream()>>>(
                    x.data_ptr<scalar_t>(),
                    packed_x.data_ptr<scalar_t>(),
                    channels,
                    spatial);
            });
        C10_CUDA_KERNEL_LAUNCH_CHECK();
    }

    auto packed_weight = torch::empty(
        {output_channels, input_per_group, kh, kw},
        weight.options().memory_format(at::MemoryFormat::ChannelsLast));

    const int64_t count = packed_weight.numel();
    const int blocks = int(std::min<int64_t>((count + 255) / 256, 4096));

    AT_DISPATCH_FLOATING_TYPES_AND2(
        at::ScalarType::Half, at::ScalarType::BFloat16,
        weight.scalar_type(), "pack_conv_weight", [&] {
            pack_weight<scalar_t><<<blocks, 256, 0, stream.stream()>>>(
                weight.data_ptr<scalar_t>(),
                packed_weight.data_ptr<scalar_t>(),
                count,
                input_per_group,
                output_per_group,
                kh,
                kw);
        });
    C10_CUDA_KERNEL_LAUNCH_CHECK();

    return {packed_x, packed_weight};
}
"""

_extension = load_inline(
    name="asymmetric_deconv_forward_pack_v1",
    cpp_sources=_cpp_source,
    cuda_sources=_cuda_source,
    functions=["prepare_conv_cuda"],
    extra_cflags=["-O3"],
    extra_cuda_cflags=["-O3"],
    verbose=False,
)


class _PrepareConv(torch.autograd.Function):
    @staticmethod
    def forward(ctx, x, weight, groups):
        ctx.groups = groups
        packed_x, packed_weight = _extension.prepare_conv_cuda(
            x, weight, groups
        )
        return packed_x, packed_weight

    @staticmethod
    def backward(ctx, grad_x, grad_weight):
        if grad_weight is not None:
            co, ci_per_group, kh, kw = grad_weight.shape
            groups = ctx.groups
            grad_weight = (
                grad_weight.reshape(
                    groups, co // groups, ci_per_group, kh, kw
                )
                .permute(0, 2, 1, 3, 4)
                .flip(-2, -1)
                .reshape(groups * ci_per_group, co // groups, kh, kw)
                .contiguous()
            )
        return grad_x, grad_weight, None


class ModelNew(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: tuple,
        stride: tuple = (1, 1),
        padding: tuple = (0, 0),
        output_padding: tuple = (0, 0),
        dilation: tuple = (1, 1),
        groups: int = 1,
        bias: bool = False,
    ):
        super().__init__()
        self.conv_transpose2d = nn.ConvTranspose2d(
            in_channels,
            out_channels,
            kernel_size,
            stride=stride,
            padding=padding,
            output_padding=output_padding,
            dilation=dilation,
            groups=groups,
            bias=bias,
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        conv = self.conv_transpose2d
        weight = conv.weight

        # Unit-stride transposed convolution is a forward convolution
        # with reversed spatial kernels and exchanged channel axes.
        ph = conv.dilation[0] * (conv.kernel_size[0] - 1) - conv.padding[0]
        pw = conv.dilation[1] * (conv.kernel_size[1] - 1) - conv.padding[1]

        supported = (
            x.is_cuda
            and x.ndim == 4
            and 0 < x.shape[0] <= 65535
            and x.shape[1] == conv.in_channels
            and x.shape[2] > 0
            and x.shape[3] > 0
            and x.dtype == weight.dtype
            and x.device == weight.device
            and x.dtype in (
                torch.float16,
                torch.bfloat16,
                torch.float32,
                torch.float64,
            )
            and (
                x.is_contiguous()
                or x.is_contiguous(memory_format=torch.channels_last)
            )
            and weight.is_contiguous()
            and conv.stride == (1, 1)
            and conv.output_padding == (0, 0)
            and ph >= 0
            and pw >= 0
        )

        if not supported:
            return conv(x)

        packed_x, packed_weight = _PrepareConv.apply(
            x, weight, conv.groups
        )
        return F.conv2d(
            packed_x,
            packed_weight,
            conv.bias,
            stride=1,
            padding=(ph, pw),
            dilation=conv.dilation,
            groups=conv.groups,
        )
```