You write custom CUDA kernels to replace the pytorch operators in the given architecture to get speedups. 

    You have complete freedom to choose the set of operators you want to replace. You may make the decision to replace some operators with custom CUDA kernels and leave others unchanged. You may replace multiple operators with custom implementations, consider operator fusion opportunities (combining multiple operators into a single kernel, for example, combining matmul+relu), or algorithmic changes (such as online softmax). You are only limited by your imagination.


        Here's an example to show you the syntax of inline embedding custom CUDA operators in torch: The example given architecture is: 

        ``` 

        # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import torch
import torch.nn as nn
import torch.nn.functional as F


class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()

    def forward(self, a, b):
        return a + b


def get_inputs():
    # randomly generate input tensors based on the model architecture
    a = torch.randn(1, 128).cuda()
    b = torch.randn(1, 128).cuda()
    return [a, b]


def get_init_inputs():
    # randomly generate tensors required for initialization based on the model architecture
    return []

        ``` 

        The example new arch with custom CUDA kernels looks like this:
        ```
        # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.cpp_extension import load_inline

# Define the custom CUDA kernel for element-wise addition
elementwise_add_source = """
#include <torch/extension.h>
#include <cuda_runtime.h>

__global__ void elementwise_add_kernel(const float* a, const float* b, float* out, int size) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < size) {
        out[idx] = a[idx] + b[idx];
    }
}

torch::Tensor elementwise_add_cuda(torch::Tensor a, torch::Tensor b) {
    auto size = a.numel();
    auto out = torch::zeros_like(a);

    const int block_size = 256;
    const int num_blocks = (size + block_size - 1) / block_size;

    elementwise_add_kernel<<<num_blocks, block_size>>>(a.data_ptr<float>(), b.data_ptr<float>(), out.data_ptr<float>(), size);

    return out;
}
"""

elementwise_add_cpp_source = (
    "torch::Tensor elementwise_add_cuda(torch::Tensor a, torch::Tensor b);"
)

# Compile the inline CUDA code for element-wise addition
elementwise_add = load_inline(
    name="elementwise_add",
    cpp_sources=elementwise_add_cpp_source,
    cuda_sources=elementwise_add_source,
    functions=["elementwise_add_cuda"],
    verbose=True,
    extra_cflags=[""],
    extra_ldflags=[""],
)


class ModelNew(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.elementwise_add = elementwise_add

    def forward(self, a, b):
        return self.elementwise_add.elementwise_add_cuda(a, b)

        ``` 

        
    You are given the following architecture: 

    ```
    # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Performs a standard 2D convolution operation with a square input and an asymmetric kernel.

    Args:
        in_channels (int): Number of channels in the input tensor.
        out_channels (int): Number of channels produced by the convolution.
        kernel_size (tuple): Size of the convolution kernel (height, width).
        stride (int, optional): Stride of the convolution. Defaults to 1.
        padding (int or tuple, optional): Padding applied to the input. Defaults to 0.
        dilation (int or tuple, optional): Spacing between kernel elements. Defaults to 1.
        groups (int, optional): Number of blocked connections from input channels to output channels. Defaults to 1.
        bias (bool, optional): If `True`, adds a learnable bias to the output. Defaults to `False`.
    """
    def __init__(self, in_channels: int, out_channels: int, kernel_size: tuple, stride: int = 1, padding: int = 0, dilation: int = 1, groups: int = 1, bias: bool = False):
        super(Model, self).__init__()
        self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)
        
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Performs the 2D convolution.

        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, in_channels, height, width).

        Returns:
            torch.Tensor: Output tensor of shape (batch_size, out_channels, height_out, width_out).
        """
        return self.conv2d(x)

# Test code
batch_size = 8
in_channels = 32
out_channels = 64
kernel_size = (5, 9)
width = 512
height = 512

def get_inputs():
    x = torch.rand(batch_size, in_channels, height, width)
    return [x]

def get_init_inputs():
    return [in_channels, out_channels, kernel_size]  # Provide in_channels, out_channels, kernel_size for initialization
    ```
    
Optimize the architecture named Model with custom CUDA operators! Name your optimized output architecture ModelNew. Output the new code in codeblocks. Please generate real code, NOT pseudocode, make sure the code compiles and is fully functional. Just output the new model code, no other text, and NO testing code! 



This is turn 2 of 6 for this task. Your previous solution (turn 1) was:
```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 asymmetric_conv_cuda(
    torch::Tensor x,
    torch::Tensor weight,
    torch::Tensor bias,
    int64_t stride_h,
    int64_t stride_w,
    int64_t pad_h,
    int64_t pad_w,
    int64_t dilation_h,
    int64_t dilation_w,
    int64_t groups,
    bool has_bias);
"""

_cuda_source = r"""
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <c10/cuda/CUDAException.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <mma.h>

namespace {

constexpr int TILE_M = 32;
constexpr int TILE_N = 64;
constexpr int TILE_K = 32;
constexpr int STRIDE_A = 40;
constexpr int STRIDE_B = 72;

#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800
__device__ __forceinline__ float round_tf32(float value) {
    unsigned int bits;
    asm("cvt.rn.tf32.f32 %0, %1;" : "=r"(bits) : "f"(value));
    return __uint_as_float(bits);
}
#endif

__global__ void asymmetric_conv_kernel(
    const float* __restrict__ x,
    const float* __restrict__ weight,
    const float* __restrict__ bias,
    float* __restrict__ output,
    int channels,
    int height,
    int width,
    int out_channels,
    int kernel_h,
    int kernel_w,
    int out_h,
    int out_w,
    int stride_h,
    int stride_w,
    int pad_h,
    int pad_w,
    int dilation_h,
    int dilation_w,
    int groups,
    bool has_bias) {

#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800
    using namespace nvcuda;

    __shared__ __align__(32) union SharedStorage {
        struct {
            float a_hi[TILE_M * STRIDE_A];
            float a_lo[TILE_M * STRIDE_A];
            float b_hi[TILE_K * STRIDE_B];
            float b_lo[TILE_K * STRIDE_B];
        } operands;
        float result[TILE_M * TILE_N];
    } shared;

    const int tid = threadIdx.x;
    const int warp = tid / 32;
    const int warp_m = (warp / 2) * 16;
    const int warp_n = (warp % 2) * 32;

    const int channels_per_group = channels / groups;
    const int outputs_per_group = out_channels / groups;
    const int group = blockIdx.z % groups;
    const int batch = blockIdx.z / groups;

    const int row_base = blockIdx.y * TILE_M;
    const int column_base = blockIdx.x * TILE_N;
    const int spatial_size = out_h * out_w;
    const int kernel_area = kernel_h * kernel_w;
    const int reduction_size = channels_per_group * kernel_area;

    wmma::fragment<wmma::accumulator, 16, 16, 8, float> acc0;
    wmma::fragment<wmma::accumulator, 16, 16, 8, float> acc1;
    wmma::fill_fragment(acc0, 0.0f);
    wmma::fill_fragment(acc1, 0.0f);

    for (int k_base = 0; k_base < reduction_size; k_base += TILE_K) {
        for (int i = tid; i < TILE_M * TILE_K; i += blockDim.x) {
            const int row = i / TILE_K;
            const int k = i % TILE_K;
            const int output_channel = row_base + row;
            const int reduction_index = k_base + k;

            float value = 0.0f;
            if (output_channel < outputs_per_group &&
                reduction_index < reduction_size) {
                const int global_channel =
                    group * outputs_per_group + output_channel;
                value = weight[
                    static_cast<int64_t>(global_channel) * reduction_size +
                    reduction_index];
            }

            const float hi = round_tf32(value);
            shared.operands.a_hi[row * STRIDE_A + k] = hi;
            shared.operands.a_lo[row * STRIDE_A + k] =
                round_tf32(value - hi);
        }

        for (int i = tid; i < TILE_K * TILE_N; i += blockDim.x) {
            const int k = i / TILE_N;
            const int column = i % TILE_N;
            const int reduction_index = k_base + k;
            const int spatial_index = column_base + column;

            float value = 0.0f;
            if (reduction_index < reduction_size &&
                spatial_index < spatial_size) {
                const int input_channel = reduction_index / kernel_area;
                const int kernel_index = reduction_index % kernel_area;
                const int kh = kernel_index / kernel_w;
                const int kw = kernel_index % kernel_w;

                const int oh = spatial_index / out_w;
                const int ow = spatial_index % out_w;
                const int ih = oh * stride_h - pad_h + kh * dilation_h;
                const int iw = ow * stride_w - pad_w + kw * dilation_w;

                if (static_cast<unsigned>(ih) <
                        static_cast<unsigned>(height) &&
                    static_cast<unsigned>(iw) <
                        static_cast<unsigned>(width)) {
                    const int global_channel =
                        group * channels_per_group + input_channel;
                    const int64_t offset =
                        ((static_cast<int64_t>(batch) * channels +
                          global_channel) * height + ih) * width + iw;
                    value = x[offset];
                }
            }

            const float hi = round_tf32(value);
            shared.operands.b_hi[k * STRIDE_B + column] = hi;
            shared.operands.b_lo[k * STRIDE_B + column] =
                round_tf32(value - hi);
        }

        __syncthreads();

        #pragma unroll
        for (int kk = 0; kk < TILE_K; kk += 8) {
            wmma::fragment<
                wmma::matrix_a, 16, 16, 8,
                wmma::precision::tf32, wmma::row_major> a_hi, a_lo;
            wmma::fragment<
                wmma::matrix_b, 16, 16, 8,
                wmma::precision::tf32, wmma::row_major> b_hi, b_lo;

            wmma::load_matrix_sync(
                a_hi,
                shared.operands.a_hi + warp_m * STRIDE_A + kk,
                STRIDE_A);
            wmma::load_matrix_sync(
                a_lo,
                shared.operands.a_lo + warp_m * STRIDE_A + kk,
                STRIDE_A);

            wmma::load_matrix_sync(
                b_hi,
                shared.operands.b_hi + kk * STRIDE_B + warp_n,
                STRIDE_B);
            wmma::load_matrix_sync(
                b_lo,
                shared.operands.b_lo + kk * STRIDE_B + warp_n,
                STRIDE_B);

            wmma::mma_sync(acc0, a_hi, b_hi, acc0);
            wmma::mma_sync(acc0, a_lo, b_hi, acc0);
            wmma::mma_sync(acc0, a_hi, b_lo, acc0);

            wmma::load_matrix_sync(
                b_hi,
                shared.operands.b_hi + kk * STRIDE_B + warp_n + 16,
                STRIDE_B);
            wmma::load_matrix_sync(
                b_lo,
                shared.operands.b_lo + kk * STRIDE_B + warp_n + 16,
                STRIDE_B);

            wmma::mma_sync(acc1, a_hi, b_hi, acc1);
            wmma::mma_sync(acc1, a_lo, b_hi, acc1);
            wmma::mma_sync(acc1, a_hi, b_lo, acc1);
        }

        __syncthreads();
    }

    wmma::store_matrix_sync(
        shared.result + warp_m * TILE_N + warp_n,
        acc0, TILE_N, wmma::mem_row_major);
    wmma::store_matrix_sync(
        shared.result + warp_m * TILE_N + warp_n + 16,
        acc1, TILE_N, wmma::mem_row_major);

    __syncthreads();

    for (int i = tid; i < TILE_M * TILE_N; i += blockDim.x) {
        const int row = i / TILE_N;
        const int column = i % TILE_N;
        const int output_channel = row_base + row;
        const int spatial_index = column_base + column;

        if (output_channel < outputs_per_group &&
            spatial_index < spatial_size) {
            const int global_channel =
                group * outputs_per_group + output_channel;
            float value = shared.result[i];
            if (has_bias) {
                value += bias[global_channel];
            }

            output[
                (static_cast<int64_t>(batch) * out_channels +
                 global_channel) * spatial_size + spatial_index] = value;
        }
    }
#endif
}

} // namespace

torch::Tensor asymmetric_conv_cuda(
    torch::Tensor x,
    torch::Tensor weight,
    torch::Tensor bias,
    int64_t stride_h,
    int64_t stride_w,
    int64_t pad_h,
    int64_t pad_w,
    int64_t dilation_h,
    int64_t dilation_w,
    int64_t groups,
    bool has_bias) {

    TORCH_CHECK(x.is_cuda() && weight.is_cuda(), "Expected CUDA tensors");
    TORCH_CHECK(x.device() == weight.device(), "Device mismatch");
    TORCH_CHECK(x.scalar_type() == torch::kFloat32 &&
                weight.scalar_type() == torch::kFloat32,
                "Expected float32 tensors");
    TORCH_CHECK(x.dim() == 4 && weight.dim() == 4,
                "Expected four-dimensional tensors");
    TORCH_CHECK(x.is_contiguous() && weight.is_contiguous(),
                "Expected contiguous tensors");
    TORCH_CHECK(groups > 0 &&
                x.size(1) == weight.size(1) * groups &&
                weight.size(0) % groups == 0,
                "Invalid convolution groups");

    c10::cuda::CUDAGuard guard(x.device());
    TORCH_CHECK(at::cuda::getCurrentDeviceProperties()->major >= 8,
                "This kernel requires compute capability 8.0 or newer");

    const int64_t out_h =
        (x.size(2) + 2 * pad_h -
         dilation_h * (weight.size(2) - 1) - 1) / stride_h + 1;
    const int64_t out_w =
        (x.size(3) + 2 * pad_w -
         dilation_w * (weight.size(3) - 1) - 1) / stride_w + 1;

    TORCH_CHECK(out_h > 0 && out_w > 0, "Invalid output size");
    TORCH_CHECK(x.size(0) * groups <= 65535, "Batch/group count too large");

    if (has_bias) {
        TORCH_CHECK(bias.device() == x.device() &&
                    bias.scalar_type() == torch::kFloat32 &&
                    bias.is_contiguous() &&
                    bias.numel() == weight.size(0),
                    "Invalid bias");
    }

    auto output = torch::empty(
        {x.size(0), weight.size(0), out_h, out_w}, x.options());

    if (output.numel() == 0) {
        return output;
    }

    const dim3 grid(
        (out_h * out_w + TILE_N - 1) / TILE_N,
        (weight.size(0) / groups + TILE_M - 1) / TILE_M,
        x.size(0) * groups);

    asymmetric_conv_kernel<<<
        grid, 128, 0, at::cuda::getCurrentCUDAStream()>>>(
        x.data_ptr<float>(),
        weight.data_ptr<float>(),
        has_bias ? bias.data_ptr<float>() : nullptr,
        output.data_ptr<float>(),
        x.size(1), x.size(2), x.size(3),
        weight.size(0), weight.size(2), weight.size(3),
        out_h, out_w,
        stride_h, stride_w,
        pad_h, pad_w,
        dilation_h, dilation_w,
        groups, has_bias);

    C10_CUDA_KERNEL_LAUNCH_CHECK();
    return output;
}
"""

_extension = None


def _get_extension():
    global _extension
    if _extension is None:
        _extension = load_inline(
            name="asymmetric_conv_tf32_split_v1",
            cpp_sources=_cpp_source,
            cuda_sources=_cuda_source,
            functions=["asymmetric_conv_cuda"],
            extra_cflags=["-O3"],
            extra_cuda_cflags=["-O3"],
            verbose=False,
        )
    return _extension


class ModelNew(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: tuple,
        stride: int = 1,
        padding: int = 0,
        dilation: int = 1,
        groups: int = 1,
        bias: bool = False,
    ):
        super().__init__()
        self.conv2d = nn.Conv2d(
            in_channels,
            out_channels,
            kernel_size,
            stride=stride,
            padding=padding,
            dilation=dilation,
            groups=groups,
            bias=bias,
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        conv = self.conv2d

        if (
            not x.is_cuda
            or x.dtype != torch.float32
            or conv.weight.dtype != torch.float32
            or x.ndim != 4
            or isinstance(conv.padding, str)
            or torch.is_autocast_enabled()
            or x.shape[0] * conv.groups > 65535
            or torch.cuda.get_device_capability(x.device)[0] < 8
        ):
            return conv(x)

        bias = conv.bias if conv.bias is not None else conv.weight

        return _get_extension().asymmetric_conv_cuda(
            x.contiguous(),
            conv.weight.contiguous(),
            bias,
            conv.stride[0],
            conv.stride[1],
            conv.padding[0],
            conv.padding[1],
            conv.dilation[0],
            conv.dilation[1],
            conv.groups,
            conv.bias is not None,
        )
```
Result of evaluating it:
The solution compiled but failed at run time. Error output:
```
Error building extension 'asymmetric_conv_tf32_split_v1': [1/3] c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=asymmetric_conv_tf32_split_v1 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /opt/kbv/v/lib/py...
```

Write an improved complete ModelNew: fix any error, and if the solution is already correct, make it faster. Output the new code in one ```python codeblock. Just the model code, no other text, and NO testing code.
