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 transposed 2D convolution with square input and square kernel.

    Args:
        in_channels (int): Number of channels in the input tensor.
        out_channels (int): Number of channels produced by the convolution.
        kernel_size (int): Size of the square convolution kernel.
        stride (int, optional): Stride of the convolution. Defaults to 1.
        padding (int, optional): Padding applied to the input. Defaults to 0.
        output_padding (int, optional): Additional size added to one side of the output shape. Defaults to 0.
        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: int, stride: int = 1, padding: int = 0, output_padding: int = 0, groups: int = 1, bias: bool = False):
        super(Model, self).__init__()
        self.conv_transpose2d = nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, output_padding=output_padding, groups=groups, bias=bias)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Performs the transposed 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.conv_transpose2d(x)

# Test code
batch_size = 8
in_channels = 64  # double channels for heavier compute
out_channels = 64
kernel_size = 3
# larger square input
height = 1024
width = 1024

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 3 of 6 for this task. Your previous solution (turn 2) was:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.cpp_extension import load_inline

torch.backends.cudnn.benchmark = True
torch.backends.cudnn.allow_tf32 = True

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

#define TH 8
#define TW 8
#define TILE 64
#define KSTEP 16
#define LDS 20

// Implicit-GEMM transposed 3x3 conv (stride=1, pad=0) using TF32 tensor cores
// via mma.sync.m16n8k8. C[cout, pixel] = sum_k W[k, cout] * B[k, pixel],
// where k enumerates (ky, kx, cin) and B is the im2col gather of the input.
__global__ void __launch_bounds__(256) tconv2d_mma_tf32_kernel(
    const float* __restrict__ in,
    const float* __restrict__ w,   // [K][Cout], k = (ky*3+kx)*Cin + cin
    float* __restrict__ out,
    int Bn, int Cin, int Cout, int H, int W, int Ho, int Wo, int K)
{
    __shared__ float Ws[TILE][LDS];  // [m][kk]
    __shared__ float Bs[TILE][LDS];  // [n][kk]

    const int mtiles = Cout / TILE;
    const int b = blockIdx.z / mtiles;
    const int m0 = (blockIdx.z % mtiles) * TILE;
    const int y0 = blockIdx.y * TH;
    const int x0 = blockIdx.x * TW;

    const int tid = threadIdx.x;
    const int warp = tid >> 5;
    const int lane = tid & 31;
    const int warp_m = warp >> 2;   // 0..1
    const int warp_n = warp & 3;    // 0..3
    const int gid = lane >> 2;
    const int tig = lane & 3;

    float acc[2][2][4];
    #pragma unroll
    for (int i = 0; i < 2; ++i)
        #pragma unroll
        for (int j = 0; j < 2; ++j)
            #pragma unroll
            for (int r = 0; r < 4; ++r) acc[i][j][r] = 0.f;

    const long long inHW = (long long)H * W;
    const float* __restrict__ inb = in + (long long)b * Cin * inHW;

    for (int k0 = 0; k0 < K; k0 += KSTEP) {
        // stage weights, coalesced global reads (consecutive tid -> consecutive m)
        #pragma unroll
        for (int i = 0; i < 4; ++i) {
            int idx = tid + i * 256;
            int kk = idx >> 6;
            int m = idx & 63;
            int k = k0 + kk;
            float v = 0.f;
            if (k < K) v = w[(long long)k * Cout + m0 + m];
            Ws[m][kk] = v;
        }
        // stage im2col input, coalesced global reads (consecutive tid -> consecutive pixel/x)
        #pragma unroll
        for (int i = 0; i < 4; ++i) {
            int idx = tid + i * 256;
            int kk = idx >> 6;
            int n = idx & 63;
            int k = k0 + kk;
            float v = 0.f;
            if (k < K) {
                int tap = k / Cin;
                int cin = k - tap * Cin;
                int ky = tap / 3;
                int kx = tap - ky * 3;
                int ty = n >> 3;
                int tx = n & 7;
                int gy = y0 + ty - ky;   // out(y,x) = sum w[ky,kx]*in(y-ky, x-kx)
                int gx = x0 + tx - kx;
                if (gy >= 0 && gy < H && gx >= 0 && gx < W)
                    v = inb[(long long)cin * inHW + (long long)gy * W + gx];
            }
            Bs[n][kk] = v;
        }
        __syncthreads();

        #pragma unroll
        for (int ks = 0; ks < KSTEP; ks += 8) {
            unsigned bf[2][2];
            #pragma unroll
            for (int ni = 0; ni < 2; ++ni) {
                int nb = warp_n * 16 + ni * 8;
                bf[ni][0] = __float_as_uint(Bs[nb + gid][ks + tig]);
                bf[ni][1] = __float_as_uint(Bs[nb + gid][ks + tig + 4]);
            }
            #pragma unroll
            for (int mi = 0; mi < 2; ++mi) {
                int mb = warp_m * 32 + mi * 16;
                unsigned a0 = __float_as_uint(Ws[mb + gid][ks + tig]);
                unsigned a1 = __float_as_uint(Ws[mb + gid + 8][ks + tig]);
                unsigned a2 = __float_as_uint(Ws[mb + gid][ks + tig + 4]);
                unsigned a3 = __float_as_uint(Ws[mb + gid + 8][ks + tig + 4]);
                #pragma unroll
                for (int ni = 0; ni < 2; ++ni) {
                    float* c = acc[mi][ni];
                    asm volatile(
                        "mma.sync.aligned.m16n8k8.row.col.f32.tf32.tf32.f32 "
                        "{%0,%1,%2,%3}, {%4,%5,%6,%7}, {%8,%9}, {%0,%1,%2,%3};\n"
                        : "+f"(c[0]), "+f"(c[1]), "+f"(c[2]), "+f"(c[3])
                        : "r"(a0), "r"(a1), "r"(a2), "r"(a3),
                          "r"(bf[ni][0]), "r"(bf[ni][1]));
                }
            }
        }
        __syncthreads();
    }

    #pragma unroll
    for (int mi = 0; mi < 2; ++mi) {
        #pragma unroll
        for (int ni = 0; ni < 2; ++ni) {
            int cb = m0 + warp_m * 32 + mi * 16;
            int pb = warp_n * 16 + ni * 8;
            #pragma unroll
            for (int r = 0; r < 4; ++r) {
                int cm = cb + gid + ((r >> 1) << 3);
                int pn = pb + tig * 2 + (r & 1);
                int y = y0 + (pn >> 3);
                int x = x0 + (pn & 7);
                if (y < Ho && x < Wo)
                    out[((long long)(b * Cout + cm) * Ho + y) * Wo + x] = acc[mi][ni][r];
            }
        }
    }
}

void tconv2d_forward(torch::Tensor in, torch::Tensor w, torch::Tensor out,
                     int64_t Bn, int64_t Cin, int64_t Cout, int64_t H, int64_t W,
                     int64_t Ho, int64_t Wo, int64_t K)
{
    dim3 grid((unsigned)((Wo + TW - 1) / TW), (unsigned)((Ho + TH - 1) / TH),
              (unsigned)(Bn * (Cout / TILE)));
    tconv2d_mma_tf32_kernel<<<grid, 256>>>(
        in.data_ptr<float>(), w.data_ptr<float>(), out.data_ptr<float>(),
        (int)Bn, (int)Cin, (int)Cout, (int)H, (int)W, (int)Ho, (int)Wo, (int)K);
}
"""

cpp_src = ("void tconv2d_forward(torch::Tensor in, torch::Tensor w, torch::Tensor out, "
           "int64_t Bn, int64_t Cin, int64_t Cout, int64_t H, int64_t W, "
           "int64_t Ho, int64_t Wo, int64_t K);")

_mod = None
try:
    _mod = load_inline(
        name="tconv2d_mma_tf32_v2",
        cpp_sources=cpp_src,
        cuda_sources=cuda_src,
        functions=["tconv2d_forward"],
        verbose=False,
        extra_cuda_cflags=["-O3"],
    )
except Exception:
    _mod = None


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

        # stride-1, pad-0 transposed conv == regular conv with 180-degree
        # flipped kernel, channel-transposed weights and padding k-1.
        self._can_conv2d = (stride == 1 and padding == 0 and output_padding == 0 and groups == 1)
        self._use_custom = (
            _mod is not None and self._can_conv2d and kernel_size == 3
            and (out_channels % 64 == 0) and not bias
        )
        self._wre = None
        self._wflip = None
        self._wflip_cl = None
        self._path = None  # 0: custom mma kernel, 1: conv2d NCHW, 2: native, 3: conv2d channels-last

    def _custom_run(self, x):
        w = self.conv_transpose2d.weight
        if self._wre is None or self._wre.device != x.device:
            k = self.kernel_size
            ci = w.shape[0]
            co = w.shape[1]
            self._wre = w.permute(2, 3, 0, 1).reshape(k * k * ci, co).contiguous()
        x = x.contiguous()
        Bn, Cin, H, W = x.shape
        Cout = w.shape[1]
        Ho = (H - 1) * self.stride - 2 * self.padding + self.kernel_size + self.output_padding
        Wo = (W - 1) * self.stride - 2 * self.padding + self.kernel_size + self.output_padding
        K = self.kernel_size * self.kernel_size * Cin
        out = torch.empty((Bn, Cout, Ho, Wo), device=x.device, dtype=x.dtype)
        _mod.tconv2d_forward(x, self._wre, out, Bn, Cin, Cout, H, W, Ho, Wo, K)
        return out

    def _conv2d_run(self, x):
        w = self.conv_transpose2d.weight
        if self._wflip is None or self._wflip.device != x.device:
            self._wflip = w.flip([2, 3]).permute(1, 0, 2, 3).contiguous()
        return F.conv2d(x, self._wflip, self.conv_transpose2d.bias, 1, self.kernel_size - 1)

    def _conv2d_cl_run(self, x):
        w = self.conv_transpose2d.weight
        if self._wflip_cl is None or self._wflip_cl.device != x.device:
            self._wflip_cl = w.flip([2, 3]).permute(1, 0, 2, 3).contiguous(
                memory_format=torch.channels_last)
        y = F.conv2d(x.contiguous(memory_format=torch.channels_last), self._wflip_cl,
                     self.conv_transpose2d.bias, 1, self.kernel_size - 1)
        return y.contiguous()

    def _autotune(self, x):
        if not x.is_cuda:
            self._path = 2
            return
        ref = self.conv_transpose2d(x)
        candidates = [(2, lambda t: self.conv_transpose2d(t))]
        if self._can_conv2d:
            try:
                o = self._conv2d_run(x)
                if o.shape == ref.shape and torch.allclose(o, ref, atol=1e-2, rtol=1e-2):
                    candidates.append((1, lambda t: self._conv2d_run(t)))
            except Exception:
                pass
            try:
                o = self._conv2d_cl_run(x)
                if o.shape == ref.shape and torch.allclose(o, ref, atol=1e-2, rtol=1e-2):
                    candidates.append((3, lambda t: self._conv2d_cl_run(t)))
            except Exception:
                pass
        if self._use_custom and x.dtype == torch.float32:
            try:
                o = self._custom_run(x)
                if o.shape == ref.shape and torch.allclose(o, ref, atol=1e-2, rtol=1e-2):
                    candidates.append((0, lambda t: self._custom_run(t)))
            except Exception:
                pass
        best_path, best_t = 2, float("inf")
        for path, fn in candidates:
            try:
                fn(x)
                torch.cuda.synchronize()
                start = torch.cuda.Event(enable_timing=True)
                end = torch.cuda.Event(enable_timing=True)
                start.record()
                for _ in range(3):
                    fn(x)
                end.record()
                torch.cuda.synchronize()
                t = start.elapsed_time(end)
                if t < best_t:
                    best_t, best_path = t, path
            except Exception:
                continue
        self._path = best_path

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        if self._path is None:
            self._autotune(x)
        if self._path == 0 and x.is_cuda and x.dtype == torch.float32:
            return self._custom_run(x)
        elif self._path == 1:
            return self._conv2d_run(x)
        elif self._path == 3:
            return self._conv2d_cl_run(x)
        return self.conv_transpose2d(x)
```
Result of evaluating it:
The solution is correct on the test inputs. Mean runtime 12.1 ms; the PyTorch reference (with TF32 enabled) takes 13.5 ms; speedup 1.116x.
Profiler, top device kernels of one forward pass of your solution:
| device kernel | total device time (us) | calls |
|---|---|---|
| sm80_xmma_fprop_implicit_gemm_tf32f32_tf32f32_f32_nhwckrsc_nchw_tilesize128x64x32_stage5_w | 8929.4 | 1 |
| void cudnn::engines_precompiled::nchwToNhwcKernel<float, float, float, false, true, (cudnn | 3212.0 | 2 |

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.


Precision rule: the timed forward must compute in FP32 (TF32 tensor-core math is allowed, as in the PyTorch reference). Do not cast inputs, weights or intermediate results to float16 or bfloat16 and do not call half-precision kernels in forward().
