L1 P65 · original

error 65_conv_transposed_2D__square_input__asymmetric_kernel.py

model

Kimi-K2.6

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wall clock

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truncated

backend

cuda fp32

variant

original

runtime

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ref runtime

0.00 μs

speedup

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started

2026-05-02T21:33:44Z

reference PyTorch source (the prompt the agent sees)

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Performs a transposed 2D convolution 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.
        output_padding (int or tuple, 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: tuple, 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
out_channels = 64
kernel_size = (3, 7)  # larger asymmetric kernel
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

turn 015.6s turn · 15.6s LLM · 0.0s tools

reasoning

We need to write a custom CUDA kernel that replaces forward of ConvTranspose2d in PyTorch. Since it's a convolution (transposed), but with asymmetric kernel (3,7), stride=1, padding=0, output_padding=0, groups=1, bias=False. The shape: batch_size=8, in_channels=64, out_channels=64, kernel=(3,7), stride=1, no padding, no output padding, height=512 width=512. So output shape: same as input because stride=1, kernel size 3 and 7, padding=0 => output size = (input_size-1)*stride - 2*padding + kernel + output_padding = (512-1)*1 -0 +3 = 514 for height, similarly width = 512-1+7=518. Wait ConvTranspose2d output formula: H_out = (H_in-1)*stride - 2*padding + dilation*(kernel_size-1)+1 + output_padding. For dilation=1 => (H_in-1)*stride - 2*padding + kernel_size + output_padding. So height_out = 511*1 +3 = 514. width_out = 511+7 = 518. So output tensor shape (8,64,514,518). Computation: transposed convolution with groups=1, no bias. We need to write a custom CUDA kernel that is faster than PyTorch's built-in cuDNN or im2col-based. Since kernel size is relatively small (3x7), manually writing a direct convolution kernel could be competitive. Input batch 8, C=64, H=W=512. Output C=64, H=514, W=518. Kernel uses standard conv transpose. We should consider writing a kernel that performs the transposed convolution as a strided convolution on the output gradient? Actually forward transposed convolution is equivalent to convolving the input with a kernel that is rotated 180 degrees and placed on the output with appropriate overlaps. Another way: ConvTranspose2d forward can be seen as a standard convolution of the input with kernel shape swapped? Wait: In PyTorch, ConvTranspose2d is implemented as a backward of Conv2d. The forward operation computes y = conv_transpose2d(x, weight) where weight shape is (in_channels, out_channels, kH, kW). Equivalent to conv2d of x with weight flipped both dims? Actually the relationship: For standard conv2d with weight (out_channels, in_channels, kH, kW) and stride s, padding p, the transpose conv uses weight (in_channels, out_channels, kH, kW) (same as the gradient w.r.t. input of conv2d). The forward operation: y[b, c_out, h, w] = sum_{c_in, i, j} x[b, c_in, h', w'] * weight[c_in, c_out, i, j], where h = h' + i (maybe with stride). For stride=1, no padding, we can view it as x convolved with weight but with 'full' padding. Specifically, transposed convolution with stride=1 and padding=0 is equivalent to standard convolution with stride=1 and padding=(kH-1, kW-1) using weight flipped vertically and horizontally? Let's verify. Standard conv: out[h,w] = sum_{i,j} in[h+i-p, w+j-p] * weight[i,j]. For stride=1. Transposed conv (forward) with stride=1, padding=0: out[h,w] = sum_{i,j} in[h-i, w-j] * weight[i,j] where h ranges [0, H+kH-2]. That is full convolution, i.e., pad input with kH-1 and kW-1 zeros, then convolve with weight flipped both dims (or equivalently cross-correlation with flipped weight). But PyTorch's ConvTranspose2d uses weight shape (in_channels, out_channels, kH, kW). So we can implement forward as for each output pixel (h,w), sum over input positions (h',w') such that h' + i = h, w' + j = w => h' = h - i, w' = w - j. For stride=1. So: y[b, c_out, h, w] = sum_{c_in} sum_{i=0}^{kH-1} sum_{j=0}^{kW-1} x[b, c_in, h - i, w - j] * weight[c_in, c_out, i, j] where out-of-bound x treated as zero. Equivalent to: y = conv2d(x_padded, weight_flipped) with stride 1? Let's not get too deep; easier to implement direct approach. Given sizes: batch=8, C=64, H=512, W=512, kH=3, kW=7, outH=514, outW=518. Total output elements = 8*64*514*518 ≈ 8*64*266,252 ≈ 8*17,040,128 ≈ 136,321,024. Each output pixel requires 64*3*7 = 1344 multiply-adds. So total FLOPs = output_elements * C_in * kH * kW = 136M * 1344 ≈ 183.4 GFLOPs. That's moderate. Memory bandwidth: reading input: 8*64*512*512 * 4 bytes ≈ 536.9 MB. Reading weight: 64*64*3*7 * 4 = 344,064 B ~ 0.34 MB. Writing output: 8*64*514*518 * 4 ≈ 545.3 MB. Total ~1.08 GB. If we achieve near memory bandwidth of ~900 GB/s (A100), then bandwidth-limited time is ~1.2ms. Compute-limited at 312 TFLOPS (A100 fp32) is 183 GF / 312 TF = 0.587 ms. So both are close, likely compute-limited or mix. Need efficient kernel. Since kernel is small (3x7), we can write a fused direct convolution kernel. We need to know GPU specs. Let's query GPU specs.

call → get_gpu_specs

callget_gpu_specs {}

result ← get_gpu_specs (ok)

okget_gpu_specs GPU specs for NVIDIA H100 80GB HBM3: total memory (runtime): 79.1 GB spec entry: H100 GPU Architecture: Hopper GPU Memory: 80GB Memory Bandwidth: 3.35 TB/s FP64 TFLOPS: 34 FP64 Tensor Core TFLOPS: 67 FP32 TFLOPS: 67 TF32 Tensor Core TFLOPS: 989 with sparsity BFLOAT16 Tensore Core TFLOPS: 1979 with sparsity FP16 Tensor Core TFLOPS: 1979 with sparsity FP8 Tensor Core TFLOPS: 3958 with sparsity INT8 Tensor Core TOPS: 3958 with sparsity Register File Size: 64K 32-bit registers per SM Maximum number of registers per thread: 255 Maximum number of thread blocks per SM: 32 Shared memory capacity per SM: 228 KB Maximum shared memory per thread block: 227 KB

turn 17184.4s turn · 7184.4s LLM · 0.0s tools

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