[BUG] Conv2D Frop Gives Wrong Output

Describe the bug
Conv2D Frop gives wrong output when calculating a small conv in FP32

Steps/Code to reproduce bug
Simply run

import torch
import numpy as np
import cutlass

inputs = torch.from_numpy(np.array([[
    [[1 ,1], [1, 1]],
    [[2 ,2], [2, 2]],
    [[3 ,3], [3, 3]],
    [[0 ,0], [0, 0]],
]]))


inputs = inputs.to(torch.float32).to(memory_format=torch.channels_last)

weights = torch.from_numpy(np.array([[[[1]], [[1]], [[1]], [[1]]]]))
weights = weights.to(torch.float32).to(memory_format=torch.channels_last)

H, C, W, H = inputs.shape
K, C, R, S = weights.shape

N, P, Q, K = cutlass.Conv2d.output_size((N, H, W, C), (K, R, S, C), padding, stride, dilation)


N, P, Q, K = cutlass.Conv2d.output_size((N, H, W, C), (K, R, S, C), padding, stride, dilation)


dtype = torch.float32

plan = cutlass.Conv2dFprop(element_input=dtype, element_weight=dtype, element_C=dtype, element_output=dtype, element_accumulator=dtype)

tensor_C = torch.empty(size=(N, K, P, Q), dtype=dtype, device="cuda").to(memory_format=torch.channels_last)
output = torch.zeros_like(torch.empty(size=(N, K, P, Q), dtype=dtype, device="cuda").to(memory_format=torch.channels_last))
plan.run(inputs, weights, tensor_C, output, stride, padding, dilation, alpha, beta, print_module=print_module)

print(output)

> 
tensor([[[[4., 6.],
          [6., 6.]]]], device='cuda:0')

Expected behavior

Output should be:

tensor([[[[6., 6.],
          [6., 6.]]]], device='cuda:0')

Environment details (please complete the following information):

• Environment location: Local

Additional context
Add any other context about the problem here.

Begin at the cutlass.Conv2dFprop entry point and reduce the supplied FP32, channels-last reproduction to a runnable case, resolving the undefined parameters shown in the snippet. Compare the CUDA output with the stated all-6 tensor; done means the reproduction returns that expected output.