1D Average Pooling

Perform 1D average pooling on an input tensor:

$$\text{output}[i] = \frac{1}{k}\sum_{m=0}^{k-1} \text{input}[S \cdot i + m - P]$$

The average pooling operation slides a window of size $k$ over the input tensor with stride $S$ and padding $P$, computing the average value within each window position.

Input:

• Matrix input of size $\text{H}$ (input tensor)
• kernel_size ($k$): Size of the pooling window
• stride ($S$): Step size between window positions
• padding ($P$): Number of zero-padding elements added on all sides

Output:

• Matrix output of size $\text{H}_{\text{out}}$ where: $\text{H}_{\text{out}} = \left\lfloor\frac{\text{H} + 2P - k}{S} + 1\right\rfloor$

Notes:

• Zero padding is applied when specified by the padding parameter
• For values outside the input boundaries (after padding), use zero values in the average computation
• The denominator ($k$) should always be the full kernel size, even when some elements are outside the input boundaries
• This problem is adapted from KernelBench

Test Case Sizes

• H=2097152, K=7, S=4, P=3
• H=4194304, K=2, S=1, P=0
• H=8388608, K=3, S=2, P=1
• H=16777216, K=4, S=2, P=1
• H=33554432, K=3, S=1, P=1
• H=67108864, K=5, S=3, P=2