I ran into a type conversion issue in the code below and am having trouble resolving it. I’m using p04_layout_tensor.mojo as a reference, and cannot see the significant difference between my code and the puzzle’s. It seems straightforward and that it should work, but…no.
This code snippet is from my hackathon project (pendulum). Any help/guidance appreciated!
Mojo 25.5.0.dev2025063020
Error: cannot implicitly convert ‘SIMD[float32, init[::Origin[::Bool(IntTuple(1), IntTuple(1)).size()]’ value to ‘SIMD[float32, 1]’
Code: sum = sum + input_buffer[0, j] * weight
from gpu import thread_idx, block_dim, block_idx
from layout import Layout, LayoutTensor
# GPU kernel with tensor indexing issues (for demonstration)
fn gpu_neural_network_kernel_with_indexing_issues(
output_buffer: LayoutTensor[
mut=True, DType.float32, Layout.row_major(1, 3)
],
input_buffer: LayoutTensor[mut=True, DType.float32, Layout.row_major(1, 4)],
):
"""GPU neural network kernel that demonstrates tensor indexing type conversion issues.
"""
idx = thread_idx.x + block_idx.x * block_dim.x
if idx < 3: # 3 outputs
# Compute neural network output exactly like CPU version
# Use identical weight formula: (i + j + 1) * 0.1
var sum: Float32 = 0.0
# This will cause tensor indexing type conversion issues
for j in range(4): # 4 inputs
weight = Float32(idx + j + 1) * 0.1
# SIMD[float32, __init__[::Origin[::Bool(IntTuple(1), IntTuple(1)).size()]
value = input_buffer[0, j]
sum = (
sum + input_buffer[0, j] * weight
) # Type conversion issue here
# Apply real tanh activation
var tanh_result: Float32
if sum > 5.0:
tanh_result = 1.0
elif sum < -5.0:
tanh_result = -1.0
else:
# High-quality tanh approximation
abs_sum = sum if sum >= 0.0 else -sum
sum_squared = sum * sum
denominator = 1.0 + abs_sum / 3.0 + sum_squared / 15.0
tanh_result = sum / denominator
# Store result - this may also have indexing issues
output_buffer[0, idx] = tanh_result