// Mechanically adapted from OA C++:
// source/cpp/lib/oa/ml/shader/compute/nn/softmax/softmaxScaledMaskedBwd.slang
import storage;
import attributes;
struct PushConstants {
uint forward_output_index;
uint output_gradient_index;
uint input_gradient_index;
uint rows;
uint columns;
float scale;
};
[[vk::push_constant]] PushConstants push;
[[vk::binding(0, 0)]] RWByteAddressBuffer storage_buffers[];
groupshared float shared_values[256];
[kernel_name("softmax_scaled_masked_backward")]
[domain("ml")]
[variant("generic")]
[dtype("f32")]
[status("experimental")]
[shader("compute")]
[numthreads(256, 1, 1)]
void main(uint3 group_id : SV_GroupID, uint3 group_thread_id : SV_GroupThreadID) {
uint row = group_id.x;
uint lane = group_thread_id.x;
if (row >= push.rows) {
return;
}
uint base = row * push.columns;
float local_dot = 0.0;
for (uint column = lane; column < push.columns; column += 256) {
local_dot +=
load_f32(storage_buffers[push.output_gradient_index], base + column) *
load_f32(storage_buffers[push.forward_output_index], base + column);
}
shared_values[lane] = local_dot;
GroupMemoryBarrierWithGroupSync();
for (uint stride = 128; stride > 0; stride >>= 1) {
if (lane < stride) {
shared_values[lane] += shared_values[lane + stride];
}
GroupMemoryBarrierWithGroupSync();
}
float dot = shared_values[0];
for (uint column = lane; column < push.columns; column += 256) {
float softmax_value = load_f32(storage_buffers[push.forward_output_index], base + column);
float gradient = load_f32(storage_buffers[push.output_gradient_index], base + column);
store_f32(
storage_buffers[push.input_gradient_index],
base + column,
softmax_value * (gradient - dot) * push.scale);
}
}