Source codes

1 files changed, 79 insertions, 0 deletions

diff --git a/ComputeShaders/softMaxFixed.hlsl b/ComputeShaders/softMaxFixed.hlsl
new file mode 100644
index 0000000..7b4add2
--- /dev/null
+++ b/ComputeShaders/softMaxFixed.hlsl
@@ -0,0 +1,79 @@
+// Special softMax shader for matrices with rows of 1500 elements.
+// Uses group shared buffer of that length to save global memory bandwidth, more than 2x faster than the original.
+// Dispatch [ nr, 1, 1 ] thread groups of this shader
+RWBuffer<float> result: register( u0 );
+
+cbuffer Constants: register( b0 )
+{
+	uint4 elements: packoffset( c0 );
+	uint4 strides: packoffset( c1 );
+	uint nr: packoffset( c2.x );
+	float inputScale: packoffset( c2.y );
+}
+
+#include "miscUtils.hlsli"
+#include "groupReduce64.hlsli"
+
+static const uint THREADS = 64;
+static const uint ROW_LENGTH = 1500;
+groupshared float rowBuffer[ ROW_LENGTH ];
+
+static const float negativeInfinity = asfloat( 0xff800000 );
+
+[ numthreads( THREADS, 1, 1 ) ]
+void main( uint3 group: SV_GroupID, uint thread : SV_GroupIndex )
+{
+	const uint p = group.x * strides[ 1 ];
+	const uint nc = ROW_LENGTH;
+	uint i;
+
+	float m = negativeInfinity;
+	// First pass: compute maximum, and copy the row into the group shared buffer
+	for( i = thread; i < nc; i += THREADS )
+	{
+		float f = result[ p + i ];
+		m = max( m, f );
+		rowBuffer[ i ] = f;
+	}
+	horizontalMaxBroadcast( thread, m );
+
+	// Second pass: apply initial scale, compute the exponent, and compute total sum over the row
+	float sum = 0;
+	for( i = thread; i < nc; i += THREADS )
+	{
+		float f = rowBuffer[ i ];
+
+		[branch]
+		if( f != negativeInfinity )
+		{
+			f = ( f - m ) * inputScale;
+#if 1
+			// At least on Radeon Graphics GPU inside Ryzen 7 5700G, computing exponent instead of loading from the buffer improves the performance
+			f = exp( f );
+#else
+			uint s = fp16Rounded( f );
+			s = lookupTable[ s ];
+			f = f16tof32( s );
+#endif
+			sum += f;
+		}
+		else
+			f = 0;
+
+		rowBuffer[ i ] = f;
+	}
+
+	horizontalSum( thread, sum );
+	if( 0 == thread )
+		sharedAccumulators[ 0 ] = 1.0 / sum;
+	GroupMemoryBarrierWithGroupSync();
+	const float scale = sharedAccumulators[ 0 ];
+
+	// Final pass: apply the final scale, and copy the row from the group shared buffer back into the global memory
+	for( i = thread; i < nc; i += THREADS )
+	{
+		float f = rowBuffer[ i ];
+		f *= scale;
+		result[ p + i ] = f;
+	}
+}
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