mirror of
https://github.com/Const-me/Whisper.git
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156 lines
4.1 KiB
HLSL
156 lines
4.1 KiB
HLSL
// matrix*row vector product, needs first argument reshaped into a sequence of horizontal column major panels
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#ifndef TILE_SIZE
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static const uint TILE_SIZE = 32;
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#endif
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#ifndef TILE_HEIGHT
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static const uint TILE_HEIGHT = 32;
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#endif
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#ifndef THREADS_Y
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static const uint THREADS_Y = 16;
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#endif
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// First tensor, reshaped into dense column major horizontal panels of size [ width, TILE_SIZE ]
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Buffer<float> arg0: register( t0 );
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// Second tensor, reshaped into dense column major horizontal panels of size [ width, TILE_SIZE ]
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Buffer<float> arg1: register( t1 );
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// FP32 output tensor, row major and continuous
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RWBuffer<float> result: register( u0 );
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cbuffer Constants: register( b0 )
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{
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uint4 arg0Size: packoffset( c0 );
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uint arg0panel: packoffset( c1.y );
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uint2 arg0LayerStrides: packoffset( c1.z );
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// uint4 arg1Size: packoffset( c2 );
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uint4 arg1Strides: packoffset( c3 );
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uint4 resultSize: packoffset( c4 );
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uint4 resultStrides: packoffset( c5 );
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}
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groupshared float tileOutput[ THREADS_Y ][ TILE_SIZE ];
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groupshared float tile0[ TILE_HEIGHT ][ TILE_SIZE ];
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groupshared float tile1[ TILE_HEIGHT ];
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void multiplyTiles( const uint3 thread )
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{
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float r = 0.0;
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for( uint i = thread.y; i < TILE_HEIGHT; i += THREADS_Y )
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{
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float a = tile0[ i ][ thread.x ];
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float b = tile1[ i ];
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r = mad( a, b, r );
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}
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tileOutput[ thread.y ][ thread.x ] += r;
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}
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void reduceOutput( const uint3 thread )
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{
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float curr = 0.0;
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[branch]
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if( thread.y < THREADS_Y / 2 )
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curr = tileOutput[ thread.y ][ thread.x ];
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for( uint i = THREADS_Y / 2; i > 0; i /= 2 )
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{
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[branch]
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if( thread.y < i )
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{
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curr += tileOutput[ thread.y + i ][ thread.x ];
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tileOutput[ thread.y ][ thread.x ] = curr;
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}
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GroupMemoryBarrierWithGroupSync();
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}
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}
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void storeTile( const uint threadFlat, const uint4 pos, const uint size )
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{
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if( threadFlat >= size )
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return;
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const uint4 prod4 = pos * resultStrides;
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const uint2 prod2 = prod4.xy + prod4.zw;
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uint rdi = prod2.x + prod2.y;
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result[ rdi + threadFlat ] = tileOutput[ 0 ][ threadFlat ];
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}
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[ numthreads( TILE_SIZE, THREADS_Y, 1 ) ]
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void main( const uint3 group: SV_GroupID, const uint3 thread : SV_GroupThreadID, uint threadFlat : SV_GroupIndex )
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{
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uint i;
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// Zero all 3 shared buffers
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tileOutput[ thread.y ][ thread.x ] = 0.0;
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for( i = thread.y; i < TILE_HEIGHT; i += THREADS_Y )
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tile0[ i ][ thread.x ] = 0.0;
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if( threadFlat < THREADS_Y )
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tile1[ threadFlat ] = 0.0;
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const uint2 layer = group.yz;
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uint rsi0 = group.x * arg0panel + layer.x * arg0LayerStrides.x + layer.y * arg0LayerStrides.y;
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uint rsi1 = layer.x * arg1Strides.z + layer.y * arg1Strides.w;
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const uint threadOffset = thread.y * TILE_SIZE + thread.x;
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rsi0 += threadOffset;
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rsi1 += threadFlat * arg1Strides.x;
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const uint completeTiles = arg0Size.x / TILE_HEIGHT;
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for( i = 0; i < completeTiles; i++ )
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{
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// Load [ TILE_SIZE, TILE_HEIGHT ] block from the first source tensor into the groupshared buffer
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for( uint j = thread.y; j < TILE_HEIGHT; j += THREADS_Y )
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{
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tile0[ j ][ thread.x ] = arg0[ rsi0 ];
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rsi0 += THREADS_Y * TILE_SIZE;
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}
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// Load [ TILE_HEIGHT ] row from the second source into another groupshared buffer
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[ branch ]
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if( threadFlat < TILE_HEIGHT )
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tile1[ threadFlat ] = arg1[ rsi1 ];
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rsi1 += TILE_HEIGHT * arg1Strides.x;
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GroupMemoryBarrierWithGroupSync();
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multiplyTiles( thread );
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GroupMemoryBarrierWithGroupSync();
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}
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const uint rem = arg0Size.x % TILE_HEIGHT;
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if( rem != 0 )
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{
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for( uint j = thread.y; j < TILE_HEIGHT; j += THREADS_Y )
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{
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float a;
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[branch]
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if( j < rem )
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{
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a = arg0[ rsi0 ];
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rsi0 += THREADS_Y * TILE_SIZE;
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}
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else
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a = 0.0;
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tile0[ j ][ thread.x ] = a;
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}
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if( threadFlat < TILE_HEIGHT )
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{
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float b;
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[branch]
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if( threadFlat < rem )
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b = arg1[ rsi1 ];
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else
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b = 0.0;
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tile1[ threadFlat ] = b;
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}
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GroupMemoryBarrierWithGroupSync();
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multiplyTiles( thread );
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GroupMemoryBarrierWithGroupSync();
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}
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reduceOutput( thread );
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const uint resultPos = group.x * TILE_SIZE;
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const uint outputSize = min( TILE_SIZE, resultSize.x - resultPos );
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storeTile( threadFlat, uint4( resultPos, 0, layer ), outputSize );
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} |