#version 450 #ifdef FLOAT16 #extension GL_EXT_shader_explicit_arithmetic_types_float16 : require #endif #extension GL_EXT_shader_explicit_arithmetic_types : require #include "mul_mat_vec_base.comp" layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; layout (constant_id = 0) const uint BLOCK_SIZE = 32; layout (constant_id = 1) const uint NUM_ROWS = 1; #if !defined(DATA_A_F32) && !defined(DATA_A_F16) #define K_PER_ITER 8 #else #define K_PER_ITER 2 #endif uint a_offset, b_offset, d_offset, y_offset; shared FLOAT_TYPE tmpsh[NUM_ROWS][BLOCK_SIZE]; void iter(inout FLOAT_TYPE temp[NUM_ROWS], const uint first_row, const uint num_rows, const uint tid, const uint i, bool lastiter) { const uint col = i*BLOCK_SIZE + K_PER_ITER*tid; const uint iqs = (col%QUANT_K)/QUANT_R; // quant index const uint iybs = col - col%QUANT_K; // y block start index #if K_PER_ITER == 8 #if QUANT_R == 2 B_TYPE_VEC4 bv02 = data_b_v4[(b_offset + iybs + iqs) / 4]; B_TYPE_VEC4 bv13 = data_b_v4[(b_offset + iybs + iqs + y_offset) / 4]; FLOAT_TYPE b0 = FLOAT_TYPE(bv02.x); FLOAT_TYPE b1 = FLOAT_TYPE(bv13.x); FLOAT_TYPE b2 = FLOAT_TYPE(bv02.y); FLOAT_TYPE b3 = FLOAT_TYPE(bv13.y); FLOAT_TYPE b4 = FLOAT_TYPE(bv02.z); FLOAT_TYPE b5 = FLOAT_TYPE(bv13.z); FLOAT_TYPE b6 = FLOAT_TYPE(bv02.w); FLOAT_TYPE b7 = FLOAT_TYPE(bv13.w); #else B_TYPE_VEC4 bv0 = data_b_v4[(b_offset + iybs + iqs) / 4]; B_TYPE_VEC4 bv1 = data_b_v4[(b_offset + iybs + iqs) / 4 + 1]; FLOAT_TYPE b0 = FLOAT_TYPE(bv0.x); FLOAT_TYPE b1 = FLOAT_TYPE(bv0.y); FLOAT_TYPE b2 = FLOAT_TYPE(bv0.z); FLOAT_TYPE b3 = FLOAT_TYPE(bv0.w); FLOAT_TYPE b4 = FLOAT_TYPE(bv1.x); FLOAT_TYPE b5 = FLOAT_TYPE(bv1.y); FLOAT_TYPE b6 = FLOAT_TYPE(bv1.z); FLOAT_TYPE b7 = FLOAT_TYPE(bv1.w); #endif #else // Check if the second of the pair of elements is OOB, and don't fetch B or // accumulate it. We still fetch a pair of elements for A, which is fine for // quantized formats since they'll be within the same block. We should // probably skip fetching the second element for F16/F32, but as of now we // still do. const bool OOB = lastiter && (iybs + iqs + y_offset >= p.ncols); FLOAT_TYPE b0 = 0, b1 = 0; b0 = FLOAT_TYPE(data_b[b_offset + iybs + iqs]); if (!OOB) { b1 = FLOAT_TYPE(data_b[b_offset + iybs + iqs + y_offset]); } #endif [[unroll]] for (uint n = 0; n < num_rows; ++n) { const uint ib = ((first_row + n)*p.ncols + col)/QUANT_K; // block index #if K_PER_ITER == 8 const vec4 v = dequantize4(ib, iqs, a_offset); const vec4 v2 = dequantize4(ib, iqs+(4/QUANT_R), a_offset); // matrix multiplication temp[n] = fma(FLOAT_TYPE(v.x), b0, temp[n]); temp[n] = fma(FLOAT_TYPE(v.y), b1, temp[n]); temp[n] = fma(FLOAT_TYPE(v.z), b2, temp[n]); temp[n] = fma(FLOAT_TYPE(v.w), b3, temp[n]); temp[n] = fma(FLOAT_TYPE(v2.x), b4, temp[n]); temp[n] = fma(FLOAT_TYPE(v2.y), b5, temp[n]); temp[n] = fma(FLOAT_TYPE(v2.z), b6, temp[n]); temp[n] = fma(FLOAT_TYPE(v2.w), b7, temp[n]); #else const vec2 v = dequantize(ib, iqs, a_offset); // matrix multiplication temp[n] = fma(FLOAT_TYPE(v.x), b0, temp[n]); if (!OOB) { temp[n] = fma(FLOAT_TYPE(v.y), b1, temp[n]); } #endif } } void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { const uint tid = gl_LocalInvocationID.x; get_offsets(a_offset, b_offset, d_offset); a_offset /= QUANT_K; y_offset = QUANT_R == 1 ? 1 : QUANT_K/2; FLOAT_TYPE temp[NUM_ROWS]; for (uint i = 0; i < NUM_ROWS; ++i) { temp[i] = FLOAT_TYPE(0); } uint num_iters = p.ncols / (K_PER_ITER * BLOCK_SIZE); if (num_iters * K_PER_ITER * BLOCK_SIZE + K_PER_ITER*tid < p.ncols) { num_iters++; } int unroll_count = 4; uint unrolled_iters = num_iters & ~(unroll_count - 1); uint i = 0; while (i < unrolled_iters) { // Manually partially unroll the loop [[unroll]] for (uint k = 0; k < unroll_count; ++k) { iter(temp, first_row, num_rows, tid, i*K_PER_ITER, false); i++; } } unroll_count = 2; unrolled_iters = num_iters & ~(unroll_count - 1); while (i < unrolled_iters) { // Manually partially unroll the loop [[unroll]] for (uint k = 0; k < unroll_count; ++k) { iter(temp, first_row, num_rows, tid, i*K_PER_ITER, false); i++; } } while (i < num_iters) { iter(temp, first_row, num_rows, tid, i*K_PER_ITER, true); i++; } // sum up partial sums and write back result [[unroll]] for (uint n = 0; n < num_rows; ++n) { tmpsh[n][tid] = temp[n]; } barrier(); [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) { if (tid < s) { [[unroll]] for (uint n = 0; n < num_rows; ++n) { tmpsh[n][tid] += tmpsh[n][tid + s]; } } barrier(); } if (tid == 0) { [[unroll]] for (uint n = 0; n < num_rows; ++n) { data_d[d_offset + first_row + n] = D_TYPE(tmpsh[n][0]); } } } void main() { const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z); #if defined(DATA_A_IQ4_NL) init_iq4nl_shmem(); #endif // do NUM_ROWS at a time, unless there aren't enough remaining rows if (first_row + NUM_ROWS <= p.stride_d) { compute_outputs(first_row, NUM_ROWS); } else { if (first_row >= p.stride_d) { return; } compute_outputs(first_row, p.stride_d - first_row); } }