mirror of
https://github.com/ggerganov/llama.cpp.git
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5fd89a70ea
* Optimize Vulkan REPEAT performance * Use Vulkan GLSL fused multiply-add instruction where possible * Add GGML_VULKAN_PERF option to output performance data per operator * Rework and fix Vulkan descriptor set and descriptor pool handling * Fix float32 concat f16 shader validation error * Add Vulkan GROUP_NORM eps parameter * Fix validation error with transfer queue memory barrier flags * Remove trailing whitespaces
75 lines
4.5 KiB
Plaintext
75 lines
4.5 KiB
Plaintext
#version 450
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#include "mul_mat_vec_base.comp"
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layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
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shared FLOAT_TYPE tmp[32];
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void main() {
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const uint row = gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z;
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uint a_offset, b_offset, d_offset;
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get_offsets(a_offset, b_offset, d_offset);
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const uint num_blocks_per_row = p.ncols / QUANT_K;
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const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row;
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const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16
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const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1
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const uint step = 16/K_QUANTS_PER_ITERATION; // 16 or 8
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const uint v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128...
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const uint v_in = tid - step*v_im; // 0...15 or 0...7
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const uint l0 = K_QUANTS_PER_ITERATION*v_in; // 0...15
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const uint q_offset = 32*v_im + l0;
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const uint s_offset = 8*v_im;
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const uint y_offset = 128*v_im + l0;
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tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp
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[[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
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const uint y_idx = i * QUANT_K + y_offset;
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const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x);
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const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y);
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FLOAT_TYPE sum1 = FLOAT_TYPE(0.0);
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FLOAT_TYPE sum2 = FLOAT_TYPE(0.0);
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for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
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sum1 = fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 0]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 0) & 3),
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fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 0) & 3),
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fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 2) & 3),
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fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 2) & 3),
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fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 4) & 3),
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fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 4) & 3),
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fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 6) & 3),
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fma(FLOAT_TYPE(data_b[b_offset + y_idx + l +112]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 6) & 3), sum1))))))));
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sum2 = fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 0]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 0] >> 4) & 0xF),
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fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 1] >> 4) & 0xF),
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fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 2] >> 4) & 0xF),
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fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 3] >> 4) & 0xF),
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fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 4] >> 4) & 0xF),
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fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 5] >> 4) & 0xF),
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fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 6] >> 4) & 0xF),
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fma(FLOAT_TYPE(data_b[b_offset + y_idx + l +112]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 7] >> 4) & 0xF), sum2))))))));
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}
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const uint tmp_idx = 16 * ix + tid;
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tmp[tmp_idx] = fma(dall, sum1, fma(-dmin, sum2, tmp[tmp_idx]));
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}
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// sum up partial sums and write back result
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barrier();
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[[unroll]] for (uint s = 16; s > 0; s >>= 1) {
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if (tid < s) {
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tmp[tid] += tmp[tid + s];
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}
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barrier();
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}
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if (tid == 0) {
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data_d[d_offset + row] = D_TYPE(tmp[0]);
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}
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}
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