* vulkan: combine duplicated fastdiv functions, rename the one optimizing small divs * remove one more fastdiv
136 lines
4.4 KiB
Plaintext
136 lines
4.4 KiB
Plaintext
#version 450
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#extension GL_EXT_control_flow_attributes : enable
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#ifdef USE_SUBGROUPS
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#extension GL_KHR_shader_subgroup_basic : enable
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#extension GL_KHR_shader_subgroup_arithmetic : enable
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#endif
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#include "types.glsl"
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#include "utils.glsl"
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layout (push_constant) uniform parameter
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{
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uint32_t ne00;
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uint32_t ne01;
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uint32_t nb00;
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uint32_t nb01;
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uint32_t a_offset;
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uint32_t n_experts;
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uint32_t hoist_row_ids;
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uint32_t ne00mp;
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uint32_t ne00L;
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} p;
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#define BLOCK_SIZE 256
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layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
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layout (binding = 0) readonly buffer A {uint data_a[];};
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layout (binding = 1) writeonly buffer D {uint data_d[];};
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shared uint vals[BLOCK_SIZE];
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shared uint offsets[BLOCK_SIZE];
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shared uint cursors[BLOCK_SIZE];
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// data_d layout when p.hoist_row_ids is set:
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// [0, n_experts) per-expert row count
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// [n_experts, 2*n_experts) per-expert start offset into the row id region
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// [2*n_experts] total row count
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// [2*n_experts + 1, ) row ids grouped by expert, packed as (i01 << 16) | (i00 & 0xffff)
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// Otherwise only data_d[expert_id] is written, holding that expert's row count.
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void main() {
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const uint expert_id = gl_WorkGroupID.x;
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const uint num_elements = p.ne00 * p.ne01;
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const uint tid = gl_LocalInvocationID.x;
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if (p.hoist_row_ids != 0) {
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if (tid < p.n_experts) {
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vals[tid] = 0;
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}
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barrier();
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for (uint idx = tid; idx < num_elements; idx += BLOCK_SIZE) {
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const uint i01 = fastdiv(idx, p.ne00mp, p.ne00L);
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const uint i00 = idx - i01 * p.ne00;
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const uint expert = data_a[p.a_offset + i01 * p.nb01 + i00 * p.nb00];
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if (expert < p.n_experts) {
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atomicAdd(vals[expert], 1);
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}
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}
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barrier();
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#ifdef USE_SUBGROUPS
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if (gl_SubgroupID == 0) {
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// pad the trip count so the subgroup ops stay in uniform control flow
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const uint n_experts_padded = (p.n_experts + gl_SubgroupSize - 1) & ~(gl_SubgroupSize - 1);
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uint base = 0;
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for (uint expert = gl_SubgroupInvocationID; expert < n_experts_padded; expert += gl_SubgroupSize) {
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const bool in_range = expert < p.n_experts;
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const uint count = in_range ? vals[expert] : 0;
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const uint offset = base + subgroupExclusiveAdd(count);
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if (in_range) {
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data_d[expert] = count;
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data_d[p.n_experts + expert] = offset;
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offsets[expert] = offset;
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cursors[expert] = 0;
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}
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base += subgroupAdd(count);
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}
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if (subgroupElect()) {
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data_d[2 * p.n_experts] = base;
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}
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}
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#else
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if (tid == 0) {
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uint offset = 0;
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for (uint expert = 0; expert < p.n_experts; ++expert) {
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const uint count = vals[expert];
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data_d[expert] = count;
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data_d[p.n_experts + expert] = offset;
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offsets[expert] = offset;
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cursors[expert] = 0;
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offset += count;
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}
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data_d[2 * p.n_experts] = offset;
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}
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#endif
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barrier();
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for (uint idx = tid; idx < num_elements; idx += BLOCK_SIZE) {
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const uint i01 = fastdiv(idx, p.ne00mp, p.ne00L);
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const uint i00 = idx - i01 * p.ne00;
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const uint expert = data_a[p.a_offset + i01 * p.nb01 + i00 * p.nb00];
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if (expert < p.n_experts) {
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const uint row = atomicAdd(cursors[expert], 1);
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const uint packed_row_id = (i01 << 16) | (i00 & 0xffffu);
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data_d[2 * p.n_experts + 1 + offsets[expert] + row] = packed_row_id;
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}
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}
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return;
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}
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uint count = 0;
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for (uint idx = tid; idx < num_elements; idx += BLOCK_SIZE) {
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const uint i01 = fastdiv(idx, p.ne00mp, p.ne00L);
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const uint i00 = idx - i01 * p.ne00;
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const uint a = data_a[p.a_offset + i01 * p.nb01 + i00 * p.nb00];
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count += uint(a == expert_id);
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}
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vals[tid] = count;
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barrier();
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[[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
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if (tid < s) {
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vals[tid] += vals[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[expert_id] = vals[0];
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}
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}
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