mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-07-15 07:14:32 +00:00
vulkan: support softmax/FA batch and broadcast (#14449)
This commit is contained in:
committed by
Georgi Gerganov
parent
ec68e84c32
commit
8875523eb3
@ -633,6 +633,7 @@ struct vk_flash_attn_push_constants {
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uint32_t nev2;
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uint32_t nev3;
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uint32_t nem1;
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uint32_t nem2;
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uint32_t nb01;
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uint32_t nb02;
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@ -643,7 +644,6 @@ struct vk_flash_attn_push_constants {
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uint32_t nb21;
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uint32_t nb22;
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uint32_t nb23;
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uint32_t nb31;
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float scale;
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float max_bias;
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@ -658,6 +658,7 @@ struct vk_flash_attn_push_constants {
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uint32_t split_kv;
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uint32_t k_num;
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};
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static_assert(sizeof(vk_flash_attn_push_constants) <= 128, "sizeof(vk_flash_attn_push_constants) must be <= 128");
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struct vk_op_push_constants {
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uint32_t KX;
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@ -756,6 +757,14 @@ struct vk_op_rope_push_constants {
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struct vk_op_soft_max_push_constants {
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uint32_t KX;
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uint32_t KY;
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uint32_t ne00;
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uint32_t ne01;
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uint32_t ne02;
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uint32_t ne12;
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uint32_t ne13;
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uint32_t nb11;
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uint32_t nb12;
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uint32_t nb13;
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float scale;
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float max_bias;
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float m0;
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@ -6040,7 +6049,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
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GGML_TENSOR_LOCALS(size_t, nb, dst, nb)
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const uint32_t nem1 = mask ? mask->ne[1] : 0;
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const uint32_t nbm1 = mask ? mask->nb[1] : 0;
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const uint32_t nem2 = mask ? mask->ne[2] : 0;
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const uint32_t D = neq0;
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uint32_t N = neq1;
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@ -6203,7 +6212,7 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
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// Try to use split_k when KV is large enough to be worth the overhead
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if (workgroups_x == 1 && shader_core_count > 0 && KV >= 512) {
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// Try to run two workgroups per SM.
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split_k = ctx->device->shader_core_count * 2 / workgroups_y;
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split_k = ctx->device->shader_core_count * 2 / (workgroups_y * workgroups_z);
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if (split_k > 1) {
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// Try to evenly split KV into split_k chunks, but it needs to be a multiple
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// of "align", so recompute split_k based on that.
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@ -6213,9 +6222,9 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
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}
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}
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// Reserve space for split_k temporaries. For each split, we need to store the O matrix (D x ne1)
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// and the per-row m and L values (ne1 rows).
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const uint64_t split_k_size = split_k > 1 ? (D * ne1 * sizeof(float) + ne1 * sizeof(float) * 2) * split_k : 0;
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// Reserve space for split_k temporaries. For each split x batch, we need to store the O matrix (D x ne1)
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// and the per-row m and L values (ne1 rows). We store all the matrices first, followed by the rows.
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const uint64_t split_k_size = split_k > 1 ? (D * ne1 * sizeof(float) + ne1 * sizeof(float) * 2) * split_k * ne3 : 0;
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if (split_k_size > ctx->device->max_memory_allocation_size) {
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GGML_ABORT("Requested preallocation size is too large");
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}
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@ -6307,11 +6316,10 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
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(uint32_t)neq2, (uint32_t)neq3,
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(uint32_t)nek2, (uint32_t)nek3,
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(uint32_t)nev2, (uint32_t)nev3,
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nem1,
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nem1, nem2,
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q_stride, (uint32_t)nbq2, (uint32_t)nbq3,
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k_stride, (uint32_t)nbk2, (uint32_t)nbk3,
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v_stride, (uint32_t)nbv2, (uint32_t)nbv3,
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nbm1,
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scale, max_bias, logit_softcap,
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mask != nullptr, n_head_log2, m0, m1,
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gqa_ratio, split_kv, split_k };
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@ -6334,13 +6342,13 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx
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pc, { workgroups_x * pipeline->wg_denoms[0], workgroups_y, workgroups_z });
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ggml_vk_sync_buffers(subctx);
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const std::array<uint32_t, 3> pc2 = { D, (uint32_t)ne1, split_k };
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const std::array<uint32_t, 4> pc2 = { D, (uint32_t)ne1, (uint32_t)ne3, split_k };
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ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_flash_attn_split_k_reduce,
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{
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vk_subbuffer{ctx->prealloc_split_k, 0, VK_WHOLE_SIZE},
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vk_subbuffer{d_D, d_buf_offset, VK_WHOLE_SIZE},
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},
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pc2, { (uint32_t)ne1, 1, 1 });
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pc2, { (uint32_t)ne1, 1, (uint32_t)ne3 });
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} else {
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ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
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{
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@ -7666,7 +7674,13 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx,
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const uint32_t nrows_x = (uint32_t)ggml_nrows(src0);
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const uint32_t nrows_y = (uint32_t)src0->ne[1];
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const uint32_t n_head_kv = nrows_x/nrows_y;
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const uint32_t ne12 = src1 ? (uint32_t)(src1->ne[2]) : 0u;
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const uint32_t ne13 = src1 ? (uint32_t)(src1->ne[3]) : 0u;
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const uint32_t nb11 = src1 ? (uint32_t)(src1->nb[1] / src1->nb[0]) : 0u;
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const uint32_t nb12 = src1 ? (uint32_t)(src1->nb[2] / src1->nb[0]) : 0u;
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const uint32_t nb13 = src1 ? (uint32_t)(src1->nb[3] / src1->nb[0]) : 0u;
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const uint32_t n_head_kv = src0->ne[2];
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const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv));
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const float m0 = powf(2.0f, -(max_bias ) / n_head_log2);
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@ -7675,6 +7689,9 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx,
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ggml_vk_op_f32<vk_op_soft_max_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_SOFT_MAX, {
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ncols,
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src1 != nullptr ? nrows_y : (uint32_t)0,
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(uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],
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ne12, ne13,
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nb11, nb12, nb13,
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scale, max_bias,
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m0, m1,
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n_head_log2,
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@ -10248,11 +10265,6 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
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if (op->src[3] && op->src[3]->type != GGML_TYPE_F16) {
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return false;
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}
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// TODO: support broadcast
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// ref: https://github.com/ggml-org/llama.cpp/pull/14435
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if (op->src[0]->ne[3] != 1) {
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return false;
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}
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// It's straightforward to support different K/V dequant, but would
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// significantly increase the number of pipelines
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if (op->src[1]->type != op->src[2]->type) {
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@ -10413,13 +10425,6 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
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case GGML_OP_DIAG_MASK_INF:
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return true;
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case GGML_OP_SOFT_MAX:
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// TODO: support batching
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if (op->src[0]->ne[3] != 1) {
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return false;
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}
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// TODO: support broadcast
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// ref: https://github.com/ggml-org/llama.cpp/pull/14435
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return !op->src[1] || (op->src[1]->ne[2] == 1 && op->src[1]->ne[3] == 1);
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case GGML_OP_SOFT_MAX_BACK:
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case GGML_OP_ARGSORT:
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case GGML_OP_SUM:
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@ -99,6 +99,10 @@ void main() {
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uint32_t k_offset = (ik2*p.nb12 + ik3*p.nb13) / 2;
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uint32_t v_offset = (iv2*p.nb22 + iv3*p.nb23) / 2;
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#endif
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uint32_t m_offset = 0;
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if (p.nem2 != 1) {
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m_offset = (iq3 % p.nem2) * p.nem1 * KV;
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}
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[[dont_unroll]]
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for (uint32_t j = start_j; j < end_j; ++j) {
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@ -150,7 +154,7 @@ void main() {
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uint32_t c = (idx + tid) % Bc;
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uint32_t r = (idx + tid) / Bc;
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if (idx + tid < Bc * Br) {
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masksh[c][r] = float(data_m[(i * Br + r) * m_stride + (j * Bc + c)]);
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masksh[c][r] = float(data_m[m_offset + (i * Br + r) * m_stride + (j * Bc + c)]);
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}
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}
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barrier();
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@ -277,7 +281,7 @@ void main() {
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// If there is split_k, then the split_k resolve shader does the final
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// division by L. Store the intermediate O value and per-row m and L values.
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if (p.k_num > 1) {
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uint32_t o_offset = D * p.ne1 * split_k_index;
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uint32_t o_offset = D * p.ne1 * (split_k_index + iq3 * p.k_num);
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[[unroll]] for (uint32_t r = 0; r < Br; ++r) {
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if (r < N) {
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@ -289,7 +293,7 @@ void main() {
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}
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}
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o_offset = D * p.ne1 * p.k_num + p.ne1 * split_k_index * 2;
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o_offset = D * p.ne1 * p.ne3 * p.k_num + p.ne1 * (split_k_index + iq3 * p.k_num) * 2;
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[[unroll]] for (uint32_t r = 0; r < Br; ++r) {
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if (r < N) {
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perElemOpStoreCol0(r, 0u, ACC_TYPE(Lf[r]), o_offset, iq2, N);
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@ -311,7 +315,7 @@ void main() {
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}
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}
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uint32_t o_offset = iq3*p.ne2*p.ne1;
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uint32_t o_offset = iq3*p.ne2*p.ne1*D;
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if (p.gqa_ratio > 1) {
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[[unroll]] for (uint32_t r = 0; r < Br; ++r) {
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@ -24,6 +24,7 @@ layout (push_constant) uniform parameter {
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uint32_t nev2;
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uint32_t nev3;
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uint32_t nem1;
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uint32_t nem2;
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uint32_t nb01;
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uint32_t nb02;
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@ -34,7 +35,6 @@ layout (push_constant) uniform parameter {
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uint32_t nb21;
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uint32_t nb22;
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uint32_t nb23;
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uint32_t nb31;
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float scale;
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float max_bias;
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@ -123,6 +123,10 @@ void main() {
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uint32_t k_offset = (ik2*p.nb12 + ik3*p.nb13) / 2;
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uint32_t v_offset = (iv2*p.nb22 + iv3*p.nb23) / 2;
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#endif
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uint32_t m_offset = 0;
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if (p.nem2 != 1) {
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m_offset = (iq3 % p.nem2) * p.nem1 * KV;
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}
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[[dont_unroll]]
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for (uint32_t j = start_j; j < end_j; ++j) {
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@ -181,7 +185,7 @@ void main() {
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uint32_t c = (idx + tid) % Bc;
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uint32_t r = (idx + tid) / Bc;
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if (idx + tid < Bc * Br || idx + gl_WorkGroupSize.x <= Bc * Br) {
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sfsh[c * sfshstride + r] += ACC_TYPE(slope[r] * float(data_m[(i * Br + r) * m_stride + (j * Bc + c)]));
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sfsh[c * sfshstride + r] += ACC_TYPE(slope[r] * float(data_m[m_offset + (i * Br + r) * m_stride + (j * Bc + c)]));
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}
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}
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barrier();
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@ -300,7 +304,7 @@ void main() {
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// If there is split_k, then the split_k resolve shader does the final
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// division by L. Store the intermediate O value and per-row m and L values.
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if (p.k_num > 1) {
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uint32_t o_offset = D * p.ne1 * split_k_index;
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uint32_t o_offset = D * p.ne1 * (split_k_index + iq3 * p.k_num);
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[[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
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if (tile_row(r) < N) {
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@ -312,7 +316,7 @@ void main() {
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}
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}
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o_offset = D * p.ne1 * p.k_num + p.ne1 * split_k_index * 2;
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o_offset = D * p.ne1 * p.ne3 * p.k_num + p.ne1 * (split_k_index + iq3 * p.k_num) * 2;
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[[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
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if (tile_row(r) < N) {
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perElemOpStoreCol0(tile_row(r), 0u, ACC_TYPE(Lf[r]), o_offset, iq2, N);
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@ -334,7 +338,7 @@ void main() {
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}
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}
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uint32_t o_offset = iq3*p.ne2*p.ne1;
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uint32_t o_offset = iq3*p.ne2*p.ne1*D;
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if (p.gqa_ratio > 1) {
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[[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) {
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@ -130,6 +130,11 @@ void main() {
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coopMatPerElementNV(slopeMat, slopeMat, perElemOpComputeSlope, iq2);
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}
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uint32_t m_offset = 0;
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if (p.nem2 != 1) {
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m_offset = (iq3 % p.nem2) * p.nem1 * KV * 2 /*sizeof(float16_t)*/;
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}
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[[dont_unroll]]
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for (uint32_t j = start_j; j < end_j; ++j) {
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@ -155,7 +160,7 @@ void main() {
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coopmat<float16_t, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator> mv;
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coopMatLoadTensorNV(mv, data_m, 0, sliceTensorLayoutNV(tensorLayoutM, i * Br, Br, j * Bc, Bc));
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coopMatLoadTensorNV(mv, data_m, m_offset, sliceTensorLayoutNV(tensorLayoutM, i * Br, Br, j * Bc, Bc));
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S += slopeMat*coopmat<ACC_TYPE, gl_ScopeWorkgroup, Br, Bc, gl_MatrixUseAccumulator>(mv);
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}
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@ -229,10 +234,10 @@ void main() {
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if (p.k_num > 1) {
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coopmat<D_TYPE, gl_ScopeWorkgroup, Br, D, gl_MatrixUseAccumulator> O_D = coopmat<D_TYPE, gl_ScopeWorkgroup, Br, D, gl_MatrixUseAccumulator>(O);
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uint32_t o_offset = D * p.ne1 * split_k_index;
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uint32_t o_offset = D * p.ne1 * (split_k_index + iq3 * p.k_num);
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coopMatPerElementNV(O_D, O_D, perElemOpGqaStore, o_offset, iq2, N);
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o_offset = D * p.ne1 * p.k_num + p.ne1 * split_k_index * 2;
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o_offset = D * p.ne1 * p.ne3 * p.k_num + p.ne1 * (split_k_index + iq3 * p.k_num) * 2;
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coopMatPerElementNV(L, L, perElemOpStoreCol0, o_offset, iq2, N);
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coopMatPerElementNV(M, M, perElemOpStoreCol0, o_offset + p.ne1, iq2, N);
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return;
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@ -250,7 +255,7 @@ void main() {
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O = Ldiag*O;
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uint32_t o_offset = iq3*p.ne2*p.ne1;
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uint32_t o_offset = iq3*p.ne2*p.ne1*D;
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coopmat<D_TYPE, gl_ScopeWorkgroup, Br, D, gl_MatrixUseAccumulator> O_D = coopmat<D_TYPE, gl_ScopeWorkgroup, Br, D, gl_MatrixUseAccumulator>(O);
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if (p.gqa_ratio > 1) {
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@ -12,6 +12,7 @@ layout (binding = 1) writeonly buffer D {float data_d[];};
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layout (push_constant) uniform parameter {
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uint D;
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uint N;
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uint ne3;
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uint k_num;
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} p;
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@ -19,13 +20,14 @@ void main() {
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// Each workgroup handles a row
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const uint n = gl_WorkGroupID.x;
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const uint tid = gl_LocalInvocationID.x;
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const uint iq3 = gl_WorkGroupID.z;
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uint D = p.D;
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uint N = p.N;
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uint k_num = p.k_num;
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uint l_offset = D * N * k_num + n;
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uint m_offset = D * N * k_num + N + n;
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uint l_offset = D * N * p.ne3 * k_num + N * iq3 * k_num * 2 + n;
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uint m_offset = D * N * p.ne3 * k_num + N * iq3 * k_num * 2 + N + n;
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uint lm_stride = N * 2;
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// Compute the max m value for the row
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@ -49,11 +51,11 @@ void main() {
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for (uint d = tid; d < D; d += BLOCK_SIZE) {
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float O = 0.0;
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[[unroll]] for (uint k = 0; k < k_num; ++k) {
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uint o_offset = D * N * k + D * n + d;
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uint o_offset = D * N * (k + iq3 * k_num) + D * n + d;
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float m = data_a[m_offset + k * lm_stride];
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O += exp(m - m_max) * data_a[o_offset];
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}
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O *= L;
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data_d[D * n + d] = O;
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data_d[iq3 * D * N + D * n + d] = O;
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}
|
||||
}
|
||||
|
@ -6,6 +6,14 @@ layout (push_constant) uniform parameter
|
||||
{
|
||||
uint KX;
|
||||
uint KY;
|
||||
uint ne00;
|
||||
uint ne01;
|
||||
uint ne02;
|
||||
uint ne12;
|
||||
uint ne13;
|
||||
uint nb11;
|
||||
uint nb12;
|
||||
uint nb13;
|
||||
float scale;
|
||||
float max_bias;
|
||||
float m0;
|
||||
@ -31,7 +39,15 @@ shared FLOAT_TYPE vals[BLOCK_SIZE];
|
||||
void soft_max(uint num_iters) {
|
||||
const uint tid = gl_LocalInvocationID.x;
|
||||
const uint rowx = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x;
|
||||
const uint rowy = (p.KY > 0) ? (rowx % p.KY) : 0;
|
||||
|
||||
const uint32_t i03 = rowx / (p.ne01 * p.ne02);
|
||||
const uint32_t i02 = (rowx - i03 * p.ne01 * p.ne02) / p.ne01;
|
||||
const uint32_t i01 = rowx % p.ne01;
|
||||
|
||||
uint rowy_start = 0;
|
||||
if (p.KY > 0) {
|
||||
rowy_start = i01 * p.nb11 + (i02 % p.ne12) * p.nb12 + (i03 % p.ne13) * p.nb13;
|
||||
}
|
||||
|
||||
if (rowx >= p.nrows_x) {
|
||||
return;
|
||||
@ -41,7 +57,7 @@ void soft_max(uint num_iters) {
|
||||
|
||||
// ALiBi
|
||||
if (p.max_bias > 0.0f) {
|
||||
const uint h = rowx/p.KY; // head index
|
||||
const uint h = (rowx / p.ne01) % p.ne02; // head index
|
||||
|
||||
const float base = h < p.n_head_log2 ? p.m0 : p.m1;
|
||||
const uint exp = h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1;
|
||||
@ -67,7 +83,7 @@ void soft_max(uint num_iters) {
|
||||
|
||||
FLOAT_TYPE b = FLOAT_TYPE(0);
|
||||
if (p.KY > 0 && col < p.KX) {
|
||||
b = data_b[rowy * p.KX + col];
|
||||
b = data_b[rowy_start + col];
|
||||
}
|
||||
|
||||
FLOAT_TYPE v = a * p.scale + slope * b;
|
||||
@ -111,7 +127,7 @@ void soft_max(uint num_iters) {
|
||||
if (idx < DATA_CACHE_SIZE) {
|
||||
val = exp(data_cache[idx] - max_val);
|
||||
} else {
|
||||
val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) - max_val);
|
||||
val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy_start + col]) : FLOAT_TYPE(0.0f)) - max_val);
|
||||
}
|
||||
sum += val;
|
||||
if (idx < DATA_CACHE_SIZE) {
|
||||
|
Reference in New Issue
Block a user