diff --git a/examples/passkey/passkey.cpp b/examples/passkey/passkey.cpp index 2cbc9e1fa..6442026c0 100644 --- a/examples/passkey/passkey.cpp +++ b/examples/passkey/passkey.cpp @@ -148,6 +148,7 @@ int main(int argc, char ** argv) { llama_kv_cache_seq_add (ctx, 0, n_past - n_batch, n_past, ib*bd); llama_kv_cache_seq_div (ctx, 0, n_past - n_batch + ib*bd, n_past + ib*bd, n_grp); + llama_kv_cache_compress(ctx, 0); llama_kv_cache_update (ctx); n_past = llama_kv_cache_seq_pos_max(ctx, 0) + 1; diff --git a/llama.cpp b/llama.cpp index 6729bb99c..94f22ad12 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1738,6 +1738,9 @@ struct llama_kv_cache { ggml_type type_k = GGML_TYPE_F16; ggml_type type_v = GGML_TYPE_F16; + // if non-negative, compress data on next update + llama_pos compress_delta = -1; + std::vector cells; std::vector k_l; // per layer @@ -2273,6 +2276,10 @@ static llama_pos llama_kv_cache_seq_pos_max(struct llama_kv_cache & cache, llama return result; } +static void llama_kv_cache_compress(struct llama_kv_cache & cache, llama_pos delta) { + cache.compress_delta = delta; +} + static void llama_kv_cache_defrag(struct llama_kv_cache & cache) { cache.do_defrag = true; } @@ -8109,6 +8116,240 @@ static int llama_decode_internal( return 0; } +// summary: +// +// - determine which KV cell pairs (i0, i1) to merge: +// +// abs(cell[i0].pos - cell[i1].pos) <= compress_delta +// +// - move the KV cache to the host memory for easier manipulation +// - processing is done layer-by-layer +// - convert the KV data to F32 +// - merge the KV data (different ways to merge) +// - convert the KV data back to the original type +// - move the KV cache back to the device memory +// - update the KV cache metadata +// +// as a side effect, the new KV cache is defragmented +// +static void llama_kv_cache_compress_internal(struct llama_context & lctx) { + auto & kv_self = lctx.kv_self; + + const auto & hparams = lctx.model.hparams; + + const uint32_t n_layer = hparams.n_layer; + const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(); + const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(); + const uint32_t n_embd_head_k = hparams.n_embd_head_k; GGML_UNUSED(n_embd_head_k); + const uint32_t n_embd_head_v = hparams.n_embd_head_v; GGML_UNUSED(n_embd_head_v); + const uint32_t n_head_kv = hparams.n_head_kv; GGML_UNUSED(n_head_kv); + const uint32_t kv_size = kv_self.size; + + const int64_t t_start = ggml_time_us(); + + std::vector buf_q; + + std::vector buf_src_f32; + std::vector buf_dst_f32; + + struct c_pair { uint32_t i0, i1; }; + struct c_info { bool merged; uint32_t id, cnt, r; }; + + std::vector infos(kv_size, { false, 0, 0, 0 }); + + // the destination cell in the new KV cache + uint32_t id = 0; + + // number of pairs merged + uint32_t n_merges = 0; + + // determine which KV cells to merge + for (uint32_t i0 = 0; i0 < kv_size; ++i0) { + const auto & cell0 = kv_self.cells[i0]; + + if (!cell0.is_empty() && !infos[i0].merged) { + infos[i0] = { true, id, 0, 0 }; + infos[id].cnt = 1; + + const llama_pos p0 = cell0.pos; + + for (uint32_t i1 = i0 + 1; i1 < kv_size; ++i1) { + const auto & cell1 = kv_self.cells[i1]; + + if (i0 != i1 && cell0.is_same_seq(cell1)) { + const llama_pos p1 = cell1.pos; + + if (std::abs(p0 - p1) <= kv_self.compress_delta) { + infos[i1] = { true, id, 0, 0 }; + infos[id].cnt++; + n_merges++; + } + } + } + + if (i0 != id) { + kv_self.cells[id] = cell0; + } + + id++; + } + } + + kv_self.head = id; + kv_self.used = id; + + for (uint32_t i = id; i < kv_size; ++i) { + kv_self.cells[i] = llama_kv_cell(); + } + + LLAMA_LOG_INFO("(tmp log) KV compress pairs: %u\n", n_merges); + + ggml_type_traits_t tt_k; + ggml_type_traits_t tt_v; + + tt_k = ggml_internal_get_type_traits(kv_self.type_k); + tt_v = ggml_internal_get_type_traits(kv_self.type_v); + + for (uint32_t il = 0; il < n_layer; ++il) { + for (uint32_t i = 0; i < kv_size; ++i) { + infos[i].r = 0; + } + + // update keys + { + const int64_t ne = n_embd_k_gqa*kv_size; + + const size_t k_size = ggml_row_size(kv_self.k_l[il]->type, ne); + + buf_q.resize(k_size); + + buf_src_f32.resize(ne); + buf_dst_f32.resize(ne); + + ggml_backend_tensor_get(kv_self.k_l[il], buf_q.data(), 0, buf_q.size()); + + tt_k.to_float(buf_q.data(), buf_src_f32.data(), ne); + + std::fill(buf_dst_f32.begin(), buf_dst_f32.end(), 0); + + for (uint32_t i = 0; i < kv_size; ++i) { + if (!infos[i].merged) { + continue; + } + + const uint32_t id = infos[i].id; + +#if 1 + // merge using averaging + { + const float scale = 1.0f/float(infos[id].cnt); + + const int64_t os = i*n_embd_k_gqa; + const int64_t od = id*n_embd_k_gqa; + + for (uint32_t j = 0; j < n_embd_k_gqa; ++j) { + buf_dst_f32[od + j] += buf_src_f32[os + j]*scale; + } + } +#else + // merge separate heads + { + for (uint32_t h = 0; h < n_head_kv; ++h) { + if ((h + il) % infos[id].cnt != infos[id].r) { + continue; + } + + const int64_t os = i*n_embd_k_gqa + h*n_embd_head_k; + const int64_t od = id*n_embd_k_gqa + h*n_embd_head_k; + + for (uint32_t j = 0; j < n_embd_head_k; ++j) { + buf_dst_f32[od + j] = buf_src_f32[os + j]; + } + } + } + + infos[id].r++; +#endif + } + + tt_k.from_float(buf_dst_f32.data(), buf_q.data(), ne); + + ggml_backend_tensor_set(kv_self.k_l[il], buf_q.data(), 0, buf_q.size()); + } + + for (uint32_t i = 0; i < kv_size; ++i) { + infos[i].r = 0; + } + + // update values (note: they are transposed) + { + const int64_t ne = n_embd_v_gqa*kv_size; + + const size_t v_size = ggml_row_size(kv_self.v_l[il]->type, ne); + + buf_q.resize(v_size); + + buf_src_f32.resize(ne); + buf_dst_f32.resize(ne); + + ggml_backend_tensor_get(kv_self.v_l[il], buf_q.data(), 0, buf_q.size()); + + tt_v.to_float(buf_q.data(), buf_src_f32.data(), ne); + + std::fill(buf_dst_f32.begin(), buf_dst_f32.end(), 0); + + for (uint32_t i = 0; i < kv_size; ++i) { + if (!infos[i].merged) { + continue; + } + + const uint32_t id = infos[i].id; + +#if 1 + // merge using averaging + { + const float scale = 1.0f/float(infos[id].cnt); + //printf("i: %d -> id: %d, scale: %f\n", i, id, scale); + + const int64_t os = i; + const int64_t od = id; + + for (uint32_t j = 0; j < n_embd_v_gqa; ++j) { + buf_dst_f32[od + j*kv_size] += buf_src_f32[os + j*kv_size]*scale; + } + } +#else + // merge separate heads + { + for (uint32_t h = 0; h < n_head_kv; ++h) { + if ((h + il) % infos[id].cnt != infos[id].r) { + continue; + } + + const int64_t os = i; + const int64_t od = id; + + for (uint32_t j = h*n_embd_head_v; j < (h + 1)*n_embd_head_v; ++j) { + buf_dst_f32[od + j*kv_size] = buf_src_f32[os + j*kv_size]; + } + } + } + + infos[id].r++; +#endif + } + + tt_v.from_float(buf_dst_f32.data(), buf_q.data(), ne); + + ggml_backend_tensor_set(kv_self.v_l[il], buf_q.data(), 0, buf_q.size()); + } + } + + const int64_t t_end = ggml_time_us(); + + LLAMA_LOG_INFO("(tmp log) KV compress time: %.3f ms\n", (t_end - t_start)/1000.0); +} + // find holes from the beginning of the KV cache and fill them by moving data from the end of the cache static void llama_kv_cache_defrag_internal(struct llama_context & lctx) { auto & kv_self = lctx.kv_self; @@ -8340,6 +8581,14 @@ static void llama_kv_cache_update_internal(struct llama_context & lctx) { } } + // compress the KV cache data if needed + if (lctx.kv_self.compress_delta >= 0) { + llama_kv_cache_compress_internal(lctx); + + lctx.kv_self.compress_delta = -1; + lctx.kv_self.do_defrag = false; + } + // defragment the KV cache if needed if (lctx.kv_self.do_defrag) { llama_kv_cache_defrag_internal(lctx); @@ -12450,6 +12699,10 @@ llama_pos llama_kv_cache_seq_pos_max(struct llama_context * ctx, llama_seq_id se return llama_kv_cache_seq_pos_max(ctx->kv_self, seq_id); } +void llama_kv_cache_compress(struct llama_context * ctx, llama_pos delta) { + llama_kv_cache_compress(ctx->kv_self, delta); +} + void llama_kv_cache_defrag(struct llama_context * ctx) { llama_kv_cache_defrag(ctx->kv_self); } diff --git a/llama.h b/llama.h index 604161808..4d9032b56 100644 --- a/llama.h +++ b/llama.h @@ -557,6 +557,14 @@ extern "C" { struct llama_context * ctx, llama_seq_id seq_id); + // [EXPERIMENTAL] Compress the data in the KV cache + // This will be applied: + // - lazily on next llama_decode() + // - explicitly with llama_kv_cache_update() + LLAMA_API void llama_kv_cache_compress( + struct llama_context * ctx, + llama_pos delta); + // Defragment the KV cache // This will be applied: // - lazily on next llama_decode()