#include "llama-kv-cache-unified-iswa.h" #include "llama-impl.h" #include "llama-batch.h" #include "llama-model.h" #include #include // // llama_kv_cache_unified_iswa // llama_kv_cache_unified_iswa::llama_kv_cache_unified_iswa( const llama_model & model, ggml_type type_k, ggml_type type_v, bool v_trans, bool offload, bool swa_full, uint32_t kv_size, uint32_t n_seq_max, uint32_t n_ubatch, uint32_t n_pad) : hparams(model.hparams) { llama_kv_cache_unified::layer_filter_cb filter_base = [&](int32_t il) { return !model.hparams.is_swa(il); }; llama_kv_cache_unified::layer_filter_cb filter_swa = [&](int32_t il) { return model.hparams.is_swa(il); }; const uint32_t size_base = kv_size; uint32_t size_swa = std::min(size_base, GGML_PAD(hparams.n_swa*n_seq_max + n_ubatch, n_pad)); // when using full-size SWA cache, we set the SWA cache size to be equal to the base cache size if (swa_full) { LLAMA_LOG_WARN("%s: using full-size SWA cache (ref: %s)\n", __func__, "https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055"); size_swa = size_base; } LLAMA_LOG_INFO("%s: creating non-SWA KV cache, size = %u cells\n", __func__, size_base); kv_base = std::make_unique( model, std::move(filter_base), type_k, type_v, v_trans, offload, size_base, n_seq_max, n_pad, 0, LLAMA_SWA_TYPE_NONE); LLAMA_LOG_INFO("%s: creating SWA KV cache, size = %u cells\n", __func__, size_swa); kv_swa = std::make_unique( model, std::move(filter_swa), type_k, type_v, v_trans, offload, size_swa, n_seq_max, n_pad, hparams.n_swa, hparams.swa_type); } void llama_kv_cache_unified_iswa::clear() { kv_base->clear(); kv_swa ->clear(); } bool llama_kv_cache_unified_iswa::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) { bool res = true; res = res & kv_base->seq_rm(seq_id, p0, p1); res = res & kv_swa ->seq_rm(seq_id, p0, p1); return res; } void llama_kv_cache_unified_iswa::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) { kv_base->seq_cp(seq_id_src, seq_id_dst, p0, p1); kv_swa ->seq_cp(seq_id_src, seq_id_dst, p0, p1); } void llama_kv_cache_unified_iswa::seq_keep(llama_seq_id seq_id) { kv_base->seq_keep(seq_id); kv_swa ->seq_keep(seq_id); } void llama_kv_cache_unified_iswa::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) { kv_base->seq_add(seq_id, p0, p1, shift); kv_swa ->seq_add(seq_id, p0, p1, shift); } void llama_kv_cache_unified_iswa::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) { kv_base->seq_div(seq_id, p0, p1, d); kv_swa ->seq_div(seq_id, p0, p1, d); } llama_pos llama_kv_cache_unified_iswa::seq_pos_min(llama_seq_id seq_id) const { // the base cache is a superset of the SWA cache, so we can just check the SWA cache return kv_swa->seq_pos_min(seq_id); } llama_pos llama_kv_cache_unified_iswa::seq_pos_max(llama_seq_id seq_id) const { return kv_swa->seq_pos_max(seq_id); } llama_memory_state_ptr llama_kv_cache_unified_iswa::init_batch(const llama_batch & batch, uint32_t n_ubatch, bool embd_pooled, bool logits_all) { GGML_UNUSED(embd_pooled); // TODO: if we fail with split_simple, we should attempt different splitting strategies // but to do that properly, we first have to refactor the batches to be more flexible auto sbatch = llama_sbatch(batch, hparams.n_embd, true, logits_all); std::vector ubatches; while (sbatch.n_tokens > 0) { auto ubatch = sbatch.split_simple(n_ubatch); ubatches.push_back(ubatch); } auto heads_base = kv_base->prepare(ubatches); if (heads_base.empty()) { return std::make_unique(LLAMA_MEMORY_STATUS_FAILED_PREPARE); } auto heads_swa = kv_swa->prepare(ubatches); if (heads_swa.empty()) { return std::make_unique(LLAMA_MEMORY_STATUS_FAILED_PREPARE); } assert(heads_base.size() == heads_swa.size()); return std::make_unique(LLAMA_MEMORY_STATUS_SUCCESS, this, std::move(sbatch), std::move(heads_base), std::move(heads_swa), std::move(ubatches)); } llama_memory_state_ptr llama_kv_cache_unified_iswa::init_full() { return std::make_unique(LLAMA_MEMORY_STATUS_SUCCESS, this); } bool llama_kv_cache_unified_iswa::update(llama_context & lctx) { bool res = false; res = res | kv_base->update(lctx); res = res | kv_swa ->update(lctx); return res; } void llama_kv_cache_unified_iswa::defrag_sched(float thold) { kv_base->defrag_sched(thold); kv_swa ->defrag_sched(thold); } bool llama_kv_cache_unified_iswa::get_can_shift() const { return kv_base->get_size() == kv_swa->get_size(); } void llama_kv_cache_unified_iswa::state_write(llama_io_write_i & io, llama_seq_id seq_id) const { kv_base->state_write(io, seq_id); kv_swa ->state_write(io, seq_id); } void llama_kv_cache_unified_iswa::state_read(llama_io_read_i & io, llama_seq_id seq_id) { kv_base->state_read(io, seq_id); kv_swa ->state_read(io, seq_id); } llama_kv_cache_unified * llama_kv_cache_unified_iswa::get_base() const { return kv_base.get(); } llama_kv_cache_unified * llama_kv_cache_unified_iswa::get_swa() const { return kv_swa.get(); } // // llama_kv_cache_unified_iswa_state // llama_kv_cache_unified_iswa_state::llama_kv_cache_unified_iswa_state(llama_memory_status status) : status(status) {} llama_kv_cache_unified_iswa_state::llama_kv_cache_unified_iswa_state( llama_memory_status status, llama_kv_cache_unified_iswa * kv) : status(status) { state_base.reset(new llama_kv_cache_unified_state(status, kv->get_base())); state_swa .reset(new llama_kv_cache_unified_state(status, kv->get_swa ())); } llama_kv_cache_unified_iswa_state::llama_kv_cache_unified_iswa_state( llama_memory_status status, llama_kv_cache_unified_iswa * kv, llama_sbatch sbatch, std::vector heads_base, std::vector heads_swa, std::vector ubatches) : status(status), sbatch(std::move(sbatch)), ubatches(std::move(ubatches)) { // note: here we copy the ubatches. not sure if this is ideal state_base.reset(new llama_kv_cache_unified_state(status, kv->get_base(), {}, std::move(heads_base), this->ubatches)); state_swa .reset(new llama_kv_cache_unified_state(status, kv->get_swa (), {}, std::move(heads_swa), this->ubatches)); } llama_kv_cache_unified_iswa_state:: ~llama_kv_cache_unified_iswa_state() = default; bool llama_kv_cache_unified_iswa_state::next() { assert(status == LLAMA_MEMORY_STATUS_SUCCESS); state_base->next(); state_swa ->next(); if (++i_next >= ubatches.size()) { return false; } return true; } bool llama_kv_cache_unified_iswa_state::apply() { assert(status == LLAMA_MEMORY_STATUS_SUCCESS); bool res = true; res = res & state_base->apply(); res = res & state_swa ->apply(); return res; } std::vector & llama_kv_cache_unified_iswa_state::out_ids() { assert(status == LLAMA_MEMORY_STATUS_SUCCESS); return sbatch.out_ids; } llama_memory_status llama_kv_cache_unified_iswa_state::get_status() const { return status; } const llama_ubatch & llama_kv_cache_unified_iswa_state::get_ubatch() const { assert(status == LLAMA_MEMORY_STATUS_SUCCESS); return ubatches[i_next]; } const llama_kv_cache_unified_state * llama_kv_cache_unified_iswa_state::get_base() const { assert(status == LLAMA_MEMORY_STATUS_SUCCESS); return state_base.get(); } const llama_kv_cache_unified_state * llama_kv_cache_unified_iswa_state::get_swa() const { assert(status == LLAMA_MEMORY_STATUS_SUCCESS); return state_swa.get(); }