#include "llama-memory-hybrid.h"

#include "llama-impl.h"
#include "llama-model.h"
#include "llama-context.h"

//
// llama_memory_hybrid
//

llama_memory_hybrid::llama_memory_hybrid(
    const llama_model & model,
                         /* attn */
            ggml_type    type_k,
            ggml_type    type_v,
                 bool    v_trans,
             uint32_t    kv_size,
             uint32_t    n_pad,
             uint32_t    n_swa,
       llama_swa_type    swa_type,
                         /* recurrent */
            ggml_type    type_r,
            ggml_type    type_s,
             uint32_t    rs_size,
                         /* common */
             uint32_t    n_seq_max,
                 bool    offload,
                         /* layer filters */
      layer_filter_cb && filter_attn,
      layer_filter_cb && filter_recr) :
    hparams(model.hparams),
    mem_attn(new llama_kv_cache_unified(
        model,
        filter_attn == nullptr ?
            [&](int32_t il) { return !hparams.is_recurrent(il); }
            : filter_attn,
        type_k,
        type_v,
        v_trans,
        offload,
        kv_size,
        n_seq_max,
        n_pad,
        n_swa,
        swa_type
    )),
    mem_recr(new llama_memory_recurrent(
        model,
        filter_recr == nullptr ?
            [&](int32_t il) { return hparams.is_recurrent(il); }
            : filter_recr,
        type_r,
        type_s,
        offload,
        rs_size,
        n_seq_max
    )) {}

llama_memory_state_ptr llama_memory_hybrid::init_batch(llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) {
    do {
        balloc.split_reset();

        // follow the recurrent pattern for creating the ubatch splits
        std::vector<llama_ubatch> ubatches;

        while (true) {
            llama_ubatch ubatch;

            if (embd_all) {
                // if all tokens are output, split by sequence
                ubatch = balloc.split_seq(n_ubatch);
            } else {
                ubatch = balloc.split_equal(n_ubatch);
            }

            if (ubatch.n_tokens == 0) {
                break;
            }

            ubatches.push_back(std::move(ubatch)); // NOLINT
        }

        // prepare the recurrent batches first
        if (!mem_recr->prepare(ubatches)) {
            // TODO: will the recurrent cache be in an undefined state at this point?
            LLAMA_LOG_ERROR("%s: failed to prepare recurrent ubatches\n", __func__);
            return std::make_unique<llama_memory_hybrid_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
        }

        // prepare the attention cache
        auto heads_attn = mem_attn->prepare(ubatches);
        if (heads_attn.empty()) {
            LLAMA_LOG_ERROR("%s: failed to prepare attention ubatches\n", __func__);
            return std::make_unique<llama_memory_hybrid_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
        }

        return std::make_unique<llama_memory_hybrid_state>(
                this, std::move(heads_attn), std::move(ubatches));
    } while(false);

    return std::make_unique<llama_memory_hybrid_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
}

llama_memory_state_ptr llama_memory_hybrid::init_full() {
    return std::make_unique<llama_memory_hybrid_state>(this);
}

llama_memory_state_ptr llama_memory_hybrid::init_update(llama_context * lctx, bool optimize) {
    return std::make_unique<llama_memory_hybrid_state>(this, lctx, optimize);
}

bool llama_memory_hybrid::get_can_shift() const {
    // Shifting is trivially supported for recurrent
    return mem_attn->get_can_shift();
}

void llama_memory_hybrid::clear(bool data) {
    mem_attn->clear(data);
    mem_recr->clear(data);
}

bool llama_memory_hybrid::seq_rm(llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
    // Try removing from the recurrent cache first since it may fail. If it does
    // fail, the cache will not have been mutated.
    if (!mem_recr->seq_rm(seq_id, p0, p1)) {
        return false;
    }
    return mem_attn->seq_rm(seq_id, p0, p1);
}

void llama_memory_hybrid::seq_cp(llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
    mem_attn->seq_cp(seq_id_src, seq_id_dst, p0, p1);
    mem_recr->seq_cp(seq_id_src, seq_id_dst, p0, p1);
}

void llama_memory_hybrid::seq_keep(llama_seq_id seq_id) {
    mem_attn->seq_keep(seq_id);
    mem_recr->seq_keep(seq_id);
}

void llama_memory_hybrid::seq_add(llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) {
    mem_attn->seq_add(seq_id, p0, p1, shift);
    mem_recr->seq_add(seq_id, p0, p1, shift);
}

void llama_memory_hybrid::seq_div(llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
    mem_attn->seq_div(seq_id, p0, p1, d);
    mem_recr->seq_div(seq_id, p0, p1, d);
}

llama_pos llama_memory_hybrid::seq_pos_min(llama_seq_id seq_id) const {
    // the min of the total cache is the max of the two caches' min values
    return std::max(mem_attn->seq_pos_min(seq_id), mem_recr->seq_pos_min(seq_id));
}

llama_pos llama_memory_hybrid::seq_pos_max(llama_seq_id seq_id) const {
    // the max of the total cache is the min of the two caches' max values
    return std::min(mem_attn->seq_pos_max(seq_id), mem_recr->seq_pos_max(seq_id));
}

void llama_memory_hybrid::state_write(llama_io_write_i & io, llama_seq_id seq_id) const {
    mem_attn->state_write(io, seq_id);
    mem_recr->state_write(io, seq_id);
}

void llama_memory_hybrid::state_read(llama_io_read_i & io, llama_seq_id seq_id) {
    mem_attn->state_read(io, seq_id);
    mem_recr->state_read(io, seq_id);
}

llama_kv_cache_unified * llama_memory_hybrid::get_mem_attn() const {
    return mem_attn.get();
}

llama_memory_recurrent * llama_memory_hybrid::get_mem_recr() const {
    return mem_recr.get();
}

llama_memory_hybrid_state::llama_memory_hybrid_state(llama_memory_status status) : status(status) {}

llama_memory_hybrid_state::llama_memory_hybrid_state(llama_memory_hybrid * mem) :
    state_attn(mem->get_mem_attn()->init_full()),
    state_recr(mem->get_mem_recr()->init_full()),
    status(llama_memory_status_combine(state_attn->get_status(), state_recr->get_status())) {
}

llama_memory_hybrid_state::llama_memory_hybrid_state(
        llama_memory_hybrid * mem,
              llama_context * lctx,
                       bool   optimize) :
    state_attn(mem->get_mem_attn()->init_update(lctx, optimize)),
    state_recr(mem->get_mem_recr()->init_update(lctx, optimize)),
    status(llama_memory_status_combine(state_attn->get_status(), state_recr->get_status())) {
}

llama_memory_hybrid_state::llama_memory_hybrid_state(
              llama_memory_hybrid * mem,
            std::vector<uint32_t>   heads_attn,
        std::vector<llama_ubatch>   ubatches) :
    ubatches(std::move(ubatches)),
    // note: here we copy the ubatches. not sure if this is ideal
    state_attn(new llama_kv_cache_unified_state(mem->get_mem_attn(), std::move(heads_attn), this->ubatches)),
    state_recr(new llama_memory_recurrent_state(mem->get_mem_recr(),                        this->ubatches)),
    status(llama_memory_status_combine(state_attn->get_status(), state_recr->get_status())) {
}

bool llama_memory_hybrid_state::next() {
    assert(status == LLAMA_MEMORY_STATUS_SUCCESS);

    state_attn->next();
    state_recr->next();

    if (++i_next >= ubatches.size()) {
        return false;
    }

    return true;
}

bool llama_memory_hybrid_state::apply() {
    assert(status == LLAMA_MEMORY_STATUS_SUCCESS);

    bool res = true;

    res = res & state_attn->apply();
    res = res & state_recr->apply();

    return res;
}

llama_memory_status llama_memory_hybrid_state::get_status() const {
    return status;
}

const llama_ubatch & llama_memory_hybrid_state::get_ubatch() const {
    assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
    return ubatches[i_next];
}

const llama_kv_cache_unified_state * llama_memory_hybrid_state::get_state_attn() const {
    return static_cast<const llama_kv_cache_unified_state *>(state_attn.get());
}

const llama_memory_recurrent_state * llama_memory_hybrid_state::get_state_recr() const {
    return static_cast<const llama_memory_recurrent_state *>(state_recr.get());
}
