#include "llama-hparams.h"

#include "ggml.h"

#include <cassert>

#include <cmath>

void llama_hparams::set_swa_pattern(uint32_t n_pattern, bool dense_first) {

    if (dense_first) {

        for (uint32_t il = 0; il < n_layer; ++il) {

            swa_layers[il] = n_pattern == 0 || (il % n_pattern != 0);

        }

    } else {

        for (uint32_t il = 0; il < n_layer; ++il) {

            swa_layers[il] = n_pattern == 0 || (il % n_pattern < (n_pattern - 1));

        }

    }

}

bool llama_hparams::is_swa_any() const {

    for (uint32_t il = 0; il < n_layer; ++il) {

        if (swa_layers[il]) {

            return true;

        }

    }

    return false;

}

uint32_t llama_hparams::n_head(uint32_t il) const {

    if (il < n_layer) {

        return n_head_arr[il];

    }

    GGML_ABORT("fatal error");

}

uint32_t llama_hparams::n_head_kv(uint32_t il) const {

    if (il < n_layer) {

        return n_head_kv_arr[il];

    }

    GGML_ABORT("fatal error");

}

uint32_t llama_hparams::n_ff(uint32_t il) const {

    if (il < n_layer) {

        return n_ff_arr[il];

    }

    GGML_ABORT("fatal error");

}

uint32_t llama_hparams::n_gqa(uint32_t il) const {

    const uint32_t n_head    = this->n_head(il);

    const uint32_t n_head_kv = this->n_head_kv(il);

    if (n_head_kv == 0) {

        return 0;

    }

    return n_head/n_head_kv;

}

uint32_t llama_hparams::n_embd_inp() const {

    uint32_t n_embd_inp = n_embd;

    if (n_deepstack_layers > 0) {

        n_embd_inp += n_embd * n_deepstack_layers;

    }

    return n_embd_inp;

}

uint32_t llama_hparams::n_embd_k_gqa(uint32_t il) const {

    const uint32_t n_head_kv = this->n_head_kv(il);

    return n_embd_head_k * n_head_kv;

}

uint32_t llama_hparams::n_embd_v_gqa(uint32_t il) const {

    const uint32_t n_head_kv = this->n_head_kv(il);

    return n_embd_head_v * n_head_kv;

}

bool llama_hparams::is_n_embd_k_gqa_variable() const {

    const uint32_t val = n_embd_k_gqa();

    for (uint32_t il = 0; il < n_layer; ++il) {

        if (val != n_embd_k_gqa(il)) {

            return true;

        }

    }

    return false;

}

bool llama_hparams::is_n_embd_v_gqa_variable() const {

    const uint32_t val = n_embd_v_gqa();

    for (uint32_t il = 0; il < n_layer; ++il) {

        if (val != n_embd_v_gqa(il)) {

            return true;

        }

    }

    return false;

}

uint32_t llama_hparams::n_embd_k_gqa_max() const {

    uint32_t val = n_embd_k_gqa();

    for (uint32_t il = 0; il < n_layer; ++il) {

        val = std::max(val, n_embd_k_gqa(il));

    }

    return val;

}

uint32_t llama_hparams::n_embd_v_gqa_max() const {

    uint32_t val = n_embd_v_gqa();

    for (uint32_t il = 0; il < n_layer; ++il) {

        val = std::max(val, n_embd_v_gqa(il));

    }

    return val;

}

uint32_t llama_hparams::n_embd_r() const {

    if (wkv_head_size != 0) {

        // for RWKV models

        return token_shift_count * n_embd;

    }

    if (n_shortconv_l_cache != 0) {

        // for LFM2 models

        return n_embd * (n_shortconv_l_cache - 1);

    }

    // TODO: maybe support other convolution strides than 1

    // NOTE: since the first column of the conv_state is shifted out each time, it's not actually needed

    // Corresponds to Mamba's conv_states size

    return (ssm_d_conv > 0 ? ssm_d_conv - 1 : 0) * (ssm_d_inner + 2*ssm_n_group*ssm_d_state);

}

uint32_t llama_hparams::n_embd_s() const {

    if (wkv_head_size != 0) {

        // corresponds to RWKV's wkv_states size

        return n_embd * wkv_head_size;

    }

    // corresponds to Mamba's ssm_states size

    return ssm_d_state * ssm_d_inner;

}

bool llama_hparams::is_recurrent(uint32_t il) const {

    if (il < n_layer) {

        return recurrent_layer_arr[il];

    }

    GGML_ABORT("%s: il (%u) out of bounds (n_layer: %u)\n", __func__, il, n_layer);

}

uint32_t llama_hparams::n_pos_per_embd() const {

    return rope_type == LLAMA_ROPE_TYPE_MROPE || rope_type == LLAMA_ROPE_TYPE_IMROPE ? 4 : 1;

}

bool llama_hparams::is_swa(uint32_t il) const {

    if (il < n_layer) {

        return swa_layers[il];

    }

    GGML_ABORT("fatal error");

}

bool llama_hparams::has_kv(uint32_t il) const {

    if (n_layer_kv_from_start >= 0) {

        if (il < (uint32_t) n_layer_kv_from_start) {

            return true;

        }

        return false;

    }

    // by default, all layers have kv

    return true;

}

uint32_t llama_hparams::n_layer_kv() const {

    uint32_t res = 0;

    for (uint32_t il = 0; il < n_layer; ++il) {

        if (has_kv(il)) {

            res++;

        }

    }

    return res;

}

bool llama_hparams::is_masked_swa(uint32_t n_swa, llama_swa_type swa_type, llama_pos p0, llama_pos p1) {

    assert(p0 >= 0 && p1 >= 0);

    switch (swa_type) {

        case LLAMA_SWA_TYPE_NONE:

            {

            } break;

        case LLAMA_SWA_TYPE_STANDARD:

            {

                if (p1 - p0 >= (int32_t) n_swa) {

                    return true;

                }

            } break;

        case LLAMA_SWA_TYPE_CHUNKED:

            {

                const llama_pos pos_chunk_start = (p1 / n_swa) * n_swa;

                if (p0 < pos_chunk_start) {

                    return true;

                }

            } break;

        case LLAMA_SWA_TYPE_SYMMETRIC:

            {

                const int32_t half_n_swa = (int32_t) n_swa / 2;

                const int32_t pos_diff = p1 - p0;

                // Mask if outside the symmetric window

                if (pos_diff < -half_n_swa || pos_diff > half_n_swa) {

                    return true;

                }

            } break;

    }

    return false;

}

float llama_hparams::yarn_attn_factor_adjust(float attn_factor, float freq_scale, float ext_factor) {

    GGML_ASSERT(ext_factor >= 0.0f);

    if (ext_factor != 0.0f) {

        attn_factor *= 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale));

    }

    return attn_factor;

}