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ggml-ci
104 lines
2.3 KiB
C++
104 lines
2.3 KiB
C++
#include "llama-hparams.h"
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#include "ggml.h"
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void llama_hparams::set_swa_pattern(uint32_t n_pattern) {
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for (uint32_t il = 0; il < n_layer; ++il) {
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swa_layers[il] = n_pattern == 0 || (il % n_pattern < (n_pattern - 1));
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}
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}
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bool llama_hparams::is_swa_any() const {
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for (uint32_t il = 0; il < n_layer; ++il) {
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if (swa_layers[il]) {
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return true;
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}
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}
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return false;
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}
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uint32_t llama_hparams::n_head(uint32_t il) const {
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if (il < n_layer) {
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return n_head_arr[il];
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}
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GGML_ABORT("fatal error");
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}
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uint32_t llama_hparams::n_head_kv(uint32_t il) const {
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if (il < n_layer) {
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return n_head_kv_arr[il];
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}
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GGML_ABORT("fatal error");
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}
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uint32_t llama_hparams::n_ff(uint32_t il) const {
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if (il < n_layer) {
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return n_ff_arr[il];
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}
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GGML_ABORT("fatal error");
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}
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uint32_t llama_hparams::n_gqa(uint32_t il) const {
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const uint32_t n_head = this->n_head(il);
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const uint32_t n_head_kv = this->n_head_kv(il);
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if (n_head_kv == 0) {
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return 0;
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}
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return n_head/n_head_kv;
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}
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uint32_t llama_hparams::n_embd_k_gqa(uint32_t il) const {
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const uint32_t n_head_kv = this->n_head_kv(il);
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return n_embd_head_k * n_head_kv;
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}
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uint32_t llama_hparams::n_embd_v_gqa(uint32_t il) const {
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const uint32_t n_head_kv = this->n_head_kv(il);
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return n_embd_head_v * n_head_kv;
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}
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uint32_t llama_hparams::n_embd_r() const {
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if (wkv_head_size != 0) {
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// for RWKV models
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return token_shift_count * n_embd;
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}
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// TODO: maybe support other convolution strides than 1
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// NOTE: since the first column of the conv_state is shifted out each time, it's not actually needed
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return (ssm_d_conv > 0 ? ssm_d_conv - 1 : 0) * ssm_d_inner;
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}
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uint32_t llama_hparams::n_embd_s() const {
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if (wkv_head_size != 0) {
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// corresponds to RWKV's wkv_states size
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return n_embd * wkv_head_size;
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}
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// corresponds to Mamba's ssm_states size
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return ssm_d_state * ssm_d_inner;
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}
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bool llama_hparams::is_recurrent(uint32_t il) const {
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return recurrent_layer_arr[il];
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}
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uint32_t llama_hparams::n_pos_per_embd() const {
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return rope_type == LLAMA_ROPE_TYPE_MROPE ? 4 : 1;
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}
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bool llama_hparams::is_swa(uint32_t il) const {
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if (il < n_layer) {
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return swa_layers[il];
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}
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GGML_ABORT("fatal error");
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}
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