mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2025-01-15 14:50:51 +01:00
afa8a9ec9b
* llama : functions -> methods (#11110) * llama : add struct llama_vocab to the API (#11156) ggml-ci * hparams : move vocab params to llama_vocab (#11159) ggml-ci * vocab : more pimpl (#11165) ggml-ci * vocab : minor tokenization optimizations (#11160) ggml-ci Co-authored-by: Diego Devesa <slarengh@gmail.com> * lora : update API names (#11167) ggml-ci * llama : update API names to use correct prefix (#11174) * llama : update API names to use correct prefix ggml-ci * cont ggml-ci * cont ggml-ci * minor [no ci] * vocab : llama_vocab_add_[be]os -> llama_vocab_get_add_[be]os (#11174) ggml-ci * vocab : llama_vocab_n_vocab -> llama_vocab_n_tokens (#11174) ggml-ci --------- Co-authored-by: Diego Devesa <slarengh@gmail.com>
3955 lines
211 KiB
C++
3955 lines
211 KiB
C++
#include "llama-model.h"
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#include "llama-impl.h"
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#include "llama-mmap.h"
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#include "llama-model-loader.h"
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#include "ggml-cpp.h"
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#include <algorithm>
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#include <cassert>
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#include <cstring>
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#include <functional>
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#include <map>
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#include <sstream>
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#include <stdexcept>
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const char * llm_type_name(llm_type type) {
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switch (type) {
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case LLM_TYPE_14M: return "14M";
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case LLM_TYPE_17M: return "17M";
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case LLM_TYPE_22M: return "22M";
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case LLM_TYPE_33M: return "33M";
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case LLM_TYPE_60M: return "60M";
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case LLM_TYPE_70M: return "70M";
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case LLM_TYPE_80M: return "80M";
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case LLM_TYPE_109M: return "109M";
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case LLM_TYPE_137M: return "137M";
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case LLM_TYPE_160M: return "160M";
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case LLM_TYPE_220M: return "220M";
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case LLM_TYPE_250M: return "250M";
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case LLM_TYPE_270M: return "270M";
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case LLM_TYPE_335M: return "335M";
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case LLM_TYPE_410M: return "410M";
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case LLM_TYPE_450M: return "450M";
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case LLM_TYPE_770M: return "770M";
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case LLM_TYPE_780M: return "780M";
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case LLM_TYPE_0_5B: return "0.5B";
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case LLM_TYPE_1B: return "1B";
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case LLM_TYPE_1_3B: return "1.3B";
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case LLM_TYPE_1_4B: return "1.4B";
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case LLM_TYPE_1_5B: return "1.5B";
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case LLM_TYPE_1_6B: return "1.6B";
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case LLM_TYPE_2B: return "2B";
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case LLM_TYPE_2_8B: return "2.8B";
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case LLM_TYPE_3B: return "3B";
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case LLM_TYPE_4B: return "4B";
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case LLM_TYPE_6B: return "6B";
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case LLM_TYPE_6_9B: return "6.9B";
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case LLM_TYPE_7B: return "7B";
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case LLM_TYPE_8B: return "8B";
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case LLM_TYPE_9B: return "9B";
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case LLM_TYPE_11B: return "11B";
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case LLM_TYPE_12B: return "12B";
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case LLM_TYPE_13B: return "13B";
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case LLM_TYPE_14B: return "14B";
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case LLM_TYPE_15B: return "15B";
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case LLM_TYPE_16B: return "16B";
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case LLM_TYPE_20B: return "20B";
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case LLM_TYPE_30B: return "30B";
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case LLM_TYPE_32B: return "32B";
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case LLM_TYPE_34B: return "34B";
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case LLM_TYPE_35B: return "35B";
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case LLM_TYPE_40B: return "40B";
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case LLM_TYPE_65B: return "65B";
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case LLM_TYPE_70B: return "70B";
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case LLM_TYPE_236B: return "236B";
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case LLM_TYPE_314B: return "314B";
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case LLM_TYPE_671B: return "671B";
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case LLM_TYPE_SMALL: return "0.1B";
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case LLM_TYPE_MEDIUM: return "0.4B";
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case LLM_TYPE_LARGE: return "0.8B";
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case LLM_TYPE_XL: return "1.5B";
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case LLM_TYPE_A1_7B: return "A1.7B";
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case LLM_TYPE_A2_7B: return "A2.7B";
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case LLM_TYPE_8x7B: return "8x7B";
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case LLM_TYPE_8x22B: return "8x22B";
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case LLM_TYPE_16x12B: return "16x12B";
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case LLM_TYPE_16x3_8B: return "16x3.8B";
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case LLM_TYPE_10B_128x3_66B: return "10B+128x3.66B";
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case LLM_TYPE_57B_A14B: return "57B.A14B";
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case LLM_TYPE_27B: return "27B";
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default: return "?B";
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}
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}
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static const char * llama_expert_gating_func_name(llama_expert_gating_func_type type) {
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switch (type) {
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case LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX: return "softmax";
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case LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID: return "sigmoid";
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default: return "unknown";
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}
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}
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static const std::map<llama_rope_scaling_type, const char *> LLAMA_ROPE_SCALING_TYPES = {
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{ LLAMA_ROPE_SCALING_TYPE_NONE, "none" },
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{ LLAMA_ROPE_SCALING_TYPE_LINEAR, "linear" },
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{ LLAMA_ROPE_SCALING_TYPE_YARN, "yarn" },
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{ LLAMA_ROPE_SCALING_TYPE_LONGROPE, "longrope" },
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};
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static llama_rope_scaling_type llama_rope_scaling_type_from_string(const std::string & name) {
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for (const auto & kv : LLAMA_ROPE_SCALING_TYPES) {
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if (kv.second == name) {
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return (llama_rope_scaling_type) kv.first;
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}
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}
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return LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
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}
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// checks if the weight tensor can be used with the specified buffer type and device
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static bool weight_buft_supported(const llama_hparams & hparams, ggml_tensor * w, ggml_op op, ggml_backend_buffer_type_t buft, ggml_backend_dev_t dev) {
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GGML_ASSERT(w != nullptr);
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if (op == GGML_OP_NONE) {
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return true;
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}
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ggml_init_params params = {
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/*.mem_size =*/ ggml_tensor_overhead()*8,
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/*.mem_buffer =*/ NULL,
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/*.no_alloc =*/ true,
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};
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ggml_context_ptr ctx_ptr { ggml_init(params) };
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if (!ctx_ptr) {
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throw std::runtime_error(format("failed to create ggml context"));
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}
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ggml_context * ctx = ctx_ptr.get();
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ggml_tensor * op_tensor = nullptr;
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switch (op) {
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case GGML_OP_GET_ROWS:
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{
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ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 512);
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op_tensor = ggml_get_rows(ctx, w, b);
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} break;
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case GGML_OP_MUL_MAT:
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{
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ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], 512, w->ne[2], w->ne[3]);
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op_tensor = ggml_mul_mat(ctx, w, b);
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} break;
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case GGML_OP_MUL_MAT_ID:
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{
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int n_expert_used = hparams.n_expert_used;
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ggml_tensor * b = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, w->ne[0], n_expert_used, 512);
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ggml_tensor * ids = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, n_expert_used, 512);
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op_tensor = ggml_mul_mat_id(ctx, w, b, ids);
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} break;
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case GGML_OP_ADD:
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{
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ggml_tensor * a = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], w->ne[1], w->ne[2], w->ne[3]);
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op_tensor = ggml_add(ctx, a, w);
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} break;
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case GGML_OP_MUL:
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{
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ggml_tensor * a = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], w->ne[1], w->ne[2], w->ne[3]);
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op_tensor = ggml_mul(ctx, a, w);
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} break;
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case GGML_OP_DIV:
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{
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ggml_tensor * a = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, w->ne[0]);
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op_tensor = ggml_div(ctx, a, w);
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} break;
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case GGML_OP_ROPE:
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{
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int n_embd_head = hparams.n_embd_head_v;
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int n_head = hparams.n_head();
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ggml_tensor * a = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd_head, n_head, 512);
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ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 512);
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op_tensor = ggml_rope_ext(
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ctx, a, b, w,
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0, 0, 0, 0, 0,
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0, 0, 0, 0
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);
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} break;
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case GGML_OP_SSM_CONV:
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{
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// FIXME
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ggml_tensor * conv_x = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 12345, w->ne[1], 6789);
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op_tensor = ggml_ssm_conv(ctx, conv_x, w);
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} break;
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case GGML_OP_SSM_SCAN:
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{
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// FIXME
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const int64_t d_state = w->ne[0];
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const int64_t d_inner = w->ne[1];
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const int64_t n_seq_tokens = 512;
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const int64_t n_seqs = 1;
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ggml_tensor * s = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, d_inner, n_seqs);
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ggml_tensor * x = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_inner, n_seq_tokens, n_seqs);
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ggml_tensor * dt = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_inner, n_seq_tokens, n_seqs);
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ggml_tensor * B = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, n_seq_tokens, n_seqs);
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ggml_tensor * C = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, n_seq_tokens, n_seqs);
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op_tensor = ggml_ssm_scan(ctx, s, x, dt, w, B, C);
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} break;
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case GGML_OP_RWKV_WKV6:
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{
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// FIXME
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const int64_t S = 123;
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const int64_t H = 123;
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const int64_t n_tokens = 123;
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const int64_t n_seqs = 123;
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ggml_tensor * k = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, S, H, n_tokens);
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ggml_tensor * v = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, S, H, n_tokens);
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ggml_tensor * r = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, S, H, n_tokens);
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ggml_tensor * tf = w;
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ggml_tensor * td = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, S, H, n_tokens);
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ggml_tensor * state = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, S, n_seqs, S, H);
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op_tensor = ggml_rwkv_wkv6(ctx, k, v, r, tf, td, state);
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} break;
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case GGML_OP_IM2COL:
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{
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const int n_embd = hparams.n_embd;
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ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, n_embd, w->ne[1], 1, 1);
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op_tensor = ggml_im2col(ctx, w, b, 1, 0, 0, 0, 1, 0, false, GGML_TYPE_F16);
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} break;
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default:
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GGML_ABORT("%s: missing test for op %s for tensor %s", __func__, ggml_op_name(op), w->name);
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}
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// create a temporary dummy buffer for the weight so that supports_op can check the buffer type
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GGML_ASSERT(w->buffer == nullptr);
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w->buffer = ggml_backend_buft_alloc_buffer(buft, 0);
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bool op_supported = ggml_backend_dev_supports_op(dev, op_tensor);
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ggml_backend_buffer_free(w->buffer);
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w->buffer = nullptr;
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return op_supported;
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}
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// lists of buffer types used for each layer
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using buft_list_t = std::vector<std::pair<ggml_backend_dev_t, ggml_backend_buffer_type_t>>;
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// find the first buffer type in the list that can use the tensor
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static ggml_backend_buffer_type_t select_weight_buft(const llama_hparams & hparams, ggml_tensor * tensor, ggml_op op, const buft_list_t & buft_list) {
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GGML_ASSERT(!buft_list.empty());
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for (const auto & cur : buft_list) {
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ggml_backend_dev_t cur_dev = cur.first;
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ggml_backend_buffer_type_t cur_buft = cur.second;
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if (weight_buft_supported(hparams, tensor, op, cur_buft, cur_dev)) {
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return cur_buft;
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}
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}
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return nullptr;
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}
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// CPU: ACCEL -> CPU extra -> GPU host -> CPU
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static buft_list_t make_cpu_buft_list(const std::vector<ggml_backend_dev_t> & devices) {
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buft_list_t buft_list;
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// add ACCEL buffer types
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for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
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ggml_backend_dev_t dev = ggml_backend_dev_get(i);
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if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_ACCEL) {
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auto * buft = ggml_backend_dev_buffer_type(dev);
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// skip
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if (buft != ggml_backend_cpu_buffer_type()) {
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buft_list.emplace_back(dev, buft);
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}
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}
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}
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// add extra buffer types
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auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
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auto * cpu_reg = ggml_backend_dev_backend_reg(cpu_dev);
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auto ggml_backend_dev_get_extra_bufts_fn = (ggml_backend_dev_get_extra_bufts_t)
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ggml_backend_reg_get_proc_address(cpu_reg, "ggml_backend_dev_get_extra_bufts");
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if (ggml_backend_dev_get_extra_bufts_fn) {
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ggml_backend_buffer_type_t * extra_bufts = ggml_backend_dev_get_extra_bufts_fn(cpu_dev);
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while (extra_bufts && *extra_bufts) {
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buft_list.emplace_back(cpu_dev, *extra_bufts);
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++extra_bufts;
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}
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}
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// add a host buffer type
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// storing the tensors in a host buffer is useful when the processing of large batches
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// is offloaded to a GPU device, since it reduces the time spent on data transfers
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// generally, this will be done using the first device in the list
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// a better approach would be to handle this on a weight-by-weight basis using the offload_op
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// function of the device to determine if it would benefit from being stored in a host buffer
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for (auto * dev : devices) {
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ggml_backend_buffer_type_t buft = ggml_backend_dev_host_buffer_type(dev);
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if (buft) {
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buft_list.emplace_back(dev, buft);
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break;
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}
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}
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// add the CPU buffer type
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for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
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ggml_backend_dev_t dev = ggml_backend_dev_get(i);
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if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_CPU) {
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buft_list.emplace_back(dev, ggml_backend_dev_buffer_type(dev));
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}
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}
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return buft_list;
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}
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// GPU: split if LLAMA_SPLIT_MODE_ROW -> GPU
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static buft_list_t make_gpu_buft_list(ggml_backend_dev_t dev, enum llama_split_mode split_mode, const float * tensor_split) {
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buft_list_t buft_list;
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// add the device split buffer type if requested and available
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if (split_mode == LLAMA_SPLIT_MODE_ROW) {
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ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(dev);
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auto ggml_backend_split_buffer_type_fn = (ggml_backend_split_buffer_type_t)
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ggml_backend_reg_get_proc_address(reg, "ggml_backend_split_buffer_type");
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if (ggml_backend_split_buffer_type_fn) {
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size_t dev_index = [&]() {
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auto * reg = ggml_backend_dev_backend_reg(dev);
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for (size_t i = 0; i < ggml_backend_reg_dev_count(reg); ++i) {
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if (ggml_backend_reg_dev_get(reg, i) == dev) {
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return i;
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}
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}
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throw std::runtime_error(format("device %s not found in its backend reg", ggml_backend_dev_name(dev)));
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}();
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auto * buft = ggml_backend_split_buffer_type_fn(dev_index, tensor_split);
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if (buft != nullptr) {
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buft_list.emplace_back(dev, buft);
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}
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}
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}
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// add the device default buffer type
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buft_list.emplace_back(dev, ggml_backend_dev_buffer_type(dev));
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return buft_list;
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}
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struct llama_model::impl {
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impl() {}
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~impl() {}
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uint64_t n_elements = 0;
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size_t n_bytes = 0;
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std::string desc_str;
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// model memory mapped files
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llama_mmaps mappings;
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// objects representing data potentially being locked in memory
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llama_mlocks mlock_bufs;
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llama_mlocks mlock_mmaps;
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// contexts where the model tensors metadata is stored
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std::vector<ggml_context_ptr> ctxs;
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// the model memory buffers for the tensor data
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std::vector<ggml_backend_buffer_ptr> bufs;
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buft_list_t cpu_buft_list;
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std::map<ggml_backend_dev_t, buft_list_t> gpu_buft_list;
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struct layer_dev {
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ggml_backend_dev_t dev;
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buft_list_t * buft_list;
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};
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layer_dev dev_input = {};
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layer_dev dev_output = {};
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std::vector<layer_dev> dev_layer;
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};
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llama_model::llama_model(const struct llama_model_params & params) : params(params), pimpl(std::make_unique<impl>()) {
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}
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llama_model::~llama_model() {}
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void llama_model::load_stats(llama_model_loader & ml) {
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pimpl->n_elements = ml.n_elements;
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pimpl->n_bytes = ml.n_bytes;
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}
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void llama_model::load_arch(llama_model_loader & ml) {
|
|
arch = ml.get_arch();
|
|
if (arch == LLM_ARCH_UNKNOWN) {
|
|
throw std::runtime_error("unknown model architecture: '" + ml.get_arch_name() + "'");
|
|
}
|
|
}
|
|
|
|
void llama_model::load_hparams(llama_model_loader & ml) {
|
|
const gguf_context * ctx = ml.meta.get();
|
|
|
|
// get metadata as string
|
|
for (int i = 0; i < gguf_get_n_kv(ctx); i++) {
|
|
enum gguf_type type = gguf_get_kv_type(ctx, i);
|
|
if (type == GGUF_TYPE_ARRAY) {
|
|
continue;
|
|
}
|
|
const char * name = gguf_get_key(ctx, i);
|
|
const std::string value = gguf_kv_to_str(ctx, i);
|
|
gguf_kv.emplace(name, value);
|
|
}
|
|
|
|
// get general kv
|
|
ml.get_key(LLM_KV_GENERAL_NAME, name, false);
|
|
|
|
// everything past this point is not vocab-related
|
|
if (hparams.vocab_only) {
|
|
return;
|
|
}
|
|
|
|
ml.get_key(LLM_KV_CONTEXT_LENGTH, hparams.n_ctx_train);
|
|
ml.get_key(LLM_KV_EMBEDDING_LENGTH, hparams.n_embd);
|
|
ml.get_key(LLM_KV_BLOCK_COUNT, hparams.n_layer);
|
|
ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert, false);
|
|
ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false);
|
|
|
|
if (arch == LLM_ARCH_WAVTOKENIZER_DEC) {
|
|
ml.get_key(LLM_KV_FEATURES_LENGTH, hparams.n_embd_features);
|
|
|
|
ml.get_key(LLM_KV_POSNET_EMBEDDING_LENGTH, hparams.posnet.n_embd);
|
|
ml.get_key(LLM_KV_POSNET_BLOCK_COUNT, hparams.posnet.n_layer);
|
|
|
|
ml.get_key(LLM_KV_CONVNEXT_EMBEDDING_LENGTH, hparams.convnext.n_embd);
|
|
ml.get_key(LLM_KV_CONVNEXT_BLOCK_COUNT, hparams.convnext.n_layer);
|
|
}
|
|
|
|
GGML_ASSERT(hparams.n_expert <= LLAMA_MAX_EXPERTS);
|
|
GGML_ASSERT(hparams.n_expert_used <= hparams.n_expert);
|
|
if (hparams.n_expert > 0) {
|
|
GGML_ASSERT(hparams.n_expert_used > 0);
|
|
} else {
|
|
GGML_ASSERT(hparams.n_expert_used == 0);
|
|
}
|
|
|
|
// zero-out the array hparams
|
|
std::fill(hparams.n_head_arr.begin(), hparams.n_head_arr.end(), 0);
|
|
std::fill(hparams.n_head_kv_arr.begin(), hparams.n_head_kv_arr.end(), 0);
|
|
std::fill(hparams.n_ff_arr.begin(), hparams.n_ff_arr.end(), 0);
|
|
|
|
ml.get_key_or_arr(LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff_arr, hparams.n_layer, false);
|
|
ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, hparams.n_layer, false);
|
|
|
|
// n_head_kv is optional, default to n_head
|
|
hparams.n_head_kv_arr = hparams.n_head_arr;
|
|
|
|
ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT_KV, hparams.n_head_kv_arr, hparams.n_layer, false);
|
|
|
|
bool rope_finetuned = false;
|
|
ml.get_key(LLM_KV_ROPE_SCALING_FINETUNED, rope_finetuned, false);
|
|
hparams.rope_finetuned = rope_finetuned;
|
|
|
|
hparams.n_ctx_orig_yarn = hparams.n_ctx_train;
|
|
ml.get_key(LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, hparams.n_ctx_orig_yarn, false);
|
|
|
|
// rope_freq_base (optional)
|
|
hparams.rope_freq_base_train = 10000.0f;
|
|
ml.get_key(LLM_KV_ROPE_FREQ_BASE, hparams.rope_freq_base_train, false);
|
|
|
|
std::string rope_scaling("linear");
|
|
ml.get_key(LLM_KV_ROPE_SCALING_TYPE, rope_scaling, false);
|
|
hparams.rope_scaling_type_train = llama_rope_scaling_type_from_string(rope_scaling);
|
|
GGML_ASSERT(hparams.rope_scaling_type_train != LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED);
|
|
|
|
// rope_freq_scale (inverse of the kv) is optional
|
|
float ropescale = 0.0f;
|
|
if (!ml.get_key(LLM_KV_ROPE_SCALING_FACTOR, ropescale, false)) {
|
|
// try the old key name
|
|
ml.get_key(LLM_KV_ROPE_SCALE_LINEAR, ropescale, false);
|
|
}
|
|
hparams.rope_freq_scale_train = ropescale == 0.0f ? 1.0f : 1.0f/ropescale;
|
|
|
|
ml.get_key(LLM_KV_ROPE_SCALING_ATTN_FACTOR, hparams.rope_attn_factor, false);
|
|
|
|
// non-transformer models do not have attention heads
|
|
if (hparams.n_head() > 0) {
|
|
// gpt-neox n_rot = rotary_pct * (n_embd / n_head)
|
|
// gpt-j n_rot = rotary_dim
|
|
|
|
hparams.n_embd_head_k = hparams.n_embd / hparams.n_head();
|
|
ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k, false);
|
|
|
|
hparams.n_embd_head_v = hparams.n_embd / hparams.n_head();
|
|
ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false);
|
|
|
|
// sanity check for n_rot (optional)
|
|
hparams.n_rot = hparams.n_embd_head_k;
|
|
|
|
ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false);
|
|
|
|
if (arch == LLM_ARCH_LLAMA || arch == LLM_ARCH_DECI || arch == LLM_ARCH_FALCON) {
|
|
if (hparams.n_rot != hparams.n_embd_head_k) {
|
|
throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd_head_k));
|
|
}
|
|
}
|
|
} else {
|
|
hparams.n_rot = 0;
|
|
hparams.n_embd_head_k = 0;
|
|
hparams.n_embd_head_v = 0;
|
|
}
|
|
|
|
// for differentiating model types
|
|
uint32_t n_vocab = 0;
|
|
ml.get_key(LLM_KV_VOCAB_SIZE, n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, n_vocab, false);
|
|
|
|
// arch-specific KVs
|
|
switch (arch) {
|
|
case LLM_ARCH_LLAMA:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
if (hparams.n_expert == 8) {
|
|
switch (hparams.n_layer) {
|
|
case 32: type = LLM_TYPE_8x7B; break;
|
|
case 56: type = LLM_TYPE_8x22B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} else {
|
|
switch (hparams.n_layer) {
|
|
case 16: type = LLM_TYPE_1B; break; // Llama 3.2 1B
|
|
case 22: type = LLM_TYPE_1B; break;
|
|
case 26: type = LLM_TYPE_3B; break;
|
|
case 28: type = LLM_TYPE_3B; break; // Llama 3.2 3B
|
|
// granite uses a vocab with len 49152
|
|
case 32: type = n_vocab == 49152 ? LLM_TYPE_3B : (n_vocab < 40000 ? LLM_TYPE_7B : LLM_TYPE_8B); break;
|
|
case 36: type = LLM_TYPE_8B; break; // granite
|
|
case 40: type = LLM_TYPE_13B; break;
|
|
case 48: type = LLM_TYPE_34B; break;
|
|
case 60: type = LLM_TYPE_30B; break;
|
|
case 80: type = hparams.n_head() == hparams.n_head_kv() ? LLM_TYPE_65B : LLM_TYPE_70B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
}
|
|
} break;
|
|
case LLM_ARCH_DECI:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
switch (hparams.n_layer) {
|
|
case 32: type = LLM_TYPE_7B; break;
|
|
case 80: type = LLM_TYPE_70B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_MINICPM:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
ml.get_key(LLM_KV_EMBEDDING_SCALE, hparams.f_embedding_scale);
|
|
ml.get_key(LLM_KV_RESIDUAL_SCALE, hparams.f_residual_scale);
|
|
ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 52: type = LLM_TYPE_1B; break;
|
|
case 40: type = LLM_TYPE_2B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_MINICPM3:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q);
|
|
ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 62: type = LLM_TYPE_4B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_GROK:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 64: type = LLM_TYPE_314B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_FALCON:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 32: type = LLM_TYPE_7B; break;
|
|
case 60: type = LLM_TYPE_40B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_BAICHUAN:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
switch (hparams.n_layer) {
|
|
case 32: type = LLM_TYPE_7B; break;
|
|
case 40: type = LLM_TYPE_13B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
|
|
if (type == LLM_TYPE_13B) {
|
|
// TODO: become GGUF KV parameter
|
|
hparams.f_max_alibi_bias = 8.0f;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_STARCODER:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
switch (hparams.n_layer) {
|
|
case 24: type = LLM_TYPE_1B; break;
|
|
case 36: type = LLM_TYPE_3B; break;
|
|
case 42: type = LLM_TYPE_7B; break;
|
|
case 40: type = LLM_TYPE_15B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_REFACT:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
switch (hparams.n_layer) {
|
|
case 32: type = LLM_TYPE_1B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
|
|
// TODO: become GGUF KV parameter
|
|
hparams.f_max_alibi_bias = 8.0f;
|
|
} break;
|
|
case LLM_ARCH_BERT:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
|
|
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 3:
|
|
type = LLM_TYPE_17M; break; // bge-micro
|
|
case 6:
|
|
type = LLM_TYPE_22M; break; // MiniLM-L6
|
|
case 12:
|
|
switch (hparams.n_embd) {
|
|
case 384: type = LLM_TYPE_33M; break; // MiniLM-L12, bge-small
|
|
case 768: type = LLM_TYPE_109M; break; // bge-base
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
} break;
|
|
case 24:
|
|
type = LLM_TYPE_335M; break; // bge-large
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_JINA_BERT_V2:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
|
|
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);
|
|
hparams.f_max_alibi_bias = 8.0f;
|
|
|
|
switch (hparams.n_layer) {
|
|
case 4: type = LLM_TYPE_33M; break; // jina-embeddings-small
|
|
case 12: type = LLM_TYPE_137M; break; // jina-embeddings-base
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_NOMIC_BERT:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
|
|
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type);
|
|
|
|
if (hparams.n_layer == 12 && hparams.n_embd == 768) {
|
|
type = LLM_TYPE_137M;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_BLOOM:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 24: type = LLM_TYPE_1B; break;
|
|
case 30:
|
|
switch (hparams.n_embd) {
|
|
case 2560: type = LLM_TYPE_3B; break;
|
|
case 4096: type = LLM_TYPE_7B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
} break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
|
|
// TODO: become GGUF KV parameter
|
|
hparams.f_max_alibi_bias = 8.0f;
|
|
} break;
|
|
case LLM_ARCH_MPT:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV, hparams.f_clamp_kqv, false);
|
|
ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 32: type = LLM_TYPE_7B; break;
|
|
case 48: type = LLM_TYPE_30B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_STABLELM:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 24: type = LLM_TYPE_1B; break;
|
|
case 32: type = LLM_TYPE_3B; break;
|
|
case 40: type = LLM_TYPE_12B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_QWEN:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 32: type = LLM_TYPE_7B; break;
|
|
case 40: type = LLM_TYPE_13B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_QWEN2VL:
|
|
{
|
|
ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, true);
|
|
}
|
|
// fall through
|
|
case LLM_ARCH_QWEN2:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
switch (hparams.n_layer) {
|
|
case 24: type = hparams.n_embd == 1024 ? LLM_TYPE_0_5B : LLM_TYPE_1B; break;
|
|
case 28: type = hparams.n_embd == 1536 ? LLM_TYPE_1_5B : LLM_TYPE_7B; break;
|
|
case 32: type = LLM_TYPE_7B; break;
|
|
case 36: type = LLM_TYPE_3B; break;
|
|
case 40: type = hparams.n_head() == 20 ? LLM_TYPE_4B : LLM_TYPE_13B; break;
|
|
case 48: type = LLM_TYPE_14B; break;
|
|
case 64: type = LLM_TYPE_32B; break;
|
|
case 80: type = LLM_TYPE_70B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_QWEN2MOE:
|
|
{
|
|
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
|
|
ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
|
|
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
switch (hparams.n_layer) {
|
|
case 24: type = LLM_TYPE_A2_7B; break;
|
|
case 28: type = LLM_TYPE_57B_A14B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_PHI2:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 24: type = LLM_TYPE_1B; break;
|
|
case 32: type = LLM_TYPE_3B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_PHI3:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 24: type = LLM_TYPE_1B; break;
|
|
case 32: type = LLM_TYPE_3B; break;
|
|
case 40: type = LLM_TYPE_14B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
|
|
// for backward compatibility ; see: https://github.com/ggerganov/llama.cpp/pull/8931
|
|
if ((hparams.n_layer == 32 || hparams.n_layer == 40) && hparams.n_ctx_train == 4096) {
|
|
// default value for Phi-3-mini-4k-instruct and Phi-3-medium-4k-instruct
|
|
hparams.n_swa = 2047;
|
|
} else if (hparams.n_layer == 32 && hparams.n_head_kv(0) == 32 && hparams.n_ctx_train == 131072) {
|
|
// default value for Phi-3-mini-128k-instruct
|
|
hparams.n_swa = 262144;
|
|
} else if (hparams.n_layer == 40 && hparams.n_ctx_train == 131072) {
|
|
// default value for Phi-3-medium-128k-instruct
|
|
hparams.n_swa = 131072;
|
|
}
|
|
bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
|
|
if (!found_swa && hparams.n_swa == 0) {
|
|
throw std::runtime_error("invalid value for sliding_window");
|
|
}
|
|
} break;
|
|
case LLM_ARCH_PHIMOE:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 32: type = LLM_TYPE_16x3_8B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_PLAMO:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 40: type = LLM_TYPE_13B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_GPT2:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
switch (hparams.n_layer) {
|
|
case 12: type = LLM_TYPE_SMALL; break;
|
|
case 24: type = LLM_TYPE_MEDIUM; break;
|
|
case 36: type = LLM_TYPE_LARGE; break;
|
|
case 48: type = LLM_TYPE_XL; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_CODESHELL:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
switch (hparams.n_layer) {
|
|
case 42: type = LLM_TYPE_7B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_ORION:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 40: type = LLM_TYPE_14B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_INTERNLM2:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
switch (hparams.n_layer) {
|
|
case 32: type = LLM_TYPE_7B; break;
|
|
case 48: type = LLM_TYPE_20B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_GEMMA:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 18: type = LLM_TYPE_2B; break;
|
|
case 28: type = LLM_TYPE_7B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_GEMMA2:
|
|
{
|
|
hparams.n_swa = 4096; // default value of gemma 2
|
|
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
ml.get_key(LLM_KV_ATTN_LOGIT_SOFTCAPPING, hparams.f_attn_logit_softcapping, false);
|
|
ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING, hparams.f_final_logit_softcapping, false);
|
|
hparams.attn_soft_cap = true;
|
|
|
|
switch (hparams.n_layer) {
|
|
case 26: type = LLM_TYPE_2B; break;
|
|
case 42: type = LLM_TYPE_9B; break;
|
|
case 46: type = LLM_TYPE_27B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_STARCODER2:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
switch (hparams.n_layer) {
|
|
case 30: type = LLM_TYPE_3B; break;
|
|
case 32: type = LLM_TYPE_7B; break;
|
|
case 40: type = LLM_TYPE_15B; break;
|
|
case 52: type = LLM_TYPE_20B; break; // granite
|
|
case 88: type = LLM_TYPE_34B; break; // granite
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_MAMBA:
|
|
{
|
|
ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv);
|
|
ml.get_key(LLM_KV_SSM_INNER_SIZE, hparams.ssm_d_inner);
|
|
ml.get_key(LLM_KV_SSM_STATE_SIZE, hparams.ssm_d_state);
|
|
ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
|
|
ml.get_key(LLM_KV_SSM_DT_B_C_RMS, hparams.ssm_dt_b_c_rms, false);
|
|
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 24:
|
|
switch (hparams.n_embd) {
|
|
case 768: type = LLM_TYPE_SMALL; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
} break;
|
|
case 48:
|
|
switch (hparams.n_embd) {
|
|
case 1024: type = LLM_TYPE_MEDIUM; break;
|
|
case 1536: type = LLM_TYPE_LARGE; break;
|
|
case 2048: type = LLM_TYPE_XL; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
} break;
|
|
case 64:
|
|
switch (hparams.n_embd) {
|
|
case 2560: type = LLM_TYPE_3B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
} break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_XVERSE:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
switch (hparams.n_layer) {
|
|
case 32: type = LLM_TYPE_7B; break;
|
|
case 40: type = LLM_TYPE_13B; break;
|
|
case 80: type = LLM_TYPE_65B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_COMMAND_R:
|
|
{
|
|
ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
switch (hparams.n_layer) {
|
|
case 40: type = LLM_TYPE_35B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_COHERE2:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
|
|
ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
switch (hparams.n_layer) {
|
|
case 32: type = LLM_TYPE_8B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_DBRX:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV, hparams.f_clamp_kqv);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 40: type = LLM_TYPE_16x12B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_OLMO:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV, hparams.f_clamp_kqv, false);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 22: type = LLM_TYPE_1B; break;
|
|
case 32: type = LLM_TYPE_7B; break;
|
|
case 80: type = LLM_TYPE_70B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_OLMO2:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 16: type = LLM_TYPE_1B; break;
|
|
case 32: type = LLM_TYPE_7B; break;
|
|
case 40: type = LLM_TYPE_13B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_OLMOE:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
switch (hparams.n_layer) {
|
|
case 16: type = LLM_TYPE_A1_7B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_OPENELM:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 16: type = LLM_TYPE_270M; break;
|
|
case 20: type = LLM_TYPE_450M; break;
|
|
case 28: type = LLM_TYPE_1B; break;
|
|
case 36: type = LLM_TYPE_3B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_GPTNEOX:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
ml.get_key(LLM_KV_USE_PARALLEL_RESIDUAL, hparams.use_par_res);
|
|
switch (hparams.n_layer) {
|
|
case 6:
|
|
switch (hparams.n_ff()) {
|
|
case 512: type = LLM_TYPE_14M; break;
|
|
case 2048: type = LLM_TYPE_70M; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
} break;
|
|
case 12:
|
|
switch (hparams.n_ff()) {
|
|
case 3072: type = LLM_TYPE_160M; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
} break;
|
|
case 16:
|
|
switch (hparams.n_ff()) {
|
|
case 8192: type = LLM_TYPE_1B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
} break;
|
|
case 24:
|
|
switch (hparams.n_ff()) {
|
|
case 4096: type = LLM_TYPE_410M; break;
|
|
case 8192: type = LLM_TYPE_1_4B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
} break;
|
|
case 32:
|
|
switch (hparams.n_ff()) {
|
|
case 10240: type = LLM_TYPE_2_8B; break;
|
|
case 16384: type = LLM_TYPE_6_9B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
} break;
|
|
case 36:
|
|
switch (hparams.n_ff()) {
|
|
case 20480: type = LLM_TYPE_12B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
} break;
|
|
case 44:
|
|
switch (hparams.n_ff()) {
|
|
case 24576: type = LLM_TYPE_20B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
} break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_ARCTIC:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
if (hparams.n_expert == 128) {
|
|
switch (hparams.n_layer) {
|
|
case 35: type = LLM_TYPE_10B_128x3_66B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} else {
|
|
type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_DEEPSEEK:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
|
|
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
|
|
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
|
|
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 28: type = LLM_TYPE_20B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_DEEPSEEK2:
|
|
{
|
|
bool is_lite = (hparams.n_layer == 27);
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
|
|
if (!is_lite) {
|
|
ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q);
|
|
}
|
|
ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv);
|
|
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
|
|
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
|
|
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
|
|
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
|
|
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
|
|
if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
|
|
// for compatibility with existing DeepSeek V2 and V2.5 GGUFs
|
|
// that have no expert_gating_func model parameter set
|
|
hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX;
|
|
}
|
|
ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 27: type = LLM_TYPE_16B; break;
|
|
case 60: type = LLM_TYPE_236B; break;
|
|
case 61: type = LLM_TYPE_671B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_CHATGLM:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
switch (hparams.n_layer) {
|
|
case 28: type = LLM_TYPE_6B; break;
|
|
case 40: type = LLM_TYPE_9B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_BITNET:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 26: type = LLM_TYPE_3B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_T5:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, hparams.n_rel_attn_bkts);
|
|
|
|
uint32_t dec_start_token_id;
|
|
if (ml.get_key(LLM_KV_DECODER_START_TOKEN_ID, dec_start_token_id, false)) {
|
|
hparams.dec_start_token_id = dec_start_token_id;
|
|
}
|
|
|
|
switch (hparams.n_layer) {
|
|
case 6: type = LLM_TYPE_60M; break; // t5-small
|
|
case 8: type = LLM_TYPE_80M; break; // flan-t5-small
|
|
case 12:
|
|
switch (hparams.n_ff()) {
|
|
case 3072: type = LLM_TYPE_220M; break; // t5-base
|
|
case 2048: type = LLM_TYPE_250M; break; // flan-t5-base
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
} break;
|
|
case 24:
|
|
switch (hparams.n_ff()) {
|
|
case 4096: type = LLM_TYPE_770M; break; // t5-large
|
|
case 2816: type = LLM_TYPE_780M; break; // flan-t5-large
|
|
case 16384: type = LLM_TYPE_3B; break; // t5-3b
|
|
case 5120: type = LLM_TYPE_3B; break; // flan-t5-xl
|
|
case 65536: type = LLM_TYPE_11B; break; // t5-11b
|
|
case 10240: type = LLM_TYPE_11B; break; // flan-t5-xxl
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
} break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_T5ENCODER:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, hparams.n_rel_attn_bkts);
|
|
type = LLM_TYPE_UNKNOWN;
|
|
} break;
|
|
case LLM_ARCH_JAIS:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 24: type = LLM_TYPE_1_3B; break;
|
|
case 40: type = LLM_TYPE_13B; break;
|
|
/* TODO: add variants */
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_NEMOTRON:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
switch (hparams.n_layer) {
|
|
case 32: type = LLM_TYPE_4B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_EXAONE:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 32: type = LLM_TYPE_8B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_RWKV6:
|
|
case LLM_ARCH_RWKV6QWEN2:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps, false);
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps, false);
|
|
ml.get_key(LLM_KV_WKV_HEAD_SIZE, hparams.wkv_head_size);
|
|
ml.get_key(LLM_KV_TIME_MIX_EXTRA_DIM, hparams.time_mix_extra_dim);
|
|
ml.get_key(LLM_KV_TIME_DECAY_EXTRA_DIM, hparams.time_decay_extra_dim);
|
|
ml.get_key(LLM_KV_RESCALE_EVERY_N_LAYERS, hparams.rescale_every_n_layers, false);
|
|
ml.get_key(LLM_KV_TOKEN_SHIFT_COUNT, hparams.token_shift_count, false);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 24: type = LLM_TYPE_1_6B; break;
|
|
case 32:
|
|
switch (hparams.n_embd) {
|
|
case 2560: type = LLM_TYPE_3B; break;
|
|
case 4096: type = LLM_TYPE_7B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
} break;
|
|
case 61: type = LLM_TYPE_14B; break;
|
|
case 64: type = LLM_TYPE_32B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_GRANITE:
|
|
case LLM_ARCH_GRANITE_MOE:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
|
|
ml.get_key(LLM_KV_RESIDUAL_SCALE, hparams.f_residual_scale);
|
|
ml.get_key(LLM_KV_EMBEDDING_SCALE, hparams.f_embedding_scale);
|
|
ml.get_key(LLM_KV_ATTENTION_SCALE, hparams.f_attention_scale);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 32: type = LLM_TYPE_3B; break;
|
|
case 40: type = LLM_TYPE_3B; break;
|
|
// Add additional layer/vocab/etc checks here for other model sizes
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_CHAMELEON:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
hparams.f_norm_eps = 1e-5; // eps for qk-norm, torch default
|
|
ml.get_key(LLM_KV_SWIN_NORM, hparams.swin_norm);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 32: type = LLM_TYPE_7B; break;
|
|
case 48: type = LLM_TYPE_34B; break;
|
|
default: type = LLM_TYPE_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_WAVTOKENIZER_DEC:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_GROUPNORM_EPS, hparams.f_norm_group_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_GROUPNORM_GROUPS, hparams.n_norm_groups);
|
|
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
|
|
} break;
|
|
default: throw std::runtime_error("unsupported model architecture");
|
|
}
|
|
|
|
pimpl->n_bytes = ml.n_bytes;
|
|
|
|
pimpl->desc_str = arch_name() + " " + type_name() + " " + ml.ftype_name();
|
|
|
|
if (hparams.f_max_alibi_bias > 0.0f) {
|
|
hparams.use_alibi = true;
|
|
}
|
|
|
|
hparams.rope_type = llama_model_rope_type(this);
|
|
}
|
|
|
|
void llama_model::load_vocab(llama_model_loader & ml) {
|
|
const auto kv = LLM_KV(arch);
|
|
|
|
vocab.load(ml, kv);
|
|
}
|
|
|
|
bool llama_model::load_tensors(llama_model_loader & ml) {
|
|
const auto & split_mode = params.split_mode;
|
|
const auto & n_gpu_layers = params.n_gpu_layers;
|
|
const auto & use_mlock = params.use_mlock;
|
|
const auto & tensor_split = params.tensor_split;
|
|
|
|
const int n_layer = hparams.n_layer;
|
|
|
|
const bool use_mmap_buffer = true;
|
|
|
|
// build a list of buffer types for the CPU and GPU devices
|
|
pimpl->cpu_buft_list = make_cpu_buft_list(devices);
|
|
for (auto * dev : devices) {
|
|
buft_list_t buft_list = make_gpu_buft_list(dev, split_mode, tensor_split);
|
|
// add CPU buffer types as a fallback
|
|
buft_list.insert(buft_list.end(), pimpl->cpu_buft_list.begin(), pimpl->cpu_buft_list.end());
|
|
pimpl->gpu_buft_list.emplace(dev, std::move(buft_list));
|
|
}
|
|
|
|
// calculate the split points
|
|
bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + n_devices(), [](float x) { return x == 0.0f; });
|
|
std::vector<float> splits(n_devices());
|
|
if (all_zero) {
|
|
// default split, by free memory
|
|
for (size_t i = 0; i < n_devices(); ++i) {
|
|
ggml_backend_dev_t dev = devices[i];
|
|
size_t total;
|
|
size_t free;
|
|
ggml_backend_dev_memory(dev, &free, &total);
|
|
splits[i] = free;
|
|
}
|
|
} else {
|
|
std::copy(tensor_split, tensor_split + n_devices(), splits.begin());
|
|
}
|
|
|
|
// sum and normalize the splits to get the split points
|
|
float split_sum = 0.0f;
|
|
for (size_t i = 0; i < n_devices(); ++i) {
|
|
split_sum += splits[i];
|
|
splits[i] = split_sum;
|
|
}
|
|
for (size_t i = 0; i < n_devices(); ++i) {
|
|
splits[i] /= split_sum;
|
|
}
|
|
|
|
ggml_backend_dev_t cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
|
|
const int i_gpu_start = std::max((int) hparams.n_layer - n_gpu_layers, (int) 0);
|
|
const int act_gpu_layers = devices.empty() ? 0 : std::min(n_gpu_layers, (int)n_layer + 1);
|
|
auto get_layer_buft_list = [&](int il) -> llama_model::impl::layer_dev {
|
|
if (il < i_gpu_start || (il - i_gpu_start) >= act_gpu_layers) {
|
|
return {cpu_dev, &pimpl->cpu_buft_list};
|
|
}
|
|
const int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + n_devices(), float(il - i_gpu_start)/act_gpu_layers) - splits.begin();
|
|
auto * dev = devices.at(layer_gpu);
|
|
return {dev, &pimpl->gpu_buft_list.at(dev)};
|
|
};
|
|
|
|
// assign the input layer
|
|
// there is very little benefit to offloading the input layer, so always keep it on the CPU
|
|
pimpl->dev_input = { cpu_dev, &pimpl->cpu_buft_list };
|
|
|
|
// assign the repeating layers to the devices according to the splits
|
|
pimpl->dev_layer.resize(n_layer);
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
pimpl->dev_layer[il] = get_layer_buft_list(il);
|
|
}
|
|
|
|
// assign the output layer
|
|
pimpl->dev_output = get_layer_buft_list(n_layer);
|
|
|
|
// one ggml context per buffer type
|
|
int max_n_tensors = ml.n_tensors;
|
|
max_n_tensors += 1; // duplicated output tensor
|
|
max_n_tensors += n_layer*2; // duplicated rope freq tensors
|
|
const size_t ctx_size = ggml_tensor_overhead()*max_n_tensors;
|
|
|
|
std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
|
|
auto ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {
|
|
auto it = ctx_map.find(buft);
|
|
if (it == ctx_map.end()) {
|
|
ggml_init_params params = {
|
|
/*.mem_size =*/ ctx_size,
|
|
/*.mem_buffer =*/ NULL,
|
|
/*.no_alloc =*/ true,
|
|
};
|
|
|
|
ggml_context * ctx = ggml_init(params);
|
|
if (!ctx) {
|
|
throw std::runtime_error(format("failed to create ggml context"));
|
|
}
|
|
|
|
ctx_map[buft] = ctx;
|
|
pimpl->ctxs.emplace_back(ctx);
|
|
|
|
return ctx;
|
|
}
|
|
return it->second;
|
|
};
|
|
|
|
const auto TENSOR_DUPLICATED = llama_model_loader::TENSOR_DUPLICATED;
|
|
const auto TENSOR_NOT_REQUIRED = llama_model_loader::TENSOR_NOT_REQUIRED;
|
|
|
|
// create tensors for the weights
|
|
{
|
|
// note: cast to int64_t since we will use these for the tensor dimensions
|
|
const int64_t n_head = hparams.n_head();
|
|
const int64_t n_head_kv = hparams.n_head_kv();
|
|
const int64_t n_embd = hparams.n_embd;
|
|
const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
|
|
const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();
|
|
const int64_t n_embd_head_k = hparams.n_embd_head_k;
|
|
const int64_t n_embd_head_v = hparams.n_embd_head_v;
|
|
const int64_t n_ff = hparams.n_ff();
|
|
const int64_t n_embd_gqa = n_embd_v_gqa;
|
|
const int64_t n_vocab = vocab.n_tokens();
|
|
const int64_t n_token_types = vocab.n_token_types();
|
|
const int64_t n_rot = hparams.n_rot;
|
|
const int64_t n_expert = hparams.n_expert;
|
|
const int64_t n_expert_used = hparams.n_expert_used;
|
|
const int64_t n_ctx_train = hparams.n_ctx_train;
|
|
|
|
if (n_expert > 0 && hparams.n_expert_used == 0) {
|
|
throw std::runtime_error("model has expert layers but no expert layers are used");
|
|
}
|
|
|
|
int n_moved_tensors = 0;
|
|
ggml_tensor * first_moved_tensor = nullptr;
|
|
ggml_backend_buffer_type_t first_moved_from_buft = nullptr;
|
|
ggml_backend_buffer_type_t first_moved_to_buft = nullptr;
|
|
|
|
auto create_tensor = [&](const LLM_TN_IMPL & tn, const std::initializer_list<int64_t> & ne, int flags) -> ggml_tensor * {
|
|
ggml_tensor * t_meta = ml.get_tensor_meta(tn.str().c_str());
|
|
|
|
if (!t_meta) {
|
|
if (flags & TENSOR_NOT_REQUIRED) {
|
|
return nullptr;
|
|
}
|
|
throw std::runtime_error(format("missing tensor '%s'", tn.str().c_str()));
|
|
}
|
|
|
|
// some models use the token embedding tensor as the output, but since these are used in different layers and with different ops
|
|
// the tensor is duplicated
|
|
// to handle this, we check if the tensor is duplicated, and if so, we assume that it is being loaded as the output tensor
|
|
llm_tensor tn_tensor = tn.tensor;
|
|
if (tn.tensor == LLM_TENSOR_TOKEN_EMBD && flags & TENSOR_DUPLICATED) {
|
|
tn_tensor = LLM_TENSOR_OUTPUT;
|
|
}
|
|
|
|
llm_tensor_info info;
|
|
try {
|
|
info = llm_tensor_info_for(tn_tensor);
|
|
} catch (const std::out_of_range & e) {
|
|
throw std::runtime_error(format("missing tensor info mapping for %s", tn.str().c_str()));
|
|
}
|
|
|
|
// tensors with "bias" suffix are always used with GGML_OP_ADD
|
|
ggml_op op;
|
|
bool bias = tn.suffix != nullptr && strcmp(tn.suffix, "bias") == 0;
|
|
if (bias) {
|
|
op = GGML_OP_ADD;
|
|
} else {
|
|
op = info.op;
|
|
}
|
|
|
|
// sanity checks
|
|
if (info.layer == LLM_TENSOR_LAYER_INPUT || info.layer == LLM_TENSOR_LAYER_OUTPUT) {
|
|
if (tn.bid != -1) {
|
|
GGML_ABORT("input/output layer tensor %s used with a layer number", tn.str().c_str());
|
|
}
|
|
} else {
|
|
if (tn.bid == -1) {
|
|
GGML_ABORT("repeating layer tensor %s used without a layer number", tn.str().c_str());
|
|
}
|
|
}
|
|
|
|
// select the buffer type for this tensor
|
|
buft_list_t * buft_list;
|
|
switch (info.layer) {
|
|
case LLM_TENSOR_LAYER_INPUT:
|
|
buft_list = pimpl->dev_input.buft_list;
|
|
break;
|
|
case LLM_TENSOR_LAYER_OUTPUT:
|
|
buft_list = pimpl->dev_output.buft_list;
|
|
break;
|
|
case LLM_TENSOR_LAYER_REPEATING:
|
|
buft_list = pimpl->dev_layer.at(tn.bid).buft_list;
|
|
break;
|
|
default:
|
|
GGML_ABORT("invalid layer %d for tensor %s", info.layer, tn.str().c_str());
|
|
}
|
|
|
|
ggml_backend_buffer_type_t buft = select_weight_buft(hparams, t_meta, op, *buft_list);
|
|
if (!buft) {
|
|
throw std::runtime_error(format("failed to find a compatible buffer type for tensor %s", tn.str().c_str()));
|
|
}
|
|
|
|
// avoid using a host buffer when using mmap
|
|
auto * buft_dev = ggml_backend_buft_get_device(buft);
|
|
if (ml.use_mmap && buft_dev && buft == ggml_backend_dev_host_buffer_type(buft_dev)) {
|
|
auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
|
|
buft = ggml_backend_dev_buffer_type(cpu_dev);
|
|
}
|
|
|
|
if (buft != buft_list->front().second) {
|
|
n_moved_tensors++;
|
|
if (!first_moved_tensor) {
|
|
first_moved_tensor = t_meta;
|
|
first_moved_from_buft = buft_list->front().second;
|
|
first_moved_to_buft = buft;
|
|
}
|
|
}
|
|
|
|
ggml_context * ctx = ctx_for_buft(buft);
|
|
|
|
// if duplicated, check if the original tensor was allocated in the same buffer type context and avoid creating a new one
|
|
if (flags & TENSOR_DUPLICATED) {
|
|
ggml_tensor * t = ggml_get_tensor(ctx, tn.str().c_str());
|
|
if (t) {
|
|
return t;
|
|
}
|
|
}
|
|
return ml.create_tensor(ctx, tn, ne, flags);
|
|
};
|
|
|
|
layers.resize(n_layer);
|
|
|
|
// TODO: move to a separate function
|
|
const auto tn = LLM_TN(arch);
|
|
switch (arch) {
|
|
case LLM_ARCH_LLAMA:
|
|
case LLM_ARCH_REFACT:
|
|
case LLM_ARCH_MINICPM:
|
|
case LLM_ARCH_GRANITE:
|
|
case LLM_ARCH_GRANITE_MOE:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
|
|
|
|
// if output is NULL, init from the input tok embed
|
|
if (output == NULL) {
|
|
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
|
|
|
|
// optional bias tensors
|
|
layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED);
|
|
layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED);
|
|
layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
|
|
layer.rope_long = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
|
|
layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
|
|
}
|
|
else {
|
|
layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
|
|
}
|
|
|
|
if (n_expert == 0) {
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
|
|
// optional MLP bias
|
|
layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
|
|
layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
|
|
} else {
|
|
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
|
|
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, TENSOR_NOT_REQUIRED);
|
|
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}, 0);
|
|
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0);
|
|
}
|
|
}
|
|
} break;
|
|
case LLM_ARCH_DECI:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
|
|
|
|
// if output is NULL, init from the input tok embed
|
|
if (output == NULL) {
|
|
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(i);
|
|
const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(i);
|
|
const int64_t n_embd_gqa = hparams.n_embd_v_gqa(i);
|
|
const int64_t n_ff = hparams.n_ff(i);
|
|
const int64_t n_head = hparams.n_head(i);
|
|
const int64_t n_head_kv = hparams.n_head_kv(i);
|
|
|
|
if (n_head_kv == 0 && n_head > 0) {
|
|
// linear attention for DeciLMCausalModel
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
}
|
|
else if (n_head_kv > 0) {
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
|
|
}
|
|
|
|
// optional bias tensors
|
|
layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED);
|
|
layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED);
|
|
layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
|
|
layer.rope_long = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
|
|
layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
|
|
}
|
|
else {
|
|
layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
|
|
}
|
|
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
|
|
// optional MLP bias
|
|
layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
|
|
layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_MINICPM3:
|
|
{
|
|
const int64_t n_embd_head_qk_rope = hparams.n_rot;
|
|
const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
|
|
|
|
const int64_t q_lora_rank = hparams.n_lora_q;
|
|
const int64_t kv_lora_rank = hparams.n_lora_kv;
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
|
|
|
|
// if output is NULL, init from the input tok embed
|
|
if (output == NULL) {
|
|
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0);
|
|
|
|
layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0);
|
|
|
|
layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0);
|
|
layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k}, 0);
|
|
|
|
layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + (n_embd_head_qk_rope)}, 0);
|
|
layer.wkv_b = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_head * ( n_embd_head_v), n_embd}, 0);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
|
|
layer.rope_long = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight", i), { n_embd_head_qk_rope/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
|
|
layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_embd_head_qk_rope/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
|
|
}
|
|
} break;
|
|
case LLM_ARCH_GROK:
|
|
{
|
|
if (n_expert == 0) {
|
|
throw std::runtime_error("Grok model cannot have zero experts");
|
|
}
|
|
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
|
|
|
|
// if output is NULL, init from the input tok embed
|
|
if (output == NULL) {
|
|
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
|
|
layer.attn_out_norm = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
|
|
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, TENSOR_NOT_REQUIRED);
|
|
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}, 0);
|
|
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0);
|
|
|
|
layer.layer_out_norm = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_DBRX:
|
|
{
|
|
if (n_expert == 0) {
|
|
throw std::runtime_error("DBRX model cannot have zero experts");
|
|
}
|
|
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
|
|
layer.attn_out_norm = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
|
|
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0);
|
|
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff, n_embd, n_expert}, 0);
|
|
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_BAICHUAN:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
{
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_FALCON:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
{
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
|
|
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
|
|
if (!output) {
|
|
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); // needs to be on GPU
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);
|
|
|
|
layer.attn_norm_2 = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
|
|
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_STARCODER:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}, 0);
|
|
|
|
// output
|
|
{
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
|
|
if (!output) {
|
|
// needs to be on GPU
|
|
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
|
|
}
|
|
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);
|
|
|
|
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
|
|
layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0);
|
|
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0);
|
|
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
|
|
layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0);
|
|
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_BERT:
|
|
case LLM_ARCH_NOMIC_BERT:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
type_embd = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_token_types}, 0);
|
|
|
|
if (arch == LLM_ARCH_BERT) {
|
|
pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}, 0);
|
|
|
|
cls = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, n_embd}, TENSOR_NOT_REQUIRED);
|
|
cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
|
|
cls_out = create_tensor(tn(LLM_TENSOR_CLS_OUT, "weight"), {n_embd, 1}, TENSOR_NOT_REQUIRED);
|
|
cls_out_b = create_tensor(tn(LLM_TENSOR_CLS_OUT, "bias"), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
|
|
tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
|
|
tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}, 0);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
if (arch == LLM_ARCH_BERT) {
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0);
|
|
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, 0);
|
|
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, 0);
|
|
} else {
|
|
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
|
|
}
|
|
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
|
|
layer.attn_out_norm = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0);
|
|
layer.attn_out_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd}, 0);
|
|
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
|
|
|
|
if (arch == LLM_ARCH_BERT) {
|
|
layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
|
|
layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0);
|
|
layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0);
|
|
} else {
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
|
}
|
|
|
|
layer.layer_out_norm = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0);
|
|
layer.layer_out_norm_b = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_JINA_BERT_V2:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // word_embeddings
|
|
type_embd = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_token_types}, 0); // token_type_embeddings
|
|
|
|
tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0); // LayerNorm
|
|
tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}, 0); //LayerNorm bias
|
|
|
|
cls = create_tensor(tn(LLM_TENSOR_CLS, "weight"), {n_embd, 1}, TENSOR_NOT_REQUIRED);
|
|
cls_b = create_tensor(tn(LLM_TENSOR_CLS, "bias"), {1}, TENSOR_NOT_REQUIRED);
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i]; // JinaBertLayer
|
|
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0);
|
|
|
|
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, 0);
|
|
|
|
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, 0);
|
|
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0); //output_dens
|
|
layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0); //output_dens
|
|
|
|
layer.attn_out_norm = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0); //output_norm
|
|
layer.attn_out_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd}, 0);
|
|
|
|
layer.attn_norm_2 = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
|
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
|
|
layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0);
|
|
|
|
layer.layer_out_norm = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0);
|
|
layer.layer_out_norm_b = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_BLOOM:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
|
|
tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);
|
|
|
|
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
|
|
layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0);
|
|
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0);
|
|
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
|
|
layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0);
|
|
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_MPT:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}, TENSOR_NOT_REQUIRED);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
|
|
if (!output) {
|
|
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); // needs to be on GPU
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
|
|
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
|
|
layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, TENSOR_NOT_REQUIRED);
|
|
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
|
|
layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
|
|
|
|
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
|
|
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
|
|
// AWQ ScaleActivation layer
|
|
layer.ffn_act = create_tensor(tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_STABLELM:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);
|
|
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
|
|
// optional bias tensors, present in Stable LM 2 1.6B
|
|
layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED);
|
|
layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED);
|
|
|
|
// optional q and k layernorms, present in StableLM 2 12B
|
|
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}, TENSOR_NOT_REQUIRED);
|
|
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, TENSOR_NOT_REQUIRED);
|
|
|
|
// optional FFN norm, not present in StableLM 2 12B which uses parallel residual
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_QWEN:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd*3}, 0);
|
|
layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd*3}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff/2}, 0);
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff/2, n_embd}, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff/2}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_QWEN2:
|
|
case LLM_ARCH_QWEN2VL:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
|
|
// if output is NULL, init from the input tok embed
|
|
if (output == NULL) {
|
|
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
|
|
// optional bias tensors
|
|
layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0);
|
|
layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, 0);
|
|
layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, 0);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_QWEN2MOE:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
|
|
// optional bias tensors
|
|
layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0);
|
|
layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, 0);
|
|
layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, 0);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
|
|
|
|
if (n_expert == 0) {
|
|
throw std::runtime_error("n_expert must be > 0 for QWEN2MOE");
|
|
}
|
|
if (n_expert_used == 0) {
|
|
throw std::runtime_error("n_expert_used must be > 0 for QWEN2MOE");
|
|
}
|
|
|
|
// MoE branch
|
|
const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
|
|
|
|
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0);
|
|
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
|
|
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0);
|
|
|
|
// Shared expert branch
|
|
const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;
|
|
|
|
layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), {n_embd}, 0);
|
|
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp}, 0);
|
|
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0);
|
|
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_PHI2:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
|
output_b = create_tensor(tn(LLM_TENSOR_OUTPUT, "bias"), {n_vocab}, 0);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);
|
|
|
|
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, TENSOR_NOT_REQUIRED);
|
|
layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, TENSOR_NOT_REQUIRED);
|
|
|
|
if (layer.wqkv == nullptr) {
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0);
|
|
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, 0);
|
|
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, 0);
|
|
}
|
|
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
|
|
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
|
|
layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0);
|
|
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_PHI3:
|
|
{
|
|
const int64_t n_embd_head = n_embd / n_head;
|
|
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), { n_embd, n_vocab }, 0);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
|
|
|
|
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, TENSOR_NOT_REQUIRED);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }, 0);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
|
|
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, 2 * n_ff }, 0);
|
|
|
|
layer.rope_long = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight", i), { n_embd_head/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
|
|
layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_embd_head/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
|
|
}
|
|
} break;
|
|
case LLM_ARCH_PLAMO:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_GPT2:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
pos_embd = create_tensor(tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);
|
|
|
|
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
|
|
layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0);
|
|
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0);
|
|
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
|
|
layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0);
|
|
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_CODESHELL:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);
|
|
|
|
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
|
|
layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0);
|
|
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0);
|
|
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
|
|
layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0);
|
|
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_ORION:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);
|
|
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0);
|
|
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_INTERNLM2:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
// layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_GEMMA:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_GEMMA2:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
|
|
layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
|
layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_STARCODER2:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
|
|
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
|
|
// if output is NULL, init from the input tok embed
|
|
if (output == NULL) {
|
|
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);
|
|
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
|
|
// optional bias tensors
|
|
layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0);
|
|
layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, 0);
|
|
layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, 0);
|
|
layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0);
|
|
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
|
|
// optional bias tensors
|
|
layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0);
|
|
layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP , "bias", i), { n_ff}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_MAMBA:
|
|
{
|
|
const int64_t d_conv = hparams.ssm_d_conv;
|
|
const int64_t d_inner = hparams.ssm_d_inner;
|
|
const int64_t d_state = hparams.ssm_d_state;
|
|
const int64_t dt_rank = hparams.ssm_dt_rank;
|
|
|
|
// only an expansion factor of 2 is supported for now
|
|
if (2 * n_embd != d_inner) {
|
|
throw std::runtime_error("only an expansion factor of 2 is supported for now");
|
|
}
|
|
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
|
|
// if output is NULL, init from the input tok embed, duplicated to allow offloading
|
|
if (output == NULL) {
|
|
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
// norm
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, 2*d_inner}, 0);
|
|
|
|
layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner}, 0);
|
|
layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner}, 0);
|
|
|
|
layer.ssm_x = create_tensor(tn(LLM_TENSOR_SSM_X, "weight", i), {d_inner, dt_rank + 2*d_state}, 0);
|
|
|
|
layer.ssm_dt = create_tensor(tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_rank, d_inner}, 0);
|
|
layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {d_inner}, 0);
|
|
|
|
// no "weight" suffix for these
|
|
layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {d_state, d_inner}, 0);
|
|
layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {d_inner}, 0);
|
|
|
|
// out_proj
|
|
layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_XVERSE:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_COMMAND_R:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
// init output from the input tok embed
|
|
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
if (n_layer >= 64){
|
|
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}, 0);
|
|
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, 0);
|
|
}
|
|
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_COHERE2:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
|
|
// init output from the input tok embed
|
|
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab },
|
|
TENSOR_DUPLICATED);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
|
|
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd }, 0);
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_gqa }, 0);
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_gqa }, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }, 0);
|
|
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, 0);
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, n_ff }, 0);
|
|
}
|
|
}
|
|
break;
|
|
case LLM_ARCH_OLMO: // adapted from LLM_ARCH_LLAMA with norm params removed
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
|
|
// if output is NULL, init from the input tok embed
|
|
if (output == NULL) {
|
|
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_OLMO2:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, 0);
|
|
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, 0);
|
|
layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
|
layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_OLMOE:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, 0);
|
|
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
|
|
|
|
if (n_expert == 0) {
|
|
throw std::runtime_error("n_expert must be > 0");
|
|
}
|
|
if (n_expert_used == 0) {
|
|
throw std::runtime_error("n_expert_used must be > 0");
|
|
}
|
|
|
|
// MoE branch
|
|
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0);
|
|
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff, n_embd, n_expert}, 0);
|
|
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_OPENELM:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
// init output from the input tok embed
|
|
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
const int64_t n_head = hparams.n_head(i);
|
|
const int64_t n_head_qkv = 2*hparams.n_head_kv(i) + n_head;
|
|
const int64_t n_ff = hparams.n_ff(i);
|
|
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_head_qkv*n_embd_head_k}, 0);
|
|
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
|
|
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head*n_embd_head_k, n_embd}, 0);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_GPTNEOX:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);
|
|
|
|
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
|
|
layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0);
|
|
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0);
|
|
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
|
|
layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0);
|
|
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_ARCTIC:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
|
|
|
|
// if output is NULL, init from the input tok embed
|
|
if (output == NULL) {
|
|
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_embd}, 0);
|
|
|
|
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
|
|
layer.ffn_norm_exps = create_tensor(tn(LLM_TENSOR_FFN_NORM_EXPS, "weight", i), {n_embd}, 0);
|
|
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, false);
|
|
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}, 0);
|
|
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_DEEPSEEK:
|
|
{
|
|
|
|
const int64_t n_ff_exp = hparams.n_ff_exp;
|
|
const int64_t n_expert_shared = hparams.n_expert_shared;
|
|
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
if (i < (int) hparams.n_layer_dense_lead) {
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
} else {
|
|
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
|
|
|
|
if (n_expert == 0) {
|
|
throw std::runtime_error("n_expert must be > 0");
|
|
}
|
|
if (n_expert_used == 0) {
|
|
throw std::runtime_error("n_expert_used must be > 0");
|
|
}
|
|
|
|
// MoE branch
|
|
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0);
|
|
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
|
|
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0);
|
|
|
|
// Shared expert branch
|
|
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
|
|
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}, 0);
|
|
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
|
|
}
|
|
}
|
|
} break;
|
|
case LLM_ARCH_DEEPSEEK2:
|
|
{
|
|
const bool is_lite = (hparams.n_layer == 27);
|
|
|
|
const int64_t n_embd_head_qk_rope = hparams.n_rot;
|
|
const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
|
|
|
|
const int64_t q_lora_rank = hparams.n_lora_q;
|
|
const int64_t kv_lora_rank = hparams.n_lora_kv;
|
|
|
|
const int64_t n_ff_exp = hparams.n_ff_exp;
|
|
const int64_t n_expert_shared = hparams.n_expert_shared;
|
|
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
if (!is_lite) {
|
|
layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, 0);
|
|
}
|
|
|
|
layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, 0);
|
|
|
|
if (!is_lite) {
|
|
layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, 0);
|
|
layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k}, 0);
|
|
} else {
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}, 0);
|
|
}
|
|
|
|
layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + (n_embd_head_qk_rope)}, 0);
|
|
layer.wkv_b = create_tensor(tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_head * ( n_embd_head_v), n_embd}, 0);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
if (i < (int) hparams.n_layer_dense_lead) {
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
} else {
|
|
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
|
|
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
|
|
|
|
if (n_expert == 0) {
|
|
throw std::runtime_error("n_expert must be > 0");
|
|
}
|
|
if (n_expert_used == 0) {
|
|
throw std::runtime_error("n_expert_used must be > 0");
|
|
}
|
|
|
|
// MoE branch
|
|
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0);
|
|
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
|
|
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0);
|
|
|
|
// Shared expert branch
|
|
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
|
|
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}, 0);
|
|
layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
|
|
}
|
|
}
|
|
} break;
|
|
case LLM_ARCH_BITNET:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.attn_sub_norm = create_tensor(tn(LLM_TENSOR_ATTN_SUB_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.wq_scale = create_tensor(tn(LLM_TENSOR_ATTN_Q, "scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wk_scale = create_tensor(tn(LLM_TENSOR_ATTN_K, "scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wv_scale = create_tensor(tn(LLM_TENSOR_ATTN_V, "scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.wo_scale = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.ffn_sub_norm = create_tensor(tn(LLM_TENSOR_FFN_SUB_NORM, "weight", i), {n_ff}, 0);
|
|
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_gate_scale = create_tensor(tn(LLM_TENSOR_FFN_GATE, "scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
|
|
layer.ffn_down_scale = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_up_scale = create_tensor(tn(LLM_TENSOR_FFN_UP, "scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_T5:
|
|
{
|
|
const auto n_rel_attn_bkts = hparams.n_rel_attn_bkts;
|
|
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output_norm = create_tensor(tn(LLM_TENSOR_DEC_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
|
|
// if output is NULL, init from the input tok embed
|
|
if (output == NULL) {
|
|
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.attn_rel_b_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, TENSOR_NOT_REQUIRED);
|
|
|
|
layer.wq_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}, 0);
|
|
layer.wk_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
|
|
layer.wv_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
|
|
layer.wo_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);
|
|
|
|
layer.ffn_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.ffn_gate_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED);
|
|
layer.ffn_down_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
|
layer.ffn_up_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_DEC_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.attn_rel_b = create_tensor(tn(LLM_TENSOR_DEC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, TENSOR_NOT_REQUIRED);
|
|
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_DEC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}, 0);
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_DEC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_DEC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_DEC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);
|
|
|
|
layer.attn_norm_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
// this tensor seems to be unused in HF transformers implementation
|
|
layer.attn_rel_b_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, TENSOR_NOT_REQUIRED);
|
|
|
|
layer.wq_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}, 0);
|
|
layer.wk_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
|
|
layer.wv_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
|
|
layer.wo_cross = create_tensor(tn(LLM_TENSOR_DEC_CROSS_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_DEC_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_DEC_FFN_GATE, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED);
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_DEC_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_DEC_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_T5ENCODER:
|
|
{
|
|
const auto n_rel_attn_bkts = hparams.n_rel_attn_bkts;
|
|
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
|
|
// if output is NULL, init from the input tok embed
|
|
if (output == NULL) {
|
|
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.attn_rel_b_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, TENSOR_NOT_REQUIRED);
|
|
|
|
layer.wq_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}, 0);
|
|
layer.wk_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
|
|
layer.wv_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
|
|
layer.wo_enc = create_tensor(tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd}, 0);
|
|
|
|
layer.ffn_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.ffn_gate_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED);
|
|
layer.ffn_down_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
|
layer.ffn_up_enc = create_tensor(tn(LLM_TENSOR_ENC_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_JAIS:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);
|
|
|
|
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
|
|
layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0);
|
|
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0);
|
|
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
|
|
layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0);
|
|
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, 0);
|
|
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_CHATGLM:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
|
|
layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0);
|
|
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff * 2}, 0);
|
|
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_NEMOTRON:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);
|
|
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
|
|
// optional bias tensors
|
|
layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED);
|
|
layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED);
|
|
layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0);
|
|
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
|
|
// optional MLP bias
|
|
layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
|
layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_EXAONE:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_RWKV6:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// Block 0, LN0
|
|
tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}, 0);
|
|
tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
const int time_mix_extra_dim = hparams.time_mix_extra_dim;
|
|
const int time_decay_extra_dim = hparams.time_decay_extra_dim;
|
|
const int head_size = hparams.wkv_head_size;
|
|
const int attn_hidden_size = n_embd;
|
|
const int ffn_size = hparams.n_ff_arr[0];
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);
|
|
|
|
layer.attn_norm_2 = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, 0);
|
|
layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, 0);
|
|
|
|
layer.time_mix_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W1, "weight", i), {n_embd, time_mix_extra_dim * 5}, 0);
|
|
layer.time_mix_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {time_mix_extra_dim, n_embd, 5}, 0);
|
|
|
|
layer.time_mix_lerp_x = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_X, "weight", i), {n_embd, 1, 1}, 0);
|
|
layer.time_mix_lerp_w = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_W, "weight", i), {n_embd, 1, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.time_mix_lerp_k = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_K, "weight", i), {n_embd, 1, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.time_mix_lerp_v = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_V, "weight", i), {n_embd, 1, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.time_mix_lerp_r = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_R, "weight", i), {n_embd, 1, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.time_mix_lerp_g = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_G, "weight", i), {n_embd, 1, 1}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.time_mix_lerp_fused = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_FUSED, "weight", i), {n_embd, 1, 1, 5}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
GGML_ASSERT(!(layer.time_mix_lerp_fused == NULL && layer.time_mix_lerp_w == NULL));
|
|
|
|
layer.time_mix_first = create_tensor(tn(LLM_TENSOR_TIME_MIX_FIRST, "weight", i), {head_size, n_embd / head_size}, 0);
|
|
layer.time_mix_decay = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY, "weight", i), {n_embd}, 0);
|
|
layer.time_mix_decay_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY_W1, "weight", i), {n_embd, time_decay_extra_dim}, 0);
|
|
layer.time_mix_decay_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY_W2, "weight", i), {time_decay_extra_dim, attn_hidden_size}, 0);
|
|
layer.time_mix_key = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {attn_hidden_size, n_embd}, 0);
|
|
layer.time_mix_value = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {attn_hidden_size, n_embd}, 0);
|
|
layer.time_mix_receptance = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {attn_hidden_size, n_embd}, 0);
|
|
layer.time_mix_gate = create_tensor(tn(LLM_TENSOR_TIME_MIX_GATE, "weight", i), {attn_hidden_size, n_embd}, 0);
|
|
|
|
layer.time_mix_ln = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "weight", i), {n_embd}, 0);
|
|
layer.time_mix_ln_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_LN, "bias", i), {n_embd}, 0);
|
|
layer.time_mix_output = create_tensor(tn(LLM_TENSOR_TIME_MIX_OUTPUT, "weight", i), {n_embd, attn_hidden_size}, 0);
|
|
|
|
layer.channel_mix_lerp_k = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_LERP_K, "weight", i), {n_embd, 1, 1}, 0);
|
|
layer.channel_mix_lerp_r = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_LERP_R, "weight", i), {n_embd, 1, 1}, 0);
|
|
|
|
layer.channel_mix_key = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_KEY, "weight", i), {n_embd, ffn_size}, 0);
|
|
layer.channel_mix_value = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_VALUE, "weight", i), {ffn_size, n_embd}, 0);
|
|
layer.channel_mix_receptance = create_tensor(tn(LLM_TENSOR_CHANNEL_MIX_RECEPTANCE, "weight", i), {n_embd, n_embd}, 0);
|
|
}
|
|
|
|
} break;
|
|
case LLM_ARCH_RWKV6QWEN2:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
const int time_mix_extra_dim = hparams.time_mix_extra_dim;
|
|
const int time_decay_extra_dim = hparams.time_decay_extra_dim;
|
|
const int head_size = hparams.wkv_head_size;
|
|
const int attn_hidden_size = n_embd;
|
|
const int n_head_kv = hparams.n_head_kv();
|
|
int attn_key_value_size;
|
|
if (n_head_kv == 0 || attn_hidden_size / head_size == n_head_kv) {
|
|
attn_key_value_size = attn_hidden_size;
|
|
} else {
|
|
attn_key_value_size = n_head_kv * head_size;
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.time_mix_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W1, "weight", i), {n_embd, time_mix_extra_dim * 5}, 0);
|
|
layer.time_mix_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {time_mix_extra_dim, n_embd, 5}, 0);
|
|
|
|
layer.time_mix_lerp_x = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_X, "weight", i), {n_embd, 1, 1}, 0);
|
|
layer.time_mix_lerp_fused = create_tensor(tn(LLM_TENSOR_TIME_MIX_LERP_FUSED, "weight", i), {n_embd, 1, 1, 5}, 0);
|
|
|
|
layer.time_mix_first = create_tensor(tn(LLM_TENSOR_TIME_MIX_FIRST, "weight", i), {head_size, n_embd / head_size}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.time_mix_decay = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY, "weight", i), {n_embd}, 0);
|
|
layer.time_mix_decay_w1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY_W1, "weight", i), {n_embd, time_decay_extra_dim}, 0);
|
|
layer.time_mix_decay_w2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_DECAY_W2, "weight", i), {time_decay_extra_dim, attn_hidden_size}, 0);
|
|
layer.time_mix_key = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {n_embd, attn_key_value_size}, 0);
|
|
layer.time_mix_value = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {n_embd, attn_key_value_size}, 0);
|
|
layer.time_mix_receptance = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {attn_hidden_size, n_embd}, 0);
|
|
layer.time_mix_gate = create_tensor(tn(LLM_TENSOR_TIME_MIX_GATE, "weight", i), {attn_hidden_size, n_embd}, 0);
|
|
// optional bias tensors
|
|
layer.time_mix_key_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_KEY, "bias", i), {attn_key_value_size}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.time_mix_value_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_VALUE, "bias", i), {attn_key_value_size}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.time_mix_receptance_b = create_tensor(tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "bias", i), {attn_hidden_size}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
layer.time_mix_output = create_tensor(tn(LLM_TENSOR_TIME_MIX_OUTPUT, "weight", i), {n_embd, attn_hidden_size}, 0);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_CHAMELEON:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
|
|
// if output is NULL, init from the input tok embed
|
|
if (output == NULL) {
|
|
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}, 0);
|
|
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, 0);
|
|
layer.attn_q_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd_head_k, n_head}, TENSOR_NOT_REQUIRED);
|
|
layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd_head_k, n_head_kv}, TENSOR_NOT_REQUIRED);
|
|
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
|
|
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
|
|
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
|
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
}
|
|
} break;
|
|
case LLM_ARCH_WAVTOKENIZER_DEC:
|
|
{
|
|
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {hparams.n_embd_features, n_vocab}, 0);
|
|
|
|
conv1d = create_tensor(tn(LLM_TENSOR_CONV1D, "weight"), {7, hparams.n_embd_features, hparams.posnet.n_embd}, 0);
|
|
conv1d_b = create_tensor(tn(LLM_TENSOR_CONV1D, "bias"), {1, hparams.posnet.n_embd}, 0);
|
|
|
|
// posnet
|
|
{
|
|
const int64_t n_embd = hparams.posnet.n_embd;
|
|
|
|
for (uint32_t i = 0; i < hparams.posnet.n_layer; ++i) {
|
|
auto & layer = layers[i].posnet;
|
|
|
|
// posnet:
|
|
//
|
|
// - resnet
|
|
// - resnet
|
|
// - attn
|
|
// - resnet
|
|
// - resnet
|
|
// - norm
|
|
//
|
|
switch (i) {
|
|
case 0:
|
|
case 1:
|
|
case 3:
|
|
case 4:
|
|
{
|
|
layer.norm1 = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "weight", i), {1, n_embd}, 0);
|
|
layer.norm1_b = create_tensor(tn(LLM_TENSOR_POS_NET_NORM1, "bias", i), {1, n_embd}, 0);
|
|
|
|
layer.conv1 = create_tensor(tn(LLM_TENSOR_POS_NET_CONV1, "weight", i), {3, n_embd, n_embd}, 0);
|
|
layer.conv1_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV1, "bias", i), {1, n_embd}, 0);
|
|
|
|
layer.norm2 = create_tensor(tn(LLM_TENSOR_POS_NET_NORM2, "weight", i), {1, n_embd}, 0);
|
|
layer.norm2_b = create_tensor(tn(LLM_TENSOR_POS_NET_NORM2, "bias", i), {1, n_embd}, 0);
|
|
|
|
layer.conv2 = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "weight", i), {3, n_embd, n_embd}, 0);
|
|
layer.conv2_b = create_tensor(tn(LLM_TENSOR_POS_NET_CONV2, "bias", i), {1, n_embd}, 0);
|
|
} break;
|
|
case 2:
|
|
{
|
|
layer.attn_norm = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "weight", i), {1, n_embd}, 0);
|
|
layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "bias", i), {1, n_embd}, 0);
|
|
|
|
layer.attn_q = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_Q, "weight", i), {1, n_embd, n_embd}, 0);
|
|
layer.attn_q_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_Q, "bias", i), {1, n_embd}, 0);
|
|
|
|
layer.attn_k = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_K, "weight", i), {1, n_embd, n_embd}, 0);
|
|
layer.attn_k_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_K, "bias", i), {1, n_embd}, 0);
|
|
|
|
layer.attn_v = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_V, "weight", i), {1, n_embd, n_embd}, 0);
|
|
layer.attn_v_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_V, "bias", i), {1, n_embd}, 0);
|
|
|
|
layer.attn_o = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_OUT, "weight", i), {1, n_embd, n_embd}, 0);
|
|
layer.attn_o_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_OUT, "bias", i), {1, n_embd}, 0);
|
|
} break;
|
|
case 5:
|
|
{
|
|
layer.norm = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "weight", i), {1, n_embd}, 0);
|
|
layer.norm_b = create_tensor(tn(LLM_TENSOR_POS_NET_ATTN_NORM, "bias", i), {1, n_embd}, 0);
|
|
} break;
|
|
default: GGML_ABORT("unknown posnet layer");
|
|
};
|
|
}
|
|
}
|
|
|
|
GGML_ASSERT(hparams.posnet.n_embd == hparams.convnext.n_embd);
|
|
|
|
tok_norm = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {hparams.posnet.n_embd}, 0);
|
|
tok_norm_b = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {hparams.posnet.n_embd}, 0);
|
|
|
|
// convnext
|
|
{
|
|
const int64_t n_embd = hparams.convnext.n_embd;
|
|
|
|
for (uint32_t i = 0; i < hparams.convnext.n_layer; ++i) {
|
|
auto & layer = layers[i].convnext;
|
|
|
|
layer.dw = create_tensor(tn(LLM_TENSOR_CONVNEXT_DW, "weight", i), {7, 1, n_embd}, 0);
|
|
layer.dw_b = create_tensor(tn(LLM_TENSOR_CONVNEXT_DW, "bias", i), {1, n_embd}, 0);
|
|
|
|
layer.norm = create_tensor(tn(LLM_TENSOR_CONVNEXT_NORM, "weight", i), {n_embd}, 0);
|
|
layer.norm_b = create_tensor(tn(LLM_TENSOR_CONVNEXT_NORM, "bias", i), {n_embd}, 0);
|
|
|
|
layer.pw1 = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW1, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.pw1_b = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW1, "bias", i), {n_ff}, 0);
|
|
|
|
layer.pw2 = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW2, "weight", i), {n_ff, n_embd}, 0);
|
|
layer.pw2_b = create_tensor(tn(LLM_TENSOR_CONVNEXT_PW2, "bias", i), {n_embd}, 0);
|
|
|
|
layer.gamma = create_tensor(tn(LLM_TENSOR_CONVNEXT_GAMMA, "weight", i), {n_embd}, 0);
|
|
}
|
|
|
|
// output
|
|
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
|
|
}
|
|
|
|
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {hparams.convnext.n_embd, n_embd}, 0);
|
|
output_b = create_tensor(tn(LLM_TENSOR_OUTPUT, "bias"), {n_embd}, 0);
|
|
} break;
|
|
default:
|
|
throw std::runtime_error("unknown architecture");
|
|
}
|
|
|
|
if (n_moved_tensors > 0) {
|
|
LLAMA_LOG_DEBUG("%s: tensor '%s' (%s) (and %d others) cannot be used with preferred buffer type %s, using %s instead\n",
|
|
__func__, first_moved_tensor->name, ggml_type_name(first_moved_tensor->type), n_moved_tensors - 1,
|
|
ggml_backend_buft_name(first_moved_from_buft), ggml_backend_buft_name(first_moved_to_buft));
|
|
}
|
|
}
|
|
|
|
ml.done_getting_tensors();
|
|
|
|
ml.init_mappings(true, use_mlock ? &pimpl->mlock_mmaps : nullptr);
|
|
pimpl->mappings.reserve(ml.mappings.size());
|
|
|
|
// create the backend buffers
|
|
std::vector<std::pair<ggml_context *, llama_buf_map>> ctx_bufs;
|
|
ctx_bufs.reserve(ctx_map.size());
|
|
|
|
// Ensure we have enough capacity for the maximum backend buffer we will potentially create
|
|
const size_t n_max_backend_buffer = ctx_map.size() * ml.files.size();
|
|
pimpl->bufs.reserve(n_max_backend_buffer);
|
|
|
|
for (auto & it : ctx_map) {
|
|
ggml_backend_buffer_type_t buft = it.first;
|
|
ggml_context * ctx = it.second;
|
|
|
|
// skip contexts without tensors
|
|
if (ggml_get_first_tensor(ctx) == nullptr) {
|
|
continue;
|
|
}
|
|
|
|
llama_buf_map buf_map;
|
|
buf_map.reserve(n_max_backend_buffer);
|
|
|
|
// check if it is possible to use buffer_from_host_ptr with this buffer type
|
|
ggml_backend_dev_t dev = ggml_backend_buft_get_device(buft);
|
|
if (!dev) {
|
|
// FIXME: workaround for CPU backend buft having a NULL device
|
|
dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
|
|
}
|
|
ggml_backend_dev_props props;
|
|
ggml_backend_dev_get_props(dev, &props);
|
|
bool buffer_from_host_ptr_supported = props.caps.buffer_from_host_ptr;
|
|
bool is_default_buft = buft == ggml_backend_dev_buffer_type(dev);
|
|
|
|
if (ml.use_mmap && use_mmap_buffer && buffer_from_host_ptr_supported && is_default_buft) {
|
|
for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
|
|
// only the mmap region containing the tensors in the model is mapped to the backend buffer
|
|
// this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, then we could just use metal for all layers
|
|
// this allows using partial offloading when the model size exceeds the metal buffer size, but not the RAM size
|
|
void * addr = nullptr;
|
|
size_t first, last; // NOLINT
|
|
ml.get_mapping_range(&first, &last, &addr, idx, ctx);
|
|
if (first >= last) {
|
|
continue;
|
|
}
|
|
const size_t max_size = ggml_get_max_tensor_size(ctx);
|
|
ggml_backend_buffer_t buf = ggml_backend_dev_buffer_from_host_ptr(dev, (char *) addr + first, last - first, max_size);
|
|
if (buf == nullptr) {
|
|
throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft)));
|
|
}
|
|
pimpl->bufs.emplace_back(buf);
|
|
buf_map.emplace(idx, buf);
|
|
}
|
|
}
|
|
else {
|
|
ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
|
|
if (buf == nullptr) {
|
|
throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft)));
|
|
}
|
|
pimpl->bufs.emplace_back(buf);
|
|
if (use_mlock && ggml_backend_buffer_is_host(buf)) {
|
|
pimpl->mlock_bufs.emplace_back(new llama_mlock);
|
|
auto & mlock_buf = pimpl->mlock_bufs.back();
|
|
mlock_buf->init (ggml_backend_buffer_get_base(buf));
|
|
mlock_buf->grow_to(ggml_backend_buffer_get_size(buf));
|
|
}
|
|
for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
|
|
buf_map.emplace(idx, buf);
|
|
}
|
|
}
|
|
|
|
if (pimpl->bufs.empty()) {
|
|
throw std::runtime_error("failed to allocate buffer");
|
|
}
|
|
|
|
for (auto & buf : buf_map) {
|
|
// indicate that this buffer contains weights
|
|
// this is used by ggml_backend_sched to improve op scheduling: ops that use a weight are preferably scheduled to the backend that contains the weight
|
|
ggml_backend_buffer_set_usage(buf.second, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
|
|
}
|
|
|
|
ctx_bufs.emplace_back(ctx, buf_map);
|
|
}
|
|
|
|
if (llama_supports_gpu_offload()) {
|
|
const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
|
|
|
|
LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU\n", __func__, n_gpu);
|
|
if (n_gpu_layers > (int) hparams.n_layer) {
|
|
LLAMA_LOG_INFO("%s: offloading output layer to GPU\n", __func__);
|
|
}
|
|
|
|
const int max_backend_supported_layers = hparams.n_layer + 1;
|
|
const int max_offloadable_layers = hparams.n_layer + 1;
|
|
|
|
LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers);
|
|
}
|
|
|
|
// print memory requirements per buffer type
|
|
for (auto & buf : pimpl->bufs) {
|
|
LLAMA_LOG_INFO("%s: %12s model buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf.get()), ggml_backend_buffer_get_size(buf.get()) / 1024.0 / 1024.0);
|
|
}
|
|
|
|
// populate tensors_by_name
|
|
for (auto & ctx : pimpl->ctxs) {
|
|
for (auto * cur = ggml_get_first_tensor(ctx.get()); cur != NULL; cur = ggml_get_next_tensor(ctx.get(), cur)) {
|
|
tensors_by_name.emplace_back(ggml_get_name(cur), cur);
|
|
}
|
|
}
|
|
|
|
// load tensor data
|
|
for (auto & it : ctx_bufs) {
|
|
ggml_context * ctx = it.first;
|
|
auto & bufs = it.second;
|
|
if (!ml.load_all_data(ctx, bufs, use_mlock ? &pimpl->mlock_mmaps : NULL, params.progress_callback, params.progress_callback_user_data)) {
|
|
return false;
|
|
}
|
|
}
|
|
|
|
if (use_mmap_buffer) {
|
|
for (auto & mapping : ml.mappings) {
|
|
pimpl->mappings.emplace_back(std::move(mapping));
|
|
}
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
std::string llama_model::arch_name() const {
|
|
return llm_arch_name(arch);
|
|
}
|
|
|
|
std::string llama_model::type_name() const {
|
|
return llm_type_name(type);
|
|
}
|
|
|
|
std::string llama_model::desc() const {
|
|
return pimpl->desc_str;
|
|
}
|
|
|
|
size_t llama_model::size() const {
|
|
return pimpl->n_bytes;
|
|
}
|
|
|
|
size_t llama_model::max_nodes() const {
|
|
return std::max<size_t>(8192, tensors_by_name.size()*5);
|
|
}
|
|
|
|
size_t llama_model::n_devices() const {
|
|
return devices.size();
|
|
}
|
|
|
|
uint64_t llama_model::n_elements() const {
|
|
return pimpl->n_elements;
|
|
}
|
|
|
|
void llama_model::print_info() const {
|
|
const char * rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train);
|
|
|
|
auto print_f = [](const std::function<uint32_t(uint32_t)> & f, uint32_t n) {
|
|
bool is_var = false;
|
|
|
|
std::vector<uint32_t> v;
|
|
for (uint32_t i = 0; i < n; ++i) {
|
|
v.push_back(f(i));
|
|
if (v[i] != v[0]) {
|
|
is_var = true;
|
|
}
|
|
}
|
|
|
|
std::stringstream ss;
|
|
|
|
if (is_var) {
|
|
ss << "[";
|
|
for (uint32_t i = 0; i < n; ++i) {
|
|
ss << v[i];
|
|
if (i < n - 1) {
|
|
ss << ", ";
|
|
}
|
|
}
|
|
ss << "]";
|
|
} else {
|
|
ss << v[0];
|
|
}
|
|
|
|
return ss.str();
|
|
};
|
|
|
|
// hparams
|
|
LLAMA_LOG_INFO("%s: arch = %s\n", __func__, arch_name().c_str());
|
|
LLAMA_LOG_INFO("%s: vocab_only = %d\n", __func__, hparams.vocab_only);
|
|
|
|
if (!hparams.vocab_only) {
|
|
LLAMA_LOG_INFO("%s: n_ctx_train = %u\n", __func__, hparams.n_ctx_train);
|
|
LLAMA_LOG_INFO("%s: n_embd = %u\n", __func__, hparams.n_embd);
|
|
LLAMA_LOG_INFO("%s: n_layer = %u\n", __func__, hparams.n_layer);
|
|
LLAMA_LOG_INFO("%s: n_head = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_head(il); }, hparams.n_layer).c_str());
|
|
LLAMA_LOG_INFO("%s: n_head_kv = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_head_kv(il); }, hparams.n_layer).c_str());
|
|
LLAMA_LOG_INFO("%s: n_rot = %u\n", __func__, hparams.n_rot);
|
|
LLAMA_LOG_INFO("%s: n_swa = %u\n", __func__, hparams.n_swa);
|
|
LLAMA_LOG_INFO("%s: n_embd_head_k = %u\n", __func__, hparams.n_embd_head_k);
|
|
LLAMA_LOG_INFO("%s: n_embd_head_v = %u\n", __func__, hparams.n_embd_head_v);
|
|
LLAMA_LOG_INFO("%s: n_gqa = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_gqa(il); }, hparams.n_layer).c_str());
|
|
LLAMA_LOG_INFO("%s: n_embd_k_gqa = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_embd_k_gqa(il); }, hparams.n_layer).c_str());
|
|
LLAMA_LOG_INFO("%s: n_embd_v_gqa = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_embd_v_gqa(il); }, hparams.n_layer).c_str());
|
|
LLAMA_LOG_INFO("%s: f_norm_eps = %.1e\n", __func__, hparams.f_norm_eps);
|
|
LLAMA_LOG_INFO("%s: f_norm_rms_eps = %.1e\n", __func__, hparams.f_norm_rms_eps);
|
|
LLAMA_LOG_INFO("%s: f_clamp_kqv = %.1e\n", __func__, hparams.f_clamp_kqv);
|
|
LLAMA_LOG_INFO("%s: f_max_alibi_bias = %.1e\n", __func__, hparams.f_max_alibi_bias);
|
|
LLAMA_LOG_INFO("%s: f_logit_scale = %.1e\n", __func__, hparams.f_logit_scale);
|
|
LLAMA_LOG_INFO("%s: n_ff = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_ff(il); }, hparams.n_layer).c_str());
|
|
LLAMA_LOG_INFO("%s: n_expert = %u\n", __func__, hparams.n_expert);
|
|
LLAMA_LOG_INFO("%s: n_expert_used = %u\n", __func__, hparams.n_expert_used);
|
|
LLAMA_LOG_INFO("%s: causal attn = %d\n", __func__, hparams.causal_attn);
|
|
LLAMA_LOG_INFO("%s: pooling type = %d\n", __func__, hparams.pooling_type);
|
|
LLAMA_LOG_INFO("%s: rope type = %d\n", __func__, hparams.rope_type);
|
|
LLAMA_LOG_INFO("%s: rope scaling = %s\n", __func__, rope_scaling_type);
|
|
LLAMA_LOG_INFO("%s: freq_base_train = %.1f\n", __func__, hparams.rope_freq_base_train);
|
|
LLAMA_LOG_INFO("%s: freq_scale_train = %g\n", __func__, hparams.rope_freq_scale_train);
|
|
LLAMA_LOG_INFO("%s: n_ctx_orig_yarn = %u\n", __func__, hparams.n_ctx_orig_yarn);
|
|
LLAMA_LOG_INFO("%s: rope_finetuned = %s\n", __func__, hparams.rope_finetuned ? "yes" : "unknown");
|
|
LLAMA_LOG_INFO("%s: ssm_d_conv = %u\n", __func__, hparams.ssm_d_conv);
|
|
LLAMA_LOG_INFO("%s: ssm_d_inner = %u\n", __func__, hparams.ssm_d_inner);
|
|
LLAMA_LOG_INFO("%s: ssm_d_state = %u\n", __func__, hparams.ssm_d_state);
|
|
LLAMA_LOG_INFO("%s: ssm_dt_rank = %u\n", __func__, hparams.ssm_dt_rank);
|
|
LLAMA_LOG_INFO("%s: ssm_dt_b_c_rms = %d\n", __func__, hparams.ssm_dt_b_c_rms);
|
|
}
|
|
|
|
LLAMA_LOG_INFO("%s: model type = %s\n", __func__, type_name().c_str());
|
|
if (pimpl->n_elements >= 1e12) {
|
|
LLAMA_LOG_INFO("%s: model params = %.2f T\n", __func__, pimpl->n_elements*1e-12);
|
|
} else if (pimpl->n_elements >= 1e9) {
|
|
LLAMA_LOG_INFO("%s: model params = %.2f B\n", __func__, pimpl->n_elements*1e-9);
|
|
} else if (pimpl->n_elements >= 1e6) {
|
|
LLAMA_LOG_INFO("%s: model params = %.2f M\n", __func__, pimpl->n_elements*1e-6);
|
|
} else {
|
|
LLAMA_LOG_INFO("%s: model params = %.2f K\n", __func__, pimpl->n_elements*1e-3);
|
|
}
|
|
|
|
// general kv
|
|
LLAMA_LOG_INFO("%s: general.name = %s\n", __func__, name.c_str());
|
|
|
|
if (arch == LLM_ARCH_DEEPSEEK) {
|
|
LLAMA_LOG_INFO("%s: n_layer_dense_lead = %d\n", __func__, hparams.n_layer_dense_lead);
|
|
LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp);
|
|
LLAMA_LOG_INFO("%s: n_expert_shared = %d\n", __func__, hparams.n_expert_shared);
|
|
LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n", __func__, hparams.expert_weights_scale);
|
|
}
|
|
|
|
if (arch == LLM_ARCH_DEEPSEEK2) {
|
|
LLAMA_LOG_INFO("%s: n_layer_dense_lead = %d\n", __func__, hparams.n_layer_dense_lead);
|
|
LLAMA_LOG_INFO("%s: n_lora_q = %d\n", __func__, hparams.n_lora_q);
|
|
LLAMA_LOG_INFO("%s: n_lora_kv = %d\n", __func__, hparams.n_lora_kv);
|
|
LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp);
|
|
LLAMA_LOG_INFO("%s: n_expert_shared = %d\n", __func__, hparams.n_expert_shared);
|
|
LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n", __func__, hparams.expert_weights_scale);
|
|
LLAMA_LOG_INFO("%s: expert_weights_norm = %d\n", __func__, hparams.expert_weights_norm);
|
|
LLAMA_LOG_INFO("%s: expert_gating_func = %s\n", __func__, llama_expert_gating_func_name((enum llama_expert_gating_func_type) hparams.expert_gating_func));
|
|
LLAMA_LOG_INFO("%s: rope_yarn_log_mul = %.4f\n", __func__, hparams.rope_yarn_log_mul);
|
|
}
|
|
|
|
if (arch == LLM_ARCH_QWEN2MOE) {
|
|
LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp);
|
|
LLAMA_LOG_INFO("%s: n_ff_shexp = %d\n", __func__, hparams.n_ff_shexp);
|
|
}
|
|
|
|
if (arch == LLM_ARCH_MINICPM || arch == LLM_ARCH_GRANITE || arch == LLM_ARCH_GRANITE_MOE) {
|
|
LLAMA_LOG_INFO("%s: f_embedding_scale = %f\n", __func__, hparams.f_embedding_scale);
|
|
LLAMA_LOG_INFO("%s: f_residual_scale = %f\n", __func__, hparams.f_residual_scale);
|
|
LLAMA_LOG_INFO("%s: f_attention_scale = %f\n", __func__, hparams.f_attention_scale);
|
|
}
|
|
|
|
vocab.print_info();
|
|
}
|
|
|
|
ggml_backend_dev_t llama_model::dev_layer(int il) const {
|
|
return pimpl->dev_layer.at(il).dev;
|
|
}
|
|
|
|
ggml_backend_dev_t llama_model::dev_output() const {
|
|
return pimpl->dev_output.dev;
|
|
}
|
|
|
|
template<typename F>
|
|
static bool buft_supported(ggml_backend_buffer_type_t buft, ggml_backend_dev_t dev, F & fn) {
|
|
ggml_init_params params = {
|
|
/*.mem_size =*/ ggml_tensor_overhead()*8,
|
|
/*.mem_buffer =*/ NULL,
|
|
/*.no_alloc =*/ true,
|
|
};
|
|
|
|
ggml_context_ptr ctx { ggml_init(params) };
|
|
if (!ctx) {
|
|
throw std::runtime_error(format("failed to create ggml context"));
|
|
}
|
|
|
|
ggml_backend_buffer_ptr buf { ggml_backend_buft_alloc_buffer(buft, 0) };
|
|
ggml_tensor * op_tensor = fn(ctx.get());
|
|
for (int i = 0; i < GGML_MAX_SRC; i++) {
|
|
if (op_tensor->src[i] != nullptr) {
|
|
assert(op_tensor->src[i]->buffer == nullptr);
|
|
op_tensor->src[i]->buffer = buf.get();
|
|
}
|
|
}
|
|
|
|
bool op_supported = ggml_backend_dev_supports_op(dev, op_tensor);
|
|
|
|
return op_supported;
|
|
}
|
|
|
|
template<typename F>
|
|
static ggml_backend_buffer_type_t select_buft(const buft_list_t & buft_list, const F & fn) {
|
|
for (const auto & cur : buft_list) {
|
|
ggml_backend_dev_t cur_dev = cur.first;
|
|
ggml_backend_buffer_type_t cur_buft = cur.second;
|
|
if (buft_supported(cur_buft, cur_dev, fn)) {
|
|
return cur_buft;
|
|
}
|
|
}
|
|
|
|
throw std::runtime_error(format("no suitable buffer type found"));
|
|
}
|
|
|
|
ggml_backend_buffer_type_t llama_model::select_buft(int il) const {
|
|
return ::select_buft(
|
|
*pimpl->dev_layer.at(il).buft_list,
|
|
[&](ggml_context * ctx) {
|
|
ggml_tensor * cur = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hparams.n_embd);
|
|
ggml_tensor * layer_dir = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hparams.n_embd);
|
|
return ggml_add(ctx, cur, layer_dir);
|
|
});
|
|
}
|
|
|
|
const struct ggml_tensor * llama_model::get_tensor(const char * name) const {
|
|
auto it = std::find_if(tensors_by_name.begin(), tensors_by_name.end(),
|
|
[name](const std::pair<std::string, struct ggml_tensor *> & it) {
|
|
return it.first == name;
|
|
});
|
|
if (it == tensors_by_name.end()) {
|
|
return nullptr;
|
|
}
|
|
|
|
return it->second;
|
|
}
|
|
|
|
//
|
|
// interface implementation
|
|
//
|
|
|
|
struct llama_model_params llama_model_default_params() {
|
|
struct llama_model_params result = {
|
|
/*.devices =*/ nullptr,
|
|
/*.n_gpu_layers =*/ 0,
|
|
/*.split_mode =*/ LLAMA_SPLIT_MODE_LAYER,
|
|
/*.main_gpu =*/ 0,
|
|
/*.tensor_split =*/ nullptr,
|
|
/*.rpc_servers =*/ nullptr,
|
|
/*.progress_callback =*/ nullptr,
|
|
/*.progress_callback_user_data =*/ nullptr,
|
|
/*.kv_overrides =*/ nullptr,
|
|
/*.vocab_only =*/ false,
|
|
/*.use_mmap =*/ true,
|
|
/*.use_mlock =*/ false,
|
|
/*.check_tensors =*/ false,
|
|
};
|
|
|
|
#ifdef GGML_USE_METAL
|
|
// note: we usually have plenty of VRAM, so by default offload all layers to the GPU
|
|
result.n_gpu_layers = 999;
|
|
#endif
|
|
|
|
return result;
|
|
}
|
|
|
|
const struct llama_vocab * llama_model_get_vocab(const struct llama_model * model) {
|
|
return &model->vocab;
|
|
}
|
|
|
|
void llama_free_model(struct llama_model * model) {
|
|
llama_model_free(model);
|
|
}
|
|
|
|
void llama_model_free(struct llama_model * model) {
|
|
delete model;
|
|
}
|
|
|
|
int32_t llama_model_n_ctx_train(const struct llama_model * model) {
|
|
return model->hparams.n_ctx_train;
|
|
}
|
|
|
|
int32_t llama_model_n_embd(const struct llama_model * model) {
|
|
return model->hparams.n_embd;
|
|
}
|
|
|
|
int32_t llama_model_n_layer(const struct llama_model * model) {
|
|
return model->hparams.n_layer;
|
|
}
|
|
|
|
int32_t llama_model_n_head(const struct llama_model * model) {
|
|
return model->hparams.n_head();
|
|
}
|
|
|
|
// deprecated
|
|
int32_t llama_n_ctx_train(const struct llama_model * model) {
|
|
return llama_model_n_ctx_train(model);
|
|
}
|
|
|
|
// deprecated
|
|
int32_t llama_n_embd(const struct llama_model * model) {
|
|
return llama_model_n_embd(model);
|
|
}
|
|
|
|
// deprecated
|
|
int32_t llama_n_layer(const struct llama_model * model) {
|
|
return llama_model_n_layer(model);
|
|
}
|
|
|
|
// deprecated
|
|
int32_t llama_n_head(const struct llama_model * model) {
|
|
return llama_model_n_head(model);
|
|
}
|
|
|
|
enum llama_rope_type llama_model_rope_type(const struct llama_model * model) {
|
|
switch (model->arch) {
|
|
// these models do not use RoPE
|
|
case LLM_ARCH_GPT2:
|
|
case LLM_ARCH_GPTJ:
|
|
case LLM_ARCH_MPT:
|
|
case LLM_ARCH_REFACT:
|
|
case LLM_ARCH_BLOOM:
|
|
case LLM_ARCH_MAMBA:
|
|
case LLM_ARCH_JINA_BERT_V2:
|
|
case LLM_ARCH_T5:
|
|
case LLM_ARCH_T5ENCODER:
|
|
case LLM_ARCH_JAIS:
|
|
case LLM_ARCH_RWKV6:
|
|
case LLM_ARCH_RWKV6QWEN2:
|
|
case LLM_ARCH_WAVTOKENIZER_DEC:
|
|
return LLAMA_ROPE_TYPE_NONE;
|
|
|
|
// use what we call a normal RoPE, operating on pairs of consecutive head values
|
|
case LLM_ARCH_LLAMA:
|
|
case LLM_ARCH_DECI:
|
|
case LLM_ARCH_BAICHUAN:
|
|
case LLM_ARCH_STARCODER:
|
|
case LLM_ARCH_PLAMO:
|
|
case LLM_ARCH_ORION:
|
|
case LLM_ARCH_INTERNLM2:
|
|
case LLM_ARCH_MINICPM:
|
|
case LLM_ARCH_XVERSE:
|
|
case LLM_ARCH_COMMAND_R:
|
|
case LLM_ARCH_COHERE2:
|
|
case LLM_ARCH_OLMO:
|
|
case LLM_ARCH_ARCTIC:
|
|
case LLM_ARCH_DEEPSEEK:
|
|
case LLM_ARCH_DEEPSEEK2:
|
|
case LLM_ARCH_CHATGLM:
|
|
case LLM_ARCH_GRANITE:
|
|
case LLM_ARCH_GRANITE_MOE:
|
|
case LLM_ARCH_CHAMELEON:
|
|
return LLAMA_ROPE_TYPE_NORM;
|
|
|
|
// the pairs of head values are offset by n_rot/2
|
|
case LLM_ARCH_FALCON:
|
|
case LLM_ARCH_GROK:
|
|
case LLM_ARCH_DBRX:
|
|
case LLM_ARCH_BERT:
|
|
case LLM_ARCH_NOMIC_BERT:
|
|
case LLM_ARCH_STABLELM:
|
|
case LLM_ARCH_BITNET:
|
|
case LLM_ARCH_QWEN:
|
|
case LLM_ARCH_QWEN2:
|
|
case LLM_ARCH_QWEN2MOE:
|
|
case LLM_ARCH_OLMO2:
|
|
case LLM_ARCH_OLMOE:
|
|
case LLM_ARCH_PHI2:
|
|
case LLM_ARCH_PHI3:
|
|
case LLM_ARCH_PHIMOE:
|
|
case LLM_ARCH_GEMMA:
|
|
case LLM_ARCH_GEMMA2:
|
|
case LLM_ARCH_STARCODER2:
|
|
case LLM_ARCH_OPENELM:
|
|
case LLM_ARCH_GPTNEOX:
|
|
case LLM_ARCH_CODESHELL:
|
|
case LLM_ARCH_NEMOTRON:
|
|
case LLM_ARCH_EXAONE:
|
|
case LLM_ARCH_MINICPM3:
|
|
return LLAMA_ROPE_TYPE_NEOX;
|
|
|
|
case LLM_ARCH_QWEN2VL:
|
|
return LLAMA_ROPE_TYPE_MROPE;
|
|
|
|
// all model arches should be listed explicitly here
|
|
case LLM_ARCH_UNKNOWN:
|
|
GGML_ABORT("unknown architecture");
|
|
}
|
|
|
|
return LLAMA_ROPE_TYPE_NONE;
|
|
}
|
|
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float llama_model_rope_freq_scale_train(const struct llama_model * model) {
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return model->hparams.rope_freq_scale_train;
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}
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int32_t llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size) {
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const auto & it = model->gguf_kv.find(key);
|
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if (it == model->gguf_kv.end()) {
|
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if (buf_size > 0) {
|
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buf[0] = '\0';
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}
|
|
return -1;
|
|
}
|
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return snprintf(buf, buf_size, "%s", it->second.c_str());
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}
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|
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int32_t llama_model_meta_count(const struct llama_model * model) {
|
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return (int)model->gguf_kv.size();
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}
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|
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int32_t llama_model_meta_key_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size) {
|
|
if (i < 0 || i >= (int)model->gguf_kv.size()) {
|
|
if (buf_size > 0) {
|
|
buf[0] = '\0';
|
|
}
|
|
return -1;
|
|
}
|
|
auto it = model->gguf_kv.begin();
|
|
std::advance(it, i);
|
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return snprintf(buf, buf_size, "%s", it->first.c_str());
|
|
}
|
|
|
|
int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size) {
|
|
if (i < 0 || i >= (int)model->gguf_kv.size()) {
|
|
if (buf_size > 0) {
|
|
buf[0] = '\0';
|
|
}
|
|
return -1;
|
|
}
|
|
auto it = model->gguf_kv.begin();
|
|
std::advance(it, i);
|
|
return snprintf(buf, buf_size, "%s", it->second.c_str());
|
|
}
|
|
|
|
int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) {
|
|
return snprintf(buf, buf_size, "%s", model->desc().c_str());
|
|
}
|
|
|
|
uint64_t llama_model_size(const struct llama_model * model) {
|
|
return model->size();
|
|
}
|
|
|
|
const char * llama_model_chat_template(const struct llama_model * model) {
|
|
const auto & it = model->gguf_kv.find(LLM_KV(model->arch)(LLM_KV_TOKENIZER_CHAT_TEMPLATE));
|
|
if (it == model->gguf_kv.end()) {
|
|
return nullptr;
|
|
}
|
|
|
|
return it->second.c_str();
|
|
}
|
|
|
|
uint64_t llama_model_n_params(const struct llama_model * model) {
|
|
return model->n_elements();
|
|
}
|
|
|
|
bool llama_model_has_encoder(const struct llama_model * model) {
|
|
switch (model->arch) {
|
|
case LLM_ARCH_T5: return true;
|
|
case LLM_ARCH_T5ENCODER: return true;
|
|
default: return false;
|
|
}
|
|
}
|
|
|
|
bool llama_model_has_decoder(const struct llama_model * model) {
|
|
switch (model->arch) {
|
|
case LLM_ARCH_T5ENCODER: return false;
|
|
default: return true;
|
|
}
|
|
}
|
|
|
|
llama_token llama_model_decoder_start_token(const struct llama_model * model) {
|
|
return model->hparams.dec_start_token_id;
|
|
}
|
|
|
|
bool llama_model_is_recurrent(const struct llama_model * model) {
|
|
switch (model->arch) {
|
|
case LLM_ARCH_MAMBA: return true;
|
|
case LLM_ARCH_RWKV6: return true;
|
|
case LLM_ARCH_RWKV6QWEN2: return true;
|
|
default: return false;
|
|
}
|
|
}
|