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llama-model.cpp
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#include "llama-model.h"
#include "llama-impl.h"
#include "llama-mmap.h"
#include "llama-batch.h"
#include "llama-cparams.h"
#include "llama-model-loader.h"
#include "llama-kv-cache.h"
#include "ggml-cpp.h"
#include <algorithm>
#include <cassert>
#include <cmath>
#include <cfloat>
#include <cstring>
#include <cmath>
#include <functional>
#include <map>
#include <sstream>
#include <stdexcept>
const char * llm_type_name(llm_type type) {
switch (type) {
case LLM_TYPE_14M: return "14M";
case LLM_TYPE_17M: return "17M";
case LLM_TYPE_22M: return "22M";
case LLM_TYPE_33M: return "33M";
case LLM_TYPE_60M: return "60M";
case LLM_TYPE_70M: return "70M";
case LLM_TYPE_80M: return "80M";
case LLM_TYPE_109M: return "109M";
case LLM_TYPE_137M: return "137M";
case LLM_TYPE_160M: return "160M";
case LLM_TYPE_190M: return "190M";
case LLM_TYPE_220M: return "220M";
case LLM_TYPE_250M: return "250M";
case LLM_TYPE_270M: return "270M";
case LLM_TYPE_335M: return "335M";
case LLM_TYPE_410M: return "410M";
case LLM_TYPE_450M: return "450M";
case LLM_TYPE_770M: return "770M";
case LLM_TYPE_780M: return "780M";
case LLM_TYPE_0_5B: return "0.5B";
case LLM_TYPE_1B: return "1B";
case LLM_TYPE_1_3B: return "1.3B";
case LLM_TYPE_1_4B: return "1.4B";
case LLM_TYPE_1_5B: return "1.5B";
case LLM_TYPE_1_6B: return "1.6B";
case LLM_TYPE_2B: return "2B";
case LLM_TYPE_2_8B: return "2.8B";
case LLM_TYPE_2_9B: return "2.9B";
case LLM_TYPE_3B: return "3B";
case LLM_TYPE_4B: return "4B";
case LLM_TYPE_6B: return "6B";
case LLM_TYPE_6_9B: return "6.9B";
case LLM_TYPE_7B: return "7B";
case LLM_TYPE_8B: return "8B";
case LLM_TYPE_9B: return "9B";
case LLM_TYPE_11B: return "11B";
case LLM_TYPE_12B: return "12B";
case LLM_TYPE_13B: return "13B";
case LLM_TYPE_14B: return "14B";
case LLM_TYPE_15B: return "15B";
case LLM_TYPE_16B: return "16B";
case LLM_TYPE_20B: return "20B";
case LLM_TYPE_30B: return "30B";
case LLM_TYPE_32B: return "32B";
case LLM_TYPE_34B: return "34B";
case LLM_TYPE_35B: return "35B";
case LLM_TYPE_40B: return "40B";
case LLM_TYPE_65B: return "65B";
case LLM_TYPE_70B: return "70B";
case LLM_TYPE_236B: return "236B";
case LLM_TYPE_314B: return "314B";
case LLM_TYPE_671B: return "671B";
case LLM_TYPE_SMALL: return "0.1B";
case LLM_TYPE_MEDIUM: return "0.4B";
case LLM_TYPE_LARGE: return "0.8B";
case LLM_TYPE_XL: return "1.5B";
case LLM_TYPE_A1_7B: return "A1.7B";
case LLM_TYPE_A2_7B: return "A2.7B";
case LLM_TYPE_8x7B: return "8x7B";
case LLM_TYPE_8x22B: return "8x22B";
case LLM_TYPE_16x12B: return "16x12B";
case LLM_TYPE_16x3_8B: return "16x3.8B";
case LLM_TYPE_10B_128x3_66B: return "10B+128x3.66B";
case LLM_TYPE_57B_A14B: return "57B.A14B";
case LLM_TYPE_27B: return "27B";
default: return "?B";
}
}
static const char * llama_expert_gating_func_name(llama_expert_gating_func_type type) {
switch (type) {
case LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX: return "softmax";
case LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID: return "sigmoid";
default: return "unknown";
}
}
static const std::map<llama_rope_scaling_type, const char *> LLAMA_ROPE_SCALING_TYPES = {
{ LLAMA_ROPE_SCALING_TYPE_NONE, "none" },
{ LLAMA_ROPE_SCALING_TYPE_LINEAR, "linear" },
{ LLAMA_ROPE_SCALING_TYPE_YARN, "yarn" },
{ LLAMA_ROPE_SCALING_TYPE_LONGROPE, "longrope" },
};
static llama_rope_scaling_type llama_rope_scaling_type_from_string(const std::string & name) {
for (const auto & kv : LLAMA_ROPE_SCALING_TYPES) {
if (kv.second == name) {
return (llama_rope_scaling_type) kv.first;
}
}
return LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
}
// checks if the weight tensor can be used with the specified buffer type and device
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) {
GGML_ASSERT(w != nullptr);
if (op == GGML_OP_NONE) {
return true;
}
ggml_init_params params = {
/*.mem_size =*/ ggml_tensor_overhead()*8,
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
};
ggml_context_ptr ctx_ptr { ggml_init(params) };
if (!ctx_ptr) {
throw std::runtime_error(format("failed to create ggml context"));
}
ggml_context * ctx = ctx_ptr.get();
ggml_tensor * op_tensor = nullptr;
switch (op) {
case GGML_OP_GET_ROWS:
{
ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 512);
op_tensor = ggml_get_rows(ctx, w, b);
} break;
case GGML_OP_MUL_MAT:
{
ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], 512, w->ne[2], w->ne[3]);
op_tensor = ggml_mul_mat(ctx, w, b);
} break;
case GGML_OP_MUL_MAT_ID:
{
int n_expert_used = hparams.n_expert_used;
ggml_tensor * b = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, w->ne[0], n_expert_used, 512);
ggml_tensor * ids = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, n_expert_used, 512);
op_tensor = ggml_mul_mat_id(ctx, w, b, ids);
} break;
case GGML_OP_ADD:
{
ggml_tensor * a = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], w->ne[1], w->ne[2], w->ne[3]);
op_tensor = ggml_add(ctx, a, w);
} break;
case GGML_OP_MUL:
{
ggml_tensor * a = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, w->ne[0], w->ne[1], w->ne[2], w->ne[3]);
op_tensor = ggml_mul(ctx, a, w);
} break;
case GGML_OP_DIV:
{
ggml_tensor * a = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, w->ne[0]);
op_tensor = ggml_div(ctx, a, w);
} break;
case GGML_OP_ROPE:
{
int n_embd_head = hparams.n_embd_head_v;
int n_head = hparams.n_head();
ggml_tensor * a = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd_head, n_head, 512);
ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 512);
op_tensor = ggml_rope_ext(
ctx, a, b, w,
0, 0, 0, 0, 0,
0, 0, 0, 0
);
} break;
case GGML_OP_SSM_CONV:
{
// FIXME
ggml_tensor * conv_x = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 12345, w->ne[1], 6789);
op_tensor = ggml_ssm_conv(ctx, conv_x, w);
} break;
case GGML_OP_SSM_SCAN:
{
// FIXME
const int64_t d_state = w->ne[0];
const int64_t d_inner = w->ne[1];
const int64_t n_seq_tokens = 512;
const int64_t n_seqs = 1;
ggml_tensor * s = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, d_inner, n_seqs);
ggml_tensor * x = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_inner, n_seq_tokens, n_seqs);
ggml_tensor * dt = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_inner, n_seq_tokens, n_seqs);
ggml_tensor * B = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, n_seq_tokens, n_seqs);
ggml_tensor * C = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_state, n_seq_tokens, n_seqs);
op_tensor = ggml_ssm_scan(ctx, s, x, dt, w, B, C);
} break;
case GGML_OP_RWKV_WKV6:
{
// FIXME
const int64_t S = 123;
const int64_t H = 123;
const int64_t n_tokens = 123;
const int64_t n_seqs = 123;
ggml_tensor * k = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, S, H, n_tokens);
ggml_tensor * v = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, S, H, n_tokens);
ggml_tensor * r = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, S, H, n_tokens);
ggml_tensor * tf = w;
ggml_tensor * td = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, S, H, n_tokens);
ggml_tensor * state = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, S, n_seqs, S, H);
op_tensor = ggml_rwkv_wkv6(ctx, k, v, r, tf, td, state);
} break;
case GGML_OP_IM2COL:
{
const int n_embd = hparams.n_embd;
ggml_tensor * b = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, n_embd, w->ne[1], 1, 1);
op_tensor = ggml_im2col(ctx, w, b, 1, 0, 0, 0, 1, 0, false, GGML_TYPE_F16);
} break;
default:
GGML_ABORT("%s: missing test for op %s for tensor %s", __func__, ggml_op_name(op), w->name);
}
// create a temporary dummy buffer for the weight so that supports_op can check the buffer type
GGML_ASSERT(w->buffer == nullptr);
w->buffer = ggml_backend_buft_alloc_buffer(buft, 0);
bool op_supported = ggml_backend_dev_supports_op(dev, op_tensor);
ggml_backend_buffer_free(w->buffer);
w->buffer = nullptr;
return op_supported;
}
// lists of buffer types used for each layer
using buft_list_t = std::vector<std::pair<ggml_backend_dev_t, ggml_backend_buffer_type_t>>;
// find the first buffer type in the list that can use the tensor
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) {
GGML_ASSERT(!buft_list.empty());
for (const auto & cur : buft_list) {
ggml_backend_dev_t cur_dev = cur.first;
ggml_backend_buffer_type_t cur_buft = cur.second;
if (weight_buft_supported(hparams, tensor, op, cur_buft, cur_dev)) {
return cur_buft;
}
}
return nullptr;
}
// CPU: ACCEL -> CPU extra -> GPU host -> CPU
static buft_list_t make_cpu_buft_list(const std::vector<ggml_backend_dev_t> & devices) {
buft_list_t buft_list;
// add ACCEL buffer types
for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
ggml_backend_dev_t dev = ggml_backend_dev_get(i);
if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_ACCEL) {
auto * buft = ggml_backend_dev_buffer_type(dev);
// skip
if (buft != ggml_backend_cpu_buffer_type()) {
buft_list.emplace_back(dev, buft);
}
}
}
// add extra buffer types
auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
auto * cpu_reg = ggml_backend_dev_backend_reg(cpu_dev);
auto ggml_backend_dev_get_extra_bufts_fn = (ggml_backend_dev_get_extra_bufts_t)
ggml_backend_reg_get_proc_address(cpu_reg, "ggml_backend_dev_get_extra_bufts");
if (ggml_backend_dev_get_extra_bufts_fn) {
ggml_backend_buffer_type_t * extra_bufts = ggml_backend_dev_get_extra_bufts_fn(cpu_dev);
while (extra_bufts && *extra_bufts) {
buft_list.emplace_back(cpu_dev, *extra_bufts);
++extra_bufts;
}
}
// add a host buffer type
// storing the tensors in a host buffer is useful when the processing of large batches
// is offloaded to a GPU device, since it reduces the time spent on data transfers
// generally, this will be done using the first device in the list
// a better approach would be to handle this on a weight-by-weight basis using the offload_op
// function of the device to determine if it would benefit from being stored in a host buffer
for (auto * dev : devices) {
ggml_backend_buffer_type_t buft = ggml_backend_dev_host_buffer_type(dev);
if (buft) {
buft_list.emplace_back(dev, buft);
break;
}
}
// add the CPU buffer type
for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
ggml_backend_dev_t dev = ggml_backend_dev_get(i);
if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_CPU) {
buft_list.emplace_back(dev, ggml_backend_dev_buffer_type(dev));
}
}
return buft_list;
}
// GPU: split if LLAMA_SPLIT_MODE_ROW -> GPU
static buft_list_t make_gpu_buft_list(ggml_backend_dev_t dev, llama_split_mode split_mode, const float * tensor_split) {
buft_list_t buft_list;
// add the device split buffer type if requested and available
if (split_mode == LLAMA_SPLIT_MODE_ROW) {
ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(dev);
auto ggml_backend_split_buffer_type_fn = (ggml_backend_split_buffer_type_t)
ggml_backend_reg_get_proc_address(reg, "ggml_backend_split_buffer_type");
if (ggml_backend_split_buffer_type_fn) {
size_t dev_index = [&]() {
auto * reg = ggml_backend_dev_backend_reg(dev);
for (size_t i = 0; i < ggml_backend_reg_dev_count(reg); ++i) {
if (ggml_backend_reg_dev_get(reg, i) == dev) {
return i;
}
}
throw std::runtime_error(format("device %s not found in its backend reg", ggml_backend_dev_name(dev)));
}();
auto * buft = ggml_backend_split_buffer_type_fn(dev_index, tensor_split);
if (buft != nullptr) {
buft_list.emplace_back(dev, buft);
}
}
}
// add the device default buffer type
buft_list.emplace_back(dev, ggml_backend_dev_buffer_type(dev));
return buft_list;
}
struct llama_model::impl {
impl() {}
~impl() {}
uint64_t n_elements = 0;
size_t n_bytes = 0;
std::string desc_str;
// model memory mapped files
llama_mmaps mappings;
// objects representing data potentially being locked in memory
llama_mlocks mlock_bufs;
llama_mlocks mlock_mmaps;
// contexts where the model tensors metadata is stored
std::vector<ggml_context_ptr> ctxs;
// the model memory buffers for the tensor data
std::vector<ggml_backend_buffer_ptr> bufs;
buft_list_t cpu_buft_list;
std::map<ggml_backend_dev_t, buft_list_t> gpu_buft_list;
struct layer_dev {
ggml_backend_dev_t dev;
buft_list_t * buft_list;
};
layer_dev dev_input = {};
layer_dev dev_output = {};
std::vector<layer_dev> dev_layer;
};
llama_model::llama_model(const llama_model_params & params) : params(params), pimpl(std::make_unique<impl>()) {
}
llama_model::~llama_model() {}
void llama_model::load_stats(llama_model_loader & ml) {
pimpl->n_elements = ml.n_elements;
pimpl->n_bytes = ml.n_bytes;
}
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++) {
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;
// by default assume that the sliding-window layers use the same scaling type as the non-sliding-window layers
hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train;
hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
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:
case LLM_ARCH_NOMIC_BERT_MOE:
{
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);
ml.get_key(LLM_KV_MOE_EVERY_N_LAYERS, hparams.moe_every_n_layers);
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
// note: this seems incorrect because the window is bigger than the train context?
hparams.n_swa = 262144;
} else if (hparams.n_layer == 40 && hparams.n_ctx_train == 131072) {
// default value for Phi-3-medium-128k-instruct
// note: this seems incorrect because the window is equal to the train context?
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
hparams.n_swa_pattern = 2;
hparams.attn_soft_cap = true;
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);
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_GEMMA3:
{
hparams.n_swa_pattern = 6;
hparams.rope_freq_base_train_swa = 10000.0f;
hparams.rope_freq_scale_train_swa = 1.0f;
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
switch (hparams.n_layer) {
case 26: type = LLM_TYPE_1B; break;
case 34: type = LLM_TYPE_4B; break;
case 48: type = LLM_TYPE_12B; break;
case 62: type = LLM_TYPE_27B; break;
default: type = LLM_TYPE_UNKNOWN;
}
hparams.f_attention_scale = type == LLM_TYPE_27B
? 1.0f / std::sqrt(float(hparams.n_embd / hparams.n_head(0)))
: 1.0f / std::sqrt(float(hparams.n_embd_head_k));
} 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:
{
hparams.n_swa_pattern = 4;
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;