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llama.cpp
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llama.cpp/src/llama-quant.cpp

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#include "llama.h"
#include "llama-impl.h"
#include "llama-model.h"
#include "llama-model-loader.h"
#include <cmath>
#include <cstring>
#include <string>
#include <cinttypes>
#include <fstream>
#include <mutex>
#include <regex>
#include <thread>
#include <unordered_map>
// result of parsing --tensor-type option
// (changes to this struct must be reflected in tools/quantize/quantize.cpp)
struct tensor_type_option {
std::string name;
ggml_type type = GGML_TYPE_COUNT;
};
// tensor categorization - used to avoid repeated string matching in quantization logic.
// this is different from LLM_TN - we want broad categories, not specific tensor names per arch.
enum class tensor_category {
TOKEN_EMBD,
ATTENTION_Q,
ATTENTION_V,
ATTENTION_K,
ATTENTION_QKV,
ATTENTION_KV_B,
ATTENTION_OUTPUT,
FFN_UP,
FFN_GATE,
FFN_DOWN,
OUTPUT,
OTHER
};
static void zeros(std::ofstream & file, size_t n) {
char zero = 0;
for (size_t i = 0; i < n; ++i) {
file.write(&zero, 1);
}
}
static std::string remap_layer(const std::string & orig_name, const std::vector<int> & prune, std::map<int, std::string> & mapped, int & next_id) {
if (prune.empty()) {
return orig_name;
}
static const std::regex pattern(R"(blk\.(\d+)\.)");
if (std::smatch match; std::regex_search(orig_name, match, pattern)) {
const int blk = std::stoi(match[1]);
std::string new_name = orig_name;
if (mapped.count(blk)) {
// Already mapped, do nothing
} else if (std::find(prune.begin(), prune.end(), blk) != prune.end()) {
mapped[blk] = "";
} else if (blk < prune.front()) {
mapped[blk] = std::to_string(blk);
next_id = blk + 1;
} else {
mapped[blk] = std::to_string(next_id);
++next_id;
}
return mapped[blk].empty() ? mapped[blk] : new_name.replace(match.position(1), match.length(1), mapped[blk]);
}
return orig_name;
}
static std::string remap_imatrix(const std::string & orig_name, const std::map<int, std::string> & mapped) {
if (mapped.empty()) {
return orig_name;
}
static const std::regex pattern(R"(blk\.(\d+)\.)");
if (std::smatch match; std::regex_search(orig_name, match, pattern)) {
const std::string blk(match[1]);
std::string new_name = orig_name;
for (const auto & p : mapped) {
if (p.second == blk) {
LLAMA_LOG_DEBUG("(blk.%d imatrix) ", p.first);
return new_name.replace(match.position(1), match.length(1), std::to_string(p.first));
}
}
GGML_ABORT("\n%s: imatrix mapping error for %s\n", __func__, orig_name.c_str());
}
return orig_name;
}
//
// helper functions for tensor name matching
//
static bool tensor_name_match_token_embd(const char * tensor_name) {
return std::strcmp(tensor_name, "token_embd.weight") == 0 ||
std::strcmp(tensor_name, "per_layer_token_embd.weight") == 0;
}
static bool tensor_name_match_output_weight(const char * tensor_name) {
return std::strcmp(tensor_name, "output.weight") == 0;
}
//
// tensor categorization for quantization
//
// (this is different from LLM_TN - we want broad categories, not specific tensor names per arch)
//
static tensor_category tensor_get_category(const std::string & tensor_name) {
if (tensor_name_match_output_weight(tensor_name.c_str())) {
return tensor_category::OUTPUT;
}
if (tensor_name_match_token_embd(tensor_name.c_str())) {
return tensor_category::TOKEN_EMBD;
}
if (tensor_name.find("attn_qkv.weight") != std::string::npos) {
return tensor_category::ATTENTION_QKV;
}
if (tensor_name.find("attn_kv_b.weight") != std::string::npos) {
return tensor_category::ATTENTION_KV_B;
}
if (tensor_name.find("attn_v.weight") != std::string::npos) {
return tensor_category::ATTENTION_V;
}
if (tensor_name.find("attn_k.weight") != std::string::npos) {
return tensor_category::ATTENTION_K;
}
if (tensor_name.find("attn_q.weight") != std::string::npos) {
return tensor_category::ATTENTION_Q;
}
if (tensor_name.find("attn_output.weight") != std::string::npos) {
return tensor_category::ATTENTION_OUTPUT;
}
if (tensor_name.find("ffn_up") != std::string::npos) {
return tensor_category::FFN_UP;
}
if (tensor_name.find("ffn_gate") != std::string::npos) {
return tensor_category::FFN_GATE;
}
if (tensor_name.find("ffn_down") != std::string::npos) {
return tensor_category::FFN_DOWN;
}
return tensor_category::OTHER;
}
// check if category is for attention-v-like tensors (more sensitive to quantization)
static bool category_is_attn_v(tensor_category cat) {
return cat == tensor_category::ATTENTION_V ||
cat == tensor_category::ATTENTION_QKV ||
cat == tensor_category::ATTENTION_KV_B;
}
//
// quantization state
//
struct quantize_state_impl {
const llama_model & model;
const llama_model_quantize_params * params;
int n_attention_wv = 0;
int n_ffn_down = 0;
int n_ffn_gate = 0;
int n_ffn_up = 0;
int i_attention_wv = 0;
int i_ffn_down = 0;
int i_ffn_gate = 0;
int i_ffn_up = 0;
int n_fallback = 0;
bool has_imatrix = false;
// used to figure out if a model has tied embeddings (tok_embd shares weights with output)
bool has_tied_embeddings = true; // assume tied until we see output.weight
// tensor type override patterns (compiled once, used twice)
std::vector<std::pair<std::regex, ggml_type>> tensor_type_patterns;
quantize_state_impl(const llama_model & model, const llama_model_quantize_params * params):
model(model), params(params)
{
// compile regex patterns once - they are expensive
if (params->tensor_types) {
const auto & tensor_types = *static_cast<const std::vector<tensor_type_option> *>(params->tensor_types);
for (const auto & [tname, qtype] : tensor_types) {
tensor_type_patterns.emplace_back(std::regex(tname), qtype);
}
}
}
};
// per-tensor metadata, computed in the preliminary loop and used in the main loop
struct tensor_metadata {
ggml_type target_type;
tensor_category category;
std::string remapped_imatrix_name;
bool allows_quantization;
bool requires_imatrix;
};
//
// dequantization
//
static void llama_tensor_dequantize_impl(
ggml_tensor * tensor, std::vector<no_init<float>> & output, std::vector<std::thread> & workers,
const size_t nelements, const int nthread
) {
if (output.size() < nelements) {
output.resize(nelements);
}
float * f32_output = (float *) output.data();
const ggml_type_traits * qtype = ggml_get_type_traits(tensor->type);
if (ggml_is_quantized(tensor->type)) {
if (qtype->to_float == NULL) {
throw std::runtime_error(format("type %s unsupported for integer quantization: no dequantization available", ggml_type_name(tensor->type)));
}
} else if (tensor->type != GGML_TYPE_F16 &&
tensor->type != GGML_TYPE_BF16) {
throw std::runtime_error(format("cannot dequantize/convert tensor type %s", ggml_type_name(tensor->type)));
}
if (nthread < 2) {
if (tensor->type == GGML_TYPE_F16) {
ggml_fp16_to_fp32_row((ggml_fp16_t *)tensor->data, f32_output, nelements);
} else if (tensor->type == GGML_TYPE_BF16) {
ggml_bf16_to_fp32_row((ggml_bf16_t *)tensor->data, f32_output, nelements);
} else if (ggml_is_quantized(tensor->type)) {
qtype->to_float(tensor->data, f32_output, nelements);
} else {
GGML_ABORT("fatal error"); // unreachable
}
return;
}
size_t block_size;
if (tensor->type == GGML_TYPE_F16 ||
tensor->type == GGML_TYPE_BF16) {
block_size = 1;
} else {
block_size = (size_t)ggml_blck_size(tensor->type);
}
size_t block_size_bytes = ggml_type_size(tensor->type);
GGML_ASSERT(nelements % block_size == 0);
size_t nblocks = nelements / block_size;
size_t blocks_per_thread = nblocks / nthread;
size_t spare_blocks = nblocks - (blocks_per_thread * nthread); // if blocks aren't divisible by thread count
size_t in_buff_offs = 0;
size_t out_buff_offs = 0;
for (int tnum = 0; tnum < nthread; tnum++) {
size_t thr_blocks = blocks_per_thread + (tnum == nthread - 1 ? spare_blocks : 0); // num blocks for this thread
size_t thr_elems = thr_blocks * block_size; // number of elements for this thread
size_t thr_block_bytes = thr_blocks * block_size_bytes; // number of input bytes for this thread
auto compute = [qtype] (ggml_type typ, uint8_t * inbuf, float * outbuf, int nels) {
if (typ == GGML_TYPE_F16) {
ggml_fp16_to_fp32_row((ggml_fp16_t *)inbuf, outbuf, nels);
} else if (typ == GGML_TYPE_BF16) {
ggml_bf16_to_fp32_row((ggml_bf16_t *)inbuf, outbuf, nels);
} else {
qtype->to_float(inbuf, outbuf, nels);
}
};
workers.emplace_back(compute, tensor->type, (uint8_t *) tensor->data + in_buff_offs, f32_output + out_buff_offs, thr_elems);
in_buff_offs += thr_block_bytes;
out_buff_offs += thr_elems;
}
for (auto & w : workers) { w.join(); }
workers.clear();
}
//
// do we allow this tensor to be quantized?
//
static bool tensor_allows_quantization(const llama_model_quantize_params * params, llm_arch arch, const ggml_tensor * tensor) {
// trivial checks first -- no string ops needed
if (params->only_copy) return false;
// quantize only 2D and 3D tensors (experts)
if (ggml_n_dims(tensor) < 2) return false;
const std::string name = ggml_get_name(tensor);
// This used to be a regex, but <regex> has an extreme cost to compile times.
bool quantize = name.rfind("weight") == name.size() - 6; // ends with 'weight'?
// do not quantize norm tensors
quantize &= name.find("_norm.weight") == std::string::npos;
quantize &= params->quantize_output_tensor || name != "output.weight";
// do not quantize expert gating tensors
// NOTE: can't use LLM_TN here because the layer number is not known
quantize &= name.find("ffn_gate_inp.weight") == std::string::npos;
// these are very small (e.g. 4x4)
quantize &= name.find("altup") == std::string::npos;
quantize &= name.find("laurel") == std::string::npos;
// these are not too big so keep them as it is
quantize &= name.find("per_layer_model_proj") == std::string::npos;
// do not quantize positional embeddings and token types (BERT)
quantize &= name != LLM_TN(arch)(LLM_TENSOR_POS_EMBD, "weight");
quantize &= name != LLM_TN(arch)(LLM_TENSOR_TOKEN_TYPES, "weight");
// do not quantize Mamba/Kimi's small conv1d weights
// NOTE: can't use LLM_TN here because the layer number is not known
quantize &= name.find("ssm_conv1d") == std::string::npos;
quantize &= name.find("shortconv.conv.weight") == std::string::npos;
// do not quantize RWKV's small yet 2D weights
quantize &= name.find("time_mix_first.weight") == std::string::npos;
quantize &= name.find("time_mix_w0.weight") == std::string::npos;
quantize &= name.find("time_mix_w1.weight") == std::string::npos;
quantize &= name.find("time_mix_w2.weight") == std::string::npos;
quantize &= name.find("time_mix_v0.weight") == std::string::npos;
quantize &= name.find("time_mix_v1.weight") == std::string::npos;
quantize &= name.find("time_mix_v2.weight") == std::string::npos;
quantize &= name.find("time_mix_a0.weight") == std::string::npos;
quantize &= name.find("time_mix_a1.weight") == std::string::npos;
quantize &= name.find("time_mix_a2.weight") == std::string::npos;
quantize &= name.find("time_mix_g1.weight") == std::string::npos;
quantize &= name.find("time_mix_g2.weight") == std::string::npos;
quantize &= name.find("time_mix_decay_w1.weight") == std::string::npos;
quantize &= name.find("time_mix_decay_w2.weight") == std::string::npos;
quantize &= name.find("time_mix_lerp_fused.weight") == std::string::npos;
// do not quantize relative position bias (T5)
quantize &= name.find("attn_rel_b.weight") == std::string::npos;
// do not quantize specific multimodal tensors
quantize &= name.find(".position_embd.") == std::string::npos;
return quantize;
}
//
// tensor type selection
//
// incompatible tensor shapes are handled here - fallback to a compatible type
static ggml_type tensor_type_fallback(quantize_state_impl & qs, const ggml_tensor * t, const ggml_type target_type) {
ggml_type return_type = target_type;
const int64_t ncols = t->ne[0];
const int64_t qk_k = ggml_blck_size(target_type);
if (ncols % qk_k != 0) { // this tensor's shape is incompatible with this quant
LLAMA_LOG_WARN("warning: %-36s - ncols %6" PRId64 " not divisible by %3" PRId64 " (required for type %7s) ",
t->name, ncols, qk_k, ggml_type_name(target_type));
++qs.n_fallback;
switch (target_type) {
// types on the left: block size 256
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ2_XXS:
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ3_S: // types on the right: block size 32
case GGML_TYPE_IQ4_XS: return_type = GGML_TYPE_IQ4_NL; break;
case GGML_TYPE_Q2_K:
case GGML_TYPE_Q3_K:
case GGML_TYPE_TQ1_0:
case GGML_TYPE_TQ2_0: return_type = GGML_TYPE_Q4_0; break;
case GGML_TYPE_Q4_K: return_type = GGML_TYPE_Q5_0; break;
case GGML_TYPE_Q5_K: return_type = GGML_TYPE_Q5_1; break;
case GGML_TYPE_Q6_K: return_type = GGML_TYPE_Q8_0; break;
default:
throw std::runtime_error(format("no tensor type fallback is defined for type %s",
ggml_type_name(target_type)));
}
if (ncols % ggml_blck_size(return_type) != 0) {
//
// the fallback return type is still not compatible for this tensor!
//
// most likely, this tensor's first dimension is not divisible by 32.
// this is very rare. we can either abort the quantization, or
// fallback to F16 / F32.
//
LLAMA_LOG_WARN("(WARNING: must use F16 due to unusual shape) ");
return_type = GGML_TYPE_F16;
}
LLAMA_LOG_WARN("-> falling back to %7s\n", ggml_type_name(return_type));
}
return return_type;
}
// internal standard logic for selecting the target tensor type based on tensor category, ftype, and model arch
static ggml_type llama_tensor_get_type_impl(quantize_state_impl & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype, tensor_category category) {
const std::string name = ggml_get_name(tensor);
// TODO: avoid hardcoded tensor names - use the TN_* constants
const llm_arch arch = qs.model.arch;
auto use_more_bits = [](int i_layer, int n_layers) -> bool {
return i_layer < n_layers/8 || i_layer >= 7*n_layers/8 || (i_layer - n_layers/8)%3 == 2;
};
const int n_expert = std::max(1, (int)qs.model.hparams.n_expert);
auto layer_info = [n_expert] (int i_layer, int n_layer, const char * name) {
if (n_expert > 1) {
// Believe it or not, "experts" in the FFN of Mixtral-8x7B are not consecutive, but occasionally randomly
// sprinkled in the model. Hence, simply dividing i_ffn_down by n_expert does not work
// for getting the current layer as I initially thought, and we need to resort to parsing the
// tensor name.
if (sscanf(name, "blk.%d.", &i_layer) != 1) {
throw std::runtime_error(format("Failed to determine layer for tensor %s", name));
}
if (i_layer < 0 || i_layer >= n_layer) {
throw std::runtime_error(format("Bad layer %d for tensor %s. Must be in [0, %d)", i_layer, name, n_layer));
}
}
return std::make_pair(i_layer, n_layer);
};
// for arches that share the same tensor between the token embeddings and the output, we quantize the token embeddings
// with the quantization of the output tensor
if (category == tensor_category::OUTPUT || (qs.has_tied_embeddings && category == tensor_category::TOKEN_EMBD)) {
if (qs.params->output_tensor_type < GGML_TYPE_COUNT) {
new_type = qs.params->output_tensor_type;
} else {
const int64_t nx = tensor->ne[0];
const int64_t qk_k = ggml_blck_size(new_type);
if (ftype == LLAMA_FTYPE_MOSTLY_MXFP4_MOE) {
new_type = GGML_TYPE_Q8_0;
}
else if (arch == LLM_ARCH_FALCON || nx % qk_k != 0) {
new_type = GGML_TYPE_Q8_0;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ||
ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
new_type = GGML_TYPE_Q5_K;
}
else if (new_type != GGML_TYPE_Q8_0) {
new_type = GGML_TYPE_Q6_K;
}
}
} else if (ftype == LLAMA_FTYPE_MOSTLY_MXFP4_MOE) {
// MoE tensors -> MXFP4
// other tensors -> Q8_0
if (tensor->ne[2] > 1) {
new_type = GGML_TYPE_MXFP4;
} else {
new_type = GGML_TYPE_Q8_0;
}
} else if (category == tensor_category::TOKEN_EMBD) {
if (qs.params->token_embedding_type < GGML_TYPE_COUNT) {
new_type = qs.params->token_embedding_type;
} else {
if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS ||
ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
new_type = GGML_TYPE_Q2_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) {
new_type = GGML_TYPE_IQ3_S;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
new_type = GGML_TYPE_IQ3_S;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_TQ1_0 || ftype == LLAMA_FTYPE_MOSTLY_TQ2_0) {
new_type = GGML_TYPE_Q4_K;
}
}
} else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
if (category_is_attn_v(category)) {
if (qs.model.hparams.n_gqa() >= 4 || qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_Q4_K;
else new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
++qs.i_attention_wv;
}
else if (qs.model.hparams.n_expert == 8 && category == tensor_category::ATTENTION_K) {
new_type = GGML_TYPE_Q4_K;
}
else if (category == tensor_category::FFN_DOWN) {
if (qs.i_ffn_down < qs.n_ffn_down/8) {
new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
}
++qs.i_ffn_down;
}
else if (category == tensor_category::ATTENTION_OUTPUT) {
if (qs.model.hparams.n_expert == 8) {
new_type = GGML_TYPE_Q5_K;
} else {
if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) new_type = GGML_TYPE_IQ2_XXS;
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) new_type = GGML_TYPE_IQ3_S;
}
}
} else if (category_is_attn_v(category)) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) {
new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && qs.model.hparams.n_gqa() >= 4) {
new_type = GGML_TYPE_Q4_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : !qs.has_imatrix ? GGML_TYPE_IQ3_S : GGML_TYPE_IQ3_XXS;
}
else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S) && qs.model.hparams.n_gqa() >= 4) {
new_type = GGML_TYPE_Q4_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
new_type = GGML_TYPE_Q4_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) && qs.model.hparams.n_gqa() >= 4) {
new_type = GGML_TYPE_Q5_K;
}
else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) new_type = GGML_TYPE_Q6_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && qs.i_attention_wv < 4) new_type = GGML_TYPE_Q5_K;
if (qs.model.type == LLM_TYPE_70B) {
// In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is
// 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with
// nearly negligible increase in model size by quantizing this tensor with more bits:
if (new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K) new_type = GGML_TYPE_Q5_K;
}
if (qs.model.hparams.n_expert == 8) {
// for the 8-expert model, bumping this to Q8_0 trades just ~128MB
// TODO: explore better strategies
new_type = GGML_TYPE_Q8_0;
}
++qs.i_attention_wv;
} else if (category == tensor_category::ATTENTION_K) {
if (qs.model.hparams.n_expert == 8) {
// for the 8-expert model, bumping this to Q8_0 trades just ~128MB
// TODO: explore better strategies
new_type = GGML_TYPE_Q8_0;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) {
new_type = GGML_TYPE_IQ3_XXS;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
new_type = GGML_TYPE_IQ2_S;
}
} else if (category == tensor_category::ATTENTION_Q) {
if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) {
new_type = GGML_TYPE_IQ3_XXS;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
new_type = GGML_TYPE_IQ2_S;
}
} else if (category == tensor_category::FFN_DOWN) {
auto info = layer_info(qs.i_ffn_down, qs.n_ffn_down, name.c_str());
int i_layer = info.first, n_layer = info.second;
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S) {
if (i_layer < n_layer/8) new_type = GGML_TYPE_Q4_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS && !qs.has_imatrix) {
new_type = i_layer < n_layer/8 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
new_type = i_layer < n_layer/16 ? GGML_TYPE_Q5_K
: arch != LLM_ARCH_FALCON || use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q4_K
: GGML_TYPE_Q3_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M && (i_layer < n_layer/8 ||
(qs.model.hparams.n_expert == 8 && use_more_bits(i_layer, n_layer)))) {
new_type = GGML_TYPE_Q4_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) {
new_type = arch == LLM_ARCH_FALCON ? GGML_TYPE_Q4_K : GGML_TYPE_Q5_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) {
if (arch == LLM_ARCH_FALCON) {
new_type = i_layer < n_layer/16 ? GGML_TYPE_Q6_K :
use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
} else {
if (use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
}
}
else if (i_layer < n_layer/8 && (ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) && !qs.has_imatrix) {
new_type = GGML_TYPE_Q5_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && arch != LLM_ARCH_FALCON && i_layer < n_layer/8) {
new_type = GGML_TYPE_Q5_K;
}
else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_0 || ftype == LLAMA_FTYPE_MOSTLY_Q5_0)
&& qs.has_imatrix && i_layer < n_layer/8) {
// Guard against craziness in the first few ffn_down layers that can happen even with imatrix for Q4_0/Q5_0.
// We only do it when an imatrix is provided because a) we want to make sure that one can always get the
// same quantization as before imatrix stuff, and b) Q4_1/Q5_1 do go crazy on ffn_down without an imatrix.
new_type = ftype == LLAMA_FTYPE_MOSTLY_Q4_0 ? GGML_TYPE_Q4_1 : GGML_TYPE_Q5_1;
}
++qs.i_ffn_down;
} else if (category == tensor_category::ATTENTION_OUTPUT) {
if (arch != LLM_ARCH_FALCON) {
if (qs.model.hparams.n_expert == 8) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL ||
ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S ||
ftype == LLAMA_FTYPE_MOSTLY_IQ3_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) {
new_type = GGML_TYPE_Q5_K;
}
} else {
if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K ) new_type = GGML_TYPE_Q3_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) new_type = GGML_TYPE_IQ3_S;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M ) new_type = GGML_TYPE_Q4_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L ) new_type = GGML_TYPE_Q5_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M ) new_type = GGML_TYPE_Q4_K;
}
} else {
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K;
}
}
else if (category == tensor_category::ATTENTION_QKV) {
if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L || ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
new_type = GGML_TYPE_Q4_K;
}
else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) new_type = GGML_TYPE_Q5_K;
else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) new_type = GGML_TYPE_Q6_K;
}
else if (category == tensor_category::FFN_GATE) {
auto info = layer_info(qs.i_ffn_gate, qs.n_ffn_gate, name.c_str());
int i_layer = info.first, n_layer = info.second;
if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) {
new_type = GGML_TYPE_IQ3_XXS;
}
++qs.i_ffn_gate;
}
else if (category == tensor_category::FFN_UP) {
auto info = layer_info(qs.i_ffn_up, qs.n_ffn_up, name.c_str());
int i_layer = info.first, n_layer = info.second;
if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) {
new_type = GGML_TYPE_IQ3_XXS;
}
++qs.i_ffn_up;
}
return new_type;
}
// outer wrapper: determine the ggml_type that this tensor should be quantized to
static ggml_type llama_tensor_get_type(quantize_state_impl & qs, const llama_model_quantize_params * params, const ggml_tensor * tensor, ggml_type default_type, const tensor_metadata & tm) {
if (!tensor_allows_quantization(params, qs.model.arch, tensor)) {
return tensor->type;
}
if (params->token_embedding_type < GGML_TYPE_COUNT && tm.category == tensor_category::TOKEN_EMBD) {
return params->token_embedding_type;
}
if (params->output_tensor_type < GGML_TYPE_COUNT && tm.category == tensor_category::OUTPUT) {
return params->output_tensor_type;
}
ggml_type new_type = default_type;
// get more optimal quantization type based on the tensor shape, layer, etc.
if (!params->pure && ggml_is_quantized(default_type)) {
// if the user provided tensor types - use those
bool manual = false;
if (!qs.tensor_type_patterns.empty()) {
const std::string tensor_name(tensor->name);
for (const auto & [pattern, qtype] : qs.tensor_type_patterns) {
if (std::regex_search(tensor_name, pattern)) {
if (qtype != new_type) {
LLAMA_LOG_WARN("%s: %-36s - applying manual override: %s -> %s\n",
__func__, tensor_name.c_str(), ggml_type_name(new_type), ggml_type_name(qtype));
new_type = qtype;
manual = true;
break;
}
}
}
}
// if not manual - use the standard logic for choosing the quantization type based on the selected mixture
if (!manual) {
new_type = llama_tensor_get_type_impl(qs, new_type, tensor, params->ftype, tm.category);
}
// incompatible tensor shapes are handled here - fallback to a compatible type
new_type = tensor_type_fallback(qs, tensor, new_type);
}
return new_type;
}
//
// quantization implementation
//
static size_t llama_tensor_quantize_impl(enum ggml_type new_type, const float * f32_data, void * new_data, const int64_t chunk_size, int64_t nrows, int64_t n_per_row, const float * imatrix, std::vector<std::thread> & workers, const int nthread) {
if (nthread < 2) {
// single-thread
size_t new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, imatrix);
if (!ggml_validate_row_data(new_type, new_data, new_size)) {
throw std::runtime_error("quantized data validation failed");
}
return new_size;
}
std::mutex mutex;
int64_t counter = 0;
size_t new_size = 0;
bool valid = true;
auto compute = [&mutex, &counter, &new_size, &valid, new_type, f32_data, new_data, chunk_size,
nrows, n_per_row, imatrix]() {
const int64_t nrows_per_chunk = chunk_size / n_per_row;
size_t local_size = 0;
while (true) {
std::unique_lock<std::mutex> lock(mutex);
int64_t first_row = counter; counter += nrows_per_chunk;
if (first_row >= nrows) {
if (local_size > 0) {
new_size += local_size;
}
break;
}
lock.unlock();
const int64_t this_nrow = std::min(nrows - first_row, nrows_per_chunk);
size_t this_size = ggml_quantize_chunk(new_type, f32_data, new_data, first_row * n_per_row, this_nrow, n_per_row, imatrix);
local_size += this_size;
// validate the quantized data
const size_t row_size = ggml_row_size(new_type, n_per_row);
void * this_data = (char *) new_data + first_row * row_size;
if (!ggml_validate_row_data(new_type, this_data, this_size)) {
std::unique_lock<std::mutex> lock(mutex);
valid = false;
break;
}
}
};
for (int it = 0; it < nthread - 1; ++it) {
workers.emplace_back(compute);
}
compute();
for (auto & w : workers) { w.join(); }
workers.clear();
if (!valid) {
throw std::runtime_error("quantized data validation failed");
}
return new_size;
}
//
// imatrix requirement check
//
static bool tensor_requires_imatrix(const char * tensor_name, const ggml_type dst_type, const llama_ftype ftype) {
if (tensor_name_match_token_embd(tensor_name) || tensor_name_match_output_weight(tensor_name)) {
return false;
}
switch (dst_type) {
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ2_XXS:
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ1_S:
return true;
case GGML_TYPE_Q2_K:
// as a general rule, the k-type quantizations don't require imatrix data.
// the only exception is Q2_K tensors that are part of a Q2_K_S file.
return ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S;
default:
return false;
}
}
//
// given a file type, get the default tensor type
//
static ggml_type llama_ftype_get_default_type(llama_ftype ftype) {
switch (ftype) {
case LLAMA_FTYPE_MOSTLY_Q4_0: return GGML_TYPE_Q4_0;
case LLAMA_FTYPE_MOSTLY_Q4_1: return GGML_TYPE_Q4_1;
case LLAMA_FTYPE_MOSTLY_Q5_0: return GGML_TYPE_Q5_0;
case LLAMA_FTYPE_MOSTLY_Q5_1: return GGML_TYPE_Q5_1;
case LLAMA_FTYPE_MOSTLY_Q8_0: return GGML_TYPE_Q8_0;
case LLAMA_FTYPE_MOSTLY_F16: return GGML_TYPE_F16;
case LLAMA_FTYPE_MOSTLY_BF16: return GGML_TYPE_BF16;
case LLAMA_FTYPE_ALL_F32: return GGML_TYPE_F32;
case LLAMA_FTYPE_MOSTLY_MXFP4_MOE: return GGML_TYPE_MXFP4;
// K-quants
case LLAMA_FTYPE_MOSTLY_Q2_K_S:
case LLAMA_FTYPE_MOSTLY_Q2_K: return GGML_TYPE_Q2_K;
case LLAMA_FTYPE_MOSTLY_IQ3_XS: return GGML_TYPE_IQ3_S;
case LLAMA_FTYPE_MOSTLY_Q3_K_S:
case LLAMA_FTYPE_MOSTLY_Q3_K_M:
case LLAMA_FTYPE_MOSTLY_Q3_K_L: return GGML_TYPE_Q3_K;
case LLAMA_FTYPE_MOSTLY_Q4_K_S:
case LLAMA_FTYPE_MOSTLY_Q4_K_M: return GGML_TYPE_Q4_K;
case LLAMA_FTYPE_MOSTLY_Q5_K_S:
case LLAMA_FTYPE_MOSTLY_Q5_K_M: return GGML_TYPE_Q5_K;
case LLAMA_FTYPE_MOSTLY_Q6_K: return GGML_TYPE_Q6_K;
case LLAMA_FTYPE_MOSTLY_TQ1_0: return GGML_TYPE_TQ1_0;
case LLAMA_FTYPE_MOSTLY_TQ2_0: return GGML_TYPE_TQ2_0;
case LLAMA_FTYPE_MOSTLY_IQ2_XXS: return GGML_TYPE_IQ2_XXS;
case LLAMA_FTYPE_MOSTLY_IQ2_XS: return GGML_TYPE_IQ2_XS;
case LLAMA_FTYPE_MOSTLY_IQ2_S: return GGML_TYPE_IQ2_XS;
case LLAMA_FTYPE_MOSTLY_IQ2_M: return GGML_TYPE_IQ2_S;
case LLAMA_FTYPE_MOSTLY_IQ3_XXS: return GGML_TYPE_IQ3_XXS;
case LLAMA_FTYPE_MOSTLY_IQ1_S: return GGML_TYPE_IQ1_S;
case LLAMA_FTYPE_MOSTLY_IQ1_M: return GGML_TYPE_IQ1_M;
case LLAMA_FTYPE_MOSTLY_IQ4_NL: return GGML_TYPE_IQ4_NL;
case LLAMA_FTYPE_MOSTLY_IQ4_XS: return GGML_TYPE_IQ4_XS;
case LLAMA_FTYPE_MOSTLY_IQ3_S:
case LLAMA_FTYPE_MOSTLY_IQ3_M: return GGML_TYPE_IQ3_S;
default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
}
}
//
// main quantization driver
//
static void llama_model_quantize_impl(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
ggml_type default_type;
llama_ftype ftype = params->ftype;
int nthread = params->nthread;
if (nthread <= 0) {
nthread = std::thread::hardware_concurrency();
}
default_type = llama_ftype_get_default_type(ftype);
// mmap consistently increases speed on Linux, and also increases speed on Windows with
// hot cache. It may cause a slowdown on macOS, possibly related to free memory.
#if defined(__linux__) || defined(_WIN32)
constexpr bool use_mmap = true;
#else
constexpr bool use_mmap = false;
#endif
llama_model_kv_override * kv_overrides = nullptr;
if (params->kv_overrides) {
auto * v = (std::vector<llama_model_kv_override>*)params->kv_overrides;
kv_overrides = v->data();
}
std::vector<std::string> splits = {};
llama_model_loader ml(/*metadata*/ nullptr, /*set_tensor_data*/ nullptr, /*set_tensor_data_ud*/ nullptr,
fname_inp, splits, use_mmap, /*use_direct_io*/ false, /*check_tensors*/ true, /*no_alloc*/ false, kv_overrides, nullptr);
ml.init_mappings(false); // no prefetching
llama_model model(llama_model_default_params());
model.load_arch (ml);
model.load_hparams(ml);
model.load_stats (ml);
quantize_state_impl qs(model, params);
// these need to be set to n_layer by default
qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)model.hparams.n_layer;
if (params->only_copy) {
ftype = ml.ftype;
}
const std::unordered_map<std::string, std::vector<float>> * imatrix_data = nullptr;
if (params->imatrix) {
imatrix_data = static_cast<const std::unordered_map<std::string, std::vector<float>>*>(params->imatrix);
if (imatrix_data) {
LLAMA_LOG_INFO("\n%s: have importance matrix data with %d entries\n",
__func__, (int)imatrix_data->size());
qs.has_imatrix = true;
// check imatrix for nans or infs
for (const auto & kv : *imatrix_data) {
for (float f : kv.second) {
if (!std::isfinite(f)) {
throw std::runtime_error(format("imatrix contains non-finite value %f\n", f));
}
}
}
}
}
const size_t align = GGUF_DEFAULT_ALIGNMENT;
gguf_context_ptr ctx_out { gguf_init_empty() };
std::vector<int> prune_list = {};
if (params->prune_layers) {
prune_list = *static_cast<const std::vector<int> *>(params->prune_layers);
}
// copy the KV pairs from the input file
gguf_set_kv (ctx_out.get(), ml.metadata);
gguf_set_val_u32(ctx_out.get(), "general.quantization_version", GGML_QNT_VERSION); // TODO: use LLM_KV
gguf_set_val_u32(ctx_out.get(), "general.file_type", ftype); // TODO: use LLM_KV
// Remove split metadata
gguf_remove_key(ctx_out.get(), ml.llm_kv(LLM_KV_SPLIT_NO).c_str());
gguf_remove_key(ctx_out.get(), ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str());
gguf_remove_key(ctx_out.get(), ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str());
if (params->kv_overrides) {
const std::vector<llama_model_kv_override> & overrides = *(const std::vector<llama_model_kv_override> *)params->kv_overrides;
for (const auto & o : overrides) {
if (o.key[0] == 0) break;
if (o.tag == LLAMA_KV_OVERRIDE_TYPE_FLOAT) {
gguf_set_val_f32(ctx_out.get(), o.key, o.val_f64);
} else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_INT) {
// Setting type to UINT32. See https://github.com/ggml-org/llama.cpp/pull/14182 for context
gguf_set_val_u32(ctx_out.get(), o.key, (uint32_t)std::abs(o.val_i64));
} else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_BOOL) {
gguf_set_val_bool(ctx_out.get(), o.key, o.val_bool);
} else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_STR) {
gguf_set_val_str(ctx_out.get(), o.key, o.val_str);
} else {
LLAMA_LOG_WARN("%s: unknown KV override type for key %s\n", __func__, o.key);
}
}
}
std::map<int, std::string> mapped;
int blk_id = 0;
// make a list of weights
std::vector<const llama_model_loader::llama_tensor_weight *> tensors;
tensors.reserve(ml.weights_map.size());
for (const auto & it : ml.weights_map) {
const std::string remapped_name(remap_layer(it.first, prune_list, mapped, blk_id));
if (remapped_name.empty()) {
LLAMA_LOG_DEBUG("%s: pruning tensor %s\n", __func__, it.first.c_str());
continue;
}
if (remapped_name != it.first) {
ggml_set_name(it.second.tensor, remapped_name.c_str());
LLAMA_LOG_DEBUG("%s: tensor %s remapped to %s\n", __func__, it.first.c_str(), ggml_get_name(it.second.tensor));
}
tensors.push_back(&it.second);
}
if (!prune_list.empty()) {
gguf_set_val_u32(ctx_out.get(), ml.llm_kv(LLM_KV_BLOCK_COUNT).c_str(), blk_id);
}
// keep_split requires that the weights are sorted by split index
if (params->keep_split) {
std::sort(tensors.begin(), tensors.end(), [](const llama_model_loader::llama_tensor_weight * a, const llama_model_loader::llama_tensor_weight * b) {
if (a->idx == b->idx) {
return a->offs < b->offs;
}
return a->idx < b->idx;
});
}
int idx = 0;
uint16_t n_split = 1;
// Assume split index is continuous
if (params->keep_split) {
for (const auto * it : tensors) {
n_split = std::max(uint16_t(it->idx + 1), n_split);
}
}
std::vector<gguf_context_ptr> ctx_outs(n_split);
ctx_outs[0] = std::move(ctx_out);
// compute tensor metadata once and cache it
std::vector<tensor_metadata> metadata(tensors.size());
// flag for --dry-run
bool will_require_imatrix = false;
//
// preliminary iteration over all weights
//
for (size_t i = 0; i < tensors.size(); ++i) {
const auto * it = tensors[i];
const struct ggml_tensor * tensor = it->tensor;
const std::string name = ggml_get_name(tensor);
metadata[i].category = tensor_get_category(name);
if (category_is_attn_v(metadata[i].category)) {
++qs.n_attention_wv;
}
if (tensor_name_match_output_weight(name.c_str())) {
qs.has_tied_embeddings = false;
}
uint16_t i_split = params->keep_split ? it->idx : 0;
if (!ctx_outs[i_split]) {
ctx_outs[i_split].reset(gguf_init_empty());
}
gguf_add_tensor(ctx_outs[i_split].get(), tensor);
metadata[i].allows_quantization = tensor_allows_quantization(params, model.arch, tensor);
if (metadata[i].allows_quantization) {
metadata[i].target_type = llama_tensor_get_type(qs, params, tensor, default_type, metadata[i]);
} else {
metadata[i].target_type = tensor->type;
}
metadata[i].requires_imatrix = tensor_requires_imatrix(tensor->name, metadata[i].target_type, ftype);
if (params->imatrix) {
metadata[i].remapped_imatrix_name = remap_imatrix(tensor->name, mapped);
} else if (metadata[i].allows_quantization && metadata[i].requires_imatrix) {
if (params->dry_run) {
will_require_imatrix = true;
} else {
LLAMA_LOG_ERROR("\n============================================================================\n"
" ERROR: this quantization requires an importance matrix!\n"
" - offending tensor: %s\n"
" - target type: %s\n"
"============================================================================\n\n",
name.c_str(), ggml_type_name(metadata[i].target_type));
throw std::runtime_error("this quantization requires an imatrix!");
}
}
}
// Set split info if needed
if (n_split > 1) {
for (size_t i = 0; i < ctx_outs.size(); ++i) {
gguf_set_val_u16(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_NO).c_str(), i);
gguf_set_val_u16(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str(), n_split);
gguf_set_val_i32(ctx_outs[i].get(), ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str(), (int32_t)tensors.size());
}
}
size_t total_size_org = 0;
size_t total_size_new = 0;
std::vector<std::thread> workers;
workers.reserve(nthread);
std::vector<no_init<uint8_t>> read_data;
std::vector<no_init<uint8_t>> work;
std::vector<no_init<float>> f32_conv_buf;
int cur_split = -1;
std::ofstream fout;
auto close_ofstream = [&]() {
// Write metadata and close file handler
if (fout.is_open()) {
fout.seekp(0);
std::vector<uint8_t> data(gguf_get_meta_size(ctx_outs[cur_split].get()));
gguf_get_meta_data(ctx_outs[cur_split].get(), data.data());
fout.write((const char *) data.data(), data.size());
fout.close();
}
};
auto new_ofstream = [&](int index) {
cur_split = index;
GGML_ASSERT(ctx_outs[cur_split] && "Find uninitialized gguf_context");
std::string fname = fname_out;
if (params->keep_split) {
std::vector<char> split_path(llama_path_max(), 0);
llama_split_path(split_path.data(), split_path.size(), fname_out.c_str(), cur_split, n_split);
fname = std::string(split_path.data());
}
fout = std::ofstream(fname, std::ios::binary);
fout.exceptions(std::ofstream::failbit); // fail fast on write errors
const size_t meta_size = gguf_get_meta_size(ctx_outs[cur_split].get());
// placeholder for the meta data
::zeros(fout, meta_size);
};
// no output file for --dry-run
if (!params->dry_run) {
new_ofstream(0);
}
//
// main loop: iterate over all weights
//
for (size_t i = 0; i < tensors.size(); ++i) {
const auto & weight = *tensors[i];
const auto & tm = metadata[i];
ggml_tensor * tensor = weight.tensor;
if (!params->dry_run && (weight.idx != cur_split && params->keep_split)) {
close_ofstream();
new_ofstream(weight.idx);
}
const std::string name = ggml_get_name(tensor);
const size_t tensor_size = ggml_nbytes(tensor);
if (!params->dry_run) {
if (!ml.use_mmap) {
if (read_data.size() < tensor_size) {
read_data.resize(tensor_size);
}
tensor->data = read_data.data();
}
ml.load_data_for(tensor);
}
LLAMA_LOG_INFO("[%4d/%4d] %-36s - [%s], type = %6s, ",
++idx, ml.n_tensors,
ggml_get_name(tensor),
llama_format_tensor_shape(tensor).c_str(),
ggml_type_name(tensor->type));
const ggml_type cur_type = tensor->type;
const ggml_type new_type = tm.target_type;
// If we've decided to quantize to the same type the tensor is already
// in then there's nothing to do.
bool quantize = cur_type != new_type;
void * new_data;
size_t new_size;
if (params->dry_run) {
// the --dry-run option calculates the final quantization size without quantizing
if (quantize) {
new_size = ggml_nrows(tensor) * ggml_row_size(new_type, tensor->ne[0]);
LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB (%s)\n",
tensor_size/1024.0/1024.0,
new_size/1024.0/1024.0,
ggml_type_name(new_type));
if (!will_require_imatrix && tm.requires_imatrix) {
will_require_imatrix = true;
}
} else {
new_size = tensor_size;
LLAMA_LOG_INFO("size = %8.3f MiB\n", new_size/1024.0/1024.0);
}
total_size_org += tensor_size;
total_size_new += new_size;
continue;
} else {
// no --dry-run, perform quantization
if (!quantize) {
new_data = tensor->data;
new_size = tensor_size;
LLAMA_LOG_INFO("size = %8.3f MiB\n", tensor_size/1024.0/1024.0);
} else {
const int64_t nelements = ggml_nelements(tensor);
const float * imatrix = nullptr;
if (imatrix_data) {
auto it = imatrix_data->find(tm.remapped_imatrix_name);
if (it == imatrix_data->end()) {
LLAMA_LOG_INFO("\n====== %s: did not find weights for %s\n", __func__, tensor->name);
} else {
if (it->second.size() == (size_t)tensor->ne[0]*tensor->ne[2]) {
imatrix = it->second.data();
} else {
LLAMA_LOG_INFO("\n====== %s: imatrix size %d is different from tensor size %d for %s\n", __func__,
int(it->second.size()), int(tensor->ne[0]*tensor->ne[2]), tensor->name);
// this can happen when quantizing an old mixtral model with split tensors with a new incompatible imatrix
// this is a significant error and it may be good idea to abort the process if this happens,
// since many people will miss the error and not realize that most of the model is being quantized without an imatrix
// tok_embd should be ignored in this case, since it always causes this warning
if (!tensor_name_match_token_embd(tensor->name)) {
throw std::runtime_error(format("imatrix size %d is different from tensor size %d for %s",
int(it->second.size()), int(tensor->ne[0]*tensor->ne[2]), tensor->name));
}
}
}
}
if (!imatrix && tm.requires_imatrix) {
LLAMA_LOG_ERROR("\n\n============================================================\n");
LLAMA_LOG_ERROR("Missing importance matrix for tensor %s in a very low-bit quantization\n", tensor->name);
LLAMA_LOG_ERROR("The result will be garbage, so bailing out\n");
LLAMA_LOG_ERROR("============================================================\n\n");
throw std::runtime_error(format("Missing importance matrix for tensor %s in a very low-bit quantization", tensor->name));
}
float * f32_data;
if (tensor->type == GGML_TYPE_F32) {
f32_data = (float *) tensor->data;
} else if (ggml_is_quantized(tensor->type) && !params->allow_requantize) {
throw std::runtime_error(format("requantizing from type %s is disabled", ggml_type_name(tensor->type)));
} else {
llama_tensor_dequantize_impl(tensor, f32_conv_buf, workers, nelements, nthread);
f32_data = (float *) f32_conv_buf.data();
}
LLAMA_LOG_INFO("converting to %s .. ", ggml_type_name(new_type));
fflush(stdout);
if (work.size() < (size_t)nelements * 4) {
work.resize(nelements * 4); // upper bound on size
}
new_data = work.data();
const int64_t n_per_row = tensor->ne[0];
const int64_t nrows = tensor->ne[1];
static const int64_t min_chunk_size = 32 * 512;
const int64_t chunk_size = (n_per_row >= min_chunk_size ? n_per_row : n_per_row * ((min_chunk_size + n_per_row - 1)/n_per_row));
const int64_t nelements_matrix = tensor->ne[0] * tensor->ne[1];
const int64_t nchunk = (nelements_matrix + chunk_size - 1)/chunk_size;
const int64_t nthread_use = nthread > 1 ? std::max((int64_t)1, std::min((int64_t)nthread, nchunk)) : 1;
// quantize each expert separately since they have different importance matrices
new_size = 0;
for (int64_t i03 = 0; i03 < tensor->ne[2]; ++i03) {
const float * f32_data_03 = f32_data + i03 * nelements_matrix;
void * new_data_03 = (char *)new_data + ggml_row_size(new_type, n_per_row) * i03 * nrows;
const float * imatrix_03 = imatrix ? imatrix + i03 * n_per_row : nullptr;
new_size += llama_tensor_quantize_impl(new_type, f32_data_03, new_data_03, chunk_size, nrows, n_per_row, imatrix_03, workers, nthread_use);
}
LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB\n", tensor_size/1024.0/1024.0, new_size/1024.0/1024.0);
}
total_size_org += tensor_size;
total_size_new += new_size;
// update the gguf meta data as we go
gguf_set_tensor_type(ctx_outs[cur_split].get(), name.c_str(), new_type);
GGML_ASSERT(gguf_get_tensor_size(ctx_outs[cur_split].get(), gguf_find_tensor(ctx_outs[cur_split].get(), name.c_str())) == new_size);
gguf_set_tensor_data(ctx_outs[cur_split].get(), name.c_str(), new_data);
// write tensor data + padding
fout.write((const char *) new_data, new_size);
zeros(fout, GGML_PAD(new_size, align) - new_size);
} // no --dry-run
} // main loop
if (!params->dry_run) {
close_ofstream();
}
LLAMA_LOG_INFO("%s: model size = %8.2f MiB (%.2f BPW)\n", __func__, total_size_org/1024.0/1024.0, total_size_org*8.0/ml.n_elements);
LLAMA_LOG_INFO("%s: quant size = %8.2f MiB (%.2f BPW)\n", __func__, total_size_new/1024.0/1024.0, total_size_new*8.0/ml.n_elements);
if (!params->imatrix && params->dry_run && will_require_imatrix) {
LLAMA_LOG_WARN("%s: WARNING: dry run completed successfully, but actually completing this quantization will require an imatrix!\n",
__func__
);
}
if (qs.n_fallback > 0) {
LLAMA_LOG_WARN("%s: WARNING: %d of %d tensor(s) required fallback quantization\n",
__func__, qs.n_fallback, ml.n_tensors);
}
}
//
// interface implementation
//
llama_model_quantize_params llama_model_quantize_default_params() {
llama_model_quantize_params result = {
/*.nthread =*/ 0,
/*.ftype =*/ LLAMA_FTYPE_MOSTLY_Q5_1,
/*.output_tensor_type =*/ GGML_TYPE_COUNT,
/*.token_embedding_type =*/ GGML_TYPE_COUNT,
/*.allow_requantize =*/ false,
/*.quantize_output_tensor =*/ true,
/*.only_copy =*/ false,
/*.pure =*/ false,
/*.keep_split =*/ false,
/*.dry_run =*/ false,
/*.imatrix =*/ nullptr,
/*.kv_overrides =*/ nullptr,
/*.tensor_type =*/ nullptr,
/*.prune_layers =*/ nullptr
};
return result;
}
uint32_t llama_model_quantize(
const char * fname_inp,
const char * fname_out,
const llama_model_quantize_params * params) {
try {
llama_model_quantize_impl(fname_inp, fname_out, params);
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: failed to quantize: %s\n", __func__, err.what());
return 1;
}
return 0;
}
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