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quantize: Handle user-defined quantization levels for additional tensors (#12511)

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* Add llama_model_quantize_params parameters

* Add new quantize parameters parsing and validation

* Update usage

* Add new parameters defaults

* Add new quantization parameters logic

* Add llama_model_quantize_params parameters

* Add new quantize parameters parsing and validation

* Update usage

* Add new parameters defaults

* Add new quantization parameters logic

* Minor refactoring as per the contributors' coding guidelines

* Update descriptions to match existing style

* Add llama_model_quantize_params parameters

* Add new quantize parameters parsing and validation

* Update usage

* Add new parameters defaults

* Add new quantization parameters logic

* Minor refactoring as per the contributors' guidelines

* Implement general --tensor-type instead of tensor-specific command option

* Fix implied type bug

* Restore missing #includes

* Add regex capability for tensor selection

* Refactor function name and update ALLOWED_TENSOR_TYPE

* Add missing #include

* Handle edge case when tensor name is cls.output

* Minor logging improvement
This commit is contained in:
Ed Addario
2025-04-13 19:29:28 +01:00
committed by GitHub
parent bc091a4dc5
commit 71e90e8813
3 changed files with 155 additions and 20 deletions
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117
examples/quantize/quantize.cpp
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@ -9,6 +9,7 @@
#include <fstream>
#include <cmath>
#include <cctype>
#include <algorithm>
struct quant_option {
std::string name;
@ -16,7 +17,7 @@ struct quant_option {
std::string desc;
};
static const std::vector<struct quant_option> QUANT_OPTIONS = {
static const std::vector<quant_option> QUANT_OPTIONS = {
{ "Q4_0", LLAMA_FTYPE_MOSTLY_Q4_0, " 4.34G, +0.4685 ppl @ Llama-3-8B", },
{ "Q4_1", LLAMA_FTYPE_MOSTLY_Q4_1, " 4.78G, +0.4511 ppl @ Llama-3-8B", },
{ "Q5_0", LLAMA_FTYPE_MOSTLY_Q5_0, " 5.21G, +0.1316 ppl @ Llama-3-8B", },
@ -105,7 +106,8 @@ static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftyp
//
[[noreturn]]
static void usage(const char * executable) {
printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights] [--exclude-weights] [--output-tensor-type] [--token-embedding-type] [--override-kv] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable);
printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights] [--exclude-weights] [--output-tensor-type]\n", executable);
printf(" [--token-embedding-type] [--tensor-type] [--keep-split] [--override-kv] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n");
printf(" --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n");
printf(" --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n");
printf(" --pure: Disable k-quant mixtures and quantize all tensors to the same type\n");
@ -114,6 +116,8 @@ static void usage(const char * executable) {
printf(" --exclude-weights tensor_name: use importance matrix for this/these tensor(s)\n");
printf(" --output-tensor-type ggml_type: use this ggml_type for the output.weight tensor\n");
printf(" --token-embedding-type ggml_type: use this ggml_type for the token embeddings tensor\n");
printf(" --tensor-type TENSOR=TYPE: quantize this tensor to this ggml_type. example: --tensor-type attn_q=q8_0\n");
printf(" Advanced option to selectively quantize tensors. May be specified multiple times.\n");
printf(" --keep-split: will generate quantized model in the same shards as input\n");
printf(" --override-kv KEY=TYPE:VALUE\n");
printf(" Advanced option to override model metadata by key in the quantized model. May be specified multiple times.\n");
@ -244,6 +248,107 @@ static ggml_type parse_ggml_type(const char * arg) {
return GGML_TYPE_COUNT;
}
// Allowed tensors for arbitrary quantization with --tensor-type option
static const std::vector<std::string> ALLOWED_TENSOR_TYPE = {
"attn_k",
"attn_kv_a_mqa",
"attn_kv_b",
"attn_o",
"attn_output",
"attn_q",
"attn_q_a",
"attn_q_b",
"attn_qkv",
"attn_v",
"channel_mix_key",
"channel_mix_receptance",
"channel_mix_value",
"cls",
"cls.output",
"cross_attn_k",
"cross_attn_o",
"cross_attn_q",
"cross_attn_v",
"ffn_act",
"ffn_down",
"ffn_down_exps",
"ffn_down_shexp",
"ffn_gate",
"ffn_gate_exps",
"ffn_gate_shexp",
"ffn_up",
"ffn_up_exps",
"ffn_up_shexp",
"ssm_in",
"ssm_out",
"time_mix_gate",
"time_mix_key",
"time_mix_output",
"time_mix_receptance",
"time_mix_value",
};
// changes to this struct must be replicated in llama-quant.cpp
struct tensor_quantization {
std::string name;
ggml_type quant = GGML_TYPE_COUNT;
};
static bool parse_tensor_type(const char * data, std::vector<tensor_quantization> & tensor_type) {
const char * sep = strchr(data, '=');
if (sep == nullptr) {
printf("\n%s: malformed tensor type '%s'\n\n", __func__, data);
return false;
}
const size_t tn_len = sep - data;
if (tn_len == 0) {
printf("\n%s: missing tensor name\n\n", __func__);
return false;
}
if (const size_t qt_len = strlen(sep); qt_len == 1) {
printf("\n%s: missing quantization type\n\n", __func__);
return false;
}
std::string tn(data, tn_len);
std::transform(tn.begin(), tn.end(), tn.begin(), tolower);
sep++;
const std::string qt(sep);
bool found = false;
for (const auto & allowed : ALLOWED_TENSOR_TYPE) {
std::string tensor;
tensor = tn.rfind('.') != std::string::npos ? tn.substr(tn.rfind('.') + 1) : tn;
// handle special case of cls.output
std::string cls_output = "cls.output";
if (tn.find(cls_output) != std::string::npos) {
tensor = "cls.output";
}
// check if an allowed tensor exists and it's at the end of the kv string
if (tensor == allowed) {
found = true;
break;
}
}
if (!found) {
printf("\n%s: invalid tensor name '%s'\n\n", __func__, tn.c_str());
return false;
}
if (parse_ggml_type(qt.c_str()) == GGML_TYPE_COUNT) {
printf("\n%s: invalid quantization type '%s'\n\n", __func__, qt.c_str());
return false;
}
tensor_quantization tqz;
tqz.name = tn;
tqz.quant = parse_ggml_type(qt.c_str());
tensor_type.emplace_back(std::move(tqz));
return true;
}
int main(int argc, char ** argv) {
if (argc < 3) {
usage(argv[0]);
@ -255,6 +360,7 @@ int main(int argc, char ** argv) {
std::string imatrix_file;
std::vector<std::string> included_weights, excluded_weights;
std::vector<llama_model_kv_override> kv_overrides;
std::vector<tensor_quantization> tensor_types;
for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) {
if (strcmp(argv[arg_idx], "--leave-output-tensor") == 0) {
@ -277,6 +383,10 @@ int main(int argc, char ** argv) {
} else {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--tensor-type") == 0) {
if (arg_idx == argc-1 || !parse_tensor_type(argv[++arg_idx], tensor_types)) {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--override-kv") == 0) {
if (arg_idx == argc-1 || !string_parse_kv_override(argv[++arg_idx], kv_overrides)) {
usage(argv[0]);
@ -361,6 +471,9 @@ int main(int argc, char ** argv) {
kv_overrides.back().key[0] = 0;
params.kv_overrides = &kv_overrides;
}
if (!tensor_types.empty()) {
params.tensor_types = &tensor_types;
}
llama_backend_init();