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Allow "quantizing" to f16 and f32 (#1787)
* Allow "quantizing" to f16 and f32 Fix an issue where quantizing didn't respect LLAMA_NO_K_QUANTS Add brief help to the list of quantization types in the quantize tool Ignore case for quantization type arguments in the quantize tool
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1
Makefile
1
Makefile
@ -127,6 +127,7 @@ endif
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ifndef LLAMA_NO_K_QUANTS
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CFLAGS += -DGGML_USE_K_QUANTS
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CXXFLAGS += -DGGML_USE_K_QUANTS
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OBJS += k_quants.o
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endif
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@ -4,43 +4,135 @@
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#include <cstdio>
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#include <cstring>
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#include <map>
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#include <vector>
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#include <string>
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static const std::map<std::string, llama_ftype> LLAMA_FTYPE_MAP = {
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{"q4_0", LLAMA_FTYPE_MOSTLY_Q4_0},
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{"q4_1", LLAMA_FTYPE_MOSTLY_Q4_1},
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{"q5_0", LLAMA_FTYPE_MOSTLY_Q5_0},
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{"q5_1", LLAMA_FTYPE_MOSTLY_Q5_1},
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{"q8_0", LLAMA_FTYPE_MOSTLY_Q8_0},
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{"q2_K", LLAMA_FTYPE_MOSTLY_Q2_K},
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{"q3_K", LLAMA_FTYPE_MOSTLY_Q3_K_M},
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{"q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S},
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{"q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M},
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{"q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L},
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{"q4_K", LLAMA_FTYPE_MOSTLY_Q4_K_M},
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{"q4_K_S", LLAMA_FTYPE_MOSTLY_Q4_K_S},
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{"q4_K_M", LLAMA_FTYPE_MOSTLY_Q4_K_M},
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{"q5_K", LLAMA_FTYPE_MOSTLY_Q5_K_M},
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{"q5_K_S", LLAMA_FTYPE_MOSTLY_Q5_K_S},
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{"q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M},
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{"q6_K", LLAMA_FTYPE_MOSTLY_Q6_K},
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struct quant_option {
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std::string name;
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llama_ftype ftype;
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std::string desc;
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};
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bool try_parse_ftype(const std::string & ftype_str, llama_ftype & ftype, std::string & ftype_str_out) {
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auto it = LLAMA_FTYPE_MAP.find(ftype_str);
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if (it != LLAMA_FTYPE_MAP.end()) {
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ftype = it->second;
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ftype_str_out = it->first;
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return true;
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static const std::vector<struct quant_option> QUANT_OPTIONS = {
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{
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"Q4_0",
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LLAMA_FTYPE_MOSTLY_Q4_0,
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" 3.50G, +0.2499 ppl @ 7B - small, very high quality loss - legacy, prefer using Q3_K_M",
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},
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{
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"Q4_1",
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LLAMA_FTYPE_MOSTLY_Q4_1,
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" 3.90G, +0.1846 ppl @ 7B - small, substantial quality loss - legacy, prefer using Q3_K_L",
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},
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{
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"Q5_0",
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LLAMA_FTYPE_MOSTLY_Q5_0,
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" 4.30G, +0.0796 ppl @ 7B - medium, balanced quality - legacy, prefer using Q4_K_M",
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},
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{
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"Q5_1",
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LLAMA_FTYPE_MOSTLY_Q5_1,
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" 4.70G, +0.0415 ppl @ 7B - medium, low quality loss - legacy, prefer using Q5_K_M",
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},
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#ifdef GGML_USE_K_QUANTS
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{
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"Q2_K",
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LLAMA_FTYPE_MOSTLY_Q2_K,
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" 2.67G, +0.8698 ppl @ 7B - smallest, extreme quality loss - not recommended",
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},
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{
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"Q3_K",
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LLAMA_FTYPE_MOSTLY_Q3_K_M,
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"alias for Q3_K_M"
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},
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{
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"Q3_K_S",
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LLAMA_FTYPE_MOSTLY_Q3_K_S,
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" 2.75G, +0.5505 ppl @ 7B - very small, very high quality loss",
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},
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{
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"Q3_K_M",
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LLAMA_FTYPE_MOSTLY_Q3_K_M,
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" 3.06G, +0.2437 ppl @ 7B - very small, very high quality loss",
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},
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{
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"Q3_K_L",
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LLAMA_FTYPE_MOSTLY_Q3_K_L,
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" 3.35G, +0.1803 ppl @ 7B - small, substantial quality loss",
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},
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{
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"Q4_K",
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LLAMA_FTYPE_MOSTLY_Q4_K_M,
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"alias for Q4_K_M",
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},
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{
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"Q4_K_S",
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LLAMA_FTYPE_MOSTLY_Q4_K_S,
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" 3.56G, +0.1149 ppl @ 7B - small, significant quality loss",
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},
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{
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"Q4_K_M",
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LLAMA_FTYPE_MOSTLY_Q4_K_M,
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" 3.80G, +0.0535 ppl @ 7B - medium, balanced quality - *recommended*",
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},
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{
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"Q5_K",
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LLAMA_FTYPE_MOSTLY_Q5_K_M,
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"alias for Q5_K_M",
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},
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{
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"Q5_K_S",
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LLAMA_FTYPE_MOSTLY_Q5_K_S,
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" 4.33G, +0.0353 ppl @ 7B - large, low quality loss - *recommended*",
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},
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{
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"Q5_K_M",
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LLAMA_FTYPE_MOSTLY_Q5_K_M,
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" 4.45G, +0.0142 ppl @ 7B - large, very low quality loss - *recommended*",
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},
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{
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"Q6_K",
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LLAMA_FTYPE_MOSTLY_Q6_K,
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" 5.15G, +0.0044 ppl @ 7B - very large, extremely low quality loss",
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},
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#endif
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{
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"Q8_0",
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LLAMA_FTYPE_MOSTLY_Q8_0,
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" 6.70G, +0.0004 ppl @ 7B - very large, extremely low quality loss - not recommended",
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},
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{
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"F16",
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LLAMA_FTYPE_MOSTLY_F16,
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"13.00G @ 7B - extremely large, virtually no quality loss - not recommended",
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},
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{
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"F32",
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LLAMA_FTYPE_ALL_F32,
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"26.00G @ 7B - absolutely huge, lossless - not recommended",
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},
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};
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bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftype, std::string & ftype_str_out) {
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std::string ftype_str;
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for (auto ch : ftype_str_in) {
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ftype_str.push_back(std::toupper(ch));
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}
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for (auto & it : QUANT_OPTIONS) {
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if (it.name == ftype_str) {
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ftype = it.ftype;
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ftype_str_out = it.name;
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return true;
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}
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}
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// try to parse as an integer
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try {
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int ftype_int = std::stoi(ftype_str);
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for (auto it = LLAMA_FTYPE_MAP.begin(); it != LLAMA_FTYPE_MAP.end(); it++) {
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if (it->second == ftype_int) {
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ftype = it->second;
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ftype_str_out = it->first;
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for (auto & it : QUANT_OPTIONS) {
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if (it.ftype == ftype_int) {
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ftype = it.ftype;
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ftype_str_out = it.name;
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return true;
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}
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}
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@ -52,15 +144,15 @@ bool try_parse_ftype(const std::string & ftype_str, llama_ftype & ftype, std::st
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}
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// usage:
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// ./quantize models/llama/ggml-model.bin [models/llama/ggml-model-quant.bin] type [nthreads]
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// ./quantize [--allow-requantize] [--leave-output-tensor] models/llama/ggml-model.bin [models/llama/ggml-model-quant.bin] type [nthreads]
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//
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void usage(const char * executable) {
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fprintf(stderr, "usage: %s [--help] [--allow-requantize] [--leave-output-tensor] model-f32.bin [model-quant.bin] type [nthreads]\n", executable);
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fprintf(stderr, "usage: %s [--help] [--allow-requantize] [--leave-output-tensor] model-f32.bin [model-quant.bin] type [nthreads]\n\n", executable);
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fprintf(stderr, " --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n");
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fprintf(stderr, " --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n");
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fprintf(stderr, "Allowed quantization types:\n");
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for (auto it = LLAMA_FTYPE_MAP.begin(); it != LLAMA_FTYPE_MAP.end(); it++) {
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fprintf(stderr, " type = \"%s\" or %d\n", it->first.c_str(), it->second);
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fprintf(stderr, "\nAllowed quantization types:\n");
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for (auto & it : QUANT_OPTIONS) {
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printf(" %2d or %-6s : %s\n", it.ftype, it.name.c_str(), it.desc.c_str());
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}
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exit(1);
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}
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12
ggml.c
12
ggml.c
@ -16301,6 +16301,18 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i
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result = ggml_quantize_q6_K(src + start, block, n, n, hist);
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} break;
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#endif
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case GGML_TYPE_F16:
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{
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int elemsize = sizeof(ggml_fp16_t);
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ggml_fp32_to_fp16_row(src + start, (ggml_fp16_t *)dst + start, n);
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result = n * elemsize;
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} break;
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case GGML_TYPE_F32:
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{
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int elemsize = sizeof(float);
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result = n * elemsize;
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memcpy((uint8_t *)dst + start * elemsize, src + start, result);
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} break;
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default:
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assert(false);
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}
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27
llama.cpp
27
llama.cpp
@ -2298,7 +2298,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
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case LLAMA_FTYPE_MOSTLY_Q5_0: quantized_type = GGML_TYPE_Q5_0; break;
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case LLAMA_FTYPE_MOSTLY_Q5_1: quantized_type = GGML_TYPE_Q5_1; break;
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case LLAMA_FTYPE_MOSTLY_Q8_0: quantized_type = GGML_TYPE_Q8_0; break;
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case LLAMA_FTYPE_MOSTLY_F16: quantized_type = GGML_TYPE_F16; break;
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case LLAMA_FTYPE_ALL_F32: quantized_type = GGML_TYPE_F32; break;
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#ifdef GGML_USE_K_QUANTS
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// K-quants
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case LLAMA_FTYPE_MOSTLY_Q2_K: quantized_type = GGML_TYPE_Q2_K; break;
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case LLAMA_FTYPE_MOSTLY_Q3_K_S:
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@ -2309,6 +2312,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
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case LLAMA_FTYPE_MOSTLY_Q5_K_S:
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case LLAMA_FTYPE_MOSTLY_Q5_K_M: quantized_type = GGML_TYPE_Q5_K; break;
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case LLAMA_FTYPE_MOSTLY_Q6_K: quantized_type = GGML_TYPE_Q6_K; break;
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#endif
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default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
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}
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@ -2320,6 +2324,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
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/*vocab_only*/ false));
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llama_file_saver file_saver(fname_out.c_str(), model_loader->file_loaders.at(0).get(), params->ftype);
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#ifdef GGML_USE_K_QUANTS
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int n_attention_wv = 0;
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int n_feed_forward_w2 = 0;
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for (auto& tensor : model_loader->tensors_map.tensors) {
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@ -2333,6 +2338,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
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int i_attention_wv = 0;
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int i_feed_forward_w2 = 0;
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#endif
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size_t total_size_org = 0;
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size_t total_size_new = 0;
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@ -2358,12 +2364,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
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// quantize only 2D tensors
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quantize &= (tensor.ne.size() == 2);
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// uncomment this to keep the output layer in FP16
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if (!params->quantize_output_tensor && tensor.name == "output.weight") {
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quantize = false;
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}
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quantize = quantize && quantized_type != tensor.type;
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quantize &= params->quantize_output_tensor || tensor.name != "output.weight";
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quantize &= quantized_type != tensor.type;
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enum ggml_type new_type;
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void * new_data;
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@ -2377,29 +2379,28 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
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printf("size = %8.3f MB\n", tensor.size/1024.0/1024.0);
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} else {
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new_type = quantized_type;
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#ifdef GGML_USE_K_QUANTS
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if (tensor.name == "output.weight") {
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new_type = GGML_TYPE_Q6_K;
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}
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else if (tensor.name.find("attention.wv.weight") != std::string::npos) {
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new_type = GGML_TYPE_Q6_K;
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} else if (tensor.name.find("attention.wv.weight") != std::string::npos) {
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if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K;
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else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
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else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
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(i_attention_wv < n_attention_wv/8 || i_attention_wv >= 7*n_attention_wv/8 ||
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(i_attention_wv - n_attention_wv/8)%3 == 2)) new_type = GGML_TYPE_Q6_K;
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++i_attention_wv;
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}
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if (tensor.name.find("feed_forward.w2.weight") != std::string::npos) {
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} else if (tensor.name.find("feed_forward.w2.weight") != std::string::npos) {
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if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K;
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else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
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else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
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(i_feed_forward_w2 < n_feed_forward_w2/8 || i_feed_forward_w2 >= 7*n_feed_forward_w2/8 ||
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(i_feed_forward_w2 - n_feed_forward_w2/8)%3 == 2)) new_type = GGML_TYPE_Q6_K;
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++i_feed_forward_w2;
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}
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if (tensor.name.find("attention.wo.weight") != std::string::npos) {
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} else if (tensor.name.find("attention.wo.weight") != std::string::npos) {
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if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K;
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else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
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}
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#endif
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float * f32_data;
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size_t nelements = tensor.ne.at(0) * tensor.ne.at(1);
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