2023-05-01 18:23:47 +02:00
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#include "build-info.h"
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2023-03-10 19:40:58 +01:00
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2023-05-20 10:06:11 +02:00
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#include "llama.h"
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2023-03-10 19:40:58 +01:00
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#include <cstdio>
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2023-04-26 18:43:27 +02:00
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#include <map>
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2023-03-10 19:40:58 +01:00
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#include <string>
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2023-03-21 18:21:50 +01:00
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2023-05-05 00:58:56 +02:00
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static const std::map<std::string, llama_ftype> LLAMA_FTYPE_MAP = {
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ggml : add SOTA 2,3,4,5,6 bit k-quantizations (#1684)
* Starting to add k-quantization to ggml
I think it is better to have quantization separate from
ggml. For now just adding the k-quants there, but it would be
better to also factor out the existing ggml quantizations.
* Adding Q3_K and Q8_K (de)-quantization
* Q3_K now working on CUDA and AVX2/scalar
CUDA is not ideal - ~50% slower than Q4_0 for
single token prediction, about the same in batch
mode (perplexity). CPU single token is ~55 ms
(on Ryzen 7950X).
* Some improvement for Q3_K on CUDA
It is now ~22.5 ms/token on my GPU, so ~30% slower than Q4_0.
* Some more CUDA optimizations for Q3_K
Single token is now 20.5 ms/token (~20% slower than Q4_0).
Perplexity is on par with Q4_0.
* Adding Q4_K - scalar, AVX2, CUDA
Performance is the same or perhaps very slightly better than Q4_0 on the CPU.
On the GPU, single token prediction is ~10% better than Q4_0,
batch mode (perplexity is about the same).
* Adding Q6_K - scalar, AVX2, CUDA
Performance is ~40% lower compared to Q4_K on the CPU.
This is to be expected, considering that we are memory bound
on the CPU and the 6-bit model is ~44% larger than the 4-bit.
On the GPU, single token prediction is ~6% lower than Q4_0,
batch mode (perplexity) is even closer (but still slower).
* Adding Q5_K - scalar, AVX2, CUDA
Performance is ~20% lower compared to Q4_K on the CPU.
This is to be expected, considering that we are memory bound
on the CPU and the 5-bit model is ~22% larger than the 4-bit.
On the GPU, single token prediction is about the same as Q4_0
for both, single token and batch prediction.
* Per convention, all QX_K quantizations use Q5_K for output.weight
* Adding quantization mixes
* Quantization mixes: didn't quite get what I wanted in the last commit
* Q4_K dot product for ARM_NEON
* Q6_K dot product for ARM_NEON
* Q5_K dot product for ARM_NEON
* Adding Q3_K dot for ARM_NEON
It is 22% slower than Q4_K, despite the smaller model size.
On x86_64, where we are memory bound, the Q3_K model is
quite a bit faster than Q4_K.
* A very slightly faster ARM_NEON Q3_K dot
* Adding Q2_K - just CUDA for now
Token prediction is pretty good - about 15.5 ms on a RTX 4080.
Perplexity is about the same as Q4_K.
* Adding scalar and AVX2 Q2_K dot
* Adding ARM_NEON Q2_K dot
About the same performance as Q4_K.
* A slightly faster ARM_NEON Q2_K dot
Single token prediction is now ~36 ms on M2 Max.
The code is much simpler too.
* Fixed bug in Q2_K CUDA dot product kernel
Stranegly enough, for the few prompts I tried with the 7B model
the responses looked perfectly reasonable. Only realized something
is not quite right when I tried the larger models and started getting
nonse back.
In any case, Q2_K single token evaluation time on an RTX 4080 in a Ryzen7950X
box iusing CUDA and model fully loaded on the GPU are
~15.5 ms for 7B, ~25.4 ms for 13B, and ~55.8 ms for 30B.
The max number of layers that fit in VRAM for The 65B is 32.
With that, we get ~330 ms per token, which is not that much faster
than just running on the CPU (~470 ms per token).
* Don't print zeros/NaNs when no count histogram has been collected
* A 10% faster CUDA vector dot kernel for Q3_K
Q3_K is now running at ~18.5 ms / token on CUDA,
so the gap to Q4_0 is only 10%.
It seems memory acccess pattern is more important for
performance than the amount of computation the kernel
does.
* A slightly daster Q4_K AVX2 dot product
For perplexity, where we are less memory bound, time per
pass drops by ~5%. Barely measurable difference for single
token prediction.
* A slightly faster ARM_NEON A4_K dot product
* Minor
* Fix quantization error test
We cannot possibly be expecting rmse < 0.002 for 2- and 3-bit
quantization variants.
* Fix docker build
I have been sloppy with vector reinterpret casts on ARM_NEON.
It seems clang is very forgiving in that regard.
* Added forgotten ggml.o dependence on k_quants.h to the Makefile
* Had unintentionally committed the Makefile with -Ofast enabled
* ggml : rename k_quants -> ggml-quants-k, use lowercase in code
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-06-05 21:56:18 +02:00
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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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2023-04-26 18:43:27 +02:00
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};
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2023-05-05 00:58:56 +02:00
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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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}
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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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return true;
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}
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}
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}
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catch (...) {
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// stoi failed
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}
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return false;
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}
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2023-03-10 19:40:58 +01:00
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// usage:
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2023-05-05 00:58:56 +02:00
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// ./quantize models/llama/ggml-model.bin [models/llama/ggml-model-quant.bin] type [nthreads]
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2023-03-10 19:40:58 +01:00
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//
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int main(int argc, char ** argv) {
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2023-05-05 00:58:56 +02:00
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if (argc < 3) {
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fprintf(stderr, "usage: %s model-f32.bin [model-quant.bin] type [nthreads]\n", argv[0]);
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2023-04-26 18:43:27 +02:00
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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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}
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2023-03-10 19:40:58 +01:00
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return 1;
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}
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2023-05-20 10:06:11 +02:00
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llama_init_backend();
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2023-03-11 16:40:14 +01:00
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2023-05-05 00:58:56 +02:00
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// parse command line arguments
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2023-03-10 19:40:58 +01:00
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const std::string fname_inp = argv[1];
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2023-05-05 00:58:56 +02:00
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std::string fname_out;
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int nthread;
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llama_ftype ftype;
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int arg_idx = 2;
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std::string ftype_str;
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if (try_parse_ftype(argv[arg_idx], ftype, ftype_str)) {
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// argv[2] is the ftype
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std::string fpath;
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const size_t pos = fname_inp.find_last_of('/');
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if (pos != std::string::npos) {
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fpath = fname_inp.substr(0, pos + 1);
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}
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// export as [inp path]/ggml-model-[ftype].bin
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fname_out = fpath + "ggml-model-" + ftype_str + ".bin";
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arg_idx++;
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}
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else {
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// argv[2] is the output path
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fname_out = argv[arg_idx];
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arg_idx++;
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2023-03-10 19:40:58 +01:00
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2023-05-05 00:58:56 +02:00
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if (argc <= arg_idx) {
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fprintf(stderr, "%s: missing ftype\n", __func__);
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return 1;
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}
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// argv[3] is the ftype
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if (!try_parse_ftype(argv[arg_idx], ftype, ftype_str)) {
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fprintf(stderr, "%s: invalid ftype '%s'\n", __func__, argv[3]);
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return 1;
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}
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arg_idx++;
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}
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// parse nthreads
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if (argc > arg_idx) {
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try {
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nthread = std::stoi(argv[arg_idx]);
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}
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catch (const std::exception & e) {
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fprintf(stderr, "%s: invalid nthread '%s' (%s)\n", __func__, argv[arg_idx], e.what());
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2023-04-26 18:43:27 +02:00
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return 1;
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}
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} else {
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nthread = 0;
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2023-04-26 18:43:27 +02:00
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}
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2023-05-01 18:23:47 +02:00
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fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
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2023-05-05 00:58:56 +02:00
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fprintf(stderr, "%s: quantizing '%s' to '%s' as %s", __func__, fname_inp.c_str(), fname_out.c_str(), ftype_str.c_str());
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if (nthread > 0) {
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fprintf(stderr, " using %d threads", nthread);
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}
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fprintf(stderr, "\n");
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2023-03-10 19:40:58 +01:00
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2023-05-20 10:06:11 +02:00
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const int64_t t_main_start_us = llama_time_us();
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2023-03-10 19:40:58 +01:00
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int64_t t_quantize_us = 0;
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// load the model
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{
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2023-05-20 10:06:11 +02:00
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const int64_t t_start_us = llama_time_us();
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2023-03-10 19:40:58 +01:00
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2023-04-20 19:42:27 +02:00
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if (llama_model_quantize(fname_inp.c_str(), fname_out.c_str(), ftype, nthread)) {
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2023-03-10 19:40:58 +01:00
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fprintf(stderr, "%s: failed to quantize model from '%s'\n", __func__, fname_inp.c_str());
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return 1;
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}
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2023-05-20 10:06:11 +02:00
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t_quantize_us = llama_time_us() - t_start_us;
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2023-03-10 19:40:58 +01:00
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}
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// report timing
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{
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2023-05-20 10:06:11 +02:00
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const int64_t t_main_end_us = llama_time_us();
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2023-03-10 19:40:58 +01:00
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printf("\n");
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2023-03-28 18:48:20 +02:00
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printf("%s: quantize time = %8.2f ms\n", __func__, t_quantize_us/1000.0);
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printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0);
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2023-03-10 19:40:58 +01:00
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}
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return 0;
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}
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