vbatts/llama.cpp
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ggml : remove old quantization functions (#5942)

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* ggml : remove old quantization functions

ggml-ci

* ggml : simplify ggml_quantize_chunk

ggml-ci

* ggml : restrict correctness

ggml-ci

* ggml : remove hist data from the quantization API

ggml-ci

* tests : remove hist usage in test-backend-ops

ggml-ci

* vulkan : remove hist and fix typo
This commit is contained in:
Georgi Gerganov 2024-03-09 15:53:59 +02:00 • committed by GitHub
parent 97c09585d6
commit 5b09797321
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GPG key ID: B5690EEEBB952194
9 changed files with 131 additions and 568 deletions
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llama.cpp
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@ -11890,17 +11890,16 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
return new_type;
}
static int32_t llama_tensor_quantize_internal(enum ggml_type new_type, const float * f32_data, void * new_data, const int chunk_size, int nrows, int n_per_row, int64_t * hist_cur, const float * imatrix, std::vector<std::thread> & workers, const int nthread) {
static int32_t llama_tensor_quantize_internal(enum ggml_type new_type, const float * f32_data, void * new_data, const int chunk_size, int nrows, int n_per_row, const float * imatrix, std::vector<std::thread> & workers, const int nthread) {
std::mutex mutex;
int counter = 0;
size_t new_size = 0;
if (nthread < 2) {
// single-thread
return ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, hist_cur, imatrix);
return ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, imatrix);
}
auto compute = [&mutex, &counter, &hist_cur, &new_size, new_type, f32_data, new_data, chunk_size,
auto compute = [&mutex, &counter, &new_size, new_type, f32_data, new_data, chunk_size,
nrows, n_per_row, imatrix]() {
std::array<int64_t, 1 << 4> local_hist = {};
const int nrows_per_chunk = chunk_size / n_per_row;
size_t local_size = 0;
while (true) {
@ -11908,17 +11907,13 @@ static int32_t llama_tensor_quantize_internal(enum ggml_type new_type, const flo
int first_row = counter; counter += nrows_per_chunk;
if (first_row >= nrows) {
if (local_size > 0) {
for (int j=0; j<int(local_hist.size()); ++j) {
hist_cur[j] += local_hist[j];
}
new_size += local_size;
}
break;
}
lock.unlock();
const int this_nrow = std::min(nrows - first_row, nrows_per_chunk);
local_size += ggml_quantize_chunk(new_type, f32_data, new_data,
first_row * n_per_row, this_nrow, n_per_row, local_hist.data(), imatrix);
local_size += ggml_quantize_chunk(new_type, f32_data, new_data, first_row * n_per_row, this_nrow, n_per_row, imatrix);
}
};
for (int it = 0; it < nthread - 1; ++it) {
@ -12041,7 +12036,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
size_t total_size_org = 0;
size_t total_size_new = 0;
std::vector<int64_t> hist_all(1 << 4, 0);
std::vector<std::thread> workers;
workers.reserve(nthread);
@ -12175,7 +12169,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
work.resize(nelements * 4); // upper bound on size
}
new_data = work.data();
std::array<int64_t, 1 << 4> hist_cur = {};
const int n_per_row = tensor->ne[0];
const int nrows = nelements / n_per_row;
@ -12185,22 +12178,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
const int nchunk = (nelements + chunk_size - 1)/chunk_size;
const int nthread_use = nthread > 1 ? std::max(1, std::min(nthread, nchunk)) : 1;
new_size = llama_tensor_quantize_internal(new_type, f32_data, new_data, chunk_size, nrows, n_per_row, hist_cur.data(), imatrix, workers, nthread_use);
new_size = llama_tensor_quantize_internal(new_type, f32_data, new_data, chunk_size, nrows, n_per_row, imatrix, workers, nthread_use);
LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0);
int64_t tot_count = 0;
for (size_t i = 0; i < hist_cur.size(); i++) {
hist_all[i] += hist_cur[i];
tot_count += hist_cur[i];
}
if (tot_count > 0) {
LLAMA_LOG_INFO(" | hist: ");
for (size_t i = 0; i < hist_cur.size(); i++) {
LLAMA_LOG_INFO("%5.3f ", hist_cur[i] / float(nelements));
}
}
LLAMA_LOG_INFO("\n");
LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB\n", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0);
}
total_size_org += ggml_nbytes(tensor);
total_size_new += new_size;
@ -12229,22 +12209,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
LLAMA_LOG_INFO("%s: model size = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0);
LLAMA_LOG_INFO("%s: quant size = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0);
// print histogram for all tensors
{
int64_t sum_all = 0;
for (size_t i = 0; i < hist_all.size(); i++) {
sum_all += hist_all[i];
}
if (sum_all > 0) {
LLAMA_LOG_INFO("%s: hist: ", __func__);
for (size_t i = 0; i < hist_all.size(); i++) {
LLAMA_LOG_INFO("%5.3f ", hist_all[i] / float(sum_all));
}
LLAMA_LOG_INFO("\n");
}
}
if (qs.n_fallback > 0) {
LLAMA_LOG_WARN("%s: WARNING: %d of %d tensor(s) incompatible with k-quants and required fallback quantization\n",
__func__, qs.n_fallback, qs.n_k_quantized + qs.n_fallback);