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Merge branch 'upstream' into concedo_experimental

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# Conflicts:
#	.github/workflows/build.yml
#	Makefile
#	docs/build.md
#	examples/rpc/rpc-server.cpp
#	examples/sycl/build.sh
#	ggml/CMakeLists.txt
#	ggml/src/ggml-cann/aclnn_ops.cpp
#	ggml/src/ggml-cann/ggml-cann.cpp
#	ggml/src/ggml-hip/CMakeLists.txt
#	scripts/sync-ggml.last
This commit is contained in:
Concedo
2025-04-17 00:52:37 +08:00
parent a5c39143a1 b43d89e311
commit 06159939d9
72 changed files with 6549 additions and 5397 deletions
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src/llama-quant.cpp
+28 -7
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@@ -10,6 +10,7 @@
#include <cinttypes>
#include <fstream>
#include <mutex>
#include <regex>
#include <thread>
#include <unordered_map>
@@ -47,8 +48,14 @@ struct quantize_state_impl {
{}
};
// changes to this struct must be replicated in quantize.cpp
struct tensor_quantization {
std::string name;
ggml_type quant = GGML_TYPE_COUNT;
};
static void llama_tensor_dequantize_impl(
struct ggml_tensor * tensor, std::vector<no_init<float>> & output, std::vector<std::thread> & workers,
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) {
@@ -539,7 +546,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
model.load_hparams(ml);
model.load_stats (ml);
struct quantize_state_impl qs(model, params);
quantize_state_impl qs(model, params);
if (params->only_copy) {
ftype = ml.ftype;
@@ -664,7 +671,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
// populate the original tensors so we get an initial meta data
for (const auto * it : tensors) {
uint16_t i_split = params->keep_split ? it->idx : 0;
struct ggml_tensor * tensor = it->tensor;
ggml_tensor * tensor = it->tensor;
if (!ctx_outs[i_split]) {
ctx_outs[i_split].reset(gguf_init_empty());
}
@@ -713,7 +720,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
new_ofstream(0);
for (const auto * it : tensors) {
const auto & weight = *it;
struct ggml_tensor * tensor = weight.tensor;
ggml_tensor * tensor = weight.tensor;
if (weight.idx != cur_split && params->keep_split) {
close_ofstream();
new_ofstream(weight.idx);
@@ -779,7 +786,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
// do not quantize relative position bias (T5)
quantize &= name.find("attn_rel_b.weight") == std::string::npos;
enum ggml_type new_type;
ggml_type new_type;
void * new_data;
size_t new_size;
@@ -789,6 +796,19 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
// get more optimal quantization type based on the tensor shape, layer, etc.
if (!params->pure && ggml_is_quantized(default_type)) {
new_type = llama_tensor_get_type(qs, new_type, tensor, ftype);
// unless the user specifies a type
if (params->tensor_types) {
const std::vector<tensor_quantization> & tensor_types = *static_cast<const std::vector<tensor_quantization> *>(params->tensor_types);
for (const auto & [tname, qtype] : tensor_types) {
if (std::regex pattern(tname); std::regex_search(tensor->name, pattern)) {
if (qtype != new_type) {
LLAMA_LOG_DEBUG("(overriding %s -> %s), ", ggml_type_name(new_type), ggml_type_name(qtype));
}
new_type = qtype;
break;
}
}
}
}
if (params->token_embedding_type < GGML_TYPE_COUNT && strcmp(tensor->name, "token_embd.weight") == 0) {
new_type = params->token_embedding_type;
@@ -913,8 +933,8 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
// interface implementation
//
struct llama_model_quantize_params llama_model_quantize_default_params() {
struct llama_model_quantize_params result = {
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,
@@ -926,6 +946,7 @@ struct llama_model_quantize_params llama_model_quantize_default_params() {
/*.keep_split =*/ false,
/*.imatrix =*/ nullptr,
/*.kv_overrides =*/ nullptr,
/*.tensor_type =*/ nullptr,
};
return result;