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talk-llama : sync llama.cpp

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This commit is contained in:
Georgi Gerganov
2025-12-12 18:15:27 +02:00
parent 48cdc06e91
commit 179d8b1c9c
28 changed files with 2163 additions and 177 deletions
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examples/talk-llama/llama-quant.cpp
+3 -24
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@@ -666,7 +666,6 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
std::map<int, std::string> mapped;
int blk_id = 0;
int pruned_attention_w = 0;
// make a list of weights
std::vector<const llama_model_loader::llama_tensor_weight *> tensors;
@@ -674,14 +673,11 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
for (const auto & it : ml.weights_map) {
const std::string remapped_name(remap_layer(it.first, prune_list, mapped, blk_id));
if (remapped_name.empty()) {
if (it.first.find("attn_v.weight") != std::string::npos ||
it.first.find("attn_qkv.weight") != std::string::npos ||
it.first.find("attn_kv_b.weight") != std::string::npos) {
pruned_attention_w++;
}
LLAMA_LOG_DEBUG("%s: pruning tensor %s\n", __func__, it.first.c_str());
continue;
} else if (remapped_name != it.first) {
}
if (remapped_name != it.first) {
ggml_set_name(it.second.tensor, remapped_name.c_str());
LLAMA_LOG_DEBUG("%s: tensor %s remapped to %s\n", __func__, it.first.c_str(), ggml_get_name(it.second.tensor));
}
@@ -701,7 +697,6 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
});
}
bool is_clip_model = false;
for (const auto * it : tensors) {
const struct ggml_tensor * tensor = it->tensor;
@@ -715,26 +710,10 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
} else if (name == LLM_TN(model.arch)(LLM_TENSOR_OUTPUT, "weight")) {
qs.has_output = true;
}
is_clip_model |= name.rfind("mm.", 0) == 0; // check the "mm." prefix
}
qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)model.hparams.n_layer;
// sanity checks for models that have attention layers
if (qs.n_attention_wv != 0 && !is_clip_model)
{
const auto & n_head_kv_iter = model.hparams.n_head_kv_arr.begin();
// attention layers have a non-zero number of kv heads
int32_t n_attn_layer = model.hparams.n_layer - std::count(n_head_kv_iter, n_head_kv_iter + model.hparams.n_layer, 0);
if (llama_model_has_encoder(&model)) {
// now n_attn_layer is the number of attention layers in the encoder
// for each decoder block, there are 2 attention layers
n_attn_layer += 2 * model.hparams.dec_n_layer;
}
GGML_ASSERT((qs.n_attention_wv == n_attn_layer - pruned_attention_w) && "n_attention_wv is unexpected");
}
size_t total_size_org = 0;
size_t total_size_new = 0;