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llama : remove quantization sanity check #17788

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Merged
danbev merged 2 commits into from
Dec 6, 2025
Merged

llama : remove quantization sanity check #17788

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0ecbb8c
llama : remove quantization sanity check
danbev Dec 5, 2025
00c5a71
llama : remove unused pruned_attention_w and is_clip_model vars
danbev Dec 5, 2025
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27 changes: 0 additions & 27 deletions src/llama-quant.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -666,19 +666,13 @@ 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;
tensors.reserve(ml.weights_map.size());
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;
}
Expand All @@ -703,7 +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;

Expand All @@ -717,30 +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)
{
int32_t n_layer_all = model.hparams.n_layer;
if (llama_model_has_encoder(&model)) {
// now n_layer_all is the number of attention layers in the encoder
// for each decoder block, there are 2 attention layers
n_layer_all += 2 * model.hparams.dec_n_layer;
}

// note: for linear-attention models (such as Qwen3 Next) this is the number of linear layers
const int32_t n_layer_recr = std::count(model.hparams.recurrent_layer_arr.begin(), model.hparams.recurrent_layer_arr.end(), true);

LLAMA_LOG_INFO("%s: n_layer_all = %d, n_layer_recr = %d, pruned_attention_w = %d\n", __func__, n_layer_all, n_layer_recr, pruned_attention_w);

GGML_ASSERT((qs.n_attention_wv == n_layer_all - pruned_attention_w - n_layer_recr) && "n_attention_wv is unexpected");
}

size_t total_size_org = 0;
size_t total_size_new = 0;

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