llama : quantize token_embd.weight using output type
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1 changed files with 4 additions and 4 deletions
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@ -10481,7 +10481,10 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
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return std::make_pair(i_layer, n_layer);
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};
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if (name == tn(LLM_TENSOR_OUTPUT, "weight")) {
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// for arches that share the same tensor between the token embeddings and the output, we quantize the token embeddings
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// with the quantization of the output tensor
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if (name == tn(LLM_TENSOR_OUTPUT, "weight") ||
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(LLM_TENSOR_NAMES.at(arch).find(LLM_TENSOR_OUTPUT) == LLM_TENSOR_NAMES.at(arch).end() && name == "token_embd.weight")) {
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int nx = tensor->ne[0];
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if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) {
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new_type = GGML_TYPE_Q8_0;
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@ -10499,9 +10502,6 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty
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else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
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new_type = GGML_TYPE_Q4_K;
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}
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else if (arch == LLM_ARCH_GEMMA) {
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new_type = GGML_TYPE_Q8_0;
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}
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} else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S) {
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if (name.find("attn_v.weight") != std::string::npos) {
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if (qs.model.hparams.n_gqa() >= 4 || qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_Q4_K;
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