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llama : temporary disable Q6_K output quantization (#1711)
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13
llama.cpp
13
llama.cpp
@ -2198,8 +2198,12 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
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printf("size = %8.3f MB\n", tensor.size/1024.0/1024.0);
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} else {
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new_type = quantized_type;
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if (tensor.name == "output.weight") new_type = GGML_TYPE_Q6_K;
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else if (tensor.name.find("attention.wv.weight") != std::string::npos) {
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// TODO: temporary disabled until Metal / OpenCL support is available
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// ref: https://github.com/ggerganov/llama.cpp/issues/1711
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//if (tensor.name == "output.weight") {
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// new_type = GGML_TYPE_Q6_K;
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//}
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if (tensor.name.find("attention.wv.weight") != std::string::npos) {
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if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K;
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else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
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else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
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@ -2207,7 +2211,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
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(i_attention_wv - n_attention_wv/8)%3 == 2)) new_type = GGML_TYPE_Q6_K;
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++i_attention_wv;
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}
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else if (tensor.name.find("feed_forward.w2.weight") != std::string::npos) {
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if (tensor.name.find("feed_forward.w2.weight") != std::string::npos) {
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if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K;
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else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
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else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
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@ -2215,10 +2219,11 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
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(i_feed_forward_w2 - n_feed_forward_w2/8)%3 == 2)) new_type = GGML_TYPE_Q6_K;
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++i_feed_forward_w2;
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}
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else if (tensor.name.find("attention.wo.weight") != std::string::npos) {
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if (tensor.name.find("attention.wo.weight") != std::string::npos) {
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if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K;
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else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
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}
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float * f32_data;
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size_t nelements = tensor.ne.at(0) * tensor.ne.at(1);
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llama_buffer f32_conv_buf;
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