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stdout : vertical align outputs for better readibility
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@ -951,8 +951,9 @@ class OutputFile:
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ndarrays = bounded_parallel_map(do_item, model.items(), concurrency=8)
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for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)):
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size = ' x '.join(map(str, lazy_tensor.shape))
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print(f"[{i+1}/{len(model)}] Writing tensor {name}, size {size}...")
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size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape)
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padi = len(str(len(model)))
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print(f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type}")
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of.write_tensor_header(name, lazy_tensor.shape, lazy_tensor.data_type)
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ndarray.tofile(of.fout)
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of.fout.close()
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10
llama.cpp
10
llama.cpp
@ -262,12 +262,12 @@ static size_t checked_div(size_t a, size_t b) {
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}
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static std::string llama_format_tensor_shape(const std::vector<uint32_t> & ne) {
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std::string ret = "[" + std::to_string(ne.at(0));
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char buf[256];
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snprintf(buf, sizeof(buf), "%5u", ne.at(0));
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for (size_t i = 1; i < ne.size(); i++) {
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ret += " x " + std::to_string(ne.at(i));
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snprintf(buf + strlen(buf), sizeof(buf) - strlen(buf), " x %5u", ne.at(i));
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}
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ret += "]";
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return ret;
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return buf;
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}
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static size_t llama_calc_tensor_size(const std::vector<uint32_t> & ne, enum ggml_type type) {
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@ -1570,7 +1570,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
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tensor.data = read_data.addr;
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model_loader->load_data_for(tensor);
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printf("[%zu/%zu] %36s - %s, type = %6s, ",
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printf("[%4zu/%4zu] %36s - %16s, type = %6s, ",
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++idx, model_loader->tensors_map.tensors.size(),
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tensor.name.c_str(), llama_format_tensor_shape(tensor.ne).c_str(),
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ggml_type_name(tensor.type));
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