llama: model_loader: add TENSOR_READ_LAZY (#27794)
* llama: model_loader: add TENSOR_GET_ROW_LAZY * add --tensor-read-lazy * rename to TENSOR_READ_LAZY * gen docs * address comments
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@@ -1282,6 +1282,18 @@ struct ggml_tensor * llama_model_loader::create_tensor(
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return NULL;
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}
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if ((flags & TENSOR_READ_LAZY) && use_mmap && tensor_read_lazy != LLAMA_TENSOR_READ_LAZY_OFF) {
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// in auto mode, small tensors are cheap enough to keep resident
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constexpr size_t auto_lazy_min_size = 4ull * 1024 * 1024 * 1024;
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if (tensor_read_lazy == LLAMA_TENSOR_READ_LAZY_ON || ggml_nbytes(cur) > auto_lazy_min_size) {
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const auto & w = require_weight(tn.str().c_str());
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lazy_tensor_ranges[w.idx].emplace_back(w.offs, w.offs + ggml_nbytes(cur));
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LLAMA_LOG_INFO("%s: tensor %s (size = %zu MiB) lazy read enabled\n",
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__func__, tn.str().c_str(), ggml_nbytes(cur)/1024/1024);
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}
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}
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ggml_tensor t_meta = *cur;
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if (flags & TENSOR_ALLOW_RESHAPE) {
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for (size_t dim = 0; dim < GGML_MAX_DIMS; dim++) {
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@@ -1349,7 +1361,9 @@ void llama_model_loader::init_mappings(bool prefetch, llama_mlocks * mlock_mmaps
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if (use_mmap) {
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mappings.reserve(files.size());
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mmaps_used.reserve(files.size());
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for (const auto & file : files) {
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for (uint32_t idx = 0; idx < files.size(); idx++) {
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const auto & file = files[idx];
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bool is_numa = false;
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auto * dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
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@@ -1361,7 +1375,11 @@ void llama_model_loader::init_mappings(bool prefetch, llama_mlocks * mlock_mmaps
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}
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}
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std::unique_ptr<llama_mmap> mapping = std::make_unique<llama_mmap>(file.get(), prefetch ? -1 : 0, is_numa);
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const auto it_lazy = lazy_tensor_ranges.find(idx);
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static const llama_mmap::ranges no_lazy_ranges;
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std::unique_ptr<llama_mmap> mapping = std::make_unique<llama_mmap>(file.get(), prefetch ? -1 : 0, is_numa,
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it_lazy != lazy_tensor_ranges.end() ? it_lazy->second : no_lazy_ranges);
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mmaps_used.emplace_back(mapping->size(), 0);
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if (mlock_mmaps) {
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std::unique_ptr<llama_mlock> mlock_mmap(new llama_mlock());
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