mirror of
https://github.com/matrix-construct/construct
synced 2024-11-15 22:41:12 +01:00
729 lines
18 KiB
C++
729 lines
18 KiB
C++
// Tensor Construct
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//
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// Copyright (C) Matrix Construct Developers, Authors & Contributors
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// Copyright (C) 2016-2021 Jason Volk <jason@zemos.net>
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//
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// Permission to use, copy, modify, and/or distribute this software for any
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// purpose with or without fee is hereby granted, provided that the above
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// copyright notice and this permission notice is present in all copies. The
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// full license for this software is available in the LICENSE file.
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namespace ircd::gpt::model
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{
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using init_func = void (*)(decoder &, const string_view &, const size_t &, const json::array &);
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using init_handler = std::pair<string_view, init_func>;
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static void
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init_f_weight(decoder &, const string_view &, const size_t &, const json::array &),
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init_f_bias(decoder &, const string_view &, const size_t &, const json::array &),
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init_wpe_weight(decoder &, const string_view &, const size_t &, const json::array &),
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init_wte_weight(decoder &, const string_view &, const size_t &, const json::array &),
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init_h_ffnn_fc_weight(decoder &, const string_view &, const size_t &, const json::array &),
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init_h_ffnn_fc_bias(decoder &, const string_view &, const size_t &, const json::array &),
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init_h_ffnn_proj_weight(decoder &, const string_view &, const size_t &, const json::array &),
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init_h_ffnn_proj_bias(decoder &, const string_view &, const size_t &, const json::array &),
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init_h_ln_1_weight(decoder &, const string_view &, const size_t &, const json::array &),
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init_h_ln_1_bias(decoder &, const string_view &, const size_t &, const json::array &),
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init_h_ln_2_weight(decoder &, const string_view &, const size_t &, const json::array &),
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init_h_ln_2_bias(decoder &, const string_view &, const size_t &, const json::array &),
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init_h_attn_attn_weight(decoder &, const string_view &, const size_t &, const json::array &),
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init_h_attn_attn_bias(decoder &, const string_view &, const size_t &, const json::array &),
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init_h_attn_proj_weight(decoder &, const string_view &, const size_t &, const json::array &),
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init_h_attn_proj_bias(decoder &, const string_view &, const size_t &, const json::array &);
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static bool init_dataset(const string_view &);
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static bool init_from_cache(const string_view &);
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static void init_from_json_handle(decoder &, const init_handler &, const size_t &);
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static void init_from_json(const string_view &, const string_view &);
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static void init(), fini() noexcept;
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extern const init_handler
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manifest[],
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manifest_h[];
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extern conf::item<bool>
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cache_locked,
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cache_shared,
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cache_hugepage;
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extern conf::item<std::string>
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path,
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cache_path,
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dataset_path;
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static fs::map
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default_model_shm,
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default_dataset_shm;
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}
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constexpr const char
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*const ircd::gpt::model::prop::ended,
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*const ircd::gpt::model::prop::id,
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*const ircd::gpt::model::prop::length,
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*const ircd::gpt::model::prop::text;
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decltype(ircd::gpt::model::manifest_h)
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ircd::gpt::model::manifest_h
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{
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{ "h.%u.mlp.c_fc.weight.json", init_h_ffnn_fc_weight, },
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{ "h.%u.mlp.c_fc.bias.json", init_h_ffnn_fc_bias, },
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{ "h.%u.mlp.c_proj.weight.json", init_h_ffnn_proj_weight, },
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{ "h.%u.mlp.c_proj.bias.json", init_h_ffnn_proj_bias, },
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{ "h.%u.ln_1.weight.json", init_h_ln_1_weight, },
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{ "h.%u.ln_1.bias.json", init_h_ln_1_bias, },
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{ "h.%u.ln_2.weight.json", init_h_ln_2_weight, },
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{ "h.%u.ln_2.bias.json", init_h_ln_2_bias, },
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{ "h.%u.attn.c_attn.weight.json", init_h_attn_attn_weight, },
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{ "h.%u.attn.c_attn.bias.json", init_h_attn_attn_bias, },
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{ "h.%u.attn.c_proj.weight.json", init_h_attn_proj_weight, },
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{ "h.%u.attn.c_proj.bias.json", init_h_attn_proj_bias },
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};
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decltype(ircd::gpt::model::manifest)
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ircd::gpt::model::manifest
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{
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{ "ln_f.weight.json", init_f_weight, },
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{ "ln_f.bias.json", init_f_bias, },
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{ "wpe.weight.json", init_wpe_weight },
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{ "wte.weight.json", init_wte_weight },
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};
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decltype(ircd::gpt::model::cache_locked)
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ircd::gpt::model::cache_locked
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{
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{ "name", "ircd.gpt.model.cache.locked" },
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{ "default", false },
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};
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decltype(ircd::gpt::model::cache_shared)
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ircd::gpt::model::cache_shared
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{
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{ "name", "ircd.gpt.model.cache.shared" },
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{ "default", false },
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};
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decltype(ircd::gpt::model::cache_hugepage)
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ircd::gpt::model::cache_hugepage
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{
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{ "name", "ircd.gpt.model.cache.hugepage" },
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{ "default", false },
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};
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decltype(ircd::gpt::model::cache_path)
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ircd::gpt::model::cache_path
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{
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{ "name", "ircd.gpt.model.cache.path" },
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{ "default", "model.cache.localhost" },
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};
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decltype(ircd::gpt::model::dataset_path)
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ircd::gpt::model::dataset_path
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{
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{ "name", "ircd.gpt.model.dataset.path" },
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{ "default", string_view{} },
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};
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decltype(ircd::gpt::model::path)
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ircd::gpt::model::path
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{
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{
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{ "name", "ircd.gpt.model.path" },
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{ "default", string_view{} },
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},
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init
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};
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decltype(ircd::gpt::model::default_model)
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ircd::gpt::model::default_model;
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decltype(ircd::gpt::model::default_moment)
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ircd::gpt::model::default_moment;
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decltype(ircd::gpt::model::default_checkpoint)
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ircd::gpt::model::default_checkpoint;
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decltype(ircd::gpt::model::default_dataset)
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ircd::gpt::model::default_dataset;
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decltype(ircd::gpt::model::default_data)
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ircd::gpt::model::default_data;
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void
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ircd::gpt::model::init()
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{
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if(!model::path)
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return;
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if(model::dataset_path)
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init_dataset(model::dataset_path);
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if(likely(init_from_cache(model::cache_path)))
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return;
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init_from_json(model::cache_path, model::path);
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if(unlikely(!init_from_cache(model::cache_path)))
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throw error
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{
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"Failed to find and/or initialize model."
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};
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}
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void
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ircd::gpt::model::fini()
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noexcept
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{
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default_checkpoint[2] = nullptr;
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default_checkpoint[1] = nullptr;
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default_checkpoint[0] = nullptr;
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default_moment[1] = nullptr;
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default_moment[0] = nullptr;
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default_model = nullptr;
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default_model_shm = {};
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default_dataset = nullptr;
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default_data.clear();
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default_dataset_shm = {};
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}
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bool
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ircd::gpt::model::init_from_cache(const string_view &cache_path)
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{
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if(!fs::is_reg(cache_path))
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return false;
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const auto file_size
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{
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fs::size(cache_path)
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};
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const auto decoder_size
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{
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sizeof(model::decoder)
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};
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const bool has_params
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{
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file_size >= decoder_size
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};
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const bool has_moments
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{
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file_size >= decoder_size * 6
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};
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if(unlikely(!has_params))
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throw error
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{
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"Cached model `%s' size %zu insufficient for decoder size %zu.",
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cache_path,
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file_size,
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decoder_size,
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};
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const auto mode
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{
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cache_shared?
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std::ios::in | std::ios::out:
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std::ios::in
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};
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const fs::fd fd
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{
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cache_path, fs::fd::opts
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{
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.mode = mode,
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},
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};
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const bool map_moments
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{
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has_moments || cache_shared
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};
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if(!has_moments && map_moments)
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{
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fs::truncate(fd, decoder_size * 6);
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fs::allocate(fd, decoder_size * 5, decoder_size);
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}
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const auto map_size
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{
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map_moments?
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decoder_size * 6:
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decoder_size
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};
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fs::map::opts map_opts
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{
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.alignment = alignof(model::decoder),
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.shared = bool(cache_shared),
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.locked = bool(cache_locked),
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.huge2mb = bool(cache_hugepage),
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};
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map_opts.mode = mode;
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default_model_shm = fs::map
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{
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fd, map_size, map_opts,
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};
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default_model = reinterpret_cast<decoder *>
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(
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data(default_model_shm)
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);
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if(map_moments)
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{
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default_moment[0] = reinterpret_cast<float *>(default_model + 1);
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default_moment[1] = reinterpret_cast<float *>(default_model + 2);
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default_checkpoint[0] = reinterpret_cast<float *>(default_model + 3);
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default_checkpoint[1] = reinterpret_cast<float *>(default_model + 4);
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default_checkpoint[2] = reinterpret_cast<float *>(default_model + 5);
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}
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allocator::lock({(const char *)default_model, sizeof(decoder)});
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fs::prefetch(default_model_shm, sizeof(decoder));
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char pbuf[48];
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log::info
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{
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log, "model(%p) mapped cached model `%s' params:%b moments:%b align:%u %s",
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data(default_model_shm),
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cache_path,
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has_params,
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has_moments,
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map_opts.alignment,
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pretty(pbuf, iec(map_size)),
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};
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return true;
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}
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void
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ircd::gpt::model::init_from_json(const string_view &cache_path,
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const string_view &model_path)
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{
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util::timer stopwatch;
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auto decoder(std::make_unique<model::decoder>());
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memset(decoder.get(), 0x0, sizeof(model::decoder));
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// Load the top level files, vocab etc
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for(size_t i(0); i < 4; ++i)
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init_from_json_handle(*decoder, manifest[i], 0);
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// Load the transformer files by layer
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const size_t layers {12};
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for(size_t i(0); i < layers; ++i)
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for(size_t j(0); j < 12; ++j)
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init_from_json_handle(*decoder, manifest_h[j], i);
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const const_buffer src
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{
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reinterpret_cast<char *>(decoder.get()), sizeof(model::decoder)
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};
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const auto wrote
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{
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fs::write(cache_path, src)
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};
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char pbuf[2][48];
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log::info
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{
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log, "model(%p) parsed `%s' cached %s to `%s' in %s",
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decoder.get(),
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model_path,
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pretty(pbuf[0], iec(size(wrote))),
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cache_path,
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stopwatch.pretty(pbuf[1]),
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};
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}
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void
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ircd::gpt::model::init_from_json_handle(decoder &d,
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const init_handler &handler,
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const size_t &layer)
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{
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const auto &[fmt, func]
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{
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handler
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};
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char namebuf[128];
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const string_view path_part[2]
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{
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model::path, fmt::sprintf
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{
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namebuf, fmt, layer
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}
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};
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const auto path
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{
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fs::path(fs::path_scratch, path_part)
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};
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const fs::fd::opts fd_opts
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{
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.mode = std::ios::in,
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.sequential = true,
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};
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const fs::fd fd
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{
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path, fd_opts
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};
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// mmap of the file
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const fs::map map
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{
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fd, size(fd), fs::map::opts{fd_opts},
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};
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// Each file is a JSON array at the top level.
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const json::array matrix
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{
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map
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};
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// Readable name for logging
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const auto &name
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{
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path_part[1]
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};
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if(likely(func))
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func(d, name, layer, matrix);
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// Check for interrupt after long operation
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ctx::interruption_point();
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log::info
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{
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log, "model(%p) loaded layer:%zu :%s",
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&d,
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layer,
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name,
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};
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}
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bool
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ircd::gpt::model::init_dataset(const string_view &path)
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{
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if(!fs::is_reg(path))
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return false;
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const auto size
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{
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fs::size(path)
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};
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const fs::fd::opts fd_opts
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{
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.mode = std::ios::in,
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};
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const fs::fd fd
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{
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path, fd_opts,
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};
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fs::map::opts map_opts{fd_opts};
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map_opts.huge2mb = bool(cache_hugepage);
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default_dataset_shm = fs::map
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{
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fd, size, map_opts
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};
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default_dataset = string_view
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(
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default_dataset_shm
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);
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size_t checkpoint(0);
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default_data.resize(260000); //TODO: XXX
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fs::prefetch(default_dataset_shm, size);
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ircd::tokens(default_dataset, '\n', [&checkpoint]
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(const string_view &line)
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{
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default_data.at(checkpoint++) = line;
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});
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char pbuf[48];
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log::info
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{
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log, "dataset(%p) mapped `%s' %s @%lu",
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data(default_dataset_shm),
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path,
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pretty(pbuf, iec(size)),
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checkpoint,
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};
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fs::evict(default_dataset_shm, size);
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return true;
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}
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void
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ircd::gpt::model::init_wpe_weight(decoder &d,
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const string_view &name,
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const size_t &layer,
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const json::array &mat)
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{
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size_t i(0);
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for(const json::array vec : mat)
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{
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size_t j(0);
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for(const auto &elem : vec)
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d.embed.pos[i].elem[j++] = lex_cast<float>(elem);
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always_assert(j == sizeof(d.embed.pos[i]) / sizeof(float));
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++i;
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}
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}
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void
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ircd::gpt::model::init_wte_weight(decoder &d,
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const string_view &name,
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const size_t &layer,
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const json::array &mat)
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{
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size_t i(0);
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for(const json::array vec : mat)
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{
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size_t j(0);
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for(const auto &elem : vec)
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d.embed.token[i].elem[j++] = lex_cast<float>(elem);
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always_assert(j == sizeof(d.embed.token[i]) / sizeof(float));
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++i;
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}
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}
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void
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ircd::gpt::model::init_f_weight(decoder &d,
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const string_view &name,
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const size_t &layer,
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const json::array &vec)
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{
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size_t i(0);
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for(const auto &elem : vec)
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d.embed.norm.weight.elem[i++] = lex_cast<float>(elem);
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always_assert(i == sizeof(d.embed.norm.weight) / sizeof(float));
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}
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void
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ircd::gpt::model::init_f_bias(decoder &d,
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const string_view &name,
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const size_t &layer,
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const json::array &vec)
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{
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size_t i(0);
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for(const auto &elem : vec)
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d.embed.norm.bias.elem[i++] = lex_cast<float>(elem);
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always_assert(i == sizeof(d.embed.norm.bias) / sizeof(float));
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}
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void
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ircd::gpt::model::init_h_ffnn_fc_weight(decoder &d,
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const string_view &name,
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const size_t &layer,
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const json::array &mat)
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{
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size_t i(0);
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for(const json::array vec : mat)
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{
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size_t j(0);
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for(const auto &elem : vec)
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d.layer[layer].ffnn.fcon_weight[i].fcon[j++] = lex_cast<float>(elem);
|
|
|
|
always_assert(j == sizeof(d.layer[layer].ffnn.fcon_weight[i]) / sizeof(float));
|
|
++i;
|
|
}
|
|
|
|
always_assert
|
|
(
|
|
i == sizeof(d.layer[layer].ffnn.fcon_weight)
|
|
/ sizeof(d.layer[layer].ffnn.fcon_weight[0])
|
|
);
|
|
}
|
|
|
|
void
|
|
ircd::gpt::model::init_h_ffnn_fc_bias(decoder &d,
|
|
const string_view &name,
|
|
const size_t &layer,
|
|
const json::array &vec)
|
|
{
|
|
size_t i(0);
|
|
for(const auto &elem : vec)
|
|
d.layer[layer].ffnn.fcon_bias.fcon[i++] = lex_cast<float>(elem);
|
|
|
|
always_assert(i == sizeof(d.layer[layer].ffnn.fcon_bias) / sizeof(float));
|
|
}
|
|
|
|
void
|
|
ircd::gpt::model::init_h_ffnn_proj_weight(decoder &d,
|
|
const string_view &name,
|
|
const size_t &layer,
|
|
const json::array &mat)
|
|
{
|
|
size_t i(0);
|
|
for(const json::array vec : mat)
|
|
{
|
|
size_t j(0);
|
|
for(const auto &elem : vec)
|
|
d.layer[layer].ffnn.proj_weight[i].elem[j++] = lex_cast<float>(elem);
|
|
|
|
always_assert(j == sizeof(d.layer[layer].ffnn.proj_weight[i]) / sizeof(float));
|
|
++i;
|
|
}
|
|
|
|
always_assert
|
|
(
|
|
i == sizeof(d.layer[layer].ffnn.proj_weight)
|
|
/ sizeof(d.layer[layer].ffnn.proj_weight[0])
|
|
);
|
|
}
|
|
|
|
void
|
|
ircd::gpt::model::init_h_ffnn_proj_bias(decoder &d,
|
|
const string_view &name,
|
|
const size_t &layer,
|
|
const json::array &vec)
|
|
{
|
|
size_t i(0);
|
|
for(const auto &elem : vec)
|
|
d.layer[layer].ffnn.proj_bias.elem[i++] = lex_cast<float>(elem);
|
|
|
|
always_assert(i == sizeof(d.layer[layer].ffnn.proj_bias) / sizeof(float));
|
|
}
|
|
|
|
void
|
|
ircd::gpt::model::init_h_ln_1_weight(decoder &d,
|
|
const string_view &name,
|
|
const size_t &layer,
|
|
const json::array &vec)
|
|
{
|
|
size_t i(0);
|
|
for(const auto &elem : vec)
|
|
d.layer[layer].attn.norm.weight.elem[i++] = lex_cast<float>(elem);
|
|
|
|
always_assert(i == sizeof(d.layer[layer].attn.norm.weight) / sizeof(float));
|
|
}
|
|
|
|
void
|
|
ircd::gpt::model::init_h_ln_1_bias(decoder &d,
|
|
const string_view &name,
|
|
const size_t &layer,
|
|
const json::array &vec)
|
|
{
|
|
size_t i(0);
|
|
for(const auto &elem : vec)
|
|
d.layer[layer].attn.norm.bias.elem[i++] = lex_cast<float>(elem);
|
|
|
|
always_assert(i == sizeof(d.layer[layer].attn.norm.bias) / sizeof(float));
|
|
}
|
|
|
|
void
|
|
ircd::gpt::model::init_h_ln_2_weight(decoder &d,
|
|
const string_view &name,
|
|
const size_t &layer,
|
|
const json::array &vec)
|
|
{
|
|
size_t i(0);
|
|
for(const auto &elem : vec)
|
|
d.layer[layer].ffnn.norm.weight.elem[i++] = lex_cast<float>(elem);
|
|
|
|
always_assert(i == sizeof(d.layer[layer].ffnn.norm.weight) / sizeof(float));
|
|
}
|
|
|
|
void
|
|
ircd::gpt::model::init_h_ln_2_bias(decoder &d,
|
|
const string_view &name,
|
|
const size_t &layer,
|
|
const json::array &vec)
|
|
{
|
|
size_t i(0);
|
|
for(const auto &elem : vec)
|
|
d.layer[layer].ffnn.norm.bias.elem[i++] = lex_cast<float>(elem);
|
|
|
|
always_assert(i == sizeof(d.layer[layer].ffnn.norm.bias) / sizeof(float));
|
|
}
|
|
|
|
void
|
|
ircd::gpt::model::init_h_attn_attn_weight(decoder &d,
|
|
const string_view &name,
|
|
const size_t &layer,
|
|
const json::array &mat)
|
|
{
|
|
size_t i(0);
|
|
for(const json::array vec : mat)
|
|
{
|
|
size_t j(0);
|
|
for(const auto &elem : vec)
|
|
d.layer[layer].attn.fcon_weight[i].fcon[j++] = lex_cast<float>(elem);
|
|
|
|
always_assert(j == sizeof(d.layer[layer].attn.fcon_weight[i]) / sizeof(float));
|
|
++i;
|
|
}
|
|
|
|
always_assert
|
|
(
|
|
i == sizeof(d.layer[layer].attn.fcon_weight)
|
|
/ sizeof(d.layer[layer].attn.fcon_weight[0])
|
|
);
|
|
}
|
|
|
|
void
|
|
ircd::gpt::model::init_h_attn_attn_bias(decoder &d,
|
|
const string_view &name,
|
|
const size_t &layer,
|
|
const json::array &vec)
|
|
{
|
|
size_t i(0);
|
|
for(const auto &elem : vec)
|
|
d.layer[layer].attn.fcon_bias.fcon[i++] = lex_cast<float>(elem);
|
|
|
|
always_assert(i == sizeof(d.layer[layer].attn.fcon_bias) / sizeof(float));
|
|
}
|
|
|
|
void
|
|
ircd::gpt::model::init_h_attn_proj_weight(decoder &d,
|
|
const string_view &name,
|
|
const size_t &layer,
|
|
const json::array &mat)
|
|
{
|
|
size_t i(0);
|
|
for(const json::array vec : mat)
|
|
{
|
|
size_t j(0);
|
|
for(const auto &elem : vec)
|
|
d.layer[layer].attn.proj_weight[i].elem[j++] = lex_cast<float>(elem);
|
|
|
|
always_assert(j == sizeof(d.layer[layer].attn.proj_weight[i]) / sizeof(float));
|
|
++i;
|
|
}
|
|
|
|
always_assert
|
|
(
|
|
i == sizeof(d.layer[layer].attn.proj_weight)
|
|
/ sizeof(d.layer[layer].attn.proj_weight[0])
|
|
);
|
|
}
|
|
|
|
void
|
|
ircd::gpt::model::init_h_attn_proj_bias(decoder &d,
|
|
const string_view &name,
|
|
const size_t &layer,
|
|
const json::array &vec)
|
|
{
|
|
size_t i(0);
|
|
for(const auto &elem : vec)
|
|
d.layer[layer].attn.proj_bias.elem[i++] = lex_cast<float>(elem);
|
|
|
|
always_assert(i == sizeof(d.layer[layer].attn.proj_bias) / sizeof(float));
|
|
}
|