mirror of
https://github.com/matrix-construct/construct
synced 2024-12-24 14:34:00 +01:00
591 lines
14 KiB
C++
591 lines
14 KiB
C++
// Matrix Construct Is All You Need Is All You Need Is AllĊĊĊĊĊĊĊĊ
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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
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{
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static void gelu(float &, const float &);
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static void gelu(float (&)[3072], const float (&)[3072]);
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static void norm(float (&)[768], const float *, const float (&)[768], const float (&)[768], const float);
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static void fmma(float (&)[768], const float (&)[3072], const float (&)[768], const float (&)[3072][768]);
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static void fmma(float (&)[3072], const float (&)[768], const float (&)[3072], const float (&)[768][3072]);
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static void fmma(float (&)[2304], const float (&)[768], const float (&)[2304], const float (&)[768][2304]);
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static void fmma(float *, const float (&)[12][1024][64], const float (&)[768], const float (&)[768][768], const size_t);
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static void vals(float (&)[12][1024][64], const float (&)[12][1024][1024], const float (&)[3][1024][12][64], const size_t);
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static void pare(float (&)[12][1024][1024], const float (&)[3][1024][12][64], const size_t);
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static void mask(float (&)[12][1024][1024], const float (&)[12][1024][1024], const bool (&)[1024][1024], const size_t);
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static void smax(float (&)[12][1024][1024], const float (&)[12][1024][1024], const size_t);
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static void ctrl(float (&)[3][1024][12][64], const float *const, const size_t, const model::block &);
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static void ffnn(float (&)[768], const float (&)[768], const model::block &);
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static void transform(float *, const size_t, const model::decoder &);
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static void logitsmax(float *, const float *);
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static void logits(float *, const float (&)[768], const model::decoder &);
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static void tail(float *, const float *, const model::decoder &);
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static u16 argmax(const float *, const opts &);
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static void embed(float *, const u16 token, const u16 position, const opts &);
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static f32
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logit alignas(64) [65536],
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scratch alignas(64) [1024 * 768];
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}
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decltype(ircd::gpt::log)
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ircd::gpt::log
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{
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"gpt"
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};
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decltype(ircd::gpt::default_opts)
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ircd::gpt::default_opts;
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namespace ircd::gpt::model
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{
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constexpr float embed_pdrop
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{
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0.1
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};
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constexpr float ln1_epsilon
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{
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0.00001
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};
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constexpr float ln2_epsilon
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{
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0.00001
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};
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constexpr float lnf_epsilon
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{
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0.00001
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};
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constexpr float attn_pdrop
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{
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0.1
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};
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constexpr float resid_pdrop
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{
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0.1
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};
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}
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ircd::string_view
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ircd::gpt::generate(const mutable_buffer &out,
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const string_view &in,
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const opts *opts,
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task *task)
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{
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u16 buf[2][256];
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const auto input_tokens
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{
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vocab::tokenize(buf[0], in)
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};
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const auto output_tokens
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{
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generate(buf[1], input_tokens, opts, task)
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};
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const auto output
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{
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vocab::detokenize(out, output_tokens)
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};
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return output;
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}
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ircd::vector_view<ircd::u16>
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ircd::gpt::generate(const vector_view<u16> &out,
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const vector_view<const u16> &in,
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const opts *opts,
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task *task)
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{
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size_t ret(0);
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bool halt(false);
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uint errc[3] {0}, accc[3] {0};
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for(uint i(0); !halt && i < out.size() && ret < opts->limit; ++i)
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{
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const size_t tokens
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{
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in.size() + i
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};
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const vector_view<f32> scratch
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{
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gpt::scratch, tokens * 768
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};
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for(uint j(0); j < in.size(); ++j)
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{
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const vector_view<f32> dst
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{
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data(scratch) + j * 768, 768
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};
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embed(data(dst), in[j], j, *opts);
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}
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for(uint j(0); j < ret; ++j)
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{
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const vector_view<f32> dst
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{
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data(scratch) + (in.size() + j) * 768, 768
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};
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embed(data(dst), out[j], in.size() + j, *opts);
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}
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transform(data(scratch), tokens, *opts->model);
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const vector_view<f32> last_embed
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{
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data(scratch) + ((tokens - 1) * 768), 768
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};
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tail(logit, data(last_embed), *opts->model);
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out[i] = argmax(logit, *opts);
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for(uint j(0); j < 3; ++j)
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{
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errc[j] = out[i] == opts->error_code[j][errc[j]]? errc[j] + 1: 0;
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accc[j] = out[i] == opts->accept_code[j][accc[j]]? accc[j] + 1: 0;
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}
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for(uint j(0); j < 3; ++j)
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{
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halt |= errc[j] >= 3 || (errc[j] && opts->error_code[j][errc[j] + 1] == -1U);
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halt |= accc[j] >= 3 || (accc[j] && opts->accept_code[j][accc[j] + 1] == -1U);
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}
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++ret;
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}
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return vector_view<u16>
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{
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out, ret
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};
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}
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void
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ircd::gpt::embed(float *const out,
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const u16 token,
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const u16 position,
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const opts &opts)
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{
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assert(opts.model);
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const auto &wpe
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{
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opts.model->wpe[position]
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};
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const auto &wte
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{
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opts.model->wte[token]
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};
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for(uint j(0); j < 768; ++j)
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out[j] = wte[j] + wpe[j];
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}
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uint16_t
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ircd::gpt::argmax(const float *const __restrict__ logit,
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const opts &opts)
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{
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static const auto max
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{
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32U
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};
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const auto top
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{
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std::clamp(opts.top_k, 1U, max - 1)
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};
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u16 best[max] {0};
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for(uint j(0); j < vocab::tokens; ++j)
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{
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best[top] = j;
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std::sort(begin(best), begin(best) + top + 1, [&logit]
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(const auto &a, const auto &b)
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{
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return logit[a] > logit[b];
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});
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}
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const auto x
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{
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top > 1?
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rand::integer(0, top - 1):
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0
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};
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return best[x];
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}
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[[gnu::noinline]]
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void
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ircd::gpt::tail(float *const __restrict__ logit,
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const float *const __restrict__ state,
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const model::decoder &d)
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{
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static float
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buf alignas(64) [768];
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norm(buf, state, d.f.bias, d.f.weight, model::lnf_epsilon);
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logits(logit, buf, d);
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//logitsmax(logit, logit);
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}
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void
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ircd::gpt::logits(float *const __restrict__ out,
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const float (&__restrict__ in)[768],
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const model::decoder &d)
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{
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for(uint j(0); j < vocab::tokens; ++j)
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out[j] = 0;
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for(uint j(0); j < vocab::tokens; ++j)
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for(uint k(0); k < 768; ++k)
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out[j] += in[k] * d.wte[j][k];
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}
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void
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ircd::gpt::logitsmax(float *const out,
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const float *const in)
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{
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static float
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exps alignas(64) [65536];
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for(uint j(0); j < vocab::tokens; ++j)
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exps[j] = exp(in[j]);
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for(uint j(0); j < vocab::tokens; ++j)
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out[j] = 0;
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for(uint j(0); j < vocab::tokens; ++j)
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for(uint k(0); k < vocab::tokens; ++k)
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out[k] += exps[j];
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for(uint j(0); j < vocab::tokens; ++j)
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out[j] = exps[j] / out[j];
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}
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[[gnu::noinline]]
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void
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ircd::gpt::transform(float *__restrict__ accum,
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const size_t tokens,
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const model::decoder &decoder)
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{
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static float
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qkv alignas(64) [3][1024][12][64],
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state alignas(64) [12][1024][1024],
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attns alignas(64) [12][1024][64],
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buf alignas(64) [768];
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for(uint i(0); i < 12; ++i)
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{
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const auto &layer
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{
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decoder.layer[i]
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};
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ctrl(qkv, accum, tokens, layer);
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pare(state, qkv, tokens);
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mask(state, state, layer.attn.bias, tokens);
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smax(state, state, tokens);
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vals(attns, state, qkv, tokens);
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fmma(accum, attns, layer.attn.proj_bias, layer.attn.proj_weight, tokens);
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for(uint j(0); j < tokens; ++j)
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{
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for(uint k(0); k < 768; ++k)
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buf[k] = accum[j * 768 + k];
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ffnn(buf, buf, layer);
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for(uint k(0); k < 768; ++k)
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accum[j * 768 + k] += buf[k];
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}
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}
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}
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void
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ircd::gpt::ffnn(float (&__restrict__ out)[768],
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const float (&__restrict__ in)[768],
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const model::block &layer)
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{
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static float
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proj alignas(64) [3072];
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norm(out, in, layer.ln2.bias, layer.ln2.weight, model::ln2_epsilon);
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fmma(proj, out, layer.ffnn.fc_bias, layer.ffnn.fc_weight);
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gelu(proj, proj);
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fmma(out, proj, layer.ffnn.proj_bias, layer.ffnn.proj_weight);
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}
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void
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ircd::gpt::ctrl(float (&__restrict__ out)[3][1024][12][64],
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const float *const __restrict__ in,
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const size_t num,
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const model::block &layer)
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{
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float
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(&__restrict__ qry)[1024][12][64] { out[0] },
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(&__restrict__ key)[1024][12][64] { out[1] },
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(&__restrict__ val)[1024][12][64] { out[2] };
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for(uint i(0); i < num; ++i)
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{
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static float
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buf alignas(64) [768],
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proj alignas(64) [2304];
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for(uint j(0); j < 768; ++j)
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buf[j] = in[i * 768 + j];
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norm(buf, buf, layer.ln1.bias, layer.ln1.weight, model::ln1_epsilon);
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fmma(proj, buf, layer.attn.attn_bias, layer.attn.attn_weight);
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#pragma clang loop unroll (disable)
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for(uint j(0); j < 12; ++j)
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for(uint k(0); k < 64; ++k)
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qry[i][j][k] = proj[768 * 0 + j * 64 + k];
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#pragma clang loop unroll (disable)
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for(uint j(0); j < 12; ++j)
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for(uint k(0); k < 64; ++k)
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key[i][j][k] = proj[768 * 1 + j * 64 + k];
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#pragma clang loop unroll (disable)
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for(uint j(0); j < 12; ++j)
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for(uint k(0); k < 64; ++k)
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val[i][j][k] = proj[768 * 2 + j * 64 + k];
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}
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}
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void
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ircd::gpt::pare(float (&__restrict__ out)[12][1024][1024],
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const float (&__restrict__ qkv)[3][1024][12][64],
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const size_t num)
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{
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const float
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(&__restrict__ qry)[1024][12][64] { qkv[0] },
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(&__restrict__ key)[1024][12][64] { qkv[1] },
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(&__restrict__ val)[1024][12][64] { qkv[2] };
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#pragma clang loop unroll (disable)
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for(uint j(0); j < 12; ++j)
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for(uint k(0); k < num; ++k)
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for(uint l(0); l < num; ++l)
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out[j][k][l] = 0;
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#pragma clang loop unroll (disable)
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for(uint j(0); j < 12; ++j)
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for(uint k(0); k < num; ++k)
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for(uint l(0); l < num; ++l)
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for(uint m(0); m < 64; ++m)
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out[j][k][l] += qry[k][j][m] * key[l][j][m];
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#pragma clang loop unroll (disable)
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for(uint j(0); j < 12; ++j)
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for(uint k(0); k < num; ++k)
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for(uint l(0); l < num; ++l)
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out[j][k][l] /= 8.0;
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}
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void
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ircd::gpt::vals(float (&__restrict__ out)[12][1024][64],
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const float (&__restrict__ in)[12][1024][1024],
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const float (&__restrict__ qkv)[3][1024][12][64],
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const size_t num)
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{
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const float
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(&__restrict__ val)[1024][12][64] { qkv[2] };
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#pragma clang loop unroll (disable)
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for(uint j(0); j < 12; ++j)
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for(uint k(0); k < num; ++k)
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for(uint l(0); l < 64; ++l)
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out[j][k][l] = 0;
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#pragma clang loop unroll (disable)
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for(uint j(0); j < 12; ++j)
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for(uint k(0); k < num; ++k)
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for(uint l(0); l < num; ++l)
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for(uint m(0); m < 64; ++m)
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out[j][k][m] += in[j][k][l] * val[l][j][m];
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}
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void
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ircd::gpt::smax(float (&__restrict__ out)[12][1024][1024],
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const float (&__restrict__ in)[12][1024][1024],
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const size_t num)
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{
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static float
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exps alignas(64) [12][1024][1024];
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#pragma clang loop unroll (disable)
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for(uint j(0); j < 12; ++j)
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for(uint k(0); k < num; ++k)
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for(uint m(0); m < num; ++m)
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exps[j][k][m] = exp(in[j][k][m]);
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#pragma clang loop unroll (disable)
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for(uint j(0); j < 12; ++j)
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for(uint k(0); k < num; ++k)
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for(uint m(0); m < num; ++m)
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out[j][k][m] = 0;
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#pragma clang loop unroll (disable)
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for(uint j(0); j < 12; ++j)
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for(uint k(0); k < num; ++k)
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for(uint m(0); m < num; ++m)
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for(uint l(0); l < num; ++l)
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out[j][k][l] += exps[j][k][m];
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#pragma clang loop unroll (disable)
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for(uint j(0); j < 12; ++j)
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for(uint k(0); k < num; ++k)
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for(uint l(0); l < num; ++l)
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out[j][k][l] = exps[j][k][l] / out[j][k][l];
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}
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void
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ircd::gpt::mask(float (&__restrict__ out)[12][1024][1024],
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const float (&__restrict__ in)[12][1024][1024],
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const bool (&__restrict__ bias)[1024][1024],
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const size_t num)
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{
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static const float masked
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{
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-10000.0
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};
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#pragma clang loop unroll (disable)
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for(uint j(0); j < 12; ++j)
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for(uint k(0); k < num; ++k)
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for(uint l(0); l < num; ++l)
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out[j][k][l] = bias[k][l]? in[j][k][l]: masked;
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}
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void
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ircd::gpt::norm(float (&__restrict__ out)[768],
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const float *const in,
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const float (&__restrict__ bias)[768],
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const float (&__restrict__ weight)[768],
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const float epsilon)
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{
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static float
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tmp alignas(64) [768];
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const float mean
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{
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math::mean<float>(vector_view<const float>{in, 768})
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};
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for(uint j(0); j < 768; ++j)
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tmp[j] = pow(in[j] - mean, 2);
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const float s
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{
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math::mean<float>(tmp)
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};
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for(uint j(0); j < 768; ++j)
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out[j] = (in[j] - mean) / sqrt(s + epsilon),
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out[j] = out[j] * weight[j] + bias[j];
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}
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void
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ircd::gpt::fmma(float *const __restrict__ out,
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const float (&__restrict__ attn)[12][1024][64],
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const float (&__restrict__ bias)[768],
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const float (&__restrict__ weight)[768][768],
|
|
const size_t num)
|
|
{
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|
static float
|
|
a alignas(64) [1024][768],
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|
b alignas(64) [1024][768];
|
|
|
|
for(uint k(0); k < 12; k++)
|
|
for(uint j(0); j < num; j++)
|
|
for(uint l(0); l < 64; l++)
|
|
a[j][k * 64 + l] = attn[k][j][l];
|
|
|
|
for(uint i(0); i < num; i++)
|
|
for(uint j(0); j < 768; j++)
|
|
b[i][j] = bias[j];
|
|
|
|
for(uint i(0); i < num; i++)
|
|
for(uint k(0); k < 768; k++)
|
|
for(uint j(0); j < 768; j++)
|
|
b[i][k] += a[i][j] * weight[j][k];
|
|
|
|
for(uint i(0); i < num; i++)
|
|
for(uint j(0); j < 768; j++)
|
|
out[i * 768 + j] += b[i][j];
|
|
}
|
|
|
|
void
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|
ircd::gpt::fmma(float (&__restrict__ out)[2304],
|
|
const float (&__restrict__ in)[768],
|
|
const float (&__restrict__ bias)[2304],
|
|
const float (&__restrict__ weight)[768][2304])
|
|
{
|
|
for(uint j(0); j < 2304; ++j)
|
|
out[j] = bias[j];
|
|
|
|
for(uint k(0); k < 768; ++k)
|
|
for(uint j(0); j < 2304; ++j)
|
|
out[j] += in[k] * weight[k][j];
|
|
}
|
|
|
|
void
|
|
ircd::gpt::fmma(float (&__restrict__ out)[768],
|
|
const float (&__restrict__ in)[3072],
|
|
const float (&__restrict__ bias)[768],
|
|
const float (&__restrict__ weight)[3072][768])
|
|
{
|
|
for(uint j(0); j < 768; ++j)
|
|
out[j] = bias[j];
|
|
|
|
for(uint k(0); k < 3072; k++)
|
|
for(uint j(0); j < 768; j++)
|
|
out[j] += in[k] * weight[k][j];
|
|
}
|
|
|
|
void
|
|
ircd::gpt::fmma(float (&__restrict__ out)[3072],
|
|
const float (&__restrict__ in)[768],
|
|
const float (&__restrict__ bias)[3072],
|
|
const float (&__restrict__ weight)[768][3072])
|
|
{
|
|
for(uint j(0); j < 3072; ++j)
|
|
out[j] = bias[j];
|
|
|
|
for(uint k(0); k < 768; ++k)
|
|
for(uint j(0); j < 3072; ++j)
|
|
out[j] += in[k] * weight[k][j];
|
|
}
|
|
|
|
void
|
|
ircd::gpt::gelu(float (&__restrict__ out)[3072],
|
|
const float (&__restrict__ in)[3072])
|
|
{
|
|
for(uint j(0); j < 3072; ++j)
|
|
gelu(out[j], in[j]);
|
|
}
|
|
|
|
void
|
|
ircd::gpt::gelu(float &out,
|
|
const float &in)
|
|
{
|
|
out = 0.5 * in * (1.0 + tanh(in * 0.7978845608 * (1.0 + 0.044715 * in * in)));
|
|
}
|