mirror of
https://github.com/matrix-construct/construct
synced 2024-12-24 14:34:00 +01:00
722 lines
16 KiB
C++
722 lines
16 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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#include <ircd/gpt/pipe/pipe.h>
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namespace ircd::gpt
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{
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static void gelu(f32x4 &, const f32x4 &);
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static void gelu(f32x4 *, const f32x4 *);
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static void norm(f32x4 *, const f32x4 *, const f32x4 *, const f32x4 *, const f32);
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static void fmma4(f32x4 *, const f32x4 *, const f32x4 *, const f32x4 *);
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static void fmma3(f32x4 *, const f32x4 *, const f32x4 *, const f32x4 *);
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static void fmma2(f32x4 *, const f32x4 *, const f32x4 *, const f32x4 *, const size_t);
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static void fmma1(f32x4 *, const f32x4 *, const f32x4 *, const f32x4 *);
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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::decoder &, const uint layer);
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static void ffnn(float *, const float *, const model::decoder &, const uint layer);
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static void coil(float *, const size_t, const model::decoder &);
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static void logitsmax(float *, const float *, const size_t);
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static void logits(float *, const float *, 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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embeds alignas(64) [1024 * 768],
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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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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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task &task)
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{
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u16 buf[2][1024];
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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, 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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task &task)
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{
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assert(task.ctrl);
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assert(task.opts);
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uint ret(0);
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bool halt(false);
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const auto &opts(*task.opts);
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auto &ctrl(*task.ctrl);
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auto &errc(ctrl.error_seq);
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auto &accc(ctrl.accept_seq);
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ctrl.tokens = in.size();
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const size_t tmax
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{
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in.size() + opts.limit
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};
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const vector_view<f32> accum
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{
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gpt::scratch, tmax * 768
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};
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const vector_view<f32> embeds
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{
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gpt::embeds, tmax * 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(embeds) + j * 768, 768
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};
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if(ircd::cl::enable)
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ctrl.token[j] = in[j];
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else
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embed(data(dst), in[j], j, opts);
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static char dbuf[512] {0};
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char report[1536] {0};
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char tmbuf[1][64] {{0}};
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const size_t report_size = snprintf
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(
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report, sizeof(report),
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"%-2u -- %-3u [%5u] --- --- %s 0 0 | %8s",
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j,
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ctrl.tokens,
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ctrl.token[j],
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vocab::debug(dbuf, ctrl.token[j]).c_str(),
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pretty(tmbuf[0], milliseconds(ctrl.elapsed), 1).c_str()
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);
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log::info
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{
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log, "%s",
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string_view{report, report_size}
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};
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}
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uint64_t cycles(0);
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milliseconds last_time {0};
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util::timer stopwatch;
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{
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const prof::scope_cycles task_cycles
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{
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cycles
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};
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generate(task);
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}
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last_time = stopwatch.at<milliseconds>();
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ctrl.elapsed += last_time.count();
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/*
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coil(data(scratch), tokens, *opts.model);
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tail(logit, data(last_embed), *opts.model);
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out[i] = argmax(logit, *opts);
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*/
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uint accc_thresh[3] {3, 3, 3};
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for(uint i(0); i < 3; ++i)
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for(uint j(3); j > 0; --j)
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if(opts.accept_code[i][j - 1] == -1U)
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--accc_thresh[i];
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else
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break;
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uint errc_thresh[3] {3, 3, 3};
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for(uint i(0); i < 3; ++i)
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for(uint j(3); j > 0; --j)
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if(opts.error_code[i][j - 1] == -1U)
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--errc_thresh[i];
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else
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break;
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for(auto &j(ret); j + in.size() < ctrl.tokens && j < out.size() && !halt; ++j)
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{
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out[j] = ctrl.token[(in.size() + j + ctrl.head) % opts.buffer_tokens];
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for(uint j(0); j < 3; ++j)
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errc[j] = opts.error_code[j][errc[j]] == out[j]?
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errc[j] + 1: 0;
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for(uint j(0); j < 3; ++j)
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accc[j] = opts.accept_code[j][accc[j]] == out[j]?
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accc[j] + 1: 0;
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for(uint j(0); j < 3; ++j)
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halt |= accc_thresh[j] && accc[j] >= accc_thresh[j],
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halt |= errc_thresh[j] && errc[j] >= errc_thresh[j];
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static char dbuf[512] {0};
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char report[1536] {0};
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char tmbuf[4][64] {0};
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const size_t bsz(ctrl.tokens - in.size());
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const size_t report_size = snprintf
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(
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report, sizeof(report),
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"%-2u %-2u %-3u %-3u %-3u [%5u] a:%u e:%u %s %8s %8s | %8s",
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j,
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j + in.size(),
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ctrl.tokens,
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ctrl.cycle,
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ctrl.epoch,
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out[j],
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accc[0] + accc[1] + accc[2],
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errc[0] + errc[1] + errc[2],
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vocab::debug(dbuf, out[j]).c_str(),
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pretty(tmbuf[0], milliseconds(last_time / bsz), 1).c_str(),
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pretty(tmbuf[1], si(cycles / bsz), 1).c_str(),
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pretty(tmbuf[2], milliseconds(ctrl.elapsed), 1).c_str()
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);
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log::info
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{
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log, "%s",
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string_view{report, report_size}
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};
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}
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ret = ctrl.tokens - in.size();
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for(uint i(0); i < 3; ++i)
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if(accc_thresh[i] && ctrl.accept_seq[i] >= accc_thresh[i])
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{
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ret -= (3 - accc_thresh[i]);
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break;
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}
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else if(errc_thresh[i] && ctrl.error_seq[i] >= errc_thresh[i])
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{
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ret -= (3 - errc_thresh[i]);
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break;
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}
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ctx::interruption_point();
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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->word.pos[position]
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};
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const auto &wte
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{
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opts.model->word.token[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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constexpr float lnf_epsilon
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{
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0.00001
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};
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static float
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buf alignas(64) [1][768];
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for(uint i(0); i < 768; ++i)
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buf[0][i] = state[i];
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norm((f32x4 *)buf[0], (const f32x4 *)state, (const f32x4 *)d.f.bias, (const f32x4 *)d.f.weight, lnf_epsilon);
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logits(logit, buf[0], d);
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//logitsmax(logit, logit, vocab::tokens);
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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 *const __restrict__ in,
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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.word.token[j][k];
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}
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[[gnu::noinline]]
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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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const size_t num)
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{
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static f64x4
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exps alignas(4096) [2][65536 / 4];
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math::smax<f32x4, f64x4>
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(
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{(f32x4 *)out, num / 4},
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{(const f32x4 *)in, num / 4},
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exps[0],
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exps[1]
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);
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}
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[[gnu::noinline]]
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void
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ircd::gpt::coil(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(4096) [3][1024][12][64],
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state alignas(4096) [12][1024][1024],
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attns alignas(4096) [12][1024][64];
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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, decoder, i);
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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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static f32 a alignas(64) [1024][768];
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memset(a, 0x0, 768 * tokens * sizeof(float));
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for(uint j(0); j < tokens; j++)
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{
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for(uint k(0); k < 12; k++)
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for(uint l(0); l < 64; l++)
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a[j][k * 64 + l] = attns[k][j][l];
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}
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for(uint j(0); j < tokens; ++j)
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fmma2((f32x4 *)(accum + j * 768), (const f32x4 *)(a[j]), (const f32x4 *)layer.attn.proj_bias, (const f32x4 *)layer.attn.proj_weight, tokens);
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for(uint j(0); j < tokens; ++j)
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ffnn(accum + j * 768, accum + j * 768, decoder, i);
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}
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}
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void
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ircd::gpt::ffnn(float *const out,
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const float *const in,
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const model::decoder &decoder,
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const uint laynum)
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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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const auto &layer
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{
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decoder.layer[laynum]
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};
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static float
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buf alignas(64) [768],
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buf2 alignas(64) [3072];
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memset(buf2, 0x0, sizeof(buf2));
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norm((f32x4 *)buf, (const f32x4 *)in, (const f32x4 *)layer.ln2.bias, (const f32x4 *)layer.ln2.weight, ln2_epsilon);
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fmma3((f32x4 *)buf2, (const f32x4 *)buf, (const f32x4 *)layer.ffnn.fc_bias, (const f32x4 *)layer.ffnn.fc_weight);
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gelu((f32x4 *)buf2, (const f32x4 *)buf2);
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fmma4((f32x4 *)out, (const f32x4 *)buf2, (const f32x4 *)layer.ffnn.proj_bias, (const f32x4 *)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::decoder &decoder,
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const uint laynum)
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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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const auto &layer
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{
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decoder.layer[laynum]
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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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norm((f32x4 *)buf, (const f32x4 *)(in + i * 768), (const f32x4 *)layer.ln1.bias, (const f32x4 *)layer.ln1.weight, ln1_epsilon);
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memset(proj, 0x0, sizeof(proj));
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fmma1((f32x4 *)proj, (const f32x4 *)buf, (const f32x4 *)layer.attn.attn_bias, (const f32x4 *)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::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
|
|
};
|
|
|
|
#pragma clang loop unroll (disable)
|
|
for(uint j(0); j < 12; ++j)
|
|
for(uint k(0); k < num; ++k)
|
|
for(uint l(0); l < num; ++l)
|
|
out[j][k][l] = bias[k][l]? in[j][k][l]: masked;
|
|
}
|
|
|
|
void
|
|
ircd::gpt::smax(float (&__restrict__ out)[12][1024][1024],
|
|
const float (&__restrict__ in)[12][1024][1024],
|
|
const size_t num)
|
|
{
|
|
static f64
|
|
tmp alignas(4096) [2][1024];
|
|
|
|
#pragma clang loop unroll (disable)
|
|
for(uint j(0); j < 12; ++j)
|
|
for(uint k(0); k < num; ++k)
|
|
math::smax<f32, f64>
|
|
(
|
|
out[j][k], { in[j][k], num }, tmp[0], tmp[1]
|
|
);
|
|
}
|
|
|
|
void
|
|
ircd::gpt::vals(float (&__restrict__ out)[12][1024][64],
|
|
const float (&__restrict__ in)[12][1024][1024],
|
|
const float (&__restrict__ qkv)[3][1024][12][64],
|
|
const size_t num)
|
|
{
|
|
const float
|
|
(&__restrict__ val)[1024][12][64] { qkv[2] };
|
|
|
|
#pragma clang loop unroll (disable)
|
|
for(uint j(0); j < 12; ++j)
|
|
for(uint k(0); k < num; ++k)
|
|
for(uint l(0); l < 64; ++l)
|
|
out[j][k][l] = 0;
|
|
|
|
#pragma clang loop unroll (disable)
|
|
for(uint j(0); j < 12; ++j)
|
|
for(uint k(0); k < num; ++k)
|
|
for(uint l(0); l < num; ++l)
|
|
for(uint m(0); m < 64; ++m)
|
|
out[j][k][m] += in[j][k][l] * val[l][j][m];
|
|
}
|
|
|
|
void
|
|
ircd::gpt::norm(f32x4 *const __restrict__ out,
|
|
const f32x4 *const __restrict__ in,
|
|
const f32x4 *const __restrict__ bias,
|
|
const f32x4 *const __restrict__ weight,
|
|
const float epsilon)
|
|
{
|
|
static f64x4
|
|
tmp alignas(64) [768 / 4];
|
|
|
|
math::norm<f32x4, f64x4>
|
|
(
|
|
{out, 192}, {in, 192}, epsilon, tmp
|
|
);
|
|
|
|
for(uint j(0); j < 768 / 4; ++j)
|
|
out[j] = out[j] * weight[j] + bias[j];
|
|
}
|
|
|
|
void
|
|
ircd::gpt::fmma1(f32x4 *const __restrict__ out,
|
|
const f32x4 *const __restrict__ in,
|
|
const f32x4 *const __restrict__ bias,
|
|
const f32x4 *const __restrict__ weight)
|
|
{
|
|
constexpr uint width
|
|
{
|
|
2304
|
|
};
|
|
|
|
constexpr uint height
|
|
{
|
|
768
|
|
};
|
|
|
|
constexpr uint lanes
|
|
{
|
|
simd::lanes<f32x4>()
|
|
};
|
|
|
|
for(uint j(0); j < width / lanes; ++j)
|
|
out[j] += bias[j];
|
|
|
|
static const math::fmma_opts opts
|
|
{
|
|
width,
|
|
height,
|
|
2U,
|
|
'y',
|
|
};
|
|
|
|
math::fmma<opts>(out, in, weight);
|
|
}
|
|
|
|
void
|
|
ircd::gpt::fmma2(f32x4 *const __restrict__ out,
|
|
const f32x4 *const __restrict__ in,
|
|
const f32x4 *const __restrict__ bias,
|
|
const f32x4 *const __restrict__ weight,
|
|
const size_t num)
|
|
{
|
|
constexpr uint width
|
|
{
|
|
768
|
|
};
|
|
|
|
constexpr uint height
|
|
{
|
|
768
|
|
};
|
|
|
|
constexpr uint lanes
|
|
{
|
|
simd::lanes<f32x4>()
|
|
};
|
|
|
|
for(uint j(0); j < width / lanes; ++j)
|
|
out[j] += bias[j];
|
|
|
|
static const math::fmma_opts opts
|
|
{
|
|
width,
|
|
height,
|
|
2U,
|
|
};
|
|
|
|
math::fmma<opts>(out, in, weight);
|
|
}
|
|
|
|
void
|
|
ircd::gpt::fmma3(f32x4 *const __restrict__ out,
|
|
const f32x4 *const __restrict__ in,
|
|
const f32x4 *const __restrict__ bias,
|
|
const f32x4 *const __restrict__ weight)
|
|
{
|
|
constexpr uint width
|
|
{
|
|
3072
|
|
};
|
|
|
|
constexpr uint height
|
|
{
|
|
768
|
|
};
|
|
|
|
constexpr uint lanes
|
|
{
|
|
simd::lanes<f32x4>()
|
|
};
|
|
|
|
for(uint j(0); j < width / lanes; ++j)
|
|
out[j] += bias[j];
|
|
|
|
static const math::fmma_opts opts
|
|
{
|
|
width,
|
|
height,
|
|
2U,
|
|
'y',
|
|
};
|
|
|
|
math::fmma<opts>(out, in, weight);
|
|
}
|
|
|
|
void
|
|
ircd::gpt::fmma4(f32x4 *const __restrict__ out,
|
|
const f32x4 *const __restrict__ in,
|
|
const f32x4 *const __restrict__ bias,
|
|
const f32x4 *const __restrict__ weight)
|
|
{
|
|
constexpr uint width
|
|
{
|
|
3072
|
|
};
|
|
|
|
constexpr uint height
|
|
{
|
|
768
|
|
};
|
|
|
|
constexpr uint lanes
|
|
{
|
|
simd::lanes<f32x4>()
|
|
};
|
|
|
|
for(uint j(0); j < height / lanes; ++j)
|
|
out[j] += bias[j];
|
|
|
|
static const math::fmma_opts opts
|
|
{
|
|
width,
|
|
height,
|
|
2U,
|
|
};
|
|
|
|
math::fmma<opts>(out, in, weight);
|
|
}
|
|
|
|
void
|
|
ircd::gpt::gelu(f32x4 *const out,
|
|
const f32x4 *const in)
|
|
{
|
|
for(uint j(0); j < 3072 / 4; ++j)
|
|
gelu(out[j], in[j]);
|
|
}
|
|
|
|
void
|
|
ircd::gpt::gelu(f32x4 &out,
|
|
const f32x4 &in)
|
|
{
|
|
out = 0.5 * in * (1.0 + tanh(in * f32(0.7978845608) * (1.0 + f32(0.044715) * in * in)));
|
|
}
|