mirror of
https://github.com/matrix-construct/construct
synced 2024-06-08 21:18:57 +02:00
653 lines
16 KiB
C++
653 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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namespace ircd::gpt
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{
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static size_t adamw(f32x4 &, f32x4 &, f32x4 &, const f32, const f32, const f32, const f32, const u32, size_t);
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static size_t adamw(task &, const f32, f32 *, const size_t, f32 *const (&)[2], const size_t);
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static size_t backprop(task &, const f32, model::norm &, f32 *const (&)[2], size_t);
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static size_t backprop(task &, const f32, model::attn &, f32 *const (&)[2], size_t);
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static size_t backprop(task &, const f32, model::ffnn &, f32 *const (&)[2], size_t);
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static size_t backprop(task &, const f32, model::block &, f32 *const (&)[2], size_t);
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static size_t backprop(task &, const f32, model::embed &, f32 *const (&)[2], size_t);
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extern size_t backprop(task &, const f32, model::decoder &, f32 *const (&)[2], size_t = 0);
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template<class T>
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static void fmma(T *out, const T *in, const T *bias, const T *weight, const math::fmma_opts &);
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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 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 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 attn(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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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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attn(qkv, accum, tokens, decoder, i);
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pare(state, qkv, tokens);
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mask(state, state, 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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static const math::fmma_opts fmma_opts
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{
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768, 768, 2U
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};
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for(uint j(0); j < tokens; ++j)
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fmma((f32x4 *)(accum + j * 768), (const f32x4 *)(a[j]), (const f32x4 *)layer.attn.proj_bias, (const f32x4 *)layer.attn.proj_weight, fmma_opts);
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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::attn(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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static const math::fmma_opts fmma_opts
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{
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768, 2304, 2U,
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};
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memset(proj, 0x0, sizeof(proj));
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fmma((f32x4 *)proj, (const f32x4 *)buf, (const f32x4 *)layer.attn.attn_bias, (const f32x4 *)layer.attn.attn_weight, fmma_opts);
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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 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] = (k < l)? in[j][k][l]: masked;
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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 f64
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tmp alignas(4096) [2][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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math::smax<f32, f64>
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(
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out[j][k], { in[j][k], num }, tmp[0], tmp[1]
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);
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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::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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static const math::fmma_opts fmma3_opts
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{
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768, 3072, 2U,
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};
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static const math::fmma_opts fmma4_opts
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{
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3072, 768, 2U,
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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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fmma((f32x4 *)buf2, (const f32x4 *)buf, (const f32x4 *)layer.ffnn.fc_bias, (const f32x4 *)layer.ffnn.fc_weight, fmma3_opts);
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gelu((f32x4 *)buf2, (const f32x4 *)buf2);
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fmma((f32x4 *)out, (const f32x4 *)buf2, (const f32x4 *)layer.ffnn.proj_bias, (const f32x4 *)layer.ffnn.proj_weight, fmma4_opts);
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}
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void
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ircd::gpt::norm(f32x4 *const __restrict__ out,
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const f32x4 *const __restrict__ in,
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const f32x4 *const __restrict__ bias,
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const f32x4 *const __restrict__ weight,
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const float epsilon)
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{
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static f64x4
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tmp alignas(64) [768 / 4];
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math::norm<f32x4, f64x4>
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(
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{out, 192}, {in, 192}, epsilon, tmp
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);
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for(uint j(0); j < 768 / 4; ++j)
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out[j] = out[j] * weight[j] + bias[j];
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}
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template<class T>
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void
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ircd::gpt::fmma(T *const __restrict__ out,
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const T *const __restrict__ in,
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const T *const __restrict__ bias,
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const T *const __restrict__ weight,
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const math::fmma_opts &opts)
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{
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for(uint i(0); i < opts.rows / simd::lanes<T>(); ++i)
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out[i] += bias[i];
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math::fmma(out, in, weight, opts);
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}
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void
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ircd::gpt::gelu(f32x4 *const out,
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const f32x4 *const in)
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{
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for(uint j(0); j < 3072 / 4; ++j)
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gelu(out[j], in[j]);
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}
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void
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ircd::gpt::gelu(f32x4 &out,
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const f32x4 &in)
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{
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out = 0.5 * in * (1.0 + tanh(in * f32(0.7978845608) * (1.0 + f32(0.044715) * in * in)));
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}
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//
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// backside
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//
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[[gnu::noinline]]
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size_t
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ircd::gpt::backprop(task &task,
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const f32 grad,
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model::decoder ¶m,
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f32 *const (&moment)[2],
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size_t off)
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{
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for(uint i(0); i < 12; ++i)
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off = backprop(task, grad, param.layer[i], moment, off);
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off = backprop(task, grad, param.f, moment, off);
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off = backprop(task, grad, param.word, moment, off);
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return off;
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}
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size_t
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ircd::gpt::backprop(task &task,
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const f32 grad,
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model::embed ¶m,
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f32 *const (&moment)[2],
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size_t off)
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{
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assert(task.opts);
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const auto &opts
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{
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*task.opts
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};
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for(uint i(0); i < opts.context_tokens; ++i)
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off = adamw(task, grad, param.pos[i], 768, moment, off);
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for(uint i(0); i < opts.logits; ++i)
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off = adamw(task, grad, param.token[i], 768, moment, off);
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return off;
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}
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size_t
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ircd::gpt::backprop(task &task,
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const f32 grad,
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model::block ¶m,
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f32 *const (&moment)[2],
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size_t off)
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{
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off = backprop(task, grad, param.ln1, moment, off);
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off = backprop(task, grad, param.attn, moment, off);
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off = backprop(task, grad, param.ln2, moment, off);
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off = backprop(task, grad, param.ffnn, moment, off);
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return off;
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}
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size_t
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ircd::gpt::backprop(task &task,
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const f32 grad,
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model::attn ¶m,
|
|
f32 *const (&moment)[2],
|
|
size_t off)
|
|
{
|
|
off = adamw(task, grad, param.attn_bias, 2304, moment, off);
|
|
|
|
for(uint i(0); i < 768; ++i)
|
|
off = adamw(task, grad, param.attn_weight[i], 2304, moment, off);
|
|
|
|
off = adamw(task, grad, param.proj_bias, 768, moment, off);
|
|
|
|
for(uint i(0); i < 768; ++i)
|
|
off = adamw(task, grad, param.proj_weight[i], 768, moment, off);
|
|
|
|
return off;
|
|
}
|
|
|
|
size_t
|
|
ircd::gpt::backprop(task &task,
|
|
const f32 grad,
|
|
model::ffnn ¶m,
|
|
f32 *const (&moment)[2],
|
|
size_t off)
|
|
{
|
|
off = adamw(task, grad, param.fc_bias, 3072, moment, off);
|
|
|
|
for(uint i(0); i < 768; ++i)
|
|
off = adamw(task, grad, param.fc_weight[i], 3072, moment, off);
|
|
|
|
off = adamw(task, grad, param.proj_bias, 768, moment, off);
|
|
|
|
for(uint i(0); i < 3072; ++i)
|
|
off = adamw(task, grad, param.proj_weight[i], 768, moment, off);
|
|
|
|
return off;
|
|
}
|
|
|
|
size_t
|
|
ircd::gpt::backprop(task &task,
|
|
const f32 grad,
|
|
model::norm ¶m,
|
|
f32 *const (&moment)[2],
|
|
size_t off)
|
|
{
|
|
off = adamw(task, grad, param.bias, 768, moment, off);
|
|
off = adamw(task, grad, param.weight, 768, moment, off);
|
|
return off;
|
|
}
|
|
|
|
[[gnu::noinline]]
|
|
size_t
|
|
ircd::gpt::adamw(task &task,
|
|
const f32 grad,
|
|
f32 *const p_,
|
|
const size_t num,
|
|
f32 *const (&__restrict__ m_)[2],
|
|
size_t off)
|
|
{
|
|
assert(task.opts);
|
|
const auto &opts
|
|
{
|
|
*task.opts
|
|
};
|
|
|
|
assert(task.ctrl);
|
|
auto &ctrl
|
|
{
|
|
*task.ctrl
|
|
};
|
|
|
|
f32x4 *const p[3]
|
|
{
|
|
reinterpret_cast<f32x4 *>(p_),
|
|
reinterpret_cast<f32x4 *>(m_[0]) + off,
|
|
reinterpret_cast<f32x4 *>(m_[1]) + off,
|
|
};
|
|
|
|
assert(num >= 4);
|
|
const uint n
|
|
{
|
|
uint(num) / 4
|
|
};
|
|
|
|
// Assume loop body always taken w/o soundness; otherwise extra branch.
|
|
assert(n > 0);
|
|
uint i(0); do
|
|
{
|
|
off = adamw
|
|
(
|
|
p[0][i],
|
|
p[1][i],
|
|
p[2][i],
|
|
grad,
|
|
opts.alpha,
|
|
opts.beta[0],
|
|
opts.beta[1],
|
|
ctrl.epic.step,
|
|
off
|
|
);
|
|
}
|
|
while(++i < n);
|
|
|
|
return off;
|
|
}
|
|
|
|
size_t
|
|
ircd::gpt::adamw(f32x4 &__restrict__ param,
|
|
f32x4 &__restrict__ moment0,
|
|
f32x4 &__restrict__ moment1,
|
|
const f32 grad,
|
|
const f32 alpha,
|
|
const f32 beta0,
|
|
const f32 beta1,
|
|
const u32 step,
|
|
const size_t off)
|
|
{
|
|
const f32x4 one
|
|
{
|
|
1.0f, 1.0f, 1.0f, 1.0f,
|
|
};
|
|
|
|
const f32x4 a[2]
|
|
{
|
|
{ one - beta0 },
|
|
{ one - beta1 },
|
|
};
|
|
|
|
const f32x4 avg_mul[2]
|
|
{
|
|
{ moment0 * beta0 },
|
|
{ moment1 * beta1 },
|
|
};
|
|
|
|
const f32x4 avg_dot[2]
|
|
{
|
|
{ avg_mul[0] + a[0] * grad },
|
|
{ avg_mul[1] + a[1] * grad * grad },
|
|
};
|
|
|
|
const f32x4 bias[2]
|
|
{
|
|
{ avg_dot[0] / (one - powf(beta0, step + 1)) },
|
|
{ avg_dot[1] / (one - powf(beta1, step + 1)) },
|
|
};
|
|
|
|
const f32x4 denom
|
|
{
|
|
sqrtf(bias[1]) + 0.000001f // epsilon
|
|
};
|
|
|
|
const f32x4 delta
|
|
{
|
|
alpha * (bias[0] / denom)
|
|
};
|
|
|
|
const f32x4 update
|
|
{
|
|
param - delta
|
|
};
|
|
|
|
moment0 = avg_dot[0];
|
|
moment1 = avg_dot[1];
|
|
param = update;
|
|
return off + 1;
|
|
}
|