mirror of
https://github.com/matrix-construct/construct
synced 2024-11-30 02:32:43 +01:00
652 lines
16 KiB
C++
652 lines
16 KiB
C++
// Matrix Construct Is All You Need Is All You Need Is AllĊĊĊĊĊĊĊĊ
|
|
//
|
|
// Copyright (C) Matrix Construct Developers, Authors & Contributors
|
|
// Copyright (C) 2016-2021 Jason Volk <jason@zemos.net>
|
|
//
|
|
// Permission to use, copy, modify, and/or distribute this software for any
|
|
// purpose with or without fee is hereby granted, provided that the above
|
|
// copyright notice and this permission notice is present in all copies. The
|
|
// full license for this software is available in the LICENSE file.
|
|
|
|
namespace ircd::gpt
|
|
{
|
|
static size_t adamw(f32x4 &, f32x4 &, f32x4 &, const f32, const f32, const f32, const f32, const u32, size_t);
|
|
static size_t adamw(task &, const f32, f32 *, const size_t, f32 *const (&)[2], const size_t);
|
|
|
|
static size_t backprop(task &, const f32, model::norm &, f32 *const (&)[2], size_t);
|
|
static size_t backprop(task &, const f32, model::attn &, f32 *const (&)[2], size_t);
|
|
static size_t backprop(task &, const f32, model::ffnn &, f32 *const (&)[2], size_t);
|
|
static size_t backprop(task &, const f32, model::block &, f32 *const (&)[2], size_t);
|
|
static size_t backprop(task &, const f32, model::embed &, f32 *const (&)[2], size_t);
|
|
extern size_t backprop(task &, const f32, model::decoder &, f32 *const (&)[2], size_t = 0);
|
|
|
|
template<class T>
|
|
static void fmma(T *out, const T *in, const T *bias, const T *weight, const math::fmma_opts &);
|
|
|
|
static void gelu(f32x4 &, const f32x4 &);
|
|
static void gelu(f32x4 *, const f32x4 *);
|
|
static void norm(f32x4 *, const f32x4 *, const f32x4 *, const f32x4 *, const f32);
|
|
static void vals(float (&)[12][1024][64], const float (&)[12][1024][1024], const float (&)[3][1024][12][64], const size_t);
|
|
static void pare(float (&)[12][1024][1024], const float (&)[3][1024][12][64], const size_t);
|
|
static void mask(float (&)[12][1024][1024], const float (&)[12][1024][1024], const size_t);
|
|
static void smax(float (&)[12][1024][1024], const float (&)[12][1024][1024], const size_t);
|
|
static void attn(float (&)[3][1024][12][64], const float *const, const size_t, const model::decoder &, const uint layer);
|
|
static void ffnn(float *, const float *, const model::decoder &, const uint layer);
|
|
static void coil(float *, const size_t, const model::decoder &);
|
|
static void logitsmax(float *, const float *, const size_t);
|
|
static void logits(float *, const float *, const model::decoder &);
|
|
static void tail(float *, const float *, const model::decoder &);
|
|
static u16 argmax(const float *, const opts &);
|
|
static void embed(float *, const u16 token, const u16 position, const opts &);
|
|
|
|
static f32
|
|
logit alignas(64) [65536],
|
|
embeds alignas(64) [1024 * 768],
|
|
scratch alignas(64) [1024 * 768];
|
|
}
|
|
|
|
void
|
|
ircd::gpt::embed(float *const out,
|
|
const u16 token,
|
|
const u16 position,
|
|
const opts &opts)
|
|
{
|
|
assert(opts.model);
|
|
const auto &wpe
|
|
{
|
|
opts.model->word.pos[position]
|
|
};
|
|
|
|
const auto &wte
|
|
{
|
|
opts.model->word.token[token]
|
|
};
|
|
|
|
for(uint j(0); j < 768; ++j)
|
|
out[j] = wte[j] + wpe[j];
|
|
}
|
|
|
|
uint16_t
|
|
ircd::gpt::argmax(const float *const __restrict__ logit,
|
|
const opts &opts)
|
|
{
|
|
static const auto max
|
|
{
|
|
32U
|
|
};
|
|
|
|
const auto top
|
|
{
|
|
std::clamp(opts.top_k, 1U, max - 1)
|
|
};
|
|
|
|
u16 best[max] {0};
|
|
for(uint j(0); j < vocab::tokens; ++j)
|
|
{
|
|
best[top] = j;
|
|
std::sort(begin(best), begin(best) + top + 1, [&logit]
|
|
(const auto &a, const auto &b)
|
|
{
|
|
return logit[a] > logit[b];
|
|
});
|
|
}
|
|
|
|
const auto x
|
|
{
|
|
top > 1?
|
|
rand::integer(0, top - 1):
|
|
0
|
|
};
|
|
|
|
return best[x];
|
|
}
|
|
|
|
[[gnu::noinline]]
|
|
void
|
|
ircd::gpt::tail(float *const __restrict__ logit,
|
|
const float *const __restrict__ state,
|
|
const model::decoder &d)
|
|
{
|
|
constexpr float lnf_epsilon
|
|
{
|
|
0.00001
|
|
};
|
|
|
|
static float
|
|
buf alignas(64) [1][768];
|
|
for(uint i(0); i < 768; ++i)
|
|
buf[0][i] = state[i];
|
|
|
|
norm((f32x4 *)buf[0], (const f32x4 *)state, (const f32x4 *)d.f.bias, (const f32x4 *)d.f.weight, lnf_epsilon);
|
|
logits(logit, buf[0], d);
|
|
//logitsmax(logit, logit, vocab::tokens);
|
|
}
|
|
|
|
void
|
|
ircd::gpt::logits(float *const __restrict__ out,
|
|
const float *const __restrict__ in,
|
|
const model::decoder &d)
|
|
{
|
|
for(uint j(0); j < vocab::tokens; ++j)
|
|
out[j] = 0;
|
|
|
|
for(uint j(0); j < vocab::tokens; ++j)
|
|
for(uint k(0); k < 768; ++k)
|
|
out[j] += in[k] * d.word.token[j][k];
|
|
}
|
|
|
|
[[gnu::noinline]]
|
|
void
|
|
ircd::gpt::logitsmax(float *const out,
|
|
const float *const in,
|
|
const size_t num)
|
|
{
|
|
static f64x4
|
|
exps alignas(4096) [2][65536 / 4];
|
|
|
|
math::smax<f32x4, f64x4>
|
|
(
|
|
{(f32x4 *)out, num / 4},
|
|
{(const f32x4 *)in, num / 4},
|
|
exps[0],
|
|
exps[1]
|
|
);
|
|
}
|
|
|
|
[[gnu::noinline]]
|
|
void
|
|
ircd::gpt::coil(float *__restrict__ accum,
|
|
const size_t tokens,
|
|
const model::decoder &decoder)
|
|
{
|
|
static float
|
|
qkv alignas(4096) [3][1024][12][64],
|
|
state alignas(4096) [12][1024][1024],
|
|
attns alignas(4096) [12][1024][64];
|
|
|
|
for(uint i(0); i < 12; ++i)
|
|
{
|
|
const auto &layer
|
|
{
|
|
decoder.layer[i]
|
|
};
|
|
|
|
attn(qkv, accum, tokens, decoder, i);
|
|
pare(state, qkv, tokens);
|
|
mask(state, state, tokens);
|
|
smax(state, state, tokens);
|
|
vals(attns, state, qkv, tokens);
|
|
|
|
static f32 a alignas(64) [1024][768];
|
|
memset(a, 0x0, 768 * tokens * sizeof(float));
|
|
for(uint j(0); j < tokens; j++)
|
|
{
|
|
for(uint k(0); k < 12; k++)
|
|
for(uint l(0); l < 64; l++)
|
|
a[j][k * 64 + l] = attns[k][j][l];
|
|
}
|
|
|
|
static const math::fmma_opts fmma_opts
|
|
{
|
|
768, 768, 2U
|
|
};
|
|
|
|
for(uint j(0); j < tokens; ++j)
|
|
fmma((f32x4 *)(accum + j * 768), (const f32x4 *)(a[j]), (const f32x4 *)layer.attn.proj_bias, (const f32x4 *)layer.attn.proj_weight, fmma_opts);
|
|
|
|
for(uint j(0); j < tokens; ++j)
|
|
ffnn(accum + j * 768, accum + j * 768, decoder, i);
|
|
}
|
|
}
|
|
|
|
void
|
|
ircd::gpt::attn(float (&__restrict__ out)[3][1024][12][64],
|
|
const float *const __restrict__ in,
|
|
const size_t num,
|
|
const model::decoder &decoder,
|
|
const uint laynum)
|
|
{
|
|
constexpr float ln1_epsilon
|
|
{
|
|
0.00001
|
|
};
|
|
|
|
const auto &layer
|
|
{
|
|
decoder.layer[laynum]
|
|
};
|
|
|
|
float
|
|
(&__restrict__ qry)[1024][12][64] { out[0] },
|
|
(&__restrict__ key)[1024][12][64] { out[1] },
|
|
(&__restrict__ val)[1024][12][64] { out[2] };
|
|
|
|
for(uint i(0); i < num; ++i)
|
|
{
|
|
static float
|
|
buf alignas(64) [768],
|
|
proj alignas(64) [2304];
|
|
|
|
norm((f32x4 *)buf, (const f32x4 *)(in + i * 768), (const f32x4 *)layer.ln1.bias, (const f32x4 *)layer.ln1.weight, ln1_epsilon);
|
|
|
|
static const math::fmma_opts fmma_opts
|
|
{
|
|
768, 2304, 2U,
|
|
};
|
|
|
|
memset(proj, 0x0, sizeof(proj));
|
|
fmma((f32x4 *)proj, (const f32x4 *)buf, (const f32x4 *)layer.attn.attn_bias, (const f32x4 *)layer.attn.attn_weight, fmma_opts);
|
|
|
|
#pragma clang loop unroll (disable)
|
|
for(uint j(0); j < 12; ++j)
|
|
for(uint k(0); k < 64; ++k)
|
|
qry[i][j][k] = proj[768 * 0 + j * 64 + k];
|
|
|
|
#pragma clang loop unroll (disable)
|
|
for(uint j(0); j < 12; ++j)
|
|
for(uint k(0); k < 64; ++k)
|
|
key[i][j][k] = proj[768 * 1 + j * 64 + k];
|
|
|
|
#pragma clang loop unroll (disable)
|
|
for(uint j(0); j < 12; ++j)
|
|
for(uint k(0); k < 64; ++k)
|
|
val[i][j][k] = proj[768 * 2 + j * 64 + k];
|
|
}
|
|
}
|
|
|
|
void
|
|
ircd::gpt::pare(float (&__restrict__ out)[12][1024][1024],
|
|
const float (&__restrict__ qkv)[3][1024][12][64],
|
|
const size_t num)
|
|
{
|
|
const float
|
|
(&__restrict__ qry)[1024][12][64] { qkv[0] },
|
|
(&__restrict__ key)[1024][12][64] { qkv[1] },
|
|
(&__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 < num; ++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][l] += qry[k][j][m] * key[l][j][m];
|
|
|
|
#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] /= 8.0;
|
|
}
|
|
|
|
void
|
|
ircd::gpt::mask(float (&__restrict__ out)[12][1024][1024],
|
|
const float (&__restrict__ in)[12][1024][1024],
|
|
const size_t num)
|
|
{
|
|
static const float masked
|
|
{
|
|
-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] = (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::ffnn(float *const out,
|
|
const float *const in,
|
|
const model::decoder &decoder,
|
|
const uint laynum)
|
|
{
|
|
static const math::fmma_opts fmma3_opts
|
|
{
|
|
768, 3072, 2U,
|
|
};
|
|
|
|
static const math::fmma_opts fmma4_opts
|
|
{
|
|
3072, 768, 2U,
|
|
};
|
|
|
|
constexpr float ln2_epsilon
|
|
{
|
|
0.00001
|
|
};
|
|
|
|
const auto &layer
|
|
{
|
|
decoder.layer[laynum]
|
|
};
|
|
|
|
static float
|
|
buf alignas(64) [768],
|
|
buf2 alignas(64) [3072];
|
|
|
|
memset(buf2, 0x0, sizeof(buf2));
|
|
norm((f32x4 *)buf, (const f32x4 *)in, (const f32x4 *)layer.ln2.bias, (const f32x4 *)layer.ln2.weight, ln2_epsilon);
|
|
fmma((f32x4 *)buf2, (const f32x4 *)buf, (const f32x4 *)layer.ffnn.fc_bias, (const f32x4 *)layer.ffnn.fc_weight, fmma3_opts);
|
|
gelu((f32x4 *)buf2, (const f32x4 *)buf2);
|
|
fmma((f32x4 *)out, (const f32x4 *)buf2, (const f32x4 *)layer.ffnn.proj_bias, (const f32x4 *)layer.ffnn.proj_weight, fmma4_opts);
|
|
}
|
|
|
|
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];
|
|
}
|
|
|
|
template<class T>
|
|
void
|
|
ircd::gpt::fmma(T *const __restrict__ out,
|
|
const T *const __restrict__ in,
|
|
const T *const __restrict__ bias,
|
|
const T *const __restrict__ weight,
|
|
const math::fmma_opts &opts)
|
|
{
|
|
for(uint i(0); i < opts.rows / simd::lanes<T>(); ++i)
|
|
out[i] += bias[i];
|
|
|
|
math::fmma(out, in, weight, opts);
|
|
}
|
|
|
|
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)));
|
|
}
|
|
|
|
//
|
|
// backside
|
|
//
|
|
|
|
[[gnu::noinline]]
|
|
size_t
|
|
ircd::gpt::backprop(task &task,
|
|
const f32 grad,
|
|
model::decoder ¶m,
|
|
f32 *const (&moment)[2],
|
|
size_t off)
|
|
{
|
|
for(uint i(0); i < 12; ++i)
|
|
off = backprop(task, grad, param.layer[i], moment, off);
|
|
|
|
off = backprop(task, grad, param.f, moment, off);
|
|
off = backprop(task, grad, param.word, moment, off);
|
|
return off;
|
|
}
|
|
|
|
size_t
|
|
ircd::gpt::backprop(task &task,
|
|
const f32 grad,
|
|
model::embed ¶m,
|
|
f32 *const (&moment)[2],
|
|
size_t off)
|
|
{
|
|
assert(task.opts);
|
|
const auto &opts
|
|
{
|
|
*task.opts
|
|
};
|
|
|
|
for(uint i(0); i < opts.context_tokens; ++i)
|
|
off = adamw(task, grad, param.pos[i], 768, moment, off);
|
|
|
|
for(uint i(0); i < opts.logits; ++i)
|
|
off = adamw(task, grad, param.token[i], 768, moment, off);
|
|
|
|
return off;
|
|
}
|
|
|
|
size_t
|
|
ircd::gpt::backprop(task &task,
|
|
const f32 grad,
|
|
model::block ¶m,
|
|
f32 *const (&moment)[2],
|
|
size_t off)
|
|
{
|
|
off = backprop(task, grad, param.ln1, moment, off);
|
|
off = backprop(task, grad, param.attn, moment, off);
|
|
off = backprop(task, grad, param.ln2, moment, off);
|
|
off = backprop(task, grad, param.ffnn, moment, off);
|
|
return off;
|
|
}
|
|
|
|
size_t
|
|
ircd::gpt::backprop(task &task,
|
|
const f32 grad,
|
|
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;
|
|
}
|