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construct/ircd/gpt.cc

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2021-03-05 02:03:33 +01:00
// 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.
decltype(ircd::gpt::log)
ircd::gpt::log
{
"gpt"
};
namespace ircd::gpt
{
static void gelu(float &, const float &);
static void gelu(float (&)[3072], const float (&)[3072]);
static void norm(float (&)[768], const float (&)[768], const float (&)[768], const float (&)[768], const float);
static void fmma(float (&)[768], const float (&)[3072], const float (&)[768], const float (&)[3072][768]);
static void fmma(float (&)[3072], const float (&)[768], const float (&)[3072], const float (&)[768][3072]);
static void fmma(float (&)[2304], const float (&)[768], const float (&)[2304], const float (&)[768][2304]);
static void fmma(float *, const float (&)[12][1024][64], const float (&)[768], const float (&)[768][768], const size_t);
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 bool (&)[1024][1024], const size_t);
static void smax(float (&)[12][1024][1024], const float (&)[12][1024][1024], const size_t);
static void ctrl(float (&)[3][1024][12][64], const float *const, const size_t, const model::block &);
static void ffnn(float (&)[768], const float (&)[768], const model::block &);
static void transform(float *, const size_t, const model::decoder &);
static void logitsmax(float *, const float *);
static void logits(float *, const float (&)[768], const model::decoder &);
static void tail(float *, const float (&)[768], const model::decoder &);
static u16 argmax(const float *);
std::unique_ptr<model::decoder> device
{
new model::decoder{}
};
static f32
logit alignas(64) [65536],
scratch alignas(64) [1024 * 768];
}
namespace ircd::gpt::model
{
constexpr float embed_pdrop
{
0.1
};
constexpr float ln1_epsilon
{
0.00001
};
constexpr float ln2_epsilon
{
0.00001
};
constexpr float lnf_epsilon
{
0.00001
};
constexpr float attn_pdrop
{
0.1
};
constexpr float resid_pdrop
{
0.1
};
}
ircd::vector_view<ircd::f32>
ircd::gpt::embed(const vector_view<f32> &out,
const vector_view<const u16> &in)
noexcept
{
assert(device);
uint i(0);
for(; i < in.size(); ++i)
{
const auto &wpe
{
device->wpe[i]
};
const auto &wte
{
device->wte[in[i]]
};
for(uint j(0); j < 768; ++j)
out[i * 768 + j] = wte[j] + wpe[j];
}
return vector_view<f32>
{
data(out), i * 768
};
}
uint16_t
ircd::gpt::generate(const vector_view<const f32> &in)
noexcept
{
always_assert(in.size() % 768 == 0);
const auto toks
{
in.size() / 768
};
const vector_view<f32> scratch
{
gpt::scratch, in.size()
};
for(uint i(0); i < in.size(); ++i)
scratch[i] = in[i];
transform(data(scratch), toks, *device);
static float
buf alignas(64) [768];
for(uint i(0); i < 768; ++i)
buf[i] = scratch[(toks - 1) * 768 + i];
tail(logit, buf, *device);
return argmax(logit);
}
uint16_t
ircd::gpt::argmax(const float *const __restrict__ logit)
{
u16 ret(0);
for(uint j(0); j < vocab::tokens; ++j)
if(logit[j] > logit[ret])
ret = j;
return ret;
}
[[gnu::noinline]]
void
ircd::gpt::tail(float *const __restrict__ logit,
const float (&__restrict__ state)[768],
const model::decoder &d)
{
static float
buf alignas(64) [768];
norm(buf, state, d.f.bias, d.f.weight, model::lnf_epsilon);
logits(logit, buf, d);
//logitsmax(logit, logit);
}
void
ircd::gpt::logits(float *const __restrict__ out,
const float (&__restrict__ in)[768],
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.wte[j][k];
}
void
ircd::gpt::logitsmax(float *const out,
const float *const in)
{
static float
exps alignas(64) [65536];
for(uint j(0); j < vocab::tokens; ++j)
exps[j] = exp(in[j]);
for(uint j(0); j < vocab::tokens; ++j)
out[j] = 0;
for(uint j(0); j < vocab::tokens; ++j)
for(uint k(0); k < vocab::tokens; ++k)
out[k] += exps[j];
for(uint j(0); j < vocab::tokens; ++j)
out[j] = exps[j] / out[j];
}
[[gnu::noinline]]
void
ircd::gpt::transform(float *__restrict__ accum,
const size_t tokens,
const model::decoder &decoder)
{
static float
qkv alignas(64) [3][1024][12][64],
state alignas(64) [12][1024][1024],
attns alignas(64) [12][1024][64],
buf alignas(64) [768];
for(uint i(0); i < 12; ++i)
{
const auto &layer
{
decoder.layer[i]
};
ctrl(qkv, accum, tokens, layer);
pare(state, qkv, tokens);
mask(state, state, layer.attn.bias, tokens);
smax(state, state, tokens);
vals(attns, state, qkv, tokens);
fmma(accum, attns, layer.attn.proj_bias, layer.attn.proj_weight, tokens);
for(uint j(0); j < tokens; ++j)
{
for(uint k(0); k < 768; ++k)
buf[k] = accum[j * 768 + k];
ffnn(buf, buf, layer);
for(uint k(0); k < 768; ++k)
accum[j * 768 + k] += buf[k];
}
}
}
void
ircd::gpt::ffnn(float (&__restrict__ out)[768],
const float (&__restrict__ in)[768],
const model::block &layer)
{
static float
proj alignas(64) [3072];
norm(out, in, layer.ln2.bias, layer.ln2.weight, model::ln2_epsilon);
fmma(proj, out, layer.ffnn.fc_bias, layer.ffnn.fc_weight);
gelu(proj, proj);
fmma(out, proj, layer.ffnn.proj_bias, layer.ffnn.proj_weight);
}
void
ircd::gpt::ctrl(float (&__restrict__ out)[3][1024][12][64],
const float *const __restrict__ in,
const size_t num,
const model::block &layer)
{
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];
for(uint j(0); j < 768; ++j)
buf[j] = in[i * 768 + j];
norm(buf, buf, layer.ln1.bias, layer.ln1.weight, model::ln1_epsilon);
fmma(proj, buf, layer.attn.attn_bias, layer.attn.attn_weight);
#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::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::smax(float (&__restrict__ out)[12][1024][1024],
const float (&__restrict__ in)[12][1024][1024],
const size_t num)
{
static float
exps alignas(64) [12][1024][1024];
#pragma clang loop unroll (disable)
for(uint j(0); j < 12; ++j)
for(uint k(0); k < num; ++k)
for(uint m(0); m < num; ++m)
exps[j][k][m] = exp(in[j][k][m]);
#pragma clang loop unroll (disable)
for(uint j(0); j < 12; ++j)
for(uint k(0); k < num; ++k)
for(uint m(0); m < num; ++m)
out[j][k][m] = 0;
#pragma clang loop unroll (disable)
for(uint j(0); j < 12; ++j)
for(uint k(0); k < num; ++k)
for(uint m(0); m < num; ++m)
for(uint l(0); l < num; ++l)
out[j][k][l] += exps[j][k][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] = exps[j][k][l] / out[j][k][l];
}
void
ircd::gpt::mask(float (&__restrict__ out)[12][1024][1024],
const float (&__restrict__ in)[12][1024][1024],
const bool (&__restrict__ bias)[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] = bias[k][l]? in[j][k][l]: masked;
}
void
ircd::gpt::norm(float (&__restrict__ out)[768],
const float (&__restrict__ in)[768],
const float (&__restrict__ bias)[768],
const float (&__restrict__ weight)[768],
const float epsilon)
{
static float
tmp alignas(64) [768];
const float mean
{
math::mean<float>(in)
};
for(uint j(0); j < 768; ++j)
tmp[j] = pow(in[j] - mean, 2);
const float s
{
math::mean<float>(tmp)
};
for(uint j(0); j < 768; ++j)
out[j] = (in[j] - mean) / sqrt(s + epsilon),
out[j] = out[j] * weight[j] + bias[j];
}
void
ircd::gpt::fmma(float *const __restrict__ out,
const float (&__restrict__ attn)[12][1024][64],
const float (&__restrict__ bias)[768],
const float (&__restrict__ weight)[768][768],
const size_t num)
{
static float
a alignas(64) [1024][768],
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
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)));
}