mirror of
https://github.com/matrix-construct/construct
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988 lines
27 KiB
Common Lisp
988 lines
27 KiB
Common Lisp
// Matrix Construct
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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/simt/simt.h>
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#include <ircd/gpt/token.h>
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#include <ircd/gpt/task/task.h>
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inline void
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__attribute__((always_inline))
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ircd_gpt_norm_fmad(__local float4 *const out,
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__local const float4 *const in,
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__global const float4 *const restrict bias,
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__global const float4 *const restrict weight,
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const uint i)
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{
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out[i] = in[i] * weight[i] + bias[i];
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}
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/// Gaussian Error Linear Unit
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inline void
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__attribute__((always_inline))
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ircd_gpt_ffnn_gelu(__local float4 *const out,
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__local const float4 *const in_,
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const uint i)
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{
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const float4
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in = in_[i];
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float4 a;
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a = 0.044715f;
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a *= in;
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a *= in;
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a += 1.0f;
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a *= 0.7978845608f;
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a *= in;
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a = tanh(a);
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a += 1.0f;
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a *= in;
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a *= 0.5f;
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out[i] = a;
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}
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// Matrix * Vector Multiply/Accumulate
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inline void
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__attribute__((flatten, always_inline))
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ircd_gpt_sgemv(__local float4 *const restrict out,
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__local const float4 *const restrict in,
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__global const float4 *const restrict bias,
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__global const float4 *const restrict weight,
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const uint width,
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const uint height,
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const uint segs)
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{
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const uint
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li = get_local_id(0),
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ln = get_local_size(0),
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lanes = 4;
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__attribute__((opencl_unroll_hint))
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for(uint i = 0; i < segs; ++i)
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{
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const uint
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col = i * ln + li;
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out[col] = bias[col];
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}
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for(uint j = 0; j < height; ++j)
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for(uint i = 0; i < segs; ++i)
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{
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const uint
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col = i * ln + li;
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float4 acc = 0.0f;
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for(uint k = 0; k < lanes; ++k)
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{
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const uint
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row = j * lanes + k,
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cell = row * width + col;
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acc += in[j][k] * weight[cell];
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}
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out[col] += acc;
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}
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}
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inline void
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__attribute__((flatten, always_inline))
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ircd_gpt_ffnn_fcon(__global const struct ircd_gpt_ctrl *const ctrl,
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__constant const struct ircd_gpt_opts *const opts,
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__local union ircd_gpt_ffnn_aperaturev *const restrict out,
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__local const union ircd_gpt_tokenv *const in,
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__global const float4 *const restrict bias,
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__global const float4 *const restrict weight)
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{
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const uint
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li = get_local_id(0),
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ln = get_local_size(0),
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width = opts->ffnn_width,
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height = opts->ffnn_height,
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tiles = opts->ffnn_mult;
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ircd_gpt_sgemv(out->fcon, in->word, bias, weight, width, height, tiles);
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for(uint i = 0; i < tiles; ++i)
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ircd_gpt_ffnn_gelu(out->fcon, out->fcon, i * ln + li);
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}
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inline void
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__attribute__((flatten, always_inline))
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ircd_gpt_ffnn(__global const struct ircd_gpt_ctrl *const ctrl,
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__constant const struct ircd_gpt_opts *const opts,
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__local union ircd_gpt_tokenv *const restrict token,
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__local union ircd_gpt_ffnn_aperaturev *const restrict buf,
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__local union ircd_gpt_ffnn_aperaturev *const restrict tmp0,
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__local union ircd_gpt_tokenv *const restrict tmp1,
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__global const float4 *const restrict norm_bias,
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__global const float4 *const restrict norm_weight,
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__global const float4 *const restrict fcon_bias,
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__global const float4 *const restrict fcon_weight,
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__global const float4 *const restrict proj_bias,
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__global const float4 *const restrict proj_weight)
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{
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const uint
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gi = get_global_id(0),
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gn = get_global_size(0),
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li = get_local_id(0),
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ln = get_local_size(0),
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wi = get_group_id(0),
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wn = get_num_groups(0),
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width = opts->ffnn_width,
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height = opts->ffnn_height;
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// Layer re-normalization
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ircd_simt_math_norm_f4lldr(token->word, token->word, buf->word);
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ircd_gpt_norm_fmad(token->word, token->word, norm_bias, norm_weight, li);
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// ln's writes are still pending but fcon reads results across threads.
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barrier(CLK_LOCAL_MEM_FENCE);
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// Fully connected
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ircd_gpt_ffnn_fcon(ctrl, opts, buf, token, fcon_bias, fcon_weight);
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// fcon's writes are still pending but proj reads results across threads.
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barrier(CLK_LOCAL_MEM_FENCE);
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// Projection
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ircd_gpt_sgemv(token->word, buf->fcon, proj_bias, proj_weight, height, width, 1);
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}
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inline void
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__attribute__((flatten, always_inline))
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ircd_gpt_attn_self_samax(__global const struct ircd_gpt_ctrl *const ctrl,
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__constant const struct ircd_gpt_opts *const opts,
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__local float self[][12])
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{
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const uint
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li = get_local_id(0),
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wn = get_num_groups(0);
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struct ircd_math_samax samax =
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{
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.mu = -10000.0f,
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.sum = 0.0f,
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};
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for(uint i = 0; i < wn; ++i)
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samax.mu = max(samax.mu, self[i][li]);
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for(uint i = 0; i < wn; ++i)
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self[i][li] = exp(self[i][li] - samax.mu);
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__attribute__((opencl_unroll_hint))
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for(uint i = 0; i < wn; ++i)
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samax.sum += self[i][li];
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samax.lambda = 1.0f / samax.sum;
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__attribute__((opencl_unroll_hint))
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for(uint i = 0; i < wn; ++i)
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self[i][li] *= samax.lambda;
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}
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inline void
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__attribute__((flatten, always_inline))
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ircd_gpt_attn_self(__global const struct ircd_gpt_ctrl *const ctrl,
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__constant const struct ircd_gpt_opts *const opts,
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__local union ircd_gpt_tokenv *const restrict out,
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__local float self[][12],
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__global const struct ircd_gpt_attn_qkvv *const restrict token,
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__global const struct ircd_gpt_attn_mask *const restrict mask) // [1024][1024],
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{
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const uint
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gi = get_global_id(0),
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gn = get_global_size(0),
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li = get_local_id(0),
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ln = get_local_size(0),
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wi = get_group_id(0),
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wn = get_num_groups(0),
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ti = li % 12,
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ki = li / 12;
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// Low-rank mask
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if(li < 12)
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{
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for(uint i = 0; i < wn; ++i)
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{
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if(!mask[wi].token[i])
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{
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self[i][li] = -10000.0f;
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continue;
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}
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float4 acc = 0.0f;
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for(uint k = 0; k < 64/4; ++k)
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{
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float4
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qry = token[wi].qry.attn[li][k],
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key = token[i].key.attn[li][k];
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acc += qry * key;
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}
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const float
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sum = ircd_simt_reduce_add_f4(acc),
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res = sum / 8.0f;
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self[i][li] = res;
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}
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// Three-piece softmax
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ircd_gpt_attn_self_samax(ctrl, opts, self);
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}
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// Propagate to full width for value dot prod.
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barrier(CLK_LOCAL_MEM_FENCE);
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float4 acc = 0.0f;
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__attribute__((opencl_unroll_hint))
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for(uint i = 0; i < wn; ++i)
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{
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const float4
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attn = self[i][ti],
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val = token[i].val.attn[ti][ki];
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acc += attn * val;
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}
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out->attn[ti][ki] = acc;
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}
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inline void
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__attribute__((flatten, always_inline))
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ircd_gpt_attn_proj(__global const struct ircd_gpt_ctrl *const ctrl,
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__constant const struct ircd_gpt_opts *const opts,
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__local union ircd_gpt_tokenv *const out,
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__local const union ircd_gpt_tokenv *const xattn,
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__global const float4 *const restrict bias,
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__global const float4 *const restrict weight)
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{
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const uint
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gi = get_global_id(0),
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gn = get_global_size(0),
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li = get_local_id(0),
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ln = get_local_size(0),
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wi = get_group_id(0),
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wn = get_num_groups(0),
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width = opts->attn_height, // same
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height = opts->attn_height;
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// Projection
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ircd_gpt_sgemv(out->word, xattn->word, bias, weight, width, height, 1);
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}
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__kernel void
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__attribute__((flatten))
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ircd_gpt_coil(__global const struct ircd_gpt_ctrl *const ctrl,
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__constant const struct ircd_gpt_opts *const opts,
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__global union ircd_gpt_tokenv *const restrict accum,
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__global const struct ircd_gpt_attn_qkvv *const restrict state,
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__global const struct ircd_gpt_attn_mask *const restrict mask, // [1024][1024],
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__global const float4 *const restrict attn_proj_bias,
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__global const float4 *const restrict attn_proj_weight,
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__global const float4 *const restrict ffnn_norm_bias,
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__global const float4 *const restrict ffnn_norm_weight,
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__global const float4 *const restrict ffnn_fcon_bias,
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__global const float4 *const restrict ffnn_fcon_weight,
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__global const float4 *const restrict ffnn_proj_bias,
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__global const float4 *const restrict ffnn_proj_weight)
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{
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const uint
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li = get_local_id(0),
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ln = get_local_size(0),
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wi = get_group_id(0);
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__local union ircd_gpt_tokenv
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buf1, buf0;
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__local union
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{
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union ircd_gpt_ffnn_aperaturev
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ffnn_fcon[2];
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float
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attn_self[512][12];
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}
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buf;
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// Self-attention backend; this computes the self-attention result now
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// that keys and values are globally visible across tokens.
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ircd_gpt_attn_self
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(
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ctrl,
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opts,
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&buf1,
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buf.attn_self,
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state,
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mask
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);
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barrier(CLK_LOCAL_MEM_FENCE);
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// Project result of self-attention.
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ircd_gpt_attn_proj
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(
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ctrl,
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opts,
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&buf0,
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&buf1,
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attn_proj_bias,
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attn_proj_weight
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);
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// Frontend accumulation
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{
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const float4
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attn = buf0.word[li],
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resid = accum[wi].word[li];
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buf0.word[li] += resid;
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accum[wi].word[li] += attn;
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}
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// Backend mlp; layer-norm acquires any pending writes, no fence required.
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ircd_gpt_ffnn
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(
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ctrl,
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opts,
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&buf0,
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buf.ffnn_fcon + 0,
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buf.ffnn_fcon + 1,
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&buf1,
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ffnn_norm_bias,
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ffnn_norm_weight,
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ffnn_fcon_bias,
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ffnn_fcon_weight,
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ffnn_proj_bias,
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ffnn_proj_weight
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);
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// Backend accumulation
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{
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const float4
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ffnn = buf0.word[li],
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resid = accum[wi].word[li],
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result = ffnn + resid;
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accum[wi].word[li] = result;
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}
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}
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__kernel void
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__attribute__((flatten))
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ircd_gpt_attn_fcon(__global const struct ircd_gpt_ctrl *const ctrl,
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__constant const struct ircd_gpt_opts *const opts,
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__global union ircd_gpt_attn_aperaturev *const restrict state,
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__global const union ircd_gpt_tokenv *const restrict accum,
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__global const float4 *const restrict norm_bias,
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__global const float4 *const restrict norm_weight,
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__global const float4 *const restrict fcon_bias,
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__global const float4 *const restrict fcon_weight)
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{
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const uint
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gi = get_global_id(0),
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gn = get_global_size(0),
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li = get_local_id(0),
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ln = get_local_size(0),
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wi = get_group_id(0),
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wn = get_num_groups(0),
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width = opts->attn_width,
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height = opts->attn_height,
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tiles = opts->attn_mult;
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__local union ircd_gpt_attn_aperaturev
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token;
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__local float4
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tmp[768/4];
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token.word[li] = accum[wi].word[li];
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// Layer re-normalization
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ircd_simt_math_norm_f4lldr(token.word, token.word, tmp);
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ircd_gpt_norm_fmad(tmp, token.word, norm_bias, norm_weight, li);
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// Ln's writes are still pending; fcon requires results across threads.
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barrier(CLK_LOCAL_MEM_FENCE);
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// Fully connected
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ircd_gpt_sgemv(token.fcon, tmp, fcon_bias, fcon_weight, width, height, tiles);
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// Export queries, keys, and values.
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for(uint i = 0; i < tiles; ++i)
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state[wi].proj[i][li] = token.proj[i][li];
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}
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//
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// frontend
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//
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inline void
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__attribute__((always_inline))
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_ircd_gpt_lm_embed(__global const struct ircd_gpt_ctrl *const ctrl,
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__constant const struct ircd_gpt_opts *const opts,
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__global union ircd_gpt_tokenv *const restrict out,
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__global const union ircd_gpt_tokenv *const restrict pos,
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__global const union ircd_gpt_tokenv *const restrict vocab,
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const uint out_idx,
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const uint tok_idx,
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const uint word_idx)
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{
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const ushort
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ring_idx = (ctrl->tokens.head + tok_idx) % opts->buffer_tokens,
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token = ctrl->token[ring_idx];
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const float4
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wte = vocab[token].word[word_idx],
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wpe = pos[tok_idx].word[word_idx];
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out[out_idx].word[word_idx] = wte + wpe;
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}
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__kernel void
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__attribute__((flatten))
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ircd_gpt_lm_embed(__global const struct ircd_gpt_ctrl *const ctrl,
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__constant const struct ircd_gpt_opts *const opts,
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__global union ircd_gpt_tokenv *const restrict accum,
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__global const union ircd_gpt_tokenv *const restrict pos,
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__global const union ircd_gpt_tokenv *const restrict vocab)
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{
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const uint
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li = get_local_id(0),
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wi = get_group_id(0),
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wn = get_num_groups(0);
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for(uint i = 0; i < ctrl->tokens.count; ++i)
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if(i % wn == wi)
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_ircd_gpt_lm_embed(ctrl, opts, accum, pos, vocab, i, i, li);
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}
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__kernel void
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__attribute__((flatten))
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ircd_gpt_lm_norm(__global const struct ircd_gpt_ctrl *const ctrl,
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__constant const struct ircd_gpt_opts *const opts,
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__global union ircd_gpt_tokenv *const restrict accum,
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__global const float4 *const restrict norm_bias,
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__global const float4 *const restrict norm_weight)
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{
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const uint
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li = get_local_id(0),
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ln = get_local_size(0),
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wi = get_global_offset(0) / ln + get_group_id(0);
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__local union ircd_gpt_tokenv
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token, tmp;
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token.word[li] = accum[wi].word[li];
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// Final re-normalization
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ircd_simt_math_norm_f4lldr(token.word, token.word, tmp.word);
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ircd_gpt_norm_fmad(token.word, token.word, norm_bias, norm_weight, li);
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accum[wi].word[li] = token.word[li];
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}
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__kernel void
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__attribute__((flatten))
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ircd_gpt_lm_logit(__global const struct ircd_gpt_ctrl *const ctrl,
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__constant const struct ircd_gpt_opts *const opts,
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__global float *const restrict logit,
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__global const union ircd_gpt_tokenv *const restrict accum,
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__global const union ircd_gpt_tokenv *const restrict token)
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{
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const uint
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gi = get_global_id(0),
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ti = ctrl->tokens.count - 1,
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|
words = opts->embed_width;
|
|
|
|
float4 acc = 0.0f;
|
|
__attribute__((opencl_unroll_hint))
|
|
for(uint j = 0; j < words; ++j)
|
|
{
|
|
const float4
|
|
in = accum[ti].word[j],
|
|
vocab = token[gi].word[j];
|
|
|
|
acc += vocab * in;
|
|
}
|
|
|
|
const float
|
|
ret = ircd_simt_reduce_add_f4(acc);
|
|
|
|
if(gi < opts->logits)
|
|
logit[gi] = ret;
|
|
else
|
|
logit[gi] = -10000.0f;
|
|
}
|
|
|
|
__kernel void
|
|
__attribute__((flatten))
|
|
ircd_gpt_lm_logsm(__global struct ircd_gpt_ctrl *const ctrl,
|
|
__constant const struct ircd_gpt_opts *const opts,
|
|
__global float4 *const restrict logsm,
|
|
__global float4 *const restrict logexp,
|
|
__global const float4 *const restrict logit)
|
|
{
|
|
const uint
|
|
gi = get_global_id(0),
|
|
li = get_local_id(0),
|
|
ln = get_local_size(0),
|
|
logits = opts->logits,
|
|
logits_alignup = logits + (ln - (logits % ln)),
|
|
tn = logits_alignup / ln / 4,
|
|
ti = tn * li;
|
|
|
|
__local float share[256];
|
|
__local float4 share4[256];
|
|
|
|
share4[li] = -10000.0f;
|
|
for(uint i = ti; i < ti + tn; ++i)
|
|
share4[li] = max(share4[li], logit[i]);
|
|
|
|
share[li] = -10000.0f;
|
|
for(uint k = 0; k < 4; ++k)
|
|
share[li] = max(share[li], share4[li][k]);
|
|
|
|
ircd_simt_reduce_max_flldr(share);
|
|
|
|
if(li == 0)
|
|
share4[li] = ctrl->samax.mu = share[li];
|
|
|
|
ircd_simt_broadcast_f4lldr(share4);
|
|
|
|
const float4
|
|
mu = share4[li];
|
|
|
|
share4[li] = 0.0f;
|
|
for(uint i = ti; i < ti + tn; ++i)
|
|
{
|
|
const float4
|
|
reg = logit[i] - mu;
|
|
|
|
float4 res;
|
|
for(uint k = 0; k < 4; ++k)
|
|
if(i * 4 + k < logits)
|
|
res[k] = exp(reg[k]);
|
|
else
|
|
res[k] = 0.0f;
|
|
|
|
share4[li] += res;
|
|
logexp[i] = res;
|
|
}
|
|
|
|
ircd_simt_reduce_add_f4lldr(share4);
|
|
|
|
if(li == 0)
|
|
{
|
|
const float
|
|
sum = ircd_simt_reduce_add_f4(share4[li]);
|
|
|
|
share4[li][0] = ctrl->samax.sum = sum;
|
|
share4[li][1] = ctrl->samax.lambda = 1.0f / sum;
|
|
}
|
|
|
|
ircd_simt_broadcast_f4lldr(share4);
|
|
|
|
const float4
|
|
sum = share4[li][0],
|
|
lambda = share4[li][1];
|
|
|
|
for(uint i = ti; i < ti + tn; ++i)
|
|
logsm[i] = logexp[i] * lambda;
|
|
}
|
|
|
|
inline void
|
|
__attribute__((always_inline))
|
|
ircd_gpt_leave(__global struct ircd_gpt_ctrl *const ctrl,
|
|
__constant const struct ircd_gpt_opts *const opts,
|
|
const uint li)
|
|
{
|
|
// No action for other threads right now
|
|
if(li != 0)
|
|
return;
|
|
|
|
if(ctrl->epic.cycle + 1 >= opts->limit)
|
|
ctrl->epic.epoch += 1;
|
|
|
|
ctrl->epic.cycle += 1;
|
|
ctrl->magic = 0xC7012C70U;
|
|
}
|
|
|
|
inline void
|
|
__attribute__((always_inline))
|
|
ircd_gpt_lm_result(__global struct ircd_gpt_ctrl *const ctrl,
|
|
__constant const struct ircd_gpt_opts *const opts,
|
|
const uint li,
|
|
__local const ushort *const restrict idx,
|
|
__global const float *const restrict logsm,
|
|
__global const float *const restrict logexp,
|
|
__global const float *const restrict logit)
|
|
{
|
|
// To read from cells other than idx[0] we need this barrier.
|
|
barrier(CLK_LOCAL_MEM_FENCE);
|
|
|
|
// Mask for write-leader
|
|
if(li != 0)
|
|
return;
|
|
|
|
const bool
|
|
buffer_full = ctrl->tokens.count >= opts->buffer_tokens;
|
|
|
|
const ulong
|
|
rnd = opts->top_k > 1?
|
|
ircd_simt_rand_xoshiro256pg(ctrl->rand): 1UL;
|
|
|
|
const ushort
|
|
entro = max(opts->top_k, 1U),
|
|
select = rnd % entro,
|
|
token = idx[select],
|
|
dest = (ctrl->tokens.head + ctrl->tokens.count) % opts->buffer_tokens,
|
|
tokens = min(ctrl->tokens.count + 1, opts->buffer_tokens),
|
|
head = buffer_full?
|
|
(ctrl->tokens.head + 1) % opts->buffer_tokens: ctrl->tokens.head;
|
|
|
|
ctrl->tokens.head = head;
|
|
ctrl->tokens.count = tokens;
|
|
ctrl->token[dest] = token;
|
|
|
|
const ushort
|
|
ln = get_local_size(0),
|
|
next_select = (select + 1) % ln,
|
|
next_token = idx[next_select],
|
|
sum_sel = ctrl->epic.epoch % 3;
|
|
|
|
const float
|
|
test_lsm = logexp[opts->label],
|
|
loss = 0.0f - log(test_lsm * ctrl->samax.lambda),
|
|
perp = (1.0f - logsm[token]) * native_log2(opts->logits),
|
|
cert = (logsm[token] - logsm[next_token]) / logsm[token],
|
|
loss_sum = ctrl->loss.sum[0] + ctrl->loss.sum[1] + ctrl->loss.sum[2] + loss,
|
|
perp_sum = ctrl->perp.sum[0] + ctrl->perp.sum[1] + ctrl->perp.sum[2] + perp,
|
|
cert_sum = ctrl->cert.sum[0] + ctrl->cert.sum[1] + ctrl->cert.sum[2] + cert,
|
|
mean_div = ctrl->epic.epoch + 1.0f,
|
|
loss_mean = loss_sum / mean_div,
|
|
perp_mean = perp_sum / mean_div,
|
|
cert_mean = cert_sum / mean_div;
|
|
|
|
ctrl->loss.last = loss;
|
|
ctrl->loss.sum[sum_sel] += loss;
|
|
ctrl->loss.mean = loss_mean;
|
|
ctrl->perp.last = perp;
|
|
ctrl->perp.sum[sum_sel] += perp;
|
|
ctrl->perp.mean = perp_mean;
|
|
ctrl->cert.last = cert;
|
|
ctrl->cert.sum[sum_sel] += cert;
|
|
ctrl->cert.mean = cert_mean;
|
|
}
|
|
|
|
__kernel void
|
|
__attribute__((flatten))
|
|
ircd_gpt_lm_select(__global struct ircd_gpt_ctrl *const ctrl,
|
|
__constant const struct ircd_gpt_opts *const opts,
|
|
__global const float *const restrict logsm,
|
|
__global const float *const restrict logexp,
|
|
__global const float *const restrict logit)
|
|
{
|
|
const uint
|
|
gi = get_global_id(0),
|
|
gn = get_global_size(0),
|
|
li = get_local_id(0),
|
|
ln = get_local_size(0),
|
|
wi = get_group_id(0),
|
|
wn = get_num_groups(0),
|
|
tn = opts->logits / ln,
|
|
ti = tn * li;
|
|
|
|
__local ushort idx[256];
|
|
|
|
idx[li] = ti;
|
|
for(uint j = ti + 1; j < ti + tn; ++j)
|
|
if(logsm[j] > logsm[idx[li]])
|
|
idx[li] = j;
|
|
|
|
ircd_simt_sort_idx16_flldr(idx, logsm);
|
|
ircd_gpt_lm_result(ctrl, opts, li, idx, logsm, logexp, logit);
|
|
ircd_gpt_leave(ctrl, opts, li);
|
|
}
|
|
|
|
//
|
|
// backpropagations
|
|
//
|
|
|
|
inline void
|
|
__attribute__((always_inline))
|
|
ircd_gpt_prop_elem(__global const struct ircd_gpt_ctrl *const ctrl,
|
|
__constant const struct ircd_gpt_opts *const opts,
|
|
__global float4 *const restrict param_,
|
|
__global float4 *const restrict exp_avg_,
|
|
__global float4 *const restrict exp_avg_sqr_)
|
|
{
|
|
const uint
|
|
li = get_local_id(0),
|
|
step = ctrl->epic.step;
|
|
|
|
const float4
|
|
param = param_[li],
|
|
grad = ctrl->loss.mean,
|
|
alpha[2] = { 1.0f - opts->beta[0], 1.0f - opts->beta[1], },
|
|
exp_avg = step? exp_avg_[li]: 0.0f,
|
|
exp_avg_sqr = step? exp_avg_sqr_[li]: 0.0f,
|
|
exp_avg_mul = exp_avg * opts->beta[0],
|
|
exp_avg_dot = exp_avg_mul + alpha[0] * grad,
|
|
exp_avg_sqr_mul = exp_avg_sqr * opts->beta[1],
|
|
exp_avg_sqr_dot = exp_avg_sqr_mul + alpha[1] * grad * grad,
|
|
denom = sqrt(exp_avg_sqr_dot) + opts->epsilon,
|
|
delta = opts->alpha * (exp_avg_dot / denom),
|
|
update = param - delta;
|
|
|
|
param_[li] = update;
|
|
exp_avg_[li] = exp_avg_dot;
|
|
exp_avg_sqr_[li] = exp_avg_sqr_dot;
|
|
}
|
|
|
|
__kernel void
|
|
__attribute__((flatten))
|
|
ircd_gpt_norm_prop(__global const struct ircd_gpt_ctrl *const ctrl,
|
|
__constant const struct ircd_gpt_opts *const opts,
|
|
__global union ircd_gpt_tokenv *const restrict bias,
|
|
__global union ircd_gpt_tokenv *const restrict bias_m0,
|
|
__global union ircd_gpt_tokenv *const restrict bias_m1,
|
|
__global union ircd_gpt_tokenv *const restrict weight,
|
|
__global union ircd_gpt_tokenv *const restrict weight_m0,
|
|
__global union ircd_gpt_tokenv *const restrict weight_m1)
|
|
{
|
|
const uint
|
|
gi = get_global_id(0),
|
|
gn = get_global_size(0),
|
|
li = get_local_id(0),
|
|
ln = get_local_size(0),
|
|
wi = get_group_id(0),
|
|
wn = get_num_groups(0);
|
|
|
|
ircd_gpt_prop_elem
|
|
(
|
|
ctrl, opts,
|
|
bias->word,
|
|
bias_m0->word,
|
|
bias_m1->word
|
|
);
|
|
|
|
ircd_gpt_prop_elem
|
|
(
|
|
ctrl, opts,
|
|
weight->word,
|
|
weight_m0->word,
|
|
weight_m1->word
|
|
);
|
|
}
|
|
|
|
__kernel void
|
|
__attribute__((flatten))
|
|
ircd_gpt_coil_prop_attn(__global const struct ircd_gpt_ctrl *const ctrl,
|
|
__constant const struct ircd_gpt_opts *const opts,
|
|
__global union ircd_gpt_tokenv *const restrict norm_bias,
|
|
__global union ircd_gpt_tokenv *const restrict norm_bias_m0,
|
|
__global union ircd_gpt_tokenv *const restrict norm_bias_m1,
|
|
__global union ircd_gpt_tokenv *const restrict norm_weight,
|
|
__global union ircd_gpt_tokenv *const restrict norm_weight_m0,
|
|
__global union ircd_gpt_tokenv *const restrict norm_weight_m1,
|
|
__global union ircd_gpt_attn_aperaturev *const restrict fcon_bias,
|
|
__global union ircd_gpt_attn_aperaturev *const restrict fcon_bias_m0,
|
|
__global union ircd_gpt_attn_aperaturev *const restrict fcon_bias_m1,
|
|
__global union ircd_gpt_attn_aperaturev *const restrict fcon_weight,
|
|
__global union ircd_gpt_attn_aperaturev *const restrict fcon_weight_m0,
|
|
__global union ircd_gpt_attn_aperaturev *const restrict fcon_weight_m1,
|
|
__global union ircd_gpt_tokenv *const restrict proj_bias,
|
|
__global union ircd_gpt_tokenv *const restrict proj_bias_m0,
|
|
__global union ircd_gpt_tokenv *const restrict proj_bias_m1,
|
|
__global union ircd_gpt_tokenv *const restrict proj_weight,
|
|
__global union ircd_gpt_tokenv *const restrict proj_weight_m0,
|
|
__global union ircd_gpt_tokenv *const restrict proj_weight_m1)
|
|
{
|
|
const uint
|
|
gi = get_global_id(0),
|
|
gn = get_global_size(0),
|
|
li = get_local_id(0),
|
|
ln = get_local_size(0),
|
|
wi = get_group_id(0),
|
|
wn = get_num_groups(0);
|
|
|
|
ircd_gpt_norm_prop
|
|
(
|
|
ctrl, opts,
|
|
norm_bias,
|
|
norm_bias_m0,
|
|
norm_bias_m1,
|
|
norm_weight,
|
|
norm_weight_m0,
|
|
norm_weight_m1
|
|
);
|
|
|
|
for(uint j = 0; j < 3; ++j)
|
|
ircd_gpt_prop_elem
|
|
(
|
|
ctrl, opts,
|
|
fcon_bias->proj[j],
|
|
fcon_bias_m0->proj[j],
|
|
fcon_bias_m1->proj[j]
|
|
);
|
|
|
|
for(uint i = 0; i < 768; ++i)
|
|
for(uint j = 0; j < 3; ++j)
|
|
ircd_gpt_prop_elem
|
|
(
|
|
ctrl, opts,
|
|
fcon_weight[i].proj[j],
|
|
fcon_weight_m0[i].proj[j],
|
|
fcon_weight_m1[i].proj[j]
|
|
);
|
|
|
|
ircd_gpt_prop_elem
|
|
(
|
|
ctrl, opts,
|
|
proj_bias->word,
|
|
proj_bias_m0->word,
|
|
proj_bias_m1->word
|
|
);
|
|
|
|
for(uint i = 0; i < 768; ++i)
|
|
ircd_gpt_prop_elem
|
|
(
|
|
ctrl, opts,
|
|
proj_weight[i].word,
|
|
proj_weight_m0[i].word,
|
|
proj_weight_m1[i].word
|
|
);
|
|
}
|
|
|
|
__kernel void
|
|
__attribute__((flatten))
|
|
ircd_gpt_coil_prop_ffnn(__global const struct ircd_gpt_ctrl *const ctrl,
|
|
__constant const struct ircd_gpt_opts *const opts,
|
|
__global union ircd_gpt_tokenv *const restrict norm_bias,
|
|
__global union ircd_gpt_tokenv *const restrict norm_bias_m0,
|
|
__global union ircd_gpt_tokenv *const restrict norm_bias_m1,
|
|
__global union ircd_gpt_tokenv *const restrict norm_weight,
|
|
__global union ircd_gpt_tokenv *const restrict norm_weight_m0,
|
|
__global union ircd_gpt_tokenv *const restrict norm_weight_m1,
|
|
__global union ircd_gpt_ffnn_aperaturev *const restrict fcon_bias,
|
|
__global union ircd_gpt_ffnn_aperaturev *const restrict fcon_bias_m0,
|
|
__global union ircd_gpt_ffnn_aperaturev *const restrict fcon_bias_m1,
|
|
__global union ircd_gpt_ffnn_aperaturev *const restrict fcon_weight,
|
|
__global union ircd_gpt_ffnn_aperaturev *const restrict fcon_weight_m0,
|
|
__global union ircd_gpt_ffnn_aperaturev *const restrict fcon_weight_m1,
|
|
__global union ircd_gpt_tokenv *const restrict proj_bias,
|
|
__global union ircd_gpt_tokenv *const restrict proj_bias_m0,
|
|
__global union ircd_gpt_tokenv *const restrict proj_bias_m1,
|
|
__global union ircd_gpt_tokenv *const restrict proj_weight,
|
|
__global union ircd_gpt_tokenv *const restrict proj_weight_m0,
|
|
__global union ircd_gpt_tokenv *const restrict proj_weight_m1)
|
|
{
|
|
const uint
|
|
gi = get_global_id(0),
|
|
gn = get_global_size(0),
|
|
li = get_local_id(0),
|
|
ln = get_local_size(0),
|
|
wi = get_group_id(0),
|
|
wn = get_num_groups(0);
|
|
|
|
ircd_gpt_norm_prop
|
|
(
|
|
ctrl, opts,
|
|
norm_bias,
|
|
norm_bias_m0,
|
|
norm_bias_m1,
|
|
norm_weight,
|
|
norm_weight_m0,
|
|
norm_weight_m1
|
|
);
|
|
|
|
for(uint j = 0; j < 4; ++j)
|
|
ircd_gpt_prop_elem
|
|
(
|
|
ctrl, opts,
|
|
fcon_bias->proj[j],
|
|
fcon_bias_m0->proj[j],
|
|
fcon_bias_m1->proj[j]
|
|
);
|
|
|
|
for(uint i = 0; i < 768; ++i)
|
|
for(uint j = 0; j < 4; ++j)
|
|
ircd_gpt_prop_elem
|
|
(
|
|
ctrl, opts,
|
|
fcon_weight[i].proj[j],
|
|
fcon_weight_m0[i].proj[j],
|
|
fcon_weight_m1[i].proj[j]
|
|
);
|
|
|
|
ircd_gpt_prop_elem
|
|
(
|
|
ctrl, opts,
|
|
proj_bias->word,
|
|
proj_bias_m0->word,
|
|
proj_bias_m1->word
|
|
);
|
|
|
|
for(uint i = 0; i < 3072; ++i)
|
|
ircd_gpt_prop_elem
|
|
(
|
|
ctrl, opts,
|
|
proj_weight[i].word,
|
|
proj_weight_m0[i].word,
|
|
proj_weight_m1[i].word
|
|
);
|
|
}
|
|
|
|
__kernel void
|
|
__attribute__((flatten))
|
|
ircd_gpt_lm_embed_prop(__global const struct ircd_gpt_ctrl *const ctrl,
|
|
__constant const struct ircd_gpt_opts *const opts,
|
|
__global union ircd_gpt_tokenv *const restrict pos,
|
|
__global union ircd_gpt_tokenv *const restrict pos_m0,
|
|
__global union ircd_gpt_tokenv *const restrict pos_m1,
|
|
__global union ircd_gpt_tokenv *const restrict token,
|
|
__global union ircd_gpt_tokenv *const restrict token_m0,
|
|
__global union ircd_gpt_tokenv *const restrict token_m1)
|
|
{
|
|
const uint
|
|
gi = get_global_id(0),
|
|
gn = get_global_size(0),
|
|
li = get_local_id(0),
|
|
ln = get_local_size(0),
|
|
wi = get_group_id(0),
|
|
wn = get_num_groups(0),
|
|
cn = opts->context_tokens / wn,
|
|
ci = cn * wi,
|
|
tn = opts->logits / wn,
|
|
ti = tn * wi;
|
|
|
|
for(uint i = ci; i < ci + cn; ++i)
|
|
ircd_gpt_prop_elem
|
|
(
|
|
ctrl, opts,
|
|
pos[i].word,
|
|
pos_m0[i].word,
|
|
pos_m1[i].word
|
|
);
|
|
|
|
for(uint i = ti; i < ti + tn; ++i)
|
|
ircd_gpt_prop_elem
|
|
(
|
|
ctrl, opts,
|
|
token[i].word,
|
|
token_m0[i].word,
|
|
token_m1[i].word
|
|
);
|
|
}
|