--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/tools/ubot-plugins/ubot-mingpt-plugin/src/main.rs Sat Jun 12 20:58:07 2021 +0200
@@ -0,0 +1,313 @@
+/* This example uses the tinyshakespeare dataset which can be downloaded at:
+ https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt
+
+ This is mostly a rust port of https://github.com/karpathy/minGPT
+*/
+
+extern crate tch;
+use anyhow::{bail, Result as AHResult};
+use std::{io, io::Write};
+use tch::data::TextData;
+use tch::nn::{ModuleT, OptimizerConfig};
+use tch::{nn, Device, IndexOp, Kind, Tensor};
+
+use futures::prelude::*;
+use lapin::{options::*, types::FieldTable, BasicProperties, Connection, ConnectionProperties};
+
+use tokio_amqp::*;
+
+const LEARNING_RATE: f64 = 0.0003;
+const BLOCK_SIZE: i64 = 128;
+const BATCH_SIZE: i64 = 64;
+const EPOCHS: i64 = 100;
+const SAMPLING_LEN: i64 = 512;
+
+#[derive(Debug, Copy, Clone)]
+struct Config {
+ vocab_size: i64,
+ n_embd: i64,
+ n_head: i64,
+ n_layer: i64,
+ block_size: i64,
+ attn_pdrop: f64,
+ resid_pdrop: f64,
+ embd_pdrop: f64,
+}
+
+// Weight decay only applies to the weight matrixes in the linear layers
+const NO_WEIGHT_DECAY_GROUP: usize = 0;
+const WEIGHT_DECAY_GROUP: usize = 1;
+
+// Custom linear layer so that different groups can be used for weight
+// and biases.
+#[derive(Debug)]
+struct Linear {
+ pub ws: Tensor,
+ pub bs: Tensor,
+}
+
+impl nn::Module for Linear {
+ fn forward(&self, xs: &Tensor) -> Tensor {
+ xs.matmul(&self.ws.tr()) + &self.bs
+ }
+}
+
+fn linear(vs: nn::Path, in_dim: i64, out_dim: i64) -> Linear {
+ let wd = vs.set_group(WEIGHT_DECAY_GROUP);
+ let no_wd = vs.set_group(NO_WEIGHT_DECAY_GROUP);
+ Linear {
+ ws: wd.randn("weight", &[out_dim, in_dim], 0.0, 0.02),
+ bs: no_wd.zeros("bias", &[out_dim]),
+ }
+}
+
+fn linear_no_bias(vs: nn::Path, in_dim: i64, out_dim: i64) -> Linear {
+ let wd = vs.set_group(WEIGHT_DECAY_GROUP);
+ let no_wd = vs.set_group(NO_WEIGHT_DECAY_GROUP);
+ Linear {
+ ws: wd.randn("weight", &[out_dim, in_dim], 0.0, 0.02),
+ bs: no_wd.zeros_no_train("bias", &[out_dim]),
+ }
+}
+
+fn causal_self_attention(p: &nn::Path, cfg: Config) -> impl ModuleT {
+ let key = linear(p / "key", cfg.n_embd, cfg.n_embd);
+ let query = linear(p / "query", cfg.n_embd, cfg.n_embd);
+ let value = linear(p / "value", cfg.n_embd, cfg.n_embd);
+ let proj = linear(p / "proj", cfg.n_embd, cfg.n_embd);
+ let mask_init =
+ Tensor::ones(&[cfg.block_size, cfg.block_size], (Kind::Float, p.device())).tril(0);
+ let mask_init = mask_init.view([1, 1, cfg.block_size, cfg.block_size]);
+ // let mask = p.var_copy("mask", &mask_init);
+ let mask = mask_init;
+ nn::func_t(move |xs, train| {
+ let (sz_b, sz_t, sz_c) = xs.size3().unwrap();
+ let sizes = [sz_b, sz_t, cfg.n_head, sz_c / cfg.n_head];
+ let k = xs.apply(&key).view(sizes).transpose(1, 2);
+ let q = xs.apply(&query).view(sizes).transpose(1, 2);
+ let v = xs.apply(&value).view(sizes).transpose(1, 2);
+ let att = q.matmul(&k.transpose(-2, -1)) * (1.0 / f64::sqrt(sizes[3] as f64));
+ let att = att.masked_fill(
+ &mask.i((.., .., ..sz_t, ..sz_t)).eq(0.),
+ std::f64::NEG_INFINITY,
+ );
+ let att = att.softmax(-1, Kind::Float).dropout(cfg.attn_pdrop, train);
+ let ys = att
+ .matmul(&v)
+ .transpose(1, 2)
+ .contiguous()
+ .view([sz_b, sz_t, sz_c]);
+ ys.apply(&proj).dropout(cfg.resid_pdrop, train)
+ })
+}
+
+fn block(p: &nn::Path, cfg: Config) -> impl ModuleT {
+ let ln1 = nn::layer_norm(p / "ln1", vec![cfg.n_embd], Default::default());
+ let ln2 = nn::layer_norm(p / "ln2", vec![cfg.n_embd], Default::default());
+ let attn = causal_self_attention(p, cfg);
+ let lin1 = linear(p / "lin1", cfg.n_embd, 4 * cfg.n_embd);
+ let lin2 = linear(p / "lin2", 4 * cfg.n_embd, cfg.n_embd);
+ nn::func_t(move |xs, train| {
+ let xs = xs + xs.apply(&ln1).apply_t(&attn, train);
+ let ys = xs
+ .apply(&ln2)
+ .apply(&lin1)
+ .gelu()
+ .apply(&lin2)
+ .dropout(cfg.resid_pdrop, train);
+ xs + ys
+ })
+}
+
+fn gpt(p: &nn::Path, cfg: Config) -> impl ModuleT {
+ let p = &p.set_group(NO_WEIGHT_DECAY_GROUP);
+ let tok_emb = nn::embedding(
+ p / "tok_emb",
+ cfg.vocab_size,
+ cfg.n_embd,
+ Default::default(),
+ );
+ let pos_emb = p.zeros("pos_emb", &[1, cfg.block_size, cfg.n_embd]);
+ let ln_f = nn::layer_norm(p / "ln_f", vec![cfg.n_embd], Default::default());
+ let head = linear_no_bias(p / "head", cfg.n_embd, cfg.vocab_size);
+ let mut blocks = nn::seq_t();
+ for block_idx in 0..cfg.n_layer {
+ blocks = blocks.add(block(&(p / block_idx), cfg));
+ }
+ nn::func_t(move |xs, train| {
+ let (_sz_b, sz_t) = xs.size2().unwrap();
+ let tok_emb = xs.apply(&tok_emb);
+ let pos_emb = pos_emb.i((.., ..sz_t, ..));
+ (tok_emb + pos_emb)
+ .dropout(cfg.embd_pdrop, train)
+ .apply_t(&blocks, train)
+ .apply(&ln_f)
+ .apply(&head)
+ })
+}
+
+/// Generates some sample string using the GPT model.
+fn sample(data: &TextData, gpt: &impl ModuleT, input: Tensor) -> String {
+ let mut input = input;
+ let mut result = String::new();
+ for _index in 0..SAMPLING_LEN {
+ let logits = input.apply_t(gpt, false).i((0, -1, ..));
+ let sampled_y = logits.softmax(-1, Kind::Float).multinomial(1, true);
+ let last_label = i64::from(&sampled_y);
+ result.push(data.label_to_char(last_label));
+ input = Tensor::cat(&[input, sampled_y.view([1, 1])], 1).narrow(1, 1, BLOCK_SIZE);
+ }
+ result
+}
+
+#[tokio::main]
+async fn main() -> AHResult<()> {
+ let device = Device::cuda_if_available();
+ let mut vs = nn::VarStore::new(device);
+ let data = TextData::new("10.log")?;
+ let labels = data.labels();
+ println!("Dataset loaded, {} labels.", labels);
+ let cfg = Config {
+ vocab_size: labels,
+ n_embd: 384, // was 512
+ n_head: 8,
+ n_layer: 8,
+ block_size: BLOCK_SIZE,
+ attn_pdrop: 0.1,
+ resid_pdrop: 0.1,
+ embd_pdrop: 0.1,
+ };
+ let gpt = gpt(&(&vs.root() / "gpt"), cfg);
+ let args: Vec<_> = std::env::args().collect();
+ if args.len() < 2 {
+ bail!("usage: main (train|predict weights.ot seqstart)")
+ }
+ match args[1].as_str() {
+ "train" => {
+ let mut opt = nn::AdamW::default().build(&vs, LEARNING_RATE)?;
+ opt.set_weight_decay_group(NO_WEIGHT_DECAY_GROUP, 0.0);
+ opt.set_weight_decay_group(WEIGHT_DECAY_GROUP, 0.1);
+ let mut idx = 0;
+ vs.load("384.ot")?;
+ for epoch in 1..(1 + EPOCHS) {
+ let mut sum_loss = 0.;
+ let mut cnt_loss = 0.;
+ for batch in data.iter_shuffle(BLOCK_SIZE + 1, BATCH_SIZE) {
+ let xs = batch
+ .narrow(1, 0, BLOCK_SIZE)
+ .to_kind(Kind::Int64)
+ .to_device(device);
+ let ys = batch
+ .narrow(1, 1, BLOCK_SIZE)
+ .to_kind(Kind::Int64)
+ .to_device(device);
+ let logits = xs.apply_t(&gpt, true);
+ let loss = logits
+ .view([BATCH_SIZE * BLOCK_SIZE, labels])
+ .cross_entropy_for_logits(&ys.view([BATCH_SIZE * BLOCK_SIZE]));
+ opt.backward_step_clip(&loss, 0.5);
+ sum_loss += f64::from(loss);
+ cnt_loss += 1.0;
+ idx += 1;
+ if idx % 10 == 0 {
+ print!("{}", '.');
+ io::stdout().flush()?;
+ }
+ if idx % 1000 == 0 {
+ println!("Epoch: {} loss: {:5.3}", epoch, sum_loss / cnt_loss);
+ let input = Tensor::zeros(&[1, BLOCK_SIZE], (Kind::Int64, device));
+ println!("Sample: {}", sample(&data, &gpt, input));
+ if let Err(err) = vs.save(format!("gpt{:08}.ot", idx)) {
+ println!("error while saving {}", err);
+ }
+ sum_loss = 0.;
+ cnt_loss = 0.;
+ }
+ }
+ }
+ }
+ "predict" => {
+ let amqp_url = std::env::var("AMQP_URL").expect("expected AMQP_URL env variabe");
+ let conn = Connection::connect(&amqp_url, ConnectionProperties::default().with_tokio())
+ .await?;
+
+ let pub_channel = conn.create_channel().await?;
+ let sub_channel = conn.create_channel().await?;
+
+ let queue = sub_channel
+ .queue_declare(
+ &"",
+ QueueDeclareOptions {
+ exclusive: true,
+ auto_delete: true,
+ ..QueueDeclareOptions::default()
+ },
+ FieldTable::default(),
+ )
+ .await?;
+
+ sub_channel
+ .queue_bind(
+ queue.name().as_str(),
+ "irc",
+ "cmd.say.hedgewars",
+ QueueBindOptions::default(),
+ FieldTable::default(),
+ )
+ .await?;
+
+ let mut subscriber = sub_channel
+ .basic_consume(
+ queue.name().as_str(),
+ &"",
+ BasicConsumeOptions::default(),
+ FieldTable::default(),
+ )
+ .await?;
+
+ vs.load(args[2].as_str())?;
+
+ while let Some(amqp_message) = subscriber.next().await {
+ let (_, delivery) = amqp_message.expect("error in consumer");
+ delivery.ack(BasicAckOptions::default()).await?;
+
+ let chat_message = String::from_utf8(delivery.data)?;
+ if let Some((_who, seed)) = chat_message.split_once('\n') {
+ let input_sample = &format!("\n{}", seed);
+ let input = Tensor::zeros(&[1, BLOCK_SIZE], (Kind::Int64, device));
+ for (idx, c) in input_sample.chars().rev().enumerate() {
+ let idx = idx as i64;
+ if idx >= BLOCK_SIZE {
+ break;
+ }
+ let _filled = input
+ .i((0, BLOCK_SIZE - 1 - idx))
+ .fill_(data.char_to_label(c).unwrap_or(0) as i64);
+ }
+
+ let proceeded_message = &sample(&data, &gpt, input);
+ let final_message = proceeded_message
+ .split_once('\n')
+ .map(|(m, _)| m)
+ .unwrap_or(proceeded_message);
+ let final_message = &format!("{}{}", seed, final_message);
+
+ println!("{} --> {}", seed, proceeded_message);
+
+ pub_channel
+ .basic_publish(
+ "irc",
+ "say.hedgewars",
+ BasicPublishOptions::default(),
+ final_message.as_bytes().to_vec(),
+ BasicProperties::default(),
+ )
+ .await?;
+ }
+ }
+ }
+ _ => bail!("usage: main (train|predict weights.ot)"),
+ };
+
+ Ok(())
+}