Add training script for ICL experiment.

Add an example training script to reproduce the in-context learning experiment from the paper (see issue #4).

An important detail is to set `--unembed_mask 0` (otherwise the model will be prevented from predicting the `unk`, which is used for this task). You may need to run the script with multiple seeds (e.g. 10) to get an initialization that learns to solve the task.

scripts/induction.sh

@@ -0,0 +1,42 @@
+#!/bin/bash 
+
+VOCAB_SIZE=10
+MIN_LENGTH=9
+MAX_LENGTH=9
+SEED=6
+
+echo "SEED=${SEED}";
+
+python src/run.py \
+     --dataset "induction" \
+     --vocab_size "${VOCAB_SIZE}" \
+     --dataset_size 20000 \
+     --min_length "${MIN_LENGTH}" \
+     --max_length "${MAX_LENGTH}" \
+     --n_epochs 500 \
+     --batch_size 512 \
+     --lr "5e-2" \
+     --gumbel_samples 1 \
+     --sample_fn "gumbel_soft" \
+     --tau_init 3.0 \
+     --tau_end 0.01 \
+     --tau_schedule "geomspace" \
+     --n_vars_cat 1 \
+     --n_vars_num 1 \
+     --n_layers 2 \
+     --n_heads_cat 1 \
+     --n_heads_num 0 \
+     --n_cat_mlps 0 \
+     --n_num_mlps 0 \
+     --attention_type "cat" \
+     --rel_pos_bias "fixed" \
+     --one_hot_embed \
+     --count_only \
+     --selector_width 0 \
+     --seed "${SEED}" \
+     --unique 1 \
+     --unembed_mask 0 \
+     --autoregressive \
+     --save \
+     --save_code \
+     --output_dir "output/induction/s${SEED}";