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import numpy as np
import math
from collections import defaultdict
task_choices = ["var-copy", "var-copy-rep", "decode-recall", "decode-recall-last", "assoc-recall", "assoc-recall-mk", "needle"]
def force_args(args):
if args.train_task in ["var-copy", "var-copy-rep"]:
pass
if args.train_task in ["decode-recall", "decode-recall-last"]:
args.num_numbers = 2
args.num_vocab = int(2 ** math.floor(math.log(args.num_vocab) / math.log(2)))
if args.train_task == "assoc-recall":
args.num_numbers = 0
if args.train_task == "assoc-recall-mk":
args.num_numbers = 0
# args.num_vocab = 1 + int(args.num_vocab ** (1./size_key))
if args.train_task == "needle":
args.num_numbers = 2
def generate_seq(tokenizer, length, task, p=0.2, mixed=False):
num_vocab = tokenizer.num_vocab
num_numbers = tokenizer.num_numbers
if task == "var-copy":
# Start with num_numbers vocab tokens
if mixed:
if np.random.rand() < 0.5:
# Hard for the TF
input_seq = rand_seq(tokenizer, length, num_vocab, num_numbers, p_numbers=0.03)
else:
# Hard for the SSM
input_seq = rand_seq(tokenizer, length, num_vocab, num_numbers, p_numbers=0.2)
input_seq[-1] = np.random.choice(tokenizer.number_tokens)
else:
input_seq = rand_seq(tokenizer, length, num_vocab, num_numbers, p_numbers=p)
# input_seq = rand_seq(tokenizer, length, num_vocab, num_numbers)
nums = [(i, int(c[1:])) for (i, c) in enumerate(input_seq) if c in tokenizer.number_tokens]
# The real task, if not degenerate
if len(nums) > 0:
output_seq = ["<null>"] * nums[0][0]
for i in range(len(nums)-1):
if nums[i][0]-nums[i][1] < 0:
if nums[i+1][0]-nums[i][1] < 0:
output_seq += ["<null>"] * (nums[i+1][0]-nums[i][0])
else:
output_seq += ["<null>"] * (nums[i][1]-nums[i][0])
output_seq += input_seq[:nums[i+1][0]-nums[i][1]]
else:
output_seq += input_seq[nums[i][0]-nums[i][1]:nums[i+1][0]-nums[i][1]]
if nums[-1][0]-nums[-1][1] < 0:
output_seq += ["<null>"] * (nums[-1][1]-nums[-1][0])
output_seq += input_seq[:-nums[-1][1]]
else:
output_seq += input_seq[nums[-1][0]-nums[-1][1]:-nums[-1][1]]
else:
output_seq = ["<null>"] * length
# output_seq = ["<bos>"] + output_seq[:length] + ["<eos>"]
elif task == "var-copy-rep":
# Start with num_numbers vocab tokens
# input_seq = rand_seq(tokenizer, length, num_vocab, num_numbers, p_numbers=p)
input_seq = rand_seq_special(tokenizer, length, num_vocab, num_numbers, p_numbers=p, special_type="repetitive_vocab")
nums = [(i, int(c[1:])) for (i, c) in enumerate(input_seq) if c in tokenizer.number_tokens]
# The real task, if not degenerate
if len(nums) > 0:
output_seq = ["<null>"] * nums[0][0]
for i in range(len(nums)-1):
if nums[i][0]-nums[i][1] < 0:
if nums[i+1][0]-nums[i][1] < 0:
output_seq += ["<null>"] * (nums[i+1][0]-nums[i][0])
else:
output_seq += ["<null>"] * (nums[i][1]-nums[i][0])
output_seq += input_seq[:nums[i+1][0]-nums[i][1]]
else:
output_seq += input_seq[nums[i][0]-nums[i][1]:nums[i+1][0]-nums[i][1]]
if nums[-1][0]-nums[-1][1] < 0:
output_seq += ["<null>"] * (nums[-1][1]-nums[-1][0])
output_seq += input_seq[:-nums[-1][1]]
else:
output_seq += input_seq[nums[-1][0]-nums[-1][1]:-nums[-1][1]]
else:
output_seq = ["<null>"] * length
input_seq = ["<bos>"] + input_seq + ["<eos>"]
output_seq = ["<bos>"] + output_seq + ["<eos>"]
# output_seq = ["<bos>"] + output_seq[:length] + ["<eos>"]
elif task == "decode-recall":
input_seq = rand_seq(tokenizer, length, num_vocab, num_numbers, p_numbers=p)
output_seq = [None for _ in range(len(input_seq))]
assoc = {v: "<null>" for v in tokenizer.vocab}
s = 0
for i in range(len(output_seq)):
if i != 0:
assoc[input_seq[i-1]] = input_seq[i]
if input_seq[i][0] == '#':
# s = (2 * s + int(input_seq[i][1:])) % num_numbers
s = (2 * s + int(input_seq[i][1:])) % num_vocab
# if i-s < 0:
# output_seq[i] = "<null>"
# else:
# output_seq[i] = input_seq[i-s]
output_seq[i] = assoc["V%d" % s]
elif task == "decode-recall-last":
input_seq = rand_seq(tokenizer, length, num_vocab, num_numbers, p_numbers=0)
output_seq = ["<null>" for _ in range(len(input_seq))]
n_bits = int(math.log(num_vocab)/math.log(2))
target = np.random.randint(0, num_vocab)
temp = target
for i in range(length-1, length-1-n_bits, -1):
input_seq[i] = "#%d" % (temp % 2)
temp = temp // 2
try:
i = length-2-n_bits - input_seq[-2-n_bits::-1].index("V%d" % target)
output_seq[-1] = input_seq[i+1]
except ValueError:
pass
elif task == "assoc-recall":
input_seq = rand_seq(tokenizer, length, num_vocab, num_numbers, p_numbers=0.2)
output_seq = [None for _ in range(len(input_seq))]
assoc = {v: "<null>" for v in tokenizer.vocab}
for i in range(len(output_seq)):
if i != 0:
assoc[input_seq[i-1]] = input_seq[i]
output_seq[i] = assoc[input_seq[i]]
elif task == "assoc-recall-mk":
size_key = 2
input_seq = rand_seq(tokenizer, length, num_vocab, 0, p_numbers=0.0)
output_seq = ["<null>" for _ in range(len(input_seq))]
assoc = defaultdict(lambda: "<null>")
for i in range(len(output_seq)):
if i > size_key:
key = tuple(input_seq[i-size_key:i])
assoc[key] = input_seq[i]
if i+1 > size_key:
key = tuple(input_seq[i-size_key+1:i+1])
output_seq[i] = assoc[key]
elif task == "needle":
needle_length = 1
input_seq = rand_seq(tokenizer, length, num_vocab, num_numbers, p_numbers=0)
input_seq[-needle_length:] = ["#1" for _ in range(needle_length)]
# Needle position
needle_pos = np.random.randint(0, length // 2)
input_seq[needle_pos] = "#0"
needle = input_seq[needle_pos + 1:needle_pos + needle_length + 1]
# Ask for the needle at the end
output_seq = ["<null>" for _ in range(len(input_seq))]
output_seq[needle_pos:] = [needle[-1] for i in range(len(input_seq) - needle_pos)]
# output_seq[-needle_length:] = needle
else:
print("Task name:", task)
assert False # Not implemented
return input_seq, output_seq
################################################################################################
# SEQUENCE GENERATION HELPERS
def rand_seq(tokenizer, length, num_vocab, num_numbers, p_numbers=-1):
if p_numbers == -1:
p_numbers = num_numbers / (num_vocab + num_numbers)
if num_numbers != 0:
props = {"V": (1-p_numbers)/num_vocab, "#": p_numbers/num_numbers, "<": 0}
else:
props = {"V": 1/num_vocab, "#": 0, "<": 0}
props = np.array([props[i[0]] for i in tokenizer.vocab])
return np.random.choice(tokenizer.vocab, size=length, p=props).tolist()
# For other special generations
def rand_seq_special(tokenizer, length, num_vocab, num_numbers, p_numbers=-1, special_type=None):
if special_type == "repetitive_vocab":
if p_numbers == -1:
p_numbers = num_numbers / (num_vocab + num_numbers)
if num_numbers != 0:
props = {"V": 0, "#": p_numbers/num_numbers, "<": 0}
else:
props = {"V": 0, "#": 0, "<": 0}
if num_numbers != 0:
props_V0 = (1-p_numbers)
else:
props_V0 = 1
props = np.array([props[i[0]] if i != "V0" else props_V0 for i in tokenizer.vocab])
tile_length = 3
props_tile = {"V": 1./num_vocab, "#": 0, "<": 0}
props_tile = np.array([props_tile[i[0]] for i in tokenizer.vocab])
ret_seq = np.random.choice(tokenizer.vocab, size=length, p=props)
ret_seq2 = np.tile(np.random.choice(tokenizer.vocab, size=tile_length, p=props_tile), (length // tile_length + 1))[:length]
return np.where(ret_seq == "V0", ret_seq2, ret_seq).tolist()
else:
assert False, "Not implemented" |