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import numpy as np
all_tasks = ["assoc_recall", "assoc_recall_mk", "binary_copy", "binary_encode", "binary_recall", "binary_recall_mix", "binary_recall_last",\
"binary_recall_rep", "median_last", "parity", "read_write", "sparse_parity", "threshold", "var_copy"]
def set_task_specific_parameters(args):
assert args.task_name in all_tasks, "Task not in list of all implemented tasks"
if args.task_name == "assoc_recall":
args.num_numbers = 0
args.vocab_size = args.num_vocab + 1
args.data_name = "data_%d_%d" % (args.sequence_len, args.num_vocab)
if args.task_name == "assoc_recall_mk":
print("Shifting the number of vocab to hits are as frequent")
args.size_query = 2
args.num_vocab = int(1 + args.num_vocab ** (1./args.size_query))
args.vocab_size = args.num_vocab + 1
args.data_name = "data_%d_%d" % (args.sequence_len, args.num_vocab)
print("Current query length:", args.size_query)
if args.task_name == "binary_copy":
args.vocab_size = 2 + args.num_vocab + 1
args.data_name = "data_%d_%d_%d" % (args.sequence_len, args.num_vocab)
print("Using %d bits" % args.num_bits)
if args.task_name == "binary_encode":
args.vocab_size = 2 ** args.num_bits + 1
args.data_name = "data_%d_%d" % (args.sequence_len, args.num_bits)
print("Using %d bits" % args.num_bits)
if args.task_name == "binary_recall":
args.vocab_size = 2 + 2 ** args.num_bits + 1
args.data_name = "data_%d_%d" % (args.sequence_len, args.num_bits)
print("Using %d bits" % args.num_bits)
if args.task_name == "binary_recall_mix":
args.vocab_size = 2 + 2 ** args.num_bits + 1
args.data_name = "data_%d_%d" % (args.sequence_len, args.num_bits)
print("Using %d bits" % args.num_bits)
if args.task_name == "binary_recall_last":
args.vocab_size = 2 + 2 ** args.num_bits + 1
args.data_name = "data_%d_%d.pt" % (args.sequence_len, args.num_bits)
print("Using %d bits" % args.num_bits)
if args.task_name == "binary_recall_rep":
args.vocab_size = 2 + 2 ** args.num_bits + 1
args.data_name = "data_%d_%d.pt" % (args.sequence_len, args.num_bits)
print("Using %d bits" % args.num_bits)
if args.task_name == "median_last":
assert args.num_numbers % 2 == 1
args.vocab_size = args.num_numbers + 1
args.data_name = "data_%d_%d" % (args.sequence_len, args.num_numbers)
if args.task_name == "parity":
args.vocab_size = 2 + 1
args.data_name = "data_%d" % (args.sequence_len,)
if args.task_name == "read_write":
args.num_numbers = 0
args.vocab_size = args.num_vocab + 3
args.data_name = "data_%d_%d" % (args.sequence_len, args.num_vocab)
if args.task_name == "sparse_parity":
args.vocab_size = 2 + 1
args.data_name = "data_%d" % (args.sequence_len,)
if args.task_name == "threshold":
args.vocab_size = 2 + args.num_numbers + args.num_vocab + 1
args.data_name = "data_%d_%d_%d" % (args.sequence_len, args.num_numbers, args.num_vocab)
if args.task_name == "var_copy":
args.min_num_token = 5
args.vocab_size = args.min_num_token + args.num_numbers + args.num_vocab + 1
args.data_name = "data_%d_%d_%d" % (args.sequence_len, args.num_numbers, args.num_vocab)
def generate_seq(args, ood=False):
if args.task_name == "assoc_recall":
seq_in = torch.randint(0, args.num_vocab, (args.sequence_len,))
seq_out = torch.zeros(args.sequence_len, dtype=torch.int32) - 1 # All empty tokens
lookup = {}
for i in range(1, args.sequence_len):
lookup[seq_in[i-1].item()] = seq_in[i].item()
if seq_in[i].item() in lookup.keys():
seq_out[i] = lookup[seq_in[i].item()]
if args.task_name == "assoc_recall_mk":
seq_in = torch.randint(0, args.num_vocab, (args.sequence_len,))
seq_out = torch.zeros(args.sequence_len, dtype=torch.int32) - 1 # All empty tokens
lookup = {}
for i in range(args.size_query, args.sequence_len):
key = tuple(seq_in[i-args.size_query:i].tolist())
lookup[key] = seq_in[i].item()
key = tuple(seq_in[i-args.size_query+1:i+1].tolist())
if key in lookup.keys():
seq_out[i] = lookup[key]
if args.task_name == "binary_copy":
seq_in = torch.randint(2, 2 + args.num_vocab, (args.sequence_len,))
seq_in[torch.multinomial(torch.tensor([1-args.p, args.p]), args.sequence_len, replacement=True).to(torch.bool)] = 0
seq_in[torch.multinomial(torch.tensor([1-args.p, args.p]), args.sequence_len, replacement=True).to(torch.bool)] = 1
seq_out = torch.zeros(args.sequence_len, dtype=torch.int32) - 1
s = 0
for i in range(args.sequence_len):
if seq_in[i] in [0, 1]:
s = (2*s + seq_in[i]) % (2 ** args.num_bits)
if i-s >= 0:
seq_out[i] = seq_in[i-s]
if args.task_name == "binary_encode":
seq_in = torch.randint(0, 2, (args.sequence_len,))
seq_out = torch.zeros(args.sequence_len, dtype=torch.int32)
s = 0
for i in range(args.sequence_len):
s = (2*s + seq_in[i]) % (2 ** args.num_bits)
seq_out[i] = s
if args.task_name == "binary_recall":
seq_in = torch.randint(2, 2 + 2 ** args.num_bits, (args.sequence_len,))
seq_in[torch.multinomial(torch.tensor([1-args.p, args.p]), args.sequence_len, replacement=True).to(torch.bool)] = 0
seq_in[torch.multinomial(torch.tensor([1-args.p, args.p]), args.sequence_len, replacement=True).to(torch.bool)] = 1
seq_out = torch.zeros(args.sequence_len, dtype=torch.int32) -1
assoc = {i: -1 for i in range(2, 2 + 2**args.num_bits)}
s = 0
for i in range(args.length):
if seq_in[i] in [0, 1]:
s = (2*s + seq_in[i].item()) % (2**args.num_bits)
if i >= 1 and 2 <= seq_in[i-1] and seq_in[i-1] < 2 + 2**args.num_bits: # Is a vocab token
assoc[seq_in[i-1].item()] = seq_in[i]
seq_out[i] = assoc[s+2]
if args.task_name == "binary_recall_mix":
target = torch.randint(0, 2 ** args.num_bits, (1,)).item()
seq_in = torch.randint(2, 2 + (2 ** args.num_bits), (args.sequence_len,)) # Only 32 possible tokens, can be set to more
temp = target
bit_positions = np.random.randint(0, 2 ** args.num_bits - args.num_bits // 2, args.num_bits // 2).tolist()
bit_positions += list(range(args.sequence_len-1, args.sequence_len-1-args.num_bits // 2, -1))
bit_positions.sort()
for i in bit_positions:
seq_in[i] = temp % 2
temp = temp // 2
assoc = {i: 0 for i in range(2 + 2 ** args.num_bits)}
for i in range(1,args.sequence_len-args.num_bits):
assoc[seq_in[i-1].item()] = seq_in[i]
seq_out = torch.zeros(args.sequence_len, dtype=torch.int32) -1
seq_out[-1] = assoc[2 + target]
if args.task_name == "binary_recall_last":
target = torch.randint(0, 2 ** args.num_bits, (1,)).item()
seq_in = torch.randint(2, 2 + (2 ** args.num_bits), (args.sequence_len,)) # Only 32 possible tokens, can be set to more
temp = target
for i in range(args.sequence_len-1, args.sequence_len-1-args.num_bits, -1):
seq_in[i] = temp % 2
temp = temp // 2
assoc = {i: 0 for i in range(2 + 2 ** args.num_bits)}
for i in range(1,args.sequence_len-args.num_bits):
assoc[seq_in[i-1].item()] = seq_in[i]
seq_out = torch.zeros(args.sequence_len, dtype=torch.int32) -1
seq_out[-1] = assoc[2 + target]
if args.task_name == "binary_recall_rep":
target = torch.randint(0, 2 ** args.num_bits, (1,)).item()
repeat_length = torch.randint(4, 10, (1,)).item()
seq_in = torch.randint(2, 2 + (2 ** args.num_bits), (repeat_length,)) # Only 32 possible tokens, can be set to more
seq_in = seq_in.repeat(int(args.sequence_len / repeat_length) + 1)[:args.sequence_len]
temp = target
for i in range(args.sequence_len-1, args.sequence_len-1-args.num_bits, -1):
seq_in[i] = temp % 2
temp = temp // 2
assoc = {i: 0 for i in range(2 + 2 ** args.num_bits)}
for i in range(1,args.sequence_len-args.num_bits):
assoc[seq_in[i-1].item()] = seq_in[i]
seq_out = torch.zeros(args.sequence_len, dtype=torch.int32) -1
seq_out[-1] = assoc[2 + target]
if args.task_name == "median_last":
seq_in = torch.randint(0, args.num_numbers, (args.sequence_len,))
seq_in[-1] = 1 # Marks that the models should be outputting a median
seq_out = torch.zeros(args.sequence_len, dtype=torch.int32) -1
seq_out[-1] = torch.median(seq_in[-args.num_numbers-1:-2])
if args.task_name == "parity":
seq_in = torch.multinomial(torch.Tensor([0.5, 0.5]), args.sequence_len, replacement=True)
seq_in[0] = 0 # Distribution choice to make code simpler
seq_out = torch.zeros(args.sequence_len, dtype=torch.int32)
for i in range(1, args.sequence_len):
seq_out[i] = (seq_out[i-1] + seq_in[i]) % 2
if args.task_name == "read_write":
# -3 is the read token
# -2 is the write token
# -1 is the ignore token
seq_in = torch.randint(0, args.num_vocab, (args.sequence_len+1,))
seq_in[::2] = torch.multinomial(torch.Tensor([0.1, 0.1, 0.8]), args.sequence_len // 2 + 1, replacement=True).to(torch.int32) - 3
lookup = {}
for i in range(0, args.sequence_len, 2):
if seq_in[i].item() == -2: # Write token
lookup[i] = seq_in[i+1].item()
elif seq_in[i].item() == -3: # Read token
if i in lookup.keys():
seq_in[i+1] = lookup[i]
# else leave it as is (random token)
seq_out = seq_in[1:] # Shifted by one
seq_in = seq_in[:-1]
if args.task_name == "sparse_parity":
seq_in = torch.multinomial(torch.Tensor([1-args.p, args.p]), args.sequence_len, replacement=True)
seq_in[0] = 0 # Distribution choice to make code simpler
seq_out = torch.zeros(args.sequence_len, dtype=torch.int32)
for i in range(1, args.sequence_len):
seq_out[i] = (seq_out[i-1] + seq_in[i]) % 2
if args.task_name == "threshold":
threshold = args.num_numbers / args.p # this just seems to work, nothing more special than that
seq_in = torch.zeros(args.sequence_len, dtype=torch.int32)
seq_out = torch.zeros(args.sequence_len, dtype=torch.int32) -1
lookup = {}
s = 0
for i in range(args.length):
if torch.rand((1,)) > args.p:
# Add a random vocab token
seq_in[i] = torch.randint(args.num_numbers+2, args.num_numbers+args.num_vocab+2, (1,))
else:
# Add a random number token
num = torch.randint(0, args.num_numbers, (1,))
seq_in[i] = num + 2
s += num # ... which is added to the sum
if s < threshold:
seq_out[i] = 0
else:
seq_out[i] = 1
if args.task_name == "var_copy":
vocab = torch.randint(args.min_num_token+args.num_numbers, args.min_num_token+args.num_numbers+args.num_vocab, (args.sequence_len,), dtype=torch.int32)
numbers = torch.randint(args.min_num_token, args.min_num_token+args.num_numbers, (args.sequence_len,), dtype=torch.int32)
mask = torch.rand_like(vocab, dtype=torch.float) < args.p
seq_in = torch.where(mask, numbers, vocab)
seq_out = torch.zeros(args.sequence_len, dtype=torch.int32) -1
number_seq = [(i, v) for i, v in enumerate(seq_in) if args.min_num_token <= v and v < args.min_num_token+args.num_numbers]
for i in range(len(number_seq)-1):
this_i = number_seq[i][0]
this_v = number_seq[i][1]
next_i = number_seq[i+1][0]
next_v = number_seq[i+1][1]
if next_i-this_v > 0:
if this_i-this_v >= 0:
seq_out[this_i:next_i] = seq_in[this_i-this_v:next_i-this_v]
else:
seq_out[this_v:next_i] = seq_in[:next_i-this_v]
if len(number_seq) > 0:
this_i = number_seq[-1][0]
this_v = number_seq[-1][1]
if this_i-this_v >= 0:
seq_out[this_i:] = seq_in[this_i-this_v:-this_v]
else:
seq_out[this_v:] = seq_in[:-this_v]
return seq_in, seq_out
def generate_data(args, all_at_once=True):
sequences_in = []
sequences_out = []
if all_at_once:
print("Generating Data")
for i in range(args.batch_size * args.batches_per_epoch):
if i % 8000 == 0:
print("- Sequence Num:", i)
seq_in, seq_out = generate_seq(args)
sequences_in.append(seq_in)
sequences_out.append(seq_out)
else:
for i in range(args.batch_size):
seq_in, seq_out = generate_seq(args)
sequences_in.append(seq_in)
sequences_out.append(seq_out)
x_in = torch.stack(sequences_in) # (batch_size, max_len)
x_out = torch.stack(sequences_out) # (batch_size, max_len)
# Shift -1
x_in = (x_in + args.vocab_size) % args.vocab_size
x_out = (x_out + args.vocab_size) % args.vocab_size
return x_in, x_out |