def encode_huffman(model,
enc,
message,
context,
bits_per_word,
finish_sent=False,
device='cpu'):
length = len(message)
context = torch.tensor(context[-1022:], device=device, dtype=torch.long)
prev = context
output = context
past = None
total_num = 0
total_num_for_stats = 0
total_log_probs = 0
total_kl = 0 # in bits
total_num_sents = 0
with torch.no_grad():
i = 0
sent_finish = False
while i < length or (finish_sent and not sent_finish):
logits, past = model(prev.unsqueeze(0), past=past)
past = limit_past(past)
logits[0, -1, -1] = -1e10 # endoftext can't happen
logits[0, -1, 628] = -1e10 # 2 newlines can't happen
logits, indices = logits[0, -1, :].sort(descending=True)
# Get the top 2**bits options
indices = indices[:2**bits_per_word]
log_probs = F.log_softmax(logits, dim=-1)[:2**bits_per_word]
probs = torch.exp(log_probs)
if i >= length:
selection = 0
sent_finish = is_sent_finish(indices[0].item(), enc)
else:
probs_array = probs.cpu().numpy()
coding = HuffmanCoding()
coding.make_heap_from_array(probs_array)
coding.merge_nodes()
root = coding.make_codes()
#print(message[i:i+10])
while root.token is None:
if i >= length or message[i] == 0:
root = root.left
else:
root = root.right
i += 1
selection = root.token
logq = torch.tensor([
-len(coding.codes[idx]) for idx in range(len(probs_array))
],
dtype=torch.float,
device=device) # in bits
logq = logq * 0.69315 # in nats
q = torch.exp(logq)
total_kl += kl(q, logq, log_probs)
total_log_probs += log_probs[selection].item()
total_num_for_stats += 1
total_num += 1
prev = indices[selection].view(1)
output = torch.cat((output, prev))
avg_NLL = -total_log_probs / total_num_for_stats
avg_KL = total_kl / total_num_for_stats
words_per_bit = total_num_for_stats / i
return output[len(context):].tolist(), avg_NLL, avg_KL, words_per_bit
def encode_arithmetic(model, enc, message, context, finish_sent=False, device='cuda', temp=1.0, precision=16, topk=50000):
context = torch.tensor(context[-1022:], device=device, dtype=torch.long)
max_val = 2**precision
threshold = 2**(-precision)
cur_interval = [0, max_val] # bottom inclusive, top exclusive
prev = context
output = context
past = None
total_num = 0
total_num_for_stats = 0
total_log_probs = 0
total_kl = 0 # in bits
total_entropy_ptau = 0
total_num_sents = 0
with torch.no_grad():
i = 0
sent_finish = False
while i < len(message) or (finish_sent and not sent_finish):
logits, past = model(prev.unsqueeze(0), past=past)
past = limit_past(past)
logits[0, -1, -1] = -1e20 # endoftext token can't happen
logits[0, -1, 628] = -1e20 # 2 newlines token can't happen
logits, indices = logits[0, -1, :].sort(descending=True)
logits = logits.double()
logits_temp = logits / temp
probs_temp = F.softmax(logits_temp, dim=0)
log_probs_temp = F.log_softmax(logits_temp, dim=0)
log_probs = F.log_softmax(logits, dim=0)
# conditions for having reached the end of the message
if i >= len(message):
selection = 0
sent_finish = is_sent_finish(indices[selection].item(), enc)
else:
# Cutoff low probabilities that would be rounded to 0
cur_int_range = cur_interval[1]-cur_interval[0]
cur_threshold = 1/cur_int_range
k = min(max(2, (probs_temp < cur_threshold).nonzero()[0].item()), topk)
probs_temp_int = probs_temp[:k] # Cutoff all but top k
# Rescale to correct range
probs_temp_int = probs_temp_int/probs_temp_int.sum()*cur_int_range
# Round probabilities to integers given precision
probs_temp_int = probs_temp_int.round().long()
cum_probs = probs_temp_int.cumsum(0)
# Remove any elements from the bottom if rounding caused the total prob to be too large
overfill_index = (cum_probs > cur_int_range).nonzero()
if len(overfill_index) > 0:
cum_probs = cum_probs[:overfill_index[0]]
# Add any mass to the top if removing/rounding causes the total prob to be too small
cum_probs += cur_int_range-cum_probs[-1] # add
# Get out resulting probabilities
probs_final = cum_probs.clone()
probs_final[1:] = cum_probs[1:] - cum_probs[:-1]
# Convert to position in range
cum_probs += cur_interval[0]
# Get selected index based on binary fraction from message bits
message_bits = message[i:i+precision]
if i+precision > len(message):
message_bits = message_bits + [0]*(i+precision-len(message))
message_idx = bits2int(reversed(message_bits))
selection = (cum_probs > message_idx).nonzero()[0].item()
# Calculate new range as ints
new_int_bottom = cum_probs[selection-1] if selection > 0 else cur_interval[0]
new_int_top = cum_probs[selection]
# Convert range to bits
new_int_bottom_bits_inc = list(reversed(int2bits(new_int_bottom, precision)))
new_int_top_bits_inc = list(reversed(int2bits(new_int_top-1, precision))) # -1 here because upper bound is exclusive
# Consume most significant bits which are now fixed and update interval
num_bits_encoded = num_same_from_beg(new_int_bottom_bits_inc, new_int_top_bits_inc)
i += num_bits_encoded
new_int_bottom_bits = new_int_bottom_bits_inc[num_bits_encoded:] + [0]*num_bits_encoded
new_int_top_bits = new_int_top_bits_inc[num_bits_encoded:] + [1]*num_bits_encoded
cur_interval[0] = bits2int(reversed(new_int_bottom_bits))
cur_interval[1] = bits2int(reversed(new_int_top_bits))+1 # +1 here because upper bound is exclusive
# Gather statistics
total_log_probs += log_probs[selection].item()
q = probs_final.double()/probs_final.sum()
logq = q.log()
total_kl += kl(q, logq, log_probs[:len(q)])
total_entropy_ptau += entropy(probs_temp, log_probs_temp)
total_num_for_stats += 1
# Update history with new token
prev = indices[selection].view(1)
output = torch.cat((output, prev))
total_num += 1
#print(enc.decode(prev.tolist()), message_bits[:num_bits_encoded])
# For text->bits->text
partial = enc.decode(output[len(context):].tolist())
if '<eos>' in partial:
break
avg_NLL = -total_log_probs/total_num_for_stats
avg_KL = total_kl/total_num_for_stats
avg_Hq = total_entropy_ptau/total_num_for_stats
words_per_bit = total_num_for_stats/i
return output[len(context):].tolist(), avg_NLL, avg_KL, words_per_bit, avg_Hq
def encode_block(model,
enc,
message,
context,
block_size,
bin2words,
words2bin,
finish_sent=False,
device='cpu'):
length = len(message)
context = torch.tensor(context[-1022:], device=device, dtype=torch.long)
prev = context
output = context
past = None
total_num = 0
total_num_for_stats = 0
total_log_probs = 0
total_kl = 0 # in bits
total_num_sents = 0
with torch.no_grad():
i = 0
sent_finish = False
while i < length or (finish_sent and not sent_finish):
logits, past = model(prev.unsqueeze(0), past=past)
past = limit_past(past)
logits[0, -1, -1] = -1e10 # endoftext can't happen
logits[0, -1, 628] = -1e10 # 2 newlines can't happen
logits = logits[0, -1, :]
log_probs = F.log_softmax(logits, dim=-1)
filtered_logits = logits.clone()
filtered_logits[:] = -1e10 # first set all to 0
if i >= length:
_, indices = logits.sort(descending=True)
sent_finish = is_sent_finish(indices[0].item(), enc)
else:
# First calculate logq
logq = logits.clone()
logq[:] = -1e10 # first set all to 0
for bin_val in range(2**block_size):
filtered_logits = logits.clone()
filtered_logits[:] = -1e10 # first set all to 0
available_tokens = bin2words[bin_val]
filtered_logits[available_tokens] = logits[
available_tokens]
filtered_logits, indices = filtered_logits.sort(
descending=True)
logq[indices[0]] = -block_size # in bits
logq = logq * 0.69315 # in nats
q = torch.exp(logq)
# Then find the actual word for the right bin
m_part = message[i:i + block_size]
filtered_logits = logits.clone()
filtered_logits[:] = -1e10 # first set all to 0
available_tokens = bin2words[bits2int(m_part)]
filtered_logits[available_tokens] = logits[available_tokens]
filtered_logits, indices = filtered_logits.sort(
descending=True)
total_kl += kl(q, logq, log_probs)
total_log_probs += log_probs[indices[0]].item()
i += block_size
total_num_for_stats += 1
total_num += 1
prev = indices[0].view(1)
output = torch.cat((output, prev))
avg_NLL = -total_log_probs / total_num_for_stats
avg_KL = total_kl / total_num_for_stats
words_per_bit = total_num_for_stats / i
return output[len(context):].tolist(), avg_NLL, avg_KL, words_per_bit