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import torch
import numpy as np
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
def add_gumbel_noise(logits, temperature):
'''
The Gumbel max is a method for sampling categorical distributions.
According to arXiv:2409.02908, for MDM, low-precision Gumbel Max improves perplexity score but reduces generation quality.
Thus, we use float64.
'''
if temperature == 0:
return logits
logits = logits.to(torch.float64)
noise = torch.rand_like(logits, dtype=torch.float64)
gumbel_noise = (- torch.log(noise)) ** temperature
return logits.exp() / gumbel_noise
def get_num_transfer_tokens(mask_index, steps):
'''
In the reverse process, the interval [0, 1] is uniformly discretized into steps intervals.
Furthermore, because LLaDA employs a linear noise schedule (as defined in Eq. (8)),
the expected number of tokens transitioned at each step should be consistent.
This function is designed to precompute the number of tokens that need to be transitioned at each step.
'''
mask_num = mask_index.sum(dim=1, keepdim=True)
base = mask_num // steps
remainder = mask_num % steps
num_transfer_tokens = torch.zeros(mask_num.size(0), steps, device=mask_index.device, dtype=torch.int64) + base
for i in range(mask_num.size(0)):
num_transfer_tokens[i, :remainder[i]] += 1
return num_transfer_tokens
@ torch.no_grad()
def generate(model, prompt, attention_mask=None, steps=128, gen_length=128, block_length=128, temperature=0.,
cfg_scale=0., remasking='low_confidence', mask_id=126336, logits_eos_inf=False, confidence_eos_eot_inf=False):
'''
Args:
model: Mask predictor.
prompt: A tensor of shape (1, L).
steps: Sampling steps, less than or equal to gen_length.
gen_length: Generated answer length.
block_length: Block length, less than or equal to gen_length. If less than gen_length, it means using semi_autoregressive remasking.
temperature: Categorical distribution sampling temperature.
cfg_scale: Unsupervised classifier-free guidance scale.
remasking: Remasking strategy. 'low_confidence' or 'random'.
mask_id: The toke id of [MASK] is 126336.
logits_eos_inf: Whether to set the logits of EOS token to -inf. See Appendix B.4 of LLaDA for details
confidence_eos_eot_inf: Whether to set the confidence of EOS and EoT token to -inf. See Appendix B.4 of LLaDA for details
'''
x = torch.full((prompt.shape[0], prompt.shape[1] + gen_length), mask_id, dtype=torch.long).to(model.device)
x[:, :prompt.shape[1]] = prompt.clone()
if attention_mask is not None:
attention_mask = torch.cat([attention_mask, torch.ones((prompt.shape[0], gen_length), dtype=attention_mask.dtype, device=model.device)], dim=-1)
prompt_index = (x != mask_id)
assert gen_length % block_length == 0
num_blocks = gen_length // block_length
assert steps % num_blocks == 0
steps = steps // num_blocks
for num_block in range(num_blocks):
block_mask_index = (x[:, prompt.shape[1] + num_block * block_length: prompt.shape[1] + (num_block + 1) * block_length:] == mask_id)
num_transfer_tokens = get_num_transfer_tokens(block_mask_index, steps)
for i in range(steps):
mask_index = (x == mask_id)
if cfg_scale > 0.:
un_x = x.clone()
un_x[prompt_index] = mask_id
x_ = torch.cat([x, un_x], dim=0)
if attention_mask is not None:
attention_mask_ = torch.cat([attention_mask, attention_mask], dim=0)
logits = model(x_, attention_mask=attention_mask_).logits
logits, un_logits = torch.chunk(logits, 2, dim=0)
logits = un_logits + (cfg_scale + 1) * (logits - un_logits)
else:
logits = model(x, attention_mask=attention_mask).logits
if logits_eos_inf:
logits[:, :, 126081] = -torch.inf
logits_with_noise = add_gumbel_noise(logits, temperature=temperature)
x0 = torch.argmax(logits_with_noise, dim=-1) # b, l
if confidence_eos_eot_inf:
logits_with_noise[:, :, 126081] = logits[:, :, 126348] = -torch.inf
if remasking == 'low_confidence':
p = F.softmax(logits, dim=-1)
x0_p = torch.squeeze(
torch.gather(p, dim=-1, index=torch.unsqueeze(x0, -1)), -1) # b, l
elif remasking == 'random':
x0_p = torch.rand((x0.shape[0], x0.shape[1]), device=x0.device)
else:
raise NotImplementedError(remasking)
x0_p[:, prompt.shape[1] + (num_block + 1) * block_length:] = -np.inf
x0 = torch.where(mask_index, x0, x)
confidence = torch.where(mask_index, x0_p, -np.inf)
transfer_index = torch.zeros_like(x0, dtype=torch.bool, device=x0.device)
for j in range(confidence.shape[0]):
_, select_index = torch.topk(confidence[j], k=num_transfer_tokens[j, i])
transfer_index[j, select_index] = True
x[transfer_index] = x0[transfer_index]
return x
def main():
device = 'cuda'
model = AutoModel.from_pretrained('GSAI-ML/LLaDA-8B-Instruct', trust_remote_code=True, torch_dtype=torch.bfloat16).to(device).eval()
tokenizer = AutoTokenizer.from_pretrained('GSAI-ML/LLaDA-8B-Instruct', trust_remote_code=True)
# The LLaDA architecture theoretically supports both left-padding and right-padding.
# However, the sampling code implementation is simpler with left-padding.
if tokenizer.padding_side != 'left':
tokenizer.padding_side = 'left'
# If the padding ID equals the mask ID, you need to modify our generate function to achieve correct inference.
assert tokenizer.pad_token_id != 126336
prompts = [ "Lily can run 12 kilometers per hour for 4 hours. After that, she runs 6 kilometers per hour. How many kilometers can she run in 8 hours?",
"Joy can read 8 pages of a book in 20 minutes. How many hours will it take her to read 120 pages?",
"Randy has 60 mango trees on his farm. He also has 5 less than half as many coconut trees as mango trees. How many trees does Randy have in all on his farm?"]
# Add special tokens for the Instruct model. The Base model does not require the following two lines.
messages = [{"role": "user", "content": prompt} for prompt in prompts]
prompts = [tokenizer.apply_chat_template([message], add_generation_prompt=True, tokenize=False) for message in messages]
encoded_outputs = tokenizer(
prompts,
add_special_tokens=False,
padding=True,
return_tensors="pt"
)
input_ids = encoded_outputs['input_ids'].to(device)
attention_mask = encoded_outputs['attention_mask'].to(device)
out = generate(model, input_ids, attention_mask, steps=128, gen_length=128, block_length=32, temperature=0., cfg_scale=0., remasking='low_confidence')
output = tokenizer.batch_decode(out[:, input_ids.shape[1]:], skip_special_tokens=True)
for o in output:
print(o)
print('-' * 50)
if __name__ == '__main__':
main()
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