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'''
This file is inspired by the code from https://github.com/ML-GSAI/SMDM
'''
import accelerate
import torch
import re
from pathlib import Path
import random
import numpy as np
import torch.nn.functional as F
from datasets import Dataset
from lm_eval.__main__ import cli_evaluate
from lm_eval.api.instance import Instance
from lm_eval.api.model import LM
from lm_eval.api.registry import register_model
from tqdm import tqdm
from transformers import AutoTokenizer, AutoModel
from generate import generate
def set_seed(seed):
torch.manual_seed(seed)
random.seed(seed)
np.random.seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
@register_model("llada_dist")
class LLaDAEvalHarness(LM):
def __init__(
self,
model_path='',
mask_id=126336,
max_length=4096,
batch_size=32,
mc_num=128,
is_check_greedy=True,
cfg=0.,
steps=1024,
gen_length=1024,
block_length=1024,
remasking='low_confidence',
device="cuda",
**kwargs,
):
'''
Args:
model_path: LLaDA-8B-Base model path.
mask_id: The token id of [MASK] is 126336.
max_length: the max sequence length.
batch_size: mini batch size.
mc_num: Monte Carlo estimation iterations
is_check_greedy: For certain metrics like LAMBADA, the evaluation requires the model to verify whether the answer
is generated through greedy sampling conditioned on the prompt (note that this differs from conditional
generation). We implement this verification through the suffix_greedy_prediction() function, which
returns a True/False judgment used for accuracy calculation.
When is_check_greedy is set to True, the lm-evaluation-harness library automatically invokes this function.
However, since none of the metrics in the LLaDA paper (https://arxiv.org/abs/2502.09992) require this functionality,
we recommend setting is_check_greedy to False. This configuration causes suffix_greedy_prediction() to return False
by default, significantly accelerating the evaluation process.
cfg_scale: Unsupervised classifier-free guidance scale.
'''
super().__init__()
accelerator = accelerate.Accelerator()
if accelerator.num_processes > 1:
self.accelerator = accelerator
else:
self.accelerator = None
model_kwargs = {}
if self.accelerator is not None:
model_kwargs.update({'device_map': {'': f'{self.accelerator.device}'}})
self.model = AutoModel.from_pretrained(model_path, trust_remote_code=True, torch_dtype=torch.bfloat16, **model_kwargs)
self.model.eval()
self.device = torch.device(device)
if self.accelerator is not None:
self.model = self.accelerator.prepare(self.model)
self.device = torch.device(f'{self.accelerator.device}')
self._rank = self.accelerator.local_process_index
self._world_size = self.accelerator.num_processes
else:
self.model = self.model.to(device)
self.mask_id = mask_id
self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
self.mc_num = mc_num
self.batch_size = int(batch_size)
assert mc_num % self.batch_size == 0
self.sampling_eps = 0.
self.max_length = max_length
self.is_check_greedy = is_check_greedy
self.cfg = cfg
self.steps = steps
self.gen_length = gen_length
self.block_length = block_length
self.remasking = remasking
@property
def rank(self):
return self._rank
@property
def world_size(self):
return self._world_size
def _forward_process(self, batch, prompt_index):
b, l = batch.shape
target_len = (l - prompt_index.sum()).item()
k = torch.randint(1, target_len + 1, (), device=batch.device)
x = torch.round(torch.linspace(float(k), k + (b - 1) * (target_len / b), steps=b, device=batch.device)).long()
x = ((x - 1) % target_len) + 1
assert x.min() >= 1 and x.max() <= target_len
indices = torch.arange(target_len, device=batch.device).repeat(b, 1)
is_mask = indices < x.unsqueeze(1)
for i in range(b):
is_mask[i] = is_mask[i][torch.randperm(target_len)]
is_mask = torch.cat((torch.zeros(b, prompt_index.sum(), dtype=torch.bool, device=batch.device), is_mask), dim=1)
noisy_batch = torch.where(is_mask, self.mask_id, batch)
return noisy_batch, (x / target_len).unsqueeze(1).repeat(1, l)
@torch.no_grad()
def get_logits(self, batch, prompt_index):
if self.cfg > 0.:
assert len(prompt_index) == batch.shape[1]
prompt_index = prompt_index.unsqueeze(0).repeat(batch.shape[0], 1)
un_batch = batch.clone()
un_batch[prompt_index] = self.mask_id
batch = torch.cat([batch, un_batch])
logits = self.model(batch).logits
if self.cfg > 0.:
logits, un_logits = torch.chunk(logits, 2, dim=0)
logits = un_logits + (self.cfg + 1) * (logits - un_logits)
return logits[:, :batch.shape[1]]
@torch.no_grad()
def get_loglikelihood(self, prefix, target):
seq = torch.concatenate([prefix, target])[None, :]
seq = seq.repeat((self.batch_size, 1)).to(self.device)
prompt_index = torch.arange(seq.shape[1], device=self.device) < len(prefix)
loss_acc = []
for _ in range(self.mc_num // self.batch_size):
perturbed_seq, p_mask = self._forward_process(seq, prompt_index)
mask_indices = perturbed_seq == self.mask_id
logits = self.get_logits(perturbed_seq, prompt_index)
loss = F.cross_entropy(logits[mask_indices], seq[mask_indices], reduction='none') / p_mask[mask_indices]
loss = loss.sum() / self.batch_size
loss_acc.append(loss.item())
return - sum(loss_acc) / len(loss_acc)
@torch.no_grad()
def suffix_greedy_prediction(self, prefix, target):
if not self.is_check_greedy:
return False
seq = torch.full((1, len(prefix) + len(target)), self.mask_id, device=self.device)
prompt_index = torch.arange(seq.shape[1], device=self.device) < len(prefix)
prefix, target = prefix.to(self.device), target.to(self.device)
seq[0, :len(prefix)] = prefix
for i in range(len(target)):
mask_index = (seq == self.mask_id)
logits = self.get_logits(seq, prompt_index)[mask_index]
x0 = torch.argmax(logits, dim=-1)
p = torch.softmax(logits.to(torch.float32), dim=-1)
confidence = torch.gather(p, dim=-1, index=torch.unsqueeze(x0, -1)).squeeze(dim=-1)
_, index = torch.sort(confidence, descending=True)
x0[index[1:]] = self.mask_id
seq[mask_index] = x0.clone()
correct = target == seq[0, len(prefix):]
correct = torch.all(correct)
return correct
def _encode_pair(self, context, continuation):
n_spaces = len(context) - len(context.rstrip())
if n_spaces > 0:
continuation = context[-n_spaces:] + continuation
context = context[:-n_spaces]
whole_enc = self.tokenizer(context + continuation)["input_ids"]
context_enc = self.tokenizer(context)["input_ids"]
context_enc_len = len(context_enc)
continuation_enc = whole_enc[context_enc_len:]
return context_enc, continuation_enc
def loglikelihood(self, requests):
def _tokenize(e):
prefix, target = self._encode_pair(e["prefix"], e["target"])
return {
"prefix_text": e["prefix"],
"target_text": e["target"],
"prefix": prefix,
"target": target,
}
ds = []
ds = [{"prefix": req.args[0], "target": req.args[1]} for req in requests]
ds = Dataset.from_list(ds)
ds = ds.map(_tokenize)
ds = ds.with_format("torch")
prompt_len = [len(x["prefix"]) + len(x["target"]) for x in ds]
assert max(prompt_len) <= 4096
out = []
with torch.no_grad():
for elem in tqdm(ds, desc="Computing likelihood..."):
prefix = elem["prefix"]
target = elem["target"]
ll = self.get_loglikelihood(prefix, target)
is_target_greedy_dec = self.suffix_greedy_prediction(prefix, target)
out.append((ll, 1.0 if is_target_greedy_dec else 0.0))
torch.cuda.empty_cache()
return out
def loglikelihood_rolling(self, requests):
raise NotImplementedError
def generate_until(self, requests: list[Instance]):
def _tokenize(e):
return {
"question": self.tokenizer(e["question"])["input_ids"],
"question_text": e["question"],
"until": e["until"],
}
ds = [{"question": req.args[0], "until": req.args[1]['until']} for req in requests]
ds = Dataset.from_list(ds)
ds = ds.map(_tokenize)
ds = ds.with_format("torch")
out = []
for elem in tqdm(ds, desc="Generating..."):
prompt = elem["question"].unsqueeze(0).to(self.device)
stop_tokens = elem["until"]
generated_answer = generate(self.model, prompt, steps=self.steps, gen_length=self.gen_length, block_length=self.block_length,
temperature=0, cfg_scale=self.cfg, remasking=self.remasking, mask_id=self.mask_id)
generated_answer = self.tokenizer.decode(generated_answer[0][prompt.shape[1]:], skip_special_tokens=False)
for stop_seq in stop_tokens:
if stop_seq in generated_answer:
generated_answer = generated_answer.split(stop_seq)[0]
# remove special tokens
generated_answer_ids = self.tokenizer(generated_answer)["input_ids"]
generated_answer = self.tokenizer.decode(generated_answer_ids, skip_special_tokens=True)
out.append(generated_answer)
self.accelerator.wait_for_everyone()
return out
if __name__ == "__main__":
set_seed(1234)
cli_evaluate()
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