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import statistics
from typing import Dict, List, Union
import jax
import jax.numpy as jnp
import numpy as np
import sacrebleu
import transformers
from flax.jax_utils import replicate
from tqdm import tqdm
from utils.logging_utils import log_for_0
# ============================================
# Text-similarity metrics (BLEU / ROUGE)
# ============================================
def _mean_std_sem(values):
n = len(values)
mean = sum(values) / n
std = statistics.pstdev(values) if n > 1 else 0.0
sem = std / math.sqrt(n) if n > 1 else 0.0
return mean, std, sem
def compute_bleu(hypotheses, references):
return sacrebleu.corpus_bleu(hypotheses, [references], lowercase=True, use_effective_order=True).score
def compute_rouge(hypotheses, references, return_std=False):
from rouge_score import rouge_scorer
scorer = rouge_scorer.RougeScorer(["rouge1", "rouge2", "rougeL"], use_stemmer=True)
r1, r2, rL = [], [], []
for hyp, ref in zip(hypotheses, references):
s = scorer.score(ref, hyp)
r1.append(s["rouge1"].fmeasure * 100)
r2.append(s["rouge2"].fmeasure * 100)
rL.append(s["rougeL"].fmeasure * 100)
m1, s1, e1 = _mean_std_sem(r1)
m2, s2, e2 = _mean_std_sem(r2)
mL, sL, eL = _mean_std_sem(rL)
means = {"rouge1": m1, "rouge2": m2, "rougeL": mL}
if not return_std:
return means
stds = {
"rouge1_std": s1, "rouge2_std": s2, "rougeL_std": sL,
"rouge1_sem": e1, "rouge2_sem": e2, "rougeL_sem": eL,
}
return means, stds
# ============================================
# JAX perplexity / entropy metrics
# ============================================
class NLL:
"""JAX implementation of NLL metric."""
def __init__(self):
self.reset()
def reset(self):
self.mean_value = jnp.array(0.0, dtype=jnp.float32)
self.weight = jnp.array(0.0, dtype=jnp.float32)
def update(self, value: Union[float, jnp.ndarray], weight: Union[float, jnp.ndarray] = 1.0):
if not isinstance(value, jnp.ndarray):
value = jnp.array(value, dtype=jnp.float32)
if weight is not None and not isinstance(weight, jnp.ndarray):
weight = jnp.array(weight, dtype=jnp.float32)
weight = jnp.broadcast_to(weight, value.shape)
if value.size == 0:
return
self.mean_value = self.mean_value + jnp.sum(value)
self.weight = self.weight + jnp.sum(weight)
class Perplexity(NLL):
def compute(self) -> jnp.ndarray:
return jnp.exp(self.mean_value / self.weight)
class MeanMetric:
def __init__(self):
self.reset()
def reset(self):
self.sum_value = jnp.array(0.0, dtype=jnp.float32)
self.count = jnp.array(0.0, dtype=jnp.float32)
def update(self, value: Union[float, jnp.ndarray]):
if not isinstance(value, jnp.ndarray):
value = jnp.array(value, dtype=jnp.float32)
self.sum_value = self.sum_value + jnp.sum(value)
self.count = self.count + value.size
def compute(self) -> jnp.ndarray:
return self.sum_value / self.count
class Metrics:
def __init__(
self,
gen_ppl_eval_model_name_or_path=None,
eval_ppl_batch_size=None,
eval_context_size=1024,
) -> None:
self.gen_ppl = Perplexity()
self.sample_entropy = MeanMetric()
self.eval_ppl_batch_size = eval_ppl_batch_size
self.gen_ppl_eval_model_name_or_path = gen_ppl_eval_model_name_or_path
self.eval_context_size = eval_context_size
self._ppl_params = None
self._ppl_compute_batch_nlls = None
# mT5 needs use_fast=False to avoid Tiktoken/SentencePiece conversion issues.
use_fast = "mt5" not in gen_ppl_eval_model_name_or_path.lower()
self.tokenizer = transformers.AutoTokenizer.from_pretrained(
gen_ppl_eval_model_name_or_path, use_fast=use_fast,
)
if self.tokenizer.pad_token is None:
self.tokenizer.pad_token = self.tokenizer.eos_token
self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
def reset(self):
self.gen_ppl.reset()
self.sample_entropy.reset()
def _eval_retokenize(self, text_samples, max_length):
"""Retokenize samples for the eval model. Returns (samples, attn_mask, eval_context_size)."""
out = self.tokenizer(
text_samples,
return_tensors="np",
return_token_type_ids=False,
return_attention_mask=True,
truncation=True,
padding=True,
max_length=max_length,
)
return out["input_ids"], out["attention_mask"], self.eval_context_size
def record_generative_perplexity(
self,
text_samples: List[str],
max_length: int,
retokenize: bool = True,
) -> Dict:
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
n_devices = jax.local_device_count()
# Load model and compile pmap once; reuse on subsequent calls
if self._ppl_params is None:
from transformers import FlaxAutoModelForCausalLM
log_for_0(f"Loading JAX/Flax model: {self.gen_ppl_eval_model_name_or_path}")
eval_model = FlaxAutoModelForCausalLM.from_pretrained(self.gen_ppl_eval_model_name_or_path)
log_for_0(f"Replicating model parameters across {n_devices} devices...")
params = replicate(eval_model.params)
@jax.pmap
def compute_batch_nlls(params, input_ids, attention_mask, eos_token_id):
logits = eval_model(input_ids, attention_mask=attention_mask, params=params).logits
targets = input_ids[:, 1:]
logits_pred = logits[:, :-1, :]
batch_indices = jnp.arange(targets.shape[0])[:, None]
seq_indices = jnp.arange(targets.shape[1])[None, :]
target_logits = logits_pred[batch_indices, seq_indices, targets]
log_normalizers = jax.nn.logsumexp(logits_pred, axis=-1)
nlls = log_normalizers - target_logits
is_eos = input_ids == eos_token_id
first_eos = jnp.cumsum(is_eos, axis=-1) == 1
token_mask = input_ids != eos_token_id
valid_tokens = first_eos[:, 1:] + token_mask[:, 1:]
return nlls, valid_tokens
self._ppl_params = params
self._ppl_compute_batch_nlls = compute_batch_nlls
log_for_0("PPL model cached for reuse")
params = self._ppl_params
compute_batch_nlls = self._ppl_compute_batch_nlls
if retokenize:
samples, attn_mask, eval_context_size = self._eval_retokenize(text_samples, max_length=max_length)
else:
samples = text_samples
attn_mask = np.ones(samples.shape)
eval_context_size = samples.shape[-1]
# Round batch size down to a multiple of n_devices (>=1).
batch_size = self.eval_ppl_batch_size or samples.shape[0]
batch_size = min(batch_size, samples.shape[0])
batch_size = (batch_size // n_devices) * n_devices or n_devices
num_batches = (samples.shape[0] + batch_size - 1) // batch_size
log_for_0(f"PPL: batch_size={batch_size} ({batch_size // n_devices}/device), {num_batches} batches")
per_sample_nll_sum = np.zeros(samples.shape[0], dtype=np.float64)
per_sample_token_count = np.zeros(samples.shape[0], dtype=np.float64)
for i in tqdm(range(num_batches), desc="Evaluating perplexity"):
batch_start = i * batch_size
batch_end = min((i + 1) * batch_size, samples.shape[0])
actual_batch_size = batch_end - batch_start
batch_samples = samples[batch_start:batch_end]
batch_attn_mask = attn_mask[batch_start:batch_end]
# Pad the last batch to full batch_size for pmap
if actual_batch_size < batch_size:
pad_size = batch_size - actual_batch_size
batch_samples = np.concatenate([
batch_samples,
np.zeros((pad_size, batch_samples.shape[1]), dtype=batch_samples.dtype),
], axis=0)
batch_attn_mask = np.concatenate([
batch_attn_mask,
np.zeros((pad_size, batch_attn_mask.shape[1]), dtype=batch_attn_mask.dtype),
], axis=0)
for chunk_start in range(0, batch_samples.shape[1], eval_context_size):
chunk_end = min(chunk_start + eval_context_size, batch_samples.shape[1])
sample_chunk = batch_samples[:, chunk_start:chunk_end]
attn_mask_chunk = batch_attn_mask[:, chunk_start:chunk_end]
# [n_devices, batch_per_device, seq_len]
sample_chunk_sharded = sample_chunk.reshape(n_devices, batch_size // n_devices, sample_chunk.shape[1])
attn_mask_chunk_sharded = attn_mask_chunk.reshape(n_devices, batch_size // n_devices, attn_mask_chunk.shape[1])
eos_token_id_replicated = jnp.array([self.tokenizer.eos_token_id] * n_devices)
nlls_sharded, valid_tokens_sharded = compute_batch_nlls(
params, sample_chunk_sharded, attn_mask_chunk_sharded, eos_token_id_replicated,
)
nlls = nlls_sharded.reshape(batch_size, nlls_sharded.shape[2])
valid_tokens = valid_tokens_sharded.reshape(batch_size, valid_tokens_sharded.shape[2])
if actual_batch_size < batch_size:
nlls = nlls[:actual_batch_size]
valid_tokens = valid_tokens[:actual_batch_size]
# Device-to-host transfer for accumulation
nlls_np = np.asarray(nlls)
valid_tokens_np = np.asarray(valid_tokens)
weighted_nlls = nlls_np * valid_tokens_np
self.gen_ppl.update(jnp.array(weighted_nlls), jnp.array(valid_tokens_np))
per_sample_nll_sum[batch_start:batch_end] += weighted_nlls.sum(axis=-1)
per_sample_token_count[batch_start:batch_end] += valid_tokens_np.sum(axis=-1)
del nlls_sharded, valid_tokens_sharded, nlls, valid_tokens
del nlls_np, valid_tokens_np, weighted_nlls
# Per-sample perplexity (NaN for zero-token samples)
with np.errstate(divide="ignore", invalid="ignore"):
per_sample_ppl = np.exp(per_sample_nll_sum / per_sample_token_count)
per_sample_ppl = np.where(per_sample_token_count > 0, per_sample_ppl, np.nan).tolist()
# Per-sample entropy (only on valid tokens, excluding padding)
per_sample_entropy = []
for i in range(samples.shape[0]):
valid_len = int(attn_mask[i].sum())
valid_tokens = samples[i, :valid_len]
_, counts = np.unique(valid_tokens, return_counts=True)
probs = counts.astype(np.float32) / counts.sum()
entropy = float(-np.sum(probs * np.log(probs + 1e-10)))
per_sample_entropy.append(entropy)
self.sample_entropy.update(entropy)
return {
"ppl": float(self.gen_ppl.compute()),
"per_sample_ppl": per_sample_ppl,
"mean_entropy": sum(per_sample_entropy) / len(per_sample_entropy),
}
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