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import torch
import torch.nn.functional as F
from typing import List, Tuple, Optional
import numpy as np
from dataclasses import dataclass
@dataclass
class BeamHypothesis:
"""Single hypothesis in beam search"""
tokens: List[int]
log_prob: float
finished: bool = False
class BeamSearch:
"""Beam search decoder for transformer models"""
def __init__(self, beam_size: int = 4, length_penalty: float = 0.6,
coverage_penalty: float = 0.0, no_repeat_ngram_size: int = 3):
self.beam_size = beam_size
self.length_penalty = length_penalty
self.coverage_penalty = coverage_penalty
self.no_repeat_ngram_size = no_repeat_ngram_size # Changed default from 0 to 3
def search(self, model, src: torch.Tensor, max_length: int = 100,
bos_id: int = 2, eos_id: int = 3, pad_id: int = 0) -> List[List[int]]:
"""
Perform beam search decoding
Args:
model: Transformer model
src: Source sequence [batch_size, src_len]
max_length: Maximum decoding length
bos_id: Beginning of sequence token
eos_id: End of sequence token
pad_id: Padding token
Returns:
List of decoded sequences
"""
batch_size = src.size(0)
device = src.device
# Encode source
src_mask = (src != pad_id).unsqueeze(1).unsqueeze(2)
memory = model.encode(src, src_mask)
# Initialize beams
beams = [[BeamHypothesis([bos_id], 0.0)] for _ in range(batch_size)]
for step in range(max_length - 1):
all_candidates = []
for batch_idx in range(batch_size):
# NEW: Stop if the BEST beam (first one after sorting) is finished
if beams[batch_idx] and beams[batch_idx][0].finished:
continue
# Also skip if all beams are finished
if all(hyp.finished for hyp in beams[batch_idx]):
continue
# Prepare input for all beams
beam_tokens = []
beam_indices = []
for beam_idx, hypothesis in enumerate(beams[batch_idx]):
if not hypothesis.finished:
beam_tokens.append(hypothesis.tokens)
beam_indices.append(beam_idx)
if not beam_tokens:
continue
# Create batch of sequences
tgt = torch.tensor(beam_tokens, device=device)
# Decode
tgt_mask = torch.ones(len(beam_tokens), 1, tgt.size(1), tgt.size(1), device=device)
tgt_mask = torch.tril(tgt_mask)
# Expand memory for beam size
expanded_memory = memory[batch_idx:batch_idx+1].expand(len(beam_tokens), -1, -1)
expanded_src_mask = src_mask[batch_idx:batch_idx+1].expand(len(beam_tokens), -1, -1, -1)
# Get predictions
decoder_output = model.decode(tgt, expanded_memory, tgt_mask, expanded_src_mask)
logits = model.output_projection(decoder_output[:, -1, :])
log_probs = F.log_softmax(logits, dim=-1)
# Get top k tokens for each beam
vocab_size = log_probs.size(-1)
top_log_probs, top_indices = torch.topk(log_probs, min(self.beam_size, vocab_size))
# Create new candidates
candidates = []
for beam_local_idx, (beam_idx, beam_log_probs, beam_indices_local) in enumerate(
zip(beam_indices, top_log_probs, top_indices)):
hypothesis = beams[batch_idx][beam_idx]
for token_rank, (token_log_prob, token_id) in enumerate(
zip(beam_log_probs, beam_indices_local)):
new_tokens = hypothesis.tokens + [token_id.item()]
# Apply no-repeat penalty
if self._has_repeated_ngram(new_tokens):
continue
new_log_prob = hypothesis.log_prob + token_log_prob.item()
# Apply length penalty
score = self._apply_length_penalty(new_log_prob, len(new_tokens))
candidates.append((
score,
BeamHypothesis(
tokens=new_tokens,
log_prob=new_log_prob,
finished=(token_id.item() == eos_id)
)
))
# Select top beam_size candidates
candidates.sort(key=lambda x: x[0], reverse=True)
new_beams = []
for score, hypothesis in candidates[:self.beam_size]:
new_beams.append(hypothesis)
# If we have no candidates, keep the old beams
if not new_beams:
new_beams = beams[batch_idx]
beams[batch_idx] = new_beams
# Extract best sequences
results = []
for batch_idx in range(batch_size):
# Sort by score
sorted_hyps = sorted(
beams[batch_idx],
key=lambda h: self._apply_length_penalty(h.log_prob, len(h.tokens)),
reverse=True
)
# Get best hypothesis
best_hyp = sorted_hyps[0]
results.append(best_hyp.tokens)
return results
def _apply_length_penalty(self, log_prob: float, length: int) -> float:
"""Apply length penalty to score"""
return log_prob / (length ** self.length_penalty)
def _has_repeated_ngram(self, tokens: List[int]) -> bool:
"""Check if sequence has repeated n-grams"""
if self.no_repeat_ngram_size <= 0:
return False
ngrams = set()
for i in range(len(tokens) - self.no_repeat_ngram_size + 1):
ngram = tuple(tokens[i:i + self.no_repeat_ngram_size])
if ngram in ngrams:
return True
ngrams.add(ngram)
return False
class GreedyDecoder:
"""Simple greedy decoder for fast inference"""
@staticmethod
def decode(model, src: torch.Tensor, max_length: int = 100,
bos_id: int = 2, eos_id: int = 3, pad_id: int = 0) -> List[List[int]]:
"""
Perform greedy decoding
Args:
model: Transformer model
src: Source sequence [batch_size, src_len]
max_length: Maximum decoding length
bos_id: Beginning of sequence token
eos_id: End of sequence token
pad_id: Padding token
Returns:
List of decoded sequences
"""
batch_size = src.size(0)
device = src.device
# Use model's built-in generate method
with torch.no_grad():
translations = model.generate(
src,
max_length=max_length,
bos_id=bos_id,
eos_id=eos_id
)
# Convert to list
results = []
for i in range(batch_size):
tokens = translations[i].cpu().tolist()
# Remove padding and special tokens if needed
if eos_id in tokens:
eos_idx = tokens.index(eos_id)
tokens = tokens[:eos_idx + 1]
results.append(tokens)
return results |