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Internal #766

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28 changes: 24 additions & 4 deletions init2winit/mt_eval/inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -93,7 +93,12 @@ def init_offline_evaluator(self,
self.mt_eval_config = mt_eval_config
self.dataset = dataset
params_rng, dropout_rng = jax.random.split(rng, num=2)
self.encoder = self.load_tokenizer(hps.vocab_path)
if not hps.vocab_path:
self.encoder = None
self.use_test_vocab = True
else:
self.encoder = self.load_tokenizer(hps.vocab_path)
self.use_test_vocab = False
self.initialize_model(model_cls, dataset_meta_data, dropout_rng, params_rng)

def init_online_evaluator(self,
Expand All @@ -110,7 +115,12 @@ def init_online_evaluator(self,
self.eval_num_batches = mt_eval_config.get('eval_num_batches')
self.mt_eval_config = mt_eval_config
self.dataset = dataset
self.encoder = self.load_tokenizer(hps.vocab_path)
if not hps.vocab_path:
self.encoder = None
self.use_test_vocab = True
else:
self.encoder = self.load_tokenizer(hps.vocab_path)
self.use_test_vocab = False
params_rng, dropout_rng = jax.random.split(rng, num=2)
self.initialize_model(model_cls, dataset_metadata, params_rng, dropout_rng)

Expand Down Expand Up @@ -189,8 +199,15 @@ def initialize_cache(self, inputs, max_length, params_rng, dropout_rng):
return init_dict['cache']

def decode_tokens(self, toks):
valid_toks = toks[:np.argmax(toks == self.eos_id) + 1].astype(np.int32)
return self.encoder.detokenize(valid_toks).numpy().decode('utf-8')
print('DEBUG: toke shape : ', toks.shape)
print('DEBUG: toks: ', toks)
if not self.use_test_vocab:
valid_toks = toks[:np.argmax(toks == self.eos_id) + 1].astype(np.int32)
return self.encoder.detokenize(valid_toks).numpy().decode('utf-8')
else:
valid_tok = toks[0]
print('DEBUG: valid_toks: ', valid_tok)
return TEST_VOCAB[valid_tok]

def current_batch_size(self, batch):
# we assume first token is non-zero in each target side example.
Expand Down Expand Up @@ -275,6 +292,9 @@ def translate_and_calculate_bleu_single_model(self, params, eval_split):
len(decode_output.reference_list),
len(decode_output.source_list))
if self.mt_eval_config.get('decoding_type') == 'beam_search':
print('DEBUG: ', decode_output.translation_list)
print('DEBUG: ', decode_output.reference_list)

bleu_score = eval_utils.compute_bleu_from_predictions(
decode_output.translation_list,
decode_output.reference_list,
Expand Down