def evaluate(args, model, tokenizer, prefix=""):
dataset, examples, features = load_and_cache_examples(args,
tokenizer,
evaluate=True,
output_examples=True)
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
# Note that DistributedSampler samples randomly
eval_sampler = SequentialSampler(dataset)
eval_dataloader = DataLoader(dataset,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
# multi-gpu evaluate
if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
model = torch.nn.DataParallel(model)
# Eval!
logger.info("***** Running evaluation {} *****".format(prefix))
logger.info(" Num examples = %d", len(dataset))
logger.info(" Batch size = %d", args.eval_batch_size)
all_results = []
start_time = timeit.default_timer()
for batch in tqdm(eval_dataloader, desc="Evaluating"):
model.eval()
batch = tuple(t.to(args.device) for t in batch)
with torch.no_grad():
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"token_type_ids": batch[2],
}
if args.model_type in [
"xlm", "roberta", "distilbert", "camembert"
]:
del inputs["token_type_ids"]
example_indices = batch[3]
# XLNet and XLM use more arguments for their predictions
if args.model_type in ["xlnet", "xlm"]:
inputs.update({"cls_index": batch[4], "p_mask": batch[5]})
# for lang_id-sensitive xlm models
if hasattr(model, "config") and hasattr(
model.config, "lang2id"):
inputs.update({
"langs":
(torch.ones(batch[0].shape, dtype=torch.int64) *
args.lang_id).to(args.device)
})
outputs = model(**inputs)
for i, example_index in enumerate(example_indices):
eval_feature = features[example_index.item()]
unique_id = int(eval_feature.unique_id)
output = [to_list(output[i]) for output in outputs]
# Some models (XLNet, XLM) use 5 arguments for their predictions, while the other "simpler"
# models only use two.
if len(output) >= 5:
start_logits = output[0]
start_top_index = output[1]
end_logits = output[2]
end_top_index = output[3]
cls_logits = output[4]
result = SquadResult(
unique_id,
start_logits,
end_logits,
start_top_index=start_top_index,
end_top_index=end_top_index,
cls_logits=cls_logits,
)
else:
start_logits, end_logits = output
result = SquadResult(unique_id, start_logits, end_logits)
all_results.append(result)
evalTime = timeit.default_timer() - start_time
logger.info(" Evaluation done in total %f secs (%f sec per example)",
evalTime, evalTime / len(dataset))
# Compute predictions
output_prediction_file = os.path.join(args.output_dir,
"predictions_{}.json".format(prefix))
output_nbest_file = os.path.join(
args.output_dir, "nbest_predictions_{}.json".format(prefix))
if args.version_2_with_negative:
output_null_log_odds_file = os.path.join(
args.output_dir, "null_odds_{}.json".format(prefix))
else:
output_null_log_odds_file = None
# XLNet and XLM use a more complex post-processing procedure
if args.model_type in ["xlnet", "xlm"]:
start_n_top = model.config.start_n_top if hasattr(
model, "config") else model.module.config.start_n_top
end_n_top = model.config.end_n_top if hasattr(
model, "config") else model.module.config.end_n_top
predictions = compute_predictions_log_probs(
examples,
features,
all_results,
args.n_best_size,
args.max_answer_length,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
start_n_top,
end_n_top,
args.version_2_with_negative,
tokenizer,
args.verbose_logging,
)
else:
predictions = compute_predictions_logits(
examples,
features,
all_results,
args.n_best_size,
args.max_answer_length,
args.do_lower_case,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
args.verbose_logging,
args.version_2_with_negative,
args.null_score_diff_threshold,
tokenizer,
)
if output_null_log_odds_file:
with open(output_null_log_odds_file, "r") as fin:
import json
no_answer_probs = json.load(fin)
else:
no_answer_probs = None
# Compute the F1 and exact scores.
results = squad_evaluate(examples, predictions, no_answer_probs)
output_eval_file = os.path.join(args.output_dir, prefix,
"eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results {} *****".format(prefix))
for key in sorted(results.keys()):
logger.info(" %s = %s", key, str(results[key]))
writer.write("%s = %s\n" % (key, str(results[key])))
return results
def evaluate(args, model, tokenizer, prefix=""):
dataset, examples, features = load_and_cache_examples(args, tokenizer, evaluate=True, output_examples=True)
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
# Note that DistributedSampler samples randomly
eval_sampler = SequentialSampler(dataset)
eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
# multi-gpu evaluate
if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
model = torch.nn.DataParallel(model)
# Eval!
logger.info("***** Running evaluation {} *****".format(prefix))
logger.info(" Num examples = %d", len(dataset))
logger.info(" Batch size = %d", args.eval_batch_size)
all_results = []
start_time = timeit.default_timer()
for batch in tqdm(eval_dataloader, desc="Evaluating"):
model.eval()
batch = tuple(t.to(args.device) for t in batch)
with torch.no_grad():
inputs = {"input_ids": batch[0], "attention_mask": batch[1]}
if args.model_type != "distilbert":
inputs["token_type_ids"] = None if args.model_type == "xlm" else batch[2] # XLM don't use segment_ids
example_indices = batch[3]
if args.model_type in ["xlnet", "xlm"]:
inputs.update({"cls_index": batch[4], "p_mask": batch[5]})
outputs = model(**inputs)
for i, example_index in enumerate(example_indices):
eval_feature = features[example_index.item()]
unique_id = int(eval_feature.unique_id)
output = [to_list(output[i]) for output in outputs]
# Some models (XLNet, XLM) use 5 arguments for their predictions, while the other "simpler"
# models only use two.
if len(output) >= 5:
start_logits = output[0]
start_top_index = output[1]
end_logits = output[2]
end_top_index = output[3]
cls_logits = output[4]
result = SquadResult(
unique_id,
start_logits,
end_logits,
start_top_index=start_top_index,
end_top_index=end_top_index,
cls_logits=cls_logits,
)
else:
start_logits, end_logits = output
result = SquadResult(unique_id, start_logits, end_logits)
all_results.append(result)
evalTime = timeit.default_timer() - start_time
logger.info(" Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(dataset))
# Compute predictions
output_prediction_file = os.path.join(args.output_dir, "predictions_{}.json".format(prefix))
output_nbest_file = os.path.join(args.output_dir, "nbest_predictions_{}.json".format(prefix))
if args.version_2_with_negative:
output_null_log_odds_file = os.path.join(args.output_dir, "null_odds_{}.json".format(prefix))
else:
output_null_log_odds_file = None
if args.model_type in ["xlnet", "xlm"]:
# XLNet uses a more complex post-processing procedure
predictions = compute_predictions_log_probs(
examples,
features,
all_results,
args.n_best_size,
args.max_answer_length,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
model.config.start_n_top,
model.config.end_n_top,
args.version_2_with_negative,
tokenizer,
args.verbose_logging,
)
else:
predictions = compute_predictions_logits(
examples,
features,
all_results,
args.n_best_size,
args.max_answer_length,
args.do_lower_case,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
args.verbose_logging,
args.version_2_with_negative,
args.null_score_diff_threshold,
tokenizer,
)
# Compute the F1 and exact scores.
results = squad_evaluate(examples, predictions)
return results
def evaluate(args, model, tokenizer, prefix=""):
dataset, examples, features = load_and_cache_examples(args, tokenizer, evaluate=True, output_examples=True)
# 만약 output_dir 존재하지 않는다면 만든다
if not os.path.exists(output_dir) and local_rank in [-1, 0]:
os.makedirs(output_dir)
# eval_batch_size 어차피 ngpu = 1이므로 개당 batch size와 같음
eval_batch_size = per_gpu_eval_batch_size
# Note that DistributedSampler samples randomly
eval_sampler = SequentialSampler(dataset)
eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=eval_batch_size)
# multi-gpu evaluate
if n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
model = torch.nn.DataParallel(model)
# Eval!
logger.info("***** Running evaluation {} *****".format(prefix))
logger.info(" Num examples = %d", len(dataset))
logger.info(" Batch size = %d", eval_batch_size)
all_results = []
start_time = timeit.default_timer()
for batch in tqdm(eval_dataloader, desc="Evaluating"):
model.eval()
batch = tuple(t.to(device) for t in batch)
with torch.no_grad():
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"token_type_ids": batch[2],
}
example_indices = batch[3]
outputs = model(**inputs)
for i, example_index in enumerate(example_indices):
eval_feature = features[example_index.item()]
unique_id = int(eval_feature.unique_id)
output = [to_list(output[i]) for output in outputs]
# Some models (XLNet, XLM) use 5 arguments for their predictions, while the other "simpler"
# models only use two.
if len(output) >= 5:
start_logits = output[0]
start_top_index = output[1]
end_logits = output[2]
end_top_index = output[3]
cls_logits = output[4]
result = SquadResult(
unique_id,
start_logits,
end_logits,
start_top_index=start_top_index,
end_top_index=end_top_index,
cls_logits=cls_logits,
)
else:
start_logits, end_logits = output
result = SquadResult(unique_id, start_logits, end_logits)
all_results.append(result)
evalTime = timeit.default_timer() - start_time
logger.info(" Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(dataset))
# Compute predictions
output_prediction_file = os.path.join(output_dir, "predictions_{}.json".format(prefix))
output_nbest_file = os.path.join(output_dir, "nbest_predictions_{}.json".format(prefix))
if version_2_with_negative:
output_null_log_odds_file = os.path.join(output_dir, "null_odds_{}.json".format(prefix))
else:
output_null_log_odds_file = None
predictions = compute_predictions_logits(
examples,
features,
all_results,
n_best_size,
max_answer_length,
do_lower_case,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
verbose_logging,
version_2_with_negative,
null_score_diff_threshold,
tokenizer,
)
# Compute the F1 and exact scores.
results = squad_evaluate(examples, predictions)
return results
def evaluate(args, model, tokenizer, prefix=""):
dataset, examples, features = load_and_cache_examples(args,
tokenizer,
evaluate=True,
output_examples=True)
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
# Note that DistributedSampler samples randomly
eval_sampler = SequentialSampler(dataset)
eval_dataloader = DataLoader(dataset,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
# Eval!
print("***** Running evaluation {} *****".format(prefix))
print(" Num examples = %d" % len(dataset))
print(" Batch size = %d" % args.eval_batch_size)
all_results = []
start_time = timeit.default_timer()
eval_pbar = tqdm(total=len(dataset),
position=0,
leave=True,
file=sys.stdout,
bar_format="{l_bar}%s{bar}%s{r_bar}" %
(Fore.GREEN, Fore.RESET))
for batch in eval_dataloader:
model.eval()
batch = tuple(t.to(args.device) for t in batch)
with torch.no_grad():
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"token_type_ids": batch[2]
}
feature_indices = batch[3]
outputs = model(**inputs)
for i, feature_index in enumerate(feature_indices):
eval_feature = features[feature_index.item()]
unique_id = int(eval_feature.unique_id)
output = [to_list(output[i]) for output in outputs]
start_logits, end_logits = output
result = SquadResult(unique_id, start_logits, end_logits)
all_results.append(result)
eval_pbar.update(batch[0].size(0)) # hiepnh
eval_pbar.close() # hiepnh
evalTime = timeit.default_timer() - start_time
print(" Evaluation done in total %f secs (%f sec per example)" %
(evalTime, evalTime / len(dataset)))
# Compute predictions
output_prediction_file = os.path.join(args.output_dir,
"predictions_{}.json".format(prefix))
output_nbest_file = os.path.join(
args.output_dir, "nbest_predictions_{}.json".format(prefix))
if args.version_2_with_negative:
output_null_log_odds_file = os.path.join(
args.output_dir, "null_odds_{}.json".format(prefix))
else:
output_null_log_odds_file = None
predictions = compute_predictions_logits(
examples,
features,
all_results,
args.n_best_size,
args.max_answer_length,
args.do_lower_case,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
args.verbose_logging,
args.version_2_with_negative,
args.null_score_diff_threshold,
tokenizer,
)
# Compute the F1 and exact scores.
results = squad_evaluate(examples, predictions)
return results
def test_mrc(args, model, tokenizer, prefix=""):
dataset, examples, features = load_and_cache_examples(args,
tokenizer,
evaluate=True,
output_examples=True)
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
# Note that DistributedSampler samples randomly
eval_sampler = SequentialSampler(dataset)
eval_dataloader = DataLoader(dataset,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
# multi-gpu evaluate
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Eval
logger.info("***** Running evaluation {} *****".format(prefix))
logger.info(" Num examples = %d", len(dataset))
logger.info(" Batch size = %d", args.eval_batch_size)
all_results = []
start_time = timeit.default_timer()
for batch in tqdm(eval_dataloader, desc="Evaluating"):
model.eval()
batch = tuple(t.to(args.device) for t in batch)
with torch.no_grad():
inputs = {
'input_ids':
batch[0],
'attention_mask':
batch[1],
'token_type_ids':
None if args.model_type in ['xlm', 'roberta', 'distilbert']
else batch[2],
}
example_indices = batch[3]
# XLNet and XLM use more arguments for their predictions
if args.model_type in ['xlnet', 'xlm']:
inputs.update({'cls_index': batch[4], 'p_mask': batch[5]})
outputs = model.mrc_forward(**inputs, output_embedding=True)
start_logits, end_logits, classification_logits, emb = outputs
for i, example_index in enumerate(example_indices):
eval_feature = features[example_index.item()]
unique_id = int(eval_feature.unique_id)
output = [to_list(output[i]) for output in outputs]
if len(output) >= 5:
start_logits = output[0]
start_top_index = output[1]
end_logits = output[2]
end_top_index = output[3]
cls_logits = output[4]
result = SquadResult(unique_id,
start_logits,
end_logits,
start_top_index=start_top_index,
end_top_index=end_top_index,
cls_logits=cls_logits)
else:
start_logits, end_logits, _ = output
result = SquadResult(unique_id, start_logits, end_logits)
all_results.append(result)
evalTime = timeit.default_timer() - start_time
logger.info(" Evaluation done in total %f secs (%f sec per example)",
evalTime, evalTime / len(dataset))
# Compute predictions
output_prediction_file = os.path.join(args.output_dir,
"predictions_{}.json".format(prefix))
output_nbest_file = os.path.join(
args.output_dir, "nbest_predictions_{}.json".format(prefix))
if args.version_2_with_negative:
output_null_log_odds_file = os.path.join(
args.output_dir, "null_odds_{}.json".format(prefix))
else:
output_null_log_odds_file = None
# XLNet and XLM use a more complex post-processing procedure
if args.model_type in ['xlnet', 'xlm']:
start_n_top = model.config.start_n_top if hasattr(
model, "config") else model.module.config.start_n_top
end_n_top = model.config.end_n_top if hasattr(
model, "config") else model.module.config.end_n_top
predictions = compute_predictions_log_probs(
examples, features, all_results, args.n_best_size,
args.max_answer_length, output_prediction_file, output_nbest_file,
output_null_log_odds_file, start_n_top, end_n_top,
args.version_2_with_negative, tokenizer, args.verbose_logging)
else:
predictions = compute_predictions_logits(
examples, features, all_results, args.n_best_size,
args.max_answer_length, args.do_lower_case, output_prediction_file,
output_nbest_file, output_null_log_odds_file, args.verbose_logging,
args.version_2_with_negative, args.null_score_diff_threshold,
tokenizer)
# Compute the F1 and exact scores.
results = squad_evaluate(examples, predictions)
return results
def evaluate(args, config, model, tokenizer, prefix="", global_step=0):
dataset, examples, features = load_and_cache_examples(args,
config,
tokenizer,
evaluate=True,
output_examples=True)
config['eval'][
'eval_batch_size'] = config.eval.per_gpu_eval_batch_size * max(
1, args.n_gpu)
# Note that DistributedSampler samples randomly
eval_sampler = SequentialSampler(dataset)
eval_dataloader = DataLoader(dataset,
sampler=eval_sampler,
batch_size=config.eval.eval_batch_size)
# multi-gpu evaluate
if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
model = torch.nn.DataParallel(model)
# Eval!
logger.info("***** Running evaluation {} *****".format(prefix))
logger.info(" Num examples = %d", len(dataset))
logger.info(" Batch size = %d", config.eval.eval_batch_size)
all_results = []
start_time = timeit.default_timer()
for batch in tqdm(eval_dataloader, desc="Evaluating"):
model.eval()
batch = tuple(t.to(args.device) for t in batch)
with torch.no_grad():
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"token_type_ids": batch[2],
}
if config.model.model_type in [
"xlm", "roberta", "distilbert", "camembert"
]:
del inputs["token_type_ids"]
example_indices = batch[3]
# XLNet and XLM use more arguments for their predictions
if config.model.model_type in ["xlnet", "xlm"]:
inputs.update({"cls_index": batch[4], "p_mask": batch[5]})
# for lang_id-sensitive xlm models
if hasattr(model, "config") and hasattr(
model.config, "lang2id"):
inputs.update({
"langs":
(torch.ones(batch[0].shape, dtype=torch.int64) *
config.input.lang_id).to(args.device)
})
outputs = model(**inputs)
for i, example_index in enumerate(example_indices):
eval_feature = features[example_index.item()]
unique_id = int(eval_feature.unique_id)
output = [to_list(output[i]) for output in outputs]
# Some models (XLNet, XLM) use 5 arguments for their predictions, while the other "simpler"
# models only use two.
if len(output) >= 5:
start_logits = output[0]
start_top_index = output[1]
end_logits = output[2]
end_top_index = output[3]
cls_logits = output[4]
result = SquadResult(
unique_id,
start_logits,
end_logits,
start_top_index=start_top_index,
end_top_index=end_top_index,
cls_logits=cls_logits,
)
else:
start_logits, end_logits = output
result = SquadResult(unique_id, start_logits, end_logits)
all_results.append(result)
evalTime = timeit.default_timer() - start_time
logger.info(" Evaluation done in total %f secs (%f sec per example)",
evalTime, evalTime / len(dataset))
# Compute predictions
output_prediction_file = os.path.join(config.output.log_dir,
f"predictions_{prefix}.json")
output_nbest_file = os.path.join(
config.output.log_dir,
f"nbest_{config.model.n_best_size}_predictions_{prefix}.json")
if config.input.version_2_with_negative:
output_null_log_odds_file = os.path.join(config.output.log_dir,
f"null_odds_{prefix}.json")
else:
output_null_log_odds_file = None
# XLNet and XLM use a more complex post-processing procedure
if config.model.model_type in ["xlnet", "xlm"]:
start_n_top = model.config.start_n_top if hasattr(
model, "config") else model.module.config.start_n_top
end_n_top = model.config.end_n_top if hasattr(
model, "config") else model.module.config.end_n_top
predictions = compute_predictions_log_probs(
examples,
features,
all_results,
config.model.n_best_size,
config.model.max_answer_length,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
start_n_top,
end_n_top,
config.input.version_2_with_negative,
tokenizer,
config.output.verbose_logging,
)
else:
predictions = compute_predictions_logits(
examples,
features,
all_results,
config.model.n_best_size,
config.model.max_answer_length,
config.model.do_lower_case,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
config.output.verbose_logging,
config.input.version_2_with_negative,
config.model.null_score_diff_threshold,
tokenizer,
)
# Compute the F1 and exact scores.
results = squad_evaluate(examples, predictions)
# Save eval results to output file as well
if prefix == "-1":
# evaluate at the end of training, store in the log_dir directly
output_eval_file = os.path.join(config.output.log_dir,
"eval_results.tsv")
else:
# there is a 'prefix' subfolder
output_eval_file = os.path.join(config.output.log_dir, prefix,
"eval_results.tsv")
if not os.path.exists(
output_eval_file): # file does not exist yet. write header first
with open(output_eval_file, "a") as writer:
writer.write("global_step\t" + "\t".join(results.keys()) +
"\n") # write header
with open(output_eval_file, "a") as writer:
logger.info("***** Eval results {} *****".format(prefix))
for key in sorted(results.keys()):
logger.info(" %s = %s", key, str(results[key]))
line = [str(global_step)] + [str(r) for r in results.values()]
writer.write("\t".join(line) + "\n")
return results
def evaluate(args, model, tokenizer, prefix=""):
dataset, examples, features = load_and_cache_examples(args,
tokenizer,
evaluate=True,
output_examples=True)
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
# Note that DistributedSampler samples randomly
eval_sampler = SequentialSampler(dataset)
eval_dataloader = DataLoader(dataset,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
# multi-gpu evaluate
if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
model = torch.nn.DataParallel(model)
# Eval!
logger.info("***** Running evaluation {} *****".format(prefix))
logger.info(" Num examples = %d", len(dataset))
logger.info(" Batch size = %d", args.eval_batch_size)
all_results = []
start_time = timeit.default_timer()
for batch in tqdm(eval_dataloader, desc="Evaluating"):
model.eval()
batch = tuple(t.to(args.device) for t in batch)
with torch.no_grad():
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"token_type_ids": batch[2],
}
if args.model_type in ["xlm", "roberta", "distilbert"]:
del inputs["token_type_ids"]
# 这里面 的 example_indices 只的是 features 中的哪几个。 （切分问题）
example_indices = batch[3]
# XLNet and XLM use more arguments for their predictions
if args.model_type in ["xlnet", "xlm"]:
inputs.update({"cls_index": batch[4], "p_mask": batch[5]})
# for lang_id-sensitive xlm models
if hasattr(model, "config") and hasattr(
model.config, "lang2id"):
inputs.update({
"langs":
(torch.ones(batch[0].shape, dtype=torch.int64) *
args.lang_id).to(args.device)
})
outputs = model(**inputs)
for i, example_index in enumerate(example_indices):
# tensor.item() only works for tensor containing only one element
eval_feature = features[example_index.item()]
unique_id = int(eval_feature.unique_id)
output = [to_list(output[i]) for output in outputs]
# Some models (XLNet, XLM) use 5 arguments for their predictions, while the other "simpler"
# models only use two.
if len(output) >= 5:
start_logits = output[0]
start_top_index = output[1]
end_logits = output[2]
end_top_index = output[3]
cls_logits = output[4]
result = SquadResult(
unique_id,
start_logits,
end_logits,
start_top_index=start_top_index,
end_top_index=end_top_index,
cls_logits=cls_logits,
)
else:
start_logits, end_logits = output
# print("size of start_logits of {0} is {1}".format(unique_id, len(start_logits)))
result = SquadResult(unique_id, start_logits, end_logits)
all_results.append(result)
evalTime = timeit.default_timer() - start_time
logger.info(" Evaluation done in total %f secs (%f sec per example)",
evalTime, evalTime / len(dataset))
# Compute predictions
output_prediction_file = os.path.join(args.output_dir,
"predictions_{}.json".format(prefix))
output_nbest_file = os.path.join(
args.output_dir, "nbest_predictions_{}.json".format(prefix))
if args.version_2_with_negative:
output_null_log_odds_file = os.path.join(
args.output_dir, "null_odds_{}.json".format(prefix))
else:
output_null_log_odds_file = None
# XLNet and XLM use a more complex post-processing procedure
if args.model_type in ["xlnet", "xlm"]:
start_n_top = model.config.start_n_top if hasattr(
model, "config") else model.module.config.start_n_top
end_n_top = model.config.end_n_top if hasattr(
model, "config") else model.module.config.end_n_top
predictions = compute_predictions_log_probs(
examples,
features,
all_results,
args.n_best_size,
args.max_answer_length,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
start_n_top,
end_n_top,
args.version_2_with_negative,
tokenizer,
args.verbose_logging,
)
else:
# print('******* length of examples is {0}*******'.format(len(examples)))
# print(len(examples))
# print(len(features))
# print(len(all_results))
predictions = compute_predictions_logits(
examples,
features,
all_results,
args.n_best_size,
args.max_answer_length,
args.do_lower_case,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
args.verbose_logging,
args.version_2_with_negative,
args.null_score_diff_threshold,
tokenizer,
real_pred=args.do_real_pred,
)
# Compute the F1 and exact scores. Skip this step when we do real prediction (without ground truth)
# Writing files are done in the previous steps,
if not args.do_real_pred:
results = squad_evaluate(examples, predictions)
# print('ok, {0}'.format(results))
return results
else:
return None
def evaluate(args, model, tokenizer, prefix=""):
dataset, examples, features = load_and_cache_examples(args,
tokenizer,
evaluate=True,
output_examples=True)
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
if args.use_jit_trace:
enable_tracing()
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
is_eval_traced = False
# Note that DistributedSampler samples randomly
eval_sampler = SequentialSampler(dataset)
eval_dataloader = DataLoader(dataset,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
# multi-gpu evaluate
if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
model = torch.nn.DataParallel(model)
# Eval!
logger.info("***** Running evaluation {} *****".format(prefix))
logger.info(" Num examples = %d", len(dataset))
logger.info(" Batch size = %d", args.eval_batch_size)
all_results = []
start_time = timeit.default_timer()
for batch in tqdm(eval_dataloader, desc="Evaluating"):
## Habana doesn't support Long tensors
## Hence we need to convert start and end positions to int
if args.use_habana:
batch[0] = batch[0].to(dtype=torch.int32)
batch[1] = batch[1].to(dtype=torch.int32)
batch[2] = batch[2].to(dtype=torch.int32)
position_ids_cpu = compute_position_ids(batch[0])
model.eval()
batch = tuple(t.to(args.device) for t in batch)
position_ids = position_ids_cpu.to(args.device)
with torch.no_grad():
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"token_type_ids": batch[2],
"position_ids": position_ids,
}
tensor_dummy = torch.zeros(1).to(args.device)
if args.model_type in [
"xlm", "roberta", "distilbert", "camembert"
]:
del inputs["token_type_ids"]
feature_indices = batch[3]
# XLNet and XLM use more arguments for their predictions
if args.model_type in ["xlnet", "xlm"]:
inputs.update({"cls_index": batch[4], "p_mask": batch[5]})
# for lang_id-sensitive xlm models
if hasattr(model, "config") and hasattr(
model.config, "lang2id"):
inputs.update({
"langs":
(torch.ones(batch[0].shape, dtype=torch.int64) *
args.lang_id).to(args.device)
})
if args.use_jit_trace and is_eval_traced == False:
model_trace = torch.jit.trace(
model, (batch[0], batch[1], batch[2], position_ids,
tensor_dummy, tensor_dummy, tensor_dummy,
tensor_dummy, tensor_dummy, tensor_dummy),
check_trace=False)
is_eval_traced = True
model_trace.eval()
if args.use_jit_trace:
outputs = model_trace(batch[0], batch[1], batch[2],
position_ids, tensor_dummy, tensor_dummy,
tensor_dummy, tensor_dummy, tensor_dummy,
tensor_dummy)
else:
outputs = model(**inputs)
feature_indices = feature_indices.to("cpu")
for i, feature_index in enumerate(feature_indices):
eval_feature = features[feature_index.item()]
unique_id = int(eval_feature.unique_id)
output = [to_list(output[i]) for output in outputs]
# Some models (XLNet, XLM) use 5 arguments for their predictions, while the other "simpler"
# models only use two.
if len(output) >= 5:
start_logits = output[0]
start_top_index = output[1]
end_logits = output[2]
end_top_index = output[3]
cls_logits = output[4]
result = SquadResult(
unique_id,
start_logits,
end_logits,
start_top_index=start_top_index,
end_top_index=end_top_index,
cls_logits=cls_logits,
)
else:
start_logits, end_logits = output
result = SquadResult(unique_id, start_logits, end_logits)
all_results.append(result)
evalTime = timeit.default_timer() - start_time
logger.info(" Evaluation done in total %f secs (%f sec per example)",
evalTime, evalTime / len(dataset))
# Compute predictions
output_prediction_file = os.path.join(args.output_dir,
"predictions_{}.json".format(prefix))
output_nbest_file = os.path.join(
args.output_dir, "nbest_predictions_{}.json".format(prefix))
if args.version_2_with_negative:
output_null_log_odds_file = os.path.join(
args.output_dir, "null_odds_{}.json".format(prefix))
else:
output_null_log_odds_file = None
# XLNet and XLM use a more complex post-processing procedure
if args.model_type in ["xlnet", "xlm"]:
start_n_top = model.config.start_n_top if hasattr(
model, "config") else model.module.config.start_n_top
end_n_top = model.config.end_n_top if hasattr(
model, "config") else model.module.config.end_n_top
predictions = compute_predictions_log_probs(
examples,
features,
all_results,
args.n_best_size,
args.max_answer_length,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
start_n_top,
end_n_top,
args.version_2_with_negative,
tokenizer,
args.verbose_logging,
)
else:
predictions = compute_predictions_logits(
examples,
features,
all_results,
args.n_best_size,
args.max_answer_length,
args.do_lower_case,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
args.verbose_logging,
args.version_2_with_negative,
args.null_score_diff_threshold,
tokenizer,
)
# Compute the F1 and exact scores.
results = squad_evaluate(examples, predictions)
return results
def evaluate(args, model, tokenizer, prefix="", global_step=None):
dataset, examples, features = load_and_cache_examples(args, tokenizer, evaluate=True, output_examples=True)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
# Note that DistributedSampler samples randomly
eval_sampler = SequentialSampler(dataset)
eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
# Eval!
logger.info("***** Running evaluation {} *****".format(prefix))
logger.info(" Num examples = %d", len(dataset))
logger.info(" Batch size = %d", args.eval_batch_size)
all_results = []
start_time = timeit.default_timer()
for batch in progress_bar(eval_dataloader):
model.eval()
batch = tuple(t.to(args.device) for t in batch)
with torch.no_grad():
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"token_type_ids": batch[2],
}
if args.model_type in ["xlm", "roberta", "distilbert", "distilkobert", "xlm-roberta"]:
del inputs["token_type_ids"]
example_indices = batch[3]
outputs = model(**inputs)
for i, example_index in enumerate(example_indices):
eval_feature = features[example_index.item()]
unique_id = int(eval_feature.unique_id)
output = [to_list(output[i]) for output in outputs]
# Some models (XLNet, XLM) use 5 arguments for their predictions, while the other "simpler"
# models only use two.
if len(output) >= 5:
start_logits = output[0]
start_top_index = output[1]
end_logits = output[2]
end_top_index = output[3]
cls_logits = output[4]
result = SquadResult(
unique_id,
start_logits,
end_logits,
start_top_index=start_top_index,
end_top_index=end_top_index,
cls_logits=cls_logits,
)
else:
start_logits, end_logits = output
result = SquadResult(unique_id, start_logits, end_logits)
all_results.append(result)
evalTime = timeit.default_timer() - start_time
logger.info(" Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(dataset))
# Compute predictions
output_prediction_file = os.path.join(args.output_dir, "predictions_{}.json".format(prefix))
output_nbest_file = os.path.join(args.output_dir, "nbest_predictions_{}.json".format(prefix))
if args.version_2_with_negative:
output_null_log_odds_file = os.path.join(args.output_dir, "null_odds_{}.json".format(prefix))
else:
output_null_log_odds_file = None
predictions = compute_predictions_logits(
examples,
features,
all_results,
args.n_best_size,
args.max_answer_length,
args.do_lower_case,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
args.verbose_logging,
args.version_2_with_negative,
args.null_score_diff_threshold,
tokenizer,
)
# Compute the F1 and exact scores.
results = squad_evaluate(examples, predictions)
# Write the result
# Write the evaluation result on file
output_dir = os.path.join(args.output_dir, 'eval')
if not os.path.exists(output_dir):
os.makedirs(output_dir)
output_eval_file = os.path.join(output_dir, "eval_result_{}_{}.txt".format(list(filter(None, args.model_name_or_path.split("/"))).pop(),
global_step))
logger.info("***** Official Eval results *****")
with open(output_eval_file, "w", encoding='utf-8') as f:
official_eval_results = eval_during_train(args)
for key in sorted(official_eval_results.keys()):
logger.info(" %s = %s", key, str(official_eval_results[key]))
f.write(" {} = {}\n".format(key, str(official_eval_results[key])))
return results
def evaluate(args, model, tokenizer, prefix="", calibration=False):
dataset, examples, features = load_and_cache_examples(args,
tokenizer,
evaluate=True,
output_examples=True)
dataset_cached = "./dataset_cached"
if not os.path.exists(dataset_cached):
os.makedirs(dataset_cached)
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
# Note that DistributedSampler samples randomly
eval_sampler = SequentialSampler(dataset)
eval_dataloader = DataLoader(dataset,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
calibation_iteration = int(
(len(dataset) * 0.05 + args.eval_batch_size - 1) /
args.eval_batch_size)
# multi-gpu evaluate
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Eval!
logger.info("***** Running evaluation {} *****".format(prefix))
logger.info(" Num examples = %d", len(dataset))
logger.info(" Batch size = %d", args.eval_batch_size)
if args.mkldnn_eval:
from torch.utils import mkldnn as mkldnn_utils
model = mkldnn_utils.to_mkldnn(model)
print(model)
all_results = []
evalTime = 0
nb_eval_steps = 0
for batch in tqdm(eval_dataloader, desc="Evaluating"):
model.eval()
batch = tuple(t.to(args.device) for t in batch)
if calibration and nb_eval_steps >= calibation_iteration:
break
with torch.no_grad():
inputs = {'input_ids': batch[0], 'attention_mask': batch[1]}
if args.model_type != 'distilbert':
inputs[
'token_type_ids'] = None if args.model_type == 'xlm' else batch[
2] # XLM don't use segment_ids
example_indices = batch[3]
# XLNet and XLM use more arguments for their predictions
if args.model_type in ['xlnet', 'xlm']:
inputs.update({'cls_index': batch[4], 'p_mask': batch[5]})
if nb_eval_steps >= args.warmup:
start_time = timeit.default_timer()
outputs = model(**inputs)
for i, example_index in enumerate(example_indices):
eval_feature = features[example_index.item()]
unique_id = int(eval_feature.unique_id)
output = [to_list(output[i]) for output in outputs]
# Some models (XLNet, XLM) use 5 arguments for their predictions, while the other "simpler"
# models only use two.
if len(output) >= 5:
start_logits = output[0]
start_top_index = output[1]
end_logits = output[2]
end_top_index = output[3]
cls_logits = output[4]
result = SquadResult(unique_id,
start_logits,
end_logits,
start_top_index=start_top_index,
end_top_index=end_top_index,
cls_logits=cls_logits)
else:
start_logits, end_logits = output
result = SquadResult(unique_id, start_logits, end_logits)
all_results.append(result)
if nb_eval_steps >= args.warmup:
evalTime += (timeit.default_timer() - start_time)
nb_eval_steps += 1
if nb_eval_steps >= args.warmup:
perf = (len(eval_dataloader) -
args.warmup) * args.eval_batch_size / evalTime
logger.info("Evaluation done in total %f secs (%f samples/sec)",
evalTime, (len(dataset) - args.warmup) *
args.eval_batch_size / evalTime)
else:
logger.info(
"*****no perfformance, please check dataset length and warmup number *****"
)
# Compute predictions
output_prediction_file = os.path.join(dataset_cached,
"predictions_{}.json".format(prefix))
output_nbest_file = os.path.join(
dataset_cached, "nbest_predictions_{}.json".format(prefix))
if args.version_2_with_negative:
output_null_log_odds_file = os.path.join(
dataset_cached, "null_odds_{}.json".format(prefix))
else:
output_null_log_odds_file = None
# XLNet and XLM use a more complex post-processing procedure
if args.model_type in ['xlnet', 'xlm']:
start_n_top = model.config.start_n_top if hasattr(
model, "config") else model.module.config.start_n_top
end_n_top = model.config.end_n_top if hasattr(
model, "config") else model.module.config.end_n_top
predictions = compute_predictions_log_probs(
examples, features, all_results, args.n_best_size,
args.max_answer_length, output_prediction_file, output_nbest_file,
output_null_log_odds_file, start_n_top, end_n_top,
args.version_2_with_negative, tokenizer, args.verbose_logging)
elif not calibration:
predictions = compute_predictions_logits(
examples, features, all_results, args.n_best_size,
args.max_answer_length, args.do_lower_case, output_prediction_file,
output_nbest_file, output_null_log_odds_file, args.verbose_logging,
args.version_2_with_negative, args.null_score_diff_threshold)
# Compute the F1 and exact scores.
if not calibration:
results = squad_evaluate(examples, predictions)
return results, perf
def run_prediction(question_texts, context_text):
"""Setup function to compute predictions"""
examples = []
for i, question_text in enumerate(question_texts):
example = SquadExample(
qas_id=str(i),
question_text=question_text,
context_text=context_text,
answer_text=None,
start_position_character=None,
title="Predict",
is_impossible=False,
answers=None,
)
examples.append(example)
features, dataset = squad_convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=384,
doc_stride=128,
max_query_length=64,
is_training=False,
return_dataset="pt",
threads=4,
)
eval_sampler = SequentialSampler(dataset)
eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=10)
all_results = []
for batch in eval_dataloader:
model.eval()
batch = tuple(t.to(device) for t in batch)
with torch.no_grad():
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"token_type_ids": batch[2],
}
example_indices = batch[3]
outputs = model(**inputs)
for i, example_index in enumerate(example_indices):
eval_feature = features[example_index.item()]
unique_id = int(eval_feature.unique_id)
output = [to_list(output[i]) for output in outputs]
start_logits, end_logits = output
result = SquadResult(unique_id, start_logits, end_logits)
all_results.append(result)
output_prediction_file = "predictions.json"
output_nbest_file = "nbest_predictions.json"
output_null_log_odds_file = "null_predictions.json"
predictions = compute_predictions_logits(
examples,
features,
all_results,
n_best_size,
max_answer_length,
do_lower_case,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
False, # verbose_logging
True, # version_2_with_negative
null_score_diff_threshold,
tokenizer,
)
return predictions
# context = "New Zealand (Māori: Aotearoa) is a sovereign island country in the southwestern Pacific Ocean. It has a total land area of 268,000 square kilometres (103,500 sq mi), and a population of 4.9 million. New Zealand's capital city is Wellington, and its most populous city is Auckland."
# questions = ["How many people live in New Zealand?",
# "What's the largest city?"]
#
# # Run method
# predictions = run_prediction(questions, context)
#
# # Print results
# for key in predictions.keys():
# print(predictions[key])
def evaluate(args, model_path1, model1, model2, model3, tokenizer, prefix=""):
dataset, examples, features = load_and_cache_examples(args,
model_path1,
tokenizer,
evaluate=True,
output_examples=True)
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
# Note that DistributedSampler samples randomly
eval_sampler = SequentialSampler(dataset)
eval_dataloader = DataLoader(dataset,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
# multi-gpu evaluate
if args.n_gpu > 1 and not isinstance(model1, torch.nn.DataParallel):
model1 = torch.nn.DataParallel(model1)
if args.n_gpu > 1 and not isinstance(model2, torch.nn.DataParallel):
model2 = torch.nn.DataParallel(model2)
if args.n_gpu > 1 and not isinstance(model3, torch.nn.DataParallel):
model3 = torch.nn.DataParallel(model3)
# Eval!
logger.info("***** Running evaluation {} *****".format(prefix))
logger.info(" Num examples = %d", len(dataset))
logger.info(" Batch size = %d", args.eval_batch_size)
all_results = []
start_time = timeit.default_timer()
for batch in tqdm(eval_dataloader, desc="Evaluating"):
model1.eval()
model2.eval()
model3.eval()
batch = tuple(t.to(args.device) for t in batch)
with torch.no_grad():
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"token_type_ids": batch[2],
}
feature_indices = batch[3]
outputs1 = model1(**inputs)
outputs2 = model2(**inputs)
outputs3 = model3(**inputs)
# print("outputs1", outputs1)
for i, feature_index in enumerate(feature_indices):
eval_feature = features[feature_index.item()]
unique_id = int(eval_feature.unique_id)
output1 = [to_list(output1[i]) for output1 in outputs1]
output2 = [to_list(output2[i]) for output2 in outputs2]
output3 = [to_list(output3[i]) for output3 in outputs3]
start_logits1, end_logits1 = output1
start_logits2, end_logits2 = output2
start_logits3, end_logits3 = output3
# TODO homework 补充模型集成代码 权重为 0.4, 0.2, 0.4 计算：start_logits， end_logits
weight = [0.4, 0.2, 0.4]
start_logits = start_logits1 * weight[0] + start_logits2 * weight[
1] + start_logits3 * weight[2]
end_logits = end_logits1 * weight[0] + end_logits2 * weight[
1] + end_logits3 * weight[2]
result = SquadResult(unique_id, start_logits, end_logits)
all_results.append(result)
evalTime = timeit.default_timer() - start_time
logger.info(
" Evaluation done in total %f secs (%f sec per example)",
evalTime,
evalTime / len(dataset),
)
# Compute predictions
output_prediction_file = os.path.join(args.output_dir,
"predictions_{}.json".format(prefix))
output_nbest_file = os.path.join(
args.output_dir, "nbest_predictions_{}.json".format(prefix))
if args.version_2_with_negative:
output_null_log_odds_file = os.path.join(
args.output_dir, "null_odds_{}.json".format(prefix))
else:
output_null_log_odds_file = None
predictions = compute_predictions_logits(
examples,
features,
all_results,
args.n_best_size,
args.max_answer_length,
args.do_lower_case,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
args.verbose_logging,
args.version_2_with_negative,
args.null_score_diff_threshold,
tokenizer,
)
# Compute the F1 and exact scores.
results = squad_evaluate(examples, predictions)
return results
def evaluate(args, model, tokenizer, prefix=""):
languages = args.language.split(',')
all_languages_results = {}
processor = MLQAProcessor()
for split, lang in itertools.product(["dev", "test"], languages):
# for split, lang in itertools.product(["dev", "test"], languages):
print("evaluating on {0} {1}".format(split, lang))
dataset, examples, features = load_and_cache_examples(
args, tokenizer, language=lang, split=split, output_examples=True)
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
args.eval_batch_size = args.per_gpu_eval_batch_size * max(
1, args.n_gpu)
# Note that DistributedSampler samples randomly
eval_sampler = SequentialSampler(dataset)
eval_dataloader = DataLoader(dataset,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
# multi-gpu evaluate
if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
model = torch.nn.DataParallel(model)
# Eval!
logger.info("***** Running evaluation {} *****".format(prefix))
logger.info(" Num examples = %d", len(dataset))
logger.info(" Batch size = %d", args.eval_batch_size)
all_results = []
start_time = timeit.default_timer()
for batch in tqdm(eval_dataloader, desc="Evaluating"):
model.eval()
batch = tuple(t.to(args.device) for t in batch)
with torch.no_grad():
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"token_type_ids": batch[2],
}
if args.model_type in [
"xlm", "roberta", "distilbert", "camembert"
]:
del inputs["token_type_ids"]
example_indices = batch[3]
# XLNet and XLM use more arguments for their predictions
if args.model_type in ["xlnet", "xlm"]:
inputs.update({"cls_index": batch[4], "p_mask": batch[5]})
# for lang_id-sensitive xlm models
if hasattr(model, "config") and hasattr(
model.config, "lang2id"):
inputs.update({
"langs":
(torch.ones(batch[0].shape, dtype=torch.int64) *
args.lang_id).to(args.device)
})
outputs = model(**inputs)
for i, example_index in enumerate(example_indices):
eval_feature = features[example_index.item()]
unique_id = int(eval_feature.unique_id)
output = [to_list(output[i]) for output in outputs]
# Some models (XLNet, XLM) use 5 arguments for their predictions, while the other "simpler"
# models only use two.
if len(output) >= 5:
start_logits = output[0]
start_top_index = output[1]
end_logits = output[2]
end_top_index = output[3]
cls_logits = output[4]
result = SquadResult(
unique_id,
start_logits,
end_logits,
start_top_index=start_top_index,
end_top_index=end_top_index,
cls_logits=cls_logits,
)
else:
start_logits, end_logits = output
result = SquadResult(unique_id, start_logits, end_logits)
all_results.append(result)
evalTime = timeit.default_timer() - start_time
logger.info(" Evaluation done in total %f secs (%f sec per example)",
evalTime, evalTime / len(dataset))
# Compute predictions
output_prediction_file = os.path.join(args.output_dir,
"{}.prediction".format(lang))
output_nbest_file = os.path.join(
args.output_dir,
"nbest_predictions_{}_{}_{}.json".format(prefix, split, lang))
if args.version_2_with_negative:
output_null_log_odds_file = os.path.join(
args.output_dir,
"null_odds_{}_{}_{}.json".format(prefix, split, lang))
else:
output_null_log_odds_file = None
# XLNet and XLM use a more complex post-processing procedure
if args.model_type in ["xlnet", "xlm"]:
start_n_top = model.config.start_n_top if hasattr(
model, "config") else model.module.config.start_n_top
end_n_top = model.config.end_n_top if hasattr(
model, "config") else model.module.config.end_n_top
predictions = compute_predictions_log_probs(
examples,
features,
all_results,
args.n_best_size,
args.max_answer_length,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
start_n_top,
end_n_top,
args.version_2_with_negative,
tokenizer,
args.verbose_logging,
)
else:
predictions = compute_predictions_logits(
examples,
features,
all_results,
args.n_best_size,
args.max_answer_length,
args.do_lower_case,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
args.verbose_logging,
args.version_2_with_negative,
args.null_score_diff_threshold,
tokenizer,
map_to_origin=not (args.model_type == "xlmr" and lang == 'zh'))
# Compute the F1 and exact scores.
# results = squad_evaluate(examples, predictions)
results = evaluate_with_path(
processor.get_dataset_path(args.data_dir, split, lang),
output_prediction_file, lang)
all_languages_results["{0}_{1}".format(split, lang)] = results
for split in ["dev", "test"]:
all_languages_results["{0}_avg".format(split)] = average_dic([
value for key, value in all_languages_results.items()
if split in key
])
return all_languages_results
def run_prediction(model, tokenizer, device, output_dir, filename, question_texts, context_text):
"""Setup function to compute predictions"""
examples = []
for i, question_text in enumerate(question_texts):
example = SquadExample(
qas_id=str(i),
question_text=question_text,
context_text=context_text,
answer_text=None,
start_position_character=None,
title="Predict",
is_impossible=False,
answers=None,
)
examples.append(example)
features, dataset = squad_convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=384,
doc_stride=128,
max_query_length=64,
is_training=False,
return_dataset="pt",
threads=1,
)
eval_sampler = SequentialSampler(dataset)
eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=10)
all_results = []
for batch in eval_dataloader:
model.eval()
batch = tuple(t.to(device) for t in batch)
with torch.no_grad():
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"token_type_ids": batch[2],
}
example_indices = batch[3]
outputs = model(**inputs)
for i, example_index in enumerate(example_indices):
eval_feature = features[example_index.item()]
unique_id = int(eval_feature.unique_id)
output = [to_list(output[i]) for output in outputs]
start_logits, end_logits = output
result = SquadResult(unique_id, start_logits, end_logits)
all_results.append(result)
output_prediction_file = os.path.join(output_dir, filename + "_predictions.json")
output_nbest_file = os.path.join(output_dir, filename + "_nbest_predictions.json")
output_null_log_odds_file = os.path.join(output_dir, filename + "_null_predictions.json")
predictions = compute_predictions_logits(
examples,
features,
all_results,
n_best_size,
max_answer_length,
do_lower_case,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
False, # verbose_logging
True, # version_2_with_negative
null_score_diff_threshold,
tokenizer,
)
return predictions
def predict(
self,
query: Union[List[str], str],
context: Union[List[str], str],
n_best_size: int = 5,
mini_batch_size: int = 32,
max_answer_length: int = 10,
do_lower_case: bool = False,
version_2_with_negative: bool = False,
verbose_logging: bool = False,
null_score_diff_threshold: float = 0.0,
max_seq_length: int = 512,
doc_stride: int = 128,
max_query_length: int = 64,
**kwargs,
) -> Tuple[Tuple[str, List[OrderedDict]], Tuple[OrderedDict, OrderedDict]]:
""" Predict method for running inference using the pre-trained question answering model
* **query** - String or list of strings that specify the ordered questions corresponding to `context`
* **context** - String or list of strings that specify the ordered contexts corresponding to `query`
* **n_best_size** - Number of top n results you want
* **mini_batch_size** - Mini batch size
* **max_answer_length** - Maximum token length for answers that are returned
* **do_lower_case** - Set as `True` if using uncased QA models
* **version_2_with_negative** - Set as True if using QA model with SQUAD2.0
* **verbose_logging** - Set True if you want prediction verbose loggings
* **null_score_diff_threshold** - Threshold for predicting null(no answer) in Squad 2.0 Model. Default is 0.0. Raise this if you want fewer null answers
* **max_seq_length** - Maximum context token length. Check model configs to see max sequence length the model was trained with
* **doc_stride** - Number of token strides to take when splitting up conext into chunks of size `max_seq_length`
* **max_query_length** - Maximum token length for queries
* ****kwargs**(Optional) - Optional arguments for the Transformers model (mostly for saving evaluations)
"""
# Make string input consistent as list
if isinstance(query, str):
query = [query]
context = [context]
assert len(query) == len(context)
examples = self._mini_squad_processor(query=query, context=context)
features, dataset = squad_convert_examples_to_features(
examples,
self.tokenizer,
max_seq_length=max_seq_length,
doc_stride=doc_stride,
max_query_length=max_query_length,
is_training=False,
return_dataset="pt",
threads=1,
)
all_results = []
with torch.no_grad():
dataloader = DataLoader(dataset, batch_size=mini_batch_size)
for batch in tqdm(dataloader, desc="Predicting answer"):
self.model.eval()
batch = tuple(t.to(self.device) for t in batch)
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"token_type_ids": batch[2],
}
example_indices = batch[3]
if isinstance(
self.model,
(
XLMForQuestionAnswering,
RobertaForQuestionAnswering,
DistilBertForQuestionAnswering,
CamembertForQuestionAnswering,
),
):
del inputs["token_type_ids"]
# XLNet and XLM use more arguments for their predictions
if isinstance(self.model, self.xmodel_instances):
inputs.update({"cls_index": batch[4], "p_mask": batch[5]})
# for lang_id-sensitive xlm models
if hasattr(self.model, "config") and hasattr(
self.model.config, "lang2id"):
# Set language id as 0 for now
inputs.update({
"langs":
(torch.ones(batch[0].shape, dtype=torch.int64) *
0).to(self.device)
})
outputs = self.model(**inputs)
# Iterate through and produce `SquadResults
for i, example_index in enumerate(example_indices):
eval_feature = features[example_index.item()]
unique_id = int(eval_feature.unique_id)
output = [self.to_list(output[i]) for output in outputs]
if isinstance(self.model, self.xmodel_instances):
# Some models like the ones in `self.xmodel_instances` use 5 arguments for their predictions
start_logits = output[0]
start_top_index = output[1]
end_logits = output[2]
end_top_index = output[3]
cls_logits = output[4]
result = SquadResult(
unique_id,
start_logits,
end_logits,
start_top_index=start_top_index,
end_top_index=end_top_index,
cls_logits=cls_logits,
)
else:
start_logits, end_logits = output
result = SquadResult(unique_id, start_logits,
end_logits)
all_results.append(result)
if isinstance(self.model, self.xmodel_instances):
start_n_top = (self.model.config.start_n_top if hasattr(
self.model, "config") else
self.model.module.config.start_n_top)
end_n_top = (self.model.config.end_n_top if hasattr(
self.model, "config") else
self.model.module.config.end_n_top)
answers, n_best = compute_predictions_log_probs(
examples,
features,
all_results,
n_best_size,
max_answer_length,
start_n_top,
end_n_top,
version_2_with_negative,
self.tokenizer,
verbose_logging,
**kwargs,
)
else:
answers, n_best = compute_predictions_logits(
examples,
features,
all_results,
n_best_size,
max_answer_length,
do_lower_case,
verbose_logging,
version_2_with_negative,
null_score_diff_threshold,
self.tokenizer,
**kwargs,
)
return answers, n_best
def evaluate(self, dataset, examples, features, prefix=""):
eval_batch_size = 8
eval_sampler = SequentialSampler(dataset)
eval_dataloader = DataLoader(dataset,
sampler=eval_sampler,
batch_size=eval_batch_size)
all_results = []
for batch in tqdm(eval_dataloader, desc="Evaluating"):
self._model.eval()
batch = tuple(t.to(self._device) for t in batch)
with torch.no_grad():
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"token_type_ids": batch[2],
}
example_indices = batch[3]
if self._model_name in ['xlnet']:
inputs.update({"cls_index": batch[4], "p_mask": batch[5]})
print("Coding: inputs ", inputs)
outputs = self._model(**inputs)
# feature is needed
for i, example_index in enumerate(example_indices):
eval_feature = features[example_index.item()]
unique_id = int(eval_feature.unique_id)
print("Coding: unique_id ", unique_id)
output = [self._to_list(output[i]) for output in outputs]
start_logits = output[0]
start_top_index = output[1]
end_logits = output[2]
end_top_index = output[3]
cls_logits = output[4]
result = SquadResult(unique_id,
start_logits,
end_logits,
start_top_index=start_top_index,
end_top_index=end_top_index,
cls_logits=cls_logits)
all_results.append(result)
# Compute predictions
output_dir = os.getcwd()
output_prediction_file = os.path.join(
output_dir, "predictions_{}.json".format(prefix))
output_nbest_file = os.path.join(
output_dir, "nbest_predictions_{}.json".format(prefix))
version_2_with_negative = True
output_null_log_odds_file = None
if version_2_with_negative:
output_null_log_odds_file = os.path.join(
output_dir, "null_odds_{}.json".format(prefix))
else:
output_null_log_odds_file = None
n_best_size = 20
max_answer_length = 30
verbose_logging = True
if self._model_name in ['xlnet']:
start_n_top = self._model.config.start_n_top
end_n_top = self._model.config.end_n_top
predictions = compute_predictions_log_probs(
examples, features, all_results, n_best_size,
max_answer_length, output_prediction_file, output_nbest_file,
output_null_log_odds_file, start_n_top, end_n_top,
version_2_with_negative, self._tokenizer, verbose_logging)
# Compute the F1 and exact scores.
results = squad_evaluate(examples, predictions)
return results
