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LLaMA-Factory-Mirror
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fix #1263

This commit is contained in:
hiyouga 2023-11-19 16:05:18 +08:00
parent 1740131d63
commit 065bfaeed4
1 changed files with 11 additions and 6 deletions
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17
src/llmtuner/train/sft/trainer.py
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@ -39,10 +39,10 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
prompt_len, label_len = inputs["input_ids"].size(-1), inputs["labels"].size(-1) prompt_len, label_len = inputs["input_ids"].size(-1), inputs["labels"].size(-1)
if prompt_len > label_len: if prompt_len > label_len:
inputs["labels"] = self._pad_tensors_to_target_len(inputs["labels"], inputs["input_ids"]) inputs["labels"] = self._pad_tensors_to_target_len(inputs["labels"], inputs["input_ids"])
if label_len > prompt_len: if label_len > prompt_len: # truncate the labels instead of padding the inputs (llama2 fp16 compatibility)
inputs["labels"] = inputs["labels"][:, :prompt_len] # truncate the labels instead of padding the inputs inputs["labels"] = inputs["labels"][:, :prompt_len]
loss, generated_tokens, _ = super().prediction_step( loss, generated_tokens, _ = super().prediction_step( # ignore the returned labels (may be truncated)
model, inputs, prediction_loss_only=prediction_loss_only, ignore_keys=ignore_keys model, inputs, prediction_loss_only=prediction_loss_only, ignore_keys=ignore_keys
) )
if generated_tokens is not None and self.args.predict_with_generate: if generated_tokens is not None and self.args.predict_with_generate:
@ -79,14 +79,19 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
output_prediction_file = os.path.join(self.args.output_dir, "generated_predictions.jsonl") output_prediction_file = os.path.join(self.args.output_dir, "generated_predictions.jsonl")
logger.info(f"Saving prediction results to {output_prediction_file}") logger.info(f"Saving prediction results to {output_prediction_file}")
preds = np.where(predict_results.predictions != IGNORE_INDEX, predict_results.predictions, self.tokenizer.pad_token_id)
labels = np.where(predict_results.label_ids != IGNORE_INDEX, predict_results.label_ids, self.tokenizer.pad_token_id) labels = np.where(predict_results.label_ids != IGNORE_INDEX, predict_results.label_ids, self.tokenizer.pad_token_id)
preds = np.where(predict_results.predictions != IGNORE_INDEX, predict_results.predictions, self.tokenizer.pad_token_id)
for i in range(len(preds)):
pad_len = np.nonzero(preds[i] != self.tokenizer.pad_token_id)[0]
if len(pad_len):
preds[i] = np.concatenate((preds[i][pad_len[0]:], preds[i][:pad_len[0]]), axis=-1) # move pad token to last
decoded_labels = self.tokenizer.batch_decode(labels, skip_special_tokens=True, clean_up_tokenization_spaces=False)
decoded_preds = self.tokenizer.batch_decode(preds, skip_special_tokens=True, clean_up_tokenization_spaces=True) decoded_preds = self.tokenizer.batch_decode(preds, skip_special_tokens=True, clean_up_tokenization_spaces=True)
decoded_labels = self.tokenizer.batch_decode(labels, skip_special_tokens=True, clean_up_tokenization_spaces=True)
with open(output_prediction_file, "w", encoding="utf-8") as writer: with open(output_prediction_file, "w", encoding="utf-8") as writer:
res: List[str] = [] res: List[str] = []
for pred, label in zip(decoded_preds, decoded_labels): for label, pred in zip(decoded_labels, decoded_preds):
res.append(json.dumps({"label": label, "predict": pred}, ensure_ascii=False)) res.append(json.dumps({"label": label, "predict": pred}, ensure_ascii=False))
writer.write("\n".join(res)) writer.write("\n".join(res))