From 2a8d041806df41fa3719505d1b3379bbbd369574 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期六, 08 六月 2024 21:35:21 +0800
Subject: [PATCH] fix bug
---
funasr/models/llm_asr/model.py | 37 ++++++++++++++++++++++++++++++-------
1 files changed, 30 insertions(+), 7 deletions(-)
diff --git a/funasr/models/llm_asr/model.py b/funasr/models/llm_asr/model.py
index 0955e84..5fde3ff 100644
--- a/funasr/models/llm_asr/model.py
+++ b/funasr/models/llm_asr/model.py
@@ -19,6 +19,7 @@
from funasr.utils.datadir_writer import DatadirWriter
from funasr.register import tables
from funasr.train_utils.device_funcs import to_device
+import traceback
@tables.register("model_classes", "LLMASR")
@@ -489,6 +490,7 @@
fbank_fake_len = fbank_fake_lens[batch_idx].item()
fbank_beg_idx = fbank_beg[batch_idx, 0].item()
min_len = min(fbank_fake_len, inputs_embeds.shape[1] - fbank_beg_idx)
+
try:
inputs_embeds[batch_idx, fbank_beg_idx : fbank_beg_idx + min_len, :] = encoder_out[
batch_idx, :min_len, :
@@ -496,10 +498,10 @@
except Exception as e:
logging.error(f"{str(e)}, {traceback.format_exc()}")
logging.info(
- f"batch_idx: {batch_idx}, inputs_embeds: {inputs_embeds.shape}, fbank_beg_idx: {fbank_beg_idx}, min_len: {min_len}, fbank_fake_len: {fbank_fake_len}"
+ f"batch_idx: {batch_idx}, inputs_embeds: {inputs_embeds.shape}, fbank_beg_idx: {fbank_beg_idx}, min_len: {min_len}, fbank_fake_len: {fbank_fake_len}, encoder_out: {encoder_out.shape}, encoder_out_lens: {encoder_out_lens[batch_idx].item()}"
)
fbank_fake_len = encoder_out_lens[batch_idx].item()
- min_len = min(fbank_fake_len, inputs_embeds.shape[1] - fbank_beg_idx)
+ min_len = min(fbank_fake_len, min_len)
inputs_embeds[batch_idx, fbank_beg_idx : fbank_beg_idx + min_len, :] = encoder_out[
batch_idx, :min_len, :
]
@@ -568,6 +570,7 @@
[],
[],
[],
+ [],
)
for i, (system_prompt, user_prompt, target_out) in enumerate(zip(system, user, assistant)):
@@ -624,7 +627,7 @@
input_ids = torch.tensor(input_ids, dtype=torch.int64) # [: self.max_token_length]
attention_mask = torch.tensor([1] * len(input_ids), dtype=torch.int32)
labels = torch.tensor(labels, dtype=torch.int64) # [: self.max_token_length]
- source_ids = torch.tensor(source_ids, dtype=torch.int64)
+ source_ids = torch.tensor(source_ids_i, dtype=torch.int64)
target_ids = torch.tensor(target_ids, dtype=torch.int64)
fbank = speech[0, :, :]
@@ -662,7 +665,7 @@
if kwargs.get("batch_size", 1) > 1:
raise NotImplementedError("batch decoding is not implemented")
- contents = self.data_template(data_in)
+ contents = self.data_template(data_in[0])
output = self.data_load_speech(contents, tokenizer, frontend, **kwargs)
batch = to_device(output, kwargs["device"])
@@ -676,7 +679,7 @@
input_ids = batch["input_ids"]
source_ids = batch["source_ids"]
- if kwargs.get("tearchforing", False):
+ if not kwargs.get("tearchforing", False):
input_ids = source_ids
input_ids[input_ids < 0] = 0
inputs_embeds = self.llm.model.get_input_embeddings()(input_ids)
@@ -691,6 +694,7 @@
batch_idx, :min_len, :
]
+ label = contents["assistant"][0]
if not kwargs.get("tearchforing", False):
generated_ids = self.llm.generate(
@@ -703,7 +707,24 @@
response = tokenizer.batch_decode(
generated_ids, skip_special_tokens=kwargs.get("skip_special_tokens", True)
)[0]
- label = contents["assistant"][0]
+
+ loss = None
+ else:
+
+ labels_ids = batch["labels_ids"]
+ labels_ids[labels_ids == -1] = -100
+ attention_mask = batch.get("attention_mask", None)
+ model_outputs = self.llm(
+ inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels_ids
+ )
+
+ preds = torch.argmax(model_outputs.logits, -1)[:, source_ids.shape[1] :]
+ response = tokenizer.batch_decode(
+ preds,
+ add_special_tokens=False,
+ skip_special_tokens=kwargs.get("skip_special_tokens", True),
+ )[0]
+ loss = model_outputs.loss.item()
ibest_writer = None
if kwargs.get("output_dir") is not None:
@@ -713,10 +734,12 @@
results = []
result_i = {"key": key[0], "text": response, "label": label}
+ if loss is not None:
+ result_i["loss"] = loss
results.append(result_i)
if ibest_writer is not None:
- ibest_writer["text"][key[0]] = text
+ ibest_writer["text"][key[0]] = response
ibest_writer["label"][key[0]] = label
return results, meta_data
--
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