From 3d5e19792cd4bb510c2c0fc5749731d52b825c15 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期六, 08 六月 2024 18:43:35 +0800
Subject: [PATCH] fix bug
---
funasr/models/llm_asr/model.py | 55 ++++++++++++++++++++++++++++++++++++++++++-------------
1 files changed, 42 insertions(+), 13 deletions(-)
diff --git a/funasr/models/llm_asr/model.py b/funasr/models/llm_asr/model.py
index 78d9340..697f78d 100644
--- a/funasr/models/llm_asr/model.py
+++ b/funasr/models/llm_asr/model.py
@@ -6,7 +6,7 @@
import torch.nn as nn
import torch.nn.functional as F
from torch.cuda.amp import autocast
-
+import re
from funasr.models.scama.utils import sequence_mask
from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
from funasr.models.ctc.ctc import CTC
@@ -532,7 +532,7 @@
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
return loss, stats, weight
- def data_template(self, data_in):
+ def data_template(self, data):
system, user, assistant = [], [], []
for i, item in enumerate(data):
role = item["role"]
@@ -560,21 +560,31 @@
user = contents["user"]
assistant = contents["assistant"]
pattern = re.compile(r"(<\|startofspeech\|>.*?<\|endofspeech\|>)")
- input_ids, labels, fbank, fbank_lens, fbank_mask, fbank_beg = [], [], [], [], [], []
+ input_ids, labels, source_ids, target_ids, fbank, fbank_lens, fbank_mask, fbank_beg = (
+ [],
+ [],
+ [],
+ [],
+ [],
+ [],
+ [],
+ [],
+ )
for i, (system_prompt, user_prompt, target_out) in enumerate(zip(system, user, assistant)):
source_input = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n"
splits = pattern.split(source_input)
- source_ids = []
+ source_ids_i = []
fbank_mask_i = []
fbank_beg_i = []
fbank_lens_i = []
+ # target_ids_i = []
for k, sub_str in enumerate(splits):
if not sub_str.startswith("<|startofspeech|>"):
sub_token = tokenizer.encode(sub_str)
- source_ids += sub_token
+ source_ids_i += sub_token
fbank_mask_i += [0] * len(sub_token)
else:
sub_str = sub_str.replace("<|startofspeech|>", "").replace(
@@ -600,14 +610,14 @@
olens = 1 + (olens - 3 + 2 * 1) // 2
sub_token_len = (olens - 1) // 2 + 1
sub_token = [0] * sub_token_len
- fbank_beg_i = [len(source_ids)]
- source_ids += sub_token
+ fbank_beg_i = [len(source_ids_i)]
+ source_ids_i += sub_token
fbank_mask_i += [1] * len(sub_token)
- source_mask = [-100] * len(source_ids)
+ source_mask = [-100] * len(source_ids_i)
target_out = f"{target_out}<|im_end|>"
target_ids = tokenizer.encode(target_out)
- input_ids += source_ids + target_ids
+ input_ids += source_ids_i + target_ids
labels += source_mask + target_ids
fbank_mask += fbank_mask_i
fbank_beg.append(fbank_beg_i)
@@ -615,7 +625,7 @@
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, :, :]
@@ -653,7 +663,7 @@
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"])
@@ -667,7 +677,7 @@
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)
@@ -695,6 +705,23 @@
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
ibest_writer = None
if kwargs.get("output_dir") is not None:
@@ -704,10 +731,12 @@
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
--
Gitblit v1.9.1