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游雁 2024-06-11 11:48:42 +08:00
parent ea85c483ad
commit 23008c7cac
1 changed files with 33 additions and 27 deletions
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60
funasr/models/llm_asr/model.py
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@ -706,37 +706,43 @@ class LLMASR2(nn.Module):
batch_idx, :min_len, :
]
label = contents["assistant"][0]
if not kwargs.get("tearchforing", False):
llm_dtype = kwargs.get("llm_dtype", "fp32")
dtype_map = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}
with torch.cuda.amp.autocast(dtype=dtype_map[llm_dtype]):
label = contents["assistant"][0]
self.llm = self.llm.to(dtype_map[llm_dtype])
inputs_embeds = inputs_embeds.to(dtype_map[llm_dtype])
attention_mask = attention_mask.to(dtype_map[llm_dtype])
if not kwargs.get("tearchforing", False):
generated_ids = self.llm.generate(
inputs_embeds=inputs_embeds, max_new_tokens=kwargs.get("max_length", 512)
)
# generated_ids = [
# output_ids[len(input_id) :]
# for input_id, output_ids in zip(input_ids, generated_ids)
# ]
response = tokenizer.batch_decode(
generated_ids, skip_special_tokens=kwargs.get("skip_special_tokens", True)
)[0]
generated_ids = self.llm.generate(
inputs_embeds=inputs_embeds, max_new_tokens=kwargs.get("max_length", 512)
)
# generated_ids = [
# output_ids[len(input_id) :]
# for input_id, output_ids in zip(input_ids, generated_ids)
# ]
response = tokenizer.batch_decode(
generated_ids, skip_special_tokens=kwargs.get("skip_special_tokens", True)
)[0]
loss = None
else:
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
)
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()
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: