From 2518f03d20caeb8f1707da49aacad37a2e76c06d Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 12 六月 2024 17:44:12 +0800
Subject: [PATCH] decoding
---
funasr/models/llm_asr/model.py | 68 ++++++++++++++++++++++++---------
1 files changed, 49 insertions(+), 19 deletions(-)
diff --git a/funasr/models/llm_asr/model.py b/funasr/models/llm_asr/model.py
index 5f15309..fb0bee3 100644
--- a/funasr/models/llm_asr/model.py
+++ b/funasr/models/llm_asr/model.py
@@ -407,38 +407,56 @@
audio_encoder = encoder_class(input_size=input_size, **audio_encoder_conf)
audio_encoder_output_size = audio_encoder.output_size()
freeze = audio_encoder_conf.get("freeze", True)
+ freeze_layer_num = int(audio_encoder_conf.get("freeze_layer_num", -1))
+ if freeze_layer_num > 0:
+ freeze_layer_num = range(freeze_layer_num)
+
if freeze:
for name, param in audio_encoder.named_parameters():
- param.requires_grad = False
+ idx = re.search(r"\.\d+\.", name)
+ if idx is not None:
+ beg, end = idx.regs[0]
+ layer_id = int(name[beg + 1 : end - 1])
+ if isinstance(freeze_layer_num, (list, tuple)):
+ if layer_id in freeze_layer_num:
+ param.requires_grad = False
+ else:
+ param.requires_grad = False
audio_encoder.eval()
self.audio_encoder = audio_encoder
# llm
- hub = llm_conf.get("hub", "hf")
self.llm = None
- if hub == "hf":
- from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
- init_param_path = llm_conf.get("init_param_path", "vicuna-7b-v1.5")
+ from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
- model = AutoModelForCausalLM.from_pretrained(
- init_param_path,
- load_in_8bit=None,
- device_map=None,
- use_cache=None,
- )
- freeze = llm_conf.get("freeze", True)
- if freeze:
- for name, param in model.named_parameters():
- param.requires_grad = False
- model.eval()
- self.llm = model
+ init_param_path = llm_conf.get("init_param_path", "vicuna-7b-v1.5")
+
+ model = AutoModelForCausalLM.from_pretrained(
+ init_param_path,
+ load_in_8bit=None,
+ device_map=None,
+ use_cache=None,
+ )
+ freeze = llm_conf.get("freeze", True)
+ if freeze:
+ for name, param in model.named_parameters():
+ param.requires_grad = False
+ model.eval()
+ self.llm = model
+ llm_dim = model.get_input_embeddings().weight.shape[-1]
# adaptor
adaptor_class = tables.adaptor_classes.get(audio_adaptor)
audio_adaptor_conf["encoder_dim"] = audio_encoder_output_size
+ audio_adaptor_conf["llm_dim"] = llm_dim
audio_adaptor = adaptor_class(**audio_adaptor_conf)
+ init_param_path = audio_adaptor_conf.get("init_param_path", None)
+ if init_param_path is not None:
+ src_state = torch.load(init_param_path, map_location="cpu")
+ flag = audio_adaptor.load_state_dict(src_state, strict=False)
+ logging.info(f"Loading audio_adaptor ckpt: {init_param_path}, status: {flag}")
self.audio_adaptor = audio_adaptor
@@ -684,6 +702,11 @@
# audio encoder
speech = batch["speech"]
speech_lengths = batch["speech_lengths"][:, 0]
+ # fp16
+ if kwargs.get("fp16", False):
+ speech = speech.to(torch.float16)
+ elif kwargs.get("bf16", False):
+ speech = speech.to(torch.bfloat16)
encoder_out, encoder_out_lens = self.audio_encoder(speech.permute(0, 2, 1), speech_lengths)
# audio_adaptor
@@ -707,12 +730,18 @@
]
llm_dtype = kwargs.get("llm_dtype", "fp32")
+ if llm_dtype == "fp32":
+ llm_dtype = "fp16" if kwargs.get("fp16", False) else llm_dtype
+ llm_dtype = "bf16" if kwargs.get("bf16", False) else llm_dtype
+
dtype_map = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}
- with torch.cuda.amp.autocast(dtype=dtype_map[llm_dtype]):
+ with torch.cuda.amp.autocast(
+ enabled=True if llm_dtype != "fp32" else False, 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(
@@ -732,6 +761,7 @@
labels_ids = batch["labels_ids"]
labels_ids[labels_ids == -1] = -100
attention_mask = batch.get("attention_mask", None)
+ # attention_mask = attention_mask.to(dtype_map[llm_dtype])
model_outputs = self.llm(
inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels_ids
)
--
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