From c553a8db1712c2a5deeef5bbb68bd1fdf8d61ab7 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期四, 13 六月 2024 17:38:01 +0800
Subject: [PATCH] decoding

---
 funasr/models/llm_asr/model.py |   18 ++++++++++--------
 1 files changed, 10 insertions(+), 8 deletions(-)

diff --git a/funasr/models/llm_asr/model.py b/funasr/models/llm_asr/model.py
index 15969e3..6e7939b 100644
--- a/funasr/models/llm_asr/model.py
+++ b/funasr/models/llm_asr/model.py
@@ -410,19 +410,19 @@
             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_layer_num > 0:
+        #     freeze_layer_num = range(freeze_layer_num)
 
         if freeze:
             for name, param in audio_encoder.named_parameters():
-                if isinstance(freeze_layer_num, (list, tuple)):
+                if freeze_layer_num > 0:
                     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 layer_id in freeze_layer_num:
+                        if layer_id < freeze_layer_num:
                             param.requires_grad = False
-                    else:
+                    elif not name.startswith("audio_encoder.ln_post"):
                         param.requires_grad = False
                 else:
                     param.requires_grad = False
@@ -449,9 +449,9 @@
             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]
         self.llm_dtype = llm_conf.get("llm_dtype", "fp32")
+        self.llm = model.to(dtype_map[self.llm_dtype])
+        llm_dim = model.get_input_embeddings().weight.shape[-1]
 
         # adaptor
         adaptor_class = tables.adaptor_classes.get(audio_adaptor)
@@ -536,7 +536,9 @@
             labels_ids[labels_ids == -1] = -100
             attention_mask[attention_mask < 0] = 0
             model_outputs = self.llm(
-                inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels_ids
+                inputs_embeds=inputs_embeds.to(dtype_map[self.llm_dtype]),
+                attention_mask=attention_mask,
+                labels=labels_ids,
             )
             loss = model_outputs.loss
 

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