Merge pull request #932 from alibaba-damo-academy/dev_lhn

hnluo

2023-09-11 9fcb3cc06b4e324f0913d2f61b89becc2baeef1b

 funasr/modules/attention.py

@@ -338,7 +338,10 @@
            else:
                self.linear_out = nn.Linear(n_feat, n_feat)
            lora_qkv_list = ["q" in lora_list, "k" in lora_list, "v" in lora_list]
            self.linear_q_k_v = lora.MergedLinear(in_feat, n_feat * 3, r=lora_rank, lora_alpha=lora_alpha, lora_dropout=lora_dropout, enable_lora=lora_qkv_list)
            if lora_qkv_list == [False, False, False]:
                self.linear_q_k_v = nn.Linear(in_feat, n_feat * 3)
            else:
                self.linear_q_k_v = lora.MergedLinear(in_feat, n_feat * 3, r=lora_rank, lora_alpha=lora_alpha, lora_dropout=lora_dropout, enable_lora=lora_qkv_list)
        else:
            self.linear_out = nn.Linear(n_feat, n_feat)
            self.linear_q_k_v = nn.Linear(in_feat, n_feat * 3)
@@ -562,11 +565,18 @@
        if lora_list is not None:
            if "q" in lora_list:
                self.linear_q = lora.Linear(n_feat, n_feat, r=lora_rank, lora_alpha=lora_alpha, lora_dropout=lora_dropout)
            else:
                self.linear_q = nn.Linear(n_feat, n_feat)
            lora_kv_list = ["k" in lora_list, "v" in lora_list]
            self.linear_k_v = lora.MergedLinear(n_feat if encoder_output_size is None else encoder_output_size, n_feat * 2, 
                                  r=lora_rank, lora_alpha=lora_alpha, lora_dropout=lora_dropout, enable_lora=lora_kv_list)
            if lora_kv_list == [False, False]:
                self.linear_k_v = nn.Linear(n_feat if encoder_output_size is None else encoder_output_size, n_feat*2)
            else:
                self.linear_k_v = lora.MergedLinear(n_feat if encoder_output_size is None else encoder_output_size, n_feat * 2, 
                                      r=lora_rank, lora_alpha=lora_alpha, lora_dropout=lora_dropout, enable_lora=lora_kv_list)
            if "o" in lora_list:
                self.linear_out = lora.Linear(n_feat, n_feat, r=lora_rank, lora_alpha=lora_alpha, lora_dropout=lora_dropout)
            else:
                self.linear_out = nn.Linear(n_feat, n_feat)
        else:
            self.linear_q = nn.Linear(n_feat, n_feat)
            self.linear_k_v = nn.Linear(n_feat if encoder_output_size is None else encoder_output_size, n_feat*2)