clean up comments

Signed-off-by: NickLucche <[email protected]>

vllm/model_executor/models/bart.py

@@ -300,21 +300,6 @@ def __init__(
                              f" and `num_heads`: {num_heads}).")
         self.scaling = self.head_dim**-0.5
 
-        # self.q_proj = ColumnParallelLinear(
-        #     input_size=self.embed_dim,
-        #     output_size=self.embed_dim,
-        #     bias=bias,
-        #     quant_config=quant_config,
-        # )
-
-        # self.kv_proj = QKVParallelLinear(
-        #     hidden_size=self.d_model,
-        #     head_size=self.d_model // self.total_num_heads,
-        #     total_num_heads=0,
-        #     total_num_kv_heads=self.total_num_kv_heads,
-        #     bias=bias,
-        #     quant_config=quant_config,
-        # )
         self.qkv_proj = QKVCrossParallelLinear(self.d_model,
                                                self.d_model //
                                                self.total_num_heads,
@@ -364,15 +349,6 @@ def forward(
     ) -> torch.Tensor:
         """Input shape: Batch x Time x Channel"""
 
-        # q, _ = self.q_proj(decoder_hidden_states)
-
-        # if encoder_hidden_states is None:
-        #     k = None
-        #     v = None
-        # else:
-        #     # Prefill, cache encoder KV.
-        #     kv_enc, _ = self.kv_proj(encoder_hidden_states)
-        #     k, v = kv_enc.split(self.kv_size, dim=-1)
         q, k, v = self.qkv_proj(decoder_hidden_states, encoder_hidden_states)
 
         attn_output = self.attn(q, k, v, kv_cache, attn_metadata)