remove used code

Author: shihaobai

lightllm/models/deepseek2/model.py

@@ -20,35 +20,6 @@
 logger = init_logger(__name__)
 
 
-class FlashAttentionStateExtraInfo:
-    def __init__(self, model):
-        num_heads = model.config["num_attention_heads"]
-        self.tp_q_head_num = num_heads // get_dp_world_size()
-        self.qk_nope_head_dim = model.qk_nope_head_dim
-        self.qk_rope_head_dim = model.qk_rope_head_dim
-        self.kv_lora_rank = model.kv_lora_rank
-        self.q_data_type = model.data_type
-        self.kv_data_type = model.data_type
-        self.workspace_buffer = torch.empty(128 * 1024 * 1024, dtype=torch.int8).to(get_current_device_id())
-        self.max_seq_length = model.max_seq_length
-        self.softmax_scale = (self.qk_nope_head_dim + self.qk_rope_head_dim) ** (-0.5)
-        self.kv_indices_buffer = [
-            torch.empty(model.graph_max_batch_size * self.max_seq_length, dtype=torch.int32).to(
-                get_current_device_id()
-            ),
-            torch.empty(model.graph_max_batch_size * self.max_seq_length, dtype=torch.int32).to(
-                get_current_device_id()
-            ),
-        ]
-        if model.config["rope_scaling"] is not None:
-            rope_scaling = model.config["rope_scaling"]
-            mscale_all_dim = rope_scaling.get("mscale_all_dim", 0)
-            scaling_factor = rope_scaling["factor"]
-            if mscale_all_dim:
-                mscale = get_deepseek_mscale(scaling_factor, mscale_all_dim)
-                self.softmax_scale = self.softmax_scale * mscale * mscale
-
-
 class FlashInferStateExtraInfo:
     def __init__(self, model):
         num_heads = model.config["num_attention_heads"]