[Model] Reuse RoPE positions for Deepseek-v2 model (#3084)

This PR updates the Deepseek-v2 model implementation with the updated
RoPE position arrays. Prior to this PR, we will query the RoPE positions
for every single layer, while in fact these arrays can be reused and
thus only one query is sufficient.

Author: MasterJH5574

python/mlc_llm/model/deepseek_v2/deepseek_v2_model.py

@@ -229,7 +229,13 @@ def __init__(self, config: DeepseekV2Config):
                 self.softmax_scale = self.softmax_scale * mscale * mscale
         self.rotary_emb = DeepseekV2YarnRotaryEmbedding(config)
 
-    def forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id: int):
+    def forward(
+        self,
+        hidden_states: Tensor,
+        paged_kv_cache: PagedKVCache,
+        layer_id: int,
+        query_positions: Tensor,
+    ):
         b, s, _ = hidden_states.shape
 
         if self.q_lora_rank is None:
@@ -260,7 +266,7 @@ def forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id:
             kv, [self.qk_nope_head_dim], axis=-1
         )  # (b, s, num_heads, qk_nope_head_dim), (b, s, num_heads, v_head_dim)
 
-        q_pe, k_pe = self.rotary_emb(q_pe, k_pe, paged_kv_cache.get_query_positions(s))
+        q_pe, k_pe = self.rotary_emb(q_pe, k_pe, query_positions)
 
         @T.prim_func
         def inplace_q(var_q: T.handle, var_pe: T.handle):
@@ -471,9 +477,15 @@ def _set(layer, hint):
         self.tensor_parallel_shards = config.tensor_parallel_shards
         _set_tp()
 
-    def forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id: int):
+    def forward(
+        self,
+        hidden_states: Tensor,
+        paged_kv_cache: PagedKVCache,
+        layer_id: int,
+        query_positions: Tensor,
+    ):
         out = self.input_layernorm(hidden_states)
-        out = self.self_attn(out, paged_kv_cache, layer_id)
+        out = self.self_attn(out, paged_kv_cache, layer_id, query_positions)
         hidden_states = self._apply_residual(out, residual=hidden_states)
         out = self.post_attention_layernorm(hidden_states)
         out = self.mlp(out)  # type: ignore[operator]
@@ -499,8 +511,10 @@ def __init__(self, config: DeepseekV2Config):
 
     def forward(self, inputs: Tensor, paged_kv_cache: PagedKVCache):
         hidden_states = inputs
+        print(f"inputs.shape = {inputs.shape}")
+        query_positions = paged_kv_cache.get_query_positions(inputs.shape[0] * inputs.shape[1])
         for layer_id, layer in enumerate(self.layers):
-            hidden_states = layer(hidden_states, paged_kv_cache, layer_id)
+            hidden_states = layer(hidden_states, paged_kv_cache, layer_id, query_positions)
         hidden_states = self.norm(hidden_states)
         return hidden_states