fix code style

python/llm/src/ipex_llm/transformers/npu_models/qwen2_mp.py

@@ -819,6 +819,17 @@ def run_prefill(
     print("finish creating all decode layers in prefill")
     result_queue.put("loading finish")
 
+    if model.config.intermediate_size == 18944:
+        # for qwen2-7b
+        from transformers.models.qwen2.modeling_qwen2 import Qwen2Attention
+        from ipex_llm.transformers.npu_models.convert_mp import convert_forward
+        qwen2_attention_forward = generate_qwen2_attention_forward(
+            max_seq_len=max_output_len,
+            transpose_value=transpose_value_cache
+        )
+        convert_forward(model, Qwen2Attention, qwen2_attention_forward)
+        deocderlayers = model.model.layers
+
     while True:
 
         result = input_queue.get()
@@ -1090,3 +1101,79 @@ def qwen2_casullm_forward(
         hidden_states=outputs.hidden_states,
         attentions=outputs.attentions,
     )
+
+from transformers.models.qwen2.modeling_qwen2 import apply_rotary_pos_emb, repeat_kv
+import math
+
+
+def generate_qwen2_attention_forward(max_seq_len, transpose_value):
+    def qwen2_attention_forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads,
+                                     self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads,
+                                         self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
+                                                        cos, sin, position_ids)
+
+        cache_kwargs = {"max_seq_len": max_seq_len, "transpose": transpose_value, }
+
+        if past_key_value is not None:
+            key_states, value_states = past_key_value.update(key_states, value_states,
+                                                             self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = None
+        if query_states.size(2) == key_states.size(2):
+            # first token
+            from intel_npu_acceleration_library.functional import scaled_dot_product_attention
+            attn_output = scaled_dot_product_attention(
+                query_states,
+                key_states,
+                value_states,
+                attn_mask=attention_mask,
+                is_causal=q_len > 1 and bsz == 1,
+            )
+        else:
+            attn_weights = torch.matmul(query_states,
+                                        key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+            if attention_mask is not None:
+                attn_weights = attn_weights + attention_mask
+            # upcast attention to fp32
+            attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1,
+                                                       dtype=torch.float32).to(query_states.dtype)
+            attn_weights = torch.nn.functional.dropout(attn_weights, p=self.attention_dropout,
+                                                       training=self.training)
+            attn_output = torch.matmul(attn_weights, value_states)
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+        return attn_output, attn_weights, past_key_value
+    return qwen2_attention_forward