update (#8359)

* change llama/modeling.py to opt npu performence

* update

* update

* Update modeling.py

* add judge

* update

* update

---------

Co-authored-by: Wang Huan <wanghuan29@baidu.com>

Author: Galaxy1458

paddlenlp/transformers/llama/modeling.py

@@ -96,7 +96,7 @@ def swiglu(x, y=None):
     "LlamaForCausalLM",
     "LlamaPretrainingCriterion",
 ]
-
+global npu_is_casual
 npu_is_casual = False
 
 def _get_interleave(n):
@@ -213,7 +213,7 @@ def scaled_dot_product_attention(
 ):
     bsz, q_len, num_heads, head_dim = query_states.shape
     _, kv_seq_len, _, _ = value_states.shape
-
+    global npu_is_casual
     if config.use_flash_attention and flash_attention:
         # Paddle Flash Attention input [ bz, seqlen, nhead, head_dim]
         # Torch Flash Attention input [ bz, nhead, seqlen, head_dim]
@@ -1613,6 +1613,7 @@ def forward(
         attention_mask = self._prepare_decoder_attention_mask(
             attention_mask, (batch_size, seq_length), cache_length, inputs_embeds.dtype
         )  # [bs, 1, seq_len, seq_len]
+        global npu_is_casual
         if self.config.use_flash_attention:
             is_casual = is_casual_mask(attention_mask)
             if get_env_device() != "npu":