use self.config to check attn

src/transformers/models/mbart/modeling_mbart.py

@@ -955,8 +955,7 @@ def __init__(self, config: MBartConfig, embed_tokens: Optional[nn.Embedding] = N
             embed_dim,
         )
         self.layers = nn.ModuleList([MBartEncoderLayer(config) for _ in range(config.encoder_layers)])
-        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
-        self._use_sdpa = config._attn_implementation == "sdpa"
+        self.config = config
         self.layernorm_embedding = nn.LayerNorm(embed_dim)
         self.layer_norm = nn.LayerNorm(config.d_model)
 
@@ -1044,9 +1043,9 @@ def forward(
 
         # expand attention_mask
         if attention_mask is not None:
-            if self._use_flash_attention_2:
+            if self.config._attn_implementation == "flash_attention_2":
                 attention_mask = attention_mask if 0 in attention_mask else None
-            elif self._use_sdpa and head_mask is None and not output_attentions:
+            elif self.config._attn_implementation == "sdpa" and head_mask is None and not output_attentions:
                 # output_attentions=True & head_mask can not be supported when using SDPA, fall back to
                 # the manual implementation that requires a 4D causal mask in all cases.
                 # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
@@ -1140,8 +1139,7 @@ def __init__(self, config: MBartConfig, embed_tokens: Optional[nn.Embedding] = N
             config.d_model,
         )
         self.layers = nn.ModuleList([MBartDecoderLayer(config) for _ in range(config.decoder_layers)])
-        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
-        self._use_sdpa = config._attn_implementation == "sdpa"
+        self.config = config
 
         self.layernorm_embedding = nn.LayerNorm(config.d_model)
         self.layer_norm = nn.LayerNorm(config.d_model)
@@ -1262,10 +1260,10 @@ def forward(
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids)
 
-        if self._use_flash_attention_2:
+        if self.config._attn_implementation == "flash_attention_2":
             # 2d mask is passed through the layers
             attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
-        elif self._use_sdpa and not output_attentions and cross_attn_head_mask is None:
+        elif self.config._attn_implementation == "sdpa" and not output_attentions and cross_attn_head_mask is None:
             # output_attentions=True & cross_attn_head_mask can not be supported when using SDPA, and we fall back on
             # the manual implementation that requires a 4D causal mask in all cases.
             attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
@@ -1282,9 +1280,9 @@ def forward(
 
         # expand encoder attention mask
         if encoder_hidden_states is not None and encoder_attention_mask is not None:
-            if self._use_flash_attention_2:
+            if self.config._attn_implementation == "flash_attention_2":
                 encoder_attention_mask = encoder_attention_mask if 0 in encoder_attention_mask else None
-            elif self._use_sdpa and cross_attn_head_mask is None and not output_attentions:
+            elif self.config._attn_implementation == "sdpa" and cross_attn_head_mask is None and not output_attentions:
                 # output_attentions=True & cross_attn_head_mask can not be supported when using SDPA, and we fall back on
                 # the manual implementation that requires a 4D causal mask in all cases.
                 # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]