replace get_seq_length

src/transformers/models/moshi/modeling_moshi.py

@@ -1160,7 +1160,7 @@ def forward(
         if use_cache and past_key_values is None and not self.training:
             past_key_values = DynamicCache.from_legacy_cache(past_key_values)
 
-        past_seen_tokens = 0 if past_key_values is None else past_key_values.get_seq_length()
+        past_seen_tokens = 0 if past_key_values is None else past_key_values.get_past_seen_tokens()
         if cache_position is None:
             cache_position = torch.arange(
                 past_seen_tokens, past_seen_tokens + input_ids.shape[1], device=input_ids.device
@@ -1496,7 +1496,7 @@ def forward(
             past_key_values = DynamicCache.from_legacy_cache(past_key_values)
 
         if cache_position is None:
-            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            past_seen_tokens = past_key_values.get_past_seen_tokens() if past_key_values is not None else 0
             cache_position = torch.arange(
                 past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
             )

src/transformers/models/mt5/modeling_mt5.py

@@ -1016,7 +1016,7 @@ def forward(
             # it messes indexing later in decoder-stack because cache object is modified in-place
             past_key_values = None
 
-        past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
+        past_key_values_length = past_key_values.get_past_seen_tokens() if past_key_values is not None else 0
         if cache_position is None:
             cache_position = torch.arange(
                 past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device

src/transformers/models/phimoe/modeling_phimoe.py

@@ -1089,7 +1089,7 @@ def forward(
             inputs_embeds = self.embed_tokens(input_ids)
 
         if cache_position is None:
-            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            past_seen_tokens = past_key_values.get_past_seen_tokens() if past_key_values is not None else 0
             cache_position = torch.arange(
                 past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
             )

src/transformers/models/pix2struct/modeling_pix2struct.py

@@ -1411,7 +1411,7 @@ def forward(
         if cache_position is not None:
             past_key_values_length = cache_position[0]
         elif past_key_values is not None:
-            past_key_values_length = past_key_values.get_seq_length()
+            past_key_values_length = past_key_values.get_past_seen_tokens()
 
         if cache_position is None:
             cache_position = torch.arange(
@@ -1421,7 +1421,7 @@ def forward(
         if attention_mask is None:
             # required mask seq length can be calculated via length of past
             mask_seq_length = (
-                past_key_values.get_seq_length() + seq_length if past_key_values is not None else seq_length
+                past_key_values.get_past_seen_tokens() + seq_length if past_key_values is not None else seq_length
             )
             attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
 

src/transformers/models/pop2piano/modeling_pop2piano.py

@@ -854,7 +854,7 @@ def forward(
             # it messes indexing later in decoder-stack because cache object is modified in-place
             past_key_values = None
 
-        past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
+        past_key_values_length = past_key_values.get_past_seen_tokens() if past_key_values is not None else 0
         if cache_position is None:
             cache_position = torch.arange(
                 past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device

src/transformers/models/switch_transformers/modeling_switch_transformers.py

@@ -976,7 +976,7 @@ def forward(
             # it messes indexing later in decoder-stack because cache object is modified in-place
             past_key_values = None
 
-        past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
+        past_key_values_length = past_key_values.get_past_seen_tokens() if past_key_values is not None else 0
         if cache_position is None:
             cache_position = torch.arange(
                 past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device

src/transformers/models/t5/modeling_t5.py

@@ -1029,7 +1029,7 @@ def forward(
             # it messes indexing later in decoder-stack because cache object is modified in-place
             past_key_values = None
 
-        past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
+        past_key_values_length = past_key_values.get_past_seen_tokens() if past_key_values is not None else 0
         if cache_position is None:
             cache_position = torch.arange(
                 past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device

src/transformers/models/udop/modeling_udop.py

@@ -1410,7 +1410,7 @@ def forward(
             # it messes indexing later in decoder-stack because cache object is modified in-place
             past_key_values = None
 
-        past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
+        past_key_values_length = past_key_values.get_past_seen_tokens() if past_key_values is not None else 0
         if cache_position is None:
             cache_position = torch.arange(
                 past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device

src/transformers/models/umt5/modeling_umt5.py

@@ -715,7 +715,7 @@ def forward(
             # it messes indexing later in decoder-stack because cache object is modified in-place
             past_key_values = None
 
-        past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
+        past_key_values_length = past_key_values.get_past_seen_tokens() if past_key_values is not None else 0
         if cache_position is None:
             cache_position = torch.arange(
                 past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device