deprecate in models

zucchini-nlp · Sep 6, 2024 · 82b6193 · 82b6193
1 parent 2b88f4b
commit 82b6193
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Showing 17 changed files with 26 additions and 26 deletions.
diff --git a/src/transformers/models/bloom/modeling_bloom.py b/src/transformers/models/bloom/modeling_bloom.py
@@ -819,7 +819,7 @@ def _update_causal_mask(
         min_dtype = torch.finfo(dtype).min
         sequence_length = input_tensor.shape[1]
         if past_key_values is not None and past_key_values.is_static:
-            target_length = past_key_values.get_max_length()
+            target_length = past_key_values.get_max_cache_shape()
         else:
             target_length = (
                 attention_mask.shape[-1]

diff --git a/src/transformers/models/chameleon/modeling_chameleon.py b/src/transformers/models/chameleon/modeling_chameleon.py
@@ -1462,7 +1462,7 @@ def _update_causal_mask(
         min_dtype = torch.finfo(dtype).min
         sequence_length = input_tensor.shape[1]
         if past_key_values is not None and past_key_values.is_static:
-            target_length = past_key_values.get_max_length()
+            target_length = past_key_values.get_max_cache_shape()
         else:
             target_length = (
                 attention_mask.shape[-1]

diff --git a/src/transformers/models/cohere/modeling_cohere.py b/src/transformers/models/cohere/modeling_cohere.py
@@ -942,7 +942,7 @@ def _update_causal_mask(
         min_dtype = torch.finfo(dtype).min
         sequence_length = input_tensor.shape[1]
         if past_key_values is not None and past_key_values.is_static:
-            target_length = past_key_values.get_max_length()
+            target_length = past_key_values.get_max_cache_shape()
         else:
             target_length = (
                 attention_mask.shape[-1]

diff --git a/src/transformers/models/gemma/modeling_gemma.py b/src/transformers/models/gemma/modeling_gemma.py
@@ -956,7 +956,7 @@ def _update_causal_mask(
         min_dtype = torch.finfo(dtype).min
         sequence_length = input_tensor.shape[1]
         if using_static_cache:
-            target_length = past_key_values.get_max_length()
+            target_length = past_key_values.get_max_cache_shape()
         else:
             target_length = (
                 attention_mask.shape[-1]
@@ -1173,7 +1173,7 @@ def prepare_inputs_for_generation(
             attention_mask = _prepare_4d_causal_attention_mask_with_cache_position(
                 attention_mask,
                 sequence_length=sequence_length,
-                target_length=past_key_values.get_max_length(),
+                target_length=past_key_values.get_max_cache_shape(),
                 dtype=dtype,
                 device=device,
                 min_dtype=min_dtype,

diff --git a/src/transformers/models/gemma2/modeling_gemma2.py b/src/transformers/models/gemma2/modeling_gemma2.py
@@ -925,7 +925,7 @@ def _update_causal_mask(
         min_dtype = torch.finfo(dtype).min
         sequence_length = input_tensor.shape[1]
         if isinstance(past_key_values, HybridCache):
-            target_length = past_key_values.get_max_length()
+            target_length = past_key_values.get_max_cache_shape()
         else:
             target_length = attention_mask.shape[-1] if attention_mask is not None else input_tensor.shape[1]
 
@@ -1141,7 +1141,7 @@ def prepare_inputs_for_generation(
             attention_mask = _prepare_4d_causal_attention_mask_with_cache_position(
                 attention_mask,
                 sequence_length=sequence_length,
-                target_length=past_key_values.get_max_length(),
+                target_length=past_key_values.get_max_cache_shape(),
                 dtype=dtype,
                 device=device,
                 min_dtype=min_dtype,

diff --git a/src/transformers/models/idefics2/modeling_idefics2.py b/src/transformers/models/idefics2/modeling_idefics2.py
@@ -1651,7 +1651,7 @@ def prepare_inputs_for_generation(
         if past_key_values is not None:
             # Past key values are always initialized with a `Cache` object -> no need for if-else anymore
             past_length = past_key_values.get_seq_length()
-            max_cache_length = past_key_values.get_max_length()
+            max_cache_length = past_key_values.get_max_cache_shape()
 
             # Keep only the unprocessed tokens:
             # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where

diff --git a/src/transformers/models/jetmoe/modeling_jetmoe.py b/src/transformers/models/jetmoe/modeling_jetmoe.py
@@ -1165,7 +1165,7 @@ def _update_causal_mask(
         min_dtype = torch.finfo(dtype).min
         sequence_length = input_tensor.shape[1]
         if past_key_values is not None and past_key_values.is_static:
-            target_length = past_key_values.get_max_length()
+            target_length = past_key_values.get_max_cache_shape()
         else:
             target_length = (
                 attention_mask.shape[-1]

diff --git a/src/transformers/models/llama/modeling_llama.py b/src/transformers/models/llama/modeling_llama.py
@@ -1067,7 +1067,7 @@ def _update_causal_mask(
         min_dtype = torch.finfo(dtype).min
         sequence_length = input_tensor.shape[1]
         if past_key_values is not None and past_key_values.is_static:
-            target_length = past_key_values.get_max_length()
+            target_length = past_key_values.get_max_cache_shape()
         else:
             target_length = (
                 attention_mask.shape[-1]
@@ -1291,7 +1291,7 @@ def prepare_inputs_for_generation(
             attention_mask = _prepare_4d_causal_attention_mask_with_cache_position(
                 attention_mask,
                 sequence_length=sequence_length,
-                target_length=past_key_values.get_max_length(),
+                target_length=past_key_values.get_max_cache_shape(),
                 dtype=dtype,
                 device=device,
                 min_dtype=min_dtype,

diff --git a/src/transformers/models/mistral/modeling_mistral.py b/src/transformers/models/mistral/modeling_mistral.py
@@ -892,7 +892,7 @@ def _update_causal_mask(
         sequence_length = input_tensor.shape[1]
         # SlidingWindowCache or StaticCache
         if past_key_values is not None and (past_key_values.is_sliding or past_key_values.is_static):
-            target_length = past_key_values.get_max_length()
+            target_length = past_key_values.get_max_cache_shape()
         # DynamicCache or no cache
         else:
             target_length = (

diff --git a/src/transformers/models/mixtral/modeling_mixtral.py b/src/transformers/models/mixtral/modeling_mixtral.py
@@ -1150,7 +1150,7 @@ def _update_causal_mask(
         min_dtype = torch.finfo(dtype).min
         sequence_length = input_tensor.shape[1]
         if past_key_values is not None and past_key_values.is_static:
-            target_length = past_key_values.get_max_length()
+            target_length = past_key_values.get_max_cache_shape()
         else:
             target_length = (
                 attention_mask.shape[-1]

diff --git a/src/transformers/models/olmo/modeling_olmo.py b/src/transformers/models/olmo/modeling_olmo.py
@@ -987,7 +987,7 @@ def _update_causal_mask(
         min_dtype = torch.finfo(dtype).min
         sequence_length = input_tensor.shape[1]
         if past_key_values is not None and past_key_values.is_static:
-            target_length = past_key_values.get_max_length()
+            target_length = past_key_values.get_max_cache_shape()
         else:
             target_length = (
                 attention_mask.shape[-1]

diff --git a/src/transformers/models/persimmon/modeling_persimmon.py b/src/transformers/models/persimmon/modeling_persimmon.py
@@ -798,7 +798,7 @@ def _update_causal_mask(
         min_dtype = torch.finfo(dtype).min
         sequence_length = input_tensor.shape[1]
         if past_key_values is not None and past_key_values.is_static:
-            target_length = past_key_values.get_max_length()
+            target_length = past_key_values.get_max_cache_shape()
         else:
             target_length = (
                 attention_mask.shape[-1]
@@ -1019,7 +1019,7 @@ def prepare_inputs_for_generation(
             attention_mask = _prepare_4d_causal_attention_mask_with_cache_position(
                 attention_mask,
                 sequence_length=sequence_length,
-                target_length=past_key_values.get_max_length(),
+                target_length=past_key_values.get_max_cache_shape(),
                 dtype=dtype,
                 device=device,
                 min_dtype=min_dtype,

diff --git a/src/transformers/models/phi/modeling_phi.py b/src/transformers/models/phi/modeling_phi.py
@@ -1083,7 +1083,7 @@ def _update_causal_mask(
         min_dtype = torch.finfo(dtype).min
         sequence_length = input_tensor.shape[1]
         if past_key_values is not None and past_key_values.is_static:
-            target_length = past_key_values.get_max_length()
+            target_length = past_key_values.get_max_cache_shape()
         else:
             target_length = (
                 attention_mask.shape[-1]
@@ -1311,7 +1311,7 @@ def prepare_inputs_for_generation(
             attention_mask = _prepare_4d_causal_attention_mask_with_cache_position(
                 attention_mask,
                 sequence_length=sequence_length,
-                target_length=past_key_values.get_max_length(),
+                target_length=past_key_values.get_max_cache_shape(),
                 dtype=dtype,
                 device=device,
                 min_dtype=min_dtype,

diff --git a/src/transformers/models/phi3/modeling_phi3.py b/src/transformers/models/phi3/modeling_phi3.py
@@ -1123,7 +1123,7 @@ def _update_causal_mask(
         min_dtype = torch.finfo(dtype).min
         sequence_length = input_tensor.shape[1]
         if past_key_values is not None and past_key_values.is_static:
-            target_length = past_key_values.get_max_length()
+            target_length = past_key_values.get_max_cache_shape()
         else:
             target_length = (
                 attention_mask.shape[-1]
@@ -1351,7 +1351,7 @@ def prepare_inputs_for_generation(
             attention_mask = _prepare_4d_causal_attention_mask_with_cache_position(
                 attention_mask,
                 sequence_length=sequence_length,
-                target_length=past_key_values.get_max_length(),
+                target_length=past_key_values.get_max_cache_shape(),
                 dtype=dtype,
                 device=device,
                 min_dtype=min_dtype,

diff --git a/src/transformers/models/qwen2/modeling_qwen2.py b/src/transformers/models/qwen2/modeling_qwen2.py
@@ -988,7 +988,7 @@ def _update_causal_mask(
         min_dtype = torch.finfo(dtype).min
         sequence_length = input_tensor.shape[1]
         if past_key_values is not None and past_key_values.is_static:
-            target_length = past_key_values.get_max_length()
+            target_length = past_key_values.get_max_cache_shape()
         else:
             target_length = (
                 attention_mask.shape[-1]
@@ -1209,7 +1209,7 @@ def prepare_inputs_for_generation(
             attention_mask = _prepare_4d_causal_attention_mask_with_cache_position(
                 attention_mask,
                 sequence_length=sequence_length,
-                target_length=past_key_values.get_max_length(),
+                target_length=past_key_values.get_max_cache_shape(),
                 dtype=dtype,
                 device=device,
                 min_dtype=min_dtype,

diff --git a/src/transformers/models/stablelm/modeling_stablelm.py b/src/transformers/models/stablelm/modeling_stablelm.py
@@ -1075,7 +1075,7 @@ def _update_causal_mask(
         min_dtype = torch.finfo(dtype).min
         sequence_length = input_tensor.shape[1]
         if past_key_values is not None and past_key_values.is_static:
-            target_length = past_key_values.get_max_length()
+            target_length = past_key_values.get_max_cache_shape()
         else:
             target_length = (
                 attention_mask.shape[-1]
@@ -1297,7 +1297,7 @@ def prepare_inputs_for_generation(
             attention_mask = _prepare_4d_causal_attention_mask_with_cache_position(
                 attention_mask,
                 sequence_length=sequence_length,
-                target_length=past_key_values.get_max_length(),
+                target_length=past_key_values.get_max_cache_shape(),
                 dtype=dtype,
                 device=device,
                 min_dtype=min_dtype,

diff --git a/src/transformers/models/starcoder2/modeling_starcoder2.py b/src/transformers/models/starcoder2/modeling_starcoder2.py
@@ -962,7 +962,7 @@ def _update_causal_mask(
         min_dtype = torch.finfo(dtype).min
         sequence_length = input_tensor.shape[1]
         if past_key_values is not None and past_key_values.is_static:
-            target_length = past_key_values.get_max_length()
+            target_length = past_key_values.get_max_cache_shape()
         else:
             target_length = (
                 attention_mask.shape[-1]
@@ -1185,7 +1185,7 @@ def prepare_inputs_for_generation(
             attention_mask = _prepare_4d_causal_attention_mask_with_cache_position(
                 attention_mask,
                 sequence_length=sequence_length,
-                target_length=past_key_values.get_max_length(),
+                target_length=past_key_values.get_max_cache_shape(),
                 dtype=dtype,
                 device=device,
                 min_dtype=min_dtype,