Fix the issues with latest transformers, add previously removed function to compute usable past KV length for cache compatibility.

The code to fix the issue is copied from another fix pull request: https://huggingface.co/it-just-works/stella_en_1.5B_v5_bf16/commit/03aedd040580357ec688f3467f1109af5e053249

modeling_qwen2_rm.py

@@ -58,6 +58,23 @@ logger = logging.get_logger(__name__)
 _CHECKPOINT_FOR_DOC = "Qwen/Qwen2-7B-beta"
 _CONFIG_FOR_DOC = "Qwen2Config"
 
+# Copied from the fix: https://huggingface.co/it-just-works/stella_en_1.5B_v5_bf16/commit/03aedd040580357ec688f3467f1109af5e053249.
+def _get_usable_past_kv_length(cache: Cache, new_seq_length: int, layer_idx: int = 0) -> int:
+    """Compute the usable past length for the given cache and upcoming new sequence length.
+
+    This mirrors the previous `get_usable_length(new_seq_length, layer_idx)` behavior that existed in
+    Transformers < 4.45, while being compatible with the new Cache API.
+    """
+    try:
+        previous_length = cache.get_seq_length(layer_idx)
+        # Dynamic layers return -1, static layers return an int
+        max_length = cache.get_max_cache_shape(layer_idx)
+        if max_length is not None and max_length != -1 and previous_length + new_seq_length > max_length:
+            return max_length - new_seq_length
+        return previous_length
+    except Exception:
+        # Best-effort fallback
+        return cache.get_seq_length(layer_idx) if hasattr(cache, "get_seq_length") else 0
 
 # Copied from transformers.models.llama.modeling_llama._prepare_4d_causal_attention_mask_with_cache_position
 def _prepare_4d_causal_attention_mask_with_cache_position(
@@ -307,7 +324,8 @@ class Qwen2Attention(nn.Module):
                     "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
                     "with a layer index."
                 )
-            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+            # kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+            kv_seq_len += _get_usable_past_kv_length(past_key_value, 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)
 
@@ -399,7 +417,8 @@ class Qwen2FlashAttention2(Qwen2Attention):
                     "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
                     "with a layer index."
                 )
-            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+            # kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+            kv_seq_len += _get_usable_past_kv_length(past_key_value, kv_seq_len, self.layer_idx)
 
         # Because the input can be padded, the absolute sequence length depends on the max position id.
         rotary_seq_len = (
@@ -549,7 +568,8 @@ class Qwen2SdpaAttention(Qwen2Attention):
 
         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)
+            # kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+            kv_seq_len += _get_usable_past_kv_length(past_key_value, 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)