XLA optimized Implementation of StaticCache with Tensor Indexing API

src/transformers/cache_utils.py

@@ -9,7 +9,6 @@
 from .configuration_utils import PretrainedConfig
 from .utils import is_hqq_available, is_quanto_available, logging
 
-
 if is_quanto_available():
     from quanto import QBitsTensor, qint2, qint4
 
@@ -935,3 +934,53 @@ def get_max_length(self) -> Optional[int]:
     def reset(self):
         self.key_cache.zero_()
         self.value_cache.zero_()
+
+class StaticCacheXLA(StaticCache):
+    """ XLA optimized Implementation of StaticCache """
+    def update(
+        self,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        layer_idx: int,
+        cache_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
+        It is VERY important to index using a tensor, otherwise you introduce a copy to the device.
+
+        Parameters:
+            key_states (`torch.Tensor`):
+                The new key states to cache.
+            value_states (`torch.Tensor`):
+                The new value states to cache.
+            layer_idx (`int`):
+                The index of the layer to cache the states for.
+            cache_kwargs (`Dict[str, Any]`, `optional`):
+                Additional arguments for the cache subclass. The `StaticCache` needs the `cache_position` input
+                to know how where to write in the cache.
+
+        Return:
+            A tuple containing the updated key and value states.
+        """
+        cache_position = cache_kwargs.get("cache_position")
+
+        k_out = self.key_cache[layer_idx]
+        v_out = self.value_cache[layer_idx]
+
+        k_out.index_copy_(2, cache_position, key_states)
+        v_out.index_copy_(2, cache_position, value_states)
+
+        return k_out, v_out
+
+    def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
+        """Returns the sequence length of the cached states that were seen by the model."""
+        # Occupied cache == any slot in the 3rd dim (sequence length) holds a non-zero value. To save on compute, let's
+        # limit the check to the first batch member and head dimension.
+        # TODO: deprecate this function in favor of `cache_position`
+        key_cache = self.key_cache[layer_idx]
+        device = key_cache.device
+
+        item = key_cache.index_select(0, torch.tensor(0, device=device))
+        head = item.index_select(1, torch.tensor(0, device=device))
+
+        return head.any(dim=-1).sum()