Add support for sliding window via ring kv cache (#81)

* Add support for sliding window via ring kv cache

Summary:
This diffs adds
- Ring kv cache for local attention where ring buffer updates cache
  with new entries in a ring fashion rather than sliding cache
- Correspondingly we have to update sdpa to accept mask that correspond
  to the state of ring buffer because slot in ring buffer may correspond
  to different token positions. Thus we cannot just use causal mask.

Now custom_kv_cache option means we support both global attention and
local/global like gemma3

Test Plan:
Test added

Reviewers:

Subscribers:

Tasks:

Tags:

* Gating imports on ET version

Tags:

* Make room for transformer changes requiring attn_mask fn

Summary:
Latest changse in 4.53 requires that custom attentin functions have
corresponding mask generation

Test Plan:
CI wont pass without changes on top of 4.53

Reviewers:

Subscribers:

Tasks:

Tags:

Author: kimishpatel

optimum/executorch/attentions/custom_kv_cache.py

@@ -9,21 +9,19 @@
 import torch
 
 
+# If transformers is not installed, raise an ImportError
 try:
-    from transformers.cache_utils import StaticCache
+    from transformers.cache_utils import HybridCache, StaticCache
 except ImportError:
-    # If transformers is not installed, raise an ImportError
-    try:
-        from transformers.cache_utils import StaticCache
-    except ImportError:
-        raise ImportError("transformers is not installed. Please install it to use StaticCache.")
+    raise ImportError("transformers is not installed. Please install it to use Static/HybridCache.")
 
 try:
     from executorch.examples.models.llama.source_transformation.custom_kv_cache import (
         CustomKVCache,
+        CustomRingKVCache,
     )
 except ImportError:
-    raise ImportError("ExecutorTorch is not installed. Please install it to use CustomKVCache.")
+    raise ImportError("ExecutorTorch is not installed. Please install it to use Custom Cache.")
 
 
 class ETCustomStaticCache(StaticCache):
@@ -55,7 +53,7 @@ def __init__(
         assert device is None or device == "cpu", "Device must be None or 'cpu'"
 
         # Create a list of CustomKVCache instances, one per layer
-        kv_cache_list = []
+        self.kv_cache = torch.nn.ModuleList()
         for _ in range(config.num_hidden_layers):
             layer_cache = CustomKVCache(
                 max_batch_size=self.max_batch_size,
@@ -64,8 +62,7 @@ def __init__(
                 head_dim=self.head_dim,
                 dtype=dtype,
             )
-            kv_cache_list.append(layer_cache)
-        self.kv_cache = torch.nn.ModuleList(kv_cache_list)
+            self.kv_cache.append(layer_cache)
 
     def update(
         self,
@@ -180,6 +177,135 @@ def from_legacy_cache(
         )
 
 
+# Need to figure out if I have to inherit from HybridCache or StaticCache
+class ETCustomHybridCache(HybridCache):
+    """
+    Custom Hybrid KV Cache implementation for ExecutorTorch that inherits from Hugging Face's HybridCache
+    but uses ExecutorTorch's CustomKVCache for global layers and CustomRingKVCache for sliding window layers.
+    """
+
+    def __init__(
+        self,
+        config,
+        max_batch_size: int,
+        max_cache_len: Optional[int] = None,
+        device: Union[torch.device, str, None] = None,
+        dtype: torch.dtype = torch.float32,
+        layer_device_map: Optional[Dict[int, Union[str, torch.device, int]]] = None,
+    ):
+        super().__init__(
+            config=config,
+            max_batch_size=max_batch_size,
+            max_cache_len=max_cache_len,
+            device=device,
+            dtype=dtype,
+            layer_device_map=layer_device_map,
+        )
+
+        # make sure layer_device_map is none
+        assert layer_device_map is None
+        assert device is None or device == "cpu", "Device must be None or 'cpu'"
+
+        self.cache_position = None
+        # Create a list of cache instances, one per layer
+        # Use CustomKVCache for global layers and CustomRingKVCache for sliding window layers
+        self.kv_cache = torch.nn.ModuleList()
+        for layer_idx in range(config.num_hidden_layers):
+            # newer version of transfomer has is_sliding defined
+            # for HybridCache
+            if self.is_sliding[layer_idx]:
+                # This is a sliding window layer
+                layer_cache = CustomRingKVCache(
+                    max_batch_size=self.max_batch_size,
+                    max_context_length=self.sliding_window_len,
+                    n_heads=self.num_key_value_heads,
+                    head_dim=self.head_dim,
+                    dtype=dtype,
+                )
+            else:
+                layer_cache = CustomKVCache(
+                    max_batch_size=self.max_batch_size,
+                    max_context_length=self.max_cache_len,
+                    n_heads=self.num_key_value_heads,
+                    head_dim=self.head_dim,
+                    dtype=dtype,
+                )
+            self.kv_cache.append(layer_cache)
+
+    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`
+        using ExecutorTorch's CustomKVCache or CustomRingKVCache depending on the layer type.
+
+        Args:
+            key_states (`torch.Tensor`):
+                The new key states to cache. Shape: [batch_size, n_heads, seq_len, head_dim]
+            value_states (`torch.Tensor`):
+                The new value states to cache. Shape: [batch_size, n_heads, seq_len, head_dim]
+            layer_idx (`int`):
+                The index of the layer to cache the states for.
+            cache_kwargs (`Dict[str, Any]`, `optional`):
+                Additional arguments for the cache update.
+
+        Returns:
+            A tuple containing the updated key and value states.
+        """
+        assert cache_kwargs is not None
+
+        # Get cache position from cache_kwargs (used by HybridCache)
+        cache_position = cache_kwargs.get("cache_position")
+        assert cache_position is not None
+        assert isinstance(cache_position, torch.Tensor)
+        self.cache_position = cache_position
+
+        # Get the cache instance for this layer (either CustomKVCache or CustomRingKVCache)
+        layer_cache = self.kv_cache[layer_idx]
+
+        # Use the cache's update method
+        # Both CustomKVCache and CustomRingKVCache have the same update interface
+        k_out, v_out = layer_cache.update(
+            input_pos=cache_position,
+            k_val=key_states,
+            v_val=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. A layer index can be optionally passed."""
+        if layer_idx is None:
+            layer_idx = 0
+
+        # For CustomRingKVCache, we need to handle the sequence length differently
+        layer_cache = self.kv_cache[layer_idx]
+        if self.is_sliding[layer_idx]:
+            # CustomRingKVCache cache_position_manager which
+            # maintains cache position for each slot in the kv cache
+            # we return the max position + 1 to indicate max position
+            # seen so far. Not sure if thats the correct interpretation
+            # of sequence length
+            return layer_cache.cache_positions_manager.cache_positions.max().item() + 1
+        return (layer_cache.k_cache[0, :, 0].any(dim=-1)).sum()
+
+    def get_layer_cache(self, layer_idx: int):
+        """
+        Get the cache for a specific layer. This method is dynamo-traceable.
+
+        Args:
+            layer_idx (int): The layer index
+
+        Returns:
+            The cache instance for the specified layer (CustomKVCache or CustomRingKVCache)
+        """
+        return self.kv_cache[layer_idx]
+
+
 def replace_with_et_custom_kv_cache(module, config, generation_config, cache_dtype):
     """
     Replace all KV caches in the module with ETCustomStaticCache.
@@ -192,22 +318,6 @@ def replace_with_et_custom_kv_cache(module, config, generation_config, cache_dty
     Returns:
         The modified module
     """
-    # Ensure custom ops are registered
-    try:
-        op = torch.ops.llama.update_cache
-        assert op is not None
-    except Exception:
-        try:
-            from executorch.extension.llm.custom_ops import custom_ops  # noqa: F401
-
-            op = torch.ops.llama.update_cache
-            assert op is not None
-        except ImportError:
-            raise ImportError(
-                "ExecutorTorch custom operations are not available. "
-                "Please install executorch with custom operations support."
-            )
-
     # Recursively replace KV caches
     return _replace_with_et_custom_kv_cache(module, config, generation_config, cache_dtype)
 
@@ -223,33 +333,73 @@ def _replace_with_et_custom_kv_cache(module, config, generation_config, cache_dt
     Returns:
         The modified module
     """
-    assert hasattr(module, "static_cache")
-    assert isinstance(
-        module.static_cache, StaticCache
-    ), "Only StaticCache transform is supported. Hybrid cache with local global attention is not yet supported"
-    # TODO: Add replace_cache to exported module
-    # in transformer's executorch.py
-    if getattr(module, "replace_cache", None) is not None:
-        static_cache = ETCustomStaticCache(
-            config=config,
-            max_batch_size=generation_config.cache_config.batch_size,
-            max_cache_len=generation_config.cache_config.max_cache_len,
-            device=generation_config.cache_config.device,
-            dtype=cache_dtype,
-        )
-        module.replace_cache(static_cache)
+    # Check if module has static_cache (TorchExportableModuleWithStaticCache)
+    if hasattr(module, "static_cache"):
+        assert isinstance(module.static_cache, StaticCache), f"Expected StaticCache, got {type(module.static_cache)}"
+
+        # TODO: Add replace_cache to exported module
+        # in transformer's executorch.py
+        if getattr(module, "replace_cache", None) is not None:
+            static_cache = ETCustomStaticCache(
+                config=config,
+                max_batch_size=generation_config.cache_config.batch_size,
+                max_cache_len=generation_config.cache_config.max_cache_len,
+                device=generation_config.cache_config.device,
+                dtype=cache_dtype,
+            )
+            module.replace_cache(static_cache)
+        else:
+            module.static_cache = ETCustomStaticCache(
+                config=config,
+                max_batch_size=generation_config.cache_config.batch_size,
+                max_cache_len=generation_config.cache_config.max_cache_len,
+                device=generation_config.cache_config.device,
+                dtype=cache_dtype,
+            )
+            # Dont know why we need to this even though
+            # CustomKVCache registers the attributes
+            for i in range(len(module.static_cache.kv_cache)):
+                setattr(module, f"key_cache_{i}", module.static_cache.kv_cache[i].k_cache)
+                setattr(module, f"value_cache_{i}", module.static_cache.kv_cache[i].v_cache)
+
+    # Check if module has cache (TorchExportableModuleWithHybridCache)
+    elif hasattr(module, "cache"):
+        assert isinstance(module.cache, HybridCache), f"Expected HybridCache, got {type(module.cache)}"
+
+        # Replace with ETCustomHybridCache
+        if getattr(module, "replace_cache", None) is not None:
+            hybrid_cache = ETCustomHybridCache(
+                config=config,
+                max_batch_size=generation_config.cache_config.batch_size,
+                max_cache_len=generation_config.cache_config.max_cache_len,
+                device=generation_config.cache_config.device,
+                dtype=cache_dtype,
+            )
+            module.replace_cache(hybrid_cache)
+        else:
+            module.cache = ETCustomHybridCache(
+                config=config,
+                max_batch_size=generation_config.cache_config.batch_size,
+                max_cache_len=generation_config.cache_config.max_cache_len,
+                device=generation_config.cache_config.device,
+                dtype=cache_dtype,
+            )
+            # Register cache attributes for each layer
+            for i in range(len(module.cache.kv_cache)):
+                setattr(module, f"key_cache_{i}", module.cache.kv_cache[i].k_cache)
+                setattr(module, f"value_cache_{i}", module.cache.kv_cache[i].v_cache)
+                if module.cache.is_sliding[i]:
+                    # Register cache_positions as buffer for sliding window layers
+                    # This prevents it from being traced as a constant
+                    module.register_buffer(
+                        f"cache_positions_{i}",
+                        module.cache.kv_cache[i].cache_positions_manager.cache_positions,
+                        persistent=False,
+                    )
     else:
-        module.static_cache = ETCustomStaticCache(
-            config=config,
-            max_batch_size=generation_config.cache_config.batch_size,
-            max_cache_len=generation_config.cache_config.max_cache_len,
-            device=generation_config.cache_config.device,
-            dtype=cache_dtype,
+        raise ValueError(
+            "Module must have either 'static_cache' (TorchExportableModuleWithStaticCache) "
+            "or 'cache' (TorchExportableModuleWithHybridCache) attribute"
         )
-        # Dont know why we need to this even though
-        # CustomKVCache registers the attributes
-        for i in range(len(module.static_cache.kv_cache)):
-            setattr(module, f"key_cache_{i}", module.static_cache.kv_cache[i].k_cache)
-            setattr(module, f"value_cache_{i}", module.static_cache.kv_cache[i].v_cache)
 
     return module