New dynamic cache for sliding window attention

docs/source/en/internal/generation_utils.md

@@ -365,22 +365,29 @@ A [`Constraint`] can be used to force the generation to include specific tokens
 
 [[autodoc]] DynamicCache
     - update
-    - get_seq_length
+    - get_past_seen_tokens
+    - reorder_cache
+    - to_legacy_cache
+    - from_legacy_cache
+
+[[autodoc]] DynamicSlidingWindowCache
+    - update
+    - get_past_seen_tokens
     - reorder_cache
     - to_legacy_cache
     - from_legacy_cache
 
 [[autodoc]] QuantizedCache
     - update
-    - get_seq_length
+    - get_past_seen_tokens
 
 [[autodoc]] QuantoQuantizedCache
 
 [[autodoc]] HQQQuantizedCache
 
 [[autodoc]] SinkCache
     - update
-    - get_seq_length
+    - get_past_seen_tokens
     - reorder_cache
 
 [[autodoc]] OffloadedCache
@@ -390,25 +397,25 @@ A [`Constraint`] can be used to force the generation to include specific tokens
 
 [[autodoc]] StaticCache
     - update
-    - get_seq_length
+    - get_past_seen_tokens
     - reset
 
 [[autodoc]] OffloadedStaticCache
     - update
-    - get_seq_length
+    - get_past_seen_tokens
     - reset
 
 [[autodoc]] HybridCache
     - update
-    - get_seq_length
+    - get_past_seen_tokens
     - reset
 
 [[autodoc]] SlidingWindowCache
     - update
     - reset
 
 [[autodoc]] EncoderDecoderCache
-    - get_seq_length
+    - get_past_seen_tokens
     - to_legacy_cache
     - from_legacy_cache
     - reset

examples/modular-transformers/modeling_dummy.py

@@ -854,7 +854,7 @@ def forward(
                 )
 
         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
             )
@@ -946,7 +946,7 @@ def _update_causal_mask(
         # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
         # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
         # to infer the attention mask.
-        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
         using_static_cache = isinstance(past_key_values, StaticCache)
 
         # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward

examples/modular-transformers/modeling_my_new_model2.py

@@ -726,7 +726,7 @@ def forward(
                 )
 
         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
             )
@@ -821,7 +821,7 @@ def _update_causal_mask(
         # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
         # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
         # to infer the attention mask.
-        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
         using_static_cache = isinstance(past_key_values, StaticCache)
 
         # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward

examples/modular-transformers/modeling_super.py

@@ -850,7 +850,7 @@ def _update_causal_mask(
         # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
         # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
         # to infer the attention mask.
-        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
         using_static_cache = isinstance(past_key_values, StaticCache)
 
         # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward

src/transformers/__init__.py

@@ -1275,6 +1275,7 @@
         "Cache",
         "CacheConfig",
         "DynamicCache",
+        "DynamicSlidingWindowCache",
         "EncoderDecoderCache",
         "HQQQuantizedCache",
         "HybridCache",
@@ -6194,6 +6195,7 @@
             Cache,
             CacheConfig,
             DynamicCache,
+            DynamicSlidingWindowCache,
             EncoderDecoderCache,
             HQQQuantizedCache,
             HybridCache,

src/transformers/cache_utils.py

@@ -64,6 +64,12 @@ def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
         # TODO: deprecate this function in favor of `cache_position`
         raise NotImplementedError("Make sure to implement `get_seq_length` in a subclass.")
 
+    def get_past_seen_tokens(self, layer_idx: Optional[int] = 0) -> int:
+        """Returns the number of already processed tokens. For all Cache classes except SlidingWindow caches, this is the same as
+        `get_seq_length()`. However, with sliding window we can process more tokens than the cache size. A layer index can be optionally passed.
+        """
+        return self.get_seq_length(layer_idx)
+
     # Deprecate in favor of max-cache-shape because we want to be specifc by what we mean with "max_length"
     # Prev some cache objects didn't have "max_length" (SlidingWindowCache or SinkCache) because the cache object technically handles
     # infinite amount of tokens. In the codebase what we really need to check is the max capacity of certain cache instances, so
@@ -545,6 +551,133 @@ def batch_select_indices(self, indices: torch.Tensor):
             self.value_cache[layer_idx] = self.value_cache[layer_idx][indices, ...]
 
 
+# TODO: (cyril) Make this the default for models with sliding window once `generate` no longer returns Cache as tuples
+class DynamicSlidingWindowCache(DynamicCache):
+    """
+    A cache that grows dynamically as more tokens are generated, but will stop growing if the sequence length is bigger than the sliding window.
+    This will be the default for generative models with sliding window attention (except for assisted decoding where `DynamicCache` is used).
+
+    It stores the Key and Value states as a list of tensors, one for each layer. The expected shape for each tensor is
+    `[batch_size, num_heads, seq_len, head_dim]` and up to `[batch_size, num_heads, sliding_window-1, head_dim]` if seq_len >= sliding_window-1.
+
+    Note: Since we only keep maximum `sliding_window-1` tokens in the cache, once this value is reached the cache can no
+    longer be roll-backed to previous states without losing information. For this reason, it should not be used with assisted decoding
+    (or contrastive search when using `low_memory=True`).
+
+    Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, AutoModelForCausalLM, DynamicSlidingWindowCache
+
+        >>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
+        >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
+
+        >>> inputs = tokenizer(text="My name is Mistral", return_tensors="pt")
+
+        >>> # Prepare a cache class and pass it to model's forward
+        >>> past_key_values = DynamicSlidingWindowCache(model.config.sliding_window)
+        >>> outputs = model(**inputs, past_key_values=past_key_values, use_cache=True)
+        >>> outputs.past_key_values # access cache filled with key/values from generation
+        DynamicSlidingWindowCache()
+        ```
+    """
+
+    def __init__(self, sliding_window: int) -> None:
+        super().__init__()
+        self.sliding_window = sliding_window
+        # We overwrite the field and maintain a list of size `num_hidden_layers` to accurately reflect the seen tokens at each layer during `update`
+        self._seen_tokens = []
+
+    def get_past_seen_tokens(self, layer_idx: Optional[int] = 0) -> int:
+        """This needs to be overriden because the number of processed tokens may be larger than the cache length."""
+        if len(self._seen_tokens) <= layer_idx:
+            return 0
+        else:
+            return self._seen_tokens[layer_idx]
+
+    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`. Discard previous
+        tokens according to the sliding window if needed.
+
+        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. No additional arguments are used in `DynamicCache`.
+
+        Return:
+            A tuple containing the updated key and value states.
+        """
+        if len(self.key_cache) <= layer_idx:
+            # Update the number of seen tokens
+            self._seen_tokens.append(key_states.shape[-2])
+            # Add only up to sliding window size if larger
+            self.key_cache.append(key_states[..., -self.sliding_window + 1 :, :])
+            self.value_cache.append(value_states[..., -self.sliding_window + 1 :, :])
+            # We should return full states during prefill even though we only save up to sliding window-1
+            return key_states, value_states
+        else:
+            self._seen_tokens[layer_idx] += key_states.shape[-2]
+            # We may need to return longer states (e.g. to continue generation with previous cache, with added tokens), but we only keep
+            # the last `sliding_window-1` states in the cache for next forward
+            full_key_states = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
+            full_value_states = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)
+            self.key_cache[layer_idx] = full_key_states[..., -self.sliding_window + 1 :, :]
+            self.value_cache[layer_idx] = full_value_states[..., -self.sliding_window + 1 :, :]
+            return full_key_states, full_value_states
+
+    def batch_split(self, full_batch_size: int, split_size: int) -> List["DynamicCache"]:
+        """Split the current instance into a list of `DynamicCache` by the batch size. This will be used by
+        `_split_model_inputs()` in `generation.utils`"""
+        out = []
+        for i in range(0, full_batch_size, split_size):
+            current_split = DynamicSlidingWindowCache(self.sliding_window)
+            current_split._seen_tokens = self._seen_tokens
+            current_split.key_cache = [tensor[i : i + split_size] for tensor in self.key_cache]
+            current_split.value_cache = [tensor[i : i + split_size] for tensor in self.value_cache]
+            out.append(current_split)
+        return out
+
+    @classmethod
+    def from_batch_splits(cls, splits: List["DynamicSlidingWindowCache"]) -> "DynamicSlidingWindowCache":
+        """This is the opposite of the above `batch_split()` method. This will be used by `stack_model_outputs` in
+        `generation.utils`"""
+        cache = cls(splits[0].sliding_window)
+        for idx in range(len(splits[0])):
+            key_cache = [current.key_cache[idx] for current in splits if current.key_cache[idx] != []]
+            value_cache = [current.key_cache[idx] for current in splits if current.key_cache[idx] != []]
+            if key_cache != []:
+                layer_keys = torch.cat(key_cache, dim=0)
+                layer_values = torch.cat(value_cache, dim=0)
+                cache.update(layer_keys, layer_values, idx)
+
+        # We need this because _seen_tokens may be bigger than what will be automatically set with `update` (if cache > sliding_window)
+        cache._seen_tokens = splits[0]._seen_tokens
+        return cache
+
+    def crop(self, max_length: int):
+        if self.get_past_seen_tokens() >= self.sliding_window - 1:
+            raise RuntimeError(
+                "The current DynamicSlidingWindowCache is full. It cannot be cropped as this would mean losing past states."
+            )
+        else:
+            super().crop(max_length)
+
+    from_legacy_cache = None
+    to_legacy_cache = None
+
+
 class OffloadedCache(DynamicCache):
     """
     A drop-in replacement for DynamicCache that conserves GPU memory at the expense of more CPU memory.

src/transformers/generation/utils.py

@@ -28,6 +28,7 @@
 from ..cache_utils import (
     Cache,
     DynamicCache,
+    DynamicSlidingWindowCache,
     EncoderDecoderCache,
     OffloadedCache,
     QuantizedCacheConfig,
@@ -455,6 +456,7 @@ def prepare_inputs_for_generation(
                     attention_mask,
                     sequence_length=sequence_length,
                     target_length=past_key_values.get_max_cache_shape(),
+                    initial_mask_position=0,
                     dtype=self.dtype,
                     device=device,
                     cache_position=cache_position,
@@ -1557,8 +1559,8 @@ def _get_initial_cache_position(self, input_ids, model_kwargs):
             past_length = 0
             if not isinstance(cache, Cache):
                 past_length = cache[0][0].shape[2]
-            elif hasattr(cache, "get_seq_length") and cache.get_seq_length() is not None:
-                past_length = cache.get_seq_length()
+            elif hasattr(cache, "get_past_seen_tokens") and cache.get_past_seen_tokens() is not None:
+                past_length = cache.get_past_seen_tokens()
 
             # TODO(joao): this is not torch.compile-friendly, find a work-around. If the cache is not empty,
             # end-to-end compilation will yield bad results because `cache_position` will be incorrect.
@@ -2158,6 +2160,8 @@ def generate(
                 raise ValueError(
                     f"assisted generation is not supported with stateful models, such as {self.__class__.__name__}"
                 )
+            if isinstance(getattr(model_kwargs, "past_key_values", None), DynamicSlidingWindowCache):
+                raise ValueError("DynamicSlidingWindowCache cannot be used in assisted generation.")
 
             # 11. Get the candidate generator, given the parameterization
             candidate_generator = self._get_candidate_generator(
@@ -2207,6 +2211,12 @@ def generate(
                 raise ValueError(
                     f"contrastive search is not supported with stateful models, such as {self.__class__.__name__}"
                 )
+            if isinstance(getattr(model_kwargs, "past_key_values", None), DynamicSlidingWindowCache) and getattr(
+                generation_config, "low_memory", False
+            ):
+                raise ValueError(
+                    "DynamicSlidingWindowCache cannot be used in contrastive generation with `low_memory=True`."
+                )
 
             result = self._contrastive_search(
                 input_ids,
@@ -2817,7 +2827,7 @@ def _contrastive_search(
             # (2) last_hidden_states; (3) logit_for_next_step; (4) update model kwargs for the next step
             if model_kwargs.get("past_key_values") is None or (
                 isinstance(model_kwargs["past_key_values"], (Cache, EncoderDecoderCache))
-                and model_kwargs["past_key_values"].get_seq_length() == 0
+                and model_kwargs["past_key_values"].get_past_seen_tokens() == 0
             ):
                 # prepare inputs
                 model_kwargs["use_cache"] = True
@@ -4655,10 +4665,8 @@ def _concat(data):
         if isinstance(data[0], torch.Tensor):
             return torch.cat(data, dim=0)
         # New cache format
-        elif isinstance(data[0], DynamicCache):
-            return DynamicCache.from_batch_splits(data, num_hidden_layers=num_hidden_layers)
-        elif isinstance(data[0], EncoderDecoderCache):
-            return EncoderDecoderCache.from_batch_splits(data, num_hidden_layers=num_hidden_layers)
+        elif isinstance(data[0], (DynamicCache, EncoderDecoderCache)):
+            return data[0].__class__.from_batch_splits(data, num_hidden_layers=num_hidden_layers)
         elif isinstance(data[0], tuple):
             # If the elements of the tuple are also tuples (e.g., past_key_values in our earlier example)
             if isinstance(data[0][0], tuple):

src/transformers/modeling_attn_mask_utils.py

@@ -275,13 +275,13 @@ def _ignore_causal_mask_sdpa(
             if (
                 (is_training or not is_tracing)
                 and (query_length == 1 or key_value_length == query_length)
-                and (sliding_window is None or key_value_length < sliding_window)
+                and (sliding_window is None or key_value_length <= sliding_window)
             ):
                 ignore_causal_mask = True
-        elif sliding_window is None or key_value_length < sliding_window:
+        elif sliding_window is None or key_value_length <= sliding_window:
             if len(attention_mask.shape) == 4:
                 return False
-            elif not is_tracing and torch.all(attention_mask == 1):
+            elif not is_tracing and torch.all(attention_mask[:, -key_value_length:] == 1):
                 if query_length == 1 or key_value_length == query_length:
                     # For query_length == 1, causal attention and bi-directional attention are the same.
                     ignore_causal_mask = True

src/transformers/models/bloom/modeling_bloom.py

@@ -646,7 +646,7 @@ def forward(
                 )
 
         batch_size, seq_length, _ = inputs_embeds.shape
-        past_length = past_key_values.get_seq_length() if past_key_values is not None else 0
+        past_length = past_key_values.get_past_seen_tokens() if past_key_values is not None else 0
         seq_length_with_past = seq_length + past_length
         if cache_position is None:
             cache_position = torch.arange(past_length, past_length + seq_length, device=inputs_embeds.device)
@@ -747,7 +747,7 @@ def _update_causal_mask(
         # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
         # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
         # to infer the attention mask.
-        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
         using_static_cache = isinstance(past_key_values, StaticCache)
 
         # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward