Transformers 4.48

optimum/exporters/onnx/_traceable_cache.py

@@ -0,0 +1,92 @@
+from typing import Any, Dict, Optional, Tuple
+
+import torch
+from transformers.cache_utils import logger
+
+
+# Simply removing the nn.Module, same as in https://github.com/huggingface/transformers/pull/35873
+class TraceableCache:
+    """
+    Base, abstract class for all caches. The actual data structure is specific to each subclass.
+    """
+
+    def __init__(self):
+        super().__init__()
+
+    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`.
+
+        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. These are specific to each subclass and allow new types of
+                cache to be created.
+
+        Return:
+            A tuple containing the updated key and value states.
+        """
+        raise NotImplementedError("Make sure to implement `update` in a subclass.")
+
+    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."""
+        # TODO: deprecate this function in favor of `cache_position`
+        raise NotImplementedError("Make sure to implement `get_seq_length` in a subclass.")
+
+    # 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
+    # we change naming to be more explicit
+    def get_max_length(self) -> Optional[int]:
+        logger.warning_once(
+            "`get_max_cache()` is deprecated for all Cache classes. Use `get_max_cache_shape()` instead. "
+            "Calling `get_max_cache()` will raise error from v4.48"
+        )
+        return self.get_max_cache_shape()
+
+    def get_max_cache_shape(self) -> Optional[int]:
+        """Returns the maximum sequence length (i.e. max capacity) of the cache object"""
+        raise NotImplementedError("Make sure to implement `get_max_cache_shape` in a subclass.")
+
+    def get_usable_length(self, new_seq_length: int, layer_idx: Optional[int] = 0) -> int:
+        """Given the sequence length of the new inputs, returns the usable length of the cache."""
+        # Cache without size limit -> all cache is usable
+        # Cache with size limit -> if the length cache plus the length of the new inputs is larger the maximum cache
+        #   length, we will need to evict part of the cache (and thus not all cache is usable)
+        max_length = self.get_max_cache_shape()
+        previous_seq_length = self.get_seq_length(layer_idx)
+        if max_length is not None and previous_seq_length + new_seq_length > max_length:
+            return max_length - new_seq_length
+        return previous_seq_length
+
+    def reorder_cache(self, beam_idx: torch.LongTensor):
+        """Reorders the cache for beam search, given the selected beam indices."""
+        for layer_idx in range(len(self.key_cache)):
+            if self.key_cache[layer_idx] != []:
+                device = self.key_cache[layer_idx].device
+                self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device))
+            if self.value_cache[layer_idx] != []:
+                device = self.value_cache[layer_idx].device
+                self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device))
+
+    @property
+    def seen_tokens(self):
+        logger.warning_once(
+            "The `seen_tokens` attribute is deprecated and will be removed in v4.41. Use the `cache_position` "
+            "model input instead."
+        )
+        if hasattr(self, "_seen_tokens"):
+            return self._seen_tokens
+        else:
+            return None

optimum/exporters/onnx/model_configs.py

@@ -843,7 +843,7 @@ class DeiTOnnxConfig(ViTOnnxConfig):
 
 
 class BeitOnnxConfig(ViTOnnxConfig):
-    DEFAULT_ONNX_OPSET = 11
+    DEFAULT_ONNX_OPSET = 14  # now uses F.scaled_dot_product_attention by default for torch>=2.1.1.
 
 
 class ConvNextOnnxConfig(ViTOnnxConfig):
@@ -1573,13 +1573,12 @@ class Data2VecTextOnnxConfig(DistilBertOnnxConfig):
 
 
 class Data2VecVisionOnnxConfig(ViTOnnxConfig):
-    DEFAULT_ONNX_OPSET = 11
+    DEFAULT_ONNX_OPSET = 14  # now uses F.scaled_dot_product_attention by default for torch>=2.1.1.
 
 
 class Data2VecAudioOnnxConfig(AudioOnnxConfig):
-    NORMALIZED_CONFIG_CLASS = NormalizedConfig
-    ATOL_FOR_VALIDATION = 1e-4
     DEFAULT_ONNX_OPSET = 14  # now uses F.scaled_dot_product_attention by default for torch>=2.1.1.
+    NORMALIZED_CONFIG_CLASS = NormalizedConfig
 
 
 class PerceiverDummyInputGenerator(DummyVisionInputGenerator):