[whisper] static kv cache (#31166)

* make work with cache abstraction

* correct for static cache

* hacks for compile

* make fast

* fix

* fix pos ids

* generate

* fix sdpa

* fix sdpa cache pos

* fix fa2

* clean fa2

* integrate cache into generate

* make style

* copies

* more copies

* update eager

* update sdpa

* update fa2

* simplify

* use cache pos

* always compute cross-cache for debug

* avoid recompiles
Co-authored-by: Arthur Zucker <arthur@huggingface.co>

* fix fix

* fix fix fix

* more fix

* try encoder-decoder cache (too messy)

* revert encoder-decoder cache

* check cross-attn cache

* use enc-dec dataclass

* use richer enc-dec dataclass

* clean-up

* revert static cache changes

* small fixes

* revert to cpu flag

* fix copies

* add static slow test

* past k/v docstring

* more docstrings

* cache_position docstrings

* add to docs

* add enc-dec cache to docs

* make style

* fix afte...

docs/source/en/internal/generation_utils.md

@@ -391,6 +391,12 @@ A [`Constraint`] can be used to force the generation to include specific tokens
     - get_seq_length
     - reset
 
+[[autodoc]] EncoderDecoderCache
+    - get_seq_length
+    - to_legacy_cache
+    - from_legacy_cache
+    - reset
+    - reorder_cache
 
 ## Watermark Utils
 

docs/source/en/model_doc/whisper.md

@@ -52,8 +52,6 @@ Here is a step-by-step guide to transcribing an audio sample using a pre-trained
 >>> # Select an audio file and read it:
 >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
 >>> audio_sample = ds[0]["audio"]
->>> waveform = audio_sample["array"]
->>> sampling_rate = audio_sample["sampling_rate"]
 
 >>> # Load the Whisper model in Hugging Face format:
 >>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
@@ -61,7 +59,7 @@ Here is a step-by-step guide to transcribing an audio sample using a pre-trained
 
 >>> # Use the model and processor to transcribe the audio:
 >>> input_features = processor(
-...     waveform, sampling_rate=sampling_rate, return_tensors="pt"
+...     audio_sample["array"], sampling_rate=audio_sample["sampling_rate"], return_tensors="pt"
 ... ).input_features
 
 >>> # Generate token ids
@@ -74,6 +72,49 @@ Here is a step-by-step guide to transcribing an audio sample using a pre-trained
 ' Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.'
 ```
 
+Whisper is compatible with the following optimisations:
+- [PyTorch Scaled Dot Product Attention (SDPA)](../perf_infer_gpu_one#pytorch-scaled-dot-product-attention): flash attention and memory-efficient attention kernels. Enabled by default for `torch>=2.1.1`.
+- [Flash Attention 2](../perf_infer_gpu_one#flashattention-2): improved implementation of flash attention through better parallelism and work partitioning. 
+- [torch.compile](../llm_optims#static-kv-cache-and-torchcompile): JIT-compile the forward pass to dispatch to efficient fused kernels.
+
+As an example, the following codesnippet enables SDPA and `torch.compile` for up to 5x faster inference:
+
+```python
+>>> from datasets import load_dataset
+>>> from transformers import WhisperProcessor, WhisperForConditionalGeneration
+
+>>> # Select an audio file and read it:
+>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+>>> audio_sample = ds[0]["audio"]
+
+>>> # Load the Whisper model with SDPA attention
+>>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
+>>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", attn_implementation="sdpa")
+
+>>> # Enable static cache and compile the forward pass
+>>> model.generation_config.cache_implementation = "static"
+>>> model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True)
+
+>>> # Use the model and processor to transcribe the audio:
+>>> input_features = processor(
+...     audio_sample["array"], sampling_rate=audio_sample["sampling_rate"], return_tensors="pt"
+... ).input_features
+
+>>> # Compile the forward pass
+>>> _ = model.generate(input_features)
+
+>>> # Generate token ids using compiled graph (fast!)
+>>> predicted_ids = model.generate(input_features)
+
+>>> # Decode token ids to text
+>>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
+
+>>> transcription[0]
+' Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.'
+```
+
+For more details on each optimisation, refer to the documentation linked above.
+
 ## Resources
 
 A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Whisper. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

src/transformers/__init__.py

@@ -1212,6 +1212,7 @@ else:
         "Cache",
         "CacheConfig",
         "DynamicCache",
+        "EncoderDecoderCache",
         "HQQQuantizedCache",
         "QuantizedCache",
         "QuantizedCacheConfig",
@@ -5895,6 +5896,7 @@ if TYPE_CHECKING:
             Cache,
             CacheConfig,
             DynamicCache,
+            EncoderDecoderCache,
             HQQQuantizedCache,
             QuantizedCache,
             QuantizedCacheConfig,

src/transformers/cache_utils.py

@@ -858,8 +858,12 @@ class StaticCache(Cache):
         k_out = self.key_cache[layer_idx]
         v_out = self.value_cache[layer_idx]
 
-        k_out[:, :, cache_position] = key_states
-        v_out[:, :, cache_position] = value_states
+        if cache_position is None:
+            k_out.copy_(key_states)
+            v_out.copy_(value_states)
+        else:
+            k_out[:, :, cache_position] = key_states
+            v_out[:, :, cache_position] = value_states
 
         return k_out, v_out
 
@@ -971,6 +975,158 @@ class SlidingWindowCache(StaticCache):
         # no matter how long the sentence is
         return None
 
+    def reset(self):
+        self.key_cache.zero_()
+        self.value_cache.zero_()
+
+
+class EncoderDecoderCache(Cache):
+    """
+    Base, abstract class for all encoder-decoder caches. Can be used to hold combinations of self-attention and
+    cross-attention caches.
+    """
+
+    def __init__(self, self_attention_cache: Cache, cross_attention_cache: Cache):
+        self.self_attention_cache = self_attention_cache
+        self.cross_attention_cache = cross_attention_cache
+
+        self.is_updated = {}
+        for layer_idx in range(len(cross_attention_cache.key_cache)):
+            self.is_updated[layer_idx] = bool(cross_attention_cache.get_seq_length(layer_idx) > 0)
+
+    def __getitem__(self, layer_idx: int) -> List[Tuple[torch.Tensor]]:
+        """
+        Support for backwards-compatible `past_key_value` indexing, e.g. `past_key_value[0][0].shape[2]` to get the
+        sequence length.
+        """
+        if layer_idx < len(self):
+            return (
+                self.self_attention_cache.key_cache[layer_idx],
+                self.self_attention_cache.value_cache[layer_idx],
+                self.cross_attention_cache.key_cache[layer_idx],
+                self.cross_attention_cache.key_cache[layer_idx],
+            )
+        else:
+            raise KeyError(f"Cache only has {len(self)} layers, attempted to access layer with index {layer_idx}")
+
+    def __len__(self):
+        """
+        Support for backwards-compatible `past_key_value` length, e.g. `len(past_key_value)`. This value corresponds
+        to the number of layers in the model.
+        """
+        return len(self.self_attention_cache)
+
+    def to_legacy_cache(self) -> Tuple[Tuple[torch.Tensor], Tuple[torch.Tensor]]:
+        """Converts the `EncoderDecoderCache` instance into  its equivalent in the legacy cache format."""
+        legacy_cache = ()
+        if len(self.cross_attention_cache) > 0:
+            for self_attn, cross_attn in zip(
+                self.self_attention_cache.to_legacy_cache(), self.cross_attention_cache.to_legacy_cache()
+            ):
+                legacy_cache += (self_attn + cross_attn,)
+        else:
+            legacy_cache = self.self_attention_cache.to_legacy_cache()
+        return legacy_cache
+
+    @classmethod
+    def from_legacy_cache(
+        cls, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
+    ) -> "EncoderDecoderCache":
+        """Converts a cache in the legacy cache format into an equivalent `EncoderDecoderCache`."""
+        cache = cls(self_attention_cache=DynamicCache(), cross_attention_cache=DynamicCache())
+        if past_key_values is not None:
+            for layer_idx in range(len(past_key_values)):
+                key_states, value_states = past_key_values[layer_idx][:2]
+                cache.self_attention_cache.update(key_states, value_states, layer_idx)
+                if len(past_key_values[layer_idx]) > 2:
+                    key_states, value_states = past_key_values[layer_idx][2:]
+                    cache.cross_attention_cache.update(key_states, value_states, layer_idx)
+                    cache.is_updated[layer_idx] = True
+        return cache
+
+    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 len(self.self_attention_cache.key_cache) <= layer_idx:
+            return 0
+        return (self.self_attention_cache.key_cache[layer_idx][0, 0].any(dim=-1)).sum()
+
+    def reset(self):
+        if hasattr(self.self_attention_cache, "reset"):
+            self.self_attention_cache.reset()
+        if hasattr(self.cross_attention_cache, "reset"):
+            self.cross_attention_cache.reset()
+        elif not hasattr(self.self_attention_cache, "reset") and not hasattr(self.cross_attention_cache, "reset"):
+            raise ValueError(
+                "Neither self nor cross-attention cache have valid `.reset()` methods. `.reset()` should "
+                "only be called on compatible cache classes, such as `StaticCache` or `SlidingWindowCache`. "
+                f"Got {self.self_attention_cache.__str__()} for the self attention cache and "
+                f"{self.cross_attention_cache.__str__()} for the cross attention cache."
+            )
+        for layer_idx in self.is_updated:
+            self.is_updated[layer_idx] = False
+
+    def reorder_cache(self, beam_idx: torch.LongTensor):
+        """Reorders the cache for beam search, given the selected beam indices."""
+        self.self_attention_cache.reorder_cache(beam_idx)
+        self.cross_attention_cache.reorder_cache(beam_idx)
+
+    def check_dynamic_cache(self, method: str):
+        if not (
+            isinstance(self.self_attention_cache, DynamicCache)
+            and isinstance(self.cross_attention_cache, DynamicCache)
+        ):
+            raise ValueError(
+                f"`{method}` is only defined for dynamic cache, got {self.self_attention_cache.__str__()} for the self "
+                f"attention cache and {self.cross_attention_cache.__str__()} for the cross attention cache."
+            )
+
+    # TODO(gante, sanchit-gandhi): move following functionality into `.generate`
+    def crop(self, maximum_length: int):
+        """Crop the past key values up to a new `maximum_length` in terms of tokens. `maximum_length` can also be
+        negative to remove `maximum_length` tokens. This is used in assisted decoding and contrastive search."""
+        self.check_dynamic_cache(self.crop.__name__)
+        self.self_attention_cache.crop(maximum_length)
+
+    def batch_split(self, full_batch_size: int, split_size: int) -> "List[EncoderDecoderCache]":
+        """Split the current instance into a list of `DynamicCache` by the batch size. This will be used by
+        `_split_model_inputs()` in `generation.utils`"""
+        self.check_dynamic_cache(self.batch_split.__name__)
+        self_attention_cache = self.self_attention_cache.batch_split(full_batch_size, split_size)
+        cross_attention_cache = self.cross_attention_cache.batch_split(full_batch_size, split_size)
+
+        out = []
+        for self_attn, cross_attn in zip(self_attention_cache, cross_attention_cache):
+            out.append(EncoderDecoderCache(self_attn, cross_attn))
+        return out
+
+    @classmethod
+    def from_batch_splits(cls, splits: List["EncoderDecoderCache"]) -> "EncoderDecoderCache":
+        """This is the opposite of the above `batch_split()` method. This will be used by `stack_model_outputs` in
+        `generation.utils`"""
+        self_attention_cache = DynamicCache()
+        cross_attention_cache = DynamicCache()
+        for idx in range(len(splits[0])):
+            layer_keys = torch.cat([current.self_attention_cache.key_cache[idx] for current in splits], dim=0)
+            layer_values = torch.cat([current.self_attention_cache.value_cache[idx] for current in splits], dim=0)
+            self_attention_cache.update(layer_keys, layer_values, idx)
+
+            layer_keys = torch.cat([current.cross_attention_cache.key_cache[idx] for current in splits], dim=0)
+            layer_values = torch.cat([current.cross_attention_cache.value_cache[idx] for current in splits], dim=0)
+            cross_attention_cache.update(layer_keys, layer_values, idx)
+        return cls(self_attention_cache, cross_attention_cache)
+
+    def batch_repeat_interleave(self, repeats: int):
+        """Repeat the cache `repeats` times in the batch dimension. Used in contrastive search."""
+        self.check_dynamic_cache(self.batch_repeat_interleave.__name__)
+        self.self_attention_cache.batch_repeat_interleave(repeats)
+        self.cross_attention_cache.batch_repeat_interleave(repeats)
+
+    def batch_select_indices(self, indices: torch.Tensor):
+        """Only keep the `indices` in the batch dimension of the cache. Used in contrastive search."""
+        self.check_dynamic_cache(self.batch_select_indices.__name__)
+        self.self_attention_cache.batch_select_indices(indices)
+        self.cross_attention_cache.batch_select_indices(indices)
+
 
 class HybridCache(Cache):
     def __init__(self, config: PretrainedConfig, max_batch_size, max_cache_len, device="cpu", dtype=None) -> None:

src/transformers/generation/utils.py

@@ -27,6 +27,7 @@ from torch import nn
 from ..cache_utils import (
     Cache,
     DynamicCache,
+    EncoderDecoderCache,
     HQQQuantizedCache,
     HybridCache,
     QuantizedCacheConfig,
@@ -1409,7 +1410,7 @@ class GenerationMixin:
         model_kwargs["cache_position"] = torch.arange(past_length, cur_len, device=input_ids.device)
         return model_kwargs
 
-    def _get_cache(self, cache_implementation: str, max_batch_size: int, max_cache_len: int) -> Cache:
+    def _get_cache(self, cache_implementation: str, max_batch_size: int, max_cache_len: int, model_kwargs) -> Cache:
         """
         Sets a cache for `generate`, that will persist across calls. A new cache will only be initialized a
         new `generate` call requires a larger cache.
@@ -1417,28 +1418,46 @@ class GenerationMixin:
         Returns the resulting cache object.
         """
         cache_cls: Cache = NEED_SETUP_CACHE_CLASSES_MAPPING[cache_implementation]
+        requires_cross_attention_cache = (
+            self.config.is_encoder_decoder or model_kwargs.get("encoder_outputs") is not None
+        )
+
+        if hasattr(self, "_cache"):
+            cache_to_check = self._cache.self_attention_cache if requires_cross_attention_cache else self._cache
+
         if cache_implementation == "sliding_window":
             max_cache_len = min(self.config.sliding_window, max_cache_len)
 
         need_new_cache = (
             not hasattr(self, "_cache")
-            or (not isinstance(self._cache, cache_cls))
-            or self._cache.max_batch_size != max_batch_size
-            or self._cache.max_cache_len < max_cache_len
+            or (not isinstance(cache_to_check, cache_cls))
+            or cache_to_check.max_batch_size != max_batch_size
+            or cache_to_check.max_cache_len < max_cache_len
         )
 
+        if requires_cross_attention_cache and hasattr(self, "_cache"):
+            need_new_cache = (
+                need_new_cache
+                or self._cache.cross_attention_cache.max_cache_len != model_kwargs["encoder_outputs"][0].shape[1]
+            )
+
         if need_new_cache:
             if hasattr(self.config, "_pre_quantization_dtype"):
                 cache_dtype = self.config._pre_quantization_dtype
             else:
                 cache_dtype = self.dtype
-            self._cache = cache_cls(
-                config=self.config,
-                max_batch_size=max_batch_size,
-                max_cache_len=max_cache_len,
-                device=self.device,
-                dtype=cache_dtype,
-            )
+            cache_kwargs = {
+                "config": self.config,
+                "max_batch_size": max_batch_size,
+                "max_cache_len": max_cache_len,
+                "device": self.device,
+                "dtype": cache_dtype,
+            }
+            self._cache = cache_cls(**cache_kwargs)
+            if requires_cross_attention_cache:
+                encoder_kwargs = cache_kwargs.copy()
+                encoder_kwargs["max_cache_len"] = model_kwargs["encoder_outputs"][0].shape[1]
+                self._cache = EncoderDecoderCache(self._cache, cache_cls(**encoder_kwargs))
         else:
             self._cache.reset()
         return self._cache
@@ -1745,6 +1764,7 @@ class GenerationMixin:
                     generation_config.cache_implementation,
                     getattr(generation_config, "num_beams", 1) * batch_size,
                     generation_config.max_length,
+                    model_kwargs,
                 )
             elif generation_config.cache_implementation == "quantized":
                 if not self._supports_quantized_cache:
@@ -1776,11 +1796,22 @@ class GenerationMixin:
         # keeps copying the cache thus using much more memory
         elif generation_config.cache_implementation is None and self._supports_default_dynamic_cache():
             past = model_kwargs.get("past_key_values", None)
+            requires_cross_attention_cache = (
+                self.config.is_encoder_decoder or model_kwargs.get("encoder_outputs") is not None
+            )
             if past is None:
-                model_kwargs["past_key_values"] = DynamicCache()
+                model_kwargs["past_key_values"] = (
+                    DynamicCache()
+                    if not requires_cross_attention_cache
+                    else EncoderDecoderCache(DynamicCache(), DynamicCache())
+                )
                 use_dynamic_cache_by_default = True
             elif isinstance(past, tuple):
-                model_kwargs["past_key_values"] = DynamicCache.from_legacy_cache(past)
+                model_kwargs["past_key_values"] = (
+                    DynamicCache.from_legacy_cache(past)
+                    if not requires_cross_attention_cache
+                    else EncoderDecoderCache.from_legacy_cache(past)
+                )
                 use_dynamic_cache_by_default = True
 
         self._validate_generated_length(generation_config, input_ids_length, has_default_max_length)
@@ -2064,7 +2095,7 @@ class GenerationMixin:
         # Convert to legacy cache if needed
         if use_dynamic_cache_by_default and generation_config.return_legacy_cache:
             if isinstance(result, ModelOutput) and hasattr(result, "past_key_values"):
-                if isinstance(result.past_key_values, DynamicCache):
+                if isinstance(result.past_key_values, (DynamicCache, EncoderDecoderCache)):
                     result.past_key_values = result.past_key_values.to_legacy_cache()
         return result
 
@@ -2234,7 +2265,7 @@ class GenerationMixin:
             # if the first step in the loop, encode all the prefix and obtain: (1) past_key_values;
             # (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)
+                isinstance(model_kwargs["past_key_values"], (Cache, EncoderDecoderCache))
                 and model_kwargs["past_key_values"].get_seq_length() == 0
             ):
                 # prepare inputs
@@ -2323,7 +2354,9 @@ class GenerationMixin:
                 # Replicates the new past_key_values to match the `top_k` candidates
                 past = model_kwargs["past_key_values"]
                 # If it is a static cache, modify it in-place layer after layer to save memory
-                if isinstance(past, DynamicCache):
+                if isinstance(past, DynamicCache) or (
+                    isinstance(past, EncoderDecoderCache) and isinstance(past.self_attention_cache, DynamicCache)
+                ):
                     past.batch_repeat_interleave(top_k)
                 else:
                     new_key_values = []
@@ -2350,7 +2383,10 @@ class GenerationMixin:
                         output_hidden_states=True,
                         output_attentions=output_attentions,
                     )
-                    if isinstance(outputs["past_key_values"], DynamicCache):
+                    if isinstance(outputs["past_key_values"], DynamicCache) or (
+                        isinstance(outputs["past_key_values"], EncoderDecoderCache)
+                        and isinstance(outputs["past_key_values"].self_attention_cache, DynamicCache)
+                    ):
                         # Remove past K-V from output since we don't need to stack later
                         outputs["past_key_values"] = None
                         # Remove last token from past K-V since we don't want to append it at this point
@@ -2425,7 +2461,10 @@ class GenerationMixin:
             else:
                 _, next_past_key_values = self._extract_past_from_model_output(outputs, standardize_cache_format=True)
                 # Do it in-place layer per layer to save memory
-                if isinstance(next_past_key_values, DynamicCache):
+                if isinstance(next_past_key_values, DynamicCache) or (
+                    isinstance(next_past_key_values, EncoderDecoderCache)
+                    and isinstance(next_past_key_values.self_attention_cache, DynamicCache)
+                ):
                     next_past_key_values.batch_select_indices(augmented_idx)
                 else:
                     new_key_values = []
@@ -2498,7 +2537,10 @@ class GenerationMixin:
             # Contrastive search works by forward looking at the next token, so we need to exclude it from
             # `past_key_values` to be consistent with the other decoding methods
             if model_kwargs.get("past_key_values") is not None:
-                if isinstance(model_kwargs["past_key_values"], DynamicCache):
+                if isinstance(model_kwargs["past_key_values"], DynamicCache) or (
+                    isinstance(model_kwargs["past_key_values"], EncoderDecoderCache)
+                    and isinstance(model_kwargs["past_key_values"].self_attention_cache, DynamicCache)
+                ):
                     model_kwargs["past_key_values"].crop(-1)
                 else:
                     past_key_values = []
@@ -2757,7 +2799,7 @@ class GenerationMixin:
         # Exception 2: models with different cache formats. These are limited to `DynamicCache` until their
         # cache format is standardized, to avoid adding complexity to the codebase.
         elif "bloom" in model_class or "gptbigcode" in model_class:
-            if not isinstance(past_key_values, DynamicCache):
+            if not isinstance(past_key_values, (DynamicCache, EncoderDecoderCache)):
                 raise ValueError(
                     f"Using an unsupported cache format with {model_class}. Currently, it only supports the "
                     "legacy tuple format or `DynamicCache`"
@@ -3703,8 +3745,12 @@ class GenerationMixin:
 
         # This is needed if return_dict_in_generate is True
         start_from_empty_dynamic_cache = False
-        if isinstance(model_kwargs.get("past_key_values", None), DynamicCache):
-            if len(model_kwargs["past_key_values"]) == 0:
+        past_key_values = model_kwargs.get("past_key_values", None)
+        if isinstance(past_key_values, DynamicCache) or (
+            isinstance(past_key_values, EncoderDecoderCache)
+            and isinstance(past_key_values.self_attention_cache, DynamicCache)
+        ):
+            if len(past_key_values) == 0:
                 start_from_empty_dynamic_cache = True
 
         this_peer_finished = False
@@ -4022,7 +4068,9 @@ def _split(data, full_batch_size: int, split_size: int = None):
     if isinstance(data, torch.Tensor):
         return [data[i : i + split_size] for i in range(0, full_batch_size, split_size)]
     # New cache format
-    elif isinstance(data, DynamicCache):
+    elif isinstance(data, DynamicCache) or (
+        isinstance(data, EncoderDecoderCache) and isinstance(data.self_attention_cache, DynamicCache)
+    ):
         return data.batch_split(full_batch_size, split_size)
     elif isinstance(data, tuple):
         # If the elements of the tuple are also tuples (e.g., past_key_values in our earlier example)
@@ -4130,6 +4178,8 @@ def stack_model_outputs(model_outputs: List[ModelOutput]) -> ModelOutput:
         # New cache format
         elif isinstance(data[0], DynamicCache):
             return DynamicCache.from_batch_splits(data)
+        elif isinstance(data[0], EncoderDecoderCache):
+            return EncoderDecoderCache.from_batch_splits(data)
         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/models/whisper/configuration_whisper.py

@@ -189,7 +189,11 @@ class WhisperConfig(PretrainedConfig):
 
     model_type = "whisper"
     keys_to_ignore_at_inference = ["past_key_values"]
-    attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
+    attribute_map = {
+        "num_key_value_heads": "encoder_attention_heads",
+        "num_attention_heads": "encoder_attention_heads",
+        "hidden_size": "d_model",
+    }
 
     def __init__(
         self,

src/transformers/utils/dummy_pt_objects.py

@@ -37,6 +37,13 @@ class DynamicCache(metaclass=DummyObject):
         requires_backends(self, ["torch"])
 
 
+class EncoderDecoderCache(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class HQQQuantizedCache(metaclass=DummyObject):
     _backends = ["torch"]
 

tests/generation/test_utils.py

@@ -57,7 +57,7 @@ if is_torch_available():
         ImageGPTForCausalImageModeling,
         SpeechEncoderDecoderModel,
     )
-    from transformers.cache_utils import DynamicCache, QuantoQuantizedCache
+    from transformers.cache_utils import DynamicCache, EncoderDecoderCache, QuantoQuantizedCache
     from transformers.generation import (
         BeamSampleDecoderOnlyOutput,
         BeamSampleEncoderDecoderOutput,
@@ -1636,7 +1636,6 @@ class GenerationTesterMixin:
 
             config, input_ids, attention_mask = self._get_input_ids_and_config()
             config.use_cache = True
-            config.is_decoder = True
 
             model = model_class(config).to(torch_device).eval()
             generation_kwargs = {
@@ -1652,15 +1651,21 @@ class GenerationTesterMixin:
             set_seed(seed)
             legacy_results = model.generate(input_ids, attention_mask=attention_mask, **generation_kwargs)
             set_seed(seed)
+            if config.is_encoder_decoder:
+                cache_cls = EncoderDecoderCache
+                past_key_values = cache_cls(DynamicCache(), DynamicCache())
+            else:
+                cache_cls = DynamicCache
+                past_key_values = cache_cls()
             new_results = model.generate(
-                input_ids, attention_mask=attention_mask, past_key_values=DynamicCache(), **generation_kwargs
+                input_ids, attention_mask=attention_mask, past_key_values=past_key_values, **generation_kwargs
             )
 
             # The two sets of generated sequences must match, despite the cache format between forward passes being
             # different
             self.assertListEqual(legacy_results.sequences.tolist(), new_results.sequences.tolist())
             self.assertTrue(isinstance(legacy_results.past_key_values, tuple))
-            self.assertTrue(isinstance(new_results.past_key_values, DynamicCache))
+            self.assertTrue(isinstance(new_results.past_key_values, cache_cls))
 
             # The contents of the two caches, when converted to the same format (in both directions!), must match
             legacy_cache = legacy_results.past_key_values
@@ -1675,7 +1680,7 @@ class GenerationTesterMixin:
                     )
 
             new_cache = new_results.past_key_values
-            legacy_cache_converted = DynamicCache.from_legacy_cache(legacy_results.past_key_values)
+            legacy_cache_converted = cache_cls.from_legacy_cache(legacy_results.past_key_values)
             for layer_idx in range(len(new_cache)):
                 for kv_idx in range(len(new_cache[layer_idx])):
                     self.assertTrue(

tests/models/whisper/test_modeling_whisper.py

@@ -1539,6 +1539,46 @@ class WhisperModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMi
     def test_longform_generate_multi_batch_cond_prev(self):
         self._check_longform_generate_multi_batch(condition_on_prev_tokens=True)
 
+    def test_custom_4d_attention_mask(self):
+        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        model = WhisperForConditionalGeneration(config).to(device=torch_device, dtype=torch.float32)
+        model.eval()
+
+        (
+            input_ids,
+            position_ids,
+            input_ids_shared_prefix,
+            mask_shared_prefix,
+            position_ids_shared_prefix,
+        ) = self._get_custom_4d_mask_test_data()
+
+        with torch.no_grad():
+            logits = model.forward(
+                decoder_input_ids=input_ids,
+                input_features=input_dict["input_features"],
+                decoder_position_ids=position_ids,
+            ).logits
+            # logits.shape == torch.Size([3, 4, ...])
+
+            logits_shared_prefix = model(
+                decoder_input_ids=input_ids_shared_prefix,
+                input_features=input_dict["input_features"],
+                decoder_attention_mask=mask_shared_prefix,
+                decoder_position_ids=position_ids_shared_prefix,
+            )[0]
+            # logits_shared_prefix.shape == torch.Size([1, 6, ...])
+
+        out_last_tokens = logits[:, -1, :]  # last tokens in each batch line
+        out_shared_prefix_last_tokens = logits_shared_prefix[0, -3:, :]  # last three tokens
+
+        # comparing greedily-chosen tokens:
+        assert torch.equal(out_last_tokens.max(axis=1).indices, out_shared_prefix_last_tokens.max(axis=1).indices)
+
+        # comparing softmax-normalized logits:
+        normalized_0 = torch.nn.functional.softmax(out_last_tokens)
+        normalized_1 = torch.nn.functional.softmax(out_shared_prefix_last_tokens)
+        torch.testing.assert_close(normalized_0, normalized_1, rtol=1e-3, atol=1e-4)
+
 
 @require_torch
 @require_torchaudio
@@ -2961,6 +3001,34 @@ class WhisperModelIntegrationTests(unittest.TestCase):
         torch.manual_seed(0)
         model.generate(**inputs, **gen_kwargs)
 
+    @slow
+    def test_tiny_static_generation(self):
+        processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
+        model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
+        model.to(torch_device)
+
+        input_speech = self._load_datasamples(4)
+        input_features = processor(input_speech, return_tensors="pt", sampling_rate=16_000).input_features
+        input_features = input_features.to(torch_device)
+        eager_generated_ids = model.generate(input_features, max_new_tokens=64)
+
+        model.generation_config.cache_implementation = "static"
+        model.forward = torch.compile(model.forward, mode="reduce-overhead", fullgraph=True)
+
+        # compile the forward pass and assert equivalence
+        static_generated_ids = model.generate(input_features, max_new_tokens=64)
+        assert (eager_generated_ids == static_generated_ids).all()
+
+        # check the compiled graph can be re-used and that the cache is correctly reset
+        # reverse the ordering of the input features
+        permutation_idx = (
+            torch.arange(input_features.shape[0], 0, step=-1, dtype=torch.long, device=input_features.device) - 1
+        )
+        input_features = input_features[permutation_idx, ...]
+        static_generated_ids = model.generate(input_features, max_new_tokens=64)
+        # assert re-ordered generations match those from eager
+        assert (eager_generated_ids[permutation_idx, :] == static_generated_ids).all()
+
 
 def prepare_whisper_encoder_inputs_dict(config, input_features, head_mask=None):
     if head_mask is None:
@@ -3564,6 +3632,10 @@ class WhisperStandaloneDecoderModelTest(ModelTesterMixin, GenerationTesterMixin,
             config=config, input_ids=inputs_dict["input_ids"]
         )
 
+    @unittest.skip(reason="Tested implicitly through the encoder-decoder tests")
+    def test_custom_4d_attention_mask(self):
+        pass
+
     @unittest.skip(reason="Generate needs input ids")
     def test_generate_without_input_ids(self):
         # generate only works with input ids for whisper