[VLM] Support caching in merged multi-modal processor #11341
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Signed-off-by: DarkLight1337 <[email protected]>
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DarkLight1337
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Signed-off-by: DarkLight1337 <[email protected]>
This was referenced Dec 19, 2024
Signed-off-by: DarkLight1337 <[email protected]>
Signed-off-by: DarkLight1337 <[email protected]>
Signed-off-by: DarkLight1337 <[email protected]>
Signed-off-by: DarkLight1337 <[email protected]>
Signed-off-by: DarkLight1337 <[email protected]>
Signed-off-by: DarkLight1337 <[email protected]>
Signed-off-by: DarkLight1337 <[email protected]>
DarkLight1337
commented
Dec 19, 2024
vllm/multimodal/processing.py
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| def _iter_bytes_to_hash(self, key: str, obj: object) -> Iterable[bytes]: | ||
| # Recursive cases | ||
| if isinstance(obj, (list, tuple)): | ||
| for elem in obj: | ||
| yield from self._iter_bytes_to_hash(key, elem) | ||
| return | ||
| if isinstance(obj, dict): | ||
| for k, v in obj.items(): | ||
| yield from self._iter_bytes_to_hash(f"{key}.{k}", v) | ||
| return | ||
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| # Simple cases | ||
| if isinstance(obj, str): | ||
| yield key.encode("utf-8") | ||
| yield obj.encode("utf-8") | ||
| return | ||
| if isinstance(obj, bytes): | ||
| yield key.encode("utf-8") | ||
| yield obj | ||
| return | ||
| if isinstance(obj, Image): | ||
| yield key.encode("utf-8") | ||
| yield obj.tobytes() | ||
| return | ||
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| # Convertible to NumPy arrays | ||
| if isinstance(obj, torch.Tensor): | ||
| obj = obj.numpy() | ||
| if isinstance(obj, (int, float)): | ||
| obj = np.array(obj) | ||
| if isinstance(obj, np.ndarray): | ||
| yield key.encode("utf-8") | ||
| yield obj.tobytes() | ||
| return | ||
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| msg = f"Unable to hash object of type {type(obj)}" | ||
| raise NotImplementedError(msg) | ||
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| def _hash_kwargs(self, **kwargs: object) -> str: | ||
| hasher = blake3() | ||
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| for k, v in kwargs.items(): | ||
| for item_bytes in self._iter_bytes_to_hash(k, v): | ||
| hasher.update(item_bytes) | ||
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| return hasher.hexdigest() |
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I'm a bit worried about unintentional hash collisions. Is there a better way to do this?
Signed-off-by: DarkLight1337 <[email protected]>
Signed-off-by: DarkLight1337 <[email protected]>
Signed-off-by: DarkLight1337 <[email protected]>
Signed-off-by: DarkLight1337 <[email protected]>
Signed-off-by: DarkLight1337 <[email protected]>
Signed-off-by: DarkLight1337 <[email protected]>
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Signed-off-by: DarkLight1337 <[email protected]>
Signed-off-by: DarkLight1337 <[email protected]>
Signed-off-by: DarkLight1337 <[email protected]>
Signed-off-by: DarkLight1337 <[email protected]>
Signed-off-by: DarkLight1337 <[email protected]>
Signed-off-by: DarkLight1337 <[email protected]>
Signed-off-by: DarkLight1337 <[email protected]>
Signed-off-by: DarkLight1337 <[email protected]>
Signed-off-by: DarkLight1337 <[email protected]>
Signed-off-by: DarkLight1337 <[email protected]>
Signed-off-by: DarkLight1337 <[email protected]>
|
Closing this in favor of #11396 |
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V1 multi-modal cache is currently incompatible with the merged multi-modal processor. To mitigate the performance hit, this PR adds a cache inside the merged multi-modal processor.
Note: Even with this PR, none of the models that currently use merged multi-modal processor actually support fine-grained caching because their HF processors all require text inputs.Now supported by using the inner modality-specific processor.