mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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[data] fix mllama (#7053)
* fix mllama * fix test Former-commit-id: 76314e6ad1ecaa44fcae4375dd0abf4ebaf1f924
This commit is contained in:
hoshi-hiyouga
GitHub
parent
ca78ba964d
commit
dca5fe14c2
@ -82,6 +82,7 @@ class MMPluginMixin:
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def _validate_input(
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self,
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processor: Optional["ProcessorMixin"],
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images: Sequence["ImageInput"],
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videos: Sequence["VideoInput"],
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audios: Sequence["AudioInput"],
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@ -89,6 +90,8 @@ class MMPluginMixin:
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r"""
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Validates if this model accepts the input modalities.
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"""
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image_processor: "BaseImageProcessor" = getattr(processor, "image_processor", None)
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feature_extractor: "SequenceFeatureExtractor" = getattr(processor, "feature_extractor", None)
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if len(images) != 0 and self.image_token is None:
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raise ValueError(
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"This model does not support image input. Please check whether the correct `template` is used."
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@ -104,6 +107,15 @@ class MMPluginMixin:
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"This model does not support audio input. Please check whether the correct `template` is used."
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)
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if self.image_token is not None and processor is None:
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raise ValueError("Processor was not found, please check and update your processor config.")
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if self.image_token is not None and image_processor is None:
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raise ValueError("Image processor was not found, please check and update your processor config.")
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if self.audio_token is not None and feature_extractor is None:
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raise ValueError("Audio feature extractor was not found, please check and update your processor config.")
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def _preprocess_image(
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self, image: "ImageObject", image_max_pixels: int, image_min_pixels: int, **kwargs
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) -> "ImageObject":
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@ -275,7 +287,7 @@ class BasePlugin(MMPluginMixin):
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r"""
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Pre-processes input messages before tokenization for VLMs.
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"""
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self._validate_input(images, videos, audios)
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self._validate_input(processor, images, videos, audios)
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return messages
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def process_token_ids(
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@ -291,7 +303,7 @@ class BasePlugin(MMPluginMixin):
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r"""
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Pre-processes token ids after tokenization for VLMs.
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"""
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self._validate_input(images, videos, audios)
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self._validate_input(processor, images, videos, audios)
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return input_ids, labels
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def get_mm_inputs(
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@ -317,7 +329,7 @@ class BasePlugin(MMPluginMixin):
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batch_ids: token ids of input samples, shape (batch_size, seq_len)
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processor: a processor for pre-processing images and videos
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"""
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self._validate_input(images, videos, audios)
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self._validate_input(processor, images, videos, audios)
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return {}
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@ -332,7 +344,7 @@ class LlavaPlugin(BasePlugin):
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audios: Sequence["AudioInput"],
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processor: Optional["ProcessorMixin"],
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) -> List[Dict[str, str]]:
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self._validate_input(images, videos, audios)
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self._validate_input(processor, images, videos, audios)
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num_image_tokens = 0
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image_seqlen = getattr(processor, "image_seqlen") if self.expand_mm_tokens else 1
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messages = deepcopy(messages)
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@ -361,7 +373,7 @@ class LlavaPlugin(BasePlugin):
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batch_ids: Sequence[List[int]],
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processor: Optional["ProcessorMixin"],
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) -> Dict[str, Union[List[int], "torch.Tensor"]]:
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self._validate_input(images, videos, audios)
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self._validate_input(processor, images, videos, audios)
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return self._get_mm_inputs(images, videos, audios, processor)
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@ -376,7 +388,7 @@ class LlavaNextPlugin(BasePlugin):
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audios: Sequence["AudioInput"],
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processor: Optional["ProcessorMixin"],
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) -> List[Dict[str, str]]:
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self._validate_input(images, videos, audios)
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self._validate_input(processor, images, videos, audios)
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num_image_tokens = 0
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messages = deepcopy(messages)
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mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
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@ -417,7 +429,7 @@ class LlavaNextPlugin(BasePlugin):
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batch_ids: Sequence[List[int]],
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processor: Optional["ProcessorMixin"],
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) -> Dict[str, Union[List[int], "torch.Tensor"]]:
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self._validate_input(images, videos, audios)
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self._validate_input(processor, images, videos, audios)
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return self._get_mm_inputs(images, videos, audios, processor)
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@ -432,7 +444,7 @@ class LlavaNextVideoPlugin(BasePlugin):
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audios: Sequence["AudioInput"],
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processor: Optional["ProcessorMixin"],
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) -> List[Dict[str, str]]:
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self._validate_input(images, videos, audios)
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self._validate_input(processor, images, videos, audios)
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num_image_tokens, num_video_tokens = 0, 0
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messages = deepcopy(messages)
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mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
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@ -493,7 +505,7 @@ class LlavaNextVideoPlugin(BasePlugin):
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batch_ids: Sequence[List[int]],
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processor: Optional["ProcessorMixin"],
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) -> Dict[str, Union[List[int], "torch.Tensor"]]:
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self._validate_input(images, videos, audios)
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self._validate_input(processor, images, videos, audios)
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return self._get_mm_inputs(images, videos, audios, processor)
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@ -508,7 +520,7 @@ class MiniCPMVPlugin(BasePlugin):
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audios: Sequence["AudioInput"],
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processor: Optional["ProcessorMixin"],
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) -> List[Dict[str, str]]:
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self._validate_input(images, videos, audios)
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self._validate_input(processor, images, videos, audios)
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num_image_tokens = 0
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num_video_tokens = 0
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num_audio_tokens = 0
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@ -611,6 +623,7 @@ class MiniCPMVPlugin(BasePlugin):
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**kwargs,
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) -> Dict[str, "torch.Tensor"]:
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image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
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feature_extractor: "SequenceFeatureExtractor" = getattr(processor, "feature_extractor", None)
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mm_inputs = {}
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if len(images) != 0:
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images = self._regularize_images(
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@ -645,21 +658,19 @@ class MiniCPMVPlugin(BasePlugin):
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mm_inputs.update(video_inputs)
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if len(audios) != 0:
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new_audios = []
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for audio in audios:
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if not isinstance(audio, np.ndarray):
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audio = librosa.load(audio, sr=processor.feature_extractor.sampling_rate)[0]
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new_audios.append(audio)
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audios = self._regularize_audios(
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audios,
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sampling_rate=getattr(feature_extractor, "sampling_rate", 16000),
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)
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if "valid_audio_nums_ls" in kwargs:
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valid_audio_nums_ls = kwargs["valid_audio_nums_ls"]
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audios_ls = []
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idx = 0
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for valid_audio_nums in valid_audio_nums_ls:
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audios_ls.append(new_audios[idx : idx + valid_audio_nums])
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audios_ls.append(audios[idx : idx + valid_audio_nums])
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idx += valid_audio_nums
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else:
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audios_ls = [new_audios]
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audios_ls = [audios]
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audio_features, audio_feature_lens, audio_phs = processor.audio_feature_extract(
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audios_ls,
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@ -685,7 +696,7 @@ class MiniCPMVPlugin(BasePlugin):
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batch_ids: Sequence[List[int]],
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processor: Optional["ProcessorMixin"],
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) -> Dict[str, Union[List[int], "torch.Tensor"]]:
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self._validate_input(images, videos, audios)
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self._validate_input(processor, images, videos, audios)
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# image bound
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image_bounds_list = []
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valid_image_nums_ls = []
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@ -753,7 +764,7 @@ class MllamaPlugin(BasePlugin):
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audios: Sequence["AudioInput"],
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processor: Optional["ProcessorMixin"],
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) -> List[Dict[str, str]]:
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self._validate_input(images, videos, audios)
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self._validate_input(processor, images, videos, audios)
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num_image_tokens = 0
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messages = deepcopy(messages)
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for message in messages:
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@ -787,17 +798,21 @@ class MllamaPlugin(BasePlugin):
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num_tiles: List[List[int]] with shape (batch_size, num_images_in_batch). For example, (2, 1).
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"""
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image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
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images = self._regularize_images(
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images,
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image_max_pixels=getattr(processor, "image_max_pixels", 768 * 768),
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image_min_pixels=getattr(processor, "image_min_pixels", 32 * 32),
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)
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batch_images = []
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for image_length in imglens:
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batch_images.append(images[:image_length])
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images = images[image_length:]
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mm_inputs = {}
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if len(images) > 0:
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images = self._regularize_images(
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images,
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image_max_pixels=getattr(processor, "image_max_pixels", 768 * 768),
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image_min_pixels=getattr(processor, "image_min_pixels", 32 * 32),
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)
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batch_images = []
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for image_length in imglens:
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batch_images.append(images[:image_length])
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images = images[image_length:]
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return image_processor(batch_images, return_tensors="pt")
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mm_inputs.update(image_processor(batch_images, return_tensors="pt"))
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return mm_inputs
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@override
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def get_mm_inputs(
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@ -811,22 +826,24 @@ class MllamaPlugin(BasePlugin):
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batch_ids: Sequence[List[int]],
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processor: Optional["ProcessorMixin"],
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) -> Dict[str, Union[List[int], "torch.Tensor"]]:
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self._validate_input(images, videos, audios)
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self._validate_input(processor, images, videos, audios)
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mm_inputs = self._get_mm_inputs(images, videos, audios, processor, imglens)
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num_tiles = mm_inputs.pop("num_tiles")
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image_token_id = getattr(processor, "image_token_id")
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max_image_tiles = getattr(processor.image_processor, "max_image_tiles")
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cross_attention_token_mask = [
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get_cross_attention_token_mask(input_ids, image_token_id) for input_ids in batch_ids
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]
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mm_inputs["cross_attention_mask"] = torch.from_numpy(
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convert_sparse_cross_attention_mask_to_dense(
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cross_attention_token_mask,
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num_tiles=num_tiles,
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max_num_tiles=max_image_tiles,
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length=max(len(input_ids) for input_ids in batch_ids),
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)
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) # shape: (batch_size, length, max_num_images, max_num_tiles)
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if mm_inputs:
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num_tiles = mm_inputs.pop("num_tiles")
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image_token_id = getattr(processor, "image_token_id")
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max_image_tiles = getattr(processor.image_processor, "max_image_tiles")
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cross_attention_token_mask = [
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get_cross_attention_token_mask(input_ids, image_token_id) for input_ids in batch_ids
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]
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mm_inputs["cross_attention_mask"] = torch.from_numpy(
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convert_sparse_cross_attention_mask_to_dense(
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cross_attention_token_mask,
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num_tiles=num_tiles,
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max_num_tiles=max_image_tiles,
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length=max(len(input_ids) for input_ids in batch_ids),
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)
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) # shape: (batch_size, length, max_num_images, max_num_tiles)
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return mm_inputs
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@ -841,7 +858,7 @@ class PaliGemmaPlugin(BasePlugin):
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audios: Sequence["AudioInput"],
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processor: Optional["ProcessorMixin"],
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) -> List[Dict[str, str]]:
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self._validate_input(images, videos, audios)
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self._validate_input(processor, images, videos, audios)
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num_image_tokens = 0
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messages = deepcopy(messages)
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for message in messages:
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@ -868,7 +885,7 @@ class PaliGemmaPlugin(BasePlugin):
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tokenizer: "PreTrainedTokenizer",
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processor: Optional["ProcessorMixin"],
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) -> Tuple[List[int], Optional[List[int]]]:
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self._validate_input(images, videos, audios)
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self._validate_input(processor, images, videos, audios)
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num_images = len(images)
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image_seqlen = num_images * getattr(processor, "image_seqlen") if self.expand_mm_tokens else 0 # skip mm token
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image_token_id = tokenizer.convert_tokens_to_ids(self.image_token)
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@ -890,7 +907,7 @@ class PaliGemmaPlugin(BasePlugin):
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batch_ids: Sequence[List[int]],
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processor: Optional["ProcessorMixin"],
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) -> Dict[str, Union[List[int], "torch.Tensor"]]:
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self._validate_input(images, videos, audios)
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self._validate_input(processor, images, videos, audios)
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seqlens = [len(input_ids) for input_ids in batch_ids]
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mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
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mm_inputs["token_type_ids"] = _get_paligemma_token_type_ids(imglens, seqlens, processor)
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@ -908,7 +925,7 @@ class PixtralPlugin(BasePlugin):
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audios: Sequence["AudioInput"],
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processor: Optional["ProcessorMixin"],
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) -> List[Dict[str, str]]:
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self._validate_input(images, videos, audios)
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self._validate_input(processor, images, videos, audios)
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patch_size = getattr(processor, "patch_size")
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image_token = getattr(processor, "image_token")
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image_break_token = getattr(processor, "image_break_token")
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@ -956,7 +973,7 @@ class PixtralPlugin(BasePlugin):
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batch_ids: Sequence[List[int]],
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processor: Optional["ProcessorMixin"],
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) -> Dict[str, Union[List[int], "torch.Tensor"]]:
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self._validate_input(images, videos, audios)
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self._validate_input(processor, images, videos, audios)
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mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
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mm_inputs.pop("image_sizes", None)
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return mm_inputs
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@ -973,7 +990,7 @@ class Qwen2AudioPlugin(BasePlugin):
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audios: Sequence["AudioInput"],
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processor: Optional["ProcessorMixin"],
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) -> List[Dict[str, str]]:
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self._validate_input(images, videos, audios)
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self._validate_input(processor, images, videos, audios)
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bos_token: str = getattr(processor, "audio_bos_token")
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eos_token: str = getattr(processor, "audio_eos_token")
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mm_inputs = self._get_mm_inputs([], [], audios, processor)
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@ -1015,7 +1032,7 @@ class Qwen2AudioPlugin(BasePlugin):
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batch_ids: Sequence[List[int]],
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processor: Optional["ProcessorMixin"],
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) -> Dict[str, Union[List[int], "torch.Tensor"]]:
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self._validate_input(images, videos, audios)
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self._validate_input(processor, images, videos, audios)
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return self._get_mm_inputs(images, videos, audios, processor)
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@ -1105,7 +1122,7 @@ class Qwen2vlPlugin(BasePlugin):
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audios: Sequence["AudioInput"],
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processor: Optional["ProcessorMixin"],
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) -> List[Dict[str, str]]:
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self._validate_input(images, videos, audios)
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self._validate_input(processor, images, videos, audios)
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image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
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merge_length: int = getattr(image_processor, "merge_size") ** 2
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if self.expand_mm_tokens:
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@ -1162,7 +1179,7 @@ class Qwen2vlPlugin(BasePlugin):
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batch_ids: Sequence[List[int]],
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processor: Optional["ProcessorMixin"],
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) -> Dict[str, Union[List[int], "torch.Tensor"]]:
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self._validate_input(images, videos, audios)
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self._validate_input(processor, images, videos, audios)
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mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
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fps_per_video = mm_inputs.pop("fps_per_video", [])
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image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
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@ -1183,7 +1200,7 @@ class VideoLlavaPlugin(BasePlugin):
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audios: Sequence["AudioInput"],
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processor: Optional["ProcessorMixin"],
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) -> List[Dict[str, str]]:
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self._validate_input(images, videos, audios)
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self._validate_input(processor, images, videos, audios)
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num_image_tokens, num_video_tokens = 0, 0
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messages = deepcopy(messages)
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mm_inputs = self._get_mm_inputs(images, videos, audios, processor)
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@ -1241,7 +1258,7 @@ class VideoLlavaPlugin(BasePlugin):
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batch_ids: Sequence[List[int]],
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processor: Optional["ProcessorMixin"],
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) -> Dict[str, Union[List[int], "torch.Tensor"]]:
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self._validate_input(images, videos, audios)
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self._validate_input(processor, images, videos, audios)
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return self._get_mm_inputs(images, videos, audios, processor)
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@ -103,15 +103,17 @@ def _check_plugin(
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expected_no_mm_inputs: Dict[str, Any] = {},
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) -> None:
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# test mm_messages
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assert plugin.process_messages(MM_MESSAGES, IMAGES, NO_VIDEOS, NO_AUDIOS, processor) == expected_mm_messages
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assert plugin.process_token_ids(INPUT_IDS, LABELS, IMAGES, NO_VIDEOS, NO_AUDIOS, tokenizer, processor) == (
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expected_input_ids,
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expected_labels,
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)
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_is_close(
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plugin.get_mm_inputs(IMAGES, NO_VIDEOS, NO_AUDIOS, IMGLENS, NO_VIDLENS, NO_AUDLENS, BATCH_IDS, processor),
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expected_mm_inputs,
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)
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if plugin.__class__.__name__ != "BasePlugin":
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assert plugin.process_messages(MM_MESSAGES, IMAGES, NO_VIDEOS, NO_AUDIOS, processor) == expected_mm_messages
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assert plugin.process_token_ids(INPUT_IDS, LABELS, IMAGES, NO_VIDEOS, NO_AUDIOS, tokenizer, processor) == (
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expected_input_ids,
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expected_labels,
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)
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_is_close(
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plugin.get_mm_inputs(IMAGES, NO_VIDEOS, NO_AUDIOS, IMGLENS, NO_VIDLENS, NO_AUDLENS, BATCH_IDS, processor),
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expected_mm_inputs,
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)
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# test text_messages
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assert plugin.process_messages(TEXT_MESSAGES, NO_IMAGES, NO_VIDEOS, NO_AUDIOS, processor) == TEXT_MESSAGES
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assert plugin.process_token_ids(INPUT_IDS, LABELS, NO_IMAGES, NO_VIDEOS, NO_AUDIOS, tokenizer, processor) == (
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@ -128,7 +130,7 @@ def _check_plugin(
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def test_base_plugin():
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tokenizer_module = _load_tokenizer_module(model_name_or_path=TINY_LLAMA)
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base_plugin = get_mm_plugin(name="base", image_token="<image>")
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base_plugin = get_mm_plugin(name="base")
|
||||
check_inputs = {"plugin": base_plugin, **tokenizer_module}
|
||||
_check_plugin(**check_inputs)
|
||||
|
||||
|
||||
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Reference in New Issue
Block a user