backends/gaudi/server/text_generation_server/models/custom_modeling/qwen2_5_vl.py [51:381]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ) import habana_frameworks.torch as htorch # Copied from: https://github.com/huggingface/transformers/blob/main/src/transformers/models/qwen2_5_vl/processing_qwen2_5_vl.py from typing import Union from transformers.feature_extraction_utils import BatchFeature from transformers.image_utils import ImageInput from transformers.video_utils import VideoInput from transformers.processing_utils import ( ProcessingKwargs, ProcessorMixin, Unpack, VideosKwargs, ) from transformers.tokenization_utils_base import PreTokenizedInput, TextInput class Qwen2_5_VLVideosProcessorKwargs(VideosKwargs, total=False): fps: Union[List[float], float] class Qwen2_5_VLProcessorKwargs(ProcessingKwargs, total=False): videos_kwargs: Qwen2_5_VLVideosProcessorKwargs _defaults = { "text_kwargs": { "padding": False, }, "videos_kwargs": {"fps": 2.0}, } class Qwen2_5_VLProcessor(ProcessorMixin): r""" Constructs a Qwen2.5-VL processor which wraps a Qwen2.5-VL image processor and a Qwen2 tokenizer into a single processor. [`Qwen2_5_VLProcessor`] offers all the functionalities of [`Qwen2VLImageProcessor`] and [`Qwen2TokenizerFast`]. See the [`~Qwen2_5_VLProcessor.__call__`] and [`~Qwen2_5_VLProcessor.decode`] for more information. Args: image_processor ([`Qwen2VLImageProcessor`], *optional*): The image processor is a required input. tokenizer ([`Qwen2TokenizerFast`], *optional*): The tokenizer is a required input. chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages in a chat into a tokenizable string. """ attributes = ["image_processor", "tokenizer"] valid_kwargs = ["chat_template"] image_processor_class = "AutoImageProcessor" tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast") def __init__( self, image_processor=None, tokenizer=None, chat_template=None, **kwargs ): self.image_token = ( "<|image_pad|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token ) self.video_token = ( "<|video_pad|>" if not hasattr(tokenizer, "video_token") else tokenizer.video_token ) super().__init__(image_processor, tokenizer, chat_template=chat_template) def __call__( self, images: ImageInput = None, text: Union[ TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput] ] = None, videos: VideoInput = None, **kwargs: Unpack[Qwen2_5_VLProcessorKwargs], ) -> BatchFeature: """ Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text` and `kwargs` arguments to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text` is not `None` to encode the text. To prepare the vision inputs, this method forwards the `vision_infos` and `kwrags` arguments to Qwen2VLImageProcessor's [`~Qwen2VLImageProcessor.__call__`] if `vision_infos` is not `None`. Args: images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`): The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch tensor. Both channels-first and channels-last formats are supported. text (`str`, `List[str]`, `List[List[str]]`): The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`): The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported. return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors of a particular framework. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return NumPy `np.ndarray` objects. - `'jax'`: Return JAX `jnp.ndarray` objects. Returns: [`BatchFeature`]: A [`BatchFeature`] with the following fields: - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not `None`). - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`. - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`. - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`. - **second_per_grid_ts** -- List of video seconds per time grid. Returned when `videos` is not `None`. """ output_kwargs = self._merge_kwargs( Qwen2_5_VLProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs, ) if images is not None: image_inputs = self.image_processor( images=images, videos=None, **output_kwargs["images_kwargs"] ) image_grid_thw = image_inputs["image_grid_thw"] else: image_inputs = {} image_grid_thw = None if videos is not None: videos_inputs = self.image_processor( images=None, videos=videos, **output_kwargs["images_kwargs"] ) video_grid_thw = videos_inputs["video_grid_thw"] fps = output_kwargs["videos_kwargs"].pop("fps", 2.0) if isinstance(fps, (int, float)): second_per_grid_ts = [ self.image_processor.temporal_patch_size / fps ] * len(video_grid_thw) elif hasattr(fps, "__len__") and len(fps) == len(video_grid_thw): second_per_grid_ts = [ self.image_processor.temporal_patch_size / tmp for tmp in fps ] else: raise ValueError( f"The length of fps ({len(fps) if hasattr(fps, '__len__') else fps}) must be equal to the length of video_grid_thw ({len(video_grid_thw)}) or fps should be a single number." ) videos_inputs.update({"second_per_grid_ts": second_per_grid_ts}) else: videos_inputs = {} video_grid_thw = None if not isinstance(text, list): text = [text] if image_grid_thw is not None: merge_length = self.image_processor.merge_size**2 index = 0 for i in range(len(text)): while self.image_token in text[i]: text[i] = text[i].replace( self.image_token, "<|placeholder|>" * (image_grid_thw[index].prod() // merge_length), 1, ) index += 1 text[i] = text[i].replace("<|placeholder|>", self.image_token) if video_grid_thw is not None: merge_length = self.image_processor.merge_size**2 index = 0 for i in range(len(text)): while self.video_token in text[i]: text[i] = text[i].replace( self.video_token, "<|placeholder|>" * (video_grid_thw[index].prod() // merge_length), 1, ) index += 1 text[i] = text[i].replace("<|placeholder|>", self.video_token) text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"]) return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}) def batch_decode(self, *args, **kwargs): """ This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.batch_decode(*args, **kwargs) def decode(self, *args, **kwargs): """ This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.decode(*args, **kwargs) def post_process_image_text_to_text(self, generated_outputs): """ Post-process the output of the model to decode the text. Args: generated_outputs (`torch.Tensor` or `np.ndarray`): The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)` or `(sequence_length,)`. Returns: `List[str]`: The decoded text. """ return self.tokenizer.batch_decode( generated_outputs, skip_special_tokens=True, clean_up_tokenization_spaces=False, ) @property def model_input_names(self): tokenizer_input_names = self.tokenizer.model_input_names image_processor_input_names = self.image_processor.model_input_names names_from_processor = list( dict.fromkeys(tokenizer_input_names + image_processor_input_names) ) return names_from_processor + ["second_per_grid_ts"] # Copied from: https://github.com/huggingface/transformers/blob/main/src/transformers/models/qwen2_5_vl/configuration_qwen2_5_vl.py class Qwen2_5_VLVisionConfig(PretrainedConfig): model_type = "qwen2_5_vl" base_config_key = "vision_config" def __init__( self, depth=32, hidden_size=3584, hidden_act="silu", intermediate_size=3420, num_heads=16, in_channels=3, patch_size=14, spatial_merge_size=2, spatial_patch_size=14, temporal_patch_size=2, tokens_per_second=4, window_size=112, out_hidden_size=3584, fullatt_block_indexes=[7, 15, 23, 31], **kwargs, ): super().__init__(**kwargs) self.depth = depth self.hidden_size = hidden_size self.hidden_act = hidden_act self.intermediate_size = intermediate_size self.num_heads = num_heads self.in_channels = in_channels self.patch_size = patch_size self.spatial_patch_size = spatial_patch_size self.spatial_merge_size = spatial_merge_size self.temporal_patch_size = temporal_patch_size self.tokens_per_second = tokens_per_second self.window_size = window_size self.fullatt_block_indexes = fullatt_block_indexes self.out_hidden_size = out_hidden_size class Qwen2_5_VLConfig(PretrainedConfig): def __init__( self, vocab_size=152064, hidden_size=8192, intermediate_size=29568, num_hidden_layers=80, num_attention_heads=64, num_key_value_heads=8, hidden_act="silu", max_position_embeddings=32768, initializer_range=0.02, rms_norm_eps=1e-05, use_cache=True, tie_word_embeddings=False, rope_theta=1000000.0, use_sliding_window=False, sliding_window=4096, max_window_layers=80, attention_dropout=0.0, vision_config=None, rope_scaling=None, **kwargs, ): if vision_config is not None: self.vision_config = Qwen2_5_VLVisionConfig(**vision_config) self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.use_sliding_window = use_sliding_window self.sliding_window = sliding_window self.max_window_layers = max_window_layers # for backward compatibility if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.rope_theta = rope_theta self.attention_dropout = attention_dropout self.rope_scaling = rope_scaling # Validate the correctness of rotary position embeddings parameters # BC: if there is a 'type' field, move it to 'rope_type'. # and change type from 'mrope' to 'default' because `mrope` does defeault RoPE calculations # one can set it to "linear"/"dynamic" etc. to have scaled RoPE # TODO: @raushan update config in the hub if self.rope_scaling is not None and "type" in self.rope_scaling: if self.rope_scaling["type"] == "mrope": self.rope_scaling["type"] = "default" self.rope_scaling["rope_type"] = self.rope_scaling["type"] super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - server/text_generation_server/models/custom_modeling/qwen2_5_vl.py [48:375]: - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ) # Copied from: https://github.com/huggingface/transformers/blob/main/src/transformers/models/qwen2_5_vl/processing_qwen2_5_vl.py from typing import Union from transformers.feature_extraction_utils import BatchFeature from transformers.image_utils import ImageInput, VideoInput from transformers.processing_utils import ( ProcessingKwargs, ProcessorMixin, Unpack, VideosKwargs, ) from transformers.tokenization_utils_base import PreTokenizedInput, TextInput class Qwen2_5_VLVideosProcessorKwargs(VideosKwargs, total=False): fps: Union[List[float], float] class Qwen2_5_VLProcessorKwargs(ProcessingKwargs, total=False): videos_kwargs: Qwen2_5_VLVideosProcessorKwargs _defaults = { "text_kwargs": { "padding": False, }, "videos_kwargs": {"fps": 2.0}, } class Qwen2_5_VLProcessor(ProcessorMixin): r""" Constructs a Qwen2.5-VL processor which wraps a Qwen2.5-VL image processor and a Qwen2 tokenizer into a single processor. [`Qwen2_5_VLProcessor`] offers all the functionalities of [`Qwen2VLImageProcessor`] and [`Qwen2TokenizerFast`]. See the [`~Qwen2_5_VLProcessor.__call__`] and [`~Qwen2_5_VLProcessor.decode`] for more information. Args: image_processor ([`Qwen2VLImageProcessor`], *optional*): The image processor is a required input. tokenizer ([`Qwen2TokenizerFast`], *optional*): The tokenizer is a required input. chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages in a chat into a tokenizable string. """ attributes = ["image_processor", "tokenizer"] valid_kwargs = ["chat_template"] image_processor_class = "AutoImageProcessor" tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast") def __init__( self, image_processor=None, tokenizer=None, chat_template=None, **kwargs ): self.image_token = ( "<|image_pad|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token ) self.video_token = ( "<|video_pad|>" if not hasattr(tokenizer, "video_token") else tokenizer.video_token ) super().__init__(image_processor, tokenizer, chat_template=chat_template) def __call__( self, images: ImageInput = None, text: Union[ TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput] ] = None, videos: VideoInput = None, **kwargs: Unpack[Qwen2_5_VLProcessorKwargs], ) -> BatchFeature: """ Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text` and `kwargs` arguments to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text` is not `None` to encode the text. To prepare the vision inputs, this method forwards the `vision_infos` and `kwrags` arguments to Qwen2VLImageProcessor's [`~Qwen2VLImageProcessor.__call__`] if `vision_infos` is not `None`. Args: images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`): The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch tensor. Both channels-first and channels-last formats are supported. text (`str`, `List[str]`, `List[List[str]]`): The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`): The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported. return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors of a particular framework. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return NumPy `np.ndarray` objects. - `'jax'`: Return JAX `jnp.ndarray` objects. Returns: [`BatchFeature`]: A [`BatchFeature`] with the following fields: - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not `None`). - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`. - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`. - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`. - **second_per_grid_ts** -- List of video seconds per time grid. Returned when `videos` is not `None`. """ output_kwargs = self._merge_kwargs( Qwen2_5_VLProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs, ) if images is not None: image_inputs = self.image_processor( images=images, videos=None, **output_kwargs["images_kwargs"] ) image_grid_thw = image_inputs["image_grid_thw"] else: image_inputs = {} image_grid_thw = None if videos is not None: videos_inputs = self.image_processor( images=None, videos=videos, **output_kwargs["images_kwargs"] ) video_grid_thw = videos_inputs["video_grid_thw"] fps = output_kwargs["videos_kwargs"].pop("fps", 2.0) if isinstance(fps, (int, float)): second_per_grid_ts = [ self.image_processor.temporal_patch_size / fps ] * len(video_grid_thw) elif hasattr(fps, "__len__") and len(fps) == len(video_grid_thw): second_per_grid_ts = [ self.image_processor.temporal_patch_size / tmp for tmp in fps ] else: raise ValueError( f"The length of fps ({len(fps) if hasattr(fps, '__len__') else fps}) must be equal to the length of video_grid_thw ({len(video_grid_thw)}) or fps should be a single number." ) videos_inputs.update({"second_per_grid_ts": second_per_grid_ts}) else: videos_inputs = {} video_grid_thw = None if not isinstance(text, list): text = [text] if image_grid_thw is not None: merge_length = self.image_processor.merge_size**2 index = 0 for i in range(len(text)): while self.image_token in text[i]: text[i] = text[i].replace( self.image_token, "<|placeholder|>" * (image_grid_thw[index].prod() // merge_length), 1, ) index += 1 text[i] = text[i].replace("<|placeholder|>", self.image_token) if video_grid_thw is not None: merge_length = self.image_processor.merge_size**2 index = 0 for i in range(len(text)): while self.video_token in text[i]: text[i] = text[i].replace( self.video_token, "<|placeholder|>" * (video_grid_thw[index].prod() // merge_length), 1, ) index += 1 text[i] = text[i].replace("<|placeholder|>", self.video_token) text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"]) return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}) def batch_decode(self, *args, **kwargs): """ This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.batch_decode(*args, **kwargs) def decode(self, *args, **kwargs): """ This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.decode(*args, **kwargs) def post_process_image_text_to_text(self, generated_outputs): """ Post-process the output of the model to decode the text. Args: generated_outputs (`torch.Tensor` or `np.ndarray`): The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)` or `(sequence_length,)`. Returns: `List[str]`: The decoded text. """ return self.tokenizer.batch_decode( generated_outputs, skip_special_tokens=True, clean_up_tokenization_spaces=False, ) @property def model_input_names(self): tokenizer_input_names = self.tokenizer.model_input_names image_processor_input_names = self.image_processor.model_input_names names_from_processor = list( dict.fromkeys(tokenizer_input_names + image_processor_input_names) ) return names_from_processor + ["second_per_grid_ts"] # Copied from: https://github.com/huggingface/transformers/blob/main/src/transformers/models/qwen2_5_vl/configuration_qwen2_5_vl.py class Qwen2_5_VLVisionConfig(PretrainedConfig): model_type = "qwen2_5_vl" base_config_key = "vision_config" def __init__( self, depth=32, hidden_size=3584, hidden_act="silu", intermediate_size=3420, num_heads=16, in_channels=3, patch_size=14, spatial_merge_size=2, spatial_patch_size=14, temporal_patch_size=2, tokens_per_second=4, window_size=112, out_hidden_size=3584, fullatt_block_indexes=[7, 15, 23, 31], **kwargs, ): super().__init__(**kwargs) self.depth = depth self.hidden_size = hidden_size self.hidden_act = hidden_act self.intermediate_size = intermediate_size self.num_heads = num_heads self.in_channels = in_channels self.patch_size = patch_size self.spatial_patch_size = spatial_patch_size self.spatial_merge_size = spatial_merge_size self.temporal_patch_size = temporal_patch_size self.tokens_per_second = tokens_per_second self.window_size = window_size self.fullatt_block_indexes = fullatt_block_indexes self.out_hidden_size = out_hidden_size class Qwen2_5_VLConfig(PretrainedConfig): def __init__( self, vocab_size=152064, hidden_size=8192, intermediate_size=29568, num_hidden_layers=80, num_attention_heads=64, num_key_value_heads=8, hidden_act="silu", max_position_embeddings=32768, initializer_range=0.02, rms_norm_eps=1e-05, use_cache=True, tie_word_embeddings=False, rope_theta=1000000.0, use_sliding_window=False, sliding_window=4096, max_window_layers=80, attention_dropout=0.0, vision_config=None, rope_scaling=None, **kwargs, ): if vision_config is not None: self.vision_config = Qwen2_5_VLVisionConfig(**vision_config) self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.use_sliding_window = use_sliding_window self.sliding_window = sliding_window self.max_window_layers = max_window_layers # for backward compatibility if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.rope_theta = rope_theta self.attention_dropout = attention_dropout self.rope_scaling = rope_scaling # Validate the correctness of rotary position embeddings parameters # BC: if there is a 'type' field, move it to 'rope_type'. # and change type from 'mrope' to 'default' because `mrope` does defeault RoPE calculations # one can set it to "linear"/"dynamic" etc. to have scaled RoPE # TODO: @raushan update config in the hub if self.rope_scaling is not None and "type" in self.rope_scaling: if self.rope_scaling["type"] == "mrope": self.rope_scaling["type"] = "default" self.rope_scaling["rope_type"] = self.rope_scaling["type"] super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -