From e4e6ea2598bb39309ab0723528755059a89d0c1b Mon Sep 17 00:00:00 2001 From: drbh Date: Mon, 17 Feb 2025 18:46:34 +0000 Subject: [PATCH] fix: vendor processor and config from transformers --- .../text_generation_server/models/__init__.py | 4 + .../models/custom_modeling/qwen2_5_vl.py | 391 ++++++++++++++++-- 2 files changed, 364 insertions(+), 31 deletions(-) diff --git a/server/text_generation_server/models/__init__.py b/server/text_generation_server/models/__init__.py index 8cd33660..af26e1c6 100644 --- a/server/text_generation_server/models/__init__.py +++ b/server/text_generation_server/models/__init__.py @@ -166,6 +166,8 @@ try: ) from text_generation_server.models.custom_modeling.qwen2_5_vl import ( Qwen2_5VLForConditionalGeneration, + Qwen2_5_VLConfig, + Qwen2_5_VLProcessor, ) from text_generation_server.layers.attention import SUPPORTS_WINDOWING except ImportError as e: @@ -1388,6 +1390,8 @@ def get_model( kv_cache_dtype=kv_cache_dtype, trust_remote_code=trust_remote_code, lora_adapter_ids=lora_adapter_ids, + config_class=Qwen2_5_VLConfig, + processor_class=Qwen2_5_VLProcessor, ) if model_type == MLLAMA: if FLASH_ATTENTION: diff --git a/server/text_generation_server/models/custom_modeling/qwen2_5_vl.py b/server/text_generation_server/models/custom_modeling/qwen2_5_vl.py index 11ec308c..0ee3a8d9 100644 --- a/server/text_generation_server/models/custom_modeling/qwen2_5_vl.py +++ b/server/text_generation_server/models/custom_modeling/qwen2_5_vl.py @@ -29,6 +29,8 @@ else: import numpy as np from transformers.activations import ACT2FN +from transformers.configuration_utils import PretrainedConfig + import torch.nn.functional as F from text_generation_server.layers.layernorm import FastRMSNorm @@ -45,6 +47,334 @@ from text_generation_server.models.custom_modeling.flash_qwen2_modeling import ( Qwen2Model, ) +# 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) + # Copied from transformers.models.llama.modeling_llama.rotate_half def rotate_half(x): @@ -273,7 +603,7 @@ class Qwen2_5VisionModel(nn.Module): self.spatial_merge_size = config.spatial_merge_size kernel_size = [config.temporal_patch_size, config.patch_size, config.patch_size] self.patch_embedding = nn.Conv3d( - in_channels=config.in_chans, + in_channels=config.in_channels, out_channels=config.hidden_size, kernel_size=kernel_size, stride=kernel_size, @@ -304,7 +634,7 @@ class Qwen2_5VisionModel(nn.Module): config=config, weights=weights, ) - + # import ipdb; ipdb.set_trace() self.temporal_patch_size = config.temporal_patch_size self.spatial_patch_size = config.spatial_patch_size self.in_channels = config.in_channels @@ -506,52 +836,52 @@ class Qwen2_5VLForConditionalGeneration(nn.Module): ) self.device = weights.device + # based on https://github.com/huggingface/transformers/blob/e284c7e954abe12c34b50461c17f8115a0afe115/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py#L1391 + # modified to first find segments then initialize position ids for each segment + # Steps: + # locate all vision and text segments + # calculate `vision_segment_lengths` for each vision segment to be use as offset + # calculate `text_segment_lengths` for each text segment to be used as offset + # create position ids for each vision segment based on the image grid + # create position ids for each text segment + # combine all the position ids + # the final segment is the difference between the last vision segment and the end of the input + # combine all the position ids and reshape to (3, input_ids_len) then swap dimensions to (input_ids_len, 3) def get_position_ids( self, input_ids: torch.Tensor, image_grid_thw: Optional[torch.Tensor] = None, ) -> torch.Tensor: if image_grid_thw is None: - # (batch_size, 3) return ( torch.arange(input_ids.shape[0], device=input_ids.device) .unsqueeze(1) .repeat(1, 3) ) - # if image grid provided than we need to calculate the position ids spatial_merge_size = self.spatial_merge_size vision_start_token_id = self.vision_start_token_id vision_end_token_id = self.vision_end_token_id - device = input_ids.device dtype = input_ids.dtype input_ids_len = input_ids.shape[0] - # capture vision segments - starts = torch.where(input_ids == vision_start_token_id)[0] - ends = torch.where(input_ids == vision_end_token_id)[0] - # ie. [[ 14, 2181], [2212, 4379]] - vision_segments = torch.stack((starts, ends), dim=1) - # capture text lengths as the space between vision segments - - prev_end = torch.cat( # shift to the left to get the previous end - [torch.zeros(1, device=ends.device, dtype=dtype), ends[:-1]] - ) # ie. [0, 2181] - - # text is the space between the end of one vision segment and the start of the next - text_lengths = vision_segments[:, 0] - prev_end + 1 # ie. [15, 32] - - # calculate the max id from the image width for each segment + vision_starts = torch.where(input_ids == vision_start_token_id)[0] + vision_ends = torch.where(input_ids == vision_end_token_id)[0] + vision_segments = torch.stack((vision_starts, vision_ends), dim=1) + prev_vision_end = torch.cat( + [torch.zeros(1, device=vision_ends.device, dtype=dtype), vision_ends[:-1]] + ) + text_lengths_between_vision = vision_segments[:, 0] - prev_vision_end + 1 vision_widths_max = torch.cat( [ torch.zeros(1, device=image_grid_thw.device, dtype=dtype), image_grid_thw[:-1, 2] // spatial_merge_size, ] ) - total_segment_lengths = vision_widths_max + text_lengths - total_segment_lengths = total_segment_lengths.cumsum(dim=0) - text_diff = total_segment_lengths - text_lengths + vision_segment_lengths = vision_widths_max + text_lengths_between_vision + vision_segment_lengths = vision_segment_lengths.cumsum(dim=0) + text_segment_lengths = vision_segment_lengths - text_lengths_between_vision # create position ids for each vision segment based on the image grid llm_pos_ids_list = [] @@ -567,29 +897,28 @@ class Qwen2_5VLForConditionalGeneration(nn.Module): image_position_ids = torch.stack([t_indices, h_indices, w_indices], dim=0) # offset by the position of the last vision segment - im = image_position_ids + total_segment_lengths[i] + im = image_position_ids + vision_segment_lengths[i] llm_pos_ids_list.append(im) # create position ids for each text segment text_ranges = [ torch.arange(seq_len, device=device).view(1, -1).expand(3, -1) - + text_diff[i] - for i, seq_len in enumerate(text_lengths) - ] # ie. [[ 0, 1, ..., 14], [2182, 2183, ..., 2213]] + + text_segment_lengths[i] + for i, seq_len in enumerate(text_lengths_between_vision) + ] - # combine by alternating text and vision segments (text, vision, text, vision, ...) full_llm_pos_ids_list = [ item for sublist in zip(text_ranges, llm_pos_ids_list) for item in sublist ] + # import ipdb - # the final segment is the difference between the last vision segment and the end of the input + # ipdb.set_trace() max_s = full_llm_pos_ids_list[-1].max() + 1 - final_text_len = input_ids_len - ends[-1] + final_text_len = input_ids_len - vision_ends[-1] if final_text_len > 0: m = torch.arange(final_text_len, device=device).view(1, -1).expand(3, -1) full_llm_pos_ids_list.append(m + max_s) - # concat and reshape to (3, input_ids_len) then swap dimensions to (input_ids_len, 3) position_ids = ( torch.cat(full_llm_pos_ids_list, dim=1).reshape(3, -1).transpose(0, 1) )