fix: vendor processor and config from transformers

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:

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)
         )