modeling_sarashina2_vision.py

# coding=utf-8
# Copyright 2025 the SB Intuitions.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Optional, Tuple, Union

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import CrossEntropyLoss
from transformers import (
    AutoConfig,
    AutoModelForCausalLM,
    GenerationMixin,
    LlamaForCausalLM,
    PreTrainedModel,
)
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.models.qwen2_vl.modeling_qwen2_vl import Qwen2VisionTransformerPretrainedModel
from transformers.utils import logging, replace_return_docstrings

from .configuration_sarashina2_vision import Sarashina2VisionConfig

logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "Sarashina2VisionConfig"


class Sarashina2VisionPreTrainedModel(PreTrainedModel):
    config_class = Sarashina2VisionConfig
    base_model_prefix = "model"
    _supports_flash_attn_2 = True
    _supports_sdpa = True
    _supports_cache_class = True
    _supports_static_cache = True

    def _init_weights(self, module):
        std = (
            self.config.initializer_range
            if hasattr(self.config, "initializer_range")
            else self.config.text_config.initializer_range
        )

        if hasattr(module, "class_embedding"):
            module.class_embedding.data.normal_(mean=0.0, std=std)

        if isinstance(module, (nn.Linear, nn.Conv3d)):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()


class Sarashina2VisionForCausalLM(Sarashina2VisionPreTrainedModel, GenerationMixin):
    def __init__(self, config: Sarashina2VisionConfig):
        super().__init__(config)
        self.visual = Qwen2VisionTransformerPretrainedModel._from_config(config.vision_config)
        self.norm = nn.LayerNorm(config.text_config.hidden_size)
        self.llm = LlamaForCausalLM._from_config(config.text_config)

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.llm.get_input_embeddings()

    def get_image_embeds(
        self,
        hidden_states: torch.Tensor,
        grid_thw: torch.Tensor,
    ) -> torch.Tensor:
        rotary_pos_emb = self.visual.rot_pos_emb(grid_thw)
        hidden_states = self.visual.patch_embed(hidden_states)

        cu_seqlens = torch.repeat_interleave(
            grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
        ).cumsum(dim=0, dtype=torch.int32)
        cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)

        for blk in self.visual.blocks:
            hidden_states = blk(
                hidden_states, cu_seqlens=cu_seqlens, rotary_pos_emb=rotary_pos_emb
            )
        return self.norm(self.visual.merger(hidden_states))

    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        pixel_values: torch.FloatTensor = None,
        image_grid_thw: Optional[torch.LongTensor] = None,
        cache_position: Optional[torch.LongTensor] = None,
        logits_to_keep: Union[int, torch.Tensor] = 0,
        **lm_kwargs,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        """
        Args:
            input_ids (torch.LongTensor, optional): Indices of input sequence tokens in the vocabulary. Defaults to None.
            attention_mask (Optional[torch.Tensor], optional):  Mask to avoid performing attention on padding token indices. Defaults to None.
            position_ids (Optional[torch.LongTensor], optional): Indices of positions of each input sequence tokens in the position embeddings. Defaults to None.
            past_key_values (Optional[List[torch.FloatTensor]], optional): _description_. Defaults to None.
            inputs_embeds (Optional[torch.FloatTensor], optional): Instead of passing `input_ids` you can choose to directly pass an embedded representation. Defaults to None.
            labels (Optional[torch.LongTensor], optional): Labels for computing the masked language modeling loss. Defaults to None.
            use_cache (Optional[bool], optional): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding. Defaults to None.
            output_attentions (Optional[bool], optional): Whether or not to return the attentions tensors of all attention layers. Defaults to None.
            output_hidden_states (Optional[bool], optional): Whether or not to return the hidden states of all layers. Defaults to None.
            return_dict (Optional[bool], optional): Whether or not to return a `CausalLMOutputWithPast` instead of a plain tuple. Defaults to None.
            pixel_values (torch.FloatTensor, optional): The tensors corresponding to the input images. Defaults to None.
            image_grid_thw (Optional[torch.LongTensor], optional): The temporal, height and width of feature shape of each image in LLM. Defaults to None.
            cache_position (Optional[torch.LongTensor], optional): Indices depicting the position of the input sequence tokens in the sequence. Defaults to None.
            logits_to_keep (Union[int, torch.Tensor]): If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
                `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
                token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
                If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
                This is useful when using packed tensor format (single dimension for batch and sequence length).
        Returns:
            CausalLMOutputWithPast: The output of the model.
        """
        output_attentions = (
            output_attentions if output_attentions is not None else self.config.output_attentions
        )
        output_hidden_states = (
            output_hidden_states
            if output_hidden_states is not None
            else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if inputs_embeds is None:
            inputs_embeds = self.get_input_embeddings()(input_ids)
            if pixel_values is not None:
                pixel_values = pixel_values.type(self.visual.get_dtype())
                image_embeds = self.get_image_embeds(pixel_values, image_grid_thw)
                n_image_tokens = (input_ids == self.config.image_token_index).sum().item()
                n_image_features = image_embeds.shape[0]
                if n_image_tokens != n_image_features:
                    raise ValueError(
                        f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
                    )
                image_mask = (
                    (input_ids == self.config.image_token_index)
                    .unsqueeze(-1)
                    .expand_as(inputs_embeds)
                    .to(inputs_embeds.device)
                )
                image_embeds = image_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
                inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)

        outputs = self.llm(
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            cache_position=cache_position,
            logits_to_keep=logits_to_keep,
            **lm_kwargs,
        )

        logits = outputs[0]

        loss = None
        if labels is not None:
            # Upcast to float if we need to compute the loss to avoid potential precision issues
            logits = logits.float()
            # Shift so that tokens < n predict n
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()
            shift_logits = shift_logits.view(-1, self.config.vocab_size)
            shift_labels = shift_labels.view(-1)
            # Enable model parallelism
            shift_labels = shift_labels.to(shift_logits.device)
            loss = loss_fct(shift_logits, shift_labels)

        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    def prepare_inputs_for_generation(
        self,
        input_ids,
        past_key_values=None,
        inputs_embeds=None,
        pixel_values=None,
        attention_mask=None,
        cache_position=None,
        logits_to_keep=None,
        image_grid_thw=None,
        **kwargs,
    ):
        model_inputs = self.llm.prepare_inputs_for_generation(
            input_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            attention_mask=attention_mask,
            cache_position=cache_position,
            logits_to_keep=logits_to_keep,
            **kwargs,
        )

        if cache_position[0] == 0:
            # If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
            # Otherwise we need pixel values to be passed to model
            model_inputs["pixel_values"] = pixel_values
            model_inputs["image_grid_thw"] = image_grid_thw

        return model_inputs


AutoConfig.register("sarashina2_vision", Sarashina2VisionConfig)
AutoModelForCausalLM.register(Sarashina2VisionConfig, Sarashina2VisionForCausalLM)
Sarashina2VisionConfig.register_for_auto_class()
Sarashina2VisionForCausalLM.register_for_auto_class("AutoModelForCausalLM")