Helios/_DEV/helios/videoalign/trainer.py

# from training.train_utils import get_peft_state_maybe_zero_3, get_peft_state_non_lora_maybe_zero_3
from typing import List, Optional

import torch
import torch.nn as nn
from transformers import Qwen2VLForConditionalGeneration
from transformers.trainer import (
    is_torch_xla_available,
)


if is_torch_xla_available():
    pass
else:
    IS_XLA_FSDPV2_POST_2_2 = False


class Qwen2VLRewardModelBT(Qwen2VLForConditionalGeneration):
    def __init__(self, config, output_dim=4, reward_token="last", special_token_ids=None):
        super().__init__(config)
        # pdb.set_trace()
        self.output_dim = output_dim
        self.rm_head = nn.Linear(config.hidden_size, output_dim, bias=False)
        self.reward_token = reward_token

        self.special_token_ids = special_token_ids
        if self.special_token_ids is not None:
            self.reward_token = "special"

    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: Optional[torch.Tensor] = None,
        pixel_values_videos: Optional[torch.FloatTensor] = None,
        image_grid_thw: Optional[torch.LongTensor] = None,
        video_grid_thw: Optional[torch.LongTensor] = None,
        rope_deltas: Optional[torch.LongTensor] = None,
    ):
        ## modified from the origin class Qwen2VLForConditionalGeneration
        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
        # pdb.set_trace()
        if inputs_embeds is None:
            # inputs_embeds = self.model.embed_tokens(input_ids)
            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.visual(pixel_values, grid_thw=image_grid_thw)
                image_mask = (input_ids == self.config.image_token_id).unsqueeze(-1).expand_as(inputs_embeds)
                image_embeds = image_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
                inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)

            if pixel_values_videos is not None:
                pixel_values_videos = pixel_values_videos.type(self.visual.get_dtype())
                video_embeds = self.visual(pixel_values_videos, grid_thw=video_grid_thw)
                video_mask = (input_ids == self.config.video_token_id).unsqueeze(-1).expand_as(inputs_embeds)
                video_embeds = video_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
                inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds)

            if attention_mask is not None:
                attention_mask = attention_mask.to(inputs_embeds.device)

        outputs = self.model(
            input_ids=None,
            position_ids=position_ids,
            attention_mask=attention_mask,
            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,
        )

        hidden_states = outputs[0]  # [B, L, D]

        logits = self.rm_head(hidden_states)  # [B, L, N]

        if input_ids is not None:
            batch_size = input_ids.shape[0]
        else:
            batch_size = inputs_embeds.shape[0]

        ## get sequence length
        if self.config.pad_token_id is None and batch_size != 1:
            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
        if self.config.pad_token_id is None:
            sequence_lengths = -1
        else:
            if input_ids is not None:
                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
                sequence_lengths = sequence_lengths % input_ids.shape[-1]
                sequence_lengths = sequence_lengths.to(logits.device)
            else:
                sequence_lengths = -1

        ## get the last token's logits
        if self.reward_token == "last":
            pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
        elif self.reward_token == "mean":
            ## get the mean of all valid tokens' logits
            valid_lengths = torch.clamp(sequence_lengths, min=0, max=logits.size(1) - 1)
            pooled_logits = torch.stack([logits[i, : valid_lengths[i]].mean(dim=0) for i in range(batch_size)])
        elif self.reward_token == "special":
            # special_token_ids = self.tokenizer.convert_tokens_to_ids(self.special_tokens)
            # create a mask for special tokens
            special_token_mask = torch.zeros_like(input_ids, dtype=torch.bool)
            for special_token_id in self.special_token_ids:
                special_token_mask = special_token_mask | (input_ids == special_token_id)
            pooled_logits = logits[special_token_mask, ...]
            pooled_logits = pooled_logits.view(batch_size, 3, -1)  # [B, 3, N] assert 3 attributes
            if self.output_dim == 3:
                pooled_logits = pooled_logits.diagonal(dim1=1, dim2=2)
            pooled_logits = pooled_logits.view(batch_size, -1)

            # pdb.set_trace()
        else:
            raise ValueError("Invalid reward_token")

        return {"logits": pooled_logits}