glm4v.py

from typing import List
from typing import Optional
from typing import Tuple
from typing import Union

import torch

from liger_kernel.transformers.model.loss_utils import LigerForCausalLMLoss
from liger_kernel.transformers.model.loss_utils import unpack_cross_entropy_result
from liger_kernel.transformers.model.output_classes import LigerCausalLMOutputWithPast


def lce_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,
    mm_token_type_ids: Optional[torch.IntTensor] = None,
    cache_position: Optional[torch.LongTensor] = None,
    logits_to_keep: Union[int, torch.Tensor] = 0,
    skip_logits: Optional[bool] = None,
    **kwargs,
) -> Union[Tuple, LigerCausalLMOutputWithPast]:
    r"""
    Args:
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

        logits_to_keep (`int` or `torch.Tensor`, *optional*):
            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:

    Example:

    ```python
    >>> from PIL import Image
    >>> from transformers import AutoTokenizer, Glm4vForConditionalGeneration

    >>> MODEL_PATH = "THUDM/GLM-4.1V-9B-Thinking"
    >>> messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "url": "https://upload.wikimedia.org/wikipedia/commons/f/fa/Grayscale_8bits_palette_sample_image.png"
                },
                {
                    "type": "text",
                    "text": "describe this image"
                }
            ],
        }
    ]
    >>> processor = AutoProcessor.from_pretrained(MODEL_PATH, use_fast=True)
    >>> model = Glm4vForConditionalGeneration.from_pretrained(
        pretrained_model_name_or_path=MODEL_PATH,
        dtype=torch.bfloat16,
        device_map="auto",
    )
    >>> inputs = processor.apply_chat_template(
        messages,
        tokenize=True,
        add_generation_prompt=True,
        return_dict=True,
        return_tensors="pt"
    ).to(model.device)
    >>> generated_ids = model.generate(**inputs, max_new_tokens=8192)
    output_text = processor.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False)
    <think>Got it, let's describe the image. First, there's a vintage car, specifically a Volkswagen Beetle
    ```"""
    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

    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
    outputs = self.model(
        input_ids=input_ids,
        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,
        mm_token_type_ids=mm_token_type_ids,
        **kwargs,
    )

    hidden_states = outputs[0]
    # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
    slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
    kept_hidden_states = hidden_states[:, slice_indices, :]

    shift_labels = kwargs.pop("shift_labels", None)
    logits = None
    loss = None
    token_accuracy = None
    predicted_tokens = None

    if skip_logits and labels is None and shift_labels is None:
        raise ValueError("skip_logits is True, but labels and shift_labels are None")

    if skip_logits is None:
        # By default, if in training mode, don't materialize logits
        skip_logits = self.training and (labels is not None or shift_labels is not None)

    # Compute loss
    if skip_logits:
        result = LigerForCausalLMLoss(
            hidden_states=kept_hidden_states,
            lm_head_weight=self.lm_head.weight,
            labels=labels,
            shift_labels=shift_labels,
            hidden_size=self.config.hidden_size,
            **kwargs,
        )
        loss, _, token_accuracy, predicted_tokens = unpack_cross_entropy_result(result)

    else:
        logits = self.lm_head(kept_hidden_states)
        if labels is not None or shift_labels is not None:
            loss = self.loss_function(
                logits=logits,
                labels=labels,
                shift_labels=shift_labels,
                vocab_size=self.config.vocab_size,
                **kwargs,
            )

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

    # Return custom output class with token_accuracy field
    return LigerCausalLMOutputWithPast(
        loss=loss,
        logits=logits,
        past_key_values=outputs.past_key_values,
        hidden_states=outputs.hidden_states,
        attentions=outputs.attentions,
        token_accuracy=token_accuracy,
        predicted_tokens=predicted_tokens,
    )