Source code for nemo_automodel.datasets.vlm.collate_fns

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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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import torch

from nemo_automodel.datasets.vlm.utils import extract_skipped_token_ids
from nemo_automodel.shared.import_utils import MISSING_QWEN_VL_UTILS_MSG
from unittest.mock import MagicMock

try:
    from qwen_vl_utils import process_vision_info

    HAVE_QWEN_VL_UTILS = True
except ImportError:
    HAVE_QWEN_VL_UTILS = False
    process_vision_info = MagicMock()




def qwen2_5_collate_fn(examples: list, processor) -> dict[str, torch.Tensor]:
    """Collate function for Qwen2.5 VL model."""
    if not HAVE_QWEN_VL_UTILS:
        raise ImportError(MISSING_QWEN_VL_UTILS_MSG)

    skipped_tokens = extract_skipped_token_ids(processor)

    texts = [processor.apply_chat_template(example["conversation"], tokenize=False) for example in examples]
    image_inputs = [process_vision_info(example["conversation"])[0] for example in examples]

    batch = processor(
        text=texts,
        images=image_inputs,
        padding=True,
        return_tensors="pt",
    )

    labels = batch["input_ids"].clone()[:, 1:]
    labels = torch.cat([labels, -100 * torch.ones_like(labels[:, :1])], dim=1)
    labels[torch.isin(labels, skipped_tokens)] = -100
    batch["labels"] = labels

    return batch





def default_collate_fn(examples: list, processor) -> dict[str, torch.Tensor]:
    """Default collate function for VLM models."""
    if not HAVE_QWEN_VL_UTILS:
        raise ImportError(MISSING_QWEN_VL_UTILS_MSG)

    skipped_tokens = extract_skipped_token_ids(processor)

    batch = processor.apply_chat_template(
        [example["conversation"] for example in examples],
        tokenize=True,
        add_generation_prompt=False,
        return_tensors="pt",
        return_dict=True,
    )

    batch["pixel_values"] = batch["pixel_values"].to(torch.bfloat16)
    labels = batch["input_ids"].clone()[:, 1:]
    labels = torch.cat([labels, -100 * torch.ones_like(labels[:, :1])], dim=1)
    labels[torch.isin(labels, skipped_tokens)] = -100
    batch["labels"] = labels

    return batch



# Mapping of processor types to their collate functions
COLLATE_FNS = {
    "Qwen2_5_VLProcessor": qwen2_5_collate_fn,
    "default": default_collate_fn,
}