LLaMA-Factory/scripts/bench_qwen.py

# Copyright 2025 the LlamaFactory team.
#
# 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.

import os
from dataclasses import dataclass
from typing import Any

import fire
import torch
from peft import PeftModel
from torch.utils.data import Dataset
from transformers import DataCollatorForSeq2Seq, Qwen2_5_VLProcessor

from llamafactory.extras.constants import IGNORE_INDEX
from llamafactory.hparams import get_train_args
from llamafactory.model import load_model, load_tokenizer
from llamafactory.train.callbacks import LogCallback
from llamafactory.train.sft.trainer import CustomSeq2SeqTrainer


class DummyDataset(Dataset):
    def __init__(self, size: int = 1000, seq_length: int = 1024, processor: Qwen2_5_VLProcessor = None):
        self.size = size
        self.seq_length = seq_length
        self.vocab_size = 32768
        self.processor = processor

        image_token_num = 18 * 18 // (2 * 2)
        image_t = 2

        self.text_seqlen = seq_length // 4  # 25% text
        video_seq_length = self.seq_length - self.text_seqlen - image_t * image_token_num
        video_t = video_seq_length // image_token_num

        self.image_size = [18 * 18 * image_t, 1176]
        self.image_grid_thw = torch.tensor([[1, 18, 18]] * image_t, dtype=torch.long)
        self.image_seqlen = image_t * image_token_num

        self.video_size = [18 * 18 * video_t, 1176]
        self.video_grid_thw = torch.tensor([[video_t, 18, 18]], dtype=torch.long)
        self.video_seqlen = video_t * image_token_num

    def __len__(self):
        return self.size

    def __getitem__(self, index: int):
        input_ids = torch.randint(low=0, high=self.vocab_size, size=(self.seq_length,))
        input_ids[: self.image_seqlen] = self.processor.image_token_id
        input_ids[self.image_seqlen : self.image_seqlen + self.video_seqlen] = self.processor.video_token_id

        attention_mask = torch.ones((self.seq_length,), dtype=torch.long)
        labels = input_ids.clone()
        labels[: self.image_seqlen + self.video_seqlen] = IGNORE_INDEX
        pixel_values = torch.rand(self.image_size, dtype=torch.float32)
        pixel_values_videos = torch.rand(self.video_size, dtype=torch.float32)
        return {
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            "labels": labels,
            "pixel_values": pixel_values,
            "pixel_values_videos": pixel_values_videos,
            "image_grid_thw": self.image_grid_thw,
            "video_grid_thw": self.video_grid_thw,
        }


@dataclass
class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
    def __post_init__(self):
        if isinstance(self.model, PeftModel):
            self.model = self.model.base_model.model

        if self.model is not None and hasattr(self.model, "get_rope_index"):  # for qwen2vl mrope
            self.get_rope_func = self.model.get_rope_index  # transformers < 4.52.0 or qwen2.5 omni
        elif self.model is not None and hasattr(self.model, "model") and hasattr(self.model.model, "get_rope_index"):
            self.get_rope_func = self.model.model.get_rope_index  # transformers >= 4.52.0
        else:
            self.get_rope_func = None

    def __call__(self, features: list[dict[str, Any]]) -> dict[str, "torch.Tensor"]:
        batch_pixel_values = [feature.pop("pixel_values") for feature in features]
        batch_pixel_values_videos = [feature.pop("pixel_values_videos") for feature in features]
        batch_image_grid_thw = [feature.pop("image_grid_thw") for feature in features]
        batch_video_grid_thw = [feature.pop("video_grid_thw") for feature in features]

        batch: dict[str, torch.Tensor] = super().__call__(features)

        batch["pixel_values"] = torch.cat(batch_pixel_values, dim=0)
        batch["pixel_values_videos"] = torch.cat(batch_pixel_values_videos, dim=0)
        batch["image_grid_thw"] = torch.cat(batch_image_grid_thw, dim=0)
        batch["video_grid_thw"] = torch.cat(batch_video_grid_thw, dim=0)

        if self.get_rope_func is not None:
            rope_index_kwargs = {
                "input_ids": batch["input_ids"],
                "image_grid_thw": batch["image_grid_thw"],
                "video_grid_thw": batch["video_grid_thw"],
                "attention_mask": (batch["attention_mask"] >= 1).float(),
            }
            batch["position_ids"], batch["rope_deltas"] = self.get_rope_func(**rope_index_kwargs)

        if "position_ids" not in batch or batch["position_ids"].dim() != 3:
            raise ValueError("Qwen2VL requires 3D position ids for mrope.")

        return batch


def bench_qwen(
    model_name_or_path: str = "Qwen/Qwen2-VL-7B-Instruct",
    batch_size: int = 1,
    seq_length: int = 2048,
    liger_kernel: bool = False,
    deepspeed_stage: int = 3,
):
    os.environ["LLAMABOARD_ENABLED"] = "true"
    os.environ["LLAMABOARD_WORKDIR"] = "output/dummy_dir"
    args = {
        "model_name_or_path": model_name_or_path,
        "enable_liger_kernel": liger_kernel,
        "stage": "sft",
        "do_train": True,
        "finetuning_type": "full",
        "dataset": "alpaca_en_demo",
        "template": "qwen2_vl",
        "cutoff_len": seq_length,
        "output_dir": "output/dummy_dir",
        "logging_steps": 10,
        "save_strategy": "no",
        "save_only_model": True,
        "overwrite_output_dir": True,
        "per_device_train_batch_size": batch_size,
        "max_steps": 1000,
        "bf16": True,
        "include_num_input_tokens_seen": True,
        "report_to": "none",
    }
    if deepspeed_stage in [2, 3]:
        args["deepspeed"] = f"examples/deepspeed/ds_z{deepspeed_stage}_config.json"

    model_args, _, training_args, finetuning_args, _ = get_train_args(args)
    tokenizer_module = load_tokenizer(model_args)
    tokenizer = tokenizer_module["tokenizer"]
    trainset = DummyDataset(size=100000, seq_length=seq_length, processor=tokenizer_module["processor"])
    model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
    data_collator = MultiModalDataCollatorForSeq2Seq(
        tokenizer=tokenizer, model=model, pad_to_multiple_of=8, label_pad_token_id=IGNORE_INDEX
    )

    trainer = CustomSeq2SeqTrainer(
        model=model,
        args=training_args,
        finetuning_args=finetuning_args,
        data_collator=data_collator,
        callbacks=[LogCallback()],
        train_dataset=trainset,
        **tokenizer_module,
    )
    trainer.train(resume_from_checkpoint=training_args.resume_from_checkpoint)


if __name__ == "__main__":
    fire.Fire(bench_qwen)