mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2026-08-17 13:35:44 +08:00
109 lines
3.3 KiB
Python
109 lines
3.3 KiB
Python
# Copyright 2026 the LlamaFactory team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import random
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import numpy as np
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import torch
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from transformers import PreTrainedTokenizer
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from transformers import set_seed as hf_set_seed
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from ..accelerator.helper import is_torch_npu_available
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from ..accelerator.interface import DistributedInterface
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from .constants import IGNORE_INDEX
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from .types import BatchInput, Processor
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def enable_full_determinism(seed: int) -> None:
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"""Enable full deterministic mode for reproducible distributed training."""
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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torch.use_deterministic_algorithms(True, warn_only=True)
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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torch.backends.cudnn.enabled = False
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if is_torch_npu_available():
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torch.npu.manual_seed(seed)
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torch.npu.manual_seed_all(seed)
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def set_seed(seed: int, full_determinism: bool = False) -> None:
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"""Set seed for reproducibility.
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Args:
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seed: Random seed.
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full_determinism: Whether to enable full deterministic mode.
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"""
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if full_determinism:
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enable_full_determinism(seed)
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else:
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hf_set_seed(seed)
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def is_tokenizer(processor: Processor) -> bool:
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"""Check if processor is tokenizer.
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Args:
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processor: Processor.
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Returns:
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Whether processor is tokenizer.
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"""
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return not hasattr(processor, "tokenizer")
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def get_tokenizer(processor: Processor) -> PreTrainedTokenizer:
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"""Get tokenizer from processor.
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Args:
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processor: Processor.
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Returns:
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Tokenizer.
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"""
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return processor.tokenizer if hasattr(processor, "tokenizer") else processor
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def compute_valid_tokens(batches: list[BatchInput]) -> int:
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"""Compute valid tokens in batches.
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Args:
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batches: Batches.
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Returns:
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Number of valid tokens.
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"""
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device = DistributedInterface().current_device
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return sum(
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(batch["labels"].to(device, non_blocking=True) != IGNORE_INDEX).sum().item()
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for batch in batches
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if "labels" in batch
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)
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def model_uses_mrope(config) -> bool:
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"""Whether the model uses multimodal RoPE (3D position ids built from grid_thw).
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Detected from the (text) config's rope settings carrying an ``mrope_section`` (Qwen2.5-VL /
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Qwen3-VL / Qwen3.5 family). Such models compute their own multimodal position ids inside
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``forward`` when ``position_ids`` is not provided.
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"""
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text_config = getattr(config, "text_config", config)
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rope = getattr(text_config, "rope_scaling", None) or getattr(text_config, "rope_parameters", None)
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return isinstance(rope, dict) and "mrope_section" in rope
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