mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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104 lines
2.9 KiB
Python
104 lines
2.9 KiB
Python
# Copyright 2025 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 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.interface import DistributedInterface
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from .constants import IGNORE_INDEX
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from .types import BatchInput, ModelInput, Processor, Tensor
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def set_seed(seed: int) -> 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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"""
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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 _pad_and_truncate(tensor: Tensor, max_seqlen: int, pad_value: int = 0) -> Tensor:
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if tensor.shape[-1] >= max_seqlen:
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return tensor[..., :max_seqlen]
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pad_shape = list(tensor.shape)
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pad_shape[-1] = max_seqlen - tensor.shape[-1]
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pad_tensor = torch.full(pad_shape, pad_value, dtype=tensor.dtype, device=tensor.device)
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return torch.cat([tensor, pad_tensor], dim=-1)
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def pad_and_truncate(samples: list[ModelInput], max_seqlen: int) -> list[BatchInput]:
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max_length = min(max(len(sample["input_ids"]) for sample in samples), max_seqlen)
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padded_samples = []
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for sample in samples:
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padded_sample = {}
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for key, value in sample.items():
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if "label" in key:
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pad_value = IGNORE_INDEX
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else:
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pad_value = 0
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if not isinstance(value, str):
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padded_sample[key] = _pad_and_truncate(torch.tensor(value), max_length, pad_value)
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else:
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padded_sample[key] = value
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padded_samples.append(padded_sample)
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return padded_samples
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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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