mirror of
https://github.com/hiyouga/LlamaFactory.git
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74 lines
2.8 KiB
Python
74 lines
2.8 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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from typing import Any
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import torch.nn.functional as F
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from mcore_adapter.trainer import McaTrainer
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from torch import Tensor
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from transformers import PreTrainedTokenizerBase
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from typing_extensions import override
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from ...extras.constants import IGNORE_INDEX
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class CustomMcaTrainer(McaTrainer):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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@override
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def _pad_batched_inputs(self, inputs: dict[str, Tensor | Any], seq_length: int):
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r"""Override to avoid padding error when handling 3d posids."""
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padding_inputs = {
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k: v.tolist() if v is not None and isinstance(v, Tensor) else v
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for k, v in inputs.items()
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if k in self._language_input_names
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}
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position_ids_3d = None
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if isinstance(inputs.get("position_ids"), Tensor) and inputs["position_ids"].dim() == 3:
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position_ids_3d = inputs["position_ids"]
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padding_inputs.pop("position_ids", None)
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if "labels" in padding_inputs:
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padding_inputs["labels"] = [
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labels + [IGNORE_INDEX] * (seq_length - len(labels)) for labels in padding_inputs["labels"]
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]
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tokenizer = (
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self.processing_class
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if isinstance(self.processing_class, PreTrainedTokenizerBase)
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else getattr(self.processing_class, "tokenizer", self.processing_class)
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)
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padding_side = getattr(tokenizer, "padding_side", "right")
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padding_inputs = tokenizer.pad(
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padding_inputs,
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padding="max_length",
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max_length=seq_length,
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return_tensors="pt",
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).to(self.args.device)
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inputs.update(padding_inputs)
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if position_ids_3d is not None:
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current_seq_len = position_ids_3d.size(-1)
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if current_seq_len < seq_length:
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pad_len = seq_length - current_seq_len
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if padding_side == "left":
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position_ids_3d = F.pad(position_ids_3d, (pad_len, 0), value=0)
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else:
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position_ids_3d = F.pad(position_ids_3d, (0, pad_len), value=0)
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inputs["position_ids"] = position_ids_3d.to(self.args.device)
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return inputs
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