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https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-02 03:32:50 +08:00
optimize predict vram
Former-commit-id: a244f143f48a01910ce1cd56c0855ef11d62a72a
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@ -172,7 +172,7 @@ Regarding the above dataset, the *dataset description* in `dataset_info.json` sh
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- [Example dataset](mllm_demo.json)
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Multimodal datasets require a `images` column containing the paths to the input images. Currently we only support one image.
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Multimodal datasets require a `images` column containing the paths to the input images.
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```json
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[
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@ -172,7 +172,7 @@ KTO 数据集需要额外添加一个 `kto_tag` 列,包含 bool 类型的人
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- [样例数据集](mllm_demo.json)
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多模态数据集需要额外添加一个 `images` 列,包含输入图像的路径。目前我们仅支持单张图像输入。
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多模态数据集需要额外添加一个 `images` 列,包含输入图像的路径。
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```json
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[
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@ -75,8 +75,8 @@ class PairwiseTrainer(Trainer):
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return super().create_scheduler(num_training_steps, optimizer)
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def compute_loss(
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self, model: "PreTrainedModel", inputs: Dict[str, torch.Tensor], return_outputs: bool = False
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) -> Union[torch.Tensor, Tuple[torch.Tensor, List[torch.Tensor]]]:
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self, model: "PreTrainedModel", inputs: Dict[str, "torch.Tensor"], return_outputs: bool = False
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) -> Union["torch.Tensor", Tuple["torch.Tensor", List["torch.Tensor"]]]:
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r"""
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Computes pairwise loss. The first n examples are chosen and the last n examples are rejected.
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@ -54,7 +54,7 @@ def eval_logit_processor(logits: "torch.Tensor", labels: "torch.Tensor") -> "tor
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if logits.dim() != 3:
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raise ValueError("Cannot process the logits.")
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return torch.argmax(logits, dim=-1)
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return torch.argmax(logits, dim=-1).cpu()
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@dataclass
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@ -78,16 +78,16 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
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def prediction_step(
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self,
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model: "torch.nn.Module",
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inputs: Dict[str, Union[torch.Tensor, Any]],
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inputs: Dict[str, Union["torch.Tensor", Any]],
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prediction_loss_only: bool,
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ignore_keys: Optional[List[str]] = None,
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) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
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) -> Tuple[Optional[float], Optional["torch.Tensor"], Optional["torch.Tensor"]]:
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r"""
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Removes the prompt part in the generated tokens.
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Subclass and override to inject custom behavior.
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"""
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labels = inputs["labels"].detach().clone() if "labels" in inputs else None # backup labels
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labels = inputs["labels"].detach().clone().cpu() if "labels" in inputs else None # backup labels (d2h)
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if self.args.predict_with_generate:
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assert self.tokenizer.padding_side == "left", "This method only accepts left-padded tensor."
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prompt_len, label_len = inputs["input_ids"].size(-1), inputs["labels"].size(-1)
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@ -101,11 +101,11 @@ class CustomSeq2SeqTrainer(Seq2SeqTrainer):
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)
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if generated_tokens is not None and self.args.predict_with_generate:
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generated_tokens[:, :prompt_len] = self.tokenizer.pad_token_id
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generated_tokens = generated_tokens.contiguous()
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generated_tokens = generated_tokens.contiguous().cpu() # d2h
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return loss, generated_tokens, labels
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def _pad_tensors_to_target_len(self, src_tensor: torch.Tensor, tgt_tensor: torch.Tensor) -> torch.Tensor:
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def _pad_tensors_to_target_len(self, src_tensor: "torch.Tensor", tgt_tensor: "torch.Tensor") -> "torch.Tensor":
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r"""
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Pads the tensor to the same length as the target tensor.
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"""
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