wehub-resource-sync
74 lines
2.9 KiB
Python
74 lines
2.9 KiB
Python
# Copyright (c) ModelScope Contributors. All rights reserved.
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import torch.nn
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from collections import namedtuple
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from functools import partial
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from swift.loss import loss_map
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from swift.metrics import eval_metrics_map
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from swift.utils import get_logger
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from .base import BaseMegatronTrainer
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logger = get_logger()
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ModelOutputs = namedtuple('ModelOutputs', ['logits'])
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class MegatronRerankerTrainer(BaseMegatronTrainer):
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def __init__(self, args, template):
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super().__init__(args, template)
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self._loss_func = loss_map[args.loss_type](args, self)
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self.eval_metrics = eval_metrics_map['reranker'](args, self)
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@staticmethod
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def _get_listwise_reranker_preds(logits, labels):
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positive_indices = torch.nonzero(labels == 1, as_tuple=False).squeeze(-1).tolist()
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positive_indices.append(labels.shape[0])
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preds = []
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for i in range(len(positive_indices) - 1):
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start, end = positive_indices[i], positive_indices[i + 1]
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preds.append(logits[start:end].argmax())
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preds = torch.stack(preds)
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labels = torch.tensor([0] * (len(positive_indices) - 1), device=preds.device)
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return preds, labels
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def loss_func(self,
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output_tensor: torch.Tensor,
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*,
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labels: torch.Tensor,
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packed_seq_params=None,
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attention_mask=None):
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training = self.unwrapped_models[0].training
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logits = self.get_last_tokens(output_tensor, packed_seq_params, attention_mask)
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loss = self._loss_func(ModelOutputs(logits=logits), labels)
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args = self.args
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logits_detach = logits.detach().squeeze(-1)
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if not training:
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self.eval_metrics.update(logits_detach, labels)
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if args.loss_type == 'listwise_reranker':
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preds, labels = self._get_listwise_reranker_preds(logits_detach, labels)
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else:
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preds = (logits_detach.detach() > 0).long()
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acc = (preds == labels).float().mean()
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metric = {'loss': loss.detach().clone(), 'acc': acc}
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metric = self._all_reduce_metric(metric)
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return loss, metric
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def prepare_model(self):
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super().prepare_model()
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for model in self.unwrapped_models:
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lm_model = model.language_model if hasattr(model, 'language_model') else model
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lm_model.tokenizer = self.template.tokenizer
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def forward_step(self, data_iterator, model):
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vp_stage = model.module.module.vp_stage
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data = self.get_batch(data_iterator, vp_stage)
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labels = data.pop('labels', None)
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output_tensor = model(**data)
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loss_func = partial(
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self.loss_func,
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labels=labels,
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packed_seq_params=data.get('packed_seq_params'),
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attention_mask=data.get('attention_mask')
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if data.get('attention_mask') is not None else data.get('attention_mask_2d'))
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return output_tensor, loss_func
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