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84 lines
3.2 KiB
84 lines
3.2 KiB
import torch
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import transformers
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from ..registry import ModelAttribute, model_zoo
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# ===============================
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# Register single-sentence OPT
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# ===============================
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BATCH_SIZE = 2
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SEQ_LENGTH = 16
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def data_gen():
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input_ids = torch.Tensor([[1, 15043, 29892, 590, 11203, 338, 274, 1082]]).long()
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attention_mask = torch.Tensor([[1, 1, 1, 1, 1, 1, 1, 1]]).long()
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return dict(input_ids=input_ids, attention_mask=attention_mask)
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def data_gen_for_causal_lm():
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# LM data gen
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# the `labels` of LM is the token of the output, cause no padding, use `input_ids` as `labels`
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data = data_gen()
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labels = data['input_ids'].clone()
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data['labels'] = labels
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return data
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def data_gen_for_sequence_classification():
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# LM data gen
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# the `labels` of LM is the token of the output, cause no padding, use `input_ids` as `labels`
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data = data_gen()
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labels = data['input_ids'].clone()
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data['labels'] = torch.tensor([1])
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return data
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def data_gen_for_question_answering():
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# LM data gen
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# the `labels` of LM is the token of the output, cause no padding, use `input_ids` as `labels`
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data = data_gen()
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data['start_positions'] = torch.tensor([0])
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data['end_positions'] = torch.tensor([1])
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return data
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output_transform_fn = lambda x: x
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loss_fn_for_opt_model = lambda x: torch.nn.functional.mse_loss(x.last_hidden_state, torch.ones_like(x.last_hidden_state)
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)
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loss_fn_for_lm = lambda x: x.loss
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config = transformers.OPTConfig(
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hidden_size=128,
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num_hidden_layers=2,
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num_attention_heads=4,
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dropout=0,
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)
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# register the following models
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# transformers.OPTModel,
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# transformers.OPTForCausalLM,
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model_zoo.register(name='transformers_opt',
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model_fn=lambda: transformers.OPTModel(config),
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data_gen_fn=data_gen,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn_for_opt_model,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name='transformers_opt_for_causal_lm',
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model_fn=lambda: transformers.OPTForCausalLM(config),
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data_gen_fn=data_gen_for_causal_lm,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn_for_lm,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name='transformers_opt_for_question_answering',
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model_fn=lambda: transformers.OPTForQuestionAnswering(config),
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data_gen_fn=data_gen_for_question_answering,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn_for_lm,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name='transformers_opt_for_sequence_classification',
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model_fn=lambda: transformers.OPTForSequenceClassification(config),
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data_gen_fn=data_gen_for_sequence_classification,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn_for_lm,
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model_attribute=ModelAttribute(has_control_flow=True))
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