mirror of https://github.com/hpcaitech/ColossalAI
47 lines
1.7 KiB
Python
47 lines
1.7 KiB
Python
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 T5
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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.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
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decoder_input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
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return dict(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
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def data_gen_for_encoder_only():
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input_ids = torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64)
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return dict(input_ids=input_ids)
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output_transform_fn = lambda x: x
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config = transformers.T5Config(d_model=128, num_layers=2)
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# register the following models
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# transformers.T5Model,
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# transformers.T5ForConditionalGeneration,
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# transformers.T5EncoderModel,
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model_zoo.register(name='transformers_t5',
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model_fn=lambda: transformers.T5Model(config),
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data_gen_fn=data_gen,
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output_transform_fn=output_transform_fn,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name='transformers_t5_for_conditional_generation',
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model_fn=lambda: transformers.T5ForConditionalGeneration(config),
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data_gen_fn=data_gen,
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output_transform_fn=output_transform_fn,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name='transformers_t5_encoder_model',
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model_fn=lambda: transformers.T5EncoderModel(config),
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data_gen_fn=data_gen_for_encoder_only,
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output_transform_fn=output_transform_fn,
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model_attribute=ModelAttribute(has_control_flow=True))
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