mirror of https://github.com/hpcaitech/ColossalAI
97 lines
3.9 KiB
Python
97 lines
3.9 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 GPT
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# ===============================
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BATCH_SIZE = 1 # it can only be 1 as GPT cannot handle batch sizes > 1 if no padding token is defined.
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SEQ_LENGTH = 16
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def data_gen():
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# Generated from following code snippet
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#
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# from transformers import GPT2Tokenizer
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# input = 'Hello, my dog is cute'
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# tokenized_input = tokenizer(input, return_tensors='pt')
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# input_ids = tokenized_input['input_ids']
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# attention_mask = tokenized_input['attention_mask']
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input_ids = torch.tensor([[15496, 11, 616, 3290, 318, 13779]], dtype=torch.int64)
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attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1]], dtype=torch.int64)
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return dict(input_ids=input_ids, attention_mask=attention_mask)
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def data_gen_for_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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data['labels'] = data['input_ids'].clone()
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return data
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def data_gen_for_token_classification():
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# token classification data gen
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# `labels` is the type not the token id for token classification, 0 or 1
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data = data_gen()
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data['labels'] = torch.tensor([[0, 0, 0, 0, 0, 0]], dtype=torch.int64)
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return data
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def data_gen_for_sequence_classification():
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# sequence classification data gen
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data = data_gen()
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data['labels'] = torch.tensor([0], dtype=torch.int64)
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return data
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# define output transform function
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output_transform_fn = lambda x: x
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# define loss function
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loss_fn_for_gpt2_model = lambda x: x.last_hidden_state.mean()
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loss_fn = lambda x: x.loss
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config = transformers.GPT2Config(n_layer=2,
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n_head=4,
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vocab_size=50258,
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attn_pdrop=0,
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embd_pdrop=0,
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resid_pdrop=0,
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summary_first_dropout=0,
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hidden_dropout=0,
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problem_type="single_label_classification")
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# register the following models
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model_zoo.register(name='transformers_gpt',
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model_fn=lambda: transformers.GPT2Model(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_gpt2_model,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name='transformers_gpt_lm',
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model_fn=lambda: transformers.GPT2LMHeadModel(config),
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data_gen_fn=data_gen_for_lm,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name='transformers_gpt_double_heads',
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model_fn=lambda: transformers.GPT2DoubleHeadsModel(config),
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data_gen_fn=data_gen_for_lm,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name='transformers_gpt_for_token_classification',
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model_fn=lambda: transformers.GPT2ForTokenClassification(config),
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data_gen_fn=data_gen_for_token_classification,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name='transformers_gpt_for_sequence_classification',
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model_fn=lambda: transformers.GPT2ForSequenceClassification(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,
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model_attribute=ModelAttribute(has_control_flow=True))
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