mirror of https://github.com/hpcaitech/ColossalAI
143 lines
6.0 KiB
Python
143 lines
6.0 KiB
Python
import copy
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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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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, 318, 13779]], dtype=torch.int64)
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attention_mask = torch.tensor([[1, 1, 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_question_answering():
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# question answering 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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start_positions = torch.tensor([0], dtype=torch.int64)
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data['start_positions'] = start_positions
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end_positions = torch.tensor([1], dtype=torch.int64)
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data['end_positions'] = end_positions
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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, 0, 1]], 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([1], dtype=torch.int64)
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return data
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def date_gen_for_double_heads():
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num_choices = 2
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batch_size = 2
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input_ids = torch.tensor(
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[[15496, 11, 616, 3290, 318, 13779, 318, 13779], [15496, 11, 616, 3290, 318, 13779, 318, 13779]],
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dtype=torch.int64)
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attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1]], dtype=torch.int64)
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mc_labels = torch.zeros(input_ids.shape[0], dtype=torch.int64)
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mc_token_ids = torch.arange(0, num_choices, dtype=torch.int64)
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mc_token_ids = mc_token_ids.expand((batch_size, num_choices))
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multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, num_choices, -1).contiguous()
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multiple_choice_input_mask = attention_mask.unsqueeze(1).expand(-1, num_choices, -1).contiguous()
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inputs = {
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"input_ids": multiple_choice_inputs_ids,
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"mc_token_ids": mc_token_ids,
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"attention_mask": multiple_choice_input_mask,
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"labels": multiple_choice_inputs_ids,
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"mc_labels": mc_labels,
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}
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return inputs
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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: 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 = 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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pad_token_id=50256)
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config_for_token_classification = copy.deepcopy(config)
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config_for_token_classification.num_labels = 2
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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=date_gen_for_double_heads,
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output_transform_fn=output_transform_fn,
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loss_fn=lambda x: x.loss + x.mc_loss,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name='transformers_gpt_for_question_answering',
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model_fn=lambda: transformers.GPT2ForQuestionAnswering(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,
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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_for_token_classification),
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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_for_token_classification),
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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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