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https://github.com/hpcaitech/ColossalAI.git
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27061426f7
* [gemini] remove distributed-related part from colotensor (#4379) * [gemini] remove process group dependency * [gemini] remove tp part from colo tensor * [gemini] patch inplace op * [gemini] fix param op hook and update tests * [test] remove useless tests * [test] remove useless tests * [misc] fix requirements * [test] fix model zoo * [test] fix model zoo * [test] fix model zoo * [test] fix model zoo * [test] fix model zoo * [misc] update requirements * [gemini] refactor gemini optimizer and gemini ddp (#4398) * [gemini] update optimizer interface * [gemini] renaming gemini optimizer * [gemini] refactor gemini ddp class * [example] update gemini related example * [example] update gemini related example * [plugin] fix gemini plugin args * [test] update gemini ckpt tests * [gemini] fix checkpoint io * [example] fix opt example requirements * [example] fix opt example * [example] fix opt example * [example] fix opt example * [gemini] add static placement policy (#4443) * [gemini] add static placement policy * [gemini] fix param offload * [test] update gemini tests * [plugin] update gemini plugin * [plugin] update gemini plugin docstr * [misc] fix flash attn requirement * [test] fix gemini checkpoint io test * [example] update resnet example result (#4457) * [example] update bert example result (#4458) * [doc] update gemini doc (#4468) * [example] update gemini related examples (#4473) * [example] update gpt example * [example] update dreambooth example * [example] update vit * [example] update opt * [example] update palm * [example] update vit and opt benchmark * [hotfix] fix bert in model zoo (#4480) * [hotfix] fix bert in model zoo * [test] remove chatglm gemini test * [test] remove sam gemini test * [test] remove vit gemini test * [hotfix] fix opt tutorial example (#4497) * [hotfix] fix opt tutorial example * [hotfix] fix opt tutorial example
125 lines
5.2 KiB
Python
125 lines
5.2 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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data = data_gen_for_lm()
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data['mc_labels'] = torch.zeros(data['input_ids'].shape[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: 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=lambda x: dict(loss=x.loss + x.mc_loss),
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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_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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