mirror of https://github.com/hpcaitech/ColossalAI
108 lines
4.5 KiB
Python
108 lines
4.5 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 Bloom
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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 BloomTokenizer
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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([[59414, 15, 2670, 35433, 632, 207595]], 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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def data_gen_for_question_answering():
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# obtained with the following code
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#
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# from transformers import AutoTokenizer
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# tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-560m")
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# question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
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# inputs = tokenizer(question, text, return_tensors="pt")
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input_ids = torch.tensor(
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[[57647, 1620, 23967, 620, 107373, 34, 91514, 620, 107373, 1620, 267, 35378, 48946, 18161]], 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]], dtype=torch.int64)
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return dict(input_ids=input_ids, attention_mask=attention_mask)
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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_bloom_model = lambda x: x.last_hidden_state.mean()
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loss_fn_for_causal_lm = lambda x: x.loss
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loss_fn_for_classification = lambda x: x.logits.mean()
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loss_fn_for_question_answering = lambda x: x.end_logits.mean()
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config = transformers.BloomConfig(n_layer=1,
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n_head=4,
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vocab_size=250880,
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hidden_dropout=0,
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attention_dropout=0,
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hidden_size=64)
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# register the following models
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model_zoo.register(name='transformers_bloom',
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model_fn=lambda: transformers.BloomModel(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_bloom_model,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name='transformers_bloom_for_causal_lm',
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model_fn=lambda: transformers.BloomForCausalLM(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_for_causal_lm,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name='transformers_bloom_for_sequence_classification',
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model_fn=lambda: transformers.BloomForSequenceClassification(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_classification,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name='transformers_bloom_for_token_classification',
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model_fn=lambda: transformers.BloomForTokenClassification(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_for_classification,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name='transformers_bloom_for_question_answering',
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model_fn=lambda: transformers.BloomForQuestionAnswering(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_question_answering,
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model_attribute=ModelAttribute(has_control_flow=True))
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