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113 lines
3.5 KiB
113 lines
3.5 KiB
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 AutoTokenizer
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# input = 'Hello, my dog is cute is cute' (last two words repeated to satisfy length requirement)
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# tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
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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_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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# 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_gptj_model = lambda x: torch.nn.functional.mse_loss(
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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.GPTJConfig(
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n_layer=2,
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n_head=4,
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vocab_size=50258,
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n_embd=256,
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hidden_size=256,
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n_positions=512,
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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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hidden_dropout=0,
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problem_type="single_label_classification",
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pad_token_id=50256,
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)
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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(
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name="transformers_gptj",
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model_fn=lambda: transformers.GPTJModel(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_gptj_model,
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model_attribute=ModelAttribute(has_control_flow=True),
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)
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model_zoo.register(
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name="transformers_gptj_lm",
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model_fn=lambda: transformers.GPTJForCausalLM(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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)
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model_zoo.register(
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name="transformers_gptj_for_question_answering",
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model_fn=lambda: transformers.GPTJForQuestionAnswering(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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)
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model_zoo.register(
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name="transformers_gptj_for_sequence_classification",
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model_fn=lambda: transformers.GPTJForSequenceClassification(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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)
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