mirror of https://github.com/hpcaitech/ColossalAI
59 lines
2.3 KiB
Python
59 lines
2.3 KiB
Python
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import torch
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import transformers
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from colossalai.shardformer.modeling.chatglm2_6b.configuration_chatglm import ChatGLMConfig
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from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import ChatGLMForConditionalGeneration, ChatGLMModel
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from ..registry import ModelAttribute, model_zoo
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# ================================
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# Register single-sentence ChatGLM
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# ================================
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def data_gen():
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input_ids = torch.tensor([[5941, 15, 2670, 3543, 632, 2075]], dtype=torch.int64)
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attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1]])
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return dict(input_ids=input_ids, attention_mask=attention_mask)
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def data_gen_for_conditional_generation():
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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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labels = data['input_ids'].clone()
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data['labels'] = labels
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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_chatglm_model = lambda x: torch.nn.functional.mse_loss(x.last_hidden_state,
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torch.ones_like(x.last_hidden_state))
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loss_fn = lambda x: x.loss
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config = ChatGLMConfig(num_layers=2,
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padded_vocab_size=65024,
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hidden_size=64,
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num_attention_heads=8,
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rmsnorm=True,
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original_rope=True,
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use_cache=True,
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torch_dtype=torch.float32)
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model_zoo.register(name='transformers_chatglm',
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model_fn=lambda: ChatGLMModel(config, empty_init=False),
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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_chatglm_model,
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model_attribute=ModelAttribute(has_control_flow=True))
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model_zoo.register(name="transformers_chatglm_for_conditional_generation",
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model_fn=lambda: ChatGLMForConditionalGeneration(config, empty_init=False),
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data_gen_fn=data_gen_for_conditional_generation,
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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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