mirror of https://github.com/hpcaitech/ColossalAI
80 lines
2.4 KiB
Python
80 lines
2.4 KiB
Python
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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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try:
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from transformers import CohereConfig
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HAS_COMMAND = True
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except ImportError:
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HAS_COMMAND = False
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if HAS_COMMAND:
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# ===============================
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# Register Command-R
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# ===============================
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def data_gen():
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input_ids = torch.Tensor(
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[
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[1, 15043, 29892, 590, 11203, 338, 274, 1082, 1, 15043, 29892, 590, 11203, 338, 274, 1082],
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[1, 15043, 29892, 590, 11203, 338, 274, 1082, 1, 15043, 29892, 590, 11203, 338, 274, 1082],
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]
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).long()
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attention_mask = torch.Tensor(
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[
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
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]
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).long()
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return dict(input_ids=input_ids, attention_mask=attention_mask)
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# label is needed for casual lm
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def data_gen_for_casual_lm():
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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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# transform the output to a dict
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output_transform_fn = lambda x: x
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# function to get the loss
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loss_fn = lambda output: output["last_hidden_state"].mean()
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loss_fn_for_casual_lm = lambda output: output["loss"]
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loss_fn_for_seq_classification = lambda output: output["logits"].mean()
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config = CohereConfig(
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num_hidden_layers=8,
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hidden_size=32,
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intermediate_size=64,
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num_attention_heads=4,
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max_position_embeddings=128,
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)
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if hasattr(config, "pad_token_id"):
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config.pad_token_id = config.eos_token_id
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# register the following models
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# transformers.CohereModel,
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# transformers.CohereForCausalLM,
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model_zoo.register(
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name="transformers_command",
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model_fn=lambda: transformers.CohereModel(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,
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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_command_for_casual_lm",
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model_fn=lambda: transformers.CohereForCausalLM(config),
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data_gen_fn=data_gen_for_casual_lm,
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output_transform_fn=output_transform_fn,
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loss_fn=loss_fn_for_casual_lm,
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model_attribute=ModelAttribute(has_control_flow=True),
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)
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