mirror of https://github.com/hpcaitech/ColossalAI
88 lines
2.9 KiB
Python
88 lines
2.9 KiB
Python
# modified from tests/kit/model_zoo/transformers/mistral.py
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import torch
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import transformers
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from transformers import MixtralConfig
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from ..registry import ModelAttribute, model_zoo
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# ===============================
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# Register single-sentence Mixtral
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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 AutoModelForCausalLM, AutoTokenizer
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# tokenizer = AutoTokenizer.from_pretrained("mixtralai/Mixtral-7B-v0.1")
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# input = 'My favourite condiment is vinegar' (last two words repeated to satisfy length requirement)
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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([[1, 22, 55, 77, 532, 349, 43, 22]], 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_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_mixtral_model = lambda x: x[0].mean()
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loss_fn = lambda x: x.loss
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loss_fn_for_seq_classification = lambda output: output.logits.mean()
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config = MixtralConfig(
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hidden_size=32,
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intermediate_size=32,
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num_attention_heads=8,
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num_hidden_layers=2,
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vocab_size=1000,
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attn_implementation="flash_attention_2",
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torch_dtype="float16",
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output_router_logits=True,
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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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model_zoo.register(
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name="transformers_mixtral",
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model_fn=lambda: transformers.MixtralModel(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_mixtral_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_mixtral_for_casual_lm",
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# model_fn=lambda: transformers.MixtralForCausalLM(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_mixtral_for_sequence_classification",
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# model_fn=lambda: transformers.MixtralForSequenceClassification(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_seq_classification,
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# model_attribute=ModelAttribute(has_control_flow=True),
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# )
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