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https://github.com/hpcaitech/ColossalAI.git
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7172459e74
* [shardformer] implement policy for all GPT-J models and test * [shardformer] support interleaved pipeline parallel for bert finetune * [shardformer] shardformer support falcon (#4883) * [shardformer]: fix interleaved pipeline for bert model (#5048) * [hotfix]: disable seq parallel for gptj and falcon, and polish code (#5093) * Add Mistral support for Shardformer (#5103) * [shardformer] add tests to mistral (#5105) --------- Co-authored-by: Pengtai Xu <henryxu880@gmail.com> Co-authored-by: ppt0011 <143150326+ppt0011@users.noreply.github.com> Co-authored-by: flybird11111 <1829166702@qq.com> Co-authored-by: eric8607242 <e0928021388@gmail.com>
79 lines
2.7 KiB
Python
79 lines
2.7 KiB
Python
import torch
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import transformers
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from transformers import MistralConfig
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from ..registry import ModelAttribute, model_zoo
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# ===============================
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# Register single-sentence Mistral
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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("mistralai/Mistral-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, 1984, 16020, 2076, 2487, 349, 21375, 4749]], 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_mistral_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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loss_fn_for_seq_classification = lambda output: output.logits.mean()
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config = MistralConfig(
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hidden_size=256, intermediate_size=256, num_attention_heads=64, num_hidden_layers=2, vocab_size=50258
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)
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model_zoo.register(
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name="transformers_mistral",
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model_fn=lambda: transformers.MistralModel(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_mistral_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_mistral_for_casual_lm",
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model_fn=lambda: transformers.MistralForCausalLM(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_mistral_for_sequence_classification",
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model_fn=lambda: transformers.MistralForSequenceClassification(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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