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76 lines
2.3 KiB
Python
76 lines
2.3 KiB
Python
# modified from tests/kit/model_zoo/transformers/mistral.py
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from types import MethodType
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import torch
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import transformers
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from transformers import AutoConfig
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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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# 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 = lambda x: x[0].mean()
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loss_fn_for_lm = lambda x: x.loss
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def init_deepseek():
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config = AutoConfig.from_pretrained(
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"deepseek-ai/DeepSeek-V3",
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hidden_size=128,
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intermediate_size=320,
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kv_lora_rank=4,
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moe_intermediate_size=32,
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num_attention_heads=4,
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num_experts_per_tok=4,
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n_group=4,
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num_hidden_layers=3,
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num_key_value_heads=4,
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first_k_dense_replace=1,
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q_lora_rank=8,
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torch_dtype="bfloat16",
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n_routed_experts=16,
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topk_group=2,
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v_head_dim=32,
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qk_nope_head_dim=32,
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qk_rope_head_dim=32,
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trust_remote_code=True,
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vocab_size=2048,
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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 = transformers.AutoModelForCausalLM.from_config(config, trust_remote_code=True)
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# enable grad for moe layers
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for m in model.modules():
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if m.__class__.__name__ == "DeepseekV3MoE":
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m.moe_infer = MethodType(m.moe_infer.__wrapped__, m)
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return model
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