Update modeling_internlm.py
Browse files- modeling_internlm.py +4 -0
modeling_internlm.py
CHANGED
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@@ -784,6 +784,7 @@ class InternLMForCausalLM(InternLMPreTrainedModel):
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do_sample: bool = True,
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temperature: float = 0.8,
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top_p: float = 0.8,
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**kwargs):
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inputs = self.build_inputs(tokenizer, query, history)
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inputs = {k: v.to(self.device) for k, v in inputs.items() if torch.is_tensor(v)}
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@@ -793,6 +794,7 @@ class InternLMForCausalLM(InternLMPreTrainedModel):
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do_sample=do_sample,
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temperature=temperature,
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top_p=top_p,
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**kwargs)
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outputs = outputs[0].cpu().tolist()[len(inputs["input_ids"][0]):]
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response = tokenizer.decode(outputs, skip_special_tokens=True)
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@@ -809,6 +811,7 @@ class InternLMForCausalLM(InternLMPreTrainedModel):
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do_sample: bool = True,
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temperature: float = 0.8,
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top_p: float = 0.8,
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**kwargs):
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class ChatStreamer(BaseStreamer):
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def __init__(self, tokenizer) -> None:
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@@ -836,6 +839,7 @@ class InternLMForCausalLM(InternLMPreTrainedModel):
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do_sample=do_sample,
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temperature=temperature,
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top_p=top_p,
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**kwargs
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)
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do_sample: bool = True,
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temperature: float = 0.8,
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top_p: float = 0.8,
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eos_token_id = (2, 103028),
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**kwargs):
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inputs = self.build_inputs(tokenizer, query, history)
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inputs = {k: v.to(self.device) for k, v in inputs.items() if torch.is_tensor(v)}
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do_sample=do_sample,
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temperature=temperature,
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top_p=top_p,
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eos_token_id=list(eos_token_id),
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**kwargs)
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outputs = outputs[0].cpu().tolist()[len(inputs["input_ids"][0]):]
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response = tokenizer.decode(outputs, skip_special_tokens=True)
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do_sample: bool = True,
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temperature: float = 0.8,
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top_p: float = 0.8,
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eos_token_id = (2, 103028),
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**kwargs):
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class ChatStreamer(BaseStreamer):
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def __init__(self, tokenizer) -> None:
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do_sample=do_sample,
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temperature=temperature,
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top_p=top_p,
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eos_token_id=eos_token_id,
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**kwargs
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)
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