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Fix Multi-GPU not working on exllama_hf (#2803)
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@ -38,7 +38,6 @@ class ExllamaHF(PreTrainedModel):
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@property
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def device(self) -> torch.device:
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# TODO: May cause problem on multi-gpu inference?
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return torch.device(0)
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def __call__(self, *args, **kwargs):
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@ -50,7 +49,7 @@ class ExllamaHF(PreTrainedModel):
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if cache is None:
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cache = ExLlamaCache(self.ex_model)
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self.ex_model.forward(torch.tensor([seq[:-1]], dtype=torch.long), cache, preprocess_only=True)
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logits = self.ex_model.forward(torch.tensor([seq[-1:]], dtype=torch.long), cache).to(self.device)
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logits = self.ex_model.forward(torch.tensor([seq[-1:]], dtype=torch.long), cache).to(kwargs['input_ids'].device)
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return CausalLMOutputWithPast(logits=logits, past_key_values=cache if use_cache else None)
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@classmethod
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@ -72,11 +71,14 @@ class ExllamaHF(PreTrainedModel):
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assert weight_path is not None, f'could not find weight in "{pretrained_model_name_or_path}"'
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config.model_path = str(weight_path)
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if shared.args.gpu_split:
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config.set_auto_map(shared.args.gpu_split)
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config.gpu_peer_fix = True
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# This slowes down a bit but align better with autogptq generation.
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# TODO: Should give user choice to tune the exllama config
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config.act_order = True
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config.fused_attn = False
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config.fused_mlp_thd = 0
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# config.fused_attn = False
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# config.fused_mlp_thd = 0
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return ExllamaHF(config)
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return ExllamaHF(config)
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