Use torch.where to inject the soft prompt instead of torch.cat
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Gnome Ann
parent
248e0bd24b
commit
1556bd32a5
64
aiserver.py
64
aiserver.py
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@ -515,17 +515,25 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly"]):
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def patch_causallm(cls):
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old_forward = cls.forward
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def new_causallm_forward(self, *args, **kwargs):
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num_embeddings = self.config.vocab_size
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if(vars.sp is not None):
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num_embeddings += vars.sp.shape[0]
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if(self.transformer.wte.num_embeddings != num_embeddings):
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self.resize_token_embeddings(num_embeddings)
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input_ids = kwargs.get('input_ids').to(self.device)
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assert input_ids is not None
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kwargs['input_ids'] = None
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inputs_embeds = self.transformer.wte(input_ids)
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input_ids -= self.config.vocab_size
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if(vars.sp is not None):
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inputs_embeds = torch.cat((
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vars.sp.tile((inputs_embeds.shape[0], 1, 1)),
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inputs_embeds
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), dim=1).to(self.device)
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vars.sp = vars.sp.to(inputs_embeds.device)
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inputs_embeds = torch.where(
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(input_ids >= 0)[:, :, None],
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vars.sp[input_ids.clamp(min=0)],
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inputs_embeds,
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)
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kwargs['inputs_embeds'] = inputs_embeds
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return old_forward(*args, **kwargs)
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return old_forward(self, *args, **kwargs)
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cls.forward = new_causallm_forward
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for cls in (GPT2LMHeadModel, GPTNeoForCausalLM):
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patch_causallm(cls)
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@ -543,13 +551,14 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly"]):
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# Is CUDA available? If so, use GPU, otherwise fall back to CPU
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if(vars.hascuda):
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if(vars.usegpu):
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generator = pipeline('text-generation', model=model, tokenizer=tokenizer, device=0)
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model = model.to(0)
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generator = model.generate
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elif(vars.breakmodel): # Use both RAM and VRAM (breakmodel)
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device_config(model)
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else:
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generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
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generator = model.generate
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else:
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generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
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generator = model.generate
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# If custom GPT2 model was chosen
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elif(vars.model == "GPT2Custom"):
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model_config = open(vars.custmodpth + "/config.json", "r")
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@ -559,9 +568,10 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly"]):
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tokenizer = GPT2Tokenizer.from_pretrained(vars.custmodpth)
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# Is CUDA available? If so, use GPU, otherwise fall back to CPU
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if(vars.hascuda and vars.usegpu):
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generator = pipeline('text-generation', model=model, tokenizer=tokenizer, device=0)
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model = model.to(0)
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generator = model.generate
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else:
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generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
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generator = model.generate
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# If base HuggingFace model was chosen
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else:
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# Is CUDA available? If so, use GPU, otherwise fall back to CPU
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@ -570,16 +580,17 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly"]):
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if(vars.usegpu):
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model = AutoModelForCausalLM.from_pretrained(vars.model, device=0)
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vars.modeldim = int(model.transformer.hidden_size)
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generator = pipeline('text-generation', model=model, device=0)
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model = model.to(0)
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generator = model.generate
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elif(vars.breakmodel): # Use both RAM and VRAM (breakmodel)
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model = AutoModelForCausalLM.from_pretrained(vars.model)
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device_config(model)
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else:
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model = AutoModelForCausalLM.from_pretrained(vars.model)
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generator = pipeline('text-generation', model=vars.model)
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generator = model.generate
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else:
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model = AutoModelForCausalLM.from_pretrained(vars.model)
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generator = pipeline('text-generation', model=vars.model)
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generator = model.generate
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# Suppress Author's Note by flagging square brackets (Old implementation)
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#vocab = tokenizer.get_vocab()
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@ -1177,6 +1188,9 @@ def calcsubmit(txt):
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else:
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budget = vars.max_length - lnsp - lnmem - lnanote - lnwi - vars.genamt
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if(vars.sp is not None):
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budget -= vars.sp.shape[0]
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if(actionlen == 0):
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# First/Prompt action
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subtxt = vars.memory + winfo + anotetxt + vars.prompt
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@ -1336,13 +1350,22 @@ def generate(txt, min, max):
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top_k = vars.top_k if vars.top_k > 0 else None
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tfs = vars.tfs if vars.tfs > 0.0 else None
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# generator() only accepts a torch tensor of tokens (long datatype) as
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# its first argument if we're using breakmodel, otherwise a string
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# is fine
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if(vars.hascuda and vars.breakmodel):
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gen_in = tokenizer.encode(txt, return_tensors="pt", truncation=True).long().to(breakmodel.primary_device)
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gen_in = tokenizer.encode(txt, return_tensors="pt", truncation=True).long()
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if(vars.sp is not None):
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soft_tokens = torch.arange(
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model.config.vocab_size,
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model.config.vocab_size + vars.sp.shape[0],
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)
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gen_in = torch.cat((soft_tokens[None], gen_in), dim=1)
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if(vars.hascuda and vars.usegpu):
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gen_in = gen_in.to(0)
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elif(vars.hascuda and vars.breakmodel):
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gen_in = gen_in.to(breakmodel.primary_device)
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elif(vars.hascuda):
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gen_in = gen_in.to(0)
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else:
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gen_in = txt
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gen_in = gen_in.to('cpu')
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with torch.no_grad():
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genout = generator(
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@ -1367,8 +1390,7 @@ def generate(txt, min, max):
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return
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# Need to manually strip and decode tokens if we're not using a pipeline
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if(vars.hascuda and vars.breakmodel):
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genout = [{"generated_text": tokenizer.decode(tokens[len(gen_in[0])-len(tokens):])} for tokens in genout]
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genout = [{"generated_text": tokenizer.decode(tokens[len(gen_in[0])-len(tokens):])} for tokens in genout]
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if(len(genout) == 1):
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genresult(genout[0]["generated_text"])
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