Add instruction mode
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Mug
parent
f1615f05e6
commit
0b32bb3d43
1 changed files with 64 additions and 37 deletions
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@ -5,24 +5,26 @@ Quirks:
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* Input is always echoed if on, so it should be turned off when using "input()"
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* The first antiprompt should be the userprompt like "\nUser:",
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because its added when n_predict is reached (aka generation ended prematurely)
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* n_predict can be set to -1 for unlimited length responses
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* n_predict can be set to -1 for unlimited length responses (or just a really high value)
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* It's always in interactive mode, generation ends either by reaching an antiprompt
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or running out of n_predict.
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* Instruction mode adds its own antiprompt
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"""
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import llama_cpp
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def toIntArray(lst):
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return [int(i) for i in lst]
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# A LLaMA interactive session
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class LLaMAInteract:
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def __init__(self,
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primer: str="",
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model: str="./models/30B/ggml-model-q4_0.bin",
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instruct: bool=False,
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n_ctx: int=1024,
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seed: int=0,
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n_threads: int=8,
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antiprompt: list[str]=[],
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input_echo: bool=True,
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n_predict: int=20,
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n_keep: int=0,
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n_batch: int=8,
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repeat_last_n: int=64,
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top_k: int=50,
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@ -31,17 +33,17 @@ class LLaMAInteract:
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repeat_penalty: float=1,
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) -> None:
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# input args
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self.instruct = instruct
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self.n_threads = n_threads
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self.input_echo = input_echo
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self.n_predict = n_predict
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self.n_keep = n_keep
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self.n_batch = n_batch
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self.repeat_last_n = repeat_last_n
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self.top_k=top_k
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self.top_p=top_p
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self.temp=temp
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self.repeat_penalty=repeat_penalty
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self.n_ctx = n_ctx
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self.seed = seed
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# runtime args
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self.input_consumed = 0
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@ -54,8 +56,8 @@ class LLaMAInteract:
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# model load
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self.lparams = llama_cpp.llama_context_default_params()
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self.lparams.n_ctx = self.n_ctx
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self.lparams.seed = self.seed
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self.lparams.n_ctx = n_ctx
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self.lparams.seed = seed
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self.ctx = llama_cpp.llama_init_from_file(model.encode("utf8"), self.lparams)
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# determine the required inference memory per token:
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@ -63,29 +65,44 @@ class LLaMAInteract:
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llama_cpp.llama_eval(self.ctx, (llama_cpp.c_int * len(tmp))(*tmp), len(tmp), 0, self.n_threads)
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# determine newline token
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self.llama_token_newline = (llama_cpp.llama_token * 1)()
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llama_cpp.llama_tokenize(self.ctx, b"\n", self.llama_token_newline, len(self.llama_token_newline), False)
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self.llama_token_newline = toIntArray(self.llama_token_newline)
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self.llama_token_newline = self._tokenize("\n", False)
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self.inp_prefix = self._tokenize("\n\n### Instruction:\n\n")
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self.inp_suffix = self._tokenize("\n\n### Response:\n\n", False)
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# add instruction as antiprompt
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if (self.instruct):
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self.first_antiprompt.append(self.inp_prefix)
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# primer feed
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if (len(primer) > 0):
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self.input(primer)
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self.n_keep = len(self.embd_inp)
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self.embd_inp += self._tokenize(primer)
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# break immediately if using instruct
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self.init_break = self.instruct
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# number of tokens to keep when resetting context
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if (self.n_keep < 0 or self.n_keep > len(self.embd_inp) or self.instruct):
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self.n_keep = len(self.embd_inp)
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# create internal context
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self.n_ctx = int(llama_cpp.llama_n_ctx(self.ctx))
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self.n_ctx = llama_cpp.llama_n_ctx(self.ctx)
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self.last_n_tokens = [0]*self.n_ctx #TODO: deque doesnt support slices
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# determine antiprompt tokens
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for i in antiprompt:
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d_antiprompt = (llama_cpp.llama_token * (len(i) + 1))()
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n_antiprompt = llama_cpp.llama_tokenize(self.ctx, i.encode("utf8"), d_antiprompt, len(d_antiprompt), False)
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self.first_antiprompt.append(toIntArray(d_antiprompt[:n_antiprompt]))
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self.first_antiprompt.append(self._tokenize(i, False))
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# tokenize a prompt
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def _tokenize(self, prompt, bos=True):
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_arr = (llama_cpp.llama_token * (len(prompt) + 1))()
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_n = llama_cpp.llama_tokenize(self.ctx, prompt.encode("utf8"), _arr, len(_arr), bos)
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return _arr[:_n]
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# if an antiprompt is present
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def use_antiprompt(self):
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return len(self.first_antiprompt) > 0
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# generate tokens
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def generate(self):
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while self.remaining_tokens > 0 or self.use_antiprompt():
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# predict
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@ -125,16 +142,16 @@ class LLaMAInteract:
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self.repeat_penalty,
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)
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self.last_n_tokens.pop(0)
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self.last_n_tokens.append(int(id))
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self.last_n_tokens.append(id)
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# replace end of text token with newline token when in interactive mode
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if (id == llama_cpp.llama_token_eos() and self.use_antiprompt()):
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if (id == llama_cpp.llama_token_eos() and self.use_antiprompt() and not self.instruct):
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id = self.llama_token_newline[0]
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# tokenize and inject first reverse prompt
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self.embd_inp += self.first_antiprompt[0]
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# add it to the context
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self.embd.append(int(id))
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self.embd.append(id)
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# echo this to console
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self.output_echo = True
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@ -147,9 +164,9 @@ class LLaMAInteract:
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# some user input remains from prompt or interaction, forward it to processing
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while len(self.embd_inp) > self.input_consumed:
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self.embd.append(int(self.embd_inp[self.input_consumed]))
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self.embd.append(self.embd_inp[self.input_consumed])
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self.last_n_tokens.pop(0)
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self.last_n_tokens.append(int(self.embd_inp[self.input_consumed]))
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self.last_n_tokens.append(self.embd_inp[self.input_consumed])
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self.input_consumed += 1
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if len(self.embd) >= self.n_batch:
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break
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@ -159,11 +176,17 @@ class LLaMAInteract:
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for id in self.embd:
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yield id
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# if antiprompt is present, stop
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if (self.use_antiprompt() and len(self.embd_inp) <= self.input_consumed):
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for i in self.first_antiprompt:
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if i == self.last_n_tokens[-len(i):]:
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return
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if (len(self.embd_inp) <= self.input_consumed):
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# if antiprompt is present, stop
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if (self.use_antiprompt()):
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for i in self.first_antiprompt:
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if i == self.last_n_tokens[-len(i):]:
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return
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# if we are using instruction mode, and we have processed the initial prompt
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if (self.init_break):
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self.init_break = False
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break
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# if end of generation
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if len(self.embd) > 0 and self.embd[-1] == llama_cpp.llama_token_eos():
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@ -174,15 +197,20 @@ class LLaMAInteract:
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self.embd_inp += self.first_antiprompt[0]
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break
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# return past text
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def past(self):
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for id in self.last_n_tokens[-self.n_past:]:
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yield llama_cpp.llama_token_to_str(self.ctx, id).decode("utf-8")
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# write input
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def input(self, prompt: str):
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embd_arr = (llama_cpp.llama_token * (len(prompt) + 1))()
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n_of_tok = llama_cpp.llama_tokenize(self.ctx, prompt.encode("utf8"), embd_arr, len(embd_arr), True)
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self.embd_inp += toIntArray(embd_arr[:n_of_tok])
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if (self.instruct):
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self.embd_inp += self.inp_prefix
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self.embd_inp += self._tokenize(prompt + "\n")
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if (self.instruct):
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self.embd_inp += self.inp_suffix
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# write output
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def output(self):
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self.remaining_tokens = self.n_predict
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for id in self.generate():
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@ -214,7 +242,7 @@ The transcript only includes text, it does not include markup like HTML and Mark
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{USER_NAME}:"""
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print("Loading model...")
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ll = LLaMAInteract(prompt,
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m = LLaMAInteract(prompt,
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model="./models/30B/ggml-model-q4_0.bin",
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n_ctx=2048,
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antiprompt=[f"\n{USER_NAME}:"],
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@ -224,12 +252,11 @@ The transcript only includes text, it does not include markup like HTML and Mark
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)
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print("Loaded model!")
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for i in ll.output():
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for i in m.output():
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print(i,end="",flush=True)
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ll.input_echo = False
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m.input_echo = False
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inp = lambda x: f" {x}\n"
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while True:
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ll.input(inp(input(' ')))
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for i in ll.output():
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m.input(" " + input('\n> ' if m.instruct else " "))
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for i in m.output():
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print(i,end="",flush=True)
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