Add instruction mode
This commit is contained in:
Mug
parent
f1615f05e6
commit
0b32bb3d43
|
|
@ -5,24 +5,26 @@ Quirks:
|
|||
* Input is always echoed if on, so it should be turned off when using "input()"
|
||||
* The first antiprompt should be the userprompt like "\nUser:",
|
||||
because its added when n_predict is reached (aka generation ended prematurely)
|
||||
* n_predict can be set to -1 for unlimited length responses
|
||||
* n_predict can be set to -1 for unlimited length responses (or just a really high value)
|
||||
* It's always in interactive mode, generation ends either by reaching an antiprompt
|
||||
or running out of n_predict.
|
||||
* Instruction mode adds its own antiprompt
|
||||
"""
|
||||
import llama_cpp
|
||||
|
||||
def toIntArray(lst):
|
||||
return [int(i) for i in lst]
|
||||
|
||||
# A LLaMA interactive session
|
||||
class LLaMAInteract:
|
||||
def __init__(self,
|
||||
primer: str="",
|
||||
model: str="./models/30B/ggml-model-q4_0.bin",
|
||||
instruct: bool=False,
|
||||
n_ctx: int=1024,
|
||||
seed: int=0,
|
||||
n_threads: int=8,
|
||||
antiprompt: list[str]=[],
|
||||
input_echo: bool=True,
|
||||
n_predict: int=20,
|
||||
n_keep: int=0,
|
||||
n_batch: int=8,
|
||||
repeat_last_n: int=64,
|
||||
top_k: int=50,
|
||||
|
|
@ -31,17 +33,17 @@ class LLaMAInteract:
|
|||
repeat_penalty: float=1,
|
||||
) -> None:
|
||||
# input args
|
||||
self.instruct = instruct
|
||||
self.n_threads = n_threads
|
||||
self.input_echo = input_echo
|
||||
self.n_predict = n_predict
|
||||
self.n_keep = n_keep
|
||||
self.n_batch = n_batch
|
||||
self.repeat_last_n = repeat_last_n
|
||||
self.top_k=top_k
|
||||
self.top_p=top_p
|
||||
self.temp=temp
|
||||
self.repeat_penalty=repeat_penalty
|
||||
self.n_ctx = n_ctx
|
||||
self.seed = seed
|
||||
|
||||
# runtime args
|
||||
self.input_consumed = 0
|
||||
|
|
@ -54,8 +56,8 @@ class LLaMAInteract:
|
|||
|
||||
# model load
|
||||
self.lparams = llama_cpp.llama_context_default_params()
|
||||
self.lparams.n_ctx = self.n_ctx
|
||||
self.lparams.seed = self.seed
|
||||
self.lparams.n_ctx = n_ctx
|
||||
self.lparams.seed = seed
|
||||
self.ctx = llama_cpp.llama_init_from_file(model.encode("utf8"), self.lparams)
|
||||
|
||||
# determine the required inference memory per token:
|
||||
|
|
@ -63,29 +65,44 @@ class LLaMAInteract:
|
|||
llama_cpp.llama_eval(self.ctx, (llama_cpp.c_int * len(tmp))(*tmp), len(tmp), 0, self.n_threads)
|
||||
|
||||
# determine newline token
|
||||
self.llama_token_newline = (llama_cpp.llama_token * 1)()
|
||||
llama_cpp.llama_tokenize(self.ctx, b"\n", self.llama_token_newline, len(self.llama_token_newline), False)
|
||||
self.llama_token_newline = toIntArray(self.llama_token_newline)
|
||||
self.llama_token_newline = self._tokenize("\n", False)
|
||||
self.inp_prefix = self._tokenize("\n\n### Instruction:\n\n")
|
||||
self.inp_suffix = self._tokenize("\n\n### Response:\n\n", False)
|
||||
|
||||
# add instruction as antiprompt
|
||||
if (self.instruct):
|
||||
self.first_antiprompt.append(self.inp_prefix)
|
||||
|
||||
# primer feed
|
||||
if (len(primer) > 0):
|
||||
self.input(primer)
|
||||
self.n_keep = len(self.embd_inp)
|
||||
self.embd_inp += self._tokenize(primer)
|
||||
|
||||
# break immediately if using instruct
|
||||
self.init_break = self.instruct
|
||||
|
||||
# number of tokens to keep when resetting context
|
||||
if (self.n_keep < 0 or self.n_keep > len(self.embd_inp) or self.instruct):
|
||||
self.n_keep = len(self.embd_inp)
|
||||
|
||||
# create internal context
|
||||
self.n_ctx = int(llama_cpp.llama_n_ctx(self.ctx))
|
||||
self.n_ctx = llama_cpp.llama_n_ctx(self.ctx)
|
||||
self.last_n_tokens = [0]*self.n_ctx #TODO: deque doesnt support slices
|
||||
|
||||
# determine antiprompt tokens
|
||||
for i in antiprompt:
|
||||
d_antiprompt = (llama_cpp.llama_token * (len(i) + 1))()
|
||||
n_antiprompt = llama_cpp.llama_tokenize(self.ctx, i.encode("utf8"), d_antiprompt, len(d_antiprompt), False)
|
||||
self.first_antiprompt.append(toIntArray(d_antiprompt[:n_antiprompt]))
|
||||
self.first_antiprompt.append(self._tokenize(i, False))
|
||||
|
||||
# tokenize a prompt
|
||||
def _tokenize(self, prompt, bos=True):
|
||||
_arr = (llama_cpp.llama_token * (len(prompt) + 1))()
|
||||
_n = llama_cpp.llama_tokenize(self.ctx, prompt.encode("utf8"), _arr, len(_arr), bos)
|
||||
return _arr[:_n]
|
||||
|
||||
# if an antiprompt is present
|
||||
def use_antiprompt(self):
|
||||
return len(self.first_antiprompt) > 0
|
||||
|
||||
# generate tokens
|
||||
def generate(self):
|
||||
while self.remaining_tokens > 0 or self.use_antiprompt():
|
||||
# predict
|
||||
|
|
@ -125,16 +142,16 @@ class LLaMAInteract:
|
|||
self.repeat_penalty,
|
||||
)
|
||||
self.last_n_tokens.pop(0)
|
||||
self.last_n_tokens.append(int(id))
|
||||
self.last_n_tokens.append(id)
|
||||
|
||||
# replace end of text token with newline token when in interactive mode
|
||||
if (id == llama_cpp.llama_token_eos() and self.use_antiprompt()):
|
||||
if (id == llama_cpp.llama_token_eos() and self.use_antiprompt() and not self.instruct):
|
||||
id = self.llama_token_newline[0]
|
||||
# tokenize and inject first reverse prompt
|
||||
self.embd_inp += self.first_antiprompt[0]
|
||||
|
||||
# add it to the context
|
||||
self.embd.append(int(id))
|
||||
self.embd.append(id)
|
||||
|
||||
# echo this to console
|
||||
self.output_echo = True
|
||||
|
|
@ -147,9 +164,9 @@ class LLaMAInteract:
|
|||
|
||||
# some user input remains from prompt or interaction, forward it to processing
|
||||
while len(self.embd_inp) > self.input_consumed:
|
||||
self.embd.append(int(self.embd_inp[self.input_consumed]))
|
||||
self.embd.append(self.embd_inp[self.input_consumed])
|
||||
self.last_n_tokens.pop(0)
|
||||
self.last_n_tokens.append(int(self.embd_inp[self.input_consumed]))
|
||||
self.last_n_tokens.append(self.embd_inp[self.input_consumed])
|
||||
self.input_consumed += 1
|
||||
if len(self.embd) >= self.n_batch:
|
||||
break
|
||||
|
|
@ -159,11 +176,17 @@ class LLaMAInteract:
|
|||
for id in self.embd:
|
||||
yield id
|
||||
|
||||
# if antiprompt is present, stop
|
||||
if (self.use_antiprompt() and len(self.embd_inp) <= self.input_consumed):
|
||||
for i in self.first_antiprompt:
|
||||
if i == self.last_n_tokens[-len(i):]:
|
||||
return
|
||||
if (len(self.embd_inp) <= self.input_consumed):
|
||||
# if antiprompt is present, stop
|
||||
if (self.use_antiprompt()):
|
||||
for i in self.first_antiprompt:
|
||||
if i == self.last_n_tokens[-len(i):]:
|
||||
return
|
||||
|
||||
# if we are using instruction mode, and we have processed the initial prompt
|
||||
if (self.init_break):
|
||||
self.init_break = False
|
||||
break
|
||||
|
||||
# if end of generation
|
||||
if len(self.embd) > 0 and self.embd[-1] == llama_cpp.llama_token_eos():
|
||||
|
|
@ -174,15 +197,20 @@ class LLaMAInteract:
|
|||
self.embd_inp += self.first_antiprompt[0]
|
||||
break
|
||||
|
||||
# return past text
|
||||
def past(self):
|
||||
for id in self.last_n_tokens[-self.n_past:]:
|
||||
yield llama_cpp.llama_token_to_str(self.ctx, id).decode("utf-8")
|
||||
|
||||
# write input
|
||||
def input(self, prompt: str):
|
||||
embd_arr = (llama_cpp.llama_token * (len(prompt) + 1))()
|
||||
n_of_tok = llama_cpp.llama_tokenize(self.ctx, prompt.encode("utf8"), embd_arr, len(embd_arr), True)
|
||||
self.embd_inp += toIntArray(embd_arr[:n_of_tok])
|
||||
if (self.instruct):
|
||||
self.embd_inp += self.inp_prefix
|
||||
self.embd_inp += self._tokenize(prompt + "\n")
|
||||
if (self.instruct):
|
||||
self.embd_inp += self.inp_suffix
|
||||
|
||||
# write output
|
||||
def output(self):
|
||||
self.remaining_tokens = self.n_predict
|
||||
for id in self.generate():
|
||||
|
|
@ -193,7 +221,7 @@ if __name__ == "__main__":
|
|||
|
||||
USER_NAME="User"
|
||||
AI_NAME="ChatLLaMa"
|
||||
|
||||
|
||||
time_now = datetime.now()
|
||||
prompt = f"""Text transcript of a never ending dialog, where {USER_NAME} interacts with an AI assistant named {AI_NAME}.
|
||||
{AI_NAME} is helpful, kind, honest, friendly, good at writing and never fails to answer {USER_NAME}’s requests immediately and with details and precision.
|
||||
|
|
@ -214,7 +242,7 @@ The transcript only includes text, it does not include markup like HTML and Mark
|
|||
{USER_NAME}:"""
|
||||
|
||||
print("Loading model...")
|
||||
ll = LLaMAInteract(prompt,
|
||||
m = LLaMAInteract(prompt,
|
||||
model="./models/30B/ggml-model-q4_0.bin",
|
||||
n_ctx=2048,
|
||||
antiprompt=[f"\n{USER_NAME}:"],
|
||||
|
|
@ -224,12 +252,11 @@ The transcript only includes text, it does not include markup like HTML and Mark
|
|||
)
|
||||
print("Loaded model!")
|
||||
|
||||
for i in ll.output():
|
||||
for i in m.output():
|
||||
print(i,end="",flush=True)
|
||||
ll.input_echo = False
|
||||
m.input_echo = False
|
||||
|
||||
inp = lambda x: f" {x}\n"
|
||||
while True:
|
||||
ll.input(inp(input(' ')))
|
||||
for i in ll.output():
|
||||
m.input(" " + input('\n> ' if m.instruct else " "))
|
||||
for i in m.output():
|
||||
print(i,end="",flush=True)
|
||||
Loading…
Reference in a new issue