baalajimaestro/llama.cpp
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Fix low level api examples

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This commit is contained in:
Juarez Bochi 2023-09-07 17:50:47 -04:00
parent 18337267c1
commit 20ac434d0f
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GPG key ID: 724DBA0D70E08401
3 changed files with 41 additions and 18 deletions
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3
README.md
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@ -187,7 +187,8 @@ Below is a short example demonstrating how to use the low-level API to tokenize
>>> import ctypes
>>> params = llama_cpp.llama_context_default_params()
# use bytes for char * params
>>> ctx = llama_cpp.llama_init_from_file(b"./models/7b/ggml-model.bin", params)
>>> model = llama_cpp.llama_load_model_from_file(b"./models/7b/ggml-model.bin", params)
>>> ctx = llama_cpp.llama_new_context_with_model(model, params)
>>> max_tokens = params.n_ctx
# use ctypes arrays for array params
>>> tokens = (llama_cpp.llama_token * int(max_tokens))()

30
examples/low_level_api/low_level_api_chat_cpp.py
View file

@ -24,6 +24,10 @@ class LLaMAInteract:
def __init__(self, params: GptParams) -> None:
# input args
self.params = params
if self.params.path_session is None:
self.params.path_session = ""
if self.params.antiprompt is None:
self.params.antiprompt = ""
if (self.params.perplexity):
raise NotImplementedError("""************
@ -66,7 +70,9 @@ specified) expect poor results""", file=sys.stderr)
self.lparams.use_mlock = self.params.use_mlock
self.lparams.use_mmap = self.params.use_mmap
self.ctx = llama_cpp.llama_init_from_file(self.params.model.encode("utf8"), self.lparams)
self.model = llama_cpp.llama_load_model_from_file(
self.params.model.encode("utf8"), self.lparams)
self.ctx = llama_cpp.llama_new_context_with_model(self.model, self.lparams)
if (not self.ctx):
raise RuntimeError(f"error: failed to load model '{self.params.model}'")
@ -181,12 +187,12 @@ prompt: '{self.params.prompt}'
number of tokens in prompt = {len(self.embd_inp)}""", file=sys.stderr)
for i in range(len(self.embd_inp)):
print(f"{self.embd_inp[i]} -> '{llama_cpp.llama_token_to_str(self.ctx, self.embd_inp[i])}'", file=sys.stderr)
print(f"{self.embd_inp[i]} -> '{self.token_to_str(self.embd_inp[i])}'", file=sys.stderr)
if (self.params.n_keep > 0):
print("static prompt based on n_keep: '")
for i in range(self.params.n_keep):
print(llama_cpp.llama_token_to_str(self.ctx, self.embd_inp[i]), file=sys.stderr)
print(self.token_to_str(self.embd_inp[i]), file=sys.stderr)
print("'", file=sys.stderr)
print(file=sys.stderr)
@ -339,7 +345,7 @@ n_keep = {self.params.n_keep}
candidates_p = llama_cpp.ctypes.pointer(llama_cpp.llama_token_data_array(_arr, len(_arr), False))
# Apply penalties
nl_logit = logits[llama_cpp.llama_token_nl()]
nl_logit = logits[llama_cpp.llama_token_nl(self.ctx)]
last_n_repeat = min(len(self.last_n_tokens), repeat_last_n, self.n_ctx)
_arr = (llama_cpp.llama_token * last_n_repeat)(*self.last_n_tokens[len(self.last_n_tokens) - last_n_repeat:])
@ -380,7 +386,7 @@ n_keep = {self.params.n_keep}
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.params.interactive and not self.params.instruct):
if (id == llama_cpp.llama_token_eos(self.ctx) and self.params.interactive and not self.params.instruct):
id = self.llama_token_newline[0]
self.embd.append(id)
if (self.use_antiprompt()):
@ -437,7 +443,7 @@ n_keep = {self.params.n_keep}
break
# end of text token
if len(self.embd) > 0 and self.embd[-1] == llama_cpp.llama_token_eos():
if len(self.embd) > 0 and self.embd[-1] == llama_cpp.llama_token_eos(self.ctx):
if (not self.params.instruct):
for i in self.llama_token_eot:
yield i
@ -464,10 +470,18 @@ n_keep = {self.params.n_keep}
llama_cpp.llama_free(self.ctx)
self.set_color(util.CONSOLE_COLOR_DEFAULT)
def token_to_str(self, token_id: int) -> bytes:
size = 32
buffer = (ctypes.c_char * size)()
n = llama_cpp.llama_token_to_piece_with_model(
self.model, llama_cpp.llama_token(token_id), buffer, size)
assert n <= size
return bytes(buffer[:n])
# 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("utf8", errors="ignore")
yield self.token_to_str(id).decode("utf8", errors="ignore")
# write input
def input(self, prompt: str):
@ -481,7 +495,7 @@ n_keep = {self.params.n_keep}
def output(self):
self.remaining_tokens = self.params.n_predict
for id in self.generate():
cur_char = llama_cpp.llama_token_to_str(self.ctx, id)
cur_char = self.token_to_str(id)
# Add remainder of missing bytes
if None in self.multibyte_fix:

26
examples/low_level_api/low_level_api_llama_cpp.py
View file

@ -1,15 +1,17 @@
import llama_cpp
import ctypes
import os
import multiprocessing
import llama_cpp
N_THREADS = multiprocessing.cpu_count()
MODEL_PATH = os.environ.get('MODEL', "../models/7B/ggml-model.bin")
prompt = b"\n\n### Instruction:\nWhat is the capital of France?\n\n### Response:\n"
lparams = llama_cpp.llama_context_default_params()
ctx = llama_cpp.llama_init_from_file(b"../models/7B/ggml-model.bin", lparams)
model = llama_cpp.llama_load_model_from_file(MODEL_PATH.encode('utf-8'), lparams)
ctx = llama_cpp.llama_new_context_with_model(model, lparams)
# determine the required inference memory per token:
tmp = [0, 1, 2, 3]
@ -58,7 +60,8 @@ while remaining_tokens > 0:
llama_cpp.llama_token_data(token_id, logits[token_id], 0.0)
for token_id in range(n_vocab)
])
candidates_p = llama_cpp.ctypes.pointer(llama_cpp.llama_token_data_array(_arr, len(_arr), False))
candidates_p = llama_cpp.ctypes.pointer(
llama_cpp.llama_token_data_array(_arr, len(_arr), False))
_arr = (llama_cpp.c_int * len(last_n_tokens_data))(*last_n_tokens_data)
llama_cpp.llama_sample_repetition_penalty(ctx, candidates_p,
@ -68,9 +71,9 @@ while remaining_tokens > 0:
_arr,
last_n_repeat, frequency_penalty, presence_penalty)
llama_cpp.llama_sample_top_k(ctx, candidates_p, 40)
llama_cpp.llama_sample_top_p(ctx, candidates_p, 0.8)
llama_cpp.llama_sample_temperature(ctx, candidates_p, 0.2)
llama_cpp.llama_sample_top_k(ctx, candidates_p, k=40, min_keep=1)
llama_cpp.llama_sample_top_p(ctx, candidates_p, p=0.8, min_keep=1)
llama_cpp.llama_sample_temperature(ctx, candidates_p, temp=0.2)
id = llama_cpp.llama_sample_token(ctx, candidates_p)
last_n_tokens_data = last_n_tokens_data[1:] + [id]
@ -86,13 +89,18 @@ while remaining_tokens > 0:
break
if not input_noecho:
for id in embd:
size = 32
buffer = (ctypes.c_char * size)()
n = llama_cpp.llama_token_to_piece_with_model(
model, llama_cpp.llama_token(id), buffer, size)
assert n <= size
print(
llama_cpp.llama_token_to_str(ctx, id).decode("utf-8", errors="ignore"),
buffer[:n].decode('utf-8'),
end="",
flush=True,
)
if len(embd) > 0 and embd[-1] == llama_cpp.llama_token_eos():
if len(embd) > 0 and embd[-1] == llama_cpp.llama_token_eos(ctx):
break
print()