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This commit is contained in:
Andrei Betlen 2023-03-24 14:35:41 -04:00
parent d29b05bb67
commit 2cc499512c
6 changed files with 121 additions and 35 deletions
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8
examples/fastapi_server.py
View file

@ -5,9 +5,11 @@ from llama_cpp import Llama
from fastapi import FastAPI
from pydantic import BaseModel, BaseSettings, Field
class Settings(BaseSettings):
model: str
app = FastAPI(
title="🦙 llama.cpp Python API",
version="0.0.1",
@ -15,6 +17,7 @@ app = FastAPI(
settings = Settings()
llama = Llama(settings.model)
class CompletionRequest(BaseModel):
prompt: str
suffix: Optional[str] = Field(None)
@ -31,12 +34,11 @@ class CompletionRequest(BaseModel):
schema_extra = {
"example": {
"prompt": "\n\n### Instructions:\nWhat is the capital of France?\n\n### Response:\n",
"stop": ["\n", "###"]
"stop": ["\n", "###"],
}
}
@app.post("/v1/completions")
def completions(request: CompletionRequest):
return llama(**request.dict())
return llama(**request.dict())

9
examples/high_level_api_basic_inference.py
View file

@ -9,6 +9,11 @@ args = parser.parse_args()
llm = Llama(model_path=args.model)
output = llm("Question: What are the names of the planets in the solar system? Answer: ", max_tokens=48, stop=["Q:", "\n"], echo=True)
output = llm(
"Question: What are the names of the planets in the solar system? Answer: ",
max_tokens=48,
stop=["Q:", "\n"],
echo=True,
)
print(json.dumps(output, indent=2))
print(json.dumps(output, indent=2))

10
examples/langchain_custom_llm.py
View file

@ -5,6 +5,7 @@ from llama_cpp import Llama
from langchain.llms.base import LLM
from typing import Optional, List, Mapping, Any
class LlamaLLM(LLM):
model_path: str
llm: Llama
@ -16,7 +17,7 @@ class LlamaLLM(LLM):
def __init__(self, model_path: str, **kwargs: Any):
model_path = model_path
llm = Llama(model_path=model_path)
super().__init__(model_path=model_path, llm=llm, **kwargs)
super().__init__(model_path=model_path, llm=llm, **kwargs)
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
response = self.llm(prompt, stop=stop or [])
@ -26,6 +27,7 @@ class LlamaLLM(LLM):
def _identifying_params(self) -> Mapping[str, Any]:
return {"model_path": self.model_path}
parser = argparse.ArgumentParser()
parser.add_argument("-m", "--model", type=str, default="./models/...")
args = parser.parse_args()
@ -34,7 +36,9 @@ args = parser.parse_args()
llm = LlamaLLM(model_path=args.model)
# Basic Q&A
answer = llm("Question: What is the capital of France? Answer: ", stop=["Question:", "\n"])
answer = llm(
"Question: What is the capital of France? Answer: ", stop=["Question:", "\n"]
)
print(f"Answer: {answer.strip()}")
# Using in a chain
@ -48,4 +52,4 @@ prompt = PromptTemplate(
chain = LLMChain(llm=llm, prompt=prompt)
# Run the chain only specifying the input variable.
print(chain.run("colorful socks"))
print(chain.run("colorful socks"))

12
examples/low_level_api_inference.py
View file

@ -27,7 +27,15 @@ embd = embd_inp
n = 8
for i in range(n):
id = llama_cpp.llama_sample_top_p_top_k(ctx, (llama_cpp.c_int * len(embd))(*embd), n_of_tok + i, 40, 0.8, 0.2, 1.0/0.85)
id = llama_cpp.llama_sample_top_p_top_k(
ctx,
(llama_cpp.c_int * len(embd))(*embd),
n_of_tok + i,
40,
0.8,
0.2,
1.0 / 0.85,
)
embd.append(id)
@ -38,4 +46,4 @@ for i in range(n):
llama_cpp.llama_free(ctx)
print(prediction.decode("utf-8"))
print(prediction.decode("utf-8"))

11
llama_cpp/llama.py
View file

@ -5,6 +5,7 @@ from typing import List, Optional
from . import llama_cpp
class Llama:
def __init__(
self,
@ -82,7 +83,10 @@ class Llama:
for i in range(max_tokens):
tokens_seen = prompt_tokens + completion_tokens
last_n_tokens = [0] * max(0, self.last_n - tokens_seen) + [self.tokens[j] for j in range(max(tokens_seen - self.last_n, 0), tokens_seen)]
last_n_tokens = [0] * max(0, self.last_n - tokens_seen) + [
self.tokens[j]
for j in range(max(tokens_seen - self.last_n, 0), tokens_seen)
]
token = llama_cpp.llama_sample_top_p_top_k(
self.ctx,
@ -128,9 +132,8 @@ class Llama:
self.ctx,
)[:logprobs]
return {
"id": f"cmpl-{str(uuid.uuid4())}", # Likely to change
"id": f"cmpl-{str(uuid.uuid4())}", # Likely to change
"object": "text_completion",
"created": int(time.time()),
"model": self.model_path,
@ -151,5 +154,3 @@ class Llama:
def __del__(self):
llama_cpp.llama_free(self.ctx)

106
llama_cpp/llama_cpp.py
View file

@ -1,6 +1,15 @@
import ctypes
from ctypes import c_int, c_float, c_double, c_char_p, c_void_p, c_bool, POINTER, Structure
from ctypes import (
c_int,
c_float,
c_double,
c_char_p,
c_void_p,
c_bool,
POINTER,
Structure,
)
import pathlib
@ -13,26 +22,32 @@ lib = ctypes.CDLL(str(libfile))
llama_token = c_int
llama_token_p = POINTER(llama_token)
class llama_token_data(Structure):
_fields_ = [
('id', llama_token), # token id
('p', c_float), # probability of the token
('plog', c_float), # log probability of the token
("id", llama_token), # token id
("p", c_float), # probability of the token
("plog", c_float), # log probability of the token
]
llama_token_data_p = POINTER(llama_token_data)
class llama_context_params(Structure):
_fields_ = [
('n_ctx', c_int), # text context
('n_parts', c_int), # -1 for default
('seed', c_int), # RNG seed, 0 for random
('f16_kv', c_bool), # use fp16 for KV cache
('logits_all', c_bool), # the llama_eval() call computes all logits, not just the last one
('vocab_only', c_bool), # only load the vocabulary, no weights
("n_ctx", c_int), # text context
("n_parts", c_int), # -1 for default
("seed", c_int), # RNG seed, 0 for random
("f16_kv", c_bool), # use fp16 for KV cache
(
"logits_all",
c_bool,
), # the llama_eval() call computes all logits, not just the last one
("vocab_only", c_bool), # only load the vocabulary, no weights
]
llama_context_params_p = POINTER(llama_context_params)
llama_context_p = c_void_p
@ -74,7 +89,15 @@ lib.llama_token_bos.restype = llama_token
lib.llama_token_eos.argtypes = []
lib.llama_token_eos.restype = llama_token
lib.llama_sample_top_p_top_k.argtypes = [llama_context_p, llama_token_p, c_int, c_int, c_double, c_double, c_double]
lib.llama_sample_top_p_top_k.argtypes = [
llama_context_p,
llama_token_p,
c_int,
c_int,
c_double,
c_double,
c_double,
]
lib.llama_sample_top_p_top_k.restype = llama_token
lib.llama_print_timings.argtypes = [llama_context_p]
@ -86,45 +109,71 @@ lib.llama_reset_timings.restype = None
lib.llama_print_system_info.argtypes = []
lib.llama_print_system_info.restype = c_char_p
# Python functions
def llama_context_default_params() -> llama_context_params:
params = lib.llama_context_default_params()
return params
def llama_init_from_file(path_model: bytes, params: llama_context_params) -> llama_context_p:
def llama_init_from_file(
path_model: bytes, params: llama_context_params
) -> llama_context_p:
"""Various functions for loading a ggml llama model.
Allocate (almost) all memory needed for the model.
Return NULL on failure """
Return NULL on failure"""
return lib.llama_init_from_file(path_model, params)
def llama_free(ctx: llama_context_p):
"""Free all allocated memory"""
lib.llama_free(ctx)
def llama_model_quantize(fname_inp: bytes, fname_out: bytes, itype: c_int, qk: c_int) -> c_int:
def llama_model_quantize(
fname_inp: bytes, fname_out: bytes, itype: c_int, qk: c_int
) -> c_int:
"""Returns 0 on success"""
return lib.llama_model_quantize(fname_inp, fname_out, itype, qk)
def llama_eval(ctx: llama_context_p, tokens: llama_token_p, n_tokens: c_int, n_past: c_int, n_threads: c_int) -> c_int:
def llama_eval(
ctx: llama_context_p,
tokens: llama_token_p,
n_tokens: c_int,
n_past: c_int,
n_threads: c_int,
) -> c_int:
"""Run the llama inference to obtain the logits and probabilities for the next token.
tokens + n_tokens is the provided batch of new tokens to process
n_past is the number of tokens to use from previous eval calls
Returns 0 on success"""
return lib.llama_eval(ctx, tokens, n_tokens, n_past, n_threads)
def llama_tokenize(ctx: llama_context_p, text: bytes, tokens: llama_token_p, n_max_tokens: c_int, add_bos: c_bool) -> c_int:
def llama_tokenize(
ctx: llama_context_p,
text: bytes,
tokens: llama_token_p,
n_max_tokens: c_int,
add_bos: c_bool,
) -> c_int:
"""Convert the provided text into tokens.
The tokens pointer must be large enough to hold the resulting tokens.
Returns the number of tokens on success, no more than n_max_tokens
Returns a negative number on failure - the number of tokens that would have been returned"""
Returns a negative number on failure - the number of tokens that would have been returned
"""
return lib.llama_tokenize(ctx, text, tokens, n_max_tokens, add_bos)
def llama_n_vocab(ctx: llama_context_p) -> c_int:
return lib.llama_n_vocab(ctx)
def llama_n_ctx(ctx: llama_context_p) -> c_int:
return lib.llama_n_ctx(ctx)
def llama_get_logits(ctx: llama_context_p):
"""Token logits obtained from the last call to llama_eval()
The logits for the last token are stored in the last row
@ -133,25 +182,42 @@ def llama_get_logits(ctx: llama_context_p):
Cols: n_vocab"""
return lib.llama_get_logits(ctx)
def llama_token_to_str(ctx: llama_context_p, token: int) -> bytes:
"""Token Id -> String. Uses the vocabulary in the provided context"""
return lib.llama_token_to_str(ctx, token)
def llama_token_bos() -> llama_token:
return lib.llama_token_bos()
def llama_token_eos() -> llama_token:
return lib.llama_token_eos()
def llama_sample_top_p_top_k(ctx: llama_context_p, last_n_tokens_data: llama_token_p, last_n_tokens_size: c_int, top_k: c_int, top_p: c_double, temp: c_double, repeat_penalty: c_double) -> llama_token:
return lib.llama_sample_top_p_top_k(ctx, last_n_tokens_data, last_n_tokens_size, top_k, top_p, temp, repeat_penalty)
def llama_sample_top_p_top_k(
ctx: llama_context_p,
last_n_tokens_data: llama_token_p,
last_n_tokens_size: c_int,
top_k: c_int,
top_p: c_double,
temp: c_double,
repeat_penalty: c_double,
) -> llama_token:
return lib.llama_sample_top_p_top_k(
ctx, last_n_tokens_data, last_n_tokens_size, top_k, top_p, temp, repeat_penalty
)
def llama_print_timings(ctx: llama_context_p):
lib.llama_print_timings(ctx)
def llama_reset_timings(ctx: llama_context_p):
lib.llama_reset_timings(ctx)
def llama_print_system_info() -> bytes:
"""Print system informaiton"""
return lib.llama_print_system_info()