740 lines
26 KiB
Python
740 lines
26 KiB
Python
from abc import ABC, abstractmethod
|
||
from enum import Enum, auto
|
||
import os
|
||
import pathlib
|
||
import copy
|
||
import re
|
||
import time
|
||
from typing import Dict, Iterable, List, Tuple, Union, Type, Callable
|
||
from utils.log import quick_log
|
||
from fastapi import HTTPException
|
||
from pydantic import BaseModel, Field
|
||
from routes import state_cache
|
||
import global_var
|
||
|
||
os.environ["TORCH_EXTENSIONS_DIR"] = f"{pathlib.Path(__file__).parent.parent.resolve()}"
|
||
|
||
|
||
class RWKVType(Enum):
|
||
NoneType = auto()
|
||
Raven = auto()
|
||
World = auto()
|
||
Music = auto()
|
||
|
||
|
||
class AbstractRWKV(ABC):
|
||
def __init__(self, model, pipeline):
|
||
self.EOS_ID = 0
|
||
|
||
self.name = "rwkv"
|
||
self.version = 4
|
||
self.model = model
|
||
self.pipeline = pipeline
|
||
self.model_state = None
|
||
self.model_tokens = []
|
||
self.rwkv_type: RWKVType = RWKVType.NoneType
|
||
self.tokenizer_len = len(model.w["emb.weight"])
|
||
|
||
self.max_tokens_per_generation = 500
|
||
self.temperature = 1
|
||
self.top_p = 0.3
|
||
self.top_k = 0
|
||
self.penalty_alpha_presence = 0
|
||
self.penalty_alpha_frequency = 1
|
||
self.penalty_decay = 0.996
|
||
self.global_penalty = False
|
||
|
||
@abstractmethod
|
||
def adjust_occurrence(self, occurrence: Dict, token: int):
|
||
pass
|
||
|
||
@abstractmethod
|
||
def adjust_forward_logits(self, logits: List[float], occurrence: Dict, i: int):
|
||
pass
|
||
|
||
# Model only saw '\n\n' as [187, 187] before, but the tokenizer outputs [535] for it at the end
|
||
@abstractmethod
|
||
def fix_tokens(self, tokens) -> List[int]:
|
||
pass
|
||
|
||
@abstractmethod
|
||
def run_rnn(
|
||
self, _tokens: List[str], newline_adj: int = 0
|
||
) -> Tuple[List[float], int]:
|
||
pass
|
||
|
||
@abstractmethod
|
||
def delta_postprocess(self, delta: str) -> str:
|
||
pass
|
||
|
||
def get_embedding(self, input: str, fast_mode: bool) -> Tuple[List[float], int]:
|
||
import numpy as np
|
||
|
||
if fast_mode:
|
||
embedding, token_len = self.__fast_embedding(
|
||
self.fix_tokens(self.pipeline.encode(input)), None
|
||
)
|
||
else:
|
||
self.model_state = None
|
||
self.model_tokens = []
|
||
_, token_len = self.run_rnn(self.fix_tokens(self.pipeline.encode(input)))
|
||
embedding = self.model_state[-11].tolist()
|
||
embedding = (embedding / np.linalg.norm(embedding)).tolist()
|
||
return embedding, token_len
|
||
|
||
def __fast_embedding(self, tokens: List[str], state):
|
||
import torch
|
||
|
||
tokens = [int(x) for x in tokens]
|
||
token_len = len(tokens)
|
||
self = self.model
|
||
|
||
with torch.no_grad():
|
||
w = self.w
|
||
args = self.args
|
||
|
||
if state == None:
|
||
state = [None] * args.n_layer * 5
|
||
for i in range(
|
||
args.n_layer
|
||
): # state: 0=att_xx 1=att_aa 2=att_bb 3=att_pp 4=ffn_xx
|
||
dd = self.strategy[i]
|
||
dev = dd.device
|
||
atype = dd.atype
|
||
state[i * 5 + 0] = torch.zeros(
|
||
args.n_embd, dtype=atype, requires_grad=False, device=dev
|
||
).contiguous()
|
||
state[i * 5 + 1] = torch.zeros(
|
||
args.n_embd, dtype=torch.float, requires_grad=False, device=dev
|
||
).contiguous()
|
||
state[i * 5 + 2] = torch.zeros(
|
||
args.n_embd, dtype=torch.float, requires_grad=False, device=dev
|
||
).contiguous()
|
||
state[i * 5 + 3] = (
|
||
torch.zeros(
|
||
args.n_embd,
|
||
dtype=torch.float,
|
||
requires_grad=False,
|
||
device=dev,
|
||
).contiguous()
|
||
- 1e30
|
||
)
|
||
state[i * 5 + 4] = torch.zeros(
|
||
args.n_embd, dtype=atype, requires_grad=False, device=dev
|
||
).contiguous()
|
||
|
||
break
|
||
|
||
seq_mode = len(tokens) > 1
|
||
|
||
x = w["emb.weight"][tokens if seq_mode else tokens[0]]
|
||
|
||
for i in range(args.n_layer):
|
||
bbb = f"blocks.{i}."
|
||
att = f"blocks.{i}.att."
|
||
ffn = f"blocks.{i}.ffn."
|
||
dd = self.strategy[i]
|
||
dev = dd.device
|
||
atype = dd.atype
|
||
wtype = dd.wtype
|
||
if seq_mode:
|
||
if "cuda" in str(dev) and os.environ["RWKV_CUDA_ON"] == "1":
|
||
ATT = (
|
||
self.cuda_att_seq
|
||
if wtype != torch.uint8
|
||
else self.cuda_att_seq_i8
|
||
)
|
||
else:
|
||
ATT = self.att_seq if wtype != torch.uint8 else self.att_seq_i8
|
||
FFN = self.ffn_seq if wtype != torch.uint8 else self.ffn_seq_i8
|
||
else:
|
||
ATT = self.att_one if wtype != torch.uint8 else self.att_one_i8
|
||
FFN = self.ffn_one if wtype != torch.uint8 else self.ffn_one_i8
|
||
|
||
x = x.to(dtype=atype, device=dev)
|
||
|
||
kw = w[f"{att}key.weight"]
|
||
vw = w[f"{att}value.weight"]
|
||
rw = w[f"{att}receptance.weight"]
|
||
ow = w[f"{att}output.weight"]
|
||
if dd.stream:
|
||
kw = kw.to(device=dev, non_blocking=True)
|
||
vw = vw.to(device=dev, non_blocking=True)
|
||
rw = rw.to(device=dev, non_blocking=True)
|
||
ow = ow.to(device=dev, non_blocking=True)
|
||
kmx = w[f"{att}key.weight_mx"] if wtype == torch.uint8 else x
|
||
krx = w[f"{att}key.weight_rx"] if wtype == torch.uint8 else x
|
||
kmy = w[f"{att}key.weight_my"] if wtype == torch.uint8 else x
|
||
kry = w[f"{att}key.weight_ry"] if wtype == torch.uint8 else x
|
||
vmx = w[f"{att}value.weight_mx"] if wtype == torch.uint8 else x
|
||
vrx = w[f"{att}value.weight_rx"] if wtype == torch.uint8 else x
|
||
vmy = w[f"{att}value.weight_my"] if wtype == torch.uint8 else x
|
||
vry = w[f"{att}value.weight_ry"] if wtype == torch.uint8 else x
|
||
rmx = w[f"{att}receptance.weight_mx"] if wtype == torch.uint8 else x
|
||
rrx = w[f"{att}receptance.weight_rx"] if wtype == torch.uint8 else x
|
||
rmy = w[f"{att}receptance.weight_my"] if wtype == torch.uint8 else x
|
||
rry = w[f"{att}receptance.weight_ry"] if wtype == torch.uint8 else x
|
||
omx = w[f"{att}output.weight_mx"] if wtype == torch.uint8 else x
|
||
orx = w[f"{att}output.weight_rx"] if wtype == torch.uint8 else x
|
||
omy = w[f"{att}output.weight_my"] if wtype == torch.uint8 else x
|
||
ory = w[f"{att}output.weight_ry"] if wtype == torch.uint8 else x
|
||
(
|
||
x,
|
||
state[i * 5 + 0],
|
||
state[i * 5 + 1],
|
||
state[i * 5 + 2],
|
||
state[i * 5 + 3],
|
||
) = ATT(
|
||
x,
|
||
state[i * 5 + 0],
|
||
state[i * 5 + 1],
|
||
state[i * 5 + 2],
|
||
state[i * 5 + 3],
|
||
w[f"{bbb}ln1.weight"],
|
||
w[f"{bbb}ln1.bias"],
|
||
w[f"{att}time_mix_k"],
|
||
w[f"{att}time_mix_v"],
|
||
w[f"{att}time_mix_r"],
|
||
w[f"{att}time_decay"],
|
||
w[f"{att}time_first"],
|
||
kw,
|
||
vw,
|
||
rw,
|
||
ow,
|
||
kmx,
|
||
krx,
|
||
kmy,
|
||
kry,
|
||
vmx,
|
||
vrx,
|
||
vmy,
|
||
vry,
|
||
rmx,
|
||
rrx,
|
||
rmy,
|
||
rry,
|
||
omx,
|
||
orx,
|
||
omy,
|
||
ory,
|
||
)
|
||
|
||
return state[0].tolist(), token_len
|
||
|
||
def generate(
|
||
self, prompt: str, stop: Union[str, List[str], None] = None
|
||
) -> Iterable[Tuple[str, str, int, int]]:
|
||
import numpy as np
|
||
|
||
quick_log(None, None, "Generation Prompt:\n" + prompt)
|
||
cache = None
|
||
delta_prompt = prompt
|
||
try:
|
||
cache = state_cache.longest_prefix_state(
|
||
state_cache.LongestPrefixStateBody(prompt=prompt), None
|
||
)
|
||
except HTTPException:
|
||
pass
|
||
if cache is None or cache["prompt"] == "" or cache["state"] is None:
|
||
self.model_state = None
|
||
self.model_tokens = []
|
||
else:
|
||
delta_prompt = prompt[len(cache["prompt"]) :]
|
||
self.model_state = cache["state"]
|
||
self.model_tokens = cache["tokens"]
|
||
logits = cache["logits"]
|
||
|
||
prompt_token_len = 0
|
||
if delta_prompt != "":
|
||
prompt_start_time = time.time()
|
||
logits, prompt_token_len = self.run_rnn(
|
||
self.fix_tokens(self.pipeline.encode(delta_prompt))
|
||
)
|
||
prompt_end_time = time.time()
|
||
tps = prompt_token_len / (prompt_end_time - prompt_start_time)
|
||
print(f"Prompt Prefill TPS: {tps:.2f}", end=" ", flush=True)
|
||
try:
|
||
state_cache.add_state(
|
||
state_cache.AddStateBody(
|
||
prompt=prompt,
|
||
tokens=self.model_tokens,
|
||
state=self.model_state,
|
||
logits=logits,
|
||
)
|
||
)
|
||
except HTTPException:
|
||
pass
|
||
|
||
begin = len(self.model_tokens)
|
||
out_last = begin
|
||
|
||
occurrence: Dict = {}
|
||
|
||
completion_token_len = 0
|
||
response = ""
|
||
for i in range(self.max_tokens_per_generation):
|
||
self.adjust_forward_logits(logits, occurrence, i)
|
||
|
||
token = self.pipeline.sample_logits(
|
||
logits, temperature=self.temperature, top_p=self.top_p, top_k=self.top_k
|
||
)
|
||
|
||
if token == self.EOS_ID:
|
||
try:
|
||
state_cache.add_state(
|
||
state_cache.AddStateBody(
|
||
prompt=prompt + response,
|
||
tokens=self.model_tokens,
|
||
state=self.model_state,
|
||
logits=logits,
|
||
)
|
||
)
|
||
except HTTPException:
|
||
pass
|
||
yield response, "", prompt_token_len, completion_token_len
|
||
break
|
||
|
||
self.adjust_occurrence(occurrence, token)
|
||
|
||
logits, _ = self.run_rnn([token])
|
||
completion_token_len = completion_token_len + 1
|
||
delta: str = self.delta_postprocess(
|
||
self.pipeline.decode(self.model_tokens[out_last:])
|
||
)
|
||
if "\ufffd" not in delta: # avoid utf-8 display issues
|
||
response += delta
|
||
if stop is not None:
|
||
if type(stop) == str:
|
||
if stop in response:
|
||
try:
|
||
state_cache.add_state(
|
||
state_cache.AddStateBody(
|
||
prompt=prompt + response,
|
||
tokens=self.model_tokens,
|
||
state=self.model_state,
|
||
logits=logits,
|
||
)
|
||
)
|
||
except HTTPException:
|
||
pass
|
||
response = response.split(stop)[0]
|
||
yield response, "", prompt_token_len, completion_token_len
|
||
break
|
||
elif type(stop) == list:
|
||
exit_flag = False
|
||
for s in stop:
|
||
if s in response:
|
||
try:
|
||
state_cache.add_state(
|
||
state_cache.AddStateBody(
|
||
prompt=prompt + response,
|
||
tokens=self.model_tokens,
|
||
state=self.model_state,
|
||
logits=logits,
|
||
)
|
||
)
|
||
except HTTPException:
|
||
pass
|
||
exit_flag = True
|
||
response = response.split(s)[0]
|
||
yield response, "", prompt_token_len, completion_token_len
|
||
break
|
||
if exit_flag:
|
||
break
|
||
out_last = begin + i + 1
|
||
if i == self.max_tokens_per_generation - 1:
|
||
try:
|
||
state_cache.add_state(
|
||
state_cache.AddStateBody(
|
||
prompt=prompt + response,
|
||
tokens=self.model_tokens,
|
||
state=self.model_state,
|
||
logits=logits,
|
||
)
|
||
)
|
||
except HTTPException:
|
||
pass
|
||
yield response, delta, prompt_token_len, completion_token_len
|
||
|
||
|
||
class TextRWKV(AbstractRWKV):
|
||
def __init__(self, model, pipeline) -> None:
|
||
super().__init__(model, pipeline)
|
||
|
||
self.CHUNK_LEN = 256
|
||
|
||
self.max_tokens_per_generation = 500
|
||
self.temperature = 1
|
||
self.top_p = 0.3
|
||
self.top_k = 0
|
||
self.penalty_alpha_presence = 0
|
||
self.penalty_alpha_frequency = 1
|
||
|
||
self.interface = ":"
|
||
if self.tokenizer_len < 65536:
|
||
self.rwkv_type = RWKVType.Raven
|
||
self.user = "Bob"
|
||
self.bot = "Alice"
|
||
self.END_OF_LINE = 187
|
||
else:
|
||
self.rwkv_type = RWKVType.World
|
||
self.user = "User"
|
||
self.bot = "Assistant"
|
||
self.END_OF_LINE = 11
|
||
|
||
self.AVOID_REPEAT_TOKENS = set()
|
||
AVOID_REPEAT = "，：？！"
|
||
for i in AVOID_REPEAT:
|
||
dd = self.pipeline.encode(i)
|
||
assert len(dd) == 1
|
||
self.AVOID_REPEAT_TOKENS.add(dd[0])
|
||
self.AVOID_PENALTY_TOKENS = set()
|
||
AVOID_PENALTY = '\n,.:?!，。：？！"“”<>[]{}/\\|;；~`@#$%^&*()_+-=0123456789 '
|
||
for i in AVOID_PENALTY:
|
||
dd = self.pipeline.encode(i)
|
||
if len(dd) == 1:
|
||
self.AVOID_PENALTY_TOKENS.add(dd[0])
|
||
|
||
self.__preload()
|
||
|
||
def adjust_occurrence(self, occurrence: Dict, token: int):
|
||
for xxx in occurrence:
|
||
occurrence[xxx] *= self.penalty_decay
|
||
if token not in occurrence:
|
||
occurrence[token] = 1
|
||
else:
|
||
occurrence[token] += 1
|
||
|
||
def adjust_forward_logits(self, logits: List[float], occurrence: Dict, i: int):
|
||
for n in occurrence:
|
||
# if n not in self.AVOID_PENALTY_TOKENS:
|
||
logits[n] -= (
|
||
self.penalty_alpha_presence
|
||
+ occurrence[n] * self.penalty_alpha_frequency
|
||
)
|
||
|
||
# set global_penalty to False to get the same generated results as the official RWKV Gradio
|
||
if self.global_penalty and i == 0:
|
||
for token in self.model_tokens:
|
||
token = int(token)
|
||
if token not in self.AVOID_PENALTY_TOKENS:
|
||
self.adjust_occurrence(occurrence, token)
|
||
|
||
# Model only saw '\n\n' as [187, 187] before, but the tokenizer outputs [535] for it at the end
|
||
def fix_tokens(self, tokens) -> List[int]:
|
||
if self.rwkv_type == RWKVType.World:
|
||
return tokens
|
||
if len(tokens) > 0 and tokens[-1] == 535:
|
||
tokens = tokens[:-1] + [self.END_OF_LINE, self.END_OF_LINE]
|
||
return tokens
|
||
|
||
def run_rnn(
|
||
self, _tokens: List[str], newline_adj: int = 0
|
||
) -> Tuple[List[float], int]:
|
||
tokens = [int(x) for x in _tokens]
|
||
token_len = len(tokens)
|
||
self.model_tokens += tokens
|
||
|
||
while len(tokens) > 0:
|
||
out, self.model_state = self.model.forward(
|
||
tokens[: self.CHUNK_LEN], self.model_state
|
||
)
|
||
tokens = tokens[self.CHUNK_LEN :]
|
||
|
||
out[self.END_OF_LINE] += newline_adj # adjust \n probability
|
||
|
||
if self.model_tokens[-1] in self.AVOID_REPEAT_TOKENS:
|
||
out[self.model_tokens[-1]] = -999999999
|
||
return out, token_len
|
||
|
||
def delta_postprocess(self, delta: str) -> str:
|
||
return delta
|
||
|
||
def __preload(self):
|
||
interface = self.interface
|
||
user = self.user
|
||
bot = self.bot
|
||
preset_system = (
|
||
f"""
|
||
The following is a coherent verbose detailed conversation between a girl named {bot} and her friend {user}. \
|
||
{bot} is very intelligent, creative and friendly. \
|
||
{bot} is unlikely to disagree with {user}, and {bot} doesn't like to ask {user} questions. \
|
||
{bot} likes to tell {user} a lot about herself and her opinions. \
|
||
{bot} usually gives {user} kind, helpful and informative advices.\n
|
||
"""
|
||
if self.rwkv_type == RWKVType.Raven
|
||
else (
|
||
f"{user}{interface} hi\n\n{bot}{interface} Hi. "
|
||
+ "I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it.\n\n"
|
||
)
|
||
)
|
||
logits, _ = self.run_rnn(self.fix_tokens(self.pipeline.encode(preset_system)))
|
||
try:
|
||
state_cache.add_state(
|
||
state_cache.AddStateBody(
|
||
prompt=preset_system,
|
||
tokens=self.model_tokens,
|
||
state=self.model_state,
|
||
logits=logits,
|
||
)
|
||
)
|
||
except HTTPException:
|
||
pass
|
||
|
||
|
||
class MusicMidiRWKV(AbstractRWKV):
|
||
def __init__(self, model, pipeline):
|
||
super().__init__(model, pipeline)
|
||
|
||
self.max_tokens_per_generation = 500
|
||
self.temperature = 1
|
||
self.top_p = 0.8
|
||
self.top_k = 8
|
||
|
||
self.rwkv_type = RWKVType.Music
|
||
|
||
def adjust_occurrence(self, occurrence: Dict, token: int):
|
||
for n in occurrence:
|
||
occurrence[n] *= 0.997 #### decay repetition penalty
|
||
if token >= 128 or token == 127:
|
||
occurrence[token] = 1 + (occurrence[token] if token in occurrence else 0)
|
||
else:
|
||
occurrence[token] = 0.3 + (occurrence[token] if token in occurrence else 0)
|
||
|
||
def adjust_forward_logits(self, logits: List[float], occurrence: Dict, i: int):
|
||
for n in occurrence:
|
||
logits[n] -= 0 + occurrence[n] * 0.5
|
||
|
||
logits[0] += (i - 2000) / 500 # try not to be too short or too long
|
||
logits[127] -= 1 # avoid "t125"
|
||
|
||
def fix_tokens(self, tokens) -> List[int]:
|
||
return tokens
|
||
|
||
def run_rnn(
|
||
self, _tokens: List[str], newline_adj: int = 0
|
||
) -> Tuple[List[float], int]:
|
||
tokens = [int(x) for x in _tokens]
|
||
token_len = len(tokens)
|
||
self.model_tokens += tokens
|
||
out, self.model_state = self.model.forward(tokens, self.model_state)
|
||
return out, token_len
|
||
|
||
def delta_postprocess(self, delta: str) -> str:
|
||
return " " + delta
|
||
|
||
|
||
class MusicAbcRWKV(AbstractRWKV):
|
||
def __init__(self, model, pipeline):
|
||
super().__init__(model, pipeline)
|
||
|
||
self.EOS_ID = 3
|
||
|
||
self.max_tokens_per_generation = 500
|
||
self.temperature = 1
|
||
self.top_p = 0.8
|
||
self.top_k = 8
|
||
|
||
self.rwkv_type = RWKVType.Music
|
||
|
||
def adjust_occurrence(self, occurrence: Dict, token: int):
|
||
pass
|
||
|
||
def adjust_forward_logits(self, logits: List[float], occurrence: Dict, i: int):
|
||
pass
|
||
|
||
def fix_tokens(self, tokens) -> List[int]:
|
||
return tokens
|
||
|
||
def run_rnn(
|
||
self, _tokens: List[str], newline_adj: int = 0
|
||
) -> Tuple[List[float], int]:
|
||
tokens = [int(x) for x in _tokens]
|
||
token_len = len(tokens)
|
||
self.model_tokens += tokens
|
||
out, self.model_state = self.model.forward(tokens, self.model_state)
|
||
return out, token_len
|
||
|
||
def delta_postprocess(self, delta: str) -> str:
|
||
return delta
|
||
|
||
|
||
def get_tokenizer(tokenizer_len: int):
|
||
tokenizer_dir = f"{pathlib.Path(__file__).parent.parent.resolve()}/rwkv_pip/"
|
||
if tokenizer_len < 2176:
|
||
return "abc_tokenizer"
|
||
if tokenizer_len < 20096:
|
||
return tokenizer_dir + "tokenizer-midipiano.json"
|
||
if tokenizer_len < 50277:
|
||
return tokenizer_dir + "tokenizer-midi.json"
|
||
elif tokenizer_len < 65536:
|
||
return tokenizer_dir + "20B_tokenizer.json"
|
||
else:
|
||
return "rwkv_vocab_v20230424"
|
||
|
||
|
||
def get_model_path(model_path: str) -> str:
|
||
if os.path.isabs(model_path):
|
||
return model_path
|
||
|
||
working_dir: pathlib.Path = pathlib.Path(os.path.abspath(os.getcwd()))
|
||
|
||
parent_paths: List[pathlib.Path] = [
|
||
working_dir, # [cwd](RWKV-Runner)/models/xxx
|
||
working_dir.parent, # [cwd](backend-python)/../models/xxx
|
||
pathlib.Path(
|
||
os.path.abspath(__file__)
|
||
).parent.parent, # backend-python/models/xxx
|
||
pathlib.Path(
|
||
os.path.abspath(__file__)
|
||
).parent.parent.parent, # RWKV-Runner/models/xxx
|
||
]
|
||
|
||
child_paths: List[Callable[[pathlib.Path], pathlib.Path]] = [
|
||
lambda p: p / model_path,
|
||
lambda p: p / "build" / "bin" / model_path, # for dev
|
||
]
|
||
|
||
for parent_path in parent_paths:
|
||
for child_path in child_paths:
|
||
full_path: pathlib.Path = child_path(parent_path)
|
||
|
||
if os.path.isfile(full_path):
|
||
return str(full_path)
|
||
|
||
return model_path
|
||
|
||
|
||
def RWKV(model: str, strategy: str, tokenizer: Union[str, None]) -> AbstractRWKV:
|
||
model = get_model_path(model)
|
||
|
||
rwkv_beta = global_var.get(global_var.Args).rwkv_beta
|
||
rwkv_cpp = getattr(global_var.get(global_var.Args), "rwkv.cpp")
|
||
webgpu = global_var.get(global_var.Args).webgpu
|
||
|
||
if "midi" in model.lower() or "abc" in model.lower():
|
||
os.environ["RWKV_RESCALE_LAYER"] = "999"
|
||
|
||
# dynamic import to make RWKV_CUDA_ON work
|
||
if rwkv_beta:
|
||
print("Using rwkv-beta")
|
||
from rwkv_pip.beta.model import (
|
||
RWKV as Model,
|
||
)
|
||
elif rwkv_cpp:
|
||
print("Using rwkv.cpp, strategy is ignored")
|
||
from rwkv_pip.cpp.model import (
|
||
RWKV as Model,
|
||
)
|
||
elif webgpu:
|
||
print("Using webgpu")
|
||
from rwkv_pip.webgpu.model import (
|
||
RWKV as Model,
|
||
)
|
||
else:
|
||
from rwkv_pip.model import (
|
||
RWKV as Model,
|
||
)
|
||
from rwkv_pip.utils import PIPELINE
|
||
|
||
filename, _ = os.path.splitext(os.path.basename(model))
|
||
model = Model(model, strategy)
|
||
if not tokenizer:
|
||
tokenizer = get_tokenizer(len(model.w["emb.weight"]))
|
||
pipeline = PIPELINE(model, tokenizer)
|
||
|
||
rwkv_map: dict[str, Type[AbstractRWKV]] = {
|
||
"20B_tokenizer": TextRWKV,
|
||
"rwkv_vocab_v20230424": TextRWKV,
|
||
"tokenizer-midi": MusicMidiRWKV,
|
||
"tokenizer-midipiano": MusicMidiRWKV,
|
||
"abc_tokenizer": MusicAbcRWKV,
|
||
}
|
||
tokenizer_name = os.path.splitext(os.path.basename(tokenizer))[0]
|
||
global_var.set(
|
||
global_var.Midi_Vocab_Config_Type,
|
||
(
|
||
global_var.MidiVocabConfig.Piano
|
||
if tokenizer_name == "tokenizer-midipiano"
|
||
else global_var.MidiVocabConfig.Default
|
||
),
|
||
)
|
||
rwkv: AbstractRWKV
|
||
if tokenizer_name in rwkv_map:
|
||
rwkv = rwkv_map[tokenizer_name](model, pipeline)
|
||
else:
|
||
tokenizer_name = tokenizer_name.lower()
|
||
if "music" in tokenizer_name or "midi" in tokenizer_name:
|
||
rwkv = MusicMidiRWKV(model, pipeline)
|
||
elif "abc" in tokenizer_name:
|
||
rwkv = MusicAbcRWKV(model, pipeline)
|
||
else:
|
||
rwkv = TextRWKV(model, pipeline)
|
||
rwkv.name = filename
|
||
rwkv.version = model.version
|
||
|
||
return rwkv
|
||
|
||
|
||
class ModelConfigBody(BaseModel):
|
||
max_tokens: int = Field(default=None, gt=0, le=102400)
|
||
temperature: float = Field(default=None, ge=0, le=3)
|
||
top_p: float = Field(default=None, ge=0, le=1)
|
||
presence_penalty: float = Field(default=None, ge=-2, le=2)
|
||
frequency_penalty: float = Field(default=None, ge=-2, le=2)
|
||
penalty_decay: float = Field(default=None, ge=0.99, le=0.999)
|
||
top_k: int = Field(default=None, ge=0, le=25)
|
||
global_penalty: bool = Field(
|
||
default=None,
|
||
description="When generating a response, whether to include the submitted prompt as a penalty factor. By turning this off, you will get the same generated results as official RWKV Gradio. If you find duplicate results in the generated results, turning this on can help avoid generating duplicates.",
|
||
)
|
||
|
||
model_config = {
|
||
"json_schema_extra": {
|
||
"example": {
|
||
"max_tokens": 1000,
|
||
"temperature": 1,
|
||
"top_p": 0.3,
|
||
"presence_penalty": 0,
|
||
"frequency_penalty": 1,
|
||
"penalty_decay": 0.996,
|
||
"global_penalty": False,
|
||
}
|
||
}
|
||
}
|
||
|
||
|
||
def set_rwkv_config(model: AbstractRWKV, body: ModelConfigBody):
|
||
if body.max_tokens is not None:
|
||
model.max_tokens_per_generation = body.max_tokens
|
||
if body.temperature is not None:
|
||
if body.temperature < 0.1:
|
||
model.temperature = 0.1
|
||
else:
|
||
model.temperature = body.temperature
|
||
if body.top_p is not None:
|
||
model.top_p = body.top_p
|
||
if body.presence_penalty is not None:
|
||
model.penalty_alpha_presence = body.presence_penalty
|
||
if body.frequency_penalty is not None:
|
||
model.penalty_alpha_frequency = body.frequency_penalty
|
||
if body.penalty_decay is not None:
|
||
model.penalty_decay = body.penalty_decay
|
||
if body.top_k is not None:
|
||
model.top_k = body.top_k
|
||
if body.global_penalty is not None:
|
||
model.global_penalty = body.global_penalty
|
||
|
||
|
||
def get_rwkv_config(model: AbstractRWKV) -> ModelConfigBody:
|
||
return ModelConfigBody(
|
||
max_tokens=model.max_tokens_per_generation,
|
||
temperature=model.temperature,
|
||
top_p=model.top_p,
|
||
presence_penalty=model.penalty_alpha_presence,
|
||
frequency_penalty=model.penalty_alpha_frequency,
|
||
penalty_decay=model.penalty_decay,
|
||
top_k=model.top_k,
|
||
global_penalty=model.global_penalty,
|
||
)
|