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RWKV_v5_demo.py
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RWKV_v5_demo.py
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########################################################################################################
# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
########################################################################################################
import numpy as np
np.set_printoptions(precision=4, suppress=True, linewidth=200)
import types, torch
import torch.nn as nn
from torch.nn import functional as F
MyModule = torch.jit.ScriptModule
MyFunction = torch.jit.script_method
class RWKV_TOKENIZER():
table: list[list[list[bytes]]]
good: list[set[int]]
wlen: list[int]
def __init__(self, file_name):
self.idx2token = {}
sorted = [] # must be already sorted
lines = open(file_name, "r", encoding="utf-8").readlines()
for l in lines:
idx = int(l[:l.index(' ')])
x = eval(l[l.index(' '):l.rindex(' ')])
x = x.encode("utf-8") if isinstance(x, str) else x
assert isinstance(x, bytes)
assert len(x) == int(l[l.rindex(' '):])
sorted += [x]
self.idx2token[idx] = x
self.token2idx = {}
for k, v in self.idx2token.items():
self.token2idx[v] = int(k)
# precompute some tables for fast matching
self.table = [[[] for j in range(256)] for i in range(256)]
self.good = [set() for i in range(256)]
self.wlen = [0 for i in range(256)]
for i in reversed(range(len(sorted))): # reverse order - match longer tokens first
s = sorted[i]
if len(s) >= 2:
s0 = int(s[0])
s1 = int(s[1])
self.table[s0][s1] += [s]
self.wlen[s0] = max(self.wlen[s0], len(s))
self.good[s0].add(s1)
def encodeBytes(self, src: bytes) -> list[int]:
src_len: int = len(src)
tokens: list[int] = []
i: int = 0
while i < src_len:
s: bytes = src[i : i + 1]
if i < src_len - 1:
s1: int = int(src[i + 1])
s0: int = int(src[i])
if s1 in self.good[s0]:
sss: bytes = src[i : i + self.wlen[s0]]
try:
s = next(filter(sss.startswith, self.table[s0][s1]))
except:
pass
tokens.append(self.token2idx[s])
i += len(s)
return tokens
def decodeBytes(self, tokens):
return b''.join(map(lambda i: self.idx2token[i], tokens))
def encode(self, src: str):
return self.encodeBytes(src.encode("utf-8"))
def decode(self, tokens):
return self.decodeBytes(tokens).decode('utf-8')
def printTokens(self, tokens):
for i in tokens:
s = self.idx2token[i]
try:
s = s.decode('utf-8')
except:
pass
print(f'{repr(s)}{i}', end=' ')
# print(repr(s), i)
print()
########################################################################################################
def sample_logits(out, temperature=1.0, top_p=0.8):
probs = F.softmax(out, dim=-1).numpy()
sorted_probs = np.sort(probs)[::-1]
cumulative_probs = np.cumsum(sorted_probs)
cutoff = float(sorted_probs[np.argmax(cumulative_probs > top_p)])
probs[probs < cutoff] = 0
if temperature != 1.0:
probs = probs.pow(1.0 / temperature)
probs = probs / np.sum(probs)
out = np.random.choice(a=len(probs), p=probs)
return out
########################################################################################################
tokenizer = RWKV_TOKENIZER("/fsx/BlinkDL/CODE/_PUBLIC_/ChatRWKV/tokenizer/rwkv_vocab_v20230424.txt")
# THIS IS NOW UPDATED TO SUPPORT LATEST RWKV-5 WORLD v2 MODELS
args = types.SimpleNamespace()
args.MODEL_NAME = '/fsx/BlinkDL/HF-MODEL/temp/RWKV-5-World-0B4-v2-OnlyForTest_71%_trained-20231104-ctx4096'
args.n_layer = 24
args.n_embd = 1024
args.vocab_size = 65536
context = "\nElon Musk has"
# context = "\n我们发现"
NUM_TRIALS = 3
LENGTH_PER_TRIAL = 100
TEMPERATURE = 1.0
TOP_P = 0.7
class RWKV_RNN(MyModule):
def __init__(self, args):
super().__init__()
self.args = args
self.eval() # set torch to inference mode
w = torch.load(args.MODEL_NAME + '.pth', map_location='cpu')
for k in w.keys():
w[k] = w[k].float() # convert to f32 type
if '.time_' in k: w[k] = w[k].squeeze()
if '.time_decay' in k: w[k] = torch.exp(-torch.exp(w[k])).unsqueeze(-1)
if '.time_faaaa' in k: w[k] = w[k].unsqueeze(-1)
self.n_head = w['blocks.0.att.time_decay'].shape[0]
self.head_size = w['blocks.0.ln1.weight'].shape[0] // self.n_head
self.w = types.SimpleNamespace() # set self.w from w
self.w.blocks = {}
for k in w.keys(): # example: "blocks.0.att.time_first" => self.w.blocks[0].att.time_first
parts = k.split('.')
last = parts.pop()
here = self.w
for p in parts:
if p.isdigit():
p = int(p)
if p not in here: here[p] = types.SimpleNamespace()
here = here[p]
else:
if not hasattr(here, p): setattr(here, p, types.SimpleNamespace())
here = getattr(here, p)
setattr(here, last, w[k])
def layer_norm(self, x, w):
return F.layer_norm(x, (self.args.n_embd,), weight=w.weight, bias=w.bias)
@MyFunction
def channel_mixing(self, x, state, i:int, time_mix_k, time_mix_r, kw, vw, rw):
i0 = (2+self.head_size)*i+0
xk = x * time_mix_k + state[i0] * (1 - time_mix_k)
xr = x * time_mix_r + state[i0] * (1 - time_mix_r)
state[i0] = x
r = torch.sigmoid(rw @ xr)
k = torch.square(torch.relu(kw @ xk)) # square relu, primer paper
return r * (vw @ k)
@MyFunction
def time_mixing(self, x, state, i:int, time_mix_k, time_mix_v, time_mix_r, time_mix_g, time_first, time_decay, kw, vw, rw, gw, ow, ln_w, ln_b):
H = self.n_head
S = self.head_size
i1 = (2+S)*i+1
xk = x * time_mix_k + state[i1] * (1 - time_mix_k)
xv = x * time_mix_v + state[i1] * (1 - time_mix_v)
xr = x * time_mix_r + state[i1] * (1 - time_mix_r)
xg = x * time_mix_g + state[i1] * (1 - time_mix_g)
state[i1] = x
r = (rw @ xr).view(H, 1, S)
k = (kw @ xk).view(H, S, 1)
v = (vw @ xv).view(H, 1, S)
g = F.silu(gw @ xg)
s = state[(2+S)*i+2:(2+S)*(i+1), :].reshape(H, S, S)
x = torch.zeros(H, S)
a = k @ v
x = r @ (time_first * a + s)
s = a + time_decay * s
state[(2+S)*i+2:(2+S)*(i+1), :] = s.reshape(S, -1)
x = x.flatten()
x = F.group_norm(x.unsqueeze(0), num_groups=H, weight=ln_w, bias=ln_b, eps = 64e-5).squeeze(0) * g # same as gn(x/8, eps=1e-5)
return ow @ x
def forward(self, token, state):
with torch.no_grad():
if state == None:
state = torch.zeros(self.args.n_layer * (2+self.head_size), self.args.n_embd)
x = self.w.emb.weight[token]
x = self.layer_norm(x, self.w.blocks[0].ln0)
for i in range(self.args.n_layer):
att = self.w.blocks[i].att
x = x + self.time_mixing(self.layer_norm(x, self.w.blocks[i].ln1), state, i,
att.time_mix_k, att.time_mix_v, att.time_mix_r, att.time_mix_g, att.time_faaaa, att.time_decay,
att.key.weight, att.value.weight, att.receptance.weight, att.gate.weight, att.output.weight,
att.ln_x.weight, att.ln_x.bias)
ffn = self.w.blocks[i].ffn
x = x + self.channel_mixing(self.layer_norm(x, self.w.blocks[i].ln2), state, i,
ffn.time_mix_k, ffn.time_mix_r,
ffn.key.weight, ffn.value.weight, ffn.receptance.weight)
x = self.w.head.weight @ self.layer_norm(x, self.w.ln_out)
return x.float(), state
print(f'\nUsing CPU. Loading {args.MODEL_NAME} ...')
model = RWKV_RNN(args)
print(f'\nPreprocessing context (slow version. see v2/rwkv/model.py for fast version)')
init_state = None
for token in tokenizer.encode(context):
init_out, init_state = model.forward(token, init_state)
for TRIAL in range(NUM_TRIALS):
print(f'\n\n--[ Trial {TRIAL} ]-----------------', context, end="")
all_tokens = []
out_last = 0
out, state = init_out.clone(), init_state.clone()
for i in range(LENGTH_PER_TRIAL):
token = sample_logits(out, TEMPERATURE, TOP_P)
all_tokens += [token]
try:
tmp = tokenizer.decode(all_tokens[out_last:])
if '\ufffd' not in tmp: # only print when we have a valid utf-8 string
print(tmp, end="", flush=True)
out_last = i + 1
except:
pass
out, state = model.forward(token, state)
print('\n')