forked from BlinkDL/ChatRWKV
-
Notifications
You must be signed in to change notification settings - Fork 0
/
RWKV_in_150_lines.py
153 lines (132 loc) · 6.28 KB
/
RWKV_in_150_lines.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
########################################################################################################
# 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
from torch.nn import functional as F
from tokenizers import Tokenizer
tokenizer = Tokenizer.from_file("20B_tokenizer.json")
args = types.SimpleNamespace()
args.MODEL_NAME = '/fsx/BlinkDL/HF-MODEL/rwkv-4-pile-430m/RWKV-4-Pile-430M-20220808-8066'
args.n_layer = 24
args.n_embd = 1024
context = "\nIn a shocking finding, scientist discovered a herd of dragons living in a remote, previously unexplored valley, in Tibet. Even more surprising to the researchers was the fact that the dragons spoke perfect Chinese."
NUM_TRIALS = 3
LENGTH_PER_TRIAL = 100
TEMPERATURE = 1.0
TOP_P = 0.85
########################################################################################################
class RWKV_RNN(torch.jit.ScriptModule):
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():
if '.time_' in k: w[k] = w[k].squeeze()
if '.time_decay' in k: w[k] = -torch.exp(w[k].float()) # the real time decay is like e^{-e^x}
else: w[k] = w[k].float() # convert to f32 type
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)
@torch.jit.script_method
def channel_mixing(self, x, state, i:int, time_mix_k, time_mix_r, kw, vw, rw):
xk = x * time_mix_k + state[5*i+0] * (1 - time_mix_k)
xr = x * time_mix_r + state[5*i+0] * (1 - time_mix_r)
state[5*i+0] = x
r = torch.sigmoid(rw @ xr)
k = torch.square(torch.relu(kw @ xk)) # square relu, primer paper
return r * (vw @ k)
@torch.jit.script_method
def time_mixing(self, x, state, i:int, time_mix_k, time_mix_v, time_mix_r, time_first, time_decay, kw, vw, rw, ow):
xk = x * time_mix_k + state[5*i+1] * (1 - time_mix_k)
xv = x * time_mix_v + state[5*i+1] * (1 - time_mix_v)
xr = x * time_mix_r + state[5*i+1] * (1 - time_mix_r)
state[5*i+1] = x
r = torch.sigmoid(rw @ xr)
k = kw @ xk
v = vw @ xv
aa = state[5*i+2]
bb = state[5*i+3]
pp = state[5*i+4]
ww = time_first + k
qq = torch.maximum(pp, ww)
e1 = torch.exp(pp - qq)
e2 = torch.exp(ww - qq)
a = e1 * aa + e2 * v
b = e1 * bb + e2
wkv = a / b
ww = pp + time_decay
qq = torch.maximum(ww, k)
e1 = torch.exp(ww - qq)
e2 = torch.exp(k - qq)
state[5*i+2] = e1 * aa + e2 * v
state[5*i+3] = e1 * bb + e2
state[5*i+4] = qq
return ow @ (r * wkv)
def forward(self, token, state):
with torch.no_grad():
if state == None:
state = torch.zeros(self.args.n_layer * 5, self.args.n_embd)
for i in range(self.args.n_layer): state[5*i+4] = -1e30 # -infinity
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_first, att.time_decay,
att.key.weight, att.value.weight, att.receptance.weight, att.output.weight)
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
##########################################################################################################
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
########################################################################################################
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).ids:
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]
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
out, state = model.forward(token, state)
print('\n')