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########################################################################################################
# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
########################################################################################################
import types, math, os, gc
import torch
from torch.nn import functional as F
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.allow_tf32 = True
torch.backends.cuda.matmul.allow_tf32 = True
MyModule = torch.nn.Module
def __nop(ob):
return ob
MyFunction = __nop
if int(os.environ["RWKV_JIT_ON"]) > 0:
MyModule = torch.jit.ScriptModule
MyFunction = torch.jit.script_method
print(f'\nRWKV_JIT_ON {os.environ["RWKV_JIT_ON"]}\n')
RWKV_RESCALE_LAYER = 6 # set x = x/2 every X layer (to avoid FP16 overflow)
############################################################################################################
class RWKV_RNN(MyModule):
def __init__(self, args):
super().__init__()
self.args = args
if args.FLOAT_MODE == 'fp32':
self.FLOAT_MODE = torch.float
elif args.FLOAT_MODE == 'fp16':
self.FLOAT_MODE = torch.half
elif args.FLOAT_MODE == 'bf16':
self.FLOAT_MODE = torch.bfloat16
self.RUN_DEVICE = args.RUN_DEVICE
with torch.no_grad():
w = torch.load(args.MODEL_NAME + '.pth', map_location='cpu')
args.n_embd = w['emb.weight'].shape[1]
args.n_layer = 0
keys = list(w.keys()) # refine weights and send to correct device
print_need_newline = False
for x in keys:
w[x].requires_grad = False
if x == 'emb.weight' or 'ln0' in x:
continue
block_id = int(x.split('.')[1]) if ('blocks.' in x) else 0
args.n_layer = max(args.n_layer, block_id + 1)
if '.time_' in x:
w[x] = w[x].squeeze()
if 'key.weight' in x or 'value.weight' in x or 'receptance.weight' in x or 'output.weight' in x:
w[x] = w[x].t()
if '.time_decay' in x:
w[x] = w[x].float()
w[x] = -torch.exp(w[x])
elif '.time_first' in x:
w[x] = w[x].float()
else:
w[x] = w[x].to(dtype=self.FLOAT_MODE)
if args.FLOAT_MODE == 'fp16':
if 'att.output.weight' in x:
w[x] = w[x] / (2 ** int(block_id // RWKV_RESCALE_LAYER))
if 'ffn.value.weight' in x:
w[x] = w[x] / (2 ** int(block_id // RWKV_RESCALE_LAYER))
if args.RUN_DEVICE == 'cuda':
w[x] = w[x].cuda()
shape = w[x].shape
shape = [i for i in shape if i != 1]
if len(shape) > 1:
shape = f" {str(shape[0]).rjust(5)} {str(shape[1]).rjust(5)}"
else:
shape = f" {str(shape[0]).rjust(5)} "
if block_id == 0:
if print_need_newline:
print('\n', end='')
print_need_newline = False
print(x.ljust(32), str(w[x].dtype).replace('torch.', '').ljust(10), w[x].device, shape)
else:
print_need_newline = True
print('.', end='', flush=True)
print(f'\nn_layer {args.n_layer} n_embd {args.n_embd} ctx_len {args.ctx_len}')
keys = list(w.keys()) # store weights in self.w
self.w = types.SimpleNamespace()
for x in keys:
xx = x.split('.')
here = self.w
for i in range(len(xx)):
if xx[i].isdigit():
ii = int(xx[i])
if ii not in here:
here[ii] = types.SimpleNamespace()
here = here[ii]
else:
if i == len(xx) - 1:
setattr(here, xx[i], w[x])
elif not hasattr(here, xx[i]):
if xx[i + 1].isdigit():
setattr(here, xx[i], {})
else:
setattr(here, xx[i], types.SimpleNamespace())
here = getattr(here, xx[i])
with torch.no_grad(): # precompute embedding
try:
x = self.LN(self.w.emb.weight, self.w.blocks[0].ln0)
except:
x = F.layer_norm(self.w.emb.weight.float(), (self.args.n_embd,),
weight=self.w.blocks[0].ln0.weight.float(), bias=self.w.blocks[0].ln0.bias.float())
self.w.emb.weight = x.to(dtype=self.FLOAT_MODE)
self.eval()
gc.collect()
torch.cuda.empty_cache()
def LN(self, x, w):
return F.layer_norm(x, (self.args.n_embd,), weight=w.weight, bias=w.bias)
# state[] 0=ffn_xx 1=att_xx 2=att_aa 3=att_bb 4=att_pp
@MyFunction
def FF_one(self, x, state, i: int, time_mix_k, time_mix_r, kw, vw, rw):
xx = state[5 * i + 0].to(dtype=self.FLOAT_MODE)
xk = x * time_mix_k + xx * (1 - time_mix_k)
xr = x * time_mix_r + xx * (1 - time_mix_r)
state[5 * i + 0] = x.float()
r = torch.sigmoid(xr @ rw)
k = torch.square(torch.relu(xk @ kw))
kv = k @ vw
return r * kv
@MyFunction
def FF_seq(self, x, state, i: int, time_mix_k, time_mix_r, kw, vw, rw):
xx = torch.cat((state[5 * i + 0].to(dtype=self.FLOAT_MODE).unsqueeze(0), x[:-1, :]))
xk = x * time_mix_k + xx * (1 - time_mix_k)
xr = x * time_mix_r + xx * (1 - time_mix_r)
state[5 * i + 0] = x[-1, :].float()
r = torch.sigmoid(xr @ rw)
k = torch.square(torch.relu(xk @ kw))
kv = k @ vw
return r * kv
@MyFunction
def SA_one(self, x, state, i: int, time_mix_k, time_mix_v, time_mix_r, time_first, time_decay, kw, vw, rw, ow):
xx = state[5 * i + 1].to(dtype=self.FLOAT_MODE)
xk = x * time_mix_k + xx * (1 - time_mix_k)
xv = x * time_mix_v + xx * (1 - time_mix_v)
xr = x * time_mix_r + xx * (1 - time_mix_r)
state[5 * i + 1] = x.float()
r = torch.sigmoid(xr @ rw)
k = (xk @ kw).float()
v = (xv @ vw).float()
aa = state[5 * i + 2]
bb = state[5 * i + 3]
pp = state[5 * i + 4]
ww = time_first + k
p = torch.maximum(pp, ww)
e1 = torch.exp(pp - p)
e2 = torch.exp(ww - p)
a = e1 * aa + e2 * v
b = e1 * bb + e2
ww = pp + time_decay
p = torch.maximum(ww, k)
e1 = torch.exp(ww - p)
e2 = torch.exp(k - p)
state[5 * i + 2] = e1 * aa + e2 * v
state[5 * i + 3] = e1 * bb + e2
state[5 * i + 4] = p
wkv = (a / b).to(dtype=self.FLOAT_MODE)
return (r * wkv) @ ow
@MyFunction
def SA_seq(self, x, state, i: int, time_mix_k, time_mix_v, time_mix_r, time_first, time_decay, kw, vw, rw, ow):
xx = torch.cat((state[5 * i + 1].to(dtype=self.FLOAT_MODE).unsqueeze(0), x[:-1, :]))
xk = x * time_mix_k + xx * (1 - time_mix_k)
xv = x * time_mix_v + xx * (1 - time_mix_v)
xr = x * time_mix_r + xx * (1 - time_mix_r)
state[5 * i + 1] = x[-1, :].float()
r = torch.sigmoid(xr @ rw)
k = (xk @ kw).float()
v = (xv @ vw).float()
aa = state[5 * i + 2]
bb = state[5 * i + 3]
pp = state[5 * i + 4]
T = x.shape[0]
for t in range(T):
ww = time_first + k[t]
p = torch.maximum(pp, ww)
e1 = torch.exp(pp - p)
e2 = torch.exp(ww - p)
a = e1 * aa + e2 * v[t]
b = e1 * bb + e2
ww = pp + time_decay
p = torch.maximum(ww, k[t])
e1 = torch.exp(ww - p)
e2 = torch.exp(k[t] - p)
if t != T - 1:
aa = e1 * aa + e2 * v[t]
bb = e1 * bb + e2
pp = p
else:
state[5 * i + 2] = e1 * aa + e2 * v[t]
state[5 * i + 3] = e1 * bb + e2
state[5 * i + 4] = p
xx[t] = (a / b).to(dtype=self.FLOAT_MODE)
return (r * xx) @ ow
def forward(self, tokens, state, preprocess_only=False):
with torch.no_grad():
w = self.w
args = self.args
seq_mode = len(tokens) > 1
x = w.emb.weight[tokens] if seq_mode else w.emb.weight[tokens[-1]]
if self.RUN_DEVICE == 'cuda':
x = x.cuda()
if state == None:
state = torch.zeros(args.n_layer * 5, args.n_embd, device=self.RUN_DEVICE)
for i in range(args.n_layer):
state[5 * i + 4] -= 1e30
SA = self.SA_seq if seq_mode else self.SA_one
FF = self.FF_seq if seq_mode else self.FF_one
for i in range(args.n_layer):
ww = w.blocks[i].att
x = x + SA(self.LN(x, w.blocks[i].ln1), state, i,
ww.time_mix_k, ww.time_mix_v, ww.time_mix_r, ww.time_first, ww.time_decay,
ww.key.weight, ww.value.weight, ww.receptance.weight, ww.output.weight)
ww = w.blocks[i].ffn
x = x + FF(self.LN(x, w.blocks[i].ln2), state, i,
ww.time_mix_k, ww.time_mix_r,
ww.key.weight, ww.value.weight, ww.receptance.weight)
if args.FLOAT_MODE == 'fp16':
if (i + 1) % RWKV_RESCALE_LAYER == 0:
x = x / 2
if preprocess_only:
return state
x = self.LN(x[-1, :], w.ln_out) if seq_mode else self.LN(x, w.ln_out)
x = w.head.weight @ x
return x.float(), state
