Format rwkv/RWKV-v7/rwkv_v7_demo.py
This commit is contained in:
Colin
parent
002f132818
commit
4f18296e40
|
|
@ -5,7 +5,9 @@
|
|||
import torch, types, os, gc, math, json
|
||||
import numpy as np
|
||||
import torch.nn as nn
|
||||
from torch.nn import Module
|
||||
from torch.nn import functional as F
|
||||
|
||||
np.set_printoptions(precision=4, suppress=True, linewidth=200)
|
||||
torch.backends.cudnn.benchmark = True
|
||||
torch.backends.cudnn.allow_tf32 = True
|
||||
|
|
@ -14,9 +16,9 @@ torch.backends.cuda.matmul.allow_tf32 = True
|
|||
# torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = True
|
||||
torch._C._jit_set_autocast_mode(False)
|
||||
|
||||
'''
|
||||
"""
|
||||
This will load RWKV-7 "Goose" x070 and inference in GPT-mode (slower than RNN-mode for autoregressive generation)
|
||||
'''
|
||||
"""
|
||||
|
||||
args = types.SimpleNamespace()
|
||||
|
||||
|
|
@ -42,28 +44,26 @@ HEAD_SIZE = args.head_size_a
|
|||
|
||||
USE_CUDA_KERNEL = True # False => UNOPTIMIZED, VERY SLOW
|
||||
|
||||
MyModule = torch.jit.ScriptModule
|
||||
MyFunction = torch.jit.script_method
|
||||
MyStatic = torch.jit.script
|
||||
|
||||
########################################################################################################
|
||||
# RWKV Tokenizer (slow version)
|
||||
########################################################################################################
|
||||
|
||||
class RWKV_TOKENIZER():
|
||||
|
||||
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(' ')])
|
||||
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(' '):])
|
||||
assert len(x) == int(l[l.rindex(" ") :])
|
||||
sorted += [x]
|
||||
self.idx2token[idx] = x
|
||||
|
||||
|
|
@ -107,25 +107,26 @@ class RWKV_TOKENIZER():
|
|||
return tokens
|
||||
|
||||
def decodeBytes(self, tokens):
|
||||
return b''.join(map(lambda i: self.idx2token[i], 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')
|
||||
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')
|
||||
s = s.decode("utf-8")
|
||||
except:
|
||||
pass
|
||||
print(f'{repr(s)}{i}', end=' ')
|
||||
print(f"{repr(s)}{i}", end=" ")
|
||||
# print(repr(s), i)
|
||||
print()
|
||||
|
||||
|
||||
tokenizer = RWKV_TOKENIZER("rwkv_vocab_v20230424.txt")
|
||||
|
||||
########################################################################################################
|
||||
|
|
@ -136,8 +137,21 @@ if USE_CUDA_KERNEL:
|
|||
|
||||
from torch.utils.cpp_extension import load
|
||||
|
||||
load(name="wkv7", sources=["cuda/wkv7_op.cpp", f"cuda/wkv7.cu"], is_python_module=False,
|
||||
verbose=True, extra_cuda_cflags=["-res-usage", "--use_fast_math", "-O3", "-Xptxas -O3", "--extra-device-vectorization", f"-D_N_={HEAD_SIZE}"])
|
||||
load(
|
||||
name="wkv7",
|
||||
sources=["cuda/wkv7_op.cpp", f"cuda/wkv7.cu"],
|
||||
is_python_module=False,
|
||||
verbose=True,
|
||||
extra_cuda_cflags=[
|
||||
"-res-usage",
|
||||
"--use_fast_math",
|
||||
"-O3",
|
||||
"-Xptxas -O3",
|
||||
"--extra-device-vectorization",
|
||||
f"-D_N_={HEAD_SIZE}",
|
||||
],
|
||||
)
|
||||
|
||||
class WKV_7(torch.autograd.Function):
|
||||
@staticmethod
|
||||
def forward(ctx, r, w, k, v, a, b):
|
||||
|
|
@ -202,11 +216,13 @@ else:
|
|||
|
||||
return out.view(B, T, C).to(dtype=DTYPE)
|
||||
|
||||
|
||||
########################################################################################################
|
||||
# RWKV TimeMix
|
||||
########################################################################################################
|
||||
|
||||
class RWKV_Tmix_x070(MyModule):
|
||||
|
||||
class RWKV_Tmix_x070(Module):
|
||||
def __init__(self, args, layer_id):
|
||||
super().__init__()
|
||||
self.args = args
|
||||
|
|
@ -253,7 +269,6 @@ class RWKV_Tmix_x070(MyModule):
|
|||
self.output = nn.Linear(C, C, bias=False)
|
||||
self.ln_x = nn.GroupNorm(H, C, eps=64e-5) # !!! notice eps value !!!
|
||||
|
||||
@MyFunction
|
||||
def forward(self, x, v_first):
|
||||
B, T, C = x.size()
|
||||
H = self.n_head
|
||||
|
|
@ -284,15 +299,19 @@ class RWKV_Tmix_x070(MyModule):
|
|||
x = RWKV7_OP(r, w, k, v, -kk, kk * a)
|
||||
x = self.ln_x(x.view(B * T, C)).view(B, T, C)
|
||||
|
||||
x = x + ((r.view(B,T,H,-1)*k.view(B,T,H,-1)*self.r_k).sum(dim=-1, keepdim=True) * v.view(B,T,H,-1)).view(B,T,C)
|
||||
x = x + (
|
||||
(r.view(B, T, H, -1) * k.view(B, T, H, -1) * self.r_k).sum(dim=-1, keepdim=True) * v.view(B, T, H, -1)
|
||||
).view(B, T, C)
|
||||
x = self.output(x * g)
|
||||
return x, v_first
|
||||
|
||||
|
||||
########################################################################################################
|
||||
# RWKV ChannelMix
|
||||
########################################################################################################
|
||||
|
||||
class RWKV_CMix_x070(MyModule):
|
||||
|
||||
class RWKV_CMix_x070(Module):
|
||||
def __init__(self, args, layer_id):
|
||||
super().__init__()
|
||||
self.args = args
|
||||
|
|
@ -305,7 +324,6 @@ class RWKV_CMix_x070(MyModule):
|
|||
self.key = nn.Linear(args.n_embd, args.dim_ffn, bias=False)
|
||||
self.value = nn.Linear(args.dim_ffn, args.n_embd, bias=False)
|
||||
|
||||
@MyFunction
|
||||
def forward(self, x):
|
||||
xx = self.time_shift(x) - x
|
||||
|
||||
|
|
@ -313,11 +331,13 @@ class RWKV_CMix_x070(MyModule):
|
|||
k = torch.relu(self.key(k)) ** 2
|
||||
return self.value(k)
|
||||
|
||||
|
||||
########################################################################################################
|
||||
# RWKV Block
|
||||
########################################################################################################
|
||||
|
||||
class Block(MyModule):
|
||||
|
||||
class Block(Module):
|
||||
def __init__(self, args, layer_id):
|
||||
super().__init__()
|
||||
self.args = args
|
||||
|
|
@ -330,7 +350,6 @@ class Block(MyModule):
|
|||
self.att = RWKV_Tmix_x070(args, layer_id)
|
||||
self.ffn = RWKV_CMix_x070(args, layer_id)
|
||||
|
||||
@MyFunction
|
||||
def forward(self, x, v_first):
|
||||
|
||||
if self.layer_id == 0:
|
||||
|
|
@ -342,10 +361,12 @@ class Block(MyModule):
|
|||
|
||||
return x, v_first
|
||||
|
||||
|
||||
########################################################################################################
|
||||
# RWKV Model
|
||||
########################################################################################################
|
||||
|
||||
|
||||
class RWKV(nn.Module):
|
||||
def __init__(self, args):
|
||||
super().__init__()
|
||||
|
|
@ -371,6 +392,7 @@ class RWKV(nn.Module):
|
|||
|
||||
return x
|
||||
|
||||
|
||||
########################################################################################################
|
||||
# RWKV Inference
|
||||
########################################################################################################
|
||||
|
|
@ -386,32 +408,32 @@ with torch.no_grad():
|
|||
|
||||
prompt = "中国的首都是在"
|
||||
input = tokenizer.encode(prompt)
|
||||
print(f'\nInput:\n{input}')
|
||||
print(f"\nInput:\n{input}")
|
||||
|
||||
out = model.forward(torch.tensor(input).reshape(1, -1).cuda())
|
||||
print(f'\nOutput:\n{out}')
|
||||
print(f"\nOutput:\n{out}")
|
||||
|
||||
# logits of the last token => prediction for the next token
|
||||
out = out[0, -1]
|
||||
|
||||
probs = F.softmax(out.float(), dim=-1) # compute softmax in float (more accurate)
|
||||
|
||||
print(f'\n{prompt}')
|
||||
print(f"\n{prompt}")
|
||||
|
||||
_, indices = torch.topk(probs, 10) # print top-10 possibilities
|
||||
for i in range(len(indices)):
|
||||
token_id = indices[i].item()
|
||||
token = tokenizer.decode([token_id])
|
||||
token_prob = probs[token_id].item()
|
||||
print(token, f'[probability {token_prob:.2%}]')
|
||||
print(token, f"[probability {token_prob:.2%}]")
|
||||
|
||||
########################################################################################################
|
||||
|
||||
with open(f"misc/lambada_test.jsonl", "r", encoding="utf-8") as f:
|
||||
todo = [json.loads(line) for line in f]
|
||||
todo = [[doc['text'].rsplit(' ', 1)[0], " " + doc['text'].rsplit(' ', 1)[1]] for doc in todo]
|
||||
todo = [[doc["text"].rsplit(" ", 1)[0], " " + doc["text"].rsplit(" ", 1)[1]] for doc in todo]
|
||||
|
||||
print('\nCheck LAMBADA...')
|
||||
print("\nCheck LAMBADA...")
|
||||
xsum = 0
|
||||
xcnt = 0
|
||||
xacc = 0
|
||||
|
|
@ -433,4 +455,4 @@ with torch.no_grad():
|
|||
xsum += logits
|
||||
xacc += 1 if correct else 0
|
||||
if xcnt % 100 == 0 or xcnt == len(todo):
|
||||
print(xcnt, 'ppl', round(math.exp(-xsum / xcnt), 2), 'acc', round(xacc/xcnt*100, 2))
|
||||
print(xcnt, "ppl", round(math.exp(-xsum / xcnt), 2), "acc", round(xacc / xcnt * 100, 2))
|
||||
|
|
|
|||
Loading…
Reference in New Issue