Format rwkv/RWKV-v7/rwkv_v7_demo.py
This commit is contained in:
Colin
parent
002f132818
commit
4f18296e40
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@ -5,7 +5,9 @@
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import torch, types, os, gc, math, json
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import numpy as np
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import torch.nn as nn
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from torch.nn import Module
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from torch.nn import functional as F
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np.set_printoptions(precision=4, suppress=True, linewidth=200)
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torch.backends.cudnn.benchmark = True
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torch.backends.cudnn.allow_tf32 = True
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@ -14,9 +16,9 @@ torch.backends.cuda.matmul.allow_tf32 = True
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# torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = True
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torch._C._jit_set_autocast_mode(False)
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'''
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"""
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This will load RWKV-7 "Goose" x070 and inference in GPT-mode (slower than RNN-mode for autoregressive generation)
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'''
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"""
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args = types.SimpleNamespace()
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@ -35,35 +37,33 @@ D_GATE_LORA = 128
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args.vocab_size = 65536
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# DTYPE = torch.bfloat16
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DTYPE = torch.half # better
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DTYPE = torch.half # better
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args.head_size_a = 64 # don't change
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args.head_size_a = 64 # don't change
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HEAD_SIZE = args.head_size_a
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USE_CUDA_KERNEL = True # False => UNOPTIMIZED, VERY SLOW
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MyModule = torch.jit.ScriptModule
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MyFunction = torch.jit.script_method
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MyStatic = torch.jit.script
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USE_CUDA_KERNEL = True # False => UNOPTIMIZED, VERY SLOW
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########################################################################################################
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# RWKV Tokenizer (slow version)
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########################################################################################################
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class RWKV_TOKENIZER():
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class RWKV_TOKENIZER:
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table: list[list[list[bytes]]]
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good: list[set[int]]
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wlen: list[int]
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def __init__(self, file_name):
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self.idx2token = {}
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sorted = [] # must be already sorted
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sorted = [] # must be already sorted
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lines = open(file_name, "r", encoding="utf-8").readlines()
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for l in lines:
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idx = int(l[:l.index(' ')])
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x = eval(l[l.index(' '):l.rindex(' ')])
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idx = int(l[: l.index(" ")])
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x = eval(l[l.index(" ") : l.rindex(" ")])
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x = x.encode("utf-8") if isinstance(x, str) else x
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assert isinstance(x, bytes)
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assert len(x) == int(l[l.rindex(' '):])
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assert len(x) == int(l[l.rindex(" ") :])
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sorted += [x]
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self.idx2token[idx] = x
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@ -76,7 +76,7 @@ class RWKV_TOKENIZER():
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self.good = [set() for i in range(256)]
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self.wlen = [0 for i in range(256)]
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for i in reversed(range(len(sorted))): # reverse order - match longer tokens first
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for i in reversed(range(len(sorted))): # reverse order - match longer tokens first
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s = sorted[i]
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if len(s) >= 2:
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s0 = int(s[0])
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@ -107,25 +107,26 @@ class RWKV_TOKENIZER():
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return tokens
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def decodeBytes(self, tokens):
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return b''.join(map(lambda i: self.idx2token[i], tokens))
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return b"".join(map(lambda i: self.idx2token[i], tokens))
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def encode(self, src: str):
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return self.encodeBytes(src.encode("utf-8"))
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def decode(self, tokens):
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return self.decodeBytes(tokens).decode('utf-8')
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return self.decodeBytes(tokens).decode("utf-8")
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def printTokens(self, tokens):
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for i in tokens:
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s = self.idx2token[i]
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try:
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s = s.decode('utf-8')
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s = s.decode("utf-8")
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except:
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pass
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print(f'{repr(s)}{i}', end=' ')
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print(f"{repr(s)}{i}", end=" ")
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# print(repr(s), i)
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print()
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tokenizer = RWKV_TOKENIZER("rwkv_vocab_v20230424.txt")
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########################################################################################################
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@ -136,8 +137,21 @@ if USE_CUDA_KERNEL:
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from torch.utils.cpp_extension import load
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load(name="wkv7", sources=["cuda/wkv7_op.cpp", f"cuda/wkv7.cu"], is_python_module=False,
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verbose=True, extra_cuda_cflags=["-res-usage", "--use_fast_math", "-O3", "-Xptxas -O3", "--extra-device-vectorization", f"-D_N_={HEAD_SIZE}"])
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load(
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name="wkv7",
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sources=["cuda/wkv7_op.cpp", f"cuda/wkv7.cu"],
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is_python_module=False,
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verbose=True,
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extra_cuda_cflags=[
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"-res-usage",
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"--use_fast_math",
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"-O3",
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"-Xptxas -O3",
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"--extra-device-vectorization",
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f"-D_N_={HEAD_SIZE}",
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],
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)
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class WKV_7(torch.autograd.Function):
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@staticmethod
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def forward(ctx, r, w, k, v, a, b):
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@ -186,7 +200,7 @@ else:
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vv = v[:, t, :].view(B, H, N, 1)
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aa = a[:, t, :].view(B, H, N, 1)
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bb = b[:, t, :].view(B, H, 1, N)
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state = state * w[: , t, :, None, :] + state @ aa @ bb + vv @ kk
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state = state * w[:, t, :, None, :] + state @ aa @ bb + vv @ kk
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out[:, t, :] = (state @ rr).view(B, H, N)
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# another method using einsum
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@ -202,11 +216,13 @@ else:
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return out.view(B, T, C).to(dtype=DTYPE)
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########################################################################################################
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# RWKV TimeMix
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########################################################################################################
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class RWKV_Tmix_x070(MyModule):
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class RWKV_Tmix_x070(Module):
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def __init__(self, args, layer_id):
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super().__init__()
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self.args = args
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@ -220,40 +236,39 @@ class RWKV_Tmix_x070(MyModule):
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N = self.head_size
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C = args.n_embd
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self.x_r = nn.Parameter(torch.empty(1,1,C))
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self.x_w = nn.Parameter(torch.empty(1,1,C))
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self.x_k = nn.Parameter(torch.empty(1,1,C))
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self.x_v = nn.Parameter(torch.empty(1,1,C))
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self.x_a = nn.Parameter(torch.empty(1,1,C))
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self.x_g = nn.Parameter(torch.empty(1,1,C))
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self.x_r = nn.Parameter(torch.empty(1, 1, C))
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self.x_w = nn.Parameter(torch.empty(1, 1, C))
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self.x_k = nn.Parameter(torch.empty(1, 1, C))
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self.x_v = nn.Parameter(torch.empty(1, 1, C))
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self.x_a = nn.Parameter(torch.empty(1, 1, C))
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self.x_g = nn.Parameter(torch.empty(1, 1, C))
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self.w0 = nn.Parameter(torch.empty(1,1,C))
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self.w0 = nn.Parameter(torch.empty(1, 1, C))
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self.w1 = nn.Parameter(torch.empty(C, D_DECAY_LORA))
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self.w2 = nn.Parameter(torch.empty(D_DECAY_LORA, C))
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self.a0 = nn.Parameter(torch.empty(1,1,C))
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self.a0 = nn.Parameter(torch.empty(1, 1, C))
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self.a1 = nn.Parameter(torch.empty(C, D_AAA_LORA))
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self.a2 = nn.Parameter(torch.empty(D_AAA_LORA, C))
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self.v0 = nn.Parameter(torch.empty(1,1,C))
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self.v0 = nn.Parameter(torch.empty(1, 1, C))
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self.v1 = nn.Parameter(torch.empty(C, D_MV_LORA))
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self.v2 = nn.Parameter(torch.empty(D_MV_LORA, C))
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self.g1 = nn.Parameter(torch.empty(C, D_GATE_LORA))
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self.g2 = nn.Parameter(torch.empty(D_GATE_LORA, C))
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self.k_k = nn.Parameter(torch.empty(1,1,C))
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self.k_a = nn.Parameter(torch.empty(1,1,C))
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self.r_k = nn.Parameter(torch.empty(H,N))
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self.k_k = nn.Parameter(torch.empty(1, 1, C))
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self.k_a = nn.Parameter(torch.empty(1, 1, C))
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self.r_k = nn.Parameter(torch.empty(H, N))
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self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
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self.receptance = nn.Linear(C, C, bias=False)
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self.key = nn.Linear(C, C, bias=False)
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self.value = nn.Linear(C, C, bias=False)
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self.output = nn.Linear(C, C, bias=False)
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self.ln_x = nn.GroupNorm(H, C, eps=64e-5) # !!! notice eps value !!!
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self.ln_x = nn.GroupNorm(H, C, eps=64e-5) # !!! notice eps value !!!
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@MyFunction
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def forward(self, x, v_first):
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B, T, C = x.size()
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H = self.n_head
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@ -267,32 +282,36 @@ class RWKV_Tmix_x070(MyModule):
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xg = x + xx * self.x_g
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r = self.receptance(xr)
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w = -F.softplus(-(self.w0 + torch.tanh(xw @ self.w1) @ self.w2)) - 0.5 # soft-clamp to (-inf, -0.5)
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w = -F.softplus(-(self.w0 + torch.tanh(xw @ self.w1) @ self.w2)) - 0.5 # soft-clamp to (-inf, -0.5)
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k = self.key(xk)
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v = self.value(xv)
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if self.layer_id == 0:
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v_first = v # store the v of the first layer
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v_first = v # store the v of the first layer
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else:
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v = v + (v_first - v) * torch.sigmoid(self.v0 + (xv @ self.v1) @ self.v2) # add value residual
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a = torch.sigmoid(self.a0 + (xa @ self.a1) @ self.a2) # a is "in-context learning rate"
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v = v + (v_first - v) * torch.sigmoid(self.v0 + (xv @ self.v1) @ self.v2) # add value residual
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a = torch.sigmoid(self.a0 + (xa @ self.a1) @ self.a2) # a is "in-context learning rate"
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g = torch.sigmoid(xg @ self.g1) @ self.g2
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kk = k * self.k_k
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kk = F.normalize(kk.view(B,T,H,-1), dim=-1, p=2.0).view(B,T,C)
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k = k * (1 + (a-1) * self.k_a)
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kk = F.normalize(kk.view(B, T, H, -1), dim=-1, p=2.0).view(B, T, C)
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k = k * (1 + (a - 1) * self.k_a)
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x = RWKV7_OP(r, w, k, v, -kk, kk*a)
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x = RWKV7_OP(r, w, k, v, -kk, kk * a)
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x = self.ln_x(x.view(B * T, C)).view(B, T, C)
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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)
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x = x + (
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(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)
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).view(B, T, C)
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x = self.output(x * g)
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return x, v_first
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########################################################################################################
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# RWKV ChannelMix
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########################################################################################################
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class RWKV_CMix_x070(MyModule):
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class RWKV_CMix_x070(Module):
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def __init__(self, args, layer_id):
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super().__init__()
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self.args = args
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@ -305,32 +324,32 @@ class RWKV_CMix_x070(MyModule):
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self.key = nn.Linear(args.n_embd, args.dim_ffn, bias=False)
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self.value = nn.Linear(args.dim_ffn, args.n_embd, bias=False)
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@MyFunction
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def forward(self, x):
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xx = self.time_shift(x) - x
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k = x + xx * self.x_k
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k = torch.relu(self.key(k)) ** 2
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return self.value(k)
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########################################################################################################
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# RWKV Block
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########################################################################################################
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class Block(MyModule):
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class Block(Module):
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def __init__(self, args, layer_id):
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super().__init__()
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self.args = args
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self.layer_id = layer_id
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self.ln0 = nn.LayerNorm(args.n_embd) # only used in block 0, should be fused with emb
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self.ln0 = nn.LayerNorm(args.n_embd) # only used in block 0, should be fused with emb
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self.ln1 = nn.LayerNorm(args.n_embd)
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self.ln2 = nn.LayerNorm(args.n_embd)
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self.att = RWKV_Tmix_x070(args, layer_id)
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self.ffn = RWKV_CMix_x070(args, layer_id)
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@MyFunction
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def forward(self, x, v_first):
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if self.layer_id == 0:
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@ -342,10 +361,12 @@ class Block(MyModule):
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return x, v_first
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########################################################################################################
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# RWKV Model
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########################################################################################################
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class RWKV(nn.Module):
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def __init__(self, args):
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super().__init__()
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@ -371,6 +392,7 @@ class RWKV(nn.Module):
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return x
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########################################################################################################
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# RWKV Inference
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########################################################################################################
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@ -380,38 +402,38 @@ model_params = torch.load(MODEL_PATH, map_location="cpu")
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with torch.no_grad():
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model = RWKV(args).to(dtype=DTYPE).cuda()
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model.load_state_dict(model_params, strict=False) # we will ignore blocks.0.att.v0/v1/v2
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model.load_state_dict(model_params, strict=False) # we will ignore blocks.0.att.v0/v1/v2
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########################################################################################################
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prompt = "中国的首都是在"
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input = tokenizer.encode(prompt)
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print(f'\nInput:\n{input}')
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print(f"\nInput:\n{input}")
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out = model.forward(torch.tensor(input).reshape(1,-1).cuda())
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print(f'\nOutput:\n{out}')
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out = model.forward(torch.tensor(input).reshape(1, -1).cuda())
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print(f"\nOutput:\n{out}")
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# logits of the last token => prediction for the next token
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# logits of the last token => prediction for the next token
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out = out[0, -1]
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probs = F.softmax(out.float(), dim=-1) # compute softmax in float (more accurate)
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print(f'\n{prompt}')
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probs = F.softmax(out.float(), dim=-1) # compute softmax in float (more accurate)
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_, indices = torch.topk(probs, 10) # print top-10 possibilities
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print(f"\n{prompt}")
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_, indices = torch.topk(probs, 10) # print top-10 possibilities
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for i in range(len(indices)):
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token_id = indices[i].item()
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token = tokenizer.decode([token_id])
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token_prob = probs[token_id].item()
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print(token, f'[probability {token_prob:.2%}]')
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print(token, f"[probability {token_prob:.2%}]")
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########################################################################################################
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with open(f"misc/lambada_test.jsonl", "r", encoding="utf-8") as f:
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todo = [json.loads(line) for line in f]
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todo = [[doc['text'].rsplit(' ', 1)[0], " " + doc['text'].rsplit(' ', 1)[1]] for doc in todo]
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todo = [[doc["text"].rsplit(" ", 1)[0], " " + doc["text"].rsplit(" ", 1)[1]] for doc in todo]
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print('\nCheck LAMBADA...')
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print("\nCheck LAMBADA...")
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xsum = 0
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xcnt = 0
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xacc = 0
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@ -421,9 +443,9 @@ with torch.no_grad():
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logits = 0
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correct = True
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out = model.forward(torch.tensor(src+dst).reshape(1,-1).cuda())
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out = model.forward(torch.tensor(src + dst).reshape(1, -1).cuda())
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for i in range(len(dst)):
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ooo = out[0,len(src)-1+i].float()
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ooo = out[0, len(src) - 1 + i].float()
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probs = F.softmax(ooo, dim=-1)
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logits += math.log(probs[dst[i]])
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if torch.argmax(probs).item() != dst[i]:
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@ -433,4 +455,4 @@ with torch.no_grad():
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xsum += logits
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xacc += 1 if correct else 0
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if xcnt % 100 == 0 or xcnt == len(todo):
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print(xcnt, 'ppl', round(math.exp(-xsum / xcnt), 2), 'acc', round(xacc/xcnt*100, 2))
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print(xcnt, "ppl", round(math.exp(-xsum / xcnt), 2), "acc", round(xacc / xcnt * 100, 2))
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