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model.py
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model.py
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########################################################################################################
# The RWKV v2-RNN Language Model - https://github.com/BlinkDL/RWKV-LM
########################################################################################################
from torch.utils.cpp_extension import load
import math
import numpy as np
import logging
import torch
import torch.nn as nn
from torch.nn import functional as F
logger = logging.getLogger(__name__)
########################################################################################################
# CUDA Kernel
########################################################################################################
T_MAX = 1024 # increase this if your ctx_len > 1024
B_GROUP_FORWARD = 4 # set to 8 for best performance
B_GROUP_BACKWARD = 2 # set to 2 for best performance
timex_cuda = load(name="timex", sources=["cuda/timex_op.cpp", "cuda/timex_cuda.cu"],
verbose=True, extra_cuda_cflags=['--use_fast_math', '--extra-device-vectorization', f'-DTmax={T_MAX}', f'-DBF={B_GROUP_FORWARD}', f'-DBB={B_GROUP_BACKWARD}'])
class TimeX(torch.autograd.Function):
@staticmethod
def forward(ctx, w, k, B, C, T, eps):
ctx.B = B
ctx.C = C
ctx.T = T
assert ctx.T % 4 == 0 and ctx.T <= T_MAX and ctx.B % B_GROUP_FORWARD == 0 and ctx.B % B_GROUP_BACKWARD == 0
w = w.contiguous()
k = k.contiguous()
ctx.save_for_backward(w, k)
wk = torch.empty((B, C, T), device='cuda',
memory_format=torch.contiguous_format)
timex_cuda.forward(w, k, wk, eps, B, C, T)
return wk
@staticmethod
def backward(ctx, gwk):
assert ctx.T % 4 == 0 and ctx.T <= T_MAX and ctx.B % B_GROUP_FORWARD == 0 and ctx.B % B_GROUP_BACKWARD == 0
w, k = ctx.saved_tensors
gw = torch.empty((ctx.B, ctx.C, ctx.T), device='cuda',
memory_format=torch.contiguous_format)
gk = torch.empty((ctx.B, ctx.C, ctx.T), device='cuda',
memory_format=torch.contiguous_format)
timex_cuda.backward(w, k, gwk.contiguous(), gw,
gk, ctx.B, ctx.C, ctx.T)
return (gw.sum(dim=0), gk, None, None, None, None)
########################################################################################################
# RWKV: RWKV Time-mix + RWKV Channel-mix
########################################################################################################
RWKV_K_CLAMP = 60 # e^60 = 1e26
RWKV_K_EPS = 1e-16
RWKV_HEAD_QK_DIM = 256
def RWKV_Init(module, config): # fancy initialization of all lin & emb layer in the module
for m in module.modules():
if not isinstance(m, (nn.Linear, nn.Embedding)):
continue
with torch.no_grad():
name = '[unknown weight]'
for name, parameter in module.named_parameters(): # find the name of the weight
if id(m.weight) == id(parameter):
break
shape = m.weight.data.shape
gain = 1.0
scale = 1.0 # extra scale for gain
if isinstance(m, nn.Embedding):
gain = math.sqrt(max(shape[0], shape[1]))
if shape[0] == config.vocab_size and shape[1] == config.n_embd: # token emb?
scale = 1e-4
else:
scale = 0
if isinstance(m, nn.Linear):
if m.bias is not None:
m.bias.data.zero_()
if shape[0] > shape[1]:
gain = math.sqrt(shape[0] / shape[1])
if shape[0] == config.vocab_size and shape[1] == config.n_embd: # final projection?
scale = 0.5
if hasattr(m, 'scale_init'):
scale = m.scale_init
# print(str(shape[0]).ljust(5), str(shape[1]).ljust(5), f'{round(scale,2):g}'.ljust(4), name)
gain *= scale
if scale == -999:
nn.init.eye_(m.weight)
elif gain == 0:
# zero init is great for some RWKV matrices
nn.init.zeros_(m.weight)
elif gain > 0:
nn.init.orthogonal_(m.weight, gain=gain)
else:
nn.init.normal_(m.weight, mean=0.0, std=-scale)
class RWKV_TimeMix(nn.Module):
def __init__(self, config, layer_id):
super().__init__()
self.layer_id = layer_id
self.ctx_len = config.ctx_len
self.n_embd = config.n_embd
attn_sz = config.n_embd
############# fancy init of time_w curves ###################################
f1_begin = 3.0
f1_end = 1.2
f2_begin = 0.65
f2_end = 0.4
with torch.no_grad(): # initial time_w curves for better convergence
decay_speed = torch.ones(attn_sz, 1)
first_sa_layer_id = 1
for h in range(attn_sz):
f1 = f1_begin + (layer_id-first_sa_layer_id) / \
(config.n_layer-1-first_sa_layer_id) * (f1_end - f1_begin)
f2 = f2_begin + (layer_id-first_sa_layer_id) / \
(config.n_layer-1-first_sa_layer_id) * (f2_end - f2_begin)
if layer_id == first_sa_layer_id:
f1 += 0.5
if layer_id == config.n_layer-2:
f2 = 0.4
if layer_id == config.n_layer-1:
f2 = 0.37
decay_speed[h][0] = math.pow(f2, h / (attn_sz-1) * 7) * f1
self.time_decay = nn.Parameter(torch.log(decay_speed)) # will use exp(self.time_decay) to ensure time_decay > 0
self.time_curve = torch.tensor(
[-(config.ctx_len - 2 - i) for i in range(config.ctx_len-1)]).unsqueeze(0)
self.time_curve = self.time_curve.to('cuda')
self.time_first = nn.Parameter(torch.ones(attn_sz, 1) * math.log(0.3))
#############################################################################
self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
with torch.no_grad(): # init to "shift half of the channels"
ww = torch.ones(1, 1, config.n_embd)
for i in range(config.n_embd // 2):
ww[0, 0, i] = 0
self.time_mix = nn.Parameter(ww)
self.key = nn.Linear(config.n_embd, attn_sz, bias=False)
self.value = nn.Linear(config.n_embd, attn_sz, bias=False)
self.receptance = nn.Linear(config.n_embd, attn_sz, bias=False)
self.output = nn.Linear(attn_sz, config.n_embd, bias=False)
self.key.scale_init = 0
self.receptance.scale_init = 0
self.output.scale_init = 0
def forward(self, x):
B, T, C = x.size()
x = x * self.time_mix + self.time_shift(x) * (1 - self.time_mix)
k = self.key(x).transpose(-1, -2)
v = self.value(x).transpose(-1, -2)
r = self.receptance(x)
# RWKV_K_CLAMP can be removed if the CUDA kernel substracts the correct k_max for each k (I will do this later)
k = torch.clamp(k, max=RWKV_K_CLAMP)
k = torch.exp(k)
kv = k * v
self.time_w = torch.cat(
[torch.exp(self.time_decay) * self.time_curve, self.time_first], dim=-1)
w = torch.exp(self.time_w)
wkv = TimeX.apply(w, kv, B, C, T, 0)
# RWKV_K_EPS can be removed if the CUDA kernel sets 0/0 = 0 (I will do this later)
wk = TimeX.apply(w, k, B, C, T, RWKV_K_EPS)
rwkv = torch.sigmoid(r) * (wkv / wk).transpose(-1, -2)
rwkv = self.output(rwkv)
return rwkv
class RWKV_ChannelMix(nn.Module):
def __init__(self, config, layer_id):
super().__init__()
self.layer_id = layer_id
self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
with torch.no_grad(): # init to "shift half of the channels"
x = torch.ones(1, 1, config.n_embd)
for i in range(config.n_embd // 2):
x[0, 0, i] = 0
self.time_mix = nn.Parameter(x)
hidden_sz = 4 * config.n_embd
self.key = nn.Linear(config.n_embd, hidden_sz, bias=False)
self.receptance = nn.Linear(config.n_embd, config.n_embd, bias=False)
self.value = nn.Linear(hidden_sz, config.n_embd, bias=False)
self.value.scale_init = 0
self.receptance.scale_init = 0
def forward(self, x):
x = x * self.time_mix + self.time_shift(x) * (1 - self.time_mix)
k = self.key(x)
k = torch.square(torch.relu(k))
kv = self.value(k)
rkv = torch.sigmoid(self.receptance(x)) * kv
return rkv
########################################################################################################
# The GPT Model with our blocks
########################################################################################################
class GPTConfig:
def __init__(self, vocab_size, ctx_len, **kwargs):
self.vocab_size = vocab_size
self.ctx_len = ctx_len
for k, v in kwargs.items():
setattr(self, k, v)
class Block(nn.Module):
def __init__(self, config, layer_id):
super().__init__()
self.config = config
self.layer_id = layer_id
self.ln1 = nn.LayerNorm(config.n_embd)
self.ln2 = nn.LayerNorm(config.n_embd)
if self.layer_id == 0 and self.config.model_type == 'RWKV-ffnPre':
self.ffnPre = RWKV_ChannelMix(config, layer_id+1000)
else:
self.att = RWKV_TimeMix(config, layer_id)
self.ffn = RWKV_ChannelMix(config, layer_id)
def forward(self, x):
x = self.ln1(x)
if self.layer_id == 0 and self.config.model_type == 'RWKV-ffnPre':
x = x + self.ffnPre(x) # better in some cases
else:
x = x + self.att(x)
x = self.ln2(x)
x = x + self.ffn(x)
return x
class GPT(nn.Module):
def __init__(self, config):
super().__init__()
self.step = 0
self.config = config
self.emb = nn.Embedding(config.vocab_size, config.n_embd)
self.blocks = nn.Sequential(*[Block(config, i)
for i in range(config.n_layer)])
self.ln_out = nn.LayerNorm(config.n_embd)
self.head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
self.head_q = nn.Linear(config.n_embd, RWKV_HEAD_QK_DIM, bias=False)
self.head_q.scale_init = 0
self.head_k = nn.Linear(config.n_embd, RWKV_HEAD_QK_DIM, bias=False)
self.head_k.scale_init = 0.1
self.register_buffer("copy_mask", torch.tril(
torch.ones(config.ctx_len, config.ctx_len)))
self.ctx_len = config.ctx_len
RWKV_Init(self, config)
logger.info("number of parameters: %e", sum(p.numel()
for p in self.parameters()))
def get_ctx_len(self):
return self.ctx_len
def _init_weights(self, module):
if isinstance(module, (nn.Linear)):
module.weight.data.normal_(mean=0.0, std=0.01)
if isinstance(module, (nn.Embedding)):
module.weight.data.normal_(mean=0.0, std=1e-5)
if isinstance(module, nn.Linear) and module.bias is not None:
module.bias.data.zero_()
def configure_optimizers(self, train_config):
# separate out all parameters to those that will and won't experience regularizing weight decay
decay = set()
no_decay = set()
for mn, m in self.named_modules(): # here we disable weight_decay
for pn, p in m.named_parameters():
fpn = '%s.%s' % (mn, pn) if mn else pn # full param name
no_decay.add(fpn)
param_dict = {pn: p for pn, p in self.named_parameters()}
inter_params = decay & no_decay
union_params = decay | no_decay
assert len(
inter_params) == 0, "parameters %s made it into both decay/no_decay sets!" % (str(inter_params), )
assert len(param_dict.keys() - union_params) == 0, "parameters %s were not separated into either decay/no_decay set!" \
% (str(param_dict.keys() - union_params), )
optim_groups = [
{"params": [param_dict[pn]
for pn in sorted(list(no_decay))], "weight_decay": 0.0},
]
optimizer = torch.optim.Adam(
optim_groups, lr=train_config.learning_rate, betas=train_config.betas, eps=train_config.eps)
return optimizer
def forward(self, idx, targets=None):
self.step += 1
B, T = idx.size()
assert T <= self.ctx_len, "Cannot forward, because len(input) > model ctx_len."
x = self.emb(idx)
x = self.blocks(x)
x = self.ln_out(x)
q = self.head_q(x)[:, :T, :]
k = self.head_k(x)[:, :T, :]
c = (q @ k.transpose(-2, -1)) * (1.0 / RWKV_HEAD_QK_DIM)
c = c.masked_fill(self.copy_mask[:T, :T] == 0, 0)
c = c @ F.one_hot(idx, num_classes=self.config.vocab_size).float()
x = self.head(x) + c
loss = None
if targets is not None:
loss = F.cross_entropy(x.view(-1, x.size(-1)), targets.view(-1))
return x, loss