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Code in this directory is taken from https://github.com/BlinkDL/RWKV-LM.
The license for this code is as follows:
Apache License
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http://www.apache.org/licenses/
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#include <stdio.h>
#include <assert.h>
#define MIN_VALUE (-1e38)
template <typename F>
__global__ void kernel_forward(const int B, const int T, const int C,
const F *__restrict__ const _w, const F *__restrict__ const _u, const F *__restrict__ const _k, const F *__restrict__ const _v,
F *__restrict__ const _y) {
const int idx = blockIdx.x * blockDim.x + threadIdx.x;
const int _b = idx / C;
const int _c = idx % C;
const int _offset = _b * T * C + _c;
F u = _u[_c];
F w = _w[_c];
const F *__restrict__ const k = _k + _offset;
const F *__restrict__ const v = _v + _offset;
F *__restrict__ const y = _y + _offset;
F p = 0, q = 0, o = MIN_VALUE;
// p and q are running sums divided by exp(o) (to avoid overflows)
for (int i = 0; i < T; i++) {
const int ii = i * C;
F no = max(o, u + k[ii]);
F A = exp(o - no);
F B = exp(u + k[ii] - no);
y[ii] = (A * p + B * v[ii]) / (A * q + B);
no = max(w + o, k[ii]);
A = exp(w + o - no);
B = exp(k[ii] - no);
p = A * p + B * v[ii];
q = A * q + B;
o = no;
}
}
template <typename F>
__global__ void kernel_backward(const int B, const int T, const int C,
const F *__restrict__ const _w, const F *__restrict__ const _u, const F *__restrict__ const _k, const F *__restrict__ const _v, const F *__restrict__ const _gy,
F *__restrict__ const _gw, F *__restrict__ const _gu, F *__restrict__ const _gk, F *__restrict__ const _gv) {
const int idx = blockIdx.x * blockDim.x + threadIdx.x;
const int _b = idx / C;
const int _c = idx % C;
const int _offset = _b * T * C + _c;
F u = _u[_c];
F w = _w[_c];
const F *__restrict__ const k = _k + _offset;
const F *__restrict__ const v = _v + _offset;
const F *__restrict__ const gy = _gy + _offset;
F *__restrict__ const gk = _gk + _offset;
F *__restrict__ const gv = _gv + _offset;
F y[Tmax], z[Tmax], zexp[Tmax];
F gw = 0, gu = 0;
F p = 0, q = 0;
F dpdw = 0, dqdw = 0;
F o = MIN_VALUE;
for (int i = 0; i < T; i++) {
const int ii = i * C;
F no = max(o, k[ii] + u);
F A = exp(o - no);
F B = exp(k[ii] + u - no);
F num = A * p + B * v[ii];
F iden = 1 / (A * q + B);
y[i] = num * iden;
z[i] = iden;
zexp[i] = k[ii] + u - no;
gw += gy[ii] * (dpdw - dqdw * y[i]) * iden * A;
gu += gy[ii] * (v[ii] - y[i]) * B * iden;
no = max(w + o, k[ii]);
A = exp(w + o - no);
B = exp(k[ii] - no);
dpdw = A * (p + dpdw);
dqdw = A * (q + dqdw);
p = A * p + B * v[ii];
q = A * q + B;
o = no;
}
F gp = 0, gq = 0;
o = MIN_VALUE;
for (int i = T - 1; i >= 0; i--) {
const int ii = i * C;
F A = gy[ii] * z[i] * exp(zexp[i]);
F B = exp(k[ii] + o);
gk[ii] = A * (v[ii] - y[i]) + B * (gp * v[ii] + gq);
gv[ii] = A + B * gp;
F no = max(w + o, zexp[i] - k[ii] - u);
A = exp(w + o - no);
B = gy[ii] * z[i] * exp(zexp[i] - k[ii] - u - no);
gp = A * gp + B;
gq = A * gq - B * y[i];
o = no;
}
// Multiply by w because the w -> -exp(w) preprocessing is halfway in the backwards pass, even though it's not in the forward pass
const int _offsetBC = _b * C + _c;
_gw[_offsetBC] += gw * _w[_c];
_gu[_offsetBC] += gu;
}
void cuda_forward(int B, int T, int C, float *w, float *u, float *k, float *v, float *y) {
dim3 threadsPerBlock( min(C, 32) ); // requires --maxrregcount 60 for optimal performance
assert(B * C % threadsPerBlock.x == 0);
dim3 numBlocks(B * C / threadsPerBlock.x);
kernel_forward<<<numBlocks, threadsPerBlock>>>(B, T, C, w, u, k, v, y);
}
void cuda_backward(int B, int T, int C, float *w, float *u, float *k, float *v, float *gy, float *gw, float *gu, float *gk, float *gv) {
dim3 threadsPerBlock( min(C, 32) ); // requires --maxrregcount 60 for optimal performance
assert(B * C % threadsPerBlock.x == 0);
dim3 numBlocks(B * C / threadsPerBlock.x);
kernel_backward<<<numBlocks, threadsPerBlock>>>(B, T, C, w, u, k, v, gy, gw, gu, gk, gv);
}

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#include <torch/extension.h>
void cuda_forward(int B, int T, int C, float *w, float *u, float *k, float *v, float *y);
void cuda_backward(int B, int T, int C, float *w, float *u, float *k, float *v, float *gy, float *gw, float *gu, float *gk, float *gv);
void forward(int64_t B, int64_t T, int64_t C, torch::Tensor &w, torch::Tensor &u, torch::Tensor &k, torch::Tensor &v, torch::Tensor &y) {
cuda_forward(B, T, C, w.data_ptr<float>(), u.data_ptr<float>(), k.data_ptr<float>(), v.data_ptr<float>(), y.data_ptr<float>());
}
void backward(int64_t B, int64_t T, int64_t C, torch::Tensor &w, torch::Tensor &u, torch::Tensor &k, torch::Tensor &v, torch::Tensor &gy, torch::Tensor &gw, torch::Tensor &gu, torch::Tensor &gk, torch::Tensor &gv) {
cuda_backward(B, T, C, w.data_ptr<float>(), u.data_ptr<float>(), k.data_ptr<float>(), v.data_ptr<float>(), gy.data_ptr<float>(), gw.data_ptr<float>(), gu.data_ptr<float>(), gk.data_ptr<float>(), gv.data_ptr<float>());
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("forward", &forward, "wkv forward");
m.def("backward", &backward, "wkv backward");
}
TORCH_LIBRARY(wkv, m) {
m.def("forward", forward);
m.def("backward", backward);
}

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# File from RWKV-v4 Repo - Small changes made for compatibility
########################################################################################################
# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
########################################################################################################
import math, os
import numpy as np
import logging
import torch
import torch.nn as nn
from torch.nn import functional as F
try:
from deepspeed.ops.adam import FusedAdam
except:
pass # some poor windows users cant install deepspeed
logger = logging.getLogger(__name__)
RWKV_HEAD_QK_DIM = 0
print(f'\nRWKV_HEAD_QK_DIM {RWKV_HEAD_QK_DIM}\n')
class L2Wrap(torch.autograd.Function):
@staticmethod
def forward(ctx, loss, y):
ctx.save_for_backward(y)
return loss
@staticmethod
def backward(ctx, grad_output):
y = ctx.saved_tensors[0]
# to encourage the logits to be close to 0
factor = 1e-4 / (y.shape[0] * y.shape[1])
maxx, ids = torch.max(y, -1, keepdim=True)
gy = torch.zeros_like(y)
gy.scatter_(-1, ids, maxx * factor)
return (grad_output, gy)
########################################################################################################
# CUDA Kernel
########################################################################################################
T_MAX = 1024 # increase this if your ctx_len is long [NOTE: TAKES LOTS OF VRAM!]
# it's possible to go beyond CUDA limitations if you slice the ctx and pass the hidden state in each slice
from torch.utils.cpp_extension import load
wkv_cuda = load(name="wkv", sources=["models/RWKV4/cuda/wkv_op.cpp", "models/RWKV4/cuda/wkv_cuda.cu"],
verbose=True, extra_cuda_cflags=['-res-usage', '--maxrregcount 60', '--use_fast_math', '-O3', '-Xptxas -O3', f'-DTmax={T_MAX}'])
class WKV(torch.autograd.Function):
@staticmethod
def forward(ctx, B, T, C, w, u, k, v):
ctx.B = B
ctx.T = T
ctx.C = C
assert T <= T_MAX
assert B * C % min(C, 1024) == 0
if '32' in os.environ['RWKV_FLOAT_MODE']:
w = -torch.exp(w.contiguous())
u = u.contiguous()
k = k.contiguous()
v = v.contiguous()
else:
w = -torch.exp(w.float().contiguous())
u = u.float().contiguous()
k = k.float().contiguous()
v = v.float().contiguous()
ctx.save_for_backward(w, u, k, v)
y = torch.empty((B, T, C), device='cuda', memory_format=torch.contiguous_format)
wkv_cuda.forward(B, T, C, w, u, k, v, y)
if '32' in os.environ['RWKV_FLOAT_MODE']:
return y
elif os.environ['RWKV_FLOAT_MODE'] == 'fp16':
return y.half()
elif os.environ['RWKV_FLOAT_MODE'] == 'bf16':
return y.bfloat16()
@staticmethod
def backward(ctx, gy):
B = ctx.B
T = ctx.T
C = ctx.C
assert T <= T_MAX
assert B * C % min(C, 1024) == 0
w, u, k, v = ctx.saved_tensors
gw = torch.zeros((B, C), device='cuda').contiguous()
gu = torch.zeros((B, C), device='cuda').contiguous()
gk = torch.zeros((B, T, C), device='cuda').contiguous()
gv = torch.zeros((B, T, C), device='cuda').contiguous()
if '32' in os.environ['RWKV_FLOAT_MODE']:
wkv_cuda.backward(B, T, C, w, u, k, v, gy.contiguous(), gw, gu, gk, gv)
else:
wkv_cuda.backward(B, T, C, w, u, k, v, gy.float().contiguous(), gw, gu, gk, gv)
gw = torch.sum(gw, dim=0)
gu = torch.sum(gu, dim=0)
if '32' in os.environ['RWKV_FLOAT_MODE']:
return (None, None, None, gw, gu, gk, gv)
elif os.environ['RWKV_FLOAT_MODE'] == 'fp16':
return (None, None, None, gw.half(), gu.half(), gk.half(), gv.half())
elif os.environ['RWKV_FLOAT_MODE'] == 'bf16':
return (None, None, None, gw.bfloat16(), gu.bfloat16(), gk.bfloat16(), gv.bfloat16())
def RUN_CUDA(B, T, C, w, u, k, v):
return WKV.apply(B, T, C, w.cuda(), u.cuda(), k.cuda(), v.cuda())
########################################################################################################
# RWKV: RWKV Time-mix + RWKV Channel-mix
########################################################################################################
def RWKV_Init(model, args): # fancy initialization of all lin & emb layer in the model
print("\n[--> first run, init model params (very slow for large models) <--]")
print("[so you shall only do it for 1 single GPU and save the checkpt and load it when using multiple GPU]\n")
for mm in model.modules():
if "RecursiveScriptModule" in str(type(mm)):
if mm.original_name not in ["Linear"]:
continue
ww = None
for name, param in mm.named_parameters():
if name == "weight":
ww = param
else:
m = mm
if not isinstance(m, (nn.Linear, nn.Embedding)):
continue
ww = m.weight
with torch.no_grad():
name = "[unknown weight]"
for name, parameter in model.named_parameters(): # find the name of the weight
if id(ww) == id(parameter):
break
shape = ww.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] == args.vocab_size and shape[1] == args.n_embd: # token emb?
scale = 1e-4
else:
scale = 0
if isinstance(m, nn.Linear):
if shape[0] > shape[1]:
gain = math.sqrt(shape[0] / shape[1])
if shape[0] == args.vocab_size and shape[1] == args.n_embd: # final projection?
scale = 0.5
if hasattr(m, "scale_init"):
scale = m.scale_init
# print(f"{str(shape[0]).ljust(5)} {str(shape[1]).ljust(5)} {str(scale).ljust(4)} {name}")
gain *= scale
if scale == -999:
nn.init.eye_(ww)
elif gain == 0:
# zero init is great for some RWKV matrices
nn.init.zeros_(ww)
elif gain > 0:
nn.init.orthogonal_(ww, gain=gain)
else:
nn.init.normal_(ww, mean=0.0, std=-scale)
class RWKV_TimeMix(torch.jit.ScriptModule):
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
with torch.no_grad(): # fancy init
ratio_0_to_1 = (layer_id / (config.n_layer - 1)) # 0 to 1
ratio_1_to_almost0 = (1.0 - (layer_id / config.n_layer)) # 1 to ~0
# fancy time_decay
decay_speed = torch.ones(attn_sz)
for h in range(attn_sz):
decay_speed[h] = -5 + 8 * (h / (attn_sz-1)) ** (0.7 + 1.3 * ratio_0_to_1)
self.time_decay = nn.Parameter(decay_speed)
# print(layer_id, self.time_decay.flatten()[:3].cpu().numpy(), '...', self.time_decay.flatten()[-3:].cpu().numpy())
# fancy time_first
zigzag = (torch.tensor([(i+1)%3 - 1 for i in range(attn_sz)]) * 0.5)
self.time_first = nn.Parameter(torch.ones(attn_sz) * math.log(0.3) + zigzag)
# fancy time_mix
x = torch.ones(1, 1, config.n_embd)
for i in range(config.n_embd):
x[0, 0, i] = i / config.n_embd
self.time_mix_k = nn.Parameter(torch.pow(x, ratio_1_to_almost0))
self.time_mix_v = nn.Parameter(torch.pow(x, ratio_1_to_almost0) + 0.3 * ratio_0_to_1)
self.time_mix_r = nn.Parameter(torch.pow(x, 0.5 * ratio_1_to_almost0))
self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
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
@torch.jit.script_method
def jit_func(self, x):
# Mix x with the previous timestep to produce xk, xv, xr
xx = self.time_shift(x)
xk = x * self.time_mix_k + xx * (1 - self.time_mix_k)
xv = x * self.time_mix_v + xx * (1 - self.time_mix_v)
xr = x * self.time_mix_r + xx * (1 - self.time_mix_r)
# Use xk, xv, xr to produce k, v, r
k = self.key(xk)
v = self.value(xv)
r = self.receptance(xr)
sr = torch.sigmoid(r)
return sr, k, v
def forward(self, x):
B, T, C = x.size() # x = (Batch,Time,Channel)
sr, k, v = self.jit_func(x)
rwkv = sr * RUN_CUDA(B, T, C, self.time_decay, self.time_first, k, v)
rwkv = self.output(rwkv)
return rwkv
class RWKV_ChannelMix(torch.jit.ScriptModule):
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(): # fancy init of time_mix
ratio_1_to_almost0 = (1.0 - (layer_id / config.n_layer)) # 1 to ~0
x = torch.ones(1, 1, config.n_embd)
for i in range(config.n_embd):
x[0, 0, i] = i / config.n_embd
self.time_mix_k = nn.Parameter(torch.pow(x, ratio_1_to_almost0))
self.time_mix_r = nn.Parameter(torch.pow(x, ratio_1_to_almost0))
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
@torch.jit.script_method
def forward(self, x):
xx = self.time_shift(x)
xk = x * self.time_mix_k + xx * (1 - self.time_mix_k)
xr = x * self.time_mix_r + xx * (1 - self.time_mix_r)
k = self.key(xk)
k = torch.square(torch.relu(k))
kv = self.value(k)
rkv = torch.sigmoid(self.receptance(xr)) * 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:
self.ln0 = nn.LayerNorm(config.n_embd)
if self.layer_id == 0 and self.config.model_type == 'RWKV-ffnPre':
self.ffnPre = RWKV_ChannelMix(config, 0)
else:
self.att = RWKV_TimeMix(config, layer_id)
self.ffn = RWKV_ChannelMix(config, layer_id)
def forward(self, x):
if self.layer_id == 0:
x = self.ln0(x)
if self.layer_id == 0 and self.config.model_type == 'RWKV-ffnPre':
x = x + self.ffnPre(self.ln1(x)) # better in some cases
else:
x = x + self.att(self.ln1(x))
x = x + self.ffn(self.ln2(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)
if RWKV_HEAD_QK_DIM > 0:
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
try:
if os.environ['RWKV_LOAD_MODEL'] == str(False):
RWKV_Init(self, config)
except:
pass
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):
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()}
optim_groups = [
{"params": [param_dict[pn]
for pn in sorted(list(no_decay))], "weight_decay": 0.0},
]
try:
optimizer = FusedAdam(optim_groups, lr=train_config.learning_rate, betas=train_config.betas, eps=train_config.eps, bias_correction=True, adam_w_mode=False, weight_decay=0, amsgrad=False)
except:
print('\n\nDeepSpeed not found. Using torch optimizer instead (probably slower)\n\n')
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):
idx = idx.to(self.emb.weight.device)
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)
if RWKV_HEAD_QK_DIM > 0:
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)
if '32' in os.environ['RWKV_FLOAT_MODE']:
c = c @ F.one_hot(idx, num_classes=self.config.vocab_size)
elif os.environ['RWKV_FLOAT_MODE'] == 'fp16':
c = c @ F.one_hot(idx, num_classes=self.config.vocab_size).half()
elif os.environ['RWKV_FLOAT_MODE'] == 'bf16':
c = c @ F.one_hot(idx, num_classes=self.config.vocab_size).bfloat16()
x = self.head(x) + c
else:
x = self.head(x)
loss = None
if targets is not None:
loss = F.cross_entropy(x.view(-1, x.size(-1)), targets.to(x.device).view(-1))
return L2Wrap.apply(loss, x)

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models/RWKV4/src/model_run.py Normal file
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########################################################################################################
# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
########################################################################################################
import types
import copy
import torch
import math, os
from torch.nn import functional as F
import torch.nn as nn
RWKV_HEAD_QK_DIM = 0
print(f'\nRWKV_HEAD_QK_DIM {RWKV_HEAD_QK_DIM}\n')
DEBUG_TIME = False # True False - show trained time-coeffs
########################################################################################################
# CUDA Kernel
########################################################################################################
if os.environ['RWKV_RUN_DEVICE'] == 'cuda':
T_MAX = 1024 # increase this if your ctx_len is long [NOTE: TAKES LOTS OF VRAM!]
# it's possible to go beyond CUDA limitations if you slice the ctx and pass the hidden state in each slice
from torch.utils.cpp_extension import load
wkv_cuda = load(name="wkv", sources=["models/RWKV4/cuda/wkv_op.cpp", "models/RWKV4/cuda/wkv_cuda.cu"],
verbose=True, extra_cuda_cflags=['-res-usage', '--maxrregcount 60', '--use_fast_math', '-O3', '-Xptxas -O3', f'-DTmax={T_MAX}'])
class WKV(torch.autograd.Function):
@staticmethod
def forward(ctx, B, T, C, w, u, k, v):
ctx.B = B
ctx.T = T
ctx.C = C
assert T <= T_MAX
assert B * C % min(C, 1024) == 0
if '32' in os.environ['RWKV_FLOAT_MODE']:
w = -torch.exp(w.contiguous())
u = u.contiguous()
k = k.contiguous()
v = v.contiguous()
else:
w = -torch.exp(w.float().contiguous())
u = u.float().contiguous()
k = k.float().contiguous()
v = v.float().contiguous()
ctx.save_for_backward(w, u, k, v)
y = torch.empty((B, T, C), device='cuda', memory_format=torch.contiguous_format)
wkv_cuda.forward(B, T, C, w, u, k, v, y)
if '32' in os.environ['RWKV_FLOAT_MODE']:
return y
elif os.environ['RWKV_FLOAT_MODE'] == 'fp16':
return y.half()
elif os.environ['RWKV_FLOAT_MODE'] == 'bf16':
return y.bfloat16()
@staticmethod
def backward(ctx, gy):
B = ctx.B
T = ctx.T
C = ctx.C
assert T <= T_MAX
assert B * C % min(C, 1024) == 0
w, u, k, v = ctx.saved_tensors
gw = torch.zeros((B, C), device='cuda').contiguous()
gu = torch.zeros((B, C), device='cuda').contiguous()
gk = torch.zeros((B, T, C), device='cuda').contiguous()
gv = torch.zeros((B, T, C), device='cuda').contiguous()
if '32' in os.environ['RWKV_FLOAT_MODE']:
wkv_cuda.backward(B, T, C, w, u, k, v, gy.contiguous(), gw, gu, gk, gv)
else:
wkv_cuda.backward(B, T, C, w, u, k, v, gy.float().contiguous(), gw, gu, gk, gv)
gw = torch.sum(gw, dim=0)
gu = torch.sum(gu, dim=0)
if '32' in os.environ['RWKV_FLOAT_MODE']:
return (None, None, None, gw, gu, gk, gv)
elif os.environ['RWKV_FLOAT_MODE'] == 'fp16':
return (None, None, None, gw.half(), gu.half(), gk.half(), gv.half())
elif os.environ['RWKV_FLOAT_MODE'] == 'bf16':
return (None, None, None, gw.bfloat16(), gu.bfloat16(), gk.bfloat16(), gv.bfloat16())
def RUN_CUDA(B, T, C, w, u, k, v):
return WKV.apply(B, T, C, w.cuda(), u.cuda(), k.cuda(), v.cuda())
############################################################################################################
RWKV_CFG = types.SimpleNamespace()
class RWKV_ChannelMix(nn.Module):
def __init__(self, layer_id):
super().__init__()
self.layer_id = layer_id
self.time_shift = nn.ZeroPad2d((0,0,1,-1))
self.time_mix_k = nn.Parameter(torch.ones(1, 1, RWKV_CFG.n_embd))
self.time_mix_r = nn.Parameter(torch.ones(1, 1, RWKV_CFG.n_embd))
hidden_sz = 4 * RWKV_CFG.n_embd
self.key = nn.Linear(RWKV_CFG.n_embd, hidden_sz, bias=False)
self.receptance = nn.Linear(RWKV_CFG.n_embd, RWKV_CFG.n_embd, bias=False)
self.value = nn.Linear(hidden_sz, RWKV_CFG.n_embd, bias=False)
def forward(self, x):
xx = self.time_shift(x)
xk = x * self.time_mix_k + xx * (1 - self.time_mix_k)
xr = x * self.time_mix_r + xx * (1 - self.time_mix_r)
k = self.key(xk)
k = torch.square(torch.relu(k))
kv = self.value(k)
rkv = torch.sigmoid(self.receptance(xr)) * kv
return rkv
class RWKV_TimeMix(nn.Module):
def __init__(self, layer_id):
super().__init__()
self.layer_id = layer_id
self.time_decay = nn.Parameter(torch.ones(RWKV_CFG.n_embd))
self.time_first = nn.Parameter(torch.ones(RWKV_CFG.n_embd) * math.log(0.3))
self.time_shift = nn.ZeroPad2d((0,0,1,-1))
self.time_mix_k = nn.Parameter(torch.ones(1,1,RWKV_CFG.n_embd))
self.time_mix_v = nn.Parameter(torch.ones(1,1,RWKV_CFG.n_embd))
self.time_mix_r = nn.Parameter(torch.ones(1,1,RWKV_CFG.n_embd))
self.key = nn.Linear(RWKV_CFG.n_embd, RWKV_CFG.n_embd, bias=False)
self.value = nn.Linear(RWKV_CFG.n_embd, RWKV_CFG.n_embd, bias=False)
self.receptance = nn.Linear(RWKV_CFG.n_embd, RWKV_CFG.n_embd, bias=False)
self.output = nn.Linear(RWKV_CFG.n_embd, RWKV_CFG.n_embd, bias=False)
def forward(self, x):
B, T, C = x.size()
xx = self.time_shift(x)
xk = x * self.time_mix_k + xx * (1 - self.time_mix_k)
xv = x * self.time_mix_v + xx * (1 - self.time_mix_v)
xr = x * self.time_mix_r + xx * (1 - self.time_mix_r)
k = self.key(xk)
v = self.value(xv)
r = self.receptance(xr)
rwkv = torch.sigmoid(r) * RUN_CUDA(B, T, C, self.time_decay, self.time_first, k, v)
rwkv = self.output(rwkv)
return rwkv
class Block(nn.Module):
def __init__(self, layer_id):
super().__init__()
self.layer_id = layer_id
self.ln1 = nn.LayerNorm(RWKV_CFG.n_embd)
self.ln2 = nn.LayerNorm(RWKV_CFG.n_embd)
if self.layer_id == 0:
self.ln0 = nn.LayerNorm(RWKV_CFG.n_embd)
if self.layer_id == 0 and RWKV_CFG.model_type == 'RWKV-ffnPre':
self.ffnPre = RWKV_ChannelMix(layer_id+1000)
else:
self.att = RWKV_TimeMix(layer_id)
self.ffn = RWKV_ChannelMix(layer_id)
def forward(self, x):
if self.layer_id == 0:
x = self.ln0(x)
if self.layer_id == 0 and RWKV_CFG.model_type == 'RWKV-ffnPre':
x = x + self.ffnPre(self.ln1(x))
else:
x = x + self.att(self.ln1(x))
x = x + self.ffn(self.ln2(x))
return x
class RWKV_GPT(nn.Module):
def __init__(self, MODEL_NAME, RUN_DEVICE, model_type, vocab_size, n_layer, n_embd, ctx_len):
global RWKV_CFG
super().__init__()
RWKV_CFG.RUN_DEVICE = RUN_DEVICE
RWKV_CFG.model_type = model_type
RWKV_CFG.vocab_size = vocab_size
RWKV_CFG.n_layer = n_layer
RWKV_CFG.n_embd = n_embd
RWKV_CFG.ctx_len = ctx_len
print('\nloading RWKV-GPT', MODEL_NAME)
self.emb = nn.Embedding(vocab_size, n_embd)
self.blocks = nn.Sequential(*[Block(i) for i in range(n_layer)])
self.ln_out = nn.LayerNorm(n_embd)
self.head = nn.Linear(n_embd, vocab_size, bias=False)
if RWKV_HEAD_QK_DIM > 0:
self.head_q = nn.Linear(n_embd, RWKV_HEAD_QK_DIM, bias=False)
self.head_q.scale_init = 0
self.head_k = nn.Linear(n_embd, RWKV_HEAD_QK_DIM, bias=False)
self.head_k.scale_init = 0.1
self.register_buffer("copy_mask", torch.tril(
torch.ones(ctx_len, ctx_len)))
self.ctx_len = ctx_len
self.eval()
self.load_state_dict(torch.load(MODEL_NAME + '.pth'))
self.eval()
def forward(self, idx):
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)
if RWKV_HEAD_QK_DIM > 0:
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)
if '32' in os.environ['RWKV_FLOAT_MODE']:
c = c @ F.one_hot(idx, num_classes=RWKV_CFG.vocab_size)
elif os.environ['RWKV_FLOAT_MODE'] == 'fp16':
c = c @ F.one_hot(idx, num_classes=RWKV_CFG.vocab_size).half()
elif os.environ['RWKV_FLOAT_MODE'] == 'bf16':
c = c @ F.one_hot(idx, num_classes=RWKV_CFG.vocab_size).bfloat16()
x = self.head(x) + c
else:
x = self.head(x)
return x
############################################################################################################
class RWKV_RNN(): # this is running in FP32 at this moment
def __init__(self, MODEL_NAME, RUN_DEVICE, model_type, n_layer, n_embd, ctx_len):
self.RUN_DEVICE = RUN_DEVICE
self.model_type = model_type
self.n_layer = n_layer
self.n_embd = n_embd
self.ctx_len = ctx_len
self.w = types.SimpleNamespace()
w = torch.load(MODEL_NAME + '.pth',
map_location=torch.device(RUN_DEVICE))
for x in w.keys():
w[x] = w[x].float()
if '.time_' in x:
w[x] = w[x].squeeze()
if '.time_decay' in x:
w[x] = -torch.exp(w[x])
if DEBUG_TIME and '.time_' in x:
print(x, w[x].squeeze().cpu().numpy())
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])
self.clear()
def clear(self):
self.xx = {}
self.aa = {}
self.bb = {}
self.pp = {}
self.hk = None
def save(self, target):
target.xx = copy.deepcopy(self.xx)
target.aa = copy.deepcopy(self.aa)
target.bb = copy.deepcopy(self.bb)
target.pp = copy.deepcopy(self.pp)
target.hk = copy.deepcopy(self.hk)
def load(self, target):
self.xx = copy.deepcopy(target.xx)
self.aa = copy.deepcopy(target.aa)
self.bb = copy.deepcopy(target.bb)
self.pp = copy.deepcopy(target.pp)
self.hk = copy.deepcopy(target.hk)
def LN(self, xx, w):
return F.layer_norm(xx, (self.n_embd,), weight=w.weight, bias=w.bias)
def FF(self, xx, w, name):
if name not in self.xx:
self.xx[name] = torch.zeros(self.n_embd, device=self.RUN_DEVICE)
xk = xx * w.time_mix_k + self.xx[name] * (1 - w.time_mix_k)
xr = xx * w.time_mix_r + self.xx[name] * (1 - w.time_mix_r)
self.xx[name] = xx
r = torch.sigmoid(w.receptance.weight @ xr)
k = torch.square(torch.relu(w.key.weight @ xk))
kv = w.value.weight @ k
return r * kv
def SA(self, xx, w, name):
if name not in self.xx:
self.xx[name] = torch.zeros(self.n_embd, device=self.RUN_DEVICE)
self.aa[name] = torch.zeros(self.n_embd, device=self.RUN_DEVICE)
self.bb[name] = torch.zeros(self.n_embd, device=self.RUN_DEVICE)
self.pp[name] = torch.zeros(self.n_embd, device=self.RUN_DEVICE) - 1e30
xk = xx * w.time_mix_k + self.xx[name] * (1 - w.time_mix_k)
xv = xx * w.time_mix_v + self.xx[name] * (1 - w.time_mix_v)
xr = xx * w.time_mix_r + self.xx[name] * (1 - w.time_mix_r)
self.xx[name] = xx
r = torch.sigmoid(w.receptance.weight @ xr)
k = w.key.weight @ xk
v = w.value.weight @ xv
pp = self.pp[name]
aa = self.aa[name]
bb = self.bb[name]
ww = w.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 + w.time_decay
p = torch.maximum(ww, k)
e1 = torch.exp(ww - p)
e2 = torch.exp(k - p)
self.aa[name] = e1 * aa + e2 * v
self.bb[name] = e1 * bb + e2
self.pp[name] = p
rwkv = r * a / b
return w.output.weight @ rwkv
def run(self, ctx):
w = self.w
x = w.emb.weight[ctx[-1]]
for i in range(self.n_layer):
if i == 0:
x = self.LN(x, w.blocks[i].ln0)
if i == 0 and self.model_type == 'RWKV-ffnPre':
x = x + self.FF(self.LN(x, w.blocks[i].ln1), w.blocks[i].ffnPre, f'ffnPre.{i}')
else:
x = x + self.SA(self.LN(x, w.blocks[i].ln1), w.blocks[i].att, f'att.{i}')
x = x + self.FF(self.LN(x, w.blocks[i].ln2), w.blocks[i].ffn, f'ffn.{i}')
x = self.LN(x, w.ln_out)
if RWKV_HEAD_QK_DIM > 0:
if self.hk == None:
self.hk = (w.head_k.weight @ x).unsqueeze(0)
else:
self.hk = torch.cat(
[self.hk, (w.head_k.weight @ x).unsqueeze(0)], dim=0)
if self.hk.shape[0] > self.ctx_len:
self.hk = self.hk[-self.ctx_len:, :]
q = w.head_q.weight @ x
x = w.head.weight @ x
x = x.cpu().numpy().tolist()
c = (self.hk @ q) / RWKV_HEAD_QK_DIM
for i in range(len(c)):
x[ctx[i]] += c[i]
else:
x = w.head.weight @ x
x = x.cpu().numpy().tolist()
return x

95
models/RWKV4/src/utils.py Normal file
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########################################################################################################
# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
########################################################################################################
import os
try:
NUM_GPUS = int(os.environ['RWKV_NUM_GPUS'])
except:
NUM_GPUS = 1
import json
import random
import numpy as np
import torch
from torch.nn import functional as F
class TOKENIZER():
def __init__(self, WORD_NAME, UNKNOWN_CHAR='\ue083'):
if 'list' in str(type(WORD_NAME)):
self.charMode = False
if WORD_NAME[0] == WORD_NAME[1]:
from transformers import PreTrainedTokenizerFast
self.tokenizer = PreTrainedTokenizerFast(tokenizer_file=WORD_NAME[0])
else:
from transformers import GPT2TokenizerFast
self.tokenizer = GPT2TokenizerFast(WORD_NAME[0], WORD_NAME[1])
self.vocab_size = len(self.tokenizer)
else:
self.charMode = True
with open(WORD_NAME + '.json', "r", encoding="utf-16") as result_file:
self.word_table = json.load(result_file)
self.vocab_size = len(self.word_table)
self.stoi = {v: int(k) for k, v in self.word_table.items()}
self.itos = {int(k): v for k, v in self.word_table.items()}
self.UNKNOWN_CHAR = self.stoi[UNKNOWN_CHAR]
def refine_context(self, context):
context = context.strip().split('\n')
for c in range(len(context)):
context[c] = context[c].strip().strip('\u3000').strip('\r')
context = list(filter(lambda c: c != '', context))
context = '\n' + ('\n'.join(context)).strip()
if context == '':
context = '\n'
return context
def sample_logits(self, out, x, ctx_len, temperature=1.0, top_p_usual=None, top_p_newline=None):
# out[self.UNKNOWN_CHAR] = -float('Inf')
lastChar = int(x[-1])
probs = F.softmax(torch.tensor(out), dim=-1)
if self.charMode:
if self.itos[lastChar] == '\n':
top_p = top_p_newline
else:
top_p = top_p_usual
else:
top_p = top_p_usual
sorted_probs, s_index = torch.sort(probs, descending=True)
# for j in range(30):
# pp = sorted_probs[j].item()
# if pp < 0.005:
# break
# ss = self.itos[int(s_index[j])].replace('\n','_')
# print(f'{math.floor(pp*100):>3.0f}{ss}', end='')
# print('')
cumulative_probs = torch.cumsum(sorted_probs, dim=-1).numpy()
cutoff = float(sorted_probs[np.argmax(cumulative_probs > top_p)])
probs[probs < cutoff] = 0
# print("[" + str(round(cutoff,4)) + ' ' + str(round(to_float(sum(probs)),3)) + "]", end = "")
if temperature != 1.0:
probs = probs.pow(1.0 / temperature)
return torch.multinomial(probs, num_samples=1)[0]
def to_float(x):
return x.cpu().detach().numpy().flatten()[0].astype(float)
def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)