RWKV-Gradio-1 / modeling_rwkv.py
BlinkDL's picture
Upload 16 files
cade2a3
########################################################################################################
# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
########################################################################################################
from typing import Optional
import types, gc, os, time, re
import torch
import torch.nn as nn
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
current_path = os.path.dirname(os.path.abspath(__file__))
########################################################################################################
if os.environ.get('RWKV_JIT_ON') != '0':
os.environ["RWKV_JIT_ON"] = '1'
MyModule = torch.jit.ScriptModule
MyFunction = torch.jit.script_method
MyStatic = torch.jit.script
else:
MyModule = torch.nn.Module
def __nop(ob):
return ob
MyFunction = __nop
MyStatic = __nop
if os.environ.get('RWKV_CUDA_ON') == '1':
from torch.utils.cpp_extension import load
try:
load(
name=f"wkv_cuda",
sources=[f"{current_path}/cuda/wrapper.cpp", f"{current_path}/cuda/operators.cu", f"{current_path}/cuda/gemm_fp16_cublas.cpp"],
verbose=True,
extra_ldflags=["cublas.lib" if os.name == "nt" else ""],
extra_cuda_cflags=["--use_fast_math", "-O3", "--extra-device-vectorization"],
is_python_module=False)
DISABLE_CUBLAS_GEMM = False
except:
print("Failed to build cuBLAS matmul, falling back to torch.matmul. Small model with fp16 will overflow.")
load(
name=f"wkv_cuda",
sources=[f"{current_path}/cuda/wrapper.cpp", f"{current_path}/cuda/operators.cu"],
verbose=True,
extra_cuda_cflags=["--use_fast_math", "-O3", "--extra-device-vectorization"],
extra_cflags=["-DDISABLE_CUBLAS_GEMM"],
is_python_module=False)
DISABLE_CUBLAS_GEMM = True
@MyStatic
def cuda_wkv(T: int, C: int, w, u, k, v, aa, bb, pp):
assert 1 * C % min(C, 32) == 0
assert k.dtype == v.dtype == torch.float16 or k.dtype == v.dtype == torch.float32
assert w.dtype == u.dtype == aa.dtype == bb.dtype == pp.dtype == torch.float32
w = w.contiguous()
u = u.contiguous()
k = k.contiguous()
v = v.contiguous()
y = torch.empty((T, C), device=w.device, memory_format=torch.contiguous_format, dtype=k.dtype)
torch.ops.rwkv.wkv_forward(1, T, C, w, u, k, v, y, aa, bb, pp)
return y, aa, bb, pp
@MyStatic
def cuda_mm8_seq(B: int, N: int, M: int, x, w, mx, rx, my, ry):
assert x.dtype == mx.dtype == rx.dtype == my.dtype == ry.dtype
assert x.dtype == torch.float32 or x.dtype == torch.float16
assert w.dtype == torch.uint8
assert x.shape == (B, N)
assert w.shape == (N, M)
assert rx.shape == mx.shape == (M,)
assert ry.shape == my.shape == (N, 1)
y = torch.empty((B, M), device=w.device, dtype=x.dtype)
torch.ops.rwkv.mm8_seq(B, N, M, x, w, mx, rx, my, ry, y)
return y
@MyStatic
def cuda_mm8_one(N: int, M: int, x, w, mx, rx, my, ry):
assert x.dtype == mx.dtype == rx.dtype == my.dtype == ry.dtype
assert x.dtype == torch.float32 or x.dtype == torch.float16
assert w.dtype == torch.uint8
assert x.shape == (N,)
assert w.shape == (N, M)
assert rx.shape == mx.shape == (M,)
assert ry.shape == my.shape == (N, 1)
y = torch.zeros((M,), device=w.device, dtype=torch.float32)
torch.ops.rwkv.mm8_one(N, M, x, w, mx, rx, my, ry, y)
return y.to(dtype=x.dtype)
else:
os.environ["RWKV_CUDA_ON"] = '0'
@MyStatic
def torch_mm8_seq(x, w, mx, rx, my, ry):
return x @ ((w.to(dtype=x.dtype) + 0.5) * ry * rx + my + mx)
@MyStatic
def torch_mm8_one(x, w, mx, rx, my, ry):
return x @ ((w.to(dtype=x.dtype) + 0.5) * ry * rx + my + mx)
if os.environ.get('RWKV_CUDA_ON') == '1':
@MyStatic
def mm8_seq(x, w, mx, rx, my, ry):
if w.device.type == 'cuda' and x.dtype == torch.float16:
B, N, M = x.shape[0], w.shape[0], w.shape[1]
return cuda_mm8_seq(B, N, M, x, w, mx, rx, my, ry)
else:
return torch_mm8_seq(x, w, mx, rx, my, ry)
@MyStatic
def mm8_one(x, w, mx, rx, my, ry):
if w.device.type == 'cuda':
N, M = w.shape[0], w.shape[1]
return cuda_mm8_one(N, M, x, w, mx, rx, my, ry)
else:
return torch_mm8_one(x, w, mx, rx, my, ry)
else:
@MyStatic
def mm8_seq(x, w, mx, rx, my, ry):
return torch_mm8_seq(x, w, mx, rx, my, ry)
@MyStatic
def mm8_one(x, w, mx, rx, my, ry):
return torch_mm8_one(x, w, mx, rx, my, ry)
def mm8(x: torch.Tensor, w: torch.Tensor, mx: torch.Tensor, rx: torch.Tensor, my: torch.Tensor, ry: torch.Tensor):
if len(x.shape) == 1:
return mm8_one(x, w, mx, rx, my, ry)
return mm8_seq(x, w, mx, rx, my, ry)
def matmul(a, b, mx: Optional[torch.Tensor]=None, rx: Optional[torch.Tensor]=None, my: Optional[torch.Tensor]=None, ry: Optional[torch.Tensor]=None, output_dtype: Optional[torch.dtype]=None) -> torch.Tensor:
if output_dtype is None:
output_dtype = a.dtype
if b.dtype in [torch.float16, torch.bfloat16, torch.float32]:
assert a.dtype == b.dtype
return matmul_float(a, b, output_dtype=output_dtype)
elif b.dtype == torch.uint8:
assert mx is not None
assert rx is not None
assert my is not None
assert ry is not None
return mm8(a, b, mx, rx, my, ry).to(output_dtype)
else:
raise ValueError("Unsupported dtype")
if os.environ.get('RWKV_CUDA_ON') == '1' and not DISABLE_CUBLAS_GEMM:
def matmul_float(a, b, output_dtype: Optional[torch.dtype]=None):
if output_dtype is None:
output_dtype = a.dtype
if a.dtype == b.dtype == torch.float16 and a.device.type == 'cuda':
if len(a.shape) == 1:
assert len(b.shape) == 2
c = torch.empty((b.shape[-1],), dtype=output_dtype, device=a.device)
a = a.unsqueeze(0)
else:
assert len(a.shape) == len(b.shape)
assert len(a.shape) == 2 or len(a.shape) == 3
# torch.empty((*a.shape[:-1], b.shape[-1])) doesn't work with jit
if len(a.shape) == 2:
c = torch.empty((a.shape[0], b.shape[-1]), dtype=output_dtype, device=a.device)
else:
c = torch.empty((a.shape[0], a.shape[1], b.shape[-1]), dtype=output_dtype, device=a.device)
torch.ops.rwkv.gemm_fp16_cublas(a, b, c)
return c
else:
return (a @ b).to(output_dtype)
else:
def matmul_float(a, b, output_dtype: Optional[torch.dtype]=None):
return (a @ b).to(output_dtype)
if os.environ.get('RWKV_DML_ON') == '1':
import torch_directml
print("PyTorch with DirectML Enabled")
########################################################################################################
class RWKV(MyModule):
def __init__(self, model, strategy, verbose = True, convert_and_save_and_exit = None):
super().__init__()
if verbose:
prxxx = lambda *args, **kwargs: print(*args, **kwargs)
else:
prxxx = lambda *args, **kwargs: None
STRATEGY_REGEX = r"^(?:(?:^|->) *(?:cuda(?::[\d]+)?|cpu|mps|dml) (?:fp(?:16|32)|bf16)(?:i8|i4|i3)?(?: \*[\d]+\+?)? *)+$"
if not re.match(STRATEGY_REGEX, strategy):
raise ValueError("Invalid strategy. Please read https://pypi.org/project/rwkv/")
strategy = ('->'.join([x.strip() for x in strategy.split('->')])).replace('->', ' -> ')
self.args = types.SimpleNamespace()
args = self.args
args.MODEL_NAME = model
args.strategy_string = strategy
# Rescale for fp16 mode: set x = x/2 every X layer (to avoid fp16 overflow)
try:
self.RESCALE_LAYER = int(os.environ["RWKV_RESCALE_LAYER"]) # !!! NOTE: SEEMS YOU SHOULD SET IT TO 999 (disable) FOR RWKV-MUSIC MODELS !!!
except:
self.RESCALE_LAYER = 6 if 'fp16' in strategy else 0
prxxx(f'RWKV_JIT_ON {os.environ["RWKV_JIT_ON"]} RWKV_CUDA_ON {os.environ["RWKV_CUDA_ON"]} RESCALE_LAYER {self.RESCALE_LAYER}\n')
args.MODEL_NAME = args.MODEL_NAME.strip()
if not args.MODEL_NAME.endswith('.pth'):
args.MODEL_NAME += '.pth'
prxxx(f'Loading {args.MODEL_NAME} ...')
with torch.no_grad():
self.w = torch.load(args.MODEL_NAME, map_location='cpu') # load model to CPU first
gc.collect()
w = self.w
ALREADY_CONVERTED = False
if '_strategy' in w:
ALREADY_CONVERTED = True
assert convert_and_save_and_exit == None # you should only convert a raw model
prxxx(f"Converted model: strategy {w['_strategy']}, version {w['_version']}\n")
assert w['_strategy'] == args.strategy_string # if you are using a new strategy, re-convert the model
assert float(w['_version']) >= 0.7 # sometimes you should re-convert using latest convert_model.py
assert w['_rescale_layer'] == self.RESCALE_LAYER # must use same RESCALE_LAYER to avoid mistakes
del w['_strategy']
del w['_version']
del w['_rescale_layer']
args.n_embd = w['emb.weight'].shape[1]
args.n_att = w['blocks.0.att.key.weight'].shape[0] # note: transposed matrix
args.n_ffn = w['blocks.0.ffn.key.weight'].shape[0] # note: transposed matrix
args.n_layer = 0
keys = list(w.keys())
self.version = 4
for x in keys:
layer_id = int(x.split('.')[1]) if ('blocks.' in x) else 0
args.n_layer = max(args.n_layer, layer_id+1)
if 'ln_x' in x:
self.version = max(5, self.version)
if 'gate.weight' in x:
self.version = max(5.1, self.version)
if int(self.version) == 5 and 'att.time_decay' in x:
args.n_head = w[x].shape[0]
if len(w[x].shape) > 1:
if w[x].shape[1] > 1:
self.version = max(5.2, self.version)
if 'time_maa' in x:
self.version = max(6, self.version)
if int(self.version) == 6 and 'time_faaaa' in x:
args.n_head = w[x].shape[0]
prxxx(f'Model detected: v{self.version:.1f}')
####################### Compute strategy
s = [x.strip().split(' ') for x in strategy.split('->')]
plan = [0] * len(s)
stream_i = -1
stream_count = 0
to_allocate = args.n_layer + 1
allocated = 0
free_slots = 0
for i in range(len(s)):
si = s[i]
si1 = si[1]
if si1.startswith('fp32'): si[1] = [torch.float]
elif si1.startswith('fp16'): si[1] = [torch.float16]
elif si1.startswith('bf16'): si[1] = [torch.bfloat16]
if si1.endswith('i8'): si[1] += [torch.uint8]
else: si[1] += [si[1][0]]
if len(si) > 2:
ss = si[2]
assert ss.startswith('*')
if ss.endswith('+'):
plan[i] = int(ss[1:-1])
stream_i = i
else:
plan[i] = int(ss[1:])
allocated += plan[i]
if allocated >= to_allocate:
plan[i] += to_allocate - allocated
break
else:
free_slots += 1
if stream_i < 0:
if free_slots > 0 and to_allocate > allocated:
for i in range(len(s)):
if plan[i] == 0:
plan[i] = (to_allocate - allocated) // free_slots
allocated += plan[i]
free_slots -= 1
if to_allocate > allocated:
plan[len(s)-1] += to_allocate - allocated
else:
if to_allocate > allocated:
stream_count = to_allocate - allocated
plan[stream_i] += stream_count
prxxx(f'Strategy: (total {args.n_layer}+1={args.n_layer+1} layers)')
for i in range(len(s)):
ss = s[i]
if i != stream_i:
prxxx(f'* {ss[0]} {str(ss[1]).replace("torch.","")}, store {plan[i]} layers')
else:
prxxx(f'* {ss[0]} {str(ss[1]).replace("torch.","")}, store {plan[i]-stream_count} layers, stream {stream_count} layers')
plan[i] += (0 if i == 0 else plan[i-1])
self.strategy = [None] * (args.n_layer + 1)
strategy = self.strategy
for n in range(args.n_layer + 1):
for i in range(len(s)):
if n < plan[i]:
strategy[n] = types.SimpleNamespace()
strategy[n].device = s[i][0]
strategy[n].atype = s[i][1][0]
strategy[n].wtype = s[i][1][1]
strategy[n].stream = False
if strategy[n].device == 'dml':
strategy[n].device = torch_directml.device()
if i == stream_i and n >= (plan[i] - stream_count):
strategy[n].stream = True
break
prxxx(f"{n}-{strategy[n].device}-{str(strategy[n].atype).replace('torch.','')}-{str(strategy[n].wtype).replace('torch.','')}{'-stream' if strategy[n].stream else ''}",end=' ')
prxxx()
####################### Load weights to self.w
if not ALREADY_CONVERTED:
try: # precompute embedding
w['emb.weight'] = F.layer_norm(w['emb.weight'], (args.n_embd,), weight=w['blocks.0.ln0.weight'], bias=w['blocks.0.ln0.bias'])
except:
w['emb.weight'] = F.layer_norm(w['emb.weight'].float(), (args.n_embd,), weight=w['blocks.0.ln0.weight'].float(), bias=w['blocks.0.ln0.bias'].float())
# del w['blocks.0.ln0.weight']
# del w['blocks.0.ln0.bias']
print_need_newline = False
REAL_TIME_FIRST = False
for x in list(w.keys()):
if '.time_faaaa' in x: REAL_TIME_FIRST = True
if REAL_TIME_FIRST:
w = {k.replace('.time_faaaa','.time_first') if '.time_faaaa' in k else k: v for k, v in w.items()}
self.w = w
keys = list(w.keys())
for x in keys:
w[x].requires_grad = False
layer_id = int(x.split('.')[1]) if ('blocks.' in x) else 0
if ('ln_out.' in x) or ('head.' in x):
layer_id = args.n_layer
dd = strategy[layer_id]
DEVICE = dd.device
ATYPE = dd.atype
WTYPE = dd.wtype
if not ALREADY_CONVERTED:
if self.RESCALE_LAYER > 0:
if 'att.output.weight' in x:
w[x] = w[x] / (2 ** int(layer_id // self.RESCALE_LAYER))
if 'ffn.value.weight' in x:
w[x] = w[x] / (2 ** int(layer_id // self.RESCALE_LAYER))
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 'gate.weight' in x or 'output.weight' in x or 'head.weight' in x:
w[x] = w[x].t()
if '.time_decay' in x and '_w' not in x: # need fp32 for this
if self.version == 4:
w[x] = -torch.exp(w[x].float())
elif int(self.version) == 5:
w[x] = torch.exp(-torch.exp(w[x].float())).reshape(-1,1,1)
if self.version == 5.2:
w[x] = w[x].reshape(args.n_head, -1, 1)
elif self.version == 6.0:
w[x] = w[x].float().reshape(args.n_head, -1, 1)
elif '.time_first' in x: # need fp32 for this
if self.version == 4:
w[x] = w[x].float()
elif int(self.version) in [5, 6]:
if REAL_TIME_FIRST:
w[x] = w[x].float().reshape(-1,1,1)
else:
w[x] = torch.exp(w[x].float()).reshape(-1,1,1)
if self.version in [5.2, 6.0]:
w[x] = w[x].reshape(args.n_head, -1, 1)
elif '.ln_x' in x: # need fp32 for group_norm
w[x] = w[x].float()
else:
if (len(w[x].shape) == 2) and ('emb' not in x):
if WTYPE != torch.uint8:
w[x] = w[x].to(dtype=WTYPE)
else:
w[x] = w[x].float()
if w[x].shape[0] > w[x].shape[1]:
w[x+'_my'] = torch.amin(w[x], dim=1).unsqueeze(1)
w[x] = w[x] - w[x+'_my']
w[x+'_mx'] = torch.amin(w[x], dim=0)
w[x] = w[x] - w[x+'_mx']
w[x+'_rx'] = torch.amax(w[x], dim=0)
w[x] = w[x] / w[x+'_rx']
w[x+'_ry'] = torch.amax(w[x], dim=1).unsqueeze(1)
w[x] = w[x] / w[x+'_ry']
else:
w[x+'_mx'] = torch.amin(w[x], dim=0)
w[x] = w[x] - w[x+'_mx']
w[x+'_my'] = torch.amin(w[x], dim=1).unsqueeze(1)
w[x] = w[x] - w[x+'_my']
w[x+'_rx'] = torch.amax(w[x], dim=0)
w[x] = w[x] / w[x+'_rx']
w[x+'_ry'] = torch.amax(w[x], dim=1).unsqueeze(1)
w[x] = w[x] / w[x+'_ry']
w[x] = torch.clip(torch.floor(w[x] * 256), min=0, max=255).to(dtype=torch.uint8)
w[x+'_mx'] = w[x+'_mx'].to(dtype=ATYPE).contiguous()
w[x+'_rx'] = (w[x+'_rx'] / 16).to(dtype=ATYPE).contiguous()
w[x+'_my'] = w[x+'_my'].to(dtype=ATYPE).contiguous()
w[x+'_ry'] = (w[x+'_ry'] / 16).to(dtype=ATYPE).contiguous()
else:
w[x] = w[x].to(dtype=ATYPE)
if convert_and_save_and_exit == None:
if 'emb.' in x:
w[x] = w[x].contiguous()
elif (dd.stream) and (x.endswith('key.weight') or x.endswith('value.weight') or x.endswith('receptance.weight') or x.endswith('output.weight')):
try:
w[x] = w[x].contiguous().pin_memory() # if you see "CUDA error: out of memory" here, that's out of CPU RAM, not VRAM. Get more RAM :)
except:
print('Note: You are running out of RAM. Get more CPU RAM. Now this will run much slower.')
elif DEVICE != 'cpu':
w[x] = w[x].to(device=DEVICE).contiguous()
if (dd.stream) or (DEVICE != 'cpu'):
try:
w[x+'_mx'] = w[x+'_mx'].to(device=DEVICE).contiguous()
w[x+'_rx'] = w[x+'_rx'].to(device=DEVICE).contiguous()
w[x+'_my'] = w[x+'_my'].to(device=DEVICE).contiguous()
w[x+'_ry'] = w[x+'_ry'].to(device=DEVICE).contiguous()
except:
pass
if 'ffn.value.weight' in x:
gc.collect()
if 'cuda' in args.strategy_string:
torch.cuda.empty_cache()
shape = [i for i in w[x].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 layer_id == 0 or layer_id >= args.n_layer-1:
if print_need_newline:
prxxx('\n', end = '')
print_need_newline = False
dt = str(w[x].dtype).replace('torch.', '')
dt = dt.replace('float32', 'f32').replace('bfloat16', 'bf16').replace('float16', 'f16').replace('uint8', 'i8')
prxxx(x.ljust(32), dt.rjust(4), str(w[x].device).rjust(8), shape, ' (pinned)' if w[x].is_pinned() else '')
else:
print_need_newline = True
prxxx('.', end = '', flush = True)
if convert_and_save_and_exit:
w['_strategy'] = args.strategy_string
w['_rescale_layer'] = self.RESCALE_LAYER
w['_version'] = '0.7'
if not convert_and_save_and_exit.endswith('.pth'):
convert_and_save_and_exit += '.pth'
prxxx(f'Saving to {convert_and_save_and_exit}...')
torch.save(w, convert_and_save_and_exit)
prxxx(f'Converted and saved. Now this will exit.')
exit(0)
if self.version == 5.2 and os.environ["RWKV_CUDA_ON"] == '1':
HEAD_SIZE = args.n_att // args.n_head
rwkv5 = load(name="rwkv5", sources=[f"{current_path}/cuda/rwkv5_op.cpp", f"{current_path}/cuda/rwkv5.cu"],
verbose=True, extra_cuda_cflags=["-res-usage", "--use_fast_math", "-O3", "-Xptxas -O3" if os.name != "nt" else "", "--extra-device-vectorization", f"-D_N_={HEAD_SIZE}"])
class RWKV_5(torch.autograd.Function):
@staticmethod
def forward(ctx, B, T, C, H, state, r, k, v, w, u):
with torch.no_grad():
assert HEAD_SIZE == C // H
ctx.B = B
ctx.T = T
ctx.C = C
ctx.H = H
assert state.dtype == torch.float32
assert w.dtype == torch.float32
assert r.is_contiguous()
assert k.is_contiguous()
assert v.is_contiguous()
assert w.is_contiguous()
assert u.is_contiguous()
assert state.is_contiguous()
y = torch.empty((B, T, C), device=w.device, dtype=r.dtype, memory_format=torch.contiguous_format)
if r.dtype == torch.bfloat16:
rwkv5.forward_bf16(B, T, C, H, state, r, k, v, w, u, y)
elif r.dtype == torch.float16:
rwkv5.forward_fp16(B, T, C, H, state, r, k, v, w, u, y)
elif r.dtype == torch.float32:
rwkv5.forward_fp32(B, T, C, H, state, r, k, v, w, u, y)
return y, state
self.RWKV_5 = RWKV_5
if self.version == 6.0 and os.environ["RWKV_CUDA_ON"] == '1':
HEAD_SIZE = args.n_att // args.n_head
rwkv6 = load(name="rwkv6", sources=[f"{current_path}/cuda/rwkv6_op.cpp", f"{current_path}/cuda/rwkv6.cu"],
verbose=True, extra_cuda_cflags=["-res-usage", "--use_fast_math", "-O3", "-Xptxas -O3", "--extra-device-vectorization", f"-D_N_={HEAD_SIZE}", f"-D_T_={4096}"])
class RWKV_6(torch.autograd.Function):
@staticmethod
def forward(ctx, B, T, C, H, state, r, k, v, w, u):
with torch.no_grad():
assert HEAD_SIZE == C // H
ctx.B = B
ctx.T = T
ctx.C = C
ctx.H = H
assert state.dtype == torch.float32
assert w.dtype == torch.float32
assert r.is_contiguous()
assert k.is_contiguous()
assert v.is_contiguous()
assert w.is_contiguous()
assert u.is_contiguous()
eew = torch.exp(-torch.exp(w.float())).contiguous()
y = torch.empty((B, T, C), device=w.device, dtype=r.dtype, memory_format=torch.contiguous_format)
if r.dtype == torch.bfloat16:
rwkv6.forward_bf16(B, T, C, H, state, r, k, v, eew, u, y)
elif r.dtype == torch.float16:
rwkv6.forward_fp16(B, T, C, H, state, r, k, v, eew, u, y)
elif r.dtype == torch.float32:
rwkv6.forward_fp32(B, T, C, H, state, r, k, v, eew, u, y)
return y, state
self.RWKV_6 = RWKV_6
gc.collect()
if 'cuda' in args.strategy_string:
torch.cuda.empty_cache()
def RUN_RWKV_5(self, B, T, C, H, state, r, k, v, w, u):
return self.RWKV_5.apply(B, T, C, H, state, r, k, v, w, u)
def RUN_RWKV_6(self, B, T, C, H, state, r, k, v, w, u):
return self.RWKV_6.apply(B, T, C, H, state, r, k, v, w, u)
########################################################################################################
@MyFunction
def ffn_one(self, x, sx, ln_w, ln_b, k_mix, r_mix, kw, vw, rw, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry):
xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
kx = xx * k_mix + sx * (1 - k_mix)
rx = xx * r_mix + sx * (1 - r_mix)
r = torch.sigmoid(matmul(rx, rw, rmx, rrx, rmy, rry))
vx = torch.relu(matmul(kx, kw, kmx, krx, kmy, kry)) ** 2
out = r * matmul(vx, vw, vmx, vrx, vmy, vry)
return x + out, xx
@MyFunction
def ffn_seq(self, x, sx, ln_w, ln_b, k_mix, r_mix, kw, vw, rw, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry):
xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
sx = torch.cat((sx.unsqueeze(0), xx[:-1,:]))
kx = xx * k_mix + sx * (1 - k_mix)
rx = xx * r_mix + sx * (1 - r_mix)
r = torch.sigmoid(matmul(rx, rw, rmx, rrx, rmy, rry))
vx = torch.relu(matmul(kx, kw, kmx, krx, kmy, kry)) ** 2
out = r * matmul(vx, vw, vmx, vrx, vmy, vry)
return x + out, xx[-1,:]
@MyFunction
def ffn_one_v6(self, x, sx, ln_w, ln_b, k_maa, r_maa, kw, vw, rw, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry):
xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
sx = sx - xx
kx = xx + sx * k_maa
rx = xx + sx * r_maa
r = torch.sigmoid(matmul(rx, rw, rmx, rrx, rmy, rry))
vx = torch.relu(matmul(kx, kw, kmx, krx, kmy, kry)) ** 2
out = r * matmul(vx, vw, vmx, vrx, vmy, vry)
return x + out, xx
@MyFunction
def ffn_seq_v6(self, x, sx, ln_w, ln_b, k_maa, r_maa, kw, vw, rw, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry):
xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
sx = torch.cat((sx.unsqueeze(0), xx[:-1,:]))
sx = sx - xx
kx = xx + sx * k_maa
rx = xx + sx * r_maa
r = torch.sigmoid(matmul(rx, rw, rmx, rrx, rmy, rry))
vx = torch.relu(matmul(kx, kw, kmx, krx, kmy, kry)) ** 2
out = r * matmul(vx, vw, vmx, vrx, vmy, vry)
return x + out, xx[-1,:]
########################################################################################################
@MyFunction
def att_one(self, x, sx, aa, bb, pp, ln_w, ln_b, k_mix, v_mix, r_mix, t_decay, t_first, kw, vw, rw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, omx, orx, omy, ory):
xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
kx = xx * k_mix + sx * (1 - k_mix)
vx = xx * v_mix + sx * (1 - v_mix)
rx = xx * r_mix + sx * (1 - r_mix)
r = torch.sigmoid(matmul(rx, rw, rmx, rrx, rmy, rry))
k = matmul(kx, kw, kmx, krx, kmy, kry, output_dtype=torch.float32)
v = matmul(vx, vw, vmx, vrx, vmy, vry, output_dtype=torch.float32)
ww = t_first + k
p = torch.maximum(pp, ww)
e1 = torch.exp(pp - p)
e2 = torch.exp(ww - p)
wkv = ((e1 * aa + e2 * v) / (e1 * bb + e2)).to(dtype=x.dtype)
ww = t_decay + pp
p = torch.maximum(ww, k)
e1 = torch.exp(ww - p)
e2 = torch.exp(k - p)
out = matmul(r * wkv, ow, omx, orx, omy, ory)
return x + out, xx, e1 * aa + e2 * v, e1 * bb + e2, p
@MyFunction
def att_seq(self, x, sx, aa, bb, pp, ln_w, ln_b, k_mix, v_mix, r_mix, t_decay, t_first, kw, vw, rw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, omx, orx, omy, ory):
xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
sx = torch.cat((sx.unsqueeze(0), xx[:-1,:]))
kx = xx * k_mix + sx * (1 - k_mix)
vx = xx * v_mix + sx * (1 - v_mix)
rx = xx * r_mix + sx * (1 - r_mix)
r = torch.sigmoid(matmul(rx, rw, rmx, rrx, rmy, rry))
k = matmul(kx, kw, kmx, krx, kmy, kry, output_dtype=torch.float32)
v = matmul(vx, vw, vmx, vrx, vmy, vry, output_dtype=torch.float32)
T = x.shape[0]
for t in range(T):
kk = k[t]
vv = v[t]
ww = t_first + kk
p = torch.maximum(pp, ww)
e1 = torch.exp(pp - p)
e2 = torch.exp(ww - p)
sx[t] = ((e1 * aa + e2 * vv) / (e1 * bb + e2)).to(dtype=x.dtype)
ww = t_decay + pp
p = torch.maximum(ww, kk)
e1 = torch.exp(ww - p)
e2 = torch.exp(kk - p)
aa = e1 * aa + e2 * vv
bb = e1 * bb + e2
pp = p
out = matmul(r * sx, ow, omx, orx, omy, ory)
return x + out, xx[-1,:], aa, bb, pp
########################################################################################################
@MyFunction
def att_one_v5(self, x, sx, s, ln_w, ln_b, lx_w, lx_b, k_mix, v_mix, r_mix, t_decay, t_first, kw, vw, rw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, omx, orx, omy, ory):
xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
kx = xx * k_mix + sx * (1 - k_mix)
vx = xx * v_mix + sx * (1 - v_mix)
rx = xx * r_mix + sx * (1 - r_mix)
H = t_decay.shape[0]
N = x.shape[-1] // H
r = matmul(rx, rw, rmx, rrx, rmy, rry, output_dtype=torch.float32).view(H, 1, N)
k = matmul(kx, kw, kmx, krx, kmy, kry, output_dtype=torch.float32).view(H, N, 1)
v = matmul(vx, vw, vmx, vrx, vmy, vry, output_dtype=torch.float32).view(H, 1, N)
a = matmul(k, v)
out = r @ (t_first * a + s)
s = a + t_decay * s
out = out.flatten()
out = F.group_norm(out.unsqueeze(0), num_groups=H, weight=lx_w, bias=lx_b, eps = 64e-5).squeeze(0)
out = out.to(dtype=x.dtype)
out = matmul(out, ow, omx, orx, omy, ory)
return x + out, xx, s
@MyFunction
def att_seq_v5(self, x, sx, s, ln_w, ln_b, lx_w, lx_b, k_mix, v_mix, r_mix, t_decay, t_first, kw, vw, rw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, omx, orx, omy, ory):
xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
sx = torch.cat((sx.unsqueeze(0), xx[:-1,:]))
kx = xx * k_mix + sx * (1 - k_mix)
vx = xx * v_mix + sx * (1 - v_mix)
rx = xx * r_mix + sx * (1 - r_mix)
H = t_decay.shape[0]
N = x.shape[-1] // H
T = x.shape[0]
w = t_decay.reshape(-1, 1)
u = t_first.reshape(-1, 1)
ws = w.pow(T).reshape(H, 1, 1)
ind = torch.arange(T-1, -1, -1, device=w.device).unsqueeze(0).repeat(H, 1)
w = w.repeat(1, T).pow(ind)
wk = w.reshape(H, 1, T)
wb = wk.transpose(-2, -1).flip(1)
w = torch.cat([w[:, 1:], u], dim=1)
w = F.pad(w, (0, T))
w = torch.tile(w, [T])
w = w[:, :-T].reshape(-1, T, 2 * T - 1)
w = w[:, :, T-1:].reshape(H, T, T)
r = matmul(rx, rw, rmx, rrx, rmy, rry, output_dtype=torch.float32).view(T, H, N).transpose(0, 1)
k = matmul(kx, kw, kmx, krx, kmy, kry, output_dtype=torch.float32).view(T, H, N).permute(1, 2, 0)
v = matmul(vx, vw, vmx, vrx, vmy, vry, output_dtype=torch.float32).view(T, H, N).transpose(0, 1)
out = ((r @ k) * w) @ v + (r @ s) * wb
s = ws * s + (k * wk) @ v
out = out.transpose(0, 1).contiguous().reshape(T, H*N)
out = F.group_norm(out, num_groups=H, weight=lx_w, bias=lx_b, eps = 64e-5)
out = out.to(dtype=x.dtype)
out = matmul(out, ow, omx, orx, omy, ory)
return x + out, xx[-1,:], s
########################################################################################################
@MyFunction
def att_one_v5_1(self, x, sx, s, ln_w, ln_b, lx_w, lx_b, k_mix, v_mix, r_mix, g_mix, t_decay, t_first, kw, vw, rw, gw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, gmx, grx, gmy, gry, omx, orx, omy, ory):
xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
kx = xx * k_mix + sx * (1 - k_mix)
vx = xx * v_mix + sx * (1 - v_mix)
rx = xx * r_mix + sx * (1 - r_mix)
gx = xx * g_mix + sx * (1 - g_mix)
H = t_decay.shape[0]
N = x.shape[-1] // H
r = matmul(rx, rw, rmx, rrx, rmy, rry, output_dtype=torch.float32).view(H, 1, N)
k = matmul(kx, kw, kmx, krx, kmy, kry, output_dtype=torch.float32).view(H, N, 1)
v = matmul(vx, vw, vmx, vrx, vmy, vry, output_dtype=torch.float32).view(H, 1, N)
g = F.silu(matmul(gx, gw, gmx, grx, gmy, gry))
a = matmul(k, v)
out = r @ (t_first * a + s)
s = a + t_decay * s
out = out.flatten()
out = F.group_norm(out.unsqueeze(0), num_groups=H, weight=lx_w, bias=lx_b, eps = 64e-5).squeeze(0)
out = out.to(dtype=x.dtype) * g
out = matmul(out, ow, omx, orx, omy, ory)
return x + out, xx, s
@MyFunction
def att_seq_v5_1(self, x, sx, s, ln_w, ln_b, lx_w, lx_b, k_mix, v_mix, r_mix, g_mix, t_decay, t_first, kw, vw, rw, gw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, gmx, grx, gmy, gry, omx, orx, omy, ory):
xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
sx = torch.cat((sx.unsqueeze(0), xx[:-1,:]))
kx = xx * k_mix + sx * (1 - k_mix)
vx = xx * v_mix + sx * (1 - v_mix)
rx = xx * r_mix + sx * (1 - r_mix)
gx = xx * g_mix + sx * (1 - g_mix)
H = t_decay.shape[0]
N = x.shape[-1] // H
T = x.shape[0]
w = t_decay.reshape(-1, 1)
u = t_first.reshape(-1, 1)
ws = w.pow(T).reshape(H, 1, 1)
ind = torch.arange(T-1, -1, -1, device=w.device).unsqueeze(0).repeat(H, 1)
w = w.repeat(1, T).pow(ind)
wk = w.reshape(H, 1, T)
wb = wk.transpose(-2, -1).flip(1)
w = torch.cat([w[:, 1:], u], dim=1)
w = F.pad(w, (0, T))
w = torch.tile(w, [T])
w = w[:, :-T].reshape(-1, T, 2 * T - 1)
w = w[:, :, T-1:].reshape(H, T, T)
r = matmul(rx, rw, rmx, rrx, rmy, rry, output_dtype=torch.float32).view(T, H, N).transpose(0, 1)
k = matmul(kx, kw, kmx, krx, kmy, kry, output_dtype=torch.float32).view(T, H, N).permute(1, 2, 0)
v = matmul(vx, vw, vmx, vrx, vmy, vry, output_dtype=torch.float32).view(T, H, N).transpose(0, 1)
g = F.silu(matmul(gx, gw, gmx, grx, gmy, gry))
out = ((r @ k) * w) @ v + (r @ s) * wb
s = ws * s + (k * wk) @ v
out = out.transpose(0, 1).contiguous().reshape(T, H*N)
out = F.group_norm(out, num_groups=H, weight=lx_w, bias=lx_b, eps = 64e-5)
out = out.to(dtype=x.dtype) * g
out = matmul(out, ow, omx, orx, omy, ory)
return x + out, xx[-1,:], s
########################################################################################################
@MyFunction
def att_seq_v5_2(self, x, sx, s, ln_w, ln_b, lx_w, lx_b, k_mix, v_mix, r_mix, g_mix, t_decay, t_first, kw, vw, rw, gw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, gmx, grx, gmy, gry, omx, orx, omy, ory):
xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
sx = torch.cat((sx.unsqueeze(0), xx[:-1,:]))
kx = xx * k_mix + sx * (1 - k_mix)
vx = xx * v_mix + sx * (1 - v_mix)
rx = xx * r_mix + sx * (1 - r_mix)
gx = xx * g_mix + sx * (1 - g_mix)
H = t_decay.shape[0]
N = x.shape[-1] // H
T = x.shape[0]
r = matmul(rx, rw, rmx, rrx, rmy, rry, output_dtype=torch.float32).view(T, H, N).transpose(0, 1)
k = matmul(kx, kw, kmx, krx, kmy, kry, output_dtype=torch.float32).view(T, H, N).permute(1, 2, 0)
v = matmul(vx, vw, vmx, vrx, vmy, vry, output_dtype=torch.float32).view(T, H, N).transpose(0, 1)
g = F.silu(matmul(gx, gw, gmx, grx, gmy, gry))
out = torch.empty((T, H, N), dtype=r.dtype, device=r.device)
for t in range(T):
rt = r[:,t:t+1,:]
kt = k[:,:,t:t+1]
vt = v[:,t:t+1,:]
at = matmul(kt, vt)
out[t] = (rt @ (t_first * at + s)).squeeze(1)
s = at + t_decay * s
out = out.reshape(T, H*N)
out = F.group_norm(out, num_groups=H, weight=lx_w, bias=lx_b, eps = 64e-5)
out = out.to(dtype=x.dtype) * g
out = matmul(out, ow, omx, orx, omy, ory)
return x + out, xx[-1,:], s
########################################################################################################
@MyFunction
def att_one_v6_0(self, x, sx, s, ln_w, ln_b, lx_w, lx_b, x_maa, w_maa, k_maa, v_maa, r_maa, g_maa, tm_w1, tm_w2, td_w1, td_w2, t_decay, t_first, kw, vw, rw, gw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, gmx, grx, gmy, gry, omx, orx, omy, ory):
xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
sx = sx - xx
xxx = xx + sx * x_maa
xxx = torch.tanh(xxx @ tm_w1).view(5, 1, -1)
xxx = torch.bmm(xxx, tm_w2).view(5, -1)
mw, mk, mv, mr, mg = xxx.unbind(dim=0)
wx = xx + sx * (w_maa + mw)
kx = xx + sx * (k_maa + mk)
vx = xx + sx * (v_maa + mv)
rx = xx + sx * (r_maa + mr)
gx = xx + sx * (g_maa + mg)
H = t_decay.shape[0]
N = x.shape[-1] // H
r = matmul(rx, rw, rmx, rrx, rmy, rry, output_dtype=torch.float32).view(H, 1, N)
k = matmul(kx, kw, kmx, krx, kmy, kry, output_dtype=torch.float32).view(H, N, 1)
v = matmul(vx, vw, vmx, vrx, vmy, vry, output_dtype=torch.float32).view(H, 1, N)
g = F.silu(matmul(gx, gw, gmx, grx, gmy, gry))
w = t_decay + (torch.tanh(wx @ td_w1) @ td_w2).float().view(H, N, 1)
w = torch.exp(-torch.exp(w.float()))
a = matmul(k, v)
out = r @ (t_first * a + s)
s = a + w * s
out = out.flatten()
out = F.group_norm(out.unsqueeze(0), num_groups=H, weight=lx_w, bias=lx_b, eps = 64e-5).squeeze(0)
out = out.to(dtype=x.dtype) * g
out = matmul(out, ow, omx, orx, omy, ory)
return x + out, xx, s
@MyFunction
def att_seq_v6_0(self, x, sx, s, ln_w, ln_b, lx_w, lx_b, x_maa, w_maa, k_maa, v_maa, r_maa, g_maa, tm_w1, tm_w2, td_w1, td_w2, t_decay, t_first, kw, vw, rw, gw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, gmx, grx, gmy, gry, omx, orx, omy, ory):
H = t_decay.shape[0]
N = x.shape[-1] // H
T = x.shape[0]
xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
sx = torch.cat((sx.unsqueeze(0), xx[:-1,:])) - xx
xxx = xx + sx * x_maa
xxx = torch.tanh(xxx @ tm_w1).view(T, 5, -1).transpose(0, 1)
xxx = torch.bmm(xxx, tm_w2).view(5, T, -1)
mw, mk, mv, mr, mg = xxx.unbind(dim=0)
wx = xx + sx * (w_maa + mw)
kx = xx + sx * (k_maa + mk)
vx = xx + sx * (v_maa + mv)
rx = xx + sx * (r_maa + mr)
gx = xx + sx * (g_maa + mg)
r = matmul(rx, rw, rmx, rrx, rmy, rry, output_dtype=torch.float32).view(T, H, N).transpose(0, 1)
k = matmul(kx, kw, kmx, krx, kmy, kry, output_dtype=torch.float32).view(T, H, N).permute(1, 2, 0)
v = matmul(vx, vw, vmx, vrx, vmy, vry, output_dtype=torch.float32).view(T, H, N).transpose(0, 1)
g = F.silu(matmul(gx, gw, gmx, grx, gmy, gry))
w = t_decay.view(1, H, N, 1) + (torch.tanh(wx @ td_w1) @ td_w2).float().view(T, H, N, 1)
w = torch.exp(-torch.exp(w.float()))
out = torch.empty((T, H, N), dtype=r.dtype, device=r.device)
for t in range(T):
rt = r[:,t:t+1,:]
kt = k[:,:,t:t+1]
vt = v[:,t:t+1,:]
at = matmul(kt, vt)
out[t] = (rt @ (t_first * at + s)).squeeze(1)
s = at + w[t] * s
out = out.reshape(T, H*N)
out = F.group_norm(out, num_groups=H, weight=lx_w, bias=lx_b, eps = 64e-5)
out = out.to(dtype=x.dtype) * g
out = matmul(out, ow, omx, orx, omy, ory)
return x + out, xx[-1,:], s
########################################################################################################
if os.environ["RWKV_CUDA_ON"] == '1':
@MyFunction
def cuda_att_seq(self, x, sx, aa, bb, pp, ln_w, ln_b, k_mix, v_mix, r_mix, t_decay, t_first, kw, vw, rw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, omx, orx, omy, ory):
T, C = x.shape
xx = F.layer_norm(x, (C,), weight=ln_w, bias=ln_b)
sx = torch.cat((sx.unsqueeze(0), xx[:-1,:]))
kx = xx * k_mix + sx * (1 - k_mix)
vx = xx * v_mix + sx * (1 - v_mix)
rx = xx * r_mix + sx * (1 - r_mix)
r = torch.sigmoid(matmul(rx, rw, rmx, rrx, rmy, rry))
k = matmul(kx, kw, kmx, krx, kmy, kry, output_dtype=torch.float32)
v = matmul(vx, vw, vmx, vrx, vmy, vry, output_dtype=torch.float32)
y, aa, bb, pp = cuda_wkv(T, C, t_decay, t_first, k, v, aa, bb, pp)
out = matmul(r * y.to(x.dtype), ow, omx, orx, omy, ory)
return x + out, xx[-1,:], aa, bb, pp
@MyFunction
def v5_2_before(self, x, sx, s, ln_w, ln_b, lx_w, lx_b, k_mix, v_mix, r_mix, g_mix, t_decay, t_first, kw, vw, rw, gw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, gmx, grx, gmy, gry, omx, orx, omy, ory):
xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
sx = torch.cat((sx.unsqueeze(0), xx[:-1,:]))
kx = xx * k_mix + sx * (1 - k_mix)
vx = xx * v_mix + sx * (1 - v_mix)
rx = xx * r_mix + sx * (1 - r_mix)
gx = xx * g_mix + sx * (1 - g_mix)
r = matmul(rx, rw, rmx, rrx, rmy, rry, output_dtype=torch.float32)
k = matmul(kx, kw, kmx, krx, kmy, kry, output_dtype=torch.float32)
v = matmul(vx, vw, vmx, vrx, vmy, vry, output_dtype=torch.float32)
g = F.silu(matmul(gx, gw, gmx, grx, gmy, gry))
return r, k, v, g, xx[-1,:], s.transpose(-1,-2).contiguous()
@MyFunction
def v5_2_after(self, t_decay, out, s, x, xxx, g, lx_w, lx_b, ow, omx, orx, omy, ory):
H = t_decay.shape[0]
N = x.shape[-1] // H
T = x.shape[0]
s = s.transpose(-1,-2)
out = out.reshape(T, H*N)
out = F.group_norm(out, num_groups=H, weight=lx_w, bias=lx_b, eps = 64e-5)
out = out.to(dtype=x.dtype) * g
out = matmul(out, ow, omx, orx, omy, ory)
return x + out, xxx, s
def cuda_att_seq_v5_2(self, x, sx, s, ln_w, ln_b, lx_w, lx_b, k_mix, v_mix, r_mix, g_mix, t_decay, t_first, kw, vw, rw, gw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, gmx, grx, gmy, gry, omx, orx, omy, ory):
H = t_decay.shape[0]
N = x.shape[-1] // H
T = x.shape[0]
r, k, v, g, xxx, ss = self.v5_2_before(x, sx, s, ln_w, ln_b, lx_w, lx_b, k_mix, v_mix, r_mix, g_mix, t_decay, t_first, kw, vw, rw, gw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, gmx, grx, gmy, gry, omx, orx, omy, ory)
out, s = self.RUN_RWKV_5(1, T, self.args.n_att, H, ss, r, k, v, w=t_decay, u=t_first)
return self.v5_2_after(t_decay, out, s, x, xxx, g, lx_w, lx_b, ow, omx, orx, omy, ory)
@MyFunction
def v6_0_before(self, x, sx, s, ln_w, ln_b, lx_w, lx_b, x_maa, w_maa, k_maa, v_maa, r_maa, g_maa, tm_w1, tm_w2, td_w1, td_w2, t_decay, t_first, kw, vw, rw, gw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, gmx, grx, gmy, gry, omx, orx, omy, ory):
H = t_decay.shape[0]
N = x.shape[-1] // H
T = x.shape[0]
xx = F.layer_norm(x, (x.shape[-1],), weight=ln_w, bias=ln_b)
sx = torch.cat((sx.unsqueeze(0), xx[:-1,:])) - xx
xxx = xx + sx * x_maa
xxx = torch.tanh(xxx @ tm_w1).view(T, 5, -1).transpose(0, 1)
xxx = torch.bmm(xxx, tm_w2).view(5, T, -1)
mw, mk, mv, mr, mg = xxx.unbind(dim=0)
wx = xx + sx * (w_maa + mw)
kx = xx + sx * (k_maa + mk)
vx = xx + sx * (v_maa + mv)
rx = xx + sx * (r_maa + mr)
gx = xx + sx * (g_maa + mg)
r = matmul(rx, rw, rmx, rrx, rmy, rry, output_dtype=torch.float32)
k = matmul(kx, kw, kmx, krx, kmy, kry, output_dtype=torch.float32)
v = matmul(vx, vw, vmx, vrx, vmy, vry, output_dtype=torch.float32)
g = F.silu(matmul(gx, gw, gmx, grx, gmy, gry))
w = t_decay.view(1, H, N, 1) + (torch.tanh(wx @ td_w1) @ td_w2).float().view(T, H, N, 1)
return r, k, v, g, w, xx[-1,:], s.transpose(-1,-2).contiguous()
def cuda_att_seq_v6_0(self, x, sx, s, ln_w, ln_b, lx_w, lx_b, x_maa, w_maa, k_maa, v_maa, r_maa, g_maa, tm_w1, tm_w2, td_w1, td_w2, t_decay, t_first, kw, vw, rw, gw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, gmx, grx, gmy, gry, omx, orx, omy, ory):
H = t_decay.shape[0]
N = x.shape[-1] // H
T = x.shape[0]
r, k, v, g, w, xxx, ss = self.v6_0_before(x, sx, s, ln_w, ln_b, lx_w, lx_b, x_maa, w_maa, k_maa, v_maa, r_maa, g_maa, tm_w1, tm_w2, td_w1, td_w2, t_decay, t_first, kw, vw, rw, gw, ow, kmx, krx, kmy, kry, vmx, vrx, vmy, vry, rmx, rrx, rmy, rry, gmx, grx, gmy, gry, omx, orx, omy, ory)
out, s = self.RUN_RWKV_6(1, T, self.args.n_att, H, ss, r, k, v, w=w, u=t_first)
return self.v5_2_after(t_decay, out, s, x, xxx, g, lx_w, lx_b, ow, omx, orx, omy, ory)
########################################################################################################
def forward(self, tokens=None, state=None, full_output=False, embs=None):
with torch.no_grad():
w = self.w
args = self.args
if state == None:
if self.version == 4:
state = [None] * args.n_layer * 5
for i in range(args.n_layer): # state: 0=att_xx 1=att_aa 2=att_bb 3=att_pp 4=ffn_xx
dd = self.strategy[i]
dev = dd.device
atype = dd.atype
state[i*5+0] = torch.zeros(args.n_embd, dtype=atype, requires_grad=False, device=dev).contiguous()
state[i*5+1] = torch.zeros(args.n_att, dtype=torch.float, requires_grad=False, device=dev).contiguous()
state[i*5+2] = torch.zeros(args.n_att, dtype=torch.float, requires_grad=False, device=dev).contiguous()
state[i*5+3] = torch.zeros(args.n_att, dtype=torch.float, requires_grad=False, device=dev).contiguous() - 1e30
state[i*5+4] = torch.zeros(args.n_embd, dtype=atype, requires_grad=False, device=dev).contiguous()
elif int(self.version) in [5,6]:
state = [None] * args.n_layer * 3
for i in range(args.n_layer): # state: 0=att_xx 1=att_kv 2=ffn_xx
dd = self.strategy[i]
dev = dd.device
atype = dd.atype
state[i*3+0] = torch.zeros(args.n_embd, dtype=atype, requires_grad=False, device=dev).contiguous()
state[i*3+1] = torch.zeros((args.n_head, args.n_att//args.n_head, args.n_att//args.n_head), dtype=torch.float, requires_grad=False, device=dev).contiguous()
state[i*3+2] = torch.zeros(args.n_embd, dtype=atype, requires_grad=False, device=dev).contiguous()
if embs is None:
seq_mode = len(tokens) > 1
x = w['emb.weight'][tokens if seq_mode else tokens[0]]
else:
x = embs
seq_mode = True
for i in range(args.n_layer):
bbb = f'blocks.{i}.'
att = f'blocks.{i}.att.'
ffn = f'blocks.{i}.ffn.'
dd = self.strategy[i]
dev = dd.device
atype = dd.atype
wtype = dd.wtype
if seq_mode:
cuda_applicable = os.environ["RWKV_CUDA_ON"] == '1' and 'cuda' in str(dev)
if cuda_applicable:
ATT = self.cuda_att_seq
else:
ATT = self.att_seq
if self.version == 5:
ATT = self.att_seq_v5
elif self.version == 5.1:
ATT = self.att_seq_v5_1
elif self.version == 5.2:
ATT = self.att_seq_v5_2
if cuda_applicable:
ATT = self.cuda_att_seq_v5_2
elif self.version == 6.0:
ATT = self.att_seq_v6_0
if cuda_applicable:
ATT = self.cuda_att_seq_v6_0
FFN = self.ffn_seq
if self.version >= 6.0:
FFN = self.ffn_seq_v6
else:
ATT = self.att_one
if self.version == 5:
ATT = self.att_one_v5
elif self.version == 5.1:
ATT = self.att_one_v5_1
elif self.version == 5.2:
ATT = self.att_one_v5_1 # same as v5.1
elif self.version == 6.0:
ATT = self.att_one_v6_0
FFN = self.ffn_one
if self.version >= 6.0:
FFN = self.ffn_one_v6
x = x.to(dtype=atype, device=dev)
kw = w[f'{att}key.weight']
vw = w[f'{att}value.weight']
rw = w[f'{att}receptance.weight']
ow = w[f'{att}output.weight']
if dd.stream:
kw = kw.to(device=dev, non_blocking=True)
vw = vw.to(device=dev, non_blocking=True)
rw = rw.to(device=dev, non_blocking=True)
ow = ow.to(device=dev, non_blocking=True)
kmx = w[f'{att}key.weight_mx'] if wtype == torch.uint8 else x
krx = w[f'{att}key.weight_rx'] if wtype == torch.uint8 else x
kmy = w[f'{att}key.weight_my'] if wtype == torch.uint8 else x
kry = w[f'{att}key.weight_ry'] if wtype == torch.uint8 else x
vmx = w[f'{att}value.weight_mx'] if wtype == torch.uint8 else x
vrx = w[f'{att}value.weight_rx'] if wtype == torch.uint8 else x
vmy = w[f'{att}value.weight_my'] if wtype == torch.uint8 else x
vry = w[f'{att}value.weight_ry'] if wtype == torch.uint8 else x
rmx = w[f'{att}receptance.weight_mx'] if wtype == torch.uint8 else x
rrx = w[f'{att}receptance.weight_rx'] if wtype == torch.uint8 else x
rmy = w[f'{att}receptance.weight_my'] if wtype == torch.uint8 else x
rry = w[f'{att}receptance.weight_ry'] if wtype == torch.uint8 else x
omx = w[f'{att}output.weight_mx'] if wtype == torch.uint8 else x
orx = w[f'{att}output.weight_rx'] if wtype == torch.uint8 else x
omy = w[f'{att}output.weight_my'] if wtype == torch.uint8 else x
ory = w[f'{att}output.weight_ry'] if wtype == torch.uint8 else x
if self.version in [5.1, 5.2, 6.0]:
gw = w[f'{att}gate.weight']
if dd.stream:
gw = gw.to(device=dev, non_blocking=True)
gmx = w[f'{att}gate.weight_mx'] if wtype == torch.uint8 else x
grx = w[f'{att}gate.weight_rx'] if wtype == torch.uint8 else x
gmy = w[f'{att}gate.weight_my'] if wtype == torch.uint8 else x
gry = w[f'{att}gate.weight_ry'] if wtype == torch.uint8 else x
if self.version == 4:
x, state[i*5+0], state[i*5+1], state[i*5+2], state[i*5+3] = ATT(
x, state[i*5+0], state[i*5+1], state[i*5+2], state[i*5+3],
w[f'{bbb}ln1.weight'], w[f'{bbb}ln1.bias'],
w[f'{att}time_mix_k'], w[f'{att}time_mix_v'], w[f'{att}time_mix_r'],
w[f'{att}time_decay'], w[f'{att}time_first'],
kw, vw, rw, ow,
kmx, krx, kmy, kry,
vmx, vrx, vmy, vry,
rmx, rrx, rmy, rry,
omx, orx, omy, ory,
)
elif self.version == 5:
x, state[i*3+0], state[i*3+1] = ATT(
x, state[i*3+0], state[i*3+1],
w[f'{bbb}ln1.weight'], w[f'{bbb}ln1.bias'],
w[f'{att}ln_x.weight'], w[f'{att}ln_x.bias'],
w[f'{att}time_mix_k'], w[f'{att}time_mix_v'], w[f'{att}time_mix_r'],
w[f'{att}time_decay'], w[f'{att}time_first'],
kw, vw, rw, ow,
kmx, krx, kmy, kry,
vmx, vrx, vmy, vry,
rmx, rrx, rmy, rry,
omx, orx, omy, ory,
)
elif self.version in [5.1, 5.2]:
x, state[i*3+0], state[i*3+1] = ATT(
x, state[i*3+0], state[i*3+1],
w[f'{bbb}ln1.weight'], w[f'{bbb}ln1.bias'],
w[f'{att}ln_x.weight'], w[f'{att}ln_x.bias'],
w[f'{att}time_mix_k'], w[f'{att}time_mix_v'], w[f'{att}time_mix_r'], w[f'{att}time_mix_g'],
w[f'{att}time_decay'], w[f'{att}time_first'],
kw, vw, rw, gw, ow,
kmx, krx, kmy, kry,
vmx, vrx, vmy, vry,
rmx, rrx, rmy, rry,
gmx, grx, gmy, gry,
omx, orx, omy, ory,
)
elif self.version == 6.0:
x, state[i*3+0], state[i*3+1] = ATT(
x, state[i*3+0], state[i*3+1],
w[f'{bbb}ln1.weight'], w[f'{bbb}ln1.bias'],
w[f'{att}ln_x.weight'], w[f'{att}ln_x.bias'],
w[f'{att}time_maa_x'], w[f'{att}time_maa_w'], w[f'{att}time_maa_k'], w[f'{att}time_maa_v'], w[f'{att}time_maa_r'], w[f'{att}time_maa_g'],
w[f'{att}time_maa_w1'], w[f'{att}time_maa_w2'], w[f'{att}time_decay_w1'], w[f'{att}time_decay_w2'],
w[f'{att}time_decay'], w[f'{att}time_first'],
kw, vw, rw, gw, ow,
kmx, krx, kmy, kry,
vmx, vrx, vmy, vry,
rmx, rrx, rmy, rry,
gmx, grx, gmy, gry,
omx, orx, omy, ory,
)
if dd.stream:
del kw, vw, rw, ow
if self.version in [5.1, 5.2, 6.0]:
del gw
kw = w[f'{ffn}key.weight']
vw = w[f'{ffn}value.weight']
rw = w[f'{ffn}receptance.weight']
if dd.stream:
kw = kw.to(device=dev, non_blocking=True)
vw = vw.to(device=dev, non_blocking=True)
rw = rw.to(device=dev, non_blocking=True)
kmx = w[f'{ffn}key.weight_mx'] if wtype == torch.uint8 else x
krx = w[f'{ffn}key.weight_rx'] if wtype == torch.uint8 else x
kmy = w[f'{ffn}key.weight_my'] if wtype == torch.uint8 else x
kry = w[f'{ffn}key.weight_ry'] if wtype == torch.uint8 else x
vmx = w[f'{ffn}value.weight_mx'] if wtype == torch.uint8 else x
vrx = w[f'{ffn}value.weight_rx'] if wtype == torch.uint8 else x
vmy = w[f'{ffn}value.weight_my'] if wtype == torch.uint8 else x
vry = w[f'{ffn}value.weight_ry'] if wtype == torch.uint8 else x
rmx = w[f'{ffn}receptance.weight_mx'] if wtype == torch.uint8 else x
rrx = w[f'{ffn}receptance.weight_rx'] if wtype == torch.uint8 else x
rmy = w[f'{ffn}receptance.weight_my'] if wtype == torch.uint8 else x
rry = w[f'{ffn}receptance.weight_ry'] if wtype == torch.uint8 else x
if self.version == 4:
offset = i*5+4
elif int(self.version) in [5,6]:
offset = i*3+2
if self.version < 6.0:
x, state[offset] = FFN(
x, state[offset],
w[f'{bbb}ln2.weight'], w[f'{bbb}ln2.bias'],
w[f'{ffn}time_mix_k'], w[f'{ffn}time_mix_r'],
kw, vw, rw,
kmx, krx, kmy, kry,
vmx, vrx, vmy, vry,
rmx, rrx, rmy, rry,
)
else:
x, state[offset] = FFN(
x, state[offset],
w[f'{bbb}ln2.weight'], w[f'{bbb}ln2.bias'],
w[f'{ffn}time_maa_k'], w[f'{ffn}time_maa_r'],
kw, vw, rw,
kmx, krx, kmy, kry,
vmx, vrx, vmy, vry,
rmx, rrx, rmy, rry,
)
if dd.stream:
del kw, vw, rw
if self.RESCALE_LAYER > 0:
if (i+1) % self.RESCALE_LAYER == 0:
x = x / 2
dd = self.strategy[args.n_layer]
x = x[-1,:] if (seq_mode and (not full_output)) else x
x = x.to(dtype=dd.atype, device=dd.device)
x = F.layer_norm(x, (args.n_embd,), weight=w['ln_out.weight'], bias=w['ln_out.bias'])
if w['head.weight'].dtype != torch.uint8:
x = x @ w['head.weight']
else:
if seq_mode and full_output:
x = mm8_seq(x, w['head.weight'], w['head.weight_mx'], w['head.weight_rx'], w['head.weight_my'], w['head.weight_ry'])
else:
x = mm8_one(x, w['head.weight'], w['head.weight_mx'], w['head.weight_rx'], w['head.weight_my'], w['head.weight_ry'])
return x.float(), state