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rwkv5 lora finetune support (https://github.com/JL-er/RWKV-v5-lora)

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This commit is contained in:
josc146 2023-12-29 12:23:36 +08:00
parent b7f4dd835e
commit 81544ca8b3
23 changed files with 2889 additions and 138 deletions
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6
finetune/get_layer_and_embd.py
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@ -32,6 +32,7 @@ cleaner_thread.start()
w = torch.load(model_file, map_location="cpu") w = torch.load(model_file, map_location="cpu")
gc.collect() gc.collect()
vocab_size = w["emb.weight"].shape[0]
n_embd = w["emb.weight"].shape[1] n_embd = w["emb.weight"].shape[1]
n_layer = 0 n_layer = 0
keys = list(w.keys()) keys = list(w.keys())
@ -52,6 +53,9 @@ for x in keys:
version = max(6, version) version = max(6, version)
if version <= expected_max_version: if version <= expected_max_version:
print(f"--n_layer {n_layer} --n_embd {n_embd}", end="") print(
f"v{int(version)}/train.py --vocab_size {vocab_size} --n_layer {n_layer} --n_embd {n_embd}",
end="",
)
else: else:
raise Exception(f"RWKV{version} is not supported") raise Exception(f"RWKV{version} is not supported")

4
finetune/install-wsl-dep-and-train.sh
View File

@ -47,10 +47,10 @@ else
fi fi
echo "loading $loadModel" echo "loading $loadModel"
modelInfo=$(python3 ./finetune/get_layer_and_embd.py $loadModel 4) modelInfo=$(python3 ./finetune/get_layer_and_embd.py $loadModel 5.2)
echo $modelInfo echo $modelInfo
if [[ $modelInfo =~ "--n_layer" ]]; then if [[ $modelInfo =~ "--n_layer" ]]; then
python3 ./finetune/lora/train.py $modelInfo $@ --proj_dir lora-models --data_type binidx --lora \ python3 ./finetune/lora/$modelInfo $@ --proj_dir lora-models --data_type binidx --lora \
--lora_parts=att,ffn,time,ln --strategy deepspeed_stage_2 --accelerator gpu --lora_parts=att,ffn,time,ln --strategy deepspeed_stage_2 --accelerator gpu
else else
echo "modelInfo is invalid" echo "modelInfo is invalid"

0
finetune/lora/cuda/wkv_cuda.cu → finetune/lora/v4/cuda/wkv_cuda.cu vendored
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0
finetune/lora/cuda/wkv_cuda_bf16.cu → finetune/lora/v4/cuda/wkv_cuda_bf16.cu vendored
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0
finetune/lora/cuda/wkv_op.cpp → finetune/lora/v4/cuda/wkv_op.cpp vendored
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0
finetune/lora/cuda/wkv_op_bf16.cpp → finetune/lora/v4/cuda/wkv_op_bf16.cpp vendored
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0
finetune/lora/src/__init__.py → finetune/lora/v4/src/__init__.py vendored
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10
finetune/lora/src/binidx.py → finetune/lora/v4/src/binidx.py vendored
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@ -7,6 +7,7 @@ import struct
from functools import lru_cache from functools import lru_cache
from itertools import accumulate from itertools import accumulate
def print_rank_0(*message): def print_rank_0(*message):
pass pass
# """If distributed is initialized print only on rank 0.""" # """If distributed is initialized print only on rank 0."""
@ -16,12 +17,14 @@ def print_rank_0(*message):
# else: # else:
# print(*message, flush=True) # print(*message, flush=True)
def _warmup_mmap_file(path): def _warmup_mmap_file(path):
pass pass
# with open(path, "rb") as stream: # with open(path, "rb") as stream:
# while stream.read(100 * 1024 * 1024): # while stream.read(100 * 1024 * 1024):
# pass # pass
dtypes = { dtypes = {
1: np.uint8, 1: np.uint8,
2: np.int8, 2: np.int8,
@ -33,18 +36,22 @@ dtypes = {
8: np.uint16, 8: np.uint16,
} }
def code(dtype): def code(dtype):
for k in dtypes.keys(): for k in dtypes.keys():
if dtypes[k] == dtype: if dtypes[k] == dtype:
return k return k
raise ValueError(dtype) raise ValueError(dtype)
def index_file_path(prefix_path): def index_file_path(prefix_path):
return prefix_path + ".idx" return prefix_path + ".idx"
def data_file_path(prefix_path): def data_file_path(prefix_path):
return prefix_path + ".bin" return prefix_path + ".bin"
class MMapIndexedDataset(torch.utils.data.Dataset): class MMapIndexedDataset(torch.utils.data.Dataset):
class Index(object): class Index(object):
_HDR_MAGIC = b"MMIDIDX\x00\x00" _HDR_MAGIC = b"MMIDIDX\x00\x00"
@ -217,8 +224,7 @@ class MMapIndexedDataset(torch.utils.data.Dataset):
elif isinstance(idx, slice): elif isinstance(idx, slice):
start, stop, step = idx.indices(len(self)) start, stop, step = idx.indices(len(self))
if step != 1: if step != 1:
raise ValueError( raise ValueError("Slices into indexed_dataset must be contiguous")
"Slices into indexed_dataset must be contiguous")
ptr = self._index._pointers[start] ptr = self._index._pointers[start]
sizes = self._index._sizes[idx] sizes = self._index._sizes[idx]
offsets = list(accumulate(sizes)) offsets = list(accumulate(sizes))

97
finetune/lora/src/dataset.py → finetune/lora/v4/src/dataset.py vendored
View File

@ -17,9 +17,11 @@ class MyDataset(Dataset):
if args.data_type == "binidx": if args.data_type == "binidx":
self.vocab_size = args.vocab_size self.vocab_size = args.vocab_size
rank_zero_info(f"Current vocab size = {self.vocab_size} (make sure it's correct)") rank_zero_info(
f"Current vocab size = {self.vocab_size} (make sure it's correct)"
)
if args.data_file.endswith('/'): if args.data_file.endswith("/"):
d_all = [] d_all = []
for p in os.listdir(args.data_file): for p in os.listdir(args.data_file):
if p.endswith(".idx"): if p.endswith(".idx"):
@ -29,33 +31,52 @@ class MyDataset(Dataset):
exit(0) exit(0)
else: else:
self.data = MMapIndexedDataset(args.data_file) self.data = MMapIndexedDataset(args.data_file)
self.data_size = len(self.data._bin_buffer) // self.data._index._dtype_size self.data_size = (
len(self.data._bin_buffer) // self.data._index._dtype_size
)
rank_zero_info(f"Data has {self.data_size} tokens.") rank_zero_info(f"Data has {self.data_size} tokens.")
if args.my_qa_mask > 0: if args.my_qa_mask > 0:
self.data_pile = MMapIndexedDataset('/fsx/BlinkDL/pile/pile_20B_tokenizer_text_document') self.data_pile = MMapIndexedDataset(
self.data_pile_size = len(self.data_pile._bin_buffer) // self.data._index._dtype_size "/fsx/BlinkDL/pile/pile_20B_tokenizer_text_document"
)
self.data_pile_size = (
len(self.data_pile._bin_buffer) // self.data._index._dtype_size
)
if args.my_pile_stage > 0: if args.my_pile_stage > 0:
# assert self.data_size == 332115325534 and self.vocab_size == 50277 # assert self.data_size == 332115325534 and self.vocab_size == 50277
self.samples_per_epoch = args.epoch_steps * args.real_bsz self.samples_per_epoch = args.epoch_steps * args.real_bsz
assert self.samples_per_epoch == 40320 assert self.samples_per_epoch == 40320
rank_zero_info(f"########## Pile 20b-tokenized stage {args.my_pile_stage} ##########") rank_zero_info(
f"########## Pile 20b-tokenized stage {args.my_pile_stage} ##########"
)
dataset_slot = self.data_size // args.ctx_len dataset_slot = self.data_size // args.ctx_len
if args.my_pile_stage != 4: if args.my_pile_stage != 4:
assert MaybeIsPrime(args.magic_prime) assert MaybeIsPrime(args.magic_prime)
assert args.magic_prime % 3 == 2 assert args.magic_prime % 3 == 2
assert args.magic_prime / dataset_slot > 0.99 and args.magic_prime / dataset_slot <= 1 assert (
args.magic_prime / dataset_slot > 0.99
and args.magic_prime / dataset_slot <= 1
)
elif args.data_type == "numpy": elif args.data_type == "numpy":
self.data = np.load(args.data_file).astype("int") self.data = np.load(args.data_file).astype("int")
self.vocab_size = args.vocab_size self.vocab_size = args.vocab_size
rank_zero_info("Current vocab size =", self.vocab_size, "(make sure it's correct)") rank_zero_info(
"Current vocab size =", self.vocab_size, "(make sure it's correct)"
)
self.data_size = len(self.data) self.data_size = len(self.data)
rank_zero_info(f"Data has {self.data_size} tokens.") rank_zero_info(f"Data has {self.data_size} tokens.")
elif args.data_type == "uint16": elif args.data_type == "uint16":
self.data = np.fromfile(args.data_file, dtype=np.uint16).astype("int32").reshape(-1, args.my_sample_len) self.data = (
np.fromfile(args.data_file, dtype=np.uint16)
.astype("int32")
.reshape(-1, args.my_sample_len)
)
self.vocab_size = args.vocab_size self.vocab_size = args.vocab_size
rank_zero_info("Current vocab size =", self.vocab_size, "(make sure it's correct)") rank_zero_info(
"Current vocab size =", self.vocab_size, "(make sure it's correct)"
)
self.data_size = self.data.shape[0] self.data_size = self.data.shape[0]
rank_zero_info(f"Data has {self.data_size} samples.") rank_zero_info(f"Data has {self.data_size} samples.")
elif args.data_type == "wds_img": elif args.data_type == "wds_img":
@ -86,10 +107,14 @@ class MyDataset(Dataset):
for u in unique: for u in unique:
xxObj[xx] = u xxObj[xx] = u
xx += 1 xx += 1
with open(f"{args.proj_dir}/vocab.json", "w", encoding="utf-16le") as vocab_file: with open(
f"{args.proj_dir}/vocab.json", "w", encoding="utf-16le"
) as vocab_file:
vocab_file.write(json.dumps(xxObj, ensure_ascii=False)) vocab_file.write(json.dumps(xxObj, ensure_ascii=False))
self.data_size = len(self.data) self.data_size = len(self.data)
rank_zero_info(f"Data has {self.data_size} tokens, {self.vocab_size} vocab size.") rank_zero_info(
f"Data has {self.data_size} tokens, {self.vocab_size} vocab size."
)
self.stoi = {ch: i for i, ch in enumerate(unique)} self.stoi = {ch: i for i, ch in enumerate(unique)}
self.itos = {i: ch for i, ch in enumerate(unique)} self.itos = {i: ch for i, ch in enumerate(unique)}
@ -104,36 +129,53 @@ class MyDataset(Dataset):
# print(f"epoch {epoch} idx {idx} rank {rank}/{world_size}") # print(f"epoch {epoch} idx {idx} rank {rank}/{world_size}")
if args.data_type == "wds_img": if args.data_type == "wds_img":
def init_wds(self, bias=0): def init_wds(self, bias=0):
def identity(x): def identity(x):
return x return x
import webdataset as wds import webdataset as wds
import torchvision.transforms as transforms import torchvision.transforms as transforms
# img_transform = transforms.Compose( # img_transform = transforms.Compose(
# [transforms.CenterCrop(256)] # [transforms.CenterCrop(256)]
# ) # )
img_transform = transforms.Compose([ img_transform = transforms.Compose(
transforms.CenterCrop(512), [transforms.CenterCrop(512), transforms.Resize((args.my_img_size))]
transforms.Resize((args.my_img_size)) )
]) self.data_raw = (
self.data_raw = wds.WebDataset(args.data_file, resampled=True).shuffle(10000, initial=1000, rng=random.Random(epoch*100000+rank+bias*1e9)).decode("torchrgb").to_tuple("jpg", "json", "txt").map_tuple(img_transform, identity, identity) wds.WebDataset(args.data_file, resampled=True)
.shuffle(
10000,
initial=1000,
rng=random.Random(epoch * 100000 + rank + bias * 1e9),
)
.decode("torchrgb")
.to_tuple("jpg", "json", "txt")
.map_tuple(img_transform, identity, identity)
)
for pp in self.data_raw.pipeline: for pp in self.data_raw.pipeline:
if 'Resampled' in str(pp): if "Resampled" in str(pp):
pp.deterministic = True pp.deterministic = True
def worker_seed(): def worker_seed():
return rank*100000+epoch+bias*1e9 return rank * 100000 + epoch + bias * 1e9
pp.worker_seed = worker_seed pp.worker_seed = worker_seed
self.data = iter(self.data_raw) self.data = iter(self.data_raw)
# print(f"WebDataset loaded for rank {rank} epoch {epoch}") # print(f"WebDataset loaded for rank {rank} epoch {epoch}")
if self.data == None: if self.data == None:
init_wds(self) init_wds(self)
trial = 0 trial = 0
while trial < 10: while trial < 10:
try: try:
dd = next(self.data) # jpg, json, txt dd = next(self.data) # jpg, json, txt
break break
except: except:
print(f'[dataloader error - epoch {epoch} rank {rank} - trying a new shuffle]') print(
f"[dataloader error - epoch {epoch} rank {rank} - trying a new shuffle]"
)
self.error_count += 1 self.error_count += 1
init_wds(self, self.error_count) init_wds(self, self.error_count)
trial += 1 trial += 1
@ -144,7 +186,7 @@ class MyDataset(Dataset):
return dd[0], dd[2] return dd[0], dd[2]
else: else:
if args.data_type == "uint16": if args.data_type == "uint16":
i = np.random.randint(0, self.data_size-1) i = np.random.randint(0, self.data_size - 1)
dix = self.data[i] dix = self.data[i]
x = torch.tensor(dix[:-1], dtype=torch.long) x = torch.tensor(dix[:-1], dtype=torch.long)
y = torch.tensor(dix[1:], dtype=torch.long) y = torch.tensor(dix[1:], dtype=torch.long)
@ -196,7 +238,12 @@ class MyDataset(Dataset):
z_sum = 0 z_sum = 0
isGood = False isGood = False
for i in range(3, ctx_len): for i in range(3, ctx_len):
if dix[i] == 27 and dix[i-1] == 34 and dix[i-2] == 187 and dix[i-3] == 187: if (
dix[i] == 27
and dix[i - 1] == 34
and dix[i - 2] == 187
and dix[i - 3] == 187
):
isGood = True isGood = True
if dix[i] == 0: if dix[i] == 0:
isGood = False isGood = False
@ -206,7 +253,9 @@ class MyDataset(Dataset):
if z_sum == 0: if z_sum == 0:
z = [1] * ctx_len z = [1] * ctx_len
i = np.random.randint(0, self.data_pile_size - req_len) i = np.random.randint(0, self.data_pile_size - req_len)
dix = self.data_pile.get(idx=0, offset=i, length=req_len).astype(int) dix = self.data_pile.get(
idx=0, offset=i, length=req_len
).astype(int)
z = torch.tensor(z, dtype=torch.bfloat16) z = torch.tensor(z, dtype=torch.bfloat16)
x = torch.tensor(dix[:-1], dtype=torch.long) x = torch.tensor(dix[:-1], dtype=torch.long)

317
finetune/lora/src/model.py → finetune/lora/v4/src/model.py vendored
View File

@ -5,6 +5,7 @@
import functools import functools
import os, math, gc, importlib import os, math, gc, importlib
import torch import torch
# torch._C._jit_set_profiling_executor(True) # torch._C._jit_set_profiling_executor(True)
# torch._C._jit_set_profiling_mode(True) # torch._C._jit_set_profiling_mode(True)
import torch.nn as nn import torch.nn as nn
@ -13,7 +14,8 @@ from torch.nn import functional as F
import pytorch_lightning as pl import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info, rank_zero_only from pytorch_lightning.utilities import rank_zero_info, rank_zero_only
from pytorch_lightning.strategies import DeepSpeedStrategy from pytorch_lightning.strategies import DeepSpeedStrategy
if importlib.util.find_spec('deepspeed'):
if importlib.util.find_spec("deepspeed"):
import deepspeed import deepspeed
from deepspeed.ops.adam import DeepSpeedCPUAdam, FusedAdam from deepspeed.ops.adam import DeepSpeedCPUAdam, FusedAdam
@ -28,9 +30,10 @@ LORA_CONFIG = {
try: try:
print('RWKV_MY_TESTING', os.environ["RWKV_MY_TESTING"]) print("RWKV_MY_TESTING", os.environ["RWKV_MY_TESTING"])
except: except:
os.environ["RWKV_MY_TESTING"] = '' os.environ["RWKV_MY_TESTING"] = ""
def __nop(ob): def __nop(ob):
return ob return ob
@ -53,7 +56,26 @@ T_MAX = int(os.environ["RWKV_T_MAX"]) # TAKES LOTS OF VRAM!
from torch.utils.cpp_extension import load from torch.utils.cpp_extension import load
if os.environ["RWKV_FLOAT_MODE"] == "bf16": if os.environ["RWKV_FLOAT_MODE"] == "bf16":
wkv_cuda = load(name=f"wkv_{T_MAX}_bf16", sources=["finetune/lora/cuda/wkv_op_bf16.cpp", "finetune/lora/cuda/wkv_cuda_bf16.cu"], verbose=True, extra_cuda_cflags=["-t 4", "-std=c++17", "-res-usage", "--maxrregcount 60", "--use_fast_math", "-O3", "-Xptxas -O3", "--extra-device-vectorization", f"-DTmax={T_MAX}"]) wkv_cuda = load(
name=f"wkv_{T_MAX}_bf16",
sources=[
"finetune/lora/v4/cuda/wkv_op_bf16.cpp",
"finetune/lora/v4/cuda/wkv_cuda_bf16.cu",
],
verbose=True,
extra_cuda_cflags=[
"-t 4",
"-std=c++17",
"-res-usage",
"--maxrregcount 60",
"--use_fast_math",
"-O3",
"-Xptxas -O3",
"--extra-device-vectorization",
f"-DTmax={T_MAX}",
],
)
class WKV(torch.autograd.Function): class WKV(torch.autograd.Function):
@staticmethod @staticmethod
def forward(ctx, B, T, C, w, u, k, v): def forward(ctx, B, T, C, w, u, k, v):
@ -66,10 +88,16 @@ if os.environ["RWKV_FLOAT_MODE"] == "bf16":
u = u.contiguous() u = u.contiguous()
k = k.contiguous() k = k.contiguous()
v = v.contiguous() v = v.contiguous()
y = torch.empty((B, T, C), device=w.device, memory_format=torch.contiguous_format, dtype=torch.bfloat16) y = torch.empty(
(B, T, C),
device=w.device,
memory_format=torch.contiguous_format,
dtype=torch.bfloat16,
)
wkv_cuda.forward(B, T, C, w, u, k, v, y) wkv_cuda.forward(B, T, C, w, u, k, v, y)
ctx.save_for_backward(w, u, k, v, y) ctx.save_for_backward(w, u, k, v, y)
return y return y
@staticmethod @staticmethod
def backward(ctx, gy): def backward(ctx, gy):
B = ctx.B B = ctx.B
@ -78,16 +106,54 @@ if os.environ["RWKV_FLOAT_MODE"] == "bf16":
assert T <= T_MAX assert T <= T_MAX
assert B * C % min(C, 32) == 0 assert B * C % min(C, 32) == 0
w, u, k, v, y = ctx.saved_tensors w, u, k, v, y = ctx.saved_tensors
gw = torch.empty((B, C), device=gy.device, memory_format=torch.contiguous_format, dtype=torch.bfloat16) gw = torch.empty(
gu = torch.empty((B, C), device=gy.device, memory_format=torch.contiguous_format, dtype=torch.bfloat16) (B, C),
gk = torch.empty((B, T, C), device=gy.device, memory_format=torch.contiguous_format, dtype=torch.bfloat16) device=gy.device,
gv = torch.empty((B, T, C), device=gy.device, memory_format=torch.contiguous_format, dtype=torch.bfloat16) memory_format=torch.contiguous_format,
dtype=torch.bfloat16,
)
gu = torch.empty(
(B, C),
device=gy.device,
memory_format=torch.contiguous_format,
dtype=torch.bfloat16,
)
gk = torch.empty(
(B, T, C),
device=gy.device,
memory_format=torch.contiguous_format,
dtype=torch.bfloat16,
)
gv = torch.empty(
(B, T, C),
device=gy.device,
memory_format=torch.contiguous_format,
dtype=torch.bfloat16,
)
wkv_cuda.backward(B, T, C, w, u, k, v, y, gy.contiguous(), gw, gu, gk, gv) wkv_cuda.backward(B, T, C, w, u, k, v, y, gy.contiguous(), gw, gu, gk, gv)
gw = torch.sum(gw, dim=0) gw = torch.sum(gw, dim=0)
gu = torch.sum(gu, dim=0) gu = torch.sum(gu, dim=0)
return (None, None, None, gw, gu, gk, gv) return (None, None, None, gw, gu, gk, gv)
else: else:
wkv_cuda = load(name=f"wkv_{T_MAX}", sources=["finetune/lora/cuda/wkv_op.cpp", "finetune/lora/cuda/wkv_cuda.cu"], verbose=True, extra_cuda_cflags=["-res-usage", "--maxrregcount 60", "--use_fast_math", "-O3", "-Xptxas -O3", "--extra-device-vectorization", f"-DTmax={T_MAX}"]) wkv_cuda = load(
name=f"wkv_{T_MAX}",
sources=[
"finetune/lora/v4/cuda/wkv_op.cpp",
"finetune/lora/v4/cuda/wkv_cuda.cu",
],
verbose=True,
extra_cuda_cflags=[
"-res-usage",
"--maxrregcount 60",
"--use_fast_math",
"-O3",
"-Xptxas -O3",
"--extra-device-vectorization",
f"-DTmax={T_MAX}",
],
)
class WKV(torch.autograd.Function): class WKV(torch.autograd.Function):
@staticmethod @staticmethod
def forward(ctx, B, T, C, w, u, k, v): def forward(ctx, B, T, C, w, u, k, v):
@ -106,7 +172,9 @@ else:
u = u.float().contiguous() u = u.float().contiguous()
k = k.float().contiguous() k = k.float().contiguous()
v = v.float().contiguous() v = v.float().contiguous()
y = torch.empty((B, T, C), device=w.device, memory_format=torch.contiguous_format) y = torch.empty(
(B, T, C), device=w.device, memory_format=torch.contiguous_format
)
wkv_cuda.forward(B, T, C, w, u, k, v, y) wkv_cuda.forward(B, T, C, w, u, k, v, y)
ctx.save_for_backward(w, u, k, v, y) ctx.save_for_backward(w, u, k, v, y)
if "32" in os.environ["RWKV_FLOAT_MODE"]: if "32" in os.environ["RWKV_FLOAT_MODE"]:
@ -115,6 +183,7 @@ else:
return y.half() return y.half()
elif os.environ["RWKV_FLOAT_MODE"] == "bf16": elif os.environ["RWKV_FLOAT_MODE"] == "bf16":
return y.bfloat16() return y.bfloat16()
@staticmethod @staticmethod
def backward(ctx, gy): def backward(ctx, gy):
B = ctx.B B = ctx.B
@ -123,14 +192,26 @@ else:
assert T <= T_MAX assert T <= T_MAX
assert B * C % min(C, 32) == 0 assert B * C % min(C, 32) == 0
w, u, k, v, y = ctx.saved_tensors w, u, k, v, y = ctx.saved_tensors
gw = torch.empty((B, C), device=gy.device, memory_format=torch.contiguous_format) gw = torch.empty(
gu = torch.empty((B, C), device=gy.device, memory_format=torch.contiguous_format) (B, C), device=gy.device, memory_format=torch.contiguous_format
gk = torch.empty((B, T, C), device=gy.device, memory_format=torch.contiguous_format) )
gv = torch.empty((B, T, C), device=gy.device, memory_format=torch.contiguous_format) gu = torch.empty(
(B, C), device=gy.device, memory_format=torch.contiguous_format
)
gk = torch.empty(
(B, T, C), device=gy.device, memory_format=torch.contiguous_format
)
gv = torch.empty(
(B, T, C), device=gy.device, memory_format=torch.contiguous_format
)
if "32" in os.environ["RWKV_FLOAT_MODE"]: if "32" in os.environ["RWKV_FLOAT_MODE"]:
wkv_cuda.backward(B, T, C, w, u, k, v, y, gy.contiguous(), gw, gu, gk, gv) wkv_cuda.backward(
B, T, C, w, u, k, v, y, gy.contiguous(), gw, gu, gk, gv
)
else: else:
wkv_cuda.backward(B, T, C, w, u, k, v, y, gy.float().contiguous(), gw, gu, gk, gv) wkv_cuda.backward(
B, T, C, w, u, k, v, y, gy.float().contiguous(), gw, gu, gk, gv
)
gw = torch.sum(gw, dim=0) gw = torch.sum(gw, dim=0)
gu = torch.sum(gu, dim=0) gu = torch.sum(gu, dim=0)
if "32" in os.environ["RWKV_FLOAT_MODE"]: if "32" in os.environ["RWKV_FLOAT_MODE"]:
@ -138,7 +219,15 @@ else:
elif os.environ["RWKV_FLOAT_MODE"] == "fp16": elif os.environ["RWKV_FLOAT_MODE"] == "fp16":
return (None, None, None, gw.half(), gu.half(), gk.half(), gv.half()) return (None, None, None, gw.half(), gu.half(), gk.half(), gv.half())
elif os.environ["RWKV_FLOAT_MODE"] == "bf16": elif os.environ["RWKV_FLOAT_MODE"] == "bf16":
return (None, None, None, gw.bfloat16(), gu.bfloat16(), gk.bfloat16(), gv.bfloat16()) return (
None,
None,
None,
gw.bfloat16(),
gu.bfloat16(),
gk.bfloat16(),
gv.bfloat16(),
)
def RUN_CUDA(B, T, C, w, u, k, v): def RUN_CUDA(B, T, C, w, u, k, v):
@ -151,15 +240,17 @@ def RUN_CUDA(B, T, C, w, u, k, v):
class LoraLinear(nn.Module): class LoraLinear(nn.Module):
def __init__(self, in_features: int, out_features: int, bias: bool): def __init__(self, in_features: int, out_features: int, bias: bool):
super().__init__() super().__init__()
self.weight = nn.Parameter(torch.empty((out_features, in_features))) self.weight = nn.Parameter(torch.empty((out_features, in_features)))
assert bias == False, "Biased LoraLinear not supported" assert bias == False, "Biased LoraLinear not supported"
r, alpha, dropout = LORA_CONFIG["r"], LORA_CONFIG[ r, alpha, dropout = (
"alpha"], LORA_CONFIG["dropout"] LORA_CONFIG["r"],
LORA_CONFIG["alpha"],
LORA_CONFIG["dropout"],
)
self.lora_A = nn.Parameter(torch.empty(r, in_features)) self.lora_A = nn.Parameter(torch.empty(r, in_features))
self.lora_B = nn.Parameter(torch.empty(out_features, r)) self.lora_B = nn.Parameter(torch.empty(out_features, r))
self.lora_dropout = nn.Dropout(dropout) self.lora_dropout = nn.Dropout(dropout)
@ -170,9 +261,9 @@ class LoraLinear(nn.Module):
nn.init.zeros_(self.lora_B) nn.init.zeros_(self.lora_B)
def forward(self, x): def forward(self, x):
return ( return F.linear(x, self.weight) + self.scaling * F.linear(
F.linear(x, self.weight) + self.scaling * F.linear(self.lora_dropout(x), self.lora_A), self.lora_B
F.linear(F.linear(self.lora_dropout(x), self.lora_A), self.lora_B)) )
@functools.wraps(LoraLinear) @functools.wraps(LoraLinear)
@ -214,17 +305,23 @@ class RWKV_TimeMix(MyModule):
# fancy time_decay # fancy time_decay
decay_speed = torch.ones(args.dim_att) decay_speed = torch.ones(args.dim_att)
for h in range(args.dim_att): for h in range(args.dim_att):
decay_speed[h] = -5 + 8 * (h / (args.dim_att - 1)) ** (0.7 + 1.3 * ratio_0_to_1) decay_speed[h] = -5 + 8 * (h / (args.dim_att - 1)) ** (
0.7 + 1.3 * ratio_0_to_1
)
self.time_decay = nn.Parameter(decay_speed) self.time_decay = nn.Parameter(decay_speed)
# print(layer_id, self.time_decay.flatten()[:3].cpu().numpy(), '...', self.time_decay.flatten()[-3:].cpu().numpy()) # print(layer_id, self.time_decay.flatten()[:3].cpu().numpy(), '...', self.time_decay.flatten()[-3:].cpu().numpy())
# fancy time_first # fancy time_first
zigzag = torch.tensor([(i + 1) % 3 - 1 for i in range(args.dim_att)]) * 0.5 zigzag = torch.tensor([(i + 1) % 3 - 1 for i in range(args.dim_att)]) * 0.5
self.time_first = nn.Parameter(torch.ones(args.dim_att) * math.log(0.3) + zigzag) self.time_first = nn.Parameter(
torch.ones(args.dim_att) * math.log(0.3) + zigzag
)
# fancy time_mix # fancy time_mix
self.time_mix_k = nn.Parameter(torch.pow(ddd, ratio_1_to_almost0)) self.time_mix_k = nn.Parameter(torch.pow(ddd, ratio_1_to_almost0))
self.time_mix_v = nn.Parameter(torch.pow(ddd, ratio_1_to_almost0) + 0.3 * ratio_0_to_1) self.time_mix_v = nn.Parameter(
torch.pow(ddd, ratio_1_to_almost0) + 0.3 * ratio_0_to_1
)
self.time_mix_r = nn.Parameter(torch.pow(ddd, 0.5 * ratio_1_to_almost0)) self.time_mix_r = nn.Parameter(torch.pow(ddd, 0.5 * ratio_1_to_almost0))
self.time_shift = nn.ZeroPad2d((0, 0, 1, -1)) self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
@ -235,8 +332,10 @@ class RWKV_TimeMix(MyModule):
self.output = nn.Linear(args.dim_att, args.n_embd, bias=False) self.output = nn.Linear(args.dim_att, args.n_embd, bias=False)
if 'a' in os.environ["RWKV_MY_TESTING"]: if "a" in os.environ["RWKV_MY_TESTING"]:
self.register_buffer("att_mask", torch.tril(torch.ones(args.ctx_len, args.ctx_len))) self.register_buffer(
"att_mask", torch.tril(torch.ones(args.ctx_len, args.ctx_len))
)
d_qkv = args.n_embd // 16 d_qkv = args.n_embd // 16
self.qq = nn.Linear(args.n_embd, d_qkv, bias=False) self.qq = nn.Linear(args.n_embd, d_qkv, bias=False)
self.kk = nn.Linear(args.n_embd, d_qkv, bias=False) self.kk = nn.Linear(args.n_embd, d_qkv, bias=False)
@ -245,12 +344,17 @@ class RWKV_TimeMix(MyModule):
with torch.no_grad(): with torch.no_grad():
self.time_mix_qq = nn.Parameter(torch.pow(ddd, ratio_1_to_almost0)) self.time_mix_qq = nn.Parameter(torch.pow(ddd, ratio_1_to_almost0))
self.time_mix_kk = nn.Parameter(torch.pow(ddd, ratio_1_to_almost0)) self.time_mix_kk = nn.Parameter(torch.pow(ddd, ratio_1_to_almost0))
self.time_mix_vv = nn.Parameter(torch.pow(ddd, ratio_1_to_almost0) + 0.3 * ratio_0_to_1) self.time_mix_vv = nn.Parameter(
torch.pow(ddd, ratio_1_to_almost0) + 0.3 * ratio_0_to_1
)
if "a" not in os.environ["RWKV_MY_TESTING"]:
if 'a' not in os.environ["RWKV_MY_TESTING"]:
@MyFunction @MyFunction
def jit_func(self, x): def jit_func(self, x):
xx = self.time_shift(x) # Mix x with the previous timestep to produce xk, xv, xr xx = self.time_shift(
x
) # Mix x with the previous timestep to produce xk, xv, xr
xk = x * self.time_mix_k + xx * (1 - self.time_mix_k) xk = x * self.time_mix_k + xx * (1 - self.time_mix_k)
xv = x * self.time_mix_v + xx * (1 - self.time_mix_v) xv = x * self.time_mix_v + xx * (1 - self.time_mix_v)
xr = x * self.time_mix_r + xx * (1 - self.time_mix_r) xr = x * self.time_mix_r + xx * (1 - self.time_mix_r)
@ -263,21 +367,26 @@ class RWKV_TimeMix(MyModule):
def forward(self, x): def forward(self, x):
B, T, C = x.size() # x = (Batch,Time,Channel) B, T, C = x.size() # x = (Batch,Time,Channel)
sr, k, v = self.jit_func(x) sr, k, v = self.jit_func(x)
rwkv = sr * RUN_CUDA(B, T, self.args.dim_att, self.time_decay, self.time_first, k, v) rwkv = sr * RUN_CUDA(
B, T, self.args.dim_att, self.time_decay, self.time_first, k, v
)
return self.output(rwkv) return self.output(rwkv)
if 'a' in os.environ["RWKV_MY_TESTING"]: if "a" in os.environ["RWKV_MY_TESTING"]:
@MyFunction @MyFunction
def QKV(self, q, k, v): def QKV(self, q, k, v):
att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1))) att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
att = att.masked_fill(self.att_mask == 0, float('-inf')) att = att.masked_fill(self.att_mask == 0, float("-inf"))
att = F.softmax(att, dim = -1) att = F.softmax(att, dim=-1)
x = att @ v x = att @ v
return x return x
@MyFunction @MyFunction
def jit_funcQKV(self, x): def jit_funcQKV(self, x):
xx = self.time_shift(x) # Mix x with the previous timestep to produce xk, xv, xr xx = self.time_shift(
x
) # Mix x with the previous timestep to produce xk, xv, xr
xk = x * self.time_mix_k + xx * (1 - self.time_mix_k) xk = x * self.time_mix_k + xx * (1 - self.time_mix_k)
xv = x * self.time_mix_v + xx * (1 - self.time_mix_v) xv = x * self.time_mix_v + xx * (1 - self.time_mix_v)
xr = x * self.time_mix_r + xx * (1 - self.time_mix_r) xr = x * self.time_mix_r + xx * (1 - self.time_mix_r)
@ -296,12 +405,16 @@ class RWKV_TimeMix(MyModule):
def forward(self, x): def forward(self, x):
B, T, C = x.size() # x = (Batch,Time,Channel) B, T, C = x.size() # x = (Batch,Time,Channel)
sr, k, v, qq, kk, vv = self.jit_funcQKV(x) sr, k, v, qq, kk, vv = self.jit_funcQKV(x)
rwkv = sr * RUN_CUDA(B, T, self.args.dim_att, self.time_decay, self.time_first, k, v) rwkv = sr * RUN_CUDA(
B, T, self.args.dim_att, self.time_decay, self.time_first, k, v
)
rwkv = self.output(rwkv) + self.oo(self.QKV(qq, kk, vv)) rwkv = self.output(rwkv) + self.oo(self.QKV(qq, kk, vv))
return rwkv return rwkv
######################################################################################################## ########################################################################################################
class RWKV_ChannelMix(MyModule): class RWKV_ChannelMix(MyModule):
def __init__(self, args, layer_id): def __init__(self, args, layer_id):
super().__init__() super().__init__()
@ -331,6 +444,7 @@ class RWKV_ChannelMix(MyModule):
kv = self.value(k) kv = self.value(k)
return torch.sigmoid(self.receptance(xr)) * kv return torch.sigmoid(self.receptance(xr)) * kv
class MishGLU(MyModule): class MishGLU(MyModule):
def __init__(self, args, layer_id): def __init__(self, args, layer_id):
super().__init__() super().__init__()
@ -360,6 +474,7 @@ class MishGLU(MyModule):
b = self.bb(xb) b = self.bb(xb)
return self.value(a * F.mish(b)) return self.value(a * F.mish(b))
######################################################################################################## ########################################################################################################
# The RWKV Model with our blocks # The RWKV Model with our blocks
######################################################################################################## ########################################################################################################
@ -377,15 +492,19 @@ class Block(nn.Module):
if self.layer_id == 0: if self.layer_id == 0:
self.ln0 = nn.LayerNorm(args.n_embd) self.ln0 = nn.LayerNorm(args.n_embd)
if args.my_pos_emb > 0: if args.my_pos_emb > 0:
self.pos_emb_x = nn.Parameter(torch.zeros((1,args.my_pos_emb,args.n_embd))) self.pos_emb_x = nn.Parameter(
self.pos_emb_y = nn.Parameter(torch.zeros((args.my_pos_emb,1,args.n_embd))) torch.zeros((1, args.my_pos_emb, args.n_embd))
)
self.pos_emb_y = nn.Parameter(
torch.zeros((args.my_pos_emb, 1, args.n_embd))
)
if self.layer_id == 0 and self.args.pre_ffn > 0: if self.layer_id == 0 and self.args.pre_ffn > 0:
self.ffnPre = RWKV_ChannelMix(args, 0) self.ffnPre = RWKV_ChannelMix(args, 0)
else: else:
self.att = RWKV_TimeMix(args, layer_id) self.att = RWKV_TimeMix(args, layer_id)
if 'g' in os.environ["RWKV_MY_TESTING"]: if "g" in os.environ["RWKV_MY_TESTING"]:
self.ffn = MishGLU(args, layer_id) self.ffn = MishGLU(args, layer_id)
else: else:
self.ffn = RWKV_ChannelMix(args, layer_id) self.ffn = RWKV_ChannelMix(args, layer_id)
@ -395,7 +514,9 @@ class Block(nn.Module):
self.tiny_q = nn.Linear(args.n_embd, args.tiny_att_dim, bias=False) self.tiny_q = nn.Linear(args.n_embd, args.tiny_att_dim, bias=False)
self.tiny_k = nn.Linear(args.n_embd, args.tiny_att_dim, bias=False) self.tiny_k = nn.Linear(args.n_embd, args.tiny_att_dim, bias=False)
self.tiny_v = nn.Linear(args.n_embd, args.n_embd, bias=False) self.tiny_v = nn.Linear(args.n_embd, args.n_embd, bias=False)
self.register_buffer("tiny_mask", torch.tril(torch.ones(args.ctx_len, args.ctx_len))) self.register_buffer(
"tiny_mask", torch.tril(torch.ones(args.ctx_len, args.ctx_len))
)
def forward(self, x, x_emb=None): def forward(self, x, x_emb=None):
args = self.args args = self.args
@ -403,7 +524,7 @@ class Block(nn.Module):
if self.layer_id == 0: if self.layer_id == 0:
x = self.ln0(x) x = self.ln0(x)
if args.my_pos_emb > 0: if args.my_pos_emb > 0:
pos_emb = (self.pos_emb_x + self.pos_emb_y).reshape(T+1, -1)[:-1,:] pos_emb = (self.pos_emb_x + self.pos_emb_y).reshape(T + 1, -1)[:-1, :]
x = x + pos_emb x = x + pos_emb
if self.layer_id == 0 and args.pre_ffn > 0: if self.layer_id == 0 and args.pre_ffn > 0:
@ -443,13 +564,13 @@ class RWKV(pl.LightningModule):
def __init__(self, args): def __init__(self, args):
super().__init__() super().__init__()
self.args = args self.args = args
if not hasattr(args, 'dim_att'): if not hasattr(args, "dim_att"):
args.dim_att = args.n_embd args.dim_att = args.n_embd
if not hasattr(args, 'dim_ffn'): if not hasattr(args, "dim_ffn"):
args.dim_ffn = args.n_embd * 4 args.dim_ffn = args.n_embd * 4
if not hasattr(args, 'tiny_att_layer'): if not hasattr(args, "tiny_att_layer"):
args.tiny_att_layer = -1 args.tiny_att_layer = -1
if not hasattr(args, 'tiny_att_dim'): if not hasattr(args, "tiny_att_dim"):
args.tiny_att_dim = -1 args.tiny_att_dim = -1
self.emb = nn.Embedding(args.vocab_size, args.n_embd) self.emb = nn.Embedding(args.vocab_size, args.n_embd)
@ -462,7 +583,9 @@ class RWKV(pl.LightningModule):
if args.head_qk > 0: if args.head_qk > 0:
self.head_q = nn.Linear(args.n_embd, args.head_qk, bias=False) self.head_q = nn.Linear(args.n_embd, args.head_qk, bias=False)
self.head_k = nn.Linear(args.n_embd, args.head_qk, bias=False) self.head_k = nn.Linear(args.n_embd, args.head_qk, bias=False)
self.register_buffer("copy_mask", torch.tril(torch.ones(args.ctx_len, args.ctx_len))) self.register_buffer(
"copy_mask", torch.tril(torch.ones(args.ctx_len, args.ctx_len))
)
def configure_optimizers(self): def configure_optimizers(self):
args = self.args args = self.args
@ -494,19 +617,46 @@ class RWKV(pl.LightningModule):
param_dict = {n: p for n, p in self.named_parameters()} param_dict = {n: p for n, p in self.named_parameters()}
if args.my_pile_stage == 2: if args.my_pile_stage == 2:
optim_groups = [ optim_groups = [
{"params": [param_dict[n] for n in lr_1x], "weight_decay": 0.0, "my_lr_scale": 1.0}, {
{"params": [param_dict[n] for n in lr_2x], "weight_decay": 0.0, "my_lr_scale": 5.0},# test: 2e-3 / args.lr_init}, "params": [param_dict[n] for n in lr_1x],
{"params": [param_dict[n] for n in lr_3x], "weight_decay": 0.0, "my_lr_scale": 5.0},# test: 3e-3 / args.lr_init}, "weight_decay": 0.0,
"my_lr_scale": 1.0,
},
{
"params": [param_dict[n] for n in lr_2x],
"weight_decay": 0.0,
"my_lr_scale": 5.0,
}, # test: 2e-3 / args.lr_init},
{
"params": [param_dict[n] for n in lr_3x],
"weight_decay": 0.0,
"my_lr_scale": 5.0,
}, # test: 3e-3 / args.lr_init},
] ]
else: else:
optim_groups = [ optim_groups = [
{"params": [param_dict[n] for n in lr_1x], "weight_decay": 0.0, "my_lr_scale": 1.0}, {
{"params": [param_dict[n] for n in lr_2x], "weight_decay": 0.0, "my_lr_scale": 2.0}, "params": [param_dict[n] for n in lr_1x],
{"params": [param_dict[n] for n in lr_3x], "weight_decay": 0.0, "my_lr_scale": 3.0}, "weight_decay": 0.0,
"my_lr_scale": 1.0,
},
{
"params": [param_dict[n] for n in lr_2x],
"weight_decay": 0.0,
"my_lr_scale": 2.0,
},
{
"params": [param_dict[n] for n in lr_3x],
"weight_decay": 0.0,
"my_lr_scale": 3.0,
},
] ]
else: else:
optim_groups = [ optim_groups = [
{"params": [p for n, p in self.named_parameters()], "weight_decay": 0.0}, {
"params": [p for n, p in self.named_parameters()],
"weight_decay": 0.0,
},
] ]
for g in optim_groups: for g in optim_groups:
@ -514,8 +664,26 @@ class RWKV(pl.LightningModule):
optim_groups = [g for g in optim_groups if len(g["params"]) > 0] optim_groups = [g for g in optim_groups if len(g["params"]) > 0]
if self.deepspeed_offload: if self.deepspeed_offload:
return DeepSpeedCPUAdam(optim_groups, lr=self.args.lr_init, betas=self.args.betas, eps=self.args.adam_eps, bias_correction=True, adamw_mode=False, weight_decay=0, amsgrad=False) return DeepSpeedCPUAdam(
return FusedAdam(optim_groups, lr=self.args.lr_init, betas=self.args.betas, eps=self.args.adam_eps, bias_correction=True, adam_w_mode=False, weight_decay=0, amsgrad=False) optim_groups,
lr=self.args.lr_init,
betas=self.args.betas,
eps=self.args.adam_eps,
bias_correction=True,
adamw_mode=False,
weight_decay=0,
amsgrad=False,
)
return FusedAdam(
optim_groups,
lr=self.args.lr_init,
betas=self.args.betas,
eps=self.args.adam_eps,
bias_correction=True,
adam_w_mode=False,
weight_decay=0,
amsgrad=False,
)
# return ZeroOneAdam(optim_groups, lr=self.args.lr_init, betas=self.args.betas, eps=self.args.adam_eps, bias_correction=True, weight_decay=0, amsgrad=False, cuda_aware=False) # return ZeroOneAdam(optim_groups, lr=self.args.lr_init, betas=self.args.betas, eps=self.args.adam_eps, bias_correction=True, weight_decay=0, amsgrad=False, cuda_aware=False)
@property @property
@ -589,10 +757,14 @@ class RWKV(pl.LightningModule):
logits = self(idx) logits = self(idx)
if sum_mask == mask.shape[0]: if sum_mask == mask.shape[0]:
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1)) loss = F.cross_entropy(
logits.view(-1, logits.size(-1)), targets.view(-1)
)
# print('rank', self.global_rank, 'loss', loss.item()) # print('rank', self.global_rank, 'loss', loss.item())
else: else:
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), reduction='none') loss = F.cross_entropy(
logits.view(-1, logits.size(-1)), targets.view(-1), reduction="none"
)
# loss_raw = loss # loss_raw = loss
loss = torch.sum(loss * mask) / sum_mask loss = torch.sum(loss * mask) / sum_mask
@ -632,7 +804,14 @@ class RWKV(pl.LightningModule):
gain = 1.0 gain = 1.0
scale = 1.0 scale = 1.0
if "ln_" in n or ".ln" in n or "time_" in n or "_mask" in n or "pos_emb" in n or '.mask.' in n: if (
"ln_" in n
or ".ln" in n
or "time_" in n
or "_mask" in n
or "pos_emb" in n
or ".mask." in n
):
m[n] = p m[n] = p
else: else:
if n == "emb.weight": if n == "emb.weight":
@ -640,7 +819,19 @@ class RWKV(pl.LightningModule):
else: else:
if shape[0] > shape[1]: if shape[0] > shape[1]:
gain = math.sqrt(shape[0] / shape[1]) gain = math.sqrt(shape[0] / shape[1])
for kk in [".att.key.", ".att.receptance.", ".att.output.", ".att.key.", ".ffn.value.", ".ffn.receptance.", ".ffnPre.value.", ".ffnPre.receptance.", "head_q.", '.oo.', '.rr.']: for kk in [
".att.key.",
".att.receptance.",
".att.output.",
".att.key.",
".ffn.value.",
".ffn.receptance.",
".ffnPre.value.",
".ffnPre.receptance.",
"head_q.",
".oo.",
".rr.",
]:
if kk in n: if kk in n:
scale = 0 scale = 0
if n == "head.weight": if n == "head.weight":
@ -650,7 +841,9 @@ class RWKV(pl.LightningModule):
if "head_q." in n: if "head_q." in n:
scale = 0 scale = 0
print(f"{str(shape[0]).ljust(5)} {str(shape[1]).ljust(5)} {str(scale).ljust(4)} {n}") print(
f"{str(shape[0]).ljust(5)} {str(shape[1]).ljust(5)} {str(scale).ljust(4)} {n}"
)
if self.args.accelerator.upper() == "GPU": if self.args.accelerator.upper() == "GPU":
m[n] = torch.empty((shape[0], shape[1]), device="cuda") m[n] = torch.empty((shape[0], shape[1]), device="cuda")

65
finetune/lora/src/trainer.py → finetune/lora/v4/src/trainer.py vendored
View File

@ -5,15 +5,17 @@ import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info, rank_zero_only from pytorch_lightning.utilities import rank_zero_info, rank_zero_only
from .model import LORA_CONFIG from .model import LORA_CONFIG
def my_save(dd, ff): def my_save(dd, ff):
if '14b-run1' not in ff: if "14b-run1" not in ff:
torch.save(dd, ff) torch.save(dd, ff)
else: else:
fn = ff.split('/')[-1] fn = ff.split("/")[-1]
fff = '/dev/shm/' + fn fff = "/dev/shm/" + fn
torch.save(dd, fff) torch.save(dd, fff)
subprocess.Popen(f" aws s3 mv {fff} s3://rwkv-14b-4k/{fn} --quiet", shell=True) subprocess.Popen(f" aws s3 mv {fff} s3://rwkv-14b-4k/{fn} --quiet", shell=True)
class train_callback(pl.Callback): class train_callback(pl.Callback):
def __init__(self, args): def __init__(self, args):
super().__init__() super().__init__()
@ -38,7 +40,9 @@ class train_callback(pl.Callback):
if args.lr_final == 0 or args.lr_init == 0: # linear decay if args.lr_final == 0 or args.lr_init == 0: # linear decay
lr = args.lr_init + (args.lr_final - args.lr_init) * progress lr = args.lr_init + (args.lr_final - args.lr_init) * progress
else: # exp decay else: # exp decay
lr = args.lr_init * math.exp(math.log(args.lr_final / args.lr_init) * pow(progress, 1)) lr = args.lr_init * math.exp(
math.log(args.lr_final / args.lr_init) * pow(progress, 1)
)
if trainer.global_step < w_step: if trainer.global_step < w_step:
lr = lr * (0.2 + 0.8 * trainer.global_step / w_step) lr = lr * (0.2 + 0.8 * trainer.global_step / w_step)
@ -60,7 +64,9 @@ class train_callback(pl.Callback):
trainer.my_loss_sum = 0 trainer.my_loss_sum = 0
trainer.my_loss_count = 0 trainer.my_loss_count = 0
trainer.my_log = open(args.proj_dir + "/train_log.txt", "a") trainer.my_log = open(args.proj_dir + "/train_log.txt", "a")
trainer.my_log.write(f"NEW RUN {args.my_timestamp}\n{vars(self.args)}\n") trainer.my_log.write(
f"NEW RUN {args.my_timestamp}\n{vars(self.args)}\n"
)
try: try:
print(f"\n{trainer.strategy.config}\n") print(f"\n{trainer.strategy.config}\n")
trainer.my_log.write(f"{trainer.strategy.config}\n") trainer.my_log.write(f"{trainer.strategy.config}\n")
@ -70,6 +76,7 @@ class train_callback(pl.Callback):
if len(args.wandb) > 0: if len(args.wandb) > 0:
print("Login to wandb...") print("Login to wandb...")
import wandb import wandb
wandb.init( wandb.init(
project=args.wandb, project=args.wandb,
name=args.run_name + " " + args.my_timestamp, name=args.run_name + " " + args.my_timestamp,
@ -102,20 +109,26 @@ class train_callback(pl.Callback):
# self.log("s", real_step, prog_bar=True, on_step=True) # self.log("s", real_step, prog_bar=True, on_step=True)
if len(args.wandb) > 0: if len(args.wandb) > 0:
lll = {"loss": trainer.my_loss, "lr": trainer.my_lr, "Gtokens": real_step * token_per_step / 1e9} lll = {
"loss": trainer.my_loss,
"lr": trainer.my_lr,
"Gtokens": real_step * token_per_step / 1e9,
}
if kt_s > 0: if kt_s > 0:
lll["kt/s"] = kt_s lll["kt/s"] = kt_s
trainer.my_wandb.log(lll, step=int(real_step)) trainer.my_wandb.log(lll, step=int(real_step))
if args.magic_prime > 0: if args.magic_prime > 0:
expand_factor = 2 if args.my_qa_mask > 0 else 1 expand_factor = 2 if args.my_qa_mask > 0 else 1
if int(real_step) == int(args.magic_prime * expand_factor // args.real_bsz) - 1: if (
int(real_step)
== int(args.magic_prime * expand_factor // args.real_bsz) - 1
):
to_save_dict = pl_module.state_dict() to_save_dict = pl_module.state_dict()
my_save( my_save(
to_save_dict, to_save_dict,
f"{args.proj_dir}/rwkv-final.pth", f"{args.proj_dir}/rwkv-final.pth",
) )
def on_train_epoch_start(self, trainer, pl_module): def on_train_epoch_start(self, trainer, pl_module):
args = self.args args = self.args
dataset = trainer.train_dataloader.dataset.datasets dataset = trainer.train_dataloader.dataset.datasets
@ -128,24 +141,28 @@ class train_callback(pl.Callback):
def on_train_epoch_end(self, trainer, pl_module): def on_train_epoch_end(self, trainer, pl_module):
args = self.args args = self.args
if trainer.is_global_zero: # logging & save state_dict if trainer.is_global_zero: # logging & save state_dict
if (args.epoch_save > 0 and trainer.current_epoch % args.epoch_save == 0) or trainer.current_epoch == args.epoch_count - 1: if (
if args.data_type == 'wds_img': args.epoch_save > 0 and trainer.current_epoch % args.epoch_save == 0
) or trainer.current_epoch == args.epoch_count - 1:
if args.data_type == "wds_img":
raw_dict = pl_module.state_dict() raw_dict = pl_module.state_dict()
to_save_dict = {} to_save_dict = {}
for k in raw_dict: for k in raw_dict:
if k.startswith('encoder.') or k.startswith('decoder.'): if k.startswith("encoder.") or k.startswith("decoder."):
to_save_dict[k] = raw_dict[k] to_save_dict[k] = raw_dict[k]
else: else:
to_save_dict = pl_module.state_dict() to_save_dict = pl_module.state_dict()
if args.lora: if args.lora:
enable_time_finetune = 'time' in LORA_CONFIG["parts"] enable_time_finetune = "time" in LORA_CONFIG["parts"]
enable_ln_finetune = 'ln' in LORA_CONFIG["parts"] enable_ln_finetune = "ln" in LORA_CONFIG["parts"]
lora_dict = {} lora_dict = {}
for name, state in to_save_dict.items(): for name, state in to_save_dict.items():
if ('.lora_' in name if (
or (enable_time_finetune and '.time_' in name) ".lora_" in name
or (enable_ln_finetune and '.ln' in name)): or (enable_time_finetune and ".time_" in name)
or (enable_ln_finetune and ".ln" in name)
):
lora_dict[name] = state lora_dict[name] = state
to_save_dict = lora_dict to_save_dict = lora_dict
@ -155,8 +172,10 @@ class train_callback(pl.Callback):
f"{args.proj_dir}/rwkv-{args.epoch_begin + trainer.current_epoch}.pth", f"{args.proj_dir}/rwkv-{args.epoch_begin + trainer.current_epoch}.pth",
) )
except Exception as e: except Exception as e:
print('Error\n\n', e, '\n\n') print("Error\n\n", e, "\n\n")
trainer.my_log.write(f"{args.epoch_begin + trainer.current_epoch} {trainer.my_epoch_loss:.6f} {math.exp(trainer.my_epoch_loss):.4f} {trainer.my_lr:.8f} {datetime.datetime.now()} {trainer.current_epoch}\n") trainer.my_log.write(
f"{args.epoch_begin + trainer.current_epoch} {trainer.my_epoch_loss:.6f} {math.exp(trainer.my_epoch_loss):.4f} {trainer.my_lr:.8f} {datetime.datetime.now()} {trainer.current_epoch}\n"
)
trainer.my_log.flush() trainer.my_log.flush()
trainer.my_loss_sum = 0 trainer.my_loss_sum = 0
@ -178,22 +197,22 @@ def generate_init_weight(model, init_weight_name):
mm[k] = src.reshape(mm[k].shape) mm[k] = src.reshape(mm[k].shape)
except: except:
tmp = mm[k].squeeze().clone() tmp = mm[k].squeeze().clone()
print(k, src.shape, '-->', mm[k].shape) print(k, src.shape, "-->", mm[k].shape)
ss = src.shape[0] ss = src.shape[0]
dd = tmp.shape[0] dd = tmp.shape[0]
for i in range(dd): for i in range(dd):
pos = i / dd * ss pos = i / dd * ss
if pos >= ss - 1: if pos >= ss - 1:
tmp[i] = src[ss-1] tmp[i] = src[ss - 1]
else: else:
p0 = int(math.floor(pos)) p0 = int(math.floor(pos))
ii = pos - p0 ii = pos - p0
tmp[i] = src[p0] * (1-ii) + src[p0+1] * (ii) tmp[i] = src[p0] * (1 - ii) + src[p0 + 1] * (ii)
mm[k] = tmp.reshape(mm[k].shape) mm[k] = tmp.reshape(mm[k].shape)
sss = src.squeeze().float().cpu().numpy() sss = src.squeeze().float().cpu().numpy()
print(sss[:10], '...', sss[-10:]) print(sss[:10], "...", sss[-10:])
mmm = mm[k].squeeze().float().cpu().numpy() mmm = mm[k].squeeze().float().cpu().numpy()
print(mmm[:10], '...', mmm[-10:]) print(mmm[:10], "...", mmm[-10:])
print(f"Save to {init_weight_name}...") print(f"Save to {init_weight_name}...")
torch.save(mm, init_weight_name) torch.save(mm, init_weight_name)

35
finetune/lora/src/utils.py → finetune/lora/v4/src/utils.py vendored
View File

@ -6,6 +6,7 @@ from torch.nn import functional as F
time_slot = {} time_slot = {}
time_ref = time.time_ns() time_ref = time.time_ns()
def record_time(name): def record_time(name):
if name not in time_slot: if name not in time_slot:
time_slot[name] = 1e20 time_slot[name] = 1e20
@ -13,20 +14,23 @@ def record_time(name):
if tt < time_slot[name]: if tt < time_slot[name]:
time_slot[name] = tt time_slot[name] = tt
class TOKENIZER():
def __init__(self, WORD_NAME, UNKNOWN_CHAR='\ue083'): class TOKENIZER:
if 'list' in str(type(WORD_NAME)): def __init__(self, WORD_NAME, UNKNOWN_CHAR="\ue083"):
if "list" in str(type(WORD_NAME)):
self.charMode = False self.charMode = False
if WORD_NAME[0] == WORD_NAME[1]: if WORD_NAME[0] == WORD_NAME[1]:
from transformers import PreTrainedTokenizerFast from transformers import PreTrainedTokenizerFast
self.tokenizer = PreTrainedTokenizerFast(tokenizer_file=WORD_NAME[0]) self.tokenizer = PreTrainedTokenizerFast(tokenizer_file=WORD_NAME[0])
else: else:
from transformers import GPT2TokenizerFast from transformers import GPT2TokenizerFast
self.tokenizer = GPT2TokenizerFast(WORD_NAME[0], WORD_NAME[1]) self.tokenizer = GPT2TokenizerFast(WORD_NAME[0], WORD_NAME[1])
self.vocab_size = len(self.tokenizer) self.vocab_size = len(self.tokenizer)
else: else:
self.charMode = True self.charMode = True
with open(WORD_NAME + '.json', "r", encoding="utf-16") as result_file: with open(WORD_NAME + ".json", "r", encoding="utf-16") as result_file:
self.word_table = json.load(result_file) self.word_table = json.load(result_file)
self.vocab_size = len(self.word_table) self.vocab_size = len(self.word_table)
@ -37,23 +41,25 @@ class TOKENIZER():
self.UNKNOWN_CHAR = self.stoi[UNKNOWN_CHAR] self.UNKNOWN_CHAR = self.stoi[UNKNOWN_CHAR]
def refine_context(self, context): def refine_context(self, context):
context = context.strip().split('\n') context = context.strip().split("\n")
for c in range(len(context)): for c in range(len(context)):
context[c] = context[c].strip().strip('\u3000').strip('\r') context[c] = context[c].strip().strip("\u3000").strip("\r")
context = list(filter(lambda c: c != '', context)) context = list(filter(lambda c: c != "", context))
context = '\n' + ('\n'.join(context)).strip() context = "\n" + ("\n".join(context)).strip()
if context == '': if context == "":
context = '\n' context = "\n"
return context return context
def sample_logits(self, out, x, ctx_len, temperature=1.0, top_p_usual=None, top_p_newline=None): 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') # out[self.UNKNOWN_CHAR] = -float('Inf')
lastChar = int(x[-1]) lastChar = int(x[-1])
probs = F.softmax(out, dim=-1) probs = F.softmax(out, dim=-1)
if self.charMode: if self.charMode:
if self.itos[lastChar] == '\n': if self.itos[lastChar] == "\n":
top_p = top_p_newline top_p = top_p_newline
else: else:
top_p = top_p_usual top_p = top_p_usual
@ -81,6 +87,7 @@ class TOKENIZER():
out = torch.multinomial(probs, num_samples=1)[0] out = torch.multinomial(probs, num_samples=1)[0]
return out return out
def MaybeIsPrime(number): def MaybeIsPrime(number):
if FermatPrimalityTest(number) and MillerRabinPrimalityTest(number): if FermatPrimalityTest(number) and MillerRabinPrimalityTest(number):
return True return True
@ -121,7 +128,9 @@ def MillerRabinPrimalityTest(number):
if (randomNumberWithPower != 1) and (randomNumberWithPower != number - 1): if (randomNumberWithPower != 1) and (randomNumberWithPower != number - 1):
iterationNumber = 1 iterationNumber = 1
while (iterationNumber <= timesTwoDividNumber - 1) and (randomNumberWithPower != number - 1): while (iterationNumber <= timesTwoDividNumber - 1) and (
randomNumberWithPower != number - 1
):
randomNumberWithPower = pow(randomNumberWithPower, 2, number) randomNumberWithPower = pow(randomNumberWithPower, 2, number)
iterationNumber = iterationNumber + 1 iterationNumber = iterationNumber + 1
if randomNumberWithPower != (number - 1): if randomNumberWithPower != (number - 1):

14
finetune/lora/train.py → finetune/lora/v4/train.py vendored
View File

@ -184,7 +184,7 @@ if __name__ == "__main__":
args.num_sanity_val_steps = 0 args.num_sanity_val_steps = 0
args.check_val_every_n_epoch = int(1e20) args.check_val_every_n_epoch = int(1e20)
args.log_every_n_steps = int(1e20) args.log_every_n_steps = int(1e20)
args.max_epochs = args.epoch_count # continue forever args.max_epochs = args.epoch_count # -1 continue forever
args.betas = (args.beta1, args.beta2) args.betas = (args.beta1, args.beta2)
args.real_bsz = int(args.num_nodes) * int(args.devices) * args.micro_bsz args.real_bsz = int(args.num_nodes) * int(args.devices) * args.micro_bsz
os.environ["RWKV_T_MAX"] = str(args.ctx_len) os.environ["RWKV_T_MAX"] = str(args.ctx_len)
@ -373,7 +373,7 @@ if __name__ == "__main__":
for param in module.parameters(): for param in module.parameters():
param.requires_grad = True param.requires_grad = True
elif enable_time_finetune and any( elif enable_time_finetune and any(
n.startswith("time") for n, _ in module.named_parameters() n.startswith("time") for n, _ in module.named_parameters()
): ):
for pname, param in module.named_parameters(): for pname, param in module.named_parameters():
if pname.startswith("time"): if pname.startswith("time"):
@ -381,7 +381,7 @@ if __name__ == "__main__":
param.requires_grad = True param.requires_grad = True
if ( if (
len(args.load_model) == 0 or args.my_pile_stage == 1 len(args.load_model) == 0 or args.my_pile_stage == 1
): # shall we build the initial weights? ): # shall we build the initial weights?
init_weight_name = f"{args.proj_dir}/rwkv-init.pth" init_weight_name = f"{args.proj_dir}/rwkv-init.pth"
generate_init_weight(model, init_weight_name) # save initial weights generate_init_weight(model, init_weight_name) # save initial weights
@ -423,8 +423,8 @@ if __name__ == "__main__":
) )
if ( if (
args.lr_init > 1e-4 args.lr_init > 1e-4
or trainer.world_size * args.micro_bsz * trainer.accumulate_grad_batches < 8 or trainer.world_size * args.micro_bsz * trainer.accumulate_grad_batches < 8
): ):
if "I_KNOW_WHAT_IM_DOING" in os.environ: if "I_KNOW_WHAT_IM_DOING" in os.environ:
if trainer.global_rank == 0: if trainer.global_rank == 0:
@ -459,10 +459,10 @@ if __name__ == "__main__":
if "deepspeed" in args.strategy: if "deepspeed" in args.strategy:
trainer.strategy.config["zero_optimization"]["allgather_bucket_size"] = ( trainer.strategy.config["zero_optimization"]["allgather_bucket_size"] = (
args.ds_bucket_mb * 1000 * 1000 args.ds_bucket_mb * 1000 * 1000
) )
trainer.strategy.config["zero_optimization"]["reduce_bucket_size"] = ( trainer.strategy.config["zero_optimization"]["reduce_bucket_size"] = (
args.ds_bucket_mb * 1000 * 1000 args.ds_bucket_mb * 1000 * 1000
) )
# must set shuffle=False, persistent_workers=False (because worker is in another thread) # must set shuffle=False, persistent_workers=False (because worker is in another thread)

202
finetune/lora/v5/cuda/wkv5_cuda.cu vendored Normal file
View File

@ -0,0 +1,202 @@
#include <stdio.h>
#include <assert.h>
#include "ATen/ATen.h"
typedef at::BFloat16 bf16;
template <typename F>
__global__ void kernel_forward(const int B, const int T, const int C, const int H,
const F *__restrict__ const _r, const F *__restrict__ const _k, const F *__restrict__ const _v, const float *__restrict__ _w, const F *__restrict__ _u,
F *__restrict__ const _y)
{
const int b = blockIdx.x / H;
const int h = blockIdx.x % H;
const int i = threadIdx.x;
_w += h*_N_;
_u += h*_N_;
__shared__ float r[_N_], k[_N_], u[_N_], w[_N_];
float state[_N_] = {0};
__syncthreads();
w[i] = _w[i];
u[i] = float(_u[i]);
__syncthreads();
for (int t = b*T*C + h*_N_ + i; t < (b+1)*T*C + h*_N_ + i; t += C)
{
__syncthreads();
r[i] = float(_r[t]);
k[i] = float(_k[t]);
__syncthreads();
const float v = float(_v[t]);
float y = 0;
#pragma unroll
for (int j = 0; j < _N_; j+=4)
{
const float4& r_ = (float4&)(r[j]);
const float4& k_ = (float4&)(k[j]);
const float4& w_ = (float4&)(w[j]);
const float4& u_ = (float4&)(u[j]);
float4& s = (float4&)(state[j]);
float4 x;
x.x = k_.x * v;
x.y = k_.y * v;
x.z = k_.z * v;
x.w = k_.w * v;
y += r_.x * (u_.x * x.x + s.x);
y += r_.y * (u_.y * x.y + s.y);
y += r_.z * (u_.z * x.z + s.z);
y += r_.w * (u_.w * x.w + s.w);
s.x = s.x * w_.x + x.x;
s.y = s.y * w_.y + x.y;
s.z = s.z * w_.z + x.z;
s.w = s.w * w_.w + x.w;
}
_y[t] = F(y);
}
}
template <typename F>
__global__ void kernel_backward(const int B, const int T, const int C, const int H,
const F *__restrict__ const _r, const F *__restrict__ const _k, const F *__restrict__ const _v, const float *__restrict__ _w, const float *__restrict__ __w, const F *__restrict__ _u, const F *__restrict__ const _gy,
F *__restrict__ const _gr, F *__restrict__ const _gk, F *__restrict__ const _gv, F *__restrict__ const _gw, F *__restrict__ const _gu)
{
const int b = blockIdx.x / H;
const int h = blockIdx.x % H;
const int i = threadIdx.x;
_w += h*_N_;
_u += h*_N_;
__w += h*_N_;
__shared__ float w_[_N_], u_[_N_];
__shared__ float r[_N_], k[_N_], v[_N_], gy[_N_];
__syncthreads();
w_[i] = _w[i];
u_[i] = float(_u[i]);
__syncthreads();
const float w = w_[i];
const float ww = __w[i];
const float u = u_[i];
float state[_N_] = {0}, saaaa[_N_] = {0}, sbbbb[_N_] = {0}, scccc[_N_] = {0}, sdddd[_N_] = {0};
float gw = 0, gu = 0;
const int t000 = b*T*C + h*_N_ + i;
const int t111 = (b+1)*T*C + h*_N_ + i;
const int t222 = t111 - 2*C;
for (int t = t000; t < t111; t += C)
{
__syncthreads();
v[i] = float(_v[t]);
gy[i] = float(_gy[t]);
__syncthreads();
const float k = float(_k[t]);
float gr = 0, gu_ = 0;
#pragma unroll
for (int j = 0; j < _N_; j++)
{
float& s = state[j];
float x = k * v[j];
gr += (u * x + s) * gy[j];
gu_ += x * gy[j];
s = s * w + x;
}
_gr[t] = F(gr);
gu += float(_r[t]) * gu_;
}
_gu[b*C + h*_N_ + i] = F(gu);
for (int t = t000; t < t222; t += C)
{
__syncthreads();
v[i] = float(_v[t]);
gy[i] = float(_gy[t + 2*C]);
__syncthreads();
const float k = float(_k[t]);
float gw_ = 0;
#pragma unroll
for (int j = 0; j < _N_; j++)
{
float& s = saaaa[j];
float& s2 = sbbbb[j];
float x = k * v[j];
float tmp = w * (x + s);
s = tmp;
s2 = tmp + w * s2;
gw_ += s2 * gy[j];
}
gw += float(_r[t + 2*C]) * gw_;
}
_gw[b*C + h*_N_ + i] = F(ww * gw);
for (int t = t111 - C; t >= t000; t -= C)
{
__syncthreads();
v[i] = float(_v[t]);
gy[i] = float(_gy[t]);
__syncthreads();
const float rr = float(_r[t]);
float gk = 0;
#pragma unroll
for (int j = 0; j < _N_; j++)
{
float& s = scccc[j];
float x = rr * gy[j];
gk += (u * x + s) * v[j];
s = x + s * w;
}
_gk[t] = F(gk);
}
for (int t = t111 - C; t >= t000; t -= C)
{
__syncthreads();
r[i] = float(_r[t]);
k[i] = float(_k[t]);
__syncthreads();
const float gyy = float(_gy[t]);
float gv = 0;
#pragma unroll
for (int j = 0; j < _N_; j++)
{
float& s = sdddd[j];
float x = gyy * r[j];
gv += (u_[j] * x + s) * k[j];
s = x + s * w_[j];
}
_gv[t] = F(gv);
}
}
void cuda_forward(int B, int T, int C, int H, bf16 *r, bf16 *k, bf16 *v, float *w, bf16 *u, bf16 *y)
{
assert(H*_N_ == C);
assert(_N_%4 == 0);
kernel_forward<<<dim3(B * H), dim3(_N_)>>>(B, T, C, H, r, k, v, w, u, y);
}
void cuda_backward(int B, int T, int C, int H, bf16 *r, bf16 *k, bf16 *v, float *w, float *ww, bf16 *u, bf16 *gy, bf16 *gr, bf16 *gk, bf16 *gv, bf16 *gw, bf16 *gu)
{
assert(H*_N_ == C);
assert(_N_%4 == 0);
kernel_backward<<<dim3(B * H), dim3(_N_)>>>(B, T, C, H, r, k, v, w, ww, u, gy, gr, gk, gv, gw, gu);
}

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#include <torch/extension.h>
#include "ATen/ATen.h"
typedef at::BFloat16 bf16;
void cuda_forward(int B, int T, int C, int H, bf16 *r, bf16 *k, bf16 *v, float *w, bf16 *u, bf16 *y);
void cuda_backward(int B, int T, int C, int H, bf16 *r, bf16 *k, bf16 *v, float *w, float *ww, bf16 *u, bf16 *gy, bf16 *gr, bf16 *gk, bf16 *gv, bf16 *gw, bf16 *gu);
void forward(int64_t B, int64_t T, int64_t C, int64_t H, torch::Tensor &r, torch::Tensor &k, torch::Tensor &v, torch::Tensor &w, torch::Tensor &u, torch::Tensor &y) {
cuda_forward(B, T, C, H, r.data_ptr<bf16>(), k.data_ptr<bf16>(), v.data_ptr<bf16>(), w.data_ptr<float>(), u.data_ptr<bf16>(), y.data_ptr<bf16>());
}
void backward(int64_t B, int64_t T, int64_t C, int64_t H, torch::Tensor &r, torch::Tensor &k, torch::Tensor &v, torch::Tensor &w, torch::Tensor &ww, torch::Tensor &u, torch::Tensor &gy, torch::Tensor &gr, torch::Tensor &gk, torch::Tensor &gv, torch::Tensor &gw, torch::Tensor &gu) {
cuda_backward(B, T, C, H, r.data_ptr<bf16>(), k.data_ptr<bf16>(), v.data_ptr<bf16>(), w.data_ptr<float>(), ww.data_ptr<float>(), u.data_ptr<bf16>(), gy.data_ptr<bf16>(), gr.data_ptr<bf16>(), gk.data_ptr<bf16>(), gv.data_ptr<bf16>(), gw.data_ptr<bf16>(), gu.data_ptr<bf16>());
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("forward", &forward, "wkv5 forward");
m.def("backward", &backward, "wkv5 backward");
}
TORCH_LIBRARY(wkv5, m) {
m.def("forward", forward);
m.def("backward", backward);
}

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finetune/lora/v5/src/__init__.py vendored Normal file
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from lib2to3.pgen2 import token
import os
import torch
import numpy as np
import shutil
import struct
from functools import lru_cache
from itertools import accumulate
def print_rank_0(*message):
pass
# """If distributed is initialized print only on rank 0."""
# if torch.distributed.is_initialized():
# if torch.distributed.get_rank() == 0:
# print(*message, flush=True)
# else:
# print(*message, flush=True)
def _warmup_mmap_file(path):
pass
# with open(path, "rb") as stream:
# while stream.read(100 * 1024 * 1024):
# pass
dtypes = {
1: np.uint8,
2: np.int8,
3: np.int16,
4: np.int32,
5: np.int64,
6: float,
7: np.double,
8: np.uint16,
}
def code(dtype):
for k in dtypes.keys():
if dtypes[k] == dtype:
return k
raise ValueError(dtype)
def index_file_path(prefix_path):
return prefix_path + ".idx"
def data_file_path(prefix_path):
return prefix_path + ".bin"
class MMapIndexedDataset(torch.utils.data.Dataset):
class Index(object):
_HDR_MAGIC = b"MMIDIDX\x00\x00"
@classmethod
def writer(cls, path, dtype):
class _Writer(object):
def __enter__(self):
self._file = open(path, "wb")
# Write Magic string so we can check the file format then opening it again.
self._file.write(cls._HDR_MAGIC)
# Write version number
# Little endian unsigned 64 Bit integer
self._file.write(struct.pack("<Q", 1))
# Little endian unsigned 8 Bit integer
self._file.write(struct.pack("<B", code(dtype)))
return self
@staticmethod
def _get_pointers(sizes):
dtype_size = dtype().itemsize
address = 0
pointers = []
for size in sizes:
pointers.append(address)
address += size * dtype_size
return pointers
def write(self, sizes, doc_idx):
pointers = self._get_pointers(sizes)
# Little endian unsigned 64 Bit integer
self._file.write(struct.pack("<Q", len(sizes)))
# Little endian unsigned 64 Bit integer
self._file.write(struct.pack("<Q", len(doc_idx)))
sizes = np.array(sizes, dtype=np.int32)
self._file.write(sizes.tobytes(order="C"))
del sizes
pointers = np.array(pointers, dtype=np.int64)
self._file.write(pointers.tobytes(order="C"))
del pointers
doc_idx = np.array(doc_idx, dtype=np.int64)
self._file.write(doc_idx.tobytes(order="C"))
def __exit__(self, exc_type, exc_val, exc_tb):
self._file.close()
return _Writer()
def __init__(self, path, skip_warmup=False):
with open(path, "rb") as stream:
magic_test = stream.read(9)
assert self._HDR_MAGIC == magic_test, (
"Index file doesn't match expected format. "
"Make sure that --dataset-impl is configured properly."
)
# Little endian unsigned 64 Bit integer
version = struct.unpack("<Q", stream.read(8))
assert (1,) == version
# Little endian unsigned 8 Bit integer
(dtype_code,) = struct.unpack("<B", stream.read(1))
self._dtype = dtypes[dtype_code]
self._dtype_size = self._dtype().itemsize
self._len = struct.unpack("<Q", stream.read(8))[0]
self._doc_count = struct.unpack("<Q", stream.read(8))[0]
offset = stream.tell()
if not skip_warmup:
print_rank_0(" warming up index mmap file...")
_warmup_mmap_file(path)
self._bin_buffer_mmap = np.memmap(path, mode="r", order="C")
self._bin_buffer = memoryview(self._bin_buffer_mmap)
print_rank_0(" reading sizes...")
self._sizes = np.frombuffer(
self._bin_buffer, dtype=np.int32, count=self._len, offset=offset
)
print_rank_0(" reading pointers...")
self._pointers = np.frombuffer(
self._bin_buffer,
dtype=np.int64,
count=self._len,
offset=offset + self._sizes.nbytes,
)
print_rank_0(" reading document index...")
self._doc_idx = np.frombuffer(
self._bin_buffer,
dtype=np.int64,
count=self._doc_count,
offset=offset + self._sizes.nbytes + self._pointers.nbytes,
)
def __del__(self):
self._bin_buffer_mmap._mmap.close()
del self._bin_buffer_mmap
@property
def dtype(self):
return self._dtype
@property
def sizes(self):
return self._sizes
@property
def doc_idx(self):
return self._doc_idx
@lru_cache(maxsize=8)
def __getitem__(self, i):
return self._pointers[i], self._sizes[i]
def __len__(self):
return self._len
def __init__(self, path, skip_warmup=False):
super().__init__()
self._path = None
self._index = None
self._bin_buffer = None
self._do_init(path, skip_warmup)
def __getstate__(self):
return self._path
def __setstate__(self, state):
self._do_init(state)
def _do_init(self, path, skip_warmup):
self._path = path
self._index = self.Index(index_file_path(self._path), skip_warmup)
if not skip_warmup:
print_rank_0(" warming up data mmap file...")
_warmup_mmap_file(data_file_path(self._path))
print_rank_0(" creating numpy buffer of mmap...")
self._bin_buffer_mmap = np.memmap(
data_file_path(self._path), mode="r", order="C"
)
print_rank_0(" creating memory view of numpy buffer...")
self._bin_buffer = memoryview(self._bin_buffer_mmap)
def __del__(self):
self._bin_buffer_mmap._mmap.close()
del self._bin_buffer_mmap
del self._index
def __len__(self):
return len(self._index)
# @lru_cache(maxsize=8)
def __getitem__(self, idx):
if isinstance(idx, int):
ptr, size = self._index[idx]
np_array = np.frombuffer(
self._bin_buffer, dtype=self._index.dtype, count=size, offset=ptr
)
return np_array
elif isinstance(idx, slice):
start, stop, step = idx.indices(len(self))
if step != 1:
raise ValueError("Slices into indexed_dataset must be contiguous")
ptr = self._index._pointers[start]
sizes = self._index._sizes[idx]
offsets = list(accumulate(sizes))
total_size = sum(sizes)
np_array = np.frombuffer(
self._bin_buffer, dtype=self._index.dtype, count=total_size, offset=ptr
)
sents = np.split(np_array, offsets[:-1])
return sents
def get(self, idx, offset=0, length=None):
"""Retrieves a single item from the dataset with the option to only
return a portion of the item.
get(idx) is the same as [idx] but get() does not support slicing.
"""
ptr, size = self._index[idx]
if length is None:
length = size - offset
ptr += offset * np.dtype(self._index.dtype).itemsize
np_array = np.frombuffer(
self._bin_buffer, dtype=self._index.dtype, count=length, offset=ptr
)
return np_array
def pad(self, idx, length=None):
ptr, size = self._index[idx]
try:
np_array = np.frombuffer(
self._bin_buffer, dtype=self._index.dtype, count=length, offset=ptr
)
except:
np_array = np.frombuffer(
self._bin_buffer, dtype=self._index.dtype, count=size, offset=ptr
)
ptr0, _ = self._index[0]
np_array0 = np.frombuffer(
self._bin_buffer,
dtype=self._index.dtype,
count=length - size,
offset=ptr0,
)
np_array = np.append(np_array, np_array0)
return np_array
def only(self, idx):
ptr, size = self._index[idx]
np_array = np.frombuffer(
self._bin_buffer, dtype=self._index.dtype, count=size, offset=ptr
)
return np_array
@property
def sizes(self):
return self._index.sizes
@property
def doc_idx(self):
return self._index.doc_idx
def get_doc_idx(self):
return self._index._doc_idx
def set_doc_idx(self, doc_idx_):
self._index._doc_idx = doc_idx_
@property
def supports_prefetch(self):
return False
@staticmethod
def exists(path):
return os.path.exists(index_file_path(path)) and os.path.exists(
data_file_path(path)
)

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########################################################################################################
# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
########################################################################################################
import json, math, random, os, sys
import numpy as np
import torch
from torch.utils.data import Dataset
from pytorch_lightning.utilities import rank_zero_info
from .binidx import MMapIndexedDataset
from .utils import MaybeIsPrime
class MyDataset(Dataset):
def __init__(self, args):
self.args = args
if args.data_type == "binidx":
self.vocab_size = args.vocab_size
rank_zero_info(
f"Current vocab size = {self.vocab_size} (make sure it's correct)"
)
if args.my_pile_version == 1:
self.data = MMapIndexedDataset(args.data_file)
self.data_size = (
len(self.data._bin_buffer) // self.data._index._dtype_size
)
rank_zero_info(f"Data has {self.data_size} tokens.")
elif args.my_pile_version == 2:
data_list = (
open(args.data_file, "r", encoding="utf-8")
.read()
.strip()
.split("\n")
)
data_list = [i.strip().split(" ") for i in data_list]
self.data = []
self.data_size = int(data_list[-1][-1])
rank_zero_info(f"Data has {self.data_size} chunks.")
for d in data_list:
data = MMapIndexedDataset(d[0])
data_size = len(data._bin_buffer) // data._index._dtype_size
assert (data_size - args.ctx_len) == int(d[1])
self.data += [[int(d[-1]), int(d[1]), data]]
# rank_zero_info(self.data)
if args.my_qa_mask > 0:
# self.data_pile = MMapIndexedDataset('/fsx/pile/pile_20B_tokenizer_text_document')
self.data_pile = MMapIndexedDataset(
"/fsx/pile_deduped/pile_0.87_deduped_text_document"
)
self.data_pile_size = (
len(self.data_pile._bin_buffer) // self.data._index._dtype_size
)
else:
self.data_pile = None
self.data_pile_size = 0
if args.my_pile_stage > 0:
# assert self.data_size == 332115325534 and self.vocab_size == 50277
self.samples_per_epoch = args.epoch_steps * args.real_bsz
assert self.samples_per_epoch == 40320
rank_zero_info(
f"########## Pile 20b-tokenized stage {args.my_pile_stage} ##########"
)
dataset_slot = self.data_size // args.ctx_len
if args.my_pile_stage != 4:
assert MaybeIsPrime(args.magic_prime)
assert args.magic_prime % 3 == 2
assert (
args.magic_prime / dataset_slot > 0.99
and args.magic_prime / dataset_slot <= 1
)
elif args.data_type == "numpy":
self.data = np.load(args.data_file).astype("int")
self.vocab_size = args.vocab_size
rank_zero_info(
f"Current vocab size = {self.vocab_size} (make sure it's correct)"
)
self.data_size = len(self.data)
rank_zero_info(f"Data has {self.data_size} tokens.")
elif args.data_type == "uint16":
self.data = (
np.fromfile(args.data_file, dtype=np.uint16)
.astype("int32")
.reshape(-1, args.my_sample_len)
)
self.vocab_size = args.vocab_size
rank_zero_info(
f"Current vocab size = {self.vocab_size} (make sure it's correct)"
)
self.data_size = self.data.shape[0]
rank_zero_info(f"Data has {self.data_size} samples.")
else:
if args.data_type == "dummy":
rank_zero_info("Building dummy data...")
self.data = ""
for i in range(100000):
aa = (i) % 10000
bb = (i * i) % 10000
cc = aa + bb
self.data += f".{aa}+{bb}={cc}."
else:
self.data = open(args.data_file, "r", encoding=args.data_type).read()
rank_zero_info("Building token list...")
unique = sorted(list(set(self.data)))
self.vocab_size = len(unique)
# rank_zero_info()
# for u in unique:
# print(u, end=' ')
# rank_zero_info('\n\n')
xx = 0
xxObj = {}
for u in unique:
xxObj[xx] = u
xx += 1
with open(
f"{args.proj_dir}/vocab.json", "w", encoding="utf-8"
) as vocab_file:
vocab_file.write(json.dumps(xxObj, ensure_ascii=False))
self.data_size = len(self.data)
rank_zero_info(
f"Data has {self.data_size} tokens, {self.vocab_size} vocab size."
)
self.stoi = {ch: i for i, ch in enumerate(unique)}
self.itos = {i: ch for i, ch in enumerate(unique)}
def __len__(self):
return self.args.epoch_steps * self.args.micro_bsz
def __getitem__(self, idx):
args = self.args
rank = self.global_rank
epoch = self.real_epoch
world_size = self.world_size
# print(f"epoch {epoch} idx {idx} rank {rank}/{world_size}")
if args.data_type == "uint16":
i = np.random.randint(0, self.data_size - 1)
dix = self.data[i]
x = torch.tensor(dix[:-1], dtype=torch.long)
y = torch.tensor(dix[1:], dtype=torch.long)
else:
ctx_len = args.ctx_len
req_len = ctx_len + 1
magic_prime = args.magic_prime
data = self.data
if args.my_pile_stage > 0:
ii = 1 + epoch * self.samples_per_epoch + (idx * world_size) + rank
if args.my_qa_mask > 0:
ii_orig = ii
if ii % 2 == 0:
ii = -1
data = self.data_pile
else:
ii = ii // 2
if data == self.data_pile:
i = np.random.randint(0, self.data_pile_size - req_len)
else:
if args.my_pile_stage == 4 or ii < args.my_random_steps:
# cheat: pick a random spot in dataset
if args.my_pile_version == 1:
i = np.random.randint(0, self.data_size - req_len)
else:
i = np.random.randint(0, self.data_size)
else:
ii = ii - args.my_random_steps
factor = (math.sqrt(5) - 1) / 2
factor = int(magic_prime * factor)
i = ((factor * ii * ii * ii) % magic_prime) * ctx_len
i = i + args.my_pile_shift
# print(f"epoch {epoch} idx {idx} rank {rank}/{world_size} ii {ii} pos {round(i / self.data_size, 3)}")
else:
# cheat: pick a random spot in dataset
i = np.random.randint(0, self.data_size - req_len)
if args.data_type == "binidx":
if args.my_pile_version == 1:
dix = data.get(idx=0, offset=i, length=req_len).astype(int)
else:
# self.data : cutoff, chunk_count, data
for j in range(len(data)):
if i < data[j][0]:
ii = i
i = (i - (data[j - 1][0] if j > 0 else 0)) % data[j][1]
dix = (
data[j][2]
.get(idx=0, offset=i, length=req_len)
.astype(int)
)
# print(ii, j, i)
break
elif args.data_type == "numpy":
dix = data[i : i + req_len]
else:
dix = [self.stoi[s] for s in data[i : i + req_len]]
if args.my_qa_mask == 1:
if data == self.data_pile:
z = [1] * ctx_len
else:
z = [0] * ctx_len
z_sum = 0
isGood = False
for i in range(3, ctx_len):
if (
dix[i] == 27
and dix[i - 1] == 34
and dix[i - 2] == 187
and dix[i - 3] == 187
):
isGood = True
if dix[i] == 0:
isGood = False
if isGood:
z[i] = 1
z_sum += 1
if z_sum == 0:
z = [1] * ctx_len
i = np.random.randint(0, self.data_pile_size - req_len)
dix = self.data_pile.get(
idx=0, offset=i, length=req_len
).astype(int)
z = torch.tensor(z, dtype=torch.bfloat16)
x = torch.tensor(dix[:-1], dtype=torch.long)
y = torch.tensor(dix[1:], dtype=torch.long)
# if ii_orig < 50:
# # if rank == 1:
# print('rank', rank, 'i', ii_orig, ii, i, 'x', x[:5], '...', x[-5:])
# else:
# exit(0)
if args.my_qa_mask == 1:
return x, y, z
return x, y

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########################################################################################################
# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
########################################################################################################
import functools
import os, math, gc, importlib
import torch
# torch._C._jit_set_profiling_executor(True)
# torch._C._jit_set_profiling_mode(True)
import torch.nn as nn
from torch.utils.checkpoint import checkpoint as torch_checkpoint
from torch.nn import functional as F
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info, rank_zero_only
from pytorch_lightning.strategies import DeepSpeedStrategy
if importlib.util.find_spec("deepspeed"):
import deepspeed
from deepspeed.ops.adam import DeepSpeedCPUAdam, FusedAdam
# from deepspeed.runtime.fp16.onebit.zoadam import ZeroOneAdam
# lora-config
LORA_CONFIG = {
"r": 0,
"alpha": 0,
"dropout": 0,
"parts": {"att", "ln", "time"},
}
try:
print("RWKV_MY_TESTING", os.environ["RWKV_MY_TESTING"])
except:
os.environ["RWKV_MY_TESTING"] = ""
def __nop(ob):
return ob
MyModule = nn.Module
MyFunction = __nop
if os.environ["RWKV_JIT_ON"] == "1":
MyModule = torch.jit.ScriptModule
MyFunction = torch.jit.script_method
########################################################################################################
# CUDA Kernel
########################################################################################################
from torch.utils.cpp_extension import load
HEAD_SIZE = int(os.environ["RWKV_HEAD_SIZE_A"])
wkv5_cuda = load(
name="wkv5",
sources=[
"finetune/lora/v5/cuda/wkv5_op.cpp",
f"finetune/lora/v5/cuda/wkv5_cuda.cu",
],
verbose=True,
extra_cuda_cflags=[
"-res-usage",
"--use_fast_math",
"-O3",
"-Xptxas -O3",
"--extra-device-vectorization",
f"-D_N_={HEAD_SIZE}",
],
)
class WKV_5(torch.autograd.Function):
@staticmethod
def forward(ctx, B, T, C, H, r, k, v, w, u):
with torch.no_grad():
assert r.dtype == torch.bfloat16
assert k.dtype == torch.bfloat16
assert v.dtype == torch.bfloat16
assert w.dtype == torch.bfloat16
assert u.dtype == torch.bfloat16
assert HEAD_SIZE == C // H
ctx.B = B
ctx.T = T
ctx.C = C
ctx.H = H
assert r.is_contiguous()
assert k.is_contiguous()
assert v.is_contiguous()
assert w.is_contiguous()
assert u.is_contiguous()
ew = (-torch.exp(w.float())).contiguous()
eew = (torch.exp(ew)).contiguous()
ctx.save_for_backward(r, k, v, eew, ew, u)
y = torch.empty(
(B, T, C),
device=r.device,
dtype=torch.bfloat16,
memory_format=torch.contiguous_format,
) # .uniform_(-1, 1)
wkv5_cuda.forward(B, T, C, H, r, k, v, eew, u, y)
return y
@staticmethod
def backward(ctx, gy):
with torch.no_grad():
assert gy.dtype == torch.bfloat16
B = ctx.B
T = ctx.T
C = ctx.C
H = ctx.H
assert gy.is_contiguous()
r, k, v, eew, ew, u = ctx.saved_tensors
gr = torch.empty(
(B, T, C),
device=gy.device,
requires_grad=False,
dtype=torch.bfloat16,
memory_format=torch.contiguous_format,
) # .uniform_(-1, 1)
gk = torch.empty(
(B, T, C),
device=gy.device,
requires_grad=False,
dtype=torch.bfloat16,
memory_format=torch.contiguous_format,
) # .uniform_(-1, 1)
gv = torch.empty(
(B, T, C),
device=gy.device,
requires_grad=False,
dtype=torch.bfloat16,
memory_format=torch.contiguous_format,
) # .uniform_(-1, 1)
gw = torch.empty(
(B, C),
device=gy.device,
requires_grad=False,
dtype=torch.bfloat16,
memory_format=torch.contiguous_format,
) # .uniform_(-1, 1)
gu = torch.empty(
(B, C),
device=gy.device,
requires_grad=False,
dtype=torch.bfloat16,
memory_format=torch.contiguous_format,
) # .uniform_(-1, 1)
wkv5_cuda.backward(B, T, C, H, r, k, v, eew, ew, u, gy, gr, gk, gv, gw, gu)
gw = torch.sum(gw, 0).view(H, C // H)
gu = torch.sum(gu, 0).view(H, C // H)
return (None, None, None, None, gr, gk, gv, gw, gu)
def RUN_CUDA_RWKV5(B, T, C, H, r, k, v, w, u):
return WKV_5.apply(B, T, C, H, r, k, v, w, u)
#################################################################
class LoraLinear(nn.Module):
def __init__(self, in_features: int, out_features: int, bias: bool):
super().__init__()
self.weight = nn.Parameter(torch.empty((out_features, in_features)))
assert bias == False, "Biased LoraLinear not supported"
r, alpha, dropout = (
LORA_CONFIG["r"],
LORA_CONFIG["alpha"],
LORA_CONFIG["dropout"],
)
self.lora_A = nn.Parameter(torch.empty(r, in_features))
self.lora_B = nn.Parameter(torch.empty(out_features, r))
self.lora_dropout = nn.Dropout(dropout)
self.scaling = alpha / r
nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
nn.init.kaiming_uniform_(self.lora_A, a=math.sqrt(5))
nn.init.zeros_(self.lora_B)
def forward(self, x):
return F.linear(x, self.weight) + self.scaling * F.linear(
F.linear(self.lora_dropout(x), self.lora_A), self.lora_B
)
@functools.wraps(LoraLinear)
def make_linear_att(*args, **kwargs):
if "att" in LORA_CONFIG["parts"] and LORA_CONFIG["r"] > 0:
return LoraLinear(*args, **kwargs)
else:
return nn.Linear(*args, **kwargs)
@functools.wraps(LoraLinear)
def make_linear_ffn(*args, **kwargs):
if "ffn" in LORA_CONFIG["parts"] and LORA_CONFIG["r"] > 0:
return LoraLinear(*args, **kwargs)
else:
return nn.Linear(*args, **kwargs)
########################################################################################################
class RWKV_TimeMix_RWKV5(MyModule):
def __init__(self, args, layer_id):
super().__init__()
self.args = args
self.layer_id = layer_id
self.head_size = args.head_size_a
assert HEAD_SIZE == self.head_size # change HEAD_SIZE to match args.head_size_a
self.n_head = args.dim_att // self.head_size
assert args.dim_att % self.n_head == 0
self.head_size_divisor = args.head_size_divisor
with torch.no_grad():
ratio_0_to_1 = layer_id / (args.n_layer - 1) # 0 to 1
ratio_1_to_almost0 = 1.0 - (layer_id / args.n_layer) # 1 to ~0
ddd = torch.ones(1, 1, args.n_embd)
for i in range(args.n_embd):
ddd[0, 0, i] = i / args.n_embd
# fancy time_mix
self.time_mix_k = nn.Parameter(torch.pow(ddd, ratio_1_to_almost0))
self.time_mix_v = nn.Parameter(
torch.pow(ddd, ratio_1_to_almost0) + 0.3 * ratio_0_to_1
)
self.time_mix_r = nn.Parameter(torch.pow(ddd, 0.5 * ratio_1_to_almost0))
self.time_mix_g = nn.Parameter(torch.pow(ddd, 0.5 * ratio_1_to_almost0))
# fancy time_decay
decay_speed = torch.ones(args.dim_att)
for n in range(args.dim_att):
decay_speed[n] = -6 + 5 * (n / (args.dim_att - 1)) ** (
0.7 + 1.3 * ratio_0_to_1
)
self.time_decay = nn.Parameter(
decay_speed.reshape(self.n_head, self.head_size)
)
# print(layer_id, self.time_decay.flatten()[:3].cpu().numpy(), '...', self.time_decay.flatten()[-3:].cpu().numpy())
tmp = torch.zeros(args.dim_att)
for n in range(args.dim_att):
zigzag = ((n + 1) % 3 - 1) * 0.1
tmp[n] = ratio_0_to_1 * (1 - (n / (args.dim_att - 1))) + zigzag
self.time_faaaa = nn.Parameter(tmp.reshape(self.n_head, self.head_size))
self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
self.receptance = make_linear_att(args.n_embd, args.dim_att, bias=False)
self.key = make_linear_att(args.n_embd, args.dim_att, bias=False)
self.value = make_linear_att(args.n_embd, args.dim_att, bias=False)
self.output = nn.Linear(args.dim_att, args.n_embd, bias=False)
self.gate = make_linear_att(args.n_embd, args.dim_att, bias=False)
self.ln_x = nn.GroupNorm(self.n_head, args.dim_att)
@MyFunction
def jit_func(self, x):
B, T, C = x.size()
xx = self.time_shift(
x
) # Mix x with the previous timestep to produce xk, xv, xr
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)
xg = x * self.time_mix_g + xx * (1 - self.time_mix_g)
r = self.receptance(xr)
k = self.key(xk)
v = self.value(xv)
g = F.silu(self.gate(xg))
return r, k, v, g
@MyFunction
def jit_func_2(self, x, g):
B, T, C = x.size()
x = x.view(B * T, C)
x = self.ln_x(x / self.head_size_divisor).view(B, T, C)
x = self.output(x * g)
return x
def forward(self, x):
B, T, C = x.size()
H = self.n_head
r, k, v, g = self.jit_func(x)
x = RUN_CUDA_RWKV5(B, T, C, H, r, k, v, w=self.time_decay, u=self.time_faaaa)
return self.jit_func_2(x, g)
########################################################################################################
class RWKV_ChannelMix(MyModule):
def __init__(self, args, layer_id):
super().__init__()
self.args = args
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 / args.n_layer) # 1 to ~0
ddd = torch.ones(1, 1, args.n_embd)
for i in range(args.n_embd):
ddd[0, 0, i] = i / args.n_embd
self.time_mix_k = nn.Parameter(torch.pow(ddd, ratio_1_to_almost0))
self.time_mix_r = nn.Parameter(torch.pow(ddd, ratio_1_to_almost0))
self.key = make_linear_ffn(args.n_embd, args.dim_ffn, bias=False)
self.receptance = make_linear_ffn(args.n_embd, args.n_embd, bias=False)
self.value = make_linear_ffn(args.dim_ffn, args.n_embd, bias=False)
@MyFunction
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.relu(k) ** 2
kv = self.value(k)
return torch.sigmoid(self.receptance(xr)) * kv
class MishGLU(MyModule):
def __init__(self, args, layer_id):
super().__init__()
self.args = args
self.layer_id = layer_id
self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
with torch.no_grad():
ratio_1_to_almost0 = 1.0 - (layer_id / args.n_layer)
x = torch.ones(1, 1, args.n_embd)
for i in range(args.n_embd):
x[0, 0, i] = i / args.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))
self.aa = nn.Linear(args.n_embd, args.dim_ffn, bias=False)
self.bb = nn.Linear(args.n_embd, args.dim_ffn, bias=False)
self.value = nn.Linear(args.dim_ffn, args.n_embd, bias=False)
@MyFunction
def forward(self, x):
xx = self.time_shift(x)
xa = x * self.time_mix_k + xx * (1 - self.time_mix_k)
xb = x * self.time_mix_r + xx * (1 - self.time_mix_r)
a = self.aa(xa)
b = self.bb(xb)
return self.value(a * F.mish(b))
########################################################################################################
# The RWKV Model with our blocks
########################################################################################################
class Block(nn.Module):
def __init__(self, args, layer_id):
super().__init__()
self.args = args
self.layer_id = layer_id
self.ln1 = nn.LayerNorm(args.n_embd)
self.ln2 = nn.LayerNorm(args.n_embd)
if self.layer_id == 0:
self.ln0 = nn.LayerNorm(args.n_embd)
if args.my_pos_emb > 0:
self.pos_emb_x = nn.Parameter(
torch.zeros((1, args.my_pos_emb, args.n_embd))
)
self.pos_emb_y = nn.Parameter(
torch.zeros((args.my_pos_emb, 1, args.n_embd))
)
if self.layer_id == 0 and self.args.pre_ffn > 0:
self.ffnPre = RWKV_ChannelMix(args, 0)
else:
self.att = RWKV_TimeMix_RWKV5(args, layer_id)
if "g" in os.environ["RWKV_MY_TESTING"]:
self.ffn = MishGLU(args, layer_id)
else:
self.ffn = RWKV_ChannelMix(args, layer_id)
if args.tiny_att_dim > 0 and self.layer_id == args.tiny_att_layer:
self.tiny_ln = nn.LayerNorm(args.n_embd)
self.tiny_q = nn.Linear(args.n_embd, args.tiny_att_dim, bias=False)
self.tiny_k = nn.Linear(args.n_embd, args.tiny_att_dim, bias=False)
self.tiny_v = nn.Linear(args.n_embd, args.n_embd, bias=False)
self.register_buffer(
"tiny_mask", torch.tril(torch.ones(args.ctx_len, args.ctx_len))
)
if args.dropout > 0:
self.drop0 = nn.Dropout(p=args.dropout)
self.drop1 = nn.Dropout(p=args.dropout)
def forward(self, x, x_emb=None):
args = self.args
B, T, C = x.size()
if self.layer_id == 0:
x = self.ln0(x)
if args.my_pos_emb > 0:
pos_emb = (self.pos_emb_x + self.pos_emb_y).reshape(T + 1, -1)[:-1, :]
x = x + pos_emb
if self.args.dropout == 0:
if self.layer_id == 0 and args.pre_ffn > 0:
x = x + self.ffnPre(self.ln1(x))
else:
x = x + self.att(self.ln1(x))
x = x + self.ffn(self.ln2(x))
else:
if self.layer_id == 0 and args.pre_ffn > 0:
x = self.drop0(x + self.ffnPre(self.ln1(x)))
else:
x = self.drop0(x + self.att(self.ln1(x)))
x = self.drop1(x + self.ffn(self.ln2(x)))
if args.tiny_att_dim > 0 and self.layer_id == args.tiny_att_layer:
xx = self.tiny_ln(x)
q = self.tiny_q(xx)[:, :T, :]
k = self.tiny_k(xx)[:, :T, :]
c = (q @ k.transpose(-2, -1)) * (args.tiny_att_dim ** (-0.5))
c = c.masked_fill(self.tiny_mask[:T, :T] == 0, 0)
x = x + c @ self.tiny_v(x_emb)
return x
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)
class RWKV(pl.LightningModule):
def __init__(self, args):
super().__init__()
self.args = args
if not hasattr(args, "dim_att"):
args.dim_att = args.n_embd
if not hasattr(args, "dim_ffn"):
args.dim_ffn = args.n_embd * 4
if not hasattr(args, "tiny_att_layer"):
args.tiny_att_layer = -1
if not hasattr(args, "tiny_att_dim"):
args.tiny_att_dim = -1
assert args.n_embd % 32 == 0
assert args.dim_att % 32 == 0
assert args.dim_ffn % 32 == 0
self.emb = nn.Embedding(args.vocab_size, args.n_embd)
self.blocks = nn.ModuleList([Block(args, i) for i in range(args.n_layer)])
self.ln_out = nn.LayerNorm(args.n_embd)
self.head = nn.Linear(args.n_embd, args.vocab_size, bias=False)
if args.head_qk > 0:
self.head_q = nn.Linear(args.n_embd, args.head_qk, bias=False)
self.head_k = nn.Linear(args.n_embd, args.head_qk, bias=False)
self.register_buffer(
"copy_mask", torch.tril(torch.ones(args.ctx_len, args.ctx_len))
)
if args.dropout > 0:
self.drop0 = nn.Dropout(p=args.dropout)
def configure_optimizers(self):
args = self.args
lr_decay = set()
lr_1x = set()
lr_2x = set()
lr_3x = set()
for n, p in self.named_parameters():
if ("time_mix" in n) and (args.layerwise_lr > 0):
if args.my_pile_stage == 2:
lr_2x.add(n)
else:
lr_1x.add(n)
elif ("time_decay" in n) and (args.layerwise_lr > 0):
if args.my_pile_stage == 2:
lr_3x.add(n)
else:
lr_2x.add(n)
elif ("time_faaaa" in n) and (args.layerwise_lr > 0):
if args.my_pile_stage == 2:
lr_2x.add(n)
else:
lr_1x.add(n)
elif ("time_first" in n) and (args.layerwise_lr > 0):
lr_3x.add(n)
elif (len(p.squeeze().shape) >= 2) and (args.weight_decay > 0):
lr_decay.add(n)
else:
lr_1x.add(n)
lr_decay = sorted(list(lr_decay))
lr_1x = sorted(list(lr_1x))
lr_2x = sorted(list(lr_2x))
lr_3x = sorted(list(lr_3x))
# print('decay', lr_decay)
# print('1x', lr_1x)
# print('2x', lr_2x)
# print('3x', lr_3x)
param_dict = {n: p for n, p in self.named_parameters()}
if args.layerwise_lr > 0:
if args.my_pile_stage == 2:
optim_groups = [
{
"params": [param_dict[n] for n in lr_1x],
"weight_decay": 0.0,
"my_lr_scale": 1.0,
},
{
"params": [param_dict[n] for n in lr_2x],
"weight_decay": 0.0,
"my_lr_scale": 5.0,
}, # test: 2e-3 / args.lr_init},
{
"params": [param_dict[n] for n in lr_3x],
"weight_decay": 0.0,
"my_lr_scale": 5.0,
}, # test: 3e-3 / args.lr_init},
]
else:
optim_groups = [
{
"params": [param_dict[n] for n in lr_1x],
"weight_decay": 0.0,
"my_lr_scale": 1.0,
},
{
"params": [param_dict[n] for n in lr_2x],
"weight_decay": 0.0,
"my_lr_scale": 2.0,
},
{
"params": [param_dict[n] for n in lr_3x],
"weight_decay": 0.0,
"my_lr_scale": 3.0,
},
]
else:
optim_groups = [
{
"params": [param_dict[n] for n in lr_1x],
"weight_decay": 0.0,
"my_lr_scale": 1.0,
}
]
if args.weight_decay > 0:
optim_groups += [
{
"params": [param_dict[n] for n in lr_decay],
"weight_decay": args.weight_decay,
"my_lr_scale": 1.0,
}
]
if self.deepspeed_offload:
return DeepSpeedCPUAdam(
optim_groups,
lr=self.args.lr_init,
betas=self.args.betas,
eps=self.args.adam_eps,
bias_correction=True,
adamw_mode=True,
amsgrad=False,
)
return FusedAdam(
optim_groups,
lr=self.args.lr_init,
betas=self.args.betas,
eps=self.args.adam_eps,
bias_correction=True,
adam_w_mode=True,
amsgrad=False,
)
else:
if self.deepspeed_offload:
return DeepSpeedCPUAdam(
optim_groups,
lr=self.args.lr_init,
betas=self.args.betas,
eps=self.args.adam_eps,
bias_correction=True,
adamw_mode=False,
weight_decay=0,
amsgrad=False,
)
return FusedAdam(
optim_groups,
lr=self.args.lr_init,
betas=self.args.betas,
eps=self.args.adam_eps,
bias_correction=True,
adam_w_mode=False,
weight_decay=0,
amsgrad=False,
)
# return ZeroOneAdam(optim_groups, lr=self.args.lr_init, betas=self.args.betas, eps=self.args.adam_eps, bias_correction=True, weight_decay=0, amsgrad=False, cuda_aware=False)
@property
def deepspeed_offload(self) -> bool:
strategy = self.trainer.strategy
if isinstance(strategy, DeepSpeedStrategy):
cfg = strategy.config["zero_optimization"]
return cfg.get("offload_optimizer") or cfg.get("offload_param")
return False
def forward(self, idx):
args = self.args
B, T = idx.size()
assert T <= args.ctx_len, "Cannot forward, model ctx_len is exhausted."
x = self.emb(idx)
x_emb = x
if args.dropout > 0:
x = self.drop0(x)
if args.tiny_att_dim > 0:
for block in self.blocks:
if args.grad_cp == 1:
if args.lora:
x = torch_checkpoint(block, x, x_emb, use_reentrant=False)
else:
x = deepspeed.checkpointing.checkpoint(block, x, x_emb)
else:
x = block(x, x_emb)
else:
for block in self.blocks:
if args.grad_cp == 1:
if args.lora:
x = torch_checkpoint(block, x, x_emb, use_reentrant=False)
else:
x = deepspeed.checkpointing.checkpoint(block, x)
else:
x = block(x)
x = self.ln_out(x)
if args.head_qk > 0:
q = self.head_q(x)[:, :T, :]
k = self.head_k(x)[:, :T, :]
c = (q @ k.transpose(-2, -1)) * (1.0 / args.head_qk)
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=args.vocab_size)
elif os.environ["RWKV_FLOAT_MODE"] == "fp16":
c = c @ F.one_hot(idx, num_classes=args.vocab_size).half()
elif os.environ["RWKV_FLOAT_MODE"] == "bf16":
c = c @ F.one_hot(idx, num_classes=args.vocab_size).bfloat16()
x = self.head(x) + c
else:
x = self.head(x)
return x
def training_step(self, batch, batch_idx):
args = self.args
if args.my_qa_mask != 1:
idx, targets = batch
logits = self(idx)
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
# if '0' in os.environ["RWKV_MY_TESTING"]:
# print('logits', logits)
# torch.set_printoptions(threshold=10000)
# print('idx', idx)
# exit(0)
else:
idx, targets, mask = batch
mask = mask.view(-1)
sum_mask = torch.sum(mask).item()
# if sum_mask == 0:
# return torch.tensor([0.0], requires_grad=True)
logits = self(idx)
if sum_mask == mask.shape[0]:
loss = F.cross_entropy(
logits.view(-1, logits.size(-1)), targets.view(-1)
)
# print('rank', self.global_rank, 'loss', loss.item())
else:
loss = F.cross_entropy(
logits.view(-1, logits.size(-1)), targets.view(-1), reduction="none"
)
# loss_raw = loss
loss = torch.sum(loss * mask) / sum_mask
# torch.set_printoptions(threshold=10000)
# if True: #self.global_rank == 1:
# tmp = ''
# sss = 0
# ccc = 0
# for i in range(mask.shape[0]):
# if mask[i] > 0:
# tmp += str(idx.view(-1)[i].item()) + ','
# sss += loss_raw.view(-1)[i].float().item()
# ccc += 1
# print('rank', self.global_rank, 'loss', loss.item(), 'lavg', sss / ccc)#, 'tmp', tmp, 'input', idx)
return L2Wrap.apply(loss, logits)
def training_step_end(self, batch_parts):
if pl.__version__[0] != "2":
all = self.all_gather(batch_parts)
if self.trainer.is_global_zero:
self.trainer.my_loss_all = all
def generate_init_weight(self):
print(
f"""
############################################################################
#
# Init model weight (slow for large models)...
#
############################################################################
"""
)
m = {}
for n in self.state_dict():
p = self.state_dict()[n]
shape = p.shape
gain = 1.0
scale = 1.0
if (
"ln_" in n
or ".ln" in n
or "time_" in n
or "_mask" in n
or "pos_emb" in n
or ".mask." in n
):
if "ln_x.weight" in n:
layer_scale = (1 + int(n.split(".")[1])) / self.args.n_layer
m[n] = (p * 0.0) + (layer_scale**0.7)
else:
m[n] = p
else:
if n == "emb.weight":
scale = -1 * self.args.lr_init
else:
if shape[0] > shape[1]:
gain = math.sqrt(shape[0] / shape[1])
zero = [
".att.output.",
".ffn.value.",
".ffn.receptance.",
".ffnPre.value.",
".ffnPre.receptance.",
"head_q.",
".oo.",
".rr.",
]
for kk in zero:
if kk in n:
scale = 0
if n == "head.weight":
scale = 0.5
if "head_k." in n:
scale = 0.1
if "head_q." in n:
scale = 0
print(
f"{str(shape[0]).ljust(5)} {str(shape[1]).ljust(5)} {str(scale).ljust(4)} {n}"
)
if self.args.accelerator.upper() == "GPU":
m[n] = torch.empty((shape[0], shape[1]), device="cuda")
else:
m[n] = torch.empty((shape[0], shape[1]))
if scale == 0:
nn.init.zeros_(m[n])
elif scale < 0:
nn.init.uniform_(m[n], a=scale, b=-scale)
else:
nn.init.orthogonal_(m[n], gain=gain * scale)
m[n] = m[n].cpu()
if os.environ["RWKV_FLOAT_MODE"] == "fp16":
m[n] = m[n].half()
elif os.environ["RWKV_FLOAT_MODE"] == "bf16":
m[n] = m[n].bfloat16()
# if n == "emb.weight":
# print(m[n])
gc.collect()
torch.cuda.empty_cache()
return m

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import os, math, time, datetime, subprocess
import torch
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info, rank_zero_only
from .model import LORA_CONFIG
def my_save(args, trainer, dd, ff):
if "14b-run1" in ff:
fn = ff.split("/")[-1]
fff = "/dev/shm/" + fn
torch.save(dd, fff)
subprocess.Popen(f" aws s3 mv {fff} s3://rwkv-14b-4k/{fn} --quiet", shell=True)
elif ("world/14b" in ff) or ("world/7b" in ff):
aa = ff.split("/")[1]
fn = ff.split("/")[-1]
fff = f"/dev/shm/{aa}-{fn}"
torch.save(dd, fff)
subprocess.Popen(
f" aws s3 mv {fff} s3://rwkv-world/{aa}-{fn} --quiet", shell=True
)
else:
if "deepspeed_stage_3" in args.strategy:
trainer.save_checkpoint(ff, weights_only=True)
else:
torch.save(dd, ff)
class train_callback(pl.Callback):
def __init__(self, args):
super().__init__()
self.args = args
def on_train_batch_start(self, trainer, pl_module, batch, batch_idx):
args = self.args
# if args.cuda_cleanup > 0:
# torch.cuda.empty_cache()
real_step = trainer.global_step + args.epoch_begin * args.epoch_steps
# LR schedule
w_step = args.warmup_steps
if args.lr_final == args.lr_init or args.epoch_count == 0:
lr = args.lr_init
else:
decay_step = real_step - args.my_pile_edecay * args.epoch_steps
decay_total = (args.epoch_count - args.my_pile_edecay) * args.epoch_steps
progress = (decay_step - w_step + 1) / (decay_total - w_step)
progress = min(1, max(0, progress))
if args.lr_final == 0 or args.lr_init == 0: # linear decay
lr = args.lr_init + (args.lr_final - args.lr_init) * progress
else: # exp decay
lr = args.lr_init * math.exp(
math.log(args.lr_final / args.lr_init) * pow(progress, 1)
)
# if trainer.is_global_zero:
# print(trainer.global_step, decay_step, decay_total, w_step, progress, lr)
if args.my_exit_tokens != 0: # cosine decay
real_tokens = real_step * args.ctx_len * args.real_bsz
warmup_tokens = w_step * args.ctx_len * args.real_bsz
progress = (real_tokens - warmup_tokens) / (
abs(args.my_exit_tokens) - warmup_tokens
)
progress = max(0, min(1, progress))
lr_final_factor = args.lr_final / args.lr_init
lr_mult = (0.5 + lr_final_factor / 2) + (
0.5 - lr_final_factor / 2
) * math.cos(math.pi * progress)
if args.my_exit_tokens > 0:
lr = args.lr_init * lr_mult
else:
lr = (lr + args.lr_init * lr_mult) / 2
if progress >= 1:
if (trainer.is_global_zero) or ("deepspeed_stage_3" in args.strategy):
my_save(
args,
trainer,
pl_module.state_dict(),
f"{args.proj_dir}/rwkv-final.pth",
)
exit(0)
if trainer.global_step < w_step:
lr = lr * (0.2 + 0.8 * trainer.global_step / w_step)
if args.weight_decay_final > 0:
wd_now = args.weight_decay * math.exp(
math.log(args.weight_decay_final / args.weight_decay) * progress
)
else:
wd_now = args.weight_decay
for param_group in trainer.optimizers[0].param_groups:
if param_group["weight_decay"] > 0:
param_group["weight_decay"] = wd_now
if args.layerwise_lr > 0:
param_group["lr"] = lr * param_group["my_lr_scale"]
# print(param_group["lr"], param_group["my_lr_scale"])
else:
param_group["lr"] = lr
trainer.my_lr = lr
trainer.my_wd = wd_now
# rank_zero_info(f"{real_step} {lr}")
if trainer.global_step == 0:
if trainer.is_global_zero: # logging
trainer.my_loss_sum = 0
trainer.my_loss_count = 0
trainer.my_log = open(args.proj_dir + "/train_log.txt", "a")
trainer.my_log.write(
f"NEW RUN {args.my_timestamp}\n{vars(self.args)}\n"
)
try:
print(f"\n{trainer.strategy.config}\n")
trainer.my_log.write(f"{trainer.strategy.config}\n")
except:
pass
trainer.my_log.flush()
if len(args.wandb) > 0:
print("Login to wandb...")
import wandb
wandb.init(
project=args.wandb,
name=args.run_name + " " + args.my_timestamp,
config=args,
save_code=False,
)
trainer.my_wandb = wandb
def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
args = self.args
token_per_step = args.ctx_len * args.real_bsz
real_step = trainer.global_step + args.epoch_begin * args.epoch_steps
if trainer.is_global_zero: # logging
t_now = time.time_ns()
kt_s = 0
try:
t_cost = (t_now - trainer.my_time_ns) / 1e9
kt_s = token_per_step / t_cost / 1000
self.log("REAL it/s", 1.0 / t_cost, prog_bar=True, on_step=True)
self.log("Kt/s", kt_s, prog_bar=True, on_step=True)
except:
pass
trainer.my_time_ns = t_now
if pl.__version__[0] == "2":
trainer.my_loss = outputs["loss"]
else:
trainer.my_loss = trainer.my_loss_all.float().mean().item()
trainer.my_loss_sum += trainer.my_loss
trainer.my_loss_count += 1
trainer.my_epoch_loss = trainer.my_loss_sum / trainer.my_loss_count
self.log("lr", trainer.my_lr, prog_bar=True, on_step=True)
self.log("loss", trainer.my_epoch_loss, prog_bar=True, on_step=True)
# self.log("s", real_step, prog_bar=True, on_step=True)
if len(args.wandb) > 0:
lll = {
"loss": trainer.my_loss,
"lr": trainer.my_lr,
"wd": trainer.my_wd,
"Gtokens": real_step * token_per_step / 1e9,
}
if kt_s > 0:
lll["kt/s"] = kt_s
trainer.my_wandb.log(lll, step=int(real_step))
if (trainer.is_global_zero) or (
"deepspeed_stage_3" in args.strategy
): # save pth
if args.magic_prime > 0:
expand_factor = 2 if args.my_qa_mask > 0 else 1
if int(real_step) == int(
args.magic_prime * expand_factor // args.real_bsz
) - 1 + int(args.my_random_steps):
to_save_dict = pl_module.state_dict()
my_save(
args,
trainer,
to_save_dict,
f"{args.proj_dir}/rwkv-final.pth",
)
# if args.batch_save==batch_idx :
# to_save_dict = pl_module.state_dict()
# for name, state in to_save_dict.items():
# if 'img' in name:
# to_save_dict[name] = state
# try:
# my_save(
# args, trainer,
# to_save_dict,
# f"{args.proj_dir}/rwkv-{args.epoch_begin + trainer.current_epoch}-{batch_idx}.pth",
# )
# except Exception as e:
# print('Error\n\n', e, '\n\n')
def on_train_epoch_start(self, trainer, pl_module):
args = self.args
if pl.__version__[0] == "2":
dataset = trainer.train_dataloader.dataset
else:
dataset = trainer.train_dataloader.dataset.datasets
assert "MyDataset" in str(dataset)
dataset.global_rank = trainer.global_rank
dataset.real_epoch = int(args.epoch_begin + trainer.current_epoch)
dataset.world_size = trainer.world_size
# print(f'########## world_size {dataset.world_size} global_rank {dataset.global_rank} real_epoch {dataset.real_epoch} ##########')
def on_train_epoch_end(self, trainer, pl_module):
args = self.args
to_save_dict = {}
if (trainer.is_global_zero) or (
"deepspeed_stage_3" in args.strategy
): # save pth
if (
args.epoch_save > 0 and trainer.current_epoch % args.epoch_save == 0
) or (trainer.current_epoch == args.epoch_count - 1):
if args.data_type == "wds_img":
raw_dict = pl_module.state_dict()
for k in raw_dict:
if k.startswith("encoder.") or k.startswith("decoder."):
to_save_dict[k] = raw_dict[k]
else:
to_save_dict = pl_module.state_dict()
if args.data_type == "img" and not args.lora:
for name, state in to_save_dict.items():
if "img" in name:
to_save_dict[name] = state
if args.lora:
enable_time_finetune = "time" in LORA_CONFIG["parts"]
enable_ln_finetune = "ln" in LORA_CONFIG["parts"]
lora_dict = {}
for name, state in to_save_dict.items():
if "img" in name:
lora_dict[name] = state
if (
".lora_" in name
or (enable_time_finetune and ".time_" in name)
or (enable_ln_finetune and ".ln" in name)
):
lora_dict[name] = state
to_save_dict = lora_dict
try:
my_save(
args,
trainer,
to_save_dict,
f"{args.proj_dir}/rwkv-{args.epoch_begin + trainer.current_epoch}.pth",
)
except Exception as e:
print("Error\n\n", e, "\n\n")
if trainer.is_global_zero: # logging
trainer.my_log.write(
f"{args.epoch_begin + trainer.current_epoch} {trainer.my_epoch_loss:.6f} {math.exp(trainer.my_epoch_loss):.4f} {trainer.my_lr:.8f} {datetime.datetime.now()} {trainer.current_epoch}\n"
)
trainer.my_log.flush()
trainer.my_loss_sum = 0
trainer.my_loss_count = 0
if (args.epoch_begin + trainer.current_epoch) >= args.my_exit:
exit(0)
@rank_zero_only
def generate_init_weight(model, init_weight_name):
mm = model.generate_init_weight()
if model.args.my_pile_stage == 1:
if len(model.args.load_model) > 0:
print(f"Combine weights from {model.args.load_model}...")
load_dict = torch.load(model.args.load_model, map_location="cpu")
for k in load_dict:
try:
assert k in mm
except:
print("missing", k)
exit(0)
src = load_dict[k]
try:
mm[k] = src.reshape(mm[k].shape)
except:
tmp = mm[k].squeeze().clone()
print(k, src.shape, "-->", mm[k].shape)
ss = src.shape[0]
dd = tmp.shape[0]
for i in range(dd):
pos = i / dd * ss
if pos >= ss - 1:
tmp[i] = src[ss - 1]
else:
p0 = int(math.floor(pos))
ii = pos - p0
tmp[i] = src[p0] * (1 - ii) + src[p0 + 1] * (ii)
mm[k] = tmp.reshape(mm[k].shape)
sss = src.squeeze().float().cpu().numpy()
print(sss[:10], "...", sss[-10:])
mmm = mm[k].squeeze().float().cpu().numpy()
print(mmm[:10], "...", mmm[-10:])
print(f"Save to {init_weight_name}...")
torch.save(mm, init_weight_name)
if model.args.my_pile_stage == 1:
print("Done. Now go for stage 2.")
exit(0)

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finetune/lora/v5/src/utils.py vendored Normal file
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import json, time, random, os
import numpy as np
import torch
from torch.nn import functional as F
time_slot = {}
time_ref = time.time_ns()
def record_time(name):
if name not in time_slot:
time_slot[name] = 1e20
tt = (time.time_ns() - time_ref) / 1e9
if tt < time_slot[name]:
time_slot[name] = tt
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(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
if os.environ["RWKV_RUN_DEVICE"] == "cpu":
probs = probs.numpy()
sorted_probs = np.sort(probs)[::-1]
cumulative_probs = np.cumsum(sorted_probs)
cutoff = float(sorted_probs[np.argmax(cumulative_probs > top_p)])
probs[probs < cutoff] = 0
if temperature != 1.0:
probs = probs.pow(1.0 / temperature)
probs = probs / np.sum(probs)
out = np.random.choice(a=len(probs), p=probs)
return out
else:
sorted_probs = torch.sort(probs, descending=True)[0]
cumulative_probs = torch.cumsum(sorted_probs, dim=-1).cpu().numpy()
cutoff = float(sorted_probs[np.argmax(cumulative_probs > top_p)])
probs[probs < cutoff] = 0
if temperature != 1.0:
probs = probs.pow(1.0 / temperature)
out = torch.multinomial(probs, num_samples=1)[0]
return out
def MaybeIsPrime(number):
if FermatPrimalityTest(number) and MillerRabinPrimalityTest(number):
return True
else:
return False
def FermatPrimalityTest(number):
if number > 1:
for time in range(3):
randomNumber = random.randint(2, number) - 1
if pow(randomNumber, number - 1, number) != 1:
return False
return True
else:
return False
def MillerRabinPrimalityTest(number):
if number == 2:
return True
elif number == 1 or number % 2 == 0:
return False
oddPartOfNumber = number - 1
timesTwoDividNumber = 0
while oddPartOfNumber % 2 == 0:
oddPartOfNumber = oddPartOfNumber // 2
timesTwoDividNumber = timesTwoDividNumber + 1
for time in range(3):
while True:
randomNumber = random.randint(2, number) - 1
if randomNumber != 0 and randomNumber != 1:
break
randomNumberWithPower = pow(randomNumber, oddPartOfNumber, number)
if (randomNumberWithPower != 1) and (randomNumberWithPower != number - 1):
iterationNumber = 1
while (iterationNumber <= timesTwoDividNumber - 1) and (
randomNumberWithPower != number - 1
):
randomNumberWithPower = pow(randomNumberWithPower, 2, number)
iterationNumber = iterationNumber + 1
if randomNumberWithPower != (number - 1):
return False
return True

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finetune/lora/v5/train.py vendored Normal file
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########################################################################################################
# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM
########################################################################################################
import logging
logging.basicConfig(level=logging.INFO)
if __name__ == "__main__":
from argparse import ArgumentParser
from pytorch_lightning import Trainer
from pytorch_lightning.utilities import rank_zero_info, rank_zero_only
import pytorch_lightning as pl
rank_zero_info("########## work in progress ##########")
parser = ArgumentParser()
parser.add_argument("--load_model", default="", type=str) # full path, with .pth
parser.add_argument(
"--wandb", default="", type=str
) # wandb project name. if "" then don't use wandb
parser.add_argument("--proj_dir", default="out", type=str)
parser.add_argument("--random_seed", default="-1", type=int)
parser.add_argument("--data_file", default="", type=str)
parser.add_argument("--data_type", default="utf-8", type=str)
parser.add_argument(
"--vocab_size", default=0, type=int
) # vocab_size = 0 means auto (for char-level LM and .txt data)
parser.add_argument("--ctx_len", default=1024, type=int)
parser.add_argument(
"--epoch_steps", default=1000, type=int
) # a mini "epoch" has [epoch_steps] steps
parser.add_argument(
"--epoch_count", default=500, type=int
) # train for this many "epochs". will continue afterwards with lr = lr_final
parser.add_argument(
"--epoch_begin", default=0, type=int
) # if you load a model trained for x "epochs", set epoch_begin = x
parser.add_argument(
"--epoch_save", default=5, type=int
) # save the model every [epoch_save] "epochs"
parser.add_argument(
"--micro_bsz", default=12, type=int
) # micro batch size (batch size per GPU)
parser.add_argument("--n_layer", default=6, type=int)
parser.add_argument("--n_embd", default=512, type=int)
parser.add_argument("--dim_att", default=0, type=int)
parser.add_argument("--dim_ffn", default=0, type=int)
parser.add_argument(
"--pre_ffn", default=0, type=int
) # replace first att layer by ffn (sometimes better)
parser.add_argument("--head_qk", default=0, type=int) # my headQK trick
parser.add_argument("--tiny_att_dim", default=0, type=int) # tiny attention dim
parser.add_argument(
"--tiny_att_layer", default=-999, type=int
) # tiny attention @ which layer
parser.add_argument(
"--lr_init", default=6e-4, type=float
) # 6e-4 for L12-D768, 4e-4 for L24-D1024, 3e-4 for L24-D2048
parser.add_argument("--lr_final", default=1e-5, type=float)
parser.add_argument(
"--warmup_steps", default=-1, type=int
) # try 50 if you load a model
parser.add_argument("--beta1", default=0.9, type=float)
parser.add_argument(
"--beta2", default=0.99, type=float
) # use 0.999 when your model is close to convergence
parser.add_argument("--adam_eps", default=1e-8, type=float)
parser.add_argument(
"--grad_cp", default=0, type=int
) # gradient checkpt: saves VRAM, but slower
parser.add_argument(
"--dropout", default=0, type=float
) # try 0.01 / 0.02 / 0.05 / 0.1
parser.add_argument(
"--weight_decay", default=0, type=float
) # try 0.1 / 0.01 / 0.001
parser.add_argument("--weight_decay_final", default=-1, type=float)
parser.add_argument(
"--my_pile_version", default=1, type=int
) # my special pile version
parser.add_argument("--my_pile_stage", default=0, type=int) # my special pile mode
parser.add_argument(
"--my_pile_shift", default=-1, type=int
) # my special pile mode - text shift
parser.add_argument("--my_pile_edecay", default=0, type=int)
parser.add_argument(
"--layerwise_lr", default=1, type=int
) # layerwise lr for faster convergence (but slower it/s)
parser.add_argument(
"--ds_bucket_mb", default=200, type=int
) # deepspeed bucket size in MB. 200 seems enough
# parser.add_argument("--cuda_cleanup", default=0, type=int) # extra cuda cleanup (sometimes helpful)
parser.add_argument("--my_sample_len", default=0, type=int)
parser.add_argument("--my_ffn_shift", default=1, type=int)
parser.add_argument("--my_att_shift", default=1, type=int)
parser.add_argument(
"--head_size_a", default=64, type=int
) # can try larger values for larger models
parser.add_argument("--head_size_divisor", default=8, type=int)
parser.add_argument("--my_pos_emb", default=0, type=int)
parser.add_argument("--load_partial", default=0, type=int)
parser.add_argument("--magic_prime", default=0, type=int)
parser.add_argument("--my_qa_mask", default=0, type=int)
parser.add_argument("--my_random_steps", default=0, type=int)
parser.add_argument("--my_testing", default="", type=str)
parser.add_argument("--my_exit", default=99999999, type=int)
parser.add_argument("--my_exit_tokens", default=0, type=int)
# LORA
parser.add_argument("--emb", action="store_true")
parser.add_argument("--lora", action="store_true")
parser.add_argument("--lora_load", default="", type=str)
parser.add_argument("--lora_r", default=8, type=int)
parser.add_argument("--lora_alpha", default=32, type=float)
parser.add_argument("--lora_dropout", default=0.01, type=float)
parser.add_argument("--lora_parts", default="att,ln,time", type=str)
if pl.__version__[0] == "2":
parser.add_argument("--accelerator", default="gpu", type=str)
parser.add_argument("--strategy", default="auto", type=str)
parser.add_argument("--devices", default=1, type=int)
parser.add_argument("--num_nodes", default=1, type=int)
parser.add_argument("--precision", default="fp16", type=str)
parser.add_argument("--accumulate_grad_batches", default=1, type=int)
else:
parser = Trainer.add_argparse_args(parser)
args = parser.parse_args()
########################################################################################################
import os, warnings, math, datetime, sys, time
import numpy as np
import torch
from torch.utils.data import DataLoader
if "deepspeed" in args.strategy:
import deepspeed
from pytorch_lightning import seed_everything
if args.random_seed >= 0:
print(
f"########## WARNING: GLOBAL SEED {args.random_seed} THIS WILL AFFECT MULTIGPU SAMPLING ##########\n"
* 3
)
seed_everything(args.random_seed)
np.set_printoptions(precision=4, suppress=True, linewidth=200)
warnings.filterwarnings(
"ignore", ".*Consider increasing the value of the `num_workers` argument*"
)
warnings.filterwarnings(
"ignore", ".*The progress bar already tracks a metric with the*"
)
# os.environ["WDS_SHOW_SEED"] = "1"
args.my_timestamp = datetime.datetime.today().strftime("%Y-%m-%d-%H-%M-%S")
args.enable_checkpointing = False
args.replace_sampler_ddp = False
args.logger = False
args.gradient_clip_val = 1.0
args.num_sanity_val_steps = 0
args.check_val_every_n_epoch = int(1e20)
args.log_every_n_steps = int(1e20)
args.max_epochs = args.epoch_count # -1 continue forever
args.betas = (args.beta1, args.beta2)
args.real_bsz = int(args.num_nodes) * int(args.devices) * args.micro_bsz
os.environ["RWKV_MY_TESTING"] = args.my_testing
os.environ["RWKV_HEAD_SIZE_A"] = str(args.head_size_a)
if args.dim_att <= 0:
args.dim_att = args.n_embd
if args.dim_ffn <= 0:
args.dim_ffn = int((args.n_embd * 3.5) // 32 * 32) # default = 3.5x emb size
if args.data_type == "wds_img":
args.run_name = f"v{args.my_img_version}-{args.my_img_size}-{args.my_img_bit}bit-{args.my_img_clip}x{args.my_img_clip_scale}"
args.proj_dir = f"{args.proj_dir}-{args.run_name}"
else:
args.run_name = (
f"{args.vocab_size} ctx{args.ctx_len} L{args.n_layer} D{args.n_embd}"
)
if not os.path.exists(args.proj_dir):
os.makedirs(args.proj_dir)
if args.my_pile_stage > 0:
magic_prime_bak = args.magic_prime
if args.my_pile_shift < 0:
args.my_pile_shift = 0
if magic_prime_bak > 0:
args.magic_prime = magic_prime_bak
if args.my_qa_mask == 2:
args.epoch_count = 2 * args.magic_prime // 40320
else:
args.epoch_count = args.magic_prime // 40320
args.epoch_steps = 40320 // args.real_bsz
assert args.epoch_steps * args.real_bsz == 40320
# if args.my_pile_stage == 2:
# assert args.lr_final == args.lr_init
if args.my_pile_stage >= 2: # find latest saved model
list_p = []
for p in os.listdir(args.proj_dir):
if p.startswith("rwkv") and p.endswith(".pth"):
p = ((p.split("-"))[1].split("."))[0]
if p != "final":
if p == "init":
p = -1
else:
p = int(p)
list_p += [p]
list_p.sort()
max_p = list_p[-1]
if len(list_p) > 1:
args.my_pile_prev_p = list_p[-2] # in case max_p is corrupted
if max_p == -1:
args.load_model = f"{args.proj_dir}/rwkv-init.pth"
else:
args.load_model = f"{args.proj_dir}/rwkv-{max_p}.pth"
if args.warmup_steps < 0:
if args.my_pile_stage == 2:
args.warmup_steps = 10
else:
args.warmup_steps = 30
args.epoch_begin = max_p + 1
samples_per_epoch = args.epoch_steps * args.real_bsz
tokens_per_epoch = samples_per_epoch * args.ctx_len
try:
deepspeed_version = deepspeed.__version__
except:
deepspeed_version = None
pass
rank_zero_info(
f"""
############################################################################
#
# RWKV-5 {args.precision.upper()} on {args.num_nodes}x{args.devices} {args.accelerator.upper()}, bsz {args.num_nodes}x{args.devices}x{args.micro_bsz}={args.real_bsz}, {args.strategy} {'with grad_cp' if args.grad_cp > 0 else ''}
#
# Data = {args.data_file} ({args.data_type}), ProjDir = {args.proj_dir}
#
# Epoch = {args.epoch_begin} to {args.epoch_begin + args.epoch_count - 1}, save every {args.epoch_save} epoch
#
# Each "epoch" = {args.epoch_steps} steps, {samples_per_epoch} samples, {tokens_per_epoch} tokens
#
# Model = {args.n_layer} n_layer, {args.n_embd} n_embd, {args.ctx_len} ctx_len
#
# Adam = lr {args.lr_init} to {args.lr_final}, warmup {args.warmup_steps} steps, beta {args.betas}, eps {args.adam_eps}
#
# Found torch {torch.__version__}, recommend 1.13.1+cu117 or newer
# Found deepspeed {deepspeed_version}, recommend 0.7.0 (faster than newer versions)
# Found pytorch_lightning {pl.__version__}, recommend 1.9.5
#
############################################################################
"""
)
rank_zero_info(str(vars(args)) + "\n")
assert args.data_type in ["utf-8", "utf-16le", "numpy", "binidx", "dummy", "uint16"]
if args.lr_final == 0 or args.lr_init == 0:
rank_zero_info(
"\n\nNote: lr_final = 0 or lr_init = 0. Using linear LR schedule instead.\n\n"
)
assert args.precision in ["fp32", "tf32", "fp16", "bf16"]
os.environ["RWKV_FLOAT_MODE"] = args.precision
if args.precision == "fp32":
for i in range(10):
rank_zero_info(
"\n\nNote: you are using fp32 (very slow). Try bf16 / tf32 for faster training.\n\n"
)
if args.precision == "fp16":
rank_zero_info(
"\n\nNote: you are using fp16 (might overflow). Try bf16 / tf32 for stable training.\n\n"
)
os.environ["RWKV_JIT_ON"] = "0"
if "deepspeed_stage_3" in args.strategy:
os.environ["RWKV_JIT_ON"] = "0"
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.enabled = True
if args.precision == "fp32":
torch.backends.cudnn.allow_tf32 = False
torch.backends.cuda.matmul.allow_tf32 = False
else:
torch.backends.cudnn.allow_tf32 = True
torch.backends.cuda.matmul.allow_tf32 = True
if "32" in args.precision:
args.precision = 32
elif args.precision == "fp16":
args.precision = 16
else:
args.precision = "bf16"
########################################################################################################
from src.trainer import train_callback, generate_init_weight
from src.dataset import MyDataset
train_data = MyDataset(args)
args.vocab_size = train_data.vocab_size
from src.model import RWKV, LORA_CONFIG, LoraLinear
if args.lora:
assert args.lora_r > 0, "LoRA should have its `r` > 0"
LORA_CONFIG["r"] = args.lora_r
LORA_CONFIG["alpha"] = args.lora_alpha
LORA_CONFIG["dropout"] = args.lora_dropout
LORA_CONFIG["parts"] = set(str(args.lora_parts).split(","))
enable_time_finetune = "time" in LORA_CONFIG["parts"]
enable_ln_finetune = "ln" in LORA_CONFIG["parts"]
model = RWKV(args)
# only train lora parameters
if args.lora:
model.requires_grad_(False)
for name, module in model.named_modules():
if any(n.startswith("lora_") for n, _ in module.named_parameters()):
print(f" LoRA additionally training module {name}")
for pname, param in module.named_parameters():
param.requires_grad = "lora_" in pname
elif enable_ln_finetune and ".ln" in name:
print(f" LoRA additionally training module {name}")
for param in module.parameters():
param.requires_grad = True
elif enable_time_finetune and any(
n.startswith("time") for n, _ in module.named_parameters()
):
for pname, param in module.named_parameters():
if pname.startswith("time"):
print(f" LoRA additionally training parameter {pname}")
param.requires_grad = True
if (
len(args.load_model) == 0 or args.my_pile_stage == 1
): # shall we build the initial weights?
init_weight_name = f"{args.proj_dir}/rwkv-init.pth"
generate_init_weight(model, init_weight_name) # save initial weights
args.load_model = init_weight_name
rank_zero_info(f"########## Loading {args.load_model}... ##########")
try:
load_dict = torch.load(args.load_model, map_location="cpu")
load_keys = list(load_dict.keys())
for k in load_keys:
if k.startswith("_forward_module."):
load_dict[k.replace("_forward_module.", "")] = load_dict[k]
del load_dict[k]
except:
rank_zero_info(f"Bad checkpoint {args.load_model}")
if args.my_pile_stage >= 2: # try again using another checkpoint
max_p = args.my_pile_prev_p
if max_p == -1:
args.load_model = f"{args.proj_dir}/rwkv-init.pth"
else:
args.load_model = f"{args.proj_dir}/rwkv-{max_p}.pth"
args.epoch_begin = max_p + 1
rank_zero_info(f"Trying {args.load_model}")
load_dict = torch.load(args.load_model, map_location="cpu")
if args.load_partial == 1:
load_keys = load_dict.keys()
for k in model.state_dict():
if k not in load_keys:
load_dict[k] = model.state_dict()[k]
# model.load_state_dict(load_dict)
model.load_state_dict(load_dict, strict=(not args.lora))
if os.path.isfile(args.lora_load):
model.load_state_dict(
torch.load(args.lora_load, map_location="cpu"), strict=False
)
if pl.__version__[0] == "2":
trainer = Trainer(
accelerator=args.accelerator,
strategy=args.strategy,
devices=args.devices,
num_nodes=args.num_nodes,
precision=args.precision,
logger=args.logger,
callbacks=[train_callback(args)],
max_epochs=args.max_epochs,
check_val_every_n_epoch=args.check_val_every_n_epoch,
num_sanity_val_steps=args.num_sanity_val_steps,
log_every_n_steps=args.log_every_n_steps,
enable_checkpointing=args.enable_checkpointing,
accumulate_grad_batches=args.accumulate_grad_batches,
gradient_clip_val=args.gradient_clip_val,
)
else:
trainer = Trainer.from_argparse_args(
args,
callbacks=[train_callback(args)],
)
if trainer.global_rank == 0:
for n in model.state_dict():
shape = model.state_dict()[n].shape
shape = [i for i in shape if i != 1]
if len(shape) > 1:
print(f"{str(shape[0]).ljust(5)} {str(shape[1]).ljust(5)} {n}")
else:
print(f"{str(shape[0]).ljust(5)} {n}")
if "deepspeed" in args.strategy:
trainer.strategy.config["zero_optimization"]["allgather_bucket_size"] = (
args.ds_bucket_mb * 1000 * 1000
)
trainer.strategy.config["zero_optimization"]["reduce_bucket_size"] = (
args.ds_bucket_mb * 1000 * 1000
)
# must set shuffle=False, persistent_workers=False (because worker is in another thread)
data_loader = DataLoader(
train_data,
shuffle=False,
pin_memory=True,
batch_size=args.micro_bsz,
num_workers=1,
persistent_workers=False,
drop_last=True,
)
trainer.fit(model, data_loader)

3
frontend/src/pages/Train.tsx
View File

@ -131,7 +131,7 @@ const showError = (e: any) => {
}; };
const errorsMap = Object.entries({ const errorsMap = Object.entries({
'python3 ./finetune/lora/train.py': 'Memory is not enough, try to increase the virtual memory (Swap of WSL) or use a smaller base model.', 'python3 ./finetune/lora/v': 'Memory is not enough, try to increase the virtual memory (Swap of WSL) or use a smaller base model.',
'cuda out of memory': 'VRAM is not enough', 'cuda out of memory': 'VRAM is not enough',
'valueerror: high <= 0': 'Training data is not enough, reduce context length or add more data for training', 'valueerror: high <= 0': 'Training data is not enough, reduce context length or add more data for training',
'+= \'+ptx\'': 'Can not find an Nvidia GPU. Perhaps the gpu driver of windows is too old, or you are using WSL 1 for training, please upgrade to WSL 2. e.g. Run "wsl --set-version Ubuntu-22.04 2"', '+= \'+ptx\'': 'Can not find an Nvidia GPU. Perhaps the gpu driver of windows is too old, or you are using WSL 1 for training, please upgrade to WSL 2. e.g. Run "wsl --set-version Ubuntu-22.04 2"',
@ -299,7 +299,6 @@ const LoraFinetune: FC = observer(() => {
(loraParams.baseModel ? `--load_model models/${loraParams.baseModel} ` : '') + (loraParams.baseModel ? `--load_model models/${loraParams.baseModel} ` : '') +
(loraParams.loraLoad ? `--lora_load lora-models/${loraParams.loraLoad} ` : '') + (loraParams.loraLoad ? `--lora_load lora-models/${loraParams.loraLoad} ` : '') +
`--data_file ${convertedDataPath} ` + `--data_file ${convertedDataPath} ` +
`--vocab_size ${loraParams.baseModel.toLowerCase().includes('world') ? '65536' : '50277'} ` +
`--ctx_len ${ctxLen} --epoch_steps ${loraParams.epochSteps} --epoch_count ${loraParams.epochCount} ` + `--ctx_len ${ctxLen} --epoch_steps ${loraParams.epochSteps} --epoch_count ${loraParams.epochCount} ` +
`--epoch_begin ${loraParams.epochBegin} --epoch_save ${loraParams.epochSave} ` + `--epoch_begin ${loraParams.epochBegin} --epoch_save ${loraParams.epochSave} ` +
`--micro_bsz ${loraParams.microBsz} --accumulate_grad_batches ${loraParams.accumGradBatches} ` + `--micro_bsz ${loraParams.microBsz} --accumulate_grad_batches ${loraParams.accumGradBatches} ` +