path stringlengths 9 117 | type stringclasses 2
values | project stringclasses 10
values | commit_hash stringlengths 40 40 | commit_message stringlengths 1 137 | ground_truth stringlengths 0 2.74k | main_code stringlengths 102 3.37k | context stringlengths 0 14.7k |
|---|---|---|---|---|---|---|---|
RWKV-v4neo.src.dataset/MyDataset.__init__ | Modified | BlinkDL~RWKV-LM | 1945cb58ed29ed1c353453bb93a7ee72d563d3bc | pile v2 | <6>:<del> if args.data_file.endswith('/'):
<7>:<del> d_all = []
<8>:<del> for p in os.listdir(args.data_file):
<9>:<del> if p.endswith(".idx"):
<10>:<del> d_all += [p[:-4]]
<11>:<del> d_all.sort()
<12>:<del> ... | # module: RWKV-v4neo.src.dataset
class MyDataset(Dataset):
def __init__(self, args):
<0> self.args = args
<1>
<2> if args.data_type == "binidx":
<3> self.vocab_size = args.vocab_size
<4> rank_zero_info(f"Current vocab size = {self.vocab_size} (mak... | ===========below chunk 0===========
# module: RWKV-v4neo.src.dataset
class MyDataset(Dataset):
def __init__(self, args):
# offset: 1
assert args.magic_prime / dataset_slot > 0.99 and args.magic_prime / dataset_slot <= 1
elif args.data_type == "numpy":
... |
RWKV-v4neo.src.dataset/MyDataset.__init__ | Modified | BlinkDL~RWKV-LM | 79915b3696fc744d410b69a09510b7161bda835b | better | <24>:<add> self.data_pile = MMapIndexedDataset('/fsx/pile/pile_20B_tokenizer_text_document')
<del> self.data_pile = MMapIndexedDataset('/fsx/BlinkDL/pile/pile_20B_tokenizer_text_document')
<25>:<add> # self.data_pile = MMapIndexedDataset('/fsx/pile_deduped/pile_0.87_... | # module: RWKV-v4neo.src.dataset
class MyDataset(Dataset):
def __init__(self, args):
<0> self.args = args
<1>
<2> if args.data_type == "binidx":
<3> self.vocab_size = args.vocab_size
<4> rank_zero_info(f"Current vocab size = {self.vocab_size} (mak... | ===========below chunk 0===========
# module: RWKV-v4neo.src.dataset
class MyDataset(Dataset):
def __init__(self, args):
# offset: 1
# assert self.data_size == 332115325534 and self.vocab_size == 50277
self.samples_per_epoch = args.epoch_steps * args.real_bsz
... |
RWKV-v4neo.src.dataset/MyDataset.__init__ | Modified | BlinkDL~RWKV-LM | 725327d667167a23ce6100e7d4c7b6fb9d6b1a40 | Merge branch 'main' of https://github.com/BlinkDL/RWKV-LM into main | <24>:<add> # self.data_pile = MMapIndexedDataset('/fsx/pile/pile_20B_tokenizer_text_document')
<del> self.data_pile = MMapIndexedDataset('/fsx/pile/pile_20B_tokenizer_text_document')
<25>:<add> self.data_pile = MMapIndexedDataset('/fsx/pile_deduped/pile_0.87_deduped_... | # module: RWKV-v4neo.src.dataset
class MyDataset(Dataset):
def __init__(self, args):
<0> self.args = args
<1>
<2> if args.data_type == "binidx":
<3> self.vocab_size = args.vocab_size
<4> rank_zero_info(f"Current vocab size = {self.vocab_size} (mak... | ===========below chunk 0===========
# module: RWKV-v4neo.src.dataset
class MyDataset(Dataset):
def __init__(self, args):
# offset: 1
if args.my_pile_stage > 0:
# assert self.data_size == 332115325534 and self.vocab_size == 50277
self.sampl... |
RWKV-v4neo.src.trainer/train_callback.on_train_batch_start | Modified | BlinkDL~RWKV-LM | 725327d667167a23ce6100e7d4c7b6fb9d6b1a40 | Merge branch 'main' of https://github.com/BlinkDL/RWKV-LM into main | <9>:<add> if trainer.global_step < w_step:
<add> lr = lr * (0.2 + 0.8 * trainer.global_step / w_step)
| # module: RWKV-v4neo.src.trainer
class train_callback(pl.Callback):
def on_train_batch_start(self, trainer, pl_module, batch, batch_idx):
<0> args = self.args
<1> # if args.cuda_cleanup > 0:
<2> # torch.cuda.empty_cache()
<3> real_step = trainer.global_s... | ===========below chunk 0===========
# module: RWKV-v4neo.src.trainer
class train_callback(pl.Callback):
def on_train_batch_start(self, trainer, pl_module, batch, batch_idx):
# offset: 1
else:
param_group["lr"] = lr
trainer.my_lr = lr
#... |
RWKV-v4neo.src.trainer/train_callback.on_train_batch_end | Modified | BlinkDL~RWKV-LM | a8ec3151b6e2160b9c46527a71ccc6f47b5125df | fix | # module: RWKV-v4neo.src.trainer
class train_callback(pl.Callback):
def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
<0> args = self.args
<1> if trainer.is_global_zero: # logging
<2> t_now = time.time_ns()
<3> token_per_s... | ===========below chunk 0===========
# module: RWKV-v4neo.src.trainer
class train_callback(pl.Callback):
def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
# offset: 1
trainer.my_wandb.log(lll, step=int(real_step))
if args.magic_prime > 0:... | |
RWKV-v4neo.src.trainer/train_callback.on_train_epoch_end | Modified | BlinkDL~RWKV-LM | 2f57660839017c3a3138cd2199df3232bf3a9a8c | misc | <2>:<add> if (args.epoch_save > 0 and trainer.current_epoch % args.epoch_save == 0) or (trainer.current_epoch == args.epoch_count - 1):
<del> if (args.epoch_save > 0 and trainer.current_epoch % args.epoch_save == 0) or trainer.current_epoch == args.epoch_count - 1:
<23>:<add> i... | # module: RWKV-v4neo.src.trainer
class train_callback(pl.Callback):
# 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):
<0> args = self.a... | ===========unchanged ref 0===========
at: RWKV-v4neo.src.trainer
my_save(dd, ff)
at: RWKV-v4neo.src.trainer.train_callback.__init__
self.args = args
at: datetime
datetime()
at: datetime.datetime
__slots__ = date.__slots__ + time.__slots__
now(t... |
RWKV-v4neo.src.dataset/MyDataset.__init__ | Modified | BlinkDL~RWKV-LM | f8c5f6c3dea1d720c5774fb0352bee373e3bc244 | misc | <10>:<add> elif args.my_pile_version == 2:
<del> else:
| # module: RWKV-v4neo.src.dataset
class MyDataset(Dataset):
def __init__(self, args):
<0> self.args = args
<1>
<2> if args.data_type == "binidx":
<3> self.vocab_size = args.vocab_size
<4> rank_zero_info(f"Current vocab size = {self.vocab_size} (mak... | ===========below chunk 0===========
# module: RWKV-v4neo.src.dataset
class MyDataset(Dataset):
def __init__(self, args):
# offset: 1
else:
self.data_pile = None
self.data_pile_size = 0
if args.my_pile_stage > 0:
... |
RWKV-v4neo.src.trainer/my_save | Modified | BlinkDL~RWKV-LM | f8c5f6c3dea1d720c5774fb0352bee373e3bc244 | misc | <0>:<add> if '14b-run1' in ff:
<del> if '14b-run1' not in ff:
<1>:<del> torch.save(dd, ff)
<2>:<del> else:
<7>:<add> elif ('world/14b' in ff) or ('world/7b' in ff):
<add> aa = ff.split('/')[1]
<add> fn = ff.split('/')[-1]
<add> fff = f'/dev/shm/{aa... | # module: RWKV-v4neo.src.trainer
def my_save(dd, ff):
<0> if '14b-run1' not in ff:
<1> torch.save(dd, ff)
<2> else:
<3> fn = ff.split('/')[-1]
<4> fff = '/dev/shm/' + fn
<5> torch.save(dd, fff)
<6> subprocess.Popen(f" aws s3 mv {fff} s3://rwkv-14... | ===========unchanged ref 0===========
at: subprocess
Popen()
at: torch.serialization
save(obj: object, f: FILE_LIKE, pickle_module: Any=pickle, pickle_protocol: int=DEFAULT_PROTOCOL, _use_new_zipfile_serialization: bool=True, _disable_byteorder_record: bool=False) -> None
========... |
RWKV-v4neo.src.dataset/MyDataset.__init__ | Modified | BlinkDL~RWKV-LM | cca1b5e8e597cf40675882bb10b46287c844e35c | misc | # module: RWKV-v4neo.src.dataset
class MyDataset(Dataset):
def __init__(self, args):
<0> self.args = args
<1>
<2> if args.data_type == "binidx":
<3> self.vocab_size = args.vocab_size
<4> rank_zero_info(f"Current vocab size = {self.vocab_size} (mak... | ===========below chunk 0===========
# module: RWKV-v4neo.src.dataset
class MyDataset(Dataset):
def __init__(self, args):
# offset: 1
else:
self.data_pile = None
self.data_pile_size = 0
if args.my_pile_stage > 0:
... | |
RWKV-v4neo.src.trainer/train_callback.on_train_batch_start | Modified | BlinkDL~RWKV-LM | cca1b5e8e597cf40675882bb10b46287c844e35c | misc | <26>:<add>
<add> if args.my_exit_tokens > 0: # cosine decay
<add> if trainer.global_step < w_step:
<add> lr = args.lr_init * (0.2 + 0.8 * trainer.global_step / w_step)
<add> else:
<add> real_tokens = real_step * args.ctx_len * arg... | # module: RWKV-v4neo.src.trainer
class train_callback(pl.Callback):
def on_train_batch_start(self, trainer, pl_module, batch, batch_idx):
<0> args = self.args
<1> # if args.cuda_cleanup > 0:
<2> # torch.cuda.empty_cache()
<3> real_step = trainer.global_s... | ===========below chunk 0===========
# module: RWKV-v4neo.src.trainer
class train_callback(pl.Callback):
def on_train_batch_start(self, trainer, pl_module, batch, batch_idx):
# offset: 1
if args.layerwise_lr > 0:
param_group["lr"] = lr * param_group["my_lr_scale"]
... |
RWKV-v4neo.src.model/RWKV.configure_optimizers | Modified | BlinkDL~RWKV-LM | a637aea61c77cedd290054449d819da5e7b19d44 | weight decay (very important for small dataset) | <1>:<add>
<add> lr_decay = set()
<del> if args.layerwise_lr > 0:
<2>:<add> lr_1x = set()
<del> lr_1x = set()
<3>:<add> lr_2x = set()
<del> lr_2x = set()
<4>:<add> lr_3x = set()
<del> lr_3x = set()
<5>:<add... | # module: RWKV-v4neo.src.model
class RWKV(pl.LightningModule):
def configure_optimizers(self):
<0> args = self.args
<1> if args.layerwise_lr > 0:
<2> lr_1x = set()
<3> lr_2x = set()
<4> lr_3x = set()
<5> for n, p in self.n... | ===========below chunk 0===========
# module: RWKV-v4neo.src.model
class RWKV(pl.LightningModule):
def configure_optimizers(self):
# offset: 1
]
else:
optim_groups = [
{"params": [param_dict[n] for n in lr_1x], "weight_d... |
RWKV-v4neo.src.model/Block.__init__ | Modified | BlinkDL~RWKV-LM | 9143748f8079e7d3c726c2b98a83681242da30f7 | RWKV5 preview | <16>:<add> if 'r' in os.environ["RWKV_MY_TESTING"]:
<add> self.att = RWKV_TimeMix_RWKV5_Preview(args, layer_id)
<add> else:
<add> self.att = RWKV_TimeMix(args, layer_id)
<del> self.att = RWKV_TimeMix(args, layer_id)
| # module: RWKV-v4neo.src.model
########################################################################################################
# The RWKV Model with our blocks
########################################################################################################
cl... | ===========below chunk 0===========
# module: RWKV-v4neo.src.model
########################################################################################################
# The RWKV Model with our blocks
###################################################################################################... |
RWKV-v4neo.src.model/RWKV.generate_init_weight | Modified | BlinkDL~RWKV-LM | 9143748f8079e7d3c726c2b98a83681242da30f7 | RWKV5 preview | <24>:<add> if 'r' in os.environ["RWKV_MY_TESTING"]:
<add> zero = [".att.output.", ".ffn.value.", ".ffn.receptance.", ".ffnPre.value.", ".ffnPre.receptance.", "head_q.", '.oo.', '.rr.']
<add> else:
<add> zero = [".att.... | # module: RWKV-v4neo.src.model
class RWKV(pl.LightningModule):
def generate_init_weight(self):
<0> print(
<1> f"""
<2> ############################################################################
<3> #
<4> # Init model weight (slow for large models)...
<5> #
<6> #... | ===========below chunk 0===========
# module: RWKV-v4neo.src.model
class RWKV(pl.LightningModule):
def generate_init_weight(self):
# offset: 1
m[n] = torch.empty((shape[0], shape[1]))
if scale == 0:
nn.init.zeros_(m[n])
... |
RWKV-v4neo.src.model/RWKV_TimeMix_RWKV5_Preview.__init__ | Modified | BlinkDL~RWKV-LM | 686c962008676809f17cf2424c193d9dc217c0e4 | rwkv5 with time_first | <6>:<add> self.head_size = 64
<del> try:
<7>:<add> self.n_head = self.n_embd // self.head_size
<del> self.n_head = self.n_embd // 96
<8>:<add> assert self.n_embd % self.n_head == 0
<del> assert self.n_embd % self.n_head == 0
<9>:<add>
<add... | # module: RWKV-v4neo.src.model
########################################################################################################
+ class RWKV_TimeMix_RWKV5_Preview(MyModule):
- class RWKV_TimeMix_RWKV5_Preview(nn.Module):
def __init__(self, args, layer_id):
<0> supe... | ===========below chunk 0===========
# module: RWKV-v4neo.src.model
########################################################################################################
+ class RWKV_TimeMix_RWKV5_Preview(MyModule):
- class RWKV_TimeMix_RWKV5_Preview(nn.Module):
def __init__(self, args, layer... |
RWKV-v4neo.src.model/RWKV_TimeMix_RWKV5_Preview.forward | Modified | BlinkDL~RWKV-LM | 686c962008676809f17cf2424c193d9dc217c0e4 | rwkv5 with time_first | <0>:<del> B, TT, C = x.size() # x = (Batch,Time,Channel)
<2>:<add> T = self.chunk_len
<del> S = self.head_size
<4>:<del> xx = self.time_shift(x) # Mix x with the previous timestep to produce xk, xv, xr
<5>:<del> xk = x * self.time_mix_k + xx * (1 - self.time_mix_k)
<6>... | # module: RWKV-v4neo.src.model
########################################################################################################
+ class RWKV_TimeMix_RWKV5_Preview(MyModule):
- class RWKV_TimeMix_RWKV5_Preview(nn.Module):
+
def forward(self, x):
<0> B, TT, ... | ===========below chunk 0===========
# module: RWKV-v4neo.src.model
########################################################################################################
+ class RWKV_TimeMix_RWKV5_Preview(MyModule):
- class RWKV_TimeMix_RWKV5_Preview(nn.Module):
+
def forward(self, x):... |
RWKV-v4neo.src.model/RWKV_TimeMix_RWKV5_Preview.__init__ | Modified | BlinkDL~RWKV-LM | 9b38a54c8e9e0cdc01e3d461a299485d3c1be6c3 | BUG FIX: rwkv5 groupnorm was wrong | <28>:<add> decay_speed[h] = -10 + 9 * (h / (self.n_head - 1)) ** (0.7 + 1.3 * ratio_0_to_1)
<del> decay_speed[h] = -9 + 8 * (h / (self.
| # module: RWKV-v4neo.src.model
########################################################################################################
class RWKV_TimeMix_RWKV5_Preview(MyModule):
def __init__(self, args, layer_id):
<0> super().__init__()
<1> self.args = args
<2> ... | ===========below chunk 0===========
# module: RWKV-v4neo.src.model
########################################################################################################
class RWKV_TimeMix_RWKV5_Preview(MyModule):
def __init__(self, args, layer_id):
# offset: 1
self.time_d... |
RWKV-v4neo.src.model/RWKV_TimeMix_RWKV5_Preview.jit_func_2 | Modified | BlinkDL~RWKV-LM | 9b38a54c8e9e0cdc01e3d461a299485d3c1be6c3 | BUG FIX: rwkv5 groupnorm was wrong | <19>:<add> x = x.transpose(1, 2).contiguous().view(B * TT, H*S) # BHTS -> BTHS -> BTC
<del> x = x.transpose(1, 2).contiguous().view(B, TT, H*S) # BHTS -> BTHS -> BTC
<20>:<add> x = self.ln_x(x).view(B, TT, H*S)
<del> x = self.ln_x(x.transpose(-2, -1)).transpose(-2, -1)
| # module: RWKV-v4neo.src.model
########################################################################################################
class RWKV_TimeMix_RWKV5_Preview(MyModule):
@MyFunction
def jit_func_2(self, r, k, v, w, wk, wb, ws):
<0> B, H, TT, S = r.size()
... | ===========unchanged ref 0===========
at: RWKV-v4neo.src.model.RWKV_TimeMix_RWKV5_Preview.__init__
self.chunk_len = 512
===========changed ref 0===========
# module: RWKV-v4neo.src.model
#######################################################################################################... |
RWKV-v4neo.src.model/RWKV_TimeMix_RWKV5_Preview.__init__ | Modified | BlinkDL~RWKV-LM | 468e608107632743c5ea3a9c23d9a18b4ee2ab59 | rwkv5 working | <28>:<add> decay_speed[h] = -8 + 7 * (h / (self.n_head - 1)) ** (0.7 + 1.3 * ratio_0_to_1)
<del> decay_speed[h] = -10 + 9 * (h / (self.
| # module: RWKV-v4neo.src.model
########################################################################################################
class RWKV_TimeMix_RWKV5_Preview(MyModule):
def __init__(self, args, layer_id):
<0> super().__init__()
<1> self.args = args
<2> ... | ===========below chunk 0===========
# module: RWKV-v4neo.src.model
########################################################################################################
class RWKV_TimeMix_RWKV5_Preview(MyModule):
def __init__(self, args, layer_id):
# offset: 1
self.time_d... |
RWKV-v4neo.src.model/RWKV.generate_init_weight | Modified | BlinkDL~RWKV-LM | bff997a64972a343ccc40afffdb850c4a89c6b56 | better rwkv5 | <17>:<add> if 'ln_x.weight' in n:
<add> layer_scale = (1+int(n.split('.')[1])) / self.args.n_layer
<add> m[n] = (p * 0.0) + (layer_scale ** 0.5)
<add> else:
<add> m[n] = p
<del> m[n] = p
| # module: RWKV-v4neo.src.model
class RWKV(pl.LightningModule):
def generate_init_weight(self):
<0> print(
<1> f"""
<2> ############################################################################
<3> #
<4> # Init model weight (slow for large models)...
<5> #
<6> #... | ===========below chunk 0===========
# module: RWKV-v4neo.src.model
class RWKV(pl.LightningModule):
def generate_init_weight(self):
# offset: 1
if self.args.accelerator.upper() == "GPU":
m[n] = torch.empty((shape[0], shape[1]), device="cuda")
... |
RWKV-v4neo.src.model/RWKV_TimeMix_RWKV5_Preview.__init__ | Modified | BlinkDL~RWKV-LM | 69e6c50001e8da742dcfdd7e53064f155a6c9ad1 | Merge branch 'main' of https://github.com/BlinkDL/RWKV-LM into main | # module: RWKV-v4neo.src.model
########################################################################################################
class RWKV_TimeMix_RWKV5_Preview(MyModule):
def __init__(self, args, layer_id):
<0> super().__init__()
<1> self.args = args
<2> ... | ===========below chunk 0===========
# module: RWKV-v4neo.src.model
########################################################################################################
class RWKV_TimeMix_RWKV5_Preview(MyModule):
def __init__(self, args, layer_id):
# offset: 1
self.time_d... | |
RWKV-v4neo.src.model/RWKV_TimeMix_RWKV5_Preview.forward | Modified | BlinkDL~RWKV-LM | 69e6c50001e8da742dcfdd7e53064f155a6c9ad1 | Merge branch 'main' of https://github.com/BlinkDL/RWKV-LM into main | <6>:<add>
<add> if 'r2' in os.environ["RWKV_MY_TESTING"]:
<add> u = self.time_faaaa.float().unsqueeze(-1)
<add> else:
<add> u = torch.exp(self.time_first.float()).unsqueeze(-1)
<del> u = torch.exp(self.time_first.float()).unsqueeze(-1)
| # module: RWKV-v4neo.src.model
########################################################################################################
class RWKV_TimeMix_RWKV5_Preview(MyModule):
def forward(self, x):
<0> H = self.n_head
<1> T = self.chunk_len
<2>
<... | ===========unchanged ref 0===========
at: RWKV-v4neo.src.model.RWKV_TimeMix_RWKV5_Preview.__init__
self.n_head = self.n_embd // self.head_size
self.chunk_len = 512
self.time_decay = nn.Parameter(decay_speed)
self.time_faaaa = nn.Parameter(torch.ones(self.n_head) * 0.05... |
RWKV-v4neo.src.model/RWKV.configure_optimizers | Modified | BlinkDL~RWKV-LM | 69e6c50001e8da742dcfdd7e53064f155a6c9ad1 | Merge branch 'main' of https://github.com/BlinkDL/RWKV-LM into main | <13>:<add> if args.my_pile_stage == 2:
<add> lr_3x.add(n)
<add> else:
<add> lr_2x.add(n)
<add> elif ("time_faaaa" in n) and (args.layerwise_lr > 0):
| # module: RWKV-v4neo.src.model
class RWKV(pl.LightningModule):
def configure_optimizers(self):
<0> args = self.args
<1>
<2> lr_decay = set()
<3> lr_1x = set()
<4> lr_2x = set()
<5> lr_3x = set()
<6> for n, p in self.named_param... | ===========below chunk 0===========
# module: RWKV-v4neo.src.model
class RWKV(pl.LightningModule):
def configure_optimizers(self):
# offset: 1
{"params": [param_dict[n] for n in lr_2x], "weight_decay": 0.0, "my_lr_scale": 5.0},# test: 2e-3 / args.lr_init},
... |
RWKV-v4neo.src.trainer/train_callback.on_train_batch_start | Modified | BlinkDL~RWKV-LM | 69e6c50001e8da742dcfdd7e53064f155a6c9ad1 | Merge branch 'main' of https://github.com/BlinkDL/RWKV-LM into main | <27>:<add> if args.my_exit_tokens != 0: # cosine decay
<del> if args.my_exit_tokens > 0: # cosine decay
| # module: RWKV-v4neo.src.trainer
class train_callback(pl.Callback):
def on_train_batch_start(self, trainer, pl_module, batch, batch_idx):
<0> args = self.args
<1> # if args.cuda_cleanup > 0:
<2> # torch.cuda.empty_cache()
<3> real_step = trainer.global_s... | ===========below chunk 0===========
# module: RWKV-v4neo.src.trainer
class train_callback(pl.Callback):
def on_train_batch_start(self, trainer, pl_module, batch, batch_idx):
# offset: 1
lr = args.lr_init * (0.2 + 0.8 * trainer.global_step / w_step)
else:
... |
RWKV-v4neo.src.trainer/train_callback.on_train_batch_end | Modified | BlinkDL~RWKV-LM | 69e6c50001e8da742dcfdd7e53064f155a6c9ad1 | Merge branch 'main' of https://github.com/BlinkDL/RWKV-LM into main | <23>:<add> lll = {"loss": trainer.my_loss, "lr": trainer.my_lr, "wd": trainer.my_wd, "Gtokens": real_step * token_per_step / 1e9}
<del> lll = {"loss": trainer.my_loss, "lr": trainer.my_lr, "Gtokens": real_step * token_per_step / 1e9}
| # module: RWKV-v4neo.src.trainer
class train_callback(pl.Callback):
def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
<0> args = self.args
<1> if trainer.is_global_zero: # logging
<2> t_now = time.time_ns()
<3> token_per_s... | ===========below chunk 0===========
# module: RWKV-v4neo.src.trainer
class train_callback(pl.Callback):
def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
# offset: 1
trainer.my_wandb.log(lll, step=int(real_step))
if args.magic_prime > 0:... |
RWKV-v4neo.src.trainer/generate_init_weight | Modified | BlinkDL~RWKV-LM | 69e6c50001e8da742dcfdd7e53064f155a6c9ad1 | Merge branch 'main' of https://github.com/BlinkDL/RWKV-LM into main | <7>:<add> try:
<add> assert k in mm
<del> assert k in mm
<8>:<add> except:
<add> print('missing', k)
<add> exit(0)
| # module: RWKV-v4neo.src.trainer
@rank_zero_only
def generate_init_weight(model, init_weight_name):
<0> mm = model.generate_init_weight()
<1>
<2> if model.args.my_pile_stage == 1:
<3> if len(model.args.load_model) > 0:
<4> print(f"Combine weights from {model.args... | ===========below chunk 0===========
# module: RWKV-v4neo.src.trainer
@rank_zero_only
def generate_init_weight(model, init_weight_name):
# offset: 1
===========unchanged ref 0===========
at: RWKV-v4neo.src.trainer.train_callback.on_train_epoch_end
args = self.args
at: datet... |
RWKV-v4neo.src.model/Block.forward | Modified | BlinkDL~RWKV-LM | b42fc101f8b8ffdee4128ac5ba6ee938d6273258 | + dropout | <8>:<add> if self.args.dropout == 0:
<add> if self.layer_id == 0 and args.pre_ffn > 0:
<del> if self.layer_id == 0 and args.pre_ffn > 0:
<9>:<add> x = x + self.ffnPre(self.ln1(x))
<del> x = x + self.ffnPre(self.ln1(x))
<10>:<add> else:... | # module: RWKV-v4neo.src.model
########################################################################################################
# The RWKV Model with our blocks
########################################################################################################
cl... | |
RWKV-v4neo.src.model/RWKV.__init__ | Modified | BlinkDL~RWKV-LM | b42fc101f8b8ffdee4128ac5ba6ee938d6273258 | + dropout | <22>:<add> if args.dropout > 0:
<add> self.drop0 = nn.Dropout(p = args.dropout)
| # module: RWKV-v4neo.src.model
class RWKV(pl.LightningModule):
def __init__(self, args):
<0> super().__init__()
<1> self.args = args
<2> if not hasattr(args, 'dim_att'):
<3> args.dim_att = args.n_embd
<4> if not hasattr(args, 'dim_ffn'):
<5> ... | ===========changed ref 0===========
# module: RWKV-v4neo.src.model
########################################################################################################
# The RWKV Model with our blocks
###################################################################################################... |
RWKV-v4neo.src.model/RWKV.forward | Modified | BlinkDL~RWKV-LM | b42fc101f8b8ffdee4128ac5ba6ee938d6273258 | + dropout | <7>:<add> if args.dropout > 0:
<add> x = self.drop0(x)
| # module: RWKV-v4neo.src.model
class RWKV(pl.LightningModule):
def forward(self, idx):
<0> args = self.args
<1> B, T = idx.size()
<2> assert T <= args.ctx_len, "Cannot forward, model ctx_len is exhausted."
<3>
<4> x = self.emb(idx)
<5> x_emb = x... | ===========below chunk 0===========
# module: RWKV-v4neo.src.model
class RWKV(pl.LightningModule):
def forward(self, idx):
# offset: 1
else:
x = self.head(x)
return x
===========changed ref 0===========
# module: RWKV-v4neo.src.model
cla... |
RWKV-v4neo.src.model/RWKV.training_step_end | Modified | BlinkDL~RWKV-LM | 087a66accf9bd698d651b5609bfad42cfca0cd69 | 增加PyTorch Lightning 2.0支持 | <0>:<add> if pl.__version__[0]!='2':
<add> all = self.all_gather(batch_parts)
<del> all = self.all_gather(batch_parts)
<1>:<add> if self.trainer.is_global_zero:
<del> if self.trainer.is_global_zero:
<2>:<add> self.trainer.my_loss_all = al... | # module: RWKV-v4neo.src.model
class RWKV(pl.LightningModule):
def training_step_end(self, batch_parts):
<0> all = self.all_gather(batch_parts)
<1> if self.trainer.is_global_zero:
<2> self.trainer.my_loss_all = all
<3>
| ===========changed ref 0===========
# module: RWKV-v4neo.train
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
+ ... |
RWKV-v4neo.src.trainer/train_callback.on_train_batch_end | Modified | BlinkDL~RWKV-LM | 087a66accf9bd698d651b5609bfad42cfca0cd69 | 增加PyTorch Lightning 2.0支持 | <14>:<add> if pl.__version__[0]=='2':
<add> trainer.my_loss = outputs["loss"]
<add> else:
<add> trainer.my_loss = trainer.my_loss_all.float().mean().item()
<del> trainer.my_loss = trainer.my_loss_all.float().mean().item()
| # module: RWKV-v4neo.src.trainer
class train_callback(pl.Callback):
def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
<0> args = self.args
<1> if trainer.is_global_zero: # logging
<2> t_now = time.time_ns()
<3> token_per_s... | ===========below chunk 0===========
# module: RWKV-v4neo.src.trainer
class train_callback(pl.Callback):
def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
# offset: 1
lll["kt/s"] = kt_s
trainer.my_wandb.log(lll, step=int(real_step... |
RWKV-v4neo.src.trainer/train_callback.on_train_epoch_start | Modified | BlinkDL~RWKV-LM | 087a66accf9bd698d651b5609bfad42cfca0cd69 | 增加PyTorch Lightning 2.0支持 | <1>:<add> if pl.__version__[0]=='2':
<add> dataset = trainer.train_dataloader.dataset
<add> else:
<add> dataset = trainer.train_dataloader.dataset.datasets
<del> dataset = trainer.train_dataloader.dataset.datasets
| # module: RWKV-v4neo.src.trainer
class train_callback(pl.Callback):
def on_train_epoch_start(self, trainer, pl_module):
<0> args = self.args
<1> dataset = trainer.train_dataloader.dataset.datasets
<2> assert "MyDataset" in str(dataset)
<... | ===========unchanged ref 0===========
at: RWKV-v4neo.src.trainer.train_callback.__init__
self.args = args
at: RWKV-v4neo.src.trainer.train_callback.on_train_batch_end
args = self.args
to_save_dict = pl_module.state_dict()
===========changed ref 0===========
# modu... |
RWKV-v4neo.src.dataset/MyDataset.__init__ | Modified | BlinkDL~RWKV-LM | 673d241cdaaf54cb927f49e2a2ca4542b3d98fab | Merge pull request #178 from fluxlinkage/main | # module: RWKV-v4neo.src.dataset
class MyDataset(Dataset):
def __init__(self, args):
<0> self.args = args
<1>
<2> if args.data_type == "binidx":
<3> self.vocab_size = args.vocab_size
<4> rank_zero_info(f"Current vocab size = {self.vocab_size} (mak... | ===========below chunk 0===========
# module: RWKV-v4neo.src.dataset
class MyDataset(Dataset):
def __init__(self, args):
# offset: 1
else:
self.data_pile = None
self.data_pile_size = 0
if args.my_pile_stage > 0:
... | |
RWKV-v4neo.src.trainer/my_save | Modified | BlinkDL~RWKV-LM | 673d241cdaaf54cb927f49e2a2ca4542b3d98fab | Merge pull request #178 from fluxlinkage/main | <12>:<add> if 'deepspeed_stage_3' in args.strategy:
<add> trainer.save_checkpoint(ff, weights_only=True)
<add> else:
<add> torch.save(dd, ff)
<del> torch.save(dd, ff)
| # module: RWKV-v4neo.src.trainer
+ def my_save(args, trainer, dd, ff):
- def my_save(dd, ff):
<0> if '14b-run1' in ff:
<1> fn = ff.split('/')[-1]
<2> fff = '/dev/shm/' + fn
<3> torch.save(dd, fff)
<4> subprocess.Popen(f" aws s3 mv {fff} s3://rwkv-14b-4k/{fn} -... | ===========unchanged ref 0===========
at: subprocess
Popen()
at: torch.serialization
save(obj: object, f: FILE_LIKE, pickle_module: Any=pickle, pickle_protocol: int=DEFAULT_PROTOCOL, _use_new_zipfile_serialization: bool=True, _disable_byteorder_record: bool=False) -> None
========... |
RWKV-v4neo.src.trainer/train_callback.on_train_batch_start | Modified | BlinkDL~RWKV-LM | 673d241cdaaf54cb927f49e2a2ca4542b3d98fab | Merge pull request #178 from fluxlinkage/main | # module: RWKV-v4neo.src.trainer
class train_callback(pl.Callback):
def on_train_batch_start(self, trainer, pl_module, batch, batch_idx):
<0> args = self.args
<1> # if args.cuda_cleanup > 0:
<2> # torch.cuda.empty_cache()
<3> real_step = trainer.global_s... | ===========below chunk 0===========
# module: RWKV-v4neo.src.trainer
class train_callback(pl.Callback):
def on_train_batch_start(self, trainer, pl_module, batch, batch_idx):
# offset: 1
lr = args.lr_init * (0.2 + 0.8 * trainer.global_step / w_step)
else:
... | |
RWKV-v4neo.src.trainer/train_callback.on_train_batch_end | Modified | BlinkDL~RWKV-LM | 673d241cdaaf54cb927f49e2a2ca4542b3d98fab | Merge pull request #178 from fluxlinkage/main | <1>:<add> token_per_step = args.ctx_len * args.real_bsz
<add> real_step = trainer.global_step + args.epoch_begin * args.epoch_steps
<3>:<del> token_per_step = args.ctx_len * args.real_bsz
<4>:<del> real_step = trainer.global_step + args.epoch_begin * args.epoch_steps
| # module: RWKV-v4neo.src.trainer
class train_callback(pl.Callback):
def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
<0> args = self.args
<1> if trainer.is_global_zero: # logging
<2> t_now = time.time_ns()
<3> token_per_s... | ===========below chunk 0===========
# module: RWKV-v4neo.src.trainer
class train_callback(pl.Callback):
def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
# offset: 1
if kt_s > 0:
lll["kt/s"] = kt_s
trainer.my_... |
RWKV-v4neo.src.trainer/train_callback.on_train_epoch_end | Modified | BlinkDL~RWKV-LM | 673d241cdaaf54cb927f49e2a2ca4542b3d98fab | Merge pull request #178 from fluxlinkage/main | <1>:<add> to_save_dict = {}
<add> if (trainer.is_global_zero) or ('deepspeed_stage_3' in args.strategy): # save pth
<del> if trainer.is_global_zero: # logging & save state_dict
<5>:<del> to_save_dict = {}
<13>:<add> args, trainer,
<18>:<a... | # module: RWKV-v4neo.src.trainer
class train_callback(pl.Callback):
# 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):
<0> args = self.a... | ===========unchanged ref 0===========
at: RWKV-v4neo.src.trainer
my_save(args, trainer, dd, ff)
at: RWKV-v4neo.src.trainer.train_callback.__init__
self.args = args
at: RWKV-v4neo.src.trainer.train_callback.on_train_epoch_start
args = self.args
===========changed r... |
RWKV-v4neo.src.model/RUN_CUDA | Modified | BlinkDL~RWKV-LM | 673d241cdaaf54cb927f49e2a2ca4542b3d98fab | Merge pull request #178 from fluxlinkage/main | <0>:<add> return WKV_5.apply(B, T, C, H, r, k, v, w, u)
<del> return WKV.apply(B, T, C, w, u, k, v)
| # module: RWKV-v4neo.src.model
+ def RUN_CUDA(B, T, C, H, r, k, v, w, u):
- def RUN_CUDA(B, T, C, w, u, k, v):
<0> return WKV.apply(B, T, C, w, u, k, v)
<1>
| ===========changed ref 0===========
# module: RWKV-v4neo.src.trainer
+ def my_save(args, trainer, dd, ff):
- def my_save(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-v4neo.src.model/RWKV_TimeMix_RWKV5_Preview.__init__ | Modified | BlinkDL~RWKV-LM | 673d241cdaaf54cb927f49e2a2ca4542b3d98fab | Merge pull request #178 from fluxlinkage/main | <3>:<del> self.ctx_len = args.ctx_len
<4>:<del> self.n_embd = args.n_embd
<7>:<add> self.n_head = args.dim_att // self.head_size
<del> self.n_head = self.n_embd // self.head_size
<8>:<add> assert args.dim_att % self.n_head == 0
<del> assert self.n_embd % sel... | # module: RWKV-v4neo.src.model
########################################################################################################
class RWKV_TimeMix_RWKV5_Preview(MyModule):
def __init__(self, args, layer_id):
<0> super().__init__()
<1> self.args = args
<2> ... | ===========below chunk 0===========
# module: RWKV-v4neo.src.model
########################################################################################################
class RWKV_TimeMix_RWKV5_Preview(MyModule):
def __init__(self, args, layer_id):
# offset: 1
self.time_d... |
RWKV-v4neo.src.model/RWKV_TimeMix_RWKV5_Preview.jit_func_2 | Modified | BlinkDL~RWKV-LM | 673d241cdaaf54cb927f49e2a2ca4542b3d98fab | Merge pull request #178 from fluxlinkage/main | <0>:<add> B, H, TT, S = r.size()
<del> B, H, TT, S = r.size()
<1>:<add> T = self.chunk_len
<del> T = self.chunk_len
<3>:<add> s = torch.zeros(B, H, S, S, device=r.device, dtype=r.dtype) # state
<del> s = torch.zeros(B, H, S, S, device=r.devi... | # module: RWKV-v4neo.src.model
########################################################################################################
class RWKV_TimeMix_RWKV5_Preview(MyModule):
+ @MyFunction
- @MyFunction
+ def jit_func_2(self, r, k, v, w, wk, wb, ws):
- ... | ===========changed ref 0===========
# module: RWKV-v4neo.src.model
+ def RUN_CUDA(B, T, C, H, r, k, v, w, u):
- def RUN_CUDA(B, T, C, w, u, k, v):
+ return WKV_5.apply(B, T, C, H, r, k, v, w, u)
- return WKV.apply(B, T, C, w, u, k, v)
===========changed ref 1===========
# module: RWKV-v4neo... |
RWKV-v4neo.src.model/RWKV_TimeMix_RWKV5_Preview.forward | Modified | BlinkDL~RWKV-LM | 673d241cdaaf54cb927f49e2a2ca4542b3d98fab | Merge pull request #178 from fluxlinkage/main | <3>:<add> if 'r3' in os.environ["RWKV_MY_TESTING"]:
<add> r, k, v, g = self.jit_func(x)
<add> else:
<add> r, k, v = self.jit_func(x)
<del> r, k, v = self.jit_func(x)
<34>:<add> if 'r3' in os.environ["RWKV_MY_TESTING"]:
<add> r... | # module: RWKV-v4neo.src.model
########################################################################################################
class RWKV_TimeMix_RWKV5_Preview(MyModule):
def forward(self, x):
<0> H = self.n_head
<1> T = self.chunk_len
<2>
<... | ===========below chunk 0===========
# module: RWKV-v4neo.src.model
########################################################################################################
class RWKV_TimeMix_RWKV5_Preview(MyModule):
def forward(self, x):
# offset: 1
===========changed ref... |
RWKV-v4neo.src.model/RWKV_ChannelMix.forward | Modified | BlinkDL~RWKV-LM | 673d241cdaaf54cb927f49e2a2ca4542b3d98fab | Merge pull request #178 from fluxlinkage/main | <4>:<add> k = torch.relu(k) ** 2
<del> k = torch.square(torch.relu(k))
| # module: RWKV-v4neo.src.model
########################################################################################################
class RWKV_ChannelMix(MyModule):
@MyFunction
def forward(self, x):
<0> xx = self.time_shift(x)
<1> xk = x * self.time_m... | ===========changed ref 0===========
# module: RWKV-v4neo.src.model
+ def RUN_CUDA(B, T, C, H, r, k, v, w, u):
- def RUN_CUDA(B, T, C, w, u, k, v):
+ return WKV_5.apply(B, T, C, H, r, k, v, w, u)
- return WKV.apply(B, T, C, w, u, k, v)
===========changed ref 1===========
# module: RWKV-v4neo... |
RWKV-v4neo.src.model/Block.__init__ | Modified | BlinkDL~RWKV-LM | 673d241cdaaf54cb927f49e2a2ca4542b3d98fab | Merge pull request #178 from fluxlinkage/main | <16>:<add> if 'r4' in os.environ["RWKV_MY_TESTING"]:
<del> if 'r' in os.environ["RWKV_MY_TESTING"]:
<17>:<add> self.att = RWKV_TimeMix_RWKV5(args, layer_id)
<add> elif 'r' in os.environ["RWKV_MY_TESTING"]:
| # module: RWKV-v4neo.src.model
########################################################################################################
# The RWKV Model with our blocks
########################################################################################################
cl... | ===========below chunk 0===========
# module: RWKV-v4neo.src.model
########################################################################################################
# The RWKV Model with our blocks
###################################################################################################... |
RWKV-v4neo.src.model/RWKV.__init__ | Modified | BlinkDL~RWKV-LM | 673d241cdaaf54cb927f49e2a2ca4542b3d98fab | Merge pull request #178 from fluxlinkage/main | <10>:<add> assert args.n_embd % 32 == 0
<add> assert args.dim_att % 32 == 0
<add> assert args.dim_ffn % 32 == 0
| # module: RWKV-v4neo.src.model
class RWKV(pl.LightningModule):
def __init__(self, args):
<0> super().__init__()
<1> self.args = args
<2> if not hasattr(args, 'dim_att'):
<3> args.dim_att = args.n_embd
<4> if not hasattr(args, 'dim_ffn'):
<5> ... | ===========changed ref 0===========
# module: RWKV-v4neo.src.model
+ ########################################################################################################
+
+ class RWKV_TimeMix_RWKV5(MyModule):
+ @MyFunction
+ def jit_func_2(self, x, g):
+ B, T, C = x.size()
+ ... |
RWKV-v4neo.src.model/RWKV.configure_optimizers | Modified | BlinkDL~RWKV-LM | 673d241cdaaf54cb927f49e2a2ca4542b3d98fab | Merge pull request #178 from fluxlinkage/main | <19>:<add> lr_2x.add(n)
<del> lr_3x.add(n)
<21>:<add> lr_1x.add(n)
<del> lr_2x.add(n)
| # module: RWKV-v4neo.src.model
class RWKV(pl.LightningModule):
def configure_optimizers(self):
<0> args = self.args
<1>
<2> lr_decay = set()
<3> lr_1x = set()
<4> lr_2x = set()
<5> lr_3x = set()
<6> for n, p in self.named_param... | ===========below chunk 0===========
# module: RWKV-v4neo.src.model
class RWKV(pl.LightningModule):
def configure_optimizers(self):
# offset: 1
param_dict = {n: p for n, p in self.named_parameters()}
if args.layerwise_lr > 0:
if args.my_pile_stage ... |
RWKV-v4neo.src.model/RWKV.generate_init_weight | Modified | BlinkDL~RWKV-LM | 673d241cdaaf54cb927f49e2a2ca4542b3d98fab | Merge pull request #178 from fluxlinkage/main | <19>:<add> m[n] = (p * 0.0) + (layer_scale ** 0.7)
<del> m[n] = (p * 0.0) + (layer_scale ** 0.5)
| # module: RWKV-v4neo.src.model
class RWKV(pl.LightningModule):
def generate_init_weight(self):
<0> print(
<1> f"""
<2> ############################################################################
<3> #
<4> # Init model weight (slow for large models)...
<5> #
<6> #... | ===========below chunk 0===========
# module: RWKV-v4neo.src.model
class RWKV(pl.LightningModule):
def generate_init_weight(self):
# offset: 1
if n == "head.weight":
scale = 0.5
if "head_k." in n:
... |
RWKV-v4neo.src.model/WKV_5.forward | Modified | BlinkDL~RWKV-LM | 653ae70a66f0a9905e232f23ff7936dbbf3b26f7 | misc | <1>:<del> assert HEAD_SIZE == C // H
<7>:<add> assert HEAD_SIZE == C // H
<11>:<add> assert r.is_contiguous()
<del> r = r.contiguous()
<12>:<add> assert k.is_contiguous()
<del> k = k.contiguous()
<13>:<add> assert v.is_conti... | # module: RWKV-v4neo.src.model
class WKV_5(torch.autograd.Function):
@staticmethod
def forward(ctx, B, T, C, H, r, k, v, w, u):
<0> with torch.no_grad():
<1> assert HEAD_SIZE == C // H
<2> assert r.dtype == torch.bfloat16
<3> ... | ===========unchanged ref 0===========
at: RWKV-v4neo.src.model
HEAD_SIZE = 64
wkv5_cuda = load(name="wkv5", sources=["cuda/wkv5_op.cpp", f"cuda/wkv5_cuda.cu"],
verbose=True, extra_cuda_cflags=["-res-usage", "--use_fast_math", "-O3", "-Xptxas -O3", "--extra-device-vectori... |
RWKV-v4neo.src.model/WKV_5.backward | Modified | BlinkDL~RWKV-LM | 653ae70a66f0a9905e232f23ff7936dbbf3b26f7 | misc | <1>:<add> assert gy.dtype == torch.bfloat16
<5>:<add> assert gy.is_contiguous()
<del> gy = gy.contiguous()
<6>:<del> assert gy.dtype == torch.bfloat16
<8>:<add> gr = torch.empty((B, T, C), device=gy.device, requires_grad=False, dtype=torch.bfloat16, m... | # module: RWKV-v4neo.src.model
class WKV_5(torch.autograd.Function):
@staticmethod
def backward(ctx, gy):
<0> with torch.no_grad():
<1> B = ctx.B
<2> T = ctx.T
<3> C = ctx.C
<4> H = ctx.H
<5> gy = ... | ===========unchanged ref 0===========
at: RWKV-v4neo.src.model
wkv5_cuda = load(name="wkv5", sources=["cuda/wkv5_op.cpp", f"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}... |
RWKV-v4neo.src.model/RWKV_TimeMix_RWKV5.__init__ | Modified | BlinkDL~RWKV-LM | 653ae70a66f0a9905e232f23ff7936dbbf3b26f7 | misc | <5>:<add> assert HEAD_SIZE == self.head_size # change HEAD_SIZE to match args.head_size_a
| # module: RWKV-v4neo.src.model
########################################################################################################
class RWKV_TimeMix_RWKV5(MyModule):
def __init__(self, args, layer_id):
<0> super().__init__()
<1> self.args = args
<2> ... | ===========below chunk 0===========
# module: RWKV-v4neo.src.model
########################################################################################################
class RWKV_TimeMix_RWKV5(MyModule):
def __init__(self, args, layer_id):
# offset: 1
self.time_decay = n... |
RWKV-v4neo.src.model/RWKV_TimeMix_RWKV5.jit_func | Modified | BlinkDL~RWKV-LM | ad848131362c68ac651b16c0bea25a7ba3f6a6ea | RWKV5 - extra silu for better performance (breaking change) | <10>:<add> v = F.silu(self.value(xv))
<del> v = self.value(xv)
| # module: RWKV-v4neo.src.model
########################################################################################################
class RWKV_TimeMix_RWKV5(MyModule):
@MyFunction
def jit_func(self, x):
<0> B, T, C = x.size()
<1>
<2> xx = self.time... | ===========unchanged ref 0===========
at: RWKV-v4neo.src.model
MyFunction = torch.jit.script_method
MyFunction = __nop
at: RWKV-v4neo.src.model.RWKV_TimeMix_RWKV5.__init__
self.time_mix_k = nn.Parameter(torch.pow(ddd, ratio_1_to_almost0))
self.time_mix_v = nn.Parameter(... |
RWKV-v4neo.src.model/RUN_CUDA | Modified | BlinkDL~RWKV-LM | e7a6e5a5362e82eb3a75998ce2f986cbc4902bcb | fix | <0>:<add> return WKV.apply(B, T, C, w, u, k, v)
<del> return WKV_5.apply(B, T, C, H, r, k, v, w, u)
| # module: RWKV-v4neo.src.model
+ def RUN_CUDA(B, T, C, w, u, k, v):
- def RUN_CUDA(B, T, C, H, r, k, v, w, u):
<0> return WKV_5.apply(B, T, C, H, r, k, v, w, u)
<1>
| ===========changed ref 0===========
# module: RWKV-v4neo.src.model
########################################################################################################
# CUDA RWKV5 Kernel
########################################################################################################
... |
RWKV-v4neo.src.model/RWKV_TimeMix_RWKV5.forward | Modified | BlinkDL~RWKV-LM | e7a6e5a5362e82eb3a75998ce2f986cbc4902bcb | fix | <5>:<add> x = RUN_CUDA_RWKV5(B, T, C, H, r, k, v, w=self.time_decay, u=self.time_faaaa)
<del> x = RUN_CUDA(B, T, C, H, r, k, v, w=self.time_decay, u=self.time_faaaa)
| # module: RWKV-v4neo.src.model
########################################################################################################
class RWKV_TimeMix_RWKV5(MyModule):
def forward(self, x):
<0> B, T, C = x.size()
<1> H = self.n_head
<2>
<3> r, k, v... | ===========unchanged ref 0===========
at: RWKV-v4neo.src.model
RUN_CUDA_RWKV5(B, T, C, H, r, k, v, w, u)
at: RWKV-v4neo.src.model.RWKV_TimeMix_RWKV5
jit_func(self, x)
jit_func(x)
at: RWKV-v4neo.src.model.RWKV_TimeMix_RWKV5.__init__
self.n_head = args.dim_att // self... |
RWKV-v4neo.src.model/WKV_5.backward | Modified | BlinkDL~RWKV-LM | 3db37a72356b736966ddd377268f02b80963af3f | Merge pull request #186 from PicoCreator/patch-1 | <11>:<add> gw = torch.empty((B, C), device=gy.device, requires_grad=False, dtype=torch.bfloat16, memory_format=torch.contiguous_format) # .uniform_(-1, 1)
<del> gw = torch.empty((B, T, C), device=gy.device, requires_grad=False, dtype=torch.bfloat16, memory_format=torch.contiguous_fo... | # module: RWKV-v4neo.src.model
class WKV_5(torch.autograd.Function):
@staticmethod
def backward(ctx, gy):
<0> with torch.no_grad():
<1> assert gy.dtype == torch.bfloat16
<2> B = ctx.B
<3> T = c... | ===========below chunk 0===========
# module: RWKV-v4neo.src.model
class WKV_5(torch.autograd.Function):
@staticmethod
def backward(ctx, gy):
# offset: 1
===========unchanged ref 0===========
at: RWKV-v4neo.src.model
wkv5_cuda = load(name="wkv5"... |
RWKV-v4neo.src.model/RWKV_TimeMix_RWKV5.jit_func | Modified | BlinkDL~RWKV-LM | 3db37a72356b736966ddd377268f02b80963af3f | Merge pull request #186 from PicoCreator/patch-1 | <10>:<add> v = self.value(xv)
<del> v = F.silu(self.value(xv))
| # module: RWKV-v4neo.src.model
########################################################################################################
class RWKV_TimeMix_RWKV5(MyModule):
@MyFunction
def jit_func(self, x):
<0> B, T, C = x.size()
<1>
<2> xx = self.time... | ===========unchanged ref 0===========
at: RWKV-v4neo.src.model
MyFunction = torch.jit.script_method
MyFunction = __nop
at: RWKV-v4neo.src.model.RWKV_TimeMix_RWKV5.__init__
self.time_mix_k = nn.Parameter(torch.pow(ddd, ratio_1_to_almost0))
self.time_mix_v = nn.Parameter(... |
RWKV-v4neo.src.trainer/train_callback.on_train_batch_start | Modified | BlinkDL~RWKV-LM | 06ef9a2199df8e47f356ed8b5195b4fc8de41b41 | bugfix & new cuda | <9>:<del> if trainer.global_step < w_step:
<10>:<del> lr = lr * (0.2 + 0.8 * trainer.global_step / w_step)
<21>:<del>
<22>:<del> if trainer.global_step < w_step:
<23>:<del> lr = lr * (0.2 + 0.8 * trainer.global_step / w_step)
| # module: RWKV-v4neo.src.trainer
class train_callback(pl.Callback):
def on_train_batch_start(self, trainer, pl_module, batch, batch_idx):
<0> args = self.args
<1> # if args.cuda_cleanup > 0:
<2> # torch.cuda.empty_cache()
<3> real_step = trainer.global_s... | ===========below chunk 0===========
# module: RWKV-v4neo.src.trainer
class train_callback(pl.Callback):
def on_train_batch_start(self, trainer, pl_module, batch, batch_idx):
# offset: 1
lr = args.lr_init * (0.2 + 0.8 * trainer.global_step / w_step)
else:
... |
RWKV-v5.src.model/Block.__init__ | Modified | BlinkDL~RWKV-LM | 8fea4ebeabc61c03d79763786a7766db65ce45e7 | v6 training | <16>:<add> if 'x060' in os.environ["RWKV_MY_TESTING"]:
<add> self.att = RWKV_Tmix_x060(args, layer_id)
<add> else:
<add> self.att = RWKV_TimeMix_RWKV5(args, layer_id)
<del> self.att = RWKV_TimeMix_RWKV5(args, layer_id)
<21>:<add> ... | # module: RWKV-v5.src.model
########################################################################################################
# The RWKV Model with our blocks
########################################################################################################
class... | ===========below chunk 0===========
# module: RWKV-v5.src.model
########################################################################################################
# The RWKV Model with our blocks
######################################################################################################... |
RWKV-v5.src.model/RWKV.configure_optimizers | Modified | BlinkDL~RWKV-LM | 8fea4ebeabc61c03d79763786a7766db65ce45e7 | v6 training | <7>:<add> if (("_w1" in n) or ("_w2" in n)) and (args.layerwise_lr > 0):
<add> lr_1x.add(n)
<add> elif (("time_mix" in n) or ("time_maa" in n)) and (args.layerwise_lr > 0):
<del> if ("time_mix" in n) and (args.layerwise_lr > 0):
<12>:<add> ... | # module: RWKV-v5.src.model
class RWKV(pl.LightningModule):
def configure_optimizers(self):
<0> args = self.args
<1>
<2> lr_decay = set()
<3> lr_1x = set()
<4> lr_2x = set()
<5> lr_3x = set()
<6> for n, p in self.named_paramete... | ===========below chunk 0===========
# module: RWKV-v5.src.model
class RWKV(pl.LightningModule):
def configure_optimizers(self):
# offset: 1
if args.layerwise_lr > 0:
if args.my_pile_stage == 2:
optim_groups = [
{"pa... |
RWKV-v5.src.model/RWKV_Tmix_x060.__init__ | Modified | BlinkDL~RWKV-LM | 62572c917d8b4d27456085cb43e43883fa9a9e52 | better | # module: RWKV-v5.src.model
class RWKV_Tmix_x060(MyModule):
def __init__(self, args, layer_id):
<0> super().__init__()
<1> self.args = args
<2> self.layer_id = layer_id
<3>
<4> self.head_size = args.head_size_a
<5> self.n_head = args.dim_att // ... | ===========below chunk 0===========
# module: RWKV-v5.src.model
class RWKV_Tmix_x060(MyModule):
def __init__(self, args, layer_id):
# offset: 1
TIME_MIX_EXTRA_DIM = 32 # generate TIME_MIX for w,k,v,r,g
self.time_maa_w1 = nn.Parameter(torch.empty(args.n_embd, TIME... | |
RWKV-v5.src.model/RWKV_Tmix_x060.__init__ | Modified | BlinkDL~RWKV-LM | cf854f356103a7848fc1f69352bebb5ec153de47 | misc | # module: RWKV-v5.src.model
class RWKV_Tmix_x060(MyModule):
def __init__(self, args, layer_id):
<0> super().__init__()
<1> self.args = args
<2> self.layer_id = layer_id
<3>
<4> self.head_size = args.head_size_a
<5> self.n_head = args.dim_att // ... | ===========below chunk 0===========
# module: RWKV-v5.src.model
class RWKV_Tmix_x060(MyModule):
def __init__(self, args, layer_id):
# offset: 1
TIME_MIX_EXTRA_DIM = 32 # generate TIME_MIX for w,k,v,r,g
self.time_maa_w1 = nn.Parameter(torch.zeros(args.n_embd, TIME... | |
RWKV-v5.src.model/RWKV.__init__ | Modified | BlinkDL~RWKV-LM | cf854f356103a7848fc1f69352bebb5ec153de47 | misc | <5>:<add> args.dim_ffn = int((args.n_embd * 3.5) // 32 * 32) # default = 3.5x emb size
<del> args.dim_ffn = args.n_embd * 4
| # module: RWKV-v5.src.model
class RWKV(pl.LightningModule):
def __init__(self, args):
<0> super().__init__()
<1> self.args = args
<2> if not hasattr(args, 'dim_att'):
<3> args.dim_att = args.n_embd
<4> if not hasattr(args, 'dim_ffn'):
<5> ... | ===========unchanged ref 0===========
at: torch._C._VariableFunctions
ones(size: _size, *, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype]=None, layout: Optional[_layout]=None, device: Optional[Union[_device, str, None]]=None, pin_memory: Optional[_bool]=False, requires_grad: ... |
RWKV-v5.src.model/Block.__init__ | Modified | BlinkDL~RWKV-LM | 461c87c7dce7e149cf61331bd7a4aba671ab924e | better guide | <18>:<add> elif 'x052' in os.environ["RWKV_MY_TESTING"]:
<del> else:
<19>:<add> self.att = RWKV_Tmix_x052(args, layer_id)
<del> self.att = RWKV_TimeMix_RWKV5(args, layer_id)
<26>:<add> elif 'x052' in os.environ["RWKV_MY_TESTING"]:
<del> ... | # module: RWKV-v5.src.model
########################################################################################################
# The RWKV Model with our blocks
########################################################################################################
class... | ===========below chunk 0===========
# module: RWKV-v5.src.model
########################################################################################################
# The RWKV Model with our blocks
######################################################################################################... |
RWKV-v5.src.model/Block.__init__ | Modified | BlinkDL~RWKV-LM | e7c27f00dde647b72636ae72f27a167a53987217 | Merge branch 'main' of https://github.com/BlinkDL/RWKV-LM | <20>:<add> elif 'mamba' in os.environ["RWKV_MY_TESTING"]:
<add> self.att = Mamba(d_model=args.n_embd, d_state=16, d_conv=4, expand=2.125) # match rwkv6 #params
<23>:<del> else:
<24>:<add> elif 'x060' in os.environ["RWKV_MY_TESTING"]:
<del> if 'x060' in o... | # module: RWKV-v5.src.model
########################################################################################################
# The RWKV Model with our blocks
########################################################################################################
class... | ===========below chunk 0===========
# module: RWKV-v5.src.model
########################################################################################################
# The RWKV Model with our blocks
######################################################################################################... |
RWKV-v5.src.model/RWKV.generate_init_weight | Modified | BlinkDL~RWKV-LM | e7c27f00dde647b72636ae72f27a167a53987217 | Merge branch 'main' of https://github.com/BlinkDL/RWKV-LM | <10>:<add> n_params = 0
<13>:<add>
<add> s0 = str(shape[0]) if len(shape) > 0 else ""
<add> s1 = str(shape[1]) if len(shape) > 1 else ""
<add> s2 = str(shape[2]) if len(shape) > 2 else ""
<add> print(f"{s0.ljust(5)} {s1.ljust(5)} {s2.ljust(5)}... | # module: RWKV-v5.src.model
class RWKV(pl.LightningModule):
def generate_init_weight(self):
<0> print(
<1> f"""
<2> ############################################################################
<3> #
<4> # Init model weight (slow for large models)...
<5> #
<6> ####... | ===========below chunk 0===========
# module: RWKV-v5.src.model
class RWKV(pl.LightningModule):
def generate_init_weight(self):
# offset: 1
if self.args.accelerator.upper() == "GPU":
m[n] = torch.empty((shape[0], shape[1]), device="cuda")
... |
RWKV-v5.src.model/RWKV.generate_init_weight | Modified | BlinkDL~RWKV-LM | 4e2e255b9ad9ec4dd075b20d70e177c3aeb135f2 | better | # module: RWKV-v5.src.model
class RWKV(pl.LightningModule):
def generate_init_weight(self):
<0> print(
<1> f"""
<2> ############################################################################
<3> #
<4> # Init model weight (slow for large models)...
<5> #
<6> ####... | ===========below chunk 0===========
# module: RWKV-v5.src.model
class RWKV(pl.LightningModule):
def generate_init_weight(self):
# offset: 1
scale = 0.5
nn.init.orthogonal_(m[n], gain=gain * scale)
print(f" [scale {sc... | |
RWKV-v5.src.model/RWKV.generate_init_weight | Modified | BlinkDL~RWKV-LM | 402dab6bd3bb22acd4b149ff04f1a8ab149317b1 | fix init | <37>:<add> if shape[1] > shape[0]: # !!! only for pytorch where linear layer weight is transposed !!!
<del> if shape[0] > shape[1]:
<38>:<add> gain = math.sqrt(shape[1] / shape[0])
<del> gain = math.sqrt(sh... | # module: RWKV-v5.src.model
class RWKV(pl.LightningModule):
def generate_init_weight(self):
<0> print(
<1> f"""
<2> ############################################################################
<3> #
<4> # Init model weight (slow for large models)...
<5> #
<6> ####... | ===========below chunk 0===========
# module: RWKV-v5.src.model
class RWKV(pl.LightningModule):
def generate_init_weight(self):
# offset: 1
scale = 0.5
nn.init.orthogonal_(m[n], gain=gain * scale)
print(f" [scale {sc... |
RWKV-v5.src.model/RWKV_Tmix_x060.__init__ | Modified | BlinkDL~RWKV-LM | 2142cb78e9bca400991008400b257176788badb4 | MODEL_TYPE "x060a-f4" (sometimes better) | # module: RWKV-v5.src.model
class RWKV_Tmix_x060(MyModule):
def __init__(self, args, layer_id):
<0> super().__init__()
<1> self.args = args
<2> self.layer_id = layer_id
<3>
<4> self.head_size = args.head_size_a
<5> self.n_head = args.dim_att // ... | ===========below chunk 0===========
# module: RWKV-v5.src.model
class RWKV_Tmix_x060(MyModule):
def __init__(self, args, layer_id):
# offset: 1
TIME_MIX_EXTRA_DIM = 32 # generate TIME_MIX for w,k,v,r,g
self.time_maa_w1 = nn.Parameter(torch.zeros(args.n_embd, TIME... | |
RWKV-v5.src.model/Block.__init__ | Modified | BlinkDL~RWKV-LM | 2142cb78e9bca400991008400b257176788badb4 | MODEL_TYPE "x060a-f4" (sometimes better) | <16>:<add> if 'x060a' in os.environ["RWKV_MY_TESTING"]:
<del> if 'x060' in os.environ["RWKV_MY_TESTING"]:
<17>:<add> self.att = RWKV_Tmix_x060a(args, layer_id)
<add> elif 'x060' in os.environ["RWKV_MY_TESTING"]:
| # module: RWKV-v5.src.model
########################################################################################################
# The RWKV Model with our blocks
########################################################################################################
class... | ===========below chunk 0===========
# module: RWKV-v5.src.model
########################################################################################################
# The RWKV Model with our blocks
######################################################################################################... |
RWKV-v5.src.model/RWKV.__init__ | Modified | BlinkDL~RWKV-LM | 2142cb78e9bca400991008400b257176788badb4 | MODEL_TYPE "x060a-f4" (sometimes better) | <5>:<add> if '-f4' in os.environ["RWKV_MY_TESTING"]:
<add> args.dim_ffn = int((args.n_embd * 4) // 32 * 32)
<add> else:
<add> args.dim_ffn = int((args.n_embd * 3.5) // 32 * 32) # default = 3.5x emb size
<del> args.dim_f... | # module: RWKV-v5.src.model
class RWKV(pl.LightningModule):
def __init__(self, args):
<0> super().__init__()
<1> self.args = args
<2> if not hasattr(args, 'dim_att'):
<3> args.dim_att = args.n_embd
<4> if not hasattr(args, 'dim_ffn'):
<5> ... | ===========unchanged ref 0===========
at: torch._C._VariableFunctions
zeros(*size: _int, out: Optional[Tensor]=None, dtype: Optional[_dtype]=None, layout: Optional[_layout]=None, device: Optional[Union[_device, str, None]]=None, pin_memory: Optional[_bool]=False, requires_grad: Optional[_bool]=False) -> Ten... |
RWKV-v5.src.model/RWKV.generate_init_weight | Modified | BlinkDL~RWKV-LM | 2142cb78e9bca400991008400b257176788badb4 | MODEL_TYPE "x060a-f4" (sometimes better) | <22>:<add> 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 or n.endswith('_w') or n.endswith('_w1') or n.endswith('_w2') or n.endswith('_bias'):
<del> if "ln_" in n or ".ln" in n or "time_" in n or "_mask" in n or "pos_emb" in n or '.mask.' in ... | # module: RWKV-v5.src.model
class RWKV(pl.LightningModule):
def generate_init_weight(self):
<0> print(
<1> f"""
<2> ############################################################################
<3> #
<4> # Init model weight (slow for large models)...
<5> #
<6> ####... | ===========below chunk 0===========
# module: RWKV-v5.src.model
class RWKV(pl.LightningModule):
def generate_init_weight(self):
# offset: 1
gain = math.sqrt(shape[1] / shape[0])
nn.init.orthogonal_(m[n], gain=gain * scale)
... |
RWKV-v5.src.model/WKV_6.forward | Modified | BlinkDL~RWKV-LM | ffb089b9b21063bb4c247f900b71a917556ad522 | faster v6 cuda | <16>:<del> ew = (-torch.exp(w.float())).contiguous()
<17>:<add> ctx.save_for_backward(r, k, v, w, u)
<del> ctx.save_for_backward(r, k, v, ew, u)
<19>:<add> wkv6_cuda.forward(B, T, C, H, r, k, v, w, u, y)
<del> wkv6_cuda.forward(B,... | # module: RWKV-v5.src.model
class WKV_6(torch.autograd.Function):
@staticmethod
def forward(ctx, B, T, C, H, r, k, v, w, u):
<0> with torch.no_grad():
<1> assert r.dtype == torch.bfloat16
<2> assert k.dtype == t... | ===========unchanged ref 0===========
at: RWKV-v5.src.model
HEAD_SIZE = int(os.environ["RWKV_HEAD_SIZE_A"])
wkv6_cuda = load(name="wkv6", sources=["cuda/wkv6_op.cpp", f"cuda/wkv6_cuda.cu"],
verbose=True, extra_cuda_cflags=["-res-usage", "--use_fast_math", "-O3", "-Xp... |
RWKV-v5.src.model/WKV_6.backward | Modified | BlinkDL~RWKV-LM | ffb089b9b21063bb4c247f900b71a917556ad522 | faster v6 cuda | <7>:<add> r, k, v, w, u = ctx.saved_tensors
<del> r, k, v, ew, u = ctx.saved_tensors
<13>:<add> wkv6_cuda.backward(B, T, C, H, r, k, v, w, u, gy, gr, gk, gv, gw, gu)
<del> wkv6_cuda.backward(B, T, C, H, r, k, v, ew, u, gy, gr, gk, gv, gw, gu)
| # module: RWKV-v5.src.model
class WKV_6(torch.autograd.Function):
@staticmethod
def backward(ctx, gy):
<0> with torch.no_grad():
<1> assert gy.dtype == torch.bfloat16
<2> B = ctx.B
<3> T = ctx.... | ===========unchanged ref 0===========
at: RWKV-v5.src.model
wkv6_cuda = load(name="wkv6", sources=["cuda/wkv6_op.cpp", f"cuda/wkv6_cuda.cu"],
verbose=True, extra_cuda_cflags=["-res-usage", "--use_fast_math", "-O3", "-Xptxas -O3", "--extra-device-vectorization", f"-D_N_={HEAD_SIZE... |
RWKV-v5.src.model/RWKV.generate_init_weight | Modified | BlinkDL~RWKV-LM | 43045b702a509eca80237d2516887a0f79dc7984 | fix | <20>:<del> gain = 1.0
<29>:<add> elif n == "emb.weight":
<add> m[n] = p
<add> scale = -1e-4
<add> nn.init.uniform_(m[n], a=scale, b=-scale)
<add> print(f" [scale {scale}]")
<add> elif n == "head.... | # module: RWKV-v5.src.model
class RWKV(pl.LightningModule):
def generate_init_weight(self):
<0> print(
<1> f"""
<2> ############################################################################
<3> #
<4> # Init model weight (slow for large models)...
<5> #
<6> ####... | ===========below chunk 0===========
# module: RWKV-v5.src.model
class RWKV(pl.LightningModule):
def generate_init_weight(self):
# offset: 1
elif n == "head.weight":
if shape[1] > shape[0]: # !!! only for pytorch where linear layer weight is transpo... |
RWKV-v5.src.model/Block.__init__ | Modified | BlinkDL~RWKV-LM | 033ce840de872dd67c42ed9f9bba08fa2d683eb7 | v6.0b & prepare for state-tuning | <18>:<add> elif 'x060b' in os.environ["RWKV_MY_TESTING"]:
<add> self.att = RWKV_Tmix_x060b(args, layer_id)
| # module: RWKV-v5.src.model
########################################################################################################
# The RWKV Model with our blocks
########################################################################################################
class... | ===========below chunk 0===========
# module: RWKV-v5.src.model
########################################################################################################
# The RWKV Model with our blocks
######################################################################################################... |
RWKV-v5.src.model/Block.__init__ | Modified | BlinkDL~RWKV-LM | 1a44eede5f6bd9610944d7c4787a8f2f867a12d6 | state-tuning (not working yet) | <21>:<add> if os.environ["RWKV_TRAIN_TYPE"] == 'states':
<add> self.att = RWKV_Tmix_x060_state(args, layer_id)
<add> else:
<add> self.att = RWKV_Tmix_x060(args, layer_id)
<del> self.att = RWKV_Tmix_x060(args, lay... | # module: RWKV-v5.src.model
########################################################################################################
# The RWKV Model with our blocks
########################################################################################################
class... | ===========below chunk 0===========
# module: RWKV-v5.src.model
########################################################################################################
# The RWKV Model with our blocks
######################################################################################################... |
RWKV-v5.src.model/RWKV.configure_optimizers | Modified | BlinkDL~RWKV-LM | 1a44eede5f6bd9610944d7c4787a8f2f867a12d6 | state-tuning (not working yet) | <7>:<add> if not p.requires_grad:
<add> continue
| # module: RWKV-v5.src.model
class RWKV(pl.LightningModule):
def configure_optimizers(self):
<0> args = self.args
<1>
<2> lr_decay = set()
<3> lr_1x = set()
<4> lr_2x = set()
<5> lr_3x = set()
<6> for n, p in self.named_paramete... | ===========below chunk 0===========
# module: RWKV-v5.src.model
class RWKV(pl.LightningModule):
def configure_optimizers(self):
# offset: 1
# print('decay', lr_decay)
# print('1x', lr_1x)
# print('2x', lr_2x)
# print('3x', lr_3x)
param_dict... |
RWKV-v5.src.dataset/MyDataset.__init__ | Modified | BlinkDL~RWKV-LM | 1a44eede5f6bd9610944d7c4787a8f2f867a12d6 | state-tuning (not working yet) | # module: RWKV-v5.src.dataset
class MyDataset(Dataset):
def __init__(self, args):
<0> self.args = args
<1>
<2> if args.data_type == "binidx":
<3> self.vocab_size = args.vocab_size
<4> rank_zero_info(f"Current vocab size = {self.vocab_size} (make s... | ===========below chunk 0===========
# module: RWKV-v5.src.dataset
class MyDataset(Dataset):
def __init__(self, args):
# offset: 1
else:
self.data_pile = None
self.data_pile_size = 0
if args.my_pile_stage > 0:
... | |
RWKV-v5.src.model/RWKV.configure_optimizers | Modified | BlinkDL~RWKV-LM | 5d856c2ce27709d1ef715608f4701f9605956376 | still testing state-tuning... | <7>:<add>
<add> # if not p.requires_grad:
<del> if not p.requires_grad:
<8>:<add> # continue
<del> continue
<9>:<add> if args.train_type == 'states':
<add> if 'time_state' not in n:
<add> ... | # module: RWKV-v5.src.model
class RWKV(pl.LightningModule):
def configure_optimizers(self):
<0> args = self.args
<1>
<2> lr_decay = set()
<3> lr_1x = set()
<4> lr_2x = set()
<5> lr_3x = set()
<6> for n, p in self.named_paramete... | ===========below chunk 0===========
# module: RWKV-v5.src.model
class RWKV(pl.LightningModule):
def configure_optimizers(self):
# offset: 1
lr_3x = sorted(list(lr_3x))
if self.trainer.is_global_zero:
print('decay', lr_decay, '\n')
print('1... |
RWKV-v5.src.model/RWKV.configure_optimizers | Modified | BlinkDL~RWKV-LM | 64b7fe4c66fcf7da37019630268075b0558f6dc5 | better state training | <11>:<add> if 'time_sta' not in n:
<del> if 'time_state' not in n:
<16>:<add> elif (("time_sta" in n) and (args.weight_decay > 0)):
<add> lr_decay.add(n)
| # module: RWKV-v5.src.model
class RWKV(pl.LightningModule):
def configure_optimizers(self):
<0> args = self.args
<1>
<2> lr_decay = set()
<3> lr_1x = set()
<4> lr_2x = set()
<5> lr_3x = set()
<6> for n, p in self.named_paramete... | ===========below chunk 0===========
# module: RWKV-v5.src.model
class RWKV(pl.LightningModule):
def configure_optimizers(self):
# offset: 1
lr_1x = sorted(list(lr_1x))
lr_2x = sorted(list(lr_2x))
lr_3x = sorted(list(lr_3x))
if self.trainer.is_glob... |
RWKV-v5.src.trainer/my_save | Modified | BlinkDL~RWKV-LM | 64b7fe4c66fcf7da37019630268075b0558f6dc5 | better state training | <15>:<add> if args.train_type == 'states':
<add> ddd = {}
<add> for k, v in dd.items():
<add> if 'time_sta' in k:
<add> ddd[k] = v.clone()
<add> torch.save(ddd, ff)
<add> els... | # module: RWKV-v5.src.trainer
def my_save(args, trainer, dd, ff):
<0> if '14b-run1' in ff:
<1> fn = ff.split('/')[-1]
<2> fff = '/dev/shm/' + fn
<3> torch.save(dd, fff)
<4> subprocess.Popen(f" aws s3 mv {fff} s3://rwkv-14b-4k/{fn} --quiet", shell=True)
<5> ... | ===========unchanged ref 0===========
at: subprocess
Popen()
at: torch.serialization
save(obj: object, f: FILE_LIKE, pickle_module: Any=pickle, pickle_protocol: int=DEFAULT_PROTOCOL, _use_new_zipfile_serialization: bool=True, _disable_byteorder_record: bool=False) -> None
========... |
RWKV-v5.src.model/RWKV_Tmix_x060c.jit_func | Modified | BlinkDL~RWKV-LM | bf86e0cec3bc876aa93305200d271fc1f0c43e92 | . | <19>:<add> # k = k * (1-(-w.exp()).exp()) # for fp32
<del> k = k * (1-(-w.exp()).exp())
<20>:<add> k = k * (1-(-w.float().exp()).exp()).to(dtype=torch.bfloat16) # for bf16
| # module: RWKV-v5.src.model
########################################################################################################
class RWKV_Tmix_x060c(MyModule):
@MyFunction
def jit_func(self, x):
<0> B, T, C = x.size()
<1>
<2> xx = self... | ===========unchanged ref 0===========
at: RWKV-v5.src.model
MyFunction = torch.jit.script_method
MyFunction = __nop
at: RWKV-v5.src.model.RWKV_Tmix_x060c.__init__
self.time_maa_x = nn.Parameter(1.0 - torch.pow(ddd, ratio_1_to_almost0))
self.time_maa_r = nn.Parameter(1.0... |
RWKV-v5.src.model/WKV_6.forward | Modified | BlinkDL~RWKV-LM | 86affd71c53425d1abc5398f940b09fd8c2a3a99 | fully compatible with torch jit | <1>:<add> B, T, C = r.size()
<add> H = C // HEAD_SIZE
<add> assert C % HEAD_SIZE == 0
<6>:<del> assert HEAD_SIZE == C // H
<18>:<add> torch.ops.wkv6.forward(B, T, C, H, r, k, v, w, u, y)
<del> ... | # module: RWKV-v5.src.model
class WKV_6(torch.autograd.Function):
@staticmethod
+ def forward(ctx, r, k, v, w, u):
- def forward(ctx, B, T, C, H, r, k, v, w, u):
<0> with torch.no_grad():
<1> ... | ===========unchanged ref 0===========
at: RWKV-v5.src.model
HEAD_SIZE = int(os.environ["RWKV_HEAD_SIZE_A"])
at: torch._C
bfloat16: dtype = ...
contiguous_format: memory_format = ...
at: torch._C._VariableFunctions
empty(size: _size, *, names: Optional[Sequence[... |
RWKV-v5.src.model/WKV_6.backward | Modified | BlinkDL~RWKV-LM | 86affd71c53425d1abc5398f940b09fd8c2a3a99 | fully compatible with torch jit | <13>:<add> torch.ops.wkv6.backward(B, T, C, H, r, k, v, w, u, gy, gr, gk, gv, gw, gu)
<del> wkv6_cuda.backward(B, T, C, H, r, k, v, w, u, gy, gr, gk, gv, gw, gu)
<15>:<add> return (gr, gk, gv, gw, gu)
<del> return (None, None, Non... | # module: RWKV-v5.src.model
class WKV_6(torch.autograd.Function):
@staticmethod
def backward(ctx, gy):
<0> with torch.no_grad():
<1> assert gy.dtype == torch.bfloat16
<2> B = ctx.B
<... | ===========unchanged ref 0===========
at: RWKV-v5.src.model.WKV_6.forward
y = torch.empty((B, T, C), device=r.device, dtype=torch.bfloat16, memory_format=torch.contiguous_format)#.uniform_(-100, 100)
at: torch._C
bfloat16: dtype = ...
contiguous_format: memory_format = ...
... |
RWKV-v5.src.model/RUN_CUDA_RWKV6 | Modified | BlinkDL~RWKV-LM | 86affd71c53425d1abc5398f940b09fd8c2a3a99 | fully compatible with torch jit | <0>:<add> return WKV_6.apply(r, k, v, w, u)
<del> return WKV_6.apply(B, T, C, H, r, k, v, w, u)
| # module: RWKV-v5.src.model
+ def RUN_CUDA_RWKV6(r, k, v, w, u):
- def RUN_CUDA_RWKV6(B, T, C, H, r, k, v, w, u):
<0> return WKV_6.apply(B, T, C, H, r, k, v, w, u)
<1>
| ===========unchanged ref 0===========
at: RWKV-v5.src.model.WKV_6.backward
gr = torch.empty((B, T, C), device=gy.device, requires_grad=False, dtype=torch.bfloat16, memory_format=torch.contiguous_format)#.uniform_(-100, 100)
gk = torch.empty((B, T, C), device=gy.device, requires_grad=False, dtyp... |
RWKV-v5.src.model/RWKV_Tmix_x060.forward | Modified | BlinkDL~RWKV-LM | 86affd71c53425d1abc5398f940b09fd8c2a3a99 | fully compatible with torch jit | <1>:<del> H = self.n_head
<3>:<del> r, k, v, g, w = self.jit_func(x)
<4>:<del> x = RUN_CUDA_RWKV6(B, T, C, H, r, k, v, w, u=self.time_faaaa)
<5>:<add> xx = self.time_shift(x) - x
<6>:<add> xxx = x + xx * self.time_maa_x
<add> xxx = torch.tanh(xxx @ self.time_maa... | # module: RWKV-v5.src.model
class RWKV_Tmix_x060(MyModule):
+ @MyFunction
def forward(self, x):
<0> B, T, C = x.size()
<1> H = self.n_head
<2>
<3> r, k, v, g, w = self.jit_func(x)
<4> x = RUN_CUDA_RWKV6(B, T, C, H, r, k, v, w, u=self.time_faaaa)
... | ===========unchanged ref 0===========
at: RWKV-v5.src.model
MyModule = nn.Module
MyModule = torch.jit.ScriptModule
at: torch.jit._script.ScriptModule
__jit_unused_properties__ = [
"code",
"code_with_constants",
"graph",
... |
RWKV-v5.src.model/RWKV_Tmix_x060a.forward | Modified | BlinkDL~RWKV-LM | 86affd71c53425d1abc5398f940b09fd8c2a3a99 | fully compatible with torch jit | <1>:<del> H = self.n_head
<3>:<del> r, k, v, g, w = self.jit_func(x)
<4>:<del> x = RUN_CUDA_RWKV6(B, T, C, H, r, k, v, w, u=self.time_faaaa)
<5>:<add> xx = self.time_shift(x) - x
<6>:<add> xxx = x + xx * self.time_maa_x
<add> xxx = torch.tanh(xxx @ self.time_maa... | # module: RWKV-v5.src.model
########################################################################################################
class RWKV_Tmix_x060a(MyModule):
+ @MyFunction
def forward(self, x):
<0> B, T, C = x.size()
<1> H = self.n_head
<2>
<3> ... | ===========unchanged ref 0===========
at: RWKV-v5.src.model.RWKV_Tmix_x060b.__init__
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
at: torch._C._VariableFunctions
ones(size: _size, *, names: Optional[Seque... |
RWKV-v5.src.model/RWKV_Tmix_x060b.forward | Modified | BlinkDL~RWKV-LM | 86affd71c53425d1abc5398f940b09fd8c2a3a99 | fully compatible with torch jit | <1>:<del> H = self.n_head
<3>:<del> r, k, v, w = self.jit_func(x)
<4>:<del> x = RUN_CUDA_RWKV6(B, T, C, H, r, k, v, w, u=self.time_faaaa)
<5>:<add> xx = self.time_shift(x) - x
<6>:<add> xxx = x + xx * self.time_maa_x
<add> xxx = torch.tanh(xxx @ self.time_maa_rk... | # module: RWKV-v5.src.model
+
########################################################################################################
class RWKV_Tmix_x060b(MyModule):
+ @MyFunction
def forward(self, x):
<0> B, T, C = x.size()
<1> H = self.n_head
... | ===========unchanged ref 0===========
at: RWKV-v5.src.model.RWKV_Tmix_x060c.__init__
ratio_0_to_1 = layer_id / (args.n_layer - 1) # 0 to 1
at: torch._C._VariableFunctions
ones(size: _size, *, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype]=None, layout: Optio... |
RWKV-v5.src.model/RWKV_Tmix_x060c.forward | Modified | BlinkDL~RWKV-LM | 86affd71c53425d1abc5398f940b09fd8c2a3a99 | fully compatible with torch jit | <1>:<del> H = self.n_head
<3>:<del> r, k, v, w = self.jit_func(x)
<4>:<del> x = RUN_CUDA_RWKV6(B, T, C, H, r, k, v, w, u=self.time_faaaa)
<5>:<add> xx = self.time_shift(x) - x
<6>:<add> xxx = x + xx * self.time_maa_x
<add> xxx = torch.tanh(xxx @ self.time_maa_rk... | # module: RWKV-v5.src.model
########################################################################################################
class RWKV_Tmix_x060c(MyModule):
+ @MyFunction
def forward(self, x):
<0> B, T, C = x.size()
<1> H = self.n_head
... | ===========unchanged ref 0===========
at: torch._C._VariableFunctions
ones(size: _size, *, names: Optional[Sequence[Union[str, ellipsis, None]]], dtype: Optional[_dtype]=None, layout: Optional[_layout]=None, device: Optional[Union[_device, str, None]]=None, pin_memory: Optional[_bool]=False, requires_grad: ... |
RWKV-v5.src.trainer/train_callback.on_train_batch_start | Modified | BlinkDL~RWKV-LM | 97c8aaee96ae7dee4c86535cf0c0e92b97595ab4 | Update trainer.py | # module: RWKV-v5.src.trainer
class train_callback(pl.Callback):
def on_train_batch_start(self, trainer, pl_module, batch, batch_idx):
<0> args = self.args
<1> # if args.cuda_cleanup > 0:
<2> # torch.cuda.empty_cache()
<3> real_step = trainer.global_step... | ===========below chunk 0===========
# module: RWKV-v5.src.trainer
class train_callback(pl.Callback):
def on_train_batch_start(self, trainer, pl_module, batch, batch_idx):
# offset: 1
lr_final_factor = args.lr_final / args.lr_init
lr_mult = (0.5 + lr_fi... | |
RWKV-v7.rwkv_v7_demo/RWKV_Tmix_x070.__init__ | Modified | BlinkDL~RWKV-LM | bd74d483b3531388a43fc2fc0ca7ffe68143da13 | rwkv-7 rc3 | # module: RWKV-v7.rwkv_v7_demo
########################################################################################################
# RWKV TimeMix
########################################################################################################
class RWKV_Tmix_x070(nn.Mod... | ===========below chunk 0===========
# module: RWKV-v7.rwkv_v7_demo
########################################################################################################
# RWKV TimeMix
########################################################################################################
cla... | |
RWKV-v7.rwkv_v7_demo/RWKV_Tmix_x070.forward | Modified | BlinkDL~RWKV-LM | bd74d483b3531388a43fc2fc0ca7ffe68143da13 | rwkv-7 rc3 | # module: RWKV-v7.rwkv_v7_demo
########################################################################################################
# RWKV TimeMix
########################################################################################################
class RWKV_Tmix_x070(nn.Mod... | ===========below chunk 0===========
# module: RWKV-v7.rwkv_v7_demo
########################################################################################################
# RWKV TimeMix
########################################################################################################
cla... | |
RWKV-v7.rwkv_v7_demo/RWKV_Tmix_x070.__init__ | Modified | BlinkDL~RWKV-LM | c453b42656baa206cf5e9441482c97448c0d221a | rwkv-7 rc4 | <24>:<add> ### TOO MANY LORAs HERE. I WILL REMOVE MOST OF THEM IN RWKV-7 FINAL :) ###
<add>
| # module: RWKV-v7.rwkv_v7_demo
########################################################################################################
# RWKV TimeMix
########################################################################################################
class RWKV_Tmix_x070(nn.Mod... | ===========below chunk 0===========
# module: RWKV-v7.rwkv_v7_demo
########################################################################################################
# RWKV TimeMix
########################################################################################################
cla... |
RWKV-v7.rwkv_v7_demo/RWKV_Tmix_x070.forward | Modified | BlinkDL~RWKV-LM | c453b42656baa206cf5e9441482c97448c0d221a | rwkv-7 rc4 | <20>:<add> if self.layer_id == 0:
<add> v0 = v
<add> else:
<add> v = v + (v0 - v) * torch.sigmoid(self.time_misc_v + (xv @ self.mv_w1) @ self.mv_w2)
<add> g = torch.sigmoid(xg @ self.gate_w1) @ self.gate_w2
<del> g = torch.tanh(xg @ self.g... | # module: RWKV-v7.rwkv_v7_demo
########################################################################################################
# RWKV TimeMix
########################################################################################################
class RWKV_Tmix_x070(nn.Mod... | ===========below chunk 0===========
# module: RWKV-v7.rwkv_v7_demo
########################################################################################################
# RWKV TimeMix
########################################################################################################
cla... |
RWKV-v7.rwkv_v7_demo/Block.__init__ | Modified | BlinkDL~RWKV-LM | c453b42656baa206cf5e9441482c97448c0d221a | rwkv-7 rc4 | <11>:<add> self.ffn = RWKV_CMix_x070(args, layer_id)
<del> self.ffn = RWKV_CMix_x060(args, layer_id)
| # module: RWKV-v7.rwkv_v7_demo
########################################################################################################
# RWKV Block
########################################################################################################
class Block(nn.Module):
... | ===========unchanged ref 0===========
at: torch.nn.modules.module
Module()
at: torch.nn.modules.module.Module
__init__(self) -> None
__init__() -> None
at: torch.nn.modules.normalization
LayerNorm(normalized_shape: _shape_t, eps: float=1e-5, elementwise_affine: bool... |
RWKV-v7.rwkv_v7_demo/Block.forward | Modified | BlinkDL~RWKV-LM | c453b42656baa206cf5e9441482c97448c0d221a | rwkv-7 rc4 | <3>:<add> xx, v0 = self.att(self.ln1(x), v0)
<del> x = x + self.att(self.ln1(x))
<4>:<add> x = x + xx
<6>:<add> # if RESCALE_LAYER > 0:
<del> if RESCALE_LAYER > 0:
<7>:<add> # if (self.layer_id+1) % RESCALE_LAYER == 0:
<del> if (self.lay... | # module: RWKV-v7.rwkv_v7_demo
########################################################################################################
# RWKV Block
########################################################################################################
class Block(nn.Module):
... | ===========unchanged ref 0===========
at: RWKV-v7.rwkv_v7_demo
RWKV_CMix_x070(args, layer_id)
at: RWKV-v7.rwkv_v7_demo.Block.__init__
self.layer_id = layer_id
self.ln1 = nn.LayerNorm(args.n_embd)
self.ln2 = nn.LayerNorm(args.n_embd)
self.att = RWKV_Tmi... |
RWKV-v7.rwkv_v7_demo/RWKV.forward | Modified | BlinkDL~RWKV-LM | c453b42656baa206cf5e9441482c97448c0d221a | rwkv-7 rc4 | <2>:<add> v0 = torch.empty_like(x)
<3>:<add> x, v0 = block(x, v0)
<del> x = block(x)
| # module: RWKV-v7.rwkv_v7_demo
########################################################################################################
# RWKV Model
########################################################################################################
class RWKV(nn.Module):
... | ===========unchanged ref 0===========
at: RWKV-v7.rwkv_v7_demo
Block(args, layer_id)
at: RWKV-v7.rwkv_v7_demo.RWKV.__init__
self.emb = nn.Embedding(args.vocab_size, args.n_embd)
at: torch._C._VariableFunctions
empty_like(input: Tensor, *, memory_format: Optional[memory_form... |
RWKV-v5.src.model/RWKV_CMix_x060.__init__ | Modified | BlinkDL~RWKV-LM | b44816c689f9d37448de1a64cc6a7fa44995d9f2 | RWKV-7 (preview) training | <10>:<add> self.time_maa_k = nn.Parameter(1.0 - torch.pow(ddd, ratio_1_to_almost0**3))
<del> self.time_maa_k = nn.Parameter(1.0 - torch.pow(ddd, ratio_1_to_almost0))
<11>:<add> self.time_maa_r = nn.Parameter(1.0 - torch.pow(ddd, ratio_1_to_almost0**3))
<del> sel... | # module: RWKV-v5.src.model
class RWKV_CMix_x060(MyModule):
def __init__(self, args, layer_id):
<0> super().__init__()
<1> self.args = args
<2> self.layer_id = layer_id
<3> self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
<4>
<5> with torch.no_grad... | ===========changed ref 0===========
# module: RWKV-v5.src.model
+ def RUN_CUDA_RWKV7g(q,w,k,v,a,b):
+ B,T,HC = q.shape
+ q,w,k,v,a,b = [i.view(B,T,HC//64,64) for i in [q,w,k,v,a,b]]
+ return WindBackstepping.apply(w,q,k,v,a,b).view(B,T,HC)
+
===========changed ref 1===========... |
RWKV-v5.src.model/Block.__init__ | Modified | BlinkDL~RWKV-LM | b44816c689f9d37448de1a64cc6a7fa44995d9f2 | RWKV-7 (preview) training | <16>:<add> if 'x070' in os.environ["RWKV_MY_TESTING"]:
<add> self.att = RWKV_Tmix_x070(args, layer_id)
<add> elif 'x060a' in os.environ["RWKV_MY_TESTING"]:
<del> if 'x060a' in os.environ["RWKV_MY_TESTING"]:
| # module: RWKV-v5.src.model
########################################################################################################
# The RWKV Model with our blocks
########################################################################################################
class... | ===========below chunk 0===========
# module: RWKV-v5.src.model
########################################################################################################
# The RWKV Model with our blocks
######################################################################################################... |
RWKV-v5.src.model/RWKV.forward | Modified | BlinkDL~RWKV-LM | b44816c689f9d37448de1a64cc6a7fa44995d9f2 | RWKV-7 (preview) training | <16>:<add> if 'x070' in os.environ["RWKV_MY_TESTING"]:
<add> v0 = torch.empty_like(x)
<add> for block in self.blocks:
<del> for block in self.blocks:
<17>:<add> if args.grad_cp == 1:
<del> if args.grad_cp == ... | # module: RWKV-v5.src.model
class RWKV(pl.LightningModule):
def forward(self, idx):
<0> args = self.args
<1> B, T = idx.size()
<2> assert T <= args.ctx_len, "Cannot forward, model ctx_len is exhausted."
<3>
<4> x = self.emb(idx)
<5> x_emb = x
<... | ===========below chunk 0===========
# module: RWKV-v5.src.model
class RWKV(pl.LightningModule):
def forward(self, idx):
# offset: 1
x = self.head(x) + c
else:
x = self.head(x)
return x
===========changed ref 0===========
... |
RWKV-v5.src.model/RWKV_Tmix_x070.__init__ | Modified | BlinkDL~RWKV-LM | 6b44d244e60143635c073507a4db4808b1731a32 | less params for x070 | <8>:<add> H = self.n_head
<add> N = self.head_size
<add> C = args.n_embd
<12>:<add> ddd = torch.ones(1, 1, C)
<del> ddd = torch.ones(1, 1, args.n_embd)
<13>:<add> for i in range(C):
<del> for i in range(args.n_embd):
<14>:<add>... | # module: RWKV-v5.src.model
########################################################################################################
class RWKV_Tmix_x070(MyModule):
def __init__(self, args, layer_id):
<0> super().__init__()
<1> self.args = args
<2> self.l... | ===========below chunk 0===========
# module: RWKV-v5.src.model
########################################################################################################
class RWKV_Tmix_x070(MyModule):
def __init__(self, args, layer_id):
# offset: 1
self.time_maa_a = nn.Param... |
RWKV-v5.src.model/RWKV_Tmix_x070.forward | Modified | BlinkDL~RWKV-LM | 6b44d244e60143635c073507a4db4808b1731a32 | less params for x070 | <18>:<add> v = v + (v0 - v) * torch.sigmoid(self.time_misc_v + (xv @ self.mv_w).view(B,T,H,1)).view(B,T,C)
<del> v = v + (v0 - v) * torch.sigmoid(self.time_misc_v + (xv @ self.mv_w1) @ self.mv_w2)
<19>:<add> a = torch.sigmoid(self.time_aaa + (xa @ self.time_aaa_w1) @ self.time_aaa_w... | <s>.n_embd**0.5))
+ # self.value.weight.data.uniform_(-0.5/(C**0.5), 0.5/(C**0.5))
- # self.value.weight.data.uniform_(-0.5/(args.n_embd**0.5), 0.5/(args.n_embd**0.5))
# self.output.weight.data.zero_()
@MyFunction
def forward(self, x, v0... | ===========below chunk 0===========
<s>**0.5))
+ # self.value.weight.data.uniform_(-0.5/(C**0.5), 0.5/(C**0.5))
- # self.value.weight.data.uniform_(-0.5/(args.n_embd**0.5), 0.5/(args.n_embd**0.5))
# self.output.weight.data.zero_()
@MyFunction
def forw... |
RWKV-v5.src.model/RWKV.generate_init_weight | Modified | BlinkDL~RWKV-LM | 6b44d244e60143635c073507a4db4808b1731a32 | less params for x070 | <18>:<add> s3 = str(shape[3]) if len(shape) > 3 else ""
<add> print(f"{s0.ljust(5)} {s1.ljust(5)} {s2.ljust(5)} {s3.ljust(5)} {n}", end="")
<del> print(f"{s0.ljust(5)} {s1.ljust(5)} {s2.ljust(5)} {n}", end="")
| # module: RWKV-v5.src.model
class RWKV(pl.LightningModule):
def generate_init_weight(self):
<0> print(
<1> f"""
<2> ############################################################################
<3> #
<4> # Init model weight (slow for large models)...
<5> #
<6> ####... | ===========below chunk 0===========
# module: RWKV-v5.src.model
class RWKV(pl.LightningModule):
def generate_init_weight(self):
# offset: 1
scale = 0.5 * math.sqrt(self.args.vocab_size / self.args.n_embd)
else:
scale = 0.5
... |
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