File size: 3,771 Bytes
e423fc2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 | import logging
import os
import sys
import torch
from torch import nn
import torch.distributed as dist
import torch.nn.functional as F
from .norm import SimpleRMSNorm as SimpleRMSNormTorch
from .srmsnorm_triton import SimpleRMSNorm as SimpleRMSNormTriton
use_triton = eval(os.environ.get("use_triton", default="True"))
debug = eval(os.environ.get("debug", default="False"))
if use_triton:
SimpleRMSNorm = SimpleRMSNormTriton
else:
SimpleRMSNorm = SimpleRMSNormTorch
logging.basicConfig(
format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
level=os.environ.get("LOGLEVEL", "INFO").upper(),
stream=sys.stdout,
)
logger = logging.getLogger("print_config")
BASE_DIM = 256
def is_dist_avail_and_initialized():
if not dist.is_available():
return False
if not dist.is_initialized():
return False
return True
def get_world_size():
if not is_dist_avail_and_initialized():
return 1
return dist.get_world_size()
def get_rank():
if not is_dist_avail_and_initialized():
return 0
return dist.get_rank()
def is_main_process():
return get_rank() == 0
def logging_info(string):
if is_main_process():
logger.info(string)
def print_params(**kwargs):
if is_main_process():
logger.info(f"start print config of {kwargs['__class__']}")
for key in kwargs:
if key in ["__class__", "self"]:
continue
logger.info(f"{key}: {kwargs[key]}")
logger.info(f"end print config of {kwargs['__class__']}")
def print_config(config):
if is_main_process():
logger.info(f"start print config of {config['__class__']}")
for key in config:
if key in ["__class__", "self"]:
continue
logger.info(f"{key}: {config[key]}")
logger.info(f"end print config of {config['__class__']}")
def print_module(module):
named_modules = set()
for p in module.named_modules():
named_modules.update([p[0]])
named_modules = list(named_modules)
string_repr = ""
for p in module.named_parameters():
name = p[0].split(".")[0]
if name not in named_modules:
string_repr = (string_repr + "(" + name + "): " + "Tensor(" +
str(tuple(p[1].shape)) + ", requires_grad=" +
str(p[1].requires_grad) + ")\n")
return string_repr.rstrip("\n")
def get_activation_fn(activation):
if debug:
logger.info(f"activation: {activation}")
if activation == "gelu":
return F.gelu
elif activation == "relu":
return F.relu
elif activation == "elu":
return F.elu
elif activation == "sigmoid":
return F.sigmoid
elif activation == "exp":
def f(x):
with torch.no_grad():
x_max = torch.max(x, dim=-1, keepdims=True).values
y = torch.exp(x - x_max)
return y
return f
elif activation == "leak":
return F.leaky_relu
elif activation == "1+elu":
def f(x):
return 1 + F.elu(x)
return f
elif activation == "2+elu":
def f(x):
return 2 + F.elu(x)
return f
elif activation == "silu" or activation == "swish":
return F.silu
elif activation == "sine":
return torch.sin
else:
logger.info(
f"activation: does not support {activation}, use Identity!!!")
return lambda x: x
def get_norm_fn(norm_type):
if norm_type == "simplermsnorm":
return SimpleRMSNorm
else:
return nn.LayerNorm
def convert_to_multiple_of_base(x):
return BASE_DIM * ((x + BASE_DIM - 1) // BASE_DIM)
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