Spaces:
Sleeping
Sleeping
File size: 8,446 Bytes
1423dc8 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 |
'''
Copied and modified from
https://github.com/state-spaces/mamba/blob/main/mamba_ssm/models/mixer_seq_simple.py
'''
import math
import torch
import torch.nn as nn
from functools import partial
from mamba_ssm import Mamba
from modules.mamba.bimamba import Mamba as BiMamba
from modules.mamba.bimamba import Block as PreNormBlock
try:
from mamba_ssm.ops.triton.layernorm import RMSNorm, layer_norm_fn, rms_norm_fn
except ImportError:
RMSNorm, layer_norm_fn, rms_norm_fn = None, None, None
def create_block(
d_model,
ssm_cls=None,
ssm_cfg=None,
norm_epsilon=1e-5,
rms_norm=False,
residual_in_fp32=False,
fused_add_norm=True,
layer_idx=None,
device=None,
dtype=None,
):
if ssm_cfg is None:
ssm_cfg = {}
factory_kwargs = {"device": device, "dtype": dtype}
mixer_cls = partial(ssm_cls, layer_idx=layer_idx, **ssm_cfg, **factory_kwargs)
norm_cls = partial(
nn.LayerNorm if not rms_norm else RMSNorm, eps=norm_epsilon, **factory_kwargs
)
block = PreNormBlock(
d_model,
mixer_cls,
norm_cls=norm_cls,
fused_add_norm=fused_add_norm,
residual_in_fp32=residual_in_fp32,
)
block.layer_idx = layer_idx
return block
# https://github.com/huggingface/transformers/blob/c28d04e9e252a1a099944e325685f14d242ecdcd/src/transformers/models/gpt2/modeling_gpt2.py#L454
def _init_weights(
module,
n_layer,
initializer_range=0.02, # Now only used for embedding layer.
rescale_prenorm_residual=True,
n_residuals_per_layer=1, # Change to 2 if we have MLP
):
if isinstance(module, nn.Linear):
if module.bias is not None:
if not getattr(module.bias, "_no_reinit", False):
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, std=initializer_range)
if rescale_prenorm_residual:
# Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
# > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
# > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
# > -- GPT-2 :: https://openai.com/blog/better-language-models/
#
# Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
for name, p in module.named_parameters():
if name in ["out_proj.weight", "fc2.weight"]:
# Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block
# Following Pytorch init, except scale by 1/sqrt(2 * n_layer)
# We need to reinit p since this code could be called multiple times
# Having just p *= scale would repeatedly scale it down
nn.init.kaiming_uniform_(p, a=math.sqrt(5))
with torch.no_grad():
p /= math.sqrt(n_residuals_per_layer * n_layer)
class LnMambaAdd(nn.Module):
def __init__(self,
d_model,
ssm_cls,
ssm_cfg,
rms_norm=False,
layer_idx=None
):
super().__init__()
if rms_norm:
self.norm = RMSNorm(d_model)
else:
self.norm = nn.LayerNorm(d_model)
self.mamba = ssm_cls(d_model=d_model, **ssm_cfg)
print(type(self.mamba))
print('Created LnMambaAdd.')
def forward(self, x, residual=None, inference_params=None):
if residual != None:
x = x + residual
return self.mamba(self.norm(x)), x
class MambaBlocksSequential(nn.Module):
"""
A wrapper for the Mamba block to replicate it
Arguments
---------
n_mamba : int
Number of Mamba blocks
d_model : int
Input dimension to Mamba (bottleneck dimension).
d_state : int
Mamba state dimension
expand: int
First linear projection d_model -> d_model * expand
d_conv: int
Kernel size of Mamba conv
norm type : str
The type of normalization, in ['gLN', 'cLN'].
---------
"""
def __init__(self,
n_mamba: int,
bidirectional: bool,
d_model: int, # bottleneck dimension (B)
d_state: int = 16,
expand: int = 2,
d_conv: int = 4, # kernel_size of 'Conv' in Mamba
dt_rank: str="auto",
conv_bias: bool = True,
bias: bool = False,
fused_add_norm: bool = True,
rms_norm: bool = False,
norm_epsilon: float = 1e-5,
initializer_cfg=None,
residual_in_fp32=False,
use_simple_block=False
):
super().__init__()
self.residual_in_fp32 = residual_in_fp32
self.bidirectional = bidirectional
# We change the order of residual and layer norm:
# Instead of LN -> Attn / MLP -> Add, we do:
# Add -> LN -> Attn / MLP / Mixer, returning both the residual branch (output of Add) and
# the main branch (output of MLP / Mixer). The model definition is unchanged.
# This is for performance reason: we can fuse add + layer_norm.
self.fused_add_norm = fused_add_norm
if self.fused_add_norm:
if layer_norm_fn is None or rms_norm_fn is None:
raise ImportError("Failed to import Triton LayerNorm / RMSNorm kernels")
self.use_simple_block = use_simple_block
ssm_cfg = {
"d_state": d_state,
"expand": expand,
"d_conv": d_conv,
"dt_rank": dt_rank,
"conv_bias": conv_bias,
"bias": bias
}
if bidirectional:
ssm_cfg["bimamba_type"] = "v2"
if use_simple_block:
self.layers = nn.Sequential(
*[
LnMambaAdd(
d_model=d_model,
ssm_cls=BiMamba if bidirectional else Mamba,
ssm_cfg=ssm_cfg,
rms_norm=rms_norm,
layer_idx=i
)
for i in range(n_mamba)
]
)
else:
self.layers = nn.Sequential(
*[
create_block(
d_model=d_model,
ssm_cls=BiMamba if bidirectional else Mamba,
ssm_cfg=ssm_cfg,
norm_epsilon=norm_epsilon,
rms_norm=rms_norm,
residual_in_fp32=residual_in_fp32,
fused_add_norm=fused_add_norm,
layer_idx=i,
)
for i in range(n_mamba)
]
)
self.norm_f = (nn.LayerNorm if not rms_norm else RMSNorm)(
d_model, eps=norm_epsilon
)
self.apply(
partial(
_init_weights,
n_layer=n_mamba,
**(initializer_cfg if initializer_cfg is not None else {}),
)
)
def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None, **kwargs):
return {
i: block.allocate_inference_cache(batch_size, max_seqlen, dtype=dtype, **kwargs)
for i, layer in enumerate(self.layers)
}
def forward(self, x, inference_params=None):
hidden_states = x
residual = None
for i, layer in enumerate(self.layers):
hidden_states, residual = layer(
hidden_states, residual, inference_params=inference_params
)
if not self.fused_add_norm:
residual = (hidden_states + residual) if residual is not None else hidden_states
hidden_states = self.norm_f(residual.to(dtype=self.norm_f.weight.dtype))
else:
# Set prenorm=False here since we don't need the residual
fused_add_norm_fn = rms_norm_fn if isinstance(self.norm_f, RMSNorm) else layer_norm_fn
hidden_states = fused_add_norm_fn(
hidden_states,
self.norm_f.weight,
self.norm_f.bias,
eps=self.norm_f.eps,
residual=residual,
prenorm=False,
residual_in_fp32=self.residual_in_fp32,
)
return hidden_states
|