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import torch
from torch import nn
from src.utils.parameter import LearnableParameter
__all__ = ['init_weights']
def init_weights(m, linear=None, rpe=None, activation='leaky_relu'):
"""Manual weight initialization. Allows setting specific init modes
for certain modules. In particular, the linear and RPE layers are
initialized with Xavier uniform initialization by default:
https://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf
Supported initializations are:
- 'xavier_uniform'
- 'xavier_normal'
- 'kaiming_uniform'
- 'kaiming_normal'
- 'trunc_normal'
"""
from src.nn import SelfAttentionBlock
linear = 'xavier_uniform' if linear is None else linear
rpe = linear if rpe is None else rpe
if isinstance(m, LearnableParameter):
nn.init.trunc_normal_(m, std=0.02)
return
if isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
return
if isinstance(m, nn.Linear):
_linear_init(m, method=linear, activation=activation)
return
if isinstance(m, SelfAttentionBlock):
if m.k_rpe is not None:
_linear_init(m.k_rpe, method=rpe, activation=activation)
if m.q_rpe is not None:
_linear_init(m.q_rpe, method=rpe, activation=activation)
return
def _linear_init(m, method='xavier_uniform', activation='leaky_relu'):
gain = torch.nn.init.calculate_gain(activation)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
if method == 'xavier_uniform':
nn.init.xavier_uniform_(m.weight, gain=gain)
elif method == 'xavier_normal':
nn.init.xavier_normal_(m.weight, gain=gain)
elif method == 'kaiming_uniform':
nn.init.kaiming_uniform_(m.weight, nonlinearity=activation)
elif method == 'kaiming_normal':
nn.init.kaiming_normal_(m.weight, nonlinearity=activation)
elif method == 'trunc_normal':
nn.init.trunc_normal_(m.weight, std=0.02)
else:
raise NotImplementedError(f"Unknown initialization method: {method}")
def build_qk_scale_func(dim, num_heads, qk_scale):
"""Builds the QK-scale function that will be used to produce
the qk-scale. This function follows the template:
f(s), where `s` is the `edge_index[0]`
even if it does not use it.
"""
# If qk_scale is not provided, the default behavior will be
# 1/(sqrt(dim)*sqrt(num))
if qk_scale is None:
def f(s):
D = (dim // num_heads) ** -0.5
G = (s.bincount() ** -0.5)[s].view(-1, 1, 1)
return D * G
return f
# If qk_scale is provided as a scalar, it will be used as is
if not isinstance(qk_scale, str):
def f(s):
return qk_scale
return f
# Convert input str to lowercase and remove spaces before
# parsing
qk_scale = qk_scale.lower().replace(' ', '')
if qk_scale in ['d+g', 'g+d']:
def f(s):
D = (dim // num_heads) ** -0.5
G = (s.bincount() ** -0.5)[s].view(-1, 1, 1)
return D + G
return f
if qk_scale in ['dg', 'gd', 'd*g', 'g*d', 'd.g', 'g.d']:
def f(s):
D = (dim // num_heads) ** -0.5
G = (s.bincount() ** -0.5)[s].view(-1, 1, 1)
return D * G
return f
if qk_scale == 'd':
def f(s):
D = (dim // num_heads) ** -0.5
return D
return f
if qk_scale == 'g':
def f(s):
G = (s.bincount() ** -0.5)[s].view(-1, 1, 1)
return G
return f
raise ValueError(
f"Unable to build QK scaling scheme for qk_scale='{qk_scale}'")
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