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All ScaledStdConv and StdConv uses default to using F.layernorm so that they work with PyTorch XLA. eps value tweaking is a WIP.

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This commit is contained in:
Ross Wightman 2021-06-07 17:14:19 -07:00
parent 54a6cca27a
commit 8e4ac3549f
3 changed files with 34 additions and 27 deletions
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52
timm/models/layers/std_conv.py
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@ -19,17 +19,22 @@ class StdConv2d(nn.Conv2d):
"""
def __init__(
self, in_channel, out_channels, kernel_size, stride=1, padding=None, dilation=1,
groups=1, bias=False, eps=1e-5):
groups=1, bias=False, eps=1e-5, use_layernorm=True):
if padding is None:
padding = get_padding(kernel_size, stride, dilation)
super().__init__(
in_channel, out_channels, kernel_size, stride=stride,
padding=padding, dilation=dilation, groups=groups, bias=bias)
self.eps = eps
self.use_layernorm = use_layernorm
def get_weight(self):
std, mean = torch.std_mean(self.weight, dim=[1, 2, 3], keepdim=True, unbiased=False)
weight = (self.weight - mean) / (std + self.eps)
if self.use_layernorm:
# NOTE F.layer_norm is being used to compute (self.weight - mean) / (sqrt(var) + self.eps) in one op
weight = F.layer_norm(self.weight, self.weight.shape[1:], eps=self.eps)
else:
std, mean = torch.std_mean(self.weight, dim=[1, 2, 3], keepdim=True, unbiased=False)
weight = (self.weight - mean) / (std + self.eps)
return weight
def forward(self, x):
@ -45,17 +50,22 @@ class StdConv2dSame(nn.Conv2d):
"""
def __init__(
self, in_channel, out_channels, kernel_size, stride=1, padding='SAME', dilation=1,
groups=1, bias=False, eps=1e-5):
groups=1, bias=False, eps=1e-5, use_layernorm=True):
padding, is_dynamic = get_padding_value(padding, kernel_size, stride=stride, dilation=dilation)
super().__init__(
in_channel, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation,
groups=groups, bias=bias)
self.same_pad = is_dynamic
self.eps = eps
self.use_layernorm = use_layernorm
def get_weight(self):
std, mean = torch.std_mean(self.weight, dim=[1, 2, 3], keepdim=True, unbiased=False)
weight = (self.weight - mean) / (std + self.eps)
if self.use_layernorm:
# NOTE F.layer_norm is being used to compute (self.weight - mean) / (sqrt(var) + self.eps) in one op
weight = F.layer_norm(self.weight, self.weight.shape[1:], eps=self.eps)
else:
std, mean = torch.std_mean(self.weight, dim=[1, 2, 3], keepdim=True, unbiased=False)
weight = (self.weight - mean) / (std + self.eps)
return weight
def forward(self, x):
@ -76,7 +86,7 @@ class ScaledStdConv2d(nn.Conv2d):
def __init__(
self, in_channels, out_channels, kernel_size, stride=1, padding=None, dilation=1, groups=1,
bias=True, gamma=1.0, eps=1e-5, gain_init=1.0, use_layernorm=False):
bias=True, gamma=1.0, eps=1e-5, gain_init=1.0, use_layernorm=True):
if padding is None:
padding = get_padding(kernel_size, stride, dilation)
super().__init__(
@ -84,16 +94,17 @@ class ScaledStdConv2d(nn.Conv2d):
groups=groups, bias=bias)
self.gain = nn.Parameter(torch.full((self.out_channels, 1, 1, 1), gain_init))
self.scale = gamma * self.weight[0].numel() ** -0.5 # gamma * 1 / sqrt(fan-in)
self.eps = eps ** 2 if use_layernorm else eps
self.eps = eps
self.use_layernorm = use_layernorm # experimental, slightly faster/less GPU memory to hijack LN kernel
def get_weight(self):
if self.use_layernorm:
weight = self.scale * F.layer_norm(self.weight, self.weight.shape[1:], eps=self.eps)
# NOTE F.layer_norm is being used to compute (self.weight - mean) / (sqrt(var) + self.eps) in one op
weight = F.layer_norm(self.weight, self.weight.shape[1:], eps=self.eps)
else:
std, mean = torch.std_mean(self.weight, dim=[1, 2, 3], keepdim=True, unbiased=False)
weight = self.scale * (self.weight - mean) / (std + self.eps)
return self.gain * weight
weight = (self.weight - mean) / (std + self.eps)
return weight.mul_(self.gain * self.scale)
def forward(self, x):
return F.conv2d(x, self.get_weight(), self.bias, self.stride, self.padding, self.dilation, self.groups)
@ -110,7 +121,7 @@ class ScaledStdConv2dSame(nn.Conv2d):
def __init__(
self, in_channels, out_channels, kernel_size, stride=1, padding='SAME', dilation=1, groups=1,
bias=True, gamma=1.0, eps=1e-5, gain_init=1.0, use_layernorm=False):
bias=True, gamma=1.0, eps=1e-5, gain_init=1.0, use_layernorm=True):
padding, is_dynamic = get_padding_value(padding, kernel_size, stride=stride, dilation=dilation)
super().__init__(
in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation,
@ -118,24 +129,17 @@ class ScaledStdConv2dSame(nn.Conv2d):
self.gain = nn.Parameter(torch.full((self.out_channels, 1, 1, 1), gain_init))
self.scale = gamma * self.weight[0].numel() ** -0.5
self.same_pad = is_dynamic
self.eps = eps ** 2 if use_layernorm else eps
self.eps = eps
self.use_layernorm = use_layernorm # experimental, slightly faster/less GPU memory to hijack LN kernel
# NOTE an alternate formulation to consider, closer to DeepMind Haiku impl but doesn't seem
# to make much numerical difference (+/- .002 to .004) in top-1 during eval.
# def get_weight(self):
# var, mean = torch.var_mean(self.weight, dim=[1, 2, 3], keepdim=True, unbiased=False)
# scale = torch.rsqrt((self.weight[0].numel() * var).clamp_(self.eps)) * self.gain
# weight = (self.weight - mean) * scale
# return self.gain * weight
def get_weight(self):
if self.use_layernorm:
weight = self.scale * F.layer_norm(self.weight, self.weight.shape[1:], eps=self.eps)
# NOTE F.layer_norm is being used to compute (self.weight - mean) / (sqrt(var) + self.eps) in one op
weight = F.layer_norm(self.weight, self.weight.shape[1:], eps=self.eps)
else:
std, mean = torch.std_mean(self.weight, dim=[1, 2, 3], keepdim=True, unbiased=False)
weight = self.scale * (self.weight - mean) / (std + self.eps)
return self.gain * weight
weight = (self.weight - mean) / (std + self.eps)
return weight.mul_(self.gain * self.scale)
def forward(self, x):
if self.same_pad:

7
timm/models/nfnet.py
View File

@ -166,6 +166,8 @@ class NfCfg:
extra_conv: bool = False # extra 3x3 bottleneck convolution for NFNet models
gamma_in_act: bool = False
same_padding: bool = False
std_conv_eps: float = 1e-5
std_conv_ln: bool = True # use layer-norm impl to normalize in std-conv, works in PyTorch XLA, slightly faster
skipinit: bool = False # disabled by default, non-trivial performance impact
zero_init_fc: bool = False
act_layer: str = 'silu'
@ -482,10 +484,11 @@ class NormFreeNet(nn.Module):
conv_layer = ScaledStdConv2dSame if cfg.same_padding else ScaledStdConv2d
if cfg.gamma_in_act:
act_layer = act_with_gamma(cfg.act_layer, gamma=_nonlin_gamma[cfg.act_layer])
conv_layer = partial(conv_layer, eps=1e-4) # DM weights better with higher eps
conv_layer = partial(conv_layer, eps=cfg.std_conv_eps, use_layernorm=cfg.std_conv_ln)
else:
act_layer = get_act_layer(cfg.act_layer)
conv_layer = partial(conv_layer, gamma=_nonlin_gamma[cfg.act_layer])
conv_layer = partial(
conv_layer, gamma=_nonlin_gamma[cfg.act_layer], eps=cfg.std_conv_eps, use_layernorm=cfg.std_conv_ln)
attn_layer = partial(get_attn(cfg.attn_layer), **cfg.attn_kwargs) if cfg.attn_layer else None
stem_chs = make_divisible((cfg.stem_chs or cfg.channels[0]) * cfg.width_factor, cfg.ch_div)

2
timm/models/vision_transformer_hybrid.py
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@ -118,7 +118,7 @@ def _resnetv2(layers=(3, 4, 9), **kwargs):
padding_same = kwargs.get('padding_same', True)
if padding_same:
stem_type = 'same'
conv_layer = StdConv2dSame
conv_layer = partial(StdConv2dSame, eps=1e-5)
else:
stem_type = ''
conv_layer = StdConv2d