You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
86 lines
4.6 KiB
86 lines
4.6 KiB
2 years ago
|
from typing import Optional, Union, Tuple
|
||
|
|
from tinygrad.tensor import Tensor
|
||
|
|
|
||
|
|
class BatchNorm2d:
|
||
|
|
def __init__(self, sz, eps=1e-5, affine=True, track_running_stats=True, momentum=0.1):
|
||
|
|
assert affine, "BatchNorm2d is only supported with affine"
|
||
|
|
self.eps, self.track_running_stats, self.momentum = eps, track_running_stats, momentum
|
||
|
|
|
||
|
|
self.weight, self.bias = Tensor.ones(sz), Tensor.zeros(sz)
|
||
|
|
|
||
|
|
self.running_mean, self.running_var = Tensor.zeros(sz, requires_grad=False), Tensor.ones(sz, requires_grad=False)
|
||
|
|
self.num_batches_tracked = Tensor.zeros(1, requires_grad=False)
|
||
|
|
|
||
|
|
def __call__(self, x:Tensor):
|
||
|
|
if Tensor.training:
|
||
|
|
# This requires two full memory accesses to x
|
||
|
|
# https://github.com/pytorch/pytorch/blob/c618dc13d2aa23625cb0d7ada694137532a4fa33/aten/src/ATen/native/cuda/Normalization.cuh
|
||
|
|
# There's "online" algorithms that fix this, like https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Welford's_Online_algorithm
|
||
|
|
x_detached = x.detach()
|
||
|
|
batch_mean = x_detached.mean(axis=(0,2,3))
|
||
|
|
y = (x_detached - batch_mean.reshape(shape=[1, -1, 1, 1]))
|
||
|
|
batch_var = (y*y).mean(axis=(0,2,3))
|
||
|
|
batch_invstd = batch_var.add(self.eps).pow(-0.5)
|
||
|
|
self.batch_invstd = None
|
||
|
|
|
||
|
|
# NOTE: wow, this is done all throughout training in most PyTorch models
|
||
|
|
if self.track_running_stats:
|
||
|
|
self.running_mean.assign((1 - self.momentum) * self.running_mean + self.momentum * batch_mean)
|
||
|
|
self.running_var.assign((1 - self.momentum) * self.running_var + self.momentum * batch_var)
|
||
|
|
self.num_batches_tracked += 1
|
||
|
|
else:
|
||
|
|
batch_mean, batch_var = self.running_mean, self.running_var
|
||
|
|
# NOTE: this can be precomputed for static inference. if you manually update running_var, you have to reset this
|
||
|
|
if not hasattr(self, "batch_invstd") or not self.batch_invstd:
|
||
|
|
self.batch_invstd = batch_var.add(self.eps).pow(-0.5)
|
||
|
|
batch_invstd = self.batch_invstd
|
||
|
|
|
||
|
|
return x.batchnorm(self.weight, self.bias, batch_mean, batch_invstd)
|
||
|
|
|
||
|
|
# TODO: is this good weight init?
|
||
|
|
class Conv2d:
|
||
|
|
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=True):
|
||
|
|
self.kernel_size = (kernel_size, kernel_size) if isinstance(kernel_size, int) else (kernel_size[0], kernel_size[1])
|
||
|
|
self.stride = (stride, stride) if isinstance(stride, int) else (stride[0], stride[1])
|
||
|
|
self.padding = (padding, ) * 4 if isinstance(padding, int) else ((padding[0], padding[0], padding[1], padding[1]) if len(padding) == 2 else padding)
|
||
|
|
# TODO: why is this realize needed? shouldn't it realize on the first run?
|
||
|
|
self.weight = Tensor.glorot_uniform(out_channels, in_channels, self.kernel_size[0], self.kernel_size[1]).realize()
|
||
|
|
self.bias = Tensor.zeros(out_channels).contiguous().realize() if bias else None
|
||
|
|
|
||
|
|
def __call__(self, x):
|
||
|
|
return x.conv2d(self.weight, self.bias, padding=self.padding, stride=self.stride)
|
||
|
|
|
||
|
|
class Linear:
|
||
|
|
def __init__(self, in_features, out_features, bias=True):
|
||
|
|
self.weight = Tensor.glorot_uniform(out_features, in_features).realize()
|
||
|
|
self.bias = Tensor.zeros(out_features).contiguous().realize() if bias else None
|
||
|
|
|
||
|
|
def __call__(self, x):
|
||
|
|
return x.linear(self.weight.transpose(), self.bias)
|
||
|
|
|
||
|
|
class GroupNorm:
|
||
|
|
def __init__(self, num_groups:int, num_channels:int, eps:float=1e-5, affine:bool=True):
|
||
|
|
self.num_groups, self.num_channels, self.eps = num_groups, num_channels, eps
|
||
|
|
self.weight : Optional[Tensor] = Tensor.ones(num_channels) if affine else None
|
||
|
|
self.bias : Optional[Tensor] = Tensor.zeros(num_channels) if affine else None
|
||
|
|
|
||
|
|
def __call__(self, x:Tensor):
|
||
|
|
# reshape for layernorm to work as group norm
|
||
|
|
# subtract mean and divide stddev
|
||
|
|
x = x.reshape(x.shape[0], self.num_groups, -1).layernorm(eps=self.eps).reshape(x.shape)
|
||
|
|
|
||
|
|
if self.weight is None or self.bias is None: return x
|
||
|
|
# elementwise_affine on channels
|
||
|
|
return x * self.weight.reshape(1, -1, 1, 1) + self.bias.reshape(1, -1, 1, 1)
|
||
|
|
|
||
|
|
class LayerNorm:
|
||
|
|
def __init__(self, normalized_shape:Union[int, Tuple[int, ...]], eps:float=1e-5, elementwise_affine:bool=True):
|
||
|
|
normalized_shape = (normalized_shape,) if isinstance(normalized_shape, int) else tuple(normalized_shape)
|
||
|
|
self.axis, self.eps, self.elementwise_affine = tuple(-1-i for i in range(len(normalized_shape))), eps, elementwise_affine
|
||
|
|
self.weight, self.bias = (Tensor.ones(*normalized_shape), Tensor.zeros(*normalized_shape)) if elementwise_affine else (None, None)
|
||
|
|
|
||
|
|
def __call__(self, x:Tensor):
|
||
|
|
x = x.layernorm(eps=self.eps, axis=self.axis)
|
||
|
|
if not self.elementwise_affine: return x
|
||
|
|
return x * self.weight + self.bias
|