Heikki Arponen harpone

I hereby claim:

To claim this, I am signing this object:

	def forward_sequential(h, xs, U, W, nu, theta):
	"""Forward pass through the network sequentially over input `xs` of any length.
	NOTE: has no batch dimension. To be batched with `vmap`.

	Args:
	h (torch.tensor): shape [D_h, ]; previous state
	xs (torch.tensor): shape [T, D_x]; input sequence
	U (torch.tensor): Parameter matrix of shape [D_h, D_x]
	W (torch.tensor): Parameter matrix of shape [D_h, D_x]
	xi (torch.tensor): Parameter vector of shape [D_h, ]

	# PARALLEL:
	from contractpool import ContractPool # imaginary 'contractpool' library, similar to python's `multiprocessing`

	W = parameter(M, N)

	def forward(x_: float32[N]) -> float32:
	# matrix-vector multiplication:
	zs = float32[M] # let's imagine we have a float32 dtype in Vyper
	with ContractPool(dot, M) as p:
	p.map(W, x_, out=zs) # launches M subcontracts asynchronously, each subcontract writes values to zs

	# SERIAL:
	W = parameter(M, N)

	def forward(x_: float32[N]) -> float32:
	# matrix-vector multiplication:
	zs = float32[M] # let's imagine we have a float32 dtype in Vyper
	for i, W_row in enumerate(W):
	zs[i] = dot(W_row, x_) # 'dot' is an external smart contract

	# summation:


	Long-lasting COVID-19 -
	Consensus statement of the expert group appointed by STM on 31 December 2021
	VN / 20672/2021



	DRAFT 7.1.2022

	from itertools import islice
	import os
	import torch
	from torch.utils.data import DataLoader
	from torchvision import transforms
	import numpy as np

	import torch_xla.distributed.parallel_loader as pl
	import torch_xla.core.xla_model as xm
	import torch_xla.distributed.xla_multiprocessing as xmp

	from itertools import islice
	from munch import Munch
	import sys, os
	from torch.utils.data import DataLoader
	from torchvision import transforms
	import time

	import webdataset as wds

	sys.path.append(os.getcwd())

	"""
	MIT License

	knn, kl_div, entropy Copyright (c) 2017 Heikki Arponen
	"""

	import torch


	def knn(x, y, k=3, last_only=False, discard_nearest=True):

	class Lamb(Optimizer):
	r"""Implements Lamb algorithm.
	It has been proposed in `Large Batch Optimization for Deep Learning: Training BERT in 76 minutes`_.
	Arguments:
	params (iterable): iterable of parameters to optimize or dicts defining
	parameter groups
	lr (float, optional): learning rate (default: 1e-3)
	betas (Tuple[float, float], optional): coefficients used for computing
	running averages of gradient and its square (default: (0.9, 0.999))
	eps (float, optional): term added to the denominator to improve

	class GCSDataset(Dataset):
	"""Generic PyTorch dataset for GCS. Streams data from GCS and (optionally) caches to local disk.

	"""

	def __init__(self,
	bucketname=None,
	path_list=None, # TODO: list bucket/path contents if None
	target_list=None,
	transform=None,