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program.py
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program.py
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from typing import Callable, NewType, Type, List
from dataclasses import dataclass
from itertools import chain
from functools import cached_property
import copy
from collections import defaultdict
import weakref
import sklearn.datasets
import torch
from nosbench.utils import deterministic
from nosbench.device import Device
Pointer = NewType("Pointer", int)
READONLY_REGION = 8
class Program(list):
__refs__: dict[str, list[Type["Program"]]] = defaultdict(list)
def __init__(self, *args, **kwargs):
self.__refs__[self.__class__].append(weakref.ref(self))
super().__init__(*args, **kwargs)
@classmethod
def get_instances(cls):
for inst_ref in cls.__refs__[cls]:
inst = inst_ref()
if inst is not None:
yield inst
def __eq__(self, other):
return hash(self) == hash(other)
@deterministic(seed=42)
def __hash__(self):
device = Device.get()
Device.set("cpu")
dim = 10
a = torch.normal(torch.zeros(dim, dim), torch.ones(dim, dim) * 1e-1)
b = torch.normal(torch.zeros(dim), torch.ones(dim) * 1e-1)
x = torch.normal(torch.zeros(dim), torch.ones(dim) * 1e-1)
# y = -torch.ones(dim) / 2.0
y = -torch.tensor(0.5)
x.requires_grad_()
optimizer_class = self.optimizer()
optimizer = optimizer_class([x])
# To eliminate 1/g optimizers
optimizer.zero_grad()
optimizer.step()
exp_avg = 0.0
try:
for _ in range(10):
z = x @ a + b
z = (z**2).sum().sqrt()
yhat = z**2 / (z**2 + 1.0)
loss = torch.nn.functional.l1_loss(yhat, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if torch.isinf(loss):
return -3
elif torch.isnan(loss):
return -2
exp_avg = loss.item() * 0.1 + exp_avg * 0.9
finally:
Device.set(device)
return hash(exp_avg)
def optimizer(self) -> Type[torch.optim.Optimizer]:
return create_optimizer(self)
class NamedProgram(Program):
def __init__(self, name, *args, **kwargs):
self.name = name
super().__init__(*args, **kwargs)
@dataclass
class Instruction:
__slots__ = "op", "inputs", "output"
op: Callable
inputs: List[Pointer]
output: Pointer
def execute(self, memory):
device = Device.get()
output = self.op([memory[inp].to(device) for inp in self.inputs])
memory[self.output].data = output.data
return output
def __str__(self):
return f"Instruction(Function({self.op}, {self.op.n_args}), inputs={self.inputs}, output={self.output})"
def __repr__(self):
return str(self)
class _TensorMemory(list):
def __init__(self, iterable=[]):
assert all([isinstance(x, torch.Tensor) for x in iterable])
super().__init__(iterable)
def append(self, item):
assert isinstance(item, torch.Tensor)
list.append(self, item)
def __getitem__(self, idx):
if idx < self.__len__():
return list.__getitem__(self, idx)
else:
device = Device.get()
while not self.__len__() > idx:
self.append(torch.tensor(0.0, device=device))
return self[idx]
def __setitem__(self, idx, value):
if idx < self.__len__():
return list.__setitem__(self, idx, value)
else:
device = Device.get()
while not self.__len__() > idx:
self.append(torch.tensor(0.0, device=device))
self[idx] = value
def create_optimizer(program):
class Optimizer(torch.optim.Optimizer):
def __init__(self, params, lr=1.0):
# Hyperparameters of the optimizer are part of the program
self.memory = {}
defaults = dict(lr=lr)
super(Optimizer, self).__init__(params, defaults)
def load_state_dict(self, state_dict):
super().load_state_dict(state_dict)
groups = self.param_groups
saved_groups = state_dict["param_groups"]
for old_id, p in zip(
chain.from_iterable((g["params"] for g in saved_groups)),
chain.from_iterable((g["params"] for g in groups)),
):
self.memory[p] = state_dict["state"][old_id]["memory"]
@torch.no_grad()
def step(self, closure=None):
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
device = Device.get()
for group_id, group in enumerate(self.param_groups):
for param_id, p in enumerate(group["params"]):
if p.grad is not None:
state = self.state[p]
if len(state) == 0:
# Initialize vector memory
state["step"] = torch.tensor(0.0, device=device)
self.memory[p] = _TensorMemory()
state["memory"] = self.memory[p]
self.memory[p][0] = p
self.memory[p][1] = p.grad
self.memory[p][2] = state["step"]
self.memory[p][3] = torch.tensor(1.0, device=device)
self.memory[p][4] = torch.tensor(0.5, device=device)
self.memory[p][5] = torch.tensor(1e-01, device=device)
self.memory[p][6] = torch.tensor(1e-02, device=device)
self.memory[p][7] = torch.tensor(1e-03, device=device)
self.memory[p][8] = torch.tensor(1e-06, device=device)
state["step"] += 1
d_p = 0.0 # If program is empty no updates
# Execute the program
for instruction in program:
assert instruction.output > READONLY_REGION
d_p = instruction.execute(self.memory[p])
# Update weights
p.add_(d_p, alpha=-group["lr"])
return loss
return Optimizer
def bruteforce_optimize(program):
i = 0
while i < len(program):
program_copy = copy.deepcopy(program)
program_copy.pop(i)
if program_copy == program:
program = program_copy
else:
i += 1
return program