decagon-pytorch/src/icosagon/fastloop.py

from .fastmodel import FastModel
from .trainprep import PreparedData
import torch
from typing import Callable
from types import FunctionType
import time
import random


class FastBatcher(object):
    def __init__(self, prep_d: PreparedData, batch_size: int,
        shuffle: bool, generator: torch.Generator,
        part_type: str) -> None:

        if not isinstance(prep_d, PreparedData):
            raise TypeError('prep_d must be an instance of PreparedData')

        if not isinstance(generator, torch.Generator):
            raise TypeError('generator must be an instance of torch.Generator')

        if part_type not in ['train', 'val', 'test']:
            raise ValueError('part_type must be set to train, val or test')

        self.prep_d = prep_d
        self.batch_size = int(batch_size)
        self.shuffle = bool(shuffle)
        self.generator = generator
        self.part_type = part_type

        self.edges = None
        self.targets = None
        self.build()

    def build(self):
        self.edges = []
        self.targets = []

        for fam in self.prep_d.relation_families:
            edges = []
            targets = []
            for i, rel in enumerate(fam.relation_types):

                edges_pos = getattr(rel.edges_pos, self.part_type)
                edges_neg = getattr(rel.edges_neg, self.part_type)
                edges_back_pos = getattr(rel.edges_back_pos, self.part_type)
                edges_back_neg = getattr(rel.edges_back_neg, self.part_type)

                e = torch.cat([ edges_pos,
                    torch.cat([edges_back_pos[:, 1], edges_back_pos[:, 0]], dim=1) ])
                e = torch.cat([torch.ones(len(e), 1, dtype=torch.long) * i , e ], dim=1)
                t = torch.ones(len(e))
                edges.append(e)
                targets.append(t)

                e = torch.cat([ edges_neg,
                    torch.cat([edges_back_neg[:, 1], edges_back_neg[:, 0]], dim=1) ])
                e = torch.cat([ torch.ones(len(e), 1, dtype=torch.long) * i, e ], dim=1)
                t = torch.zeros(len(e))
                edges.append(e)
                targets.append(t)

            edges = torch.cat(edges)
            targets = torch.cat(targets)

            self.edges.append(edges)
            self.targets.append(targets)

        # print(self.edges)
        # print(self.targets)

        if self.shuffle:
            self.shuffle_families()

    def shuffle_families(self):
        for i in range(len(self.edges)):
            edges = self.edges[i]
            targets = self.targets[i]
            order = torch.randperm(len(edges), generator=self.generator)
            self.edges[i] = edges[order]
            self.targets[i] = targets[order]

    def __iter__(self):
        offsets = [ 0 for _ in self.edges ]

        while True:
            choice = [ i for i in range(len(offsets)) \
                if offsets[i] < len(self.edges[i]) ]
            if len(choice) == 0:
                break
            fam_idx = torch.randint(len(choice), (1,), generator=self.generator).item()
            ofs = offsets[fam_idx]
            edges = self.edges[fam_idx][ofs:ofs + self.batch_size]
            targets = self.targets[fam_idx][ofs:ofs + self.batch_size]
            offsets[fam_idx] += self.batch_size
            yield (fam_idx, edges, targets)


class FastLoop(object):
    def __init__(
        self,
        model: FastModel,
        lr: float = 0.001,
        loss: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] = \
            torch.nn.functional.binary_cross_entropy_with_logits,
        batch_size: int = 100,
        shuffle: bool = True,
        generator: torch.Generator = None) -> None:

        self._check_params(model, loss, generator)

        self.model = model
        self.lr = float(lr)
        self.loss = loss
        self.batch_size = int(batch_size)
        self.shuffle = bool(shuffle)
        self.generator = generator or torch.default_generator

        self.opt = None

        self.build()

    def _check_params(self, model, loss, generator):
        if not isinstance(model, FastModel):
            raise TypeError('model must be an instance of FastModel')

        if not isinstance(loss, FunctionType):
            raise TypeError('loss must be a function')

        if generator is not None and not isinstance(generator, torch.Generator):
            raise TypeError('generator must be an instance of torch.Generator')

    def build(self) -> None:
        opt = torch.optim.Adam(self.model.parameters(), lr=self.lr)
        self.opt = opt

    def run_epoch(self):
        prep_d = self.model.prep_d

        batcher = FastBatcher(self.model.prep_d, batch_size=self.batch_size,
            shuffle = self.shuffle, generator=self.generator)
        # pred = self.model(None)
        # n = len(list(iter(batch)))
        loss_sum = 0
        for fam_idx, edges, targets in batcher:
            self.opt.zero_grad()
            pred = self.model(None)

            # process pred, get input and targets
            input = pred[fam_idx][edges[:, 0], edges[:, 1]]

            loss = self.loss(input, targets)
            loss.backward()
            self.opt.step()
            loss_sum += loss.detach().cpu().item()
        return loss_sum


    def train(self, max_epochs):
        best_loss = None
        best_epoch = None
        for i in range(max_epochs):
            loss = self.run_epoch()
            if best_loss is None or loss < best_loss:
                best_loss = loss
                best_epoch = i
        return loss, best_loss, best_epoch