MegEngine/imperative/python/megengine/autodiff/grad_manager.py

from collections import defaultdict
from contextlib import contextmanager
from typing import Callable

from ..core.autodiff.grad import Grad
from ..tensor import tensor
from ..utils.future import Future

backwarding_grad_manager = None


def get_backwarding_grad_manager():
    return backwarding_grad_manager


class GradManager:
    def __init__(self):
        self._call_back_dict = defaultdict(list)
        self._param_dict = dict()
        self._recording = False
        self._grad = None
        self._after_backward_callback = []
        self._gradients = dict()

    def attach(self, params, callbacks=None):
        if callbacks is None:
            callbacks = []
        if isinstance(callbacks, Callable):
            callbacks = [callbacks]
        for p in params:
            self._param_dict[id(p)] = p
            for cb in callbacks:
                self._call_back_dict[id(p)].append(cb)
        return self

    def _register_after_backward_callback(self, callback):
        self._after_backward_callback.append(callback)
        return self

    def backward(self, ys, dys=None):
        global backwarding_grad_manager
        cache = backwarding_grad_manager
        backwarding_grad_manager = self
        if not self._recording:
            raise RuntimeError(
                "no computation history. "
                "did you forget record() or "
                "call a method that clears the history?"
            )
        assert self._grad is not None
        if not isinstance(ys, (tuple, list)):
            ys = [ys]
        if dys is None:
            dys = [tensor(1.0).broadcast(y.shape) for y in ys]
        if not isinstance(dys, (tuple, list)):
            dys = [dys]
        try:
            self._grad(ys, dys)
            for callback in self._after_backward_callback:
                callback()
            for p, grad in self._gradients.items():
                if isinstance(grad, Future):
                    grad = grad.get()
                param = self._param_dict[p]
                if param.grad is None:
                    param.grad = grad
                else:
                    param.grad += grad
        finally:
            self._stop_record()
            backwarding_grad_manager = cache

    def record(self):
        if self._recording:
            raise RuntimeError("already recording")
        grad = Grad()
        self._recording = True
        self._grad = grad
        for param_id in self._param_dict.keys():
            param_wrapper = self._param_dict[param_id]
            callbacks = self._call_back_dict[param_id]

            def callback(param, grad, callbacks=callbacks, p=param_wrapper, gm=self):
                ret = grad
                for cb in callbacks:
                    ret = cb(param, ret)
                gm._gradients[id(p)] = ret

            grad.wrt(param_wrapper, callback=callback)
        grad.__enter__()

    def release(self):
        if not self._recording:
            raise RuntimeError("not recording")
        self._stop_record()

    def _stop_record(self):
        if self._grad is not None:
            self._grad.__exit__(None, None, None)
        self._recording = False
        self._grad = None
        self._gradients = dict()

    def __enter__(self):
        self.record()
        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        self._stop_record()