annotator/oneformer/detectron2/solver/build.py

# Copyright (c) Facebook, Inc. and its affiliates.
import copy
import itertools
import logging
from collections import defaultdict
from enum import Enum
from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Type, Union
import torch
from fvcore.common.param_scheduler import (
    CosineParamScheduler,
    MultiStepParamScheduler,
    StepWithFixedGammaParamScheduler,
)

from annotator.oneformer.detectron2.config import CfgNode
from annotator.oneformer.detectron2.utils.env import TORCH_VERSION

from .lr_scheduler import LRMultiplier, LRScheduler, WarmupParamScheduler

_GradientClipperInput = Union[torch.Tensor, Iterable[torch.Tensor]]
_GradientClipper = Callable[[_GradientClipperInput], None]


class GradientClipType(Enum):
    VALUE = "value"
    NORM = "norm"


def _create_gradient_clipper(cfg: CfgNode) -> _GradientClipper:
    """
    Creates gradient clipping closure to clip by value or by norm,
    according to the provided config.
    """
    cfg = copy.deepcopy(cfg)

    def clip_grad_norm(p: _GradientClipperInput):
        torch.nn.utils.clip_grad_norm_(p, cfg.CLIP_VALUE, cfg.NORM_TYPE)

    def clip_grad_value(p: _GradientClipperInput):
        torch.nn.utils.clip_grad_value_(p, cfg.CLIP_VALUE)

    _GRADIENT_CLIP_TYPE_TO_CLIPPER = {
        GradientClipType.VALUE: clip_grad_value,
        GradientClipType.NORM: clip_grad_norm,
    }
    return _GRADIENT_CLIP_TYPE_TO_CLIPPER[GradientClipType(cfg.CLIP_TYPE)]


def _generate_optimizer_class_with_gradient_clipping(
    optimizer: Type[torch.optim.Optimizer],
    *,
    per_param_clipper: Optional[_GradientClipper] = None,
    global_clipper: Optional[_GradientClipper] = None,
) -> Type[torch.optim.Optimizer]:
    """
    Dynamically creates a new type that inherits the type of a given instance
    and overrides the `step` method to add gradient clipping
    """
    assert (
        per_param_clipper is None or global_clipper is None
    ), "Not allowed to use both per-parameter clipping and global clipping"

    def optimizer_wgc_step(self, closure=None):
        if per_param_clipper is not None:
            for group in self.param_groups:
                for p in group["params"]:
                    per_param_clipper(p)
        else:
            # global clipper for future use with detr
            # (https://github.com/facebookresearch/detr/pull/287)
            all_params = itertools.chain(*[g["params"] for g in self.param_groups])
            global_clipper(all_params)
        super(type(self), self).step(closure)

    OptimizerWithGradientClip = type(
        optimizer.__name__ + "WithGradientClip",
        (optimizer,),
        {"step": optimizer_wgc_step},
    )
    return OptimizerWithGradientClip


def maybe_add_gradient_clipping(
    cfg: CfgNode, optimizer: Type[torch.optim.Optimizer]
) -> Type[torch.optim.Optimizer]:
    """
    If gradient clipping is enabled through config options, wraps the existing
    optimizer type to become a new dynamically created class OptimizerWithGradientClip
    that inherits the given optimizer and overrides the `step` method to
    include gradient clipping.

    Args:
        cfg: CfgNode, configuration options
        optimizer: type. A subclass of torch.optim.Optimizer

    Return:
        type: either the input `optimizer` (if gradient clipping is disabled), or
            a subclass of it with gradient clipping included in the `step` method.
    """
    if not cfg.SOLVER.CLIP_GRADIENTS.ENABLED:
        return optimizer
    if isinstance(optimizer, torch.optim.Optimizer):
        optimizer_type = type(optimizer)
    else:
        assert issubclass(optimizer, torch.optim.Optimizer), optimizer
        optimizer_type = optimizer

    grad_clipper = _create_gradient_clipper(cfg.SOLVER.CLIP_GRADIENTS)
    OptimizerWithGradientClip = _generate_optimizer_class_with_gradient_clipping(
        optimizer_type, per_param_clipper=grad_clipper
    )
    if isinstance(optimizer, torch.optim.Optimizer):
        optimizer.__class__ = OptimizerWithGradientClip  # a bit hacky, not recommended
        return optimizer
    else:
        return OptimizerWithGradientClip


def build_optimizer(cfg: CfgNode, model: torch.nn.Module) -> torch.optim.Optimizer:
    """
    Build an optimizer from config.
    """
    params = get_default_optimizer_params(
        model,
        base_lr=cfg.SOLVER.BASE_LR,
        weight_decay_norm=cfg.SOLVER.WEIGHT_DECAY_NORM,
        bias_lr_factor=cfg.SOLVER.BIAS_LR_FACTOR,
        weight_decay_bias=cfg.SOLVER.WEIGHT_DECAY_BIAS,
    )
    sgd_args = {
        "params": params,
        "lr": cfg.SOLVER.BASE_LR,
        "momentum": cfg.SOLVER.MOMENTUM,
        "nesterov": cfg.SOLVER.NESTEROV,
        "weight_decay": cfg.SOLVER.WEIGHT_DECAY,
    }
    if TORCH_VERSION >= (1, 12):
        sgd_args["foreach"] = True
    return maybe_add_gradient_clipping(cfg, torch.optim.SGD(**sgd_args))


def get_default_optimizer_params(
    model: torch.nn.Module,
    base_lr: Optional[float] = None,
    weight_decay: Optional[float] = None,
    weight_decay_norm: Optional[float] = None,
    bias_lr_factor: Optional[float] = 1.0,
    weight_decay_bias: Optional[float] = None,
    lr_factor_func: Optional[Callable] = None,
    overrides: Optional[Dict[str, Dict[str, float]]] = None,
) -> List[Dict[str, Any]]:
    """
    Get default param list for optimizer, with support for a few types of
    overrides. If no overrides needed, this is equivalent to `model.parameters()`.

    Args:
        base_lr: lr for every group by default. Can be omitted to use the one in optimizer.
        weight_decay: weight decay for every group by default. Can be omitted to use the one
            in optimizer.
        weight_decay_norm: override weight decay for params in normalization layers
        bias_lr_factor: multiplier of lr for bias parameters.
        weight_decay_bias: override weight decay for bias parameters.
        lr_factor_func: function to calculate lr decay rate by mapping the parameter names to
            corresponding lr decay rate. Note that setting this option requires
            also setting ``base_lr``.
        overrides: if not `None`, provides values for optimizer hyperparameters
            (LR, weight decay) for module parameters with a given name; e.g.
            ``{"embedding": {"lr": 0.01, "weight_decay": 0.1}}`` will set the LR and
            weight decay values for all module parameters named `embedding`.

    For common detection models, ``weight_decay_norm`` is the only option
    needed to be set. ``bias_lr_factor,weight_decay_bias`` are legacy settings
    from Detectron1 that are not found useful.

    Example:
    ::
        torch.optim.SGD(get_default_optimizer_params(model, weight_decay_norm=0),
                       lr=0.01, weight_decay=1e-4, momentum=0.9)
    """
    if overrides is None:
        overrides = {}
    defaults = {}
    if base_lr is not None:
        defaults["lr"] = base_lr
    if weight_decay is not None:
        defaults["weight_decay"] = weight_decay
    bias_overrides = {}
    if bias_lr_factor is not None and bias_lr_factor != 1.0:
        # NOTE: unlike Detectron v1, we now by default make bias hyperparameters
        # exactly the same as regular weights.
        if base_lr is None:
            raise ValueError("bias_lr_factor requires base_lr")
        bias_overrides["lr"] = base_lr * bias_lr_factor
    if weight_decay_bias is not None:
        bias_overrides["weight_decay"] = weight_decay_bias
    if len(bias_overrides):
        if "bias" in overrides:
            raise ValueError("Conflicting overrides for 'bias'")
        overrides["bias"] = bias_overrides
    if lr_factor_func is not None:
        if base_lr is None:
            raise ValueError("lr_factor_func requires base_lr")
    norm_module_types = (
        torch.nn.BatchNorm1d,
        torch.nn.BatchNorm2d,
        torch.nn.BatchNorm3d,
        torch.nn.SyncBatchNorm,
        # NaiveSyncBatchNorm inherits from BatchNorm2d
        torch.nn.GroupNorm,
        torch.nn.InstanceNorm1d,
        torch.nn.InstanceNorm2d,
        torch.nn.InstanceNorm3d,
        torch.nn.LayerNorm,
        torch.nn.LocalResponseNorm,
    )
    params: List[Dict[str, Any]] = []
    memo: Set[torch.nn.parameter.Parameter] = set()
    for module_name, module in model.named_modules():
        for module_param_name, value in module.named_parameters(recurse=False):
            if not value.requires_grad:
                continue
            # Avoid duplicating parameters
            if value in memo:
                continue
            memo.add(value)

            hyperparams = copy.copy(defaults)
            if isinstance(module, norm_module_types) and weight_decay_norm is not None:
                hyperparams["weight_decay"] = weight_decay_norm
            if lr_factor_func is not None:
                hyperparams["lr"] *= lr_factor_func(f"{module_name}.{module_param_name}")

            hyperparams.update(overrides.get(module_param_name, {}))
            params.append({"params": [value], **hyperparams})
    return reduce_param_groups(params)


def _expand_param_groups(params: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    # Transform parameter groups into per-parameter structure.
    # Later items in `params` can overwrite parameters set in previous items.
    ret = defaultdict(dict)
    for item in params:
        assert "params" in item
        cur_params = {x: y for x, y in item.items() if x != "params"}
        for param in item["params"]:
            ret[param].update({"params": [param], **cur_params})
    return list(ret.values())


def reduce_param_groups(params: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    # Reorganize the parameter groups and merge duplicated groups.
    # The number of parameter groups needs to be as small as possible in order
    # to efficiently use the PyTorch multi-tensor optimizer. Therefore instead
    # of using a parameter_group per single parameter, we reorganize the
    # parameter groups and merge duplicated groups. This approach speeds
    # up multi-tensor optimizer significantly.
    params = _expand_param_groups(params)
    groups = defaultdict(list)  # re-group all parameter groups by their hyperparams
    for item in params:
        cur_params = tuple((x, y) for x, y in item.items() if x != "params")
        groups[cur_params].extend(item["params"])
    ret = []
    for param_keys, param_values in groups.items():
        cur = {kv[0]: kv[1] for kv in param_keys}
        cur["params"] = param_values
        ret.append(cur)
    return ret


def build_lr_scheduler(cfg: CfgNode, optimizer: torch.optim.Optimizer) -> LRScheduler:
    """
    Build a LR scheduler from config.
    """
    name = cfg.SOLVER.LR_SCHEDULER_NAME

    if name == "WarmupMultiStepLR":
        steps = [x for x in cfg.SOLVER.STEPS if x <= cfg.SOLVER.MAX_ITER]
        if len(steps) != len(cfg.SOLVER.STEPS):
            logger = logging.getLogger(__name__)
            logger.warning(
                "SOLVER.STEPS contains values larger than SOLVER.MAX_ITER. "
                "These values will be ignored."
            )
        sched = MultiStepParamScheduler(
            values=[cfg.SOLVER.GAMMA**k for k in range(len(steps) + 1)],
            milestones=steps,
            num_updates=cfg.SOLVER.MAX_ITER,
        )
    elif name == "WarmupCosineLR":
        end_value = cfg.SOLVER.BASE_LR_END / cfg.SOLVER.BASE_LR
        assert end_value >= 0.0 and end_value <= 1.0, end_value
        sched = CosineParamScheduler(1, end_value)
    elif name == "WarmupStepWithFixedGammaLR":
        sched = StepWithFixedGammaParamScheduler(
            base_value=1.0,
            gamma=cfg.SOLVER.GAMMA,
            num_decays=cfg.SOLVER.NUM_DECAYS,
            num_updates=cfg.SOLVER.MAX_ITER,
        )
    else:
        raise ValueError("Unknown LR scheduler: {}".format(name))

    sched = WarmupParamScheduler(
        sched,
        cfg.SOLVER.WARMUP_FACTOR,
        min(cfg.SOLVER.WARMUP_ITERS / cfg.SOLVER.MAX_ITER, 1.0),
        cfg.SOLVER.WARMUP_METHOD,
        cfg.SOLVER.RESCALE_INTERVAL,
    )
    return LRMultiplier(optimizer, multiplier=sched, max_iter=cfg.SOLVER.MAX_ITER)