Dubhe/dubhe-tadl/classic_nas/fixed.py

# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.

import json
import logging

import sys
sys.path.append('..'+ '/' + '..')
from pytorch.mutables import InputChoice, LayerChoice, MutableScope
from pytorch.mutator import Mutator
from pytorch.utils import to_list


_logger = logging.getLogger(__name__)
#_logger.setLevel(logging.INFO)

class FixedArchitecture(Mutator):
    """
    Fixed architecture mutator that always selects a certain graph.

    Parameters
    ----------
    model : nn.Module
        A mutable network.
    fixed_arc : dict
        Preloaded architecture object.
    strict : bool
        Force everything that appears in ``fixed_arc`` to be used at least once.
    """

    def __init__(self, model, fixed_arc, strict=True):
        super().__init__(model)
        self._fixed_arc = fixed_arc

        mutable_keys = set([mutable.key for mutable in self.mutables if not isinstance(mutable, MutableScope)])
        fixed_arc_keys = set(self._fixed_arc.keys())
        if fixed_arc_keys - mutable_keys:
            raise RuntimeError("Unexpected keys found in fixed architecture: {}.".format(fixed_arc_keys - mutable_keys))
        if mutable_keys - fixed_arc_keys:
            raise RuntimeError("Missing keys in fixed architecture: {}.".format(mutable_keys - fixed_arc_keys))
        self._fixed_arc = self._from_human_readable_architecture(self._fixed_arc)

    def _from_human_readable_architecture(self, human_arc):
        # convert from an exported architecture
        #print('human_arc',human_arc)
        result_arc = {k: to_list(v) for k, v in human_arc.items()}  # there could be tensors, numpy arrays, etc.
        #print('result_arc',result_arc)
        # First, convert non-list to list, because there could be {"op1": 0} or {"op1": "conv"},
        # which means {"op1": [0, ]} ir {"op1": ["conv", ]}
        result_arc = {k: v['_value'] if isinstance(v['_value'], list) else [v['_value']] for k, v in result_arc.items()}
        # Second, infer which ones are multi-hot arrays and which ones are in human-readable format.
        # This is non-trivial, since if an array in [0, 1], we cannot know for sure it means [false, true] or [true, true].
        # Here, we assume an multihot array has to be a boolean array or a float array and matches the length.
        for mutable in self.mutables:

            if mutable.key not in result_arc:
                continue  # skip silently
            choice_arr = result_arc[mutable.key]

            if all(isinstance(v, bool) for v in choice_arr) or all(isinstance(v, float) for v in choice_arr):
                if (isinstance(mutable, LayerChoice) and len(mutable) == len(choice_arr)) or \
                        (isinstance(mutable, InputChoice) and mutable.n_candidates == len(choice_arr)):
                    # multihot, do nothing
                    continue
            if isinstance(mutable, LayerChoice):

                choice_arr = [mutable.names.index(val) if isinstance(val, str) else val for val in choice_arr]

                choice_arr = [i in choice_arr for i in range(len(mutable))]

            elif isinstance(mutable, InputChoice):
                choice_arr = [mutable.choose_from.index(val) if isinstance(val, str) else val for val in choice_arr]
                choice_arr = [i in choice_arr for i in range(mutable.n_candidates)]
            result_arc[mutable.key] = choice_arr
        return result_arc

    def sample_search(self):
        """
        Always returns the fixed architecture.
        """
        return self._fixed_arc

    def sample_final(self):
        """
        Always returns the fixed architecture.
        """
        return self._fixed_arc

    def replace_layer_choice(self, module=None, prefix=""):
        """
        Replace layer choices with selected candidates. It's done with best effort.
        In case of weighted choices or multiple choices. if some of the choices on weighted with zero, delete them.
        If single choice, replace the module with a normal module.

        Parameters
        ----------
        module : nn.Module
            Module to be processed.
        prefix : str
            Module name under global namespace.
        """
        if module is None:
            module = self.model
        for name, mutable in module.named_children():
            global_name = (prefix + "." if prefix else "") + name
            if isinstance(mutable, LayerChoice):
                chosen = self._fixed_arc[mutable.key]
                if sum(chosen) == 1 and max(chosen) == 1 and not mutable.return_mask:
                    # sum is one, max is one, there has to be an only one
                    # this is compatible with both integer arrays, boolean arrays and float arrays
                    _logger.info("Replacing %s with candidate number %d.", global_name, chosen.index(1))
                    setattr(module, name, mutable[chosen.index(1)])
                else:
                    if mutable.return_mask:
                        _logger.info("`return_mask` flag of %s is true. As it relies on the behavior of LayerChoice, " \
                                     "LayerChoice will not be replaced.")
                    # remove unused parameters
                    for ch, n in zip(chosen, mutable.names):
                        if ch == 0 and not isinstance(ch, float):
                            setattr(mutable, n, None)
            else:
                self.replace_layer_choice(mutable, global_name)


def apply_fixed_architecture(model, fixed_arc):
    """
    Load architecture from `fixed_arc` and apply to model.

    Parameters
    ----------
    model : torch.nn.Module
        Model with mutables.
    fixed_arc : str or dict
        Path to the JSON that stores the architecture, or dict that stores the exported architecture.

    Returns
    -------
    FixedArchitecture
        Mutator that is responsible for fixes the graph.
    """

    if isinstance(fixed_arc, str):
        with open(fixed_arc) as f:
            fixed_arc = json.load(f)
    architecture = FixedArchitecture(model, fixed_arc)
    architecture.reset()

    # for the convenience of parameters counting
    architecture.replace_layer_choice()
    return architecture