ofa.py

#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import logging
import numpy as np
from collections import namedtuple
import paddle
#import paddle.nn as nn
import paddle.fluid as fluid
from paddle.fluid.dygraph import Conv2D
from .layers import BaseBlock, Block, SuperConv2D, SuperBatchNorm
from .utils.utils import search_idx
from ...common import get_logger

_logger = get_logger(__name__, level=logging.INFO)

__all__ = ['OFA', 'RunConfig', 'DistillConfig']

RunConfig = namedtuple('RunConfig', [
    'train_batch_size', 'n_epochs', 'save_frequency', 'eval_frequency',
    'init_learning_rate', 'total_images', 'elastic_depth', 'dynamic_batch_size'
])
RunConfig.__new__.__defaults__ = (None, ) * len(RunConfig._fields)

DistillConfig = namedtuple('DistillConfig', [
    'lambda_distill', 'teacher_model', 'mapping_layers', 'teacher_model_path',
    'distill_fn'
])
DistillConfig.__new__.__defaults__ = (None, ) * len(DistillConfig._fields)


class OFABase(fluid.dygraph.Layer):
    def __init__(self, model):
        super(OFABase, self).__init__()
        self.model = model
        self._layers, self._elastic_task = self.get_layers()

    def get_layers(self):
        layers = dict()
        elastic_task = set()
        for name, sublayer in self.model.named_sublayers():
            if isinstance(sublayer, BaseBlock):
                sublayer.set_supernet(self)
                if not sublayer.fixed:
                    layers[sublayer.key] = sublayer.candidate_config
                    for k in sublayer.candidate_config.keys():
                        elastic_task.add(k)
        return layers, elastic_task

    def forward(self, *inputs, **kwargs):
        raise NotImplementedError

    def layers_forward(self, block, *inputs, **kwargs):
        if getattr(self, 'current_config', None) != None:
            ### if block is fixed, donnot join key into candidate
            ### concrete config as parameter in kwargs
            if block.fixed == False:
                assert block.key in self.current_config, 'DONNT have {} layer in config.'.format(
                    block.key)
                config = self.current_config[block.key]
            else:
                config = dict()
                config.update(kwargs)
        else:
            config = dict()
        logging.debug(self.model, config)

        return block.fn(*inputs, **config)

    @property
    def layers(self):
        return self._layers


class OFA(OFABase):
    def __init__(self,
                 model,
                 run_config=None,
                 net_config=None,
                 distill_config=None,
                 elastic_order=None,
                 train_full=False):
        super(OFA, self).__init__(model)
        self.net_config = net_config
        self.run_config = run_config
        self.distill_config = distill_config
        self.elastic_order = elastic_order
        self.train_full = train_full
        self.iter = 0
        self.dynamic_iter = 0
        self.manual_set_task = False
        self.task_idx = 0
        self._add_teacher = False
        self.netAs_param = []

        ### if elastic_order is none, use default order
        if self.elastic_order is not None:
            assert isinstance(self.elastic_order,
                              list), 'elastic_order must be a list'

            if getattr(self.run_config, 'elastic_depth', None) != None:
                depth_list = list(set(self.run_config.elastic_depth))
                depth_list.sort()
                self.layers['depth'] = depth_list

        if self.elastic_order is None:
            self.elastic_order = []
            # zero, elastic resulotion, write in demo
            # first, elastic kernel size
            if 'kernel_size' in self._elastic_task:
                self.elastic_order.append('kernel_size')

            # second, elastic depth, such as: list(2, 3, 4)
            if getattr(self.run_config, 'elastic_depth', None) != None:
                depth_list = list(set(self.run_config.elastic_depth))
                depth_list.sort()
                self.layers['depth'] = depth_list
                self.elastic_order.append('depth')

            # final, elastic width
            if 'expand_ratio' in self._elastic_task:
                self.elastic_order.append('width')

            if 'channel' in self._elastic_task and 'width' not in self.elastic_order:
                self.elastic_order.append('width')

        if getattr(self.run_config, 'n_epochs', None) != None:
            assert len(self.run_config.n_epochs) == len(self.elastic_order)
            for idx in range(len(run_config.n_epochs)):
                assert isinstance(
                    run_config.n_epochs[idx],
                    list), "each candidate in n_epochs must be list"

            if self.run_config.dynamic_batch_size != None:
                assert len(self.run_config.n_epochs) == len(
                    self.run_config.dynamic_batch_size)
            if self.run_config.init_learning_rate != None:
                assert len(self.run_config.n_epochs) == len(
                    self.run_config.init_learning_rate)
                for idx in range(len(run_config.n_epochs)):
                    assert isinstance(
                        run_config.init_learning_rate[idx], list
                    ), "each candidate in init_learning_rate must be list"

        ### =================  add distill prepare ======================
        if self.distill_config != None:
            self._add_teacher = True
            self._prepare_distill()

        self.model.train()

    def _prepare_distill(self):
        self.Tacts, self.Sacts = {}, {}

        if self.distill_config.teacher_model == None:
            logging.error(
                'If you want to add distill, please input instance of teacher model'
            )

        ### instance model by user can input super-param easily.
        assert isinstance(self.distill_config.teacher_model,
                          paddle.fluid.dygraph.Layer)

        # load teacher parameter
        if self.distill_config.teacher_model_path != None:
            param_state_dict, _ = paddle.load_dygraph(
                self.distill_config.teacher_model_path)
            self.distill_config.teacher_model.set_dict(param_state_dict)

        self.ofa_teacher_model = OFABase(self.distill_config.teacher_model)
        self.ofa_teacher_model.model.eval()

        # add hook if mapping layers is not None
        # if mapping layer is None, return the output of the teacher model,
        # if mapping layer is NOT None, add hook and compute distill loss about mapping layers.
        mapping_layers = getattr(self.distill_config, 'mapping_layers', None)
        if mapping_layers != None:
            self.netAs = []
            for name, sublayer in self.model.named_sublayers():
                if name in mapping_layers:
                    netA = SuperConv2D(
                        sublayer._num_filters,
                        sublayer._num_filters,
                        filter_size=1)
                    self.netAs_param.extend(netA.parameters())
                    self.netAs.append(netA)

            def get_activation(mem, name):
                def get_output_hook(layer, input, output):
                    mem[name] = output

                return get_output_hook

            def add_hook(net, mem, mapping_layers):
                for idx, (n, m) in enumerate(net.named_sublayers()):
                    if n in mapping_layers:
                        m.register_forward_post_hook(get_activation(mem, n))

            add_hook(self.model, self.Sacts, mapping_layers)
            add_hook(self.ofa_teacher_model.model, self.Tacts, mapping_layers)

    def _compute_epochs(self):
        if getattr(self, 'epoch', None) == None:
            assert self.run_config.total_images is not None, \
                "if not use set_epoch() to set epoch, please set total_images in run_config."
            assert self.run_config.train_batch_size is not None, \
                "if not use set_epoch() to set epoch, please set train_batch_size in run_config."
            assert self.run_config.n_epochs is not None, \
                "if not use set_epoch() to set epoch, please set n_epochs in run_config."
            self.iter_per_epochs = self.run_config.total_images // self.run_config.train_batch_size
            epoch = self.iter // self.iter_per_epochs
        else:
            epoch = self.epoch
        return epoch

    def _sample_from_nestdict(self, cands, sample_type, task, phase):
        sample_cands = dict()
        for k, v in cands.items():
            if isinstance(v, dict):
                sample_cands[k] = self._sample_from_nestdict(
                    v, sample_type=sample_type, task=task, phase=phase)
            elif isinstance(v, list) or isinstance(v, set) or isinstance(v,
                                                                         tuple):
                if sample_type == 'largest':
                    sample_cands[k] = v[-1]
                elif sample_type == 'smallest':
                    sample_cands[k] = v[0]
                else:
                    if k not in task:
                        # sort and deduplication in candidate_config
                        # fixed candidate not in task_list
                        sample_cands[k] = v[-1]
                    else:
                        # phase == None -> all candidate; phase == number, append small candidate in each phase
                        # phase only affect last task in current task_list
                        if phase != None and k == task[-1]:
                            start = -(phase + 2)
                        else:
                            start = 0
                        sample_cands[k] = np.random.choice(v[start:])

        return sample_cands

    def _sample_config(self, task, sample_type='random', phase=None):
        config = self._sample_from_nestdict(
            self.layers, sample_type=sample_type, task=task, phase=phase)
        return config

    def set_task(self, task=None, phase=None):
        self.manual_set_task = True
        self.task = task
        self.phase = phase

    def set_epoch(self, epoch):
        self.epoch = epoch

    def _progressive_shrinking(self):
        epoch = self._compute_epochs()
        self.task_idx, phase_idx = search_idx(epoch, self.run_config.n_epochs)
        self.task = self.elastic_order[:self.task_idx + 1]
        if 'width' in self.task:
            ### change width in task to concrete config
            self.task.remove('width')
            if 'expand_ratio' in self._elastic_task:
                self.task.append('expand_ratio')
            if 'channel' in self._elastic_task:
                self.task.append('channel')
        if len(self.run_config.n_epochs[self.task_idx]) == 1:
            phase_idx = None
        return self._sample_config(task=self.task, phase=phase_idx)

    def calc_distill_loss(self):
        losses = []
        assert len(self.netAs) > 0
        for i, netA in enumerate(self.netAs):
            assert isinstance(netA, SuperConv2D)
            n = self.distill_config.mapping_layers[i]
            Tact = self.Tacts[n]
            Sact = self.Sacts[n]
            Sact = netA(Sact, channel=netA._num_filters)
            if self.distill_config.distill_fn == None:
                loss = fluid.layers.mse_loss(Sact, Tact)
            else:
                loss = distill_fn(Sact, Tact)
            losses.append(loss)
        return sum(losses) * self.distill_config.lambda_distill

    ### TODO: complete it
    def search(self, eval_func, condition):
        pass

    ### TODO: complete it
    def export(self, config):
        pass

    def set_net_config(self, net_config):
        self.net_config = net_config

    def forward(self, *inputs, **kwargs):
        # =====================  teacher process  =====================
        teacher_output = None
        if self._add_teacher:
            teacher_output = self.ofa_teacher_model.model.forward(*inputs,
                                                                  **kwargs)
        # ============================================================

        # ====================   student process  =====================
        if getattr(self.run_config, 'dynamic_batch_size', None) != None:
            self.dynamic_iter += 1
            if self.dynamic_iter == self.run_config.dynamic_batch_size[
                    self.task_idx]:
                self.iter += 1
                self.dynamic_iter = 0

        if self.net_config == None:
            if self.train_full == True:
                self.current_config = self._sample_config(
                    task=None, sample_type='largest')
            else:
                if self.manual_set_task == False:
                    self.current_config = self._progressive_shrinking()
                else:
                    self.current_config = self._sample_config(
                        self.task, phase=self.phase)
        else:
            self.current_config = self.net_config

        _logger.debug("Current config is {}".format(self.current_config))
        if 'depth' in self.current_config:
            kwargs['depth'] = self.current_config['depth']

        return self.model.forward(*inputs, **kwargs), teacher_output