How to use is_parameter method in Slash

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mpgen_catalog.py

Source:mpgen_catalog.py

...75			Sets is_checkable_kernel in the MP template header76		"""77		self.is_checkable_kernel = 078def kprobes(config):79	if (   config.is_parameter('CONFIG_FTRACE', 'y')80		or config.is_parameter('CONFIG_KPROBES', 'y')) :81		return True82	else :83		return False84def errata(config):85	if (   config.is_parameter('CONFIG_ARM64_ERRATUM_826319', 'y')86		or config.is_parameter('CONFIG_ARM64_ERRATUM_827319', 'y')87		or config.is_parameter('CONFIG_ARM64_ERRATUM_824069', 'y')88		or config.is_parameter('CONFIG_ARM64_ERRATUM_819472', 'y')89		or config.is_parameter('CONFIG_ARM64_ERRATUM_832075', 'y')90		or config.is_parameter('CONFIG_ARM64_ERRATUM_845719', 'y')91		or config.is_parameter('CONFIG_ARM64_ERRATUM_843419', 'y')92		or config.is_parameter('CONFIG_CAVIUM_ERRATUM_22375', 'y')93		or config.is_parameter('CONFIG_CAVIUM_ERRATUM_23154', 'y')94		or config.is_parameter('CONFIG_CAVIUM_ERRATUM_27456', 'y')95		or config.is_parameter('CONFIG_QCOM_QDF2400_ERRATUM_0065', 'y')) :96		return True97	else : return False98def get_catalog(config):99	cat = Catalog()100	# TEST CATALOG101	# For production catalog contact Qualcomm Technologies, Inc.102	if 1 == 0 :103		cat.ro_section('populate_all_ro_sections')104	else :105		if (kprobes(config)):106			cat.mark_kernel_non_checkable()107			cat.mp_warning('report is not trusted due to kprobes or tracing')108			cat.ro_section('.head.text', attrs = 'MP_SECTION_ATTRIBUTE_NQHEE | MP_SECTION_ATTRIBUTE_NONSTRICT')109			...

parameter_api.py

Source:parameter_api.py

1import logging2import nginx3from datetime import datetime, timedelta4from api.base import BaseAPI5from uuid import uuid46from core.settings_utils import settings7from models import Parameter, ApiErrorCode, ApiParameter, Category, Function, SiteParameter, EventParameter, HttpParameter, LocationParameter, ParameterOptionsField, ParameterVariable, ParameterType, ServerParameter, UpstreamParameter8from mongoengine.queryset.visitor import Q9logger = logging.getLogger(__name__)10model_map = {11    'category': {12        "model": Category13    },14    'function': {15        "model": Function16    },17    'parameter_type': {18        "model": ParameterType19    },20    'parameter': {21        "model": Parameter22    },23    'api_parameter': {24        "model": ApiParameter,25        "is_parameter": True # æ¯å¦å±äºå¼ç¨å±æ§26    },27    'site_parameter': {28        "model": SiteParameter,29        "is_parameter": True30    },31    'event_parameter': {32        "model": EventParameter,33        "is_parameter": True34    },35    'http_parameter': {36        "model": HttpParameter,37        "is_parameter": True38    },39    'location_parameter': {40        "model": LocationParameter,41        "is_parameter": True42    },43    'parameter_variable': {44        "model": ParameterVariable,45    },46    'server_parameter': {47        "model": ServerParameter,48        "is_parameter": True49    },50    'upstream_parameter': {51        "model": UpstreamParameter,52        "is_parameter": True53    }54}55class ListAPI(BaseAPI):56    def parse_url_args(self, args, kwargs):57        self.modelCls = model_map[args[0]]['model']58        self.is_parameter = model_map[args[0]].get('is_parameter', False)59    def api(self, params):60        print(params)61        page = params.get("page", 1)62        pagesize = params.get("pagesize", 10)63        query_args = Q(is_enable=True) 64        keyword = params.get("keyword", "")65        if keyword:66            query_args = query_args & Q(key__contains=params.keyword)67        count, total_page, records = self.modelCls.paginate(page, pagesize, query_args)68        data = []69        records = records.order_by('id')70        for item in records:71            item_dct = item.to_dict()72            data.append(item_dct)73        if self.is_parameter is True:74            for item in data:75                parameter = Parameter.get_first(key=item['key'], is_enable=True)76                if parameter:77                    item['_parameter'] = parameter.to_dict()78        pageData =  {79            'data': data,80            'total': count,81            'total_page': total_page,82            'pagesize': pagesize,83            'page': page84        }85        pageData['keyword'] = keyword86        return pageData87class GetAPI(BaseAPI):88    def parse_url_args(self, args, kwargs):89        self.modelCls = model_map[args[0]]['model']90        self.is_parameter = model_map[args[0]].get('is_parameter', False)91    def api(self, params):92        entity = None93        parameter = None94        if params.get('id'):95            entity = self.modelCls.get_first(id=params.id, is_enable=True)96        elif params.get('key'):97            entity = self.modelCls.get_first(key=params.key, is_enable=True)98        else:99            self.fail(code=-1, message='id_or_key_required')100            return101        if not entity:102            self.fail(code=-1, message='entity_not_found')103            return104        if self.is_parameter is True:105            parameter = Parameter.get_first(key=entity.key, is_enable=True)106        data = {107            "entity": entity.to_dict(),108        }109        if parameter is not None:110            data.update(_parameter=parameter)111        return data112fields_exclude = ['id', '_cls', 'is_import', 'is_enable', 'create_at', 'update_at', 'status']113class CreateAPI(BaseAPI):114    def parse_url_args(self, args, kwargs):115        self.modelCls = model_map[args[0]]['model']116        self.is_parameter = model_map[args[0]].get('is_parameter', False)117    def api(self, params):118        now = datetime.now()119        fields = {120            "update_at": now,121            "create_at": now122        }123        for field in self.modelCls._fields.keys():124            if field not in fields_exclude:125                fields[field] = params.entity.get(field, '')126        entity = self.modelCls(**fields)127        entity.save()128        return {129            "entity": entity.to_dict()130        }131class UpdateAPI(BaseAPI):132    def parse_url_args(self, args, kwargs):133        self.modelCls = model_map[args[0]]['model']134        self.is_parameter = model_map[args[0]].get('is_parameter', False)135    def api(self, params):136        print(params)137        now = datetime.now()138        entity = None139        if params.get('id'):140            entity = self.modelCls.get_first(id=params.id, is_enable=True)141        elif params.get('key'):142            entity = self.modelCls.get_first(key=params.key, is_enable=True)143        else:144            self.fail(code=-1, message='id_or_key_required')145            return146        if not entity:147            self.fail(code=-1, message='entity_not_found')148            return149        150        for field in dict(params.entity).keys():151            if field not in fields_exclude:152                if field != 'options':153                    value = params.entity.get(field, '')154                else:155                    print('****')156                    print(value)157                    value = params.entity.get(field, [])158                    value = [ParameterOptionsField(**v.to_dict()) for v in value]159                setattr(entity, field, value)160        entity.update_at = now161        entity.save()162        return {163            "entity": entity.to_dict()164        }165class DeleteAPI(BaseAPI):166    def parse_url_args(self, args, kwargs):167        self.modelCls = model_map[args[0]]['model']168        self.is_parameter = model_map[args[0]].get('is_parameter', False)169    def api(self, params):170        now = datetime.now()171        entity = None172        if params.get('id'):173            entity = self.modelCls.get_first(id=params.id, is_enable=True)174        elif params.get('key'):175            entity = self.modelCls.get_first(key=params.key, is_enable=True)176        else:177            self.fail(code=-1, message='id_or_key_required')178            return179        entity.update_at = now180        entity.is_enable = False181        entity.save()182        return {183            "id": str(entity.id)...

gru.py

Source:gru.py

1import numpy as np2from mytorch import tensor3from mytorch.tensor import Tensor4from mytorch.nn.module import Module5from mytorch.nn.activations import Tanh, ReLU, Sigmoid6from mytorch.nn.util import PackedSequence, pack_sequence, unpack_sequence7from mytorch.nn.rnn import TimeIterator8class GRUUnit(Module):9    """10    This class defines a single GRU Unit block.11    12    NOTE: *args is placed just to ignore the nonlinearity parameter that it recevies from GRU module as GRUs have fixed set of activation functions that are called unlike RNNs. Given we will be using the same rnn.TimeIterator class to construct GRU we need to ignore the nonlinearity parameter. 13    Args:14        input_size (int): # features in each timestep15        hidden_size (int): # features generated by the GRUUnit at each timestep16    """17    def __init__(self, input_size, hidden_size, *args, **kwargs):18        super(GRUUnit, self).__init__()19        # Initializing parameters20        self.weight_ir = Tensor(21            np.random.randn(hidden_size, input_size),22            requires_grad=True,23            is_parameter=True,24        )25        self.bias_ir = Tensor(26            np.zeros(hidden_size), requires_grad=True, is_parameter=True27        )28        self.weight_iz = Tensor(29            np.random.randn(hidden_size, input_size),30            requires_grad=True,31            is_parameter=True,32        )33        self.bias_iz = Tensor(34            np.zeros(hidden_size), requires_grad=True, is_parameter=True35        )36        self.weight_in = Tensor(37            np.random.randn(hidden_size, input_size),38            requires_grad=True,39            is_parameter=True,40        )41        self.bias_in = Tensor(42            np.zeros(hidden_size), requires_grad=True, is_parameter=True43        )44        self.weight_hr = Tensor(45            np.random.randn(hidden_size, hidden_size),46            requires_grad=True,47            is_parameter=True,48        )49        self.bias_hr = Tensor(50            np.zeros(hidden_size), requires_grad=True, is_parameter=True51        )52        self.weight_hz = Tensor(53            np.random.randn(hidden_size, hidden_size),54            requires_grad=True,55            is_parameter=True,56        )57        self.bias_hz = Tensor(58            np.zeros(hidden_size), requires_grad=True, is_parameter=True59        )60        self.weight_hn = Tensor(61            np.random.randn(hidden_size, hidden_size),62            requires_grad=True,63            is_parameter=True,64        )65        self.bias_hn = Tensor(66            np.zeros(hidden_size), requires_grad=True, is_parameter=True67        )68        self.hidden_size = hidden_size69    def __call__(self, input, hidden=None):70        return self.forward(input, hidden)71    def forward(self, input: Tensor, hidden=None):72        """73        Args:74            input (Tensor): (effective_batch_size,input_size)75            hidden (Tensor,None): (effective_batch_size,hidden_size)76        Return:77            Tensor: (effective_batch_size,hidden_size)78        """79        # TODO: INSTRUCTIONS80        # Perform matrix operations to construct the intermediary value from input and hidden tensors81        # Remeber to handle the case when hidden = None. Construct a tensor of appropriate size, filled with 0s to use as the hidden.82        if not hidden:83            hidden_shape = (input.shape[0], self.hidden_size)84            hidden = Tensor(85                np.zeros(hidden_shape), requires_grad=True, is_parameter=True,86            )87        self.sigmoid = Sigmoid()88        self.tanh = Tanh()89        r_t = self.sigmoid(90            (input @ self.weight_ir.T())91            + self.bias_ir92            + (hidden @ self.weight_hr.T())93            + self.bias_hr94        )95        z_t = self.sigmoid(96            (input @ self.weight_iz.T())97            + self.bias_iz98            + (hidden @ self.weight_hz.T())99            + self.bias_hz100        )101        n_t = self.tanh(102            (input @ self.weight_in.T())103            + self.bias_in104            + (r_t * ((hidden @ self.weight_hn.T()) + self.bias_hn))105        )106        one = Tensor(1, requires_grad=True, is_parameter=True,)107        h_t = (one - z_t) * n_t + (z_t * hidden)108        return h_t109class GRU(TimeIterator):110    """111    Child class for TimeIterator which appropriately initializes the parent class to construct an GRU.112    Args:113        input_size (int): # features in each timestep114        hidden_size (int): # features generated by the GRUUnit at each timestep115    """116    def __init__(self, input_size, hidden_size):117        super(GRU, self).__init__(118            basic_unit=GRUUnit, input_size=input_size, hidden_size=hidden_size,...

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