Source code for mindquantum.parameterresolver.parameterresolver

# Copyright 2021 Huawei Technologies Co., Ltd
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"""Parameter resolver."""

from collections.abc import Iterable
from copy import deepcopy
import numpy as np


[docs]class ParameterResolver(dict): """ A ParameterRsolver can set the parameter of parameterized quantum gate or parameterized quantum circuit. By specific which part of parameters needs to calculate gradient, the PQC operator can only calculate gradient of these parameters. Args: data (dict): initial parameter names and its values. Examples: >>> pr = ParameterResolver({'a': 0.3}) >>> pr['b'] = 0.5 >>> pr.no_grad_part('a') >>> pr *= 2 >>> pr {'a': 0.6, 'b': 1.0} >>> pr.no_grad_parameters {'a'} """ def __init__(self, data=None): if data is None: data = {} if not isinstance(data, (dict, ParameterResolver)): raise TypeError( "Require a dict or a ParameterResolver, but get {}!".format( type(data))) for k, v in data.items(): if not isinstance(k, str): raise TypeError( "Parameter name should be a string, but get {}!".format( type(k))) if not isinstance(v, _num_type): raise TypeError( "Require a real number, but get {}, which is {}!".format( v, type(v))) super(ParameterResolver, self).__init__(data) self.no_grad_parameters = set() self.requires_grad_parameters = set(self.para_name) def __setitem__(self, keys, values): """ Set parameter or as list of parameters of this parameter resolver. By default, the parameter you set requires gradient. Args: keys (Union[str, list[str]]): The name of parameters. values (Union[number, list[number]]): The value of parameters. Raises: TypeError: If the key that you set is not a string or a iterable of string. """ if isinstance(keys, str): if not isinstance(values, _num_type): raise TypeError( "Parameter value should be a real number, but get {}, which is {}!" .format(values, type(values))) super().__setitem__(keys, values) self.requires_grad_parameters.add(keys) elif isinstance(keys, Iterable): assert isinstance(values, Iterable) assert len(keys) == len(values) for i, k in enumerate(keys): self.__setitem__(k, values[i]) else: raise TypeError( "Parameter name should be a string, but get {}!".format( type(keys))) def __imul__(self, num): """ Parameter support inplace multiply. Returns: :class:`mindquantum.parameterresolver.ParameterResolver` Args: num (number): Multiply factor. Examples: >>> pr = ParameterResolver({'a': 1, 'b': 2}) >>> pr *= 2 >>> pr {'a': 2, 'b': 4} """ no_grad_parameters = deepcopy(self.no_grad_parameters) requires_grad_parameters = deepcopy(self.requires_grad_parameters) for k in self.keys(): self[k] = self[k] * num self.no_grad_parameters = no_grad_parameters self.requires_grad_parameters = requires_grad_parameters return self def __mul__(self, num): """ Multiply num with every value of parameter resolver. Returns: :class:`mindquantum.parameterresolver.ParameterResolver` Args: num (number): Multiply factor. Examples: >>> pr1 = ParameterResolver({'a': 1, 'b': 2}) >>> pr2 = pr1 * 2 >>> pr2 {'a': 2, 'b': 4} """ no_grad_parameters = deepcopy(self.no_grad_parameters) requires_grad_parameters = deepcopy(self.requires_grad_parameters) out = deepcopy(self) out *= num out.no_grad_parameters = no_grad_parameters out.requires_grad_parameters = requires_grad_parameters return out def __rmul__(self, num): """ See :class:`mindquantum.parameterresolver.ParameterResolver.__mul__`. """ return self.__mul__(num) def __eq__(self, other): _check_pr_type(other) no_grad_eq = self.no_grad_parameters == other.no_grad_parameters requires_grad_eq = self.requires_grad_parameters == other.requires_grad_parameters return super().__eq__(other) and no_grad_eq and requires_grad_eq @property def para_name(self): """ Get the parameters name. Returns: list[str], Parameters name. Examples: >>> pr = ParameterResolver({'a': 1, 'b': 2}) >>> pr.para_name ['a', 'b'] """ return list(self.keys()) @property def para_value(self): """ Get the parameters value. Returns: list[float], Parameters value. Examples: >>> pr = ParameterResolver({'a': 1, 'b': 2}) >>> pr.para_value [1, 2] """ return list(self.values())
[docs] def requires_grad(self): """ Set all parameters of this parameter resolver to require gradient calculation. Returns: :class:`mindquantum.parameterresolver.ParameterResolver` Examples: >>> pr = ParameterResolver({'a': 1, 'b': 2}) >>> pr.no_grad_part('a') >>> pr.requires_grad() >>> pr.requires_grad_parameters {'a', 'b'} """ self.no_grad_parameters = set() self.requires_grad_parameters = set(self.para_name) return self
[docs] def no_grad(self): """ Set all parameters to not require gradient calculation. Returns: :class:`mindquantum.parameterresolver.ParameterResolver` Examples: >>> pr = ParameterResolver({'a': 1, 'b': 2}) >>> pr.no_grad() >>> pr.requires_grad_parameters set() """ self.no_grad_parameters = set(self.para_name) self.requires_grad_parameters = set() return self
[docs] def requires_grad_part(self, *names): """ Set part of parameters that requires grad. Args: names (tuple[str]): Parameters that requires grad. Returns: :class:`mindquantum.parameterresolver.ParameterResolver` Examples: >>> pr = ParameterResolver({'a': 1, 'b': 2}) >>> pr.no_grad() >>> pr.requires_grad_part('a') >>> pr.requires_grad_parameters {'a'} """ for name in names: if not isinstance(name, str): raise TypeError("name should be a string, but get {}!".format( type(name))) if name not in self: raise KeyError( "Parameter {} not in this parameter resolver!".format( name)) while name in self.no_grad_parameters: self.no_grad_parameters.remove(name) while name not in self.requires_grad_parameters: self.requires_grad_parameters.add(name) return self
[docs] def no_grad_part(self, *names): """ Set part of parameters that not requires grad. Args: names (tuple[str]): Parameters that not requires grad. Returns: :class:`mindquantum.parameterresolver.ParameterResolver` Examples: >>> pr = ParameterResolver({'a': 1, 'b': 2}) >>> pr.no_grad_part('a') >>> pr.requires_grad_parameters {'b'} """ for name in names: if not isinstance(name, str): raise TypeError("name should be a string, but get {}!".format( type(name))) if name not in self: raise KeyError( "Parameter {} not in this parameter resolver!".format( name)) while name not in self.no_grad_parameters: self.no_grad_parameters.add(name) while name in self.requires_grad_parameters: self.requires_grad_parameters.remove(name) return self
[docs] def update(self, others): """ Update this parameter resolver with other parameter resolver. Args: others (:class:`mindquantum.parameterresolver.ParameterResolver`): other parameter resolver. Raises: ValueError: If some parameters require grad and not require grad in other parameter resolver and vise versa. Examples: >>> pr1 = ParameterResolver({'a': 1}) >>> pr2 = ParameterResolver({'b': 2}) >>> pr2.no_grad() >>> pr1.update(pr2) >>> pr1 {'a': 1, 'b': 2} >>> pr1.no_grad_parameters {'b'} """ _check_pr_type(others) super().update(others) conflict = (self.no_grad_parameters & others.requires_grad_parameters ) | (others.no_grad_parameters & self.requires_grad_parameters) if conflict: raise ValueError( "Parameter conflict, {} require grad in some parameter \ resolver and not require grad in other parameter resolver ".format(conflict)) self.no_grad_parameters.update(others.no_grad_parameters) self.requires_grad_parameters.update(others.requires_grad_parameters)
[docs] def mindspore_data(self): """ Generate data for PQC operator. Returns: Dict. """ m_data = { 'gate_params_names': [], 'gate_coeff': [], 'gate_requires_grad': [] } for k, v in self.items(): m_data['gate_params_names'].append(k) m_data['gate_coeff'].append(float(v)) m_data['gate_requires_grad'].append( k in self.requires_grad_parameters) return m_data
def _check_pr_type(pr): if not isinstance(pr, ParameterResolver): raise TypeError("Require a ParameterResolver, but get {}".format( type(pr))) _num_type = (int, float, np.int32, np.int64, np.float32, np.float64)