Source code for quantify_scheduler.backends.qblox.compiler_abc

# Repository: https://gitlab.com/quantify-os/quantify-scheduler
# Licensed according to the LICENCE file on the main branch
"""Compiler base and utility classes for Qblox backend."""

from __future__ import annotations

import json
import logging
import warnings
from abc import ABC, abstractmethod
from collections import defaultdict
from enum import Enum, auto
from os import makedirs, path
from typing import (
    TYPE_CHECKING,
    Any,
    Callable,
    Generic,
    Hashable,
    Iterator,
    Protocol,
    TypeVar,
)

from pathvalidate import sanitize_filename
from qcodes.utils.json_utils import NumpyJSONEncoder

from quantify_core.data.handling import gen_tuid, get_datadir
from quantify_scheduler.backends.qblox import (
    constants,
    driver_version_check,
    helpers,
    q1asm_instructions,
    register_manager,
)
from quantify_scheduler.backends.qblox.exceptions import NcoOperationTimingError
from quantify_scheduler.backends.qblox.operation_handling.acquisitions import (
    AcquisitionStrategyPartial,
)
from quantify_scheduler.backends.qblox.operation_handling.base import IOperationStrategy
from quantify_scheduler.backends.qblox.operation_handling.pulses import (
    DigitalOutputStrategy,
)
from quantify_scheduler.backends.qblox.operation_handling.virtual import (
    ConditionalStrategy,
    ControlFlowReturnStrategy,
    LoopStrategy,
    UpdateParameterStrategy,
)
from quantify_scheduler.backends.qblox.qasm_program import QASMProgram
from quantify_scheduler.backends.types.qblox import (
    OpInfo,
    SequencerSettings,
    StaticHardwareProperties,
)
from quantify_scheduler.enums import BinMode
from quantify_scheduler.helpers.schedule import (
    extract_acquisition_metadata_from_acquisition_protocols,
)

if TYPE_CHECKING:
    from quantify_scheduler.schedules.schedule import AcquisitionMetadata

[docs] logger = logging.getLogger(__name__)
logger.setLevel(logging.WARNING)
[docs] class InstrumentCompiler(ABC): """ Abstract base class that defines a generic instrument compiler. The subclasses that inherit from this are meant to implement the compilation steps needed to compile the lists of :class:`~quantify_scheduler.backends.types.qblox.OpInfo` representing the pulse and acquisition information to device-specific instructions. Each device that needs to be part of the compilation process requires an associated ``InstrumentCompiler``. Parameters ---------- parent: :class:`~quantify_scheduler.backends.qblox.compiler_container.CompilerContainer` Reference to the parent object. name Name of the `QCoDeS` instrument this compiler object corresponds to. total_play_time Total time execution of the schedule should go on for. This parameter is used to ensure that the different devices, potentially with different clock rates, can work in a synchronized way when performing multiple executions of the schedule. instrument_cfg The part of the hardware configuration dictionary referring to this device. This is one of the inner dictionaries of the overall hardware config. latency_corrections Dict containing the delays for each port-clock combination. This is specified in the top layer of hardware config. """ def __init__( self, parent, # No type hint due to circular import, added to docstring name: str, total_play_time: float, instrument_cfg: dict[str, Any], latency_corrections: dict[str, float] | None = None, ) -> None: self.parent = parent self.name = name self.total_play_time = total_play_time self.instrument_cfg = instrument_cfg self.latency_corrections = latency_corrections or {}
[docs] def prepare(self) -> None: """ Method that can be overridden to implement logic before the main compilation starts. This step is to extract all settings for the devices that are dependent on settings of other devices. This step happens after instantiation of the compiler object but before the start of the main compilation. """
@abstractmethod
[docs] def compile(self, debug_mode: bool, repetitions: int) -> Any: """ An abstract method that should be overridden in a subclass to implement the actual compilation. It should turn the pulses and acquisitions added to the device into device-specific instructions. Parameters ---------- debug_mode Debug mode can modify the compilation process, so that debugging of the compilation process is easier. repetitions Number of times execution of the schedule is repeated. Returns ------- : A data structure representing the compiled program. The type is dependent on implementation. """
[docs] class SequencerCompiler(ABC): """ Class that performs the compilation steps on the sequencer level. Abstract base class for different sequencer types. Parameters ---------- parent A reference to the module compiler this sequencer belongs to. index Index of the sequencer. portclock Tuple that specifies the unique port and clock combination for this sequencer. The first value is the port, second is the clock. static_hw_properties The static properties of the hardware. This effectively gathers all the differences between the different modules. settings The settings set to this sequencer. latency_corrections Dict containing the delays for each port-clock combination. qasm_hook_func Allows the user to inject custom Q1ASM code into the compilation, just prior to returning the final string. """ def __init__( self, parent: ClusterModuleCompiler, index: int, portclock: tuple[str, str], static_hw_properties: StaticHardwareProperties, settings: SequencerSettings, latency_corrections: dict[str, float], qasm_hook_func: Callable | None = None, ) -> None: self.parent = parent self.index = index self.port = portclock[0] self.clock = portclock[1] self.op_strategies: list[IOperationStrategy] = [] self._num_acquisitions = 0 self.static_hw_properties = static_hw_properties self.register_manager = register_manager.RegisterManager() self._settings = settings
[docs] self.qasm_hook_func = qasm_hook_func
"""Allows the user to inject custom Q1ASM code into the compilation, just prior to returning the final string.""" portclock_key = f"{self.port}-{self.clock}"
[docs] self.latency_correction: float = latency_corrections.get(portclock_key, 0)
"""Latency correction accounted for by delaying the start of the program.""" @property
[docs] def connected_output_indices(self) -> tuple[int, ...]: """ Return the connected output indices associated with the output name specified in the hardware config. For the baseband modules, output index 'n' corresponds to physical module output 'n+1'. For RF modules, output indices '0' and '1' (or: '2' and '3') correspond to 'path_I' and 'path_Q' of some sequencer, and both these paths are routed to the **same** physical module output '1' (or: '2'). """ return self._settings.connected_output_indices
@property
[docs] def connected_input_indices(self) -> tuple[int, ...]: """ Return the connected input indices associated with the input name specified in the hardware config. For the baseband modules, input index 'n' corresponds to physical module input 'n+1'. For RF modules, input indices '0' and '1' correspond to 'path_I' and 'path_Q' of some sequencer, and both paths are connected to physical module input '1'. """ return self._settings.connected_input_indices
@property
[docs] def portclock(self) -> tuple[str, str]: """ A tuple containing the unique port and clock combination for this sequencer. Returns ------- : The portclock. """ return self.port, self.clock
@property
[docs] def settings(self) -> SequencerSettings: """ Gives the current settings. Returns ------- : The settings set to this sequencer. """ return self._settings
@property
[docs] def name(self) -> str: """ The name assigned to this specific sequencer. Returns ------- : The name. """ return f"seq{self.index}"
@property
[docs] def has_data(self) -> bool: """ Whether or not the sequencer has any data (meaning pulses or acquisitions) assigned to it or not. Returns ------- : Has data been assigned to this sequencer? """ return len(self.op_strategies) > 0
@abstractmethod
[docs] def get_operation_strategy( self, operation_info: OpInfo, ) -> IOperationStrategy: """ Determines and instantiates the correct strategy object. Parameters ---------- operation_info The operation we are building the strategy for. Returns ------- : The instantiated strategy object. """
[docs] def add_operation_strategy(self, op_strategy: IOperationStrategy) -> None: """ Adds the operation strategy to the sequencer compiler. Parameters ---------- op_strategy The operation strategy. """ self.op_strategies.append(op_strategy) if op_strategy.operation_info.is_parameter_instruction: update_parameters_strategy = UpdateParameterStrategy( OpInfo( name="UpdateParameters", data={ "t0": 0, "port": self.port, "clock": self.clock, "duration": 0, "instruction": q1asm_instructions.UPDATE_PARAMETERS, }, timing=op_strategy.operation_info.timing, ) ) self.op_strategies.append(update_parameters_strategy)
[docs] def _generate_awg_dict(self) -> dict[str, Any]: """ Generates the dictionary that contains the awg waveforms in the format accepted by the driver. Notes ----- The final dictionary to be included in the json that is uploaded to the module is of the form: .. code-block:: program awg waveform_name data index acq waveform_name data index This function generates the awg dictionary. Returns ------- : The awg dictionary. Raises ------ ValueError I or Q amplitude is being set outside of maximum range. RuntimeError When the total waveform size specified for a port-clock combination exceeds the waveform sample limit of the hardware. """ wf_dict: dict[str, Any] = {} for op_strategy in self.op_strategies: if not op_strategy.operation_info.is_acquisition: op_strategy.generate_data(wf_dict=wf_dict) self._validate_awg_dict(wf_dict=wf_dict) return wf_dict
[docs] def _generate_weights_dict(self) -> dict[str, Any]: """ Generates the dictionary that corresponds that contains the acq weights waveforms in the format accepted by the driver. Notes ----- The final dictionary to be included in the json that is uploaded to the module is of the form: .. code-block:: program awg waveform_name data index acq waveform_name data index This function generates the acq dictionary. Returns ------- : The acq dictionary. Raises ------ NotImplementedError Currently, only two one dimensional waveforms can be used as acquisition weights. This exception is raised when either or both waveforms contain both a real and imaginary part. """ wf_dict: dict[str, Any] = {} for op_strategy in self.op_strategies: if op_strategy.operation_info.is_acquisition: op_strategy.generate_data(wf_dict) return wf_dict
[docs] def _validate_awg_dict(self, wf_dict: dict[str, Any]) -> None: total_size = 0 for waveform in wf_dict.values(): total_size += len(waveform["data"]) if total_size > constants.MAX_SAMPLE_SIZE_WAVEFORMS: raise RuntimeError( f"Total waveform size specified for port-clock {self.port}-" f"{self.clock} is {total_size} samples, which exceeds the sample " f"limit of {constants.MAX_SAMPLE_SIZE_WAVEFORMS}. The compiled " f"schedule cannot be uploaded to the sequencer.", )
@abstractmethod
[docs] def _prepare_acq_settings( self, acquisitions: list[IOperationStrategy], acq_metadata: AcquisitionMetadata, ) -> None: """ Sets sequencer settings that are specific to certain acquisitions. For example for a TTL acquisition strategy. Parameters ---------- acquisitions List of the acquisitions assigned to this sequencer. acq_metadata Acquisition metadata. """
[docs] def _generate_acq_declaration_dict( self, repetitions: int, acq_metadata: AcquisitionMetadata, ) -> dict[str, Any]: """ Generates the "acquisitions" entry of the program json. It contains declaration of the acquisitions along with the number of bins and the corresponding index. For the name of the acquisition (in the hardware), the acquisition channel (cast to str) is used, and is thus identical to the index. Number of bins is taken to be the highest acq_index specified for that channel. Parameters ---------- repetitions The number of times to repeat execution of the schedule. acq_metadata Acquisition metadata. Returns ------- : The "acquisitions" entry of the program json as a dict. The keys correspond to the names of the acquisitions (i.e. the acq_channel in the scheduler). """ # initialize an empty dictionary for the format required by module acq_declaration_dict = {} for ( qblox_acq_index, acq_channel_metadata, ) in acq_metadata.acq_channels_metadata.items(): acq_indices: list[int] = acq_channel_metadata.acq_indices acq_channel: Hashable = acq_channel_metadata.acq_channel # Some sanity checks on the input for easier debugging. if min(acq_indices) != 0: raise ValueError( f"Please make sure the lowest acquisition index used is 0. " f"Found: {min(acq_indices)} as lowest index for channel " f"{acq_channel}. Problem occurred for port {self.port} with" f" clock {self.clock}, which corresponds to {self.name} of " f"{self.parent.name}." ) if len(acq_indices) != max(acq_indices) + 1: raise ValueError( f"Found {max(acq_indices)} as the highest index out of " f"{len(acq_indices)} for channel {acq_channel}, indicating " f"an acquisition index was skipped or an acquisition index was repeated. " f"Please make sure the used indices increment by 1 starting from 0. " f"Problem occurred for port {self.port} with clock {self.clock}, " f"which corresponds to {self.name} of {self.parent.name}." ) unique_acq_indices = len(set(acq_indices)) if len(acq_indices) != unique_acq_indices: raise ValueError( f"Found {unique_acq_indices} unique indices out of " f"{len(acq_indices)} for channel {acq_channel}, indicating " f"an acquisition index was skipped or an acquisition index was repeated. " f"Please make sure the used indices increment by 1 starting from 0. " f"Problem occurred for port {self.port} with clock {self.clock}, " f"which corresponds to {self.name} of {self.parent.name}." ) # Add the acquisition metadata to the acquisition declaration dict if acq_metadata.bin_mode == BinMode.APPEND: num_bins = repetitions * self._num_acquisitions elif acq_metadata.bin_mode == BinMode.AVERAGE: if acq_metadata.acq_protocol == "TriggerCount": num_bins = constants.MAX_NUMBER_OF_BINS else: num_bins = max(acq_indices) + 1 else: # currently the BinMode enum only has average and append. # this check exists to catch unexpected errors if we add more # BinModes in the future. raise NotImplementedError(f"Unknown bin mode {acq_metadata.bin_mode}.") acq_declaration_dict[str(qblox_acq_index)] = { "num_bins": num_bins, "index": qblox_acq_index, } return acq_declaration_dict
[docs] def generate_qasm_program( self, ordered_op_strategies: list[IOperationStrategy], total_sequence_time: float, align_qasm_fields: bool, acq_metadata: AcquisitionMetadata | None, repetitions: int, ) -> str: """ Generates a QASM program for a sequencer. Requires the awg and acq dicts to already have been generated. Example of a program generated by this function: .. code-block:: wait_sync 4 set_mrk 1 move 10,R0 # iterator for loop with label start start: wait 4 set_awg_gain 22663,10206 # setting gain for 9056793381316377208 play 0,1,4 wait 176 loop R0,@start set_mrk 0 upd_param 4 stop Parameters ---------- ordered_op_strategies A sorted list of operations, in order of execution. total_sequence_time Total time the program needs to play for. If the sequencer would be done before this time, a wait is added at the end to ensure synchronization. align_qasm_fields If True, make QASM program more human-readable by aligning its fields. acq_metadata Acquisition metadata. repetitions Number of times to repeat execution of the schedule. Returns ------- : The generated QASM program. Warns ----- RuntimeWarning When number of instructions in the generated QASM program exceeds the maximum supported number of instructions for sequencers in the type of module. Raises ------ RuntimeError Upon ``total_sequence_time`` exceeding :attr:`.QASMProgram.elapsed_time`. """ loop_label = "start" qasm = QASMProgram( static_hw_properties=self.static_hw_properties, register_manager=self.register_manager, align_fields=align_qasm_fields, acq_metadata=acq_metadata, ) self._write_pre_wait_sync_instructions(qasm) # program header qasm.set_latch(self.op_strategies) qasm.emit(q1asm_instructions.WAIT_SYNC, constants.MIN_TIME_BETWEEN_OPERATIONS) qasm.emit( q1asm_instructions.UPDATE_PARAMETERS, constants.MIN_TIME_BETWEEN_OPERATIONS ) self._initialize_append_mode_registers(qasm, ordered_op_strategies) # Program body. The operations must be ordered such that real-time IO operations # always come after any other operations. E.g., an offset instruction should # always come before the parameter update, play, or acquisition instruction. # Adds the latency correction, this needs to be a minimum of 4 ns, # so all sequencers get delayed by at least that. latency_correction_ns: int = self._get_latency_correction_ns( self.latency_correction ) qasm.auto_wait( wait_time=constants.MIN_TIME_BETWEEN_OPERATIONS + latency_correction_ns, count_as_elapsed_time=False, comment=f"latency correction of {constants.MIN_TIME_BETWEEN_OPERATIONS} + " f"{latency_correction_ns} ns", ) with qasm.loop(label=loop_label, repetitions=repetitions): self._write_repetition_loop_header(qasm) last_operation_end = {True: 0.0, False: 0.0} for operation in ordered_op_strategies: # Check if there is an overlapping pulse or overlapping acquisition if operation.operation_info.is_real_time_io_operation: start_time = operation.operation_info.timing is_acquisition = operation.operation_info.is_acquisition if helpers.to_grid_time(start_time) < helpers.to_grid_time( last_operation_end[is_acquisition] ): warnings.warn( f"Operation is interrupting previous" f" {'Acquisition' if is_acquisition else 'Pulse'}" f" because it starts before the previous ends," f" offending operation:" f" {str(operation.operation_info)}", RuntimeWarning, ) last_operation_end[is_acquisition] = ( start_time + operation.operation_info.duration ) self._parse_operations(iter(ordered_op_strategies), qasm, 1) end_time = helpers.to_grid_time(total_sequence_time) wait_time = end_time - qasm.elapsed_time if wait_time < 0: raise RuntimeError( f"Invalid timing detected, attempting to insert wait " f"of {wait_time} ns. The total duration of the " f"schedule is {end_time} but {qasm.elapsed_time} ns " f"already processed." ) qasm.auto_wait(wait_time=wait_time) # program footer qasm.emit(q1asm_instructions.STOP) # If we loop, and the duration of the loop is not on the 4 ns grid, NCO # operations may be misaligned with the grid. So we check for this. if repetitions > 1: try: helpers.to_grid_time(total_sequence_time, constants.NCO_TIME_GRID) except ValueError as e: raise NcoOperationTimingError( f"The schedule is repeated with a duration of {end_time} ns per " "iteration, which does not align with the grid time of " f"{constants.NCO_TIME_GRID} ns for NCO operations. The duration " "must adhere to this grid time to ensure proper alignment of NCO " "operations for each iteration." ) from e if self.qasm_hook_func: self.qasm_hook_func(qasm) if ( num_instructions := len(qasm.instructions) ) > self.parent.max_number_of_instructions: warnings.warn( f"Number of instructions ({num_instructions}) compiled for " f"'{self.name}' of {self.parent.__class__.__name__} " f"'{self.parent.name}' exceeds the maximum supported number of " f"instructions in Q1ASM programs for {self.parent.__class__.__name__} " f"({self.parent.max_number_of_instructions}).", RuntimeWarning, ) return str(qasm)
[docs] class ParseOperationStatus(Enum): """Return status of the stack."""
[docs] COMPLETED_ITERATION = auto()
"""The iterator containing operations is exhausted."""
[docs] EXITED_CONTROL_FLOW = auto()
"""The end of a control flow scope is reached."""
[docs] def _parse_operations( self, operations_iter: Iterator[IOperationStrategy], qasm: QASMProgram, acquisition_multiplier: int, ) -> ParseOperationStatus: """Handle control flow and insert Q1ASM.""" while (operation := next(operations_iter, None)) is not None: qasm.wait_till_start_operation(operation.operation_info) if isinstance(operation, LoopStrategy): loop_label = f"loop{len(qasm.instructions)}" repetitions = operation.operation_info.data["repetitions"] with qasm.loop(label=loop_label, repetitions=repetitions): returned_from_return_stack = self._parse_operations( operations_iter=operations_iter, qasm=qasm, acquisition_multiplier=acquisition_multiplier * repetitions, ) assert returned_from_return_stack in self.ParseOperationStatus elif isinstance(operation, ConditionalStrategy): with qasm.conditional(operation): returned_from_return_stack = self._parse_operations( operations_iter=operations_iter, qasm=qasm, acquisition_multiplier=acquisition_multiplier, ) assert returned_from_return_stack in self.ParseOperationStatus elif isinstance(operation, ControlFlowReturnStrategy): return self.ParseOperationStatus.EXITED_CONTROL_FLOW else: if operation.operation_info.is_acquisition: self._num_acquisitions += acquisition_multiplier qasm.conditional_manager.update(operation) self._insert_qasm(operation, qasm) return self.ParseOperationStatus.EXITED_CONTROL_FLOW
@abstractmethod
[docs] def _insert_qasm( self, op_strategy: IOperationStrategy, qasm_program: QASMProgram ) -> None: """Get Q1ASM instruction(s) from ``op_strategy`` and insert them into ``qasm_program``."""
@abstractmethod
[docs] def _write_pre_wait_sync_instructions(self, qasm: QASMProgram) -> None: """ Write instructions to the QASM program that must come before the first wait_sync. The duration must be equal for all module types. """
@abstractmethod
[docs] def _write_repetition_loop_header(self, qasm: QASMProgram) -> None: """ Write the Q1ASM that should appear at the start of the repetition loop. The duration must be equal for all module types. """
[docs] def _get_ordered_operations(self) -> list[IOperationStrategy]: """Get the class' operation strategies in order of scheduled execution.""" return sorted( self.op_strategies, key=lambda op: helpers.to_grid_time(op.operation_info.timing), )
[docs] def _initialize_append_mode_registers( self, qasm: QASMProgram, op_strategies: list[IOperationStrategy] ) -> None: """ Adds the instructions to initialize the registers needed to use the append bin mode to the program. This should be added in the header. Parameters ---------- qasm: The program to add the instructions to. op_strategies: An operations list including all the acquisitions to consider. """ channel_to_reg: dict[str, str] = {} for op_strategy in op_strategies: if not op_strategy.operation_info.is_acquisition: continue # Help the type checker. assert isinstance(op_strategy, AcquisitionStrategyPartial) if op_strategy.operation_info.data["bin_mode"] != BinMode.APPEND: continue channel = op_strategy.operation_info.data["acq_channel"] if channel in channel_to_reg: acq_bin_idx_reg = channel_to_reg[channel] else: acq_bin_idx_reg = self.register_manager.allocate_register() channel_to_reg[channel] = acq_bin_idx_reg qasm.emit( q1asm_instructions.MOVE, 0, acq_bin_idx_reg, comment=f"Initialize acquisition bin_idx for " f"ch{op_strategy.operation_info.data['acq_channel']}", ) op_strategy.bin_idx_register = acq_bin_idx_reg
[docs] def _get_latency_correction_ns(self, latency_correction: float) -> int: if latency_correction == 0: return 0 latency_correction_ns = int(round(latency_correction * 1e9)) return latency_correction_ns
[docs] def _remove_redundant_update_parameters(self) -> None: """ Removing redundant update parameter instructions. If multiple update parameter instructions happen at the same time, directly after each other in order, then it's safe to only keep one of them. Also, real time io operations act as update parameter instructions too. If a real time io operation happen ((just after or just before) and at the same time) as an update parameter instruction, then the update parameter instruction is redundant. """ def _removal_pass(is_reversed: bool) -> None: indices_to_be_removed: set[int] = set() last_updated_timing: int | None = None # Cannot use self._get_ordered_operations here because of the `enumerate`. sorted_op_strategies = sorted( enumerate(self.op_strategies), key=lambda op: helpers.to_grid_time(op[1].operation_info.timing), ) if is_reversed: sorted_op_strategies = reversed(sorted_op_strategies) for index, op_strategy in sorted_op_strategies: op_timing = helpers.to_grid_time(op_strategy.operation_info.timing) if ( op_strategy.operation_info.is_parameter_update or op_strategy.operation_info.is_real_time_io_operation ): if ( op_strategy.operation_info.is_parameter_update and last_updated_timing is not None and last_updated_timing == op_timing ): indices_to_be_removed.add(index) else: last_updated_timing = op_timing elif ( ( not is_reversed and op_strategy.operation_info.is_parameter_instruction ) or (is_reversed and isinstance(op_strategy, ConditionalStrategy)) or isinstance( op_strategy, (LoopStrategy, ControlFlowReturnStrategy) ) ): # If a parameter instruction happens while we're iterating through the operations not in reverse, # that invalidates all the other update parameters (and real time io instructions) that were before it, # because that potentially means the parameter is not updated. # # For conditionals and loops we cannot eliminate the update parameter just before them, # because these control flows might not even run their bodies (for loops if repetition is 0). # # For loops, we cannot eliminate the first update parameter in the body, # because we directly can jump there from the end of the body, # not necessarily from the instruction just before the loop. last_updated_timing = None self.op_strategies = [ op for i, op in enumerate(self.op_strategies) if i not in indices_to_be_removed ] # We can remove all redundant update parameters which # happen at the same time and after each other, # and remove all update parameters which happen **after** a real time io operation, # if no parameter instruction is between them. _removal_pass(is_reversed=False) # We can remove all update parameters which # happen **before** a real time io operation. _removal_pass(is_reversed=True)
[docs] def _validate_update_parameters_alignment(self) -> None: last_upd_params_incompatible_op_info: OpInfo | None = None total_play_time = helpers.to_grid_time(self.parent.total_play_time) sorted_op_strategies = self._get_ordered_operations() for op_strategy in reversed(sorted_op_strategies): op_timing = helpers.to_grid_time(op_strategy.operation_info.timing) if op_strategy.operation_info.is_parameter_update: if total_play_time == op_timing: raise RuntimeError( f"Parameter operation {op_strategy.operation_info} with start time " f"{op_strategy.operation_info.timing} cannot be scheduled at the very end " "of a Schedule. The Schedule can be extended by adding an " "IdlePulse operation with a duration of at least " f"{constants.MIN_TIME_BETWEEN_OPERATIONS} ns, or the Parameter operation can be " "replaced by another operation." ) elif ( last_upd_params_incompatible_op_info is not None and helpers.to_grid_time( last_upd_params_incompatible_op_info.timing ) == op_timing ): raise RuntimeError( f"Parameter operation {op_strategy.operation_info} with start time " f"{op_strategy.operation_info.timing} cannot be scheduled exactly before " f"the operation {last_upd_params_incompatible_op_info} with the same start time. " "Insert an IdlePulse operation with a duration of at least " f"{constants.MIN_TIME_BETWEEN_OPERATIONS} ns, or the Parameter operation can be " "replaced by another operation." ) elif op_strategy.operation_info.is_control_flow_end or isinstance( op_strategy, (LoopStrategy, ConditionalStrategy) ): last_upd_params_incompatible_op_info = op_strategy.operation_info
@staticmethod
[docs] def _replace_digital_pulses( op_strategies: list[IOperationStrategy], ) -> list[IOperationStrategy]: """Replaces MarkerPulse operations by explicit high and low operations.""" new_op_strategies: list[IOperationStrategy] = [] for op_strategy in op_strategies: if isinstance(op_strategy, DigitalOutputStrategy): high_op_info = OpInfo( name=op_strategy.operation_info.name, data=op_strategy.operation_info.data.copy(), timing=op_strategy.operation_info.timing, ) duration = op_strategy.operation_info.data["duration"] high_op_info.data["enable"] = True high_op_info.data["duration"] = 0 new_op_strategies.append( op_strategy.__class__( operation_info=high_op_info, channel_name=op_strategy.channel_name, ) ) new_op_strategies.append( UpdateParameterStrategy( OpInfo( name="UpdateParameters", data={ "t0": 0, "port": high_op_info.data["port"], "clock": high_op_info.data["clock"], "duration": 0, "instruction": q1asm_instructions.UPDATE_PARAMETERS, }, timing=high_op_info.timing, ) ) ) low_op_info = OpInfo( name=op_strategy.operation_info.name, data=op_strategy.operation_info.data.copy(), timing=op_strategy.operation_info.timing + duration, ) low_op_info.data["enable"] = False low_op_info.data["duration"] = 0 new_op_strategies.append( op_strategy.__class__( operation_info=low_op_info, channel_name=op_strategy.channel_name, ) ) new_op_strategies.append( UpdateParameterStrategy( OpInfo( name="UpdateParameters", data={ "t0": 0, "port": low_op_info.data["port"], "clock": low_op_info.data["clock"], "duration": 0, "instruction": q1asm_instructions.UPDATE_PARAMETERS, }, timing=low_op_info.timing, ) ) ) else: new_op_strategies.append(op_strategy) return new_op_strategies
@staticmethod
[docs] def _generate_waveforms_and_program_dict( program: str, waveforms_dict: dict[str, Any], weights_dict: dict[str, Any] | None = None, acq_decl_dict: dict[str, Any] | None = None, ) -> dict[str, Any]: """ Generates the full waveforms and program dict that is to be uploaded to the sequencer from the program string and the awg and acq dicts, by combining them and assigning the appropriate keys. Parameters ---------- program The compiled QASM program as a string. waveforms_dict The dictionary containing all the awg data and indices. This is expected to be of the form generated by the ``generate_awg_dict`` method. weights_dict The dictionary containing all the acq data and indices. This is expected to be of the form generated by the ``generate_acq_dict`` method. acq_decl_dict The dictionary containing all the acq declarations. This is expected to be of the form generated by the ``generate_acq_decl_dict`` method. Returns ------- : The combined program. """ compiled_dict: dict[str, Any] = {} compiled_dict["program"] = program compiled_dict["waveforms"] = waveforms_dict if weights_dict is not None: compiled_dict["weights"] = weights_dict if acq_decl_dict is not None: compiled_dict["acquisitions"] = acq_decl_dict return compiled_dict
@staticmethod
[docs] def _dump_waveforms_and_program_json( wf_and_pr_dict: dict[str, Any], label: str | None = None ) -> str: """ Takes a combined waveforms and program dict and dumps it as a json file. Parameters ---------- wf_and_pr_dict The dict to dump as a json file. label A label that is appended to the filename. Returns ------- : The full absolute path where the json file is stored. """ data_dir = get_datadir() folder = path.join(data_dir, "schedules") makedirs(folder, exist_ok=True) filename = ( f"{gen_tuid()}.json" if label is None else f"{gen_tuid()}_{label}.json" ) filename = sanitize_filename(filename) file_path = path.join(folder, filename) with open(file_path, "w") as file: json.dump(wf_and_pr_dict, file, cls=NumpyJSONEncoder, indent=4) return file_path
[docs] def prepare(self) -> None: """ Perform necessary operations on this sequencer's data before :meth:`~SequencerCompiler.compile` is called. """ self.op_strategies = self._replace_digital_pulses(self.op_strategies) self._remove_redundant_update_parameters() self._validate_update_parameters_alignment()
[docs] def compile( self, sequence_to_file: bool, align_qasm_fields: bool, repetitions: int = 1, ) -> tuple[dict[str, Any] | None, AcquisitionMetadata | None]: """ Performs the full sequencer level compilation based on the assigned data and settings. If no data is assigned to this sequencer, the compilation is skipped and None is returned instead. Parameters ---------- sequence_to_file Dump waveforms and program dict to JSON file, filename stored in `SequencerCompiler.settings.seq_fn`. align_qasm_fields If True, make QASM program more human-readable by aligning its fields. repetitions Number of times execution the schedule is repeated. Returns ------- : The compiled program and the acquisition metadata. If no data is assigned to this sequencer, the compilation is skipped and None is returned instead. """ if not self.has_data: return None, None awg_dict = self._generate_awg_dict() weights_dict = None acq_declaration_dict = None acq_metadata: AcquisitionMetadata | None = None # the program needs _generate_weights_dict for the waveform indices if self.parent.supports_acquisition: weights_dict = {} acquisitions = [ op_strategy for op_strategy in self.op_strategies if op_strategy.operation_info.is_acquisition ] if len(acquisitions) > 0: acq_metadata = extract_acquisition_metadata_from_acquisition_protocols( acquisition_protocols=[ acq.operation_info.data for acq in acquisitions ], repetitions=repetitions, ) self._prepare_acq_settings( acquisitions=acquisitions, acq_metadata=acq_metadata, ) weights_dict = self._generate_weights_dict() # acq_declaration_dict needs to count number of acquires in the program operation_list = self._get_ordered_operations() qasm_program = self.generate_qasm_program( ordered_op_strategies=operation_list, total_sequence_time=self.parent.total_play_time, align_qasm_fields=align_qasm_fields, acq_metadata=acq_metadata, repetitions=repetitions, ) if self.parent.supports_acquisition: acq_declaration_dict = {} if acq_metadata is not None: acq_declaration_dict = self._generate_acq_declaration_dict( repetitions=repetitions, acq_metadata=acq_metadata, ) wf_and_prog = self._generate_waveforms_and_program_dict( qasm_program, awg_dict, weights_dict, acq_declaration_dict ) self._settings.sequence = wf_and_prog self._settings.seq_fn = None if sequence_to_file: self._settings.seq_fn = self._dump_waveforms_and_program_json( wf_and_pr_dict=wf_and_prog, label=f"{self.port}_{self.clock}" ) sequencer_cfg = self._settings.to_dict() return sequencer_cfg, acq_metadata
[docs] _SequencerT_co = TypeVar("_SequencerT_co", bound=SequencerCompiler, covariant=True)
""" A generic SequencerCompiler type for typehints in :class:`ClusterModuleCompiler`. Covariant so that subclasses of ClusterModuleCompiler can use subclassses of :class:`SequencerCompiler` in their typehints. """
[docs] class _ModuleSettingsType(Protocol): """ A typehint for the various module settings (e.g. :class:`~quantify_scheduler.backends.types.qblox.BasebandModuleSettings`) classes. """
[docs] def to_dict(self) -> dict[str, Any]: """Convert the settings to a dictionary.""" ...
[docs] class ClusterModuleCompiler(InstrumentCompiler, Generic[_SequencerT_co], ABC): """ Base class for all cluster modules, and an interface for those modules to the :class:`~quantify_scheduler.backends.qblox.instrument_compilers.ClusterCompiler`. This class is defined as an abstract base class since the distinctions between the different devices are defined in subclasses. Effectively, this base class contains the functionality shared by all Qblox devices and serves to avoid repeated code between them. Parameters ---------- parent: :class:`~quantify_scheduler.backends.qblox.compiler_container.CompilerContainer` Reference to the parent object. name Name of the `QCoDeS` instrument this compiler object corresponds to. total_play_time Total time execution of the schedule should go on for. This parameter is used to ensure that the different devices, potentially with different clock rates, can work in a synchronized way when performing multiple executions of the schedule. instrument_cfg The part of the hardware configuration dictionary referring to this device. This is one of the inner dictionaries of the overall hardware config. latency_corrections Dict containing the delays for each port-clock combination. This is specified in the top layer of hardware config. distortion_corrections Dict containing the distortion corrections for each output. """
[docs] _settings: _ModuleSettingsType
def __init__( self, parent, # No type hint due to circular import, added to docstring name: str, total_play_time: float, instrument_cfg: dict[str, Any], latency_corrections: dict[str, float] | None = None, distortion_corrections: dict[int, Any] | None = None, ) -> None: driver_version_check.verify_qblox_instruments_version() super().__init__( parent=parent, name=name, total_play_time=total_play_time, instrument_cfg=instrument_cfg, latency_corrections=latency_corrections, ) self.distortion_corrections = distortion_corrections or {} self._op_infos: dict[tuple[str, str], list[OpInfo]] = defaultdict(list) self.sequencers: dict[str, _SequencerT_co] = {} @property
[docs] def portclocks(self) -> list[tuple[str, str]]: """Returns all the port-clock combinations that this device can target.""" portclocks = [] for channel_name in helpers.find_channel_names(self.instrument_cfg): portclock_configs = self.instrument_cfg[channel_name].get( "portclock_configs", [] ) if not portclock_configs: raise KeyError( f"No 'portclock_configs' entry found in '{channel_name}' of {self.name}." ) portclocks += [ (target["port"], target["clock"]) for target in portclock_configs ] return portclocks
@property @abstractmethod
[docs] def supports_acquisition(self) -> bool: """Specifies whether the device can perform acquisitions."""
@property @abstractmethod
[docs] def max_number_of_instructions(self) -> int: """The maximum number of Q1ASM instructions supported by this module type."""
[docs] def add_op_info(self, port: str, clock: str, op_info: OpInfo) -> None: """ Assigns a certain pulse or acquisition to this device. Parameters ---------- port The port this waveform is sent to (or acquired from). clock The clock for modulation of the pulse or acquisition. Can be a BasebandClock. op_info Data structure containing all the information regarding this specific pulse or acquisition operation. """ if op_info.is_acquisition and not self.supports_acquisition: raise RuntimeError( f"{self.__class__.__name__} {self.name} does not support acquisitions. " f"Attempting to add acquisition {repr(op_info)} " f"on port {port} with clock {clock}." ) self._op_infos[(port, clock)].append(op_info)
@property
[docs] def _portclocks_with_data(self) -> set[tuple[str, str]]: """ All the port-clock combinations associated with at least one pulse and/or acquisition. Returns ------- : A set containing all the port-clock combinations that are used by this InstrumentCompiler. """ portclocks_used: set[tuple[str, str]] = { portclock for portclock, op_infos in self._op_infos.items() if not all(op_info.data.get("name") == "LatchReset" for op_info in op_infos) } return portclocks_used
@property @abstractmethod
[docs] def static_hw_properties(self) -> StaticHardwareProperties: """ The static properties of the hardware. This effectively gathers all the differences between the different modules. """
[docs] def _construct_all_sequencer_compilers(self) -> None: """ Constructs :class:`~SequencerCompiler` objects for each port and clock combination belonging to this device. Raises ------ ValueError When the output names do not conform to the `complex_output_X`/`real_output_X` norm, where X is the index of the output. KeyError Raised if no 'portclock_configs' entry is found in the specific outputs of the hardware config. ValueError Raised when the same port-clock is multiply assigned in the hardware config. ValueError Attempting to use more sequencers than available. """ # Setup each sequencer. sequencers: dict[str, _SequencerT_co] = {} portclock_to_channel: dict[tuple, str] = {} for channel_name, channel_cfg in sorted( self.instrument_cfg.items() ): # Sort to ensure deterministic sequencer order if not isinstance(channel_cfg, dict): continue portclock_configs: list[dict[str, Any]] = channel_cfg.get( "portclock_configs", [] ) if not portclock_configs: raise KeyError( f"No 'portclock_configs' entry found in '{channel_name}' of {self.name}." ) for sequencer_cfg in portclock_configs: portclock = (sequencer_cfg["port"], sequencer_cfg["clock"]) if portclock in self._portclocks_with_data: new_seq = self._construct_sequencer_compiler( index=len(sequencers), portclock=portclock, channel_name=channel_name, sequencer_cfg=sequencer_cfg, channel_cfg=channel_cfg, ) sequencers[new_seq.name] = new_seq # Check if the portclock was not multiply specified, which is not allowed if portclock in portclock_to_channel: raise ValueError( f"Portclock {portclock} was assigned to multiple " f"portclock_configs of {self.name}. This portclock was " f"used in channel '{channel_name}' despite being already previously " f"used in channel '{portclock_to_channel[portclock]}'. When using " f"the same portclock for output and input, assigning only " f"the output suffices." ) portclock_to_channel[portclock] = channel_name # Check if more portclock_configs than sequencers are active if len(sequencers) > self.static_hw_properties.max_sequencers: raise ValueError( "Number of simultaneously active port-clock combinations exceeds " "number of sequencers. " f"Maximum allowed for {self.name} ({self.__class__.__name__}) is " f"{self.static_hw_properties.max_sequencers}!" ) self.sequencers = sequencers
@abstractmethod
[docs] def _construct_sequencer_compiler( self, index: int, portclock: tuple[str, str], channel_name: str, sequencer_cfg: dict[str, Any], channel_cfg: dict[str, Any], ) -> _SequencerT_co: """Create the sequencer object of the correct sequencer type belonging to the module."""
[docs] def distribute_data(self) -> None: """ Distributes the pulses and acquisitions assigned to this module over the different sequencers based on their portclocks. Raises an exception in case the device does not support acquisitions. """ for seq in self.sequencers.values(): if seq.op_strategies is None: seq.op_strategies = [] for portclock, op_info_list in self._op_infos.items(): if seq.portclock == portclock or ( portclock[0] is None and portclock[1] == seq.clock ): for op_info in op_info_list: if not op_info.is_acquisition or not ( portclock[0] is None and portclock[1] == seq.clock ): op_strategy = seq.get_operation_strategy( operation_info=op_info, ) seq.add_operation_strategy(op_strategy)
[docs] def compile( self, debug_mode: bool, repetitions: int = 1, sequence_to_file: bool | None = None, ) -> dict[str, Any]: """ Performs the actual compilation steps for this module, by calling the sequencer level compilation functions and combining them into a single dictionary. Parameters ---------- debug_mode Debug mode can modify the compilation process, so that debugging of the compilation process is easier. repetitions Number of times execution the schedule is repeated. sequence_to_file Dump waveforms and program dict to JSON file, filename stored in `SequencerCompiler.settings.seq_fn`. Returns ------- : The compiled program corresponding to this module. It contains an entry for every sequencer under the key `"sequencers"`, and acquisition metadata under the key `"acq_metadata"`, and the `"repetitions"` is an integer with the number of times the defined schedule is repeated. All the other generic settings are under the key `"settings"`. If the device is not actually used, and an empty program is compiled, None is returned instead. """ program: dict[str, Any] = {} # `sequence_to_file` of a module can be `True` even if its `False` for a cluster if sequence_to_file is None or sequence_to_file is False: # Explicit cast to bool to help type checker. sequence_to_file = bool(self.instrument_cfg.get("sequence_to_file", False)) align_qasm_fields = debug_mode if self.supports_acquisition: program["acq_metadata"] = {} program["sequencers"] = {} for seq_name, seq in self.sequencers.items(): seq_program, acq_metadata = seq.compile( repetitions=repetitions, sequence_to_file=sequence_to_file, align_qasm_fields=align_qasm_fields, ) if seq_program is not None: program["sequencers"][seq_name] = seq_program if acq_metadata is not None: program["acq_metadata"][seq_name] = acq_metadata if len(program) == 0: return {} program["settings"] = self._settings.to_dict() program["repetitions"] = repetitions return program