Sampling functions

Sampling functions for model inference.

Generation

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get_batch_samples


def get_batch_samples(
    samples:int, auto_batch_size:int=512
)->list:

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batched_sampling


def batched_sampling(
    pipeline:Pipeline, cond_kwargs:dict, samples:int, system_size:int, num_of_qubits:int, max_gates:int, g:float=1.0,
    init_latents:Optional=None, no_bar:bool=True, unique:bool=False, auto_batch_size:int=512,
    enable_params:bool=True, reduce_spatial:bool=True, return_predicted_x0:bool=False
):

e.g. cond_kwargs.keys = {“c”, “micro_cond”, “negative_c”, “U”}

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prepare_prompts


def prepare_prompts(
    pipeline:Pipeline, prompt:Union, negative_prompt:Union=None
):

Task specific sampling

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generate_tensors


def generate_tensors(
    pipeline:Pipeline, prompt:Union, samples:int, system_size:int, num_of_qubits:int, max_gates:int, g:float=1.0,
    init_latents:Optional=None, no_bar:bool=True, unique:bool=False, auto_batch_size:int=512,
    enable_params:bool=False, reduce_spatial:bool=True, return_predicted_x0:bool=False, negative_prompt:Union=None,
    micro_cond:Optional=None
)->Tensor:

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generate_compilation_tensors


def generate_compilation_tensors(
    pipeline:Pipeline, prompt:Union, U:Tensor, samples:int, system_size:int, num_of_qubits:int, max_gates:int,
    g:float=1.0, tensor_prod_pad:bool=True, init_latents:Optional=None, no_bar:bool=True, unique:bool=False,
    auto_batch_size:int=512, enable_params:bool=True, reduce_spatial:bool=True, return_predicted_x0:bool=False,
    negative_prompt:Union=None, negative_u:Optional=None, micro_cond:Optional=None
)->Tensor:

Samples tensor encodings from the DM for the given sample parameters.

What kind of unitary padding we have depends on what we used for model training, so it depends on the concrete model weights.

Convertion

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decode_tensors_to_backend


def decode_tensors_to_backend(
    simulator:Simulator, tokenizer:BaseTokenizer, tensors:Tensor, params:Optional=None, silent:bool=True,
    n_jobs:int=1, filter_errs:bool=True, return_tensors:bool=False
)->tuple[typing.Sequence[<built-in function any>], int] | tuple[typing.Sequence[<built-in function any>], int, torch.Tensor]:
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