# Copyright 2021 The NetKet Authors - All rights reserved.
#
# 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.
from typing import Any, Optional
from functools import partial
import jax
from flax import linen as nn
from jax import numpy as jnp
from netket import config
from netket.hilbert import DiscreteHilbert
from netket.nn import to_array
from netket.utils.types import PyTree, SeedT, DType
from netket.jax.sharding import sharding_decorator
from .base import Sampler, SamplerState
class ExactSamplerState(SamplerState):
pdf: Any
rng: Any
def __init__(self, pdf: Any, rng: Any):
self.pdf = pdf
self.rng = rng
super().__init__()
def __repr__(self):
return f"ExactSamplerState(rng state={self.rng})"
[docs]
class ExactSampler(Sampler):
"""
This sampler generates i.i.d. samples from :math:`|\\Psi(\\sigma)|^2`.
In order to perform exact sampling, :math:`|\\Psi(\\sigma)|^2` is precomputed an all
the possible values of the quantum numbers :math:`\\sigma`. This sampler has thus an
exponential cost with the number of degrees of freedom, and cannot be used
for large systems, where Metropolis-based sampling are instead a viable
option.
"""
[docs]
def __init__(
self,
hilbert: DiscreteHilbert,
machine_pow: int = 2,
dtype: DType = float,
):
"""
Construct an exact sampler.
Args:
hilbert: The Hilbert space to sample.
machine_pow: The power to which the machine should be exponentiated to generate the pdf (default = 2).
dtype: The dtype of the states sampled (default = np.float64).
"""
super().__init__(hilbert, machine_pow=machine_pow, dtype=dtype)
@property
def is_exact(sampler):
return True
def _init_state(
sampler,
machine: nn.Module,
parameters: PyTree,
seed: Optional[SeedT] = None,
):
pdf = jnp.zeros(sampler.hilbert.n_states, dtype=jnp.float32)
return ExactSamplerState(pdf=pdf, rng=seed)
def _reset(sampler, machine, parameters, state):
pdf = jnp.absolute(
to_array(sampler.hilbert, machine.apply, parameters) ** sampler.machine_pow
)
pdf = pdf / pdf.sum()
return state.replace(pdf=pdf)
@partial(jax.jit, static_argnums=(1, 4))
def _sample_chain(
sampler,
machine: nn.Module,
parameters: PyTree,
state: SamplerState,
chain_length: int,
) -> tuple[jnp.ndarray, SamplerState]:
# Reimplement sample_chain because we can sample the whole 'chain' in one
# go, since it's not really a chain anyway. This will be much faster because
# we call into python only once.
new_rng, rng = jax.random.split(state.rng)
numbers = jax.random.choice(
rng,
sampler.hilbert.n_states,
shape=(
sampler.n_batches,
chain_length,
),
replace=True,
p=state.pdf,
)
samples = sampler.hilbert.numbers_to_states(numbers).astype(sampler.dtype)
# TODO run the part above in parallel
if config.netket_experimental_sharding:
samples = jax.lax.with_sharding_constraint(
samples,
jax.sharding.PositionalSharding(jax.devices()).reshape(-1, 1, 1),
)
return samples, state.replace(rng=new_rng)
