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positive_wavefunction.py
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positive_wavefunction.py
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# Copyright 2019 PIQuIL - 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.
import torch
from qucumber import _warn_on_missing_gpu
from qucumber.rbm import BinaryRBM
from .wavefunction import WaveFunctionBase
class PositiveWaveFunction(WaveFunctionBase):
"""Class capable of learning wavefunctions with no phase.
:param num_visible: The number of visible units, ie. the size of the system being learned.
:type num_visible: int
:param num_hidden: The number of hidden units in the internal RBM. Defaults to
the number of visible units.
:type num_hidden: int
:param gpu: Whether to perform computations on the default GPU.
:type gpu: bool
:param module: An instance of a BinaryRBM module to use for density estimation.
Will be copied to the default GPU if `gpu=True` (if it
isn't already there). If `None`, will initialize a BinaryRBM
from scratch.
:type module: qucumber.rbm.BinaryRBM
"""
_rbm_am = None
_device = None
def __init__(self, num_visible, num_hidden=None, gpu=True, module=None):
if module is None:
self.rbm_am = BinaryRBM(
int(num_visible),
int(num_hidden) if num_hidden else int(num_visible),
gpu=gpu,
)
else:
_warn_on_missing_gpu(gpu)
gpu = gpu and torch.cuda.is_available()
device = torch.device("cuda") if gpu else torch.device("cpu")
self.rbm_am = module.to(device)
self.rbm_am.device = device
self.num_visible = self.rbm_am.num_visible
self.num_hidden = self.rbm_am.num_hidden
self.device = self.rbm_am.device
@property
def networks(self):
return ["rbm_am"]
@property
def rbm_am(self):
return self._rbm_am
@rbm_am.setter
def rbm_am(self, new_val):
self._rbm_am = new_val
@property
def device(self):
return self._device
@device.setter
def device(self, new_val):
self._device = new_val
def amplitude(self, v):
r"""Compute the (unnormalized) amplitude of a given vector/matrix of visible states.
.. math::
\text{amplitude}(\bm{\sigma})=|\psi_{\bm{\lambda}}(\bm{\sigma})|=
e^{-\mathcal{E}_{\bm{\lambda}}(\bm{\sigma})/2}
:param v: visible states :math:`\bm{\sigma}`
:type v: torch.Tensor
:returns: Matrix/vector containing the amplitudes of v
:rtype: torch.Tensor
"""
return super().amplitude(v)
def phase(self, v):
r"""Compute the phase of a given vector/matrix of visible states.
In the case of a PositiveWaveFunction, the phase is just zero.
:param v: visible states :math:`\bm{\sigma}`
:type v: torch.Tensor
:returns: Matrix/vector containing the phases of v
:rtype: torch.Tensor
"""
if v.dim() == 1:
v = v.unsqueeze(0)
unsqueezed = True
else:
unsqueezed = False
phase = torch.zeros(v.shape[0])
if unsqueezed:
return phase.squeeze_(0)
else:
return phase
def psi(self, v):
r"""Compute the (unnormalized) wavefunction of a given vector/matrix of visible states.
.. math::
\psi_{\bm{\lambda}}(\bm{\sigma})
= e^{-\mathcal{E}_{\bm{\lambda}}(\bm{\sigma})/2}
:param v: visible states :math:`\bm{\sigma}`
:type v: torch.Tensor
:returns: Complex object containing the value of the wavefunction for
each visible state
:rtype: torch.Tensor
"""
# vector/tensor of shape (len(v),)
amplitude = self.amplitude(v)
# complex vector; shape: (2, len(v))
psi = torch.zeros((2,) + amplitude.shape).to(
dtype=torch.double, device=self.device
)
psi[0] = amplitude
return psi
def gradient(self, v):
r"""Compute the gradient of the effective energy for a batch of states.
:math:`\nabla_{\bm{\lambda}}\mathcal{E}_{\bm{\lambda}}(\bm{\sigma})`
:param v: visible states :math:`\bm{\sigma}`
:type v: torch.Tensor
:returns: A single tensor containing all of the parameter gradients.
:rtype: torch.Tensor
"""
return self.rbm_am.effective_energy_gradient(v)
def compute_batch_gradients(self, k, samples_batch, neg_batch):
"""Compute the gradients of a batch of the training data (`samples_batch`).
:param k: Number of contrastive divergence steps in training.
:type k: int
:param samples_batch: Batch of the input samples.
:type samples_batch: torch.Tensor
:param neg_batch: Batch of the input samples for computing the
negative phase.
:type neg_batch: torch.Tensor
:returns: List containing the gradients of the parameters.
:rtype: list
"""
return super().compute_batch_gradients(k, samples_batch, neg_batch)
def fit(
self,
data,
epochs=100,
pos_batch_size=100,
neg_batch_size=None,
k=1,
lr=1e-3,
progbar=False,
starting_epoch=1,
time=False,
callbacks=None,
optimizer=torch.optim.SGD,
**kwargs
):
"""Train the WaveFunction.
:param data: The training samples
:type data: np.array
:param epochs: The number of full training passes through the dataset.
Technically, this specifies the index of the *last* training
epoch, which is relevant if `starting_epoch` is being set.
:type epochs: int
:param pos_batch_size: The size of batches for the positive phase
taken from the data.
:type pos_batch_size: int
:param neg_batch_size: The size of batches for the negative phase
taken from the data. Defaults to `pos_batch_size`.
:type neg_batch_size: int
:param k: The number of contrastive divergence steps.
:type k: int
:param lr: Learning rate
:type lr: float
:param progbar: Whether or not to display a progress bar. If "notebook"
is passed, will use a Jupyter notebook compatible
progress bar.
:type progbar: bool or str
:param starting_epoch: The epoch to start from. Useful if continuing training
from a previous state.
:type starting_epoch: int
:param callbacks: Callbacks to run while training.
:type callbacks: list[qucumber.callbacks.CallbackBase]
:param optimizer: The constructor of a torch optimizer.
:type optimizer: torch.optim.Optimizer
:param kwargs: Keyword arguments to pass to the optimizer
"""
return super().fit(
data=data,
epochs=epochs,
pos_batch_size=pos_batch_size,
neg_batch_size=neg_batch_size,
k=k,
lr=lr,
progbar=progbar,
starting_epoch=starting_epoch,
time=time,
callbacks=callbacks,
optimizer=optimizer,
**kwargs
)
def compute_normalization(self, space):
r"""Compute the normalization constant of the wavefunction.
.. math::
Z_{\bm{\lambda}}=\sqrt{\sum_{\bm{\sigma}}|\psi_{\bm{\lambda}}|^2}=
\sqrt{\sum_{\bm{\sigma}} p_{\bm{\lambda}}(\bm{\sigma})}
:param space: A rank 2 tensor of the entire visible space.
:type space: torch.Tensor
"""
return super().compute_normalization(space)
@staticmethod
def autoload(location, gpu=False):
if not gpu:
state_dict = torch.load(location, map_location=lambda storage, loc: storage)
else:
state_dict = torch.load(location)
wvfn = PositiveWaveFunction(
num_visible=len(state_dict["rbm_am"]["visible_bias"]),
num_hidden=len(state_dict["rbm_am"]["hidden_bias"]),
gpu=gpu,
)
wvfn.load(location)
return wvfn