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grads_utils.py
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grads_utils.py
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# Copyright 2018 PIQuIL - All Rights Reserved
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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 numpy as np
import torch
from qucumber.utils import cplx
class PosGradsUtils:
def __init__(self, nn_state):
self.nn_state = nn_state
def compute_numerical_kl(self, target_psi, vis, Z):
KL = 0.0
for i in range(len(vis)):
KL += ((target_psi[i, 0]) ** 2) * ((target_psi[i, 0]) ** 2).log()
KL -= ((target_psi[i, 0]) ** 2) * (
self.nn_state.probability(vis[i], Z)
).log().item()
return KL
def compute_numerical_NLL(self, data, Z):
NLL = 0
batch_size = len(data)
for i in range(batch_size):
NLL -= self.nn_state.probability(data[i], Z).log().item() / float(
batch_size
)
return NLL
def algorithmic_gradKL(self, target_psi, vis, **kwargs):
Z = self.nn_state.compute_normalization(vis)
grad_KL = torch.zeros(
self.nn_state.rbm_am.num_pars,
dtype=torch.double,
device=self.nn_state.device,
)
for i in range(len(vis)):
grad_KL += ((target_psi[i, 0]) ** 2) * self.nn_state.gradient(vis[i])
grad_KL -= self.nn_state.probability(vis[i], Z) * self.nn_state.gradient(
vis[i]
)
return [grad_KL]
def algorithmic_gradNLL(self, data, k, **kwargs):
return self.nn_state.compute_batch_gradients(k, data, data)
def numeric_gradKL(self, target_psi, param, vis, eps, **kwargs):
num_gradKL = []
for i in range(len(param)):
param[i] += eps
Z = self.nn_state.compute_normalization(vis)
KL_p = self.compute_numerical_kl(target_psi, vis, Z)
param[i] -= 2 * eps
Z = self.nn_state.compute_normalization(vis)
KL_m = self.compute_numerical_kl(target_psi, vis, Z)
param[i] += eps
num_gradKL.append((KL_p - KL_m) / (2 * eps))
return torch.stack(num_gradKL).to(param)
def numeric_gradNLL(self, param, data, vis, eps, **kwargs):
num_gradNLL = []
for i in range(len(param)):
param[i] += eps
Z = self.nn_state.compute_normalization(vis)
NLL_p = self.compute_numerical_NLL(data, Z)
param[i] -= 2 * eps
Z = self.nn_state.compute_normalization(vis)
NLL_m = self.compute_numerical_NLL(data, Z)
param[i] += eps
num_gradNLL.append((NLL_p - NLL_m) / (2 * eps))
return torch.tensor(np.array(num_gradNLL), dtype=torch.double).to(param)
class ComplexGradsUtils:
def __init__(self, nn_state):
self.nn_state = nn_state
def load_target_psi(self, bases, psi_data):
psi_dict = {}
D = int(len(psi_data) / float(len(bases)))
if isinstance(psi_data, torch.Tensor):
psi_data = psi_data.clone().detach().to(dtype=torch.double)
else:
psi_data = torch.tensor(psi_data, dtype=torch.double)
for b in range(len(bases)):
psi = torch.zeros(2, D, dtype=torch.double)
psi_real = psi_data[b * D : (b + 1) * D, 0]
psi_imag = psi_data[b * D : (b + 1) * D, 1]
psi[0] = psi_real
psi[1] = psi_imag
psi_dict[bases[b]] = psi
return psi_dict
def transform_bases(self, bases_data):
bases = []
for i in range(len(bases_data)):
tmp = ""
for j in range(len(bases_data[i])):
if bases_data[i][j] is not " ":
tmp += bases_data[i][j]
bases.append(tmp)
return bases
def rotate_psi_full(self, basis, full_unitary_dict, psi):
U = full_unitary_dict[basis]
Upsi = cplx.matmul(U, psi)
return Upsi
def rotate_psi(self, basis, unitary_dict, vis):
N = self.nn_state.num_visible
v = torch.zeros(N, dtype=torch.double, device=self.nn_state.device)
psi_r = torch.zeros(2, 1 << N, dtype=torch.double, device=self.nn_state.device)
for x in range(1 << N):
Upsi = torch.zeros(2, dtype=torch.double, device=self.nn_state.device)
num_nontrivial_U = 0
nontrivial_sites = []
for j in range(N):
if basis[j] is not "Z":
num_nontrivial_U += 1
nontrivial_sites.append(j)
sub_state = self.nn_state.generate_hilbert_space(num_nontrivial_U)
for xp in range(1 << num_nontrivial_U):
cnt = 0
for j in range(N):
if basis[j] is not "Z":
v[j] = sub_state[xp][cnt]
cnt += 1
else:
v[j] = vis[x, j]
U = torch.tensor(
[1.0, 0.0], dtype=torch.double, device=self.nn_state.device
)
for ii in range(num_nontrivial_U):
tmp = unitary_dict[basis[nontrivial_sites[ii]]]
tmp = tmp[
:,
int(vis[x][nontrivial_sites[ii]]),
int(v[nontrivial_sites[ii]]),
]
U = cplx.scalar_mult(U, tmp)
Upsi += cplx.scalar_mult(U, self.nn_state.psi(v))
psi_r[:, x] = Upsi
return psi_r
def compute_numerical_NLL(self, data_samples, data_bases, Z, unitary_dict, vis):
NLL = 0
batch_size = len(data_samples)
b_flag = 0
for i in range(batch_size):
bitstate = []
for j in range(self.nn_state.num_visible):
ind = 0
if data_bases[i][j] != "Z":
b_flag = 1
bitstate.append(int(data_samples[i, j].item()))
ind = int("".join(str(i) for i in bitstate), 2)
if b_flag == 0:
NLL -= (
self.nn_state.probability(data_samples[i], Z)
).log().item() / batch_size
else:
psi_r = self.rotate_psi(data_bases[i], unitary_dict, vis)
NLL -= (
cplx.norm_sqr(psi_r[:, ind]).log() - Z.log()
).item() / batch_size
return NLL
def compute_numerical_kl(self, psi_dict, vis, Z, unitary_dict, bases):
N = self.nn_state.num_visible
psi_r = torch.zeros(2, 1 << N, dtype=torch.double, device=self.nn_state.device)
KL = 0.0
for i in range(len(vis)):
KL += (
cplx.norm_sqr(psi_dict[bases[0]][:, i])
* cplx.norm_sqr(psi_dict[bases[0]][:, i]).log()
/ float(len(bases))
)
KL -= (
cplx.norm_sqr(psi_dict[bases[0]][:, i])
* self.nn_state.probability(vis[i], Z).log().item()
/ float(len(bases))
)
for b in range(1, len(bases)):
psi_r = self.rotate_psi(bases[b], unitary_dict, vis)
for ii in range(len(vis)):
if cplx.norm_sqr(psi_dict[bases[b]][:, ii]) > 0.0:
KL += (
cplx.norm_sqr(psi_dict[bases[b]][:, ii])
* cplx.norm_sqr(psi_dict[bases[b]][:, ii]).log()
/ float(len(bases))
)
KL -= (
cplx.norm_sqr(psi_dict[bases[b]][:, ii])
* cplx.norm_sqr(psi_r[:, ii]).log()
/ float(len(bases))
)
KL += (
cplx.norm_sqr(psi_dict[bases[b]][:, ii])
* Z.log()
/ float(len(bases))
)
return KL
def algorithmic_gradNLL(self, data_samples, data_bases, k, **kwargs):
return self.nn_state.compute_batch_gradients(
k, data_samples, data_samples, data_bases
)
def numeric_gradNLL(
self, data_samples, data_bases, unitary_dict, param, vis, eps, **kwargs
):
num_gradNLL = []
for i in range(len(param)):
param[i] += eps
Z = self.nn_state.compute_normalization(vis)
NLL_p = self.compute_numerical_NLL(
data_samples, data_bases, Z, unitary_dict, vis
)
param[i] -= 2 * eps
Z = self.nn_state.compute_normalization(vis)
NLL_m = self.compute_numerical_NLL(
data_samples, data_bases, Z, unitary_dict, vis
)
param[i] += eps
num_gradNLL.append((NLL_p - NLL_m) / (2 * eps))
return torch.tensor(np.array(num_gradNLL), dtype=torch.double).to(param)
def numeric_gradKL(self, param, psi_dict, vis, unitary_dict, bases, eps, **kwargs):
num_gradKL = []
for i in range(len(param)):
param[i] += eps
Z = self.nn_state.compute_normalization(vis)
KL_p = self.compute_numerical_kl(psi_dict, vis, Z, unitary_dict, bases)
param[i] -= 2 * eps
Z = self.nn_state.compute_normalization(vis)
KL_m = self.compute_numerical_kl(psi_dict, vis, Z, unitary_dict, bases)
param[i] += eps
num_gradKL.append((KL_p - KL_m) / (2 * eps))
return torch.stack(num_gradKL).to(param)
def algorithmic_gradKL(self, psi_dict, vis, unitary_dict, bases, **kwargs):
grad_KL = [
torch.zeros(
self.nn_state.rbm_am.num_pars,
dtype=torch.double,
device=self.nn_state.device,
),
torch.zeros(
self.nn_state.rbm_ph.num_pars,
dtype=torch.double,
device=self.nn_state.device,
),
]
Z = self.nn_state.compute_normalization(vis).to(device=self.nn_state.device)
for i in range(len(vis)):
grad_KL[0] += (
cplx.norm_sqr(psi_dict[bases[0]][:, i])
* self.nn_state.rbm_am.effective_energy_gradient(vis[i])
/ float(len(bases))
)
grad_KL[0] -= (
self.nn_state.probability(vis[i], Z)
* self.nn_state.rbm_am.effective_energy_gradient(vis[i])
/ float(len(bases))
)
for b in range(1, len(bases)):
for i in range(len(vis)):
rotated_grad = self.nn_state.gradient(bases[b], vis[i])
grad_KL[0] += (
cplx.norm_sqr(psi_dict[bases[b]][:, i])
* rotated_grad[0]
/ float(len(bases))
)
grad_KL[1] += (
cplx.norm_sqr(psi_dict[bases[b]][:, i])
* rotated_grad[1]
/ float(len(bases))
)
grad_KL[0] -= (
self.nn_state.probability(vis[i], Z)
* self.nn_state.rbm_am.effective_energy_gradient(vis[i])
/ float(len(bases))
)
return grad_KL