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min_norm_solvers_numpy.py
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min_norm_solvers_numpy.py
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"""
:author: hqx
"""
import numpy as np
MAX_ITER = 250
STOP_CRIT = 1e-6
class MinNormSolver:
"""[summary]
Returns:
[type]: [description]
"""
def __init__(self) -> None:
super().__init__()
self.counter = 0
def min_norm_element_from2(self, v1v1, v1v2, v2v2):
"""
Analytical solution for min_{c} |cx_1 + (1-c)x_2|_2^2
d is the distance (objective) optimzed
v1v1 = <x1,x1>
v1v2 = <x1,x2>
v2v2 = <x2,x2>
"""
self.counter += 1
#print('v1v1', v1v1, 'v2v2',v2v2)
if v1v2 >= v1v1:
# Case: Fig 1, third column
gamma = 0.999
cost = v1v1
return gamma, cost
if v1v2 >= v2v2:
# Case: Fig 1, first column
gamma = 0.001
cost = v2v2
return gamma, cost
# Case: Fig 1, second column
gamma = -1.0 * ((v1v2 - v2v2) / (v1v1 + v2v2 - 2*v1v2))
cost = v2v2 + gamma*(v1v2 - v2v2)
return gamma, cost
def _min_norm_2d(self, vecs, dps):
r"""
Find the minimum norm solution as combination of two points
This solution is correct if vectors(gradients) lie in 2D
ie. min_c |\sum c_i x_i|_2^2 st. \sum c_i = 1 , 1 >= c_1 >= 0
for all i, c_i + c_j = 1.0 for some i, j
"""
dmin = 1e8
for i in range(len(vecs)): # for num_tasks:
for j in range(i+1, len(vecs)):
#print('vecs[i], vecs[j]', vecs[i], vecs[j])
if (i, j) not in dps:
dps[(i, j)] = 0.0
dps[(i, j)] = np.dot(vecs[i], vecs[j])
dps[(j, i)] = dps[(i, j)]
if (i, i) not in dps:
dps[(i, i)] = 0.0
dps[(i, i)] = np.dot(vecs[i], vecs[i])
if (j, j) not in dps:
dps[(j, j)] = 0.0
dps[(j, j)] = np.dot(vecs[j], vecs[j])
c_val, d_val = self.min_norm_element_from2(dps[(i, i)], dps[(i, j)], dps[(j, j)])
if d_val < dmin:
dmin = d_val
sol = [(i, j), c_val, d_val]
return sol, dps
def _projection2simplex(self, output):
r"""
Given y, it solves argmin_z |y-z|_2 st \sum z = 1 , 1 >= z_i >= 0 for all i
"""
self.counter += 1
length = len(output)
sorted_y = np.flip(np.sort(output), axis=0)
tmpsum = 0.0
tmax_f = (np.sum(output) - 1.0)/length
for i in range(length-1):
tmpsum += sorted_y[i]
tmax = (tmpsum - 1)/ (i+1.0)
if tmax > sorted_y[i+1]:
tmax_f = tmax
break
return np.maximum(output - tmax_f, np.zeros(output.shape))
def _next_point(self, cur_val, grad, num):
"""next_point"""
proj_grad = grad - (np.sum(grad) / num)
tm1 = -1.0 * cur_val[proj_grad < 0] / proj_grad[proj_grad < 0]
tm2 = (1.0 - cur_val[proj_grad > 0]) / (proj_grad[proj_grad > 0])
# skippers = np.sum(tm1<1e-7) + np.sum(tm2<1e-7)
temp = 1
if tm1[tm1 > 1e-7].shape[0] > 0:
temp = np.min(tm1[tm1 > 1e-7])
if tm2[tm2 > 1e-7].shape[0] > 0:
temp = min(temp, np.min(tm2[tm2 > 1e-7]))
next_point = proj_grad*temp + cur_val
next_point = self._projection2simplex(next_point)
return next_point
def find_min_norm_element(self, vecs):
r"""
Given a list of vectors (vecs), this method finds the minimum norm element in the
convex hull as min |u|_2 st. u = \sum c_i vecs[i] and \sum c_i = 1.
vecs[i]是第i个任务的gradients
It is quite geometric, and the main idea is th e fact that if d_{ij} =
min |u|_2 st u = c x_i + (1-c) x_j; the solution lies in (0, d_{i,j})
Hence, we find the best 2-task solution, and then run the projected
gradient descent until convergence
"""
# Solution lying at the combination of two points
dps = {}
init_sol, dps = self._min_norm_2d(vecs, dps)
num = len(vecs) #num_tasks
sol_vec = np.zeros(num)
sol_vec[init_sol[0][0]] = init_sol[1]
sol_vec[init_sol[0][1]] = 1 - init_sol[1]
if num < 3:
# This is optimal for n=2, so return the solution
return sol_vec, init_sol[2]
iter_count = 0
grad_mat = np.zeros((num, num))
for i in range(num):
for j in range(num):
grad_mat[i, j] = dps[(i, j)].asnumpy()
while iter_count < MAX_ITER:
grad_dir = -1.0*np.dot(grad_mat, sol_vec)
new_point = self._next_point(sol_vec, grad_dir, num)
# Re-compute the inner products for line search
v1v1 = 0.0
v1v2 = 0.0
v2v2 = 0.0
for i in range(num):
for j in range(num):
v1v1 += sol_vec[i] * sol_vec[j] * dps[(i, j)]
v1v2 += sol_vec[i] * new_point[j] * dps[(i, j)]
v2v2 += new_point[i] * new_point[j] * dps[(i, j)]
nc_, nd_ = self.min_norm_element_from2(v1v1, v1v2, v2v2)
new_sol_vec = nc_*sol_vec + (1-nc_)*new_point
change = new_sol_vec - sol_vec
if np.sum(np.abs(change)) < STOP_CRIT:
return sol_vec, nd_
sol_vec = new_sol_vec
return sol_vec, nd_
def find_min_norm_element_fw(self, vecs):
r"""
Given a list of vectors (vecs), this method finds the minimum norm element
in the convex hull
as min |u|_2 st. u = \sum c_i vecs[i] and \sum c_i = 1.
It is quite geometric, and the main idea is the fact that if d_{ij} =
min |u|_2 st u = c x_i + (1-c) x_j; the solution lies in (0, d_{i,j})
Hence, we find the best 2-task solution,
and then run the Frank Wolfe until convergence
"""
# Solution lying at the combination of two points
dps = {}
init_sol, dps = self._min_norm_2d(vecs, dps)
num = len(vecs)
sol_vec = np.zeros(num)
sol_vec[init_sol[0][0]] = init_sol[1]
sol_vec[init_sol[0][1]] = 1 - init_sol[1]
if num < 3:
# This is optimal for n=2, so return the solution
return sol_vec, init_sol[2]
iter_count = 0
grad_mat = np.zeros((num, num))
for i in range(num):
for j in range(num):
grad_mat[i, j] = dps[(i, j)]
while iter_count < MAX_ITER:
t_iter = np.argmin(np.dot(grad_mat, sol_vec))
v1v1 = np.dot(sol_vec, np.dot(grad_mat, sol_vec))
v1v2 = np.dot(sol_vec, grad_mat[:, t_iter])
v2v2 = grad_mat[t_iter, t_iter]
nc_, nd_ = self.min_norm_element_from2(v1v1, v1v2, v2v2)
new_sol_vec = nc_*sol_vec
new_sol_vec[t_iter] += 1 - nc_
change = new_sol_vec - sol_vec
if np.sum(np.abs(change)) < STOP_CRIT:
return sol_vec, nd_
sol_vec = new_sol_vec
return sol_vec, nd_
def gradient_normalizers(self, grads, losses, normalization_type):
"""[summary]
Args:
grads ([type]): [description]
losses ([type]): [description]
normalization_type ([type]): [description]
Returns:
[type]: [description]
"""
self.counter += 1
gn_ = {}
num_tsk = len(grads)
if normalization_type == 'l2':
for temp in range(num_tsk):
gn_[temp] = np.sqrt(np.sum([np.sum(np.power(gr, 2)) for gr in grads[temp]]))
elif normalization_type == 'loss':
for temp in range(num_tsk):
gn_[temp] = losses[temp]
elif normalization_type == 'loss+':
for temp in range(num_tsk):
gn_[temp] = losses[temp] * np.sqrt(np.sum([np.sum(np.power(gr, 2)) \
for gr in grads[temp]]))
elif normalization_type == 'none':
for temp in range(num_tsk):
gn_[temp] = 1.0
else:
print('ERROR: Invalid Normalization Type')
return gn_