def text_split_transform(fsplit_lst, irrep_dict, alpha, parts, mem_dict=None):
'''
fsplit_pkl: list of split file names of the distance values for a chunk of the total distance values
irrep_dict: irrep dict
alpha: weak partition
parts: list/iterable of partitions of the parts of alpha
'''
print(' Computing transform on splits: {}'.format(fsplit_lst))
cos_reps = coset_reps(sn(8), young_subgroup_perm(alpha))
save_dict = {}
cyc_irrep_func = cyclic_irreps(alpha)
pid = os.getpid()
for split_f in fsplit_lst:
with open(split_f, 'r') as f:
for line in tqdm(f):
otup, perm_tup, dist = clean_line(line)
perm_rep = irrep_dict[
perm_tup] # perm_rep is a dict of (i, j) -> matrix
block_cyclic_rep = block_cyclic_irreps(otup, cos_reps,
cyc_irrep_func)
mult_yor_block(perm_rep, dist, block_cyclic_rep, save_dict)
if mem_dict is not None:
mem_dict[pid] = max(check_memory(verbose=False),
mem_dict.get(pid, 0))
block_size = wreath_dim(parts)
n_cosets = coset_size(alpha)
mat = convert_yor_matrix(save_dict, block_size, n_cosets)
return mat
def split_transform(fsplit_lst, irrep_dict, alpha, parts, mem_dict=None):
'''
fsplit_pkl: list of pkl file names of the distance values for a chunk of the total distance values
irrep_dict: irrep dict
alpha: weak partition
parts: list/iterable of partitions of the parts of alpha
'''
print(' Computing transform on splits: {}'.format(fsplit_lst))
cos_reps = coset_reps(sn(8), young_subgroup_perm(alpha))
save_dict = {}
cyc_irrep_func = cyclic_irreps(alpha)
pid = os.getpid()
for fsplit_pkl in fsplit_lst:
with open(fsplit_pkl, 'r') as f:
# dict of function values
pkl_dict = load_pkl(fsplit_pkl)
for perm_tup, tup_dict in pkl_dict.items():
for tup, dists in tup_dict.items():
dist_tot = sum(dists)
perm_rep = irrep_dict[
perm_tup] # perm_rep is a dict of (i, j) -> matrix
block_cyclic_rep = block_cyclic_irreps(
tup, cos_reps, cyc_irrep_func)
mult_yor_block(perm_rep, dist_tot, block_cyclic_rep,
save_dict)
if mem_dict is not None:
mem_dict[pid] = max(check_memory(verbose=False),
mem_dict.get(pid, 0))
del pkl_dict
block_size = wreath_dim(parts)
n_cosets = coset_size(alpha)
mat = convert_yor_matrix(save_dict, block_size, n_cosets)
return mat
def compute_cyclic_irreps(self):
cyc_irreps = {}
for otup in cube2_orientations():
o_irrep = block_cyclic_irreps(otup, self.cos_reps,
self.cyc_irrep_func)
cyc_irreps[otup] = o_irrep
return cyc_irreps
def all_cyc_irreps(cos_reps, cyc_irrep_func):
otups = []
irreps = {}
xs = [(0, 1, 2) for _ in range(8)]
opts = product(*xs)
opts = [o for o in opts if sum(o) % 3 == 0]
for otup in opts:
irreps[otup] = block_cyclic_irreps(otup, cos_reps, cyc_irrep_func)
return irreps
def tup_to_irrep_np(self, otup, ptup):
'''
otup: tuple of orientations in Z/3Z
ptup: tuple of permutation of S_8
Returns: numpy matrix
'''
block_scalars = block_cyclic_irreps(otup, self.cos_reps,
self.cyc_irrep_func)
rep = get_mat(ptup, self.yor_dict, block_scalars)
return rep
def fill_cyclic_irreps(self):
'''
Stores the cyclic irrep for every single 2-cube orientation
so fetching a 2-cube state representation becomes
two dictionary lookups and a sparse elementwise multiplication.
'''
for tup in cube2_orientations():
cyc_irrep = block_cyclic_irreps(tup, self.cos_reps,
self.cyc_irrep_func)
self.cyclic_irreps_re[tup] = torch.FloatTensor(
self.block_pad(cyc_irrep.real))
self.cyclic_irreps_im[tup] = torch.FloatTensor(
self.block_pad(cyc_irrep.imag))
def par_cube_ft(rank, size, alpha, parts):
start = time.time()
try:
df = load_df('/scratch/hopan/cube/')
irrep_dict = load_irrep('/scratch/hopan/cube/', alpha, parts)
except Exception as e:
print('rank {} | memory usg: {} | exception {}'.format(rank, check_memory(verbose=False), e))
print('Rank {:3d} / {} | load irrep: {:.2f}s | mem: {}mb'.format(rank, size, time.time() - start, check_memory(verbose=False)))
cos_reps = coset_reps(sn(8), young_subgroup_perm(alpha))
save_dict = {}
cyc_irrep_func = cyclic_irreps(alpha)
chunk_size = len(df) // size
start_idx = chunk_size * rank
#print('Rank {} | {:7d}-{:7d}'.format(rank, start_idx, start_idx + chunk_size))
if rank == 0:
print('Rank {} | elapsed: {:.2f}s | {:.2f}mb | done load'.format(rank, time.time() - start, check_memory(verbose=False)))
for idx in range(start_idx, start_idx + chunk_size):
row = df.loc[idx]
otup = tuple(int(i) for i in row[0])
perm_tup = tuple(int(i) for i in row[1])
dist = int(row[2])
perm_rep = irrep_dict[perm_tup] # perm_rep is a dict of (i, j) -> matrix
block_cyclic_rep = block_cyclic_irreps(otup, cos_reps, cyc_irrep_func)
mult_yor_block(perm_rep, dist, block_cyclic_rep, save_dict)
if rank == 0:
print('Rank {} | elapsed: {:.2f}s | {:.2f}mb | done add'.format(rank, time.time() - start, check_memory(verbose=False)))
del irrep_dict
block_size = wreath_dim(parts)
n_cosets = coset_size(alpha)
mat = convert_yor_matrix(save_dict, block_size, n_cosets)
if rank == 0:
print('Rank {} | elapsed: {:.2f}s | {:.2f}mb | done matrix conversion'.format(rank, time.time() - start, check_memory(verbose=False)))
return mat
def tup_to_irrep_th(self, otup, ptup):
block_scalars = block_cyclic_irreps(otup, self.cos_reps,
self.cyc_irrep_func)
rep_re, rep_im = get_sparse_mat(ptup, self.np_yor_dict, block_scalars)
return rep_re, rep_im
def test_main(alpha, parts):
'''
Computes the ft via the sparse wreath rep and the non-sparse wreath rep
to double check that the sparse wreath rep is actually correct.
'''
_start = time.time()
st = time.time()
sp_irrep_dict = load_pkl(
'/scratch/hopan/cube/pickles_sparse/{}/{}.pkl'.format(alpha, parts))
end = time.time()
print('Loading sparse irrep dict: {:.2f}s'.format(time.time() - st))
check_memory()
st = time.time()
irrep_dict = load_irrep('/scratch/hopan/cube/', alpha, parts)
print('Loading irrep dict: {:.2f}s'.format(time.time() - st))
check_memory()
# generate a random group element?
st = time.time()
df = load_df('/scratch/hopan/cube/')
fhat = np.load('/scratch/hopan/cube/fourier/{}/{}.npy'.format(
alpha, parts))
print('Loading df: {:.2f}s'.format(time.time() - st))
check_memory()
cyc_irrep_func = cyclic_irreps(alpha)
cos_reps = coset_reps(sn(8), young_subgroup_perm(alpha))
st = time.time()
cyc_irrs = all_cyc_irreps(cos_reps, cyc_irrep_func)
print('Time to compute all cyc irreps: {:.5f}s'.format(time.time() - st))
sp_times = []
sp_mult_times = []
sp_results = np.zeros(len(df), dtype=np.complex128)
coo_times = []
th_sp_times = []
times = []
mult_times = []
z3_irreps = []
results = np.zeros(len(df), dtype=np.complex128)
fhat_t_ravel = fhat.T.ravel()
loop_start = time.time()
for idx in range(len(df)):
row = df.loc[idx]
otup = tuple(int(i) for i in row[0])
perm_tup = tuple(int(i) for i in row[1])
# compute wreath rep
st = time.time()
wmat = wreath_rep(otup, perm_tup, irrep_dict, cos_reps, cyc_irrep_func)
reg_time = time.time() - st
# compute wreath rep multiply
st = time.time()
wmat_inv = wmat.conj().T
feval = np.dot(fhat_t_ravel, wmat_inv.ravel())
reg_mult_time = time.time() - st
results[idx] = feval
# compute sparse wreath rep
st = time.time()
wmat_sp = wreath_rep_sp(otup, perm_tup, sp_irrep_dict, cos_reps,
cyc_irrep_func, cyc_irrs)
sp_time = time.time() - st
if not np.allclose(wmat, wmat_sp.todense()):
print('unequal! | idx = {}'.format(idx))
pdb.set_trace()
# compute sparse wreath rep multiply
st = time.time()
wmat_inv_sp = wmat_sp.conj().T
feval_sp = (wmat_inv_sp.multiply(fhat.T)).sum()
sp_mult_time = time.time() - st
sp_results[idx] = feval_sp
times.append(reg_time)
sp_times.append(sp_time)
mult_times.append(reg_mult_time)
sp_mult_times.append(sp_mult_time)
st = time.time()
coo = wmat_sp.tocoo()
end = time.time()
coo_times.append(end - st)
st = time.time()
ix = torch.LongTensor([coo.row, coo.col])
th_sp_re = torch.sparse.FloatTensor(ix,
torch.FloatTensor(coo.data.real),
torch.Size(coo.shape))
th_sp_cplx = torch.sparse.FloatTensor(ix,
torch.FloatTensor(coo.data.imag),
torch.Size(coo.shape))
end = time.time()
th_sp_times.append(end - st)
st = time.time()
block_scalars = block_cyclic_irreps(otup, cos_reps, cyc_irrep_func)
end = time.time()
z3_irreps.append(end - st)
if idx > 200:
break
print('Normal time: {:.6f}s | Sparse time: {:.6f}s'.format(
np.mean(times), np.mean(sp_times)))
print('Mult time: {:.6f}s | Spmult time: {:.6f}s'.format(
np.mean(mult_times), np.mean(sp_mult_times)))
print('To coo time: {:.6f}s | Torchsptime: {:.6f}s'.format(
np.mean(coo_times), np.mean(th_sp_times)))
print('irrep time: {:.6f}s'.format(np.mean(z3_irreps)))
print('Loop time: {:.2f}s'.format(time.time() - loop_start))
print('Total time: {:.2f}s'.format(time.time() - _start))
