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test_case.py
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test_case.py
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"""
See 'README.md' for details.
Expected output from 'python test_case.py':
Compiling our c++/cuda code, this usually takes 1-2 min.
Finished compilation, took: 0.0701s
---------- Assertions ----------
Multiplication: PASSED!
Inverse Multiplication: PASSED!
Reconstruction: PASSED!
---------- Check Orthogonal Inverse ----------
Multiplication: PASSED!
---------- Time Algorithms ----------
Printing time of both algorithms as [mean +- std].
[100 / 100] Seq: [0.066833 +- 0.002570] Ours: [0.001502 +- 0.000026]
Speed-up: 0.066833 / 0.001502 = 44.50 times faster
---------- Time Neural Network ----------
Taking time of a single gradient step for a
Neural Network with orthogonal matrices of size
[512, 512, 512].
Sequential: 0.3743
Our approach: 0.0212
Speed-up: 17.6459 times faster
"""
import numpy as np
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load
import time
from fasth_wrapper import *
torch.manual_seed(42)
torch.set_default_tensor_type('torch.cuda.FloatTensor')
# Define problem size
d = 512
m = 32
batch_size = 32
# Initialize problem.
V = torch.zeros((d,d)).normal_(0, 1).cuda()
X = torch.zeros((d,batch_size)).normal_(0, 1).cuda()
norms = normalize(V)
# 1. Demonstrate that both algorithms computes the same thing.
print("\n---------- Assertions ----------")
# Compute multiplication with sequential algorithm.
mult_seq = sequential_mult (V, X)
# Compute multiplication with our algorithm.
Y = algo_compute_dec(V, m)
mult_algo = algo_mult(V, X, Y, m)
# Check that both algorithms computed the same.
assert torch.allclose(mult_seq, mult_algo, atol=10**(-5))
print("Multiplication:\t\t\tPASSED!")
# Compute inverse multiplication with sequential algorithm.
imult_seq = sequential_inv_mult(V, mult_seq)
# Compute inverse multiplication with our algorithm.
imult_algo = algo_inv_mult(V, mult_algo, Y, m)
# Check that both algorithms computed the same.
assert torch.allclose(imult_seq, imult_algo, atol=10**(-5))
print("Inverse Multiplication: \tPASSED!")
# Multiplication and inverse multiplication is identity.
# Check that both algorithms also succesfully computed identity
assert torch.allclose(imult_algo, X, atol=10**(-5))
assert torch.allclose(imult_seq, X, atol=10**(-5))
print("Reconstruction: \t\tPASSED!")
# 1. Check invertibility of orthogonal matrix.
print("\n---------- Check Orthogonal Inverse ----------")
orth = Orthogonal(d, 32, "fast")
X = torch.zeros((d, 32)).normal_(0, 1)
enc = orth.forward(X)
rec = orth.inverse(enc)
assert torch.allclose(rec, X, atol=10**(-5))
assert not torch.allclose(enc, X, atol=10**(-5))
print("Multiplication:\t\t\tPASSED!")
# 2. Take time of both algorithms.
print("\n---------- Time Algorithms ----------")
print("Printing time of both algorithms as [mean +- std]. ")
time_seq = []
time_our = []
for i in range(100):
# Initialize a new problem each time.
V = torch.zeros((d,d)).normal_(0, 1).cuda()
X = torch.zeros((d,batch_size)).normal_(0, 1).cuda()
norms = normalize(V)
torch.cuda.synchronize()
t0 = time.time()
sequential_mult(V, X)
sequential_inv_mult(V, X)
torch.cuda.synchronize()
t1 = time.time()
Y = algo_compute_dec(V, m)
mult_algo = algo_mult(V, X, Y, m)
mult_algo = algo_inv_mult(V, X, Y, m)
torch.cuda.synchronize()
t2 = time.time()
time_seq.append(t1-t0)
time_our.append(t2-t1)
print("\r[%i / %i]\tSeq: \t[%.6f +- %.6f] \tOurs: [%.6f +- %.6f] \t "%(
i+1, 100,
np.mean(time_seq), np.std(time_seq),
np.mean(time_our), np.std(time_our)),
end="", flush=True)
print("")
print("Speed-up: \t%.6f / %.6f = %.2f times faster"%(
np.mean(time_seq) , np.mean(time_our),
np.mean(time_seq)/np.mean(time_our)))
# 3. Compare FastH in a Neural Network.
print("\n---------- Time Neural Network ----------")
print("Taking time of a single gradient step for a")
print("Neural Network with orthogonal matrices of size ")
print("[%i, %i, %i]. "%(d, d, d))
seq_net = OrthNet(d, 32, "sequential")
our_net = OrthNet(d, 32, "fast")
X = torch.zeros((d, 32)).normal_(0, 1)
Y = torch.zeros((d, 32)).normal_(0, 1)
# warmup round.
pred = seq_net(X)
error = torch.mean( (pred - Y)**2)
error.backward()
torch.cuda.synchronize()
t0 = time.time()
pred = seq_net(X)
error = torch.mean( (pred - Y)**2)
error.backward()
torch.cuda.synchronize()
t1 = time.time()
time_seq = t1-t0
print("Sequential: \t%.4f"%(t1-t0))
t0 = time.time()
pred = our_net(X)
error = torch.mean( (pred - Y)**2)
error.backward()
torch.cuda.synchronize()
t1 = time.time()
time_ours = t1-t0
print("Our approach: \t%.4f"%(t1-t0))
print("Speed-up: \t%.4f times faster"%(time_seq/time_ours))