def benchmark_convnet(ctx, timer):
image_size = BASE_IMG_SIZE
minibatch = 64
#minibatch = ctx.num_workers
hint = util.divup(image_size, sqrt(ctx.num_workers))
tile_hint = (util.divup(minibatch, ctx.num_workers), N_COLORS, image_size, image_size)
util.log_info('Hint: %s', tile_hint)
images = expr.eager(expr.ones((minibatch, N_COLORS, image_size, image_size),
tile_hint=tile_hint))
w1 = expr.eager(expr.ones((N_FILTERS, N_COLORS) + FILTER_SIZE,
tile_hint=ONE_TILE))
w2 = expr.eager(expr.ones((N_FILTERS, N_FILTERS) + FILTER_SIZE,
tile_hint=ONE_TILE))
w3 = expr.eager(expr.ones((N_FILTERS, N_FILTERS) + FILTER_SIZE,
tile_hint=ONE_TILE))
def _():
conv1 = stencil.stencil(images, w1, 2)
pool1 = stencil.maxpool(conv1)
conv2 = stencil.stencil(pool1, w2, 2)
pool2 = stencil.maxpool(conv2)
conv3 = stencil.stencil(pool2, w3, 2)
pool3 = stencil.maxpool(conv3)
expr.force(pool3)
# force parakeet functions to compile before timing.
_()
for i in range(2):
timer.time_op('convnet', _)
def test_convnet(ctx):
hint = util.divup(64, sqrt(ctx.num_workers))
images = expr.eager(
expr.ones((N_IMGS, ) + IMG_SIZE,
tile_hint=(N_IMGS, N_COLORS, hint, hint)))
w1 = expr.eager(
expr.ones((N_FILTERS, N_COLORS) + FILTER_SIZE, tile_hint=ONE_TILE))
conv1 = stencil.stencil(images, w1, 2)
pool1 = stencil.maxpool(conv1)
w2 = expr.eager(
expr.ones((N_FILTERS, N_FILTERS) + FILTER_SIZE, tile_hint=ONE_TILE))
conv2 = stencil.stencil(pool1, w2, 2)
pool2 = stencil.maxpool(conv2)
w3 = expr.eager(
expr.ones((N_FILTERS, N_FILTERS) + FILTER_SIZE, tile_hint=ONE_TILE))
conv3 = stencil.stencil(pool2, w3, 2)
pool3 = stencil.maxpool(conv3)
util.log_info(pool3.shape)
def _(axis):
util.log_info('Testing sum over axis %s', axis)
a = expr.ones((TEST_SIZE, TEST_SIZE)) + expr.ones(
(TEST_SIZE, TEST_SIZE))
b = a.sum(axis=axis)
Assert.all_eq(b.glom(), 2 * np.ones(
(TEST_SIZE, TEST_SIZE)).sum(axis))
def fn2(): a = expr.ones((N, N)) b = expr.ones((N, N)) x = expr.dot(a, b) g = a + b + x return g
def fn2():
a = expr.ones((N, N))
b = expr.ones((N, N))
x = expr.dot(a, b)
g = a + b + x
return g
def test_optimization_map_with_location(self):
FLAGS.opt_parakeet_gen = 1
def mapper(tile, ex):
return tile + 10
a = expr.map_with_location(expr.ones((5, 5)), mapper) + expr.ones((5, 5))
Assert.isinstance(a.optimized().op, expr.local.ParakeetExpr)
def benchmark_convnet(ctx, timer):
image_size = BASE_IMG_SIZE
minibatch = 64
#minibatch = ctx.num_workers
hint = util.divup(image_size, sqrt(ctx.num_workers))
tile_hint = (util.divup(minibatch, ctx.num_workers), N_COLORS, image_size, image_size)
util.log_info('Hint: %s', tile_hint)
images = expr.eager(expr.ones((minibatch, N_COLORS, image_size, image_size),
tile_hint=tile_hint))
w1 = expr.eager(expr.ones((N_FILTERS, N_COLORS) + FILTER_SIZE,
tile_hint=ONE_TILE))
w2 = expr.eager(expr.ones((N_FILTERS, N_FILTERS) + FILTER_SIZE,
tile_hint=ONE_TILE))
w3 = expr.eager(expr.ones((N_FILTERS, N_FILTERS) + FILTER_SIZE,
tile_hint=ONE_TILE))
def _():
conv1 = stencil.stencil(images, w1, 2)
pool1 = stencil.maxpool(conv1)
conv2 = stencil.stencil(pool1, w2, 2)
pool2 = stencil.maxpool(conv2)
conv3 = stencil.stencil(pool2, w3, 2)
pool3 = stencil.maxpool(conv3)
pool3.evaluate()
# force parakeet functions to compile before timing.
_()
for i in range(2):
timer.time_op('convnet', _)
def test_add_many(self):
a = expr.ones((TEST_SIZE, TEST_SIZE))
b = expr.ones((TEST_SIZE, TEST_SIZE))
add_many = (a + b + a + b + a + b + a + b + a + b)
#print add_many
#print add_many.dag()
Assert.all_eq(add_many.glom(), np.ones((TEST_SIZE, TEST_SIZE)) * 10)
def test_numexpr_opt(ctx): a = expr.ones((10, 10)) b = expr.ones((10, 10)) c = expr.ones((10, 10)) d = expr.ones((10, 10)) e = expr.ones((10, 10)) f = a + b + c + d + e f.evaluate()
def test_optimization_map_with_location(self):
FLAGS.opt_parakeet_gen = 1
def mapper(tile, ex):
return tile + 10
a = expr.map_with_location(expr.ones((5, 5)), mapper) + expr.ones(
(5, 5))
Assert.isinstance(a.optimized().op, expr.operator.local.ParakeetExpr)
def test_add_many(self):
a = expr.ones((TEST_SIZE, TEST_SIZE))
b = expr.ones((TEST_SIZE, TEST_SIZE))
add_many = (a + b + a + b + a + b + a + b + a + b)
#print add_many
#print add_many.dag()
Assert.all_eq(add_many.glom(),
np.ones((TEST_SIZE, TEST_SIZE)) * 10)
def test_optimization_region_map(self):
def mapper(tile, ex):
return tile + 10
ex = array.extent.create((0, 0), (1, 5), (5, 5))
a = expr.region_map(expr.ones((5, 5)), ex, mapper) + expr.ones((5, 5))*10
for child in a.optimized().op.deps:
Assert.true(not isinstance(child, expr.local.LocalInput))
def test_optimization_region_map(self):
def mapper(tile, ex):
return tile + 10
ex = array.extent.create((0, 0), (1, 5), (5, 5))
a = expr.region_map(expr.ones((5, 5)), ex, mapper) + expr.ones(
(5, 5)) * 10
for child in a.optimized().op.deps:
Assert.true(not isinstance(child, expr.operator.local.LocalInput))
def fn2(): a = expr.ones((N, N)) b = expr.ones((N, N/2)) g = expr.dot(a, b) + expr.dot(expr.sum(a, axis=1).reshape((1, N)), b) t1 = time.time() g_opt = g.optimized() #g_opt.force() t2 = time.time() print t2 - t1 print g_opt
def fn2(): a = expr.ones((N, N)) b = expr.ones((N, N/2)) g = expr.dot(a, b) + expr.dot(expr.sum(a, axis=1).reshape((1, N)), b) t1 = time.time() g_opt = g.optimized() #g_opt.evaluate() t2 = time.time() print t2 - t1 print g_opt
def test_numexpr_opt(ctx): a = expr.ones((10, 10)) b = expr.ones((10, 10)) c = expr.ones((10, 10)) d = expr.ones((10, 10)) e = expr.ones((10, 10)) f = a + b + c + d + e f.force() #print f.dag() #print f.force()
def test_numexpr_opt(ctx):
a = expr.ones((10, 10))
b = expr.ones((10, 10))
c = expr.ones((10, 10))
d = expr.ones((10, 10))
e = expr.ones((10, 10))
f = a + b + c + d + e
f.force()
#print f.dag()
#print f.force()
def fn1(): a = expr.ones((N, N)) b = expr.ones((N, N)) x = expr.dot(a, b) g = a + b + x t1 = time.time() print g.optimized() t2 = time.time() print t2 - t1
def fn1(): a = expr.ones((N, N)) b = expr.ones((N, N)) x = expr.dot(a, b) g = a + b + x t1 = time.time() print g.optimized() t2 = time.time() print t2 - t1
def test_broadcast(self):
a = expr.ones((100, 1, 100, 100)).force()
b = expr.ones((10, 100, 1)).force()
a, b = broadcast.broadcast((a, b))
c = expr.add(a, b).force()
d = expr.sub(a, b).force()
n = np.ones((100, 10, 100, 100))
n1 = n + n
n2 = n - n
Assert.all_eq(n1, c.glom())
Assert.all_eq(n2, d.glom())
def test_broadcast(self):
a = expr.ones((100, 1, 100, 100)).force()
b = expr.ones((10, 100, 1)).force()
a, b = broadcast.broadcast((a, b))
c = expr.add(a, b).force()
d = expr.sub(a, b).force()
n = np.ones((100, 10, 100, 100))
n1 = n + n
n2 = n - n
Assert.all_eq(n1, c.glom())
Assert.all_eq(n2, d.glom())
def test1(self):
a = expr.ones(ARRAY_SIZE)
b = expr.ones(ARRAY_SIZE)
c = expr.ones(ARRAY_SIZE)
x = a + b + c
y = x + x
z = y + y
z = expr.checkpoint(z, mode='disk')
z.evaluate()
failed_worker_id = 0
ctx = blob_ctx.get()
ctx.local_worker.mark_failed_worker(failed_worker_id)
res = z + z
Assert.all_eq(res.glom(), np.ones(ARRAY_SIZE)*24)
def benchmark_optimization(ctx, timer):
FLAGS.optimization = 0
DATA_SIZE = 5 * 1000 * 1000
current = eager(zeros((DATA_SIZE * ctx.num_workers,),
dtype=np.float32, tile_hint = (DATA_SIZE,)))
strike = eager(ones((DATA_SIZE * ctx.num_workers,),
dtype=np.float32, tile_hint=(DATA_SIZE,)))
maturity = eager(strike * 12)
rate = eager(strike * 0.05)
volatility = eager(strike * 0.01)
timer.time_op('opt-none', lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-none', lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-none', lambda: bs_step(current, strike, maturity, rate, volatility))
FLAGS.optimization = 1
FLAGS.opt_parakeet_gen = 0
FLAGS.opt_map_fusion = 1
timer.time_op('opt-fusion', lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-fusion', lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-fusion', lambda: bs_step(current, strike, maturity, rate, volatility))
FLAGS.opt_parakeet_gen = 1
timer.time_op('opt-parakeet', lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-parakeet', lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-parakeet', lambda: bs_step(current, strike, maturity, rate, volatility))
def test1(self): a = expr.ones(ARRAY_SIZE) b = expr.ones(ARRAY_SIZE) c = expr.ones(ARRAY_SIZE) x = a + b + c y = x + x z = y + y z = expr.checkpoint(z, mode='disk') z.force() failed_worker_id = 0 ctx = blob_ctx.get() ctx.local_worker.mark_failed_worker(failed_worker_id) res = z + z Assert.all_eq(res.glom(), np.ones(ARRAY_SIZE)*24)
def benchmark_optimization(ctx, timer):
FLAGS.optimization = 0
DATA_SIZE = 5 * 1000 * 1000
current = eager(
zeros((DATA_SIZE * ctx.num_workers, ),
dtype=np.float32,
tile_hint=(DATA_SIZE, )))
strike = eager(
ones((DATA_SIZE * ctx.num_workers, ),
dtype=np.float32,
tile_hint=(DATA_SIZE, )))
maturity = eager(strike * 12)
rate = eager(strike * 0.05)
volatility = eager(strike * 0.01)
timer.time_op('opt-none',
lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-none',
lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-none',
lambda: bs_step(current, strike, maturity, rate, volatility))
FLAGS.optimization = 1
FLAGS.opt_parakeet_gen = 0
FLAGS.opt_map_fusion = 1
timer.time_op('opt-fusion',
lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-fusion',
lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-fusion',
lambda: bs_step(current, strike, maturity, rate, volatility))
FLAGS.opt_parakeet_gen = 1
timer.time_op('opt-parakeet',
lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-parakeet',
lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-parakeet',
lambda: bs_step(current, strike, maturity, rate, volatility))
FLAGS.opt_parakeet_gen = 0
FLAGS.opt_auto_tiling = 0
timer.time_op('opt-tiling = 0',
lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-tiling = 0',
lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-tiling = 0',
lambda: bs_step(current, strike, maturity, rate, volatility))
FLAGS.opt_auto_tiling = 1
timer.time_op('opt-tiling',
lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-tiling',
lambda: bs_step(current, strike, maturity, rate, volatility))
timer.time_op('opt-tiling',
lambda: bs_step(current, strike, maturity, rate, volatility))
def test_index(self):
a = expr.arange((TEST_SIZE, TEST_SIZE))
b = expr.ones((10, ), dtype=np.int)
z = a[b]
val = expr.evaluate(z)
nx = np.arange(TEST_SIZE * TEST_SIZE).reshape(TEST_SIZE, TEST_SIZE)
ny = np.ones((10, ), dtype=np.int)
Assert.all_eq(val.glom(), nx[ny])
def test_index(self):
a = expr.arange((TEST_SIZE, TEST_SIZE))
b = expr.ones((10,), dtype=np.int)
z = a[b]
val = expr.evaluate(z)
nx = np.arange(TEST_SIZE * TEST_SIZE).reshape(TEST_SIZE, TEST_SIZE)
ny = np.ones((10,), dtype=np.int)
Assert.all_eq(val.glom(), nx[ny])
def benchmark_matmul(ctx, timer):
N = int(1000 * math.pow(ctx.num_workers, 1.0 / 3.0))
# N = 4000
M = util.divup(N, ctx.num_workers)
T = util.divup(N, math.sqrt(ctx.num_workers))
util.log_info("Testing with %d workers, N = %d, tile_size=%s", ctx.num_workers, N, T)
# x = expr.eager(expr.ones((N, N), dtype=np.double, tile_hint=(N, M)))
# y = expr.eager(expr.ones((N, N), dtype=np.double, tile_hint=(N, M)))
x = expr.eager(expr.ones((N, N), dtype=np.double, tile_hint=(T, T)))
y = expr.eager(expr.ones((N, N), dtype=np.double, tile_hint=(T, T)))
# print expr.glom(expr.dot(x, y))
# print expr.dag(expr.dot(x, y))
def _step():
expr.evaluate(expr.dot(x, y))
timer.time_op("matmul", _step)
def test_stencil(ctx):
st = time.time()
IMG_SIZE = int(8 * math.sqrt(ctx.num_workers))
FILT_SIZE = 8
N = 8
F = 32
tile_size = util.divup(IMG_SIZE, math.sqrt(ctx.num_workers))
images = expr.ones((N, 3, IMG_SIZE, IMG_SIZE),
dtype=np.float,
tile_hint=(N, 3, tile_size, tile_size))
filters = expr.ones((F, 3, FILT_SIZE, FILT_SIZE),
dtype=np.float,
tile_hint=ONE_TILE)
result = stencil.stencil(images, filters, 1)
ed = time.time()
print ed - st
def test_tilesharing(ctx):
print "#worker:", ctx.num_workers
N_EXAMPLES = 5 * ctx.num_workers
x = expr.ones((N_EXAMPLES, 1), tile_hint=(N_EXAMPLES / ctx.num_workers, 1))
y = expr.region_map(x, extent.create((0, 0), (3, 1), (N_EXAMPLES, 1)), fn=lambda data, ex, a: data+a, fn_kw={'a': 1})
npx = np.ones((N_EXAMPLES, 1))
npy = np.ones((N_EXAMPLES, 1))
npy[0:3, 0] += 1
assert np.all(np.equal(x.glom(), npx))
assert np.all(np.equal(y.glom(), npy))
def benchmark_matmul(ctx, timer):
N = int(1000 * math.pow(ctx.num_workers, 1.0 / 3.0))
#N = 4000
M = util.divup(N, ctx.num_workers)
T = util.divup(N, math.sqrt(ctx.num_workers))
util.log_info('Testing with %d workers, N = %d, tile_size=%s',
ctx.num_workers, N, T)
#x = expr.eager(expr.ones((N, N), dtype=np.double, tile_hint=(N, M)))
#y = expr.eager(expr.ones((N, N), dtype=np.double, tile_hint=(N, M)))
x = expr.eager(expr.ones((N, N), dtype=np.double, tile_hint=(T, T)))
y = expr.eager(expr.ones((N, N), dtype=np.double, tile_hint=(T, T)))
#print expr.glom(expr.dot(x, y))
#print expr.dag(expr.dot(x, y))
def _step():
expr.evaluate(expr.dot(x, y))
timer.time_op('matmul', _step)
def center_data(X, y, fit_intercept, normalize=False):
"""
Centers data to have mean zero along axis 0. This is here because
nearly all linear models will want their data to be centered.
"""
if fit_intercept:
X_mean = X.mean(axis = 0)
X_mean = expr.reshape(X_mean, (1, X_mean.shape[0]))
X -= X_mean
if normalize:
X_std = expr.sqrt(expr.sum(X ** 2, axis=0)).force()
X_std[X_std == 0] = 1
X /= X_std
else:
X_std = expr.ones(X.shape[1])
y_mean = y.mean(axis=0)
y -= y_mean
else:
X_mean = expr.zeros(X.shape[1])
X_std = expr.ones(X.shape[1])
y_mean = 0. if y.ndim == 1 else expr.zeros(y.shape[1], dtype=X.dtype)
return X, y, X_mean, y_mean, X_std
def test_convnet(ctx):
hint = util.divup(64, sqrt(ctx.num_workers))
images = expr.eager(expr.ones((N_IMGS,) + IMG_SIZE,
tile_hint=(N_IMGS, N_COLORS, hint, hint)))
w1 = expr.eager(expr.ones((N_FILTERS, N_COLORS) + FILTER_SIZE,
tile_hint=ONE_TILE))
conv1 = stencil.stencil(images, w1, 2)
pool1 = stencil.maxpool(conv1)
w2 = expr.eager(expr.ones((N_FILTERS, N_FILTERS) + FILTER_SIZE,
tile_hint=ONE_TILE))
conv2 = stencil.stencil(pool1, w2, 2)
pool2 = stencil.maxpool(conv2)
w3 = expr.eager(expr.ones((N_FILTERS, N_FILTERS) + FILTER_SIZE,
tile_hint=ONE_TILE))
conv3 = stencil.stencil(pool2, w3, 2)
pool3 = stencil.maxpool(conv3)
util.log_info(pool3.shape)
def benchmark_pagerank(ctx, timer):
num_pages = PAGES_PER_WORKER * ctx.num_workers
util.log_info('Total pages: %s', num_pages)
wts = eager(
expr.shuffle(
expr.ndarray(
(num_pages, num_pages),
dtype=np.float32,
tile_hint=(num_pages, PAGES_PER_WORKER / 8)),
make_weights,
))
p = eager(expr.ones((num_pages, 1),
tile_hint=(PAGES_PER_WORKER / 8, 1),
dtype=np.float32))
for i in range(3):
timer.time_op('pagerank', lambda: expr.dot(wts, p).force())
def conj_gradient(A, num_iter=15):
'''
NAS Conjugate Gradient benchmark
This function is similar to the NAS CG benchmark described in:
http://www.nas.nasa.gov/News/Techreports/1994/PDF/RNR-94-007.pdf
See code on page 19-20 for the pseudo code.
Args:
A(Expr): matrix to be processed.
num_iter(int): max iteration to run.
'''
x = expr.ones((A.shape[1],1))
for iter in range(num_iter):
#util.log_warn('iteration:%d', iter)
z = cgit(A, x)
x = z / expr.norm(z)
return x
def conj_gradient(A, num_iter=15):
'''
NAS Conjugate Gradient benchmark
This function is similar to the NAS CG benchmark described in:
http://www.nas.nasa.gov/News/Techreports/1994/PDF/RNR-94-007.pdf
See code on page 19-20 for the pseudo code.
Args:
A(Expr): matrix to be processed.
num_iter(int): max iteration to run.
'''
x = expr.ones((A.shape[1], 1))
for iter in range(num_iter):
#util.log_warn('iteration:%d', iter)
z = cgit(A, x)
x = z / expr.norm(z)
return x
def benchmark_pagerank(ctx, timer):
num_pages = PAGES_PER_WORKER * ctx.num_workers
util.log_info('Total pages: %s', num_pages)
wts = eager(
expr.shuffle(
expr.ndarray((num_pages, num_pages),
dtype=np.float32,
tile_hint=(num_pages, PAGES_PER_WORKER / 8)),
make_weights,
))
p = eager(
expr.ones((num_pages, 1),
tile_hint=(PAGES_PER_WORKER / 8, 1),
dtype=np.float32))
for i in range(3):
timer.time_op('pagerank', lambda: expr.dot(wts, p).force())
def pagerankDistributed(ctx, numPage, numIters, alpha):
sGenerate = time.time()
rank = eager(expr.ones((numPage, 1), tile_hint = (numPage / ctx.num_workers, 1), dtype = np.float32))
linkMatrix = eager(
expr.shuffle(
expr.ndarray(
(numPage, numPage),
dtype = np.float32,
tile_hint = (numPage, numPage / ctx.num_workers)),
make_weights,
))
eGenerate = time.time()
util.log_info("**pagerank** rank init finished")
startCompute = time.time()
for i in range(numIters):
#rank = ((1 - alpha) * expr.dot(linkMatrix, rank,tile_hint = (numPage, numPage/10))) + belta
rank = expr.dot(linkMatrix, rank, tile_hint = (numPage, numPage/10))
rank.evaluate()
endCompute = time.time()
util.log_info("**pagerank** compute finished")
return (eGenerate - sGenerate, endCompute - startCompute)
def test_pagerank(self):
_skip_if_travis()
OUTLINKS_PER_PAGE = 10
PAGES_PER_WORKER = 1000000
num_pages = PAGES_PER_WORKER * self.ctx.num_workers
wts = expr.shuffle(
expr.ndarray(
(num_pages, num_pages),
dtype=np.float32,
tile_hint=(num_pages, PAGES_PER_WORKER / 8)),
make_weights,
)
start = time.time()
p = expr.eager(expr.ones((num_pages, 1), tile_hint=(PAGES_PER_WORKER / 8, 1),
dtype=np.float32))
expr.dot(wts, p, tile_hint=(PAGES_PER_WORKER / 8, 1)).evaluate()
cost = time.time() - start
self._verify_cost("pagerank", cost)
def test_add3(self): a = expr.ones((TEST_SIZE, TEST_SIZE)) b = expr.ones((TEST_SIZE, TEST_SIZE)) c = expr.ones((TEST_SIZE, TEST_SIZE)) Assert.all_eq((a + b + c).glom(), np.ones((TEST_SIZE, TEST_SIZE)) * 3)
def test_broadcast(self): a = expr.ones((2, 1)) b = expr.ones((2, 5)) Assert.all_eq((a / b).glom(), np.ones((2, 5))) Assert.all_eq((b / a).glom(), np.ones((2, 5)))
def test_map_1(self):
a = expr.ones((20, 20))
b = expr.ones((20, 20))
c = a + b
Assert.all_eq(2 * np.ones((20, 20)), expr.glom(c))
def test_ln(self):
a = 1.0 + expr.ones((100, ), dtype=np.float32)
b = 1.0 + np.ones(100).astype(np.float32)
Assert.all_close(expr.ln(a).glom(), np.log(b))
def test_add2(self):
a = expr.ones((TEST_SIZE, TEST_SIZE))
b = expr.ones((TEST_SIZE, TEST_SIZE))
Assert.all_eq((a + b).glom(), np.ones((TEST_SIZE, TEST_SIZE)) * 2)
def test_add3(self):
a = expr.ones((TEST_SIZE, TEST_SIZE))
b = expr.ones((TEST_SIZE, TEST_SIZE))
c = expr.ones((TEST_SIZE, TEST_SIZE))
Assert.all_eq((a + b + c).glom(), np.ones((TEST_SIZE, TEST_SIZE)) * 3)
def fn3(): a = expr.ones((10,)) g = expr.diag(a) g += expr.ones((10,10)) g = expr.diagonal(g) print g.optimized()
def test_broadcast(self):
a = expr.ones((2, 1))
b = expr.ones((2, 5))
Assert.all_eq((a / b).glom(), np.ones((2, 5)))
Assert.all_eq((b / a).glom(), np.ones((2, 5)))
def simulate(ts_all, te_all, lamb_all, num_paths):
"""Range over a number of independent products.
:param ts_all: DistArray
Start dates for a series of swaptions.
:param te_all: DistArray
End dates for a series of swaptions.
:param lamb_all: DistArray
Parameter values for a series of swaptions.
:param num_paths: Int
Number of paths used in random walk.
:rtype: DistArray
"""
swaptions = []
i = 0
for ts_a, te, lamb in zip(ts_all, te_all, lamb_all):
for ts in ts_a:
# start = time()
print i
time_structure = arange(None, 0, ts + DELTA, DELTA)
maturity_structure = arange(None, 0, te, DELTA)
############# MODEL ###############
# Variance reduction technique - Antithetic Variates.
eps_tmp = randn(time_structure.shape[0] - 1, num_paths)
eps = concatenate(eps_tmp, -eps_tmp, 1)
# Forward LIBOR rates for the construction of the spot measure.
f_kk = zeros((time_structure.shape[0], 2 * num_paths))
f_kk = assign(f_kk, np.s_[0, :], F_0)
# Plane kxN of simulated LIBOR rates.
f_kn = ones((maturity_structure.shape[0], 2 * num_paths)) * F_0
# Simulations of the plane f_kn for each time step.
for t in xrange(1, time_structure.shape[0]):
f_kn_new = f_kn[1:, :] * exp(
lamb * mu(f_kn, lamb) * DELTA - 0.5 * lamb * lamb * DELTA + lamb * eps[t - 1, :] * sqrt(DELTA)
)
f_kk = assign(f_kk, np.s_[t, :], f_kn_new[0])
f_kn = f_kn_new
############## PRODUCT ###############
# Value of zero coupon bonds.
zcb = ones((int((te - ts) / DELTA) + 1, 2 * num_paths))
f_kn_modified = 1 + DELTA * f_kn
for j in xrange(zcb.shape[0] - 1):
zcb = assign(zcb, np.s_[j + 1], zcb[j] / f_kn_modified[j])
# Swaption price at maturity.
last_row = zcb[zcb.shape[0] - 1, :].reshape((20,))
swap_ts = maximum(1 - last_row - THETA * DELTA * expr.sum(zcb[1:], 0), 0)
# Spot measure used for discounting.
b_ts = ones((2 * num_paths,))
tmp = 1 + DELTA * f_kk
for j in xrange(int(ts / DELTA)):
b_ts *= tmp[j].reshape((20,))
# Swaption price at time 0.
swaption = swap_ts / b_ts
# Save expected value in bps and std.
me = mean((swaption[0:num_paths] + swaption[num_paths:]) / 2) * 10000
st = std((swaption[0:num_paths] + swaption[num_paths:]) / 2) / sqrt(num_paths) * 10000
swaptions.append([me.optimized().force(), st.optimized().force()])
# print time() - start
i += 1
return swaptions
def gen_array(shape):
return expr.ones(shape)
def fn3():
a = expr.ones((N, N))
b = expr.ones((N, N / 2))
g = expr.dot(a, b) + expr.dot(expr.sum(a, axis=1).reshape((1, N)), b)
return g
def fn3(): a = expr.ones((N, N)) b = expr.ones((N, N/2)) g = expr.dot(a, b) + expr.dot(expr.sum(a, axis=1).reshape((1, N)), b) return g
def train_smo_2005(self, data, labels):
'''
Train an SVM model using the SMO (2005) algorithm.
Args:
data(Expr): points to be trained
labels(Expr): the correct labels of the training data
'''
N = data.shape[0] # Number of instances
D = data.shape[1] # Number of features
self.b = 0.0
alpha = expr.zeros((N,1), dtype=np.float64, tile_hint=[N/self.ctx.num_workers, 1]).force()
# linear kernel
kernel_results = expr.dot(data, expr.transpose(data), tile_hint=[N/self.ctx.num_workers, N])
gradient = expr.ones((N, 1), dtype=np.float64, tile_hint=[N/self.ctx.num_workers, 1]) * -1.0
expr_labels = expr.lazify(labels)
util.log_info("Starting SMO")
pv1 = pv2 = -1
it = 0
while it < self.maxiter:
util.log_info("Iteration:%d", it)
minObj = 1e100
expr_alpha = expr.lazify(alpha)
G = expr.multiply(labels, gradient) * -1.0
v1_mask = ((expr_labels > self.tol) * (expr_alpha < self.C) + (expr_labels < -self.tol) * (expr_alpha > self.tol))
v1 = expr.argmax(G[v1_mask-True]).glom().item()
maxG = G[v1,0].glom()
print 'maxv1:', v1, 'maxG:', maxG
v2_mask = ((expr_labels > self.tol) * (expr_alpha > self.tol) + (expr_labels < -self.tol) * (expr_alpha < self.C))
min_v2 = expr.argmin(G[v2_mask-True]).glom().item()
minG = G[min_v2,0].glom()
#print 'minv2:', min_v2, 'minG:', minG
set_v2 = v2_mask.glom().nonzero()[0]
#print 'actives:', set_v2.shape[0]
v2 = -1
for v in set_v2:
b = maxG - G[v,0].glom()
if b > self.tol:
na = (kernel_results[v1,v1] + kernel_results[v,v] - 2*kernel_results[v1,v]).glom()[0][0]
if na < self.tol: na = 1e12
obj = -(b*b)/na
if obj <= minObj and v1 != pv1 or v != pv2:
v2 = v
a = na
minObj = obj
if v2 == -1: break
if maxG - minG < self.tol: break
print 'opt v1:', v1, 'v2:', v2
pv1 = v1
pv2 = v2
y1 = labels[v1,0]
y2 = labels[v2,0]
oldA1 = alpha[v1,0]
oldA2 = alpha[v2,0]
# Calculate new alpha values, to reduce the objective function...
b = y2*expr.glom(gradient[v2,0]) - y1*expr.glom(gradient[v1,0])
if y1 != y2:
a += 4 * kernel_results[v1,v2].glom()
newA1 = oldA1 + y1*b/a
newA2 = oldA2 - y2*b/a
# Correct for alpha being out of range...
sum = y1*oldA1 + y2*oldA2;
if newA1 < self.tol: newA1 = 0.0
elif newA1 > self.C: newA1 = self.C
newA2 = y2 * (sum - y1 * newA1)
if newA2 < self.tol: newA2 = 0.0;
elif newA2 > self.C: newA2 = self.C
newA1 = y1 * (sum - y2 * newA2)
# Update the gradient...
dA1 = newA1 - oldA1
dA2 = newA2 - oldA2
gradient += expr.multiply(labels, kernel_results[:,v1]) * y1 * dA1 + expr.multiply(labels, kernel_results[:,v2]) * y2 * dA2
alpha[v1,0] = newA1
alpha[v2,0] = newA2
#print 'alpha:', alpha.glom().T
it += 1
#print 'gradient:', gradient.glom().T
self.w = expr.zeros((D, 1), dtype=np.float64).force()
for i in xrange(D):
self.w[i,0] = expr.reduce(alpha, axis=None, dtype_fn=lambda input: input.dtype,
local_reduce_fn=margin_mapper,
accumulate_fn=np.add,
fn_kw=dict(label=labels, data=expr.force(data[:,i]))).glom()
self.b = 0.0
E = (labels - self.margins(data)).force()
minB = -1e100
maxB = 1e100
actualB = 0.0
numActualB = 0
for i in xrange(N):
ai = alpha[i,0]
yi = labels[i,0]
Ei = E[i,0]
if ai < 1e-3:
if yi < self.tol:
maxB = min((maxB,Ei))
else:
minB = max((minB,Ei))
elif ai > self.C - 1e-3:
if yi < self.tol:
minB = max((minB,Ei))
else:
maxB = min((maxB,Ei))
else:
numActualB += 1
actualB += (Ei - actualB) / float(numActualB)
if numActualB > 0:
self.b = actualB
else:
self.b = 0.5*(minB + maxB)
self.usew_ = True
print 'iteration finish:', it
print 'b:', self.b
print 'w:', self.w.glom()
def test_add2(self): a = expr.ones((TEST_SIZE, TEST_SIZE)) b = expr.ones((TEST_SIZE, TEST_SIZE)) Assert.all_eq((a + b).glom(), np.ones((TEST_SIZE, TEST_SIZE)) * 2)
def gen_array(shape): return expr.ones(shape)
def simulate(ts_all, te_all, lamb_all, num_paths):
'''Range over a number of independent products.
:param ts_all: DistArray
Start dates for a series of swaptions.
:param te_all: DistArray
End dates for a series of swaptions.
:param lamb_all: DistArray
Parameter values for a series of swaptions.
:param num_paths: Int
Number of paths used in random walk.
:rtype: DistArray
'''
swaptions = []
i = 0
for ts_a, te, lamb in zip(ts_all, te_all, lamb_all):
for ts in ts_a:
#start = time()
print i
time_structure = arange(None, 0, ts + DELTA, DELTA)
maturity_structure = arange(None, 0, te, DELTA)
############# MODEL ###############
# Variance reduction technique - Antithetic Variates.
eps_tmp = randn(time_structure.shape[0] - 1, num_paths)
eps = concatenate(eps_tmp, -eps_tmp, 1)
# Forward LIBOR rates for the construction of the spot measure.
f_kk = zeros((time_structure.shape[0], 2*num_paths))
f_kk = assign(f_kk, np.s_[0, :], F_0)
# Plane kxN of simulated LIBOR rates.
f_kn = ones((maturity_structure.shape[0], 2*num_paths))*F_0
# Simulations of the plane f_kn for each time step.
for t in xrange(1, time_structure.shape[0]):
f_kn_new = f_kn[1:, :]*exp(lamb*mu(f_kn, lamb)*DELTA-0.5*lamb*lamb *
DELTA + lamb*eps[t - 1, :]*sqrt(DELTA))
f_kk = assign(f_kk, np.s_[t, :], f_kn_new[0])
f_kn = f_kn_new
############## PRODUCT ###############
# Value of zero coupon bonds.
zcb = ones((int((te-ts)/DELTA)+1, 2*num_paths))
f_kn_modified = 1 + DELTA*f_kn
for j in xrange(zcb.shape[0] - 1):
zcb = assign(zcb, np.s_[j + 1], zcb[j] / f_kn_modified[j])
# Swaption price at maturity.
last_row = zcb[zcb.shape[0] - 1, :].reshape((20, ))
swap_ts = maximum(1 - last_row - THETA*DELTA*expr.sum(zcb[1:], 0), 0)
# Spot measure used for discounting.
b_ts = ones((2*num_paths, ))
tmp = 1 + DELTA * f_kk
for j in xrange(int(ts/DELTA)):
b_ts *= tmp[j].reshape((20, ))
# Swaption price at time 0.
swaption = swap_ts/b_ts
# Save expected value in bps and std.
me = mean((swaption[0:num_paths] + swaption[num_paths:])/2) * 10000
st = std((swaption[0:num_paths] + swaption[num_paths:])/2)/sqrt(num_paths)*10000
swaptions.append([me.optimized().force(), st.optimized().force()])
#print time() - start
i += 1
return swaptions
