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提交 7a515454 authored 作者: Pascal Lamblin's avatar Pascal Lamblin
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Add tests for strides with abs value > 1

上级 a42afaf6
隐藏空白字符变更
内嵌 并排
正在显示 包含 135 行增加 和 123 行删除
theano/sandbox/cuda/tests/test_blas.py
浏览文件 @ 7a515454
......@@ -195,29 +195,31 @@ def test_dot22_strides():
mode=mode_with_gpu)
# Try with all stride patterns, and all transposed pattern
for steps in itertools.product((-1, 1), repeat=4):
b_step1, b_step2, c_step1, c_step2 = steps
b.set_value(b_gpu.copy()[::b_step1, ::b_step2], borrow=True)
c.set_value(c_gpu.copy()[::c_step1, ::c_step2], borrow=True)
b_t.set_value(bt_gpu.copy()[::b_step2, ::b_step1], borrow=True)
c_t.set_value(ct_gpu.copy()[::c_step2, ::c_step1], borrow=True)
# Numpy result
a_n = numpy.dot(bv[::b_step1, ::b_step2],
cv[::c_step1, ::c_step2])
for step_signs in itertools.product((-1, 1), repeat=4):
for step in (1, 2):
b_step1, b_step2, c_step1, c_step2 = (s * step
for s in step_signs)
b.set_value(b_gpu.copy()[::b_step1, ::b_step2], borrow=True)
c.set_value(c_gpu.copy()[::c_step1, ::c_step2], borrow=True)
b_t.set_value(bt_gpu.copy()[::b_step2, ::b_step1], borrow=True)
c_t.set_value(ct_gpu.copy()[::c_step2, ::c_step1], borrow=True)
# Numpy result
a_n = numpy.dot(bv[::b_step1, ::b_step2],
cv[::c_step1, ::c_step2])
f_nn()
assert numpy.allclose(a.get_value(), a_n)
f_nn()
assert numpy.allclose(a.get_value(), a_n)
f_nt()
assert numpy.allclose(a.get_value(), a_n)
f_nt()
assert numpy.allclose(a.get_value(), a_n)
f_tn()
assert numpy.allclose(a.get_value(), a_n)
f_tn()
assert numpy.allclose(a.get_value(), a_n)
f_tt()
assert numpy.allclose(a.get_value(), a_n)
f_tt()
assert numpy.allclose(a.get_value(), a_n)
cmp((3, 4), (4, 5))
cmp((1, 4), (4, 5))
......@@ -263,29 +265,31 @@ def test_dot22scalar_strides():
mode=mode_with_gpu)
# Try with all stride patterns, and all transposed pattern
for steps in itertools.product((-1, 1), repeat=4):
b_step1, b_step2, c_step1, c_step2 = steps
for step_signs in itertools.product((-1, 1), repeat=4):
for step in (1, 2):
b_step1, b_step2, c_step1, c_step2 = (s * step
for s in step_signs)
b.set_value(b_gpu.copy()[::b_step1, ::b_step2], borrow=True)
c.set_value(c_gpu.copy()[::c_step1, ::c_step2], borrow=True)
b_t.set_value(bt_gpu.copy()[::b_step2, ::b_step1], borrow=True)
c_t.set_value(ct_gpu.copy()[::c_step2, ::c_step1], borrow=True)
b.set_value(b_gpu.copy()[::b_step1, ::b_step2], borrow=True)
c.set_value(c_gpu.copy()[::c_step1, ::c_step2], borrow=True)
b_t.set_value(bt_gpu.copy()[::b_step2, ::b_step1], borrow=True)
c_t.set_value(ct_gpu.copy()[::c_step2, ::c_step1], borrow=True)
# Numpy result
a_n = l * numpy.dot(bv[::b_step1, ::b_step2],
cv[::c_step1, ::c_step2])
# Numpy result
a_n = l * numpy.dot(bv[::b_step1, ::b_step2],
cv[::c_step1, ::c_step2])
f_nn()
assert numpy.allclose(a.get_value(), a_n)
f_nn()
assert numpy.allclose(a.get_value(), a_n)
f_nt()
assert numpy.allclose(a.get_value(), a_n)
f_nt()
assert numpy.allclose(a.get_value(), a_n)
f_tn()
assert numpy.allclose(a.get_value(), a_n)
f_tn()
assert numpy.allclose(a.get_value(), a_n)
f_tt()
assert numpy.allclose(a.get_value(), a_n)
f_tt()
assert numpy.allclose(a.get_value(), a_n)
cmp((3, 4), (4, 5))
cmp((1, 4), (4, 5))
......@@ -341,58 +345,60 @@ def test_gemm_strides():
mode=mode_with_gpu)
# Try with all stride patterns, and all transposed pattern
for steps in itertools.product((-1, 1), repeat=6):
a_step1, a_step2, b_step1, b_step2, c_step1, c_step2 = steps
b.set_value(b_gpu.copy()[::b_step1, ::b_step2], borrow=True)
c.set_value(c_gpu.copy()[::c_step1, ::c_step2], borrow=True)
b_t.set_value(bt_gpu.copy()[::b_step2, ::b_step1], borrow=True)
c_t.set_value(ct_gpu.copy()[::c_step2, ::c_step1], borrow=True)
# Numpy results
a_n = (l * av[::a_step1, ::a_step2]
+ numpy.dot(bv[::b_step1, ::b_step2],
cv[::c_step1, ::c_step2]))
at_n = (l * av[::a_step1, ::a_step2].T
+ numpy.dot(bv[::b_step1, ::b_step2],
cv[::c_step1, ::c_step2]).T)
# a's value is updated, so we need to reinitialize it each time
a.set_value(a_gpu.copy()[::a_step1, ::a_step2], borrow=True)
f_nnn()
assert numpy.allclose(a.get_value(), a_n)
a.set_value(a_gpu.copy()[::a_step1, ::a_step2], borrow=True)
f_nnt()
assert numpy.allclose(a.get_value(), a_n)
a.set_value(a_gpu.copy()[::a_step1, ::a_step2], borrow=True)
f_ntn()
assert numpy.allclose(a.get_value(), a_n)
a.set_value(a_gpu.copy()[::a_step1, ::a_step2], borrow=True)
f_ntt()
assert numpy.allclose(a.get_value(), a_n)
a_t.set_value(transpose(a_gpu.copy())[::a_step2, ::a_step1],
borrow=True)
f_tnn()
assert numpy.allclose(a_t.get_value(), at_n)
a_t.set_value(transpose(a_gpu.copy())[::a_step2, ::a_step1],
borrow=True)
f_tnt()
assert numpy.allclose(a_t.get_value(), at_n)
a_t.set_value(transpose(a_gpu.copy())[::a_step2, ::a_step1],
borrow=True)
f_ttn()
assert numpy.allclose(a_t.get_value(), at_n)
a_t.set_value(transpose(a_gpu.copy())[::a_step2, ::a_step1],
borrow=True)
f_ttt()
assert numpy.allclose(a_t.get_value(), at_n)
for step_signs in itertools.product((-1, 1), repeat=6):
for step in (1, 2):
a_step1, a_step2, b_step1, b_step2, c_step1, c_step2 = \
(s * step for s in step_signs)
b.set_value(b_gpu.copy()[::b_step1, ::b_step2], borrow=True)
c.set_value(c_gpu.copy()[::c_step1, ::c_step2], borrow=True)
b_t.set_value(bt_gpu.copy()[::b_step2, ::b_step1], borrow=True)
c_t.set_value(ct_gpu.copy()[::c_step2, ::c_step1], borrow=True)
# Numpy results
a_n = (l * av[::a_step1, ::a_step2]
+ numpy.dot(bv[::b_step1, ::b_step2],
cv[::c_step1, ::c_step2]))
at_n = (l * av[::a_step1, ::a_step2].T
+ numpy.dot(bv[::b_step1, ::b_step2],
cv[::c_step1, ::c_step2]).T)
# a's value is updated, so we need to reinitialize it each time
a.set_value(a_gpu.copy()[::a_step1, ::a_step2], borrow=True)
f_nnn()
assert numpy.allclose(a.get_value(), a_n)
a.set_value(a_gpu.copy()[::a_step1, ::a_step2], borrow=True)
f_nnt()
assert numpy.allclose(a.get_value(), a_n)
a.set_value(a_gpu.copy()[::a_step1, ::a_step2], borrow=True)
f_ntn()
assert numpy.allclose(a.get_value(), a_n)
a.set_value(a_gpu.copy()[::a_step1, ::a_step2], borrow=True)
f_ntt()
assert numpy.allclose(a.get_value(), a_n)
a_t.set_value(transpose(a_gpu.copy())[::a_step2, ::a_step1],
borrow=True)
f_tnn()
assert numpy.allclose(a_t.get_value(), at_n)
a_t.set_value(transpose(a_gpu.copy())[::a_step2, ::a_step1],
borrow=True)
f_tnt()
assert numpy.allclose(a_t.get_value(), at_n)
a_t.set_value(transpose(a_gpu.copy())[::a_step2, ::a_step1],
borrow=True)
f_ttn()
assert numpy.allclose(a_t.get_value(), at_n)
a_t.set_value(transpose(a_gpu.copy())[::a_step2, ::a_step1],
borrow=True)
f_ttt()
assert numpy.allclose(a_t.get_value(), at_n)
cmp((3, 5), (3, 4), (4, 5))
......@@ -433,24 +439,26 @@ def test_gemv_strides():
mode=mode_with_gpu)
# Try with all stride patterns, and all transposed pattern
for steps in itertools.product((1, -1), repeat=4):
a_step, b_step1, b_step2, c_step = steps
a.set_value(a_gpu.copy()[::a_step], borrow=True)
b.set_value(b_gpu.copy()[::b_step1, ::b_step2],
borrow=True)
b_t.set_value(transpose(b_gpu.copy())[::b_step2, ::b_step1],
borrow=True)
c.set_value(c_gpu.copy()[::c_step], borrow=True)
a_n = (av[::a_step]
+ l * numpy.dot(bv[::b_step1, ::b_step2], cv[::c_step]))
f_n()
assert numpy.allclose(a.get_value(), a_n), (a.get_value(), a_n)
a.set_value(a_gpu.copy()[::a_step], borrow=True)
f_t()
assert numpy.allclose(a.get_value(), a_n), (a.get_value(), a_n)
for step_signs in itertools.product((1, -1), repeat=4):
for step in (1, 2):
a_step, b_step1, b_step2, c_step = (s * step
for s in step_signs)
a.set_value(a_gpu.copy()[::a_step], borrow=True)
b.set_value(b_gpu.copy()[::b_step1, ::b_step2],
borrow=True)
b_t.set_value(transpose(b_gpu.copy())[::b_step2, ::b_step1],
borrow=True)
c.set_value(c_gpu.copy()[::c_step], borrow=True)
a_n = (av[::a_step]
+ l * numpy.dot(bv[::b_step1, ::b_step2], cv[::c_step]))
f_n()
assert numpy.allclose(a.get_value(), a_n), (a.get_value(), a_n)
a.set_value(a_gpu.copy()[::a_step], borrow=True)
f_t()
assert numpy.allclose(a.get_value(), a_n), (a.get_value(), a_n)
cmp(3, (3, 5), 5)
cmp(1, (1, 5), 5)
......@@ -485,23 +493,27 @@ def test_ger_strides():
mode=mode_with_gpu)
# Try with all stride patterns, and all transposed patterns
for steps in itertools.product((1, -1), repeat=4):
a_step1, a_step2, b_step, c_step = steps
a.set_value(a_gpu.copy()[::a_step1, ::a_step2], borrow=True)
a_t.set_value(transpose(a_gpu.copy())[::a_step1, ::a_step2],
borrow=True)
b.set_value(b_gpu.copy()[::b_step], borrow=True)
c.set_value(c_gpu.copy()[::c_step], borrow=True)
f_n()
n_n = (av[::a_step1, ::a_step2]
+ l * numpy.outer(bv[::b_step], cv[::c_step]))
assert numpy.allclose(a.get_value(), n_n), (a.get_value(), n_n)
f_t()
n_t = (av.T[::a_step1, ::a_step2]
+ l * numpy.outer(bv[::b_step], cv[::c_step]).T)
assert numpy.allclose(a_t.get_value(), n_t), (a_t.get_value(), n_t)
for step_signs in itertools.product((1, -1), repeat=4):
for step in (1, 2):
a_step1, a_step2, b_step, c_step = (s * step
for s in step_signs)
a.set_value(a_gpu.copy()[::a_step1, ::a_step2], borrow=True)
a_t.set_value(transpose(a_gpu.copy())[::a_step1, ::a_step2],
borrow=True)
b.set_value(b_gpu.copy()[::b_step], borrow=True)
c.set_value(c_gpu.copy()[::c_step], borrow=True)
f_n()
n_n = (av[::a_step1, ::a_step2]
+ l * numpy.outer(bv[::b_step], cv[::c_step]))
assert numpy.allclose(a.get_value(), n_n), (a.get_value(), n_n)
f_t()
n_t = (av.T[::a_step1, ::a_step2]
+ l * numpy.outer(bv[::b_step], cv[::c_step]).T)
assert numpy.allclose(a_t.get_value(), n_t),\
(a_t.get_value(), n_t)
cmp((3, 5), 3, 5)
cmp((1, 5), 1, 5)
......
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