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提交 af6361cd authored 作者: Ian Goodfellow's avatar Ian Goodfellow
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made test_blas use deterministic interface to theano.function

上级 8ebe4381
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正在显示 包含 33 行增加 和 33 行删除
theano/tensor/tests/test_blas.py
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......@@ -185,8 +185,8 @@ class t_gemm(TestCase):
l2_reg = T.constant(0.0001).astype(config.floatX)
#test constant merge with gemm
f = theano.function([a, b], updates={s: lr1 * T.dot(a, b) +
l2_reg * lr2 * s},
f = theano.function([a, b], updates=[(s, lr1 * T.dot(a, b) +
l2_reg * lr2 * s)],
mode=mode_not_fast_compile).maker.fgraph.toposort()
#[Gemm{inplace}(<TensorType(float64, matrix)>, 0.01,
# <TensorType(float64, matrix)>, <TensorType(float64, matrix)>,
......@@ -195,8 +195,8 @@ class t_gemm(TestCase):
assert f[0].op == gemm_inplace
#test factored scalar with merge
f = theano.function([a, b], updates={s: lr1 * (T.dot(a, b) -
l2_reg * s)},
f = theano.function([a, b], updates=[(s, lr1 * (T.dot(a, b) -
l2_reg * s))],
mode=mode_not_fast_compile).maker.fgraph.toposort()
#[Gemm{inplace}(<TensorType(float64, matrix)>, 0.01,
# <TensorType(float64, matrix)>, <TensorType(float64, matrix)>,
......@@ -206,7 +206,7 @@ class t_gemm(TestCase):
#test factored scalar with merge and neg
f = theano.function([a, b],
updates={s: s - lr1 * (s * .0002 + T.dot(a, b))},
updates=[(s, s - lr1 * (s * .0002 + T.dot(a, b)))],
mode=mode_not_fast_compile).maker.fgraph.toposort()
#[Gemm{inplace}(<TensorType(float64, matrix)>, -0.01,
# <TensorType(float64, matrix)>, <TensorType(float64, matrix)>,
......@@ -368,7 +368,7 @@ class t_gemm(TestCase):
tz_i = gemm_no_inplace(tz[:, :, i], ta, tx[
:, :, i], ty[:, :, i], tb)
g_i = theano.function([], tz_i,
updates={tz: T.set_subtensor(tz[:, :, i], tz_i)},
updates=[(tz, T.set_subtensor(tz[:, :, i], tz_i))],
mode=compile.Mode(optimizer=None, linker=l))
for j in xrange(3):
g_i()
......@@ -801,7 +801,7 @@ def test_gemm_unrolled():
cur_V = update_V(cur_H)
cur_H = update_H(cur_V)
unrolled_theano = theano.function([], updates={V: cur_V, H: cur_H},
unrolled_theano = theano.function([], updates=[(V, cur_V), (H, cur_H)],
name='unrolled_theano')
nb_dot = sum([1 for node in unrolled_theano.maker.fgraph.toposort()
if isinstance(node.op, (theano.tensor.Dot,
......@@ -1032,7 +1032,7 @@ def test_dot_w_self():
p = T.dot(A, A) * B
grad = T.grad(T.mean(p), A)
f = theano.function([B], p, updates={A: A - grad})
f = theano.function([B], p, updates=[(A, A - grad)])
# tests correctness in debugmode
f(numpy.asarray([[0, 1], [2, 3]], dtype=config.floatX))
......@@ -1119,7 +1119,7 @@ class TestGemv(TestCase, unittest_tools.TestOptimizationMixin):
assert topo[0].op.inplace == False
#test the inplace version
g = theano.function([], [], updates={v2: v2 + theano.dot(m, v1)},
g = theano.function([], [], updates=[(v2, v2 + theano.dot(m, v1))],
mode=mode_blas_opt)
# Assert they produce the same output
......@@ -1169,7 +1169,7 @@ class TestGemv(TestCase, unittest_tools.TestOptimizationMixin):
assert topo[-1].op.inplace == False
#test the inplace version
g = theano.function([], [], updates={v2: v2 + theano.dot(v1, m)},
g = theano.function([], [], updates=[(v2, v2 + theano.dot(v1, m))],
mode=mode_blas_opt)
# Assert they produce the same output
......@@ -1575,7 +1575,7 @@ class TestGer(TestCase, unittest_tools.TestOptimizationMixin):
def function(self, inputs, outputs, updates=None):
if updates is None:
updates = {}
updates = []
return theano.function(inputs, outputs, self.mode, updates=updates)
def b(self, bval):
......@@ -1691,8 +1691,8 @@ class TestGer(TestCase, unittest_tools.TestOptimizationMixin):
def test_inplace(self):
A = self.shared(numpy.random.rand(4, 5).astype(self.dtype))
f = self.function([self.x, self.y], [],
updates={A: A + T.constant(0.1, dtype=self.dtype) *
T.outer(self.x, self.y)})
updates=[(A, A + T.constant(0.1, dtype=self.dtype) *
T.outer(self.x, self.y))])
self.assertFunctionContains(f, self.ger_destructive)
f(numpy.random.rand(4).astype(self.dtype),
numpy.random.rand(5).astype(self.dtype))
......@@ -1731,15 +1731,15 @@ class TestBlasStrides(TestCase):
bt_dev = b_t.get_value(borrow=False, return_internal_type=True)
ct_dev = c_t.get_value(borrow=False, return_internal_type=True)
f_nn = theano.function([], [], updates={a: tensor.dot(b, c)},
f_nn = theano.function([], [], updates=[(a, tensor.dot(b, c))],
mode=self.mode)
#print 'class name:', self.__class__.__name__
#theano.printing.debugprint(f_nn)
f_nt = theano.function([], [], updates={a: tensor.dot(b, c_t.T)},
f_nt = theano.function([], [], updates=[(a, tensor.dot(b, c_t.T))],
mode=self.mode)
f_tn = theano.function([], [], updates={a: tensor.dot(b_t.T, c)},
f_tn = theano.function([], [], updates=[(a, tensor.dot(b_t.T, c))],
mode=self.mode)
f_tt = theano.function([], [], updates={a: tensor.dot(b_t.T, c_t.T)},
f_tt = theano.function([], [], updates=[(a, tensor.dot(b_t.T, c_t.T))],
mode=self.mode)
# Try with all stride patterns, and all transposed pattern
......@@ -1802,14 +1802,14 @@ class TestBlasStrides(TestCase):
bt_dev = b_t.get_value(borrow=False, return_internal_type=True)
ct_dev = c_t.get_value(borrow=False, return_internal_type=True)
f_nn = theano.function([], [], updates={a: l * tensor.dot(b, c)},
f_nn = theano.function([], [], updates=[(a, l * tensor.dot(b, c))],
mode=self.mode)
f_nt = theano.function([], [], updates={a: l * tensor.dot(b, c_t.T)},
f_nt = theano.function([], [], updates=[(a, l * tensor.dot(b, c_t.T))],
mode=self.mode)
f_tn = theano.function([], [], updates={a: l * tensor.dot(b_t.T, c)},
f_tn = theano.function([], [], updates=[(a, l * tensor.dot(b_t.T, c))],
mode=self.mode)
f_tt = theano.function([], [],
updates={a: l * tensor.dot(b_t.T, c_t.T)},
updates=[(a, l * tensor.dot(b_t.T, c_t.T))],
mode=self.mode)
# Try with all stride patterns, and all transposed pattern
......@@ -1875,28 +1875,28 @@ class TestBlasStrides(TestCase):
ct_dev = c_t.get_value(borrow=False, return_internal_type=True)
f_nnn = theano.function([], [],
updates={a: (l * a + tensor.dot(b, c))},
updates=[(a, (l * a + tensor.dot(b, c)))],
mode=self.mode)
f_nnt = theano.function([], [],
updates={a: (l * a + tensor.dot(b, c_t.T))},
updates=[(a, (l * a + tensor.dot(b, c_t.T)))],
mode=self.mode)
f_ntn = theano.function([], [],
updates={a: (l * a + tensor.dot(b_t.T, c))},
updates=[(a, (l * a + tensor.dot(b_t.T, c)))],
mode=self.mode)
f_ntt = theano.function([], [],
updates={a: (l * a + tensor.dot(b_t.T, c_t.T))},
updates=[(a, (l * a + tensor.dot(b_t.T, c_t.T)))],
mode=self.mode)
f_tnn = theano.function([], [],
updates={a_t: (l * a_t + tensor.dot(b, c).T)},
updates=[(a_t, (l * a_t + tensor.dot(b, c).T))],
mode=self.mode)
f_tnt = theano.function([], [],
updates={a_t: (l * a_t + tensor.dot(b, c_t.T).T)},
updates=[(a_t, (l * a_t + tensor.dot(b, c_t.T).T))],
mode=self.mode)
f_ttn = theano.function([], [],
updates={a_t: (l * a_t + tensor.dot(b_t.T, c).T)},
updates=[(a_t, (l * a_t + tensor.dot(b_t.T, c).T))],
mode=self.mode)
f_ttt = theano.function([], [],
updates={a_t: (l * a_t + tensor.dot(b_t.T, c_t.T).T)},
updates=[(a_t, (l * a_t + tensor.dot(b_t.T, c_t.T).T))],
mode=self.mode)
# Try with all stride patterns, and all transposed pattern
......@@ -1985,11 +1985,11 @@ class TestBlasStrides(TestCase):
b_dev = b.get_value(borrow=False, return_internal_type=True)
c_dev = c.get_value(borrow=False, return_internal_type=True)
f_n = theano.function([], [], updates={a: (a + l * tensor.dot(b, c))},
f_n = theano.function([], [], updates=[(a, (a + l * tensor.dot(b, c)))],
mode=self.mode)
f_t = theano.function([], [],
updates={a: (a + l * tensor.dot(b_t.T, c))},
updates=[(a, (a + l * tensor.dot(b_t.T, c)))],
mode=self.mode)
# Try with all stride patterns, and all transposed pattern
......@@ -2041,11 +2041,11 @@ class TestBlasStrides(TestCase):
c_dev = c.get_value(borrow=False, return_internal_type=True)
f_n = theano.function([], [],
updates={a: (a + l * tensor.outer(b, c))},
updates=[(a, (a + l * tensor.outer(b, c)))],
mode=self.mode)
f_t = theano.function([], [],
updates={a_t: (a_t + l * tensor.outer(b, c).T)},
updates=[(a_t, (a_t + l * tensor.outer(b, c).T))],
mode=self.mode)
# Try with all stride patterns, and all transposed patterns
......
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