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提交 2ebc24ff authored 作者: Iulian Vlad Serban's avatar Iulian Vlad Serban
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Continued work on #3018. Fixed stack trace copy over for additional…

Continued work on #3018. Fixed stack trace copy over for additional optimizations, fixed errors in previous commit and inclduded addditional tests.
上级 daf7ea76
隐藏空白字符变更
内嵌 并排
正在显示 包含 104 行增加 和 7 行删除
theano/gof/utils.py
浏览文件 @ 2ebc24ff
......@@ -77,6 +77,7 @@ def add_tag_trace(thing, user_line=1):
if limit == -1:
limit = None
tr = simple_extract_stack(limit=limit)[:-1]
# Different python version use different sementic for
# limit. python 2.7 include the call to extrack_stack. The -1 get
# rid of it.
......@@ -93,7 +94,11 @@ def add_tag_trace(thing, user_line=1):
"theano/sparse/", "theano\\sparse\\",
"theano/typed_list/", "theano\\typed_list\\",
]:
if p in file_path:
# Julian: I added the 'tests' exception together with Arnaud.
# Otherwise, we'd lose the stack trace during in our test cases
# (e.g. in test_opt.py). We're not sure this is the right way to
# do it though.
if p in file_path and 'tests' not in file_path:
tr = tr[:-1]
rm = True
break
......
theano/tensor/opt.py
浏览文件 @ 2ebc24ff
......@@ -91,8 +91,8 @@ def copy_stack_trace(from_var, to_var):
tr += getattr(v.tag, 'trace', [])
else:
# If from_var is not a list, it must be a single tensor
# variable, so just store that particular stack trace
# If from_var is not a list, it must be a single tensor variable,
# so just store that particular stack trace
tr = getattr(from_var.tag, 'trace', [])
# Copy over stack traces to to_var
......@@ -2565,7 +2565,7 @@ def local_subtensor_lift(node):
ret = u.owner.op(x_idx)
# Copy over previous output stacktrace
# and stacktrace from previous unary operation
copy_stack_trace([node.outputs, node.inputs[0]], ret)
copy_stack_trace([node.outputs[0], node.inputs[0]], ret)
return [ret]
if isinstance(u.owner.op, T.Elemwise):
......@@ -2574,7 +2574,14 @@ def local_subtensor_lift(node):
# There is no broadcastable in the inputs
idx = node.inputs[1:]
new_inputs = [node.op(i, *idx) for i in u.owner.inputs]
return [u.owner.op(*new_inputs)]
# Copy over previous output stacktrace
copy_stack_trace(node.outputs[0], new_inputs)
ret = u.owner.op(*new_inputs)
# Copy over previous output stacktrace
# and stacktrace from previous unary operation
copy_stack_trace([node.outputs[0], node.inputs[0]], ret)
return [ret]
elif all([sum(i.type.broadcastable) in [i.ndim, 0]
for i in u.owner.inputs]):
# There is no broadcastable in the inputs or it is scalar
......@@ -2591,7 +2598,15 @@ def local_subtensor_lift(node):
else:
new_inputs.append(
i.dimshuffle(['x'] * node.outputs[0].ndim))
return [u.owner.op(*new_inputs)]
# Copy over previous output stacktrace
copy_stack_trace(node.outputs[0], new_inputs)
ret = u.owner.op(*new_inputs)
# Copy over previous output stacktrace
# and stacktrace from previous unary operation
copy_stack_trace([node.outputs[0], node.inputs[0]], ret)
return [ret]
if isinstance(u.owner.op, T.Rebroadcast):
# make sure that Rebroadcast has only 1 input
......@@ -2617,7 +2632,13 @@ def local_subtensor_lift(node):
j += 1
subt_x = node.op(u.owner.inputs[0], *node.inputs[1:])
# Copy over previous output stacktrace
copy_stack_trace(node.outputs[0], subt_x)
rbcast_subt_x = T.Rebroadcast(*new_axis)(subt_x)
# Copy over previous output stacktrace
# and stacktrace from previous unary operation
copy_stack_trace([node.outputs[0], node.inputs[0]], rbcast_subt_x)
return [rbcast_subt_x]
......@@ -2809,11 +2830,18 @@ def local_subtensor_merge(node):
merged_slices = make_constant(merged_slices)
subtens = Subtensor(merged_slices)
sl_ins = Subtensor.collapse(
merged_slices,
lambda x: isinstance(x, T.Variable))
# Do not call make_node for test_value
out = subtens(x, *sl_ins)
# Copy over previous output stacktrace
# and stacktrace from previous slicing operation.
# Why? Because, the merged slicing operation could have failed
# because of either of the two original slicing operations
copy_stack_trace([node.outputs[0], node.inputs[0]], out)
return [out]
......@@ -2821,6 +2849,7 @@ def local_subtensor_merge(node):
@register_specialize
@gof.local_optimizer([Subtensor])
def local_subtensor_of_alloc(node):
#TODO Julian: Document this better!
"""alloc[x:y] -> alloc"""
if not isinstance(node.op, Subtensor):
return False
......
theano/tensor/tests/test_opt.py
浏览文件 @ 2ebc24ff
......@@ -1864,11 +1864,18 @@ class test_local_subtensor_make_vector(unittest.TestCase):
#self.assertTrue(hasattr(f.outputs[0].tag, 'trace'))
class test_local_subtensor_lift(unittest.TestCase):
def _verify_stack_trace(self, f):
for output in f.outputs:
# Check stacktrace was copied over correctly after opt was applied
self.assertTrue(hasattr(output.variable.tag, 'trace'))
def test0(self):
# basic test that the Op works
x = tensor.matrix('x')
f = function([x], tensor.exp(x)[0], mode=mode_opt)
self._verify_stack_trace(f)
prog = f.maker.fgraph.toposort()
assert isinstance(prog[0].op, tensor.Subtensor) # first subtensor
assert prog[1].op == tensor.exp
......@@ -1881,6 +1888,8 @@ class test_local_subtensor_lift(unittest.TestCase):
x = tensor.matrix('x')
f = function([x], [tensor.exp(x)[0], tensor.exp(x)], mode=mode_opt)
self._verify_stack_trace(f)
prog = f.maker.fgraph.toposort()
assert prog[0].op == tensor.exp
assert isinstance(prog[1].op, tensor.Subtensor) # first subtensor
......@@ -1895,6 +1904,8 @@ class test_local_subtensor_lift(unittest.TestCase):
z = tensor.matrix('z')
f = function([x, y, z], tensor.exp(x + y + z)[0], mode=mode_opt)
self._verify_stack_trace(f)
prog = f.maker.fgraph.toposort()
assert isinstance(prog[0].op, tensor.DimShuffle)
assert isinstance(prog[1].op, tensor.Subtensor) # first subtensor
......@@ -1912,6 +1923,8 @@ class test_local_subtensor_lift(unittest.TestCase):
z = tensor.matrix('z')
f = function([x, y, z], tensor.exp(x + y + z)[0:2], mode=mode_opt)
self._verify_stack_trace(f)
prog = f.maker.fgraph.toposort()
assert isinstance(prog[0].op, tensor.DimShuffle)
assert isinstance(prog[1].op, tensor.Subtensor) # first subtensor
......@@ -1928,6 +1941,8 @@ class test_local_subtensor_lift(unittest.TestCase):
y = tensor.vector('y')
f = function([y], tensor.exp(y.dimshuffle(0, 'x'))[0], mode=mode_opt)
self._verify_stack_trace(f)
prog = f.maker.fgraph.toposort()
assert isinstance(prog[0].op, tensor.DimShuffle)
assert isinstance(prog[1].op, tensor.Subtensor)
......@@ -1943,6 +1958,8 @@ class test_local_subtensor_lift(unittest.TestCase):
y = tensor.vector('y')
f = function([x, y], tensor.exp(x + y)[0], mode=mode_opt)
self._verify_stack_trace(f)
prog = f.maker.fgraph.toposort()
assert isinstance(prog[0].op, tensor.DimShuffle)
assert prog[1].op == tensor.add
......@@ -1959,6 +1976,8 @@ class test_local_subtensor_lift(unittest.TestCase):
f = function([x, y], [tensor.exp(x + y)[0], tensor.exp(x + y) + x],
mode=mode_opt)
self._verify_stack_trace(f)
prog = f.maker.fgraph.toposort()
assert isinstance(prog[0].op, tensor.DimShuffle)
assert isinstance(prog[1].op.scalar_op, theano.scalar.
......@@ -1977,6 +1996,8 @@ class test_local_subtensor_lift(unittest.TestCase):
y = tensor.scalar('y')
f = function([x, y], tensor.exp(x + y)[0], mode=mode_opt)
self._verify_stack_trace(f)
prog = f.maker.fgraph.toposort()
assert isinstance(prog[0].op, tensor.Subtensor)
# Composite{add,exp}
......@@ -1996,6 +2017,7 @@ class test_local_subtensor_lift(unittest.TestCase):
assert newx.broadcastable == (True, False)
f1 = function([x], newx[:2, :5], mode=mode_opt)
self._verify_stack_trace(f1)
prog = f1.maker.fgraph.toposort()
assert isinstance(prog[0].op, tensor.Subtensor)
assert isinstance(prog[1].op, tensor.Rebroadcast)
......@@ -2009,6 +2031,7 @@ class test_local_subtensor_lift(unittest.TestCase):
assert newy.broadcastable == (True, False, True, False)
f2 = function([y], newy[:, 3, 0, :], mode=mode_opt)
self._verify_stack_trace(f2)
prog = f2.maker.fgraph.toposort()
assert isinstance(prog[0].op, tensor.Subtensor)
assert isinstance(prog[1].op, tensor.Rebroadcast)
......@@ -2016,6 +2039,7 @@ class test_local_subtensor_lift(unittest.TestCase):
# corner case 2: subtensor idx_list is shorter than resulting broadcast pattern
f3 = function([y], newy[:, 3, 0], mode=mode_opt)
self._verify_stack_trace(f3)
prog = f3.maker.fgraph.toposort()
assert isinstance(prog[0].op, tensor.Subtensor)
assert isinstance(prog[1].op, tensor.Rebroadcast)
......@@ -2030,6 +2054,7 @@ class test_local_subtensor_lift(unittest.TestCase):
out = newz[:, 3, 0]
f4 = function([z], newz[:, 3, 0], mode=mode_opt)
self._verify_stack_trace(f4)
prog = f4.maker.fgraph.toposort()
assert isinstance(prog[0].op, tensor.Subtensor)
assert isinstance(prog[1].op, tensor.Rebroadcast)
......@@ -2037,6 +2062,11 @@ class test_local_subtensor_lift(unittest.TestCase):
class test_local_subtensor_merge(unittest.TestCase):
def _verify_stack_trace(self, f):
for output in f.outputs:
# Check stacktrace was copied over correctly after opt was applied
self.assertTrue(hasattr(output.variable.tag, 'trace'))
def setUp(self):
utt.seed_rng()
self.x_shapes = [(2, 2), (5, 3), (4, 1), (1, 2),
......@@ -2051,6 +2081,8 @@ class test_local_subtensor_merge(unittest.TestCase):
g = function([x], x[idx::][-1], mode=mode_opt.excluding(
'local_subtensor_merge'))
self._verify_stack_trace(f)
topo = f.maker.fgraph.toposort()
assert len([t for t in topo
if isinstance(t.op, tensor.Subtensor)]) == 1
......@@ -2077,6 +2109,8 @@ class test_local_subtensor_merge(unittest.TestCase):
mode=mode_opt.excluding('local_subtensor_merge'))
#theano.printing.debugprint(f, print_type=True)
self._verify_stack_trace(f)
topo = f.maker.fgraph.toposort()
# print [t for t in topo if isinstance(t.op, tensor.Subtensor)]
assert len([t for t in topo
......@@ -2104,6 +2138,8 @@ class test_local_subtensor_merge(unittest.TestCase):
g = function([x], x[::-1][idx],
mode=mode_opt.excluding('local_subtensor_merge'))
self._verify_stack_trace(f)
#theano.printing.debugprint(f, print_type=True)
topo = f.maker.fgraph.toposort()
# print [t for t in topo if isinstance(t.op, tensor.Subtensor)]
......@@ -2132,6 +2168,8 @@ class test_local_subtensor_merge(unittest.TestCase):
mode=mode_opt.excluding('local_subtensor_merge'))
#theano.printing.debugprint(f, print_type=True)
self._verify_stack_trace(f)
topo = f.maker.fgraph.toposort()
# print [t for t in topo if isinstance(t.op, tensor.Subtensor)]
assert len([t for t in topo
......@@ -2154,6 +2192,8 @@ class test_local_subtensor_merge(unittest.TestCase):
for idx in xrange(-9, 8):
f = function([x], x[::-1][:idx], mode=mode_opt)
self._verify_stack_trace(f)
#theano.printing.debugprint(f, print_type=True)
topo = f.maker.fgraph.toposort()
# print [t for t in topo if isinstance(t.op, tensor.Subtensor)]
......@@ -2171,6 +2211,9 @@ class test_local_subtensor_merge(unittest.TestCase):
x = tensor.matrix('x')
y = tensor.iscalar('y')
f = function([x, y], x[::-1][:y], mode=mode_opt)
self._verify_stack_trace(f)
#theano.printing.debugprint(f, print_type=True)
topo = f.maker.fgraph.toposort()
......@@ -2192,6 +2235,8 @@ class test_local_subtensor_merge(unittest.TestCase):
for idx2 in xrange(-7, 7):
f = function([x], x[idx1:][:idx2], mode=mode_opt)
self._verify_stack_trace(f)
#theano.printing.debugprint(f, print_type=True)
topo = f.maker.fgraph.toposort()
# print [t for t in topo if isinstance(t.op, tensor.Subtensor)]
......@@ -2210,6 +2255,9 @@ class test_local_subtensor_merge(unittest.TestCase):
y = tensor.iscalar('y')
z = tensor.iscalar('y')
f = function([x, y, z], x[y:][:z], mode=mode_opt)
self._verify_stack_trace(f)
#theano.printing.debugprint(f, print_type=True)
topo = f.maker.fgraph.toposort()
......@@ -2239,6 +2287,9 @@ class test_local_subtensor_merge(unittest.TestCase):
z = x[slice(*sl1)][slice(*sl2)]
f = function([x], z, mode=mode_opt)
self._verify_stack_trace(f)
x_val = self.rng.uniform(size=shape).astype(config.floatX)
f(x_val)
......@@ -2254,6 +2305,9 @@ class test_local_subtensor_merge(unittest.TestCase):
s2 = tensor.iscalar('s2')
f = function([x, b1, e1, s1, b2, e2, s2], x[b1:e1:s1][b2:e2:s2],
mode=mode_opt)
self._verify_stack_trace(f)
#theano.printing.debugprint(f, print_type=True)
topo = f.maker.fgraph.toposort()
......@@ -2292,6 +2346,7 @@ class test_local_subtensor_merge(unittest.TestCase):
t = theano.shared(numpy.int64(0))
fun = theano.function([x], y[t])
val = fun(data)
assert val == data[7:1:-1][0]
......@@ -2337,6 +2392,9 @@ class test_local_subtensor_merge(unittest.TestCase):
s = tensor.iscalar('s')
i = tensor.iscalar('i')
f = function([x, b, e, s, i], x[b:e:s][i], mode=mode_opt)
self._verify_stack_trace(f)
#theano.printing.debugprint(f, print_type=True)
topo = f.maker.fgraph.toposort()
......@@ -2428,6 +2486,9 @@ class test_local_subtensor_merge(unittest.TestCase):
sub_x = x[slice1][slice2]
f = theano.function([x] + input_vars, sub_x, mode=mode_opt)
self._verify_stack_trace(f)
topo = f.maker.fgraph.toposort()
# print [t for t in topo if isinstance(t.op, tensor.Subtensor)]
assert len([t for t in topo if isinstance(t.op,
......@@ -2485,6 +2546,8 @@ class test_local_subtensor_merge(unittest.TestCase):
sub_x = x[symbol_slice][i]
f = theano.function([x] + input_vars, sub_x, mode=mode_opt)
self._verify_stack_trace(f)
topo = f.maker.fgraph.toposort()
# print [t for t in topo if isinstance(t.op, tensor.Subtensor)]
assert len([t for t in topo if isinstance(t.op,
......@@ -2911,7 +2974,7 @@ class Test_local_elemwise_alloc(unittest.TestCase):
def _verify_stack_trace(self, f):
for output in f.outputs:
# Check stacktrace was copied over correctly after opt was applied
self.assertTrue(hasattr(output.tag, 'trace'))
self.assertTrue(hasattr(output.variable.tag, 'trace'))
def test_remove_alloc_wo_dimshuffle(self):
# No optimization on alloc
......
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