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提交 6a2259f4 authored 作者: Brandon T. Willard's avatar Brandon T. Willard
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Remove constant caching

Closes #99
上级 b99dea2f
隐藏空白字符变更
内嵌 并排
正在显示 包含 137 行增加 和 252 行删除
tests/gof/test_fg.py
浏览文件 @ 6a2259f4
......@@ -5,25 +5,14 @@ import pytest
import theano
from theano.compat import PY3
from theano.gof import CachedConstantError, FunctionGraph
from theano.gof.fg import FunctionGraph
from theano import tensor as tt
class TFunctionGraph:
def test_constant_cache_error(self):
v = theano.tensor.constant(1)
assert v.cached
with pytest.raises(CachedConstantError):
FunctionGraph([], [v + 1], clone=False)
def test_clone(self):
v = theano.tensor.constant(1)
assert v.cached
FunctionGraph([], [v + 1])
class TestFunctionGraph:
def test_pickle(self):
v = tt.vector()
func = theano.gof.FunctionGraph([v], [v + 1])
func = FunctionGraph([v], [v + 1])
s = pickle.dumps(func)
pickle.loads(s)
......@@ -31,6 +20,7 @@ class TFunctionGraph:
@pytest.mark.skipif(
not theano.config.cxx, reason="G++ not available, so we need to skip this test."
)
@pytest.mark.slow
def test_node_outputs_not_used(self):
# In the past, we where removing some not used variable from
# fgraph.variables event if the apply had other output used in
......
tests/gof/test_graph.py
浏览文件 @ 6a2259f4
......@@ -266,27 +266,14 @@ class TestAutoName:
assert r2.auto_name == "auto_" + str(autoname_id + 1)
def test_constant(self):
# Make sure the value we will use for the test aren't yet in the cache.
r1 = tensor.constant(1.5)
del tensor.constant_cache[r1.signature()]
r1 = tensor.constant(1.6)
del tensor.constant_cache[r1.signature()]
# Get counter value
autoname_id = next(Variable.__count__)
Variable.__count__ = count(autoname_id)
r1 = tensor.constant(1.5)
r2 = tensor.constant(1.5)
assert r1.auto_name == "auto_" + str(autoname_id), (
r1.auto_name,
"auto_" + str(autoname_id),
)
# We reuse the same variable
assert r2.auto_name == "auto_" + str(autoname_id), (
r2.auto_name,
"auto_" + str(autoname_id),
)
assert r1 is r2
r3 = tensor.constant(1.6)
assert r3.auto_name == "auto_" + str(autoname_id + 1)
......
tests/scan_module/test_scan.py
浏览文件 @ 6a2259f4
......@@ -1673,72 +1673,72 @@ class TestScan:
| |<RandomStateType> [id DD]
| |Shape [id DE] ''
| | |Subtensor{int64::} [id DA] ''
| |TensorConstant{0.1} [id CW]
| |TensorConstant{0.9} [id CX]
|Sum{acc_dtype=float64} [id DF] ''
|Elemwise{mul,no_inplace} [id DG] ''
| |TensorConstant{0.1} [id DF]
| |TensorConstant{0.9} [id DG]
|Sum{acc_dtype=float64} [id DH] ''
|Elemwise{mul,no_inplace} [id DI] ''
|for{cpu,scan_fn}.2 [id H] ''
|RandomFunction{uniform}.1 [id DH] ''
|<RandomStateType> [id DI]
|Shape [id DJ] ''
|RandomFunction{uniform}.1 [id DJ] ''
|<RandomStateType> [id DK]
|Shape [id DL] ''
| |for{cpu,scan_fn}.2 [id H] ''
|TensorConstant{0.1} [id CW]
|TensorConstant{0.9} [id CX]
|TensorConstant{0.1} [id DM]
|TensorConstant{0.9} [id DN]
Inner graphs of the scan ops:
for{cpu,scan_fn}.1 [id H] ''
>Elemwise{Composite{((i0 + i1) * i2)}} [id DK] ''
> |y0[t-1] [id DL] -> [id BR]
> |y0[t-3] [id DM] -> [id BR]
> |InplaceDimShuffle{} [id DN] ''
> |CGemv{inplace} [id DO] ''
> |AllocEmpty{dtype='%(float)s'} [id DP] ''
> | |TensorConstant{1} [id DQ]
> |TensorConstant{1.0} [id DR]
> |InplaceDimShuffle{x,0} [id DS] ''
> | |wout_copy [id DT] -> [id CQ]
> |x0[t-1] [id DU] -> [id CB]
> |TensorConstant{0.0} [id DV]
>Elemwise{Composite{(i0 + ((i1 + (i2 * i3)) * i4) + i5)}} [id DW] ''
> |CGemv{no_inplace} [id DX] ''
> | |AllocEmpty{dtype='%(float)s'} [id DY] ''
> | | |Shape_i{1} [id DZ] ''
> | | |win_copy [id EA] -> [id CR]
> | |TensorConstant{1.0} [id DR]
> | |InplaceDimShuffle{1,0} [id EB] 'win_copy.T'
> | | |win_copy [id EA] -> [id CR]
> | |u1[t] [id EC] -> [id BJ]
> | |TensorConstant{0.0} [id DV]
> |u2[t] [id ED] -> [id BN]
> |u2[t-1] [id EE] -> [id BL]
> |u2[t+1] [id EF] -> [id BP]
> |win2_copy [id EG] -> [id CO]
> |CGemv{inplace} [id EH] ''
> |AllocEmpty{dtype='%(float)s'} [id EI] ''
> | |Shape_i{1} [id EJ] ''
> | |w_copy [id EK] -> [id CP]
> |TensorConstant{1.0} [id DR]
> |InplaceDimShuffle{1,0} [id EL] 'w_copy.T'
> | |w_copy [id EK] -> [id CP]
> |x0[t-1] [id DU] -> [id CB]
> |TensorConstant{0.0} [id DV]
>CGemv{no_inplace} [id DX] ''
>Elemwise{Composite{((i0 + i1) * i2)}} [id DO] ''
> |y0[t-1] [id DP] -> [id BR]
> |y0[t-3] [id DQ] -> [id BR]
> |InplaceDimShuffle{} [id DR] ''
> |CGemv{inplace} [id DS] ''
> |AllocEmpty{dtype='%(float)s'} [id DT] ''
> | |TensorConstant{1} [id DU]
> |TensorConstant{1.0} [id DV]
> |InplaceDimShuffle{x,0} [id DW] ''
> | |wout_copy [id DX] -> [id CQ]
> |x0[t-1] [id DY] -> [id CB]
> |TensorConstant{0.0} [id DZ]
>Elemwise{Composite{(i0 + ((i1 + (i2 * i3)) * i4) + i5)}} [id EA] ''
> |CGemv{no_inplace} [id EB] ''
> | |AllocEmpty{dtype='%(float)s'} [id EC] ''
> | | |Shape_i{1} [id ED] ''
> | | |win_copy [id EE] -> [id CR]
> | |TensorConstant{1.0} [id DV]
> | |InplaceDimShuffle{1,0} [id EF] 'win_copy.T'
> | | |win_copy [id EE] -> [id CR]
> | |u1[t] [id EG] -> [id BJ]
> | |TensorConstant{0.0} [id DZ]
> |u2[t] [id EH] -> [id BN]
> |u2[t-1] [id EI] -> [id BL]
> |u2[t+1] [id EJ] -> [id BP]
> |win2_copy [id EK] -> [id CO]
> |CGemv{inplace} [id EL] ''
> |AllocEmpty{dtype='%(float)s'} [id EM] ''
> | |Shape_i{1} [id EN] ''
> | |w_copy [id EO] -> [id CP]
> |TensorConstant{1.0} [id DV]
> |InplaceDimShuffle{1,0} [id EP] 'w_copy.T'
> | |w_copy [id EO] -> [id CP]
> |x0[t-1] [id DY] -> [id CB]
> |TensorConstant{0.0} [id DZ]
>CGemv{no_inplace} [id EB] ''
for{cpu,scan_fn}.0 [id H] ''
>Elemwise{Composite{((i0 + i1) * i2)}} [id DK] ''
>Elemwise{Composite{(i0 + ((i1 + (i2 * i3)) * i4) + i5)}} [id DW] ''
>CGemv{no_inplace} [id DX] ''
>Elemwise{Composite{((i0 + i1) * i2)}} [id DO] ''
>Elemwise{Composite{(i0 + ((i1 + (i2 * i3)) * i4) + i5)}} [id EA] ''
>CGemv{no_inplace} [id EB] ''
for{cpu,scan_fn}.2 [id H] ''
>Elemwise{Composite{((i0 + i1) * i2)}} [id DK] ''
>Elemwise{Composite{(i0 + ((i1 + (i2 * i3)) * i4) + i5)}} [id DW] ''
>CGemv{no_inplace} [id DX] ''
>Elemwise{Composite{((i0 + i1) * i2)}} [id DO] ''
>Elemwise{Composite{(i0 + ((i1 + (i2 * i3)) * i4) + i5)}} [id EA] ''
>CGemv{no_inplace} [id EB] ''
for{cpu,scan_fn}.2 [id H] ''
>Elemwise{Composite{((i0 + i1) * i2)}} [id DK] ''
>Elemwise{Composite{(i0 + ((i1 + (i2 * i3)) * i4) + i5)}} [id DW] ''
>CGemv{no_inplace} [id DX] ''
>Elemwise{Composite{((i0 + i1) * i2)}} [id DO] ''
>Elemwise{Composite{(i0 + ((i1 + (i2 * i3)) * i4) + i5)}} [id EA] ''
>CGemv{no_inplace} [id EB] ''
""" % {
"float": theano.config.floatX
}
......
tests/tensor/test_basic.py
浏览文件 @ 6a2259f4
......@@ -2761,6 +2761,10 @@ class TestAsTensorVariable:
def setup_method(self):
self.x = tensor.scalar("x")
def test_tensor_from_scalar(self):
y = as_tensor_variable(scal.int8())
assert isinstance(y.owner.op, TensorFromScalar)
def test_one_output(self):
good_apply_var = ApplyDefaultTestOp(0).make_node(self.x)
as_tensor_variable(good_apply_var)
......@@ -5747,81 +5751,50 @@ class TestDot:
assert g.broadcastable == y.broadcastable
class TestTensorfromscalar:
def test_basic(self):
s = scal.constant(56)
t = tensor_from_scalar(s)
assert t.owner.op is tensor_from_scalar
assert t.type.broadcastable == (), t.type.broadcastable
assert t.type.ndim == 0, t.type.ndim
assert t.type.dtype == s.type.dtype
v = eval_outputs([t])
assert v == 56, v
assert isinstance(v, np.ndarray)
assert v.shape == (), v.shape
def test_basic_1(self):
s = scal.constant(56)
t = as_tensor_variable(s)
assert t.owner.op is tensor_from_scalar
assert t.type.broadcastable == (), t.type.broadcastable
assert t.type.ndim == 0, t.type.ndim
assert t.type.dtype == s.type.dtype
v = eval_outputs([t])
assert v == 56, v
assert isinstance(v, np.ndarray)
assert v.shape == (), v.shape
def test_TensorFromScalar():
s = scal.constant(56)
t = tensor_from_scalar(s)
assert t.owner.op is tensor_from_scalar
assert t.type.broadcastable == (), t.type.broadcastable
assert t.type.ndim == 0, t.type.ndim
assert t.type.dtype == s.type.dtype
g = grad(t, s)
assert eval_outputs([g]) == 0.0
v = eval_outputs([t])
def test_basic_2(self):
s = scal.constant(56.0)
t = as_tensor_variable(s)
assert t.owner.op is tensor_from_scalar
assert t.type.broadcastable == (), t.type.broadcastable
assert t.type.ndim == 0, t.type.ndim
assert t.type.dtype == s.type.dtype
v = eval_outputs([t])
assert v == 56, v
assert isinstance(v, np.ndarray)
assert v.shape == (), v.shape
assert v == 56.0, v
assert isinstance(v, np.ndarray)
assert v.shape == (), v.shape
g = grad(t, s)
assert eval_outputs([g]) == 0.0
g = grad(t, s)
assert eval_outputs([g]) == 1.0
def test_ScalarFromTensor():
tt = constant(56) # scal.constant(56)
ss = scalar_from_tensor(tt)
assert ss.owner.op is scalar_from_tensor
assert ss.type.dtype == tt.type.dtype
class TestScalarfromtensor:
def test_basic(self):
tt = constant(56) # scal.constant(56)
ss = scalar_from_tensor(tt)
assert ss.owner.op is scalar_from_tensor
assert ss.type.dtype == tt.type.dtype
v = eval_outputs([ss])
v = eval_outputs([ss])
assert v == 56
assert v.shape == ()
assert v == 56
if config.cast_policy == "custom":
assert isinstance(v, np.int8)
elif config.cast_policy in ("numpy", "numpy+floatX"):
assert isinstance(v, str(np.asarray(56).dtype))
else:
raise NotImplementedError(config.cast_policy)
assert v.shape == ()
tt = lscalar()
ss = scalar_from_tensor(tt)
ss.owner.op.grad([tt], [ss])
fff = function([tt], ss)
v = fff(np.asarray(5))
assert v == 5
assert isinstance(v, np.int64)
assert v.shape == ()
if config.cast_policy == "custom":
assert isinstance(v, np.int8)
elif config.cast_policy in ("numpy", "numpy+floatX"):
assert isinstance(v, str(np.asarray(56).dtype))
else:
raise NotImplementedError(config.cast_policy)
tt = lscalar()
ss = scalar_from_tensor(tt)
ss.owner.op.grad([tt], [ss])
fff = function([tt], ss)
v = fff(np.asarray(5))
assert v == 5
assert isinstance(v, np.int64)
assert v.shape == ()
class TestGrad:
......
tests/test_printing.py
浏览文件 @ 6a2259f4
......@@ -656,61 +656,61 @@ def test_scan_debugprint5():
| | | | | | |for{cpu,scan_fn} [id F] ''
| | | | | | |Constant{1} [id BT]
| | | | | |InplaceDimShuffle{x,x} [id BU] ''
| | | | | |TensorConstant{0.0} [id BP]
| | | | |Elemwise{second} [id BV] ''
| | | | | |Subtensor{int64} [id BW] ''
| | | | | |TensorConstant{0.0} [id BV]
| | | | |Elemwise{second} [id BW] ''
| | | | | |Subtensor{int64} [id BX] ''
| | | | | | |Subtensor{int64::} [id BS] ''
| | | | | | |Constant{-1} [id BX]
| | | | | |InplaceDimShuffle{x} [id BY] ''
| | | | | |Elemwise{second,no_inplace} [id BZ] ''
| | | | | |Sum{acc_dtype=float64} [id CA] ''
| | | | | | |Subtensor{int64} [id BW] ''
| | | | | |TensorConstant{1.0} [id R]
| | | | |Constant{-1} [id BX]
| | | | | | |Constant{-1} [id BY]
| | | | | |InplaceDimShuffle{x} [id BZ] ''
| | | | | |Elemwise{second,no_inplace} [id CA] ''
| | | | | |Sum{acc_dtype=float64} [id CB] ''
| | | | | | |Subtensor{int64} [id BX] ''
| | | | | |TensorConstant{1.0} [id CC]
| | | | |Constant{-1} [id BY]
| | | |Constant{1} [id BT]
| | |Constant{-1} [id CB]
| |Alloc [id CC] ''
| | |TensorConstant{0.0} [id BP]
| | |Elemwise{add,no_inplace} [id CD] ''
| | |Constant{-1} [id CD]
| |Alloc [id CE] ''
| | |TensorConstant{0.0} [id CF]
| | |Elemwise{add,no_inplace} [id CG] ''
| | | |Elemwise{sub,no_inplace} [id C] ''
| | | |TensorConstant{1} [id Y]
| | |Subtensor{int64} [id CE] ''
| | |Shape [id CF] ''
| | | |TensorConstant{1} [id CH]
| | |Subtensor{int64} [id CI] ''
| | |Shape [id CJ] ''
| | | |A [id P]
| | |Constant{0} [id CG]
| | |Constant{0} [id CK]
| |A [id P]
|Constant{-1} [id CH]
|Constant{-1} [id CL]
Inner graphs of the scan ops:
for{cpu,grad_of_scan_fn}.1 [id B] ''
>Elemwise{add,no_inplace} [id CI] ''
> |Elemwise{mul} [id CJ] ''
> | |<TensorType(float64, vector)> [id CK] -> [id BL]
> | |A_copy [id CL] -> [id P]
> |<TensorType(float64, vector)> [id CM] -> [id BL]
>Elemwise{add,no_inplace} [id CN] ''
> |Elemwise{mul} [id CO] ''
> | |<TensorType(float64, vector)> [id CK] -> [id BL]
> | |<TensorType(float64, vector)> [id CP] -> [id Z]
> |<TensorType(float64, vector)> [id CQ] -> [id CC]
>Elemwise{add,no_inplace} [id CM] ''
> |Elemwise{mul} [id CN] ''
> | |<TensorType(float64, vector)> [id CO] -> [id BL]
> | |A_copy [id CP] -> [id P]
> |<TensorType(float64, vector)> [id CQ] -> [id BL]
>Elemwise{add,no_inplace} [id CR] ''
> |Elemwise{mul} [id CS] ''
> | |<TensorType(float64, vector)> [id CO] -> [id BL]
> | |<TensorType(float64, vector)> [id CT] -> [id Z]
> |<TensorType(float64, vector)> [id CU] -> [id CE]
for{cpu,scan_fn} [id F] ''
>Elemwise{mul,no_inplace} [id CR] ''
> |<TensorType(float64, vector)> [id CP] -> [id H]
> |A_copy [id CL] -> [id P]
>Elemwise{mul,no_inplace} [id CV] ''
> |<TensorType(float64, vector)> [id CT] -> [id H]
> |A_copy [id CP] -> [id P]
for{cpu,scan_fn} [id F] ''
>Elemwise{mul,no_inplace} [id CR] ''
>Elemwise{mul,no_inplace} [id CV] ''
for{cpu,scan_fn} [id F] ''
>Elemwise{mul,no_inplace} [id CR] ''
>Elemwise{mul,no_inplace} [id CV] ''
for{cpu,scan_fn} [id F] ''
>Elemwise{mul,no_inplace} [id CR] ''
>Elemwise{mul,no_inplace} [id CV] ''
for{cpu,scan_fn} [id F] ''
>Elemwise{mul,no_inplace} [id CR] ''"""
>Elemwise{mul,no_inplace} [id CV] ''"""
for truth, out in zip(expected_output.split("\n"), lines):
assert truth.strip() == out.strip()
......
theano/gof/__init__.py
浏览文件 @ 6a2259f4
......@@ -40,7 +40,6 @@ e-mail thread "What is gof?".
from theano.gof.cc import CLinker, OpWiseCLinker, DualLinker, HideC
from theano.gof.fg import (
CachedConstantError,
InconsistencyError,
MissingInputError,
FunctionGraph,
......
theano/gof/fg.py
浏览文件 @ 6a2259f4
......@@ -20,17 +20,6 @@ from theano.misc.ordered_set import OrderedSet
NullType = None
class CachedConstantError(Exception):
"""
An exception thrown when we put in a FunctionGraph a Constant
that is cached. This should not happen as the user can reuse this
cached constant in other FunctionGraph.
"""
pass
class InconsistencyError(Exception):
"""
This exception should be thrown by listeners to FunctionGraph when the
......@@ -186,15 +175,7 @@ class FunctionGraph(utils.object2):
self.__setup_r__(input)
self.variables.add(input)
# Setup a Variable #
def __setup_r__(self, r):
# sets up r so it belongs to this fgraph
if getattr(r, "cached", False):
raise CachedConstantError(
"You manually constructed a FunctionGraph, but you passed it a"
" graph that has a cached constant. This should not happen."
" Clone the graph before building the FunctionGraph."
)
if hasattr(r, "fgraph") and r.fgraph is not None and r.fgraph is not self:
raise Exception("%s is already owned by another fgraph" % r)
r.fgraph = self
......
theano/gof/opt.py
浏览文件 @ 6a2259f4
......@@ -93,12 +93,7 @@ class Optimizer(object):
"""
self.add_requirements(fgraph)
try:
orig = theano.tensor.basic.constant.enable
theano.tensor.basic.constant.enable = False
ret = self.apply(fgraph, *args, **kwargs)
finally:
theano.tensor.basic.constant.enable = orig
ret = self.apply(fgraph, *args, **kwargs)
return ret
def __call__(self, fgraph):
......
theano/tensor/basic.py
浏览文件 @ 6a2259f4
......@@ -26,7 +26,6 @@ from theano.tensor.var import (
AsTensorError,
TensorVariable,
TensorConstant,
TensorConstantSignature,
_tensor_py_operators,
)
from theano.tensor.type import TensorType, values_eq_approx_always_true
......@@ -217,7 +216,7 @@ as_tensor = as_tensor_variable
def constant(x, name=None, ndim=None, dtype=None):
"""Return a symbolic `Constant` with value `x`.
"""Return a `TensorConstant` with value `x`.
Raises
------
......@@ -226,16 +225,6 @@ def constant(x, name=None, ndim=None, dtype=None):
ValueError
`x` could not be expanded to have ndim dimensions.
Notes
-----
We create a small cache of frequently used constant.
This speed up the Merge optimization for big graph.
We want to cache all scalar to don't merge as frequently constants.
But we don't want to cache too much stuff.
So we cache integer with dtype [u]int and float where the value is
between -10 and 10.
We cache all broadcast pattern for scalar.
"""
x_ = scal.convert(x, dtype=dtype)
......@@ -252,40 +241,11 @@ def constant(x, name=None, ndim=None, dtype=None):
try:
ttype = TensorType(dtype=x_.dtype, broadcastable=bcastable)
if not constant.enable:
return TensorConstant(ttype, x_, name=name)
sig = TensorConstantSignature((ttype, x_))
if sig in constant_cache:
return constant_cache[sig]
ret = TensorConstant(ttype, x_, name=name)
if (
x_.size == 1
and (-10) <= x_ <= 10
and (
x_.dtype in int_dtypes
or x_.dtype in uint_dtypes
or (
x_.dtype in float_dtypes
and
# Limit the size of the cache.
len(constant_cache) < 10000
)
)
):
constant_cache[sig] = ret
# This is needed to raise a good error to the user.
ret.cached = True
return ret
return TensorConstant(ttype, x_, name=name)
except Exception:
raise TypeError("Could not convert %s to TensorType" % x, type(x))
constant.enable = True
constant_cache = {}
def _obj_is_wrappable_as_tensor(x):
try:
constant(x)
......
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