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提交 a1388f92 authored 作者: CloudChaoszero's avatar CloudChaoszero 提交者： Thomas Wiecki
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🎉 Start removal of circular deps

🔥 Remove circular aesara imports in tests/compile/ section 🎨 Replace self-import dep changes on tests/d3viz 📌 Continue deps change config for import aesara cycles 📌 Continue deps change config for import aesara cycles 🎨 Re-config module import specification 🎨 redefine imports as aesara.XXX 🔥 Remove duplicate config import in test_pfunc ✏ Fix precommit formatting on tests ⏪ Revert value == 0 change due to consideration of data types on numerics, during pytests ✏ change import order from rebase
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aesara/compile/builders.py
浏览文件 @ a1388f92
...@@ -3,15 +3,20 @@ ...@@ -3,15 +3,20 @@
from collections import OrderedDict from collections import OrderedDict
from functools import partial, reduce from functools import partial, reduce
import aesara import aesara.tensor as aet
from aesara import tensor as aet
from aesara.compile.function.pfunc import rebuild_collect_shared from aesara.compile.function.pfunc import rebuild_collect_shared
from aesara.compile.function.types import orig_function from aesara.compile.function.types import orig_function
from aesara.compile.mode import optdb from aesara.compile.mode import optdb
from aesara.compile.sharedvalue import SharedVariable from aesara.compile.sharedvalue import SharedVariable
from aesara.configdefaults import config from aesara.configdefaults import config
from aesara.gradient import DisconnectedType from aesara.gradient import DisconnectedType, Rop, grad
from aesara.graph.basic import Apply, Variable, graph_inputs, io_connection_pattern from aesara.graph.basic import (
Apply,
Variable,
clone_replace,
graph_inputs,
io_connection_pattern,
)
from aesara.graph.fg import FunctionGraph from aesara.graph.fg import FunctionGraph
from aesara.graph.null_type import NullType from aesara.graph.null_type import NullType
from aesara.graph.op import Op, ops_with_inner_function from aesara.graph.op import Op, ops_with_inner_function
...@@ -441,7 +446,7 @@ class OpFromGraph(Op): ...@@ -441,7 +446,7 @@ class OpFromGraph(Op):
output_grads = [out_t() for out_t in self.output_types] output_grads = [out_t() for out_t in self.output_types]
fn_grad = partial( fn_grad = partial(
aesara.gradient.grad, grad,
cost=None, cost=None,
disconnected_inputs="ignore", disconnected_inputs="ignore",
return_disconnected="Disconnected", return_disconnected="Disconnected",
...@@ -559,7 +564,7 @@ class OpFromGraph(Op): ...@@ -559,7 +564,7 @@ class OpFromGraph(Op):
return return
eval_points = [inp_t() for inp_t in self.input_types] eval_points = [inp_t() for inp_t in self.input_types]
fn_rop = partial(aesara.gradient.Rop, wrt=local_inputs, eval_points=eval_points) fn_rop = partial(Rop, wrt=local_inputs, eval_points=eval_points)
TYPE_ERR_MSG = ( TYPE_ERR_MSG = (
"R_op overrides should be (single or list of)" "R_op overrides should be (single or list of)"
"OpFromGraph | 'default' | None | 0 | callable, got %s" "OpFromGraph | 'default' | None | 0 | callable, got %s"
...@@ -786,7 +791,7 @@ class OpFromGraph(Op): ...@@ -786,7 +791,7 @@ class OpFromGraph(Op):
# each shape call. Aesara optimizer will clean this up later, but this # each shape call. Aesara optimizer will clean this up later, but this
# will ask extra work to the optimizer. # will ask extra work to the optimizer.
repl = dict(zip(self.local_inputs, node.inputs)) repl = dict(zip(self.local_inputs, node.inputs))
cloned = aesara.clone_replace(reduce(tuple.__add__, out_shp), replace=repl) cloned = clone_replace(reduce(tuple.__add__, out_shp), replace=repl)
ret = [] ret = []
used = 0 used = 0
for i in range(len(out_shp)): for i in range(len(out_shp)):
...@@ -824,7 +829,7 @@ def inline_ofg_expansion(fgraph, node): ...@@ -824,7 +829,7 @@ def inline_ofg_expansion(fgraph, node):
return False return False
if not op.is_inline: if not op.is_inline:
return False return False
return aesara.clone_replace( return clone_replace(
op.local_outputs, {u: v for u, v in zip(node.op.local_inputs, node.inputs)} op.local_outputs, {u: v for u, v in zip(node.op.local_inputs, node.inputs)}
) )
......
aesara/compile/compiledir.py
浏览文件 @ a1388f92
...@@ -9,8 +9,9 @@ import shutil ...@@ -9,8 +9,9 @@ import shutil
import numpy as np import numpy as np
import aesara
from aesara.configdefaults import config from aesara.configdefaults import config
from aesara.graph.op import Op
from aesara.graph.type import CType
from aesara.utils import flatten from aesara.utils import flatten
...@@ -54,9 +55,9 @@ def cleanup(): ...@@ -54,9 +55,9 @@ def cleanup():
have_npy_abi_version = True have_npy_abi_version = True
elif obj.startswith("c_compiler_str="): elif obj.startswith("c_compiler_str="):
have_c_compiler = True have_c_compiler = True
elif isinstance( elif isinstance(obj, (Op, CType)) and hasattr(
obj, (aesara.graph.op.Op, aesara.graph.type.CType) obj, "c_code_cache_version"
) and hasattr(obj, "c_code_cache_version"): ):
v = obj.c_code_cache_version() v = obj.c_code_cache_version()
if v not in [(), None] and v not in key[0]: if v not in [(), None] and v not in key[0]:
# Reuse have_npy_abi_version to # Reuse have_npy_abi_version to
...@@ -125,13 +126,7 @@ def print_compiledir_content(): ...@@ -125,13 +126,7 @@ def print_compiledir_content():
with open(filename, "rb") as file: with open(filename, "rb") as file:
try: try:
keydata = pickle.load(file) keydata = pickle.load(file)
ops = list( ops = list({x for x in flatten(keydata.keys) if isinstance(x, Op)})
{
x
for x in flatten(keydata.keys)
if isinstance(x, aesara.graph.op.Op)
}
)
# Whatever the case, we count compilations for OP classes. # Whatever the case, we count compilations for OP classes.
for op_class in {op.__class__ for op in ops}: for op_class in {op.__class__ for op in ops}:
table_op_class.setdefault(op_class, 0) table_op_class.setdefault(op_class, 0)
...@@ -140,11 +135,7 @@ def print_compiledir_content(): ...@@ -140,11 +135,7 @@ def print_compiledir_content():
zeros_op += 1 zeros_op += 1
else: else:
types = list( types = list(
{ {x for x in flatten(keydata.keys) if isinstance(x, CType)}
x
for x in flatten(keydata.keys)
if isinstance(x, aesara.graph.type.CType)
}
) )
compile_start = compile_end = float("nan") compile_start = compile_end = float("nan")
for fn in os.listdir(os.path.join(compiledir, dir)): for fn in os.listdir(os.path.join(compiledir, dir)):
......
aesara/link/c/cmodule.py
浏览文件 @ a1388f92
...@@ -23,16 +23,17 @@ from typing import Dict, List, Set ...@@ -23,16 +23,17 @@ from typing import Dict, List, Set
import numpy.distutils import numpy.distutils
import aesara
# we will abuse the lockfile mechanism when reading and writing the registry # we will abuse the lockfile mechanism when reading and writing the registry
from aesara.compile.compilelock import lock_ctx from aesara.compile.compilelock import lock_ctx
from aesara.configdefaults import config, gcc_version_str from aesara.configdefaults import config, gcc_version_str
from aesara.configparser import BoolParam, StrParam
from aesara.graph.op import Op
from aesara.link.c.exceptions import CompileError, MissingGXX from aesara.link.c.exceptions import CompileError, MissingGXX
from aesara.utils import ( from aesara.utils import (
LOCAL_BITWIDTH, LOCAL_BITWIDTH,
flatten, flatten,
hash_from_code, hash_from_code,
maybe_add_to_os_environ_pathlist,
output_subprocess_Popen, output_subprocess_Popen,
subprocess_Popen, subprocess_Popen,
) )
...@@ -1126,7 +1127,7 @@ class ModuleCache: ...@@ -1126,7 +1127,7 @@ class ModuleCache:
self.loaded_key_pkl.add(key_pkl) self.loaded_key_pkl.add(key_pkl)
elif config.cmodule__warn_no_version: elif config.cmodule__warn_no_version:
key_flat = flatten(key) key_flat = flatten(key)
ops = [k for k in key_flat if isinstance(k, aesara.graph.op.Op)] ops = [k for k in key_flat if isinstance(k, Op)]
_logger.warning( _logger.warning(
"not all the" "not all the"
" following op(s) implement" " following op(s) implement"
...@@ -1993,7 +1994,7 @@ def try_march_flag(flags): ...@@ -1993,7 +1994,7 @@ def try_march_flag(flags):
""" """
) )
cflags = flags + ["-L" + d for d in aesara.link.c.cmodule.std_lib_dirs()] cflags = flags + ["-L" + d for d in std_lib_dirs()]
compilation_result, execution_result = GCC_compiler.try_compile_tmp( compilation_result, execution_result = GCC_compiler.try_compile_tmp(
test_code, tmp_prefix="try_march_", flags=cflags, try_run=True test_code, tmp_prefix="try_march_", flags=cflags, try_run=True
) )
...@@ -2773,7 +2774,7 @@ def default_blas_ldflags(): ...@@ -2773,7 +2774,7 @@ def default_blas_ldflags():
res = try_blas_flag(flags) res = try_blas_flag(flags)
if res: if res:
check_mkl_openmp() check_mkl_openmp()
aesara.utils.maybe_add_to_os_environ_pathlist("PATH", lib_path[0]) maybe_add_to_os_environ_pathlist("PATH", lib_path[0])
return res return res
# to support path that includes spaces, we need to wrap it with double quotes on Windows # to support path that includes spaces, we need to wrap it with double quotes on Windows
...@@ -2844,7 +2845,7 @@ def add_blas_configvars(): ...@@ -2844,7 +2845,7 @@ def add_blas_configvars():
config.add( config.add(
"blas__ldflags", "blas__ldflags",
"lib[s] to include for [Fortran] level-3 blas implementation", "lib[s] to include for [Fortran] level-3 blas implementation",
aesara.configparser.StrParam(default_blas_ldflags), StrParam(default_blas_ldflags),
# Added elsewhere in the c key only when needed. # Added elsewhere in the c key only when needed.
in_c_key=False, in_c_key=False,
) )
...@@ -2852,7 +2853,7 @@ def add_blas_configvars(): ...@@ -2852,7 +2853,7 @@ def add_blas_configvars():
config.add( config.add(
"blas__check_openmp", "blas__check_openmp",
"Check for openmp library conflict.\nWARNING: Setting this to False leaves you open to wrong results in blas-related operations.", "Check for openmp library conflict.\nWARNING: Setting this to False leaves you open to wrong results in blas-related operations.",
aesara.configparser.BoolParam(True), BoolParam(True),
in_c_key=False, in_c_key=False,
) )
......
tests/compile/function/test_function.py
浏览文件 @ a1388f92
...@@ -8,7 +8,7 @@ from collections import OrderedDict ...@@ -8,7 +8,7 @@ from collections import OrderedDict
import numpy as np import numpy as np
import pytest import pytest
import aesara from aesara.compile import shared
from aesara.compile.function import function, function_dump from aesara.compile.function import function, function_dump
from aesara.compile.io import In from aesara.compile.io import In
from aesara.configdefaults import config from aesara.configdefaults import config
...@@ -60,7 +60,7 @@ class TestFunctionIn: ...@@ -60,7 +60,7 @@ class TestFunctionIn:
def test_in_strict(self): def test_in_strict(self):
a = dvector() a = dvector()
b = aesara.shared(7) b = shared(7)
out = a + b out = a + b
f = function([In(a, strict=False)], out) f = function([In(a, strict=False)], out)
...@@ -79,14 +79,14 @@ class TestFunctionIn: ...@@ -79,14 +79,14 @@ class TestFunctionIn:
def test_explicit_shared_input(self): def test_explicit_shared_input(self):
# This is not a test of the In class per se, but the In class relies # This is not a test of the In class per se, but the In class relies
# on the fact that shared variables cannot be explicit inputs # on the fact that shared variables cannot be explicit inputs
a = aesara.shared(1.0) a = shared(1.0)
with pytest.raises(TypeError): with pytest.raises(TypeError):
function([a], a + 1) function([a], a + 1)
def test_in_shared_variable(self): def test_in_shared_variable(self):
# Ensure that an error is raised if the In wrapped is used to wrap # Ensure that an error is raised if the In wrapped is used to wrap
# a shared variable # a shared variable
a = aesara.shared(1.0) a = shared(1.0)
a_wrapped = In(a, update=a + 1) a_wrapped = In(a, update=a + 1)
with pytest.raises(TypeError): with pytest.raises(TypeError):
function([a_wrapped]) function([a_wrapped])
...@@ -130,7 +130,7 @@ class TestFunctionIn: ...@@ -130,7 +130,7 @@ class TestFunctionIn:
def test_in_update_shared(self): def test_in_update_shared(self):
# Test that using both In() with updates and shared variables with # Test that using both In() with updates and shared variables with
# updates in the same function behaves as expected # updates in the same function behaves as expected
shared_var = aesara.shared(1.0) shared_var = shared(1.0)
a = dscalar("a") a = dscalar("a")
a_wrapped = In(a, value=0.0, update=shared_var) a_wrapped = In(a, value=0.0, update=shared_var)
f = function([a_wrapped], [], updates={shared_var: a}, mode="FAST_RUN") f = function([a_wrapped], [], updates={shared_var: a}, mode="FAST_RUN")
...@@ -244,14 +244,14 @@ def test_pickle_unpickle_with_reoptimization(): ...@@ -244,14 +244,14 @@ def test_pickle_unpickle_with_reoptimization():
mode = "FAST_RUN" mode = "FAST_RUN"
x1 = fmatrix("x1") x1 = fmatrix("x1")
x2 = fmatrix("x2") x2 = fmatrix("x2")
x3 = aesara.shared(np.ones((10, 10), dtype=floatX)) x3 = shared(np.ones((10, 10), dtype=floatX))
x4 = aesara.shared(np.ones((10, 10), dtype=floatX)) x4 = shared(np.ones((10, 10), dtype=floatX))
y = aet_sum(aet_sum(aet_sum(x1 ** 2 + x2) + x3) + x4) y = aet_sum(aet_sum(aet_sum(x1 ** 2 + x2) + x3) + x4)
updates = OrderedDict() updates = OrderedDict()
updates[x3] = x3 + 1 updates[x3] = x3 + 1
updates[x4] = x4 + 1 updates[x4] = x4 + 1
f = aesara.function([x1, x2], y, updates=updates, mode=mode) f = function([x1, x2], y, updates=updates, mode=mode)
# now pickle the compiled aesara fn # now pickle the compiled aesara fn
string_pkl = pickle.dumps(f, -1) string_pkl = pickle.dumps(f, -1)
...@@ -276,14 +276,14 @@ def test_pickle_unpickle_without_reoptimization(): ...@@ -276,14 +276,14 @@ def test_pickle_unpickle_without_reoptimization():
mode = "FAST_RUN" mode = "FAST_RUN"
x1 = fmatrix("x1") x1 = fmatrix("x1")
x2 = fmatrix("x2") x2 = fmatrix("x2")
x3 = aesara.shared(np.ones((10, 10), dtype=floatX)) x3 = shared(np.ones((10, 10), dtype=floatX))
x4 = aesara.shared(np.ones((10, 10), dtype=floatX)) x4 = shared(np.ones((10, 10), dtype=floatX))
y = aet_sum(aet_sum(aet_sum(x1 ** 2 + x2) + x3) + x4) y = aet_sum(aet_sum(aet_sum(x1 ** 2 + x2) + x3) + x4)
updates = OrderedDict() updates = OrderedDict()
updates[x3] = x3 + 1 updates[x3] = x3 + 1
updates[x4] = x4 + 1 updates[x4] = x4 + 1
f = aesara.function([x1, x2], y, updates=updates, mode=mode) f = function([x1, x2], y, updates=updates, mode=mode)
# now pickle the compiled aesara fn # now pickle the compiled aesara fn
string_pkl = pickle.dumps(f, -1) string_pkl = pickle.dumps(f, -1)
......
tests/compile/function/test_pfunc.py
浏览文件 @ a1388f92
import numpy as np import numpy as np
import pytest import pytest
import aesara import aesara.tensor as aet
from aesara import tensor as aet from aesara.compile import UnusedInputError
from aesara.compile.function import pfunc from aesara.compile.function import function, pfunc
from aesara.compile.io import In from aesara.compile.io import In
from aesara.compile.sharedvalue import shared from aesara.compile.sharedvalue import shared
from aesara.configdefaults import config from aesara.configdefaults import config
from aesara.graph.fg import MissingInputError
from aesara.misc.safe_asarray import _asarray from aesara.misc.safe_asarray import _asarray
from aesara.tensor.math import sum as aet_sum from aesara.tensor.math import sum as aet_sum
from aesara.tensor.type import ( from aesara.tensor.type import (
...@@ -380,7 +381,7 @@ class TestPfunc: ...@@ -380,7 +381,7 @@ class TestPfunc:
# the update_var has type matrix, and the update expression # the update_var has type matrix, and the update expression
# is a broadcasted scalar, and that should be allowed. # is a broadcasted scalar, and that should be allowed.
with pytest.raises(TypeError): with pytest.raises(TypeError):
aesara.function( function(
inputs=[], inputs=[],
outputs=[], outputs=[],
updates={output_var: output_var.sum().dimshuffle("x", "x")}, updates={output_var: output_var.sum().dimshuffle("x", "x")},
...@@ -390,7 +391,7 @@ class TestPfunc: ...@@ -390,7 +391,7 @@ class TestPfunc:
x, y = dmatrices("x", "y") x, y = dmatrices("x", "y")
z = shared(np.ones((2, 3))) z = shared(np.ones((2, 3)))
with pytest.raises(ValueError): with pytest.raises(ValueError):
aesara.function([x, y], [z], updates=[(z, (z + x + y)), (z, (z - x))]) function([x, y], [z], updates=[(z, (z + x + y)), (z, (z - x))])
def test_givens(self): def test_givens(self):
x = shared(0) x = shared(0)
...@@ -623,7 +624,7 @@ class TestPfunc: ...@@ -623,7 +624,7 @@ class TestPfunc:
assert y.get_value() == 2 assert y.get_value() == 2
# a is needed as input if y.default_update is used # a is needed as input if y.default_update is used
with pytest.raises(aesara.graph.fg.MissingInputError): with pytest.raises(MissingInputError):
pfunc([], x) pfunc([], x)
def test_default_updates_partial_graph(self): def test_default_updates_partial_graph(self):
...@@ -657,8 +658,8 @@ class TestPfunc: ...@@ -657,8 +658,8 @@ class TestPfunc:
def test_duplicate_inputs(self): def test_duplicate_inputs(self):
x = lscalar("x") x = lscalar("x")
with pytest.raises(aesara.compile.UnusedInputError): with pytest.raises(UnusedInputError):
aesara.function([x, x, x], x) function([x, x, x], x)
def test_update_same(self): def test_update_same(self):
# There was a bug in CVM, triggered when a shared variable # There was a bug in CVM, triggered when a shared variable
...@@ -675,8 +676,8 @@ class TestPfunc: ...@@ -675,8 +676,8 @@ class TestPfunc:
# Is that all the comment above meant, or is the CVM intended # Is that all the comment above meant, or is the CVM intended
# to add extra non-determinism? Or is the CVM meant to # to add extra non-determinism? Or is the CVM meant to
# deterministically but arbitrarily pick an order for the updates? # deterministically but arbitrarily pick an order for the updates?
f = aesara.function([], [], updates=[(a, a), (b, (2 * b))]) f = function([], [], updates=[(a, a), (b, (2 * b))])
g = aesara.function([], [], updates=[(a, (a * 2)), (b, b)]) g = function([], [], updates=[(a, (a * 2)), (b, b)])
f() f()
assert a.get_value(borrow=True).shape == (), a.get_value() assert a.get_value(borrow=True).shape == (), a.get_value()
...@@ -693,8 +694,8 @@ class TestPfunc: ...@@ -693,8 +694,8 @@ class TestPfunc:
# See comment in test_update_same about why we try both # See comment in test_update_same about why we try both
# shared variables. # shared variables.
f = aesara.function([], [], updates=[(a, a), (b, (2 * b - b))]) f = function([], [], updates=[(a, a), (b, (2 * b - b))])
g = aesara.function([], [], updates=[(a, (a * 2 - a)), (b, b)]) g = function([], [], updates=[(a, (a * 2 - a)), (b, b)])
f() f()
assert a.get_value(borrow=True).shape == (), a.get_value() assert a.get_value(borrow=True).shape == (), a.get_value()
...@@ -727,7 +728,7 @@ class TestAliasingRules: ...@@ -727,7 +728,7 @@ class TestAliasingRules:
# library code. # library code.
def shared(self, x): def shared(self, x):
return aesara.shared(x) return shared(x)
def test_shared_constructor_copies(self): def test_shared_constructor_copies(self):
# shared constructor makes copy # shared constructor makes copy
...@@ -751,9 +752,7 @@ class TestAliasingRules: ...@@ -751,9 +752,7 @@ class TestAliasingRules:
x = sparse.SparseType("csc", dtype="float64")() x = sparse.SparseType("csc", dtype="float64")()
y = sparse.SparseType("csc", dtype="float64")() y = sparse.SparseType("csc", dtype="float64")()
f = aesara.function( f = function([In(x, mutable=True), In(y, mutable=True)], (x + y) + (x + y))
[In(x, mutable=True), In(y, mutable=True)], (x + y) + (x + y)
)
# Test 1. If the same variable is given twice # Test 1. If the same variable is given twice
# Compute bogus values # Compute bogus values
...@@ -800,7 +799,7 @@ class TestAliasingRules: ...@@ -800,7 +799,7 @@ class TestAliasingRules:
y = dvector() y = dvector()
m1 = dmatrix() m1 = dmatrix()
m2 = dmatrix() m2 = dmatrix()
f = aesara.function( f = function(
[ [
In(x, mutable=True), In(x, mutable=True),
In(y, mutable=True), In(y, mutable=True),
...@@ -862,7 +861,7 @@ class TestAliasingRules: ...@@ -862,7 +861,7 @@ class TestAliasingRules:
# and a shares memory with b, b shares memory with c, but # and a shares memory with b, b shares memory with c, but
# c does not share memory with a # c does not share memory with a
f = aesara.function( f = function(
[ [
In(x, mutable=True), In(x, mutable=True),
In(y, mutable=True), In(y, mutable=True),
...@@ -1011,7 +1010,7 @@ class TestAliasingRules: ...@@ -1011,7 +1010,7 @@ class TestAliasingRules:
assert not np.may_share_memory(data_of(A), data_of(B)) assert not np.may_share_memory(data_of(A), data_of(B))
# aesara should have been smart enough to not make copies # aesara should have been smart enough to not make copies
if aesara.config.mode not in ["DebugMode", "DEBUG_MODE", "FAST_COMPILE"]: if config.mode not in ["DebugMode", "DEBUG_MODE", "FAST_COMPILE"]:
# We don't ask DebugMode and FAST_COMPILE not to make copy. # We don't ask DebugMode and FAST_COMPILE not to make copy.
# We have the right to do so. # We have the right to do so.
assert np.all(data_of(A) < 5) assert np.all(data_of(A) < 5)
...@@ -1042,7 +1041,7 @@ class TestRebuildStrict: ...@@ -1042,7 +1041,7 @@ class TestRebuildStrict:
w = imatrix() w = imatrix()
x, y = ivectors("x", "y") x, y = ivectors("x", "y")
z = x * y z = x * y
f = aesara.function([w, y], z, givens=[(x, w)], rebuild_strict=False) f = function([w, y], z, givens=[(x, w)], rebuild_strict=False)
z_val = f(np.ones((3, 5), dtype="int32"), np.arange(5, dtype="int32")) z_val = f(np.ones((3, 5), dtype="int32"), np.arange(5, dtype="int32"))
assert z_val.ndim == 2 assert z_val.ndim == 2
assert np.all(z_val == np.ones((3, 5)) * np.arange(5)) assert np.all(z_val == np.ones((3, 5)) * np.arange(5))
tests/compile/test_builders.py
浏览文件 @ a1388f92
...@@ -3,12 +3,11 @@ from functools import partial ...@@ -3,12 +3,11 @@ from functools import partial
import numpy as np import numpy as np
import pytest import pytest
import aesara from aesara.compile import shared
from aesara import shared
from aesara.compile.builders import OpFromGraph from aesara.compile.builders import OpFromGraph
from aesara.compile.function import function from aesara.compile.function import function
from aesara.configdefaults import config from aesara.configdefaults import config
from aesara.gradient import DisconnectedType, Rop, grad from aesara.gradient import DisconnectedType, Rop, disconnected_type, grad
from aesara.graph.null_type import NullType from aesara.graph.null_type import NullType
from aesara.tensor.math import dot, exp from aesara.tensor.math import dot, exp
from aesara.tensor.math import round as aet_round from aesara.tensor.math import round as aet_round
...@@ -300,7 +299,7 @@ class TestOpFromGraph(unittest_tools.InferShapeTester): ...@@ -300,7 +299,7 @@ class TestOpFromGraph(unittest_tools.InferShapeTester):
return y + aet_round(y) return y + aet_round(y)
def f1_back(inputs, output_gradients): def f1_back(inputs, output_gradients):
return [output_gradients[0], aesara.gradient.disconnected_type()] return [output_gradients[0], disconnected_type()]
op = cls_ofg( op = cls_ofg(
inputs=[x, y], inputs=[x, y],
...@@ -312,7 +311,7 @@ class TestOpFromGraph(unittest_tools.InferShapeTester): ...@@ -312,7 +311,7 @@ class TestOpFromGraph(unittest_tools.InferShapeTester):
c = op(x, y) c = op(x, y)
g1 = aesara.grad(c.sum(), x) g1 = grad(c.sum(), x)
out = g1.eval( out = g1.eval(
{x: np.ones((5,), dtype=np.float32), y: np.ones((5,), dtype=np.float32)} {x: np.ones((5,), dtype=np.float32), y: np.ones((5,), dtype=np.float32)}
......
tests/compile/test_debugmode.py
浏览文件 @ a1388f92
...@@ -3,7 +3,6 @@ import sys ...@@ -3,7 +3,6 @@ import sys
import numpy as np import numpy as np
import pytest import pytest
import aesara
import aesara.tensor as aet import aesara.tensor as aet
from aesara.compile.debugmode import ( from aesara.compile.debugmode import (
BadDestroyMap, BadDestroyMap,
...@@ -13,6 +12,8 @@ from aesara.compile.debugmode import ( ...@@ -13,6 +12,8 @@ from aesara.compile.debugmode import (
InvalidValueError, InvalidValueError,
StochasticOrder, StochasticOrder,
) )
from aesara.compile.function import function
from aesara.compile.mode import predefined_modes
from aesara.configdefaults import config from aesara.configdefaults import config
from aesara.graph.basic import Apply, Variable from aesara.graph.basic import Apply, Variable
from aesara.graph.features import BadOptimization from aesara.graph.features import BadOptimization
...@@ -26,7 +27,7 @@ from tests import unittest_tools as utt ...@@ -26,7 +27,7 @@ from tests import unittest_tools as utt
def test_debugmode_basic(): def test_debugmode_basic():
x = dvector() x = dvector()
f = aesara.function([x], ((2.0 * x) + 7) / 2.0, mode=DebugMode()) f = function([x], ((2.0 * x) + 7) / 2.0, mode=DebugMode())
f([1, 2]) f([1, 2])
...@@ -214,12 +215,12 @@ def test_badthunkoutput(): ...@@ -214,12 +215,12 @@ def test_badthunkoutput():
a = dvector() a = dvector()
b = dvector() b = dvector()
f_good = aesara.function( f_good = function(
[a, b], [a, b],
off_by_half(a, b), off_by_half(a, b),
mode=DebugMode(check_c_code=config.cxx), mode=DebugMode(check_c_code=config.cxx),
) )
f_inconsistent = aesara.function( f_inconsistent = function(
[a, b], [a, b],
inconsistent(a, b), inconsistent(a, b),
mode=DebugMode(check_c_code=config.cxx), mode=DebugMode(check_c_code=config.cxx),
...@@ -248,7 +249,7 @@ def test_badoptimization(): ...@@ -248,7 +249,7 @@ def test_badoptimization():
a = dvector() a = dvector()
b = dvector() b = dvector()
f = aesara.function([a, b], a + b, mode=DebugMode(optimizer=opt)) f = function([a, b], a + b, mode=DebugMode(optimizer=opt))
with pytest.raises(BadOptimization) as einfo: with pytest.raises(BadOptimization) as einfo:
f( f(
...@@ -289,7 +290,7 @@ def test_badoptimization_opt_err(): ...@@ -289,7 +290,7 @@ def test_badoptimization_opt_err():
a = dvector() a = dvector()
b = dvector() b = dvector()
f = aesara.function([a, b], a + b, mode=DebugMode(optimizer=opt)) f = function([a, b], a + b, mode=DebugMode(optimizer=opt))
with pytest.raises(ValueError, match=r"insert_bigger_b_add"): with pytest.raises(ValueError, match=r"insert_bigger_b_add"):
f( f(
[1.0, 2.0, 3.0], [1.0, 2.0, 3.0],
...@@ -299,7 +300,7 @@ def test_badoptimization_opt_err(): ...@@ -299,7 +300,7 @@ def test_badoptimization_opt_err():
# Test that opt that do an illegal change still get the error from graph. # Test that opt that do an illegal change still get the error from graph.
with pytest.raises(TypeError) as einfo: with pytest.raises(TypeError) as einfo:
with config.change_flags(on_opt_error="raise"): with config.change_flags(on_opt_error="raise"):
f2 = aesara.function( f2 = function(
[a, b], [a, b],
a + b, a + b,
mode=DebugMode(optimizer=opt2, stability_patience=1), mode=DebugMode(optimizer=opt2, stability_patience=1),
...@@ -340,7 +341,7 @@ def test_stochasticoptimization(): ...@@ -340,7 +341,7 @@ def test_stochasticoptimization():
b = dvector() b = dvector()
with pytest.raises(StochasticOrder): with pytest.raises(StochasticOrder):
aesara.function( function(
[a, b], [a, b],
add(a, b), add(a, b),
mode=DebugMode( mode=DebugMode(
...@@ -356,7 +357,7 @@ def test_stochasticoptimization(): ...@@ -356,7 +357,7 @@ def test_stochasticoptimization():
) )
def test_just_c_code(): def test_just_c_code():
x = dvector() x = dvector()
f = aesara.function([x], wb2(x), mode=DebugMode(check_py_code=False)) f = function([x], wb2(x), mode=DebugMode(check_py_code=False))
assert np.all(f([1, 2]) == [2, 4]) assert np.all(f([1, 2]) == [2, 4])
...@@ -374,7 +375,7 @@ def test_baddestroymap(): ...@@ -374,7 +375,7 @@ def test_baddestroymap():
x = dvector() x = dvector()
y = dvector() y = dvector()
f = aesara.function([x, y], BadAdd()(x, y), mode="DEBUG_MODE") f = function([x, y], BadAdd()(x, y), mode="DEBUG_MODE")
with pytest.raises(BadDestroyMap): with pytest.raises(BadDestroyMap):
f([1, 2], [3, 4]) f([1, 2], [3, 4])
...@@ -385,7 +386,7 @@ def test_baddestroymap(): ...@@ -385,7 +386,7 @@ def test_baddestroymap():
) )
def test_baddestroymap_c(): def test_baddestroymap_c():
x = dvector() x = dvector()
f = aesara.function([x], wb2i(x), mode=DebugMode(check_py_code=False)) f = function([x], wb2i(x), mode=DebugMode(check_py_code=False))
with pytest.raises(BadDestroyMap): with pytest.raises(BadDestroyMap):
assert np.all(f([1, 2]) == [2, 4]) assert np.all(f([1, 2]) == [2, 4])
...@@ -414,14 +415,14 @@ class TestViewMap: ...@@ -414,14 +415,14 @@ class TestViewMap:
def test_badviewmap_ref(self): def test_badviewmap_ref(self):
x = dvector() x = dvector()
y = dvector() y = dvector()
f = aesara.function([x, y], self.BadAddRef()(x, y), mode="DEBUG_MODE") f = function([x, y], self.BadAddRef()(x, y), mode="DEBUG_MODE")
with pytest.raises(BadViewMap): with pytest.raises(BadViewMap):
f([1, 2], [3, 4]) f([1, 2], [3, 4])
def test_badviewmap_slice(self): def test_badviewmap_slice(self):
x = dvector() x = dvector()
y = dvector() y = dvector()
f = aesara.function([x, y], self.BadAddSlice()(x, y), mode="DEBUG_MODE") f = function([x, y], self.BadAddSlice()(x, y), mode="DEBUG_MODE")
with pytest.raises(BadViewMap): with pytest.raises(BadViewMap):
f([1, 2], [3, 4]) f([1, 2], [3, 4])
...@@ -430,7 +431,7 @@ class TestViewMap: ...@@ -430,7 +431,7 @@ class TestViewMap:
goodop.view_map = {0: [1]} goodop.view_map = {0: [1]}
x = dvector() x = dvector()
y = dvector() y = dvector()
f = aesara.function([x, y], goodop(x, y), mode="DEBUG_MODE") f = function([x, y], goodop(x, y), mode="DEBUG_MODE")
# Shouldn't raise an error # Shouldn't raise an error
f([1, 5, 1], [3, 4, 2, 1, 4]) f([1, 5, 1], [3, 4, 2, 1, 4])
...@@ -439,7 +440,7 @@ class TestViewMap: ...@@ -439,7 +440,7 @@ class TestViewMap:
) )
def test_badviewmap_c(self): def test_badviewmap_c(self):
x = dvector() x = dvector()
f = aesara.function([x], wb1i(x), mode=DebugMode(check_py_code=False)) f = function([x], wb1i(x), mode=DebugMode(check_py_code=False))
with pytest.raises(BadViewMap): with pytest.raises(BadViewMap):
f([1, 2]) f([1, 2])
...@@ -462,7 +463,7 @@ class TestViewMap: ...@@ -462,7 +463,7 @@ class TestViewMap:
x = dvector("x") x = dvector("x")
y = dvector("y") y = dvector("y")
f = aesara.function([x, y], CustomOp()(x, y), mode="DEBUG_MODE") f = function([x, y], CustomOp()(x, y), mode="DEBUG_MODE")
r0, r1 = f([1, 2, 3, 4], [5, 6, 7, 8]) r0, r1 = f([1, 2, 3, 4], [5, 6, 7, 8])
...@@ -487,7 +488,7 @@ class TestViewMap: ...@@ -487,7 +488,7 @@ class TestViewMap:
x = dvector() x = dvector()
y = dvector() y = dvector()
f = aesara.function([x, y], CustomOp()(x, y), mode="DEBUG_MODE") f = function([x, y], CustomOp()(x, y), mode="DEBUG_MODE")
r0, r1 = f([1, 2, 3, 4], [5, 6, 7, 8]) r0, r1 = f([1, 2, 3, 4], [5, 6, 7, 8])
...@@ -513,7 +514,7 @@ class TestViewMap: ...@@ -513,7 +514,7 @@ class TestViewMap:
x = dvector("x") x = dvector("x")
y = dvector("y") y = dvector("y")
f = aesara.function([x, y], CustomOp()(x, y)[0] * 2, mode="DEBUG_MODE") f = function([x, y], CustomOp()(x, y)[0] * 2, mode="DEBUG_MODE")
r0 = f([1, 2, 3, 4], [5, 6, 7, 8]) r0 = f([1, 2, 3, 4], [5, 6, 7, 8])
...@@ -542,7 +543,7 @@ class TestViewMap: ...@@ -542,7 +543,7 @@ class TestViewMap:
y = dvector() y = dvector()
bad_xy0, bad_xy1 = custom_op(x, y) bad_xy0, bad_xy1 = custom_op(x, y)
out = bad_xy0 * 2 + bad_xy1 * 2 out = bad_xy0 * 2 + bad_xy1 * 2
f = aesara.function([x, y], out, mode="DEBUG_MODE") f = function([x, y], out, mode="DEBUG_MODE")
with pytest.raises(BadViewMap): with pytest.raises(BadViewMap):
f([1, 2, 3, 4], [5, 6, 7, 8]) f([1, 2, 3, 4], [5, 6, 7, 8])
...@@ -558,16 +559,16 @@ class TestViewMap: ...@@ -558,16 +559,16 @@ class TestViewMap:
class TestCheckIsfinite: class TestCheckIsfinite:
def setup_method(self): def setup_method(self):
self.old_ts = TensorType.filter_checks_isfinite self.old_ts = TensorType.filter_checks_isfinite
self.old_dm = aesara.compile.mode.predefined_modes["DEBUG_MODE"].check_isfinite self.old_dm = predefined_modes["DEBUG_MODE"].check_isfinite
def teardown_method(self): def teardown_method(self):
TensorType.filter_checks_isfinite = self.old_ts TensorType.filter_checks_isfinite = self.old_ts
aesara.compile.mode.predefined_modes["DEBUG_MODE"].check_isfinite = self.old_dm predefined_modes["DEBUG_MODE"].check_isfinite = self.old_dm
def test_check_isfinite(self): def test_check_isfinite(self):
x = vector() x = vector()
f = aesara.function([x], (x + 2) * 5, mode="DEBUG_MODE") f = function([x], (x + 2) * 5, mode="DEBUG_MODE")
g = aesara.function([x], log(x), mode="DEBUG_MODE") g = function([x], log(x), mode="DEBUG_MODE")
# this should work # this should work
f(np.log([3, 4, 5]).astype(config.floatX)) f(np.log([3, 4, 5]).astype(config.floatX))
...@@ -590,13 +591,13 @@ class TestCheckIsfinite: ...@@ -590,13 +591,13 @@ class TestCheckIsfinite:
# this should disable the exception # this should disable the exception
TensorType.filter_checks_isfinite = False TensorType.filter_checks_isfinite = False
aesara.compile.mode.predefined_modes["DEBUG_MODE"].check_isfinite = False predefined_modes["DEBUG_MODE"].check_isfinite = False
# insert several Inf # insert several Inf
f(np.asarray(np.asarray([1.0, 1.0, 1.0]) / 0, dtype=config.floatX)) f(np.asarray(np.asarray([1.0, 1.0, 1.0]) / 0, dtype=config.floatX))
def test_check_isfinite_disabled(self): def test_check_isfinite_disabled(self):
x = dvector() x = dvector()
f = aesara.function([x], (x + 2) * 5, mode=DebugMode(check_isfinite=False)) f = function([x], (x + 2) * 5, mode=DebugMode(check_isfinite=False))
# nan should go through # nan should go through
f(np.log([3, -4, 5])) f(np.log([3, -4, 5]))
...@@ -750,7 +751,7 @@ class TestPreallocatedOutput: ...@@ -750,7 +751,7 @@ class TestPreallocatedOutput:
# Should work # Should work
mode = DebugMode(check_preallocated_output=["c_contiguous"]) mode = DebugMode(check_preallocated_output=["c_contiguous"])
f = aesara.function([a, b], out, mode=mode) f = function([a, b], out, mode=mode)
f(a_val, b_val) f(a_val, b_val)
# print 'out_val =', out_val # print 'out_val =', out_val
# print out_val.strides # print out_val.strides
...@@ -759,7 +760,7 @@ class TestPreallocatedOutput: ...@@ -759,7 +760,7 @@ class TestPreallocatedOutput:
# used incorrectly. # used incorrectly.
mode = DebugMode(check_preallocated_output=["f_contiguous"]) mode = DebugMode(check_preallocated_output=["f_contiguous"])
f = aesara.function([a, b], out, mode=mode) f = function([a, b], out, mode=mode)
if config.cxx: if config.cxx:
with pytest.raises(BadThunkOutput): with pytest.raises(BadThunkOutput):
...@@ -781,7 +782,7 @@ class TestPreallocatedOutput: ...@@ -781,7 +782,7 @@ class TestPreallocatedOutput:
# Should work # Should work
mode = DebugMode(check_preallocated_output=["c_contiguous"]) mode = DebugMode(check_preallocated_output=["c_contiguous"])
f = aesara.function([a, b], out, mode=mode) f = function([a, b], out, mode=mode)
f(a_val, b_val) f(a_val, b_val)
# print 'out_val =', out_val # print 'out_val =', out_val
# print out_val.strides # print out_val.strides
...@@ -790,7 +791,7 @@ class TestPreallocatedOutput: ...@@ -790,7 +791,7 @@ class TestPreallocatedOutput:
# used incorrectly. # used incorrectly.
mode = DebugMode(check_preallocated_output=["f_contiguous"]) mode = DebugMode(check_preallocated_output=["f_contiguous"])
f = aesara.function([a, b], out, mode=mode) f = function([a, b], out, mode=mode)
if config.cxx: if config.cxx:
with pytest.raises(BadThunkOutput): with pytest.raises(BadThunkOutput):
...@@ -802,7 +803,7 @@ class TestPreallocatedOutput: ...@@ -802,7 +803,7 @@ class TestPreallocatedOutput:
def test_output_broadcast_tensor(self): def test_output_broadcast_tensor(self):
v = fvector("v") v = fvector("v")
c, r = VecAsRowAndCol()(v) c, r = VecAsRowAndCol()(v)
f = aesara.function([v], [c, r]) f = function([v], [c, r])
v_val = self.rng.randn(5).astype("float32") v_val = self.rng.randn(5).astype("float32")
f(v_val) f(v_val)
tests/compile/test_mode.py
浏览文件 @ a1388f92
import pytest import pytest
import aesara from aesara.compile.function import function
from aesara.compile.mode import AddFeatureOptimizer, Mode from aesara.compile.mode import AddFeatureOptimizer, Mode
from aesara.configdefaults import config
from aesara.graph.features import NoOutputFromInplace from aesara.graph.features import NoOutputFromInplace
from aesara.tensor.math import dot, tanh from aesara.tensor.math import dot, tanh
from aesara.tensor.type import matrix from aesara.tensor.type import matrix
@pytest.mark.skipif( @pytest.mark.skipif(
not aesara.config.cxx, reason="G++ not available, so we need to skip this test." not config.cxx, reason="G++ not available, so we need to skip this test."
) )
def test_no_output_from_implace(): def test_no_output_from_implace():
x = matrix() x = matrix()
...@@ -18,7 +19,7 @@ def test_no_output_from_implace(): ...@@ -18,7 +19,7 @@ def test_no_output_from_implace():
# Ensure that the elemwise op that produces the output is inplace when # Ensure that the elemwise op that produces the output is inplace when
# using a mode that does not include the optimization # using a mode that does not include the optimization
fct_no_opt = aesara.function([x, y], b, mode="FAST_RUN") fct_no_opt = function([x, y], b, mode="FAST_RUN")
op = fct_no_opt.maker.fgraph.outputs[0].owner.op op = fct_no_opt.maker.fgraph.outputs[0].owner.op
assert op.destroy_map and 0 in op.destroy_map assert op.destroy_map and 0 in op.destroy_map
...@@ -27,7 +28,7 @@ def test_no_output_from_implace(): ...@@ -27,7 +28,7 @@ def test_no_output_from_implace():
opt = AddFeatureOptimizer(NoOutputFromInplace()) opt = AddFeatureOptimizer(NoOutputFromInplace())
mode_opt = Mode(linker="cvm", optimizer="fast_run").register((opt, 49.9)) mode_opt = Mode(linker="cvm", optimizer="fast_run").register((opt, 49.9))
fct_opt = aesara.function([x, y], b, mode=mode_opt) fct_opt = function([x, y], b, mode=mode_opt)
op = fct_opt.maker.fgraph.outputs[0].owner.op op = fct_opt.maker.fgraph.outputs[0].owner.op
assert not op.destroy_map or 0 not in op.destroy_map assert not op.destroy_map or 0 not in op.destroy_map
......
tests/compile/test_modes.py
浏览文件 @ a1388f92
...@@ -4,8 +4,9 @@ Test compilation modes ...@@ -4,8 +4,9 @@ Test compilation modes
import copy import copy
import aesara from aesara.compile.function import function
from aesara.compile.mode import Mode from aesara.compile.mode import Mode, get_default_mode
from aesara.configdefaults import config
from aesara.tensor.type import matrix, vector from aesara.tensor.type import matrix, vector
...@@ -18,7 +19,7 @@ class TestBunchOfModes: ...@@ -18,7 +19,7 @@ class TestBunchOfModes:
predef_modes = ["FAST_COMPILE", "FAST_RUN", "DEBUG_MODE"] predef_modes = ["FAST_COMPILE", "FAST_RUN", "DEBUG_MODE"]
# Linkers to use with regular Mode # Linkers to use with regular Mode
if aesara.config.cxx: if config.cxx:
linkers = ["py", "c|py", "c|py_nogc", "vm", "vm_nogc", "cvm", "cvm_nogc"] linkers = ["py", "c|py", "c|py_nogc", "vm", "vm_nogc", "cvm", "cvm_nogc"]
else: else:
linkers = ["py", "c|py", "c|py_nogc", "vm", "vm_nogc"] linkers = ["py", "c|py", "c|py_nogc", "vm", "vm_nogc"]
...@@ -27,7 +28,7 @@ class TestBunchOfModes: ...@@ -27,7 +28,7 @@ class TestBunchOfModes:
for mode in modes: for mode in modes:
x = matrix() x = matrix()
y = vector() y = vector()
f = aesara.function([x, y], x + y, mode=mode) f = function([x, y], x + y, mode=mode)
# test that it runs something # test that it runs something
f([[1, 2], [3, 4]], [5, 6]) f([[1, 2], [3, 4]], [5, 6])
linker_classes_involved.append(f.maker.mode.linker.__class__) linker_classes_involved.append(f.maker.mode.linker.__class__)
...@@ -45,7 +46,7 @@ class TestBunchOfModes: ...@@ -45,7 +46,7 @@ class TestBunchOfModes:
class TestOldModesProblem: class TestOldModesProblem:
def test_modes(self): def test_modes(self):
# Then, build a mode with the same linker, and a modified optimizer # Then, build a mode with the same linker, and a modified optimizer
default_mode = aesara.compile.mode.get_default_mode() default_mode = get_default_mode()
modified_mode = default_mode.including("specialize") modified_mode = default_mode.including("specialize")
# The following line used to fail, with Python 2.4, in July 2012, # The following line used to fail, with Python 2.4, in July 2012,
...@@ -53,5 +54,5 @@ class TestOldModesProblem: ...@@ -53,5 +54,5 @@ class TestOldModesProblem:
copy.deepcopy(modified_mode) copy.deepcopy(modified_mode)
# More straightforward test # More straightforward test
linker = aesara.compile.mode.get_default_mode().linker linker = get_default_mode().linker
assert not hasattr(linker, "fgraph") or linker.fgraph is None assert not hasattr(linker, "fgraph") or linker.fgraph is None
tests/compile/test_monitormode.py
浏览文件 @ a1388f92
...@@ -3,7 +3,10 @@ from io import StringIO ...@@ -3,7 +3,10 @@ from io import StringIO
import numpy as np import numpy as np
import aesara from aesara.compile import MonitorMode
from aesara.compile.function import function
from aesara.printing import debugprint
from aesara.tensor import log, outer
from aesara.tensor.type import dscalar, vector from aesara.tensor.type import dscalar, vector
...@@ -16,17 +19,17 @@ def test_detect_nan(): ...@@ -16,17 +19,17 @@ def test_detect_nan():
for output in fn.outputs: for output in fn.outputs:
if np.isnan(output[0]).any(): if np.isnan(output[0]).any():
print("*** NaN detected ***") print("*** NaN detected ***")
aesara.printing.debugprint(node) debugprint(node)
print("Inputs : %s" % [input[0] for input in fn.inputs]) print("Inputs : %s" % [input[0] for input in fn.inputs])
print("Outputs: %s" % [output[0] for output in fn.outputs]) print("Outputs: %s" % [output[0] for output in fn.outputs])
nan_detected[0] = True nan_detected[0] = True
break break
x = dscalar("x") x = dscalar("x")
f = aesara.function( f = function(
[x], [x],
[aesara.tensor.log(x) * x], [log(x) * x],
mode=aesara.compile.MonitorMode(post_func=detect_nan), mode=MonitorMode(post_func=detect_nan),
) )
try: try:
old_stdout = sys.stdout old_stdout = sys.stdout
...@@ -46,16 +49,16 @@ def test_optimizer(): ...@@ -46,16 +49,16 @@ def test_optimizer():
for output in fn.outputs: for output in fn.outputs:
if np.isnan(output[0]).any(): if np.isnan(output[0]).any():
print("*** NaN detected ***") print("*** NaN detected ***")
aesara.printing.debugprint(node) debugprint(node)
print("Inputs : %s" % [input[0] for input in fn.inputs]) print("Inputs : %s" % [input[0] for input in fn.inputs])
print("Outputs: %s" % [output[0] for output in fn.outputs]) print("Outputs: %s" % [output[0] for output in fn.outputs])
nan_detected[0] = True nan_detected[0] = True
break break
x = dscalar("x") x = dscalar("x")
mode = aesara.compile.MonitorMode(post_func=detect_nan) mode = MonitorMode(post_func=detect_nan)
mode = mode.excluding("fusion") mode = mode.excluding("fusion")
f = aesara.function([x], [aesara.tensor.log(x) * x], mode=mode) f = function([x], [log(x) * x], mode=mode)
# Test that the fusion wasn't done # Test that the fusion wasn't done
assert len(f.maker.fgraph.apply_nodes) == 2 assert len(f.maker.fgraph.apply_nodes) == 2
try: try:
...@@ -78,19 +81,19 @@ def test_not_inplace(): ...@@ -78,19 +81,19 @@ def test_not_inplace():
for output in fn.outputs: for output in fn.outputs:
if np.isnan(output[0]).any(): if np.isnan(output[0]).any():
print("*** NaN detected ***") print("*** NaN detected ***")
aesara.printing.debugprint(node) debugprint(node)
print("Inputs : %s" % [input[0] for input in fn.inputs]) print("Inputs : %s" % [input[0] for input in fn.inputs])
print("Outputs: %s" % [output[0] for output in fn.outputs]) print("Outputs: %s" % [output[0] for output in fn.outputs])
nan_detected[0] = True nan_detected[0] = True
break break
x = vector("x") x = vector("x")
mode = aesara.compile.MonitorMode(post_func=detect_nan) mode = MonitorMode(post_func=detect_nan)
# mode = mode.excluding('fusion', 'inplace') # mode = mode.excluding('fusion', 'inplace')
mode = mode.excluding("local_elemwise_fusion", "inplace_elemwise_optimizer") mode = mode.excluding("local_elemwise_fusion", "inplace_elemwise_optimizer")
o = aesara.tensor.outer(x, x) o = outer(x, x)
out = aesara.tensor.log(o) * o out = log(o) * o
f = aesara.function([x], [out], mode=mode) f = function([x], [out], mode=mode)
# Test that the fusion wasn't done # Test that the fusion wasn't done
assert len(f.maker.fgraph.apply_nodes) == 5 assert len(f.maker.fgraph.apply_nodes) == 5
......
tests/compile/test_nanguardmode.py
浏览文件 @ a1388f92
...@@ -7,9 +7,11 @@ import logging ...@@ -7,9 +7,11 @@ import logging
import numpy as np import numpy as np
import pytest import pytest
import aesara
import aesara.tensor as aet import aesara.tensor as aet
from aesara.compile import shared
from aesara.compile.function import function
from aesara.compile.nanguardmode import NanGuardMode from aesara.compile.nanguardmode import NanGuardMode
from aesara.configdefaults import config
from aesara.tensor.math import dot from aesara.tensor.math import dot
from aesara.tensor.type import matrix, tensor3 from aesara.tensor.type import matrix, tensor3
...@@ -19,16 +21,14 @@ def test_NanGuardMode(): ...@@ -19,16 +21,14 @@ def test_NanGuardMode():
# intentionally. A working implementation should be able to capture all # intentionally. A working implementation should be able to capture all
# the abnormalties. # the abnormalties.
x = matrix() x = matrix()
w = aesara.shared(np.random.randn(5, 7).astype(aesara.config.floatX)) w = shared(np.random.randn(5, 7).astype(config.floatX))
y = dot(x, w) y = dot(x, w)
fun = aesara.function( fun = function([x], y, mode=NanGuardMode(nan_is_error=True, inf_is_error=True))
[x], y, mode=NanGuardMode(nan_is_error=True, inf_is_error=True) a = np.random.randn(3, 5).astype(config.floatX)
) infa = np.tile((np.asarray(100.0) ** 1000000).astype(config.floatX), (3, 5))
a = np.random.randn(3, 5).astype(aesara.config.floatX) nana = np.tile(np.asarray(np.nan).astype(config.floatX), (3, 5))
infa = np.tile((np.asarray(100.0) ** 1000000).astype(aesara.config.floatX), (3, 5)) biga = np.tile(np.asarray(1e20).astype(config.floatX), (3, 5))
nana = np.tile(np.asarray(np.nan).astype(aesara.config.floatX), (3, 5))
biga = np.tile(np.asarray(1e20).astype(aesara.config.floatX), (3, 5))
fun(a) # normal values fun(a) # normal values
...@@ -46,18 +46,14 @@ def test_NanGuardMode(): ...@@ -46,18 +46,14 @@ def test_NanGuardMode():
_logger.propagate = True _logger.propagate = True
# slices # slices
a = np.random.randn(3, 4, 5).astype(aesara.config.floatX) a = np.random.randn(3, 4, 5).astype(config.floatX)
infa = np.tile( infa = np.tile((np.asarray(100.0) ** 1000000).astype(config.floatX), (3, 4, 5))
(np.asarray(100.0) ** 1000000).astype(aesara.config.floatX), (3, 4, 5) nana = np.tile(np.asarray(np.nan).astype(config.floatX), (3, 4, 5))
) biga = np.tile(np.asarray(1e20).astype(config.floatX), (3, 4, 5))
nana = np.tile(np.asarray(np.nan).astype(aesara.config.floatX), (3, 4, 5))
biga = np.tile(np.asarray(1e20).astype(aesara.config.floatX), (3, 4, 5))
x = tensor3() x = tensor3()
y = x[:, aet.arange(2), aet.arange(2), None] y = x[:, aet.arange(2), aet.arange(2), None]
fun = aesara.function( fun = function([x], y, mode=NanGuardMode(nan_is_error=True, inf_is_error=True))
[x], y, mode=NanGuardMode(nan_is_error=True, inf_is_error=True)
)
fun(a) # normal values fun(a) # normal values
try: try:
_logger.propagate = False _logger.propagate = False
......
tests/compile/test_profiling.py
浏览文件 @ a1388f92
...@@ -5,8 +5,10 @@ from io import StringIO ...@@ -5,8 +5,10 @@ from io import StringIO
import numpy as np import numpy as np
import aesara
import aesara.tensor as aet import aesara.tensor as aet
from aesara.compile import ProfileStats
from aesara.compile.function import function
from aesara.configdefaults import config
from aesara.ifelse import ifelse from aesara.ifelse import ifelse
from aesara.tensor.type import fvector, scalars from aesara.tensor.type import fvector, scalars
...@@ -16,13 +18,13 @@ class TestProfiling: ...@@ -16,13 +18,13 @@ class TestProfiling:
def test_profiling(self): def test_profiling(self):
config1 = aesara.config.profile config1 = config.profile
config2 = aesara.config.profile_memory config2 = config.profile_memory
config3 = aesara.config.profiling__min_peak_memory config3 = config.profiling__min_peak_memory
try: try:
aesara.config.profile = True config.profile = True
aesara.config.profile_memory = True config.profile_memory = True
aesara.config.profiling__min_peak_memory = True config.profiling__min_peak_memory = True
x = [fvector("val%i" % i) for i in range(3)] x = [fvector("val%i" % i) for i in range(3)]
...@@ -30,14 +32,14 @@ class TestProfiling: ...@@ -30,14 +32,14 @@ class TestProfiling:
z += [aet.outer(x[i], x[i + 1]).sum(axis=1) for i in range(len(x) - 1)] z += [aet.outer(x[i], x[i + 1]).sum(axis=1) for i in range(len(x) - 1)]
z += [x[i] + x[i + 1] for i in range(len(x) - 1)] z += [x[i] + x[i + 1] for i in range(len(x) - 1)]
p = aesara.ProfileStats(False, gpu_checks=False) p = ProfileStats(False, gpu_checks=False)
if aesara.config.mode in ["DebugMode", "DEBUG_MODE", "FAST_COMPILE"]: if config.mode in ["DebugMode", "DEBUG_MODE", "FAST_COMPILE"]:
m = "FAST_RUN" m = "FAST_RUN"
else: else:
m = None m = None
f = aesara.function(x, z, profile=p, name="test_profiling", mode=m) f = function(x, z, profile=p, name="test_profiling", mode=m)
inp = [np.arange(1024, dtype="float32") + 1 for i in range(len(x))] inp = [np.arange(1024, dtype="float32") + 1 for i in range(len(x))]
f(*inp) f(*inp)
...@@ -49,7 +51,7 @@ class TestProfiling: ...@@ -49,7 +51,7 @@ class TestProfiling:
the_string = buf.getvalue() the_string = buf.getvalue()
lines1 = [l for l in the_string.split("\n") if "Max if linker" in l] lines1 = [l for l in the_string.split("\n") if "Max if linker" in l]
lines2 = [l for l in the_string.split("\n") if "Minimum peak" in l] lines2 = [l for l in the_string.split("\n") if "Minimum peak" in l]
if aesara.config.device == "cpu": if config.device == "cpu":
assert "CPU: 4112KB (4104KB)" in the_string, (lines1, lines2) assert "CPU: 4112KB (4104KB)" in the_string, (lines1, lines2)
assert "CPU: 8204KB (8196KB)" in the_string, (lines1, lines2) assert "CPU: 8204KB (8196KB)" in the_string, (lines1, lines2)
assert "CPU: 8208KB" in the_string, (lines1, lines2) assert "CPU: 8208KB" in the_string, (lines1, lines2)
...@@ -68,33 +70,31 @@ class TestProfiling: ...@@ -68,33 +70,31 @@ class TestProfiling:
), (lines1, lines2) ), (lines1, lines2)
finally: finally:
aesara.config.profile = config1 config.profile = config1
aesara.config.profile_memory = config2 config.profile_memory = config2
aesara.config.profiling__min_peak_memory = config3 config.profiling__min_peak_memory = config3
def test_ifelse(self): def test_ifelse(self):
config1 = aesara.config.profile config1 = config.profile
config2 = aesara.config.profile_memory config2 = config.profile_memory
try: try:
aesara.config.profile = True config.profile = True
aesara.config.profile_memory = True config.profile_memory = True
a, b = scalars("a", "b") a, b = scalars("a", "b")
x, y = scalars("x", "y") x, y = scalars("x", "y")
z = ifelse(aet.lt(a, b), x * 2, y * 2) z = ifelse(aet.lt(a, b), x * 2, y * 2)
p = aesara.ProfileStats(False, gpu_checks=False) p = ProfileStats(False, gpu_checks=False)
if aesara.config.mode in ["DebugMode", "DEBUG_MODE", "FAST_COMPILE"]: if config.mode in ["DebugMode", "DEBUG_MODE", "FAST_COMPILE"]:
m = "FAST_RUN" m = "FAST_RUN"
else: else:
m = None m = None
f_ifelse = aesara.function( f_ifelse = function([a, b, x, y], z, profile=p, name="test_ifelse", mode=m)
[a, b, x, y], z, profile=p, name="test_ifelse", mode=m
)
val1 = 0.0 val1 = 0.0
val2 = 1.0 val2 = 1.0
...@@ -104,5 +104,5 @@ class TestProfiling: ...@@ -104,5 +104,5 @@ class TestProfiling:
f_ifelse(val1, val2, big_mat1, big_mat2) f_ifelse(val1, val2, big_mat1, big_mat2)
finally: finally:
aesara.config.profile = config1 config.profile = config1
aesara.config.profile_memory = config2 config.profile_memory = config2
tests/compile/test_shared.py
浏览文件 @ a1388f92
import numpy as np import numpy as np
import pytest import pytest
import aesara import aesara.tensor
from aesara.compile.sharedvalue import SharedVariable, generic, shared from aesara.compile.sharedvalue import SharedVariable, generic, shared
from aesara.configdefaults import config
from aesara.misc.safe_asarray import _asarray from aesara.misc.safe_asarray import _asarray
from aesara.tensor.type import ( from aesara.tensor.type import (
TensorType, TensorType,
...@@ -329,8 +330,8 @@ class TestSharedVariable: ...@@ -329,8 +330,8 @@ class TestSharedVariable:
# assert b.type == dvector # assert b.type == dvector
# f(b,[8]) # f(b,[8])
b = shared(np.asarray([7.234], dtype=aesara.config.floatX), allow_downcast=True) b = shared(np.asarray([7.234], dtype=config.floatX), allow_downcast=True)
assert b.dtype == aesara.config.floatX assert b.dtype == config.floatX
f(b, [8]) f(b, [8])
assert b.get_value() == 8 assert b.get_value() == 8
......
tests/d3viz/models.py
浏览文件 @ a1388f92
import numpy as np import numpy as np
import aesara
import aesara.tensor as aet import aesara.tensor as aet
from aesara import shared
from aesara.compile.builders import OpFromGraph
from aesara.tensor.type import dmatrix, scalars from aesara.tensor.type import dmatrix, scalars
...@@ -17,13 +18,13 @@ class Mlp: ...@@ -17,13 +18,13 @@ class Mlp:
self.nhiddens = nhiddens self.nhiddens = nhiddens
x = dmatrix("x") x = dmatrix("x")
wh = aesara.shared(self.rng.normal(0, 1, (nfeatures, nhiddens)), borrow=True) wh = shared(self.rng.normal(0, 1, (nfeatures, nhiddens)), borrow=True)
bh = aesara.shared(np.zeros(nhiddens), borrow=True) bh = shared(np.zeros(nhiddens), borrow=True)
h = aesara.tensor.sigmoid(aet.dot(x, wh) + bh) h = aet.nnet.sigmoid(aet.dot(x, wh) + bh)
wy = aesara.shared(self.rng.normal(0, 1, (nhiddens, noutputs))) wy = shared(self.rng.normal(0, 1, (nhiddens, noutputs)))
by = aesara.shared(np.zeros(noutputs), borrow=True) by = shared(np.zeros(noutputs), borrow=True)
y = aesara.tensor.nnet.softmax(aet.dot(h, wy) + by) y = aet.nnet.softmax(aet.dot(h, wy) + by)
self.inputs = [x] self.inputs = [x]
self.outputs = [y] self.outputs = [y]
...@@ -33,9 +34,9 @@ class OfgNested: ...@@ -33,9 +34,9 @@ class OfgNested:
def __init__(self): def __init__(self):
x, y, z = scalars("xyz") x, y, z = scalars("xyz")
e = x * y e = x * y
op = aesara.compile.builders.OpFromGraph([x, y], [e]) op = OpFromGraph([x, y], [e])
e2 = op(x, y) + z e2 = op(x, y) + z
op2 = aesara.compile.builders.OpFromGraph([x, y, z], [e2]) op2 = OpFromGraph([x, y, z], [e2])
e3 = op2(x, y, z) + z e3 = op2(x, y, z) + z
self.inputs = [x, y, z] self.inputs = [x, y, z]
...@@ -45,8 +46,8 @@ class OfgNested: ...@@ -45,8 +46,8 @@ class OfgNested:
class Ofg: class Ofg:
def __init__(self): def __init__(self):
x, y, z = scalars("xyz") x, y, z = scalars("xyz")
e = aesara.tensor.sigmoid((x + y + z) ** 2) e = aet.nnet.sigmoid((x + y + z) ** 2)
op = aesara.compile.builders.OpFromGraph([x, y, z], [e]) op = OpFromGraph([x, y, z], [e])
e2 = op(x, y, z) + op(z, y, x) e2 = op(x, y, z) + op(z, y, x)
self.inputs = [x, y, z] self.inputs = [x, y, z]
...@@ -56,8 +57,8 @@ class Ofg: ...@@ -56,8 +57,8 @@ class Ofg:
class OfgSimple: class OfgSimple:
def __init__(self): def __init__(self):
x, y, z = scalars("xyz") x, y, z = scalars("xyz")
e = aesara.tensor.sigmoid((x + y + z) ** 2) e = aet.nnet.sigmoid((x + y + z) ** 2)
op = aesara.compile.builders.OpFromGraph([x, y, z], [e]) op = OpFromGraph([x, y, z], [e])
e2 = op(x, y, z) e2 = op(x, y, z)
self.inputs = [x, y, z] self.inputs = [x, y, z]
......
tests/d3viz/test_d3viz.py
浏览文件 @ a1388f92
...@@ -5,8 +5,10 @@ import tempfile ...@@ -5,8 +5,10 @@ import tempfile
import numpy as np import numpy as np
import pytest import pytest
import aesara as th
import aesara.d3viz as d3v import aesara.d3viz as d3v
from aesara import compile
from aesara.compile.function import function
from aesara.configdefaults import config
from aesara.d3viz.formatting import pydot_imported, pydot_imported_msg from aesara.d3viz.formatting import pydot_imported, pydot_imported_msg
from tests.d3viz import models from tests.d3viz import models
...@@ -32,30 +34,30 @@ class TestD3Viz: ...@@ -32,30 +34,30 @@ class TestD3Viz:
def test_mlp(self): def test_mlp(self):
m = models.Mlp() m = models.Mlp()
f = th.function(m.inputs, m.outputs) f = function(m.inputs, m.outputs)
self.check(f) self.check(f)
def test_mlp_profiled(self): def test_mlp_profiled(self):
if th.config.mode in ("DebugMode", "DEBUG_MODE"): if config.mode in ("DebugMode", "DEBUG_MODE"):
pytest.skip("Can't profile in DebugMode") pytest.skip("Can't profile in DebugMode")
m = models.Mlp() m = models.Mlp()
profile = th.compile.profiling.ProfileStats(False) profile = compile.profiling.ProfileStats(False)
f = th.function(m.inputs, m.outputs, profile=profile) f = function(m.inputs, m.outputs, profile=profile)
x_val = self.rng.normal(0, 1, (1000, m.nfeatures)) x_val = self.rng.normal(0, 1, (1000, m.nfeatures))
f(x_val) f(x_val)
self.check(f) self.check(f)
def test_ofg(self): def test_ofg(self):
m = models.Ofg() m = models.Ofg()
f = th.function(m.inputs, m.outputs) f = function(m.inputs, m.outputs)
self.check(f) self.check(f)
def test_ofg_nested(self): def test_ofg_nested(self):
m = models.OfgNested() m = models.OfgNested()
f = th.function(m.inputs, m.outputs) f = function(m.inputs, m.outputs)
self.check(f) self.check(f)
def test_ofg_simple(self): def test_ofg_simple(self):
m = models.OfgSimple() m = models.OfgSimple()
f = th.function(m.inputs, m.outputs) f = function(m.inputs, m.outputs)
self.check(f) self.check(f)
tests/d3viz/test_formatting.py
浏览文件 @ a1388f92
import numpy as np import numpy as np
import pytest import pytest
import aesara as th from aesara import config, function
from aesara.d3viz.formatting import PyDotFormatter, pydot_imported, pydot_imported_msg from aesara.d3viz.formatting import PyDotFormatter, pydot_imported, pydot_imported_msg
...@@ -23,16 +23,16 @@ class TestPyDotFormatter: ...@@ -23,16 +23,16 @@ class TestPyDotFormatter:
def test_mlp(self): def test_mlp(self):
m = models.Mlp() m = models.Mlp()
f = th.function(m.inputs, m.outputs) f = function(m.inputs, m.outputs)
pdf = PyDotFormatter() pdf = PyDotFormatter()
graph = pdf(f) graph = pdf(f)
expected = 11 expected = 11
if th.config.mode == "FAST_COMPILE": if config.mode == "FAST_COMPILE":
expected = 12 expected = 12
assert len(graph.get_nodes()) == expected assert len(graph.get_nodes()) == expected
nc = self.node_counts(graph) nc = self.node_counts(graph)
if th.config.mode == "FAST_COMPILE": if config.mode == "FAST_COMPILE":
assert nc["apply"] == 6 assert nc["apply"] == 6
else: else:
assert nc["apply"] == 5 assert nc["apply"] == 5
...@@ -40,14 +40,14 @@ class TestPyDotFormatter: ...@@ -40,14 +40,14 @@ class TestPyDotFormatter:
def test_ofg(self): def test_ofg(self):
m = models.Ofg() m = models.Ofg()
f = th.function(m.inputs, m.outputs) f = function(m.inputs, m.outputs)
pdf = PyDotFormatter() pdf = PyDotFormatter()
graph = pdf(f) graph = pdf(f)
assert len(graph.get_nodes()) == 10 assert len(graph.get_nodes()) == 10
sub_graphs = graph.get_subgraph_list() sub_graphs = graph.get_subgraph_list()
assert len(sub_graphs) == 2 assert len(sub_graphs) == 2
ofg1, ofg2 = sub_graphs ofg1, ofg2 = sub_graphs
if th.config.mode == "FAST_COMPILE": if config.mode == "FAST_COMPILE":
assert len(ofg1.get_nodes()) == 9 assert len(ofg1.get_nodes()) == 9
else: else:
assert len(ofg1.get_nodes()) == 5 assert len(ofg1.get_nodes()) == 5
...@@ -55,7 +55,7 @@ class TestPyDotFormatter: ...@@ -55,7 +55,7 @@ class TestPyDotFormatter:
def test_ofg_nested(self): def test_ofg_nested(self):
m = models.OfgNested() m = models.OfgNested()
f = th.function(m.inputs, m.outputs) f = function(m.inputs, m.outputs)
pdf = PyDotFormatter() pdf = PyDotFormatter()
graph = pdf(f) graph = pdf(f)
assert len(graph.get_nodes()) == 7 assert len(graph.get_nodes()) == 7
......
tests/link/c/test_basic.py
浏览文件 @ a1388f92
import numpy as np import numpy as np
import pytest import pytest
import aesara from aesara.compile import shared
from aesara.compile.function import function
from aesara.compile.mode import Mode from aesara.compile.mode import Mode
from aesara.configdefaults import config
from aesara.graph.basic import Apply, Constant, Variable from aesara.graph.basic import Apply, Constant, Variable
from aesara.graph.fg import FunctionGraph from aesara.graph.fg import FunctionGraph
from aesara.graph.op import COp from aesara.graph.op import COp
...@@ -181,7 +183,7 @@ def inputs(): ...@@ -181,7 +183,7 @@ def inputs():
@pytest.mark.skipif( @pytest.mark.skipif(
not aesara.config.cxx, reason="G++ not available, so we need to skip this test." not config.cxx, reason="G++ not available, so we need to skip this test."
) )
def test_clinker_straightforward(): def test_clinker_straightforward():
x, y, z = inputs() x, y, z = inputs()
...@@ -192,7 +194,7 @@ def test_clinker_straightforward(): ...@@ -192,7 +194,7 @@ def test_clinker_straightforward():
@pytest.mark.skipif( @pytest.mark.skipif(
not aesara.config.cxx, reason="G++ not available, so we need to skip this test." not config.cxx, reason="G++ not available, so we need to skip this test."
) )
def test_cthunk_str(): def test_cthunk_str():
x = double("x") x = double("x")
...@@ -205,7 +207,7 @@ def test_cthunk_str(): ...@@ -205,7 +207,7 @@ def test_cthunk_str():
@pytest.mark.skipif( @pytest.mark.skipif(
not aesara.config.cxx, reason="G++ not available, so we need to skip this test." not config.cxx, reason="G++ not available, so we need to skip this test."
) )
def test_clinker_literal_inlining(): def test_clinker_literal_inlining():
x, y, z = inputs() x, y, z = inputs()
...@@ -221,7 +223,7 @@ def test_clinker_literal_inlining(): ...@@ -221,7 +223,7 @@ def test_clinker_literal_inlining():
@pytest.mark.skipif( @pytest.mark.skipif(
not aesara.config.cxx, reason="G++ not available, so we need to skip this test." not config.cxx, reason="G++ not available, so we need to skip this test."
) )
def test_clinker_literal_cache(): def test_clinker_literal_cache():
mode = Mode(linker="c") mode = Mode(linker="c")
...@@ -235,15 +237,13 @@ def test_clinker_literal_cache(): ...@@ -235,15 +237,13 @@ def test_clinker_literal_cache():
[-4.664007e-07, 9.468691e-01, -3.18862e-02], [-4.664007e-07, 9.468691e-01, -3.18862e-02],
[-2.562651e-06, -3.188625e-02, 1.05226e00], [-2.562651e-06, -3.188625e-02, 1.05226e00],
], ],
dtype=aesara.config.floatX, dtype=config.floatX,
) )
orientationi = np.array( orientationi = np.array([59.36276866, 1.06116353, 0.93797339], dtype=config.floatX)
[59.36276866, 1.06116353, 0.93797339], dtype=aesara.config.floatX
)
for out1 in [A - input1[0] * np.identity(3), input1[0] * np.identity(3)]: for out1 in [A - input1[0] * np.identity(3), input1[0] * np.identity(3)]:
benchmark = aesara.function( benchmark = function(
inputs=[A, input1], outputs=[out1], on_unused_input="ignore", mode=mode inputs=[A, input1], outputs=[out1], on_unused_input="ignore", mode=mode
) )
...@@ -251,7 +251,7 @@ def test_clinker_literal_cache(): ...@@ -251,7 +251,7 @@ def test_clinker_literal_cache():
@pytest.mark.skipif( @pytest.mark.skipif(
not aesara.config.cxx, reason="G++ not available, so we need to skip this test." not config.cxx, reason="G++ not available, so we need to skip this test."
) )
def test_clinker_single_node(): def test_clinker_single_node():
x, y, z = inputs() x, y, z = inputs()
...@@ -262,7 +262,7 @@ def test_clinker_single_node(): ...@@ -262,7 +262,7 @@ def test_clinker_single_node():
@pytest.mark.skipif( @pytest.mark.skipif(
not aesara.config.cxx, reason="G++ not available, so we need to skip this test." not config.cxx, reason="G++ not available, so we need to skip this test."
) )
def test_clinker_dups(): def test_clinker_dups():
# Testing that duplicate inputs are allowed. # Testing that duplicate inputs are allowed.
...@@ -275,7 +275,7 @@ def test_clinker_dups(): ...@@ -275,7 +275,7 @@ def test_clinker_dups():
@pytest.mark.skipif( @pytest.mark.skipif(
not aesara.config.cxx, reason="G++ not available, so we need to skip this test." not config.cxx, reason="G++ not available, so we need to skip this test."
) )
def test_clinker_not_used_inputs(): def test_clinker_not_used_inputs():
# Testing that unused inputs are allowed. # Testing that unused inputs are allowed.
...@@ -287,7 +287,7 @@ def test_clinker_not_used_inputs(): ...@@ -287,7 +287,7 @@ def test_clinker_not_used_inputs():
@pytest.mark.skipif( @pytest.mark.skipif(
not aesara.config.cxx, reason="G++ not available, so we need to skip this test." not config.cxx, reason="G++ not available, so we need to skip this test."
) )
def test_clinker_dups_inner(): def test_clinker_dups_inner():
# Testing that duplicates are allowed inside the graph # Testing that duplicates are allowed inside the graph
...@@ -304,7 +304,7 @@ def test_opwiseclinker_straightforward(): ...@@ -304,7 +304,7 @@ def test_opwiseclinker_straightforward():
e = add(mul(add(x, y), div(x, y)), bad_sub(bad_sub(x, y), z)) e = add(mul(add(x, y), div(x, y)), bad_sub(bad_sub(x, y), z))
lnk = OpWiseCLinker().accept(FunctionGraph([x, y, z], [e])) lnk = OpWiseCLinker().accept(FunctionGraph([x, y, z], [e]))
fn = lnk.make_function() fn = lnk.make_function()
if aesara.config.cxx: if config.cxx:
assert fn(2.0, 2.0, 2.0) == 2.0 assert fn(2.0, 2.0, 2.0) == 2.0
else: else:
# The python version of bad_sub always return -10. # The python version of bad_sub always return -10.
...@@ -340,7 +340,7 @@ def test_duallinker_straightforward(): ...@@ -340,7 +340,7 @@ def test_duallinker_straightforward():
@pytest.mark.skipif( @pytest.mark.skipif(
not aesara.config.cxx, reason="G++ not available, so we need to skip this test." not config.cxx, reason="G++ not available, so we need to skip this test."
) )
def test_duallinker_mismatch(): def test_duallinker_mismatch():
x, y, z = inputs() x, y, z = inputs()
...@@ -382,7 +382,7 @@ add_fail = AddFail() ...@@ -382,7 +382,7 @@ add_fail = AddFail()
@pytest.mark.skipif( @pytest.mark.skipif(
not aesara.config.cxx, reason="G++ not available, so we need to skip this test." not config.cxx, reason="G++ not available, so we need to skip this test."
) )
def test_c_fail_error(): def test_c_fail_error():
x, y, z = inputs() x, y, z = inputs()
...@@ -395,19 +395,19 @@ def test_c_fail_error(): ...@@ -395,19 +395,19 @@ def test_c_fail_error():
@pytest.mark.skipif( @pytest.mark.skipif(
not aesara.config.cxx, reason="G++ not available, so we need to skip this test." not config.cxx, reason="G++ not available, so we need to skip this test."
) )
def test_shared_input_output(): def test_shared_input_output():
# Test bug reported on the mailing list by Alberto Orlandi # Test bug reported on the mailing list by Alberto Orlandi
# https://groups.google.com/d/topic/theano-users/6dLaEqc2R6g/discussion # https://groups.google.com/d/topic/theano-users/6dLaEqc2R6g/discussion
# The shared variable is both an input and an output of the function. # The shared variable is both an input and an output of the function.
inc = iscalar("inc") inc = iscalar("inc")
state = aesara.shared(0) state = shared(0)
state.name = "state" state.name = "state"
linker = CLinker() linker = CLinker()
mode = Mode(linker=linker) mode = Mode(linker=linker)
f = aesara.function([inc], state, updates=[(state, state + inc)], mode=mode) f = function([inc], state, updates=[(state, state + inc)], mode=mode)
g = aesara.function([inc], state, updates=[(state, state + inc)]) g = function([inc], state, updates=[(state, state + inc)])
# Initial value # Initial value
f0 = f(0) f0 = f(0)
...@@ -428,10 +428,10 @@ def test_shared_input_output(): ...@@ -428,10 +428,10 @@ def test_shared_input_output():
g0 = g(0) g0 = g(0)
assert f0 == g0 == 5, (f0, g0) assert f0 == g0 == 5, (f0, g0)
vstate = aesara.shared(np.zeros(3, dtype="int32")) vstate = shared(np.zeros(3, dtype="int32"))
vstate.name = "vstate" vstate.name = "vstate"
fv = aesara.function([inc], vstate, updates=[(vstate, vstate + inc)], mode=mode) fv = function([inc], vstate, updates=[(vstate, vstate + inc)], mode=mode)
gv = aesara.function([inc], vstate, updates=[(vstate, vstate + inc)]) gv = function([inc], vstate, updates=[(vstate, vstate + inc)])
# Initial value # Initial value
fv0 = fv(0) fv0 = fv(0)
......
tests/link/c/test_cmodule.py
浏览文件 @ a1388f92
...@@ -11,8 +11,9 @@ from unittest.mock import patch ...@@ -11,8 +11,9 @@ from unittest.mock import patch
import numpy as np import numpy as np
import pytest import pytest
import aesara from aesara.compile.function import function
from aesara.compile.ops import DeepCopyOp from aesara.compile.ops import DeepCopyOp
from aesara.configdefaults import config
from aesara.link.c.cmodule import GCC_compiler, default_blas_ldflags from aesara.link.c.cmodule import GCC_compiler, default_blas_ldflags
from aesara.link.c.exceptions import CompileError from aesara.link.c.exceptions import CompileError
from aesara.tensor.type import dvectors from aesara.tensor.type import dvectors
...@@ -35,9 +36,7 @@ class MyOp(DeepCopyOp): ...@@ -35,9 +36,7 @@ class MyOp(DeepCopyOp):
rand = np.random.rand() rand = np.random.rand()
return ('printf("%(rand)s\\n");' + code) % locals() return ('printf("%(rand)s\\n");' + code) % locals()
# Else, no C code # Else, no C code
return super(aesara.compile.ops.DeepCopyOp, self).c_code( return super(DeepCopyOp, self).c_code(node, name, inames, onames, sub)
node, name, inames, onames, sub
)
def test_compiler_error(): def test_compiler_error():
...@@ -56,18 +55,18 @@ def test_inter_process_cache(): ...@@ -56,18 +55,18 @@ def test_inter_process_cache():
# node.inputs[*].owner like the name of the variable. # node.inputs[*].owner like the name of the variable.
x, y = dvectors("xy") x, y = dvectors("xy")
f = aesara.function([x, y], [MyOp()(x), MyOp()(y)]) f = function([x, y], [MyOp()(x), MyOp()(y)])
f(np.arange(60), np.arange(60)) f(np.arange(60), np.arange(60))
if aesara.config.mode == "FAST_COMPILE" or aesara.config.cxx == "": if config.mode == "FAST_COMPILE" or config.cxx == "":
assert MyOp.nb_called == 0 assert MyOp.nb_called == 0
else: else:
assert MyOp.nb_called == 1 assert MyOp.nb_called == 1
# What if we compile a new function with new variables? # What if we compile a new function with new variables?
x, y = dvectors("xy") x, y = dvectors("xy")
f = aesara.function([x, y], [MyOp()(x), MyOp()(y)]) f = function([x, y], [MyOp()(x), MyOp()(y)])
f(np.arange(60), np.arange(60)) f(np.arange(60), np.arange(60))
if aesara.config.mode == "FAST_COMPILE" or aesara.config.cxx == "": if config.mode == "FAST_COMPILE" or config.cxx == "":
assert MyOp.nb_called == 0 assert MyOp.nb_called == 0
else: else:
assert MyOp.nb_called == 1 assert MyOp.nb_called == 1
......
tests/test_breakpoint.py
浏览文件 @ a1388f92
import numpy as np import numpy as np
import aesara
from aesara.breakpoint import PdbBreakpoint from aesara.breakpoint import PdbBreakpoint
from aesara.compile.function import function
from aesara.gradient import grad
from aesara.tensor.math import dot, gt from aesara.tensor.math import dot, gt
from aesara.tensor.type import fmatrix, fscalar from aesara.tensor.type import fmatrix, fscalar
from tests import unittest_tools as utt from tests import unittest_tools as utt
...@@ -46,16 +47,14 @@ class TestPdbBreakpoint(utt.InferShapeTester): ...@@ -46,16 +47,14 @@ class TestPdbBreakpoint(utt.InferShapeTester):
input2_value = 10.0 input2_value = 10.0
grads = [ grads = [
aesara.grad(self.monitored_input1.sum(), self.input1), grad(self.monitored_input1.sum(), self.input1),
aesara.grad(self.monitored_input2.sum(), self.input2), grad(self.monitored_input2.sum(), self.input2),
] ]
# Add self.monitored_input1 as an output to the Aesara function to # Add self.monitored_input1 as an output to the Aesara function to
# prevent Aesara from optimizing the PdbBreakpoint op out of the # prevent Aesara from optimizing the PdbBreakpoint op out of the
# function graph # function graph
fct = aesara.function( fct = function([self.input1, self.input2], grads + [self.monitored_input1])
[self.input1, self.input2], grads + [self.monitored_input1]
)
gradients = fct(input1_value, input2_value)[:-1] gradients = fct(input1_value, input2_value)[:-1]
...@@ -71,7 +70,7 @@ class TestPdbBreakpoint(utt.InferShapeTester): ...@@ -71,7 +70,7 @@ class TestPdbBreakpoint(utt.InferShapeTester):
input1_value = np.arange(9).reshape(3, 3).astype("float32") input1_value = np.arange(9).reshape(3, 3).astype("float32")
input2_value = 10.0 input2_value = 10.0
fct = aesara.function( fct = function(
[self.input1, self.input2], [self.monitored_input1, self.monitored_input2] [self.input1, self.input2], [self.monitored_input1, self.monitored_input2]
) )
......
tests/test_determinism.py
浏览文件 @ a1388f92
...@@ -4,9 +4,10 @@ from io import StringIO ...@@ -4,9 +4,10 @@ from io import StringIO
import numpy as np import numpy as np
import aesara from aesara.compile import shared
from aesara import shared from aesara.compile.function import function
from aesara.configdefaults import config from aesara.configdefaults import config
from aesara.graph import basic
from aesara.printing import var_descriptor from aesara.printing import var_descriptor
from tests.record import Record, RecordMode from tests.record import Record, RecordMode
...@@ -78,16 +79,14 @@ def test_determinism_1(): ...@@ -78,16 +79,14 @@ def test_determinism_1():
s = sharedX(0.0, name="s_" + str(i)) s = sharedX(0.0, name="s_" + str(i))
updates.append((s, val)) updates.append((s, val))
for var in aesara.graph.basic.ancestors(update for _, update in updates): for var in basic.ancestors(update for _, update in updates):
if var.name is not None and var.name != "b": if var.name is not None and var.name != "b":
if var.name[0] != "s" or len(var.name) != 2: if var.name[0] != "s" or len(var.name) != 2:
var.name = None var.name = None
for key in channels: for key in channels:
updates.append((s, channels[key])) updates.append((s, channels[key]))
f = aesara.function( f = function([], mode=mode, updates=updates, on_unused_input="ignore", name="f")
[], mode=mode, updates=updates, on_unused_input="ignore", name="f"
)
for output in f.maker.fgraph.outputs: for output in f.maker.fgraph.outputs:
mode.record.handle_line(var_descriptor(output) + "\n") mode.record.handle_line(var_descriptor(output) + "\n")
disturb_mem() disturb_mem()
......
tests/test_dictionary_output.py
浏览文件 @ a1388f92
import pytest import pytest
import aesara from aesara import function
from aesara.tensor.type import scalar from aesara.tensor.type import scalar
class TestDictionaryOutput: class TestDictionaryOutput:
def test_output_dictionary(self): def test_output_dictionary(self):
# Tests that aesara.function works when outputs is a dictionary # Tests that function works when outputs is a dictionary
x = scalar() x = scalar()
f = aesara.function([x], outputs={"a": x, "c": x * 2, "b": x * 3, "1": x * 4}) f = function([x], outputs={"a": x, "c": x * 2, "b": x * 3, "1": x * 4})
outputs = f(10.0) outputs = f(10.0)
...@@ -24,7 +24,7 @@ class TestDictionaryOutput: ...@@ -24,7 +24,7 @@ class TestDictionaryOutput:
x = scalar("x") x = scalar("x")
y = scalar("y") y = scalar("y")
f = aesara.function([x, y], outputs={"a": x + y, "b": x * y}) f = function([x, y], outputs={"a": x + y, "b": x * y})
assert f(2, 4) == {"a": 6, "b": 8} assert f(2, 4) == {"a": 6, "b": 8}
assert f(2, y=4) == f(2, 4) assert f(2, y=4) == f(2, 4)
...@@ -39,7 +39,7 @@ class TestDictionaryOutput: ...@@ -39,7 +39,7 @@ class TestDictionaryOutput:
e1 = scalar("1") e1 = scalar("1")
e2 = scalar("2") e2 = scalar("2")
f = aesara.function( f = function(
[x, y, z, e1, e2], outputs={"x": x, "y": y, "z": z, "1": e1, "2": e2} [x, y, z, e1, e2], outputs={"x": x, "y": y, "z": z, "1": e1, "2": e2}
) )
...@@ -59,7 +59,7 @@ class TestDictionaryOutput: ...@@ -59,7 +59,7 @@ class TestDictionaryOutput:
a = x + y a = x + y
b = x * y b = x * y
f = aesara.function([x, y], outputs={"a": a, "b": b}) f = function([x, y], outputs={"a": a, "b": b})
a = scalar("a") a = scalar("a")
b = scalar("b") b = scalar("b")
...@@ -67,7 +67,7 @@ class TestDictionaryOutput: ...@@ -67,7 +67,7 @@ class TestDictionaryOutput:
l = a + b l = a + b
r = a * b r = a * b
g = aesara.function([a, b], outputs=[l, r]) g = function([a, b], outputs=[l, r])
result = g(**f(5, 7)) result = g(**f(5, 7))
...@@ -75,11 +75,11 @@ class TestDictionaryOutput: ...@@ -75,11 +75,11 @@ class TestDictionaryOutput:
assert result[1] == 420.0 assert result[1] == 420.0
def test_output_list_still_works(self): def test_output_list_still_works(self):
# Test that aesara.function works if outputs is a list. # Test that function works if outputs is a list.
x = scalar("x") x = scalar("x")
f = aesara.function([x], outputs=[x * 3, x * 2, x * 4, x]) f = function([x], outputs=[x * 3, x * 2, x * 4, x])
result = f(5.0) result = f(5.0)
...@@ -93,9 +93,7 @@ class TestDictionaryOutput: ...@@ -93,9 +93,7 @@ class TestDictionaryOutput:
x = scalar("x") x = scalar("x")
f = aesara.function( f = function([x], outputs={"1": x, "2": 2 * x, "3": 3 * x}, mode="DEBUG_MODE")
[x], outputs={"1": x, "2": 2 * x, "3": 3 * x}, mode="DEBUG_MODE"
)
result = f(3.0) result = f(3.0)
...@@ -108,7 +106,7 @@ class TestDictionaryOutput: ...@@ -108,7 +106,7 @@ class TestDictionaryOutput:
x = scalar("x") x = scalar("x")
f = aesara.function([x], outputs=[x, 2 * x, 3 * x], mode="DEBUG_MODE") f = function([x], outputs=[x, 2 * x, 3 * x], mode="DEBUG_MODE")
result = f(5.0) result = f(5.0)
...@@ -122,10 +120,10 @@ class TestDictionaryOutput: ...@@ -122,10 +120,10 @@ class TestDictionaryOutput:
x = scalar("x") x = scalar("x")
with pytest.raises(AssertionError): with pytest.raises(AssertionError):
aesara.function([x], outputs={1.0: x}) function([x], outputs={1.0: x})
with pytest.raises(AssertionError): with pytest.raises(AssertionError):
aesara.function([x], outputs={1.0: x, "a": x ** 2}) function([x], outputs={1.0: x, "a": x ** 2})
with pytest.raises(AssertionError): with pytest.raises(AssertionError):
aesara.function([x], outputs={(1, "b"): x, 1.0: x ** 2}) function([x], outputs={(1, "b"): x, 1.0: x ** 2})
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