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pytensor
提交 b128f2b8 authored 作者: Olivier Delalleau's avatar Olivier Delalleau
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.hgignore
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...@@ -2,6 +2,7 @@ syntax: glob ...@@ -2,6 +2,7 @@ syntax: glob
*.pyo *.pyo
*~ *~
\#*\# \#*\#
doc/oplist.txt
compiled/*.cpp compiled/*.cpp
cutils_ext.cpp cutils_ext.cpp
html html
......
__init__.py
浏览文件 @ b128f2b8
...@@ -19,6 +19,8 @@ To learn more, check out: ...@@ -19,6 +19,8 @@ To learn more, check out:
- Index of Howto documents (:wiki:`IndexHowto`) - Index of Howto documents (:wiki:`IndexHowto`)
- Op List (:doc:`oplist`)
""" """
__docformat__ = "restructuredtext en" __docformat__ = "restructuredtext en"
......
_test_compile.py
浏览文件 @ b128f2b8
...@@ -77,7 +77,7 @@ class T_Function(unittest.TestCase): ...@@ -77,7 +77,7 @@ class T_Function(unittest.TestCase):
def test_closure(self): def test_closure(self):
x, y, z = tensor.scalars('xyz') x, y, z = tensor.scalars('xyz')
v = tensor.value(numpy.zeros(())) v = tensor.value(numpy.zeros(()))
e = x + tensor.add_inplace(v, 1) e = x + tensor._add_inplace(v, 1)
f = function([x], [e]) f = function([x], [e])
assert f(1.) == 2. assert f(1.) == 2.
assert f(1.) == 3. assert f(1.) == 3.
...@@ -109,7 +109,7 @@ class T_Function(unittest.TestCase): ...@@ -109,7 +109,7 @@ class T_Function(unittest.TestCase):
def test_borrow_false_through_inplace(self): def test_borrow_false_through_inplace(self):
x, y, z = tensor.scalars('xyz') x, y, z = tensor.scalars('xyz')
# if borrow_outputs is False, we must not reuse the temporary created for x+y # if borrow_outputs is False, we must not reuse the temporary created for x+y
e = tensor.add_inplace(x + y, z) e = tensor._add_inplace(x + y, z)
for linker in 'py c c|py c&py'.split(): for linker in 'py c c|py c&py'.split():
f = function([x, y, z], [e], borrow_outputs = False, linker = linker) f = function([x, y, z], [e], borrow_outputs = False, linker = linker)
res1 = f(1.0, 2.0, 3.0) res1 = f(1.0, 2.0, 3.0)
......
_test_tensor.py
浏览文件 @ b128f2b8
...@@ -21,9 +21,16 @@ def _numpy_checker(x, y): ...@@ -21,9 +21,16 @@ def _numpy_checker(x, y):
Used in DualLinker to compare C version with Python version. Used in DualLinker to compare C version with Python version.
""" """
x, y = x[0], y[0] x, y = x[0], y[0]
if x.dtype != y.dtype or x.shape != y.shape or numpy.any(abs(x - y) > 1e-10): if x.dtype != y.dtype or x.shape != y.shape or numpy.any(numpy.abs(x - y) > 1e-10):
raise Exception("Output mismatch.", {'performlinker': x, 'clinker': y}) raise Exception("Output mismatch.", {'performlinker': x, 'clinker': y})
def safe_make_node(op, *inputs):
"""Emulate the behaviour of make_node when op is a function instead of an Op instance."""
node = op(*inputs)
if isinstance(node, list):
return node[0].owner
else:
return node.owner
def make_tester(name, op, expected, checks = {}, good = {}, bad_build = {}, bad_runtime = {}, grad = {}): def make_tester(name, op, expected, checks = {}, good = {}, bad_build = {}, bad_runtime = {}, grad = {}):
if grad is True: if grad is True:
...@@ -46,7 +53,8 @@ def make_tester(name, op, expected, checks = {}, good = {}, bad_build = {}, bad_ ...@@ -46,7 +53,8 @@ def make_tester(name, op, expected, checks = {}, good = {}, bad_build = {}, bad_
inputs = [copy(input) for input in inputs] inputs = [copy(input) for input in inputs]
inputrs = [value(input) for input in inputs] inputrs = [value(input) for input in inputs]
try: try:
node = self.op.make_node(*inputrs) #node = self.op.make_node(*inputrs)
node = safe_make_node(self.op, *inputrs)
except: except:
type, exc_value, traceback = sys.exc_info() type, exc_value, traceback = sys.exc_info()
err_msg = "Test %s::%s: Error occurred while making a node with inputs %s" \ err_msg = "Test %s::%s: Error occurred while making a node with inputs %s" \
...@@ -80,7 +88,8 @@ def make_tester(name, op, expected, checks = {}, good = {}, bad_build = {}, bad_ ...@@ -80,7 +88,8 @@ def make_tester(name, op, expected, checks = {}, good = {}, bad_build = {}, bad_
if not isinstance(expecteds, (list, tuple)): if not isinstance(expecteds, (list, tuple)):
expecteds = (expecteds, ) expecteds = (expecteds, )
for i, (result, expected) in enumerate(zip(results, expecteds)): for i, (result, expected) in enumerate(zip(results, expecteds)):
if result.dtype != expected.dtype or result.shape != expected.shape or numpy.any(abs(result - expected) > 1e-10): if result.dtype != expected.dtype or result.shape != expected.shape or \
numpy.any(numpy.abs(result - expected) > 1e-10):
self.fail("Test %s::%s: Output %s gave the wrong value. With inputs %s, expected %s, got %s." self.fail("Test %s::%s: Output %s gave the wrong value. With inputs %s, expected %s, got %s."
% (self.op, testname, i, inputs, expected, result)) % (self.op, testname, i, inputs, expected, result))
...@@ -94,7 +103,7 @@ def make_tester(name, op, expected, checks = {}, good = {}, bad_build = {}, bad_ ...@@ -94,7 +103,7 @@ def make_tester(name, op, expected, checks = {}, good = {}, bad_build = {}, bad_
inputs = [copy(input) for input in inputs] inputs = [copy(input) for input in inputs]
inputrs = [value(input) for input in inputs] inputrs = [value(input) for input in inputs]
try: try:
node = self.op.make_node(*inputrs) node = safe_make_node(self.op,*inputrs)
except: except:
return return
self.fail("Test %s::%s: %s was successfully instantiated on the following bad inputs: %s" self.fail("Test %s::%s: %s was successfully instantiated on the following bad inputs: %s"
...@@ -105,7 +114,7 @@ def make_tester(name, op, expected, checks = {}, good = {}, bad_build = {}, bad_ ...@@ -105,7 +114,7 @@ def make_tester(name, op, expected, checks = {}, good = {}, bad_build = {}, bad_
inputs = [copy(input) for input in inputs] inputs = [copy(input) for input in inputs]
inputrs = [value(input) for input in inputs] inputrs = [value(input) for input in inputs]
try: try:
node = self.op.make_node(*inputrs) node = safe_make_node(self.op,*inputrs)
except: except:
type, exc_value, traceback = sys.exc_info() type, exc_value, traceback = sys.exc_info()
err_msg = "Test %s::%s: Error occurred while trying to make a node with inputs %s" \ err_msg = "Test %s::%s: Error occurred while trying to make a node with inputs %s" \
...@@ -204,7 +213,7 @@ AddTester = make_broadcast_tester(op = add, ...@@ -204,7 +213,7 @@ AddTester = make_broadcast_tester(op = add,
**_good_broadcast_binary_normal), **_good_broadcast_binary_normal),
bad_build = _bad_build_broadcast_binary_normal, bad_build = _bad_build_broadcast_binary_normal,
bad_runtime = _bad_runtime_broadcast_binary_normal) bad_runtime = _bad_runtime_broadcast_binary_normal)
AddInplaceTester = make_broadcast_tester(op = add_inplace, AddInplaceTester = make_broadcast_tester(op = tensor._add_inplace,
expected = lambda x, y: x + y, expected = lambda x, y: x + y,
good = _good_broadcast_binary_normal, good = _good_broadcast_binary_normal,
bad_build = _bad_build_broadcast_binary_normal, bad_build = _bad_build_broadcast_binary_normal,
...@@ -218,7 +227,7 @@ SubTester = make_broadcast_tester(op = sub, ...@@ -218,7 +227,7 @@ SubTester = make_broadcast_tester(op = sub,
bad_runtime = _bad_runtime_broadcast_binary_normal, bad_runtime = _bad_runtime_broadcast_binary_normal,
grad = _grad_broadcast_binary_normal) grad = _grad_broadcast_binary_normal)
SubInplaceTester = make_broadcast_tester(op = sub_inplace, SubInplaceTester = make_broadcast_tester(op = tensor._sub_inplace,
expected = lambda x, y: x - y, expected = lambda x, y: x - y,
good = _good_broadcast_binary_normal, good = _good_broadcast_binary_normal,
bad_build = _bad_build_broadcast_binary_normal, bad_build = _bad_build_broadcast_binary_normal,
...@@ -236,7 +245,7 @@ MulTester = make_broadcast_tester(op = mul, ...@@ -236,7 +245,7 @@ MulTester = make_broadcast_tester(op = mul,
grad = dict(three_inputs_same_shapes = (rand(2, 3), rand(2, 3), rand(2, 3)), grad = dict(three_inputs_same_shapes = (rand(2, 3), rand(2, 3), rand(2, 3)),
four_inputs_broadcast = (rand(2, 3), rand(1, 3), rand(2, 1), rand(1, 1)), four_inputs_broadcast = (rand(2, 3), rand(1, 3), rand(2, 1), rand(1, 1)),
**_grad_broadcast_binary_normal)) **_grad_broadcast_binary_normal))
MulInplaceTester = make_broadcast_tester(op = mul_inplace, MulInplaceTester = make_broadcast_tester(op = tensor._mul_inplace,
expected = lambda x, y: x * y, expected = lambda x, y: x * y,
good = _good_broadcast_binary_normal, good = _good_broadcast_binary_normal,
bad_build = _bad_build_broadcast_binary_normal, bad_build = _bad_build_broadcast_binary_normal,
...@@ -262,7 +271,7 @@ DivTester = make_broadcast_tester(op = div, ...@@ -262,7 +271,7 @@ DivTester = make_broadcast_tester(op = div,
scalar = (rand(2, 3), rand(1, 1)), scalar = (rand(2, 3), rand(1, 1)),
row = (rand(2, 3), rand(1, 3)), row = (rand(2, 3), rand(1, 3)),
column = (rand(2, 3), rand(2, 1)))) column = (rand(2, 3), rand(2, 1))))
DivInplaceTester = make_broadcast_tester(op = div_inplace, DivInplaceTester = make_broadcast_tester(op = tensor._div_inplace,
expected = lambda x, y: x / y, expected = lambda x, y: x / y,
good = dict(same_shapes = (rand(2, 3), rand(2, 3)), good = dict(same_shapes = (rand(2, 3), rand(2, 3)),
scalar = (rand(2, 3), rand(1, 1)), scalar = (rand(2, 3), rand(1, 1)),
...@@ -292,7 +301,7 @@ ModTester = make_broadcast_tester(op = mod, ...@@ -292,7 +301,7 @@ ModTester = make_broadcast_tester(op = mod,
# dtype_mixup_1 = (rand(2, 3), randint_nonzero(2, 3)), # dtype_mixup_1 = (rand(2, 3), randint_nonzero(2, 3)),
# dtype_mixup_2 = (randint_nonzero(2, 3), rand(2, 3))), # dtype_mixup_2 = (randint_nonzero(2, 3), rand(2, 3))),
) )
ModInplaceTester = make_broadcast_tester(op = mod_inplace, ModInplaceTester = make_broadcast_tester(op = tensor._mod_inplace,
expected = lambda x, y: x % y, expected = lambda x, y: x % y,
good = dict(same_shapes = (rand(2, 3), rand(2, 3)), good = dict(same_shapes = (rand(2, 3), rand(2, 3)),
scalar = (rand(2, 3), rand(1, 1)), scalar = (rand(2, 3), rand(1, 1)),
...@@ -315,7 +324,7 @@ PowTester = make_broadcast_tester(op = pow, ...@@ -315,7 +324,7 @@ PowTester = make_broadcast_tester(op = pow,
row = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (1, 3))), row = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (1, 3))),
column = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (2, 1)))) column = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (2, 1))))
) )
PowInplaceTester = make_broadcast_tester(op = pow_inplace, PowInplaceTester = make_broadcast_tester(op = tensor._pow_inplace,
expected = lambda x, y: x ** y, expected = lambda x, y: x ** y,
good = dict(same_shapes = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (2, 3))), good = dict(same_shapes = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (2, 3))),
scalar = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (1, 1))), scalar = (rand_ranged(1, 5, (2, 3)), rand_ranged(-3, 3, (1, 1))),
...@@ -340,8 +349,8 @@ AbsTester = make_broadcast_tester(op = tensor._abs, ...@@ -340,8 +349,8 @@ AbsTester = make_broadcast_tester(op = tensor._abs,
expected = lambda x: abs(x), expected = lambda x: abs(x),
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal) grad = _grad_broadcast_unary_normal)
AbsInplaceTester = make_broadcast_tester(op = abs_inplace, AbsInplaceTester = make_broadcast_tester(op = tensor.__abs_inplace,
expected = lambda x: abs(x), expected = lambda x: numpy.abs(x),
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal, grad = _grad_broadcast_unary_normal,
inplace = True) inplace = True)
...@@ -350,7 +359,7 @@ NegTester = make_broadcast_tester(op = neg, ...@@ -350,7 +359,7 @@ NegTester = make_broadcast_tester(op = neg,
expected = lambda x: -x, expected = lambda x: -x,
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal) grad = _grad_broadcast_unary_normal)
NegInplaceTester = make_broadcast_tester(op = neg_inplace, NegInplaceTester = make_broadcast_tester(op = tensor._neg_inplace,
expected = lambda x: -x, expected = lambda x: -x,
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal, grad = _grad_broadcast_unary_normal,
...@@ -359,7 +368,7 @@ NegInplaceTester = make_broadcast_tester(op = neg_inplace, ...@@ -359,7 +368,7 @@ NegInplaceTester = make_broadcast_tester(op = neg_inplace,
SgnTester = make_broadcast_tester(op = sgn, SgnTester = make_broadcast_tester(op = sgn,
expected = numpy.sign, expected = numpy.sign,
good = _good_broadcast_unary_normal) good = _good_broadcast_unary_normal)
SgnInplaceTester = make_broadcast_tester(op = sgn_inplace, SgnInplaceTester = make_broadcast_tester(op = tensor._sgn_inplace,
expected = numpy.sign, expected = numpy.sign,
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
inplace = True) inplace = True)
...@@ -368,7 +377,7 @@ SqrTester = make_broadcast_tester(op = sqr, ...@@ -368,7 +377,7 @@ SqrTester = make_broadcast_tester(op = sqr,
expected = numpy.square, expected = numpy.square,
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal) grad = _grad_broadcast_unary_normal)
SqrInplaceTester = make_broadcast_tester(op = sqr_inplace, SqrInplaceTester = make_broadcast_tester(op = tensor._sqr_inplace,
expected = numpy.square, expected = numpy.square,
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal, grad = _grad_broadcast_unary_normal,
...@@ -378,7 +387,7 @@ ExpTester = make_broadcast_tester(op = exp, ...@@ -378,7 +387,7 @@ ExpTester = make_broadcast_tester(op = exp,
expected = numpy.exp, expected = numpy.exp,
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal) grad = _grad_broadcast_unary_normal)
ExpInplaceTester = make_broadcast_tester(op = exp_inplace, ExpInplaceTester = make_broadcast_tester(op = tensor._exp_inplace,
expected = numpy.exp, expected = numpy.exp,
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal, grad = _grad_broadcast_unary_normal,
...@@ -394,7 +403,7 @@ LogTester = make_broadcast_tester(op = log, ...@@ -394,7 +403,7 @@ LogTester = make_broadcast_tester(op = log,
expected = numpy.log, expected = numpy.log,
good = _good_broadcast_unary_positive, good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive) grad = _grad_broadcast_unary_positive)
LogInplaceTester = make_broadcast_tester(op = log_inplace, LogInplaceTester = make_broadcast_tester(op = tensor._log_inplace,
expected = numpy.log, expected = numpy.log,
good = _good_broadcast_unary_positive, good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive, grad = _grad_broadcast_unary_positive,
...@@ -404,7 +413,7 @@ Log2Tester = make_broadcast_tester(op = log2, ...@@ -404,7 +413,7 @@ Log2Tester = make_broadcast_tester(op = log2,
expected = numpy.log2, expected = numpy.log2,
good = _good_broadcast_unary_positive, good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive) grad = _grad_broadcast_unary_positive)
Log2InplaceTester = make_broadcast_tester(op = log2_inplace, Log2InplaceTester = make_broadcast_tester(op = tensor._log2_inplace,
expected = numpy.log2, expected = numpy.log2,
good = _good_broadcast_unary_positive, good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive, grad = _grad_broadcast_unary_positive,
...@@ -414,7 +423,7 @@ SqrtTester = make_broadcast_tester(op = sqrt, ...@@ -414,7 +423,7 @@ SqrtTester = make_broadcast_tester(op = sqrt,
expected = numpy.sqrt, expected = numpy.sqrt,
good = _good_broadcast_unary_positive, good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive) grad = _grad_broadcast_unary_positive)
SqrtInplaceTester = make_broadcast_tester(op = sqrt_inplace, SqrtInplaceTester = make_broadcast_tester(op = tensor._sqrt_inplace,
expected = numpy.sqrt, expected = numpy.sqrt,
good = _good_broadcast_unary_positive, good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive, grad = _grad_broadcast_unary_positive,
...@@ -432,7 +441,7 @@ SinTester = make_broadcast_tester(op = sin, ...@@ -432,7 +441,7 @@ SinTester = make_broadcast_tester(op = sin,
expected = numpy.sin, expected = numpy.sin,
good = _good_broadcast_unary_wide, good = _good_broadcast_unary_wide,
grad = _grad_broadcast_unary_wide) grad = _grad_broadcast_unary_wide)
SinInplaceTester = make_broadcast_tester(op = sin_inplace, SinInplaceTester = make_broadcast_tester(op = tensor._sin_inplace,
expected = numpy.sin, expected = numpy.sin,
good = _good_broadcast_unary_wide, good = _good_broadcast_unary_wide,
grad = _grad_broadcast_unary_wide, grad = _grad_broadcast_unary_wide,
...@@ -442,7 +451,7 @@ CosTester = make_broadcast_tester(op = cos, ...@@ -442,7 +451,7 @@ CosTester = make_broadcast_tester(op = cos,
expected = numpy.cos, expected = numpy.cos,
good = _good_broadcast_unary_wide, good = _good_broadcast_unary_wide,
grad = _grad_broadcast_unary_wide) grad = _grad_broadcast_unary_wide)
CosInplaceTester = make_broadcast_tester(op = cos_inplace, CosInplaceTester = make_broadcast_tester(op = tensor._cos_inplace,
expected = numpy.cos, expected = numpy.cos,
good = _good_broadcast_unary_wide, good = _good_broadcast_unary_wide,
grad = _grad_broadcast_unary_wide, grad = _grad_broadcast_unary_wide,
...@@ -454,7 +463,7 @@ TanTester = make_broadcast_tester(op = tan, ...@@ -454,7 +463,7 @@ TanTester = make_broadcast_tester(op = tan,
shifted = (rand_ranged(3.15, 6.28, (2, 3)),)), shifted = (rand_ranged(3.15, 6.28, (2, 3)),)),
grad = dict(normal = (rand_ranged(-3.14, 3.14, (2, 3)),), grad = dict(normal = (rand_ranged(-3.14, 3.14, (2, 3)),),
shifted = (rand_ranged(3.15, 6.28, (2, 3)),))) shifted = (rand_ranged(3.15, 6.28, (2, 3)),)))
TanInplaceTester = make_broadcast_tester(op = tan_inplace, TanInplaceTester = make_broadcast_tester(op = tensor._tan_inplace,
expected = numpy.tan, expected = numpy.tan,
good = dict(normal = (rand_ranged(-3.14, 3.14, (2, 3)),), good = dict(normal = (rand_ranged(-3.14, 3.14, (2, 3)),),
shifted = (rand_ranged(3.15, 6.28, (2, 3)),)), shifted = (rand_ranged(3.15, 6.28, (2, 3)),)),
...@@ -467,7 +476,7 @@ CoshTester = make_broadcast_tester(op = cosh, ...@@ -467,7 +476,7 @@ CoshTester = make_broadcast_tester(op = cosh,
expected = numpy.cosh, expected = numpy.cosh,
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal) grad = _grad_broadcast_unary_normal)
CoshInplaceTester = make_broadcast_tester(op = cosh_inplace, CoshInplaceTester = make_broadcast_tester(op = tensor._cosh_inplace,
expected = numpy.cosh, expected = numpy.cosh,
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal, grad = _grad_broadcast_unary_normal,
...@@ -477,7 +486,7 @@ SinhTester = make_broadcast_tester(op = sinh, ...@@ -477,7 +486,7 @@ SinhTester = make_broadcast_tester(op = sinh,
expected = numpy.sinh, expected = numpy.sinh,
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal) grad = _grad_broadcast_unary_normal)
SinhInplaceTester = make_broadcast_tester(op = sinh_inplace, SinhInplaceTester = make_broadcast_tester(op = tensor._sinh_inplace,
expected = numpy.sinh, expected = numpy.sinh,
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal, grad = _grad_broadcast_unary_normal,
...@@ -487,7 +496,7 @@ TanhTester = make_broadcast_tester(op = tanh, ...@@ -487,7 +496,7 @@ TanhTester = make_broadcast_tester(op = tanh,
expected = numpy.tanh, expected = numpy.tanh,
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal) grad = _grad_broadcast_unary_normal)
TanhInplaceTester = make_broadcast_tester(op = tanh_inplace, TanhInplaceTester = make_broadcast_tester(op = tensor._tanh_inplace,
expected = numpy.tanh, expected = numpy.tanh,
good = _good_broadcast_unary_normal, good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal, grad = _grad_broadcast_unary_normal,
...@@ -519,7 +528,9 @@ def verify_grad(testcase, op, pt, n_tests=1, rng=numpy.random, eps=0.0000001, to ...@@ -519,7 +528,9 @@ def verify_grad(testcase, op, pt, n_tests=1, rng=numpy.random, eps=0.0000001, to
for test_num in xrange(n_tests): for test_num in xrange(n_tests):
# tensor_pt = [as_tensor(p,name='input %i'%i) for i,p in enumerate(pt)] # tensor_pt = [as_tensor(p,name='input %i'%i) for i,p in enumerate(pt)]
tensor_pt = [constant(p).type('input %i'%i) for i,p in enumerate(pt)] tensor_pt = [constant(p).type('input %i'%i) for i,p in enumerate(pt)]
o = op.make_node(*[tpt.copy() for tpt in tensor_pt]) #o = op.make_node(*[tpt.copy() for tpt in tensor_pt])
o = safe_make_node(op, *[tpt.copy() for tpt in tensor_pt])
if hasattr(o, 'outputs'): if hasattr(o, 'outputs'):
o_outputs = o.outputs o_outputs = o.outputs
else: else:
...@@ -689,7 +700,7 @@ class T_transpose(unittest.TestCase): ...@@ -689,7 +700,7 @@ class T_transpose(unittest.TestCase):
def test0(self): def test0(self):
n = as_tensor(numpy.ones(())) n = as_tensor(numpy.ones(()))
t = transpose(n) t = transpose(n)
self.failUnless(t.owner.op == transpose_inplace) self.failUnless(t.owner.op == tensor._transpose_inplace)
f = function([n], [t]) f = function([n], [t])
tval = f(n.data) tval = f(n.data)
self.failUnless(tval.shape == n.data.shape) self.failUnless(tval.shape == n.data.shape)
...@@ -701,7 +712,7 @@ class T_transpose(unittest.TestCase): ...@@ -701,7 +712,7 @@ class T_transpose(unittest.TestCase):
def test1(self): def test1(self):
n = as_tensor(numpy.ones(5)) n = as_tensor(numpy.ones(5))
t = transpose(n) t = transpose(n)
self.failUnless(t.owner.op == transpose_inplace) self.failUnless(t.owner.op == tensor._transpose_inplace)
f = function([n], [t]) f = function([n], [t])
tval = f(n.data) tval = f(n.data)
self.failUnless(tval.shape == n.data.shape) self.failUnless(tval.shape == n.data.shape)
...@@ -712,7 +723,7 @@ class T_transpose(unittest.TestCase): ...@@ -712,7 +723,7 @@ class T_transpose(unittest.TestCase):
def test2(self): def test2(self):
n = as_tensor(numpy.ones((5,3))) n = as_tensor(numpy.ones((5,3)))
t = transpose(n) t = transpose(n)
self.failUnless(t.owner.op == transpose_inplace) self.failUnless(t.owner.op == tensor._transpose_inplace)
f = function([n], [t]) f = function([n], [t])
tval = f(n.data) tval = f(n.data)
self.failUnless(tval.shape == (3,5)) self.failUnless(tval.shape == (3,5))
...@@ -723,8 +734,8 @@ class T_transpose(unittest.TestCase): ...@@ -723,8 +734,8 @@ class T_transpose(unittest.TestCase):
def test3(self): def test3(self):
"""Test transpose of tensor, inplace version""" """Test transpose of tensor, inplace version"""
n = as_tensor(numpy.ones((5,3,2))) n = as_tensor(numpy.ones((5,3,2)))
t = transpose_inplace(n) t = tensor._transpose_inplace(n)
self.failUnless(t.owner.op == transpose_inplace) self.failUnless(t.owner.op == tensor._transpose_inplace)
f = function([n], [t]) f = function([n], [t])
tval = f(n.data) tval = f(n.data)
self.failUnless(tval.shape == (2,3,5)) self.failUnless(tval.shape == (2,3,5))
...@@ -732,8 +743,8 @@ class T_transpose(unittest.TestCase): ...@@ -732,8 +743,8 @@ class T_transpose(unittest.TestCase):
tval += 55.0 tval += 55.0
self.failUnless(n.data[0,0,0] == 56.0) self.failUnless(n.data[0,0,0] == 56.0)
def test_grad(self): def test_grad(self):
verify_grad(self, transpose_inplace, [numpy.random.rand(2, 3)]) verify_grad(self, tensor._transpose_inplace, [numpy.random.rand(2, 3)])
verify_grad(self, transpose_inplace, [numpy.ones(3)]) verify_grad(self, tensor._transpose_inplace, [numpy.ones(3)])
class T_subtensor(unittest.TestCase): class T_subtensor(unittest.TestCase):
def setUp(self): def setUp(self):
...@@ -1066,7 +1077,7 @@ class T_exp(unittest.TestCase): ...@@ -1066,7 +1077,7 @@ class T_exp(unittest.TestCase):
numpy.asarray([[ 1.5089518 , 1.48439076, -4.7820262 ], numpy.asarray([[ 1.5089518 , 1.48439076, -4.7820262 ],
[ 2.04832468, 0.50791564, -1.58892269]])]) [ 2.04832468, 0.50791564, -1.58892269]])])
def test_grad_1(self): def test_grad_1(self):
verify_grad(self, exp_inplace, [ verify_grad(self, tensor._exp_inplace, [
numpy.asarray([[ 1.5089518 , 1.48439076, -4.7820262 ], numpy.asarray([[ 1.5089518 , 1.48439076, -4.7820262 ],
[ 2.04832468, 0.50791564, -1.58892269]])]) [ 2.04832468, 0.50791564, -1.58892269]])])
...@@ -1434,7 +1445,7 @@ class t_gemm(unittest.TestCase): ...@@ -1434,7 +1445,7 @@ class t_gemm(unittest.TestCase):
Z = as_tensor(self.rand(2,2)) Z = as_tensor(self.rand(2,2))
A = as_tensor(self.rand(2,2)) A = as_tensor(self.rand(2,2))
try: try:
gemm(Z, 1.0, A, transpose_inplace(Z), 1.0) gemm(Z, 1.0, A, tensor._transpose_inplace(Z), 1.0)
except ValueError, e: except ValueError, e:
if e[0] == Gemm.E_z_uniq: if e[0] == Gemm.E_z_uniq:
return return
...@@ -1444,7 +1455,7 @@ class t_gemm(unittest.TestCase): ...@@ -1444,7 +1455,7 @@ class t_gemm(unittest.TestCase):
Z = as_tensor(self.rand(2,2)) Z = as_tensor(self.rand(2,2))
A = as_tensor(self.rand(2,2)) A = as_tensor(self.rand(2,2))
try: try:
gemm(Z, 1.0, transpose_inplace(Z), A, 1.0) gemm(Z, 1.0, tensor._transpose_inplace(Z), A, 1.0)
except ValueError, e: except ValueError, e:
if e[0] == Gemm.E_z_uniq: if e[0] == Gemm.E_z_uniq:
return return
......
_test_tensor_opt.py
浏览文件 @ b128f2b8
...@@ -3,12 +3,12 @@ ...@@ -3,12 +3,12 @@
import unittest import unittest
from theano import gof import gof
from theano.tensor_opt import * from tensor_opt import *
from theano import tensor import tensor
from theano.tensor import Tensor from tensor import Tensor
from theano.gof import Env from gof import Env
from theano.elemwise import DimShuffle from elemwise import DimShuffle
import numpy import numpy
#import scalar_opt #import scalar_opt
...@@ -43,7 +43,7 @@ def inputs(xbc = (0, 0), ybc = (0, 0), zbc = (0, 0)): ...@@ -43,7 +43,7 @@ def inputs(xbc = (0, 0), ybc = (0, 0), zbc = (0, 0)):
# def test_user_inplace(self): # def test_user_inplace(self):
# x, y, z = inputs() # x, y, z = inputs()
# e0 = x + y # e0 = x + y
# e1 = tensor.mul_inplace(x, y) # e1 = tensor._mul_inplace(x, y)
# g = Env([x, y], [e0, e1]) # g = Env([x, y], [e0, e1])
# self.failUnless(str(g) == "[Broadcast{Add}(x, y), Broadcast{Mul}{0: 0}(x, y)]") # self.failUnless(str(g) == "[Broadcast{Add}(x, y), Broadcast{Mul}{0: 0}(x, y)]")
# inplace_optimizer.optimize(g) # inplace_optimizer.optimize(g)
...@@ -52,7 +52,7 @@ def inputs(xbc = (0, 0), ybc = (0, 0), zbc = (0, 0)): ...@@ -52,7 +52,7 @@ def inputs(xbc = (0, 0), ybc = (0, 0), zbc = (0, 0)):
# def test_inplace_on_second_argument(self): # def test_inplace_on_second_argument(self):
# x, y, z = inputs() # x, y, z = inputs()
# e0 = x + y # e0 = x + y
# e1 = tensor.mul_inplace(x, z) # e1 = tensor._mul_inplace(x, z)
# g = Env([x, y], [e0, e1]) # g = Env([x, y], [e0, e1])
# self.failUnless(str(g) == "[Broadcast{Add}(x, y), Broadcast{Mul}{0: 0}(x, z)]") # self.failUnless(str(g) == "[Broadcast{Add}(x, y), Broadcast{Mul}{0: 0}(x, z)]")
# inplace_optimizer.optimize(g) # inplace_optimizer.optimize(g)
...@@ -98,9 +98,9 @@ class _test_dimshuffle_lift(unittest.TestCase): ...@@ -98,9 +98,9 @@ class _test_dimshuffle_lift(unittest.TestCase):
from theano.tensor import * from tensor import *
from theano.sandbox import pprint from sandbox import pprint
class _test_greedy_distribute(unittest.TestCase): class _test_greedy_distribute(unittest.TestCase):
def test_main(self): def test_main(self):
...@@ -279,8 +279,8 @@ class _test_canonize(unittest.TestCase): ...@@ -279,8 +279,8 @@ class _test_canonize(unittest.TestCase):
# # def test_inplace(self): # # def test_inplace(self):
# # x, y, z = inputs() # # x, y, z = inputs()
# # #e = tensor.add_inplace(x, y + z) # # #e = tensor._add_inplace(x, y + z)
# # e = x + tensor.add_inplace(y, z) # # e = x + tensor._add_inplace(y, z)
# # g = Env([x, y, z], [e]) # # g = Env([x, y, z], [e])
# # opt = CliqueOptimizer(through_broadcast = False, # # opt = CliqueOptimizer(through_broadcast = False,
# # scalar_optimizer = None, # # scalar_optimizer = None,
......
doc/apirst2html.py 0 → 100755
浏览文件 @ b128f2b8
#!/usr/bin/python
# -*- coding: iso-8859-1 -*-
"""An HTML writer supporting link to external documentation.
This module is a frontend for the Docutils_ HTML writer. It allows a document
to reference objects documented in the API documentation generated by
extraction tools such as Doxygen_ or Epydoc_.
.. _Docutils: http://docutils.sourceforge.net/
.. _Doxygen: http://www.doxygen.org/
.. _Epydoc: http://epydoc.sourceforge.net/
"""
# $Id: apirst2html.py 1531 2007-02-18 23:07:25Z dvarrazzo $
__version__ = "$Revision: 1531 $"[11:-2]
__author__ = "Daniele Varrazzo"
__copyright__ = "Copyright (C) 2007 by Daniele Varrazzo"
__docformat__ = 'reStructuredText en'
try:
import locale
locale.setlocale(locale.LC_ALL, '')
except:
pass
# We have to do some path magic to prevent Python from getting
# confused about the difference between the ``epydoc.py`` script, and the
# real ``epydoc`` package. So remove ``sys.path[0]``, which contains the
# directory of the script.
import sys, os.path
script_path = os.path.abspath(sys.path[0])
sys.path = [p for p in sys.path if os.path.abspath(p) != script_path]
import epydoc.docwriter.xlink as xlink
from docutils.core import publish_cmdline, default_description
description = ('Generates (X)HTML documents with API documentation links. '
+ default_description)
publish_cmdline(reader=xlink.ApiLinkReader(), writer_name='html',
description=description)
doc/doc.idx
浏览文件 @ b128f2b8
...@@ -2,3 +2,4 @@ graph graph.html ...@@ -2,3 +2,4 @@ graph graph.html
tensor graph.html tensor graph.html
result graph.html result graph.html
howto graph.html howto graph.html
oplist oplist.html
epydoc 100644 → 100755
浏览文件 @ b128f2b8
# TODO: #!/usr/bin/python
# Get all graphs to work! #
# Call the command line interface for Epydoc.
#
# Make sure that we don't get confused between an epydoc.py script and
# the real epydoc package.
import sys, os.path, inspect
if os.path.exists(os.path.join(sys.path[0], 'epydoc.py')):
del sys.path[0]
from epydoc import docintrospecter
from epydoc.apidoc import RoutineDoc
def Op_to_RoutineDoc(op, routine_doc, module_name=None):
routine_doc.specialize_to(RoutineDoc)
#NB: this code is lifted from
# /u/bergstrj/pub/prefix/x86_64-unknown-linux-gnu-Fedora_release_7__Moonshine_/lib/python2.5/site-packages/epydoc
# /u/bergstrj/pub/prefix/x86_64-unknown-linux-gnu-Fedora_release_7__Moonshine_/lib/python2.5/site-packages/epydoc/docintrospecter.py
# op should be an op instance
assert hasattr(op, 'perform')
# Record the function's docstring.
routine_doc.docstring = getattr(op, '__doc__', '')
# Record the function's signature.
func = op.__epydoc_asRoutine
if isinstance(func, type(Op_to_RoutineDoc)):
(args, vararg, kwarg, defaults) = inspect.getargspec(func)
# Add the arguments.
routine_doc.posargs = args
routine_doc.vararg = vararg
routine_doc.kwarg = kwarg
# Set default values for positional arguments.
routine_doc.posarg_defaults = [None]*len(args)
# Set the routine's line number.
if hasattr(func, 'func_code'):
routine_doc.lineno = func.func_code.co_firstlineno
else:
# [XX] I should probably use UNKNOWN here??
# dvarrazzo: if '...' is to be changed, also check that
# `docstringparser.process_arg_field()` works correctly.
# See SF bug #1556024.
routine_doc.posargs = ['...']
routine_doc.posarg_defaults = [None]
routine_doc.kwarg = None
routine_doc.vararg = None
return routine_doc
docintrospecter.register_introspecter(
lambda value: getattr(value, '__epydoc_asRoutine', False),
Op_to_RoutineDoc,
priority=-1)
from epydoc.cli import cli
cli()
[epydoc] # Epydoc section marker (required by ConfigParser)
# The list of objects to document. Objects can be named using
# dotted names, module filenames, or package directory names.
# Alases for this option include "objects" and "values".
modules: *.py, gof/*.py, theano/*.py, theano/gof/*.py, joseph/*.py, pylearn/*.py, scipy, numpy
# The type of output that should be generated. Should be one
# of: html, text, latex, dvi, ps, pdf.
output: html
# The path to the output directory. May be relative or absolute.
target: html/
# An integer indicating how verbose epydoc should be. The default
# value is 0; negative values will supress warnings and errors;
# positive values will give more verbose output.
verbosity: 1
# A boolean value indicating that Epydoc should show a tracaback
# in case of unexpected error. By default don't show tracebacks
debug: 0
# If True, don't try to use colors or cursor control when doing
# textual output. The default False assumes a rich text prompt
simple-term: 0
### Generation options
# The default markup language for docstrings, for modules that do
# not define __docformat__. Defaults to epytext.
docformat: epytext
# Whether or not parsing should be used to examine objects.
parse: yes
# Whether or not introspection should be used to examine objects.
introspect: yes
# Don't examine in any way the modules whose dotted name match this
# regular expression pattern.
#exclude
# Don't perform introspection on the modules whose dotted name match this
# regular expression pattern.
#exclude-introspect
# Don't perform parsing on the modules whose dotted name match this
# regular expression pattern.
#exclude-parse
# The format for showing inheritance objects.
# It should be one of: 'grouped', 'listed', 'included'.
inheritance: grouped
# Whether or not to inclue private variables. (Even if included,
# private variables will be hidden by default.)
private: yes
# Whether or not to list each module's imports.
imports: yes
# Whether or not to include syntax highlighted source code in
# the output (HTML only).
sourcecode: yes
# Whether or not to includea a page with Epydoc log, containing
# effective option at the time of generation and the reported logs.
include-log: yes
### Output options
# The documented project's name.
name: Theano
# The CSS stylesheet for HTML output. Can be the name of a builtin
# stylesheet, or the name of a file.
css: white
# The documented project's URL.
url: http://lgcm.iro.umontreal.ca/theano/
# HTML code for the project link in the navigation bar. If left
# unspecified, the project link will be generated based on the
# project's name and URL.
#link: <a href="somewhere">My Cool Project</a>
# The "top" page for the documentation. Can be a URL, the name
# of a module or class, or one of the special names "trees.html",
# "indices.html", or "help.html"
#top: os.path
# An alternative help file. The named file should contain the
# body of an HTML file; navigation bars will be added to it.
#help: my_helpfile.html
# Whether or not to include a frames-based table of contents.
#frames: yes
frames: no
# Whether each class should be listed in its own section when
# generating LaTeX or PDF output.
separate-classes: no
### API linking options
# Define a new API document. A new interpreted text role
# will be created
#external-api: epydoc
# Use the records in this file to resolve objects in the API named NAME.
#external-api-file: epydoc:api-objects.txt
# Use this URL prefix to configure the string returned for external API.
#external-api-root: epydoc:http://epydoc.sourceforge.net/api
### Graph options
# The list of graph types that should be automatically included
# in the output. Graphs are generated using the Graphviz "dot"
# executable. Graph types include: "classtree", "callgraph",
# "umlclass". Use "all" to include all graph types
graph: all
# The path to the Graphviz "dot" executable, used to generate
# graphs.
dotpath: /usr/bin/dot
# The name of one or more pstat files (generated by the profile
# or hotshot module). These are used to generate call graphs.
pstat: autotest.pstat
# Specify the font used to generate Graphviz graphs.
# (e.g., helvetica or times).
graph-font: Helvetica
# Specify the font size used to generate Graphviz graphs.
graph-font-size: 10
### Return value options
# The condition upon which Epydoc should exit with a non-zero
# exit status. Possible values are error, warning, docstring_warning
#fail-on: error
gen_oplist.py 0 → 100644
浏览文件 @ b128f2b8
"""script to generate doc/oplist.txt, which compiles to :doc:`oplist`. """
__docformat__ = "restructuredtext en"
import sys
import gof
def isOpClass(thing):
return hasattr(thing, 'perform') and not isinstance(thing, gof.Op)
def isOpConstructor(thing, module):
return hasattr(thing, 'perform') and isinstance(thing, gof.Op)\
or thing in getattr(module, '_constructor_list', [])
def print_title(title_string, under_char):
print title_string
print under_char * len(title_string)
print ""
def chomp(s):
"""interpret and left-align a docstring"""
if 'subtensor' in s:
debug = 0
else:
debug = 0
r = []
leadspace = True
for c in s:
if leadspace and c in ' \n\t':
continue
else:
leadspace = False
if c == '\n':
if debug:
print >> sys.stderr, 'breaking'
break
if c == '\t':
c = ' ';
r.append(c)
if debug:
print >> sys.stderr, r
return "".join(r)
def generate():
"""Generate the op list"""
import scalar, sparse, tensor
print_title("Theano Op List", "~")
print ""
print ".. contents:: "
print ""
for module in [scalar, sparse, tensor]:
print_title('module: `%s`' % module.__name__, '=')
print_title('Op Classes', '-')
symbol_name_list = [s for s in dir(module) if not s[0] == '_']
for symbol_name in symbol_name_list:
symbol = getattr(module, symbol_name)
if isOpClass(symbol):
print ""
print "- :api:`%s.%s`" % (symbol.__module__, symbol_name)
docstring = getattr(symbol, '__doc__', "")
if not docstring:
print " ", '(no doc)'
elif len(docstring) < 50:
print " ", chomp(docstring)
else:
print " ", chomp(docstring[:40]), "..."
# a little trailing whitespace
print ""
print_title('Op Constructors', '-')
for symbol_name in symbol_name_list:
symbol = getattr(module, symbol_name)
if isOpConstructor(symbol, module):
print ""
print "- :api:`%s.%s`" % (symbol.__module__, symbol_name)
docstring = getattr(symbol, '__doc__', "")
if not docstring:
print " ", 'No documentation'
elif len(docstring) < 50:
print " ", chomp(docstring)
else:
print " ", chomp(docstring[:40]), "..."
# a little trailing whitespace
print ""
if __name__ == "__main__":
generate()
gof/op.py
浏览文件 @ b128f2b8
...@@ -29,18 +29,18 @@ class CLinkerOp(object): ...@@ -29,18 +29,18 @@ class CLinkerOp(object):
given names for the inputs and outputs. given names for the inputs and outputs.
:Parameters: :Parameters:
`node`: Apply instance `node` : Apply instance
WRITEME WRITEME
`name`: WRITEME `name` : WRITEME
WRITEME WRITEME
`inputs`: list of strings `inputs` : list of strings
There is a string for each input of the function, and the string is the name of a C There is a string for each input of the function, and the string is the name of a C
`PyObject` variable pointing to that input. `PyObject` variable pointing to that input.
`outputs`: list of strings `outputs` : list of strings
Each string is the name of a `PyObject` pointer where the Op should store its Each string is the name of a `PyObject` pointer where the Op should store its
results. The `CLinker` guarantees that on entry to this code block, each pointer results. The `CLinker` guarantees that on entry to this code block, each pointer
is either NULL or is unchanged from the end of the previous execution. is either NULL or is unchanged from the end of the previous execution.
`sub`: dict of strings `sub` : dict of strings
extra symbols defined in `CLinker` sub symbols (such as 'fail'). extra symbols defined in `CLinker` sub symbols (such as 'fail').
WRITEME WRITEME
...@@ -59,18 +59,18 @@ class CLinkerOp(object): ...@@ -59,18 +59,18 @@ class CLinkerOp(object):
This is a convenient place to clean up things allocated by c_code(). This is a convenient place to clean up things allocated by c_code().
:Parameters: :Parameters:
`node`: Apply instance `node` : Apply instance
WRITEME WRITEME
`name`: WRITEME `name` : WRITEME
WRITEME WRITEME
`inputs`: list of strings `inputs` : list of strings
There is a string for each input of the function, and the string is the name of a C There is a string for each input of the function, and the string is the name of a C
`PyObject` variable pointing to that input. `PyObject` variable pointing to that input.
`outputs`: list of strings `outputs` : list of strings
Each string is the name of a `PyObject` pointer where the Op should store its Each string is the name of a `PyObject` pointer where the Op should store its
results. The `CLinker` guarantees that on entry to this code block, each pointer results. The `CLinker` guarantees that on entry to this code block, each pointer
is either NULL or is unchanged from the end of the previous execution. is either NULL or is unchanged from the end of the previous execution.
`sub`: dict of strings `sub` : dict of strings
extra symbols defined in `CLinker` sub symbols (such as 'fail'). extra symbols defined in `CLinker` sub symbols (such as 'fail').
WRITEME WRITEME
...@@ -225,11 +225,11 @@ class PureOp(object): ...@@ -225,11 +225,11 @@ class PureOp(object):
output storage. Return None. output storage. Return None.
:Parameters: :Parameters:
`node`: Apply instance `node` : Apply instance
contains the symbolic inputs and outputs contains the symbolic inputs and outputs
`inputs`: list `inputs` : list
sequence of inputs (immutable) sequence of inputs (immutable)
`output_storage`: list `output_storage` : list
list of mutable 1-element lists (do not change the length of these lists) list of mutable 1-element lists (do not change the length of these lists)
The `output_storage` list might contain data. If an element of The `output_storage` list might contain data. If an element of
......
local.build_html.sh 100644 → 100755
浏览文件 @ b128f2b8
#!/bin/bash #!/bin/bash
mkdir -p html/api APIRST2HTML=doc/apirst2html.py
epydoc --config local.epydoc EPYDOC_ARGS='--external-api=api --external-api-file=api:html/api/api-objects.txt --external-api-root=api:../api/'
cd doc
sh build_html.sh mkdir -p html/api && mkdir -p html/doc
cd ../
rm -Rf html/doc # this builds some stuff or something... basically makes the rest work properly
mv doc/html html/doc # for a reason I don't understand. -JB 20080924
python __init__.py
if [ " $1" != " rst" ]; then
./epydoc --config local.epydoc
fi
if [ " $1" != " epydoc" ]; then
python gen_oplist.py > doc/oplist.txt
for RST in graph oplist ; do
$APIRST2HTML $EPYDOC_ARGS doc/$RST.txt html/doc/$RST.html
done
fi
local.epydoc
浏览文件 @ b128f2b8
...@@ -7,17 +7,12 @@ ...@@ -7,17 +7,12 @@
# The list of objects to document. Objects can be named using # The list of objects to document. Objects can be named using
# dotted names, module filenames, or package directory names. # dotted names, module filenames, or package directory names.
# Alases for this option include "objects" and "values". # Alases for this option include "objects" and "values".
modules: *.py, gof/__init__.py, modules: __init__.py,
gof/a*, [a-z]*.py,
gof/c*, [A-Z]*.py,
gof/d*, gof/__init__.py,
gof/e*, gof/[a-z]*.py,
gof/g*, gof/[A-Z]*.py
gof/l*,
gof/o*,
gof/p*,
gof/t*,
gof/u*
# The type of output that should be generated. Should be one # The type of output that should be generated. Should be one
# of: html, text, latex, dvi, ps, pdf. # of: html, text, latex, dvi, ps, pdf.
......
scalar.py
浏览文件 @ b128f2b8
...@@ -586,7 +586,7 @@ class Abs(UnaryScalarOp): ...@@ -586,7 +586,7 @@ class Abs(UnaryScalarOp):
return "%(z)s = fabs(%(x)s);" % locals() return "%(z)s = fabs(%(x)s);" % locals()
#complex, other? #complex, other?
raise NotImplementedError('type not supported', type) raise NotImplementedError('type not supported', type)
abs = Abs(same_out) _abs = Abs(same_out)
class Sgn(UnaryScalarOp): class Sgn(UnaryScalarOp):
def impl(self, x): def impl(self, x):
......
tensor.py
浏览文件 @ b128f2b8
"""A L{Result} to store L{numpy.ndarray} with basic accompanying L{Op}s""" """A `Type` and `Op` classes to work with numpy.ndarrays symbolically."""
__docformat__ = "restructuredtext en"
import sys # for sys.maxint import sys # for sys.maxint
import inspect import inspect
import functools import functools
...@@ -23,8 +26,36 @@ from elemwise import Elemwise, DimShuffle, CAReduce, Sum ...@@ -23,8 +26,36 @@ from elemwise import Elemwise, DimShuffle, CAReduce, Sum
import tensor_random as random import tensor_random as random
_constructor_list = []
"""List of functions to be listed as op constructors in the oplist (`gen_oplist`, doc/oplist.txt)."""
def constructor(f):
"""Make `f` appear as a constructor in the oplist (`gen_oplist`, doc/oplist.txt)."""
_constructor_list.append(f)
return f
def as_tensor(x, name = None): def as_tensor(x, name = None):
"""Return `x`, transformed into a `Tensor`
This function is often used by `make_node` methods of `Op` subclasses to
turn ndarrays, numbers, `Scalar` instances, `Apply` instances and `Tensor`
instances into valid input list elemnts.
:Parameters:
- `x`: Apply instance, Result instance, numpy.ndarray, or number
This thing will be transformed into a `Result` in a sensible way. An
ndarray argument will not be copied, but a list of numbers will be copied
to make an ndarray.
- `name`: str or None
If a new `Result` instance is created, it will be named with this string.
:Exceptions:
- `ValueError`: raised if an `Apply` with no default output is fetched
- `TypeError`: raised if `x` cannot be converted to a Tensor Result
"""
if isinstance(x, gof.Apply): if isinstance(x, gof.Apply):
#TODO: use Apply's default output mechanism
if len(x.outputs) != 1: if len(x.outputs) != 1:
raise ValueError("It is ambiguous which output of a multi-output Op has to be fetched.", x) raise ValueError("It is ambiguous which output of a multi-output Op has to be fetched.", x)
else: else:
...@@ -39,56 +70,74 @@ def as_tensor(x, name = None): ...@@ -39,56 +70,74 @@ def as_tensor(x, name = None):
return constant(x) return constant(x)
except TypeError: except TypeError:
raise TypeError("Cannot convert %s to Tensor" % x, type(x)) raise TypeError("Cannot convert %s to Tensor" % x, type(x))
# this has a different name, because _as_tensor is the function which ops use # this has a different name, because _as_tensor is the function which ops use
# to upcast their arguments... this internal-use function is a good place to put debugging stuff, better than the global astensor. # to upcast their arguments... this internal-use function is a good place to put debugging stuff, better than the global astensor.
_as_tensor = as_tensor _as_tensor = as_tensor
def constant(_x): def constant(x):
if not isinstance(_x, numpy.ndarray): """Return a symbolic `Constant` with value `x`
x = numpy.asarray(_x)
:Exceptions:
- `TypeError`: `x` could not be converted to a numpy.ndarray
"""
if isinstance(x, numpy.ndarray):
x_ = x
else: else:
x = _x x_ = numpy.asarray(x)
try: try:
return TensorConstant(Tensor(dtype = x.dtype, return TensorConstant(Tensor(dtype = x_.dtype,
broadcastable = [d == 1 for d in x.shape]), x) broadcastable = [d == 1 for d in x_.shape]), x_)
except: except:
raise TypeError("Could not convert %s to Tensor" % _x, type(_x)) raise TypeError("Could not convert %s to Tensor" % x, type(x))
def value(x): def value(x):
if not isinstance(x, numpy.ndarray): """Return a symbolic `Value` with default value `x`
x = numpy.asarray(x)
:Exceptions:
- `TypeError`: `x` could not be converted to a numpy.ndarray
"""
if isinstance(x, numpy.ndarray):
x_ = x
else:
x_ = numpy.asarray(x)
try: try:
return TensorValue(Tensor(dtype = x.dtype, return TensorValue(Tensor(dtype = x_.dtype,
broadcastable = [d == 1 for d in x.shape]), x) broadcastable = [d == 1 for d in x_.shape]), x_)
except: except:
raise TypeError("Could not convert %s to Tensor" % _x, type(_x)) raise TypeError("Could not convert %s to Tensor" % x, type(x))
class Tensor(Type): class Tensor(Type):
""" """Symbolic `Type` representing a numpy.ndarray value."""
L{Type} representing L{numpy.ndarray} in Theano.
@todo: At some point we should document a glossary, such as terms like
broadcasting and shape.
@type dtype: numpy dtype string such as 'int64' or 'float64' (among others)
@type broadcastable: tuple or list or array of boolean values, whose length
is the number of dimensions of the L{ndarray} represented by this Type.
@ivar broadcastable: Each element of the broadcastable vector tells us
something about the corresponding dimension:
- False means the dimension can be anything.
- True means the dimension must be 1. Also, this dimension will be considered
for L{broadcasting}, as described and implemented in Numpy.
"""
def __init__(self, dtype, broadcastable): def __init__(self, dtype, broadcastable):
"""Initialize self.dtype and self.broadcastable.
:Parameters:
- `dtype`: str corresponding to numpy dtype (e.g., 'int64')
The value (ndarray) associated to a `Result` of this `Type` will have
this dtype.
- `broadcastable`: tuple, list, or array of boolean values
This argument serves two purposes. First, the True elements of this
list indicate the dimensions where the shape of an associated value
must be 1. Secondly, the length of this list is the number of
dimensions that an associated value must have. See
:doc:`broadcasting` for an explanation of how this list is used.
"""
self.dtype = str(dtype) self.dtype = str(dtype)
self.broadcastable = tuple(broadcastable) self.broadcastable = tuple(broadcastable)
self.dtype_specs() # error checking is done there self.dtype_specs() # error checking is done there
def filter(self, data, strict = False): def filter(self, data, strict = False):
"""Convert `data` to something which can be associated to a `TensorResult`.
This function is not meant to be called in user code. It is for
`Linker` instances to use when running a compiled graph.
"""
_data = data _data = data
if strict: if strict:
if not isinstance(data, numpy.ndarray): if not isinstance(data, numpy.ndarray):
...@@ -107,10 +156,10 @@ class Tensor(Type): ...@@ -107,10 +156,10 @@ class Tensor(Type):
return data return data
def dtype_specs(self): def dtype_specs(self):
"""Return python - C type correspondance tuple for self.data """Return a tuple (python type, c type, numpy typenum) that corresponds to
self.dtype.
Return a tuple (python type, c type, numpy typenum) that corresponds to This function is used internally as part of C code generation.
L{self.dtype}. It is for use in C code generation.
""" """
#TODO: add more type correspondances for e.g. int32, int64, float32, #TODO: add more type correspondances for e.g. int32, int64, float32,
#complex64, etc. #complex64, etc.
...@@ -131,14 +180,29 @@ class Tensor(Type): ...@@ -131,14 +180,29 @@ class Tensor(Type):
raise TypeError("Unsupported dtype for %s: %s" % (self.__class__.__name__, self.dtype)) raise TypeError("Unsupported dtype for %s: %s" % (self.__class__.__name__, self.dtype))
def __eq__(self, other): def __eq__(self, other):
"""Compare True iff other is the same kind of Tensor"""
return type(self) == type(other) and other.dtype == self.dtype and other.broadcastable == self.broadcastable return type(self) == type(other) and other.dtype == self.dtype and other.broadcastable == self.broadcastable
def __hash__(self): def __hash__(self):
"""Hash equal for same kinds of Tensor"""
return hash(self.dtype) ^ hash(self.broadcastable) return hash(self.dtype) ^ hash(self.broadcastable)
ndim = property(lambda self: len(self.broadcastable), doc = "read-only access to the number of dimensions") ndim = property(lambda self: len(self.broadcastable), doc = "number of dimensions")
"""Number of dimensions
This read-only property is the preferred way to get the number of dimensions
of a `Tensor`.
"""
def make_result(self, name = None): def make_result(self, name = None):
"""Return a `TensorResult` of this type
:Parameters:
- `name`: str
A pretty name to identify this `Result` when printing and debugging
"""
return TensorResult(self, name = name) return TensorResult(self, name = name)
def __str__(self): def __str__(self):
...@@ -148,6 +212,7 @@ class Tensor(Type): ...@@ -148,6 +212,7 @@ class Tensor(Type):
return "Tensor{%s, %s}" % (str(self.dtype), str(self.broadcastable)) return "Tensor{%s, %s}" % (str(self.dtype), str(self.broadcastable))
def c_declare(self, name, sub): def c_declare(self, name, sub):
"""Override `CLinkerOp.c_declare` """
return """ return """
PyArrayObject* %(name)s; PyArrayObject* %(name)s;
int type_num_%(name)s; int type_num_%(name)s;
...@@ -155,12 +220,14 @@ class Tensor(Type): ...@@ -155,12 +220,14 @@ class Tensor(Type):
""" % dict(sub, name = name, dtype = self.dtype_specs()[1]) """ % dict(sub, name = name, dtype = self.dtype_specs()[1])
def c_init(self, name, sub): def c_init(self, name, sub):
"""Override `CLinkerOp.c_init` """
return """ return """
%(name)s = NULL; %(name)s = NULL;
type_num_%(name)s = %(type_num)s; type_num_%(name)s = %(type_num)s;
""" % dict(sub, name = name, type_num = self.dtype_specs()[2]) """ % dict(sub, name = name, type_num = self.dtype_specs()[2])
def c_extract(self, name, sub): def c_extract(self, name, sub):
"""Override `CLinkerOp.c_extract` """
return """ return """
%(name)s = NULL; %(name)s = NULL;
type_num_%(name)s = %(type_num)s; type_num_%(name)s = %(type_num)s;
...@@ -186,6 +253,7 @@ class Tensor(Type): ...@@ -186,6 +253,7 @@ class Tensor(Type):
""" % dict(sub, name = name, type_num = self.dtype_specs()[2]) """ % dict(sub, name = name, type_num = self.dtype_specs()[2])
def c_cleanup(self, name, sub): def c_cleanup(self, name, sub):
"""Override `CLinkerOp.c_cleanup` """
return """ return """
if (%(name)s) { if (%(name)s) {
Py_XDECREF(%(name)s); Py_XDECREF(%(name)s);
...@@ -193,6 +261,7 @@ class Tensor(Type): ...@@ -193,6 +261,7 @@ class Tensor(Type):
""" % locals() """ % locals()
def c_sync(self, name, sub): def c_sync(self, name, sub):
"""Override `CLinkerOp.c_sync` """
return """ return """
Py_XDECREF(py_%(name)s); Py_XDECREF(py_%(name)s);
if (!%(name)s) { if (!%(name)s) {
...@@ -205,12 +274,14 @@ class Tensor(Type): ...@@ -205,12 +274,14 @@ class Tensor(Type):
""" % locals() """ % locals()
def c_headers(self): def c_headers(self):
"""Override `CLinkerOp.c_headers` """
return [] return []
def c_libraries(self): def c_libraries(self):
return [] return []
def c_support_code(cls): def c_support_code(cls):
"""Override `CLinkerOp.c_support_code` """
template = """ template = """
struct theano_complex%(nbits)s : public npy_complex%(nbits)s struct theano_complex%(nbits)s : public npy_complex%(nbits)s
{ {
...@@ -344,9 +415,9 @@ class _tensor_py_operators: ...@@ -344,9 +415,9 @@ class _tensor_py_operators:
def __rand__(self,other): return and_(other,self) def __rand__(self,other): return and_(other,self)
def __ror__(self,other): return or_(other, self) def __ror__(self,other): return or_(other, self)
def __rxor__(self,other): return xor(other, self) def __rxor__(self,other): return xor(other, self)
def __iand__(self, other): return and_inplace(self, other) def __iand__(self, other): return _and_inplace(self, other)
def __ior__(self, other): return or_inplace(self, other) def __ior__(self, other): return _or_inplace(self, other)
def __ixor__(self, other): return xor_inplace(self, other) def __ixor__(self, other): return _xor_inplace(self, other)
#ARITHMETIC - NORMAL #ARITHMETIC - NORMAL
def __add__(self,other): return add(self,other) def __add__(self,other): return add(self,other)
...@@ -357,11 +428,11 @@ class _tensor_py_operators: ...@@ -357,11 +428,11 @@ class _tensor_py_operators:
def __mod__(self,other): return mod(self,other) def __mod__(self,other): return mod(self,other)
#ARITHMETIC - INPLACE #ARITHMETIC - INPLACE
def __iadd__(self,other): return add_inplace(self,other) def __iadd__(self,other): return _add_inplace(self,other)
def __isub__(self,other): return sub_inplace(self,other) def __isub__(self,other): return _sub_inplace(self,other)
def __imul__(self,other): return mul_inplace(self,other) def __imul__(self,other): return _mul_inplace(self,other)
def __idiv__(self,other): return div_inplace(self,other) def __idiv__(self,other): return _div_inplace(self,other)
def __ipow__(self,other): return pow_inplace(self,other) def __ipow__(self,other): return _pow_inplace(self,other)
#ARITHMETIC - RIGHT-OPERAND #ARITHMETIC - RIGHT-OPERAND
def __radd__(self,other): return add(other,self) def __radd__(self,other): return add(other,self)
...@@ -406,6 +477,7 @@ class TensorConstant(Constant, _tensor_py_operators): ...@@ -406,6 +477,7 @@ class TensorConstant(Constant, _tensor_py_operators):
class TensorValue(Value, _tensor_py_operators): class TensorValue(Value, _tensor_py_operators):
pass pass
#QUESTION: why are we doing this!?
elemwise.as_tensor = as_tensor elemwise.as_tensor = as_tensor
elemwise.Tensor = Tensor elemwise.Tensor = Tensor
elemwise.TensorResult = TensorResult elemwise.TensorResult = TensorResult
...@@ -418,12 +490,60 @@ elemwise.TensorValue = TensorValue ...@@ -418,12 +490,60 @@ elemwise.TensorValue = TensorValue
# Utilities # Utilities
######################### #########################
def _elemwise(scalar_op, name): def _elemwise(scalar_op, name, doc_prefix=''):
straight = elemwise.Elemwise(scalar_op, name = name) straight = elemwise.Elemwise(scalar_op, name = name)
inplace_scalar_op = scalar_op.__class__(scal.transfer_type(0)) inplace_scalar_op = scalar_op.__class__(scal.transfer_type(0))
inplace = elemwise.Elemwise(inplace_scalar_op, {0: 0}, name = name+"_inplace") inplace = elemwise.Elemwise(inplace_scalar_op, {0: 0}, name = '_'+name+"_inplace")
# don't add the inplace versions, they aren't supposed to be part of the user interface
_constructor_list.append(straight)
# This is here so that gen_oplist can detect which module declared these variables.
straight.__module__ = 'tensor'
inplace.__module__ = 'tensor'
if doc_prefix:
straight.__doc__ = doc_prefix + '\n' + straight.__doc__
return straight, inplace return straight, inplace
def _redefine(real_symbol_value):
"""Replace the value associated with a function symbol.
This is useful to trick epydoc into doing what we want. It's a hack.
"""
def decorator(f):
return real_symbol_value
return decorator
def _redefine_asRoutine(real_symbol_value):
real_symbol_value.__epydoc_asRoutine = True
def decorator(f):
return real_symbol_value
return decorator
def _scal_elemwise(symbol):
"""Replace a symbol definition with an elementwise version of the corresponding scalar Op"""
symbolname = symbol.__name__
inplace = symbolname.endswith('_inplace')
if inplace:
scalar_op = getattr(scal, symbolname[1:-len('_inplace')])
inplace_scalar_op = scalar_op.__class__(scal.transfer_type(0))
rval = elemwise.Elemwise(inplace_scalar_op, {0: 0}, name=symbolname)
else:
scalar_op = getattr(scal, symbolname)
rval = elemwise.Elemwise(scalar_op, name=symbolname)
if getattr(symbol, '__doc__', False):
rval.__doc__ = symbol.__doc__ + '\n' + rval.__doc__
#for the meaning of this see the ./epydoc script
# it makes epydoc display rval as if it were a function, not an object
rval.__epydoc_asRoutine = symbol
return rval
######################### #########################
...@@ -457,6 +577,7 @@ class ScalarFromTensor(Op): ...@@ -457,6 +577,7 @@ class ScalarFromTensor(Op):
scalar_from_tensor = ScalarFromTensor() scalar_from_tensor = ScalarFromTensor()
@constructor
def cast(t, dtype): def cast(t, dtype):
mapping = {'int8': convert_to_int8, mapping = {'int8': convert_to_int8,
'int16': convert_to_int16, 'int16': convert_to_int16,
...@@ -468,14 +589,33 @@ def cast(t, dtype): ...@@ -468,14 +589,33 @@ def cast(t, dtype):
'complex128': convert_to_complex128} 'complex128': convert_to_complex128}
return mapping[dtype](t) return mapping[dtype](t)
convert_to_int8 = elemwise.Elemwise(scal.Identity(scal.specific_out(scal.int8))) #to be removed as we get the epydoc routine-documenting thing going -JB 20080924
convert_to_int16 = elemwise.Elemwise(scal.Identity(scal.specific_out(scal.int16))) def _conversion(real_value):
convert_to_int32 = elemwise.Elemwise(scal.Identity(scal.specific_out(scal.int32))) return real_value
convert_to_int64 = elemwise.Elemwise(scal.Identity(scal.specific_out(scal.int64)))
convert_to_float32 = elemwise.Elemwise(scal.Identity(scal.specific_out(scal.float32))) convert_to_int8 = _conversion(elemwise.Elemwise(scal.Identity(scal.specific_out(scal.int8))))
convert_to_float64 = elemwise.Elemwise(scal.Identity(scal.specific_out(scal.float64))) """Cast to 8-bit integer"""
convert_to_complex64 = elemwise.Elemwise(scal.Identity(scal.specific_out(scal.complex64)))
convert_to_complex128 = elemwise.Elemwise(scal.Identity(scal.specific_out(scal.complex128))) convert_to_int16 = _conversion(elemwise.Elemwise(scal.Identity(scal.specific_out(scal.int16))))
"""Cast to 16-bit integer"""
convert_to_int32 = _conversion(elemwise.Elemwise(scal.Identity(scal.specific_out(scal.int32))))
"""Cast to 32-bit integer"""
convert_to_int64 = _conversion(elemwise.Elemwise(scal.Identity(scal.specific_out(scal.int64))))
"""Cast to 64-bit integer"""
convert_to_float32 = _conversion(elemwise.Elemwise(scal.Identity(scal.specific_out(scal.float32))))
"""Cast to single-precision floating point"""
convert_to_float64 = _conversion(elemwise.Elemwise(scal.Identity(scal.specific_out(scal.float64))))
"""Cast to double-precision floating point"""
convert_to_complex64 = _conversion(elemwise.Elemwise(scal.Identity(scal.specific_out(scal.complex64))))
"""Cast to single-precision complex"""
convert_to_complex128 = _conversion(elemwise.Elemwise(scal.Identity(scal.specific_out(scal.complex128))))
"""Cast to double-precision complex"""
...@@ -496,7 +636,9 @@ class Shape(Op): ...@@ -496,7 +636,9 @@ class Shape(Op):
out[0] = numpy.asarray(x.shape) out[0] = numpy.asarray(x.shape)
def grad(self, (x,), (gz,)): def grad(self, (x,), (gz,)):
return [None] return [None]
shape = Shape() @_redefine_asRoutine(Shape())
def shape(a):
pass
class MaxAndArgmax(Op): class MaxAndArgmax(Op):
"""Calculate the max and argmax over a given axis""" """Calculate the max and argmax over a given axis"""
...@@ -529,10 +671,12 @@ class MaxAndArgmax(Op): ...@@ -529,10 +671,12 @@ class MaxAndArgmax(Op):
assert axis.data == 0 assert axis.data == 0
g_x = eq(max(x, axis), x) * g_max g_x = eq(max(x, axis), x) * g_max
return g_x, None return g_x, None
max_and_argmax = MaxAndArgmax() @_redefine_asRoutine(MaxAndArgmax())
def max_and_argmax(a):
pass
@constructor
def max(x, axis=None): def max(x, axis=None):
"""Return indexes of maximum elements obtained by iterating over given axis """Return indexes of maximum elements obtained by iterating over given axis
...@@ -543,6 +687,7 @@ def max(x, axis=None): ...@@ -543,6 +687,7 @@ def max(x, axis=None):
# but when Argmax.c_impl() is in place, it should be fine. # but when Argmax.c_impl() is in place, it should be fine.
return max_and_argmax(x,axis)[0] return max_and_argmax(x,axis)[0]
@constructor
def argmax(x, axis=None): def argmax(x, axis=None):
"""Return maximum elements obtained by iterating over given axis """Return maximum elements obtained by iterating over given axis
...@@ -558,58 +703,237 @@ def argmax(x, axis=None): ...@@ -558,58 +703,237 @@ def argmax(x, axis=None):
# Comparison # Comparison
########################## ##########################
lt, lt_inplace = _elemwise(scal.lt, 'lt') @_scal_elemwise
gt, gt_inplace = _elemwise(scal.gt, 'gt') def lt(a, b):
le, le_inplace = _elemwise(scal.le, 'le') """a < b"""
ge, ge_inplace = _elemwise(scal.ge, 'ge')
eq, eq_inplace = _elemwise(scal.eq, 'eq') @_scal_elemwise
neq, neq_inplace = _elemwise(scal.neq, 'neq') def _lt_inplace(a,b):
"""a < b (inplace on a)"""
@_scal_elemwise
def gt(a, b):
"""a > b"""
@_scal_elemwise
def _gt_inplace(a,b):
"""a > b (inplace on a)"""
@_scal_elemwise
def le(a, b):
"""a <= b"""
@_scal_elemwise
def _le_inplace(a,b):
"""a <= b (inplace on a)"""
@_scal_elemwise
def ge(a, b):
"""a >= b"""
@_scal_elemwise
def _ge_inplace(a,b):
"""a >= b (inplace on a)"""
@_scal_elemwise
def eq(a, b):
"""a == b"""
@_scal_elemwise
def _eq_inplace(a,b):
"""a == b (inplace on a)"""
@_scal_elemwise
def neq(a, b):
"""a != b"""
@_scal_elemwise
def _neq_inplace(a,b):
"""a != b (inplace on a)"""
########################## ##########################
# Bit-wise # Bit-wise
########################## ##########################
and_, and_inplace = _elemwise(scal.and_, 'and_') @_scal_elemwise
or_, or_inplace = _elemwise(scal.or_, 'or_') def and_(a,b):
xor, xor_inplace = _elemwise(scal.xor, 'xor') """bitwise a & b"""
invert, invert_inplace = _elemwise(scal.invert, 'invert')
@_scal_elemwise
def _and__inplace(a,b):
"""bitwise a & b (inplace on a)"""
@_scal_elemwise
def or_(a,b):
"""bitwise a | b"""
@_scal_elemwise
def _or__inplace(a,b):
"""bitwise a | b (inplace on a)"""
@_scal_elemwise
def xor(a,b):
"""bitwise a ^ b"""
@_scal_elemwise
def _xor_inplace(a,b):
"""bitwise a ^ b (inplace on a)"""
@_scal_elemwise
def invert(a):
"""bitwise ~a"""
@_scal_elemwise
def _invert_inplace(a):
"""bitwise ~a (inplace on a)"""
########################## ##########################
# Math # Math
########################## ##########################
_abs, abs_inplace = _elemwise(scal.abs, 'abs') @_scal_elemwise
exp, exp_inplace = _elemwise(scal.exp, 'exp') def _abs(a):
neg, neg_inplace = _elemwise(scal.neg, 'neg') """|`a`|
inv, inv_inplace = _elemwise(scal.inv, 'inv')
log, log_inplace = _elemwise(scal.log, 'log') _abs has a leading underscore because abs() is a builtin. TensorResult overloads the
log2, log2_inplace = _elemwise(scal.log2, 'log2') `TensorResult.__abs__` operator so that this function is called when you type abs(a).
sgn, sgn_inplace = _elemwise(scal.sgn, 'sgn')
sqr, sqr_inplace = _elemwise(scal.sqr, 'sqr') """
sqrt, sqrt_inplace = _elemwise(scal.sqrt, 'sqrt')
cos, cos_inplace = _elemwise(scal.cos, 'cos') @_scal_elemwise
sin, sin_inplace = _elemwise(scal.sin, 'sin') def __abs_inplace(a):
tan, tan_inplace = _elemwise(scal.tan, 'tan') """|`a`| (inplace on `a`)"""
cosh, cosh_inplace = _elemwise(scal.cosh, 'cosh')
sinh, sinh_inplace = _elemwise(scal.sinh, 'sinh') @_scal_elemwise
tanh, tanh_inplace = _elemwise(scal.tanh, 'tanh') def exp(a):
"""e^`a`"""
@_scal_elemwise
def _exp_inplace(a):
"""e^`a` (inplace on `a`)"""
@_scal_elemwise
def neg(a):
"""-a"""
@_scal_elemwise
def _neg_inplace(a):
"""-a (inplace on a)"""
@_scal_elemwise
def inv(a):
"""1.0/a (inplace on a)"""
@_scal_elemwise
def _inv_inplace(a):
"""1.0/a (inplace on a)"""
@_scal_elemwise
def log(a):
"""base e logarithm of a"""
@_scal_elemwise
def _log_inplace(a):
"""base e logarithm of a (inplace on a)"""
@_scal_elemwise
def log2(a):
"""base 2 logarithm of a"""
@_scal_elemwise
def _log2_inplace(a):
"""base 2 logarithm of a (inplace on a)"""
@_scal_elemwise
def sgn(a):
"""sign of a"""
@_scal_elemwise
def _sgn_inplace(a):
"""sign of `a` (inplace on `a`)"""
@_scal_elemwise
def sqr(a):
"""square of a"""
@_scal_elemwise
def _sqr_inplace(a):
"""square of `a` (inplace on `a`)"""
@_scal_elemwise
def sqrt(a):
"""square root of a"""
@_scal_elemwise
def _sqrt_inplace(a):
"""square root of `a` (inplace on `a`)"""
@_scal_elemwise
def cos(a):
"""cosine of a"""
@_scal_elemwise
def _cos_inplace(a):
"""cosine of `a` (inplace on `a`)"""
@_scal_elemwise
def sin(a):
"""sine of a"""
@_scal_elemwise
def _sin_inplace(a):
"""sine of `a` (inplace on `a`)"""
@_scal_elemwise
def tan(a):
"""tangent of a"""
@_scal_elemwise
def _tan_inplace(a):
"""tangent of `a` (inplace on `a`)"""
@_scal_elemwise
def cosh(a):
"""hyperbolic cosine of a"""
@_scal_elemwise
def _cosh_inplace(a):
"""hyperbolic cosine of `a` (inplace on `a`)"""
@_scal_elemwise
def sinh(a):
"""hyperbolic sine of a"""
@_scal_elemwise
def _sinh_inplace(a):
"""hyperbolic sine of `a` (inplace on `a`)"""
@_scal_elemwise
def tanh(a):
"""hyperbolic tangent of a"""
@_scal_elemwise
def _tanh_inplace(a):
"""hyperbolic tangent of `a` (inplace on `a`)"""
########################## ##########################
# Misc # Misc
########################## ##########################
fill, fill_inplace = _elemwise(scal.second, 'fill') #fill, _fill_inplace = _elemwise(scal.second, 'fill',
#"""fill WRITEME (elemwise)""")
@_scal_elemwise
def second(a, b):
"""Create a matrix by filling the shape of a with b"""
@_scal_elemwise
def _second_inplace(a):
"""Fill `a` with `b`"""
fill = second
_fill_inplace = _second_inplace
@constructor
def ones_like(model): def ones_like(model):
"""WRITEME"""
#return Ones(model.type.ndim)(shape(model)) #return Ones(model.type.ndim)(shape(model))
return fill(model, 1.0) return fill(model, 1.0)
@constructor
def zeros_like(model): def zeros_like(model):
"""WRITEME"""
#return Zeros(model.type.ndim)(shape(model)) #return Zeros(model.type.ndim)(shape(model))
return fill(model, 0.0) return fill(model, 0.0)
class Filler(gof.Op): class Filler(gof.Op):
"""WRITEME"""
def __init__(self, value, ndim, dtype = 'float64'): def __init__(self, value, ndim, dtype = 'float64'):
self.value = value self.value = value
self.ndim = ndim self.ndim = ndim
...@@ -643,25 +967,40 @@ class Filler(gof.Op): ...@@ -643,25 +967,40 @@ class Filler(gof.Op):
return hash(self.ndim) ^ hash(self.dtype) return hash(self.ndim) ^ hash(self.dtype)
Zeros = functools.partial(Filler, 0) Zeros = functools.partial(Filler, 0)
"""WRITEME"""
Ones = functools.partial(Filler, 1) Ones = functools.partial(Filler, 1)
"""WRITEME"""
@constructor
def zero(): def zero():
""" """
Return a scalar zero, e.g. for initializing sums. Return a scalar zero, e.g. for initializing sums.
""" """
return Zeros(0)([]) return Zeros(0)([])
@constructor
def one(): def one():
"""WRITEME"""
return Ones(0)([]) return Ones(0)([])
tensor_copy = elemwise.Elemwise(scal.identity) @_redefine(elemwise.Elemwise(scal.identity))
identity = elemwise.Elemwise(scal.identity, inplace_pattern = {0: [0]}) def tensor_copy(a):
"""Create a duplicate of `a` (with duplicated storage)"""
@_redefine(elemwise.Elemwise(scal.identity, inplace_pattern = {0: [0]}))
def view(a):
"""Create a duplicate of `a` (with shared storage)"""
@constructor
def sum(input, axis = None): def sum(input, axis = None):
"""WRITEME"""
return elemwise.Sum(axis)(input) return elemwise.Sum(axis)(input)
@constructor
def mean(input, axis = None): def mean(input, axis = None):
"""WRITEME"""
s = sum(input, axis) s = sum(input, axis)
shp = shape(input) shp = shape(input)
if axis is None: if axis is None:
...@@ -697,12 +1036,47 @@ repeat = Repeat() ...@@ -697,12 +1036,47 @@ repeat = Repeat()
# Arithmetics # Arithmetics
########################## ##########################
add, add_inplace = _elemwise(scal.add, 'add') @_scal_elemwise
sub, sub_inplace = _elemwise(scal.sub, 'sub') def add(a, b):
mul, mul_inplace = _elemwise(scal.mul, 'mul') """elementwise addition"""
div, div_inplace = _elemwise(scal.div, 'div') @_scal_elemwise
mod, mod_inplace = _elemwise(scal.mod, 'mod') def _add_inplace(a, b):
pow, pow_inplace = _elemwise(scal.pow, 'pow') """elementwise addition (inplace on `a`)"""
@_scal_elemwise
def sub(a, b):
"""elementwise subtraction"""
@_scal_elemwise
def _sub_inplace(a, b):
"""elementwise subtraction (inplace on `a`)"""
@_scal_elemwise
def mul(a, b):
"""elementwise multiplication"""
@_scal_elemwise
def _mul_inplace(a, b):
"""elementwise multiplication (inplace on `a`)"""
@_scal_elemwise
def div(a, b):
"""elementwise division"""
@_scal_elemwise
def _div_inplace(a, b):
"""elementwise division (inplace on `a`)"""
@_scal_elemwise
def mod(a, b):
"""elementwise modulo"""
@_scal_elemwise
def _mod_inplace(a, b):
"""elementwise modulo (inplace on `a`)"""
@_scal_elemwise
def pow(a, b):
"""elementwise power"""
@_scal_elemwise
def _pow_inplace(a, b):
"""elementwise power (inplace on `a`)"""
########################## ##########################
...@@ -734,9 +1108,12 @@ class TransposeInplace(Op): ...@@ -734,9 +1108,12 @@ class TransposeInplace(Op):
def __str__(self): def __str__(self):
return "TransposeView" return "TransposeView"
transpose_inplace = TransposeInplace() _transpose_inplace = TransposeInplace()
"""WRITEME"""
def transpose(x, **kwargs): def transpose(x, **kwargs):
return transpose_inplace(tensor_copy(x), **kwargs) """WRITEME"""
return _transpose_inplace(tensor_copy(x), **kwargs)
...@@ -871,6 +1248,7 @@ class Subtensor(Op): ...@@ -871,6 +1248,7 @@ class Subtensor(Op):
class SetSubtensor(Subtensor): class SetSubtensor(Subtensor):
"""WRITEME"""
view_map = {} view_map = {}
destroy_map = {0: [0]} destroy_map = {0: [0]}
...@@ -923,6 +1301,7 @@ class SetSubtensor(Subtensor): ...@@ -923,6 +1301,7 @@ class SetSubtensor(Subtensor):
class MakeVector(Op): class MakeVector(Op):
"""WRITEME"""
def __init__(self, stype): def __init__(self, stype):
self.stype = stype self.stype = stype
def make_node(self, *inputs): def make_node(self, *inputs):
...@@ -935,6 +1314,7 @@ class MakeVector(Op): ...@@ -935,6 +1314,7 @@ class MakeVector(Op):
return [None]*len(inputs) return [None]*len(inputs)
make_lvector = MakeVector(lscalar) make_lvector = MakeVector(lscalar)
"""WRITEME"""
class Concatenate(Op): class Concatenate(Op):
""" """
......
tensor_opt.py
浏览文件 @ b128f2b8
...@@ -20,7 +20,7 @@ def in2out(*local_opts): ...@@ -20,7 +20,7 @@ def in2out(*local_opts):
# gemm: (d,a,b,c,s) -> d = d*s + a*dot(b,c) # gemm: (d,a,b,c,s) -> d = d*s + a*dot(b,c)
# Transforms d -= a * dot(b, c) into gemm(d, -a, b, c, 1.0) # Transforms d -= a * dot(b, c) into gemm(d, -a, b, c, 1.0)
gemm_pattern_1 = gof.PatternSub((T.sub_inplace, gemm_pattern_1 = gof.PatternSub((T._sub_inplace,
'd', 'd',
(T.mul, (T.mul,
dict(pattern = (T.DimShuffle((), ['x', 'x'], inplace = True), 'a'), dict(pattern = (T.DimShuffle((), ['x', 'x'], inplace = True), 'a'),
......
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