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pytensor
提交 2ea6bd45 authored 作者: Olivier Breuleux's avatar Olivier Breuleux
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上级 5af58c40 d1cc7c4b
隐藏空白字符变更
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正在显示 包含 307 行增加 和 216 行删除
_test_tensor.py
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...@@ -22,8 +22,8 @@ def _numpy_checker(x, y): ...@@ -22,8 +22,8 @@ def _numpy_checker(x, y):
raise Exception("Output mismatch.", {'performlinker': x, 'clinker': y}) raise Exception("Output mismatch.", {'performlinker': x, 'clinker': y})
def make_tester(name, op_class, expected, checks = {}, good = {}, bad_build = {}, bad_runtime = {}, grad = None): def make_tester(name, op_class, expected, checks = {}, good = {}, bad_build = {}, bad_runtime = {}, grad = {}):
if grad is None: if grad is True:
grad = good grad = good
_op_class, _expected, _checks, _good, _bad_build, _bad_runtime, _grad = op_class, expected, checks, good, bad_build, bad_runtime, grad _op_class, _expected, _checks, _good, _bad_build, _bad_runtime, _grad = op_class, expected, checks, good, bad_build, bad_runtime, grad
...@@ -134,8 +134,8 @@ def make_tester(name, op_class, expected, checks = {}, good = {}, bad_build = {} ...@@ -134,8 +134,8 @@ def make_tester(name, op_class, expected, checks = {}, good = {}, bad_build = {}
verify_grad(self, self.op_class, inputs) verify_grad(self, self.op_class, inputs)
except: except:
type, value, traceback = sys.exc_info() type, value, traceback = sys.exc_info()
err_msg = "With data %s::%s: This error occurred while computing the gradient for %s" \ err_msg = "With data %s::%s: This error occurred while computing the gradient for %s on the following inputs: %s" \
% (self.op_class.__name__, testname, self.op_class) % (self.op_class.__name__, testname, self.op_class, inputs)
value.args = value.args + (err_msg, ) value.args = value.args + (err_msg, )
raise type, value, traceback raise type, value, traceback
...@@ -144,111 +144,20 @@ def make_tester(name, op_class, expected, checks = {}, good = {}, bad_build = {} ...@@ -144,111 +144,20 @@ def make_tester(name, op_class, expected, checks = {}, good = {}, bad_build = {}
rand = lambda *shape: 2 * numpy.random.rand(*shape) - 1 rand = lambda *shape: 2 * numpy.random.rand(*shape) - 1
randint = lambda *shape: numpy.random.random_integers(-10, 10, shape) randint = lambda *shape: numpy.random.random_integers(-5, 5, shape)
def randint_notzero(*shape):
r = numpy.random.random_integers(-10, 9, shape)
return r + (r == 0) * 10
# randplus = numpy.random.rand def randint_nonzero(*shape):
# randintplus = r = numpy.random.random_integers(-5, 4, shape)
return r + (r == 0) * 5
# def banzero(f): def rand_ranged(min, max, shape):
# def f2(*shape): return numpy.random.rand(*shape) * (max - min) + min
# res = f(*shape)
# while numpy.any(res == 0):
# res = f(*shape)
# return res
# return f2
# def banneg(f): def randint_ranged(min, max, shape):
# def f2(*shape): return numpy.random.random_integers(min, max, shape)
# res = f(*shape)
# while numpy.any(res < 0):
# res = f(*shape)
# return res
# return f2
def make_broadcast_tester(op_class, expected, checks = {}, **kwargs): def make_broadcast_tester(op_class, expected, checks = {}, **kwargs):
_randint = randint
_rand = rand
if kwargs.has_key('nonzero'):
if kwargs['nonzero']:
_randint = banzero(_randint)
_rand = banzero(_rand)
del kwargs['nonzero']
if kwargs.has_key('positive'):
if kwargs['positive']:
_randint = banneg(_randint)
_rand = banneg(_rand)
del kwargs['positive']
_good_broadcast = dict(same_shapes = (_rand(2, 3), _rand(2, 3)),
scalar = (_rand(2, 3), _rand(1, 1)),
row = (_rand(2, 3), _rand(1, 3)),
column = (_rand(2, 3), _rand(2, 1)),
integers = (_randint(2, 3), _randint(2, 3)),
dtype_mixup_1 = (_rand(2, 3), _randint(2, 3)),
dtype_mixup_2 = (_randint(2, 3), _rand(2, 3)))
_bad_build_broadcast = dict(not_same_dimensions = (_rand(2), _rand(2, 2)))
_bad_runtime_broadcast = dict(bad_shapes = (_rand(2, 3), _rand(3, 2)),
bad_row = (_rand(2, 3), _rand(1, 2)))
_grad_broadcast = dict(same_shapes = (_rand(2, 3), _rand(2, 3)),
scalar = (_rand(2, 3), _rand(1, 1)),
row = (_rand(2, 3), _rand(1, 3)),
column = (_rand(2, 3), _rand(2, 1)))
kwargs.setdefault('good', _good_broadcast)
kwargs.setdefault('bad_build', _bad_build_broadcast)
kwargs.setdefault('bad_runtime', _bad_runtime_broadcast)
kwargs.setdefault('grad', _grad_broadcast)
name = op_class.__name__ + "Tester"
if kwargs.has_key('inplace'):
if kwargs['inplace']:
_expected = expected
expected = lambda *inputs: numpy.array(_expected(*inputs), dtype = inputs[0].dtype)
checks = dict(checks,
inplace_check = lambda inputs, outputs: inputs[0] is outputs[0])
del kwargs['inplace']
return make_tester(name, op_class, expected, checks, **kwargs)
def make_broadcast_tester_unary(op_class, expected, checks = {}, **kwargs):
_randint = randint
_rand = rand
if kwargs.has_key('nonzero'):
if kwargs['nonzero']:
_randint = banzero(_randint)
_rand = banzero(_rand)
del kwargs['nonzero']
if kwargs.has_key('positive'):
if kwargs['positive']:
_randint = banneg(_randint)
_rand = banneg(_rand)
del kwargs['positive']
_good_broadcast = dict(normal = (_rand(2, 3), ),
int = (_rand(2, 3), ))
_bad_build_broadcast = dict()
_bad_runtime_broadcast = dict()
_grad_broadcast = dict(normal = (_rand(2, 3), ),
int = (_rand(2, 3), ))
kwargs.setdefault('good', _good_broadcast)
kwargs.setdefault('bad_build', _bad_build_broadcast)
kwargs.setdefault('bad_runtime', _bad_runtime_broadcast)
kwargs.setdefault('grad', _grad_broadcast)
name = op_class.__name__ + "Tester" name = op_class.__name__ + "Tester"
if kwargs.has_key('inplace'): if kwargs.has_key('inplace'):
if kwargs['inplace']: if kwargs['inplace']:
...@@ -261,108 +170,288 @@ def make_broadcast_tester_unary(op_class, expected, checks = {}, **kwargs): ...@@ -261,108 +170,288 @@ def make_broadcast_tester_unary(op_class, expected, checks = {}, **kwargs):
_good_broadcast_binary_normal = dict(same_shapes = (rand(2, 3), rand(2, 3)),
scalar = (rand(2, 3), rand(1, 1)),
row = (rand(2, 3), rand(1, 3)),
column = (rand(2, 3), rand(2, 1)),
integers = (randint(2, 3), randint(2, 3)),
dtype_mixup_1 = (rand(2, 3), randint(2, 3)),
dtype_mixup_2 = (randint(2, 3), rand(2, 3)))
_bad_build_broadcast_binary_normal = dict(not_same_dimensions = (rand(2), rand(2, 2)))
_bad_runtime_broadcast_binary_normal = dict(bad_shapes = (rand(2, 3), rand(3, 2)),
bad_row = (rand(2, 3), rand(1, 2)))
_grad_broadcast_binary_normal = dict(same_shapes = (rand(2, 3), rand(2, 3)),
scalar = (rand(2, 3), rand(1, 1)),
row = (rand(2, 3), rand(1, 3)),
column = (rand(2, 3), rand(2, 1)))
AddTester = make_broadcast_tester(op_class = Add,
expected = lambda *inputs: reduce(lambda x, y: x + y, inputs),
good = 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)),
**_good_broadcast_binary_normal),
bad_build = _bad_build_broadcast_binary_normal,
bad_runtime = _bad_runtime_broadcast_binary_normal)
AddInplaceTester = make_broadcast_tester(op_class = AddInplace,
expected = lambda x, y: x + y,
good = _good_broadcast_binary_normal,
bad_build = _bad_build_broadcast_binary_normal,
bad_runtime = _bad_runtime_broadcast_binary_normal,
inplace = True)
SubTester = make_broadcast_tester(op_class = Sub,
expected = lambda x, y: x - y,
good = _good_broadcast_binary_normal,
bad_build = _bad_build_broadcast_binary_normal,
bad_runtime = _bad_runtime_broadcast_binary_normal,
grad = _grad_broadcast_binary_normal)
SubInplaceTester = make_broadcast_tester(op_class = SubInplace,
expected = lambda x, y: x - y,
good = _good_broadcast_binary_normal,
bad_build = _bad_build_broadcast_binary_normal,
bad_runtime = _bad_runtime_broadcast_binary_normal,
grad = _grad_broadcast_binary_normal,
inplace = True)
MulTester = make_broadcast_tester(op_class = Mul,
expected = lambda *inputs: reduce(lambda x, y: x * y, inputs),
good = 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)),
**_good_broadcast_binary_normal),
bad_build = _bad_build_broadcast_binary_normal,
bad_runtime = _bad_runtime_broadcast_binary_normal,
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)),
**_grad_broadcast_binary_normal))
MulInplaceTester = make_broadcast_tester(op_class = MulInplace,
expected = lambda x, y: x * y,
good = _good_broadcast_binary_normal,
bad_build = _bad_build_broadcast_binary_normal,
bad_runtime = _bad_runtime_broadcast_binary_normal,
grad = _grad_broadcast_binary_normal,
inplace = True)
DivTester = make_broadcast_tester(op_class = Div,
expected = lambda x, y: x / y,
good = dict(same_shapes = (rand(2, 3), rand(2, 3)),
scalar = (rand(2, 3), rand(1, 1)),
row = (rand(2, 3), rand(1, 3)),
column = (rand(2, 3), rand(2, 1)),
dtype_mixup_1 = (rand(2, 3), randint_nonzero(2, 3)),
dtype_mixup_2 = (randint_nonzero(2, 3), rand(2, 3)),
# integers_positive = (randint_ranged(4, 10, (2, 3)), randint_ranged(1, 6, (2, 3))),
# integers_known_to_fail = (numpy.array(-1), numpy.array(5))
),
# integers = (randint(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))),
grad = dict(same_shapes = (rand(2, 3), rand(2, 3)),
scalar = (rand(2, 3), rand(1, 1)),
row = (rand(2, 3), rand(1, 3)),
column = (rand(2, 3), rand(2, 1))))
DivInplaceTester = make_broadcast_tester(op_class = DivInplace,
expected = lambda x, y: x / y,
good = dict(same_shapes = (rand(2, 3), rand(2, 3)),
scalar = (rand(2, 3), rand(1, 1)),
row = (rand(2, 3), rand(1, 3)),
column = (rand(2, 3), rand(2, 1)),
dtype_mixup_1 = (rand(2, 3), randint_nonzero(2, 3)),
dtype_mixup_2 = (randint_nonzero(2, 3), rand(2, 3))
),
grad = dict(same_shapes = (rand(2, 3), rand(2, 3)),
scalar = (rand(2, 3), rand(1, 1)),
row = (rand(2, 3), rand(1, 3)),
column = (rand(2, 3), rand(2, 1))),
inplace = True)
PowTester = make_broadcast_tester(op_class = Pow,
expected = lambda x, y: x ** y,
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))),
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))),
dtype_mixup = (rand_ranged(-3, 3, (2, 3)), randint_ranged(-3, 3, (2, 3)))),
grad = 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))),
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))))
)
PowTester = make_broadcast_tester(op_class = PowInplace,
expected = lambda x, y: x ** y,
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))),
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))),
dtype_mixup = (rand_ranged(-3, 3, (2, 3)), randint_ranged(-3, 3, (2, 3)))),
grad = 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))),
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)))),
inplace = True)
_good_broadcast_unary_normal = dict(normal = (rand_ranged(-5, 5, (2, 3)),),
integers = (randint_ranged(-5, 5, (2, 3)),))
_grad_broadcast_unary_normal = dict(normal = (rand_ranged(-5, 5, (2, 3)),))
AbsTester = make_broadcast_tester(op_class = Abs,
expected = lambda x: abs(x),
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
AbsInplaceTester = make_broadcast_tester(op_class = AbsInplace,
expected = lambda x: abs(x),
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal,
inplace = True)
NegTester = make_broadcast_tester(op_class = Neg,
expected = lambda x: -x,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
NegInplaceTester = make_broadcast_tester(op_class = NegInplace,
expected = lambda x: -x,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal,
inplace = True)
SgnTester = make_broadcast_tester(op_class = Sgn,
expected = numpy.sign,
good = _good_broadcast_unary_normal)
SgnInplaceTester = make_broadcast_tester(op_class = SgnInplace,
expected = numpy.sign,
good = _good_broadcast_unary_normal,
inplace = True)
SqrTester = make_broadcast_tester(op_class = Sqr,
expected = numpy.square,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
SqrInplaceTester = make_broadcast_tester(op_class = SqrInplace,
expected = numpy.square,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal,
inplace = True)
ExpTester = make_broadcast_tester(op_class = Exp,
expected = numpy.exp,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
ExpInplaceTester = make_broadcast_tester(op_class = ExpInplace,
expected = numpy.exp,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal,
inplace = True)
_good_broadcast_unary_positive = dict(normal = (rand_ranged(0.001, 5, (2, 3)),),
integers = (randint_ranged(1, 5, (2, 3)),))
_grad_broadcast_unary_positive = dict(normal = (rand_ranged(0.001, 5, (2, 3)),))
LogTester = make_broadcast_tester(op_class = Log,
expected = numpy.log,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive)
LogInplaceTester = make_broadcast_tester(op_class = LogInplace,
expected = numpy.log,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive,
inplace = True)
Log2Tester = make_broadcast_tester(op_class = Log2,
expected = numpy.log2,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive)
Log2InplaceTester = make_broadcast_tester(op_class = Log2Inplace,
expected = numpy.log2,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive,
inplace = True)
SqrtTester = make_broadcast_tester(op_class = Sqrt,
expected = numpy.sqrt,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive)
SqrtInplaceTester = make_broadcast_tester(op_class = SqrtInplace,
expected = numpy.sqrt,
good = _good_broadcast_unary_positive,
grad = _grad_broadcast_unary_positive,
inplace = True)
_good_broadcast_unary_wide = dict(normal = (rand_ranged(-1000, 1000, (2, 3)),),
integers = (randint_ranged(-1000, 1000, (2, 3)),))
_grad_broadcast_unary_wide = dict(normal = (rand_ranged(-1000, 1000, (2, 3)),))
SinTester = make_broadcast_tester(op_class = Sin,
expected = numpy.sin,
good = _good_broadcast_unary_wide,
grad = _grad_broadcast_unary_wide)
SinInplaceTester = make_broadcast_tester(op_class = SinInplace,
expected = numpy.sin,
good = _good_broadcast_unary_wide,
grad = _grad_broadcast_unary_wide,
inplace = True)
CosTester = make_broadcast_tester(op_class = Cos,
expected = numpy.cos,
good = _good_broadcast_unary_wide,
grad = _grad_broadcast_unary_wide)
CosInplaceTester = make_broadcast_tester(op_class = CosInplace,
expected = numpy.cos,
good = _good_broadcast_unary_wide,
grad = _grad_broadcast_unary_wide,
inplace = True)
TanTester = make_broadcast_tester(op_class = Tan,
expected = numpy.tan,
good = dict(normal = (rand_ranged(-3.14, 3.14, (2, 3)),),
shifted = (rand_ranged(3.15, 6.28, (2, 3)),)))
TanInplaceTester = make_broadcast_tester(op_class = CosInplace,
expected = numpy.cos,
good = dict(normal = (rand_ranged(-3.14, 3.14, (2, 3)),),
shifted = (rand_ranged(3.15, 6.28, (2, 3)),)),
inplace = True)
CoshTester = make_broadcast_tester(op_class = Cosh,
expected = numpy.cosh,
good = _good_broadcast_unary_normal)
CoshInplaceTester = make_broadcast_tester(op_class = CoshInplace,
expected = numpy.cosh,
good = _good_broadcast_unary_normal,
inplace = True)
SinhTester = make_broadcast_tester(op_class = Sinh,
expected = numpy.sinh,
good = _good_broadcast_unary_normal)
SinhInplaceTester = make_broadcast_tester(op_class = SinhInplace,
expected = numpy.sinh,
good = _good_broadcast_unary_normal,
inplace = True)
TanhTester = make_broadcast_tester(op_class = Tanh,
expected = numpy.tanh,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal)
TanhInplaceTester = make_broadcast_tester(op_class = TanhInplace,
expected = numpy.tanh,
good = _good_broadcast_unary_normal,
grad = _grad_broadcast_unary_normal,
inplace = True)
# AddTester = make_broadcast_tester(op_class = Add,
# expected = lambda x, y: x + y,
# grad = {})
# AddInplaceTester = make_broadcast_tester(op_class = AddInplace,
# expected = lambda x, y: x + y,
# inplace = True,
# grad = {})
# SubTester = make_broadcast_tester(op_class = Sub,
# expected = lambda x, y: x - y)
# SubInplaceTester = make_broadcast_tester(op_class = SubInplace,
# expected = lambda x, y: x - y,
# inplace = True)
# MulTester = make_broadcast_tester(op_class = Mul,
# expected = lambda x, y: x * y)
# MulInplaceTester = make_broadcast_tester(op_class = MulInplace,
# expected = lambda x, y: x * y,
# inplace = True)
# DivTester = make_broadcast_tester(op_class = Div,
# expected = lambda x, y: x / y,
# nonzero = True,
# positive = True)
# DivInplaceTester = make_broadcast_tester(op_class = DivInplace,
# expected = lambda x, y: x / y,
# inplace = True,
# nonzero = True,
# positive = True)
# _pow_good = dict(normal = ()
# _pow_grad =
# PowTester = make_broadcast_tester(op_class = Pow,
# expected = lambda x, y: x ** y,
# good = _pow_good)
# PowInplaceTester = make_broadcast_tester(op_class = PowInplace,
# expected = lambda x, y: x ** y,
# good = _pow_good)
AbsTester = make_broadcast_tester_unary(op_class = Abs,
expected = lambda x: abs(x))
AbsInplaceTester = make_broadcast_tester_unary(op_class = AbsInplace,
expected = lambda x: abs(x),
inplace = True)
# ExpTester = make_broadcast_tester(op_class = Exp,
# expected = lambda x: numpy.exp(x))
# ExpInplaceTester = make_broadcast_tester(op_class = ExpInplace,
# expected = lambda x: numpy.exp(x),
# inplace = True)
# Abs, _abs, AbsInplace, abs_inplace = broadcast(scal.Abs, 'Abs')
# Exp, exp, ExpInplace, exp_inplace = broadcast(scal.Exp, 'Exp')
# Neg, neg, NegInplace, neg_inplace = broadcast(scal.Neg, 'Neg')
# Log, log, LogInplace, log_inplace = broadcast(scal.Log, 'Log')
# Log2, log2, Log2Inplace, log2_inplace = broadcast(scal.Log2, 'Log2')
# Sgn, sgn, SgnInplace, sgn_inplace = broadcast(scal.Sgn, 'Sgn')
# Sqr, sqr, SqrInplace, sqr_inplace = broadcast(scal.Sqr, 'Sqr')
# Sqrt, sqrt, SqrtInplace, sqrt_inplace = broadcast(scal.Sqrt, 'Sqrt')
# AddTester = make_broadcast_tester(op_class = Add,
# expected = lambda x, y: x + y,
# checks = {},
# grad = {})
# AddInplaceTester = make_broadcast_tester(op_class = AddInplace,
# expected = lambda x, y: numpy.array(x + y, dtype = x.dtype),
# checks = dict(inplace_check = lambda (x, y), (z, ): x is z),
# grad = {})
# AddTester = make_tester(name = 'AddTester',
# op_class = Add,
# expected = lambda x, y: x + y,
# checks = {},
# good = dict(same_shapes = (rand(5, 6), rand(5, 6)),
# scalar = (rand(5, 6), rand(1, 1)),
# row = (rand(5, 6), rand(1, 6)),
# column = (rand(5, 6), rand(5, 1)),
# integers = (randint(5, 6), randint(5, 6)),
# dtype_mixup = (rand(5, 6), randint(5, 6))),
# bad_build = dict(not_same_dimensions = (rand(5), rand(5, 5))),
# bad_runtime = dict(bad_shapes = (rand(5, 6), rand(6, 5)),
# bad_row = (rand(5, 6), rand(1, 5))),
# grad = {})
# AddInplaceTester = make_tester(name = 'AddInplaceTester',
# op_class = AddInplace,
# expected = lambda x, y: numpy.array(x + y, dtype = x.dtype),
# checks = dict(inplace_check = lambda (x, y), (z, ): x is z),
# good = dict(same_shapes = (rand(5, 6), rand(5, 6)),
# dtype_mixup = (randint(5, 6), rand(5, 6))),
# bad_build = dict(not_same_dimensions = (rand(5), rand(5, 5))),
# bad_runtime = dict(bad_shapes = (rand(5, 6), rand(6, 5)),
# bad_row = (rand(5, 6), rand(1, 5))),
# grad = {})
DotTester = make_tester(name = 'DotTester', DotTester = make_tester(name = 'DotTester',
op_class = Dot, op_class = Dot,
...@@ -414,11 +503,8 @@ def verify_grad(testcase, op_cls, pt, n_tests=1, rng=numpy.random, eps=0.0000001 ...@@ -414,11 +503,8 @@ def verify_grad(testcase, op_cls, pt, n_tests=1, rng=numpy.random, eps=0.0000001
print '----------' print '----------'
for op in gof.graph.io_toposort(tensor_pt, symbolic_grad): for op in gof.graph.io_toposort(tensor_pt, symbolic_grad):
print op print op
try:
grad_fn = Function(tensor_pt, symbolic_grad) grad_fn = Function(tensor_pt, symbolic_grad)
except:
print gof.graph.as_string(tensor_pt, symbolic_grad)
raise
analytic_grad = grad_fn(*pt) analytic_grad = grad_fn(*pt)
if not isinstance(analytic_grad, (list, tuple)): if not isinstance(analytic_grad, (list, tuple)):
......
elemwise.py
浏览文件 @ 2ea6bd45
...@@ -149,13 +149,13 @@ class Broadcast(Op, Destroyer): ...@@ -149,13 +149,13 @@ class Broadcast(Op, Destroyer):
if ib and not ob: if ib and not ob:
raise ValueError("Operation cannot be done inplace on an input with broadcasted dimensions.") raise ValueError("Operation cannot be done inplace on an input with broadcasted dimensions.")
upcasted = upcast(*[input.dtype for input in inputs]) out_dtypes = [t.dtype for t in self.shadow.outputs]
def get_dtype(i): def get_dtype(i):
input_idx = inplace_pattern.get(i, None) input_idx = inplace_pattern.get(i, None)
if input_idx is not None: if input_idx is not None:
return inputs[input_idx].dtype return inputs[input_idx].dtype
else: else:
return upcasted return out_dtypes[i]
out_dtypes = map(get_dtype, xrange(self.nout)) out_dtypes = map(get_dtype, xrange(self.nout))
self.inputs = inputs self.inputs = inputs
self.outputs = [Tensor(dtype = dtype, broadcastable = broadcastable) for dtype, broadcastable in zip(out_dtypes, out_broadcastables)] self.outputs = [Tensor(dtype = dtype, broadcastable = broadcastable) for dtype, broadcastable in zip(out_dtypes, out_broadcastables)]
...@@ -201,6 +201,9 @@ class Broadcast(Op, Destroyer): ...@@ -201,6 +201,9 @@ class Broadcast(Op, Destroyer):
return bcasted return bcasted
ret = [] ret = []
for scalar_igrad, input in zip(scalar_igrads, inputs): for scalar_igrad, input in zip(scalar_igrads, inputs):
if scalar_igrad is None:
ret.append(None)
continue
r = transform(scalar_igrad) r = transform(scalar_igrad)
to_sum = [i for i, bcast in enumerate(input.broadcastable) if bcast] to_sum = [i for i, bcast in enumerate(input.broadcastable) if bcast]
if to_sum: if to_sum:
......
scalar.py
浏览文件 @ 2ea6bd45
...@@ -282,6 +282,8 @@ class Div(BinaryScalarOp): ...@@ -282,6 +282,8 @@ class Div(BinaryScalarOp):
def impl(self, x, y): def impl(self, x, y):
return x / y return x / y
def c_code(self, (x, y), (z, ), sub): def c_code(self, (x, y), (z, ), sub):
if 'int' in self.inputs[0].dtype and 'int' in self.inputs[1].dtype:
raise NotImplementedError("For integer arguments the behavior of division in C and in Python differ when the quotient is negative (to implement).")
return "%(z)s = %(x)s / %(y)s;" % locals() return "%(z)s = %(x)s / %(y)s;" % locals()
def grad(self, (x, y), (gz, )): def grad(self, (x, y), (gz, )):
return gz / y, -(gz * x) / (y * y) return gz / y, -(gz * x) / (y * y)
...@@ -346,13 +348,13 @@ class Abs(UnaryScalarOp): ...@@ -346,13 +348,13 @@ class Abs(UnaryScalarOp):
class Sgn(UnaryScalarOp): class Sgn(UnaryScalarOp):
def impl(self, x): def impl(self, x):
#casting to output type is handled by filter #casting to output type is handled by filter
return 1.0 if x >= 0 else -1.0 return numpy.sign(x)
def grad(self, (x, ), (gz, )): def grad(self, (x, ), (gz, )):
return None, return None,
def c_code(self, (x, ), (z, ), sub): def c_code(self, (x, ), (z, ), sub):
#casting is done by compiler #casting is done by compiler
#TODO: use copysign #TODO: use copysign
return "%(z)s = (%(x)s >= 0) ? 1.0 : -1.0;" % locals() return "%(z)s = (%(x)s >= 0) ? (%(x)s == 0) ? 0.0 : 1.0 : -1.0;" % locals()
class Inv(FloatUnaryScalarOp): class Inv(FloatUnaryScalarOp):
def impl(self, x): def impl(self, x):
...@@ -406,7 +408,7 @@ class Cos(FloatUnaryScalarOp): ...@@ -406,7 +408,7 @@ class Cos(FloatUnaryScalarOp):
def impl(self, x): def impl(self, x):
return math.cos(x) return math.cos(x)
def grad(self, (x, ), (gz, )): def grad(self, (x, ), (gz, )):
return gz * sin(x), return -gz * sin(x),
def c_code(self, (x, ), (z, ), sub): def c_code(self, (x, ), (z, ), sub):
return "%(z)s = cos(%(x)s);" % locals() return "%(z)s = cos(%(x)s);" % locals()
...@@ -414,7 +416,7 @@ class Sin(FloatUnaryScalarOp): ...@@ -414,7 +416,7 @@ class Sin(FloatUnaryScalarOp):
def impl(self, x): def impl(self, x):
return math.sin(x) return math.sin(x)
def grad(self, (x, ), (gz, )): def grad(self, (x, ), (gz, )):
return -gz * cos(x), return gz * cos(x),
def c_code(self, (x, ), (z, ), sub): def c_code(self, (x, ), (z, ), sub):
return "%(z)s = sin(%(x)s);" % locals() return "%(z)s = sin(%(x)s);" % locals()
...@@ -440,13 +442,13 @@ class Sinh(FloatUnaryScalarOp): ...@@ -440,13 +442,13 @@ class Sinh(FloatUnaryScalarOp):
def grad(self, (x, ), (gz, )): def grad(self, (x, ), (gz, )):
raise NotImplementedError() raise NotImplementedError()
def c_code(self, (x, ), (z, ), sub): def c_code(self, (x, ), (z, ), sub):
return "%(z)s = sin(%(x)s);" % locals() return "%(z)s = sinh(%(x)s);" % locals()
class Tanh(FloatUnaryScalarOp): class Tanh(FloatUnaryScalarOp):
def impl(self, x): def impl(self, x):
return math.tanh(x) return math.tanh(x)
def grad(self, (x, ), (gz, )): def grad(self, (x, ), (gz, )):
return gz * (1 - tanh(x))**2 return gz * (1 - tanh(x)**2),
def c_code(self, (x, ), (z, ), sub): def c_code(self, (x, ), (z, ), sub):
return "%(z)s = tanh(%(x)s);" % locals() return "%(z)s = tanh(%(x)s);" % locals()
......
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