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pytensor
提交 bc361363 authored 作者: Olivier Breuleux's avatar Olivier Breuleux
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上级 a8cfe8f2
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正在显示 包含 208 行增加 和 63 行删除
compile.py
浏览文件 @ bc361363
......@@ -11,7 +11,7 @@ import core
def to_func(inputs, outputs):
print gof.Env(inputs, outputs)
# print gof.Env(inputs, outputs).io_toposort()
p = prog(inputs, outputs)
print p.env
def f(*args):
......@@ -42,7 +42,7 @@ class prog(gof.Prog):
inputs,
outputs,
opt.optimizer,
gof.link.thunk_linker,
gof.link.perform_linker,
[])
# core.pop_mode()
......
core.py
浏览文件 @ bc361363
......@@ -71,7 +71,7 @@ def wrap(x):
elif isinstance(x, Proxy):
return wrap(x._obj)
else:
return input(x)
return literal(x)
# elif isinstance(x, numpy.ndarray):
# return NumpyR(x)
# elif isinstance(x, (int, float)):
......@@ -82,8 +82,10 @@ def wrap(x):
def literal(x):
try:
present = x in gof.literals_db
hashable = True
except TypeError: # x is unhashable
present = False
hashable = False
if present:
return gof.literals_db.get(x)
......@@ -95,7 +97,10 @@ def literal(x):
raise TypeError("%s is already a result." % x)
else:
return PythonR(x, constant = True)
if hashable:
gof.literals_db[x] = ret
return ret
......@@ -103,9 +108,19 @@ def literal(x):
inplace = gof.Destroyer
view = gof.Viewer
def assert_same_shapes(impl):
def ret(x, *rest):
shape = x.shape
for other in rest:
if other.shape != shape:
raise TypeError("The dimensions of the inputs do not match.")
return impl(x, *rest)
return ret
class omega_op(gof.PythonOp):
broadcast_op = False
@staticmethod
def __clsinit__(cls, name, bases, dct):
# make grad a static method
......@@ -114,6 +129,10 @@ class omega_op(gof.PythonOp):
grad = grad.im_func
cls.grad = staticmethod(grad)
# adjust impl
if cls.broadcast_op:
cls.impl = assert_same_shapes(cls.impl)
# make impl a static method
gof.PythonOp.__clsinit__(cls, name, bases, dct)
......@@ -135,6 +154,15 @@ class omega_op(gof.PythonOp):
return UNDEFINED
def scalar_switch(x, y, normal_f, scalar_f):
x, y = wrap(x), wrap(y)
if x.constant and not x.data.shape:
return scalar_f(y, x)
if y.constant and not y.data.shape:
return scalar_f(x, y)
return normal_f(x, y)
class NumpyR(gof.PythonR):
def set_value(self, value):
......@@ -145,29 +173,25 @@ class NumpyR(gof.PythonR):
else:
self.data = numpy.array(value)
def __add__(self, y): return add(self, y)
def __radd__(self, x): return add(x, self)
def __iadd__(self, y): return iadd(self, y)
def __sub__(self, y): return sub(self, y)
def __rsub__(self, x): return sub(x, self)
def __isub__(self, y): return isub(self, y)
def __add__(self, y): return scalar_switch(self, y, add, add_scalar)
def __radd__(self, x): return scalar_switch(x, self, add, add_scalar)
def __iadd__(self, y): return scalar_switch(self, y, iadd, iadd_scalar)
def __mul__(self, y): return mul(self, y)
def __rmul__(self, x): return mul(x, self)
def __imul__(self, y): return imul(self, y)
def __sub__(self, y): return scalar_switch(self, y, sub, sub_scalar)
def __rsub__(self, x): return scalar_switch(x, self, sub, sub_scalar)
def __isub__(self, y): return scalar_switch(self, y, isub, isub_scalar)
def __div__(self, y): return div(self, y)
def __rdiv__(self, x): return div(x, self)
def __idiv__(self, y): return idiv(self, y)
def __mul__(self, y): return scalar_switch(self, y, mul, scale)
def __rmul__(self, x): return scalar_switch(x, self, mul, scale)
def __imul__(self, y): return scalar_switch(self, y, imul, iscale)
def __mod__(self, y): return mod(self, y)
def __rmod__(self, x): return mod(x, self)
def __imod__(self, y): return imod(self, y)
def __div__(self, y): return scalar_switch(self, y, div, inv_scale)
def __rdiv__(self, x): return scalar_switch(x, self, div, inv_scale)
def __idiv__(self, y): return scalar_switch(self, y, idiv, iinv_scale)
def __pow__(self, y): return pow(self, y)
def __rpow__(self, x): return pow(x, self)
def __ipow__(self, y): return ipow(self, y)
def __pow__(self, y): return scalar_switch(self, y, pow_elemwise, pow)
def __rpow__(self, x): return scalar_switch(x, self, pow_elemwise, pow)
def __ipow__(self, y): return scalar_switch(self, y, ipow_elemwise, ipow)
def __neg__(self): return neg(self)
......@@ -194,6 +218,7 @@ ones = wrap_producer(numpy.ones)
## Addition ##
class proto_add(omega_op):
broadcast_op = True
def grad(x, y, gz):
return gz
......@@ -203,6 +228,9 @@ class add(proto_add):
class iadd(proto_add, inplace):
impl = numpy.ndarray.__iadd__
class add_scalar(omega_op):
impl = numpy.ndarray.__add__
class proto_twice(omega_op):
def grad(x, gz):
......@@ -230,6 +258,12 @@ class sub(proto_sub):
class isub(proto_sub, inplace):
impl = numpy.ndarray.__isub__
class sub_scalar(omega_op):
impl = numpy.ndarray.__sub__
class isub_scalar(omega_op, inplace):
impl = numpy.ndarray.__isub__
## Element-wise multiplication ##
......@@ -252,7 +286,7 @@ class sqr(proto_sqr):
impl = lambda x: numpy.multiply(x, x)
class isqr(proto_sqr, inplace):
impl = lambda x: x.__imul__(x),
impl = lambda x: x.__imul__(x)
class proto_sqrt(omega_op):
......@@ -284,17 +318,23 @@ class div(proto_div):
class idiv(proto_div, inplace):
impl = numpy.ndarray.__idiv__
class inv_scale(omega_op):
impl = numpy.ndarray.__div__
class iinv_scale(omega_op, inplace):
impl = numpy.ndarray.__idiv__
## Scaling ##
class proto_scal(omega_op):
class proto_scale(omega_op):
def grad(x, a, gz):
return scal(a, gz), sum(mul(x, gz))
class scal(omega_op):
class scale(omega_op):
impl = numpy.ndarray.__mul__
class iscal(omega_op, inplace):
class iscale(omega_op, inplace):
impl = numpy.ndarray.__imul__
......@@ -335,6 +375,30 @@ class array_copy(omega_op):
grad = lambda x, gz: gz
## Power ##
class proto_pow(omega_op):
def grad(x, y, gz):
pass
class pow(proto_pow):
impl = numpy.ndarray.__pow__
class ipow(proto_pow, inplace):
impl = numpy.ndarray.__ipow__
class proto_pow_elemwise(omega_op):
def grad(x, y, gz):
pass
class pow_elemwise(proto_pow_elemwise):
impl = numpy.ndarray.__pow__
class ipow_elemwise(proto_pow_elemwise, inplace):
impl = numpy.ndarray.__ipow__
## Others ##
class minmax(omega_op):
......@@ -342,6 +406,14 @@ class minmax(omega_op):
def impl(x):
return x.min, x.max
class fill(omega_op):
impl = lambda model, value: (model * 0) + value
class sum(omega_op):
impl = numpy.sum
def grad(x, gz):
return fill(x, gz)
# array_copy = wrapper("copy",
# numpy.array,
......
grad.py
浏览文件 @ bc361363
......@@ -31,6 +31,7 @@ def expand_grad(i, o, cost_derivs):
def grad(cost, wrt, cost_grad = 1.0):
assert core.current_mode() != 'eval'
# cost, wrt = core.wrap(cost), core.wrap(wrt)
cost_derivs = expand_grad([wrt], [cost], {cost: core.wrap(cost_grad)})
# print wrt
......
opt.py
浏览文件 @ bc361363
......@@ -44,7 +44,7 @@ opts = [
['twice_to_itwice', op_sub(twice, itwice)],
['mulxx_to_twice', pattern_opt((mul, 'x', 'x'),
['mulxx_to_sqr', pattern_opt((mul, 'x', 'x'),
(sqr, 'x'))],
['sqr_to_isqr', op_sub(sqr, isqr)],
......@@ -65,3 +65,16 @@ export_opts(opts) # publish the optimizations performed under individual names
optimizer = gof.PythonOpt(gof.MergeOptMerge(gof.SeqOptimizer([opt for name, opt in opts])))
#optimizer = gof.PythonOpt(gof.SeqOptimizer([opt for name, opt in opts]))
#[isub(1.0, mul(0.1, iadd(transpose(dot(transpose(*2 -> sigmoid(dot(0.0, 1.0))), *4 -> mul(mul(neg(scal(mul(*3 -> sub(0.0, *1 -> sigmoid(dot(*2, transpose(1.0)))), fill(isqr(*3), 1.0)), 2.0)), *1), sub(1, *1)))), dot(transpose(0.0), mul(mul(dot(*4, 1.0), *2), sub(1, *2))))))]
#[isub(1.0, mul(0.1, iadd(dot(transpose(0.0), mul(mul(dot(*4 -> mul(mul(neg(scal(mul(*1 -> sub(0.0, *2 -> sigmoid(dot(*3 -> sigmoid(dot(0.0, 1.0)), transpose(1.0)))), fill(sqr(*1), 1.0)), 2.0)), *2), sub(1, *2)), 1.0), *3), sub(1, *3))), transpose(dot(transpose(*3), *4)))))]
#[isub(1.0, mul(0.1, iadd(dot(transpose(0.0), mul(mul(dot(*2 -> mul(mul(neg(scal(mul(*4 -> sub(0.0, *1 -> sigmoid(dot(*3 -> sigmoid(dot(0.0, 1.0)), transpose(1.0)))), fill(sqr(*4), 1.0)), 2.0)), *1), sub(1, *1)), 1.0), *3), sub(1, *3))), transpose(dot(transpose(*3), *2)))))]
# [ sqr(sub(0.0, sigmoid(dot(sigmoid(dot(0.0, 1.0)), transpose(1.0)))))]
# [isqr(sub(0.0, sigmoid(dot(sigmoid(dot(0.0, 1.0)), transpose(1.0)))))]
test.py
浏览文件 @ bc361363
......@@ -63,32 +63,32 @@ import grad
############################
#core.build_mode()
dim = core.wrap(())
dim2 = core.wrap((2, 2))
a = core.zeros(dim, dtype='int32') #(core.NumpyR(numpy.ones((3, 3))))
b = core.ones(dim2, 'int32') #(core.NumpyR(numpy.ones((3, 3))))
c = core.zeros(dim, dtype='int32')
# #core.build_mode()
# dim = core.wrap(())
# dim2 = core.wrap((2, 2))
# a = core.zeros(dim, dtype='int32') #(core.NumpyR(numpy.ones((3, 3))))
# b = core.ones(dim2, 'int32') #(core.NumpyR(numpy.ones((3, 3))))
# c = core.zeros(dim, dtype='int32')
d = a + (b + b) + c + numpy.ones(())
e = d + (b * c)
# d = a + (b + b) + c + numpy.ones(())
# e = d + (b * c)
#core.pop_mode()
# #core.pop_mode()
print e
# print e
#print e
#print gof.graph.ops([dim], [e])
#1/0
# #print e
# #print gof.graph.ops([dim], [e])
# #1/0
#print gof.Env([dim], [e])
# #print gof.Env([dim], [e])
#f = compile.to_func([dim], [e])
# #f = compile.to_func([dim], [e])
# f = compile.to_func([a, b, c], [e])
# # f = compile.to_func([a, b, c], [e])
# print f(1, 2, 3)
# #print f((2,2))
# # print f(1, 2, 3)
# # #print f((2,2))
############################
......@@ -129,29 +129,88 @@ print e
# core.print_graph(r)
############################
# def dataset_1hot(x, targ, n):
# """Return an looping iterator over 1-hot vectors
# This function is a generator for the integers range(n) that works by
# side-effect on the numpy ndarray mat.
# On each iteration, mat is set (in-place) to the next element of an infinite
# sequence of 1-hot vectors.
# """
# assert targ.size == 1
# for i in xrange(n):
# idx = i % x.shape[1]
# x[:] = 0
# x[0,idx] = 1
# targ[0] = idx
# yield i
# class sigmoid(core.omega_op):
# def impl(x):
# return 1.0 / (1.0 + numpy.exp(-x))
# def grad(x, gz):
# return gz * sigmoid(x) * (1 - sigmoid(x))
# x = core.zeros((1, 10))
# w = core.input(numpy.random.rand(10, 15))
# #print x.data, w.data
# def autoassociator(w, x):
# forward = sigmoid(core.dot(sigmoid(core.dot(x, w)), w.T))
# rec_error = core.sum(core.sqr(x - forward))
# w -= 0.1 * grad.grad(rec_error, w)
# return w, rec_error
# w2, rec_error = core.build(autoassociator, w, x)
# f = compile.to_func([w, x], [w2, rec_error])
# for i in dataset_1hot(x.data, numpy.ndarray((1, )), 10000):
# w2, rec_error = f(w.data, x.data)
# if not(i % 1000):
# print rec_error
class sigmoid(core.omega_op):
def impl(x):
return 1.0 / (1.0 + numpy.exp(-x))
def grad(x, gz):
return gz * sigmoid(x) * (1 - sigmoid(x))
# print "done!"
# print w.data
def autoassociator(w, x):
forward = sigmoid(core.dot(w.T, sigmoid(core.dot(w, x))))
w -= 0.01 * grad.grad(core.sqr(x - forward(x)), w)
w = core.input(numpy.random.rand())
x = core.input(numpy.random.rand())
# # 1 = mul(mul(neg(scal(mul(sub(0.736213102665, sigmoid(*3)), 1.0), 2.0)), sigmoid(*3)), sub(1, sigmoid(*3)))
# # 2 = transpose(0.11474051836)
# # 3 = dot(*2, *5)
# # 4 = dot(0.11474051836, 0.736213102665)
# # 5 = sigmoid(*4)
# # add(transpose(dot(*1, transpose(*5))), dot(mul(mul(dot(transpose(*2), *1), sigmoid(*4)), sub(1, sigmoid(*4))), transpose(0.736213102665)))
############################
# def fun():
# a = core.NumpyR(numpy.zeros(()) + 200)
# # b = numpy.ones(())
# # a = a * core.sqrt(core.isqr(a))
# a = a * core.isqr(a)
# return a
# f = core.build(fun)
# g = compile.to_func(gof.graph.inputs([f]), [f])
############################
for i in xrange(10000):
autoassociator(w, dataset.next())
print core.ones((2, 2)) + 1
print numpy.ones((2, 2)) ** numpy.ones((2, 2))
# 1 = mul(mul(neg(scal(mul(sub(0.736213102665, sigmoid(*3)), 1.0), 2.0)), sigmoid(*3)), sub(1, sigmoid(*3)))
# 2 = transpose(0.11474051836)
# 3 = dot(*2, *5)
# 4 = dot(0.11474051836, 0.736213102665)
# 5 = sigmoid(*4)
# add(transpose(dot(*1, transpose(*5))), dot(mul(mul(dot(transpose(*2), *1), sigmoid(*4)), sub(1, sigmoid(*4))), transpose(0.736213102665)))
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