def test_values_eq_approx():
a = rand_gpuarray(20, dtype="float32")
assert GpuArrayType.values_eq_approx(a, a)
b = a.copy()
b[0] = np.asarray(b[0]) + 1.0
assert not GpuArrayType.values_eq_approx(a, b)
b = a.copy()
b[0] = -np.asarray(b[0])
assert not GpuArrayType.values_eq_approx(a, b)
def test_rebroadcast():
for dtype in ["float16", "float32"]:
a = rand_gpuarray(1, dtype=dtype)
g = GpuArrayType(dtype=dtype, broadcastable=(False, ))("g")
f = theano.function([g], Rebroadcast((0, True))(g))
assert isinstance(f.maker.fgraph.toposort()[0].op, Rebroadcast)
res = f(a)
assert GpuArrayType.values_eq(res, a)
def test_deep_copy():
for dtype in ["float16", "float32"]:
a = rand_gpuarray(20, dtype=dtype)
g = GpuArrayType(dtype=dtype, broadcastable=(False, ))("g")
f = theano.function([g], g)
assert isinstance(f.maker.fgraph.toposort()[0].op, DeepCopyOp)
res = f(a)
assert GpuArrayType.values_eq(res, a)
def test_view():
for dtype in ["float16", "float32"]:
a = rand_gpuarray(20, dtype=dtype)
g = GpuArrayType(dtype=dtype, broadcastable=(False, ))("g")
m = theano.compile.get_default_mode().excluding("local_view_op")
f = theano.function([g], ViewOp()(g), mode=m)
assert isinstance(f.maker.fgraph.toposort()[0].op, ViewOp)
res = f(a)
assert GpuArrayType.values_eq(res, a)
def rand_cval(self, shp):
return rand_gpuarray(*shp, **dict(cls=gpuarray))
def get_gpu_value(self):
return rand_gpuarray(*self.shape)
def test_specify_shape():
for dtype in ["float16", "float32"]:
a = rand_gpuarray(20, dtype=dtype)
g = GpuArrayType(dtype=dtype, broadcastable=(False, ))("g")
f = theano.function([g], theano.tensor.specify_shape(g, [20]))
f(a)
def test_specify_shape():
for dtype in ("float16", "float32"):
a = rand_gpuarray(20, dtype=dtype)
g = GpuArrayType(dtype=dtype, broadcastable=(False, ))("g")
f = aesara.function([g], specify_shape(g, [20]))
f(a)
def rand_cval(self, shp):
return rand_gpuarray(*shp, cls=gpuarray)
