def test_hostfromgpu_shape_i():
# Test that the shape is lifted over hostfromgpu
m = mode_with_gpu.including(
"local_dot_to_dot22", "local_dot22_to_dot22scalar", "specialize"
)
a = tt.fmatrix("a")
ca = aesara.gpuarray.type.GpuArrayType("float32", (False, False))()
av = np.asarray(np.random.rand(5, 4), dtype="float32")
cv = gpuarray.asarray(
np.random.rand(5, 4), dtype="float32", context=get_context(test_ctx_name)
)
f = aesara.function([a], GpuFromHost(test_ctx_name)(a), mode=m)
assert any(isinstance(x.op, GpuFromHost) for x in f.maker.fgraph.toposort())
f = aesara.function([a], GpuFromHost(test_ctx_name)(a).shape, mode=m)
topo = f.maker.fgraph.toposort()
assert isinstance(topo[0].op, tt.opt.Shape_i)
assert isinstance(topo[1].op, tt.opt.Shape_i)
assert isinstance(topo[2].op, tt.opt.MakeVector)
assert tuple(f(av)) == (5, 4)
f = aesara.function([ca], host_from_gpu(ca), mode=m)
assert host_from_gpu in [x.op for x in f.maker.fgraph.toposort()]
f = aesara.function([ca], host_from_gpu(ca).shape, mode=m)
topo = f.maker.fgraph.toposort()
assert isinstance(topo[0].op, aesara.compile.Shape_i)
assert isinstance(topo[1].op, aesara.compile.Shape_i)
assert isinstance(topo[2].op, tt.opt.MakeVector)
assert tuple(f(cv)) == (5, 4)
def local_gpua_mrg_graph(fgraph, op, context_name, inputs, outputs):
if (type(op) == mrg_uniform and isinstance(inputs[0].type, GpuArrayType)
and (inputs[0].owner is None
or not isinstance(inputs[0].owner.op, GpuFromHost))):
outs = GPUA_mrg_uniform.new(inputs[0], op.output_type.ndim,
op.output_type.dtype, inputs[1])
return [outs[0], host_from_gpu(outs[1])]
def test_transfer_cpu_gpu():
a = tt.fmatrix("a")
g = GpuArrayType(dtype="float32", broadcastable=(False, False))("g")
av = np.asarray(rng.rand(5, 4), dtype="float32")
gv = gpuarray.array(av, context=get_context(test_ctx_name))
f = aesara.function([a], GpuFromHost(test_ctx_name)(a))
fv = f(av)
assert GpuArrayType.values_eq(fv, gv)
f = aesara.function([g], host_from_gpu(g))
fv = f(gv)
assert np.all(fv == av)
def test_transfer_strided():
# This is just to ensure that it works in aesara
# libgpuarray has a much more comprehensive suit of tests to
# ensure correctness
a = tt.fmatrix("a")
g = GpuArrayType(dtype="float32", broadcastable=(False, False))("g")
av = np.asarray(rng.rand(5, 8), dtype="float32")
gv = gpuarray.array(av, context=get_context(test_ctx_name))
av = av[:, ::2]
gv = gv[:, ::2]
f = aesara.function([a], GpuFromHost(test_ctx_name)(a))
fv = f(av)
assert GpuArrayType.values_eq(fv, gv)
f = aesara.function([g], host_from_gpu(g))
fv = f(gv)
assert np.all(fv == av)
def _as_tensor_operators(x, **kwargs):
from aesara.gpuarray.basic_ops import host_from_gpu
return host_from_gpu(x)