def test_gpu(self):
with xdl.device("GPU"):
grps = np.array([],dtype=np.int32)
ksum_grad = xdl.ksum_grad(embeds, idx, values, segs, grps, grads)
ksum_grad = xdl.execute(ksum_grad)
res = np.array([[0.4],[0.3],[0.6],[0.2],[0.3],[0.3]],dtype=np.float)
self.assertTrue(np.allclose(ksum_grad, res))
def test_merged_gpu(self):
with xdl.device("GPU"):
ksum_grad = xdl.ksum_grad(embeds_shape, idx, values, segs, grps,
sidx_nogrp, sseg, merged_grads)
ksum_grad = xdl.execute(ksum_grad)
res = np.array([[7], [5], [12], [3], [5], [6]], dtype=np.float)
self.assertTrue(np.allclose(ksum_grad, res))
def test_merged_cpu_with_values(self):
ksum_grad = xdl.ksum_grad(embeds_shape, idx, values, segs, grps, sidx,
sseg, merged_grads)
ksum_grad = xdl.execute(ksum_grad)
res = np.array([[7], [5], [12], [3], [5], [6]], dtype=np.float)
self.assertTrue(np.allclose(ksum_grad, res))
ksum_grad = xdl.ksum_grad(embeds_shape,
idx,
values,
segs,
grps,
sidx,
sseg,
merged_grads,
average=True)
ksum_grad = xdl.execute(ksum_grad)
res = np.array([[2.5], [2.5], [6], [3], [5], [2]], dtype=np.float)
self.assertTrue(np.allclose(ksum_grad, res))
def test_cpu_with_values(self):
grps = np.array([], dtype=np.int32)
ksum_grad = xdl.ksum_grad(embeds_shape, idx, values, segs, grps,
sidx_nogrp, sseg, grads)
ksum_grad = xdl.execute(ksum_grad)
res = np.array([[4], [3], [6], [2], [3], [3]], dtype=np.float)
self.assertTrue(np.allclose(ksum_grad, res))
ksum_grad = xdl.ksum_grad(embeds_shape,
idx,
values,
segs,
grps,
sidx_nogrp,
sseg,
grads,
average=True)
ksum_grad = xdl.execute(ksum_grad)
res = np.array(
[[1.0833333], [1], [1.75], [0.66666666], [0.75], [0.75]],
dtype=np.float)
self.assertTrue(np.allclose(ksum_grad, res))
def KSumGrad(op, grad):
with xdl.device('CPU'):
shape = xdl.shape_op(op.inputs[0])
return [
xdl.ksum_grad(shape,
op.inputs[1],
op.inputs[2],
op.inputs[3],
op.inputs[4],
op.inputs[5],
op.inputs[6],
grad[0],
average=op.attrs['average'])
]
def test_merged_gpu(self):
with xdl.device("GPU"):
ksum_grad = xdl.ksum_grad(embeds, idx, values, segs, grps, merged_grads)
ksum_grad = xdl.execute(ksum_grad)
res = np.array([[0.7],[0.5],[1.2],[0.3],[0.5],[0.6]],dtype=np.float)
self.assertTrue(np.allclose(ksum_grad, res))
