def test_all(self):
var = xdl.Variable(name="w",
dtype=DataType.float,
shape=[4, 2],
initializer=xdl.Zeros())
execute(xdl.variable_registers())
execute(xdl.global_initializers())
op = xdl.ps_sparse_assign_op(var_name="w",
var_type="index",
ids=np.array([1, 2], dtype=np.int32),
values=np.array([[1, 2], [3, 4]],
dtype=np.float32))
execute(op)
ret = execute(var.value)
self.assertTrue((ret == np.array([[0, 0], [1, 2], [3, 4], [0, 0]],
dtype=np.float32)).all())
def test_all(self):
var = xdl.Variable(name="w", dtype=DataType.float, shape=[4,1], initializer=xdl.Ones())
execute(xdl.variable_registers())
execute(xdl.global_initializers())
op = xdl.ps_sparse_apply_adagrad_op(
learning_rate=np.array(0.5, dtype=np.float),
initial_accumulator_value=np.array(0.0, dtype=np.float),
grad=np.array([[1],[2]], dtype=np.float32),
indices=np.array([1,2], dtype=np.int32),
var_name="w",
var_type="index")
execute(op)
ret = execute(var.value)
self.assertTrue((ret == np.array([[1],[0.5],[0.5],[1]], dtype=np.float32)).all())
execute(op)
ret = execute(var.value)
self.assertTrue((ret == np.array([[1],[0.14644662],[0.14644662],[1]], dtype=np.float32)).all())
def test_all(self):
var = xdl.Variable(name="w", dtype=xdl.DT_FLOAT, shape=[4], initializer=xdl.Ones())
execute(xdl.variable_registers())
execute(xdl.global_initializers())
op = xdl.ps_dense_apply_adagrad_op(
learning_rate=np.array(0.5, dtype=np.float),
initial_accumulator_value=np.array(0.0, dtype=np.float),
grad=np.array([1,2,3,4], dtype=np.float32),
var_name="w",
var_type="index")
execute(op)
ret = execute(var.value)
self.assertTrue((ret == np.array([0.5,0.5,0.5,0.5])).all())
execute(op)
ret = execute(var.value)
print(ret)
self.assertTrue((ret == np.array([0.14644662,0.14644662,0.14644662,0.14644662], dtype=np.float32)).all())
def test_all(self):
var = xdl.Variable(name="w", dtype=DataType.float, shape=[4,1], initializer=xdl.Ones())
execute(xdl.variable_registers())
execute(xdl.global_initializers())
op = xdl.ps_sparse_apply_momentum_op(
learning_rate=np.array(0.5, dtype=np.float),
momentum=np.array(0.9, dtype=np.float),
grad=np.array([[1],[2]], dtype=np.float32),
indices=np.array([1,2], dtype=np.int32),
var_name="w",
var_type="index",
use_nesterov=False)
execute(op)
ret = execute(var.value)
self.assertTrue((ret == np.array([[1],[0.5],[0],[1]], dtype=np.float32)).all())
execute(op)
ret = execute(var.value)
self.assertTrue((ret == np.array([[1],[-0.45],[-1.9],[1]], dtype=np.float32)).all())
def test_all(self):
var = xdl.Variable(name="w", dtype=DataType.float, shape=[4], initializer=xdl.Ones())
execute(xdl.variable_registers())
execute(xdl.global_initializers())
op = xdl.ps_dense_apply_ftrl_op(
learning_rate=np.array(0.1, dtype=np.float),
learning_rate_power=np.array(-0.5, dtype=np.float),
initial_accumulator_value=np.array(0.1, dtype=np.float),
l1_reg=np.array(0, dtype=np.float),
l2_reg=np.array(0, dtype=np.float),
grad=np.array([1,2,3,4], dtype=np.float32),
var_name="w",
var_type="index")
execute(op)
ret = execute(var.value)
self.assertTrue((ret == np.array([0.6031424,0.7450533,0.7957225,0.8215], dtype=np.float32)).all())
execute(op)
ret = execute(var.value)
self.assertTrue((ret == np.array([0.5341358,0.6747804,0.7252074,0.75089955], dtype=np.float32)).all())
def test_all(self):
var = xdl.Variable(name="w", dtype=DataType.float, shape=[4,1], initializer=xdl.Ones())
execute(xdl.variable_registers())
execute(xdl.global_initializers())
op = xdl.ps_sparse_apply_adam_op(
beta1=np.array(0.9, dtype=np.float),
beta2=np.array(0.999, dtype=np.float),
epsilon=np.array(1e-08, dtype=np.float),
learning_rate=np.array(0.1, dtype=np.float),
grad=np.array([[1],[2]], dtype=np.float32),
indices=np.array([1,2], dtype=np.int32),
lr_decay=True,
var_name="w",
var_type="index")
execute(op)
ret = execute(var.value)
self.assertTrue((ret == np.array([[1],[0.90000004],[0.90000004],[1]], dtype=np.float32)).all())
execute(op)
ret = execute(var.value)
self.assertTrue((ret == np.array([[1],[0.8000001],[0.8000001],[1]], dtype=np.float32)).all())
def test_all(self):
var = xdl.Variable(name="w",
dtype=DataType.int32,
shape=[4],
initializer=xdl.Zeros())
execute(xdl.variable_registers())
execute(xdl.global_initializers())
save_op = xdl.ps_save_op(ckpt_version=np.array(123, dtype=np.int8))
execute(save_op)
add_op = xdl.ps_assign_add_op(var_name="w",
var_type="index",
delta=np.array([1, 2, 3, 4],
dtype=np.int32))
execute(add_op)
ret = execute(var.value)
self.assertTrue((ret == np.array([1, 2, 3, 4])).all())
restore_op = xdl.ps_restore_op(
ckpt_version=np.array(123, dtype=np.int8))
execute(restore_op)
ret = execute(var.value)
self.assertTrue((ret == np.array([0, 0, 0, 0])).all())
def test_all(self):
var = xdl.Variable(name="w",
dtype=DataType.float,
shape=[4],
initializer=xdl.Ones())
execute(xdl.variable_registers())
execute(xdl.global_initializers())
op = xdl.ps_dense_apply_momentum_op(learning_rate=np.array(
0.5, dtype=np.float),
momentum=np.array(0.9,
dtype=np.float),
grad=np.array([1, 2, 3, 4],
dtype=np.float32),
var_name="w",
var_type="index",
use_nesterov=False)
execute(op)
ret = execute(var.value)
self.assertTrue((ret == np.array([0.5, 0, -0.5, -1],
dtype=np.float32)).all())
execute(op)
ret = execute(var.value)
self.assertTrue((ret == np.array([-0.45, -1.9, -3.35, -4.8],
dtype=np.float32)).all())