def test_broadcast():
a = sym.Variable("a")
b = sym.Variable("b")
shape = {'a': (3, 4, 5), 'b': (1, 5)}
def _collapse(g):
return g.reshape(-1, shape['b'][-1]).sum(0, keepdims=True)
y = sym.broadcast_add(a, b)
def _backward_add(head_grads, a, b):
da = head_grads
db = _collapse(head_grads)
return da, db
check_function(y, lambda a, b: a + b, _backward_add, shape=shape)
y = sym.broadcast_sub(a, b)
def _backward_sub(head_grads, a, b):
da = head_grads
db = -_collapse(head_grads)
return da, db
check_function(y, lambda a, b: a - b, _backward_sub, shape=shape)
y = sym.broadcast_mul(a, b)
def _backward_mul(head_grads, a, b):
da = head_grads * b
db = _collapse(head_grads * a)
return da, db
check_function(y, lambda a, b: a * b, _backward_mul, shape=shape)
y = sym.broadcast_div(a, b)
def _backward_div(head_grads, a, b):
da = head_grads / b
db = _collapse(-head_grads * a / b**2)
return da, db
# We avoid computing numerical derivatives too close to zero here
check_function(y,
lambda a, b: a / b,
_backward_div,
shape=shape,
numerical_grads=False)
check_function(y,
lambda a, b: a / b,
_backward_div,
shape=shape,
in_range={'b': (0.1, 20)})
y = sym.broadcast_mod(a, b)
check_function(y,
lambda a, b: np.mod(a, b),
in_range={
'a': (0.001, 100),
'b': (1, 100)
},
dtype='int32',
shape=shape)
y = sym.broadcast_max(a, b)
check_function(y, lambda a, b: np.maximum(a, b), shape=shape)
y = sym.broadcast_min(a, b)
check_function(y, lambda a, b: np.minimum(a, b), shape=shape)
y = sym.broadcast_pow(a, b)
check_function(y,
lambda a, b: np.power(a, b),
in_range={
'a': (0.001, 100),
'b': (0.001, 2)
},
shape=shape)
y = sym.broadcast_left_shift(a, b)
check_function(y, lambda a, b: a << b, dtype='int32', shape=shape)
y = sym.broadcast_right_shift(a, b)
check_function(y, lambda a, b: a >> b, dtype='int32', shape=shape)
y = sym.broadcast_greater(a, b)
check_function(y, lambda a, b: np.greater(a, b), shape=shape)
y = sym.broadcast_less(a, b)
check_function(y, lambda a, b: np.less(a, b), shape=shape)
y = sym.broadcast_equal(a, b)
check_function(y,
lambda a, b: np.equal(a, b),
in_range={
'a': (-2, 2),
'b': (-2, 2)
},
dtype='int32',
shape=shape)
y = sym.broadcast_not_equal(a, b)
check_function(y,
lambda a, b: np.not_equal(a, b),
in_range={
'a': (-2, 2),
'b': (-2, 2)
},
dtype='int32',
shape=shape)
y = sym.broadcast_greater_equal(a, b)
check_function(y,
lambda a, b: np.greater_equal(a, b),
in_range={
'a': (-3, 3),
'b': (-3, 3)
},
dtype='int32',
shape=shape)
y = sym.broadcast_less_equal(a, b)
check_function(y,
lambda a, b: np.less_equal(a, b),
in_range={
'a': (-3, 3),
'b': (-3, 3)
},
dtype='int32',
shape=shape)
def nnvm_bool_not(c, arg):
"""Implementation of boolean not."""
t = arg.abstract.dtype()
zero = c.make_constant(0, nnvm_type=type_to_np_dtype(t))
return sym.broadcast_equal(zero, c.ref(arg))
def nnvm_bool_not(c, arg):
"""Implementation of boolean not."""
zero = c.make_constant(0, nnvm_type=nnvm_type_map(arg.type))
return sym.broadcast_equal(zero, c.ref(arg))
def test_broadcast():
a = sym.Variable("a")
b = sym.Variable("b")
inputs = [('a', (3, 4, 5), a),
('b', (1, 5), b)]
dtype = "float32"
def _collapse(g):
return g.reshape(-1, inputs[-1][1][-1]).sum(0, keepdims=True)
y = sym.broadcast_add(a, b)
def _backward_add(head_grads, a, b):
da = head_grads
db = _collapse(head_grads)
return da, db
helper(y, inputs, dtype, lambda a, b: a + b, _backward_add)
y = sym.broadcast_sub(a, b)
def _backward_sub(head_grads, a, b):
da = head_grads
db = -_collapse(head_grads)
return da, db
helper(y, inputs, dtype, lambda a, b: a - b, _backward_sub)
y = sym.broadcast_mul(a, b)
def _backward_mul(head_grads, a, b):
da = head_grads * b
db = _collapse(head_grads * a)
return da, db
helper(y, inputs, dtype, lambda a, b: a * b, _backward_mul)
y = sym.broadcast_div(a, b)
def _backward_div(head_grads, a, b):
da = head_grads / b
db = _collapse(head_grads * a / (2 * b**2))
return da, db
helper(y, inputs, dtype, lambda a, b: a / b, _backward_div)
y = sym.broadcast_mod(a, b)
helper(y, inputs, 'int32',
lambda a, b: np.mod(a, b),
in_range={'a': (0.001, 100), 'b': (1, 100)})
y = sym.broadcast_max(a, b)
helper(y, inputs, dtype, lambda a, b: np.maximum(a, b))
y = sym.broadcast_min(a, b)
helper(y, inputs, dtype, lambda a, b: np.minimum(a, b))
y = sym.broadcast_pow(a, b)
helper(y, inputs, dtype,
lambda a, b: np.power(a, b),
in_range={'a': (0.001, 100), 'b': (0.001, 2)})
y = sym.broadcast_left_shift(a, b)
helper(y, inputs, 'int32', lambda a, b: a << b)
y = sym.broadcast_right_shift(a, b)
helper(y, inputs, 'int32', lambda a, b: a >> b)
y = sym.broadcast_greater(a, b)
helper(y, inputs, dtype, lambda a, b: np.greater(a, b))
y = sym.broadcast_less(a, b)
helper(y, inputs, dtype, lambda a, b: np.less(a, b))
y = sym.broadcast_equal(a, b)
helper(y, inputs, 'int32', lambda a, b: np.equal(a, b),
in_range={'a': (-2, 2), 'b': (-2, 2)})
y = sym.broadcast_not_equal(a, b)
helper(y, inputs, 'int32', lambda a, b: np.not_equal(a, b),
in_range={'a': (-2, 2), 'b': (-2, 2)})
y = sym.broadcast_greater_equal(a, b)
helper(y, inputs, 'int32', lambda a, b: np.greater_equal(a, b),
in_range={'a': (-3, 3), 'b': (-3, 3)})
y = sym.broadcast_less_equal(a, b)
helper(y, inputs, 'int32', lambda a, b: np.less_equal(a, b),
in_range={'a': (-3, 3), 'b': (-3, 3)})
def test_broadcast():
a = sym.Variable("a")
b = sym.Variable("b")
shape = {'a': (3, 4, 5), 'b': (1, 5)}
def _collapse(g):
return g.reshape(-1, shape['b'][-1]).sum(0, keepdims=True)
y = sym.broadcast_add(a, b)
def _backward_add(head_grads, a, b):
da = head_grads
db = _collapse(head_grads)
return da, db
check_function(y, lambda a, b: a + b, _backward_add, shape=shape)
y = sym.broadcast_sub(a, b)
def _backward_sub(head_grads, a, b):
da = head_grads
db = -_collapse(head_grads)
return da, db
check_function(y, lambda a, b: a - b, _backward_sub, shape=shape)
y = sym.broadcast_mul(a, b)
def _backward_mul(head_grads, a, b):
da = head_grads * b
db = _collapse(head_grads * a)
return da, db
check_function(y, lambda a, b: a * b, _backward_mul, shape=shape)
y = sym.broadcast_div(a, b)
def _backward_div(head_grads, a, b):
da = head_grads / b
db = _collapse(- head_grads * a / b**2)
return da, db
# We avoid computing numerical derivatives too close to zero here
check_function(y, lambda a, b: a / b, _backward_div, shape=shape, numerical_grads=False)
check_function(y, lambda a, b: a / b, _backward_div, shape=shape,
in_range={'b': (0.1, 20)})
y = sym.broadcast_mod(a, b)
check_function(y,
lambda a, b: np.mod(a, b),
in_range={'a': (0.001, 100), 'b': (1, 100)}, dtype='int32', shape=shape)
y = sym.broadcast_max(a, b)
check_function(y, lambda a, b: np.maximum(a, b), shape=shape)
y = sym.broadcast_min(a, b)
check_function(y, lambda a, b: np.minimum(a, b), shape=shape)
y = sym.broadcast_pow(a, b)
check_function(y,
lambda a, b: np.power(a, b),
in_range={'a': (0.001, 100), 'b': (0.001, 2)}, shape=shape)
y = sym.broadcast_left_shift(a, b)
check_function(y, lambda a, b: a << b, dtype='int32', shape=shape)
y = sym.broadcast_right_shift(a, b)
check_function(y, lambda a, b: a >> b, dtype='int32', shape=shape)
y = sym.broadcast_greater(a, b)
check_function(y, lambda a, b: np.greater(a, b), shape=shape)
y = sym.broadcast_less(a, b)
check_function(y, lambda a, b: np.less(a, b), shape=shape)
y = sym.broadcast_equal(a, b)
check_function(y, lambda a, b: np.equal(a, b),
in_range={'a': (-2, 2), 'b': (-2, 2)}, dtype='int32', shape=shape)
y = sym.broadcast_not_equal(a, b)
check_function(y, lambda a, b: np.not_equal(a, b),
in_range={'a': (-2, 2), 'b': (-2, 2)}, dtype='int32', shape=shape)
y = sym.broadcast_greater_equal(a, b)
check_function(y, lambda a, b: np.greater_equal(a, b),
in_range={'a': (-3, 3), 'b': (-3, 3)}, dtype='int32', shape=shape)
y = sym.broadcast_less_equal(a, b)
check_function(y, lambda a, b: np.less_equal(a, b),
in_range={'a': (-3, 3), 'b': (-3, 3)}, dtype='int32', shape=shape)