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Fix rtrue_divide grad #16769

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Nov 10, 2019
Merged

Fix rtrue_divide grad #16769

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18ce563
Fix rtrue_divide_scalar
reminisce Nov 10, 2019
7ecf820
More tests
reminisce Nov 10, 2019
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2 changes: 1 addition & 1 deletion src/operator/numpy/np_true_divide.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -126,7 +126,7 @@ NNVM_REGISTER_OP(_npi_rtrue_divide_scalar)
})
#endif
.set_attr<FCompute>("FCompute<cpu>", TrueDivideScalarCompute<cpu, mshadow_op::rtrue_divide>)
.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_rdiv_scalar"})
.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseIn{"_backward_rdiv_scalar"})
.add_argument("data", "NDArray-or-Symbol", "source input")
.add_argument("scalar", "float", "scalar input");

Expand Down
107 changes: 95 additions & 12 deletions tests/python/unittest/test_numpy_ndarray.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,7 @@
from mxnet.gluon import HybridBlock
from mxnet.test_utils import same, assert_almost_equal, rand_shape_nd, rand_ndarray, retry, use_np
from common import with_seed, TemporaryDirectory
from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf, assert_exception, is_op_runnable
from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf, assert_exception, is_op_runnable, collapse_sum_like
from mxnet.ndarray.ndarray import py_slice
from mxnet.base import integer_types
import scipy.stats as ss
Expand Down Expand Up @@ -281,6 +281,62 @@ def test_np_ndarray_binary_element_wise_ops():
'<=': _np.less_equal
})

def _get_grad_func(op, scalar=None, reverse=False):
if op == '+':
if scalar is None:
return lambda ograd, x1, x2, out: (collapse_sum_like(ograd, x1.shape),
collapse_sum_like(ograd, x2.shape))
elif not reverse:
return lambda ograd, x1, x2, out: ograd
else:
return lambda ograd, x1, x2, out: ograd
elif op == '-':
if scalar is None:
return lambda ograd, x1, x2, out: (collapse_sum_like(ograd, x1.shape),
-collapse_sum_like(ograd, x2.shape))
elif not reverse:
return lambda ograd, x1, x2, out: ograd
else:
return lambda ograd, x1, x2, out: -ograd
elif op == '*':
if scalar is None:
return lambda ograd, x1, x2, out: (collapse_sum_like(ograd * x2, x1.shape),
collapse_sum_like(ograd * x1, x2.shape))
elif not reverse:
return lambda ograd, x1, x2, out: ograd * x2
else:
return lambda ograd, x1, x2, out: ograd * x1
elif op == '/':
if scalar is None:
return lambda ograd, x1, x2, out: (collapse_sum_like(ograd / x2, x1.shape),
collapse_sum_like(-x1 * ograd / (x2 * x2), x2.shape))
elif not reverse:
return lambda ograd, x1, x2, out: ograd / x2
else:
return lambda ograd, x1, x2, out: -x1 * ograd / (x2 * x2)
elif op == 'mod':
if scalar is None:
return lambda ograd, x1, x2, out: (collapse_sum_like(ograd, x1.shape),
collapse_sum_like(-ograd * _np.floor(x1 / x2), x2.shape))
elif not reverse:
return lambda ograd, x1, x2, out: ograd
else:
return lambda ograd, x1, x2, out: -ograd * _np.floor(x1 / x2)
elif op == 'pow':
if scalar is None:
return lambda ograd, x1, x2, out: (collapse_sum_like(ograd * x2 * _np.power(x1, x2 - 1), x1.shape),
collapse_sum_like(ograd * out * _np.log(x1), x2.shape))
elif not reverse:
return lambda ograd, x1, x2, out: ograd * x2 * _np.power(x1, x2 - 1)
else:
return lambda ograd, x1, x2, out: ograd * out * _np.log(x1)
elif op in ('==', '!=', '<', '<=', '>', '>='):
if scalar is None:
return lambda ograd, x1, x2, out: (_np.zeros_like(x1), _np.zeros_like(x2))
else:
return lambda ograd, x1, x2, out: _np.zeros_like(ograd)
return None

def get_np_ret(x1, x2, op):
return np_op_map[op](x1, x2)

Expand Down Expand Up @@ -364,13 +420,15 @@ def check_binary_op_result(shape1, shape2, op, dtype=None):
mx_input1 = abs(_np.random.uniform()) + 1
np_input1 = mx_input1
else:
mx_input1 = rand_ndarray(shape1, dtype=dtype).abs() + 1
mx_input1 = (rand_ndarray(shape1, dtype=dtype).abs() + 1).as_np_ndarray()
mx_input1.attach_grad()
np_input1 = mx_input1.asnumpy()
if shape2 is None:
mx_input2 = abs(_np.random.uniform()) + 1
np_input2 = mx_input2
else:
mx_input2 = rand_ndarray(shape2, dtype=dtype).abs() + 1
mx_input2 = (rand_ndarray(shape2, dtype=dtype).abs() + 1).as_np_ndarray()
mx_input2.attach_grad()
np_input2 = mx_input2.asnumpy()

scalar = None
Expand All @@ -382,34 +440,59 @@ def check_binary_op_result(shape1, shape2, op, dtype=None):
scalar = mx_input1
reverse = True

grad_func = _get_grad_func(op, scalar, reverse)
np_out = get_np_ret(np_input1, np_input2, op)
ograd = _np.ones_like(np_out)
for hybridize in [True, False]:
if scalar is None:
get_mx_ret_np = TestBinaryElementWiseOp(op)
get_mx_ret_classic = TestBinaryElementWiseOp(op)
if hybridize:
get_mx_ret_np.hybridize()
get_mx_ret_classic.hybridize()
mx_out = get_mx_ret_np(mx_input1.as_np_ndarray(), mx_input2.as_np_ndarray())
if grad_func is None:
mx_out = get_mx_ret_np(mx_input1, mx_input2)
else:
with mx.autograd.record():
mx_out = get_mx_ret_np(mx_input1, mx_input2)
mx_out.backward()
assert type(mx_out) == np.ndarray
assert np_out.shape == mx_out.shape
if op in logic_ops:
assert np_out.dtype == mx_out.dtype
assert_almost_equal(mx_out.asnumpy(), np_out, atol=1e-6, rtol=1e-5)
assert_almost_equal(mx_out.asnumpy(), np_out, atol=1e-6, rtol=1e-5, use_broadcast=False)

if grad_func is not None:
x1_grad_expected, x2_grad_expected = grad_func(ograd, np_input1, np_input2, np_out)
assert_almost_equal(mx_input1.grad.asnumpy(), x1_grad_expected, atol=1e-5, rtol=1e-3,
use_broadcast=False)
assert_almost_equal(mx_input2.grad.asnumpy(), x2_grad_expected, atol=1e-5, rtol=1e-3,
use_broadcast=False)
else:
get_mx_ret = TestBinaryElementWiseOp(op, scalar=scalar, reverse=reverse)
if hybridize:
get_mx_ret.hybridize()
if reverse:
mx_out = get_mx_ret(mx_input2.as_np_ndarray())
assert type(mx_out) == np.ndarray
mx_input = mx_input2
else:
mx_out = get_mx_ret(mx_input1.as_np_ndarray())
assert type(mx_out) == np.ndarray
assert np_out.shape == mx_out.shape
mx_input = mx_input1

if grad_func is None:
mx_out = get_mx_ret(mx_input)
else:
with mx.autograd.record():
mx_out = get_mx_ret(mx_input)
mx_out.backward()
assert type(mx_out) == np.ndarray

if op in logic_ops:
assert np_out.dtype == mx_out.dtype
assert_almost_equal(mx_out.asnumpy(), np_out, atol=1e-6, rtol=1e-5)
assert_almost_equal(mx_out.asnumpy(), np_out, atol=1e-6, rtol=1e-5, use_broadcast=False)

# check grad
if grad_func is not None:
x_grad_expected = grad_func(ograd, np_input1, np_input2, np_out)
assert_almost_equal(mx_input.grad.asnumpy(), x_grad_expected, atol=1e-5, rtol=1e-3,
use_broadcast=False)

dtypes = [_np.float32, _np.float64, None]
ops = np_op_map.keys()
Expand Down
7 changes: 3 additions & 4 deletions tests/python/unittest/test_numpy_op.py
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Expand Up @@ -1572,8 +1572,8 @@ def hybrid_forward(self, F, a, b, *args, **kwargs):
rtol=1e-1, atol=1e-2, equal_nan=True, use_broadcast=False)
if rgrads is None:
assert_almost_equal(mx_test_x2.grad.asnumpy(),
collapse_sum_like(rgrad(y.asnumpy(), np_test_x2, np_test_x1), mx_test_x2.shape),
rtol=1e-1, atol=1e-2, equal_nan=True, use_broadcast=False)
collapse_sum_like(rgrad(y.asnumpy(), np_test_x2, np_test_x1), mx_test_x2.shape),
rtol=1e-1, atol=1e-2, equal_nan=True, use_broadcast=False)
else:
assert_almost_equal(mx_test_x2.grad.asnumpy(),
collapse_sum_like(rgrad(y.asnumpy(), np_test_x1, np_test_x2), mx_test_x2.shape),
Expand All @@ -1594,7 +1594,6 @@ def hybrid_forward(self, F, a, b, *args, **kwargs):
assertRaises(NotImplementedError, getattr(np, func), mx_test_x1, mx_test_x2, order='C')
assertRaises(NotImplementedError, getattr(np, func), mx_test_x1, mx_test_x2, order='mxnet')


funcs = {
'add': (-1.0, 1.0, [lambda y, x1, x2: _np.ones(y.shape)], None),
'subtract':
Expand All @@ -1603,7 +1602,7 @@ def hybrid_forward(self, F, a, b, *args, **kwargs):
'multiply': (-1.0, 1.0, [lambda y, x1, x2: _np.broadcast_to(x2, y.shape)],
[lambda y, x1, x2: _np.broadcast_to(x1, y.shape)]),
'divide': (0.1, 1.0, [lambda y, x1, x2: _np.ones(y.shape) / x2],
[lambda y, x1, x2: -x1 / (x2 * x2)]),
[lambda y, x1, x2: -x1 / (x2 * x2)]),
'mod': (1.0, 10.0,
[lambda y, x1, x2: _np.ones(y.shape),
lambda y, x1, x2: _np.zeros(y.shape)],
Expand Down