leezu commented on a change in pull request #18545:
URL: https://github.com/apache/incubator-mxnet/pull/18545#discussion_r440470722
##########
File path: tests/python/unittest/test_numpy_op.py
##########
@@ -1477,6 +1477,165 @@ def index_add_bwd(out_grad, a_grad, ind, val_grad,
ind_ndim, ind_num, grad_req_a
assert_almost_equal(mx_out.asnumpy(), expected_ret, rtol=eps,
atol=eps)
+@with_seed()
+@use_np
+def test_npx_index_update():
+ class TestIndexUpdate(HybridBlock):
+ def __init__(self):
+ super(TestIndexUpdate, self).__init__()
+
+ def hybrid_forward(self, F, a, ind, val):
+ return F.npx.index_update(a, ind, val)
+
+ def check_index_update_forward(mx_ret, a, ind, val, ind_ndim, ind_num,
eps):
+ if val.dtype != a.dtype:
+ val = val.astype(a.dtype)
+ ind_arr = ind.transpose()
+ if ind_arr.ndim == 0:
+ ind_arr = _np.array([ind_arr])
+ for i in range(ind_arr.shape[0]):
+ t_ind = ind_arr[i]
+ t_ind = tuple(t_ind.tolist()) if type(t_ind) is _np.ndarray else
t_ind.tolist()
+ if val.ndim + ind_ndim > a.ndim:
+ t_val = val[tuple([0 if val.shape[0]==1 else i])]
+ if type(t_val) is _np.ndarray and t_val.shape[0] == 1:
+ expect_tmp = _np.squeeze(t_val, axis=0)
+ else:
+ expect_tmp = t_val
+ else:
+ expect_tmp = val
+ mx_tmp = mx_ret[t_ind]
+ if _np.allclose(expect_tmp, mx_tmp, rtol=eps, atol=eps):
+ mx_ret[t_ind] = 0
+ a[t_ind] = 0
+ assert_almost_equal(mx_ret, a, rtol=eps, atol=eps)
+
+ def index_update_bwd(out_grad, a_grad, ind, val_grad, ind_ndim, ind_num,
grad_req_a, grad_req_val):
+ if grad_req_a == 'add':
+ init_a_grad = _np.array(a_grad)
+ if grad_req_val == 'add':
+ init_val_grad = _np.array(val_grad)
+ a_grad = _np.zeros(a_grad.shape) + out_grad
+ a_grad = a_grad.astype(a_grad.dtype)
+ val_grad = _np.zeros(val_grad.shape).astype(val_grad.dtype)
+
+ ind_arr = ind.transpose()
+ if ind_arr.ndim == 0:
+ ind_arr = _np.array([ind_arr])
+ for i in range(ind_arr.shape[0]):
+ t_ind = ind_arr[i]
+ t_ind = tuple(ind_arr[i].tolist()) if type(ind_arr[i]) is
_np.ndarray else ind_arr[i].tolist()
+ a_grad[t_ind] = 0
+ if val_grad.ndim + ind_ndim > a_grad.ndim:
+ idx = 0 if val_grad.shape[0]==1 else i
+ t_grad = out_grad[t_ind]
+ t_grad_shape = _np.array(t_grad.shape)
+ val_grad_shape = _np.array(val_grad[idx].shape)
+ if type(val_grad[idx]) is not _np.ndarray:
+ t_grad = _np.sum(t_grad)
+ else:
+ is_not_equal = t_grad_shape - val_grad_shape
+ if _np.any(is_not_equal):
+ broadcast_dim = _np.nonzero(_np.where(is_not_equal, 1,
0))
+ t_grad = _np.sum(t_grad,
axis=tuple(broadcast_dim[0].reshape(1, -1)[0]), keepdims=True)
+ val_grad[idx] += t_grad
+ else:
+ t_grad = out_grad[t_ind]
+ if type(val_grad) is not _np.ndarray or val_grad.shape == ():
+ t_grad = _np.sum(t_grad)
+ else:
+ if type(t_grad) is _np.ndarray:
+ ext_dim = t_grad.ndim() - val_grad.ndim()
+ if ext_dim:
+ t_grad = _np.sum(t_grad,
axis=tuple(_np.arange(ext_dim)))
+ t_grad_shape = _np.array(t_grad.shape)
+ val_grad_shape = _np.array(val_grad.shape)
+ is_not_equal = t_grad_shape - val_grad_shape
+ if _np.any(is_not_equal):
+ broadcast_dim =
_np.nonzero(_np.where(is_not_equal, 1, 0))
+ t_grad = _np.sum(t_grad,
axis=tuple(broadcast_dim.reshape(1, -1)[0]), keepdims=True)
+ val_grad += t_grad
+ if grad_req_a == 'add':
+ a_grad += init_a_grad
+ if grad_req_val == 'add':
+ val_grad += init_val_grad
+ return a_grad, val_grad
+
+ # a.shape, ind.shape, val.shape, ind_ndim, ind_num
+ configs = [((2, ), np.array(1, dtype=_np.int32), (1, ), 1, 1)]
+ shape = tuple(_np.random.randint(1, 6, size=(4))) # a.shape
+ for ind_ndim in range(1, 5): # ind.shape: (ind_ndim, ind_num)
+ ind_num = _np.random.randint(1, 7)
+ ind = []
+ for ind_dim in range(ind_ndim):
+ ind.append(_np.random.randint(0, shape[ind_dim], size=(ind_num)))
+ ind = _np.array(ind).astype(_np.int32)
+ # case: val is scalar
+ configs.append(tuple([shape, ind, (), ind_ndim, ind_num]))
+ for val_ndim in range(1, 5 - ind_ndim):
+ val_shape = [1 if _np.random.randint(0, 5)==0 else ind_num]
+ for val_dim in range(ind_ndim, 4):
+ val_shape.append(1 if _np.random.randint(0, 5)==0 else
shape[val_dim])
+ # case: val is tensor
+ configs.append(tuple([shape, ind, tuple(val_shape), ind_ndim,
ind_num]))
+
+ dtypes = ['float32', 'float64', 'int32', 'int64']
+ grad_req = ['write', 'null', 'add']
+ for hybridize, grad_req_a, grad_req_val, dtype, indtype in \
+ itertools.product([True, False], grad_req, grad_req, dtypes, ['int32',
'int64']):
+ for a_shape, ind, val_shape ,ind_ndim, ind_num in configs:
+ eps = 1e-3
+ if sys.platform.startswith('linux'):
+ eps = 1e-2
Review comment:
eps should not depend on the platform. What's the root-cause here?
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