提交 b7b95ee2 authored 作者: Frederic's avatar Frederic

small fix following code review.

上级 be9826d9
...@@ -53,8 +53,8 @@ New Features ...@@ -53,8 +53,8 @@ New Features
(Frederic B., Simon McGregor) (Frederic B., Simon McGregor)
* MRG random now raises an error with a clear message when the passed shape * MRG random now raises an error with a clear message when the passed shape
contains dimensions with bad value like 0. (Frédéric B. reported by Ian G.) contains dimensions with bad value like 0. (Frédéric B. reported by Ian G.)
* "CudaNdarra[*] = ndarray" work in more case (Frederic B.) * "CudaNdarray[*] = ndarray" work in more case (Frederic B.)
* "CudaNdarra[*] += ndarray" work in more case (Frederic B.) * "CudaNdarray[*] += ndarray" work in more case (Frederic B.)
* We add dimensions to CudaNdarray to automatically broadcast more frequently. * We add dimensions to CudaNdarray to automatically broadcast more frequently.
(Frederic B.) (Frederic B.)
......
...@@ -1028,7 +1028,7 @@ def _get_preallocated_maps(node, thunk, prealloc_modes, def_val, ...@@ -1028,7 +1028,7 @@ def _get_preallocated_maps(node, thunk, prealloc_modes, def_val,
# Build a C-contiguous buffer # Build a C-contiguous buffer
new_buf = r.type.value_zeros(r_vals[r].shape) new_buf = r.type.value_zeros(r_vals[r].shape)
# CudaNdarray don't have flags field # CudaNdarray don't have flags field
# assert new_buf.flags["C_CONTIGUOUS"] # assert new_buf.flags["C_CONTIGUOUS"]
new_buf += numpy.asarray(def_val).astype(r.type.dtype) new_buf += numpy.asarray(def_val).astype(r.type.dtype)
c_cont_outputs[r] = new_buf c_cont_outputs[r] = new_buf
......
...@@ -1937,9 +1937,10 @@ class GpuAdvancedIncSubtensor1(tensor.AdvancedIncSubtensor1, GpuOp): ...@@ -1937,9 +1937,10 @@ class GpuAdvancedIncSubtensor1(tensor.AdvancedIncSubtensor1, GpuOp):
return Apply(self, [x_, y_, ilist_], [x_.type()]) return Apply(self, [x_, y_, ilist_], [x_.type()])
#def perform(self, node, inp, out_): # CudaNdarray_Subscript() don't support Advanced slicing.
# CudaNdarray_Subscript() don't support Advanced slicing. # But we can't use the parent version that loop on each indices
# so we use the parent version that loop on each indices. # as we also need to loop when set_instead_of_inc is True and the
# parent don't look in that case.
def perform(self, node, inp, out_): def perform(self, node, inp, out_):
# TODO opt to make this inplace # TODO opt to make this inplace
x, y, idx = inp x, y, idx = inp
......
...@@ -1065,7 +1065,9 @@ CudaNdarray_inplace_elemwise(PyObject* py_self, PyObject * py_other, operator_t ...@@ -1065,7 +1065,9 @@ CudaNdarray_inplace_elemwise(PyObject* py_self, PyObject * py_other, operator_t
{ {
PyErr_SetString( PyErr_SetString(
PyExc_ValueError, PyExc_ValueError,
"CudaNdarray_inplace_elemwise cannot work inplace on un-initialized array when the new value have more then 0 or 1 broadcastable dimensions"); "CudaNdarray_inplace_elemwise cannot work inplace on"
" un-initialized array when the new value have more then"
" 0 or 1 broadcastable dimensions");
Py_XDECREF(new_other); Py_XDECREF(new_other);
return 0; return 0;
} }
...@@ -2802,7 +2804,7 @@ int CudaNdarray_CopyFromCudaNdarray(CudaNdarray * self, ...@@ -2802,7 +2804,7 @@ int CudaNdarray_CopyFromCudaNdarray(CudaNdarray * self,
} }
else if (self->nd != other->nd) else if (self->nd != other->nd)
{ {
CudaNdarray * new_other = (CudaNdarray *) CudaNdarray_View(other); new_other = (CudaNdarray *) CudaNdarray_View(other);
int added_dims = self->nd - other->nd; int added_dims = self->nd - other->nd;
int pattern[self->nd]; int pattern[self->nd];
for(int i = 0; i < added_dims; i++) for(int i = 0; i < added_dims; i++)
......
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