Skip to content
This repository has been archived by the owner on Nov 17, 2023. It is now read-only.

Commit

Permalink
Enable large tensor support for padding (#19403)
Browse files Browse the repository at this point in the history 
Co-authored-by: Rohit Kumar Srivastava <[email protected]>
  • Loading branch information
@access2rohit
access2rohit and Rohit Kumar Srivastava committed Oct 24, 2020
1 parent 8e5a16c commit 3dda8e9
Show file tree
Hide file tree
Showing 2 changed files with 81 additions and 36 deletions.
72 changes: 36 additions & 36 deletions src/operator/numpy/np_pad_op-inl.h
View file
Original file line number Diff line number Diff line change
Expand Up @@ -59,12 +59,12 @@ MSHADOW_XINLINE index_t rravel(const mshadow::Shape<ndim>& coord,

/* Compute coordinates from flattened index given shape */
template<int ndim>
MSHADOW_XINLINE mshadow::Shape<ndim> uunravel(const int idx,
MSHADOW_XINLINE mshadow::Shape<ndim> uunravel(const index_t idx,
const index_t* shape) {
mshadow::Shape<ndim> ret;
#pragma unroll
for (int i = ndim-1, j = idx; i >=0; --i) {
auto tmp = j / shape[i];
for (index_t i = ndim-1, j = idx; i >=0; --i) {
index_t tmp = j / shape[i];
ret[i] = j - tmp*shape[i];
j = tmp;
}
Expand Down Expand Up @@ -158,13 +158,13 @@ struct NumpyPadParam : public dmlc::Parameter<NumpyPadParam> {
inline mxnet::TShape NumpyPadShapeImpl(const mxnet::TShape& ishape,
const mxnet::Tuple<Tuple<int>> pad_width) {
if (ishape.ndim() == 1) {
auto s = ishape[0] + pad_width[0][0] + pad_width[1][0];
index_t s = ishape[0] + pad_width[0][0] + pad_width[1][0];
return mxnet::TShape({s});
} else if (ishape.ndim() >= 2) {
int i;
mxnet::TShape oshape(ishape.ndim(), -1);
for (i = ishape.ndim() - 1; i >=0; i--) {
int base = ishape[i];
index_t base = ishape[i];
base = base + pad_width[i][0] + pad_width[i][1];
oshape[i] = base;
}
Expand Down Expand Up @@ -231,7 +231,7 @@ struct pad_copy {
for (m = 0; m < ndim; m++) {
indexshape[m] = indexshape[m] - indexwidth[m * 2];
}
int l = rravel<ndim>(j, ishape);
index_t l = rravel<ndim>(j, ishape);
KERNEL_ASSIGN(out[i], req, a[l]);
} else {
return;
Expand Down Expand Up @@ -274,11 +274,11 @@ struct symmetric_pad {
}
if (indexshape[index] < indexwidth[index * 2]) {
// we need to do the assignment
int distance = indexwidth[index * 2] - indexshape[index];
int total = ishape[index];
index_t distance = indexwidth[index * 2] - indexshape[index];
index_t total = ishape[index];
// the round of this element
int round = (distance - 1) / total;
int position = distance % total;
index_t round = (distance - 1) / total;
index_t position = distance % total;
if (position == 0) {
position = ishape[index];
}
Expand All @@ -287,13 +287,13 @@ struct symmetric_pad {
} else {
indexshape[index] = indexwidth[index * 2] + ishape[index] - 1 - (position - 1);
}
int l = rravel<ndim>(j, oshape);
index_t l = rravel<ndim>(j, oshape);
KERNEL_ASSIGN(out[i], req, out[l]);
} else if (indexshape[index] >= (indexwidth[index * 2]+ishape[index])) {
int distance = (indexshape[index]+1) - (indexwidth[index * 2]+ishape[index]);
int total = ishape[index];
int position = distance % total;
int round = (distance - 1) / total;
index_t distance = (indexshape[index]+1) - (indexwidth[index * 2]+ishape[index]);
index_t total = ishape[index];
index_t position = distance % total;
index_t round = (distance - 1) / total;
if (position == 0) {
position = ishape[index];
}
Expand All @@ -302,7 +302,7 @@ struct symmetric_pad {
} else {
indexshape[index] = indexwidth[index * 2] + position - 1;
}
int l = rravel<ndim>(j, oshape);
index_t l = rravel<ndim>(j, oshape);
KERNEL_ASSIGN(out[i], req, out[l]);
}
}
Expand Down Expand Up @@ -345,11 +345,11 @@ struct edge_pad {
if (indexshape[index] < indexwidth[index * 2]) {
// we need to do the assignment
indexshape[index] = indexwidth[index * 2];
int l = rravel<ndim>(j, oshape);
index_t l = rravel<ndim>(j, oshape);
KERNEL_ASSIGN(out[i], req, out[l]);
} else if (indexshape[index] >= (indexwidth[index * 2]+ishape[index])) {
indexshape[index] = indexwidth[index * 2] + ishape[index] - 1;
int l = rravel<ndim>(j, oshape);
index_t l = rravel<ndim>(j, oshape);
KERNEL_ASSIGN(out[i], req, out[l]);
}
}
Expand Down Expand Up @@ -391,42 +391,42 @@ struct reflect_pad {
}
if (indexshape[index] < indexwidth[index * 2]) {
// we need to do the assignment
int distance = indexwidth[index * 2] - indexshape[index];
int total = ishape[index];
index_t distance = indexwidth[index * 2] - indexshape[index];
index_t total = ishape[index];
if (total == 1) {
indexshape[index] = indexwidth[index * 2];
int l = rravel<ndim>(j, oshape);
index_t l = rravel<ndim>(j, oshape);
KERNEL_ASSIGN(out[i], req, out[l]);
return;
}
int round = (distance - 1) / (total - 1);
index_t round = (distance - 1) / (total - 1);
if (round % 2 == 0) {
int position = (distance + round) % total;
index_t position = (distance + round) % total;
indexshape[index] = indexwidth[index * 2] + position;
} else {
int position = (distance + round) % total;
index_t position = (distance + round) % total;
indexshape[index] = indexwidth[index * 2] + ishape[index] - 1 - (position);
}
int l = rravel<ndim>(j, oshape);
index_t l = rravel<ndim>(j, oshape);
KERNEL_ASSIGN(out[i], req, out[l]);
} else if (indexshape[index] >= (indexwidth[index * 2] + ishape[index])) {
int distance = (indexshape[index]+1) - (indexwidth[index * 2] + ishape[index]);
int total = ishape[index];
index_t distance = (indexshape[index]+1) - (indexwidth[index * 2] + ishape[index]);
index_t total = ishape[index];
if (total == 1) {
indexshape[index] = indexwidth[index * 2];
int l = rravel<ndim>(j, oshape);
index_t l = rravel<ndim>(j, oshape);
KERNEL_ASSIGN(out[i], req, out[l]);
return;
}
int round = (distance - 1) / (total - 1);
index_t round = (distance - 1) / (total - 1);
if (round % 2 == 0) {
int position = (distance + round) % total;
index_t position = (distance + round) % total;
indexshape[index] = indexwidth[index * 2] + ishape[index] - 1 - (position);
} else {
int position = (distance + round) % total;
index_t position = (distance + round) % total;
indexshape[index] = indexwidth[index * 2] + position;
}
int l = rravel<ndim>(j, oshape);
index_t l = rravel<ndim>(j, oshape);
KERNEL_ASSIGN(out[i], req, out[l]);
}
}
Expand Down Expand Up @@ -470,8 +470,8 @@ struct max_pad {
if (indexshape[index] < indexwidth[index * 2] ||
indexshape[index] >= indexwidth[index * 2] + ishape[index]) {
indexshape[index] = indexwidth[index * 2];
int l = rravel<ndim>(j, oshape);
int max_count = 0;
index_t l = rravel<ndim>(j, oshape);
index_t max_count = 0;
auto max_value = out[l];
for (max_count = 0; max_count < ishape[index]; max_count++) {
indexshape[index] = indexwidth[index * 2] + max_count;
Expand Down Expand Up @@ -522,8 +522,8 @@ struct min_pad {
if (indexshape[index] < indexwidth[index * 2] ||
indexshape[index] >= (indexwidth[index * 2] + ishape[index])) {
indexshape[index] = indexwidth[index * 2];
int l = rravel<ndim>(j, oshape);
int min_count = 0;
index_t l = rravel<ndim>(j, oshape);
index_t min_count = 0;
auto min_value = out[l];
for (min_count = 0; min_count < ishape[index]; min_count++) {
indexshape[index] = indexwidth[index * 2] + min_count;
Expand Down
45 changes: 45 additions & 0 deletions tests/nightly/test_np_large_array.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -2150,3 +2150,48 @@ def test_interp():
assert inp.grad.shape == inp.shape
assert inp.grad[-1, -1] == 0


@use_np
def test_edge_padding():
inp = create_2d_np_tensor(rows=INT_OVERFLOW, columns=4, dtype=np.int64)
out = np.pad(inp, ((1, 1), (1, 1)), "edge")
assert out[0][0] == 0
assert out[-1][-1] == INT_OVERFLOW - 1
assert out.shape == (INT_OVERFLOW + 2, 4 + 2)


@use_np
def test_constant_padding():
inp = create_2d_np_tensor(rows=INT_OVERFLOW, columns=4, dtype=np.int64)
out = np.pad(inp, ((1, 1), (1, 1)), "constant")
assert out[0][0] == 0
assert out[-1][-1] == 0
assert out.shape == (INT_OVERFLOW + 2, 4 + 2)


@use_np
def test_minimum_padding():
inp = create_2d_np_tensor(rows=INT_OVERFLOW, columns=4, dtype=np.int64)
out = np.pad(inp, ((1, 1), (1, 1)), "minimum")
assert out[0][-1] == 0
assert out[-1][-1] == 0
assert out.shape == (INT_OVERFLOW + 2, 4 + 2)


@use_np
def test_reflection_padding():
inp = create_2d_np_tensor(rows=INT_OVERFLOW, columns=4, dtype=np.int64)
out = np.pad(inp, ((1, 1), (1, 1)), "reflect")
assert out[0][-1] == 0 + 1
assert out[-1][0] == INT_OVERFLOW - 1 - 1
assert out.shape == (INT_OVERFLOW + 2, 4 + 2)


@use_np
def test_symmetric_padding():
inp = create_2d_np_tensor(rows=INT_OVERFLOW, columns=4, dtype=np.int64)
out = np.pad(inp, ((1, 1), (1, 1)), "symmetric")
assert out[0][0] == 0
assert out[-1][-1] == INT_OVERFLOW - 1
assert out.shape == (INT_OVERFLOW + 2, 4 + 2)

0 comments on commit 3dda8e9

Please sign in to comment.