TaoLv closed pull request #13555: [MXNET-1253] fix control_flow_op
URL: https://github.com/apache/incubator-mxnet/pull/13555
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diff --git a/src/operator/tensor/control_flow_op.h
b/src/operator/tensor/control_flow_op.h
index 07252963c87..9d0e8cf9081 100644
--- a/src/operator/tensor/control_flow_op.h
+++ b/src/operator/tensor/control_flow_op.h
@@ -46,7 +46,7 @@ struct where {
// DType is the output data type
// CType is condition data type
template<typename DType, typename CType>
- MSHADOW_XINLINE static void Map(int i, DType* out, const CType* cond,
+ MSHADOW_XINLINE static void Map(index_t i, DType* out, const CType* cond,
const DType* x, const DType* y) {
KERNEL_ASSIGN(out[i], req, (0 != cond[i]? x[i] : y[i]));
}
@@ -64,7 +64,7 @@ struct where_csr {
// CType is condition data type
// i is for i-th row in the output
template<typename DType, typename CType, typename IType>
- MSHADOW_XINLINE static void Map(int i, DType* out, const IType* cond_idx,
+ MSHADOW_XINLINE static void Map(index_t i, DType* out, const IType* cond_idx,
const IType* cond_indptr, const CType*
cond_data,
const nnvm::dim_t num_cols, const DType* x) {
using nnvm::dim_t;
@@ -92,8 +92,8 @@ struct where_batch {
// DType is the output data type
// CType is the condition data type
template<typename DType, typename CType>
- MSHADOW_XINLINE static void Map(int i, DType* out, const CType* cond,
- const DType* x, const DType* y, int M) {
+ MSHADOW_XINLINE static void Map(index_t i, DType* out, const CType* cond,
+ const DType* x, const DType* y, index_t M) {
KERNEL_ASSIGN(out[i], req, (0 != cond[i/M]? x[i] : y[i]));
}
};
@@ -109,7 +109,7 @@ struct where_backward {
// DType is the output data type
// CType is condition data type
template<typename DType, typename CType>
- MSHADOW_XINLINE static void Map(int i, DType* grad_out,
+ MSHADOW_XINLINE static void Map(index_t i, DType* grad_out,
const DType* grad_in,
const CType* cond) {
KERNEL_ASSIGN(grad_out[i], req,
@@ -130,7 +130,7 @@ struct where_backward_csr {
// CType is condition data type
// IType is condition aux data type
template<typename DType, typename CType, typename IType>
- MSHADOW_XINLINE static void Map(int i, DType* grad_out,
+ MSHADOW_XINLINE static void Map(index_t i, DType* grad_out,
const DType* grad_in,
const CType* cond_data,
const IType* cond_idx,
@@ -161,9 +161,9 @@ struct where_batch_backward {
// DType is the output data type
// CType is condition data type
template<typename DType, typename CType>
- MSHADOW_XINLINE static void Map(int i, DType* grad_out,
+ MSHADOW_XINLINE static void Map(index_t i, DType* grad_out,
const DType* grad_in,
- const CType* cond, int M) {
+ const CType* cond, index_t M) {
KERNEL_ASSIGN(grad_out[i], req,
((0 == cond[i/M])^negate)? grad_in[i] : static_cast<DType>(0));
}
diff --git a/tests/nightly/test_large_array.py
b/tests/nightly/test_large_array.py
index a301362f2db..696fdb1d417 100644
--- a/tests/nightly/test_large_array.py
+++ b/tests/nightly/test_large_array.py
@@ -134,6 +134,17 @@ def test_Dense(ctx=mx.cpu(0)):
res.wait_to_read()
assert res.shape == (50000000, 100)
+def test_where():
+ a = nd.ones(shape=(LARGE_X, SMALL_Y))
+ b = nd.arange(0, LARGE_X).reshape(LARGE_X, 1)
+ b = nd.broadcast_to(b, shape=(b.shape[0], SMALL_Y))
+ res = nd.where(b > 100, a, b)
+ assert np.sum(res[-1].asnumpy() == 1) == b.shape[1]
+
+ csr_cond = nd.sparse.cast_storage(b < 10, 'csr')
+ res = nd.sparse.where(csr_cond, a, b)
+ assert np.sum(res[0].asnumpy() == 1) == b.shape[1]
+
if __name__ == '__main__':
import nose
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