add sum for boolean type in mainline

3rdparty/mshadow/mshadow/base.h

@@ -650,6 +650,11 @@ template<>
 MSHADOW_XINLINE int64_t MinValue<int64_t>(void) {
  return LLONG_MIN;
 }
+/*! \brief minimum value of bool */
+template<>
+MSHADOW_XINLINE bool MinValue<bool>(void) {
+ return false;
+}
 
 /*!
  * \brief negative infinity of certain types
@@ -711,6 +716,11 @@ template<>
 MSHADOW_XINLINE int64_t MaxValue<int64_t>(void) {
  return LLONG_MAX;
 }
+/*! \brief maximum value of bool */
+template<>
+MSHADOW_XINLINE bool MaxValue<bool>(void) {
+ return true;
+}
 
 /*!
  * \brief positive infinity of certain types

src/operator/mxnet_op.h

@@ -314,6 +314,14 @@ struct AccType<mshadow::half::half_t> {
  "floating point types, not int64"; \
  } \
  break; \
+ case mshadow::kBool: \
+ { \
+ typedef bool DType; \
+ typedef int64_t AType; \
+ LOG(FATAL) << "This operation only support " \
+ "floating point types, not bool"; \
+ } \
+ break; \
  default: \
  LOG(FATAL) << "Unknown type enum " << type; \
  }
@@ -369,6 +377,13 @@ struct AccType<mshadow::half::half_t> {
  {__VA_ARGS__} \
  } \
  break; \
+ case mshadow::kBool: \
+ { \
+ typedef bool DType; \
+ typedef int64_t AType; \
+ {__VA_ARGS__} \
+ } \
+ break; \
  default: \
  LOG(FATAL) << "Unknown type enum " << type; \
  }
@@ -608,16 +623,11 @@ template <typename xpu>
 MSHADOW_CINLINE void copy(mshadow::Stream<xpu> *s, const TBlob& to, const TBlob& from) {
  CHECK_EQ(from.Size(), to.Size());
  CHECK_EQ(from.dev_mask(), to.dev_mask());
- if (from.type_flag_ == mshadow::kBool || to.type_flag_ == mshadow::kBool) {
- CHECK_EQ(from.type_flag_, to.type_flag_) << "Only supports copying between boolean ndarrays.";
- mshadow::Copy(to.FlatTo1D<xpu, bool>(s), from.FlatTo1D<xpu, bool>(s), s);
- return;
- }
  MSHADOW_TYPE_SWITCH(to.type_flag_, DType, {
  if (to.type_flag_ == from.type_flag_) {
  mshadow::Copy(to.FlatTo1D<xpu, DType>(s), from.FlatTo1D<xpu, DType>(s), s);
  } else {
- MSHADOW_TYPE_SWITCH(from.type_flag_, SrcDType, {
+ MSHADOW_TYPE_SWITCH_WITH_BOOL(from.type_flag_, SrcDType, {
  to.FlatTo1D<xpu, DType>(s) = mshadow::expr::tcast<DType>(from.FlatTo1D<xpu, SrcDType>(s));
  })
  }

src/operator/numpy/np_broadcast_reduce_op.h

@@ -226,7 +226,7 @@ void NumpyReduceAxesCompute(const nnvm::NodeAttrs& attrs,
  if (param.initial.has_value()) {
  LOG(FATAL) << "initial is not supported yet";
  }
- if (inputs[0].shape_.Size() == 0) {
+ if (inputs[0].shape_.Size() == 0 && outputs[0].shape_.Size() != 0) {
  using namespace mxnet_op;
  using namespace mshadow;
  Stream<xpu>* s = ctx.get_stream<xpu>();
@@ -236,6 +236,7 @@ void NumpyReduceAxesCompute(const nnvm::NodeAttrs& attrs,
  return;
  }
  CHECK_NE(req[0], kWriteInplace) << "Reduce does not support write in-place";
+#if MXNET_USE_TVM_OP
  // If boolean ndarray, use the kernel generated by TVM
  if (inputs[0].type_flag_ == mshadow::kBool) {
  std::string reducer_name;
@@ -247,6 +248,7 @@ void NumpyReduceAxesCompute(const nnvm::NodeAttrs& attrs,
  TVMOpReduce(ctx, inputs[0], param.axis, outputs[0], req[0], reducer_name);
  return;
  }
+#endif
  if (param.axis.has_value() && param.axis.value().ndim() == 0) {
  UnaryOp::IdentityCompute<xpu>(attrs, ctx, inputs, req, outputs);
  }

src/operator/tensor/broadcast_reduce_op.h

@@ -616,7 +616,7 @@ void ReduceAxesComputeImpl(const OpContext& ctx,
  mxnet::TShape src_shape, dst_shape;
  BroadcastReduceShapeCompact(inputs[0].shape_, small, &src_shape, &dst_shape);
  Stream<xpu> *s = ctx.get_stream<xpu>();
- MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, DType, {
+ MSHADOW_TYPE_SWITCH_WITH_BOOL(inputs[0].type_flag_, DType, {
  MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, OType, {
  const TBlob in_data = inputs[0].reshape(src_shape);
  const TBlob out_data = outputs[0].reshape(dst_shape);

tests/python/unittest/test_numpy_op.py

@@ -32,7 +32,8 @@
 from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf, retry
 from mxnet.runtime import Features
 from mxnet.numpy_op_signature import _get_builtin_op
-from mxnet.test_utils import verify_generator, gen_buckets_probs_with_ppf, has_tvm_ops
+from mxnet.test_utils import current_context, verify_generator, gen_buckets_probs_with_ppf
+from mxnet.test_utils import is_op_runnable, has_tvm_ops
 import platform
 
 
@@ -495,13 +496,15 @@ def is_int(dtype):
  shape = rand_shape_nd(in_data_dim, dim=3)
  acc_type = {'float16': 'float32', 'float32': 'float64', 'float64': 'float64',
  'int8': 'int32', 'int32': 'int64', 'int64': 'int64', 'bool': 'int64'}
+ is_windows = sys.platform.startswith('win')
  for hybridize in [False, True]:
  for keepdims in [True, False]:
  for axis in ([i for i in range(in_data_dim)] + [(), None]):
  for itype in ['float16', 'float32', 'float64', 'int8', 'int32', 'int64', 'bool']:
  for dtype in ['float16', 'float32', 'float64', 'int8', 'int32', 'int64']:
  if (is_int(dtype) and not is_int(itype))\
- or (itype == 'bool' and dtype not in ('float32', 'float64', 'int32', 'int64')):
+ or (itype == 'bool' and\
+ (dtype not in ('float32', 'float64', 'int32', 'int64') or is_windows)):
  continue
  # test gluon
  test_sum = TestSum(axis=axis, dtype=dtype, keepdims=keepdims)
@@ -517,8 +520,8 @@ def is_int(dtype):
  x = np.random.uniform(-1.0, 1.0, size=shape, dtype=itype)
  expected_ret = _np.sum(x.asnumpy(), axis=axis, dtype=acc_type[itype], keepdims=keepdims)
  expected_ret = expected_ret.astype(dtype)
- if itype == 'bool': # special handling of boolean ndarray
- if has_tvm_ops():
+ if itype == 'bool':
+ if is_op_runnable() and (not is_windows): # special handling of boolean ndarray
  y = test_sum(x)
  assert y.dtype == expected_ret.dtype
  assert_almost_equal(y.asnumpy(), expected_ret, rtol=1e-4, atol=1e-5,
@@ -540,7 +543,7 @@ def is_int(dtype):
  x_sym = mx.sym.Variable("x").as_np_ndarray()
  mx_sym = mx.sym.np.sum(x_sym, axis=axis, dtype=dtype, keepdims=keepdims).as_nd_ndarray()
  check_numeric_gradient(mx_sym, [x.as_nd_ndarray()],
- numeric_eps=1e-3, rtol=1e-3, atol=1e-4, dtype=_np.float32)
+ numeric_eps=1e-3, rtol=1e-2, atol=1e-3, dtype=_np.float32)
 
  # test imperative
  mx_out = np.sum(x, axis=axis, dtype=dtype, keepdims=keepdims)