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Posted to commits@mxnet.apache.org by la...@apache.org on 2020/04/24 01:48:02 UTC
[incubator-mxnet] branch v1.7.x updated: [v1.7.x] Backport #17177
to 1.7.x (Fix incorrect calculation results when the C locale is set to a
locale that uses commas as the decimal separator) (#18147)
This is an automated email from the ASF dual-hosted git repository.
lausen pushed a commit to branch v1.7.x
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/v1.7.x by this push:
new b2e0cce [v1.7.x] Backport #17177 to 1.7.x (Fix incorrect calculation results when the C locale is set to a locale that uses commas as the decimal separator) (#18147)
b2e0cce is described below
commit b2e0cce9bc3917948a714e71a0394b5653ea6476
Author: Nick Guletskii <ni...@nickguletskii.com>
AuthorDate: Fri Apr 24 04:46:43 2020 +0300
[v1.7.x] Backport #17177 to 1.7.x (Fix incorrect calculation results when the C locale is set to a locale that uses commas as the decimal separator) (#18147)
* Add a test for floating point parsing locale invariance
* Use locale-invariant dmlc:stod/stof instead of std:stod/stof
* Change the new operator tutorial to use dmlc:stod instead of std::stod
* Rename locale invariance test
* Skip test_scalarop_locale_invariance if the locales aren't available
* Fix linter errors due to incorrect include order
---
cpp-package/include/mxnet-cpp/optimizer.h | 3 +-
cpp-package/include/mxnet-cpp/optimizer.hpp | 25 +++++++-------
docs/static_site/src/pages/api/faq/new_op.md | 2 +-
plugin/torch/torch_function.h | 3 +-
src/nnvm/legacy_op_util.cc | 3 +-
src/operator/contrib/gradient_multiplier_op.cc | 3 +-
src/operator/numpy/np_boolean_mask_assign.cc | 3 +-
src/operator/numpy/np_boolean_mask_assign.cu | 3 +-
.../numpy/np_elemwise_broadcast_logic_op.cc | 3 +-
src/operator/numpy/np_elemwise_broadcast_op.cc | 3 +-
.../numpy/np_elemwise_broadcast_op_extended.cc | 17 +++++-----
src/operator/numpy/np_true_divide.cc | 5 +--
.../mkldnn_post_quantize_align_scale_property.h | 5 +--
src/operator/tensor/elemwise_binary_scalar_op.h | 3 +-
.../tensor/elemwise_binary_scalar_op_basic.cc | 9 ++---
.../tensor/elemwise_binary_scalar_op_extended.cc | 15 +++++----
tests/python/unittest/test_operator.py | 38 ++++++++++++++++++++++
17 files changed, 98 insertions(+), 45 deletions(-)
diff --git a/cpp-package/include/mxnet-cpp/optimizer.h b/cpp-package/include/mxnet-cpp/optimizer.h
index 320b13e..ac84287 100644
--- a/cpp-package/include/mxnet-cpp/optimizer.h
+++ b/cpp-package/include/mxnet-cpp/optimizer.h
@@ -27,6 +27,7 @@
#ifndef MXNET_CPP_OPTIMIZER_H_
#define MXNET_CPP_OPTIMIZER_H_
+#include <dmlc/strtonum.h>
#include <map>
#include <vector>
#include <string>
@@ -84,7 +85,7 @@ class Optimizer {
Optimizer *SetLRScheduler(std::unique_ptr<LRScheduler> lrScheduler) {
CHECK(lrScheduler);
lrScheduler_ = std::move(lrScheduler);
- lrScheduler_->SetLR(std::stof(params_["lr"]));
+ lrScheduler_->SetLR(dmlc::stof(params_["lr"]));
return this;
}
/*!
diff --git a/cpp-package/include/mxnet-cpp/optimizer.hpp b/cpp-package/include/mxnet-cpp/optimizer.hpp
index 26fd00f..f3c71df 100644
--- a/cpp-package/include/mxnet-cpp/optimizer.hpp
+++ b/cpp-package/include/mxnet-cpp/optimizer.hpp
@@ -26,6 +26,7 @@
#ifndef MXNET_CPP_OPTIMIZER_HPP_
#define MXNET_CPP_OPTIMIZER_HPP_
+#include <dmlc/strtonum.h>
#include <algorithm>
#include <utility>
#include <numeric>
@@ -116,11 +117,11 @@ inline float Optimizer::GetLR_(int index) {
if (nullptr != lrScheduler_) {
return lrScheduler_->GetLR(num_update_);
}
- return std::stof(params_["lr"]);
+ return dmlc::stof(params_["lr"]);
}
inline float Optimizer::GetWD_(int index) {
- float wd = std::stof(params_["wd"]);
+ float wd = dmlc::stof(params_["wd"]);
return wd;
}
@@ -362,9 +363,9 @@ inline void AdamOptimizer::Update(int index, NDArray weight, NDArray grad) {
auto values = GetParamValues_();
CHECK_EQ(keys.size(), values.size());
- float lr = std::stof(params_["lr"]);
- float b1 = std::stof(params_["beta1"]);
- float b2 = std::stof(params_["beta2"]);
+ float lr = dmlc::stof(params_["lr"]);
+ float b1 = dmlc::stof(params_["beta1"]);
+ float b2 = dmlc::stof(params_["beta2"]);
float t = count_[index];
float coef1 = 1.0f - std::pow(b1, t);
float coef2 = 1.0f - std::pow(b2, t);
@@ -407,15 +408,15 @@ inline void AdaGradOptimizer::Update(int index, NDArray weight, NDArray grad) {
CreateState_(index, weight);
}
- float eps = std::stof(params_["eps"]);
+ float eps = dmlc::stof(params_["eps"]);
float lr = GetLR_(index);
float wd = GetWD_(index);
UpdateCount_(index);
if (params_.count("rescale_grad") > 0) {
- grad *= std::stof(params_["rescale_grad"]);
+ grad *= dmlc::stof(params_["rescale_grad"]);
}
if (params_.count("clip_gradient") > 0) {
- _clip(grad, std::stof(params_["clip_gradient"]));
+ _clip(grad, dmlc::stof(params_["clip_gradient"]));
}
auto& history = *history_[index];
history += grad * grad;
@@ -448,16 +449,16 @@ inline void AdaDeltaOptimizer::Update(int index, NDArray weight, NDArray grad) {
CreateState_(index, weight);
}
- float rho = std::stof(params_["rho"]);
- float epsilon = std::stof(params_["epsilon"]);
+ float rho = dmlc::stof(params_["rho"]);
+ float epsilon = dmlc::stof(params_["epsilon"]);
float wd = GetWD_(index);
UpdateCount_(index);
if (params_.count("rescale_grad") > 0) {
- grad *= std::stof(params_["rescale_grad"]);
+ grad *= dmlc::stof(params_["rescale_grad"]);
}
if (params_.count("clip_gradient") > 0) {
- _clip(grad, std::stof(params_["clip_gradient"]));
+ _clip(grad, dmlc::stof(params_["clip_gradient"]));
}
auto& acc_g = *acc_g_[index];
diff --git a/docs/static_site/src/pages/api/faq/new_op.md b/docs/static_site/src/pages/api/faq/new_op.md
index 787b403..053182b 100644
--- a/docs/static_site/src/pages/api/faq/new_op.md
+++ b/docs/static_site/src/pages/api/faq/new_op.md
@@ -204,7 +204,7 @@ Simple arguments can be parsed like
NNVM_REGISTER_OP(scalar_op)
.set_attr_parser(
[](NodeAttrs* attrs) {
- attrs->parsed = std::stod(attrs->dict["scalar"]);
+ attrs->parsed = dmlc::stod(attrs->dict["scalar"]);
})
```
diff --git a/plugin/torch/torch_function.h b/plugin/torch/torch_function.h
index f6f7602..6a33e70 100644
--- a/plugin/torch/torch_function.h
+++ b/plugin/torch/torch_function.h
@@ -28,6 +28,7 @@
#include "./torch_base.h"
#include <mxnet/base.h>
#include <mxnet/ndarray.h>
+#include <dmlc/strtonum.h>
#include <stdio.h>
#include <stdlib.h>
#include <string>
@@ -69,7 +70,7 @@ void TorchRunOp(std::vector<NDArray> arr_in,
lua_pushinteger(L, std::stoi(val));
break;
case 'f':
- lua_pushnumber(L, std::stof(val));
+ lua_pushnumber(L, dmlc::stof(val));
break;
case 's':
lua_pushstring(L, val.c_str());
diff --git a/src/nnvm/legacy_op_util.cc b/src/nnvm/legacy_op_util.cc
index 851552a..f66350f 100644
--- a/src/nnvm/legacy_op_util.cc
+++ b/src/nnvm/legacy_op_util.cc
@@ -23,6 +23,7 @@
* \brief Utility to adapt OpProperty to the new NNVM registery
*/
#include <dmlc/base.h>
+#include <dmlc/strtonum.h>
#include <mxnet/base.h>
#include <mxnet/operator.h>
#include <mxnet/op_attr_types.h>
@@ -511,7 +512,7 @@ void RegisterLegacyNDFunc() {
const std::string& name = reg->arguments[i+reg->num_use_vars].name;
auto s = dict.find(name);
CHECK(s != dict.end()) << "Missing scalar param " << name;
- scalars.push_back(std::stof(s->second));
+ scalars.push_back(dmlc::stof(s->second));
dict.erase(s);
}
diff --git a/src/operator/contrib/gradient_multiplier_op.cc b/src/operator/contrib/gradient_multiplier_op.cc
index 47f891e..0a49ec1 100644
--- a/src/operator/contrib/gradient_multiplier_op.cc
+++ b/src/operator/contrib/gradient_multiplier_op.cc
@@ -23,6 +23,7 @@
* \brief
* \author Istvan Fehervari
*/
+#include <dmlc/strtonum.h>
#include "../tensor/elemwise_unary_op.h"
#include "../tensor/elemwise_binary_scalar_op.h"
@@ -77,7 +78,7 @@ multiplies the gradient from the subsequent level by a scalar factor lambda and
the preceding layer.
)code" ADD_FILELINE)
.set_attr_parser([](NodeAttrs* attrs) {
- attrs->parsed = std::stod(attrs->dict["scalar"]);
+ attrs->parsed = dmlc::stod(attrs->dict["scalar"]);
})
.set_attr<FInferStorageType>("FInferStorageType", ElemwiseStorageType<1, 1, false, true, true>)
.set_attr<FCompute>("FCompute<cpu>", UnaryOp::IdentityCompute<cpu>)
diff --git a/src/operator/numpy/np_boolean_mask_assign.cc b/src/operator/numpy/np_boolean_mask_assign.cc
index e01ebb7..ef7cce4 100644
--- a/src/operator/numpy/np_boolean_mask_assign.cc
+++ b/src/operator/numpy/np_boolean_mask_assign.cc
@@ -22,6 +22,7 @@
* \brief CPU implementation of Boolean Mask Assign
*/
+#include <dmlc/strtonum.h>
#include "../../common/utils.h"
#include "../contrib/boolean_mask-inl.h"
@@ -272,7 +273,7 @@ void NumpyBooleanAssignForwardCPU(const nnvm::NodeAttrs& attrs,
MSHADOW_TYPE_SWITCH_WITH_BOOL(data.type_flag_, DType, {
Kernel<BooleanAssignCPUKernel<true>, cpu>::Launch(
s, valid_num, data.dptr<DType>(), prefix_sum.data(), prefix_sum.size(),
- leading, middle, trailing, static_cast<DType>(std::stod(attrs.dict.at("value"))));
+ leading, middle, trailing, static_cast<DType>(dmlc::stod(attrs.dict.at("value"))));
});
}
}
diff --git a/src/operator/numpy/np_boolean_mask_assign.cu b/src/operator/numpy/np_boolean_mask_assign.cu
index e1e6452..6fa59be 100644
--- a/src/operator/numpy/np_boolean_mask_assign.cu
+++ b/src/operator/numpy/np_boolean_mask_assign.cu
@@ -23,6 +23,7 @@
*/
#include <cub/cub.cuh>
+#include <dmlc/strtonum.h>
#include "../../common/utils.h"
#include "../contrib/boolean_mask-inl.h"
@@ -252,7 +253,7 @@ void NumpyBooleanAssignForwardGPU(const nnvm::NodeAttrs& attrs,
}
} else {
CHECK(attrs.dict.find("value") != attrs.dict.end()) << "value is not provided";
- double value = std::stod(attrs.dict.at("value"));
+ double value = dmlc::stod(attrs.dict.at("value"));
MSHADOW_TYPE_SWITCH_WITH_BOOL(data.type_flag_, DType, {
Kernel<BooleanAssignGPUKernel<true>, gpu>::Launch(
s, leading * valid_num * trailing, data.dptr<DType>(), prefix_sum, mask_size + 1,
diff --git a/src/operator/numpy/np_elemwise_broadcast_logic_op.cc b/src/operator/numpy/np_elemwise_broadcast_logic_op.cc
index 8395caf..74db52d 100644
--- a/src/operator/numpy/np_elemwise_broadcast_logic_op.cc
+++ b/src/operator/numpy/np_elemwise_broadcast_logic_op.cc
@@ -30,6 +30,7 @@
#include "../tvmop/op_module.h"
#endif // MXNET_USE_TVM_OP
+#include <dmlc/strtonum.h>
#include "../tensor/elemwise_binary_broadcast_op.h"
#include "../tensor/elemwise_binary_scalar_op.h"
@@ -225,7 +226,7 @@ struct TVMBinaryBroadcastScalarCompute {
.set_num_inputs(1) \
.set_num_outputs(1) \
.set_attr_parser([](NodeAttrs* attrs) { \
- attrs->parsed = std::stod(attrs->dict["scalar"]); \
+ attrs->parsed = dmlc::stod(attrs->dict["scalar"]); \
}) \
.set_attr<nnvm::FListInputNames>("FListInputNames", \
[](const NodeAttrs& attrs) { \
diff --git a/src/operator/numpy/np_elemwise_broadcast_op.cc b/src/operator/numpy/np_elemwise_broadcast_op.cc
index 6409d43..ae285ca 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op.cc
+++ b/src/operator/numpy/np_elemwise_broadcast_op.cc
@@ -23,6 +23,7 @@
* \brief CPU Implementation of basic functions for elementwise numpy binary broadcast operator.
*/
+#include <dmlc/strtonum.h>
#include "./np_elemwise_broadcast_op.h"
namespace mxnet {
@@ -33,7 +34,7 @@ namespace op {
.set_num_inputs(1) \
.set_num_outputs(1) \
.set_attr_parser([](NodeAttrs* attrs) { \
- attrs->parsed = std::stod(attrs->dict["scalar"]); \
+ attrs->parsed = dmlc::stod(attrs->dict["scalar"]); \
}) \
.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>) \
.set_attr<nnvm::FInferType>("FInferType", NumpyBinaryScalarType) \
diff --git a/src/operator/numpy/np_elemwise_broadcast_op_extended.cc b/src/operator/numpy/np_elemwise_broadcast_op_extended.cc
index 70233a5..52d6818 100644
--- a/src/operator/numpy/np_elemwise_broadcast_op_extended.cc
+++ b/src/operator/numpy/np_elemwise_broadcast_op_extended.cc
@@ -23,6 +23,7 @@
* \brief CPU Implementation of extended functions for elementwise numpy binary broadcast operator.
*/
+#include <dmlc/strtonum.h>
#include "../../common/utils.h"
#include "./np_elemwise_broadcast_op.h"
@@ -34,7 +35,7 @@ namespace op {
.set_num_inputs(1) \
.set_num_outputs(1) \
.set_attr_parser([](NodeAttrs* attrs) { \
- attrs->parsed = std::stod(attrs->dict["scalar"]); \
+ attrs->parsed = dmlc::stod(attrs->dict["scalar"]); \
}) \
.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>) \
.set_attr<nnvm::FInferType>("FInferType", NumpyBinaryScalarType) \
@@ -87,7 +88,7 @@ NNVM_REGISTER_OP(_npi_lcm_scalar)
.set_num_inputs(1)
.set_num_outputs(1)
.set_attr_parser([](NodeAttrs* attrs) {
- attrs->parsed = std::stod(attrs->dict["scalar"]);
+ attrs->parsed = dmlc::stod(attrs->dict["scalar"]);
})
.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
.set_attr<nnvm::FInferType>("FInferType", ElemwiseIntType<1, 1>)
@@ -175,7 +176,7 @@ NNVM_REGISTER_OP(_npi_bitwise_xor_scalar)
.set_num_inputs(1)
.set_num_outputs(1)
.set_attr_parser([](NodeAttrs* attrs) {
- attrs->parsed = std::stod(attrs->dict["scalar"]);
+ attrs->parsed = dmlc::stod(attrs->dict["scalar"]);
})
.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
.set_attr<nnvm::FInferType>("FInferType", ElemwiseIntType<1, 1>)
@@ -192,7 +193,7 @@ NNVM_REGISTER_OP(_npi_bitwise_or_scalar)
.set_num_inputs(1)
.set_num_outputs(1)
.set_attr_parser([](NodeAttrs* attrs) {
- attrs->parsed = std::stod(attrs->dict["scalar"]);
+ attrs->parsed = dmlc::stod(attrs->dict["scalar"]);
})
.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
.set_attr<nnvm::FInferType>("FInferType", ElemwiseIntType<1, 1>)
@@ -275,13 +276,13 @@ MXNET_OPERATOR_REGISTER_NP_BINARY_SCALAR(_npi_rarctan2_scalar)
MXNET_OPERATOR_REGISTER_BINARY(_backward_npi_arctan2_scalar)
.add_argument("scalar", "float", "scalar value")
-.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = std::stod(attrs->dict["scalar"]); })
+.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = dmlc::stod(attrs->dict["scalar"]); })
.set_attr<FCompute>("FCompute<cpu>",
BinaryScalarOp::Backward<cpu, mshadow_op::arctan2_grad>);
MXNET_OPERATOR_REGISTER_BINARY(_backward_npi_rarctan2_scalar)
.add_argument("scalar", "float", "scalar value")
-.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = std::stod(attrs->dict["scalar"]); })
+.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = dmlc::stod(attrs->dict["scalar"]); })
.set_attr<FCompute>("FCompute<cpu>",
BinaryScalarOp::Backward<cpu, mshadow_op::arctan2_rgrad>);
@@ -363,12 +364,12 @@ NNVM_REGISTER_OP(_backward_npi_ldexp)
MXNET_OPERATOR_REGISTER_BINARY(_backward_npi_ldexp_scalar)
.add_argument("scalar", "float", "scalar value")
-.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = std::stod(attrs->dict["scalar"]); })
+.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = dmlc::stod(attrs->dict["scalar"]); })
.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Backward<cpu, mshadow_op::ldexp_grad>);
MXNET_OPERATOR_REGISTER_BINARY(_backward_npi_rldexp_scalar)
.add_argument("scalar", "float", "scalar value")
-.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = std::stod(attrs->dict["scalar"]); })
+.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = dmlc::stod(attrs->dict["scalar"]); })
.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Backward<cpu, mshadow_op::rldexp_grad>);
} // namespace op
diff --git a/src/operator/numpy/np_true_divide.cc b/src/operator/numpy/np_true_divide.cc
index 1e46cc9..6edfb4d 100644
--- a/src/operator/numpy/np_true_divide.cc
+++ b/src/operator/numpy/np_true_divide.cc
@@ -23,6 +23,7 @@
* \brief CPU Implementation of true_divide operator.
*/
+#include <dmlc/strtonum.h>
#include "./np_true_divide-inl.h"
namespace mxnet {
@@ -88,7 +89,7 @@ NNVM_REGISTER_OP(_npi_true_divide_scalar)
.set_num_inputs(1)
.set_num_outputs(1)
.set_attr_parser([](NodeAttrs* attrs) {
- attrs->parsed = std::stod(attrs->dict["scalar"]);
+ attrs->parsed = dmlc::stod(attrs->dict["scalar"]);
})
.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
.set_attr<nnvm::FInferType>("FInferType", TrueDivideType<1>)
@@ -111,7 +112,7 @@ NNVM_REGISTER_OP(_npi_rtrue_divide_scalar)
.set_num_inputs(1)
.set_num_outputs(1)
.set_attr_parser([](NodeAttrs* attrs) {
- attrs->parsed = std::stod(attrs->dict["scalar"]);
+ attrs->parsed = dmlc::stod(attrs->dict["scalar"]);
})
.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>)
.set_attr<nnvm::FInferType>("FInferType", TrueDivideType<1>)
diff --git a/src/operator/subgraph/mkldnn/mkldnn_post_quantize_align_scale_property.h b/src/operator/subgraph/mkldnn/mkldnn_post_quantize_align_scale_property.h
index c05c2a8..85691c1 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_post_quantize_align_scale_property.h
+++ b/src/operator/subgraph/mkldnn/mkldnn_post_quantize_align_scale_property.h
@@ -21,6 +21,7 @@
#define MXNET_OPERATOR_SUBGRAPH_MKLDNN_MKLDNN_POST_QUANTIZE_ALIGN_SCALE_PROPERTY_H_
#if MXNET_USE_MKLDNN == 1
+#include <dmlc/strtonum.h>
#include <string>
#include <vector>
#include "../common.h"
@@ -146,8 +147,8 @@ class SgMKLDNNPostQuantizeAlignScaleProperty : public SubgraphProperty {
float min_calib = 0.0f;
float max_calib = 0.0f;
for (size_t i = 0; i < subgraph_nodes.size(); ++i) {
- auto this_min_calib = std::stof(subgraph_nodes[i]->attrs.dict["min_calib_range"]);
- auto this_max_calib = std::stof(subgraph_nodes[i]->attrs.dict["max_calib_range"]);
+ auto this_min_calib = dmlc::stof(subgraph_nodes[i]->attrs.dict["min_calib_range"]);
+ auto this_max_calib = dmlc::stof(subgraph_nodes[i]->attrs.dict["max_calib_range"]);
if (min_calib > this_min_calib) min_calib = this_min_calib;
if (max_calib < this_max_calib) max_calib = this_max_calib;
}
diff --git a/src/operator/tensor/elemwise_binary_scalar_op.h b/src/operator/tensor/elemwise_binary_scalar_op.h
index f974332..53161ee 100644
--- a/src/operator/tensor/elemwise_binary_scalar_op.h
+++ b/src/operator/tensor/elemwise_binary_scalar_op.h
@@ -26,6 +26,7 @@
#define MXNET_OPERATOR_TENSOR_ELEMWISE_BINARY_SCALAR_OP_H_
#include <mxnet/operator_util.h>
+#include <dmlc/strtonum.h>
#include <vector>
#include <utility>
#include "../mshadow_op.h"
@@ -400,7 +401,7 @@ class BinaryScalarOp : public UnaryOp {
.set_num_inputs(1) \
.set_num_outputs(1) \
.set_attr_parser([](NodeAttrs* attrs) { \
- attrs->parsed = std::stod(attrs->dict["scalar"]); \
+ attrs->parsed = dmlc::stod(attrs->dict["scalar"]); \
}) \
.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>) \
.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>) \
diff --git a/src/operator/tensor/elemwise_binary_scalar_op_basic.cc b/src/operator/tensor/elemwise_binary_scalar_op_basic.cc
index ae356de..13014b3 100644
--- a/src/operator/tensor/elemwise_binary_scalar_op_basic.cc
+++ b/src/operator/tensor/elemwise_binary_scalar_op_basic.cc
@@ -22,6 +22,7 @@
* \file elemwise_binary_scalar_op_basic.cc
* \brief CPU Implementation of basic binary scalar functions.
*/
+#include <dmlc/strtonum.h>
#include "../../common/utils.h"
#include "./elemwise_binary_op.h"
#include "./elemwise_binary_scalar_op.h"
@@ -31,7 +32,7 @@
.set_num_inputs(1) \
.set_num_outputs(1) \
.set_attr_parser([](NodeAttrs* attrs) { \
- attrs->parsed = std::stod(attrs->dict["scalar"]); \
+ attrs->parsed = dmlc::stod(attrs->dict["scalar"]); \
}) \
.set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>) \
.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>) \
@@ -189,7 +190,7 @@ MXNET_OPERATOR_REGISTER_BINARY_SCALAR(_rdiv_scalar)
MXNET_OPERATOR_REGISTER_BINARY(_backward_rdiv_scalar)
.add_argument("scalar", "float", "scalar value")
-.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = std::stod(attrs->dict["scalar"]); })
+.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = dmlc::stod(attrs->dict["scalar"]); })
.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Backward<
cpu, mshadow_op::rdiv_grad>);
@@ -200,7 +201,7 @@ MXNET_OPERATOR_REGISTER_BINARY_SCALAR(_mod_scalar)
MXNET_OPERATOR_REGISTER_BINARY(_backward_mod_scalar)
.add_argument("scalar", "float", "scalar value")
-.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = std::stod(attrs->dict["scalar"]); })
+.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = dmlc::stod(attrs->dict["scalar"]); })
.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Backward<
cpu, mshadow_op::mod_grad>);
@@ -211,7 +212,7 @@ MXNET_OPERATOR_REGISTER_BINARY_SCALAR(_rmod_scalar)
MXNET_OPERATOR_REGISTER_BINARY(_backward_rmod_scalar)
.add_argument("scalar", "float", "scalar value")
-.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = std::stod(attrs->dict["scalar"]); })
+.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = dmlc::stod(attrs->dict["scalar"]); })
.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Backward<
cpu, mshadow_op::rmod_grad>);
diff --git a/src/operator/tensor/elemwise_binary_scalar_op_extended.cc b/src/operator/tensor/elemwise_binary_scalar_op_extended.cc
index 4ada2f0..7dd8cf4 100644
--- a/src/operator/tensor/elemwise_binary_scalar_op_extended.cc
+++ b/src/operator/tensor/elemwise_binary_scalar_op_extended.cc
@@ -22,6 +22,7 @@
* \file elemwise_binary_scalar_op_extended.cc
* \brief CPU Implementation of extended binary scalar functions.
*/
+#include <dmlc/strtonum.h>
#include "./elemwise_unary_op.h"
#include "./elemwise_binary_op.h"
#include "./elemwise_binary_scalar_op.h"
@@ -36,7 +37,7 @@ MXNET_OPERATOR_REGISTER_BINARY_SCALAR(_maximum_scalar)
MXNET_OPERATOR_REGISTER_BINARY(_backward_maximum_scalar)
.add_argument("scalar", "float", "scalar value")
-.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = std::stod(attrs->dict["scalar"]); })
+.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = dmlc::stod(attrs->dict["scalar"]); })
.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Backward<cpu, mshadow_op::ge>);
MXNET_OPERATOR_REGISTER_BINARY_SCALAR(_minimum_scalar)
@@ -47,7 +48,7 @@ MXNET_OPERATOR_REGISTER_BINARY_SCALAR(_minimum_scalar)
MXNET_OPERATOR_REGISTER_BINARY(_backward_minimum_scalar)
.add_argument("scalar", "float", "scalar value")
-.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = std::stod(attrs->dict["scalar"]); })
+.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = dmlc::stod(attrs->dict["scalar"]); })
.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Backward<cpu, mshadow_op::le>);
MXNET_OPERATOR_REGISTER_BINARY_SCALAR(_power_scalar)
@@ -57,7 +58,7 @@ MXNET_OPERATOR_REGISTER_BINARY_SCALAR(_power_scalar)
MXNET_OPERATOR_REGISTER_BINARY(_backward_power_scalar)
.add_argument("scalar", "float", "scalar value")
-.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = std::stod(attrs->dict["scalar"]); })
+.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = dmlc::stod(attrs->dict["scalar"]); })
.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Backward<
cpu, mshadow_op::power_grad>);
@@ -69,7 +70,7 @@ MXNET_OPERATOR_REGISTER_BINARY_SCALAR(_rpower_scalar)
MXNET_OPERATOR_REGISTER_BINARY(_backward_rpower_scalar)
.add_argument("scalar", "float", "scalar value")
-.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = std::stod(attrs->dict["scalar"]); })
+.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = dmlc::stod(attrs->dict["scalar"]); })
.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Backward<
cpu, mshadow_op::rpower_grad>);
@@ -82,7 +83,7 @@ MXNET_OPERATOR_REGISTER_BINARY_SCALAR(_hypot_scalar)
MXNET_OPERATOR_REGISTER_BINARY(_backward_hypot_scalar)
.add_argument("scalar", "float", "scalar value")
-.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = std::stod(attrs->dict["scalar"]); })
+.set_attr_parser([](NodeAttrs *attrs) { attrs->parsed = dmlc::stod(attrs->dict["scalar"]); })
.set_attr<FCompute>("FCompute<cpu>", BinaryScalarOp::Backward<
cpu, mshadow_op::hypot_grad_left>);
@@ -110,7 +111,7 @@ Example::
.set_num_outputs(1)
.set_attr_parser([](NodeAttrs* attrs) {
if (attrs->dict.find("scalar") != attrs->dict.end()) {
- attrs->parsed = std::stod(attrs->dict["scalar"]);
+ attrs->parsed = dmlc::stod(attrs->dict["scalar"]);
} else {
attrs->parsed = 1.0;
}
@@ -129,7 +130,7 @@ Example::
MXNET_OPERATOR_REGISTER_BINARY(_backward_smooth_l1)
.set_attr_parser([](NodeAttrs *attrs) {
if (attrs->dict.find("scalar") != attrs->dict.end()) {
- attrs->parsed = std::stod(attrs->dict["scalar"]);
+ attrs->parsed = dmlc::stod(attrs->dict["scalar"]);
} else {
attrs->parsed = 1.0;
}
diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py
index 481cd00..e22d529 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -34,6 +34,7 @@ from common import run_in_spawned_process
from nose.tools import assert_raises, ok_
import unittest
import os
+import locale
def check_rnn_consistency(cell1, cell2, T, N, I, H, grad_req, rtol=1e-2, atol=1e-4):
dshape = (N, T, I)
@@ -9973,6 +9974,43 @@ def test_broadcast_ops_on_misaligned_input_oneside():
mx.nd.waitall()
assert_almost_equal(f, expected)
+def test_scalarop_locale_invariance():
+ arr = mx.nd.zeros((1,))
+ prev = locale.getlocale(locale.LC_NUMERIC)
+ try:
+ locales_to_try = [
+ "en_DK.UTF-8", # Non-standard locale that uses , as the decimal separator, installed
+ # on English Ubuntu by default.
+ "ru_RU.UTF-8", # Uses , as the decimal separator. May not be installed on the system.
+ "German" # A Windows locale. Uses , as the decimal separator.
+ ]
+
+ locale_set = False
+ for loc in locales_to_try:
+ try:
+ locale.setlocale(locale.LC_NUMERIC, loc)
+ locale_set = True
+ break
+ except locale.Error as e:
+ print("Couldn't enable locale", loc, ": ", str(e))
+
+ if locale_set:
+ scalar = 0.3
+ assert "," in locale.str(scalar)
+ assert_almost_equal(
+ arr.asnumpy() + scalar,
+ (arr + scalar).asnumpy(),
+ rtol=1e-5,
+ atol=1e-5
+ )
+ else:
+ # Shouldn't happen on Windows: "German" should always be available.
+ raise unittest.SkipTest("Couldn't find a locale suitable for "
+ "test_scalarop_locale_invariance. Please install en_DK.UTF-8 "
+ "or ru_RU.UTF-8 to run this test.")
+ finally:
+ locale.setlocale(locale.LC_NUMERIC, prev)
+
if __name__ == '__main__':
import nose
nose.runmodule()