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wenleix wants to merge 4 commits into from
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codemode everything #53468

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8af368c
codemode everything
wenleix Mar 7, 2021
a9ebf84
Update on "codemode everything"
wenleix Mar 7, 2021
62824c5
Update on "codemode everything"
wenleix Mar 8, 2021
4a348f9
Update on "codemode everything"
wenleix Mar 8, 2021
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10 changes: 5 additions & 5 deletions aten/src/ATen/LegacyTHFunctionsCPU.cpp
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Expand Up @@ -442,7 +442,7 @@ Tensor _th_std(const Tensor & self, bool unbiased) {
AT_ERROR("_th_std not supported on CPUType for ", dispatch_scalar_type);
}
}
Tensor & _th_renorm_out(Tensor & result, const Tensor & self, Scalar p, int64_t dim, Scalar maxnorm) {
Tensor & _th_renorm_out(Tensor & result, const Tensor & self, const Scalar& p, int64_t dim, const Scalar& maxnorm) {
// DeviceGuard omitted
auto dispatch_scalar_type = infer_scalar_type(self);

Expand All @@ -468,7 +468,7 @@ Tensor & _th_renorm_out(Tensor & result, const Tensor & self, Scalar p, int64_t
}
return result;
}
Tensor _th_renorm(const Tensor & self, Scalar p, int64_t dim, Scalar maxnorm) {
Tensor _th_renorm(const Tensor & self, const Scalar& p, int64_t dim, const Scalar& maxnorm) {
// DeviceGuard omitted
auto dispatch_scalar_type = infer_scalar_type(self);
auto result_ = c10::make_intrusive<TensorImpl, UndefinedTensorImpl>(c10::Storage(c10::Storage::use_byte_size_t(), 0, allocator(), true),DispatchKey::CPU, scalarTypeToTypeMeta(dispatch_scalar_type)).release();
Expand All @@ -493,7 +493,7 @@ Tensor _th_renorm(const Tensor & self, Scalar p, int64_t dim, Scalar maxnorm) {
}
return result;
}
Tensor & _th_renorm_(Tensor & self, Scalar p, int64_t dim, Scalar maxnorm) {
Tensor & _th_renorm_(Tensor & self, const Scalar& p, int64_t dim, const Scalar& maxnorm) {
// DeviceGuard omitted
auto dispatch_scalar_type = infer_scalar_type(self);

Expand All @@ -517,7 +517,7 @@ Tensor & _th_renorm_(Tensor & self, Scalar p, int64_t dim, Scalar maxnorm) {
}
return self;
}
Tensor & _th_histc_out(Tensor & result, const Tensor & self, int64_t bins, Scalar min, Scalar max) {
Tensor & _th_histc_out(Tensor & result, const Tensor & self, int64_t bins, const Scalar& min, const Scalar& max) {
// DeviceGuard omitted
auto dispatch_scalar_type = infer_scalar_type(self);

Expand All @@ -543,7 +543,7 @@ Tensor & _th_histc_out(Tensor & result, const Tensor & self, int64_t bins, Scala
}
return result;
}
Tensor _th_histc(const Tensor & self, int64_t bins, Scalar min, Scalar max) {
Tensor _th_histc(const Tensor & self, int64_t bins, const Scalar& min, const Scalar& max) {
// DeviceGuard omitted
auto dispatch_scalar_type = infer_scalar_type(self);
auto result_ = c10::make_intrusive<TensorImpl, UndefinedTensorImpl>(c10::Storage(c10::Storage::use_byte_size_t(), 0, allocator(), true),DispatchKey::CPU, scalarTypeToTypeMeta(dispatch_scalar_type)).release();
Expand Down
10 changes: 5 additions & 5 deletions aten/src/ATen/LegacyTHFunctionsCPU.h
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Expand Up @@ -30,11 +30,11 @@ std::tuple<Tensor &,Tensor &> _th_mode_out(Tensor & values, Tensor & indices, co
std::tuple<Tensor,Tensor> _th_mode(const Tensor & self, int64_t dim, bool keepdim);
Tensor _th_var(const Tensor & self, bool unbiased);
Tensor _th_std(const Tensor & self, bool unbiased);
Tensor & _th_renorm_out(Tensor & result, const Tensor & self, Scalar p, int64_t dim, Scalar maxnorm);
Tensor _th_renorm(const Tensor & self, Scalar p, int64_t dim, Scalar maxnorm);
Tensor & _th_renorm_(Tensor & self, Scalar p, int64_t dim, Scalar maxnorm);
Tensor & _th_histc_out(Tensor & result, const Tensor & self, int64_t bins, Scalar min, Scalar max);
Tensor _th_histc(const Tensor & self, int64_t bins, Scalar min, Scalar max);
Tensor & _th_renorm_out(Tensor & result, const Tensor & self, const Scalar& p, int64_t dim, const Scalar& maxnorm);
Tensor _th_renorm(const Tensor & self, const Scalar& p, int64_t dim, const Scalar& maxnorm);
Tensor & _th_renorm_(Tensor & self, const Scalar& p, int64_t dim, const Scalar& maxnorm);
Tensor & _th_histc_out(Tensor & result, const Tensor & self, int64_t bins, const Scalar& min, const Scalar& max);
Tensor _th_histc(const Tensor & self, int64_t bins, const Scalar& min, const Scalar& max);
std::tuple<Tensor &,Tensor &> _th_gels_out(Tensor & res1, Tensor & res2, const Tensor & self, const Tensor & A);
std::tuple<Tensor,Tensor> _th_gels(const Tensor & self, const Tensor & A);
std::tuple<Tensor &,Tensor &> _th_geqrf_out(Tensor & res1, Tensor & res2, const Tensor & self);
Expand Down
26 changes: 13 additions & 13 deletions aten/src/ATen/LegacyTHFunctionsCUDA.h
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Expand Up @@ -18,8 +18,8 @@ namespace native {
namespace legacy {
namespace cuda {

Tensor & _th_masked_fill_(Tensor & self, const Tensor & mask, Scalar value);
Tensor & _th_masked_fill_bool_(Tensor & self, const Tensor & mask, Scalar value);
Tensor & _th_masked_fill_(Tensor & self, const Tensor & mask, const Scalar& value);
Tensor & _th_masked_fill_bool_(Tensor & self, const Tensor & mask, const Scalar& value);
Tensor & _th_index_copy_(Tensor & self, int64_t dim, const Tensor & index, const Tensor & source);
Tensor & _th_take_out(Tensor & result, const Tensor & self, const Tensor & index);
Tensor _th_take(const Tensor & self, const Tensor & index);
Expand All @@ -32,9 +32,9 @@ std::tuple<Tensor &,Tensor &> _th_sort_out_stable(Tensor & values, Tensor & indi
std::tuple<Tensor,Tensor> _th_sort_stable(const Tensor & self, c10::optional<bool> stable, int64_t dim, bool descending);
std::tuple<Tensor &,Tensor &> _th_topk_out(Tensor & values, Tensor & indices, const Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted);
std::tuple<Tensor,Tensor> _th_topk(const Tensor & self, int64_t k, int64_t dim, bool largest, bool sorted);
Tensor & _th_renorm_out(Tensor & result, const Tensor & self, Scalar p, int64_t dim, Scalar maxnorm);
Tensor _th_renorm(const Tensor & self, Scalar p, int64_t dim, Scalar maxnorm);
Tensor & _th_renorm_(Tensor & self, Scalar p, int64_t dim, Scalar maxnorm);
Tensor & _th_renorm_out(Tensor & result, const Tensor & self, const Scalar& p, int64_t dim, const Scalar& maxnorm);
Tensor _th_renorm(const Tensor & self, const Scalar& p, int64_t dim, const Scalar& maxnorm);
Tensor & _th_renorm_(Tensor & self, const Scalar& p, int64_t dim, const Scalar& maxnorm);
Tensor & _th_cross_kernel_out(Tensor & result, const Tensor & self, const Tensor & other, int64_t dim);
Tensor _th_cross_kernel(const Tensor & self, const Tensor & other, int64_t dim);
std::tuple<Tensor &,Tensor &> _th_gels_out(Tensor & res1, Tensor & res2, const Tensor & self, const Tensor & A);
Expand All @@ -44,10 +44,10 @@ Tensor _th_potri(const Tensor & self, bool upper);
std::tuple<Tensor &,Tensor &> _th_geqrf_out(Tensor & res1, Tensor & res2, const Tensor & self);
std::tuple<Tensor,Tensor> _th_geqrf(const Tensor & self);
Tensor & _th_copy_ignoring_overlaps_(Tensor & self, const Tensor & src);
Tensor & _thnn_multi_margin_loss_forward_out(Tensor & output, const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, int64_t reduction);
Tensor _thnn_multi_margin_loss_forward(const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, int64_t reduction);
Tensor & _thnn_multi_margin_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, int64_t reduction);
Tensor _thnn_multi_margin_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, Scalar p, Scalar margin, const Tensor & weight, int64_t reduction);
Tensor & _thnn_multi_margin_loss_forward_out(Tensor & output, const Tensor & self, const Tensor & target, const Scalar& p, const Scalar& margin, const Tensor & weight, int64_t reduction);
Tensor _thnn_multi_margin_loss_forward(const Tensor & self, const Tensor & target, const Scalar& p, const Scalar& margin, const Tensor & weight, int64_t reduction);
Tensor & _thnn_multi_margin_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, const Scalar& p, const Scalar& margin, const Tensor & weight, int64_t reduction);
Tensor _thnn_multi_margin_loss_backward(const Tensor & grad_output, const Tensor & self, const Tensor & target, const Scalar& p, const Scalar& margin, const Tensor & weight, int64_t reduction);
std::tuple<Tensor &,Tensor &> _thnn_multilabel_margin_loss_forward_out(Tensor & output, Tensor & is_target, const Tensor & self, const Tensor & target, int64_t reduction);
std::tuple<Tensor,Tensor> _thnn_multilabel_margin_loss_forward(const Tensor & self, const Tensor & target, int64_t reduction);
Tensor & _thnn_multilabel_margin_loss_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & target, int64_t reduction, const Tensor & is_target);
Expand All @@ -68,10 +68,10 @@ std::tuple<Tensor &,Tensor &> _thnn_log_sigmoid_forward_out(Tensor & output, Ten
std::tuple<Tensor,Tensor> _thnn_log_sigmoid_forward(const Tensor & self);
Tensor & _thnn_log_sigmoid_backward_out(Tensor & grad_input, const Tensor & grad_output, const Tensor & self, const Tensor & buffer);
Tensor _thnn_log_sigmoid_backward(const Tensor & grad_output, const Tensor & self, const Tensor & buffer);
Tensor & _thnn_rrelu_with_noise_forward_out(Tensor & output, const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training, c10::optional<at::Generator> generator);
Tensor _thnn_rrelu_with_noise_forward(const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training, c10::optional<at::Generator> generator);
Tensor _thnn_rrelu_with_noise_backward(const Tensor & grad_output, const Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training);
Tensor & _thnn_rrelu_with_noise_forward_(Tensor & self, const Tensor & noise, Scalar lower, Scalar upper, bool training, c10::optional<at::Generator> generator);
Tensor & _thnn_rrelu_with_noise_forward_out(Tensor & output, const Tensor & self, const Tensor & noise, const Scalar& lower, const Scalar& upper, bool training, c10::optional<at::Generator> generator);
Tensor _thnn_rrelu_with_noise_forward(const Tensor & self, const Tensor & noise, const Scalar& lower, const Scalar& upper, bool training, c10::optional<at::Generator> generator);
Tensor _thnn_rrelu_with_noise_backward(const Tensor & grad_output, const Tensor & self, const Tensor & noise, const Scalar& lower, const Scalar& upper, bool training);
Tensor & _thnn_rrelu_with_noise_forward_(Tensor & self, const Tensor & noise, const Scalar& lower, const Scalar& upper, bool training, c10::optional<at::Generator> generator);
std::tuple<Tensor &,Tensor &,Tensor &> _thnn_conv2d_forward_out(Tensor & output, Tensor & columns, Tensor & ones, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding);
std::tuple<Tensor,Tensor,Tensor> _thnn_conv2d_forward(const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, const Tensor & bias, IntArrayRef stride, IntArrayRef padding);
std::tuple<Tensor &,Tensor &,Tensor &> _thnn_conv2d_backward_out(Tensor & grad_input, Tensor & grad_weight, Tensor & grad_bias, const Tensor & grad_output, const Tensor & self, const Tensor & weight, IntArrayRef kernel_size, IntArrayRef stride, IntArrayRef padding, const Tensor & columns, const Tensor & ones);
Expand Down
6 changes: 3 additions & 3 deletions aten/src/ATen/ScalarOps.cpp
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Expand Up @@ -13,23 +13,23 @@
namespace at {
namespace {
template <typename scalar_t>
inline void fill_inplace(Tensor& self, Scalar value_scalar) {
inline void fill_inplace(Tensor& self, const Scalar& value_scalar) {
auto value = value_scalar.to<scalar_t>();
scalar_t* dptr = static_cast<scalar_t*>(self.data_ptr());
*dptr = value;
}
}

namespace detail {
Tensor& scalar_fill(Tensor& self, Scalar value) {
Tensor& scalar_fill(Tensor& self, const Scalar& value) {
AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND3(
kHalf, kBool, kBFloat16, self.scalar_type(), "fill_out", [&]() {
fill_inplace<scalar_t>(self, value);
});
return self;
}

Tensor scalar_tensor_static(Scalar s, c10::optional<ScalarType> dtype_opt, c10::optional<Device> device_opt) {
Tensor scalar_tensor_static(const Scalar& s, c10::optional<ScalarType> dtype_opt, c10::optional<Device> device_opt) {
at::tracer::impl::NoTracerDispatchMode tracer_guard;
at::AutoNonVariableTypeMode non_var_type_mode(true);
auto result = at::detail::empty_cpu({}, dtype_opt, c10::nullopt, device_opt, c10::nullopt, c10::nullopt);
Expand Down
4 changes: 2 additions & 2 deletions aten/src/ATen/ScalarOps.h
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Expand Up @@ -11,8 +11,8 @@ namespace detail {
// Ideally this fast pass should be implemented in TensorIterator,
// but we also want to skip compute_types which in not avoidable
// in TensorIterator for now.
Tensor& scalar_fill(Tensor& self, Scalar value);
TORCH_API Tensor scalar_tensor_static(Scalar s, c10::optional<ScalarType> dtype_opt, c10::optional<Device> device_opt);
Tensor& scalar_fill(Tensor& self, const Scalar& value);
TORCH_API Tensor scalar_tensor_static(const Scalar& s, c10::optional<ScalarType> dtype_opt, c10::optional<Device> device_opt);
} // namespace detail
} // namespace at

Expand Down
6 changes: 3 additions & 3 deletions aten/src/ATen/TensorIndexing.cpp
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Expand Up @@ -41,7 +41,7 @@ std::ostream& operator<<(std::ostream& stream, const std::vector<TensorIndex>& t

// This mirrors `THPVariable_setitem` in torch/csrc/autograd/python_variable_indexing.cpp
// for "the assigned value is a Scalar" case
static inline void set_item(Tensor& self, ArrayRef<TensorIndex> indices, Scalar v) {
static inline void set_item(Tensor& self, ArrayRef<TensorIndex> indices, const Scalar& v) {
Tensor value;

{
Expand Down Expand Up @@ -74,7 +74,7 @@ Tensor & Tensor::index_put_(ArrayRef<at::indexing::TensorIndex> indices, Tensor
at::indexing::set_item(*this, indices, rhs);
return *this;
}
Tensor & Tensor::index_put_(ArrayRef<at::indexing::TensorIndex> indices, Scalar v) {
Tensor & Tensor::index_put_(ArrayRef<at::indexing::TensorIndex> indices, const Scalar& v) {
TORCH_CHECK(indices.size() > 0, "Passing an empty index list to Tensor::index_put_() is not valid syntax");
OptionalDeviceGuard device_guard(device_of(*this));
at::indexing::set_item(*this, indices, v);
Expand All @@ -83,7 +83,7 @@ Tensor & Tensor::index_put_(ArrayRef<at::indexing::TensorIndex> indices, Scalar
Tensor & Tensor::index_put_(std::initializer_list<at::indexing::TensorIndex> indices, Tensor const & rhs) {
return index_put_(ArrayRef<at::indexing::TensorIndex>(indices), rhs);
}
Tensor & Tensor::index_put_(std::initializer_list<at::indexing::TensorIndex> indices, Scalar v) {
Tensor & Tensor::index_put_(std::initializer_list<at::indexing::TensorIndex> indices, const Scalar& v) {
return index_put_(ArrayRef<at::indexing::TensorIndex>(indices), v);
}

Expand Down
4 changes: 2 additions & 2 deletions aten/src/ATen/TensorIndexing.h
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Expand Up @@ -252,7 +252,7 @@ static inline Tensor boolToIndexingTensor(const Tensor& self, bool value, const
}
}

static inline Tensor scalarToTensorNonNativeDeviceType(Scalar v, const TensorOptions& options) {
static inline Tensor scalarToTensorNonNativeDeviceType(const Scalar& v, const TensorOptions& options) {
return at::scalar_tensor(v, options);
}

Expand Down Expand Up @@ -319,7 +319,7 @@ static inline int64_t count_specified_dimensions(const ArrayRef<TensorIndex>& in
//
// The rest of the functions are in `at::indexing::impl` namespace, signifying
// that they shouldn't be used from Python indexing implementation.
static inline Tensor scalarToTensor(Scalar v, const TensorOptions& options, const at::Device& self_device) {
static inline Tensor scalarToTensor(const Scalar& v, const TensorOptions& options, const at::Device& self_device) {
if (self_device == at::kCPU) {
return at::detail::scalar_tensor_static(v, options.dtype_opt()->toScalarType(), self_device);
} else {
Expand Down
4 changes: 2 additions & 2 deletions aten/src/ATen/TensorOperators.h
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Expand Up @@ -99,10 +99,10 @@ _(!=,x.ne(y), y.ne(x))
static inline Tensor operator op(const Tensor & x, const Tensor & y) { \
return body; \
} \
static inline Tensor operator op(const Tensor & x, Scalar y) { \
static inline Tensor operator op(const Tensor & x, const Scalar& y) { \
return body; \
} \
static inline Tensor operator op(Scalar x, const Tensor & y) { \
static inline Tensor operator op(const Scalar& x, const Tensor & y) { \
return reverse_scalar_body; \
}

Expand Down
2 changes: 1 addition & 1 deletion aten/src/ATen/core/ivalue.h
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Expand Up @@ -661,7 +661,7 @@ struct TORCH_API IValue final {
}

// Scalar, which gets encoded as either an Int, a Double or a ComplexDouble
IValue(at::Scalar s) : IValue() {
IValue(const at::Scalar& s) : IValue() {
if (s.isFloatingPoint()) {
*this = s.toDouble();
} else if (s.isComplex()) {
Expand Down
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