Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Revert "[Reland]Change ATEN generator argument type to const std::optional<Generator>&" #6819

Merged
merged 1 commit into from
Mar 26, 2024
Merged

Revert "[Reland]Change ATEN generator argument type to const std::optional<Generator>&" #6819

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
41 changes: 18 additions & 23 deletions torch_xla/csrc/aten_xla_type.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -920,7 +920,7 @@ at::Tensor XLANativeFunctions::baddbmm(const at::Tensor& self,
}

at::Tensor XLANativeFunctions::bernoulli(
const at::Tensor& self, const std::optional<at::Generator>& generator) {
const at::Tensor& self, c10::optional<at::Generator> generator) {
TORCH_LAZY_FN_COUNTER_TIMED_TRACING("xla::");
if (generator.has_value() && generator->defined()) {
return at::native::call_fallback_fn<&xla_cpu_fallback,
Expand All @@ -932,8 +932,7 @@ at::Tensor XLANativeFunctions::bernoulli(
}

at::Tensor XLANativeFunctions::bernoulli(
const at::Tensor& self, double p,
const std::optional<at::Generator>& generator) {
const at::Tensor& self, double p, c10::optional<at::Generator> generator) {
TORCH_LAZY_FN_COUNTER_TIMED_TRACING("xla::");
if (generator.has_value() && generator->defined()) {
return at::native::call_fallback_fn<
Expand All @@ -945,7 +944,7 @@ at::Tensor XLANativeFunctions::bernoulli(

at::Tensor& XLANativeFunctions::bernoulli_(
at::Tensor& self, const at::Tensor& p,
const std::optional<at::Generator>& generator) {
c10::optional<at::Generator> generator) {
TORCH_LAZY_FN_COUNTER_TIMED_TRACING("xla::");
if (generator.has_value() && generator->defined()) {
return at::native::call_fallback_fn<
Expand Down Expand Up @@ -1348,8 +1347,7 @@ at::Tensor XLANativeFunctions::expand_copy_symint(const at::Tensor& self,
}

at::Tensor& XLANativeFunctions::exponential_(
at::Tensor& self, double lambd,
const std::optional<at::Generator>& generator) {
at::Tensor& self, double lambd, c10::optional<at::Generator> generator) {
TORCH_LAZY_FN_COUNTER_TIMED_TRACING("xla::");
if (generator.has_value() && generator->defined()) {
return at::native::call_fallback_fn<&xla_cpu_fallback,
Expand Down Expand Up @@ -2032,7 +2030,7 @@ at::Tensor XLANativeFunctions::mul(const at::Tensor& self,

at::Tensor XLANativeFunctions::multinomial(
const at::Tensor& self, int64_t num_samples, bool replacement,
const std::optional<at::Generator>& generator) {
c10::optional<at::Generator> generator) {
XLA_CHECK(num_samples > 0)
<< "Multinomial number of samples must be greater than 0";
XLA_CHECK(at::isFloatingType(self.scalar_type()))
Expand Down Expand Up @@ -2346,9 +2344,8 @@ at::Tensor XLANativeFunctions::norm(const at::Tensor& self,
bridge::GetXlaTensor(self), p, c10::nullopt, dim, keepdim));
}

at::Tensor XLANativeFunctions::normal(
const at::Tensor& mean, double std,
const std::optional<at::Generator>& generator) {
at::Tensor XLANativeFunctions::normal(const at::Tensor& mean, double std,
c10::optional<at::Generator> generator) {
TORCH_LAZY_FN_COUNTER_TIMED_TRACING("xla::");
if (generator.has_value() && generator->defined()) {
return at::native::call_fallback_fn<
Expand All @@ -2359,9 +2356,8 @@ at::Tensor XLANativeFunctions::normal(
tensor_methods::normal(bridge::GetXlaTensor(mean), std));
}

at::Tensor XLANativeFunctions::normal(
double mean, const at::Tensor& std,
const std::optional<at::Generator>& generator) {
at::Tensor XLANativeFunctions::normal(double mean, const at::Tensor& std,
c10::optional<at::Generator> generator) {
TORCH_LAZY_FN_COUNTER_TIMED_TRACING("xla::");
if (generator.has_value() && generator->defined()) {
return at::native::call_fallback_fn<
Expand All @@ -2372,9 +2368,9 @@ at::Tensor XLANativeFunctions::normal(
tensor_methods::normal(mean, bridge::GetXlaTensor(std)));
}

at::Tensor XLANativeFunctions::normal(
const at::Tensor& mean, const at::Tensor& std,
const std::optional<at::Generator>& generator) {
at::Tensor XLANativeFunctions::normal(const at::Tensor& mean,
const at::Tensor& std,
c10::optional<at::Generator> generator) {
TORCH_LAZY_FN_COUNTER_TIMED_TRACING("xla::");
if (generator.has_value() && generator->defined()) {
return at::native::call_fallback_fn<
Expand All @@ -2387,7 +2383,7 @@ at::Tensor XLANativeFunctions::normal(

at::Tensor& XLANativeFunctions::normal_(
at::Tensor& self, double mean, double std,
const std::optional<at::Generator>& generator) {
c10::optional<at::Generator> generator) {
TORCH_LAZY_FN_COUNTER_TIMED_TRACING("xla::");
if (generator.has_value() && generator->defined()) {
return at::native::call_fallback_fn<&xla_cpu_fallback,
Expand Down Expand Up @@ -2531,7 +2527,7 @@ std::tuple<at::Tensor, at::Tensor> XLANativeFunctions::qr(
// The value generated should be within (from, to].
at::Tensor& XLANativeFunctions::random_(
at::Tensor& self, int64_t from, c10::optional<int64_t> to,
const std::optional<at::Generator>& generator) {
c10::optional<at::Generator> generator) {
TORCH_LAZY_FN_COUNTER_TIMED_TRACING("xla::");
if (generator.has_value() && generator->defined()) {
return at::native::call_fallback_fn<
Expand All @@ -2551,8 +2547,7 @@ at::Tensor& XLANativeFunctions::random_(

// The value generated should be in (0, to].
at::Tensor& XLANativeFunctions::random_(
at::Tensor& self, int64_t to,
const std::optional<at::Generator>& generator) {
at::Tensor& self, int64_t to, c10::optional<at::Generator> generator) {
TORCH_LAZY_FN_COUNTER_TIMED_TRACING("xla::");
if (generator.has_value() && generator->defined()) {
return at::native::call_fallback_fn<&xla_cpu_fallback,
Expand All @@ -2568,7 +2563,7 @@ at::Tensor& XLANativeFunctions::random_(

// The value generated should be in (self_type_min, self_type_max).
at::Tensor& XLANativeFunctions::random_(
at::Tensor& self, const std::optional<at::Generator>& generator) {
at::Tensor& self, c10::optional<at::Generator> generator) {
TORCH_LAZY_FN_COUNTER_TIMED_TRACING("xla::");
if (generator.has_value() && generator->defined()) {
return at::native::call_fallback_fn<&xla_cpu_fallback,
Expand Down Expand Up @@ -2741,7 +2736,7 @@ at::Tensor XLANativeFunctions::roll(const at::Tensor& self,
at::Tensor XLANativeFunctions::rrelu_with_noise(
const at::Tensor& self, const at::Tensor& noise, const at::Scalar& lower,
const at::Scalar& upper, bool training,
const std::optional<at::Generator>& generator) {
c10::optional<at::Generator> generator) {
TORCH_LAZY_FN_COUNTER_TIMED_TRACING("xla::");
if (generator.has_value() && generator->defined()) {
// The fallback path for rrelu_with_noise when training=true is wrong
Expand Down Expand Up @@ -3280,7 +3275,7 @@ std::vector<at::Tensor> XLANativeFunctions::unbind_copy(const at::Tensor& self,

at::Tensor& XLANativeFunctions::uniform_(
at::Tensor& self, double from, double to,
const std::optional<at::Generator>& generator) {
c10::optional<at::Generator> generator) {
TORCH_LAZY_FN_COUNTER_TIMED_TRACING("xla::");
if (generator.has_value() && generator->defined()) {
return at::native::call_fallback_fn<&xla_cpu_fallback,
Expand Down
Loading