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((self.device().is_cuda() && cuda_enabled) ||
(self.device().is_cpu() && cpu_enabled))
) {
at::ScalarType target = at::ScalarType::Undefined;
if (self.device().is_cuda()) {
target = cuda_dtype;
} else if (self.device().is_cpu()) {
target = cpu_dtype;
}
TORCH_INTERNAL_ASSERT(target != at::ScalarType::Undefined, "_autocast_to_reduced_precision requires legit ScalarType argument for given device");
return to_impl(
self, target, c10::nullopt, c10::nullopt, c10::nullopt, false, false, c10::nullopt);
} else {
return self;
}
}
// If input tensor is fp16, cast it to fp32, otherwise leave it alone.
// (this is intended to be used internally by the JIT autocast implementation)
Tensor _autocast_to_full_precision(const Tensor& self, bool cuda_enabled, bool cpu_enabled) {
if ((self.dtype() == at::ScalarType::Half || self.dtype() == at::ScalarType::BFloat16) &&
((self.device().is_cuda() && cuda_enabled) ||
(self.device().is_cpu() && cpu_enabled))
) {
return to_impl(
self, at::ScalarType::Float, c10::nullopt, c10::nullopt, c10::nullopt, false, false, c10::nullopt);
} else {
return self;
}
}
Tensor to(
const Tensor& self,
c10::optional<ScalarType> dtype,
c10::optional<Layout> layout,
c10::optional<Device> device,
c10::optional<bool> pin_memory,
bool non_blocking,
bool copy,
c10::optional<c10::MemoryFormat> optional_memory_format
) {
return to_impl(
self,
dtype,
layout,
ensure_has_index(device),
pin_memory,
non_blocking,
copy,
optional_memory_format);
}
Tensor to(const Tensor& self, Device device, ScalarType dtype, bool non_blocking, bool copy, c10::optional<c10::MemoryFormat> optional_memory_format) {
return to_impl(
self,
dtype,
nullopt,
ensure_has_index(device),
nullopt,
non_blocking,
copy,
optional_memory_format);
}
Tensor to(const Tensor& self, ScalarType dtype, bool non_blocking, bool copy, c10::optional<c10::MemoryFormat> optional_memory_format) {
return to_impl(
self,
dtype,
nullopt,
nullopt,
nullopt,
non_blocking,
copy,
optional_memory_format);
}
Tensor to(const Tensor& self, const Tensor& other, bool non_blocking, bool copy, c10::optional<c10::MemoryFormat> optional_memory_format) {
auto options = other.options();
return to_impl(
self,
options.dtype().toScalarType(),
options.layout(),
options.device(),
options.pinned_memory(),
non_blocking,
copy,
optional_memory_format);
}
// This op is important primarily for lazy / graph-based backends.
// While this vanilla implementation loops through each tensor and independently converts it to cpu,
// a lazy backend like XLA might need to tell sync updates across tensors.
std::vector<Tensor> _to_cpu(TensorList tensors) {
std::vector<Tensor> cpu_tensors;
for (const auto& t : tensors) {
cpu_tensors.push_back(t.cpu());
}
return cpu_tensors;