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Normalization.cu
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#include <ATen/native/cuda/Normalization.cuh>
namespace at { namespace native {
std::tuple<Tensor&, Tensor&, Tensor&> batch_norm_cuda_out(Tensor& output, Tensor& save_mean, Tensor& save_invstd, const Tensor& self, const Tensor& weight, const Tensor& bias,
const Tensor& running_mean, const Tensor& running_var, bool train, double momentum, double epsilon) {
AT_DISPATCH_FLOATING_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, self.scalar_type(), "batch_norm_cuda", [&] {
auto mean_st = running_mean.dtype();
auto var_st = running_var.dtype();
TORCH_CHECK(mean_st == var_st, "running_mean and running_var need to have the same data types");
bool is_half_float = std::is_same<scalar_t, at::Half>::value && mean_st == at::kFloat;
bool is_bfloat16_float = std::is_same<scalar_t, at::BFloat16>::value && mean_st == at::kFloat;
if (cuda::detail::canUse32BitIndexMath(self)) {
if (is_half_float || is_bfloat16_float) {
batch_norm_cuda_template<scalar_t, float, int32_t>(output, save_mean, save_invstd, self, weight, bias, running_mean, running_var, train, momentum, epsilon);
} else {
batch_norm_cuda_template<scalar_t, scalar_t, int32_t>(output, save_mean, save_invstd, self, weight, bias, running_mean, running_var, train, momentum, epsilon);
}
} else {
if (is_half_float || is_bfloat16_float) {
batch_norm_cuda_template<scalar_t, float, int64_t>(output, save_mean, save_invstd, self, weight, bias, running_mean, running_var, train, momentum, epsilon);
} else {
batch_norm_cuda_template<scalar_t, scalar_t, int64_t>(output, save_mean, save_invstd, self, weight, bias, running_mean, running_var, train, momentum, epsilon);
}
}
});
return std::tuple<Tensor&, Tensor&, Tensor&>(output, save_mean, save_invstd);
}
std::tuple<Tensor, Tensor, Tensor> batch_norm_cuda(const Tensor& self, const Tensor& weight, const Tensor& bias,
const Tensor& running_mean, const Tensor& running_var, bool train, double momentum, double epsilon) {
auto output = at::empty_like(self, at::MemoryFormat::Contiguous);
int64_t n_input = self.size(1);
auto input_options = self.options();
// Accumulate in higher precision if input is half/bfloat16
if (self.scalar_type() == at::ScalarType::Half || self.scalar_type() == at::ScalarType::BFloat16) {
input_options = input_options.dtype(ScalarType::Float);
}
Tensor save_mean, save_invstd;
if (train) {
save_mean = at::empty({n_input}, input_options);
save_invstd = at::empty({n_input}, input_options);
} else {
save_mean = at::empty({0}, input_options);
save_invstd = at::empty({0}, input_options);
}
batch_norm_cuda_out(output, save_mean, save_invstd, self, weight, bias, running_mean, running_var, train, momentum, epsilon);
return std::make_tuple(output, save_mean, save_invstd);
}
std::tuple<Tensor, Tensor, Tensor> batch_norm_backward_cuda(const Tensor& grad_out, const Tensor& self, const Tensor& weight, const Tensor& running_mean, const Tensor& running_var,
const Tensor& save_mean, const Tensor& save_invstd, bool train, double epsilon, std::array<bool,3> grad_input_mask) {
return AT_DISPATCH_FLOATING_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, self.scalar_type(), "batch_norm_backward_cuda", [&] {
auto mean_st = running_mean.dtype();
auto var_st = running_var.dtype();
TORCH_CHECK(mean_st == var_st, "running_mean and running_var need to have the same data types");
bool is_half_float = std::is_same<scalar_t, at::Half>::value && mean_st == at::kFloat;
bool is_bfloat16_float = std::is_same<scalar_t, at::BFloat16>::value && mean_st == at::kFloat;
if (cuda::detail::canUse32BitIndexMath(self)) {
if (is_half_float || is_bfloat16_float) {
return batch_norm_backward_cuda_template<scalar_t, float, int32_t>(grad_out, self, weight, running_mean, running_var, save_mean, save_invstd, train, epsilon, grad_input_mask);
} else {
return batch_norm_backward_cuda_template<scalar_t, scalar_t, int32_t>(grad_out, self, weight, running_mean, running_var, save_mean, save_invstd, train, epsilon, grad_input_mask);
}
} else {
if (is_half_float || is_bfloat16_float) {
return batch_norm_backward_cuda_template<scalar_t, float, int64_t>(grad_out, self, weight, running_mean, running_var, save_mean, save_invstd, train, epsilon, grad_input_mask);
} else {
return batch_norm_backward_cuda_template<scalar_t, scalar_t, int64_t>(grad_out, self, weight, running_mean, running_var, save_mean, save_invstd, train, epsilon, grad_input_mask);
}
}
});
}
std::tuple<Tensor, Tensor> batch_norm_stats_cuda(const Tensor& self, double epsilon) {
return AT_DISPATCH_FLOATING_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, self.scalar_type(), "batch_norm_stats_cuda", [&] {
if (cuda::detail::canUse32BitIndexMath(self)) {
return batch_norm_stats_cuda_template<scalar_t, int32_t>(self, epsilon);
} else {
return batch_norm_stats_cuda_template<scalar_t, int64_t>(self, epsilon);
}
});
}
Tensor batch_norm_elemt_cuda(const Tensor& self, const Tensor& weight, const Tensor& bias,
const Tensor& mean, const Tensor& invstd, double epsilon) {
auto output = at::empty_like(self, LEGACY_CONTIGUOUS_MEMORY_FORMAT);
batch_norm_elemt_cuda_out(output, self, weight, bias, mean, invstd, epsilon);
return output;
}
Tensor& batch_norm_elemt_cuda_out(Tensor& output, const Tensor& self, const Tensor& weight, const Tensor& bias,
const Tensor& mean, const Tensor& invstd, double epsilon) {
AT_DISPATCH_FLOATING_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, self.scalar_type(), "batch_norm_elemt", [&] {
auto mean_st = mean.dtype();
auto invstd_st = invstd.dtype();
TORCH_CHECK(mean_st == invstd_st, "mean and invstd need to have the same data types");
bool is_half_float = std::is_same<scalar_t, at::Half>::value && mean_st == at::kFloat;
bool is_bfloat16_float = std::is_same<scalar_t, at::BFloat16>::value && mean_st == at::kFloat;
if (cuda::detail::canUse32BitIndexMath(self)) {
if (is_half_float || is_bfloat16_float) {
batch_norm_elemt_cuda_template<scalar_t, float, int32_t>(output, self, weight, bias, mean, invstd, epsilon);
} else {
batch_norm_elemt_cuda_template<scalar_t, scalar_t, int32_t>(output, self, weight, bias, mean, invstd, epsilon);
}
} else {
if (is_half_float || is_bfloat16_float) {
batch_norm_elemt_cuda_template<scalar_t, float, int64_t>(output, self, weight, bias, mean, invstd, epsilon);
} else {
batch_norm_elemt_cuda_template<scalar_t, scalar_t, int64_t>(output, self, weight, bias, mean, invstd, epsilon);
}
}
});
return output;
}
// accepting input(self) here to determine template data types, since running_mean/running_var are optional
std::tuple<Tensor, Tensor> batch_norm_gather_stats_cuda(const Tensor& self, const Tensor& mean, const Tensor& invstd, const Tensor& running_mean,
const Tensor& running_var, double momentum, double epsilon, int64_t count) {
std::vector<int64_t> counts(mean.size(0), count);
Tensor counts_ = at::from_blob((void*)counts.data(), {(int64_t)counts.size()}, self.options().dtype(at::kLong).device(at::kCPU));
counts_ = counts_.to(self.device()).to(running_mean.defined() ? running_mean.dtype() : self.dtype());
return batch_norm_gather_stats_with_counts_cuda(self, mean, invstd, running_mean, running_var, momentum, epsilon, counts_);
}
std::tuple<Tensor, Tensor> batch_norm_gather_stats_with_counts_cuda(
const Tensor& self, const Tensor& mean, const Tensor& invstd, const Tensor& running_mean /* optional */,
const Tensor& running_var /* optional */, double momentum, double epsilon, const Tensor& counts) {
auto scalar_type = running_mean.defined() ? running_mean.scalar_type() : self.scalar_type();
return AT_DISPATCH_FLOATING_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, scalar_type, "batch_norm_update_stats_cuda", [&] {
using accscalar_t = at::acc_type<scalar_t, true>;
if (cuda::detail::canUse32BitIndexMath(self)) {
return batch_norm_gather_stats_cuda_template<scalar_t, accscalar_t, int32_t>(mean, invstd, running_mean, running_var, momentum, epsilon, counts);
} else {
return batch_norm_gather_stats_cuda_template<scalar_t, accscalar_t, int64_t>(mean, invstd, running_mean, running_var, momentum, epsilon, counts);
}
});
}
std::tuple<Tensor, Tensor, Tensor, Tensor> batch_norm_backward_reduce_cuda(const Tensor& self, const Tensor& input, const Tensor& mean, const Tensor& invstd,
const Tensor& weight, bool input_g, bool weight_g, bool bias_g) {
return AT_DISPATCH_FLOATING_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, self.scalar_type(), "batch_norm_backward_reduce", [&] {
auto mean_st = mean.dtype();
auto invstd_st = invstd.dtype();
TORCH_CHECK(mean_st == invstd_st, "mean and invstd need to have the same data types");
bool is_half_float = std::is_same<scalar_t, at::Half>::value && mean_st == at::kFloat;
bool is_bfloat16_float = std::is_same<scalar_t, at::BFloat16>::value && mean_st == at::kFloat;
if (cuda::detail::canUse32BitIndexMath(self)) {
if (is_half_float || is_bfloat16_float) {
return batch_norm_backward_reduce_cuda_template<scalar_t, float, int32_t>(self, input, mean, invstd, weight, input_g, weight_g, bias_g);
} else {
return batch_norm_backward_reduce_cuda_template<scalar_t, scalar_t, int32_t>(self, input, mean, invstd, weight, input_g, weight_g, bias_g);
}
} else {
if (is_half_float || is_bfloat16_float) {
return batch_norm_backward_reduce_cuda_template<scalar_t, float, int64_t>(self, input, mean, invstd, weight, input_g, weight_g, bias_g);
} else {
return batch_norm_backward_reduce_cuda_template<scalar_t, scalar_t, int64_t>(self, input, mean, invstd, weight, input_g, weight_g, bias_g);
}
}
});
}
Tensor batch_norm_backward_elemt_cuda(const Tensor& self, const Tensor& input, const Tensor& mean, const Tensor& invstd,
const Tensor& weight, const Tensor& mean_dy, const Tensor& mean_dy_xmu) {
return AT_DISPATCH_FLOATING_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, self.scalar_type(), "batch_norm_backward_elemt", [&] {
auto mean_st = mean.dtype();
auto invstd_st = invstd.dtype();
TORCH_CHECK(mean_st == invstd_st, "mean and invstd need to have the same data types");
bool is_half_float = std::is_same<scalar_t, at::Half>::value && mean_st == at::kFloat;
bool is_bfloat16_float = std::is_same<scalar_t, at::BFloat16>::value && mean_st == at::kFloat;
if (cuda::detail::canUse32BitIndexMath(self)) {
if (is_half_float || is_bfloat16_float) {
return batch_norm_backward_elemt_cuda_template<scalar_t, float, int32_t>(self, input, mean, invstd, weight, mean_dy, mean_dy_xmu);
} else {
return batch_norm_backward_elemt_cuda_template<scalar_t, scalar_t, int32_t>(self, input, mean, invstd, weight, mean_dy, mean_dy_xmu);
}
} else {
if (is_half_float || is_bfloat16_float) {
return batch_norm_backward_elemt_cuda_template<scalar_t, float, int64_t>(self, input, mean, invstd, weight, mean_dy, mean_dy_xmu);
} else {
return batch_norm_backward_elemt_cuda_template<scalar_t, scalar_t, int64_t>(self, input, mean, invstd, weight, mean_dy, mean_dy_xmu);
}
}
});
}
std::tuple<Tensor, Tensor> batch_norm_update_stats_cuda(
const Tensor& self, const Tensor& running_mean, const Tensor& running_var, double momentum) {
return AT_DISPATCH_FLOATING_TYPES_AND2(at::ScalarType::Half, at::ScalarType::BFloat16, self.scalar_type(), "batch_norm_backward", [&] {
auto mean_st = running_mean.dtype();
auto var_st = running_var.dtype();
TORCH_CHECK(mean_st == var_st, "running_mean and running_var need to have the same data types");
// <sigh> Some workloads depend on passing in half input and float stats, which is
// usually handled by cuDNN. However, the JIT sometimes replaces cuDNN calls with this
// one so it needs to support the same case, or people start to complain.
bool is_half_float = std::is_same<scalar_t, at::Half>::value && mean_st == at::kFloat;
bool is_bfloat16_float = std::is_same<scalar_t, at::BFloat16>::value && mean_st == at::kFloat;
if (cuda::detail::canUse32BitIndexMath(self)) {
if (is_half_float || is_bfloat16_float) {
return batch_norm_update_stats_cuda_template<scalar_t, float, int32_t>(self, running_mean, running_var, momentum);
} else {
return batch_norm_update_stats_cuda_template<scalar_t, scalar_t, int32_t>(self, running_mean, running_var, momentum);
}
} else {
if (is_half_float || is_bfloat16_float) {
return batch_norm_update_stats_cuda_template<scalar_t, float, int64_t>(self, running_mean, running_var, momentum);
} else {
return batch_norm_update_stats_cuda_template<scalar_t, scalar_t, int64_t>(self, running_mean, running_var, momentum);
}
}
});
}
} } // namespace at::native