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Normalization.cpp
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Normalization.cpp
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#include <ATen/ATen.h>
#include <ATen/Config.h>
#include <ATen/NativeFunctions.h>
#include <tuple>
#if !AT_MKLDNN_ENABLED()
namespace at {
namespace native {
std::tuple<Tensor, Tensor, Tensor> mkldnn_batch_norm(
const Tensor& self, const c10::optional<Tensor>& weight_opt, const c10::optional<Tensor>& bias_opt, const c10::optional<Tensor>& running_mean_opt, const c10::optional<Tensor>& running_var_opt,
bool train,
double momentum,
double eps) {
TORCH_CHECK(false, "mkldnn_batch_norm: ATen not compiled with MKLDNN support");
}
std::tuple<Tensor, Tensor, Tensor> mkldnn_batch_norm_backward(
const Tensor& grad_output,
const Tensor& input, const c10::optional<Tensor>& weight_opt, const c10::optional<Tensor>& running_mean_opt, const c10::optional<Tensor>& running_var_opt, const c10::optional<Tensor>& save_mean_opt, const c10::optional<Tensor>& save_invstd_opt,
bool train,
double eps,
std::array<bool,3> grad_input_mask) {
TORCH_CHECK(false, "mkldnn_batch_norm_backward: ATen not compiled with MKLDNN support");
}
std::tuple<Tensor, Tensor, Tensor> mkldnn_layer_norm_last_index_weight_bias_f32(
const Tensor& input,
IntArrayRef normalized_shape, const Tensor& weight, const Tensor& bias,
double eps, bool inplace) {
TORCH_CHECK(false, "mkldnn_layer_norm_last_index_weight_bias_f32: ATen not compiled with MKLDNN support");
}
} // namespace native
} // namespace at
#else // AT_MKLDNN_ENABLED
#include <ATen/native/mkldnn/MKLDNNCommon.h>
#include <ATen/native/mkldnn/Utils.h>
#include <ATen/native/layer_norm.h>
#include <ideep/abstract_types.hpp>
namespace at {
namespace native {
std::tuple<Tensor, Tensor, Tensor> mkldnn_layer_norm_last_index_weight_bias_f32(
const Tensor& input,
IntArrayRef normalized_shape, const Tensor& weight, const Tensor& bias,
double eps, bool inplace) {
TORCH_INTERNAL_ASSERT(normalized_shape.size() == 1, "only accept shapes with the last dimension");
TORCH_INTERNAL_ASSERT(input.scalar_type() == at::kFloat);
auto M_N = at::native::_check_layer_norm_inputs(input, normalized_shape, weight, bias);
auto M = M_N.first;
auto mean = empty_mkldnn(
{M},
input.scalar_type(),
input.options().layout_opt(),
input.options().device_opt(),
input.options().pinned_memory_opt());
auto rstd = empty_mkldnn(
{M},
input.scalar_type(),
input.options().layout_opt(),
input.options().device_opt(),
input.options().pinned_memory_opt());
auto mean_it = at::native::itensor_from_mkldnn(mean);
auto rstd_it = at::native::itensor_from_mkldnn(rstd);
auto input_it = at::native::itensor_from_mkldnn(input);
auto weight_it = at::native::itensor_from_mkldnn(weight);
auto bias_it = at::native::itensor_from_mkldnn(bias);
auto out_it = inplace ? input_it : ideep::tensor(input_it.get_desc());
ideep::layer_normalization_forward::compute(input_it, weight_it, bias_it, out_it, mean_it, rstd_it, static_cast<float>(eps));
auto dst = at::native::new_with_itensor_mkldnn(
std::move(out_it),
optTypeMetaToScalarType(input.options().dtype_opt()),
input.options().device_opt());
return std::make_tuple(dst, mean, rstd);
}
std::tuple<Tensor, Tensor, Tensor> mkldnn_batch_norm(
const Tensor& input, const c10::optional<Tensor>& weight_opt, const c10::optional<Tensor>& bias_opt, const c10::optional<Tensor>& running_mean_opt, const c10::optional<Tensor>& running_var_opt,
bool train,
double momentum,
double eps) {
// See [Note: hacky wrapper removal for optional tensor]
c10::MaybeOwned<Tensor> weight_maybe_owned = at::borrow_from_optional_tensor(weight_opt);
const Tensor& weight = *weight_maybe_owned;
const Tensor& bias = c10::value_or_else(bias_opt, [] {return Tensor();});
const Tensor& running_mean = c10::value_or_else(running_mean_opt, [] {return Tensor();});
const Tensor& running_var = c10::value_or_else(running_var_opt, [] {return Tensor();});
if (input.scalar_type() == ScalarType::BFloat16) {
TORCH_CHECK(mkldnn_bf16_device_check(),
"mkldnn_batch_norm: bf16 path needs the cpu support avx512bw, avx512vl and avx512dq");
}
TORCH_CHECK(weight.defined() && bias.defined(),
"mkldnn_batch_norm: currently mkldnn only support affine model");
ideep::tensor& x = itensor_from_mkldnn(input);
ideep::tensor w = itensor_from_tensor(weight);
ideep::tensor b = itensor_from_tensor(bias);
bool use_running_stat = (running_mean.defined() && running_var.defined());
ideep::tensor y;
if (train) {
// TODO: enable 3d batchnorm.
TORCH_CHECK(input.dim() == 4,
"mkldnn_batch_norm: currently mkldnn training only support 2d batchnorm");
ideep::tensor saved_mean;
ideep::tensor saved_var;
ideep::batch_normalization_forward_training::compute(
// NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
x, w, b, y, saved_mean, saved_var, momentum, eps);
if (use_running_stat) {
auto len = x.get_nelems() / w.get_nelems(); // n*h*w
ideep::tensor m = itensor_from_tensor(running_mean);
ideep::tensor v = itensor_from_tensor(running_var);
const std::vector<float> scales_mean{static_cast<float>(1 - momentum),
static_cast<float>(momentum)};
const std::vector<float> scales_var{static_cast<float>(1 - momentum),
// NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
static_cast<float>(momentum * len / (len - 1))};
ideep::sum::compute(scales_mean, {m, saved_mean}, m);
ideep::sum::compute(scales_var, {v, saved_var}, v);
}
return std::make_tuple(
new_with_itensor_mkldnn(std::move(y), optTypeMetaToScalarType(input.options().dtype_opt()),
input.options().device_opt()),
new_with_itensor_mkldnn(std::move(saved_mean), optTypeMetaToScalarType(weight.options().dtype_opt()),
weight.options().device_opt()),
new_with_itensor_mkldnn(std::move(saved_var), optTypeMetaToScalarType(weight.options().dtype_opt()),
weight.options().device_opt()));
} else {
TORCH_CHECK(input.dim() == 4 || input.dim() == 5,
"mkldnn_batch_norm: currently mkldnn inference only support 2d and 3d batchnorm");
if (use_running_stat) {
ideep::tensor m = itensor_from_tensor(running_mean);
ideep::tensor v = itensor_from_tensor(running_var);
ideep::batch_normalization_forward_inference::compute(
// NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
x, m, v, w, b, y, eps);
} else {
// TODO: keep running estimates.
TORCH_CHECK(false, "mkldnn_batch_norm: mkldnn inference is not keep running estimates.");
}
return std::make_tuple(
new_with_itensor_mkldnn(std::move(y), optTypeMetaToScalarType(input.options().dtype_opt()),
input.options().device_opt()),
new_with_itensor_mkldnn(ideep::tensor{}, optTypeMetaToScalarType(weight.options().dtype_opt()),
weight.options().device_opt()),
new_with_itensor_mkldnn(ideep::tensor{}, optTypeMetaToScalarType(weight.options().dtype_opt()),
weight.options().device_opt()));
}
}
std::tuple<Tensor, Tensor, Tensor> mkldnn_batch_norm_backward(const Tensor& grad_output,
const Tensor& input, const c10::optional<Tensor>& weight_opt, const c10::optional<Tensor>& running_mean_opt, const c10::optional<Tensor>& running_var_opt, const c10::optional<Tensor>& save_mean_opt, const c10::optional<Tensor>& save_invstd_opt,
bool train,
double eps,
std::array<bool,3> grad_input_mask) {
// See [Note: hacky wrapper removal for optional tensor]
c10::MaybeOwned<Tensor> weight_maybe_owned = at::borrow_from_optional_tensor(weight_opt);
const Tensor& weight = *weight_maybe_owned;
const Tensor& save_mean = c10::value_or_else(save_mean_opt, [] {return Tensor();});
const Tensor& save_invstd = c10::value_or_else(save_invstd_opt, [] {return Tensor();});
TORCH_CHECK(train, "mkldnn_batch_norm_backward: currently mkldnn only support train model");
ideep::tensor& grady = itensor_from_mkldnn(grad_output);
ideep::tensor& x = itensor_from_mkldnn(input);
ideep::tensor w = itensor_from_tensor(weight);
ideep::tensor& m = itensor_from_mkldnn(save_mean);
ideep::tensor& v = itensor_from_mkldnn(save_invstd);
ideep::tensor gradx, gradw, gradb;
ideep::batch_normalization_backward::compute(
// NOLINTNEXTLINE(cppcoreguidelines-narrowing-conversions,bugprone-narrowing-conversions)
x, m, v, grady, w, gradx, gradw, gradb, eps);
return std::make_tuple(
new_with_itensor_mkldnn(std::move(gradx), optTypeMetaToScalarType(input.options().dtype_opt()),
input.options().device_opt()),
mkldnn_to_dense(new_with_itensor_mkldnn(std::move(gradw),
optTypeMetaToScalarType(weight.options().dtype_opt()),
weight.options().device_opt())),
mkldnn_to_dense(new_with_itensor_mkldnn(std::move(gradb),
optTypeMetaToScalarType(weight.options().dtype_opt()),
weight.options().device_opt())));
}
} // namespace native
} // namespace at
#endif // AT_MKLDNN_ENABLED