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提交 e0333735 编写于 作者: C chengduoZH
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unifid GPU and CPU implementation

上级 76e188e5
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Showing with 206 addition and 413 deletion
paddle/operators/layer_norm_op.cc
浏览文件 @ e0333735
...@@ -13,8 +13,6 @@ See the License for the specific language governing permissions and ...@@ -13,8 +13,6 @@ See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "paddle/operators/layer_norm_op.h" #include "paddle/operators/layer_norm_op.h"
#include "paddle/operators/elementwise_op_function.h"
#include "paddle/operators/math/math_function.h"
namespace paddle { namespace paddle {
namespace operators { namespace operators {
...@@ -23,13 +21,6 @@ using Tensor = framework::Tensor; ...@@ -23,13 +21,6 @@ using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor; using LoDTensor = framework::LoDTensor;
using DataLayout = framework::DataLayout; using DataLayout = framework::DataLayout;
template <typename T>
using EigenMatrixMapRowMajor = Eigen::Map<
Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>;
template <typename T>
using ConstEigenMatrixMapRowMajor = Eigen::Map<
const Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>;
class LayerNormOp : public framework::OperatorWithKernel { class LayerNormOp : public framework::OperatorWithKernel {
public: public:
using framework::OperatorWithKernel::OperatorWithKernel; using framework::OperatorWithKernel::OperatorWithKernel;
...@@ -118,75 +109,6 @@ https://arxiv.org/abs/1607.06450 ...@@ -118,75 +109,6 @@ https://arxiv.org/abs/1607.06450
} }
}; };
template <typename T>
class LayerNormKernel<platform::CPUDeviceContext, T>
: public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
const float epsilon = ctx.Attr<float>("epsilon");
const auto *scale = ctx.Input<Tensor>("Scale");
const auto *bias = ctx.Input<Tensor>("Bias");
const auto *x = ctx.Input<Tensor>("X");
const auto &x_dims = x->dims();
const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
auto *output = ctx.Output<Tensor>("Y");
auto *mean = ctx.Output<Tensor>("Mean");
auto *var = ctx.Output<Tensor>("Variance");
output->mutable_data<T>(ctx.GetPlace());
mean->mutable_data<T>(ctx.GetPlace());
var->mutable_data<T>(ctx.GetPlace());
auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
int left = static_cast<int>(matrix_dim[0]);
int right = static_cast<int>(matrix_dim[1]);
auto input_map = ConstEigenMatrixMapRowMajor<T>(x->data<T>(), left, right);
auto mean_map = EigenMatrixMapRowMajor<T>(mean->data<T>(), left, 1);
auto var_map = EigenMatrixMapRowMajor<T>(var->data<T>(), left, 1);
auto output_map = EigenMatrixMapRowMajor<T>(output->data<T>(), left, right);
auto squre = [](T ele) { return ele * ele; };
auto add_epslion = [epsilon](T ele) { return ele + epsilon; };
mean_map = input_map.rowwise().mean();
var_map = (input_map - mean_map.replicate(1, right))
.unaryExpr(squre)
.rowwise()
.mean()
.unaryExpr(add_epslion);
auto inv_std_func = [](T ele) { return std::sqrt(1 / ele); };
// TODO(zcd): Some thinking about output_map, is it appropriate that
// `output_map` and `input_map` point to the same memory.
auto inv_std = var_map.unaryExpr(inv_std_func);
if (scale && bias) {
auto scale_map =
ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), 1, right);
auto bias_map = ConstEigenMatrixMapRowMajor<T>(bias->data<T>(), 1, right);
output_map = (input_map - mean_map.replicate(1, right))
.cwiseProduct(inv_std.replicate(1, right))
.cwiseProduct(scale_map.replicate(left, 1)) +
bias_map.replicate(left, 1);
} else if (scale) {
auto scale_map =
ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), 1, right);
output_map = (input_map - mean_map.replicate(1, right))
.cwiseProduct(inv_std.replicate(1, right))
.cwiseProduct(scale_map.replicate(left, 1));
} else if (bias) {
auto bias_map = ConstEigenMatrixMapRowMajor<T>(bias->data<T>(), 1, right);
output_map = (input_map - mean_map.replicate(1, right))
.cwiseProduct(inv_std.replicate(1, right)) +
bias_map.replicate(left, 1);
} else {
output_map = (input_map - mean_map.replicate(1, right))
.cwiseProduct(inv_std.replicate(1, right));
}
}
};
class LayerNormGradOp : public framework::OperatorWithKernel { class LayerNormGradOp : public framework::OperatorWithKernel {
public: public:
using framework::OperatorWithKernel::OperatorWithKernel; using framework::OperatorWithKernel::OperatorWithKernel;
...@@ -239,115 +161,6 @@ class LayerNormGradOp : public framework::OperatorWithKernel { ...@@ -239,115 +161,6 @@ class LayerNormGradOp : public framework::OperatorWithKernel {
} }
}; };
template <typename T>
class LayerNormGradKernel<platform::CPUDeviceContext, T>
: public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
const auto *x = ctx.Input<Tensor>("X");
const auto *mean = ctx.Input<Tensor>("Mean");
const auto *var = ctx.Input<Tensor>("Variance");
const auto *scale = ctx.Input<Tensor>("Scale");
const auto *d_y = ctx.Input<Tensor>(framework::GradVarName("Y"));
const auto &x_dims = x->dims();
const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
int left = static_cast<int>(matrix_dim[0]);
int right = static_cast<int>(matrix_dim[1]);
// init output
auto *d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
auto *d_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
auto *d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
auto x_map = ConstEigenMatrixMapRowMajor<T>(x->data<T>(), left, right);
auto d_y_map = ConstEigenMatrixMapRowMajor<T>(d_y->data<T>(), left, right);
auto mean_map = ConstEigenMatrixMapRowMajor<T>(mean->data<T>(), left, 1);
auto var_map = ConstEigenMatrixMapRowMajor<T>(var->data<T>(), left, 1);
if (d_bias) {
d_bias->mutable_data<T>(ctx.GetPlace());
auto d_bias_map = EigenMatrixMapRowMajor<T>(d_bias->data<T>(), 1, right);
d_bias_map = d_y_map.colwise().sum();
}
if (d_scale) {
d_scale->mutable_data<T>(ctx.GetPlace());
auto d_scale_map =
EigenMatrixMapRowMajor<T>(d_scale->data<T>(), 1, right);
auto inv_std_func = [](T ele) { return std::sqrt(1 / ele); };
// There are two equation to compute d_scale. One uses "Y" and the other
// does not use "Y"
d_scale_map =
((x_map - mean_map.replicate(1, right))
.cwiseProduct(
var_map.unaryExpr(inv_std_func).replicate(1, right))
.cwiseProduct(d_y_map))
.colwise()
.sum();
}
if (d_x) {
d_x->mutable_data<T>(ctx.GetPlace());
auto d_x_map = EigenMatrixMapRowMajor<T>(d_x->data<T>(), left, right);
auto triple_product_func = [](T ele) { return ele * ele * ele; };
auto inv_std_func = [](T ele) { return std::sqrt(1 / ele); };
auto inv_std_map = var_map.unaryExpr(inv_std_func).eval();
// TODO(zcd): these code can be refined
if (d_scale) {
auto scale_map =
ConstEigenMatrixMapRowMajor<T>(scale->data<T>(), 1, right);
// dy_dx
auto dx_end =
inv_std_map.replicate(1, right).cwiseProduct(d_y_map).cwiseProduct(
scale_map.replicate(left, 1));
// dy_dmean_dx
auto dx_mean =
(T(-1.0) / right) * dx_end.rowwise().sum().replicate(1, right);
// dy_var_dx
auto dvar_end_part = (x_map - mean_map.replicate(1, right))
.cwiseProduct(scale_map.replicate(left, 1))
.cwiseProduct(d_y_map)
.rowwise()
.sum();
auto dvar_end = inv_std_map.unaryExpr(triple_product_func)
.cwiseProduct(dvar_end_part)
.replicate(1, right);
auto dx_var =
(T(-1.0) / right) *
(x_map - mean_map.replicate(1, right)).cwiseProduct(dvar_end);
d_x_map = dx_end + dx_mean + dx_var;
} else {
// dy_dx
auto dx_end = inv_std_map.replicate(1, right).cwiseProduct(d_y_map);
// dy_dmean_dx
auto dx_mean =
(T(-1.0) / right) * dx_end.rowwise().sum().replicate(1, right);
// dy_var_dx
auto dvar_end_part = (x_map - mean_map.replicate(1, right))
.cwiseProduct(d_y_map)
.rowwise()
.sum();
auto dvar_end = inv_std_map.unaryExpr(triple_product_func)
.cwiseProduct(dvar_end_part)
.replicate(1, right);
auto dx_var =
(T(-1.0) / right) *
(x_map - mean_map.replicate(1, right)).cwiseProduct(dvar_end);
d_x_map = dx_end + dx_mean + dx_var;
}
}
}
};
} // namespace operators } // namespace operators
} // namespace paddle } // namespace paddle
......
paddle/operators/layer_norm_op.cu
浏览文件 @ e0333735
...@@ -12,234 +12,14 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. ...@@ -12,234 +12,14 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and See the License for the specific language governing permissions and
limitations under the License. */ limitations under the License. */
#include "paddle/operators/elementwise_op_function.h"
#include "paddle/operators/layer_norm_op.h" #include "paddle/operators/layer_norm_op.h"
#include "paddle/operators/math/math_function.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
using DataLayout = framework::DataLayout;
namespace {
template <typename T>
struct SubAndSquareFunctor {
inline HOSTDEVICE T operator()(T a, T b) const { return (a - b) * (a - b); }
};
template <typename T>
struct DivAndSqrtFunctor {
explicit DivAndSqrtFunctor(T epsilon) { epsilon_ = epsilon; }
inline HOSTDEVICE T operator()(T a, T b) const {
return a / (sqrt(b) + epsilon_);
}
private:
T epsilon_;
};
template <typename T>
struct MulFunctor {
inline HOSTDEVICE T operator()(T a, T b) const { return a * b; }
};
template <typename T>
struct AddFunctor {
inline HOSTDEVICE T operator()(T a, T b) const { return a + b; }
};
template <typename T>
struct SubFunctor {
inline HOSTDEVICE T operator()(T a, T b) const { return a - b; }
};
template <typename T>
struct MulInvVarFunctor {
inline HOSTDEVICE T operator()(T a, T b) const {
return a * std::sqrt(1.0 / b);
}
};
} // namespace
template <typename DeviceContext, typename T>
class LayerNormCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
const float epsilon = ctx.Attr<float>("epsilon");
auto *scale = ctx.Input<Tensor>("Scale");
auto *bias = ctx.Input<Tensor>("Bias");
auto x = *ctx.Input<Tensor>("X");
auto *y = ctx.Output<Tensor>("Y");
auto *mean = ctx.Output<Tensor>("Mean");
auto *var = ctx.Output<Tensor>("Variance");
const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
const auto &x_dims = x.dims();
y->mutable_data<T>(ctx.GetPlace());
mean->mutable_data<T>(ctx.GetPlace());
var->mutable_data<T>(ctx.GetPlace());
auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
int left = static_cast<int>(matrix_dim[0]);
int right = static_cast<int>(matrix_dim[1]);
framework::DDim matrix_shape({left, right});
x.Resize(matrix_shape);
y->Resize(matrix_shape);
auto &dev_ctx = ctx.template device_context<DeviceContext>();
math::RowwiseMean<DeviceContext, T> row_mean;
// functor-> get mean
row_mean(dev_ctx, x, mean);
// functor-> get variance
ElementwiseComputeEx<SubAndSquareFunctor<T>, DeviceContext, T>(
ctx, &x, mean, /*axis*/ 0, SubAndSquareFunctor<T>(), y);
row_mean(dev_ctx, *y, var);
// functor-> get norm_out
ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
ctx, &x, mean, /*axis*/ 0, SubFunctor<T>(), y);
ElementwiseComputeEx<DivAndSqrtFunctor<T>, DeviceContext, T>(
ctx, y, var, /*axis*/ 0, DivAndSqrtFunctor<T>(static_cast<T>(epsilon)),
y);
framework::DDim scale_shape({right});
if (scale) {
Tensor scale_matrix = *scale;
scale_matrix.Resize(scale_shape);
ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
ctx, y, &scale_matrix, /*axis*/ 1, MulFunctor<T>(), y);
}
if (bias) {
Tensor bias_matrix = *bias;
bias_matrix.Resize(scale_shape);
ElementwiseComputeEx<AddFunctor<T>, DeviceContext, T>(
ctx, y, &bias_matrix, /*axis*/ 1, AddFunctor<T>(), y);
}
y->Resize(x_dims);
}
};
template <typename DeviceContext, typename T>
class LayerNormCUDAGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &ctx) const override {
const float epsilon = ctx.Attr<float>("epsilon");
auto x = *ctx.Input<Tensor>("X");
auto mean = *ctx.Input<Tensor>("Mean");
auto var = *ctx.Input<Tensor>("Variance");
auto scale = *ctx.Input<Tensor>("Scale");
auto d_y = *ctx.Input<Tensor>(framework::GradVarName("Y"));
const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
// init output
auto *d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
auto *d_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
auto *d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
const auto &x_dims = x.dims();
auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
int left = static_cast<int>(matrix_dim[0]);
int right = static_cast<int>(matrix_dim[1]);
framework::DDim matrix_shape({left, right});
d_y.Resize(matrix_shape);
auto &dev_ctx = ctx.template device_context<DeviceContext>();
math::ColwiseSum<DeviceContext, T> colwise_sum;
Tensor temp;
Tensor temp_norm;
if (d_scale || d_x) {
x.Resize(matrix_shape);
temp.mutable_data<T>(matrix_shape, ctx.GetPlace());
temp_norm.mutable_data<T>(matrix_shape, ctx.GetPlace());
// get x_norm
ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
ctx, &x, &mean, /*axis*/ 0, SubFunctor<T>(), &temp_norm);
ElementwiseComputeEx<DivAndSqrtFunctor<T>, DeviceContext, T>(
ctx, &temp_norm, &var, /*axis*/ 0,
DivAndSqrtFunctor<T>(static_cast<T>(epsilon)), &temp_norm);
}
if (d_bias) {
d_bias->mutable_data<T>(ctx.GetPlace());
colwise_sum(dev_ctx, d_y, d_bias);
}
if (d_scale) {
d_scale->mutable_data<T>(ctx.GetPlace());
ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
ctx, &temp_norm, &d_y, /*axis*/ 0, MulFunctor<T>(), &temp);
colwise_sum(dev_ctx, temp, d_scale);
}
if (d_x) {
framework::DDim vec_shape({left});
d_x->mutable_data<T>(ctx.GetPlace());
Tensor temp_vec;
temp_vec.mutable_data<T>(vec_shape, ctx.GetPlace());
auto &dev_ctx = ctx.template device_context<DeviceContext>();
math::RowwiseMean<DeviceContext, T> row_mean;
if (d_scale) {
// dy_dx
ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
ctx, &d_y, &scale, /*axis*/ 1, MulFunctor<T>(), &temp);
framework::Copy(temp, ctx.GetPlace(), ctx.device_context(), d_x);
// dy_dmean_dx
row_mean(dev_ctx, temp, &temp_vec);
ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
ctx, d_x, &temp_vec, /*axis*/ 0, SubFunctor<T>(), d_x);
// dy_var_dx
ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
ctx, &temp, &temp_norm, /*axis*/ 0, MulFunctor<T>(), &temp);
} else {
// dy_dx
framework::Copy(d_y, ctx.GetPlace(), ctx.device_context(), d_x);
// dy_dmean_dx
row_mean(dev_ctx, d_y, &temp_vec);
ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
ctx, d_x, &temp_vec, /*axis*/ 0, SubFunctor<T>(), d_x);
// dy_var_dx
ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
ctx, &d_y, &temp_norm, /*axis*/ 0, MulFunctor<T>(), &temp);
}
// dy_var_dx
row_mean(dev_ctx, temp, &temp_vec);
ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
ctx, &temp_norm, &temp_vec, /*axis*/ 0, MulFunctor<T>(), &temp_norm);
ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
ctx, d_x, &temp_norm, /*axis*/ 0, SubFunctor<T>(), d_x);
ElementwiseComputeEx<DivAndSqrtFunctor<T>, DeviceContext, T>(
ctx, d_x, &var, /*axis*/ 0,
DivAndSqrtFunctor<T>(static_cast<T>(epsilon)), d_x);
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators; namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL( REGISTER_OP_CUDA_KERNEL(
layer_norm, layer_norm,
ops::LayerNormCUDAKernel<paddle::platform::CUDADeviceContext, float>, ops::LayerNormKernel<paddle::platform::CUDADeviceContext, float>,
ops::LayerNormCUDAKernel<paddle::platform::CUDADeviceContext, double>); ops::LayerNormKernel<paddle::platform::CUDADeviceContext, double>);
REGISTER_OP_CUDA_KERNEL( REGISTER_OP_CUDA_KERNEL(
layer_norm_grad, layer_norm_grad,
ops::LayerNormCUDAGradKernel<paddle::platform::CUDADeviceContext, float>, ops::LayerNormGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::LayerNormCUDAGradKernel<paddle::platform::CUDADeviceContext, double>); ops::LayerNormGradKernel<paddle::platform::CUDADeviceContext, double>);
paddle/operators/layer_norm_op.h
浏览文件 @ e0333735
...@@ -16,19 +16,219 @@ limitations under the License. */ ...@@ -16,19 +16,219 @@ limitations under the License. */
#include "paddle/framework/eigen.h" #include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h" #include "paddle/framework/op_registry.h"
#include "paddle/operators/elementwise_op_function.h"
#include "paddle/operators/math/math_function.h"
namespace paddle { namespace paddle {
namespace operators { namespace operators {
template <typename T>
struct SubAndSquareFunctor {
inline HOSTDEVICE T operator()(T a, T b) const { return (a - b) * (a - b); }
};
template <typename T>
struct DivAndSqrtFunctor {
explicit DivAndSqrtFunctor(T epsilon) { epsilon_ = epsilon; }
inline HOSTDEVICE T operator()(T a, T b) const {
return a / (sqrt(b) + epsilon_);
}
private:
T epsilon_;
};
template <typename T>
struct MulFunctor {
inline HOSTDEVICE T operator()(T a, T b) const { return a * b; }
};
template <typename T>
struct AddFunctor {
inline HOSTDEVICE T operator()(T a, T b) const { return a + b; }
};
template <typename T>
struct SubFunctor {
inline HOSTDEVICE T operator()(T a, T b) const { return a - b; }
};
template <typename T>
struct MulInvVarFunctor {
inline HOSTDEVICE T operator()(T a, T b) const {
return a * std::sqrt(1.0 / b);
}
};
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
using DataLayout = framework::DataLayout;
template <typename DeviceContext, typename T> template <typename DeviceContext, typename T>
class LayerNormKernel : public framework::OpKernel<T> { class LayerNormKernel : public framework::OpKernel<T> {
public: public:
void Compute(const framework::ExecutionContext& ctx) const override; void Compute(const framework::ExecutionContext &ctx) const override {
const float epsilon = ctx.Attr<float>("epsilon");
auto *scale = ctx.Input<Tensor>("Scale");
auto *bias = ctx.Input<Tensor>("Bias");
auto x = *ctx.Input<Tensor>("X");
auto *y = ctx.Output<Tensor>("Y");
auto *mean = ctx.Output<Tensor>("Mean");
auto *var = ctx.Output<Tensor>("Variance");
const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
const auto &x_dims = x.dims();
y->mutable_data<T>(ctx.GetPlace());
mean->mutable_data<T>(ctx.GetPlace());
var->mutable_data<T>(ctx.GetPlace());
auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
int left = static_cast<int>(matrix_dim[0]);
int right = static_cast<int>(matrix_dim[1]);
framework::DDim matrix_shape({left, right});
x.Resize(matrix_shape);
y->Resize(matrix_shape);
auto &dev_ctx = ctx.template device_context<DeviceContext>();
math::RowwiseMean<DeviceContext, T> row_mean;
// functor-> get mean
row_mean(dev_ctx, x, mean);
// functor-> get variance
ElementwiseComputeEx<SubAndSquareFunctor<T>, DeviceContext, T>(
ctx, &x, mean, /*axis*/ 0, SubAndSquareFunctor<T>(), y);
row_mean(dev_ctx, *y, var);
// functor-> get norm_out
ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
ctx, &x, mean, /*axis*/ 0, SubFunctor<T>(), y);
ElementwiseComputeEx<DivAndSqrtFunctor<T>, DeviceContext, T>(
ctx, y, var, /*axis*/ 0, DivAndSqrtFunctor<T>(static_cast<T>(epsilon)),
y);
framework::DDim scale_shape({right});
if (scale) {
Tensor scale_matrix = *scale;
scale_matrix.Resize(scale_shape);
ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
ctx, y, &scale_matrix, /*axis*/ 1, MulFunctor<T>(), y);
}
if (bias) {
Tensor bias_matrix = *bias;
bias_matrix.Resize(scale_shape);
ElementwiseComputeEx<AddFunctor<T>, DeviceContext, T>(
ctx, y, &bias_matrix, /*axis*/ 1, AddFunctor<T>(), y);
}
y->Resize(x_dims);
}
}; };
template <typename DeviceContext, typename T> template <typename DeviceContext, typename T>
class LayerNormGradKernel : public framework::OpKernel<T> { class LayerNormGradKernel : public framework::OpKernel<T> {
public: public:
void Compute(const framework::ExecutionContext& ctx) const override; void Compute(const framework::ExecutionContext &ctx) const override {
const float epsilon = ctx.Attr<float>("epsilon");
auto x = *ctx.Input<Tensor>("X");
auto mean = *ctx.Input<Tensor>("Mean");
auto var = *ctx.Input<Tensor>("Variance");
auto scale = *ctx.Input<Tensor>("Scale");
auto d_y = *ctx.Input<Tensor>(framework::GradVarName("Y"));
const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
// init output
auto *d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
auto *d_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
auto *d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
const auto &x_dims = x.dims();
auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
int left = static_cast<int>(matrix_dim[0]);
int right = static_cast<int>(matrix_dim[1]);
framework::DDim matrix_shape({left, right});
d_y.Resize(matrix_shape);
auto &dev_ctx = ctx.template device_context<DeviceContext>();
math::ColwiseSum<DeviceContext, T> colwise_sum;
Tensor temp;
Tensor temp_norm;
if (d_scale || d_x) {
x.Resize(matrix_shape);
temp.mutable_data<T>(matrix_shape, ctx.GetPlace());
temp_norm.mutable_data<T>(matrix_shape, ctx.GetPlace());
// get x_norm
ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
ctx, &x, &mean, /*axis*/ 0, SubFunctor<T>(), &temp_norm);
ElementwiseComputeEx<DivAndSqrtFunctor<T>, DeviceContext, T>(
ctx, &temp_norm, &var, /*axis*/ 0,
DivAndSqrtFunctor<T>(static_cast<T>(epsilon)), &temp_norm);
}
if (d_bias) {
d_bias->mutable_data<T>(ctx.GetPlace());
colwise_sum(dev_ctx, d_y, d_bias);
}
if (d_scale) {
d_scale->mutable_data<T>(ctx.GetPlace());
ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
ctx, &temp_norm, &d_y, /*axis*/ 0, MulFunctor<T>(), &temp);
colwise_sum(dev_ctx, temp, d_scale);
}
if (d_x) {
framework::DDim vec_shape({left});
d_x->mutable_data<T>(ctx.GetPlace());
Tensor temp_vec;
temp_vec.mutable_data<T>(vec_shape, ctx.GetPlace());
math::RowwiseMean<DeviceContext, T> row_mean;
if (d_scale) {
// dy_dx
ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
ctx, &d_y, &scale, /*axis*/ 1, MulFunctor<T>(), &temp);
framework::Copy(temp, ctx.GetPlace(), ctx.device_context(), d_x);
// dy_dmean_dx
row_mean(dev_ctx, temp, &temp_vec);
ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
ctx, d_x, &temp_vec, /*axis*/ 0, SubFunctor<T>(), d_x);
// dy_var_dx
ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
ctx, &temp, &temp_norm, /*axis*/ 0, MulFunctor<T>(), &temp);
} else {
// dy_dx
framework::Copy(d_y, ctx.GetPlace(), ctx.device_context(), d_x);
// dy_dmean_dx
row_mean(dev_ctx, d_y, &temp_vec);
ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
ctx, d_x, &temp_vec, /*axis*/ 0, SubFunctor<T>(), d_x);
// dy_var_dx
ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
ctx, &d_y, &temp_norm, /*axis*/ 0, MulFunctor<T>(), &temp);
}
// dy_var_dx
row_mean(dev_ctx, temp, &temp_vec);
ElementwiseComputeEx<MulFunctor<T>, DeviceContext, T>(
ctx, &temp_norm, &temp_vec, /*axis*/ 0, MulFunctor<T>(), &temp_norm);
ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
ctx, d_x, &temp_norm, /*axis*/ 0, SubFunctor<T>(), d_x);
ElementwiseComputeEx<DivAndSqrtFunctor<T>, DeviceContext, T>(
ctx, d_x, &var, /*axis*/ 0,
DivAndSqrtFunctor<T>(static_cast<T>(epsilon)), d_x);
}
}
}; };
} // namespace operators } // namespace operators
......
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