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115 lines
4.9 KiB
115 lines
4.9 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#ifdef PADDLE_WITH_XPU
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#include "paddle/fluid/operators/layer_norm_op.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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using DDim = framework::DDim;
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template <typename DeviceContext, typename T>
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class LayerNormXPUKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
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const auto epsilon = ctx.Attr<float>("epsilon");
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const auto* x = ctx.Input<Tensor>("X");
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const auto& x_dims = x->dims();
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auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
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int left = static_cast<int>(matrix_dim[0]);
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int right = static_cast<int>(matrix_dim[1]);
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const auto* scale = ctx.Input<Tensor>("Scale");
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const auto* bias = ctx.Input<Tensor>("Bias");
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auto* y = ctx.Output<Tensor>("Y");
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auto* mean = ctx.Output<Tensor>("Mean");
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auto* variance = ctx.Output<Tensor>("Variance");
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const auto* x_data = x->data<T>();
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const auto* scale_data = (scale == nullptr ? nullptr : scale->data<T>());
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const auto* bias_data = (bias == nullptr ? nullptr : bias->data<T>());
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auto* y_data = y->mutable_data<T>(ctx.GetPlace());
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auto* mean_data = mean->mutable_data<T>(ctx.GetPlace());
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auto* variance_data = variance->mutable_data<T>(ctx.GetPlace());
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auto& dev_ctx = ctx.template device_context<DeviceContext>();
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int r = xpu::layer_norm(dev_ctx.x_context(), left, right, x_data, y_data,
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scale_data, bias_data, epsilon, mean_data,
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variance_data, false);
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PADDLE_ENFORCE_EQ(
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r, XPU_SUCCESS,
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platform::errors::External("XPU API(layer_norm) return wrong "
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"value[%d], please check whether Baidu "
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"Kunlun Card is properly installed.",
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r));
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}
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};
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template <typename DeviceContext, typename T>
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class LayerNormGradXPUKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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const auto begin_norm_axis = ctx.Attr<int>("begin_norm_axis");
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const auto epsilon = ctx.Attr<float>("epsilon");
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const auto* x = ctx.Input<Tensor>("X");
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const auto& x_dims = x->dims();
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auto matrix_dim = framework::flatten_to_2d(x_dims, begin_norm_axis);
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int left = static_cast<int>(matrix_dim[0]);
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int right = static_cast<int>(matrix_dim[1]);
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const auto* mean = ctx.Input<Tensor>("Mean");
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const auto* variance = ctx.Input<Tensor>("Variance");
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const auto* scale = ctx.Input<Tensor>("Scale");
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const auto* dy = ctx.Input<Tensor>(framework::GradVarName("Y"));
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auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
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auto* dscale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
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auto* dbias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
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const auto* x_data = x->data<T>();
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const auto* dy_data = dy->data<T>();
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const auto* mean_data = mean->data<T>();
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const auto* variance_data = variance->data<T>();
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const auto* scale_data = (scale == nullptr ? nullptr : scale->data<T>());
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auto* dscale_data =
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(dscale == nullptr ? nullptr : dscale->mutable_data<T>(ctx.GetPlace()));
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auto* dbias_data =
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(dbias == nullptr ? nullptr : dbias->mutable_data<T>(ctx.GetPlace()));
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auto* dx_data =
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(dx == nullptr ? nullptr : dx->mutable_data<T>(ctx.GetPlace()));
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auto& dev_ctx = ctx.template device_context<DeviceContext>();
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int r = xpu::layer_norm_backward(
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dev_ctx.x_context(), left, right, x_data, scale_data, variance_data,
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mean_data, dy_data, dx_data, dscale_data, dbias_data, epsilon);
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PADDLE_ENFORCE_EQ(
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r, XPU_SUCCESS,
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platform::errors::External("XPU API(layer_norm_backward) return wrong "
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"value[%d], please check whether Baidu "
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"Kunlun Card is properly installed.",
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r));
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_XPU_KERNEL(
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layer_norm,
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ops::LayerNormXPUKernel<paddle::platform::XPUDeviceContext, float>);
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REGISTER_OP_XPU_KERNEL(
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layer_norm_grad,
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ops::LayerNormGradXPUKernel<paddle::platform::XPUDeviceContext, float>);
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#endif // PADDLE_WITH_XPU
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