wehub-resource-sync
128 lines
4.5 KiB
C++
128 lines
4.5 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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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#include "paddle/phi/kernels/instance_norm_kernel.h"
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#include "paddle/phi/backends/xpu/enforce_xpu.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/full_kernel.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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namespace phi {
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template <typename T, typename Context>
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void InstanceNormKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const optional<DenseTensor>& scale,
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const optional<DenseTensor>& bias,
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float epsilon,
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DenseTensor* y,
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DenseTensor* saved_mean,
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DenseTensor* saved_var) {
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using XPUType = typename XPUTypeTrait<T>::Type;
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const auto& x_dims = x.dims();
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int64_t n = x_dims[0];
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int64_t c = x_dims[1];
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int64_t h = x_dims[2];
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int64_t w = x_dims[3];
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dev_ctx.template Alloc<T>(y);
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dev_ctx.template Alloc<float>(saved_mean);
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dev_ctx.template Alloc<float>(saved_var);
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if (x.numel() == 0) {
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if (y) {
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Full<T, Context>(dev_ctx, y->dims(), 0, y);
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}
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if (saved_mean) {
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Full<float, Context>(dev_ctx, saved_mean->dims(), 0.f, saved_mean);
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}
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if (saved_var) {
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Full<float, Context>(dev_ctx, saved_var->dims(), 0.f, saved_var);
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}
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return;
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}
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xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
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// scale
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const auto scale_ptr = scale.get_ptr();
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const float* scale_data_fp32 = nullptr;
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if (scale_ptr == nullptr) {
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float* scale_data_temp = RAII_GUARD.alloc_l3_or_gm<float>(c);
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int r = xpu::constant<float>(dev_ctx.x_context(), scale_data_temp, c, 1.f);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
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scale_data_fp32 = scale_data_temp;
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} else if (scale_ptr->dtype() ==
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phi::CppTypeToDataType<phi::float16>::Type()) {
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float* scale_data_temp =
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RAII_GUARD.alloc_l3_or_gm<float>(scale_ptr->numel());
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int r = xpu::cast<XPUType, float>(
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dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(scale_ptr->data<T>()),
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scale_data_temp,
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scale_ptr->numel());
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast");
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scale_data_fp32 = scale_data_temp;
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} else {
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// no need to cast
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scale_data_fp32 = scale_ptr->data<float>();
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}
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// bias
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const float* bias_data_fp32 = nullptr;
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const auto* bias_ptr = bias.get_ptr();
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if (bias_ptr == nullptr) {
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float* bias_data_temp = RAII_GUARD.alloc_l3_or_gm<float>(c);
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int r = xpu::constant<float>(dev_ctx.x_context(), bias_data_temp, c, 1.f);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant");
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bias_data_fp32 = bias_data_temp;
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} else if (bias_ptr->dtype() ==
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phi::CppTypeToDataType<phi::float16>::Type()) {
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float* bias_data_temp = RAII_GUARD.alloc_l3_or_gm<float>(bias_ptr->numel());
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int r = xpu::cast<XPUType, float>(
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dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(bias_ptr->data<T>()),
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bias_data_temp,
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bias_ptr->numel());
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast");
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bias_data_fp32 = bias_data_temp;
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} else {
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// no need to cast
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bias_data_fp32 = bias_ptr->data<float>();
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}
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int r = xpu::instance_norm(dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(x.data<T>()),
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reinterpret_cast<XPUType*>(y->data<T>()),
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n,
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c,
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h,
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w,
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epsilon,
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scale_data_fp32,
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bias_data_fp32,
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saved_mean->data<float>(),
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saved_var->data<float>(),
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true);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "instance_norm");
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}
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} // namespace phi
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PD_REGISTER_KERNEL(instance_norm,
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XPU,
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ALL_LAYOUT,
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phi::InstanceNormKernel,
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float,
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phi::float16) {}
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