wehub-resource-sync
140 lines
4.8 KiB
C++
140 lines
4.8 KiB
C++
// Copyright (c) 2023 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/group_norm_kernel.h"
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#include <algorithm>
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#include <array>
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#include <numeric>
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#include <string>
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#include "paddle/common/layout.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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namespace phi {
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template <typename T, typename Context>
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void GroupNormKernel(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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double epsilon,
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int groups,
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const std::string& data_layout_str,
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DenseTensor* y,
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DenseTensor* mean,
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DenseTensor* var) {
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if (y && y->numel() == 0) {
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dev_ctx.template Alloc<T>(y);
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if (mean) {
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Full<T, Context>(dev_ctx, mean->dims(), 0, mean);
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}
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if (var) {
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Full<T, Context>(dev_ctx, var->dims(), 0, var);
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}
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return;
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}
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using XPUType = typename XPUTypeTrait<T>::Type;
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const DataLayout data_layout = StringToDataLayout(data_layout_str);
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const auto scale_ptr = scale.get_ptr();
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const auto bias_ptr = bias.get_ptr();
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const auto x_dims = vectorize<int64_t>(x.dims());
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const int64_t N = x_dims[0];
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const bool channel_first =
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data_layout == DataLayout::NCHW || data_layout == DataLayout::NCDHW;
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const int64_t C = (channel_first ? x_dims[1] : x_dims[x_dims.size() - 1]);
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const int64_t L =
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(channel_first ? std::accumulate(x_dims.begin() + 2,
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x_dims.end(),
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1,
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std::multiplies<int64_t>())
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: std::accumulate(x_dims.begin() + 1,
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x_dims.end() - 1,
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1,
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std::multiplies<int64_t>()));
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dev_ctx.template Alloc<T>(y);
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dev_ctx.template Alloc<float>(mean);
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dev_ctx.template Alloc<float>(var);
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auto* x_data = x.data<T>();
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auto* y_data = y->data<T>();
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auto* mean_data = mean->data<float>();
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auto* var_data = var->data<float>();
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xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
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const float* scale_data = nullptr;
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if (scale_ptr) {
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if (std::is_same<T, float>::value) {
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scale_data = scale_ptr->data<float>();
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} else {
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float* scale_fp32 = 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_fp32,
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scale_ptr->numel());
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast");
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scale_data = scale_fp32;
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}
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}
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const float* bias_data = nullptr;
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if (bias_ptr) {
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if (std::is_same<T, float>::value) {
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bias_data = bias_ptr->data<float>();
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} else {
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float* bias_fp32 = 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_fp32,
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bias_ptr->numel());
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast");
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bias_data = bias_fp32;
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}
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}
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int r = xpu::group_norm<XPUType>(dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(x_data),
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reinterpret_cast<XPUType*>(y_data),
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N,
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C,
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L,
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1,
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groups,
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epsilon,
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scale_data,
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bias_data,
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mean_data,
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var_data,
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channel_first, // is_nchw
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false); // is_rstd
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "group_norm");
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}
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} // namespace phi
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PD_REGISTER_KERNEL(group_norm,
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XPU,
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ALL_LAYOUT,
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phi::GroupNormKernel,
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float,
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phi::float16,
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phi::bfloat16) {}
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