wehub-resource-sync
129 lines
5.2 KiB
C++
129 lines
5.2 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 "glog/logging.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/funcs/norm_utils.h"
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namespace phi {
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namespace fusion {
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template <typename T, typename Context>
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void BNActXPUKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& mean,
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const DenseTensor& variance,
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const DenseTensor& scale,
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const DenseTensor& bias,
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float momentum,
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float epsilon,
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const std::string& data_layout_str,
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int act_type,
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DenseTensor* y) {
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using XPUType = typename XPUTypeTrait<T>::Type;
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const auto data_layout = StringToDataLayout(data_layout_str);
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PADDLE_ENFORCE_EQ(data_layout_str == "NCHW" || data_layout_str == "NHWC",
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true,
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common::errors::InvalidArgument(
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"The 'data_layout' attribute must be NCHW or NHWC. "
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"But received 'data_layout' is [%s].",
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data_layout_str));
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const auto& x_dims = x.dims();
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PADDLE_ENFORCE_EQ(
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x_dims.size() >= 2 && x_dims.size() <= 5,
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true,
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common::errors::InvalidArgument(
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"The size of input's dimensions should be between 2 and 5"
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"But received: the size of input's dimensions is [%d]",
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x_dims.size()));
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int N = -1, C = -1, H = -1, W = -1, D = -1;
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funcs::ExtractNCWHD(x_dims, data_layout, &N, &C, &H, &W, &D);
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N = (N == 0) ? 1 : N;
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C = (C == 0) ? 1 : C;
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H = (H == 0) ? 1 : H;
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W = (W == 0) ? 1 : W;
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D = (D == 0) ? 1 : D;
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W = W * D;
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const auto* x_data = reinterpret_cast<const XPUType*>(x.data<T>());
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const auto* scale_data = scale.data<float>();
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const auto* bias_data = bias.data<float>();
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// alloc memory
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auto* y_data = reinterpret_cast<XPUType*>(dev_ctx.template Alloc<T>(y));
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PADDLE_ENFORCE_LE(
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x_dims.size(),
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5,
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common::errors::InvalidArgument(
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"The size of input X's dimensions should be less than 6."
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"But received: the size of input X's dimensions is [%d]",
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x_dims.size()));
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bool is_nchw = data_layout_str == "NCHW";
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const auto* mean_data = mean.data<float>();
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const auto* variance_data = variance.data<float>();
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#ifndef PADDLE_WITH_XPU_PLUGIN
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LOG(WARNING) << "Add -DWITH_XPU_PLUGIN=ON to build "
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"xpu::plugin::bn_act_fusion_infer(), which will lead high "
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"performance.";
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xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
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XPUType* temp_data = RAII_GUARD.alloc_l3_or_gm<XPUType>(x.numel());
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int r = xpu::batch_norm_infer<XPUType>(dev_ctx.x_context(),
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x_data,
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temp_data,
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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,
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bias_data,
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mean_data,
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variance_data,
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is_nchw);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "batch_norm_infer");
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r = xpu::relu(
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dev_ctx.x_context(), temp_data, y_data, x.numel(), nullptr, nullptr);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "relu");
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#else
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int r = xpu::plugin::bn_act_fusion_infer<XPUType>(dev_ctx.x_context(),
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x_data,
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y_data,
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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,
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bias_data,
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mean_data,
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variance_data,
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is_nchw,
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act_type);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "bn_act_fusion_infer");
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#endif
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}
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} // namespace fusion
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} // namespace phi
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PD_REGISTER_KERNEL(bn_act_xpu,
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XPU,
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ALL_LAYOUT,
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phi::fusion::BNActXPUKernel,
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float,
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phi::float16) {}
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