wehub-resource-sync
79 lines
2.5 KiB
C++
79 lines
2.5 KiB
C++
// Copyright (c) 2024 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/affine_channel_kernel.h"
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#include <string>
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#include <unordered_map>
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#include <vector>
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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/eigen/common.h"
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namespace phi {
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template <typename T, typename Context>
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void AffineChannelXPUKernel(const Context& dev_ctx,
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const DenseTensor& x_in,
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const DenseTensor& scale_in,
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const DenseTensor& bias_in,
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const std::string& data_layout,
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DenseTensor* out) {
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auto* x = &x_in;
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auto* scale = &scale_in;
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auto* bias = &bias_in;
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auto* y = out;
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dev_ctx.template Alloc<T>(y);
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const DataLayout layout = StringToDataLayout(data_layout);
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auto dims = x->dims();
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int64_t N = dims[0];
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int64_t C = layout == DataLayout::NCHW ? dims[1] : dims[dims.size() - 1];
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int64_t HxW = x->numel() / N / C;
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auto* scale_d = scale->data<T>();
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auto* bias_d = bias->data<T>();
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auto* x_d = x->data<T>();
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auto* y_d = y->data<T>();
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std::vector<int64_t> x_shape;
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std::vector<int64_t> b_shape;
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if (layout == DataLayout::NCHW) {
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x_shape.push_back(N);
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x_shape.push_back(C);
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x_shape.push_back(HxW);
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b_shape.push_back(1);
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b_shape.push_back(C);
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b_shape.push_back(1);
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} else {
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x_shape.push_back(N * HxW);
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x_shape.push_back(C);
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b_shape.push_back(1);
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b_shape.push_back(C);
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}
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int r = 0;
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r = xpu::broadcast_mul(
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dev_ctx.x_context(), x_d, scale_d, y_d, x_shape, b_shape);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "broadcast_mul");
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r = xpu::broadcast_add(
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dev_ctx.x_context(), y_d, bias_d, y_d, x_shape, b_shape);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "broadcast_add");
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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affine_channel, XPU, ALL_LAYOUT, phi::AffineChannelXPUKernel, float) {}
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