chore: import upstream snapshot with attribution
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wehub-resource-sync
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// 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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#pragma once
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/fusion/gpu/cudnn_bn_stats_finalize.cu.h"
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#include "paddle/phi/kernels/fusion/gpu/cudnn_norm_conv.cu.h"
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#include "paddle/phi/kernels/fusion/gpu/cudnn_scale_bias_add_relu.cu.h"
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#include "paddle/utils/optional.h"
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#if CUDNN_VERSION >= 8000
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namespace phi {
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template <typename T, typename Context>
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void ResNetUnitKernel(const Context &dev_ctx,
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const DenseTensor &x_in,
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const DenseTensor &filter_x_in,
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const DenseTensor &scale_x_in,
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const DenseTensor &bias_x_in,
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const DenseTensor &mean_x_in,
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const DenseTensor &var_x_in,
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const optional<DenseTensor> &z_in,
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const optional<DenseTensor> &filter_z_in,
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const optional<DenseTensor> &scale_z_in,
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const optional<DenseTensor> &bias_z_in,
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const optional<DenseTensor> &mean_z_in,
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const optional<DenseTensor> &var_z_in,
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int stride,
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int stride_z,
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int padding,
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int dilation,
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int group,
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float momentum_in,
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float epsilon,
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const std::string &data_format,
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bool fuse_add,
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bool has_shortcut,
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bool use_global_stats,
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bool is_test,
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bool use_addto,
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const std::string &act_type,
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DenseTensor *out,
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DenseTensor *bit_mask,
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DenseTensor *conv_x,
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DenseTensor *saved_mean_x,
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DenseTensor *saved_invstd_x,
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DenseTensor *running_mean_x,
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DenseTensor *running_var_x,
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DenseTensor *conv_z,
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DenseTensor *saved_mean_z,
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DenseTensor *saved_invstd_z,
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DenseTensor *running_mean_z,
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DenseTensor *running_var_z) {
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PADDLE_ENFORCE_EQ(backends::gpu::CudnnDataType<T>::type,
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CUDNN_DATA_HALF,
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common::errors::Unavailable(
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"ResNetUnitOp only supports float16 for now."));
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// input x
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const DenseTensor *input_x = &x_in;
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const DenseTensor *filter_x = &filter_x_in;
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const DenseTensor *scale_x = &scale_x_in;
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const DenseTensor *bias_x = &bias_x_in;
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// norm conv
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DenseTensor *conv_out_x = conv_x;
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// sbar
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DenseTensor *output = out;
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DenseTensor *bitmask = bit_mask;
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// attrs
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double eps = static_cast<double>(epsilon);
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double momentum = static_cast<double>(momentum_in);
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bool is_train = !is_test && !use_global_stats;
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auto input_x_shape = vectorize<int>(input_x->dims());
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auto filter_x_shape = vectorize<int>(filter_x->dims());
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// std::swap used to convert shape of filter from conv2d when kernel size is
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// 1.
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if (filter_x_shape[1] != filter_x_shape[2] && 1 == filter_x_shape[2]) {
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std::swap(filter_x_shape[1], filter_x_shape[3]);
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}
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auto param_dims = scale_x->dims();
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auto param_shape = vectorize<int>(scale_x->dims());
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if (1 == param_shape.size()) {
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param_shape = {1, 1, 1, param_shape[0]};
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}
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auto output_shape = vectorize<int>(output->dims());
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auto bitmask_shape = vectorize<int>(bitmask->dims());
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int output_channel = filter_x_shape[0];
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int64_t ele_count =
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std::accumulate(
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output_shape.begin(), output_shape.end(), 1, std::multiplies<int>()) /
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output_channel;
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// 1. Conv
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DenseTensor sum_x;
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DenseTensor sum_of_squares_x;
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sum_x.Resize(param_dims);
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sum_of_squares_x.Resize(param_dims);
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phi::fusion::CudnnNormConvolution<T> conv_x_op(dev_ctx,
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input_x_shape,
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filter_x_shape,
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output_shape,
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padding,
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stride,
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dilation,
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group);
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conv_x_op.Forward(
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dev_ctx, *input_x, *filter_x, conv_out_x, &sum_x, &sum_of_squares_x);
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// 2. BN
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DenseTensor equiv_scale_x;
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DenseTensor equiv_bias_x;
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equiv_scale_x.Resize(param_dims);
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equiv_bias_x.Resize(param_dims);
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phi::fusion::CudnnBNStatsFinalize<T> bn_x_op(dev_ctx, param_shape);
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bn_x_op.Forward(dev_ctx,
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sum_x,
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sum_of_squares_x,
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*scale_x,
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*bias_x,
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saved_mean_x,
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saved_invstd_x,
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running_mean_x,
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running_var_x,
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&equiv_scale_x,
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&equiv_bias_x,
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eps,
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momentum,
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ele_count,
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is_train);
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// 3. scale + bias + add + relu
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phi::fusion::CudnnScaleBiasAddRelu<T> sbar_op(dev_ctx,
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act_type,
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fuse_add,
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has_shortcut,
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output_shape,
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param_shape,
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bitmask_shape);
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if (has_shortcut) {
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// input z
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const DenseTensor *input_z = z_in.get_ptr();
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const DenseTensor *filter_z = filter_z_in.get_ptr();
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const DenseTensor *scale_z = scale_z_in.get_ptr();
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const DenseTensor *bias_z = bias_z_in.get_ptr();
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// norm conv
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DenseTensor *conv_out_z = conv_z;
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auto input_z_shape = vectorize<int>(input_z->dims());
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auto filter_z_shape = vectorize<int>(filter_z->dims());
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// 3.1 Conv for second input
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DenseTensor sum_z;
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DenseTensor sum_of_squares_z;
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sum_z.Resize(param_dims);
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sum_of_squares_z.Resize(param_dims);
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phi::fusion::CudnnNormConvolution<T> conv_z_op(dev_ctx,
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input_z_shape,
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filter_z_shape,
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output_shape,
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padding,
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stride_z,
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dilation,
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group);
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conv_z_op.Forward(
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dev_ctx, *input_z, *filter_z, conv_out_z, &sum_z, &sum_of_squares_z);
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// 3.2 BN for second input
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DenseTensor equiv_scale_z;
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DenseTensor equiv_bias_z;
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equiv_scale_z.Resize(param_dims);
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equiv_bias_z.Resize(param_dims);
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phi::fusion::CudnnBNStatsFinalize<T> bn_z_op(dev_ctx, param_shape);
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bn_z_op.Forward(dev_ctx,
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sum_z,
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sum_of_squares_z,
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*scale_z,
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*bias_z,
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saved_mean_z,
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saved_invstd_z,
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running_mean_z,
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running_var_z,
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&equiv_scale_z,
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&equiv_bias_z,
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eps,
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momentum,
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ele_count,
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is_train);
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// 3.3 sbar
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sbar_op.Forward(dev_ctx,
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*conv_out_x,
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equiv_scale_x,
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equiv_bias_x,
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conv_out_z,
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&equiv_scale_z,
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&equiv_bias_z,
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output,
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bitmask);
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} else {
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const DenseTensor *input_z = fuse_add ? z_in.get_ptr() : nullptr;
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sbar_op.Forward(dev_ctx,
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*conv_out_x,
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equiv_scale_x,
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equiv_bias_x,
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input_z,
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nullptr,
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nullptr,
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output,
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bitmask);
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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resnet_unit, GPU, ALL_LAYOUT, phi::ResNetUnitKernel, phi::float16) {}
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#else
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namespace phi {
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template <typename T, typename Context>
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void ResNetUnitEmptyKernel(const Context &dev_ctx,
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const DenseTensor &x_in,
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const DenseTensor &filter_x_in,
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const DenseTensor &scale_x_in,
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const DenseTensor &bias_x_in,
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const DenseTensor &mean_x_in,
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const DenseTensor &var_x_in,
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const optional<DenseTensor> &z_in,
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const optional<DenseTensor> &filter_z_in,
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const optional<DenseTensor> &scale_z_in,
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const optional<DenseTensor> &bias_z_in,
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const optional<DenseTensor> &mean_z_in,
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const optional<DenseTensor> &var_z_in,
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int stride,
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int stride_z,
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int padding,
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int dilation,
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int group,
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float momentum_in,
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float epsilon,
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const std::string &data_format,
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bool fuse_add,
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bool has_shortcut,
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bool use_global_stats,
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bool is_test,
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bool use_addto,
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const std::string &act_type,
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DenseTensor *out,
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DenseTensor *bit_mask,
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DenseTensor *conv_x,
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DenseTensor *saved_mean_x,
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DenseTensor *saved_invstd_x,
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DenseTensor *running_mean_x,
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DenseTensor *running_var_x,
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DenseTensor *conv_z,
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DenseTensor *saved_mean_z,
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DenseTensor *saved_invstd_z,
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DenseTensor *running_mean_z,
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DenseTensor *running_var_z) {
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PADDLE_THROW(common::errors::Unavailable(
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"ResNetUnitOp only supports CUDNN_VERSION >= 8000 for now."));
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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resnet_unit, GPU, ALL_LAYOUT, phi::ResNetUnitEmptyKernel, phi::float16) {}
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#endif
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