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* init changes * bnorm * method signature * change order * bnorm * removed unused args
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| // Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. | ||
| // | ||
| // Licensed under the Apache License, Version 2.0 (the "License"); | ||
| // you may not use this file except in compliance with the License. | ||
| // You may obtain a copy of the License at | ||
| // | ||
| // http://www.apache.org/licenses/LICENSE-2.0 | ||
| // | ||
| // Unless required by applicable law or agreed to in writing, software | ||
| // distributed under the License is distributed on an "AS IS" BASIS, | ||
| // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| // See the License for the specific language governing permissions and | ||
| // limitations under the License. | ||
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| #include "paddle/phi/kernels/batch_norm_kernel.h" | ||
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| #include "paddle/phi/backends/onednn/onednn_reuse.h" | ||
| #include "paddle/phi/core/kernel_registry.h" | ||
| #include "paddle/phi/kernels/funcs/eigen/common.h" | ||
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| namespace phi { | ||
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| template <typename T> | ||
| using EigenVectorArrayMap = Eigen::Map<Eigen::Array<T, Eigen::Dynamic, 1>>; | ||
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| template <typename T, typename Context> | ||
| void BatchNormKernel(const Context &dev_ctx, | ||
| const DenseTensor &x, | ||
| const DenseTensor &mean, | ||
| const DenseTensor &variance, | ||
| const DenseTensor &scale, | ||
| const DenseTensor &bias, | ||
| bool is_test, | ||
| float momentum, | ||
| float epsilon, | ||
| const std::string &data_layout, | ||
| bool use_global_stats, | ||
| bool trainable_statistics, | ||
| DenseTensor *y, | ||
| DenseTensor *mean_out, | ||
| DenseTensor *variance_out, | ||
| DenseTensor *saved_mean, | ||
| DenseTensor *saved_variance, | ||
| DenseTensor *reserve_space) { | ||
| const bool test_mode = is_test && (!trainable_statistics); | ||
| const bool global_stats = test_mode || use_global_stats; | ||
| const bool fuse_with_relu = | ||
| dev_ctx.HasDnnAttr("fuse_with_relu") | ||
| ? PADDLE_GET_CONST(bool, dev_ctx.GetDnnAttr("fuse_with_relu")) | ||
| : false; | ||
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| funcs::BatchNormOneDNNHandler<T> handler(dev_ctx.GetEngine(), | ||
| dev_ctx.GetPlace(), | ||
| &x, | ||
| epsilon, | ||
| fuse_with_relu, | ||
| global_stats, | ||
| test_mode); | ||
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| auto src_memory = handler.AcquireSrcMemory(&x); | ||
| auto scaleshift_memory = handler.AcquireScaleShiftMemory(&scale, &bias); | ||
| auto dst_memory = handler.AcquireDstMemory(y); | ||
| auto batch_norm_p = handler.AcquireForwardPrimitive(); | ||
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| std::shared_ptr<dnnl::memory> mean_memory; | ||
| std::shared_ptr<dnnl::memory> variance_memory; | ||
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| // mean and variance can be taken either from input or output Tensor | ||
| if (global_stats) { | ||
| mean_memory = handler.AcquireMeanMemory(&mean); | ||
| variance_memory = handler.AcquireVarianceMemory(&variance); | ||
| } else { | ||
| mean_memory = handler.AcquireMeanMemory(saved_mean); | ||
| variance_memory = handler.AcquireVarianceMemory(saved_variance); | ||
| } | ||
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| y->set_mem_desc(dst_memory->get_desc()); | ||
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| auto &astream = OneDNNContext::tls().get_stream(); | ||
| batch_norm_p->execute(astream, | ||
| {{DNNL_ARG_SRC, *src_memory}, | ||
| {DNNL_ARG_SCALE_SHIFT, *scaleshift_memory}, | ||
| {DNNL_ARG_MEAN, *mean_memory}, | ||
| {DNNL_ARG_VARIANCE, *variance_memory}, | ||
| {DNNL_ARG_DST, *dst_memory}}); | ||
| astream.wait(); | ||
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| if (!global_stats) { | ||
| const unsigned int C = phi::vectorize(scale.dims())[0]; | ||
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| // mkldnn only compute stats for current batch | ||
| // so we need compute momentum stats via Eigen lib | ||
| EigenVectorArrayMap<T> batch_mean_e(dev_ctx.template Alloc<T>(saved_mean), | ||
| C); | ||
| EigenVectorArrayMap<T> batch_variance_e( | ||
| dev_ctx.template Alloc<T>(saved_variance), C); | ||
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| EigenVectorArrayMap<T> running_mean_e(dev_ctx.template Alloc<T>(mean_out), | ||
| C); | ||
| EigenVectorArrayMap<T> running_variance_e( | ||
| dev_ctx.template Alloc<T>(variance_out), C); | ||
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| running_mean_e = running_mean_e * momentum + batch_mean_e * (1. - momentum); | ||
| running_variance_e = | ||
| running_variance_e * momentum + batch_variance_e * (1. - momentum); | ||
| } | ||
| } | ||
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| template <typename T, typename Context> | ||
| void BatchNormInferKernel(const Context &dev_ctx, | ||
| const DenseTensor &x, | ||
| const DenseTensor &mean, | ||
| const DenseTensor &variance, | ||
| const DenseTensor &scale, | ||
| const DenseTensor &bias, | ||
| float momentum, | ||
| float epsilon, | ||
| const std::string &data_layout, | ||
| DenseTensor *y, | ||
| DenseTensor *mean_out, | ||
| DenseTensor *variance_out) { | ||
| BatchNormKernel<T, Context>(dev_ctx, | ||
| x, | ||
| mean, | ||
| variance, | ||
| scale, | ||
| bias, | ||
| /*is_test=*/true, | ||
| momentum, | ||
| epsilon, | ||
| data_layout, | ||
| /*use_global_stats=*/false, | ||
| /*trainable_statistics=*/false, | ||
| y, | ||
| mean_out, | ||
| variance_out, | ||
| /*saved_mean*/ nullptr, | ||
| /*saved_variance*/ nullptr, | ||
| /*reserve_space=*/nullptr); | ||
| } | ||
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| } // namespace phi | ||
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| PD_REGISTER_KERNEL(batch_norm, OneDNN, ONEDNN, phi::BatchNormKernel, float) {} | ||
| PD_REGISTER_KERNEL( | ||
| batch_norm_infer, OneDNN, ONEDNN, phi::BatchNormInferKernel, float) {} |