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Added PRelu BF16/FP32 FWD/BWD kernels (#33878)
* added prelu bf16/fp32 fwd/bwd kernel
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| /* Copyright (c) 2021 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/fluid/platform/mkldnn_reuse.h" | ||
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| namespace paddle { | ||
| namespace operators { | ||
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| using dnnl::memory; | ||
| using framework::Tensor; | ||
| using platform::GetMKLDNNFormat; | ||
| using platform::MKLDNNDeviceContext; | ||
| using platform::MKLDNNGetDataType; | ||
| using platform::to_void_cast; | ||
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| namespace { | ||
| template <typename T> | ||
| class PReluMKLDNNHandler | ||
| : public platform::MKLDNNHandlerT<T, dnnl::prelu_forward, | ||
| dnnl::prelu_backward> { | ||
| public: | ||
| PReluMKLDNNHandler(const MKLDNNDeviceContext& dev_ctx, | ||
| const mkldnn::engine engine, platform::Place cpu_place, | ||
| const Tensor* x, const Tensor* weights, | ||
| const std::string& uniq_name, const std::string& mode, | ||
| bool is_test = false) | ||
| : platform::MKLDNNHandlerT<T, dnnl::prelu_forward, dnnl::prelu_backward>( | ||
| dev_ctx, engine, cpu_place, | ||
| platform::CreateKey(dev_ctx, framework::vectorize(x->dims()), | ||
| uniq_name)) { | ||
| if (!this->isCached()) { | ||
| auto x_md = memory::desc(framework::vectorize(x->dims()), | ||
| MKLDNNGetDataType<T>(), x->format()); | ||
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| auto weights_dims = framework::vectorize(weights->dims()); | ||
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| // weights must have same size as X only for "element" case | ||
| if (weights->dims().size() != x->dims().size()) { | ||
| auto new_weights_dims = std::vector<int64_t>(x->dims().size(), 1); | ||
| if (mode == "channel") { | ||
| new_weights_dims[1] = | ||
| *std::max_element(weights_dims.begin(), weights_dims.end()); | ||
| } | ||
| weights_dims = std::move(new_weights_dims); | ||
| } | ||
| auto weights_md = memory::desc(weights_dims, MKLDNNGetDataType<T>(), | ||
| memory::format_tag::any); | ||
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| this->AcquireForwardPrimitiveDescriptor(dnnl::prop_kind::forward_training, | ||
| x_md, weights_md); | ||
| if (!is_test) | ||
| this->AcquireBackwardPrimitiveDescriptor(x_md, weights_md, x_md, | ||
| weights_md); | ||
| } | ||
| } | ||
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| std::shared_ptr<memory> AcquireWeightsMemoryPossiblyWithReorder( | ||
| const Tensor* input, const bool is_test) { | ||
| const T* input_data = input->data<T>(); | ||
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| // if weights are 1D, every format tag is correct, so we accept | ||
| // format_tag::any's output and no reorder is needed | ||
| if (input->dims().size() == 1) { | ||
| return this->AcquireMemoryFromPrimitive(this->fwd_pd_->weights_desc(), | ||
| to_void_cast<T>(input_data), | ||
| "@alpha_mem_p"); | ||
| } | ||
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| auto user_weights_md = | ||
| memory::desc(framework::vectorize(input->dims()), | ||
| MKLDNNGetDataType<T>(), input->format()); | ||
| return this->AcquireMemoryWithReorder( | ||
| user_weights_md, this->fwd_pd_->weights_desc(), | ||
| to_void_cast<T>(input_data), "@alpha_mem_p", is_test); | ||
| } | ||
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| std::shared_ptr<memory> AcquireDiffWeightsMemory(Tensor* output) { | ||
| T* output_data = output->mutable_data<T>( | ||
| this->place_, this->bwd_pd_->diff_weights_desc().get_size()); | ||
| return this->AcquireMemoryFromPrimitive(this->bwd_pd_->diff_weights_desc(), | ||
| output_data, "@diff_weights_mem_p"); | ||
| } | ||
| }; | ||
| } // anonymous namespace | ||
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| template <typename T> | ||
| class PReluMKLDNNKernel : public framework::OpKernel<T> { | ||
| public: | ||
| void Compute(const framework::ExecutionContext& ctx) const override { | ||
| this->RunKernel(ctx); | ||
| } | ||
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| void RunKernel(const framework::ExecutionContext& ctx) const { | ||
| const auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>(); | ||
| const auto& onednn_engine = dev_ctx.GetEngine(); | ||
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| const auto* x = ctx.Input<Tensor>("X"); | ||
| const auto* alpha = ctx.Input<Tensor>("Alpha"); | ||
| auto* out = ctx.Output<Tensor>("Out"); | ||
| const bool is_test = ctx.Attr<bool>("is_test"); | ||
| const auto mode = ctx.Attr<std::string>("mode"); | ||
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| PReluMKLDNNHandler<T> handler(dev_ctx, onednn_engine, ctx.GetPlace(), x, | ||
| alpha, ctx.InputName("X"), mode, is_test); | ||
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| auto src_memory_p = handler.AcquireSrcMemory(x); | ||
| auto weights_memory_p = | ||
| handler.AcquireWeightsMemoryPossiblyWithReorder(alpha, is_test); | ||
| auto dst_memory_p = handler.AcquireDstMemory(out); | ||
| auto prelu_p = handler.AcquireForwardPrimitive(); | ||
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| auto& astream = MKLDNNDeviceContext::tls().get_stream(); | ||
| prelu_p->execute(astream, {{DNNL_ARG_SRC, *src_memory_p}, | ||
| {DNNL_ARG_WEIGHTS, *weights_memory_p}, | ||
| {DNNL_ARG_DST, *dst_memory_p}}); | ||
| astream.wait(); | ||
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| out->set_layout(framework::DataLayout::kMKLDNN); | ||
| out->set_format(GetMKLDNNFormat(*dst_memory_p)); | ||
| } | ||
| }; | ||
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| template <typename T> | ||
| class PReluGradMKLDNNKernel : public framework::OpKernel<T> { | ||
| public: | ||
| void Compute(const framework::ExecutionContext& ctx) const override { | ||
| this->RunKernel(ctx); | ||
| } | ||
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| void RunKernel(const framework::ExecutionContext& ctx) const { | ||
| const auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>(); | ||
| const auto& onednn_engine = dev_ctx.GetEngine(); | ||
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| auto* x = ctx.Input<Tensor>("X"); | ||
| auto* dx = ctx.Output<Tensor>(framework::GradVarName("X")); | ||
| auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out")); | ||
| auto* dalpha = ctx.Output<Tensor>(framework::GradVarName("Alpha")); | ||
| auto* alpha = ctx.Input<Tensor>("Alpha"); | ||
| const bool is_test = ctx.Attr<bool>("is_test"); | ||
| const auto mode = ctx.Attr<std::string>("mode"); | ||
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| PReluMKLDNNHandler<T> handler(dev_ctx, onednn_engine, ctx.GetPlace(), x, | ||
| alpha, framework::GradVarName("X"), mode); | ||
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| auto src_memory_p = handler.AcquireSrcMemory(x); | ||
| auto weights_memory_p = | ||
| handler.AcquireWeightsMemoryPossiblyWithReorder(alpha, is_test); | ||
| auto diff_src_memory_p = handler.AcquireDiffSrcMemory(dx); | ||
| auto diff_weights_memory_p = handler.AcquireDiffWeightsMemory(dalpha); | ||
| auto diff_dst_memory_p = handler.AcquireDiffDstMemory(dout); | ||
| auto prelu_p = handler.AcquireBackwardPrimitive(); | ||
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| auto& astream = MKLDNNDeviceContext::tls().get_stream(); | ||
| prelu_p->execute(astream, | ||
| {{DNNL_ARG_SRC, *src_memory_p}, | ||
| {DNNL_ARG_WEIGHTS, *weights_memory_p}, | ||
| {DNNL_ARG_DIFF_DST, *diff_dst_memory_p}, | ||
| {DNNL_ARG_DIFF_SRC, *diff_src_memory_p}, | ||
| {DNNL_ARG_DIFF_WEIGHTS, *diff_weights_memory_p}}); | ||
| astream.wait(); | ||
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| dx->set_layout(framework::DataLayout::kMKLDNN); | ||
| dx->set_format(GetMKLDNNFormat(*diff_src_memory_p)); | ||
| } | ||
| }; | ||
| } // namespace operators | ||
| } // namespace paddle | ||
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| namespace ops = paddle::operators; | ||
| REGISTER_OP_KERNEL(prelu, MKLDNN, paddle::platform::CPUPlace, | ||
| ops::PReluMKLDNNKernel<float>, | ||
| ops::PReluMKLDNNKernel<paddle::platform::bfloat16>); | ||
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| REGISTER_OP_KERNEL(prelu_grad, MKLDNN, paddle::platform::CPUPlace, | ||
| ops::PReluGradMKLDNNKernel<float>, | ||
| ops::PReluGradMKLDNNKernel<paddle::platform::bfloat16>); |
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