/* Copyright (c) 2018 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. */ #include "paddle/fluid/memory/memcpy.h" #include "paddle/fluid/operators/elementwise/elementwise_add_op.h" #include "paddle/fluid/operators/elementwise/elementwise_op_function.h" #include "paddle/fluid/framework/data_layout_transform.h" #include "paddle/fluid/platform/mkldnn_reuse.h" namespace paddle { namespace operators { using framework::DataLayout; using framework::Tensor; using mkldnn::memory; using mkldnn::reorder; using mkldnn::primitive; using mkldnn::stream; using mkldnn::sum; template class EltwiseAddMKLDNNKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { auto& dev_ctx = ctx.template device_context(); const auto& mkldnn_engine = dev_ctx.GetEngine(); auto* x = ctx.Input("X"); auto* y = ctx.Input("Y"); auto* z = ctx.Output("Out"); const T* x_data = x->data(); const T* y_data = y->data(); T* z_data = z->mutable_data(ctx.GetPlace()); int axis = ctx.Attr("axis"); auto x_dims = x->dims(); auto y_dims_untrimed = y->dims(); auto z_dims = z->dims(); // Execute default elementwise_add operator when // broadcast operations need to performed. if (x_dims != y_dims_untrimed) { Tensor _x; MKLDNNMemoryFormat format; std::vector src_x_tz = framework::vectorize(x_dims); if ((src_x_tz.size() == 3 && x->format() != (format = MKLDNNMemoryFormat::ncw)) || (src_x_tz.size() == 4 && x->format() != (format = MKLDNNMemoryFormat::nchw)) || (src_x_tz.size() == 5 && x->format() != (format = MKLDNNMemoryFormat::ncdhw))) { _x.Resize(x_dims); mkldnn::memory::data_type in_type = platform::MKLDNNGetDataType(); auto out_format = platform::MKLDNNFormatForSize( x_dims.size(), MKLDNNMemoryFormat::nchw); const std::string key = platform::CreateKey( src_x_tz, x->format(), out_format, std::to_string(in_type)); platform::ReorderMKLDNNHandler handler(src_x_tz, x->type(), in_type, dev_ctx, mkldnn_engine, key); auto user_x_memory_p = handler.AcquireSrcMemory( x->format(), paddle::platform::to_void_cast(x_data)); auto x_memory_p = handler.AcquireDstMemory(&_x, out_format, ctx.GetPlace()); auto x_reorder = handler.AcquireReorder(x_memory_p, user_x_memory_p); std::vector pipeline; pipeline.push_back(*x_reorder); stream(stream::kind::eager).submit(pipeline).wait(); } else { format = x->format(); _x.ShareDataWith(*x); } auto sum_func = [](T a, T b) -> T { return a + b; }; TransformFunctor functor( &_x, y, z, ctx.template device_context(), sum_func); axis = (axis == -1 ? x_dims.size() - y_dims_untrimed.size() : axis); PADDLE_ENFORCE(axis >= 0 && axis < x_dims.size(), "Axis should be in range [0, x_dims)"); auto y_dims = trim_trailing_singular_dims(y_dims_untrimed); axis = (y_dims.size() == 0) ? x_dims.size() : axis; int pre, n, post; get_mid_dims(x_dims, y_dims, axis, &pre, &n, &post); if (post == 1) { functor.RunRowWise(n, pre); } else { functor.RunMidWise(n, pre, post); } z->set_layout(DataLayout::kMKLDNN); z->set_format(format); } else { PADDLE_ENFORCE_EQ(x->layout(), DataLayout::kMKLDNN, "Wrong layout set for X tensor"); PADDLE_ENFORCE_NE(x->format(), MKLDNNMemoryFormat::format_undef, "Wrong format set for X tensor"); PADDLE_ENFORCE_EQ(y->layout(), DataLayout::kMKLDNN, "Wrong layout set for Y tensor"); PADDLE_ENFORCE_NE(y->format(), MKLDNNMemoryFormat::format_undef, "Wrong format set for Y tensor"); std::vector src_x_tz = framework::vectorize(x_dims); std::vector src_y_tz = framework::vectorize(y_dims_untrimed); std::vector dst_tz = framework::vectorize(z_dims); std::vector srcs_pd; std::vector scales = {1.0f, 1.0f}; const std::string key = platform::CreateKey( src_x_tz, ctx.op().Output("Out") + std::to_string(x->format()) + std::to_string(y->format())); platform::SumMKLDNNHandler handler(dev_ctx, mkldnn_engine, key); auto src_x_memory = handler.AcquireSrcMemory( {{src_x_tz}, platform::MKLDNNGetDataType(), x->format()}, paddle::platform::to_void_cast(x_data)); auto src_y_memory = handler.AcquireSecondSrcMemory( {{src_y_tz}, platform::MKLDNNGetDataType(), y->format()}, paddle::platform::to_void_cast(y_data)); auto dst_md = memory::desc({dst_tz}, platform::MKLDNNGetDataType(), MKLDNNMemoryFormat::any); auto sum_pd = handler.AcquireSumPrimitiveDescriptor( {src_x_memory, src_y_memory}, scales, dst_md); auto dst_memory = handler.AcquireDstMemoryFromPrimitive(z_data); std::vector inputs({*src_x_memory, *src_y_memory}); auto sum_prim = handler.AcquireSum(dst_memory, &inputs); std::vector pipeline; pipeline.push_back(*sum_prim); stream(stream::kind::eager).submit(pipeline).wait(); z->set_layout(DataLayout::kMKLDNN); z->set_format((MKLDNNMemoryFormat)dst_memory->get_primitive_desc() .desc() .data.format); } } }; template class EltwiseAddMKLDNNGradKernel : public ElemwiseGradKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { ElemwiseGradKernel::Compute(ctx); using Tensor = framework::Tensor; auto* dout = ctx.Input(framework::GradVarName("Out")); auto* dx = ctx.Output(framework::GradVarName("X")); auto* dy = ctx.Output(framework::GradVarName("Y")); int axis = ctx.Attr("axis"); // skip out, x, y, // dout length is larger or equal than dx, dy. auto* out = dout; auto *x = dout, *y = dout; auto set_mkldnn_format = [](Tensor* in, const Tensor* out) { in->set_layout(DataLayout::kMKLDNN); in->set_format(out->format()); }; if (dx != nullptr && dy != nullptr && dx->dims() == dy->dims()) { if (dx->dims() == dy->dims()) { auto blas = math::GetBlas(ctx); if (dx) { blas.VCOPY(dout->numel(), dout->data(), dx->mutable_data(ctx.GetPlace())); set_mkldnn_format(dx, dout); } if (dy) { blas.VCOPY(dout->numel(), dout->data(), dy->mutable_data(ctx.GetPlace())); set_mkldnn_format(dy, dout); } } } else { // Execute default kernel when broadcast is needed ElemwiseExplicitGradCompute, IdentityGrad>( ctx, *x, *y, *out, *dout, axis, dx, dy, IdentityGrad(), IdentityGrad()); } } }; } // namespace operators } // namespace paddle namespace ops = paddle::operators; REGISTER_OP_KERNEL(elementwise_add, MKLDNN, ::paddle::platform::CPUPlace, ops::EltwiseAddMKLDNNKernel) REGISTER_OP_KERNEL(elementwise_add_grad, MKLDNN, ::paddle::platform::CPUPlace, ops::EltwiseAddMKLDNNGradKernel)