bgawrych commented on a change in pull request #20862: URL: https://github.com/apache/incubator-mxnet/pull/20862#discussion_r814047681
########## File path: src/operator/nn/dnnl/dnnl_where.cc ########## @@ -0,0 +1,209 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you 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. + */ + +/*! + * \file dnnl_where.cc + */ + +#if MXNET_USE_ONEDNN == 1 + +#include <algorithm> +#include <set> +#include <unordered_set> +#include "dnnl_where-inl.h" +#include "operator/operator_common.h" + +namespace mxnet { +namespace op { + +bool SupportDNNLWhere(const std::vector<NDArray>& inputs) { + static const std::set<int> supported_dtypes = { + mshadow::kFloat32, mshadow::kBfloat16, mshadow::kInt8, mshadow::kUint8}; + for (int i = 0; i < inputs.size(); ++i) { + if (!supported_dtypes.count(inputs[i].dtype()) || inputs[i].shape().Size() <= 0 || + inputs[i].shape().ndim() <= 0) { + return false; + } + } + return true; +} + +void DNNLWhereForward(const nnvm::NodeAttrs& attrs, + const OpContext& ctx, + const std::vector<NDArray>& inputs, + const std::vector<OpReqType>& req, + const std::vector<NDArray>& outputs) { + TmpMemMgr::Get()->Init(ctx.requested[0]); + const auto tensors = DNNLWhereFwd::Tensors(inputs, outputs[0]); + const auto fwd = DNNLWhereFwd::GetCached(tensors); + fwd.Execute(tensors, req, ctx); +} + +DNNLWhereFwd::Tensors::Tensors(const std::vector<NDArray>& inputs, const NDArray& output) + : condition(inputs[0]), left(inputs[1]), right(inputs[2]), output(output) {} + +DNNLWhereFwd DNNLWhereFwd::GetCached(const Tensors& tensors) { + using where_op_fwd_map = std::unordered_map<OpSignature, DNNLWhereFwd, OpHash>; +#if DMLC_CXX11_THREAD_LOCAL + static thread_local where_op_fwd_map fwds; +#else + static MX_THREAD_LOCAL where_op_fwd_map fwds; +#endif + + OpSignature key; + key.AddSign(tensors.condition); + key.AddSign(tensors.left); + key.AddSign(tensors.right); + key.AddSign(tensors.output); + + auto it = fwds.find(key); + if (it == fwds.end()) { + DNNLWhereFwd fwd(tensors); + it = AddToCache(&fwds, key, fwd); + } + return it->second; +} + +static mxnet::TShape GetBroadcastableShape(const mxnet::TShape& in_shape, + const mxnet::TShape& out_shape) { + if (in_shape == out_shape) { + return in_shape; + } + + mxnet::TShape broadcastable_in_shape(out_shape.ndim(), 1); + const int lack_dims = out_shape.ndim() - in_shape.ndim(); + for (int i = lack_dims; i < out_shape.ndim(); ++i) { + broadcastable_in_shape[i] = in_shape[i - lack_dims]; + } + return broadcastable_in_shape; +} + +DNNLWhereFwd::DNNLWhereFwd(const Tensors& tensors) { + const auto cpu_engine = CpuEngine::Get()->get_engine(); + + const auto cnd = tensors.condition; + const auto lhs = tensors.left; + const auto rhs = tensors.right; + const auto out = tensors.output; + + const auto cnd_shape = GetBroadcastableShape(cnd.shape(), out.shape()); + const auto lhs_shape = GetBroadcastableShape(lhs.shape(), out.shape()); + const auto rhs_shape = GetBroadcastableShape(rhs.shape(), out.shape()); + + const auto& cnd_dtype = get_dnnl_type(cnd.dtype()); + const auto& inp_dtype = get_dnnl_type(lhs.dtype()); + const auto& def_ft = static_cast<dnnl::memory::format_tag>(GetDefaultFormat(lhs_shape.ndim())); + + const auto& cnd_dims = dnnl::memory::dims(cnd_shape.begin(), cnd_shape.end()); + const auto& lhs_dims = dnnl::memory::dims(lhs_shape.begin(), lhs_shape.end()); + const auto& rhs_dims = dnnl::memory::dims(rhs_shape.begin(), rhs_shape.end()); + const auto& out_dims = dnnl::memory::dims(out.shape().begin(), out.shape().end()); + const auto& scalar_dims = dnnl::memory::dims(cnd_shape.ndim(), 1); // broadcastable scalar + + auto cnd_md = dnnl::memory::desc(cnd_dims, cnd_dtype, def_ft); + auto lhs_md = dnnl::memory::desc(lhs_dims, inp_dtype, def_ft); + auto rhs_md = dnnl::memory::desc(rhs_dims, inp_dtype, def_ft); + auto out_md = dnnl::memory::desc(out_dims, inp_dtype, def_ft); + auto scalar_md = dnnl::memory::desc(scalar_dims, cnd_dtype, def_ft); + + binary_eq_zero_pd = dnnl::binary::primitive_desc( + dnnl::binary::desc(dnnl::algorithm::binary_ne, cnd_md, scalar_md, cnd_md), cpu_engine); Review comment: done ########## File path: src/operator/nn/dnnl/dnnl_where.cc ########## @@ -0,0 +1,209 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you 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. + */ + +/*! + * \file dnnl_where.cc + */ + +#if MXNET_USE_ONEDNN == 1 + +#include <algorithm> +#include <set> +#include <unordered_set> +#include "dnnl_where-inl.h" +#include "operator/operator_common.h" + +namespace mxnet { +namespace op { + +bool SupportDNNLWhere(const std::vector<NDArray>& inputs) { + static const std::set<int> supported_dtypes = { + mshadow::kFloat32, mshadow::kBfloat16, mshadow::kInt8, mshadow::kUint8}; + for (int i = 0; i < inputs.size(); ++i) { + if (!supported_dtypes.count(inputs[i].dtype()) || inputs[i].shape().Size() <= 0 || + inputs[i].shape().ndim() <= 0) { + return false; + } + } + return true; +} + +void DNNLWhereForward(const nnvm::NodeAttrs& attrs, + const OpContext& ctx, + const std::vector<NDArray>& inputs, + const std::vector<OpReqType>& req, + const std::vector<NDArray>& outputs) { + TmpMemMgr::Get()->Init(ctx.requested[0]); + const auto tensors = DNNLWhereFwd::Tensors(inputs, outputs[0]); Review comment: done -- This is an automated message from the Apache Git Service. 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