| /* |
| * 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 log_softmax.cc |
| * \brief CPU Implementation of log_softmax |
| */ |
| #include "./softmax-inl.h" |
| #include "../tensor/elemwise_unary_op.h" |
| #include "../tensor/elemwise_binary_op.h" |
| #include "../operator_common.h" |
| #if MXNET_USE_ONEDNN == 1 |
| #include "operator/nn/dnnl/dnnl_base-inl.h" |
| #include "operator/nn/dnnl/dnnl_softmax-inl.h" |
| #endif |
| |
| namespace mxnet { |
| namespace op { |
| |
| #if MXNET_USE_ONEDNN == 1 |
| static void LogSoftmaxComputeExCPU(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const std::vector<NDArray>& inputs, |
| const std::vector<OpReqType>& req, |
| const std::vector<NDArray>& outputs) { |
| const SoftmaxParam& param = nnvm::get<SoftmaxParam>(attrs.parsed); |
| if (SupportDNNLLogSoftmax(param, inputs[0])) { |
| DNNL_OPCHECK_INIT(false, outputs.size(), inputs, outputs); |
| DNNLRun(DNNLLogSoftmaxForward, attrs, ctx, inputs[0], req[0], outputs[0]); |
| auto fn = SoftmaxCompute<cpu, mxnet_op::log_softmax_fwd>; |
| DNNL_OPCHECK_RUN(fn, attrs, ctx, inputs, req, outputs); |
| return; |
| } |
| FallBackCompute(SoftmaxCompute<cpu, mxnet_op::log_softmax_fwd>, attrs, ctx, inputs, req, outputs); |
| } |
| |
| static void LogSoftmaxGradComputeExCPU(const nnvm::NodeAttrs& attrs, |
| const OpContext& ctx, |
| const std::vector<NDArray>& inputs, |
| const std::vector<OpReqType>& req, |
| const std::vector<NDArray>& outputs) { |
| const SoftmaxParam& param = nnvm::get<SoftmaxParam>(attrs.parsed); |
| if (SupportDNNLLogSoftmax(param, inputs[1])) { |
| DNNL_OPCHECK_INIT(false, outputs.size(), inputs, outputs); |
| DNNLRun(DNNLLogSoftmaxBackward, attrs, ctx, inputs, req, outputs); |
| auto fn = SoftmaxGradCompute<cpu, op::mshadow_op::left, mxnet_op::log_softmax_bwd>; |
| DNNL_OPCHECK_RUN(fn, attrs, ctx, inputs, req, outputs); |
| return; |
| } |
| FallBackCompute(SoftmaxGradCompute<cpu, op::mshadow_op::left, mxnet_op::log_softmax_bwd>, |
| attrs, |
| ctx, |
| inputs, |
| req, |
| outputs); |
| } |
| |
| inline static bool LogSoftmaxStorageType(const nnvm::NodeAttrs& attrs, |
| const int dev_mask, |
| DispatchMode* dispatch_mode, |
| std::vector<int>* in_attrs, |
| std::vector<int>* out_attrs) { |
| CHECK_EQ(in_attrs->size(), 1U); |
| CHECK_EQ(out_attrs->size(), 1U); |
| |
| return DNNLStorageType(attrs, dev_mask, true, dispatch_mode, in_attrs, out_attrs); |
| } |
| |
| inline static bool LogSoftmaxGradStorageType(const nnvm::NodeAttrs& attrs, |
| const int dev_mask, |
| DispatchMode* dispatch_mode, |
| std::vector<int>* in_attrs, |
| std::vector<int>* out_attrs) { |
| bool support = true; |
| int num_inputs = 2U; |
| if (softmax_has_dtype_override(attrs)) { |
| support = false; |
| num_inputs = 3U; |
| } |
| |
| CHECK_EQ(in_attrs->size(), num_inputs); |
| CHECK_EQ(out_attrs->size(), 1U); |
| return DNNLStorageType(attrs, dev_mask, support, dispatch_mode, in_attrs, out_attrs); |
| } |
| #endif |
| |
| NNVM_REGISTER_OP(log_softmax) |
| .add_alias("_npx_log_softmax") |
| .describe(R"code(Computes the log softmax of the input. |
| This is equivalent to computing softmax followed by log. |
| |
| Examples:: |
| |
| >>> x = mx.nd.array([1, 2, .1]) |
| >>> mx.nd.log_softmax(x).asnumpy() |
| array([-1.41702998, -0.41702995, -2.31702995], dtype=float32) |
| |
| >>> x = mx.nd.array( [[1, 2, .1],[.1, 2, 1]] ) |
| >>> mx.nd.log_softmax(x, axis=0).asnumpy() |
| array([[-0.34115392, -0.69314718, -1.24115396], |
| [-1.24115396, -0.69314718, -0.34115392]], dtype=float32) |
| |
| |
| )code") |
| .set_attr_parser(ParamParser<SoftmaxParam>) |
| .set_attr<nnvm::FListInputNames>("FListInputNames", |
| [](const NodeAttrs& attrs) { |
| return std::vector<std::string>{"data"}; |
| }) |
| .set_attr<FCompute>("FCompute<cpu>", SoftmaxCompute<cpu, mxnet_op::log_softmax_fwd>) |
| #if MXNET_USE_ONEDNN == 1 |
| .set_attr<bool>("TIsDNNL", true) |
| .set_attr<FComputeEx>("FComputeEx<cpu>", LogSoftmaxComputeExCPU) |
| .set_attr<FInferStorageType>("FInferStorageType", LogSoftmaxStorageType) |
| #endif |
| .set_attr<nnvm::FGradient>("FGradient", SoftmaxFGradient{"_backward_log_softmax"}) |
| .set_attr<nnvm::FInferType>("FInferType", SoftmaxOpType) |
| .set_num_inputs(1) |
| .set_num_outputs(1) |
| .set_attr<mxnet::FInferShape>("FInferShape", ElemwiseShape<1, 1>) |
| .set_attr<nnvm::FInplaceOption>("FInplaceOption", |
| [](const NodeAttrs& attrs) { |
| return std::vector<std::pair<int, int> >{{0, 0}}; |
| }) |
| .add_argument("data", "NDArray-or-Symbol", "The input array.") |
| .add_arguments(SoftmaxParam::__FIELDS__()); |
| |
| NNVM_REGISTER_OP(_backward_log_softmax) |
| .set_num_inputs(SoftmaxGradOpNumInputs) |
| .set_num_outputs(1) |
| .set_attr<nnvm::FListInputNames>("FListInputNames", SoftmaxGradOpInputNames) |
| .set_attr<mxnet::FInferShape>("FInferShape", SoftmaxGradOpShape) |
| .set_attr<nnvm::FInferType>("FInferType", SoftmaxGradOpType) |
| .set_attr<nnvm::FInplaceOption>("FInplaceOption", SoftmaxGradOpInplaceOption) |
| .add_argument("args", "NDArray-or-Symbol[]", "Positional input arguments") |
| .set_attr_parser(ParamParser<SoftmaxParam>) |
| #if MXNET_USE_ONEDNN == 1 |
| .set_attr<bool>("TIsDNNL", true) |
| .set_attr<FComputeEx>("FComputeEx<cpu>", LogSoftmaxGradComputeExCPU) |
| .set_attr<FInferStorageType>("FInferStorageType", LogSoftmaxGradStorageType) |
| #endif |
| .set_attr<FCompute>("FCompute<cpu>", |
| SoftmaxGradCompute<cpu, mshadow_op::left, mxnet_op::log_softmax_bwd>); |
| |
| NNVM_REGISTER_OP(masked_log_softmax) |
| .add_alias("_npx_masked_log_softmax") |
| .describe(R"code(Computes the masked log softmax of the input. |
| This is equivalent to computing masked softmax followed by log.)code") |
| .set_attr_parser(ParamParser<MaskedSoftmaxParam>) |
| .set_attr<nnvm::FListOutputNames>("FListInputNames", |
| [](const NodeAttrs& attrs) { |
| return std::vector<std::string>{"data", "mask"}; |
| }) |
| .set_attr<FCompute>("FCompute<cpu>", MaskedSoftmaxCompute<cpu, mxnet_op::log_softmax_fwd, true>) |
| .set_attr<nnvm::FGradient>( |
| "FGradient", |
| [](const nnvm::ObjectPtr& n, const std::vector<nnvm::NodeEntry>& ograds) { |
| auto data_grad = MakeNode("_backward_masked_log_softmax", |
| n->attrs.name + "_backward_data", |
| {ograds[0], n->inputs[1], nnvm::NodeEntry(n, 0, 0)}, |
| &n->attrs.dict, |
| &n); |
| auto mask_grad = |
| MakeNode("zeros_like", n->attrs.name + "_backward_mask", {n->inputs[1]}, nullptr, &n); |
| std::vector<nnvm::NodeEntry> ret; |
| ret.emplace_back(data_grad); |
| ret.emplace_back(mask_grad); |
| return ret; |
| }) |
| .set_attr<nnvm::FInferType>("FInferType", MaskedSoftmaxOpType) |
| .set_num_inputs(2) |
| .set_num_outputs(1) |
| .set_attr<mxnet::FInferShape>("FInferShape", MaskedSoftmaxOpShape) |
| .set_attr<FResourceRequest>("FResourceRequest", |
| [](const NodeAttrs& attrs) { |
| return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; |
| }) |
| .set_attr<nnvm::FInplaceOption>("FInplaceOption", |
| [](const NodeAttrs& attrs) { |
| return std::vector<std::pair<int, int> >{{0, 0}}; |
| }) |
| .add_argument("data", "NDArray-or-Symbol", "The input array.") |
| .add_argument("mask", "NDArray-or-Symbol", "Mask to apply.") |
| .add_arguments(MaskedSoftmaxParam::__FIELDS__()); |
| |
| NNVM_REGISTER_OP(_backward_masked_log_softmax) |
| .set_num_inputs(3) |
| .set_num_outputs(1) |
| .set_attr<nnvm::FListOutputNames>("FListInputNames", |
| [](const NodeAttrs& attrs) { |
| return std::vector<std::string>{"ograd", "mask", "output"}; |
| }) |
| .set_attr<mxnet::FInferShape>("FInferShape", MaskedSoftmaxGradOpShape) |
| .set_attr<nnvm::FInferType>("FInferType", MaskedSoftmaxGradOpType) |
| .set_attr<FResourceRequest>("FResourceRequest", |
| [](const NodeAttrs& attrs) { |
| return std::vector<ResourceRequest>{ResourceRequest::kTempSpace}; |
| }) |
| .set_attr<nnvm::FInplaceOption>("FInplaceOption", MaskedSoftmaxGradOpInplaceOption) |
| .add_argument("args", "NDArray-or-Symbol[]", "Positional input arguments") |
| .set_attr_parser(ParamParser<MaskedSoftmaxParam>) |
| .set_attr<FCompute>("FCompute<cpu>", |
| MaskedSoftmaxGradCompute<cpu, mshadow_op::left, mxnet_op::log_softmax_bwd>); |
| |
| } // namespace op |
| } // namespace mxnet |
