122 lines
4.1 KiB
C++
122 lines
4.1 KiB
C++
//*****************************************************************************
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// Copyright 2017-2020 Intel Corporation
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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//*****************************************************************************
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#include "pass/shape_relevance.hpp"
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#include "ngraph/graph_util.hpp"
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#include "ngraph/op/constant.hpp"
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#include "ngraph/op/util/op_types.hpp"
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using namespace ngraph;
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//
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// This pass refreshes the "is_relevant_to_shape" flag on each parameter. A parameter will be
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// flagged as relevant to shapes if there is any path from that parameter to a shape-relevant
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// input that does _not_ pass through a value-irrelevant input. For example:
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//
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// N0[Parameter] N1[Parameter]
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// | |
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// | |
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// | |
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// N2[v1::Reshape]
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//
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// N1 (but not N0) will be flagged as shape-relevant, because N1 feeds into the "shape" input
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// of N2.
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//
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// N0[Parameter] N1[Parameter]
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// | |
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// | N2[ShapeOf]
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// | |
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// N3[v1::Reshape]
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//
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// Neither N0 nor N1 will be flagged as shape-relevant. (N1 does feed into the "shape" input of N3,
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// but only via the value-irrelevant input of ShapeOf.)
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//
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bool pass::ShapeRelevance::run_on_function(std::shared_ptr<Function> f)
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{
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// TODO(amprocte): We are probably reinventing the wheel with the graph traversal here; the
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// reason is that we need to cut the traversal short in cases where input values are
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// irrelevant. See if there is a way to reduce this duplication.
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// Set of nodes that must be evaluated to determine the value of shape-relevant inputs.
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std::set<Node*> shape_determinants;
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// Step 1: Find root nodes (these are nodes with an output connected to a shape-relevant
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// input).
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for (auto& n : f->get_ops())
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{
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for (auto& output : n->outputs())
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{
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for (auto& input : output.get_target_inputs())
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{
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if (input.get_is_relevant_to_shapes())
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{
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shape_determinants.insert(n.get());
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break;
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}
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}
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}
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}
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// Step 2: Find all shape determinants. This is the transitive closure of R, where n1 R n2
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// iff there is a data flow edge from n2 to n1 and that data flow edge is not
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// value-irrelevant.
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bool changes_made = false;
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{
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std::list<Node*> to_visit{shape_determinants.begin(), shape_determinants.end()};
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std::set<Node*> already_visited;
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while (!to_visit.empty())
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{
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auto node = to_visit.front();
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to_visit.pop_front();
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if (already_visited.count(node) > 0)
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{
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continue;
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}
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shape_determinants.insert(node);
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already_visited.insert(node);
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if (op::is_parameter(node))
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{
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auto node_as_param = static_cast<op::Parameter*>(node);
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if (!node_as_param->is_relevant_to_shapes())
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{
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node_as_param->set_is_relevant_to_shapes(true);
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changes_made = true;
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}
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}
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for (size_t i = 0; i < node->get_input_size(); i++)
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{
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if (!node->input(i).get_is_relevant_to_values())
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{
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continue;
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}
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auto source_node = node->get_input_node_ptr(i);
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if (already_visited.count(source_node) == 0)
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{
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to_visit.push_front(source_node);
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}
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}
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}
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}
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return changes_made;
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}
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