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/*
* Copyright (c) 2018-2020 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#include "arm_compute/graph/mutators/GroupedConvolutionMutator.h"
#include "arm_compute/graph/Graph.h"
#include "arm_compute/graph/GraphBuilder.h"
#include "arm_compute/graph/Logger.h"
#include "arm_compute/graph/Utils.h"
#include "arm_compute/graph/backends/BackendRegistry.h"
#include "arm_compute/graph/nodes/Nodes.h"
#include "arm_compute/core/utils/misc/Cast.h"
#include "support/StringSupport.h"
#include <set>
namespace arm_compute
{
namespace graph
{
namespace
{
NodeID create_grouped_convolution(Graph &g, const NodeParams ¶ms, NodeIdxPair input, NodeID weights, NodeID bias,
PadStrideInfo conv_info, ConvolutionMethod method, ActivationLayerInfo fused_act, FastMathHint fast_math_hint, unsigned int num_groups)
{
bool has_bias = (bias != EmptyNodeID);
// Split input
const TensorDescriptor input_tensor_desc = get_tensor_descriptor(g, g.node(input.node_id)->outputs()[0]);
const unsigned int input_idx = get_dimension_idx(input_tensor_desc.layout, DataLayoutDimension::CHANNEL);
NodeID input_split = GraphBuilder::add_split_node(g, params, input, num_groups, input_idx);
// Split weights
const TensorDescriptor weights_tensor_desc = get_tensor_descriptor(g, g.node(weights)->outputs()[0]);
const unsigned int batch_idx = get_dimension_idx(weights_tensor_desc.layout, DataLayoutDimension::BATCHES);
NodeID weights_split = GraphBuilder::add_split_node(g, params, { weights, 0 }, num_groups, batch_idx);
// Split bias
NodeID bias_split = EmptyNodeID;
if(has_bias)
{
// Split bias
bias_split = GraphBuilder::add_split_node(g, params, { bias, 0 }, num_groups, 0);
}
std::vector<NodeIdxPair> convolution_outputs;
for(unsigned int i = 0; i < num_groups; ++i)
{
NodeParams group_params = params;
NodeID conv_nid = g.add_node<ConvolutionLayerNode>(conv_info, 1, method, fast_math_hint);
g.add_connection(input_split, i, conv_nid, 0);
g.add_connection(weights_split, i, conv_nid, 1);
if(has_bias)
{
g.add_connection(bias_split, i, conv_nid, 2);
}
// Add group name
if(!group_params.name.empty())
{
group_params.name.append("_g" + arm_compute::support::cpp11::to_string(i));
}
// Set node parameters
INode *node = g.node(conv_nid);
ARM_COMPUTE_ERROR_ON(node == nullptr);
node->set_common_node_parameters(group_params);
// Down-cast node
auto *conv_node = arm_compute::utils::cast::polymorphic_downcast<ConvolutionLayerNode *>(node);
conv_node->set_fused_activation(fused_act);
convolution_outputs.push_back({ conv_nid, 0 });
}
// Depth concatenate output
return GraphBuilder::add_concatenate_node(g, params, convolution_outputs, DataLayoutDimension::CHANNEL);
}
} // namespace
const char *GroupedConvolutionMutator::name()
{
return "GroupedConvolutionMutator";
}
IGraphMutator::MutationType GroupedConvolutionMutator::type() const
{
return IGraphMutator::MutationType::Backend;
}
void GroupedConvolutionMutator::mutate(Graph &g)
{
// Early exit if no Convolution layers exist in graph
if(g.nodes(NodeType::ConvolutionLayer).empty())
{
return;
}
// Total nodes
size_t total_nodes = g.nodes().size();
// Iterate over convolution nodes
for(unsigned int i = 0; i < total_nodes; ++i)
{
INode *node = g.node(i);
if(node != nullptr && node->type() == NodeType::ConvolutionLayer && arm_compute::utils::cast::polymorphic_downcast<ConvolutionLayerNode *>(node)->num_groups() != 1)
{
// Validate node
backends::IDeviceBackend &backend = backends::BackendRegistry::get().get_backend(node->assigned_target());
Status status = backend.validate_node(*node);
// If grouped convolution is not supported
if(!bool(status))
{
// Down-cast node
auto *conv_node = arm_compute::utils::cast::polymorphic_downcast<ConvolutionLayerNode *>(node);
// Get internal convolution info
// TODO (geopin01) : Create a descriptor or a clone interface
const PadStrideInfo conv_info = conv_node->convolution_info();
const ConvolutionMethod conv_method = conv_node->convolution_method();
const ActivationLayerInfo fused_act_info = conv_node->fused_activation();
const FastMathHint fast_math_hint = conv_node->fast_math_hint();
const unsigned int num_groups = conv_node->num_groups();
const NodeParams params = conv_node->common_node_params();
const Target assigned_target = conv_node->assigned_target();
// Extract node ids
ARM_COMPUTE_ERROR_ON(conv_node->input_edge(0) == nullptr || conv_node->input_edge(1) == nullptr);
const NodeID input_id = conv_node->input_edge(0)->producer()->id();
const NodeID weights_id = conv_node->input_edge(1)->producer()->id();
const NodeID bias_id = (conv_node->input_edge(2) != nullptr) ? conv_node->input_edge(2)->producer()->id() : EmptyNodeID;
// Get driving nodes
std::vector<NodeIdxPair> driving_nodes = get_driving_nodes(*node);
// Extract activation node accessor if any
auto node_accessor = conv_node->output(0)->extract_accessor();
// Current max tensor and node id
TensorID latest_tid = g.tensors().size();
NodeID latest_nid = g.nodes().size();
// Create grouped convolution node
NodeID grouped_conv_id = create_grouped_convolution(g, params, { input_id, 0 }, weights_id, bias_id,
conv_info, conv_method, fused_act_info, fast_math_hint, num_groups);
// Remove convolution node
g.remove_node(node->id());
// Update batch normalization node outputs
for(auto &driving_node : driving_nodes)
{
g.add_connection(grouped_conv_id, 0, driving_node.node_id, driving_node.index);
}
// Update accessor to batch normalization node
g.node(grouped_conv_id)->output(0)->set_accessor(std::move(node_accessor));
// Configure new tensors and nodes
std::for_each(g.tensors().begin() + latest_tid, g.tensors().end(), [](std::unique_ptr<Tensor> &t)
{
configure_tensor(t.get());
});
std::for_each(g.nodes().begin() + latest_nid, g.nodes().end(), [&assigned_target](std::unique_ptr<INode> &n)
{
if(n != nullptr)
{
n->set_assigned_target(assigned_target);
}
});
}
}
}
}
} // namespace graph
} // namespace arm_compute
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