From bffb41e06c1276af00e1605ef934d05fa61f7127 Mon Sep 17 00:00:00 2001 From: Manuel Bottini Date: Thu, 20 Jun 2019 16:00:27 +0100 Subject: COMPMID-2273: Fuse Batch Normalization with Depthwise Convolution layer at graph level (only for CL) Change-Id: I1d941c6e66722f39583bf68148c980bb28ff89a1 Signed-off-by: Manuel Bottini Reviewed-on: https://review.mlplatform.org/c/1423 Comments-Addressed: Arm Jenkins Reviewed-by: Michele Di Giorgio Tested-by: Arm Jenkins --- arm_compute/graph/INodeVisitor.h | 9 ++ arm_compute/graph/TypePrinter.h | 3 + arm_compute/graph/Types.h | 1 + arm_compute/graph/backends/FunctionHelpers.h | 57 +++++++-- .../FusedConvolutionBatchNormalizationFunction.h | 2 +- ...epthwiseConvolutionBatchNormalizationFunction.h | 131 +++++++++++++++++++ .../nodes/FusedConvolutionBatchNormalizationNode.h | 4 +- ...sedDepthwiseConvolutionBatchNormalizationNode.h | 136 ++++++++++++++++++++ arm_compute/graph/nodes/Nodes.h | 1 + arm_compute/graph/nodes/NodesFwd.h | 1 + arm_compute/graph/printers/DotGraphPrinter.h | 1 + src/graph/backends/CL/CLFunctionsFactory.cpp | 7 +- src/graph/backends/NEON/NEFunctionFactory.cpp | 5 +- src/graph/mutators/NodeFusionMutator.cpp | 87 ++++++++++++- .../FusedConvolutionBatchNormalizationNode.cpp | 8 +- ...dDepthwiseConvolutionBatchNormalizationNode.cpp | 140 +++++++++++++++++++++ src/graph/printers/DotGraphPrinter.cpp | 8 ++ 17 files changed, 578 insertions(+), 23 deletions(-) create mode 100644 arm_compute/graph/backends/FusedDepthwiseConvolutionBatchNormalizationFunction.h create mode 100644 arm_compute/graph/nodes/FusedDepthwiseConvolutionBatchNormalizationNode.h create mode 100644 src/graph/nodes/FusedDepthwiseConvolutionBatchNormalizationNode.cpp diff --git a/arm_compute/graph/INodeVisitor.h b/arm_compute/graph/INodeVisitor.h index be43b57e48..5c5b777ac9 100644 --- a/arm_compute/graph/INodeVisitor.h +++ b/arm_compute/graph/INodeVisitor.h @@ -96,6 +96,11 @@ public: * @param[in] n Node to visit. */ virtual void visit(FusedConvolutionBatchNormalizationNode &n) = 0; + /** Visit FusedDepthwiseConvolutionBatchNormalizationNode. + * + * @param[in] n Node to visit. + */ + virtual void visit(FusedDepthwiseConvolutionBatchNormalizationNode &n) = 0; /** Visit InputNode. * * @param[in] n Node to visit. @@ -214,6 +219,10 @@ public: { default_visit(); } + virtual void visit(FusedDepthwiseConvolutionBatchNormalizationNode &n) override + { + default_visit(); + } virtual void visit(InputNode &n) override { default_visit(); diff --git a/arm_compute/graph/TypePrinter.h b/arm_compute/graph/TypePrinter.h index 4fb5b73333..9da0e6157c 100644 --- a/arm_compute/graph/TypePrinter.h +++ b/arm_compute/graph/TypePrinter.h @@ -101,6 +101,9 @@ inline ::std::ostream &operator<<(::std::ostream &os, const NodeType &node_type) case NodeType::FusedConvolutionBatchNormalizationLayer: os << "FusedConvolutionBatchNormalizationLayer"; break; + case NodeType::FusedDepthwiseConvolutionBatchNormalizationLayer: + os << "FusedDepthwiseConvolutionBatchNormalizationLayer"; + break; case NodeType::GenerateProposalsLayer: os << "GenerateProposalsLayer"; break; diff --git a/arm_compute/graph/Types.h b/arm_compute/graph/Types.h index 2f09abbbab..9f962425b3 100644 --- a/arm_compute/graph/Types.h +++ b/arm_compute/graph/Types.h @@ -141,6 +141,7 @@ enum class NodeType FlattenLayer, FullyConnectedLayer, FusedConvolutionBatchNormalizationLayer, + FusedDepthwiseConvolutionBatchNormalizationLayer, GenerateProposalsLayer, NormalizationLayer, NormalizePlanarYUVLayer, diff --git a/arm_compute/graph/backends/FunctionHelpers.h b/arm_compute/graph/backends/FunctionHelpers.h index 5ac4fdaed9..ed5b35c0d1 100644 --- a/arm_compute/graph/backends/FunctionHelpers.h +++ b/arm_compute/graph/backends/FunctionHelpers.h @@ -30,6 +30,7 @@ #include "arm_compute/graph/Types.h" #include "arm_compute/graph/Utils.h" #include "arm_compute/graph/backends/FusedConvolutionBatchNormalizationFunction.h" +#include "arm_compute/graph/backends/FusedDepthwiseConvolutionBatchNormalizationFunction.h" #include "arm_compute/graph/backends/Utils.h" #include "arm_compute/graph/nodes/Nodes.h" @@ -197,12 +198,6 @@ std::unique_ptr create_fused_convolution_batch_normalization_layer(Fu const ActivationLayerInfo fused_act = node.fused_activation(); const float epsilon = node.epsilon(); - const bool is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); - if(is_quantized && biases != nullptr) - { - biases->info()->set_data_type(DataType::S32); - } - // Create and configure function auto func = support::cpp14::make_unique>(); func->configure(input, weights, biases, output, mean, var, beta, gamma, epsilon, conv_info, num_groups, fast_math, fused_act); @@ -210,7 +205,55 @@ std::unique_ptr create_fused_convolution_batch_normalization_layer(Fu // Log info ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << node.name() - << " Type: " << node.name() + << " Type: " << node.type() + << " Target: " << TargetInfo::TargetType + << " Data Type: " << input->info()->data_type() + << " Input shape: " << input->info()->tensor_shape() + << " Weights shape: " << weights->info()->tensor_shape() + << " Output shape: " << output->info()->tensor_shape() + << (fused_act.enabled() ? " " + to_string(fused_act.activation()) : "") + << std::endl); + return std::move(func); +} + +/** Create a backend fused depthwise convolution batch normalization layer function + * + * @tparam FusedLayerTypes Fused layer types + * @tparam TargetInfo Target-specific information + * + * @param[in] node Node to create the backend function for + * + * @return Backend fused depthwise convolution batch normalization layer function + */ +template +std::unique_ptr create_fused_depthwise_convolution_batch_normalization_layer(FusedDepthwiseConvolutionBatchNormalizationNode &node) +{ + validate_node(node, 7 /* expected inputs */, 1 /* expected outputs */); + + // Extract IO and info + typename TargetInfo::TensorType *input = get_backing_tensor(node.input(0)); + typename TargetInfo::TensorType *weights = get_backing_tensor(node.input(1)); + typename TargetInfo::TensorType *biases = get_backing_tensor(node.input(2)); + typename TargetInfo::TensorType *mean = get_backing_tensor(node.input(3)); + typename TargetInfo::TensorType *var = get_backing_tensor(node.input(4)); + typename TargetInfo::TensorType *beta = get_backing_tensor(node.input(5)); + typename TargetInfo::TensorType *gamma = get_backing_tensor(node.input(6)); + + typename TargetInfo::TensorType *output = get_backing_tensor(node.output(0)); + + const PadStrideInfo conv_info = node.convolution_info(); + const unsigned int depth_multiplier = node.depth_multiplier(); + const ActivationLayerInfo fused_act = node.fused_activation(); + const float epsilon = node.epsilon(); + + // Create and configure function + auto func = support::cpp14::make_unique>(); + func->configure(input, weights, biases, output, mean, var, beta, gamma, epsilon, conv_info, depth_multiplier, fused_act); + + // Log info + ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " + << node.name() + << " Type: " << node.type() << " Target: " << TargetInfo::TargetType << " Data Type: " << input->info()->data_type() << " Input shape: " << input->info()->tensor_shape() diff --git a/arm_compute/graph/backends/FusedConvolutionBatchNormalizationFunction.h b/arm_compute/graph/backends/FusedConvolutionBatchNormalizationFunction.h index 92af17b227..a6da76bb06 100644 --- a/arm_compute/graph/backends/FusedConvolutionBatchNormalizationFunction.h +++ b/arm_compute/graph/backends/FusedConvolutionBatchNormalizationFunction.h @@ -54,7 +54,7 @@ public: * Data types supported: QASYMM8/F16/F32. * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input. * @param[in] bias Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. - * Data type supported: Should match @p input data type, except for input of QASYMM8 type where biases should be of S32 type. + * Data type supported: Should match @p input data type. * @param[out] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. * Data types supported: Same as @p input. * @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input diff --git a/arm_compute/graph/backends/FusedDepthwiseConvolutionBatchNormalizationFunction.h b/arm_compute/graph/backends/FusedDepthwiseConvolutionBatchNormalizationFunction.h new file mode 100644 index 0000000000..6f70d3c3a0 --- /dev/null +++ b/arm_compute/graph/backends/FusedDepthwiseConvolutionBatchNormalizationFunction.h @@ -0,0 +1,131 @@ +/* + * Copyright (c) 2019 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. + */ + +#ifndef __ARM_COMPUTE_GRAPH_BACKENDS_FUSED_DEPTHWISE_CONVOLUTION_BATCH_NORMALIZATION_FUNCTION_H__ +#define __ARM_COMPUTE_GRAPH_BACKENDS_FUSED_DEPTHWISE_CONVOLUTION_BATCH_NORMALIZATION_FUNCTION_H__ + +#include "arm_compute/core/Types.h" +#include "arm_compute/runtime/IFunction.h" + +namespace arm_compute +{ +namespace graph +{ +namespace backends +{ +/** Wrapper function to first apply {NE, CL}BatchNormalizationLayer on the weights and then run {NE, CL}DepthwiseConvolutionLayer with the modified weights */ +template +class FusedDepthwiseConvolutionBatchNormalizationFunction : public IFunction +{ +public: + using TensorType = typename TargetInfo::TensorType; + using TensorConcreteType = typename TargetInfo::TensorConcreteType; + + FusedDepthwiseConvolutionBatchNormalizationFunction() + : _depth_conv_layer(), _fused_batch_norm_layer(), _fused_bias(), _is_prepared(false) + { + } + + /** Set the input and output tensors. + * + * @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM], + * while every optional dimension from 4 and above represent a batch of inputs. + * Data types supported: F16/F32. + * @param[in] weights Weights tensor. These are 3D tensors with shape [kernel_x, kernel_y, IFM]. Data type supported: Same as @p input. + * @param[in] bias Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [IFM]. + * Data type supported: Should match @p input data type. + * @param[out] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. + * Data types supported: Same as @p input. + * @param[in] mean Mean values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input + * @param[in] var Variance values tensor. 1 dimension with size equal to the feature maps [FM]. Data types supported: Same as @p input + * @param[in] beta Beta values tensor info. 1 dimension with size equal to the feature maps [FM]. If not provided, default value for beta is 0. Data types supported: Same as @p input + * @param[in] gamma Gamma values tensor info. 1 dimension with size equal to the feature maps [FM]. If not provided, default value for gamma is 1. Data types supported: Same as @p input + * @param[in] epsilon Small value to avoid division with zero. Default value is 0.001f. + * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. + * @param[in] depth_multiplier Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. + * @param[in] fused_act Activation layer information in case of a fused activation. + * + */ + void configure(TensorType *input, + TensorType *weights, + TensorType *bias, + TensorType *output, + const TensorType *mean, + const TensorType *var, + const TensorType *beta, + const TensorType *gamma, + float epsilon, const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo const &fused_act) + { + // We don't run any validate, as we assume that the layers have been already validated + const bool has_bias = (bias != nullptr); + const TensorType *bias_to_use; + + // We check if the layer has a bias. If yes, use it in-place. If not, we need to create one + // as batch normalization might end up with a bias != 0 + if(has_bias) + { + _fused_batch_norm_layer.configure(weights, mean, var, nullptr, nullptr, bias, beta, gamma, epsilon, FuseBatchNormalizationType::DEPTHWISECONVOLUTION); + bias_to_use = bias; + } + else + { + _fused_batch_norm_layer.configure(weights, mean, var, nullptr, &_fused_bias, nullptr, beta, gamma, epsilon, FuseBatchNormalizationType::DEPTHWISECONVOLUTION); + bias_to_use = &_fused_bias; + } + + _depth_conv_layer.configure(input, weights, bias_to_use, output, conv_info, depth_multiplier, fused_act.enabled() ? fused_act : ActivationLayerInfo()); + + if(!has_bias) + { + _fused_bias.allocator()->allocate(); + } + } + + // Inherited methods overridden: + void run() + { + prepare(); + _depth_conv_layer.run(); + } + + void prepare() + { + if(!_is_prepared) + { + _fused_batch_norm_layer.run(); + _is_prepared = true; + } + } + +private: + typename FusedLayerTypes::DepthwiseConvolutionLayer _depth_conv_layer; + typename FusedLayerTypes::FuseBatchNormalization _fused_batch_norm_layer; + TensorConcreteType _fused_bias; + bool _is_prepared; +}; +} // namespace backends +} // namespace graph +} // namespace arm_compute + +#endif /* __ARM_COMPUTE_GRAPH_BACKENDS_FUSED_DEPTHWISE_CONVOLUTION_BATCH_NORMALIZATION_FUNCTION_H__ */ diff --git a/arm_compute/graph/nodes/FusedConvolutionBatchNormalizationNode.h b/arm_compute/graph/nodes/FusedConvolutionBatchNormalizationNode.h index 9b0f5b7ade..c124c982cd 100644 --- a/arm_compute/graph/nodes/FusedConvolutionBatchNormalizationNode.h +++ b/arm_compute/graph/nodes/FusedConvolutionBatchNormalizationNode.h @@ -41,14 +41,13 @@ public: * @param[in] num_groups (Optional) Number of groups (Defaults to 1) * @param[in] method (Optional) Convolution method to use * @param[in] fast_math_hint (Optional) Fast math hint - * @param[in] out_quant_info (Optional) Output quantization info * @param[in] fused_activation (Optional) Fused activation layer. Disabled if not specified */ FusedConvolutionBatchNormalizationNode(float epsilon, PadStrideInfo info, unsigned int num_groups = 1, ConvolutionMethod method = ConvolutionMethod::Default, FastMathHint fast_math_hint = FastMathHint::Disabled, - QuantizationInfo out_quant_info = QuantizationInfo(), ActivationLayerInfo fused_activation = ActivationLayerInfo()); + ActivationLayerInfo fused_activation = ActivationLayerInfo()); /** Epsilon parameter accessor * @@ -135,7 +134,6 @@ private: unsigned int _num_groups; ConvolutionMethod _method; FastMathHint _fast_math_hint; - QuantizationInfo _out_quant_info; ActivationLayerInfo _fused_activation; }; diff --git a/arm_compute/graph/nodes/FusedDepthwiseConvolutionBatchNormalizationNode.h b/arm_compute/graph/nodes/FusedDepthwiseConvolutionBatchNormalizationNode.h new file mode 100644 index 0000000000..a2241ef64d --- /dev/null +++ b/arm_compute/graph/nodes/FusedDepthwiseConvolutionBatchNormalizationNode.h @@ -0,0 +1,136 @@ +/* + * Copyright (c) 2019 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. + */ +#ifndef __ARM_COMPUTE_GRAPH_FUSED_DEPTHWISE_CONVOLUTION_BATCH_NORMALIZATION_NODE_H__ +#define __ARM_COMPUTE_GRAPH_FUSED_DEPTHWISE_CONVOLUTION_BATCH_NORMALIZATION_NODE_H__ + +#include "arm_compute/graph/INode.h" + +namespace arm_compute +{ +namespace graph +{ +/** Fused Depthwise Convolution Batch Normalization node */ +class FusedDepthwiseConvolutionBatchNormalizationNode final : public INode +{ +public: + /** Constructor + * + * @param[in] epsilon Epsilon parameter. + * @param[in] info Convolution layer attributes. + * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. + * @param[in] method (Optional) Convolution method to use + * @param[in] fused_activation (Optional) Fused activation layer. Disabled if not specified + */ + FusedDepthwiseConvolutionBatchNormalizationNode(float epsilon, + PadStrideInfo info, + unsigned int depth_multiplier, + DepthwiseConvolutionMethod method, + ActivationLayerInfo fused_activation = ActivationLayerInfo()); + + /** Sets the depthwise convolution layer method to use + * + * @param[in] method Method to use for depthwise convolution + */ + void set_depthwise_convolution_method(DepthwiseConvolutionMethod method); + + /** Depthwise convolution layer method accessor + * + * @note This is an indication on which depthwise convolution layer implementation to use, + * if it fails to be created the library's heuristic approach will be used + * + * @return Depthwise convolution layer method to be used by the node + */ + DepthwiseConvolutionMethod depthwise_convolution_method() const; + + /** Epsilon parameter accessor + * + * @return Epsilon parameter + */ + float epsilon() const; + + /** Returns fused activation + * + * @return Fused activation + */ + ActivationLayerInfo fused_activation() const; + + /** Sets fused activation + * + * @param[in] fused_activation Fused activation to set + */ + void set_fused_activation(ActivationLayerInfo fused_activation); + + /** Computes convolution output descriptor + * + * @param[in] input_descriptor Input descriptor + * @param[in] weights_descriptor Weights descriptor + * @param[in] info Convolution operation attributes + * @param[in] depth_multiplier Depth multiplier + * + * @return Output descriptor + */ + static TensorDescriptor compute_output_descriptor(const TensorDescriptor &input_descriptor, + const TensorDescriptor &weights_descriptor, + const PadStrideInfo &info, + int depth_multiplier); + + /** Sets the convolution layer method to use + * + * @param[in] method Method to use for convolution + */ + void set_convolution_method(ConvolutionMethod method); + + /** Depth multiplier accessor + * + * @return Depth multiplier + */ + unsigned int depth_multiplier() const; + + /** Convolution metadata accessor + * + * @return Convolution information + */ + PadStrideInfo convolution_info() const; + + // Inherited overridden methods: + NodeType type() const override; + bool forward_descriptors() override; + TensorDescriptor configure_output(size_t idx) const override; + void accept(INodeVisitor &v) override; + +public: + static constexpr NodeType node_type = NodeType::FusedDepthwiseConvolutionBatchNormalizationLayer; + +private: + float _epsilon; + + PadStrideInfo _info; + unsigned int _depth_multiplier; + DepthwiseConvolutionMethod _method; + ActivationLayerInfo _fused_activation; +}; + +} // namespace graph +} // namespace arm_compute +#endif /* __ARM_COMPUTE_GRAPH_FUSED_DEPTHWISE_CONVOLUTION_BATCH_NORMALIZATION_NODE_H__ */ diff --git a/arm_compute/graph/nodes/Nodes.h b/arm_compute/graph/nodes/Nodes.h index c891bc2ca2..52e2f88528 100644 --- a/arm_compute/graph/nodes/Nodes.h +++ b/arm_compute/graph/nodes/Nodes.h @@ -39,6 +39,7 @@ #include "arm_compute/graph/nodes/FlattenLayerNode.h" #include "arm_compute/graph/nodes/FullyConnectedLayerNode.h" #include "arm_compute/graph/nodes/FusedConvolutionBatchNormalizationNode.h" +#include "arm_compute/graph/nodes/FusedDepthwiseConvolutionBatchNormalizationNode.h" #include "arm_compute/graph/nodes/GenerateProposalsLayerNode.h" #include "arm_compute/graph/nodes/InputNode.h" #include "arm_compute/graph/nodes/NormalizationLayerNode.h" diff --git a/arm_compute/graph/nodes/NodesFwd.h b/arm_compute/graph/nodes/NodesFwd.h index 0f3450b08f..2c89679902 100644 --- a/arm_compute/graph/nodes/NodesFwd.h +++ b/arm_compute/graph/nodes/NodesFwd.h @@ -45,6 +45,7 @@ class EltwiseLayerNode; class FlattenLayerNode; class FullyConnectedLayerNode; class FusedConvolutionBatchNormalizationNode; +class FusedDepthwiseConvolutionBatchNormalizationNode; class GenerateProposalsLayerNode; class InputNode; class NormalizationLayerNode; diff --git a/arm_compute/graph/printers/DotGraphPrinter.h b/arm_compute/graph/printers/DotGraphPrinter.h index 9d2ea46fde..c28a17b21a 100644 --- a/arm_compute/graph/printers/DotGraphPrinter.h +++ b/arm_compute/graph/printers/DotGraphPrinter.h @@ -57,6 +57,7 @@ public: void visit(DepthwiseConvolutionLayerNode &n) override; void visit(EltwiseLayerNode &n) override; void visit(FusedConvolutionBatchNormalizationNode &n) override; + void visit(FusedDepthwiseConvolutionBatchNormalizationNode &n) override; void visit(NormalizationLayerNode &n) override; void visit(PoolingLayerNode &n) override; void default_visit() override; diff --git a/src/graph/backends/CL/CLFunctionsFactory.cpp b/src/graph/backends/CL/CLFunctionsFactory.cpp index c14100ab42..b9f22f6199 100644 --- a/src/graph/backends/CL/CLFunctionsFactory.cpp +++ b/src/graph/backends/CL/CLFunctionsFactory.cpp @@ -74,8 +74,9 @@ struct CLEltwiseFunctions /** Function and tensor types to be used inside a CL fused convolution/batch normalization layer */ struct CLFusedLayerTypes { - using ConvolutionLayer = CLConvolutionLayer; - using FuseBatchNormalization = CLFuseBatchNormalization; + using ConvolutionLayer = CLConvolutionLayer; + using DepthwiseConvolutionLayer = CLDepthwiseConvolutionLayer; + using FuseBatchNormalization = CLFuseBatchNormalization; }; // TODO (isagot01): Remove once we support heterogeneous scheduling at function level @@ -203,6 +204,8 @@ std::unique_ptr CLFunctionFactory::create(INode *node, GraphContext & return detail::create_fully_connected_layer(*polymorphic_downcast(node), ctx); case NodeType::FusedConvolutionBatchNormalizationLayer: return detail::create_fused_convolution_batch_normalization_layer(*polymorphic_downcast(node)); + case NodeType::FusedDepthwiseConvolutionBatchNormalizationLayer: + return detail::create_fused_depthwise_convolution_batch_normalization_layer(*polymorphic_downcast(node)); case NodeType::GenerateProposalsLayer: return detail::create_generate_proposals_layer(*polymorphic_downcast(node), ctx); case NodeType::NormalizationLayer: diff --git a/src/graph/backends/NEON/NEFunctionFactory.cpp b/src/graph/backends/NEON/NEFunctionFactory.cpp index d4892f53a6..ad96240a4b 100644 --- a/src/graph/backends/NEON/NEFunctionFactory.cpp +++ b/src/graph/backends/NEON/NEFunctionFactory.cpp @@ -80,8 +80,9 @@ struct NEEltwiseFunctions /** Function and tensor types to be used inside a NEON fused convolution/batch normalization layer */ struct NEFusedLayerTypes { - using ConvolutionLayer = NEConvolutionLayer; - using FuseBatchNormalization = NEFuseBatchNormalization; + using ConvolutionLayer = NEConvolutionLayer; + using DepthwiseConvolutionLayer = NEDepthwiseConvolutionLayer; + using FuseBatchNormalization = NEFuseBatchNormalization; }; namespace detail diff --git a/src/graph/mutators/NodeFusionMutator.cpp b/src/graph/mutators/NodeFusionMutator.cpp index 427d7b5095..83177a8431 100644 --- a/src/graph/mutators/NodeFusionMutator.cpp +++ b/src/graph/mutators/NodeFusionMutator.cpp @@ -64,7 +64,6 @@ void fuse_convolution_with_batch_normalization(Graph &g, const Edge *output_edge // Extract conv inputs const auto conv_input_id = conv_node->input_edge(0)->producer_id(); const auto conv_weights_id = conv_node->input_edge(1)->producer_id(); - const auto out_quant_info = conv_node->output(0)->desc().quant_info; const auto conv_info = conv_node->convolution_info(); const auto conv_method = conv_node->convolution_method(); const auto num_groups = conv_node->num_groups(); @@ -79,7 +78,7 @@ void fuse_convolution_with_batch_normalization(Graph &g, const Edge *output_edge const auto epsilon = bn_node->epsilon(); // Create the fused node - const NodeID fused_id = g.add_node(epsilon, conv_info, num_groups, conv_method, fast_math_hint, out_quant_info, act_info); + const NodeID fused_id = g.add_node(epsilon, conv_info, num_groups, conv_method, fast_math_hint, act_info); if(conv_node->input_edge(2) != nullptr) { @@ -125,6 +124,83 @@ void fuse_convolution_with_batch_normalization(Graph &g, const Edge *output_edge } } +void fuse_depthwise_convolution_with_batch_normalization(Graph &g, const Edge *output_edge) +{ + ARM_COMPUTE_ERROR_ON(output_edge == nullptr); + + auto *depth_conv_node = arm_compute::utils::cast::polymorphic_downcast(output_edge->producer()); + auto *bn_node = arm_compute::utils::cast::polymorphic_downcast(output_edge->consumer()); + + ARM_COMPUTE_LOG_GRAPH_VERBOSE("Fusing depthwise convolution node with ID : " << output_edge->producer_id() + << " with BatchNormalization Layer node with ID : " << output_edge->consumer_id() << std::endl); + + // Prevent fusion if fused node has an output accessor + if(depth_conv_node->output(0)->accessor() == nullptr) + { + const Target assigned_target = depth_conv_node->assigned_target(); + + // Extract conv inputs + const auto depth_conv_input_id = depth_conv_node->input_edge(0)->producer_id(); + const auto conv_weights_id = depth_conv_node->input_edge(1)->producer_id(); + const auto conv_info = depth_conv_node->convolution_info(); + const auto depth_conv_method = depth_conv_node->depthwise_convolution_method(); + const auto depth_multiplier = depth_conv_node->depth_multiplier(); + const auto act_info = bn_node->fused_activation(); + + // Extract bn inputs + const auto bn_mean_id = bn_node->input_edge(1)->producer_id(); + const auto bn_var_id = bn_node->input_edge(2)->producer_id(); + const auto bn_beta_id = bn_node->input_edge(3)->producer_id(); + const auto bn_gamma_id = bn_node->input_edge(4)->producer_id(); + const auto epsilon = bn_node->epsilon(); + + // Create the fused node + const NodeID fused_id = g.add_node(epsilon, conv_info, depth_multiplier, depth_conv_method, act_info); + + if(depth_conv_node->input_edge(2) != nullptr) + { + const auto conv_bias_id = depth_conv_node->input_edge(2)->producer_id(); + g.add_connection(conv_bias_id, 0, fused_id, 2); + } + + // Add connections from the conv/batch_norm inputs to the fused node + g.add_connection(depth_conv_input_id, 0, fused_id, 0); + g.add_connection(conv_weights_id, 0, fused_id, 1); + g.add_connection(bn_mean_id, 0, fused_id, 3); + g.add_connection(bn_var_id, 0, fused_id, 4); + g.add_connection(bn_beta_id, 0, fused_id, 5); + g.add_connection(bn_gamma_id, 0, fused_id, 6); + + auto fused_node = g.node(fused_id); + std::vector bn_driving_nodes = get_driving_nodes(*bn_node); + + // Extract batch normalization node accessor if any + auto bn_node_accessor = bn_node->output(0)->extract_accessor(); + auto bn_node_name = bn_node->name(); + + // Remove batch normalization node + g.remove_node(bn_node->id()); + + // Get driving nodes of batch normalization node + for(auto &driving_node : bn_driving_nodes) + { + g.add_connection(fused_id, 0, driving_node.node_id, driving_node.index); + configure_tensor(fused_node->output(0)); + } + // Update fused node outputs + fused_node->output(0)->set_accessor(std::move(bn_node_accessor)); + fused_node->set_assigned_target(assigned_target); + fused_node->set_common_node_parameters(NodeParams{ depth_conv_node->name() + "+" + bn_node_name, assigned_target }); + + // Remove convolution node + g.remove_node(depth_conv_node->id()); + } + else + { + ARM_COMPUTE_LOG_GRAPH_VERBOSE("Prevented fusion of depthwise convolution with batch normalization due to the presence of an output accessor\n"); + } +} + template void fuse_node_with_activation(Graph &g, const Edge *output_edge, const std::set &supported_fused_activations) { @@ -224,6 +300,8 @@ void NodeFusionMutator::mutate(Graph &g) return (output_qasymm8 && same_qinfo) || !output_qasymm8; }; + Target target = g.nodes()[0].get()->output(0)->desc().target; + // Fusion mutations detail::fuse_layer(g, empty_prec, detail::fuse_node_with_activation, supported_fused_activations); detail::fuse_layer(g, empty_prec, detail::fuse_node_with_activation, supported_fused_activations); @@ -231,6 +309,11 @@ void NodeFusionMutator::mutate(Graph &g) // TODO (COMPMID-2055): re-enable once we fuse bias and activations to convolution // detail::fuse_layer(g, empty_prec, detail::fuse_convolution_with_batch_normalization); + if(target == Target::CL) + { + //Depthwise Convolution and Batch Normalization Fusion active only for CL + detail::fuse_layer(g, empty_prec, detail::fuse_depthwise_convolution_with_batch_normalization); + } } } // namespace graph } // namespace arm_compute diff --git a/src/graph/nodes/FusedConvolutionBatchNormalizationNode.cpp b/src/graph/nodes/FusedConvolutionBatchNormalizationNode.cpp index 6496a71251..0a0c0c50e8 100644 --- a/src/graph/nodes/FusedConvolutionBatchNormalizationNode.cpp +++ b/src/graph/nodes/FusedConvolutionBatchNormalizationNode.cpp @@ -36,8 +36,8 @@ FusedConvolutionBatchNormalizationNode::FusedConvolutionBatchNormalizationNode(f unsigned int num_groups, ConvolutionMethod method, FastMathHint fast_math_hint, - QuantizationInfo out_quant_info, ActivationLayerInfo fused_activation) - : _epsilon(epsilon), _info(std::move(info)), _num_groups(num_groups), _method(method), _fast_math_hint(fast_math_hint), _out_quant_info(std::move(out_quant_info)), _fused_activation(fused_activation) + ActivationLayerInfo fused_activation) + : _epsilon(epsilon), _info(std::move(info)), _num_groups(num_groups), _method(method), _fast_math_hint(fast_math_hint), _fused_activation(fused_activation) { _input_edges.resize(7, EmptyEdgeID); _outputs.resize(1, NullTensorID); @@ -132,10 +132,6 @@ TensorDescriptor FusedConvolutionBatchNormalizationNode::configure_output(size_t ARM_COMPUTE_ERROR_ON(src == nullptr || weights == nullptr); TensorDescriptor output_info = compute_output_descriptor(src->desc(), weights->desc(), _info); - if(!_out_quant_info.empty()) - { - output_info.quant_info = _out_quant_info; - } return output_info; } diff --git a/src/graph/nodes/FusedDepthwiseConvolutionBatchNormalizationNode.cpp b/src/graph/nodes/FusedDepthwiseConvolutionBatchNormalizationNode.cpp new file mode 100644 index 0000000000..a04d75407a --- /dev/null +++ b/src/graph/nodes/FusedDepthwiseConvolutionBatchNormalizationNode.cpp @@ -0,0 +1,140 @@ +/* + * Copyright (c) 2019 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/nodes/FusedDepthwiseConvolutionBatchNormalizationNode.h" + +#include "arm_compute/core/Utils.h" +#include "arm_compute/graph/Graph.h" +#include "arm_compute/graph/INodeVisitor.h" +#include "arm_compute/graph/Utils.h" + +namespace arm_compute +{ +namespace graph +{ +FusedDepthwiseConvolutionBatchNormalizationNode::FusedDepthwiseConvolutionBatchNormalizationNode(float epsilon, + PadStrideInfo info, + unsigned int depth_multiplier, + DepthwiseConvolutionMethod method, + ActivationLayerInfo fused_activation) + : _epsilon(epsilon), _info(std::move(info)), _depth_multiplier(depth_multiplier), _method(method), _fused_activation(fused_activation) +{ + _input_edges.resize(7, EmptyEdgeID); + _outputs.resize(1, NullTensorID); +} + +void FusedDepthwiseConvolutionBatchNormalizationNode::set_depthwise_convolution_method(DepthwiseConvolutionMethod method) +{ + _method = method; +} + +DepthwiseConvolutionMethod FusedDepthwiseConvolutionBatchNormalizationNode::depthwise_convolution_method() const +{ + return _method; +} + +float FusedDepthwiseConvolutionBatchNormalizationNode::epsilon() const +{ + return _epsilon; +} + +PadStrideInfo FusedDepthwiseConvolutionBatchNormalizationNode::convolution_info() const +{ + return _info; +} + +unsigned int FusedDepthwiseConvolutionBatchNormalizationNode::depth_multiplier() const +{ + return _depth_multiplier; +} + +ActivationLayerInfo FusedDepthwiseConvolutionBatchNormalizationNode::fused_activation() const +{ + return _fused_activation; +} + +void FusedDepthwiseConvolutionBatchNormalizationNode::set_fused_activation(ActivationLayerInfo fused_activation) +{ + _fused_activation = fused_activation; +} + +TensorDescriptor FusedDepthwiseConvolutionBatchNormalizationNode::compute_output_descriptor(const TensorDescriptor &input_descriptor, + const TensorDescriptor &weights_descriptor, + const PadStrideInfo &info, + int depth_multiplier) +{ + unsigned int output_width = 0; + unsigned int output_height = 0; + + const unsigned int input_width = get_dimension_size(input_descriptor, DataLayoutDimension::WIDTH); + const unsigned int input_height = get_dimension_size(input_descriptor, DataLayoutDimension::HEIGHT); + const unsigned int input_channels = get_dimension_size(input_descriptor, DataLayoutDimension::CHANNEL); + const unsigned int kernel_width = get_dimension_size(weights_descriptor, DataLayoutDimension::WIDTH); + const unsigned int kernel_height = get_dimension_size(weights_descriptor, DataLayoutDimension::HEIGHT); + + std::tie(output_width, output_height) = scaled_dimensions(input_width, input_height, kernel_width, kernel_height, info); + + TensorDescriptor output_descriptor = input_descriptor; + output_descriptor.shape.set(get_dimension_idx(output_descriptor.layout, DataLayoutDimension::WIDTH), output_width); + output_descriptor.shape.set(get_dimension_idx(output_descriptor.layout, DataLayoutDimension::HEIGHT), output_height); + output_descriptor.shape.set(get_dimension_idx(output_descriptor.layout, DataLayoutDimension::CHANNEL), input_channels * depth_multiplier); + + return output_descriptor; +} + +bool FusedDepthwiseConvolutionBatchNormalizationNode::forward_descriptors() +{ + if((input_id(0) != NullTensorID) && (input_id(1) != NullTensorID) && (output_id(0) != NullTensorID)) + { + Tensor *dst = output(0); + ARM_COMPUTE_ERROR_ON(dst == nullptr); + dst->desc() = configure_output(0); + return true; + } + return false; +} + +TensorDescriptor FusedDepthwiseConvolutionBatchNormalizationNode::configure_output(size_t idx) const +{ + ARM_COMPUTE_UNUSED(idx); + const Tensor *src = input(0); + const Tensor *weights = input(1); + + ARM_COMPUTE_ERROR_ON(src == nullptr || weights == nullptr); + + TensorDescriptor output_info = compute_output_descriptor(src->desc(), weights->desc(), _info, _depth_multiplier); + + return output_info; +} + +NodeType FusedDepthwiseConvolutionBatchNormalizationNode::type() const +{ + return FusedDepthwiseConvolutionBatchNormalizationNode::node_type; +} + +void FusedDepthwiseConvolutionBatchNormalizationNode::accept(INodeVisitor &v) +{ + v.visit(*this); +} +} // namespace graph +} // namespace arm_compute diff --git a/src/graph/printers/DotGraphPrinter.cpp b/src/graph/printers/DotGraphPrinter.cpp index c939de1b64..46f6ee828e 100644 --- a/src/graph/printers/DotGraphPrinter.cpp +++ b/src/graph/printers/DotGraphPrinter.cpp @@ -85,6 +85,14 @@ void DotGraphVisitor::visit(FusedConvolutionBatchNormalizationNode &n) _info = ss.str(); } +void DotGraphVisitor::visit(FusedDepthwiseConvolutionBatchNormalizationNode &n) +{ + ARM_COMPUTE_UNUSED(n); + std::stringstream ss; + ss << "FusedDepthwiseConvolutionBatchNormalizationNode"; + _info = ss.str(); +} + void DotGraphVisitor::visit(NormalizationLayerNode &n) { std::stringstream ss; -- cgit v1.2.1