diff options
author | Giorgio Arena <giorgio.arena@arm.com> | 2018-04-05 17:20:34 +0100 |
---|---|---|
committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:49:37 +0000 |
commit | bb54e4e40b7b08c509e234cd91ebd3087af66c23 (patch) | |
tree | 5e0b6bdf58bb129ef2b3b26e6e65515bc8b76f83 /src | |
parent | 4d33630096c769dd43716dd5607f151e3d5abef7 (diff) | |
download | ComputeLibrary-bb54e4e40b7b08c509e234cd91ebd3087af66c23.tar.gz |
COMPMID-797 Integrate Mobilenet QASYMM8 with new graph.
Change-Id: I4df63ec2f4eb27a8a6eec2bea27741bf8dec6910
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/126966
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Diffstat (limited to 'src')
-rw-r--r-- | src/core/TensorInfo.cpp | 2 | ||||
-rw-r--r-- | src/graph/GraphBuilder.cpp | 24 | ||||
-rw-r--r-- | src/graph/backends/CL/CLDeviceBackend.cpp | 2 | ||||
-rw-r--r-- | src/graph/backends/CL/CLFunctionsFactory.cpp | 32 | ||||
-rw-r--r-- | src/graph/backends/GLES/GCDeviceBackend.cpp | 2 | ||||
-rw-r--r-- | src/graph/backends/GLES/GCFunctionsFactory.cpp | 32 | ||||
-rw-r--r-- | src/graph/backends/NEON/NEDeviceBackend.cpp | 2 | ||||
-rw-r--r-- | src/graph/backends/NEON/NEFunctionFactory.cpp | 32 | ||||
-rw-r--r-- | src/graph/nodes/ConvolutionLayerNode.cpp | 10 | ||||
-rw-r--r-- | src/graph/nodes/SoftmaxLayerNode.cpp | 5 | ||||
-rw-r--r-- | src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp | 10 | ||||
-rw-r--r-- | src/runtime/NEON/functions/NEGEMM.cpp | 5 | ||||
-rw-r--r-- | src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp | 16 | ||||
-rw-r--r-- | src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp | 20 |
14 files changed, 138 insertions, 56 deletions
diff --git a/src/core/TensorInfo.cpp b/src/core/TensorInfo.cpp index 539d0f84b3..676938a231 100644 --- a/src/core/TensorInfo.cpp +++ b/src/core/TensorInfo.cpp @@ -322,7 +322,7 @@ ITensorInfo &TensorInfo::set_data_type(DataType data_type) { _data_type = data_type; _format = Format::UNKNOWN; - return *this; + return set_tensor_shape(tensor_shape()); // Force total size and strides to update } ITensorInfo &TensorInfo::set_num_channels(int num_channels) diff --git a/src/graph/GraphBuilder.cpp b/src/graph/GraphBuilder.cpp index 0d1bdc3596..4ad34e789c 100644 --- a/src/graph/GraphBuilder.cpp +++ b/src/graph/GraphBuilder.cpp @@ -206,7 +206,9 @@ NodeID GraphBuilder::add_batch_normalization_node(Graph &g, NodeParams params, N NodeID GraphBuilder::add_convolution_node(Graph &g, NodeParams params, NodeIdxPair input, Size2D kernel_spatial_extend, unsigned int depth, PadStrideInfo conv_info, unsigned int num_groups, ConvolutionMethod method, - ITensorAccessorUPtr weights_accessor, ITensorAccessorUPtr bias_accessor) + ITensorAccessorUPtr weights_accessor, ITensorAccessorUPtr bias_accessor, + const QuantizationInfo weights_quant_info, + const QuantizationInfo out_quant_info) { CHECK_NODEIDX_PAIR(input, g); ARM_COMPUTE_ERROR_ON(depth == 0); @@ -220,7 +222,13 @@ NodeID GraphBuilder::add_convolution_node(Graph &g, NodeParams params, NodeIdxPa // Create weights node TensorDescriptor w_desc = input_tensor_desc; w_desc.shape = TensorShape(kernel_spatial_extend.width, kernel_spatial_extend.height, w_desc.shape.z() / num_groups, depth); - NodeID w_nid = add_const_node_with_name(g, params, "Weights", w_desc, std::move(weights_accessor)); + + if(!weights_quant_info.empty()) + { + w_desc.quant_info = weights_quant_info; + } + + NodeID w_nid = add_const_node_with_name(g, params, "Weights", w_desc, std::move(weights_accessor)); // Create bias nodes NodeID b_nid = EmptyNodeID; @@ -234,7 +242,7 @@ NodeID GraphBuilder::add_convolution_node(Graph &g, NodeParams params, NodeIdxPa if(num_groups == 1) { // Create convolution node and connect - NodeID conv_nid = g.add_node<ConvolutionLayerNode>(conv_info, method); + NodeID conv_nid = g.add_node<ConvolutionLayerNode>(conv_info, method, out_quant_info); g.add_connection(input.node_id, input.index, conv_nid, 0); g.add_connection(w_nid, 0, conv_nid, 1); if(has_bias) @@ -270,7 +278,7 @@ NodeID GraphBuilder::add_depth_concatenate_node(Graph &g, NodeParams params, std NodeID GraphBuilder::add_depthwise_convolution_node(Graph &g, NodeParams params, NodeIdxPair input, Size2D kernel_spatial_extend, PadStrideInfo conv_info, DepthwiseConvolutionMethod method, - ITensorAccessorUPtr weights_accessor, ITensorAccessorUPtr bias_accessor) + ITensorAccessorUPtr weights_accessor, ITensorAccessorUPtr bias_accessor, const QuantizationInfo quant_info) { CHECK_NODEIDX_PAIR(input, g); ARM_COMPUTE_ERROR_ON((kernel_spatial_extend.width == 0) || (kernel_spatial_extend.height == 0)); @@ -283,7 +291,13 @@ NodeID GraphBuilder::add_depthwise_convolution_node(Graph &g, NodeParams params, // Create weights node TensorDescriptor w_desc = input_tensor_desc; w_desc.shape = TensorShape(kernel_spatial_extend.width, kernel_spatial_extend.height, w_desc.shape.z()); - NodeID w_nid = add_const_node_with_name(g, params, "Weights", w_desc, std::move(weights_accessor)); + + if(!quant_info.empty()) + { + w_desc.quant_info = quant_info; + } + + NodeID w_nid = add_const_node_with_name(g, params, "Weights", w_desc, std::move(weights_accessor)); // Create bias nodes NodeID b_nid = EmptyNodeID; diff --git a/src/graph/backends/CL/CLDeviceBackend.cpp b/src/graph/backends/CL/CLDeviceBackend.cpp index f10eb33a98..92cb6936c3 100644 --- a/src/graph/backends/CL/CLDeviceBackend.cpp +++ b/src/graph/backends/CL/CLDeviceBackend.cpp @@ -126,7 +126,7 @@ std::unique_ptr<ITensorHandle> CLDeviceBackend::create_tensor(const Tensor &tens ARM_COMPUTE_ERROR_ON(tensor_desc.target != Target::CL); // Create backend tensor handle - TensorInfo info(tensor_desc.shape, 1, tensor_desc.data_type); + TensorInfo info(tensor_desc.shape, 1, tensor_desc.data_type, tensor_desc.quant_info); auto backend_tensor_handle = support::cpp14::make_unique<CLTensorHandle>(info); return std::move(backend_tensor_handle); diff --git a/src/graph/backends/CL/CLFunctionsFactory.cpp b/src/graph/backends/CL/CLFunctionsFactory.cpp index 1b448fefd2..ad73a797e3 100644 --- a/src/graph/backends/CL/CLFunctionsFactory.cpp +++ b/src/graph/backends/CL/CLFunctionsFactory.cpp @@ -154,10 +154,16 @@ std::unique_ptr<IFunction> create_convolution_layer(ConvolutionLayerNode &node, ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1); // Extract IO and info - ICLTensor *input = get_backing_tensor(node.input(0)); - ICLTensor *weights = get_backing_tensor(node.input(1)); - ICLTensor *biases = get_backing_tensor(node.input(2)); - ICLTensor *output = get_backing_tensor(node.output(0)); + ICLTensor *input = get_backing_tensor(node.input(0)); + ICLTensor *weights = get_backing_tensor(node.input(1)); + ICLTensor *biases = get_backing_tensor(node.input(2)); + ICLTensor *output = get_backing_tensor(node.output(0)); + + if(is_data_type_quantized_asymmetric(input->info()->data_type())) + { + biases->info()->set_data_type(DataType::S32); + } + const PadStrideInfo conv_info = node.convolution_info(); const ConvolutionMethod conv_algorithm = node.convolution_method(); @@ -190,6 +196,8 @@ std::unique_ptr<IFunction> create_convolution_layer(ConvolutionLayerNode &node, // Log info ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name << " Data Type: " << input->info()->data_type() + << " Input QuantInfo: " << input->info()->quantization_info() + << " Weights QuantInfo: " << weights->info()->quantization_info() << " Input shape: " << input->info()->tensor_shape() << " Weights shape: " << weights->info()->tensor_shape() << " Output shape: " << output->info()->tensor_shape() @@ -251,10 +259,16 @@ std::unique_ptr<IFunction> create_depthwise_convolution_layer(DepthwiseConvoluti ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1); // Extract IO and info - ICLTensor *input = get_backing_tensor(node.input(0)); - ICLTensor *weights = get_backing_tensor(node.input(1)); - ICLTensor *biases = get_backing_tensor(node.input(2)); - ICLTensor *output = get_backing_tensor(node.output(0)); + ICLTensor *input = get_backing_tensor(node.input(0)); + ICLTensor *weights = get_backing_tensor(node.input(1)); + ICLTensor *biases = get_backing_tensor(node.input(2)); + ICLTensor *output = get_backing_tensor(node.output(0)); + + if(is_data_type_quantized_asymmetric(input->info()->data_type())) + { + biases->info()->set_data_type(DataType::S32); + } + const PadStrideInfo conv_info = node.convolution_info(); const DepthwiseConvolutionMethod dwc_algorithm = node.depthwise_convolution_method(); @@ -275,6 +289,8 @@ std::unique_ptr<IFunction> create_depthwise_convolution_layer(DepthwiseConvoluti // Log info ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name << " Data Type: " << input->info()->data_type() + << " Input QuantInfo: " << input->info()->quantization_info() + << " Weights QuantInfo: " << weights->info()->quantization_info() << " Input shape: " << input->info()->tensor_shape() << " Weights shape: " << weights->info()->tensor_shape() << " Output shape: " << output->info()->tensor_shape() diff --git a/src/graph/backends/GLES/GCDeviceBackend.cpp b/src/graph/backends/GLES/GCDeviceBackend.cpp index 8cd9994744..a55215f058 100644 --- a/src/graph/backends/GLES/GCDeviceBackend.cpp +++ b/src/graph/backends/GLES/GCDeviceBackend.cpp @@ -87,7 +87,7 @@ std::unique_ptr<ITensorHandle> GCDeviceBackend::create_tensor(const Tensor &tens ARM_COMPUTE_ERROR_ON(tensor_desc.target != Target::GC); // Create backend tensor handle - TensorInfo info(tensor_desc.shape, 1, tensor_desc.data_type); + TensorInfo info(tensor_desc.shape, 1, tensor_desc.data_type, tensor_desc.quant_info); auto backend_tensor_handle = support::cpp14::make_unique<GCTensorHandle>(info); return std::move(backend_tensor_handle); diff --git a/src/graph/backends/GLES/GCFunctionsFactory.cpp b/src/graph/backends/GLES/GCFunctionsFactory.cpp index 12e7c042d4..d3c5737e68 100644 --- a/src/graph/backends/GLES/GCFunctionsFactory.cpp +++ b/src/graph/backends/GLES/GCFunctionsFactory.cpp @@ -154,10 +154,16 @@ std::unique_ptr<IFunction> create_convolution_layer(ConvolutionLayerNode &node, ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1); // Extract IO and info - IGCTensor *input = get_backing_tensor(node.input(0)); - IGCTensor *weights = get_backing_tensor(node.input(1)); - IGCTensor *biases = get_backing_tensor(node.input(2)); - IGCTensor *output = get_backing_tensor(node.output(0)); + IGCTensor *input = get_backing_tensor(node.input(0)); + IGCTensor *weights = get_backing_tensor(node.input(1)); + IGCTensor *biases = get_backing_tensor(node.input(2)); + IGCTensor *output = get_backing_tensor(node.output(0)); + + if(is_data_type_quantized_asymmetric(input->info()->data_type())) + { + biases->info()->set_data_type(DataType::S32); + } + const PadStrideInfo conv_info = node.convolution_info(); const ConvolutionMethod conv_algorithm = node.convolution_method(); @@ -180,6 +186,8 @@ std::unique_ptr<IFunction> create_convolution_layer(ConvolutionLayerNode &node, // Log info ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name << " Data Type: " << input->info()->data_type() + << " Input QuantInfo: " << input->info()->quantization_info() + << " Weights QuantInfo: " << weights->info()->quantization_info() << " Input shape: " << input->info()->tensor_shape() << " Weights shape: " << weights->info()->tensor_shape() << " Output shape: " << output->info()->tensor_shape() @@ -241,10 +249,16 @@ std::unique_ptr<IFunction> create_depthwise_convolution_layer(DepthwiseConvoluti ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1); // Extract IO and info - IGCTensor *input = get_backing_tensor(node.input(0)); - IGCTensor *weights = get_backing_tensor(node.input(1)); - IGCTensor *biases = get_backing_tensor(node.input(2)); - IGCTensor *output = get_backing_tensor(node.output(0)); + IGCTensor *input = get_backing_tensor(node.input(0)); + IGCTensor *weights = get_backing_tensor(node.input(1)); + IGCTensor *biases = get_backing_tensor(node.input(2)); + IGCTensor *output = get_backing_tensor(node.output(0)); + + if(is_data_type_quantized_asymmetric(input->info()->data_type())) + { + biases->info()->set_data_type(DataType::S32); + } + const PadStrideInfo conv_info = node.convolution_info(); const DepthwiseConvolutionMethod dwc_algorithm = node.depthwise_convolution_method(); @@ -264,6 +278,8 @@ std::unique_ptr<IFunction> create_depthwise_convolution_layer(DepthwiseConvoluti // Log info ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name << " Data Type: " << input->info()->data_type() + << " Input QuantInfo: " << input->info()->quantization_info() + << " Weights QuantInfo: " << weights->info()->quantization_info() << " Input shape: " << input->info()->tensor_shape() << " Weights shape: " << weights->info()->tensor_shape() << " Output shape: " << output->info()->tensor_shape() diff --git a/src/graph/backends/NEON/NEDeviceBackend.cpp b/src/graph/backends/NEON/NEDeviceBackend.cpp index aaf05829bb..9123196540 100644 --- a/src/graph/backends/NEON/NEDeviceBackend.cpp +++ b/src/graph/backends/NEON/NEDeviceBackend.cpp @@ -93,7 +93,7 @@ std::unique_ptr<ITensorHandle> NEDeviceBackend::create_tensor(const Tensor &tens ARM_COMPUTE_ERROR_ON(tensor_desc.target != Target::NEON); // Create backend tensor handle - TensorInfo info(tensor_desc.shape, 1, tensor_desc.data_type); + TensorInfo info(tensor_desc.shape, 1, tensor_desc.data_type, tensor_desc.quant_info); auto backend_tensor_handle = support::cpp14::make_unique<NETensorHandle>(info); return std::move(backend_tensor_handle); diff --git a/src/graph/backends/NEON/NEFunctionFactory.cpp b/src/graph/backends/NEON/NEFunctionFactory.cpp index 228af9ca6f..906378c565 100644 --- a/src/graph/backends/NEON/NEFunctionFactory.cpp +++ b/src/graph/backends/NEON/NEFunctionFactory.cpp @@ -140,10 +140,16 @@ std::unique_ptr<IFunction> create_convolution_layer(ConvolutionLayerNode &node, ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1); // Extract IO and info - ITensor *input = get_backing_tensor(node.input(0)); - ITensor *weights = get_backing_tensor(node.input(1)); - ITensor *biases = get_backing_tensor(node.input(2)); - ITensor *output = get_backing_tensor(node.output(0)); + ITensor *input = get_backing_tensor(node.input(0)); + ITensor *weights = get_backing_tensor(node.input(1)); + ITensor *biases = get_backing_tensor(node.input(2)); + ITensor *output = get_backing_tensor(node.output(0)); + + if(is_data_type_quantized_asymmetric(input->info()->data_type())) + { + biases->info()->set_data_type(DataType::S32); + } + const PadStrideInfo conv_info = node.convolution_info(); const ConvolutionMethod conv_algorithm = node.convolution_method(); @@ -175,6 +181,8 @@ std::unique_ptr<IFunction> create_convolution_layer(ConvolutionLayerNode &node, // Log info ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name << " Data Type: " << input->info()->data_type() + << " Input QuantInfo: " << input->info()->quantization_info() + << " Weights QuantInfo: " << weights->info()->quantization_info() << " Input shape: " << input->info()->tensor_shape() << " Weights shape: " << weights->info()->tensor_shape() << " Output shape: " << output->info()->tensor_shape() @@ -234,10 +242,16 @@ std::unique_ptr<IFunction> create_depthwise_convolution_layer(DepthwiseConvoluti ARM_COMPUTE_ERROR_ON(node.num_outputs() != 1); // Extract IO and info - ITensor *input = get_backing_tensor(node.input(0)); - ITensor *weights = get_backing_tensor(node.input(1)); - ITensor *biases = get_backing_tensor(node.input(2)); - ITensor *output = get_backing_tensor(node.output(0)); + ITensor *input = get_backing_tensor(node.input(0)); + ITensor *weights = get_backing_tensor(node.input(1)); + ITensor *biases = get_backing_tensor(node.input(2)); + ITensor *output = get_backing_tensor(node.output(0)); + + if(is_data_type_quantized_asymmetric(input->info()->data_type())) + { + biases->info()->set_data_type(DataType::S32); + } + const PadStrideInfo conv_info = node.convolution_info(); const DepthwiseConvolutionMethod dwc_algorithm = node.depthwise_convolution_method(); @@ -258,6 +272,8 @@ std::unique_ptr<IFunction> create_depthwise_convolution_layer(DepthwiseConvoluti // Log info ARM_COMPUTE_LOG_GRAPH_INFO("Instantiated " << func_name << " Data Type: " << input->info()->data_type() + << " Input QuantInfo: " << input->info()->quantization_info() + << " Weights QuantInfo: " << weights->info()->quantization_info() << " Input shape: " << input->info()->tensor_shape() << " Weights shape: " << weights->info()->tensor_shape() << " Output shape: " << output->info()->tensor_shape() diff --git a/src/graph/nodes/ConvolutionLayerNode.cpp b/src/graph/nodes/ConvolutionLayerNode.cpp index 461728487f..eb0c6a1c1a 100644 --- a/src/graph/nodes/ConvolutionLayerNode.cpp +++ b/src/graph/nodes/ConvolutionLayerNode.cpp @@ -31,8 +31,8 @@ namespace arm_compute { namespace graph { -ConvolutionLayerNode::ConvolutionLayerNode(PadStrideInfo info, ConvolutionMethod method) - : _info(std::move(info)), _method(method) +ConvolutionLayerNode::ConvolutionLayerNode(PadStrideInfo info, ConvolutionMethod method, QuantizationInfo out_quant_info) + : _info(std::move(info)), _method(method), _out_quant_info(out_quant_info) { _input_edges.resize(3, EmptyEdgeID); _outputs.resize(1, NullTensorID); @@ -90,6 +90,12 @@ TensorDescriptor ConvolutionLayerNode::configure_output(size_t idx) const TensorDescriptor output_info = src->desc(); TensorShape output_shape = compute_output_shape(src->desc().shape, weights->desc().shape, _info); output_info.shape = output_shape; + + if(!_out_quant_info.empty()) + { + output_info.quant_info = _out_quant_info; + } + return output_info; } diff --git a/src/graph/nodes/SoftmaxLayerNode.cpp b/src/graph/nodes/SoftmaxLayerNode.cpp index 4c21ac6ad0..b6241e6654 100644 --- a/src/graph/nodes/SoftmaxLayerNode.cpp +++ b/src/graph/nodes/SoftmaxLayerNode.cpp @@ -63,7 +63,10 @@ TensorDescriptor SoftmaxLayerNode::configure_output(size_t idx) const const Tensor *src = input(0); ARM_COMPUTE_ERROR_ON(src == nullptr); - return src->desc(); + TensorDescriptor out_desc = src->desc(); + out_desc.quant_info = QuantizationInfo(1.f / 256.f, 0); + + return out_desc; } Status SoftmaxLayerNode::validate() diff --git a/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp index 5f747bc477..711b006ede 100644 --- a/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp +++ b/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp @@ -102,7 +102,10 @@ void CLGEMMLowpMatrixMultiplyCore::configure(const ICLTensor *a, const ICLTensor matrix_b = &_tmp_b; _memory_group.manage(&_tmp_a); - _memory_group.manage(&_tmp_b); + if(!_reshape_b_only_on_first_run) + { + _memory_group.manage(&_tmp_b); + } // Configure interleave kernel _mtx_a_reshape_kernel.configure(a, &_tmp_a, mult_interleave4x4_height); @@ -119,7 +122,10 @@ void CLGEMMLowpMatrixMultiplyCore::configure(const ICLTensor *a, const ICLTensor { TensorInfo info_vector_sum_col(compute_reductionA_shape(*b->info()), 1, DataType::S32); _vector_sum_col.allocator()->init(info_vector_sum_col); - _memory_group.manage(&_vector_sum_col); + if(!_reshape_b_only_on_first_run) + { + _memory_group.manage(&_vector_sum_col); + } // Configure Matrix B reduction kernel _mtx_b_reduction_kernel.configure(b, &_vector_sum_col); diff --git a/src/runtime/NEON/functions/NEGEMM.cpp b/src/runtime/NEON/functions/NEGEMM.cpp index 18e6e919c3..e0859be93e 100644 --- a/src/runtime/NEON/functions/NEGEMM.cpp +++ b/src/runtime/NEON/functions/NEGEMM.cpp @@ -107,7 +107,10 @@ void NEGEMM::configure(const ITensor *a, const ITensor *b, const ITensor *c, ITe // Manage intermediate buffers _memory_group.manage(&_tmp_a); - _memory_group.manage(&_tmp_b); + if(!_reshape_b_only_on_first_run) + { + _memory_group.manage(&_tmp_b); + } int m = a->info()->dimension(1); int n = b->info()->dimension(0); diff --git a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp index 7f25c2e717..3c48d691ed 100644 --- a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp +++ b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp @@ -109,14 +109,6 @@ Status NEConvolutionLayerReshapeWeights::validate(const ITensorInfo *weights, co ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); } - // Checks performed when biases are present - if(append_bias) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); - ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3)); - ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); - } - if(transpose1xW) { TensorInfo weights_reshaped = weights->clone()->set_tensor_shape(get_reshaped_weights_shape(weights, append_bias)); @@ -344,7 +336,7 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig _memory_group.manage(&_input_im2col_reshaped); // Create tensor (interleave) to prepare input tensor for GEMM - if(!_is_fully_connected_convolution && !run_optimised) + if(!_is_fully_connected_convolution && !run_optimised && _is_interleaved) { TensorShape shape_interleaved(shape_im2col); shape_interleaved.set(0, shape_interleaved.x() * 4); @@ -362,7 +354,9 @@ void NEGEMMConvolutionLayer::configure(const ITensor *input, const ITensor *weig TensorInfo info_gemm(shape_gemm, 1, gemm_data_type, input->info()->fixed_point_position()); info_gemm.set_quantization_info(output->info()->quantization_info()); _gemm_output.allocator()->init(info_gemm); - _memory_group.manage(&_gemm_output); + + // FIXME: enabling memory manager for _gemm_output gives incorrect results (maybe bound to the assembly kernel in GEMMLowp?) + // _memory_group.manage(&_gemm_output); // Configure kernels // Configure im2col @@ -491,7 +485,7 @@ Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI reshaped_weights->set_tensor_shape(get_reshaped_weights_shape_conv(weights, append_bias, is_fully_connected_convolution)); ARM_COMPUTE_RETURN_ON_ERROR(NEConvolutionLayerReshapeWeights::validate(weights, biases, reshaped_weights.get(), !is_fully_connected_convolution /* 1xW transpose */)); } - else + else if(!is_quantized) { TensorShape reshaped_weights_shape; diff --git a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp index 7372c6ca57..cbec73fc31 100644 --- a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp +++ b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp @@ -43,19 +43,19 @@ using namespace arm_compute::misc::shape_calculator; NEGEMMLowpMatrixMultiplyCore::NEGEMMLowpMatrixMultiplyCore(std::shared_ptr<IMemoryManager> memory_manager) : _memory_group(std::move(memory_manager)), _asm_glue_unsigned(), _asm_glue_signed(), _mm_kernel(nullptr), _mtx_a_reshape_kernel(nullptr), _mtx_b_reshape_kernel(nullptr), _mtx_a_reduction_kernel(), _mtx_b_reduction_kernel(), _offset_contribution_kernel(), _vector_sum_col(), _vector_sum_row(), _tmp_a(), _tmp_b(), _workspace(), _a_offset(0), _b_offset(0), _run_vector_matrix_multiplication(false), - _dot_product_path(false) + _dot_product_path(false), _is_first_run(true), _reshape_b_only_on_first_run(false) { } void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b, ITensor *output, const GEMMInfo &gemm_info) { ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output); - ARM_COMPUTE_UNUSED(gemm_info); ARM_COMPUTE_ERROR_THROW_ON(NEGEMMLowpMatrixMultiplyCore::validate(a->info(), b->info(), output->info(), gemm_info)); _a_offset = a->info()->quantization_info().offset; _b_offset = b->info()->quantization_info().offset; _run_vector_matrix_multiplication = a->info()->dimension(1) < 2; + _reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run(); #ifdef __aarch64__ switch(a->info()->data_type()) @@ -98,7 +98,10 @@ void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b, _tmp_a.allocator()->init(info_a); _tmp_b.allocator()->init(info_b); _memory_group.manage(&_tmp_a); - _memory_group.manage(&_tmp_b); + if(!_reshape_b_only_on_first_run) + { + _memory_group.manage(&_tmp_b); + } // Configure interleave kernel { @@ -129,7 +132,10 @@ void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b, TensorInfo info_vector_sum_col(compute_reductionA_shape(*b->info()), 1, DataType::S32); _vector_sum_col.allocator()->init(info_vector_sum_col); - _memory_group.manage(&_vector_sum_col); + if(!_reshape_b_only_on_first_run) + { + _memory_group.manage(&_vector_sum_col); + } // Configure Matrix B reduction kernel _mtx_b_reduction_kernel.configure(b, &_vector_sum_col, a->info()->dimension(0), false); @@ -252,7 +258,7 @@ void NEGEMMLowpMatrixMultiplyCore::run() NEScheduler::get().schedule(_mtx_a_reshape_kernel.get(), Window::DimY); } - if(_mtx_b_reshape_kernel) + if(_mtx_b_reshape_kernel && (_is_first_run || !_reshape_b_only_on_first_run)) { NEScheduler::get().schedule(_mtx_b_reshape_kernel.get(), Window::DimY); } @@ -278,7 +284,7 @@ void NEGEMMLowpMatrixMultiplyCore::run() } // Run matrix B reduction kernel only if _a_offset is not equal to 0 - if(_a_offset != 0) + if(_a_offset != 0 && (_is_first_run || !_reshape_b_only_on_first_run)) { NEScheduler::get().schedule(&_mtx_b_reduction_kernel, Window::DimX); } @@ -287,4 +293,6 @@ void NEGEMMLowpMatrixMultiplyCore::run() NEScheduler::get().schedule(&_offset_contribution_kernel, Window::DimY); _memory_group.release(); + + _is_first_run = false; } |