diff options
| -rw-r--r-- | examples/graph_squeezenet_v1_1.cpp | 2 | ||||
| -rw-r--r-- | src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp | 23 | ||||
| -rw-r--r-- | tests/datasets/LargeGEMMDataset.h | 8 | ||||
| -rw-r--r-- | tests/datasets/SmallGEMMDataset.h | 2 |
4 files changed, 26 insertions, 9 deletions
diff --git a/examples/graph_squeezenet_v1_1.cpp b/examples/graph_squeezenet_v1_1.cpp index bf6324698f..e52062927b 100644 --- a/examples/graph_squeezenet_v1_1.cpp +++ b/examples/graph_squeezenet_v1_1.cpp @@ -87,7 +87,6 @@ public: graph << common_params.target << common_params.fast_math_hint << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor))) - << ConvolutionMethod::Direct << ConvolutionLayer( 3U, 3U, 64U, get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/conv1_w.npy", weights_layout), @@ -95,7 +94,6 @@ public: PadStrideInfo(2, 2, 0, 0)) << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))) - << ConvolutionMethod::Default << ConvolutionLayer( 1U, 1U, 16U, get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/fire2_squeeze1x1_w.npy", weights_layout), diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp b/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp index c9e6bb34b2..79e2f8b11a 100644 --- a/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp +++ b/src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.cpp @@ -57,6 +57,7 @@ inline Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *i ARM_COMPUTE_RETURN_ERROR_ON_MSG(input0->num_dimensions() > 4, "The number of dimensions for the matrix A must be <= 4"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 3, "The number of dimensions for the matrix B must be <= 3"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(is_interleaved_transposed && reshape_info.reinterpret_input_as_3d(), "The input tensor cannot be reinterpreted as 3D if is_interleaved_transposed is true"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 2 && reshape_info.reinterpret_input_as_3d(), "The input1 tensor cannot have more than 2 dimensions if input0 has to be reinterpreted as 3D"); if(!is_interleaved_transposed) { @@ -109,13 +110,23 @@ inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *inpu const DataType data_type = input0->data_type(); unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0]; unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1]; + bool reinterpret_input_as_3d = reshape_info.reinterpret_input_as_3d(); + bool reinterpret_output_as_3d = (reshape_info.depth_output_gemm3d() != 1); + + // In case both input and output have to be reinterpreted as 3D tensors, + // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false. + if(reinterpret_input_as_3d == reinterpret_output_as_3d) + { + reinterpret_input_as_3d = false; + reinterpret_output_as_3d = false; + } // Output tensor auto inizialitation if not yet initialized auto_init_if_empty(*output, input0->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, is_interleaved_transposed, reshape_info))); TensorInfo tmp_info(*output); - if(reshape_info.depth_output_gemm3d() != 1) + if(reinterpret_output_as_3d) { // Since the output tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM, // the window needs to be constructed on the 2D collapsed version of the tensor @@ -162,7 +173,7 @@ inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *inpu // Note: bottom paddings are calculated manually as the output can be reinterpreted as 3D tensor // The only way to set properly the paddings, it is to set those explicitly through the AccessWindowStatic - const int m = reshape_info.reinterpret_input_as_3d() ? input0->tensor_shape()[1] * input0->tensor_shape()[2] : input0->tensor_shape()[1]; + const int m = reinterpret_input_as_3d ? input0->tensor_shape()[1] * input0->tensor_shape()[2] : input0->tensor_shape()[1]; const int bottom_pad = (num_elems_processed_per_iteration_y - (m % num_elems_processed_per_iteration_y)) % num_elems_processed_per_iteration_y; // Create kernels according to the architecture, data type and input size. @@ -219,6 +230,14 @@ void CLGEMMMatrixMultiplyKernel::configure(const ICLTensor *input0, const ICLTen _reinterpret_input_as_3d = reshape_info.reinterpret_input_as_3d(); _reinterpret_output_as_3d = (reshape_info.depth_output_gemm3d() != 1); + // In case both input and output have to be reinterpreted as 3D tensors, + // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false. + if(_reinterpret_input_as_3d == _reinterpret_output_as_3d) + { + _reinterpret_input_as_3d = false; + _reinterpret_output_as_3d = false; + } + // Check if we need to slide the matrix B const unsigned int num_dimensions_input0 = _reinterpret_input_as_3d ? _input0->info()->num_dimensions() - 1 : _input0->info()->num_dimensions(); diff --git a/tests/datasets/LargeGEMMDataset.h b/tests/datasets/LargeGEMMDataset.h index 9c45cb0657..bbf362ca21 100644 --- a/tests/datasets/LargeGEMMDataset.h +++ b/tests/datasets/LargeGEMMDataset.h @@ -71,11 +71,11 @@ public: { add_config(TensorShape(923U, 143U, 3U), TensorShape(871U, 923U), TensorShape(871U, 143U, 3U), TensorShape(871U, 143U, 3U), 1.0f, 0.0f); add_config(TensorShape(681U, 205U, 5U), TensorShape(213U, 681U), TensorShape(213U, 205U, 5U), TensorShape(213U, 205U, 5U), 1.0f, 0.0f); - add_config(TensorShape(364U, 605U, 5U), TensorShape(96U, 364U), TensorShape(96U, 605U, 5U), TensorShape(96U, 605U, 5U), 1.0f, 0.0f); + add_config(TensorShape(364U, 605U, 5U), TensorShape(96U, 364U), TensorShape(96U, 605U, 5U), TensorShape(96U, 605U, 5U), 0.2f, 1.2f); add_config(TensorShape(1201U, 243U, 3U), TensorShape(128U, 1201U), TensorShape(128U, 243U, 3U), TensorShape(128U, 243U, 3U), 1.0f, 0.0f); - add_config(TensorShape(2305U, 13U, 13U), TensorShape(384U, 2305U), TensorShape(384U, 13U, 13U), TensorShape(384U, 13U, 13U), 1.0f, 0.0f); - add_config(TensorShape(1729U, 85U, 2U, 2U), TensorShape(192U, 1729U, 2U), TensorShape(192U, 85U, 2U, 2U), TensorShape(192U, 85U, 2U, 2U), 1.0f, 0.0f); - add_config(TensorShape(1729U, 17U, 10U, 3U), TensorShape(128U, 1729U, 3U), TensorShape(128U, 17U, 10U, 3U), TensorShape(128U, 17U, 10U, 3U), 1.0f, 0.0f); + add_config(TensorShape(2305U, 13U, 13U), TensorShape(384U, 2305U), TensorShape(384U, 13U, 13U), TensorShape(384U, 13U, 13U), 0.4f, 0.7f); + add_config(TensorShape(1729U, 85U, 2U, 2U), TensorShape(192U, 1729U), TensorShape(192U, 85U, 2U, 2U), TensorShape(192U, 85U, 2U, 2U), 1.0f, 0.0f); + add_config(TensorShape(1729U, 17U, 10U, 3U), TensorShape(128U, 1729U), TensorShape(128U, 17U, 10U, 3U), TensorShape(128U, 17U, 10U, 3U), 1.0f, 0.3f); } }; } // namespace datasets diff --git a/tests/datasets/SmallGEMMDataset.h b/tests/datasets/SmallGEMMDataset.h index e108fcc1ca..15a3504e85 100644 --- a/tests/datasets/SmallGEMMDataset.h +++ b/tests/datasets/SmallGEMMDataset.h @@ -73,7 +73,7 @@ public: add_config(TensorShape(38U, 12U, 2U), TensorShape(21U, 38U), TensorShape(21U, 12U, 2U), TensorShape(21U, 12U, 2U), 0.2f, 1.2f); add_config(TensorShape(32U, 1U, 4U, 3U), TensorShape(17U, 32U), TensorShape(17U, 1U, 4U, 3U), TensorShape(17U, 1U, 4U, 3U), 0.4f, 0.7f); add_config(TensorShape(16U, 16U, 3U, 2U), TensorShape(8U, 16U), TensorShape(8U, 16U, 3U, 2U), TensorShape(8U, 16U, 3U, 2U), 1.0f, 0.0f); - add_config(TensorShape(16U, 16U, 5U, 3U), TensorShape(8U, 16U, 3U), TensorShape(8U, 16U, 5U, 3U), TensorShape(8U, 16U, 5U, 3U), 1.0f, 0.0f); + add_config(TensorShape(16U, 16U, 5U, 3U), TensorShape(8U, 16U), TensorShape(8U, 16U, 5U, 3U), TensorShape(8U, 16U, 5U, 3U), 1.0f, 0.3f); } }; } // namespace datasets |