From 164b65d3c8f61f1d6d404fb484c1998a20a2cbda Mon Sep 17 00:00:00 2001 From: Michele Di Giorgio Date: Fri, 13 Apr 2018 14:28:08 +0100 Subject: COMPMID-1043: Rework GCGEMMMatrixMultiplyKernel interface and allow auto initialization of the tensors This patch also: - removes support for already reshaped weights in GCConvolutionLayer - makes GCConvolutionLayer similar to CLGEMMConvolutionLayer - enables usage of the GCGEMM function in GCConvolution instead of calling the GEMM kernels directly Change-Id: I3e4a64335555e86e18585d38d8fda4bfdb44e265 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/127696 Tested-by: Jenkins Reviewed-by: Anthony Barbier --- arm_compute/core/CL/CLTypes.h | 22 +- .../kernels/GCGEMMMatrixMultiplyKernel.h | 23 +- arm_compute/core/GPUTarget.h | 49 ++++ .../GLES_COMPUTE/functions/GCConvolutionLayer.h | 74 ++++-- .../runtime/GLES_COMPUTE/functions/GCGEMM.h | 16 ++ .../kernels/GCGEMMMatrixMultiplyKernel.cpp | 247 +++++++++++++++------ .../kernels/GCWeightsReshapeKernel.cpp | 11 +- .../GLES_COMPUTE/functions/GCConvolutionLayer.cpp | 180 +++++---------- src/runtime/GLES_COMPUTE/functions/GCGEMM.cpp | 105 ++++++--- tests/validation/GLES_COMPUTE/ConvolutionLayer.cpp | 8 +- 10 files changed, 445 insertions(+), 290 deletions(-) create mode 100644 arm_compute/core/GPUTarget.h diff --git a/arm_compute/core/CL/CLTypes.h b/arm_compute/core/CL/CLTypes.h index ca487814a7..4a03cc9637 100644 --- a/arm_compute/core/CL/CLTypes.h +++ b/arm_compute/core/CL/CLTypes.h @@ -24,6 +24,8 @@ #ifndef __ARM_COMPUTE_CL_TYPES_H__ #define __ARM_COMPUTE_CL_TYPES_H__ +#include "arm_compute/core/GPUTarget.h" + #include namespace arm_compute @@ -31,26 +33,6 @@ namespace arm_compute /** Default string for the CLKernel configuration id */ static const std::string default_config_id = "no_config_id"; -/** Available GPU Targets */ -enum class GPUTarget -{ - UNKNOWN = 0x101, - GPU_ARCH_MASK = 0xF00, - MIDGARD = 0x100, - BIFROST = 0x200, - T600 = 0x110, - T700 = 0x120, - T800 = 0x130, - G71 = 0x210, - G72 = 0x220, - G51 = 0x230, - G51BIG = 0x231, - G51LIT = 0x232, - TNOX = 0x240, - TTRX = 0x250, - TBOX = 0x260 -}; - /** Available OpenCL Version */ enum class CLVersion { diff --git a/arm_compute/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.h b/arm_compute/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.h index 3a0b22f148..cea03a9357 100644 --- a/arm_compute/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.h +++ b/arm_compute/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2017-2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -25,15 +25,13 @@ #define __ARM_COMPUTE_GCGEMMMATRIXMULTIPLYKERNEL_H__ #include "arm_compute/core/GLES_COMPUTE/IGCKernel.h" +#include "arm_compute/core/GPUTarget.h" namespace arm_compute { class IGCTensor; /** GLES Compute kernel to multiply two input matrices "A" and "B" or to multiply a vector "A" by a matrix "B". All elements of the output matrix/vector will be multiplied by alpha - * - * @note If the output tensor is a matrix, the implementation assumes that the input tensors @p input0 and @p input1 are both matrices and reshaped respectively with @ref GCGEMMInterleave4x4Kernel" and @ref GCGEMMTranspose1xWKernel - * @note If the output tensor is a vector and the data type is F32, the implementation assumes that the first input tensor @p input0 is a vector and the second input tensor @p input1 a matrix. The implementation also assumes that both tensors have not been reshaped * * @attention The second input tensor must have at least 2 dimensions (matrix) * @@ -64,8 +62,23 @@ public: * @param[out] output Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0 * @param[in] alpha Weight of the matrix product * @param[in] is_interleaved_transposed (Optional) True if input0 and input1 have been reshaped respectively using @ref GCGEMMInterleave4x4Kernel and @ref GCGEMMTranspose1xWKernel + * @param[in] reshape_info (Optional) GEMM reshape info. If is_interleaved_transposed = true, this object must contain the information to understand how the matrix A and matrix B have been reshaped + */ + void configure(const IGCTensor *input0, const IGCTensor *input1, IGCTensor *output, float alpha, bool is_interleaved_transposed = true, const GEMMReshapeInfo &reshape_info = GEMMReshapeInfo()); + /** Static function to check if given info will lead to a valid configuration of @ref GCGEMMMatrixMultiplyKernel + * + * @param[in] input0 Input tensor containing the Matrix A. Data types supported: F16/F32 + * @param[in] input1 Input tensor containing the Matrix B. Data type supported: same as @p input0 + * @param[in] output Output tensor to store the result of matrix multiplication. Data type supported: same as @p input0 + * @param[in] alpha Weight of the matrix product + * @param[in] is_interleaved_transposed True if input0 and input1 have been reshaped respectively using @ref GCGEMMInterleave4x4Kernel and @ref GCGEMMTranspose1xWKernel + * @param[in] reshape_info GEMM reshape info. If is_interleaved_transposed = true, this object must contain the information to understand how the matrix A and matrix B have been reshaped + * @param[in] gpu_target GPU Target + * + * @return a status */ - void configure(const IGCTensor *input0, const IGCTensor *input1, IGCTensor *output, float alpha, bool is_interleaved_transposed = true); + static Status validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, + GPUTarget gpu_target); // Inherited methods overridden: void run(const Window &window) override; diff --git a/arm_compute/core/GPUTarget.h b/arm_compute/core/GPUTarget.h new file mode 100644 index 0000000000..8a5ca80f49 --- /dev/null +++ b/arm_compute/core/GPUTarget.h @@ -0,0 +1,49 @@ +/* + * Copyright (c) 2018 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_GPUTARGET_H__ +#define __ARM_COMPUTE_GPUTARGET_H__ + +namespace arm_compute +{ +/** Available GPU Targets */ +enum class GPUTarget +{ + UNKNOWN = 0x101, + GPU_ARCH_MASK = 0xF00, + MIDGARD = 0x100, + BIFROST = 0x200, + T600 = 0x110, + T700 = 0x120, + T800 = 0x130, + G71 = 0x210, + G72 = 0x220, + G51 = 0x230, + G51BIG = 0x231, + G51LIT = 0x232, + TNOX = 0x240, + TTRX = 0x250, + TBOX = 0x260 +}; +} // namespace arm_compute +#endif /* __ARM_COMPUTE_GPUTARGET_H__ */ diff --git a/arm_compute/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.h b/arm_compute/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.h index 54b17b40bb..fa29f447c8 100644 --- a/arm_compute/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.h +++ b/arm_compute/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.h @@ -27,15 +27,13 @@ #include "arm_compute/core/GLES_COMPUTE/kernels/GCCol2ImKernel.h" #include "arm_compute/core/GLES_COMPUTE/kernels/GCFillBorderKernel.h" -#include "arm_compute/core/GLES_COMPUTE/kernels/GCGEMMInterleave4x4Kernel.h" -#include "arm_compute/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.h" -#include "arm_compute/core/GLES_COMPUTE/kernels/GCGEMMTranspose1xWKernel.h" #include "arm_compute/core/GLES_COMPUTE/kernels/GCIm2ColKernel.h" #include "arm_compute/core/GLES_COMPUTE/kernels/GCWeightsReshapeKernel.h" #include "arm_compute/core/Types.h" #include "arm_compute/runtime/GLES_COMPUTE/GCMemoryGroup.h" #include "arm_compute/runtime/GLES_COMPUTE/GCTensor.h" #include "arm_compute/runtime/GLES_COMPUTE/functions/GCActivationLayer.h" +#include "arm_compute/runtime/GLES_COMPUTE/functions/GCGEMM.h" #include "arm_compute/runtime/IFunction.h" #include @@ -46,7 +44,6 @@ class IGCTensor; /** Function to reshape and transpose the weights. This function calls the following kernels: * -# @ref GCWeightsReshapeKernel - * -# @ref GCGEMMTranspose1xWKernel */ class GCConvolutionLayerReshapeWeights : public IFunction { @@ -55,22 +52,18 @@ public: GCConvolutionLayerReshapeWeights(); /** Set the input and output tensors. * - * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. - * Data type supported: F16/F32. - * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights. - * @param[out] output Destination tensor. Data types supported: Same as @p weights. - * @param[in] transpose1xW True if the weights are to undergo a 1xW transposition after reshaping (in case of GEMM operation), false otherwise. - * Data types supported: Same as @p weights. + * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. + * Data type supported: F16/F32. + * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights. + * @param[out] output Destination tensor. Data types supported: Same as @p weights. */ - void configure(const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, bool transpose1xW); + void configure(const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output); // Inherited methods overridden: void run() override; private: - GCWeightsReshapeKernel _weights_reshape_kernel; - GCGEMMTranspose1xWKernel _weights_transposed_kernel; - GCTensor _weights_reshaped; - bool _transpose1xW; + GCWeightsReshapeKernel _weights_reshape_kernel; + GCTensor _weights_reshaped; }; /** Basic function to compute the convolution layer. This function calls the following GLES kernels: @@ -86,7 +79,14 @@ class GCConvolutionLayer : public IFunction public: /** Default constructor */ GCConvolutionLayer(std::shared_ptr memory_manager = nullptr); - + /** Prevent instances of this class from being copied (As this class contains pointers) */ + GCConvolutionLayer(const GCConvolutionLayer &) = delete; + /** Default move constructor */ + GCConvolutionLayer(GCConvolutionLayer &&) = default; + /** Prevent instances of this class from being copied (As this class contains pointers) */ + GCConvolutionLayer &operator=(const GCConvolutionLayer &) = delete; + /** Default move assignment operator */ + GCConvolutionLayer &operator=(GCConvolutionLayer &&) = default; /** Set the input and output tensors. * * @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM], @@ -105,6 +105,26 @@ public: */ void configure(const IGCTensor *input, const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo(), const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo()); + /** Static function to check if given info will lead to a valid configuration of @ref GCConvolutionLayer. + * + * @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: QS8/QASYMM8/QS16/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] biases 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. + * @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] conv_info Contains padding and stride information described in @ref PadStrideInfo. + * @param[in] weights_info Specifies if the weights tensor has been reshaped with GCWeightsReshapeKernel. If this is not part of the fully connected layer the weights + * tensor has also been transposed with GCGEMMTranspose1xWKernel. Data type supported: Same as @p input. + * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). + * @param[in] act_info (Optional) Activation layer information in case of a fused activation. + * + * @return a status + */ + static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, + const WeightsInfo &weights_info = WeightsInfo(), const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo()); // Inherited methods overridden: void run() override; @@ -115,20 +135,30 @@ private: * @param input Input tensor. Data types supported: F16/F32. * @param weights Weights tensor. Data type supported: Same as @p input. * @param output Output tensor. Data types supported: Same as @p input, - * @param is_interleaved_transposed Flag that signals if matrix is interleaved transposed */ - void configure_mm(const IGCTensor *input, const IGCTensor *weights, IGCTensor *output, bool is_interleaved_transposed = true); + void configure_mm(const IGCTensor *input, const IGCTensor *weights, IGCTensor *output); + /** Static function to check if given info will lead to a valid configuration of @ref GCGEMMConvolutionLayer matrix multiply routines + * + * @param[in] input Input tensor. Data types supported: QS8/QASYMM8/QS16/F16/F32. + * @param[in] weights Weights tensor. Data type supported: Same as @p input. + * @param[in] output Output tensor. Data types supported: Same as @p input, + * except for input of QASYMM8 type where output should be of S32 type. + * + * @return a status + */ + static Status validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output); private: GCMemoryGroup _memory_group; GCConvolutionLayerReshapeWeights _reshape_weights; GCIm2ColKernel _input_im2col_kernel; - GCGEMMInterleave4x4Kernel _input_interleave_kernel; - GCGEMMMatrixMultiplyKernel _mm_kernel; + GCGEMM _mm_gemm; GCCol2ImKernel _output_col2im_kernel; GCFillBorderKernel _fill_border; GCActivationLayer _activationlayer_function; + const IGCTensor *_original_weights; + GCTensor _input_im2col_reshaped; GCTensor _input_interleaved_reshaped; GCTensor _weights_reshaped; @@ -136,9 +166,7 @@ private: GCTensor _gemm_output; GCTensor _tmp_output; - bool _append_bias; - bool _is_fully_connected_convolution; - bool _are_weights_reshaped; + bool _is_first_run; bool _is_activationlayer_enabled; }; } diff --git a/arm_compute/runtime/GLES_COMPUTE/functions/GCGEMM.h b/arm_compute/runtime/GLES_COMPUTE/functions/GCGEMM.h index 31ad0abaa0..a1d6c8a438 100644 --- a/arm_compute/runtime/GLES_COMPUTE/functions/GCGEMM.h +++ b/arm_compute/runtime/GLES_COMPUTE/functions/GCGEMM.h @@ -69,6 +69,20 @@ public: * if the reshape of matrix B should happen only for the first run */ void configure(const IGCTensor *a, const IGCTensor *b, const IGCTensor *c, IGCTensor *output, float alpha, float beta, const GEMMInfo &gemm_info = GEMMInfo()); + /** Static function to check if given info will lead to a valid configuration of @ref GCGEMM. + * + * @param[in] a First input tensor (Matrix or Vector A). Data types supported: F16/F32 + * @param[in] b Second input tensor (Matrix B). Data type supported: same as @p a. + * @param[in] c Third input tensor (Matrix C). It can be a nullptr if just the multiplication between @p a and @p b is needed. Data type supported: same as @p a. + * @param[out] output Output tensor. Data type supported: same as @p a + * @param[in] alpha Weight of the matrix product + * @param[in] beta Weight of matrix C + * @param[in] gemm_info (Optional) Specifies if the matrix A and/or matrix B have been reshaped and + * if the reshape of matrix B should happen only for the first run + * + * @return a status + */ + static Status validate(const ITensorInfo *a, const ITensorInfo *b, const IGCTensor *c, const ITensorInfo *output, const float alpha, const float beta, const GEMMInfo &gemm_info = GEMMInfo()); // Inherited methods overridden: void run() override; @@ -83,6 +97,8 @@ private: GCTensor _tmp_b; bool _is_interleaved_transposed; bool _run_addition; + bool _is_first_run; + bool _reshape_b_only_on_first_run; }; } diff --git a/src/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.cpp b/src/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.cpp index a5f09e8eac..b4bb5470ad 100644 --- a/src/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.cpp +++ b/src/core/GLES_COMPUTE/kernels/GCGEMMMatrixMultiplyKernel.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017, 2018 ARM Limited. + * Copyright (c) 2017-2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -31,37 +31,180 @@ #include "arm_compute/core/GLES_COMPUTE/IGCTensor.h" #include "arm_compute/core/GLES_COMPUTE/OpenGLES.h" #include "arm_compute/core/Helpers.h" +#include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/Types.h" #include "arm_compute/core/Utils.h" #include "arm_compute/core/Validate.h" #include "arm_compute/core/Window.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" #include #include using namespace arm_compute; using namespace arm_compute::gles_compute; +using namespace arm_compute::misc::shape_calculator; -GCGEMMMatrixMultiplyKernel::GCGEMMMatrixMultiplyKernel() - : _input0(nullptr), _input1(nullptr), _output(nullptr) +namespace { -} +using ElementsProcessed = Steps; -void GCGEMMMatrixMultiplyKernel::configure(const IGCTensor *input0, const IGCTensor *input1, IGCTensor *output, float alpha, bool is_interleaved_transposed) +inline Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info) { - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F32, DataType::F16); - ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1, output); + ARM_COMPUTE_UNUSED(reshape_info); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32); + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1); if(!is_interleaved_transposed) { - ARM_COMPUTE_ERROR_ON(input0->info()->dimension(0) != input1->info()->dimension(1)); + ARM_COMPUTE_ERROR_ON(input0->dimension(0) != input1->dimension(1)); + + if(output->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON(input1->dimension(0) != output->dimension(0)); + ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) != output->dimension(1)); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output); + } + } + else + { + const int m = reshape_info.m(); + const int n = reshape_info.n(); + const int k = reshape_info.k(); + const int mult_transpose1xW_width = reshape_info.mult_transpose1xW_width(); + const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height(); + + TensorShape tensor_shape0{ input0->tensor_shape() }; + tensor_shape0.set(0, k); + tensor_shape0.set(1, m); + + TensorShape tensor_shape1{ input1->tensor_shape() }; + tensor_shape1.set(0, n); + tensor_shape1.set(1, k); + + const TensorInfo tensor_info0 = input0->clone()->set_tensor_shape(tensor_shape0); + const TensorInfo tensor_info1 = input1->clone()->set_tensor_shape(tensor_shape1); + + const TensorInfo tensor_info_reshaped0 = input0->clone()->set_tensor_shape(compute_interleaved_shape(tensor_info0, mult_interleave4x4_height)); + const TensorInfo tensor_info_reshaped1 = input1->clone()->set_tensor_shape(compute_transpose1xW_with_element_size_shape(tensor_info1, mult_transpose1xW_width)); + + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input0, &tensor_info_reshaped0); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, &tensor_info_reshaped1); + + if(output->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(0) != static_cast(n)); + ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(1) != static_cast(m)); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input0, output); + } + } + + return Status{}; +} + +inline std::pair validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output, + bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info, + GPUTarget gpu_target, ElementsProcessed &num_elements_processed) +{ + ARM_COMPUTE_UNUSED(gpu_target); + + // Output tensor auto inizialitation if not yet initialized + TensorShape tensor_shape{ input0->tensor_shape() }; + tensor_shape.set(0, is_interleaved_transposed ? reshape_info.n() : input1->dimension(0)); + tensor_shape.set(1, is_interleaved_transposed ? reshape_info.m() : input0->dimension(1)); + + auto_init_if_empty(*output, input0->clone()->set_tensor_shape(tensor_shape)); + + bool window_changed = false; + Window win{}; + + 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]; + + if(is_interleaved_transposed) + { + // Configure window kernel + num_elems_processed_per_iteration_x = max_gc_vector_width / data_size_from_type(data_type); + num_elems_processed_per_iteration_y = 4; + + win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); + + AccessWindowRectangle input0_access(input0, 0, 0, num_elems_processed_per_iteration_y, 1, 1.f, 0.25f); + AccessWindowTranspose input1_access(input1, 0, 0, num_elems_processed_per_iteration_x, 1, 0.f, 0.25f); + AccessWindowRectangle output_access(output, 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y); + + update_window_and_padding(win, input0_access, input1_access, output_access); + + output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); + } + else // The input tensors have not been reshaped + { + // Special case for 1xN, 2xN, 3xN and 4xN input0 tensor + + switch(data_type) + { + case DataType::F16: + num_elems_processed_per_iteration_x = 4; + num_elems_processed_per_iteration_y = std::min(static_cast(output->dimension(1)), 4); + break; + + case DataType::F32: + num_elems_processed_per_iteration_x = max_gc_vector_width / data_size_from_type(data_type); + num_elems_processed_per_iteration_y = std::min(static_cast(output->dimension(1)), 4); + break; + + default: + ARM_COMPUTE_ERROR("Current data type is not supported"); + break; + } + + win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); + + AccessWindowStatic input0_access(input0, 0, 0, ceil_to_multiple(input0->dimension(0), 8), ceil_to_multiple(input0->dimension(1), num_elems_processed_per_iteration_y)); + AccessWindowStatic input1_access(input1, 0, 0, ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x), input1->dimension(1)); + AccessWindowRectangle output_access(output, 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y); + + update_window_and_padding(win, input0_access, input1_access, output_access); + + Coordinates coord; + coord.set_num_dimensions(output->num_dimensions()); + output_access.set_valid_region(win, ValidRegion(coord, output->tensor_shape())); } + Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; + return std::make_pair(err, win); +} +} // namespace + +GCGEMMMatrixMultiplyKernel::GCGEMMMatrixMultiplyKernel() + : _input0(nullptr), _input1(nullptr), _output(nullptr) +{ +} + +void GCGEMMMatrixMultiplyKernel::configure(const IGCTensor *input0, const IGCTensor *input1, IGCTensor *output, float alpha, bool is_interleaved_transposed, const GEMMReshapeInfo &reshape_info) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output); + + // Perform validate step + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info(), is_interleaved_transposed, reshape_info)); + _input0 = input0; _input1 = input1; _output = output; + ElementsProcessed num_elements_processed{}; + + // Configure kernel window + auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info(), is_interleaved_transposed, reshape_info, GPUTarget::UNKNOWN, num_elements_processed); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + IGCKernel::configure(win_config.second); + + // Create build options std::set build_opts; + std::string kernel_name; Window win; build_opts.emplace("#define LOCAL_SIZE_X " + support::cpp11::to_string(1)); @@ -74,6 +217,12 @@ void GCGEMMMatrixMultiplyKernel::configure(const IGCTensor *input0, const IGCTen // Check if the output tensor is a vector. If so,the kernel runs the vector-matrix multiplication if(is_interleaved_transposed) { + const int mult_transpose1xW_width = reshape_info.mult_transpose1xW_width(); + const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height(); + + build_opts.emplace("#define MULT_TRANSPOSE1XW_WIDTH " + support::cpp11::to_string(mult_transpose1xW_width)); + build_opts.emplace("#define MULT_INTERLEAVE4X4_HEIGHT " + support::cpp11::to_string(mult_interleave4x4_height)); + switch(input0->info()->data_type()) { case DataType::F16: @@ -91,56 +240,20 @@ void GCGEMMMatrixMultiplyKernel::configure(const IGCTensor *input0, const IGCTen build_opts.emplace("#define GEMM_MM_INTERLEAVED_TRANSPOSED"); - // Create kernel - _kernel = GCKernelLibrary::get().create_kernel(("gemm_mm_interleaved_transposed"), build_opts); - - // Configure window kernel - const unsigned int num_elems_processed_per_iteration_x = max_gc_vector_width / data_size_from_type(input0->info()->data_type()); - constexpr unsigned int num_elems_processed_per_iteration_y = 4; - - win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); - - AccessWindowRectangle input0_access(input0->info(), 0, 0, num_elems_processed_per_iteration_y, 1, 1.f, 0.25f); - AccessWindowTranspose input1_access(input1->info(), 0, 0, num_elems_processed_per_iteration_x, 1, 0.f, 0.25f); - AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y); - - update_window_and_padding(win, input0_access, input1_access, output_access); - - output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape())); + kernel_name = "gemm_mm_interleaved_transposed"; } else { - ARM_COMPUTE_ERROR_ON(input0->info()->dimension(0) != input1->info()->dimension(1)); - // Special case for 1xN, 2xN, 3xN and 4xN input0 tensor - unsigned int num_elems_processed_per_iteration_x; - unsigned int num_elems_processed_per_iteration_y; switch(input0->info()->data_type()) { case DataType::F16: build_opts.emplace("#define DATA_TYPE_FP16"); - -#define MM_PROCESS_4X_OPTIMIZED - -#if defined(MM_PROCESS_4X) - num_elems_processed_per_iteration_x = 4; - num_elems_processed_per_iteration_y = std::min(static_cast(output->info()->dimension(1)), 4); - build_opts.emplace("#define MM_PROCESS_4X"); -#elif defined(MM_PROCESS_4X_OPTIMIZED) /* MM_PROCESS_4X */ - num_elems_processed_per_iteration_x = 4; - num_elems_processed_per_iteration_y = std::min(static_cast(output->info()->dimension(1)), 4); build_opts.emplace("#define MM_PROCESS_4X_OPTIMIZED"); -#elif defined(MM_PROCESS_8X) /* MM_PROCESS_4X */ - num_elems_processed_per_iteration_x = 8; - num_elems_processed_per_iteration_y = 1; - build_opts.emplace("#define MM_PROCESS_8X"); -#endif /* MM_PROCESS_4X */ break; case DataType::F32: - num_elems_processed_per_iteration_x = max_gc_vector_width / data_size_from_type(input0->info()->data_type()); - num_elems_processed_per_iteration_y = std::min(static_cast(output->info()->dimension(1)), 4); build_opts.emplace("#define DATA_TYPE_FP32"); break; @@ -150,31 +263,31 @@ void GCGEMMMatrixMultiplyKernel::configure(const IGCTensor *input0, const IGCTen } build_opts.emplace("#define GEMM_MM_FLOATING_POINT"); - build_opts.emplace("#define NUM_ELEMS_PROCESSED_PER_THREAD_X " + support::cpp11::to_string(num_elems_processed_per_iteration_x)); - build_opts.emplace("#define NUM_ELEMS_PROCESSED_PER_THREAD_Y " + support::cpp11::to_string(num_elems_processed_per_iteration_y)); - - // Create kernel - _kernel = GCKernelLibrary::get().create_kernel("gemm_mm_floating_point", build_opts); + build_opts.emplace("#define NUM_ELEMS_PROCESSED_PER_THREAD_X " + support::cpp11::to_string(num_elements_processed.x())); + build_opts.emplace("#define NUM_ELEMS_PROCESSED_PER_THREAD_Y " + support::cpp11::to_string(num_elements_processed.y())); - win = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y)); - -#if defined(MM_PROCESS_4X_OPTIMIZED) - AccessWindowStatic input0_access(input0->info(), 0, 0, ceil_to_multiple(input0->info()->dimension(0), 8), ceil_to_multiple(input0->info()->dimension(1), num_elems_processed_per_iteration_y)); -#else /* MM_PROCESS_4X_OPTIMIZED */ - AccessWindowStatic input0_access(input0->info(), 0, 0, ceil_to_multiple(input0->info()->dimension(0), num_elems_processed_per_iteration_x), ceil_to_multiple(input0->info()->dimension(1), - num_elems_processed_per_iteration_y)); -#endif /* MM_PROCESS_4X_OPTIMIZED */ - AccessWindowStatic input1_access(input1->info(), 0, 0, ceil_to_multiple(input1->info()->dimension(0), num_elems_processed_per_iteration_x), input1->info()->dimension(1)); - AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y); - - update_window_and_padding(win, input0_access, input1_access, output_access); - - Coordinates coord; - coord.set_num_dimensions(output->info()->num_dimensions()); - output_access.set_valid_region(win, ValidRegion(coord, output->info()->tensor_shape())); + kernel_name = "gemm_mm_floating_point"; } - IGCKernel::configure(win); + // Create kernel + _kernel = GCKernelLibrary::get().create_kernel(kernel_name, build_opts); +} + +Status GCGEMMMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, bool is_interleaved_transposed, + const GEMMReshapeInfo &reshape_info, GPUTarget gpu_target) +{ + ARM_COMPUTE_UNUSED(alpha); + ElementsProcessed num_elements_processed{}; + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output, is_interleaved_transposed, reshape_info)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(), + input1->clone().get(), + output->clone().get(), + is_interleaved_transposed, + reshape_info, + gpu_target, + num_elements_processed) + .first); + return Status{}; } void GCGEMMMatrixMultiplyKernel::run(const Window &window) diff --git a/src/core/GLES_COMPUTE/kernels/GCWeightsReshapeKernel.cpp b/src/core/GLES_COMPUTE/kernels/GCWeightsReshapeKernel.cpp index 4c08873dcf..55bf9b754b 100644 --- a/src/core/GLES_COMPUTE/kernels/GCWeightsReshapeKernel.cpp +++ b/src/core/GLES_COMPUTE/kernels/GCWeightsReshapeKernel.cpp @@ -31,11 +31,13 @@ #include "arm_compute/core/Helpers.h" #include "arm_compute/core/Types.h" #include "arm_compute/core/Validate.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/core/GLES_COMPUTE/GCHelpers.h" using namespace arm_compute; using namespace arm_compute::gles_compute; +using namespace arm_compute::misc::shape_calculator; GCWeightsReshapeKernel::GCWeightsReshapeKernel() : _input(nullptr), _biases(nullptr), _output(nullptr) @@ -47,15 +49,8 @@ void GCWeightsReshapeKernel::configure(const IGCTensor *input, const IGCTensor * ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); ARM_COMPUTE_ERROR_ON_NULLPTR(output); - // Calculate output shape - TensorShape output_shape{ input->info()->tensor_shape() }; - output_shape.collapse(3); - const size_t tmp_dim = output_shape[0]; - output_shape.set(0, output_shape[1]); - output_shape.set(1, tmp_dim + (biases != nullptr ? 1 : 0)); - // Output tensor auto inizialitation if not yet initialized - auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape)); + auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(compute_weights_reshaped_shape(*input->info(), (biases != nullptr)))); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); diff --git a/src/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.cpp b/src/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.cpp index b1c8665216..dc73eb85e6 100644 --- a/src/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.cpp +++ b/src/runtime/GLES_COMPUTE/functions/GCConvolutionLayer.cpp @@ -37,14 +37,14 @@ using namespace arm_compute; GCConvolutionLayerReshapeWeights::GCConvolutionLayerReshapeWeights() - : _weights_reshape_kernel(), _weights_transposed_kernel(), _weights_reshaped(), _transpose1xW(false) + : _weights_reshape_kernel(), _weights_reshaped() { } -void GCConvolutionLayerReshapeWeights::configure(const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, bool transpose1xW) +void GCConvolutionLayerReshapeWeights::configure(const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output) { + ARM_COMPUTE_ERROR_ON_NULLPTR(weights, output); ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::F16, DataType::F32); - ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output); ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4); if(biases != nullptr) @@ -56,75 +56,62 @@ void GCConvolutionLayerReshapeWeights::configure(const IGCTensor *weights, const } const bool append_biases = (biases != nullptr) && !is_data_type_quantized_asymmetric(weights->info()->data_type()); - const unsigned bias_element = (append_biases) ? 1 : 0; const IGCTensor *biases_to_use = (append_biases) ? biases : nullptr; - _transpose1xW = transpose1xW; - - if(transpose1xW) - { - // Create tensor to store the reshaped weights - const unsigned int mat_weights_cols = weights->info()->dimension(3); - const unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + bias_element; - TensorShape shape_wr(mat_weights_cols, mat_weights_rows); - const DataType dt = weights->info()->data_type(); - const int fixed_point_position = weights->info()->fixed_point_position(); - TensorInfo info_wr(shape_wr, 1, dt, fixed_point_position); - - _weights_reshaped.allocator()->init(info_wr); - _weights_reshape_kernel.configure(weights, biases_to_use, &_weights_reshaped); - _weights_transposed_kernel.configure(&_weights_reshaped, output); - _weights_reshaped.allocator()->allocate(); - } - else - { - _weights_reshape_kernel.configure(weights, biases_to_use, output); - } + _weights_reshape_kernel.configure(weights, biases_to_use, output); } void GCConvolutionLayerReshapeWeights::run() { GCScheduler::get().dispatch(_weights_reshape_kernel); - if(_transpose1xW) - { - GCScheduler::get().dispatch(_weights_transposed_kernel); - } } GCConvolutionLayer::GCConvolutionLayer(std::shared_ptr memory_manager) - : _memory_group(std::move(memory_manager)), _reshape_weights(), _input_im2col_kernel(), _input_interleave_kernel(), _mm_kernel(), _output_col2im_kernel(), _fill_border(), _activationlayer_function(), - _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _append_bias(false), _is_fully_connected_convolution(false), - _are_weights_reshaped(false), _is_activationlayer_enabled(false) + : _memory_group(std::move(memory_manager)), _reshape_weights(), _input_im2col_kernel(), _mm_gemm(), _output_col2im_kernel(), _fill_border(), _activationlayer_function(), _original_weights(nullptr), + _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _is_first_run(true), _is_activationlayer_enabled(false) { } -void GCConvolutionLayer::configure_mm(const IGCTensor *input, const IGCTensor *weights, IGCTensor *output, bool is_interleaved_transposed) +void GCConvolutionLayer::configure_mm(const IGCTensor *input, const IGCTensor *weights, IGCTensor *output) { - _mm_kernel.configure(input, weights, output, 1.f, is_interleaved_transposed); + ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights); + ARM_COMPUTE_ERROR_THROW_ON(validate_mm(input->info(), weights->info(), output->info())); + + _mm_gemm.configure(input, weights, nullptr, output, 1.f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run */)); +} + +Status GCConvolutionLayer::validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output) +{ + // Perform validation step on Matrix multiply function + GCGEMM::validate(input, weights, nullptr, output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run */)); + return Status{}; } void GCConvolutionLayer::configure(const IGCTensor *input, const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, const Size2D &dilation, const ActivationLayerInfo &act_info) { + ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights); ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); - ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && weights->info()->dimension(2) != input->info()->dimension(2)); + ARM_COMPUTE_ERROR_ON_MSG(weights_info.are_reshaped(), "Weights already reshaped are not supported!"); + ARM_COMPUTE_ERROR_ON(weights->info()->dimension(2) != input->info()->dimension(2)); ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4); + _is_first_run = true; + _original_weights = weights; + if(biases != nullptr) { ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); - ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && biases->info()->dimension(0) != weights->info()->dimension(3)); + ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3)); ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1); } const DataType dt = input->info()->data_type(); - _append_bias = (biases != nullptr); - _are_weights_reshaped = weights_info.are_reshaped(); - - const unsigned bias_element = (_append_bias) ? 1 : 0; - const IGCTensor *biases_to_use = (_append_bias) ? biases : nullptr; + const bool append_bias = (biases != nullptr); + const unsigned bias_element = (append_bias) ? 1 : 0; + const IGCTensor *biases_to_use = (append_bias) ? biases : nullptr; // Get parameters from conv_info unsigned int stride_x = 0; @@ -135,57 +122,19 @@ void GCConvolutionLayer::configure(const IGCTensor *input, const IGCTensor *weig unsigned int conv_w = 0; unsigned int conv_h = 0; - const unsigned int kernel_width = (_are_weights_reshaped) ? weights_info.kernel_size().first : weights->info()->dimension(0); - const unsigned int kernel_height = (_are_weights_reshaped) ? weights_info.kernel_size().second : weights->info()->dimension(1); + const unsigned int kernel_width = weights->info()->dimension(0); + const unsigned int kernel_height = weights->info()->dimension(1); std::tie(conv_w, conv_h) = scaled_dimensions(input->info()->dimension(0), input->info()->dimension(1), kernel_width, kernel_height, conv_info, dilation); - // Check if its a "fully connected" convolution - _is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1)); - const bool run_interleaved = (!_is_fully_connected_convolution); - unsigned int mat_weights_cols = weights->info()->dimension(3); unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + bias_element; - // Reshape weights if needed - if(_are_weights_reshaped) - { - if(_is_fully_connected_convolution) - { - mat_weights_cols = weights->info()->dimension(0); - mat_weights_rows = weights->info()->dimension(1); - } - else - { - mat_weights_cols = weights_info.num_kernels(); - const unsigned int quarter_reshaped_cols = weights->info()->dimension(0) / 4; - mat_weights_rows = quarter_reshaped_cols + bias_element; - } - } - else - { - if(_is_fully_connected_convolution) - { - // Create tensor to store the reshaped weights - int num_elems_read_per_iteration_x = 1; - if(dt == DataType::F16) - { - num_elems_read_per_iteration_x = 2; - } - TensorShape shape_wr((ceil_to_multiple(mat_weights_cols, num_elems_read_per_iteration_x)), mat_weights_rows); - _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_wr)); - _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, false /* 1xW transpose */); - } - else - { - // Create tensor to store transposed weights - const float transpose_width = 16.0f / input->info()->element_size(); - TensorShape shape_wt(mat_weights_rows * static_cast(transpose_width), static_cast(std::ceil(mat_weights_cols / transpose_width))); - _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_wt)); - _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, true /* 1xW transpose */); - } - weights = &_weights_reshaped; - } + // _weights_reshaped will be auto configured in the kernel. + // Just append biases and do not transpose 1xW as it will be reshaped in GCGEMM + _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped); + + weights = &_weights_reshaped; // Create tensor to store im2col reshaped inputs const unsigned int mat_input_cols = mat_weights_rows; @@ -200,19 +149,6 @@ void GCConvolutionLayer::configure(const IGCTensor *input, const IGCTensor *weig _input_im2col_reshaped.allocator()->init(im2col_reshaped_info); _memory_group.manage(&_input_im2col_reshaped); - // Create tensor (interleave) to prepare input tensor for GEMM - if(run_interleaved) - { - TensorShape shape_interleaved = shape_im2col; - shape_interleaved.set(0, shape_interleaved.x() * 4); - shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f)); - - // FIXME: input->clone() doesn't work with subtensors for grouped convolutions. - TensorInfo interleaved_info(shape_interleaved, 1, dt, input->info()->fixed_point_position()); - _input_interleaved_reshaped.allocator()->init(interleaved_info); - _memory_group.manage(&_input_interleaved_reshaped); - } - // Create GEMM output tensor TensorShape shape_gemm = _input_im2col_reshaped.info()->tensor_shape(); shape_gemm.set(0, mat_weights_cols); @@ -224,26 +160,18 @@ void GCConvolutionLayer::configure(const IGCTensor *input, const IGCTensor *weig _gemm_output.allocator()->init(info_gemm); _memory_group.manage(&_gemm_output); - // Configure kernels if(dt == DataType::F16) { BorderSize border_size = BorderSize(conv_info.pad_top(), conv_info.pad_right(), conv_info.pad_bottom(), conv_info.pad_left()); input->info()->extend_padding(border_size); _fill_border.configure(input, border_size, BorderMode::CONSTANT, PixelValue(0)); // for PAD of im2col fp16: consider it as border } - _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias, dilation); + // Configure im2col + _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, append_bias, dilation); + + // Configure GEMM + configure_mm(&_input_im2col_reshaped, weights, &_gemm_output); - // Configure matrix multiply - if(run_interleaved) - { - _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped); - configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output); - _input_interleaved_reshaped.allocator()->allocate(); - } - else - { - configure_mm(&_input_im2col_reshaped, weights, &_gemm_output, false); - } _input_im2col_reshaped.allocator()->allocate(); // Configure Col2Im @@ -253,10 +181,7 @@ void GCConvolutionLayer::configure(const IGCTensor *input, const IGCTensor *weig ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) || (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one"); // Allocate intermediate tensor - if(!_are_weights_reshaped) - { - _weights_reshaped.allocator()->allocate(); - } + _weights_reshaped.allocator()->allocate(); //Configure Activation Layer _is_activationlayer_enabled = act_info.enabled(); @@ -265,15 +190,22 @@ void GCConvolutionLayer::configure(const IGCTensor *input, const IGCTensor *weig { _activationlayer_function.configure(output, nullptr, act_info); } + + ARM_COMPUTE_UNUSED(weights_info); } void GCConvolutionLayer::run() { // Run weights reshaping (Runs once for every configure) - if(!_are_weights_reshaped) + if(_is_first_run) { - _are_weights_reshaped = true; + ARM_COMPUTE_ERROR_ON(!_original_weights->is_used()); + _reshape_weights.run(); + _is_first_run = false; + + // Mark original weights tensor as unused + _original_weights->mark_as_unused(); } _memory_group.acquire(); @@ -283,16 +215,8 @@ void GCConvolutionLayer::run() GCScheduler::get().memory_barrier(); GCScheduler::get().dispatch(_input_im2col_kernel); - if(!_is_fully_connected_convolution) - { - GCScheduler::get().memory_barrier(); - // Run interleave4x4 - GCScheduler::get().dispatch(_input_interleave_kernel); - } - - GCScheduler::get().memory_barrier(); - // Runs matrix multiply on reshaped matrices - GCScheduler::get().dispatch(_mm_kernel); + // Run gemm on reshaped matrices + _mm_gemm.run(); GCScheduler::get().memory_barrier(); // Reshape output matrix diff --git a/src/runtime/GLES_COMPUTE/functions/GCGEMM.cpp b/src/runtime/GLES_COMPUTE/functions/GCGEMM.cpp index 9c8568a329..0a75a38c50 100644 --- a/src/runtime/GLES_COMPUTE/functions/GCGEMM.cpp +++ b/src/runtime/GLES_COMPUTE/functions/GCGEMM.cpp @@ -40,62 +40,82 @@ using namespace arm_compute; using namespace arm_compute::gles_compute; -GCGEMM::GCGEMM(std::shared_ptr memory_manager) - : _memory_group(std::move(memory_manager)), _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _ma_kernel(), _tmp_a(), _tmp_b(), _is_interleaved_transposed(false), _run_addition(false) +namespace { -} - -void GCGEMM::configure(const IGCTensor *a, const IGCTensor *b, const IGCTensor *c, IGCTensor *output, float alpha, float beta, const GEMMInfo &gemm_info) +Status validate_arguments(const ITensorInfo *a, const ITensorInfo *b, const IGCTensor *c, const ITensorInfo *output, const float alpha, const float beta, const GEMMInfo &gemm_info = GEMMInfo()) { - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::F32); + ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output); + + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::F16, DataType::F32); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, b, output); ARM_COMPUTE_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported"); ARM_COMPUTE_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported"); - ARM_COMPUTE_ERROR_ON_MSG(gemm_info.reshape_b_only_on_first_run(), "Reshape matrix B only on first run is not supported"); - ARM_COMPUTE_UNUSED(gemm_info); if(c != nullptr) { - ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, c); - ARM_COMPUTE_ERROR_ON_MSG(a->info()->dimension(1) != c->info()->dimension(1), "The C matrix must have the same number of rows as the matrix A"); - ARM_COMPUTE_ERROR_ON_MSG(b->info()->dimension(0) != c->info()->dimension(0), "The C matrix must have the same number of columns as the matrix C"); - ARM_COMPUTE_ERROR_ON_MSG(c->info()->dimension(0) != output->info()->dimension(0), "The C matrix must have the same number of rows as the output matrix"); - ARM_COMPUTE_ERROR_ON_MSG(c->info()->dimension(1) != output->info()->dimension(1), "The C matrix must have the same number of columns as the output matrix"); + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(a, c->info()); + ARM_COMPUTE_ERROR_ON_MSG(a->dimension(1) != c->info()->dimension(1), "The C matrix must have the same number of rows as the matrix A"); + ARM_COMPUTE_ERROR_ON_MSG(b->dimension(0) != c->info()->dimension(0), "The C matrix must have the same number of columns as the matrix B"); } - ARM_COMPUTE_ERROR_ON_MSG(a->info()->dimension(0) != b->info()->dimension(1), "The product AB is defined only if the number of columns in A is equal to the number of rows in B"); + if(output->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_MSG(b->dimension(0) != output->dimension(0), "The output matrix must have the same number of columns as the matrix B"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->dimension(1) != output->dimension(1), "The output matrix must have the same number of rows as the matrix A"); + } - // If the input tensor has less than 16 rows, we run a special version of GEMM without reshaping the input tensors - _is_interleaved_transposed = a->info()->dimension(1) > 16; + ARM_COMPUTE_RETURN_ERROR_ON_MSG(a->dimension(0) != b->dimension(1), "The product AB is defined only if the number of columns in A is equal to the number of rows in B"); + + ARM_COMPUTE_UNUSED(alpha); + ARM_COMPUTE_UNUSED(beta); + ARM_COMPUTE_UNUSED(gemm_info); + return Status{}; +} +} // namespace + +GCGEMM::GCGEMM(std::shared_ptr memory_manager) + : _memory_group(std::move(memory_manager)), _interleave_kernel(), _transpose_kernel(), _mm_kernel(), _ma_kernel(), _tmp_a(), _tmp_b(), _is_interleaved_transposed(false), _run_addition(false), + _is_first_run(true), _reshape_b_only_on_first_run(false) +{ +} + +void GCGEMM::configure(const IGCTensor *a, const IGCTensor *b, const IGCTensor *c, IGCTensor *output, float alpha, float beta, const GEMMInfo &gemm_info) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output); + + // Perform validation step + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(a->info(), b->info(), c, output->info(), alpha, beta, gemm_info)); + + // Check if we need to reshape the matrix B only on the first run + _reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run(); const IGCTensor *matrix_a = a; const IGCTensor *matrix_b = b; + // Arguments used by GEMMReshapeInfo + // If we pass the matrix A and matrix B reshaped to GCGEMMMatrixMultiplyKernel, we need to pass m, n, k, mult_transpose1xW_width and mult_interleave4x4_height to GCGEMMReshapeInfo + // in order to know how the matrices have been reshaped + const int m = a->info()->dimension(1); + const int n = b->info()->dimension(0); + const int k = a->info()->dimension(0); + int mult_transpose1xW_width = 1; + int mult_interleave4x4_height = 1; + + // If the input tensor has less than 16 rows, we run a special version of GEMM without reshaping the input tensors + _is_interleaved_transposed = a->info()->dimension(1) > 16; + if(_is_interleaved_transposed) { matrix_a = &_tmp_a; matrix_b = &_tmp_b; - TensorShape shape_tmp_a = a->info()->tensor_shape(); - TensorShape shape_tmp_b = b->info()->tensor_shape(); - - shape_tmp_a.set(0, a->info()->dimension(0) * 4); - shape_tmp_a.set(1, std::ceil(a->info()->dimension(1) / 4.0f)); - - const unsigned int transpose_w = max_gc_vector_width / data_size_from_type(b->info()->data_type()); - shape_tmp_b.set(0, b->info()->dimension(1) * transpose_w); - shape_tmp_b.set(1, std::ceil(b->info()->dimension(0) / static_cast(transpose_w))); - - TensorInfo info_a(shape_tmp_a, 1, a->info()->data_type(), a->info()->fixed_point_position()); - _tmp_a.allocator()->init(info_a); + // Manage intermediate buffers _memory_group.manage(&_tmp_a); - - TensorInfo info_b(shape_tmp_b, 1, b->info()->data_type(), b->info()->fixed_point_position()); - _tmp_b.allocator()->init(info_b); - if(!gemm_info.reshape_b_only_on_first_run()) + if(!_reshape_b_only_on_first_run) { _memory_group.manage(&_tmp_b); } + // _tmp_a and _tmp_b will be auto configured in _interleave_kernel and in _transpose_kernel // Configure interleave kernel _interleave_kernel.configure(a, &_tmp_a); @@ -104,7 +124,7 @@ void GCGEMM::configure(const IGCTensor *a, const IGCTensor *b, const IGCTensor * _transpose_kernel.configure(b, &_tmp_b); } - _mm_kernel.configure(matrix_a, matrix_b, output, alpha, _is_interleaved_transposed); + _mm_kernel.configure(matrix_a, matrix_b, output, alpha, _is_interleaved_transposed, GEMMReshapeInfo(m, n, k, mult_transpose1xW_width, mult_interleave4x4_height)); if(_is_interleaved_transposed) { @@ -121,6 +141,12 @@ void GCGEMM::configure(const IGCTensor *a, const IGCTensor *b, const IGCTensor * } } +Status GCGEMM::validate(const ITensorInfo *a, const ITensorInfo *b, const IGCTensor *c, const ITensorInfo *output, const float alpha, const float beta, const GEMMInfo &gemm_info) +{ + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(a, b, c, output, alpha, beta, gemm_info)); + return Status{}; +} + void GCGEMM::run() { _memory_group.acquire(); @@ -129,8 +155,17 @@ void GCGEMM::run() // Run interleave kernel GCScheduler::get().dispatch(_interleave_kernel, false); - // Run transpose kernel - GCScheduler::get().dispatch(_transpose_kernel, false); + if(_is_first_run) + { + // Run transpose kernel + GCScheduler::get().dispatch(_transpose_kernel, false); + _is_first_run = false; + } + else if(!_reshape_b_only_on_first_run) + { + // Run transpose kernel + GCScheduler::get().dispatch(_transpose_kernel, false); + } GCScheduler::get().memory_barrier(); } diff --git a/tests/validation/GLES_COMPUTE/ConvolutionLayer.cpp b/tests/validation/GLES_COMPUTE/ConvolutionLayer.cpp index a23c3ec4d7..bc0170fa06 100644 --- a/tests/validation/GLES_COMPUTE/ConvolutionLayer.cpp +++ b/tests/validation/GLES_COMPUTE/ConvolutionLayer.cpp @@ -118,7 +118,7 @@ using GCConvolutionLayerFixture = ConvolutionValidationFixture, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallConvolutionLayerDataset(), - framework::dataset::make("ReshapeWeights", { true, false })), + framework::dataset::make("ReshapeWeights", { true })), framework::dataset::make("DataType", DataType::F16)), ActivationFunctionsDataset)) @@ -127,7 +127,7 @@ FIXTURE_DATA_TEST_CASE(RunSmall, GCConvolutionLayerFixture, framework::Dat validate(GCAccessor(_target), _reference, tolerance_f16, tolerance_num); } FIXTURE_DATA_TEST_CASE(RunLarge, GCConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeConvolutionLayerDataset(), - framework::dataset::make("ReshapeWeights", { true, false })), + framework::dataset::make("ReshapeWeights", { true })), framework::dataset::make("DataType", DataType::F16)), ActivationFunctionsDataset)) @@ -139,7 +139,7 @@ TEST_SUITE_END() TEST_SUITE(FP32) FIXTURE_DATA_TEST_CASE(RunSmall, GCConvolutionLayerFixture, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallConvolutionLayerDataset(), - framework::dataset::make("ReshapeWeights", { true, false })), + framework::dataset::make("ReshapeWeights", { true })), framework::dataset::make("DataType", DataType::F32)), ActivationFunctionsDataset)) { @@ -147,7 +147,7 @@ FIXTURE_DATA_TEST_CASE(RunSmall, GCConvolutionLayerFixture, framework::Da validate(GCAccessor(_target), _reference, tolerance_f32, tolerance_num); } FIXTURE_DATA_TEST_CASE(RunLarge, GCConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeConvolutionLayerDataset(), - framework::dataset::make("ReshapeWeights", { true, false })), + framework::dataset::make("ReshapeWeights", { true })), framework::dataset::make("DataType", DataType::F32)), ActivationFunctionsDataset)) { -- cgit v1.2.1