COMPMID-790 - NEON: Add QASYMM8 support to Convolution
Change-Id: Iec82a91ad351cfe8d07d0976a24bd42f4703177a
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/116833
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
diff --git a/arm_compute/runtime/NEON/functions/NEConvolutionLayer.h b/arm_compute/runtime/NEON/functions/NEConvolutionLayer.h
index a79f21f..f7a83d8 100644
--- a/arm_compute/runtime/NEON/functions/NEConvolutionLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEConvolutionLayer.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -36,8 +36,9 @@
#include "arm_compute/core/NEON/kernels/NEWeightsReshapeKernel.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/runtime/MemoryGroup.h"
+#include "arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h"
+#include "arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h"
#include "arm_compute/runtime/Tensor.h"
-
#include <memory>
namespace arm_compute
@@ -55,7 +56,7 @@
NEConvolutionLayerReshapeWeights(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
/** 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: QS8/QS16/F32.
+ * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: QS8/QASYMM8/QS16/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.
@@ -64,7 +65,7 @@
void configure(const ITensor *weights, const ITensor *biases, ITensor *output, bool transpose1xW);
/** Static function to check if given info will lead to a valid configuration of @ref NEConvolutionLayerReshapeWeights
*
- * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: QS8/QS16/F16/F32.
+ * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: QS8/QASYMM8/QS16/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[in] 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.
@@ -89,7 +90,8 @@
* -# @ref NEWeightsReshapeKernel (executed only once for each configuration)
* -# @ref NEIm2ColKernel
* -# @ref NEGEMMInterleave4x4Kernel (executed only in case GEMM is required for the operation)
- * -# @ref NEGEMMMatrixMultiplyKernel
+ * -# @ref NEGEMMMatrixMultiplyKernel or @ref NEGEMMLowpMatrixMultiplyCore (if quantized asymmetric)
+ * -# @ref NEGEMMLowpQuantizeDownInt32ToUint8Scale (if quantized asymmetric)
* -# @ref NECol2ImKernel
*/
class NEConvolutionLayer : public IFunction
@@ -102,9 +104,10 @@
*
* @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/QS16/F32.
+ * Data types supported: QS8/QASYMM8/QS16/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: 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.
@@ -116,9 +119,10 @@
*
* @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/QS16/F16/F32.
+ * 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: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[in] 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.
@@ -134,21 +138,38 @@
void run() override;
private:
- MemoryGroup _memory_group;
- NEIm2ColKernel _input_im2col_kernel;
- NEGEMMInterleave4x4Kernel _input_interleave_kernel;
- NEConvolutionLayerReshapeWeights _reshape_weights;
- NEGEMMMatrixMultiplyKernel _mm_kernel;
- std::unique_ptr<NEGEMMAssemblyBaseKernel> _mm_optimised_kernel;
- NECol2ImKernel _output_col2im_kernel;
- Tensor _input_im2col_reshaped;
- Tensor _input_interleaved_reshaped;
- Tensor _weights_reshaped;
- Tensor _gemm_output;
- Tensor _workspace;
- bool _has_bias;
- bool _is_fully_connected_convolution;
- bool _are_weights_reshaped;
+ /** Configures the appropriate matrix multiply routine
+ *
+ * @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[out] output Output tensor. Data types supported: Same as @p input,
+ * except for input of QASYMM8 type where output should be of S32 type.
+ */
+ void configure_mm(const ITensor *input, const ITensor *weights, ITensor *output);
+
+private:
+ MemoryGroup _memory_group;
+ NEIm2ColKernel _input_im2col_kernel;
+ NEGEMMInterleave4x4Kernel _input_interleave_kernel;
+ NEConvolutionLayerReshapeWeights _reshape_weights;
+ NEGEMMMatrixMultiplyKernel _mm_kernel;
+ std::unique_ptr<NEGEMMAssemblyBaseKernel> _mm_optimised_kernel;
+ NEGEMMLowpMatrixMultiplyCore _mm_gemmlowp;
+ NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint _gemmlowp_output_stage;
+ NECol2ImKernel _output_col2im_kernel;
+
+ Tensor _input_im2col_reshaped;
+ Tensor _input_interleaved_reshaped;
+ Tensor _weights_reshaped;
+ Tensor _gemm_output;
+ Tensor _tmp_output;
+ Tensor _workspace;
+
+ bool _append_bias;
+ bool _is_fully_connected_convolution;
+ bool _are_weights_reshaped;
+ bool _is_quantized;
+ bool _is_interleaved_transposed;
};
}
#endif /* __ARM_COMPUTE_NECONVOLUTIONLAYER_H__ */