COMPMID-1413 - Improve the performance of GEMMLowp with 8 bit dot product on OpenCL
COMPMID-1424 - Add dot product support for CLDepthwise QASYMM8 3x3 NHWC non-unit stride
With this patch we are able to improve the performance of MobileNet v1-qasymm8 by 37 %
Tried to use the dot product instruction in CLDepthwise QASYMM8 3x3 NHWC non-unit stride
but I have not seen any benefit (maybe because we have few arithemtic operation and we
do not have more load instructions). However Depthwise convolution has been improved by
30%
Change-Id: Id768a99c2e53a04276707e427af5d0ec93419ada
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/155082
Tested-by: bsgcomp <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
diff --git a/arm_compute/core/CL/CLKernels.h b/arm_compute/core/CL/CLKernels.h
index 1e456fa..36abb7b 100644
--- a/arm_compute/core/CL/CLKernels.h
+++ b/arm_compute/core/CL/CLKernels.h
@@ -70,6 +70,7 @@
#include "arm_compute/core/CL/kernels/CLGEMMInterleave4x4Kernel.h"
#include "arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.h"
#include "arm_compute/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.h"
+#include "arm_compute/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.h"
#include "arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h"
#include "arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloatKernel.h"
#include "arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.h"
diff --git a/arm_compute/core/CL/kernels/CLGEMMInterleave4x4Kernel.h b/arm_compute/core/CL/kernels/CLGEMMInterleave4x4Kernel.h
index 4592fc2..96b01b0 100644
--- a/arm_compute/core/CL/kernels/CLGEMMInterleave4x4Kernel.h
+++ b/arm_compute/core/CL/kernels/CLGEMMInterleave4x4Kernel.h
@@ -68,14 +68,15 @@
* @param[out] output Output tensor. Data type supported: same as @p input
* @param[in] mult_interleave4x4_height (Optional) Multiplication factor for the height of the 4x4 interleave block
* @param[in] reinterpret_input_as_3d (Optional) True if the input has to be reinterpreted as 3D tensor
+ * @param[in] unroll_block (Optional) True if the 4x4 block has to be unrolled rather than transposed
*/
- void configure(const ICLTensor *input, ICLTensor *output, int mult_interleave4x4_height = 1, bool reinterpret_input_as_3d = false);
+ void configure(const ICLTensor *input, ICLTensor *output, int mult_interleave4x4_height = 1, bool reinterpret_input_as_3d = false, bool unroll_block = false);
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMInterleave4x4Kernel
*
* @param[in] input Input tensor info. Data types supported: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32
* @param[in] output Output tensor info which stores the interleaved matrix. Data type supported: same as @p input.
* @param[in] mult_interleave4x4_height Multiplication factor for the height of the 4x4 interleave block
- * @param[in] reinterpret_input_as_3d (Optional) True if the input has to be reinterpreted as 3D tensor
+ * @param[in] reinterpret_input_as_3d True if the input has to be reinterpreted as 3D tensor
*
* @return a status
*/
diff --git a/arm_compute/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.h b/arm_compute/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.h
index 871b97c..e6b7917 100644
--- a/arm_compute/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.h
+++ b/arm_compute/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -58,16 +58,18 @@
CLGEMMLowpOffsetContributionKernel &operator=(CLGEMMLowpOffsetContributionKernel &&) = default;
/** Initialise the kernel's input and output.
*
- * @param[in, out] mm_result Input tensor containing the result of @ref CLGEMMLowpMatrixMultiplyKernel. Data type supported: S32
+ * @param[in, out] mm_result Input tensor containing the result of @ref CLGEMMLowpMatrixMultiplyKernel
* @param[in] vector_sum_col Input row-vector of sums of all the entries in each column of matrix B.
* Note: vector_sum_col can be a nullptr in case a_offset = 0. Data type supported: same as @p mm_result
* @param[in] vector_sum_row Input row-vector of sums of all the entries in each row of matrix A.
* Note: vector_sum_row can be a nullptr in case b_offset = 0. Data type supported: same as @p mm_result
+ * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the addition of biases is not required.
+ * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
* @param[in] k Number of matrix A columns or Matrix B rows
* @param[in] a_offset Offset to be added to each element of the matrix A.
* @param[in] b_offset Offset to be added to each element of the matrix B.
*/
- void configure(ICLTensor *mm_result, const ICLTensor *vector_sum_col, const ICLTensor *vector_sum_row, int32_t k, int32_t a_offset, int32_t b_offset);
+ void configure(ICLTensor *mm_result, const ICLTensor *vector_sum_col, const ICLTensor *vector_sum_row, const ICLTensor *bias, int32_t k, int32_t a_offset, int32_t b_offset);
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpOffsetContributionKernel
*
* @param[in] mm_result Input tensor containing the result of @ref CLGEMMLowpOffsetContributionKernel. Data type supported: S32
@@ -75,12 +77,14 @@
* Note: vector_sum_col can be a nullptr in case a_offset = 0. Data type supported: same as @p mm_result
* @param[in] vector_sum_row Input row-vector of sums of all the entries in each row of matrix A.
* Note: vector_sum_row can be a nullptr in case b_offset = 0. Data type supported: same as @p mm_result
+ * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the addition of biases is not required.
+ * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
* @param[in] a_offset Offset to be added to each element of the matrix A.
* @param[in] b_offset Offset to be added to each element of the matrix B.
*
* @return a status
*/
- static Status validate(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, int32_t a_offset, int32_t b_offset);
+ static Status validate(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias, int32_t a_offset, int32_t b_offset);
// Inherited methods overridden:
void run(const Window &window, cl::CommandQueue &queue) override;
@@ -89,6 +93,7 @@
const ICLTensor *_vector_sum_col;
const ICLTensor *_vector_sum_row;
ICLTensor *_mm_result;
+ const ICLTensor *_bias;
};
} // namespace arm_compute
diff --git a/arm_compute/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.h b/arm_compute/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.h
new file mode 100644
index 0000000..de06c88
--- /dev/null
+++ b/arm_compute/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.h
@@ -0,0 +1,100 @@
+/*
+ * 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_CLGEMMLOWPOFFSETCONTRIBUTIONOUTPUTSTAGEKERNEL_H__
+#define __ARM_COMPUTE_CLGEMMLOWPOFFSETCONTRIBUTIONOUTPUTSTAGEKERNEL_H__
+
+#include "arm_compute/core/CL/ICLKernel.h"
+
+namespace arm_compute
+{
+class ICLTensor;
+
+/** OpenCL kernel used to add the offset contribution after @ref CLGEMMLowpMatrixMultiplyKernel and perform the output stage.
+ *
+ * This kernel takes a final int32 accumulator value (the output of @ref CLGEMMLowpMatrixMultiplyKernel), adds to it the offset contribution
+ * of matrix A and matrix B and performs the output stage defined by the output_stage argument
+ *
+ */
+class CLGEMMLowpOffsetContributionOutputStageKernel : public ICLKernel
+{
+public:
+ /** Constructor */
+ CLGEMMLowpOffsetContributionOutputStageKernel();
+ /** Prevent instances of this class from being copied (As this class contains pointers)*/
+ CLGEMMLowpOffsetContributionOutputStageKernel(const CLGEMMLowpOffsetContributionOutputStageKernel &) = delete;
+ /** Prevent instances of this class from being copied (As this class contains pointers)*/
+ CLGEMMLowpOffsetContributionOutputStageKernel &operator=(const CLGEMMLowpOffsetContributionOutputStageKernel &) = delete;
+ /** Allow instances of this class to be moved */
+ CLGEMMLowpOffsetContributionOutputStageKernel(CLGEMMLowpOffsetContributionOutputStageKernel &&) = default;
+ /** Allow instances of this class to be moved */
+ CLGEMMLowpOffsetContributionOutputStageKernel &operator=(CLGEMMLowpOffsetContributionOutputStageKernel &&) = default;
+ /** Initialise the kernel's input and output.
+ *
+ * @param[in] mm_result Input tensor containing the result of @ref CLGEMMLowpMatrixMultiplyKernel. Data type supported: S32
+ * @param[in] vector_sum_col Input row-vector of sums of all the entries in each column of matrix B.
+ * Note: vector_sum_col can be a nullptr in case a_offset = 0. Data type supported: same as @p mm_result
+ * @param[in] vector_sum_row Input row-vector of sums of all the entries in each row of matrix A.
+ * Note: vector_sum_row can be a nullptr in case b_offset = 0. Data type supported: same as @p mm_result
+ * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the addition of biases is not required.
+ * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
+ * @param[out] output Output tensor. Data type supported: QASYMM8
+ * @param[in] k Number of matrix A columns or Matrix B rows
+ * @param[in] a_offset Offset to be added to each element of the matrix A.
+ * @param[in] b_offset Offset to be added to each element of the matrix B.
+ * @param[in] output_stage GEMMLowp output stage info
+ */
+ void configure(const ICLTensor *mm_result, const ICLTensor *vector_sum_col, const ICLTensor *vector_sum_row, const ICLTensor *bias, ICLTensor *output, int32_t k, int32_t a_offset, int32_t b_offset,
+ const GEMMLowpOutputStageInfo &output_stage);
+ /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpOffsetContributionKernel
+ *
+ * @param[in] mm_result Input tensor containing the result of @ref CLGEMMLowpOffsetContributionKernel. Data type supported: S32 or QASYMM8 if output_stage != NONE
+ * @param[in] vector_sum_col Input row-vector of sums of all the entries in each column of matrix B.
+ * Note: vector_sum_col can be a nullptr in case a_offset = 0. Data type supported: same as @p mm_result
+ * @param[in] vector_sum_row Input row-vector of sums of all the entries in each row of matrix A.
+ * Note: vector_sum_row can be a nullptr in case b_offset = 0. Data type supported: same as @p mm_result
+ * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the addition of biases is not required.
+ * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
+ * @param[in] output Output tensor. Data type supported: QASYMM8
+ * @param[in] a_offset Offset to be added to each element of the matrix A.
+ * @param[in] b_offset Offset to be added to each element of the matrix B.
+ * @param[in] output_stage GEMMLowp output stage info
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *mm_result, const ITensorInfo *vector_sum_col, const ITensorInfo *vector_sum_row, const ITensorInfo *bias, const ITensorInfo *output, int32_t a_offset,
+ int32_t b_offset, const GEMMLowpOutputStageInfo &output_stage);
+
+ // Inherited methods overridden:
+ void run(const Window &window, cl::CommandQueue &queue) override;
+
+private:
+ const ICLTensor *_mm_result;
+ const ICLTensor *_vector_sum_col;
+ const ICLTensor *_vector_sum_row;
+ const ICLTensor *_bias;
+ ICLTensor *_output;
+};
+} // namespace arm_compute
+
+#endif /* __ARM_COMPUTE_CLGEMMLOWPOFFSETCONTRIBUTIONOUTPUTSTAGEKERNEL_H__ */
diff --git a/arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h b/arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h
index 1206206..72373b5 100644
--- a/arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h
+++ b/arm_compute/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.h
@@ -67,25 +67,22 @@
* @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8
* @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
* Along with @p min, this value can be used to implement "rectified linear unit" activation functions
- * @param[in] output_3d_depth (Optional) Depth of output in 3D (Defaults to 1)
*/
void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, int result_fixedpoint_multiplier, int result_shift, int result_offset_after_shift,
- int min = 0, int max = 0, unsigned int output_3d_depth = 1);
+ int min = 0, int max = 0);
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel
*
- * @param[in] input Input tensor. Data type supported: S32
- * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the biases addition is not required.
- * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
- * @param[in] output Output tensor. Data type supported: Data type supported: QASYMM8
- * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8
- * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
+ * @param[in] input Input tensor. Data type supported: S32
+ * @param[in] bias Biases tensor. Only shared biases supported and it can be a nullptr if the biases addition is not required.
+ * Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p input.
+ * @param[in] output Output tensor. Data type supported: Data type supported: QASYMM8
+ * @param[in] min (Optional) Min value used to saturate down the output result before converting back to QASYMM8
+ * @param[in] max (Optional) Max value used to saturate up the output result before converting back to QASYMM8,
* Along with @p min, this value can be used to implement "rectified linear unit" activation functions
- * @param[in] output_3d_depth (Optional) Depth of output in 3D (Defaults to 1)
*
* @return a status
*/
- static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output,
- int min = 0, int max = 0, unsigned int output_3d_depth = 1);
+ static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min = 0, int max = 0);
// Inherited methods overridden:
void run(const Window &window, cl::CommandQueue &queue) override;
@@ -94,7 +91,6 @@
const ICLTensor *_input;
const ICLTensor *_bias;
ICLTensor *_output;
- bool _reinterpret_as_3d;
};
} // namespace arm_compute
#endif /* __ARM_COMPUTE_CLGEMMLOWPQUANTIZEDOWNINT32TOUINT8SCALEBYFIXEDPOINTKERNEL_H__ */