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__ */
diff --git a/arm_compute/core/Types.h b/arm_compute/core/Types.h
index 5e04bcd..134b8e2 100644
--- a/arm_compute/core/Types.h
+++ b/arm_compute/core/Types.h
@@ -1205,6 +1205,26 @@
const bool _reinterpret_input_as_3d;
};
+/** GEMMLowp output stage type */
+enum class GEMMLowpOutputStageType
+{
+ NONE, /**< No quantization to uint8 */
+ QUANTIZE_DOWN, /**< Quantize to uint8 using an integer multiplication */
+ QUANTIZE_DOWN_FIXEDPOINT, /**< Quantize to uint8 using a fixed point multiplication */
+ QUANTIZE_DOWN_FLOAT /**< Quantize to uint8 using a floating point multiplication */
+};
+
+/** GEMMLowp output stage info */
+struct GEMMLowpOutputStageInfo
+{
+ GEMMLowpOutputStageType type{ GEMMLowpOutputStageType::NONE }; /**< GEMMLowp output stage type */
+ int gemmlowp_offset{ 0 }; /**< GEMMLowp output stage offset used for quantizing to QASYMM8 */
+ int gemmlowp_multiplier{ 0 }; /**< GEMMLowp output stage multiplier used for quantizing to QASYMM8 */
+ int gemmlowp_shift{ 0 }; /**< GEMMLowp output stage shift used for quantizing to uint8 */
+ int gemmlowp_min_bound{ 0 }; /**< GEMMLowp min value used to saturate down the output result before converting back to QASYMM8 */
+ int gemmlowp_max_bound{ 0 }; /**< GEMMLowp max value used to saturate down the output result before converting back to QASYMM8 */
+};
+
/** GEMM information class. This class stores the necessary information to compute GEMM functions
*
* This object also contains the information about how matrix A and matrix B have been reshaped
@@ -1215,7 +1235,7 @@
public:
/** Default constructor */
GEMMInfo()
- : _is_a_reshaped(false), _is_b_reshaped(false), _reshape_b_only_on_first_run(false), _depth_output_gemm3d(1), _reinterpret_input_as_3d(false), _retain_internal_weights(false)
+ : _is_a_reshaped(false), _is_b_reshaped(false), _reshape_b_only_on_first_run(false), _depth_output_gemm3d(1), _reinterpret_input_as_3d(false), _retain_internal_weights(false), _gemmlowp_output_stage()
{
}
/** Constructor
@@ -1227,11 +1247,13 @@
* @param[in] reinterpret_input_as_3d (Optional) Reinterpret the input as 3D tensor. (i.e. this flag should be set to true when GEMM is used
* to perform 1x1 convolutions with the NHWC data layout)
* @param[in] retain_internal_weights (Optional) Retain the weights tensor from previous run
+ * @param[in] gemmlowp_output_stage (Optional) GEMMLowp Output stage info
*
*/
- GEMMInfo(bool is_a_reshaped, bool is_b_reshaped, bool reshape_b_only_on_first_run, int depth_output_gemm3d = 1, bool reinterpret_input_as_3d = false, bool retain_internal_weights = false)
+ GEMMInfo(bool is_a_reshaped, bool is_b_reshaped, bool reshape_b_only_on_first_run, int depth_output_gemm3d = 1, bool reinterpret_input_as_3d = false, bool retain_internal_weights = false,
+ GEMMLowpOutputStageInfo gemmlowp_output_stage = GEMMLowpOutputStageInfo())
: _is_a_reshaped(is_a_reshaped), _is_b_reshaped(is_b_reshaped), _reshape_b_only_on_first_run(reshape_b_only_on_first_run), _depth_output_gemm3d(depth_output_gemm3d),
- _reinterpret_input_as_3d(reinterpret_input_as_3d), _retain_internal_weights(retain_internal_weights)
+ _reinterpret_input_as_3d(reinterpret_input_as_3d), _retain_internal_weights(retain_internal_weights), _gemmlowp_output_stage(gemmlowp_output_stage)
{
}
/** Flag which specifies if the matrix A has been reshaped
@@ -1284,14 +1306,23 @@
{
return _retain_internal_weights;
};
+ /** GEMMLowp output stage
+ *
+ * @return the GEMMLowp output stage info
+ */
+ GEMMLowpOutputStageInfo gemmlowp_output_stage() const
+ {
+ return _gemmlowp_output_stage;
+ };
private:
- const bool _is_a_reshaped;
- const bool _is_b_reshaped;
- const bool _reshape_b_only_on_first_run;
- const int _depth_output_gemm3d;
- const bool _reinterpret_input_as_3d;
- const bool _retain_internal_weights;
+ const bool _is_a_reshaped;
+ const bool _is_b_reshaped;
+ const bool _reshape_b_only_on_first_run;
+ const int _depth_output_gemm3d;
+ const bool _reinterpret_input_as_3d;
+ const bool _retain_internal_weights;
+ const GEMMLowpOutputStageInfo _gemmlowp_output_stage;
};
/** Winograd information */
diff --git a/arm_compute/core/Utils.h b/arm_compute/core/Utils.h
index cfd2736..e7fbbfe 100644
--- a/arm_compute/core/Utils.h
+++ b/arm_compute/core/Utils.h
@@ -927,6 +927,13 @@
* @return The string describing the pooling type.
*/
const std::string &string_from_pooling_type(PoolingType type);
+/** Translates a given GEMMLowp output stage to a string.
+ *
+ * @param[in] output_stage @ref GEMMLowpOutputStageInfo to be translated to string.
+ *
+ * @return The string describing the GEMMLowp output stage
+ */
+const std::string &string_from_gemmlowp_output_stage(GEMMLowpOutputStageType output_stage);
/** Convert a PixelValue to a string, represented through the specific data type
*
* @param[in] value The PixelValue to convert
diff --git a/arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h b/arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h
index 48b8801..fbf0c08 100644
--- a/arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h
+++ b/arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h
@@ -157,43 +157,48 @@
private:
/** Configures the appropriate matrix multiply routine
*
- * @param[in] input Input tensor. Data types supported: QASYMM8/F16/F32.
- * @param[in] weights Weights tensor. Data type supported: Same as @p input.
- * @param[in, out] output Output tensor. Data types supported: Same as @p input,
- * except for input of QASYMM8 type where output should be of S32 type.
- * @param[in] gemm_3d_depth (Optional) Depth of GEMM 3D (Defaults to 1)
+ * @param[in] input Input tensor. Data types supported: QASYMM8/F16/F32.
+ * @param[in] weights Weights tensor. 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, out] output Output tensor. Data types supported: Same as @p input,
+ * except for input of QASYMM8 type where output should be of S32 type.
+ * @param[in] gemmlowp_output_stage GEMMLowp output stage info
+ * @param[in] gemm_3d_depth (Optional) Depth of GEMM 3D (Defaults to 1)
*/
- void configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, int gemm_3d_depth = 1);
+ void configure_mm(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const GEMMLowpOutputStageInfo &gemmlowp_output_stage, int gemm_3d_depth = 1);
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMConvolutionLayer matrix multiply routines
*
- * @param[in] input Input tensor. Data types supported: QASYMM8/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.
- * @param[in] gemm_3d_depth (Optional) Depth of GEMM 3D (Defaults to 1)
- * @param[in] skip_im2col (Optional) Flag which specifies if im2col has to be skipped. i.e. 1x1 convolution with NHWC data layout. (Default to false)
+ * @param[in] input Input tensor. Data types supported: QASYMM8/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.
+ * @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] gemmlowp_output_stage GEMMLowp output stage info
+ * @param[in] gemm_3d_depth (Optional) Depth of GEMM 3D (Defaults to 1)
+ * @param[in] skip_im2col (Optional) Flag which specifies if im2col has to be skipped. i.e. 1x1 convolution with NHWC data layout. (Default to false)
*
* @return a status
*/
- static Status validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, int gemm_3d_depth = 1, bool skip_im2col = false);
+ static Status validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const GEMMLowpOutputStageInfo &gemmlowp_output_stage,
+ int gemm_3d_depth = 1, bool skip_im2col = false);
private:
- CLMemoryGroup _memory_group;
- CLConvolutionLayerReshapeWeights _reshape_weights;
- CLIm2ColKernel _im2col_kernel;
- CLGEMM _mm_gemm;
- CLGEMMLowpMatrixMultiplyCore _mm_gemmlowp;
- CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloat _gemmlowp_output_stage;
- CLCol2ImKernel _col2im_kernel;
- CLActivationLayer _activationlayer_function;
- CLArithmeticAdditionKernel _add_bias_kernel;
+ CLMemoryGroup _memory_group;
+ CLConvolutionLayerReshapeWeights _reshape_weights;
+ CLIm2ColKernel _im2col_kernel;
+ CLGEMM _mm_gemm;
+ CLGEMMLowpMatrixMultiplyCore _mm_gemmlowp;
+ CLCol2ImKernel _col2im_kernel;
+ CLActivationLayer _activationlayer_function;
+ CLArithmeticAdditionKernel _add_bias_kernel;
const ICLTensor *_original_weights;
CLTensor _im2col_output;
CLTensor _weights_reshaped;
CLTensor _gemm_output;
- CLTensor _tmp_output;
DataLayout _data_layout;
diff --git a/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h b/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h
index f404ccd..82f307a 100644
--- a/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h
+++ b/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h
@@ -27,6 +27,7 @@
#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/CLGEMMLowpReductionKernel.h"
#include "arm_compute/core/CL/kernels/CLGEMMTranspose1xWKernel.h"
#include "arm_compute/runtime/CL/CLMemoryGroup.h"
@@ -45,7 +46,8 @@
* -# @ref CLGEMMLowpMatrixMultiplyKernel
* -# @ref CLGEMMLowpMatrixAReductionKernel (if the offset of matrix B is not 0)
* -# @ref CLGEMMLowpMatrixBReductionKernel (if the offset of matrix A is not 0)
- * -# @ref CLGEMMLowpOffsetContributionKernel
+ * -# @ref CLGEMMLowpOffsetContributionKernel (if gemm_info.gemmlowp_output_stage == NONE)
+ * -# @ref CLGEMMLowpOffsetContributionOutputStageKernel (if gemm_info.gemmlowp_output_stage != NONE)
*
*/
class CLGEMMLowpMatrixMultiplyCore : public IFunction
@@ -63,54 +65,60 @@
CLGEMMLowpMatrixMultiplyCore &operator=(CLGEMMLowpMatrixMultiplyCore &&) = default;
/** Initialise the kernel's inputs, output
*
- * @note GEMM_LOWP: low precision GEMM kernel
+ * @note GEMMLowp: low precision GEMM kernel. [A * B + C]
* This kernel performs the following computations:
*
* -# Convert a values from QASYMM8 to int32 and add a_offset to each of them.
* -# Convert b values from QASYMM8 to int32 add b_offset to each of them.
* -# Compute the matrix product of the resulting a * b in int32.
+ * -# Quantize to uint8 if gemm_info.gemmlowp_output_stage != NONE
*
* @param[in] a First input tensor (Matrix A). Data type supported: QASYMM8.
* @param[in] b Second input tensor (Matrix B). Data type supported: same as @p a
- * @param[out] output Output tensor. Data type supported: Data type supported: S32
+ * @param[in] c Third input tensor (Matrix C). It can be a nullptr. Data type supported: S32
+ * @param[out] output Output tensor. Data type supported: S32 or QASYMM8 if gemm_info.gemmlowp_output_stage != NONE
* @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 be executed only for the first run
*/
- void configure(const ICLTensor *a, const ICLTensor *b, ICLTensor *output, const GEMMInfo &gemm_info = GEMMInfo());
+ void configure(const ICLTensor *a, const ICLTensor *b, const ICLTensor *c, ICLTensor *output, const GEMMInfo &gemm_info = GEMMInfo());
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMLowpMatrixMultiplyCore
*
* @param[in] a First input tensor (Matrix A). Data type supported: QASYMM8.
* @param[in] b Second input tensor (Matrix B). Data type supported: same as @p a
- * @param[in] output Output tensor. Data type supported: Data type supported: S32
+ * @param[in] c Third input tensor (Matrix C). It can be a nullptr. Data type supported: S32
+ * @param[in] output Output tensor. Data type supported: S32 or QASYMM8 if gemm_info.gemmlowp_output_stage != NONE
* @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 be executed only for the first run
*
* @return a status
*/
- static Status validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *output, const GEMMInfo &gemm_info = GEMMInfo());
+ static Status validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, const GEMMInfo &gemm_info = GEMMInfo());
// Inherited methods overridden:
void run() override;
void prepare() override;
private:
- CLMemoryGroup _memory_group;
- CLGEMMLowpMatrixMultiplyKernel _mm_kernel;
- CLGEMMInterleave4x4Kernel _mtx_a_reshape_kernel;
- CLGEMMTranspose1xWKernel _mtx_b_reshape_kernel;
- CLGEMMLowpMatrixAReductionKernel _mtx_a_reduction_kernel;
- CLGEMMLowpMatrixBReductionKernel _mtx_b_reduction_kernel;
- CLGEMMLowpOffsetContributionKernel _offset_contribution_kernel;
- CLTensor _vector_sum_col;
- CLTensor _vector_sum_row;
- CLTensor _tmp_a;
- CLTensor _tmp_b;
- const ICLTensor *_original_b;
- int32_t _a_offset;
- int32_t _b_offset;
- bool _is_interleaved_transposed;
- bool _reshape_b_only_on_first_run;
- bool _is_prepared;
+ CLMemoryGroup _memory_group;
+ CLGEMMLowpMatrixMultiplyKernel _mm_kernel;
+ CLGEMMInterleave4x4Kernel _mtx_a_reshape_kernel;
+ CLGEMMTranspose1xWKernel _mtx_b_reshape_kernel;
+ CLGEMMLowpMatrixAReductionKernel _mtx_a_reduction_kernel;
+ CLGEMMLowpMatrixBReductionKernel _mtx_b_reduction_kernel;
+ CLGEMMLowpOffsetContributionKernel _offset_contribution_kernel;
+ CLGEMMLowpOffsetContributionOutputStageKernel _offset_contribution_output_stage_kernel;
+ CLTensor _vector_sum_col;
+ CLTensor _vector_sum_row;
+ CLTensor _tmp_a;
+ CLTensor _tmp_b;
+ CLTensor _mm_result_s32;
+ const ICLTensor *_original_b;
+ int32_t _a_offset;
+ int32_t _b_offset;
+ bool _is_interleaved_transposed;
+ bool _reshape_b_only_on_first_run;
+ bool _is_prepared;
+ bool _fuse_output_stage;
};
}
#endif /*__ARM_COMPUTE_CLGEMMLOWPMATRIXMULTIPLYCORE_H__ */
diff --git a/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h b/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h
index 51fcbe9..3330b40 100644
--- a/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h
+++ b/arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h
@@ -131,24 +131,22 @@
* @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 CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint
*
- * @param[in] input Input tensor. It is the output of @ref CLGEMMLowpMatrixMultiplyCore function. Data type supported: S32
- * @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: 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. It is the output of @ref CLGEMMLowpMatrixMultiplyCore function. Data type supported: S32
+ * @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: 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);
};
/** Basic function to execute CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFloat on OpenCL.
diff --git a/arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h b/arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h
index 3db76f4..682475c 100644
--- a/arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h
+++ b/arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h
@@ -75,22 +75,24 @@
*
* @param[in] a First input tensor (Matrix A). Data type supported: QASYMM8.
* @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. Data type supported: S32
* @param[out] output Output tensor. Data type supported: Data type supported: S32
* @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 be executed only for the first run
*/
- void configure(const ITensor *a, const ITensor *b, ITensor *output, const GEMMInfo &gemm_info = GEMMInfo());
+ void configure(const ITensor *a, const ITensor *b, const ITensor *c, ITensor *output, const GEMMInfo &gemm_info = GEMMInfo());
/** Static function to check if given info will lead to a valid configuration of @ref NEGEMMLowpMatrixMultiplyCore
*
* @param[in] a First input tensor (Matrix A). Data type supported: QASYMM8.
* @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. Data type supported: S32
* @param[in] output Output tensor. Data type supported: Data type supported: S32
* @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 be executed only for the first run
*
* @return a status
*/
- static Status validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *output, const GEMMInfo &gemm_info = GEMMInfo());
+ static Status validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, const GEMMInfo &gemm_info = GEMMInfo());
// Inherited methods overridden
void run() override;