COMPMID-2309 : CLConvolutionLayer: support QUANT8_SYMM_PER_CHANNEL filters
Change-Id: I16f6758b768ede404a064db057302ded706e1e8a
Signed-off-by: Vidhya Sudhan Loganathan <vidhyasudhan.loganathan@arm.com>
Signed-off-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-on: https://review.mlplatform.org/c/2215
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/arm_compute/core/CL/kernels/CLDepthConvertLayerKernel.h b/arm_compute/core/CL/kernels/CLDepthConvertLayerKernel.h
index 7475d8d..cce7b69 100644
--- a/arm_compute/core/CL/kernels/CLDepthConvertLayerKernel.h
+++ b/arm_compute/core/CL/kernels/CLDepthConvertLayerKernel.h
@@ -41,6 +41,7 @@
*
* Valid conversions Input -> Output :
*
+ * - QSYMM8_PER_CHANNEL -> QASYMM8 (ATTENTION: it is the user's responsibility to keep track of the quantization info in the TensorInfo meta-data)
* - U8 -> S8, U16, S16, U32, S32, F16, F32
* - U16 -> U8, S8, S16, U32, S32, F16, F32
* - S16 -> U8, S8, U16, U32, S32, F16, F32
@@ -49,16 +50,16 @@
* - F16 -> U8, S8, U16, S16, U32, F32
* - F32 -> U8, S8, U16, S16, U32, F16
*
- * @param[in] input The input tensor to convert. Data types supported: U8/S8/U16/S16/U32/S32/F16/F32.
- * @param[out] output The output tensor. Data types supported: U8/S8/U16/S16/U32/S32/F16/F32.
+ * @param[in] input The input tensor to convert. Data types supported: U8/S8/QSYMM8_PER_CHANNEL/U16/S16/U32/S32/F16/F32.
+ * @param[out] output The output tensor. Data types supported: U8/S8/QASYMM8/U16/S16/U32/S32/F16/F32.
* @param[in] policy Conversion policy
* @param[in] shift Value for down/up conversions. Must be 0 <= shift < 8.
*/
void configure(const ICLTensor *input, ICLTensor *output, ConvertPolicy policy, uint32_t shift);
/** Static function to check if given info will lead to a valid configuration of @ref CLDepthConvertLayerKernel
*
- * @param[in] input Source tensor info. Data types supported: U8/S8/U16/S16/U32/S32/F16/F32.
- * @param[in] output Destination tensor info. Data type supported: U8/S8/U16/S16/U32/S32/F16/F32.
+ * @param[in] input Source tensor info. Data types supported: U8/S8/QSYMM8_PER_CHANNEL/U16/S16/U32/S32/F16/F32.
+ * @param[in] output Destination tensor info. Data type supported: U8/S8/QASYMM8/U16/S16/U32/S32/F16/F32.
* @param[in] policy Conversion policy
* @param[in] shift Value for down/up conversions. Must be 0 <= shift < 8.
*
diff --git a/arm_compute/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.h b/arm_compute/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.h
index de06c88..301c673 100644
--- a/arm_compute/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.h
+++ b/arm_compute/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2018 ARM Limited.
+ * Copyright (c) 2018-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -51,39 +51,47 @@
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
+ * @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
+ * @param[in] output_multipliers Output multipliers tensor. In case of per-channel quantization, the number of multipliers must be equal to the number of filters (OFM).
+ * Supported data types: S32
+ * @param[in] output_shifts Output shifts tensor. In case of per-channel quantization, the number of multipliers must be equal to the number of filters (OFM).
+ * Supported data types: S32
*/
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);
+ const GEMMLowpOutputStageInfo &output_stage, const ICLTensor *output_multipliers, const ICLTensor *output_shifts);
/** 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
+ * @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
+ * @param[in] output_multipliers Output multipliers tensor info. In case of per-channel quantization, the number of multipliers must be equal to the number of filters (OFM).
+ * Supported data types: S32
+ * @param[in] output_shifts Output shifts tensor info. In case of per-channel quantization, the number of multipliers must be equal to the number of filters (OFM).
+ * Supported data types: S32
*
* @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);
+ int32_t b_offset, const GEMMLowpOutputStageInfo &output_stage, const ITensorInfo *output_multipliers, const ITensorInfo *output_shifts);
// Inherited methods overridden:
void run(const Window &window, cl::CommandQueue &queue) override;
@@ -94,6 +102,9 @@
const ICLTensor *_vector_sum_row;
const ICLTensor *_bias;
ICLTensor *_output;
+ const ICLTensor *_output_multipliers;
+ const ICLTensor *_output_shifts;
+ bool _is_quantized_per_channel;
};
} // namespace arm_compute
diff --git a/arm_compute/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h b/arm_compute/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h
index 26ab210..937f6a9 100644
--- a/arm_compute/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h
+++ b/arm_compute/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h
@@ -48,7 +48,7 @@
CLGEMMReshapeRHSMatrixKernel &operator=(CLGEMMReshapeRHSMatrixKernel &&) = default;
/** Initialise the kernel's input and output.
*
- * @param[in] input Input tensor. Data types supported: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32
+ * @param[in] input Input tensor. Data types supported: All
* @param[out] output Output tensor. Data type supported: same as @p input
* @param[in] rhs_info RHS matrix information to be used for reshaping. This object contains all the necessary
* information to reshape the input tensor. Only the following values are supported:
@@ -61,7 +61,7 @@
void configure(const ICLTensor *input, ICLTensor *output, const GEMMRHSMatrixInfo &rhs_info);
/** Static function to check if given info will lead to a valid configuration of @ref CLGEMMReshapeRHSMatrixKernel
*
- * @param[in] input Input tensor info. Data types supported: U8/S8/QASYMM8/U16/S16/F16/U32/S32/F32
+ * @param[in] input Input tensor info. Data types supported: All
* @param[in] output Output tensor info which stores the interleaved matrix. Data type supported: same as @p input.
* @param[in] rhs_info RHS matrix information to be used for reshaping. This object contains all the necessary
* information to reshape the input tensor. Only the following values are supported:
diff --git a/arm_compute/core/CL/kernels/CLWeightsReshapeKernel.h b/arm_compute/core/CL/kernels/CLWeightsReshapeKernel.h
index bdc5792..59740b9 100644
--- a/arm_compute/core/CL/kernels/CLWeightsReshapeKernel.h
+++ b/arm_compute/core/CL/kernels/CLWeightsReshapeKernel.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2018 ARM Limited.
+ * Copyright (c) 2017-2019 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -69,9 +69,9 @@
/** Set the input and output of the kernel.
*
* @param[in] input The input tensor to convert. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] if shared,
- * and 5D tensor with dimensions [kernel_x, kernel_y, IFM, OFM, num_patches] if unshared. Data types supported: QASYMM8/F16/F32
+ * and 5D tensor with dimensions [kernel_x, kernel_y, IFM, OFM, num_patches] if unshared. Data types supported: All
* @param[in] biases The shared biases tensor to append. Bias is 1D tensor with dimensions [OFM] if shared and 2D tensor with
- * dimensions [OFM, num_patches] if unshared. Data types supported: Same as @p input
+ * dimensions [OFM, num_patches] if unshared. Data types supported: F16/F32, for quantized types this must be nullptr.
* @warning Appending biases to weights reshaped matrix is not supported for quantized asymmetric types.
* @param[out] output The output tensor. Should be a 2D Tensor if there are no groups and the weights are not shared; a 3D Tensor otherwise.
* Data types supported: Same as @p input
@@ -82,9 +82,9 @@
/** Static function to check if given info will lead to a valid configuration of @ref CLWeightsReshapeKernel
*
* @param[in] input The input tensor to convert. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] if shared,
- * and 5D tensor with dimensions [kernel_x, kernel_y, IFM, OFM, num_patches] if unshared. Data types supported: QASYMM8/F16/F32
+ * and 5D tensor with dimensions [kernel_x, kernel_y, IFM, OFM, num_patches] if unshared. Data types supported: All
* @param[in] biases The shared biases tensor to append. Bias is 1D tensor with dimensions [OFM] if shared and 2D tensor with
- * dimensions [OFM, num_patches] if unshared. Data types supported: Same as @p input
+ * dimensions [OFM, num_patches] if unshared. Data types supported: F16/F32, for quantized types this must be nullptr.
* @warning Appending biases to weights reshaped matrix is not supported for quantized asymmetric types.
* @param[in] output The output tensor. Should be a 2D Tensor if there are no groups and the weights are not shared; a 3D Tensor otherwise.
* Data types supported: Same as @p input
diff --git a/arm_compute/core/Types.h b/arm_compute/core/Types.h
index 851292f..38d7897 100644
--- a/arm_compute/core/Types.h
+++ b/arm_compute/core/Types.h
@@ -1883,6 +1883,7 @@
int gemmlowp_max_bound{ 0 }; /**< GEMMLowp max value used to saturate down the output result before converting back to QASYMM8 */
std::vector<int32_t> gemmlowp_multipliers{}; /**< GEMMLowp output stage multiplier used for quantizing to QASYMM8 */
std::vector<int32_t> gemmlowp_shifts{}; /**< GEMMLowp output stage multiplier used for quantizing to QASYMM8 */
+ bool is_quantized_per_channel{ false }; /**< GEMMLowp quantized per-channel flag */
};
/** GEMM LHS (Left Hand Side) matrix information */
diff --git a/arm_compute/core/utils/quantization/AsymmHelpers.h b/arm_compute/core/utils/quantization/AsymmHelpers.h
index 6b6cb00..0bf6ff5 100644
--- a/arm_compute/core/utils/quantization/AsymmHelpers.h
+++ b/arm_compute/core/utils/quantization/AsymmHelpers.h
@@ -84,15 +84,21 @@
* per-channel, multipliers and shifts will end up being the same for each
* channel.
*
- * @param[in] input Input tensor.
- * @param[in] weights Weights tensor.
- * @param[in] output Output tensor.
+ * @param[in] input Input tensor info.
+ * @param[in] weights Weights tensor info.
+ * @param[in] output Output tensor info.
+ * @param[in] idx_ofms Dimension index to get OFMs from the weights tensor.
* @param[out] output_multipliers_ptr Pointer to the buffer where to store per-channel multipliers.
* @param[out] output_shifts_ptr Pointer to the buffer where to store per-channel shifts.
*
* @return min and max values for the quantized data type
*/
-void compute_quantized_multipliers_and_shifts(const ITensor *input, const ITensor *weights, const ITensor *output, int32_t *output_multipliers_ptr, int32_t *output_shifts_ptr);
+void compute_quantized_multipliers_and_shifts(const ITensorInfo *input,
+ const ITensorInfo *weights,
+ const ITensorInfo *output,
+ unsigned int idx_ofms,
+ int32_t *output_multipliers_ptr,
+ int32_t *output_shifts_ptr);
} // namespace quantization
} // namespace arm_compute
#endif /* __ARM_COMPUTE_IO_FILE_HANDLER_H__ */
diff --git a/arm_compute/runtime/CL/functions/CLConvolutionLayer.h b/arm_compute/runtime/CL/functions/CLConvolutionLayer.h
index 04ce1cf..8dfb6c8 100644
--- a/arm_compute/runtime/CL/functions/CLConvolutionLayer.h
+++ b/arm_compute/runtime/CL/functions/CLConvolutionLayer.h
@@ -78,7 +78,8 @@
* @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: QASYMM8/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] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
+ * Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8.
* @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.
@@ -98,7 +99,8 @@
* @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: QASYMM8/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] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
+ * Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8.
* @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported:Same as @p input.
* @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.
@@ -120,7 +122,8 @@
* @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: QASYMM8/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] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
+ * Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8.
* @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.
diff --git a/arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h b/arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h
index 017bf78..3392f11 100644
--- a/arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h
+++ b/arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h
@@ -60,7 +60,7 @@
/** 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: QASYMM8/F16/F32.
+ * Data type supported: QASYMM8/QSYMM8_PER_CHANNEL/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] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
@@ -69,7 +69,7 @@
/** Static function to check if given info will lead to a valid configuration of @ref CLConvolutionLayerReshapeWeights
*
* @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
- * Data type supported: QASYMM8/F16/F32.
+ * Data type supported: QASYMM8/QSYMM8_PER_CHANNEL/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] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout
@@ -168,7 +168,8 @@
* @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: QASYMM8/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] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
+ * Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8.
* @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.
@@ -187,7 +188,8 @@
* @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: QASYMM8/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] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
+ * Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8.
* @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.
@@ -212,7 +214,7 @@
/** 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] weights Weights tensor. Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8.
* @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,
@@ -225,12 +227,12 @@
const ActivationLayerInfo &act_info);
/** 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] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
+ * @param[in] input Input tensor info. Data types supported: QASYMM8/F16/F32.
+ * @param[in] weights Weights tensor info. Data type supported: Same as @p input or QASYMM8/QSYMM8_PER_CHANNEL when @p input is QASYMM8.
+ * @param[in] biases Biases tensor info. 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 Output tensor info. 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 Depth of GEMM 3D
* @param[in] skip_im2col Flag which specifies if im2col has to be skipped. i.e. 1x1 convolution with NHWC data layout.
diff --git a/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h b/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h
index 6aacbf6..b364653 100644
--- a/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h
+++ b/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h
@@ -24,6 +24,7 @@
#ifndef __ARM_COMPUTE_CLGEMMLOWPMATRIXMULTIPLYCORE_H__
#define __ARM_COMPUTE_CLGEMMLOWPMATRIXMULTIPLYCORE_H__
+#include "arm_compute/core/CL/kernels/CLDepthConvertLayerKernel.h"
#include "arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyKernel.h"
#include "arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyNativeKernel.h"
#include "arm_compute/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.h"
@@ -49,6 +50,7 @@
* -# @ref CLGEMMLowpMatrixBReductionKernel (if the offset of matrix A is not 0)
* -# @ref CLGEMMLowpOffsetContributionKernel (if gemm_info.gemmlowp_output_stage == NONE)
* -# @ref CLGEMMLowpOffsetContributionOutputStageKernel (if gemm_info.gemmlowp_output_stage != NONE)
+ * -# @ref CLDepthConvertLayerKernel
*
*/
class CLGEMMLowpMatrixMultiplyCore : public IFunction
@@ -84,10 +86,10 @@
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] 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] a First input tensor info (Matrix A). Data type supported: QASYMM8.
+ * @param[in] b Second input tensor info (Matrix B). Data type supported: same as @p a
+ * @param[in] c Third input tensor info (Matrix C). It can be a nullptr. Data type supported: S32
+ * @param[in] output Output tensor info. 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
*
@@ -100,7 +102,10 @@
void prepare() override;
private:
- MemoryGroup _memory_group;
+ MemoryGroup _memory_group;
+
+ // Kernels used
+ CLDepthConvertLayerKernel _weights_to_qasymm8;
CLGEMMLowpMatrixMultiplyKernel _mm_midgard_kernel;
CLGEMMLowpMatrixMultiplyNativeKernel _mm_native_kernel;
CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel _mm_reshaped_only_rhs_kernel;
@@ -109,18 +114,29 @@
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_b;
- CLTensor _mm_result_s32;
- const ICLTensor *_original_b;
- int32_t _a_offset;
- int32_t _b_offset;
- bool _is_gemm_reshaped;
- bool _is_midgard;
- bool _reshape_b_only_on_first_run;
- bool _is_prepared;
- bool _fuse_output_stage;
+
+ // Temporary tensors
+ CLTensor _qasymm8_weights;
+ CLTensor _vector_sum_col;
+ CLTensor _vector_sum_row;
+ CLTensor _tmp_b;
+ CLTensor _mm_result_s32;
+ CLTensor _gemm_output_stage_multipliers;
+ CLTensor _gemm_output_stage_shifts;
+
+ // Tensor pointers
+ const ICLTensor *_matrix_a;
+ const ICLTensor *_original_b;
+ const ICLTensor *_output;
+
+ int32_t _a_offset;
+ int32_t _b_offset;
+ bool _is_gemm_reshaped;
+ bool _is_midgard;
+ bool _reshape_b_only_on_first_run;
+ bool _is_prepared;
+ bool _fuse_output_stage;
+ bool _convert_to_qasymm8;
};
-}
+} // namespace arm_compute
#endif /*__ARM_COMPUTE_CLGEMMLOWPMATRIXMULTIPLYCORE_H__ */
\ No newline at end of file