diff options
Diffstat (limited to 'arm_compute/core')
6 files changed, 60 insertions, 41 deletions
diff --git a/arm_compute/core/CL/kernels/CLDepthConvertLayerKernel.h b/arm_compute/core/CL/kernels/CLDepthConvertLayerKernel.h index 7475d8d41d..cce7b69a0e 100644 --- a/arm_compute/core/CL/kernels/CLDepthConvertLayerKernel.h +++ b/arm_compute/core/CL/kernels/CLDepthConvertLayerKernel.h @@ -41,6 +41,7 @@ public: * * 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 @@ public: * - 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 de06c88d5c..301c67331e 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 @@ public: 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 @@ private: 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 26ab210b21..937f6a9b89 100644 --- a/arm_compute/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h +++ b/arm_compute/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h @@ -48,7 +48,7 @@ public: 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 @@ public: 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 bdc5792641..59740b9db9 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 @@ public: /** 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 @@ public: /** 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 851292f1e1..38d78971ef 100644 --- a/arm_compute/core/Types.h +++ b/arm_compute/core/Types.h @@ -1883,6 +1883,7 @@ struct GEMMLowpOutputStageInfo 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 6b6cb007e3..0bf6ff5c95 100644 --- a/arm_compute/core/utils/quantization/AsymmHelpers.h +++ b/arm_compute/core/utils/quantization/AsymmHelpers.h @@ -84,15 +84,21 @@ std::pair<int, int> get_min_max_values_from_quantized_data_type(DataType data_ty * 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__ */ |