From f4cb81be294a1075ce3ce7d11dd60bdee5505ce9 Mon Sep 17 00:00:00 2001 From: Vidhya Sudhan Loganathan Date: Wed, 4 Jul 2018 15:13:14 +0100 Subject: COMPMID-970 : Remove QS8 / QS16 support Removed QS32 references Change-Id: Ic7df02c08ae7aa1b7dcae15bdda113321af851b8 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/138703 Tested-by: Jenkins Reviewed-by: Anthony Barbier --- arm_compute/core/CL/kernels/CLConvertFullyConnectedWeightsKernel.h | 4 ++-- .../core/CL/kernels/CLDirectConvolutionLayerOutputStageKernel.h | 4 ++-- arm_compute/core/NEON/kernels/NEConvertFullyConnectedWeightsKernel.h | 4 ++-- arm_compute/core/NEON/kernels/NEDirectConvolutionLayerKernel.h | 2 +- .../core/NEON/kernels/NEDirectConvolutionLayerOutputStageKernel.h | 4 ++-- arm_compute/core/Types.h | 1 - arm_compute/core/Utils.h | 3 --- arm_compute/runtime/CL/functions/CLConvertFullyConnectedWeights.h | 4 ++-- arm_compute/runtime/NEON/functions/NEConvertFullyConnectedWeights.h | 4 ++-- 9 files changed, 13 insertions(+), 17 deletions(-) (limited to 'arm_compute') diff --git a/arm_compute/core/CL/kernels/CLConvertFullyConnectedWeightsKernel.h b/arm_compute/core/CL/kernels/CLConvertFullyConnectedWeightsKernel.h index f5e2f0de89..b85f93e992 100644 --- a/arm_compute/core/CL/kernels/CLConvertFullyConnectedWeightsKernel.h +++ b/arm_compute/core/CL/kernels/CLConvertFullyConnectedWeightsKernel.h @@ -55,7 +55,7 @@ public: ~CLConvertFullyConnectedWeightsKernel() = default; /** Set the input and output tensor. * - * @param[in] input Source weights tensor to convert. Must be 2 dimensional. Data types supported: U8/S8/QASYMM8/U16/S16/U32/S32/QS32/F16/F32. + * @param[in] input Source weights tensor to convert. Must be 2 dimensional. Data types supported: U8/S8/QASYMM8/U16/S16/U32/S32/F16/F32. * @param[out] output The converted weights tensor. Shape and Data Type: Same as @p input. * @param[in] original_input_shape Shape of the original input tensor (the one entering fully connected layer). Must be in NCHW format. * @param[in] data_layout The data layout the weights have been trained in. @@ -63,7 +63,7 @@ public: void configure(const ICLTensor *input, ICLTensor *output, const TensorShape &original_input_shape, DataLayout data_layout); /** Static function to check if given info will lead to a valid configuration of @ref CLConvertFullyConnectedWeightsKernel * - * @param[in] input Source weights tensor info to convert. Must be 2 dimensional. Data types supported: U8/S8/QASYMM8/U16/S16/U32/S32/QS32/F16/F32. + * @param[in] input Source weights tensor info to convert. Must be 2 dimensional. Data types supported: U8/S8/QASYMM8/U16/S16/U32/S32/F16/F32. * @param[in] output The converted weights tensor info. Shape and Data Type: Same as @p input. * @param[in] original_input_shape Shape of the original input tensor (the one entering fully connected layer). Must be in NCHW format. * @param[in] data_layout The data layout the weights have been trained in. diff --git a/arm_compute/core/CL/kernels/CLDirectConvolutionLayerOutputStageKernel.h b/arm_compute/core/CL/kernels/CLDirectConvolutionLayerOutputStageKernel.h index 1947a98ba3..d90a2cf4b8 100644 --- a/arm_compute/core/CL/kernels/CLDirectConvolutionLayerOutputStageKernel.h +++ b/arm_compute/core/CL/kernels/CLDirectConvolutionLayerOutputStageKernel.h @@ -51,7 +51,7 @@ public: /** Set the accumulate buffer and the biases of the kernel. * * @param[in, out] input Input to add the bias to. If @p output is not specified then accumulation is done in-place. - * Data type supported: S32/QS32/F16/F32 + * Data type supported: S32/F16/F32 * @param[in] bias (Optional) The shared bias tensor to add. It must be 1D Tensor. Data type supported: Same as @p input * @param[out] output (Optional) If the output tensor is specified the accumulation is done out-of-place. (Defaults to nullptr) * Required parameter if output is of QASYMM8 type. @@ -65,7 +65,7 @@ public: /** Static function to check if given info will lead to a valid configuration of @ref CLDirectConvolutionLayerOutputStageKernel * * @param[in] input Input to add the bias to. If @p output is not specified then accumulation is done in-place. - * Data type supported: QS32/F16/F32 + * Data type supported: F16/F32 * @param[in] bias (Optional) The shared bias tensor to add. It must be 1D Tensor. Data type supported: Same as @p input * @param[in] output (Optional) If the output tensor is specified the accumulation is done out-of-place. (Defaults to nullptr) * Data type supported: F16/F32 diff --git a/arm_compute/core/NEON/kernels/NEConvertFullyConnectedWeightsKernel.h b/arm_compute/core/NEON/kernels/NEConvertFullyConnectedWeightsKernel.h index d5c9e3bbe9..1a276c353e 100644 --- a/arm_compute/core/NEON/kernels/NEConvertFullyConnectedWeightsKernel.h +++ b/arm_compute/core/NEON/kernels/NEConvertFullyConnectedWeightsKernel.h @@ -59,7 +59,7 @@ public: ~NEConvertFullyConnectedWeightsKernel() = default; /** Set the input and output tensor. * - * @param[in] input Source weights tensor to convert. Must be 2 dimensional. Data types supported: U8/S8/QASYMM8/U16/S16/U32/S32/QS32/F16/F32. + * @param[in] input Source weights tensor to convert. Must be 2 dimensional. Data types supported: U8/S8/QASYMM8/U16/S16/U32/S32/F16/F32. * @param[out] output The converted weights tensor. Shape and Data Type: Same as @p input. * @param[in] original_input_shape Shape of the original input tensor (the one entering fully connected layer). Must be in NCHW format. * @param[in] data_layout The data layout the weights have been trained in. @@ -67,7 +67,7 @@ public: void configure(const ITensor *input, ITensor *output, const TensorShape &original_input_shape, DataLayout data_layout); /** Static function to check if given info will lead to a valid configuration of @ref NEConvertFullyConnectedWeightsKernel * - * @param[in] input Source weights tensor info to convert. Must be 2 dimensional. Data types supported: U8/S8/QASYMM8/U16/S16/U32/S32/QS32/F16/F32. + * @param[in] input Source weights tensor info to convert. Must be 2 dimensional. Data types supported: U8/S8/QASYMM8/U16/S16/U32/S32/F16/F32. * @param[in] output The converted weights tensor info. Shape and Data Type: Same as @p input. * @param[in] original_input_shape Shape of the original input tensor (the one entering fully connected layer). Must be in NCHW format. * @param[in] data_layout The data layout the weights have been trained in. diff --git a/arm_compute/core/NEON/kernels/NEDirectConvolutionLayerKernel.h b/arm_compute/core/NEON/kernels/NEDirectConvolutionLayerKernel.h index 589725ab01..e9349a3197 100644 --- a/arm_compute/core/NEON/kernels/NEDirectConvolutionLayerKernel.h +++ b/arm_compute/core/NEON/kernels/NEDirectConvolutionLayerKernel.h @@ -74,7 +74,7 @@ public: * The 3rd dimension must be the same as the input's volume 3rd dimension. * Data type supported:Same as @p input. * @param[in] output Output tensor. - * The 3rd dimensions must be equal to the 4th dimension of the @p kernels tensor. Data types supported: QS32/F16/F32 + * The 3rd dimensions must be equal to the 4th dimension of the @p kernels tensor. Data types supported: F16/F32 * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. * * @return a status diff --git a/arm_compute/core/NEON/kernels/NEDirectConvolutionLayerOutputStageKernel.h b/arm_compute/core/NEON/kernels/NEDirectConvolutionLayerOutputStageKernel.h index 7fd1d70374..9af3de5ffe 100644 --- a/arm_compute/core/NEON/kernels/NEDirectConvolutionLayerOutputStageKernel.h +++ b/arm_compute/core/NEON/kernels/NEDirectConvolutionLayerOutputStageKernel.h @@ -55,7 +55,7 @@ public: /** Set the accumulate buffer and the biases of the kernel. * * @param[in, out] input Input to add the bias to. If @p output is not specified then accumulation is done in-place. - * Data type supported: QS32/F16/F32 + * Data type supported: F16/F32 * @param[in] bias (Optional) The shared bias tensor to add. It must be 1D Tensor. Data type supported: Same as @p input * @param[out] output (Optional) If the output tensor is specified the accumulation is done out-of-place. (Defaults to nullptr) * Data type supported: F16/F32 @@ -68,7 +68,7 @@ public: /** Static function to check if given info will lead to a valid configuration of @ref NEDirectConvolutionLayerOutputStageKernel * * @param[in] input Input to add the bias to. If @p output is not specified then accumulation is done in-place. - * Data type supported: QS32/F16/F32 + * Data type supported: F16/F32 * @param[in] bias (Optional) The shared bias tensor to add. It must be 1D Tensor. Data type supported: Same as @p input * @param[in] output (Optional) If the output tensor is specified the accumulation is done out-of-place. (Defaults to nullptr) * Data type supported: F16/F32 diff --git a/arm_compute/core/Types.h b/arm_compute/core/Types.h index 89fd4b8bb4..1363324e3b 100644 --- a/arm_compute/core/Types.h +++ b/arm_compute/core/Types.h @@ -79,7 +79,6 @@ enum class DataType S16, /**< signed 16-bit number */ U32, /**< unsigned 32-bit number */ S32, /**< signed 32-bit number */ - QS32, /**< quantized, symmetric fixed-point 32-bit number */ U64, /**< unsigned 64-bit number */ S64, /**< signed 64-bit number */ F16, /**< 16-bit floating-point number */ diff --git a/arm_compute/core/Utils.h b/arm_compute/core/Utils.h index cfebfa1506..729a46fe3f 100644 --- a/arm_compute/core/Utils.h +++ b/arm_compute/core/Utils.h @@ -119,7 +119,6 @@ inline size_t data_size_from_type(DataType data_type) case DataType::F32: case DataType::U32: case DataType::S32: - case DataType::QS32: return 4; case DataType::F64: case DataType::U64: @@ -192,7 +191,6 @@ inline size_t element_size_from_data_type(DataType dt) case DataType::U32: case DataType::S32: case DataType::F32: - case DataType::QS32: return 4; default: ARM_COMPUTE_ERROR("Undefined element size for given data type"); @@ -527,7 +525,6 @@ inline DataType get_promoted_data_type(DataType dt) case DataType::U32: case DataType::S32: case DataType::F32: - case DataType::QS32: ARM_COMPUTE_ERROR("Unsupported data type promotions!"); default: ARM_COMPUTE_ERROR("Undefined data type!"); diff --git a/arm_compute/runtime/CL/functions/CLConvertFullyConnectedWeights.h b/arm_compute/runtime/CL/functions/CLConvertFullyConnectedWeights.h index ae0c9d6459..77e9d15e7f 100644 --- a/arm_compute/runtime/CL/functions/CLConvertFullyConnectedWeights.h +++ b/arm_compute/runtime/CL/functions/CLConvertFullyConnectedWeights.h @@ -37,7 +37,7 @@ class CLConvertFullyConnectedWeights : public ICLSimpleFunction public: /** Initialize the function. * - * @param[in] input Source weights tensor to convert. Must be 2 dimensional. Data types supported: U8/S8/QASYMM8/U16/S16/U32/S32/QS32/F16/F32. + * @param[in] input Source weights tensor to convert. Must be 2 dimensional. Data types supported: U8/S8/QASYMM8/U16/S16/U32/S32/F16/F32. * @param[out] output The converted weights tensor. Shape and Data Type: Same as @p input. * @param[in] original_input_shape Shape of the original input tensor (the one entering fully connected layer). Must be in NCHW format. * @param[in] data_layout The data layout the weights have been trained in. @@ -45,7 +45,7 @@ public: void configure(const ICLTensor *input, ICLTensor *output, const TensorShape &original_input_shape, DataLayout data_layout); /** Static function to check if given info will lead to a valid configuration of @ref CLConvertFullyConnectedWeights * - * @param[in] input Source weights tensor info to convert. Must be 2 dimensional. Data types supported: U8/S8/QASYMM8/U16/S16/U32/S32/QS32/F16/F32. + * @param[in] input Source weights tensor info to convert. Must be 2 dimensional. Data types supported: U8/S8/QASYMM8/U16/S16/U32/S32/F16/F32. * @param[in] output The converted weights tensor info. Shape and Data Type: Same as @p input. * @param[in] original_input_shape Shape of the original input tensor (the one entering fully connected layer). Must be in NCHW format. * @param[in] data_layout The data layout the weights have been trained in. diff --git a/arm_compute/runtime/NEON/functions/NEConvertFullyConnectedWeights.h b/arm_compute/runtime/NEON/functions/NEConvertFullyConnectedWeights.h index 3ec0390124..acbba28040 100644 --- a/arm_compute/runtime/NEON/functions/NEConvertFullyConnectedWeights.h +++ b/arm_compute/runtime/NEON/functions/NEConvertFullyConnectedWeights.h @@ -40,7 +40,7 @@ public: NEConvertFullyConnectedWeights(); /** Initialize the function. * - * @param[in] input Source weights tensor to convert. Must be 2 dimensional. Data types supported: U8/S8/QASYMM8/U16/S16/U32/S32/QS32/F16/F32. + * @param[in] input Source weights tensor to convert. Must be 2 dimensional. Data types supported: U8/S8/QASYMM8/U16/S16/U32/S32/F16/F32. * @param[out] output The converted weights tensor. Shape and Data Type: Same as @p input. * @param[in] original_input_shape Shape of the original input tensor (the one entering fully connected layer). Must be in NCHW format. * @param[in] data_layout The data layout the weights have been trained in. @@ -48,7 +48,7 @@ public: void configure(const ITensor *input, ITensor *output, const TensorShape &original_input_shape, DataLayout data_layout); /** Static function to check if given info will lead to a valid configuration of @ref NEConvertFullyConnectedWeights * - * @param[in] input Source weights tensor info to convert. Must be 2 dimensional. Data types supported: U8/S8/QASYMM8/U16/S16/U32/S32/QS32/F16/F32. + * @param[in] input Source weights tensor info to convert. Must be 2 dimensional. Data types supported: U8/S8/QASYMM8/U16/S16/U32/S32/F16/F32. * @param[in] output The converted weights tensor info. Shape and Data Type: Same as @p input. * @param[in] original_input_shape Shape of the original input tensor (the one entering fully connected layer). Must be in NCHW format. * @param[in] data_layout The data layout the weights have been trained in. -- cgit v1.2.1