diff options
| author | Chunosov <N.Chunosov@yandex.ru> | 2017-11-22 20:42:13 +0700 |
|---|---|---|
| committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:42:17 +0000 |
| commit | 5124be5d1caa70964d452cf9a8cc7c67df31fa9d (patch) | |
| tree | 77d74963e9c3f52050cbc264a692133395182e98 | |
| parent | 9873ea3f1ea238ba7abfb635807614517c52be4b (diff) | |
| download | ComputeLibrary-5124be5d1caa70964d452cf9a8cc7c67df31fa9d.tar.gz | |
COMPMID-661: Convolution quantized (#32)
Change-Id: Id69df4ce98d1d89bdf9c9aa5c4d909659909b30f
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/110456
Tested-by: BSG Visual Compute Jenkins server to access repositories on http://mpd-gerrit.cambridge.arm.com <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
27 files changed, 521 insertions, 200 deletions
diff --git a/arm_compute/core/CL/kernels/CLWeightsReshapeKernel.h b/arm_compute/core/CL/kernels/CLWeightsReshapeKernel.h index ec8940ef7e..6c84ded49e 100644 --- a/arm_compute/core/CL/kernels/CLWeightsReshapeKernel.h +++ b/arm_compute/core/CL/kernels/CLWeightsReshapeKernel.h @@ -50,6 +50,7 @@ public: * and 5D tensor with dimensions [kernel_x, kernel_y, IFM, OFM, num_patches] if unshared. Data types supported: QS8/QS16/QASYMM8/F16/F32 * @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 + * @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. Data types supported: Same as @p input */ void configure(const ICLTensor *input, const ICLTensor *biases, ICLTensor *output); diff --git a/arm_compute/core/Types.h b/arm_compute/core/Types.h index c77f1d4157..beaec143ef 100644 --- a/arm_compute/core/Types.h +++ b/arm_compute/core/Types.h @@ -827,6 +827,58 @@ private: const unsigned int _num_kernels; }; +/** GEMM Information class. This class stores the necessary information to compute GEMM functions */ +class GEMMInfo +{ +public: + /** Default constructor */ + GEMMInfo() + : _is_a_reshaped(false), _is_b_reshaped(false), _reshape_b_only_on_first_run(false) + { + } + /** Constructor + * + * @param[in] is_a_reshaped True if the matrix A has been reshaped + * @param[in] is_b_reshaped True if the matrix B has been reshaped + * @param[in] reshape_b_only_on_first_run Reshape matrix B only for the first run + */ + GEMMInfo(bool is_a_reshaped, bool is_b_reshaped, bool reshape_b_only_on_first_run) + : _is_a_reshaped(is_a_reshaped), _is_b_reshaped(is_b_reshaped), _reshape_b_only_on_first_run(reshape_b_only_on_first_run) + { + } + /** Flag which specifies if the matrix A has been reshaped + * + * @return True if the matrix A has been reshaped + */ + bool is_a_reshaped() const + { + return _is_a_reshaped; + }; + /** Flag which specifies if the matrix B has been reshaped + * + * @return True if the matrix B has been reshaped + */ + bool is_b_reshaped() const + { + return _is_b_reshaped; + }; + /** Flag which specifies if the reshape of matrix B should executed only for the first + * + * @note This flag could be set to TRUE when GEMM is used to accelerate convolution layer + * + * @return True if the reshaped of matrix B happens only for the first run + */ + bool reshape_b_only_on_first_run() const + { + return _reshape_b_only_on_first_run; + }; + +private: + const bool _is_a_reshaped; + const bool _is_b_reshaped; + const bool _reshape_b_only_on_first_run; +}; + /** IO formatting information class*/ struct IOFormatInfo { diff --git a/arm_compute/runtime/CL/functions/CLConvolutionLayer.h b/arm_compute/runtime/CL/functions/CLConvolutionLayer.h index cd1ea70a23..74c124421a 100644 --- a/arm_compute/runtime/CL/functions/CLConvolutionLayer.h +++ b/arm_compute/runtime/CL/functions/CLConvolutionLayer.h @@ -36,6 +36,8 @@ #include "arm_compute/core/Types.h" #include "arm_compute/runtime/CL/CLMemoryGroup.h" #include "arm_compute/runtime/CL/CLTensor.h" +#include "arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h" +#include "arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h" #include "arm_compute/runtime/IMemoryManager.h" #include <memory> @@ -55,7 +57,8 @@ public: CLConvolutionLayerReshapeWeights(std::shared_ptr<IMemoryManager> memory_manager = nullptr); /** 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: QS8/QS16/F16/F32. + * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. + * Data type supported: QS8/QASYMM8/QS16/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] transpose1xW True if the weights are to undergo a 1xW transposition after reshaping (in case of GEMM operation), false otherwise. @@ -79,7 +82,8 @@ private: * -# @ref CLGEMMTranspose1xWKernel (executed only once for each configuration) * -# @ref CLIm2ColKernel * -# @ref CLGEMMInterleave4x4Kernel - * -# @ref CLGEMMMatrixMultiplyKernel + * -# @ref CLGEMMMatrixMultiplyKernel or @ref CLGEMMLowpMatrixMultiplyCore (if quantized asymmetric) + * -# @ref CLGEMMLowpQuantizeDownInt32ToUint8Scale (if quantized asymmetric) * -# @ref CLCol2ImKernel */ class CLConvolutionLayer : public IFunction @@ -91,9 +95,10 @@ public: * * @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: QS8/QS16/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] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported:Same as @p input. + * Data types supported: QS8/QASYMM8/QS16/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] 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. * Data types supported: Same as @p input. * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. @@ -106,20 +111,37 @@ public: void run() override; private: - CLMemoryGroup _memory_group; - CLConvolutionLayerReshapeWeights _reshape_weights; - CLIm2ColKernel _input_im2col_kernel; - CLGEMMInterleave4x4Kernel _input_interleave_kernel; - CLGEMMMatrixMultiplyKernel _mm_kernel; - CLCol2ImKernel _output_col2im_kernel; - CLTensor _input_im2col_reshaped; - CLTensor _input_interleaved_reshaped; - CLTensor _weights_reshaped; - CLTensor _weights_transposed; - CLTensor _gemm_output; - bool _has_bias; - bool _is_fully_connected_convolution; - bool _are_weights_reshaped; + /** Configures the appropriate matrix multiply routine + * + * @param input Input tensor. Data types supported: QS8/QASYMM8/QS16/F16/F32. + * @param weights Weights tensor. Data type supported: Same as @p input. + * @param output Output tensor. Data types supported: Same as @p input, + * except for input of QASYMM8 type where output should be of S32 type. + * @param is_interleaved_transposed Flag that signals if matrix is interleaved transposed + */ + void configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, bool is_interleaved_transposed = true); + +private: + CLMemoryGroup _memory_group; + CLConvolutionLayerReshapeWeights _reshape_weights; + CLIm2ColKernel _input_im2col_kernel; + CLGEMMInterleave4x4Kernel _input_interleave_kernel; + CLGEMMMatrixMultiplyKernel _mm_kernel; + CLGEMMLowpMatrixMultiplyCore _mm_gemmlowp; + CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint _gemmlowp_output_stage; + CLCol2ImKernel _output_col2im_kernel; + + CLTensor _input_im2col_reshaped; + CLTensor _input_interleaved_reshaped; + CLTensor _weights_reshaped; + CLTensor _weights_transposed; + CLTensor _gemm_output; + CLTensor _tmp_output; + + bool _append_bias; + bool _is_fully_connected_convolution; + bool _are_weights_reshaped; + bool _is_quantized; }; } #endif /* __ARM_COMPUTE_CLCONVOLUTIONLAYER_H__ */ diff --git a/arm_compute/runtime/CL/functions/CLGEMMInterleave4x4.h b/arm_compute/runtime/CL/functions/CLGEMMInterleave4x4.h index 04f55c1ee4..ae05b0fd9c 100644 --- a/arm_compute/runtime/CL/functions/CLGEMMInterleave4x4.h +++ b/arm_compute/runtime/CL/functions/CLGEMMInterleave4x4.h @@ -40,11 +40,11 @@ class CLGEMMInterleave4x4 : public ICLSimpleFunction public: /** Initialise the kernel's inputs, output * - * @param[in] input First input tensor. Data types supported: U8/S8/QS8/QASYMM8/U16/S16/F16/U32/S32/F32 + * @param[in] input First input tensor. Data types supported: U8/S8/QS8/QASYMM8/QS16/U16/S16/F16/U32/S32/F32 * @param[out] output Output tensor. Data type supported: same as @p input */ void configure(const ICLTensor *input, ICLTensor *output); }; } -#endif /* __ARM_COMPUTE_CLGEMMINTERLEAVE4X4_H__ */
\ No newline at end of file +#endif /* __ARM_COMPUTE_CLGEMMINTERLEAVE4X4_H__ */ diff --git a/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h b/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h index 9944afeac7..e316144548 100644 --- a/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h +++ b/arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h @@ -62,11 +62,13 @@ public: * -# 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. * - * @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] 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] 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); + void configure(const ICLTensor *a, const ICLTensor *b, ICLTensor *output, const GEMMInfo &gemm_info = GEMMInfo()); // Inherited methods overridden: void run() override; @@ -86,6 +88,8 @@ private: int32_t _a_offset; int32_t _b_offset; bool _is_interleaved_transposed; + bool _is_first_run; + bool _reshape_b_only_on_first_run; }; } #endif /*__ARM_COMPUTE_CLGEMMLOWPMATRIXMULTIPLYCORE_H__ */ diff --git a/arm_compute/runtime/CL/functions/CLGEMMTranspose1xW.h b/arm_compute/runtime/CL/functions/CLGEMMTranspose1xW.h index 3d02aa931e..ae56548c27 100644 --- a/arm_compute/runtime/CL/functions/CLGEMMTranspose1xW.h +++ b/arm_compute/runtime/CL/functions/CLGEMMTranspose1xW.h @@ -38,7 +38,7 @@ class CLGEMMTranspose1xW : public ICLSimpleFunction public: /** Initialise the kernel's inputs, output * - * @param[in] input First input tensor. Data type supported: U8/S8/QS8/QASYMM8/U16/S16/F16/U32/S32/F32/ + * @param[in] input First input tensor. Data type supported: U8/S8/QS8/QASYMM8/QS16/U16/S16/F16/U32/S32/F32 * @param[out] output Output tensor. Data type supported: same as @p input */ void configure(const ICLTensor *input, ICLTensor *output); diff --git a/arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h b/arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h index cc513ade10..a4f35117c0 100644 --- a/arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h +++ b/arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h @@ -66,20 +66,24 @@ public: * -# 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. * - * @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] 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] 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); + void configure(const ITensor *a, const ITensor *b, 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[out] output Output tensor. Data type supported: Data type supported: S32 + * @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] 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 an error status */ - static Error validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *output); + static Error validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *output, const GEMMInfo &gemm_info = GEMMInfo()); // Inherited methods overridden: void run() override; diff --git a/src/core/CL/cl_kernels/convolution_layer.cl b/src/core/CL/cl_kernels/convolution_layer.cl index ce0849bf7a..77b9b64945 100644 --- a/src/core/CL/cl_kernels/convolution_layer.cl +++ b/src/core/CL/cl_kernels/convolution_layer.cl @@ -97,13 +97,14 @@ __kernel void reshape_to_columns( } } -#if defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP) && defined(PAD_RIGHT) && defined(PAD_BOTTOM) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(KERNEL_DEPTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) +#if defined(CONVOLVED_WIDTH) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP) && defined(PAD_RIGHT) && defined(PAD_BOTTOM) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(KERNEL_DEPTH) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(PAD_VALUE) /** This kernel performs a reshaping of the input tensor to a tensor used to perform convolution using GEMM. * * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float + * @note The value to use for the paddings must be passed at compile time using -DPAD_VALUE: e.g. -DPAD_VALUE=0 * @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row. * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QS16/F16/F32 + * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QASYMM8/QS16/F16/F32 * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) @@ -149,14 +150,10 @@ __kernel void im2col_generic( { #if PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0 *output_ptr = *((__global DATA_TYPE *)(input_ptr + x * src_stride_x + y * src_stride_y)); -#else // PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0 +#else // PAD_LEFT == 0 && PAD_TOP == 0 && PAD_RIGHT == 0 && PAD_BOTTOM == 0 if(x < 0 || x >= SRC_WIDTH || y < 0 || y >= SRC_HEIGHT) { -#if defined(OFFSET) - *output_ptr = OFFSET; -#else /* OFFSET */ - *output_ptr = 0; -#endif /* OFFSET */ + *output_ptr = PAD_VALUE; } else { @@ -183,7 +180,7 @@ __kernel void im2col_generic( * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float * @note In case biases will be added to the convolution -DHAS_BIAS has to be passed to append the final matrix with 1 in each row. * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QS16/F16/F32 + * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QASYMM8/QS16/F16/F32 * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) @@ -252,7 +249,7 @@ __kernel void im2col_kernel3x3_padx0_pady0( * * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QS16/F16/F32 + * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QASYMM8/QS16/F16/F32 * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) @@ -291,7 +288,7 @@ __kernel void col2im( * @note Datatype should be given as a preprocessor argument using -DDATA_TYPE=type. e.g. -DDATA_TYPE=float * @note In case biases will be added in late stage, -DHAS_BIAS has to be passed to append the final matrix with 1 in each row. * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/F16/F32 + * @param[in] src_ptr Pointer to the source tensor. Supported data types: QS8/QASYMM8/QS16/F16/F32 * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) diff --git a/src/core/CL/cl_kernels/gemmlowp.cl b/src/core/CL/cl_kernels/gemmlowp.cl index a8e8e600fe..a92881320e 100644 --- a/src/core/CL/cl_kernels/gemmlowp.cl +++ b/src/core/CL/cl_kernels/gemmlowp.cl @@ -380,6 +380,7 @@ __kernel void gemmlowp_matrix_b_reduction(TENSOR3D_DECLARATION(src), * @attention The k_offset = a_offset * b_offset * k (where k is the number of matrix A columns) needs to be passed at compile time using -DK_OFFSET (i.e. -DK_OFFSET=1200) * @note In case the offset contribution due to a_offset is required, a_offset needs to be passed at compile time using -DA_OFFSET (i.e. -DA_OFFSET=1) * @note In case the offset contribution due to b_offset is required, b_offset needs to be passed at compile time using -DB_OFFSET (i.e. -DB_OFFSET=6) + * @note In case sum_col has batches, -DSUM_COL_HAS_BATCHES must be passed at compile time. Usually if gemmlowp is used to accelerate convolution layer, sum_col will not have batches * * The final result is: * @@ -429,7 +430,12 @@ __kernel void gemmlowp_offset_contribution(TENSOR3D_DECLARATION(mm_result) Image sum_col = CONVERT_TO_IMAGE_STRUCT(sum_col); // Compute the offset contribution due to A_OFFSET +#if defined(SUM_COL_HAS_BATCHES) + a_offset_s32 = vload16(0, (__global int *)(sum_col.ptr + get_global_id(2) * sum_col_stride_y)); +#else // defined(MATRIX_B_HAS_BATCHES) a_offset_s32 = vload16(0, (__global int *)(sum_col.ptr)); +#endif // defined(MATRIX_B_HAS_BATCHES) + a_offset_s32 *= (int16)A_OFFSET; #endif // defined(A_OFFSET) @@ -615,4 +621,4 @@ __kernel void gemmlowp_output_stage_quantize_down_fixedpoint(TENSOR3D_DECLARATIO // Store the result vstore16(res, 0, dst.ptr); } -#endif // defined(RESULT_OFFSET_AFTER_SHIFT) && defined(RESULT_FIXEDPOINT_MULTIPLIER) && defined(RESULT_SHIFT)
\ No newline at end of file +#endif // defined(RESULT_OFFSET_AFTER_SHIFT) && defined(RESULT_FIXEDPOINT_MULTIPLIER) && defined(RESULT_SHIFT) diff --git a/src/core/CL/kernels/CLCol2ImKernel.cpp b/src/core/CL/kernels/CLCol2ImKernel.cpp index f2886c569a..499e1e8fe0 100644 --- a/src/core/CL/kernels/CLCol2ImKernel.cpp +++ b/src/core/CL/kernels/CLCol2ImKernel.cpp @@ -43,7 +43,7 @@ CLCol2ImKernel::CLCol2ImKernel() void CLCol2ImKernel::configure(const ICLTensor *input, ICLTensor *output, std::pair<unsigned int, unsigned int> convolved_dims) { - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::QASYMM8, DataType::F16, DataType::F32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32); ARM_COMPUTE_ERROR_ON_NULLPTR(output); TensorShape output_shape = input->info()->tensor_shape(); @@ -52,7 +52,7 @@ void CLCol2ImKernel::configure(const ICLTensor *input, ICLTensor *output, std::p output_shape.set(2, input->info()->tensor_shape()[0]); // Output auto inizialitation if not yet initialized - auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position(), input->info()->quantization_info()); + auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape)); ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); @@ -62,15 +62,15 @@ void CLCol2ImKernel::configure(const ICLTensor *input, ICLTensor *output, std::p _output = output; _convolved_dims = convolved_dims; + const DataType data_type = input->info()->data_type(); + // Create kernel - std::set<std::string> build_opts = { ("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())) }; - build_opts.emplace("-DWIDTH_OUTPUT=" + support::cpp11::to_string(_convolved_dims.first)); - if(is_data_type_fixed_point(input->info()->data_type())) - { - build_opts.emplace("-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position())); - } + CLBuildOptions build_opts; + build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)); + build_opts.add_option("-DWIDTH_OUTPUT=" + support::cpp11::to_string(_convolved_dims.first)); + build_opts.add_option_if(is_data_type_fixed_point(data_type), "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position())); - _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("col2im", build_opts)); + _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("col2im", build_opts.options())); // Configure the local work size for Bifrost with a value obtained // via exhaustive autotuning over 30 representative tensor shapes. diff --git a/src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.cpp b/src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.cpp index d49aed3171..2877a74be8 100644 --- a/src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.cpp +++ b/src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.cpp @@ -63,6 +63,7 @@ void CLGEMMLowpOffsetContributionKernel::configure(ICLTensor *mm_result, const I ARM_COMPUTE_ERROR_ON(vector_sum_col->info()->dimension(0) != mm_result->info()->dimension(0)); build_opts.add_option("-DA_OFFSET=" + support::cpp11::to_string(a_offset)); + build_opts.add_option_if(vector_sum_col->info()->tensor_shape().num_dimensions() > 1, "-DSUM_COL_HAS_BATCHES"); } // If b_offset == 0, vector_sum_row can be a nullptr diff --git a/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp index 37a430e8b0..62288cb771 100644 --- a/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp +++ b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp @@ -41,8 +41,6 @@ namespace Error validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); ARM_COMPUTE_RETURN_ERROR_ON(max > 255); ARM_COMPUTE_RETURN_ERROR_ON(min < 0 || min > max); @@ -53,6 +51,13 @@ Error validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, cons ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1); ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0)); } + + if(output->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); + } + return Error{}; } @@ -64,11 +69,17 @@ std::pair<Error, Window> validate_and_configure_window(ITensorInfo *input, ITens Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration)); AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration); - AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration); bool window_changed = update_window_and_padding(win, - input_access, - output_result_access); + input_access); + + if(output->total_size() != 0) + { + AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration); + window_changed = window_changed || update_window_and_padding(win, output_result_access); + + output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); + } if(bias != nullptr) { @@ -76,8 +87,6 @@ std::pair<Error, Window> validate_and_configure_window(ITensorInfo *input, ITens window_changed = window_changed || update_window_and_padding(win, bias_access); } - output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); - Error err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Error{}; return std::make_pair(err, win); } @@ -93,6 +102,7 @@ CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::CLGEMMLowpQuantizeDow Error CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) { + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max)); ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), (bias != nullptr) ? bias->clone().get() : nullptr, diff --git a/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp index 343c31c73d..5d4b25c142 100644 --- a/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp +++ b/src/core/CL/kernels/CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp @@ -41,8 +41,6 @@ namespace Error validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); ARM_COMPUTE_RETURN_ERROR_ON(max > 255); ARM_COMPUTE_RETURN_ERROR_ON(min < 0 || min > max); @@ -53,6 +51,13 @@ Error validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, cons ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1); ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0)); } + + if(output->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); + } + return Error{}; } @@ -64,11 +69,17 @@ std::pair<Error, Window> validate_and_configure_window(ITensorInfo *input, ITens Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration)); AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration); - AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration); bool window_changed = update_window_and_padding(win, - input_access, - output_result_access); + input_access); + + if(output->total_size() != 0) + { + AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration); + window_changed = window_changed || update_window_and_padding(win, output_result_access); + + output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); + } if(bias != nullptr) { @@ -76,8 +87,6 @@ std::pair<Error, Window> validate_and_configure_window(ITensorInfo *input, ITens window_changed = window_changed || update_window_and_padding(win, bias_access); } - output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); - Error err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Error{}; return std::make_pair(err, win); } @@ -92,6 +101,7 @@ CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::CLGEMMLowpQuantizeDownInt32ToUint } Error CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) { + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max)); ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), (bias != nullptr) ? bias->clone().get() : nullptr, @@ -163,4 +173,4 @@ void CLGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::run(const Window &window, cl enqueue(queue, *this, slice); } while(collapsed.slide_window_slice_3D(slice)); -}
\ No newline at end of file +} diff --git a/src/core/CL/kernels/CLGEMMLowpReductionKernel.cpp b/src/core/CL/kernels/CLGEMMLowpReductionKernel.cpp index 6f410d3b14..bcf04b0982 100644 --- a/src/core/CL/kernels/CLGEMMLowpReductionKernel.cpp +++ b/src/core/CL/kernels/CLGEMMLowpReductionKernel.cpp @@ -126,7 +126,7 @@ void CLGEMMLowpMatrixBReductionKernel::configure(const ICLTensor *mtx_b, ICLTens // Configure kernel window Window win = calculate_max_window(*vector_sum_col->info(), Steps(num_elems_processed_per_iteration)); - AccessWindowStatic input_access(_input->info(), 0, 0, ceil_to_multiple(_input->info()->dimension(0), 16), _input->info()->dimension(1)); + AccessWindowStatic input_access(_input->info(), 0, 0, ceil_to_multiple(_input->info()->dimension(0), num_elems_processed_per_iteration), _input->info()->dimension(1)); AccessWindowHorizontal output_access(_output->info(), 0, num_elems_processed_per_iteration); update_window_and_padding(win, diff --git a/src/core/CL/kernels/CLIm2ColKernel.cpp b/src/core/CL/kernels/CLIm2ColKernel.cpp index f7cf9a3cb4..6514d6cf91 100644 --- a/src/core/CL/kernels/CLIm2ColKernel.cpp +++ b/src/core/CL/kernels/CLIm2ColKernel.cpp @@ -61,7 +61,6 @@ void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const build_opts.add_option(("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type))); build_opts.add_option_if(has_bias, "-DHAS_BIAS"); build_opts.add_option_if(is_data_type_fixed_point(data_type), "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position())); - build_opts.add_option_if(is_data_type_quantized_asymmetric(data_type), "-DOFFSET=" + support::cpp11::to_string(input->info()->quantization_info().offset)); int stride_x = 0; int stride_y = 0; @@ -95,6 +94,7 @@ void CLIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const build_opts.add_option("-DPAD_BOTTOM=" + support::cpp11::to_string(conv_info.pad_bottom())); build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(input->info()->dimension(0))); build_opts.add_option("-DSRC_HEIGHT=" + support::cpp11::to_string(input->info()->dimension(1))); + build_opts.add_option_if_else(is_data_type_quantized(data_type), "-DPAD_VALUE=" + support::cpp11::to_string(input->info()->quantization_info().offset), "-DPAD_VALUE=0"); if(kernel_dims.width == 3 && kernel_dims.height == 3 && !conv_info.has_padding()) { diff --git a/src/core/CL/kernels/CLWeightsReshapeKernel.cpp b/src/core/CL/kernels/CLWeightsReshapeKernel.cpp index be633b2418..3a9a32e58f 100644 --- a/src/core/CL/kernels/CLWeightsReshapeKernel.cpp +++ b/src/core/CL/kernels/CLWeightsReshapeKernel.cpp @@ -41,12 +41,12 @@ CLWeightsReshapeKernel::CLWeightsReshapeKernel() void CLWeightsReshapeKernel::configure(const ICLTensor *input, const ICLTensor *biases, ICLTensor *output) { - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::QASYMM8, DataType::F16, DataType::F32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32); ARM_COMPUTE_ERROR_ON_NULLPTR(output); - const DataType dt = input->info()->data_type(); - const int fixed_point_position = input->info()->fixed_point_position(); + const DataType data_type = input->info()->data_type(); + // Calculate output shape TensorShape output_shape{ input->info()->tensor_shape() }; output_shape.collapse(3); const size_t tmp_dim = output_shape[0]; @@ -54,7 +54,7 @@ void CLWeightsReshapeKernel::configure(const ICLTensor *input, const ICLTensor * output_shape.set(1, tmp_dim + (biases != nullptr ? 1 : 0)); // Output tensor auto inizialitation if not yet initialized - auto_init_if_empty(*output->info(), output_shape, 1, dt, fixed_point_position, input->info()->quantization_info()); + auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape)); ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); @@ -62,6 +62,7 @@ void CLWeightsReshapeKernel::configure(const ICLTensor *input, const ICLTensor * if(biases != nullptr) { + ARM_COMPUTE_ERROR_ON(is_data_type_quantized_asymmetric(data_type)); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases); ARM_COMPUTE_ERROR_ON((input->info()->num_dimensions() == 4) && (biases->info()->num_dimensions() != 1)); @@ -75,16 +76,13 @@ void CLWeightsReshapeKernel::configure(const ICLTensor *input, const ICLTensor * _input = input; // Create build options - std::set<std::string> build_opts; - build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()))); - build_opts.emplace(((biases != nullptr) ? "-DHAS_BIAS" : "")); - if(is_data_type_fixed_point(input->info()->data_type())) - { - build_opts.emplace("-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position())); - } + CLBuildOptions build_opts; + build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)); + build_opts.add_option_if(biases != nullptr, "-DHAS_BIAS"); + build_opts.add_option_if(is_data_type_fixed_point(data_type), "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position())); // Create kernel - _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("reshape_to_columns", build_opts)); + _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("reshape_to_columns", build_opts.options())); // Set static arguments unsigned int idx = num_arguments_per_3D_tensor() + num_arguments_per_2D_tensor(); diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp index 102d08c7ba..c6f7ca4fb2 100644 --- a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp +++ b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel.cpp @@ -44,8 +44,6 @@ namespace Error validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); ARM_COMPUTE_RETURN_ERROR_ON(max > 255); ARM_COMPUTE_RETURN_ERROR_ON(min < 0 || min > max); @@ -56,6 +54,13 @@ Error validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, cons ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1); ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0)); } + + if(output->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); + } + return Error{}; } @@ -67,11 +72,17 @@ std::pair<Error, Window> validate_and_configure_window(ITensorInfo *input, ITens Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration)); AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration); - AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration); bool window_changed = update_window_and_padding(win, - input_access, - output_result_access); + input_access); + + if(output->total_size() != 0) + { + AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration); + window_changed = window_changed || update_window_and_padding(win, output_result_access); + + output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); + } if(bias != nullptr) { @@ -79,8 +90,6 @@ std::pair<Error, Window> validate_and_configure_window(ITensorInfo *input, ITens window_changed = window_changed || update_window_and_padding(win, bias_access); } - output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); - Error err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Error{}; return std::make_pair(err, win); } @@ -255,6 +264,7 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::configure(const Error NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPointKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) { + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max)); ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), (bias != nullptr) ? bias->clone().get() : nullptr, diff --git a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp index edd6a9fd80..68b81d8a79 100644 --- a/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp +++ b/src/core/NEON/kernels/NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel.cpp @@ -43,8 +43,6 @@ namespace Error validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); ARM_COMPUTE_RETURN_ERROR_ON(max > 255); ARM_COMPUTE_RETURN_ERROR_ON(min < 0 || min > max); @@ -55,6 +53,13 @@ Error validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, cons ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1); ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(0) != bias->dimension(0)); } + + if(output->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output); + } + return Error{}; } @@ -66,11 +71,17 @@ std::pair<Error, Window> validate_and_configure_window(ITensorInfo *input, ITens Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration)); AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration); - AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration); bool window_changed = update_window_and_padding(win, - input_access, - output_result_access); + input_access); + + if(output->total_size() != 0) + { + AccessWindowHorizontal output_result_access(output, 0, num_elems_processed_per_iteration); + window_changed = window_changed || update_window_and_padding(win, output_result_access); + + output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); + } if(bias != nullptr) { @@ -78,8 +89,6 @@ std::pair<Error, Window> validate_and_configure_window(ITensorInfo *input, ITens window_changed = window_changed || update_window_and_padding(win, bias_access); } - output_result_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); - Error err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Error{}; return std::make_pair(err, win); } @@ -262,6 +271,7 @@ void NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::configure(const ITensor *inp Error NEGEMMLowpQuantizeDownInt32ToUint8ScaleKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, int min, int max) { + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, bias, output, min, max)); ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), (bias != nullptr) ? bias->clone().get() : nullptr, diff --git a/src/runtime/CL/functions/CLConvolutionLayer.cpp b/src/runtime/CL/functions/CLConvolutionLayer.cpp index 8d45416b30..66548d19b2 100644 --- a/src/runtime/CL/functions/CLConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLConvolutionLayer.cpp @@ -27,6 +27,7 @@ #include "arm_compute/core/Size2D.h" #include "arm_compute/core/Utils.h" #include "arm_compute/core/Validate.h" +#include "arm_compute/core/utils/quantization/AsymmHelpers.h" #include "arm_compute/runtime/CL/CLScheduler.h" #include <cmath> @@ -42,19 +43,22 @@ CLConvolutionLayerReshapeWeights::CLConvolutionLayerReshapeWeights(std::shared_p void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, bool transpose1xW) { - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(weights, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, output); ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(weights, output); ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4); if(biases != nullptr) { + ARM_COMPUTE_ERROR_ON(is_data_type_quantized_asymmetric(weights->info()->data_type())); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(3)); ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1); } - const bool _has_bias = (biases != nullptr); + const bool append_biases = (biases != nullptr) && !is_data_type_quantized_asymmetric(weights->info()->data_type()); + const unsigned bias_element = (append_biases) ? 1 : 0; + const ICLTensor *biases_to_use = (append_biases) ? biases : nullptr; _transpose1xW = transpose1xW; @@ -62,7 +66,7 @@ void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const { // Create tensor to store the reshaped weights const unsigned int mat_weights_cols = weights->info()->dimension(3); - const unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + (_has_bias ? 1 : 0); + const unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + bias_element; TensorShape shape_wr(mat_weights_cols, mat_weights_rows); const DataType dt = weights->info()->data_type(); const int fixed_point_position = weights->info()->fixed_point_position(); @@ -70,13 +74,13 @@ void CLConvolutionLayerReshapeWeights::configure(const ICLTensor *weights, const _weights_reshaped.allocator()->init(info_wr); _memory_group.manage(&_weights_reshaped); - _weights_reshape_kernel.configure(weights, biases, &_weights_reshaped); + _weights_reshape_kernel.configure(weights, biases_to_use, &_weights_reshaped); _weights_transposed_kernel.configure(&_weights_reshaped, output); _weights_reshaped.allocator()->allocate(); } else { - _weights_reshape_kernel.configure(weights, biases, output); + _weights_reshape_kernel.configure(weights, biases_to_use, output); } } @@ -95,36 +99,73 @@ void CLConvolutionLayerReshapeWeights::run() } CLConvolutionLayer::CLConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) - : _memory_group(std::move(memory_manager)), _reshape_weights(), _input_im2col_kernel(), _input_interleave_kernel(), _mm_kernel(), _output_col2im_kernel(), _input_im2col_reshaped(), - _input_interleaved_reshaped(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _has_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false) + : _memory_group(memory_manager), _reshape_weights(), _input_im2col_kernel(), _input_interleave_kernel(), _mm_kernel(), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(), _output_col2im_kernel(), + _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _weights_transposed(), _gemm_output(), _tmp_output(), _append_bias(false), _is_fully_connected_convolution(false), + _are_weights_reshaped(false), _is_quantized(false) { } +void CLConvolutionLayer::configure_mm(const ICLTensor *input, const ICLTensor *weights, ICLTensor *output, bool is_interleaved_transposed) +{ + if(_is_quantized) + { + // Since we need negative offsets for computing convolution, we need to change QuantizationInfo() + // Extract and negate input and weights offset + const QuantizationInfo input_quantization_info = input->info()->quantization_info(); + const QuantizationInfo weights_quantization_info = weights->info()->quantization_info(); + + input->info()->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset)); + weights->info()->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset)); + + _mm_gemmlowp.configure(input, weights, output, GEMMInfo(false, false, true /* Reshape weights only for the first run*/)); + + // Revert back QuantizatioInfo as input and weights could be used in other convolution layers + input->info()->set_quantization_info(input_quantization_info); + weights->info()->set_quantization_info(weights_quantization_info); + } + else + { + _mm_kernel.configure(input, weights, output, 1.f, is_interleaved_transposed); + } +} + void CLConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info) { - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32); + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights); ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && weights->info()->dimension(2) != input->info()->dimension(2)); ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4); + ARM_COMPUTE_ERROR_ON(weights_info.are_reshaped() && is_data_type_quantized_asymmetric(input->info()->data_type())); + + _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); if(biases != nullptr) { - ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); + if(_is_quantized) + { + ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); + } + else + { + ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases); + } ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, biases); ARM_COMPUTE_ERROR_ON(!weights_info.are_reshaped() && biases->info()->dimension(0) != weights->info()->dimension(3)); ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1); } - const DataType dt = input->info()->data_type(); - const int fixed_point_position = input->info()->fixed_point_position(); + const DataType dt = input->info()->data_type(); // Set the GPU target for matrix multiply _mm_kernel.set_target(CLScheduler::get().target()); - _has_bias = (biases != nullptr); + _append_bias = (biases != nullptr) && (!_is_quantized); _are_weights_reshaped = weights_info.are_reshaped(); + const unsigned bias_element = (_append_bias) ? 1 : 0; + const ICLTensor *biases_to_use = (_append_bias) ? biases : nullptr; + // Get parameters from conv_info unsigned int stride_x = 0; unsigned int stride_y = 0; @@ -141,36 +182,36 @@ void CLConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weig // Check if its a "fully connected" convolution _is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1)); + const bool run_interleaved = (!_is_fully_connected_convolution && !_is_quantized); unsigned int mat_weights_cols = weights->info()->dimension(3); - unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + (_has_bias ? 1 : 0); + unsigned int mat_weights_rows = weights->info()->dimension(0) * weights->info()->dimension(1) * weights->info()->dimension(2) + bias_element; // Reshape weights if needed if(_are_weights_reshaped) { mat_weights_cols = weights_info.num_kernels(); const unsigned int quarter_reshaped_cols = weights->info()->dimension(0) / 4; - mat_weights_rows = (_has_bias ? 1 + quarter_reshaped_cols : quarter_reshaped_cols); + mat_weights_rows = quarter_reshaped_cols + bias_element; } else { - if(_is_fully_connected_convolution) + if(_is_fully_connected_convolution || _is_quantized) { // Create tensor to store the reshaped weights TensorShape shape_wr(mat_weights_cols, mat_weights_rows); - TensorInfo info_wr(shape_wr, 1, dt, fixed_point_position); - _weights_reshaped.allocator()->init(info_wr); - _reshape_weights.configure(weights, biases, &_weights_reshaped, false /* 1xW transpose */); + _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_wr)); + _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, false /* 1xW transpose */); } else { // Create tensor to store transposed weights const float transpose_width = 16.0f / input->info()->element_size(); TensorShape shape_wt(mat_weights_rows * static_cast<unsigned int>(transpose_width), static_cast<unsigned int>(std::ceil(mat_weights_cols / transpose_width))); - TensorInfo info_wt(shape_wt, 1, dt, fixed_point_position); - _weights_reshaped.allocator()->init(info_wt); - _reshape_weights.configure(weights, biases, &_weights_reshaped, true /* 1xW transpose */); + _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_wt)); + _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, true /* 1xW transpose */); } + _weights_reshaped.info()->set_quantization_info(weights->info()->quantization_info()); weights = &_weights_reshaped; } @@ -181,16 +222,16 @@ void CLConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weig shape_im2col.set(0, mat_input_cols); shape_im2col.set(1, mat_input_rows); shape_im2col.set(2, 1); - _input_im2col_reshaped.allocator()->init(TensorInfo(shape_im2col, 1, dt, fixed_point_position)); + _input_im2col_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col)); _memory_group.manage(&_input_im2col_reshaped); // Create tensor (interleave) to prepare input tensor for GEMM - if(!_is_fully_connected_convolution) + if(run_interleaved) { TensorShape shape_interleaved = shape_im2col; shape_interleaved.set(0, shape_interleaved.x() * 4); shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f)); - _input_interleaved_reshaped.allocator()->init(TensorInfo(shape_interleaved, 1, dt, fixed_point_position)); + _input_interleaved_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_interleaved)); _memory_group.manage(&_input_interleaved_reshaped); } @@ -198,30 +239,51 @@ void CLConvolutionLayer::configure(const ICLTensor *input, const ICLTensor *weig TensorShape shape_gemm = _input_im2col_reshaped.info()->tensor_shape(); shape_gemm.set(0, mat_weights_cols); shape_gemm.set(1, mat_input_rows); - _gemm_output.allocator()->init(TensorInfo(shape_gemm, 1, dt, fixed_point_position)); + const DataType gemm_data_type = _is_quantized ? DataType::S32 : dt; + // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input. + TensorInfo info_gemm(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_gemm).set_data_type(gemm_data_type).set_quantization_info( + output->info()->quantization_info())); + _gemm_output.allocator()->init(info_gemm); _memory_group.manage(&_gemm_output); // Configure kernels - _input_im2col_kernel.set_target(CLScheduler::get().target()); - _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _has_bias); + _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias); // Configure matrix multiply - if(_is_fully_connected_convolution) + if(run_interleaved) { - // The matrix A and Matrix B have not been reshaped - _mm_kernel.configure(&_input_im2col_reshaped, weights, &_gemm_output, 1.0f, false); + _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped); + configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output); + _input_interleaved_reshaped.allocator()->allocate(); } else { - _input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped); - _mm_kernel.configure(&_input_interleaved_reshaped, weights, &_gemm_output, 1.0f); - _input_interleaved_reshaped.allocator()->allocate(); + configure_mm(&_input_im2col_reshaped, weights, &_gemm_output, false); } _input_im2col_reshaped.allocator()->allocate(); + + // Configure output stage for quantized case + if(_is_quantized) + { + float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output->info()->quantization_info().scale; + int output_multiplier, output_shift; + quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); + _gemmlowp_output_stage.configure(&_gemm_output, biases, &_tmp_output, output_multiplier, output_shift, output->info()->quantization_info().offset); + _gemm_output.allocator()->allocate(); + } + + // Configure Col2Im _output_col2im_kernel.set_target(CLScheduler::get().target()); - _output_col2im_kernel.configure(&_gemm_output, output, std::make_pair(conv_w, conv_h)); - _gemm_output.allocator()->allocate(); + _output_col2im_kernel.configure(_is_quantized ? &_tmp_output : &_gemm_output, output, std::make_pair(conv_w, conv_h)); + if(_is_quantized) + { + _tmp_output.allocator()->allocate(); + } + else + { + _gemm_output.allocator()->allocate(); + } ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) || (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one"); @@ -243,15 +305,30 @@ void CLConvolutionLayer::run() _memory_group.acquire(); - // Run input reshaping + // Run im2col CLScheduler::get().enqueue(_input_im2col_kernel); - if(!_is_fully_connected_convolution) + + if(!_is_fully_connected_convolution && !_is_quantized) { + // Run interleave4x4 CLScheduler::get().enqueue(_input_interleave_kernel); } // Runs matrix multiply on reshaped matrices - CLScheduler::get().enqueue(_mm_kernel); + if(_is_quantized) + { + _mm_gemmlowp.run(); + } + else + { + CLScheduler::get().enqueue(_mm_kernel); + } + + // Run output stage for quantized case + if(_is_quantized) + { + _gemmlowp_output_stage.run(); + } // Reshape output matrix CLScheduler::get().enqueue(_output_col2im_kernel, false); diff --git a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp index 6cc2f4bdb7..7fd81cdb94 100644 --- a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp +++ b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp @@ -50,13 +50,20 @@ void CLFullyConnectedLayer::configure_mm(const ICLTensor *input, const ICLTensor { if(_is_quantized) { + // Since we need negative offsets for computing convolution, we need to change QuantizationInfo() // Extract and negate input and weights offset - QuantizationInfo input_quantization_info = input->info()->quantization_info(); - QuantizationInfo weights_quantization_info = weights->info()->quantization_info(); + const QuantizationInfo input_quantization_info = input->info()->quantization_info(); + const QuantizationInfo weights_quantization_info = weights->info()->quantization_info(); + input->info()->set_quantization_info(QuantizationInfo(input_quantization_info.scale, -input_quantization_info.offset)); weights->info()->set_quantization_info(QuantizationInfo(weights_quantization_info.scale, -weights_quantization_info.offset)); + // Configure gemmlowp function _mm_gemmlowp.configure(input, weights, output); + + // Revert back QuantizatioInfo as input and weights could be used in other fully connected layers + input->info()->set_quantization_info(input_quantization_info); + weights->info()->set_quantization_info(weights_quantization_info); } else { diff --git a/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp index 5d2d13e243..5c6f5b4ed0 100644 --- a/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp +++ b/src/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.cpp @@ -35,11 +35,11 @@ using namespace arm_compute; CLGEMMLowpMatrixMultiplyCore::CLGEMMLowpMatrixMultiplyCore(std::shared_ptr<IMemoryManager> memory_manager) : _memory_group(std::move(memory_manager)), _mm_kernel(), _mtx_a_reshape_kernel(), _mtx_b_reshape_kernel(), _mtx_a_reduction_kernel(), _mtx_b_reduction_kernel(), _offset_contribution_kernel(), - _vector_sum_col(), _vector_sum_row(), _tmp_a(), _tmp_b(), _a_offset(0), _b_offset(0), _is_interleaved_transposed(true) + _vector_sum_col(), _vector_sum_row(), _tmp_a(), _tmp_b(), _a_offset(0), _b_offset(0), _is_interleaved_transposed(true), _is_first_run(true), _reshape_b_only_on_first_run(false) { } -void CLGEMMLowpMatrixMultiplyCore::configure(const ICLTensor *a, const ICLTensor *b, ICLTensor *output) +void CLGEMMLowpMatrixMultiplyCore::configure(const ICLTensor *a, const ICLTensor *b, ICLTensor *output, const GEMMInfo &gemm_info) { ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::QASYMM8); ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32); @@ -47,9 +47,12 @@ void CLGEMMLowpMatrixMultiplyCore::configure(const ICLTensor *a, const ICLTensor ARM_COMPUTE_ERROR_ON_MSG((a)->info()->dimension(0) != (b)->info()->dimension(1), "The product AB is defined only if the number of columns in A is equal to the number of rows in B"); ARM_COMPUTE_ERROR_ON_MSG((a)->info()->dimension(1) != (output)->info()->dimension(1), "The output matrix must have the same number of rows as the matrix A"); ARM_COMPUTE_ERROR_ON_MSG((b)->info()->dimension(0) != (output)->info()->dimension(0), "The output matrix must have the same number of columns as the matrix B"); + ARM_COMPUTE_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported"); + ARM_COMPUTE_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported"); - _a_offset = a->info()->quantization_info().offset; - _b_offset = b->info()->quantization_info().offset; + _reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run(); + _a_offset = a->info()->quantization_info().offset; + _b_offset = b->info()->quantization_info().offset; // If the input tensor has less than 16 rows, we run a special version of GEMMLowp without reshaping the input tensors _is_interleaved_transposed = a->info()->dimension(1) > 16; @@ -93,7 +96,8 @@ void CLGEMMLowpMatrixMultiplyCore::configure(const ICLTensor *a, const ICLTensor if(_a_offset != 0) { TensorShape shape_vector_sum_col = b->info()->tensor_shape(); - if(b->info()->num_dimensions() > 1) + + if(shape_vector_sum_col.num_dimensions() > 1) { shape_vector_sum_col.remove_dimension(1); } @@ -152,8 +156,21 @@ void CLGEMMLowpMatrixMultiplyCore::run() // Run reshape matrix A CLScheduler::get().enqueue(_mtx_a_reshape_kernel, false); - // Run reshape matrix B - CLScheduler::get().enqueue(_mtx_b_reshape_kernel, false); + if(_is_first_run || !_reshape_b_only_on_first_run) + { + // Run reshape matrix B + CLScheduler::get().enqueue(_mtx_b_reshape_kernel, false); + } + } + + // Note: if _reshape_b_only_on_first_run = true, the reduction kernel can be executed only once + if(_is_first_run || !_reshape_b_only_on_first_run) + { + // Run matrix B reduction kernel only if _a_offset is not equal to 0 + if(_a_offset != 0) + { + CLScheduler::get().enqueue(_mtx_b_reduction_kernel, false); + } } // Run matrix multiply @@ -165,14 +182,10 @@ void CLGEMMLowpMatrixMultiplyCore::run() CLScheduler::get().enqueue(_mtx_a_reduction_kernel, false); } - // Run matrix B reduction kernel only if _a_offset is not equal to 0 - if(_a_offset != 0) - { - CLScheduler::get().enqueue(_mtx_b_reduction_kernel, false); - } - // Run offset contribution kernel CLScheduler::get().enqueue(_offset_contribution_kernel, true); _memory_group.release(); + + _is_first_run = false; } diff --git a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp index 2c6515c1df..a18f48d9a7 100644 --- a/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp +++ b/src/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.cpp @@ -52,10 +52,11 @@ NEGEMMLowpMatrixMultiplyCore::NEGEMMLowpMatrixMultiplyCore(std::shared_ptr<IMemo { } -void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b, ITensor *output) +void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b, ITensor *output, const GEMMInfo &gemm_info) { ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output); - ARM_COMPUTE_ERROR_THROW_ON(NEGEMMLowpMatrixMultiplyCore::validate(a->info(), b->info(), output->info())); + ARM_COMPUTE_UNUSED(gemm_info); + ARM_COMPUTE_ERROR_THROW_ON(NEGEMMLowpMatrixMultiplyCore::validate(a->info(), b->info(), output->info(), gemm_info)); _a_offset = a->info()->quantization_info().offset; _b_offset = b->info()->quantization_info().offset; @@ -198,7 +199,7 @@ void NEGEMMLowpMatrixMultiplyCore::configure(const ITensor *a, const ITensor *b, } } -Error NEGEMMLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *output) +Error NEGEMMLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *output, const GEMMInfo &gemm_info) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::QASYMM8); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32); @@ -209,6 +210,9 @@ Error NEGEMMLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITensor "The output matrix must have the same number of rows as the matrix A"); ARM_COMPUTE_RETURN_ERROR_ON_MSG((b)->dimension(0) != (output)->dimension(0), "The output matrix must have the same number of columns as the matrix B"); + ARM_COMPUTE_UNUSED(gemm_info); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported"); int32_t a_offset = a->quantization_info().offset; int32_t b_offset = b->quantization_info().offset; diff --git a/tests/validation/CL/ConvolutionLayer.cpp b/tests/validation/CL/ConvolutionLayer.cpp index a6e07248aa..56e10f01b3 100644 --- a/tests/validation/CL/ConvolutionLayer.cpp +++ b/tests/validation/CL/ConvolutionLayer.cpp @@ -45,7 +45,8 @@ namespace { RelativeTolerance<float> tolerance_f32(0.05f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */ RelativeTolerance<half_float::half> tolerance_f16(half_float::half(0.2)); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */ -constexpr AbsoluteTolerance<float> tolerance_q(1.0f); /**< Tolerance value for comparing reference's output against implementation's output for fixed point data types */ +constexpr AbsoluteTolerance<float> tolerance_fixed(1.0f); /**< Tolerance value for comparing reference's output against implementation's output for fixed point data types */ +constexpr AbsoluteTolerance<float> tolerance_qasymm8(0.0); /**< Tolerance value for comparing reference's output against implementation's output for quantized data types */ constexpr float tolerance_num = 0.07f; /**< Tolerance number */ /** CNN data types */ @@ -55,6 +56,7 @@ const auto CNNDataTypes = framework::dataset::make("DataType", DataType::F32, DataType::QS8, DataType::QS16, + DataType::QASYMM8, }); } // namespace @@ -67,17 +69,22 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::da // Set fixed point position data type allowed int fixed_point_position = is_data_type_fixed_point(data_type) ? 3 : 0; + auto bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type; + // Create tensors - CLTensor src = create_tensor<CLTensor>(input_shape, data_type, 1, fixed_point_position); - CLTensor weights = create_tensor<CLTensor>(weights_shape, data_type, 1, fixed_point_position); - CLTensor bias = create_tensor<CLTensor>(bias_shape, data_type, 1, fixed_point_position); - CLTensor dst = create_tensor<CLTensor>(output_shape, data_type, 1, fixed_point_position); + CLTensor src = create_tensor<CLTensor>(input_shape, data_type, 1, fixed_point_position, QuantizationInfo(2.f / 255.f, 127)); + CLTensor weights = create_tensor<CLTensor>(weights_shape, data_type, 1, fixed_point_position, QuantizationInfo(2.f / 255.f, 127)); + CLTensor bias = create_tensor<CLTensor>(bias_shape, bias_data_type, 1, fixed_point_position, QuantizationInfo(2.f / 255.f, 127)); + CLTensor dst = create_tensor<CLTensor>(output_shape, data_type, 1, fixed_point_position, QuantizationInfo(2.f / 255.f, 127)); ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); + const QuantizationInfo src_quantization_info = src.info()->quantization_info(); + const QuantizationInfo weights_quantization_info = weights.info()->quantization_info(); + // Create and configure function CLConvolutionLayer conv; conv.configure(&src, &weights, &bias, &dst, info); @@ -93,6 +100,10 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::da validate(bias.info()->valid_region(), bias_valid_region); validate(dst.info()->valid_region(), dst_valid_region); + // Validate QuantizationInfo + ARM_COMPUTE_EXPECT(src.info()->quantization_info() == src_quantization_info, framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(weights.info()->quantization_info() == weights_quantization_info, framework::LogLevel::ERRORS); + // Validate padding //TODO(COMPMID-415) Need to validate padding? } @@ -143,7 +154,7 @@ TEST_SUITE_END() template <typename T> using CLConvolutionLayerFixedPointFixture = ConvolutionValidationFixedPointFixture<CLTensor, CLAccessor, CLConvolutionLayer, T>; -TEST_SUITE(Quantized) +TEST_SUITE(FixedPoint) TEST_SUITE(QS8) // We test for fixed point precision [4,6] FIXTURE_DATA_TEST_CASE(RunSmall, CLConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallConvolutionLayerDataset(), @@ -153,7 +164,7 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLConvolutionLayerFixedPointFixture<int8_t>, fr framework::dataset::make("FractionalBits", 4, 7))) { // Validate output - validate(CLAccessor(_target), _reference, tolerance_q); + validate(CLAccessor(_target), _reference, tolerance_fixed); } FIXTURE_DATA_TEST_CASE(RunLarge, CLConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeConvolutionLayerDataset(), framework::dataset::make("ReshapeWeights", { true, false })), @@ -162,7 +173,7 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLConvolutionLayerFixedPointFixture<int8_t>, fr framework::dataset::make("FractionalBits", 4, 7))) { // Validate output - validate(CLAccessor(_target), _reference, tolerance_q); + validate(CLAccessor(_target), _reference, tolerance_fixed); } TEST_SUITE_END() @@ -175,7 +186,7 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLConvolutionLayerFixedPointFixture<int16_t>, f framework::dataset::make("FractionalBits", 1, 14))) { // Validate output - validate(CLAccessor(_target), _reference, tolerance_q); + validate(CLAccessor(_target), _reference, tolerance_fixed); } FIXTURE_DATA_TEST_CASE(RunLarge, CLConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeConvolutionLayerDataset(), framework::dataset::make("ReshapeWeights", { true, false })), @@ -184,7 +195,31 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLConvolutionLayerFixedPointFixture<int16_t>, f framework::dataset::make("FractionalBits", 1, 14))) { // Validate output - validate(CLAccessor(_target), _reference, tolerance_q); + validate(CLAccessor(_target), _reference, tolerance_fixed); +} +TEST_SUITE_END() +TEST_SUITE_END() + +template <typename T> +using CLConvolutionLayerQuantizedFixture = ConvolutionValidationQuantizedFixture<CLTensor, CLAccessor, CLConvolutionLayer, T>; + +TEST_SUITE(Quantized) +TEST_SUITE(QASYMM8) +FIXTURE_DATA_TEST_CASE(RunSmall, CLConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallConvolutionLayerDataset(), + framework::dataset::make("ReshapeWeights", { true })), + framework::dataset::make("DataType", DataType::QASYMM8)), + framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 10) }))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_qasymm8); +} +FIXTURE_DATA_TEST_CASE(RunLarge, CLConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeConvolutionLayerDataset(), + framework::dataset::make("ReshapeWeights", { true })), + framework::dataset::make("DataType", DataType::QASYMM8)), + framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 0) }))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_qasymm8); } TEST_SUITE_END() TEST_SUITE_END() diff --git a/tests/validation/CL/DirectConvolutionLayer.cpp b/tests/validation/CL/DirectConvolutionLayer.cpp index 84e1bca6a5..4b747d64f7 100644 --- a/tests/validation/CL/DirectConvolutionLayer.cpp +++ b/tests/validation/CL/DirectConvolutionLayer.cpp @@ -232,4 +232,4 @@ TEST_SUITE_END() TEST_SUITE_END() } // namespace validation } // namespace test -} // namespace arm_compute +} // namespace arm_compute
\ No newline at end of file diff --git a/tests/validation/CL/FullyConnectedLayer.cpp b/tests/validation/CL/FullyConnectedLayer.cpp index e53f5fd407..0d8c8774b8 100644 --- a/tests/validation/CL/FullyConnectedLayer.cpp +++ b/tests/validation/CL/FullyConnectedLayer.cpp @@ -99,6 +99,9 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(frame ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); + const QuantizationInfo src_quantization_info = src.info()->quantization_info(); + const QuantizationInfo weights_quantization_info = weights.info()->quantization_info(); + // Create and configure function. CLFullyConnectedLayer fc; fc.configure(&src, &weights, &bias, &dst, transpose_weights, !reshape_weights); @@ -106,6 +109,10 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(frame // Validate valid region const ValidRegion dst_valid_region = shape_to_valid_region(dst_shape); validate(dst.info()->valid_region(), dst_valid_region); + + // Validate QuantizationInfo + ARM_COMPUTE_EXPECT(src.info()->quantization_info() == src_quantization_info, framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(weights.info()->quantization_info() == weights_quantization_info, framework::LogLevel::ERRORS); } template <typename T> diff --git a/tests/validation/fixtures/ConvolutionLayerFixture.h b/tests/validation/fixtures/ConvolutionLayerFixture.h index 859780812a..48b4665fe7 100644 --- a/tests/validation/fixtures/ConvolutionLayerFixture.h +++ b/tests/validation/fixtures/ConvolutionLayerFixture.h @@ -47,17 +47,24 @@ namespace test namespace validation { template <typename TensorType, typename AccessorType, typename FunctionType, typename T> -class ConvolutionValidationFixedPointFixture : public framework::Fixture +class ConvolutionValidationGenericFixture : public framework::Fixture { public: + using TBias = typename std::conditional<std::is_same<typename std::decay<T>::type, uint8_t>::value, int32_t, T>::type; + +public: template <typename...> - void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, bool reshape_weights, DataType data_type, int fractional_bits) + void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, bool reshape_weights, + DataType data_type, int fractional_bits, QuantizationInfo quantization_info) { - _fractional_bits = fractional_bits; - _data_type = data_type; - - _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, reshape_weights, data_type, fractional_bits); - _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, fractional_bits); + _data_type = data_type; + _is_quantized = is_data_type_quantized_asymmetric(data_type); + _bias_data_type = _is_quantized ? DataType::S32 : data_type; + _fractional_bits = fractional_bits; + _quantization_info = quantization_info; + + _target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, reshape_weights); + _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info); } protected: @@ -66,6 +73,18 @@ protected: { switch(tensor.data_type()) { + case DataType::QASYMM8: + { + std::uniform_int_distribution<uint8_t> distribution(0, 3); + library->fill(tensor, distribution, i); + break; + } + case DataType::S32: + { + std::uniform_int_distribution<int32_t> distribution(-100, 100); + library->fill(tensor, distribution, i); + break; + } case DataType::F16: case DataType::F32: { @@ -79,7 +98,7 @@ protected: } TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info, - bool reshape_weights, DataType data_type, int fixed_point_position) + bool reshape_weights) { WeightsInfo weights_info(!reshape_weights, weights_shape.x(), weights_shape.y(), weights_shape[3]); TensorShape reshaped_weights_shape(weights_shape); @@ -90,15 +109,16 @@ protected: const bool is_fully_connected_convolution = (output_shape.x() == 1 && output_shape.y() == 1); bool is_optimised = false; #if defined(__arm__) - is_optimised = std::is_same<FunctionType, NEConvolutionLayer>::value && NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && data_type == DataType::F32; + is_optimised = std::is_same<FunctionType, NEConvolutionLayer>::value && NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && _data_type == DataType::F32; #elif defined(__aarch64__) - is_optimised = std::is_same<FunctionType, NEConvolutionLayer>::value && NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && data_type == DataType::F32; + is_optimised = std::is_same<FunctionType, NEConvolutionLayer>::value && NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && _data_type == DataType::F32; #endif /* defined(__arm__) || defined(__aarch64__) */ reshaped_weights_shape.collapse(3); - if(bias_shape.total_size() > 0) + if(bias_shape.total_size() > 0 && !_is_quantized) { + // Add bias to the weights reshaped matrix reshaped_weights_shape.set(0, reshaped_weights_shape.x() + 1); } @@ -110,17 +130,17 @@ protected: } else { - const int interleave_width = 16 / data_size_from_type(data_type); + const int interleave_width = 16 / data_size_from_type(_data_type); reshaped_weights_shape.set(0, reshaped_weights_shape.x() * interleave_width); reshaped_weights_shape.set(1, static_cast<unsigned int>(std::ceil(reshaped_weights_shape.y() / static_cast<float>(interleave_width)))); } } // Create tensors - TensorType src = create_tensor<TensorType>(input_shape, data_type, 1, fixed_point_position); - TensorType weights = create_tensor<TensorType>(reshaped_weights_shape, data_type, 1, fixed_point_position); - TensorType bias = create_tensor<TensorType>(bias_shape, data_type, 1, fixed_point_position); - TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, fixed_point_position); + TensorType src = create_tensor<TensorType>(input_shape, _data_type, 1, _fractional_bits, _quantization_info); + TensorType weights = create_tensor<TensorType>(reshaped_weights_shape, _data_type, 1, _fractional_bits, _quantization_info); + TensorType bias = create_tensor<TensorType>(bias_shape, _bias_data_type, 1, _fractional_bits, _quantization_info); + TensorType dst = create_tensor<TensorType>(output_shape, _data_type, 1, _fractional_bits, _quantization_info); // Create and configure function FunctionType conv; @@ -150,20 +170,28 @@ protected: const bool is_fully_connected_convolution = (output_shape.x() == 1 && output_shape.y() == 1); bool is_optimised = false; #if defined(__arm__) - is_optimised = std::is_same<FunctionType, NEConvolutionLayer>::value && NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && data_type == DataType::F32; + is_optimised = std::is_same<FunctionType, NEConvolutionLayer>::value && NEScheduler::get().cpu_info().CPU == CPUTarget::ARMV7 && _data_type == DataType::F32; #elif defined(__aarch64__) - is_optimised = std::is_same<FunctionType, NEConvolutionLayer>::value && NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && data_type == DataType::F32; + is_optimised = std::is_same<FunctionType, NEConvolutionLayer>::value && NEScheduler::get().cpu_info().CPU >= CPUTarget::ARMV8 && _data_type == DataType::F32; #endif /* defined(__arm__) || defined(__aarch64__) */ TensorShape tmp_weights_shape(weights_shape); - SimpleTensor<T> tmp_weights(tmp_weights_shape, data_type, 1, fixed_point_position); - SimpleTensor<T> tmp_bias(bias_shape, data_type, 1, fixed_point_position); + SimpleTensor<T> tmp_weights(tmp_weights_shape, _data_type, 1, _fractional_bits, _quantization_info); // Fill with original shape fill(tmp_weights, 1); - fill(tmp_bias, 2); - tmp_weights = linearise_weights(tmp_weights, &tmp_bias); + if(_is_quantized) + { + fill(AccessorType(bias), 2); + tmp_weights = linearise_weights(tmp_weights); + } + else + { + SimpleTensor<T> tmp_bias(bias_shape, _bias_data_type, 1, _fractional_bits, _quantization_info); + fill(tmp_bias, 2); + tmp_weights = linearise_weights(tmp_weights, &tmp_bias); + } if(!is_fully_connected_convolution && !is_optimised) { @@ -192,13 +220,12 @@ protected: return dst; } - SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info, - DataType data_type, int fixed_point_position) + SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info) { // Create reference - SimpleTensor<T> src{ input_shape, data_type, 1, fixed_point_position }; - SimpleTensor<T> weights{ weights_shape, data_type, 1, fixed_point_position }; - SimpleTensor<T> bias{ bias_shape, data_type, 1, fixed_point_position }; + SimpleTensor<T> src{ input_shape, _data_type, 1, _fractional_bits, _quantization_info }; + SimpleTensor<T> weights{ weights_shape, _data_type, 1, _fractional_bits, _quantization_info }; + SimpleTensor<TBias> bias{ bias_shape, _bias_data_type, 1, _fractional_bits, _quantization_info }; // Fill reference fill(src, 0); @@ -208,10 +235,13 @@ protected: return reference::convolution_layer<T>(src, weights, bias, output_shape, info); } - TensorType _target{}; - SimpleTensor<T> _reference{}; - int _fractional_bits{}; - DataType _data_type{}; + TensorType _target{}; + SimpleTensor<T> _reference{}; + DataType _data_type{}; + DataType _bias_data_type{}; + int _fractional_bits{}; + QuantizationInfo _quantization_info{}; + bool _is_quantized = false; private: template <typename U> @@ -241,7 +271,6 @@ private: dst[dst_idx] = weights[weights_idx]; } - if(biases != nullptr) { // Fill last row with biases @@ -260,13 +289,37 @@ private: }; template <typename TensorType, typename AccessorType, typename FunctionType, typename T> -class ConvolutionValidationFixture : public ConvolutionValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T> +class ConvolutionValidationFixture : public ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T> { public: template <typename...> void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, bool reshape_weights, DataType data_type) { - ConvolutionValidationFixedPointFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, reshape_weights, data_type, 0); + ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, reshape_weights, data_type, 0, QuantizationInfo()); + } +}; + +template <typename TensorType, typename AccessorType, typename FunctionType, typename T> +class ConvolutionValidationFixedPointFixture : public ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T> +{ +public: + template <typename...> + void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, bool reshape_weights, DataType data_type, int fractional_bits) + { + ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, reshape_weights, data_type, fractional_bits, + QuantizationInfo()); + } +}; + +template <typename TensorType, typename AccessorType, typename FunctionType, typename T> +class ConvolutionValidationQuantizedFixture : public ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T> +{ +public: + template <typename...> + void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, bool reshape_weights, DataType data_type, + QuantizationInfo quantization_info) + { + ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, reshape_weights, data_type, 0, quantization_info); } }; } // namespace validation diff --git a/tests/validation/fixtures/GEMMLowpFixture.h b/tests/validation/fixtures/GEMMLowpFixture.h index c504e2e10d..06d6be3fa4 100644 --- a/tests/validation/fixtures/GEMMLowpFixture.h +++ b/tests/validation/fixtures/GEMMLowpFixture.h @@ -302,4 +302,4 @@ protected: } // namespace validation } // namespace test } // namespace arm_compute -#endif /* ARM_COMPUTE_TEST_GEMMLOWP_FIXTURE */ +#endif /* ARM_COMPUTE_TEST_GEMMLOWP_FIXTURE */
\ No newline at end of file |