diff options
author | Manuel Bottini <manuel.bottini@arm.com> | 2019-10-21 17:59:07 +0100 |
---|---|---|
committer | Manuel Bottini <manuel.bottini@arm.com> | 2019-12-03 13:58:56 +0000 |
commit | 7b9998d0fe1f98768b690ead10ebfa166d1b873d (patch) | |
tree | d3f6b81fb2e414a9e0f8ed9597eab27ef970d725 | |
parent | f9179d393a07eb9eed753e315df79d22391906c6 (diff) | |
download | ComputeLibrary-7b9998d0fe1f98768b690ead10ebfa166d1b873d.tar.gz |
COMPMID-1816: Use parallel reduction on 0 axis in CL ARG_MIN/ARG_MAX
Introducing new CLArgMinMax kernel
Change-Id: I0b8254207cc3859d19ceef9b6429cf5c1c586db0
Signed-off-by: Manuel Bottini <manuel.bottini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/2202
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michalis Spyrou <michalis.spyrou@arm.com>
-rw-r--r-- | arm_compute/core/CL/CLHelpers.h | 11 | ||||
-rw-r--r-- | arm_compute/core/CL/CLKernels.h | 7 | ||||
-rw-r--r-- | arm_compute/core/CL/kernels/CLArgMinMaxLayerKernel.h | 94 | ||||
-rw-r--r-- | arm_compute/core/CL/kernels/CLReductionOperationKernel.h | 19 | ||||
-rw-r--r-- | arm_compute/runtime/CL/functions/CLArgMinMaxLayer.h | 27 | ||||
-rw-r--r-- | arm_compute/runtime/CL/functions/CLReductionOperation.h | 10 | ||||
-rw-r--r-- | arm_compute/runtime/Utils.h | 13 | ||||
-rw-r--r-- | src/core/CL/CLHelpers.cpp | 8 | ||||
-rw-r--r-- | src/core/CL/CLKernelLibrary.cpp | 8 | ||||
-rw-r--r-- | src/core/CL/cl_kernels/arg_min_max.cl | 431 | ||||
-rw-r--r-- | src/core/CL/cl_kernels/helpers.h | 13 | ||||
-rw-r--r-- | src/core/CL/cl_kernels/reduction_operation.cl | 111 | ||||
-rw-r--r-- | src/core/CL/kernels/CLArgMinMaxLayerKernel.cpp | 283 | ||||
-rw-r--r-- | src/core/CL/kernels/CLReductionOperationKernel.cpp | 27 | ||||
-rw-r--r-- | src/core/Utils.cpp | 3 | ||||
-rw-r--r-- | src/runtime/CL/functions/CLArgMinMaxLayer.cpp | 128 | ||||
-rw-r--r-- | src/runtime/CL/functions/CLReductionOperation.cpp | 54 | ||||
-rw-r--r-- | src/runtime/Utils.cpp | 17 | ||||
-rw-r--r-- | tests/validation/CL/ArgMinMax.cpp | 36 |
19 files changed, 1098 insertions, 202 deletions
diff --git a/arm_compute/core/CL/CLHelpers.h b/arm_compute/core/CL/CLHelpers.h index cd65eafc9c..7e549be989 100644 --- a/arm_compute/core/CL/CLHelpers.h +++ b/arm_compute/core/CL/CLHelpers.h @@ -190,5 +190,16 @@ bool preferred_dummy_work_items_support(const cl::Device &device); * @return An opencl kernel */ cl::Kernel create_opencl_kernel(CLCoreRuntimeContext *ctx, const std::string &kernel_name, const CLBuildOptions &build_opts); + +/** Creates a suitable LWS hint object for parallel implementations. Sets the number of WG based on the input size. + * If input width is smaller than 128 we can use fewer threads than 8. + * + * @param[in] input_dimension number of elements along the dimension to apply the parallellization + * @param[in] vector_size size of the vector in OpenCL + * + * @return An LWS hint object + */ +cl::NDRange create_lws_hint_parallel_implementations(unsigned int input_dimension, unsigned int vector_size); + } // namespace arm_compute #endif /* __ARM_COMPUTE_CLHELPERS_H__ */ diff --git a/arm_compute/core/CL/CLKernels.h b/arm_compute/core/CL/CLKernels.h index c3c485db7c..78437beffb 100644 --- a/arm_compute/core/CL/CLKernels.h +++ b/arm_compute/core/CL/CLKernels.h @@ -21,13 +21,14 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef __ARM_COMPUTE_CLKERNELS_H__ -#define __ARM_COMPUTE_CLKERNELS_H__ +#ifndef ARM_COMPUTE_CLKERNELS_H +#define ARM_COMPUTE_CLKERNELS_H /* Header regrouping all the CL kernels */ #include "arm_compute/core/CL/kernels/CLAbsoluteDifferenceKernel.h" #include "arm_compute/core/CL/kernels/CLAccumulateKernel.h" #include "arm_compute/core/CL/kernels/CLActivationLayerKernel.h" +#include "arm_compute/core/CL/kernels/CLArgMinMaxLayerKernel.h" #include "arm_compute/core/CL/kernels/CLBatchConcatenateLayerKernel.h" #include "arm_compute/core/CL/kernels/CLBatchNormalizationLayerKernel.h" #include "arm_compute/core/CL/kernels/CLBatchToSpaceLayerKernel.h" @@ -160,4 +161,4 @@ #include "arm_compute/core/CL/kernels/CLYOLOLayerKernel.h" #include "arm_compute/core/CL/kernels/ICLDepthwiseConvolutionLayer3x3Kernel.h" -#endif /* __ARM_COMPUTE_CLKERNELS_H__ */ +#endif /* ARM_COMPUTE_CLKERNELS_H */ diff --git a/arm_compute/core/CL/kernels/CLArgMinMaxLayerKernel.h b/arm_compute/core/CL/kernels/CLArgMinMaxLayerKernel.h new file mode 100644 index 0000000000..7f4cfe3edc --- /dev/null +++ b/arm_compute/core/CL/kernels/CLArgMinMaxLayerKernel.h @@ -0,0 +1,94 @@ +/* + * Copyright (c) 2019 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#ifndef ARM_COMPUTE_CLARGMINMAXLAYERKERNEL_H +#define ARM_COMPUTE_CLARGMINMAXLAYERKERNEL_H + +#include "arm_compute/core/CL/ICLKernel.h" +#include "arm_compute/core/Types.h" + +namespace arm_compute +{ +class ICLTensor; + +/** Interface for the reduction operation kernel + * + * @note The default data type for an uninitialized output tensor is + * signed 32-bit integer (S32). It is the user's responsibility to check + * that the results do not overflow because the indices are computed + * in unsigned 32-bit (U32). + */ +class CLArgMinMaxLayerKernel : public ICLKernel +{ +public: + /** Default constructor */ + CLArgMinMaxLayerKernel(); + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLArgMinMaxLayerKernel(const CLArgMinMaxLayerKernel &) = delete; + /** Prevent instances of this class from being copied (As this class contains pointers) */ + CLArgMinMaxLayerKernel &operator=(const CLArgMinMaxLayerKernel &) = delete; + /** Allow instances of this class to be moved */ + CLArgMinMaxLayerKernel(CLArgMinMaxLayerKernel &&) = default; + /** Allow instances of this class to be moved */ + CLArgMinMaxLayerKernel &operator=(CLArgMinMaxLayerKernel &&) = default; + /** Default destructor */ + ~CLArgMinMaxLayerKernel() = default; + + /** Set the input and output tensors. + * + * @param[in] input Source tensor. Data types supported: S32/F16/F32. + * @param[in] prev_output Destination tensor of the previous iterations of @ref CLArgMinMaxLayerKernel. Data types supported: U32/S32 + * Has to be nullptr for the first iteration + * @param[out] output Destination tensor. Data types supported: U32/S32 + * Output will have the same number of dimensions as input. + * @param[in] axis Axis along which to reduce. Supported reduction axis : 0,1,2,3 + * @param[in] op Reduction operation to perform. Only ArgMin and ArgMax are supported. + */ + void configure(const ICLTensor *input, const ICLTensor *prev_output, ICLTensor *output, unsigned int axis, ReductionOperation op); + + /** Static function to check if given info will lead to a valid configuration of @ref CLArgMinMaxLayerKernel. + * + * @param[in] input Source tensor info. Data types supported: S32/F16/F32. + * @param[in] prev_output Destination tensor info of the previous iterations. Data types supported: U32/S32 + * Has to be nullptr for the first iteration + * @param[in] output Destination tensor info. Data types supported: U32/S32 + * Output will have the same number of dimensions as input. + * @param[in] axis Axis along which to reduce. Supported reduction axis : 0,1,2,3 + * @param[in] op Reduction operation to perform. Only ArgMin and ArgMax are supported. + * + * @return a status + */ + static Status validate(const ITensorInfo *input, const ITensorInfo *prev_output, const ITensorInfo *output, unsigned int axis, ReductionOperation op); + + // Inherited methods overridden: + void run(const Window &window, cl::CommandQueue &queue) override; + +private: + const ICLTensor *_input; + const ICLTensor *_prev_output; + ICLTensor *_output; + unsigned int _reduction_axis; + ReductionOperation _op; +}; +} // namespace arm_compute +#endif /* ARM_COMPUTE_CLARGMINMAXLAYERKERNEL_H */ diff --git a/arm_compute/core/CL/kernels/CLReductionOperationKernel.h b/arm_compute/core/CL/kernels/CLReductionOperationKernel.h index 172ed8985a..1ed7e6e5aa 100644 --- a/arm_compute/core/CL/kernels/CLReductionOperationKernel.h +++ b/arm_compute/core/CL/kernels/CLReductionOperationKernel.h @@ -21,8 +21,8 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef __ARM_COMPUTE_CLREDUCTIONOPERATIONKERNEL_H__ -#define __ARM_COMPUTE_CLREDUCTIONOPERATIONKERNEL_H__ +#ifndef ARM_COMPUTE_CLREDUCTIONOPERATIONKERNEL_H +#define ARM_COMPUTE_CLREDUCTIONOPERATIONKERNEL_H #include "arm_compute/core/CL/ICLKernel.h" #include "arm_compute/core/Types.h" @@ -32,11 +32,6 @@ namespace arm_compute class ICLTensor; /** Interface for the reduction operation kernel - * - * @note For ARG_MIN/ARG_MAX reduction, the default data type for an uninitialized - * output tensor is signed 32-bit integer (S32). It is the user's responsibility - * to check that the results do not overflow because the indices are computed - * in unsigned 32-bit (U32). */ class CLReductionOperationKernel : public ICLKernel { @@ -57,10 +52,10 @@ public: /** Set the input and output tensors. * * @param[in] input Source tensor. Data types supported: QASYMM8/S32/F16/F32. - * @param[out] output Destination tensor. Data types and data layouts supported: Same as @p input, U32/S32 for ARG_MIX/ARG_MAX. + * @param[out] output Destination tensor. Data types and data layouts supported: Same as @p input. * Output will have the same number of dimensions as input. * @param[in] axis Axis along which to reduce. Supported reduction axis : 0,1,2,3 - * @param[in] op Reduction operation to perform. + * @param[in] op Reduction operation to perform. Operations supported: MEAN_SUM, PROD, SUM_SQUARE, SUM, MIN, MAX * @param[in] width (Optional) In case of x-axis we also need to provide the width of the input image. */ void configure(const ICLTensor *input, ICLTensor *output, unsigned int axis, ReductionOperation op, unsigned int width = 0); @@ -68,10 +63,10 @@ public: /** Static function to check if given info will lead to a valid configuration of @ref CLReductionOperationKernel. * * @param[in] input Source tensor info. Data types supported: QASYMM8/S32/F16/F32. - * @param[in] output Destination tensor info. Data types and data layouts supported: Same as @p input, U32/S32 for ARG_MIX/ARG_MAX. + * @param[in] output Destination tensor info. Data types and data layouts supported: Same as @p input. * Output will have the same number of dimensions as input. * @param[in] axis Axis along which to reduce. Supported reduction axis : 0,1,2,3 - * @param[in] op Reduction operation to perform. + * @param[in] op Reduction operation to perform. Operations supported: MEAN_SUM, PROD, SUM_SQUARE, SUM, MIN, MAX * @param[in] width (Optional) In case of x-axis we also need to provide the width of the input image. * * @return a status @@ -90,4 +85,4 @@ private: BorderSize _border_size; }; } // namespace arm_compute -#endif /*__ARM_COMPUTE_CLREDUCTIONOPERATIONKERNEL_H__ */ +#endif /*ARM_COMPUTE_CLREDUCTIONOPERATIONKERNEL_H */ diff --git a/arm_compute/runtime/CL/functions/CLArgMinMaxLayer.h b/arm_compute/runtime/CL/functions/CLArgMinMaxLayer.h index 1b465a4866..21cded0417 100644 --- a/arm_compute/runtime/CL/functions/CLArgMinMaxLayer.h +++ b/arm_compute/runtime/CL/functions/CLArgMinMaxLayer.h @@ -21,10 +21,13 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef __ARM_COMPUTE_CLARGMINMAXLAYER_H__ -#define __ARM_COMPUTE_CLARGMINMAXLAYER_H__ +#ifndef ARM_COMPUTE_CLARGMINMAXLAYER_H +#define ARM_COMPUTE_CLARGMINMAXLAYER_H +#include "arm_compute/core/CL/kernels/CLArgMinMaxLayerKernel.h" +#include "arm_compute/core/CL/kernels/CLReshapeLayerKernel.h" #include "arm_compute/core/Types.h" +#include "arm_compute/runtime/CL/CLTensor.h" #include "arm_compute/runtime/IFunction.h" #include "arm_compute/runtime/IMemoryManager.h" #include "arm_compute/runtime/MemoryGroup.h" @@ -33,7 +36,6 @@ namespace arm_compute { class ITensorInfo; class ICLTensor; -class CLReductionOperation; /** Function to calculate the index of the minimum or maximum values in a * tensor based on an axis. @@ -53,19 +55,18 @@ public: CLArgMinMaxLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr); /** Set the input and output tensors. * - * @param[in] input Input source tensor, this could be written if @ref CLReductionOperation - * manipulates its border for better performance. Data types supported: F16/F32. + * @param[in] input Input source tensor. Data types supported: F16/F32. * @param[in] axis Axis to find max/min index. * @param[out] output Output source tensor. Data types supported: U32/S32. - * @param[in] op Operation to perform: min or max + * @param[in] op Reduction operation to perform. Operations supported: ARG_IDX_MAX, ARG_IDX_MIN */ - void configure(ICLTensor *input, int axis, ICLTensor *output, const ReductionOperation &op); + void configure(const ICLTensor *input, int axis, ICLTensor *output, const ReductionOperation &op); /** Static function to check if given info will lead to a valid configuration of @ref CLArgMinMaxLayer * * @param[in] input Input source tensor info. Data types supported: F16/F32. * @param[in] axis Axis to find max/min index. * @param[in] output Output source tensor info. Data types supported: U32/S32. - * @param[in] op Operation to perform: min or max + * @param[in] op Reduction operation to perform. Operations supported: ARG_IDX_MAX, ARG_IDX_MIN * * @return a status */ @@ -75,7 +76,13 @@ public: void run() override; private: - std::unique_ptr<CLReductionOperation> _reduction_function; + MemoryGroup _memory_group; + std::vector<CLTensor> _results_vector; + CLTensor _not_reshaped_output; + std::vector<CLArgMinMaxLayerKernel> _reduction_kernels_vector; + CLReshapeLayerKernel _reshape_kernel; + unsigned int _num_of_stages; + unsigned int _reduction_axis; }; } // namespace arm_compute -#endif /* __ARM_COMPUTE_CLARGMINMAXLAYER_H__ */ +#endif /* ARM_COMPUTE_CLARGMINMAXLAYER_H */ diff --git a/arm_compute/runtime/CL/functions/CLReductionOperation.h b/arm_compute/runtime/CL/functions/CLReductionOperation.h index 405e1177fd..9e0bf03ffe 100644 --- a/arm_compute/runtime/CL/functions/CLReductionOperation.h +++ b/arm_compute/runtime/CL/functions/CLReductionOperation.h @@ -21,8 +21,8 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef __ARM_COMPUTE_CLREDUCTIONOPERATION_H__ -#define __ARM_COMPUTE_CLREDUCTIONOPERATION_H__ +#ifndef ARM_COMPUTE_CLREDUCTIONOPERATION_H +#define ARM_COMPUTE_CLREDUCTIONOPERATION_H #include "arm_compute/core/CL/kernels/CLFillBorderKernel.h" #include "arm_compute/core/CL/kernels/CLReductionOperationKernel.h" @@ -57,7 +57,7 @@ public: * @param[in] input Source tensor. Data types supported: QASYMM8/F16/F32. * @param[out] output Destination tensor. Data types and data layouts supported: Same as @p input. * @param[in] axis Axis along which to reduce. Supported reduction axis : 0, 1, 2, 3 - * @param[in] op Reduction operation to perform. + * @param[in] op Reduction operation to perform. Operations supported: MEAN_SUM, PROD, SUM_SQUARE, SUM, MIN, MAX * @param[in] keep_dims (Optional) Whether to keep the reduced dimension after the operation. Defaults to true. */ void configure(ICLTensor *input, ICLTensor *output, unsigned int axis, ReductionOperation op, bool keep_dims = true); @@ -67,7 +67,7 @@ public: * @param[in] input Source tensor info. Data types supported: QASYMM8/F16/F32. * @param[in] output Destination tensor info. Data types and data layouts supported: Same as @p input. * @param[in] axis Axis along which to reduce. Supported reduction axis : 0, 1, 2, 3 - * @param[in] op Reduction operation to perform. + * @param[in] op Reduction operation to perform. Operations supported: MEAN_SUM, PROD, SUM_SQUARE, SUM, MIN, MAX * @param[in] keep_dims (Optional) Whether to keep the reduced dimension after the operation. Defaults to true. * * @return a status @@ -92,4 +92,4 @@ private: bool _is_reshape_required; }; } // namespace arm_compute -#endif /*__ARM_COMPUTE_CLREDUCTIONOPERATION_H__ */ +#endif /* ARM_COMPUTE_CLREDUCTIONOPERATION_H */
\ No newline at end of file diff --git a/arm_compute/runtime/Utils.h b/arm_compute/runtime/Utils.h index 15c0042a33..9a5b20eb26 100644 --- a/arm_compute/runtime/Utils.h +++ b/arm_compute/runtime/Utils.h @@ -21,8 +21,8 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef __ARM_COMPUTE_RUNTIME_UTILS_H__ -#define __ARM_COMPUTE_RUNTIME_UTILS_H__ +#ifndef ARM_COMPUTE_RUNTIME_UTILS_H +#define ARM_COMPUTE_RUNTIME_UTILS_H #include "arm_compute/runtime/IRuntimeContext.h" #include "arm_compute/runtime/Scheduler.h" @@ -46,5 +46,12 @@ const std::string &string_from_scheduler_type(Scheduler::Type t); * @param[in] hints Hints to use. */ void schedule_kernel_on_ctx(IRuntimeContext *ctx, ICPPKernel *kernel, const IScheduler::Hints &hints); + +/** Calculate number of stages for parallel implementations + * + * @param[in] input_x_dimension input tensor x dimension + * @param[in] axis axis to be used + */ +unsigned int calculate_number_of_stages_only_x_axis(size_t input_x_dimension, unsigned int axis); } // namespace arm_compute -#endif /* __ARM_COMPUTE_RUNTIME_UTILS_H__ */ +#endif /* ARM_COMPUTE_RUNTIME_UTILS_H */ diff --git a/src/core/CL/CLHelpers.cpp b/src/core/CL/CLHelpers.cpp index 28b1a3224f..9754bebd18 100644 --- a/src/core/CL/CLHelpers.cpp +++ b/src/core/CL/CLHelpers.cpp @@ -365,4 +365,12 @@ cl::Kernel create_opencl_kernel(CLCoreRuntimeContext *ctx, const std::string &ke return static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); } } + +cl::NDRange create_lws_hint_parallel_implementations(unsigned int input_dimension, unsigned int vector_size) +{ + const unsigned int width_leftover = input_dimension % vector_size; + const unsigned int border_width = (width_leftover != 0) ? vector_size - width_leftover : 0; + const unsigned int num_of_threads = ((input_dimension + border_width) / 16); + return cl::NDRange(std::min(8U, num_of_threads)); +} } // namespace arm_compute diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp index 5d5205439e..5b59094c81 100644 --- a/src/core/CL/CLKernelLibrary.cpp +++ b/src/core/CL/CLKernelLibrary.cpp @@ -150,6 +150,10 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map = { "activation_layer", "activation_layer.cl" }, { "activation_layer_quant", "activation_layer_quant.cl" }, { "activation_layer_quant_f32", "activation_layer_quant.cl" }, + { "arg_min_max_x", "arg_min_max.cl" }, + { "arg_min_max_y", "arg_min_max.cl" }, + { "arg_min_max_z", "arg_min_max.cl" }, + { "arg_min_max_w", "arg_min_max.cl" }, { "batch_to_space_nchw", "batch_to_space.cl" }, { "batch_to_space_static_nchw", "batch_to_space.cl" }, { "batch_to_space_nhwc", "batch_to_space.cl" }, @@ -585,6 +589,10 @@ const std::map<std::string, std::string> CLKernelLibrary::_program_source_map = #include "./cl_kernels/activation_layer_quant.clembed" }, { + "arg_min_max.cl", +#include "./cl_kernels/arg_min_max.clembed" + }, + { "batch_to_space.cl", #include "./cl_kernels/batch_to_space.clembed" }, diff --git a/src/core/CL/cl_kernels/arg_min_max.cl b/src/core/CL/cl_kernels/arg_min_max.cl new file mode 100644 index 0000000000..3f75377636 --- /dev/null +++ b/src/core/CL/cl_kernels/arg_min_max.cl @@ -0,0 +1,431 @@ +/* + * Copyright (c) 2019 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "helpers.h" + +#if defined(ARG_MAX) +#define CONDITION_TO_USE(x, y) ISGREATER(x, y) +#elif defined(ARG_MIN) +#define CONDITION_TO_USE(x, y) ISLESS(x, y) +#else // !(defined(ARG_MAX) || defined(ARG_MIN)) +#error "Unsupported reduction operation!" +#endif // defined(ARG_MAX) + +#if defined(DATA_TYPE_OUTPUT) +#if defined(WIDTH) +#if defined(ARG_MIN) +#if defined(PREV_OUTPUT) +/** Find index minimum value of a vector + * + * @param[in] input Pointer to the first value. + * + * @return index of the vector. + */ +inline DATA_TYPE_OUTPUT arg_idx_min_prev_out(__global const DATA_TYPE *input, __global const DATA_TYPE_OUTPUT *prev_res, const int x_idx) +{ + int end_elem = (x_idx + 1) * 16; + if(end_elem > WIDTH) + { + end_elem = WIDTH - x_idx * 16; + } + DATA_TYPE_OUTPUT res = prev_res[0]; + for(int x_v = 1; x_v < end_elem; ++x_v) + { + res = select(res, prev_res[x_v], *(input + prev_res[x_v]) < * (input + res)); + } + return res; +} +#else // !defined(PREV_OUTPUT) +/** Find index minimum value of a vector + * + * @param[in] input Pointer to the first value. + * + * @return index of the vector. + */ +inline DATA_TYPE_OUTPUT arg_idx_min(__global const DATA_TYPE *input, const int x_idx) +{ +#if WIDTH < 16 + DATA_TYPE_OUTPUT res = 0; + for(DATA_TYPE_OUTPUT x_v = res + 1; x_v < WIDTH; ++x_v) + { + res = select(res, x_v, *(input + x_v) < * (input + res)); + } + return res; +#else // WIDTH >= 16 + int x_elem = x_idx * 16; + const int x_goback = select(0, 16 - WIDTH % 16, x_elem + 16 > WIDTH); + x_elem -= x_goback; + + VEC_DATA_TYPE(DATA_TYPE, 16) + in = vload16(0, input - x_goback); + VEC_DATA_TYPE(DATA_TYPE_OUTPUT, 16) + res = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 }; + + VEC_DATA_TYPE(COND_DATA_TYPE, 8) + idx_sel = (in.s01234567 <= in.s89abcdef); + in.s01234567 = select(in.s89abcdef, in.s01234567, idx_sel); + res.s01234567 = select(res.s89abcdef, res.s01234567, CONVERT(idx_sel, int8)); + + idx_sel.s0123 = (in.s0123 < in.s4567) || (in.s0123 == in.s4567 && CONVERT((res.s0123 < res.s4567), VEC_DATA_TYPE(COND_DATA_TYPE, 4))); + in.s0123 = select(in.s4567, in.s0123, idx_sel.s0123); + res.s0123 = select(res.s4567, res.s0123, CONVERT(idx_sel.s0123, int4)); + + idx_sel.s01 = (in.s01 < in.s23) || (in.s01 == in.s23 && CONVERT((res.s01 < res.s23), VEC_DATA_TYPE(COND_DATA_TYPE, 2))); + in.s01 = select(in.s23, in.s01, idx_sel.s01); + res.s01 = select(res.s23, res.s01, CONVERT(idx_sel.s01, int2)); + + idx_sel.s0 = (in.s0 < in.s1) || (in.s0 == in.s1 && CONVERT((res.s0 < res.s1), COND_DATA_TYPE)); + res.s0 = select(res.s1, res.s0, CONVERT(idx_sel.s0, int)); + + return res.s0 + x_elem; +#endif // WIDTH < 16 +} +#endif // defined(PREV_OUTPUT) +#endif // defined(ARG_MIN) +#if defined(ARG_MAX) +#if defined(PREV_OUTPUT) +/** Find index maximum value of a vector + * + * @param[in] input Pointer to the first value. + * + * @return index of the vector. + */ +inline DATA_TYPE_OUTPUT arg_idx_max_prev_out(__global const DATA_TYPE *input, __global const DATA_TYPE_OUTPUT *prev_res, const int x_idx) +{ + int end_elem = (x_idx + 1) * 16; + if(end_elem > WIDTH) + { + end_elem = WIDTH - x_idx * 16; + } + DATA_TYPE_OUTPUT res = prev_res[0]; + for(int x_v = 1; x_v < end_elem; ++x_v) + { + res = select(res, prev_res[x_v], *(input + prev_res[x_v]) > *(input + res)); + } + return res; +} +#else // !defined(PREV_OUTPUT) +/** Find index maximum value of a vector + * + * @param[in] input Pointer to the first value. + * + * @return index of the vector. + */ +inline DATA_TYPE_OUTPUT arg_idx_max(__global const DATA_TYPE *input, const int x_idx) +{ +#if WIDTH < 16 + DATA_TYPE_OUTPUT res = 0; + for(DATA_TYPE_OUTPUT x_v = res + 1; x_v < WIDTH; ++x_v) + { + res = select(res, x_v, *(input + x_v) > *(input + res)); + } + return res; +#else // WIDTH >= 16 + int x_elem = x_idx * 16; + const int x_goback = select(0, 16 - WIDTH % 16, x_elem + 16 > WIDTH); + x_elem -= x_goback; + + VEC_DATA_TYPE(DATA_TYPE, 16) + in = vload16(0, input - x_goback); + VEC_DATA_TYPE(DATA_TYPE_OUTPUT, 16) + res = { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 }; + + VEC_DATA_TYPE(COND_DATA_TYPE, 8) + idx_sel = (in.s01234567 >= in.s89abcdef); + in.s01234567 = select(in.s89abcdef, in.s01234567, idx_sel); + res.s01234567 = select(res.s89abcdef, res.s01234567, CONVERT(idx_sel, int8)); + + idx_sel.s0123 = (in.s0123 > in.s4567) || (in.s0123 == in.s4567 && CONVERT((res.s0123 < res.s4567), VEC_DATA_TYPE(COND_DATA_TYPE, 4))); + in.s0123 = select(in.s4567, in.s0123, idx_sel.s0123); + res.s0123 = select(res.s4567, res.s0123, CONVERT(idx_sel.s0123, int4)); + + idx_sel.s01 = (in.s01 > in.s23) || (in.s01 == in.s23 && CONVERT((res.s01 < res.s23), VEC_DATA_TYPE(COND_DATA_TYPE, 2))); + in.s01 = select(in.s23, in.s01, idx_sel.s01); + res.s01 = select(res.s23, res.s01, CONVERT(idx_sel.s01, int2)); + + idx_sel.s0 = (in.s0 > in.s1) || (in.s0 == in.s1 && CONVERT((res.s0 < res.s1), COND_DATA_TYPE)); + res.s0 = select(res.s1, res.s0, CONVERT(idx_sel.s0, int)); + + return res.s0 + x_elem; +#endif // WIDTH < 16 +} +#endif // defined(PREV_OUTPUT) +#endif // defined(ARG_MAX) + +/** This kernel performs parallel reduction given an operation on x-axis. + * + * @note In case the results of previous stages are passed the flag PREV_OUTPUT has to be passed using -DPREV_OUTPUT + * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float + * @note The data type of the output must be passed at compile time using -DDATA_TYPE_OUTPUT: e.g. -DDATA_TYPE_OUTPUT=uint + * @note The arg_max flag must be passed at compile time using -DARG_MAX if we want to compute the ArgMax + * @note The arg_min flag must be passed at compile time using -DARG_MIN if we want to compute the ArgMin + * + * @param[in] src_ptr Pointer to the source tensor. Supported data types: S32/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) + * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[in] prev_res_ptr (Optional) Pointer to previous results tensor. Supported data types: U32/S32 + * @param[in] prev_res_stride_x (Optional) Stride of the output tensor in X dimension (in bytes) + * @param[in] prev_res_step_x (Optional) prev_res_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] prev_res_stride_y (Optional) Stride of the output tensor in Y dimension (in bytes) + * @param[in] prev_res_step_y (Optional) prev_res_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] prev_res_offset_first_element_in_bytes (Optional) The offset of the first element in the previous results tensor + * @param[in] partial_res_ptr The local buffer to hold partial result values. Supported data types: U32/S32 + * @param[in] partial_res_stride_x Stride of the output tensor in X dimension (in bytes) + * @param[in] partial_res_step_x partial_res_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] partial_res_stride_y Stride of the output tensor in Y dimension (in bytes) + * @param[in] partial_res_step_y partial_res_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] partial_res_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[in] local_results Local buffer for storing the partial result + */ +__kernel void arg_min_max_x( + IMAGE_DECLARATION(src), +#if defined(PREV_OUTPUT) + IMAGE_DECLARATION(prev_res), +#endif // defined(PREV_OUTPUT) + IMAGE_DECLARATION(partial_res), + __local DATA_TYPE_OUTPUT *local_results) +{ +#if defined(PREV_OUTPUT) + Image src = CONVERT_TO_IMAGE_STRUCT_NO_STEP(src); + Image prev_res = CONVERT_TO_IMAGE_STRUCT(prev_res); +#else // !defined(PREV_OUTPUT) + Image src = CONVERT_TO_IMAGE_STRUCT(src); +#endif // defined(PREV_OUTPUT) + Image partial_res = CONVERT_TO_IMAGE_STRUCT(partial_res); + + unsigned int lsize = get_local_size(0); + unsigned int lid = get_local_id(0); + + const uint x_idx = get_global_id(0); + const uint y_idx = get_global_id(1); + const __global DATA_TYPE *src_in_row = (const __global DATA_TYPE *)(src_ptr + src_offset_first_element_in_bytes + y_idx * src_step_y); + + for(unsigned int y = 0; y < get_local_size(1); ++y) + { +#if defined(ARG_MAX) +#if defined(PREV_OUTPUT) + local_results[lid] = arg_idx_max_prev_out(src_in_row, (__global DATA_TYPE_OUTPUT *)offset(&prev_res, 0, y), x_idx); +#else // !defined(PREV_OUTPUT) + local_results[lid] = arg_idx_max((__global DATA_TYPE *)offset(&src, 0, y), x_idx); +#endif // defined(PREV_OUTPUT) +#else // defined(ARG_MIN) +#if defined(PREV_OUTPUT) + local_results[lid] = arg_idx_min_prev_out(src_in_row, (__global DATA_TYPE_OUTPUT *)offset(&prev_res, 0, y), x_idx); +#else // !defined(PREV_OUTPUT) + local_results[lid] = arg_idx_min((__global DATA_TYPE *)offset(&src, 0, y), x_idx); +#endif // defined(PREV_OUTPUT) +#endif // defined(ARG_MAX) || defined(ARG_MIN) + + barrier(CLK_LOCAL_MEM_FENCE); + + // Perform parallel reduction + for(unsigned int i = lsize >> 1; i > 0; i >>= 1) + { + if(lid < i) + { + DATA_TYPE tmp0 = *(src_in_row + local_results[lid]); + DATA_TYPE tmp1 = *(src_in_row + local_results[lid + i]); +#if defined(ARG_MAX) + local_results[lid] = select( + local_results[lid], + local_results[lid + i], + ((tmp0 == tmp1) && (local_results[lid + i] < local_results[lid])) || (tmp0 < tmp1)); +#else // defined(ARG_MIN) + local_results[lid] = select( + local_results[lid], + local_results[lid + i], + ((tmp0 == tmp1) && (local_results[lid + i] < local_results[lid])) || (tmp0 > tmp1)); +#endif // defined(ARG_MAX) || defined(ARG_MIN) + } + barrier(CLK_LOCAL_MEM_FENCE); + } + + if(lid == 0) + { + ((__global DATA_TYPE_OUTPUT *)offset(&partial_res, get_group_id(0), y))[0] = local_results[0]; + } + } +} +#endif // defined(WIDTH) + +#if defined(HEIGHT) +/** This kernel performs reduction on y-axis. + * + * @note The input data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float + * @note The data type of the output must be passed at compile time using -DDATA_TYPE_OUTPUT: e.g. -DDATA_TYPE_OUTPUT=uint + * @note The data type of the intermediate results must be passed at compile time using -DDATA_TYPE_PROMOTED: e.g. -DDATA_TYPE_PROMOTED=uint + * @note The height size must be passed at compile time using -DHEIGHT e.g. -DHEIGHT=128 + * + * @param[in] src_ptr Pointer to the source tensor. Supported data types: S32/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) + * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[in] output_ptr The local buffer to hold sumed values. Supported data types: U32/S32 + * @param[in] output_stride_x Stride of the output tensor in X dimension (in bytes) + * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y Stride of the output tensor in Y dimension (in bytes) + * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes The offset of the first element in the source tensor + */ +__kernel void arg_min_max_y( + IMAGE_DECLARATION(src), + IMAGE_DECLARATION(output)) +{ + Image src = CONVERT_TO_IMAGE_STRUCT(src); + Image output = CONVERT_TO_IMAGE_STRUCT(output); + + VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16) + res = CONVERT(vload16(0, (__global DATA_TYPE *)offset(&src, 0, 0)), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16)); + + VEC_DATA_TYPE(DATA_TYPE_OUTPUT, 16) + indx = 0; + for(unsigned int y = 1; y < HEIGHT; ++y) + { + VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16) + in = CONVERT(vload16(0, (__global DATA_TYPE *)offset(&src, 0, y)), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16)); + + VEC_DATA_TYPE(DATA_TYPE_OUTPUT, 16) + cond_conv = CONVERT(CONDITION_TO_USE(in, res), VEC_DATA_TYPE(DATA_TYPE_OUTPUT, 16)); + indx = select(indx, y, cond_conv); + res = select(res, in, CONDITION_TO_USE(in, res)); + } + + // Store result + vstore16(indx, 0, (__global DATA_TYPE_OUTPUT *)output.ptr); +} +#endif // defined(HEIGHT) + +#if defined(DEPTH) +/** This kernel performs reduction on z-axis. + * + * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float + * @note The data type of the intermediate results must be passed at compile time using -DDATA_TYPE_PROMOTED: e.g. -DDATA_TYPE_PROMOTED=uint + * @note The depth size must be passed at compile time using -DDEPTH e.g. -DDEPTH=128 + * + * @param[in] input_ptr Pointer to the source tensor. Supported data types: S32/F16/F32 + * @param[in] input_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] input_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[in] output_ptr The local buffer to hold sumed values. Supported data types: U32/S32 + * @param[in] output_stride_x Stride of the output tensor in X dimension (in bytes) + * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y Stride of the output tensor in Y dimension (in bytes) + * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z Stride of the output tensor in Z dimension (in bytes) + * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes The offset of the first element in the source tensor + */ +__kernel void arg_min_max_z( + TENSOR3D_DECLARATION(input), + TENSOR3D_DECLARATION(output)) +{ + Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); + Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); + + VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16) + res = CONVERT(vload16(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0)), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16)); + + VEC_DATA_TYPE(DATA_TYPE_OUTPUT, 16) + indx = 0; + for(DATA_TYPE_OUTPUT z = 1; z < DEPTH; ++z) + { + VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16) + in = CONVERT(vload16(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, z)), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16)); + + VEC_DATA_TYPE(DATA_TYPE_OUTPUT, 16) + cond_conv = CONVERT(CONDITION_TO_USE(in, res), VEC_DATA_TYPE(DATA_TYPE_OUTPUT, 16)); + indx = select(indx, z, cond_conv); + res = select(res, in, CONDITION_TO_USE(in, res)); + } + + // Store result + vstore16(indx, 0, (__global DATA_TYPE_OUTPUT *)output.ptr); +} +#endif /* defined(DEPTH) */ + +#if defined(BATCH) && defined(DEPTH) +/** This kernel performs reduction on w-axis. + * + * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float + * @note The data type of the intermediate results must be passed at compile time using -DDATA_TYPE_PROMOTED: e.g. -DDATA_TYPE_PROMOTED=uint + * @note The batch size must be passed at compile time using -DBATCH e.g. -DBATCH=128 + * @note The depth size must be passed at compile time using -DBATCH e.g. -DDEPTH=128 + * + * @param[in] input_ptr Pointer to the source tensor. Supported data types: S32/F16/F32 + * @param[in] input_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] input_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] input_stride_w Stride of the source tensor in W dimension (in bytes) + * @param[in] input_step_w input_stride_w * number of elements along W processed per workitem(in bytes) + * @param[in] input_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[in] output_ptr The local buffer to hold sumed values. Supported data types: U32/S32 + * @param[in] output_stride_x Stride of the output tensor in X dimension (in bytes) + * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] output_stride_y Stride of the output tensor in Y dimension (in bytes) + * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] output_stride_z Stride of the output tensor in Z dimension (in bytes) + * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] output_stride_w Stride of the output tensor in W dimension (in bytes) + * @param[in] output_step_w output_stride_w * number of elements along W processed per workitem(in bytes) + * @param[in] output_offset_first_element_in_bytes The offset of the first element in the source tensor + */ +__kernel void arg_min_max_w( + TENSOR4D_DECLARATION(input), + TENSOR4D_DECLARATION(output)) +{ + Tensor4D input = CONVERT_TO_TENSOR4D_STRUCT(input, DEPTH); + Tensor4D output = CONVERT_TO_TENSOR4D_STRUCT(output, DEPTH); + + VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16) + res = CONVERT(vload16(0, (__global DATA_TYPE *)tensor4D_offset(&input, 0, 0, 0, 0)), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16)); + + VEC_DATA_TYPE(DATA_TYPE_OUTPUT, 16) + indx = 0; + for(DATA_TYPE_OUTPUT w = 1; w < BATCH; ++w) + { + VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16) + in = CONVERT(vload16(0, (__global DATA_TYPE *)tensor4D_offset(&input, 0, 0, 0, w)), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16)); + + VEC_DATA_TYPE(DATA_TYPE_OUTPUT, 16) + cond_conv = CONVERT(CONDITION_TO_USE(in, res), VEC_DATA_TYPE(DATA_TYPE_OUTPUT, 16)); + indx = select(indx, w, cond_conv); + res = select(res, in, CONDITION_TO_USE(in, res)); + } + + // Store result + vstore16(indx, 0, (__global DATA_TYPE_OUTPUT *)output.ptr); +} +#endif /* defined(BATCH) && defined(DEPTH) */ +#endif // defined(DATA_TYPE_OUTPUT)
\ No newline at end of file diff --git a/src/core/CL/cl_kernels/helpers.h b/src/core/CL/cl_kernels/helpers.h index eaeaa6034d..ec5701dc69 100644 --- a/src/core/CL/cl_kernels/helpers.h +++ b/src/core/CL/cl_kernels/helpers.h @@ -266,6 +266,19 @@ #define CONVERT_SAT_ROUND_STR(x, type, round) (convert_##type##_sat_##round((x))) #define CONVERT_SAT_ROUND(x, type, round) CONVERT_SAT_ROUND_STR(x, type, round) +#if FLOAT_DATA_TYPE +#define ISGREATER(x, y) isgreater(x, y) +#define ISLESS(x, y) isless(x, y) +#else // !FLOAT_DATA_TYPE +#if defined(WIDTH) +#define ISGREATER(x, y) (x > y) ? 1 : 0 +#define ISLESS(x, y) (x < y) ? 1 : 0 +#else // !defined(WIDTH) +#define ISGREATER(x, y) select((int16)0, (int16)-1, x > y) +#define ISLESS(x, y) select((int16)0, (int16)-1, x < y) +#endif // defined(WIDTH) +#endif // FLOAT_DATA_TYPE + #define VECTOR_DECLARATION(name) \ __global uchar *name##_ptr, \ uint name##_stride_x, \ diff --git a/src/core/CL/cl_kernels/reduction_operation.cl b/src/core/CL/cl_kernels/reduction_operation.cl index 5a4bb9ff4c..451b962b01 100644 --- a/src/core/CL/cl_kernels/reduction_operation.cl +++ b/src/core/CL/cl_kernels/reduction_operation.cl @@ -23,19 +23,6 @@ */ #include "helpers.h" -#if FLOAT_DATA_TYPE -#define ISGREATER(x, y) isgreater(x, y) -#define ISLESS(x, y) isless(x, y) -#else // !FLOAT_DATA_TYPE -#if defined(WIDTH) -#define ISGREATER(x, y) (x > y) ? 1 : 0 -#define ISLESS(x, y) (x < y) ? 1 : 0 -#else // !defined(WIDTH) -#define ISGREATER(x, y) select((int16)0, (int16)-1, x > y) -#define ISLESS(x, y) select((int16)0, (int16)-1, x < y) -#endif // defined(WIDTH) -#endif // FLOAT_DATA_TYPE - /** Calculate square sum of a vector * * @param[in] input Pointer to the first pixel. @@ -164,7 +151,7 @@ __kernel void reduction_operation_x( * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float * @note The width size must be passed at compile time using -DWIDTH e.g. -DWIDTH=128 * @note The product flag must be passed at compile time using -DPROD if we want to compute the product, otherwise sum will be used - * @note In case of ARG_MIN and ARG_MAX the condition data type must be passed at compile time using -DCOND_DATA_TYPE e.g. -DCOND_DATA_TYPE=short + * @note In case of MIN and MAX the condition data type must be passed at compile time using -DCOND_DATA_TYPE e.g. -DCOND_DATA_TYPE=short * * @param[in] src_ptr Pointer to the source tensor. Supported data types: S32/F16/F32 and QASYMM8 for operation MEAN * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) @@ -184,32 +171,19 @@ __kernel void reduction_operation_non_parallel_x( DATA_TYPE_PROMOTED res = *((__global DATA_TYPE *)vector_offset(&src, 0)); -#if defined(ARG_MAX) || defined(ARG_MIN) - uint indx = 0; -#endif // defined(ARG_MAX) || defined(ARG_MIN) - for(unsigned int x = 1; x < WIDTH; ++x) { DATA_TYPE_PROMOTED in = *((__global DATA_TYPE *)vector_offset(&src, x)); -#if defined(ARG_MAX) - indx = select(indx, x, ISGREATER(in, res)); - res = select(res, in, CONVERT(ISGREATER(in, res), COND_DATA_TYPE)); -#elif defined(ARG_MIN) - indx = select(indx, x, ISLESS(in, res)); - res = select(res, in, CONVERT(ISLESS(in, res), COND_DATA_TYPE)); -#elif defined(MIN) +#if defined(MIN) res = select(res, in, CONVERT(ISLESS(in, res), COND_DATA_TYPE)); #elif defined(MAX) - res = select(res, in, CONVERT(ISGREATER(in, res), COND_DATA_TYPE)); -#else // !(defined(ARG_MAX) || defined(ARG_MIN)) + res = select(res, in, CONVERT(ISGREATER(in, res), COND_DATA_TYPE)); +#else // !(defined(MAX) || defined(MIN)) res += in; -#endif // defined(ARG_MAX) || defined(ARG_MIN) +#endif // defined(MAX) || defined(MIN) } // Store result -#if defined(ARG_MAX) || defined(ARG_MIN) - *((__global uint *)output.ptr) = indx; -#else // !(defined(ARG_MAX) || defined(ARG_MIN)) #if defined(MEAN) res /= WIDTH; #endif // defined(MEAN) @@ -218,7 +192,6 @@ __kernel void reduction_operation_non_parallel_x( #else // defined(MIN) || defined(MAX) *((__global uchar *)output.ptr) = convert_uchar(res); #endif // defined(MIN) || defined(MAX) -#endif // defined(ARG_MAX) || defined(ARG_MIN) } #endif // defined(WIDTH) @@ -255,27 +228,15 @@ __kernel void reduction_operation_y( res *= res; #endif // defined(SUM_SQUARE) -#if defined(ARG_MAX) || defined(ARG_MIN) - uint16 indx = 0; -#endif // defined(ARG_MAX) || defined(ARG_MIN) - for(unsigned int y = 1; y < HEIGHT; ++y) { VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16) in = CONVERT(vload16(0, (__global DATA_TYPE *)offset(&src, 0, y)), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16)); -#if defined(ARG_MAX) - uint16 cond_conv = CONVERT(ISGREATER(in, res), uint16); - indx = select(indx, y, cond_conv); - res = select(res, in, ISGREATER(in, res)); -#elif defined(ARG_MIN) - uint16 cond_conv = CONVERT(ISLESS(in, res), uint16); - indx = select(indx, y, cond_conv); - res = select(res, in, ISLESS(in, res)); -#elif defined(MIN) +#if defined(MIN) res = select(res, in, ISLESS(in, res)); #elif defined(MAX) - res = select(res, in, ISGREATER(in, res)); -#else // !(defined(ARG_MAX) || defined(ARG_MIN)) + res = select(res, in, ISGREATER(in, res)); +#else // !(defined(MAX) || defined(MIN)) #if defined(SUM_SQUARE) in *= in; #endif // defined(SUM_SQUARE) @@ -284,18 +245,14 @@ __kernel void reduction_operation_y( #else // !defined(PROD) res += in; #endif // defined(PROD) -#endif // defined(ARG_MAX) || defined(ARG_MIN) +#endif // defined(MAX) || defined(MIN) } // Store result -#if defined(ARG_MAX) || defined(ARG_MIN) - vstore16(indx, 0, (__global uint *)output.ptr); -#else // !(defined(ARG_MAX) || defined(ARG_MIN)) #if defined(MEAN) res /= HEIGHT; #endif // defined(MEAN) vstore16(CONVERT(res, VEC_DATA_TYPE(DATA_TYPE, 16)), 0, (__global DATA_TYPE *)output.ptr); -#endif // defined(ARG_MAX) || defined(ARG_MIN) } #endif // defined(HEIGHT) @@ -340,10 +297,6 @@ __kernel void reduction_operation_z( res *= res; #endif // defined(SUM_SQUARE) -#if defined(ARG_MAX) || defined(ARG_MIN) - uint16 indx = 0; -#endif // defined(ARG_MAX) || defined(ARG_MIN) - for(unsigned int z = 1; z < DEPTH; ++z) { VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16) @@ -354,19 +307,11 @@ __kernel void reduction_operation_z( in1 = CONVERT(vload16(0, (__global DATA_TYPE *)tensor3D_offset(&input, 8, 0, z)), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16)); #endif // defined(COMPLEX) -#if defined(ARG_MAX) - uint16 cond_conv = CONVERT(ISGREATER(in, res), uint16); - indx = select(indx, z, cond_conv); - res = select(res, in, ISGREATER(in, res)); -#elif defined(ARG_MIN) - uint16 cond_conv = CONVERT(ISLESS(in, res), uint16); - indx = select(indx, z, cond_conv); - res = select(res, in, ISLESS(in, res)); -#elif defined(MIN) +#if defined(MIN) res = select(res, in, ISLESS(in, res)); #elif defined(MAX) - res = select(res, in, ISGREATER(in, res)); -#else // !(defined(ARG_MAX) || defined(ARG_MIN)) + res = select(res, in, ISGREATER(in, res)); +#else // !(defined(MAX) || defined(MIN)) #if defined(SUM_SQUARE) in *= in; #endif // defined(SUM_SQUARE) @@ -377,14 +322,11 @@ __kernel void reduction_operation_z( #if defined(COMPLEX) res1 += in1; #endif // defined(COMPLEX) -#endif //defined(PROD) -#endif // defined(ARG_MAX) || defined(ARG_MIN) +#endif // defined(PROD) +#endif // defined(MAX) || defined(MIN) } // Store result -#if defined(ARG_MAX) || defined(ARG_MIN) - vstore16(indx, 0, (__global uint *)output.ptr); -#else // !(defined(ARG_MAX) || defined(ARG_MIN)) #if defined(MEAN) res /= DEPTH; #endif // defined(MEAN) @@ -392,7 +334,6 @@ __kernel void reduction_operation_z( #if defined(COMPLEX) vstore16(CONVERT(res1, VEC_DATA_TYPE(DATA_TYPE, 16)), 0, (__global DATA_TYPE *)tensor3D_offset(&output, 8, 0, 0)); #endif // defined(COMPLEX) -#endif // defined(ARG_MAX) || defined(ARG_MIN) } #endif /* defined(DEPTH) */ @@ -438,28 +379,16 @@ __kernel void reduction_operation_w( res *= res; #endif // defined(SUM_SQUARE) -#if defined(ARG_MAX) || defined(ARG_MIN) - uint16 indx = 0; -#endif // defined(ARG_MAX) || defined(ARG_MIN) - for(unsigned int w = 1; w < BATCH; ++w) { VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16) in = CONVERT(vload16(0, (__global DATA_TYPE *)tensor4D_offset(&input, 0, 0, 0, w)), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16)); -#if defined(ARG_MAX) - uint16 cond_conv = CONVERT(ISGREATER(in, res), uint16); - indx = select(indx, w, cond_conv); - res = select(res, in, ISGREATER(in, res)); -#elif defined(ARG_MIN) - uint16 cond_conv = CONVERT(ISLESS(in, res), uint16); - indx = select(indx, w, cond_conv); - res = select(res, in, ISLESS(in, res)); -#elif defined(MIN) +#if defined(MIN) res = select(res, in, ISLESS(in, res)); #elif defined(MAX) - res = select(res, in, ISGREATER(in, res)); -#else // !(defined(ARG_MAX) || defined(ARG_MIN)) + res = select(res, in, ISGREATER(in, res)); +#else // !(defined(MAX) || defined(MIN)) #if defined(SUM_SQUARE) in *= in; #endif // defined(SUM_SQUARE) @@ -468,17 +397,13 @@ __kernel void reduction_operation_w( #else //!defined(PROD) res += in; #endif //defined(PROD) -#endif // defined(ARG_MAX) || defined(ARG_MIN) +#endif // defined(MAX) || defined(MIN) } // Store result -#if defined(ARG_MAX) || defined(ARG_MIN) - vstore16(indx, 0, (__global uint *)output.ptr); -#else // !(defined(ARG_MAX) || defined(ARG_MIN)) #if defined(MEAN) res /= BATCH; #endif // defined(MEAN) vstore16(CONVERT(res, VEC_DATA_TYPE(DATA_TYPE, 16)), 0, (__global DATA_TYPE *)output.ptr); -#endif // defined(ARG_MAX) || defined(ARG_MIN) } #endif /* defined(BATCH) && defined(DEPTH) */ diff --git a/src/core/CL/kernels/CLArgMinMaxLayerKernel.cpp b/src/core/CL/kernels/CLArgMinMaxLayerKernel.cpp new file mode 100644 index 0000000000..c8e87ba5ce --- /dev/null +++ b/src/core/CL/kernels/CLArgMinMaxLayerKernel.cpp @@ -0,0 +1,283 @@ +/* + * Copyright (c) 2019 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "arm_compute/core/CL/kernels/CLArgMinMaxLayerKernel.h" + +#include "arm_compute/core/AccessWindowStatic.h" +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibrary.h" +#include "arm_compute/core/CL/CLValidate.h" +#include "arm_compute/core/CL/ICLTensor.h" +#include "arm_compute/core/Helpers.h" +#include "arm_compute/core/TensorInfo.h" +#include "arm_compute/core/Utils.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/core/Window.h" + +#include "support/ToolchainSupport.h" + +namespace arm_compute +{ +namespace +{ +constexpr unsigned int vector_size = 16; + +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *prev_output, const ITensorInfo *output, unsigned int axis, ReductionOperation op) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); + ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(op != ReductionOperation::ARG_IDX_MAX && op != ReductionOperation::ARG_IDX_MIN, "Only ARG_IDX_MAX and ARG_IDX_MIN are supported"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis >= TensorShape::num_max_dimensions, "Reduction axis greater than max number of dimensions"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis > 3, "Unsupported reduction axis"); + + if(output->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U32, DataType::S32); + } + if(prev_output != nullptr && prev_output->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(prev_output, 1, DataType::U32, DataType::S32); + if(output->total_size() != 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(prev_output, output); + } + } + + return Status{}; +} + +std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *prev_output, ITensorInfo *output, unsigned int axis, ReductionOperation op) +{ + ARM_COMPUTE_UNUSED(op); + // Output tensor auto initialization if not yet initialized + TensorShape output_shape{ input->tensor_shape() }; + output_shape.set(axis, 1); + DataType output_data_type = DataType::S32; + auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape).set_data_type(output_data_type).reset_padding().set_is_resizable(true)); + + Window win = calculate_max_window((prev_output != nullptr) ? (*prev_output) : (*input), Steps(vector_size)); + bool window_changed = false; + + switch(axis) + { + case 0: + { + ITensorInfo *input_tensor_access = prev_output != nullptr ? prev_output : input; + AccessWindowStatic input_access(input_tensor_access, 0, 0, static_cast<int>(input_tensor_access->dimension(0)), 1); + AccessWindowHorizontal output_access(output, 0, 1); + window_changed = update_window_and_padding(win, input_access, output_access); + output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); + } + break; + case 1: + case 2: + case 3: + { + AccessWindowHorizontal input_access(input, 0, vector_size); + AccessWindowHorizontal output_access(output, 0, vector_size); + window_changed = update_window_and_padding(win, input_access, output_access); + output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); + } + break; + default: + ARM_COMPUTE_ERROR("Not supported"); + } + + Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; + return std::make_tuple(err, win); +} +} // namespace + +CLArgMinMaxLayerKernel::CLArgMinMaxLayerKernel() + : _input(nullptr), _prev_output(nullptr), _output(nullptr), _reduction_axis(0), _op(ReductionOperation::ARG_IDX_MAX) +{ +} + +void CLArgMinMaxLayerKernel::configure(const ICLTensor *input, const ICLTensor *prev_output, ICLTensor *output, unsigned int axis, ReductionOperation op) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (prev_output != nullptr) ? prev_output->info() : nullptr, output->info(), axis, op)); + auto win_config = validate_and_configure_window(input->info(), (prev_output != nullptr) ? prev_output->info() : nullptr, output->info(), axis, op); + ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config)); + + _input = input; + _prev_output = prev_output; + _output = output; + _reduction_axis = axis; + _op = op; + + // Set build options + CLBuildOptions build_opts; + const std::string data_type_promoted = get_cl_type_from_data_type(input->info()->data_type()); + + build_opts.add_option_if(_prev_output != nullptr, "-DPREV_OUTPUT"); + build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); + build_opts.add_option("-DDATA_TYPE_PROMOTED=" + data_type_promoted); + build_opts.add_option_if(is_data_type_float(input->info()->data_type()), "-DFLOAT_DATA_TYPE"); + build_opts.add_option_if(op == ReductionOperation::ARG_IDX_MAX, "-DARG_MAX"); + build_opts.add_option_if(op == ReductionOperation::ARG_IDX_MIN, "-DARG_MIN"); + build_opts.add_option("-DCOND_DATA_TYPE=" + get_cl_select_type_from_data_type(input->info()->data_type())); + build_opts.add_option("-DDATA_TYPE_OUTPUT=" + get_cl_type_from_data_type(output->info()->data_type())); + + // Create kernel + cl::NDRange lws_hint = CLKernelLibrary::get().default_ndrange(); + std::string kernel_axis_name; + switch(axis) + { + case 0: + { + const ICLTensor *input_for_width = prev_output != nullptr ? _prev_output : _input; + build_opts.add_option("-DWIDTH=" + support::cpp11::to_string(input_for_width->info()->dimension(0))); + + kernel_axis_name = "x"; + lws_hint = create_lws_hint_parallel_implementations(input_for_width->info()->dimension(0), vector_size); + } + break; + case 1: + build_opts.add_option("-DHEIGHT=" + support::cpp11::to_string(input->info()->dimension(1))); + kernel_axis_name = "y"; + break; + case 2: + build_opts.add_option("-DDEPTH=" + support::cpp11::to_string(input->info()->dimension(2))); + kernel_axis_name = "z"; + break; + case 3: + build_opts.add_option("-DDEPTH=" + support::cpp11::to_string(input->info()->dimension(2))); + build_opts.add_option("-DBATCH=" + support::cpp11::to_string(input->info()->dimension(3))); + kernel_axis_name = "w"; + break; + default: + ARM_COMPUTE_ERROR("Not supported"); + } + _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("arg_min_max_" + kernel_axis_name, build_opts.options())); + + // Configure kernel window + ICLKernel::configure_internal(std::get<1>(win_config), lws_hint); +} + +Status CLArgMinMaxLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *prev_output, const ITensorInfo *output, unsigned int axis, ReductionOperation op) +{ + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, prev_output, output, axis, op)); + ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), (prev_output != nullptr) ? prev_output->clone().get() : nullptr, output->clone().get(), axis, op))); + return Status{}; +} + +void CLArgMinMaxLayerKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); + + switch(_reduction_axis) + { + case 0: + { + // Set out window + Window out_window(window); + out_window.set(Window::DimX, Window::Dimension(0, 0, 0)); + + // Get first input and output slices + Window in_slice = window.first_slice_window_2D(); + Window out_slice = out_window.first_slice_window_2D(); + + // Reshape window + const unsigned int border_width = ((in_slice.x().end() % vector_size) != 0) ? vector_size - in_slice.x().end() % vector_size : 0; + in_slice.set(Window::DimX, Window::Dimension(in_slice.x().start(), in_slice.x().end() + border_width, in_slice.x().step())); + const unsigned int num_tensors = _prev_output != nullptr ? 3 : 2; + + // Set local sums buffer + unsigned int local_res_size = lws_hint()[0] * _output->info()->element_size(); + _kernel.setArg(num_arguments_per_2D_tensor() * num_tensors, local_res_size, nullptr); + do + { + unsigned int idx = 0; + add_2D_tensor_argument(idx, _input, in_slice); + if(_prev_output != nullptr) + { + add_2D_tensor_argument(idx, _prev_output, in_slice); + } + add_2D_tensor_argument(idx, _output, out_slice); + enqueue(queue, *this, in_slice, lws_hint()); + } + while(window.slide_window_slice_2D(in_slice) && window.slide_window_slice_2D(out_slice)); + } + break; + case 1: + { + // Get first input and output slices + Window window_in{ window }; + window_in.set(Window::DimY, Window::Dimension(0, _input->info()->dimension(1), _input->info()->dimension(1))); + Window in_slice = window_in.first_slice_window_2D(); + Window out_slice = window.first_slice_window_2D(); + + do + { + unsigned int idx = 0; + add_2D_tensor_argument(idx, _input, in_slice); + add_2D_tensor_argument(idx, _output, out_slice); + enqueue(queue, *this, in_slice, lws_hint()); + } + while(window_in.slide_window_slice_2D(in_slice) && window.slide_window_slice_2D(out_slice)); + } + break; + case 2: + { + // Get first input and output slices + Window window_in{ window }; + window_in.set(Window::DimZ, Window::Dimension(0, _input->info()->dimension(2), _input->info()->dimension(2))); + Window in_slice = window_in.first_slice_window_3D(); + Window out_slice = window.first_slice_window_3D(); + + do + { + unsigned int idx = 0; + add_3D_tensor_argument(idx, _input, in_slice); + add_3D_tensor_argument(idx, _output, out_slice); + enqueue(queue, *this, in_slice, lws_hint()); + } + while(window_in.slide_window_slice_3D(in_slice) && window.slide_window_slice_3D(out_slice)); + } + break; + case 3: + { + // Get first input and output slices + Window window_in{ window }; + window_in.set(3, Window::Dimension(0, 1, 1)); + Window in_slice = window_in.first_slice_window_4D(); + Window out_slice = window.first_slice_window_4D(); + + do + { + unsigned int idx = 0; + add_4D_tensor_argument(idx, _input, in_slice); + add_4D_tensor_argument(idx, _output, out_slice); + enqueue(queue, *this, in_slice, lws_hint()); + } + while(window_in.slide_window_slice_4D(in_slice) && window.slide_window_slice_4D(out_slice)); + } + break; + default: + ARM_COMPUTE_ERROR("Not supported"); + } +} +} // namespace arm_compute diff --git a/src/core/CL/kernels/CLReductionOperationKernel.cpp b/src/core/CL/kernels/CLReductionOperationKernel.cpp index cbf3923243..91ee83e530 100644 --- a/src/core/CL/kernels/CLReductionOperationKernel.cpp +++ b/src/core/CL/kernels/CLReductionOperationKernel.cpp @@ -60,19 +60,12 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, u ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis >= TensorShape::num_max_dimensions, "Reduction axis greater than max number of dimensions"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis > 3, "Unsupported reduction axis"); ARM_COMPUTE_RETURN_ERROR_ON(op == ReductionOperation::MEAN_SUM && axis == 0 && width == 0 && input->data_type() != DataType::QASYMM8); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(op == ReductionOperation::ARG_IDX_MAX || op == ReductionOperation::ARG_IDX_MIN, "Not supported reduction operation, use CLArgMinMaxLayer"); if(output->total_size() != 0) { - if(op == ReductionOperation::ARG_IDX_MAX || op == ReductionOperation::ARG_IDX_MIN) - { - ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_type() == DataType::QASYMM8, "Not supported operation for QASYMM8"); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U32, DataType::S32); - } - else - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output); - } + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output); } return Status{}; @@ -81,9 +74,8 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, u std::tuple<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, unsigned int axis, ReductionOperation op) { // Output tensor auto initialization if not yet initialized - const bool is_arg_min_max = (op == ReductionOperation::ARG_IDX_MIN || op == ReductionOperation::ARG_IDX_MAX); - const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_reduced_shape(input->tensor_shape(), axis, !is_arg_min_max); - const DataType output_data_type = is_arg_min_max ? DataType::S32 : input->data_type(); + const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_reduced_shape(input->tensor_shape(), axis, true); + DataType output_data_type = input->data_type(); auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape).set_data_type(output_data_type).reset_padding().set_is_resizable(true)); const unsigned int num_elems_processed_per_iteration = (is_data_type_quantized(input->data_type()) && (axis == 0)) ? 1 : 16; @@ -166,8 +158,6 @@ void CLReductionOperationKernel::configure(const ICLTensor *input, ICLTensor *ou build_opts.add_option_if(is_data_type_float(input->info()->data_type()), "-DFLOAT_DATA_TYPE"); build_opts.add_option_if(op == ReductionOperation::SUM_SQUARE, "-DSUM_SQUARE"); build_opts.add_option_if(op == ReductionOperation::MEAN_SUM, "-DMEAN"); - build_opts.add_option_if(op == ReductionOperation::ARG_IDX_MAX, "-DARG_MAX"); - build_opts.add_option_if(op == ReductionOperation::ARG_IDX_MIN, "-DARG_MIN"); build_opts.add_option_if(op == ReductionOperation::PROD, "-DPROD"); build_opts.add_option_if(op == ReductionOperation::MIN, "-DMIN"); build_opts.add_option_if(op == ReductionOperation::MAX, "-DMAX"); @@ -182,8 +172,6 @@ void CLReductionOperationKernel::configure(const ICLTensor *input, ICLTensor *ou case ReductionOperation::MEAN_SUM: build_opts.add_option(("-DOPERATION=sum")); break; - case ReductionOperation::ARG_IDX_MAX: - case ReductionOperation::ARG_IDX_MIN: case ReductionOperation::MIN: case ReductionOperation::MAX: break; @@ -214,12 +202,9 @@ void CLReductionOperationKernel::configure(const ICLTensor *input, ICLTensor *ou build_opts.add_option_if(op == ReductionOperation::MEAN_SUM, "-DWIDTH=" + support::cpp11::to_string(width)); const unsigned int width_leftover = input->info()->dimension(0) % border_val; const unsigned int border_width = (width_leftover != 0) ? border_val - width_leftover : 0; - const unsigned int num_of_threads = ((input->info()->dimension(0) + border_width) / 16); kernel_axis_name = "x"; - // Set the number of WG based on the input size. If input width is < 128 - // we can use fewer threads than 8. - lws_hint = cl::NDRange(std::min(8U, num_of_threads)); + lws_hint = create_lws_hint_parallel_implementations(input->info()->dimension(0), border_val); _border_size = BorderSize(0, border_width, 0, 0); } } diff --git a/src/core/Utils.cpp b/src/core/Utils.cpp index cbf6e48375..fa56118587 100644 --- a/src/core/Utils.cpp +++ b/src/core/Utils.cpp @@ -431,12 +431,11 @@ std::pair<unsigned int, unsigned int> arm_compute::scaled_dimensions(unsigned in bool arm_compute::needs_serialized_reduction(ReductionOperation op, DataType dt, unsigned int axis) { - const bool is_arg_min_max = (op == ReductionOperation::ARG_IDX_MAX || op == ReductionOperation::ARG_IDX_MIN); const bool is_min_max = (op == ReductionOperation::MAX || op == ReductionOperation::MIN); const bool is_quantized_type = is_data_type_quantized(dt); const bool is_first_dim = (axis == 0); - return !is_first_dim || is_arg_min_max || is_min_max || is_quantized_type; + return !is_first_dim || is_min_max || is_quantized_type; } #ifdef ARM_COMPUTE_ASSERTS_ENABLED diff --git a/src/runtime/CL/functions/CLArgMinMaxLayer.cpp b/src/runtime/CL/functions/CLArgMinMaxLayer.cpp index fd172d5f2c..4ac6d25d75 100644 --- a/src/runtime/CL/functions/CLArgMinMaxLayer.cpp +++ b/src/runtime/CL/functions/CLArgMinMaxLayer.cpp @@ -23,33 +23,145 @@ */ #include "arm_compute/runtime/CL/functions/CLArgMinMaxLayer.h" -#include "arm_compute/runtime/CL/functions/CLReductionOperation.h" #include "arm_compute/core/Error.h" #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/Types.h" #include "arm_compute/core/Validate.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "arm_compute/runtime/Utils.h" namespace arm_compute { CLArgMinMaxLayer::CLArgMinMaxLayer(std::shared_ptr<IMemoryManager> memory_manager) - : _reduction_function(support::cpp14::make_unique<CLReductionOperation>(std::move(memory_manager))) + : _memory_group(std::move(memory_manager)), _results_vector(), _not_reshaped_output(), _reduction_kernels_vector(), _reshape_kernel(), _num_of_stages(), _reduction_axis() { } -void CLArgMinMaxLayer::configure(ICLTensor *input, int axis, ICLTensor *output, const ReductionOperation &op) +Status CLArgMinMaxLayer::validate(const ITensorInfo *input, int axis, const ITensorInfo *output, const ReductionOperation &op) { - _reduction_function->configure(input, output, axis, op, false); + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(op != ReductionOperation::ARG_IDX_MAX && op != ReductionOperation::ARG_IDX_MIN, "Invalid reduction operation"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis >= static_cast<int>(TensorShape::num_max_dimensions), "Reduction axis greater than max number of dimensions"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis > 3, "Unsupported reduction axis"); + const unsigned int num_of_stages = calculate_number_of_stages_only_x_axis(input->dimension(0), axis); + + DataType output_data_type = DataType::S32; + TensorInfo not_reshaped_output; + const auto input_num_channles = input->num_channels(); + const auto input_qinfo = input->quantization_info(); + + if(output->total_size() != 0) + { + output_data_type = output->data_type(); + const TensorInfo expected_output_shape = output->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_reduced_shape(input->tensor_shape(), axis, false)); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&expected_output_shape, output); + } + + auto shape_before_reshape = input->tensor_shape(); + shape_before_reshape.set(axis, 1); + auto initialize_tensorinfo = [](TensorInfo & ti, TensorShape shape, DataType data_type, int num_channels, QuantizationInfo qinfo) + { + ti.set_data_type(data_type).set_tensor_shape(shape).set_num_channels(num_channels).set_quantization_info(qinfo); + }; + + initialize_tensorinfo(not_reshaped_output, shape_before_reshape, output_data_type, input_num_channles, input_qinfo); + + if(num_of_stages == 1) + { + ARM_COMPUTE_RETURN_ON_ERROR(CLArgMinMaxLayerKernel::validate(input, nullptr, ¬_reshaped_output, axis, op)); + } + else + { + // Create temporary tensor infos + std::vector<TensorInfo> sums_vector(num_of_stages - 1); + + // Create intermediate tensor info + TensorShape shape{ input->tensor_shape() }; + + for(unsigned int i = 0; i < num_of_stages - 1; i++) + { + shape.set(0, ceil(shape.x() / 128.f)); + sums_vector[i].set_data_type(input->data_type()); + sums_vector[i].set_tensor_shape(shape); + sums_vector[i].set_num_channels(input->num_channels()); + } + + // Validate ReductionOperation only on first kernel + ARM_COMPUTE_RETURN_ON_ERROR(CLArgMinMaxLayerKernel::validate(input, nullptr, &sums_vector[0], axis, op)); + + // Validate ReductionOperation on intermediate stages + for(unsigned int i = 1; i < num_of_stages - 1; ++i) + { + ARM_COMPUTE_RETURN_ON_ERROR(CLArgMinMaxLayerKernel::validate(input, &sums_vector[i - 1], &sums_vector[i], axis, op)); + } + + // Validate ReductionOperation on the last stage + const unsigned int last_stage = num_of_stages - 1; + ARM_COMPUTE_RETURN_ON_ERROR(CLArgMinMaxLayerKernel::validate(input, &sums_vector[last_stage - 1], ¬_reshaped_output, axis, op)); + } + ARM_COMPUTE_RETURN_ON_ERROR(CLReshapeLayerKernel::validate(¬_reshaped_output, output)); + return Status{}; } -Status CLArgMinMaxLayer::validate(const ITensorInfo *input, int axis, const ITensorInfo *output, const ReductionOperation &op) +void CLArgMinMaxLayer::configure(const ICLTensor *input, int axis, ICLTensor *output, const ReductionOperation &op) { - ARM_COMPUTE_RETURN_ERROR_ON_MSG(op != ReductionOperation::ARG_IDX_MAX && op != ReductionOperation::ARG_IDX_MIN, "Invalid operation"); - return CLReductionOperation::validate(input, output, axis, op, false); + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + _num_of_stages = calculate_number_of_stages_only_x_axis(input->info()->dimension(0), axis); + _reduction_axis = axis; + + const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_reduced_shape(input->info()->tensor_shape(), axis, false); + DataType output_data_type = (output->info()->data_type() == DataType::UNKNOWN) ? DataType::S32 : output->info()->data_type(); + auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape).set_data_type(output_data_type).reset_padding().set_is_resizable(true)); + + // Configure reduction operation kernels + _reduction_kernels_vector.resize(_num_of_stages); + + _memory_group.manage(&_not_reshaped_output); + // Create temporary tensors + if(_num_of_stages == 1) + { + _reduction_kernels_vector[0].configure(input, nullptr, &_not_reshaped_output, axis, op); + } + else + { + _results_vector.resize(_num_of_stages - 1); + TensorShape shape{ input->info()->tensor_shape() }; + for(unsigned int i = 0; i < _num_of_stages - 1; i++) + { + shape.set(0, ceil(shape.x() / 128.f)); + _results_vector[i].allocator()->init(input->info()->clone()->set_tensor_shape(shape).set_data_type(output_data_type)); + } + + // Apply ReductionOperation only on first kernel + _memory_group.manage(&_results_vector[0]); + _reduction_kernels_vector[0].configure(input, nullptr, &_results_vector[0], axis, op); + + // Apply ReductionOperation on intermediate stages + for(unsigned int i = 1; i < _num_of_stages - 1; ++i) + { + _memory_group.manage(&_results_vector[i]); + _reduction_kernels_vector[i].configure(input, &_results_vector[i - 1], &_results_vector[i], axis, op); + _results_vector[i - 1].allocator()->allocate(); + } + + // Apply ReductionOperation on the last stage + const unsigned int last_stage = _num_of_stages - 1; + _reduction_kernels_vector[last_stage].configure(input, &_results_vector[last_stage - 1], &_not_reshaped_output, axis, op); + _results_vector[last_stage - 1].allocator()->allocate(); + } + _reshape_kernel.configure(&_not_reshaped_output, output); + _not_reshaped_output.allocator()->allocate(); } void CLArgMinMaxLayer::run() { - _reduction_function->run(); + MemoryGroupResourceScope scope_mg(_memory_group); + + for(unsigned int i = 0; i < _num_of_stages; ++i) + { + CLScheduler::get().enqueue(_reduction_kernels_vector[i], false); + } + CLScheduler::get().enqueue(_reshape_kernel, false); } } // namespace arm_compute
\ No newline at end of file diff --git a/src/runtime/CL/functions/CLReductionOperation.cpp b/src/runtime/CL/functions/CLReductionOperation.cpp index 3aa5a813b6..2f9a38601d 100644 --- a/src/runtime/CL/functions/CLReductionOperation.cpp +++ b/src/runtime/CL/functions/CLReductionOperation.cpp @@ -33,30 +33,11 @@ #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/runtime/CL/CLScheduler.h" #include "arm_compute/runtime/Tensor.h" +#include "arm_compute/runtime/Utils.h" #include "support/ToolchainSupport.h" namespace arm_compute { -namespace -{ -unsigned int calculate_number_of_stages(const ITensorInfo *input, unsigned int axis) -{ - // We need only 1 stage for all axis except x-axis and x-axis for QASYMM8. - if(axis != 0 || (axis == 0 && is_data_type_quantized(input->data_type()))) - { - return 1; - } - // Calculate number of WGs. 16 elements per thread, 8 threads per WG - const unsigned int num_of_wg = ceil(input->dimension(0) / 128.f); - - // Calculate number of stages. First stage performs op and the rest reduction sum - // depending on the size of the input. Last stage should have only 1 WG. - const unsigned int num_of_stages = num_of_wg / 128 + 2; - - return num_of_stages; -} -} // namespace - CLReductionOperation::CLReductionOperation(std::shared_ptr<IMemoryManager> memory_manager) : _memory_group(std::move(memory_manager)), _results_vector(), _reduction_kernels_vector(), _border_handlers_vector(), _reshape_kernel(), _op(), _num_of_stages(), _reduction_axis(), _is_serial(), _is_reshape_required(false) @@ -65,15 +46,15 @@ CLReductionOperation::CLReductionOperation(std::shared_ptr<IMemoryManager> memor Status CLReductionOperation::validate(const ITensorInfo *input, const ITensorInfo *output, unsigned int axis, ReductionOperation op, bool keep_dims) { + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis >= TensorShape::num_max_dimensions, "Reduction axis greater than max number of dimensions"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis > 3, "Unsupported reduction axis"); - const unsigned int num_of_stages = calculate_number_of_stages(input, axis); + const unsigned int num_of_stages = calculate_number_of_stages_only_x_axis(input->dimension(0), axis); const bool is_serial = needs_serialized_reduction(op, input->data_type(), axis); - const bool is_arg_min_max = (op == ReductionOperation::ARG_IDX_MAX) || (op == ReductionOperation::ARG_IDX_MIN); - const bool is_reshape_required = !keep_dims || is_arg_min_max; + const bool is_reshape_required = !keep_dims; - if(is_reshape_required) + if(is_reshape_required && output->total_size() != 0) { const TensorInfo expected_output_shape = output->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_reduced_shape(input->tensor_shape(), axis, keep_dims)); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&expected_output_shape, output); @@ -86,7 +67,7 @@ Status CLReductionOperation::validate(const ITensorInfo *input, const ITensorInf const auto input_data_type = input->data_type(); const auto input_num_channles = input->num_channels(); const auto input_qinfo = input->quantization_info(); - const auto output_data_type = is_arg_min_max ? DataType::S32 : output->data_type(); + const auto output_data_type = output->data_type(); auto initialize_tensorinfo = [](TensorInfo & ti, TensorShape shape, DataType data_type, int num_channels, QuantizationInfo qinfo) { @@ -184,8 +165,7 @@ ICLTensor *CLReductionOperation::configure_intermediate_result_vector(ICLTensor return output; } - auto intermediate_result_vector_size = _is_serial ? 1 : _num_of_stages; - const auto is_arg_min_max = (_op == ReductionOperation::ARG_IDX_MAX || _op == ReductionOperation::ARG_IDX_MIN); + auto intermediate_result_vector_size = _is_serial ? 1 : _num_of_stages; if(!_is_reshape_required) { @@ -206,30 +186,24 @@ ICLTensor *CLReductionOperation::configure_intermediate_result_vector(ICLTensor v.allocator()->init(input->info()->clone()->set_tensor_shape(shape)); } - if(is_arg_min_max) - { - _results_vector.back().info()->set_data_type(DataType::S32).set_is_resizable(true).reset_padding(); - } - return _is_reshape_required ? &_results_vector.back() : output; } void CLReductionOperation::configure(ICLTensor *input, ICLTensor *output, unsigned int axis, ReductionOperation op, bool keep_dims) { - _op = op; - _num_of_stages = calculate_number_of_stages(input->info(), axis); - _reduction_axis = axis; - _is_serial = needs_serialized_reduction(op, input->info()->data_type(), axis); - const bool is_arg_min_max = (op == ReductionOperation::ARG_IDX_MAX) || (op == ReductionOperation::ARG_IDX_MIN); - _is_reshape_required = !keep_dims || is_arg_min_max; + ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); + _op = op; + _num_of_stages = calculate_number_of_stages_only_x_axis(input->info()->dimension(0), axis); + _reduction_axis = axis; + _is_serial = needs_serialized_reduction(op, input->info()->data_type(), axis); + _is_reshape_required = !keep_dims; auto *output_internal = configure_intermediate_result_vector(input, output); - // ArgMinMax might not give initialized output tensor, so initialize here. if(_is_reshape_required) { const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_reduced_shape(input->info()->tensor_shape(), axis, false); - const auto output_data_type = is_arg_min_max ? DataType::S32 : input->info()->data_type(); + const auto output_data_type = input->info()->data_type(); auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape).set_data_type(output_data_type).reset_padding().set_is_resizable(true)); } diff --git a/src/runtime/Utils.cpp b/src/runtime/Utils.cpp index 70494be05c..2204ec11d7 100644 --- a/src/runtime/Utils.cpp +++ b/src/runtime/Utils.cpp @@ -25,6 +25,7 @@ #include "arm_compute/runtime/NEON/NEScheduler.h" +#include <cmath> #include <map> #include <string> @@ -61,4 +62,20 @@ void schedule_kernel_on_ctx(IRuntimeContext *ctx, ICPPKernel *kernel, const ISch NEScheduler::get().schedule(kernel, hints); } } + +unsigned int calculate_number_of_stages_only_x_axis(size_t input_x_dimension, unsigned int axis) +{ + // We need only 1 stage for all axis except x-axis + if(axis != 0) + { + return 1; + } + // Calculate number of WGs. 16 elements per thread, 8 threads per WG + const auto num_of_wg = static_cast<unsigned int>(ceil(input_x_dimension / 128.f)); + + // Calculate number of stages. First stage performs op and the rest reduction sum + // depending on the size of the input. Last stage should have only 1 WG. + const unsigned int num_of_stages = num_of_wg / 128 + 2; + return num_of_stages; +} } // namespace arm_compute diff --git a/tests/validation/CL/ArgMinMax.cpp b/tests/validation/CL/ArgMinMax.cpp index 5b2e6f34c6..275641cb35 100644 --- a/tests/validation/CL/ArgMinMax.cpp +++ b/tests/validation/CL/ArgMinMax.cpp @@ -42,6 +42,18 @@ namespace test { namespace validation { +namespace +{ +const auto ArgMinMaxSmallDataset = framework::dataset::make("Shape", +{ + TensorShape{ 2U, 7U, 1U, 3U }, + TensorShape{ 128U, 64U, 21U, 3U }, + TensorShape{ 2560, 2U, 2U, 2U }, +}); + +const auto ArgMinMaxLargeDataset = framework::dataset::make("Shape", +{ TensorShape{ 517U, 123U, 13U, 2U } }); +} // namespace TEST_SUITE(CL) TEST_SUITE(ArgMinMax) @@ -98,7 +110,17 @@ TEST_SUITE(S32) FIXTURE_DATA_TEST_CASE(RunSmall, CLArgMinMaxValidationFixture<int32_t>, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(datasets::Small4DShapes(), framework::dataset::make("DataType", DataType::S32)), framework::dataset::make("Axis", { 0, 1, 2, 3 })), framework::dataset::make("Operation", { ReductionOperation::ARG_IDX_MIN, ReductionOperation::ARG_IDX_MAX }))) + combine(combine(combine(ArgMinMaxSmallDataset, framework::dataset::make("DataType", DataType::S32)), framework::dataset::make("Axis", { 0, 1, 2, 3 })), + framework::dataset::make("Operation", { ReductionOperation::ARG_IDX_MIN, ReductionOperation::ARG_IDX_MAX }))) +{ + // Validate output + validate(CLAccessor(_target), _reference); +} +FIXTURE_DATA_TEST_CASE(RunLarge, + CLArgMinMaxValidationFixture<int32_t>, + framework::DatasetMode::NIGHTLY, + combine(combine(combine(ArgMinMaxLargeDataset, framework::dataset::make("DataType", DataType::S32)), framework::dataset::make("Axis", { 0, 1, 2, 3 })), + framework::dataset::make("Operation", { ReductionOperation::ARG_IDX_MIN, ReductionOperation::ARG_IDX_MAX }))) { // Validate output validate(CLAccessor(_target), _reference); @@ -110,7 +132,8 @@ TEST_SUITE(FP16) FIXTURE_DATA_TEST_CASE(RunSmall, CLArgMinMaxValidationFixture<half>, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(datasets::Small4DShapes(), framework::dataset::make("DataType", DataType::F16)), framework::dataset::make("Axis", { 0, 1, 2, 3 })), framework::dataset::make("Operation", { ReductionOperation::ARG_IDX_MIN, ReductionOperation::ARG_IDX_MAX }))) + combine(combine(combine(ArgMinMaxSmallDataset, framework::dataset::make("DataType", DataType::F16)), framework::dataset::make("Axis", { 0, 1, 2, 3 })), + framework::dataset::make("Operation", { ReductionOperation::ARG_IDX_MIN, ReductionOperation::ARG_IDX_MAX }))) { // Validate output validate(CLAccessor(_target), _reference); @@ -119,7 +142,8 @@ FIXTURE_DATA_TEST_CASE(RunSmall, FIXTURE_DATA_TEST_CASE(RunLarge, CLArgMinMaxValidationFixture<half>, framework::DatasetMode::NIGHTLY, - combine(combine(combine(datasets::Large4DShapes(), framework::dataset::make("DataType", DataType::F16)), framework::dataset::make("Axis", { 0, 1, 2, 3 })), framework::dataset::make("Operation", { ReductionOperation::ARG_IDX_MIN, ReductionOperation::ARG_IDX_MAX }))) + combine(combine(combine(ArgMinMaxLargeDataset, framework::dataset::make("DataType", DataType::F16)), framework::dataset::make("Axis", { 0, 1, 2, 3 })), + framework::dataset::make("Operation", { ReductionOperation::ARG_IDX_MIN, ReductionOperation::ARG_IDX_MAX }))) { // Validate output validate(CLAccessor(_target), _reference); @@ -130,7 +154,8 @@ TEST_SUITE(FP32) FIXTURE_DATA_TEST_CASE(RunSmall, CLArgMinMaxValidationFixture<float>, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(datasets::Small4DShapes(), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("Axis", { 0, 1, 2, 3 })), framework::dataset::make("Operation", { ReductionOperation::ARG_IDX_MIN, ReductionOperation::ARG_IDX_MAX }))) + combine(combine(combine(ArgMinMaxSmallDataset, framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("Axis", { 0, 1, 2, 3 })), + framework::dataset::make("Operation", { ReductionOperation::ARG_IDX_MIN, ReductionOperation::ARG_IDX_MAX }))) { // Validate output validate(CLAccessor(_target), _reference); @@ -139,7 +164,8 @@ FIXTURE_DATA_TEST_CASE(RunSmall, FIXTURE_DATA_TEST_CASE(RunLarge, CLArgMinMaxValidationFixture<float>, framework::DatasetMode::NIGHTLY, - combine(combine(combine(datasets::Large4DShapes(), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("Axis", { 0, 1, 2, 3 })), framework::dataset::make("Operation", { ReductionOperation::ARG_IDX_MIN, ReductionOperation::ARG_IDX_MAX }))) + combine(combine(combine(ArgMinMaxLargeDataset, framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("Axis", { 0, 1, 2, 3 })), + framework::dataset::make("Operation", { ReductionOperation::ARG_IDX_MIN, ReductionOperation::ARG_IDX_MAX }))) { // Validate output validate(CLAccessor(_target), _reference); |