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author | Francesco Petrogalli <francesco.petrogalli@arm.com> | 2022-06-30 10:22:01 +0000 |
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committer | Francesco Petrogalli <francesco.petrogalli@arm.com> | 2022-07-19 09:26:27 +0000 |
commit | 553f6953fe3bdfad53c11c25f305a16d79d83b24 (patch) | |
tree | 73642b948b79662096f593458c6138d2f7f48ec6 /arm_compute/runtime/NEON | |
parent | 99c46475daf277aa53e6747f9e41209f418fed33 (diff) | |
download | ComputeLibrary-553f6953fe3bdfad53c11c25f305a16d79d83b24.tar.gz |
[ONCPUML-951] Variable weight support for Convolution.
API changes for NEGEMMConvolutionLayer and CpuGemmConv2d
Built with:
scons neon=1 opencl=0 os=linux arch=armv8.2-a multi_isa=1 \
build=native -j32 Werror=false validation_tests=1 build_dir=opt \
standalone=1 asserts=1 experimental_fixed_format_kernels=1 .
Tested with:
./build/opt/tests/arm_compute_validation
Hardware where the test executable was run:
Neoverse N1
Test coverage:
* NEGEMMConvolutionLayer, CpuGemmConv2d
* NHWC (the only one supported by the fixed-format kernels)
* F16, F32
* Shapes: RunSmall
Change-Id: I4fd3e495a7cbf61210ea02d37440ba9652934e99
Signed-off-by: Francesco Petrogalli <francesco.petrogalli@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/7632
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'arm_compute/runtime/NEON')
-rw-r--r-- | arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h | 61 |
1 files changed, 60 insertions, 1 deletions
diff --git a/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h b/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h index cf5fb82398..2af11ad656 100644 --- a/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h +++ b/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2021 Arm Limited. + * Copyright (c) 2017-2022 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -122,6 +122,65 @@ public: const WeightsInfo &weights_info = WeightsInfo(), const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false, unsigned int num_groups = 1); + /** Static function to check if there is an optimized version of + * GEMM available for the input parameters. + * + * The method is intended to be used to find out the optimal + * memory layout to be used for the weights tensor when running + * variable weights execution. + * + * The user can query the database of optimised kernels in + * arm_gemm by specifying one of the enumerations of + * arm_gemm::WeightFormat in the weight_format field of the input + * parameter weights_info. In case of success, the method + * writes the expected format in the output parameter + * expected_weight_format. The expected_weight_format can than be + * used in the configure method of the class for retrieving the + * best optimal kernel. + * + * Use case one - query for a specific format: + * + * WeightInfo weights_info(..., arm_gemm::WeightFormat::OHWIo4, ...); // Set the value of the input query. + * if (NEGEMMConvolutionlayer::has_opt_impl(WeightFormat(), ...., weights_info, ...)) + * { + * auto conv = std::unique_ptr<NEGEMMConvolutionlayer>(); + * conv->configure(..., weights_info, ...); // uses the same WeightFormat the user wanted originally, OHWYo4. + * conv->run(...); + * } + * + * Use case two - query for any format that would be optimal for the GEMM to execute: + * + * WeightInfo weights_info(..., arm_gemm::WeightFormat::ANY, ...); // Set the value of the input query. + * arm_gemm::WeightFormat expected_wf; + * if (NEGEMMConvolutionlayer::has_opt_impl(expected_wf, ...., weights_info, ...)) + * { + * auto conv = std::unique_ptr<NEGEMMConvolutionlayer>(); + * // ... code to convert the layout of the weights tensor to the layout returned by has_opt_impl + * WeightInfo new_weights_info(..., expected_wf, ...); // Set the value of the WeightFormat returned by has_opt_impl. + * conv->configure(..., new_weights_info, ...); + * conv->run(...); + * } + * + * Notice that a GEMM configured with a WeightFormat other than + * UNSPECIFIED will run GEMM with variable weights mode. + * + * @param[out] expected_weight_format The arm_compute::WeightFormat expected by the kernel. + * @param[in] src Source tensor info. + * @param[in] weights Weights tensor info. + * @param[in] biases Biases tensor info. Shared biases supported. + * @param[in] dst Destination tensor info. + * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. + * @param[in] weights_info (optional) Specifies additional configuration parameters for the weights of the GEMM computation. + * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). + * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported. And no activation (i.e. Linear) which is the default value. + * @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation + * + * @return a Status + */ + static Status has_opt_impl(arm_gemm::WeightFormat &expected_weight_format, const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, + const PadStrideInfo &conv_info, + const WeightsInfo &weights_info = WeightsInfo(), const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), + bool enable_fast_math = false); // Inherited methods overridden: void run() override; void prepare() override; |