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2024-04-25Add memory stress tests for per channel quantized convolutionGunes Bayir
Partially Resolves: MLCE-1255 Change-Id: Ibadcfedd43530232c65f05e571bc8b4568a63e67 Signed-off-by: Gunes Bayir <gunes.bayir@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/11499 Benchmark: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: Pablo Marquez Tello <pablo.tello@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
2024-04-16Fix v7 test failure when core matmul result is dequantized into fp32Gunes Bayir
Partially Resolves: ONCPUML-1444, MLINFSW-439 Change-Id: Ic7498d6944df2848f3e82eaf4e11cc5cb6ef5754 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/11424 Reviewed-by: Anitha Raj <Anitha.Raj@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com>
2024-04-15Add s8f32 kernels and dynamic QuantizationInfoJonathan Deakin
- Add support for QASYMM_SIGNED*QASYMM8_SIGNED->F32 in CpuGemmLowpMatrixMultiplyCore - Add s8f32 kernel using existing s8->s32 kernels with a new DequantizeFloat OutputStage, the structure is similar to Requantize32 but the opposite way around. - Add SME s8f32 kernels with integrated support for DequantizeFloat. - Add scale to CpuGemmLowpOffsetContributionKernel. - Add virtual dequantize scale to gemm_common, only implemented for gemm_interleaved. - Update year to 2024 in generate_build_files. - Add dynamic flag to QuantizationInfo which signals to operators that it can change after configuration - Add support for dynamic quantization in NEGEMMLowpMatrixMultiplyCore - Add dynamic quantization fixture by extending GEMMLowpGenericMatrixMultiplyCoreValidationFixture - Add GEMMLowpDequantizedMatrixMultiplyValidationFixture - Store k (number of cols of A) rather than k_offset in the offset contribution kernels so that we can recompute it when the other offsets change relates to: ONCPUML-1444 MLINFSW-439 Co-authored-by: Milos Puzovic <Milos.Puzovic@arm.com> Co-authored-by: David Mansell <David.Mansell@arm.com> Change-Id: I58a3acf2c09289a303e52eea6b336a696a5bc8da Signed-off-by: Jonathan Deakin <jonathan.deakin@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/11022 Reviewed-by: Gunes Bayir <gunes.bayir@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
2024-04-15Add guarding for accumulation validation test in aarch32Radu Salavat
Partially Resolves: ONCPUML-1442 Signed-off-by: Radu Salavat <radu.salavat@arm.com> Change-Id: I681df5e9c399996fbc7dc362b906af151588ca44 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/11416 Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
2024-04-12Runtime checks for bf16 fixed format testsDavid Svantesson-Yeung
Add checks for bf16 support for bf16 fixed format tests. This ensures tests pass in multi_isa setting where library was compiled with bf16 support, even on systems that do not support bf16. Also adds runtime check to GEMMConvolutionLayer/Float/BFLOAT16/RunSmall. Resolves: COMPMID-6922 Signed-off-by: David Svantesson-Yeung <david.svantesson-yeung@arm.com> Change-Id: Ic0f09ba34b5a2c64be8bfc848a4457a6b1c4d1c3 Signed-off-by: David Svantesson-Yeung <david.svantesson-yeung@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/11408 Reviewed-by: Gunes Bayir <gunes.bayir@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
2024-04-12Accumulation in Cpu Gemm kernels is not supported for quantized kernels in ↵Radu Salavat
aarch32. This patch guards the relevant tests. Partially Resolves: ONCPUML-1442 Signed-off-by: Radu Salavat <radu.salavat@arm.com> Change-Id: I8eed80db4b522185c3c50c13f0f701aa48961057 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/11410 Reviewed-by: Gunes Bayir <gunes.bayir@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
2024-04-11Add SME2 implementation of softmax for FP16Gunes Bayir
In addition to the softmax kernel, this patch fixes minor issues in the fp32 implementation. Resolves: COMPMID-6920 Change-Id: Ibbd9f0af5f2a93fba0e92d72ba437279c34149d3 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/11402 Benchmark: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: Viet-Hoa Do <viet-hoa.do@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
2024-04-11Add in place summation to CPU GEMM kernelsRadu Salavat
Instead of dispatching the sum postop for GEMM kernels to a separate kernel + add, that requires an extra destination sized allocation, plus 3 extra load/stores per element, just do it in the GEMM kernel. Resolves: ONCPUML-1442 Signed-off-by: Radu Salavat <radu.salavat@arm.com> Co-authored-by: Milos Puzovic <milos.puzovic@arm.com> Change-Id: I7a1f2da3300875fa1ac88b705a34390969518077 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/11298 Reviewed-by: Gunes Bayir <gunes.bayir@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
2024-03-22[ONCPUML-1451] Guard bf16 to bf16 tests with ↵Renato Arantes
ARM_COMPUTE_ENABLE_FIXED_FORMAT_KERNELS Change-Id: I6a01fe1e19a9d3e38908309d766fe7fc43775490 Signed-off-by: Renato Arantes <renato.arantes@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/11338 Benchmark: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
2024-03-21[ONCPUML-1451] Add matmul kernel to enable bf16 to bf16 operations via ↵Renato Arantes
PyTorch® autocast() function The full range of tests must be added with [MLINFSW-482] epic due to the lack of reordering kernels implemented in Acl. Co-Authored-By: David Mansell <David.Mansell@arm.com> Change-Id: I820d316295a1ec94fdc89c37e4144a268f914c36 Signed-off-by: Renato Arantes <renato.arantes@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/11169 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>
2024-03-20Make Cpu/Gpu/Ref scalar/vectoral S32 division consistentGunes Bayir
- Neon(TM) implementation converts integers to float and performs the division because there is no vector integer division instructions. However, leftover loop still uses integer division, which makes results inconsistent depending on where we are in the tensor. - SVE path does it in integer domain. - OpenCL(TM) does it similar to Neon(TM) vector path. - Reference implementation does it in integer domain. These differences cause intermittent mismatches. This patch ensures all follow the same logic. On the other hand, the provided Neon(TM) implementation is faster than the Fp32 converted version. Resolves: COMPMID-6925 Change-Id: Ia12606d57f40a7d331b9b698f87fd4321496b275 Signed-off-by: Gunes Bayir <gunes.bayir@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/11316 Tested-by: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: Pablo Marquez Tello <pablo.tello@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
2024-03-20Increase tolerance_num of Cpu RNNLayer testsGunes Bayir
Instead of increasing the tolerance amount, we increase the number of elements we tolerate to 0.02 % of the whole tensor. This ensures we do not affect the tolerance for smaller tests. This amount is set according to the number of elements above the threshold. it was 1 over 512 elements, 1/512 ~ 0.02 %. Resolves: COMPMID-6932 Change-Id: I9d3ce29a3972aa8b9daea5288005a0a41a266328 Signed-off-by: Gunes Bayir <gunes.bayir@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/11321 Benchmark: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: Pablo Marquez Tello <pablo.tello@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
2024-03-19Increase MatMul and DilatedConv test Q8 thresholds to 1Gunes Bayir
Tolerance for quantized tests is better to be 1 due to possible rounding differences between the Acl and reference implementations. Resolves: COMPMID-6929 Change-Id: I6f317631322b702e6a9579593befff65bbf46151 Signed-off-by: Gunes Bayir <gunes.bayir@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/11319 Reviewed-by: Pablo Marquez Tello <pablo.tello@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com>
2024-03-14Fix validation in pool2d assembly wrapperPablo Marquez Tello
* Validate output shape in CpuPool2dAssemblyWrapperKernel * Resolves ARMCL-625 Change-Id: I4fd91c1b15ecb17efc39fd3e82a92210e4f182b2 Signed-off-by: Pablo Marquez Tello <pablo.tello@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/11290 Reviewed-by: Gunes Bayir <gunes.bayir@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
2024-03-11Prefer indirect Gemm vs. Direct convolution if supportedGunes Bayir
Indirect GEMM uses optimized assembly path while Direct Conv uses the fallback Acl kernel for convolution. In certain cases, where the input tensor is large and filter size is greater than 7 (e.g. 9x9 filters), heuristics fall back to Direct Conv algorithm where it could still prefer the assembly path if the data layout is NHWC. This is more important when SME2 kernels are present. Resolves: COMPMID-6900 Change-Id: Ia611c975eee0423615113fcaeaa8f9eef0421456 Signed-off-by: Gunes Bayir <gunes.bayir@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/11254 Benchmark: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: Anitha Raj <Anitha.Raj@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
2024-03-11Set int8 test tolerance in FullyConnected to int8Gunes Bayir
Int8 data types should be compared with Int8 tolerances. Same holds for UInt8 as well. O/w the differences between reference and target can be larger than it is because of casting. Resolves: COMPMID-6930 Change-Id: I4940d821b7fecc21cf6b167e161dffceb764b909 Signed-off-by: Gunes Bayir <gunes.bayir@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/11269 Benchmark: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: <felixjohnny.thomasmathibalan@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com>
2024-02-20Requantization cases for offset changes onlyMohammed Suhail Munshi
Resolves: [COMPMID-6681] Signed-off-by: Mohammed Suhail Munshi <MohammedSuhail.Munshi@arm.com> Change-Id: I325b9d478dd1d04a45533bb7708cf76e98ee0cee Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/11058 Reviewed-by: Gunes Bayir <gunes.bayir@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
2024-02-14[QTest] Use dynamic output quantization in Depthwise Conv testsOmar Al Khatib
Resolves: COMPMID-6483 Signed-off-by: Omar Al Khatib <omar.alkhatib@arm.com> Change-Id: I512102f5e27743098168101b5e02382f4ad4a22a Signed-off-by: Gunes Bayir <gunes.bayir@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/11068 Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: Jakub Sujak <jakub.sujak@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
2024-02-06Disable FP16 tests compilation on Multi-Isa v8aMohammed Suhail Munshi
Resolves: [COMPMID-6791] Signed-off-by: Mohammed Suhail Munshi <MohammedSuhail.Munshi@arm.com> Change-Id: Idae6ddb0e9655ec096f25917f0a44eb57aaef908 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/11076 Benchmark: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: Pablo Marquez Tello <pablo.tello@arm.com>
2024-01-24Fix tolerance issue in BF16 MatMul testsGunes Bayir
BF16 kernels are not expected to have the same tolerance/accuracy standards as full float kernels. The reference implementation is a standard floating point implementation, thus resulting in small mismatches. We increase the tolerance of the MatMul BF16 tests, and add more tests to cover more shapes. Previously, the only tested bf16 kernel was a64_hybrid_fp32bf16fp32_mmla_4x24. With the inclusion of new shapes, heuristics also choose a64_hybrid_fp32bf16fp32_mmla_6x16 and stress this kernel as well, covering every implementation. Resolves: COMPMID-6654 Signed-off-by: Gunes Bayir <gunes.bayir@arm.com> Change-Id: I15342606912013c123b94c7e0ea2e6bbb25680d7 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/11014 Benchmark: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: Jakub Sujak <jakub.sujak@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com>
2024-01-04Implement dynamic quantization for GEMMLowp testsSiCong Li
This patch calculates the output quantization info based on the inputs' quantization information. The previous approach was using the same quantization information for input, weights and output. Remove QSYMM8_PER_CHANNEL path from the fixture as there are no related tests Remove repeated shapes from the dataset now that we get rid of the quantization info from the dataset. Combine signed and unsigned SmallGEMMLowpFusedBatchedMatMulDataset into one as they become identical Resolves COMPMID-6481, COMPMID-6634 Change-Id: I9f5a20f4bb45c3e5adab388564135ae8a5c0a9ea Signed-off-by: SiCong Li <sicong.li@arm.com> Signed-off-by: Gunes Bayir <gunes.bayir@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10680 Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: Jakub Sujak <jakub.sujak@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
2023-12-08Adjust NEReduceMean test toleranceSiCong Li
Resolves COMPMID-6728 Signed-off-by: SiCong Li <sicong.li@arm.com> Change-Id: Ic0682550a09db9aa420057a90ee65386e16e6034 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10853 Reviewed-by: Gunes Bayir <gunes.bayir@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
2023-12-05Optimize CpuSoftmaxKernel for axis=0Gunes Bayir
Implement a single kernel instead of having two consecutive ones. In the previous setup, one kernel was calculating the maximum value in the axis, and this maximum was being subtracted from each data while calculating the softmax, i.e. softmax(x_i) = exp(x_i - max) / sum_i( exp(x_i - max) ) This patch integrates these two stages into a single kernel for Neon™ for all data types. This will save some memory because we don't need to hold the max values in a separate auxiliary tensor. It also introduces some other optimizations that will ease memory pressure when the data type is float/half, by using the dst tensor as temporary storage for already exponentiated inputs. It removes the references to SVE and SVE2 implementations, and most of the associated files; but, it leaves the implementations as these may be used in the future. Resolves: COMPMID-6500 Signed-off-by: Gunes Bayir <gunes.bayir@arm.com> Change-Id: Icff9976d1214c4c6cbe15a62ca60b8a77d3784cc Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10688 Reviewed-by: SiCong Li <sicong.li@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
2023-11-08Optimize CpuGemmConv2d start-up timeSiCong Li
When weight has no holes, we can replace CpuWeightsReshapeKernel with: - Collapse by reinterpreting weight's 3 spatial dimensions - Perform CpuTranspose For more details see the documentation in src/cpu/operators/CpuGemmConv2d.cpp This is one optimization since the CpuTranspose is better performing than CpuWeightsReshapeKernel A second optimization is to fuse this transpose with other weight transformations (e.g. pretranspose_B_array in CpuGemmAssemblyDispatch) However this second optimization depends on how the underlying gemm methods (the fall back path: CpuGemmMatrixMultiplyKernel or the assembly path: CpuGemmAssemblyDispatch) chooses to fuse the transpose. Therefore, this patch moves the transpose down from CpuGemmConv2d, to the individual gemm operators where the fusion decision needs to be made, by passing an extra "transpose_b" flag to CpuGemm New transpose_b flag in different scopes (they are all the same, but with different names because pretranspose_b has a different meaning in GemmAssemblyDispatch): GEMMInfo::pretranspose_B -> AsmGemmInfo::transpose_b New auxilliary tensors holding the transposed b result: - CpuGemm optimized path: CpuGemmAssemblyDispatch::PrePretransposedB - CpuGemm fallback path: CpuGemm::PreTransposedRHS Note that this patch does not yet have the second optimization (COMPMID-6595), but it prepares for it. Relates to COMPMID-6595 Resolves COMPMID-6499 Change-Id: I999a2da9da4b2b15369a3cc06d7872c86e0190ea Signed-off-by: SiCong Li <sicong.li@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10526 Tested-by: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: Anitha Raj <Anitha.Raj@arm.com> Reviewed-by: Gunes Bayir <gunes.bayir@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
2023-11-03Add Dynamic Quantization tests to Fully Connected LayerMohammed Suhail Munshi
This patch calculates the output quantization info based on the inputs' quantization information. The previous approach was using the same quantization information for input, weights and output. This implementation does not cover the cases where we have fused activation function. Resolves: [COMPMID-6484] Signed-off-by: Mohammed Suhail Munshi <MohammedSuhail.Munshi@arm.com> Change-Id: Ib58143165191e82ae8547e661ac7c8d077bda200 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10539 Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: SiCong Li <sicong.li@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
2023-10-31Use dynamic quantization in Convolution and Dilated Convolution testsGunes Bayir
This patch calculates the output quantization info based on the inputs' quantization information. The previous approach was using the same quantization information for input, weights and output. This implementation does not cover the cases where we have fused activation function. Resolves: COMPMID-6482 Change-Id: I4a9d87cfef8ad18ef241d457d23f44c8519a1389 Signed-off-by: SiCong Li <sicong.li@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10541 Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: Gunes Bayir <gunes.bayir@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
2023-10-17Fix memory Error in Reverse Fixture.Adnan AlSinan
Resolves: COMPMID-6581 Signed-off-by: Adnan AlSinan <adnan.alsinan@arm.com> Change-Id: I0a634e064377e54b9190241c01fc75c212522ba7 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10481 Reviewed-by: Viet-Hoa Do <viet-hoa.do@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
2023-10-10Optimize NEStackLayerGunes Bayir
Optimize the stack operation in Cpu by leveraging block memcpy. Resolves: COMPMID-6498 Change-Id: I49d79d179f0375a73d654edd59fb33072112569b Signed-off-by: Gunes Bayir <gunes.bayir@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10451 Reviewed-by: SiCong Li <sicong.li@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
2023-10-10Optimize CL and Neon depthwise convolution testsGunes Bayir
Resolves: COMPMID-6465 Change-Id: I5bbf4596dd5e34e806dc51de9be14df9b6fa320a Signed-off-by: Gunes Bayir <gunes.bayir@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10452 Benchmark: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
2023-10-04NEDeconvolutionLayer validation fixPablo Marquez Tello
* Added a new test to make sure we support the following configuration: NCHW InputInfo=Shape=2,2 WeightsInfo=Shape=3,3 OutputInfo=Shape=4,4, PadStrideInfo=1,1;0,0,0,0' * Fixed the validate() method to allow this configuration * Resolves MLCE-1120 Change-Id: I6874ad57bb81384185984741b983bf5e19ba150c Signed-off-by: Pablo Marquez Tello <pablo.tello@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10417 Reviewed-by: Gunes Bayir <gunes.bayir@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
2023-10-02Optimize CL and Neon Winograd testsGunes Bayir
Several test optimizations have been introduced into Winograd tests for Gpu and Cpu backends. The testing strategy has been detailed as a comment header in the test design files. In summary - Very large shapes in the nightly are made smaller - If the underlying kernel is the same for different data types, we only need to stress some key aspects of the kernels (e.g. read/write lengths in case of fp32/fp16). - In case the underlying kernel is the same (OpenCL), Fp16 is tested on a subset of the shapes - In Cpu, there is no need to test every combination for both NCHW and NHWC as we just permute the inputs and use NHWC kernels anyways - All activations does not need to be tested for each and every shape Resolves: COMPMID-6464 Change-Id: Ie25fded85c65b9c7386dc21b23f9b695b1e77b07 Signed-off-by: Gunes Bayir <gunes.bayir@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10393 Reviewed-by: Jakub Sujak <jakub.sujak@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
2023-09-27Implement tflite compliant reverse for CPUAdnan AlSinan
- Add support for negative axis values. - Add option to use opposite ACL convention for dimension addressing. - Add validation tests for the mentioned additions. Resolves COMPMID-6497 Change-Id: I9174b201c3adc070766cc6cffcbe4ec1fe5ec1c3 Signed-off-by: Adnan AlSinan <adnan.alsinan@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10335 Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: SiCong Li <sicong.li@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
2023-09-15Remove deprecated support for BF16 in CpuCastAdnan AlSinan
Resolves : [COMPMID-6212] Signed-off-by: Omar Al Khatib <omar.alkhatib@arm.com> Signed-off-by: Adnan AlSinan <adnan.alsinan@arm.com> Change-Id: I29bbd9a3d96af462faf7f0ee13b9849f75e05356 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10319 Benchmark: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: Viet-Hoa Do <viet-hoa.do@arm.com>
2023-09-04Make zip and combine variadicViet-Hoa Do
* Illustrate the benefit by writing CPU MatMul test dataset in a more readable way. Part of: COMPMID-6353 Signed-off-by: Viet-Hoa Do <viet-hoa.do@arm.com> Change-Id: Id5dbc13a051709237bbcc4dd88716d0b24ecfd5d Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10227 Tested-by: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: Jakub Sujak <jakub.sujak@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
2023-08-30Disable NEArgMinMaxLayer RunSmall_F32_S64 for armv7aPablo Marquez Tello
* When the output is S64 the function NEArgMinMaxLayer uses CpuCast to convert the output to S64 and this is only supported on aarch64. * Disable this test case for non aarch64 builds * Resolves COMPMID-6536 Change-Id: I554c21ce9a029af086e9137b5369b7951b779997 Signed-off-by: Pablo Marquez Tello <pablo.tello@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10212 Benchmark: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: Viet-Hoa Do <viet-hoa.do@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com>
2023-08-22Optimize CpuReshapeKernelAnitha Raj
Resolves COMPMID-5279 Change-Id: Id9b007eed62c200702bbfcc83b94dab7b5de1714 Signed-off-by: Anitha Raj <anitha.raj@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9962 Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: SiCong Li <sicong.li@arm.com> Reviewed-by: Viet-Hoa Do <viet-hoa.do@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
2023-08-08Add support for S64 output in NEArgMinMaxLayerPablo Marquez Tello
* NEArgMinMaxLayer uses NEReductionOperation to compute its result in S32 * We need to call NECast to convert from S32 to S64 * Resolves MLCE-1089 Change-Id: I6fded869b6076d7af1b9b3e70eb384f4ee82fd8a Signed-off-by: Pablo Marquez Tello <pablo.tello@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10054 Tested-by: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: Viet-Hoa Do <viet-hoa.do@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
2023-08-08Fix failure in MeanReduce layerViet-Hoa Do
Resolves: COMPMID-6423 Signed-off-by: Viet-Hoa Do <viet-hoa.do@arm.com> Change-Id: I9cec051a7d1a2956218f8a6d8263bd5424f6d389 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10072 Reviewed-by: Gunes Bayir <gunes.bayir@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com>
2023-08-01Improved testing for ArgMinMaxPablo Marquez Tello
* ArgMinMax output was fixed to S32, this patch makes the changes required to allow other output types like U64/S64 * Made changes to the ArgMinMax fixture and tests to allow specifying output data type. * Made changes to the reference reduction_operation to allow specifying the output type * Added tests case to output S64 for the CL backend. * Added missing test cases in the neon backend. * Partially resolves MLCE-1089 Change-Id: I6f1cbc7093669d12c2a3aff6974cf19d83b2ecda Signed-off-by: Pablo Marquez Tello <pablo.tello@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10003 Reviewed-by: Viet-Hoa Do <viet-hoa.do@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
2023-07-19Add support for input S64/U64 in CpuCastKernelPablo Marquez Tello
* The kernel now supports the following conversions: S64 -> F32 U64 -> F32 * Resolves MLCE-1089 Change-Id: I277cf58b78d919fde25947520d2056e1412c7f82 Signed-off-by: Pablo Marquez Tello <pablo.tello@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9935 Tested-by: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: Viet-Hoa Do <viet-hoa.do@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
2023-07-18Break up core/Utils.h to reduce unused code being included everywhereMatthew Bentham
Makes a small difference to compile times and opens up other opportunities to simplify code. Change-Id: I232876910bbe4fa9719f4a0ce4a54c090faeb5ef Signed-off-by: Matthew Bentham <Matthew.Bentham@arm.com> Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/c/VisualCompute/ComputeLibrary/+/532429 Tested-by: bsgcomp <bsgcomp@arm.com> Reviewed-by: Pablo Tello <pablo.tello@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9856 Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: Viet-Hoa Do <viet-hoa.do@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
2023-07-07Enable transpose convolution with non-square kernelsViet-Hoa Do
Resolves: COMPMID-6319 Signed-off-by: Viet-Hoa Do <viet-hoa.do@arm.com> Change-Id: I49a17ff973efc88b7ce0334c47ecf076c03f4cc3 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9829 Reviewed-by: Jakub Sujak <jakub.sujak@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
2023-06-12Refactor activation LUT computationPablo Marquez Tello
* Moving the code out of Types.h will help with the compilation time. * Added LUT support for all other activation functions. * Resolves COMPMID-6292 Change-Id: I1b5f0b21f03237447163276b8796b2aeb3fdd45c Signed-off-by: Pablo Marquez Tello <pablo.tello@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9749 Tested-by: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: Viet-Hoa Do <viet-hoa.do@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
2023-05-15Raise tolerance number for NEDeconvolutionLayer fp16 testsSiCong Li
Tolerance number was too strict for fp16 Resolves COMPMID-6254 Signed-off-by: SiCong Li <sicong.li@arm.com> Change-Id: I42a5df21c2545c38ea7234497effd232b43aabf8 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9635 Reviewed-by: Jakub Sujak <jakub.sujak@arm.com> Reviewed-by: Omar Al Khatib <omar.alkhatib@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com>
2023-05-10Remove inclusion of NEReorderKernel header from NEReorderLayerRamy Elgammal
Resolves: COMPMID-6235 Change-Id: I7a094a23244286090415ee2788632cfa7bd6c037 Signed-off-by: Ramy Elgammal <ramy.elgammal@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9608 Benchmark: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: Pablo Marquez Tello <pablo.tello@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
2023-05-10Fix validation issue with CPU scale on FP16Viet-Hoa Do
* In multi-isa build with v8a base arch, FP16 is only available in SVE kernels, hence the support for FP16 depends on runtime environment. * To simplify the test, the data type test is disable on FP16 if the base arch doesn't have FP16 support. Resolves: COMPMID-6232 Signed-off-by: Viet-Hoa Do <viet-hoa.do@arm.com> Change-Id: Iee6179e1b87d3e7f4349e228479ff170285e4841 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9599 Tested-by: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: Jakub Sujak <jakub.sujak@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
2023-05-03Guards to make NEReorder aarch64 onlyDavid Svantesson
Resolves COMPMID-6151 Signed-off-by: David Svantesson <david.svantesson@arm.com> Change-Id: I0e8c957f3460633c32ef57be0cdc44a53b8c3e88 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9553 Reviewed-by: Viet-Hoa Do <viet-hoa.do@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
2023-04-28Reorder addedDavid Svantesson
Adds Reorder kernel exposing blocking reorders from arm_gemm Resolves ONCPUML-1232 Change-Id: I42bf4166311fe1771565134d3ed7039fc8e30230 Signed-off-by: David Svantesson <david.svantesson@arm.com> Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9500 Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: SiCong Li <sicong.li@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Benchmark: Arm Jenkins <bsgcomp@arm.com>
2023-04-26Only define validation test tolerance for quantized types in case of aarch64 ↵Ramy Elgammal
for Neon™ Matmul Partially Resovles: COMPMID-6026 Signed-off-by: Ramy Elgammal <ramy.elgammal@arm.com> Change-Id: I273b213abba275f1609eae33058e3acbee2a7146 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9489 Benchmark: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: Viet-Hoa Do <viet-hoa.do@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
2023-04-24Disable Neon/MatMul/Quantized for armv7aRamy Elgammal
- All the GeMM CPU assembly kernels for integer datatypes require aarch64 Resolves: COMPMID-6026 Signed-off-by: Ramy Elgammal <ramy.elgammal@arm.com> Change-Id: I34bb0d5ca5cc3684b996df851227fcd0ad452586 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9481 Benchmark: Arm Jenkins <bsgcomp@arm.com> Tested-by: Arm Jenkins <bsgcomp@arm.com> Comments-Addressed: Arm Jenkins <bsgcomp@arm.com> Reviewed-by: Mohmun02 <MohammedSuhail.Munshi@arm.com>
generated by cgit v1.2.1 (git 2.23.0) at 2024-06-26 04:54:27 +0000