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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>
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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>
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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>
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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>
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* 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>
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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>
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- 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>
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Resolves: COMPMID-5899
Signed-off-by: Viet-Hoa Do <viet-hoa.do@arm.com>
Change-Id: I89d96e292c3492ba9b1900a3e5683f9dcd11dfc6
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9440
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>
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- Implements MatMul function and operator for floating point datatype FP16/FP32
- Includes support for transposing dynamic tensors prior to matrix multiplication.
- Adds tests for 2D/3D/4D+ tensors in MatMul with F32/F16 datatype (with all combinations of transposed/not-transposed tensors)
- Updates fixture to allow for testing fused activation in MatMul
- Adds tests for matmul with and without fused activation
Resolved: [COMPMID-5898]
Signed-off-by: Mohammed Suhail Munshi <MohammedSuhail.Munshi@arm.com>
Change-Id: Iefa84b26dd723c9a51e6c3f91023152c6c31ace2
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9411
Reviewed-by: SiCong Li <sicong.li@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>
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