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| author | Gunes Bayir <gunes.bayir@arm.com> | 2023-09-28 10:30:18 +0100 |
|---|---|---|
| committer | Gunes Bayir <gunes.bayir@arm.com> | 2023-10-02 16:07:22 +0000 |
| commit | c2a51bd2cc7c4148d9444e7377af44b2f6c264ba (patch) | |
| tree | e8f66188d7e048a3f61d660c236ef66b33a0bf35 /arm_compute | |
| parent | a396da19ee6e5c36ae07c11e4f16a6787e9bc143 (diff) | |
| download | ComputeLibrary-c2a51bd2cc7c4148d9444e7377af44b2f6c264ba.tar.gz | |
Optimize CL and Neon Winograd tests
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>
Diffstat (limited to 'arm_compute')
| -rw-r--r-- | arm_compute/runtime/NEON/functions/NEWinogradConvolutionLayer.h | 9 |
1 files changed, 5 insertions, 4 deletions
diff --git a/arm_compute/runtime/NEON/functions/NEWinogradConvolutionLayer.h b/arm_compute/runtime/NEON/functions/NEWinogradConvolutionLayer.h index 7f4e354362..6caa2aeb59 100644 --- a/arm_compute/runtime/NEON/functions/NEWinogradConvolutionLayer.h +++ b/arm_compute/runtime/NEON/functions/NEWinogradConvolutionLayer.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_NEWINOGRADCONVOLUTIONLAYER_H -#define ARM_COMPUTE_NEWINOGRADCONVOLUTIONLAYER_H +#ifndef ACL_ARM_COMPUTE_RUNTIME_NEON_FUNCTIONS_NEWINOGRADCONVOLUTIONLAYER_H +#define ACL_ARM_COMPUTE_RUNTIME_NEON_FUNCTIONS_NEWINOGRADCONVOLUTIONLAYER_H #include "arm_compute/core/Types.h" #include "arm_compute/function_info/ActivationLayerInfo.h" @@ -77,7 +77,8 @@ public: * while every optional dimension from 4 and above represent a batch of inputs. * Data types supported: F16/F32. * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input. - * Currently only 3x3 and 5x5 kernels are supported. + * Supported kernel sizes: (height, width) -> 3x3, 1x3, 3x1, 5x5, 1x5, 5x1 for Fp32 + * -> 3x3 for Fp16 * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights. * @param[out] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. * Data types supported: Same as @p input. @@ -117,4 +118,4 @@ private: std::unique_ptr<Impl> _impl; }; } // namespace arm_compute -#endif /* ARM_COMPUTE_NEWINOGRADCONVOLUTIONLAYER_H */ +#endif // ACL_ARM_COMPUTE_RUNTIME_NEON_FUNCTIONS_NEWINOGRADCONVOLUTIONLAYER_H |