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author | Mohammed Suhail Munshi <MohammedSuhail.Munshi@arm.com> | 2023-06-30 15:43:29 +0100 |
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committer | Mohmun02 <MohammedSuhail.Munshi@arm.com> | 2023-07-06 09:49:03 +0000 |
commit | c9eeee5c84ad817360a1719c538c6e6c0812ec13 (patch) | |
tree | 6c80020617e83b0889e092d685940c7937f41d2c /src/core/CL/cl_kernels/tile_helpers.h | |
parent | ce3c48c7af02555f81c0f5e7ef2677916cecef34 (diff) | |
download | ComputeLibrary-c9eeee5c84ad817360a1719c538c6e6c0812ec13.tar.gz |
Fix nightly failures in MatMulLowpNativeKernel when using bounded activation functions
- Added checks for supported activation functions in MatMulLowpKernel validate
- Replaced incorrect float activation macro with quantized implementation in mat_mul_quantized
Resolves: [COMPMID-6339]
Signed-off-by: Mohammed Suhail Munshi <MohammedSuhail.Munshi@arm.com>
Change-Id: I15661f14877f1d3305644e6473feb5482a67e773
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/c/VisualCompute/ComputeLibrary/+/532858
Tested-by: bsgcomp <bsgcomp@arm.com>
Reviewed-by: Pablo Tello <pablo.tello@arm.com>
Comments-Addressed: bsgcomp <bsgcomp@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9855
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>
Diffstat (limited to 'src/core/CL/cl_kernels/tile_helpers.h')
-rw-r--r-- | src/core/CL/cl_kernels/tile_helpers.h | 26 |
1 files changed, 14 insertions, 12 deletions
diff --git a/src/core/CL/cl_kernels/tile_helpers.h b/src/core/CL/cl_kernels/tile_helpers.h index 85bd59afd4..8129606277 100644 --- a/src/core/CL/cl_kernels/tile_helpers.h +++ b/src/core/CL/cl_kernels/tile_helpers.h @@ -1144,19 +1144,21 @@ }) \ }) + +// NOTE : A_VAL and B_VAL should be quantized values (using same quantization info as x) // RELU Activation -#define relu_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) (max((DATA_TYPE)ZERO_VALUE, x)) +#define relu_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_POINT, A_VAL, B_VAL, x) (max((DATA_TYPE)ZERO_POINT, x)) // Bounded RELU Activation -#define brelu_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) (min((DATA_TYPE)A_VAL, max((DATA_TYPE)ZERO_VALUE, x))) +#define brelu_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_POINT, A_VAL, B_VAL, x) (min((DATA_TYPE)A_VAL, max((DATA_TYPE)ZERO_POINT, x))) // Lower Upper Bounded RELU Activation -#define lu_brelu_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) (min(max(x, (DATA_TYPE)B_VAL), (DATA_TYPE)A_VAL)) +#define lu_brelu_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_POINT, A_VAL, B_VAL, x) (min(max(x, (DATA_TYPE)B_VAL), (DATA_TYPE)A_VAL)) // Hard Swish Activation -#define hard_swish_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) (x * ((min(max((DATA_TYPE)(x + (DATA_TYPE)3.f), (DATA_TYPE)0.f), (DATA_TYPE)6.f)) * (DATA_TYPE)0.166666667f)) +#define hard_swish_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_POINT, A_VAL, B_VAL, x) (x * ((min(max((DATA_TYPE)(x + (DATA_TYPE)3.f), (DATA_TYPE)0.f), (DATA_TYPE)6.f)) * (DATA_TYPE)0.166666667f)) // Identity Activation -#define identity_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) (x) +#define identity_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_POINT, A_VAL, B_VAL, x) (x) -#define ACT_OP_QUANTIZED(op, DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) op##_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) -#define ACTIVATION_QUANTIZED(op, DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) ACT_OP_QUANTIZED(op, DATA_TYPE, VEC_SIZE, ZERO_VALUE, A_VAL, B_VAL, x) +#define ACT_OP_QUANTIZED(op, DATA_TYPE, VEC_SIZE, ZERO_POINT, A_VAL, B_VAL, x) op##_op_quantized(DATA_TYPE, VEC_SIZE, ZERO_POINT, A_VAL, B_VAL, x) +#define ACTIVATION_QUANTIZED(op, DATA_TYPE, VEC_SIZE, ZERO_POINT, A_VAL, B_VAL, x) ACT_OP_QUANTIZED(op, DATA_TYPE, VEC_SIZE, ZERO_POINT, A_VAL, B_VAL, x) #define V_ADD(A_VAL, B_VAL) ((A_VAL) + (B_VAL)) #define V_SUB(A_VAL, B_VAL) ((A_VAL) - (B_VAL)) @@ -1171,17 +1173,17 @@ * @param[in] M0 Number of SRC/DST rows * @param[in] N0 Number of SRC/DST columns * @param[in] ACTIVATION_TYPE Activation type - * @param[in] ZERO_VALUE The zero value to consider in the computation - * @param[in] A_VAL A value used for the activation (e.g. tanh_op, brelu,..) - * @param[in] B_VAL B value used for the activation (e.g. tanh_op, brelu,..) + * @param[in] ZERO_POINT The zero value to consider in the computation + * @param[in] A_VAL Quantized A value used for the activation (e.g. tanh_op, brelu,..) + * @param[in] B_VAL Quantized B value used for the activation (e.g. tanh_op, brelu,..) * @param[out] src SRC tile * @param[out] dst DST tile */ -#define T_ACTIVATION_QUANTIZED(DATA_TYPE, M0, N0, ACTIVATION_TYPE, ZERO_VALUE, A_VAL, B_VAL, src, dst) \ +#define T_ACTIVATION_QUANTIZED(DATA_TYPE, M0, N0, ACTIVATION_TYPE, ZERO_POINT, A_VAL, B_VAL, src, dst) \ ({ \ LOOP_UNROLLING(int, _m0, 0, 1, M0, \ { \ - dst[_m0].v = ACTIVATION_QUANTIZED(ACTIVATION_TYPE, DATA_TYPE, N0, ZERO_VALUE, A_VAL, B_VAL, src[_m0].v); \ + dst[_m0].v = ACTIVATION_QUANTIZED(ACTIVATION_TYPE, DATA_TYPE, N0, ZERO_POINT, A_VAL, B_VAL, src[_m0].v); \ }) \ }) |