COMPMID-2194: Refactor activation function macro in OpenCL. Change all activation calls to macro from activation_float_helpers.h
The different kernels now call the macro from
activation_float_helpers.h. activation_helpers.h is now removed.
Change-Id: I2e1314c6bc891809e88590d99e048072541cca14
Signed-off-by: Usama Arif <usama.arif@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1123
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/core/CL/cl_kernels/batchnormalization_layer.cl b/src/core/CL/cl_kernels/batchnormalization_layer.cl
index 0bb6cd7..66d371c 100644
--- a/src/core/CL/cl_kernels/batchnormalization_layer.cl
+++ b/src/core/CL/cl_kernels/batchnormalization_layer.cl
@@ -29,19 +29,14 @@
#define INVSQRT_OP(a) rsqrt((a))
#define SQCVT_SAT(a) (a)
-#if defined(VEC_SIZE) && defined(DATA_TYPE)
-
-#if defined(FUSED_ACTIVATION)
-#define SELECT_TYPE VEC_DATA_TYPE(SELECT_DATA_TYPE, VEC_SIZE)
-#define TYPE VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
-#include "activation_helpers.h"
-#define ACTIVATION_FUNC(x) ACTIVATION_OP(FUSED_ACTIVATION, x)
-#else /* defined(FUSED_ACTIVATION) */
-#define ACTIVATION_FUNC(x) (x)
-#endif /* defined(FUSED_ACTIVATION) */
+#if defined(VEC_SIZE) && defined(DATA_TYPE) && defined(ACTIVATION_TYPE)
+#include "activation_float_helpers.h"
/** Apply batch normalization.
*
+ * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu
+ * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively
+ *
* @param[in] input_ptr Pointer to the first source tensor. Supported data types: F16/F32
* @param[in] input_stride_x Stride of the first source tensor in X dimension (in bytes)
* @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes)
@@ -144,7 +139,7 @@
res = ADD_OP(res, beta_vec);
#endif /* USE_DEFAULT_BETA */
- res = ACTIVATION_FUNC(res);
+ res = ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, res, A_VAL, B_VAL);
VSTORE(VEC_SIZE)
(res, 0, (__global DATA_TYPE *)out.ptr);
@@ -152,6 +147,9 @@
/** Apply batch normalization on tensors with NHWC format.
*
+ * @note It is possible to select the activation function to apply using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu
+ * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively
+ *
* @param[in] input_ptr Pointer to the first source tensor. Supported data types: F16/F32
* @param[in] input_stride_x Stride of the first source tensor in X dimension (in bytes)
* @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes)
@@ -254,12 +252,12 @@
res = ADD_OP(res, beta_vec);
#endif /* USE_DEFAULT_BETA */
- res = ACTIVATION_FUNC(res);
+ res = ACTIVATION(ACTIVATION_TYPE, DATA_TYPE, res, A_VAL, B_VAL);
VSTORE(VEC_SIZE)
(res, 0, (__global DATA_TYPE *)out.ptr);
}
-#endif /* defined(VEC_SIZE) && defined(DATA_TYPE) */
+#endif /* defined(VEC_SIZE) && defined(DATA_TYPE) && defined(DATA_TYPE)*/
#if defined(NUM_CHANNELS) && defined(DATA_TYPE) && defined(EPSILON)
/** Fuse batchnorm parameters to convolution layer parameters