Add Fused Activation to OpenCL MatMul
- Added fused activation to MatMul function interface
- Added fused activation to CL backend
- Includes tests for supported Activation Functions in MatMul
Resolves: [COMPMID-6192]
Signed-off-by: Mohammed Suhail Munshi <MohammedSuhail.Munshi@arm.com>
Change-Id: Ie103212b600b60699eaf6a6394d609e6e1f5aba6
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/c/VisualCompute/ComputeLibrary/+/522465
Comments-Addressed: bsgcomp <bsgcomp@arm.com>
Reviewed-by: Viet-Hoa Do <viet-hoa.do@arm.com>
Tested-by: bsgcomp <bsgcomp@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9714
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Jakub Sujak <jakub.sujak@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>
diff --git a/src/core/CL/cl_kernels/common/mat_mul.cl b/src/core/CL/cl_kernels/common/mat_mul.cl
index 90d485e..9656a59 100644
--- a/src/core/CL/cl_kernels/common/mat_mul.cl
+++ b/src/core/CL/cl_kernels/common/mat_mul.cl
@@ -21,6 +21,7 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
+#include "activation_float_helpers.h"
#include "helpers.h"
#include "tile_helpers.h"
@@ -31,6 +32,7 @@
* should NOT be confused with the batch size of the model. For NHWC the "batch" is the "H" dimension
* @note The data type must be passed at compile time using -DDATA_TYPE (e.g. -DDATA_TYPE=float)
* @note The block's dimensions used for the LHS and RHS matrices (M0, N0 and K0) must be passed at compile time using -DN0, -DM0 and -DK0 (e.g. -DN0=8, -DM0=4, -DK0=4).
+ * @note The fused activation function used should be passed with -DACTIVATION_TYPE, -DA_VAL and -DB_VAL are used for min and max output bounded activation functions.
* @note The number of leftover outputs rows/columns must be passed using -DPARTIAL_STORE_N0 and -DPARTIAL_STORE_M0 (e.g. -DPARTIAL_STORE_N0=2, -DPARTIAL_STORE_M0=3)
* @note The dimension K must be passed at compile time using -DK (e.g. -DK=6)
* @note The tensor type ("BUFFER" or "IMAGE") of the rhs tensor must be passed at compile time using -DRHS_TENSOR_TYPE (e.g. -DRHS_TENSOR_TYPE=BUFFER)
@@ -86,7 +88,7 @@
})
const int rhs_z = z * rhs_h;
- int k;
+ int k;
for(k = 0; k <= K - K0; k += K0)
{
TILE(DATA_TYPE, M0, K0, a);
@@ -111,7 +113,7 @@
lhs_offset_first_element_in_bytes += K0 * sizeof(DATA_TYPE);
}
-#ifdef K % K0 != 0
+#if K % K0 != 0
/* Leftover Loop */
for(; k < K; ++k)
{
@@ -147,6 +149,8 @@
indirect_buffer[_i].v = min(_i, select(M0 - 1, PARTIAL_STORE_M0 - 1, y_cond));
});
+ T_ACTIVATION(DATA_TYPE, M0, N0, ACTIVATION_TYPE, A_VAL, B_VAL, acc, acc);
+
T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, M0, N0, PARTIAL_STORE_N0, BUFFER, dst, 0, dst_stride_y, x_cond, acc, indirect_buffer);
}
#endif // defined(MAT_MUL_NATIVE_NT_NT)
@@ -158,6 +162,7 @@
* should NOT be confused with the batch size of the model. For NHWC the "batch" is the "H" dimension
* @note The data type must be passed at compile time using -DDATA_TYPE (e.g. -DDATA_TYPE=float)
* @note The block's dimensions used for the LHS and RHS matrices (M0, N0 and K0) must be passed at compile time using -DN0, -DM0 and -DK0 (e.g. -DN0=8, -DM0=4, -DK0=4).
+ * @note The fused activation function used should be passed with -DACTIVATION_TYPE, -DA_VAL and -DB_VAL are used for min and max output bounded activation functions.
* @note The number of leftover outputs rows/columns must be passed using -DPARTIAL_STORE_N0 and -DPARTIAL_STORE_M0 (e.g. -DPARTIAL_STORE_N0=2, -DPARTIAL_STORE_M0=3)
* @note The dimension K must be passed at compile time using -DK (e.g. -DK=6)
* @note The tensor type ("BUFFER" or "IMAGE") of the rhs tensor must be passed at compile time using -DRHS_TENSOR_TYPE (e.g. -DRHS_TENSOR_TYPE=BUFFER)
@@ -213,7 +218,7 @@
})
const int rhs_z = z * rhs_h;
- int k;
+ int k;
for(k = 0; k <= K - K0; k += K0)
{
TILE(DATA_TYPE, M0, K0, a);
@@ -301,6 +306,8 @@
indirect_buffer[_i].v = min(_i, select(M0 - 1, PARTIAL_STORE_M0 - 1, y_cond));
});
+ T_ACTIVATION(DATA_TYPE, M0, N0, ACTIVATION_TYPE, A_VAL, B_VAL, acc, acc);
+
T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, M0, N0, PARTIAL_STORE_N0, BUFFER, dst, 0, dst_stride_y, x_cond, acc, indirect_buffer);
}
#endif // defined(MAT_MUL_NATIVE_NT_T)
@@ -312,6 +319,7 @@
* should NOT be confused with the batch size of the model. For NHWC the "batch" is the "H" dimension
* @note The data type must be passed at compile time using -DDATA_TYPE (e.g. -DDATA_TYPE=float)
* @note The block's dimensions used for the LHS and RHS matrices (M0, N0 and K0) must be passed at compile time using -DN0, -DM0 and -DK0 (e.g. -DN0=8, -DM0=4, -DK0=4).
+ * @note The fused activation function used should be passed with -DACTIVATION_TYPE, -DA_VAL and -DB_VAL are used for min and max output bounded activation functions.
* @note The number of leftover outputs rows/columns must be passed using -DPARTIAL_STORE_N0 and -DPARTIAL_STORE_M0 (e.g. -DPARTIAL_STORE_N0=2, -DPARTIAL_STORE_M0=3)
* @note The dimension K must be passed at compile time using -DK (e.g. -DK=6)
* @note The tensor type ("BUFFER" or "IMAGE") of the rhs tensor must be passed at compile time using -DRHS_TENSOR_TYPE (e.g. -DRHS_TENSOR_TYPE=BUFFER)
@@ -367,7 +375,7 @@
})
const int rhs_z = z * rhs_h;
- int k;
+ int k;
for(k = 0; k <= K - K0; k += K0)
{
TILE(DATA_TYPE, K0, M0, a);
@@ -405,7 +413,7 @@
lhs_offset_first_element_in_bytes += K0 * lhs_stride_y;
}
-#ifdef K % K0 != 0
+#if K % K0 != 0
/* Leftover Loop */
for(; k < K; ++k)
{
@@ -451,6 +459,8 @@
indirect_buffer[_i].v = min(_i, select(M0 - 1, PARTIAL_STORE_M0 - 1, y_cond));
});
+ T_ACTIVATION(DATA_TYPE, M0, N0, ACTIVATION_TYPE, A_VAL, B_VAL, acc, acc);
+
T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, M0, N0, PARTIAL_STORE_N0, BUFFER, dst, 0, dst_stride_y, x_cond, acc, indirect_buffer);
}
#endif // defined(MAT_MUL_NATIVE_T_NT)
@@ -462,6 +472,7 @@
* should NOT be confused with the batch size of the model. For NHWC the "batch" is the "H" dimension
* @note The data type must be passed at compile time using -DDATA_TYPE (e.g. -DDATA_TYPE=float)
* @note The block's dimensions used for the LHS and RHS matrices (M0, N0 and K0) must be passed at compile time using -DN0, -DM0 and -DK0 (e.g. -DN0=8, -DM0=4, -DK0=4).
+ * @note The fused activation function used should be passed with -DACTIVATION_TYPE, -DA_VAL and -DB_VAL are used for min and max output bounded activation functions.
* @note The number of leftover outputs rows/columns must be passed using -DPARTIAL_STORE_N0 and -DPARTIAL_STORE_M0 (e.g. -DPARTIAL_STORE_N0=2, -DPARTIAL_STORE_M0=3)
* @note The dimension K must be passed at compile time using -DK (e.g. -DK=6)
* @note The tensor type ("BUFFER" or "IMAGE") of the rhs tensor must be passed at compile time using -DRHS_TENSOR_TYPE (e.g. -DRHS_TENSOR_TYPE=BUFFER)
@@ -517,7 +528,7 @@
})
const int rhs_z = z * rhs_h;
- int k;
+ int k;
for(k = 0; k <= K - K0; k += K0)
{
TILE(DATA_TYPE, K0, M0, a);
@@ -565,7 +576,7 @@
lhs_offset_first_element_in_bytes += K0 * lhs_stride_y;
}
-#ifdef K % K0 != 0
+#if K % K0 != 0
/* Leftover Loop */
for(; k < K; ++k)
{
@@ -619,6 +630,8 @@
indirect_buffer[_i].v = min(_i, select(M0 - 1, PARTIAL_STORE_M0 - 1, y_cond));
});
+ T_ACTIVATION(DATA_TYPE, M0, N0, ACTIVATION_TYPE, A_VAL, B_VAL, acc, acc);
+
T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, M0, N0, PARTIAL_STORE_N0, BUFFER, dst, 0, dst_stride_y, x_cond, acc, indirect_buffer);
}
#endif // defined(MAT_MUL_NATIVE_T_T)