Add Bias to MatMul Kernels and add support for use in Fully Connected Layer
Resolves: [COMPMID-6316]
Signed-off-by: Mohammed Suhail Munshi <MohammedSuhail.Munshi@arm.com>
Change-Id: I08e6bac9e6b46b76978da0dc6a48ccfe3dde5086
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9833
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>
diff --git a/src/core/CL/cl_kernels/common/mat_mul.cl b/src/core/CL/cl_kernels/common/mat_mul.cl
index 9656a59..c7ef8ae 100644
--- a/src/core/CL/cl_kernels/common/mat_mul.cl
+++ b/src/core/CL/cl_kernels/common/mat_mul.cl
@@ -25,6 +25,21 @@
#include "helpers.h"
#include "tile_helpers.h"
+#ifdef BIAS
+// This function performs in-place bias addition for float/half datatype when bias is enabled.
+// Note The tile's dimensions used for the LHS and RHS matrices (M0, N0 and K0) must be passed at compile time using -DN0, -DM0 (e.g. -DN0=8, -DM0=4).
+inline void perform_bias_addition(uchar *bias_ptr, uint bias_offset_first_element_in_bytes, TILE(DATA_TYPE, M0, N0, acc), uint x)
+{
+ TILE(DATA_TYPE, 1, N0, bias_tile);
+
+ // below expands to use bias_ptr and bias_offset_first_element_in_bytes
+ T_LOAD(DATA_TYPE, 1, N0, BUFFER, bias, x, 0, 1, 0, bias_tile);
+
+ // c = c + bias[broadcasted]
+ T_ELTWISE_BROADCAST_ADD_X(DATA_TYPE, M0, N0, acc, bias_tile, acc);
+}
+#endif // defined(BIAS)
+
#if defined(MAT_MUL_NATIVE_NT_NT)
/** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul): LHS non-transposed, RHS non-transposed - buffer only
*
@@ -43,32 +58,42 @@
* - K0 = 1, 2, 3, 4, 8, 16
* @note Values > 8 for M0 are not expected to be efficient
*
- * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16
- * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes)
- * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes)
- * @param[in] lhs_w The width of the lhs tensor
- * @param[in] lhs_h The height of the lhs tensor
- * @param[in] lhs_n Number of the matrices (buffers) in the batch
- * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
- * @param[in] rhs_img (Optional) Read only cl_image object for the rhs tensor. Included when RHS_TENSOR_TYPE=IMAGE
- * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
- * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes)
- * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes)
- * @param[in] rhs_w The width of the rhs tensor
- * @param[in] rhs_h The height of the rhs tensor
- * @param[in] rhs_n Number of the matrices (buffers) in the batch
- * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
- * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
- * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes)
- * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes)
- * @param[in] dst_w The width of the dst tensor
- * @param[in] dst_h The height of the dst tensor
- * @param[in] dst_n Number of the matrices (buffers) in the batch
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
+ * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16
+ * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes)
+ * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes)
+ * @param[in] lhs_w The width of the lhs tensor
+ * @param[in] lhs_h The height of the lhs tensor
+ * @param[in] lhs_n Number of the matrices (buffers) in the batch
+ * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
+ * @param[in] rhs_img (Optional) Read only cl_image object for the rhs tensor. Included when RHS_TENSOR_TYPE=IMAGE
+ * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
+ * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes)
+ * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes)
+ * @param[in] rhs_w The width of the rhs tensor
+ * @param[in] rhs_h The height of the rhs tensor
+ * @param[in] rhs_n Number of the matrices (buffers) in the batch
+ * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
+ * @param[in] bias_ptr (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr
+ * @param[in] bias_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes)
+ * @param[in] bias_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes)
+ * @param[in] bias_w (Optional) The size of the width dimension of the bias tensor
+ * @param[in] bias_h (Optional) The size of the height dimension of the bias tensor
+ * @param[in] bias_n (Optional) The size of the depth dimension of the bias tensor
+ * @param[in] bias_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor
+ * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
+ * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes)
+ * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes)
+ * @param[in] dst_w The width of the dst tensor
+ * @param[in] dst_h The height of the dst tensor
+ * @param[in] dst_n Number of the matrices (buffers) in the batch
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
*/
__kernel void mat_mul_native_nt_nt(
TENSOR3D_T(lhs, BUFFER),
TENSOR3D_T(rhs, RHS_TENSOR_TYPE),
+#ifdef BIAS
+ TENSOR3D_T(bias, BUFFER),
+#endif // defined(BIAS)
TENSOR3D_T(dst, BUFFER))
{
const uint x = GET_SPATIAL_IDX(0, N0, PARTIAL_STORE_N0);
@@ -149,6 +174,10 @@
indirect_buffer[_i].v = min(_i, select(M0 - 1, PARTIAL_STORE_M0 - 1, y_cond));
});
+#ifdef BIAS
+ perform_bias_addition(bias_ptr, bias_offset_first_element_in_bytes, acc, x);
+#endif // defined(BIAS)
+
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);
@@ -173,31 +202,41 @@
* - K0 = 1, 2, 3, 4, 8, 16 (only 4, 8, 16 if RHS_TENSOR_TYPE=IMAGE)
* @note Values > 8 for M0, N0 and K0 are not expected to be efficient
*
- * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16
- * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes)
- * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes)
- * @param[in] lhs_w The width of the lhs tensor
- * @param[in] lhs_h The height of the lhs tensor
- * @param[in] lhs_n Number of the matrices (buffers) in the batch
- * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
- * @param[in] rhs_img (Optional) Read only cl_image object for the rhs tensor. Included when RHS_TENSOR_TYPE=IMAGE
- * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
- * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes)
- * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes)
- * @param[in] rhs_w The width of the rhs tensor
- * @param[in] rhs_h The height of the rhs tensor
- * @param[in] rhs_n Number of the matrices (buffers) in the batch
- * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
- * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
- * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes)
- * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes)
- * @param[in] dst_w The width of the dst tensor
- * @param[in] dst_h The height of the dst tensor
- * @param[in] dst_n Number of the matrices (buffers) in the batch
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
+ * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16
+ * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes)
+ * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes)
+ * @param[in] lhs_w The width of the lhs tensor
+ * @param[in] lhs_h The height of the lhs tensor
+ * @param[in] lhs_n Number of the matrices (buffers) in the batch
+ * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
+ * @param[in] rhs_img (Optional) Read only cl_image object for the rhs tensor. Included when RHS_TENSOR_TYPE=IMAGE
+ * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
+ * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes)
+ * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes)
+ * @param[in] rhs_w The width of the rhs tensor
+ * @param[in] rhs_h The height of the rhs tensor
+ * @param[in] rhs_n Number of the matrices (buffers) in the batch
+ * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
+ * @param[in] bias_ptr (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr
+ * @param[in] bias_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes)
+ * @param[in] bias_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes)
+ * @param[in] bias_w (Optional) The size of the width dimension of the bias tensor
+ * @param[in] bias_h (Optional) The size of the height dimension of the bias tensor
+ * @param[in] bias_n (Optional) The size of the depth dimension of the bias tensor
+ * @param[in] bias_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor
+ * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
+ * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes)
+ * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes)
+ * @param[in] dst_w The width of the dst tensor
+ * @param[in] dst_h The height of the dst tensor
+ * @param[in] dst_n Number of the matrices (buffers) in the batch
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
*/
__kernel void mat_mul_native_nt_t(TENSOR3D_T(lhs, BUFFER),
TENSOR3D_T(rhs, RHS_TENSOR_TYPE),
+#ifdef BIAS
+ TENSOR3D_T(bias, BUFFER),
+#endif // defined(BIAS)
TENSOR3D_T(dst, BUFFER))
{
@@ -306,6 +345,10 @@
indirect_buffer[_i].v = min(_i, select(M0 - 1, PARTIAL_STORE_M0 - 1, y_cond));
});
+#ifdef BIAS
+ perform_bias_addition(bias_ptr, bias_offset_first_element_in_bytes, acc, x);
+#endif // defined(BIAS)
+
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);
@@ -330,32 +373,42 @@
* - K0 > 0
* * @note Values > 8 for M0, and K0 are not expected to be efficient
*
- * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16
- * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes)
- * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes)
- * @param[in] lhs_w The width of the lhs tensor
- * @param[in] lhs_h The height of the lhs tensor
- * @param[in] lhs_n Number of the matrices (buffers) in the batch
- * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
- * @param[in] rhs_img (Optional) Read only cl_image object for the rhs tensor. Included when RHS_TENSOR_TYPE=IMAGE
- * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
- * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes)
- * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes)
- * @param[in] rhs_w The width of the rhs tensor
- * @param[in] rhs_h The height of the rhs tensor
- * @param[in] rhs_n Number of the matrices (buffers) in the batch
- * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
- * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
- * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes)
- * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes)
- * @param[in] dst_w The width of the dst tensor
- * @param[in] dst_h The height of the dst tensor
- * @param[in] dst_n Number of the matrices (buffers) in the batch
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
+ * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16
+ * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes)
+ * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes)
+ * @param[in] lhs_w The width of the lhs tensor
+ * @param[in] lhs_h The height of the lhs tensor
+ * @param[in] lhs_n Number of the matrices (buffers) in the batch
+ * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
+ * @param[in] rhs_img (Optional) Read only cl_image object for the rhs tensor. Included when RHS_TENSOR_TYPE=IMAGE
+ * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
+ * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes)
+ * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes)
+ * @param[in] rhs_w The width of the rhs tensor
+ * @param[in] rhs_h The height of the rhs tensor
+ * @param[in] rhs_n Number of the matrices (buffers) in the batch
+ * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
+ * @param[in] bias_ptr (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr
+ * @param[in] bias_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes)
+ * @param[in] bias_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes)
+ * @param[in] bias_w (Optional) The size of the width dimension of the bias tensor
+ * @param[in] bias_h (Optional) The size of the height dimension of the bias tensor
+ * @param[in] bias_n (Optional) The size of the depth dimension of the bias tensor
+ * @param[in] bias_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor
+ * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
+ * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes)
+ * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes)
+ * @param[in] dst_w The width of the dst tensor
+ * @param[in] dst_h The height of the dst tensor
+ * @param[in] dst_n Number of the matrices (buffers) in the batch
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
*/
__kernel void mat_mul_native_t_nt(
TENSOR3D_T(lhs, BUFFER),
TENSOR3D_T(rhs, RHS_TENSOR_TYPE),
+#ifdef BIAS
+ TENSOR3D_T(bias, BUFFER),
+#endif // defined(BIAS)
TENSOR3D_T(dst, BUFFER))
{
const uint x = GET_SPATIAL_IDX(0, N0, PARTIAL_STORE_N0);
@@ -459,6 +512,10 @@
indirect_buffer[_i].v = min(_i, select(M0 - 1, PARTIAL_STORE_M0 - 1, y_cond));
});
+#ifdef BIAS
+ perform_bias_addition(bias_ptr, bias_offset_first_element_in_bytes, acc, x);
+#endif // defined(BIAS)
+
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);
@@ -483,32 +540,42 @@
* - K0 = 1, 2, 3, 4, 8, 16 (only 4, 8, 16 if RHS_TENSOR_TYPE=IMAGE)
* @note Values > 8 for M0, N0 and K0 are not expected to be efficient
*
- * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16
- * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes)
- * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes)
- * @param[in] lhs_w The width of the lhs tensor
- * @param[in] lhs_h The height of the lhs tensor
- * @param[in] lhs_n Number of the matrices (buffers) in the batch
- * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
- * @param[in] rhs_img (Optional) Read only cl_image object for the rhs tensor. Included when RHS_TENSOR_TYPE=IMAGE
- * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
- * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes)
- * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes)
- * @param[in] rhs_w The width of the rhs tensor
- * @param[in] rhs_h The height of the rhs tensor
- * @param[in] rhs_n Number of the matrices (buffers) in the batch
- * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
- * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
- * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes)
- * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes)
- * @param[in] dst_w The width of the dst tensor
- * @param[in] dst_h The height of the dst tensor
- * @param[in] dst_n Number of the matrices (buffers) in the batch
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
+ * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16
+ * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes)
+ * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes)
+ * @param[in] lhs_w The width of the lhs tensor
+ * @param[in] lhs_h The height of the lhs tensor
+ * @param[in] lhs_n Number of the matrices (buffers) in the batch
+ * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
+ * @param[in] rhs_img (Optional) Read only cl_image object for the rhs tensor. Included when RHS_TENSOR_TYPE=IMAGE
+ * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
+ * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes)
+ * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes)
+ * @param[in] rhs_w The width of the rhs tensor
+ * @param[in] rhs_h The height of the rhs tensor
+ * @param[in] rhs_n Number of the matrices (buffers) in the batch
+ * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
+ * @param[in] bias_ptr (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr
+ * @param[in] bias_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes)
+ * @param[in] bias_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes)
+ * @param[in] bias_w (Optional) The size of the width dimension of the bias tensor
+ * @param[in] bias_h (Optional) The size of the height dimension of the bias tensor
+ * @param[in] bias_n (Optional) The size of the depth dimension of the bias tensor
+ * @param[in] bias_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor
+ * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr,
+ * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes)
+ * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes)
+ * @param[in] dst_w The width of the dst tensor
+ * @param[in] dst_h The height of the dst tensor
+ * @param[in] dst_n Number of the matrices (buffers) in the batch
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
*/
__kernel void mat_mul_native_t_t(
TENSOR3D_T(lhs, BUFFER),
TENSOR3D_T(rhs, RHS_TENSOR_TYPE),
+#ifdef BIAS
+ TENSOR3D_T(bias, BUFFER),
+#endif // defined(BIAS)
TENSOR3D_T(dst, BUFFER))
{
const uint x = GET_SPATIAL_IDX(0, N0, PARTIAL_STORE_N0);
@@ -630,6 +697,10 @@
indirect_buffer[_i].v = min(_i, select(M0 - 1, PARTIAL_STORE_M0 - 1, y_cond));
});
+#ifdef BIAS
+ perform_bias_addition(bias_ptr, bias_offset_first_element_in_bytes, acc, x);
+#endif // defined(BIAS)
+
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);
diff --git a/src/core/CL/cl_kernels/common/mat_mul_mmul.cl b/src/core/CL/cl_kernels/common/mat_mul_mmul.cl
index a53db27..e549da8 100644
--- a/src/core/CL/cl_kernels/common/mat_mul_mmul.cl
+++ b/src/core/CL/cl_kernels/common/mat_mul_mmul.cl
@@ -24,6 +24,21 @@
#include "helpers.h"
#include "tile_helpers.h"
+#ifdef BIAS
+// This function performs in-place bias addition for float and half datatypes when bias is enabled.
+// Note The tile's dimensions used for the LHS and RHS matrices (M0, N0) must be passed at compile time using -DN0, -DM0 (e.g. -DN0=8, -DM0=4).
+inline void perform_bias_addition(uchar *bias_ptr, uint bias_offset_first_element_in_bytes, TILE(DATA_TYPE, M0, N0, acc), uint x)
+{
+ TILE(DATA_TYPE, 1, N0, bias_tile);
+
+ // below expands to use bias_ptr and bias_offset_first_element_in_bytes
+ T_LOAD(DATA_TYPE, 1, N0, BUFFER, bias, x, 0, 1, 0, bias_tile);
+
+ // c = c + bias[broadcasted]
+ T_ELTWISE_BROADCAST_ADD_X(DATA_TYPE, M0, N0, acc, bias_tile, acc);
+}
+#endif // defined(BIAS)
+
#if defined(MAT_MUL_NATIVE_MMUL_NT_NT)
/** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul) using MMUL: LHS non-transposed, RHS non-transposed - buffer only
*
@@ -40,34 +55,44 @@
* - K0 = 1
* @note Values > 8 for M0 are not expected to be efficient
*
- * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16
- * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes)
- * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes)
- * @param[in] lhs_w The width of the lhs tensor
- * @param[in] lhs_h The height of the lhs tensor
- * @param[in] lhs_n Number of the matrices (buffers) in the batch
- * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
- * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
- * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes)
- * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes)
- * @param[in] rhs_w The width of the rhs tensor
- * @param[in] rhs_h The height of the rhs tensor
- * @param[in] rhs_n Number of the matrices (buffers) in the batch
- * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
- * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
- * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes)
- * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes)
- * @param[in] dst_w The width of the dst tensor
- * @param[in] dst_h The height of the dst tensor
- * @param[in] dst_n Number of the matrices (buffers) in the batch
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
- * @param[in] M Number of rows in LHS matrix
- * @param[in] N Number of columns in RHS matrix
- * @param[in] K Number of columns in LHS matrix and rows in RHS matrix, which is multiple of MMUL_K0.
+ * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16
+ * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes)
+ * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes)
+ * @param[in] lhs_w The width of the lhs tensor
+ * @param[in] lhs_h The height of the lhs tensor
+ * @param[in] lhs_n Number of the matrices (buffers) in the batch
+ * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
+ * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
+ * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes)
+ * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes)
+ * @param[in] rhs_w The width of the rhs tensor
+ * @param[in] rhs_h The height of the rhs tensor
+ * @param[in] rhs_n Number of the matrices (buffers) in the batch
+ * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
+ * @param[in] bias_ptr (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr
+ * @param[in] bias_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes)
+ * @param[in] bias_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes)
+ * @param[in] bias_w (Optional) The size of the width dimension of the bias tensor
+ * @param[in] bias_h (Optional) The size of the height dimension of the bias tensor
+ * @param[in] bias_n (Optional) The size of the depth dimension of the bias tensor
+ * @param[in] bias_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor
+ * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
+ * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes)
+ * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes)
+ * @param[in] dst_w The width of the dst tensor
+ * @param[in] dst_h The height of the dst tensor
+ * @param[in] dst_n Number of the matrices (buffers) in the batch
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
+ * @param[in] M Number of rows in LHS matrix
+ * @param[in] N Number of columns in RHS matrix
+ * @param[in] K Number of columns in LHS matrix and rows in RHS matrix, which is multiple of MMUL_K0.
*/
__kernel void mat_mul_native_mmul_nt_nt(
TENSOR3D_T(lhs, BUFFER),
TENSOR3D_T(rhs, BUFFER),
+#ifdef BIAS
+ TENSOR3D_T(bias, BUFFER),
+#endif // defined(BIAS)
TENSOR3D_T(dst, BUFFER),
const int M,
const int N,
@@ -90,7 +115,7 @@
// x = [0, ((N / N0) / MMUL_N0) * MMUL_N0 * MMUL_M0)
// x = [0, (N / N0) * MMUL_MO)
const uint x0 = get_global_id(0); // [0, (N / N0) * MMUL_M0)
- // The upper limit is a simplified version of (N / N0) / MMUL_N0) * MMUL_BLOCK_SIZE)
+ // The upper limit is a simplified version of (N / N0) / MMUL_N0) * MMUL_BLOCK_SIZE)
const uint y0 = get_global_id(1); // [0, (M / M0) / MMUL_M0)
const uint z = get_global_id(2); // Batch
@@ -347,6 +372,10 @@
#define c c_f32
#endif // defined(HALF_PRECISION)
+#ifdef BIAS
+ perform_bias_addition(bias_ptr, bias_offset_first_element_in_bytes, c, dst_x);
+#endif // defined(BIAS)
+
if(dst_x + N0 <= N || N0_LEFTOVER == 0)
{
LOOP_UNROLLING(int, m0, 0, 1, M0,
@@ -391,34 +420,44 @@
* - K0 = 1
* @note Values > 8 for M0 are not expected to be efficient
*
- * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16
- * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes)
- * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes)
- * @param[in] lhs_w The width of the lhs tensor
- * @param[in] lhs_h The height of the lhs tensor
- * @param[in] lhs_n Number of the matrices (buffers) in the batch
- * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
- * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
- * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes)
- * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes)
- * @param[in] rhs_w The width of the rhs tensor
- * @param[in] rhs_h The height of the rhs tensor
- * @param[in] rhs_n Number of the matrices (buffers) in the batch
- * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
- * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
- * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes)
- * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes)
- * @param[in] dst_w The width of the dst tensor
- * @param[in] dst_h The height of the dst tensor
- * @param[in] dst_n Number of the matrices (buffers) in the batch
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
- * @param[in] M Number of rows in DST matrix
- * @param[in] N Number of columns in DST matrix
- * @param[in] K Number of rows in LHS and RHS matrices, which is multiple of MMUL_K0.
+ * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16
+ * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes)
+ * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes)
+ * @param[in] lhs_w The width of the lhs tensor
+ * @param[in] lhs_h The height of the lhs tensor
+ * @param[in] lhs_n Number of the matrices (buffers) in the batch
+ * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
+ * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
+ * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes)
+ * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes)
+ * @param[in] rhs_w The width of the rhs tensor
+ * @param[in] rhs_h The height of the rhs tensor
+ * @param[in] rhs_n Number of the matrices (buffers) in the batch
+ * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
+ * @param[in] bias_ptr (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr
+ * @param[in] bias_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes)
+ * @param[in] bias_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes)
+ * @param[in] bias_w (Optional) The size of the width dimension of the bias tensor
+ * @param[in] bias_h (Optional) The size of the height dimension of the bias tensor
+ * @param[in] bias_n (Optional) The size of the depth dimension of the bias tensor
+ * @param[in] bias_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor
+ * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
+ * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes)
+ * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes)
+ * @param[in] dst_w The width of the dst tensor
+ * @param[in] dst_h The height of the dst tensor
+ * @param[in] dst_n Number of the matrices (buffers) in the batch
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
+ * @param[in] M Number of rows in DST matrix
+ * @param[in] N Number of columns in DST matrix
+ * @param[in] K Number of rows in LHS and RHS matrices, which is multiple of MMUL_K0.
*/
__kernel void mat_mul_native_mmul_t_nt(
TENSOR3D_T(lhs, BUFFER),
TENSOR3D_T(rhs, BUFFER),
+#ifdef BIAS
+ TENSOR3D_T(bias, BUFFER),
+#endif // defined(BIAS)
TENSOR3D_T(dst, BUFFER),
const int M,
const int N,
@@ -428,7 +467,7 @@
// For explanations on how this kernel works, please refer to NT/NT kernel. This kernel makes little modifications to it.
const uint x0 = get_global_id(0); // [0, (N / N0) * MMUL_M0)
- // The upper limit is a simplified version of (N / N0) / MMUL_N0) * MMUL_BLOCK_SIZE)
+ // The upper limit is a simplified version of (N / N0) / MMUL_N0) * MMUL_BLOCK_SIZE)
const uint y0 = get_global_id(1); // [0, (M / M0) / MMUL_M0)
const uint z = get_global_id(2); // Batch
@@ -511,6 +550,10 @@
#define c c_f32
#endif // defined(HALF_PRECISION)
+#ifdef BIAS
+ perform_bias_addition(bias_ptr, bias_offset_first_element_in_bytes, c, dst_x);
+#endif // defined(BIAS)
+
if(dst_x + N0 <= N || N0_LEFTOVER == 0)
{
LOOP_UNROLLING(int, m0, 0, 1, M0,
@@ -554,34 +597,44 @@
* - K0 = 1
* @note Values > 8 for M0 are not expected to be efficient
*
- * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16
- * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes)
- * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes)
- * @param[in] lhs_w The width of the lhs tensor
- * @param[in] lhs_h The height of the lhs tensor
- * @param[in] lhs_n Number of the matrices (buffers) in the batch
- * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
- * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
- * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes)
- * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes)
- * @param[in] rhs_w The width of the rhs tensor
- * @param[in] rhs_h The height of the rhs tensor
- * @param[in] rhs_n Number of the matrices (buffers) in the batch
- * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
- * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
- * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes)
- * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes)
- * @param[in] dst_w The width of the dst tensor
- * @param[in] dst_h The height of the dst tensor
- * @param[in] dst_n Number of the matrices (buffers) in the batch
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
- * @param[in] M Number of rows in LHS matrix
- * @param[in] N Number of columns in RHS matrix
- * @param[in] K Number of columns in LHS matrix and columns in RHS matrix, which is multiple of MMUL_K0.
+ * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16
+ * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes)
+ * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes)
+ * @param[in] lhs_w The width of the lhs tensor
+ * @param[in] lhs_h The height of the lhs tensor
+ * @param[in] lhs_n Number of the matrices (buffers) in the batch
+ * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
+ * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
+ * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes)
+ * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes)
+ * @param[in] rhs_w The width of the rhs tensor
+ * @param[in] rhs_h The height of the rhs tensor
+ * @param[in] rhs_n Number of the matrices (buffers) in the batch
+ * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
+ * @param[in] bias_ptr (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr
+ * @param[in] bias_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes)
+ * @param[in] bias_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes)
+ * @param[in] bias_w (Optional) The size of the width dimension of the bias tensor
+ * @param[in] bias_h (Optional) The size of the height dimension of the bias tensor
+ * @param[in] bias_n (Optional) The size of the depth dimension of the bias tensor
+ * @param[in] bias_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor
+ * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
+ * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes)
+ * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes)
+ * @param[in] dst_w The width of the dst tensor
+ * @param[in] dst_h The height of the dst tensor
+ * @param[in] dst_n Number of the matrices (buffers) in the batch
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
+ * @param[in] M Number of rows in LHS matrix
+ * @param[in] N Number of columns in RHS matrix
+ * @param[in] K Number of columns in LHS matrix and columns in RHS matrix, which is multiple of MMUL_K0.
*/
__kernel void mat_mul_native_mmul_nt_t(
TENSOR3D_T(lhs, BUFFER),
TENSOR3D_T(rhs, BUFFER),
+#ifdef BIAS
+ TENSOR3D_T(bias, BUFFER),
+#endif // defined(BIAS)
TENSOR3D_T(dst, BUFFER),
const int M,
const int N,
@@ -591,7 +644,7 @@
// For explanations on how this kernel works, please refer to NT/NT kernel. This kernel makes little modifications to it.
const uint x0 = get_global_id(0); // [0, (N / N0) * MMUL_M0)
- // The upper limit is a simplified version of (N / N0) / MMUL_N0) * MMUL_BLOCK_SIZE)
+ // The upper limit is a simplified version of (N / N0) / MMUL_N0) * MMUL_BLOCK_SIZE)
const uint y0 = get_global_id(1); // [0, (M / M0) / MMUL_M0)
const uint z = get_global_id(2); // Batch
@@ -679,6 +732,10 @@
#define c c_f32
#endif // defined(HALF_PRECISION)
+#ifdef BIAS
+ perform_bias_addition(bias_ptr, bias_offset_first_element_in_bytes, c, dst_x);
+#endif // defined(BIAS)
+
if(dst_x + N0 <= N || N0_LEFTOVER == 0)
{
LOOP_UNROLLING(int, m0, 0, 1, M0,
@@ -722,34 +779,44 @@
* - K0 = 1
* @note Values > 8 for M0 are not expected to be efficient
*
- * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16
- * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes)
- * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes)
- * @param[in] lhs_w The width of the lhs tensor
- * @param[in] lhs_h The height of the lhs tensor
- * @param[in] lhs_n Number of the matrices (buffers) in the batch
- * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
- * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
- * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes)
- * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes)
- * @param[in] rhs_w The width of the rhs tensor
- * @param[in] rhs_h The height of the rhs tensor
- * @param[in] rhs_n Number of the matrices (buffers) in the batch
- * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
- * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
- * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes)
- * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes)
- * @param[in] dst_w The width of the dst tensor
- * @param[in] dst_h The height of the dst tensor
- * @param[in] dst_n Number of the matrices (buffers) in the batch
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
- * @param[in] M Number of rows in LHS matrix
- * @param[in] N Number of columns in RHS matrix
- * @param[in] K Number of rows in LHS matrix and columns in RHS matrix, which is multiple of MMUL_K0.
+ * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16
+ * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes)
+ * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes)
+ * @param[in] lhs_w The width of the lhs tensor
+ * @param[in] lhs_h The height of the lhs tensor
+ * @param[in] lhs_n Number of the matrices (buffers) in the batch
+ * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
+ * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
+ * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes)
+ * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes)
+ * @param[in] rhs_w The width of the rhs tensor
+ * @param[in] rhs_h The height of the rhs tensor
+ * @param[in] rhs_n Number of the matrices (buffers) in the batch
+ * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
+ * @param[in] bias_ptr (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr
+ * @param[in] bias_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes)
+ * @param[in] bias_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes)
+ * @param[in] bias_w (Optional) The size of the width dimension of the bias tensor
+ * @param[in] bias_h (Optional) The size of the height dimension of the bias tensor
+ * @param[in] bias_n (Optional) The size of the depth dimension of the bias tensor
+ * @param[in] bias_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor
+ * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
+ * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes)
+ * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes)
+ * @param[in] dst_w The width of the dst tensor
+ * @param[in] dst_h The height of the dst tensor
+ * @param[in] dst_n Number of the matrices (buffers) in the batch
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
+ * @param[in] M Number of rows in LHS matrix
+ * @param[in] N Number of columns in RHS matrix
+ * @param[in] K Number of rows in LHS matrix and columns in RHS matrix, which is multiple of MMUL_K0.
*/
__kernel void mat_mul_native_mmul_t_t(
TENSOR3D_T(lhs, BUFFER),
TENSOR3D_T(rhs, BUFFER),
+#ifdef BIAS
+ TENSOR3D_T(bias, BUFFER),
+#endif // defined(BIAS)
TENSOR3D_T(dst, BUFFER),
const int M,
const int N,
@@ -759,7 +826,7 @@
// For explanations on how this kernel works, please refer to NT/NT kernel. This kernel makes little modifications to it.
const uint x0 = get_global_id(0); // [0, (N / N0) * MMUL_M0)
- // The upper limit is a simplified version of (N / N0) / MMUL_N0) * MMUL_BLOCK_SIZE)
+ // The upper limit is a simplified version of (N / N0) / MMUL_N0) * MMUL_BLOCK_SIZE)
const uint y0 = get_global_id(1); // [0, (M / M0) / MMUL_M0)
const uint z = get_global_id(2); // Batch
@@ -847,6 +914,10 @@
#define c c_f32
#endif // defined(HALF_PRECISION)
+#ifdef BIAS
+ perform_bias_addition(bias_ptr, bias_offset_first_element_in_bytes, c, dst_x);
+#endif // defined(BIAS)
+
if(dst_x + N0 <= N || N0_LEFTOVER == 0)
{
LOOP_UNROLLING(int, m0, 0, 1, M0,
diff --git a/src/core/CL/cl_kernels/common/mat_mul_quantized.cl b/src/core/CL/cl_kernels/common/mat_mul_quantized.cl
index 7029af2..7f81ac4 100644
--- a/src/core/CL/cl_kernels/common/mat_mul_quantized.cl
+++ b/src/core/CL/cl_kernels/common/mat_mul_quantized.cl
@@ -25,6 +25,21 @@
#include "helpers.h"
#include "tile_helpers.h"
+#ifdef BIAS
+// This function performs in-place bias addition for integer datatype when bias is enabled.
+// Note The tile's dimensions used for the LHS and RHS matrices (M0, N0) must be passed at compile time using -DN0, -DM0 (e.g. -DN0=8, -DM0=4).
+inline void perform_bias_addition(uchar *bias_ptr, uint bias_offset_first_element_in_bytes, TILE(int, M0, N0, acc), uint x)
+{
+ TILE(int, 1, N0, bias_tile);
+
+ // below expands to use bias_ptr and bias_offset_first_element_in_bytes
+ T_LOAD(int, 1, N0, BUFFER, bias, x, 0, 1, 0, bias_tile);
+
+ // c = c + bias[broadcasted]
+ T_ELTWISE_BROADCAST_ADD_X(int, M0, N0, acc, bias_tile, acc);
+}
+#endif // defined(BIAS)
+
#if defined(MAT_MUL_NATIVE_QUANTIZED_NT_NT)
/** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul): LHS non-transposed, RHS non-transposed - buffer only
*
@@ -43,31 +58,41 @@
* - K0 = 1, 2, 3, 4, 8, 16
* @note Values > 8 for M0 are not expected to be efficient
*
- * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: QASYMM8_SIGNED/QASYMM8
- * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes)
- * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes)
- * @param[in] lhs_w The width of the lhs tensor
- * @param[in] lhs_h The height of the lhs tensor
- * @param[in] lhs_n Number of the matrices (buffers) in the batch
- * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
- * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
- * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes)
- * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes)
- * @param[in] rhs_w The width of the rhs tensor
- * @param[in] rhs_h The height of the rhs tensor
- * @param[in] rhs_n Number of the matrices (buffers) in the batch
- * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
- * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
- * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes)
- * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes)
- * @param[in] dst_w The width of the dst tensor
- * @param[in] dst_h The height of the dst tensor
- * @param[in] dst_n Number of the matrices (buffers) in the batch
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
+ * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: QASYMM8_SIGNED/QASYMM8
+ * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes)
+ * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes)
+ * @param[in] lhs_w The width of the lhs tensor
+ * @param[in] lhs_h The height of the lhs tensor
+ * @param[in] lhs_n Number of the matrices (buffers) in the batch
+ * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
+ * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
+ * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes)
+ * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes)
+ * @param[in] rhs_w The width of the rhs tensor
+ * @param[in] rhs_h The height of the rhs tensor
+ * @param[in] rhs_n Number of the matrices (buffers) in the batch
+ * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
+ * @param[in] bias_ptr (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr
+ * @param[in] bias_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes)
+ * @param[in] bias_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes)
+ * @param[in] bias_w (Optional) The size of the width dimension of the bias tensor
+ * @param[in] bias_h (Optional) The size of the height dimension of the bias tensor
+ * @param[in] bias_n (Optional) The size of the depth dimension of the bias tensor
+ * @param[in] bias_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor
+ * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
+ * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes)
+ * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes)
+ * @param[in] dst_w The width of the dst tensor
+ * @param[in] dst_h The height of the dst tensor
+ * @param[in] dst_n Number of the matrices (buffers) in the batch
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
*/
__kernel void mat_mul_native_quantized_nt_nt(
TENSOR3D_T(lhs, BUFFER),
TENSOR3D_T(rhs, BUFFER),
+#ifdef BIAS
+ TENSOR3D_T(bias, BUFFER),
+#endif // defined(BIAS)
TENSOR3D_T(dst, BUFFER))
{
const uint x = GET_SPATIAL_IDX(0, N0, PARTIAL_STORE_N0);
@@ -197,6 +222,10 @@
const bool x_cond = PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0;
const bool y_cond = PARTIAL_STORE_M0 != 0 && get_global_id(1) == 0;
+#ifdef BIAS
+ perform_bias_addition(bias_ptr, bias_offset_first_element_in_bytes, acc, x);
+#endif // defined(BIAS)
+
// Quantize the tile
TILE(DATA_TYPE, M0, N0, accq);
T_QUANTIZE8_ASYMMETRIC(int, DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, acc, accq);
@@ -231,31 +260,41 @@
* - K0 = 1, 2, 3, 4, 8, 16
* @note Values > 8 for M0, N0, K0 are not expected to be efficient
*
- * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: QASYMM8/QASYMM8_SIGNED
- * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes)
- * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes)
- * @param[in] lhs_w The width of the lhs tensor
- * @param[in] lhs_h The height of the lhs tensor
- * @param[in] lhs_n Number of the matrices (buffers) in the batch
- * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
- * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
- * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes)
- * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes)
- * @param[in] rhs_w The width of the rhs tensor
- * @param[in] rhs_h The height of the rhs tensor
- * @param[in] rhs_n Number of the matrices (buffers) in the batch
- * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
- * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
- * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes)
- * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes)
- * @param[in] dst_w The width of the dst tensor
- * @param[in] dst_h The height of the dst tensor
- * @param[in] dst_n Number of the matrices (buffers) in the batch
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
+ * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: QASYMM8/QASYMM8_SIGNED
+ * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes)
+ * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes)
+ * @param[in] lhs_w The width of the lhs tensor
+ * @param[in] lhs_h The height of the lhs tensor
+ * @param[in] lhs_n Number of the matrices (buffers) in the batch
+ * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
+ * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
+ * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes)
+ * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes)
+ * @param[in] rhs_w The width of the rhs tensor
+ * @param[in] rhs_h The height of the rhs tensor
+ * @param[in] rhs_n Number of the matrices (buffers) in the batch
+ * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
+ * @param[in] bias_ptr (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr
+ * @param[in] bias_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes)
+ * @param[in] bias_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes)
+ * @param[in] bias_w (Optional) The size of the width dimension of the bias tensor
+ * @param[in] bias_h (Optional) The size of the height dimension of the bias tensor
+ * @param[in] bias_n (Optional) The size of the depth dimension of the bias tensor
+ * @param[in] bias_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor
+ * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
+ * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes)
+ * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes)
+ * @param[in] dst_w The width of the dst tensor
+ * @param[in] dst_h The height of the dst tensor
+ * @param[in] dst_n Number of the matrices (buffers) in the batch
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
*/
__kernel void mat_mul_native_quantized_nt_t(
TENSOR3D_T(lhs, BUFFER),
TENSOR3D_T(rhs, BUFFER),
+#ifdef BIAS
+ TENSOR3D_T(bias, BUFFER),
+#endif // defined(BIAS)
TENSOR3D_T(dst, BUFFER))
{
const uint x = GET_SPATIAL_IDX(0, N0, PARTIAL_STORE_N0);
@@ -377,6 +416,10 @@
const bool x_cond = PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0;
const bool y_cond = PARTIAL_STORE_M0 != 0 && get_global_id(1) == 0;
+#ifdef BIAS
+ perform_bias_addition(bias_ptr, bias_offset_first_element_in_bytes, acc, x);
+#endif // defined(BIAS)
+
// Quantize the tile
TILE(DATA_TYPE, M0, N0, accq);
T_QUANTIZE8_ASYMMETRIC(int, DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, acc, accq);
@@ -411,31 +454,41 @@
* - K0 = 1, 2, 3, 4, 8, 16
* @note Values > 8 for M0, N0 and K0 are not expected to be efficient
*
- * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: QASYMM8/QASYMM8_SIGNED
- * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes)
- * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes)
- * @param[in] lhs_w The width of the lhs tensor
- * @param[in] lhs_h The height of the lhs tensor
- * @param[in] lhs_n Number of the matrices (buffers) in the batch
- * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
- * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
- * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes)
- * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes)
- * @param[in] rhs_w The width of the rhs tensor
- * @param[in] rhs_h The height of the rhs tensor
- * @param[in] rhs_n Number of the matrices (buffers) in the batch
- * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
- * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
- * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes)
- * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes)
- * @param[in] dst_w The width of the dst tensor
- * @param[in] dst_h The height of the dst tensor
- * @param[in] dst_n Number of the matrices (buffers) in the batch
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
+ * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: QASYMM8/QASYMM8_SIGNED
+ * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes)
+ * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes)
+ * @param[in] lhs_w The width of the lhs tensor
+ * @param[in] lhs_h The height of the lhs tensor
+ * @param[in] lhs_n Number of the matrices (buffers) in the batch
+ * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
+ * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
+ * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes)
+ * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes)
+ * @param[in] rhs_w The width of the rhs tensor
+ * @param[in] rhs_h The height of the rhs tensor
+ * @param[in] rhs_n Number of the matrices (buffers) in the batch
+ * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
+ * @param[in] bias_ptr (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr
+ * @param[in] bias_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes)
+ * @param[in] bias_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes)
+ * @param[in] bias_w (Optional) The size of the width dimension of the bias tensor
+ * @param[in] bias_h (Optional) The size of the height dimension of the bias tensor
+ * @param[in] bias_n (Optional) The size of the depth dimension of the bias tensor
+ * @param[in] bias_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor
+ * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
+ * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes)
+ * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes)
+ * @param[in] dst_w The width of the dst tensor
+ * @param[in] dst_h The height of the dst tensor
+ * @param[in] dst_n Number of the matrices (buffers) in the batch
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
*/
__kernel void mat_mul_native_quantized_t_nt(
TENSOR3D_T(lhs, BUFFER),
TENSOR3D_T(rhs, BUFFER),
+#ifdef BIAS
+ TENSOR3D_T(bias, BUFFER),
+#endif // defined(BIAS)
TENSOR3D_T(dst, BUFFER))
{
const uint x = GET_SPATIAL_IDX(0, N0, PARTIAL_STORE_N0);
@@ -559,6 +612,10 @@
const bool x_cond = PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0;
const bool y_cond = PARTIAL_STORE_M0 != 0 && get_global_id(1) == 0;
+#ifdef BIAS
+ perform_bias_addition(bias_ptr, bias_offset_first_element_in_bytes, acc, x);
+#endif // defined(BIAS)
+
// Quantize the tile
TILE(DATA_TYPE, M0, N0, accq);
T_QUANTIZE8_ASYMMETRIC(int, DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, acc, accq);
@@ -593,31 +650,41 @@
* - K0 = 1, 2, 3, 4, 8, 16
* @note Values > 8 for M0, N0 and K0 are not expected to be efficient
*
- * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: QASYMM8/QASYMM8_SIGNED
- * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes)
- * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes)
- * @param[in] lhs_w The width of the lhs tensor
- * @param[in] lhs_h The height of the lhs tensor
- * @param[in] lhs_n Number of the matrices (buffers) in the batch
- * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
- * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
- * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes)
- * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes)
- * @param[in] rhs_w The width of the rhs tensor
- * @param[in] rhs_h The height of the rhs tensor
- * @param[in] rhs_n Number of the matrices (buffers) in the batch
- * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
- * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
- * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes)
- * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes)
- * @param[in] dst_w The width of the dst tensor
- * @param[in] dst_h The height of the dst tensor
- * @param[in] dst_n Number of the matrices (buffers) in the batch
- * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
+ * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: QASYMM8/QASYMM8_SIGNED
+ * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes)
+ * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes)
+ * @param[in] lhs_w The width of the lhs tensor
+ * @param[in] lhs_h The height of the lhs tensor
+ * @param[in] lhs_n Number of the matrices (buffers) in the batch
+ * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix
+ * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr
+ * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes)
+ * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes)
+ * @param[in] rhs_w The width of the rhs tensor
+ * @param[in] rhs_h The height of the rhs tensor
+ * @param[in] rhs_n Number of the matrices (buffers) in the batch
+ * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix
+ * @param[in] bias_ptr (Optional) Pointer to the bias tensor. Supported data type: same as @p lhs_ptr
+ * @param[in] bias_stride_y (Optional) Stride of the bias tensor in Y dimension (in bytes)
+ * @param[in] bias_stride_z (Optional) Stride of the bias tensor in Z dimension (in bytes)
+ * @param[in] bias_w (Optional) The size of the width dimension of the bias tensor
+ * @param[in] bias_h (Optional) The size of the height dimension of the bias tensor
+ * @param[in] bias_n (Optional) The size of the depth dimension of the bias tensor
+ * @param[in] bias_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor
+ * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr
+ * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes)
+ * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes)
+ * @param[in] dst_w The width of the dst tensor
+ * @param[in] dst_h The height of the dst tensor
+ * @param[in] dst_n Number of the matrices (buffers) in the batch
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix
*/
__kernel void mat_mul_native_quantized_t_t(
TENSOR3D_T(lhs, BUFFER),
TENSOR3D_T(rhs, BUFFER),
+#ifdef BIAS
+ TENSOR3D_T(bias, BUFFER),
+#endif // defined(BIAS)
TENSOR3D_T(dst, BUFFER))
{
const uint x = GET_SPATIAL_IDX(0, N0, PARTIAL_STORE_N0);
@@ -745,6 +812,10 @@
const bool x_cond = PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0;
const bool y_cond = PARTIAL_STORE_M0 != 0 && get_global_id(1) == 0;
+#ifdef BIAS
+ perform_bias_addition(bias_ptr, bias_offset_first_element_in_bytes, acc, x);
+#endif // defined(BIAS)
+
// Quantize the tile
TILE(DATA_TYPE, M0, N0, accq);
T_QUANTIZE8_ASYMMETRIC(int, DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, acc, accq);
diff --git a/src/gpu/cl/kernels/ClMatMulLowpNativeKernel.cpp b/src/gpu/cl/kernels/ClMatMulLowpNativeKernel.cpp
index 02c5754..a0eb3f2 100644
--- a/src/gpu/cl/kernels/ClMatMulLowpNativeKernel.cpp
+++ b/src/gpu/cl/kernels/ClMatMulLowpNativeKernel.cpp
@@ -100,7 +100,8 @@
{
_type = CLKernelType::GEMM;
}
-Status ClMatMulLowpNativeKernel::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info, const ActivationLayerInfo &act_info)
+Status ClMatMulLowpNativeKernel::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *bias, const ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info,
+ const ActivationLayerInfo &act_info)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lhs, rhs, dst);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
@@ -111,24 +112,32 @@
ARM_COMPUTE_RETURN_ERROR_ON_MSG((act_info.activation() != ActivationFunction::IDENTITY && act_info.activation() != ActivationFunction::RELU
&& act_info.activation() != ActivationFunction::LU_BOUNDED_RELU && act_info.activation() != ActivationFunction::BOUNDED_RELU),
"Activation Function specified is unsupported.");
+ const TensorShape expected_output_shape = misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info);
if(dst->total_size() != 0)
{
- const TensorInfo tensor_info_output = dst->clone()->set_tensor_shape(misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info));
+ const TensorInfo tensor_info_output = dst->clone()->set_tensor_shape(expected_output_shape);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, dst);
}
+ if(bias != nullptr)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(bias, 1, DataType::S32);
+ ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() > 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(expected_output_shape[0] != bias->dimension(0));
+ }
+
return Status{};
}
-void ClMatMulLowpNativeKernel::configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info,
+void ClMatMulLowpNativeKernel::configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *bias, ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info,
const ActivationLayerInfo &act_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst, &compile_context, &matmul_kernel_info);
- ARM_COMPUTE_LOG_PARAMS(lhs, rhs, dst, matmul_kernel_info);
- ARM_COMPUTE_ERROR_THROW_ON(validate(lhs, rhs, dst, matmul_kernel_info));
+ ARM_COMPUTE_LOG_PARAMS(lhs, rhs, bias, dst, matmul_kernel_info);
+ ARM_COMPUTE_ERROR_THROW_ON(validate(lhs, rhs, bias, dst, matmul_kernel_info));
- // output tensor auto initialization if not yet initialized
+ // dst tensor auto initialization if not yet initialized
auto_init_if_empty(*dst, lhs->clone()->set_tensor_shape(misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info)));
const int m = dst->dimension(1);
@@ -172,7 +181,8 @@
// Note : Offset is not negated, unlike gemmlowp kernels
build_opts.add_option("-DLHS_OFFSET=" + support::cpp11::to_string(lqinfo.offset));
build_opts.add_option("-DRHS_OFFSET=" + support::cpp11::to_string(rqinfo.offset));
- build_opts.add_option("-DDST_OFFSET=" + support::cpp11::to_string(dqinfo.offset)); // Passed as positive (unlike the above two)
+ build_opts.add_option("-DDST_OFFSET=" + support::cpp11::to_string(dqinfo.offset));
+ build_opts.add_option_if(bias != nullptr, "-DBIAS");
// Floating point boundaries are quantized prior to being passed as arguments.
// Note: We expect the input and output tensors to always adopt a per-tensor quantization approach
@@ -222,17 +232,22 @@
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
- const ICLTensor *lhs = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
- const ICLTensor *rhs = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
- ICLTensor *dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
+ const ICLTensor *lhs = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
+ const ICLTensor *rhs = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
+ const ICLTensor *bias = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2));
+ ICLTensor *dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst);
- ARM_COMPUTE_LOG_PARAMS(lhs, rhs, dst);
+ ARM_COMPUTE_LOG_PARAMS(lhs, rhs, bias, dst);
unsigned int idx = 0;
Window window_collapsed = window.collapse(ICLKernel::window(), Window::DimZ);
add_3d_tensor_nhw_argument(idx, lhs);
add_3d_tensor_nhw_argument(idx, rhs);
+ if(bias != nullptr)
+ {
+ add_3d_tensor_nhw_argument(idx, bias);
+ }
add_3d_tensor_nhw_argument(idx, dst);
enqueue(queue, *this, window_collapsed, lws_hint());
diff --git a/src/gpu/cl/kernels/ClMatMulLowpNativeKernel.h b/src/gpu/cl/kernels/ClMatMulLowpNativeKernel.h
index 67d1a66..c908280 100644
--- a/src/gpu/cl/kernels/ClMatMulLowpNativeKernel.h
+++ b/src/gpu/cl/kernels/ClMatMulLowpNativeKernel.h
@@ -45,15 +45,16 @@
/** Initialise the kernel's input and output.
*
* @param[in] compile_context The compile context to be used.
- * @param[in] lhs Input tensor for the LHS matrix. Data type supported: QASYMM8_SIGNED/QASYMM8.
+ * @param[in] lhs Input tensor info for the LHS matrix. Data type supported: QASYMM8_SIGNED/QASYMM8.
* Dimensions above 2 are collapsed onto dimension 2 and represent the batch.
- * @param[in] rhs Input tensor for the RHS matrix. Data type supported: same as @p lhs.
+ * @param[in] rhs Input tensor info for the RHS matrix. Data type supported: same as @p lhs.
* Dimensions above 2 are collapsed onto dimension 2 and represent the batch.
+ * @param[in] bias Bias tensor info. Can be nullptr. Data type supported: S32.
* @param[out] dst Output tensor info. Data type supported: same as @p lhs
* @param[in] matmul_kernel_info Attributes for Batch MatMul Kernel
- * @param[in] act_info Class containing information about fused activation function.
+ * @param[in] act_info (Optional) Class containing information about fused activation function.
*/
- void configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info,
+ void configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *bias, ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info,
const ActivationLayerInfo &act_info = ActivationLayerInfo());
/** Static function to check if given info will lead to a valid configuration
*
@@ -61,7 +62,8 @@
*
* @return a status
*/
- static Status validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info, const ActivationLayerInfo &act_info = ActivationLayerInfo());
+ static Status validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *bias, const ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info,
+ const ActivationLayerInfo &act_info = ActivationLayerInfo());
// Inherited methods overridden:
void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override;
diff --git a/src/gpu/cl/kernels/ClMatMulNativeKernel.cpp b/src/gpu/cl/kernels/ClMatMulNativeKernel.cpp
index 205396a..545a5b2 100644
--- a/src/gpu/cl/kernels/ClMatMulNativeKernel.cpp
+++ b/src/gpu/cl/kernels/ClMatMulNativeKernel.cpp
@@ -120,7 +120,8 @@
_type = CLKernelType::GEMM;
}
-Status ClMatMulNativeKernel::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info, const ActivationLayerInfo &act_info)
+Status ClMatMulNativeKernel::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *bias, const ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info,
+ const ActivationLayerInfo &act_info)
{
ARM_COMPUTE_UNUSED(act_info);
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lhs, rhs, dst);
@@ -130,21 +131,30 @@
ARM_COMPUTE_RETURN_ON_ERROR(validate_input_shapes(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info));
ARM_COMPUTE_RETURN_ON_ERROR(validate_export_to_cl_image(rhs, matmul_kernel_info));
+ const TensorShape expected_output_shape = misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info);
+
if(dst->total_size() != 0)
{
- const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info));
+ const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(expected_output_shape);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, dst);
}
+ if(bias != nullptr)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(bias, lhs);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((bias->num_dimensions() > 1), "Multi dimensional bias is unsupported.");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(bias->dimension(0) != expected_output_shape[0], "First dimension of bias and output tensors must match.");
+ }
+
return Status{};
}
-void ClMatMulNativeKernel::configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info,
+void ClMatMulNativeKernel::configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *bias, ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info,
const ActivationLayerInfo &act_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst, &compile_context, &matmul_kernel_info);
- ARM_COMPUTE_LOG_PARAMS(lhs, rhs, dst, matmul_kernel_info);
- ARM_COMPUTE_ERROR_THROW_ON(validate(lhs, rhs, dst, matmul_kernel_info));
+ ARM_COMPUTE_LOG_PARAMS(lhs, rhs, bias, dst, matmul_kernel_info);
+ ARM_COMPUTE_ERROR_THROW_ON(validate(lhs, rhs, bias, dst, matmul_kernel_info));
// dst tensor auto initialization if not yet initialized
auto_init_if_empty(*dst, lhs->clone()->set_tensor_shape(misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info)));
@@ -176,6 +186,7 @@
build_opts.add_option("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0));
build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0));
build_opts.add_option("-DK=" + support::cpp11::to_string(k));
+ build_opts.add_option_if(bias != nullptr, "-DBIAS");
build_opts.add_option_if_else(_export_rhs_to_cl_image, "-DRHS_TENSOR_TYPE=IMAGE", "-DRHS_TENSOR_TYPE=BUFFER");
// Define values for activation function
@@ -225,11 +236,12 @@
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
- const ICLTensor *lhs = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
- const ICLTensor *rhs = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
- ICLTensor *dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
+ const ICLTensor *lhs = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
+ const ICLTensor *rhs = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
+ const ICLTensor *bias = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2)); // nullptr if bias is not present
+ ICLTensor *dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst);
- ARM_COMPUTE_LOG_PARAMS(lhs, rhs, dst);
+ ARM_COMPUTE_LOG_PARAMS(lhs, rhs, bias, dst);
unsigned int idx = 0;
Window window_collapsed = window.collapse(ICLKernel::window(), Window::DimZ);
@@ -250,6 +262,10 @@
}
add_3d_tensor_nhw_argument(idx, rhs);
+ if(bias != nullptr)
+ {
+ add_3d_tensor_nhw_argument(idx, bias);
+ }
add_3d_tensor_nhw_argument(idx, dst);
enqueue(queue, *this, window_collapsed, lws_hint());
diff --git a/src/gpu/cl/kernels/ClMatMulNativeKernel.h b/src/gpu/cl/kernels/ClMatMulNativeKernel.h
index 02d8ac3..fe2b787 100644
--- a/src/gpu/cl/kernels/ClMatMulNativeKernel.h
+++ b/src/gpu/cl/kernels/ClMatMulNativeKernel.h
@@ -43,15 +43,16 @@
/** Initialise the kernel's input and output.
*
* @param[in] compile_context The compile context to be used.
- * @param[in] lhs Input tensor for the LHS matrix. Data type supported: F32/F16.
+ * @param[in] lhs Input tensor info for the LHS matrix. Data type supported: F32/F16.
* Dimensions above 2 are collapsed onto dimension 2 and represent the batch.
- * @param[in] rhs Input tensor for the RHS matrix. Data type supported: same as @p lhs.
+ * @param[in] rhs Input tensor info for the RHS matrix. Data type supported: same as @p lhs.
* Dimensions above 2 are collapsed onto dimension 2 and represent the batch.
+ * @param[in] bias Bias tensor info for bias matrix. Can be nullptr. Data type supported: same as @p lhs.
* @param[out] dst Output tensor info. Data type supported: same as @p lhs
* @param[in] matmul_kernel_info Attributes for Batch MatMul Kernel
- * @param[in] act_info Specifies activation function to use after Matrix multiplication. Default is Identity function.
+ * @param[in] act_info (Optional) Specifies activation function to use after Matrix multiplication. Default is Identity function.
*/
- void configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info,
+ void configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *bias, ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info,
const ActivationLayerInfo &act_info = ActivationLayerInfo());
/** Static function to check if given info will lead to a valid configuration
*
@@ -59,7 +60,8 @@
*
* @return a status
*/
- static Status validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info, const ActivationLayerInfo &act_info = ActivationLayerInfo());
+ static Status validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *bias, const ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info,
+ const ActivationLayerInfo &act_info = ActivationLayerInfo());
// Inherited methods overridden:
void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override;
diff --git a/src/gpu/cl/kernels/ClMatMulNativeMMULKernel.cpp b/src/gpu/cl/kernels/ClMatMulNativeMMULKernel.cpp
index 4630ec0..0efcfb1 100644
--- a/src/gpu/cl/kernels/ClMatMulNativeMMULKernel.cpp
+++ b/src/gpu/cl/kernels/ClMatMulNativeMMULKernel.cpp
@@ -60,9 +60,9 @@
Status validate_matmul_kernel_info(const MatMulKernelInfo &matmul_kernel_info)
{
const bool adj_lhs = matmul_kernel_info.adj_lhs;
- const int m0 = matmul_kernel_info.m0;
- const int n0 = matmul_kernel_info.n0;
- const int k0 = matmul_kernel_info.k0;
+ const int m0 = matmul_kernel_info.m0;
+ const int n0 = matmul_kernel_info.n0;
+ const int k0 = matmul_kernel_info.k0;
// Validate M0
ARM_COMPUTE_RETURN_ERROR_ON_MSG(m0 < 1, "Only positive integers are supported for M0");
@@ -149,7 +149,7 @@
_type = CLKernelType::GEMM;
}
-Status ClMatMulNativeMMULKernel::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info)
+Status ClMatMulNativeMMULKernel::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *bias, const ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lhs, rhs, dst);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::F32, DataType::F16);
@@ -158,20 +158,29 @@
ARM_COMPUTE_RETURN_ON_ERROR(validate_matmul_kernel_info(matmul_kernel_info));
ARM_COMPUTE_RETURN_ON_ERROR(validate_input_shapes(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info));
+ const TensorShape expected_output_shape = misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info);
+
if(dst->total_size() != 0)
{
- const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info));
+ const TensorInfo tensor_info_dst = dst->clone()->set_tensor_shape(expected_output_shape);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, dst);
}
+ if(bias != nullptr)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((bias->num_dimensions() > 1), "Multi dimensional bias is unsupported.");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(bias->dimension(0) != expected_output_shape[0], "First dimension of bias and output tensors must match.");
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, bias);
+ }
+
return Status{};
}
-void ClMatMulNativeMMULKernel::configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info)
+void ClMatMulNativeMMULKernel::configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *bias, ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst);
- ARM_COMPUTE_LOG_PARAMS(lhs, rhs, dst, matmul_kernel_info);
- ARM_COMPUTE_ERROR_THROW_ON(validate(lhs, rhs, dst, matmul_kernel_info));
+ ARM_COMPUTE_LOG_PARAMS(lhs, rhs, bias, dst, matmul_kernel_info);
+ ARM_COMPUTE_ERROR_THROW_ON(validate(lhs, rhs, bias, dst, matmul_kernel_info));
// dst tensor auto initialization if not yet initialized
auto_init_if_empty(*dst, lhs->clone()->set_tensor_shape(misc::shape_calculator::compute_matmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info)));
@@ -207,6 +216,7 @@
build_opts.add_option("-DMMUL_M0=" + support::cpp11::to_string(mmul_m0));
build_opts.add_option("-DMMUL_N0=" + support::cpp11::to_string(mmul_n0));
build_opts.add_option("-DMMUL_K0=" + support::cpp11::to_string(mmul_k0));
+ build_opts.add_option_if(bias != nullptr, "-DBIAS");
std::string kernel_name("mat_mul_native_mmul");
kernel_name += matmul_kernel_info.adj_lhs ? "_t" : "_nt";
@@ -239,15 +249,20 @@
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
- const ICLTensor *lhs = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
- const ICLTensor *rhs = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
- ICLTensor *dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
+ const ICLTensor *lhs = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
+ const ICLTensor *rhs = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
+ const ICLTensor *bias = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2)); // nullptr if bias is not present
+ ICLTensor *dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst);
- ARM_COMPUTE_LOG_PARAMS(lhs, rhs, dst);
+ ARM_COMPUTE_LOG_PARAMS(lhs, rhs, bias, dst);
unsigned int idx = 0;
add_3d_tensor_nhw_argument(idx, lhs);
add_3d_tensor_nhw_argument(idx, rhs);
+ if(bias != nullptr)
+ {
+ add_3d_tensor_nhw_argument(idx, bias);
+ }
add_3d_tensor_nhw_argument(idx, dst);
// Pass m and n at runtime as signed ints, to ensure results of any subtractions they could be operand in, would still be signed.
diff --git a/src/gpu/cl/kernels/ClMatMulNativeMMULKernel.h b/src/gpu/cl/kernels/ClMatMulNativeMMULKernel.h
index 79f675d..8044897 100644
--- a/src/gpu/cl/kernels/ClMatMulNativeMMULKernel.h
+++ b/src/gpu/cl/kernels/ClMatMulNativeMMULKernel.h
@@ -66,19 +66,20 @@
* - No broadcasting in batch dimensions. I.e. batch dims must be the same across lhs, rhs and dst
*
* @param[in] compile_context The compile context to be used.
- * @param[in] lhs Input tensor for the LHS matrix.
- * @param[in] rhs Input tensor for the RHS matrix.
+ * @param[in] lhs Input tensor info for the LHS matrix.
+ * @param[in] rhs Input tensor info for the RHS matrix.
+ * @param[in] bias Bias tensor info. Can be nullptr. Data type supported: Same as @p lhs.
* @param[out] dst Output tensor info.
* @param[in] matmul_info Attributes for Batch MatMul Kernel
*/
- void configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *dst, const MatMulKernelInfo &matmul_info);
+ void configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *bias, ITensorInfo *dst, const MatMulKernelInfo &matmul_info);
/** Static function to check if given info will lead to a valid configuration
*
* Similar to @ref ClMatMulNativeMMULKernel::configure()
*
* @return a status
*/
- static Status validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *dst, const MatMulKernelInfo &matmul_info);
+ static Status validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *bias, const ITensorInfo *dst, const MatMulKernelInfo &matmul_info);
// Inherited methods overridden:
void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override;
diff --git a/src/gpu/cl/operators/ClFullyConnected.cpp b/src/gpu/cl/operators/ClFullyConnected.cpp
index b7ba8b8..0be3f0f 100644
--- a/src/gpu/cl/operators/ClFullyConnected.cpp
+++ b/src/gpu/cl/operators/ClFullyConnected.cpp
@@ -113,22 +113,25 @@
Status validate_mm(const ITensorInfo &src, const ITensorInfo &weights, const ITensorInfo *bias, const ITensorInfo &dst, const FullyConnectedLayerInfo &fc_info)
{
- // If weights are dynamic, data is not batched, and bias is nullptr validate using matmul.
- const bool weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true;
- const bool use_matmul = !weights.are_values_constant() && !weights_reshaped && !(dst.dimension(1) > 1) && (bias == nullptr);
+ // Note : If input is dynamic and data is not batched, use matmul, else use gemm
+ const bool transpose_weights = fc_info.transpose_weights ? !fc_info.are_weights_reshaped : false;
+ const bool use_matmul = !weights.are_values_constant() && !(dst.dimension(1) > 1);
+ const bool use_dynamic_gemm = !use_matmul && !weights.are_values_constant() && transpose_weights; // use dynamic gemm as fallback for matmul
+ const bool is_quantized = is_data_type_quantized_asymmetric(src.data_type());
if(use_matmul)
{
- MatMulInfo m_info{};
- m_info.adj_rhs(fc_info.transpose_weights);
+ const MatMulInfo m_info = MatMulInfo().adj_rhs(transpose_weights);
- // Note: Currently, shape is [M, B0, B1]
- // LHS is reshaped here to match ClMatMul expectations of batch index in format - [M, 1, B0, B1, .. ]
- TensorInfo lhs_to_use{ src };
- lhs_to_use.set_tensor_shape(get_reshaped_matmul_tensor(src.tensor_shape()));
+ // Note: LHS is reshaped here to match ClMatMul expectations of batch index - From [M, B0, B1] to [M, 1, B0, B1]
+ TensorInfo lhs_to_use = src.clone()->set_tensor_shape(get_reshaped_matmul_tensor(src.tensor_shape()));
- // Operator level validation.
- ARM_COMPUTE_RETURN_ON_ERROR(ClMatMul::validate(&lhs_to_use, &weights, &dst, m_info, fc_info.activation_info));
+ const GPUTarget gpu_target = CLScheduler::get().target();
+ std::unique_ptr<cl_matmul::IClMatMulNativeKernelConfig> t = cl_matmul::ClMatMulNativeKernelConfigurationFactory::create(gpu_target);
+ const MatMulKernelInfo kernel_info = t->configure(&lhs_to_use, &weights, m_info);
+
+ return is_quantized ? kernels::ClMatMulLowpNativeKernel::validate(&lhs_to_use, &weights, bias, &dst, kernel_info, fc_info.activation_info) :
+ kernels::ClMatMulNativeKernel::validate(&lhs_to_use, &weights, bias, &dst, kernel_info, fc_info.activation_info);
}
else
{
@@ -137,7 +140,7 @@
const GEMMInfo &gemm_info = GEMMInfo(false, // is_a_reshaped
false, // is_b_reshaped
- true, // reshape_b_only_on_first_run
+ !use_dynamic_gemm, // reshape_b_only_on_first_run
0, // depth_output_gemm3d
false, // reinterpret_input_as_3d
fc_info.retain_internal_weights, // retain_internal_weights
@@ -147,7 +150,7 @@
true, // broadcast_bias
ActivationLayerInfo()); // activation_info
- if(is_data_type_quantized_asymmetric(src.data_type()))
+ if(is_quantized)
{
const UniformQuantizationInfo iq_info = src.quantization_info().uniform();
const UniformQuantizationInfo wq_info = weights.quantization_info().uniform();
@@ -191,35 +194,33 @@
void ClFullyConnected::configure_mm(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *bias, ITensorInfo *dst,
const FullyConnectedLayerInfo &fc_info)
{
- // If weights are dynamic, configure matmul operator - else use gemm
+ // If weights are dynamic and matmul is supported use matmul, else use gemm
if(_use_matmul)
{
- // Transpose RHS as _are_weights_reshaped == false when mat_mul is used.
- const MatMulInfo mat_info = MatMulInfo().adj_rhs(fc_info.transpose_weights);
+ // Specify whether transpose weights is necessary in matmul info
+ const MatMulInfo mat_info = MatMulInfo().adj_rhs(_transpose_weights);
// Note: MatMul does not need offset negation unlike gemm
// 1. Change shape when calling matmul to fit batch expectations.
- _lhs_to_use = *src->clone();
- _lhs_to_use.set_tensor_shape(get_reshaped_matmul_tensor(_lhs_to_use.tensor_shape())); // Collapse all dims > 2 into final dimension.
- _is_quantized = is_data_type_quantized_asymmetric(_lhs_to_use.data_type());
+ _lhs_to_use = src->clone()->set_tensor_shape(get_reshaped_matmul_tensor(_lhs_to_use.tensor_shape()));
- // 2. Call kernel for matmul directly.
+ // 2. Use heuristics to get kernel info object
const GPUTarget gpu_target = CLScheduler::get().target();
std::unique_ptr<cl_matmul::IClMatMulNativeKernelConfig> kernel_config = cl_matmul::ClMatMulNativeKernelConfigurationFactory::create(gpu_target);
+ MatMulKernelInfo kernel_info = kernel_config->configure(src, weights, mat_info);
- // Configure relevant matmul kernel
- MatMulKernelInfo kernel_info = kernel_config->configure(src, weights, mat_info);
+ // 3. Configure relevant matmul kernel
if(_is_quantized)
{
_matmul_lowp_native_kernel = std::make_unique<kernels::ClMatMulLowpNativeKernel>();
_matmul_lowp_native_kernel->set_target(gpu_target);
- _matmul_lowp_native_kernel->configure(compile_context, src, weights, dst, kernel_info, fc_info.activation_info);
+ _matmul_lowp_native_kernel->configure(compile_context, src, weights, bias, dst, kernel_info, fc_info.activation_info);
}
else
{
_matmul_native_kernel = std::make_unique<kernels::ClMatMulNativeKernel>();
_matmul_native_kernel->set_target(gpu_target);
- _matmul_native_kernel->configure(compile_context, src, weights, dst, kernel_info, fc_info.activation_info);
+ _matmul_native_kernel->configure(compile_context, src, weights, bias, dst, kernel_info, fc_info.activation_info);
}
}
else
@@ -230,7 +231,7 @@
const GEMMInfo &gemm_info = GEMMInfo(false, // is_a_reshaped
false, // is_b_reshaped
- !_dynamic_weights, // reshape_b_only_on_first_run
+ !_dynamic_gemm, // reshape_b_only_on_first_run
0, // depth_output_gemm3d
false, // reinterpret_input_as_3d
fc_info.retain_internal_weights, // retain_internal_weights
@@ -269,7 +270,8 @@
void ClFullyConnected::configure_conv_fc(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *bias, ITensorInfo *dst,
const FullyConnectedLayerInfo &fc_info)
{
- ARM_COMPUTE_ERROR_ON((weights->dimension((_use_matmul) ? 0 : 1) != (src->dimension(0) * src->dimension(1) * src->dimension(2))));
+ // MatMul fuses transpose operation, so we use the first dimension for comparison where appropriate.
+ ARM_COMPUTE_ERROR_ON((weights->dimension((_use_matmul && _transpose_weights) ? 0 : 1) != (src->dimension(0) * src->dimension(1) * src->dimension(2))));
// If the fully connected layer is called after a convolution layer, the input tensor must be linearized
@@ -288,8 +290,8 @@
void ClFullyConnected::configure_fc_fc(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *bias, ITensorInfo *dst,
const FullyConnectedLayerInfo &fc_info)
{
- // Compare first dimension when using matmul, as it performs transpose operation
- ARM_COMPUTE_ERROR_ON(src->dimension(0) != weights->dimension((_use_matmul) ? 0 : 1));
+ // MatMul fuses transpose operation, so we use the first dimension for comparison where appropriate.
+ ARM_COMPUTE_ERROR_ON(src->dimension(0) != weights->dimension((_use_matmul && _transpose_weights) ? 0 : 1));
// Configure matrix multiply kernel
configure_mm(compile_context, src, weights, bias, dst, fc_info);
@@ -304,20 +306,18 @@
ARM_COMPUTE_ERROR_THROW_ON(ClFullyConnected::validate(src, weights, biases, dst, fc_info));
ARM_COMPUTE_LOG_PARAMS(src, weights, biases, dst, fc_info);
- _are_weights_converted = true;
- _are_weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true;
- _is_fc_after_conv = true;
- _is_quantized = is_data_type_quantized_asymmetric(src->data_type());
- _is_prepared = fc_info.retain_internal_weights;
- _weights_to_use = TensorInfo(*weights);
- _weights_to_use_idx = ACL_SRC_1;
+ _transpose_weights = fc_info.transpose_weights ? !fc_info.are_weights_reshaped : false;
+ _is_fc_after_conv = true;
+ _is_quantized = is_data_type_quantized_asymmetric(src->data_type());
+ _is_prepared = fc_info.retain_internal_weights;
+ _weights_to_use = TensorInfo(*weights);
+ _weights_to_use_idx = ACL_SRC_1;
// When using dynamic weights - use matmul kernels.
- // Note: We don't appear to support dynamic weights with pre-reshaped RHS.
- // Note: No matmul with biases for the moment.
+ // Note: MatMul does not support broadcasting batch dimension, and therefore is disabled if fc is batched. Gemm is used as fallback.
const bool is_batched_fc_layer = dst->dimension(1) > 1;
- _dynamic_weights = !weights->are_values_constant() && !_are_weights_reshaped;
- _use_matmul = _dynamic_weights && !is_batched_fc_layer && (biases == nullptr);
+ _use_matmul = !weights->are_values_constant() && !is_batched_fc_layer;
+ _dynamic_gemm = !weights->are_values_constant() && _transpose_weights && !_use_matmul;
// With the Fully Connected layer we can have 4 different cases:
// 1) Convolution layer -> Fully Connected layer without batches
@@ -339,9 +339,8 @@
ITensorInfo *weights_used = weights;
- // Reshape weights if needed
- // Not needed when matmul is in use - MatMul has transpose RHS flags.
- if(!_are_weights_reshaped && !_use_matmul)
+ // Reshape weights if needed - Not needed when matmul is in use as matmul fuses transpose op.
+ if(_transpose_weights && !_use_matmul)
{
// Reshape the weights
_reshape_weights = std::make_unique<ClTranspose>();
@@ -361,9 +360,9 @@
src->tensor_shape(),
fc_info.weights_trained_layout);
- weights_used = &_converted_weights;
- _weights_to_use_idx = offset_int_vec(ConvertedWeights);
- _are_weights_converted = false;
+ weights_used = &_converted_weights;
+ _weights_to_use_idx = offset_int_vec(ConvertedWeights);
+ _run_convert_weights = true;
}
if(_is_fc_after_conv)
@@ -398,11 +397,11 @@
// Keep all the auxiliary tensors in case of dynamic weights as they are recalculated every time
_aux_mem[TransposedWeights] = MemoryInfo(
offset_int_vec(TransposedWeights),
- _dynamic_weights ? MemoryLifetime::Temporary : MemoryLifetime::Prepare,
+ _dynamic_gemm ? MemoryLifetime::Temporary : MemoryLifetime::Prepare,
_reshaped_weights.total_size());
_aux_mem[ConvertedWeights] = MemoryInfo(
offset_int_vec(ConvertedWeights),
- _dynamic_weights ? MemoryLifetime::Temporary : MemoryLifetime::Prepare,
+ _dynamic_gemm ? MemoryLifetime::Temporary : MemoryLifetime::Prepare,
_converted_weights.total_size());
}
else
@@ -413,11 +412,11 @@
_aux_mem[TransposedWeights] = MemoryInfo(
offset_int_vec(TransposedWeights),
- _dynamic_weights ? MemoryLifetime::Temporary : transposed_wei_lft,
+ _dynamic_gemm ? MemoryLifetime::Temporary : transposed_wei_lft,
_reshaped_weights.total_size());
_aux_mem[ConvertedWeights] = MemoryInfo(
offset_int_vec(ConvertedWeights),
- _dynamic_weights ? MemoryLifetime::Temporary : converted_wei_lft,
+ _dynamic_gemm ? MemoryLifetime::Temporary : converted_wei_lft,
_converted_weights.total_size());
}
}
@@ -434,19 +433,17 @@
ARM_COMPUTE_RETURN_ERROR_ON(fc_info.activation_info.enabled() && is_data_type_quantized(src->data_type()) && fc_info.activation_info.activation() != ActivationLayerInfo::ActivationFunction::RELU
&& fc_info.activation_info.activation() != ActivationLayerInfo::ActivationFunction::BOUNDED_RELU && fc_info.activation_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU);
- const bool weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true;
- bool is_fc_after_conv = true;
+ const bool transpose_weights = fc_info.transpose_weights ? !fc_info.are_weights_reshaped : false;
+ bool is_fc_after_conv = true;
// When using dynamic weights - use matmul kernels.
- // Note: MatMul does not support broadcasting or biases so fallback with batched cases or when biases != nullptr.
- // Note: Pre-Shaped RHS is a deprecated use case and is therefore not supported with matmul.
- const bool dynamic_weights = !weights->are_values_constant() && !weights_reshaped;
+ // Note: MatMul does not support broadcasting so fallback with batched cases.
const bool is_batched_fc_layer = dst->dimension(1) > 1;
- const bool use_matmul = dynamic_weights && !is_batched_fc_layer && (biases == nullptr);
+ const bool use_matmul = !weights->are_values_constant() && !is_batched_fc_layer;
const ITensorInfo &flatten_src = TensorInfo(src->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_flatten_shape(src)).set_data_layout(DataLayout::NCHW));
const ITensorInfo &reshaped_weights = TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_transposed_shape(*weights)));
- const ITensorInfo &converted_weights = weights_reshaped ? TensorInfo(weights->clone()->set_is_resizable(true).reset_padding()) : TensorInfo(*reshaped_weights.clone());
+ const ITensorInfo &converted_weights = transpose_weights ? TensorInfo(*reshaped_weights.clone()) : TensorInfo(weights->clone()->set_is_resizable(true).reset_padding());
// With the Fully Connected layer we can have 4 different cases:
// 1) Convolution layer -> Fully Connected layer without batches
@@ -482,7 +479,8 @@
is_fc_after_conv = src->num_dimensions() > 1;
}
- if(!weights_reshaped && !use_matmul)
+ // Transpose kernel does not run when matmul is supported as matmul fuses transpose op.
+ if(transpose_weights && !use_matmul)
{
// Validate reshape weights kernel
ARM_COMPUTE_RETURN_ON_ERROR(ClTranspose::validate(weights, &reshaped_weights));
@@ -502,14 +500,9 @@
if(is_fc_after_conv)
{
// Fully Connected layer after a Convolution Layer without batches
- if(use_matmul)
- {
- ARM_COMPUTE_RETURN_ERROR_ON((weights_to_use->dimension(0) != (src->dimension(0) * src->dimension(1) * src->dimension(2))));
- }
- else
- {
- ARM_COMPUTE_RETURN_ERROR_ON((weights_to_use->dimension(1) != (src->dimension(0) * src->dimension(1) * src->dimension(2))));
- }
+ // K Index of matrix multiplication. MatMul performs transpose in kernel, so index is 0 when matmul and transpose enabled
+ const int weight_idx = (use_matmul && transpose_weights) ? 0 : 1;
+ ARM_COMPUTE_RETURN_ERROR_ON((weights_to_use->dimension(weight_idx) != (src->dimension(0) * src->dimension(1) * src->dimension(2))));
// Validate flatten kernel
ARM_COMPUTE_RETURN_ON_ERROR(ClFlatten::validate(src, &flatten_src));
@@ -518,7 +511,9 @@
else
{
// Fully Connected layer after a Fully Connected Layer without batches
- ARM_COMPUTE_RETURN_ERROR_ON(src->dimension(0) != weights_to_use->dimension((use_matmul) ? 0 : 1));
+ // K Index of matrix multiplication. MatMul performs transpose in kernel, so index is 0 when matmul and transpose enabled
+ const int weight_idx = (use_matmul && transpose_weights) ? 0 : 1;
+ ARM_COMPUTE_RETURN_ERROR_ON(src->dimension(0) != weights_to_use->dimension(weight_idx));
}
// Validate matrix multiply kernel
@@ -533,7 +528,7 @@
#ifdef ARM_COMPUTE_ASSERTS_ENABLED
++_asrt_run_count;
- ARM_COMPUTE_ERROR_ON(_dynamic_weights && _asrt_prepare_count != _asrt_run_count);
+ ARM_COMPUTE_ERROR_ON(_dynamic_gemm && _asrt_prepare_count != _asrt_run_count);
#endif // ARM_COMPUTE_ASSERTS_ENABLED
auto src = tensors.get_const_tensor(ACL_SRC_0);
@@ -584,11 +579,12 @@
void ClFullyConnected::prepare(ITensorPack &tensors)
{
- if(!_is_prepared || _dynamic_weights)
+ // Note : Running prepare() each run when _use_matmul is true is unnecessary unless weights conversion is needed.
+ if(!_is_prepared || _dynamic_gemm || (_use_matmul && _run_convert_weights))
{
#ifdef ARM_COMPUTE_ASSERTS_ENABLED
++_asrt_prepare_count;
- ARM_COMPUTE_ERROR_ON(!_dynamic_weights && _asrt_prepare_count > 1);
+ ARM_COMPUTE_ERROR_ON(!_dynamic_gemm && !_use_matmul && _asrt_prepare_count > 1);
#endif // ARM_COMPUTE_ASSERTS_ENABLED
auto weights = tensors.get_const_tensor(ACL_SRC_1);
@@ -599,8 +595,8 @@
// Pointer to current weights
const ITensor *cur_weights = weights;
- // Reshape of the weights if needed
- if(!_are_weights_reshaped && !_use_matmul)
+ // Reshape weights if needed. Disabled when matmul kernels are enabled as matmul fuses transpose.
+ if(_transpose_weights && !_use_matmul)
{
// Run reshape weights kernel and mark weights as unused
ITensorPack transpose_pack{ { ACL_SRC, weights }, { ACL_DST, reshaped_weights.get() } };
@@ -611,7 +607,7 @@
}
// Convert weights if needed
- if(!_are_weights_converted)
+ if(_run_convert_weights)
{
ITensorPack convert_pack{ { ACL_SRC, cur_weights }, { ACL_DST, converted_weights.get() } };
_convert_weights->run(convert_pack);
@@ -623,8 +619,8 @@
ITensorPack gemm_pack = tensors;
gemm_pack.add_const_tensor(ACL_SRC_1, cur_weights);
- // Prepare GEMM prepare and release unused weights (If not using matmul)
- if(!_use_matmul)
+ // Prepare GEMM prepare and release unused weights
+ if(_dynamic_gemm || !_use_matmul)
{
if(!_is_quantized)
{
diff --git a/src/gpu/cl/operators/ClFullyConnected.h b/src/gpu/cl/operators/ClFullyConnected.h
index 9a5ba40..5a71bd2 100644
--- a/src/gpu/cl/operators/ClFullyConnected.h
+++ b/src/gpu/cl/operators/ClFullyConnected.h
@@ -132,17 +132,18 @@
TensorInfo _flattened_src{};
TensorInfo _converted_weights{};
TensorInfo _reshaped_weights{};
- TensorInfo _lhs_to_use{};
+ TensorInfo _lhs_to_use{};
TensorInfo _weights_to_use{};
int _weights_to_use_idx{ ACL_SRC_1 };
- bool _are_weights_converted{ true };
- bool _are_weights_reshaped{ true };
+ bool _run_convert_weights{ false };
+ bool _transpose_weights{ false };
+ bool _dynamic_gemm{ false };
+ bool _use_matmul{ false };
+
bool _is_fc_after_conv{ true };
bool _is_quantized{ false };
bool _is_prepared{ false };
- bool _dynamic_weights{ false };
- bool _use_matmul{ false };
#ifdef ARM_COMPUTE_ASSERTS_ENABLED
int _asrt_run_count {};
diff --git a/src/gpu/cl/operators/ClMatMul.cpp b/src/gpu/cl/operators/ClMatMul.cpp
index c453761..49d1412 100644
--- a/src/gpu/cl/operators/ClMatMul.cpp
+++ b/src/gpu/cl/operators/ClMatMul.cpp
@@ -61,8 +61,8 @@
const bool is_quantized = is_data_type_quantized_asymmetric(lhs->data_type());
- return is_quantized ? ClMatMulLowpNativeKernel::validate(lhs, rhs, dst, kernel_info, act_info) :
- ClMatMulNativeKernel::validate(lhs, rhs, dst, kernel_info, act_info);
+ return is_quantized ? ClMatMulLowpNativeKernel::validate(lhs, rhs, nullptr /* bias */, dst, kernel_info, act_info) :
+ ClMatMulNativeKernel::validate(lhs, rhs, nullptr /* bias */, dst, kernel_info, act_info);
}
void ClMatMul::configure(const CLCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *dst, const MatMulInfo &matmul_info, const ActivationLayerInfo &act_info)
@@ -86,14 +86,14 @@
_matmul_lowp_native_kernel->set_target(gpu_target);
// Configure the low-precision native matrix multiply kernel
- _matmul_lowp_native_kernel->configure(compile_context, lhs, rhs, dst, kernel_info, act_info);
+ _matmul_lowp_native_kernel->configure(compile_context, lhs, rhs, nullptr /* bias */, dst, kernel_info, act_info);
}
else
{
_matmul_native_kernel->set_target(gpu_target);
// Configure the native matrix multiply kernel
- _matmul_native_kernel->configure(compile_context, lhs, rhs, dst, kernel_info, act_info);
+ _matmul_native_kernel->configure(compile_context, lhs, rhs, nullptr /* bias */, dst, kernel_info, act_info);
}
}
diff --git a/src/runtime/heuristics/matmul_native/ClMatMulNativeHelpers.cpp b/src/runtime/heuristics/matmul_native/ClMatMulNativeHelpers.cpp
index b9e0d5a..1e06e84 100644
--- a/src/runtime/heuristics/matmul_native/ClMatMulNativeHelpers.cpp
+++ b/src/runtime/heuristics/matmul_native/ClMatMulNativeHelpers.cpp
@@ -52,7 +52,7 @@
if(rhs_lock_padding == false)
{
- if(bool(opencl::kernels::ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, &dst_info, info0)))
+ if(bool(opencl::kernels::ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, nullptr, &dst_info, info0)))
{
return info0;
}
diff --git a/tests/validation/CL/MatMulKernel.cpp b/tests/validation/CL/MatMulKernel.cpp
index ff872aa..b47f8bc 100644
--- a/tests/validation/CL/MatMulKernel.cpp
+++ b/tests/validation/CL/MatMulKernel.cpp
@@ -75,6 +75,9 @@
template <typename T>
using CLMatMulKernelFixture = MatMulKernelValidationFixture<T, ClMatMulNativeKernel>;
+template <typename T>
+using CLMatMulKernelBiasFixture = MatMulKernelWithBiasValidation<T, ClMatMulNativeKernel>;
+
TEST_SUITE(CL)
TEST_SUITE(MatMulKernel)
TEST_SUITE(Validate)
@@ -162,7 +165,7 @@
for(auto &pair : supported_block_sizes)
{
TensorInfo output_info;
- Status status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, &output_info, pair.first);
+ Status status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, nullptr, &output_info, pair.first);
if(!pair.first.export_rhs_to_cl_image || export_to_cl_image_supported)
{
@@ -222,7 +225,7 @@
};
TensorInfo output_info;
- Status status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info);
+ Status status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, nullptr, &output_info, matmul_kernel_info);
const bool expected = std::get<4>(tuple);
ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
@@ -233,22 +236,25 @@
TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL)
{
// Configurations are assumed to be Nt/Nt, but will be transposed inside the test to test other configurations
- using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, bool>;
+ using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, TensorShape, bool>;
const std::vector<ShapeConfigurationTuple> shape_configurations =
{
- { TensorShape(5U, 1U), TensorShape(3U, 5U), true },
- { TensorShape(10U, 12U), TensorShape(3U, 10U), true },
- { TensorShape(8U, 4U), TensorShape(2U, 8U), true },
- { TensorShape(8U, 4U), TensorShape(2U, 5U), false }, // Mismatch in the K dimension
- { TensorShape(5U, 0U), TensorShape(2U, 5U), false }, // Invalid dimension
- { TensorShape(5U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), true },
- { TensorShape(5U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // no batch broadcasting
- { TensorShape(5U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // mismatch in batch dimension
+ { TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(3U), true },
+ { TensorShape(10U, 12U), TensorShape(3U, 10U), TensorShape(3U), true },
+ { TensorShape(8U, 4U), TensorShape(2U, 8U), TensorShape(2U), true },
+ { TensorShape(8U, 4U), TensorShape(2U, 5U), TensorShape(2U), false }, // Mismatch in the K dimension
+ { TensorShape(5U, 0U), TensorShape(2U, 5U), TensorShape(2U), false }, // Invalid dimension
+ { TensorShape(5U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), true },
+ { TensorShape(5U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), false }, // no batch broadcasting
+ { TensorShape(5U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), false }, // mismatch in batch dimension
+ { TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(1U), false }, // Unsupported bias broadcasting.
+ { TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(3U, 3U), false }, // 2D bias is unsupported.
+ { TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(6U), false }, // bias first dimension != dst first dimension
};
for(auto &tuple : shape_configurations)
{
- const bool expected = std::get<2>(tuple);
+ const bool expected = std::get<3>(tuple);
for(bool adj_lhs :
{
@@ -262,6 +268,7 @@
{
TensorShape lhs_shape = std::get<0>(tuple);
TensorShape rhs_shape = std::get<1>(tuple);
+ TensorShape bia_shape = std::get<2>(tuple);
if(adj_lhs)
{
@@ -275,11 +282,12 @@
const TensorInfo lhs_info = TensorInfo(lhs_shape, 1, DataType::F32);
const TensorInfo rhs_info = TensorInfo(rhs_shape, 1, DataType::F32);
+ const TensorInfo bia_info = TensorInfo(bia_shape, 1, DataType::F32);
TensorInfo output_info;
MatMulKernelInfo matmul_kernel_info{ adj_lhs, adj_rhs, 1, 1, 1, false /* export_rhs_to_cl_image */ };
- Status status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info);
+ Status status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, &bia_info, &output_info, matmul_kernel_info);
ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
}
}
@@ -322,7 +330,7 @@
const TensorInfo rhs_info(shape, 1, std::get<1>(tuple));
TensorInfo output_info(shape, 1, std::get<2>(tuple));
- Status status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info);
+ Status status = ClMatMulNativeKernel::validate(&lhs_info, &rhs_info, nullptr, &output_info, matmul_kernel_info);
ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
}
}
@@ -356,6 +364,19 @@
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
}
+FIXTURE_DATA_TEST_CASE(RunWithBias, CLMatMulKernelBiasFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(),
+ framework::dataset::make("TransposeA", { false, true })),
+ framework::dataset::make("TransposeB", { false, true })),
+ m0_values_precommit),
+ n0_values_precommit),
+ k0_values_precommit),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
+ framework::dataset::make("DataType", DataType::F32)))
+
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
+}
FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
framework::dataset::make("TransposeA", { false })),
framework::dataset::make("TransposeB", { false })),
diff --git a/tests/validation/CL/MatMulLowpNativeKernel.cpp b/tests/validation/CL/MatMulLowpNativeKernel.cpp
index fd7a4cb..90eee4fb 100644
--- a/tests/validation/CL/MatMulLowpNativeKernel.cpp
+++ b/tests/validation/CL/MatMulLowpNativeKernel.cpp
@@ -49,6 +49,9 @@
template <typename T>
using CLMatMulLowpNativeKernelFixture = MatMulKernelValidationFixture<T, ClMatMulLowpNativeKernel>;
+template <typename T>
+using CLMatMulLowpKernelWithBiasFixture = MatMulKernelWithBiasValidation<T, ClMatMulLowpNativeKernel>;
+
/** M0 values to test --precommit*/
const auto m0_values_precommit = framework::dataset::make("M0", { 1, 3 });
@@ -103,7 +106,7 @@
for(auto &pair : supported_block_sizes)
{
TensorInfo output_info;
- Status status = ClMatMulLowpNativeKernel::validate(&lhs_info, &rhs_info, &output_info, pair.first);
+ Status status = ClMatMulLowpNativeKernel::validate(&lhs_info, &rhs_info, nullptr, &output_info, pair.first);
ARM_COMPUTE_EXPECT(bool(status) == pair.second, framework::LogLevel::ERRORS);
}
@@ -112,22 +115,24 @@
TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL)
{
// Configurations are assumed to be Nt/Nt, but will be transposed inside the test to test other configurations
- using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, bool>;
+ using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, TensorShape, bool>;
const std::vector<ShapeConfigurationTuple> shape_configurations =
{
- { TensorShape(5U, 1U), TensorShape(3U, 5U), true },
- { TensorShape(10U, 12U), TensorShape(3U, 10U), true },
- { TensorShape(8U, 4U), TensorShape(2U, 8U), true },
- { TensorShape(8U, 4U), TensorShape(2U, 5U), false }, // Mismatch in the K dimension
- { TensorShape(5U, 0U), TensorShape(2U, 5U), false }, // Invalid dimension
- { TensorShape(5U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), true },
- { TensorShape(5U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // no batch broadcasting
- { TensorShape(5U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // mismatch in batch dimension
+ { TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(3U), true },
+ { TensorShape(10U, 12U), TensorShape(3U, 10U), TensorShape(3U), true },
+ { TensorShape(8U, 4U), TensorShape(2U, 8U), TensorShape(2U), true },
+ { TensorShape(8U, 4U), TensorShape(2U, 5U), TensorShape(2U), false }, // Mismatch in the K dimension
+ { TensorShape(5U, 0U), TensorShape(2U, 5U), TensorShape(2U), false }, // Invalid dimension
+ { TensorShape(5U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), true },
+ { TensorShape(5U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), false }, // no batch broadcasting
+ { TensorShape(5U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), false }, // mismatch in batch dimension
+ { TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(1U), false }, // invalid broadcast of bias
+ { TensorShape(5U, 1U), TensorShape(3U, 5U), TensorShape(3U, 3U), false }, // 2d bias is invalid
};
for(auto &tuple : shape_configurations)
{
- const bool expected = std::get<2>(tuple);
+ const bool expected = std::get<3>(tuple);
for(bool adj_lhs :
{
@@ -141,6 +146,7 @@
{
TensorShape lhs_shape = std::get<0>(tuple);
TensorShape rhs_shape = std::get<1>(tuple);
+ TensorShape bia_shape = std::get<2>(tuple);
if(adj_lhs)
{
@@ -154,11 +160,12 @@
const TensorInfo lhs_info = TensorInfo(lhs_shape, 1, DataType::QASYMM8_SIGNED);
const TensorInfo rhs_info = TensorInfo(rhs_shape, 1, DataType::QASYMM8_SIGNED);
+ const TensorInfo bia_info = TensorInfo(bia_shape, 1, DataType::S32);
TensorInfo output_info;
MatMulKernelInfo matmul_kernel_info{ adj_lhs, adj_rhs, 1, 1, 1, false /* export_rhs_to_cl_image */ };
- Status status = ClMatMulLowpNativeKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info);
+ Status status = ClMatMulLowpNativeKernel::validate(&lhs_info, &rhs_info, &bia_info, &output_info, matmul_kernel_info);
ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
}
}
@@ -167,41 +174,44 @@
TEST_CASE(ValidateDataTypes, framework::DatasetMode::ALL)
{
- using DataTypeConfigurationTuple = std::tuple<DataType, DataType, DataType, bool>;
+ using DataTypeConfigurationTuple = std::tuple<DataType, DataType, DataType, DataType, bool>;
const std::vector<DataTypeConfigurationTuple> data_type_configurations =
{
- { DataType::F32, DataType::F32, DataType::F32, false }, // no floating point types
- { DataType::F16, DataType::F16, DataType::F16, false }, // no floating point types
- { DataType::F64, DataType::F64, DataType::F64, false }, // no double precision
- { DataType::QASYMM8, DataType::QASYMM8, DataType::QASYMM8, true },
- { DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, true },
- { DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, false }, // only qasymm8/qasymm8_signed is supported
- { DataType::QASYMM16, DataType::QASYMM16, DataType::QASYMM16, false }, // only qasymm8/qasymm8_signed is supported
- { DataType::QSYMM16, DataType::QSYMM16, DataType::QSYMM16, false }, // only qasymm8/qasymm8_signed is supported
- { DataType::QSYMM8, DataType::QSYMM8, DataType::QSYMM8, false }, // only qasymm8/qasymm8_signed is supported
- { DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QASYMM8, false }, // no mixed data types
- { DataType::S64, DataType::S64, DataType::S64, false }, // no integral types
- { DataType::S32, DataType::S32, DataType::S32, false }, // no integral types
- { DataType::S16, DataType::S16, DataType::S16, false }, // no integral types
- { DataType::S8, DataType::S8, DataType::S8, false }, // no integral types
- { DataType::U64, DataType::U64, DataType::U64, false }, // no integral types
- { DataType::U32, DataType::U32, DataType::U32, false }, // no integral types
- { DataType::U16, DataType::U16, DataType::U16, false }, // no integral types
- { DataType::U8, DataType::U8, DataType::U8, false }, // no integral types
+ { DataType::F32, DataType::F32, DataType::F32, DataType::F32, false }, // no floating point types
+ { DataType::F16, DataType::F16, DataType::F16, DataType::F16, false }, // no floating point types
+ { DataType::F64, DataType::F64, DataType::F64, DataType::F64, false }, // no double precision
+ { DataType::QASYMM8, DataType::QASYMM8, DataType::S32, DataType::QASYMM8, true },
+ { DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, DataType::S32, DataType::QASYMM8_SIGNED, true },
+ { DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, DataType::S32, DataType::QSYMM8_PER_CHANNEL, false }, // only qasymm8/qasymm8_signed is supported
+ { DataType::QASYMM16, DataType::QASYMM16, DataType::S32, DataType::QASYMM16, false }, // only qasymm8/qasymm8_signed is supported
+ { DataType::QSYMM16, DataType::QSYMM16, DataType::S32, DataType::QSYMM16, false }, // only qasymm8/qasymm8_signed is supported
+ { DataType::QSYMM8, DataType::QSYMM8, DataType::S32, DataType::QSYMM8, false }, // only qasymm8/qasymm8_signed is supported
+ { DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::S32, DataType::QASYMM8, false }, // no mixed data types
+ { DataType::S64, DataType::S64, DataType::S64, DataType::S64, false }, // no integral types
+ { DataType::S32, DataType::S32, DataType::S32, DataType::S32, false }, // no integral types
+ { DataType::S16, DataType::S16, DataType::S16, DataType::S16, false }, // no integral types
+ { DataType::S8, DataType::S8, DataType::S8, DataType::S8, false }, // no integral types
+ { DataType::U64, DataType::U64, DataType::U64, DataType::U64, false }, // no integral types
+ { DataType::U32, DataType::U32, DataType::U32, DataType::U32, false }, // no integral types
+ { DataType::U16, DataType::U16, DataType::U16, DataType::U16, false }, // no integral types
+ { DataType::U8, DataType::U8, DataType::U8, DataType::U8, false }, // no integral types
+ { DataType::QASYMM8, DataType::QASYMM8, DataType::F32, DataType::QASYMM8, false } // Only S32 bias is supported
};
// It's enough to test a single shape and block size configuration while checking data types
- const TensorShape shape = TensorShape(10U, 10U);
+ const TensorShape shape = TensorShape(10U, 10U);
+ const TensorShape bia_shape = TensorShape(10U);
const MatMulKernelInfo matmul_kernel_info{ false, false, 1, 1, 1, false };
for(auto &tuple : data_type_configurations)
{
- const bool expected = std::get<3>(tuple);
+ const bool expected = std::get<4>(tuple);
const TensorInfo lhs_info(shape, 1, std::get<0>(tuple));
const TensorInfo rhs_info(shape, 1, std::get<1>(tuple));
- TensorInfo output_info(shape, 1, std::get<2>(tuple));
+ const TensorInfo bia_info(bia_shape, 1, std::get<2>(tuple));
+ TensorInfo output_info(shape, 1, std::get<3>(tuple));
- Status status = ClMatMulLowpNativeKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info);
+ Status status = ClMatMulLowpNativeKernel::validate(&lhs_info, &rhs_info, &bia_info, &output_info, matmul_kernel_info);
ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
}
}
@@ -234,6 +244,18 @@
// Validate output
validate(CLAccessor(_target), _reference, tolerance_quant);
}
+FIXTURE_DATA_TEST_CASE(RunWithBias, CLMatMulLowpKernelWithBiasFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(),
+ framework::dataset::make("TransposeA", { true, false })),
+ framework::dataset::make("TransposeB", { true, false })),
+ m0_values_precommit),
+ n0_values_precommit),
+ k0_values_precommit),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
+ framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, tolerance_quant);
+}
FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulLowpNativeKernelFixture<int8_t>, framework::DatasetMode::NIGHTLY,
combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(),
framework::dataset::make("TransposeA", { false })),
diff --git a/tests/validation/CL/MatMulNativeMMULKernel.cpp b/tests/validation/CL/MatMulNativeMMULKernel.cpp
index b63af75..70c8098 100644
--- a/tests/validation/CL/MatMulNativeMMULKernel.cpp
+++ b/tests/validation/CL/MatMulNativeMMULKernel.cpp
@@ -70,6 +70,9 @@
template <typename T>
using CLMatMulNativeMMULKernelFixture = MatMulKernelValidationFixture<T, ClMatMulNativeMMULKernel, true /*use_mmul*/>;
+template <typename T>
+using CLMatMulKernelBiasFixture = MatMulKernelWithBiasValidation<T, ClMatMulNativeMMULKernel, true /*use_mmul*/>;
+
TEST_SUITE(CL)
TEST_SUITE(MatMulNativeMMULKernel)
TEST_SUITE(Validate)
@@ -117,7 +120,7 @@
{ MatMulKernelInfo(true, true, 3, 7, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16}
{ MatMulKernelInfo(true, true, 6, 3, 1), false }, // M0 not in {1, 2, 3, 4, 8, 16}
{ MatMulKernelInfo(true, true, 5, 3, 1), false }, // M0 not in {1, 2, 3, 4, 8, 16}
- { MatMulKernelInfo(true, true, 4, 8, 2), false }, // K0 is not 1
+ { MatMulKernelInfo(true, true, 4, 8, 2), false }, // K0 is not 1
{ MatMulKernelInfo(true, true, 4, 8, 1), true },
{ MatMulKernelInfo(true, true, 3, 3, 1), true },
{ MatMulKernelInfo(true, true, 16, 4, 1), true },
@@ -132,7 +135,7 @@
for(auto &pair : supported_block_sizes)
{
TensorInfo output_info;
- Status status = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, &output_info, pair.first);
+ Status status = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, nullptr, &output_info, pair.first);
ARM_COMPUTE_EXPECT(bool(status) == pair.second, framework::LogLevel::ERRORS);
}
}
@@ -148,28 +151,30 @@
if(arm_matrix_multiply_supported(CLKernelLibrary::get().get_device()))
{
// Configurations are assumed to be Nt/Nt, but will be transposed inside the test to test other configurations
- using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, bool>;
+ using ShapeConfigurationTuple = std::tuple<TensorShape, TensorShape, TensorShape, bool>; // lhs, rhs, bias, result
const std::vector<ShapeConfigurationTuple> shape_configurations =
{
- { TensorShape(4U, 1U), TensorShape(3U, 4U), true },
- { TensorShape(12U, 12U), TensorShape(3U, 12U), true },
- { TensorShape(8U, 4U), TensorShape(2U, 8U), true },
- { TensorShape(8U, 4U), TensorShape(2U, 4U), false }, // Mismatch in the K dimension
- { TensorShape(5U, 0U), TensorShape(2U, 5U), false }, // Invalid dimension
- { TensorShape(5U, 7U), TensorShape(2U, 5U), false }, // K not a multiple of 4 (MMUL_K0)
- { TensorShape(8U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 8U, 3U, 4U, 5U, 6U), true },
- { TensorShape(5U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // No batch broadcasting
- { TensorShape(5U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), false }, // Mismatch in batch dimension
+ { TensorShape(4U, 1U), TensorShape(3U, 4U), TensorShape(3U), true },
+ { TensorShape(12U, 12U), TensorShape(3U, 12U), TensorShape(3U), true },
+ { TensorShape(8U, 4U), TensorShape(2U, 8U), TensorShape(2U), true },
+ { TensorShape(8U, 4U), TensorShape(2U, 4U), TensorShape(2U), false }, // Mismatch in the K dimension
+ { TensorShape(5U, 0U), TensorShape(2U, 5U), TensorShape(2U), false }, // Invalid dimension
+ { TensorShape(5U, 7U), TensorShape(2U, 5U), TensorShape(2U), false }, // K not a multiple of 4 (MMUL_K0)
+ { TensorShape(8U, 4U, 3U, 4U, 5U, 6U), TensorShape(2U, 8U, 3U, 4U, 5U, 6U), TensorShape(2U), true },
+ { TensorShape(5U, 4U, 3U, 4U, 5U, 1U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), false }, // No batch broadcasting
+ { TensorShape(5U, 4U, 3U, 4U, 9U, 6U), TensorShape(2U, 5U, 3U, 4U, 5U, 6U), TensorShape(2U), false }, // Mismatch in batch dimension
+ { TensorShape(4U, 1U), TensorShape(3U, 4U), TensorShape(1U), false }, // Bias first dimensions != dst first dimension.
+ { TensorShape(4U, 1U), TensorShape(3U, 4U), TensorShape(5U, 6U), false }, // Bias is 2d which is invalid.
};
for(auto &tuple : shape_configurations)
{
- const bool expected = std::get<2>(tuple);
+ const bool expected = std::get<3>(tuple);
- for(bool adj_lhs :
- {
- false, true
- })
+ for(bool adj_lhs :
+ {
+ false, true
+ })
{
for(bool adj_rhs :
{
@@ -178,6 +183,7 @@
{
TensorShape lhs_shape = std::get<0>(tuple);
TensorShape rhs_shape = std::get<1>(tuple);
+ TensorShape bia_shape = std::get<2>(tuple);
if(adj_lhs)
{
@@ -191,11 +197,12 @@
const TensorInfo lhs_info = TensorInfo(lhs_shape, 1, DataType::F32);
const TensorInfo rhs_info = TensorInfo(rhs_shape, 1, DataType::F32);
+ const TensorInfo bia_info = TensorInfo(bia_shape, 1, DataType::F32);
TensorInfo output_info;
MatMulKernelInfo matmul_kernel_info{ adj_lhs, adj_rhs, 1, 1, 1, false /* export_rhs_to_cl_image */ };
- Status status = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info);
+ Status status = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, &bia_info, &output_info, matmul_kernel_info);
ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
}
}
@@ -213,40 +220,44 @@
if(arm_matrix_multiply_supported(CLKernelLibrary::get().get_device()))
{
// Configurations are assumed to be Nt/Nt, but will be transposed inside the test to test other configurations
- using DataTypeConfigurationTuple = std::tuple<DataType, DataType, DataType, bool>;
+ using DataTypeConfigurationTuple = std::tuple<DataType, DataType, DataType, DataType, bool>;
const std::vector<DataTypeConfigurationTuple> data_type_configurations =
{
- { DataType::F32, DataType::F32, DataType::F32, true },
- { DataType::F16, DataType::F16, DataType::F16, true },
- { DataType::F16, DataType::F32, DataType::F32, false }, // no mixed precision
- { DataType::F64, DataType::F64, DataType::F64, false }, // no double precision
- { DataType::QASYMM8, DataType::QASYMM8, DataType::QASYMM8, false }, // no quantized types
- { DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, false }, // no quantized types
- { DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, false }, // no quantized types
- { DataType::QASYMM16, DataType::QASYMM16, DataType::QASYMM16, false }, // no quantized types
- { DataType::QSYMM16, DataType::QSYMM16, DataType::QSYMM16, false }, // no quantized types
- { DataType::QSYMM8, DataType::QSYMM8, DataType::QSYMM8, false }, // no quantized types
- { DataType::S64, DataType::S64, DataType::S64, false }, // no integral types
- { DataType::S32, DataType::S32, DataType::S32, false }, // no integral types
- { DataType::S16, DataType::S16, DataType::S16, false }, // no integral types
- { DataType::S8, DataType::S8, DataType::S8, false }, // no integral types
- { DataType::U64, DataType::U64, DataType::U64, false }, // no integral types
- { DataType::U32, DataType::U32, DataType::U32, false }, // no integral types
- { DataType::U16, DataType::U16, DataType::U16, false }, // no integral types
- { DataType::U8, DataType::U8, DataType::U8, false }, // no integral types
+ { DataType::F32, DataType::F32, DataType::F32, DataType::F32, true },
+ { DataType::F16, DataType::F16, DataType::F16, DataType::F16, true },
+ { DataType::F32, DataType::F32, DataType::F32, DataType::F32, true },
+ { DataType::F32, DataType::F32, DataType::F16, DataType::F32, false }, // incorrect bias type
+ { DataType::F16, DataType::F32, DataType::F32, DataType::F32, false }, // no mixed precision
+ { DataType::F64, DataType::F64, DataType::F64, DataType::F64, false }, // no double precision
+ { DataType::QASYMM8, DataType::QASYMM8, DataType::S32, DataType::QASYMM8, false }, // no quantized types
+ { DataType::QASYMM8_SIGNED, DataType::QASYMM8_SIGNED, DataType::S32, DataType::QASYMM8_SIGNED, false }, // no quantized types
+ { DataType::QSYMM8_PER_CHANNEL, DataType::QSYMM8_PER_CHANNEL, DataType::S32, DataType::QSYMM8_PER_CHANNEL, false }, // no quantized types
+ { DataType::QASYMM16, DataType::QASYMM16, DataType::S32, DataType::QASYMM16, false }, // no quantized types
+ { DataType::QSYMM16, DataType::QSYMM16, DataType::S32, DataType::QSYMM16, false }, // no quantized types
+ { DataType::QSYMM8, DataType::QSYMM8, DataType::S32, DataType::QSYMM8, false }, // no quantized types
+ { DataType::S64, DataType::S64, DataType::S64, DataType::S64, false }, // no integral types
+ { DataType::S32, DataType::S32, DataType::S32, DataType::S32, false }, // no integral types
+ { DataType::S16, DataType::S16, DataType::S16, DataType::S16, false }, // no integral types
+ { DataType::S8, DataType::S8, DataType::S8, DataType::S8, false }, // no integral types
+ { DataType::U64, DataType::U64, DataType::U64, DataType::U64, false }, // no integral types
+ { DataType::U32, DataType::U32, DataType::U32, DataType::U32, false }, // no integral types
+ { DataType::U16, DataType::U16, DataType::U16, DataType::U16, false }, // no integral types
+ { DataType::U8, DataType::U8, DataType::U8, DataType::U8, false }, // no integral types
};
- const TensorShape shape = TensorShape(8U, 8U);
+ const TensorShape shape = TensorShape(8U, 8U);
+ const TensorShape bia_shape = TensorShape(8U);
const MatMulKernelInfo matmul_kernel_info{ false, false, 1, 1, 1, false };
for(auto &tuple : data_type_configurations)
{
- const bool expected = std::get<3>(tuple);
+ const bool expected = std::get<4>(tuple);
const TensorInfo lhs_info(shape, 1, std::get<0>(tuple));
const TensorInfo rhs_info(shape, 1, std::get<1>(tuple));
- TensorInfo output_info(shape, 1, std::get<2>(tuple));
+ const TensorInfo bia_info(bia_shape, 1, std::get<2>(tuple));
+ TensorInfo output_info(shape, 1, std::get<3>(tuple));
- Status status = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info);
+ Status status = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, &bia_info, &output_info, matmul_kernel_info);
ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
}
}
@@ -292,7 +303,23 @@
validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
}
}
-FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
+FIXTURE_DATA_TEST_CASE(RunWithBias, CLMatMulKernelBiasFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulMMULDataset(),
+ framework::dataset::make("TransposeA", { false, true })),
+ framework::dataset::make("TransposeB", { false, true })),
+ m0_values_precommit),
+ n0_values_precommit),
+ k0_value),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
+ framework::dataset::make("DataType", DataType::F32)))
+{
+ // Validate output
+ if(_device_supports_mmul)
+ {
+ validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
+ }
+}
+FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
framework::dataset::make("TransposeA", { false })),
framework::dataset::make("TransposeB", { false })),
m0_values_nightly_lhs_nt),
@@ -308,7 +335,8 @@
}
}
-FIXTURE_DATA_TEST_CASE(RunLargeRhsTranspose, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
+FIXTURE_DATA_TEST_CASE(RunLargeRhsTranspose, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
framework::dataset::make("TransposeA", { false })),
framework::dataset::make("TransposeB", { true })),
m0_values_nightly_lhs_nt),
@@ -323,14 +351,15 @@
validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32);
}
}
-FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
- framework::dataset::make("TransposeA", { true })),
- framework::dataset::make("TransposeB", { false })),
- m0_values_nightly_lhs_t),
- n0_values_nightly_rhs_nt),
- k0_value),
- framework::dataset::make("ExportRhsToCLImage", { false })),
- framework::dataset::make("DataType", DataType::F32)))
+FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
+ framework::dataset::make("TransposeA", { true })),
+ framework::dataset::make("TransposeB", { false })),
+ m0_values_nightly_lhs_t),
+ n0_values_nightly_rhs_nt),
+ k0_value),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
+ framework::dataset::make("DataType", DataType::F32)))
{
// Validate output
// Validate output
@@ -395,7 +424,8 @@
validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
}
}
-FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulNativeMMULKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
+FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulNativeMMULKernelFixture<half>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
framework::dataset::make("TransposeA", { false })),
framework::dataset::make("TransposeB", { false })),
m0_values_nightly_lhs_nt),
@@ -410,7 +440,8 @@
validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
}
}
-FIXTURE_DATA_TEST_CASE(RunLargeRhsTranspose, CLMatMulNativeMMULKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
+FIXTURE_DATA_TEST_CASE(RunLargeRhsTranspose, CLMatMulNativeMMULKernelFixture<half>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
framework::dataset::make("TransposeA", { false })),
framework::dataset::make("TransposeB", { true })),
m0_values_nightly_lhs_nt),
@@ -425,14 +456,15 @@
validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
}
}
-FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulNativeMMULKernelFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
- framework::dataset::make("TransposeA", { true })),
- framework::dataset::make("TransposeB", { false })),
- m0_values_nightly_lhs_t),
- n0_values_nightly_rhs_nt),
- k0_value),
- framework::dataset::make("ExportRhsToCLImage", { false })),
- framework::dataset::make("DataType", DataType::F16)))
+FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulNativeMMULKernelFixture<half>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulMMULDataset(),
+ framework::dataset::make("TransposeA", { true })),
+ framework::dataset::make("TransposeB", { false })),
+ m0_values_nightly_lhs_t),
+ n0_values_nightly_rhs_nt),
+ k0_value),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
+ framework::dataset::make("DataType", DataType::F16)))
{
// Validate output
// Validate output
diff --git a/tests/validation/fixtures/MatMulKernelFixture.h b/tests/validation/fixtures/MatMulKernelFixture.h
index 59bcfe5..88fdf8b 100644
--- a/tests/validation/fixtures/MatMulKernelFixture.h
+++ b/tests/validation/fixtures/MatMulKernelFixture.h
@@ -36,7 +36,7 @@
#include "tests/validation/reference/GEMMLowp.h"
#include "tests/validation/reference/Permute.h"
#include "tests/validation/reference/ReshapeLayer.h"
-
+#include <cmath>
#include <random>
namespace arm_compute
@@ -48,12 +48,16 @@
using namespace arm_compute::opencl::kernels;
template <typename T, typename KernelType, bool use_mmul = false>
-class MatMulKernelValidationFixture : public framework::Fixture
+class MatMulKernelGenericValidationFixture : public framework::Fixture
{
public:
template <typename...>
- void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, int M0, int N0, int K0, bool export_rhs_to_cl_image, DataType data_type)
+ void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, int M0, int N0, int K0, bool export_rhs_to_cl_image, DataType data_type,
+ bool enable_bias)
{
+ // Flag to create a bias
+ _enable_bias = enable_bias;
+
// For brevity, the input shapes are assumed to be not-transposed for both Lhs and Rhs matrices.
QuantizationInfo lhs_q_info;
QuantizationInfo rhs_q_info;
@@ -138,6 +142,16 @@
}
}
+ template <typename U>
+ void fill_bias_s32(U &&tensor, int i, const UniformQuantizationInfo &q_info)
+ {
+ // For quantized cases, fill the S32 bias according to the following to avoid saturation of test cases.
+ // The following code limits size of bias values to within expected range of output quantization.
+ const unsigned int bound = std::abs(q_info.scale * 256); // 256 is size of 8 bit datatype
+ std::uniform_int_distribution<int32_t> distribution(-(bound / 10), (bound / 10));
+ library->fill(tensor, distribution, i);
+ }
+
template <typename U, typename D>
void fill_constant(U &&tensor, D value)
{
@@ -156,12 +170,15 @@
matmul_info.k0 = K0;
matmul_info.export_rhs_to_cl_image = export_rhs_to_cl_image;
- // Create tensors
- CLTensor a = create_tensor<CLTensor>(shape_a, data_type, 1, lhs_q_info);
- CLTensor b = create_tensor<CLTensor>(shape_b, data_type, 1, rhs_q_info);
- CLTensor dst = create_tensor<CLTensor>(output_shape, data_type, 1, dst_q_info);
+ bool is_quantized = is_data_type_quantized(data_type);
- matMul.configure(a.info(), b.info(), dst.info(), matmul_info);
+ // Create tensors
+ CLTensor a = create_tensor<CLTensor>(shape_a, data_type, 1, lhs_q_info);
+ CLTensor b = create_tensor<CLTensor>(shape_b, data_type, 1, rhs_q_info);
+ CLTensor bias = create_tensor<CLTensor>(output_shape[0], (is_quantized) ? DataType::S32 : data_type, 1, dst_q_info);
+ CLTensor dst = create_tensor<CLTensor>(output_shape, data_type, 1, dst_q_info);
+
+ matMul.configure(a.info(), b.info(), (_enable_bias) ? bias.info() : nullptr, dst.info(), matmul_info);
ARM_COMPUTE_ASSERT(a.info()->is_resizable());
ARM_COMPUTE_ASSERT(b.info()->is_resizable());
ARM_COMPUTE_ASSERT(dst.info()->is_resizable());
@@ -184,6 +201,22 @@
{ ACL_SRC_1, &b },
{ ACL_DST, &dst }
});
+
+ if(_enable_bias)
+ {
+ // Allocate, fill and add bias to TensorPack obj
+ bias.allocator()->allocate();
+ if(is_quantized)
+ {
+ fill_bias_s32(CLAccessor(bias), 2, dst_q_info.uniform());
+ }
+ else
+ {
+ fill(CLAccessor(bias), 2);
+ }
+ tensors_pack.add_tensor(ACL_SRC_2, &bias);
+ }
+
matMul.run(tensors_pack);
return dst;
@@ -252,9 +285,21 @@
template <typename U = T>
typename std::enable_if < std::is_same<U, float>::value || std::is_same<U, half>::value, SimpleTensor<U >>::type gemm_reference(SimpleTensor<U> &a, SimpleTensor<U> &b, SimpleTensor<U> &c)
{
+ // Fill bias, then copy first dimension into subsequent dimensions to mimic broadcast
+ // of bias tensor from shape [dst.dimension(0)] to [dst.tensor_shape()] in target kernel
+ if(_enable_bias)
+ {
+ fill(c, 2);
+ const int n = c.shape().x();
+ const int other_dims = c.shape().collapsed_from(1)[1];
+ for(int i = 1; i < other_dims; ++i) // For all data, copy first n elements into remaining batches
+ {
+ memcpy(c.data() + i * n, c.data(), n * sizeof(T));
+ }
+ }
// Setting beta to 0 will effectively disable C for the
// computation of the reference: alpha * A * B + 0 * C
- return reference::gemm<U>(a, b, c, 1.0f, 0.f);
+ return reference::gemm<U>(a, b, c, 1.0f, (_enable_bias) ? 1.0f : 0.f);
}
template <typename U = T>
@@ -276,19 +321,59 @@
constexpr int32_t gemmlowp_max_bound = std::numeric_limits<int32_t>::max();
SimpleTensor<int> bias{ c.shape(), DataType::S32 };
- fill_constant(bias, static_cast<int32_t>(0));
+ if(_enable_bias)
+ {
+ // Identical to float implementation, fill and copy values of bias first dimension
+ fill_bias_s32(bias, 2, cq);
+ const int n = bias.shape().x();
+ const int other_dims = bias.shape().collapsed_from(1)[1];
+ const unsigned int dt_size = sizeof(int32_t);
+ for(int i = 1; i < other_dims; ++i)
+ {
+ memcpy(bias.data() + i * n, bias.data(), n * dt_size);
+ }
+ }
+ else
+ {
+ fill_constant(bias, static_cast<int32_t>(0)); // effectively disable bias
+ }
const SimpleTensor<U> final_result = reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, U>(result, bias,
gemmlowp_multipliers, gemmlowp_shifts, gemmlowp_offset, gemmlowp_min_bound, gemmlowp_max_bound);
+
return final_result;
}
CLTensor _target{};
SimpleTensor<T> _reference{};
+ bool _enable_bias{ false };
bool _device_supports_export_to_cl_image{ true };
bool _device_supports_mmul{ true };
};
+template <typename T, typename KernelType, bool use_mmul = false>
+class MatMulKernelValidationFixture : public MatMulKernelGenericValidationFixture<T, KernelType, use_mmul>
+{
+public:
+ template <typename...>
+ void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, int M0, int N0, int K0, bool export_rhs_to_cl_image, DataType data_type)
+ {
+ MatMulKernelGenericValidationFixture<T, KernelType, use_mmul>::setup(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, M0, N0, K0, export_rhs_to_cl_image, data_type,
+ false /* enable bias */);
+ }
+};
+
+template <typename T, typename KernelType, bool use_mmul = false>
+class MatMulKernelWithBiasValidation : public MatMulKernelGenericValidationFixture<T, KernelType, use_mmul>
+{
+public:
+ template <typename...>
+ void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, int M0, int N0, int K0, bool export_rhs_to_cl_image, DataType data_type)
+ {
+ MatMulKernelGenericValidationFixture<T, KernelType, use_mmul>::setup(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, M0, N0, K0, export_rhs_to_cl_image, data_type,
+ true /* enable bias */);
+ }
+};
} // namespace validation
} // namespace test
} // namespace arm_compute