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-rw-r--r--arm_compute/core/CL/CLKernels.h1
-rw-r--r--arm_compute/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.h95
-rw-r--r--src/core/CL/CLKernelLibrary.cpp1
-rw-r--r--src/core/CL/cl_kernels/gemm.cl319
-rw-r--r--src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.cpp321
-rw-r--r--tests/validation/CL/ElementwisePower.cpp1
-rw-r--r--tests/validation/CL/GEMMMatrixMultiplyNative.cpp305
-rw-r--r--tests/validation/fixtures/GEMMFixture.h196
8 files changed, 1237 insertions, 2 deletions
diff --git a/arm_compute/core/CL/CLKernels.h b/arm_compute/core/CL/CLKernels.h
index 3f5a7dc241..70ffe3030b 100644
--- a/arm_compute/core/CL/CLKernels.h
+++ b/arm_compute/core/CL/CLKernels.h
@@ -86,6 +86,7 @@
#include "arm_compute/core/CL/kernels/CLGEMMMatrixAccumulateBiasesKernel.h"
#include "arm_compute/core/CL/kernels/CLGEMMMatrixAdditionKernel.h"
#include "arm_compute/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h"
+#include "arm_compute/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.h"
#include "arm_compute/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h"
#include "arm_compute/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h"
#include "arm_compute/core/CL/kernels/CLGEMMMatrixVectorMultiplyKernel.h"
diff --git a/arm_compute/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.h b/arm_compute/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.h
new file mode 100644
index 0000000000..c611dc4c1f
--- /dev/null
+++ b/arm_compute/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.h
@@ -0,0 +1,95 @@
+/*
+ * Copyright (c) 2019 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef __ARM_COMPUTE_CLGEMMMATRIXMULTIPLYNATIVEKERNEL_H__
+#define __ARM_COMPUTE_CLGEMMMATRIXMULTIPLYNATIVEKERNEL_H__
+
+#include "arm_compute/core/CL/ICLKernel.h"
+
+namespace arm_compute
+{
+class ICLTensor;
+
+/** OpenCL kernel to multiply matrices when neither of the input matrices have been reshaped */
+class CLGEMMMatrixMultiplyNativeKernel : public ICLKernel
+{
+public:
+ /** Default Constructor */
+ CLGEMMMatrixMultiplyNativeKernel();
+ /** Prevent instances of this class from being copied (As this class contains pointers) */
+ CLGEMMMatrixMultiplyNativeKernel(const CLGEMMMatrixMultiplyNativeKernel &) = delete;
+ /** Prevent instances of this class from being copied (As this class contains pointers) */
+ CLGEMMMatrixMultiplyNativeKernel &operator=(const CLGEMMMatrixMultiplyNativeKernel &) = delete;
+ /** Allow instances of this class to be moved */
+ CLGEMMMatrixMultiplyNativeKernel(CLGEMMMatrixMultiplyNativeKernel &&) = default;
+ /** Allow instances of this class to be moved */
+ CLGEMMMatrixMultiplyNativeKernel &operator=(CLGEMMMatrixMultiplyNativeKernel &&) = default;
+ /** Initialise the kernel's input and output.
+ *
+ * @param[in] input0 Input tensor for the LHS matrix. Data type supported: F32/F16. The number of dimensions for the LHS matrix must be less or equal than 4.
+ * @param[in] input1 Input tensor for the RHS matrix. Data type supported: same as @p input0. The number of dimensions for the RHS matrix must be less or equal than 3.
+ * @param[out] output Output tensor info. Data type supported: same as @p input0
+ * @param[in] alpha Weight of the matrix product
+ * @param[in] lhs_info LHS matrix information used to retrieve the number of rows and accumulations to be processed by each thread. Only the following values are supported:
+ * lhs_info.m0: 1,2,3,4,5,6,7,8
+ * lhs_info.k0: 2,3,4,8,16
+ * @param[in] rhs_info RHS matrix information used to retrieve the number of columns and accumulations to be processed by each thread. Only the following values are supported:
+ * rhs_info.n0: 2,3,4,8,16
+ * rhs_info.k0: same of lhs_info.k0
+ * @param[in] gemm_info GEMM information used to retrieve the original dimensions of the input matrices
+ */
+ void configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, float alpha, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
+ const GEMMReshapeInfo &gemm_info);
+ /** Static function to check if given info will lead to a valid configuration of @ref CLGEMMMatrixMultiplyNativeKernel
+ *
+ * @param[in] input0 Input tensor info for the LHS matrix. Data type supported: F32/F16. The number of dimensions for the LHS matrix must be less or equal than 4.
+ * @param[in] input1 Input tensor info for the RHS matrix. Data type supported: same as @p input0. The number of dimensions for the RHS matrix must be less or equal than 3.
+ * @param[in] output Output tensor info. Data type supported: same as @p input0
+ * @param[in] alpha Weight of the matrix product
+ * @param[in] lhs_info LHS matrix information used to retrieve the number of rows and accumulations to be processed by each thread. Only the following values are supported:
+ * lhs_info.m0: 1,2,3,4,5,6,7,8
+ * lhs_info.k0: 2,3,4,8,16
+ * @param[in] rhs_info RHS matrix information used to retrieve the number of columns and accumulations to be processed by each thread. Only the following values are supported:
+ * rhs_info.n0: 2,3,4,8,16
+ * rhs_info.k0: same of lhs_info.k0
+ * @param[in] gemm_info GEMM information used to retrieve the original dimensions of the input matrices
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
+ const GEMMReshapeInfo &gemm_info);
+
+ // Inherited methods overridden:
+ void run(const Window &window, cl::CommandQueue &queue) override;
+
+private:
+ const ICLTensor *_input0;
+ const ICLTensor *_input1;
+ ICLTensor *_output;
+ bool _slide_matrix_b;
+ bool _reinterpret_input_as_3d;
+ bool _reinterpret_output_as_3d;
+ bool _use_dummy_work_items;
+};
+} // namespace arm_compute
+#endif /*__ARM_COMPUTE_CLGEMMMATRIXMULTIPLYNATIVEKERNEL_H__*/
diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index e102f44b88..dfcbfa7cc5 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -314,6 +314,7 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map =
{ "gemm_mm_floating_point_f16_bifrost_acc32", "gemm.cl" },
{ "gemm_mm_floating_point_f32_bifrost", "gemm.cl" },
{ "gemm_mm_floating_point_f32_bifrost_1000", "gemm.cl" },
+ { "gemm_mm_native", "gemm.cl" },
{ "gemm_mm_reshaped_lhs_nt_rhs_t", "gemm.cl" },
{ "gemm_mm_reshaped_only_rhs_nt", "gemm.cl" },
{ "gemm_mm_reshaped_only_rhs_t", "gemm.cl" },
diff --git a/src/core/CL/cl_kernels/gemm.cl b/src/core/CL/cl_kernels/gemm.cl
index c3107a20f2..da45d0fc18 100644
--- a/src/core/CL/cl_kernels/gemm.cl
+++ b/src/core/CL/cl_kernels/gemm.cl
@@ -1535,7 +1535,7 @@ __kernel void gemm_mm_reshaped_only_rhs_nt(IMAGE_DECLARATION(lhs),
#endif // M0 > 6
#if M0 > 7
VEC_DATA_TYPE(DATA_TYPE, 2)
- a7 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 7 * lhs_stride_y + zin));
+ a7 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 7 * lhs_stride_y + zin7));
#endif // M0 > 7
LD_RHS_VFMA_M0xN0(0, a, c);
@@ -1886,8 +1886,325 @@ __kernel void gemm_mm_reshaped_lhs_nt_rhs_t(IMAGE_DECLARATION(lhs),
#undef RHS_OFFSET_X
#undef RHS_STEP_X
}
+
#endif // defined(M0) && defined(N0) && defined(K0) && defined(V0) && defined(H0) && defined(K) && defined(DATA_TYPE)
+#if defined(M0) && defined(N0) && defined(K0) && defined(K) && defined(DATA_TYPE)
+
+#define VFMA(a, b, c) \
+ ({ \
+ c = fma(a, b, c); \
+ })
+
+#if M0 == 1
+#define RHS_VFMA_M0xN0(i, a, b, c) \
+ ({ \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##0).s##i), b, (c##0)); \
+ })
+#elif M0 == 2 // M0 == 2
+#define RHS_VFMA_M0xN0(i, a, b, c) \
+ ({ \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##0).s##i), b, (c##0)); \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##1).s##i), b, (c##1)); \
+ })
+#elif M0 == 3 // M0 == 3
+#define RHS_VFMA_M0xN0(i, a, b, c) \
+ ({ \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##0).s##i), b, (c##0)); \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##1).s##i), b, (c##1)); \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##2).s##i), b, (c##2)); \
+ })
+#elif M0 == 4 // M0 == 4
+#define RHS_VFMA_M0xN0(i, a, b, c) \
+ ({ \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##0).s##i), b, (c##0)); \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##1).s##i), b, (c##1)); \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##2).s##i), b, (c##2)); \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##3).s##i), b, (c##3)); \
+ })
+#elif M0 == 5 // M0 == 5
+#define RHS_VFMA_M0xN0(i, a, b, c) \
+ ({ \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##0).s##i), b, (c##0)); \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##1).s##i), b, (c##1)); \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##2).s##i), b, (c##2)); \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##3).s##i), b, (c##3)); \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##4).s##i), b, (c##4)); \
+ })
+#elif M0 == 6 // M0 == 6
+#define RHS_VFMA_M0xN0(i, a, b, c) \
+ ({ \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##0).s##i), b, (c##0)); \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##1).s##i), b, (c##1)); \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##2).s##i), b, (c##2)); \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##3).s##i), b, (c##3)); \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##4).s##i), b, (c##4)); \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##5).s##i), b, (c##5)); \
+ })
+#elif M0 == 7 // M0 == 7
+#define RHS_VFMA_M0xN0(i, a, b, c) \
+ ({ \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##0).s##i), b, (c##0)); \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##1).s##i), b, (c##1)); \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##2).s##i), b, (c##2)); \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##3).s##i), b, (c##3)); \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##4).s##i), b, (c##4)); \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##5).s##i), b, (c##5)); \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##6).s##i), b, (c##6)); \
+ })
+#elif M0 == 8 // M0 == 8
+#define RHS_VFMA_M0xN0(i, a, b, c) \
+ ({ \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##0).s##i), b, (c##0)); \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##1).s##i), b, (c##1)); \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##2).s##i), b, (c##2)); \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##3).s##i), b, (c##3)); \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##4).s##i), b, (c##4)); \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##5).s##i), b, (c##5)); \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##6).s##i), b, (c##6)); \
+ VFMA((VEC_DATA_TYPE(DATA_TYPE, N0))((a##7).s##i), b, (c##7)); \
+ })
+#else // M0 not supported
+#error "M0 not supported"
+#endif // M0 not supported
+
+/** This OpenCL kernel computes the matrix multiplication between 2 matrices.
+ * The LHS matrix is NOT reshaped
+ * The RHS matrix is NOT reshaped
+ *
+ * @note If the first two dimensions of NDRange have been dispatched with "dummy_work_items" support, the option -DDUMMY_WORK_ITEMS must be passed at compile time.
+ * @note The GEMM's dimensions (M,N and K) must be passed at compile time using -DM, -DN and and -DK (i.e. -DM=52, -DN=30 and -DK=90)
+ * @note The number of columns of LHS matrix must be passed at compile time using -DK (i.e. -DK=64)
+ * @note The number of M0 rows to process must be passed at compile time using -DM0 (i.e. -DM0=2)
+ * @note The number of K0 partial accumulations must be passed at compile time using -DK0 (i.e., -DK0=2)
+ * @note The number of N0 columns to process must be passed at compile time using -DN0 (i.e. -DN0=2)
+ * @note Only the following configurations of M0, N0 and K0 are currently supported:
+ * - M0 = 1, 2, 3, 4, 5, 6, 7, 8
+ * - N0 = 2, 3, 4, 8, 16
+ * - K0 = 2, 3, 4, 8, 16
+ *
+ * @note In case the input or output have to be reinterpreted as a 3D tensor, the following information must be passed at compile time:
+ * -# REINTERPRET_INPUT_AS_3D: To reinterpret the input as 3D
+ * -# REINTERPRET_OUTPUT_AS_3D: To reinterpret the output as 3D
+ * -# HEIGHT_GEMM3D: The height of the output in case it has to be reinterpreted as a 3D tensor.
+ * -# DEPTH_GEMM3D: The depth of the output in case it has to be reinterpreted as a 3D tensor
+ * (HEIGHT_GEMM3D * DEPTH_GEMM3D) = columns LHS matrix
+ *
+ * @param[in] lhs_ptr Pointer to the LHS reshaped matrix. Supported data type: F16/F32
+ * @param[in] lhs_stride_x Stride of the LHS reshaped matrix in X dimension (in bytes)
+ * @param[in] lhs_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] lhs_stride_y Stride of the LHS reshaped matrix in Y dimension (in bytes)
+ * @param[in] lhs_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the LHS reshaped matrix
+ * @param[in] rhs_ptr Pointer to the RHS reshaped matrix. Supported data type: same as @p lhs_ptr
+ * @param[in] rhs_stride_x Stride of the RHS reshaped matrix in X dimension (in bytes)
+ * @param[in] rhs_step_x src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] rhs_stride_y Stride of the RHS reshaped matrix in Y dimension (in bytes)
+ * @param[in] rhs_step_y src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the RHS reshaped matrix
+ * @param[out] dst_ptr Pointer to the destination matrix Supported data type: same as @p lhs_ptr
+ * @param[in] dst_stride_x Stride of the destination matrix in X dimension (in bytes)
+ * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y Stride of the destination matrix in Y dimension (in bytes)
+ * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination matrix
+ * @param[in] lhs_stride_z Stride of the LHS reshaped matrix in Z dimension (in bytes)
+ * @param[in] rhs_stride_z Stride of the RHS reshaped matrix in Z dimension (in bytes)
+ * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in] lhs_cross_plane_pad (Optional) Bottom paddings for LHS matrix in unit of elements (only if defined REINTERPRET_INPUT_AS_3D)
+ * @param[in] dst_cross_plane_pad (Optional) Bottom paddings for the output matrix in unit of elements (only if defined REINTERPRET_OUTPUT_AS_3D)
+ */
+__kernel void gemm_mm_native(IMAGE_DECLARATION(lhs),
+ IMAGE_DECLARATION(rhs),
+ IMAGE_DECLARATION(dst),
+ uint lhs_stride_z,
+ uint rhs_stride_z,
+ uint dst_stride_z
+#if defined(REINTERPRET_INPUT_AS_3D)
+ ,
+ uint lhs_cross_plane_pad
+#endif // REINTERPRET_INPUT_AS_3D
+#if defined(REINTERPRET_OUTPUT_AS_3D)
+ ,
+ uint dst_cross_plane_pad
+#endif // REINTERPRET_OUTPUT_AS_3D
+ )
+{
+ // Block size
+#define RHS_BLOCK_SIZE ((K0) * (N0))
+
+ // RHS offset and step X
+#define RHS_OFFSET_X (RHS_BLOCK_SIZE)
+
+ uint x = get_global_id(0);
+ uint y = get_global_id(1);
+ uint z = get_global_id(2);
+
+#if defined(DUMMY_WORK_ITEMS)
+ if((x * N0 >= N) || (y * M0 >= M))
+ {
+ return;
+ }
+#endif // defined(DUMMY_WORK_ITEMS)
+
+ // Compute LHS matrix address
+ uint lhs_offset = lhs_offset_first_element_in_bytes + y * M0 * (uint)lhs_stride_y;
+
+ // Compute RHS matrix address
+ uint rhs_offset = rhs_offset_first_element_in_bytes + x * N0 * sizeof(DATA_TYPE);
+
+#if defined(MATRIX_B_DEPTH)
+ // Do not slide matrix B if the matrix B has 3 dimensions and matrix A more than 3
+ rhs_offset += (z % MATRIX_B_DEPTH) * rhs_stride_z;
+#else // defined(MATRIX_B_DEPTH)
+ rhs_offset += z * rhs_stride_z;
+#endif // defined(MATRIX_B_DEPTH)
+
+ REPEAT_VAR_INIT_TO_CONST(8, uint, zlhs, 0); //uint zlhs0=0,zlhs1=0,zlhs2=0,... zlhs7=0;
+ REPEAT_VAR_INIT_TO_CONST(16, uint, zrhs, 0);
+
+#if defined(REINTERPRET_INPUT_AS_3D)
+ // The plane (zlhs) is calculated dividing M (y * M0) by HEIGHT_GEMM3D
+ CALCULATE_Z_OFFSET(M0, uint, zlhs, y, HEIGHT_GEMM3D, DEPTH_GEMM3D, lhs_cross_plane_pad, lhs_stride_y);
+
+ // Add offset for batched GEMM. The batches will be in the fourth dimension and for this reason we
+ // multiply lhs_stride_z by DEPTH_GEMM3D
+ lhs_offset += z * lhs_stride_z * DEPTH_GEMM3D;
+
+#else // defined(REINTERPRET_INPUT_AS_3D)
+
+ // Add offset for batched GEMM
+ lhs_offset += z * lhs_stride_z;
+
+#endif // defined(REINTERPRET_INPUT_AS_3D)
+
+ // Initialize the accumulators
+ REPEAT_VAR_INIT_TO_CONST(M0, VEC_DATA_TYPE(DATA_TYPE, N0), c, 0); //VEC_DATA_TYPE(DATA_TYPE, N0) c0=0,c1=0,c2=0,... c(N0-1)=0;
+
+ int i = 0;
+ for(; i <= (K - K0); i += K0)
+ {
+ // Supported cases (M0, K0):
+ // 1,2 - 1,3 - 1,4 - 1,8 - 1,16
+ // 2,2 - 2,3 - 2,4 - 2,8 - 2,16
+ // 3,2 - 3,3 - 3,4 - 3,8 - 3,16
+ // 4,2 - 4,3 - 4,4 - 4,8 - 4,16
+ // 5,2 - 5,3 - 5,4 - 5,8 - 5,16
+ // 6,2 - 6,3 - 6,4 - 6,8 - 6,16
+ // 7,2 - 7,3 - 7,4 - 7,8 - 7,16
+ // 8,2 - 8,3 - 8,4 - 8,8 - 8,16
+ // Load values from LHS matrix
+ LOAD_BLOCK(M0, K0, DATA_TYPE, a, lhs_ptr, lhs_offset, lhs_stride_y, zlhs);
+
+ // Load values from RHS matrix
+ LOAD_BLOCK(K0, N0, DATA_TYPE, b, rhs_ptr, rhs_offset, rhs_stride_y, zrhs);
+
+ RHS_VFMA_M0xN0(0, a, b0, c);
+ RHS_VFMA_M0xN0(1, a, b1, c);
+#if K0 > 2
+ RHS_VFMA_M0xN0(2, a, b2, c);
+#endif // K0 > 2
+#if K0 > 3
+ RHS_VFMA_M0xN0(3, a, b3, c);
+#endif // K0 > 3
+#if K0 > 4
+ RHS_VFMA_M0xN0(4, a, b4, c);
+ RHS_VFMA_M0xN0(5, a, b5, c);
+ RHS_VFMA_M0xN0(6, a, b6, c);
+ RHS_VFMA_M0xN0(7, a, b7, c);
+#endif // K0 > 4
+#if K0 > 8
+ RHS_VFMA_M0xN0(8, a, b8, c);
+ RHS_VFMA_M0xN0(9, a, b9, c);
+ RHS_VFMA_M0xN0(A, a, b10, c);
+ RHS_VFMA_M0xN0(B, a, b11, c);
+ RHS_VFMA_M0xN0(C, a, b12, c);
+ RHS_VFMA_M0xN0(D, a, b13, c);
+ RHS_VFMA_M0xN0(E, a, b14, c);
+ RHS_VFMA_M0xN0(F, a, b15, c);
+#endif // K0 > 8
+
+ lhs_offset += K0 * sizeof(DATA_TYPE);
+ rhs_offset += K0 * rhs_stride_y;
+ }
+
+ // Left-over accumulations
+ for(; i < K; ++i)
+ {
+ // Load values from LHS matrix
+ VEC_DATA_TYPE(DATA_TYPE, 2)
+ a0 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 0 * lhs_stride_y + zlhs0));
+#if M0 > 1
+ VEC_DATA_TYPE(DATA_TYPE, 2)
+ a1 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 1 * lhs_stride_y + zlhs1));
+#endif // M0 > 1
+#if M0 > 2
+ VEC_DATA_TYPE(DATA_TYPE, 2)
+ a2 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 2 * lhs_stride_y + zlhs2));
+#endif // M0 > 2
+#if M0 > 3
+ VEC_DATA_TYPE(DATA_TYPE, 2)
+ a3 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 3 * lhs_stride_y + zlhs3));
+#endif // M0 > 3
+#if M0 > 4
+ VEC_DATA_TYPE(DATA_TYPE, 2)
+ a4 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 4 * lhs_stride_y + zlhs4));
+#endif // M0 > 4
+#if M0 > 5
+ VEC_DATA_TYPE(DATA_TYPE, 2)
+ a5 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 5 * lhs_stride_y + zlhs5));
+#endif // M0 > 5
+#if M0 > 6
+ VEC_DATA_TYPE(DATA_TYPE, 2)
+ a6 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 6 * lhs_stride_y + zlhs6));
+#endif // M0 > 6
+#if M0 > 7
+ VEC_DATA_TYPE(DATA_TYPE, 2)
+ a7 = *((__global DATA_TYPE *)(lhs_ptr + lhs_offset + 7 * lhs_stride_y + zlhs7));
+#endif // M0 > 7
+
+ VEC_DATA_TYPE(DATA_TYPE, N0)
+ b = VLOAD(N0)(0, (__global DATA_TYPE *)(rhs_ptr + rhs_offset + 0 * rhs_stride_y));
+ RHS_VFMA_M0xN0(0, a, b, c);
+
+ lhs_offset += sizeof(DATA_TYPE);
+ rhs_offset += rhs_stride_y;
+ }
+
+ __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + (x * (uint)N0 * sizeof(DATA_TYPE)) + (y * (uint)M0 * dst_stride_y);
+
+ REPEAT_VAR_INIT_TO_CONST(8, uint, zout, 0); //uint zout0=0,zout1=0,zout2=0,... zout7=0;
+
+#if defined(REINTERPRET_OUTPUT_AS_3D)
+ // The plane (zout) is calculated dividing M (y * M0) by HEIGHT_GEMM3D
+ CALCULATE_Z_OFFSET(M0, uint, zout, y, HEIGHT_GEMM3D, DEPTH_GEMM3D, dst_cross_plane_pad, dst_stride_y);
+
+ // Add offset for batched GEMM. The batches will be in the fourth dimension and for this reason we
+ // multiply dst_stride_z by DEPTH_GEMM3D
+ dst_addr += z * dst_stride_z * DEPTH_GEMM3D;
+
+#else // defined(REINTERPRET_OUTPUT_AS_3D)
+
+ // Add offset for batched GEMM
+ dst_addr += z * dst_stride_z;
+
+#endif // defined(REINTERPRET_OUTPUT_AS_3D)
+
+ // Multiply by the weight of matrix-matrix product and store the result
+ // Multiply by the weight of matrix-matrix product and store the result
+#if defined(ALPHA)
+ SCALE_BLOCK(M0, DATA_TYPE, c, ALPHA);
+#endif // defined(ALPHA)
+
+ // Store output block
+ STORE_BLOCK(M0, N0, DATA_TYPE, c, dst_addr, dst_stride_y, zout);
+
+#undef RHS_BLOCK_SIZE
+#undef RHS_OFFSET_X
+#undef RHS_STEP_X
+}
+#endif // defined(M0) && defined(N0) && defined(K0) && defined(K) && defined(DATA_TYPE)
+
#if defined(TRANSPOSE_W) && defined(MULT_TRANSPOSE1XW_WIDTH)
#if ELEMENT_SIZE == 1
diff --git a/src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.cpp b/src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.cpp
new file mode 100644
index 0000000000..85d882c865
--- /dev/null
+++ b/src/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.cpp
@@ -0,0 +1,321 @@
+/*
+ * Copyright (c) 2019 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.h"
+
+#include "arm_compute/core/AccessWindowStatic.h"
+#include "arm_compute/core/CL/CLHelpers.h"
+#include "arm_compute/core/CL/CLKernelLibrary.h"
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/CL/OpenCL.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/Window.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "support/ToolchainSupport.h"
+
+#include <cstddef>
+#include <cstdint>
+#include <tuple>
+
+using namespace arm_compute::misc::shape_calculator;
+
+namespace arm_compute
+{
+namespace
+{
+using ElementsProcessed = Steps;
+
+Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
+ const GEMMReshapeInfo &gemm_info)
+{
+ ARM_COMPUTE_UNUSED(alpha);
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input0, input1, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F32, DataType::F16);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(input0->num_dimensions() > 4, "The number of dimensions for the LHS matrix must be <= 4");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(input1->num_dimensions() > 3, "The number of dimensions for the RHS matrix must be <= 3");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(((rhs_info.k0 & (rhs_info.k0 - 1)) && rhs_info.k0 != 3), "Only 2,3,4,8,16 are supported for k0");
+ ARM_COMPUTE_RETURN_ERROR_ON(rhs_info.k0 > 16);
+ ARM_COMPUTE_RETURN_ERROR_ON(lhs_info.m0 < 1 || lhs_info.m0 > 8);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(((rhs_info.n0 & (rhs_info.n0 - 1)) && rhs_info.n0 != 3), "Only 2,3,4,8,16 are supported for n0");
+
+ const int m = gemm_info.m();
+ const int n = gemm_info.n();
+ const int k = gemm_info.k();
+
+ ARM_COMPUTE_UNUSED(m);
+ ARM_COMPUTE_UNUSED(n);
+ ARM_COMPUTE_UNUSED(k);
+
+ ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != static_cast<unsigned int>(k));
+ ARM_COMPUTE_RETURN_ERROR_ON(input1->dimension(0) != static_cast<unsigned int>(n));
+ ARM_COMPUTE_RETURN_ERROR_ON(input1->dimension(1) != static_cast<unsigned int>(k));
+ if(gemm_info.reinterpret_input_as_3d())
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) * input0->dimension(2) != static_cast<unsigned int>(m));
+ }
+ else
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) != static_cast<unsigned int>(m));
+ }
+
+ if(output->total_size() != 0)
+ {
+ const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, gemm_info));
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output);
+ }
+
+ return Status{};
+}
+
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info,
+ const GEMMReshapeInfo &gemm_info, ElementsProcessed &num_elements_processed)
+{
+ unsigned int &num_elems_processed_per_iteration_x = num_elements_processed[0];
+ unsigned int &num_elems_processed_per_iteration_y = num_elements_processed[1];
+ bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
+ bool reinterpret_output_as_3d = (gemm_info.depth_output_gemm3d() != 0);
+
+ Window win{};
+ Window win_out{};
+ bool window_changed = false;
+
+ // In case both input and output have to be reinterpreted as 3D tensors,
+ // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
+ if(reinterpret_input_as_3d == reinterpret_output_as_3d)
+ {
+ reinterpret_output_as_3d = false;
+ }
+
+ // Output tensor auto initialization if not yet initialized
+ auto_init_if_empty(*output, input0->clone()->set_tensor_shape(compute_mm_shape(*input0, *input1, gemm_info)));
+
+ TensorInfo tmp_info(*output);
+
+ if(reinterpret_output_as_3d)
+ {
+ // Since the output tensor has to be reinterpreted as 3D and the execute window is based on a 2D GEMM,
+ // the window needs to be constructed on the 2D collapsed version of the tensor
+ TensorShape tmp_shape(output->tensor_shape());
+ tmp_shape.collapse(2U, 1U);
+ tmp_info.set_tensor_shape(tmp_shape);
+ }
+
+ // Configure kernel window
+ num_elems_processed_per_iteration_x = rhs_info.n0;
+ num_elems_processed_per_iteration_y = lhs_info.m0;
+
+ // Note: bottom paddings are calculated manually as the output can be reinterpreted as 3D tensor
+ // The only way to set properly the paddings, it is to set those explicitly through the AccessWindowStatic
+ const int m = gemm_info.m();
+ const int bottom_pad = (num_elems_processed_per_iteration_y - (m % num_elems_processed_per_iteration_y)) % num_elems_processed_per_iteration_y;
+
+ win = calculate_max_window(tmp_info, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+ win_out = calculate_max_window(*output, Steps(num_elems_processed_per_iteration_x, num_elems_processed_per_iteration_y));
+
+ AccessWindowStatic input0_access(input0, 0, 0,
+ input0->dimension(0),
+ input0->dimension(1) + bottom_pad);
+ AccessWindowStatic input1_access(input1, 0, 0,
+ ceil_to_multiple(input1->dimension(0), num_elems_processed_per_iteration_x),
+ input1->dimension(1));
+ AccessWindowStatic output_access(output, 0, 0,
+ ceil_to_multiple(output->dimension(0), num_elems_processed_per_iteration_x),
+ output->dimension(1) + bottom_pad);
+
+ window_changed = update_window_and_padding(win, input0_access, input1_access) || // window used by the execute_window_loop
+ update_window_and_padding(win_out, output_access); // window used to update the padding requirements of output tensor
+
+ output_access.set_valid_region(win_out, ValidRegion(Coordinates(), output->tensor_shape()));
+
+ // Collapse along the Z direction
+ // This collapse needs to be here in order to tune the Z dimension of LWS
+ Window collapsed = win;
+ const unsigned int dimension_to_collapse = std::min(static_cast<unsigned int>(output->num_dimensions()), 2u);
+ collapsed = win.collapse(win, dimension_to_collapse);
+
+ Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+ return std::make_pair(err, collapsed);
+}
+} // namespace
+
+CLGEMMMatrixMultiplyNativeKernel::CLGEMMMatrixMultiplyNativeKernel()
+ : _input0(nullptr), _input1(nullptr), _output(nullptr), _slide_matrix_b(true), _reinterpret_input_as_3d(false), _reinterpret_output_as_3d(false), _use_dummy_work_items(false)
+{
+}
+
+void CLGEMMMatrixMultiplyNativeKernel::configure(const ICLTensor *input0, const ICLTensor *input1, ICLTensor *output, float alpha, const GEMMLHSMatrixInfo &lhs_info,
+ const GEMMRHSMatrixInfo &rhs_info, const GEMMReshapeInfo &gemm_info)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);
+
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info(), alpha, lhs_info, rhs_info, gemm_info));
+
+ _input0 = input0;
+ _input1 = input1;
+ _output = output;
+ _reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d();
+ _reinterpret_output_as_3d = (gemm_info.depth_output_gemm3d() != 0);
+ _use_dummy_work_items = preferred_dummy_work_items_support(CLKernelLibrary::get().get_device());
+
+ // In case both input and output have to be reinterpreted as 3D tensors,
+ // force reinterpret_input_as_3d and reinterpret_output_as_3d to be false.
+ if(_reinterpret_input_as_3d == _reinterpret_output_as_3d)
+ {
+ _reinterpret_input_as_3d = false;
+ _reinterpret_output_as_3d = false;
+ }
+
+ // Check if we need to slide the matrix B
+ const unsigned int num_dimensions_input0 = _input0->info()->num_dimensions();
+ _slide_matrix_b = (_input1->info()->num_dimensions() >= num_dimensions_input0);
+
+ ElementsProcessed num_elements_processed{};
+
+ // Configure kernel window
+ auto win_config = validate_and_configure_window(input0->info(), input1->info(), output->info(), lhs_info, rhs_info, gemm_info, num_elements_processed);
+ ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+ ICLKernel::configure_internal(win_config.second);
+
+ // Create build options
+ CLBuildOptions build_opts;
+ build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input0->info()->data_type()));
+ build_opts.add_option_if(std::abs(1.0f - alpha) > 0.00001f, "-DALPHA=" + float_to_string_with_full_precision(alpha));
+ build_opts.add_option_if(_reinterpret_input_as_3d, "-DREINTERPRET_INPUT_AS_3D");
+ build_opts.add_option_if(_reinterpret_output_as_3d, "-DREINTERPRET_OUTPUT_AS_3D");
+ build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DHEIGHT_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(1)));
+ build_opts.add_option_if(_reinterpret_input_as_3d || _reinterpret_output_as_3d, "-DDEPTH_GEMM3D=" + support::cpp11::to_string(output->info()->dimension(2)));
+ build_opts.add_option_if(!_slide_matrix_b, "-DMATRIX_B_DEPTH=" + support::cpp11::to_string(input1->info()->dimension(2)));
+ build_opts.add_option_if(_use_dummy_work_items, "-DDUMMY_WORK_ITEMS");
+ build_opts.add_option("-DM=" + support::cpp11::to_string(input0->info()->dimension(1)));
+ build_opts.add_option("-DN=" + support::cpp11::to_string(gemm_info.n()));
+ build_opts.add_option("-DK=" + support::cpp11::to_string(gemm_info.k()));
+ build_opts.add_option("-DM0=" + support::cpp11::to_string(lhs_info.m0));
+ build_opts.add_option("-DN0=" + support::cpp11::to_string(rhs_info.n0));
+ build_opts.add_option("-DK0=" + support::cpp11::to_string(rhs_info.k0));
+
+ std::string kernel_name("gemm_mm_native");
+
+ // Create kernel
+ _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
+
+ // Set config_id for enabling LWS tuning
+ _config_id = kernel_name;
+ _config_id += "_";
+ _config_id += (_reinterpret_input_as_3d ? "3di_" : "");
+ _config_id += (_reinterpret_output_as_3d ? "3do_" : "");
+ _config_id += lower_string(string_from_data_type(input0->info()->data_type()));
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(output->info()->dimension(1));
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(output->info()->dimension(0));
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(gemm_info.k());
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(output->info()->dimension(2));
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(lhs_info.m0);
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(rhs_info.n0);
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(rhs_info.k0);
+}
+
+Status CLGEMMMatrixMultiplyNativeKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, const GEMMLHSMatrixInfo &lhs_info,
+ const GEMMRHSMatrixInfo &rhs_info, const GEMMReshapeInfo &gemm_info)
+{
+ ElementsProcessed num_elements_processed{};
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output, alpha, lhs_info, rhs_info, gemm_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(),
+ input1->clone().get(),
+ output->clone().get(),
+ lhs_info,
+ rhs_info,
+ gemm_info,
+ num_elements_processed)
+ .first);
+
+ return Status{};
+}
+
+void CLGEMMMatrixMultiplyNativeKernel::run(const Window &window, cl::CommandQueue &queue)
+{
+ ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+ ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
+
+ if(_input1->info()->num_dimensions() < 3)
+ {
+ // The stride_z for matrix B must be zero if we do not slice
+ ARM_COMPUTE_ERROR_ON(_input1->info()->strides_in_bytes()[3] != 0);
+ }
+
+ Window slice = window.first_slice_window_3D();
+ Window slice_matrix_b = slice;
+
+ slice_matrix_b.set(Window::DimX, Window::Dimension(0, 1, 1));
+ slice_matrix_b.set(Window::DimY, Window::Dimension(0, 1, 1));
+
+ if(_reinterpret_input_as_3d)
+ {
+ // Pass bottom paddings to the kernel if the input has to be reinterpreted as 3D tensor
+ const unsigned int idx0 = 3 * num_arguments_per_2D_tensor() + 3;
+ const unsigned int total_cross_plane_pad = _input0->info()->padding().top + _input0->info()->padding().bottom;
+ _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));
+ }
+
+ if(_reinterpret_output_as_3d)
+ {
+ // Pass bottom paddings to the kernel if the output has to be reinterpreted as 3D tensor
+ const unsigned int idx0 = 3 * num_arguments_per_2D_tensor() + 3 + (_reinterpret_input_as_3d ? 1 : 0);
+ const unsigned int total_cross_plane_pad = _output->info()->padding().top + _output->info()->padding().bottom;
+ _kernel.setArg<cl_uint>(idx0, static_cast<unsigned int>(total_cross_plane_pad));
+ }
+
+ do
+ {
+ Window slice_b = slice;
+ // Don't slice matrix B along the z dimension if matrix B has just 2 dimensions and matrix A more than 2
+ // This scenario can happen when the matrix multiplication is used to perform a convolution operation
+ if(!_slide_matrix_b)
+ {
+ slice_b = slice_matrix_b;
+ }
+
+ unsigned int idx = 0;
+ add_2D_tensor_argument(idx, _input0, slice);
+ add_2D_tensor_argument(idx, _input1, slice_b);
+ add_2D_tensor_argument(idx, _output, slice);
+ _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input0->info()->strides_in_bytes()[2]));
+ _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_input1->info()->strides_in_bytes()[2]));
+ _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(_output->info()->strides_in_bytes()[2]));
+ enqueue(queue, *this, slice, lws_hint(), _use_dummy_work_items);
+ }
+ while(window.slide_window_slice_3D(slice));
+}
+} // namespace arm_compute
diff --git a/tests/validation/CL/ElementwisePower.cpp b/tests/validation/CL/ElementwisePower.cpp
index af36e1768b..46509a2870 100644
--- a/tests/validation/CL/ElementwisePower.cpp
+++ b/tests/validation/CL/ElementwisePower.cpp
@@ -45,7 +45,6 @@ namespace
RelativeTolerance<float> tolerance_fp32(0.000001f);
RelativeTolerance<float> tolerance_fp16(0.001f);
-constexpr unsigned int num_elems_processed_per_iteration = 16;
/** Input data sets **/
const auto ElementwisePowerFP16Dataset = combine(combine(framework::dataset::make("DataType", DataType::F16), framework::dataset::make("DataType", DataType::F16)),
framework::dataset::make("DataType", DataType::F16));
diff --git a/tests/validation/CL/GEMMMatrixMultiplyNative.cpp b/tests/validation/CL/GEMMMatrixMultiplyNative.cpp
new file mode 100644
index 0000000000..c7c390353a
--- /dev/null
+++ b/tests/validation/CL/GEMMMatrixMultiplyNative.cpp
@@ -0,0 +1,305 @@
+/*
+ * Copyright (c) 2019 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/CL/kernels/CLGEMMMatrixMultiplyNativeKernel.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "arm_compute/runtime/CL/CLTensor.h"
+#include "arm_compute/runtime/CL/CLTensorAllocator.h"
+#include "tests/CL/CLAccessor.h"
+#include "tests/CL/Helper.h"
+#include "tests/PaddingCalculator.h"
+#include "tests/datasets/ShapeDatasets.h"
+#include "tests/framework/Asserts.h"
+#include "tests/framework/Macros.h"
+#include "tests/framework/datasets/Datasets.h"
+#include "tests/validation/Validation.h"
+#include "tests/validation/fixtures/GEMMFixture.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+using namespace arm_compute::misc::shape_calculator;
+
+// Create function for CLGEMMMatrixMultiplyNativeKernel
+using CLGEMMMatrixMultiplyNative = CLSynthetizeFunction<CLGEMMMatrixMultiplyNativeKernel>;
+
+// Fixture for CLGEMMMatrixMultiplyNative
+template <typename T>
+using CLGEMMMatrixMultiplyNativeFixture = GEMMMatrixMultiplyNativeValidationFixture<CLTensor, CLAccessor, T, CLGEMMMatrixMultiplyNative>;
+
+// Fixture for CLGEMMMatrixMultiplyNative3D
+template <typename T>
+using CLGEMMMatrixMultiplyNative3DFixture = GEMMMatrixMultiplyNative3DValidationFixture<CLTensor, CLAccessor, T, CLGEMMMatrixMultiplyNative>;
+
+namespace
+{
+// *INDENT-OFF*
+// clang-format off
+RelativeTolerance<float> rel_tolerance_f32(0.001f);
+constexpr float abs_tolerance_f32(0.0001f);
+
+RelativeTolerance<half> rel_tolerance_f16(half(0.2));
+constexpr float tolerance_num_f16 = 0.02f;
+
+/** Alpha values to test - Precommit */
+const auto a_values = framework::dataset::make("alpha", {1.0f, -0.75f} );
+
+/** M values to test */
+const auto m_values = framework::dataset::make("M", 37);
+
+/** M_W values to test */
+const auto m_w_values = framework::dataset::make("M_W", 5);
+
+/** M_H values to test */
+const auto m_h_values = framework::dataset::make("M_H", 7);
+
+/** N values to test */
+const auto n_values = framework::dataset::make("N", 51);
+
+/** K values to test */
+const auto k_values = framework::dataset::make("K", 23);
+
+/** Batch size values to test */
+const auto b_values = framework::dataset::make("batch_size", 1, 3);
+
+/** M0 values to test - Precommit */
+const auto m0_values_precommit = framework::dataset::make("M0", {4, 6});
+
+/** N0 values to test - Precommit */
+const auto n0_values_precommit = framework::dataset::make("N0", { 2, 4 });
+
+/** K0 values to test - Precommit */
+const auto k0_values_precommit = framework::dataset::make("K0", { 4 });
+
+/** H0 values to test - Precommit */
+const auto h0_values_precommit = framework::dataset::make("H0", 1, 3);
+
+/** M0 values to test - Nightly */
+const auto m0_values_nightly = framework::dataset::make("M0", 1, 8);
+
+/** N0 values to test - Nightly */
+const auto n0_values_nightly = framework::dataset::make("N0", { 2, 3, 4, 8 });
+
+/** K0 values to test - Nightly */
+const auto k0_values_nightly = framework::dataset::make("K0", { 2, 3, 4, 8 });
+
+/** Configuration test */
+void validate_configuration(unsigned int m_value, unsigned int n_value, unsigned int k_value, unsigned int b_value, unsigned int m0_value, unsigned int n0_value, unsigned int k0_value, DataType data_type)
+{
+ const unsigned int M = m_value;
+ const unsigned int N = n_value;
+ const unsigned int K = k_value;
+
+ GEMMLHSMatrixInfo lhs_info;
+ lhs_info.m0 = m0_value;
+ lhs_info.k0 = k0_value;
+
+ GEMMRHSMatrixInfo rhs_info;
+ rhs_info.n0 = n0_value;
+ rhs_info.k0 = k0_value;
+
+ GEMMReshapeInfo gemm_info(M, N, K);
+
+ const TensorShape lhs_shape(K, M, b_value);
+ const TensorShape rhs_shape(N, K, b_value);
+
+ const TensorShape dst_shape = compute_mm_shape(TensorInfo(lhs_shape, 1, data_type),
+ TensorInfo(rhs_shape, 1, data_type),
+ gemm_info);
+
+ // Create tensors
+ CLTensor lhs = create_tensor<CLTensor>(lhs_shape, data_type);
+ CLTensor rhs = create_tensor<CLTensor>(rhs_shape, data_type);
+ CLTensor dst = create_tensor<CLTensor>(dst_shape, data_type);
+
+ ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Create and configure function
+ CLGEMMMatrixMultiplyNative gemm;
+ gemm.configure(&lhs, &rhs, &dst, 1.0f, lhs_info, rhs_info, gemm_info);
+}
+} // namespace
+
+TEST_SUITE(CL)
+TEST_SUITE(GEMMMatrixMultiplyNative)
+TEST_SUITE(Float)
+TEST_SUITE(FP32)
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(
+ m_values,
+ n_values),
+ k_values),
+ framework::dataset::make("batch_size", 1)),
+ m0_values_precommit),
+ n0_values_precommit),
+ k0_values_precommit),
+m_value, n_value, k_value, b_value, m0_value, n0_value, k0_value)
+{
+ validate_configuration(m_value, n_value, k_value, b_value, m0_value, n0_value, k0_value, DataType::F32);
+}
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyNativeFixture<float>, framework::DatasetMode::ALL,
+ combine(combine(combine(combine(combine(combine(combine(combine(
+ m_values,
+ n_values),
+ k_values),
+ b_values),
+ m0_values_precommit),
+ n0_values_precommit),
+ k0_values_precommit),
+ framework::dataset::make("DataType", DataType::F32)),
+ a_values))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMMatrixMultiplyNativeFixture<float>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(combine(combine(
+ m_values,
+ n_values),
+ k_values),
+ b_values),
+ m0_values_nightly),
+ n0_values_nightly),
+ k0_values_nightly),
+ framework::dataset::make("DataType", DataType::F32)),
+ a_values))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
+}
+
+FIXTURE_DATA_TEST_CASE(RunSmall3D, CLGEMMMatrixMultiplyNative3DFixture<float>, framework::DatasetMode::ALL,
+ combine(combine(combine(combine(combine(combine(combine(combine(combine(
+ m_w_values,
+ m_h_values),
+ n_values),
+ k_values),
+ b_values),
+ m0_values_precommit),
+ n0_values_precommit),
+ k0_values_precommit),
+ framework::dataset::make("DataType", DataType::F32)),
+ a_values))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyNative3DFixture<float>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(combine(combine(combine(
+ m_w_values,
+ m_h_values),
+ n_values),
+ k_values),
+ b_values),
+ m0_values_nightly),
+ n0_values_nightly),
+ k0_values_nightly),
+ framework::dataset::make("DataType", DataType::F32)),
+ a_values))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
+}
+TEST_SUITE_END() // FP32
+
+TEST_SUITE(FP16)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMMatrixMultiplyNativeFixture<half>, framework::DatasetMode::ALL,
+ combine(combine(combine(combine(combine(combine(combine(combine(
+ m_values,
+ n_values),
+ k_values),
+ b_values),
+ m0_values_precommit),
+ n0_values_precommit),
+ k0_values_precommit),
+ framework::dataset::make("DataType", DataType::F16)),
+ a_values))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMMatrixMultiplyNativeFixture<half>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(combine(combine(
+ m_values,
+ n_values),
+ k_values),
+ b_values),
+ m0_values_nightly),
+ n0_values_nightly),
+ k0_values_nightly),
+ framework::dataset::make("DataType", DataType::F16)),
+ a_values))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16);
+}
+
+FIXTURE_DATA_TEST_CASE(RunSmall3D, CLGEMMMatrixMultiplyNative3DFixture<half>, framework::DatasetMode::ALL,
+ combine(combine(combine(combine(combine(combine(combine(combine(combine(
+ m_w_values,
+ m_h_values),
+ n_values),
+ k_values),
+ b_values),
+ m0_values_precommit),
+ n0_values_precommit),
+ k0_values_precommit),
+ framework::dataset::make("DataType", DataType::F16)),
+ a_values))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge3D, CLGEMMMatrixMultiplyNative3DFixture<half>, framework::DatasetMode::NIGHTLY,
+ combine(combine(combine(combine(combine(combine(combine(combine(combine(
+ m_w_values,
+ m_h_values),
+ n_values),
+ k_values),
+ b_values),
+ m0_values_nightly),
+ n0_values_nightly),
+ k0_values_nightly),
+ framework::dataset::make("DataType", DataType::F16)),
+ a_values))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, rel_tolerance_f16, tolerance_num_f16);
+}
+TEST_SUITE_END() // FP16
+TEST_SUITE_END() // Float
+TEST_SUITE_END() // GEMMMatrixMulipltyNative
+TEST_SUITE_END() // CL
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
diff --git a/tests/validation/fixtures/GEMMFixture.h b/tests/validation/fixtures/GEMMFixture.h
index 77d2ca61fb..b7976104aa 100644
--- a/tests/validation/fixtures/GEMMFixture.h
+++ b/tests/validation/fixtures/GEMMFixture.h
@@ -490,6 +490,100 @@ protected:
SimpleTensor<T> _reference{};
};
+template <typename TensorType, typename AccessorType, typename T, typename GEMMFunctionType>
+class GEMMMatrixMultiplyNativeValidationFixture : public framework::Fixture
+{
+public:
+ template <typename...>
+ void setup(unsigned int m, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, DataType data_type, float alpha)
+ {
+ GEMMLHSMatrixInfo lhs_info;
+ lhs_info.m0 = m0;
+ lhs_info.k0 = k0;
+
+ GEMMRHSMatrixInfo rhs_info;
+ rhs_info.n0 = n0;
+ rhs_info.k0 = k0;
+
+ // Set the tensor shapes for LHS and RHS matrices
+ const TensorShape lhs_shape(k, m, batch_size);
+ const TensorShape rhs_shape(n, k, batch_size);
+
+ _target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, data_type, alpha);
+ _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha);
+ }
+
+protected:
+ template <typename U>
+ void fill(U &&tensor, int i)
+ {
+ std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
+ library->fill(tensor, distribution, i);
+
+ // Fill border with infinity in order to check the presence of NaN values (i.e. inf * 0)
+ std::uniform_real_distribution<> distribution_inf(std::numeric_limits<float>::infinity(), std::numeric_limits<float>::infinity());
+ library->fill_borders_with_garbage(tensor, distribution_inf, i);
+ }
+
+ TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, DataType data_type, float alpha)
+ {
+ // Create tensors
+ TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
+ TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1);
+ TensorType dst;
+
+ const unsigned int M = lhs_shape[1];
+ const unsigned int N = rhs_shape[0];
+ const unsigned int K = lhs_shape[0];
+
+ // Create and configure function
+ GEMMFunctionType gemm;
+ gemm.configure(&lhs, &rhs, &dst, alpha, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K));
+
+ ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Allocate tensors
+ lhs.allocator()->allocate();
+ rhs.allocator()->allocate();
+ dst.allocator()->allocate();
+
+ ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Fill tensors
+ fill(AccessorType(lhs), 0);
+ fill(AccessorType(rhs), 1);
+
+ // Compute GEMM
+ gemm.run();
+
+ return dst;
+ }
+
+ SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha)
+ {
+ TensorShape dst_shape = lhs_shape;
+ dst_shape[0] = rhs_shape[0];
+ dst_shape[1] = lhs_shape[1];
+
+ // Create reference
+ SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
+ SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
+ SimpleTensor<T> c{ dst_shape, data_type, 1 };
+
+ // Fill reference
+ fill(lhs, 0);
+ fill(rhs, 1);
+
+ return reference::gemm<T>(lhs, rhs, c, alpha, 0.0f);
+ }
+
+ TensorType _target{};
+ SimpleTensor<T> _reference{};
+};
+
template <typename TensorType, typename AccessorType, typename T, typename ReshapeRHSFunctionType, typename GEMMFunctionType>
class GEMMMatrixMultiplyReshapedOnlyRHS3DValidationFixture : public framework::Fixture
{
@@ -597,6 +691,108 @@ protected:
TensorType _target{};
SimpleTensor<T> _reference{};
};
+
+template <typename TensorType, typename AccessorType, typename T, typename GEMMFunctionType>
+class GEMMMatrixMultiplyNative3DValidationFixture : public framework::Fixture
+{
+public:
+ template <typename...>
+ void setup(unsigned int m_w, unsigned int m_h, unsigned int n, unsigned int k, unsigned int batch_size, unsigned int m0, unsigned int n0, unsigned int k0, DataType data_type, float alpha)
+ {
+ GEMMLHSMatrixInfo lhs_info;
+ lhs_info.m0 = m0;
+ lhs_info.k0 = k0;
+
+ GEMMRHSMatrixInfo rhs_info;
+ rhs_info.n0 = n0;
+ rhs_info.k0 = k0;
+
+ // In case of GEMM3D, m is the product between m_w and m_h
+ const unsigned int m = m_w * m_h;
+
+ // Set the tensor shapes for LHS and RHS matrices
+ const TensorShape lhs_shape(k, m, batch_size);
+ const TensorShape rhs_shape(n, k, batch_size);
+
+ _target = compute_target(lhs_shape, rhs_shape, lhs_info, rhs_info, data_type, alpha, m_h);
+ _reference = compute_reference(lhs_shape, rhs_shape, data_type, alpha, m_h);
+ }
+
+protected:
+ template <typename U>
+ void fill(U &&tensor, int i)
+ {
+ std::uniform_real_distribution<> distribution(-1.0f, 1.0f);
+ library->fill(tensor, distribution, i);
+ }
+
+ TensorType compute_target(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, DataType data_type, float alpha,
+ unsigned int m_h)
+ {
+ // Create tensors
+ TensorType lhs = create_tensor<TensorType>(lhs_shape, data_type, 1);
+ TensorType rhs = create_tensor<TensorType>(rhs_shape, data_type, 1);
+ TensorType rhs_reshaped;
+ TensorType dst;
+
+ const unsigned int M = lhs_shape[1];
+ const unsigned int N = rhs_shape[0];
+ const unsigned int K = lhs_shape[0];
+
+ // The output tensor will be auto-initialized within the function
+
+ // Create and configure function
+ GEMMFunctionType gemm;
+ gemm.configure(&lhs, &rhs, &dst, alpha, lhs_info, rhs_info, GEMMReshapeInfo(M, N, K, 1, 1, m_h));
+
+ ARM_COMPUTE_EXPECT(lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Allocate tensors
+ lhs.allocator()->allocate();
+ rhs.allocator()->allocate();
+ rhs_reshaped.allocator()->allocate();
+ dst.allocator()->allocate();
+
+ ARM_COMPUTE_EXPECT(!lhs.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!rhs.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!rhs_reshaped.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+ // Fill tensors
+ fill(AccessorType(lhs), 0);
+ fill(AccessorType(rhs), 1);
+
+ // Compute GEMM
+ gemm.run();
+
+ return dst;
+ }
+
+ SimpleTensor<T> compute_reference(const TensorShape &lhs_shape, const TensorShape &rhs_shape, DataType data_type, float alpha, unsigned int m_h)
+ {
+ TensorShape dst_shape = lhs_shape;
+ dst_shape.set(0, rhs_shape[0]);
+ dst_shape.set(1, lhs_shape[1] / m_h);
+ dst_shape.set(2, m_h);
+ dst_shape.set(3, lhs_shape[2]);
+
+ // Create reference
+ SimpleTensor<T> lhs{ lhs_shape, data_type, 1 };
+ SimpleTensor<T> rhs{ rhs_shape, data_type, 1 };
+ SimpleTensor<T> c{ dst_shape, data_type, 1 };
+
+ // Fill reference
+ fill(lhs, 0);
+ fill(rhs, 1);
+
+ return reference::gemm<T>(lhs, rhs, c, alpha, 0.0f);
+ }
+
+ TensorType _target{};
+ SimpleTensor<T> _reference{};
+};
+
} // namespace validation
} // namespace test
} // namespace arm_compute
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