Implement FP32/FP16 MatMul NT/NT kernel using the MMUL extension
Resolves COMPMID-6194
Signed-off-by: SiCong Li <sicong.li@arm.com>
Change-Id: Ie45e2aa9533948b2e5235563cef1d3834494eccf
Signed-off-by: SiCong Li <sicong.li@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9739
Reviewed-by: Gunes Bayir <gunes.bayir@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>
diff --git a/Android.bp b/Android.bp
index b634a06..cfddf6e 100644
--- a/Android.bp
+++ b/Android.bp
@@ -51,6 +51,7 @@
"src/core/CL/cl_kernels/common/instance_normalization.cl",
"src/core/CL/cl_kernels/common/l2_normalize.cl",
"src/core/CL/cl_kernels/common/mat_mul.cl",
+ "src/core/CL/cl_kernels/common/mat_mul_mmul.cl",
"src/core/CL/cl_kernels/common/mat_mul_quantized.cl",
"src/core/CL/cl_kernels/common/mean_stddev_normalization.cl",
"src/core/CL/cl_kernels/common/memset.cl",
@@ -698,6 +699,7 @@
"src/gpu/cl/kernels/ClIndirectConv2dKernel.cpp",
"src/gpu/cl/kernels/ClMatMulLowpNativeKernel.cpp",
"src/gpu/cl/kernels/ClMatMulNativeKernel.cpp",
+ "src/gpu/cl/kernels/ClMatMulNativeMMULKernel.cpp",
"src/gpu/cl/kernels/ClMulKernel.cpp",
"src/gpu/cl/kernels/ClPermuteKernel.cpp",
"src/gpu/cl/kernels/ClPool2dKernel.cpp",
diff --git a/SConscript b/SConscript
index 904d5ba..320cb2d 100644
--- a/SConscript
+++ b/SConscript
@@ -395,6 +395,7 @@
'src/core/CL/cl_kernels/common/instance_normalization.cl',
'src/core/CL/cl_kernels/common/l2_normalize.cl',
'src/core/CL/cl_kernels/common/mat_mul.cl',
+ 'src/core/CL/cl_kernels/common/mat_mul_mmul.cl',
'src/core/CL/cl_kernels/common/mat_mul_quantized.cl',
'src/core/CL/cl_kernels/common/mean_stddev_normalization.cl',
'src/core/CL/cl_kernels/common/memset.cl',
diff --git a/filelist.json b/filelist.json
index 6c5b78f..f354e69 100644
--- a/filelist.json
+++ b/filelist.json
@@ -515,6 +515,7 @@
"common": [
"src/gpu/cl/kernels/ClMatMulLowpNativeKernel.cpp",
"src/gpu/cl/kernels/ClMatMulNativeKernel.cpp",
+ "src/gpu/cl/kernels/ClMatMulNativeMMULKernel.cpp",
"src/gpu/cl/operators/ClMatMul.cpp",
"src/runtime/CL/functions/CLMatMul.cpp",
"src/runtime/heuristics/matmul_native/ClMatMulNativeDefaultConfigValhall.cpp",
diff --git a/src/core/CL/cl_kernels/common/mat_mul_mmul.cl b/src/core/CL/cl_kernels/common/mat_mul_mmul.cl
new file mode 100644
index 0000000..1d94767
--- /dev/null
+++ b/src/core/CL/cl_kernels/common/mat_mul_mmul.cl
@@ -0,0 +1,191 @@
+/*
+ * Copyright (c) 2023 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 "helpers.h"
+#include "tile_helpers.h"
+
+#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
+ *
+ * @note the "batch" here expresses the number of matrix multiplications to run in parallel. However, it
+ * should NOT be confused with the batch size of the model. For NHWC the "batch" is the "H" dimension
+ * @note The data type must be passed at compile time using -DDATA_TYPE (e.g. -DDATA_TYPE=float)
+ * @note The tile's dimensions used for the LHS and RHS matrices (M0, N0 and K0) must be passed at compile time using -DN0, -DM0 and -DK0 (e.g. -DN0=8, -DM0=4, -DK0=1).
+ * @note The number of leftover outputs rows/columns must be passed using -DN0_LEFTOVER and -DM0_LEFTOVER (e.g. -DN0_LEFTOVER=2, -DM0_LEFTOVER=3)
+ * @note The MMUL block dimension (MMUL_M0, MMUL_N0, MMUL_K0) must be passed at compile time using -DMMUL_M0, -DMMUL_N0 and -DMMUL_K0 (e.g. -DMMUL_M0=4, -DMMUL_N0=4, -DMMUL_K0=4).
+ * @note The number of leftover outputs rows/columns must be passed using -DN0_LEFTOVER and -DM0_LEFTOVER (e.g. -DN0_LEFTOVER=2, -DM0_LEFTOVER=3)
+ * @note The dimension K must be passed at compile time using -DK (e.g. -DK=4). K must be a multiple of MMUL_K0
+ * @note The kernel name in uppercase must be passed at compile time (e.g. -DMAT_MUL_NATIVE_MMUL_NT_NT)
+ * @note Only the following configurations of M0, N0 and K0 are currently supported:
+ * - M0 > 0
+ * - N0 = 1, 2, 3, 4, 8, 16
+ * - 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
+ */
+__kernel void mat_mul_native_mmul_nt_nt(
+ TENSOR3D_T(lhs, BUFFER),
+ TENSOR3D_T(rhs, BUFFER),
+ TENSOR3D_T(dst, BUFFER),
+ const int M,
+ const int N)
+{
+#define MMUL_BLOCK_SIZE (MMUL_M0 * MMUL_N0)
+
+ const uint x0 = get_global_id(0); // (N / N0) * MMUL_M0
+ const uint y0 = get_global_id(1); // (M / M0) / MMUL_M0
+ const uint z = get_global_id(2); // Batch
+
+ // Get block coordinates
+ const uint block_x = (x0 / MMUL_BLOCK_SIZE);
+ const uint block_y = y0;
+
+ // Get thread coordinates within a block
+ const uint thread_id = (x0 % MMUL_BLOCK_SIZE);
+ const uint thread_x = thread_id % MMUL_N0;
+ const uint thread_y = (thread_id / MMUL_N0);
+
+ // Starting destination coordinates
+ // Note: We need to clamp dst_x and dst_y because we always need to execute a complete MMUL block! Only after the matrix multiplication
+ // part can we exit the kernel if it is out-of-bound. Remember, we have a cooperative matrix multiplication. Therefore, we need a full block to get the correct results
+ // Although we will never write out-of-bound, we still need this clamp to ensure that we do not read out-of-bound either.
+ const uint dst_x_unclamped = thread_x * N0 + block_x * N0 * MMUL_N0;
+ const uint dst_y_unclamped = thread_y * M0 + block_y * M0 * MMUL_M0;
+ const uint dst_x = min(dst_x_unclamped, (uint)(N - N0));
+ const uint dst_y = min(dst_y_unclamped, (uint)(M - M0));
+
+ // Starting LHS coordinates
+ const uint lhs_x = thread_x;
+ const uint lhs_y = dst_y;
+
+ // Starting RHS coordinates
+ const uint rhs_x = dst_x;
+ const uint rhs_y = thread_y;
+
+ // Compute LHS/RHS/DST matrix address
+ lhs_offset_first_element_in_bytes += lhs_x * sizeof(DATA_TYPE) + lhs_y * lhs_stride_y + z * lhs_stride_z;
+ rhs_offset_first_element_in_bytes += rhs_x * sizeof(DATA_TYPE) + rhs_y * rhs_stride_y + z * rhs_stride_z;
+ dst_offset_first_element_in_bytes += dst_x * sizeof(DATA_TYPE) + dst_y * dst_stride_y + z * dst_stride_z;
+
+ // Initialize the accumulators
+ // MMUL extension accumulate the result in F32 for both F32 and F16
+ TILE(float, M0, N0, c_f32);
+
+ LOOP_UNROLLING(int, i, 0, 1, M0,
+ {
+ c_f32[i].v = 0;
+ })
+
+ for(int k = 0; k < K; k += MMUL_K0)
+ {
+ // A tile of M0xK0 but K0 must be set to 1
+ TILE(DATA_TYPE, M0, 1, a);
+ // A tile of K0xN0 but K0 must be set to 1
+ TILE(DATA_TYPE, 1, N0, b);
+
+ // Load tile from the lhs/rhs tensors
+ T_LOAD(DATA_TYPE, M0, 1, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a);
+ T_LOAD(DATA_TYPE, 1, N0, BUFFER, rhs, 0, 0, 1, rhs_stride_y, b);
+
+ LOOP_UNROLLING(int, m0, 0, 1, M0,
+ {
+ LOOP_UNROLLING(int, n0, 0, 1, N0,
+ {
+ c_f32[m0].s[n0] = arm_matrix_multiply(a[m0].s[0], b[0].s[n0], c_f32[m0].s[n0]);
+ })
+ })
+
+ lhs_offset_first_element_in_bytes += MMUL_K0 * sizeof(DATA_TYPE);
+ rhs_offset_first_element_in_bytes += MMUL_K0 * rhs_stride_y;
+ }
+
+ // For threads "outside" of the dst bound, we do not write but we have to "read" (arm_matrix_multiply). That's why this needs to happen after arm_matrix_multiply
+ if(dst_x_unclamped >= N || dst_y_unclamped >= M)
+ {
+ return;
+ }
+
+#if defined(HALF_PRECISION)
+ TILE(DATA_TYPE, M0, N0, c);
+
+ // Conversion required for the half precision
+ LOOP_UNROLLING(int, m0, 0, 1, M0,
+ {
+ LOOP_UNROLLING(int, n0, 0, 1, N0,
+ {
+ c[m0].s[n0] = c_f32[m0].s[n0];
+ })
+ })
+#else // defined(HALF_PRECISION)
+#define c c_f32
+#endif // defined(HALF_PRECISION)
+
+ if(dst_x + N0 <= N || N0_LEFTOVER == 0)
+ {
+ LOOP_UNROLLING(int, m0, 0, 1, M0,
+ {
+ if(dst_y + m0 < M || M0_LEFTOVER == 0)
+ {
+ VSTORE(N0)
+ (c[m0].v, 0, (__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + m0 * dst_stride_y));
+ }
+ })
+ }
+ else
+ {
+ LOOP_UNROLLING(int, m0, 0, 1, M0,
+ {
+ if(dst_y + m0 < M || M0_LEFTOVER == 0)
+ {
+ VSTORE_PARTIAL(N0, N0_LEFTOVER)
+ (c[m0].v, 0, (__global DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + m0 * dst_stride_y));
+ }
+ })
+ }
+
+#undef MMUL_BLOCK_SIZE
+}
+#endif // defined(MAT_MUL_NATIVE_MMUL_NT_NT)
diff --git a/src/gpu/cl/ClKernelLibrary.cpp b/src/gpu/cl/ClKernelLibrary.cpp
index a908004..408f1f7 100644
--- a/src/gpu/cl/ClKernelLibrary.cpp
+++ b/src/gpu/cl/ClKernelLibrary.cpp
@@ -319,6 +319,7 @@
{ "l2_normalize_x", "common/l2_normalize.cl" },
{ "l2_normalize_y", "common/l2_normalize.cl" },
{ "l2_normalize_z", "common/l2_normalize.cl" },
+ { "mat_mul_native_mmul_nt_nt", "common/mat_mul_mmul.cl" },
{ "mat_mul_native_nt_nt", "common/mat_mul.cl" },
{ "mat_mul_native_nt_t", "common/mat_mul.cl" },
{ "mat_mul_native_t_nt", "common/mat_mul.cl" },
@@ -799,6 +800,10 @@
#include "./cl_kernels/common/mat_mul.clembed"
},
{
+ "common/mat_mul_mmul.cl",
+#include "./cl_kernels/common/mat_mul_mmul.clembed"
+ },
+ {
"common/mat_mul_quantized.cl",
#include "./cl_kernels/common/mat_mul_quantized.clembed"
},
diff --git a/src/gpu/cl/kernels/ClMatMulNativeMMULKernel.cpp b/src/gpu/cl/kernels/ClMatMulNativeMMULKernel.cpp
new file mode 100644
index 0000000..32e69ca
--- /dev/null
+++ b/src/gpu/cl/kernels/ClMatMulNativeMMULKernel.cpp
@@ -0,0 +1,261 @@
+/*
+ * Copyright (c) 2023 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 "src/gpu/cl/kernels/ClMatMulNativeMMULKernel.h"
+
+#include "arm_compute/core/CL/CLHelpers.h"
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/ITensorPack.h"
+#include "arm_compute/core/KernelDescriptors.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+
+#include "src/common/utils/Log.h"
+#include "src/core/helpers/AutoConfiguration.h"
+#include "src/core/helpers/WindowHelpers.h"
+
+#include "support/Cast.h"
+#include "support/StringSupport.h"
+
+namespace arm_compute
+{
+namespace opencl
+{
+namespace kernels
+{
+namespace
+{
+// Block size dimensions for the MMUL extension
+constexpr int mmul_m0 = 4;
+constexpr int mmul_n0 = 4;
+constexpr int mmul_k0 = 4;
+
+inline std::pair<int, int> adjust_m0_n0(int m0, int n0, int m, int n)
+{
+ m0 = std::min(m0, m);
+ n0 = adjust_vec_size(n0, n);
+ return { m0, n0 };
+}
+
+Status validate_matmul_kernel_info(const MatMulKernelInfo &matmul_kernel_info)
+{
+ const bool adj_lhs = matmul_kernel_info.adj_lhs;
+ const bool adj_rhs = matmul_kernel_info.adj_rhs;
+ const int m0 = matmul_kernel_info.m0;
+ const int n0 = matmul_kernel_info.n0;
+ const int k0 = matmul_kernel_info.k0;
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((adj_lhs || adj_rhs), "adj_lhs and adj_rhs are not supported yet");
+
+ // Validate M0
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(m0 < 1, "Only positive integers are supported for M0");
+
+ // Validate N0
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(n0 < 1, "Only positive integers are supported for N0");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(((n0 & (n0 - 1)) && (n0 != 3)) || (n0 > 16), "Only 1,2,3,4,8,16 are supported for N0");
+
+ // Validate K0
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((k0 != 1), "Only 1 is supported for k0");
+
+ return Status{};
+}
+
+Status validate_input_shapes(const TensorShape &lhs_shape, const TensorShape &rhs_shape, const MatMulKernelInfo &matmul_kernel_info)
+{
+ ARM_COMPUTE_UNUSED(matmul_kernel_info);
+ const size_t lhs_k = lhs_shape.x();
+ const size_t rhs_k = rhs_shape.y();
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(lhs_k != rhs_k, "K dimension in Lhs and Rhs matrices must match.");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG_VAR((lhs_k % mmul_k0) != 0, "K dimension must be a multiple of %d", mmul_k0);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(lhs_shape.total_size() == 0, "Lhs tensor can't be empty");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(rhs_shape.total_size() == 0, "Rhs tensor can't be empty");
+
+ constexpr size_t batch_dim_start = 2;
+ for(size_t i = batch_dim_start; i < Coordinates::num_max_dimensions; ++i)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(lhs_shape[i] != rhs_shape[i], "Batch dimension broadcasting is not supported");
+ }
+
+ return Status{};
+}
+
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *dst, const MatMulKernelInfo &matmul_kernel_info)
+{
+ ARM_COMPUTE_UNUSED(lhs, rhs);
+
+ const Window win = calculate_max_window(*dst, Steps(1, 1));
+
+ // Collapse along the Z direction
+ // This collapse needs to be here in order to tune the Z dimension of LWS
+ Window collapsed = win.collapse(win, Window::DimZ);
+
+ // Reconfigure window size, one arm_matrix_multiply call needs 16 threads to finish.
+ Window::Dimension x_dimension = collapsed.x();
+ Window::Dimension y_dimension = collapsed.y();
+
+ const int m = dst->dimension(1);
+ const int n = dst->dimension(0);
+
+ int m0{};
+ int n0{};
+ std::tie(m0, n0) = adjust_m0_n0(matmul_kernel_info.m0, matmul_kernel_info.n0, m, n);
+
+ // Make M and N multiple of M0 and N0 respectively
+ const unsigned int ceil_to_multiple_n_n0 = ceil_to_multiple(n, n0);
+ const unsigned int ceil_to_multiple_m_m0 = ceil_to_multiple(m, m0);
+
+ // Divide M and N by M0 and N0 respectively
+ const unsigned int n_div_n0 = ceil_to_multiple_n_n0 / n0;
+ const unsigned int m_div_m0 = ceil_to_multiple_m_m0 / m0;
+
+ // Make n_div_n0 and m_div_m0 multiple of mmul_n0 and mmul_m0 respectively
+ const unsigned int ceil_to_multiple_n_div_n0_mmul_n0 = ceil_to_multiple(n_div_n0, mmul_n0);
+ const unsigned int ceil_to_multiple_m_div_m0_mmul_m0 = ceil_to_multiple(m_div_m0, mmul_m0);
+
+ // Ensure x_dimension is multiple of MMUL block size (mmul_m0 * mmul_n0)
+ x_dimension.set_end(ceil_to_multiple_n_div_n0_mmul_n0 * mmul_m0);
+ y_dimension.set_end(ceil_to_multiple_m_div_m0_mmul_m0 / mmul_m0);
+
+ collapsed.set(Window::DimX, x_dimension);
+ collapsed.set(Window::DimY, y_dimension);
+
+ return std::make_pair(Status{}, collapsed);
+}
+}
+ClMatMulNativeMMULKernel::ClMatMulNativeMMULKernel()
+{
+ _type = CLKernelType::GEMM;
+}
+
+Status ClMatMulNativeMMULKernel::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, 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);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(!arm_matrix_multiply_supported(CLKernelLibrary::get().get_device()), "The extension cl_arm_matrix_multiply is not supported on the target platform");
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, rhs);
+ 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));
+
+ 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));
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(dst, &tensor_info_dst);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, dst);
+ }
+
+ return Status{};
+}
+void ClMatMulNativeMMULKernel::configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, 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));
+
+ // 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);
+ const int n = dst->dimension(0);
+ const int k = lhs->tensor_shape().x();
+ _m = m;
+ _n = n;
+
+ int m0{};
+ int n0{};
+ std::tie(m0, n0) = adjust_m0_n0(matmul_kernel_info.m0, matmul_kernel_info.n0, m, n);
+
+ // Configure kernel window
+ const auto win_config = validate_and_configure_window(lhs, rhs, dst, matmul_kernel_info);
+ ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+ IClKernel::configure_internal(win_config.second);
+
+ // Calculate partial (store instead of load) M0 and partial N0 for the partial blocks at the end of a row/column if any. This is to avoid padding.
+ const unsigned int m0_leftover = m % m0;
+ const unsigned int n0_leftover = n % n0;
+
+ CLBuildOptions build_opts;
+ build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(lhs->data_type()));
+ build_opts.add_option_if(lhs->data_type() == DataType::F16, "-DHALF_PRECISION");
+ build_opts.add_option("-DM0=" + support::cpp11::to_string(m0));
+ build_opts.add_option("-DN0=" + support::cpp11::to_string(n0));
+ build_opts.add_option("-DK0=" + support::cpp11::to_string(matmul_kernel_info.k0));
+ build_opts.add_option("-DM0_LEFTOVER=" + support::cpp11::to_string(m0_leftover));
+ build_opts.add_option("-DN0_LEFTOVER=" + support::cpp11::to_string(n0_leftover));
+ 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("-DK=" + support::cpp11::to_string(k));
+
+ std::string kernel_name("mat_mul_native_mmul_nt_nt");
+
+ // A macro guard to compile ONLY the kernel of interest
+ build_opts.add_option("-D" + upper_string(kernel_name));
+
+ // Create kernel
+ _kernel = create_kernel(compile_context, kernel_name, build_opts.options());
+
+ // Set config_id for enabling LWS tuning
+ _config_id = kernel_name;
+ _config_id += "_";
+ _config_id += lower_string(string_from_data_type(lhs->data_type()));
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(k);
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(dst->dimension(2));
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(m0);
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(n0);
+ _config_id += "_";
+ _config_id += support::cpp11::to_string(matmul_kernel_info.k0);
+}
+
+void ClMatMulNativeMMULKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
+{
+ 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));
+ ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, dst);
+ ARM_COMPUTE_LOG_PARAMS(lhs, rhs, dst);
+ unsigned int idx = 0;
+
+ add_3d_tensor_nhw_argument(idx, lhs);
+ add_3d_tensor_nhw_argument(idx, rhs);
+ 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.
+ _kernel.setArg<cl_int>(idx++, _m);
+ _kernel.setArg<cl_int>(idx++, _n);
+
+ // LWS_x should be multiple of 16 at least. (32, 2) has been chosen to have more work-items on a single core
+ // LWS also enforces the order of execution of the work items which improves cache utilization
+ enqueue(queue, *this, window, cl::NDRange(32, 2), false);
+}
+
+} // namespace kernels
+} // namespace opencl
+} // namespace arm_compute
diff --git a/src/gpu/cl/kernels/ClMatMulNativeMMULKernel.h b/src/gpu/cl/kernels/ClMatMulNativeMMULKernel.h
new file mode 100644
index 0000000..26fe08c
--- /dev/null
+++ b/src/gpu/cl/kernels/ClMatMulNativeMMULKernel.h
@@ -0,0 +1,93 @@
+/*
+ * Copyright (c) 2023 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 ACL_SRC_GPU_CL_KERNELS_CLMATMULNATIVEMMULKERNEL
+#define ACL_SRC_GPU_CL_KERNELS_CLMATMULNATIVEMMULKERNEL
+
+#include "src/core/common/Macros.h"
+#include "src/gpu/cl/ClCompileContext.h"
+#include "src/gpu/cl/IClKernel.h"
+
+namespace arm_compute
+{
+struct MatMulKernelInfo;
+namespace opencl
+{
+namespace kernels
+{
+class ClMatMulNativeMMULKernel : public IClKernel
+{
+public:
+ ClMatMulNativeMMULKernel();
+ ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(ClMatMulNativeMMULKernel);
+ /** Initialize the kernel's input and output.
+ *
+ * This kernel performs matrix multiplication of lhs and rhs:
+ *
+ * dst = matmul(lhs, rhs)
+ *
+ * Valid data layouts:
+ * - All
+ *
+ * Valid data type configurations:
+ * |lhs |rhs |dst |
+ * |:--------------|:--------------|:--------------|
+ * |F32 |F32 |F32 |
+ * |F16 |F16 |F16 |
+ *
+ * Shape definitions:
+ * Dim0, Dim1, Dim2...
+ * lhs: [ K, M, Batch dims...]
+ * rhs: [ N, K, Batch dims...]
+ * dst: [ N, M, Batch dims...]
+ *
+ * Valid shape configurations:
+ * - K must be a multiple of 4 (MMUL_K0).
+ * - 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[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);
+ /** 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);
+
+ // Inherited methods overridden:
+ void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override;
+
+private:
+ int _m{ 1 };
+ int _n{ 1 };
+};
+} // namespace kernels
+} // namespace opencl
+} // namespace arm_compute
+#endif /* ACL_SRC_GPU_CL_KERNELS_CLMATMULNATIVEMMULKERNEL */
diff --git a/tests/datasets/LargeMatMulMMULDataset.h b/tests/datasets/LargeMatMulMMULDataset.h
new file mode 100644
index 0000000..23e0b3e
--- /dev/null
+++ b/tests/datasets/LargeMatMulMMULDataset.h
@@ -0,0 +1,64 @@
+/*
+ * Copyright (c) 2023 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 ACL_TESTS_DATASETS_LARGEMATMULMMULDATASET
+#define ACL_TESTS_DATASETS_LARGEMATMULMMULDATASET
+
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/Types.h"
+#include "tests/datasets/MatMulDataset.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace datasets
+{
+/** MatMul MMUL shapes are similar to MatMul shapes except that K has to be a multiple of MMUL_K0 which is 4 (e.g. see src/gpu/cl/kernels/ClMatMulNativeMMULKernel.cpp for the definition)
+ */
+class LargeMatMulMMULDataset final : public MatMulDataset
+{
+public:
+ LargeMatMulMMULDataset()
+ {
+ add_config(TensorShape(24U, 13U, 3U, 2U), TensorShape(33U, 24U, 3U, 2U), TensorShape(33U, 13U, 3U, 2U));
+ add_config(TensorShape(36U, 12U, 1U, 5U), TensorShape(21U, 36U, 1U, 5U), TensorShape(21U, 12U, 1U, 5U));
+ add_config(TensorShape(44U, 38U, 3U, 2U), TensorShape(21U, 44U, 3U, 2U), TensorShape(21U, 38U, 3U, 2U));
+ }
+};
+
+class HighDimensionalMatMulMMULDataset final : public MatMulDataset
+{
+public:
+ HighDimensionalMatMulMMULDataset()
+ {
+ add_config(TensorShape(4U, 5U, 2U, 2U, 2U, 2U), TensorShape(5U, 4U, 2U, 2U, 2U, 2U), TensorShape(5U, 5U, 2U, 2U, 2U, 2U)); // 6D tensor
+ }
+};
+
+} // namespace datasets
+} // namespace test
+} // namespace arm_compute
+
+#endif /* ACL_TESTS_DATASETS_LARGEMATMULMMULDATASET */
diff --git a/tests/datasets/SmallMatMulMMULDataset.h b/tests/datasets/SmallMatMulMMULDataset.h
new file mode 100644
index 0000000..9e51748
--- /dev/null
+++ b/tests/datasets/SmallMatMulMMULDataset.h
@@ -0,0 +1,66 @@
+/*
+ * Copyright (c) 2023 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 ACL_TESTS_DATASETS_SMALLMATMULMMULDATASET
+#define ACL_TESTS_DATASETS_SMALLMATMULMMULDATASET
+
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/Types.h"
+#include "tests/datasets/MatMulDataset.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace datasets
+{
+/** MatMul MMUL shapes are similar to MatMul shapes except that K has to be a multiple of MMUL_K0 which is 4 (e.g. see src/gpu/cl/kernels/ClMatMulNativeMMULKernel.cpp for the definition)
+ */
+class SmallMatMulMMULDataset final : public MatMulDataset
+{
+public:
+ SmallMatMulMMULDataset()
+ {
+ add_config(TensorShape(8U, 4U, 2U, 2U), TensorShape(2U, 8U, 2U, 2U), TensorShape(2U, 4U, 2U, 2U));
+ add_config(TensorShape(28U, 1U), TensorShape(23U, 28U), TensorShape(23U, 1U));
+ add_config(TensorShape(8U, 4U, 2U), TensorShape(16U, 8U, 2U), TensorShape(16U, 4U, 2U));
+ add_config(TensorShape(32U, 2U), TensorShape(17U, 32U), TensorShape(17U, 2U));
+ add_config(TensorShape(8U, 6U), TensorShape(7U, 8U), TensorShape(7U, 6U));
+ }
+};
+
+class TinyMatMulMMULDataset final : public MatMulDataset
+{
+public:
+ TinyMatMulMMULDataset()
+ {
+ add_config(TensorShape(4U, 4U), TensorShape(4U, 4U), TensorShape(4U, 4U));
+ }
+};
+
+} // namespace datasets
+} // namespace test
+} // namespace arm_compute
+
+#endif /* ACL_TESTS_DATASETS_SMALLMATMULMMULDATASET */
diff --git a/tests/validation/CL/MatMulNativeMMULKernel.cpp b/tests/validation/CL/MatMulNativeMMULKernel.cpp
new file mode 100644
index 0000000..b33a4fa
--- /dev/null
+++ b/tests/validation/CL/MatMulNativeMMULKernel.cpp
@@ -0,0 +1,348 @@
+/*
+ * Copyright (c) 2023 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/runtime/CL/CLTensor.h"
+#include "src/gpu/cl/kernels/ClMatMulNativeMMULKernel.h"
+#include "tests/datasets/LargeMatMulMMULDataset.h"
+#include "tests/datasets/SmallMatMulMMULDataset.h"
+#include "tests/framework/Macros.h"
+#include "tests/framework/datasets/Datasets.h"
+#include "tests/validation/Validation.h"
+#include "tests/validation/fixtures/MatMulKernelFixture.h"
+#include "tests/validation/reference/Permute.h"
+
+#include <tuple>
+
+namespace arm_compute
+{
+namespace test
+{
+namespace validation
+{
+namespace
+{
+RelativeTolerance<float> tolerance_f32(0.001f); /**< Tolerance value for comparing reference's output against implementation's output for floating point data types */
+constexpr float abs_tolerance_f32(
+ 0.0001f); /**< Absolute tolerance value for comparing reference's output against implementation's output for floating point data types in case using relative tolerance fails because of small values */
+constexpr float abs_tolerance_f16(
+ 0.001f); /**< Absolute tolerance value for comparing reference's output against implementation's output for fp16 data types in case using relative tolerance fails because of small values */
+RelativeTolerance<half_float::half> tolerance_f16(half(0.01)); /**< Tolerance value for comparing reference's output against implementation's output for floating point data types */
+} // namespace
+
+/** M0 values to test --precommit*/
+const auto m0_values_precommit = framework::dataset::make("M0", { 1, 3 });
+
+/** N0 values to test --precommit*/
+const auto n0_values_precommit = framework::dataset::make("N0", { 2, 4 });
+
+/** M0 values to test --nightly*/
+const auto m0_values_nightly_lhs_nt = framework::dataset::make("M0", { 1, 2, 3, 4, 5, 6, 7, 8 });
+
+/** N0 values to test --nightly*/
+const auto n0_values_nightly_rhs_nt = framework::dataset::make("N0", { 1, 2, 3, 4, 8, 16 });
+
+/** K0 value -- Fixed to 1 */
+const auto k0_value = framework::dataset::make("K0", { 1 });
+
+template <typename T>
+using CLMatMulNativeMMULKernelFixture = MatMulKernelValidationFixture<T, ClMatMulNativeMMULKernel, true /*use_mmul*/>;
+
+TEST_SUITE(CL)
+TEST_SUITE(MatMulNativeMMULKernel)
+TEST_SUITE(Validate)
+
+TEST_CASE(SupportedBlockSizes, framework::DatasetMode::ALL)
+{
+ if(arm_matrix_multiply_supported(CLKernelLibrary::get().get_device()))
+ {
+ using MatMulConfigurationPair = std::pair<MatMulKernelInfo, bool>;
+
+ const std::vector<MatMulConfigurationPair> supported_block_sizes =
+ {
+ // MatMulKernelInfo(adj_lhs, adj_rhs, M0, N0, K0, export_rhs_to_cl_image = false)
+ // Lhs not-transposed, Rhs-not-transposed
+ { MatMulKernelInfo(false, false, 0, 1, 1), false }, // M0 should be > 0
+ { MatMulKernelInfo(false, false, 3, 5, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16}
+ { MatMulKernelInfo(false, false, 3, 6, 1), false }, // N0 not in {1, 2, 3, 4, 8, 16}
+ { MatMulKernelInfo(false, false, 3, 3, 4), false }, // K0 not 1
+ { MatMulKernelInfo(false, false, 9, 1, 2), true },
+ { MatMulKernelInfo(false, false, 3, 16, 3), true },
+ { MatMulKernelInfo(false, false, 7, 3, 4), true },
+
+ // Lhs not-transposed, Rhs transposed
+ // TODO: COMPMID-6195
+
+ // Lhs transposed, Rhs-not-transposed
+ // TODO: COMPMID-6196
+
+ // Lhs transposed, Rhs-transposed
+ // TODO: COMPMID-6197
+ };
+
+ // Set big enough shapes so that block sizes are not truncated. Also, set all dimensions equal
+ // so that it doesn't fail for different NT/T configurations. We aim to test the block sizes here,
+ // not the shapes themselves.
+ const TensorInfo lhs_info = TensorInfo(TensorShape(100U, 100U), 1, DataType::F32);
+ const TensorInfo rhs_info = TensorInfo(TensorShape(100U, 100U), 1, DataType::F32);
+
+ for(auto &pair : supported_block_sizes)
+ {
+ TensorInfo output_info;
+ Status status = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, &output_info, pair.first);
+ }
+ }
+ else
+ {
+ ARM_COMPUTE_TEST_INFO("cl_arm_matrix_multiply not supported. TEST skipped");
+ framework::ARM_COMPUTE_PRINT_INFO();
+ }
+}
+
+TEST_CASE(ValidateInputShapes, framework::DatasetMode::ALL)
+{
+ 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>;
+ 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
+ };
+
+ for(auto &tuple : shape_configurations)
+ {
+ const bool expected = std::get<2>(tuple);
+
+ for(bool adj_lhs :
+ {
+ false // TODO: COMPMID-6195, COMPMID-6196, COMPMID-6197
+ })
+ {
+ for(bool adj_rhs :
+ {
+ false // TODO: COMPMID-6195, COMPMID-6196, COMPMID-6197
+ })
+ {
+ TensorShape lhs_shape = std::get<0>(tuple);
+ TensorShape rhs_shape = std::get<1>(tuple);
+
+ if(adj_lhs)
+ {
+ permute(lhs_shape, PermutationVector(1U, 0U));
+ }
+
+ if(adj_rhs)
+ {
+ permute(rhs_shape, PermutationVector(1U, 0U));
+ }
+
+ const TensorInfo lhs_info = TensorInfo(lhs_shape, 1, DataType::F32);
+ const TensorInfo rhs_info = TensorInfo(rhs_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);
+ ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
+ }
+ }
+ }
+ }
+ else
+ {
+ ARM_COMPUTE_TEST_INFO("cl_arm_matrix_multiply not supported. TEST skipped");
+ framework::ARM_COMPUTE_PRINT_INFO();
+ }
+}
+
+TEST_CASE(ValidateDataTypes, framework::DatasetMode::ALL)
+{
+ 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>;
+ 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
+ };
+
+ const TensorShape shape = TensorShape(8U, 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 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));
+
+ Status status = ClMatMulNativeMMULKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_kernel_info);
+ ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
+ }
+ }
+ else
+ {
+ ARM_COMPUTE_TEST_INFO("cl_arm_matrix_multiply not supported. TEST skipped");
+ framework::ARM_COMPUTE_PRINT_INFO();
+ }
+}
+
+TEST_SUITE_END() // Validate
+
+TEST_SUITE(Float)
+TEST_SUITE(FP32)
+TEST_SUITE(Buffer)
+FIXTURE_DATA_TEST_CASE(RunTiny, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::TinyMatMulMMULDataset(),
+ framework::dataset::make("TransposeA", { false })),
+ framework::dataset::make("TransposeB", { false })),
+ 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(RunSmall, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulMMULDataset(),
+ framework::dataset::make("TransposeA", { false })),
+ framework::dataset::make("TransposeB", { false })),
+ 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(RunLarge, 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),
+ n0_values_nightly_rhs_nt),
+ 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);
+ }
+}
+// Running High Dimensional test is enough for FP32, because we're stressing the number of dimensions, not data type or M0/N0/K0
+// It's a good idea to test for each Lhs/Rhs T/NT combinations because they're different CL kernels
+FIXTURE_DATA_TEST_CASE(RunHighDimensional, CLMatMulNativeMMULKernelFixture<float>, framework::DatasetMode::ALL,
+ combine(combine(combine(combine(combine(combine(combine(datasets::HighDimensionalMatMulMMULDataset(),
+ framework::dataset::make("TransposeA", { false })),
+ framework::dataset::make("TransposeB", { false })),
+ framework::dataset::make("M0", { 2 })),
+ framework::dataset::make("N0", { 2 })),
+ framework::dataset::make("K0", { 1 })),
+ 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);
+ }
+}
+TEST_SUITE_END() // Buffer
+
+TEST_SUITE_END() // FP32
+
+TEST_SUITE(FP16)
+TEST_SUITE(Buffer)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulNativeMMULKernelFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulMMULDataset(),
+ framework::dataset::make("TransposeA", { false })),
+ framework::dataset::make("TransposeB", { false })),
+ m0_values_precommit),
+ n0_values_precommit),
+ k0_value),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
+ framework::dataset::make("DataType", DataType::F16)))
+{
+ // Validate output
+ if(_device_supports_mmul)
+ {
+ validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
+ }
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, 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),
+ n0_values_nightly_rhs_nt),
+ k0_value),
+ framework::dataset::make("ExportRhsToCLImage", { false })),
+ framework::dataset::make("DataType", DataType::F16)))
+{
+ // Validate output
+ if(_device_supports_mmul)
+ {
+ validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
+ }
+}
+TEST_SUITE_END() // Buffer
+
+TEST_SUITE_END() // FP16
+TEST_SUITE_END() // Float
+TEST_SUITE_END() // MatMulNativeMMULKernel
+TEST_SUITE_END() // CL
+} // namespace validation
+} // namespace test
+} // namespace arm_compute
diff --git a/tests/validation/fixtures/MatMulKernelFixture.h b/tests/validation/fixtures/MatMulKernelFixture.h
index 7d0b1a4..59bcfe5 100644
--- a/tests/validation/fixtures/MatMulKernelFixture.h
+++ b/tests/validation/fixtures/MatMulKernelFixture.h
@@ -47,7 +47,7 @@
{
using namespace arm_compute::opencl::kernels;
-template <typename T, typename KernelType>
+template <typename T, typename KernelType, bool use_mmul = false>
class MatMulKernelValidationFixture : public framework::Fixture
{
public:
@@ -94,13 +94,25 @@
permute(shape_b, PermutationVector(1U, 0U));
}
+ // Skip configurations unsupported by the device.
_device_supports_export_to_cl_image = image2d_from_buffer_supported(CLKernelLibrary::get().get_device());
-
- if(!export_rhs_to_cl_image || _device_supports_export_to_cl_image)
+ if(!_device_supports_export_to_cl_image && export_rhs_to_cl_image)
{
- _target = compute_target(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, M0, N0, K0, export_rhs_to_cl_image, data_type, lhs_q_info, rhs_q_info, dst_q_info);
- _reference = compute_reference(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, data_type, lhs_q_info, rhs_q_info, dst_q_info);
+ ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped");
+ framework::ARM_COMPUTE_PRINT_INFO();
+ return; // Note: Also need to skip the validate in corresponding FIXTURE_DATA_TEST_CASEs.
}
+
+ _device_supports_mmul = arm_matrix_multiply_supported(CLKernelLibrary::get().get_device());
+ if(!_device_supports_mmul && use_mmul)
+ {
+ ARM_COMPUTE_TEST_INFO("cl_arm_matrix_multiply not supported. TEST skipped");
+ framework::ARM_COMPUTE_PRINT_INFO();
+ return; // Note: Also need to skip the validate in corresponding FIXTURE_DATA_TEST_CASEs.
+ }
+
+ _target = compute_target(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, M0, N0, K0, export_rhs_to_cl_image, data_type, lhs_q_info, rhs_q_info, dst_q_info);
+ _reference = compute_reference(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, data_type, lhs_q_info, rhs_q_info, dst_q_info);
}
protected:
@@ -274,6 +286,7 @@
CLTensor _target{};
SimpleTensor<T> _reference{};
bool _device_supports_export_to_cl_image{ true };
+ bool _device_supports_mmul{ true };
};
} // namespace validation