From 2b6ebfe4270b06b09e45f306e8384950aeca7e4e Mon Sep 17 00:00:00 2001 From: Ramy Elgammal Date: Thu, 9 Mar 2023 21:15:37 +0000 Subject: Implement OpenCL MatMul for Lhs NT Rhs T/NT FP32/16 - Implement ClNativeMatMulKernel class - Implement opencl kernel for LHS non-transposed and RHS non-transposed - Implement opencl kernel for LHS non-transposed and RHS transposed - Add test fixture and dataset for matmul - Implement transpose_tensor() for reference implementation to transpose high dimensional tensors Resolves: COMPMID-5944, COMPMID-5951 Co-authored-by: Gunes Bayir Co-authored-by: Ramy Elgammal Change-Id: I1d5b8978f41be27baddb3153ade880472141573f Signed-off-by: Gunes Bayir Signed-off-by: Ramy Elgammal Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9333 Tested-by: Arm Jenkins Reviewed-by: Gian Marco Iodice Benchmark: Arm Jenkins --- Android.bp | 2 + SConscript | 19 +- arm_compute/core/KernelDescriptors.h | 20 +- arm_compute/core/utils/misc/ShapeCalculator.h | 34 ++- filelist.json | 7 + src/core/CL/cl_kernels/common/mat_mul.cl | 299 +++++++++++++++++++++++++ src/core/CL/cl_kernels/tile_helpers.h | 21 +- src/gpu/cl/ClKernelLibrary.cpp | 8 +- src/gpu/cl/kernels/ClNativeMatMulKernel.cpp | 192 ++++++++++++++++ src/gpu/cl/kernels/ClNativeMatMulKernel.h | 70 ++++++ tests/datasets/BatchMatMulDataset.h | 110 +++++++++ tests/datasets/LargeBatchMatMulDataset.h | 60 +++++ tests/datasets/SmallBatchMatMulDataset.h | 52 +++++ tests/validation/CL/BatchMatMul.cpp | 239 ++++++++++++++++++++ tests/validation/fixtures/BatchMatMulFixture.h | 203 +++++++++++++++++ utils/TypePrinter.h | 116 ++++++---- 16 files changed, 1393 insertions(+), 59 deletions(-) create mode 100644 src/core/CL/cl_kernels/common/mat_mul.cl create mode 100644 src/gpu/cl/kernels/ClNativeMatMulKernel.cpp create mode 100644 src/gpu/cl/kernels/ClNativeMatMulKernel.h create mode 100644 tests/datasets/BatchMatMulDataset.h create mode 100644 tests/datasets/LargeBatchMatMulDataset.h create mode 100644 tests/datasets/SmallBatchMatMulDataset.h create mode 100644 tests/validation/CL/BatchMatMul.cpp create mode 100644 tests/validation/fixtures/BatchMatMulFixture.h diff --git a/Android.bp b/Android.bp index 28bc7e20dc..42116d78ca 100644 --- a/Android.bp +++ b/Android.bp @@ -49,6 +49,7 @@ opencl_srcs = [ "src/core/CL/cl_kernels/common/generate_proposals_quantized.cl", "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/mean_stddev_normalization.cl", "src/core/CL/cl_kernels/common/memset.cl", "src/core/CL/cl_kernels/common/minmax_layer.cl", @@ -689,6 +690,7 @@ cc_library_static { "src/gpu/cl/kernels/ClIndirectConv2dAddressPrecalculationKernel.cpp", "src/gpu/cl/kernels/ClIndirectConv2dKernel.cpp", "src/gpu/cl/kernels/ClMulKernel.cpp", + "src/gpu/cl/kernels/ClNativeMatMulKernel.cpp", "src/gpu/cl/kernels/ClPermuteKernel.cpp", "src/gpu/cl/kernels/ClPool2dKernel.cpp", "src/gpu/cl/kernels/ClPool3dKernel.cpp", diff --git a/SConscript b/SConscript index a480c45d62..205764b9a7 100644 --- a/SConscript +++ b/SConscript @@ -359,6 +359,7 @@ if env['opencl'] and env['embed_kernels']: 'src/core/CL/cl_kernels/common/cast.cl', 'src/core/CL/cl_kernels/common/comparisons.cl', 'src/core/CL/cl_kernels/common/concatenate.cl', + 'src/core/CL/cl_kernels/common/convolution_layer.cl', 'src/core/CL/cl_kernels/common/col2im.cl', 'src/core/CL/cl_kernels/common/convert_fc_weights.cl', 'src/core/CL/cl_kernels/common/copy_tensor.cl', @@ -368,6 +369,9 @@ if env['opencl'] and env['embed_kernels']: 'src/core/CL/cl_kernels/common/elementwise_operation.cl', 'src/core/CL/cl_kernels/common/elementwise_operation_quantized.cl', 'src/core/CL/cl_kernels/common/elementwise_unary.cl', + 'src/core/CL/cl_kernels/common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_native.cl', + 'src/core/CL/cl_kernels/common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_reshaped.cl', + 'src/core/CL/cl_kernels/common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_reshaped_only_rhs.cl', 'src/core/CL/cl_kernels/common/fft_digit_reverse.cl', 'src/core/CL/cl_kernels/common/fft.cl', 'src/core/CL/cl_kernels/common/fft_scale.cl', @@ -377,21 +381,18 @@ if env['opencl'] and env['embed_kernels']: 'src/core/CL/cl_kernels/common/gemm.cl', 'src/core/CL/cl_kernels/common/gemm_reshaped_only_rhs_mmul.cl', 'src/core/CL/cl_kernels/common/gemm_utils.cl', - 'src/core/CL/cl_kernels/common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_native.cl', - 'src/core/CL/cl_kernels/common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_reshaped.cl', - 'src/core/CL/cl_kernels/common/experimental/gemm_fused_post_ops/act_eltwise_op_act/gemm_mm_reshaped_only_rhs.cl', - 'src/core/CL/cl_kernels/common/gemv.cl', 'src/core/CL/cl_kernels/common/gemmlowp.cl', 'src/core/CL/cl_kernels/common/gemmlowp_reshaped_only_rhs_mmul.cl', + 'src/core/CL/cl_kernels/common/gemv.cl', 'src/core/CL/cl_kernels/common/generate_proposals.cl', 'src/core/CL/cl_kernels/common/generate_proposals_quantized.cl', '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/mean_stddev_normalization.cl', - 'src/core/CL/cl_kernels/common/unpooling_layer.cl', 'src/core/CL/cl_kernels/common/memset.cl', - 'src/core/CL/cl_kernels/common/nonmax.cl', 'src/core/CL/cl_kernels/common/minmax_layer.cl', + 'src/core/CL/cl_kernels/common/nonmax.cl', 'src/core/CL/cl_kernels/common/pad_layer.cl', 'src/core/CL/cl_kernels/common/permute.cl', 'src/core/CL/cl_kernels/common/pixelwise_mul_float.cl', @@ -401,18 +402,18 @@ if env['opencl'] and env['embed_kernels']: 'src/core/CL/cl_kernels/common/range.cl', 'src/core/CL/cl_kernels/common/reduction_operation.cl', 'src/core/CL/cl_kernels/common/reshape_layer.cl', - 'src/core/CL/cl_kernels/common/convolution_layer.cl', 'src/core/CL/cl_kernels/common/reverse.cl', 'src/core/CL/cl_kernels/common/roi_align_layer.cl', 'src/core/CL/cl_kernels/common/roi_align_layer_quantized.cl', 'src/core/CL/cl_kernels/common/roi_pooling_layer.cl', 'src/core/CL/cl_kernels/common/select.cl', + 'src/core/CL/cl_kernels/common/slice_ops.cl', 'src/core/CL/cl_kernels/common/softmax_layer.cl', 'src/core/CL/cl_kernels/common/softmax_layer_quantized.cl', 'src/core/CL/cl_kernels/common/stack_layer.cl', - 'src/core/CL/cl_kernels/common/slice_ops.cl', 'src/core/CL/cl_kernels/common/tile.cl', - 'src/core/CL/cl_kernels/common/transpose.cl' + 'src/core/CL/cl_kernels/common/transpose.cl', + 'src/core/CL/cl_kernels/common/unpooling_layer.cl' ] # NCHW kernels diff --git a/arm_compute/core/KernelDescriptors.h b/arm_compute/core/KernelDescriptors.h index 19ac254c04..016e03d88e 100644 --- a/arm_compute/core/KernelDescriptors.h +++ b/arm_compute/core/KernelDescriptors.h @@ -21,8 +21,8 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef ARM_COMPUTE_CORE_KERNEL_DESCRIPTORS_H -#define ARM_COMPUTE_CORE_KERNEL_DESCRIPTORS_H +#ifndef ACL_ARM_COMPUTE_CORE_KERNELDESCRIPTORS +#define ACL_ARM_COMPUTE_CORE_KERNELDESCRIPTORS #include "arm_compute/core/PixelValue.h" #include "arm_compute/core/Types.h" @@ -223,5 +223,19 @@ struct ScaleKernelInfo bool align_corners; /**< Align corners of input and output */ DataLayout data_layout; /**< Data layout to use */ }; + +struct MatMulKernelInfo +{ + MatMulKernelInfo(bool adj_lhs = false, bool adj_rhs = false, int m0 = 1, int n0 = 1, int k0 = 1, bool export_rhs_to_cl_image = false) + : adj_lhs{ adj_lhs }, adj_rhs{ adj_rhs }, m0{ m0 }, n0{ n0 }, k0{ k0 }, export_rhs_to_cl_image{ export_rhs_to_cl_image } + { + } + bool adj_lhs{ false }; /**< Get Adjoint LHS flag value */ + bool adj_rhs{ false }; /**< Get Adjoint RHS flag value */ + int m0{ 1 }; /**< Number of output rows processed by each work-item*/ + int n0{ 1 }; /**< Number of output columns processed by each work-item*/ + int k0{ 1 }; /**< Number of inner accumulations */ + bool export_rhs_to_cl_image{ false }; /**< Flag to know whether the RHS tensor should be exported to cl_image*/ +}; } // namespace arm_compute -#endif /* ARM_COMPUTE_CORE_KERNEL_DESCRIPTORS_H */ +#endif /* ACL_ARM_COMPUTE_CORE_KERNELDESCRIPTORS */ diff --git a/arm_compute/core/utils/misc/ShapeCalculator.h b/arm_compute/core/utils/misc/ShapeCalculator.h index 6655cc1439..75a063f75c 100644 --- a/arm_compute/core/utils/misc/ShapeCalculator.h +++ b/arm_compute/core/utils/misc/ShapeCalculator.h @@ -21,8 +21,8 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef ARM_COMPUTE_MISC_SHAPE_CALCULATOR_H -#define ARM_COMPUTE_MISC_SHAPE_CALCULATOR_H +#ifndef ACL_ARM_COMPUTE_CORE_UTILS_MISC_SHAPECALCULATOR +#define ACL_ARM_COMPUTE_CORE_UTILS_MISC_SHAPECALCULATOR #include "arm_compute/core/Helpers.h" #include "arm_compute/core/ITensorInfo.h" @@ -1006,6 +1006,34 @@ inline TensorShape compute_mm_shape(const ITensorInfo &input0, const ITensorInfo return output_shape; } +/** Calculate the matrix multiplication output shape of two tensors + * + * @param[in] input0 First input tensor info + * @param[in] input1 Second input tensor info + * @param[in] matmul_info Batch MatMul Kernel info to know which matrix is transposed + * + * @return the calculated shape + */ +inline TensorShape compute_batchmatmul_shape(const TensorShape &input0, const TensorShape &input1, const MatMulKernelInfo &matmul_info) +{ + TensorShape output_shape{ input0 }; + + if(matmul_info.adj_lhs) + { + output_shape.set(1, input0[0]); // The vertical (M) dimension + } + + if(matmul_info.adj_rhs) + { + output_shape.set(0, input1[1]); // The horizontal (N) dimension + } + else + { + output_shape.set(0, input1[0]); // The horizontal (N) dimension + } + + return output_shape; +} /** Calculate the matrix multiplication output shape of two tensors * * @param[in] input Input tensor info @@ -1579,4 +1607,4 @@ inline TensorShape compute_gather_shape(const TensorShape &input_shape, const Te } // namespace shape_calculator } // namespace misc } // namespace arm_compute -#endif /* ARM_COMPUTE_MISC_SHAPE_CALCULATOR_H */ +#endif /* ACL_ARM_COMPUTE_CORE_UTILS_MISC_SHAPECALCULATOR */ diff --git a/filelist.json b/filelist.json index 1b0d07bc42..f858c6a29f 100644 --- a/filelist.json +++ b/filelist.json @@ -509,6 +509,13 @@ ] } }, + "MatMul": { + "files": { + "common": [ + "src/gpu/cl/kernels/ClNativeMatMulKernel.cpp" + ] + } + }, "GenerateProposals": { "deps": [ "BoundingBoxTransform", "Dequantize", "Pad", "Permute", "Quantize", "Reshape" ], "files": { diff --git a/src/core/CL/cl_kernels/common/mat_mul.cl b/src/core/CL/cl_kernels/common/mat_mul.cl new file mode 100644 index 0000000000..7c74e9d07b --- /dev/null +++ b/src/core/CL/cl_kernels/common/mat_mul.cl @@ -0,0 +1,299 @@ +/* + * 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_NT_NT) +/** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul): 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 block's dimensions used for the LHS and RHS matrices (M0, N0 and K0) must be passed at compile time using -DN0, -DM0 and -DK0 (e.g. -DN0=8, -DM0=4, -DK0=4). + * @note The dimension K must be passed at compile time using -DK (e.g. -DK=6) + * @note Only the following configurations of M0, N0 and K0 are currently supported: + * - M0 > 0 + * - N0 = 1, 2, 3, 4, 8, 16 + * - K0 = 1, 2, 3, 4, 8, 16 + * @note Values > 8 for M0 are not expected to be efficient + * + * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: F32/F16 + * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes) + * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes) + * @param[in] lhs_w The width of the lhs tensor + * @param[in] lhs_h The height of the lhs tensor + * @param[in] lhs_n Number of the matrices (buffers) in the batch + * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix + * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: F32/F16 + * @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: F32/F16 + * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes) + * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes) + * @param[in] dst_w The width of the dst tensor + * @param[in] dst_h The height of the dst tensor + * @param[in] dst_n Number of the matrices (buffers) in the batch + * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix + */ +__kernel void mat_mul_native_nt_nt( + TENSOR3D_T(lhs, BUFFER), + TENSOR3D_T(rhs, BUFFER), + TENSOR3D_T(dst, BUFFER)) +{ + const uint x = GET_SPATIAL_IDX(0, N0, PARTIAL_STORE_N0); + const uint y = GET_SPATIAL_IDX(1, M0, PARTIAL_STORE_M0); + const uint z = GET_SPATIAL_IDX(2, 1, 0); + + // Compute LHS/RHS/DST matrix address + lhs_offset_first_element_in_bytes += y * lhs_stride_y + z * lhs_stride_z; + rhs_offset_first_element_in_bytes += x * sizeof(DATA_TYPE) + z * rhs_stride_z; + dst_offset_first_element_in_bytes += x * sizeof(DATA_TYPE) + y * dst_stride_y + z * dst_stride_z; + + // Initialize the accumulators + TILE(DATA_TYPE, M0, N0, acc); + + LOOP_UNROLLING(int, i, 0, 1, M0, + { + acc[i].v = 0.f; + }) + + int k; + for(k = 0; k <= K - K0; k += K0) + { + TILE(DATA_TYPE, M0, K0, a); + TILE(DATA_TYPE, K0, N0, b); + + LOOP_UNROLLING(int, i, 0, 1, M0, + { + a[i].v = 0.f; + }) + + LOOP_UNROLLING(int, i, 0, 1, K0, + { + b[i].v = 0.f; + }) + + // Load tile from the lhs/rhs tensors + T_LOAD(DATA_TYPE, M0, K0, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a); + T_LOAD(DATA_TYPE, K0, N0, BUFFER, rhs, 0, 0, 1, rhs_stride_y, b); + + T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, K0, NT, NT, a, b, acc); + + lhs_offset_first_element_in_bytes += K0 * sizeof(DATA_TYPE); + rhs_offset_first_element_in_bytes += K0 * rhs_stride_y; + } + +#ifdef K % K0 != 0 + for(; k < K; ++k) + { + TILE(DATA_TYPE, M0, 1, a); + TILE(DATA_TYPE, 1, N0, b); + + LOOP_UNROLLING(int, i, 0, 1, M0, + { + a[i].v = 0.f; + }) + + LOOP_UNROLLING(int, i, 0, 1, 1, + { + b[i].v = 0.f; + }) + + // 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); + + T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, 1, NT, NT, a, b, acc); + + lhs_offset_first_element_in_bytes += 1 * sizeof(DATA_TYPE); + rhs_offset_first_element_in_bytes += 1 * rhs_stride_y; + } +#endif // K % K0 != 0 + + const bool x_cond = PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0; + const bool y_cond = PARTIAL_STORE_M0 != 0 && get_global_id(1) == 0; + + TILE(int, M0, 1, indirect_buffer); + LOOP_UNROLLING(int, _i, 0, 1, M0, + { + indirect_buffer[_i].v = min(_i, select(M0 - 1, PARTIAL_STORE_M0 - 1, y_cond)); + }); + + T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, M0, N0, PARTIAL_STORE_N0, BUFFER, dst, 0, dst_stride_y, x_cond, acc, indirect_buffer); +} +#endif // defined(MAT_MUL_NATIVE_NT_NT) + +#if defined(MAT_MUL_NATIVE_NT_T) +/** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul): LHS non-transposed, RHS 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 block's dimensions used for the LHS and RHS matrices (M0, N0 and K0) must be passed at compile time using -DN0, -DM0 and -DK0 (e.g. -DN0=8, -DM0=4, -DK0=4). + * @note The dimension K must be passed at compile time using -DK (e.g. -DK=6) + * @note Only the following configurations of M0, N0 and K0 are currently supported: + * - M0 > 0 + * - N0 = 1, 2, 3, 4, 8, 16 + * - K0 = 1, 2, 3, 4, 8, 16 + * @note Values > 8 for M0, N0 and K0 are not expected to be efficient + * + * @param[in] lhs_ptr Pointer to the lhs matrix. Supported data types: 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: F32/F16 + * @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: F32/F16 + * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes) + * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes) + * @param[in] dst_w The width of the dst tensor + * @param[in] dst_h The height of the dst tensor + * @param[in] dst_n Number of the matrices (buffers) in the batch + * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix + */ +__kernel void mat_mul_native_nt_t(TENSOR3D_T(lhs, BUFFER), + TENSOR3D_T(rhs, BUFFER), + TENSOR3D_T(dst, BUFFER)) + +{ + const uint x = GET_SPATIAL_IDX(0, N0, PARTIAL_STORE_N0); + const uint y = GET_SPATIAL_IDX(1, M0, PARTIAL_STORE_M0); + const uint z = GET_SPATIAL_IDX(2, 1, 0); + + // Compute LHS/RHS/DST matrix address + lhs_offset_first_element_in_bytes += y * lhs_stride_y + z * lhs_stride_z; + rhs_offset_first_element_in_bytes += x * rhs_stride_y + z * rhs_stride_z; + dst_offset_first_element_in_bytes += x * sizeof(DATA_TYPE) + y * dst_stride_y + z * dst_stride_z; + + // Initialize the accumulators + TILE(DATA_TYPE, M0, N0, acc); + + LOOP_UNROLLING(int, i, 0, 1, M0, + { + acc[i].v = 0.f; + }) + + int k; + for(k = 0; k <= K - K0; k += K0) + { + TILE(DATA_TYPE, M0, K0, a); + TILE(DATA_TYPE, N0, K0, b); + + LOOP_UNROLLING(int, i, 0, 1, M0, + { + a[i].v = 0.f; + }) + + LOOP_UNROLLING(int, i, 0, 1, N0, + { + b[i].v = 0.f; + }) + + // Load tile from the lhs/rhs tensors + T_LOAD(DATA_TYPE, M0, K0, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a); + T_LOAD(DATA_TYPE, N0, K0, BUFFER, rhs, 0, 0, 1, rhs_stride_y, b); + +#if GPU_ARCH == GPU_ARCH_MIDGARD + // This part is written to decrease the number of loop unrollings caused + // by T_MMUL. The NT/NT version is partly vectorized and uses less number + // of loop unrollings, and code behaves as expected. Although this is not + // a performant solution for the specified architecture, it is necessary + // to overcome some limitations. + TILE(DATA_TYPE, K0, N0, bt); + LOOP_UNROLLING(int, i, 0, 1, N0, + { + LOOP_UNROLLING(int, j, 0, 1, K0, + { + bt[j].s[i] = b[i].s[j]; + }) + }) + T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, K0, NT, NT, a, bt, acc); +#else // GPU_ARCH == GPU_ARCH_MIDGARD + T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, K0, NT, T, a, b, acc); +#endif // GPU_ARCH == GPU_ARCH_MIDGARD + + lhs_offset_first_element_in_bytes += K0 * sizeof(DATA_TYPE); + rhs_offset_first_element_in_bytes += K0 * sizeof(DATA_TYPE); + } + +#if K % K0 != 0 + /* Leftover Loop */ + for(; k < K; ++k) + { + TILE(DATA_TYPE, M0, 1, a); + TILE(DATA_TYPE, N0, 1, b); + + LOOP_UNROLLING(int, i, 0, 1, M0, + { + a[i].v = 0.f; + }) + + LOOP_UNROLLING(int, i, 0, 1, N0, + { + b[i].v = 0.f; + }) + + // 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, N0, 1, BUFFER, rhs, 0, 0, 1, rhs_stride_y, b); + +#if GPU_ARCH == GPU_ARCH_MIDGARD + // See the main loop for the explanation of this part + TILE(DATA_TYPE, 1, N0, bt); + LOOP_UNROLLING(int, i, 0, 1, N0, + { + bt[0].s[i] = b[i].s[0]; + }) + T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, 1, NT, NT, a, bt, acc); +#else // GPU_ARCH == GPU_ARCH_MIDGARD + T_MMUL(DATA_TYPE, DATA_TYPE, DATA_TYPE, M0, N0, 1, NT, T, a, b, acc); +#endif // GPU_ARCH == GPU_ARCH_MIDGARD + + lhs_offset_first_element_in_bytes += 1 * sizeof(DATA_TYPE); + rhs_offset_first_element_in_bytes += 1 * sizeof(DATA_TYPE); + } +#endif // K % K0 != 0 + + const bool x_cond = PARTIAL_STORE_N0 != 0 && get_global_id(0) == 0; + const bool y_cond = PARTIAL_STORE_M0 != 0 && get_global_id(1) == 0; + + TILE(int, M0, 1, indirect_buffer); + LOOP_UNROLLING(int, _i, 0, 1, M0, + { + indirect_buffer[_i].v = min(_i, select(M0 - 1, PARTIAL_STORE_M0 - 1, y_cond)); + }); + + T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, M0, N0, PARTIAL_STORE_N0, BUFFER, dst, 0, dst_stride_y, x_cond, acc, indirect_buffer); +} +#endif // defined(MAT_MUL_NATIVE_NT_T) \ No newline at end of file diff --git a/src/core/CL/cl_kernels/tile_helpers.h b/src/core/CL/cl_kernels/tile_helpers.h index 1e4dddd2db..5d397ad333 100644 --- a/src/core/CL/cl_kernels/tile_helpers.h +++ b/src/core/CL/cl_kernels/tile_helpers.h @@ -21,8 +21,8 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef SRC_CORE_CL_CL_KERNELS_TILE_HELPERS -#define SRC_CORE_CL_CL_KERNELS_TILE_HELPERS +#ifndef ACL_SRC_CORE_CL_CL_KERNELS_TILE_HELPERS +#define ACL_SRC_CORE_CL_CL_KERNELS_TILE_HELPERS // *INDENT-OFF* // clang-format off @@ -1282,6 +1282,21 @@ }) \ } +#define T_MMUL_NT_NT(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_NT_##LHS_DATA_TYPE##_##RHS_DATA_TYPE##_##DST_DATA_TYPE(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) +#define T_MMUL_NT_NT_float_float_float(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_NT_FLOAT(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) +#define T_MMUL_NT_NT_half_half_float(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_NT_FLOAT(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) +#define T_MMUL_NT_NT_half_half_half(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) T_MMUL_NT_NT_FLOAT(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) +#define T_MMUL_NT_NT_FLOAT(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) \ + { \ + LOOP_UNROLLING(int, _m, 0, 1, M0, \ + { \ + LOOP_UNROLLING(int, _k, 0, 1, K0, \ + { \ + dst[_m].v = fma((DST_DATA_TYPE)(lhs[_m].s[_k]), (rhs[_k].v), dst[_m].v); \ + }) \ + }) \ + } + #define T_MMUL_NT_T_INTEGER8(LHS_DATA_TYPE, RHS_DATA_TYPE, DST_DATA_TYPE, M0, N0, K0, lhs, rhs, dst) \ ({ \ LOOP_UNROLLING(int, _m, 0, 1, M0, \ @@ -1293,4 +1308,4 @@ }) \ }) -#endif /* SRC_CORE_CL_CL_KERNELS_TILE_HELPERS */ +#endif /* ACL_SRC_CORE_CL_CL_KERNELS_TILE_HELPERS */ diff --git a/src/gpu/cl/ClKernelLibrary.cpp b/src/gpu/cl/ClKernelLibrary.cpp index f788bedc34..482e8c341d 100644 --- a/src/gpu/cl/ClKernelLibrary.cpp +++ b/src/gpu/cl/ClKernelLibrary.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2016-2022 Arm Limited. + * Copyright (c) 2016-2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -358,6 +358,8 @@ const std::map ClKernelLibrary::_kernel_program_map = { "strided_slice", "common/slice_ops.cl" }, { "tile", "common/tile.cl" }, { "transpose", "common/transpose.cl" }, + { "mat_mul_native_nt_nt", "common/mat_mul.cl" }, + { "mat_mul_native_nt_t", "common/mat_mul.cl" }, #ifdef ENABLE_NCHW_KERNELS { "batch_to_space_nchw", "nchw/batch_to_space.cl" }, { "batch_to_space_static_nchw", "nchw/batch_to_space.cl" }, @@ -781,6 +783,10 @@ const std::map ClKernelLibrary::_program_source_map = "common/unpooling_layer.cl", #include "./cl_kernels/common/unpooling_layer.clembed" }, + { + "common/mat_mul.cl", +#include "./cl_kernels/common/mat_mul.clembed" + }, #ifdef ENABLE_NCHW_KERNELS { "nchw/batch_to_space.cl", diff --git a/src/gpu/cl/kernels/ClNativeMatMulKernel.cpp b/src/gpu/cl/kernels/ClNativeMatMulKernel.cpp new file mode 100644 index 0000000000..6a4db65922 --- /dev/null +++ b/src/gpu/cl/kernels/ClNativeMatMulKernel.cpp @@ -0,0 +1,192 @@ +/* + * 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/ClNativeMatMulKernel.h" +#include "arm_compute/core/CL/ICLTensor.h" +#include "arm_compute/core/TensorInfo.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "src/core/helpers/AutoConfiguration.h" + +#include "arm_compute/core/ITensorPack.h" +#include "src/common/utils/Log.h" +#include "src/core/helpers/WindowHelpers.h" +#include "support/Cast.h" +#include "utils/TypePrinter.h" + +namespace arm_compute +{ +namespace opencl +{ +namespace kernels +{ +namespace +{ +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; + + // Validate M0 + if(!adj_lhs) + { + // We support any positive integer, but will test & benchmark only 1 to 8 because > 8 will not efficient + ARM_COMPUTE_RETURN_ERROR_ON_MSG(m0 < 1, "Only positive integers are supported for M0 for Lhs non-transposed"); + } + else + { + ARM_COMPUTE_RETURN_ERROR_ON_MSG((m0 & (m0 - 1)) && (m0 != 3) && (m0 > 16), "Only 1,2,3,4,8,16 are supported for N0 for Lhs transposed"); + } + + // Validate 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 + if(adj_lhs && !adj_rhs) + { + // We support any positive integer, but will test & benchmark only 1 to 8 because > 8 will not efficient + ARM_COMPUTE_RETURN_ERROR_ON_MSG(k0 < 1, "Only positive integers are supported for K0 for Lhs transposed & Rhs non-transposed"); + } + else + { + ARM_COMPUTE_RETURN_ERROR_ON_MSG((k0 & (k0 - 1)) && (k0 != 3) && (k0 > 16), "Only 1,2,3,4,8,16 are supported for K0"); + } + + return Status{}; +} +} +ClNativeMatMulKernel::ClNativeMatMulKernel() +{ + _type = CLKernelType::GEMM; +} +Status ClNativeMatMulKernel::validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *output, const MatMulKernelInfo &matmul_kernel_info) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(lhs, rhs, output); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(lhs, 1, DataType::F32, DataType::F16); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, rhs); + ARM_COMPUTE_RETURN_ON_ERROR(validate_matmul_kernel_info(matmul_kernel_info)); + + if(output->total_size() != 0) + { + const TensorInfo tensor_info_output = output->clone()->set_tensor_shape(misc::shape_calculator::compute_batchmatmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info)); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(output, &tensor_info_output); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, output); + } + ARM_COMPUTE_RETURN_ERROR_ON_MSG(matmul_kernel_info.adj_lhs && matmul_kernel_info.adj_rhs, "LHS T and RHS T not implemented"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(matmul_kernel_info.adj_lhs && !matmul_kernel_info.adj_rhs, "LHS T and RHS NT not implemented"); + + return Status{}; +} +void ClNativeMatMulKernel::configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *output, const MatMulKernelInfo &matmul_kernel_info) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, output, &compile_context, &matmul_kernel_info); + ARM_COMPUTE_LOG_PARAMS(lhs, rhs, output, matmul_kernel_info); + + // output tensor auto initialization if not yet initialized + auto_init_if_empty(*output, lhs->clone()->set_tensor_shape(misc::shape_calculator::compute_batchmatmul_shape(lhs->tensor_shape(), rhs->tensor_shape(), matmul_kernel_info))); + ARM_COMPUTE_ERROR_THROW_ON(validate(lhs, rhs, output, matmul_kernel_info)); + + const int m = output->dimension(1); + const int n = output->dimension(0); + const int k = matmul_kernel_info.adj_lhs ? lhs->tensor_shape().y() : lhs->tensor_shape().x(); + + int m0 = std::min(matmul_kernel_info.m0, m); + int n0 = adjust_vec_size(matmul_kernel_info.n0, n); + + // Configure kernel window + Window win = calculate_max_window(*output, Steps(n0, m0)); + win = win.collapse(win, Window::DimZ); + IClKernel::configure_internal(win); + + // 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 partial_store_m0 = m % m0; // M is output->dimension(1) + const unsigned int partial_store_n0 = n % n0; // N is output->dimension(0) + + CLBuildOptions build_opts; + build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(lhs->data_type())); + 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("-DPARTIAL_STORE_M0=" + support::cpp11::to_string(partial_store_m0)); + build_opts.add_option("-DPARTIAL_STORE_N0=" + support::cpp11::to_string(partial_store_n0)); + build_opts.add_option("-DK=" + support::cpp11::to_string(k)); + + std::string kernel_name("mat_mul_native"); + kernel_name += matmul_kernel_info.adj_lhs ? "_t" : "_nt"; + kernel_name += matmul_kernel_info.adj_rhs ? "_t" : "_nt"; + + if(matmul_kernel_info.adj_lhs) + { + ARM_COMPUTE_ERROR("Only Implemented LHS non-transposed kernels"); + } + + // 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(output->dimension(1)); + _config_id += "_"; + _config_id += support::cpp11::to_string(output->dimension(0)); + _config_id += "_"; + _config_id += support::cpp11::to_string(output->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 ClNativeMatMulKernel::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(tensors.get_const_tensor(TensorType::ACL_SRC_0)); + const ICLTensor *rhs = utils::cast::polymorphic_downcast(tensors.get_const_tensor(TensorType::ACL_SRC_1)); + ICLTensor *output = utils::cast::polymorphic_downcast(tensors.get_tensor(TensorType::ACL_DST)); + ARM_COMPUTE_ERROR_ON_NULLPTR(lhs, rhs, output); + ARM_COMPUTE_LOG_PARAMS(lhs, rhs, output); + + unsigned int idx = 0; + Window window_collapsed = window.collapse(ICLKernel::window(), Window::DimZ); + + add_3d_tensor_nhw_argument(idx, lhs); + add_3d_tensor_nhw_argument(idx, rhs); + add_3d_tensor_nhw_argument(idx, output); + + enqueue(queue, *this, window_collapsed, lws_hint()); +} + +} // namespace kernels +} // namespace opencl +} // namespace arm_compute diff --git a/src/gpu/cl/kernels/ClNativeMatMulKernel.h b/src/gpu/cl/kernels/ClNativeMatMulKernel.h new file mode 100644 index 0000000000..1cd74365df --- /dev/null +++ b/src/gpu/cl/kernels/ClNativeMatMulKernel.h @@ -0,0 +1,70 @@ +/* + * 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_CLNATIVEMATMULKERNEL +#define ACL_SRC_GPU_CL_KERNELS_CLNATIVEMATMULKERNEL + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLKernelLibrary.h" +#include "arm_compute/core/KernelDescriptors.h" +#include "src/core/common/Macros.h" +#include "src/gpu/cl/ClCompileContext.h" +#include "src/gpu/cl/IClKernel.h" + +namespace arm_compute +{ +namespace opencl +{ +namespace kernels +{ +class ClNativeMatMulKernel : public IClKernel +{ +public: + ClNativeMatMulKernel(); + ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(ClNativeMatMulKernel); + /** Initialise the kernel's input and output. + * + * @param[in] compile_context The compile context to be used. + * @param[in] lhs Input tensor for the LHS matrix. Data type supported: F32/F16. + * Dimensions above 2 are collapsed onto dimension 2 and represent the batch. + * @param[in] rhs Input tensor for the RHS matrix. Data type supported: same as @p lhs. + * Dimensions above 2 are collapsed onto dimension 2 and represent the batch. + * @param[out] output Output tensor info. Data type supported: same as @p lhs + * @param[in] matmul_info Attributes for Batch MatMul Kernel + */ + void configure(const ClCompileContext &compile_context, ITensorInfo *lhs, ITensorInfo *rhs, ITensorInfo *output, const MatMulKernelInfo &matmul_info); + /** Static function to check if given info will lead to a valid configuration + * + * Similar to @ref ClNativeMatMulKernel::configure() + * + * @return a status + */ + static Status validate(const ITensorInfo *lhs, const ITensorInfo *rhs, const ITensorInfo *output, const MatMulKernelInfo &matmul_info); + + // Inherited methods overridden: + void run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) override; +}; +} // namespace kernels +} // namespace opencl +} // namespace arm_compute +#endif /* ACL_SRC_GPU_CL_KERNELS_CLNATIVEMATMULKERNEL */ diff --git a/tests/datasets/BatchMatMulDataset.h b/tests/datasets/BatchMatMulDataset.h new file mode 100644 index 0000000000..dad7cc0af4 --- /dev/null +++ b/tests/datasets/BatchMatMulDataset.h @@ -0,0 +1,110 @@ +/* + * 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 TESTS_DATASETS_BATCHMATMULDATASET +#define TESTS_DATASETS_BATCHMATMULDATASET + +#include "arm_compute/core/TensorShape.h" +#include "utils/TypePrinter.h" + +namespace arm_compute +{ +namespace test +{ +namespace datasets +{ +class BatchMatMulDataset +{ +public: + using type = std::tuple; + + struct iterator + { + iterator(std::vector::const_iterator a_it, + std::vector::const_iterator b_it, + std::vector::const_iterator dst_it) + : _a_it{ std::move(a_it) }, + _b_it{ std::move(b_it) }, + _dst_it{ std::move(dst_it) } + { + } + + std::string description() const + { + std::stringstream description; + description << "A=" << *_a_it << ":"; + description << "B=" << *_b_it << ":"; + description << "Out=" << *_dst_it << ":"; + return description.str(); + } + + BatchMatMulDataset::type operator*() const + { + return std::make_tuple(*_a_it, *_b_it, *_dst_it); + } + + iterator &operator++() + { + ++_a_it; + ++_b_it; + ++_dst_it; + + return *this; + } + + private: + std::vector::const_iterator _a_it; + std::vector::const_iterator _b_it; + std::vector::const_iterator _dst_it; + }; + + iterator begin() const + { + return iterator(_a_shapes.begin(), _b_shapes.begin(), _dst_shapes.begin()); + } + + int size() const + { + return std::min(_a_shapes.size(), std::min(_b_shapes.size(), _dst_shapes.size())); + } + + void add_config(TensorShape a, TensorShape b, TensorShape dst) + { + _a_shapes.emplace_back(std::move(a)); + _b_shapes.emplace_back(std::move(b)); + _dst_shapes.emplace_back(std::move(dst)); + } + +protected: + BatchMatMulDataset() = default; + BatchMatMulDataset(BatchMatMulDataset &&) = default; + +private: + std::vector _a_shapes{}; + std::vector _b_shapes{}; + std::vector _dst_shapes{}; +}; +} // namespace datasets +} // namespace test +} // namespace arm_compute +#endif /* TESTS_DATASETS_BATCHMATMULDATASET */ diff --git a/tests/datasets/LargeBatchMatMulDataset.h b/tests/datasets/LargeBatchMatMulDataset.h new file mode 100644 index 0000000000..0d8ff913cf --- /dev/null +++ b/tests/datasets/LargeBatchMatMulDataset.h @@ -0,0 +1,60 @@ +/* + * 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_LARGEBATCHMATMULDATASET +#define ACL_TESTS_DATASETS_LARGEBATCHMATMULDATASET + +#include "arm_compute/core/TensorShape.h" +#include "arm_compute/core/Types.h" +#include "tests/datasets/BatchMatMulDataset.h" + +namespace arm_compute +{ +namespace test +{ +namespace datasets +{ +class LargeBatchMatMulDataset final : public BatchMatMulDataset +{ +public: + LargeBatchMatMulDataset() + { + add_config(TensorShape(21U, 13U, 3U, 2U), TensorShape(33U, 21U, 3U, 2U), TensorShape(33U, 13U, 3U, 2U)); + add_config(TensorShape(38U, 12U, 1U, 5U), TensorShape(21U, 38U, 1U, 5U), TensorShape(21U, 12U, 1U, 5U)); + add_config(TensorShape(45U, 38U, 3U, 2U), TensorShape(21U, 45U, 3U, 2U), TensorShape(21U, 38U, 3U, 2U)); + } +}; + +class HighDimensionalBatchMatMulDataset final : public BatchMatMulDataset +{ +public: + HighDimensionalBatchMatMulDataset() + { + add_config(TensorShape(5U, 5U, 2U, 2U, 2U, 2U), TensorShape(5U, 5U, 2U, 2U, 2U, 2U), TensorShape(5U, 5U, 2U, 2U, 2U, 2U)); // 6D tensor + } +}; + +} // namespace datasets +} // namespace test +} // namespace arm_compute +#endif /* ACL_TESTS_DATASETS_LARGEBATCHMATMULDATASET */ diff --git a/tests/datasets/SmallBatchMatMulDataset.h b/tests/datasets/SmallBatchMatMulDataset.h new file mode 100644 index 0000000000..cfe76bea6d --- /dev/null +++ b/tests/datasets/SmallBatchMatMulDataset.h @@ -0,0 +1,52 @@ +/* + * 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_SMALLBATCHMATMULDATASET +#define ACL_TESTS_DATASETS_SMALLBATCHMATMULDATASET + +#include "arm_compute/core/TensorShape.h" +#include "arm_compute/core/Types.h" +#include "tests/datasets/BatchMatMulDataset.h" + +namespace arm_compute +{ +namespace test +{ +namespace datasets +{ +class SmallBatchMatMulDataset final : public BatchMatMulDataset +{ +public: + SmallBatchMatMulDataset() + { + add_config(TensorShape(3U, 4U, 2U, 2U), TensorShape(2U, 3U, 2U, 2U), TensorShape(2U, 4U, 2U, 2U)); + add_config(TensorShape(9U, 6U), TensorShape(5U, 9U), TensorShape(5U, 6U)); + add_config(TensorShape(31U, 1U), TensorShape(23U, 31U), 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)); + } +}; +} // namespace datasets +} // namespace test +} // namespace arm_compute +#endif /* ACL_TESTS_DATASETS_SMALLBATCHMATMULDATASET */ diff --git a/tests/validation/CL/BatchMatMul.cpp b/tests/validation/CL/BatchMatMul.cpp new file mode 100644 index 0000000000..fd84526000 --- /dev/null +++ b/tests/validation/CL/BatchMatMul.cpp @@ -0,0 +1,239 @@ +/* + * 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/ClNativeMatMulKernel.h" +#include "tests/datasets/LargeBatchMatMulDataset.h" +#include "tests/datasets/SmallBatchMatMulDataset.h" +#include "tests/framework/Macros.h" +#include "tests/framework/datasets/Datasets.h" +#include "tests/validation/Validation.h" +#include "tests/validation/fixtures/BatchMatMulFixture.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace +{ +RelativeTolerance 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 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 }); + +/** K0 values to test --precommit*/ +const auto k0_values_precommit = framework::dataset::make("K0", { 2, 3 }); + +/** M0 values to test --nightly*/ +const auto m0_values_nightly_lhs_nt = framework::dataset::make("M0", { 1, 2, 3, 4, 5, 6, 7, 8 }); +// const auto m0_values_nightly_lhs_t = framework::dataset::make("M0", { 1, 2, 3, 4, 8 }); // To be enabled + +/** N0 values to test --nightly*/ +const auto n0_values_nightly_rhs_nt = framework::dataset::make("N0", { 1, 2, 3, 4, 8, 16 }); +const auto n0_values_nightly_rhs_t = framework::dataset::make("N0", { 1, 2, 3, 4, 8 }); + +/** K0 values to test --nightly*/ +const auto k0_values_nightly_lhs_nt_rhs_nt = framework::dataset::make("K0", { 1, 2, 3, 4, 8, 16 }); +const auto k0_values_nightly_lhs_nt_rhs_t = framework::dataset::make("K0", { 1, 2, 3, 4, 8 }); +// const auto k0_values_nightly_lhs_t_rhs_nt = framework::dataset::make("K0", { 1, 2, 3, 4, 5, 6, 7, 8 }); // To be enabled + +template +using CLBatchMatMulFixture = BatchMatMulValidationFixture; + +TEST_SUITE(CL) +TEST_SUITE(BatchMatMul) +DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip( + framework::dataset::make("LhsInfo", +{ + TensorInfo(TensorShape(27U, 13U), 1, DataType::S32), // Unsupported data type + TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), + TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), + TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), + TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), + TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), + TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), + TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), + TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), + TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), +}), +framework::dataset::make("RhsInfo", +{ + TensorInfo(TensorShape(8U, 27U), 1, DataType::S32), TensorInfo(TensorShape(8U, 27U), 1, DataType::F32), TensorInfo(TensorShape(8U, 27U), 1, DataType::F32), TensorInfo(TensorShape(8U, 27U), 1, DataType::F32), TensorInfo(TensorShape(8U, 27U), 1, DataType::F32), TensorInfo(TensorShape(8U, 27U), 1, DataType::F32), TensorInfo(TensorShape(8U, 27U), 1, DataType::F32), TensorInfo(TensorShape(8U, 27U), 1, DataType::F32), TensorInfo(TensorShape(8U, 27U), 1, DataType::F32), TensorInfo(TensorShape(8U, 27U), 1, DataType::F32), +})), +framework::dataset::make("OutputInfo", +{ + TensorInfo(TensorShape(8U, 13U), 1, DataType::S32), TensorInfo(TensorShape(8U, 13U), 1, DataType::F32), TensorInfo(TensorShape(8U, 13U), 1, DataType::F32), TensorInfo(TensorShape(8U, 13U), 1, DataType::F32), TensorInfo(TensorShape(8U, 13U), 1, DataType::F32), TensorInfo(TensorShape(8U, 13U), 1, DataType::F32), TensorInfo(TensorShape(8U, 13U), 1, DataType::F32), TensorInfo(TensorShape(8U, 13U), 1, DataType::F32), TensorInfo(TensorShape(8U, 13U), 1, DataType::F32), TensorInfo(TensorShape(8U, 13U), 1, DataType::F32), +})), +framework::dataset::make("MatMulInfo", +{ + MatMulKernelInfo(false, false, 2, 2, 2, false), MatMulKernelInfo(false, false, 2, 2, 2, false), MatMulKernelInfo(false, false, 9, 2, 2, false), MatMulKernelInfo(false, false, 0, 2, 2, false), // M0 cannot be < 1 + MatMulKernelInfo(false, true, 4, 5, 2, false), // For LHS NT RHS NT: N0 cannot be 5 + MatMulKernelInfo(false, true, 4, 6, 2, false), // For LHS NT RHS NT: N0 cannot be 6 + MatMulKernelInfo(false, true, 4, 9, 2, false), // For LHS NT RHS NT: N0 cannot be 9 + MatMulKernelInfo(false, true, 4, 10, 2, false), // For LHS NT RHS NT: N0 cannot be 10 + MatMulKernelInfo(false, true, 4, 11, 2, false), // For LHS NT RHS NT: N0 cannot be 11 + MatMulKernelInfo(false, true, 4, 17, 2, false), // For LHS NT RHS NT: N0 cannot be 17 +})), +framework::dataset::make("Expected", { false, true, true, false, false, false, false, false, false, false })), +lhs_info, rhs_info, output_info, matmul_info, expected) +{ + bool is_valid = bool(ClNativeMatMulKernel::validate(&lhs_info, &rhs_info, &output_info, matmul_info)); + ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS); +} +TEST_SUITE(Float) +TEST_SUITE(FP32) +FIXTURE_DATA_TEST_CASE(RunSmallNoTranspose, CLBatchMatMulFixture, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(datasets::SmallBatchMatMulDataset(), + framework::dataset::make("pretransose_A", { false })), + framework::dataset::make("pretransose_B", { false })), + m0_values_precommit), + n0_values_precommit), + k0_values_precommit), + framework::dataset::make("DataType", DataType::F32))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); +} +FIXTURE_DATA_TEST_CASE(RunSmallRhsTransposed, CLBatchMatMulFixture, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(datasets::SmallBatchMatMulDataset(), + framework::dataset::make("pretransose_A", { false })), + framework::dataset::make("pretransose_B", { true })), + m0_values_precommit), + n0_values_precommit), + k0_values_precommit), + framework::dataset::make("DataType", DataType::F32))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); +} +FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLBatchMatMulFixture, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(datasets::LargeBatchMatMulDataset(), + framework::dataset::make("pretransose_A", { false })), + framework::dataset::make("pretransose_B", { false })), + m0_values_nightly_lhs_nt), + n0_values_nightly_rhs_nt), + k0_values_nightly_lhs_nt_rhs_nt), + framework::dataset::make("DataType", DataType::F32))) +{ + // Validate output + 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(RunHighDimNoTranspose, CLBatchMatMulFixture, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(datasets::HighDimensionalBatchMatMulDataset(), + framework::dataset::make("pretransose_A", { false })), + framework::dataset::make("pretransose_B", { false })), + framework::dataset::make("M0", { 2 })), + framework::dataset::make("N0", { 2 })), + framework::dataset::make("K0", { 2 })), + framework::dataset::make("DataType", DataType::F32))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); +} +FIXTURE_DATA_TEST_CASE(RunLargeRhsTransposed, CLBatchMatMulFixture, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(datasets::LargeBatchMatMulDataset(), + framework::dataset::make("pretransose_A", { false })), + framework::dataset::make("pretransose_B", { true })), + m0_values_nightly_lhs_nt), + n0_values_nightly_rhs_t), + k0_values_nightly_lhs_nt_rhs_t), + framework::dataset::make("DataType", DataType::F32))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); +} +FIXTURE_DATA_TEST_CASE(RunHighDimRhsTransposed, CLBatchMatMulFixture, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(datasets::HighDimensionalBatchMatMulDataset(), + framework::dataset::make("pretransose_A", { false })), + framework::dataset::make("pretransose_B", { true })), + framework::dataset::make("M0", { 2 })), + framework::dataset::make("N0", { 2 })), + framework::dataset::make("K0", { 2 })), + framework::dataset::make("DataType", DataType::F32))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f32, 0.f, abs_tolerance_f32); +} +TEST_SUITE_END() // FP32 + +TEST_SUITE(FP16) +FIXTURE_DATA_TEST_CASE(RunSmallNoTranspose, CLBatchMatMulFixture, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(datasets::SmallBatchMatMulDataset(), + framework::dataset::make("pretransose_A", { false })), + framework::dataset::make("pretransose_B", { false })), + m0_values_precommit), + n0_values_precommit), + k0_values_precommit), + framework::dataset::make("DataType", DataType::F16))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16); +} +FIXTURE_DATA_TEST_CASE(RunSmallRhsTransposed, CLBatchMatMulFixture, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(datasets::SmallBatchMatMulDataset(), + framework::dataset::make("pretransose_A", { false })), + framework::dataset::make("pretransose_B", { true })), + m0_values_precommit), + n0_values_precommit), + k0_values_precommit), + framework::dataset::make("DataType", DataType::F16))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16); +} +FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLBatchMatMulFixture, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(datasets::LargeBatchMatMulDataset(), + framework::dataset::make("pretransose_A", { false })), + framework::dataset::make("pretransose_B", { false })), + m0_values_nightly_lhs_nt), + n0_values_nightly_rhs_nt), + k0_values_nightly_lhs_nt_rhs_nt), + framework::dataset::make("DataType", DataType::F16))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16); +} +FIXTURE_DATA_TEST_CASE(RunLargeRhsTransposed, CLBatchMatMulFixture, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(datasets::LargeBatchMatMulDataset(), + framework::dataset::make("pretransose_A", { false })), + framework::dataset::make("pretransose_B", { true })), + m0_values_nightly_lhs_nt), + n0_values_nightly_rhs_t), + k0_values_nightly_lhs_nt_rhs_t), + framework::dataset::make("DataType", DataType::F16))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16); +} +TEST_SUITE_END() // FP16 + +TEST_SUITE_END() // Float +TEST_SUITE_END() // BatchMatMul +TEST_SUITE_END() // CL +} // namespace validation +} // namespace test +} // namespace arm_compute diff --git a/tests/validation/fixtures/BatchMatMulFixture.h b/tests/validation/fixtures/BatchMatMulFixture.h new file mode 100644 index 0000000000..9fb2dcc1b7 --- /dev/null +++ b/tests/validation/fixtures/BatchMatMulFixture.h @@ -0,0 +1,203 @@ +/* + * 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_VALIDATION_FIXTURES_BATCHMATMULFIXTURE +#define ACL_TESTS_VALIDATION_FIXTURES_BATCHMATMULFIXTURE + +#include "arm_compute/core/KernelDescriptors.h" +#include "src/gpu/cl/kernels/ClNativeMatMulKernel.h" +#include "tests/CL/CLAccessor.h" +#include "tests/CL/Helper.h" +#include "tests/framework/Fixture.h" +#include "tests/validation/reference/GEMM.h" +#include "tests/validation/reference/Permute.h" +#include "tests/validation/reference/ReshapeLayer.h" + +#include + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +using namespace arm_compute::opencl::kernels; + +template +class BatchMatMulValidationFixture : public framework::Fixture +{ +public: + template + void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, const int M0, const int N0, const int K0, DataType data_type) + { + // For brevity, the input shapes are assumed to be not-transposed for both Lhs and Rhs matrices. + if(pretranspose_a) + { + permute(shape_a, PermutationVector(1U, 0U)); + } + + if(pretranspose_b) + { + permute(shape_b, PermutationVector(1U, 0U)); + } + + _target = compute_target(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, M0, N0, K0, data_type); + _reference = compute_reference(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, data_type); + } + +protected: + template + void fill(U &&tensor, int i, float lo = -1.f, float hi = 1.f) + { + switch(tensor.data_type()) + { + case DataType::F16: + { + arm_compute::utils::uniform_real_distribution_16bit distribution{ float(lo), float(hi) }; + library->fill(tensor, distribution, i); + break; + } + case DataType::F32: + { + std::uniform_real_distribution distribution(lo, hi); + library->fill(tensor, distribution, i); + break; + } + default: + library->fill_tensor_uniform(tensor, i); + } + } + + CLTensor compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &output_shape, bool pretranspose_a, bool pretranspose_b, const int M0, const int N0, const int K0, + DataType data_type) + { + // Create tensors + CLTensor a = create_tensor(shape_a, data_type, 1); + CLTensor b = create_tensor(shape_b, data_type, 1); + CLTensor dst = create_tensor(output_shape, data_type, 1); + + CLSynthetizeOperator batchMatMul{}; + MatMulKernelInfo matmul_info; + matmul_info.adj_lhs = pretranspose_a; + matmul_info.adj_rhs = pretranspose_b; + matmul_info.m0 = M0; + matmul_info.n0 = N0; + matmul_info.k0 = K0; + + batchMatMul.configure(a.info(), b.info(), dst.info(), matmul_info); + ARM_COMPUTE_ASSERT(a.info()->is_resizable()); + ARM_COMPUTE_ASSERT(b.info()->is_resizable()); + ARM_COMPUTE_ASSERT(dst.info()->is_resizable()); + + // Allocate tensors + a.allocator()->allocate(); + b.allocator()->allocate(); + dst.allocator()->allocate(); + + ARM_COMPUTE_ASSERT(!a.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!b.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!dst.info()->is_resizable()); + + // Fill tensors + fill(CLAccessor(a), 0); + fill(CLAccessor(b), 1); + + // Compute batchMatMul kernel + ITensorPack tensors_pack({ { ACL_SRC_0, &a }, + { ACL_SRC_1, &b }, + { ACL_DST, &dst } + }); + batchMatMul.run(tensors_pack); + + return dst; + } + + SimpleTensor compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &output_shape, bool pretranspose_a, bool pretranspose_b, DataType data_type) + { + // We collapse dimensions > 3 onto dimension 3, i.e. 5D+ tensors will look like 4D + // This is necessary unless we choose to extend gemm reference for 5D+ tensors + TensorShape output_shape_collapsed = output_shape.collapsed_from(Window::DimW); + TensorShape shape_a_collapsed = shape_a.collapsed_from(Window::DimW); + TensorShape shape_b_collapsed = shape_b.collapsed_from(Window::DimW); + + // Create reference + SimpleTensor a{ shape_a_collapsed, data_type, 1 }; + SimpleTensor b{ shape_b_collapsed, data_type, 1 }; + SimpleTensor c{ output_shape_collapsed, data_type, 1 }; + + // Fill reference + fill(a, 0); + fill(b, 1); + + /* Note: Assuming the usual batch matmul dimensions A = (B x M x K), B = (B x K x N), if pretranspose_A is set to true, then A is assumed to be (B x K x M), + therefore, A must be pre-transposed before passing it to the fixture. And, we transpose A again in the fixture to make it (B x M x K) + in order to be able to call reference implementation that works with (B x M x K) input. + Similarly, if pretranspose_B is set to true, then B is assumed to be (B x N x K), B must be pre-transposed before passing it to the fixture. */ + + // Define transposed shapes + TensorShape a_transposed_shape(a.shape()); + a_transposed_shape.set(0, a.shape().y()); + a_transposed_shape.set(1, a.shape().x()); + + TensorShape b_transposed_shape(b.shape()); + b_transposed_shape.set(0, b.shape().y()); + b_transposed_shape.set(1, b.shape().x()); + + // Define transposed tensors + SimpleTensor a_transposed{ a_transposed_shape, data_type }; + SimpleTensor b_transposed{ b_transposed_shape, data_type }; + + // pretranspose a if necessary + if(pretranspose_a) + { + a_transposed = reference::permute(a, PermutationVector(1U, 0U)); + } + + // pretranspose b if necessary + if(pretranspose_b) + { + b_transposed = reference::permute(b, PermutationVector(1U, 0U)); + } + + // Setting beta to 0 will effectively disable C for the + // computation of the reference: alpha * A * B + 0 * C + // Use transposed tensors if boolean enabled else use original tensors + SimpleTensor result = reference::gemm((pretranspose_a) ? a_transposed : a, (pretranspose_b) ? b_transposed : b, c, 1.0f, 0.f); + + // We reshape the gemm output back if the tensor is high dimensional + if(output_shape_collapsed != output_shape) + { + result = reference::reshape_layer(result, output_shape); + } + + return result; + } + + CLTensor _target{}; + SimpleTensor _reference{}; +}; + +} // namespace validation +} // namespace test +} // namespace arm_compute +#endif /* ACL_TESTS_VALIDATION_FIXTURES_BATCHMATMULFIXTURE */ diff --git a/utils/TypePrinter.h b/utils/TypePrinter.h index db27ddccde..c3af0a2419 100644 --- a/utils/TypePrinter.h +++ b/utils/TypePrinter.h @@ -421,7 +421,8 @@ inline ::std::ostream &operator<<(::std::ostream &os, const GEMMLHSMatrixInfo &g */ inline ::std::ostream &operator<<(::std::ostream &os, const GEMMRHSMatrixInfo &gemm_info) { - os << "( n0=" << (unsigned int)gemm_info.n0 << " k0=" << gemm_info.k0 << " h0=" << gemm_info.h0 << " trans=" << gemm_info.transpose << " inter=" << gemm_info.interleave << " exp_img=" << gemm_info.export_to_cl_image << "})"; + os << "( n0=" << (unsigned int)gemm_info.n0 << " k0=" << gemm_info.k0 << " h0=" << gemm_info.h0 << " trans=" << gemm_info.transpose << " inter=" << gemm_info.interleave << " exp_img=" << + gemm_info.export_to_cl_image << "})"; return os; } @@ -474,7 +475,8 @@ inline std::string to_string(const GEMMKernelInfo &gemm_info) inline ::std::ostream &operator<<(::std::ostream &os, const BoundingBoxTransformInfo &bbox_info) { auto weights = bbox_info.weights(); - os << "(" << bbox_info.img_width() << "x" << bbox_info.img_height() << ")~" << bbox_info.scale() << "(weights={" << weights[0] << ", " << weights[1] << ", " << weights[2] << ", " << weights[3] << "})"; + os << "(" << bbox_info.img_width() << "x" << bbox_info.img_height() << ")~" << bbox_info.scale() << "(weights={" << weights[0] << ", " << weights[1] << ", " << weights[2] << ", " << weights[3] << + "})"; return os; } @@ -3333,46 +3335,46 @@ inline std::string to_string(const Conv3dInfo &conv3d_info) inline std::string to_string(const WeightFormat wf) { #define __CASE_WEIGHT_FORMAT(wf) \ - case WeightFormat::wf: \ - return #wf; +case WeightFormat::wf: \ + return #wf; switch(wf) { - __CASE_WEIGHT_FORMAT(UNSPECIFIED) - __CASE_WEIGHT_FORMAT(ANY) - __CASE_WEIGHT_FORMAT(OHWI) - __CASE_WEIGHT_FORMAT(OHWIo2) - __CASE_WEIGHT_FORMAT(OHWIo4) - __CASE_WEIGHT_FORMAT(OHWIo8) - __CASE_WEIGHT_FORMAT(OHWIo16) - __CASE_WEIGHT_FORMAT(OHWIo32) - __CASE_WEIGHT_FORMAT(OHWIo64) - __CASE_WEIGHT_FORMAT(OHWIo128) - __CASE_WEIGHT_FORMAT(OHWIo4i2) - __CASE_WEIGHT_FORMAT(OHWIo4i2_bf16) - __CASE_WEIGHT_FORMAT(OHWIo8i2) - __CASE_WEIGHT_FORMAT(OHWIo8i2_bf16) - __CASE_WEIGHT_FORMAT(OHWIo16i2) - __CASE_WEIGHT_FORMAT(OHWIo16i2_bf16) - __CASE_WEIGHT_FORMAT(OHWIo32i2) - __CASE_WEIGHT_FORMAT(OHWIo32i2_bf16) - __CASE_WEIGHT_FORMAT(OHWIo64i2) - __CASE_WEIGHT_FORMAT(OHWIo64i2_bf16) - __CASE_WEIGHT_FORMAT(OHWIo4i4) - __CASE_WEIGHT_FORMAT(OHWIo4i4_bf16) - __CASE_WEIGHT_FORMAT(OHWIo8i4) - __CASE_WEIGHT_FORMAT(OHWIo8i4_bf16) - __CASE_WEIGHT_FORMAT(OHWIo16i4) - __CASE_WEIGHT_FORMAT(OHWIo16i4_bf16) - __CASE_WEIGHT_FORMAT(OHWIo32i4) - __CASE_WEIGHT_FORMAT(OHWIo32i4_bf16) - __CASE_WEIGHT_FORMAT(OHWIo64i4) - __CASE_WEIGHT_FORMAT(OHWIo64i4_bf16) - __CASE_WEIGHT_FORMAT(OHWIo2i8) - __CASE_WEIGHT_FORMAT(OHWIo4i8) - __CASE_WEIGHT_FORMAT(OHWIo8i8) - __CASE_WEIGHT_FORMAT(OHWIo16i8) - __CASE_WEIGHT_FORMAT(OHWIo32i8) - __CASE_WEIGHT_FORMAT(OHWIo64i8) + __CASE_WEIGHT_FORMAT(UNSPECIFIED) + __CASE_WEIGHT_FORMAT(ANY) + __CASE_WEIGHT_FORMAT(OHWI) + __CASE_WEIGHT_FORMAT(OHWIo2) + __CASE_WEIGHT_FORMAT(OHWIo4) + __CASE_WEIGHT_FORMAT(OHWIo8) + __CASE_WEIGHT_FORMAT(OHWIo16) + __CASE_WEIGHT_FORMAT(OHWIo32) + __CASE_WEIGHT_FORMAT(OHWIo64) + __CASE_WEIGHT_FORMAT(OHWIo128) + __CASE_WEIGHT_FORMAT(OHWIo4i2) + __CASE_WEIGHT_FORMAT(OHWIo4i2_bf16) + __CASE_WEIGHT_FORMAT(OHWIo8i2) + __CASE_WEIGHT_FORMAT(OHWIo8i2_bf16) + __CASE_WEIGHT_FORMAT(OHWIo16i2) + __CASE_WEIGHT_FORMAT(OHWIo16i2_bf16) + __CASE_WEIGHT_FORMAT(OHWIo32i2) + __CASE_WEIGHT_FORMAT(OHWIo32i2_bf16) + __CASE_WEIGHT_FORMAT(OHWIo64i2) + __CASE_WEIGHT_FORMAT(OHWIo64i2_bf16) + __CASE_WEIGHT_FORMAT(OHWIo4i4) + __CASE_WEIGHT_FORMAT(OHWIo4i4_bf16) + __CASE_WEIGHT_FORMAT(OHWIo8i4) + __CASE_WEIGHT_FORMAT(OHWIo8i4_bf16) + __CASE_WEIGHT_FORMAT(OHWIo16i4) + __CASE_WEIGHT_FORMAT(OHWIo16i4_bf16) + __CASE_WEIGHT_FORMAT(OHWIo32i4) + __CASE_WEIGHT_FORMAT(OHWIo32i4_bf16) + __CASE_WEIGHT_FORMAT(OHWIo64i4) + __CASE_WEIGHT_FORMAT(OHWIo64i4_bf16) + __CASE_WEIGHT_FORMAT(OHWIo2i8) + __CASE_WEIGHT_FORMAT(OHWIo4i8) + __CASE_WEIGHT_FORMAT(OHWIo8i8) + __CASE_WEIGHT_FORMAT(OHWIo16i8) + __CASE_WEIGHT_FORMAT(OHWIo32i8) + __CASE_WEIGHT_FORMAT(OHWIo64i8) default: return "invalid value"; } @@ -3677,6 +3679,40 @@ inline std::string to_string(const experimental::dynamic_fusion::SoftmaxAttribut return str.str(); } +/** Formatted output of the arm_compute::MatMulKernelInfo type. + * + * @param[out] os Output stream. + * @param[in] matmul_info arm_compute::MatMulKernelInfo type to output. + * + * @return Modified output stream. + */ +inline ::std::ostream &operator<<(::std::ostream &os, const arm_compute::MatMulKernelInfo &matmul_info) +{ + os << "MatMulKernelInfo=" + << "[" + << "adj_lhs=" << matmul_info.adj_lhs << ", " + << "adj_rhs=" << matmul_info.adj_rhs << ", " + << "M0=" << matmul_info.m0 << ", " + << "N0=" << matmul_info.n0 << ", " + << "K0=" << matmul_info.k0 << ", " + << "export_rhs_to_cl_image=" << matmul_info.export_rhs_to_cl_image + << "]"; + + return os; +} +/** Formatted output of the arm_compute::MatMulKernelInfo type. + * + * @param[in] matmul_info arm_compute::MatMulKernelInfo type to output. + * + * @return Formatted string. + */ +inline std::string to_string(const arm_compute::MatMulKernelInfo &matmul_info) +{ + std::stringstream str; + str << matmul_info; + return str.str(); +} + } // namespace arm_compute #endif /* __ARM_COMPUTE_TYPE_PRINTER_H__ */ -- cgit v1.2.1