From 467daef993fe29cc4319058200b7ad797398e4b0 Mon Sep 17 00:00:00 2001 From: Omar Al Khatib Date: Thu, 13 Apr 2023 14:56:23 +0100 Subject: Implement CL kernel for a native batched matmul Quantized - LHS transposed, RHS transposed Resolves: [COMPMID-5924] Signed-off-by: Omar Al Khatib Change-Id: I9ba657737eb1e3a096c8341ad4ad311571f8edeb Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9454 Benchmark: Arm Jenkins Tested-by: Arm Jenkins Reviewed-by: SiCong Li Comments-Addressed: Arm Jenkins --- src/core/CL/cl_kernels/common/mat_mul_quantized.cl | 182 +++++++++++++++++++++ src/gpu/cl/ClKernelLibrary.cpp | 1 + tests/validation/CL/MatMulLowpNativeKernel.cpp | 93 ++++++++++- 3 files changed, 273 insertions(+), 3 deletions(-) diff --git a/src/core/CL/cl_kernels/common/mat_mul_quantized.cl b/src/core/CL/cl_kernels/common/mat_mul_quantized.cl index c250b4b988..5c931d2fc1 100644 --- a/src/core/CL/cl_kernels/common/mat_mul_quantized.cl +++ b/src/core/CL/cl_kernels/common/mat_mul_quantized.cl @@ -385,3 +385,185 @@ __kernel void mat_mul_native_quantized_t_nt( T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, M0, N0, PARTIAL_STORE_N0, BUFFER, dst, 0, dst_stride_y, x_cond, accq, indirect_buffer); } #endif // defined(MAT_MUL_NATIVE_QUANTIZED_T_NT) + +#if defined(MAT_MUL_NATIVE_QUANTIZED_T_T) +/** This OpenCL kernel performs the batch matrix multiplication (BatchMatMul): LHS transposed, RHS transposed + * + * @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=uchar) + * @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 number of leftover outputs rows/columns must be passed using -DPARTIAL_STORE_N0 and -DPARTIAL_STORE_M0 (e.g. -DPARTIAL_STORE_N0=2, -DPARTIAL_STORE_M0=3) + * @note The dimension K must be passed at compile time using -DK (e.g. -DK=6) + * @note The kernel name in uppercase must be passed at compile time (e.g. -DMAT_MUL_NATIVE_QUANTIZED_T_T) + * @note Only the following configurations of M0, N0 and K0 are currently supported: + * - M0 = 1, 2, 3, 4, 8, 16 + * - 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: QASYMM8/QASYMM8_SIGNED + * @param[in] lhs_stride_y Stride of the lhs matrix in Y (2nd) dimension (in bytes) + * @param[in] lhs_stride_z Stride of the lhs tensor in Z (3rd) dimension (in bytes) + * @param[in] lhs_w The width of the lhs tensor + * @param[in] lhs_h The height of the lhs tensor + * @param[in] lhs_n Number of the matrices (buffers) in the batch + * @param[in] lhs_offset_first_element_in_bytes The offset of the first element in the lhs matrix + * @param[in] rhs_ptr Pointer to the rhs matrix. Supported data types: same as @p lhs_ptr + * @param[in] rhs_stride_y Stride of the rhs matrix in Y (2nd) dimension (in bytes) + * @param[in] rhs_stride_z Stride of the rhs tensor in Z (3rd) dimension (in bytes) + * @param[in] rhs_w The width of the rhs tensor + * @param[in] rhs_h The height of the rhs tensor + * @param[in] rhs_n Number of the matrices (buffers) in the batch + * @param[in] rhs_offset_first_element_in_bytes The offset of the first element in the rhs matrix + * @param[out] dst_ptr Pointer to the dst matrix. Supported data types: same as @p lhs_ptr + * @param[in] dst_stride_y Stride of the dst matrix in Y (2nd) dimension (in bytes) + * @param[in] dst_stride_z Stride of the dst tensor in Z (3rd) dimension (in bytes) + * @param[in] dst_w The width of the dst tensor + * @param[in] dst_h The height of the dst tensor + * @param[in] dst_n Number of the matrices (buffers) in the batch + * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the dst matrix + */ +__kernel void mat_mul_native_quantized_t_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 * sizeof(DATA_TYPE) + 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(int, M0, N0, acc); + LOOP_UNROLLING(int, i, 0, 1, M0, + { + acc[i].v = K * ((int)LHS_OFFSET) * ((int)RHS_OFFSET); + }) + + TILE(int, 1, M0, a_sum); + a_sum[0].v = 0; + + TILE(int, 1, N0, b_sum); + b_sum[0].v = 0; + + 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; + }) + + LOOP_UNROLLING(int, i, 0, 1, N0, + { + b[i].v = 0; + }) + + // Load tile from the lhs tensor in a transposed fashion + // see mat_mul_native_quantized_nt_nt main loop for more explanation + T_LOAD_TRANSPOSED(DATA_TYPE, K0, M0, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a); + + // Load tile from the rhs tensor + T_LOAD(DATA_TYPE, N0, K0, BUFFER, rhs, 0, 0, 1, rhs_stride_y, b); + + T_MMUL(DATA_TYPE, DATA_TYPE, int, M0, N0, K0, NT, T, a, b, acc); + + LOOP_UNROLLING(int, i, 0, 1, K0, + { + LOOP_UNROLLING(int, j, 0, 1, M0, + { + a_sum[0].s[j] += (int)a[j].s[i]; + }) + }) + + LOOP_UNROLLING(int, i, 0, 1, N0, + { + LOOP_UNROLLING(int, j, 0, 1, K0, + { + b_sum[0].s[i] += (int)b[i].s[j]; + }) + }) + + lhs_offset_first_element_in_bytes += K0 * lhs_stride_y; + 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; + }) + + LOOP_UNROLLING(int, i, 0, 1, N0, + { + b[i].v = 0; + }) + + // Load tile from the lhs tensor in a transposed fashion + // see mat_mul_native_quantized_nt_nt main loop for more explanation + T_LOAD_TRANSPOSED(DATA_TYPE, 1, M0, BUFFER, lhs, 0, 0, 1, lhs_stride_y, a); + + // Load tile from the rhs tensor + T_LOAD(DATA_TYPE, N0, 1, BUFFER, rhs, 0, 0, 1, rhs_stride_y, b); + + T_MMUL(DATA_TYPE, DATA_TYPE, int, M0, N0, 1, NT, T, a, b, acc); + + LOOP_UNROLLING(int, i, 0, 1, 1, + { + LOOP_UNROLLING(int, j, 0, 1, M0, + { + a_sum[0].s[j] += (int)a[j].s[i]; + }) + }) + + LOOP_UNROLLING(int, i, 0, 1, N0, + { + LOOP_UNROLLING(int, j, 0, 1, 1, + { + b_sum[0].s[i] += (int)b[i].s[j]; + }) + }) + + lhs_offset_first_element_in_bytes += 1 * lhs_stride_y; + rhs_offset_first_element_in_bytes += 1 * sizeof(DATA_TYPE); + } +#endif // ((K % K0) != 0) + + LOOP_UNROLLING(int, i, 0, 1, M0, + { + LOOP_UNROLLING(int, j, 0, 1, N0, + { + acc[i].s[j] += ((int)RHS_OFFSET) * a_sum[0].s[i] + ((int)(LHS_OFFSET)) * b_sum[0].s[j]; + }) + }) + + 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; + + // Quantize the tile + TILE(DATA_TYPE, M0, N0, accq); + T_QUANTIZE8_ASYMMETRIC(int, DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, acc, accq); + + 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, accq, indirect_buffer); +} +#endif // defined(MAT_MUL_NATIVE_QUANTIZED_T_T) diff --git a/src/gpu/cl/ClKernelLibrary.cpp b/src/gpu/cl/ClKernelLibrary.cpp index e657687887..4612ca35b8 100644 --- a/src/gpu/cl/ClKernelLibrary.cpp +++ b/src/gpu/cl/ClKernelLibrary.cpp @@ -325,6 +325,7 @@ const std::map ClKernelLibrary::_kernel_program_map = { "mat_mul_native_t_t", "common/mat_mul.cl" }, { "mat_mul_native_quantized_nt_nt", "common/mat_mul_quantized.cl" }, { "mat_mul_native_quantized_t_nt", "common/mat_mul_quantized.cl" }, + { "mat_mul_native_quantized_t_t", "common/mat_mul_quantized.cl" }, { "max_unpooling_layer_2", "common/unpooling_layer.cl" }, { "mean_stddev_normalization", "common/mean_stddev_normalization.cl" }, { "memset", "common/memset.cl" }, diff --git a/tests/validation/CL/MatMulLowpNativeKernel.cpp b/tests/validation/CL/MatMulLowpNativeKernel.cpp index 5932fa7c21..a0b2a37b4b 100644 --- a/tests/validation/CL/MatMulLowpNativeKernel.cpp +++ b/tests/validation/CL/MatMulLowpNativeKernel.cpp @@ -64,12 +64,12 @@ const auto m0_values_nightly_lhs_t = framework::dataset::make("M0", { 1, 2, 3, /** 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 }); +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_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 }); +const auto k0_values_nightly_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 }); TEST_SUITE(CL) TEST_SUITE(MatMulLowpNativeKernel) @@ -234,6 +234,30 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulLowpNativeKernelFixture, framew // Validate output validate(CLAccessor(_target), _reference, tolerance_quant); } +FIXTURE_DATA_TEST_CASE(RunTiny_T_T, CLMatMulLowpNativeKernelFixture, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::TinyMatMulDataset(), + framework::dataset::make("TransposeA", { true })), + framework::dataset::make("TransposeB", { true })), + m0_values_precommit), + n0_values_precommit), + k0_values_precommit), + framework::dataset::make("ExportRhsToCLImage", { false })), + framework::dataset::make("DataType", DataType::QASYMM8_SIGNED))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_quant); +} +FIXTURE_DATA_TEST_CASE(RunSmall_T_T, CLMatMulLowpNativeKernelFixture, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(), + framework::dataset::make("TransposeA", { true })), + framework::dataset::make("TransposeB", { true })), + m0_values_precommit), + n0_values_precommit), + k0_values_precommit), + framework::dataset::make("ExportRhsToCLImage", { false })), + framework::dataset::make("DataType", DataType::QASYMM8_SIGNED))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_quant); +} FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulLowpNativeKernelFixture, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(), framework::dataset::make("TransposeA", { false })), @@ -260,6 +284,19 @@ FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulLowpNativeKernelFixture, framework::DatasetMode::NIGHTLY, + combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(), + framework::dataset::make("TransposeA", { true })), + framework::dataset::make("TransposeB", { true })), + m0_values_nightly_lhs_t), + n0_values_nightly_rhs_t), + k0_values_nightly_rhs_t), + framework::dataset::make("ExportRhsToCLImage", { false })), + framework::dataset::make("DataType", DataType::QASYMM8_SIGNED))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_quant); +} // Running High Dimensional test is enough for qasymm8_signed, 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, CLMatMulLowpNativeKernelFixture, framework::DatasetMode::ALL, @@ -275,6 +312,19 @@ FIXTURE_DATA_TEST_CASE(RunHighDimensional, CLMatMulLowpNativeKernelFixture, framework::DatasetMode::ALL, + combine(combine(combine(combine(combine(combine(combine(datasets::HighDimensionalMatMulDataset(), + framework::dataset::make("TransposeA", { true })), + framework::dataset::make("TransposeB", { true })), + framework::dataset::make("M0", { 2 })), + framework::dataset::make("N0", { 2 })), + framework::dataset::make("K0", { 2 })), + framework::dataset::make("ExportRhsToCLImage", { false })), + framework::dataset::make("DataType", DataType::QASYMM8_SIGNED))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_quant); +} TEST_SUITE_END() // QASYMM8_SIGNED TEST_SUITE(QASYMM8) @@ -302,6 +352,30 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLMatMulLowpNativeKernelFixture, frame // Validate output validate(CLAccessor(_target), _reference, tolerance_quant); } +FIXTURE_DATA_TEST_CASE(RunTiny_T_T, CLMatMulLowpNativeKernelFixture, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::TinyMatMulDataset(), + framework::dataset::make("TransposeA", { true })), + framework::dataset::make("TransposeB", { true })), + m0_values_precommit), + n0_values_precommit), + k0_values_precommit), + framework::dataset::make("ExportRhsToCLImage", { false })), + framework::dataset::make("DataType", DataType::QASYMM8))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_quant); +} +FIXTURE_DATA_TEST_CASE(RunSmall_T_T, CLMatMulLowpNativeKernelFixture, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(combine(combine(datasets::SmallMatMulDataset(), + framework::dataset::make("TransposeA", { true })), + framework::dataset::make("TransposeB", { true })), + m0_values_precommit), + n0_values_precommit), + k0_values_precommit), + framework::dataset::make("ExportRhsToCLImage", { false })), + framework::dataset::make("DataType", DataType::QASYMM8))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_quant); +} FIXTURE_DATA_TEST_CASE(RunLargeNoTranspose, CLMatMulLowpNativeKernelFixture, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(), framework::dataset::make("TransposeA", { false })), @@ -328,6 +402,19 @@ FIXTURE_DATA_TEST_CASE(RunLargeLhsTransposed, CLMatMulLowpNativeKernelFixture, framework::DatasetMode::NIGHTLY, + combine(combine(combine(combine(combine(combine(combine(datasets::LargeMatMulDataset(), + framework::dataset::make("TransposeA", { true })), + framework::dataset::make("TransposeB", { true })), + m0_values_nightly_lhs_t), + n0_values_nightly_rhs_t), + k0_values_nightly_rhs_t), + framework::dataset::make("ExportRhsToCLImage", { false })), + framework::dataset::make("DataType", DataType::QASYMM8))) +{ + // Validate output + validate(CLAccessor(_target), _reference, tolerance_quant); +} TEST_SUITE_END() // QASYMM8 TEST_SUITE_END() // Quantized TEST_SUITE_END() // MatMulLowpNativeKernel -- cgit v1.2.1