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author | Gian Marco Iodice <gianmarco.iodice@arm.com> | 2019-09-27 09:23:15 +0100 |
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committer | Gian Marco Iodice <gianmarco.iodice@arm.com> | 2019-09-30 08:28:43 +0000 |
commit | 0c17aa25a4f7bc812707150b91930f0cf8e75294 (patch) | |
tree | 29088e00bd7ba443dc122ad3436b0a4ef369a102 /tests/validation/reference/GEMM.cpp | |
parent | 40958adf8bad8fd9fefe591ee55a381f7bbb6fea (diff) | |
download | ComputeLibrary-0c17aa25a4f7bc812707150b91930f0cf8e75294.tar.gz |
COMPMID-2571: Add mixed-precision support in CLGEMMReshaped for FP16
Change-Id: I5ba90d4de4594ed784c7230aa6b10503be67c001
Signed-off-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1991
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'tests/validation/reference/GEMM.cpp')
-rw-r--r-- | tests/validation/reference/GEMM.cpp | 55 |
1 files changed, 54 insertions, 1 deletions
diff --git a/tests/validation/reference/GEMM.cpp b/tests/validation/reference/GEMM.cpp index 2feab89950..3c72b94143 100644 --- a/tests/validation/reference/GEMM.cpp +++ b/tests/validation/reference/GEMM.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2018 ARM Limited. + * Copyright (c) 2017-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -84,8 +84,61 @@ SimpleTensor<T> gemm(const SimpleTensor<T> &a, const SimpleTensor<T> &b, const S return dst; } +template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type> +SimpleTensor<T> gemm_mixed_precision(const SimpleTensor<T> &a, const SimpleTensor<T> &b, const SimpleTensor<T> &c, float alpha, float beta) +{ + // GEMM mixed-precision combines F32 accumulators with F16 multiplications + // Create reference + SimpleTensor<T> dst{ c.shape(), c.data_type(), 1 }; + + // Compute reference + const int M = a.shape().y(); + const int N = b.shape().x(); + const int K = a.shape().x(); + const int D = a.shape().z(); // Number of matrices in a batch + const int W = a.shape()[3]; // Number of batched-gemm (Winograd case) + + const int a_stride_z = K * M; + const int a_stride_w = K * M * D; + + const int b_stride_z = b.shape().num_dimensions() > 2 ? N * K : 0; // Do not slide the matrix B along the 3th dimension in case matrix B has less than 3 dimensions + const int b_stride_w = b.shape().num_dimensions() > 3 ? K * N * D : 0; // Do not slide the matrix B along the 4th dimension in case matrix B has less than 4 dimensions + + const int c_stride_z = N * M; + const int c_stride_w = N * M * D; + + for(int w = 0; w < W; ++w) + { + for(int depth = 0; depth < D; ++depth) + { + const int base_addr_a = depth * a_stride_z + w * a_stride_w; + const int base_addr_b = depth * b_stride_z + w * b_stride_w; + const int base_addr_c = depth * c_stride_z + w * c_stride_w; + + for(int row = 0; row < M; ++row) + { + for(int col = 0; col < N; ++col) + { + float acc(0); + + for(int k = 0; k < K; ++k) + { + acc += static_cast<float>(a[base_addr_a + k + row * K] * b[base_addr_b + col + k * N]); + } + + // Finalize the result: alpha * A * B + beta * C + dst[base_addr_c + col + row * N] = static_cast<T>(alpha * acc + beta * c[base_addr_c + col + row * N]); + } + } + } + } + + return dst; +} + template SimpleTensor<float> gemm(const SimpleTensor<float> &a, const SimpleTensor<float> &b, const SimpleTensor<float> &c, float alpha, float beta); template SimpleTensor<half> gemm(const SimpleTensor<half> &a, const SimpleTensor<half> &b, const SimpleTensor<half> &c, float alpha, float beta); +template SimpleTensor<half> gemm_mixed_precision(const SimpleTensor<half> &a, const SimpleTensor<half> &b, const SimpleTensor<half> &c, float alpha, float beta); } // namespace reference } // namespace validation } // namespace test |