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-rw-r--r--tests/validation/reference/GEMM.cpp79
1 files changed, 52 insertions, 27 deletions
diff --git a/tests/validation/reference/GEMM.cpp b/tests/validation/reference/GEMM.cpp
index f7e97e47b8..20f1139a02 100644
--- a/tests/validation/reference/GEMM.cpp
+++ b/tests/validation/reference/GEMM.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2021 Arm Limited.
+ * Copyright (c) 2017-2021,2024 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -35,10 +35,11 @@ namespace validation
namespace reference
{
template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type>
-SimpleTensor<T> gemm(const SimpleTensor<T> &a, const SimpleTensor<T> &b, const SimpleTensor<T> &c, float alpha, float beta)
+SimpleTensor<T>
+gemm(const SimpleTensor<T> &a, const SimpleTensor<T> &b, const SimpleTensor<T> &c, float alpha, float beta)
{
// Create reference
- SimpleTensor<T> dst{ c.shape(), c.data_type(), 1 };
+ SimpleTensor<T> dst{c.shape(), c.data_type(), 1};
// Compute reference
const int M = a.shape().y();
@@ -50,15 +51,22 @@ SimpleTensor<T> gemm(const SimpleTensor<T> &a, const SimpleTensor<T> &b, const S
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
- 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 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
+ 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
// Note: There are 3 gemm types: batched-gemm, multi-gemm, and batched of multi-gemms. The third dimension of tensor b is overloaded when tensor b has exactly 3 dimensions:
// it can be either number of batches or multis. Batched-GEMM computation is detected only when the third dimension of "a" and "c" tensors is 1 and the number of dimensions is 4
- const bool is_batched_gemm = b.shape().num_dimensions() == 3 && a.shape().num_dimensions() == 4 && c.shape().num_dimensions() == 4 && a.shape()[2] == 1 && c.shape()[2] == 1;
+ const bool is_batched_gemm = b.shape().num_dimensions() == 3 && a.shape().num_dimensions() == 4 &&
+ c.shape().num_dimensions() == 4 && a.shape()[2] == 1 && c.shape()[2] == 1;
// Batched-GEMM
- if(is_batched_gemm)
+ if (is_batched_gemm)
{
b_stride_w = b_stride_z;
}
@@ -69,21 +77,21 @@ SimpleTensor<T> gemm(const SimpleTensor<T> &a, const SimpleTensor<T> &b, const S
#if defined(_OPENMP) && !(defined(__arm__) && defined(__ANDROID__))
#pragma omp parallel for collapse(2)
#endif /* _OPENMP */
- for(int w = 0; w < W; ++w)
+ for (int w = 0; w < W; ++w)
{
- for(int depth = 0; depth < D; ++depth)
+ 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 row = 0; row < M; ++row)
{
- for(int col = 0; col < N; ++col)
+ for (int col = 0; col < N; ++col)
{
T acc(0);
- for(int k = 0; k < K; ++k)
+ for (int k = 0; k < K; ++k)
{
acc += a[base_addr_a + k + row * K] * b[base_addr_b + col + k * N];
}
@@ -99,11 +107,12 @@ SimpleTensor<T> gemm(const SimpleTensor<T> &a, const SimpleTensor<T> &b, const S
}
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)
+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 };
+ SimpleTensor<T> dst{c.shape(), c.data_type(), 1};
// Compute reference
const int M = a.shape().y();
@@ -115,15 +124,22 @@ SimpleTensor<T> gemm_mixed_precision(const SimpleTensor<T> &a, const SimpleTenso
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
- 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 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
+ 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
// Note: There are 3 gemm types: batched-gemm, multi-gemm, and batched of multi-gemms. The third dimension of tensor b is overloaded when tensor b has exactly 3 dimensions:
// it can be either number of batches or multis. Batched-GEMM computation is detected only when the third dimension of "a" and "c" tensors is 1 and the number of dimensions is 4
- const bool is_batched_gemm = b.shape().num_dimensions() == 3 && a.shape().num_dimensions() == 4 && c.shape().num_dimensions() == 4 && a.shape()[2] == 1 && c.shape()[2] == 1;
+ const bool is_batched_gemm = b.shape().num_dimensions() == 3 && a.shape().num_dimensions() == 4 &&
+ c.shape().num_dimensions() == 4 && a.shape()[2] == 1 && c.shape()[2] == 1;
// Batched-GEMM
- if(is_batched_gemm)
+ if (is_batched_gemm)
{
b_stride_w = b_stride_z;
}
@@ -134,27 +150,28 @@ SimpleTensor<T> gemm_mixed_precision(const SimpleTensor<T> &a, const SimpleTenso
#if defined(_OPENMP) && !(defined(__arm__) && defined(__ANDROID__))
#pragma omp parallel for collapse(2)
#endif /* _OPENMP */
- for(int w = 0; w < W; ++w)
+ for (int w = 0; w < W; ++w)
{
- for(int depth = 0; depth < D; ++depth)
+ 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 row = 0; row < M; ++row)
{
- for(int col = 0; col < N; ++col)
+ for (int col = 0; col < N; ++col)
{
float acc(0);
- for(int k = 0; k < K; ++k)
+ 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]);
+ dst[base_addr_c + col + row * N] =
+ static_cast<T>(alpha * acc + beta * c[base_addr_c + col + row * N]);
}
}
}
@@ -163,9 +180,17 @@ SimpleTensor<T> gemm_mixed_precision(const SimpleTensor<T> &a, const SimpleTenso
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);
+template SimpleTensor<float>
+gemm(const SimpleTensor<float> &a, const SimpleTensor<float> &b, const SimpleTensor<float> &c, float alpha, float beta);
+template SimpleTensor<bfloat16> gemm(const SimpleTensor<bfloat16> &a,
+ const SimpleTensor<bfloat16> &b,
+ const SimpleTensor<bfloat16> &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
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-15 06:15:14 +0000