Compute Library
 21.08
GEMM.cpp
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24 #include "GEMM.h"
25 
27 #include "arm_compute/core/Types.h"
28 
29 namespace arm_compute
30 {
31 namespace test
32 {
33 namespace validation
34 {
35 namespace reference
36 {
37 template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type>
38 SimpleTensor<T> gemm(const SimpleTensor<T> &a, const SimpleTensor<T> &b, const SimpleTensor<T> &c, float alpha, float beta)
39 {
40  // Create reference
41  SimpleTensor<T> dst{ c.shape(), c.data_type(), 1 };
42 
43  // Compute reference
44  const int M = a.shape().y();
45  const int N = b.shape().x();
46  const int K = a.shape().x();
47  const int D = a.shape().z(); // Number of matrices in a batch
48  const int W = a.shape()[3]; // Number of batched-gemm (Winograd case)
49 
50  const int a_stride_z = K * M;
51  const int a_stride_w = K * M * D;
52 
53  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
54  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
55 
56  // 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:
57  // 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
58  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;
59 
60  // Batched-GEMM
61  if(is_batched_gemm)
62  {
63  b_stride_w = b_stride_z;
64  }
65 
66  const int c_stride_z = N * M;
67  const int c_stride_w = N * M * D;
68 
69 #if defined(_OPENMP) && !(defined(__arm__) && defined(__ANDROID__))
70  #pragma omp parallel for collapse(2)
71 #endif /* _OPENMP */
72  for(int w = 0; w < W; ++w)
73  {
74  for(int depth = 0; depth < D; ++depth)
75  {
76  const int base_addr_a = depth * a_stride_z + w * a_stride_w;
77  const int base_addr_b = depth * b_stride_z + w * b_stride_w;
78  const int base_addr_c = depth * c_stride_z + w * c_stride_w;
79 
80  for(int row = 0; row < M; ++row)
81  {
82  for(int col = 0; col < N; ++col)
83  {
84  T acc(0);
85 
86  for(int k = 0; k < K; ++k)
87  {
88  acc += a[base_addr_a + k + row * K] * b[base_addr_b + col + k * N];
89  }
90 
91  // Finalize the result: alpha * A * B + beta * C
92  dst[base_addr_c + col + row * N] = alpha * acc + beta * c[base_addr_c + col + row * N];
93  }
94  }
95  }
96  }
97 
98  return dst;
99 }
100 
101 template <typename T, typename std::enable_if<is_floating_point<T>::value, int>::type>
102 SimpleTensor<T> gemm_mixed_precision(const SimpleTensor<T> &a, const SimpleTensor<T> &b, const SimpleTensor<T> &c, float alpha, float beta)
103 {
104  // GEMM mixed-precision combines F32 accumulators with F16 multiplications
105  // Create reference
106  SimpleTensor<T> dst{ c.shape(), c.data_type(), 1 };
107 
108  // Compute reference
109  const int M = a.shape().y();
110  const int N = b.shape().x();
111  const int K = a.shape().x();
112  const int D = a.shape().z(); // Number of matrices in a batch
113  const int W = a.shape()[3]; // Number of batched-gemm (Winograd case)
114 
115  const int a_stride_z = K * M;
116  const int a_stride_w = K * M * D;
117 
118  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
119  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
120 
121  // 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:
122  // 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
123  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;
124 
125  // Batched-GEMM
126  if(is_batched_gemm)
127  {
128  b_stride_w = b_stride_z;
129  }
130 
131  const int c_stride_z = N * M;
132  const int c_stride_w = N * M * D;
133 
134 #if defined(_OPENMP) && !(defined(__arm__) && defined(__ANDROID__))
135  #pragma omp parallel for collapse(2)
136 #endif /* _OPENMP */
137  for(int w = 0; w < W; ++w)
138  {
139  for(int depth = 0; depth < D; ++depth)
140  {
141  const int base_addr_a = depth * a_stride_z + w * a_stride_w;
142  const int base_addr_b = depth * b_stride_z + w * b_stride_w;
143  const int base_addr_c = depth * c_stride_z + w * c_stride_w;
144 
145  for(int row = 0; row < M; ++row)
146  {
147  for(int col = 0; col < N; ++col)
148  {
149  float acc(0);
150 
151  for(int k = 0; k < K; ++k)
152  {
153  acc += static_cast<float>(a[base_addr_a + k + row * K] * b[base_addr_b + col + k * N]);
154  }
155 
156  // Finalize the result: alpha * A * B + beta * C
157  dst[base_addr_c + col + row * N] = static_cast<T>(alpha * acc + beta * c[base_addr_c + col + row * N]);
158  }
159  }
160  }
161  }
162 
163  return dst;
164 }
165 
166 template SimpleTensor<float> gemm(const SimpleTensor<float> &a, const SimpleTensor<float> &b, const SimpleTensor<float> &c, float alpha, float beta);
167 template SimpleTensor<half> gemm(const SimpleTensor<half> &a, const SimpleTensor<half> &b, const SimpleTensor<half> &c, float alpha, float beta);
168 template SimpleTensor<half> gemm_mixed_precision(const SimpleTensor<half> &a, const SimpleTensor<half> &b, const SimpleTensor<half> &c, float alpha, float beta);
169 } // namespace reference
170 } // namespace validation
171 } // namespace test
172 } // namespace arm_compute
SimpleTensor< float > w
Definition: DFT.cpp:156
SimpleTensor< float > b
Definition: DFT.cpp:157
DataType data_type() const override
Data type of the tensor.
Definition: SimpleTensor.h:357
unsigned int M
TensorShape shape() const override
Shape of the tensor.
Definition: SimpleTensor.h:320
decltype(strategy::transforms) typedef type
Copyright (c) 2017-2021 Arm Limited.
unsigned int N
Simple tensor object that stores elements in a consecutive chunk of memory.
Definition: SimpleTensor.h:58
SimpleTensor< T > gemm_mixed_precision(const SimpleTensor< T > &a, const SimpleTensor< T > &b, const SimpleTensor< T > &c, float alpha, float beta)
Definition: GEMM.cpp:102
SimpleTensor< T > gemm(const SimpleTensor< T > &a, const SimpleTensor< T > &b, const SimpleTensor< T > &c, float alpha, float beta)
Definition: GEMM.cpp:38
unsigned int K