diff options
Diffstat (limited to 'tests/validation/fixtures/MatMulKernelFixture.h')
| -rw-r--r-- | tests/validation/fixtures/MatMulKernelFixture.h | 47 |
1 files changed, 29 insertions, 18 deletions
diff --git a/tests/validation/fixtures/MatMulKernelFixture.h b/tests/validation/fixtures/MatMulKernelFixture.h index 91ac77d5af..50d194c43a 100644 --- a/tests/validation/fixtures/MatMulKernelFixture.h +++ b/tests/validation/fixtures/MatMulKernelFixture.h @@ -21,8 +21,8 @@ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ -#ifndef ACL_TESTS_VALIDATION_FIXTURES_MATMULKERNELFIXTURE -#define ACL_TESTS_VALIDATION_FIXTURES_MATMULKERNELFIXTURE +#ifndef ACL_TESTS_VALIDATION_FIXTURES_MATMULKERNELFIXTURE_H +#define ACL_TESTS_VALIDATION_FIXTURES_MATMULKERNELFIXTURE_H #include "arm_compute/core/KernelDescriptors.h" #include "arm_compute/core/Utils.h" @@ -54,6 +54,12 @@ public: void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, int M0, int N0, int K0, bool export_rhs_to_cl_image, DataType data_type, bool enable_bias) { + // This hash is used by random generators. There may be hash collisions but + // this is intentional as it's a very easy way to make the the current + // random generation process almost different for many test configurations, + // which were using the same set of values before. + _hash = M0 + N0 + K0 + shape_a[0] + shape_a[1] + shape_b[0] + shape_b[1] + enable_bias + export_rhs_to_cl_image; + // Flag to create a bias _enable_bias = enable_bias; @@ -67,7 +73,7 @@ public: const int32_t t_max = static_cast<int32_t>(std::numeric_limits<T>::max()); const int32_t t_min = static_cast<int32_t>(std::numeric_limits<T>::min()); - std::mt19937 generator(library->seed()); + std::mt19937 generator(library->seed() + _hash); std::uniform_real_distribution<float> distribution_float(-5.0f, 3.0f); std::uniform_int_distribution<int32_t> distribution_t(t_min, t_max); @@ -84,7 +90,12 @@ public: const int n = shape_b.x(); const int k = shape_a.x(); - dst_q_info = calculate_mat_mul_dst_q_info(lhs_q_info, rhs_q_info, m, n, k, data_type); + const float bias_fraction = enable_bias ? 0.5f : 0.f; + + QuantizationHint q_hint = suggest_matmul_dst_q_info_and_bias(lhs_q_info, rhs_q_info, m, n, k, data_type, bias_fraction); + dst_q_info = q_hint.q_info; + _min_bias = q_hint.bias_min; + _max_bias = q_hint.bias_max; } if(pretranspose_a) @@ -142,12 +153,9 @@ protected: } template <typename U> - void fill_bias_s32(U &&tensor, int i, const UniformQuantizationInfo &q_info) + void fill_bias_s32(U &&tensor, int i, int32_t min, int32_t max) { - // For quantized cases, fill the S32 bias according to the following to avoid saturation of test cases. - // The following code limits size of bias values to within expected range of output quantization. - const unsigned int bound = std::abs(q_info.scale * 256); // 256 is size of 8 bit datatype - std::uniform_int_distribution<int32_t> distribution(-(bound / 10), (bound / 10)); + std::uniform_int_distribution<int32_t> distribution(min, max); library->fill(tensor, distribution, i); } @@ -192,8 +200,8 @@ protected: ARM_COMPUTE_ASSERT(!dst.info()->is_resizable()); // Fill tensors - fill(CLAccessor(a), 0); - fill(CLAccessor(b), 1); + fill(CLAccessor(a), _hash + 1); + fill(CLAccessor(b), _hash + 2); // Compute matMul kernel ITensorPack tensors_pack({ { ACL_SRC_0, &a }, @@ -207,11 +215,11 @@ protected: bias.allocator()->allocate(); if(is_quantized) { - fill_bias_s32(CLAccessor(bias), 2, dst_q_info.uniform()); + fill_bias_s32(CLAccessor(bias), _hash + 3, _min_bias, _max_bias); } else { - fill(CLAccessor(bias), 2); + fill(CLAccessor(bias), _hash + 3); } tensors_pack.add_tensor(ACL_SRC_2, &bias); } @@ -236,8 +244,8 @@ protected: SimpleTensor<T> c{ output_shape_collapsed, data_type, 1, dst_q_info }; // Fill reference - fill(a, 0); - fill(b, 1); + fill(a, _hash + 1); + fill(b, _hash + 2); /* 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) @@ -288,7 +296,7 @@ protected: // of bias tensor from shape [dst.dimension(0)] to [dst.tensor_shape()] in target kernel if(_enable_bias) { - fill(c, 2); + fill(c, _hash + 3); const int n = c.shape().x(); const int other_dims = c.shape().collapsed_from(1)[1]; for(int i = 1; i < other_dims; ++i) // For all data, copy first n elements into remaining batches @@ -323,7 +331,7 @@ protected: if(_enable_bias) { // Identical to float implementation, fill and copy values of bias first dimension - fill_bias_s32(bias, 2, cq); + fill_bias_s32(bias, _hash + 3, _min_bias, _max_bias); const int n = bias.shape().x(); const int other_dims = bias.shape().collapsed_from(1)[1]; const unsigned int dt_size = sizeof(int32_t); @@ -348,6 +356,9 @@ protected: bool _enable_bias{ false }; bool _device_supports_export_to_cl_image{ true }; bool _device_supports_mmul{ true }; + int32_t _min_bias{ 0 }; + int32_t _max_bias{ 0 }; + int32_t _hash{ 0 }; }; template <typename T, typename KernelType, bool use_mmul = false> @@ -374,4 +385,4 @@ public: } // namespace validation } // namespace test } // namespace arm_compute -#endif /* ACL_TESTS_VALIDATION_FIXTURES_MATMULKERNELFIXTURE */ +#endif // ACL_TESTS_VALIDATION_FIXTURES_MATMULKERNELFIXTURE_H |