| # RUN: %PYTHON %s | FileCheck %s |
| |
| import torch |
| import numpy as np |
| |
| from mpact.mpactbackend import mpact_linalg |
| |
| from mpact.models.kernels import MMNet |
| |
| |
| net = MMNet() |
| |
| X = torch.arange(0, 16, dtype=torch.float32).view(4, 4) |
| Y = torch.arange(16, 32, dtype=torch.float32).view(4, 4) |
| |
| # |
| # CHECK: module { |
| # CHECK: func.func @main(%[[A0:.*]]: tensor<4x4xf32>, %[[A1:.*]]: tensor<4x4xf32>) -> tensor<4x4xf32> { |
| # CHECK: %[[C0:.*]] = arith.constant 0.000000e+00 : f32 |
| # CHECK: %[[T0:.*]] = tensor.empty() : tensor<4x4xf32> |
| # CHECK: %[[T1:.*]] = linalg.fill ins(%[[C0]] : f32) outs(%[[T0]] : tensor<4x4xf32>) -> tensor<4x4xf32> |
| # CHECK: %[[T2:.*]] = linalg.matmul |
| # CHECK-SAME: ins(%[[A0]], %[[A1]] : tensor<4x4xf32>, tensor<4x4xf32>) |
| # CHECK-SAME: outs(%[[T1]] : tensor<4x4xf32>) -> tensor<4x4xf32> |
| # CHECK: return %2 : tensor<4x4xf32> |
| # CHECK: } |
| # CHECK: } |
| # |
| |
| linalg = mpact_linalg(net, X, Y) |
| print(linalg) |
