| // Test large aligned linalg matmul to make sure we go through the optimized |
| // path for GPUs. |
| |
| // Problem size : 2048x512x1024 |
| // Input type : F32 |
| // Accumulation type : F32 |
| func.func @matmul_2048x512x1024_f32_f32() { |
| %lhs = util.unfoldable_constant dense<1.0> : tensor<2048x1024xf32> |
| %rhs = util.unfoldable_constant dense<0.4> : tensor<1024x512xf32> |
| %c0 = arith.constant 0.0 : f32 |
| %init = tensor.empty() : tensor<2048x512xf32> |
| %CC = linalg.fill ins(%c0 : f32) outs(%init : tensor<2048x512xf32>) -> tensor<2048x512xf32> |
| %D = linalg.matmul ins(%lhs, %rhs: tensor<2048x1024xf32>, tensor<1024x512xf32>) |
| outs(%CC: tensor<2048x512xf32>) -> tensor<2048x512xf32> |
| check.expect_almost_eq_const(%D, dense<409.596> : tensor<2048x512xf32>) : tensor<2048x512xf32> |
| return |
| } |
| |
| // Problem size : 3456x1024x2048 |
| // Input type : F16 |
| // Accumulation type : F16 |
| func.func @matmul_3456x1024x2048_f16_f16() { |
| %lhs = util.unfoldable_constant dense<1.00> : tensor<3456x2048xf16> |
| %rhs = util.unfoldable_constant dense<0.01> : tensor<2048x1024xf16> |
| %c0 = arith.constant 0.0 : f16 |
| %init = tensor.empty() : tensor<3456x1024xf16> |
| %CC = linalg.fill ins(%c0 : f16) outs(%init : tensor<3456x1024xf16>) -> tensor<3456x1024xf16> |
| %D = linalg.matmul ins(%lhs, %rhs: tensor<3456x2048xf16>, tensor<2048x1024xf16>) |
| outs(%CC: tensor<3456x1024xf16>) -> tensor<3456x1024xf16> |
| check.expect_almost_eq_const(%D, dense<20.2812> : tensor<3456x1024xf16>) : tensor<3456x1024xf16> |
| return |
| } |
