| # RUN: %PYTHON %s | FileCheck %s |
| |
| from mlir.ir import * |
| from mlir.dialects import builtin |
| from mlir.dialects import linalg |
| from mlir.dialects import std |
| |
| from mlir.dialects.linalg.opdsl.lang import * |
| |
| T1 = TV.T1 |
| T2 = TV.T2 |
| |
| |
| @linalg_structured_op |
| def conv_poly( |
| I=TensorDef(T1, S.N, S.IH, S.IW, S.C), |
| K=TensorDef(T2, S.KH, S.KW, S.C), |
| O=TensorDef(U, S.N, S.OH, S.OW, S.C, output=True), |
| strides=AttributeDef(S.SH, S.SW), |
| dilations=AttributeDef(S.DH, S.DW)): |
| domain(D.n, D.oh, D.ow, D.kh, D.kw, D.c) |
| O[D.n, D.oh, D.ow, D.c] += cast( |
| U, I[D.n, D.oh * S.SH + D.kh * S.DH, D.ow * S.SW + D.kw * S.DW, |
| D.c]) * cast(U, K[D.kh, D.kw, D.c]) |
| |
| |
| with Context() as ctx, Location.unknown(): |
| module = Module.create() |
| f32 = F32Type.get() |
| i32 = IntegerType.get_signless(32) |
| with InsertionPoint(module.body): |
| |
| # Convolution indexing maps. |
| # CHECK: #[[$CONV_MAP_I:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d0, d1 * 2 + d3, d2 * 4 + d4 * 2, d5)> |
| # CHECK: #[[$CONV_MAP_K:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d3, d4, d5)> |
| # CHECK: #[[$CONV_MAP_O:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d0, d1, d2, d5)> |
| |
| # CHECK-LABEL: @test_f32i32_conv |
| # CHECK: linalg.generic |
| # CHECK-SAME: indexing_maps = [#[[$CONV_MAP_I]], #[[$CONV_MAP_K]], #[[$CONV_MAP_O]]] |
| # CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "reduction", "reduction", "parallel"] |
| # CHECK: ^{{.*}}(%[[IN:.+]]: f32, %[[FILTER:.+]]: f32, %[[OUT:.+]]: i32) |
| # CHECK-NEXT: %[[IN_CAST:.+]] = arith.fptosi %[[IN:.+]] : f32 to i32 |
| # CHECK-NEXT: %[[FILTER_CAST:.+]] = arith.fptosi %[[FILTER:.+]] : f32 to i32 |
| # CHECK-NEXT: %[[PROD:.+]] = arith.muli %[[IN_CAST]], %[[FILTER_CAST]] : i32 |
| # CHECK-NEXT: %[[SUM:.+]] = arith.addi %[[OUT]], %[[PROD]] : i32 |
| # CHECK-NEXT: linalg.yield %[[SUM]] : i32 |
| # CHECK-NEXT: -> tensor<1x2x4x1xi32> |
| @builtin.FuncOp.from_py_func( |
| RankedTensorType.get((1, 4, 16, 1), f32), |
| RankedTensorType.get((2, 2, 1), f32), |
| RankedTensorType.get((1, 2, 4, 1), i32)) |
| def test_f32i32_conv(input, filter, init_result): |
| return conv_poly( |
| input, filter, outs=[init_result], strides=[2, 4], dilations=[1, 2]) |
| |
| |
| print(module) |