llvm
/
torch-mlir
mirror of https://github.com/llvm/torch-mlir
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity
torch-mlir/test/Conversion/TorchToLinalg/gridsampler.mlir

89 lines
5.2 KiB
MLIR
Raw Normal View History

[torch] GridSample TorchToLinalg lowering (#2883) Lowers `torch.grid_sample` to the equilvalent `linalg` representation.
2024-02-24 01:14:38 +08:00
// RUN: torch-mlir-opt <%s -convert-torch-to-linalg -split-input-file -verify-diagnostics | FileCheck %s
// CHECK: #map
// CHECK-LABEL: func @grid_sampler
// CHECK-DAG: %[[TC0:.*]] = torch_c.to_builtin_tensor %[[ARG0:.*]] : !torch.vtensor<[4,10,10,4],f32> -> tensor<4x10x10x4xf32>
// CHECK-DAG: %[[TC1:.*]] = torch_c.to_builtin_tensor %[[ARG1:.*]] : !torch.vtensor<[4,6,8,2],f32> -> tensor<4x6x8x2xf32>
// CHECK-DAG: %[[FALSE:.*]] = torch.constant.bool false
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index
// CHECK-DAG: %[[CST:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[CST1:.*]] = arith.constant 1.000000e+00 : f32
// CHECK-DAG: %[[CST2:.*]] = arith.constant 2.000000e+00 : f32
// CHECK-DAG: %[[C2_3:.*]] = arith.constant 2 : index
// CHECK-DAG: %[[DIM:.*]] = tensor.dim %[[TC0]], %[[C2_3]] : tensor<4x10x10x4xf32>
// CHECK-DAG: %[[C3:.*]] = arith.constant 3 : index
// CHECK-DAG: %[[DIM_4:.*]] = tensor.dim %[[TC0]], %[[C3]] : tensor<4x10x10x4xf32>
// CHECK-DAG: %[[X2:.*]] = arith.subi %[[DIM:.*]], %[[C1]] : index
// CHECK-DAG: %[[X3:.*]] = arith.subi %[[DIM_4]], %[[C1:.*]] : index
// CHECK-DAG: %[[X4:.*]] = arith.index_cast %[[X2]] : index to i64
// CHECK-DAG: %[[X5:.*]] = arith.index_cast %[[X3]] : index to i64
// CHECK-DAG: %[[X6:.*]] = arith.sitofp %[[X4]] : i64 to f32
// CHECK-DAG: %[[X7:.*]] = arith.sitofp %[[X5]] : i64 to f32
// CHECK-DAG: %[[X8:.*]] = arith.divf %[[X6]], %[[CST2]] : f32
// CHECK-DAG: %[[X9:.*]] = arith.divf %[[X7]], %[[CST2]] : f32
func.func @grid_sampler(%arg0: !torch.vtensor<[4,10,10,4],f32>, %arg1: !torch.vtensor<[4,6,8,2],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
%true = torch.constant.bool 0
%int0 = torch.constant.int 0
%int1 = torch.constant.int 0
%4 = torch.aten.grid_sampler %arg0, %arg1, %int0, %int1, %true : !torch.vtensor<[4,10,10,4],f32>, !torch.vtensor<[4,6,8,2],f32>, !torch.int, !torch.int, !torch.bool -> !torch.vtensor<[?,?,?,?],f32>
return %4 : !torch.vtensor<[?,?,?,?],f32>
}
// -----
// CHECK-LABEL: func @grid_sampler2
// CHECK: #map
// CHECK-DAG: %[[X15:.*]] = arith.mulf %[[X13:.*]], %[[X8:.*]] : f32
// CHECK-DAG: %[[X16:.*]] = arith.mulf %[[X14:.*]], %[[X9:.*]] : f32
// CHECK-DAG: %[[X40:.*]] = arith.mulf %[[EXTRACTED:.*]], %[[X39:.*]] : f32
// CHECK-DAG: %[[X41:.*]] = arith.mulf %[[X31:.*]], %[[X37:.*]] : f32
// CHECK-DAG: %[[X42:.*]] = arith.addf %[[X40:.*]], %[[X41]] : f32
// CHECK-DAG: %[[X43:.*]] = arith.subf %[[CST_1:.*]], %[[X37]] : f32
// CHECK-DAG: %[[X45:.*]] = arith.mulf %[[X34:.*]], %[[X37]] : f32
// CHECK-DAG: %[[X46:.*]] = arith.addf %[[X44:.*]], %[[X45]] : f32
// CHECK-DAG: %[[X47:.*]] = arith.subf %[[CST_1]], %[[X38:.*]] : f32
// CHECK-DAG: %[[X48:.*]] = arith.mulf %[[X42]], %[[XX47:.*]] : f32
// CHECK-DAG: %[[X49:.*]] = arith.mulf %[[X46]], %[[XX38:.*]] : f32
// CHECK-DAG: %[[X50:.*]] = arith.addf %[[X48]], %[[X49]] : f32
// CHECK-DAG: linalg.yield %[[X50]] : f32
// CHECK: } -> tensor<?x?x?x?xf32>
// CHECK: %[[X12:.*]] = torch_c.from_builtin_tensor %[[X11:.*]] : tensor<?x?x?x?xf32> -> !torch.vtensor<[?,?,?,?],f32>
// CHECK: return %[[X12]] : !torch.vtensor<[?,?,?,?],f32>
func.func @grid_sampler2(%arg0: !torch.vtensor<[?,?,?,?],f32>, %arg1: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
%true = torch.constant.bool 0
%int0 = torch.constant.int 0
%int1 = torch.constant.int 0
%4 = torch.aten.grid_sampler %arg0, %arg1, %int0, %int1, %true : !torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[?,?,?,?],f32>, !torch.int, !torch.int, !torch.bool -> !torch.vtensor<[?,?,?,?],f32>
return %4 : !torch.vtensor<[?,?,?,?],f32>
[onnx][torch][linalg] Implementing align-corner modes for gridsampler (#3171) Align corner modes which select what the corners mean. Either the center of the corner points or the edges of the edge points. --------- Co-authored-by: Rob Suderman <rob.suderman@gmail.com>
2024-04-18 04:38:19 +08:00
}
// -----
// CHECK-LABEL: func @grid_sampler3
// CHECK: #map
// CHECK-DAG: %[[X15:.*]] = arith.mulf %[[X13:.*]], %[[X8:.*]] : f32
// CHECK-DAG: %[[X16:.*]] = arith.mulf %[[X14:.*]], %[[X9:.*]] : f32
// CHECK-DAG: %[[X40:.*]] = arith.mulf %[[EXTRACTED:.*]], %[[X39:.*]] : f32
// CHECK-DAG: %[[X41:.*]] = arith.mulf %[[X31:.*]], %[[X37:.*]] : f32
// CHECK-DAG: %[[X42:.*]] = arith.addf %[[X40:.*]], %[[X41]] : f32
// CHECK-DAG: %[[X43:.*]] = arith.subf %[[CST_1:.*]], %[[X37]] : f32
// CHECK-DAG: %[[X45:.*]] = arith.mulf %[[X34:.*]], %[[X37]] : f32
// CHECK-DAG: %[[X46:.*]] = arith.addf %[[X44:.*]], %[[X45]] : f32
// CHECK-DAG: %[[X47:.*]] = arith.subf %[[CST_1]], %[[X38:.*]] : f32
// CHECK-DAG: %[[X48:.*]] = arith.mulf %[[X42]], %[[XX47:.*]] : f32
// CHECK-DAG: %[[X49:.*]] = arith.mulf %[[X46]], %[[XX38:.*]] : f32
// CHECK-DAG: %[[X50:.*]] = arith.addf %[[X48]], %[[X49]] : f32
// CHECK-DAG: linalg.yield %[[X50]] : f32
// CHECK: } -> tensor<?x?x?x?xf32>
// CHECK: %[[X12:.*]] = torch_c.from_builtin_tensor %[[X11:.*]] : tensor<?x?x?x?xf32> -> !torch.vtensor<[?,?,?,?],f32>
// CHECK: return %[[X12]] : !torch.vtensor<[?,?,?,?],f32>
func.func @grid_sampler3(%arg0: !torch.vtensor<[?,?,?,?],f32>, %arg1: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
%false = torch.constant.bool 1
%int0 = torch.constant.int 0
%int1 = torch.constant.int 0
%4 = torch.aten.grid_sampler %arg0, %arg1, %int0, %int1, %false : !torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[?,?,?,?],f32>, !torch.int, !torch.int, !torch.bool -> !torch.vtensor<[?,?,?,?],f32>
return %4 : !torch.vtensor<[?,?,?,?],f32>
}