mirror of https://github.com/llvm/torch-mlir
30 lines
2.0 KiB
MLIR
30 lines
2.0 KiB
MLIR
// RUN: torch-mlir-opt <%s -convert-torch-to-linalg -split-input-file -verify-diagnostics | FileCheck %s
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// CHECK-LABEL: func @forward
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func.func @forward(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
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%int1 = torch.constant.int 1
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%int2 = torch.constant.int 2
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%int3 = torch.constant.int 3
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%int4 = torch.constant.int 4
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%int5 = torch.constant.int 5
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%int6 = torch.constant.int 6
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%int7 = torch.constant.int 7
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%int8 = torch.constant.int 8
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%false = torch.constant.bool false
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// CHECK: %[[C1:.*]] = torch_c.to_i64 %int1
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// CHECK: %[[C2:.*]] = torch_c.to_i64 %int2
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// CHECK: %[[NEUTRAL:.*]] = arith.constant -3.40282347E+38 : f32
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// CHECK: %[[PADDED:.*]] = tensor.pad %{{.*}} low[0, 0, 5, 6] high[0, 0, 5, 6]
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// CHECK: %[[OUT:.*]] = linalg.fill ins(%[[NEUTRAL]] : f32) outs(%{{.*}} : tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
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// CHECK: %[[T1:.*]] = arith.index_cast %[[C1]] : i64 to index
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// CHECK: %[[T2:.*]] = arith.index_cast %[[C2]] : i64 to index
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// CHECK: %[[INIT:.*]] = tensor.empty(%[[T1]], %[[T2]]) : tensor<?x?xf32>
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// CHECK: linalg.pooling_nchw_max {dilations = dense<[7, 8]> : vector<2xi64>, strides = dense<[3, 4]> : vector<2xi64>} ins(%[[PADDED]], %[[INIT]] : tensor<?x?x?x?xf32>, tensor<?x?xf32>) outs(%[[OUT]] : tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
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%kernel_size = torch.prim.ListConstruct %int1, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
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%stride = torch.prim.ListConstruct %int3, %int4 : (!torch.int, !torch.int) -> !torch.list<int>
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%padding = torch.prim.ListConstruct %int5, %int6 : (!torch.int, !torch.int) -> !torch.list<int>
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%dilation = torch.prim.ListConstruct %int7, %int8 : (!torch.int, !torch.int) -> !torch.list<int>
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%4 = torch.aten.max_pool2d %arg0, %kernel_size, %stride, %padding, %dilation, %false : !torch.vtensor<[?,?,?,?],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool -> !torch.vtensor<[?,?,?,?],f32>
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return %4 : !torch.vtensor<[?,?,?,?],f32>
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}
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