mirror of https://github.com/llvm/torch-mlir
94 lines
4.4 KiB
MLIR
94 lines
4.4 KiB
MLIR
// RUN: npcomp-opt %s | npcomp-opt | FileCheck %s
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// CHECK-LABEL: func @torch.operator(
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func @torch.operator(%arg0: !torch.tensor, %arg1: !torch.tensor) -> !torch.tensor {
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// CHECK: torch.operator "ns.unqual.overload"(%arg0, %arg1) : (!torch.tensor, !torch.tensor) -> !torch.tensor
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%0 = torch.operator "ns.unqual.overload"(%arg0, %arg1) : (!torch.tensor, !torch.tensor) -> !torch.tensor
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return %0 : !torch.tensor
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}
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func @torch.linear_params.create(%arg0: !torch.tensor, %arg1: !torch.tensor) -> (!torch.LinearParams, !torch.LinearParams) {
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%with_bias = torch.linear_params.create %arg0, %arg1 : !torch.tensor, !torch.tensor
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%without_bias = torch.linear_params.create %arg0 : !torch.tensor
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return %with_bias, %without_bias : !torch.LinearParams, !torch.LinearParams
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}
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// CHECK-LABEL: func @builtin_tensor_interop(
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func @builtin_tensor_interop(%arg0: tensor<*xf32>, %arg1: tensor<3x?xsi8>, %arg2: !torch.vtensor<*,f32>, %arg3: !torch.vtensor<[3,?],si8>) {
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// CHECK: torch.from_builtin_tensor %arg0 : tensor<*xf32> -> !torch.vtensor<*,f32>
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%0 = torch.from_builtin_tensor %arg0 : tensor<*xf32> -> !torch.vtensor<*,f32>
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// CHECK: torch.from_builtin_tensor %arg1 : tensor<3x?xsi8> -> !torch.vtensor<[3,?],si8>
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%1 = torch.from_builtin_tensor %arg1 : tensor<3x?xsi8> -> !torch.vtensor<[3,?],si8>
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// CHECK: torch.to_builtin_tensor %arg2 : !torch.vtensor<*,f32> -> tensor<*xf32>
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%2 = torch.to_builtin_tensor %arg2 : !torch.vtensor<*,f32> -> tensor<*xf32>
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// CHECK: torch.to_builtin_tensor %arg3 : !torch.vtensor<[3,?],si8> -> tensor<3x?xsi8>
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%3 = torch.to_builtin_tensor %arg3 : !torch.vtensor<[3,?],si8> -> tensor<3x?xsi8>
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return
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}
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// CHECK: @tensor.default() -> !torch.tensor
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func private @tensor.default() -> !torch.tensor
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// CHECK: @tensor.default_explicit() -> !torch.tensor{{$}}
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func private @tensor.default_explicit() -> !torch.tensor<*,unk>
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// CHECK: @tensor.value_semantic() -> !torch.vtensor{{$}}
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func private @tensor.value_semantic() -> !torch.vtensor<*,unk>
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// CHECK: @tensor.dtype() -> !torch.tensor<*,si32>
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func private @tensor.dtype() -> !torch.tensor<*,si32>
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// CHECK: @tensor.ranked() -> !torch.tensor<[?,?,?],unk>
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func private @tensor.ranked() -> !torch.tensor<[?,?,?],unk>
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// CHECK: @tensor.some_sizes_known() -> !torch.tensor<[?,2,?,4],unk>
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func private @tensor.some_sizes_known() -> !torch.tensor<[?,2,?,4],unk>
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// CHECK: @tensor.fully_determined() -> !torch.vtensor<[1,2,3,4],f32>
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func private @tensor.fully_determined() -> !torch.vtensor<[1,2,3,4],f32>
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// CHECK: @tuple.empty() -> !torch.tuple<>
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func private @tuple.empty() -> !torch.tuple<>
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// CHECK: @tuple.one_element() -> !torch.tuple<!torch.tensor>
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func private @tuple.one_element() -> !torch.tuple<!torch.tensor>
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// CHECK: @tuple.two_elements() -> !torch.tuple<!torch.tensor, !torch.tensor>
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func private @tuple.two_elements() -> !torch.tuple<!torch.tensor, !torch.tensor>
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// CHECK-LABEL: func @torch.tensor() {
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func @torch.tensor() {
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// CHECK: torch.tensor(dense<4.200000e+01> : tensor<3x2xf32>) : !torch.vtensor<[3,2],f32>
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%0 = torch.tensor(dense<42.0> : tensor<3x2xf32>) : !torch.vtensor<[3,2],f32>
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// CHECK: torch.tensor(dense<4.200000e+01> : tensor<3x2xf32>) : !torch.tensor<[3,2],f32>
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%1 = torch.tensor(dense<42.0> : tensor<3x2xf32>) : !torch.tensor<[3,2],f32>
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return
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}
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func @derefine(%arg0: !torch.tensor) -> !torch.optional<!torch.tensor> {
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%0 = torch.derefine %arg0 : !torch.tensor to !torch.optional<!torch.tensor>
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return %0 : !torch.optional<!torch.tensor>
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}
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%bool_true = basicpy.bool_constant true
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%num3_i64 = basicpy.numeric_constant 3 : i64
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%num = basicpy.numeric_constant 4.250000e+01 : f64
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%tensor = torch.tensor(dense<1.000000e+00> : tensor<1xf32>) : !torch.tensor
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%none = torch.constant.none
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func private @f(%arg0: !torch.nn.Module<"test">) {
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return
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}
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torch.class_type @empty {}
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%submodule = torch.nn_module {} : !torch.nn.Module<"empty">
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torch.class_type @test {
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torch.attr "b" : !basicpy.BoolType
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torch.attr "i" : i64
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torch.attr "f" : f64
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torch.attr "t" : !torch.tensor
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torch.attr "submodule" : !torch.nn.Module<"empty">
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torch.attr "ob" : !torch.optional<!basicpy.BoolType>
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torch.method "method", @f
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}
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torch.nn_module {
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torch.slot "b", %bool_true : !basicpy.BoolType
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torch.slot "i", %num3_i64 : i64
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torch.slot "f", %num : f64
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torch.slot "t", %tensor : !torch.tensor
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torch.slot "submodule", %submodule : !torch.nn.Module<"empty">
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torch.slot "ob", %none : !torch.none
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} : !torch.nn.Module<"test">
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