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4817 lines (4448 loc) · 397 KB
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// RUN: torch-mlir-opt %s -convert-torch-to-tosa -split-input-file -verify-diagnostics | FileCheck %s --check-prefix=CHECK
// CHECK-LABEL: func.func @torch.aten.tanh$basic(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[ARG_BUILTIN:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[RESULT_BUILTIN:.*]] = tosa.tanh %[[ARG_BUILTIN]] : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[RESULT_BUILTIN]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[RESULT]] : !torch.vtensor<[?,?],f32>
func.func @torch.aten.tanh$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.tanh %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func.func @conv2d_io_insert_reshape(
// CHECK-DAG: torch_c.to_builtin_tensor %arg0
// CHECK-DAG: torch_c.to_builtin_tensor %arg1
// CHECK: tosa.reshape
// CHECK: tosa.reshape
// CHECK: tosa.conv2d
// CHECK-NOT: torch.aten.convolution
func.func @conv2d_io_insert_reshape(%arg0: !torch.vtensor<[256],f32>, %arg1: !torch.vtensor<[256],f32>, %arg2: !torch.vtensor<[1],f32>) -> !torch.vtensor<[1,1,1,1],f32> {
%int0 = torch.constant.int 0
%int1 = torch.constant.int 1
%int16 = torch.constant.int 16
%false = torch.constant.bool false
%shape = torch.prim.ListConstruct %int1, %int1, %int16, %int16 : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
%wshape = torch.prim.ListConstruct %int1, %int1, %int16, %int16 : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
%input_tmpl = torch.aten.reshape %arg0, %shape : !torch.vtensor<[256],f32>, !torch.list<int> -> !torch.vtensor<[1,1,16,16],f32>
%weight_tmpl = torch.aten.reshape %arg1, %wshape : !torch.vtensor<[256],f32>, !torch.list<int> -> !torch.vtensor<[1,1,16,16],f32>
%stride = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
%padding = torch.prim.ListConstruct %int0, %int0 : (!torch.int, !torch.int) -> !torch.list<int>
%dilation = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
%output_padding = torch.prim.ListConstruct %int0, %int0 : (!torch.int, !torch.int) -> !torch.list<int>
%conv = torch.aten.convolution %input_tmpl, %weight_tmpl, %arg2, %stride, %padding, %dilation, %false, %output_padding, %int1 : !torch.vtensor<[1,1,16,16],f32>, !torch.vtensor<[1,1,16,16],f32>, !torch.vtensor<[1],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,1,1,1],f32>
return %conv : !torch.vtensor<[1,1,1,1],f32>
}
// CHECK-LABEL: func.func @depthwise_conv2d_io_insert_reshape(
// CHECK-DAG: torch_c.to_builtin_tensor %arg0
// CHECK-DAG: torch_c.to_builtin_tensor %arg1
// CHECK: tosa.reshape
// CHECK: tosa.reshape
// CHECK: tosa.depthwise_conv2d
// CHECK-NOT: torch.aten.convolution
func.func @depthwise_conv2d_io_insert_reshape(%arg0: !torch.vtensor<[9],f32>, %arg1: !torch.vtensor<[9],f32>, %arg2: !torch.vtensor<[3],f32>) -> !torch.vtensor<[1,3,1,1],f32> {
%int0 = torch.constant.int 0
%int1 = torch.constant.int 1
%int3 = torch.constant.int 3
%false = torch.constant.bool false
%shape = torch.prim.ListConstruct %int1, %int3, %int3, %int1 : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
%wshape = torch.prim.ListConstruct %int3, %int1, %int3, %int1 : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
%input_tmpl = torch.aten.reshape %arg0, %shape : !torch.vtensor<[9],f32>, !torch.list<int> -> !torch.vtensor<[1,3,3,1],f32>
%weight_tmpl = torch.aten.reshape %arg1, %wshape : !torch.vtensor<[9],f32>, !torch.list<int> -> !torch.vtensor<[3,1,3,1],f32>
%stride = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
%padding = torch.prim.ListConstruct %int0, %int0 : (!torch.int, !torch.int) -> !torch.list<int>
%dilation = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
%output_padding = torch.prim.ListConstruct %int0, %int0 : (!torch.int, !torch.int) -> !torch.list<int>
%conv = torch.aten.convolution %input_tmpl, %weight_tmpl, %arg2, %stride, %padding, %dilation, %false, %output_padding, %int3 : !torch.vtensor<[1,3,3,1],f32>, !torch.vtensor<[3,1,3,1],f32>, !torch.vtensor<[3],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,3,1,1],f32>
return %conv : !torch.vtensor<[1,3,1,1],f32>
}
// CHECK-LABEL: func.func @transpose_conv2d_io_insert_reshape(
// CHECK-DAG: torch_c.to_builtin_tensor %arg0
// CHECK-DAG: torch_c.to_builtin_tensor %arg1
// CHECK: tosa.reshape
// CHECK: tosa.reshape
// CHECK: tosa.transpose_conv2d
// CHECK-NOT: torch.aten.convolution
func.func @transpose_conv2d_io_insert_reshape(%arg0: !torch.vtensor<[9],f32>, %arg1: !torch.vtensor<[9],f32>, %arg2: !torch.vtensor<[1],f32>) -> !torch.vtensor<[1,1,5,5],f32> {
%int0 = torch.constant.int 0
%int1 = torch.constant.int 1
%int3 = torch.constant.int 3
%true = torch.constant.bool true
%shape = torch.prim.ListConstruct %int1, %int1, %int3, %int3 : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
%wshape = torch.prim.ListConstruct %int1, %int1, %int3, %int3 : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
%input_tmpl = torch.aten.reshape %arg0, %shape : !torch.vtensor<[9],f32>, !torch.list<int> -> !torch.vtensor<[1,1,3,3],f32>
%weight_tmpl = torch.aten.reshape %arg1, %wshape : !torch.vtensor<[9],f32>, !torch.list<int> -> !torch.vtensor<[1,1,3,3],f32>
%stride = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
%padding = torch.prim.ListConstruct %int0, %int0 : (!torch.int, !torch.int) -> !torch.list<int>
%dilation = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
%output_padding = torch.prim.ListConstruct %int0, %int0 : (!torch.int, !torch.int) -> !torch.list<int>
%conv = torch.aten.convolution %input_tmpl, %weight_tmpl, %arg2, %stride, %padding, %dilation, %true, %output_padding, %int1 : !torch.vtensor<[1,1,3,3],f32>, !torch.vtensor<[1,1,3,3],f32>, !torch.vtensor<[1],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,1,5,5],f32>
return %conv : !torch.vtensor<[1,1,5,5],f32>
}
// CHECK-LABEL: func.func @conv3d_io_insert_reshape(
// CHECK-DAG: torch_c.to_builtin_tensor %arg0
// CHECK-DAG: torch_c.to_builtin_tensor %arg1
// CHECK: tosa.reshape
// CHECK: tosa.reshape
// CHECK: tosa.conv3d
// CHECK-NOT: torch.aten.convolution
func.func @conv3d_io_insert_reshape(%arg0: !torch.vtensor<[64],f32>, %arg1: !torch.vtensor<[1],f32>, %arg2: !torch.vtensor<[1],f32>) -> !torch.vtensor<[1,1,4,4,4],f32> {
%int0 = torch.constant.int 0
%int1 = torch.constant.int 1
%int4 = torch.constant.int 4
%false = torch.constant.bool false
%shape = torch.prim.ListConstruct %int1, %int1, %int4, %int4, %int4 : (!torch.int, !torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
%wshape = torch.prim.ListConstruct %int1, %int1, %int1, %int1, %int1 : (!torch.int, !torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
%input_tmpl = torch.aten.reshape %arg0, %shape : !torch.vtensor<[64],f32>, !torch.list<int> -> !torch.vtensor<[1,1,4,4,4],f32>
%weight_tmpl = torch.aten.reshape %arg1, %wshape : !torch.vtensor<[1],f32>, !torch.list<int> -> !torch.vtensor<[1,1,1,1,1],f32>
%stride = torch.prim.ListConstruct %int1, %int1, %int1 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
%padding = torch.prim.ListConstruct %int0, %int0, %int0 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
%dilation = torch.prim.ListConstruct %int1, %int1, %int1 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
%output_padding = torch.prim.ListConstruct %int0, %int0, %int0 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
%conv = torch.aten.convolution %input_tmpl, %weight_tmpl, %arg2, %stride, %padding, %dilation, %false, %output_padding, %int1 : !torch.vtensor<[1,1,4,4,4],f32>, !torch.vtensor<[1,1,1,1,1],f32>, !torch.vtensor<[1],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,1,4,4,4],f32>
return %conv : !torch.vtensor<[1,1,4,4,4],f32>
}
// CHECK-LABEL: func.func @torch.aten.sigmoid$basic(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[ARG_BUILTIN:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[RESULT_BUILTIN:.*]] = tosa.sigmoid %[[ARG_BUILTIN]] : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[RESULT_BUILTIN]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[RESULT]] : !torch.vtensor<[?,?],f32>
func.func @torch.aten.sigmoid$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.sigmoid %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.relu$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_2:.*]] = tosa.clamp %[[VAL_1]] {max_val = 3.40282347E+38 : f32, min_val = 0.000000e+00 : f32} : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.from_builtin_tensor %[[VAL_2]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_3]] : !torch.vtensor<[?,?],f32>
// CHECK: }
func.func @torch.aten.relu$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.relu %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.leaky_relu$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.float 1.000000e-01
// CHECK: %[[VAL_3:.*]] = "tosa.const"() <{values = dense<1.000000e-01> : tensor<f32>}> : () -> tensor<f32>
// CHECK: %[[VAL_4:.*]] = tosa.const_shape {values = dense<1> : tensor<2xindex>} : () -> !tosa.shape<2>
// CHECK: %[[VAL_5:.*]] = tosa.reshape %[[VAL_3]], %[[VAL_4]] : (tensor<f32>, !tosa.shape<2>) -> tensor<1x1xf32>
// CHECK: %[[VAL_6:.*]] = "tosa.const"() <{values = dense<0.000000e+00> : tensor<f32>}> : () -> tensor<f32>
// CHECK: %[[VAL_7:.*]] = tosa.const_shape {values = dense<1> : tensor<2xindex>} : () -> !tosa.shape<2>
// CHECK: %[[VAL_8:.*]] = tosa.reshape %[[VAL_6]], %[[VAL_7]] : (tensor<f32>, !tosa.shape<2>) -> tensor<1x1xf32>
// CHECK: %[[VAL_9:.*]] = tosa.greater_equal %[[VAL_1]], %[[VAL_8]] : (tensor<?x?xf32>, tensor<1x1xf32>) -> tensor<?x?xi1>
// CHECK: %[[VAL_10:.*]] = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: %[[VAL_11:.*]] = tosa.mul %[[VAL_1]], %[[VAL_5]], %[[VAL_10]] : (tensor<?x?xf32>, tensor<1x1xf32>, tensor<1xi8>) -> tensor<?x?xf32>
// CHECK: %[[VAL_12:.*]] = tosa.select %[[VAL_9]], %[[VAL_1]], %[[VAL_11]] : (tensor<?x?xi1>, tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_13:.*]] = torch_c.from_builtin_tensor %[[VAL_12]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_13]] : !torch.vtensor<[?,?],f32>
// CHECK: }
func.func @torch.aten.leaky_relu$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%fp0 = torch.constant.float 1.000000e-01
%0 = torch.aten.leaky_relu %arg0, %fp0 : !torch.vtensor<[?,?],f32>, !torch.float -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.log$basic(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[ARG_BUILTIN:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[RESULT_BUILTIN:.*]] = tosa.log %[[ARG_BUILTIN]] : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[RESULT_BUILTIN]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[RESULT]] : !torch.vtensor<[?,?],f32>
func.func @torch.aten.log$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.log %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.exp$basic(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[ARG_BUILTIN:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[RESULT_BUILTIN:.*]] = tosa.exp %[[ARG_BUILTIN]] : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[RESULT_BUILTIN]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[RESULT]] : !torch.vtensor<[?,?],f32>
func.func @torch.aten.exp$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.exp %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.neg$basic(
// CHECK-SAME: %[[VAL_0:[0-9]+|[a-zA-Z$._-][a-zA-Z0-9$._-]*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_2:.*]] = "tosa.const"() <{values = dense<0.000000e+00> : tensor<1xf32>}> : () -> tensor<1xf32>
// CHECK: %[[VAL_3:.*]] = "tosa.const"() <{values = dense<0.000000e+00> : tensor<1xf32>}> : () -> tensor<1xf32>
// CHECK: %[[VAL_4:.*]] = tosa.negate %[[VAL_1]], %[[VAL_2]], %[[VAL_3]] : (tensor<?x?xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_5]] : !torch.vtensor<[?,?],f32>
// CHECK: }
func.func @torch.aten.neg$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.neg %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.floor$basic(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[ARG_BUILTIN:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[RESULT_BUILTIN:.*]] = tosa.floor %[[ARG_BUILTIN]] : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[RESULT_BUILTIN]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[RESULT]] : !torch.vtensor<[?,?],f32>
func.func @torch.aten.floor$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.floor %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.bitwise_not$basic(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[ARG_BUILTIN:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[RESULT_BUILTIN:.*]] = tosa.bitwise_not %[[ARG_BUILTIN]] : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[RESULT_BUILTIN]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[RESULT]] : !torch.vtensor<[?,?],f32>
func.func @torch.aten.bitwise_not$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.bitwise_not %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.bitwise_not$bool(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[2,3],i1>) -> !torch.vtensor<[2,3],i1> {
// CHECK: %[[ARG_BUILTIN:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[2,3],i1> -> tensor<2x3xi1>
// CHECK: %[[RESULT_BUILTIN:.*]] = tosa.logical_not %[[ARG_BUILTIN]] : (tensor<2x3xi1>) -> tensor<2x3xi1>
// CHECK: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[RESULT_BUILTIN]] : tensor<2x3xi1> -> !torch.vtensor<[2,3],i1>
// CHECK: return %[[RESULT]] : !torch.vtensor<[2,3],i1>
// CHECK: }
func.func @torch.aten.bitwise_not$bool(%arg0: !torch.vtensor<[2,3],i1>) -> !torch.vtensor<[2,3],i1> {
%0 = torch.aten.bitwise_not %arg0 : !torch.vtensor<[2,3],i1> -> !torch.vtensor<[2,3],i1>
return %0 : !torch.vtensor<[2,3],i1>
}
// -----
// CHECK-LABEL: func.func @torch.aten.ceil$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_2:.*]] = tosa.ceil %[[VAL_1]] : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.from_builtin_tensor %[[VAL_2]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_3]] : !torch.vtensor<[?,?],f32>
// CHECK: }
func.func @torch.aten.ceil$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.ceil %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.reciprocal$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_2:.*]] = tosa.reciprocal %[[VAL_1]] : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.from_builtin_tensor %[[VAL_2]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_3]] : !torch.vtensor<[?,?],f32>
// CHECK: }
func.func @torch.aten.reciprocal$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.reciprocal %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.add$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_4:.*]] = torch.constant.int 1
// CHECK: %[[VAL_5:.*]] = "tosa.const"() <{values = dense<1.000000e+00> : tensor<f32>}> : () -> tensor<f32>
// CHECK: %[[VAL_6:.*]] = tosa.const_shape {values = dense<1> : tensor<2xindex>} : () -> !tosa.shape<2>
// CHECK: %[[VAL_7:.*]] = tosa.reshape %[[VAL_5]], %[[VAL_6]] : (tensor<f32>, !tosa.shape<2>) -> tensor<1x1xf32>
// CHECK: %[[VAL_8:.*]] = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: %[[VAL_9:.*]] = tosa.mul %[[VAL_2]], %[[VAL_7]], %[[VAL_8]] : (tensor<?x?xf32>, tensor<1x1xf32>, tensor<1xi8>) -> tensor<?x?xf32>
// CHECK: %[[VAL_10:.*]] = tosa.add %[[VAL_3]], %[[VAL_9]] : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_11:.*]] = torch_c.from_builtin_tensor %[[VAL_10]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_11]] : !torch.vtensor<[?,?],f32>
// CHECK: }
func.func @torch.aten.add$basic(%arg0: !torch.vtensor<[?, ?],f32>, %arg1: !torch.vtensor<[?, ?],f32>) -> !torch.vtensor<[?, ?],f32> {
%int1 = torch.constant.int 1
%0 = torch.aten.add.Tensor %arg0, %arg1, %int1 : !torch.vtensor<[?, ?],f32>, !torch.vtensor<[?, ?],f32>, !torch.int -> !torch.vtensor<[?, ?],f32>
return %0 : !torch.vtensor<[?, ?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.sub$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_4:.*]] = torch.constant.int 1
// CHECK: %[[VAL_5:.*]] = "tosa.const"() <{values = dense<1.000000e+00> : tensor<f32>}> : () -> tensor<f32>
// CHECK: %[[VAL_6:.*]] = tosa.const_shape {values = dense<1> : tensor<2xindex>} : () -> !tosa.shape<2>
// CHECK: %[[VAL_7:.*]] = tosa.reshape %[[VAL_5]], %[[VAL_6]] : (tensor<f32>, !tosa.shape<2>) -> tensor<1x1xf32>
// CHECK: %[[VAL_8:.*]] = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: %[[VAL_9:.*]] = tosa.mul %[[VAL_2]], %[[VAL_7]], %[[VAL_8]] : (tensor<?x?xf32>, tensor<1x1xf32>, tensor<1xi8>) -> tensor<?x?xf32>
// CHECK: %[[VAL_10:.*]] = tosa.sub %[[VAL_3]], %[[VAL_9]] : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_11:.*]] = torch_c.from_builtin_tensor %[[VAL_10]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_11]] : !torch.vtensor<[?,?],f32>
// CHECK: }
func.func @torch.aten.sub$basic(%arg0: !torch.vtensor<[?, ?],f32>, %arg1: !torch.vtensor<[?, ?],f32>) -> !torch.vtensor<[?, ?],f32> {
%int1 = torch.constant.int 1
%0 = torch.aten.sub.Tensor %arg0, %arg1, %int1 : !torch.vtensor<[?, ?],f32>, !torch.vtensor<[?, ?],f32>, !torch.int -> !torch.vtensor<[?, ?],f32>
return %0 : !torch.vtensor<[?, ?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.mul$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_4:.*]] = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: %[[VAL_5:.*]] = tosa.mul %[[VAL_3]], %[[VAL_2]], %[[VAL_4]] : (tensor<?x?xf32>, tensor<?x?xf32>, tensor<1xi8>) -> tensor<?x?xf32>
// CHECK: %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_6]] : !torch.vtensor<[?,?],f32>
// CHECK: }
func.func @torch.aten.mul$basic(%arg0: !torch.vtensor<[?, ?],f32>, %arg1: !torch.vtensor<[?, ?],f32>) -> !torch.vtensor<[?, ?],f32> {
%0 = torch.aten.mul.Tensor %arg0, %arg1 : !torch.vtensor<[?, ?],f32>, !torch.vtensor<[?, ?],f32> -> !torch.vtensor<[?, ?],f32>
return %0 : !torch.vtensor<[?, ?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.div$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_4:.*]] = tosa.reciprocal %[[VAL_2]] : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_5:.*]] = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: %[[VAL_6:.*]] = tosa.mul %[[VAL_3]], %[[VAL_4]], %[[VAL_5]] : (tensor<?x?xf32>, tensor<?x?xf32>, tensor<1xi8>) -> tensor<?x?xf32>
// CHECK: %[[VAL_7:.*]] = torch_c.from_builtin_tensor %[[VAL_6]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_7]] : !torch.vtensor<[?,?],f32>
// CHECK: }
func.func @torch.aten.div$basic(%arg0: !torch.vtensor<[?, ?],f32>, %arg1: !torch.vtensor<[?, ?],f32>) -> !torch.vtensor<[?, ?],f32> {
%0 = torch.aten.div.Tensor %arg0, %arg1 : !torch.vtensor<[?, ?],f32>, !torch.vtensor<[?, ?],f32> -> !torch.vtensor<[?, ?],f32>
return %0 : !torch.vtensor<[?, ?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.rsqrt$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_2:.*]] = tosa.rsqrt %[[VAL_1]] : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.from_builtin_tensor %[[VAL_2]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_3]] : !torch.vtensor<[?,?],f32>
// CHECK: }
func.func @torch.aten.rsqrt$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.rsqrt %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func.func @test_reduce_mean_dim$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[3,4,5,6],f32>) -> !torch.vtensor<[4,5,6],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[3,4,5,6],f32> -> tensor<3x4x5x6xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.int 0
// CHECK: %[[VAL_3:.*]] = torch.prim.ListConstruct %[[VAL_2]] : (!torch.int) -> !torch.list<int>
// CHECK: %[[VAL_4:.*]] = torch.constant.bool false
// CHECK: %[[VAL_5:.*]] = torch.constant.none
// CHECK: %[[VAL_6:.*]] = tosa.reduce_sum %[[VAL_1]] {axis = 0 : i32} : (tensor<3x4x5x6xf32>) -> tensor<1x4x5x6xf32>
// CHECK: %[[VAL_7:.*]] = tosa.const_shape {values = dense<[4, 5, 6]> : tensor<3xindex>} : () -> !tosa.shape<3>
// CHECK: %[[VAL_8:.*]] = tosa.reshape %[[VAL_6]], %[[VAL_7]] : (tensor<1x4x5x6xf32>, !tosa.shape<3>) -> tensor<4x5x6xf32>
// CHECK: %[[VAL_9:.*]] = "tosa.const"() <{values = dense<0.333333343> : tensor<f32>}> : () -> tensor<f32>
// CHECK: %[[VAL_10:.*]] = tosa.const_shape {values = dense<1> : tensor<3xindex>} : () -> !tosa.shape<3>
// CHECK: %[[VAL_11:.*]] = tosa.reshape %[[VAL_9]], %[[VAL_10]] : (tensor<f32>, !tosa.shape<3>) -> tensor<1x1x1xf32>
// CHECK: %[[VAL_12:.*]] = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: %[[VAL_13:.*]] = tosa.mul %[[VAL_8]], %[[VAL_11]], %[[VAL_12]] : (tensor<4x5x6xf32>, tensor<1x1x1xf32>, tensor<1xi8>) -> tensor<4x5x6xf32>
// CHECK: %[[VAL_14:.*]] = torch_c.from_builtin_tensor %[[VAL_13]] : tensor<4x5x6xf32> -> !torch.vtensor<[4,5,6],f32>
// CHECK: return %[[VAL_14]] : !torch.vtensor<[4,5,6],f32>
// CHECK: }
func.func @test_reduce_mean_dim$basic(%arg0: !torch.vtensor<[3,4,5,6],f32>) -> !torch.vtensor<[4,5,6],f32> {
%dim0 = torch.constant.int 0
%reducedims = torch.prim.ListConstruct %dim0 : (!torch.int) -> !torch.list<int>
%keepdims = torch.constant.bool false
%dtype = torch.constant.none
%0 = torch.aten.mean.dim %arg0, %reducedims, %keepdims, %dtype : !torch.vtensor<[3,4,5,6],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[4,5,6],f32>
return %0 : !torch.vtensor<[4,5,6],f32>
}
// -----
// CHECK-LABEL: func.func @test_reduce_sum_dims$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[3,4,5,6],f32>) -> !torch.vtensor<[4,5,6],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[3,4,5,6],f32> -> tensor<3x4x5x6xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.none
// CHECK: %[[VAL_3:.*]] = torch.constant.bool false
// CHECK: %[[VAL_4:.*]] = torch.constant.int 0
// CHECK: %[[VAL_5:.*]] = torch.prim.ListConstruct %[[VAL_4]] : (!torch.int) -> !torch.list<int>
// CHECK: %[[VAL_6:.*]] = tosa.reduce_sum %[[VAL_1]] {axis = 0 : i32} : (tensor<3x4x5x6xf32>) -> tensor<1x4x5x6xf32>
// CHECK: %[[VAL_7:.*]] = tosa.const_shape {values = dense<[4, 5, 6]> : tensor<3xindex>} : () -> !tosa.shape<3>
// CHECK: %[[VAL_8:.*]] = tosa.reshape %[[VAL_6]], %[[VAL_7]] : (tensor<1x4x5x6xf32>, !tosa.shape<3>) -> tensor<4x5x6xf32>
// CHECK: %[[VAL_9:.*]] = torch_c.from_builtin_tensor %[[VAL_8]] : tensor<4x5x6xf32> -> !torch.vtensor<[4,5,6],f32>
// CHECK: return %[[VAL_9]] : !torch.vtensor<[4,5,6],f32>
// CHECK: }
func.func @test_reduce_sum_dims$basic(%arg0: !torch.vtensor<[3,4,5,6],f32>) -> !torch.vtensor<[4,5,6],f32> {
%none = torch.constant.none
%false = torch.constant.bool false
%int0 = torch.constant.int 0
%0 = torch.prim.ListConstruct %int0 : (!torch.int) -> !torch.list<int>
%1 = torch.aten.sum.dim_IntList %arg0, %0, %false, %none : !torch.vtensor<[3,4,5,6],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[4,5,6],f32>
return %1 : !torch.vtensor<[4,5,6],f32>
}
// -----
// CHECK-LABEL: func.func @test_reduce_sum_empty_dims$basic(
// CHECK-SAME: %[[INPUT_F32:.*]]: !torch.vtensor<[2,3,4],f32>) -> !torch.vtensor<[],f32> {
// CHECK: %[[INPUT_F32_TENSOR:.*]] = torch_c.to_builtin_tensor %[[INPUT_F32]] : !torch.vtensor<[2,3,4],f32> -> tensor<2x3x4xf32>
// CHECK: %[[NONE:.*]] = torch.constant.none
// CHECK: %[[EMPTY_DIMS:.*]] = torch.prim.ListConstruct : () -> !torch.list<int>
// CHECK: %[[SUM_DIM0:.*]] = tosa.reduce_sum %[[INPUT_F32_TENSOR]] {axis = 0 : i32} : (tensor<2x3x4xf32>) -> tensor<1x3x4xf32>
// CHECK: %[[SUM_DIM1:.*]] = tosa.reduce_sum %[[SUM_DIM0]] {axis = 1 : i32} : (tensor<1x3x4xf32>) -> tensor<1x1x4xf32>
// CHECK: %[[SUM_DIM2:.*]] = tosa.reduce_sum %[[SUM_DIM1]] {axis = 2 : i32} : (tensor<1x1x4xf32>) -> tensor<1x1x1xf32>
// CHECK: %[[SCALAR_SHAPE:.*]] = tosa.const_shape
// CHECK: %[[RESHAPED_SCALAR:.*]] = tosa.reshape %[[SUM_DIM2]], %[[SCALAR_SHAPE]] : (tensor<1x1x1xf32>, !tosa.shape<0>) -> tensor<f32>
// CHECK: %[[RESULT_F32:.*]] = torch_c.from_builtin_tensor %[[RESHAPED_SCALAR]] : tensor<f32> -> !torch.vtensor<[],f32>
// CHECK: return %[[RESULT_F32]] : !torch.vtensor<[],f32>
// CHECK: }
func.func @test_reduce_sum_empty_dims$basic(%arg0: !torch.vtensor<[2,3,4],f32>) -> !torch.vtensor<[],f32> {
%dtype_none = torch.constant.none
%keep_dims_false = torch.constant.bool false
%all_dims_list = torch.prim.ListConstruct : () -> !torch.list<int>
%sum_all_dims = torch.aten.sum.dim_IntList %arg0, %all_dims_list, %keep_dims_false, %dtype_none : !torch.vtensor<[2,3,4],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[],f32>
return %sum_all_dims : !torch.vtensor<[],f32>
}
// -----
// CHECK-LABEL: func.func @test_reduce_sum_empty_dims_i8_to_i32$basic(
// CHECK-SAME: %[[INPUT_I8:.*]]: !torch.vtensor<[2,3,4],si8>) -> !torch.vtensor<[],si32> {
// CHECK: %[[INPUT_I8_TENSOR:.*]] = torch_c.to_builtin_tensor %[[INPUT_I8]] : !torch.vtensor<[2,3,4],si8> -> tensor<2x3x4xi8>
// CHECK: %[[DTYPE_I32:.*]] = torch.constant.int 3
// CHECK: %[[EMPTY_DIMS:.*]] = torch.prim.ListConstruct : () -> !torch.list<int>
// CHECK: %[[CAST_INPUT_TO_I32:.*]] = tosa.cast %[[INPUT_I8_TENSOR]] : (tensor<2x3x4xi8>) -> tensor<2x3x4xi32>
// CHECK: %[[SUM_DIM0:.*]] = tosa.reduce_sum %[[CAST_INPUT_TO_I32]] {axis = 0 : i32} : (tensor<2x3x4xi32>) -> tensor<1x3x4xi32>
// CHECK: %[[SUM_DIM1:.*]] = tosa.reduce_sum %[[SUM_DIM0]] {axis = 1 : i32} : (tensor<1x3x4xi32>) -> tensor<1x1x4xi32>
// CHECK: %[[SUM_DIM2:.*]] = tosa.reduce_sum %[[SUM_DIM1]] {axis = 2 : i32} : (tensor<1x1x4xi32>) -> tensor<1x1x1xi32>
// CHECK: %[[SCALAR_SHAPE:.*]] = tosa.const_shape
// CHECK: %[[RESHAPED_SCALAR:.*]] = tosa.reshape %[[SUM_DIM2]], %[[SCALAR_SHAPE]] : (tensor<1x1x1xi32>, !tosa.shape<0>) -> tensor<i32>
// CHECK: %[[RESULT_I32:.*]] = torch_c.from_builtin_tensor %[[RESHAPED_SCALAR]] : tensor<i32> -> !torch.vtensor<[],si32>
// CHECK: return %[[RESULT_I32]] : !torch.vtensor<[],si32>
// CHECK: }
func.func @test_reduce_sum_empty_dims_i8_to_i32$basic(%arg0: !torch.vtensor<[2,3,4],si8>) -> !torch.vtensor<[],si32> {
%dtype_i32 = torch.constant.int 3
%keep_dims_false = torch.constant.bool false
%all_dims_list = torch.prim.ListConstruct : () -> !torch.list<int>
%sum_all_dims_to_i32 = torch.aten.sum.dim_IntList %arg0, %all_dims_list, %keep_dims_false, %dtype_i32 : !torch.vtensor<[2,3,4],si8>, !torch.list<int>, !torch.bool, !torch.int -> !torch.vtensor<[],si32>
return %sum_all_dims_to_i32 : !torch.vtensor<[],si32>
}
// -----
// CHECK-LABEL: func.func @test_linalg_vector_norm$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[3,151,64],f32>) -> !torch.vtensor<[3,151,1],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[3,151,64],f32> -> tensor<3x151x64xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.float 2.000000e+00
// CHECK: %[[VAL_3:.*]] = torch.constant.int -1
// CHECK: %[[VAL_4:.*]] = torch.constant.bool true
// CHECK: %[[VAL_5:.*]] = torch.constant.none
// CHECK: %[[VAL_6:.*]] = torch.prim.ListConstruct %[[VAL_3]] : (!torch.int) -> !torch.list<int>
// CHECK: %[[VAL_7:.*]] = "tosa.const"() <{values = dense<2.000000e+00> : tensor<f32>}> : () -> tensor<f32>
// CHECK: %[[VAL_8:.*]] = tosa.const_shape {values = dense<1> : tensor<3xindex>} : () -> !tosa.shape<3>
// CHECK: %[[VAL_9:.*]] = tosa.reshape %[[VAL_7]], %[[VAL_8]] : (tensor<f32>, !tosa.shape<3>) -> tensor<1x1x1xf32>
// CHECK: %[[VAL_10:.*]] = tosa.abs %[[VAL_1]] : (tensor<3x151x64xf32>) -> tensor<3x151x64xf32>
// CHECK: %[[VAL_11:.*]] = tosa.pow %[[VAL_10]], %[[VAL_9]] : (tensor<3x151x64xf32>, tensor<1x1x1xf32>) -> tensor<3x151x64xf32>
// CHECK: %[[VAL_12:.*]] = tosa.reduce_sum %[[VAL_11]] {axis = 2 : i32} : (tensor<3x151x64xf32>) -> tensor<3x151x1xf32>
// CHECK: %[[VAL_13:.*]] = tosa.reciprocal %[[VAL_7]] : (tensor<f32>) -> tensor<f32>
// CHECK: %[[VAL_14:.*]] = tosa.const_shape {values = dense<1> : tensor<3xindex>} : () -> !tosa.shape<3>
// CHECK: %[[VAL_15:.*]] = tosa.reshape %[[VAL_13]], %[[VAL_14]] : (tensor<f32>, !tosa.shape<3>) -> tensor<1x1x1xf32>
// CHECK: %[[VAL_16:.*]] = tosa.pow %[[VAL_12]], %[[VAL_15]] : (tensor<3x151x1xf32>, tensor<1x1x1xf32>) -> tensor<3x151x1xf32>
// CHECK: %[[VAL_17:.*]] = torch_c.from_builtin_tensor %[[VAL_16]] : tensor<3x151x1xf32> -> !torch.vtensor<[3,151,1],f32>
// CHECK: return %[[VAL_17]] : !torch.vtensor<[3,151,1],f32>
// CHECK: }
func.func @test_linalg_vector_norm$basic(%arg0: !torch.vtensor<[3,151,64],f32>) -> (!torch.vtensor<[3,151,1],f32>) {
%float2.000000e00 = torch.constant.float 2.000000e+00
%int-1 = torch.constant.int -1
%true = torch.constant.bool true
%none = torch.constant.none
%1 = torch.prim.ListConstruct %int-1 : (!torch.int) -> !torch.list<int>
%2 = torch.aten.linalg_vector_norm %arg0, %float2.000000e00, %1, %true, %none : !torch.vtensor<[3,151,64],f32>, !torch.float, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[3,151,1],f32>
return %2 : !torch.vtensor<[3,151,1],f32>
}
// -----
// CHECK-LABEL: func.func @test_reduce_sum$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[1],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?,?,?],f32> -> tensor<?x?x?x?xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.none
// CHECK: %[[VAL_3:.*]] = tosa.reduce_sum %[[VAL_1]] {axis = 0 : i32} : (tensor<?x?x?x?xf32>) -> tensor<1x?x?x?xf32>
// CHECK: %[[VAL_4:.*]] = tosa.reduce_sum %[[VAL_3]] {axis = 1 : i32} : (tensor<1x?x?x?xf32>) -> tensor<1x1x?x?xf32>
// CHECK: %[[VAL_5:.*]] = tosa.reduce_sum %[[VAL_4]] {axis = 2 : i32} : (tensor<1x1x?x?xf32>) -> tensor<1x1x1x?xf32>
// CHECK: %[[VAL_6:.*]] = tosa.reduce_sum %[[VAL_5]] {axis = 3 : i32} : (tensor<1x1x1x?xf32>) -> tensor<1x1x1x1xf32>
// CHECK: %[[VAL_7:.*]] = tosa.const_shape {values = dense<1> : tensor<1xindex>} : () -> !tosa.shape<1>
// CHECK: %[[VAL_8:.*]] = tosa.reshape %[[VAL_6]], %[[VAL_7]] : (tensor<1x1x1x1xf32>, !tosa.shape<1>) -> tensor<1xf32>
// CHECK: %[[VAL_9:.*]] = torch_c.from_builtin_tensor %[[VAL_8]] : tensor<1xf32> -> !torch.vtensor<[1],f32>
// CHECK: return %[[VAL_9]] : !torch.vtensor<[1],f32>
// CHECK: }
func.func @test_reduce_sum$basic(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[1],f32> {
%none = torch.constant.none
%0 = torch.aten.sum %arg0, %none : !torch.vtensor<[?,?,?,?],f32>, !torch.none -> !torch.vtensor<[1],f32>
return %0 : !torch.vtensor<[1],f32>
}
// -----
// CHECK-LABEL: func.func @test_reduce_all$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?,?,?],i1>) -> !torch.vtensor<[1],i1> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?,?,?],i1> -> tensor<?x?x?x?xi1>
// CHECK: %[[VAL_2:.*]] = tosa.reduce_all %[[VAL_1]] {axis = 0 : i32} : (tensor<?x?x?x?xi1>) -> tensor<1x?x?x?xi1>
// CHECK: %[[VAL_3:.*]] = tosa.reduce_all %[[VAL_2]] {axis = 1 : i32} : (tensor<1x?x?x?xi1>) -> tensor<1x1x?x?xi1>
// CHECK: %[[VAL_4:.*]] = tosa.reduce_all %[[VAL_3]] {axis = 2 : i32} : (tensor<1x1x?x?xi1>) -> tensor<1x1x1x?xi1>
// CHECK: %[[VAL_5:.*]] = tosa.reduce_all %[[VAL_4]] {axis = 3 : i32} : (tensor<1x1x1x?xi1>) -> tensor<1x1x1x1xi1>
// CHECK: %[[VAL_6:.*]] = tosa.const_shape {values = dense<1> : tensor<1xindex>} : () -> !tosa.shape<1>
// CHECK: %[[VAL_7:.*]] = tosa.reshape %[[VAL_5]], %[[VAL_6]] : (tensor<1x1x1x1xi1>, !tosa.shape<1>) -> tensor<1xi1>
// CHECK: %[[VAL_8:.*]] = torch_c.from_builtin_tensor %[[VAL_7]] : tensor<1xi1> -> !torch.vtensor<[1],i1>
// CHECK: return %[[VAL_8]] : !torch.vtensor<[1],i1>
// CHECK: }
func.func @test_reduce_all$basic(%arg0: !torch.vtensor<[?,?,?,?],i1>) -> !torch.vtensor<[1],i1> {
%0 = torch.aten.all %arg0 : !torch.vtensor<[?,?,?,?],i1> -> !torch.vtensor<[1],i1>
return %0 : !torch.vtensor<[1],i1>
}
// -----
// CHECK-LABEL: func.func @test_reduce_any_dim$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[3,4,5,6],i1>) -> !torch.vtensor<[4,5,6],i1> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[3,4,5,6],i1> -> tensor<3x4x5x6xi1>
// CHECK: %[[VAL_2:.*]] = torch.constant.int 0
// CHECK: %[[VAL_3:.*]] = torch.constant.bool false
// CHECK: %[[VAL_4:.*]] = tosa.reduce_any %[[VAL_1]] {axis = 0 : i32} : (tensor<3x4x5x6xi1>) -> tensor<1x4x5x6xi1>
// CHECK: %[[VAL_5:.*]] = tosa.const_shape {values = dense<[4, 5, 6]> : tensor<3xindex>} : () -> !tosa.shape<3>
// CHECK: %[[VAL_6:.*]] = tosa.reshape %[[VAL_4]], %[[VAL_5]] : (tensor<1x4x5x6xi1>, !tosa.shape<3>) -> tensor<4x5x6xi1>
// CHECK: %[[VAL_7:.*]] = torch_c.from_builtin_tensor %[[VAL_6]] : tensor<4x5x6xi1> -> !torch.vtensor<[4,5,6],i1>
// CHECK: return %[[VAL_7]] : !torch.vtensor<[4,5,6],i1>
// CHECK: }
func.func @test_reduce_any_dim$basic(%arg0: !torch.vtensor<[3,4,5,6],i1>) -> !torch.vtensor<[4,5,6],i1> {
%int0 = torch.constant.int 0
%false = torch.constant.bool false
%0 = torch.aten.any.dim %arg0, %int0, %false : !torch.vtensor<[3,4,5,6],i1>, !torch.int, !torch.bool -> !torch.vtensor<[4,5,6],i1>
return %0 : !torch.vtensor<[4,5,6],i1>
}
// -----
// CHECK-LABEL: func.func @test_reduce_any$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?,?,?],i1>) -> !torch.vtensor<[1],i1> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?,?,?],i1> -> tensor<?x?x?x?xi1>
// CHECK: %[[VAL_2:.*]] = tosa.reduce_any %[[VAL_1]] {axis = 0 : i32} : (tensor<?x?x?x?xi1>) -> tensor<1x?x?x?xi1>
// CHECK: %[[VAL_3:.*]] = tosa.reduce_any %[[VAL_2]] {axis = 1 : i32} : (tensor<1x?x?x?xi1>) -> tensor<1x1x?x?xi1>
// CHECK: %[[VAL_4:.*]] = tosa.reduce_any %[[VAL_3]] {axis = 2 : i32} : (tensor<1x1x?x?xi1>) -> tensor<1x1x1x?xi1>
// CHECK: %[[VAL_5:.*]] = tosa.reduce_any %[[VAL_4]] {axis = 3 : i32} : (tensor<1x1x1x?xi1>) -> tensor<1x1x1x1xi1>
// CHECK: %[[VAL_6:.*]] = tosa.const_shape {values = dense<1> : tensor<1xindex>} : () -> !tosa.shape<1>
// CHECK: %[[VAL_7:.*]] = tosa.reshape %[[VAL_5]], %[[VAL_6]] : (tensor<1x1x1x1xi1>, !tosa.shape<1>) -> tensor<1xi1>
// CHECK: %[[VAL_8:.*]] = torch_c.from_builtin_tensor %[[VAL_7]] : tensor<1xi1> -> !torch.vtensor<[1],i1>
// CHECK: return %[[VAL_8]] : !torch.vtensor<[1],i1>
// CHECK: }
func.func @test_reduce_any$basic(%arg0: !torch.vtensor<[?,?,?,?],i1>) -> !torch.vtensor<[1],i1> {
%0 = torch.aten.any %arg0 : !torch.vtensor<[?,?,?,?],i1> -> !torch.vtensor<[1],i1>
return %0 : !torch.vtensor<[1],i1>
}
// -----
// CHECK-LABEL: func.func @torch.aten.rsqrt$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_2:.*]] = tosa.rsqrt %[[VAL_1]] : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.from_builtin_tensor %[[VAL_2]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_3]] : !torch.vtensor<[?,?],f32>
// CHECK: }
func.func @torch.aten.rsqrt$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.rsqrt %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.maximum$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK-DAG: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK-DAG: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_4:.*]] = tosa.maximum %[[VAL_2]], %[[VAL_3]] : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_5]] : !torch.vtensor<[?,?],f32>
// CHECK: }
func.func @torch.aten.maximum$basic(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.maximum %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.minimum$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK-DAG: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK-DAG: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_4:.*]] = tosa.minimum %[[VAL_2]], %[[VAL_3]] : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_5]] : !torch.vtensor<[?,?],f32>
// CHECK: }
func.func @torch.aten.minimum$basic(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.minimum %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.pow.Tensor_Scalar$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.float 3.123400e+00
// CHECK: %[[VAL_3:.*]] = "tosa.const"() <{values = dense<3.123400e+00> : tensor<f32>}> : () -> tensor<f32>
// CHECK: %[[VAL_4:.*]] = tosa.const_shape {values = dense<1> : tensor<2xindex>} : () -> !tosa.shape<2>
// CHECK: %[[VAL_5:.*]] = tosa.reshape %[[VAL_3]], %[[VAL_4]] : (tensor<f32>, !tosa.shape<2>) -> tensor<1x1xf32>
// CHECK: %[[VAL_6:.*]] = tosa.pow %[[VAL_1]], %[[VAL_5]] : (tensor<?x?xf32>, tensor<1x1xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_7:.*]] = torch_c.from_builtin_tensor %[[VAL_6]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_7]] : !torch.vtensor<[?,?],f32>
// CHECK: }
func.func @torch.aten.pow.Tensor_Scalar$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%fp0 = torch.constant.float 3.123400e+00
%0 = torch.aten.pow.Tensor_Scalar %arg0, %fp0 : !torch.vtensor<[?,?],f32>, !torch.float -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.rsub.Scalar$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.float 3.123400e+00
// CHECK: %[[VAL_3:.*]] = torch.constant.float 6.432100e+00
// CHECK: %[[VAL_4:.*]] = "tosa.const"() <{values = dense<3.123400e+00> : tensor<f32>}> : () -> tensor<f32>
// CHECK: %[[VAL_5:.*]] = "tosa.const"() <{values = dense<6.432100e+00> : tensor<f32>}> : () -> tensor<f32>
// CHECK: %[[VAL_6:.*]] = tosa.const_shape {values = dense<1> : tensor<2xindex>} : () -> !tosa.shape<2>
// CHECK: %[[VAL_7:.*]] = tosa.reshape %[[VAL_4]], %[[VAL_6]] : (tensor<f32>, !tosa.shape<2>) -> tensor<1x1xf32>
// CHECK: %[[VAL_8:.*]] = tosa.const_shape {values = dense<1> : tensor<2xindex>} : () -> !tosa.shape<2>
// CHECK: %[[VAL_9:.*]] = tosa.reshape %[[VAL_5]], %[[VAL_8]] : (tensor<f32>, !tosa.shape<2>) -> tensor<1x1xf32>
// CHECK: %[[VAL_10:.*]] = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: %[[VAL_11:.*]] = tosa.mul %[[VAL_1]], %[[VAL_9]], %[[VAL_10]] : (tensor<?x?xf32>, tensor<1x1xf32>, tensor<1xi8>) -> tensor<?x?xf32>
// CHECK: %[[VAL_12:.*]] = tosa.sub %[[VAL_7]], %[[VAL_11]] : (tensor<1x1xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_13:.*]] = torch_c.from_builtin_tensor %[[VAL_12]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_13]] : !torch.vtensor<[?,?],f32>
// CHECK: }
func.func @torch.aten.rsub.Scalar$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%other = torch.constant.float 3.123400e+00
%alpha = torch.constant.float 6.432100e+00
%0 = torch.aten.rsub.Scalar %arg0, %other, %alpha : !torch.vtensor<[?,?],f32>, !torch.float, !torch.float -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.rsub.Scalar$float_int(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.float 3.123400e+00
// CHECK: %[[VAL_3:.*]] = torch.constant.int 1
// CHECK: %[[VAL_4:.*]] = "tosa.const"() <{values = dense<3.123400e+00> : tensor<f32>}> : () -> tensor<f32>
// CHECK: %[[VAL_5:.*]] = "tosa.const"() <{values = dense<1.000000e+00> : tensor<f32>}> : () -> tensor<f32>
// CHECK: %[[VAL_6:.*]] = tosa.const_shape {values = dense<1> : tensor<2xindex>} : () -> !tosa.shape<2>
// CHECK: %[[VAL_7:.*]] = tosa.reshape %[[VAL_4]], %[[VAL_6]] : (tensor<f32>, !tosa.shape<2>) -> tensor<1x1xf32>
// CHECK: %[[VAL_8:.*]] = tosa.const_shape {values = dense<1> : tensor<2xindex>} : () -> !tosa.shape<2>
// CHECK: %[[VAL_9:.*]] = tosa.reshape %[[VAL_5]], %[[VAL_8]] : (tensor<f32>, !tosa.shape<2>) -> tensor<1x1xf32>
// CHECK: %[[VAL_10:.*]] = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: %[[VAL_11:.*]] = tosa.mul %[[VAL_1]], %[[VAL_9]], %[[VAL_10]] : (tensor<?x?xf32>, tensor<1x1xf32>, tensor<1xi8>) -> tensor<?x?xf32>
// CHECK: %[[VAL_12:.*]] = tosa.sub %[[VAL_7]], %[[VAL_11]] : (tensor<1x1xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_13:.*]] = torch_c.from_builtin_tensor %[[VAL_12]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_13]] : !torch.vtensor<[?,?],f32>
// CHECK: }
func.func @torch.aten.rsub.Scalar$float_int(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%other = torch.constant.float 3.123400e+00
%alpha = torch.constant.int 1
%0 = torch.aten.rsub.Scalar %arg0, %other, %alpha : !torch.vtensor<[?,?],f32>, !torch.float, !torch.int -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.gt.Tensor$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],i1> {
// CHECK-DAG: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK-DAG: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_4:.*]] = tosa.greater %[[VAL_2]], %[[VAL_3]] : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xi1>
// CHECK: %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xi1> -> !torch.vtensor<[?,?],i1>
// CHECK: return %[[VAL_5]] : !torch.vtensor<[?,?],i1>
// CHECK: }
func.func @torch.aten.gt.Tensor$basic(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],i1> {
%0 = torch.aten.gt.Tensor %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],i1>
return %0 : !torch.vtensor<[?,?],i1>
}
// -----
// CHECK-LABEL: func.func @torch.aten.lt.Tensor$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],i1> {
// CHECK-DAG: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK-DAG: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_4:.*]] = tosa.greater %[[VAL_3]], %[[VAL_2]] : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xi1>
// CHECK: %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xi1> -> !torch.vtensor<[?,?],i1>
// CHECK: return %[[VAL_5]] : !torch.vtensor<[?,?],i1>
// CHECK: }
func.func @torch.aten.lt.Tensor$basic(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],i1> {
%0 = torch.aten.lt.Tensor %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],i1>
return %0 : !torch.vtensor<[?,?],i1>
}
// -----
// CHECK-LABEL: func.func @torch.aten.eq.Tensor$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],i1> {
// CHECK-DAG: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK-DAG: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_4:.*]] = tosa.equal %[[VAL_2]], %[[VAL_3]] : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xi1>
// CHECK: %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xi1> -> !torch.vtensor<[?,?],i1>
// CHECK: return %[[VAL_5]] : !torch.vtensor<[?,?],i1>
// CHECK: }
func.func @torch.aten.eq.Tensor$basic(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],i1> {
%0 = torch.aten.eq.Tensor %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],i1>
return %0 : !torch.vtensor<[?,?],i1>
}
// -----
// CHECK-LABEL: func.func @torch.aten.reshape$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?,?,?],f32> -> tensor<?x?x?x?xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.int -1
// CHECK: %[[VAL_3:.*]] = torch.prim.ListConstruct %[[VAL_2]] : (!torch.int) -> !torch.list<int>
// CHECK: %[[VAL_4:.*]] = tosa.const_shape {values = dense<-1> : tensor<1xindex>} : () -> !tosa.shape<1>
// CHECK: %[[VAL_5:.*]] = tosa.reshape %[[VAL_1]], %[[VAL_4]] : (tensor<?x?x?x?xf32>, !tosa.shape<1>) -> tensor<?xf32>
// CHECK: %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<?xf32> -> !torch.vtensor<[?],f32>
// CHECK: return %[[VAL_6]] : !torch.vtensor<[?],f32>
// CHECK: }
func.func @torch.aten.reshape$basic(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?],f32> {
%dim0 = torch.constant.int -1
%shape = torch.prim.ListConstruct %dim0 : (!torch.int) -> !torch.list<int>
%0 = torch.aten.reshape %arg0, %shape : !torch.vtensor<[?,?,?,?],f32>, !torch.list<int> -> !torch.vtensor<[?],f32>
return %0 : !torch.vtensor<[?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.native_batch_norm$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[10,4,3],f32>) -> !torch.vtensor<[10,4,3],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[10,4,3],f32> -> tensor<10x4x3xf32>
// CHECK: %[[VAL_2:.*]] = "tosa.const"() <{values = dense<[5.000000e-01, 4.000000e-01, 3.000000e-01, 6.000000e-01]> : tensor<4xf32>}> : () -> tensor<4xf32>
// CHECK: %[[VAL_3:.*]] = "tosa.const"() <{values = dense<[3.000000e+00, 2.000000e+00, 4.000000e+00, 5.000000e+00]> : tensor<4xf32>}> : () -> tensor<4xf32>
// CHECK: %[[VAL_4:.*]] = torch.constant.float 1.000000e-01
// CHECK: %[[VAL_5:.*]] = torch.constant.float 1.000000e-05
// CHECK: %[[VAL_6:.*]] = torch.constant.bool true
// CHECK: %[[VAL_7:.*]] = torch.constant.bool false
// CHECK: %[[VAL_8:.*]] = tosa.const_shape {values = dense<[4, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
// CHECK: %[[VAL_9:.*]] = tosa.reshape %[[VAL_2]], %[[VAL_8]] : (tensor<4xf32>, !tosa.shape<2>) -> tensor<4x1xf32>
// CHECK: %[[VAL_10:.*]] = tosa.const_shape {values = dense<[4, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
// CHECK: %[[VAL_11:.*]] = tosa.reshape %[[VAL_3]], %[[VAL_10]] : (tensor<4xf32>, !tosa.shape<2>) -> tensor<4x1xf32>
// CHECK: %[[VAL_12:.*]] = tosa.const_shape {values = dense<[4, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
// CHECK: %[[VAL_13:.*]] = tosa.reshape %[[VAL_3]], %[[VAL_12]] : (tensor<4xf32>, !tosa.shape<2>) -> tensor<4x1xf32>
// CHECK: %[[VAL_14:.*]] = tosa.const_shape {values = dense<[4, 1]> : tensor<2xindex>} : () -> !tosa.shape<2>
// CHECK: %[[VAL_15:.*]] = tosa.reshape %[[VAL_2]], %[[VAL_14]] : (tensor<4xf32>, !tosa.shape<2>) -> tensor<4x1xf32>
// CHECK: %[[VAL_16:.*]] = "tosa.const"() <{values = dense<9.99999974E-6> : tensor<f32>}> : () -> tensor<f32>
// CHECK: %[[VAL_17:.*]] = tosa.const_shape {values = dense<[1, 4, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
// CHECK: %[[VAL_18:.*]] = tosa.reshape %[[VAL_9]], %[[VAL_17]] : (tensor<4x1xf32>, !tosa.shape<3>) -> tensor<1x4x1xf32>
// CHECK: %[[VAL_19:.*]] = tosa.const_shape {values = dense<[1, 4, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
// CHECK: %[[VAL_20:.*]] = tosa.reshape %[[VAL_11]], %[[VAL_19]] : (tensor<4x1xf32>, !tosa.shape<3>) -> tensor<1x4x1xf32>
// CHECK: %[[VAL_21:.*]] = tosa.const_shape {values = dense<1> : tensor<3xindex>} : () -> !tosa.shape<3>
// CHECK: %[[VAL_22:.*]] = tosa.reshape %[[VAL_16]], %[[VAL_21]] : (tensor<f32>, !tosa.shape<3>) -> tensor<1x1x1xf32>
// CHECK: %[[VAL_23:.*]] = tosa.const_shape {values = dense<[1, 4, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
// CHECK: %[[VAL_24:.*]] = tosa.reshape %[[VAL_13]], %[[VAL_23]] : (tensor<4x1xf32>, !tosa.shape<3>) -> tensor<1x4x1xf32>
// CHECK: %[[VAL_25:.*]] = tosa.const_shape {values = dense<[1, 4, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
// CHECK: %[[VAL_26:.*]] = tosa.reshape %[[VAL_15]], %[[VAL_25]] : (tensor<4x1xf32>, !tosa.shape<3>) -> tensor<1x4x1xf32>
// CHECK: %[[VAL_27:.*]] = tosa.sub %[[VAL_1]], %[[VAL_18]] : (tensor<10x4x3xf32>, tensor<1x4x1xf32>) -> tensor<10x4x3xf32>
// CHECK: %[[VAL_28:.*]] = tosa.add %[[VAL_20]], %[[VAL_22]] : (tensor<1x4x1xf32>, tensor<1x1x1xf32>) -> tensor<1x4x1xf32>
// CHECK: %[[VAL_29:.*]] = tosa.rsqrt %[[VAL_28]] : (tensor<1x4x1xf32>) -> tensor<1x4x1xf32>
// CHECK: %[[VAL_30:.*]] = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: %[[VAL_31:.*]] = tosa.mul %[[VAL_27]], %[[VAL_29]], %[[VAL_30]] : (tensor<10x4x3xf32>, tensor<1x4x1xf32>, tensor<1xi8>) -> tensor<10x4x3xf32>
// CHECK: %[[VAL_32:.*]] = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: %[[VAL_33:.*]] = tosa.mul %[[VAL_31]], %[[VAL_24]], %[[VAL_32]] : (tensor<10x4x3xf32>, tensor<1x4x1xf32>, tensor<1xi8>) -> tensor<10x4x3xf32>
// CHECK: %[[VAL_34:.*]] = tosa.add %[[VAL_33]], %[[VAL_26]] : (tensor<10x4x3xf32>, tensor<1x4x1xf32>) -> tensor<10x4x3xf32>
// CHECK: %[[VAL_35:.*]] = torch_c.from_builtin_tensor %[[VAL_34]] : tensor<10x4x3xf32> -> !torch.vtensor<[10,4,3],f32>
// CHECK: return %[[VAL_35]] : !torch.vtensor<[10,4,3],f32>
// CHECK: }
func.func @torch.aten.native_batch_norm$basic(%arg0: !torch.vtensor<[10,4,3],f32> ) -> !torch.vtensor<[10,4,3],f32> {
%0 = torch.vtensor.literal(dense<[5.000000e-01, 4.000000e-01, 3.000000e-01, 6.000000e-01]> : tensor<4xf32>) : !torch.vtensor<[4],f32>
%1 = torch.vtensor.literal(dense<[3.000000e+00, 2.000000e+00, 4.000000e+00, 5.000000e+00]> : tensor<4xf32>) : !torch.vtensor<[4],f32>
%float1.000000e-01 = torch.constant.float 1.000000e-01
%float1.000000e-05 = torch.constant.float 1.000000e-05
%true = torch.constant.bool true
%false = torch.constant.bool false
%2 = torch.aten.batch_norm %arg0, %1, %0, %0, %1, %false, %float1.000000e-01, %float1.000000e-05, %true : !torch.vtensor<[10,4,3],f32>, !torch.vtensor<[4],f32>, !torch.vtensor<[4],f32>, !torch.vtensor<[4],f32>, !torch.vtensor<[4],f32>, !torch.bool, !torch.float, !torch.float, !torch.bool -> !torch.vtensor<[10,4,3],f32>
return %2 : !torch.vtensor<[10,4,3],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.flatten.using_ints$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[10,3,8,9,3,4],f32>) -> !torch.vtensor<[10,3,?,4],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[10,3,8,9,3,4],f32> -> tensor<10x3x8x9x3x4xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.int 4
// CHECK: %[[VAL_3:.*]] = torch.constant.int 2
// CHECK: %[[VAL_4:.*]] = tosa.const_shape {values = dense<[10, 3, 216, 4]> : tensor<4xindex>} : () -> !tosa.shape<4>
// CHECK: %[[VAL_5:.*]] = tosa.reshape %[[VAL_1]], %[[VAL_4]] : (tensor<10x3x8x9x3x4xf32>, !tosa.shape<4>) -> tensor<10x3x216x4xf32>
// CHECK: %[[VAL_6:.*]] = tensor.cast %[[VAL_5]] : tensor<10x3x216x4xf32> to tensor<10x3x?x4xf32>
// CHECK: %[[VAL_7:.*]] = torch_c.from_builtin_tensor %[[VAL_6]] : tensor<10x3x?x4xf32> -> !torch.vtensor<[10,3,?,4],f32>
// CHECK: return %[[VAL_7]] : !torch.vtensor<[10,3,?,4],f32>
// CHECK: }
func.func @torch.aten.flatten.using_ints$basic(%arg0: !torch.vtensor<[10,3,8,9,3,4],f32> ) -> !torch.vtensor<[10,3,?,4],f32> {
%int4 = torch.constant.int 4
%int2 = torch.constant.int 2
%0 = torch.aten.flatten.using_ints %arg0, %int2, %int4 : !torch.vtensor<[10,3,8,9,3,4],f32>, !torch.int, !torch.int -> !torch.vtensor<[10,3,?,4],f32>
return %0 : !torch.vtensor<[10,3,?,4],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.unflatten.int$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[1,6,4],f32>) -> !torch.vtensor<[1,2,3,4],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[1,6,4],f32> -> tensor<1x6x4xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.int 1
// CHECK: %[[VAL_3:.*]] = torch.constant.int 2
// CHECK: %[[VAL_4:.*]] = torch.constant.int 3
// CHECK: %[[VAL_5:.*]] = torch.prim.ListConstruct %[[VAL_3]], %[[VAL_4]] : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[VAL_6:.*]] = tosa.const_shape {values = dense<[1, 2, 3, 4]> : tensor<4xindex>} : () -> !tosa.shape<4>
// CHECK: %[[VAL_7:.*]] = tosa.reshape %[[VAL_1]], %[[VAL_6]] : (tensor<1x6x4xf32>, !tosa.shape<4>) -> tensor<1x2x3x4xf32>
// CHECK: %[[VAL_8:.*]] = torch_c.from_builtin_tensor %[[VAL_7]] : tensor<1x2x3x4xf32> -> !torch.vtensor<[1,2,3,4],f32>
// CHECK: return %[[VAL_8]] : !torch.vtensor<[1,2,3,4],f32>
// CHECK: }
func.func @torch.aten.unflatten.int$basic(%arg0: !torch.vtensor<[1,6,4],f32> ) -> !torch.vtensor<[1,2,3,4],f32> {
%int1 = torch.constant.int 1
%int2 = torch.constant.int 2
%int3 = torch.constant.int 3
%0 = torch.prim.ListConstruct %int2, %int3 : (!torch.int, !torch.int) -> !torch.list<int>
%1 = torch.aten.unflatten.int %arg0, %int1, %0 : !torch.vtensor<[1,6,4],f32>, !torch.int, !torch.list<int> -> !torch.vtensor<[1,2,3,4],f32>
return %1 : !torch.vtensor<[1,2,3,4],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.native_layer_norm$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[5,2,2,3],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[2,2,3],f32>,
// CHECK-SAME: %[[VAL_2:.*]]: !torch.vtensor<[2,2,3],f32>) -> !torch.vtensor<[5,2,2,3],f32> {
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_2]] : !torch.vtensor<[2,2,3],f32> -> tensor<2x2x3xf32>
// CHECK: %[[VAL_4:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[2,2,3],f32> -> tensor<2x2x3xf32>
// CHECK: %[[VAL_5:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[5,2,2,3],f32> -> tensor<5x2x2x3xf32>
// CHECK: %[[VAL_6:.*]] = torch.constant.float 5.000000e-01
// CHECK: %[[VAL_7:.*]] = torch.constant.int 3
// CHECK: %[[VAL_8:.*]] = torch.constant.int 2
// CHECK: %[[VAL_9:.*]] = torch.prim.ListConstruct %[[VAL_8]], %[[VAL_8]], %[[VAL_7]] : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[VAL_10:.*]] = "tosa.const"() <{values = dense<1.200000e+01> : tensor<1xf32>}> : () -> tensor<1xf32>
// CHECK: %[[VAL_11:.*]] = tosa.reciprocal %[[VAL_10]] : (tensor<1xf32>) -> tensor<1xf32>
// CHECK: %[[VAL_12:.*]] = tosa.const_shape {values = dense<1> : tensor<4xindex>} : () -> !tosa.shape<4>
// CHECK: %[[VAL_13:.*]] = tosa.reshape %[[VAL_11]], %[[VAL_12]] : (tensor<1xf32>, !tosa.shape<4>) -> tensor<1x1x1x1xf32>
// CHECK: %[[VAL_14:.*]] = tosa.reduce_sum %[[VAL_5]] {axis = 3 : i32} : (tensor<5x2x2x3xf32>) -> tensor<5x2x2x1xf32>
// CHECK: %[[VAL_15:.*]] = tosa.reduce_sum %[[VAL_14]] {axis = 2 : i32} : (tensor<5x2x2x1xf32>) -> tensor<5x2x1x1xf32>
// CHECK: %[[VAL_16:.*]] = tosa.reduce_sum %[[VAL_15]] {axis = 1 : i32} : (tensor<5x2x1x1xf32>) -> tensor<5x1x1x1xf32>
// CHECK: %[[VAL_17:.*]] = tosa.const_shape {values = dense<[5, 1, 1, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
// CHECK: %[[VAL_18:.*]] = tosa.reshape %[[VAL_16]], %[[VAL_17]] : (tensor<5x1x1x1xf32>, !tosa.shape<4>) -> tensor<5x1x1x1xf32>
// CHECK: %[[VAL_19:.*]] = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: %[[VAL_20:.*]] = tosa.mul %[[VAL_18]], %[[VAL_13]], %[[VAL_19]] : (tensor<5x1x1x1xf32>, tensor<1x1x1x1xf32>, tensor<1xi8>) -> tensor<5x1x1x1xf32>
// CHECK: %[[VAL_21:.*]] = tosa.sub %[[VAL_5]], %[[VAL_20]] : (tensor<5x2x2x3xf32>, tensor<5x1x1x1xf32>) -> tensor<5x2x2x3xf32>
// CHECK: %[[VAL_22:.*]] = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: %[[VAL_23:.*]] = tosa.mul %[[VAL_21]], %[[VAL_21]], %[[VAL_22]] : (tensor<5x2x2x3xf32>, tensor<5x2x2x3xf32>, tensor<1xi8>) -> tensor<5x2x2x3xf32>
// CHECK: %[[VAL_24:.*]] = tosa.reduce_sum %[[VAL_23]] {axis = 3 : i32} : (tensor<5x2x2x3xf32>) -> tensor<5x2x2x1xf32>
// CHECK: %[[VAL_25:.*]] = tosa.reduce_sum %[[VAL_24]] {axis = 2 : i32} : (tensor<5x2x2x1xf32>) -> tensor<5x2x1x1xf32>
// CHECK: %[[VAL_26:.*]] = tosa.reduce_sum %[[VAL_25]] {axis = 1 : i32} : (tensor<5x2x1x1xf32>) -> tensor<5x1x1x1xf32>
// CHECK: %[[VAL_27:.*]] = tosa.const_shape {values = dense<[5, 1, 1, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
// CHECK: %[[VAL_28:.*]] = tosa.reshape %[[VAL_26]], %[[VAL_27]] : (tensor<5x1x1x1xf32>, !tosa.shape<4>) -> tensor<5x1x1x1xf32>
// CHECK: %[[VAL_29:.*]] = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: %[[VAL_30:.*]] = tosa.mul %[[VAL_28]], %[[VAL_13]], %[[VAL_29]] : (tensor<5x1x1x1xf32>, tensor<1x1x1x1xf32>, tensor<1xi8>) -> tensor<5x1x1x1xf32>
// CHECK: %[[VAL_31:.*]] = tosa.const_shape {values = dense<[1, 2, 2, 3]> : tensor<4xindex>} : () -> !tosa.shape<4>
// CHECK: %[[VAL_32:.*]] = tosa.reshape %[[VAL_4]], %[[VAL_31]] : (tensor<2x2x3xf32>, !tosa.shape<4>) -> tensor<1x2x2x3xf32>
// CHECK: %[[VAL_33:.*]] = tosa.const_shape {values = dense<[1, 2, 2, 3]> : tensor<4xindex>} : () -> !tosa.shape<4>
// CHECK: %[[VAL_34:.*]] = tosa.reshape %[[VAL_3]], %[[VAL_33]] : (tensor<2x2x3xf32>, !tosa.shape<4>) -> tensor<1x2x2x3xf32>
// CHECK: %[[VAL_35:.*]] = "tosa.const"() <{values = dense<5.000000e-01> : tensor<f32>}> : () -> tensor<f32>
// CHECK: %[[VAL_36:.*]] = tosa.const_shape {values = dense<1> : tensor<4xindex>} : () -> !tosa.shape<4>
// CHECK: %[[VAL_37:.*]] = tosa.reshape %[[VAL_35]], %[[VAL_36]] : (tensor<f32>, !tosa.shape<4>) -> tensor<1x1x1x1xf32>
// CHECK: %[[VAL_38:.*]] = tosa.sub %[[VAL_5]], %[[VAL_20]] : (tensor<5x2x2x3xf32>, tensor<5x1x1x1xf32>) -> tensor<5x2x2x3xf32>
// CHECK: %[[VAL_39:.*]] = tosa.add %[[VAL_30]], %[[VAL_37]] : (tensor<5x1x1x1xf32>, tensor<1x1x1x1xf32>) -> tensor<5x1x1x1xf32>
// CHECK: %[[VAL_40:.*]] = tosa.rsqrt %[[VAL_39]] : (tensor<5x1x1x1xf32>) -> tensor<5x1x1x1xf32>
// CHECK: %[[VAL_41:.*]] = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: %[[VAL_42:.*]] = tosa.mul %[[VAL_38]], %[[VAL_40]], %[[VAL_41]] : (tensor<5x2x2x3xf32>, tensor<5x1x1x1xf32>, tensor<1xi8>) -> tensor<5x2x2x3xf32>
// CHECK: %[[VAL_43:.*]] = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
// CHECK: %[[VAL_44:.*]] = tosa.mul %[[VAL_42]], %[[VAL_32]], %[[VAL_43]] : (tensor<5x2x2x3xf32>, tensor<1x2x2x3xf32>, tensor<1xi8>) -> tensor<5x2x2x3xf32>
// CHECK: %[[VAL_45:.*]] = tosa.add %[[VAL_44]], %[[VAL_34]] : (tensor<5x2x2x3xf32>, tensor<1x2x2x3xf32>) -> tensor<5x2x2x3xf32>
// CHECK: %[[VAL_46:.*]] = torch_c.from_builtin_tensor %[[VAL_45]] : tensor<5x2x2x3xf32> -> !torch.vtensor<[5,2,2,3],f32>
// CHECK: return %[[VAL_46]] : !torch.vtensor<[5,2,2,3],f32>
// CHECK: }
func.func @torch.aten.native_layer_norm$basic(%arg0: !torch.vtensor<[5,2,2,3],f32> , %arg1: !torch.vtensor<[2,2,3],f32> , %arg2: !torch.vtensor<[2,2,3],f32> ) -> !torch.vtensor<[5,2,2,3],f32> {
%float5.000000e-01 = torch.constant.float 5.000000e-01
%int3 = torch.constant.int 3
%int2 = torch.constant.int 2
%0 = torch.prim.ListConstruct %int2, %int2, %int3 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
%result0, %result1, %result2 = torch.aten.native_layer_norm %arg0, %0, %arg1, %arg2, %float5.000000e-01 : !torch.vtensor<[5,2,2,3],f32>, !torch.list<int>, !torch.vtensor<[2,2,3],f32>, !torch.vtensor<[2,2,3],f32>, !torch.float -> !torch.vtensor<[5,2,2,3],f32>, !torch.vtensor<[3],f32>, !torch.vtensor<[3],f32>
return %result0 : !torch.vtensor<[5,2,2,3],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.ne.Tensor$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],i1> {
// CHECK-DAG: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK-DAG: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_4:.*]] = tosa.equal %[[VAL_2]], %[[VAL_3]] : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xi1>
// CHECK: %[[VAL_5:.*]] = tosa.logical_not %[[VAL_4]] : (tensor<?x?xi1>) -> tensor<?x?xi1>
// CHECK: %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<?x?xi1> -> !torch.vtensor<[?,?],i1>
// CHECK: return %[[VAL_6]] : !torch.vtensor<[?,?],i1>
// CHECK: }
func.func @torch.aten.ne.Tensor$basic(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],i1> {
%0 = torch.aten.ne.Tensor %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],i1>
return %0 : !torch.vtensor<[?,?],i1>
}
// -----
// CHECK-LABEL: func.func @torch.aten.logical_or$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],i1>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],i1>) -> !torch.vtensor<[?,?],i1> {
// CHECK-DAG: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],i1> -> tensor<?x?xi1>
// CHECK-DAG: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],i1> -> tensor<?x?xi1>
// CHECK: %[[VAL_4:.*]] = tosa.logical_or %[[VAL_2]], %[[VAL_3]] : (tensor<?x?xi1>, tensor<?x?xi1>) -> tensor<?x?xi1>
// CHECK: %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xi1> -> !torch.vtensor<[?,?],i1>
// CHECK: return %[[VAL_5]] : !torch.vtensor<[?,?],i1>
// CHECK: }
func.func @torch.aten.logical_or$basic(%arg0: !torch.vtensor<[?,?],i1>, %arg1: !torch.vtensor<[?,?],i1>) -> !torch.vtensor<[?,?],i1> {
%0 = torch.aten.logical_or %arg0, %arg1 : !torch.vtensor<[?,?],i1>, !torch.vtensor<[?,?],i1> -> !torch.vtensor<[?,?],i1>
return %0 : !torch.vtensor<[?,?],i1>
}
// -----
// CHECK-LABEL: func.func @torch.aten.permute$basic(
// CHECK-SAME: %[[VAL_0:[0-9]+|[a-zA-Z$._-][a-zA-Z0-9$._-]*]]: !torch.vtensor<[3,4,2],f32>) -> !torch.vtensor<[3,2,4],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[3,4,2],f32> -> tensor<3x4x2xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.int 1
// CHECK: %[[VAL_3:.*]] = torch.constant.int 2
// CHECK: %[[VAL_4:.*]] = torch.constant.int 0
// CHECK: %[[VAL_5:.*]] = torch.prim.ListConstruct %[[VAL_4]], %[[VAL_3]], %[[VAL_2]] : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[VAL_6:.*]] = tosa.transpose %[[VAL_1]] {perms = array<i32: 0, 2, 1>} : (tensor<3x4x2xf32>) -> tensor<3x2x4xf32>
// CHECK: %[[VAL_7:.*]] = torch_c.from_builtin_tensor %[[VAL_6]] : tensor<3x2x4xf32> -> !torch.vtensor<[3,2,4],f32>
// CHECK: return %[[VAL_7]] : !torch.vtensor<[3,2,4],f32>
// CHECK: }
func.func @torch.aten.permute$basic(%arg0: !torch.vtensor<[3,4,2],f32> ) -> !torch.vtensor<[3,2,4],f32> {
%int1 = torch.constant.int 1
%int2 = torch.constant.int 2
%int0 = torch.constant.int 0
%0 = torch.prim.ListConstruct %int0, %int2, %int1 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
%1 = torch.aten.permute %arg0, %0 : !torch.vtensor<[3,4,2],f32>, !torch.list<int> -> !torch.vtensor<[3,2,4],f32>
return %1 : !torch.vtensor<[3,2,4],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.bitwise_and.Tensor$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],si32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],si32>) -> !torch.vtensor<[?,?],si32> {
// CHECK-DAG: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],si32> -> tensor<?x?xi32>
// CHECK-DAG: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],si32> -> tensor<?x?xi32>
// CHECK: %[[VAL_4:.*]] = tosa.bitwise_and %[[VAL_2]], %[[VAL_3]] : (tensor<?x?xi32>, tensor<?x?xi32>) -> tensor<?x?xi32>
// CHECK: %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xi32> -> !torch.vtensor<[?,?],si32>
// CHECK: return %[[VAL_5]] : !torch.vtensor<[?,?],si32>
// CHECK: }
func.func @torch.aten.bitwise_and.Tensor$basic(%arg0: !torch.vtensor<[?,?],si32>, %arg1: !torch.vtensor<[?,?],si32>) -> !torch.vtensor<[?,?],si32> {
%0 = torch.aten.bitwise_and.Tensor %arg0, %arg1 : !torch.vtensor<[?,?],si32>, !torch.vtensor<[?,?],si32> -> !torch.vtensor<[?,?],si32>
return %0 : !torch.vtensor<[?,?],si32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.log2$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_2:.*]] = "tosa.const"() <{values = dense<0.693147182> : tensor<1x1xf32>}> : () -> tensor<1x1xf32>
// CHECK: %[[VAL_3:.*]] = tosa.reciprocal %[[VAL_2]] : (tensor<1x1xf32>) -> tensor<1x1xf32>
// CHECK: %[[VAL_4:.*]] = tosa.log %[[VAL_1]] : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_5:.*]] = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>