torch-mlir/test/Conversion/TorchToMhlo/linear.mlir

// RUN: torch-mlir-opt <%s -convert-torch-to-mhlo -split-input-file -verify-diagnostics | FileCheck %s

// CHECK-LABEL:  func.func @torch.aten.mm$basic$static(
// CHECK-SAME:         %[[ARG0:.*]]: !torch.vtensor<[2,3],f32>, %[[ARG1:.*]]: !torch.vtensor<[3,3],f32>) -> !torch.vtensor<[2,3],f32> {
// CHECK:         %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[2,3],f32> -> tensor<2x3xf32>
// CHECK:         %[[T1:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[3,3],f32> -> tensor<3x3xf32>
// CHECK:         %[[T2:.*]] = "mhlo.dot"(%[[T0]], %[[T1]]) : (tensor<2x3xf32>, tensor<3x3xf32>) -> tensor<2x3xf32>
// CHECK:         %[[T3:.*]] = mhlo.convert %[[T2]] : tensor<2x3xf32>
// CHECK:         %[[T4:.*]] = torch_c.from_builtin_tensor %[[T3]] : tensor<2x3xf32> -> !torch.vtensor<[2,3],f32>
// CHECK:         return %[[T4]] : !torch.vtensor<[2,3],f32>
func.func @torch.aten.mm$basic$static(%arg0: !torch.vtensor<[2,3],f32>, %arg1: !torch.vtensor<[3,3],f32>) -> !torch.vtensor<[2,3],f32> {
  %0 = torch.aten.mm %arg0, %arg1 : !torch.vtensor<[2,3],f32>, !torch.vtensor<[3,3],f32> -> !torch.vtensor<[2,3],f32>
  return %0 : !torch.vtensor<[2,3],f32>
}

// -----

// CHECK-LABEL:  func.func @torch.aten.mm$basic$dynamic(
// CHECK-SAME:         %[[ARG0:.*]]: !torch.vtensor<[?,3],f32>, %[[ARG1:.*]]: !torch.vtensor<[3,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK:         %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[?,3],f32> -> tensor<?x3xf32>
// CHECK:         %[[T1:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[3,?],f32> -> tensor<3x?xf32>
// CHECK:         %[[T2:.*]] = "mhlo.dot"(%[[T0]], %[[T1]]) : (tensor<?x3xf32>, tensor<3x?xf32>) -> tensor<?x?xf32>
// CHECK:         %[[T3:.*]] = mhlo.convert %[[T2]] : tensor<?x?xf32>
// CHECK:         %[[T4:.*]] = torch_c.from_builtin_tensor %[[T3]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK:         return %[[T4]] : !torch.vtensor<[?,?],f32>
func.func @torch.aten.mm$basic$dynamic(%arg0: !torch.vtensor<[?,3],f32>, %arg1: !torch.vtensor<[3,?],f32>) -> !torch.vtensor<[?,?],f32> {
  %0 = torch.aten.mm %arg0, %arg1 : !torch.vtensor<[?,3],f32>, !torch.vtensor<[3,?],f32> -> !torch.vtensor<[?,?],f32>
  return %0 : !torch.vtensor<[?,?],f32>
}

// -----

// CHECK-LABEL:  func.func @torch.aten.bmm$basic$static(
// CHECK-SAME:         %[[ARG0:.*]]: !torch.vtensor<[10,3,4],f32>, %[[ARG1:.*]]: !torch.vtensor<[10,4,5],f32>) -> !torch.vtensor<[10,3,5],f32> {
// CHECK:         %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[10,3,4],f32> -> tensor<10x3x4xf32>
// CHECK:         %[[T1:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[10,4,5],f32> -> tensor<10x4x5xf32>
// CHECK:         %[[C0:.*]] = arith.constant 0 : index
// CHECK:         %[[T2:.*]] = tensor.dim %[[T1]], %[[C0]] : tensor<10x4x5xf32>
// CHECK:         %[[T3:.*]] = arith.index_cast %[[T2]] : index to i64
// CHECK:         %[[C1:.*]] = arith.constant 1 : index
// CHECK:         %[[T4:.*]] = tensor.dim %[[T1]], %[[C1]] : tensor<10x4x5xf32>
// CHECK:         %[[T5:.*]] = arith.index_cast %[[T4]] : index to i64
// CHECK:         %[[C2:.*]] = arith.constant 2 : index
// CHECK:         %[[T6:.*]] = tensor.dim %[[T1]], %[[C2]] : tensor<10x4x5xf32>
// CHECK:         %[[T7:.*]] = arith.index_cast %[[T6]] : index to i64
// CHECK:         %[[T8:.*]] = tensor.from_elements %[[T3]], %[[T5]], %[[T7]] : tensor<3xi64>
// CHECK:         %[[T9:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[T1]], %[[T8]]) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<10x4x5xf32>, tensor<3xi64>) -> tensor<10x4x5xf32>
// CHECK:         %[[T10:.*]] = "mhlo.dot_general"(%[[T0]], %[[T9]]) {dot_dimension_numbers = #mhlo.dot<lhs_batching_dimensions = [0], rhs_batching_dimensions = [0], lhs_contracting_dimensions = [2], rhs_contracting_dimensions = [1]>} : (tensor<10x3x4xf32>, tensor<10x4x5xf32>) -> tensor<10x3x5xf32>
// CHECK:         %[[T11:.*]] = mhlo.convert %[[T10]] : tensor<10x3x5xf32>
// CHECK:         %[[T12:.*]] = torch_c.from_builtin_tensor %[[T11]] : tensor<10x3x5xf32> -> !torch.vtensor<[10,3,5],f32>
// CHECK:         return %[[T12]] : !torch.vtensor<[10,3,5],f32>
func.func @torch.aten.bmm$basic$static(%arg0: !torch.vtensor<[10,3,4],f32>, %arg1: !torch.vtensor<[10,4,5],f32>) -> !torch.vtensor<[10,3,5],f32> {
  %0 = torch.aten.bmm %arg0, %arg1 : !torch.vtensor<[10,3,4],f32>, !torch.vtensor<[10,4,5],f32> -> !torch.vtensor<[10,3,5],f32>
  return %0 : !torch.vtensor<[10,3,5],f32>
}

// -----

// CHECK-LABEL:  func.func @torch.aten.bmm$basic$dynamic(
// CHECK-SAME:         %[[ARG0:.*]]: !torch.vtensor<[?,?,4],f32>, %[[ARG1:.*]]: !torch.vtensor<[?,4,?],f32>) -> !torch.vtensor<[?,?,?],f32> {
// CHECK:         %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[?,?,4],f32> -> tensor<?x?x4xf32>
// CHECK:         %[[T1:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[?,4,?],f32> -> tensor<?x4x?xf32>
// CHECK:         %[[C0:.*]] = arith.constant 0 : index
// CHECK:         %[[T2:.*]] = tensor.dim %[[T1]], %[[C0]] : tensor<?x4x?xf32>
// CHECK:         %[[T3:.*]] = arith.index_cast %[[T2]] : index to i64
// CHECK:         %[[C1:.*]] = arith.constant 1 : index
// CHECK:         %[[T4:.*]] = tensor.dim %[[T1]], %[[C1]] : tensor<?x4x?xf32>
// CHECK:         %[[T5:.*]] = arith.index_cast %[[T4]] : index to i64
// CHECK:         %[[C2:.*]] = arith.constant 2 : index
// CHECK:         %[[T6:.*]] = tensor.dim %[[T1]], %[[C2]] : tensor<?x4x?xf32>
// CHECK:         %[[T7:.*]] = arith.index_cast %[[T6]] : index to i64
// CHECK:         %[[T8:.*]] = tensor.from_elements %[[T3]], %[[T5]], %[[T7]] : tensor<3xi64>
// CHECK:         %[[T9:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[T1]], %[[T8]]) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<?x4x?xf32>, tensor<3xi64>) -> tensor<?x4x?xf32>
// CHECK:         %[[T10:.*]] = "mhlo.dot_general"(%[[T0]], %[[T9]]) {dot_dimension_numbers = #mhlo.dot<lhs_batching_dimensions = [0], rhs_batching_dimensions = [0], lhs_contracting_dimensions = [2], rhs_contracting_dimensions = [1]>} : (tensor<?x?x4xf32>, tensor<?x4x?xf32>) -> tensor<?x?x?xf32>
// CHECK:         %[[T11:.*]] = mhlo.convert %[[T10]] : tensor<?x?x?xf32>
// CHECK:         %[[T12:.*]] = torch_c.from_builtin_tensor %[[T11]] : tensor<?x?x?xf32> -> !torch.vtensor<[?,?,?],f32>
// CHECK:         return %[[T12]] : !torch.vtensor<[?,?,?],f32>
func.func @torch.aten.bmm$basic$dynamic(%arg0: !torch.vtensor<[?,?,4],f32>, %arg1: !torch.vtensor<[?,4,?],f32>) -> !torch.vtensor<[?,?,?],f32> {
  %0 = torch.aten.bmm %arg0, %arg1 : !torch.vtensor<[?,?,4],f32>, !torch.vtensor<[?,4,?],f32> -> !torch.vtensor<[?,?,?],f32>
  return %0 : !torch.vtensor<[?,?,?],f32>
}

// -----

// CHECK-LABEL:  func.func @torch.aten.matmul$basic$static(
// CHECK-SAME:         %[[ARG0:.*]]: !torch.vtensor<[256,120],f32>, %[[ARG1:.*]]: !torch.vtensor<[4,120,256],f32>) -> !torch.vtensor<[4,256,256],f32> {
// CHECK:         %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[256,120],f32> -> tensor<256x120xf32>
// CHECK:         %[[T1:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[4,120,256],f32> -> tensor<4x120x256xf32>
// CHECK:         %[[C0:.*]] = arith.constant 0 : index
// CHECK:         %[[T2:.*]] = tensor.dim %[[T1]], %[[C0]] : tensor<4x120x256xf32>
// CHECK:         %[[T3:.*]] = arith.index_cast %[[T2]] : index to i64
// CHECK:         %[[C0_0:.*]] = arith.constant 0 : index
// CHECK:         %[[T4:.*]] = tensor.dim %[[T0]], %[[C0_0]] : tensor<256x120xf32>
// CHECK:         %[[T5:.*]] = arith.index_cast %[[T4]] : index to i64
// CHECK:         %[[C1:.*]] = arith.constant 1 : index
// CHECK:         %[[T6:.*]] = tensor.dim %[[T0]], %[[C1]] : tensor<256x120xf32>
// CHECK:         %[[T7:.*]] = arith.index_cast %[[T6]] : index to i64
// CHECK:         %[[T8:.*]] = tensor.from_elements %[[T3]], %[[T5]], %[[T7]] : tensor<3xi64>
// CHECK:         %[[T9:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[T0]], %[[T8]]) {broadcast_dimensions = dense<[1, 2]> : tensor<2xi64>} : (tensor<256x120xf32>, tensor<3xi64>) -> tensor<4x256x120xf32>
// CHECK:         %[[T10:.*]] = "mhlo.dot_general"(%[[T9]], %[[T1]]) {dot_dimension_numbers = #mhlo.dot<lhs_batching_dimensions = [0], rhs_batching_dimensions = [0], lhs_contracting_dimensions = [2], rhs_contracting_dimensions = [1]>} : (tensor<4x256x120xf32>, tensor<4x120x256xf32>) -> tensor<4x256x256xf32>
// CHECK:         %[[T11:.*]] = mhlo.convert %[[T10]] : tensor<4x256x256xf32>
// CHECK:         %[[T12:.*]] = torch_c.from_builtin_tensor %[[T11]] : tensor<4x256x256xf32> -> !torch.vtensor<[4,256,256],f32>
// CHECK:         return %[[T12]] : !torch.vtensor<[4,256,256],f32>
func.func @torch.aten.matmul$basic$static(%arg0: !torch.vtensor<[256,120],f32>, %arg1: !torch.vtensor<[4,120,256],f32>) -> !torch.vtensor<[4,256,256],f32> {
  %0 = torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[256,120],f32>, !torch.vtensor<[4,120,256],f32> -> !torch.vtensor<[4,256,256],f32>
  return %0 : !torch.vtensor<[4,256,256],f32>
}

// -----

// CHECK-LABEL:  func.func @torch.aten.matmul$basic$dynamic(
// CHECK-SAME:         %[[ARG0:.*]]: !torch.vtensor<[4,?,256],f32>, %[[ARG1:.*]]: !torch.vtensor<[256,?],f32>) -> !torch.vtensor<[4,?,?],f32> {
// CHECK:         %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[4,?,256],f32> -> tensor<4x?x256xf32>
// CHECK:         %[[T1:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[256,?],f32> -> tensor<256x?xf32>
// CHECK:         %[[C0:.*]] = arith.constant 0 : index
// CHECK:         %[[T2:.*]] = tensor.dim %[[T0]], %[[C0]] : tensor<4x?x256xf32>
// CHECK:         %[[T3:.*]] = arith.index_cast %[[T2]] : index to i64
// CHECK:         %[[C0_0:.*]] = arith.constant 0 : index
// CHECK:         %[[T4:.*]] = tensor.dim %[[T1]], %[[C0_0]] : tensor<256x?xf32>
// CHECK:         %[[T5:.*]] = arith.index_cast %[[T4]] : index to i64
// CHECK:         %[[C1:.*]] = arith.constant 1 : index
// CHECK:         %[[T6:.*]] = tensor.dim %[[T1]], %[[C1]] : tensor<256x?xf32>
// CHECK:         %[[T7:.*]] = arith.index_cast %[[T6]] : index to i64
// CHECK:         %[[T8:.*]] = tensor.from_elements %[[T3]], %[[T5]], %[[T7]] : tensor<3xi64>
// CHECK:         %[[T9:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[T1]], %[[T8]]) {broadcast_dimensions = dense<[1, 2]> : tensor<2xi64>} : (tensor<256x?xf32>, tensor<3xi64>) -> tensor<4x256x?xf32>
// CHECK:         %[[T10:.*]] = "mhlo.dot_general"(%[[T0]], %[[T9]]) {dot_dimension_numbers = #mhlo.dot<lhs_batching_dimensions = [0], rhs_batching_dimensions = [0], lhs_contracting_dimensions = [2], rhs_contracting_dimensions = [1]>} : (tensor<4x?x256xf32>, tensor<4x256x?xf32>) -> tensor<4x?x?xf32>
// CHECK:         %[[T11:.*]] = mhlo.convert %[[T10]] : tensor<4x?x?xf32>
// CHECK:         %[[T12:.*]] = torch_c.from_builtin_tensor %[[T11]] : tensor<4x?x?xf32> -> !torch.vtensor<[4,?,?],f32>
// CHECK:         return %[[T12]] : !torch.vtensor<[4,?,?],f32>
func.func @torch.aten.matmul$basic$dynamic(%arg0: !torch.vtensor<[4,?,256],f32>, %arg1: !torch.vtensor<[256,?],f32>) -> !torch.vtensor<[4,?,?],f32> {
  %0 = torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[4,?,256],f32>, !torch.vtensor<[256,?],f32> -> !torch.vtensor<[4,?,?],f32>
  return %0 : !torch.vtensor<[4,?,?],f32>
}

// -----

// CHECK-LABEL:  func.func @torch.aten.matmul$3dx1d(
// CHECK-SAME:         %[[ARG0:.*]]: !torch.vtensor<[1,?,256],f32>, %[[ARG1:.*]]: !torch.vtensor<[256],f32>) -> !torch.vtensor<[1,?],f32> {
// CHECK:         %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[1,?,256],f32> -> tensor<1x?x256xf32>
// CHECK:         %[[T1:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[256],f32> -> tensor<256xf32>
// CHECK:         %[[C0:.*]] = arith.constant 0 : index
// CHECK:         %[[T2:.*]] = tensor.dim %[[T0]], %[[C0]] : tensor<1x?x256xf32>
// CHECK:         %[[T3:.*]] = arith.index_cast %[[T2]] : index to i64
// CHECK:         %[[C0_0:.*]] = arith.constant 0 : index
// CHECK:         %[[T4:.*]] = tensor.dim %[[T1]], %[[C0_0]] : tensor<256xf32>
// CHECK:         %[[T5:.*]] = arith.index_cast %[[T4]] : index to i64
// CHECK:         %[[T6:.*]] = tensor.from_elements %[[T3]], %[[T5]] : tensor<2xi64>
// CHECK:         %[[T7:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[T1]], %[[T6]]) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<256xf32>, tensor<2xi64>) -> tensor<1x256xf32>
// CHECK:         %[[T8:.*]] = "mhlo.dot_general"(%[[T0]], %[[T7]]) {dot_dimension_numbers = #mhlo.dot<lhs_batching_dimensions = [0], rhs_batching_dimensions = [0], lhs_contracting_dimensions = [2], rhs_contracting_dimensions = [1]>} : (tensor<1x?x256xf32>, tensor<1x256xf32>) -> tensor<1x?xf32>
// CHECK:         %[[T9:.*]] = mhlo.convert %[[T8]] : tensor<1x?xf32>
// CHECK:         %[[T10:.*]] = torch_c.from_builtin_tensor %[[T9]] : tensor<1x?xf32> -> !torch.vtensor<[1,?],f32>
// CHECK:         return %[[T10]] : !torch.vtensor<[1,?],f32>
func.func @torch.aten.matmul$3dx1d(%arg0: !torch.vtensor<[1,?,256],f32>, %arg1: !torch.vtensor<[256],f32>) -> !torch.vtensor<[1,?],f32> {
  %0 = torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[1,?,256],f32>, !torch.vtensor<[256],f32> -> !torch.vtensor<[1,?],f32>
  return %0 : !torch.vtensor<[1,?],f32>
}

// -----

// CHECK-LABEL:  func.func @torch.aten.matmul$1dx3d(
// CHECK-SAME:         %[[ARG0:.*]]: !torch.vtensor<[256],f32>, %[[ARG1:.*]]: !torch.vtensor<[?,256,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK:         %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[256],f32> -> tensor<256xf32>
// CHECK:         %[[T1:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[?,256,?],f32> -> tensor<?x256x?xf32>
// CHECK:         %[[C0:.*]] = arith.constant 0 : index
// CHECK:         %[[T2:.*]] = tensor.dim %[[T1]], %[[C0]] : tensor<?x256x?xf32>
// CHECK:         %[[T3:.*]] = arith.index_cast %[[T2]] : index to i64
// CHECK:         %[[C0_0:.*]] = arith.constant 0 : index
// CHECK:         %[[T4:.*]] = tensor.dim %[[T0]], %[[C0_0]] : tensor<256xf32>
// CHECK:         %[[T5:.*]] = arith.index_cast %[[T4]] : index to i64
// CHECK:         %[[T6:.*]] = tensor.from_elements %[[T3]], %[[T5]] : tensor<2xi64>
// CHECK:         %[[T7:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[T0]], %[[T6]]) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<256xf32>, tensor<2xi64>) -> tensor<?x256xf32>
// CHECK:         %[[T8:.*]] = "mhlo.dot_general"(%[[T7]], %[[T1]]) {dot_dimension_numbers = #mhlo.dot<lhs_batching_dimensions = [0], rhs_batching_dimensions = [0], lhs_contracting_dimensions = [1], rhs_contracting_dimensions = [1]>} : (tensor<?x256xf32>, tensor<?x256x?xf32>) -> tensor<?x?xf32>
// CHECK:         %[[T9:.*]] = mhlo.convert %[[T8]] : tensor<?x?xf32>
// CHECK:         %[[T10:.*]] = torch_c.from_builtin_tensor %[[T9]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK:         return %[[T10]] : !torch.vtensor<[?,?],f32>
func.func @torch.aten.matmul$1dx3d(%arg0: !torch.vtensor<[256],f32>, %arg1: !torch.vtensor<[?,256,?],f32>) -> !torch.vtensor<[?,?],f32> {
  %0 = torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[256],f32>, !torch.vtensor<[?,256,?],f32> -> !torch.vtensor<[?,?],f32>
  return %0 : !torch.vtensor<[?,?],f32>
}

// -----

// CHECK-LABEL:  func.func @torch.aten.matmul$2dx1d(
// CHECK-SAME:         %[[ARG0:.*]]: !torch.vtensor<[?,256],f32>, %[[ARG1:.*]]: !torch.vtensor<[256],f32>) -> !torch.vtensor<[?],f32> {
// CHECK:         %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[?,256],f32> -> tensor<?x256xf32>
// CHECK:         %[[T1:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[256],f32> -> tensor<256xf32>
// CHECK:         %[[T2:.*]] = "mhlo.dot"(%[[T0]], %[[T1]]) : (tensor<?x256xf32>, tensor<256xf32>) -> tensor<?xf32>
// CHECK:         %[[T3:.*]] = mhlo.convert %[[T2]] : tensor<?xf32>
// CHECK:         %[[T4:.*]] = torch_c.from_builtin_tensor %[[T3]] : tensor<?xf32> -> !torch.vtensor<[?],f32>
// CHECK:         return %[[T4]] : !torch.vtensor<[?],f32>
func.func @torch.aten.matmul$2dx1d(%arg0: !torch.vtensor<[?,256],f32>, %arg1: !torch.vtensor<[256],f32>) -> !torch.vtensor<[?],f32> {
  %0 = torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[?,256],f32>, !torch.vtensor<[256],f32> -> !torch.vtensor<[?],f32>
  return %0 : !torch.vtensor<[?],f32>
}

// -----

// CHECK-LABEL:  func.func @torch.aten.matmul$1dx2d(
// CHECK-SAME:         %[[ARG0:.*]]: !torch.vtensor<[256],f32>, %[[ARG1:.*]]: !torch.vtensor<[256,?],f32>) -> !torch.vtensor<[?],f32> {
// CHECK:         %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[256],f32> -> tensor<256xf32>
// CHECK:         %[[T1:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[256,?],f32> -> tensor<256x?xf32>
// CHECK:         %[[T2:.*]] = "mhlo.dot"(%[[T0]], %[[T1]]) : (tensor<256xf32>, tensor<256x?xf32>) -> tensor<?xf32>
// CHECK:         %[[T3:.*]] = mhlo.convert %[[T2]] : tensor<?xf32>
// CHECK:         %[[T4:.*]] = torch_c.from_builtin_tensor %[[T3]] : tensor<?xf32> -> !torch.vtensor<[?],f32>
// CHECK:         return %[[T4]] : !torch.vtensor<[?],f32>
func.func @torch.aten.matmul$1dx2d(%arg0: !torch.vtensor<[256],f32>, %arg1: !torch.vtensor<[256,?],f32>) -> !torch.vtensor<[?],f32> {
  %0 = torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[256],f32>, !torch.vtensor<[256,?],f32> -> !torch.vtensor<[?],f32>
  return %0 : !torch.vtensor<[?],f32>
}

// -----

// CHECK-LABEL:  func.func @torch.aten.matmul$1dx1d(
// CHECK-SAME:         %[[ARG0:.*]]: !torch.vtensor<[256],f32>, %[[ARG1:.*]]: !torch.vtensor<[256],f32>) -> !torch.vtensor<[],f32> {
// CHECK:         %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[256],f32> -> tensor<256xf32>
// CHECK:         %[[T1:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[256],f32> -> tensor<256xf32>
// CHECK:         %[[T2:.*]] = "mhlo.dot"(%[[T0]], %[[T1]]) : (tensor<256xf32>, tensor<256xf32>) -> tensor<f32>
// CHECK:         %[[T3:.*]] = mhlo.convert %[[T2]] : tensor<f32>
// CHECK:         %[[T4:.*]] = torch_c.from_builtin_tensor %[[T3]] : tensor<f32> -> !torch.vtensor<[],f32>
// CHECK:         return %[[T4]] : !torch.vtensor<[],f32>
func.func @torch.aten.matmul$1dx1d(%arg0: !torch.vtensor<[256],f32>, %arg1: !torch.vtensor<[256],f32>) -> !torch.vtensor<[],f32> {
  %0 = torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[256],f32>, !torch.vtensor<[256],f32> -> !torch.vtensor<[],f32>
  return %0 : !torch.vtensor<[],f32>
}

// -----

// CHECK-LABEL:  func.func @torch.aten.matmul$proj(
// CHECK-SAME:         %[[ARG0:.*]]: !torch.vtensor<[?,?,256],f32>) -> !torch.vtensor<[?,?,256],f32> {
// CHECK:         %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[?,?,256],f32> -> tensor<?x?x256xf32>
// CHECK:         %[[T1:.*]] = mhlo.constant dense<1.000000e+00> : tensor<256x256xf32>
// CHECK:         %[[C0:.*]] = arith.constant 0 : index
// CHECK:         %[[T2:.*]] = tensor.dim %[[T0]], %[[C0]] : tensor<?x?x256xf32>
// CHECK:         %[[T3:.*]] = arith.index_cast %[[T2]] : index to i64
// CHECK:         %[[C0_0:.*]] = arith.constant 0 : index
// CHECK:         %[[T4:.*]] = tensor.dim %[[T1]], %[[C0_0]] : tensor<256x256xf32>
// CHECK:         %[[T5:.*]] = arith.index_cast %[[T4]] : index to i64
// CHECK:         %[[C1:.*]] = arith.constant 1 : index
// CHECK:         %[[T6:.*]] = tensor.dim %[[T1]], %[[C1]] : tensor<256x256xf32>
// CHECK:         %[[T7:.*]] = arith.index_cast %[[T6]] : index to i64
// CHECK:         %[[T8:.*]] = tensor.from_elements %[[T3]], %[[T5]], %[[T7]] : tensor<3xi64>
// CHECK:         %[[T9:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[T1]], %[[T8]]) {broadcast_dimensions = dense<[1, 2]> : tensor<2xi64>} : (tensor<256x256xf32>, tensor<3xi64>) -> tensor<?x256x256xf32>
// CHECK:         %[[T10:.*]] = "mhlo.dot_general"(%[[T0]], %[[T9]]) {dot_dimension_numbers = #mhlo.dot<lhs_batching_dimensions = [0], rhs_batching_dimensions = [0], lhs_contracting_dimensions = [2], rhs_contracting_dimensions = [1]>} : (tensor<?x?x256xf32>, tensor<?x256x256xf32>) -> tensor<?x?x256xf32>
// CHECK:         %[[T11:.*]] = mhlo.convert %[[T10]] : tensor<?x?x256xf32>
// CHECK:         %[[T12:.*]] = torch_c.from_builtin_tensor %[[T11]] : tensor<?x?x256xf32> -> !torch.vtensor<[?,?,256],f32>
// CHECK:         return %[[T12]] : !torch.vtensor<[?,?,256],f32>
func.func @torch.aten.matmul$proj(%arg0: !torch.vtensor<[?,?,256],f32>) -> !torch.vtensor<[?,?,256],f32> {
  %0 = torch.vtensor.literal(dense<1.000000e+00> : tensor<256x256xf32>) : !torch.vtensor<[256,256],f32>
  %1 = torch.aten.matmul %arg0, %0 : !torch.vtensor<[?,?,256],f32>, !torch.vtensor<[256,256],f32> -> !torch.vtensor<[?,?,256],f32>
  return %1 : !torch.vtensor<[?,?,256],f32>
}

// -----

// CHECK-LABEL:  func.func @torch.aten.mm$proj(
// CHECK-SAME:         %[[ARG0:.*]]: !torch.vtensor<[?,256],f32>) -> !torch.vtensor<[?,256],f32> {
// CHECK:         %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[?,256],f32> -> tensor<?x256xf32>
// CHECK:         %[[T1:.*]] = mhlo.constant dense<1.000000e+00> : tensor<256x256xf32>
// CHECK:         %[[T2:.*]] = "mhlo.dot"(%[[T0]], %[[T1]]) : (tensor<?x256xf32>, tensor<256x256xf32>) -> tensor<?x256xf32>
// CHECK:         %[[T3:.*]] = mhlo.convert %[[T2]] : tensor<?x256xf32>
// CHECK:         %[[T4:.*]] = torch_c.from_builtin_tensor %[[T3]] : tensor<?x256xf32> -> !torch.vtensor<[?,256],f32>
// CHECK:         return %[[T4]] : !torch.vtensor<[?,256],f32>
func.func @torch.aten.mm$proj(%arg0: !torch.vtensor<[?,256],f32>) -> !torch.vtensor<[?,256],f32> {
  %0 = torch.vtensor.literal(dense<1.000000e+00> : tensor<256x256xf32>) : !torch.vtensor<[256,256],f32>
  %1 = torch.aten.mm %arg0, %0 : !torch.vtensor<[?,256],f32>, !torch.vtensor<[256,256],f32> -> !torch.vtensor<[?,256],f32>
  return %1 : !torch.vtensor<[?,256],f32>
}

// -----

// CHECK-LABEL:   func.func @torch.aten.convolution(
// CHECK-SAME:                                 %[[ARG_0:.*]]: !torch.vtensor<[?,?,?,?],f32>, 
// CHECK-SAME:                                 %[[ARG_1:.*]]: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
// CHECK:           %[[T_0:.*]] = torch_c.to_builtin_tensor %[[ARG_0]] : !torch.vtensor<[?,?,?,?],f32> -> tensor<?x?x?x?xf32>
// CHECK:           %[[T_1:.*]] = torch_c.to_builtin_tensor %[[ARG_1]] : !torch.vtensor<[?,?,?,?],f32> -> tensor<?x?x?x?xf32>
// CHECK:           %[[T_2:.*]] = torch.constant.none
// CHECK:           %[[T_4:.*]] = torch.constant.int 2
// CHECK:           %[[T_5:.*]] = torch.constant.int 1
// CHECK:           %[[T_6:.*]] = torch.constant.int 4
// CHECK:           %[[T_7:.*]] = torch.constant.int 3
// CHECK:           %[[T_8:.*]] = torch_c.to_i64 %[[T_7]]
// CHECK:           %[[T_9:.*]] = torch.prim.ListConstruct %[[T_4]], %[[T_5]] : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK:           %[[T_10:.*]] = torch.prim.ListConstruct %[[T_6]], %[[T_4]] : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK:           %[[T_11:.*]] = torch.prim.ListConstruct %[[T_7]], %[[T_5]] : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK:           %[[T_12:.*]] = torch.prim.ListConstruct  : () -> !torch.list<int>
// CHECK:           %[[T_13:.*]] = torch.constant.bool false
// CHECK:           %[[T_14:.*]] = mhlo.convolution(%[[T_0]], %[[T_1]]) 
// CHECK-SAME{LITERAL}:                                                 dim_numbers = [b, f, 0, 1]x[o, i, 0, 1]->[b, f, 0, 1], window = {stride = [2, 1], pad = [[4, 4], [2, 2]], rhs_dilate = [3, 1]} {batch_group_count = 1 : i64, feature_group_count = 3 : i64} : (tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
// CHECK:           %[[T_15:.*]] = torch_c.from_builtin_tensor %[[T_14]] : tensor<?x?x?x?xf32> -> !torch.vtensor<[?,?,?,?],f32>
// CHECK:           return %[[T_15]] : !torch.vtensor<[?,?,?,?],f32>
func.func @torch.aten.convolution(%arg0: !torch.vtensor<[?,?,?,?],f32>, %arg1: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
  %none = torch.constant.none
  %int2 = torch.constant.int 2
  %int1 = torch.constant.int 1
  %int4 = torch.constant.int 4
  %int3 = torch.constant.int 3
  %1 = torch.prim.ListConstruct %int2, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
  %2 = torch.prim.ListConstruct %int4, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
  %3 = torch.prim.ListConstruct %int3, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
  %4 = torch.prim.ListConstruct  : () -> !torch.list<int>
  %false = torch.constant.bool false
  %5 = torch.aten.convolution %arg0, %arg1, %none, %1, %2, %3, %false, %4, %int3 : !torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[?,?,?,?],f32>, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[?,?,?,?],f32>
  return %5 : !torch.vtensor<[?,?,?,?],f32>
}

// -----

// CHECK-LABEL:   func.func @torch.aten.convolution$bias(
// CHECK-SAME:                                           %[[ARG_0:.*]]: !torch.vtensor<[?,?,?,?],f32>, %[[ARG_1:.*]]: !torch.vtensor<[?,?,?,?],f32>, 
// CHECK-SAME:                                           %[[ARG_2:.*]]: !torch.vtensor<[?],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
// CHECK:           %[[T_0:.*]] = torch_c.to_builtin_tensor %[[ARG_0]] : !torch.vtensor<[?,?,?,?],f32> -> tensor<?x?x?x?xf32>
// CHECK:           %[[T_1:.*]] = torch_c.to_builtin_tensor %[[ARG_1]] : !torch.vtensor<[?,?,?,?],f32> -> tensor<?x?x?x?xf32>
// CHECK:           %[[T_2:.*]] = torch_c.to_builtin_tensor %[[ARG_2]] : !torch.vtensor<[?],f32> -> tensor<?xf32>
// CHECK:           %int2 = torch.constant.int 2
// CHECK:           %int1 = torch.constant.int 1
// CHECK:           %int4 = torch.constant.int 4
// CHECK:           %int3 = torch.constant.int 3
// CHECK:           %[[T_3:.*]] = torch_c.to_i64 %int3
// CHECK:           %[[T_4:.*]] = torch.prim.ListConstruct %int2, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK:           %[[T_5:.*]] = torch.prim.ListConstruct %int4, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK:           %[[T_6:.*]] = torch.prim.ListConstruct %int3, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK:           %[[T_7:.*]] = torch.prim.ListConstruct  : () -> !torch.list<int>
// CHECK:           %false = torch.constant.bool false
// CHECK:           %[[T_8:.*]] = mhlo.convolution(%[[T_0]], %[[T_1]]) 
// CHECK{LITERAL}:                                                     dim_numbers = [b, f, 0, 1]x[o, i, 0, 1]->[b, f, 0, 1], window = {stride = [2, 1], pad = [[4, 4], [2, 2]], rhs_dilate = [3, 1]} {batch_group_count = 1 : i64, feature_group_count = 3 : i64} : (tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
// CHECK:           %[[IDX_0:.*]] = arith.constant 0 : index
// CHECK:           %[[T_9:.*]] = tensor.dim %[[T_2]], %[[IDX_0]] : tensor<?xf32>
// CHECK:           %[[T_10:.*]] = arith.index_cast %[[T_9]] : index to i64
// CHECK:           %[[VAL_0:.*]] = arith.constant 1 : i64
// CHECK:           %[[T_11:.*]] = tensor.from_elements %[[T_10]], %[[VAL_0]], %[[VAL_0]] : tensor<3xi64>
// CHECK:           %[[T_12:.*]] = "mhlo.dynamic_reshape"(%[[T_2]], %[[T_11]]) : (tensor<?xf32>, tensor<3xi64>) -> tensor<?x1x1xf32>
// CHECK:           %[[T_13:.*]] = chlo.broadcast_add %[[T_8]], %[[T_12]] : (tensor<?x?x?x?xf32>, tensor<?x1x1xf32>) -> tensor<?x?x?x?xf32>
// CHECK:           %[[T_14:.*]] = torch_c.from_builtin_tensor %[[T_13]] : tensor<?x?x?x?xf32> -> !torch.vtensor<[?,?,?,?],f32>
// CHECK:           return %[[T_14]] : !torch.vtensor<[?,?,?,?],f32>
func.func @torch.aten.convolution$bias(%arg0: !torch.vtensor<[?,?,?,?],f32>, %arg1: !torch.vtensor<[?,?,?,?],f32>, %arg2: !torch.vtensor<[?],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
  %int2 = torch.constant.int 2
  %int1 = torch.constant.int 1
  %int4 = torch.constant.int 4
  %int3 = torch.constant.int 3
  %1 = torch.prim.ListConstruct %int2, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
  %2 = torch.prim.ListConstruct %int4, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
  %3 = torch.prim.ListConstruct %int3, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
  %4 = torch.prim.ListConstruct  : () -> !torch.list<int>
  %false = torch.constant.bool false
  %5 = torch.aten.convolution %arg0, %arg1, %arg2, %1, %2, %3, %false, %4, %int3 : !torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[?],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[?,?,?,?],f32>
  return %5 : !torch.vtensor<[?,?,?,?],f32>
}