2022-08-04 10:10:54 +08:00
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// RUN: torch-mlir-opt <%s -convert-torch-to-mhlo -split-input-file -verify-diagnostics | FileCheck %s
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// CHECK-LABEL: func.func @torch.aten.mm$basic$static(
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// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[2,3],f32>, %[[ARG1:.*]]: !torch.vtensor<[3,3],f32>) -> !torch.vtensor<[2,3],f32> {
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// CHECK: %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[2,3],f32> -> tensor<2x3xf32>
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// CHECK: %[[T1:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[3,3],f32> -> tensor<3x3xf32>
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// CHECK: %[[T2:.*]] = "mhlo.dot"(%[[T0]], %[[T1]]) : (tensor<2x3xf32>, tensor<3x3xf32>) -> tensor<2x3xf32>
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// CHECK: %[[T3:.*]] = mhlo.convert %[[T2]] : tensor<2x3xf32>
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// CHECK: %[[T4:.*]] = torch_c.from_builtin_tensor %[[T3]] : tensor<2x3xf32> -> !torch.vtensor<[2,3],f32>
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// CHECK: return %[[T4]] : !torch.vtensor<[2,3],f32>
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func.func @torch.aten.mm$basic$static(%arg0: !torch.vtensor<[2,3],f32>, %arg1: !torch.vtensor<[3,3],f32>) -> !torch.vtensor<[2,3],f32> {
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%0 = torch.aten.mm %arg0, %arg1 : !torch.vtensor<[2,3],f32>, !torch.vtensor<[3,3],f32> -> !torch.vtensor<[2,3],f32>
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return %0 : !torch.vtensor<[2,3],f32>
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}
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// -----
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// CHECK-LABEL: func.func @torch.aten.mm$basic$dynamic(
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// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[?,3],f32>, %[[ARG1:.*]]: !torch.vtensor<[3,?],f32>) -> !torch.vtensor<[?,?],f32> {
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// CHECK: %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[?,3],f32> -> tensor<?x3xf32>
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// CHECK: %[[T1:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[3,?],f32> -> tensor<3x?xf32>
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// CHECK: %[[T2:.*]] = "mhlo.dot"(%[[T0]], %[[T1]]) : (tensor<?x3xf32>, tensor<3x?xf32>) -> tensor<?x?xf32>
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// CHECK: %[[T3:.*]] = mhlo.convert %[[T2]] : tensor<?x?xf32>
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// CHECK: %[[T4:.*]] = torch_c.from_builtin_tensor %[[T3]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
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// CHECK: return %[[T4]] : !torch.vtensor<[?,?],f32>
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func.func @torch.aten.mm$basic$dynamic(%arg0: !torch.vtensor<[?,3],f32>, %arg1: !torch.vtensor<[3,?],f32>) -> !torch.vtensor<[?,?],f32> {
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%0 = torch.aten.mm %arg0, %arg1 : !torch.vtensor<[?,3],f32>, !torch.vtensor<[3,?],f32> -> !torch.vtensor<[?,?],f32>
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return %0 : !torch.vtensor<[?,?],f32>
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}
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// -----
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// CHECK-LABEL: func.func @torch.aten.bmm$basic$static(
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// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[10,3,4],f32>, %[[ARG1:.*]]: !torch.vtensor<[10,4,5],f32>) -> !torch.vtensor<[10,3,5],f32> {
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// CHECK: %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[10,3,4],f32> -> tensor<10x3x4xf32>
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// CHECK: %[[T1:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[10,4,5],f32> -> tensor<10x4x5xf32>
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// CHECK: %[[C0:.*]] = arith.constant 0 : index
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// CHECK: %[[T2:.*]] = tensor.dim %[[T1]], %[[C0]] : tensor<10x4x5xf32>
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// CHECK: %[[T3:.*]] = arith.index_cast %[[T2]] : index to i64
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// CHECK: %[[C1:.*]] = arith.constant 1 : index
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// CHECK: %[[T4:.*]] = tensor.dim %[[T1]], %[[C1]] : tensor<10x4x5xf32>
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// CHECK: %[[T5:.*]] = arith.index_cast %[[T4]] : index to i64
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// CHECK: %[[C2:.*]] = arith.constant 2 : index
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// CHECK: %[[T6:.*]] = tensor.dim %[[T1]], %[[C2]] : tensor<10x4x5xf32>
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// CHECK: %[[T7:.*]] = arith.index_cast %[[T6]] : index to i64
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// CHECK: %[[T8:.*]] = tensor.from_elements %[[T3]], %[[T5]], %[[T7]] : tensor<3xi64>
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// CHECK: %[[T9:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[T1]], %[[T8]]) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<10x4x5xf32>, tensor<3xi64>) -> tensor<10x4x5xf32>
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// 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>
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// CHECK: %[[T11:.*]] = mhlo.convert %[[T10]] : tensor<10x3x5xf32>
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// CHECK: %[[T12:.*]] = torch_c.from_builtin_tensor %[[T11]] : tensor<10x3x5xf32> -> !torch.vtensor<[10,3,5],f32>
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// CHECK: return %[[T12]] : !torch.vtensor<[10,3,5],f32>
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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> {
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%0 = torch.aten.bmm %arg0, %arg1 : !torch.vtensor<[10,3,4],f32>, !torch.vtensor<[10,4,5],f32> -> !torch.vtensor<[10,3,5],f32>
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return %0 : !torch.vtensor<[10,3,5],f32>
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}
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// -----
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// CHECK-LABEL: func.func @torch.aten.bmm$basic$dynamic(
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// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[?,?,4],f32>, %[[ARG1:.*]]: !torch.vtensor<[?,4,?],f32>) -> !torch.vtensor<[?,?,?],f32> {
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// CHECK: %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[?,?,4],f32> -> tensor<?x?x4xf32>
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// CHECK: %[[T1:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[?,4,?],f32> -> tensor<?x4x?xf32>
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// CHECK: %[[C0:.*]] = arith.constant 0 : index
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// CHECK: %[[T2:.*]] = tensor.dim %[[T1]], %[[C0]] : tensor<?x4x?xf32>
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// CHECK: %[[T3:.*]] = arith.index_cast %[[T2]] : index to i64
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// CHECK: %[[C1:.*]] = arith.constant 1 : index
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// CHECK: %[[T4:.*]] = tensor.dim %[[T1]], %[[C1]] : tensor<?x4x?xf32>
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// CHECK: %[[T5:.*]] = arith.index_cast %[[T4]] : index to i64
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// CHECK: %[[C2:.*]] = arith.constant 2 : index
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// CHECK: %[[T6:.*]] = tensor.dim %[[T1]], %[[C2]] : tensor<?x4x?xf32>
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// CHECK: %[[T7:.*]] = arith.index_cast %[[T6]] : index to i64
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// CHECK: %[[T8:.*]] = tensor.from_elements %[[T3]], %[[T5]], %[[T7]] : tensor<3xi64>
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// 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>
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// 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>
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// CHECK: %[[T11:.*]] = mhlo.convert %[[T10]] : tensor<?x?x?xf32>
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// CHECK: %[[T12:.*]] = torch_c.from_builtin_tensor %[[T11]] : tensor<?x?x?xf32> -> !torch.vtensor<[?,?,?],f32>
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// CHECK: return %[[T12]] : !torch.vtensor<[?,?,?],f32>
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func.func @torch.aten.bmm$basic$dynamic(%arg0: !torch.vtensor<[?,?,4],f32>, %arg1: !torch.vtensor<[?,4,?],f32>) -> !torch.vtensor<[?,?,?],f32> {
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%0 = torch.aten.bmm %arg0, %arg1 : !torch.vtensor<[?,?,4],f32>, !torch.vtensor<[?,4,?],f32> -> !torch.vtensor<[?,?,?],f32>
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return %0 : !torch.vtensor<[?,?,?],f32>
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}
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// -----
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// CHECK-LABEL: func.func @torch.aten.matmul$basic$static(
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// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[256,120],f32>, %[[ARG1:.*]]: !torch.vtensor<[4,120,256],f32>) -> !torch.vtensor<[4,256,256],f32> {
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// CHECK: %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[256,120],f32> -> tensor<256x120xf32>
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// CHECK: %[[T1:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[4,120,256],f32> -> tensor<4x120x256xf32>
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// CHECK: %[[C0:.*]] = arith.constant 0 : index
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// CHECK: %[[T2:.*]] = tensor.dim %[[T1]], %[[C0]] : tensor<4x120x256xf32>
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// CHECK: %[[T3:.*]] = arith.index_cast %[[T2]] : index to i64
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// CHECK: %[[C0_0:.*]] = arith.constant 0 : index
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// CHECK: %[[T4:.*]] = tensor.dim %[[T0]], %[[C0_0]] : tensor<256x120xf32>
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// CHECK: %[[T5:.*]] = arith.index_cast %[[T4]] : index to i64
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// CHECK: %[[C1:.*]] = arith.constant 1 : index
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// CHECK: %[[T6:.*]] = tensor.dim %[[T0]], %[[C1]] : tensor<256x120xf32>
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// CHECK: %[[T7:.*]] = arith.index_cast %[[T6]] : index to i64
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// CHECK: %[[T8:.*]] = tensor.from_elements %[[T3]], %[[T5]], %[[T7]] : tensor<3xi64>
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// CHECK: %[[T9:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[T0]], %[[T8]]) {broadcast_dimensions = dense<[1, 2]> : tensor<2xi64>} : (tensor<256x120xf32>, tensor<3xi64>) -> tensor<4x256x120xf32>
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// 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>
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// CHECK: %[[T11:.*]] = mhlo.convert %[[T10]] : tensor<4x256x256xf32>
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// CHECK: %[[T12:.*]] = torch_c.from_builtin_tensor %[[T11]] : tensor<4x256x256xf32> -> !torch.vtensor<[4,256,256],f32>
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// CHECK: return %[[T12]] : !torch.vtensor<[4,256,256],f32>
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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> {
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%0 = torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[256,120],f32>, !torch.vtensor<[4,120,256],f32> -> !torch.vtensor<[4,256,256],f32>
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return %0 : !torch.vtensor<[4,256,256],f32>
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}
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// -----
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// CHECK-LABEL: func.func @torch.aten.matmul$basic$dynamic(
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// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[4,?,256],f32>, %[[ARG1:.*]]: !torch.vtensor<[256,?],f32>) -> !torch.vtensor<[4,?,?],f32> {
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// CHECK: %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[4,?,256],f32> -> tensor<4x?x256xf32>
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// CHECK: %[[T1:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[256,?],f32> -> tensor<256x?xf32>
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// CHECK: %[[C0:.*]] = arith.constant 0 : index
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// CHECK: %[[T2:.*]] = tensor.dim %[[T0]], %[[C0]] : tensor<4x?x256xf32>
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// CHECK: %[[T3:.*]] = arith.index_cast %[[T2]] : index to i64
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// CHECK: %[[C0_0:.*]] = arith.constant 0 : index
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// CHECK: %[[T4:.*]] = tensor.dim %[[T1]], %[[C0_0]] : tensor<256x?xf32>
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// CHECK: %[[T5:.*]] = arith.index_cast %[[T4]] : index to i64
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// CHECK: %[[C1:.*]] = arith.constant 1 : index
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// CHECK: %[[T6:.*]] = tensor.dim %[[T1]], %[[C1]] : tensor<256x?xf32>
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// CHECK: %[[T7:.*]] = arith.index_cast %[[T6]] : index to i64
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// CHECK: %[[T8:.*]] = tensor.from_elements %[[T3]], %[[T5]], %[[T7]] : tensor<3xi64>
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// CHECK: %[[T9:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[T1]], %[[T8]]) {broadcast_dimensions = dense<[1, 2]> : tensor<2xi64>} : (tensor<256x?xf32>, tensor<3xi64>) -> tensor<4x256x?xf32>
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// 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>
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// CHECK: %[[T11:.*]] = mhlo.convert %[[T10]] : tensor<4x?x?xf32>
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// CHECK: %[[T12:.*]] = torch_c.from_builtin_tensor %[[T11]] : tensor<4x?x?xf32> -> !torch.vtensor<[4,?,?],f32>
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// CHECK: return %[[T12]] : !torch.vtensor<[4,?,?],f32>
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func.func @torch.aten.matmul$basic$dynamic(%arg0: !torch.vtensor<[4,?,256],f32>, %arg1: !torch.vtensor<[256,?],f32>) -> !torch.vtensor<[4,?,?],f32> {
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%0 = torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[4,?,256],f32>, !torch.vtensor<[256,?],f32> -> !torch.vtensor<[4,?,?],f32>
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return %0 : !torch.vtensor<[4,?,?],f32>
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}
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// -----
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// CHECK-LABEL: func.func @torch.aten.matmul$3dx1d(
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// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[1,?,256],f32>, %[[ARG1:.*]]: !torch.vtensor<[256],f32>) -> !torch.vtensor<[1,?],f32> {
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// CHECK: %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[1,?,256],f32> -> tensor<1x?x256xf32>
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// CHECK: %[[T1:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[256],f32> -> tensor<256xf32>
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// CHECK: %[[C0:.*]] = arith.constant 0 : index
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// CHECK: %[[T2:.*]] = tensor.dim %[[T0]], %[[C0]] : tensor<1x?x256xf32>
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// CHECK: %[[T3:.*]] = arith.index_cast %[[T2]] : index to i64
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// CHECK: %[[C0_0:.*]] = arith.constant 0 : index
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// CHECK: %[[T4:.*]] = tensor.dim %[[T1]], %[[C0_0]] : tensor<256xf32>
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// CHECK: %[[T5:.*]] = arith.index_cast %[[T4]] : index to i64
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// CHECK: %[[T6:.*]] = tensor.from_elements %[[T3]], %[[T5]] : tensor<2xi64>
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// CHECK: %[[T7:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[T1]], %[[T6]]) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<256xf32>, tensor<2xi64>) -> tensor<1x256xf32>
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// 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>
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// CHECK: %[[T9:.*]] = mhlo.convert %[[T8]] : tensor<1x?xf32>
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// CHECK: %[[T10:.*]] = torch_c.from_builtin_tensor %[[T9]] : tensor<1x?xf32> -> !torch.vtensor<[1,?],f32>
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// CHECK: return %[[T10]] : !torch.vtensor<[1,?],f32>
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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>
|
|
|
|
|
}
|
|
|
|
|
|
2022-08-04 15:41:35 +08:00
|
|
|
// -----
|
|
|
|
|
|
|
|
|
|
// CHECK-LABEL: func.func @torch.aten.convolution(
|
|
|
|
|
// CHECK-SAME: %[[ARG_0:.*]]: !torch.vtensor<[?,?,?,?],f32>,
|
2022-08-09 09:50:07 +08:00
|
|
|
// CHECK-SAME: %[[ARG_1:.*]]: !torch.vtensor<[?,?,3,3],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
|
2022-08-04 15:41:35 +08:00
|
|
|
// CHECK: %[[T_0:.*]] = torch_c.to_builtin_tensor %[[ARG_0]] : !torch.vtensor<[?,?,?,?],f32> -> tensor<?x?x?x?xf32>
|
2022-08-09 09:50:07 +08:00
|
|
|
// CHECK: %[[T_1:.*]] = torch_c.to_builtin_tensor %[[ARG_1]] : !torch.vtensor<[?,?,3,3],f32> -> tensor<?x?x3x3xf32>
|
2022-08-04 15:41:35 +08:00
|
|
|
// 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]])
|
2022-08-09 09:50:07 +08:00
|
|
|
// 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?x3x3xf32>) -> tensor<?x?x?x?xf32>
|
2022-08-04 15:41:35 +08:00
|
|
|
// 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>
|
2022-08-09 09:50:07 +08:00
|
|
|
func.func @torch.aten.convolution(%arg0: !torch.vtensor<[?,?,?,?],f32>, %arg1: !torch.vtensor<[?,?,3,3],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
|
2022-08-04 15:41:35 +08:00
|
|
|
%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
|
2022-08-09 09:50:07 +08:00
|
|
|
%5 = torch.aten.convolution %arg0, %arg1, %none, %1, %2, %3, %false, %4, %int3 : !torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[?,?,3,3],f32>, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[?,?,?,?],f32>
|
2022-08-04 15:41:35 +08:00
|
|
|
return %5 : !torch.vtensor<[?,?,?,?],f32>
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
// -----
|
|
|
|
|
|
|
|
|
|
// CHECK-LABEL: func.func @torch.aten.convolution$bias(
|
2022-08-09 09:50:07 +08:00
|
|
|
// CHECK-SAME: %[[ARG_0:.*]]: !torch.vtensor<[?,?,?,?],f32>, %[[ARG_1:.*]]: !torch.vtensor<[?,?,3,3],f32>,
|
2022-08-04 15:41:35 +08:00
|
|
|
// 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>
|
2022-08-09 09:50:07 +08:00
|
|
|
// CHECK: %[[T_1:.*]] = torch_c.to_builtin_tensor %[[ARG_1]] : !torch.vtensor<[?,?,3,3],f32> -> tensor<?x?x3x3xf32>
|
2022-08-04 15:41:35 +08:00
|
|
|
// 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]])
|
2022-08-09 09:50:07 +08:00
|
|
|
// 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?x3x3xf32>) -> tensor<?x?x?x?xf32>
|
2022-08-04 15:41:35 +08:00
|
|
|
// 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>
|
2022-08-09 09:50:07 +08:00
|
|
|
func.func @torch.aten.convolution$bias(%arg0: !torch.vtensor<[?,?,?,?],f32>, %arg1: !torch.vtensor<[?,?,3,3],f32>, %arg2: !torch.vtensor<[?],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
|
2022-08-04 15:41:35 +08:00
|
|
|
%int2 = torch.constant.int 2
|
|
|
|
|
%int1 = torch.constant.int 1
|
|
|
|
|
%int4 = torch.constant.int 4
|
|
|
|
|
%int3 = torch.constant.int 3
|
|
|
|
|
%1 = torch.prim.ListConstruct %int2, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
|
|
|
|
|
%2 = torch.prim.ListConstruct %int4, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
|
|
|
|
|
%3 = torch.prim.ListConstruct %int3, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
|
|
|
|
|
%4 = torch.prim.ListConstruct : () -> !torch.list<int>
|
|
|
|
|
%false = torch.constant.bool false
|
2022-08-09 09:50:07 +08:00
|
|
|
%5 = torch.aten.convolution %arg0, %arg1, %arg2, %1, %2, %3, %false, %4, %int3 : !torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[?,?,3,3],f32>, !torch.vtensor<[?],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[?,?,?,?],f32>
|
2022-08-04 15:41:35 +08:00
|
|
|
return %5 : !torch.vtensor<[?,?,?,?],f32>
|
2022-08-09 09:50:07 +08:00
|
|
|
}
|
|
|
|
|
|
|
|
|
|
// -----
|
|
|
|
|
|
|
|
|
|
// CHECK-LABEL: func.func @torch.aten.convolution$transposed_basic(
|
|
|
|
|
// CHECK-SAME: %[[ARG_0:.*]]: !torch.vtensor<[1,2,7,7],f32>,
|
|
|
|
|
// CHECK-SAME: %[[ARG_1:.*]]: !torch.vtensor<[2,4,3,3],f32>) -> !torch.vtensor<[1,4,9,9],f32> {
|
|
|
|
|
// CHECK: %[[T_0:.*]] = torch_c.to_builtin_tensor %[[ARG_0]] : !torch.vtensor<[1,2,7,7],f32> -> tensor<1x2x7x7xf32>
|
|
|
|
|
// CHECK: %[[T_1:.*]] = torch_c.to_builtin_tensor %[[ARG_1]] : !torch.vtensor<[2,4,3,3],f32> -> tensor<2x4x3x3xf32>
|
|
|
|
|
// CHECK: %true = torch.constant.bool true
|
|
|
|
|
// CHECK: %none = torch.constant.none
|
|
|
|
|
// CHECK: %int0 = torch.constant.int 0
|
|
|
|
|
// CHECK: %int1 = torch.constant.int 1
|
|
|
|
|
// CHECK: %[[T_2:.*]] = torch_c.to_i64 %int1
|
|
|
|
|
// CHECK: %[[T_3:.*]] = torch.prim.ListConstruct %int0, %int0 : (!torch.int, !torch.int) -> !torch.list<int>
|
|
|
|
|
// CHECK: %[[T_4:.*]] = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
|
|
|
|
|
// CHECK: %[[T_5:.*]] = "mhlo.reverse"(%[[T_1]]) {dimensions = dense<[2, 3]> : tensor<2xi64>} : (tensor<2x4x3x3xf32>) -> tensor<2x4x3x3xf32>
|
|
|
|
|
// CHECK: %[[T_6:.*]] = mhlo.convolution(%[[T_0]], %[[T_5]])
|
|
|
|
|
// CHECK{LITERAL}: dim_numbers = [b, f, 0, 1]x[i, o, 0, 1]->[b, f, 0, 1], window = {stride = [1, 1], pad = [[2, 2], [2, 2]], lhs_dilate = [1, 1], rhs_dilate = [1, 1]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x2x7x7xf32>, tensor<2x4x3x3xf32>) -> tensor<1x4x9x9xf32>
|
|
|
|
|
// CHECK: %[[T_7:.*]] = torch_c.from_builtin_tensor %[[T_6]] : tensor<1x4x9x9xf32> -> !torch.vtensor<[1,4,9,9],f32>
|
|
|
|
|
// CHECK: return %[[T_7]] : !torch.vtensor<[1,4,9,9],f32>
|
|
|
|
|
func.func @torch.aten.convolution$transposed_basic(%arg0: !torch.vtensor<[1,2,7,7],f32>, %arg1: !torch.vtensor<[2,4,3,3],f32>) -> !torch.vtensor<[1,4,9,9],f32> {
|
|
|
|
|
%true = torch.constant.bool true
|
|
|
|
|
%none = torch.constant.none
|
|
|
|
|
%int0 = torch.constant.int 0
|
|
|
|
|
%int1 = torch.constant.int 1
|
|
|
|
|
%0 = torch.prim.ListConstruct %int0, %int0 : (!torch.int, !torch.int) -> !torch.list<int>
|
|
|
|
|
%1 = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
|
|
|
|
|
%2 = torch.aten.convolution %arg0, %arg1, %none, %1, %0, %1, %true, %0, %int1 : !torch.vtensor<[1,2,7,7],f32>, !torch.vtensor<[2,4,3,3],f32>, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,4,9,9],f32>
|
|
|
|
|
return %2 : !torch.vtensor<[1,4,9,9],f32>
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
// -----
|
|
|
|
|
|
|
|
|
|
// CHECK-LABEL: func.func @torch.aten.convolution$transposed_stride(
|
|
|
|
|
// CHECK-SAME: %[[ARG_0:.*]]: !torch.vtensor<[1,2,7,7],f32>,
|
|
|
|
|
// CHECK-SAME: %[[ARG_1:.*]]: !torch.vtensor<[2,4,3,3],f32>) -> !torch.vtensor<[1,4,15,15],f32> {
|
|
|
|
|
// CHECK: %[[T_0:.*]] = torch_c.to_builtin_tensor %[[ARG_0]] : !torch.vtensor<[1,2,7,7],f32> -> tensor<1x2x7x7xf32>
|
|
|
|
|
// CHECK: %[[T_1:.*]] = torch_c.to_builtin_tensor %[[ARG_1]] : !torch.vtensor<[2,4,3,3],f32> -> tensor<2x4x3x3xf32>
|
|
|
|
|
// CHECK: %true = torch.constant.bool true
|
|
|
|
|
// CHECK: %none = torch.constant.none
|
|
|
|
|
// CHECK: %int0 = torch.constant.int 0
|
|
|
|
|
// CHECK: %int1 = torch.constant.int 1
|
|
|
|
|
// CHECK: %[[T_2:.*]] = torch_c.to_i64 %int1
|
|
|
|
|
// CHECK: %int2 = torch.constant.int 2
|
|
|
|
|
// CHECK: %[[T_3:.*]] = torch.prim.ListConstruct %int0, %int0 : (!torch.int, !torch.int) -> !torch.list<int>
|
|
|
|
|
// CHECK: %[[T_4:.*]] = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
|
|
|
|
|
// CHECK: %[[T_5:.*]] = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
|
|
|
|
|
// CHECK: %[[T_6:.*]] = "mhlo.reverse"(%1) {dimensions = dense<[2, 3]> : tensor<2xi64>} : (tensor<2x4x3x3xf32>) -> tensor<2x4x3x3xf32>
|
|
|
|
|
// CHECK: %[[T_7:.*]] = mhlo.convolution(%[[T_0]], %[[T_6]])
|
|
|
|
|
// CHECK{LITERAL}: dim_numbers = [b, f, 0, 1]x[i, o, 0, 1]->[b, f, 0, 1], window = {stride = [1, 1], pad = [[2, 2], [2, 2]], lhs_dilate = [2, 2], rhs_dilate = [1, 1]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x2x7x7xf32>, tensor<2x4x3x3xf32>) -> tensor<1x4x15x15xf32>
|
|
|
|
|
// CHECK: %[[T_8:.*]] = torch_c.from_builtin_tensor %[[T_7]] : tensor<1x4x15x15xf32> -> !torch.vtensor<[1,4,15,15],f32>
|
|
|
|
|
// CHECK: return %[[T_8]] : !torch.vtensor<[1,4,15,15],f32>
|
|
|
|
|
func.func @torch.aten.convolution$transposed_stride(%arg0: !torch.vtensor<[1,2,7,7],f32>, %arg1: !torch.vtensor<[2,4,3,3],f32>) -> !torch.vtensor<[1,4,15,15],f32> {
|
|
|
|
|
%true = torch.constant.bool true
|
|
|
|
|
%none = torch.constant.none
|
|
|
|
|
%int0 = torch.constant.int 0
|
|
|
|
|
%int1 = torch.constant.int 1
|
|
|
|
|
%int2 = torch.constant.int 2
|
|
|
|
|
%0 = torch.prim.ListConstruct %int0, %int0 : (!torch.int, !torch.int) -> !torch.list<int>
|
|
|
|
|
%1 = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
|
|
|
|
|
%2 = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
|
|
|
|
|
%3 = torch.aten.convolution %arg0, %arg1, %none, %2, %0, %1, %true, %0, %int1 : !torch.vtensor<[1,2,7,7],f32>, !torch.vtensor<[2,4,3,3],f32>, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,4,15,15],f32>
|
|
|
|
|
return %3 : !torch.vtensor<[1,4,15,15],f32>
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
// -----
|
|
|
|
|
|
|
|
|
|
// CHECK-LABEL: func.func @torch.aten.convolution$transposed_outputpadding(
|
|
|
|
|
// CHECK-SAME: %[[ARG_0:.*]]: !torch.vtensor<[1,2,7,7],f32>,
|
|
|
|
|
// CHECK-SAME: %[[ARG_1:.*]]: !torch.vtensor<[2,4,3,3],f32>) -> !torch.vtensor<[1,4,16,16],f32> {
|
|
|
|
|
// CHECK: %[[T_0:.*]] = torch_c.to_builtin_tensor %[[ARG_0]] : !torch.vtensor<[1,2,7,7],f32> -> tensor<1x2x7x7xf32>
|
|
|
|
|
// CHECK: %[[T_1:.*]] = torch_c.to_builtin_tensor %[[ARG_1]] : !torch.vtensor<[2,4,3,3],f32> -> tensor<2x4x3x3xf32>
|
|
|
|
|
// CHECK: %true = torch.constant.bool true
|
|
|
|
|
// CHECK: %none = torch.constant.none
|
|
|
|
|
// CHECK: %int0 = torch.constant.int 0
|
|
|
|
|
// CHECK: %int1 = torch.constant.int 1
|
|
|
|
|
// CHECK: %[[T_2:.*]] = torch_c.to_i64 %int1
|
|
|
|
|
// CHECK: %int2 = torch.constant.int 2
|
|
|
|
|
// CHECK: %[[T_3:.*]] = torch.prim.ListConstruct %int0, %int0 : (!torch.int, !torch.int) -> !torch.list<int>
|
|
|
|
|
// CHECK: %[[T_4:.*]] = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
|
|
|
|
|
// CHECK: %[[T_5:.*]] = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
|
|
|
|
|
// CHECK: %[[T_6:.*]] = "mhlo.reverse"(%[[T_1]]) {dimensions = dense<[2, 3]> : tensor<2xi64>} : (tensor<2x4x3x3xf32>) -> tensor<2x4x3x3xf32>
|
|
|
|
|
// CHECK: %[[T_7:.*]] = mhlo.convolution(%[[T_0]], %[[T_6]])
|
|
|
|
|
// CHECK{LITERAL}: dim_numbers = [b, f, 0, 1]x[i, o, 0, 1]->[b, f, 0, 1], window = {stride = [1, 1], pad = [[2, 2], [2, 2]], lhs_dilate = [2, 2], rhs_dilate = [1, 1]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x2x7x7xf32>, tensor<2x4x3x3xf32>) -> tensor<1x4x15x15xf32>
|
|
|
|
|
// CHECK: %[[T_8:.*]] = mhlo.constant dense<0.000000e+00> : tensor<f32>
|
|
|
|
|
// CHECK: %[[T_9:.*]] = "mhlo.pad"(%[[T_7]], %[[T_8]]) {edge_padding_high = dense<[0, 0, 1, 1]> : vector<4xi64>, edge_padding_low = dense<0> : vector<4xi64>, interior_padding = dense<0> : vector<4xi64>} : (tensor<1x4x15x15xf32>, tensor<f32>) -> tensor<1x4x16x16xf32>
|
|
|
|
|
// CHECK: %[[T_10:.*]] = torch_c.from_builtin_tensor %[[T_9:.*]] : tensor<1x4x16x16xf32> -> !torch.vtensor<[1,4,16,16],f32>
|
|
|
|
|
// CHECK: return %[[T_10]] : !torch.vtensor<[1,4,16,16],f32>
|
|
|
|
|
func.func @torch.aten.convolution$transposed_outputpadding(%arg0: !torch.vtensor<[1,2,7,7],f32>, %arg1: !torch.vtensor<[2,4,3,3],f32>) -> !torch.vtensor<[1,4,16,16],f32> {
|
|
|
|
|
%true = torch.constant.bool true
|
|
|
|
|
%none = torch.constant.none
|
|
|
|
|
%int0 = torch.constant.int 0
|
|
|
|
|
%int1 = torch.constant.int 1
|
|
|
|
|
%int2 = torch.constant.int 2
|
|
|
|
|
%0 = torch.prim.ListConstruct %int0, %int0 : (!torch.int, !torch.int) -> !torch.list<int>
|
|
|
|
|
%1 = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
|
|
|
|
|
%2 = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
|
|
|
|
|
%3 = torch.aten.convolution %arg0, %arg1, %none, %2, %0, %1, %true, %1, %int1 : !torch.vtensor<[1,2,7,7],f32>, !torch.vtensor<[2,4,3,3],f32>, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,4,16,16],f32>
|
|
|
|
|
return %3 : !torch.vtensor<[1,4,16,16],f32>
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
// -----
|
|
|
|
|
|
|
|
|
|
// CHECK-LABEL: func.func @torch.aten.convolution$transposed_groups(
|
|
|
|
|
// CHECK-SAME: %[[ARG_0:.*]]: !torch.vtensor<[1,2,7,7],f32>,
|
|
|
|
|
// CHECK-SAME: %[[ARG_1:.*]]: !torch.vtensor<[2,2,3,3],f32>) -> !torch.vtensor<[1,4,15,15],f32> {
|
|
|
|
|
// CHECK: %[[T_0:.*]] = torch_c.to_builtin_tensor %[[ARG_0]] : !torch.vtensor<[1,2,7,7],f32> -> tensor<1x2x7x7xf32>
|
|
|
|
|
// CHECK: %[[T_1:.*]] = torch_c.to_builtin_tensor %[[ARG_1]] : !torch.vtensor<[2,2,3,3],f32> -> tensor<2x2x3x3xf32>
|
|
|
|
|
// CHECK: %true = torch.constant.bool true
|
|
|
|
|
// CHECK: %none = torch.constant.none
|
|
|
|
|
// CHECK: %int0 = torch.constant.int 0
|
|
|
|
|
// CHECK: %int1 = torch.constant.int 1
|
|
|
|
|
// CHECK: %int2 = torch.constant.int 2
|
|
|
|
|
// CHECK: %[[T_2:.*]] = torch_c.to_i64 %int2
|
|
|
|
|
// CHECK: %[[T_3:.*]] = torch.prim.ListConstruct %int0, %int0 : (!torch.int, !torch.int) -> !torch.list<int>
|
|
|
|
|
// CHECK: %[[T_4:.*]] = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
|
|
|
|
|
// CHECK: %[[T_5:.*]] = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
|
|
|
|
|
// CHECK: %[[T_6:.*]] = "mhlo.reverse"(%1) {dimensions = dense<[2, 3]> : tensor<2xi64>} : (tensor<2x2x3x3xf32>) -> tensor<2x2x3x3xf32>
|
|
|
|
|
// CHECK: %[[IDX_0:.*]] = arith.constant 0 : index
|
|
|
|
|
// CHECK: %[[T_7:.*]] = tensor.dim %[[T_6]], %[[IDX_0]] : tensor<2x2x3x3xf32>
|
|
|
|
|
// CHECK: %[[T_8:.*]] = arith.index_cast %[[T_7]] : index to i64
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// CHECK: %[[IDX_1:.*]] = arith.constant 1 : index
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// CHECK: %[[T_9:.*]] = tensor.dim %[[T_6]], %[[IDX_1]] : tensor<2x2x3x3xf32>
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// CHECK: %[[T_10:.*]] = arith.index_cast %[[T_9]] : index to i64
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// CHECK: %[[IDX_2:.*]] = arith.constant 2 : index
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// CHECK: %[[T_11:.*]] = tensor.dim %[[T_6]], %[[IDX_2]] : tensor<2x2x3x3xf32>
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// CHECK: %[[T_12:.*]] = arith.index_cast %[[T_11]] : index to i64
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// CHECK: %[[IDX_3:.*]] = arith.constant 3 : index
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// CHECK: %[[T_13:.*]] = tensor.dim %[[T_6]], %[[IDX_3]] : tensor<2x2x3x3xf32>
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// CHECK: %[[T_14:.*]] = arith.index_cast %[[T_13]] : index to i64
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// CHECK: %[[T_24:.*]] = arith.constant 2 : i64
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// CHECK: %[[T_15:.*]] = arith.divsi %[[T_8]], %[[T_24]] : i64
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// CHECK: %[[T_16:.*]] = arith.muli %[[T_10]], %[[T_24]] : i64
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// CHECK: %[[T_17:.*]] = tensor.from_elements %[[T_24]], %[[T_15]], %[[T_10]], %[[T_12]], %[[T_14]] : tensor<5xi64>
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// CHECK: %[[T_18:.*]] = "mhlo.dynamic_reshape"(%[[T_6]], %[[T_17]]) : (tensor<2x2x3x3xf32>, tensor<5xi64>) -> tensor<2x1x2x3x3xf32>
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// CHECK: %[[T_19:.*]] = "mhlo.transpose"(%[[T_18]]) {permutation = dense<[1, 0, 2, 3, 4]> : tensor<5xi64>} : (tensor<2x1x2x3x3xf32>) -> tensor<1x2x2x3x3xf32>
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// CHECK: %[[T_20:.*]] = tensor.from_elements %[[T_15]], %[[T_16]], %[[T_12]], %[[T_14]] : tensor<4xi64>
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// CHECK: %[[T_21:.*]] = "mhlo.dynamic_reshape"(%[[T_19]], %[[T_20]]) : (tensor<1x2x2x3x3xf32>, tensor<4xi64>) -> tensor<1x4x3x3xf32>
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// CHECK: %[[T_22:.*]] = mhlo.convolution(%[[T_0]], %[[T_21]])
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// CHECK{LITERAL}: dim_numbers = [b, f, 0, 1]x[i, o, 0, 1]->[b, f, 0, 1], window = {stride = [1, 1], pad = [[2, 2], [2, 2]], lhs_dilate = [2, 2], rhs_dilate = [1, 1]} {batch_group_count = 1 : i64, feature_group_count = 2 : i64} : (tensor<1x2x7x7xf32>, tensor<1x4x3x3xf32>) -> tensor<1x4x15x15xf32>
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// CHECK: %[[T_23:.*]] = torch_c.from_builtin_tensor %[[T_22]] : tensor<1x4x15x15xf32> -> !torch.vtensor<[1,4,15,15],f32>
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// CHECK: return %[[T_23]] : !torch.vtensor<[1,4,15,15],f32>
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func.func @torch.aten.convolution$transposed_groups(%arg0: !torch.vtensor<[1,2,7,7],f32>, %arg1: !torch.vtensor<[2,2,3,3],f32>) -> !torch.vtensor<[1,4,15,15],f32> {
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%true = torch.constant.bool true
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%none = torch.constant.none
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%int0 = torch.constant.int 0
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%int1 = torch.constant.int 1
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%int2 = torch.constant.int 2
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%0 = torch.prim.ListConstruct %int0, %int0 : (!torch.int, !torch.int) -> !torch.list<int>
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%1 = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
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%2 = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
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%3 = torch.aten.convolution %arg0, %arg1, %none, %2, %0, %1, %true, %0, %int2 : !torch.vtensor<[1,2,7,7],f32>, !torch.vtensor<[2,2,3,3],f32>, !torch.none, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,4,15,15],f32>
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return %3 : !torch.vtensor<[1,4,15,15],f32>
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2022-08-04 15:41:35 +08:00
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}
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