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[Stablehlo] Bump stablehlo to ab92adeda9119a6c3914cd42367b0a2b70765e91 (
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llvm#3285)
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@qingyunqu
qingyunqu authored May 5, 2024
1 parent 321b844 commit 53299eb
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Showing 6 changed files with 31 additions and 28 deletions.
2 changes: 1 addition & 1 deletion externals/stablehlo
Submodule stablehlo updated 171 files
10 changes: 5 additions & 5 deletions test/Conversion/TorchToStablehlo/basic.mlir
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Expand Up @@ -55,7 +55,7 @@ func.func @torch.aten.contiguous(%arg0: !torch.vtensor<[4,64],f32>) -> !torch.vt
// CHECK-LABEL: func.func @torch.aten.reciprocal(
// 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?xf32>
// CHECK: %[[VAL_2:.*]] = "chlo.constant_like"(%[[VAL_1]]) {value = 1.000000e+00 : f32} : (tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
// CHECK: %[[VAL_2:.*]] = "chlo.constant_like"(%[[VAL_1]]) <{value = 1.000000e+00 : f32}> : (tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
// CHECK: %[[VAL_3:.*]] = stablehlo.divide %[[VAL_2]], %[[VAL_1]] : tensor<?x?x?xf32>
// CHECK: %[[VAL_4:.*]] = torch_c.from_builtin_tensor %[[VAL_3]] : tensor<?x?x?xf32> -> !torch.vtensor<[?,?,?],f32>
// CHECK: return %[[VAL_4]] : !torch.vtensor<[?,?,?],f32>
Expand Down Expand Up @@ -124,7 +124,7 @@ func.func @torch.aten.broadcast_to$dynamic_implicit(%arg0: !torch.vtensor<[?,?],
// CHECK: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_5:.*]] = tensor.dim %[[VAL_1]], %[[VAL_4]] : tensor<?x3x?x?xf32>
// CHECK: %[[VAL_6:.*]] = tensor.from_elements %[[VAL_5]] : tensor<1xindex>
// CHECK: %[[VAL_7:.*]], %[[VAL_8:.*]], %[[VAL_9:.*]] = "stablehlo.batch_norm_training"(%[[VAL_1]], %[[VAL_3]], %[[VAL_2]]) {epsilon = 9.99999974E-6 : f32, feature_index = 1 : i64} : (tensor<?x3x?x?xf32>, tensor<3xf32>, tensor<3xf32>) -> (tensor<?x3x?x?xf32>, tensor<3xf32>, tensor<3xf32>)
// CHECK: %[[VAL_7:.*]], %[[VAL_8:.*]], %[[VAL_9:.*]] = "stablehlo.batch_norm_training"(%[[VAL_1]], %[[VAL_3]], %[[VAL_2]]) <{epsilon = 9.99999974E-6 : f32, feature_index = 1 : i64}> : (tensor<?x3x?x?xf32>, tensor<3xf32>, tensor<3xf32>) -> (tensor<?x3x?x?xf32>, tensor<3xf32>, tensor<3xf32>)
// CHECK: %[[VAL_8:.*]] = torch_c.from_builtin_tensor %[[VAL_7]] : tensor<?x3x?x?xf32> -> !torch.vtensor<[?,3,?,?],f32>
// CHECK: return %[[VAL_8]] : !torch.vtensor<[?,3,?,?],f32>
func.func @torch.aten.batch_norm$training(%arg0: !torch.vtensor<[?,3,?,?],f32>) -> !torch.vtensor<[?,3,?,?],f32> {
Expand Down Expand Up @@ -152,7 +152,7 @@ func.func @torch.aten.batch_norm$training(%arg0: !torch.vtensor<[?,3,?,?],f32>)
// CHECK: %[[T3:.*]] = tensor.dim %[[T0]], %[[C1]] : tensor<?x3x?x?xf32>
// CHECK: %[[T4:.*]] = tensor.from_elements %[[T3]] : tensor<1xindex>
// CHECK: %[[T5:.*]] = tensor.cast %[[T0]] : tensor<?x3x?x?xf32> to tensor<?x3x?x?xf32>
// CHECK: %[[T6:.*]] = "stablehlo.batch_norm_inference"(%[[T5]], %[[T2]], %[[T1]], %[[T1]], %[[T2]]) {epsilon = 9.99999974E-6 : f32, feature_index = 1 : i64} : (tensor<?x3x?x?xf32>, tensor<3xf32>, tensor<3xf32>, tensor<3xf32>, tensor<3xf32>) -> tensor<?x3x?x?xf32>
// CHECK: %[[T6:.*]] = "stablehlo.batch_norm_inference"(%[[T5]], %[[T2]], %[[T1]], %[[T1]], %[[T2]]) <{epsilon = 9.99999974E-6 : f32, feature_index = 1 : i64}> : (tensor<?x3x?x?xf32>, tensor<3xf32>, tensor<3xf32>, tensor<3xf32>, tensor<3xf32>) -> tensor<?x3x?x?xf32>
// CHECK: %[[T7:.*]] = tensor.cast %[[T6]] : tensor<?x3x?x?xf32> to tensor<?x3x?x?xf32>
// CHECK: %[[T8:.*]] = torch_c.from_builtin_tensor %[[T7]] : tensor<?x3x?x?xf32> -> !torch.vtensor<[?,3,?,?],f32>
// CHECK: return %[[T8]] : !torch.vtensor<[?,3,?,?],f32>
Expand Down Expand Up @@ -185,7 +185,7 @@ func.func @torch.aten.batch_norm$inference(%arg0: !torch.vtensor<[?,3,?,?],f32>)
// CHECK: %[[VAL_8:.*]] = stablehlo.dynamic_broadcast_in_dim %[[VAL_7]], %[[VAL_6]], dims = [] : (tensor<f32>, tensor<1xindex>) -> tensor<3xf32>
// CHECK: %[[VAL_9:.*]] = stablehlo.constant dense<0.000000e+00> : tensor<f32>
// CHECK: %[[VAL_10:.*]] = stablehlo.dynamic_broadcast_in_dim %[[VAL_9]], %[[VAL_6]], dims = [] : (tensor<f32>, tensor<1xindex>) -> tensor<3xf32>
// CHECK: %[[VAL_11:.*]], %[[VAL_12:.*]], %[[VAL_13:.*]] = "stablehlo.batch_norm_training"(%[[VAL_1]], %[[VAL_8]], %[[VAL_10]]) {epsilon = 9.99999974E-6 : f32, feature_index = 1 : i64} : (tensor<?x3x?x?xf32>, tensor<3xf32>, tensor<3xf32>) -> (tensor<?x3x?x?xf32>, tensor<3xf32>, tensor<3xf32>)
// CHECK: %[[VAL_11:.*]], %[[VAL_12:.*]], %[[VAL_13:.*]] = "stablehlo.batch_norm_training"(%[[VAL_1]], %[[VAL_8]], %[[VAL_10]]) <{epsilon = 9.99999974E-6 : f32, feature_index = 1 : i64}> : (tensor<?x3x?x?xf32>, tensor<3xf32>, tensor<3xf32>) -> (tensor<?x3x?x?xf32>, tensor<3xf32>, tensor<3xf32>)
// CHECK: %[[VAL_14:.*]] = torch_c.from_builtin_tensor %[[VAL_11]] : tensor<?x3x?x?xf32> -> !torch.vtensor<[?,3,?,?],f32>
// CHECK: return %[[VAL_14]] : !torch.vtensor<[?,3,?,?],f32>
func.func @torch.aten.batch_norm$no_bias_weight(%arg0: !torch.vtensor<[?,3,?,?],f32>) -> !torch.vtensor<[?,3,?,?],f32> {
Expand Down Expand Up @@ -214,7 +214,7 @@ func.func @torch.aten.batch_norm$no_bias_weight(%arg0: !torch.vtensor<[?,3,?,?],
// CHECK: %[[VAL_6:.*]] = stablehlo.dynamic_reshape %[[VAL_1]], %[[VAL_5]] : (tensor<3x7x4x5xf32>, tensor<3xi64>) -> tensor<1x21x20xf32>
// CHECK: %[[VAL_7:.*]] = stablehlo.constant dense<1.000000e+00> : tensor<21xf32>
// CHECK: %[[VAL_8:.*]] = stablehlo.constant dense<0.000000e+00> : tensor<21xf32>
// CHECK: %[[VAL_9:.*]], %[[VAL_10:.*]], %[[VAL_11:.*]] = "stablehlo.batch_norm_training"(%[[VAL_6]], %[[VAL_7]], %[[VAL_8]]) {epsilon = 9.99999974E-6 : f32, feature_index = 1 : i64} : (tensor<1x21x20xf32>, tensor<21xf32>, tensor<21xf32>) -> (tensor<1x21x20xf32>, tensor<21xf32>, tensor<21xf32>)
// CHECK: %[[VAL_9:.*]], %[[VAL_10:.*]], %[[VAL_11:.*]] = "stablehlo.batch_norm_training"(%[[VAL_6]], %[[VAL_7]], %[[VAL_8]]) <{epsilon = 9.99999974E-6 : f32, feature_index = 1 : i64}> : (tensor<1x21x20xf32>, tensor<21xf32>, tensor<21xf32>) -> (tensor<1x21x20xf32>, tensor<21xf32>, tensor<21xf32>)
// CHECK: %[[VAL_12:.*]] = stablehlo.constant dense<[3, 7, 4, 5]> : tensor<4xi64>
// CHECK: %[[VAL_13:.*]] = stablehlo.dynamic_reshape %[[VAL_9]], %[[VAL_12]] : (tensor<1x21x20xf32>, tensor<4xi64>) -> tensor<3x7x4x5xf32>
// CHECK: %[[VAL_14:.*]] = stablehlo.constant dense<[3, 7, 1, 1]> : tensor<4xi64>
Expand Down
8 changes: 4 additions & 4 deletions test/Conversion/TorchToStablehlo/elementwise.mlir
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Expand Up @@ -4,9 +4,9 @@
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[STR:.*]] = torch.constant.str "none"
// CHECK: %[[T1:.*]] = "chlo.constant_like"(%[[T0]]) {value = 1.000000e+00 : f32} : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[T2:.*]] = "chlo.constant_like"(%[[T0]]) {value = 2.000000e+00 : f32} : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[T3:.*]] = "chlo.constant_like"(%[[T0]]) {value = 5.000000e-01 : f32} : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[T1:.*]] = "chlo.constant_like"(%[[T0]]) <{value = 1.000000e+00 : f32}> : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[T2:.*]] = "chlo.constant_like"(%[[T0]]) <{value = 2.000000e+00 : f32}> : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[T3:.*]] = "chlo.constant_like"(%[[T0]]) <{value = 5.000000e-01 : f32}> : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[T4:.*]] = stablehlo.rsqrt %[[T2]] : tensor<?x?xf32>
// CHECK: %[[T5:.*]] = stablehlo.multiply %[[T0]], %[[T4]] : tensor<?x?xf32>
// CHECK: %[[T6:.*]] = chlo.erf %[[T5]] : tensor<?x?xf32> -> tensor<?x?xf32>
Expand Down Expand Up @@ -487,7 +487,7 @@ func.func @torch.aten.permute$basic(%arg0: !torch.vtensor<[4,64],f32>) -> !torch
// CHECK-LABEL: func.func @torch.aten.relu(
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[T1:.*]] = "chlo.constant_like"(%[[T0]]) {value = 0.000000e+00 : f32} : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[T1:.*]] = "chlo.constant_like"(%[[T0]]) <{value = 0.000000e+00 : f32}> : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[T2:.*]] = stablehlo.maximum %[[T0]], %[[T1]] : tensor<?x?xf32>
// CHECK: %[[T3:.*]] = torch_c.from_builtin_tensor %[[T2]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[T3]] : !torch.vtensor<[?,?],f32>
Expand Down
6 changes: 3 additions & 3 deletions test/Conversion/TorchToStablehlo/gather.mlir
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@
// CHECK: %[[T2:.*]] = tensor.dim %[[T0]], %[[C1]] : tensor<?x4xf32>
// CHECK: %[[T3:.*]] = arith.index_cast %[[T2]] : index to i64
// CHECK: %[[T4:.*]] = tensor.from_elements %[[C1_I64]], %[[T3]] : tensor<2xi64>
// CHECK: %[[T5:.*]] = "stablehlo.dynamic_gather"(%[[T0]], %[[T1]], %[[T4]]) {dimension_numbers = #stablehlo.gather<offset_dims = [1], collapsed_slice_dims = [0], start_index_map = [0], index_vector_dim = 1>, indices_are_sorted = false} : (tensor<?x4xf32>, tensor<2xi64>, tensor<2xi64>) -> tensor<2x4xf32>
// CHECK: %[[T5:.*]] = "stablehlo.dynamic_gather"(%[[T0]], %[[T1]], %[[T4]]) <{dimension_numbers = #stablehlo.gather<offset_dims = [1], collapsed_slice_dims = [0], start_index_map = [0], index_vector_dim = 1>, indices_are_sorted = false}> : (tensor<?x4xf32>, tensor<2xi64>, tensor<2xi64>) -> tensor<2x4xf32>
// CHECK: %[[T6:.*]] = stablehlo.convert %[[T5]] : tensor<2x4xf32>
// CHECK: %[[T7:.*]] = torch_c.from_builtin_tensor %[[T6]] : tensor<2x4xf32> -> !torch.vtensor<[2,4],f32>
// CHECK: return %[[T7]] : !torch.vtensor<[2,4],f32>
Expand All @@ -31,7 +31,7 @@ func.func @torch.aten.index_select$basic(%arg0: !torch.vtensor<[?,4],f32>, %arg1
// CHECK: %[[T2:.*]] = tensor.dim %[[T0]], %[[C1]] : tensor<?x?xf32>
// CHECK: %[[T3:.*]] = arith.index_cast %[[T2]] : index to i64
// CHECK: %[[T4:.*]] = tensor.from_elements %[[C1_I64]], %[[T3]] : tensor<2xi64>
// CHECK: %[[T5:.*]] = "stablehlo.dynamic_gather"(%[[T0]], %[[T1]], %[[T4]]) {dimension_numbers = #stablehlo.gather<offset_dims = [1], collapsed_slice_dims = [0], start_index_map = [0], index_vector_dim = 1>, indices_are_sorted = false} : (tensor<?x?xf32>, tensor<?xi64>, tensor<2xi64>) -> tensor<?x?xf32>
// CHECK: %[[T5:.*]] = "stablehlo.dynamic_gather"(%[[T0]], %[[T1]], %[[T4]]) <{dimension_numbers = #stablehlo.gather<offset_dims = [1], collapsed_slice_dims = [0], start_index_map = [0], index_vector_dim = 1>, indices_are_sorted = false}> : (tensor<?x?xf32>, tensor<?xi64>, tensor<2xi64>) -> tensor<?x?xf32>
// CHECK: %[[T6:.*]] = stablehlo.convert %[[T5]] : tensor<?x?xf32>
// CHECK: %[[T7:.*]] = torch_c.from_builtin_tensor %[[T6]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[T7]] : !torch.vtensor<[?,?],f32>
Expand All @@ -53,7 +53,7 @@ func.func @torch.aten.embedding$basic(%weight: !torch.vtensor<[?,?],f32>, %indic
// CHECK: %[[T2:.*]] = tensor.dim %[[T0]], %[[C1]] : tensor<?x?xf32>
// CHECK: %[[T3:.*]] = arith.index_cast %[[T2]] : index to i64
// CHECK: %[[T4:.*]] = tensor.from_elements %[[C1_I64]], %[[T3]] : tensor<2xi64>
// CHECK: %[[T5:.*]] = "stablehlo.dynamic_gather"(%[[T0]], %[[T1]], %[[T4]]) {dimension_numbers = #stablehlo.gather<offset_dims = [2], collapsed_slice_dims = [0], start_index_map = [0], index_vector_dim = 2>, indices_are_sorted = false} : (tensor<?x?xf32>, tensor<?x1xi64>, tensor<2xi64>) -> tensor<?x1x?xf32>
// CHECK: %[[T5:.*]] = "stablehlo.dynamic_gather"(%[[T0]], %[[T1]], %[[T4]]) <{dimension_numbers = #stablehlo.gather<offset_dims = [2], collapsed_slice_dims = [0], start_index_map = [0], index_vector_dim = 2>, indices_are_sorted = false}> : (tensor<?x?xf32>, tensor<?x1xi64>, tensor<2xi64>) -> tensor<?x1x?xf32>
// CHECK: %[[T6:.*]] = stablehlo.convert %[[T5]] : tensor<?x1x?xf32>
// CHECK: %[[T7:.*]] = torch_c.from_builtin_tensor %[[T6]] : tensor<?x1x?xf32> -> !torch.vtensor<[?,1,?],f32>
// CHECK: return %[[T7]] : !torch.vtensor<[?,1,?],f32>
Expand Down
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