Update to LLVM 029313c

- mergeBlockBefore -> inlineBlockBefore - move tosa-to-tensor pass ordering llvm#1178 (comment)
marvin-Yu · Mar 21, 2023 · c319a20 · c319a20
1 parent 3698a95
commit c319a20
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Showing 6 changed files with 12 additions and 12 deletions.
diff --git a/externals/llvm-project b/externals/llvm-project
diff --git a/externals/mlir-hlo b/externals/mlir-hlo
diff --git a/lib/Dialect/Torch/IR/TorchOps.cpp b/lib/Dialect/Torch/IR/TorchOps.cpp
@@ -409,7 +409,7 @@ static void replaceOpWithRegion(PatternRewriter &rewriter, Operation *op,
   Block *block = &region.front();
   Operation *terminator = block->getTerminator();
   ValueRange results = terminator->getOperands();
-  rewriter.mergeBlockBefore(block, op, blockArgs);
+  rewriter.inlineBlockBefore(block, op, blockArgs);
   rewriter.replaceOp(op, results);
   rewriter.eraseOp(terminator);
 }

diff --git a/lib/Dialect/Torch/Transforms/DropAbstractInterpCalculations.cpp b/lib/Dialect/Torch/Transforms/DropAbstractInterpCalculations.cpp
@@ -29,7 +29,7 @@ class DropCalculateOp : public OpConversionPattern<CalculateOp> {
     Block *block = &op.getBody().front();
     Operation *terminator = block->getTerminator();
     ValueRange results = terminator->getOperands();
-    rewriter.mergeBlockBefore(block, op);
+    rewriter.inlineBlockBefore(block, op);
     rewriter.replaceOp(op, results);
     rewriter.eraseOp(terminator);
     return success();

diff --git a/python/torch_mlir_e2e_test/tosa_backends/linalg_on_tensors.py b/python/torch_mlir_e2e_test/tosa_backends/linalg_on_tensors.py
@@ -29,8 +29,8 @@
     "tosa-to-linalg-named",
     # TOSA-to-LinAlg may generate tosa.const() ops, so we want to lower them
     # to arith.constants here before proceeding further.
-    "tosa-to-tensor",
     "tosa-to-linalg",
+    "tosa-to-tensor",
     "tosa-to-arith",
 ])
 

diff --git a/test/Conversion/TorchToMhlo/linear.mlir b/test/Conversion/TorchToMhlo/linear.mlir
@@ -45,7 +45,7 @@ func.func @torch.aten.mm$basic$dynamic(%arg0: !torch.vtensor<[?,3],f32>, %arg1:
 // CHECK:         %[[T7:.*]] = arith.index_cast %[[T6]] : index to i64
 // CHECK:         %[[T8:.*]] = tensor.from_elements %[[T3]], %[[T5]], %[[T7]] : tensor<3xi64>
 // CHECK:         %[[T9:.*]] = stablehlo.dynamic_broadcast_in_dim %[[T1]], %[[T8]], dims = [0, 1, 2] : (tensor<10x4x5xf32>, tensor<3xi64>) -> tensor<10x4x5xf32>
-// CHECK:         %[[T10:.*]] = "stablehlo.dot_general"(%[[T0]], %[[T9]]) {dot_dimension_numbers = #stablehlo.dot<lhs_batching_dimensions = [0], rhs_batching_dimensions = [0], lhs_contracting_dimensions = [2], rhs_contracting_dimensions = [1]>} : (tensor<10x3x4xf32>, tensor<10x4x5xf32>) -> tensor<10x3x5xf32>
+// CHECK:         %[[T10:.*]] = stablehlo.dot_general %[[T0]], %[[T9]], batching_dims = [0] x [0], contracting_dims = [2] x [1] : (tensor<10x3x4xf32>, tensor<10x4x5xf32>) -> tensor<10x3x5xf32>
 // CHECK:         %[[T11:.*]] = tensor.cast %[[T10]] : tensor<10x3x5xf32> to 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>
@@ -71,7 +71,7 @@ func.func @torch.aten.bmm$basic$static(%arg0: !torch.vtensor<[10,3,4],f32>, %arg
 // CHECK:         %[[T7:.*]] = arith.index_cast %[[T6]] : index to i64
 // CHECK:         %[[T8:.*]] = tensor.from_elements %[[T3]], %[[T5]], %[[T7]] : tensor<3xi64>
 // CHECK:         %[[T9:.*]] = stablehlo.dynamic_broadcast_in_dim %[[T1]], %[[T8]], dims = [0, 1, 2] : (tensor<?x4x?xf32>, tensor<3xi64>) -> tensor<?x4x?xf32>
-// CHECK:         %[[T10:.*]] = "stablehlo.dot_general"(%[[T0]], %[[T9]]) {dot_dimension_numbers = #stablehlo.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:         %[[T10:.*]] = stablehlo.dot_general %[[T0]], %[[T9]], batching_dims = [0] x [0], contracting_dims = [2] x [1] : (tensor<?x?x4xf32>, tensor<?x4x?xf32>) -> tensor<?x?x?xf32>
 // CHECK:         %[[T11:.*]] = tensor.cast %[[T10]] : tensor<?x?x?xf32> to 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>
@@ -97,7 +97,7 @@ func.func @torch.aten.bmm$basic$dynamic(%arg0: !torch.vtensor<[?,?,4],f32>, %arg
 // CHECK:         %[[T7:.*]] = arith.index_cast %[[T6]] : index to i64
 // CHECK:         %[[T8:.*]] = tensor.from_elements %[[T3]], %[[T5]], %[[T7]] : tensor<3xi64>
 // CHECK:         %[[T9:.*]] = stablehlo.dynamic_broadcast_in_dim %[[T0]], %[[T8]], dims = [1, 2] : (tensor<256x120xf32>, tensor<3xi64>) -> tensor<4x256x120xf32>
-// CHECK:         %[[T10:.*]] = "stablehlo.dot_general"(%[[T9]], %[[T1]]) {dot_dimension_numbers = #stablehlo.dot<lhs_batching_dimensions = [0], rhs_batching_dimensions = [0], lhs_contracting_dimensions = [2], rhs_contracting_dimensions = [1]>} : (tensor<4x256x120xf32>, tensor<4x120x256xf32>) -> tensor<4x256x256xf32>
+// CHECK:         %[[T10:.*]] = stablehlo.dot_general %[[T9]], %[[T1]], batching_dims = [0] x [0], contracting_dims = [2] x [1] : (tensor<4x256x120xf32>, tensor<4x120x256xf32>) -> tensor<4x256x256xf32>
 // CHECK:         %[[T11:.*]] = tensor.cast %[[T10]] : tensor<4x256x256xf32> to 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>
@@ -123,7 +123,7 @@ func.func @torch.aten.matmul$basic$static(%arg0: !torch.vtensor<[256,120],f32>,
 // CHECK:         %[[T7:.*]] = arith.index_cast %[[T6]] : index to i64
 // CHECK:         %[[T8:.*]] = tensor.from_elements %[[T3]], %[[T5]], %[[T7]] : tensor<3xi64>
 // CHECK:         %[[T9:.*]] = stablehlo.dynamic_broadcast_in_dim %[[T1]], %[[T8]], dims = [1, 2] : (tensor<256x?xf32>, tensor<3xi64>) -> tensor<4x256x?xf32>
-// CHECK:         %[[T10:.*]] = "stablehlo.dot_general"(%[[T0]], %[[T9]]) {dot_dimension_numbers = #stablehlo.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:         %[[T10:.*]] = stablehlo.dot_general %[[T0]], %[[T9]], batching_dims = [0] x [0], contracting_dims = [2] x [1] : (tensor<4x?x256xf32>, tensor<4x256x?xf32>) -> tensor<4x?x?xf32>
 // CHECK:         %[[T11:.*]] = tensor.cast %[[T10]] : tensor<4x?x?xf32> to 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>
@@ -146,7 +146,7 @@ func.func @torch.aten.matmul$basic$dynamic(%arg0: !torch.vtensor<[4,?,256],f32>,
 // CHECK:         %[[T5:.*]] = arith.index_cast %[[T4]] : index to i64
 // CHECK:         %[[T6:.*]] = tensor.from_elements %[[T3]], %[[T5]] : tensor<2xi64>
 // CHECK:         %[[T7:.*]] = stablehlo.dynamic_broadcast_in_dim %[[T1]], %[[T6]], dims = [1] : (tensor<256xf32>, tensor<2xi64>) -> tensor<1x256xf32>
-// CHECK:         %[[T8:.*]] = "stablehlo.dot_general"(%[[T0]], %[[T7]]) {dot_dimension_numbers = #stablehlo.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:         %[[T8:.*]] = stablehlo.dot_general %[[T0]], %[[T7]], batching_dims = [0] x [0], contracting_dims = [2] x [1] : (tensor<1x?x256xf32>, tensor<1x256xf32>) -> tensor<1x?xf32>
 // CHECK:         %[[T9:.*]] = tensor.cast %[[T8]] : tensor<1x?xf32> to 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>
@@ -169,7 +169,7 @@ func.func @torch.aten.matmul$3dx1d(%arg0: !torch.vtensor<[1,?,256],f32>, %arg1:
 // CHECK:         %[[T5:.*]] = arith.index_cast %[[T4]] : index to i64
 // CHECK:         %[[T6:.*]] = tensor.from_elements %[[T3]], %[[T5]] : tensor<2xi64>
 // CHECK:         %[[T7:.*]] = stablehlo.dynamic_broadcast_in_dim %[[T0]], %[[T6]], dims = [1] : (tensor<256xf32>, tensor<2xi64>) -> tensor<?x256xf32>
-// CHECK:         %[[T8:.*]] = "stablehlo.dot_general"(%[[T7]], %[[T1]]) {dot_dimension_numbers = #stablehlo.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:         %[[T8:.*]] = stablehlo.dot_general %[[T7]], %[[T1]], batching_dims = [0] x [0], contracting_dims = [1] x [1] : (tensor<?x256xf32>, tensor<?x256x?xf32>) -> tensor<?x?xf32>
 // CHECK:         %[[T9:.*]] = tensor.cast %[[T8]] : tensor<?x?xf32> to tensor<?x?xf32>
 // CHECK:         %[[T10:.*]] = torch_c.from_builtin_tensor %[[T9]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
 // CHECK:         return %[[T10]] : !torch.vtensor<[?,?],f32>
@@ -240,7 +240,7 @@ func.func @torch.aten.matmul$1dx1d(%arg0: !torch.vtensor<[256],f32>, %arg1: !tor
 // CHECK:         %[[T7:.*]] = arith.index_cast %[[T6]] : index to i64
 // CHECK:         %[[T8:.*]] = tensor.from_elements %[[T3]], %[[T5]], %[[T7]] : tensor<3xi64>
 // CHECK:         %[[T9:.*]] = stablehlo.dynamic_broadcast_in_dim %[[T1]], %[[T8]], dims = [1, 2] : (tensor<256x256xf32>, tensor<3xi64>) -> tensor<?x256x256xf32>
-// CHECK:         %[[T10:.*]] = "stablehlo.dot_general"(%[[T0]], %[[T9]]) {dot_dimension_numbers = #stablehlo.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:         %[[T10:.*]] = stablehlo.dot_general %[[T0]], %[[T9]], batching_dims = [0] x [0], contracting_dims = [2] x [1] : (tensor<?x?x256xf32>, tensor<?x256x256xf32>) -> tensor<?x?x256xf32>
 // CHECK:         %[[T11:.*]] = tensor.cast %[[T10]] : tensor<?x?x256xf32> to 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>