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Update to LLVM 029313cc979ae71877b65794b1063d4e51184cc8 

- mergeBlockBefore -> inlineBlockBefore
- move tosa-to-tensor pass ordering

https://github.com/llvm/torch-mlir/issues/1178#issuecomment-1476217922
pull/1965/head
Sean Silva 2023-03-20 17:31:05 +00:00
parent 3698a95586
commit c319a20828
6 changed files with 12 additions and 12 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
externals/llvm-project vendored

@ -1 +1 @@
Subproject commit 21f4b84c456b471cc52016cf360e14d45f7f2960
Subproject commit 029313cc979ae71877b65794b1063d4e51184cc8

2
externals/mlir-hlo vendored

@ -1 +1 @@
Subproject commit b1ac0403ee2a40fc648ada6b9f11096f3d50fd19
Subproject commit 0045830508ff7be4f99f7130400339ac88fbc829

2
lib/Dialect/Torch/IR/TorchOps.cpp
View File

@ -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);
}

2
lib/Dialect/Torch/Transforms/DropAbstractInterpCalculations.cpp
View File

@ -29,7 +29,7 @@ public:
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();

2
python/torch_mlir_e2e_test/tosa_backends/linalg_on_tensors.py
View File

@ -29,8 +29,8 @@ TOSA_TO_LINALG_FUNC_PIPELINE = ",".join([
"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",
])

14
test/Conversion/TorchToMhlo/linear.mlir
View File

@ -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>