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Differential D90656

[mlir] Fix materializations for unranked tensors.
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Authored by silvas on Nov 2 2020, 5:40 PM.

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rriddle
Commits
rGf556af965f11: [mlir] Fix materializations for unranked tensors.

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silvas created this revision.Nov 2 2020, 5:40 PM
Herald added a project: Restricted Project. · View Herald TranscriptNov 2 2020, 5:40 PM
Herald added subscribers: rdzhabarov, tatianashp, msifontes and 12 others. · View Herald Transcript
silvas requested review of this revision.Nov 2 2020, 5:40 PM
Herald added subscribers: stephenneuendorffer, nicolasvasilache. · View Herald TranscriptNov 2 2020, 5:40 PM
Harbormaster completed remote builds in B77345: Diff 302440.Nov 2 2020, 6:02 PM
rriddle accepted this revision.Nov 3 2020, 12:15 PM
This revision is now accepted and ready to land.Nov 3 2020, 12:15 PM
This revision was landed with ongoing or failed builds.Nov 4 2020, 10:17 AM
Closed by commit rGf556af965f11: [mlir] Fix materializations for unranked tensors. (authored by silvas). · Explain Why
This revision was automatically updated to reflect the committed changes.
silvas added a commit: rGf556af965f11: [mlir] Fix materializations for unranked tensors..

Revision Contents

PathSize
mlir/
lib/
Transforms/
4 lines
test/
Dialect/
Standard/
22 lines

Diff 302892

mlir/lib/Transforms/Bufferize.cpp

Show All 21 LinesBufferizeTypeConverter::BufferizeTypeConverter() {
// Convert RankedTensorType to MemRefType. // Convert RankedTensorType to MemRefType.
addConversion([](RankedTensorType type) -> Type { addConversion([](RankedTensorType type) -> Type {
return MemRefType::get(type.getShape(), type.getElementType()); return MemRefType::get(type.getShape(), type.getElementType());
}); });
// Convert UnrankedTensorType to UnrankedMemRefType. // Convert UnrankedTensorType to UnrankedMemRefType.
addConversion([](UnrankedTensorType type) -> Type { addConversion([](UnrankedTensorType type) -> Type {
return UnrankedMemRefType::get(type.getElementType(), 0); return UnrankedMemRefType::get(type.getElementType(), 0);
}); });
addSourceMaterialization([](OpBuilder &builder, RankedTensorType type, addSourceMaterialization([](OpBuilder &builder, TensorType type,
ValueRange inputs, Location loc) -> Value { ValueRange inputs, Location loc) -> Value {
assert(inputs.size() == 1); assert(inputs.size() == 1);
assert(inputs[0].getType().isa<BaseMemRefType>()); assert(inputs[0].getType().isa<BaseMemRefType>());
return builder.create<TensorLoadOp>(loc, type, inputs[0]); return builder.create<TensorLoadOp>(loc, type, inputs[0]);
}); });
addTargetMaterialization([](OpBuilder &builder, MemRefType type, addTargetMaterialization([](OpBuilder &builder, BaseMemRefType type,
ValueRange inputs, Location loc) -> Value { ValueRange inputs, Location loc) -> Value {
assert(inputs.size() == 1); assert(inputs.size() == 1);
assert(inputs[0].getType().isa<TensorType>()); assert(inputs[0].getType().isa<TensorType>());
return builder.create<TensorToMemrefOp>(loc, type, inputs[0]); return builder.create<TensorToMemrefOp>(loc, type, inputs[0]);
}); });
} }
/// This method tries to decompose a value of a certain type using provided /// This method tries to decompose a value of a certain type using provided
▲ Show 20 Lines • Show All 258 Lines • Show Last 20 Lines

mlir/test/Dialect/Standard/bufferize.mlir

Show First 20 Lines • Show All 80 Lines • ▼ Show 20 Lines
// CHECK: %[[CASTED:.*]] = memref_cast %[[MEMREF]] : memref<?xindex> to memref<2xindex> // CHECK: %[[CASTED:.*]] = memref_cast %[[MEMREF]] : memref<?xindex> to memref<2xindex>
// CHECK: %[[RET:.*]] = tensor_load %[[CASTED]] // CHECK: %[[RET:.*]] = tensor_load %[[CASTED]]
// CHECK: return %[[RET]] : tensor<2xindex> // CHECK: return %[[RET]] : tensor<2xindex>
func @tensor_cast(%arg0: tensor<?xindex>) -> tensor<2xindex> { func @tensor_cast(%arg0: tensor<?xindex>) -> tensor<2xindex> {
%0 = tensor_cast %arg0 : tensor<?xindex> to tensor<2xindex> %0 = tensor_cast %arg0 : tensor<?xindex> to tensor<2xindex>
return %0 : tensor<2xindex> return %0 : tensor<2xindex>
} }
// CHECK-LABEL: func @tensor_cast_from_unranked(
// CHECK-SAME: %[[TENSOR:.*]]: tensor<*xf32>) -> tensor<2xf32> {
// CHECK: %[[MEMREF:.*]] = tensor_to_memref %[[TENSOR]] : memref<*xf32>
// CHECK: %[[CASTED_MEMREF:.*]] = memref_cast %[[MEMREF]] : memref<*xf32> to memref<2xf32>
// CHECK: %[[RET:.*]] = tensor_load %[[CASTED_MEMREF]] : memref<2xf32>
// CHECK: return %[[RET]] : tensor<2xf32>
func @tensor_cast_from_unranked(%arg0: tensor<*xf32>) -> tensor<2xf32> {
%0 = tensor_cast %arg0 : tensor<*xf32> to tensor<2xf32>
return %0 : tensor<2xf32>
}
// CHECK-LABEL: func @tensor_cast_to_unranked(
// CHECK-SAME: %[[TENSOR:.*]]: tensor<2xf32>) -> tensor<*xf32> {
// CHECK: %[[MEMREF:.*]] = tensor_to_memref %[[TENSOR]] : memref<2xf32>
// CHECK: %[[CASTED_MEMREF:.*]] = memref_cast %[[MEMREF]] : memref<2xf32> to memref<*xf32>
// CHECK: %[[RET:.*]] = tensor_load %[[CASTED_MEMREF]] : memref<*xf32>
// CHECK: return %[[RET]] : tensor<*xf32>
func @tensor_cast_to_unranked(%arg0: tensor<2xf32>) -> tensor<*xf32> {
%0 = tensor_cast %arg0 : tensor<2xf32> to tensor<*xf32>
return %0 : tensor<*xf32>
}
// CHECK-LABEL: func @tensor_from_elements( // CHECK-LABEL: func @tensor_from_elements(
// CHECK-SAME: %[[ELEM0:.*]]: index, // CHECK-SAME: %[[ELEM0:.*]]: index,
// CHECK-SAME: %[[ELEM1:.*]]: index) -> tensor<2xindex> { // CHECK-SAME: %[[ELEM1:.*]]: index) -> tensor<2xindex> {
// CHECK: %[[MEMREF:.*]] = alloc() // CHECK: %[[MEMREF:.*]] = alloc()
// CHECK: %[[C0:.*]] = constant 0 : index // CHECK: %[[C0:.*]] = constant 0 : index
// CHECK: store %[[ELEM0]], %[[MEMREF]][%[[C0]]] // CHECK: store %[[ELEM0]], %[[MEMREF]][%[[C0]]]
// CHECK: %[[C1:.*]] = constant 1 : index // CHECK: %[[C1:.*]] = constant 1 : index
// CHECK: store %[[ELEM1]], %[[MEMREF]][%[[C1]]] // CHECK: store %[[ELEM1]], %[[MEMREF]][%[[C1]]]
Show All 24 Lines