[clang-tools-extra] [llvm] [flang] [mlir] [libc] [clang] [compiler-rt] port fixes from local llvm (PR #78484)
ian Bearman via cfe-commits
cfe-commits at lists.llvm.org
Fri Jan 19 09:42:57 PST 2024
https://github.com/manbearian updated https://github.com/llvm/llvm-project/pull/78484
>From b83074fa260d2ce4b876b71d507224cb9476a944 Mon Sep 17 00:00:00 2001
From: Ian Bearman <ianb at microsoft.com>
Date: Wed, 17 Jan 2024 17:54:25 +0000
Subject: [PATCH] port fixes from local llvm
---
.../IR/BufferizableOpInterface.h | 13 +++++++
.../BufferizableOpInterfaceImpl.cpp | 13 ++++---
.../IR/BufferizableOpInterface.cpp | 12 +++---
.../Bufferization/IR/BufferizationOps.cpp | 4 +-
.../FuncBufferizableOpInterfaceImpl.cpp | 4 +-
mlir/lib/Dialect/Tensor/IR/TensorOps.cpp | 38 ++++++++++++++-----
.../BufferizableOpInterfaceImpl.cpp | 8 ++--
mlir/test/Dialect/Linalg/collapse-dim.mlir | 14 +++----
8 files changed, 70 insertions(+), 36 deletions(-)
diff --git a/mlir/include/mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h b/mlir/include/mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h
index 63e2d19e68ef97c..478cdab8298754c 100644
--- a/mlir/include/mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h
+++ b/mlir/include/mlir/Dialect/Bufferization/IR/BufferizableOpInterface.h
@@ -257,6 +257,9 @@ struct BufferizationOptions {
/// Parameters: Value, memory space, bufferization options
using UnknownTypeConverterFn = std::function<BaseMemRefType(
Value, Attribute memorySpace, const BufferizationOptions &)>;
+ // Produce a MemorySpace attribute from a tensor type
+ using GetMemorySpaceFn =
+ std::function<std::optional<Attribute>(TensorType t)>;
BufferizationOptions();
@@ -351,6 +354,16 @@ struct BufferizationOptions {
/// used.
UnknownTypeConverterFn unknownTypeConverterFn = nullptr;
+ // Use during type conversion to determine the memory space for memref based
+ // on the originanl tensor type
+ GetMemorySpaceFn getMemorySpaceFn = nullptr;
+
+ std::optional<Attribute> getMemorySpace(TensorType t) const {
+ if (getMemorySpaceFn)
+ return getMemorySpaceFn(t);
+ return defaultMemorySpace;
+ }
+
/// Seed for the analysis fuzzer. If set to `0`, the fuzzer is deactivated.
/// Should be used only with `testAnalysisOnly = true`.
unsigned analysisFuzzerSeed = 0;
diff --git a/mlir/lib/Dialect/Arith/Transforms/BufferizableOpInterfaceImpl.cpp b/mlir/lib/Dialect/Arith/Transforms/BufferizableOpInterfaceImpl.cpp
index f69b2557eec922e..337ac0c0761440e 100644
--- a/mlir/lib/Dialect/Arith/Transforms/BufferizableOpInterfaceImpl.cpp
+++ b/mlir/lib/Dialect/Arith/Transforms/BufferizableOpInterfaceImpl.cpp
@@ -26,17 +26,18 @@ struct ConstantOpInterface
LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
const BufferizationOptions &options) const {
auto constantOp = cast<arith::ConstantOp>(op);
+ auto type = constantOp.getType().dyn_cast<RankedTensorType>();
+
+ // Only ranked tensors are supported.
+ if (!type)
+ return failure();
Attribute memorySpace;
- if (options.defaultMemorySpace.has_value())
- memorySpace = *options.defaultMemorySpace;
+ if (options.getMemorySpace(type))
+ memorySpace = *options.getMemorySpace(type);
else
return constantOp->emitError("could not infer memory space");
- // Only ranked tensors are supported.
- if (!isa<RankedTensorType>(constantOp.getType()))
- return failure();
-
// Only constants inside a module are supported.
auto moduleOp = constantOp->getParentOfType<ModuleOp>();
if (!moduleOp)
diff --git a/mlir/lib/Dialect/Bufferization/IR/BufferizableOpInterface.cpp b/mlir/lib/Dialect/Bufferization/IR/BufferizableOpInterface.cpp
index 4b1dfee4a2b926f..1a849155abed028 100644
--- a/mlir/lib/Dialect/Bufferization/IR/BufferizableOpInterface.cpp
+++ b/mlir/lib/Dialect/Bufferization/IR/BufferizableOpInterface.cpp
@@ -682,11 +682,11 @@ bufferization::getBufferType(Value value, const BufferizationOptions &options,
return bufferizableOp.getBufferType(value, options, invocationStack);
// Op is not bufferizable.
- if (!options.defaultMemorySpace.has_value())
+ auto memSpace = options.getMemorySpace(value.getType().cast<TensorType>());
+ if (!memSpace.has_value())
return op->emitError("could not infer memory space");
- return getMemRefType(value, options, /*layout=*/{},
- *options.defaultMemorySpace);
+ return getMemRefType(value, options, /*layout=*/{}, *memSpace);
}
bool bufferization::hasTensorSemantics(Operation *op) {
@@ -943,11 +943,11 @@ FailureOr<BaseMemRefType> bufferization::detail::defaultGetBufferType(
// If we do not know the memory space and there is no default memory space,
// report a failure.
- if (!options.defaultMemorySpace.has_value())
+ auto memSpace = options.getMemorySpace(value.getType().cast<TensorType>());
+ if (!memSpace.has_value())
return op->emitError("could not infer memory space");
- return getMemRefType(value, options, /*layout=*/{},
- *options.defaultMemorySpace);
+ return getMemRefType(value, options, /*layout=*/{}, *memSpace);
}
bool bufferization::detail::defaultIsRepetitiveRegion(
diff --git a/mlir/lib/Dialect/Bufferization/IR/BufferizationOps.cpp b/mlir/lib/Dialect/Bufferization/IR/BufferizationOps.cpp
index 253fcf2525121b8..8618436ff993828 100644
--- a/mlir/lib/Dialect/Bufferization/IR/BufferizationOps.cpp
+++ b/mlir/lib/Dialect/Bufferization/IR/BufferizationOps.cpp
@@ -234,8 +234,8 @@ AllocTensorOp::getBufferType(Value value, const BufferizationOptions &options,
if (failed(copyBufferType))
return failure();
memorySpace = copyBufferType->getMemorySpace();
- } else if (options.defaultMemorySpace.has_value()) {
- memorySpace = *options.defaultMemorySpace;
+ } else if (auto x = options.getMemorySpace(getType()); x.has_value()) {
+ memorySpace = *x;
} else {
return getOperation()->emitError("could not infer memory space");
}
diff --git a/mlir/lib/Dialect/Bufferization/Transforms/FuncBufferizableOpInterfaceImpl.cpp b/mlir/lib/Dialect/Bufferization/Transforms/FuncBufferizableOpInterfaceImpl.cpp
index 07cd1f90b17df4c..4a06bac31961b13 100644
--- a/mlir/lib/Dialect/Bufferization/Transforms/FuncBufferizableOpInterfaceImpl.cpp
+++ b/mlir/lib/Dialect/Bufferization/Transforms/FuncBufferizableOpInterfaceImpl.cpp
@@ -66,7 +66,7 @@ getBufferizedFunctionArgType(FuncOp funcOp, int64_t index,
assert(tensorType && "expected TensorType");
BaseMemRefType memrefType = options.functionArgTypeConverterFn(
- tensorType, *options.defaultMemorySpace, funcOp, options);
+ tensorType, *options.getMemorySpace(tensorType), funcOp, options);
auto layoutAttr = funcOp.getArgAttrOfType<AffineMapAttr>(
index, BufferizationDialect::kBufferLayoutAttrName);
@@ -443,7 +443,7 @@ struct FuncOpInterface
// Note: If `inferFunctionResultLayout = true`, cast are later folded
// away.
BaseMemRefType resultType = options.functionArgTypeConverterFn(
- tensorType, *options.defaultMemorySpace, funcOp, options);
+ tensorType, *options.getMemorySpace(tensorType), funcOp, options);
Value toMemrefOp = rewriter.create<bufferization::ToMemrefOp>(
loc, resultType, returnVal);
returnValues.push_back(toMemrefOp);
diff --git a/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp b/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
index b2fe58099b2fb39..5eb7f6ef24721cf 100644
--- a/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
+++ b/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
@@ -21,6 +21,7 @@
#include "mlir/IR/IRMapping.h"
#include "mlir/IR/Matchers.h"
#include "mlir/IR/OpDefinition.h"
+#include "mlir/IR/TensorEncoding.h"
#include "mlir/IR/TypeUtilities.h"
#include "mlir/Interfaces/DestinationStyleOpInterface.h"
#include "mlir/Interfaces/LoopLikeInterface.h"
@@ -1622,7 +1623,20 @@ CollapseShapeOp::inferCollapsedType(RankedTensorType type,
currentDim += dim;
}
- return RankedTensorType::get(newShape, type.getElementType());
+ auto encoding = type.getEncoding();
+ if (auto v = encoding.dyn_cast_or_null<VerifiableTensorEncoding>()) {
+ auto ignoreError = [&] {
+ auto emitter = mlir::emitError(UnknownLoc::get(type.getContext()));
+ emitter.abandon();
+ return emitter;
+ };
+ if (failed(
+ v.verifyEncoding(newShape, type.getElementType(), ignoreError))) {
+ // strip the encoding if it is not valid for the new shape.
+ encoding = Attribute();
+ }
+ }
+ return RankedTensorType::get(newShape, type.getElementType(), encoding);
}
void CollapseShapeOp::build(OpBuilder &b, OperationState &result, Value src,
@@ -1902,7 +1916,8 @@ RankedTensorType ExtractSliceOp::inferResultType(
assert(static_cast<int64_t>(staticSizes.size()) ==
sourceTensorType.getRank() &&
"unexpected staticSizes not equal to rank of source");
- return RankedTensorType::get(staticSizes, sourceTensorType.getElementType());
+ return RankedTensorType::get(staticSizes, sourceTensorType.getElementType(),
+ sourceTensorType.getEncoding());
}
RankedTensorType ExtractSliceOp::inferResultType(
@@ -1943,7 +1958,8 @@ RankedTensorType ExtractSliceOp::inferCanonicalRankReducedResultType(
if (!dimsToProject.test(pos))
projectedShape.push_back(shape[pos]);
inferredType =
- RankedTensorType::get(projectedShape, inferredType.getElementType());
+ RankedTensorType::get(projectedShape, inferredType.getElementType(),
+ inferredType.getEncoding());
}
return inferredType;
}
@@ -2663,8 +2679,8 @@ struct InsertSliceOpSourceCastInserter final
if (!hasValidSizesOffsets(newSrcShape))
return failure();
- RankedTensorType newSrcType =
- RankedTensorType::get(newSrcShape, srcType.getElementType());
+ RankedTensorType newSrcType = RankedTensorType::get(
+ newSrcShape, srcType.getElementType(), srcType.getEncoding());
if (srcType == newSrcType ||
!preservesStaticInformation(srcType, newSrcType) ||
!tensor::CastOp::areCastCompatible(srcType, newSrcType))
@@ -2815,7 +2831,8 @@ RankedTensorType PadOp::inferResultType(RankedTensorType sourceType,
}
}
- return RankedTensorType::get(inferredShape, sourceType.getElementType());
+ return RankedTensorType::get(inferredShape, sourceType.getElementType(),
+ sourceType.getEncoding());
}
void PadOp::build(OpBuilder &b, OperationState &result, Type resultType,
@@ -3597,9 +3614,9 @@ static LogicalResult commonVerifierPackAndUnPackOp(OpTy packOrUnPack) {
"tiling factors must equal the number of dimensions to tile");
}
- ShapedType packedType = (std::is_same<OpTy, PackOp>::value)
- ? packOrUnPack.getDestType()
- : packOrUnPack.getSourceType();
+ RankedTensorType packedType = (std::is_same<OpTy, PackOp>::value)
+ ? packOrUnPack.getDestType()
+ : packOrUnPack.getSourceType();
size_t packedRank = packedType.getRank();
// Require output rank to match input rank + number of blocking factors.
if (unpackedRank + mixedTiles.size() != packedRank) {
@@ -3866,7 +3883,8 @@ RankedTensorType PackOp::inferPackedType(RankedTensorType sourceType,
ArrayRef<int64_t> outerDimsPerm) {
SmallVector<int64_t> resultShape = getPackOpResultTypeShape(
sourceType.getShape(), innerTileSizes, innerDimsPos, outerDimsPerm);
- return RankedTensorType::get(resultShape, sourceType.getElementType());
+ return RankedTensorType::get(resultShape, sourceType.getElementType(),
+ sourceType.getEncoding());
}
Value PackOp::createDestinationTensor(OpBuilder &b, Location loc, Value source,
diff --git a/mlir/lib/Dialect/Tensor/Transforms/BufferizableOpInterfaceImpl.cpp b/mlir/lib/Dialect/Tensor/Transforms/BufferizableOpInterfaceImpl.cpp
index 2cd57e7324b4dc5..907f6bf23b01417 100644
--- a/mlir/lib/Dialect/Tensor/Transforms/BufferizableOpInterfaceImpl.cpp
+++ b/mlir/lib/Dialect/Tensor/Transforms/BufferizableOpInterfaceImpl.cpp
@@ -473,14 +473,14 @@ struct FromElementsOpInterface
LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
const BufferizationOptions &options) const {
auto fromElementsOp = cast<tensor::FromElementsOp>(op);
+ auto tensorType = cast<RankedTensorType>(fromElementsOp.getType());
// TODO: Implement memory space for this op.
- if (options.defaultMemorySpace != Attribute())
+ if (options.getMemorySpace(tensorType) != Attribute())
return op->emitError("memory space not implemented yet");
// Allocate a buffer for the result.
Location loc = op->getLoc();
- auto tensorType = cast<RankedTensorType>(fromElementsOp.getType());
auto shape = tensorType.getShape();
// TODO: Create alloc_tensor ops during TensorCopyInsertion.
FailureOr<Value> tensorAlloc = allocateTensorForShapedValue(
@@ -588,8 +588,10 @@ struct GenerateOpInterface
const BufferizationOptions &options) const {
auto generateOp = cast<tensor::GenerateOp>(op);
+ auto type = generateOp.getResult().getType();
+
// TODO: Implement memory space for this op.
- if (options.defaultMemorySpace != Attribute())
+ if (options.getMemorySpace(type) != Attribute())
return op->emitError("memory space not implemented yet");
// Allocate memory.
diff --git a/mlir/test/Dialect/Linalg/collapse-dim.mlir b/mlir/test/Dialect/Linalg/collapse-dim.mlir
index 547320f53387477..dc3b202c8ea9c43 100644
--- a/mlir/test/Dialect/Linalg/collapse-dim.mlir
+++ b/mlir/test/Dialect/Linalg/collapse-dim.mlir
@@ -122,13 +122,13 @@ func.func @uncollapsable_strided_memref(%arg0: memref<2x6x24x48xi32>, %arg1: mem
// CHECK-LABEL: func.func @linalg_copy(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<1x2x3x4x5xf32, 1 : i64>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<1x2x3x4x5xf32, 3 : i64>) -> tensor<1x2x3x4x5xf32, 3 : i64> {
-// CHECK: %[[VAL_2:.*]] = tensor.collapse_shape %[[VAL_0]] {{\[\[}}0], [1], [2, 3], [4]] : tensor<1x2x3x4x5xf32, 1 : i64> into tensor<1x2x12x5xf32>
-// CHECK: %[[VAL_3:.*]] = tensor.collapse_shape %[[VAL_1]] {{\[\[}}0], [1], [2, 3], [4]] : tensor<1x2x3x4x5xf32, 3 : i64> into tensor<1x2x12x5xf32>
-// CHECK: %[[VAL_4:.*]] = tensor.collapse_shape %[[VAL_2]] {{\[\[}}0], [1], [2, 3]] : tensor<1x2x12x5xf32> into tensor<1x2x60xf32>
-// CHECK: %[[VAL_5:.*]] = tensor.collapse_shape %[[VAL_3]] {{\[\[}}0], [1], [2, 3]] : tensor<1x2x12x5xf32> into tensor<1x2x60xf32>
-// CHECK: %[[VAL_6:.*]] = linalg.copy ins(%[[VAL_4]] : tensor<1x2x60xf32>) outs(%[[VAL_5]] : tensor<1x2x60xf32>) -> tensor<1x2x60xf32>
-// CHECK: %[[VAL_7:.*]] = tensor.expand_shape %[[VAL_6]] {{\[\[}}0], [1], [2, 3]] : tensor<1x2x60xf32> into tensor<1x2x12x5xf32>
-// CHECK: %[[VAL_8:.*]] = tensor.expand_shape %[[VAL_7]] {{\[\[}}0], [1], [2, 3], [4]] : tensor<1x2x12x5xf32> into tensor<1x2x3x4x5xf32, 3 : i64>
+// CHECK: %[[VAL_2:.*]] = tensor.collapse_shape %[[VAL_0]] {{\[\[}}0], [1], [2, 3], [4]] : tensor<1x2x3x4x5xf32, 1 : i64> into tensor<1x2x12x5xf32, 1 : i64>
+// CHECK: %[[VAL_3:.*]] = tensor.collapse_shape %[[VAL_1]] {{\[\[}}0], [1], [2, 3], [4]] : tensor<1x2x3x4x5xf32, 3 : i64> into tensor<1x2x12x5xf32, 3 : i64>
+// CHECK: %[[VAL_4:.*]] = tensor.collapse_shape %[[VAL_2]] {{\[\[}}0], [1], [2, 3]] : tensor<1x2x12x5xf32, 1 : i64> into tensor<1x2x60xf32, 1 : i64>
+// CHECK: %[[VAL_5:.*]] = tensor.collapse_shape %[[VAL_3]] {{\[\[}}0], [1], [2, 3]] : tensor<1x2x12x5xf32, 3 : i64> into tensor<1x2x60xf32, 3 : i64>
+// CHECK: %[[VAL_6:.*]] = linalg.copy ins(%[[VAL_4]] : tensor<1x2x60xf32, 1 : i64>) outs(%[[VAL_5]] : tensor<1x2x60xf32, 3 : i64>) -> tensor<1x2x60xf32, 3 : i64>
+// CHECK: %[[VAL_7:.*]] = tensor.expand_shape %[[VAL_6]] {{\[\[}}0], [1], [2, 3]] : tensor<1x2x60xf32, 3 : i64> into tensor<1x2x12x5xf32, 3 : i64>
+// CHECK: %[[VAL_8:.*]] = tensor.expand_shape %[[VAL_7]] {{\[\[}}0], [1], [2, 3], [4]] : tensor<1x2x12x5xf32, 3 : i64> into tensor<1x2x3x4x5xf32, 3 : i64>
// CHECK: return %[[VAL_8]] : tensor<1x2x3x4x5xf32, 3 : i64>
// CHECK: }
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