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/llvm-project/mlir/test/Dialect/Shape/
H A D	ops.mlir	7 func.func @shape_num_elements(%shape : !shape.shape) -> !shape.size { 8 %init = shape.const_size 1 9 %num_elements = shape.reduce(%shape, %init) : !shape.shape -> !shape.size { 10 ^bb0(%index : index, %extent : !shape.size, %acc : !shape.size): 11 %acc_next = shape.mul %acc, %extent 12 : !shape.size, !shape.size -> !shape.size 13 shape.yield %acc_next : !shape.size 15 return %num_elements : !shape.size 19 func.func @extent_tensor_num_elements(%shape : tensor<?xindex>) -> index { 21 %num_elements = shape.reduce(%shape, %init) : tensor<?xindex> -> index { [all …]
H A D	canonicalize.mlir	`6 // CHECK: shape.const_shape [2, 3, 4] : tensor<3xindex> 7 %0 = shape.shape_of %arg0 : tensor<2x3x4xf32> -> tensor<3xindex> 15 func.func @f() -> (!shape.shape, !shape.shape) { 16 // CHECK-DAG: shape.const_shape [2, 3] : !shape.shape 17 // CHECK-DAG: shape [all...]`
H A D	invalid.mlir	3 func.func @reduce_op_args_num_mismatch(%shape : !shape.shape, %init : !shape.size) { 5 %num_elements = shape.reduce(%shape, %init) : !shape.shape -> !shape.size { 6 ^bb0(%index: index, %dim: !shape.size): 7 shape.yield %dim : !shape.size 14 func.func @reduce_op_arg0_wrong_type(%shape : !shape.shape, %init : !shape.size) { 16 %num_elements = shape.reduce(%shape, %init) : !shape.shape -> !shape.size { 17 ^bb0(%index: f32, %dim: !shape.size, %acc: !shape.size): 18 %new_acc = "shape.add"(%acc, %dim) 19 : (!shape.size, !shape.size) -> !shape.size 20 shape.yield %new_acc : !shape.size [all …]
H A D	arg_with_shape.mlir	1 // RUN: mlir-opt -outline-shape-computation -split-input-file %s 2>%t \| FileCheck %s 3 func.func @func1(%arg0: !shape.value_shape, %arg1: !shape.value_shape) -> !shape.shape { 4 %0 = shape.shape_of %arg0 : !shape.value_shape -> !shape.shape 5 %1 = shape.shape_of %arg1 : !shape.value_shape -> !shape.shape 6 %2 = shape.meet %0, %1 : !shape.shape, !shape.shape -> !shape.shape 7 return %2 : !shape.shape 11 func.func @func(%arg0: !shape.value_shape, %arg1: !shape.value_shape) -> !shape.shape { 12 %0 = shape.shape_of %arg0 : !shape.value_shape -> !shape.shape 13 %1 = shape.with_shape %arg1, %0 : !shape.value_shape, !shape.shape 14 %2 = call @func1(%arg0, %1) : (!shape.value_shape, !shape.value_shape) -> !shape.shape [all …]
H A D	remove-shape-constraints.mlir	1 // RUN: mlir-opt -allow-unregistered-dialect -split-input-file -remove-shape-constraints -canonical… 2 // RUN: mlir-opt -allow-unregistered-dialect -split-input-file -remove-shape-constraints <%s \| File… 8 func.func @f(%arg0 : !shape.shape, %arg1 : !shape.shape) -> index { 9 // REPLACE-NEXT: %[[WITNESS:.+]] = shape.const_witness true 10 // REPLACE-NOT: shape.cstr_eq 11 // REPLACE: shape.assuming %[[WITNESS]] 14 %0 = shape.cstr_broadcastable %arg0, %arg1 : !shape.shape, !shape.shape 15 %1 = shape.assuming %0 -> index { 17 shape.assuming_yield %2 : index 26 func.func @f(%arg0 : !shape.shape, %arg1 : !shape.shape) -> index { [all …]
H A D	outline-shape-computation.mlir	1 // RUN: mlir-opt -outline-shape-computation -test-print-shape-mapping -split-input-file %s 2>%t \| F… 2 // RUN: cat %t \| FileCheck %s --check-prefix SHAPE 4 // Two dynamic shapes: one of direct shape.shape_of(arg) and the other. 6 …// SHAPE-DAG: Shape for {{.}} = "test.abs"({{.}}> :: @shape_cal_0(<block argument> of type 'tens… 7 …// SHAPE-DAG: Shape for {{.}} = "test.concat"({{.}}> :: @shape_cal_1(<block argument> of type 't… 11 %0 = shape.shape_of %arg0 : tensor<?x4x?xf32> -> tensor<3xindex> 12 %1 = shape.get_extent %0, %c2 : tensor<3xindex>, index -> index 14 %3 = shape.with_shape %2, %0 : tensor<?x4x?xf32>, tensor<3xindex> 15 %4 = shape.value_of %3 : tensor<?x4x?xf32> 17 %6 = shape.get_extent %0, %c0 : tensor<3xindex>, index -> index [all …]
H A D	shape-to-shape.mlir	1 // RUN: mlir-opt -shape-to-shape-lowering -split-input-file %s \| FileCheck %s 4 // CHECK-SAME: ([[ARG:%.]]: !shape.shape) -> !shape.size 5 func.func @num_elements_to_reduce(%shape : !shape.shape) -> !shape.size { 6 %num_elements = shape.num_elements %shape : !shape.shape -> !shape.size 7 return %num_elements : !shape.size 9 // CHECK: [[C1:%.]] = shape.const_size 1 10 // CHECK: [[NUM_ELEMENTS:%.]] = shape.reduce([[ARG]], [[C1]]) : !shape.shape -> !shape.size 11 // CHECK: ^bb0({{.}}: index, [[DIM:%.]]: !shape.size, [[ACC:%.]]: !shape.size 12 // CHECK: [[NEW_ACC:%.*]] = shape.mul [[DIM]], [[ACC]] 13 // CHECK: shape.yield [[NEW_ACC]] : !shape.size [all …]
/llvm-project/mlir/docs/Dialects/
H A D	ShapeDialect.md	1 # 'shape' Dialect 3 Description of operations & types within the Shape dialect as well as their 4 [usage](#different-stages-of-lowering-shape-dialect). 8 ## Different stages of lowering Shape dialect 11 shape dialect and the lowering between these uses. Currently we have 3 worlds / 12 stages of lowering of shape functions: 15 This "input" form carries both the shape and whether in error state as 31 Starting from the shape function of matmul in the error monadic form 35 shape.function_library @shplib { 37 func.func @matmul(%lhs: !shape.value_shape, %rhs: !shape.value_shape) -> !shape.shape { [all …]
/llvm-project/mlir/docs/
H A D	ShapeInference.md	1 # Shape Inference 3 Shape inference as discussed here is considered a specific instance of type 6 dimensions. While some operations have no compile time fixed shape (e.g., output 7 shape is dictated by data) we could still have some knowledge of 11 shape. 15 `InferShapedTypeOpInterface` is used to implement the shape and element type 16 inference. The return type can often be deduced from the deduced return shape 22 ## Shape functions 24 The C++ interfaces are the base mechanism whereby shape inference is queried and 25 executed, but not the intended way to specify shape constraints in general. [all …]
/llvm-project/mlir/include/mlir/Dialect/Shape/IR/
H A D	ShapeOps.td	1 //===- ShapeOps.td - Shape operations definition ------------ tablegen --===// 9 // This is the operation definition file for Shape dialect operations. 16 include "mlir/Dialect/Shape/IR/ShapeBase.td" 27 // Shape op definitions 58 let summary = "Returns the broadcasted output shape of two or more inputs"; 60 Returns the broadcasted shape for input shapes or extent tensors. The rest 62 to more inputs. Both operands can be of type `shape.shape` or 63 `tensor<?xindex>`. The result is of type `shape.shape` an [all...]
H A D	ShapeBase.td	9 // Base definitions for the `shape` dialect. 20 // Shape Inference dialect definitions 24 let name = "shape"; 26 let summary = "Types and operations for shape dialect"; 28 This dialect contains operations for shape inference. 30 Note: Unless explicitly stated, all functions that return a shape and take 31 shapes as input, return the invalid shape if one of its operands is an 32 invalid shape. This avoids flagging multiple errors for one verification 38 let cppNamespace = "::mlir::shape"; 50 def Shape_ShapeType : Shape_Type<"Shape", "shape"> { [all …]
/llvm-project/mlir/lib/Dialect/Shape/Transforms/
H A D	OutlineShapeComputation.cpp	10 #include "mlir/Dialect/Shape/Analysis/ShapeMappingAnalysis.h" 11 #include "mlir/Dialect/Shape/IR/Shape.h" 12 #include "mlir/Dialect/Shape/Transforms/Passes.h" 27 #include "mlir/Dialect/Shape/Transforms/Passes.h.inc" 30 #define DEBUG_TYPE "outline-shape-computation" 63 // Create a shape.func representing the shape computation for `shape`. 64 std::pair<shape 66 createFuncFromCluster(OpBuilder & b,const SmallVector<Operation ,8> & cluster,Value shape,StringRef fnName,Location loc) createFuncFromCluster() argument 101 Value shape = it.first; getOrderedClusters() local 114 for (Value shape : it->second) getOrderedClusters() local 135 Value shape = withOp.getShape(); constructShapeFunc() local 267 Value shape = withOp.getShape(); constructClustersForEachShape() local 277 getClusterFromValue(Value shape,DenseMap<Value,DenseSet<Operation >> & clusters) getClusterFromValue() argument [all...]
/llvm-project/mlir/include/mlir/Dialect/Shape/Transforms/
H A D	Passes.td	14 def OutlineShapeComputation : Pass<"outline-shape-computation", "ModuleOp"> { 15 let summary = "Using shape.func to preserve shape computation"; 17 This pass outlines the shape computation part in high level IR by adding 18 shape.func and populate corresponding mapping infoemation into 19 ShapeMappingAnalysis. The shape computation part is usually introduced by 20 shape reification, and each single dynamic shape is denoted by shape.with_shape. 22 There're two main reasons this shape-outline pass is needed: 23 1. Many passes don't take shape reification part into consideration. 24 Therefore we need to "remove" the shape reification part temporarily for 26 2. Sometimes we cannot redo shape reification after converting from dialect [all …]
/llvm-project/llvm/unittests/Analysis/Inputs/ir2native_x86_64_model/
H A D	saved_model.pbtxt	`34 name: "shape" 35 type: "shape" 37 shape { 131 name: "shape" 132 type: "shape" 150 shape { 162 key: "shape" 164 shape { 189 shape { 214 shape { [all …]`
/llvm-project/llvm/test/Transforms/Attributor/
H A D	depgraph.ll	60 ; DOT-DAG: Node[[Node0:0x[a-z0-9]+]] [shape=record,label="{[AAIsDead] 61 ; DOT-DAG: Node[[Node1:0x[a-z0-9]+]] [shape=record,label="{[AAPotentialValues] 62 ; DOT-DAG: Node[[Node2:0x[a-z0-9]+]] [shape=record,label="{[AAPotentialValues] 63 ; DOT-DAG: Node[[Node3:0x[a-z0-9]+]] [shape=record,label="{[AAPotentialValues] 64 ; DOT-DAG: Node[[Node4:0x[a-z0-9]+]] [shape=record,label="{[AAPotentialValues] 65 ; DOT-DAG: Node[[Node5:0x[a-z0-9]+]] [shape=record,label="{[AANoReturn] 66 ; DOT-DAG: Node[[Node6:0x[a-z0-9]+]] [shape=record,label="{[AANoReturn] 67 ; DOT-DAG: Node[[Node7:0x[a-z0-9]+]] [shape=record,label="{[AAIsDead] 68 ; DOT-DAG: Node[[Node8:0x[a-z0-9]+]] [shape=record,label="{[AAWillReturn] 69 ; DOT-DAG: Node[[Node9:0x[a-z0-9]+]] [shape=record,label="{[AAIsDead] [all …]
/llvm-project/llvm/lib/Transforms/Coroutines/
H A D	CoroSplit.cpp	104 coro::Shape &Shape) { 160 Shape.SymmetricTransfers.push_back(ResumeCall); 169 static void lowerAwaitSuspends(Function &F, coro::Shape &Shape) { 171 for (auto AWS : Shape.CoroAwaitSuspends) 172 lowerAwaitSuspend(Builder, AWS, Shape); 176 const coro::Shape &Shape, Value FramePtr, 178 assert(Shape in lowerAwaitSuspend() argument 106 coro::Shape &Shape; global() member in __anon631b365a0111::CoroCloner 120 CoroCloner(Function & OrigF,const Twine & Suffix,coro::Shape & Shape,Kind FKind,TargetTransformInfo & TTI) CoroCloner() argument 128 CoroCloner(Function & OrigF,const Twine & Suffix,coro::Shape & Shape,Function * NewF,AnyCoroSuspendInst * ActiveSuspend,TargetTransformInfo & TTI) CoroCloner() argument 243 lowerAwaitSuspends(Function & F,coro::Shape & Shape) lowerAwaitSuspends() argument 250 maybeFreeRetconStorage(IRBuilder<> & Builder,const coro::Shape & Shape,Value * FramePtr,CallGraph * CG) maybeFreeRetconStorage() argument 306 replaceFallthroughCoroEnd(AnyCoroEndInst * End,const coro::Shape & Shape,Value * FramePtr,bool InResume,CallGraph * CG) replaceFallthroughCoroEnd() argument 404 markCoroutineAsDone(IRBuilder<> & Builder,const coro::Shape & Shape,Value * FramePtr) markCoroutineAsDone() argument 438 replaceUnwindCoroEnd(AnyCoroEndInst * End,const coro::Shape & Shape,Value * FramePtr,bool InResume,CallGraph * CG) replaceUnwindCoroEnd() argument 476 replaceCoroEnd(AnyCoroEndInst * End,const coro::Shape & Shape,Value * FramePtr,bool InResume,CallGraph * CG) replaceCoroEnd() argument 540 createCloneDeclaration(Function & OrigF,coro::Shape & Shape,const Twine & Suffix,Module::iterator InsertBefore,AnyCoroSuspendInst * ActiveSuspend) createCloneDeclaration() argument 656 replaceSwiftErrorOps(Function & F,coro::Shape & Shape,ValueToValueMapTy * VMap) replaceSwiftErrorOps() argument 1167 updateAsyncFuncPointerContextSize(coro::Shape & Shape) updateAsyncFuncPointerContextSize() argument 1182 replaceFrameSizeAndAlignment(coro::Shape & Shape) replaceFrameSizeAndAlignment() argument 1222 handleNoSuspendCoroutine(coro::Shape & Shape) handleNoSuspendCoroutine() argument 1382 simplifySuspendPoints(coro::Shape & Shape) simplifySuspendPoints() argument 1677 splitAsyncCoroutine(Function & F,coro::Shape & Shape,SmallVectorImpl<Function * > & Clones,TargetTransformInfo & TTI) splitAsyncCoroutine() argument 1772 splitRetconCoroutine(Function & F,coro::Shape & Shape,SmallVectorImpl<Function * > & Clones,TargetTransformInfo & TTI) splitRetconCoroutine() argument 1925 coro::Shape Shape(F, OptimizeFrame); splitCoroutine() local 1973 removeCoroEnds(const coro::Shape & Shape) removeCoroEnds() argument 1980 updateCallGraphAfterCoroutineSplit(LazyCallGraph::Node & N,const coro::Shape & Shape,const SmallVectorImpl<Function * > & Clones,LazyCallGraph::SCC & C,LazyCallGraph & CG,CGSCCAnalysisManager & AM,CGSCCUpdateResult & UR,FunctionAnalysisManager & FAM) updateCallGraphAfterCoroutineSplit() argument 2126 const coro::Shape Shape = run() local [all...]
/llvm-project/mlir/docs/Traits/
H A D	Broadcastable.md	15 - A shape inference mechanism is able to compute the result shape solely based on input operand sha… 19 - The operation's result shape is compatible with —though not necessarily identical to—… 57 ## Shape inference 59 The shape inference process begins by correcting rank differences in input operands. A shape is exp… 62 ExpandRank(shape, rank): 63 while len(shape) < rank: 64 shape.prepend(1) 67 Given the shapes of two ranked input operands, the result's shape is inferred by equalizing input r… 77 # Infer shape 85 …shape for an operation with an arbitrary number of input operands is then inferred by discarding u… [all …]
/llvm-project/flang/include/flang/Evaluate/
H A D	shape.h	1 //===-- include/flang/Evaluate/shape.h --------------------------- C++ --===// 9 // GetShape() analyzes an expression and determines its shape, if possible, 34 using Shape = std::vector<MaybeExtentExpr>; variable 40 std::optional<ExtentExpr> AsExtentArrayExpr(const Shape &); 43 FoldingContext &, const Shape &); 46 // AsConstantExtents returns a constant shape. It may contain 50 FoldingContext &, const Shape &); in AsConstantExtents() 52 FoldingContext &foldingContext, const std::optional<Shape> &maybeShape) { in AsConstantExtents() 59 Shape AsShape(const ConstantSubscripts &); in GetRank() 60 std::optional<Shape> AsShap in GetRank() [all...]
/llvm-project/mlir/test/Analysis/
H A D	test-shape-fn-report.mlir	1 // RUN: mlir-opt %s --test-shape-function-report -verify-diagnostics 3 module attributes {shape.lib = [@shape_lib]} { 5 // expected-remark@+1 {{associated shape function: same_result_shape}} 7 attributes {shape.function = @shape_lib::@same_result_shape} { 15 // The shape function library with some local functions. 16 shape.function_library @shape_lib { 17 // Test shape function that returns the shape of input arg as result shape. 18 func @same_result_shape(%arg: !shape.value_shape) -> !shape.shape { 19 %0 = shape_of %arg : !shape.value_shape -> !shape.shape 20 return %0 : !shape.shape
/llvm-project/flang/test/HLFIR/
H A D	shapeof.fir	`7 func.func @shapeof(%arg0: !hlfir.expr<2x2xi32>) -> !fir.shape<2> { 8 %shape = hlfir.shape_of %arg0 : (!hlfir.expr<2x2xi32>) -> !fir.shape<2> 9 return %shape : !fir.shape<2> 14 // CHECK-NEXT: %[[SHAPE:.]] = hlfir.shape_of %[[EXPR]] : (!hlfir.expr<2x2xi32>) -> !fir.shape<2> 17 // CHECK-CANON-NEXT: %[[SHAPE:.]] = fir.shape %[[C2]], %[[C2]] : (index, index) -> !fir.shape< [all...]`
/llvm-project/flang/lib/Evaluate/
H A D	shape.cpp	1 //===-- lib/Evaluate/shape.cpp --------------------------------------------===// 9 #include "flang/Evaluate/shape.h" 35 details->shape().CanBeImpliedShape(); in IsExplicitShape() 41 const auto &shape{details->shape()}; in IsExplicitShape() 42 return shape.Rank() == 0 \|\| in IsExplicitShape() 43 shape.IsExplicitShape(); // true when scalar, too in IsExplicitShape() 46 .has<semantics::AssocEntityDetails>(); // exprs have explicit shape in ConstantShape() 50 Shape GetShapeHelper::ConstantShape(const Constant<ExtentType> &arrayConstant) { in ConstantShape() 52 Shape resul in ConstantShape() 36 const auto &shape{details->shape()}; IsExplicitShape() local 80 Shape shape; CreateShape() local 87 AsExtentArrayExpr(const Shape & shape) AsExtentArrayExpr() argument 100 AsConstantShape(FoldingContext & context,const Shape & shape) AsConstantShape() argument 110 AsConstantShape(const ConstantSubscripts & shape) AsConstantShape() argument 119 AsConstantExtents(const Constant<ExtentType> & shape) AsConstantExtents() argument 128 AsConstantExtents(FoldingContext & context,const Shape & shape) AsConstantExtents() argument 136 AsShape(const ConstantSubscripts & shape) AsShape() argument 144 AsShape(const std::optional<ConstantSubscripts> & shape) AsShape() argument 152 Fold(FoldingContext & context,Shape && shape) Fold() argument 160 Fold(FoldingContext & context,std::optional<Shape> && shape) Fold() argument 198 GetSize(Shape && shape) GetSize() argument 210 GetSize(const ConstantSubscripts & shape) GetSize() argument 484 if (auto shape{GetShape(assoc->expr())}; GetAssociatedExtent() local 520 if (auto shape{GetShape(symbol, invariantOnly)}) { GetExtent() local 569 if (auto shape{GetShape(subs.value())}) { GetExtent() local 811 Shape shape; operator ()() local 833 Shape shape; operator ()() local 1081 if (auto shape{(*this)(call.arguments().at(0))}) { operator ()() local [all...]
/llvm-project/mlir/test/Conversion/ShapeToStandard/
H A D	convert-shape-constraints.mlir	1 // RUN: mlir-opt -pass-pipeline="builtin.module(func.func(convert-shape-constraints))" <%s \| FileCh… 6 // CHECK-SAME: %[[RHS:.]]: tensor<?xindex>) -> !shape.witness { 7 // CHECK: %[[RET:.]] = shape.const_witness true 8 // CHECK: %[[BROADCAST_IS_VALID:.]] = shape.is_broadcastable %[[LHS]], %[[RHS]] 10 // CHECK: return %[[RET]] : !shape.witness 12 func.func @cstr_broadcastable(%arg0: tensor<?xindex>, %arg1: tensor<?xindex>) -> !shape.witness { 13 %witness = shape.cstr_broadcastable %arg0, %arg1 : tensor<?xindex>, tensor<?xindex> 14 return %witness : !shape.witness 19 // CHECK-SAME: %[[RHS:.]]: tensor<?xindex>) -> !shape.witness { 20 // CHECK: %[[RET:.*]] = shape.const_witness true [all …]
/llvm-project/lldb/test/API/functionalities/vtable/
H A D	TestVTableValue.py	`25 # Test a shape instance to make sure we get the vtable correctly. 26 shape = self.frame().FindVariable("shape") 27 vtable = shape.GetVTable() 28 self.assertEqual(vtable.GetName(), "vtable for Shape") 29 self.assertEqual(vtable.GetTypeName(), "vtable for Shape") 31 # for the shape class. 36 expected_addr = self.expected_vtable_addr(shape) 42 # Test a shape reference to make sure we get the vtable correctly. 43 shape [all...]`
/llvm-project/mlir/python/mlir/extras/
H A D	types.py	99 def _shaped(shape, element_type: Type = None, type_constructor=None): 102 if (element_type is None and shape and not isinstance(shape[-1], Type)) or ( 103 shape and isinstance(shape[-1], Type) and element_type is not None 110 sizes = shape 112 type = shape[-1] 113 sizes = shape[:-1] 121 shape, argument 127 shape, 85 _shaped(shape, element_type: Type = None, type_constructor=None) global() argument 107 vector( shape, element_type: Type = None, scalable: Optional[List[bool]] = None, scalable_dims: Optional[List[int]] = None, ) global() argument 138 memref( shape, element_type: Type = None, memory_space: Optional[int] = None, layout: Optional[StridedLayoutAttr] = None, ) global() argument [all...]
/llvm-project/flang/docs/
H A D	ArrayComposition.md	51 and the shape of the array delimits the domain of the map. 61 but expressions have rank and shape, and one can view array expressions 121 * `RESHAPE(A,SHAPE=s)` without `ORDER=` must precompute the shape 123 in the storage order of `A` (whose shape must also be captured). 138 ## Determination of rank and shape 141 temporary storage is determining the shape of the result prior to, 149 * `SHAPE(SUM(X,DIM=d))` is `SHAPE(X)` with one element removed: 150 `PACK(SHAPE(X),[(j,j=1,RANK(X))]/=d)` in general. 152 * `SHAPE(MAXLOC(X))` is `[RANK(X)]`. 153 * `SHAPE(MAXLOC(X,DIM=d))` is `SHAPE(X)` with one element removed. [all …]

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