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/llvm-project/polly/docs/experiments/matmul/
H A D	runall.sh	4 clang -S -emit-llvm matmul.c -Xclang -disable-O0-optnone -o matmul.ll 7 opt -S -polly-canonicalize matmul.ll -o matmul.preopt.ll 10 opt -basic-aa -polly-ast -analyze matmul.preopt.ll \ 16 # opt -basic-aa -view-scops -disable-output matmul.preopt.ll 17 opt -basic-aa -dot-scops -disable-output matmul.preopt.ll -polly-use-llvm-names 23 # opt -basic-aa -view-scops-only -disable-output matmul.preopt.ll 24 opt -basic-aa -dot-scops-only -disable-output matmul.preopt.ll -polly-use-llvm-names 30 opt -basic-aa -polly-scops -analyze matmul.preopt.ll \ 34 opt -basic-aa -polly-dependences -analyze matmul.preopt.ll \ 38 opt -basic-aa -polly-export-jscop matmul.preopt.ll \ [all …]
/llvm-project/polly/docs/
H A D	HowToManuallyUseTheIndividualPiecesOfPolly.rst	24 clang -S -emit-llvm matmul.c -Xclang -disable-O0-optnone -o matmul.ll 37 opt -S -polly-canonicalize matmul.ll -o matmul.preopt.ll 48 …$ opt -basic-aa -polly-ast -analyze matmul.preopt.ll -polly-process-unprofitable -polly-use-llvm-n… 87 $ opt -polly-use-llvm-names -basic-aa -view-scops -disable-output matmul.preopt.ll 88 … $ opt -polly-use-llvm-names -basic-aa -view-scops-only -disable-output matmul.preopt.ll 95 .. _main: http://polly.llvm.org/experiments/matmul/scops.main.dot.png 96 .. _init_array: http://polly.llvm.org/experiments/matmul/scops.init_array.dot.png 97 .. _print_array: http://polly.llvm.org/experiments/matmul/scops.print_array.dot.png 98 .. _main-scopsonly: http://polly.llvm.org/experiments/matmul/scopsonly.main.dot.png 99 .. _init_array-scopsonly: http://polly.llvm.org/experiments/matmul/scopsonly.init_array.dot.png [all …]
/llvm-project/mlir/test/Examples/transform/Ch4/
H A D	multiple.mlir	3 // Matmul+ReLU. 9 // expected-remark @below {{matmul # 0}} 10 %matmul = linalg.matmul ins(%lhs, %rhs: tensor<512x512xf32>, tensor<512x512xf32>) 16 ins(%matmul, %bias : tensor<512x512xf32>, tensor<512x512xf32>) 28 // Matmul+ReLU with swapped operands. 34 // expected-remark @below {{matmul # 1}} 35 %matmul = linalg.matmul ins(%lhs, %rhs: tensor<512x512xf32>, tensor<512x512xf32>) 41 ins(%matmul, %bias : tensor<512x512xf32>, tensor<512x512xf32>) 78 %matmul: !transform.any_op {transform.readonly}, 82 transform.debug.emit_param_as_remark %pos, "matmul #" at %matmul [all …]
H A D	sequence.mlir	19 // expected-remark @below {{matmul}} 20 %matmul = linalg.matmul ins(%lhs, %rhs: tensor<512x512xf32>, tensor<512x512xf32>) 26 ins(%matmul, %bias : tensor<512x512xf32>, tensor<512x512xf32>) 51 %matmul = transform.collect_matching @match_matmul in %root 56 transform.include @print_matmul failures(propagate) (%matmul) 67 %matmul, %el1, %el2 = transform.collect_matching @match_matmul_elemwise in %root 73 transform.include @print_matmul failures(propagate) (%matmul) 91 transform.match.operation_name %entry ["linalg.matmul"] : !transform.any_op 103 %matmul: !transform.any_op {transform.readonly}) { 104 transform.debug.emit_remark_at %matmul, "matmul" : !transform.any_op [all …]
H A D	features.mlir	`3 // Matmul as a named operation. 8 // expected-remark @below {{matmul}} 9 %matmul = linalg.matmul ins(%lhs, %rhs: tensor<512x512xf32>, tensor<512x512xf32>) 11 func.return %matmul : tensor<512x512xf32> 14 // Matmul as a generic operation. 19 // expected-remark @below {{matmul}} 20 %matmul = linalg.generic { 33 return %matmul : tensor<512x512xf32> 61 %matmul: !transform.any_op {transform.readonly}) { 62 transform.debug.emit_remark_at %matmul, "matmul" : !transform.any_op`
/llvm-project/mlir/test/Dialect/Linalg/
H A D	transform-op-hoist-pad.mlir	8 %0 = linalg.matmul ins(%arg0, %arg1 : tensor<24x12xf32>, tensor<12x25xf32>) outs(%arg2 : tensor<24x25xf32>) -> tensor<24x25xf32> 14 %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1 18 %matmul_l1, %loops_l1 = transform.structured.tile_using_for %matmul tile_sizes [5] : (!transform.any_op) -> (!transform.any_op, !transform.any_op) 46 %0 = linalg.matmul ins(%arg0, %arg1 : tensor<24x12xf32>, tensor<12x25xf32>) outs(%arg2 : tensor<24x25xf32>) -> tensor<24x25xf32> 52 %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1 56 %matmul_l1, %loops_l1 = transform.structured.tile_using_for %matmul tile_sizes [5] : (!transform.any_op) -> (!transform.any_op, !transform.any_op) 90 // CHECK: linalg.matmul ins(%[[PADDED]] 91 %0 = linalg.matmul in [all...]
H A D	transform-op-hoist-pad-build-packing-loop-nest.mlir	9 %0 = linalg.matmul ins(%arg0, %arg1 : tensor<24x12xf32>, tensor<12x25xf32>) outs(%arg2 : tensor<24x25xf32>) -> tensor<24x25xf32> 15 %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1 18 %matmul_l1, %loops_l1 = transform.structured.tile_using_for %matmul tile_sizes [5] : (!transform.any_op) -> (!transform.any_op, !transform.any_op) 43 %0 = linalg.matmul ins(%arg0, %arg1 : tensor<24x12xf32>, tensor<12x25xf32>) outs(%arg2 : tensor<24x25xf32>) -> tensor<24x25xf32> 49 %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1 52 %matmul_l1, %loops_l1 = transform.structured.tile_using_for %matmul tile_sizes [5] : (!transform.any_op) -> (!transform.any_op, !transform.any_op) 83 %0 = linalg.matmul ins(%arg0, %arg1 : tensor<24x12xf32>, tensor<12x25xf32>) outs(%arg2 : tensor<24x25xf32>) -> tensor<24x25xf32> 89 %matmul [all...]
H A D	transform-op-pad.mlir	25 // CHECK: %[[T5:.]] = linalg.matmul 31 %4 = linalg.matmul ins(%1, %2 : tensor<4x?xf32>, tensor<?x5xf32>) outs(%3 : tensor<4x5xf32>) -> tensor<4x5xf32> 38 %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op 65 // CHECK: linalg.matmul 68 %4 = linalg.matmul ins(%1, %2 : tensor<4x?xf32>, tensor<?x5xf32>) outs(%3 : tensor<4x5xf32>) -> tensor<4x5xf32> 75 %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op 99 // CHECK: linalg.matmul 102 %4 = linalg.matmul ins(%1, %2 : tensor<4x?xf32>, tensor<?x5xf32>) outs(%3 : tensor<4x5xf32>) -> tensor<4x5xf32> 110 %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op 144 // CHECK: %[[T5:.]] = linalg.matmul [all...]
H A D	continuous-tiling-multiway-split.mlir	5 // The list of split points is consumed by splitOp to split the linalg.matmul op 6 // along dimension 1 to produce as many split-up linalg.matmul ops. 9 %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op 19 %0 = linalg.matmul ins(%arg0, %arg1: tensor<25x34xf32>, tensor<34x25xf32>) 30 // CHECK %[[MM0:.+]] = linalg.matmul ins(%[[IN1]], %[[SLICE]] : tensor<25x34xf32>, tensor<34x18xf32>) outs(%[[SLICE0]] : tensor<25x18xf32>) -> tensor<25x18xf32> 36 // CHECK %[[MM1:.+]] = linalg.matmul ins(%[[IN1]], %[[SLICE3]] : tensor<25x34xf32>, tensor<34x4xf32>) outs(%[[SLICE4]] : tensor<25x4xf32>) -> tensor<25x4xf32> 42 // CHECK %[[MM2:.+]] = linalg.matmul ins(%[[IN1]], %[[SLICE7]] : tensor<25x34xf32>, tensor<34x2xf32>) outs(%[[SLICE8]] : tensor<25x2xf32>) -> tensor<25x2xf32> 46 // CHECK %[[MM3:.+]] = linalg.matmul ins(%[[IN1]], %[[SLICE9]] : tensor<25x34xf32>, tensor<34x1xf32>) outs(%[[SLICE10]] : tensor<25x1xf32>) -> tensor<25x1xf32> 59 %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op 69 %0 = linalg.matmul in [all...]
H A D	matmul-shared-memory-padding.mlir	23 // CHECK: linalg.matmul 36 %1 = linalg.matmul ins(%A, %B : tensor<1024x1024xf32>, tensor<1024x1024xf32>) 44 // Fuse linalg.fill into linalg.matmul and tile. 45 %matmul_op = transform.structured.match ops{["linalg.matmul"]} in %arg1 54 // Tile linalg.matmul a second time. 57 // Pad linalg.matmul. 143 // CHECK: linalg.matmul 155 %1 = linalg.matmul ins(%A, %B : tensor<1023x1023xf32>, tensor<1023x1023xf32>) 163 // Fuse linalg.fill into linalg.matmul and tile. 164 %matmul_op = transform.structured.match ops{["linalg.matmul"]} i [all...]
H A D	continuous-tiling-full.mlir	5 %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op 20 %0 = linalg.matmul ins(%arg0, %arg1: tensor<25x34xf32>, tensor<34x25xf32>) 37 // CHECK: %[[MATMUL:.+]] = linalg.matmul ins(%[[XSIN19]], %[[IN2]] : tensor<9x34xf32>, tensor<34x25xf32>) outs(%[[XSOUT9]] : tensor<9x25xf32>) -> tensor<9x25xf32> 38 // CHECK: %[[INS9:.+]] = tensor.insert_slice %[[MATMUL]] into %[[XSOUT18ARG]][%[[IDX]], 0] [9, 25] [1, 1] : tensor<9x25xf32> into tensor<18x25xf32> 46 // CHECK: %[[R1:.+]] = linalg.matmul ins(%[[XS3]], %[[IN2]] : tensor<4x34xf32>, tensor<34x25xf32>) outs(%[[XS4]] : tensor<4x25xf32>) -> tensor<4x25xf32> 52 // CHECK: %[[R2:.+]] = linalg.matmul ins(%[[XS8]], %[[IN2]] : tensor<2x34xf32>, tensor<34x25xf32>) outs(%[[XS9]] : tensor<2x25xf32>) -> tensor<2x25xf32> 56 // CHECK: %[[R3:.+]] = linalg.matmul ins(%[[XS11]], %[[IN2]] : tensor<1x34xf32>, tensor<34x25xf32>) outs(%[[XS12]] : tensor<1x25xf32>) -> tensor<1x25xf32> 66 %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op 81 %0 = linalg.matmul in [all...]
H A D	transform-op-multitile-sizes.mlir	7 …%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transfor… 17 %0 = linalg.matmul ins(%arg0, %arg1: tensor<13x34xf32>, tensor<34x42xf32>) 33 …%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transfor… 51 %0 = linalg.matmul ins(%arg0, %arg1: tensor<13x34xf32>, tensor<34x42xf32>) 62 …%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transfor… 78 // For matmul, the extent of the first iteration space dimension is equal to 94 %0 = linalg.matmul ins(%arg0, %arg1: tensor<?x?xf32>, tensor<?x?xf32>) 105 …%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transfor… 117 %0 = linalg.matmul ins(%arg0, %arg1: tensor<?x?xf32>, tensor<?x?xf32>)
/llvm-project/flang/test/Evaluate/
H A D	fold-matmul.f90	`2 ! Tests folding of MATMUL() 8 integer, parameter :: iab(,) = matmul(ia, ib) 9 integer, parameter :: ixa() = matmul(ix, ia) 10 integer, parameter :: iay() = matmul(ia, iy) 19 real, parameter :: rab(,) = matmul(ra, rb) 20 real, parameter :: rxa() = matmul(rx, ra) 21 real, parameter :: ray() = matmul(ra, ry) 30 complex, parameter :: zab(,) = matmul(za, zb) 31 complex, parameter :: zxa() = matmul(zx, za) 32 complex, parameter :: zay() = matmul(za, zy) [all …]`
/llvm-project/mlir/docs/Tutorials/transform/
H A D	Ch1.md	18 %matmul = linalg.matmul ins(%lhs, %rhs: tensor<512x512xf32>, tensor<512x512xf32>) 23 ins(%matmul, %bias : tensor<512x512xf32>, tensor<512x512xf32>) 43 %arg1: !transform.op<"linalg.matmul">, 74 %arg1: !transform.op<"linalg.matmul">, 76 transform.debug.emit_remark_at %arg1, "matmul" 77 : !transform.op<"linalg.matmul"> 92 debug-bind-trailing-args=linalg.matmul,linalg.elemwise_binary})" 95 …ciate the two extra arguments of the top-level sequence with all `linalg.matmul` and `linalg.elemw… 98 sequence.mlir:7:13: remark: matmul 99 %matmul = linalg.matmul ins(%lhs, %rhs: tensor<512x512xf32>, tensor<512x512xf32>) [all …]
H A D	Ch4.md	40 %matmul = linalg.matmul 46 ins(%matmul, %bias : tensor<512x512xf32>, tensor<512x512xf32>) 61 additional arguments, `bind-first-extra-to-ops=linalg.matmul 84 %matmul = transform.collect_matching @match_matmul in %root 88 transform.include @print_matmul failures(propagate) (%matmul) 106 transform.match.operation_name %entry ["linalg.matmul"] : !transform.any_op 118 %matmul: !transform.any_op {transform.readonly}) { 119 transform.debug.emit_remark_at %matmul, "matmul" : !transform.any_op 130 `linalg.elemwise_binary` and `linalg.matmul` operations. In debug builds, the 139 [transform-matcher] matching %0 = linalg.matmul ins(%arg0, %arg1 : tensor<512x512xf32>, tensor<512x… [all …]
/llvm-project/flang/test/HLFIR/
H A D	matmul.fir	1 // Test hlfir.matmul operation parse, verify (no errors), and unparse 7 …%res = hlfir.matmul %arg0 %arg1 : (!hlfir.expr<2x2xi32>, !hlfir.expr<2x2xi32>) -> !hlfir.expr<2x2x… 13 // CHECK-NEXT: %[[RES:.]] = hlfir.matmul %[[ARG0]] %[[ARG1]] : (!hlfir.expr<2x2xi32>, !hlfir.ex… 19 …%res = hlfir.matmul %arg0 %arg1 : (!hlfir.expr<?x?xi32>, !hlfir.expr<?x?xi32>) -> !hlfir.expr<?x?x… 25 // CHECK-NEXT: %[[RES:.]] = hlfir.matmul %[[ARG0]] %[[ARG1]] : (!hlfir.expr<?x?xi32>, !hlfir.ex… 31 …%res = hlfir.matmul %arg0 %arg1 : (!hlfir.expr<2x?xi32>, !hlfir.expr<?x2xi32>) -> !hlfir.expr<2x2x… 37 // CHECK-NEXT: %[[RES:.]] = hlfir.matmul %[[ARG0]] %[[ARG1]] : (!hlfir.expr<2x?xi32>, !hlfir.ex… 43 …%res = hlfir.matmul %arg0 %arg1 : (!hlfir.expr<?x2xi32>, !hlfir.expr<2x?xi32>) -> !hlfir.expr<?x?x… 49 // CHECK-NEXT: %[[RES:.]] = hlfir.matmul %[[ARG0]] %[[ARG1]] : (!hlfir.expr<?x2xi32>, !hlfir.ex… 55 …%res = hlfir.matmul %arg0 %arg1 : (!hlfir.expr<?x?x!fir.logical<4>>, !hlfir.expr<?x?x!fir.logical<… [all …]
/llvm-project/mlir/test/Examples/transform/Ch1/
H A D	invalidation-1.mlir	3 // RUN: debug-bind-trailing-args=linalg.matmul,linalg.elemwise_binary},\ 18 %arg1: !transform.op<"linalg.matmul">, 23 : (!transform.op<"linalg.matmul">) -> (!transform.any_op, !transform.any_op) 27 transform.debug.emit_remark_at %arg1, "remark" : !transform.op<"linalg.matmul"> 38 %matmul = linalg.matmul ins(%lhs, %rhs: tensor<512x512xf32>, tensor<512x512xf32>) 43 ins(%matmul, %bias : tensor<512x512xf32>, tensor<512x512xf32>) 59 %arg1: !transform.op<"linalg.matmul">, 64 %casted = transform.cast %arg1 : !transform.op<"linalg.matmul"> to 70 : (!transform.op<"linalg.matmul">) -> (!transform.any_op, !transform.any_op) 87 %matmul = linalg.matmul ins(%lhs, %rhs: tensor<512x512xf32>, tensor<512x512xf32>) [all …]
H A D	sequence.mlir	3 // RUN: debug-bind-trailing-args=linalg.matmul,linalg.elemwise_binary},\ 19 %matmul = linalg.matmul ins(%lhs, %rhs: tensor<512x512xf32>, tensor<512x512xf32>) 24 ins(%matmul, %bias : tensor<512x512xf32>, tensor<512x512xf32>) 36 // CHECK: linalg.matmul 48 // CHECK-NOT: linalg.matmul 65 %arg1: !transform.op<"linalg.matmul">, 87 … : (!transform.op<"linalg.matmul">, !transform.any_op) -> (!transform.any_op, !transform.any_op) 90 // "add" operation and fuses matmul into the loop, but doesn't affect the
/llvm-project/mlir/test/Dialect/Transform/
H A D	selective-targeting.mlir	11 // CHECK: linalg.matmul 13 %0 = linalg.matmul { test.attrA } 29 // CHECK-NOT: linalg.matmul 31 %0 = linalg.matmul { test.attrA, test.attrC } 48 %0 = linalg.matmul { test.attrC } 59 // Match matmul operations inside @matmul_tensors with test.attrA set. 64 …%0 = operation "linalg.matmul"(%args : !pdl.range<value>) {"test.attrA" = %attr}-> (%results : !pd… 69 // Match matmul operations inside @matmul_tensors with test.attrC set. 74 …%0 = operation "linalg.matmul"(%args : !pdl.range<value>) {"test.attrC" = %attr}-> (%results : !pd… 100 %0 = linalg.matmul {test.attrA} [all …]
/llvm-project/mlir/test/Integration/Dialect/Transform/
H A D	match_matmul.mlir	14 transform.match.operation_name %entry ["linalg.matmul", "linalg.generic"] : !transform.any_op 16 %fill, %matmul, %dims, %lhs_type, %rhs_type, %res_type, %kinds:4 = 23 transform.yield %fill, %matmul, %dims, %lhs_type, %rhs_type, %res_type 29 %matmul: !transform.any_op {transform.readonly}, 35 transform.debug.emit_remark_at %matmul, "matmul" : !transform.any_op 36 …transform.debug.emit_param_as_remark %dims, "dimensions" at %matmul : !transform.param<i64>, !tran… 37 …transform.debug.emit_param_as_remark %lhs_type, "LHS type" at %matmul : !transform.type, !transfor… 38 …transform.debug.emit_param_as_remark %rhs_type, "RHS type" at %matmul : !transform.type, !transfor… 39 …transform.debug.emit_param_as_remark %res_type, "result type" at %matmul : !transform.type, !trans… 56 // expected-remark @below {{matmul}} [all …]
/llvm-project/flang/include/flang/Runtime/
H A D	matmul.h	`1 //===-- include/flang/Runtime/matmul.h --------------------------- C++ --===// 9 // API for the transformational intrinsic function MATMUL. 20 // The most general MATMUL. All type and shape information is taken from the 22 void RTDECL(Matmul)(Descriptor &, const Descriptor &, const Descriptor &, 30 // MATMUL versions specialized by the categories of the operand types. 34 void RTDECL(Matmul##XCAT##XKIND##YCAT##YKIND)(Descriptor & result, \ 44 #include "matmul-instances.inc"`
/llvm-project/llvm/lib/Transforms/Scalar/
H A D	LowerMatrixIntrinsics.cpp	960 // If we have a TT matmul or a TT add, lift the transpose. We may be able in Visit() 1355 /// Special case for MatMul lowering. Prevents scalar loads of row-major in lowerDotProduct() 1358 void lowerDotProduct(CallInst MatMul, in lowerDotProduct() 1361 if (FusedInsts.contains(MatMul) \|\| in lowerDotProduct() 1364 ShapeInfo LShape(MatMul->getArgOperand(2), MatMul->getArgOperand(3)); in lowerDotProduct() 1365 ShapeInfo RShape(MatMul->getArgOperand(3), MatMul->getArgOperand(4)); in lowerDotProduct() 1370 Value LHS = MatMul->getArgOperand(0); in lowerDotProduct() 1371 Value RHS = MatMul in lowerDotProduct() 1332 lowerDotProduct(CallInst MatMul,SmallPtrSet<Instruction ,16> & FusedInsts,FastMathFlags FMF) lowerDotProduct() argument 1608 getNonAliasingPointer(LoadInst Load,StoreInst * Store,CallInst * MatMul) getNonAliasingPointer() argument 1692 isFusionProfitable(CallInst * MatMul) isFusionProfitable() argument 1736 createTiledLoops(CallInst * MatMul,Value * LPtr,ShapeInfo LShape,Value * RPtr,ShapeInfo RShape,StoreInst * Store) createTiledLoops() argument 1795 emitSIMDTiling(CallInst * MatMul,LoadInst * LoadOp0,LoadInst * LoadOp1,StoreInst * Store,SmallPtrSetImpl<Instruction * > & FusedInsts) emitSIMDTiling() argument 1864 LowerMatrixMultiplyFused(CallInst * MatMul,SmallPtrSetImpl<Instruction * > & FusedInsts,SmallVector<IntrinsicInst ,16> & LifetimeEnds) LowerMatrixMultiplyFused() argument 2010 LowerMultiply(CallInst MatMul) LowerMultiply() argument [all...]
/llvm-project/mlir/test/Integration/Dialect/Linalg/CPU/ArmSVE/
H A D	matmul.mlir	34 // Matmul 35 %C_out = linalg.matmul ins(%A, %B: tensor<?x?xf32>, tensor<?x?xf32>) outs(%C_in: tensor<?x?xf32>) -> tensor<?x?xf32> 71 // Matmul 72 %C_out = linalg.matmul ins(%A, %B: tensor<?x?xi8>, tensor<?x?xi8>) outs(%C_in: tensor<?x?xi32>) -> tensor<?x?xi32> 90 // A sequence that will tile and vectorise a Matmul Op 94 // Step 0: Get a handle to the matmul Op 95 %matmul = transform.structured.match ops{["linalg.matmul"]} in %func 99 %tiled_matmul, %loops:3 = transform.structured.tile_using_for %matmul tile_sizes [2, [4], 1]
/llvm-project/mlir/test/Integration/Dialect/PDL/CPU/
H A D	multiroot.mlir	19 …%op0 = pdl.operation "tf.MatMul" (%rxact, %weight : !pdl.value, !pdl.value) {"transpose_a" = %attr… 24 pdl.replace %op0 with (%val1 : !pdl.value) // tf.MatMul 40 …%op0 = pdl.operation "tf.MatMul" (%rxact, %rxdelta : !pdl.value, !pdl.value) {"transpose_a" = %att… 54 pdl.erase %op0 // tf.MatMul 106 …%3 = "tf.MatMul"(%arg0, %arg2) {transpose_a = false, transpose_b = false} : (tensor<2x20xf32>, ten… 111 …%7 = "tf.MatMul"(%arg0, %6) {transpose_a = true, transpose_b = false} : (tensor<2x20xf32>, tensor<… 160 …%op0 = pdl.operation "tf.MatMul" (%rxact, %weight : !pdl.value, !pdl.value) {"transpose_a" = %attr… 169 …%op4 = pdl.operation "tf.MatMul" (%rxact, %val3 : !pdl.value, !pdl.value) {"transpose_a" = %attr1,… 184 pdl.erase %op4 // tf.MatMul 188 pdl.erase %op0 // tf.MatMul [all …]
/llvm-project/mlir/test/Dialect/Mesh/
H A D	sharding-propagation.mlir	107 // CHECK-NEXT: %[[V2:.]] = tosa.matmul %[[V0]], %[[V1]] 108 %0 = tosa.matmul %arg0, %arg1 : (tensor<2x16x8xf32>, tensor<2x8x32xf32>) -> tensor<2x16x32xf32> 124 // CHECK-NEXT: %[[V2:.]] = tosa.matmul %[[V0]], %[[V1]] 125 %0 = tosa.matmul %arg0, %arg1 : (tensor<2x16x8xf32>, tensor<2x8x32xf32>) -> tensor<2x16x32xf32> 143 // CHECK-NEXT: %[[V2:.]] = tosa.matmul %[[V0]], %[[V1]] 144 %1 = tosa.matmul %0, %arg1 : (tensor<2x16x8xf32>, tensor<2x8x32xf32>) -> tensor<2x16x32xf32> 162 // CHECK-NEXT: %[[V2:.]] = tosa.matmul %[[V0]], %[[V1]] 163 %2 = tosa.matmul %0, %1 : (tensor<2x16x8xf32>, tensor<2x8x32xf32>) -> tensor<2x16x32xf32> 188 // CHECK: %[[MATMUL:.*]] = linalg.matmul in [all...]

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