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Revert "[Codegen] Add vector transfer + slice foldings in GenericVect…
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…orization (iree-org#17613)" (iree-org#17997)

This reverts commit 8b83425.

This change is hurting SVE+SME performance pretty badly. See
iree-org#17613 for context.

Signed-off-by: Andrzej Warzynski <[email protected]>
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@banach-space
banach-space authored Jul 24, 2024
1 parent 2af25b5 commit 10877f6
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Showing 11 changed files with 114 additions and 100 deletions.
13 changes: 1 addition & 12 deletions compiler/src/iree/compiler/Codegen/Common/GenericVectorization.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -322,8 +322,6 @@ class GenericVectorizationPass
this->generateContract.setValue(options.generateContract);
this->foldCastIntoContract.setValue(options.foldCastIntoContract);
this->maxVectorSize.setValue(options.maxVectorSize);
this->earlySubsetTransferFolding.setValue(
options.earlySubsetTransferFolding);
}

void getDependentDialects(DialectRegistry &registry) const override {
Expand Down Expand Up @@ -386,17 +384,8 @@ void GenericVectorizationPass::runOnOperation() {
};

{
// Canonicalize mask related ops before we lower them. Also run patterns
// for vector transfers on tensor subset ops, since they can be folded if
// not handled here.
// Canonicalize mask related ops before we lower them.
RewritePatternSet maskCanonPatterns(funcOp.getContext());
if (earlySubsetTransferFolding) {
// It is important to add these vector transfer on tensor subset patterns
// in the first greedy pattern rewrite, since transfer foldings can remove
// vectorized reads and writes by folding them into tensor ops.
tensor::populateFoldTensorSubsetIntoVectorTransferPatterns(
maskCanonPatterns);
}
vector::CreateMaskOp::getCanonicalizationPatterns(maskCanonPatterns,
funcOp.getContext());
vector::ConstantMaskOp::getCanonicalizationPatterns(maskCanonPatterns,
Expand Down
2 changes: 0 additions & 2 deletions compiler/src/iree/compiler/Codegen/Common/Passes.h
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Original file line number Diff line number Diff line change
Expand Up @@ -174,8 +174,6 @@ struct GenericVectorizationPassOptions {
bool foldCastIntoContract = false;
// Max vector size allowed to avoid creating large vectors.
int64_t maxVectorSize = std::numeric_limits<int64_t>::max();
// Enable early folding of tensor subset ops into vector transfer ops.
bool earlySubsetTransferFolding = true;
};
/// Creates a pass to perform vectorization on LinAlg and tensor ops.
std::unique_ptr<InterfacePass<FunctionOpInterface>>
Expand Down
4 changes: 1 addition & 3 deletions compiler/src/iree/compiler/Codegen/Common/Passes.td
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Original file line number Diff line number Diff line change
Expand Up @@ -288,9 +288,7 @@ def GenericVectorization :
"Enable folding casting ops into vector.contract.">,
Option<"maxVectorSize", "max-vector-size", "int64_t",
/*default=*/"2147483647",
"Max vector size allowed to avoid creating large vectors.">,
Option<"earlySubsetTransferFolding", "early-subset-transfer-folding", "bool",/*default=*/"true",
"Enable early folding of tensor subset ops into vector transfer ops.">
"Max vector size allowed to avoid creating large vectors.">
];
let constructor =
"mlir::iree_compiler::createGenericVectorizationPass()";
Expand Down
113 changes: 38 additions & 75 deletions compiler/src/iree/compiler/Codegen/Common/test/generic_vectorization.mlir
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Original file line number Diff line number Diff line change
Expand Up @@ -64,12 +64,12 @@ func.func @single_static_pack_infer_vector_size(%arg0: tensor<101x201xi8>, %arg1
// CHECK-LABEL: func.func @single_static_pack_infer_vector_size
// CHECK: tensor.pack

// CHECK-MASK-DAG: #[[$MAP0:.+]] = affine_map<(d0) -> (-d0 + 13, 2)>
// CHECK-MASK-DAG: #[[$MAP1:.+]] = affine_map<(d0) -> (-d0 + 51, 4)>
// CHECK-MASK-DAG: #[[$MAP2:.+]] = affine_map<(d0) -> (d0 * 2)>
// CHECK-MASK-DAG: #[[$MAP3:.+]] = affine_map<(d0, d1) -> (d1 * -2 + 101, d0 * 2)>
// CHECK-MASK-DAG: #[[$MAP4:.+]] = affine_map<(d0) -> (d0 * 16)>
// CHECK-MASK-DAG: #[[$MAP5:.+]] = affine_map<(d0, d1) -> (d1 * -16 + 201, d0 * 16)>
// CHECK-MASK: #[[$MAP0:.+]] = affine_map<(d0) -> (-d0 + 13, 2)>
// CHECK-MASK: #[[$MAP1:.+]] = affine_map<(d0) -> (-d0 + 51, 4)>
// CHECK-MASK: #[[$MAP2:.+]] = affine_map<(d0) -> (d0 * 2)>
// CHECK-MASK: #[[$MAP3:.+]] = affine_map<(d0, d1) -> (d1 * -2 + 101, d0 * 2)>
// CHECK-MASK: #[[$MAP4:.+]] = affine_map<(d0) -> (d0 * 16)>
// CHECK-MASK: #[[$MAP5:.+]] = affine_map<(d0, d1) -> (d1 * -16 + 201, d0 * 16)>
// CHECK-MASK-LABEL: func.func @single_static_pack_infer_vector_size
// CHECK-MASK-SAME: %[[SRC:[a-zA-Z0-9]+]]
// CHECK-MASK: %[[C0:.+]] = arith.constant 0 : i8
Expand All @@ -79,8 +79,9 @@ func.func @single_static_pack_infer_vector_size(%arg0: tensor<101x201xi8>, %arg1
// CHECK-MASK: %[[WRITE_SZ1:.+]] = affine.min #[[$MAP1]]
// CHECK-MASK: %[[READ_SZ0:.+]] = affine.min #[[$MAP3]]
// CHECK-MASK: %[[READ_SZ1:.+]] = affine.min #[[$MAP5]]
// CHECK-MASK: %[[SLICE:.+]] = tensor.extract_slice %[[SRC]][{{.+}}] [%[[READ_SZ0]], %[[READ_SZ1]]]
// CHECK-MASK: %[[READ_MASK:.+]] = vector.create_mask %[[READ_SZ0]], %[[READ_SZ1]] : vector<8x32xi1>
// CHECK-MASK: %[[READ:.+]] = vector.transfer_read %[[SRC]][%{{.+}}], %[[C0]], %[[READ_MASK]]
// CHECK-MASK: %[[READ:.+]] = vector.transfer_read %[[SLICE]][%{{.+}}], %[[C0]], %[[READ_MASK]]
// CHECK-MASK: %[[CAST:.+]] = vector.shape_cast %[[READ]] : vector<8x32xi8> to vector<4x2x2x16xi8>
// CHECK-MASK: %[[TRANSP:.+]] = vector.transpose %[[CAST]], [2, 0, 3, 1]
// CHECK-MASK: %[[EMPTY:.+]] = tensor.empty(%[[WRITE_SZ0]], %[[WRITE_SZ1]]) : tensor<?x?x16x2xi8>
Expand Down Expand Up @@ -129,12 +130,12 @@ func.func @single_dynamic_pack_infer_vector_size(%arg0: tensor<?x?xi8>, %arg1: t
// CHECK-LABEL: func.func @single_dynamic_pack_infer_vector_size
// CHECK: tensor.pack

// CHECK-MASK-DAG: #[[$MAP0:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 2)>
// CHECK-MASK-DAG: #[[$MAP1:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 4)>
// CHECK-MASK-DAG: #[[$MAP2:.+]] = affine_map<(d0) -> (d0 * 2)>
// CHECK-MASK-DAG: #[[$MAP3:.+]] = affine_map<(d0, d1)[s0] -> (d1 * -2 + s0, d0 * 2)>
// CHECK-MASK-DAG: #[[$MAP4:.+]] = affine_map<(d0) -> (d0 * 16)>
// CHECK-MASK-DAG: #[[$MAP5:.+]] = affine_map<(d0, d1)[s0] -> (d1 * -16 + s0, d0 * 16)>
// CHECK-MASK: #[[$MAP0:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 2)>
// CHECK-MASK: #[[$MAP1:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 4)>
// CHECK-MASK: #[[$MAP2:.+]] = affine_map<(d0) -> (d0 * 2)>
// CHECK-MASK: #[[$MAP3:.+]] = affine_map<(d0, d1)[s0] -> (d1 * -2 + s0, d0 * 2)>
// CHECK-MASK: #[[$MAP4:.+]] = affine_map<(d0) -> (d0 * 16)>
// CHECK-MASK: #[[$MAP5:.+]] = affine_map<(d0, d1)[s0] -> (d1 * -16 + s0, d0 * 16)>
// CHECK-MASK-LABEL: func.func @single_dynamic_pack_infer_vector_size
// CHECK-MASK-SAME: %[[SRC:[a-zA-Z0-9]+]]
// CHECK-MASK: %[[C0:.+]] = arith.constant 0 : i8
Expand All @@ -144,8 +145,9 @@ func.func @single_dynamic_pack_infer_vector_size(%arg0: tensor<?x?xi8>, %arg1: t
// CHECK-MASK: %[[WRITE_SZ1:.+]] = affine.min #[[$MAP1]]
// CHECK-MASK: %[[READ_SZ0:.+]] = affine.min #[[$MAP3]]
// CHECK-MASK: %[[READ_SZ1:.+]] = affine.min #[[$MAP5]]
// CHECK-MASK: %[[SLICE:.+]] = tensor.extract_slice %[[SRC]][{{.+}}] [%[[READ_SZ0]], %[[READ_SZ1]]]
// CHECK-MASK: %[[READ_MASK:.+]] = vector.create_mask %[[READ_SZ0]], %[[READ_SZ1]] : vector<8x32xi1>
// CHECK-MASK: %[[READ:.+]] = vector.transfer_read %[[SRC]][%{{.+}}], %[[C0]], %[[READ_MASK]]
// CHECK-MASK: %[[READ:.+]] = vector.transfer_read %[[SLICE]][%{{.+}}], %[[C0]], %[[READ_MASK]]
// CHECK-MASK: %[[CAST:.+]] = vector.shape_cast %[[READ]] : vector<8x32xi8> to vector<4x2x2x16xi8>
// CHECK-MASK: %[[TRANSP:.+]] = vector.transpose %[[CAST]], [2, 0, 3, 1]
// CHECK-MASK: %[[EMPTY:.+]] = tensor.empty(%[[WRITE_SZ0]], %[[WRITE_SZ1]]) : tensor<?x?x16x2xi8>
Expand Down Expand Up @@ -202,13 +204,13 @@ func.func @generic_pack_infer_vector_size(%arg0: tensor<?x32x128xf32>) -> tensor
}
return %3 : tensor<32x?x64x16x2xbf16>
}
// CHECK-MASK-DAG: #[[$MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 16)>
// CHECK-MASK-DAG: #[[$MAP1:.+]] = affine_map<(d0)[s0] -> (4, -d0 + s0 ceildiv 16)>
// CHECK-MASK-DAG: #[[$MAP2:.+]] = affine_map<(d0) -> (-d0 + 64, 6)>
// CHECK-MASK-DAG: #[[$MAP3:.+]] = affine_map<(d0, d1) -> (d1 * -2 + 128, d0 * 2)>
// CHECK-MASK-DAG: #[[$MAP4:.+]] = affine_map<(d0, d1)[s0] -> (d1 * -16 + s0, d0 * 16)>
// CHECK-MASK-DAG: #[[$MAP5:.+]] = affine_map<(d0) -> (d0 * 16)>
// CHECK-MASK-DAG: #[[$MAP6:.+]] = affine_map<(d0) -> (d0 * 2)>
// CHECK-MASK: #[[$MAP0:.+]] = affine_map<()[s0] -> (s0 ceildiv 16)>
// CHECK-MASK: #[[$MAP1:.+]] = affine_map<(d0)[s0] -> (4, -d0 + s0 ceildiv 16)>
// CHECK-MASK: #[[$MAP2:.+]] = affine_map<(d0) -> (-d0 + 64, 6)>
// CHECK-MASK: #[[$MAP3:.+]] = affine_map<(d0, d1) -> (d1 * -2 + 128, d0 * 2)>
// CHECK-MASK: #[[$MAP4:.+]] = affine_map<(d0, d1)[s0] -> (d1 * -16 + s0, d0 * 16)>
// CHECK-MASK: #[[$MAP5:.+]] = affine_map<(d0) -> (d0 * 16)>
// CHECK-MASK: #[[$MAP6:.+]] = affine_map<(d0) -> (d0 * 2)>
// CHECK-MASK-LABEL: func.func @generic_pack_infer_vector_size
// CHECK-MASK-SAME: %[[SRC:[a-zA-Z0-9]+]]
// CHECK-MASK-DAG: %[[C0_BF16:.+]] = arith.constant 0.000000e+00 : bf16
Expand All @@ -227,8 +229,9 @@ func.func @generic_pack_infer_vector_size(%arg0: tensor<?x32x128xf32>) -> tensor
// CHECK-MASK-DAG: %[[SRC_SZ0:.+]] = affine.min #[[$MAP4]]
// CHECK-MASK-DAG: %[[SRC_SZ2:.+]] = affine.min #[[$MAP3]]
// CHECK-MASK-DAG: %[[ITER_SLICE:.+]] = tensor.extract_slice %[[GENERIC_EMPTY]]
// CHECK-MASK-DAG: %[[SRC_SLICE:.+]] = tensor.extract_slice %[[SRC]][{{.+}}] [%[[SRC_SZ0]], 2, %[[SRC_SZ2]]]
// CHECK-MASK-DAG: %[[READ_MASK:.+]] = vector.create_mask %[[SRC_SZ0]], %[[C2]], %[[SRC_SZ2]] : vector<64x2x12xi1>
// CHECK-MASK: %[[GENERIC_READ:.+]] = vector.transfer_read %[[SRC]]{{.+}} %[[READ_MASK]]
// CHECK-MASK: %[[GENERIC_READ:.+]] = vector.transfer_read %[[SRC_SLICE]]{{.+}} %[[READ_MASK]]
// CHECK-MASK-DAG: %[[WRITE_MASK:.+]] = vector.create_mask %[[C2]], %[[SRC_SZ2]], %[[SRC_SZ0]] : vector<2x12x64xi1>
// CHECK-MASK: %[[TRUNC:.+]] = arith.truncf %[[GENERIC_READ]]
// CHECK-MASK: %[[TRANSP:.+]] = vector.transpose %[[TRUNC]], [1, 2, 0]
Expand Down Expand Up @@ -275,10 +278,10 @@ func.func @single_dynamic_unpack_infer_vector_size(%arg0: tensor<?x?x16x16xf32>,
}
return %0 : tensor<?x?xf32>
}
// CHECK-MASK-DAG: #[[$MAP0:.+]] = affine_map<(d0)[s0] -> (16, -d0 + s0)>
// CHECK-MASK-DAG: #[[$MAP1:.+]] = affine_map<(d0)[s0] -> (32, -d0 + s0)>
// CHECK-MASK-DAG: #[[$MAP2:.+]] = affine_map<(d0) -> (d0 floordiv 16)>
// CHECK-MASK-DAG: #[[$MAP3:.+]] = affine_map<(d0) -> (d0 ceildiv 16)>
// CHECK-MASK: #[[$MAP0:.+]] = affine_map<(d0)[s0] -> (16, -d0 + s0)>
// CHECK-MASK: #[[$MAP1:.+]] = affine_map<(d0)[s0] -> (32, -d0 + s0)>
// CHECK-MASK: #[[$MAP2:.+]] = affine_map<(d0) -> (d0 floordiv 16)>
// CHECK-MASK: #[[$MAP3:.+]] = affine_map<(d0) -> (d0 ceildiv 16)>
// CHECK-MASK-LABEL: func.func @single_dynamic_unpack_infer_vector_size
// CHECK-MASK-SAME: %[[SRC:[a-zA-Z0-9]+]]
// CHECK-MASK-DAG: %[[C0:.+]] = arith.constant 0 : index
Expand All @@ -289,8 +292,9 @@ func.func @single_dynamic_unpack_infer_vector_size(%arg0: tensor<?x?x16x16xf32>,
// CHECK-MASK-DAG: %[[DEST_SZ0:.+]] = affine.min #[[$MAP0]]
// CHECK-MASK-DAG: %[[DEST_SZ1:.+]] = affine.min #[[$MAP1]]
// CHECK-MASK-DAG: %[[SRC_SZ1:.+]] = affine.apply #[[$MAP3]]
// CHECK-MASK: %[[SRC_SLICE:.+]] = tensor.extract_slice %[[SRC]]
// CHECK-MASK: %[[READ_MASK:.+]] = vector.create_mask %[[C1]], %[[SRC_SZ1]], %[[C16]], %[[C16]] : vector<1x2x16x16xi1>
// CHECK-MASK: %[[READ:.+]] = vector.transfer_read %[[SRC]]{{.+}}, %[[READ_MASK]]
// CHECK-MASK: %[[READ:.+]] = vector.transfer_read %[[SRC_SLICE]]{{.+}}, %[[READ_MASK]]
// CHECK-MASK: %[[TRANSP:.+]] = vector.transpose %[[READ]], [0, 2, 1, 3]
// CHECK-MASK: %[[SHAPE_CAST:.+]] = vector.shape_cast %[[TRANSP]] : vector<1x16x2x16xf32> to vector<16x32xf32>
// CHECK-MASK: %[[EMPTY:.+]] = tensor.empty(%[[DEST_SZ0]], %[[DEST_SZ1]]) : tensor<?x?xf32>
Expand Down Expand Up @@ -334,10 +338,10 @@ func.func @generic_unpack_infer_vector_size(%arg0: tensor<?x?x16x16xf32>, %arg1:
}
return %0 : tensor<?x?xf32>
}
// CHECK-MASK-DAG: #[[$MAP0:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 16)>
// CHECK-MASK-DAG: #[[$MAP1:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 32)>
// CHECK-MASK-DAG: #[[$MAP2:.+]] = affine_map<(d0) -> (d0 floordiv 16)>
// CHECK-MASK-DAG: #[[$MAP3:.+]] = affine_map<(d0) -> (d0 ceildiv 16)>
// CHECK-MASK: #[[$MAP0:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 16)>
// CHECK-MASK: #[[$MAP1:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 32)>
// CHECK-MASK: #[[$MAP2:.+]] = affine_map<(d0) -> (d0 floordiv 16)>
// CHECK-MASK: #[[$MAP3:.+]] = affine_map<(d0) -> (d0 ceildiv 16)>
// CHECK-MASK-LABEL: func.func @generic_unpack_infer_vector_size
// CHECK-MASK-SAME: %[[SRC:[a-zA-Z0-9]+]]
// CHECK-MASK-DAG: %[[C0:.+]] = arith.constant 0 : index
Expand All @@ -348,8 +352,9 @@ func.func @generic_unpack_infer_vector_size(%arg0: tensor<?x?x16x16xf32>, %arg1:
// CHECK-MASK-DAG: %[[DEST_SZ0:.+]] = affine.min #[[$MAP0]]
// CHECK-MASK-DAG: %[[DEST_SZ1:.+]] = affine.min #[[$MAP1]]
// CHECK-MASK-DAG: %[[SRC_SZ1:.+]] = affine.apply #[[$MAP3]]
// CHECK-MASK: %[[SRC_SLICE:.+]] = tensor.extract_slice %[[SRC]]
// CHECK-MASK: %[[READ_MASK:.+]] = vector.create_mask %[[C1]], %[[SRC_SZ1]], %[[C16]], %[[C16]] : vector<1x2x16x16xi1>
// CHECK-MASK: %[[READ:.+]] = vector.transfer_read %[[SRC]]{{.+}}, %[[READ_MASK]]
// CHECK-MASK: %[[READ:.+]] = vector.transfer_read %[[SRC_SLICE]]{{.+}}, %[[READ_MASK]]
// CHECK-MASK: %[[TRANSP:.+]] = vector.transpose %[[READ]], [0, 2, 1, 3]
// CHECK-MASK: %[[SHAPE_CAST:.+]] = vector.shape_cast %[[TRANSP]] : vector<1x16x2x16xf32> to vector<16x32xf32>
// CHECK-MASK: %[[EMPTY:.+]] = tensor.empty(%[[DEST_SZ0]], %[[DEST_SZ1]]) : tensor<?x?xf32>
Expand Down Expand Up @@ -399,46 +404,4 @@ func.func @dynamic_fill_with_scalable_tiling_infer_vector_size(%arg0: tensor<1x6
// CHECK-MASK: scf.for
// CHECK-MASK: scf.for
// CHECK-MASK: scf.for
// CHECK-MASK: vector.transfer_write %[[CST]], {{.*}} {in_bounds = [true, true, true, true]} : vector<1x1x4x[4]xf32>, tensor<1x67x120x176xf32>

// -----

#map = affine_map<(d0)[s0] -> (-d0 + s0, 16)>
#map1 = affine_map<(d0)[s0] -> (-d0 + s0, 32)>
func.func @tiled_linalg_copy(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -> tensor<?x?xf32> {
%c32 = arith.constant 32 : index
%c16 = arith.constant 16 : index
%c1 = arith.constant 1 : index
%c0 = arith.constant 0 : index
%dim = tensor.dim %arg1, %c0 : tensor<?x?xf32>
%dim_0 = tensor.dim %arg1, %c1 : tensor<?x?xf32>
%0 = scf.for %arg3 = %c0 to %dim step %c16 iter_args(%arg4 = %arg1) -> (tensor<?x?xf32>) {
%1 = scf.for %arg5 = %c0 to %dim_0 step %c32 iter_args(%arg6 = %arg4) -> (tensor<?x?xf32>) {
%2 = affine.min #map(%arg3)[%dim]
%3 = affine.min #map1(%arg5)[%dim_0]
%extracted_slice_0 = tensor.extract_slice %arg0[%arg3, %arg5] [%2, %3] [1, 1] : tensor<?x?xf32> to tensor<?x?xf32>
%extracted_slice_1 = tensor.extract_slice %arg1[%arg3, %arg5] [%2, %3] [1, 1] : tensor<?x?xf32> to tensor<?x?xf32>
%copy = linalg.copy ins(%extracted_slice_0 : tensor<?x?xf32>) outs(%extracted_slice_1 : tensor<?x?xf32>) -> tensor<?x?xf32>
%inserted_slice = tensor.insert_slice %copy into %arg6[%arg3, %arg5] [%2, %3] [1, 1] : tensor<?x?xf32> into tensor<?x?xf32>
scf.yield %inserted_slice : tensor<?x?xf32>
}
scf.yield %1 : tensor<?x?xf32>
}
return %0 : tensor<?x?xf32>
}
// CHECK-MASK-DAG: #[[$MAP0:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 16)>
// CHECK-MASK-DAG: #[[$MAP1:.+]] = affine_map<(d0)[s0] -> (-d0 + s0, 32)>
// CHECK-MASK-LABEL: func.func @tiled_linalg_copy
// CHECK-MASK-SAME: %[[SRC:[a-zA-Z0-9]+]]: tensor<?x?xf32>, %[[DST:[a-zA-Z0-9]+]]
// CHECK-MASK-DAG: %[[C0:.+]] = arith.constant 0 : index
// CHECK-MASK-DAG: %[[C1:.+]] = arith.constant 1 : index
// CHECK-MASK-DAG: %[[C16:.+]] = arith.constant 16 : index
// CHECK-MASK-DAG: %[[C32:.+]] = arith.constant 32 : index
// CHECK-MASK: scf.for %[[IV0:.+]] = %[[C0]]
// CHECK-MASK: scf.for %[[IV1:.+]] = %[[C0]] {{.*}} iter_args(%[[ITER_ARG:.+]] = {{.*}})
// CHECK-MASK-DAG: %[[DST_SZ0:.+]] = affine.min #[[$MAP0]]
// CHECK-MASK-DAG: %[[DST_SZ1:.+]] = affine.min #[[$MAP1]]
// CHECK-MASK: %[[DST_SLICE:.+]] = tensor.extract_slice %[[DST]][%[[IV0]], %[[IV1]]] [%[[DST_SZ0]], %[[DST_SZ1]]] [1, 1] : tensor<?x?xf32> to tensor<?x?xf32>
// CHECK-MASK: %[[MASK:.+]] = vector.create_mask %[[DST_SZ0]], %[[DST_SZ1]] : vector<16x32xi1>
// CHECK-MASK: %[[READ:.+]] = vector.transfer_read %[[SRC]][%[[IV0]], %[[IV1]]],{{.*}} %[[MASK]]{{.*}} : tensor<?x?xf32>, vector<16x32xf32>
// CHECK-MASK: vector.transfer_write %[[READ]], %[[DST_SLICE]]{{.+}}, %[[MASK]]
// CHECK-MASK: vector.transfer_write %[[CST]], {{.*}} {in_bounds = [true, true, true, true]} : vector<1x1x4x[4]xf32>, tensor<1x1x4x?xf32>
4 changes: 3 additions & 1 deletion compiler/src/iree/compiler/Codegen/LLVMCPU/test/pipeline_pad_tests.mlir
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Original file line number Diff line number Diff line change
Expand Up @@ -33,11 +33,13 @@ module {
// CHECK: scf.for
// CHECK: scf.for
// CHECK: scf.for
// CHECK: %[[OUTPUT_SLICE:.+]] = memref.subview %[[OUTPUT_SUBVIEW]]
// CHECK: %[[RESULT_VEC:.+]] = scf.if %{{.+}} -> (vector<4xf32>) {
// CHECK: %[[VEC_LOAD:.+]] = vector.load %[[INPUT_SUBVIEW]]
// CHECK: scf.yield %[[VEC_LOAD]]
// CHECK: }
// CHECK: vector.store %[[RESULT_VEC]], %[[OUTPUT_SUBVIEW]]
// CHECK: %[[DROP_UNIT_OUTPUT_SLICE:.+]] = memref.subview %[[OUTPUT_SLICE]]
// CHECK: vector.store %[[RESULT_VEC]], %[[DROP_UNIT_OUTPUT_SLICE]]

// -----
#executable_target_embedded_elf_x86_64_ = #hal.executable.target<"llvm-cpu", "embedded-elf-x86_64", {cpu = "generic", cpu_features = "", data_layout = "e-m:e-p270:32:32-p271:32:32-p272:64:64-i64:64-f80:128-n8:16:32:64-S128", native_vector_size = 16 : index, target_triple = "x86_64-none-elf"}>
Expand Down
6 changes: 4 additions & 2 deletions compiler/src/iree/compiler/Codegen/LLVMCPU/test/vectorize_with_masking_and_hoist.mlir
View file
Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,7 @@
// CHECK: scf.for {{.*}} iter_args(%[[OUT_TENSOR:.*]] = {{.*}}) -> (tensor<1024x1024xf32>) {
// CHECK-NEXT: scf.for {{.*}} iter_args(%[[OUT_TENSOR_1:.*]] = %[[OUT_TENSOR]]) -> (tensor<1024x1024xf32>) {
// CHECK-NEXT: %[[OUT_SLICE:.*]] = tensor.extract_slice %[[OUT_TENSOR_1]]{{.*}} : tensor<1024x1024xf32> to tensor<8x?xf32>
// CHECK-NEXT: %[[OUT_SLICE_1:.*]] = tensor.extract_slice %[[OUT_SLICE]]{{.*}} : tensor<8x?xf32> to tensor<8x?xf32>
// CHECK-NEXT: %[[OUT_VEC:.*]] = vector.transfer_read %[[OUT_TENSOR_1]]{{.*}} : tensor<1024x1024xf32>, vector<8x[16]xf32>
// CHECK-NEXT: %[[INNER_LOOP:.*]] = scf.for {{.*}} iter_args(%[[RES:.*]] = %[[OUT_VEC]]) -> (vector<8x[16]xf32>) {
// CHECK-NEXT: %[[LHS:.*]] = vector.transfer_read {{.*}} : tensor<1024x1024xf32>, vector<8x1xf32>
Expand All @@ -29,8 +30,9 @@
// CHECK-SAME: %[[LHS]], %[[RHS]], %[[RES]] : vector<8x1xf32>, vector<1x[16]xf32> into vector<8x[16]xf32>
// CHECK-NEXT: scf.yield %[[CONTRACT]] : vector<8x[16]xf32>
// CHECK-NEXT: }
// CHECK-NEXT: %[[OUT_WRITE:.*]] = vector.transfer_write %[[INNER_LOOP]], %[[OUT_SLICE]]{{.*}} {{.*}} : vector<8x[16]xf32>, tensor<8x?xf32>
// CHECK-NEXT: %[[INSERT_SLICE:.*]] = tensor.insert_slice %[[OUT_WRITE]] into %[[OUT_TENSOR_1]]{{.*}} : tensor<8x?xf32> into tensor<1024x1024xf32>
// CHECK-NEXT: %[[OUT_WRITE:.*]] = vector.transfer_write %[[INNER_LOOP]], %[[OUT_SLICE_1]]{{.*}} {{.*}} : vector<8x[16]xf32>, tensor<8x?xf32>
// CHECK-NEXT: %[[INSERT_SLICE:.*]] = tensor.insert_slice %[[OUT_WRITE]] into %[[OUT_SLICE]]{{.*}} : tensor<8x?xf32> into tensor<8x?xf32>
// CHECK-NEXT: tensor.insert_slice %[[INSERT_SLICE]] into %[[OUT_TENSOR_1]]{{.*}} : tensor<8x?xf32> into tensor<1024x1024xf32>

func.func @pipeline() {
%c1 = arith.constant 1 : index
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