39ad84e4d1
At the moment, `GenericPadOpVectorizationPattern` implements two
orthogonal transformations:
1. Rewrites `tensor::PadOp` into a sequence of `tensor::EmptyOp`,
`linalg::FillOp` and `tensor::InsertSliceOp`.
2. Vectorizes (where possible) `tensor::InsertSliceOp` (see
`tryVectorizeCopy`).
This patch splits `GenericPadOpVectorizationPattern` into two separate
patterns:
1. `GeneralizePadOpPattern` for the first transformation (note that
currently `GenericPadOpVectorizationPattern` inherits from
`GeneralizePadOpPattern`).
2. `InsertSliceVectorizePattern` to vectorize `tensor::InsertSliceOp`.
With this change, we gain the following:
* a clear separation between pre-processing and vectorization
transformations/stages,
* a path to support masked vectorisation for `tensor.insert_slice`
(with a dedicated pattern for vectorization, it is much easier to
specify the input vector sizes used in masking),
* more opportunities to vectorize `tensor.insert_slice`.
Note for downstream users:
--------------------------
If you were using `populatePadOpVectorizationPatterns`, following this
change you will also have to add
`populateInsertSliceVectorizationPatterns`.
Finer implementation details:
-----------------------------
1. The majority of changes in this patch are copy & paste + some edits.
1.1. The only functional change is that the vectorization of
`tensor.insert_slice` is now broadly available (as opposed to being
constrained to the pad vectorization pattern:
`GenericPadOpVectorizationPattern`).
1.2. Following-on from the above, `@pad_and_insert_slice_dest` is
updated. As expected, the input `tensor.insert_slice` Op is no
longer "preserved" and instead gets vectorized successfully.
2. The `linalg.fill` case in `getConstantPadVal` works under the
assumption that only _scalar_ source values can be used. That's
consistent with the definition of the Op, but it's not tested at the
moment. Hence a test case in Linalg/invalid.mlir is added.
3. The behaviour of the two TD vectorization Ops,
`transform.structured.vectorize_children_and_apply_patterns` and
`transform.structured.vectorize` is preserved.
278 lines
12 KiB
MLIR
278 lines
12 KiB
MLIR
// RUN: mlir-opt %s -transform-interpreter -split-input-file | FileCheck %s
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///----------------------------------------------------------------------------------------
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/// [Pattern: PadOpVectorizationWithTransferReadPattern]
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///----------------------------------------------------------------------------------------
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// CHECK-LABEL: func @pad_and_transfer_read
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// CHECK-SAME: %[[ARG0:.*]]: tensor<5x6xf32>
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// CHECK-NOT: tensor.pad
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// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
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// CHECK-DAG: %[[C5:.*]] = arith.constant 5.0
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// CHECK: %[[RESULT:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]]], %[[C5]] : tensor<5x6xf32>, vector<7x9xf32>
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// CHECK: return %[[RESULT]]
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func.func @pad_and_transfer_read(%arg0: tensor<5x6xf32>) -> vector<7x9xf32> {
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%c0 = arith.constant 0 : index
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%c5 = arith.constant 5.0 : f32
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%c6 = arith.constant 6.0 : f32
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%0 = tensor.pad %arg0 low[0, 0] high[5, 7] {
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^bb0(%arg1: index, %arg2: index):
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tensor.yield %c5 : f32
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} : tensor<5x6xf32> to tensor<10x13xf32>
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%1 = vector.transfer_read %0[%c0, %c0], %c6
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: tensor<10x13xf32>, vector<7x9xf32>
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return %1 : vector<7x9xf32>
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}
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module attributes {transform.with_named_sequence} {
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transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
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%func_op = transform.structured.match ops{["func.func"]} in %arg1 : (!transform.any_op) -> !transform.op<"func.func">
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transform.apply_patterns to %func_op {
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transform.apply_patterns.linalg.pad_vectorization
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} : !transform.op<"func.func">
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transform.yield
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}
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}
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// -----
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///----------------------------------------------------------------------------------------
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/// [Pattern: PadOpVectorizationWithTransferWritePattern]
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///----------------------------------------------------------------------------------------
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func.func private @make_vector() -> vector<7x9xf32>
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// CHECK-LABEL: func @pad_and_transfer_write_static_low_and_high
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// CHECK-SAME: %[[ARG0:.*]]: tensor<5x6xf32>
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// CHECK-NOT: tensor.pad
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// CHECK: %[[C0:.*]] = arith.constant 0 : index
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// CHECK: %[[VEC0:.*]] = call @make_vector() : () -> vector<7x9xf32>
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// CHECK: %[[RESULT:.*]] = vector.transfer_write %[[VEC0]], %[[ARG0]][%[[C0]], %[[C0]]] : vector<7x9xf32>, tensor<5x6xf32>
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// CHECK: return %[[RESULT]]
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func.func @pad_and_transfer_write_static_low_and_high(
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%arg0: tensor<5x6xf32>) -> tensor<5x6xf32> {
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%c0 = arith.constant 0 : index
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%c5 = arith.constant 5.0 : f32
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%0 = tensor.pad %arg0 low[0, 0] high[5, 7] {
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^bb0(%arg2: index, %arg3: index):
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tensor.yield %c5 : f32
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} : tensor<5x6xf32> to tensor<10x13xf32>
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%1 = call @make_vector() : () -> vector<7x9xf32>
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%2 = vector.transfer_write %1, %0[%c0, %c0]
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: vector<7x9xf32>, tensor<10x13xf32>
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%3 = tensor.extract_slice %2[0, 0] [5, 6] [1, 1] : tensor<10x13xf32> to tensor<5x6xf32>
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return %3 : tensor<5x6xf32>
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}
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module attributes {transform.with_named_sequence} {
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transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
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%func_op = transform.structured.match ops{["func.func"]} in %arg1 : (!transform.any_op) -> !transform.op<"func.func">
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transform.apply_patterns to %func_op {
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transform.apply_patterns.linalg.pad_vectorization
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} : !transform.op<"func.func">
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transform.yield
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}
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}
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// -----
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func.func private @make_vector() -> vector<7x9xf32>
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// CHECK-LABEL: func @pad_and_transfer_write_static_low_dynamic_high
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// CHECK-SAME: %[[ARG0:.*]]: tensor<?x?xf32>, %[[SIZE:.*]]: index, %[[PADDING:.*]]: index
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// CHECK-NOT: tensor.pad
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// CHECK: %[[C0:.*]] = arith.constant 0 : index
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// CHECK: %[[SUB:.*]] = tensor.extract_slice %[[ARG0]][0, 0] [%[[SIZE]], 6] [1, 1] : tensor<?x?xf32> to tensor<?x6xf32>
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// CHECK: %[[VEC0:.*]] = call @make_vector() : () -> vector<7x9xf32>
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// CHECK: %[[RESULT:.*]] = vector.transfer_write %[[VEC0]], %[[SUB]][%[[C0]], %[[C0]]] : vector<7x9xf32>, tensor<?x6xf32>
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// CHECK: return %[[RESULT]]
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func.func @pad_and_transfer_write_static_low_dynamic_high(
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%arg0: tensor<?x?xf32>, %size: index, %padding: index) -> tensor<?x6xf32> {
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%c0 = arith.constant 0 : index
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%c5 = arith.constant 5.0 : f32
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%s = tensor.extract_slice %arg0[0, 0] [%size, 6] [1, 1]
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: tensor<?x?xf32> to tensor<?x6xf32>
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%0 = tensor.pad %s low[0, 0] high[%padding, 7] {
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^bb0(%arg2: index, %arg3: index):
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tensor.yield %c5 : f32
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} : tensor<?x6xf32> to tensor<?x13xf32>
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%1 = call @make_vector() : () -> vector<7x9xf32>
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%2 = vector.transfer_write %1, %0[%c0, %c0]
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: vector<7x9xf32>, tensor<?x13xf32>
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%3 = tensor.extract_slice %2[0, 0] [%size, 6] [1, 1] : tensor<?x13xf32> to tensor<?x6xf32>
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return %3 : tensor<?x6xf32>
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}
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module attributes {transform.with_named_sequence} {
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transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
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%func_op = transform.structured.match ops{["func.func"]} in %arg1 : (!transform.any_op) -> !transform.op<"func.func">
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transform.apply_patterns to %func_op {
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transform.apply_patterns.linalg.pad_vectorization
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} : !transform.op<"func.func">
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transform.yield
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}
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}
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// -----
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///----------------------------------------------------------------------------------------
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/// [Pattern: PadOpVectorizationWithInsertSlicePattern]
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///----------------------------------------------------------------------------------------
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func.func private @make_vector() -> tensor<12x13xf32>
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// CHECK-LABEL: func @pad_and_insert_slice_source
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// CHECK-SAME: %[[ARG0:.*]]: tensor<5x6xf32>
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// CHECK-NOT: tensor.pad
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// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
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// CHECK-DAG: %[[C5:.*]] = arith.constant 5.0
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// CHECK: %[[VEC0:.*]] = call @make_vector() : () -> tensor<12x13xf32>
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// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]]], %[[C5]] : tensor<5x6xf32>, vector<7x9xf32>
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// CHECK: %[[WRITE:.*]] = vector.transfer_write %[[READ]], %[[VEC0]][%[[C0]], %[[C0]]] {in_bounds = [true, true]} : vector<7x9xf32>, tensor<12x13xf32>
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// CHECK: return %[[WRITE]]
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func.func @pad_and_insert_slice_source(
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%arg0: tensor<5x6xf32>) -> tensor<12x13xf32> {
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%c0 = arith.constant 0 : index
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%c5 = arith.constant 5.0 : f32
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%0 = tensor.pad %arg0 low[0, 0] high[2, 3] {
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^bb0(%arg2: index, %arg3: index):
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tensor.yield %c5 : f32
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} : tensor<5x6xf32> to tensor<7x9xf32>
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%1 = call @make_vector() : () -> tensor<12x13xf32>
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%r = tensor.insert_slice %0 into %1[0, 0][7, 9][1, 1] : tensor<7x9xf32> into tensor<12x13xf32>
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return %r : tensor<12x13xf32>
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}
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module attributes {transform.with_named_sequence} {
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transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
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%func_op = transform.structured.match ops{["func.func"]} in %arg1 : (!transform.any_op) -> !transform.op<"func.func">
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transform.apply_patterns to %func_op {
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transform.apply_patterns.linalg.pad_vectorization
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} : !transform.op<"func.func">
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transform.yield
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}
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}
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// -----
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///----------------------------------------------------------------------------------------
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/// tensor::PadOp -> tensor::EmptyOp + linalg::FillOp/tensor::GenerateOp + tensor::InsertSliceOp
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/// [Pattern: GenericPadOpVectorizationPattern + InsertSliceVectorizePattern]
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/// TODO: Split the test into two, one for each pattern.
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///----------------------------------------------------------------------------------------
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func.func private @make_vector() -> tensor<12x13xf32>
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// Same as @pad_and_insert_slice_dest in vectorization-with-patterns.mlir, but
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// over here linalg::fill is not vectorized (patterns for linalg.fill are not
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// included here)
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// CHECK-LABEL: func.func @pad_and_insert_slice_dest(
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// CHECK-SAME: %[[ARG_0:.*]]: tensor<1x5x6xf32>) -> tensor<1x12x13xf32> {
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// CHECK-NOT: tensor.pad
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// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
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// CHECK-DAG: %[[PAD:.*]] = arith.constant 5.000000e+00 : f32
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// CHECK-DAG: %[[PAD_READ:.*]] = arith.constant 0.000000e+00 : f32
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// CHECK: %[[EMPTY:.*]] = tensor.empty() : tensor<1x12x13xf32>
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// CHECK: %[[FILL:.*]] = linalg.fill ins(%[[PAD]] : f32) outs(%[[EMPTY]] : tensor<1x12x13xf32>) -> tensor<1x12x13xf32>
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// CHECK: %[[READ_1:.*]] = vector.transfer_read %[[ARG_0]]{{\[}}%[[C0]], %[[C0]], %[[C0]]], %[[PAD]] {in_bounds = [true, true, true]} : tensor<1x5x6xf32>, vector<1x5x6xf32>
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// CHECK: %[[WRITE_1:.*]] = vector.transfer_write %[[READ_1]], %[[FILL]]{{\[}}%[[C0]], %[[C0]], %[[C0]]] {in_bounds = [true, true, true]} : vector<1x5x6xf32>, tensor<1x12x13xf32>
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// CHECK: %[[VEC:.*]] = call @make_vector() : () -> tensor<12x13xf32>
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// CHECK: %[[READ_2:.*]] = vector.transfer_read %[[VEC]]{{\[}}%[[C0]], %[[C0]]], %[[PAD_READ]] {in_bounds = [true, true]} : tensor<12x13xf32>, vector<12x13xf32>
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// CHECK: %[[RES:.*]] = vector.transfer_write %[[READ_2]], %[[WRITE_1]]{{\[}}%[[C0]], %[[C0]], %[[C0]]] {in_bounds = [true, true]} : vector<12x13xf32>, tensor<1x12x13xf32>
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// CHECK: return %[[RES]] : tensor<1x12x13xf32>
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func.func @pad_and_insert_slice_dest(
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%arg0: tensor<1x5x6xf32>) -> tensor<1x12x13xf32> {
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%c5 = arith.constant 5.0 : f32
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%0 = tensor.pad %arg0 low[0, 0, 0] high[0, 7, 7] {
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^bb0(%arg2: index, %arg3: index, %arg4: index):
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tensor.yield %c5 : f32
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} : tensor<1x5x6xf32> to tensor<1x12x13xf32>
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%1 = call @make_vector() : () -> tensor<12x13xf32>
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%r = tensor.insert_slice %1 into %0[0, 0, 0][1, 12, 13][1, 1, 1] : tensor<12x13xf32> into tensor<1x12x13xf32>
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return %r : tensor<1x12x13xf32>
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}
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module attributes {transform.with_named_sequence} {
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transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
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%func_op = transform.structured.match ops{["func.func"]} in %arg1 : (!transform.any_op) -> !transform.op<"func.func">
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transform.apply_patterns to %func_op {
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transform.apply_patterns.linalg.pad_vectorization
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} : !transform.op<"func.func">
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transform.yield
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}
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}
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// -----
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func.func private @make_vector() -> vector<7x9xf32>
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// Variant of @pad_and_transfer_write_static
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// CHECK-LABEL: func @pad_and_transfer_write_static_non_zero_low_pad
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// CHECK-NOT: tensor.pad
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// CHECK: linalg.fill
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func.func @pad_and_transfer_write_static_non_zero_low_pad(
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%arg0: tensor<5x6xf32>) -> tensor<5x6xf32> {
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%c0 = arith.constant 0 : index
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%c5 = arith.constant 5.0 : f32
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%0 = tensor.pad %arg0 low[0, 1] high[5, 6] {
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^bb0(%arg2: index, %arg3: index):
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tensor.yield %c5 : f32
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} : tensor<5x6xf32> to tensor<10x13xf32>
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%1 = call @make_vector() : () -> vector<7x9xf32>
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%2 = vector.transfer_write %1, %0[%c0, %c0]
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: vector<7x9xf32>, tensor<10x13xf32>
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%3 = tensor.extract_slice %2[0, 0] [5, 6] [1, 1] : tensor<10x13xf32> to tensor<5x6xf32>
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return %3 : tensor<5x6xf32>
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}
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module attributes {transform.with_named_sequence} {
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transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
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%func_op = transform.structured.match ops{["func.func"]} in %arg1 : (!transform.any_op) -> !transform.op<"func.func">
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transform.apply_patterns to %func_op {
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transform.apply_patterns.linalg.pad_vectorization
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} : !transform.op<"func.func">
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transform.yield
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}
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}
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// -----
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func.func private @make_vector() -> vector<7x9xf32>
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// Variant of @pad_and_transfer_write_static
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// CHECK-LABEL: func @pad_and_transfer_write_static_non_zero_offset
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// CHECK-NOT: tensor.pad
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// CHECK: linalg.fill
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func.func @pad_and_transfer_write_static_non_zero_offset(
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%arg0: tensor<5x6xf32>) -> tensor<5x6xf32> {
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%c0 = arith.constant 0 : index
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%c5 = arith.constant 5.0 : f32
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%0 = tensor.pad %arg0 low[0, 1] high[5, 6] {
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^bb0(%arg2: index, %arg3: index):
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tensor.yield %c5 : f32
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} : tensor<5x6xf32> to tensor<10x13xf32>
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%1 = call @make_vector() : () -> vector<7x9xf32>
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%2 = vector.transfer_write %1, %0[%c0, %c0]
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: vector<7x9xf32>, tensor<10x13xf32>
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%3 = tensor.extract_slice %2[0, 1] [5, 6] [1, 1] : tensor<10x13xf32> to tensor<5x6xf32>
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return %3 : tensor<5x6xf32>
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}
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module attributes {transform.with_named_sequence} {
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transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
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%func_op = transform.structured.match ops{["func.func"]} in %arg1 : (!transform.any_op) -> !transform.op<"func.func">
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transform.apply_patterns to %func_op {
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transform.apply_patterns.linalg.pad_vectorization
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} : !transform.op<"func.func">
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transform.yield
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}
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}
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