d68a4b93d3
For context, `tensor.insert_slice` is vectorized using a
`vector.transfer_read` + `vector.transfer_write` pair.
An unmasked example is shown below:
```mlir
// BEFORE VECTORIZATION
%res = tensor.insert_slice
%slice into %dest[0, %c2]
[5, 1] [1, 1] : tensor<5x1xi32> into tensor<5x3xi32>
// AFTER VECTORIZATION
%read = vector.transfer_read %source[%c0, %c0], %pad
: tensor<5x1xi32>, vector<8x1xi32>
%res = vector.transfer_write %read, %dest[%c0, %c2]
: vector<8x1xi32>, tensor<5x3xi32>
```
This PR refactors `InsertSliceVectorizePattern` (which is used to
vectorize `tensor.extract_slice`) to enable masked vectorization. ATM,
only `vector.transfer_read` is masked. If `vector.transfer_write` also
requires masking, the vectorizer will bail out. This will be addressed
in a sub-sequent PR.
Summary of changes:
* Added an argument to specify vector sizes (behavior remains
unchanged if vector sizes are not specified).
* Renamed `InsertSliceVectorizePattern` to `vectorizeAsInsertSliceOp`
and integrated into (alongside other hooks for vectorization) in
`linalg::vectorize`.
* Removed `populateInsertSliceVectorizationPatterns`, as
`InsertSliceVectorizePattern` was its only pattern.
* Updated `vectorizeAsInsertSliceOp` to support masking for the
"read" operation.
* Updated `@pad_and_insert_slice_dest` in
"vectorization-pad-patterns.mlir" to reflect the removal of
`populateInsertSliceVectorizationPatterns` from
`ApplyPadVectorizationPatternsOps`.
260 lines
11 KiB
MLIR
260 lines
11 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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func.func private @make_vector() -> vector<7x9xf32>
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// Negative test - low pad is non-zero
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// CHECK-LABEL: func @pad_and_transfer_write_static_non_zero_low_pad
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// CHECK: tensor.pad
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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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// Negative test - TransferWriteOp result is not _directly_ consumed by an
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// ExtractSliceOp (noet the non-zero offset).
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func.func private @make_vector() -> vector<7x9xf32>
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// CHECK-LABEL: func @pad_and_transfer_write_static_non_zero_offset
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// CHECK: tensor.pad
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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, 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, 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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// -----
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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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func.func private @make_vector() -> tensor<12x13xf32>
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// The destination of tensor.insert_slice matches the result of tensor.pad -
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// not supported.
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// CHECK-LABEL: func.func @pad_and_insert_slice_dest(
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// CHECK-NOT: vector.transfer_read
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// CHECK-NOT: vector.transfer_write
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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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