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[tosa][mlir] Lowering for dynamic shapes in the reduce_x ops in tosa-to-linalg 

Reviewed By: rsuderman

Differential Revision: https://reviews.llvm.org/D117691
This commit is contained in:
natashaknk 2022-01-19 11:06:00 -08:00 committed by Rob Suderman
parent a9935b5db7
commit b9b10c0e61
2 changed files with 75 additions and 6 deletions
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15
mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
View File

@ -830,19 +830,22 @@ static LogicalResult reduceMatchAndRewriteHelper(Operation *op, uint64_t axis,
Value input = op->getOperand(0); Value input = op->getOperand(0);
llvm::SmallVector<int64_t> reduceShape; llvm::SmallVector<int64_t> reduceShape;
SmallVector<Value> dynDims;
for (unsigned i = 0; i < inputTy.getRank(); i++) { for (unsigned i = 0; i < inputTy.getRank(); i++) {
if (axis != i) if (axis != i) {
reduceShape.push_back(inputTy.getDimSize(i)); reduceShape.push_back(inputTy.getDimSize(i));
if (inputTy.isDynamicDim(i))
dynDims.push_back(rewriter.create<tensor::DimOp>(loc, input, i));
}
} }
Type reduceTy = RankedTensorType::get(reduceShape, resultTy.getElementType()); Type reduceTy = RankedTensorType::get(reduceShape, resultTy.getElementType());
// First fill the output buffer with the init value. // First fill the output buffer with the init value.
auto initTensor = auto initTensor = rewriter
rewriter .create<linalg::InitTensorOp>(loc, dynDims, reduceShape,
.create<linalg::InitTensorOp>(loc, ArrayRef<Value>({}), reduceShape, resultTy.getElementType())
resultTy.getElementType()) .result();
.result();
auto fillValueAttr = createInitialValueForReduceOp(op, elementTy, rewriter); auto fillValueAttr = createInitialValueForReduceOp(op, elementTy, rewriter);
if (!fillValueAttr) if (!fillValueAttr)

66
mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
View File

@ -736,6 +736,72 @@ func @reduce_float(%arg0: tensor<5x4xf32>) -> () {
// ----- // -----
// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1, d2) -> (d0, d2)>
// CHECK-LABEL: @reduce_float_dyn
func @reduce_float_dyn(%arg0: tensor<?x5x4xf32>) -> () {
// CHECK: %[[C0:.+]] = arith.constant 0
// CHECK: %[[DYN:.+]] = tensor.dim %arg0, %[[C0]]
// CHECK: %[[INIT:.+]] = linalg.init_tensor [%[[DYN]], 4]
// CHECK: %[[CST0:.+]] = arith.constant 0.0
// CHECK: %[[FILL:.+]] = linalg.fill(%[[CST0]], %[[INIT]])
// CHECK: %[[GENERIC:.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "reduction", "parallel"]} ins(%arg0 : tensor<?x5x4xf32>) outs(%[[FILL]] : tensor<?x4xf32>)
// CHECK: ^bb0(%arg1: f32, %arg2: f32)
// CHECK: %[[RES:.+]] = arith.addf %arg1, %arg2 : f32
// CHECK: linalg.yield %[[RES]] : f32
// CHECK: %[[COLLAPSED:.+]] = tensor.collapse_shape %[[GENERIC]] {{\[}}[0, 1]] : tensor<?x4xf32> into tensor<?xf32>
// CHECK: tensor.expand_shape %[[COLLAPSED]] {{\[}}[0, 1, 2]] : tensor<?xf32> into tensor<?x1x4xf32>
%0 = "tosa.reduce_sum"(%arg0) {axis = 1 : i64} : (tensor<?x5x4xf32>) -> tensor<?x1x4xf32>
return
}
// -----
// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)>
// CHECK-LABEL: @reduce_float_dyn_nonzero_batch
func @reduce_float_dyn_nonzero_batch(%arg0: tensor<5x?x4xf32>) -> () {
// CHECK: %[[C1:.+]] = arith.constant 1
// CHECK: %[[DYN:.+]] = tensor.dim %arg0, %[[C1]]
// CHECK: %[[INIT:.+]] = linalg.init_tensor [5, %[[DYN]]]
// CHECK: %[[CST1:.+]] = arith.constant 1.0
// CHECK: %[[FILL:.+]] = linalg.fill(%[[CST1]], %[[INIT]])
// CHECK: %[[GENERIC:.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "reduction"]} ins(%arg0 : tensor<5x?x4xf32>) outs(%[[FILL]] : tensor<5x?xf32>)
// CHECK: ^bb0(%arg1: f32, %arg2: f32)
// CHECK: %[[RES:.+]] = arith.mulf %arg1, %arg2 : f32
// CHECK: linalg.yield %[[RES]] : f32
// CHECK: %[[COLLAPSED:.+]] = tensor.collapse_shape %[[GENERIC]] {{\[}}[0, 1]] : tensor<5x?xf32> into tensor<?xf32>
// CHECK: tensor.expand_shape %[[COLLAPSED]] {{\[}}[0, 1, 2]] : tensor<?xf32> into tensor<5x?x1xf32>
%0 = "tosa.reduce_prod"(%arg0) {axis = 2 : i64} : (tensor<5x?x4xf32>) -> tensor<5x?x1xf32>
return
}
// -----
// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)>
// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1) -> (d0)>
// CHECK-LABEL: @reduce_float_dyn_multiple
func @reduce_float_dyn_multiple(%arg0: tensor<?x?xf32>) -> () {
// CHECK: %[[C0:.+]] = arith.constant 0
// CHECK: %[[DYN:.+]] = tensor.dim %arg0, %[[C0]]
// CHECK: %[[INIT:.+]] = linalg.init_tensor [%[[DYN]]]
// CHECK: %[[CMIN:.+]] = arith.constant -3.40282347E+38
// CHECK: %[[FILL:.+]] = linalg.fill(%[[CMIN]], %[[INIT]])
// CHECK: %[[GENERIC:.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["parallel", "reduction"]} ins(%arg0 : tensor<?x?xf32>) outs(%[[FILL]] : tensor<?xf32>)
// CHECK: ^bb0(%arg1: f32, %arg2: f32)
// CHECK: %[[CMP:.+]] = arith.cmpf ogt, %arg1, %arg2 : f32
// CHECK: %[[RES:.+]] = select %[[CMP]], %arg1, %arg2 : f32
// CHECK: linalg.yield %[[RES]] : f32
// CHECK: tensor.expand_shape %[[GENERIC]] {{\[}}[0, 1]] : tensor<?xf32> into tensor<?x1xf32>
%0 = "tosa.reduce_max"(%arg0) {axis = 1 : i64} : (tensor<?x?xf32>) -> tensor<?x1xf32>
return
}
// -----
// CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)> // CHECK: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)>
// CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1) -> (d1)> // CHECK: #[[$MAP1:.*]] = affine_map<(d0, d1) -> (d1)>
// CHECK: #[[$MAP2:.*]] = affine_map<(d0, d1) -> (d0)> // CHECK: #[[$MAP2:.*]] = affine_map<(d0, d1) -> (d0)>