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[mlir][sparse] support sparse bufferization.alloc_tensor with copy argument.

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Reviewed By: aartbik

Differential Revision: https://reviews.llvm.org/D147358
This commit is contained in:
Peiming Liu 2023-03-31 21:17:57 +00:00
parent 94a74b9dbb
commit 7b86f7c5d4
2 changed files with 64 additions and 2 deletions
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22
mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorCodegen.cpp
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@ -749,11 +749,29 @@ public:
const auto resType = getSparseTensorType(op);
if (!resType.hasEncoding())
return failure();
if (op.getCopy())
return rewriter.notifyMatchFailure(op, "tensor copy not implemented");
// Construct allocation for each field.
const Location loc = op.getLoc();
if (op.getCopy()) {
auto desc = getDescriptorFromTensorTuple(adaptor.getCopy());
SmallVector<Value> fields;
fields.reserve(desc.getNumFields());
// Memcpy on memref fields.
for (auto field : desc.getMemRefFields()) {
auto memrefTp = field.getType().cast<MemRefType>();
auto size = rewriter.create<memref::DimOp>(loc, field, 0);
auto copied =
rewriter.create<memref::AllocOp>(loc, memrefTp, ValueRange{size});
rewriter.create<memref::CopyOp>(loc, field, copied);
fields.push_back(copied);
}
// Reuses specifier.
fields.push_back(desc.getSpecifier());
assert(fields.size() == desc.getNumFields());
rewriter.replaceOp(op, genTuple(rewriter, loc, resType, fields));
return success();
}
const Value sizeHint = op.getSizeHint();
const ValueRange dynSizes = adaptor.getDynamicSizes();
const size_t found = dynSizes.size();

44
mlir/test/Dialect/SparseTensor/codegen_sparse_alloc.mlir Normal file
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@ -0,0 +1,44 @@
// RUN: mlir-opt %s --sparse-tensor-codegen --canonicalize --cse | FileCheck %s
#CSR = #sparse_tensor.encoding<{ dimLevelType = ["dense", "compressed"]}>
#COO = #sparse_tensor.encoding<{ dimLevelType = ["compressed-nu", "singleton"]}>
// CHECK-LABEL: func.func @sparse_alloc_copy_CSR(
// CHECK-SAME: %[[VAL_0:.*0]]: memref<?xindex>,
// CHECK-SAME: %[[VAL_1:.*1]]: memref<?xindex>,
// CHECK-SAME: %[[VAL_2:.*2]]: memref<?xf32>,
// CHECK-SAME: %[[VAL_3:.*]]: !sparse_tensor.storage_specifier<#{{.*}}>) -> (memref<?xindex>, memref<?xindex>, memref<?xf32>, !sparse_tensor.storage_specifier<#{{.*}}>) {
// CHECK: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_5:.*]] = memref.dim %[[VAL_0]], %[[VAL_4]] : memref<?xindex>
// CHECK: %[[VAL_6:.*]] = memref.alloc(%[[VAL_5]]) : memref<?xindex>
// CHECK: memref.copy %[[VAL_0]], %[[VAL_6]] : memref<?xindex> to memref<?xindex>
// CHECK: %[[VAL_7:.*]] = memref.dim %[[VAL_1]], %[[VAL_4]] : memref<?xindex>
// CHECK: %[[VAL_8:.*]] = memref.alloc(%[[VAL_7]]) : memref<?xindex>
// CHECK: memref.copy %[[VAL_1]], %[[VAL_8]] : memref<?xindex> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = memref.dim %[[VAL_2]], %[[VAL_4]] : memref<?xf32>
// CHECK: %[[VAL_10:.*]] = memref.alloc(%[[VAL_9]]) : memref<?xf32>
// CHECK: memref.copy %[[VAL_2]], %[[VAL_10]] : memref<?xf32> to memref<?xf32>
func.func @sparse_alloc_copy_CSR(%arg0: tensor<2x2xf32, #CSR>) -> tensor<2x2xf32, #CSR> {
%0 = bufferization.alloc_tensor() copy(%arg0) : tensor<2x2xf32, #CSR>
"test.sink"(%0) : (tensor<2x2xf32, #CSR>) -> ()
}
// CHECK-LABEL: func.func @sparse_alloc_copy_COO(
// CHECK-SAME: %[[VAL_0:.*0]]: memref<?xindex>,
// CHECK-SAME: %[[VAL_1:.*1]]: memref<?xindex>,
// CHECK-SAME: %[[VAL_2:.*2]]: memref<?xf32>,
// CHECK-SAME: %[[VAL_3:.*]]: !sparse_tensor.storage_specifier<#{{.*}}>) -> (memref<?xindex>, memref<?xindex>, memref<?xf32>, !sparse_tensor.storage_specifier<#{{.*}}>) {
// CHECK: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_5:.*]] = memref.dim %[[VAL_0]], %[[VAL_4]] : memref<?xindex>
// CHECK: %[[VAL_6:.*]] = memref.alloc(%[[VAL_5]]) : memref<?xindex>
// CHECK: memref.copy %[[VAL_0]], %[[VAL_6]] : memref<?xindex> to memref<?xindex>
// CHECK: %[[VAL_7:.*]] = memref.dim %[[VAL_1]], %[[VAL_4]] : memref<?xindex>
// CHECK: %[[VAL_8:.*]] = memref.alloc(%[[VAL_7]]) : memref<?xindex>
// CHECK: memref.copy %[[VAL_1]], %[[VAL_8]] : memref<?xindex> to memref<?xindex>
// CHECK: %[[VAL_9:.*]] = memref.dim %[[VAL_2]], %[[VAL_4]] : memref<?xf32>
// CHECK: %[[VAL_10:.*]] = memref.alloc(%[[VAL_9]]) : memref<?xf32>
// CHECK: memref.copy %[[VAL_2]], %[[VAL_10]] : memref<?xf32> to memref<?xf32>
func.func @sparse_alloc_copy_COO(%arg0: tensor<2x2xf32, #COO>) -> tensor<2x2xf32, #COO> {
%0 = bufferization.alloc_tensor() copy(%arg0) : tensor<2x2xf32, #COO>
"test.sink"(%0) : (tensor<2x2xf32, #COO>) -> ()
}