a0615d020a
This commit is part of the migration of towards the new STEA syntax/design. In particular, this commit includes the following changes:
* Renaming compiler-internal functions/methods:
* `SparseTensorEncodingAttr::{getDimLevelType => getLvlTypes}`
* `Merger::{getDimLevelType => getLvlType}` (for consistency)
* `sparse_tensor::{getDimLevelType => buildLevelType}` (to help reduce confusion vs actual getter methods)
* Renaming external facets to match:
* the STEA parser and printer
* the C and Python bindings
* PyTACO
However, the actual renaming of the `DimLevelType` itself (along with all the "dlt" names) will be handled in a separate commit.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D150330
32 lines
2.7 KiB
MLIR
32 lines
2.7 KiB
MLIR
// RUN: mlir-opt %s --sparse-tensor-codegen=enable-buffer-initialization=true --canonicalize --cse | FileCheck %s
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#SV = #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>
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// CHECK-LABEL: func.func @sparse_alloc_sparse_vector(
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// CHECK-SAME: %[[VAL_0:.*]]: index) -> (memref<?xindex>, memref<?xindex>, memref<?xf64>, !sparse_tensor.storage_specifier
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// CHECK: %[[VAL_1:.*]] = arith.constant 1 : index
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// CHECK: %[[VAL_2:.*]] = arith.constant 0.000000e+00 : f64
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// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index
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// CHECK: %[[VAL_4:.*]] = memref.alloc() : memref<16xindex>
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// CHECK: %[[VAL_5:.*]] = memref.cast %[[VAL_4]] : memref<16xindex> to memref<?xindex>
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// CHECK: linalg.fill ins(%[[VAL_3]] : index) outs(%[[VAL_4]] : memref<16xindex>)
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// CHECK: %[[VAL_6:.*]] = memref.alloc() : memref<16xindex>
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// CHECK: %[[VAL_7:.*]] = memref.cast %[[VAL_6]] : memref<16xindex> to memref<?xindex>
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// CHECK: linalg.fill ins(%[[VAL_3]] : index) outs(%[[VAL_6]] : memref<16xindex>)
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// CHECK: %[[VAL_8:.*]] = memref.alloc() : memref<16xf64>
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// CHECK: %[[VAL_9:.*]] = memref.cast %[[VAL_8]] : memref<16xf64> to memref<?xf64>
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// CHECK: linalg.fill ins(%[[VAL_2]] : f64) outs(%[[VAL_8]] : memref<16xf64>)
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// CHECK: %[[VAL_10:.*]] = sparse_tensor.storage_specifier.init : !sparse_tensor.storage_specifier
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// CHECK: %[[VAL_12:.*]] = sparse_tensor.storage_specifier.set %[[VAL_10]] lvl_sz at 0 with %[[VAL_0]] : !sparse_tensor.storage_specifier
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// CHECK: %[[VAL_14:.*]] = sparse_tensor.storage_specifier.get %[[VAL_12]] pos_mem_sz at 0 : !sparse_tensor.storage_specifier
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// CHECK: %[[VAL_15:.*]], %[[VAL_17:.*]] = sparse_tensor.push_back %[[VAL_14]], %[[VAL_5]], %[[VAL_3]] : index, memref<?xindex>, index
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// CHECK: %[[VAL_18:.*]] = sparse_tensor.storage_specifier.set %[[VAL_12]] pos_mem_sz at 0 with %[[VAL_17]] : !sparse_tensor.storage_specifier
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// CHECK: %[[VAL_19:.*]], %[[VAL_21:.*]] = sparse_tensor.push_back %[[VAL_17]], %[[VAL_15]], %[[VAL_3]], %[[VAL_1]] : index, memref<?xindex>, index, index
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// CHECK: %[[VAL_22:.*]] = sparse_tensor.storage_specifier.set %[[VAL_18]] pos_mem_sz at 0 with %[[VAL_21]] : !sparse_tensor.storage_specifier
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// CHECK: return %[[VAL_19]], %[[VAL_7]], %[[VAL_9]], %[[VAL_22]] : memref<?xindex>, memref<?xindex>, memref<?xf64>, !sparse_tensor.storage_specifier
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func.func @sparse_alloc_sparse_vector(%arg0: index) -> tensor<?xf64, #SV> {
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%0 = bufferization.alloc_tensor(%arg0) : tensor<?xf64, #SV>
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%1 = sparse_tensor.load %0 : tensor<?xf64, #SV>
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return %1 : tensor<?xf64, #SV>
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}
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