c66303c287
This change removes the partial bufferization passes from the sparse compilation pipeline and replaces them with One-Shot Bufferize. One-Shot Analysis (and TensorCopyInsertion) is used to resolve all out-of-place bufferizations, dense and sparse. Dense ops are then bufferized with BufferizableOpInterface. Sparse ops are still bufferized in the Sparsification pass. Details: * Dense allocations are automatically deallocated, unless they are yielded from a block. (In that case the alloc would leak.) All test cases are modified accordingly. E.g., some funcs now have an "out" tensor argument that is returned from the function. (That way, the allocation happens at the call site.) * Sparse allocations are *not* automatically deallocated. They must be "released" manually. (No change, this will be addressed in a future change.) * Sparse tensor copies are not supported yet. (Future change) * Sparsification no longer has to consider inplacability. If necessary, allocations and/or copies are inserted during TensorCopyInsertion. All tensors are inplaceable by the time Sparsification is running. Instead of marking a tensor as "not inplaceable", it can be marked as "not writable", which will trigger an allocation and/or copy during TensorCopyInsertion. Differential Revision: https://reviews.llvm.org/D129356
403 lines
23 KiB
MLIR
403 lines
23 KiB
MLIR
// NOTE: Assertions have been autogenerated by utils/generate-test-checks.py
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// RUN: mlir-opt %s -sparsification | FileCheck %s
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#SV = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
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#trait1 = {
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indexing_maps = [
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affine_map<(i) -> (i)>, // a
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affine_map<(i) -> (i)> // x (out)
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],
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iterator_types = ["parallel"],
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doc = "x(i) = OP a(i)"
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}
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#trait2 = {
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indexing_maps = [
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affine_map<(i) -> (i)>, // a
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affine_map<(i) -> (i)>, // b
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affine_map<(i) -> (i)> // x (out)
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],
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iterator_types = ["parallel"],
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doc = "x(i) = a(i) OP b(i)"
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}
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#traitc = {
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indexing_maps = [
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affine_map<(i) -> (i)>, // a
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affine_map<(i) -> (i)> // x (out)
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],
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iterator_types = ["parallel"],
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doc = "x(i) = a(i) OP c"
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}
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// CHECK-LABEL: func @abs(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>,
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// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf64>) -> tensor<32xf64> {
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// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
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// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
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// CHECK: %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_2]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
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// CHECK: %[[VAL_5:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_2]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
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// CHECK: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf64>
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// CHECK: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf64>
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// CHECK: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
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// CHECK: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_10:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] {
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// CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_10]]] : memref<?xindex>
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// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_10]]] : memref<?xf64>
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// CHECK: %[[VAL_13:.*]] = math.abs %[[VAL_12]] : f64
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// CHECK: memref.store %[[VAL_13]], %[[VAL_7]]{{\[}}%[[VAL_11]]] : memref<32xf64>
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// CHECK: }
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// CHECK: %[[VAL_14:.*]] = bufferization.to_tensor %[[VAL_7]] : memref<32xf64>
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// CHECK: return %[[VAL_14]] : tensor<32xf64>
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// CHECK: }
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func.func @abs(%arga: tensor<32xf64, #SV>,
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%argx: tensor<32xf64>) -> tensor<32xf64> {
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%0 = linalg.generic #trait1
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ins(%arga: tensor<32xf64, #SV>)
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outs(%argx: tensor<32xf64>) {
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^bb(%a: f64, %x: f64):
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%0 = math.abs %a : f64
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linalg.yield %0 : f64
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} -> tensor<32xf64>
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return %0 : tensor<32xf64>
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}
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// CHECK-LABEL: func @ceil(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>,
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// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf64>) -> tensor<32xf64> {
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// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
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// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
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// CHECK: %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_2]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
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// CHECK: %[[VAL_5:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_2]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
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// CHECK: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf64>
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// CHECK: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf64>
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// CHECK: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
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// CHECK: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_10:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] {
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// CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_10]]] : memref<?xindex>
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// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_10]]] : memref<?xf64>
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// CHECK: %[[VAL_13:.*]] = math.ceil %[[VAL_12]] : f64
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// CHECK: memref.store %[[VAL_13]], %[[VAL_7]]{{\[}}%[[VAL_11]]] : memref<32xf64>
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// CHECK: }
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// CHECK: %[[VAL_14:.*]] = bufferization.to_tensor %[[VAL_7]] : memref<32xf64>
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// CHECK: return %[[VAL_14]] : tensor<32xf64>
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// CHECK: }
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func.func @ceil(%arga: tensor<32xf64, #SV>,
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%argx: tensor<32xf64>) -> tensor<32xf64> {
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%0 = linalg.generic #trait1
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ins(%arga: tensor<32xf64, #SV>)
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outs(%argx: tensor<32xf64>) {
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^bb(%a: f64, %x: f64):
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%0 = math.ceil %a : f64
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linalg.yield %0 : f64
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} -> tensor<32xf64>
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return %0 : tensor<32xf64>
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}
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// CHECK-LABEL: func @floor(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>,
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// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf64>) -> tensor<32xf64> {
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// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
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// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
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// CHECK: %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_2]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
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// CHECK: %[[VAL_5:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_2]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
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// CHECK: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf64>
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// CHECK: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf64>
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// CHECK: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
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// CHECK: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_10:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] {
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// CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_10]]] : memref<?xindex>
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// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_10]]] : memref<?xf64>
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// CHECK: %[[VAL_13:.*]] = math.floor %[[VAL_12]] : f64
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// CHECK: memref.store %[[VAL_13]], %[[VAL_7]]{{\[}}%[[VAL_11]]] : memref<32xf64>
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// CHECK: }
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// CHECK: %[[VAL_14:.*]] = bufferization.to_tensor %[[VAL_7]] : memref<32xf64>
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// CHECK: return %[[VAL_14]] : tensor<32xf64>
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// CHECK: }
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func.func @floor(%arga: tensor<32xf64, #SV>,
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%argx: tensor<32xf64>) -> tensor<32xf64> {
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%0 = linalg.generic #trait1
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ins(%arga: tensor<32xf64, #SV>)
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outs(%argx: tensor<32xf64>) {
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^bb(%a: f64, %x: f64):
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%0 = math.floor %a : f64
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linalg.yield %0 : f64
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} -> tensor<32xf64>
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return %0 : tensor<32xf64>
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}
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// CHECK-LABEL: func @neg(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>,
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// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf64>) -> tensor<32xf64> {
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// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
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// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
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// CHECK: %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_2]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
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// CHECK: %[[VAL_5:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_2]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
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// CHECK: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf64>
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// CHECK: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf64>
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// CHECK: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
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// CHECK: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_10:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] {
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// CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_10]]] : memref<?xindex>
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// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_10]]] : memref<?xf64>
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// CHECK: %[[VAL_13:.*]] = arith.negf %[[VAL_12]] : f64
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// CHECK: memref.store %[[VAL_13]], %[[VAL_7]]{{\[}}%[[VAL_11]]] : memref<32xf64>
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// CHECK: }
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// CHECK: %[[VAL_14:.*]] = bufferization.to_tensor %[[VAL_7]] : memref<32xf64>
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// CHECK: return %[[VAL_14]] : tensor<32xf64>
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// CHECK: }
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func.func @neg(%arga: tensor<32xf64, #SV>,
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%argx: tensor<32xf64>) -> tensor<32xf64> {
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%0 = linalg.generic #trait1
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ins(%arga: tensor<32xf64, #SV>)
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outs(%argx: tensor<32xf64>) {
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^bb(%a: f64, %x: f64):
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%0 = arith.negf %a : f64
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linalg.yield %0 : f64
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} -> tensor<32xf64>
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return %0 : tensor<32xf64>
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}
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// CHECK-LABEL: func @add(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>,
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// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf64>,
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// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf64>) -> tensor<32xf64> {
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// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
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// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
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// CHECK-DAG: %[[VAL_5:.*]] = arith.constant true
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// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
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// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf64>
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// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64>
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// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xindex>
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// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref<?xindex>
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// CHECK: %[[VAL_14:.*]]:2 = scf.while (%[[VAL_15:.*]] = %[[VAL_12]], %[[VAL_16:.*]] = %[[VAL_4]]) : (index, index) -> (index, index) {
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// CHECK: %[[VAL_17:.*]] = arith.cmpi ult, %[[VAL_15]], %[[VAL_13]] : index
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// CHECK: scf.condition(%[[VAL_17]]) %[[VAL_15]], %[[VAL_16]] : index, index
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// CHECK: } do {
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// CHECK: ^bb0(%[[VAL_18:.*]]: index, %[[VAL_19:.*]]: index):
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// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_18]]] : memref<?xindex>
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// CHECK: %[[VAL_21:.*]] = arith.cmpi eq, %[[VAL_20]], %[[VAL_19]] : index
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// CHECK: scf.if %[[VAL_21]] {
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// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_18]]] : memref<?xf64>
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// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_19]]] : memref<32xf64>
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// CHECK: %[[VAL_24:.*]] = arith.addf %[[VAL_22]], %[[VAL_23]] : f64
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// CHECK: memref.store %[[VAL_24]], %[[VAL_11]]{{\[}}%[[VAL_19]]] : memref<32xf64>
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// CHECK: } else {
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// CHECK: scf.if %[[VAL_5]] {
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// CHECK: %[[VAL_25:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_19]]] : memref<32xf64>
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// CHECK: memref.store %[[VAL_25]], %[[VAL_11]]{{\[}}%[[VAL_19]]] : memref<32xf64>
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// CHECK: } else {
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// CHECK: }
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// CHECK: }
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// CHECK: %[[VAL_26:.*]] = arith.cmpi eq, %[[VAL_20]], %[[VAL_19]] : index
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// CHECK: %[[VAL_27:.*]] = arith.addi %[[VAL_18]], %[[VAL_6]] : index
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// CHECK: %[[VAL_28:.*]] = arith.select %[[VAL_26]], %[[VAL_27]], %[[VAL_18]] : index
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// CHECK: %[[VAL_29:.*]] = arith.addi %[[VAL_19]], %[[VAL_6]] : index
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// CHECK: scf.yield %[[VAL_28]], %[[VAL_29]] : index, index
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// CHECK: }
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// CHECK: scf.for %[[VAL_30:.*]] = %[[VAL_31:.*]]#1 to %[[VAL_3]] step %[[VAL_6]] {
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// CHECK: %[[VAL_32:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_30]]] : memref<32xf64>
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// CHECK: memref.store %[[VAL_32]], %[[VAL_11]]{{\[}}%[[VAL_30]]] : memref<32xf64>
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// CHECK: }
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// CHECK: %[[VAL_33:.*]] = bufferization.to_tensor %[[VAL_11]] : memref<32xf64>
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// CHECK: return %[[VAL_33]] : tensor<32xf64>
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// CHECK: }
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func.func @add(%arga: tensor<32xf64, #SV>,
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%argb: tensor<32xf64>,
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%argx: tensor<32xf64>) -> tensor<32xf64> {
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%0 = linalg.generic #trait2
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ins(%arga, %argb: tensor<32xf64, #SV>, tensor<32xf64>)
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outs(%argx: tensor<32xf64>) {
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^bb(%a: f64, %b: f64, %x: f64):
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%0 = arith.addf %a, %b : f64
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linalg.yield %0 : f64
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} -> tensor<32xf64>
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return %0 : tensor<32xf64>
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}
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// CHECK-LABEL: func @sub(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>,
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// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf64>,
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// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf64>) -> tensor<32xf64> {
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// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index
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// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
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// CHECK-DAG: %[[VAL_5:.*]] = arith.constant true
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// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
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// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
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// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
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// CHECK: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf64>
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// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf64>
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// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64>
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// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xindex>
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// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref<?xindex>
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// CHECK: %[[VAL_14:.*]]:2 = scf.while (%[[VAL_15:.*]] = %[[VAL_12]], %[[VAL_16:.*]] = %[[VAL_4]]) : (index, index) -> (index, index) {
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// CHECK: %[[VAL_17:.*]] = arith.cmpi ult, %[[VAL_15]], %[[VAL_13]] : index
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// CHECK: scf.condition(%[[VAL_17]]) %[[VAL_15]], %[[VAL_16]] : index, index
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// CHECK: } do {
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// CHECK: ^bb0(%[[VAL_18:.*]]: index, %[[VAL_19:.*]]: index):
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// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_18]]] : memref<?xindex>
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// CHECK: %[[VAL_21:.*]] = arith.cmpi eq, %[[VAL_20]], %[[VAL_19]] : index
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// CHECK: scf.if %[[VAL_21]] {
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// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_18]]] : memref<?xf64>
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// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_19]]] : memref<32xf64>
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// CHECK: %[[VAL_24:.*]] = arith.subf %[[VAL_22]], %[[VAL_23]] : f64
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// CHECK: memref.store %[[VAL_24]], %[[VAL_11]]{{\[}}%[[VAL_19]]] : memref<32xf64>
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// CHECK: } else {
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// CHECK: scf.if %[[VAL_5]] {
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// CHECK: %[[VAL_25:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_19]]] : memref<32xf64>
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// CHECK: %[[VAL_26:.*]] = arith.negf %[[VAL_25]] : f64
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// CHECK: memref.store %[[VAL_26]], %[[VAL_11]]{{\[}}%[[VAL_19]]] : memref<32xf64>
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// CHECK: } else {
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// CHECK: }
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// CHECK: }
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// CHECK: %[[VAL_27:.*]] = arith.cmpi eq, %[[VAL_20]], %[[VAL_19]] : index
|
|
// CHECK: %[[VAL_28:.*]] = arith.addi %[[VAL_18]], %[[VAL_6]] : index
|
|
// CHECK: %[[VAL_29:.*]] = arith.select %[[VAL_27]], %[[VAL_28]], %[[VAL_18]] : index
|
|
// CHECK: %[[VAL_30:.*]] = arith.addi %[[VAL_19]], %[[VAL_6]] : index
|
|
// CHECK: scf.yield %[[VAL_29]], %[[VAL_30]] : index, index
|
|
// CHECK: }
|
|
// CHECK: scf.for %[[VAL_31:.*]] = %[[VAL_32:.*]]#1 to %[[VAL_3]] step %[[VAL_6]] {
|
|
// CHECK: %[[VAL_33:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_31]]] : memref<32xf64>
|
|
// CHECK: %[[VAL_34:.*]] = arith.negf %[[VAL_33]] : f64
|
|
// CHECK: memref.store %[[VAL_34]], %[[VAL_11]]{{\[}}%[[VAL_31]]] : memref<32xf64>
|
|
// CHECK: }
|
|
// CHECK: %[[VAL_35:.*]] = bufferization.to_tensor %[[VAL_11]] : memref<32xf64>
|
|
// CHECK: return %[[VAL_35]] : tensor<32xf64>
|
|
// CHECK: }
|
|
func.func @sub(%arga: tensor<32xf64, #SV>,
|
|
%argb: tensor<32xf64>,
|
|
%argx: tensor<32xf64>) -> tensor<32xf64> {
|
|
%0 = linalg.generic #trait2
|
|
ins(%arga, %argb: tensor<32xf64, #SV>, tensor<32xf64>)
|
|
outs(%argx: tensor<32xf64>) {
|
|
^bb(%a: f64, %b: f64, %x: f64):
|
|
%0 = arith.subf %a, %b : f64
|
|
linalg.yield %0 : f64
|
|
} -> tensor<32xf64>
|
|
return %0 : tensor<32xf64>
|
|
}
|
|
|
|
// CHECK-LABEL: func @mul(
|
|
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>,
|
|
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf64>,
|
|
// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf64>) -> tensor<32xf64> {
|
|
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
|
|
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
|
|
// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
|
|
// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
|
|
// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
|
|
// CHECK: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf64>
|
|
// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64>
|
|
// CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
|
|
// CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
|
|
// CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_4]] {
|
|
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex>
|
|
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_12]]] : memref<?xf64>
|
|
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_13]]] : memref<32xf64>
|
|
// CHECK: %[[VAL_16:.*]] = arith.mulf %[[VAL_14]], %[[VAL_15]] : f64
|
|
// CHECK: memref.store %[[VAL_16]], %[[VAL_9]]{{\[}}%[[VAL_13]]] : memref<32xf64>
|
|
// CHECK: }
|
|
// CHECK: %[[VAL_17:.*]] = bufferization.to_tensor %[[VAL_9]] : memref<32xf64>
|
|
// CHECK: return %[[VAL_17]] : tensor<32xf64>
|
|
// CHECK: }
|
|
func.func @mul(%arga: tensor<32xf64, #SV>,
|
|
%argb: tensor<32xf64>,
|
|
%argx: tensor<32xf64>) -> tensor<32xf64> {
|
|
%0 = linalg.generic #trait2
|
|
ins(%arga, %argb: tensor<32xf64, #SV>, tensor<32xf64>)
|
|
outs(%argx: tensor<32xf64>) {
|
|
^bb(%a: f64, %b: f64, %x: f64):
|
|
%0 = arith.mulf %a, %b : f64
|
|
linalg.yield %0 : f64
|
|
} -> tensor<32xf64>
|
|
return %0 : tensor<32xf64>
|
|
}
|
|
|
|
// CHECK-LABEL: func @divbyc(
|
|
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>,
|
|
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf64>) -> tensor<32xf64> {
|
|
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 2.000000e+00 : f64
|
|
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
|
|
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
|
|
// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
|
|
// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
|
|
// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
|
|
// CHECK: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf64>
|
|
// CHECK: %[[VAL_9:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
|
|
// CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
|
|
// CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_9]] to %[[VAL_10]] step %[[VAL_4]] {
|
|
// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xindex>
|
|
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_11]]] : memref<?xf64>
|
|
// CHECK: %[[VAL_14:.*]] = arith.divf %[[VAL_13]], %[[VAL_2]] : f64
|
|
// CHECK: memref.store %[[VAL_14]], %[[VAL_8]]{{\[}}%[[VAL_12]]] : memref<32xf64>
|
|
// CHECK: }
|
|
// CHECK: %[[VAL_15:.*]] = bufferization.to_tensor %[[VAL_8]] : memref<32xf64>
|
|
// CHECK: return %[[VAL_15]] : tensor<32xf64>
|
|
// CHECK: }
|
|
func.func @divbyc(%arga: tensor<32xf64, #SV>,
|
|
%argx: tensor<32xf64>) -> tensor<32xf64> {
|
|
%c = arith.constant 2.0 : f64
|
|
%0 = linalg.generic #traitc
|
|
ins(%arga: tensor<32xf64, #SV>)
|
|
outs(%argx: tensor<32xf64>) {
|
|
^bb(%a: f64, %x: f64):
|
|
%0 = arith.divf %a, %c : f64
|
|
linalg.yield %0 : f64
|
|
} -> tensor<32xf64>
|
|
return %0 : tensor<32xf64>
|
|
}
|
|
|
|
// CHECK-LABEL: func @zero_preserving_math(
|
|
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> {
|
|
// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 0 : index
|
|
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 1 : index
|
|
// CHECK: %[[VAL_4:.*]] = bufferization.alloc_tensor() : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
|
|
// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_1]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
|
|
// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_1]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
|
|
// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf64>
|
|
// CHECK: %[[VAL_8:.*]] = memref.alloca(%[[VAL_2]]) : memref<?xindex>
|
|
// CHECK: %[[BUF:.*]] = memref.alloca() : memref<f64>
|
|
// CHECK: %[[VAL_9:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_1]]] : memref<?xindex>
|
|
// CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_2]]] : memref<?xindex>
|
|
// CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_9]] to %[[VAL_10]] step %[[VAL_2]] {
|
|
// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xindex>
|
|
// CHECK: memref.store %[[VAL_12]], %[[VAL_8]]{{\[}}%[[VAL_1]]] : memref<?xindex>
|
|
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_11]]] : memref<?xf64>
|
|
// CHECK: %[[VAL_14:.*]] = math.abs %[[VAL_13]] : f64
|
|
// CHECK: %[[VAL_15:.*]] = math.ceil %[[VAL_14]] : f64
|
|
// CHECK: %[[VAL_16:.*]] = math.floor %[[VAL_15]] : f64
|
|
// CHECK: %[[VAL_17:.*]] = math.sqrt %[[VAL_16]] : f64
|
|
// CHECK: %[[VAL_18:.*]] = math.expm1 %[[VAL_17]] : f64
|
|
// CHECK: %[[VAL_19:.*]] = math.log1p %[[VAL_18]] : f64
|
|
// CHECK: %[[VAL_20:.*]] = math.sin %[[VAL_19]] : f64
|
|
// CHECK: %[[VAL_21:.*]] = math.tanh %[[VAL_20]] : f64
|
|
// CHECK: memref.store %[[VAL_21]], %[[BUF]][] : memref<f64>
|
|
// CHECK: sparse_tensor.lex_insert %[[VAL_4]], %[[VAL_8]], %[[BUF]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>, memref<?xindex>, memref<f64>
|
|
// CHECK: }
|
|
// CHECK: %[[VAL_22:.*]] = sparse_tensor.load %[[VAL_4]] hasInserts : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
|
|
// CHECK: return %[[VAL_22]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
|
|
// CHECK: }
|
|
func.func @zero_preserving_math(%arga: tensor<32xf64, #SV>) -> tensor<32xf64, #SV> {
|
|
%c32 = arith.constant 32 : index
|
|
%xinp = bufferization.alloc_tensor() : tensor<32xf64, #SV>
|
|
%0 = linalg.generic #trait1
|
|
ins(%arga: tensor<32xf64, #SV>)
|
|
outs(%xinp: tensor<32xf64, #SV>) {
|
|
^bb(%a: f64, %x: f64):
|
|
%0 = math.abs %a : f64
|
|
%1 = math.ceil %0 : f64
|
|
%2 = math.floor %1 : f64
|
|
%3 = math.sqrt %2 : f64
|
|
%4 = math.expm1 %3 : f64
|
|
%5 = math.log1p %4 : f64
|
|
%6 = math.sin %5 : f64
|
|
%7 = math.tanh %6 : f64
|
|
linalg.yield %7 : f64
|
|
} -> tensor<32xf64, #SV>
|
|
return %0 : tensor<32xf64, #SV>
|
|
}
|