96a23911f6
A very elaborate, but also very fun revision because all puzzle pieces are finally "falling in place". 1. replaces lingalg annotations + flags with proper sparse tensor types 2. add rigorous verification on sparse tensor type and sparse primitives 3. removes glue and clutter on opaque pointers in favor of sparse tensor types 4. migrates all tests to use sparse tensor types NOTE: next CL will remove *all* obsoleted sparse code in Linalg Reviewed By: bixia Differential Revision: https://reviews.llvm.org/D102095
101 lines
7.8 KiB
MLIR
101 lines
7.8 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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// Example with cyclic iteration graph with sparse and dense constraints,
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// but an acyclic iteration graph using sparse constraints only.
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#SparseTensor = #sparse_tensor.encoding<{
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dimLevelType = [ "dense", "dense", "dense", "compressed",
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"compressed", "dense", "dense", "dense" ]
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}>
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#trait_mul = {
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indexing_maps = [
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affine_map<(i,j,k,l,m,n,o,p) -> (i,j,k,l,m,n,o,p)>, // A
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affine_map<(i,j,k,l,m,n,o,p) -> (p,o,n,m,l,k,j,i)>, // B
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affine_map<(i,j,k,l,m,n,o,p) -> (i,j,k,l,m,n,o,p)> // X
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],
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iterator_types = ["parallel", "parallel", "parallel", "parallel",
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"parallel", "parallel", "parallel", "parallel"],
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doc = "X(i,j,k,l,m,n,o,p) = A(i,j,k,l,m,n,o,p) * B(p,o,n,m,l,k,j,i)"
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}
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// CHECK-LABEL: func @mul(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20x30x40x50x60x70x80xf32>,
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// CHECK-SAME: %[[VAL_1:.*]]: tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>,
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// CHECK-SAME: %[[VAL_2:.*]]: tensor<10x20x30x40x50x60x70x80xf32>) -> tensor<10x20x30x40x50x60x70x80xf32> {
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// CHECK: %[[VAL_3:.*]] = constant 3 : index
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// CHECK: %[[VAL_4:.*]] = constant 4 : index
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// CHECK: %[[VAL_5:.*]] = constant 10 : index
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// CHECK: %[[VAL_6:.*]] = constant 20 : index
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// CHECK: %[[VAL_7:.*]] = constant 30 : index
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// CHECK: %[[VAL_8:.*]] = constant 60 : index
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// CHECK: %[[VAL_9:.*]] = constant 70 : index
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// CHECK: %[[VAL_10:.*]] = constant 80 : index
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// CHECK: %[[VAL_11:.*]] = constant 0 : index
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// CHECK: %[[VAL_12:.*]] = constant 1 : index
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// CHECK: %[[VAL_13:.*]] = memref.buffer_cast %[[VAL_0]] : memref<10x20x30x40x50x60x70x80xf32>
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// CHECK: %[[VAL_14:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
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// CHECK: %[[VAL_15:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
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// CHECK: %[[VAL_16:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
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// CHECK: %[[VAL_17:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xindex>
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// CHECK: %[[VAL_18:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref<?xf32>
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// CHECK: %[[VAL_19:.*]] = memref.buffer_cast %[[VAL_2]] : memref<10x20x30x40x50x60x70x80xf32>
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// CHECK: %[[VAL_20:.*]] = memref.alloc() : memref<10x20x30x40x50x60x70x80xf32>
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// CHECK: linalg.copy(%[[VAL_19]], %[[VAL_20]]) : memref<10x20x30x40x50x60x70x80xf32>, memref<10x20x30x40x50x60x70x80xf32>
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// CHECK: scf.for %[[VAL_21:.*]] = %[[VAL_11]] to %[[VAL_10]] step %[[VAL_12]] {
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// CHECK: scf.for %[[VAL_22:.*]] = %[[VAL_11]] to %[[VAL_9]] step %[[VAL_12]] {
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// CHECK: %[[VAL_23:.*]] = muli %[[VAL_21]], %[[VAL_9]] : index
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// CHECK: %[[VAL_24:.*]] = addi %[[VAL_23]], %[[VAL_22]] : index
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// CHECK: scf.for %[[VAL_25:.*]] = %[[VAL_11]] to %[[VAL_8]] step %[[VAL_12]] {
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// CHECK: %[[VAL_26:.*]] = muli %[[VAL_24]], %[[VAL_8]] : index
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// CHECK: %[[VAL_27:.*]] = addi %[[VAL_26]], %[[VAL_25]] : index
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// CHECK: %[[VAL_28:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_27]]] : memref<?xindex>
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// CHECK: %[[VAL_29:.*]] = addi %[[VAL_27]], %[[VAL_12]] : index
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// CHECK: %[[VAL_30:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_29]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_31:.*]] = %[[VAL_28]] to %[[VAL_30]] step %[[VAL_12]] {
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// CHECK: %[[VAL_32:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_31]]] : memref<?xindex>
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// CHECK: %[[VAL_33:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_31]]] : memref<?xindex>
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// CHECK: %[[VAL_34:.*]] = addi %[[VAL_31]], %[[VAL_12]] : index
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// CHECK: %[[VAL_35:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_34]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_36:.*]] = %[[VAL_33]] to %[[VAL_35]] step %[[VAL_12]] {
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// CHECK: %[[VAL_37:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_36]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_38:.*]] = %[[VAL_11]] to %[[VAL_7]] step %[[VAL_12]] {
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// CHECK: %[[VAL_39:.*]] = muli %[[VAL_36]], %[[VAL_7]] : index
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// CHECK: %[[VAL_40:.*]] = addi %[[VAL_39]], %[[VAL_38]] : index
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// CHECK: scf.for %[[VAL_41:.*]] = %[[VAL_11]] to %[[VAL_6]] step %[[VAL_12]] {
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// CHECK: %[[VAL_42:.*]] = muli %[[VAL_40]], %[[VAL_6]] : index
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// CHECK: %[[VAL_43:.*]] = addi %[[VAL_42]], %[[VAL_41]] : index
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// CHECK: scf.for %[[VAL_44:.*]] = %[[VAL_11]] to %[[VAL_5]] step %[[VAL_12]] {
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// CHECK: %[[VAL_45:.*]] = muli %[[VAL_43]], %[[VAL_5]] : index
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// CHECK: %[[VAL_46:.*]] = addi %[[VAL_45]], %[[VAL_44]] : index
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// CHECK: %[[VAL_47:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_44]], %[[VAL_41]], %[[VAL_38]], %[[VAL_37]], %[[VAL_32]], %[[VAL_25]], %[[VAL_22]], %[[VAL_21]]] : memref<10x20x30x40x50x60x70x80xf32>
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// CHECK: %[[VAL_48:.*]] = memref.load %[[VAL_18]]{{\[}}%[[VAL_46]]] : memref<?xf32>
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// CHECK: %[[VAL_49:.*]] = mulf %[[VAL_47]], %[[VAL_48]] : f32
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// CHECK: memref.store %[[VAL_49]], %[[VAL_20]]{{\[}}%[[VAL_44]], %[[VAL_41]], %[[VAL_38]], %[[VAL_37]], %[[VAL_32]], %[[VAL_25]], %[[VAL_22]], %[[VAL_21]]] : memref<10x20x30x40x50x60x70x80xf32>
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// CHECK: }
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// CHECK: }
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// CHECK: }
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// CHECK: }
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// CHECK: }
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// CHECK: }
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// CHECK: }
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// CHECK: }
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// CHECK: %[[VAL_50:.*]] = memref.tensor_load %[[VAL_20]] : memref<10x20x30x40x50x60x70x80xf32>
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// CHECK: return %[[VAL_50]] : tensor<10x20x30x40x50x60x70x80xf32>
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// CHECK: }
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func @mul(%arga: tensor<10x20x30x40x50x60x70x80xf32>,
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%argb: tensor<80x70x60x50x40x30x20x10xf32, #SparseTensor>,
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%argx: tensor<10x20x30x40x50x60x70x80xf32>)
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-> tensor<10x20x30x40x50x60x70x80xf32> {
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%0 = linalg.generic #trait_mul
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ins(%arga, %argb: tensor<10x20x30x40x50x60x70x80xf32>,
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tensor<80x70x60x50x40x30x20x10xf32, #SparseTensor>)
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outs(%argx: tensor<10x20x30x40x50x60x70x80xf32>) {
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^bb(%a: f32, %b: f32, %x: f32):
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%0 = mulf %a, %b : f32
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linalg.yield %0 : f32
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} -> tensor<10x20x30x40x50x60x70x80xf32>
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return %0 : tensor<10x20x30x40x50x60x70x80xf32>
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}
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