a924fcc7c3
This test makes sure kernels map to efficient sparse code, i.e. all compressed for-loops, no co-iterating while loops. In addition, this revision removes the special constant folding inside the sparse compiler in favor of Mahesh' new generic linalg folding. Thanks! NOTE: relies on Mahesh fix, which needs to be rebased first Reviewed By: bixia Differential Revision: https://reviews.llvm.org/D110001
158 lines
11 KiB
MLIR
158 lines
11 KiB
MLIR
// NOTE: Assertions have been autogenerated by utils/generate-test-checks.py
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// RUN: mlir-opt %s \
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// RUN: --linalg-generalize-named-ops --linalg-fuse-elementwise-ops \
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// RUN: --sparsification | FileCheck %s
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#DCSR = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>
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// CHECK-LABEL: func @matmul(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>>,
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// CHECK-SAME: %[[VAL_1:.*]]: tensor<20x30xf32>,
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// CHECK-SAME: %[[VAL_2:.*]]: tensor<10x30xf32>) -> tensor<10x30xf32> {
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// CHECK-DAG: %[[VAL_3:.*]] = constant 0 : index
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// CHECK-DAG: %[[VAL_4:.*]] = constant 1 : index
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// CHECK-DAG: %[[VAL_5:.*]] = constant 30 : index
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// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_11:.*]] = memref.buffer_cast %[[VAL_1]] : memref<20x30xf32>
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// CHECK: %[[VAL_12:.*]] = memref.buffer_cast %[[VAL_2]] : memref<10x30xf32>
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// CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<10x30xf32>
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// CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<10x30xf32> to memref<10x30xf32>
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// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_3]]] : memref<?xindex>
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// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_14]] to %[[VAL_15]] step %[[VAL_4]] {
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// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xindex>
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// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xindex>
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// CHECK: %[[VAL_19:.*]] = addi %[[VAL_16]], %[[VAL_4]] : index
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// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_19]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_21:.*]] = %[[VAL_18]] to %[[VAL_20]] step %[[VAL_4]] {
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// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_21]]] : memref<?xindex>
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// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_21]]] : memref<?xf32>
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// CHECK: scf.for %[[VAL_24:.*]] = %[[VAL_3]] to %[[VAL_5]] step %[[VAL_4]] {
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// CHECK: %[[VAL_25:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_17]], %[[VAL_24]]] : memref<10x30xf32>
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// CHECK: %[[VAL_26:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_22]], %[[VAL_24]]] : memref<20x30xf32>
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// CHECK: %[[VAL_27:.*]] = mulf %[[VAL_23]], %[[VAL_26]] : f32
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// CHECK: %[[VAL_28:.*]] = addf %[[VAL_25]], %[[VAL_27]] : f32
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// CHECK: memref.store %[[VAL_28]], %[[VAL_13]]{{\[}}%[[VAL_17]], %[[VAL_24]]] : memref<10x30xf32>
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// CHECK: }
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// CHECK: }
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// CHECK: }
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// CHECK: %[[VAL_29:.*]] = memref.tensor_load %[[VAL_13]] : memref<10x30xf32>
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// CHECK: return %[[VAL_29]] : tensor<10x30xf32>
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// CHECK: }
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func @matmul(%a: tensor<10x20xf32, #DCSR>,
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%b: tensor<20x30xf32>,
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%c: tensor<10x30xf32>) -> tensor<10x30xf32> {
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%0 = linalg.matmul
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ins(%a, %b: tensor<10x20xf32, #DCSR>, tensor<20x30xf32>)
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outs(%c: tensor<10x30xf32>) -> tensor<10x30xf32>
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return %0 : tensor<10x30xf32>
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}
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// CHECK-LABEL: func @conv2d(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<8x8xi32>,
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// CHECK-SAME: %[[VAL_1:.*]]: tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>>,
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// CHECK-SAME: %[[VAL_2:.*]]: tensor<6x6xi32>) -> tensor<6x6xi32> {
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// CHECK-DAG: %[[VAL_3:.*]] = constant 0 : index
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// CHECK-DAG: %[[VAL_4:.*]] = constant 1 : index
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// CHECK-DAG: %[[VAL_5:.*]] = constant 6 : index
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// CHECK: %[[VAL_6:.*]] = memref.buffer_cast %[[VAL_0]] : memref<8x8xi32>
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// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_12:.*]] = memref.buffer_cast %[[VAL_2]] : memref<6x6xi32>
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// CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<6x6xi32>
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// CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<6x6xi32> to memref<6x6xi32>
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// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_3]]] : memref<?xindex>
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// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_14]] to %[[VAL_15]] step %[[VAL_4]] {
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// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xindex>
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// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_16]]] : memref<?xindex>
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// CHECK: %[[VAL_19:.*]] = addi %[[VAL_16]], %[[VAL_4]] : index
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// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_19]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_21:.*]] = %[[VAL_18]] to %[[VAL_20]] step %[[VAL_4]] {
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// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_21]]] : memref<?xindex>
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// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_21]]] : memref<?xi32>
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// CHECK: scf.for %[[VAL_24:.*]] = %[[VAL_3]] to %[[VAL_5]] step %[[VAL_4]] {
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// CHECK: scf.for %[[VAL_25:.*]] = %[[VAL_3]] to %[[VAL_5]] step %[[VAL_4]] {
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// CHECK: %[[VAL_26:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_25]], %[[VAL_24]]] : memref<6x6xi32>
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// CHECK: %[[VAL_27:.*]] = addi %[[VAL_25]], %[[VAL_17]] : index
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// CHECK: %[[VAL_28:.*]] = addi %[[VAL_24]], %[[VAL_22]] : index
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// CHECK: %[[VAL_29:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_27]], %[[VAL_28]]] : memref<8x8xi32>
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// CHECK: %[[VAL_30:.*]] = muli %[[VAL_29]], %[[VAL_23]] : i32
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// CHECK: %[[VAL_31:.*]] = addi %[[VAL_26]], %[[VAL_30]] : i32
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// CHECK: memref.store %[[VAL_31]], %[[VAL_13]]{{\[}}%[[VAL_25]], %[[VAL_24]]] : memref<6x6xi32>
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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_32:.*]] = memref.tensor_load %[[VAL_13]] : memref<6x6xi32>
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// CHECK: return %[[VAL_32]] : tensor<6x6xi32>
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// CHECK: }
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func @conv2d(%input: tensor<8x8xi32>,
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%filter: tensor<3x3xi32, #DCSR>,
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%output: tensor<6x6xi32>) -> tensor<6x6xi32> {
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%0 = linalg.conv_2d
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ins (%input, %filter: tensor<8x8xi32>, tensor<3x3xi32, #DCSR>)
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outs (%output: tensor<6x6xi32>) -> tensor<6x6xi32>
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return %0 : tensor<6x6xi32>
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}
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// CHECK-LABEL: func @quantized_matmul(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<5x3xi8>,
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// CHECK-SAME: %[[VAL_1:.*]]: tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>>,
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// CHECK-SAME: %[[VAL_2:.*]]: tensor<5x6xi64>) -> tensor<5x6xi64> {
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// CHECK-DAG: %[[VAL_3:.*]] = constant 2 : i64
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// CHECK-DAG: %[[VAL_4:.*]] = constant 0 : index
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// CHECK-DAG: %[[VAL_5:.*]] = constant 1 : index
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// CHECK-DAG: %[[VAL_6:.*]] = constant 5 : index
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// CHECK: %[[VAL_7:.*]] = memref.buffer_cast %[[VAL_0]] : memref<5x3xi8>
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// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_5]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_5]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_13:.*]] = memref.buffer_cast %[[VAL_2]] : memref<5x6xi64>
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// CHECK: %[[VAL_14:.*]] = memref.alloc() : memref<5x6xi64>
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// CHECK: memref.copy %[[VAL_13]], %[[VAL_14]] : memref<5x6xi64> to memref<5x6xi64>
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// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<?xindex>
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// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_5]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_17:.*]] = %[[VAL_15]] to %[[VAL_16]] step %[[VAL_5]] {
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// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_17]]] : memref<?xindex>
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// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_17]]] : memref<?xindex>
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// CHECK: %[[VAL_20:.*]] = addi %[[VAL_17]], %[[VAL_5]] : index
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// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_20]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_22:.*]] = %[[VAL_19]] to %[[VAL_21]] step %[[VAL_5]] {
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// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_22]]] : memref<?xindex>
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// CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_22]]] : memref<?xi8>
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// CHECK: scf.for %[[VAL_25:.*]] = %[[VAL_4]] to %[[VAL_6]] step %[[VAL_5]] {
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// CHECK: %[[VAL_26:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_25]], %[[VAL_23]]] : memref<5x6xi64>
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// CHECK: %[[VAL_27:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_25]], %[[VAL_18]]] : memref<5x3xi8>
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// CHECK: %[[VAL_28:.*]] = sexti %[[VAL_27]] : i8 to i64
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// CHECK: %[[VAL_29:.*]] = subi %[[VAL_28]], %[[VAL_3]] : i64
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// CHECK: %[[VAL_30:.*]] = sexti %[[VAL_24]] : i8 to i64
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// CHECK: %[[VAL_31:.*]] = muli %[[VAL_29]], %[[VAL_30]] : i64
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// CHECK: %[[VAL_32:.*]] = addi %[[VAL_26]], %[[VAL_31]] : i64
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// CHECK: memref.store %[[VAL_32]], %[[VAL_14]]{{\[}}%[[VAL_25]], %[[VAL_23]]] : memref<5x6xi64>
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// CHECK: }
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// CHECK: }
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// CHECK: }
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// CHECK: %[[VAL_33:.*]] = memref.tensor_load %[[VAL_14]] : memref<5x6xi64>
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// CHECK: return %[[VAL_33]] : tensor<5x6xi64>
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// CHECK: }
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func @quantized_matmul(%input1: tensor<5x3xi8>,
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%input2: tensor<3x6xi8, #DCSR>,
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%output: tensor<5x6xi64>) -> tensor<5x6xi64> {
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%c0 = constant 0 : i32
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%c2 = constant 2 : i32
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%0 = linalg.quantized_matmul
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ins(%input1, %input2, %c2, %c0 : tensor<5x3xi8>, tensor<3x6xi8, #DCSR>, i32, i32)
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outs(%output : tensor<5x6xi64>) -> tensor<5x6xi64>
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return %0: tensor<5x6xi64>
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}
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