forked from OSchip/llvm-project
59 lines
3.3 KiB
MLIR
59 lines
3.3 KiB
MLIR
// RUN: mlir-opt %s -test-linalg-greedy-fusion -split-input-file | FileCheck %s
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#map0 = affine_map<(d0)[s0] -> (2, -d0 + s0)>
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#map1 = affine_map<(d0)[s0] -> (4, -d0 + s0)>
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#map2 = affine_map<(d0)[s0] -> (3, -d0 + s0)>
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#map3 = affine_map<(d0, d1) -> (2, d0 - d1)>
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#map4 = affine_map<(d0, d1) -> (3, d0 - d1)>
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func @matmul_tensors(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>, %arg2: tensor<?x?xf32>) -> tensor<?x?xf32> {
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%t0 = linalg.matmul ins(%arg0, %arg1: tensor<?x?xf32>, tensor<?x?xf32>)
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init(%arg2: tensor<?x?xf32>)
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-> tensor<?x?xf32>
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%c4 = constant 4 : index
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%c2 = constant 2 : index
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%c0 = constant 0 : index
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%c3 = constant 3 : index
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%c1 = constant 1 : index
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%0 = dim %t0, %c0 : tensor<?x?xf32>
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%1 = dim %t0, %c1 : tensor<?x?xf32>
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%2 = dim %arg1, %c1 : tensor<?x?xf32>
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%3 = scf.for %arg3 = %c0 to %0 step %c2 iter_args(%arg4 = %arg2) -> (tensor<?x?xf32>) {
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%4 = scf.for %arg5 = %c0 to %2 step %c3 iter_args(%arg6 = %arg4) -> (tensor<?x?xf32>) {
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%5 = scf.for %arg7 = %c0 to %1 step %c4 iter_args(%arg8 = %arg6) -> (tensor<?x?xf32>) {
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%6 = subtensor %t0[%arg3, %arg7][%c2, 4][1, 1] : tensor<?x?xf32> to tensor<?x4xf32>
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%7 = subtensor %arg1[%arg7, %arg5][4, %c3][1, 1] : tensor<?x?xf32> to tensor<4x?xf32>
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%8 = subtensor %arg8[%arg3, %arg5][%c2, %c3][1, 1] : tensor<?x?xf32> to tensor<?x?xf32>
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%9 = linalg.matmul ins(%6, %7 : tensor<?x4xf32>, tensor<4x?xf32>) init(%8 : tensor<?x?xf32>) -> tensor<?x?xf32>
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%10 = subtensor_insert %9 into %arg8[%arg3, %arg5] [%c2, %c3] [1, 1] : tensor<?x?xf32> into tensor<?x?xf32>
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scf.yield %10 : tensor<?x?xf32>
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}
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scf.yield %5 : tensor<?x?xf32>
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}
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scf.yield %4 : tensor<?x?xf32>
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}
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return %3 : tensor<?x?xf32>
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}
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// CHECK-LABEL: func @matmul_tensors(
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// CHECK-SAME: %[[A:[0-9a-z]*]]: tensor<?x?xf32>
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// CHECK-SAME: %[[B:[0-9a-z]*]]: tensor<?x?xf32>
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// CHECK-SAME: %[[C:[0-9a-z]*]]: tensor<?x?xf32>
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// CHECK-DAG: %[[C0:.*]] = constant 0 : index
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// CHECK-DAG: %[[C1:.*]] = constant 1 : index
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// CHECK-DAG: %[[dA1:.*]] = dim %[[A]], %[[C1]] : tensor<?x?xf32>
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// CHECK: scf.for %[[I:[0-9a-z]*]]
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// CHECK: %[[stA:.*]] = subtensor %[[A]][%[[I]], 0] [2, %[[dA1]]] [1, 1] : tensor<?x?xf32> to tensor<2x?xf32>
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// CHECK-NEXT: scf.for %[[J:[0-9a-z]*]]
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// CHECK-NEXT: scf.for %[[K:[0-9a-z]*]] {{.*}} iter_args(%[[RES:[0-9a-z]*]]
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// CHECK-DAG: %[[stB1:.*]] = subtensor %[[B]][%[[K]], %[[J]]] [4, 3] [1, 1] : tensor<?x?xf32> to tensor<4x3xf32>
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// CHECK-DAG: %[[stF:.*]] = subtensor %[[RES]][%[[I]], %[[J]]] [2, 3] [1, 1] : tensor<?x?xf32> to tensor<2x3xf32>
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//
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// subtensors of the producing matmul.
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// CHECK-DAG: %[[stB2:.*]] = subtensor %[[B]][0, %[[K]]] [%[[dA1]], 4] [1, 1] : tensor<?x?xf32> to tensor<?x4xf32>
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// CHECK-DAG: %[[stC:.*]] = subtensor %[[C]][%[[I]], %[[K]]] [2, 4] [1, 1] : tensor<?x?xf32> to tensor<2x4xf32>
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// CHECK: %[[stD:.*]] = linalg.matmul ins(%[[stA]], %[[stB2]] : tensor<2x?xf32>, tensor<?x4xf32>) init(%[[stC]] : tensor<2x4xf32>) -> tensor<2x4xf32>
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// CHECK-NEXT: %[[stG:.*]] = linalg.matmul ins(%[[stD]], %[[stB1]] : tensor<2x4xf32>, tensor<4x3xf32>) init(%[[stF]] : tensor<2x3xf32>) -> tensor<2x3xf32>
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// CHECK-NEXT: subtensor_insert %[[stG]] into %[[RES]][%[[I]], %[[J]]]
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