70e227a404
This is a proof of concept recognition of the most basic forms of ReLu operations, used to show-case sparsification of end-to-end PyTorch models. In the long run, we must avoid lowering such constructs too early (with this need for raising them back). See discussion at https://discourse.llvm.org/t/min-max-abs-relu-recognition-starter-project/78918
35 lines
1.1 KiB
MLIR
35 lines
1.1 KiB
MLIR
// RUN: mlir-opt %s --sparsification-and-bufferization | FileCheck %s
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#map = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
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#sparse = #sparse_tensor.encoding<{
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map = (d0, d1, d2) -> (d0 : dense, d1 : dense, d2 : compressed)
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}>
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//
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// Make sure a simple ReLU passes the sparsifier
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//
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// CHECK-LABEL: func.func @relu
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// CHECK: scf.for
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// CHECK: scf.for
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// CHECK: scf.for
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// CHECK: arith.cmpf ugt
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// CHECK: arith.select
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//
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func.func @relu(%arg0: tensor<10x20x30xf64, #sparse>) -> tensor<10x20x30xf64, #sparse> {
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%cst = arith.constant 0.000000e+00 : f64
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%0 = tensor.empty() : tensor<10x20x30xf64>
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%1 = linalg.generic {
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indexing_maps = [#map, #map],
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iterator_types = ["parallel", "parallel", "parallel"]}
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ins(%arg0 : tensor<10x20x30xf64, #sparse>)
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outs(%0 : tensor<10x20x30xf64>) {
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^bb0(%in: f64, %out: f64):
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%2 = arith.cmpf ugt, %in, %cst : f64
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%3 = arith.select %2, %in, %cst : f64
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linalg.yield %3 : f64
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} -> tensor<10x20x30xf64>
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%cast = tensor.cast %1 : tensor<10x20x30xf64> to tensor<10x20x30xf64, #sparse>
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return %cast : tensor<10x20x30xf64, #sparse>
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}
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