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torch-mlir/test/Dialect/ATen/aten_add.mlir

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Add ATen Dialect (#16) This patch adds a dialect intended to be used as a frontend dialect to facilitate lowering from "A Tensor Library" in torch/pytorch. This patch includes several passes that are useful in conjuction with the dialect: --aten-layer-name: Generates layer names for each operation, which are not present in the original pytorch. --aten-to-std: Lower the ATen dialect into standard dialect function calls. --return-elimination-pass: convert functions (primarily the toplevel function) to pass return values by reference. This simplifies pytorch integration. --aten-op-report: generate a textual report about the model --liveness-report Future patches will implement actual integration with the pytorch jit to intercept and generates MLIR in this dialect, then lower the resulting MLIR into function calls through aten-layer-name -> aten-to-std -> return-elimination -> std-to-llvm. The result would then jitted using the LLVM jit, linked against a runtime library which makes calls back into pytorch to implement all the layers. Co-authored-by: Jeff Fifield <jeff.fifield@xilinx.com> Co-authored-by: Jeff Fifield <jeff.fifield@xilinx.com>
2020-08-13 10:28:04 +08:00
// RUN: npcomp-opt %s -aten-layer-name -aten-op-report |& FileCheck %s
// CHECK-LABEL: "L0-add-0": {
// CHECK-NEXT: "activation_in": 12,
// CHECK-NEXT: "activation_out": 6,
// CHECK-NEXT: "ops:+": 6,
// CHECK-NEXT: "reads": 12,
// CHECK-NEXT: "writes": 6
// RUN: npcomp-opt %s -aten-to-std |& FileCheck %s --check-prefix=CHECK-CONVERSION
// CHECK-CONVERSION-LABEL: @graph
func @graph(%arg0: tensor<1x2x3xf32>, %arg1: tensor<1x2x3xf32>) -> tensor<1x2x3xf32> {
%1 = "aten.constant"() {type = "i32", value = 1 : i32} : () -> i32
%2 = "aten.add"(%arg0, %arg1, %1) : (tensor<1x2x3xf32>, tensor<1x2x3xf32>, i32) -> tensor<1x2x3xf32>
"std.return"(%2) : (tensor<1x2x3xf32>) -> ()
}