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torch-mlir/test/Dialect/ATen/aten_resA.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-native_batch_norm-0": {
// CHECK-LABEL: "L1-relu-0": {
// CHECK-LABEL: "L2-_convolution-0": {
// CHECK-LABEL: "L3-native_batch_norm-1": {
// CHECK-LABEL: "L4-relu-1": {
// CHECK-LABEL: "L5-_convolution-1": {
// CHECK-LABEL: "L6-native_batch_norm-2": {
// CHECK-LABEL: "L7-relu-2": {
// CHECK-LABEL: "L8-_convolution-2": {
// CHECK-LABEL: "L9-add-0": {
module {
func @graph(%arg0: tensor<1x16x128x128xf32>, %arg1: tensor<1x16x128x128xf32>, %arg2: tensor<16xf32>, %arg3: tensor<16xf32>, %arg4: tensor<16xf32>, %arg5: tensor<16xf32>, %arg6: tensor<8x16x1x1xf32>, %arg7: tensor<8xf32>, %arg8: tensor<8xf32>, %arg9: tensor<8xf32>, %arg10: tensor<8xf32>, %arg11: tensor<8xf32>, %arg12: tensor<8x8x3x3xf32>, %arg13: tensor<8xf32>, %arg14: tensor<8xf32>, %arg15: tensor<8xf32>, %arg16: tensor<8xf32>, %arg17: tensor<8xf32>, %arg18: tensor<16x8x1x1xf32>, %arg19: tensor<16xf32>) -> tensor<1x16x128x128xf32> {
%0 = "aten.constant"() {type = "bool", value = 1 : i1} : () -> i1
%1 = "aten.constant"() {type = "f32", value = 1.000000e-01 : f32} : () -> f32
%2 = "aten.constant"() {type = "f32", value = 9.99999974E-6 : f32} : () -> f32
%3:3 = "aten.native_batch_norm"(%arg1, %arg2, %arg3, %arg4, %arg5, %0, %1, %2) : (tensor<1x16x128x128xf32>, tensor<16xf32>, tensor<16xf32>, tensor<16xf32>, tensor<16xf32>, i1, f32, f32) -> (tensor<1x16x128x128xf32>, tensor<16xf32>, tensor<16xf32>)
%4 = "aten.relu"(%3#0) : (tensor<1x16x128x128xf32>) -> tensor<1x16x128x128xf32>
%5 = "aten.constant"() {type = "List[i32]", value = dense<1> : vector<2xi32>} : () -> !aten.list<i32>
%6 = "aten.constant"() {type = "List[i32]", value = dense<0> : vector<2xi32>} : () -> !aten.list<i32>
%7 = "aten.constant"() {type = "List[i32]", value = dense<1> : vector<2xi32>} : () -> !aten.list<i32>
%8 = "aten.constant"() {type = "bool", value = 0 : i1} : () -> i1
%9 = "aten.constant"() {type = "List[i32]", value = dense<0> : vector<2xi32>} : () -> !aten.list<i32>
%10 = "aten.constant"() {type = "i32", value = 1 : i32} : () -> i32
%11 = "aten.constant"() {type = "bool", value = 0 : i1} : () -> i1
%12 = "aten.constant"() {type = "bool", value = 1 : i1} : () -> i1
%13 = "aten._convolution"(%4, %arg6, %arg7, %5, %6, %7) : (tensor<1x16x128x128xf32>, tensor<8x16x1x1xf32>, tensor<8xf32>, !aten.list<i32>, !aten.list<i32>, !aten.list<i32>) -> tensor<1x8x128x128xf32>
%14 = "aten.constant"() {type = "bool", value = 1 : i1} : () -> i1
%15 = "aten.constant"() {type = "f32", value = 1.000000e-01 : f32} : () -> f32
%16 = "aten.constant"() {type = "f32", value = 9.99999974E-6 : f32} : () -> f32
%17:3 = "aten.native_batch_norm"(%13, %arg8, %arg9, %arg10, %arg11, %14, %15, %16) : (tensor<1x8x128x128xf32>, tensor<8xf32>, tensor<8xf32>, tensor<8xf32>, tensor<8xf32>, i1, f32, f32) -> (tensor<1x8x128x128xf32>, tensor<8xf32>, tensor<8xf32>)
%18 = "aten.relu"(%17#0) : (tensor<1x8x128x128xf32>) -> tensor<1x8x128x128xf32>
%19 = "aten.constant"() {type = "List[i32]", value = dense<1> : vector<2xi32>} : () -> !aten.list<i32>
%20 = "aten.constant"() {type = "List[i32]", value = dense<1> : vector<2xi32>} : () -> !aten.list<i32>
%21 = "aten.constant"() {type = "List[i32]", value = dense<1> : vector<2xi32>} : () -> !aten.list<i32>
%22 = "aten.constant"() {type = "bool", value = 0 : i1} : () -> i1
%23 = "aten.constant"() {type = "List[i32]", value = dense<0> : vector<2xi32>} : () -> !aten.list<i32>
%24 = "aten.constant"() {type = "i32", value = 1 : i32} : () -> i32
%25 = "aten.constant"() {type = "bool", value = 0 : i1} : () -> i1
%26 = "aten.constant"() {type = "bool", value = 1 : i1} : () -> i1
%27 = "aten._convolution"(%18, %arg12, %arg13, %19, %20, %21) : (tensor<1x8x128x128xf32>, tensor<8x8x3x3xf32>, tensor<8xf32>, !aten.list<i32>, !aten.list<i32>, !aten.list<i32>) -> tensor<1x8x128x128xf32>
%28 = "aten.constant"() {type = "bool", value = 1 : i1} : () -> i1
%29 = "aten.constant"() {type = "f32", value = 1.000000e-01 : f32} : () -> f32
%30 = "aten.constant"() {type = "f32", value = 9.99999974E-6 : f32} : () -> f32
%31:3 = "aten.native_batch_norm"(%27, %arg14, %arg15, %arg16, %arg17, %28, %29, %30) : (tensor<1x8x128x128xf32>, tensor<8xf32>, tensor<8xf32>, tensor<8xf32>, tensor<8xf32>, i1, f32, f32) -> (tensor<1x8x128x128xf32>, tensor<8xf32>, tensor<8xf32>)
%32 = "aten.relu"(%31#0) : (tensor<1x8x128x128xf32>) -> tensor<1x8x128x128xf32>
%33 = "aten.constant"() {type = "List[i32]", value = dense<1> : vector<2xi32>} : () -> !aten.list<i32>
%34 = "aten.constant"() {type = "List[i32]", value = dense<0> : vector<2xi32>} : () -> !aten.list<i32>
%35 = "aten.constant"() {type = "List[i32]", value = dense<1> : vector<2xi32>} : () -> !aten.list<i32>
%36 = "aten.constant"() {type = "bool", value = 0 : i1} : () -> i1
%37 = "aten.constant"() {type = "List[i32]", value = dense<0> : vector<2xi32>} : () -> !aten.list<i32>
%38 = "aten.constant"() {type = "i32", value = 1 : i32} : () -> i32
%39 = "aten.constant"() {type = "bool", value = 0 : i1} : () -> i1
%40 = "aten.constant"() {type = "bool", value = 1 : i1} : () -> i1
%41 = "aten._convolution"(%32, %arg18, %arg19, %33, %34, %35) : (tensor<1x8x128x128xf32>, tensor<16x8x1x1xf32>, tensor<16xf32>, !aten.list<i32>, !aten.list<i32>, !aten.list<i32>) -> tensor<1x16x128x128xf32>
%42 = "aten.constant"() {type = "i32", value = 1 : i32} : () -> i32
%43 = "aten.add"(%arg0, %41, %42) : (tensor<1x16x128x128xf32>, tensor<1x16x128x128xf32>, i32) -> tensor<1x16x128x128xf32>
return %43 : tensor<1x16x128x128xf32>
}
}