mirror of https://github.com/llvm/torch-mlir
153 lines
6.3 KiB
Python
153 lines
6.3 KiB
Python
# -*- Python -*-
|
|
# This file is licensed under a pytorch-style license
|
|
# See frontends/pytorch/LICENSE for license information.
|
|
|
|
import typing
|
|
|
|
import torch
|
|
import torch_mlir
|
|
|
|
import typing
|
|
|
|
# RUN: %PYTHON %s | npcomp-opt | FileCheck %s
|
|
|
|
mb = torch_mlir.ModuleBuilder()
|
|
|
|
|
|
# CHECK-LABEL: func @__torch__.prim_NumToTensor(
|
|
# CHECK-SAME: %[[ARG:.*]]: i64) -> !torch.tensor {
|
|
# CHECK: %[[RET:.*]] = torch.prim.NumToTensor.Scalar %[[ARG]] : i64 -> !torch.tensor
|
|
# CHECK: return %[[RET]] : !torch.tensor
|
|
# CHECK: }
|
|
|
|
@mb.import_function
|
|
@torch.jit.script
|
|
def prim_NumToTensor(i: int):
|
|
return _to_tensor(i)
|
|
|
|
# CHECK-LABEL: func @__torch__.prim_Print(
|
|
# CHECK-SAME: %[[ARG:.*]]: !torch.tensor) -> !torch.none {
|
|
# CHECK: %[[STR:.*]] = basicpy.bytes_constant "x"
|
|
# CHECK: torch.prim.Print(%[[STR]], %[[ARG]]) : !basicpy.BytesType, !torch.tensor
|
|
@mb.import_function
|
|
@torch.jit.script
|
|
def prim_Print(x):
|
|
print("x", x)
|
|
|
|
# CHECK-LABEL: func @__torch__.prim_RaiseException() -> !torch.none {
|
|
# CHECK: %[[ERRORSTR:.*]] = basicpy.bytes_constant "Error"
|
|
# CHECK: %[[NONE:.*]] = torch.prim.Uninitialized : !torch.none
|
|
# CHECK: torch.prim.RaiseException %[[ERRORSTR]]
|
|
# CHECK: return %[[NONE]] : !torch.none
|
|
@mb.import_function
|
|
@torch.jit.script
|
|
def prim_RaiseException():
|
|
raise Exception("Error")
|
|
|
|
# CHECK-LABEL: func @__torch__.prim_unchecked_cast(
|
|
# CHECK-SAME: %[[ARG:.*]]: !torch.optional<i64>) -> i64 {
|
|
# CHECK: %[[NONE:.*]] = torch.constant.none
|
|
# CHECK: %[[C3:.*]] = constant 3 : i64
|
|
# CHECK: %[[IS_NONE:.*]] = torch.aten.__is__ %[[ARG]], %[[NONE]] : !torch.optional<i64>, !torch.none -> !basicpy.BoolType
|
|
# CHECK: %[[COND:.*]] = basicpy.bool_cast %[[IS_NONE]] : !basicpy.BoolType -> i1
|
|
# CHECK: %[[RESULT:.*]] = scf.if %[[COND]] -> (i64) {
|
|
# CHECK: scf.yield %[[C3]] : i64
|
|
# CHECK: } else {
|
|
# CHECK: %[[CASTED:.*]] = torch.prim.unchecked_cast %[[ARG]] : !torch.optional<i64> -> i64
|
|
# CHECK: scf.yield %[[CASTED]] : i64
|
|
# CHECK: }
|
|
# CHECK: return %[[RESULT:.*]] : i64
|
|
@mb.import_function
|
|
@torch.jit.script
|
|
def prim_unchecked_cast(i: typing.Optional[int]):
|
|
if i is None:
|
|
return 3
|
|
return i
|
|
|
|
# CHECK-LABEL: func @__torch__.prim_TupleUnpack(
|
|
# CHECK-SAME: %[[ARG:.*]]: !basicpy.TupleType) -> i64 {
|
|
# CHECK: %[[RET:.*]]:2 = torch.prim.TupleUnpack %[[ARG]] : !basicpy.TupleType -> i64, i64
|
|
# CHECK: return %[[RET]]#0 : i64
|
|
@mb.import_function
|
|
@torch.jit.script
|
|
def prim_TupleUnpack(tup: typing.Tuple[int, int]):
|
|
val, _ = tup
|
|
return val
|
|
|
|
# CHECK-LABEL: func @__torch__.prim_TupleIndex(
|
|
# CHECK-SAME: %[[ARG:.*]]: !basicpy.TupleType) -> i64 {
|
|
# CHECK: %[[RET:.*]] = torch.prim.TupleIndex %[[ARG]], %[[IDX:.*]] : !basicpy.TupleType, i64 -> i64
|
|
# CHECK: return %[[RET]] : i64
|
|
@mb.import_function
|
|
@torch.jit.script
|
|
def prim_TupleIndex(tup: typing.Tuple[int, int]):
|
|
return tup[0]
|
|
|
|
# CHECK-LABEL: func @__torch__.prim_ListUnpack(
|
|
# CHECK-SAME: %[[ARG:.*]]: !torch.list<i64>) -> i64 {
|
|
# CHECK: %[[RET:.*]]:3 = torch.prim.ListUnpack %[[ARG]] : !torch.list<i64> -> i64, i64
|
|
# CHECK: return %[[RET]]#1 : i64
|
|
@mb.import_function
|
|
@torch.jit.script
|
|
def prim_ListUnpack(l: typing.List[int]):
|
|
_, val, _ = l
|
|
return val
|
|
|
|
# CHECK-LABEL: func @__torch__.prim_dtype(
|
|
# CHECK-SAME: %[[ARG:.*]]: !torch.tensor) -> i64 {
|
|
# CHECK: %[[RET:.*]] = torch.prim.dtype %[[ARG]] : !torch.tensor -> i64
|
|
# CHECK: return %[[RET]] : i64
|
|
@mb.import_function
|
|
@torch.jit.script
|
|
def prim_dtype(x):
|
|
return x.dtype
|
|
|
|
# CHECK-LABEL: func @__torch__.prim_layout(
|
|
# CHECK-SAME: %[[ARG:.*]]: !torch.tensor) -> i64 {
|
|
# CHECK: %[[RET:.*]] = torch.prim.layout %[[ARG]] : !torch.tensor -> i64
|
|
# CHECK: return %[[RET]] : i64
|
|
@mb.import_function
|
|
@torch.jit.script
|
|
def prim_layout(x):
|
|
return x.layout
|
|
|
|
# CHECK-LABEL: func @__torch__.prim_device(
|
|
# CHECK-SAME: %[[ARG:.*]]: !torch.tensor) -> !torch.Device {
|
|
# CHECK: %[[RET:.*]] = torch.prim.device %[[ARG]] : !torch.tensor -> !torch.Device
|
|
# CHECK: return %[[RET]] : !torch.Device
|
|
@mb.import_function
|
|
@torch.jit.script
|
|
def prim_device(x):
|
|
return x.device
|
|
|
|
# CHECK-LABEL: func @__torch__.prim_min(
|
|
# CHECK-SAME: %[[ARG:.*]]: i64) -> !basicpy.TupleType {
|
|
# CHECK: %[[SINGLETON:.*]] = torch.prim.ListConstruct %[[ARG]] : (i64) -> !torch.list<i64>
|
|
# CHECK: %[[MIN1:.*]] = torch.prim.min.self_int %[[SINGLETON]] : !torch.list<i64> -> i64
|
|
# CHECK: %[[MIN2:.*]] = torch.prim.min.int %[[ARG]], %[[ARG]] : i64, i64 -> i64
|
|
# CHECK: %[[ARG_3_TIMES:.*]] = torch.prim.ListConstruct %[[ARG]], %[[ARG]], %[[ARG]] : (i64, i64, i64) -> !torch.list<i64>
|
|
# CHECK: %[[MIN3:.*]] = torch.prim.min.self_int %[[ARG_3_TIMES]] : !torch.list<i64> -> i64
|
|
# CHECK: %[[RET:.*]] = basicpy.build_tuple %[[MIN1]], %[[MIN2]], %[[MIN3]] : (i64, i64, i64) -> !basicpy.TupleType
|
|
# CHECK: return %[[RET]] : !basicpy.TupleType
|
|
@mb.import_function
|
|
@torch.jit.script
|
|
def prim_min(x: int):
|
|
return min(x), min(x,x), min(x, x, x)
|
|
|
|
# CHECK-LABEL: func @__torch__.prim_max(
|
|
# CHECK-SAME: %[[ARG:.*]]: i64) -> !basicpy.TupleType {
|
|
# CHECK: %[[SINGLETON:.*]] = torch.prim.ListConstruct %[[ARG]] : (i64) -> !torch.list<i64>
|
|
# CHECK: %[[MAX1:.*]] = torch.prim.max.self_int %[[SINGLETON]] : !torch.list<i64> -> i64
|
|
# CHECK: %[[MAX2:.*]] = torch.prim.max.int %[[ARG]], %[[ARG]] : i64, i64 -> i64
|
|
# CHECK: %[[ARG_3_TIMES:.*]] = torch.prim.ListConstruct %[[ARG]], %[[ARG]], %[[ARG]] : (i64, i64, i64) -> !torch.list<i64>
|
|
# CHECK: %[[MAX3:.*]] = torch.prim.max.self_int %[[ARG_3_TIMES]] : !torch.list<i64> -> i64
|
|
# CHECK: %[[RET:.*]] = basicpy.build_tuple %[[MAX1]], %[[MAX2]], %[[MAX3]] : (i64, i64, i64) -> !basicpy.TupleType
|
|
# CHECK: return %[[RET]] : !basicpy.TupleType
|
|
@mb.import_function
|
|
@torch.jit.script
|
|
def prim_max(x: int):
|
|
return max(x), max(x,x), max(x, x, x)
|
|
|
|
mb.module.operation.print()
|
|
print()
|