stellaraccident

module  {
  func @binary_func(%arg0: tensor<16xf32>, %arg1: tensor<16xf32>) -> (tensor<16xf32>, tensor<16xf32>) attributes {iree.abi = "{\22a\22:[[\22ndarray\22,\22f32\22,1,16],[\22ndarray\22,\22f32\22,1,16]],\22r\22:[[\22ndarray\22,\22f32\22,1,16],[\22ndarray\22,\22f32\22,1,16]],\22v\22:1}", iree.module.export} {
    return %arg0, %arg1 : tensor<16xf32>, tensor<16xf32>
  }
  func @dict_nest(%arg0: !iree.list<?>, %arg1: tensor<f32>) -> !iree.list<?> attributes {iree.abi = "{\22a\22:[[\22sdict\22,[\22dict\22,[\22sdict\22,[\22a\22,[\22ndarray\22,\22f32\22,1,16]],[\22b\22,[\22ndarray\22,\22f32\22,1,16]]]],[\22list\22,[\22slist\22,[\22ndarray\22,\22f32\22,1,16],[\22ndarray\22,\22f32\22,1,16]]]],[\22ndarray\22,\22f32\22,0]],\22r\22:[[\22sdict\22,[\22dict\22,[\22sdict\22,[\22a\22,[\22ndarray\22,\22f32\22,1,16]],[\22b\22,[\22ndarray\22,\22f32\22,1,16]]]],[\22list\22,[\22slist\22,[\22ndarray\22,\22f32\22,1,16],[\22ndarray\22,\22f32\22,1,16]]]]],\22v\22:1}", iree.module.export} {
    %c2 = constant 2 : index
    %c0 =

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iree_tf_compiler/TF/test/saved_model_to_iree_abi.mlir:19:3: error: list contains 'none' and cannot be accessed as '!iree.list<none>'
  func @__inference_dict_nest_190(%arg0: tensor<16xf32> {tf._user_specified_name = "mapping", tf_saved_model.index_path = [0, "dict", "a"]}, %arg1: tensor<16xf32> {tf._user_specified_name = "mapping", tf_saved_model.index_path = [0, "dict", "b"]}, %arg2: tensor<16xf32> {tf._user_specified_name = "mapping", tf_saved_model.index_path = [0, "list", 0]}, %arg3: tensor<16xf32> {tf._user_specified_name = "mapping", tf_saved_model.index_path = [0, "list", 1]}, %arg4: tensor<f32> {tf._user_specified_name = "scalar", tf_saved_model.index_path = [1]}) -> (tensor<16xf32> {tf_saved_model.index_path = ["dict", "a"]}, tensor<16xf32> {tf_saved_model.index_path = ["dict", "b"]}, tensor<16xf32> {tf_saved_model.index_path = ["list", 0]}, tensor<16xf32> {tf_saved_model.index_path = ["list", 1]}) attributes {tf._construction_context = "kEagerRuntime", tf._input_shapes = [#tf.shape<16>, #tf.shape<16

stellaraccident

IREE V1 ABI

Native ABI

The IREE ABI natively allows monomorphic functions to be exported where arguments and results are composed of the following types:

Value Types:

• Byte aligned integer type (i8, i16, i32, i64)
• Floating point value (f16, f32, f64)

stellaraccident

import ctypes

# See here for the types of ABIs ctypes can dispatch to:
# https://docs.python.org/3/library/ctypes.html#ctypes.CFUNCTYPE

func_address = get_function_voidstar_from_jit("foobar")

# Create a callable for the void* function ad func_address, taking a
# void* and returning an int.
my_compiled_func = ctypes.CFUNCTYPE(ctypes.c_uint32, ctypes.c_voidp).prototype(func_address)

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---- REAL GENERATION HERE ----

//===----------------------------------------------------------------------===//
// Op definition for MatmulOp
//===----------------------------------------------------------------------===//

def MatmulOp : LinalgStructuredBase_Op<"matmul", [
  AttrSizedOperandSegments,
  DeclareOpInterfaceMethods<MemoryEffectsOpInterface>,
  SingleBlockImplicitTerminator<"YieldOp">

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File sizes on Linux x64 Release mode (not bundling into a monolithic libLLVM.so):

`lib/`:

```
28K     lib/BugpointPasses.so                                                                                                                                                        28K     lib/Bye.so                                                                                                                                                                   16K     lib/LLVMHello.so                                                                                                                                                             8.0K    lib/libEngine.so                                                                                                                                                             116K    lib/libLLVMAggressiveInstCombine.so                                                                                                                                          7.0M    lib/libLLVMAnal

stellaraccident

# Autogenerated by mlir-tblgen; don't manually edit.

import array as _ods_array
from . import _cext as _ods_cext
from . import _segmented_accessor as _ods_segmented_accessor, _equally_sized_accessor as _ods_equally_sized_accessor, _get_default_loc_context as _ods_get_default_loc_context
_ods_ir = _ods_cext.ir

@_ods_cext.register_dialect
class _Dialect(_ods_ir.Dialect):

stellaraccident

Registered operations:
  SimpleOp('aten::abs'[1] -> aten.abs, operands=[(0, TensorValue('input'))], results=[(0, TensorValue('result'))])
  SimpleOp('aten::abs'[2] -> aten.abs_outref, operands=[(0, TensorValue('input')), (1, TensorOutRef('out'))], results=[(0, TensorOutRef('result'))])
  SimpleOp('aten::acos'[1] -> aten.acos, operands=[(0, TensorValue('input'))], results=[(0, TensorValue('result'))])
  SimpleOp('aten::acos'[2] -> aten.acos_outref, operands=[(0, TensorValue('input')), (1, TensorOutRef('out'))], results=[(0, TensorOutRef('result'))])
  SimpleOp('aten::angle'[1] -> aten.angle, operands=[(0, TensorValue('input'))], results=[(0, TensorValue('result'))])
  SimpleOp('aten::angle'[2] -> aten.angle_outref, operands=[(0, TensorValue('input')), (1, TensorOutRef('out'))], results=[(0, TensorOutRef('result'))])
  SimpleOp('aten::asin'[1] -> aten.asin, operands=[(0, TensorValue('input'))], results=[(0, TensorValue('result'))])
  SimpleOp('aten::asin'[2] -> aten.asin_outref, operands=[(0, TensorValue('input

stellaraccident

Patch sequencing

• Needs a README, updated build instructions, etc. Some notes on the design and layering.
  • Install LibTorch: https://pytorch.org/
  • -DTorch_DIR=.../libtorch/share/cmake/Torch
  • Missing deps:
    • Python lark (python3 -m pip install lark)
    • #include "nnpack.h" (appears just in the aten_ops.cpp)
      • -DCMAKE_CXX_FLAGS=-I$HOME/bin/NNPACK/include

stellaraccident

Today I implemented enough boilerplate to parse and import a (very) simple python function to a corresponding MLIR representation.

As an example:

from npcomp.compiler.frontend import *

def binary_expression():
  a = 1