llvm
/
torch-mlir
mirror of https://github.com/llvm/torch-mlir
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity
torch-mlir/frontends/pytorch/test/ivalue_import/primitives.py

41 lines
1.3 KiB
Python
Raw Normal View History

Add initial TorchScript module importer It turns out that this was easiest to structure as a general IValue importer, since torch module are just one of the possible IValue's. We import the IValue object graph in a braindead fashion into basicpy ops and a new `torch.nn_module` op that is used to model the attributes/methods of a torch::jit::Module IValue. See `Torch/ops.mlir` for an example, and also check out the .py import tests in `frontends/pytorch/test/module_import`. As part of this change, a few housekeeping tasks: - extract some helpers from graph_importer.cpp - more helpers around the C API - misc touchups
2021-01-28 08:35:44 +08:00
# -*- Python -*-
# This file is licensed under a pytorch-style license
# See frontends/pytorch/LICENSE for license information.
import typing
import torch
import torch_mlir
# RUN: %PYTHON %s | npcomp-opt | FileCheck %s
mb = torch_mlir.ModuleBuilder()
class TestModule(torch.nn.Module):
def __init__(self):
super().__init__()
self.i = 3
self.f = 42.5
Implement GlobalizeObjectGraph transformation. This required restructuring of how we model TorchScript on import. The main difference is that now we split out a `torch.class_type` that holds methods and declarations of the types of each slot. This is more consistent with TorchScript (our previous representation was "denormalized"). Recommended reading order: 1. check out the description of `torch.class_type` in `TorchOps.td` and look at `test/Dialect/Torch/ops.mlir` and `frontends/pytorch/test/module_import/` to familiarize with the new representation. - Just look at the new IR. The diff between the old names and new names is confusing. 2. check out `test/Dialect/Torch/globalize-object-graph*.mlir` and read along with the pass description in `include/npcomp/Dialect/Torch/Transforms/Passes.td` 3. Read the code in `GlobalizeObjectGraph.cpp` and miscellaneous changes in `ivalue_importer.cpp`, `TorchOps.cpp`, etc.
2021-02-18 03:28:51 +08:00
# CHECK: torch.class_type @[[CLASSTYPE:.*]] {
Add `!torch.bool` type. This finishes removing the dependence on the basicpy dialect! Changes: - Add `!torch.bool` type and replace use of `!basicpy.BoolType` in Torch-related code. - Rename BuiltinTensorize to BackendTypeConversion since now it handles bool conversions (and, when we add !torch.int and !torch.float, it will handle those as well), and generalize the related utilities (I also moved them to Torch/Transforms since they aren't really part of Torch/IR). - Add `torch.to_i1` and `torch.from_i1` ops for materializations - [cleanup] Reorganize `torch.constant.*` ops in TorchOps.td - Remove dependency of `torch` dialect on `basicpy` dialect and also `std` dialect. For `std`, we use some call related ops, but the `torch` dialect itself never produces them (we have passes that do though). This is fairly mechanical. Recommended review order: - New stuff in Torch/IR - New BuiltinTypeConversion files. - Mechnical fixups elsewhere.
2021-06-16 07:47:53 +08:00
# CHECK: torch.attr "training" : !torch.bool
Add `!torch.int` type. This replaces the ad-hoc use of `i64` throughout the Torch layer, and helps to keep it crystal clear the distinction between `!torch.int` (which is modeling the Python `int` type) and the various types that serve as dtypes of tensors, which are a totally different type universe. Changes: - `!torch.int` type and C bindings. - Change `torch.constant.int` parser to not need the `: i64` at the end. - `m_TorchConstantInt` matcher to aid with matching constants. - BackendTypeConversion changes for `!torch.int` -> `i64` type conversion. - Refactor finalizing patterns in FinalizingBackendTypeConversionPass (they were getting very repetitive). - Mechanical rewriting of `!torch.int` to `i64` in all the tests, and `AnyTorchIntType` to `Torch_IntType` in the `.td` files.
2021-06-17 06:53:15 +08:00
# CHECK: torch.attr "i" : !torch.int
Add a !torch.float type. This removes the dependence of the `torch` dialect on the low-level builtin types. Now the `torch` dialect is a standalone layer, suitable for targeting from higher-level Python abstractions without any premature lowering to primitive types.
2021-06-17 23:24:31 +08:00
# CHECK: torch.attr "f" : !torch.float
Implement GlobalizeObjectGraph transformation. This required restructuring of how we model TorchScript on import. The main difference is that now we split out a `torch.class_type` that holds methods and declarations of the types of each slot. This is more consistent with TorchScript (our previous representation was "denormalized"). Recommended reading order: 1. check out the description of `torch.class_type` in `TorchOps.td` and look at `test/Dialect/Torch/ops.mlir` and `frontends/pytorch/test/module_import/` to familiarize with the new representation. - Just look at the new IR. The diff between the old names and new names is confusing. 2. check out `test/Dialect/Torch/globalize-object-graph*.mlir` and read along with the pass description in `include/npcomp/Dialect/Torch/Transforms/Passes.td` 3. Read the code in `GlobalizeObjectGraph.cpp` and miscellaneous changes in `ivalue_importer.cpp`, `TorchOps.cpp`, etc.
2021-02-18 03:28:51 +08:00
# CHECK: }
Add `!torch.bool` type. This finishes removing the dependence on the basicpy dialect! Changes: - Add `!torch.bool` type and replace use of `!basicpy.BoolType` in Torch-related code. - Rename BuiltinTensorize to BackendTypeConversion since now it handles bool conversions (and, when we add !torch.int and !torch.float, it will handle those as well), and generalize the related utilities (I also moved them to Torch/Transforms since they aren't really part of Torch/IR). - Add `torch.to_i1` and `torch.from_i1` ops for materializations - [cleanup] Reorganize `torch.constant.*` ops in TorchOps.td - Remove dependency of `torch` dialect on `basicpy` dialect and also `std` dialect. For `std`, we use some call related ops, but the `torch` dialect itself never produces them (we have passes that do though). This is fairly mechanical. Recommended review order: - New stuff in Torch/IR - New BuiltinTypeConversion files. - Mechnical fixups elsewhere.
2021-06-16 07:47:53 +08:00
# CHECK: %[[TRUE:.*]] = torch.constant.bool true
Add `!torch.int` type. This replaces the ad-hoc use of `i64` throughout the Torch layer, and helps to keep it crystal clear the distinction between `!torch.int` (which is modeling the Python `int` type) and the various types that serve as dtypes of tensors, which are a totally different type universe. Changes: - `!torch.int` type and C bindings. - Change `torch.constant.int` parser to not need the `: i64` at the end. - `m_TorchConstantInt` matcher to aid with matching constants. - BackendTypeConversion changes for `!torch.int` -> `i64` type conversion. - Refactor finalizing patterns in FinalizingBackendTypeConversionPass (they were getting very repetitive). - Mechanical rewriting of `!torch.int` to `i64` in all the tests, and `AnyTorchIntType` to `Torch_IntType` in the `.td` files.
2021-06-17 06:53:15 +08:00
# CHECK: %[[N3:.*]] = torch.constant.int 3
Add `torch.constant.int` and `torch.constant.float`. - This removes reliance on basicpy.numeric_constant. - Also, add OpAsmOpInterface to the `torch.constant.none` and `torch.constant.str` ops.
2021-06-16 03:42:51 +08:00
# CHECK: %[[N42:.*]] = torch.constant.float 4.250000e+01
Add initial TorchScript module importer It turns out that this was easiest to structure as a general IValue importer, since torch module are just one of the possible IValue's. We import the IValue object graph in a braindead fashion into basicpy ops and a new `torch.nn_module` op that is used to model the attributes/methods of a torch::jit::Module IValue. See `Torch/ops.mlir` for an example, and also check out the .py import tests in `frontends/pytorch/test/module_import`. As part of this change, a few housekeeping tasks: - extract some helpers from graph_importer.cpp - more helpers around the C API - misc touchups
2021-01-28 08:35:44 +08:00
# CHECK: %[[MODULE:.*]] = torch.nn_module {
# Note: for some reason, Torch always adds a "training" property to all modules.
Add `!torch.bool` type. This finishes removing the dependence on the basicpy dialect! Changes: - Add `!torch.bool` type and replace use of `!basicpy.BoolType` in Torch-related code. - Rename BuiltinTensorize to BackendTypeConversion since now it handles bool conversions (and, when we add !torch.int and !torch.float, it will handle those as well), and generalize the related utilities (I also moved them to Torch/Transforms since they aren't really part of Torch/IR). - Add `torch.to_i1` and `torch.from_i1` ops for materializations - [cleanup] Reorganize `torch.constant.*` ops in TorchOps.td - Remove dependency of `torch` dialect on `basicpy` dialect and also `std` dialect. For `std`, we use some call related ops, but the `torch` dialect itself never produces them (we have passes that do though). This is fairly mechanical. Recommended review order: - New stuff in Torch/IR - New BuiltinTypeConversion files. - Mechnical fixups elsewhere.
2021-06-16 07:47:53 +08:00
# CHECK: torch.slot "training", %[[TRUE]] : !torch.bool
Add `!torch.int` type. This replaces the ad-hoc use of `i64` throughout the Torch layer, and helps to keep it crystal clear the distinction between `!torch.int` (which is modeling the Python `int` type) and the various types that serve as dtypes of tensors, which are a totally different type universe. Changes: - `!torch.int` type and C bindings. - Change `torch.constant.int` parser to not need the `: i64` at the end. - `m_TorchConstantInt` matcher to aid with matching constants. - BackendTypeConversion changes for `!torch.int` -> `i64` type conversion. - Refactor finalizing patterns in FinalizingBackendTypeConversionPass (they were getting very repetitive). - Mechanical rewriting of `!torch.int` to `i64` in all the tests, and `AnyTorchIntType` to `Torch_IntType` in the `.td` files.
2021-06-17 06:53:15 +08:00
# CHECK: torch.slot "i", %[[N3]] : !torch.int
Add a !torch.float type. This removes the dependence of the `torch` dialect on the low-level builtin types. Now the `torch` dialect is a standalone layer, suitable for targeting from higher-level Python abstractions without any premature lowering to primitive types.
2021-06-17 23:24:31 +08:00
# CHECK: torch.slot "f", %[[N42]] : !torch.float
Implement GlobalizeObjectGraph transformation. This required restructuring of how we model TorchScript on import. The main difference is that now we split out a `torch.class_type` that holds methods and declarations of the types of each slot. This is more consistent with TorchScript (our previous representation was "denormalized"). Recommended reading order: 1. check out the description of `torch.class_type` in `TorchOps.td` and look at `test/Dialect/Torch/ops.mlir` and `frontends/pytorch/test/module_import/` to familiarize with the new representation. - Just look at the new IR. The diff between the old names and new names is confusing. 2. check out `test/Dialect/Torch/globalize-object-graph*.mlir` and read along with the pass description in `include/npcomp/Dialect/Torch/Transforms/Passes.td` 3. Read the code in `GlobalizeObjectGraph.cpp` and miscellaneous changes in `ivalue_importer.cpp`, `TorchOps.cpp`, etc.
2021-02-18 03:28:51 +08:00
# CHECK: } : !torch.nn.Module<"[[CLASSTYPE:.*]]">
Add initial TorchScript module importer It turns out that this was easiest to structure as a general IValue importer, since torch module are just one of the possible IValue's. We import the IValue object graph in a braindead fashion into basicpy ops and a new `torch.nn_module` op that is used to model the attributes/methods of a torch::jit::Module IValue. See `Torch/ops.mlir` for an example, and also check out the .py import tests in `frontends/pytorch/test/module_import`. As part of this change, a few housekeeping tasks: - extract some helpers from graph_importer.cpp - more helpers around the C API - misc touchups
2021-01-28 08:35:44 +08:00
test_module = TestModule()
recursivescriptmodule = torch.jit.script(test_module)
# TODO: Automatically handle unpacking Python class RecursiveScriptModule into the underlying ScriptModule.
mb.import_module(recursivescriptmodule._c)
mb.module.operation.print()