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torch-mlir/test/Dialect/Torch/GlobalizeObjectGraph/basic.mlir

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[torch-mlir earthmoving (1/N)] C/C++ code movement. This creates the `external/torch-mlir` directory as an LLVM_EXTERNAL_PROJECTS-compatible project (analogous to `iree-dialects`) and completes movement/rename of all pure MLIR C/C++ compiler code into there. The next step will be to move all the Python code / code that links/includes PyTorch C++ code (which currently lives in `frontends/pytorch`) into a subdirectory here. I call this "earthmoving" because it is mostly mechanical changes and renames. As a quick summary (we can change this down the road easily) - C++ `mlir::NPCOMP::Torch -> mlir::torch::Torch` - CAPI `npcompTorchListTypeGet -> torchMlirTorchListTypeGet` - preprocessor `#ifndef NPCOMP_ -> #ifndef TORCHMLIR_` - CMake `NPCOMPFoo -> TorchMLIRFoo` The goal of this is to create a standalone project creating a center of mass for entry into the MLIR ecosystem from PyTorch, suitable in scope for eventual inclusion/ownership in PyTorch. The idea is that `external/torch-mlir` will some day be pulled out into its own repository, and then npcomp will simply pull it in as a submodule. Layering-wise, what lives in `torch-mlir` lowers code from PyTorch (currently TorchScript, but TorchFX or pytorch/xla-style tracing are possible extensions) down to what we have been calling the "Torch backend contract" which is cleaned up IR (inlining, simplifcation, conversion to value tensors, ...) entirely in the `torch` dialect. This is the branching off point for further lowering, of which npcomp takes one opinion (outside `torch-mlir` of course!), namely the `TorchConversion` dialect/transforms which lower to IR suitable for IREE and other linalg-on-tensors based lower-level compilers. Summary of changes: - move `{include,lib,test}/Dialect/Torch` into `torch-mlir` - move relevant parts of CAPI into `torch-mlir`. - leave a few things related to the `torch-mlir` Python build commented out, which should be resolved in a subsequent change.
2021-09-10 03:24:10 +08:00
// RUN: torch-mlir-opt -torch-globalize-object-graph -split-input-file %s | FileCheck %s
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
// Basic case.
Rework how global slot initializers work. Rather than a per-global-slot initializer region, we now have one for the whole module. For example, it might look like this: ``` torch.global_slot "private" @tensor : !torch.tensor torch.global_slot "private" @list : !torch.list<tensor> torch.global_slot.module_initializer { %0 = torch.tensor.literal(dense<0.0> : tensor<f32>) : !torch.tensor %1 = torch.prim.ListConstruct %0 : (!torch.tensor) -> !torch.list<tensor> torch.initialize.global_slots [ @tensor(%0 : !torch.tensor) @list(%1 : !torch.list<tensor>) ] } ``` This new structure allows GlobalizeObjectGraph to create the initializer in a much simpler way, avoiding the need to reason about whether different slots alias each other. Reasoning about whether slots alias each other now is the responsibility of InlineGlobalSlots, which has to do a much more complicated analysis, implemented using MLIR's dataflow analysis framework. Recommended review order: - Check out the new IR constructs in the .mlir files of various passes - Op definitions (*.td) - Changes to GlobalizeObjectGraph pass. - InlineGlobalSlots pass (~total rewrite) - Misc changes: - Moving torchMlirAdjustStaticInformation for sharing with C++ code. - EraseModuleInitializer pass To make this a bit nicer, it would be good to have a `torch.module` op with an initializer region attached. That would be more invasive though. This change has highlighted certain aspects of our project layering which are worth calling out. None of our backends can handle global slots, so we enforce that there are no global slots before backend lowering. At an earlier stage in the project, we had aspirations of transparently handling mutable global state and such, but for reasons described below, that is no longer a goal. So really global slots should be seen as a progressive lowering step as part of inlining all the IValue's in the original program (GlobalizeObjectGraph is also one such step). Over time, with insights from work like IREE-JAX, it has become clear that there isn't a reliable programming model we can compile for users where we just transparently handle mutable global state (and some other things, like lists and dictionaries). There is a need for an "outer program" that orchestrates more restricted subroutines of the kind we can handle in our compile flow here. The benefit of that is that it decouples considerations like shapes, dtypes, etc. from the program constructs used in the outer program. As long as the outer program can efficiently invoke (pipelining/async/etc.) high-performance data-parallel numerical subroutines of the kind we compile in our flow here, then there is a complete programming model. This is also consistent with the direction of upstream PyTorch which is becoming more tracing-based (which inherently loses a lot of program structure, which then has to be applied back with an "outer program" orchestrating the traced subroutines).
2022-07-14 02:45:56 +08:00
// CHECK-LABEL: torch.global_slot.module_initializer {
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: %[[INT3:.*]] = torch.constant.int 3
// CHECK: %[[FLOAT4:.*]] = torch.constant.float 4.250000e+01
// CHECK: %[[TENSOR:.*]] = torch.tensor.literal(dense<1.000000e+00> : tensor<1xf32>) : !torch.tensor
// CHECK: torch.initialize.global_slots [
// CHECK: @b(%[[TRUE]] : !torch.bool)
// CHECK: @i(%[[INT3]] : !torch.int)
// CHECK: @f(%[[FLOAT4]] : !torch.float)
// CHECK: @t(%[[TENSOR]] : !torch.tensor)
// CHECK: ]
Give torch.global_slot an initializer region. This is a much simpler representation than the ad-hoc initializer function we had before. It is also less general, but given the rationale in Passes.td it seems like the right tradeoff right now. We can probably carry this representation for quite a while, and when we can't, it likely means that TorchScript has fixed their object identity bug and we probably need to just upgrade to a more general object graph modeling (more general than GlobalizeObjectGraph). In particular, we don't want to deal with defining and carrying around this initializer function concept until we need it. For example, if we want to constant-fold the global slots into uses, this is a much better representation, and it plays better with symbol-dce (the initializer function counts as a "use" of the symbol). (the alternative would have been to write a pass that converts the initializer function to this form when possible, but I realized that lots of information had been lost which made that fairly annoying -- it was all self-inflicted anyway, so best to just go to the source (GlobalizeObjectGraph) before the information is lost) Now symbol-dce works nicely (no more "training" bools) ``` pt_util ~/tmp/classifier.pt --import --exported-name forward \ | npcomp-opt -torch-globalize-object-graph -inline -symbol-dce ``` IR: https://gist.github.com/silvasean/8abe63d70d24e29d6db9170ccc8d512b
2021-02-26 07:54:51 +08:00
// CHECK: }
Rework how global slot initializers work. Rather than a per-global-slot initializer region, we now have one for the whole module. For example, it might look like this: ``` torch.global_slot "private" @tensor : !torch.tensor torch.global_slot "private" @list : !torch.list<tensor> torch.global_slot.module_initializer { %0 = torch.tensor.literal(dense<0.0> : tensor<f32>) : !torch.tensor %1 = torch.prim.ListConstruct %0 : (!torch.tensor) -> !torch.list<tensor> torch.initialize.global_slots [ @tensor(%0 : !torch.tensor) @list(%1 : !torch.list<tensor>) ] } ``` This new structure allows GlobalizeObjectGraph to create the initializer in a much simpler way, avoiding the need to reason about whether different slots alias each other. Reasoning about whether slots alias each other now is the responsibility of InlineGlobalSlots, which has to do a much more complicated analysis, implemented using MLIR's dataflow analysis framework. Recommended review order: - Check out the new IR constructs in the .mlir files of various passes - Op definitions (*.td) - Changes to GlobalizeObjectGraph pass. - InlineGlobalSlots pass (~total rewrite) - Misc changes: - Moving torchMlirAdjustStaticInformation for sharing with C++ code. - EraseModuleInitializer pass To make this a bit nicer, it would be good to have a `torch.module` op with an initializer region attached. That would be more invasive though. This change has highlighted certain aspects of our project layering which are worth calling out. None of our backends can handle global slots, so we enforce that there are no global slots before backend lowering. At an earlier stage in the project, we had aspirations of transparently handling mutable global state and such, but for reasons described below, that is no longer a goal. So really global slots should be seen as a progressive lowering step as part of inlining all the IValue's in the original program (GlobalizeObjectGraph is also one such step). Over time, with insights from work like IREE-JAX, it has become clear that there isn't a reliable programming model we can compile for users where we just transparently handle mutable global state (and some other things, like lists and dictionaries). There is a need for an "outer program" that orchestrates more restricted subroutines of the kind we can handle in our compile flow here. The benefit of that is that it decouples considerations like shapes, dtypes, etc. from the program constructs used in the outer program. As long as the outer program can efficiently invoke (pipelining/async/etc.) high-performance data-parallel numerical subroutines of the kind we compile in our flow here, then there is a complete programming model. This is also consistent with the direction of upstream PyTorch which is becoming more tracing-based (which inherently loses a lot of program structure, which then has to be applied back with an "outer program" orchestrating the traced subroutines).
2022-07-14 02:45:56 +08:00
// CHECK-LABEL: torch.global_slot @b : !torch.bool
// CHECK-LABEL: torch.global_slot @i : !torch.int
// CHECK-LABEL: torch.global_slot @f : !torch.float
// CHECK-LABEL: torch.global_slot @t : !torch.tensor
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
torch.class_type @c {
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
torch.attr "b" : !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
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
torch.attr "f" : !torch.float
Introduce `!torch.tensor` / `!torch.vtensor` types. This removes our reliance on the numpy dialect and avoids our off-label use of the builtin tnesor type for modeling unknown dtypes. The `!torch.vtensor` (`ValueTensorType`) type is a value-semantic tensor. The `!torch.tensor` (`NonValueTensorType`) type is a non-value-semantic tensor. The new types look as follows syntactically: ``` // Least-static-information, non-value-semantic tensor. !torch.tensor // Explicit form of least-static-information variant. !torch.tensor<*,unk> // Least-static-information, value-semantic tensor. !torch.vtensor // Explicit form of least-static-information variant. !torch.vtensor<*,unk> // Fixed-set of allowable element types, with first-class support for // Torch's frontend signedness semantics. !torch.tensor<*,si32> // First-class support for unknown dtypes. !torch.tensor<[?,?,?],unk> // Standard MLIR representation of `?` for unknown dimensions. !torch.tensor<[?,2,?,4],unk> // Statically shaped / dtyped example. !torch.vtensor<[1,2,3,4],f32> ``` This required fairly significant changes throughout the compiler, but overall it is a big cleanup. We now have a much clearer layering of "the Torch frontend lowering" vs "lowering to std + linalg + etc.". At the C++ level, there is `ValueTensorType`, `NonValueTensorType`. We also have a helper `BaseTensorType` (kind of like ShapedType) which interoperates with those two. Included changes: - New `torch.tensor(dense<0.0> : tensor<5xf32>) : !torch.tensor` op for creating torch tensor literals in the frontend. - Consistently use signedness for the types (except i1 which I didn't touch -- we need to sort out the situation with !basicpy.BoolType there anyway so will be attending to that soon) - Frontend can annotate whether an argument to the function has value semantics. We currently require this, as our backend contract does not currently allow us to even model the non-value-semantic case. Before, the value-semantic assumption was randomly injected in the middle of the pass pipeline. - Move ArrayToTensor (now called MaximizeValueSemantics) and RefinePublicReturn passes to torch dialect. - The TorchToStd and TorchToLinalg passes are now type conversions from `!torch.vtensor` to `tensor` and use the dialect conversion infra. The overall conversion pipeline is set up following the best practices of the "Type Conversions the Not-So-Hard Way" talk. This required introducing `torch-func-builtin-tensorize` and `torch-finalizing-builtin-tensorize` passes analogous to the upstream bufferization passes with the corresponding names (mostly just copypasta from there). - Misc Torch-level canonicalizations -- we now cleanly layer the lowering to std later in the pipeline, so we are gradually lessening our reliance on random std constant folding before we get to that point. Recommended review order: - New types in TorchTypes.td/TorchTypes.h/TorchDialect.cpp - New ops in TorchOps.td / TorchOps.cpp - Less important / more mechanical stuff - Frontend changes. - Pass changes/additions in `Torch/Transforms` and `Conversion/`
2021-05-21 08:07:18 +08:00
torch.attr "t" : !torch.tensor
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
}
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
%bool_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
%i = 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
%f = torch.constant.float 4.250000e+01
Add `torch.vtensor.literal` op. This op is much better behaved than the `torch.tensor.literal` op (which is the new name of the `torch.tensor` op). In particular `torch.tensor.literal`: - always has a maximally refined type. - always has value semantics. - can be constant folded / CSE'd. ReduceOpVariants is changed to perform the transformation from `torch.tensor.literal` to `torch.vtensor.literal` (which in general involves static information casts and copies. This new op also allowed tightening up `torch.tensor.literal` to only accept NonValueTensorType (instead of any tensor type). This new ".literal" name is more descriptive. It was getting too confusing seeing an op called just `torch.tensor` (we originally called it that because that's the name of the similar function in the Torch Python API, but it just doesn't fit here).
2021-06-17 23:52:13 +08:00
%t = torch.tensor.literal(dense<1.0> : tensor<1xf32>) : !torch.tensor
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
torch.nn_module {
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
torch.slot "b", %bool_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
torch.slot "i", %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
torch.slot "f", %f : !torch.float
Introduce `!torch.tensor` / `!torch.vtensor` types. This removes our reliance on the numpy dialect and avoids our off-label use of the builtin tnesor type for modeling unknown dtypes. The `!torch.vtensor` (`ValueTensorType`) type is a value-semantic tensor. The `!torch.tensor` (`NonValueTensorType`) type is a non-value-semantic tensor. The new types look as follows syntactically: ``` // Least-static-information, non-value-semantic tensor. !torch.tensor // Explicit form of least-static-information variant. !torch.tensor<*,unk> // Least-static-information, value-semantic tensor. !torch.vtensor // Explicit form of least-static-information variant. !torch.vtensor<*,unk> // Fixed-set of allowable element types, with first-class support for // Torch's frontend signedness semantics. !torch.tensor<*,si32> // First-class support for unknown dtypes. !torch.tensor<[?,?,?],unk> // Standard MLIR representation of `?` for unknown dimensions. !torch.tensor<[?,2,?,4],unk> // Statically shaped / dtyped example. !torch.vtensor<[1,2,3,4],f32> ``` This required fairly significant changes throughout the compiler, but overall it is a big cleanup. We now have a much clearer layering of "the Torch frontend lowering" vs "lowering to std + linalg + etc.". At the C++ level, there is `ValueTensorType`, `NonValueTensorType`. We also have a helper `BaseTensorType` (kind of like ShapedType) which interoperates with those two. Included changes: - New `torch.tensor(dense<0.0> : tensor<5xf32>) : !torch.tensor` op for creating torch tensor literals in the frontend. - Consistently use signedness for the types (except i1 which I didn't touch -- we need to sort out the situation with !basicpy.BoolType there anyway so will be attending to that soon) - Frontend can annotate whether an argument to the function has value semantics. We currently require this, as our backend contract does not currently allow us to even model the non-value-semantic case. Before, the value-semantic assumption was randomly injected in the middle of the pass pipeline. - Move ArrayToTensor (now called MaximizeValueSemantics) and RefinePublicReturn passes to torch dialect. - The TorchToStd and TorchToLinalg passes are now type conversions from `!torch.vtensor` to `tensor` and use the dialect conversion infra. The overall conversion pipeline is set up following the best practices of the "Type Conversions the Not-So-Hard Way" talk. This required introducing `torch-func-builtin-tensorize` and `torch-finalizing-builtin-tensorize` passes analogous to the upstream bufferization passes with the corresponding names (mostly just copypasta from there). - Misc Torch-level canonicalizations -- we now cleanly layer the lowering to std later in the pipeline, so we are gradually lessening our reliance on random std constant folding before we get to that point. Recommended review order: - New types in TorchTypes.td/TorchTypes.h/TorchDialect.cpp - New ops in TorchOps.td / TorchOps.cpp - Less important / more mechanical stuff - Frontend changes. - Pass changes/additions in `Torch/Transforms` and `Conversion/`
2021-05-21 08:07:18 +08:00
torch.slot "t", %t : !torch.tensor
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
} : !torch.nn.Module<"c">
Fix compilation errors from MT model With the following changes the compilation can continue until RefineTypes pass: - Add operators without ODS into `torch_ods_gen.py` - Add some new optional and list types in `TorchTypes.td` - Add some folders for aten int type comparator ops - Modify GlobalizeObjectGraph.cpp. For global slots that's not used, dont check if an aliased value is stored in more than one of global slots. This can work around a failure where the same tensor is stored in multiple "version" slots which are not used.
2021-08-11 09:28:50 +08:00
GlobalizeObjectGraph: Clean up handling of unused slots The way we did it previously still created the slot and copied the initializer even if unused.
2022-07-12 09:07:24 +08:00
func.func private @ensure_all_slots_are_used(%arg0: !torch.nn.Module<"c">) {
%0 = torch.prim.GetAttr %arg0["b"] : !torch.nn.Module<"c"> -> !torch.bool
%1 = torch.prim.GetAttr %arg0["i"] : !torch.nn.Module<"c"> -> !torch.int
%2 = torch.prim.GetAttr %arg0["f"] : !torch.nn.Module<"c"> -> !torch.float
%3 = torch.prim.GetAttr %arg0["t"] : !torch.nn.Module<"c"> -> !torch.tensor
return
Fix compilation errors from MT model With the following changes the compilation can continue until RefineTypes pass: - Add operators without ODS into `torch_ods_gen.py` - Add some new optional and list types in `TorchTypes.td` - Add some folders for aten int type comparator ops - Modify GlobalizeObjectGraph.cpp. For global slots that's not used, dont check if an aliased value is stored in more than one of global slots. This can work around a failure where the same tensor is stored in multiple "version" slots which are not used.
2021-08-11 09:28:50 +08:00
}