Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25059
This fixes the cases where a type annotated with optional cannot
be conditionally assigned to none:
```
x : Optional[int] = 4
if ...:
x = None
```
Test Plan: Imported from OSS
Differential Revision: D16975166
Pulled By: zdevito
fbshipit-source-id: 5a7a81224d08b9447e1f4d957fcd882091e02f32
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24989
This fixes the cases where a type annotated with optional cannot
be conditionally assigned to none:
```
x : Optional[int] = 4
if ...:
x = None
```
Test Plan: Imported from OSS
Differential Revision: D16949314
Pulled By: zdevito
fbshipit-source-id: 7f63d88b30a3f5b024c2a539aa74967c9202af00
Summary:
Canonicalize the ordering of outputs of if and loop nodes based on their first usage. Previously we were able to canonicalize output order by sorting on variable name, but this breaks down with outputs added in an early return pass.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20015
Differential Revision: D15266066
Pulled By: eellison
fbshipit-source-id: ba5340c068a68b1ffc73f056db194b92d3274dc4
Summary:
Fixes#16591
This uses uniqueBaseName so that parameters do not end up with suffixes. It changes next_id to be per-base-name rather than global to fix jittering issues when re-importing a re-numbered graph.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16750
Differential Revision: D13960282
Pulled By: zdevito
fbshipit-source-id: 2156f581d9b95d77bf1f1252074e800b19116555
Summary:
This PR changes Method (just Method not all graphs) to always have a single
return argument.
This is part 1 in a set of changes that will enable us to have better handling if early return statements.
The simplification that this change provides greatly reduces the work for the next step.
This change makes it so that Method and Python handle multiple returns in the same way:
* 0 - None
* 1 - <single value>
* many - Tuple[...]
The result is that a lot of special-case handling in compiler.cpp and its
bindings can be removed. It also fixes several bugs in return handling,
including one where return values were not always checked against their
attributed values.
Notes:
* inferTypeFrom is renamed to be more accurate and discourage use.
* This has uncovered some bugs in other components, which are noted in
the diff.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15289
Differential Revision: D13481649
Pulled By: zdevito
fbshipit-source-id: 0e2242a40bb28cca2d0e8be48bede96195e4858c
Summary:
Stacked on https://github.com/pytorch/pytorch/pull/14378, only look at the last commit.
This changes the way methods are defined in TorchScript archives to use
PythonPrint rather than ONNX protobufs.
It also updates torch.proto to directly document the tensor data
structure actually being serialized.
Notes:
* because PythonPrint prints all the methods at once per module, this
removes MethodDef in favor of a single torchscript_area and a separate
caffe2_graphs entry. Note that NetDef's already have method names,
so there is no need or a separate method name entry.
* This switches cpp/pickle area to RecordRef (references to a file in
the container format) since it is possible the data in these arenas
may be large and not suited to json ouput.
* Removes 'annotations' -- annotations should be re-added on the first
commit that actually has a practical use for them. In the current state
it is unlikely they are representing the right information.
* Some expect files have changed because PythonPrint is preserving more
debug name information for parameter names.
* MethodEncoder (the ONNX output format) has been deleted. There is still
some cleanup possible combining EncoderBase and GraphEncode now that there
is only a single pathway using EncoderBase.
* This incorporates the changes from #14397
to define TensorDef
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14400
Reviewed By: suo
Differential Revision: D13231800
Pulled By: zdevito
fbshipit-source-id: af5c1152d0bd6bca8b06c4703f59b161bb19f571
Summary:
[Stacked commit, only review the last commit]
This PR adds support for printing default values in python printing as well as the logic
for parsing default values back in using the parser. For simplicity, this PR simply
creates a subgraph of the constant expressions and then runs that graph to generate the defaults.
A more lightweight approach should be possible later, but would require more machinery.
To make reading code in the printer easier, this also add ir_views.h.
Similar to tree_views.h these classes can provide views of some commonly used IR nodes
that have complicated structure and common operations on that structure.
Currently it has only read-only views for prim::If and prim::Loop,
but we should eventually add helpers to manipulate If/Loop nodes as well.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14176
Differential Revision: D13198455
Pulled By: zdevito
fbshipit-source-id: dc99ab9692804ccaedb60a55040c0b89ac7a6a6d
Summary:
export - print a method with python_print
import - import a method with import_method
We want to ensure:
export(g) == export(import(export(g)))
That is after after exporting/importing once, the graph will stay exactly
the same. This is less strict that g == import(export(g)) which would
require us to maintain a lot more information about the structure of the
IR and about the names of debug symbols.
This PR addresses this with the following fixes:
* print out double-precision numbers with high enough precision such
that they always parse in the same way
* when creating loop-carried dependencies, sort them
by variable name, ensuring a consistent order
* parse nan correctly
* DCE: remove unused outputs of if statements, and loop-carried dependencies
in loops that are dead both after the loop and inside the body of the
loop.
* Do not set uniqueName for variables whose names are _[0-9]+, these
are probably rare in user code, and we need a way to communicate
that we do not care about a variable name when re-parsing the graph.
Otherwise temporary variable names will jitter around.
* Expand the definition of a constant in printing code to None,
and family.
* Allow re-treeing to work as long as the only thing in its way is a
constant node. These do not have side effects but are sometimes
inserted in a different order when tracing compared to how we print them.
* Print all constant nodes out first in the order in which they are used_val
(or, if they are inlined, ensure they get assigned CONSTANT.cX number
in a consistent order). Cleanup tuples (this is done in the compiler,
but not in the tracer, leading to some tuple indexing jitter if not
done).
* use strtod_l, not std::stod which can throw exceptions
Other:
* Add REL_WITH_DEB_INFO to setup.py. It already existed for the
cmake files. Threading it into setup.py allows us to turn on
debug symbols with optimization everywhere.
* enable round trip testing for all generated graphs. This only adds
~6 seconds to total build time but tests printing for every graph.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14064
Differential Revision: D13094637
Pulled By: zdevito
fbshipit-source-id: 0a1c6912194d965f15d6b0c6cf838ccc551f161d
Summary:
* Correctly adds annotate when needed for lists
* Parser/Emitter handles octal escapes so we do not fail for some strings.
* more complete keyword list in pretty printer
* floating point numbers are always printed with a decimal to ensure
we never mistake them in parsing
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13879
Differential Revision: D13037860
Pulled By: zdevito
fbshipit-source-id: f09ab174fc33402a429b21a5bfaf72e15c802cad
Summary:
Get pretty printer ready for use as a serialization format
This PR adds a bunch of functionality to the pretty printer (now called python_printer to reflect
the fact that it will be used to output valid python source). The idea is to get the printer
ready for use as serialization format. This PR does not have tests beyond what the pretty
printer already had. PRs stacked on this one will do round-trip export/import to test this functionality more robustly.
Notes:
* PythonPrinter is an evolution of the original pretty printer. However, much of it has changed so it is best just to
read it as a new implementation. Trying to correlate it to the original implementation is probably not much help.
* The printer tries to get reasonably close to how the original function was likely written, such as
writing expressions rather than making intermediates when possible. We may decide to turn this off
for the actual serialization, but it is useful for pretty printing.
* tensor field access was changed so that prim::device and family have schema
* fixed a bug in the compiler where setUniqueName gets called even when a value already has one.
this sometimes assigned really poor names to graph inputs
* Graph::insert gains an optional range argument to make range-preserving inserts easier.
* prim:: ops that can have schema now have schema. This is because when we parse them back in,
we will need the schema to correctly set their output types.
* there is code in the python printer to complain if you try to add a prim op and do not update the printer.
* BuiltinModule is generalized to take an operator namespace and a version number for work in future commits.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13616
Reviewed By: goldsborough
Differential Revision: D13008252
Pulled By: zdevito
fbshipit-source-id: 32b33bc6410d6ca1c6f02bd6e050f8d5eea32083
Summary:
This PR changes the compiler to correctly emit in-place operators for augmented assignments (`+=` and friends).
- To better match the Python AST structure, add an `AugAssign` tree view and make `Assign` apply only to `=` assignments.
- Emit those `AugAssign` exprs in the compiler, dispatching to in-place aten ops for tensors and lowering to simple assignments for scalar types.
- In order to preserve (suspect) ONNX export semantics, add a pass to lower the in-place operators to out-of-place operators.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13364
Differential Revision: D12899734
Pulled By: suo
fbshipit-source-id: bec83be0062cb0235eb129aed78d6110a9e2c146
Summary:
* Replaces `prim::PythonOp` with the name of the function being called
* Delays printing values used in `prim::Return` nodes until the return
node itself if that is the only place the value is used to remove some
useless assigns
zdevito apaszke ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12179
Differential Revision: D10132661
Pulled By: driazati
fbshipit-source-id: cbc4ac34137ed5872049082e25d19eb1ebc71208
Summary:
This PR adds a bool type to `IValue` and puts it into place.
* changes conds for `prim::If` and `prim::Loop` to use `bool` type
* changes operators that take `bool`s to match their native ops
* fixes ambiguous `aten` ops `aten::std` and `aten::var`
* fixes tests in `test_jit.py TestJitGenerated`
```
'test_std_dim',
'test_std_dim_1d',
'test_std_dim_1d_neg0',
'test_std_dim_neg0',
'test_var_dim',
'test_var_dim_1d',
'test_var_dim_1d_neg0',
'test_var_dim_neg0'
```
* adds `prim::BoolToTensor` and `prim::TensorToBool`
apaszke zdevito
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11834
Differential Revision: D9928570
Pulled By: driazati
fbshipit-source-id: 373c53df2f1a8ffa9e33d9a517002fbeef25f3eb
Summary:
Adds some pretty-printing capability to the IR graph to make debugging easier/more human readable, see `torch/csrc/jit/test_jit.cpp:925` and onwards for example outputs. Results aren't perfect yet but it's a start.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10319
Reviewed By: zdevito
Differential Revision: D9558402
Pulled By: driazati
fbshipit-source-id: 1d61c02818daa4c9bdca36d1477d1734cfc7d043
