Summary:
This whitelists train/eval functions in script modules, and tests that nested nn.Modules still work.
This also changes the code for calling python functions from script to allow non-tensor inputs/outputs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11505
Differential Revision: D9765466
Pulled By: zdevito
fbshipit-source-id: 1177bff931324422b69e18fa0bbaa82e3c98ec69
Summary:
Operators like aten::chunk used to return a number of tensors, but
now return a list. To make it easier to do shape prop through
aten::chunk and fuse it, I've also introduced prim::ConstantChunk,
which behaves like the previous implementation (has a variable length
output list).
The downside of this PR is that the introduction of more lists to the IR causes the LSTM and MiLSTM graphs to be considered as non-differentiable by the graph executor. I verified that they are still optimize correctly, and my next patch (that changes how the specializations/differentiation works) will restore those.
zdevito
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10949
Reviewed By: zdevito
Differential Revision: D9556823
Pulled By: apaszke
fbshipit-source-id: 33e63b17fc7247cac6cfc05eb7eb9bf069b499ee
Summary:
Changes the approach for resolving builtin ops so that the following works
```
add = torch.add
script
def foo(x):
return add(x, x)
```
This handles cases when people alias torch and torch.nn.functional to
shorter names.
This works by building a table of id -> builtin name for the know builtin
ops in torch, torch.nn.functional, and for any user-defined
op created by accessing in torch.ops.foo.bar
This allows us to clean up many SugaredValue types in the compiler.
Notes:
* we now consider any attributes on python modules to be constants
(e.g. math.pi, and torch.double).
* fixes a bug where we incorrectly allowed attribute lookup on arbitrary
pyton objects. It is now restricted to modules only.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10927
Differential Revision: D9527522
Pulled By: zdevito
fbshipit-source-id: 0280422af08b4b0f48f302766d5a9c0deee47660
Summary:
* Fix the necessary pathways so that tuples and lists can be inputs to the script.
* prevent linear algebra functions from being run in shape prop because
they frequently will error out for nonsense data.
* favor schema-driven python input conversion where possible.
remaining cases where we directly create Stacks without schema are
only for debugging
* Make the error messages when calling script/trace functions more pythonic
* Simplify FlattenTuples -- now that tuples are supported we can choose to only flatten tuples when needed. This may have to be revisited pending onnx test results, but is necessary for making tuple io work.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10812
Differential Revision: D9477982
Pulled By: zdevito
fbshipit-source-id: ed06fc426e6ef6deb404602a26c435a7fc40ea0c
Summary:
When matching schema, first try to match without adding TensorToNum conversions. Then make another pass where TensorToNum conversions are allowed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10180
Differential Revision: D9438153
Pulled By: eellison
fbshipit-source-id: 80541b5abd06e9d4187e89dda751f44dab6f58c5
Summary:
This PR adds support for using custom ops in ScriptModules, the last step for our custom op strategy. You can now write
```
import torch
torch.ops.load_library('libcustom_ops.so')
class Model(torch.jit.ScriptModule):
def __init__(self):
super(Model, self).__init__()
torch.jit.script_method
def forward(self, input):
return torch.ops.custom.op(input) + 1
model = Model()
model.forward(torch.ones(5)) # Works
model.save("model.pt") # Works
model = torch.jit.load("model.pt") # Works
```
You can then load the `model.pt` in C++ and execute its `forward` method!
Missing for this was the fact that the script compiler didn't know to convert `ops.custom.op` into a `BuiltinFunction` which then emits a function call. For this I came up with the following strategy inside `torch/csrc/jit/scrip/init.cpp`:
1. When we access `torch.ops`, we return a `CustomOpValue` (subclass of `PythonValue`), whose purpose is only to return a `CustomOpNamespaceValue` (subclass of `PythonValue`) whenever something under it is accessed.
2. `CustomOpNamespaceValue` will then for each field accessed on it return a `BuiltinFunction`.
This doesn't reduce performance for any calls that are not to `torch.ops` (as opposed to inspecting every function call's name the call site, for example).
I also had to fix `BuiltinFunction` to not assume the namespace is always `aten::`.
A lot of other changes are just tidying up the Python and C++ test harness before I integrate it in CI.
zdevito dzhulgakov
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10610
Differential Revision: D9387832
Pulled By: goldsborough
fbshipit-source-id: c00f431db56c7502a66fe1f813fe78067f428ecb
Summary:
After this, all combinations of {String frontend, Python AST Frontend}{Python 3-style type annotations, MyPy-style type comments}{Script method, Script function} should properly accept type annotations.
Possible TODOs:
- Clean up the functions marked HACK
- Clean up the Subscript tree-view to better match the Python AST versions
- Can we use this for Python functions? That's the only place annotations.get_signature() is still needed
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10279
Differential Revision: D9319726
Pulled By: jamesr66a
fbshipit-source-id: b13f7d4f066b0283d4fc1421a1abb9305c3b28fa
Summary:
- Exposed get_debug_graph for ScriptModule (gets the debug graph for its
forward Method)
- Added forward/backward expect tests for lstm and milstm cells. These
are intended to prevent regressions
cc apaszke zdevito
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10506
Differential Revision: D9316590
Pulled By: zou3519
fbshipit-source-id: 3c2510d8363e9733ccbc5c7cc015cd1d028efecf
Summary:
This commit adds the ability to insert a node with inputs, using the schema to check the inputs are valid types, fill in any default values, and perform standard implicit conversions. Since it is schema based, it will discover and use the right overload.
Constructors to `NamedValue` enable it to be constructed using `IValue` constants so it is possible to use constant values in the input list as well:
```
g.insert(aten::add, {v, 3});
```
Keyword arguments are also supported:
```
g.insert(aten::add, {v}, {{"other", t}, {"scalar", 1}});
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10198
Differential Revision: D9307252
Pulled By: zdevito
fbshipit-source-id: 644620aa85047d1eae1288383a619d50fec44d9b
Summary:
Copy of #10191 because these changes didn't land with the diff.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10394
Differential Revision: D9260816
Pulled By: li-roy
fbshipit-source-id: 7dc16919cfab6221fda1d44e98c5b900cfb40558
Summary:
* Changes `insertConstant(g, val)` to `g.insertConstant(val)`.
* Moves SourceRange to its own file to enable it.
* Cleans up dead attribute code in schema matching and graph.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10177
Differential Revision: D9137789
Pulled By: zdevito
fbshipit-source-id: 8a73cfb01a576f02e7e4dce019be9c0a0002989d
Summary:
This lays out initial support for taking and returning a richer set
of types than only tensors. Floats and ints are already valid, lists are
straightforward to add, tuples need some discussion.
Based on top of #9948. Review only the last commit.
zdevito
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9969
Reviewed By: zdevito
Differential Revision: D9076973
Pulled By: apaszke
fbshipit-source-id: 5a1fe912ea6b79ab2bfd0dcce265eb05855b5ff0
Summary:
Follow up task of #9584.
Commit 1:
- change expect/cast to return shared pointers instead of raw pointer
- isSubtypeOf accept TypePtr instead. Use `x->isSubtypeOf(NumberType::get())` rather than `x->isSubtypeOf(*NumberType::get())`
Commit 2:
- to address enable_shared_from_this pitfalls, we make the constructor private and expose the factory method to make sure user can only create it using our factory method.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9786
Reviewed By: zdevito
Differential Revision: D8980441
Pulled By: wanchaol
fbshipit-source-id: e5c923fc57a701014310e77cf29985b43bb25364
Summary:
This is blocking the IR operator unification, because I need to be able to pass scalars to backward functions.
zdevito
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9763
Reviewed By: zou3519
Differential Revision: D8978457
Pulled By: apaszke
fbshipit-source-id: 570b4c3409322459cb0f2592069730a7d586ab20
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9718
This patch switches the interpreter to use IValue's primitive numbers rather than tensors for computing on integers and floats. In addition to preparing the interpreter for first-class support of other types, this cleans up the handling of primitive numbers, making it possible to just use the normal operator overloading dispatch to find the right implementation for numbers. As a result of this change, a lot of other functionality needed to be updated since it was the first time we use non-tensors in a lot of places in the code base.
Notes:
* Fixes code_template.py so that multi-line strings are indented correctly when used on a standalone line
* Cast operators (`int(x)`) now are functional. Some tests have addition conversions to integers because
we no longer allow implicit tensor -> integer conversions following the same convention as in python
* prim::ListConstruct/createList has been added to the interpreter for creating lists and this has
replaced aten::stack for integers lists
* gen_jit_dispatch.py has been refactored so that non-tensor types use operators on IValues to extract
the primitives
* IValue gains a .to<T> method that is the equivalent of tensor_as but for IValue instead of at::Tensor
* `constant_as<T>` is switched over to using IValues's `.to<T>` method, to make conversion from constant->IValue->C++ type
more consistent. This functionality combined with `toIValue(Value*)` replaces the `tensor_as` and `as_tensor` family of functions.
* conditional expressions (if, loop) and operators related to them are now computed on integers rather than tensors
* IValue gains constructors for constructing from at::Scalar and converting to it. However, IValue itself will always store
the scalars as a double or int64.
* To align with python 3 syntax, TK_INT, TK_FLOAT, and TK_BOOL have been removed from the parser, and int/float/bool are just treated as special identifiers in the compiler,
along with print. These are represented as special sugared values with a `call` method implemented. For int/float/bool this implements casting behavior.
* Dropped shared_from_this from Type/Module. They were not needed and they making debugging harder because they internally throw/catch exceptions.
* Shape propagation has been updated to support running nodes that include floating point primitive types, this required some refactoring of internal functions.
* TensorToNum and NumToTensor have actual implementations as operators now
* regster_prim_ops now contains implementations of math operators for float/int primitive types, and for mixed (prim <+> tensor) versions. This removes the need for special handling in compiler.cpp
* Primitive math is now entirely handled by letting the compiler choose the right overloads. This removes tons of special casing in the compiler.
* incorporates eellison's change to allow casting from return values. Due to the addition of primitive support, the code need slight modifications, so I just pre-merged it here.
* stack.h gains generic vararg versions of push/pop that know how to convert to/from C++ types:
```
at::Tensor a;
at::Scalar b;
pop(stack, a, b);
at::Tensor c = a + b;
push(stack, c);
```
apaszke
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9584
Reviewed By: apaszke
Differential Revision: D8910546
Pulled By: zdevito
fbshipit-source-id: 0f3e60d4d22217f196a8f606549430e43b7e7e30
Summary:
Use decorator `torch.jit.batch` to implement auto-batching (call `to_batch` pass to do IR tranformation).
- `to_batch` pass: "to_batch.h/cpp" in csrc/jit/passess to transform a graph to a new batched graph.
- Write several basic operators for BatchTensor (add, mul, sigmoid, tanh, mm, matmul, select).
- Register the operators in a lookup table `<std::string, std::shared_ptr<Graph>>`. (use the Graph to replace the original node in IR graph)
Move BatchTensor in python from torch.BatchTensor to torch.jit.BatchTensor
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9198
Reviewed By: zdevito
Differential Revision: D8744466
Pulled By: ChunliF
fbshipit-source-id: 9ea56a30f55cb870f13a2069a47cc635419763ff
Summary:
This is a series of two commits that should probably be read separately. They are stacked on top of #9018 since the second commit requires it for correctness.
Commit 1
=======
This commit is the first in a series that will clean up how we handle declaring operators and intrinsics in the JIT to make it more modular and readable. This introduces readable declarations that can be used to register operators and switches gen_jit_dispatch to generate this schema. A follow up PR will remove the dispatch keys like "add-3" and resolve ops directly based on the registered schema, further simplifying the generation process.
* Switches schema over to parsed declarations, in the future this will allow something like:
```
registry.register_intrinsic("foo(Tensor a, Tensor b) -> Tensor", [](Stack& stack) {
...
})
```
This will allow the scalable registration of intrinsics for lists, tuples, and other ops, as long as meta-data for these ops (e.g. derivatives and size propagation routines).
The declarations resemble those used by PythonArgParser but have been singificantly cleaned up to minimize the number of types that can appear in the declaration. We should strive to get the other parts of PyTorch switched over to this restricted declaration set when possible, but it is too much to do in a single PR. My hope is that eventually we will use a very similar language to describe declarations in C10, and this can serve as a guide for that.
Parsing is done using the script lexer, so it is very robust to whitespace and extensible for future types.
This removes the other way we encoded schema, and makes it easier to see what schema are registered.
Current generated declarations: https://gist.github.com/zdevito/a96a17766fb3a098d69a91ee00abaaf6
* Switches how we handle attempting to use an integer in the place of a fixed-sized int list, such as in conv (e.g. 'int[3] stride=1'). Now that we can statically distinguish between int and Tensor, we handle the expansion as an implicit conversion in the compiler. This allows us to simplify the interpreter since it no longer needs to handle the conversion itself.
* Schema declarations have been changed so that they match the type system in the IR exactly. In particular, attribute_info which was used by liftConstantAttributes has been dropped and constant attributes are lifted purely based on the type of the input. Type conversions in compiler have been simplified due to this change.
* Error highlighting in ErrorReport now only reports at most 20 lines of code, to make reading where an error occurred easier.
Commit 2
=======
This commit unifies aten_dispatch and aten_schema into a single Operator object that both contains schema and implementation information. In the future we can use this object to also contain functionality like shape prop and autodiff needed by all operators. Operators are registered globally, and dispatch logic uses the schema information to figure out which variant to use. Descriptor keys, a frequent source of inscrutable debug errors, have been removed.
* Introduce Operator, to replace TensorOp. Unlike TensorOp, we use Operator for all op implementations, including primitives that may occur in the graphs. The only exceptions are ops that are only known to the interpreter like jumps, and GraphExecutors where we need to record additional debug info.
* Adds a global registry for Operator implementations. aten_dispatch.cpp turns into register_aten_ops.cpp, which registers all the Operators for aten with the operator registry. register_prim_ops.cpp now contains the implementations for primitive operators that used to be in the interpreter. This means that it is now safe to use `getOperation(node)` to lookup the true interpreter function for the node, which will simplify const-propagation passes.
* Remove addInterpreterOpHandler in favor of global operator registry.
* Instead of descriptors, we match Node arguments directly against FunctionSchema describing expected inputs in `matchSchema`. `matchSchema` knows how parse both attributes and positional inputs from a node and match it to the appropriate registered operator. Debug error messages when we try to run an invalid operator are significantly improved: they now automatically display the schema for the op with the same name that are registered.
* Merge aten_schema into regsiter_aten_ops. Each Operator takes a string schema which is parsed to determine when to dispatch to that op.
* Cleans up gen_jit_dispatch.py now that we do not need to write out descriptors. In particular, skip_scalar_overloads can be removed since Richard's code sorts declarations to put Tensor, Tensor declarations first.
* remove matchSchemaAndLiftConstantAttributes and use emitBuiltinCall instead to remove code duplication
* refactor stack manipulation functions into a separate header file.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/8885
Reviewed By: jamesr66a
Differential Revision: D8751048
Pulled By: zdevito
fbshipit-source-id: 312aabfbf88307c5f6ab947b6caf691468b94557
* Created TensorOptions
Storing the type in TensorOptions to solve the Variable problem
Created convenience creation functions for TensorOptions and added tests
Converted zeros to TensorOptions
Converted rand to TensorOptions
Fix codegen for TensorOptions and multiple arguments
Put TensorOptions convenience functions into torch namespace too
All factory functions except *_like support TensorOptions
Integrated with recent JIT changes
Support *_like functions
Fix in place modification
Some cleanups and fixes
Support sparse_coo_tensor
Fix bug in Type.cpp
Fix .empty calls in C++ API
Fix bug in Type.cpp
Trying to fix device placement
Make AutoGPU CPU compatible
Remove some auto_gpu.h uses
Fixing some headers
Fix some remaining CUDA/AutoGPU issues
Fix some AutoGPU uses
Fixes to dispatch_tensor_conversion
Reset version of new variables to zero
Implemented parsing device strings
Random fixes to tests
Self review cleanups
flake8
Undo changes to variable.{h,cpp} because they fail on gcc7.2
Add [cuda] tag to tensor_options_cuda.cpp
Move AutoGPU::set_index_from into .cpp file because Windows is stupid and sucks
Fix linker error in AutoGPU.cpp
Fix bad merge conflict in native_functions.yaml
Fixed caffe2/contrib/aten
Fix new window functions added to TensorFactories.cpp
* Removed torch::TensorOptions
Added code to generate wrapper functions for factory methods
Add implicit constructor from Backend to TensorOptions
Remove Var() from C++ API and use torch:: functions
Use torch:: functions more subtly in C++ API
Make AutoGPU::set_device more exception safe
Check status directly in DynamicCUDAHooksInterface
Rename AutoGPU to DeviceGuard
Removed set_requires_grad from python_variables.h and warn appropriately in Variable::set_requires_grad
remove python_default_init: self.type()
Add back original factory functions, but with deprecation warnings
Disable DeviceGuard for a couple functions in ATen
Remove print statement
Fix DeviceGuard construction from undefined tensor
Fixing CUDA device compiler issues
Moved as many methods as possible into header files
Dont generate python functions for deprecated factories
Remove merge conflict artefact
Fix tensor_options_cuda.cpp
Fix set_requires_grad not being checked
Fix tensor_new.h
TEMPORARILY put some methods in .cpp files to see if it solves issues on windows and mac
Fix bug in DeviceGuard.h
Missing includes
TEMPORARILY moving a few more methods into .cpp to see if it fixes windows
Fixing linker errors
* Fix up SummaryOps to use new factories
Undo device agnostic behavior of DeviceGuard
Use -1 instead of optional for default device index
Also move DeviceGuard methods into header
Fixes around device index after optional -> int32_t switch
Fix use of DeviceGuard in new_with_tensor_copy
Fix tensor_options.cpp
* Fix Type::copy(
* Remove test_non_float_params from ONNX tests
* Set requires_grad=False in ONNX tests that use ints
* Put layout/dtype/device on Tensor
* Post merge fixes
* Change behavior of DeviceGuard to match AutoGPU
* Fix C++ API integration tests
* Fix flip functions
* docstring support for @script and @script_method
* make it python2 compatible
* improve according to review
* improve build_stmts
* use filter instead of list comprehension
* improve the way wrap is handled for script_method
* stash the original method instead
* allow dynamic attr for ScriptMethod and GraphExecutor
* a bit comment on build_Expr
* remove _build_wrap
* a bit improve on comments
* rename to __original_methods
* should be _original_methods
* [script] Add support for torch.zeros, torch.ones, etc.
* modifies gen_jit_dispatch to creating bindings for functions that do
not take tensor arguments, but do have an initial type argument
* adds tensor attributes to these functions for device, layout, and
dtype specification
* extends the list of valid compiler constants to include device, layout,
and dtype.
* allows functions with Generators, but only using the default generator
Known limitations:
* when using `torch.float`, we convert it to a scalar tensor and make
no checks that it is actually used only in a dtype specification.
This is similar to how we handle Python numbers, creating some situations
where the script is more permissive. Fixing this requires much more
significant changes to the IR, so is lower priority for now.
* devices specified using string literals e.g. 'cuda:1' do not work,
since we do not support string literals in general.
* Implement nn.Sequential that can be inlined into script modules
* fix bugs
* add comment
* add _ConstSequential class
* add script_method for forward in ConstSequential
* fix build bug
* refactor
Improve script builtin checking using schema
* This add aten_schema.h which provides a barebones amount of type and
argument information about each builtin operator
* emitBuiltinCall is updated to use this information rather than
aten_dispatch to ensure the operator is correct.
* handling of keyword and position arguments now matches python behavior
* There is no longer a requirement that kwargs be constant or that the
attributes of an op must be entirely constant or non-constant
* compiler now constructs a non-attributed version of the op first and
then turns it into the constant-attribute version if all attributes
are constants.
* default arguments for builtins now work
* SugaredValue::call and similar functions now have SourceRange information
for their arguments so that error reporting is more accurate
Notes:
* This does not try to merge the builtin checking with python arg parser.
Given that we will eventually have C10 schema which will replace aten_schema,
we will eventually have a C++ description of the schema and working of that
description directly will be the easiest form to understand.
* python function calls and script method calls do not support keyword arguments yet.
When we add this support we should refactor the handling in tryEmitSchema
that resolves keywords into a common function.
* default arguments work
* keyword arguments to builtins work (still need to extend to calling python and other script methods)
* much better error reporting for incorrect builtins
Lift any constants to attributes on nodes when possible
* Schema is usable internally in the compiler as
the function signatures of script functions as well as for builtin
operators.
* Adds a List[T] class to better represent the arguments to cat/stack
as a type rather than with custom checking.
* Support kwargs for calls of script methods
A future commit will be needed to add support for:
* calls to script _functions_ which are currently are GraphExecutors without schema info.
* kwargs to python functions, which will require refactoring python op
* Allow `__constant__` values in a ScriptModule to be used as attributes for builtin functions
* Fix bugs in @script loops
1. while loops run shape propagation multiple times until the shapes have converged.
There were two bugs here. (a) First the 'changed' condition was not checking if it actually
changed the output, and instead would mark changed = true if the two inputs were different.
This incorrect because the output of the block and the input of the block may always have different shapes.
Now it actually checks if it is about to change the output entry that it is writing to.
(b) expand nodes were being inserted into the graph even inside the while loop body. However, if
we iteratively discover that the input shape to one of these expands is actual dynamic, then
it was incorrect to insert the expand in the first place. This changes it so that we only insert expands
after we have converged on the shapes.
2. the way deleteExtraInputs removed loop-carried dependencies was unsafe because it would lookup
Value* elements in the loop body's environment that were previously invalidated when deleteExtraInputs
remove another input to the loop. This changes the way deleteExtraInputs works so that it never has to
read a value out of the loop body's environment to avoid using the invalidated pointers.
The long-term fix is to remove the handling-creating pathways and
remove all the modes from PythonOp making it into an op that simply
calls a PyObject. Right now ONNX expects PythonOp to hold a
nn.Function, not a generic callable, so completely removing the legacy
pathway will also require changes to how ONNX symbolics are found.
* [jit][script] Fix a bug combining sizes/unsized tensors
This add an isSubtypeOf method to reflect that sized tensors are a subtype
of Dynamic[Tensors]. It updates the typechecking code to reflect this
relationship.
* Add index_select to shape prop
* Support list and tuple literals: Adds support for [a, b], (a, b) and "a, "
* Allow non-tensors to reach emitBuiltinCall, each SugaredValue::call
is now responsible for checking the types of its inputs.
Add support for calling cat with a tuple to emitBuiltinOp
This modifies the registration process so that all script methods
in a ScriptModule are defined at once.
Method gains a `method_creator` callback that gets invoked when the
method is first called to define it if it has not already been defined.
Recursive cycles in this `method_creator` are checked.
This approach was chosen over first creating all the graphs and then
inlining the call sites because it will combine better with type
propagation for non-tensor types like tuples. e.g.
```
a = foo(b)
return bar(*a)
```
* Allow tuples to be re-assigned
This commit improves our support of tuples by making them more first-class.
In particular, it allows tuples to be re-assigned across loops and ifs.
It does this by making them first-class values in the Graph IR, and then
removing the tuples in a LowerTuples pass.
An alternative approach would have added more support for desugaring tuples
in the Environment object as they were emitted. Instead,
the current approach was chosen anticipating a future when tuples are
fully supported (including the interpreter). In that future, the current
code can be completly reused with the LowerTuples pass just becoming
a optimization that removes unneeded tuple allocations.
* Fixes to the way script handles multiple values, and other minor fixes.
This commit improves our handling of operators that return multiple values.
Builtins are now checked so that they return the right number of values,
and support for TupleValue is extended to all things that can return
multiple values.
This resolves issues where the compiler accepted things like:
a, b = c + c
This would cause the interpreter to crash. Now each operator knows
how many results it will produce and can check it against the number
of requested inputs.
Notes:
* Allow True/False literals in constant expressions
* make handling of keyword constants more consistent to support True/False
* make parsing constants match the way we construct constants from python
* improve the error messages when accessing bad graph attributes.
* switch findTensorOp to return an optional.
* check that attribute types are correct in findTensorOp
* Check the correct number of outputs for builtins
This also changes emitExpr to return a single SugaredValue
Rather than possibly returning multiple values, emitExpr now
always returns a single value, which _might_ be a tuple. This approach
more closely follows python making the code easier to follow.
Checks for returning the right number of values are now located in
the assignment operator, and occur when unpacking the tuple.
We still pass `n_binders` to function calls so that calls into python
know how many values they should return.
* Something that works
* Tuple sugared value
* Works with commenting out input size check
* support string frontend
* Initial starred assignment
* Fix parser
* Fixup tests
* clang-format
* fix rebase error
* lint
* move star assign test to string frontend to make py2 happy
* Py2 fix: parse starargs from Call node
* Address some comments
* Fixup merge
* Remove overloaded unary operators
* Bugfix and test case
* Address a few more comments
* asValues -> asTuple
* Remove unrolledFor stuff
* Fixup getValues
* Pass CallsiteDescriptor struct and have different behavior for different call types
* Address comments and lint
* some type checks
* Address comments
* lint
* Fix mistake
Like `__slots__` the `__constants__` property changes the set/getattr behavior of a script module for the keys listed so they behave as constants.
This enables script methods to use them in way that are otherwise not allowed.
* Python numbers/bools can be inlined as constants in script code.
* List of numbers can be iterated over using for loops
* nn.ModuleLists can be used in for loops as well, unrolling their content.
Script functions can now have no return statements, empty
return statements, or return one or more values.
Additionally fix the lexer to always emit TK_NEWLINE before
TK_DEDENT, which simplifies the parser.
* allow calls to non-script methods, allow python non-script attributes in methods
* add test to make sure submodules are not reassigned
* Test that we can change python attributes
* Have ScriptModule inherit from Module
This is accomplished by created replacement _parameters, _buffers,
and _modules which implement the OrderedDict APIs but which
actually get/set their members inside script::Module
* Merge TracedModule with ScriptModule
* Move logic of attribute handling into Python bindings rather than
make script::Module handle it. This was redundant with nn.Module,
which already handles attribute.
* Make TracedModule a subclass of ScriptModule
* Move handling of attribute kind logic into bindings.
* Allow ScriptModule to contain non-script module submodules.
Add script::Module C++ class to represent script modules
switch AST -> IR conversion to work on Modules/Methods rather than raw graphs
function-only AST -> IR conversion is just a simplified case where there is
only one module with a single method and no parameters.
introduce SugaredValue in compiler.h to represent values in scope in a script
function that are not first-class and that get desugared. This is used to
represent the module's self parameter, as well as python function calls,
and method calls on tensor
provide a Python ScriptModule that provides a nice API on top of script::Module
allowing for the definition of script modules with methods, parameters,
and submodules
Not in this PR but intended for the future:
ScriptModule actually subclasses nn.Module, with most methods implemented
Unification of tracedmodule and script module functionality into one container class.
Detailed changelog:
* Switch compiler over to using Module, but don't
use them yet.
* Remove intermediate attribute encoding in compiler
* Create SugaredValue object to handle resolution
of compiled module.
* switch to_ir to modules, implement Select
* hacky python wrappers
* Private ScriptModule
* Add `define` to script module
* Attributes use TK_LIST_LITERAL
this anticipates adding a real list literal expression to the language.
* Add a metaclass to make sure script stubs are registered
* Add a test
* Doc createResolutionCallback
* Docs and minor editing
* Address PR comments
* Document
* Fix unicode issue
