Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12862
This is a redo of the previous move in a way that doesn't migrate the namespace -- also will check for the windows cudnn build failure
Reviewed By: Yangqing
Differential Revision: D10459665
fbshipit-source-id: 563dec9987aa979702e6d71072ee2f4b2d969d69
Summary:
Future now is an IValue. prim::Wait now is replaced by aten::wait
This PR is built on top of #12925
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12976
Differential Revision: D10861483
Pulled By: highker
fbshipit-source-id: 9e17926a625bc502fb12335ef9ce819f25776be7
Summary:
This PR adds optional type to ATen native, autograd, JIT schema and Python Arg parser, closes#9513. It allows us to use optional default values (including None) for function signature and implementations like clamp, etc., and also let us remove the python_default_init hack.
Follow up:
remove python_default_init completely.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12582
Differential Revision: D10417423
Pulled By: wanchaol
fbshipit-source-id: 1c80f0727bb528188b47c595629e2996be269b89
Summary:
There are still a few work to be done:
- Move logging and unify AT_WARN with LOG(ERROR).
- A few header files are still being plumbed through, need cleaning.
- caffe2::EnforceNotMet aliasing is not done yet.
- need to unify the macros. See c10/util/Exception.h
This is mainly a codemod and not causing functional changes. If you find your job failing and trace back to this diff, usually it can be fixed by the following approaches:
(1) add //caffe2/c10:c10 to your dependency (or transitive dependency).
(2) change objects such as at::Error, at::Optional to the c10 namespace.
(3) change functions to the c10 namespace. Especially, caffe2::MakeString is not overridden by the unified c10::str function. Nothing else changes.
Please kindly consider not reverting this diff - it involves multiple rounds of rebasing and the fix is usually simple. Contact jiayq@ or AI Platform Dev for details.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12354
Reviewed By: orionr
Differential Revision: D10238910
Pulled By: Yangqing
fbshipit-source-id: 7794d5bf2797ab0ca6ebaccaa2f7ebbd50ff8f32
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:
This PR implements the design that we discussed. Changes:
- Added a World token IValue and type. The IValue is basically a dummy struct for now, in the future we may extend it (say, add thread-local state).
- Effectful ops explicitly declare they are mutable by having World tokens as inputs and outputs in their schema.
- Purely functional ops that use mutable values will get "fenced" and the world token will be threaded through the fences
- AnnotateEffects pass which wires up all the world tokens together.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10700
Reviewed By: eellison
Differential Revision: D9547881
Pulled By: michaelsuo
fbshipit-source-id: ebbd786c31f15bf45e2ddb0c188438ff2f5f3c88
Summary:
We generate specialized list operations for int, float, and Tensor lists so that small lists of integers like the arguments to conv do not involve tons of boxing code.
This PR adds a fallback GenericList for List types that contain any other type. It does so by adding type variables to `jit::Type`, and machinery for matching/replacing the type variables during `tryMatchSchema` and operator lookup.
It also modifies the builtin list ops to include a fallback that works on a GenericList object that simply holds IValues. This is distinguished from IValue's tuple type so that conversion to/from Python still happens losslessly.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12040
Differential Revision: D10037098
Pulled By: zdevito
fbshipit-source-id: 0c5f2864d12e7d33554bf34cc29e5fb700dde150
Summary:
As pytuple should be a constant type (since obj is constant), potential errors would occur without
this const decorator, e.g., when compiling against PyPy. Although PyPy is not supported yet, it
would still be useful if we remove this compilation issue (out of very few numbers of compilation
issues) to allow hackers playing with them.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11857
Differential Revision: D10024149
Pulled By: soumith
fbshipit-source-id: aa7e08e58f6369233a11477113351dccd3854ba8
Summary:
This is pretty important because a common situation of passing LSTM hidden states as a tuple completely trashes performance of a network.
Cleans up all our propagation/undef specialization passes, at a cost of increased complexity of `ArgumentSpec` and `GraphExecutor`. An alternative would be to simply flatten all tuple inputs to a graph ahead of time, but that might just end up being confusing in the future (you never know if you're working with a graph that can have tuple or not).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11863
Differential Revision: D9992814
Pulled By: apaszke
fbshipit-source-id: 0a565a3b23e32f8fa72c0534e07c1ce6187739fc
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
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:
In addition to documentation, this cleans up a few error message formats.
It also adds infra to find which operators are supported by the JIT automatically, which is then used in the generation of the docs.
The wording and formatting of the docs is not yet polished, but having this will allow our document writers to make faster progress.
Followup PRs will polish the docs and fix formatting issues.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11357
Differential Revision: D9721277
Pulled By: zdevito
fbshipit-source-id: 153a0d5be1efb314511bcfc0cec48643d78ea48b
Summary:
Things like torch.zeros now appear in traces rather than constants.
To continue to support our current level of ONNX export, we run
constant prop to turn these back into constants where possible before
export.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10935
Differential Revision: D9527427
Pulled By: zdevito
fbshipit-source-id: 552a8bcc01b911251dab7d7026faafdd7a3c758a
Summary:
TODO: integrate into torch.onnx.export -- separate PR
*Problem:* We have a facility to trace PyTorch operations on Python code, but there are several failure modes where the trace is not representative of the actual underlying computation:
* The tracer encountered dynamic control flow
* Some computation escaped the tracer, and appeared as a Constant tensor node in the graph
* Some stateful function was traced, e.g. someone did an optimization in Python by memoizing function outputs
*Objective*: In an ideal world, this whole process would be automated and the user can trust that the system will magically capture the intended semantics from the program. Realistically speaking, we will likely have to settle with a human-in-the-loop error reporting system, allowing for the user to identify problems and modify the source code to allow for tracing.
*Stage 1* (this PR): Output-level checking & graph diff. torch.jit.trace gains a kwarg 'check_inputs', which is a list of tuples of input arguments. We will iterate through the list and trace the function again for each set of check inputs. We'll also interpret the original trace with these inputs and compare output values and graphs, printing a diff of the graph if there is a difference.
Examples:
```
torch.jit.trace(torch.rand(3, 4), check_inputs=[(torch.rand(4, 5),)])
def foo(x):
y = torch.arange(0, x.shape[0]).float()
return x + y.unsqueeze(1)
```
```
torch.jit.TracingCheckError: Tracing failed sanity checks!
ERROR: Graphs differed across invocations!
Graph diff:
graph(%0 : Dynamic) {
- %1 : Dynamic = prim::Constant[value= 0 1 2 [ CPULongType{3} ]]()
? ^
+ %1 : Dynamic = prim::Constant[value= 0 1 2 3 [ CPULongType{4} ]]()
? +++ ^
%2 : int = prim::Constant[value=0]()
%3 : Dynamic = aten::_cast_Float(%1, %2)
%4 : int = prim::Constant[value=1]()
%5 : Dynamic = aten::unsqueeze(%3, %4)
%6 : int = prim::Constant[value=1]()
%7 : Dynamic = aten::add(%0, %5, %6)
return (%7);
}
Node diff:
- %1 : Dynamic = prim::Constant[value= 0 1 2 [ CPULongType{3} ]]()
? ^
+ %1 : Dynamic = prim::Constant[value= 0 1 2 3 [ CPULongType{4} ]]()
? +++ ^
Trace source location:
dank.py(5): foo
/Users/jamesreed/onnx-fairseq/pytorch/torch/jit/__init__.py(402): wrapper
dank.py(3): <module>
Check source location:
dank.py(5): foo
/Users/jamesreed/onnx-fairseq/pytorch/torch/jit/__init__.py(281): check_trace
/Users/jamesreed/onnx-fairseq/pytorch/torch/jit/__init__.py(408): wrapper
dank.py(3): <module>
ERROR: Tensor-valued Constant nodes differed in value across invocations. This often indicates that the tracer has encountered untraceable code.
Node:
%1 : Dynamic = prim::Constant[value= 0 1 2 [ CPULongType{3} ]]()
Source Location:
dank.py(5): foo
/Users/jamesreed/onnx-fairseq/pytorch/torch/jit/__init__.py(402): wrapper
dank.py(3): <module>
Comparison exception:
Not equal to tolerance rtol=1e-07, atol=0
(shapes (3,), (4,) mismatch)
x: array([0, 1, 2])
y: array([0, 1, 2, 3])
```
==
```
torch.jit.trace(torch.rand(3, 4), check_inputs=[(torch.rand(3, 4),)])
def foo(x):
y = x.data
return x + y
```
```
torch.jit.TracingCheckError: Tracing failed sanity checks!
ERROR: Traced function outputs do not match the Python function outputs.
ERROR: Tensor-valued Constant nodes differed in value across invocations. This often indicates that the tracer has encountered untraceable code.
Node:
%1 : Dynamic = prim::Constant[value=<Tensor>]()
Source Location:
dank.py(6): foo
/Users/jamesreed/onnx-fairseq/pytorch/torch/jit/__init__.py(402): wrapper
dank.py(3): <module>
Comparison exception:
Not equal to tolerance rtol=1e-07, atol=0
(mismatch 100.0%)
x: array([0.397137, 0.956105, 0.169478, 0.560292, 0.392568, 0.108441,
0.97645 , 0.34412 , 0.951246, 0.793061, 0.557595, 0.770245],
dtype=float32)
y: array([0.243178, 0.315964, 0.972041, 0.0215 , 0.927751, 0.457512,
0.951092, 0.97883 , 0.048688, 0.118066, 0.779345, 0.271272],
dtype=float32)
```
==
```
import torch
torch.jit.trace(torch.rand(3, 4), check_inputs=[(torch.rand(4, 4),)])
def foo(x):
for _ in range(x.size(0)):
x = torch.neg(x)
return x
```
```
torch.jit.TracingCheckError: Tracing failed sanity checks!
ERROR: Traced function outputs do not match the Python function outputs.
ERROR: Graphs differed across invocations!
Graph diff:
graph(%0 : Dynamic) {
%1 : Dynamic = aten::neg(%0)
%2 : Dynamic = aten::neg(%1)
%3 : Dynamic = aten::neg(%2)
+ %4 : Dynamic = aten::neg(%3)
- return (%3);
? ^
+ return (%4);
? ^
}
```
==
```
import torch
def foo(x):
if not hasattr(foo, 'cache'):
foo.cache = torch.neg(x)
return x + foo.cache
traced = torch.jit.trace(torch.rand(3, 4), check_inputs=[(torch.rand(3, 4),)])(foo)
```
```
torch.jit.TracingCheckError: Tracing failed sanity checks!
ERROR: Traced function outputs do not match the Python function outputs.
ERROR: Graphs differed across invocations!
Graph diff:
graph(%0 : Dynamic) {
- %1 : Dynamic = aten::neg(%0)
+ %1 : Dynamic = prim::Constant[value=<Tensor>]()
%2 : int = prim::Constant[value=1]()
%3 : Dynamic = aten::add(%0, %1, %2)
return (%3);
}
Node diff:
- %1 : Dynamic = aten::neg(%0)
+ %1 : Dynamic = prim::Constant[value=<Tensor>]()
Trace source location:
test.py(5): foo
/Users/jamesreed/onnx-fairseq/pytorch/torch/jit/__init__.py(402): wrapper
test.py(8): <module>
Check source location:
test.py(6): foo
/Users/jamesreed/onnx-fairseq/pytorch/torch/jit/__init__.py(281): check_trace
/Users/jamesreed/onnx-fairseq/pytorch/torch/jit/__init__.py(408): wrapper
test.py(8): <module>
```
The following two examples show instances where program semantics are lost in the Python -> trace transformation, and repeated invocation does not give us useful debug information. Further design in underway for catching these scenarios.
```
import torch
torch.jit.trace(torch.rand(3, 4), check_inputs=[(torch.rand(3, 4),)])
def foo(x):
for i in range(3):
x[i, :] = torch.zeros(4)
return x
```
```
torch.jit.TracingCheckError: Tracing failed sanity checks!
ERROR: Traced function outputs do not match the Python function outputs.
Exception:
Not equal to tolerance rtol=1e-07, atol=0
(mismatch 100.0%)
x: array([0.830221, 0.915481, 0.940281, 0.555241], dtype=float32)
y: array([0., 0., 0., 0.], dtype=float32)
```
==
```
import torch
torch.jit.trace(torch.rand(3, 4), check_inputs=[(torch.rand(5, 6),)])
def foo(x):
x.view(-1).add_(-x.view(-1))
return x
```
```
torch.jit.TracingCheckError: Tracing failed sanity checks!
ERROR: Traced function outputs do not match the Python function outputs.
Exception:
Not equal to tolerance rtol=1e-07, atol=0
(mismatch 100.0%)
x: array([0.734441, 0.445327, 0.640592, 0.30076 , 0.891674, 0.124771],
dtype=float32)
y: array([0., 0., 0., 0., 0., 0.], dtype=float32)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10841
Differential Revision: D9499945
Pulled By: jamesr66a
fbshipit-source-id: 1f842a32d0b0645259cc43b29700b86d99c59a45
Summary:
Please review the expects carefully to make sure there are no regressions. I tried to go over them one by one when they changed, but it's sometimes easy to miss finer details.
Summary of changes:
- Renamed `TensorType` to `CompleteTensorType`. Added a new `TensorType` which records only the scalar type, number of dimensions, and device of a value. The argument behind the rename is to encourage people to use `CompleteTensorType` less, as most passes will only have limited information available. To make transition easier `complete_type->cast<TensorType>()` works, and makes our passes work with both kinds of specialization if they don't need extra the extra detail.
- Renamed `ArgumentSpec` to `CompleteArgumentSpec`. Added a new `ArgumentSpec`, which matches argument only at the level of the new `TensorType`.
- Shape analysis can process graphs with both `CompleteTensorType` and `TensorType`.
- Fuser was a part that heavily relied on full shape information being available. Now, we simply try to fuse the largest possible graphs, and have to do run-time checks to make sure they match the code we generate. If they don't, we fall back to regular interpretation. The shape checks are implementing using an optimized method exploiting algebraic properties of shapes with broadcasting, and the relations of broadcasting with pointwise ops. A full written proof of correctness of the shape checking algorithm is included in a comment in `graph_fuser.cpp`.
zdevito ezyang mruberry ngimel csarofeen
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10844
Differential Revision: D9498705
Pulled By: apaszke
fbshipit-source-id: 0c53c2fcebd871cc2a29c260f8d012276479cc61
Summary:
Now, run `python test/onnx/test_operators.py --no-onnx`, we won't introduce any onnx python dependence. (No onnx/protobuf python packages needs to be installed)
The major changes:
- output pbtxt from C++ exporter directly, so the floating format may be slightly different. (This should be fine, since it's just to guard ONNX exporting.)
- ONNX python packages are only imported if we run the ONNX related checks. Those checks are disabled when using `--no-onnx` flag.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10151
Reviewed By: jamesr66a
Differential Revision: D9130706
Pulled By: houseroad
fbshipit-source-id: ea28cf5db8399929179698ee535137f209e9ce6f
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:
Based on top of #9763 (first 3 commits belong to that PR). The first commits from this PR are "Stop using attributes ..."
I tried to separate the changes into fairly meaningful commits. I can't split them up into smaller PRs, because everything starts working and all tests pass only after the whole sequence, but hopefully this will make reviewing somewhat easier.
Known issues/regressions/future tasks:
- `aten::lerp` and `aten::clamp` are no longer fusable
- `CreateAutodiffSubgraphs` needs a rewrite
- It is much more strict now, and will miss a lot of opportunities, especially when viewing ops are involved. Our previous approach was "ignore the assumption on shape availability in gradient formulas to determine differentiability, and hope that shape prop will be robust enough to actually deliver them before we differentiate", which obviously doesn't scale well to more complex cases. We should either work on reducing the size dependency of grad formulas (feasible e.g. for `view`/`reshape`, unfeasible for `squeeze`/`unsqueeze`), or make `CreateAutodiffSubgraphs` integrate some kind of "I could integrate this node into an AD subgraph, but will I be able to infer the shape of its input" reasoning (kind of like a limited shape prop, that doesn't infer anything, and only tells if it *could* infer something).
- It sometimes creates constant-only (or constants + one node) graphs, which is useless
- Broken `aten::add` in auto-batching, because it gained a non-tensor input. I changed the test for pointwise operations to use `aten::mul` instead, but I needed to disable the LSTM cell test. I'm not sure how scalar constants should be implemented in this case, because I don't fully understand our format. cc: ChunliF
- Graph import does some hacks to recover type of constants. This code should be removed once we'll gain the ability to export the IR along with value types.
- There's still a fair amount of dead code that can be removed. I didn't want to make this diff any bigger, and removing it is an easy task.
- Graph fuser could be improved to use signature matching (possibly using `OperatorSet`) instead of basing on node kinds.
- Manual constant propagation for the `ListConstruct` node in `torch/onnx/utils.py` should be replaced with a proper constant propagation pass (or we should ensure that the one we have handles at least this case before we remove this code).
zdevito
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9807
Reviewed By: ezyang
Differential Revision: D9004285
Pulled By: apaszke
fbshipit-source-id: fe88026a765f6b687354add034c86402362508b7
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:
I got some tensor->variable conversion exceptions from `torch/csrc/autograd/variable.h`, which used the `TORCH_ASSERTM` macros instead of `AT_CHECK`, so they didn't have backtraces. This was such a substantial loss for debugability that I decided to update the whole codebase to use the backtrace-enabled ATen macros instead of `TORCH_ASSERT` and `JIT_ASSERT`, the latter having been an alias of the former.
ezyang apaszke zdevito
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9575
Differential Revision: D8924566
Pulled By: goldsborough
fbshipit-source-id: 7a4013b13eec9dbf024cef94cf49fca72f61d441
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:
As in the title. Lets us simplify a lot of code.
Depends on #9363, so please review only the last commit.
zdevito
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9414
Reviewed By: zdevito
Differential Revision: D8836496
Pulled By: apaszke
fbshipit-source-id: 9b3c3d1f001a9dc522f8478abc005b6b86cfa3e3
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
* this removes the flag controlling whether the interpreter works on variables.
* now the interpreter _always_ works on variables
* constants in the IR are still _always_ non-variables, and an assert was added to ensure this.
* as_tensor was split into as_variable and as_tensor since it is sometimes used
to construct constants in the IR
* I tried changing the IR to also always use variables but that change was much more
cross cutting and fragile and I never got it working
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.
* 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.
* Better warnings
* Remove -Wc++14-extensions because gcc does not know it
* Warning fix in input_buffer.cpp
* Remove pedantic for torch/csrc/
* Also use Wextra and Wall for ATen
* Use check_env_flag
* Undo changes in shape_analysis.cpp
* Remove C linkage flag
* Unit test for pack_padded tracing
* Move monkeypatching stuff
* Switch symbolic
* Fix stack traces and update test
* Fixup and confirm e2e working
* lint
* Move monkeypatch back to onnx
* Address comments
* remove extraneous import
* Add gradient checking
* lint
* Address comments
* improve test case
* Namespaced symbols
- Our interned strings now have structure, "ns::symname" rather than just
"symname" before. We support efficient namespace testing for uniques
by encoding the namespace in one byte in the Symbol internal representation.
See torch/csrc/jit/interned_strings.h for a more in-depth implementation
discussion.
- All uses of ksymbol are now attr::symbol (or some appropriate namespace).
The valid namespaces are prim, attr, onnx and aten.
- Symbol is bound in Python as a qualified string "attr::symbol", EXCEPT for the
attribute setting/getting API, whose symbols must always be attr
symbols; they get special cased to assume strings are passed.
There's a little bit of naughtiness in the implementation, maybe you know
how to solve it.
- However, the g.op() convenience function assumes that you're generating
ONNX operators, unless you explicitly qualify.
- All ATen operators and nodes have built-in interned strings generated
for them, so you should never have to write a string literal ever again.
The tracing code is adjusted to use it.
- ONNX exporter now properly tests to see that all operators are in
onnx namespace before accepting the export. This is way more
robust than the previous exporter, which would be willing to
export capitalized operators which were not actually ONNX operators.
- A slight organizational change for symbolic.py; this module now ONLY
contains aten operators. In particular, the exporter for Constant
has moved into utils.py (along with Undefined, from the C++ side),
since primitive ops get "special treatment."
- The un-inplacing logic in recording is more robust, so that we don't
delete a trailing underscore from __and__. This never affected us
before because we didn't have any tests for it.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
* PyObject* <--> at::Tensor no longer unwraps variables, instead we expect end uses to always work with variable types, and we will only unwrap the variables when we optimize.
* Add torch::CPU, torch::CUDA and torch::getType
* at::CPU -> torch::CPU in extensions
Support shape propagation with control-flow
* This allows us to enable optimization in the GraphExecutor for most
script tests.
* Changes Type to always be present (non-null) on a Value, removing `hasType()`
and `typeOption()`. A new type kind 'DynamicType' now represents when
a specific type has not been determined.
* If/Loop nodes propagate shapes/types in the simple cases where types of
outputs do not change depending on where control flows. In other
cases, we propagate DynamicType to indicate we do not know what
the shape will be.
* Remove the `cond` input to the body of Loop to simplify handling in
interpreter and shape propagation.
* Bugfix for zero-dim contiguousStridesOf
