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:
We need this, for example, to properly call `_unpack` when we have a traced module in the hierarchy
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15101
Differential Revision: D13468467
Pulled By: jamesr66a
fbshipit-source-id: c2b6740b12cde6e23395d12e42d4fc2c4c7ca3f2
Summary:
This PR adds the final set of clang-tidy checks we should add for our codebase: a last set of performance-related checks. Most fixes here are around changing `auto` to `const auto&` in a few places where unnecessary copies were made, and adding `reserve()` calls before loops doing repeated `push_back()`. Also a few cases of calling `std::string::find` with a single-character string literal instead of a single char, which uses a less efficient string search algorithm meant for searching larger substrings.
ezyang apaszke
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15198
Differential Revision: D13468797
Pulled By: goldsborough
fbshipit-source-id: 2bed1ea1c7c162b7f3e0e1026f17125e88c4d5b2
Summary:
This PR fixes around 250 places in the codebase where we were making unnecessary copies of objects (some large, some small).
ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15026
Differential Revision: D13458784
Pulled By: goldsborough
fbshipit-source-id: be5148b2ce09493588d70952e6f6d6ff5ec5199b
Summary:
Documents what is supported in the script standard library.
* Adds `my_script_module._get_method('forward').schema()` method to get function schema from a `ScriptModule`
* Removes `torch.nn.functional` from the list of builtins. The only functions not supported are `nn.functional.fold` and `nn.functional.unfold`, but those currently just dispatch to their corresponding aten ops, so from a user's perspective it looks like they work.
* Allow printing of `IValue::Device` by getting its string representation
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14912
Differential Revision: D13385928
Pulled By: driazati
fbshipit-source-id: e391691b2f87dba6e13be05d4aa3ed2f004e31da
Summary:
This removes FloatToInt style names replacing it with just the destination
name (e.g. FloatToInt -> Float). This makes it more consistent with the
syntax and makes it easier to add type conversions (just add a new
prim::Int op, for instance).
None of these ops get serialized so this should not effect loading of
old models.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14947
Differential Revision: D13408409
Pulled By: zdevito
fbshipit-source-id: d773fe863f14d9de893f686832769f8cc8903a8e
Summary:
This can be use to initialize state that is not necessarily eligible for serialization/is implementation-specific. Concretely, I'm going to use this to pack the weight matrices for quantized Linear modules according to the FBGEMM APIs
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14655
Differential Revision: D13404438
Pulled By: jamesr66a
fbshipit-source-id: 2d327cef5520fdd716b5b1b29effd60a049e8a4a
Summary:
Anywhere we used #include "foo.h", we now say #include <foo.h>
Paths are adjusted to be rooted out of aten/src, torch/lib, or
the root level directory.
I modified CMakeLists.txt by hand to remove TH and THC from
the include paths.
I used the following script to do the canonicalization:
```
import subprocess
import re
import os.path
files = subprocess.check_output(['git', 'ls-files']).decode('utf-8').rstrip().split('\n')
for fn in files:
if not any(fn.endswith(suff) for suff in ['.cu', '.cpp', '.in', '.h', '.hpp', '.cu', '.cuh', '.cc']):
continue
if not any(fn.startswith(pref) for pref in ["aten/", "torch/"]):
continue
with open(fn, 'r') as f:
c = f.read()
def fmt(p):
return "#include <{}>".format(p)
def repl(m):
p = m.group(1)
if p in ["dlfcn.h", "unistd.h", "nvrtc.h", "cuda.h", "cuda_runtime.h", "cstdint", "cudnn.h", "Python.h", "cusparse.h", "cuda_runtime_api.h", "cuda_fp16.h", "cublas_v2.h", "stdint.h", "curand_kernel.h"]:
return fmt(p)
if any(p.startswith(pref) for pref in ["torch/csrc", "c10/", "ATen/", "caffe2/", "TH/", "THC/", "Eigen/", "gtest/", "zdl/", "gloo/", "onnx/", "miopen/"]):
return fmt(p)
for root in ["aten/src", "torch/lib", ""]:
for bad_root in [os.path.dirname(fn), "aten/src/TH", "aten/src/THC", "torch/csrc"]:
new_p = os.path.relpath(os.path.join(bad_root, p), root)
if not new_p.startswith("../") and (os.path.exists(os.path.join(root, new_p)) or os.path.exists(os.path.join(root, new_p + ".in"))):
return fmt(new_p)
print("ERROR: ", fn, p)
return m.group(0)
new_c = re.sub(r'#include "([^"]+)"', repl, c)
if new_c != c:
print(fn)
with open(fn, 'w') as f:
f.write(new_c)
```
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14849
Reviewed By: dzhulgakov
Differential Revision: D13363445
Pulled By: ezyang
fbshipit-source-id: 52361f878a672785f9306c9e9ab2513128092b68
Summary:
simple change to allow `print(foo.code)` to give a pretty-printed description of all the methods on a module.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14735
Differential Revision: D13317619
Pulled By: zdevito
fbshipit-source-id: dc7f7ba12ba070f2dfccf362995c2a9e0e573cb7
Summary:
To enable self.training in script modules, this PR automatically adds a buffer called 'training' if a script method requests self.training. Assignment to self.training is overloaded to assign both to the boolean property and the tensor value.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14719
Differential Revision: D13310569
Pulled By: zdevito
fbshipit-source-id: 406387bb602f8ce5794eeff37642863c75928be5
Summary:
[ note: stacked on expect files changes, will unstack once they land ]
This adds DeviceObjType (cannot use DeviceType it is already an enum)
to the type hierarchy and an isDevice/toDevice pair to IValue.
Previous hacks which used an int[] to represent Device are removed
and at::Device is used instead.
Note: the behavior or .to is only a subset of python, we need to
fix the aten op so that it accepts Option[Device] and Optional[ScalarType].
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14666
Reviewed By: suo
Differential Revision: D13290405
Pulled By: zdevito
fbshipit-source-id: 68b4381b292f5418a6a46aaa077f1c902750b134
Summary:
We align the restore logic to `torch.load`, we try to restore to the right device, and if the device is not available, an exception is raised. We allow user to remap the device through a parameter `map_location`, it can be 1) a string like 'cuda:0`, `cpu`, 2) a device, torch.device('cpu'), 3) a dict, {'cuda:1', 'cuda:0'}, and a function, and its signature looks like string map_location(tensor, saved_device_string).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14454
Reviewed By: zrphercule
Differential Revision: D13271956
Pulled By: houseroad
fbshipit-source-id: dfd6b6049b0dc07549ddeddf2dea03ac53ba6d49
Summary:
JIT world only have double, not float, so when insertConstant, we need to cast the python `float_` to double instead of float. This will fix the incorrect `math.pi` and other high precision constants value
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14417
Differential Revision: D13282975
Pulled By: wanchaol
fbshipit-source-id: 26a4c89ffc044d28598af673aebfec95153a869e
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:
To convert `max_unpool` functions to weak script, this PR adds support
for `T` as default arguments for `BroadcastingListN[T]`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14361
Differential Revision: D13192231
Pulled By: driazati
fbshipit-source-id: a25b75a0e88ba3dfa22d6a83775e9778d735e249
Summary:
Resubmitting PR #14415
The tests added for Embedding + EmbeddingBag had random numbers as input, which affected the random number generator & caused the flakey test to break.
Everything but the last two commits have already been accepted
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14509
Differential Revision: D13247917
Pulled By: eellison
fbshipit-source-id: ea6963c47f666c07687787e2fa82020cddc6aa15
Summary:
Add support for Embedding and EmbeddingBag in script. Both functions require with torch.no_grad(), which we don't have any plans to support in the near future. To work around this, I added a embedding_renorm function without derivatives.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14415
Reviewed By: wanchaol
Differential Revision: D13219647
Pulled By: eellison
fbshipit-source-id: c90706aa6fbd48686eb10f3efdb65844be7b8717
Summary:
This PR allows to overload functions based on the value of a parameter (so long as it is a constant). See max_pool1d for an example usage.
This is the first step in enabling the use of max_pool functions for the standard library that can return `Tensor` or `Tuple[Tensor, Tensor]` based on the `return_indices` flag. This will give the JIT identical results to the Python versions of the functions.
Fixes#14081
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14425
Differential Revision: D13222104
Pulled By: driazati
fbshipit-source-id: 8cb676b8b13ebcec3262234698edf4a7d7dcbbe1
Summary:
Stacked on #14176, review only the last commit.
* Print parameters to methods as self.weight rather than as extra inputs.
* Print entire set of methods out as a single string
* Update test code to test the module-at-a-time export/import
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14378
Differential Revision: D13198463
Pulled By: zdevito
fbshipit-source-id: 3fab02e8239cfd6f40d6ab6399047bd02cf0a8c8
Summary:
This PR adds a `try_outplace` option to the tracer. When `try_outplace` is true, the tracer will attempt to out-of-place ops (similar to how things are done today). When it's false, the correct in-place op is emitted.
I made `try_outplace` false by default, but flipped it to true for ONNX export utils. zdevito jamesr66a, anywhere else I should preserve the existing behavior?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14254
Reviewed By: eellison
Differential Revision: D13166691
Pulled By: suo
fbshipit-source-id: ce39fdf73ac39811c55100e567466d53108e856b
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:
This PR allows to overload functions based on the value of a parameter (so long as it is a constant). See `max_pool1d` for an example usage.
This is the first step in enabling the use of `max_pool` functions for the standard library that can return `Tensor` or `Tuple[Tensor, Tensor]` based on the `return_indices` flag. This will give the JIT identical results to the Python versions of the functions.
Depends on #14232 for `Optional[BroadcastingList[T]]`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14081
Differential Revision: D13192228
Pulled By: driazati
fbshipit-source-id: fce33c400c1fd06e59747d98507c5fdcd8d4c113
Summary:
* Add hooks to get a callback whenever a valid graph is produced in the compiler or through tracing. These hooks can be used to pretty_print and then reparse every graph our tests produce to check that the serialization function works correctly. Currently this is guarded by an environment variable since there are a few remaining failures.
* Fix printing bugs: True and False rather than 1 and 0, print 0. for floating point zero
* Change behavior of NoneType. It is now no longer a subtype of Optional but instead implicitly converts to it, returning a prim::Node with an Option[T] type for some specific T. This allows functions like `_unwrap_optional` to correctly match against a None while still deriving the right type.
* Fix a bug where empty blocks did not correctly emit "pass" in printer.
* Fix a bug where prim::Undefine sometimes cannot be printed as None because it is being used in a schema-less op. This should be fixable once Optional[T] always uses the same None object.
* Other minor printing bugs
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13959
Reviewed By: jamesr66a
Differential Revision: D13073519
Pulled By: zdevito
fbshipit-source-id: 4167a6b614f2e87b4d21823275a26be5ba4fc3dd
Summary:
This adds torch.jit.annotate for annotating the type of an intermediate.
This is Py2/3 compatible, e.g.:
```
from torch.jit import annotate
from typing import List
torch.jit.script
def foo():
a = annotate(List[int], [])
```
This is needed to output valid python programs from our IR. It removes
the need for the empty list constructors.
A future patch can add support to the C++ parser and Python 3,
via desugaring:
```
a : int = b
a = anntoate(int, b)
```
But this functionality is not required for serialization so is not added in this patch.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13752
Differential Revision: D12989885
Pulled By: zdevito
fbshipit-source-id: 161573a7352094543dc0d33a892f2a3b9103d847
Summary:
This is probably slow but it should make the traces more understandable and make debugging easier. Any suggestions for how to make it faster (i.e. make it so we don't have to traverse all of locals() and globals()) would be appreciated
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13441
Differential Revision: D12879763
Pulled By: jamesr66a
fbshipit-source-id: b84133dc2ef9ca6cfbfaf2e3f9106784cc42951e
Summary:
This is a pre-cursor diff to Python <-> C++ frontend integration -- I have a follow-up PR coming for that. This PR changes the C++ frontend module interface to replace the custom "cursor"s I introduced some time ago with `OrderedDict`. I introduced cursors at the time as a convenient way of applying functions and query operations on a modules' parameters, buffers and modules, allowing things like `module.parameters().map(my_func)`. However, I noticed that (1) this functionality is easily implement-able on top of a regular data structure and (2) more importantly, using OrderedDicts is much, much easier for Python integration. This is especially true given that ScriptModule today also uses OrderedDict. Since C++ frontend modules and ScriptModules will soon too share as many implementation details as possible, it is overall the best move to ditch the custom cursor datastructure and pervasively use OrderedDict everywhere.
For this I did:
1. Changed the C++ frontend module interface to more closely match the Python one by providing `parameters()`, `named_parameters()` and other methods Python provides. This is very important for the following diff which binds these into Python for inter-op with Python modules.
2. In lieu of the `Cursor::apply()` method I added `nn::Module::apply`. This again is one more unifying step between Python and C++, since Python modules have an apply function too.
3. Deleted all uses of Cursor.
4. Tidied and beefed up the `OrderedDict` class. In particular, I made `OrderedDict::Item` store an `std::pair` under the hood, because that is trivial to bind into Python and saved me a lot of headaches. `key` and `value` become methods instead of fields, which they should have been from the very start anyway because it allows exactly these kinds of changes, as per usual good software engineering principle of encapsulation.
5. Added many tests for the OrderedDict use in `nn::Module`.
ebetica ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13427
Differential Revision: D12894092
Pulled By: goldsborough
fbshipit-source-id: 715770c95a9643753a1db26d7f9da9a78619a15d
Summary:
This finishes a TODO to get torch.jit.script to go through the same
pathway as methods, removing the need for forward_schema and
for compileFunction.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13640
Differential Revision: D12949713
Pulled By: zdevito
fbshipit-source-id: 3d1a5f14910d97a68670a3fd416bdbfe457f621d
Summary:
Depends on #13072
Adds support for tuples as variables instead of just as literals. Before, tuples would give the error `python value of type 'tuple' cannot be used as a value`. This PR adds a flag on `SugaredValue` to determine in a value is a tuple or not.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13086
Differential Revision: D10846632
Pulled By: driazati
fbshipit-source-id: 7b5d6ae9426ca3dd476fee3f929357d7b180faa7
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:
We are beginning to use this class in a wider reaching set of use-cases. This PR refactors it so that we always access schema properties through methods. This will make adding extra information like alias information easier (i.e. we can a version of `type()` that returns the type with alias information and another version that returns a type without that information).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12967
Differential Revision: D10502674
Pulled By: zdevito
fbshipit-source-id: a88783ed8f20ab3be6460c12da95f9f940891c44
Summary:
Adds support for weak script modules created that get compiled to `ScriptModule`s once added as a submodule of a `ScriptModule`:
```python
weak_module
class Test(torch.nn.Module):
...
weak_script_method
def forward(self, x):
...
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12682
Differential Revision: D10458626
Pulled By: driazati
fbshipit-source-id: 10ae23cb83cdafc4646cee58f399e14b2e60acd4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12777
Enables JIT tests in FBCode. Changes pybind11 code to avoid mixing py::args with positinally matched arguments because old versions of PyBind11 leak memory in this case.
Reviewed By: jamesr66a
Differential Revision: D10419708
fbshipit-source-id: 74bc466001b5d363132d1af32e96841b38601827
Summary:
Where is declared as:
```
where(Tensor condition, Tensor self, Tensor other)
```
Previously the compiler assumed that self must be the first argument.
But this is not true in practice for `where` and for a few other exceptions.
This changes the compiler to take an explicit self argument which gets matched
to the `self` that appears in the schema.
Note that this requires renaming a variant of pow, which referred to
an exponent Tensor as `self` because otherwise that would cause `t^3`
to match against `t` being the exponent.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12385
Differential Revision: D10364658
Pulled By: zdevito
fbshipit-source-id: 39e030c6912dd19b4b0b9e35fcbabc167b4cc255
Summary:
... they are basically the same class and I didn't see it in the initial PR. I also got resolvers back onto std::functions by keeping the function_table logic local to defineMethodInModules.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12589
Differential Revision: D10383103
Pulled By: zdevito
fbshipit-source-id: 1b0a85eb4f112bc28256cac44446d671d803d3a2
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 is the start of weak script mode for functions
Weak scripts allow you to compile a graph from Python code at runtime by annotating with `torch.jit.weak_script` for use in the JIT without affecting eager execution. Scripts are compiled lazily on the first call in a graph to avoid long Python startup times.
apaszke zdevito ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11963
Differential Revision: D10183451
Pulled By: driazati
fbshipit-source-id: 128750994d5eb148a984f8aba4113525c3e248c8
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 replaces the use of `std::FILE` with `istream`/`ostream` for JIT serialization.
It uses this mechanism to add the possibility to serialize to/from binary buffers, in addition to files, both in `libtorch` and from Python.
`getExportImportCopy` in `test_jit.py` has been updated so that both file and buffer codepaths are exercised during tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11932
Differential Revision: D10084303
Pulled By: apaszke
fbshipit-source-id: b850801b3932922fa1dbac6fdaed5063d58bc20d
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:
This PR does a few things:
Previously test_jit.py only tested autograd on backward graphs.
This is because we borrow from test_autograd and construct graphs with a small
number of nodes. Because the number of nodes is small (typically 1-2), those graph
do not end up containing autodiff subgraphs, so autodiff never gets tested.
This PR enables autodiff testing by doing the following:
- added disableDebugAutodiffSubgraphInlining fn to graph_executor to disable
autodiff subgraph inlining.
- (implementation) added autodiffSubgraphNodeThreshold and autodiffSubgraphInlineThreshold.
These are set to their default values (2, 5) but disableDebugAutodiffSubgraphInlining()
sets both to 1, disabling subgraph inlining and allowing 1-node autodiff subgraphs.
- The relevant backward jit tests disable autodiff subgraph inlining so they
will test the autodiff versions of the operators instead of autograd whenever
an autodiff variant exists.
- We don't run the tests that do inline autodiff subgraphs anymore.
This has no impact on testing correctness because the assumption is
that autograd functions are correct and are tested in test_autograd.py
This allows the graph fuser to work better because a lot of these ops were previously not autodiff-compatible but fusible. On a more concrete example, lstm backward contains a lot of tensor-scalar operations; these autodiff formulas help its double backward pass.
Included:
- arithmetic overloads
- abs, acos, asin, atan, ceil, cos, cosh, exp, expm1, floor, fmod, frac, log, log10, log1p, log2 reciprocal, remainder, round, sin, sinh, tan, trunc, rsqrt
TestJitGenerated tests autodiff for all of the added operations.
cc apaszke zdevito
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11832
Differential Revision: D10031256
Pulled By: zou3519
fbshipit-source-id: 9daf9900a5ad187743609cd0fbbd10b15411ad93
Summary:
This changes the way module import works so that when a module
is reloaded in python it becomes a ScriptModule and not a _C.ScriptModule
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11552
Differential Revision: D9782751
Pulled By: zdevito
fbshipit-source-id: 9576850b75494b228ce3def94c0d371a4a44b11d
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
