This is a policy update for meta registration. **We now prefer python meta implementation over C++ meta function.** This is a flip of the previous policy, where we prefer C++ meta function over python meta function if they both exist.
Here's the meta registration process:
1. register_meta and register_decomposition will place the python meta/decomp functions into the `global_decomp_table`. However, they will NOT register them into dispatcher.
2. After global_decomp_table is populated, we will compile an `active_meta_table`. For a given op, we pick the most specific decomp function from `global_decomp_table` in the preference order of Meta > PostAutograd > PreAutograd.
3. We will unconditionally register all of them into python dispatcher. And register them into C++ dispatcher, unless it one of the following 3 cases
- 1. the op is a CompositeImplicitAutograd, and should rely on decomposed op's meta
- 2. the op is a view op, as the MetaTensor doesn't support aliased storage
- 3. the op is in the blocklist (due to UT failures, and we will burn down this list op by op)
Over the long run, we wish to implement all meta functions in python. With this PR, 321 op_overloads will have cpp meta overridden by python meta. There are still 400 op_overloads is using cpp meta. The exact list can be found here https://gist.github.com/SherlockNoMad/d20bb736178df8eebd3b054c8bb7cdc5
cc @ngimel @jansel @lezcano @fdrocha @mlazos @soumith @voznesenskym @yanboliang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87426
Approved by: https://github.com/ezyang, https://github.com/jansel
Based on @ezyang's suggestion, mode stack now has "one true mode" which is the _only_ mode that can ever be active at the C++ level. That mode's torch dispatch is just to take the top mode in the stack, reenable itself (if we aren't at the end of the mode stack), and run the top mode's torch_{dispatch|function}
This maintains that in the middle of a mode's torch dispatch, the mode itself will not be active. It changes the function the user has to call to see what the current mode is (no longer queries the C++, it's python only) but allows the user to also see the entire mode stack easily
Removes `enable_torch_dispatch_mode` and `.restore()` since neither makes sense in this new setup
### Background
Why do we want this? Well, a pretty common pattern that was coming up was that users had to do something like
```python
## PRE-PR UX
def f(mode):
with mode.restore(): # user needs to understand this restore thing?
...
with Mode() as m:
pass
f(m)
```
Many users were getting error from forgetting to call `.restore` or from forgetting to add the (tbh weird) "mode instantiation" step where they use the mode as a context manager with an empty body. Really, they wanted to treat modes like context managers and just write
```python
## FROM FEEDBACK, USER DESIRED CODE. POSSIBLE POST-PR
def f(mode):
with mode:
...
f(Mode())
```
** Technical Details **
With the old mode stack, we basically had a linked list so the mode itself could only be used once and had a fixed parent. In this new design, the mode stack is just a python list that we're pushing to and popping from. There's only one mode that's ever active at the C++ level and it runs the next mode in the Python list. The modes don't have state on them anymore
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84774
Approved by: https://github.com/ezyang, https://github.com/zou3519
Instead of calling into the Python dispatcher for EVERY dispatcher
call, we now have a two step process. First, we
getattr(op: OpOverload, dispatch_key) to "load" the handler for the
function. This can either be a conventional function (in which
case we will call it, in the same way the old Python dispatcher
worked), or it can be a DispatchKey, in which case we will directly
call that DispatchKey in C++, bypassing marshalling between Python
and C++ entirely. OpOverload.__getattr__ is carefully written so
that it will cache the
A further optimization would be to define __slots__ on OpOverload,
and ensuring that the DispatchKey strings are interned.
The resulting Python dispatcher is less flexible: after the first
lookup, the handler is cached and we won't recompute it. Furthermore,
by default, dispatches will not go into Python, and so you won't
get stack frames for the Python dispatcher by default. But we get
a huge performance improvement: on the following microbenchmark
we go from 2.5s to 1.9s.
```
import time
import torch
from functorch import make_fx
def f(x):
for i in range(1000):
x = x * x
return x
begin = time.time()
res = make_fx(f, tracing_mode="symbolic")(torch.randn(10, 20))
print(time.time()-begin)
```
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85133
Approved by: https://github.com/wconstab
Signed-off-by: Edward Z. Yang <ezyangfb.com>
From @ezyang's original PR:
There are a number of situations where we have non-backend kernels (e.g., CompositeImplicitAutograd, batching rules) which we would like to port to Python, but we have no way to integrate these ports with the overall system while using preexisting C++ registrations otherwise. This PR changes that by introducing a Python dispatcher (which can have its own kernels directly in Python), which can be interpose over ordinary C++ dispatch. The ingredients:
We introduce a new PythonDispatcher dispatch key, that has the same tenor as FuncTorchDynamicLayerFrontMode: it works by getting triggered before every other dispatch key in the dispatch key, and shunting to a Python implementation
The Python dispatcher is a per-interpreter global object that is enabled/disabled via the guard EnablePythonDispatcher/DisablePythonDispatcher. We don't make it compositional as I have no idea what a compositional version of this feature would look like. Because it is global, we don't need to memory manage it and so I use a simpler SafePyHandle (newly added) to control access to this pointer from non-Python C++. Like __torch_dispatch__, we use PyInterpreter to get to the Python interpreter to handle the dispatch.
I need to reimplement dispatch table computation logic in Python. To do this, I expose a lot more helper functions for doing computations on alias dispatch keys and similar. I also improve the pybind11 handling for DispatchKey so that you can either accept the pybind11 bound enum or a string; this simplifies our binding code. See https://github.com/pybind/pybind11/issues/483#issuecomment-1237418106 for how this works; the technique is generally useful.
I need to be able to call backend fallbacks. I do this by permitting you to call at a dispatch key which doesn't have a kernel for the operator; if the kernel doesn't exist, we check the backend fallback table instead.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84826
Approved by: https://github.com/ezyang
This allows you to directly call into the CompositeImplicitAutograd
implementation of an operator, *without* changing any aspects of the
dispatcher state. In particular, you can use this to recursively call
into a decomposition, dispatching back to your tensor subclass/mode
as desired.
Hypothetically, we should also make these available in the
decompositions dictionary, but I'm leaving this as future work as
enumerating these decompositions is annoying (as operators are lazily
registered.)
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83075
Approved by: https://github.com/albanD
Summary:
This is an attempt to fix error:
```
...
File "/mnt/xarfuse/uid-202090/bfc19816-seed-6bb89c2f-e1c0-4c48-8376-657c47aee7ea-ns-4026533122/torchvision/__init__.py", line 5, in <module>
from torchvision import datasets
File "/mnt/xarfuse/uid-202090/bfc19816-seed-6bb89c2f-e1c0-4c48-8376-657c47aee7ea-ns-4026533122/torchvision/datasets/__init__.py", line 1, in <module>
from ._optical_flow import KittiFlow, Sintel, FlyingChairs, FlyingThings3D, HD1 (2d884f2263)K
File "/mnt/xarfuse/uid-202090/bfc19816-seed-6bb89c2f-e1c0-4c48-8376-657c47aee7ea-ns-4026533122/torchvision/datasets/_optical_flow.py", line 12, in <module>
from ..io.image import _read_png_16
File "/mnt/xarfuse/uid-202090/bfc19816-seed-6bb89c2f-e1c0-4c48-8376-657c47aee7ea-ns-4026533122/torchvision/io/__init__.py", line 11, in <module>
from ._video_opt import (
File "/mnt/xarfuse/uid-202090/bfc19816-seed-6bb89c2f-e1c0-4c48-8376-657c47aee7ea-ns-4026533122/torchvision/io/_video_opt.py", line 8, in <module>
from ..extension import _load_library
File "/mnt/xarfuse/uid-202090/bfc19816-seed-6bb89c2f-e1c0-4c48-8376-657c47aee7ea-ns-4026533122/torchvision/extension.py", line 20, in <module>
torch.ops.load_library(lib_path)
File "/mnt/xarfuse/uid-202090/bfc19816-seed-6bb89c2f-e1c0-4c48-8376-657c47aee7ea-ns-4026533122/torch/_ops.py", line 250, in load_library
path = torch._utils_internal.resolve_library_path(path)
AttributeError: module 'torch' has no attribute '_utils_internal'
```
I don't completely understand the import mechanism, but I tried this in notebook:
```
# module 'torch' has no attribute '_torch_docs'
import torch._torch_docs
torch._torch_docs.common_args
# works
import torch._torch_docs as t
t.common_args
```
Figured I'd try it to see if it works, because in these files, there does exist a `import torch._utils_internal` at the top which didn't fail.
Test Plan: sc
Differential Revision: D36795248
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78597
Approved by: https://github.com/ngimel
This allows us to provide OpOverloadPacket.overloads method that
lists all of the overloads.
This isn't tested; will be exercised in the next PR.
Signed-off-by: Edward Z. Yang <ezyangfb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76814
Approved by: https://github.com/mruberry
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71443
cogwheel test inline_cvr_infer_canary_pyper_model_publish is timing out.
The convert_fx call takes > 20 mins for local and local_ro sub modules, which used to take ~ 2 mins.
Test Plan:
Fblearn flow run
* the following cmd took 1113 seconds before the diff and 5002 seconds after.
flow-cli clone-locally 320014219 --run-as-secure-group pytorch_at_scale --operators pyper_model_publish_workflow.pyper_model_publish_workflow.process_torch_package_model_files.process_non_sparse_parameters[0]
Cogwheel test
* Cogwheel test with packages in B3588 (the last good run) took 4694.48s
* Cogwheel test with packages in B3590 (the first timeout) took 13975.83s
* Cogwheel test with the following packages took 4535.04s
* all packages in B3588 except the model publish
* the model publish built with D33469839 (043e84b3d2) reversed (created D33633570)
Reviewed By: albanD, jerryzh168
Differential Revision: D33633570
fbshipit-source-id: dc5e777c48a90c551641a3f79126461f6a60449e
(cherry picked from commit 03ab65023a)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67254
Fixes https://github.com/pytorch/pytorch/issues/65997
BC breaking:
`output = torch.ops._test.leaky_relu(self=torch.tensor(-1.0))` now fails with the error `TypeError: __call__() got multiple values for argument 'self'` since we call into `OpOverloadBundle`'s `__call__` method that has `self` bound to it as its first argument.
Follow up work:
1. disallow `default` as an overload name for aten operators.
2. Add a method to obtain a list of all overloads (exclude the ones registered by JIT)
3. Add methods/properties to `OpOverload` to access more schema information (types of input and output args etc)
cc ezyang gchanan
Test Plan: Imported from OSS
Reviewed By: pbelevich
Differential Revision: D33469839
Pulled By: anjali411
fbshipit-source-id: c3fc43460f1c7c9651c64b4d46337be21c400621
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67254
Fixes https://github.com/pytorch/pytorch/issues/65997
TODO: disallow `default` as an overload name for aten operators.
BC breaking:
`output = torch.ops._test.leaky_relu(self=torch.tensor(-1.0))` now fails with the error `TypeError: __call__() got multiple values for argument 'self'` since we call into `OpOverloadBundle`'s `__call__` method that has `self` bound to it as its first argument.
cc ezyang gchanan
Test Plan: Imported from OSS
Reviewed By: albanD
Differential Revision: D33262228
Pulled By: anjali411
fbshipit-source-id: 600dbf511514ea9b41aea3e6b1bc1102dab08909
Summary:
Fixes https://github.com/pytorch/pytorch/issues/54420
When I tested on master, with the testing code, there were multiple objects on the garbage collector that cannot be printed.
Testing code:
```
import torch
import gc
import os
import sys
print(torch.__version__)
a = torch.rand(10)
print(a)
objects = gc.get_objects()
for i in range(len(objects)):
print(objects[i])
```
### 1
```
print(torch.classes)
```
Like SplitInfinity has mentioned in the GitHub issue, the solution here is to set `__file__` for `torch.classes` to something. Similar to [_ops.py](https://github.com/pytorch/pytorch/blob/master/torch/_ops.py#L69), where `__file__` is set to `_ops.py`, we could set `__file__` for torch.classes to `_classes.py`.
### 2
```
print(torch._ops.ops.quantized)
print(torch._ops.ops.atan)
```
When we try to print these two modules, it will call `_OpNamespace::__getattr__`, but the `op_name` is `__file__`. This becomes a problem when `torch._C._jit_get_operation(qualified_op_name)` [(link)](https://github.com/pytorch/pytorch/blob/master/torch/_ops.py#L60) tries to look for an actual op on the native C++ side.
Only when we get the attribute for an actual op, e.g. `print(torch._ops.ops.quantized.elu)`, the `op_name` becomes proper (e.g. `elu`).
My current solution is to return a hardcoded string (i.e. “torch.ops”) if `op_name` is `"__file__"`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62447
Reviewed By: saketh-are
Differential Revision: D30234654
Pulled By: yidawang-oss
fbshipit-source-id: de43a8f599739c749fb3307eea015cc61f1da60e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58933
**Summary**
This commit makes load_library calls no-ops inside packages run with
deploy. Libraries containing custom C++ operators and classes are statically linked in C++
and don't need to be loaded. This commit takes advantage of the fact that sys.executable is
set to torch_deploy in deploy and uses that to exit early in load_library if
the program is running inside deploy.
**Test Plan**
This commit adds a test to `generate_examples`/`test_deploy` that
packages and runs a function that calls `load_library`. The library
doesn't exist, but that's okay because the function should be a no-op
anyway.
Test Plan: Imported from OSS
Reviewed By: Lilyjjo
Differential Revision: D28687159
Pulled By: SplitInfinity
fbshipit-source-id: 4a61fc636698e44f204334e338c5ce35257e7ae2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51124
Original commit changeset: 1c7133627da2
Test Plan: Test locally with interpreter_test and on CI
Reviewed By: suo
Differential Revision: D26077905
fbshipit-source-id: fae83bf9822d79e9a9b5641bc5191a7f3fdea78d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50458
libinterpreter.so contains a frozen python distribution including
torch-python bindings.
Freezing refers to serializing bytecode of python standard library modules as
well as the torch python library and embedding them in the library code. This
library can then be dlopened multiple times in one process context, each
interpreter having its own python state and GIL. In addition, each python
environment is sealed off from the filesystem and can only import the frozen
modules included in the distribution.
This change relies on newly added frozenpython, a cpython 3.8.6 fork built for this purpose. Frozenpython provides libpython3.8-frozen.a which
contains frozen bytecode and object code for the python standard library.
Building on top of frozen python, the frozen torch-python bindings are added in
this diff, providing each embedded interpreter with a copy of the torch
bindings. Each interpreter is intended to share one instance of libtorch and
the underlying tensor libraries.
Known issues
- Autograd is not expected to work with the embedded interpreter currently, as it manages
its own python interactions and needs to coordinate with the duplicated python
states in each of the interpreters.
- Distributed and cuda stuff is disabled in libinterpreter.so build, needs to be revisited
- __file__ is not supported in the context of embedded python since there are no
files for the underlying library modules.
using __file__
- __version__ is not properly supported in the embedded torch-python, just a
workaround for now
Test Plan: tested locally and on CI with cmake and buck builds running torch::deploy interpreter_test
Reviewed By: ailzhang
Differential Revision: D25850783
fbshipit-source-id: a4656377caff25b73913daae7ae2f88bcab8fd88
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32260
This makes it so you can actually pass the custom class as an arg to ScriptFunctions
Test Plan: Imported from OSS
Differential Revision: D19424252
Pulled By: jamesr66a
fbshipit-source-id: c3530186619655781dedbea03c2ad321aaff1cb8
Summary:
I have some test code in there as well, along with a script "test_libtorch" to run it. You'll need to modify `test_libtorch` to point to where you have `pytorch` built. I currently require that `pybind11` is included as a subdirectory of the test, but added it to the `.gitignore` to make this reviewable.
Currently, something like this works:
```cpp
struct Foo {
int x, y;
Foo(): x(2), y(5){}
Foo(int x_, int y_) : x(x_), y(y_) {}
void display() {
cout<<"x: "<<x<<' '<<"y: "<<y<<endl;
}
int64_t add(int64_t z) {
return (x+y)*z;
}
};
static auto test = torch::jit::class_<Foo>("Foo")
.def(torch::jit::init<int64_t, int64_t>())
.def("display", &Foo::display)
.def("add", &Foo::add)
.def("combine", &Foo::combine);
```
with
```py
torch.jit.script
def f(x):
val = torch._C.Foo(5, 3)
val.display()
print(val.add(3))
```
results in
```
x: 5 y: 3
24
```
Current issues:
- [x] The python class created by torchscript doesn't interactly properly with the surrounding code.
```
torch.jit.script
def f(x):
val = torch._C.Foo(5, 3)
return val
```
- [x] Doesn't properly take in non-pointer classes. Can't define this function signature in cpp (We don't want to support this I believe).
```cpp
void combine(Foo x) {
```
- [x] Has some issues with memory for blobs when constructing multiple objects (fix constant propagation pass to not treat capsules as the same object).
```py
torch.jit.script
def f(x):
val = torch._C.Foo(5, 3)
val2 = torch._C.Foo(100, 0)
val.display()
print(val.add(3))
```
- [ ] Can't define multiple constructors (need to define overload string. Currently not possible since we don't support overloaded methods).
- [x] `init` is a little bit different syntax than `pybind`. `.init<...>()` instead of `.def(py::init<>())`
- [x] I couldn't figure out how to add some files into the build so they'd be copied to the `include/` directories, so I symlinked them manually.
- [ ] Currently, the conversion from Python into Torchscript doesn't work.
- [ ] Torchbind also currently requires Python/Pybind dependency. Fixing this would probably involve some kind of macro to bind into Python when possible.
- [ ] We pass back into Python by value, currently. There's no way of passing by reference.
- [x] Currently can only register one method with the same type signature. This is because we create a `static auto opRegistry`, and the function is templated on the type signature.
Somewhat blocked on https://github.com/pytorch/pytorch/pull/21177. We currently use some structures that will be refactored by his PR (namely `return_type_to_ivalue` and `ivalue_to_arg_type`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21098
Differential Revision: D16634872
Pulled By: Chillee
fbshipit-source-id: 1408bb89ea649c27d560df59e2cf9920467fe1de
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:
This is the last step in the custom operator implementation: providing a way to build from C++ and Python. For this I:
1. Created a `FindTorch.cmake` taken largely from ebetica with a CMake function to easily create simple custom op libraries
2. Created a ` torch/op.h` header for easy inclusion of necessary headers,
3. Created a test directory `pytorch/test/custom_operator` which includes the basic setup for a custom op.
1. It defines an op in `op.{h,cpp}`
2. Registers it with the JIT using `RegisterOperators`
3. Builds it into a shared library via a `CMakeLists.txt`
4. Binds it into Python using a `setup.py`. This step makes use of our C++ extension setup that we already have. No work, yey!
The pure C++ and the Python builds are separate and not coupled in any way.
zdevito soumith dzhulgakov
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10226
Differential Revision: D9296839
Pulled By: goldsborough
fbshipit-source-id: 32f74cafb6e3d86cada8dfca8136d0dfb1f197a0
