Fixes#102375
Sequence_nr increments in the forward pass and decrements in the backward pass. Backward ops with the same sequence_nr as a forward op represent the backward implementation for the op. The long term goal is to make this information available to the profiler so users can observe which ops are fused by the inductor openai triton kernels.
Added a test for this feature **test/dynamo/test_aot_autograd.py::AotAutogradFallbackTests::test_aot_sequence_nr**. The test case uses **aot_export_module()** to create a joint fwd/bwd fx graph. Then it walks all the nodes in fx graph using fx_graph.graph.nodes. The seq_nr of each node is recorded in node.meta. During the fwd pass the seq_nr increments and it decrements during the bwd pass. This allows the user to map forward ops to their corresponding bwd ops which is useful for performance analysis.
Expected output from the test case.
SeqNr|OrigAten|SrcFn
0|aten.convolution.default|l__self___conv1
0|aten.add.Tensor|l__self___bn1
1|aten._native_batch_norm_legit_functional.default|l__self___bn1
2|aten.relu.default|l__self___relu1
3|aten.add.Tensor|add
4|aten.view.default|flatten
5|aten.t.default|l__self___fc1
6|aten.unsqueeze.default|l__self___fc1
7|aten.mm.default|l__self___fc1
8|aten.squeeze.dim|l__self___fc1
9|aten.add.Tensor|l__self___fc1
10|aten.sub.Tensor|l__self___loss_fn
11|aten.abs.default|l__self___loss_fn
12|aten.mean.default|l__self___loss_fn
12|aten.ones_like.default|
12|aten.expand.default|
12|aten.div.Scalar|
11|aten.sgn.default|
11|aten.mul.Tensor|
8|aten.unsqueeze.default|
7|aten.t.default|
7|aten.mm.default|
7|aten.t.default|
7|aten.t.default|
7|aten.mm.default|
6|aten.squeeze.dim|
5|aten.t.default|
4|aten.view.default|
2|aten.threshold_backward.default|
1|aten.native_batch_norm_backward.default|
0|aten.convolution_backward.default|
0|aten.add.Tensor|
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103129
Approved by: https://github.com/soulitzer
remove casts to `getter` in python_cpp_function.h
Summary:
These were triggering the warning `-Wcast-function-type-strict` and
breaking the build on my machine.
Test Plan: Rely on CI.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100065
Approved by: https://github.com/ezyang
Addresses: https://github.com/pytorch/pytorch/issues/83617
This PR a way to query the TLS graph task's exec_info which is a map mapping the Node to a bool indicating whether it will be executed in the current backward pass (as determined by the inputs= argument for .grad of .backward).
- this works with both custom Function nodes and normal codegened nodes
- to be able to verify whether the pyobject passed is an actual node, we now store pointers to PyTypeObjects into a set on registration.
- error out when .backward without inputs= to avoid silently returning True
Alternatives:
- not sure if it is possible to bind to Python from a raw pointer to Node. At least we wouldn't be able to use existing logic, and the Python object should only hold a weak reference to the Node.
- other solutions to the motivating issue seem to require more extensive modification to the engine
See the issue linked for an example of usage
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84773
Approved by: https://github.com/albanD
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46227
Follow up from https://github.com/pytorch/pytorch/issues/45419, in
this PR I've removed as many PyCFunction casts as I could from the codebase.
The only ones I didn't remove were the ones with `METH_VARARGS | METH_KEYWORDS`
which have 3 parameters instead of 2 and had to be casted. Example: `
{"copy_", (PyCFunction)(void(*)(void))THPStorage_(copy_), METH_VARARGS |
METH_KEYWORDS, nullptr},`
ghstack-source-id: 114632704
Test Plan: waitforbuildbot
Reviewed By: albanD
Differential Revision: D24269435
fbshipit-source-id: 025cfd43a9a2a3e59f6b2951c1a78749193d77cf
Summary:
Follow-up to gh-25483, more of the same fixes for warnings like:
```
../torch/csrc/autograd/python_variable.cpp:503:31: warning: cast between incompatible function types from ‘PyObject* (*)(THPVariable*)’ {aka ‘_object* (*)(THPVariable*)’} to ‘getter’ {aka ‘_object* (*)(_object*, void*)’} [-Wcast-function-type]
503 | {"_backward_hooks", (getter)THPVariable_get_backwards_hooks, (setter)THPVariable_set_backwards_hooks, nullptr, nullptr},
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
```
This takes the build log output for a full rebuild with GCC 9.1 from ~10,000 to ~7,000 lines.
`clang-tidy` is going to complain, no way around that - see discussion at the end of gh-25483.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26104
Differential Revision: D17396831
Pulled By: ezyang
fbshipit-source-id: d71696bfe4dbe25519e4bcb7753151c118bd39f7
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:
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:
This is needed because the JIT declares some custom autograd functions.
colesbury
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11082
Differential Revision: D9580456
Pulled By: apaszke
fbshipit-source-id: 6bf00c1188a20b2ee6ecf60e5a0099f8263ad55a
The core autograd Variable, Function, and Engine no longer depend on the
Python API. This let's us implement functions in C++. In the future, we
can also multithread engine and release the GIL for most of the
non-Python backwards.
