Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65345
FooType::get() can return a const reference. Inconveniently, converting shared_ptr<FooType> to shared_ptr<Type> requires a copy & refcount bump, so to properly take advantage of this in unshapedType() we need to take a const Type& in isSubtypeOf(), which is good practice anyway -- don't require a shared_ptr if you don't need to take ownership.
ghstack-source-id: 140044165
Test Plan:
CI
perf says c10::unshapedType time decreased from 2.8% to 2.2% during static runtime startup, though I expect this to be generally beneficial.
Reviewed By: hlu1
Differential Revision: D31027361
fbshipit-source-id: 676feb81db9f74ad7b8651d8774f4ecb4cfa6ab8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62794
This pr updates jit serialization to support pickling Sparse COO tensors.
This pr updates message.cpp to support Sparse COO tensors.
A bug was filed a few years ago https://github.com/pytorch/pytorch/issues/30807.
I tested the fix by adding sparse tensor tests to rpc_test.py and dist_autograd_test.py.
cc pietern mrshenli pritamdamania87 zhaojuanmao satgera rohan-varma gqchen aazzolini osalpekar jiayisuse agolynski SciPioneer H-Huang mrzzd cbalioglu gcramer23 gmagogsfm
Test Plan: Imported from OSS
Reviewed By: soulitzer
Differential Revision: D30608848
Pulled By: gcramer23
fbshipit-source-id: 629ba8e4a3d8365875a709c9b87447c7a71204fb
Summary:
## Note:
**This change will include the feature, but the feature is not on. It will be enabled and bytecode version will be bumped in D27844651 (8c04593c0a).**
Jit will generate constant tensor, and it locates in the constant folder (can find them after unzip model.ptl). Bytecode generated by lite interpreter also includes constant tensor, which are almost the same with the constant tensor value from jit. This pr will let lite interpreter reuses the constant tensor from jit, instead of reproducing the similar tensor values. The reading and writing session will be as following.
More details and background can found in [Lite Interpreter Model Size Issue](https://fb.quip.com/OSidAcjhL9LS).
Data size comparison can be found in [Model size analysis](https://fb.quip.com/oEm6A4bhbo06)
### Write
1. In `export_module.cpp`, store all constant tensor value from jit in an `unordered_map constants_from_jit`, where the tensor value use tensor string as a hash. constants_from_jit is a map: (tensor) => (archive_name, index). When writing bytecode archive `writeByteCode()`, the map `constants_from_jit` will also be passed all the way to it's pickler.
2. In `pickler.cpp`, a new map tensors_archive_table_ is added. It is also a map: (tensor) => (archive_name, index). The corresponding function to update the map is `updateTensorsArchiveTable`. When pushing the storage of a tensor, if the tensor exists in `tensors_archive_table_`, the root key will be `{archive_name}/{index}`, instead of `{index}`. For example, the tensor
```
torch._utils._rebuild_tensor_v2(pers.obj(('storage', torch.FloatStorage, '0', 'cpu', 90944),),
0,
(1, 116, 28, 28),
(90944, 784, 28, 1),
False,
collections.OrderedDict()),
```
will be like following instead
```
torch._utils._rebuild_tensor_v2(pers.obj(('storage', torch.FloatStorage, 'constants/0', 'cpu', 90944),),
0,
(1, 116, 28, 28),
(90944, 784, 28, 1),
False,
collections.OrderedDict()),
```
**Note**: Only tensors in bytecode archive will be different. The tensors in other archive remains the same, because `updateTensorsArchiveTable()` is only called when `use_tensors_archive_table_` is `true`, and `tensors_archive_table_` is only set as `true` when `bytecode_version` is a valid number.
### Read
1. In `import.cpp`, the function `read_record` passed to Unpickler is updated. The argument of `read_record` is the root key. In version 4, the root key will just be index, and `archive_name_plus_slash` + `name` will be used to get the tensor. With this change (version 5+), `read_record` will check if slash exists in the argument `name`. If it does, it means the argument is `archive_name/index`, and it can be used to get tensor directly.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56002
ghstack-source-id: 128498244
Test Plan:
### Verify the new model generated from this pr can reuse constant table and the numerical result is the same.
1. Build pytorch locally. `MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ USE_CUDA=0 DEBUG=1 MAX_JOBS=16 python setup.py develop`
2. Run `python save_lite.py`
```
import torch
# ~/Documents/pytorch/data/dog.jpg
model = torch.hub.load('pytorch/vision:v0.6.0', 'shufflenet_v2_x1_0', pretrained=True)
model.eval()
# sample execution (requires torchvision)
from PIL import Image
from torchvision import transforms
import pathlib
import tempfile
import torch.utils.mobile_optimizer
input_image = Image.open('~/Documents/pytorch/data/dog.jpg')
preprocess = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
input_tensor = preprocess(input_image)
input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
# move the input and model to GPU for speed if available
if torch.cuda.is_available():
input_batch = input_batch.to('cuda')
model.to('cuda')
with torch.no_grad():
output = model(input_batch)
# Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes
print(output[0])
# The output has unnormalized scores. To get probabilities, you can run a softmax on it.
print(torch.nn.functional.softmax(output[0], dim=0))
traced = torch.jit.trace(model, input_batch)
sum(p.numel() * p.element_size() for p in traced.parameters())
tf = pathlib.Path('~/Documents/pytorch/data/data/example_debug_map_with_tensorkey.ptl')
torch.jit.save(traced, tf.name)
print(pathlib.Path(tf.name).stat().st_size)
traced._save_for_lite_interpreter(tf.name)
print(pathlib.Path(tf.name).stat().st_size)
print(tf.name)
```
3. Run `python test_lite.py`
```
import torch
from torch.jit.mobile import _load_for_lite_interpreter
# sample execution (requires torchvision)
from PIL import Image
from torchvision import transforms
input_image = Image.open('~/Documents/pytorch/data/dog.jpg')
preprocess = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
input_tensor = preprocess(input_image)
input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
reload_lite_model = _load_for_lite_interpreter('~/Documents/pytorch/experiment/example_debug_map_with_tensorkey.ptl')
with torch.no_grad():
output_lite = reload_lite_model(input_batch)
# Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes
print(output_lite[0])
# The output has unnormalized scores. To get probabilities, you can run a softmax on it.
print(torch.nn.functional.softmax(output_lite[0], dim=0))
```
4. Compare the result with pytorch in master and pytorch built locally with this change, and see the same output.
5. The model size was 16.1 MB and becomes 12.9 with this change.
Size comparison in production models:
{F603127047}
Reviewed By: iseeyuan
Differential Revision: D27759891
fbshipit-source-id: 34e0cb8149011c46c1910165b545c137d7a0b855
Summary:
This is an automatic change generated by the following script:
```
#!/usr/bin/env python3
from subprocess import check_output, check_call
import os
def get_compiled_files_list():
import json
with open("build/compile_commands.json") as f:
data = json.load(f)
files = [os.path.relpath(node['file']) for node in data]
for idx, fname in enumerate(files):
if fname.startswith('build/') and fname.endswith('.DEFAULT.cpp'):
files[idx] = fname[len('build/'):-len('.DEFAULT.cpp')]
return files
def run_clang_tidy(fname):
check_call(["python3", "tools/clang_tidy.py", "-c", "build", "-x", fname,"-s"])
changes = check_output(["git", "ls-files", "-m"])
if len(changes) == 0:
return
check_call(["git", "commit","--all", "-m", f"NOLINT stubs for {fname}"])
def main():
git_files = check_output(["git", "ls-files"]).decode("ascii").split("\n")
compiled_files = get_compiled_files_list()
for idx, fname in enumerate(git_files):
if fname not in compiled_files:
continue
if fname.startswith("caffe2/contrib/aten/"):
continue
print(f"[{idx}/{len(git_files)}] Processing {fname}")
run_clang_tidy(fname)
if __name__ == "__main__":
main()
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56892
Reviewed By: H-Huang
Differential Revision: D27991944
Pulled By: malfet
fbshipit-source-id: 5415e1eb2c1b34319a4f03024bfaa087007d7179
Summary:
Add a new device type 'XPU' ('xpu' for lower case) to PyTorch. Changes are needed for code related to device model and kernel dispatch, e.g. DeviceType, Backend and DispatchKey etc.
https://github.com/pytorch/pytorch/issues/48246
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49786
Reviewed By: mrshenli
Differential Revision: D25893962
Pulled By: ezyang
fbshipit-source-id: 7ff0a316ee34cf0ed6fc7ead08ecdeb7df4b0052
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48868
Building on the previous diff, we can make `toTensor()` return a
`const Tensor&`, which should make it easier to avoid reference
counting.
ghstack-source-id: 119327372
Test Plan: internal benchmarks.
Reviewed By: bwasti
Differential Revision: D25325379
fbshipit-source-id: ca699632901691bcee432f595f75b0a4416d55dd
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43088
Create quantized module that the user can use to perform embedding bag quantization
The module uses the EmbeddingPackedParams to store the weights which can be serialized /deserialized
using TorchBind custom classes (C++ get/setstate code)
Following PR will add support for `from_float` to convert from float to quantized module
Test Plan:
python test/test_quantization.py TestDynamicQuantizedModule.test_embedding_bag_api
Imported from OSS
Reviewed By: vkuzo
Differential Revision: D23167519
fbshipit-source-id: 029d7bb44debf78c4ef08bfebf267580ed94d033
Summary:
Clearly expressing a type is inferred by PyTorch instead of explicitly annotated by user makes many error messages more user-friendly
Currently Type has two string conversion methods. str() for IR printing and python_str() for serialization and error message generation. If we want to include more information in type printing while maintaining serialization/deserialization correctness, we need to split python_str() into annotation_str() and repr_str().
annotation_str is solely responsible for serialization, it strictly matches format of python type annotation. repr_str() is responsible for generating a human-readable error message that includes information like "this type is inferred, not explicitly annotated"
Closes https://github.com/pytorch/pytorch/issues/39449
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39544
Differential Revision: D21978759
Pulled By: gmagogsfm
fbshipit-source-id: 733566f5a62e748b5ca4bb3c5943ebb6d5b664d0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38898
Pickling will pickle the tensor meta info, and its up to the jit
exporter or other upstream who use the pickler to decide how to write
the actual tensor data.
This PR make we call getWritableTensorData in upper level so that rpc
and TensorPipe can leverge it with only pickling tensor meta data without
converting the tensor from GPU to CPU.
Test Plan: Imported from OSS
Differential Revision: D21879866
Pulled By: wanchaol
fbshipit-source-id: 75f7ff4073e4ad15b6588973dcbdc48f97a8329f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37472
Our convention is for `findX` to return an optional version and `getX`
to assert that the X is there. Fix up `getMethod` to be consistent with
this convention.
Test Plan: Imported from OSS
Differential Revision: D21297543
Pulled By: suo
fbshipit-source-id: b40f56231cc8183e61bbb01fe5c0c113bcb6464d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37776
* Remove type-specific size tracking in favor of byte size tracking in Storage and StorageImpl
* Changed numel() and set_numel() to nbytes() and set_nbytes()
* Added enum argument to Storage/StorageImpl constructor to indicate new meaning of the size parameter
* Update all callers of the changed API
Part of issue https://github.com/pytorch/pytorch/issues/33950
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37028
Differential Revision: D21171334
Pulled By: ezyang
fbshipit-source-id: 37329a379de9a3a83cc5e9007e455a3e1c2d10b8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36609
This PR remove iterationOrder that backed by CompareKeys, we universally
use Dict inseration order default backed by c10::Dict to match the
python behaviors
Test Plan: Imported from OSS
Reviewed By: houseroad
Differential Revision: D21056963
Pulled By: wanchaol
fbshipit-source-id: 487961c2db2cdc27461b2fbd6df91faafc6920b5
Summary:
Just run `./tools/clang_format.py --verbose` and `git commit --all`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35969
Test Plan: CI
Differential Revision: D20845626
Pulled By: malfet
fbshipit-source-id: 0ae9a91dfa33417a021e7e9d233baba4188daf81
Summary:
This enables the serialization part of this change (the deserialization stuff is already landed #33255)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35741
Pulled By: driazati
Differential Revision: D20758124
fbshipit-source-id: e2cdefa99c3bec991491e5e967e7f1661ca7ffd9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35720
When modules are saved, all relevant types are serialized according to
their qualified name with a compilation unit. Since qualified names are
guaranteed to be unique within a compilation unit, this normally works
fine.
On load, all types are registered in a compilation unit owned by the
script::Module. Type names are not unique across compilation units, so
if you load two modules with colliding type names, make them submodules
of yet another module, and save that module, there is the potential of a
name collision. See the added tests for examples if that description is
confusing.
The solution is to unique type names when serializing code by mangling
them if we detect a name collision.
Test Plan: Imported from OSS
Differential Revision: D20749423
Pulled By: suo
fbshipit-source-id: a8827ff1d4a89f3e7964dbbb49b4381863da3e6a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35115
This commit runs the newly added tools/clang_format.py on the JIT
codebase and includes all of the formatting changes thus produced.
Testing:
Ran the script, CI.
Test Plan: Imported from OSS
Reviewed By: eellison
Differential Revision: D20568523
Pulled By: SplitInfinity
fbshipit-source-id: e09bdb982ccf090eecfb7c7b461b8d0681eef82b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34689
rref JIT pickling is only allowed inside rpc calls. enforcing this by adding a thread local variable isInRpcCall and set it as True when converting rpc requests or responses to message, before calling JIT::pickle(). Inside JIT::pickle(), it allowes to pickle RRef only when the isInRpcCall is true.
ghstack-source-id: 100481001
Test Plan: unit tests
Differential Revision: D20429826
fbshipit-source-id: dbc04612ed15de5d6c7d75a4732041ccd4ef3f8c
Summary:
Stacked PRs
* #33474 - [jit] Remove list specializations from pickler
* **#33255 - [jit] Add type tags to lists/dicts in pickle**
This adds a global call to `torch.jit._pickle.restore_type_tags` for
lists and dicts so that we can preserve their types after serialization.
](https://our.intern.facebook.com/intern/diff/20346780/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33255
Pulled By: driazati
Differential Revision: D20346780
fbshipit-source-id: c8534954ef4adb2e3c880401acbee30cd284f3db
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33921
**NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.intern.facebook.com/intern/diff/D20153092/)!
Test Plan: Imported from OSS
Differential Revision: D20177227
Pulled By: jamesr66a
fbshipit-source-id: 87f3e484c4f873d60f76f50f6789c1b4a73bdfde
Summary:
Stacked PRs
* #33474 - [jit] Remove list specializations from pickler
* **#33255 - [jit] Add type tags to lists/dicts in pickle**
This adds a global call to `torch.jit._pickle.restore_type_tags` for
lists and dicts so that we can preserve their types after serialization.
](https://our.intern.facebook.com/intern/diff/19868637/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33255
Pulled By: driazati
Reviewed By: xman1979, Tianshu-Bao
Differential Revision: D19868637
fbshipit-source-id: 2f1826e6679a786ca209198690269f399a542c04
