This functionality does not seem to be used
and there are some requests to update dependency.
Add `third_party` to torch_cpu include directories if compiling with
Caffe2 support, as `caffe2/quantization/server/conv_dnnlowp_op.cc` depends on `third_party/fbgemm/src/RefImplementations.h`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75394
Approved by: https://github.com/janeyx99, https://github.com/seemethere
When testing composite compliance, the conj bit and neg bit are not
propagated to the wrapper tensor. This leads to problems when a
composite operator has two paths depending on whether one of these
bits are set, since the non-conjugated path will always be taken.
For example, `at::real` effectively does
```cpp
view_as_real(tensor.is_conj() ? tensor.conj() : tensor)
```
which will never call `conj()` because the `CompositeCompliantTensor`
never has has the conj bit set. The result is `view_as_real` fails
when `r.elem` does have the conj bit set.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75830
Approved by: https://github.com/zou3519
I was working on an explanation of how to call into the "super"
implementation of some given ATen operation inside of __torch_dispatch__
(https://github.com/albanD/subclass_zoo/blob/main/trivial_tensors.py)
and I kept thinking to myself "Why doesn't just calling super() on
__torch_dispatch__ work"? Well, after this patch, it does! The idea
is if you don't actually unwrap the input tensors, you can call
super().__torch_dispatch__ to get at the original behavior.
Internally, this is implemented by disabling PythonKey and then
redispatching. This implementation of disabled_torch_dispatch is
not /quite/ right, and some reasons why are commented in the code.
There is then some extra work I have to do to make sure we recognize
disabled_torch_dispatch as the "default" implementation (so we don't
start slapping PythonKey on all tensors, including base Tensors),
which is modeled the same way as how disabled_torch_function is done.
Signed-off-by: Edward Z. Yang <ezyangfb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73684
Approved by: albanD
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71746
This PR contains the following improvements:
- It exposes a new environment variable `TORCH_CPP_LOG_LEVEL` that enables users to set the log level of c10 logging facility (supports both GLOG and c10 loggers). Valid values are `INFO`, `WARNING`, `ERROR`, and `FATAL` or their numerical equivalents `0`, `1`, `2`, and `3`.
- It implements an `initLogging()` function and calls it as part of `torch._C` module import to ensure that the underlying logging facility is correctly initialized in Python.
With these changes a user can dynamically set the log level of c10 as in the following example:
```
$ TORCH_CPP_LOG_LEVEL=INFO python my_torch_script.py
```
ghstack-source-id: 149822703
Test Plan: Run existing tests.
Reviewed By: malfet
Differential Revision: D33756252
fbshipit-source-id: 7fd078c03a598595d992de0b474a23cec91838af
(cherry picked from commit 01d6ec6207faedf259ed1368730e9e197cb3e1c6)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72607
since python isn't available from libtorch, most of lazy tensor
code can't depend on python.
separate python_util into libtorch_python library
make debug_util and IR dump work with or without python by providing
a default function for 'maybe getting python stacktrace' that returns
an empty stacktrace
use a registration mechanism on libtorch_python library load to update
the 'maybe' function to use the real python stacktrace getter
Test Plan:
OSS build tests:
- test_ptltc by itself works
- LTC_SAVE_TENSORS_FILE=log test_ptltc works, and log contains
empty stacktrces
- python examply.py by itself works
- LTC_SAVE_TENSORS_FILE=log test_ptltc works, and log contains
real stacktraces
fbcode build: rely on CI to run test/lazy
Reviewed By: desertfire
Differential Revision: D34115046
fbshipit-source-id: 8d6222963c146da36b3c1b5ff8a638bbc3f1442e
(cherry picked from commit 3717688adee1bba1314640f93594181e8a2b3831)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69567
This exposes torch.monitor events and stats via pybind11 to the underlying C++ implementation.
* The registration interface is a tad different since it takes a lambda function in Python where as in C++ it's a full class.
* This has a small amount of changes to the counter interfaces since there's no way to create an initializer list at runtime so they now also take a vector.
* Only double based stats are provided in Python since it's intended more for high level stats where float imprecision shouldn't be an issue. This can be changed down the line if need arises.
```
events = []
def handler(event):
events.append(event)
handle = register_event_handler(handler)
log_event(Event(type="torch.monitor.TestEvent", timestamp=datetime.now(), metadata={"foo": 1.0}))
```
D32969391 is now included in this diff.
This cleans up the naming for events. type is now name, message is gone, and metadata is renamed data.
Test Plan: buck test //caffe2/test:monitor //caffe2/test/cpp/monitor:monitor
Reviewed By: kiukchung
Differential Revision: D32924141
fbshipit-source-id: 563304c2e3261a4754e40cca39fc64c5a04b43e8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69041
`TH_CONCAT_{N}` is still being used by THP so I've moved that into
it's own header but all the compiled code is gone.
Test Plan: Imported from OSS
Reviewed By: anjali411
Differential Revision: D32872477
Pulled By: ngimel
fbshipit-source-id: 06c82d8f96dbcee0715be407c61dfc7d7e8be47a
Summary:
Per title.
This PR introduces a global flag that lets pytorch prefer one of the many backend implementations while calling linear algebra functions on GPU.
Usage:
```python
torch.backends.cuda.preferred_linalg_library('cusolver')
```
Available options (str): `'default'`, `'cusolver'`, `'magma'`.
Issue https://github.com/pytorch/pytorch/issues/63992 inspired me to write this PR. No heuristic is perfect on all devices, library versions, matrix shapes, workloads, etc. We can obtain better performance if we can conveniently switch linear algebra backends at runtime.
Performance of linear algebra operators after this PR should be no worse than before. The flag is set to **`'default'`** by default, which makes everything the same as before this PR.
The implementation of this PR is basically following that of https://github.com/pytorch/pytorch/pull/67790.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67980
Reviewed By: mruberry
Differential Revision: D32849457
Pulled By: ngimel
fbshipit-source-id: 679fee7744a03af057995aef06316306073010a6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/69032
I am removing it because, for packaging-related reasons, it's easier if
torch.fx is a pure Python module.
I don't think there is much reason to keep it: this functionality was
experimental, has no known users currently, and we didn't have a clear
path to turning it on by default due to regressions in tracing
performance. Also, it only was ever enabled for `rand` and friends.
Technically the removal of the `enable_cpatching` arguments on
`symbolic_trace` and `Tracer.__init__` are BC-breaking, but the
docstrings clearly state that the argument is experimental and BC is not
guaranteed, so I think it's fine.
Test Plan: Imported from OSS
Reviewed By: soulitzer
Differential Revision: D32706344
Pulled By: suo
fbshipit-source-id: 501648b5c3610ae71829b5e7db74e3b8c9e1a480
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68672
This PR adds `python_module: sparse` to `native_function.yaml`.
These functions would appear in `torch._C._sparse` namespace instead of
just `torch`.
Test Plan: Imported from OSS
Reviewed By: mruberry
Differential Revision: D32517813
fbshipit-source-id: 7c3d6df57a24d7c7354d0fefe1b628dc89be9431
Summary:
This PR introduces a new function `_select_conv_backend` that returns a `ConvBackend` enum representing the selected backend for a given set of convolution inputs and params.
The function and enum are exposed to python for testing purposes through `torch/csrc/Module.cpp` (please let me know if there's a better place to do this).
A new set of tests validates that the correct backend is selected for several sets of inputs + params. Some backends aren't tested yet:
* nnpack (for mobile)
* xnnpack (for mobile)
* winograd 3x3 (for mobile)
Some flowcharts for reference:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67790
Reviewed By: zou3519
Differential Revision: D32280878
Pulled By: jbschlosser
fbshipit-source-id: 0ce55174f470f65c9b5345b9980cf12251f3abbb
Summary:
https://github.com/pytorch/pytorch/issues/67578 disabled reduced precision reductions for FP16 GEMMs. After benchmarking, we've found that this has substantial performance impacts for common GEMM shapes (e.g., those found in popular instantiations of multiheaded-attention) on architectures such as Volta. As these performance regressions may come as a surprise to current users, this PR adds a toggle to disable reduced precision reductions
`torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = `
rather than making it the default behavior.
CC ngimel ptrblck
stas00 Note that the behavior after the previous PR can be replicated with
`torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = False`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67946
Reviewed By: zou3519
Differential Revision: D32289896
Pulled By: ngimel
fbshipit-source-id: a1ea2918b77e27a7d9b391e030417802a0174abe
Summary:
Fixes https://github.com/pytorch/pytorch/issues/64883
Adds a `warn_only` kwarg to `use_deterministic_algorithms`. When enabled, calling an operation that does not have a deterministic implementation will raise a warning, rather than an error.
`torch.testing._internal.common_device_type.expectedAlertNondeterministic` is also refactored and documented in this PR to make it easier to use and understand.
cc mruberry kurtamohler
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66233
Reviewed By: bdhirsh
Differential Revision: D31616481
Pulled By: mruberry
fbshipit-source-id: 059634a82d54407492b1d8df08f059c758d0a420
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62030
Remove dtype tracking from Python Storage interface, remove all the different `<type>Storage` classes except for `ByteStorage`, and update serialization accordingly, while maintaining as much FC/BC as possible
Fixes https://github.com/pytorch/pytorch/issues/47442
* **THE SERIALIZATION FORMAT IS FULLY FC/BC.** We worked very hard to make sure this is the case. We will probably want to break FC at some point to make the serialization structure of tensors make more sense, but not today.
* There is now only a single torch.ByteStorage class. Methods like `Tensor.set_` no longer check that the dtype of storage is appropriate.
* As we no longer know what dtype of a storage is, we've **removed** the size method from Storage, replacing it with nbytes. This is to help catch otherwise silent errors where you confuse number of elements with number of bytes.
* `Storage._new_shared` takes a `nbytes` kwarg and will reject previous positional only calls. `Storage._new_with_file` and `_set_from_file` require explicit element size arguments.
* It's no longer possible to convert storages to different types using the float/double/etc methods. Instead, do the conversion using a tensor.
* It's no longer possible to allocate a typed storage directly using FloatStorage/DoubleStorage/etc constructors. Instead, construct a tensor and extract its storage. The classes still exist but they are used purely for unpickling.
* The preexisting serialization format stores dtype with storage, and in fact this dtype is used to determine the dtype of the tensor overall.
To accommodate this case, we introduce a new TypedStorage concept that exists only during unpickling time which is used to temporarily store the dtype so we can construct a tensor. **If you overrode the handling of pickling/unpickling, you MUST add handling for TypedStorage** or your serialization code will degrade to standard file-based serialization.
Original pull request: https://github.com/pytorch/pytorch/pull/59671
Reviewed By: soulitzer, ngimel
Differential Revision: D29466819
Pulled By: ezyang
fbshipit-source-id: 4a14e5d3c2b08e06e558683d97f7378a3180b00e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/65610
- Replace HIP_PLATFORM_HCC with USE_ROCM
- Dont rely on CUDA_VERSION or HIP_VERSION and use USE_ROCM and ROCM_VERSION.
- In the next PR
- Will be removing the mapping from CUDA_VERSION to HIP_VERSION and CUDA to HIP in hipify.
- HIP_PLATFORM_HCC is deprecated, so will add HIP_PLATFORM_AMD to support HIP host code compilation on gcc.
cc jeffdaily sunway513 jithunnair-amd ROCmSupport amathews-amd
Reviewed By: jbschlosser
Differential Revision: D30909053
Pulled By: ezyang
fbshipit-source-id: 224a966ebf1aaec79beccbbd686fdf3d49267e06
Summary:
As GoogleTest `TEST` macro is non-compliant with it as well as `DEFINE_DISPATCH`
All changes but the ones to `.clang-tidy` are generated using following script:
```
for i in `find . -type f -iname "*.c*" -or -iname "*.h"|xargs grep cppcoreguidelines-avoid-non-const-global-variables|cut -f1 -d:|sort|uniq`; do sed -i "/\/\/ NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)/d" $i; done
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62008
Reviewed By: driazati, r-barnes
Differential Revision: D29838584
Pulled By: malfet
fbshipit-source-id: 1b2f8602c945bd4ce50a9bfdd204755556e31d13
Summary:
Needed on platforms, that do not have MKL, such as aarch64 and M1
- Add `AT_POCKETFFT_ENABLED()` to Config.h.in
- Introduce torch._C.has_spectral that is true if PyTorch was compiled with either MKL or PocketFFT
- Modify spectral test to use skipCPUIfNoFFT instead of skipCPUIfNoMKL
Share implementation of `_out` functions as well as fft_fill_with_conjugate_symmetry_stub between MKL and PocketFFT implementations
Fixes https://github.com/pytorch/pytorch/issues/41592
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60976
Reviewed By: walterddr, driazati, janeyx99, samestep
Differential Revision: D29466530
Pulled By: malfet
fbshipit-source-id: ac5edb3d40e7c413267825f92a5e8bc4bb249caf
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/58059
Add CUDA.used vital sign which is true only if CUDA was "used" which technically means the context was created.
Also adds the following features:
- Force vitals to be written even if vitals are disabled, to enable testing when the env variable is not set from the start of execution
- Add a read_vitals call for python to read existing vital signs.
Test Plan: buck test mode/dbg caffe2/test:torch -- --regex basic_vitals
Reviewed By: xuzhao9
Differential Revision: D28357615
fbshipit-source-id: 681bf9ef63cb1458df9f1c241d301a3ddf1e5252
Summary:
Switches most of the simple for loops outside of `jit` directories to use `c10::irange`.
Generated with D28874212.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59481
Test Plan: Sandcastle
Reviewed By: ngimel
Differential Revision: D28909681
fbshipit-source-id: ec9ab1bd602933238d9d0f73d4d8d027b75d9d85
Summary:
This PR
* adds the breakpad build to most of the remaining docker images (except the mobile + slim ones)
* pins to a [fork of breakpad](https://github.com/google/breakpad/compare/master...driazati:master?expand=1) to enable dasiy chaining on signal handlers
* renames the API to be nicer
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59236
Reviewed By: malfet
Differential Revision: D28792511
Pulled By: driazati
fbshipit-source-id: 83723e74b7f0a00e1695210ac2620a0c91ab4bf2
Summary:
After stealing the ownership of the tensor passed via DLPack capsule, PyTorch should immediately mark it as used (by changing its name to `used_dltensor`). This fix is needed because the following line may raise an exception:
```cpp
py::module::import("torch.cuda").attr("init")();
```
When an exception is raised, Tensor created by `at::fromDLPack` calls the `deleter`. However as the causple is not consumed, the producer (a library that created the capsule) also calls the `deleter`, causing a double free.
Reprodcuer (I'm running this code on A100 GPU + PyTorch wheel which does not include `sm_80` support; in this configuration `torch.cuda.init` will raise a warning):
```py
$ python -Werror
>>> import torch.utils.dlpack
>>> import cupy
>>> tensor = torch.utils.dlpack.from_dlpack(cupy.arange(10).toDlpack())
free(): double free detected in tcache 2
zsh: abort (core dumped) python -Werror
```
Once this fix is merged users can now see the exception correctly:
```
A100-PCIE-40GB with CUDA capability sm_80 is not compatible with the current PyTorch installation.
The current PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60 sm_70.
If you want to use the A100-PCIE-40GB GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56789
Reviewed By: astaff
Differential Revision: D28118512
Pulled By: mruberry
fbshipit-source-id: 56992f7a3fc78d94c69513e864a473ae9587a9c8
Summary:
In my last PR I've missed CUDA and distributed folders, fixing this now
This change is autogenerated by `python tool/clang_tidy.py -s`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57235
Reviewed By: janeyx99
Differential Revision: D28084444
Pulled By: malfet
fbshipit-source-id: bf222f69ee90c7872c3cb0931e8cdb84f0cb3cda
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/55647
This adds [breakpad](https://github.com/google/breakpad) which comes with out-of-the-box utilities to register a signal handler that writes out a minidump on an unhandled exception. Right now this is gated behind a flag in `torch.utils`, but in the future it could be on by default. Sizewise this adds aboute 500k to `libtorch_cpu.so` (187275968 B to 187810016 B).
```bash
$ cat <<EOF > test.py
import torch
torch.utils.enable_minidump_collection()
# temporary util that just segfaults
torch._C._crash()
EOF
$ python test.py
Wrote minidump to /tmp/pytorch_crashes/6a829041-50e9-4247-ea992f99-a74cf47a.dmp
fish: “python test.py” terminated by signal SIGSEGV (Address boundary error)
$ minidump-2-core /tmp/pytorch_crashes/6a829041-50e9-4247-ea992f99-a74cf47a.dmp -o core.dmp
$ gdb python core.dmp
... commence debugging ...
```
Right now all exceptions that get passed up to Python don't trigger the signal handler (which by default only
handles [these](https://github.com/google/breakpad/blob/main/src/client/linux/handler/exception_handler.cc#L115)). It would be possible for PyTorch exceptions to explicitly write a minidump when passed up to Python (maybe only when the exception is unhandled or something).
Test Plan: Imported from OSS
Reviewed By: ailzhang
Differential Revision: D27679767
Pulled By: driazati
fbshipit-source-id: 1ab3b5160b6dc405f5097eb25acc644d533358d7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53238
There is a tension for the Vitals design: (1) we want a macro based logging API for C++ and (2) we want a clean python API. Furthermore, we want to this to work with "print on destruction" semantics.
The unfortunate resolution is that there are (2) ways to define vitals:
(1) Use the macros for local use only within C++ - this keeps the semantics people enjoy
(2) For vitals to be used through either C++ or Python, we use a global VitalsAPI object.
Both these go to the same place for the user: printing to stdout as the globals are destructed.
The long history on this diff shows many different ways to try to avoid having 2 different paths... we tried weak pointers & shared pointers, verbose switch cases, etc. Ultimately each ran into an ugly trade-off and this cuts the difference better the alternatives.
Test Plan:
buck test mode/dev caffe2/test:torch -- --regex vital
buck test //caffe2/aten:vitals
Reviewed By: orionr
Differential Revision: D26736443
fbshipit-source-id: ccab464224913edd07c1e8532093f673cdcb789f
Summary:
Fixes https://github.com/pytorch/pytorch/issues/52253
In the issue reproducer we can replace `torch.sparse.sum(S)` with `S.coalesce()` and get the same memory leak. The reason is that calling `coalesce()` on an already coalesced tensor returns `self`. With autograd, the result gets it's `grad_fn` set to a node that contains a reference to the input tensor, creating a reference cycle. Cloning the tensor fixes this, so `coalesce` always returns a new tensor.
As an aside, `torch.sparse.sum(S)` doesn't need to coalesce. The result should be the same either way.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52874
Reviewed By: bdhirsh
Differential Revision: D27246997
Pulled By: albanD
fbshipit-source-id: 0fe6c11043501a7874a50982afd42964f47470d3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54034Fixes#53544
I had to touch a bunch of lines but the refactoring was fairly
mechanical. Here's how it works.
The basic concept behind this PR is that tensor_new.cpp was previously
abusing DispatchKey when it actually meant TensorOptions. The provided
DispatchKey argument to most of the constructor functions typically
comes from torch::tensors::get_default_dispatch_key(); it doesn't
really make sense for people to set the default dispatch key, but
this got grandfathered in due to the old API set_default_tensor_type
(where the "Type" concept got refactored into "DispatchKey" concept
over time). See also #53124. But the upshot is that, semantically,
what we refer to as the default dispatch key really is more like
torch.set_default_tensor_type(torch.Tensor) versus
torch.set_default_tensor_type(torch.cuda.Tensor): clearly the user
wants to do something about *construction* of the tensor, and
TensorOptions captures that exactly.
So, how exactly to translate from one to the other?
- Sources (things that used to PRODUCE DispatchKey)
- Most top level functions take a DispatchKey as their argument. I
use the new function dispatchKeyToTensorOptions to convert it into
a TensorOptions
- typeIdWithDefault now produces a TensorOptions (probably could do
with a rename, though I didn't)
- Sinks (things that used to CONSUME DispatchKey)
- Previously, the function options() was typically used to convert the
DispatchKey into a TensorOptions. Now its replacement build_options
just takes a TensorOptions and sets some extra fields on it.
Irritatingly, I can't just replace
`build_options(options, scalar_type, device)` with
`options.dtype(scalar_type).device(device)` because the semantics
are slightly different: if device is nullopt, we should preserve
the usage of the device specified in options (what options.device()
does is overwrite the device unconditionally; e.g., if device is
nullopt, unset device from options)
- The other major sink for DispatchKey was `internal_new_from_data`,
but it turns out it only really extracts the device type from
the dispatch key. Now it just pulls out the device from
TensorOptions.
- To actually do the translation of DispatchKey to TensorOptions, I
introduce new functions dispatchKeyToLayout (replicating
layout_from_backend--there are still a few uses of this function
so I couldn't delete it) and dispatchKeyToDeviceType (replacing
computeDeviceType)
- In all internal functions, whenever DispatchKey is taken as an argument,
I instead take TensorOptions as an argument, and pass it along.
- Anywhere `legacyExtractDispatchKey(other.key_set())` equality was
previously used, I now do `other.options().type_equal()`, which
is the intended BC for doing "backend to backend" comparisons
- There are a few places in the sparse constructors where we allocated
a tensor for values, and then read out the dispatch key from the
result to allocate the keys. As best as I can tell, this is totally
equivalent to just passing in the options to both values and indices
(the only difference is dtype, which is captured via a separate
argument)
This refactor doesn't really go far enough: for example, there are now
functions that take both TensorOptions and ScalarType, when really
the TensorOptions can capture this all. I kept it solely just
s/DispatchKey/TensorOptions/ to reduce the number of possible bugs;
also, a lot of this will be mooted by a proper fix to #53124.
Even with this limited refactor, the payoff is sweet. I can delete:
- backendToCPU
- backendToXPU
- backendToCUDA
- backendToHIP
- backendToBackendOfDeviceType
The reason I can do this is because I can simply overwrite layout in TensorOptions
to do the conversion, rather than having to type out each backend case
explicitly.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Reviewed By: bhosmer
Differential Revision: D27109509
Pulled By: ezyang
fbshipit-source-id: 91d16cfbc390127770362ac04fb43f7e070077e9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53295
A lot of the time spent in `collect_callgrind` is spinning up Valgrind and executing the initial `import torch`. In most cases the actual run loop is a much smaller fraction. As a result, we can reuse the same process to do multiple replicates and do a much better job amortizing that startup cost. This also tends to result in more stable measurements: the kth run is more repeatable than the first because everything has been given a chance to settle into a steady state. The instruction microbenchmarks lean heavily on this behavior. I found that in practice doing several `n=100` replicates to be more reliable than one monolithic 10,000+ iteration run. (Since rare cases like memory consolidation will just contaminate that one replicate, as opposed to getting mixed into the entire long run.)
Test Plan: Imported from OSS
Reviewed By: ngimel
Differential Revision: D26907093
Pulled By: robieta
fbshipit-source-id: 72e5b48896911f5dbde96c8387845d7f9882fdb2
Summary:
Context: https://github.com/pytorch/pytorch/pull/53299#discussion_r587882857
These are the only hand-written parts of this diff:
- the addition to `.github/workflows/lint.yml`
- the file endings changed in these four files (to appease FB-internal land-blocking lints):
- `GLOSSARY.md`
- `aten/src/ATen/core/op_registration/README.md`
- `scripts/README.md`
- `torch/csrc/jit/codegen/fuser/README.md`
The rest was generated by running this command (on macOS):
```
git grep -I -l ' $' -- . ':(exclude)**/contrib/**' ':(exclude)third_party' | xargs gsed -i 's/ *$//'
```
I looked over the auto-generated changes and didn't see anything that looked problematic.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53406
Test Plan:
This run (after adding the lint but before removing existing trailing spaces) failed:
- https://github.com/pytorch/pytorch/runs/2043032377
This run (on the tip of this PR) succeeded:
- https://github.com/pytorch/pytorch/runs/2043296348
Reviewed By: walterddr, seemethere
Differential Revision: D26856620
Pulled By: samestep
fbshipit-source-id: 3f0de7f7c2e4b0f1c089eac9b5085a58dd7e0d97
Summary:
Move NumPy initialization from `initModule()` to singleton inside
`torch::utils::is_numpy_available()` function.
This singleton will print a warning, that NumPy integration is not
available, rather than fails to import torch altogether.
The warning be printed only once, and will look something like the
following:
```
UserWarning: Failed to initialize NumPy: No module named 'numpy.core' (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:66.)
```
This is helpful if PyTorch was compiled with wrong NumPy version, of
NumPy is not commonly available on the platform (which is often the case
on AARCH64 or Apple M1)
Test that PyTorch is usable after numpy is uninstalled at the end of
`_test1` CI config.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52794
Reviewed By: seemethere
Differential Revision: D26650509
Pulled By: malfet
fbshipit-source-id: a2d98769ef873862c3704be4afda075d76d3ad06
Summary:
- Allows build process to build with MLC enabled if subrepo folder mlc is in path and we can link against ML Compute on macOS BigSur
- To build with MLC enabled you will need to clone the mlc repo inside the pytorch repository.
- We need both this change and https://github.com/pytorch/pytorch/pull/50634 on pytorch/pytorch to enable the `mlc` device.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51326
Reviewed By: glaringlee
Differential Revision: D26533138
Pulled By: malfet
fbshipit-source-id: 0baa06b4eb2d62dbfc0f6fc922096cb0db1cc7d1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/52323
Using default cpu allocator for ops executed on qnnpack backend will result in
asan failures with heap overflow since qnnpack (and xnnpack) can access input
beyond their and/beginning.
Here we are enabling this feature specifically to enable dynamic sparse linear op test
using qnnpack engine. In dynamic linear op, the fp32 bias is not packed and
hence can result in out-of-bound access.
Test Plan: test_set_default_mobile_cpu_allocator.py
Reviewed By: z-a-f
Differential Revision: D26263481
fbshipit-source-id: a49227cac7e6781b0db4a156ca734d7671972d9f
