Looks like one of the first failures seen is `test_causal_variants_compile_causal_variant_CausalVariant_LOWER_RIGHT_shape0_cuda` when `test_causal_variants_causal_variant_CausalVariant_LOWER_RIGHT_shape0_cuda` passes.
What seems interesting here is that the `torch.compile` version fails while the eager version passes. Not sure what the difference would be here...
Nevertheless, is there a recommended mechanism to skip cuDNN SDPA as a backend for this test? CC @drisspg
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125343
Approved by: https://github.com/Skylion007
Looks like one of the first failures seen is `test_causal_variants_compile_causal_variant_CausalVariant_LOWER_RIGHT_shape0_cuda` when `test_causal_variants_causal_variant_CausalVariant_LOWER_RIGHT_shape0_cuda` passes.
What seems interesting here is that the `torch.compile` version fails while the eager version passes. Not sure what the difference would be here...
Nevertheless, is there a recommended mechanism to skip cuDNN SDPA as a backend for this test? CC @drisspg
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125343
Approved by: https://github.com/Skylion007
Looks like one of the first failures seen is `test_causal_variants_compile_causal_variant_CausalVariant_LOWER_RIGHT_shape0_cuda` when `test_causal_variants_causal_variant_CausalVariant_LOWER_RIGHT_shape0_cuda` passes.
What seems interesting here is that the `torch.compile` version fails while the eager version passes. Not sure what the difference would be here...
Nevertheless, is there a recommended mechanism to skip cuDNN SDPA as a backend for this test? CC @drisspg
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125343
Approved by: https://github.com/Skylion007
Now torch.dtype can pass through pybind11, so modify function _group_tensors_by_device_and_dtype to using scalar type. And without convert torch.dtype and string in python and c++ side.
@ezyang @bdhirsh
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127869
Approved by: https://github.com/ezyang
### Before this PR:
`torch.utils.swap_tensors(a, b)` required the `use_count` of `a` and `b` to be 1
```python
a = torch.randn(2, 3, requires_grad=True)
b = torch.randn(2, 4)
out = a * 2
out.sum().backward()
# Calling swap_tensors here would fail due to the reference held by AccumulateGrad node, which is not cleaned up after backward
# torch.utils.swap_tensors(a, b)
del out
# Calling swap_tensors here would pass
torch.utils.swap_tensors(a, b)
```
### After this PR:
`torch.utils.swap_tensors(a, b)` requires the `use_count` of `a` and `b` to be 1 or 2 IF the second reference is held by `AccumulateGrad`
A pre-hook will be registered on the `AccumulateGrad` node so that it will fail if it is called (i.e. if user attempts to backward through the graph).
```python
a = torch.randn(2, 3, requires_grad=True)
b = torch.randn(2, 4)
out = a * 2
out.sum().backward()
# Calling swap_tensors here is ok
torch.utils.swap_tensors(a, b)
# If we ever backward to the AccumulateGrad node it will error that it was poisoned by swap_tensors
```
### Application to `nn.Module`
This issue is especially pertinent in context of `nn.Module` where parameters will have `AccumulateGrad` nodes initialized after forward. Specifically, this is intended to address https://github.com/pytorch/pytorch/pull/126814#issuecomment-2127777866. Previously, this would fail at the `m.cpu()` but we want users to be able to do something like the following, and instead raise an error if the user ever attempts to backward through the poisoned `AccumulateGrad` node
```python
import torch
import torch.nn as nn
m = nn.Linear(3, 5)
inp = torch.randn(2, 3)
out = m(inp)
out.sum().backward()
m.cpu()
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127313
Approved by: https://github.com/soulitzer
#### Conditions for allowlisting tensor subclasses
We allow tensor subclasses types that
(1) Do not override `__setstate__`, `__getattr__`, `__setattr__`, `__get__`, `__set__` or `__getattribute__` of `torch.Tensor` (`torch.Tensor` does not have a definition of `__getattr__`, `__get__` or `__set__` so we check that these are `None`)
(2) Use the generic `tp_alloc`
(3) Are in a module that *has been imported by the user*
to be pushed onto the stack as strings by `GLOBAL` instructions, while storing the type in a dict
The strings will be converted to the classes as appropriate when executing `REBUILD` with `_rebuild_from_type_v2`
*Note that we use `inspect.getattr_static(sys.modules[module], name)` to get the class/function as this method claims to have no code execution.
The rationale for the 3 conditions above is as follows:
The rebuild func provided by `Tensor.__reduce_ex__` is `torch._tensor._rebuild_from_type_v2`, which is defined as such (note the call to `getattr`, `Tensor.__setstate__` and the call to `as_subclass` as well as the call to `_set_obj_state` which calls `setattr`)
4e66aaa010/torch/_tensor.py (L57-L71)
`as_subclass` is implemented with a call to `THPVariable_NewWithVar`
that will eventually call `tp_alloc` here
4e66aaa010/torch/csrc/autograd/python_variable.cpp (L2053)
The `func` arg to `_rebuild_from_type_v2` for wrapper subclasses is `Tensor.rebuild_wrapper_subclass`, which will similarly call into `THPVariable_NewWithVar` and hit the above `tp_alloc`
**Note that we do not call `tp_init` or `tp_new` (i.e. `cls.__init__` or `cls.__new__`) when unpickling**
### How do we check something is a tensor subclass/constraints around imports
In order to check whether `bla` is a tensor subclass in the bytecode `GLOBAL module.name`, we need to do an `issubclass` check, which entails converting the global string to the appropriate type. We *do not* arbitrarily import modules but will perform this check as long as the given subclass (given by `module.name`) has already been imported by the user (i.e. `module in sys.modules` and `issubclass(getattr(sys[modules], name), torch.Tensor)`
This PR also allowlisted `torch._utils._rebuild_wrapper_subclass` and `torch.device` (used by `_rebuild_wrapper_subclass`)
### API for allow listing
This PR also added `torch.serialization.{add/get/clear}_safe_globals` that enables user to allowlist globals they have deemed safe and manipulate this list (for example they could allowlist a tensor subclass with a custom `__setstate__` if they have checked that this is safe).
Next steps:
- Add testing and allowlist required classes for all in-core tensor subclasses (e.g. `DTensor`, `FakeTensor` etc.)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124331
Approved by: https://github.com/albanD
We've had issues using addr2line. On certain versions of
CentOS it is on a version that has a performance regression making it very slow,
and even normallly it is not that fast, taking several seconds even when parallelized
for a typical memory trace dump.
Folly Symbolize or LLVMSymbolize are fast but it requires PyTorch take a dependency on those libraries to do this, and given the number of environments we run stuff in, we end up hitting cases where we fallback to slow addr2line behavior.
This adds a standalone symbolizer to PyTorch similar to the unwinder which has
no external dependencies and is ~20x faster than addr2line for unwinding PyTorch frames.
I've tested this on some memory profiling runs using all combinations of {gcc, clang} x {dwarf4, dwarf5} and it seems to do a good job at getting line numbers and function names right. It is also careful to route all reads of library data through the `CheckedLexer` object, which ensure it is not reading out of bounds of the section. Errors are routed through UnwindError so that those exceptions get caught and we produce a ?? frame rather than crash. I also added a fuzz test which gives all our symbolizer options random addresses in the process to make sure they do not crash.
Differential Revision: [D56828968](https://our.internmc.facebook.com/intern/diff/D56828968)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123966
Approved by: https://github.com/ezyang, https://github.com/aaronenyeshi
By using `Py_NewRef`
Also, wrap `THPDtype_to_real`/`THPDtype_to_complex` calls with `HANDLE_TH_ERRORS`
Add regression test for the above issues, by calling to_complex for integral dtypes, that raises an exception and by preserving reference count to the same to_complex/to_real call to detect if leak is happeneing.
Replace
```cpp
auto dtype = (PyObject*)torch::getTHPDtype(current_dtype);
Py_INCREF(dtype);
return dtype;
```
with a more compact/streamlined equivalent
```cpp
return Py_NewRef(torch::getTHPDtype(current_dtype));
```
Fixes https://github.com/pytorch/pytorch/issues/124868
Pull Request resolved: https://github.com/pytorch/pytorch/pull/125154
Approved by: https://github.com/Skylion007, https://github.com/albanD
MTIA device has its own Module in PyTorch now.
torch.mtia has following APIs similar to other backends. The lazy_init is also supported.
```
__all__ = [
"init",
"is_available",
"synchronize",
"device_count",
"current_device",
"current_stream",
"default_stream",
"set_stream",
"stream",
"device",
]
```
------------
For device management. We expand AccleratorHooksInterface to support generic device management and it can be used in both C++ and PyThon.
```
def _accelerator_hooks_device_count() -> _int: ...
def _accelerator_hooks_set_current_device(device_index: _int) -> None: ...
def _accelerator_hooks_get_current_device() -> _int : ...
def _accelerator_hooks_exchange_device(device_index: _int) -> _int : ...
def _accelerator_hooks_maybe_exchange_device(device_index: _int) -> _int : ...
```
---------
Adding get_device_module API to retrieve device modules for different device types.
```
def get_device_module(device: Optional[Union[torch.device, str]] = None)
```
---------
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123612
Approved by: https://github.com/albanD
ghstack dependencies: #123611
MTIA device has its own Module in PyTorch now.
torch.mtia has following APIs similar to other backends. The lazy_init is also supported.
```
__all__ = [
"init",
"is_available",
"synchronize",
"device_count",
"current_device",
"current_stream",
"default_stream",
"set_stream",
"stream",
"device",
]
```
------------
For device management. We expand AccleratorHooksInterface to support generic device management and it can be used in both C++ and PyThon.
```
def _accelerator_hooks_device_count() -> _int: ...
def _accelerator_hooks_set_current_device(device_index: _int) -> None: ...
def _accelerator_hooks_get_current_device() -> _int : ...
def _accelerator_hooks_exchange_device(device_index: _int) -> _int : ...
def _accelerator_hooks_maybe_exchange_device(device_index: _int) -> _int : ...
```
---------
Adding get_device_module API to retrieve device modules for different device types.
```
def get_device_module(device: Optional[Union[torch.device, str]] = None)
```
---------
Differential Revision: [D56443356](https://our.internmc.facebook.com/intern/diff/D56443356)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123612
Approved by: https://github.com/albanD
ghstack dependencies: #123611
This diff intends to build device generic torch.Stream and torch.Event for newly added accelerators in PyTorch.
------------
**torch.Stream APIs**
```
# Defined in torch/csrc/Stream.cpp
class Stream(_StreamBase):
stream_id: _int # Stream id
device_index: _int
device_type: _int
device: _device # The device of the stream
@overload
def __new__(self, device: Optional[DeviceLikeType] = None, priority: _int = 0) -> Stream: ...
@overload
def __new__(self, stream_id: _int, device_index: _int, device_type: _int, priority: _int = 0) -> Stream: ...
def wait_event(self, event: Event) -> None: ...
def wait_stream(self, other: Stream) -> None: ...
def record_event(self, event: Optional[Event] = None) -> Event: ...
def query(self) -> None: ...
def synchronize(self) -> None: ...
def __hash__(self) -> _int: ...
def __repr__(self) -> str: ...
def __eq__(self, other: object) -> _bool: ...
```
------------------
**torch.Event APIs**:
- IPC related APIs are not implemented, since many device backends don't support it, but we leave interfaces there for future adaption of torch.cuda.Stream.
- currently only the enable_timing is supported, since it is the most common one used in other device backends. We have to refactor the event flag system in PyTorch to support more fancy flag.
- elapsedTime API is added to c10::Event
```
# Defined in torch/csrc/Event.cpp
class Event(_EventBase):
device: _device # The device of the Event
event_id: _int # The raw event created by device backend
def __new__(self,
device: Optional[DeviceLikeType] = None,
enable_timing: _bool = False,
blocking: _bool = False,
interprocess: _bool = False) -> Event: ...
@classmethod
def from_ipc_handle(self, device: DeviceLikeType, ipc_handle: bytes) -> Event: ...
def record(self, stream: Optional[Stream] = None) -> None: ...
def wait(self, stream: Optional[Stream] = None) -> None: ...
def query(self) -> _bool: ...
def elapsed_time(self, other: Event) -> _float: ...
def synchronize(self) -> None: ...
def ipc_handle(self) -> bytes: ...
def __repr__(self) -> str: ...
```
-----------
c10::Event provides new APIs
- calculate **elapsedTime**.
- Get raw event id
- Synchronize event.
```
double elapsedTime(const Event& event) const {
return impl_.elapsedTime(event.impl_);
}
void* eventId() const {
return impl_.eventId();
}
void synchronize() const {
return impl_.synchronize();
}
```
----------
TODO: need to find a good way to test them in PyTorch with API mocks.
Differential Revision: [D56443357](https://our.internmc.facebook.com/intern/diff/D56443357)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123611
Approved by: https://github.com/albanD, https://github.com/jeffdaily
We've had issues using addr2line. On certain versions of
CentOS it is on a version that has a performance regression making it very slow,
and even normallly it is not that fast, taking several seconds even when parallelized
for a typical memory trace dump.
Folly Symbolize or LLVMSymbolize are fast but it requires PyTorch take a dependency on those libraries to do this, and given the number of environments we run stuff in, we end up hitting cases where we fallback to slow addr2line behavior.
This adds a standalone symbolizer to PyTorch similar to the unwinder which has
no external dependencies and is ~20x faster than addr2line for unwinding PyTorch frames.
I've tested this on some memory profiling runs using all combinations of {gcc, clang} x {dwarf4, dwarf5} and it seems to do a good job at getting line numbers and function names right. It is also careful to route all reads of library data through the `CheckedLexer` object, which ensure it is not reading out of bounds of the section. Errors are routed through UnwindError so that those exceptions get caught and we produce a ?? frame rather than crash. I also added a fuzz test which gives all our symbolizer options random addresses in the process to make sure they do not crash.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123966
Approved by: https://github.com/ezyang
Following the example of PyTorch supporting a preferred Linalg library (cusolver or magma), this PR introduces a preferred blas library selector of either cublas or cublaslt for CUDA and hipblas or hipblaslt for ROCm via normal hipification of sources.
The default blas implementation remains cublas or hipblas. cublaslt or hipblaslt can be enabled using environment variable TORCH_BLAS_PREFER_CUBLASLT=1 (or TORCH_BLAS_PREFER_HIPBLASLT=1 as an alias) or by calling `torch.backends.cuda.preferred_blas_library(backend="cublaslt")` or as an alias `backend="hipblaslt"`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/122106
Approved by: https://github.com/lezcano
MTIA device has its own Module in PyTorch now.
torch.mtia has following APIs similar to other backends. The lazy_init is also supported.
```
__all__ = [
"init",
"is_available",
"synchronize",
"device_count",
"current_device",
"current_stream",
"default_stream",
"set_stream",
"stream",
"device",
]
```
------------
For device management. We expand AccleratorHooksInterface to support generic device management and it can be used in both C++ and PyThon.
```
def _accelerator_hooks_device_count() -> _int: ...
def _accelerator_hooks_set_current_device(device_index: _int) -> None: ...
def _accelerator_hooks_get_current_device() -> _int : ...
def _accelerator_hooks_exchange_device(device_index: _int) -> _int : ...
def _accelerator_hooks_maybe_exchange_device(device_index: _int) -> _int : ...
```
---------
Adding get_device_module API to retrieve device modules for different device types.
```
def get_device_module(device: Optional[Union[torch.device, str]] = None)
```
---------
@exported-using-ghexport
Differential Revision: [D52923602](https://our.internmc.facebook.com/intern/diff/D52923602/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123612
Approved by: https://github.com/albanD
ghstack dependencies: #123611
This diff intends to build device generic torch.Stream and torch.Event for newly added accelerators in PyTorch.
------------
**torch.Stream APIs**
```
# Defined in torch/csrc/Stream.cpp
class Stream(_StreamBase):
stream_id: _int # Stream id
device_index: _int
device_type: _int
device: _device # The device of the stream
@overload
def __new__(self, device: Optional[DeviceLikeType] = None, priority: _int = 0) -> Stream: ...
@overload
def __new__(self, stream_id: _int, device_index: _int, device_type: _int, priority: _int = 0) -> Stream: ...
def query(self) -> _bool: ...
def synchronize(self) -> None: ...
def wait_event(self, event: Event) -> None: ...
def wait_stream(self, other: Stream) -> None: ...
def record_event(self, event: Optional[Event] = None) -> Event: ...
def query(self) -> None: ...
def synchronize(self) -> None: ...
def __hash__(self) -> _int: ...
def __repr__(self) -> str: ...
def __eq__(self, other: object) -> _bool: ...
```
------------------
**torch.Event APIs**:
- IPC related APIs are not implemented, since many device backends don't support it, but we leave interfaces there for future adaption of torch.cuda.Stream.
- currently only the enable_timing is supported, since it is the most common one used in other device backends. We have to refactor the event flag system in PyTorch to support more fancy flag.
- elapsedTime API is added to c10::Event
```
# Defined in torch/csrc/Event.cpp
class Event(_EventBase):
device: _device # The device of the Event
event_id: _int # The raw event created by device backend
def __new__(self,
device: Optional[DeviceLikeType] = None,
enable_timing: _bool = False,
blocking: _bool = False,
interprocess: _bool = False) -> Event: ...
@classmethod
def from_ipc_handle(self, device: DeviceLikeType, ipc_handle: bytes) -> Event: ...
def record(self, stream: Optional[Stream] = None) -> None: ...
def wait(self, stream: Optional[Stream] = None) -> None: ...
def query(self) -> _bool: ...
def elapsed_time(self, other: Event) -> _float: ...
def synchronize(self) -> None: ...
def ipc_handle(self) -> bytes: ...
def __repr__(self) -> str: ...
```
-----------
c10::Event provides new APIs
- calculate **elapsedTime**.
- Get raw event id
- Synchronize event.
```
double elapsedTime(const Event& event) const {
return impl_.elapsedTime(event.impl_);
}
void* eventId() const {
return impl_.eventId();
}
void synchronize() const {
return impl_.synchronize();
}
```
----------
TODO: need to find a good way to test them in PyTorch with API mocks.
Differential Revision: [D55351839](https://our.internmc.facebook.com/intern/diff/D55351839/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123611
Approved by: https://github.com/albanD
**Summary:**
This commit simplifies the existing decomposition hierarchy
of batch norm ops by adding a single, backend agnostic op:
`batch_norm_with_update`. The existing hierarchy looks like:
```
aten.batch_norm ->
aten._batch_norm_impl_index ->
[
aten.native_batch_norm ->
aten._native_batch_norm_legit (export only) ->
_batch_norm_legit_cpu/cuda (kernels, export only) ->
_batch_norm_cpu/cuda (kernels)
] OR
[ aten.cudnn_batch_norm ] OR
[ aten.miopen_batch_norm ]
```
Aside from complexity, an important problem with the
above decomposition hierarchy is cuda numerics in
export flows. We observed significantly worse convergence
when training a mobilenetv2-like model when using the
`_batch_norm_cuda` kernel instead of the `cudnn_batch_norm`
kernel. This means users who export their models on CPU
first then move the models to cuda later may silently
see worse accuracies even when cudnn is installed,
because they are using the worse kernel. This issue is
summarized in https://github.com/pytorch/pytorch/issues/111384.
Instead, the new hierarchy proposed by consolidating
existing batch norm ops will look like:
```
aten.batch_norm ->
aten.batch_norm_with_update ->
[ _batch_norm_cpu (kernel) ] OR
[ _batch_norm_cuda (kernel) ] OR
[ cudnn_batch_norm (kernel) ] OR
[ miopen_batch_norm (kernel) ]
```
The new op `batch_norm_with_update` hides backend
implementation details and automatically picks the right
kernel based on what is installed. This commit also adds
the following variants to this op:
```
batch_norm_with_update_functional
batch_norm_with_update.out
batch_norm_no_update
batch_norm_no_update.out
batch_norm_backward
```
Note that this commit only adds this op and its variants,
but does not actually change the decomps to produce these
ops in the graph. This will be done after the 2 week FC
window, and the ops used in the old stack is planned to
be removed after the 6 month BC window.
Test Plan: `OpInfo` tests for `batch_norm_with_update`.
Reviewers: albanD, bdhirsh
Subscribers: albanD, bdhirsh, supriyar
Tasks: https://github.com/pytorch/pytorch/issues/111384
Differential Revision: [D54805279](https://our.internmc.facebook.com/intern/diff/D54805279)
Co-authored-by: Tugsbayasgalan Manlaibaatar <tmanlaibaatar@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116092
Approved by: https://github.com/bdhirsh, https://github.com/albanD
This reduces `torch.mv` time for 256x768 matrix by 256 element vector from 209 usec to 16 usec for nontransposed case and from 104 to 18 usec if transposed
Also, add fp16-accumulation flavor to the same ops (controlled by private `torch._C._set_cpu_allow_fp16_reduced_precision_reduction` which yields a slightly better numbers), summarized in the following table
| op | original | F32+NEON | F16+NEON|
| ---| -------- | ---------- | ----- |
| torch.mv(m, v) | 209.53 usec | 16.25 usec | 14.68 usec |
| torch.mv(m.t(), v) | 104.80 usec | 28.68 usec | 24.82 usec |
Test plan: CI on MacOS for both CPU and MPS test fp32<->fp16 matmul consistency ( For example "test_output_grad_match_nn_functional_linear_cpu_float16" passes if fp32-reductions are performed, but fails if fp16 accumulation is used)
To investigate:
- why replacing `sum0Vec = vaddq_f32(sum0Vec, vmulq_f32(a0Vec, xVec));` with `sum0Vec = vfmaq_f32(sum0Vec, a0Vec, xVec);` slows down gemv from 16.2 to 18.2 usec
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119992
Approved by: https://github.com/mikekgfb
**Summary:**
This commit simplifies the existing decomposition hierarchy
of batch norm ops by adding a single, backend agnostic op:
`batch_norm_with_update`. The existing hierarchy looks like:
```
aten.batch_norm ->
aten._batch_norm_impl_index ->
[
aten.native_batch_norm ->
aten._native_batch_norm_legit (export only) ->
_batch_norm_legit_cpu/cuda (kernels, export only) ->
_batch_norm_cpu/cuda (kernels)
] OR
[ aten.cudnn_batch_norm ] OR
[ aten.miopen_batch_norm ]
```
Aside from complexity, an important problem with the
above decomposition hierarchy is cuda numerics in
export flows. We observed significantly worse convergence
when training a mobilenetv2-like model when using the
`_batch_norm_cuda` kernel instead of the `cudnn_batch_norm`
kernel. This means users who export their models on CPU
first then move the models to cuda later may silently
see worse accuracies even when cudnn is installed,
because they are using the worse kernel. This issue is
summarized in https://github.com/pytorch/pytorch/issues/111384.
Instead, the new hierarchy proposed by consolidating
existing batch norm ops will look like:
```
aten.batch_norm ->
aten.batch_norm_with_update ->
[ _batch_norm_cpu (kernel) ] OR
[ _batch_norm_cuda (kernel) ] OR
[ cudnn_batch_norm (kernel) ] OR
[ miopen_batch_norm (kernel) ]
```
The new op `batch_norm_with_update` hides backend
implementation details and automatically picks the right
kernel based on what is installed. This commit also adds
the following variants to this op:
```
batch_norm_with_update_functional
batch_norm_with_update.out
batch_norm_no_update
batch_norm_no_update.out
batch_norm_backward
```
Note that this commit only adds this op and its variants,
but does not actually change the decomps to produce these
ops in the graph. This will be done after the 2 week FC
window, and the ops used in the old stack is planned to
be removed after the 6 month BC window.
Test Plan: `OpInfo` tests for `batch_norm_with_update`.
Reviewers: albanD, bdhirsh
Subscribers: albanD, bdhirsh, supriyar
Tasks: https://github.com/pytorch/pytorch/issues/111384
Co-authored-by: Tugsbayasgalan Manlaibaatar <tmanlaibaatar@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116092
Approved by: https://github.com/bdhirsh, https://github.com/albanD
**Summary:**
This commit simplifies the existing decomposition hierarchy
of batch norm ops by adding a single, backend agnostic op:
`batch_norm_with_update`. The existing hierarchy looks like:
```
aten.batch_norm ->
aten._batch_norm_impl_index ->
[
aten.native_batch_norm ->
aten._native_batch_norm_legit (export only) ->
_batch_norm_legit_cpu/cuda (kernels, export only) ->
_batch_norm_cpu/cuda (kernels)
] OR
[ aten.cudnn_batch_norm ] OR
[ aten.miopen_batch_norm ]
```
Aside from complexity, an important problem with the
above decomposition hierarchy is cuda numerics in
export flows. We observed significantly worse convergence
when training a mobilenetv2-like model when using the
`_batch_norm_cuda` kernel instead of the `cudnn_batch_norm`
kernel. This means users who export their models on CPU
first then move the models to cuda later may silently
see worse accuracies even when cudnn is installed,
because they are using the worse kernel. This issue is
summarized in https://github.com/pytorch/pytorch/issues/111384.
Instead, the new hierarchy proposed by consolidating
existing batch norm ops will look like:
```
aten.batch_norm ->
aten.batch_norm_with_update ->
[ _batch_norm_cpu (kernel) ] OR
[ _batch_norm_cuda (kernel) ] OR
[ cudnn_batch_norm (kernel) ] OR
[ miopen_batch_norm (kernel) ]
```
The new op `batch_norm_with_update` hides backend
implementation details and automatically picks the right
kernel based on what is installed. This commit also adds
the following variants to this op:
```
batch_norm_with_update_functional
batch_norm_with_update.out
batch_norm_no_update
batch_norm_no_update.out
batch_norm_backward
```
Note that this commit only adds this op and its variants,
but does not actually change the decomps to produce these
ops in the graph. This will be done after the 2 week FC
window, and the ops used in the old stack is planned to
be removed after the 6 month BC window.
Test Plan: `OpInfo` tests for `batch_norm_with_update`.
Reviewers: albanD, bdhirsh
Subscribers: albanD, bdhirsh, supriyar
Tasks: https://github.com/pytorch/pytorch/issues/111384
Co-authored-by: Tugsbayasgalan Manlaibaatar <tmanlaibaatar@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116092
Approved by: https://github.com/bdhirsh, https://github.com/albanD
The main thread and the autograd one are latency critical threads. They launch CPU/GPU/Accelerator kernels and if for some reason they get preempted, the rank can become a straggler in a distributed training application. By naming these threads we can debug performance issues that impact the latency sensitive threads.
I used Kineto traces to verify if the thread names were propagated:
<img width="851" alt="Screenshot 2024-03-04 at 3 07 43 PM" src="https://github.com/pytorch/pytorch/assets/23515689/68b4a09c-b8e5-4f14-a5c0-6593f866c03f">
Also:
```
nvidia-smi
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 3065920 C ...me#python#py_version_3_10 1968MiB |
| 1 N/A N/A 3065926 C ...me#python#py_version_3_10 1978MiB |
| 2 N/A N/A 3065930 C ...me#python#py_version_3_10 2084MiB |
| 3 N/A N/A 3065936 C ...me#python#py_version_3_10 2016MiB |
| 4 N/A N/A 3065939 C ...me#python#py_version_3_10 1998MiB |
| 5 N/A N/A 3065943 C ...me#python#py_version_3_10 2070MiB |
| 6 N/A N/A 3065948 C ...me#python#py_version_3_10 2026MiB |
| 7 N/A N/A 3065952 C ...me#python#py_version_3_10 2070MiB |
+-----------------------------------------------------------------------------+
[me@myhost ~]$ ps -T -p 3065920
PID SPID TTY TIME CMD
3065920 3065920 pts/14 00:01:04 pt_main_thread
...
3065920 3092181 pts/14 00:00:40 pt_autograd_d0
3065920 3092182 pts/14 00:00:00 pt_autograd_d1
3065920 3092183 pts/14 00:00:00 pt_autograd_d2
3065920 3092184 pts/14 00:00:00 pt_autograd_d3
3065920 3092185 pts/14 00:00:00 pt_autograd_d4
3065920 3092186 pts/14 00:00:00 pt_autograd_d5
3065920 3092187 pts/14 00:00:00 pt_autograd_d6
3065920 3092188 pts/14 00:00:00 pt_autograd_d7
...
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121170
Approved by: https://github.com/albanD
Fixes#115331.
This PR increases the number of valid GPU devices to 512 (from 64) in order to future-proof PyTorch for providers that offer [single nodes with a large device count](https://www.tensorwave.com/). Until now, `DeviceIndex` was an `int8_t`, thus multiple changes were necessary:
- `DeviceIndex` changed to `int16_t`. Updated consumers that assume it to be an `int8_t`.
- Updated bounds checking for `torch.device()` in the Python frontend. Right now, we allow funny things like `torch.device('cpu', 200).index == -56`, which is undefined behavior. I inserted some checks to only allow values between 0 and `c10::Device::MAX_NUM_DEVICES - 1`.
- Updated the `ArgumentInfo` struct as it hardcodes the device index as 8 bit field [^1]. Might be a breaking change, not sure if users rely on this.
- Introduced `c10::Device::MAX_NUM_DEVICES` as a replacement for the old `C10_COMPILE_TIME_MAX_GPUS`
[^1]: This field was unsigned, so I guess this has also been undef behavior the whole time? Our default device index is -1, so this always wrapped around to 255 when written to the `ArgumentInfo` struct. When I switched the `DeviceIndex` to `int16_t`, it actually stayed 255 after unpacking from `ArgumentInfo` again, as the `DeviceIndex` was now wide enough that it didn't wrap back to -1.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119639
Approved by: https://github.com/cyyever, https://github.com/albanD, https://github.com/huydhn
# Motivation
As mentioned in [[RFC] Intel GPU Runtime Upstreaming](https://github.com/pytorch/pytorch/issues/114842), the next runtime component we would like to upstream is `Event` which handles the status of an operation that is being executed. Typically, in some circumstances, we can fine-grain control of the operation execution via `Event`.
# Design
`XPUEvent` is a movable but not a copyable wrapper around sycl event. It should be created lazily on an XPU device when recording an `XPUStream`. Meanwhile, `XPUEvent` can wait for another `XPUEvent` or all the submitted kernels on an `XPUStream` to complete. Align to the other backend, the C++ files related to `Event` will be placed in `aten/src/ATen/xpu` folder. For frontend code, `XPUEvent` runtime API will be bound to Python `torch.xpu.Event`. The corresponding C++ code will be placed in `torch/csrc/xpu/Event.cpp` and Python code will be placed in `torch/xpu/streams.py` respectively.
# Additional Context
It is worth mentioning that the `elapsed_time` method is temporarily not supported by `XPUEvent`. We will be adding support for it soon. Meanwhile `XPUEvent` doesn't support IPC from different processes. For the other parts, we have almost a 1:1 mapping with CUDA.
lack of the below APIs:
- `torch.cuda.Event.ipc_handle`
- `CUDAEvent`'s constructor with `IpcEventHandle`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117734
Approved by: https://github.com/EikanWang, https://github.com/gujinghui, https://github.com/jgong5, https://github.com/malfet
ghstack dependencies: #117611, #117619
# Motivation
According to [[1/2] Intel GPU Runtime Upstreaming for Stream](https://github.com/pytorch/pytorch/pull/117611), as mentioned in [[RFC] Intel GPU Runtime Upstreaming](https://github.com/pytorch/pytorch/issues/114842), the second PR covers the changes under `python frontend`.
# Design
Currently, it primarily offers stream-related APIs, including
- `torch.xpu.StreamContext`
- `torch.xpu.current_stream`
- `torch.xpu.set_stream`
- `torch.xpu.synchronize`
- `torch._C._xpu_getCurrentRawStream`
# Additional Context
We will implement functions like `torch.xpu.Stream.wait_event`, `torch.xpu.Stream.wait_stream`, and `torch.xpu.Stream.record_event` in the next PR related with `Event`.
The differences with CUDA:
no default and external stream in XPU and lack of below APIs:
- `torch.cuda.ExternalStream`
- `torch.cuda.default_stream`
- `toch.cuda.is_current_stream_capturing`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117619
Approved by: https://github.com/EikanWang, https://github.com/jgong5, https://github.com/gujinghui, https://github.com/albanD
ghstack dependencies: #117611
# Motivation
According to [[1/4] Intel GPU Runtime Upstreaming for Device](https://github.com/pytorch/pytorch/pull/116019), As mentioned in [[RFC] Intel GPU Runtime Upstreaming](https://github.com/pytorch/pytorch/issues/114842), this third PR covers the changes under `libtorch_python`.
# Design
This PR primarily offers device-related APIs in python frontend, including
- `torch.xpu.is_available`
- `torch.xpu.device_count`
- `torch.xpu.current_device`
- `torch.xpu.set_device`
- `torch.xpu.device`
- `torch.xpu.device_of`
- `torch.xpu.get_device_name`
- `torch.xpu.get_device_capability`
- `torch.xpu.get_device_properties`
- ====================
- `torch.xpu._DeviceGuard`
- `torch.xpu._is_compiled`
- `torch.xpu._get_device`
# Additional Context
We will implement the support of lazy initialization in the next PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116850
Approved by: https://github.com/EikanWang, https://github.com/jgong5, https://github.com/gujinghui, https://github.com/malfet
# Summary
Simplification of Backend Selection
This PR deprecates the `torch.backends/cuda/sdp_kernel` context manager and replaces it with a new context manager `torch.nn.attention.sdpa_kernel`. This context manager also changes the api for this context manager.
For `sdp_kernel` one would specify the backend choice by taking the negation of what kernel they would like to run. The purpose of this backend manager was to only to be a debugging tool, "turn off the math backend" and see if you can run one of the fused implementations.
Problems:
- This pattern makes sense if majority of users don't care to know anything about the backends that can be run. However, if users are seeking to use this context manager then they are explicitly trying to run a specific backend.
- This is not scalable. We are working on adding the cudnn backend and this API makes it so so that more implementations will need to be turned off if user wants to explicitly run a given backend.
- Discoverability of the current context manager. It is somewhat un-intutive that this backend manager is in backends/cuda/init when this now also controls the CPU fused kernel behavior. I think centralizing to attention namespace will be helpful.
Other concerns:
- Typically backends (kernels) for operators are entirely hidden from users and implementation details of the framework. We have exposed this to users already, albeit not by default and with beta warnings. Does making backends choices even more explicit lead to problems when we potentially want to remove existing backends, (perhaps inputs shapes will get covered by newer backends).
A nice side effect is now that we aren't using the `BACKEND_MAP` in test_transformers many, many dynamo failures are passing for CPU tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114689
Approved by: https://github.com/cpuhrsch
This PR intends to fix the following issue when swapping two tensors
```python
>>> import torch
>>> torch.manual_seed(5)
>>> t1 = torch.randn(2)
>>> t2 = torch.randn(3)
>>> t1
tensor([-0.4868, -0.6038])
>>> t2
tensor([-0.5581, 0.6675, -0.1974])
>>> torch.utils.swap_tensors(t1, t2)
>>> t1
tensor([-0.5581, 0.6675, -0.1974])
>>> t2
tensor([-0.4868, -0.6038])
>>> t1.fill_(0.5) # t1 back to its unswapped state :o
tensor([-0.4868, -0.6038])
```
What happens here is that in `THPVariable_Wrap` (which is used when going back from C++ --> Python), we check if the TensorImpl of the tensor to be returned already has a pointer to a PyObject in its PyObject slot. If this is the case then this object is returned.
57491d2046/torch/csrc/autograd/python_variable.cpp (L271-L292)
When we run any operation that returns the same TensorImpl (e.g. inplace op, `t.to(dtype=t.dtype)`, etc.), although `t1` now has `t2`'s TensorImpl, `t2`'s TensorImpl still has a reference to `t2`, so when we do the op on `t1` and `THPVariable_Wrap` attempts to return the pointer to the TensorImpl's PyObject, we return a pointer to `t2` instead.
The TensorImpl should have the PyObjects in their PyObjectSlots swapped as well in `swap_tensors`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116955
Approved by: https://github.com/albanD
Summary: Now we can allocate an AOTIModelContainerRunner object instead of relying on torch.utils.cpp_extension.load_inline. Also renamed AOTInductorModelRunner to AOTIRunnerUtil in this PR.
Test Plan: CI
Reviewed By: khabinov
Differential Revision: D52339116
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116269
Approved by: https://github.com/khabinov
When exporting a model with a convolution kernel on cpu, if mkldnn is disabled and nnpack is enabled, export will go down the nnpack optimized convolution kernel for certain shapes ((code pointer)[cd449e260c/aten/src/ATen/native/Convolution.cpp (L542-L552)]). This means that we will automatically create a guard on that certain shape. If users want to export without any restrictions, one option is to disable nnpack. However, no config function exists for this, so this PR is adding a config function, similar to the `set_mkldnn_enabled` function.
Original context is in https://fb.workplace.com/groups/1075192433118967/posts/1349589822345892/?comment_id=1349597102345164&reply_comment_id=1349677642337110.
To test the flag, the following script runs successfully:
```
import os
import torch
from torchvision.models import ResNet18_Weights, resnet18
torch.set_float32_matmul_precision("high")
model = resnet18(weights=ResNet18_Weights.DEFAULT)
model.eval()
with torch.no_grad():
# device = "cuda" if torch.cuda.is_available() else "cpu"
torch.backends.mkldnn.set_flags(False)
torch.backends.nnpack.set_flags(False) # <--- Added config
device = "cpu"
model = model.to(device=device)
example_inputs = (torch.randn(2, 3, 224, 224, device=device),)
batch_dim = torch.export.Dim("batch", min=2, max=32)
so_path = torch._export.aot_compile(
model,
example_inputs,
# Specify the first dimension of the input x as dynamic
dynamic_shapes={"x": {0: batch_dim}},
# Specify the generated shared library path
options={
"aot_inductor.output_path": os.path.join(os.getcwd(), "resnet18_pt2.so"),
"max_autotune": True,
},
)
```
I'm not sure who to add as reviewer, so please feel free to add whoever is relevant!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116152
Approved by: https://github.com/malfet
Fixes#50051.
This PR is based on #50320 and I address the last feedback.
On Windows it is enabled by default. Can be enabled or disabled via USE_CUSTOM_TERMINATE env variable.
This PR adds support for overriding the terminate handler in order to log uncaught exceptions in the threads.
If an exception is thrown and not caught, it will print <Unhandled exception caught in c10/util/AbortHandler.h>
The point of doing this is that in issue #50051, exceptions were thrown but not logged. With this logging system it will be easier to debug it in the future.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/101332
Approved by: https://github.com/albanD, https://github.com/malfet
This PR is proposing a new approach to solve the nn/optim only linked by python object identity problem.
The idea is to have a function that can swap the content of two Tensors t1 and t2 while preserving all the old references.
This would allow us to swap the `model.weight` with a new Tensor (can be any subclass of Tensor and any TensorImpl (xla, sparse, nested tensorimpl would work)). The use within nn will be done in a follow up.
This is done by swapping the whole content of the PyObject and then putting back the fields associated with external references (refcount, gc tracking and weakrefs).
Note that we have to properly handle all the cases where there is memory used before the public pointer PyObject* and where the PyObject is bigger due to dict/weakref being inlined (older CPython version) or due to slots.
The main limitation of this approach is that the number of slots need to match for the objects being swapped and thus limit usage of slots in subclasses.
Draft right now to see what @colesbury thinks about doing this?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111747
Approved by: https://github.com/colesbury
This is the cheap and cheerful implementation, which is only enabled on TORCH_SHOW_CPP_STACKTRACES, because it *eagerly* symbolizes immediately at exception throw time, even if the exception will end up getting caught. It would be better to do this lazily and only symbolize when we try to print the exception, but that requires a more involved refactor of c10::Error that I don't feel like doing.
Compare the output before:
```
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0x95 (0x7fa21b99d975 in /data/users/ezyang/c/pytorch/torch/lib/libc10.so)
frame #1: c10::TensorImpl::throw_cannot_call_with_symbolic(char const*) const + 0x8d (0x7fa21b951269 in /data/users/ezyang/c/pytorch/torch/lib/libc10.so)
frame #2: c10::TensorImpl::sizes_custom() const + 0x9f (0x7fa21b9770df in /data/users/ezyang/c/pytorch/torch/lib/libc10.so)
frame #3: at::meta::structured_mm::meta(at::Tensor const&, at::Tensor const&) + 0x31e (0x7fa20a202a8e in /data/users/ezyang/c/pytorch/torch/lib/libtorch_cpu.so)
frame #4: <unknown function> + 0x29f34de (0x7fa20b5f34de in /data/users/ezyang/c/pytorch/torch/lib/libtorch_cpu.so)
frame #5: <unknown function> + 0x2a1fd8e (0x7fa20b61fd8e in /data/users/ezyang/c/pytorch/torch/lib/libtorch_cpu.so)
frame #6: <unknown function> + 0x6b907b (0x7fa2142b907b in /data/users/ezyang/c/pytorch/torch/lib/libtorch_python.so)
frame #7: <unknown function> + 0x6b6175 (0x7fa2142b6175 in /data/users/ezyang/c/pytorch/torch/lib/libtorch_python.so)
```
and after:
```
#4 c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) from ??:0
#5 c10::TensorImpl::throw_cannot_call_with_symbolic(char const*) const from ??:0
#6 c10::TensorImpl::sizes_custom() const [clone .localalias] from TensorImpl.cpp:0
#7 at::meta::structured_mm::meta(at::Tensor const&, at::Tensor const&) from ??:0
#8 at::(anonymous namespace)::wrapper_Meta_mm_out_out(at::Tensor const&, at::Tensor const&, at::Tensor&) from RegisterMeta.cpp:0
#9 c10::impl::make_boxed_from_unboxed_functor<c10::impl::detail::WrapFunctionIntoFunctor_<c10::CompileTimeFunctionPointer<at::Tensor& (at::Tensor const&, at::Tensor const&, at::Tensor&), &at::(anonymous namespace)::wrapper_Meta_mm_out_out>, at::Tensor&, c10::guts::typelist::typelist<at::Tensor const&, at::Tensor const&, at::Tensor&> >, false>::call(c10::OperatorKernel*, c10::OperatorHandle const&, c10::DispatchKeySet, std::vector<c10::IValue, std::allocator<c10::IValue> >*) from RegisterMeta.cpp:0
```
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113207
Approved by: https://github.com/Skylion007
In almost all cases this is only included for writing the output formatter, which
only uses `std::ostream` so including `<ostream>` is sufficient.
The istream header is ~1000 lines so the difference is non-trivial.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106914
Approved by: https://github.com/lezcano
In almost all cases this is only included for writing the output formatter, which
only uses `std::ostream` so including `<ostream>` is sufficient.
The istream header is ~1000 lines so the difference is non-trivial.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106914
Approved by: https://github.com/lezcano
This is a reland of https://github.com/pytorch/pytorch/pull/100007 with a build fix for Windows debug builds.
`at::native::ParamsHash` only works on structs with standard layout, but `std::string` isn't one in Visual C++ debug builds, which one can easily verified by running something like:
```cpp
#define _DEBUG
#include <type_traits>
#include <string>
static_assert(std::is_standard_layout_v<std::string>, "Oh noes");
```
If above conditon is not met, instead of printing a static_assert output, VC++ raises a very cryptic compilation errors, see https://github.com/pytorch/pytorch/pull/100007#discussion_r1227116292 for more detail.
Also, using `std::hash` for string should result in a faster hash function.
(cherry picked from commit 74b7a6c75e)
<!--
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### <samp>🤖 Generated by Copilot at 5914771</samp>
This pull request introduces a new function `_group_tensors_by_device_and_dtype` that can group tensors by their device and dtype, and updates the `foreach` utilities and several optimizers to use this function. The goal is to improve the performance, readability, and compatibility of the code that handles tensors with different properties. The pull request also adds a test case and type annotations for the new function, and some error checks for the `fused` argument in Adam and AdamW.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103912
Approved by: https://github.com/janeyx99
**Summary**
- Update the quantization document that default qconfig with oneDNN backend is recommended to be used on CPUs with Vector Neural Network Instruction support.
- Add the warning message when user uses default qconfig with oneDNN backend on CPU without Vector Neural Network Instruction support.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103653
Approved by: https://github.com/jgong5, https://github.com/malfet
Summary: The new logger allows passing metadata into the api usage logger. The immediate use case is to pass the serialization_id to the save and load events to be enable tracking serialized models in API events. It could be extended to add more metadata in the future.
Test Plan:
```
buck2 test @//mode/dev //caffe2/caffe2/serialize:inline_container_test
```
Reviewed By: davidberard98
Differential Revision: D45683697
Pull Request resolved: https://github.com/pytorch/pytorch/pull/101762
Approved by: https://github.com/davidberard98
Description:
- As suggested by Nikita, created `torch.backends.cpu` submodule and exposed `get_cpu_capability`.
- In torchvision Resize method we want to know current cpu capability in order to pick appropriate codepath depending on cpu capablities
Newly coded vectorized resize of uint8 images on AVX2 supported CPUs is now faster than older way (uint8->float->resize->uint8). However, on non-avx hardware (e.g. Mac M1) certain configs are slower using native uint8.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100164
Approved by: https://github.com/albanD, https://github.com/malfet
This PR introduces **-Wmissing-prototypes** of clang-tidy to prevent further coding errors such as the one fixed by PR #96714.
<!--
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### <samp>🤖 Generated by Copilot at fd2cf2a</samp>
This pull request makes several internal functions static to improve performance and avoid name clashes. It also fixes some typos, formatting, and missing includes in various files. It adds a new .clang-tidy check to warn about missing prototypes for non-static functions.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96805
Approved by: https://github.com/malfet, https://github.com/albanD
Fixes #ISSUE_NUMBER
1、add amp support for custom backend
2、optimize the file `backend_registration.py`, and rename it with `custom_backend_registration.py`. And then we would register other funcs for custom backend.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96188
Approved by: https://github.com/bdhirsh
Fixes #ISSUE_NUMBER
1、add amp support for custom backend
2、optimize the file `backend_registration.py`, and rename it with `custom_backend_registration.py`. And then we would register other funcs for custom backend.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96188
Approved by: https://github.com/bdhirsh
When we checkpoint the state of the private pool allocator, we will need to make sure that its current live allocated blocks will get properly cleaned up when the tensors they correspond to die. Return DataPtrs for these new allocated blocks that the callee can swap onto live Tensors.
The exact api for setting the checkpoint can be manipulated after this as the cudagraph implementation is built out, but this at least shows its sufficiently general.
This should be the last PR touching cuda caching allocator necessary for new cudagraphs integration.
Differential Revision: [D43999888](https://our.internmc.facebook.com/intern/diff/D43999888)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95020
Approved by: https://github.com/zdevito
This PR do two things:
1. It moves some Windows warning suppression from various CMake files into the main CMakeList.txt, following the conventions of gcc and clang.
2. It fixes some Windows warnings in the source code. Most importantly, it fixes lots of dll warnings by adjusting C10_API to TORCH_API or TORCH_PYTHON_API. There are still some dll warnings because some TORCH_API functions are actually built as part of libtorch_python
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94927
Approved by: https://github.com/malfet
- This PR is a prerequisite for the upcoming Memory Leak Detection PR.
- Enable global manual seeding via `torch.manual_seed()` + test case
- Add `torch.mps.synchronize()` to wait for MPS stream to finish + test case
- Enable the following python interfaces for MPS:
`torch.mps.[get_rng_state(), set_rng_state(), synchronize(), manual_seed(), seed()]`
- Added some test cases in test_mps.py
- Added `mps.rst` to document the `torch.mps` module.
- Fixed the failure with `test_public_bindings.py`
Description of new files added:
- `torch/csrc/mps/Module.cpp`: implements `torch._C` module functions for `torch.mps` and `torch.backends.mps`.
- `torch/mps/__init__.py`: implements Python bindings for `torch.mps` module.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94417
Approved by: https://github.com/albanD
- This PR is a prerequisite for the upcoming Memory Leak Detection PR.
- Enable global manual seeding via `torch.manual_seed()` + test case
- Add `torch.mps.synchronize()` to wait for MPS stream to finish + test case
- Enable the following python interfaces for MPS:
`torch.mps.[get_rng_state(), set_rng_state(), synchronize(), manual_seed(), seed()]`
- Added some test cases in test_mps.py
- Added `mps.rst` to document the `torch.mps` module.
- Fixed the failure with `test_public_bindings.py`
Description of new files added:
- `torch/csrc/mps/Module.cpp`: implements `torch._C` module functions for `torch.mps` and `torch.backends.mps`.
- `torch/mps/__init__.py`: implements Python bindings for `torch.mps` module.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94417
Approved by: https://github.com/albanD
For the cudagraphs implementation, we would like to reuse objects that are defined in python across the forward and backward. The backward is run in a different thread, so to handle this we add an api for copying over arbitrary python objects in pytorch's thread local state, in the same way that C++ objects are copied over currently.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89169
Approved by: https://github.com/albanD
This PR is a copy of https://github.com/pytorch/pytorch/pull/90849 that merge was reverted.
The PR adds "check sparse tensor invariants" flag to Context that when enabled will trigger sparse tensor data invariants checks in unsafe methods of constructing sparse COO/CSR/CSC/BSR/BSC tensors. The feature includes the following changes to UI:
`torch.sparse.check_sparse_tensor_invariants` class provides different ways to enable/disable the invariant checking.
`torch.sparse_coo/csr/csc/bsr/bsc/compressed_tensor` functions have a new optional argument `check_invariants` to enable/disable the invariant checks explicitly. When the `check_invariants` argument is specified, the global state of the feature is temporarily overridden.
The PR fixes https://github.com/pytorch/pytorch/issues/90833
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92094
Approved by: https://github.com/cpuhrsch
This PR adds "check sparse tensor invariants" flag to Context that when enabled will trigger sparse tensor data invariants checks in unsafe methods of constructing sparse COO/CSR/CSC/BSR/BSC tensors. The feature includes the following changes to UI:
- `torch.enable_check_sparse_tensor_invariants` and `torch.is_check_sparse_tensor_invariants_enabled` functions to globally enable/disable the invariant checks and to retrieve the state of the feature, respectively
- `torch.sparse_coo/csr/csc/bsr/bsc/compressed_tensor` functions have a new optional argument `check_invariants` to enable/disable the invariant checks explicitly. When the `check_invariants` argument is specified, the global state of the feature is temporarily overridden.
The PR also fixes https://github.com/pytorch/pytorch/issues/90833
# Main issue
*The following content is outdated after merging the PRs in this ghstack but kept for the record.*
The importance of this feature is that when enabling the invariants checks by default, say, via
<details>
```
$ git diff
diff --git a/torch/__init__.py b/torch/__init__.py
index c8543057c7..19a91d0482 100644
--- a/torch/__init__.py
+++ b/torch/__init__.py
@@ -1239,3 +1239,8 @@ if 'TORCH_CUDA_SANITIZER' in os.environ:
# Populate magic methods on SymInt and SymFloat
import torch.fx.experimental.symbolic_shapes
+
+# temporarily enable sparse tensor arguments validation in unsafe
+# constructors:
+
+torch._C._set_check_sparse_tensor_invariants(True)
```
</details>
a massive number of test failures/errors occur in test_sparse_csr.py tests:
```
$ pytest -sv test/test_sparse_csr.py
<snip>
==== 4293 failed, 1557 passed, 237 skipped, 2744 errors in 69.71s (0:01:09) ====
```
that means that we are silently constructing sparse compressed tensors that do not satisfy the sparse tensor invariants. In particular, the following errors are raised:
```
AssertionError: "resize_as_sparse_compressed_tensor_: self and src must have the same layout" does not match "expected values to be a strided and contiguous tensor"
RuntimeError: CUDA error: device-side assert triggered
RuntimeError: `col_indices[..., crow_indices[..., i - 1]:crow_indices[..., i]] for all i = 1, ..., nrows are sorted and distinct along the last dimension values` is not satisfied.
RuntimeError: expected col_indices to be a strided and contiguous tensor
RuntimeError: expected row_indices to be a strided and contiguous tensor
RuntimeError: expected values to be a strided and contiguous tensor
RuntimeError: for_each: failed to synchronize: cudaErrorAssert: device-side assert triggered
RuntimeError: tensor dimensionality must be sum of batch, base, and dense dimensionalities (=0 + 2 + 0) but got 3
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90849
Approved by: https://github.com/amjames, https://github.com/cpuhrsch
Essentially the same change as #67946, except that the default is to disallow reduced precision reductions in `BFloat16` GEMMs (for now). If performance is severely regressed, we can change the default, but this option appears to be necessary to pass some `addmm` `BFloat16` tests on H100.
CC @ptrblck @ngimel
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89172
Approved by: https://github.com/ngimel
This allows to know at any point during the backward pass what is running and where the Node currently running was created at:
```python
import torch
from torch.utils._python_dispatch import TorchDispatchMode
from torch.autograd import detect_anomaly
class MyMode(TorchDispatchMode):
def __torch_dispatch__(self, func, types, args, kwargs=None):
node = torch._C._current_autograd_node()
print(f"Running {func} from within {node}")
if node is not None:
print("The Node was created at:")
print("\n ".join(node.metadata["traceback_"]))
return func(*args, **kwargs or {})
with MyMode(), detect_anomaly():
print("FW")
a = torch.rand(10, requires_grad=True)
b = a.mul(2)
b = b.div(3)
b = b.sum()
print("BW")
b.backward()
```
Gives
```
$ python foo.py
foo.py:15: UserWarning: Anomaly Detection has been enabled. This mode will increase the runtime and should only be enabled for debugging.
with MyMode(), detect_anomaly():
FW
Running aten.rand.default from within None
Running aten.mul.Tensor from within None
Running aten.div.Tensor from within None
Running aten.sum.default from within None
BW
Running aten.ones_like.default from within None
Running aten.expand.default from within <SumBackward0 object at 0x7fa40c0c6dc0>
The Node was created at:
File "foo.py", line 20, in <module>
b = b.sum()
Running aten.isnan.default from within <SumBackward0 object at 0x7fa40c0c6500>
The Node was created at:
File "foo.py", line 20, in <module>
b = b.sum()
Running aten.any.default from within <SumBackward0 object at 0x7fa32b23a780>
The Node was created at:
File "foo.py", line 20, in <module>
b = b.sum()
Running aten._local_scalar_dense.default from within <SumBackward0 object at 0x7fa40c0c9190>
The Node was created at:
File "foo.py", line 20, in <module>
b = b.sum()
Running aten.div.Tensor from within <DivBackward0 object at 0x7fa40c0c9190>
The Node was created at:
File "foo.py", line 19, in <module>
b = b.div(3)
Running aten.isnan.default from within <DivBackward0 object at 0x7fa40c0c9190>
The Node was created at:
File "foo.py", line 19, in <module>
b = b.div(3)
Running aten.any.default from within <DivBackward0 object at 0x7fa40c0c9190>
The Node was created at:
File "foo.py", line 19, in <module>
b = b.div(3)
Running aten._local_scalar_dense.default from within <DivBackward0 object at 0x7fa40c0c9190>
The Node was created at:
File "foo.py", line 19, in <module>
b = b.div(3)
Running aten.mul.Tensor from within <MulBackward0 object at 0x7fa40c0c9190>
The Node was created at:
File "foo.py", line 18, in <module>
b = a.mul(2)
Running aten.isnan.default from within <MulBackward0 object at 0x7fa40c0c9190>
The Node was created at:
File "foo.py", line 18, in <module>
b = a.mul(2)
Running aten.any.default from within <MulBackward0 object at 0x7fa40c0c9190>
The Node was created at:
File "foo.py", line 18, in <module>
b = a.mul(2)
Running aten._local_scalar_dense.default from within <MulBackward0 object at 0x7fa40c0c9190>
The Node was created at:
File "foo.py", line 18, in <module>
b = a.mul(2)
Running aten.detach.default from within <AccumulateGrad object at 0x7fa40c0c9730>
The Node was created at:
File "foo.py", line 18, in <module>
b = a.mul(2)
Running aten.detach.default from within <AccumulateGrad object at 0x7fa40c0c94b0>
The Node was created at:
File "foo.py", line 18, in <module>
b = a.mul(2)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90867
Approved by: https://github.com/soulitzer
We have an older torch.vmap implementation. It is no longer supported.
It still needs to exist somewhere for the sake of BC with
torch.autograd.functional.
This PR makes it clear what files are meant for implementing the old
vmap implementation. I've seen a couple of PRs recently adding support
for the old vmap implementation, so this will lessen the confusion.
Test Plan:
- CI
Pull Request resolved: https://github.com/pytorch/pytorch/pull/90324
Approved by: https://github.com/samdow
We will need this to implement a convolution meta function that
is SymInt aware. I use templates so that regular convolution code
is not affected by the change. No tests for symbolic ints directly; that will
come in a subsequent PR which also needs to refactor fake tensors.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89069
Approved by: https://github.com/SherlockNoMad
Fixes#81690
TODO:
* [x] C++ Unpickler Fix (locally tested pickled in Python and unpickled in C++)
* [x] C++ Pickler Fix (locally tested pickled in C++ and unpickled in Python)
* [x] Do quant_tensor, sparse_tensor, etc require similar changes? (Sparse and Quant don't need this)
* [x] Add Comments
* [x] How to make sure C++ and Python are in sync? (Functions in `pickler.h` help in getting and setting Tensor Metadata (math-bits for now) on a tensor. They are the only place which should handle this.)
Notes:
Quant Tensor don't support complex dtypes and for float they segfault with `_neg_view` : https://github.com/pytorch/pytorch/issues/88484
Sparse Tensor:
```python
>>> a = torch.tensor([[0, 2.], [3j, 0]]).to_sparse()
>>> a.conj().is_conj()
False
>>> a._neg_view()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NotImplementedError: Cannot access storage of SparseTensorImpl
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88182
Approved by: https://github.com/ezyang, https://github.com/anjali411
The logic for determine conv backend and therefore output striding is very complex. It depends on build settings, input striding/contiguity, sizes, etc. Eventually we should port that logic to the meta impl for dynamic shapes but that will require a lot more work and keeping the implementations in sync. See https://github.com/pytorch/torchdynamo/issues/1701
This is a prerequisite to removing the inductor conv stride propagation and more general fake tensor for inductor propagation. In that PR, the meta impls for cpu conv give incorrect striding which led to test failures (https://github.com/pytorch/pytorch/pull/87083).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87305
Approved by: https://github.com/ezyang
# Summary
Add in a torch.backends.cuda flag and update context manager to pic between the three implementations of the scaled_dot_product_attention.
cc @cpuhrsch @jbschlosser @bhosmer @mikaylagawarecki
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87946
Approved by: https://github.com/cpuhrsch
In this PR:
- graph_task stores graph roots on construction so that we can later traverse through the graph
- before the nodes are returned, they needed to be converted from raw_ptr to shared_ptr, and this should be OK because the graph is guaranteed to be alive
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87507
Approved by: https://github.com/albanD
This API adds some improvements to external backends who are building C++ backends out of tree using the `PrivateUse1` dispatch key.
The docs and linked examples go over the API in more detail, but you should be able to use it like:
```
# This should probably be in the __init__.py file of a external backend's python package
> torch.register_privateuse1_backend("foo")`
# And it will allow the user to do this:
> a = torch.ones(2, device="foo")
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86992
Approved by: https://github.com/albanD
# Summary
- This code creates the runtime dispatch system for choosing a performant fused SDP kernel. The only choice of fused kernel is flash_attention. It also creates python flags and a context manager that can be used to turn off and on behavior for dispatch.
- This also adds support for flash_attention with dense tensors.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85984
Approved by: https://github.com/cpuhrsch
If you e.g. printed within a decomp which would call `in_kernel_invocation_manager`, on the exit from the manager it would unilaterally remove meta from the tls / set the tensor to return its real device. We should just restore what the existing state was.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85920
Approved by: https://github.com/ezyang, https://github.com/bdhirsh
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
This moves functorch's python bindings to torch/csrc/functorch/init.cpp.
Coming next is the torchdim move. I didn't do torchdim yet because
moving functorch's python bindings unblocks some other things that I
want to do first.
Test Plan:
- tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85426
Approved by: https://github.com/ezyang
From PR:
```
Note: [Fake Tensor Dispatch Keys]
In order to model the behavior of device-specific autocast
and autograd logic, we update the dispatch keys of FakeTensors
to reflect their fake device. This includes the BackendComponent
(DispatchKey::Meta -> DispatchKey::CUDA), and also the BackendComponent
related Autocast and Autograd keys. __torch__dispatch__ sits below
Autocast and Autograd, and is only invoked when we are at the
kernel for the BackendComponent. Then, we add Meta to the
thread-local dispatch include set to hit the meta kernel
instead of the kernel of the BackendComponent for the fake device.
```
Also adds the `conv1/2/3d.padding` operators to the Autocast rule set. Without that fix, the FakeTensor dtype would diverge.
See: https://github.com/pytorch/pytorch/issues/81608
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82449
Approved by: https://github.com/ezyang
We define specializations for pybind11 defined templates
(in particular, PYBIND11_DECLARE_HOLDER_TYPE) and consequently
it is important that these specializations *always* be #include'd
when making use of pybind11 templates whose behavior depends on
these specializations, otherwise we can cause an ODR violation.
The easiest way to ensure that all the specializations are always
loaded is to designate a header (in this case, torch/csrc/util/pybind.h)
that ensures the specializations are defined, and then add a lint
to ensure this header is included whenever pybind11 headers are
included.
The existing grep linter didn't have enough knobs to do this
conveniently, so I added some features. I'm open to suggestions
for how to structure the features better. The main changes:
- Added an --allowlist-pattern flag, which turns off the grep lint
if some other line exists. This is used to stop the grep
lint from complaining about pybind11 includes if the util
include already exists.
- Added --match-first-only flag, which lets grep only match against
the first matching line. This is because, even if there are multiple
includes that are problematic, I only need to fix one of them.
We don't /really/ need this, but when I was running lintrunner -a
to fixup the preexisting codebase it was annoying without this,
as the lintrunner overall driver fails if there are multiple edits
on the same file.
I excluded any files that didn't otherwise have a dependency on
torch/ATen, this was mostly caffe2 and the valgrind wrapper compat
bindings.
Note the grep replacement is kind of crappy, but clang-tidy lint
cleaned it up in most cases.
See also https://github.com/pybind/pybind11/issues/4099
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82552
Approved by: https://github.com/albanD
**RFC:
Problem statement**
Intel oneMKL and oneDNN are used to accelerate performance on Intel platforms. Both these 2 libraries provide verbose functionality to dump detailed operator execution information as well as execution time. These verbose messages are very helpful to performance profiling. However, the verbose functionality works for the entire execution. In many scenarios, though, we only would like to profile partial of the execution process. This feature is to expose PyTorch API functions to control oneDNN and oneMKL verbose functionality in runtime.
**Additional context**
The most used performance profiling steps are shown as the following code snippet:
```
def inference(model, inputs):
# step0 (optional): jit
model = torch.jit.trace(model, inputs)
# step1: warmup
for _ in range(100):
model(inputs)
# step2: performance profiling. We only care the profiling result, as well as oneDNN and oneMKL verbose messages, of this step
model(inputs)
# step3 (optional): benchmarking
t0 = time.time()
for _ in range(100):
model(inputs)
t1 = time.time()
print(‘dur: {}’.format((t1-t0)/100))
return model(inputs)
```
Since environment variables MKL_VERBOSE and DNNL_VERBOSE will be effect to the entire progress, we will get a great number of verbose messages for all of 101 iterations (if step3 is not involved). However, we only care about the verbose messages dumped in step2. It is very difficult to filter unnecessary verbose messages out if we are running into a complicated usages scenario. Also, jit trace will also bring more undesired verbose messages.
Furthermore, there are more complicated topologies or usages like cascaded topologies as below:
```
model1 = Model1()
model2 = Model2()
model3 = Model3()
x1 = inference(model1, x)
x2 = inference(model2, x1)
y = inference(model3, x2)
```
There are many cases that it is very hard to split these child topologies out. In this scenario, it is not possible to investigate performance of each individual topology with `DNNL_VERBOSE` and `MKL_VERBOSE`.
To solve this issue, oneDNN and oneMKL provide API functions to make it possible to control verbose functionality in runtime.
```
int mkl_verbose (int enable)
status dnnl::set_verbose(int level)
```
oneDNN and oneMKL print verbose messages to stdout when oneMKL or oneDNN ops are executed.
Sample verbose messages:
```
MKL_VERBOSE SGEMM(t,n,768,2048,3072,0x7fff64115800,0x7fa1aca58040,3072,0x1041f5c0,3072,0x7fff64115820,0x981f0c0,768) 8.52ms CNR:OFF Dyn:1 FastMM:1 TID:0 NThr:44
dnnl_verbose,exec,cpu,inner_product,brgemm:avx512_core,forward_training,src_f32::blocked:ab:f0 wei_f32::blocked:AB16b64a:f0 bia_f32::blocked:a:f0 dst_f32::blocked:ab:f0,,,mb16ic768oc768,0.0839844
```
**Design and implementation**
The design is to make python-interfaced wrap functions to invoke mkl_verbose and dnnl::set_verbose functions.
**Design concern**
- Need to add wrapper C++ functions for mkl_verbose and dnnl::set_verbose functions in torch/csrc and aten/csrc.
- Python API functions will be added to device-specific backends
- with torch.backends.mkl.verbose(1):
- with torch.backends.mkldnn.verbose(1):
**Use cases**
```
def inference(model, inputs):
# step0 (optional): jit
model = torch.jit.trace(model, inputs)
# step1: warmup
for _ in range(100):
model(inputs)
# step2: performance profiling
with torch.backends.mkl.verbose(1), torch.backends.mkldnn.verbose(1):
model(inputs)
# step3 (optional): benchmarking
t0 = time.time()
for _ in range(100):
model(inputs)
t1 = time.time()
print(‘dur: {}’.format((t1-t0)/100))
return model(inputs)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63212
Approved by: https://github.com/VitalyFedyunin, https://github.com/malfet
(reopening due to botched merge)
The cuDNN V8 API (main support merged in https://github.com/pytorch/pytorch/pull/60755) potentially exposes many more kernels with benchmark=True. While these additional kernels can improve performance, it is often unnecessary to run every kernel returned by the heuristic and doing so may degrade the user experience by causing the first model iteration to be very slow. To alleviate this issue, this PR introduces torch.backends.cudnn.benchmark_limit. benchmark_limit specifies the maximum number of working cuDNN kernels to try for a given workload, with the default being 10 (similar to what TensorFlow does). benchmark_limit = 0 yields the current behavior of trying every kernel returned by the heuristic.
CC @ptrblck @ngimel @xwang233
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77002
Approved by: https://github.com/ngimel
Fixes#68172. Generally, this corrects multiple flaky convolution unit test behavior seen on ROCm.
The MIOpen integration has been forcing benchmark=True when calling `torch._C._set_cudnn_benchmark(False)`, typically called by `torch.backends.cudnn.set_flags(enabled=True, benchmark=False)`. We now add support for MIOpen immediate mode to avoid benchmarking during MIOpen solution selection.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77438
Approved by: https://github.com/ngimel, https://github.com/malfet
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 3717688ade)
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
