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
Summary:
Rename _device_mesh.py to device_mesh.py, update all callsites, add documentation.
We created stubs for public class and methods in torch.distributed.device_mesh so that torch.distributed.device_mesh can be imported with or without distributed is available().
Original diff reverted: D51629761
Original PR reverted: https://github.com/pytorch/pytorch/pull/115099
Prior to landing, CI signals are all passed. Shipit added the "ci/trunk" label to the PR and DID NOT wait for it and went ahead committing. More context can be found in the reverted PR above.
Test Plan: CI.
Differential Revision: D51861018
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115193
Approved by: https://github.com/fegin
Adds a useful high level wrapper for calling `dist.save/load` with the correct storage readers and writers.
Instead of doing:
```
DCP.save(
state_dict={...},
storage_writer=StorageWriter(...)
)
DCP.load(
state_dict={...},
storage_reader=StorageReader(...)
)
```
We can now do:
```
checkpointer = Checkpointer(...)
checkpointer.save(state_dict={...})
checkpointer.load(state_dict={...})
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114603
Approved by: https://github.com/fegin, https://github.com/wz337
Summary:
Documenting the `Work` object
For a collective (broadcast, all_reduce, etc.) when async_op=True we return a `Work` object to which users can call `.wait()`, `.is_success()`, among other things but this class is not documented
Test Plan: Preview the docs build in OSS
Differential Revision: D51854974
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115172
Approved by: https://github.com/wconstab
# Summary
This PR introduces a new Tensor subclass that is designed to be used with torch.nn.functional.scaled_dot_product_attention. Currently we have a boolean `is_causal` flag that allows users to do do causal masking without the need to actually create the "realized" attention bias and pass into sdpa. We originally added this flag since there is native support in both fused kernels we support. This provides a big performance gain ( the kernels only need to iterate over ~0.5x the sequence, and for very large sequence lengths this can provide vary large memory improvements.
The flag was introduced when the early on in the kernel development and at the time it was implicitly meant to "upper_left" causal attention. This distinction only matters when the attention_bias is not square. For a more detailed break down see: https://github.com/pytorch/pytorch/issues/108108. The kernels default behavior has since changed, largely due to the rise of autogressive text generation. And unfortunately this would lead to a BC break. In the long term it may actually be beneficial to change the default meaning of `is_causal` to represent lower_right causal masking.
The larger theme though is laid here: https://github.com/pytorch/pytorch/issues/110681. The thesis being that there is alot of innovation in SDPA revolving around the attention_bias being used. This is the first in hopefully a few more attention_biases that we would like to add. The next interesting one would be `sliding_window` which is used by the popular mistral model family.
Results from benchmarking, I improved the meff_attention perf hence the slightly decreased max perf.
```Shell
+---------+--------------------+------------+-----------+-----------+-----------+-----------+----------------+----------+
| Type | Speedup | batch_size | num_heads | q_seq_len | k_seq_len | embed_dim | dtype | head_dim |
+---------+--------------------+------------+-----------+-----------+-----------+-----------+----------------+----------+
| Average | 1.2388050062214226 | | | | | | | |
| Max | 1.831672915579016 | 128 | 32 | 1024 | 2048 | 2048 | torch.bfloat16 | 64 |
| Min | 0.9430534166730135 | 1 | 16 | 256 | 416 | 2048 | torch.bfloat16 | 128 |
+---------+--------------------+------------+-----------+-----------+-----------+-----------+----------------+----------+
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114823
Approved by: https://github.com/cpuhrsch
Continuation of #112185, following the design in this [doc](https://docs.google.com/document/d/1ipSxcTzEMMOAPvxP-YJlD5JBZZmIGgh8Q34ixtOUCRo).
Summary:
* Introduce `SubclassSymbolicPolicy` containing separate dynamic dim / constraint policies for the outer and inner tensors
* Expand the automatic dynamic algorithm to recurse into inner tensors and produce one of these for a subclass instance
* Maintain legacy behavior for subclasses by recursively calling `mark_dynamic()` on inner tensors *of the same dim as outer* when `mark_dynamic(outer, ...)` is called
* Addresses this: 6a86cf00ad/torch/_dynamo/variables/builder.py (L1750)
* Add `outer_size` and `outer_stride` arguments to `__tensor_unflatten__()` so that you can find out what symbols were allocated for the outer size / stride (you are expected to return a tensor that compares equal to the outer symbols)
* Signatures now:
```python
# attrs is a list of inner tensor attributes on x; inner_tensor = getattr(x, attr)
# ctx is anything useful for rebuilding the class we want to guard on
attrs, ctx = x.__tensor_flatten__()
...
# inner_tensors is a dict of {attr -> tensor}
# ctx is taken unmodified from flattening and (eventually) guarded on
# outer_size is the expected size of the output; possibly symbolic
# outer_stride is the expected strides of the output; possibly symbolic
y = MySubclass.__tensor_unflatten__(inner_tensors, ctx, outer_size, outer_stride)
# at the __tensor_unflatten__() call-site in PT2, we assert y.shape == outer_size and y.stride() == outer_stride
# the assert simplifies symbols when there are relationships between outer and inner symbols
```
* Size info needed for `NestedTensor` at least, stride info needed for `DTensor` at least
* Punting on `outer_storage_offset` because storage_offset handling is horribly broken in PT2 right now
* ~~Add new `__tensor_mark_dynamic__()` to allow overriding the behavior of mark_dynamic on a per-subclass basis~~ (booted to future work)
* ~~Add guards for tensor subclasses by calling `__tensor_flatten__()` in the guard to test equality on `ctx`~~
* Now handled in #114469
* Next PR: add TENSOR_MATCH guards on inner tensors
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114311
Approved by: https://github.com/ezyang, https://github.com/drisspg, https://github.com/voznesenskym, https://github.com/bdhirsh
Summary:
Rename _device_mesh.py to device_mesh.py, update all callsites, adds documentation.
Original diff reverted: D51629761
Original PR reverted: https://github.com/pytorch/pytorch/pull/114991
It was failing because failing a public module binding tests in MacOS, and this is due to the change in import order for torch/distributed/fsdp/_common_utils.py. Since this original import would still work, we remove the changes in this file.
Test Plan: CI.
Differential Revision: D51825114
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115099
Approved by: https://github.com/wanchaol, https://github.com/fegin
This PR rewrites Tensor Parallel implementation. Tensor Parallel APIs
supposed to be a very thin-wrapper to DTensor APIs, but the current
implementation got too messy and buggy. It's really hard to debug what
went wrong when using it. It's crucially important for advanced users or
developers to understand the API and its implementation easily without
going through all different types of functions and utils, so that
they could trust what happen under the hood.
In particular this PR:
* Make ParallelStyle to be a real contract API for parallelize_module to
take, each concrete ParallelStyle only needs to implement `apply` to
apply the sharding to nn.Module, remove all non-necessary fields. This
also enable easier ParallelStyle authoring going forward.
* Keep the ColwiseParallel and RowwiseParallel public interface, but
refactor them in a way that makes the parameter sharding, inputs and
outputs handling lives within the style itself, so that it's easy to
understand how Linear/Embedding layers are sharded and how the inputs/outputs
transformations are performed.
* remove all those private _prepare_input/_prepare_output_fn fields for
both ColwiseParallel/RowwiseParallel. Since we throw deprecation
messages in nightly for a while and TP is on prototype release, the
fields are also private, it should be safe to remove them
* Refactor the recently landed PrepareModuleInput/Output style, change
output_layouts to desired_input/output_layouts, group
the function inside the style itself, no default arguments for these
two styles and user need to specify them to think about the sharding
layouts. Fixed bugs about not handling
`use_local_output` flag.
* Make default arguments be None instead of Placement object, this is
standard python practice to not have custom object instance as default
argument
* Remove all dead APIs (i.e. PairwiseParallel and SequenceParallel
style, all prepare input/output functions) as we throw deprecation
msgs for a while, and in the progress of removing all of them from the tests.
* throw deprecation warning for `tp_mesh_dim` as we recomemnd use device
mesh slice/indexing instead of manually specify mesh dim
* Rewrite all documentations for every ParallelStyle and make the
documentation more clear about what each style is doing
TODOs:
* Rewrite TP tests to adjust for the changes we have in this PR
* add more tests to guard the bug fixes
Differential Revision: [D51761183](https://our.internmc.facebook.com/intern/diff/D51761183)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114732
Approved by: https://github.com/wz337, https://github.com/fduwjj
Summary:
This is a util for numeric suite in pt2 export so that we can build
a more streamlined UX for numerical debugging in quant + executorch stack
Test Plan:
python test/test_quantization.py TestGenerateNumericDebugHandle
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114315
Approved by: https://github.com/zhxchen17
Summary:
The primary problem we are setting out to solve here is fake tensor freshness. Before this PR, fake tensors after dynamo represented fake tensors *at the end* of trace, so subsequent retraces like aot_autograd would start off with fake tensors in the wrong (end result) state, rather than their expected fresh state. The solution here is to start a fresh fake mode, and re-fakify the tensors. The nuance comes from ensuring that symbols are uniformly created for the symbolic sizes and strides of the tensor.
This PR is the result of *a lot* of back and forth with ezyang and eellison. Initially, the first pass at this was not super different from what we have in the PR - the broad strokes were the same:
1) We cache source->symbol in shape_env
2) We pass policy objects around, stored at dynamo fakificaiton time, and reused for later fakification
3) We create a new fake mode for backends
(from https://github.com/pytorch/pytorch/pull/113605/files)
This is ugly, and has some layering violations. We detoured our decision making through a few other alternatives. Immutable/mutable fake tensor mode was the most interesting alternative, https://github.com/pytorch/pytorch/pull/113653, and was struck down on concerns of complexity in fake mode combined with it not covering all edge cases. We also detoured on what to do about tensor memoization returning back potentially different tensors than requested, and if that was an anti pattern (it is) we want to hack in with the symbol cache (we don't).
We went back to the drawing board here, but with a few concessions:
1) the cache for source->symbol must live outside of shape_env, for both lifecycle, and layering reasons
2) A good amount of work needs to be done to pipe policy around fake_mode and meta_utils correctly, to cover all the cases (ezyang did this)
cc penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx chenyang78 aakhundov kadeng
imported-using-ghimport
Test Plan: Imported from OSS
Reviewed By: huydhn, Chillee
Differential Revision: D51566250
Pulled By: voznesenskym
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114526
Approved by: https://github.com/Chillee, https://github.com/huydhn
If you copy and paste the env var in the docs:
```console
TORCHDYNAMO_REPRO_AFTER=“aot”
```
it leads to this error:
```python
@functools.wraps(unconfigured_compiler_fn)
def debug_wrapper(gm, example_inputs, **kwargs):
compiler_fn = functools.partial(unconfigured_compiler_fn, **kwargs)
> assert config.repro_after in ("dynamo", "aot", None)
E torch._dynamo.exc.BackendCompilerFailed: backend='inductor' raised:
E AssertionError:
```
because `config.repro_after` is being `'“aot”'` but not `'aot'`.
---
It would've saved a few minutes of my time 😄
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114530
Approved by: https://github.com/Chillee
Currently the user can use torch.onnx.dynamo_export to export the model.
to ONNX.
```python
import torch
class Model(torch.nn.Module):
def forward(self, x):
return x + 1.0
onnx_program = torch.onnx.dynamo_export(
Model(),
torch.randn(1, 1, 2, dtype=torch.float),
)
```
The next step would be instantiating a ONNX runtime to execute it.
```python
import onnxruntime # type: ignore[import]
onnx_input = self.adapt_torch_inputs_to_onnx(*args, **kwargs)
options = options or {}
providers = options.get("providers", onnxruntime.get_available_providers())
onnx_model = self.model_proto.SerializeToString()
ort_session = onnxruntime.InferenceSession(onnx_model, providers=providers)
def to_numpy(tensor):
return (
tensor.detach().cpu().numpy()
if tensor.requires_grad
else tensor.cpu().numpy()
)
onnxruntime_input = {
k.name: to_numpy(v) for k, v in zip(ort_session.get_inputs(), onnx_input)
}
return ort_session.run(None, onnxruntime_input)
```
This PR provides the `ONNXProgram.__call__` method as facilitator to use ONNX Runtime under the hood, similar to how `torch.export.ExportedProgram.__call__` which allows the underlying `torch.fx.GraphModule` to be executed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113495
Approved by: https://github.com/titaiwangms
Summary: our docs were saying dynamic embedding bag wasn't supported but
it actually is (at least at the same level as embeddings were) it just wasn't previously tested/listed.
Test Plan: python test/test_quantization.py -k "test_embedding"
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107623
Approved by: https://github.com/jerryzh168
The primary problem we are setting out to solve here is fake tensor freshness. Before this PR, fake tensors after dynamo represented fake tensors *at the end* of trace, so subsequent retraces like aot_autograd would start off with fake tensors in the wrong (end result) state, rather than their expected fresh state. The solution here is to start a fresh fake mode, and re-fakify the tensors. The nuance comes from ensuring that symbols are uniformly created for the symbolic sizes and strides of the tensor.
This PR is the result of *a lot* of back and forth with @ezyang and @eellison. Initially, the first pass at this was not super different from what we have in the PR - the broad strokes were the same:
1) We cache source->symbol in shape_env
2) We pass policy objects around, stored at dynamo fakificaiton time, and reused for later fakification
3) We create a new fake mode for backends
(from https://github.com/pytorch/pytorch/pull/113605/files)
This is ugly, and has some layering violations. We detoured our decision making through a few other alternatives. Immutable/mutable fake tensor mode was the most interesting alternative, https://github.com/pytorch/pytorch/pull/113653, and was struck down on concerns of complexity in fake mode combined with it not covering all edge cases. We also detoured on what to do about tensor memoization returning back potentially different tensors than requested, and if that was an anti pattern (it is) we want to hack in with the symbol cache (we don't).
We went back to the drawing board here, but with a few concessions:
1) the cache for source->symbol must live outside of shape_env, for both lifecycle, and layering reasons
2) A good amount of work needs to be done to pipe policy around fake_mode and meta_utils correctly, to cover all the cases (@ezyang did this)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113926
Approved by: https://github.com/ezyang, https://github.com/eellison
